[
  {
    "path": ".github/workflows/push.yml",
    "content": "name: Lint & Tests\n\non: [push, pull_request]\n\njobs:\n  lint-and-tests:\n\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: [3.6] # build only for 3.6 for now\n\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python ${{ matrix.python-version }}\n      uses: actions/setup-python@v2\n      with:\n        python-version: ${{ matrix.python-version }}\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        pip install --upgrade setuptools==50.3.0\n        python setup.py install\n        pip install -r requirements.opt.txt\n        pip install flake8\n        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n    - name: Lint with flake8\n      run: |\n        # stop the build if there are Python syntax errors or undefined names\n        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n        flake8 . --count --exit-zero --statistics\n    - name: Unit tests\n      run: |\n        python -m unittest discover\n    - name: Test vocabulary build\n      run: |\n        python onmt/bin/build_vocab.py \\\n          -config data/data.yaml \\\n          -save_data /tmp/onmt \\\n          -n_sample 5000 \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          && rm -rf /tmp/sample\n    - name: Test field/transform dump\n      run: |\n        # The dumped fields are used later when testing tools\n        python train.py \\\n          -config data/data.yaml \\\n          -save_data /tmp/onmt.train.check \\\n          -dump_fields \\\n          -dump_transforms \\\n          -n_sample 30 \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000\n    - name: Test RNN training\n      run: |\n        python train.py \\\n          -config data/data.yaml \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000 \\\n          -rnn_size 2 \\\n          -batch_size 10 \\\n          -word_vec_size 5 \\\n          -report_every 5\\\n          -rnn_size 10 \\\n          -train_steps 10\n    - name: Test RNN training with copy\n      run: |\n        python train.py \\\n          -config data/data.yaml \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000 \\\n          -rnn_size 2 \\\n          -batch_size 10 \\\n          -word_vec_size 5 \\\n          -report_every 5 \\\n          -rnn_size 10 \\\n          -train_steps 10 \\\n          -copy_attn\n    - name: Test RNN training with coverage\n      run: |\n        python train.py \\\n          -config data/data.yaml \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000 \\\n          -rnn_size 2 -batch_size 10 \\\n          -word_vec_size 5 -report_every 5        \\\n          -coverage_attn true -lambda_coverage 0.1 \\\n          -rnn_size 10 -train_steps 10\n    - name: Test Transformer training with align\n      run: |\n        python train.py \\\n          -config data/align_data.yaml \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.tgt \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000 \\\n          -max_generator_batches 0 \\\n          -encoder_type transformer \\\n          -decoder_type transformer \\\n          -layers 4 \\\n          -word_vec_size 16 \\\n          -rnn_size 16 \\\n          -heads 2 \\\n          -transformer_ff 64 \\\n          -lambda_align 0.05 \\\n          -alignment_layer 2 \\\n          -alignment_heads 0 \\\n          -report_every 5 \\\n          -train_steps 10\n    - name: Test LM training\n      run: |\n        python train.py \\\n            -config data/lm_data.yaml \\\n            -src_vocab /tmp/onmt.vocab.src \\\n            -tgt_vocab /tmp/onmt.vocab.src \\\n            -model_task lm \\\n            -encoder_type transformer_lm \\\n            -decoder_type transformer_lm \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -dec_layers 2 -batch_size 10 \\\n            -heads 4 -transformer_ff 64 \\\n            -word_vec_size 16 -report_every 5 \\\n            -rnn_size 16 -train_steps 10\n    - name: Test LM training with copy\n      run: |\n        python train.py \\\n          -config data/lm_data.yaml \\\n          -src_vocab /tmp/onmt.vocab.src \\\n          -tgt_vocab /tmp/onmt.vocab.src \\\n          -model_task lm \\\n          -encoder_type transformer_lm \\\n          -decoder_type transformer_lm \\\n          -src_vocab_size 1000 \\\n          -tgt_vocab_size 1000 \\\n          -dec_layers 2 -batch_size 10 \\\n          -heads 4 -transformer_ff 64 \\\n          -word_vec_size 16 -report_every 5        \\\n          -rnn_size 16 -train_steps 10 \\\n          -copy_attn\n    - name: Test Graph neural network training\n      run: |\n        python train.py \\\n          -config data/ggnn_data.yaml \\\n          -src_seq_length 1000 \\\n          -tgt_seq_length 30 \\\n          -encoder_type ggnn \\\n          -layers 2 \\\n          -decoder_type rnn \\\n          -rnn_size 256 \\\n          -learning_rate 0.1 \\\n          -learning_rate_decay 0.8 \\\n          -global_attention general \\\n          -batch_size 32 \\\n          -word_vec_size 256 \\\n          -bridge \\\n          -train_steps 10 \\\n          -n_edge_types 9 \\\n          -state_dim 256 \\\n          -n_steps 10 \\\n          -n_node 64\n    - name: Test RNN translation\n      run: |\n        head data/src-test.txt > /tmp/src-test.txt\n        python translate.py \\\n          -model onmt/tests/test_model.pt \\\n          -src /tmp/src-test.txt \\\n          -verbose\n    - name: Test RNN ensemble translation\n      run: |\n        head data/src-test.txt > /tmp/src-test.txt\n        python translate.py \\\n          -model onmt/tests/test_model.pt \\\n          onmt/tests/test_model.pt \\\n          -src /tmp/src-test.txt \\\n          -verbose\n    - name: Test RNN translation with beam search\n      run: |\n        python translate.py \\\n          -model onmt/tests/test_model2.pt \\\n          -src data/morph/src.valid \\\n          -verbose \\\n          -batch_size 10 \\\n          -beam_size 10 \\\n          -tgt data/morph/tgt.valid \\\n          -out /tmp/trans\n        diff data/morph/tgt.valid /tmp/trans && rm /tmp/trans\n    - name: Test RNN translation with random sampling\n      run: |\n        python translate.py \\\n          -model onmt/tests/test_model2.pt \\\n          -src data/morph/src.valid \\\n          -verbose \\\n          -batch_size 10 \\\n          -beam_size 1 \\\n          -seed 1 \\\n          -random_sampling_topk \"-1\" \\\n          -random_sampling_temp 0.0001 \\\n          -tgt data/morph/tgt.valid \\\n          -out /tmp/trans\n        diff data/morph/tgt.valid /tmp/trans && rm /tmp/trans\n    - name: Test LM generation\n      run: |\n        head data/src-test.txt > /tmp/src-test.txt\n        python translate.py \\\n          -model onmt/tests/test_model_lm.pt \\\n          -src data/src-test.txt \\\n          -verbose\n    - name: Test LM generation with beam search\n      run: |\n        python translate.py \\\n          -model onmt/tests/test_model_lm.pt \\\n          -src data/data_lm/src-gen.txt \\\n          -verbose -batch_size 10 \\\n          -beam_size 10 \\\n          -out /tmp/gen\n        diff data/data_lm/gen-beam-sol.txt /tmp/gen && rm /tmp/gen\n    - name: Test LM generation with random sampling\n      run: |\n        python translate.py -model onmt/tests/test_model_lm.pt \\\n          -src data/data_lm/src-gen.txt \\\n          -verbose -batch_size 10 \\\n          -beam_size 1 \\\n          -seed 1 \\\n          -random_sampling_topk=-1 \\\n          -random_sampling_temp=0.0001 \\\n          -out /tmp/gen\n        diff data/data_lm/gen-sampling-sol.txt /tmp/gen && rm /tmp/gen\n    - name: Test extract_vocabulary tool\n      run: |\n        python tools/extract_vocabulary.py \\\n          -file /tmp/onmt.train.check.vocab.pt \\\n          -file_type field \\\n          -side src \\\n          -out_file /tmp/onmt.vocab.txt\n        if ! wc -l /tmp/onmt.vocab.txt | grep -qF  \"1002\"\n          then echo \"wrong word count\" && exit 1\n        else\n          echo \"create vocabulary pass\"\n        fi\n    - name: Test embeddings_to_torch tool\n      run: |\n        python tools/embeddings_to_torch.py \\\n          -emb_file_enc onmt/tests/sample_glove.txt \\\n          -emb_file_dec onmt/tests/sample_glove.txt \\\n          -dict_file /tmp/onmt.train.check.vocab.pt \\\n          -output_file /tmp/q_gloveembeddings \\\n          && rm /tmp/q_gloveembeddings*\n        rm /tmp/onmt.train.check.*.pt\n    - name: Test extract_embeddings tool\n      run: |\n        python tools/extract_embeddings.py \\\n          -model onmt/tests/test_model.pt\n  build-docs:\n    runs-on: ubuntu-latest\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python 3.6\n      uses: actions/setup-python@v2\n      with:\n        python-version: 3.6\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        pip install --upgrade setuptools\n        python setup.py install\n        pip install -r docs/requirements.txt\n    - name: Build docs\n      run: |\n        set -e\n        # Check that docs are built without errors\n        cd docs/ && make html && cd ..\n"
  },
  {
    "path": ".github/workflows/release.yml",
    "content": "name: Deploy Docs & Publish to PyPi\n\non:\n  release:\n    types: [published]\n\njobs:\n  deploy-docs:\n    runs-on: ubuntu-latest\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python 3.6\n      uses: actions/setup-python@v2\n      with:\n        python-version: 3.6\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        pip install --upgrade setuptools\n        python setup.py install\n        pip install -r docs/requirements.txt\n    - name: Build docs\n      run: |\n        set -e\n        # Check that docs are built without errors\n        cd docs/ && make html && cd ..\n    - name: Deploy docs\n      uses: JamesIves/github-pages-deploy-action@3.7.1\n      with:\n        GITHUB_TOKEN:  ${{ secrets.GITHUB_TOKEN }}\n        BRANCH: gh-pages\n        FOLDER: docs/build/html\n        CLEAN: true\n  publish-pypi:\n    runs-on: ubuntu-latest\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python 3.6\n      uses: actions/setup-python@v2\n      with:\n        python-version: 3.6\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        pip install --upgrade setuptools wheel\n    - name: Build a binary wheel and a source tarball\n      run: |\n        python setup.py sdist bdist_wheel\n    - name: Publish release to PyPi\n      uses: pypa/gh-action-pypi-publish@54b39fb\n      with:\n        user: __token__\n        password: ${{ secrets.pypi_password }}\n        verbose: true\n"
  },
  {
    "path": ".gitignore",
    "content": "# repo-specific stuff\n*.pkl\nmulti-bleu.perl\n*.pt\n\\#*#\n.idea\n*.sublime-*\n.DS_Store\ndata/\nruns/\noutput/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n"
  },
  {
    "path": "CHANGELOG.md",
    "content": "\n**Notes on versioning**\n\n\n## [Unreleased]\n\n## [2.0.0rc2](https://github.com/OpenNMT/OpenNMT-py/tree/2.0.0rc2) (2020-11-10)\n\n### Fixes and improvements\n* Parallelize onmt_build_vocab (422d824)\n* Some fixes to the on-the-fly transforms\n* Some CTranslate2 related updates\n* Some fixes to the docs\n\n## [2.0.0rc1](https://github.com/OpenNMT/OpenNMT-py/tree/2.0.0rc1) (2020-09-25)\n\nThis is the first release candidate for OpenNMT-py major upgdate to 2.0.0!\n\nThe major idea behind this release is the -- almost -- complete **makeover of the data loading pipeline** . A new 'dynamic' paradigm is introduced, allowing to apply on the fly transforms to the data.\n\nThis has a few advantages, amongst which:\n\n* remove or drastically reduce the preprocessing required to train a model;\n* increase and simplify the possibilities of data augmentation and manipulation through on-the fly transforms.\n\nThese transforms can be specific **tokenization** methods, **filters**, **noising**, or **any custom transform** users may want to implement. Custom transform implementation is quite straightforward thanks to the existing base class and example implementations.\n\nYou can check out how to use this new data loading pipeline in the updated [docs and examples](https://opennmt.net/OpenNMT-py).\n\nAll the **readily available transforms** are described [here](https://opennmt.net/OpenNMT-py/FAQ.html#what-are-the-readily-available-on-the-fly-data-transforms).\n\n### Performance\n\nGiven sufficient CPU resources according to GPU computing power, most of the transforms should not slow the training down. (Note: for now, one producer process per GPU is spawned -- meaning you would ideally need 2N CPU threads for N GPUs).\n\n### Breaking changes\n\nA few features are dropped, at least for now:\n\n* audio, image and video inputs;\n* source word features.\n\nSome very old checkpoints with previous fields and vocab structure are also incompatible with this new version.\n\nFor any user that still need some of these features, the previous codebase will be retained as [`legacy` in a separate branch](https://github.com/OpenNMT/OpenNMT-py/tree/legacy). It will no longer receive extensive development from the core team but PRs may still be accepted.\n\n\n-----\n\n## [1.2.0](https://github.com/OpenNMT/OpenNMT-py/tree/1.2.0) (2020-08-17)\n### Fixes and improvements\n* Support pytorch 1.6 (e813f4d, eaaae6a)\n* Support official torch 1.6 AMP for mixed precision training (2ac1ed0)\n* Flag to override batch_size_multiple in FP16 mode, useful in some memory constrained setups (23e5018)\n* Pass a dict and allow custom options in preprocess/postprocess functions of REST server (41f0c02, 8ec54d2)\n* Allow different tokenization for source and target in REST server (bb2d045, 4659170)\n* Various bug fixes\n\n### New features\n* Gated Graph Sequence Neural Networks encoder (11e8d0), thanks @SteveKommrusch\n* Decoding with a target prefix (95aeefb, 0e143ff, 91ab592), thanks @Zenglinxiao\n\n## [1.1.1](https://github.com/OpenNMT/OpenNMT-py/tree/1.1.1) (2020-03-20)\n### Fixes and improvements\n* Fix backcompatibility when no 'corpus_id' field (c313c28)\n\n## [1.1.0](https://github.com/OpenNMT/OpenNMT-py/tree/1.1.0) (2020-03-19)\n### New features\n* Support CTranslate2 models in REST server (91d5d57)\n* Extend support for custom preprocessing/postprocessing function in REST server by using return dictionaries (d14613d, 9619ac3, 92a7ba5)\n* Experimental: BART-like source noising (5940dcf)\n\n### Fixes and improvements\n* Add options to CTranslate2 release (e442f3f)\n* Fix dataset shard order (458fc48)\n* Rotate only the server logs, not training (189583a)\n* Fix alignment error with empty prediction (91287eb)\n\n## [1.0.2](https://github.com/OpenNMT/OpenNMT-py/tree/1.0.2) (2020-03-05)\n### Fixes and improvements\n* Enable CTranslate2 conversion of Transformers with relative position (db11135)\n* Adapt `-replace_unk` to use with learned alignments if they exist (7625b53)\n\n## [1.0.1](https://github.com/OpenNMT/OpenNMT-py/tree/1.0.1) (2020-02-17)\n### Fixes and improvements\n* Ctranslate2 conversion handled in release script (1b50e0c)\n* Use `attention_dropout` properly in MHA (f5c9cd4)\n* Update apex FP16_Optimizer path (d3e2268)\n* Some REST server optimizations\n* Fix and add some docs\n\n## [1.0.0](https://github.com/OpenNMT/OpenNMT-py/tree/1.0.0) (2019-10-01)\n### New features\n* Implementation of \"Jointly Learning to Align & Translate with Transformer\" (@Zenglinxiao)\n\n### Fixes and improvements\n* Add nbest support to REST server (@Zenglinxiao)\n* Merge greedy and beam search codepaths (@Zenglinxiao)\n* Fix \"block ngram repeats\" (@KaijuML, @pltrdy)\n* Small fixes, some more docs\n\n## [1.0.0.rc2](https://github.com/OpenNMT/OpenNMT-py/tree/1.0.0.rc1) (2019-10-01)\n* Fix Apex / FP16 training (Apex new API is buggy)\n* Multithread preprocessing way faster (Thanks @francoishernandez)\n* Pip Installation v1.0.0.rc1 (thanks @pltrdy)\n\n## [0.9.2](https://github.com/OpenNMT/OpenNMT-py/tree/0.9.2) (2019-09-04)\n* Switch to Pytorch 1.2\n* Pre/post processing on the translation server\n* option to remove the FFN layer in AAN + AAN optimization (faster)\n* Coverage loss (per Abisee paper 2017) implementation\n* Video Captioning task: Thanks Dylan Flaute!\n* Token batch at inference\n* Small fixes and add-ons\n\n\n## [0.9.1](https://github.com/OpenNMT/OpenNMT-py/tree/0.9.1) (2019-06-13)\n* New mechanism for MultiGPU training \"1 batch producer / multi batch consumers\"\n  resulting in big memory saving when handling huge datasets\n* New APEX AMP (mixed precision) API\n* Option to overwrite shards when preprocessing\n* Small fixes and add-ons\n\n## [0.9.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.9.0) (2019-05-16)\n* Faster vocab building when processing shards (no reloading)\n* New dataweighting feature\n* New dropout scheduler.\n* Small fixes and add-ons\n\n## [0.8.2](https://github.com/OpenNMT/OpenNMT-py/tree/0.8.2) (2019-02-16)\n* Update documentation and Library example\n* Revamp args\n* Bug fixes, save moving average in FP32\n* Allow FP32 inference for FP16 models\n\n## [0.8.1](https://github.com/OpenNMT/OpenNMT-py/tree/0.8.1) (2019-02-12)\n* Update documentation\n* Random sampling scores fixes\n* Bug fixes\n\n## [0.8.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.8.0) (2019-02-09)\n* Many fixes and code cleaning thanks @flauted, @guillaumekln\n* Datasets code refactor (thanks @flauted) you need to r-preeprocess datasets\n\n### New features\n* FP16 Support: Experimental, using Apex, Checkpoints may break in future version.\n* Continuous exponential moving average (thanks @francoishernandez, and Marian)\n* Relative positions encoding (thanks @francoishernanndez, and Google T2T)\n* Deprecate the old beam search, fast batched beam search supports all options\n\n\n## [0.7.2](https://github.com/OpenNMT/OpenNMT-py/tree/0.7.2) (2019-01-31)\n* Many fixes and code cleaning thanks @bpopeters, @flauted, @guillaumekln\n\n### New features\n* Multilevel fields for better handling of text featuer embeddinggs. \n\n\n## [0.7.1](https://github.com/OpenNMT/OpenNMT-py/tree/0.7.1) (2019-01-24)\n* Many fixes and code refactoring thanks @bpopeters, @flauted, @guillaumekln\n\n### New features\n* Random sampling thanks @daphnei\n* Enable sharding for huge files at translation\n\n## [0.7.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.7.0) (2019-01-02)\n* Many fixes and code refactoring thanks @benopeters\n* Migrated to Pytorch 1.0\n\n## [0.6.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.6.0) (2018-11-28)\n* Many fixes and code improvements\n* New: Ability to load a yml config file. See examples in config folder.\n\n## [0.5.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.5.0) (2018-10-24)\n* Fixed advance n_best beam in translate_batch_fast\n* Fixed remove valid set vocab from total vocab\n* New: Ability to reset optimizer when using train_from\n* New: create_vocabulary tool + fix when loading existing vocab.\n\n## [0.4.1](https://github.com/OpenNMT/OpenNMT-py/tree/0.4.1) (2018-10-11)\n* Fixed preprocessing files names, cleaning intermediary files.\n\n## [0.4.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.4.0) (2018-10-08)\n* Fixed Speech2Text training (thanks Yuntian)\n\n* Removed -max_shard_size, replaced by -shard_size = number of examples in a shard.\n  Default value = 1M which works fine in most Text dataset cases. (will avoid Ram OOM in most cases)\n\n\n## [0.3.0](https://github.com/OpenNMT/OpenNMT-py/tree/0.3.0) (2018-09-27)\n* Now requires Pytorch 0.4.1\n\n* Multi-node Multi-GPU with Torch Distributed\n\n  New options are:\n  -master_ip: ip address of the master node\n  -master_port: port number of th emaster node\n  -world_size = total number of processes to be run (total GPUs accross all nodes)\n  -gpu_ranks = list of indices of processes accross all nodes\n\n* gpuid is deprecated\nSee examples in https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/FAQ.md\n\n* Fixes to img2text now working\n\n* New sharding based on number of examples\n\n* Fixes to avoid 0.4.1 deprecated functions.\n\n\n## [0.2.1](https://github.com/OpenNMT/OpenNMT-py/tree/0.2.1) (2018-08-31)\n\n### Fixes and improvements\n\n* First compatibility steps with Pytorch 0.4.1 (non breaking)\n* Fix TranslationServer (when various request try to load the same model at the same time)\n* Fix StopIteration error (python 3.7)\n\n### New features\n* Ensemble at inference (thanks @Waino)\n\n## [0.2](https://github.com/OpenNMT/OpenNMT-py/tree/v0.2) (2018-08-28)\n\n### improvements\n\n* Compatibility fixes with Pytorch 0.4 / Torchtext 0.3\n* Multi-GPU based on Torch Distributed\n* Average Attention Network (AAN) for the Transformer (thanks @francoishernandez )\n* New fast beam search (see -fast in translate.py) (thanks @guillaumekln)\n* Sparse attention / sparsemax (thanks to @bpopeters)\n* Refactoring of many parts of the code base:\n - change from -epoch to -train_steps -valid_steps (see opts.py)\n - reorg of the logic train => train_multi / train_single => trainer\n* Many fixes / improvements in the translationserver (thanks @pltrdy @francoishernandez)\n* fix BPTT\n\n## [0.1](https://github.com/OpenNMT/OpenNMT-py/tree/v0.1) (2018-06-08)\n\n### First and Last Release using Pytorch 0.3.x\n\n\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "# Contributors\n\nOpenNMT-py is a community developed project and we love developer contributions.\n\n## Guidelines\nBefore sending a PR, please do this checklist first:\n\n- Please run `onmt/tests/pull_request_chk.sh` and fix any errors. When adding new functionality, also add tests to this script. Included checks:\n    1. flake8 check for coding style;\n    2. unittest;\n    3. continuous integration tests listed in `.travis.yml`.\n- When adding/modifying class constructor, please make the arguments as same naming style as its superclass in PyTorch.\n- If your change is based on a paper, please include a clear comment and reference in the code (more on that below).\n\n### Docstrings\nAbove all, try to follow the Google docstring format\n([Napoleon example](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html),\n[Google styleguide](http://google.github.io/styleguide/pyguide.html)).\nThis makes it easy to include your contributions in the Sphinx documentation. And, do feel free\nto autodoc your contributions in the API ``.rst`` files in the `docs/source` folder! If you do, check that\nyour additions look right.\n\n```bash\ncd docs\n# install some dependencies if necessary:\n# recommonmark, sphinx_rtd_theme, sphinxcontrib-bibtex\nmake html\nfirefox build/html/main.html  # or your browser of choice\n```\n\nSome particular advice:\n- Try to follow Python 3 [``typing`` module](https://docs.python.org/3/library/typing.html) conventions when documenting types.\n    - Exception: use \"or\" instead of unions for more readability\n    - For external types, use the full \"import name\". Common abbreviations (e.g. ``np``) are acceptable.\n      For ``torch.Tensor`` types, the ``torch.`` is optional.\n    - Please don't use tics like `` (`str`) `` or rst directives like `` (:obj:`str`) ``. Napoleon handles types\n      very well without additional help, so avoid the clutter.\n- [Google docstrings don't support multiple returns](https://stackoverflow.com/questions/29221551/can-sphinx-napoleon-document-function-returning-multiple-arguments).\nFor multiple returns, the following works well with Sphinx and is still very readable.\n  ```python\n  def foo(a, b):\n      \"\"\"This is my docstring.\n\n      Args:\n          a (object): Something.\n          b (class): Another thing.\n\n      Returns:\n          (object, class):\n\n          * a: Something or rather with a long\n            description that spills over.\n          * b: And another thing.\n      \"\"\"\n\n      return a, b\n  ```\n- When citing a paper, avoid directly linking in the docstring! Add a Bibtex entry to `docs/source/refs.bib`.\nE.g., to cite \"Attention Is All You Need\", visit [arXiv](https://arxiv.org/abs/1706.03762), choose the\n[bibtext](https://dblp.uni-trier.de/rec/bibtex/journals/corr/VaswaniSPUJGKP17) link, search `docs/source/refs.bib`\nusing `CTRL-F` for `DBLP:journals/corr/VaswaniSPUJGKP17`, and if you do not find it then copy-paste the\ncitation into `refs.bib`. Then, in your docstring, use ``:cite:`DBLP:journals/corr/VaswaniSPUJGKP17` ``.\n    - However, a link is better than nothing.\n- Please document tensor shapes. Prefer the format\n  ``` ``(a, b, c)`` ```. This style is easy to read, allows using ``x`` for multplication, and is common\n  (PyTorch uses a few variations on the parentheses format, AllenNLP uses exactly this format, Fairseq uses\n  the parentheses format with single ticks).\n    - Again, a different style is better than no shape documentation.\n- Please avoid unnecessary space characters, try to capitalize, and try to punctuate.\n\n  For multi-line docstrings, add a blank line after the closing ``\"\"\"``.\n  Don't use a blank line before the closing quotes.\n\n  ``\"\"\" not this \"\"\"`` ``\"\"\"This.\"\"\"``\n\n  ```python\n  \"\"\"\n      Not this.\n  \"\"\"\n  ```\n  ```python\n  \"\"\"This.\"\"\"\n  ```\n\n  This note is the least important. Focus on content first, but remember that consistent docs look good.\n- Be sensible about the first line. Generally, one stand-alone summary line (per the Google guidelines) is good.\n  Sometimes, it's better to cut directly to the args or an extended description. It's always acceptable to have a\n  \"trailing\" citation.\n"
  },
  {
    "path": "Dockerfile",
    "content": "FROM pytorch/pytorch:latest\nRUN git clone https://github.com/OpenNMT/OpenNMT-py.git && cd OpenNMT-py && pip install -r requirements.txt && python setup.py install\n"
  },
  {
    "path": "LICENSE.md",
    "content": "MIT License\n\nCopyright (c) 2017-Present OpenNMT\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Keyphrase Generation (built on OpenNMT-py)\n\nThis is a repository providing code and datasets for keyphrase generation.\n\n## Update (October 2022)\nRelease the resources of paper [**General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation**](https://arxiv.org/pdf/2208.09606.pdf). \n\nAll datasets and selected model checkpoints in the papers can be downloaded from Huggingface Hub ([data](https://huggingface.co/memray) and [ckpt](https://huggingface.co/memray/opennmt-kpg/tree/main)).\nConfig files can be found at [script/](https://github.com/memray/OpenNMT-kpg-release/tree/master/script/transfer/train_fulldata). \n\nFor example, you can start training a Transformer model on KP20k using OpenNMT:\n```bash\nCUDA_VISIBLE_DEVICES=0 python train.py -config config/transfer_kp/train/transformer-presabs-kp20k.yml\n```\nTo train a BART model on KP20k using [fairseq-kpg](https://github.com/memray/fairseq-kpg), vocab can be downloaded [here](https://huggingface.co/memray/opennmt-kpg/blob/main/roberta-base-kp.zip):\n```bash\ncd $FAIRSEQ_DIR\nCUDA_VISIBLE_DEVICES=0 python train.py data/kp/json/kp20k/ --save-dir exps/kp/bartFT_presabs_kp20k_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab hf_vocab/roberta-base-kp/vocab.json --bpe-merges hf_vocab/roberta-base-kp/merges.txt --dict-path hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --update-freq 8 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1024 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project kp-project\n```\n\n\n## Update (April 2022)\nSeveral pretrained checkpoints are available at Huggingface model repos.\n- BART-large-KPG model pretrained with wikipedia phrases: [https://huggingface.co/memray/bart_wikikp](https://huggingface.co/memray/bart_wikikp/tree/main)\n- BART/Transformers trained on four different keyphrase datasets: [https://huggingface.co/memray/opennmt-kpg/](https://huggingface.co/memray/opennmt-kpg/tree/main)\n\nTwo examples:\n- Run kpg inference with Huggingface Transformers\n```bash\nCUDA_VISIBLE_DEVICES=0 python onmt/keyphrase/run_infer_hfkpg.py --config_name memray/bart_wikikp --model_name_or_path memray/bart_wikikp --tokenizer_name memray/bart_wikikp --dataset_name midas/duc2001 --do_predict --output_dir kp_output/duc2001/ --overwrite_output_dir --per_device_eval_batch_size 8 --predict_with_generate --text_column document --keyphrase_column extractive_keyphrases --source_prefix <present>10<header>5<category>5<seealso>2<infill>0<s> --num_beams 5 --generation_max_length 60\n```\n- Run kpg inference with OpenNMT-kpg (parameters are hard-coded)\n```bash\nCUDA_VISIBLE_DEVICES=0 python onmt/keyphrase/kpg_example_hfdatasets.py\n```\n\n## Update (Jan 2022)\n\nMerged with OpenNMT v2 and integrated a new pre-processing pipeline. Now training/inference can directly load **JSON data** from disk, without any hassle of tokenization or conversion to tensor files.\nPlease check out Huggingface [repo](https://huggingface.co/datasets/memray/keyphrase/) for all resources.\n~~- Paper datasets and DUC: KP20k/Inspec/Krapivin/NUS/SemEval2010/DUC2001.~~\n~~- 4 large annotated datasets: KP20k, OpenKP, KPTimes+JPTimes, StackExchange.~~ \n\nSome config examples can be of help for you to kick off:\n- [Configs](https://github.com/memray/OpenNMT-kpg-release/tree/master/script/transfer/train_fulldata) using RoBERTa subword tokenization. Vocab (including merges.txt/vocab.json/tokenizer.json) can be found [here](https://huggingface.co/memray/opennmt-kpg/blob/main/roberta-base-kp.zip).\n- [Configs](https://github.com/memray/OpenNMT-kpg-release/tree/master/script/empirical_study/diverse) using word tokenization. Vocab (magkp20k.vocab.json, 50k most frequent words in KP20k and MagKP) can be found [here](https://huggingface.co/memray/opennmt-kpg/blob/main/magkp20k.vocab.json).\n\nPlease note that hf_vocab.tar.gz contains the vocab of subword tokenization (RoBERTa vocab with some new special tokens such as <SEP>), and [magkp20k.vocab.json](https://huggingface.co/memray/opennmt-kpg/blob/main/magkp20k.vocab.json) is for previous word tokenization based models (top 50k frequent words in magcs and kp20k).\n\n\n## Quickstart\n\nAll the config files used for training and evaluation can be found in folder `config/`.\nFor more examples, you can refer to scripts placed in folder `script/`.\n\n\n### Train a One2Seq model\n\n```bash\npython train.py -config config/transfer_kp/train/transformer-presabs-kp20k.yml\n```\n\n### Train a One2One model\n\n```bash\npython train.py -config config/transfer_kp/train/transformer-one2one-kp20k.yml\n```\n\n### Run generation and evaluation\n\n```bash\n# beam search (beamwidth=50)\npython kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred eval -data_dir kp/data/kp/json/ -exp_root_dir kp/exps/transformer_exp_devbest/ -gpu 0 -batch_size 16 -beam_size 50 -max_length 40 -testsets kp20k openkp kptimes jptimes stackex kp20k_valid2k openkp_valid2k kptimes_valid2k jptimes_valid2k stackex_valid2k duc -splits test --data_format jsonl -gpu 0\n\n# greedy decoding (beamwidth=1)\npython kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred eval -data_dir kp/data/kp/json/ -exp_root_dir kp/exps/transformer_exp_devbest/ -gpu 0 -batch_size 16 -beam_size 1 -max_length 40 -testsets kp20k openkp kptimes jptimes stackex kp20k_valid2k openkp_valid2k kptimes_valid2k jptimes_valid2k stackex_valid2k duc -splits test --data_format jsonl -gpu 0\n```\n\n## Evaluation and Datasets\nYou may refer to `notebook/json_process.ipynb` to have a glance at the pre-processing.\n\nWe follow the data pre-processing and evaluation protocols in [Meng et al. 2017](https://arxiv.org/pdf/1704.06879.pdf). We pre-process both document texts and ground-truth keyphrases, including word segmentation, lowercasing and replacing all digits with symbol \\<digit\\>.\n\nWe manually clean the data examples in the valid/test set of KP20k (clean noisy text, replace erroneous keyphrases with actual author keyphrases, remove examples without any ground-truth keyphrases) and use scripts to remove invalid training examples (without any author keyphrase).\n\nWe evaluate models' performance on predicting present and absent phrases separately. Specifically, we first tokenize, lowercase and stem (using the Porter Stemmer of [NLTK](https://www.nltk.org/api/nltk.stem.html\\#module-nltk.stem.porter)) the text, then we determine the presence of each ground-truth keyphrase by checking whether its words can be found verbatim in the source text.\n\nTo evaluate present phrase performance, we compute Precision/Recall/F1-score for each document taking only present ground-truth keyphrases as target and ignore the absent ones. We report the macro-averaged scores over documents that have at least one present ground-truth phrases (corresponding to the column \\#PreDoc in the Table below, and similarly to the case of absent phrase evaluation.\n\n\n![metrics](images/metric_formula.gif \"metrics\")\n\nwhere #(pred) and #(target) are the number of predicted and ground-truth keyphrases respectively; and #(correct@k) is the number of correct predictions among the first k results.\n\n\nWe clarify that, since our study mainly focuses on keyword/keyphrase extraction/generation on short text, we only used the abstract of Semeval and NUS as source text. Therefore statistics like #PreKP may be different from the ones computed with fulltext, which also affect the final F1-scores. For the ease of reproduction, we post the detailed statistics in the following table and processed testsets with present/absent phrases split can be found in the released data (e.g. `data/json/kp20k/kp20k_test_meng17token.json`).\n\n\n| **Dataset** | **#Train** | **#Valid** | **#Test** | **#KP** | **#PreDoc** | **#PreKP** | **#AbsDoc** | **#AbsKP** |\n| :---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: \n| **KP20k** | 514k | 19,992 | 19,987 | 105,181 | 19,048 | 66,595 | 16,357 | 38,586|\n| **Inspec** | -- | 1,500 | 500| 4,913 | 497 | 3,858 | 381 | 1,055 |\n| **Krapivin** | -- | 1,844 | 460 | 2,641 | 437 | 1,485 | 417 | 1,156 |\n| **NUS** | -- | - | 211 | 2,461 | 207 | 1,263 | 195 | 1,198 |\n| **Semeval** | -- | 144 | 100 | 1,507 | 100 | 671 | 99 | 836|\n| **StackEx** | 298k | 16,000 | 16,000 | 43,131 | 13,475 | 24,809 | 10,984 | 18,322 |\n| **DUC** | -- | -- | 308 | 2,484 | 308 | 2,421 | 38 | 63 |\n\n\n\n\n## Contributers\nMajor contributors are:\n- [Rui Meng](https://github.com/memray/) (Salesforce Research, previously at University of Pittsburgh)\n- [Eric Yuan](https://github.com/xingdi-eric-yuan) (Microsoft Research, Montréal)\n- [Tong Wang](https://github.com/wangtong106) (Microsoft Research, Montréal)\n- [Khushboo Thaker](https://github.com/khushsi) (University of Pittsburgh)\n\n\n## Citation\n\nPlease cite the following papers if you are interested in using our code and datasets.\n```\n@article{meng2022general2specific,\n  title={General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation},\n  author={Meng, Rui and Wang, Tong and Yuan, Xingdi and Zhou, Yingbo and He, Daqing},\n  journal={arXiv preprint arXiv:2208.09606},\n  year={2022}\n}\n```\n```\n@inproceedings{meng2021empirical,\n  title={An Empirical Study on Neural Keyphrase Generation},\n  author={Meng, Rui and Yuan, Xingdi and Wang, Tong and Zhao, Sanqiang and Trischler, Adam and He, Daqing},\n  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},\n  pages={4985--5007},\n  year={2021}\n}\n```\n```\n@article{yuan2018onesizenotfit,\n  title={One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases},\n  author={Yuan, Xingdi and Wang, Tong and Meng, Rui and Thaker, Khushboo and He, Daqing and Trischler, Adam},\n  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},\n  url={https://arxiv.org/pdf/1810.05241.pdf},\n  year={2020}\n}\n```\n```\n@article{meng2019ordermatters,\n  title={Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence},\n  author={Meng, Rui and Yuan, Xingdi and Wang, Tong and Brusilovsky, Peter and Trischler, Adam and He, Daqing},\n  journal={arXiv preprint arXiv:1909.03590},\n  url={https://arxiv.org/pdf/1909.03590.pdf},\n  year={2019}\n}\n```\n```\n@inproceedings{meng2017kpgen,\n  title={Deep keyphrase generation},\n  author={Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},\n  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n  pages={582--592},\n  url={https://arxiv.org/pdf/1704.06879.pdf},\n  year={2017}\n}\n```\n"
  },
  {
    "path": "available_models/example.conf.json",
    "content": "{\n    \"models_root\": \"./available_models\",\n    \"models\": [\n        {\n            \"id\": 100,\n            \"model\": \"model_0.pt\",\n            \"timeout\": 600,\n            \"on_timeout\": \"to_cpu\",\n            \"load\": true,\n            \"opt\": {\n                \"gpu\": 0,\n                \"beam_size\": 5\n            },\n            \"tokenizer\": {\n                \"type\": \"sentencepiece\",\n                \"model\": \"wmtenfr.model\"\n            }\n        },{\n            \"model\": \"model_0.light.pt\",\n            \"timeout\": -1,\n            \"on_timeout\": \"unload\",\n            \"model_root\": \"../other_models\",\n            \"opt\": {\n                \"batch_size\": 1,\n                \"beam_size\": 10\n            }\n        }\n    ]\n}\n"
  },
  {
    "path": "build_vocab.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.build_vocab import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "config/diversity/keyphrase-one2one-diversity.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata:\n    valid:\n        path_src: none\n        path_tgt: none\n        path_align: none\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n        weight: 1\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\nbeam_terminate: full\nuse_given_inputs: false\n\n#### Subword and vocab\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Data options\n#model_dtype: fp16\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\n\n# Evaluation options\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Decoding\nrandom_sampling_temp: 0.0\n\n# Efficiency\n#gpu: 0\ngpu: -1\n\nseed: 97\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/diversity/keyphrase-one2seq-diversity.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata:\n    valid:\n        path_src: none\n        path_tgt: none\n        path_align: none\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n        weight: 1\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nuse_given_inputs: false\n\nbeam_terminate: full\n#### Subword and vocab\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Data options\n#model_dtype: fp16\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\n\n# Evaluation options\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Decoding\nrandom_sampling_temp: 0.0\n\n# Efficiency\n#gpu: 0\ngpu: -1\n\nseed: 97\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-keyphrase-kp20k.yml",
    "content": "data_type: keyphrase\n\ntrain_src: data/keyphrase/meng17/kp20k/kp20k_train.src\ntrain_tgt: data/keyphrase/meng17/kp20k/kp20k_train.tgt\nvalid_src: data/keyphrase/meng17/kp20k/kp20k_valid.src\nvalid_tgt: data/keyphrase/meng17/kp20k/kp20k_valid.tgt\n\nsave_data: data/keyphrase/meng17/kp20k_pt_shard10k/\nshard_size: 10000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_vocab: data/keyphrase/meng17/magkp20k.vocab.pt # use the pre-generated vocab\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 8\nlower: 'true'\nfilter_valid: 'false'\n\nreport_every: 1000\nlog_file: data/keyphrase/meng17/preprocess.kp20k_shard10k.log\n\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-keyphrase-magkp.yml",
    "content": "data_type: keyphrase\n\ntrain_src: data/keyphrase/meng17/magkp/magkp_train.src\ntrain_tgt: data/keyphrase/meng17/magkp/magkp_train.tgt\nvalid_src: data/keyphrase/meng17/kp20k/kp20k_valid.src\nvalid_tgt: data/keyphrase/meng17/kp20k/kp20k_valid.tgt\n\nsave_data: data/keyphrase/meng17/magkp_pt_shard10k/\nshard_size: 10000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_vocab: data/keyphrase/meng17/magkp20k.vocab.pt # use the pre-generated vocab\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 8\nlower: 'true'\nfilter_valid: 'false'\n\nreport_every: 1000\nlog_file: data/keyphrase/meng17/preprocess.magkp_shard10k.log\n\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-keyphrase-magkp_subset.yml",
    "content": "data_type: keyphrase\n\ntrain_src: data/keyphrase/meng17/magkp_Nsmall/magkp_Nsmall_train.src\ntrain_tgt: data/keyphrase/meng17/magkp_Nsmall/magkp_Nsmall_train.tgt\nvalid_src: data/keyphrase/meng17/kp20k/kp20k_valid.src\nvalid_tgt: data/keyphrase/meng17/kp20k/kp20k_valid.tgt\n\nsave_data: data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\nshard_size: 10000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_vocab: data/keyphrase/meng17/magkp20k.vocab.pt # use the pre-generated vocab\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 8\nlower: 'true'\nfilter_valid: 'false'\n\nreport_every: 1000\nlog_file: data/keyphrase/meng17/preprocess.magkp_Nsmall_shard10k.log\n\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-keyphrase-small.yml",
    "content": "data_type: keyphrase\n\ntrain_src: data/keyphrase/meng17/kp20k_small/kp20k_small_train.src\ntrain_tgt: data/keyphrase/meng17/kp20k_small/kp20k_small_train.tgt\nvalid_src: data/keyphrase/meng17/kp20k_small/kp20k_small_valid.src\nvalid_tgt: data/keyphrase/meng17/kp20k_small/kp20k_small_valid.tgt\n\nsave_data: data/keyphrase/meng17/kp20k_small\nshard_size: 100000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_seq_length_trunc: 300\ntgt_seq_length_trunc: 8\nlower: 'true'\nfilter_valid: 'false'\n\nreport_every: 1000\nlog_file: data/keyphrase/kp20k/preprocess.small.log\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-keyphrase-stackexchange.yml",
    "content": "data_type: keyphrase\n\ntrain_src: data/keyphrase/meng17/stackexchange/stackexchange_train.src\ntrain_tgt: data/keyphrase/meng17/stackexchange/stackexchange_train.tgt\nvalid_src: data/keyphrase/meng17/stackexchange/stackexchange_valid.src\nvalid_tgt: data/keyphrase/meng17/stackexchange/stackexchange_valid.tgt\n\nsave_data: data/keyphrase/meng17/stackexchange\nshard_size: 100000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_seq_length_trunc: 500\ntgt_seq_length_trunc: 8\nlower: 'true'\nfilter_valid: 'false'\n\nreport_every: 1000\nlog_file: data/keyphrase/meng17/preprocess.stackexchange.log\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-mt_demo.yml",
    "content": "data_type: text\ntrain_src: data/mt/demo/src-train.txt\ntrain_tgt: data/mt/demo/tgt-train.txt\nvalid_src: data/mt/demo/src-val.txt\nvalid_tgt: data/mt/demo/tgt-val.txt\n\nsave_data: data/mt/demo/demo\nshard_size: 1000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_seq_length: 10000\ntgt_seq_length: 10000\nsrc_seq_length_trunc: 400\ntgt_seq_length_trunc: 100\nlower: 'true'\n\nreport_every: 1000\nlog_file: data/mt/demo/preprocess.log\n"
  },
  {
    "path": "config/empirical/preprocess/config-preprocess-summarization.yml",
    "content": "data_type: text\ntrain_src: data/summarization/cnndm/train.txt.src\ntrain_tgt: data/summarization/cnndm/train.txt.tgt.tagged\nvalid_src: data/summarization/cnndm/val.txt.src\nvalid_tgt: data/summarization/cnndm/val.txt.tgt.tagged\n\nsave_data: data/summarization/cnndm/cnndm\nshard_size: 100000\ndynamic_dict: 'true'\nshare_vocab: 'true'\n\nsrc_seq_length: 10000\ntgt_seq_length: 10000\nsrc_seq_length_trunc: 400\ntgt_seq_length_trunc: 100\nlower: 'true'\n\nreport_every: 1000\nlog_file: data/summarization/cnndm/preprocess.log\n"
  },
  {
    "path": "config/empirical/test/config-test-keyphrase-one2one.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\n\n# Data options\ndata_type: keyphrase\n#src: data/keyphrase/kp20k/kp20k_test_small.meng17.lower.src\n#tgt: data/keyphrase/kp20k/kp20k_test_small.meng17.lower.tgt\nshard_size: 0\n#output: output/pred/kp20k/kp20k.one2one_step_40000.pred\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'false'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\n#beam_size: 32\n#beam_size: 200\nmin_length: 1\n#max_length: 6\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\n#batch_size: 16\n#gpu: 0\ngpu: -1\n\ntgt_type: multiple"
  },
  {
    "path": "config/empirical/test/config-test-keyphrase-one2seq.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\n\n# Data options\ndata_type: keyphrase\n#src: data/keyphrase/kp20k/kp20k_test_small.meng17.lower.src\n#tgt: data/keyphrase/kp20k/kp20k_test_small.meng17.lower.tgt\nshard_size: 0\n#output: output/pred/kp20k/kp20k.one2one_step_40000.pred\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'false'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\n#batch_size: 32\n#gpu: 0\ngpu: -1\n\ntgt_type: multiple\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/empirical/train/jsonl/config-rnn-keyphrase.yml",
    "content": "data_type: keyphrase\ntgt_type: random\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-diverse-local\nexp_dir: output/meng17-one2seq-debug/kp20k-one2seq-birnn-GRU150-EMB100-diverse-local\nwandb_project: kp20k-meng17-one2seq\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n#data_format: jsonl\n#data: data/keyphrase/jsonl_tokenized/kp20k/\ndata_format: pt\nvocab: data/keyphrase/meng17/kp20k.vocab.pt\ndata: data/keyphrase/meng17/kp20k\n#save_checkpoint_steps: 10000\n#keep_checkpoint: 20\nseed: 3435\n#train_steps: 100000\n#valid_steps: 20000\nreport_every: 10\n\nencoder_type: brnn\nrnn_type: GRU\n#word_vec_size: 100\n#rnn_size: 150\n#layers: 1\n\noptim: adam\n#learning_rate: 0.002\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1\n\n#batch_size: 128\n#valid_batch_size: 128\n#dropout: 0.0\nbatch_type: sents\nnormalization: sents\n\nglobal_attention: mlp\n#copy_attn: 'true'\n#reuse_copy_attn: 'true'\n#bridge: 'true'\ntensorboard: 'false'\nlog_file_level: DEBUG\n\nworld_size: 1\ngpu_ranks:\n#[]\n- 0\n#- 1\n#master_port: 5000\n"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase-magkp.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/magkp/magkp_train.one2many\nsave_model: models/magkp/magkp.one2one.birnn\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 256\nvalid_batch_size: 128\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\ncopy_loss_by_seqlength: 'true'\nbridge: 'true'\n\nlog_file: output/magkp.one2one.birnn.log\nlog_file_level: DEBUG\nexp: magkp-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout0.0\ntensorboard: 'true'\ntensorboard_log_dir: runs/magkp.one2one.birnn/\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 5001"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase-one2one-stackexchange.yml",
    "content": "model_type: keyphrase\ntgt_type: one2one\n\n\ndata: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 1\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 8\nvalid_batch_size: 128\ndropout: 0.0\n\nglobal_attention: mlp\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nexp: kp20k-one2one-birnn-GRU150-EMB100-ATTNmlp-local\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.local\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.local.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.local/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\n#master_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase-one2seq-diverse.yml",
    "content": "model_type: keyphrase\ntgt_type: verbatim_append\n\ndata: data/keyphrase/meng17/kp20k\n# data: data/keyphrase/meng17/kp20k_small\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 5000\nkeep_checkpoint: 10000\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 200000 # no validation\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adam\nlearning_rate: 1e-3\nmax_grad_norm: 2\n\nbatch_size: 64\nvalid_batch_size: 128\ndropout: 0.1\n\nglobal_attention: mlp\n\ntensorboard: 'true'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nbridge: 'true'\n\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'true'\nlambda_sem_cov: 0.1\n\ntgt_enc: 'rnn'\ndetach_tgt_enc: 'true'\nnum_negsample: 16\nuse_ending_state: 'true'\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00\nlog_file: output/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase-one2seq.yml",
    "content": "model_type: keyphrase\ntgt_type: random\n\ndata: data/keyphrase/meng17/kp20k\n#data: data/keyphrase/meng17/kp20k_small\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 8\nvalid_batch_size: 128\ndropout: 0.0\n\nglobal_attention: mlp\n\ntensorboard: 'true'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\nposition_encoding: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\ncopy_loss_by_seqlength: 'false'\nshare_embeddings: 'true'\nbridge: 'true'\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00\nlog_file: output/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase.drop00.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout00\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase.drop05.coverage.noreuse.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'false'\nbridge: 'true'\ncoverage_attn: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05-CovATT-NoReuse\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5003"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase.drop05.coverage.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ncoverage_attn: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.log\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05-CovATT\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5002"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-keyphrase.drop05.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.log\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5001"
  },
  {
    "path": "config/empirical/train/pt/1st/config-rnn-summarization.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 10000\nreport_every: 100\n\nencoder_type: brnn\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 16\nvalid_batch_size: 16\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\ncopy_loss_by_seqlength: 'true'\nbridge: 'true'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-base-1GPU.yml",
    "content": "data: exp/dataset.de-en\nsave_model: exp/model.de-en\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 500000\nvalid_steps: 10000\nwarmup_steps: 8000\nreport_every: 100\n\ndecoder_type: transformer\nencoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\ntransformer_ff: 2048\nheads: 8\n\naccum_count: 8\noptim: adam\nadam_beta1: 0.9\nadam_beta2: 0.998\ndecay_method: noam\nlearning_rate: 2.0\nmax_grad_norm: 0.0\n\nbatch_size: 4096\nbatch_type: tokens\nnormalization: tokens\ndropout: 0.1\nlabel_smoothing: 0.1\n\nmax_generator_batches: 2\n\nparam_init: 0.0\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\nworld_size: 1\ngpu_ranks:\n- 0\n\n"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-base-4GPU.yml",
    "content": "data: exp/dataset.de-en\nsave_model: exp/model.de-en\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 10000\nwarmup_steps: 8000\nreport_every: 100\n\ndecoder_type: transformer\nencoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\ntransformer_ff: 2048\nheads: 8\n\naccum_count: 2\noptim: adam\nadam_beta1: 0.9\nadam_beta2: 0.998\ndecay_method: noam\nlearning_rate: 2.0\nmax_grad_norm: 0.0\n\nbatch_size: 4096\nbatch_type: tokens\nnormalization: tokens\ndropout: 0.1\nlabel_smoothing: 0.1\n\nmax_generator_batches: 2\n\nparam_init: 0.0\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n\n"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-keyphrase-local.yml",
    "content": "model_type: keyphrase\n#tgt_type: one2one\ntgt_type: no_sort\n\n#data: data/keyphrase/kp20k/kp20k.one2one\ndata: data/keyphrase/kp20k/kp20k.one2many.small.meng17\nsave_model: models/kp20k/kp20k.one2one.transformer\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n#batch_size: 4096\nbatch_size: 1000\n#batch_size: 24576\nvalid_batch_size: 20\n\ntrain_steps: 200000\nvalid_steps: 10\nreport_every: 10\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nlog_file: output/kp20k/kp20k.one2one.transformer.log\nlog_file_level: DEBUG\nexp: keyphrase-one2one-transformer-Layer4-Dim512-Emb512--Dropout0.2\ntensorboard: 'true'\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks: []\nmaster_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-keyphrase-magkp.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/magkp/magkp_train.one2many\nsave_model: models/magkp/magkp.one2one.transformer\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 400000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nlog_file: output/magkp.one2one.transformer.log\nlog_file_level: DEBUG\nexp: magkp-one2one-transformer-Layer4-Dim512-Emb512--Dropout0.2\ntensorboard: 'true'\ntensorboard_log_dir: runs/magkp.one2one.transformer/\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\nmaster_port: 10001"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-keyphrase-test.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/magkp\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 300000\nvalid_steps: 10000\nreport_every: 100\n\n#dropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\nlog_file_level: DEBUG\ntensorboard: 'true'\n\nexp: kp20k-one2one-transformer-Layer4-Dim512-Emb512-Dropout0.2\nsave_model: models/kp20k/kp20k.one2one.transformer\nlog_file: output/kp20k.one2one.transformer.log\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-keyphrase.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 300000\nvalid_steps: 10000\nreport_every: 100\n\n#dropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\nlog_file_level: DEBUG\ntensorboard: 'true'\n\nexp: kp20k-one2one-transformer-Layer4-Dim512-Emb512-Dropout0.2\nsave_model: models/kp20k/kp20k.one2one.transformer\nlog_file: output/kp20k.one2one.transformer.log\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-summarization-local.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n#batch_size: 4096\nbatch_size: 256\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\nvalid_batch_size: 16\naccum_count: 4\n\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nworld_size: 1\ngpu_ranks: []\n"
  },
  {
    "path": "config/empirical/train/pt/1st/config-transformer-summarization.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n#batch_size: 4096\nbatch_size: 256\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\nvalid_batch_size: 16\naccum_count: 4\n\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2one-transformer-copycovfalse-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-one2one-transformer-L6H8-Copyfalse-Covfalse\nexp_dir: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nsave_model: models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nlog_file: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: one2one\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'false'\nreuse_copy_attn: 'false'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2one-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-one2one-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: one2one\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2one-transformer-magkp+kp20kFT.yml",
    "content": "exp: kpgen-meng17-magkp_kp20k_finetune-one2one-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: one2one\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2one-transformer-magkp.yml",
    "content": "exp: kpgen-meng17-magkp-one2one-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: one2one\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2one-transformer-magkp20k.yml",
    "content": "exp: kpgen-meng17-magkp20k-one2one-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: one2one\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-alphabetical-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-alphabetical-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: alphabetical \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-alphabetical_reverse-rnn-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: alphabetical_reverse\n\ndata: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard100k/\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.0\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2.0\n\nbatch_size: 64\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-alphabetical_reverse-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: alphabetical_reverse\n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-length-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-length-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: length \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-length_reverse-rnn-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: length_reverse\n\ndata: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard100k/\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.0\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2.0\n\nbatch_size: 64\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-length_reverse-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-length_reverse-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: length_reverse \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-no_sort-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-no_sort-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: no_sort \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-no_sort_reverse-rnn-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: no_sort_reverse\n\ndata: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard100k/\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.0\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2.0\n\nbatch_size: 64\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-no_sort_reverse-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: no_sort_reverse \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-random-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-random-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: random \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_LN+kp20kFT.yml",
    "content": "exp: kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_300000.pt\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nreset_optim: 'all'\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 1.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_LN.yml",
    "content": "exp: kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge+kp20kFT.yml",
    "content": "exp: kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_300000.pt\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nreset_optim: 'all'\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 1.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge.yml",
    "content": "exp: kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall+kp20kFT.yml",
    "content": "exp: kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_300000.pt\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nreset_optim: 'all'\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 1.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall.yml",
    "content": "exp: kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-copycovfalse-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-Copyfalse-Covfalse\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'false'\nreuse_copy_attn: 'false'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-copycovfalse-magkp20k.yml",
    "content": "exp: kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-Copyfalse-Covfalse\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'false'\nreuse_copy_attn: 'false'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_LN.yml",
    "content": "exp: kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nlarge.yml",
    "content": "exp: kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n#   - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nsmall.yml",
    "content": "exp: kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp+kp20kFT.yml",
    "content": "exp: kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_300000.pt\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nreset_optim: 'all'\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 1.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp.yml",
    "content": "exp: kpgen-meng17-magkp-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp20k+kp20kFT.yml",
    "content": "exp: kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_300000.pt\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nreset_optim: 'all'\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 1.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp20k.yml",
    "content": "exp: kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_append \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/3rd/config-kpgen-one2seq-verbatim_prepend-transformer-kp20k.yml",
    "content": "exp: kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-Copytrue-Covtrue\nexp_dir: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nsave_model: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\nlog_file: output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue/log.txt\nwandb_project: kp20k-meng17-one2one\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: verbatim_prepend \n\nmulti_dataset: true\nshuffle_shards: true\ndata: none\nvocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.pt\ndata_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/\n#  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_LN_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nsmall_pt_shard10k/\n  # - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp_Nlarge_pt_shard10k/\n\ndata_weights:\n  - 1\n#  - 1\n  # - 1\n  # - 1\n  # - 1\n\nvalid_data_ids:\n  - /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/kp20k_pt_shard10k/valid\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\nvalid_batch_size: 64\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase-magkp.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/magkp/magkp_train.one2many\nsave_model: models/magkp/magkp.one2one.birnn\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 256\nvalid_batch_size: 128\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\ncopy_loss_by_seqlength: 'true'\nbridge: 'true'\n\nlog_file: output/magkp.one2one.birnn.log\nlog_file_level: DEBUG\nexp: magkp-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout0.0\ntensorboard: 'true'\ntensorboard_log_dir: runs/magkp.one2one.birnn/\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 5001"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase-one2one-stackexchange.yml",
    "content": "model_type: keyphrase\ntgt_type: one2one\n\n\ndata: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 1\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 8\nvalid_batch_size: 128\ndropout: 0.0\n\nglobal_attention: mlp\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nexp: kp20k-one2one-birnn-GRU150-EMB100-ATTNmlp-local\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.local\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.local.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.local/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\n#master_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase-one2seq-diverse.yml",
    "content": "model_type: keyphrase\ntgt_type: verbatim_append\n\ndata: data/keyphrase/meng17/kp20k\n# data: data/keyphrase/meng17/kp20k_small\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 5000\nkeep_checkpoint: 10000\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 200000 # no validation\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adam\nlearning_rate: 1e-3\nmax_grad_norm: 2\n\nbatch_size: 64\nvalid_batch_size: 128\ndropout: 0.1\n\nglobal_attention: mlp\n\ntensorboard: 'true'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nbridge: 'true'\n\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'true'\nlambda_sem_cov: 0.1\n\ntgt_enc: 'rnn'\ndetach_tgt_enc: 'true'\nnum_negsample: 16\nuse_ending_state: 'true'\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00\nlog_file: output/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase-one2seq-localtest.yml",
    "content": "model_type: keyphrase\ndata_type: keyphrase\ndata_format: pt\ntgt_type: length_reverse\n\ntrain_from: models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000.pt\n\ndata: data/keyphrase/meng17/kp20k_pt_shard10k/\n#data: data/keyphrase/meng17/kp20k_pt_shard100k/kp20k\n#data: data/keyphrase/meng17/kp20k_small\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 5000\nkeep_checkpoint: 10000\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 200000 # no validation\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adam\nlearning_rate: 1e-3\nmax_grad_norm: 2\nreset_optim: all\n\nbatch_size: 64\nvalid_batch_size: 128\ndropout: 0.1\n\nglobal_attention: mlp\n\ntensorboard: 'true'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nbridge: 'true'\n\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'false'\nlambda_sem_cov: 0.0\n\ntgt_enc: 'rnn'\ndetach_tgt_enc: 'true'\nnum_negsample: 16\nuse_ending_state: 'true'\n\nexp: 'kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-test'\nexp_dir: 'output/keyphrase/meng17-one2seq-test/kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-test/'\nsave_model: 'models/keyphrase/meng17-one2seq-test/kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-test'\nlog_file: 'output/keyphrase/meng17-one2seq-test/kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-test.log'\ntensorboard_log_dir: 'runs/keyphrase/meng17-one2seq-test/kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-test/'\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase-one2seq.yml",
    "content": "model_type: keyphrase\ntgt_type: random\n\ndata: data/keyphrase/meng17/kp20k\n#data: data/keyphrase/meng17/kp20k_small\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 8\nvalid_batch_size: 128\ndropout: 0.0\n\nglobal_attention: mlp\n\ntensorboard: 'true'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\nposition_encoding: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\ncopy_loss_by_seqlength: 'false'\nshare_embeddings: 'true'\nbridge: 'true'\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00\nlog_file: output/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17-one2seq/kp20k.one2seq.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks: []\n#- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase.drop00.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout00\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout00\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout00.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout00/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase.drop05.coverage.noreuse.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'false'\nbridge: 'true'\ncoverage_attn: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05-CovATT-NoReuse\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse.log\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.NoReuse/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5003"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase.drop05.coverage.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ncoverage_attn: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT.log\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05-CovATT\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.CovATT/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5002"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-keyphrase.drop05.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 20\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.5\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\nbridge: 'true'\ntensorboard: 'true'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\ncontext_gate: 'both'\ninput_feed: 1\nshare_embeddings: 'true'\nposition_encoding: 'true'\ncopy_loss_by_seqlength: 'false'\n\nsave_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout05\nlog_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout05.log\nexp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout05\ntensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout05/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 5001"
  },
  {
    "path": "config/empirical/train/pt/base/config-rnn-summarization.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 100000\nvalid_steps: 10000\nreport_every: 100\n\nencoder_type: brnn\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\n\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2\n\nbatch_size: 16\nvalid_batch_size: 16\ndropout: 0.0\n\ncopy_attn: 'true'\nglobal_attention: mlp\nreuse_copy_attn: 'true'\ncopy_loss_by_seqlength: 'true'\nbridge: 'true'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-base-1GPU.yml",
    "content": "data: exp/dataset.de-en\nsave_model: exp/model.de-en\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 500000\nvalid_steps: 10000\nwarmup_steps: 8000\nreport_every: 100\n\ndecoder_type: transformer\nencoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\ntransformer_ff: 2048\nheads: 8\n\naccum_count: 8\noptim: adam\nadam_beta1: 0.9\nadam_beta2: 0.998\ndecay_method: noam\nlearning_rate: 2.0\nmax_grad_norm: 0.0\n\nbatch_size: 4096\nbatch_type: tokens\nnormalization: tokens\ndropout: 0.1\nlabel_smoothing: 0.1\n\nmax_generator_batches: 2\n\nparam_init: 0.0\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\nworld_size: 1\ngpu_ranks:\n- 0\n\n"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-base-4GPU.yml",
    "content": "data: exp/dataset.de-en\nsave_model: exp/model.de-en\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 10000\nwarmup_steps: 8000\nreport_every: 100\n\ndecoder_type: transformer\nencoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\ntransformer_ff: 2048\nheads: 8\n\naccum_count: 2\noptim: adam\nadam_beta1: 0.9\nadam_beta2: 0.998\ndecay_method: noam\nlearning_rate: 2.0\nmax_grad_norm: 0.0\n\nbatch_size: 4096\nbatch_type: tokens\nnormalization: tokens\ndropout: 0.1\nlabel_smoothing: 0.1\n\nmax_generator_batches: 2\n\nparam_init: 0.0\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n\n"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-keyphrase-local.yml",
    "content": "model_type: keyphrase\n#tgt_type: one2one\ntgt_type: no_sort\n\n#data: data/keyphrase/kp20k/kp20k.one2one\ndata: data/keyphrase/kp20k/kp20k.one2many.small.meng17\nsave_model: models/kp20k/kp20k.one2one.transformer\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n#batch_size: 4096\nbatch_size: 1000\n#batch_size: 24576\nvalid_batch_size: 20\n\ntrain_steps: 200000\nvalid_steps: 10\nreport_every: 10\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nlog_file: output/kp20k/kp20k.one2one.transformer.log\nlog_file_level: DEBUG\nexp: keyphrase-one2one-transformer-Layer4-Dim512-Emb512--Dropout0.2\ntensorboard: 'true'\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks: []\nmaster_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-keyphrase-magkp.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/magkp/magkp_train.one2many\nsave_model: models/magkp/magkp.one2one.transformer\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 400000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nlog_file: output/magkp.one2one.transformer.log\nlog_file_level: DEBUG\nexp: magkp-one2one-transformer-Layer4-Dim512-Emb512--Dropout0.2\ntensorboard: 'true'\ntensorboard_log_dir: runs/magkp.one2one.transformer/\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\nmaster_port: 10001"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-keyphrase-test.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/magkp\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 300000\nvalid_steps: 10000\nreport_every: 100\n\n#dropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\nlog_file_level: DEBUG\ntensorboard: 'true'\n\nexp: kp20k-one2one-transformer-Layer4-Dim512-Emb512-Dropout0.2\nsave_model: models/kp20k/kp20k.one2one.transformer\nlog_file: output/kp20k.one2one.transformer.log\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-keyphrase.yml",
    "content": "model_type: keyphrase\n\ndata: data/keyphrase/meng17/kp20k\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\naccum_count: 4\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\nvalid_batch_size: 256\n\ntrain_steps: 300000\nvalid_steps: 10000\nreport_every: 100\n\n#dropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\nlog_file_level: DEBUG\ntensorboard: 'true'\n\nexp: kp20k-one2one-transformer-Layer4-Dim512-Emb512-Dropout0.2\nsave_model: models/kp20k/kp20k.one2one.transformer\nlog_file: output/kp20k.one2one.transformer.log\ntensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-summarization-local.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n#batch_size: 4096\nbatch_size: 256\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\nvalid_batch_size: 16\naccum_count: 4\n\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nworld_size: 1\ngpu_ranks: []\n"
  },
  {
    "path": "config/empirical/train/pt/base/config-transformer-summarization.yml",
    "content": "data: data/summarization/cnndm/cnndm\nsave_model: models/cnndm\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 4\n\nposition_encoding: true\n\noptim: adam\nlearning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n#batch_size: 4096\nbatch_size: 256\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 2\nvalid_batch_size: 16\naccum_count: 4\n\ntrain_steps: 200000\nvalid_steps: 10000\nreport_every: 100\n\ndropout: 0.2\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nparam_init_glorot: 'true'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n"
  },
  {
    "path": "config/empirical/train/pt/config-rnn-keyphrase-crc.yml",
    "content": "model_type: keyphrase\n\n#data: data/keyphrase/meng17/kp20k\n#save_checkpoint_steps: 10000\n#keep_checkpoint: 20\nseed: 3435\n#train_steps: 100000\n#valid_steps: 20000\nreport_every: 100\n\nencoder_type: brnn\nrnn_type: GRU\n#word_vec_size: 100\n#rnn_size: 150\n#layers: 1\n\noptim: adagrad\n#learning_rate: 0.002\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 2.0\n\n#batch_size: 128\n#valid_batch_size: 128\n#dropout: 0.0\nbatch_type: sents\nnormalization: sents\n\nglobal_attention: mlp\n#copy_attn: 'true'\n#reuse_copy_attn: 'true'\n#bridge: 'true'\ntensorboard: 'false'\nlog_file_level: DEBUG\n\n# new added on April 17, 2019\n#context_gate: 'both'\n#input_feed: 1\n#share_embeddings: 'true'\n#position_encoding: 'true'\n#copy_loss_by_seqlength: 'false'\n\n#exp: kp20k-one2one-birnn-GRU512-EMB128-ATTNmlp-Dropout00\n#save_model: models/keyphrase/meng17/kp20k.one2one.birnn.Dropout00\n#log_file: output/keyphrase/meng17/kp20k.one2one.birnn.Dropout00.log\n#tensorboard_log_dir: runs/keyphrase/meng17/kp20k.one2one.birnn.Dropout00/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#master_port: 5000"
  },
  {
    "path": "config/empirical/train/pt/config-rnn-keyphrase-one2seq-debug.yml",
    "content": "model_type: keyphrase\ntgt_type: random\n\nexp: kp20k-one2seq-birnn-GRU150-EMB100-diverse-test\nexp_dir: output/meng17-one2seq-debug/kp20k-one2seq-birnn-GRU150-EMB100-diverse-test\nwandb_project: kp20k-meng17-one2seq\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n#data: data/keyphrase/meng17/kp20k\ndata: data/keyphrase/meng17/magkp_pt_shard1k/\nvocab: data/keyphrase/meng17/magkp20k.vocab.pt\n#data: data/keyphrase/meng17/stackexchange\nsave_checkpoint_steps: 10000\nkeep_checkpoint: 10\nseed: 3435\ntrain_steps: 200000\nvalid_steps: 100000\nreport_every: 10\n\nencoder_type: brnn\nrnn_type: GRU\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\n\noptim: adam\nlearning_rate: 1e-3\nmax_grad_norm: 2\n\nbatch_size: 128\nvalid_batch_size: 128\ndropout: 0.0\nbatch_type: sents\nnormalization: sents\n\nglobal_attention: mlp\n\ntensorboard: 'false'\nlog_file_level: DEBUG\n\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\nposition_encoding: 'true'\n\ncontext_gate: 'both'\ninput_feed: 1\ncopy_loss_by_seqlength: 'false'\nshare_embeddings: 'true'\nbridge: 'true'\n\north_reg: 'false'\nlambda_orth_reg: 0.1\nsem_cov: 'false'\nlambda_sem_cov: 1.0\n\nnum_negsample: 32\nuse_ending_state: 'true'\ntgt_enc: 'rnn'\ndetach_tgt_enc: 'true'\n\nworld_size: 1\ngpu_ranks: #[]\n  - 0\n#- 1\nmaster_port: 5000\n"
  },
  {
    "path": "config/empirical/train/pt/config-transformer-keyphrase-crc.yml",
    "content": "model_type: keyphrase\n\n#data: data/keyphrase/meng17/kp20k\n#save_checkpoint_steps: 10000\n#keep_checkpoint: 40\nseed: 3435\n\nencoder_type: transformer\ndecoder_type: transformer\n#word_vec_size: 512\n#rnn_size: 512\n#layers: 4\n\nposition_encoding: true\n\noptim: adam\n#learning_rate: 2\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\n#batch_size: 4096\n#batch_size: 8192\n#batch_size: 24576\n#valid_batch_size: 256\n\n#train_steps: 300000\n#valid_steps: 10000\nreport_every: 100\n\n#dropout: 0.2\n\n#share_embeddings: 'true'\n#copy_attn: 'true'\nparam_init_glorot: 'true'\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#exp: kp20k-one2one-transformer-Layer4-Dim512-Emb512-Dropout0.2\n#save_model: models/kp20k/kp20k.one2one.transformer\n#log_file: output/kp20k.one2one.transformer.log\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "config/transfer_kp/infer/keyphrase-one2one.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata:\n    valid:\n        path_src: none\n        path_tgt: none\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        weight: 1\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\nbeam_terminate: full\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n\n# Data options\n#model_dtype: fp16\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\n\n# Evaluation options\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\n#gpu: 0\ngpu: -1\n\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/transfer_kp/infer/keyphrase-one2seq-controlled.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata:\n    valid:\n        path_src: none\n        path_tgt: none\n        path_align: none\n        type: keyphrase\n        transforms: [keyphrase, add_control_prefix, roberta_tokenize_kpg]\n        weight: 1\n\n### Transform related opts:\n#### Keyphrase specific\nsrc_control_prefix: '<present>10<header>5<category>5<seealso>2<infill>0<s>'\ntgt_control_prefix: ''\n\n\nkp_concat_type: pres_abs\nbeam_terminate: full\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n\n# Data options\n#model_dtype: fp16\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\n\n# Evaluation options\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\n#gpu: 0\ngpu: -1\n\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/transfer_kp/infer/keyphrase-one2seq.yml",
    "content": "# Translation / inference options\n#model: models/keyphrase/kp20k/kp20k.one2one.birnn.test_step_40000.pt\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nmodel_type: keyphrase\ndata_type: keyphrase\ndata:\n    valid:\n        path_src: none\n        path_tgt: none\n        path_align: none\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        weight: 1\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nuse_given_inputs: false\nlowercase: false\n\nbeam_terminate: full\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n\n# Data options\n#model_dtype: fp16\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\n\n# Evaluation options\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\n\n# Beam search\n#beam_size: 8\nmin_length: 1\n#max_length: 32\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Decoding\nrandom_sampling_temp: 0.0\n\n# Efficiency\n#gpu: 0\ngpu: -1\n\nseed: 97\n#beam_terminate: full/topbeam"
  },
  {
    "path": "config/translate/config-rnn-keyphrase.yml",
    "content": "# Translation / inference options\nmodel: models/keyphrase/meng17/kp20k.one2one.birnn.test_step_100000.pt\n\n# Data options\ndata_type: keyphrase\nsrc: data/keyphrase/meng17/kp20k_small/kp20k_small_test.src\ntgt: data/keyphrase/meng17/kp20k_small/kp20k_small_test.tgt\nshard_size: 0\noutput: output/pred/kp20k/kp20k.one2one_step_100000.pred\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'true'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\nbeam_size: 32\n#beam_size: 200\nmin_length: 1\nmax_length: 6\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'true'\nlog_file: output/pred/kp20k/kp20k.one2one_step_100000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 100\n\n# Efficiency\nbatch_size: 10\ngpu: -1\n\ntgt_type: multiple"
  },
  {
    "path": "docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS    =\nSPHINXBUILD   = python3 -msphinx\nSPHINXPROJ    = OpenNMT-py\nSOURCEDIR     = source\nBUILDDIR      = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n"
  },
  {
    "path": "docs/requirements.txt",
    "content": "sphinx==2.4.*\nsphinxcontrib.bibtex\nsphinxcontrib.mermaid\nsphinx-rtd-theme==0.4.*\nrecommonmark==0.6.*\nsphinx-argparse==0.2.*\nsphinx_markdown_tables==0.0.12\n"
  },
  {
    "path": "docs/source/CONTRIBUTING.md",
    "content": "# Contributors\n\nOpenNMT-py is a community developed project and we love developer contributions.\n\n## Guidelines\nBefore sending a PR, please do this checklist first:\n\n- Please run `onmt/tests/pull_request_chk.sh` and fix any errors. When adding new functionality, also add tests to this script. Included checks:\n    1. flake8 check for coding style;\n    2. unittest;\n    3. continuous integration tests listed in `.travis.yml`.\n- When adding/modifying class constructor, please make the arguments as same naming style as its superclass in PyTorch.\n- If your change is based on a paper, please include a clear comment and reference in the code (more on that below). \n\n### Docstrings\nAbove all, try to follow the Google docstring format\n([Napoleon example](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html),\n[Google styleguide](http://google.github.io/styleguide/pyguide.html)).\nThis makes it easy to include your contributions in the Sphinx documentation. And, do feel free\nto autodoc your contributions in the API ``.rst`` files in the `docs/source` folder! If you do, check that\nyour additions look right.\n\n**How to build the docs locally?**\n```bash\ncd docs\n# install some dependencies if necessary:\n# recommonmark, sphinx_rtd_theme, sphinxcontrib-bibtex\npip install requirements.txt\nmake html\nfirefox build/html/main.html  # or your browser of choice\n```\n\nSome particular advice:\n- Try to follow Python 3 [``typing`` module](https://docs.python.org/3/library/typing.html) conventions when documenting types.\n    - Exception: use \"or\" instead of unions for more readability\n    - For external types, use the full \"import name\". Common abbreviations (e.g. ``np``) are acceptable.\n      For ``torch.Tensor`` types, the ``torch.`` is optional.\n    - Please don't use tics like `` (`str`) `` or rst directives like `` (:obj:`str`) ``. Napoleon handles types\n      very well without additional help, so avoid the clutter.\n- [Google docstrings don't support multiple returns](https://stackoverflow.com/questions/29221551/can-sphinx-napoleon-document-function-returning-multiple-arguments).\nFor multiple returns, the following works well with Sphinx and is still very readable.\n  ```python\n  def foo(a, b):\n      \"\"\"This is my docstring.\n      \n      Args:\n          a (object): Something.\n          b (class): Another thing.\n    \n      Returns:\n          (object, class):\n        \n          * a: Something or rather with a long\n            description that spills over.\n          * b: And another thing.\n      \"\"\"\n    \n      return a, b\n  ```\n- When citing a paper, avoid directly linking in the docstring! Add a Bibtex entry to `docs/source/refs.bib`.\nE.g., to cite \"Attention Is All You Need\", visit [arXiv](https://arxiv.org/abs/1706.03762), choose the\n[bibtext](https://dblp.uni-trier.de/rec/bibtex/journals/corr/VaswaniSPUJGKP17) link, search `docs/source/refs.bib`\nusing `CTRL-F` for `DBLP:journals/corr/VaswaniSPUJGKP17`, and if you do not find it then copy-paste the\ncitation into `refs.bib`. Then, in your docstring, use ``:cite:`DBLP:journals/corr/VaswaniSPUJGKP17` ``.\n    - However, a link is better than nothing.\n- Please document tensor shapes. Prefer the format\n  ``` ``(a, b, c)`` ```. This style is easy to read, allows using ``x`` for multplication, and is common\n  (PyTorch uses a few variations on the parentheses format, AllenNLP uses exactly this format, Fairseq uses\n  the parentheses format with single ticks).\n    - Again, a different style is better than no shape documentation.\n- Please avoid unnecessary space characters, try to capitalize, and try to punctuate.\n    \n  For multi-line docstrings, add a blank line after the closing ``\"\"\"``.\n  Don't use a blank line before the closing quotes.\n  \n  ``\"\"\" not this \"\"\"`` ``\"\"\"This.\"\"\"``\n  \n  ```python\n  \"\"\"\n      Not this.\n  \"\"\"\n  ```\n  ```python\n  \"\"\"This.\"\"\"\n  ```\n\n  This note is the least important. Focus on content first, but remember that consistent docs look good.\n- Be sensible about the first line. Generally, one stand-alone summary line (per the Google guidelines) is good.\n  Sometimes, it's better to cut directly to the args or an extended description. It's always acceptable to have a\n  \"trailing\" citation."
  },
  {
    "path": "docs/source/FAQ.md",
    "content": "\nAll the example YAML configurations are partial. To get an overview of what this YAML configuration is you can start by reading the [Quickstart](quickstart) section.\n\n## How do I use Pretrained embeddings (e.g. GloVe)?\n\nThis is handled in the initial steps of the `onmt_train` execution.\n\nPretrained embeddings can be configured in the main YAML configuration file.\n\n### Example\n\n1. Get GloVe files:\n\n```bash\nmkdir \"glove_dir\"\nwget http://nlp.stanford.edu/data/glove.6B.zip\nunzip glove.6B.zip -d \"glove_dir\"\n```\n\n2. Adapt the configuration:\n\n```yaml\n# <your_config>.yaml\n\n<Your data config...>\n\n...\n\n# this means embeddings will be used for both encoder and decoder sides\nboth_embeddings: glove_dir/glove.6B.100d.txt\n# to set src and tgt embeddings separately:\n# src_embeddings: ...\n# tgt_embeddings: ...\n\n# supported types: GloVe, word2vec\nembeddings_type: \"GloVe\"\n\n# word_vec_size need to match with the pretrained embeddings dimensions\nword_vec_size: 100\n\n```\n\n3. Train:\n\n```bash\nonmt_train -config <your_config>.yaml\n```\n\nNotes:\n\n- the matched embeddings will be saved at `<save_data>.enc_embeddings.pt` and `<save_data>.dec_embeddings.pt`;\n- additional flags `freeze_word_vecs_enc` and `freeze_word_vecs_dec` are available to freeze the embeddings.\n\n## How do I use the Transformer model?\n\nThe transformer model is very sensitive to hyperparameters. To run it\neffectively you need to set a bunch of different options that mimic the [Google](https://arxiv.org/abs/1706.03762) setup. We have confirmed the following configuration can replicate their WMT results.\n\n```yaml\n<data configuration>\n...\n\n# General opts\nsave_model: foo\nsave_checkpoint_steps: 10000\nvalid_steps: 10000\ntrain_steps: 200000\n\n# Batching\nqueue_size: 10000\nbucket_size: 32768\nworld_size: 4\ngpu_ranks: [0, 1, 2, 3]\nbatch_type: \"tokens\"\nbatch_size: 4096\nvalid_batch_size: 8\nmax_generator_batches: 2\naccum_count: [4]\naccum_steps: [0]\n\n# Optimization\nmodel_dtype: \"fp32\"\noptim: \"adam\"\nlearning_rate: 2\nwarmup_steps: 8000\ndecay_method: \"noam\"\nadam_beta2: 0.998\nmax_grad_norm: 0\nlabel_smoothing: 0.1\nparam_init: 0\nparam_init_glorot: true\nnormalization: \"tokens\"\n\n# Model\nencoder_type: transformer\ndecoder_type: transformer\nposition_encoding: true\nenc_layers: 6\ndec_layers: 6\nheads: 8\nrnn_size: 512\nword_vec_size: 512\ntransformer_ff: 2048\ndropout_steps: [0]\ndropout: [0.1]\nattention_dropout: [0.1]\n```\n\nHere are what the most important parameters mean:\n\n* `param_init_glorot` & `param_init 0`: correct initialization of parameters;\n* `position_encoding`: add sinusoidal position encoding to each embedding;\n* `optim adam`, `decay_method noam`, `warmup_steps 8000`: use special learning rate;\n* `batch_type tokens`, `normalization tokens`: batch and normalize based on number of tokens and not sentences;\n* `accum_count 4`: compute gradients based on four batches;\n* `label_smoothing 0.1`: use label smoothing loss.\n\n## Do you support multi-gpu?\n\nFirst you need to make sure you `export CUDA_VISIBLE_DEVICES=0,1,2,3`.\n\nIf you want to use GPU id 1 and 3 of your OS, you will need to `export CUDA_VISIBLE_DEVICES=1,3`\n\nBoth `-world_size` and `-gpu_ranks` need to be set. E.g. `-world_size 4 -gpu_ranks 0 1 2 3` will use 4 GPU on this node only.\n\n**Warning - Deprecated**\n\nMulti-node distributed training is not properly implemented in OpenNMT-py 2.0 yet.\n\nIf you want to use 2 nodes with 2 GPU each, you need to set `-master_ip` and `-master_port`, and\n\n* `-world_size 4 -gpu_ranks 0 1`: on the first node\n* `-world_size 4 -gpu_ranks 2 3`: on the second node\n* `-accum_count 2`: This will accumulate over 2 batches before updating parameters.\n\nIf you use a regular network card (1 Gbps) then we suggest to use a higher `-accum_count` to minimize the inter-node communication.\n\n**Note:**\n\nIn the legacy version, when training on several GPUs, you couldn't have them in 'Exclusive' compute mode (`nvidia-smi -c 3`).\n\nThe multi-gpu setup relied on a Producer/Consumer setup. This setup means there will be `2<n_gpu> + 1` processes spawned, with 2 processes per GPU, one for model training and one (Consumer) that hosts a `Queue` of batches that will be processed next. The additional process is the Producer, creating batches and sending them to the Consumers. This setup is beneficial for both wall time and memory, since it loads data shards 'in advance', and does not require to load it for each GPU process.\n\nThe new codebase allows GPUs to be in exclusive mode, because batches are moved to the device later in the process. Hence, there is no 'producer' process on each GPU.\n\n## How can I ensemble Models at inference?\n\nYou can specify several models in the `onmt_translate` command line: `-model model1_seed1 model2_seed2`\nBear in mind that your models must share the same target vocabulary.\n\n## How can I weight different corpora at training?\n\nThis is naturally embedded in the data configuration format introduced in OpenNMT-py 2.0. Each entry of the `data` configuration will have its own *weight*. When building batches, we'll sequentially take *weight* example from each corpus.\n\n**Note**: don't worry about batch homogeneity/heterogeneity, the pooling mechanism is here for that reason. Instead of building batches one at a time, we will load `pool_factor` of batches worth of examples, sort them by length, build batches and then yield them in a random order.\n\n### Example\n\nIn the following example, we will sequentially sample 7 examples from *corpus_1*, and 3 examples from *corpus_2*, and so on:\n\n```yaml\n# <your_config>.yaml\n\n...\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: toy-ende/src-train1.txt\n        path_tgt: toy-ende/tgt-train1.txt\n        weight: 7\n    corpus_2:\n        path_src: toy-ende/src-train1.txt\n        path_tgt: toy-ende/tgt-train1.txt\n        weight: 3\n    valid:\n        path_src: toy-ende/src-val.txt\n        path_tgt: toy-ende/tgt-val.txt\n...\n\n```\n\n## How can I apply on-the-fly tokenization and subword regularization when training?\n\nThis is naturally embedded in the data configuration format introduced in OpenNMT-py 2.0. Each entry of the `data` configuration will have its own `transforms`. `transforms` basically is a `list` of functions that will be applied sequentially to the examples when read from file.\n\n### Example\n\nThis example applies sentencepiece tokenization with `pyonmttok`, with `nbest=20` and `alpha=0.1`.\n\n```yaml\n# <your_config>.yaml\n\n...\n\n# Tokenization options\nsrc_subword_type: sentencepiece\nsrc_subword_model: examples/subword.spm.model\ntgt_subword_type: sentencepiece\ntgt_subword_model: examples/subword.spm.model\n\n# Number of candidates for SentencePiece sampling\nsubword_nbest: 20\n# Smoothing parameter for SentencePiece sampling\nsubword_alpha: 0.1\n# Specific arguments for pyonmttok\nsrc_onmttok_kwargs: \"{'mode': 'none', 'spacer_annotate': True}\"\ntgt_onmttok_kwargs: \"{'mode': 'none', 'spacer_annotate': True}\"\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: toy-ende/src-train1.txt\n        path_tgt: toy-ende/tgt-train1.txt\n        transforms: [onmt_tokenize]\n        weight: 1\n    valid:\n        path_src: toy-ende/src-val.txt\n        path_tgt: toy-ende/tgt-val.txt\n        transforms: [onmt_tokenize]\n...\n\n```\n\nOther tokenization methods and transforms are readily available. See the dedicated docs for more details.\n\n## What are the readily available on-the-fly data transforms?\n\nIt's your lucky day! We already embedded several transforms that can be used easily.\n\nNote: all the details about every flag and options for each transform can be found in the [train](#train) section.\n\n### General purpose\n\n#### Filter examples by length\n\nTransform name: `filtertoolong`\n\nClass: `onmt.transforms.misc.FilterTooLongTransform`\n\nThe following options can be added to the configuration :\n- `src_seq_length`: maximum source sequence length;\n- `tgt_seq_length`: maximum target sequence length.\n\n#### Add custom prefix to examples\n\nTransform name: `prefix`\n\nClass: `onmt.transforms.misc.PrefixTransform`\n\nFor each dataset that the `prefix` transform is applied to, you can set the additional `src_prefix` and `tgt_prefix` parameters in its data configuration:\n\n```yaml\ndata:\n    corpus_1:\n        path_src: toy-ende/src-train1.txt\n        path_tgt: toy-ende/tgt-train1.txt\n        transforms: [prefix]\n        weight: 1\n        src_prefix: __some_src_prefix__\n        tgt_prefix: __some_tgt_prefix__\n```\n\n\n\n### Tokenization\n\nCommon options for the tokenization transforms are the following:\n\n- `src_subword_model`: path of source side (or both if shared) subword model;\n- `tgt_subword_model`: path of target side subword model;\n- `src_subword_nbest`: number of candidates for subword regularization (sentencepiece), source side;\n- `tgt_subword_nbest`: number of candidates for subword regularization (sentencepiece), target_side;\n- `src_subword_alpha`: smoothing parameter for sentencepiece regularization / dropout probability for BPE, source side;\n- `tgt_subword_alpha`: smoothing parameter for sentencepiece regularization / dropout probability for BPE, target side.\n\n#### [OpenNMT Tokenizer](https://github.com/opennmt/Tokenizer)\n\nTransform name: `onmt_tokenize`\n\nClass: `onmt.transforms.misc.ONMTTokenizerTransform`\n\nAdditional options are available:\n- `src_subword_type`: type of subword model for source side (from `[\"none\", \"sentencepiece\", \"bpe\"]`);\n- `tgt_subword_type`: type of subword model for target side (from `[\"none\", \"sentencepiece\", \"bpe\"]`);\n- `src_onmttok_kwargs`: additional kwargs for pyonmttok Tokenizer class, source side;\n- `tgt_onmttok_kwargs`: additional kwargs for pyonmttok Tokenizer class, target side.\n\n#### [SentencePiece](https://github.com/google/sentencepiece)\n\nTransform name: `sentencepiece`\n\nClass: `onmt.transforms.misc.SentencePieceTransform`\n\nThe `src_subword_model` and `tgt_subword_model` should be valid sentencepiece models.\n\n#### BPE ([subword-nmt](https://github.com/rsennrich/subword-nmt))\n\nTransform name: `bpe`\n\nClass: `onmt.transforms.misc.BPETransform`\n\nThe `src_subword_model` and `tgt_subword_model` should be valid BPE models.\n\n### BART-style noise\n\nBART-style noise is composed of several parts, as described in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461).\n\nThese different types of noise can be controlled with the following options:\n\n- `permute_sent_ratio`: proportion of sentences to permute (default boundaries are \".\", \"?\" and \"!\");\n- `rotate_ratio`: proportion of inputs to permute;\n- `insert_ratio`: proportion of additional random tokens to insert;\n- `random_ratio`: proportion of tokens to replace with random;\n- `mask_ratio`: proportion of words/subwords to mask;\n- `mask_length`: length of masking window (from `[\"subword\", \"word\", \"span-poisson\"]`);\n- `poisson_lambda`: $\\lambda$ value for Poisson distribution to sample span length (in the case of `mask_length` set to `span-poisson`);\n- `replace_length`: when masking N tokens, replace with 0, 1, \" \"or N tokens. (set to -1 for N).\n\n### SwitchOut and sampling\n\n#### [SwitchOut](https://arxiv.org/abs/1808.07512)\n\nTransform name: `switchout`\n\nClass: `onmt.transforms.misc.SwitchOutTransform`\n\nOptions:\n\n- `switchout_temperature`: sampling temperature for SwitchOut.\n\n#### Drop some tokens\n\nTransform name: `tokendrop`\n\nClass: `onmt.transforms.misc.TokenDropTransform`\n\nOptions:\n\n- `tokendrop_temperature`: sampling temperature for token deletion.\n\n#### Mask some tokens\n\nTransform name: `tokenmask`\n\nClass: `onmt.transforms.misc.TokenMaskTransform`\n\nOptions:\n\n- `tokenmask_temperature`: sampling temperature for token masking.\n\n## How can I create custom on-the-fly data transforms?\n\nThe code is easily extendable with custom transforms inheriting from the `Transform` base class.\n\nYou can for instance have a look at the `FilterTooLongTransform` class as a template:\n\n```python\n@register_transform(name='filtertoolong')\nclass FilterTooLongTransform(Transform):\n    \"\"\"Filter out sentence that are too long.\"\"\"\n\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.src_seq_length = opts.src_seq_length\n        self.tgt_seq_length = opts.tgt_seq_length\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to this Transform.\"\"\"\n        group = parser.add_argument_group(\"Transform/Filter\")\n        group.add(\"--src_seq_length\", \"-src_seq_length\", type=int, default=200,\n                  help=\"Maximum source sequence length.\")\n        group.add(\"--tgt_seq_length\", \"-tgt_seq_length\", type=int, default=200,\n                  help=\"Maximum target sequence length.\")\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Return None if too long else return as is.\"\"\"\n        if (len(example['src']) > self.src_seq_length or\n                len(example['tgt']) > self.tgt_seq_length):\n            if stats is not None:\n                stats.filter_too_long()\n            return None\n        else:\n            return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}, {}={}'.format(\n            'src_seq_length', self.src_seq_length,\n            'tgt_seq_length', self.tgt_seq_length\n        )\n```\n\nMethods:\n- `add_options` allows to add custom options that would be necessary for the transform configuration;\n- `apply` is where the transform happens;\n- `_repr_args` is for clean logging purposes.\n\nAs you can see, there is the `@register_transform` wrapper before the class definition. This will allow for the class to be automatically detected (if put in the proper `transforms` folder) and usable in your training configurations through its `name` argument.\n\nThe `example` argument of `apply` is a `dict` of the form:\n```\n{\n\t\"src\": <source string>,\n\t\"tgt\": <target string>,\n\t\"align\": <alignment pharaoh string> # optional\n}\n```\n\nThis is defined in `onmt.inputters.corpus.ParallelCorpus.load`. This class is not easily extendable for now but it can be considered for future developments. For instance, we could create some `CustomParallelCorpus` class that would handle other kind of inputs.\n\n\n## Can I get word alignments while translating?\n\n### Raw alignments from averaging Transformer attention heads\n\nCurrently, we support producing word alignment while translating for Transformer based models. Using `-report_align` when calling `translate.py` will output the inferred alignments in Pharaoh format. Those alignments are computed from an argmax on the average of the attention heads of the *second to last* decoder layer. The resulting alignment src-tgt (Pharaoh) will be pasted to the translation sentence, separated by ` ||| `.\nNote: The *second to last* default behaviour was empirically determined. It is not the same as the paper (they take the *penultimate* layer), probably because of slight differences in the architecture.\n\n* alignments use the standard \"Pharaoh format\", where a pair `i-j` indicates the i<sub>th</sub> word of source language is aligned to j<sub>th</sub> word of target language.\n* Example: {'src': 'das stimmt nicht !'; 'output': 'that is not true ! ||| 0-0 0-1 1-2 2-3 1-4 1-5 3-6'}\n* Using the`-tgt` option when calling `translate.py`, we output alignments between the source and the gold target rather than the inferred target, assuming we're doing evaluation.\n* To convert subword alignments to word alignments, or symetrize bidirectional alignments, please refer to the [lilt scripts](https://github.com/lilt/alignment-scripts).\n\n### Supervised learning on a specific head\n\nThe quality of output alignments can be further improved by providing reference alignments while training. This will invoke multi-task learning on translation and alignment. This is an implementation based on the paper [Jointly Learning to Align and Translate with Transformer Models](https://arxiv.org/abs/1909.02074).\n\nThe data need to be preprocessed with the reference alignments in order to learn the supervised task.\nThe reference alignment file(s) can for instance be generated by [GIZA++](https://github.com/moses-smt/mgiza/) or [fast_align](https://github.com/clab/fast_align).\n\nIn order to learn the supervised task, you can set for each dataset the path of its alignment file in the YAML configuration file:\n\n```yaml\n<your_config>.yaml\n\n...\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: toy-ende/src-train1.txt\n        path_tgt: toy-ende/tgt-train1.txt\n        # src - tgt alignments in pharaoh format\n        path_align: toy-ende/src-tgt.align\n        transforms: []\n        weight: 1\n    valid:\n        path_src: toy-ende/src-val.txt\n        path_tgt: toy-ende/tgt-val.txt\n        transforms: []\n\n...\n```\n\n**Notes**:\n- Most of the transforms are for now incompatible with the joint alignment learning pipeline, because most of them make modifications at the token level, hence alignments would be made invalid.\n- There should be no blank lines in the alignment files provided.\n\nTraining options to learn such alignments are:\n\n* `-lambda_align`: set the value > 0.0 to enable joint align training, the paper suggests 0.05;\n* `-alignment_layer`: indicate the index of the decoder layer;\n* `-alignment_heads`:  number of alignment heads for the alignment task - should be set to 1 for the supervised task, and preferably kept to default (or same as `num_heads`) for the average task;\n* `-full_context_alignment`: do full context decoder pass (no future mask) when computing alignments. This will slow down the training (~12% in terms of tok/s) but will be beneficial to generate better alignment.\n"
  },
  {
    "path": "docs/source/_static/theme_overrides.css",
    "content": "/* override table width restrictions */\n@media screen and (min-width: 767px) {\n\n   .wy-table-responsive table td {\n      /* !important prevents the common CSS stylesheets from overriding\n         this as on RTD they are loaded after this stylesheet */\n      white-space: normal !important;\n   }\n\n   .wy-table-responsive {\n      overflow: visible !important;\n   }\n}"
  },
  {
    "path": "docs/source/conf.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# OpenNMT-py documentation build configuration file, created by\n# sphinx-quickstart on Sun Dec 17 12:07:14 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\n# import os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\n\nfrom recommonmark.parser import CommonMarkParser\nimport sphinx_rtd_theme\nfrom recommonmark.transform import AutoStructify\n\nsource_parsers = {\n    '.md': CommonMarkParser,\n}\n\nsource_suffix = ['.rst', '.md']\nextensions = ['sphinx.ext.autodoc',\n              'sphinx.ext.mathjax',\n              'sphinx.ext.viewcode',\n              'sphinx.ext.coverage',\n              'sphinx.ext.githubpages',\n              'sphinx.ext.napoleon',\n              'sphinxcontrib.mermaid',\n              'sphinxcontrib.bibtex',\n              'sphinxarg.ext',\n              'sphinx_markdown_tables']\n\n# Show base classes\nautodoc_default_options = {\n    'show-inheritance': True\n}\n\n# Use \"variables\" section for Attributes instead of weird block things\n# mimicking the function style.\nnapoleon_use_ivar = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['.templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'OpenNMT-py'\ncopyright = '2017, srush'\nauthor = 'srush'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = ''\n# The full version, including alpha/beta/rc tags.\nrelease = ''\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = []\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\n\n\n# html_theme = 'sphinx_materialdesign_theme'\n# html_theme_path = [sphinx_materialdesign_theme.get_path()]\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\nhtml_context = {\n    'css_files': [\n        '_static/theme_overrides.css',  # override wide tables in RTD theme\n        ],\n     }\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n    '**': [\n        'relations.html',  # needs 'show_related': True theme option to display\n        'searchbox.html',\n    ]\n}\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'OpenNMT-pydoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #\n    # 'papersize': 'letterpaper',\n\n    # The font size ('10pt', '11pt' or '12pt').\n    #\n    # 'pointsize': '10pt',\n\n    # Additional stuff for the LaTeX preamble.\n    #\n    # 'preamble': '',\n\n    # Latex figure (float) alignment\n    #\n    # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (master_doc, 'OpenNMT-py.tex', 'OpenNMT-py Documentation',\n     'srush', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n    (master_doc, 'opennmt-py', 'OpenNMT-py Documentation',\n     [author], 1)\n]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (master_doc, 'OpenNMT-py', 'OpenNMT-py Documentation',\n     author, 'OpenNMT-py', 'One line description of project.',\n     'Miscellaneous'),\n]\n\ngithub_doc_root = 'https://github.com/opennmt/opennmt-py/tree/master/doc/'\n\n\ndef setup(app):\n    print(\"hello\")\n    app.add_config_value('recommonmark_config', {\n        'enable_eval_rst': True\n    }, True)\n    app.add_transform(AutoStructify)\n"
  },
  {
    "path": "docs/source/examples/GGNN.md",
    "content": "# Gated Graph Sequence Neural Networks\n\nGraph-to-sequence networks allow information represtable as a graph (such as an annotated NLP sentence or computer code structure as an AST) to be connected to a sequence generator to produce output which can benefit from the graph structure of the input.\n\nThe training option `-encoder_type ggnn` implements a GGNN (Gated Graph Neural Network) based on github.com/JamesChuanggg/ggnn.pytorch.git which is based on the paper \"Gated Graph Sequence Neural Networks\" by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel.\n\nThe ggnn encoder is used for program equivalence proof generation in the paper [Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model](https://arxiv.org/abs/2002.06799). That paper shows the benefit of the graph-to-sequence model over a sequence-to-sequence model for this problem which can be well represented with graphical input. The integration of the ggnn network into the OpenNMT-py system supports attention on the nodes as well as a copy mechanism.\n\n### Dependencies\n\n* There are no additional dependencies beyond the rnn-to-rnn sequence2sequence requirements.\n\n### Quick Start\n\nTo get started, we provide a toy graph-to-sequence example. We assume that the working directory is `OpenNMT-py` throughout this document.\n\n0) Download the data to a sibling directory.\n\n```\ncd ..\ngit clone https://github.com/SteveKommrusch/OpenNMT-py-ggnn-example\nsource OpenNMT-py-ggnn-example/env.sh\ncd OpenNMT-py\n```\n\n\nThe YAML configuration for this example is the following:\n\n```yaml\n# ggnn_example.yaml\n## Where the necessary objects will be written\nsave_data: <path_to>/OpenNMT-py-ggnn-example/run/example\n\n# Filter long examples\nsrc_seq_length: 1000\ntgt_seq_length: 30\n\n# Data definition\ndata:\n    cnndm:\n        path_src: <path_to>/OpenNMT-py-ggnn-example/src-train.txt\n        path_tgt: <path_to>/OpenNMT-py-ggnn-example/tgt-train.txt\n        transforms: [filtertoolong]\n        weight: 1\n    valid:\n        path_src: <path_to>/OpenNMT-py-ggnn-example/src-val.txt\n        path_tgt: <path_to>/OpenNMT-py-ggnn-example/tgt-val.txt\n\nsrc_vocab: <path_to>/OpenNMT-py-ggnn-example/srcvocab.txt\ntgt_vocab: <path_to>/OpenNMT-py-ggnn-example/tgtvocab.txt\n\nsave_model: <path_to>/OpenNMT-py-ggnn-example/run/model\n\n# Model options\ntrain_steps: 10000\nsave_checkpoint_steps: 5000\nencoder_type: ggnn\nlayers: 2\ndecoder_type: rnn\nrnn_size: 256\nlearning_rate: 0.1\nstart_decay_steps: 5000\nlearning_rate_decay: 0.8\nglobal_attention: general\nbatch_size: 32\nword_vec_size: 256\nbridge: true\ngpu_ranks: 0\nn_edge_types: 9\nstate_dim: 256\nn_steps: 10\nn_node: 64\n```\n\n2) Train the model.\n\nYou can simply run the following command:\n\n```\npython train.py -config ggnn_example.yaml\n```\n\n3) Translate the graph of 2 equivalent linear algebra expressions into the axiom list which proves them equivalent.\n\n```\npython translate.py \\\n    -model <path_to>/OpenNMT-py-ggnn-example/run/model_step_10000.pt \\\n    -src <path_to>/OpenNMT-py-ggnn-example$data_path/src-test.txt \\\n    -beam_size 5 -n_best 5 \\\n    -gpu 0 \\\n    -output <path_to>/OpenNMT-py-ggnn-example/pred-test_beam5.txt \\\n    2>&1 > <path_to>/OpenNMT-py-ggnn-example/translate5.out\n```\n\n### Graph data format\n\nThe GGNN implementation leverages the sequence processing and vocabulary\ninterface of OpenNMT. Each graph is provided on an input line, much like\na sentence is provided on an input line. A graph nearal network input line\nincludes `sentence tokens`, `feature values`, and `edges` separated by\n`<EOT>` (end of tokens) tokens. Below is example of the input for a pair\nof algebraic equations structured as a graph:\n\n```\nSentence tokens       Feature values           Edges\n---------------       ------------------       -------------------------------------------------------\n- - - 0 a a b b <EOT> 0 1 2 3 4 4 2 3 12 <EOT> 0 2 1 3 2 4 , 0 6 1 7 2 5 , 0 4 , 0 5 , , , , 8 0 , 8 1\n```\n\nThe equations being represented are `((a - a) - b)` and `(0 - b)`, the \n`sentence tokens` of which are provided before the first `<EOT>`. After\nthe first `<EOT>`, the `features values` are provided. These are extra\nflags with information on each node in the graph. In this case, the 8\nsentence tokens have feature flags ranging from 0 to 4; the 9th feature\nflag defines a 9th node in the graph which does not have sentence token\ninformation, just feature data. Nodes with any non-number flag (such as\n`-` or `.`) will not have a feature added. Multiple groups of features\ncan be provided by using the `,` delimiter between the first and second\n'<EOT>' tokens. After the second `<EOT>` token, edge information is provided.\nEdge data is given as node pairs, hence `<EOT> 0 2 1 3` indicates that there\nare edges from node 0 to node 2 and from node 1 to node 3. The GGNN supports\nmultiple edge types (which result mathematically in multiple weight matrices\nfor the model) and the edge types are separated by `,` tokens after the\nsecond `<EOT>` token.\n\nNote that the source vocabulary file needs to include the '<EOT>' token,\nthe ',' token, and all of the numbers used for feature flags and node\nidentifiers in the edge list.\n\n\n### Options\n\n* `-rnn_type (str)`: style of recurrent unit to use, one of [LSTM]\n* `-state_dim (int)`: Number of state dimensions in nodes\n* `-n_edge_types (int)`: Number of edge types\n* `-bidir_edges (bool)`: True if reverse edges should be automatically created\n* `-n_node (int)`: Max nodes in graph\n* `-bridge_extra_node (bool)`: True indicates only the vector from the 1st extra node (after token listing) should be used for decoder initialization; False indicates all node vectors should be averaged together for decoder initialization\n* `-n_steps (int)`: Steps to advance graph encoder for stabilization\n* `-src_vocab (int)`: Path to source vocabulary\n\n### Acknowledgement\n\nThis gated graph neural network is leveraged from https://github.com/JamesChuanggg/ggnn.pytorch.git which is based on the paper [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493) by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel.\n"
  },
  {
    "path": "docs/source/examples/LanguageModelGeneration.md",
    "content": "# Language Model Generation\n\n\n## Step 0: Download and clean the data\n\nPreliminary steps are defined in the [`examples/scripts/prepare_wikitext-103_data.sh`](https://github.com/OpenNMT/OpenNMT-py/tree/master/examples/scripts/prepare_wikitext-103_data.sh). The following command will download the [WikiText103 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/), remove empty lines and shuffle the training corpus:\n```bash\nchmod u+x prepare_wikitext-103_data.sh\n./prepare_wikitext-103_data.sh\n```\n\n## Step 1: Prepare the subword model - BPE with pyonmttok\n\nThis snippet will train a bpe of 40000 symbols on the training dataset using pyonmttok. The bpe model will be stored in `subwords.bpe` and the train/valid/test sets will be tokenized and saved.\n\nThe tokenized files won't be used for training. Indeed, dynamic iteration over the training dataset enables on the fly tokenization using transforms (see step 2).\n\n```python\nimport pyonmttok\n\nargs = {\n    \"mode\": \"aggressive\",\n    \"joiner_annotate\": True,\n    \"preserve_placeholders\": True,\n    \"case_markup\": True,\n    \"soft_case_regions\": True,\n    \"preserve_segmented_tokens\": True,\n}\nn_symbols = 40000\n\ntokenizer_default = pyonmttok.Tokenizer(**args)\nlearner = pyonmttok.BPELearner(tokenizer=tokenizer_default, symbols=n_symbols)\n# load training corpus\nlearner.ingest_file(\"wiki.train.raw\")\n\n# learn and store bpe model\ntokenizer = learner.learn(\"subwords.bpe\")\n\n# tokenize corpus and save results\nfor data_file in [\"wiki.valid\", \"wiki.test\", \"wiki.train\"]:\n    tokenizer.tokenize_file(f\"{data_file}.raw\", f\"{data_file}.bpe\")\n```\n\n## Step 2: Build the vocabulary\nAn example of yaml configuration for language modeling task is available in [`examples/wiki_103.yaml`](https://github.com/OpenNMT/OpenNMT-py/tree/master/examples/wiki_103.yaml). This configuration will be used for building the vocabulary and training the model.\nBPE and language modeling specificities are explained in the following sections.\n\n### Language Model specificities\n\nIn LM tasks we expect a single source, therefore path_tgt is not required for LM tasks.\n\n```yaml\ndata:\n    corpus_1:\n        path_src: data/wikitext-103-raw/wiki.train.raw\n```\n\n### BPE specificities\n\nTo use BPE tokenization on the fly, the following parameters must be in the config file.\nSlight differences between on the fly tokenization and outputed tokenized files from step 1 can be observed.\n\n```yaml\nsrc_subword_type: bpe\nsrc_subword_model: data/wikitext-103-raw/subwords.bpe\nsrc_onmttok_kwargs: '{\"mode\": \"aggressive\", \"joiner_annotate\": True, \"preserve_placeholders\":\n  True, \"case_markup\": True, \"soft_case_regions\": True, \"preserve_segmented_tokens\":\n  True}'\ntransforms: [onmt_tokenize]\n```\n\n### Build vocabulary command\nThe vocabulary is built using:\n```bash\nonmt_build_vocab -config examples/wiki_103.yaml -n_sample -1\n```\n\n## Step 3: Train the model\nTo train a model for LM tasks, the following parameters are required:\n\n* *model_task* is used to specify that the task will be language modeling (decoder only model with tansformer_lm decoder type, source only dataset expected)\n* *decoder_type* must be transformer_lm. This transformer is the one used in GPT-2: [**Language Models are Unsupervised Multitask Learners**](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). Basically, it is a transformer without an encoder attention block\n* *encoder_type* is not useful but need to be mentionned\n* *share_vocab* must be true. The slided source will play the role of the target hence vocabulary must be shared. \n```yaml\nmodel_task: lm\nencoder_type: transformer_lm\ndecoder_type: transformer_lm\n\nshare_vocab: true\n```\n\nThe training is launched using:\n```bash\nonmt_train -config examples/wiki_103.yaml\n```\nTensorboard can be used to monitor the training.\n\n**Expected results:** perplexity of 20-22 on the validation set.\n\n## Step 4: Generate output\nOptions contained in the loaded model will trigger language modeling specific inference.\n\n`input.txt` must contain already tokenized examples, with the same method as the training data. Here, part of validation data will be used:\n```bash\nhead data/wikitext-103-raw/wiki.valid.bpe | cut -d\" \" -f-15 > data/wikitext-103-raw/lm_input.txt\n```\n\nTo proceed with inference:\n```bash\nonmt_translate -model data/wikitext-103-raw/run/model-lm_step_1000000.pt -src data/wikitext-103-raw/lm_input.txt -output data/wikitext-103-raw/lm_pred_input.txt -verbose -n_best 3\n```"
  },
  {
    "path": "docs/source/examples/Library.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# How to use OpenNMT-py as a Library\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The example notebook (available [here](https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/examples/Library.ipynb)) should be able to run as a standalone execution, provided `onmt` is in the path (installed via `pip` for instance).\\n\",\n    \"\\n\",\n    \"Some parts may not be 100% 'library-friendly' but it's mostly workable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Import a few modules and functions that will be necessary\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import yaml\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from argparse import Namespace\\n\",\n    \"from collections import defaultdict, Counter\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import onmt\\n\",\n    \"from onmt.inputters.inputter import _load_vocab, _build_fields_vocab, get_fields, IterOnDevice\\n\",\n    \"from onmt.inputters.corpus import ParallelCorpus\\n\",\n    \"from onmt.inputters.dynamic_iterator import DynamicDatasetIter\\n\",\n    \"from onmt.translate import GNMTGlobalScorer, Translator, TranslationBuilder\\n\",\n    \"from onmt.utils.misc import set_random_seed\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Enable logging\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<RootLogger root (INFO)>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# enable logging\\n\",\n    \"from onmt.utils.logging import init_logger, logger\\n\",\n    \"init_logger()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Set random seed\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"is_cuda = torch.cuda.is_available()\\n\",\n    \"set_random_seed(1111, is_cuda)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Retrieve data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To make a proper example, we will need some data, as well as some vocabulary(ies).\\n\",\n    \"\\n\",\n    \"Let's take the same data as in the [quickstart](https://opennmt.net/OpenNMT-py/quickstart.html):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--2020-09-25 15:28:05--  https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz\\n\",\n      \"Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.18.38\\n\",\n      \"Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.18.38|:443... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 1662081 (1,6M) [application/x-gzip]\\n\",\n      \"Saving to: ‘toy-ende.tar.gz.5’\\n\",\n      \"\\n\",\n      \"toy-ende.tar.gz.5   100%[===================>]   1,58M  2,33MB/s    in 0,7s    \\n\",\n      \"\\n\",\n      \"2020-09-25 15:28:07 (2,33 MB/s) - ‘toy-ende.tar.gz.5’ saved [1662081/1662081]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!wget https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!tar xf toy-ende.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"config.yaml  src-test.txt   src-val.txt   tgt-train.txt\\r\\n\",\n      \"\\u001b[0m\\u001b[01;34mrun\\u001b[0m/         src-train.txt  tgt-test.txt  tgt-val.txt\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ls toy-ende\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Prepare data and vocab\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As for any use case of OpenNMT-py 2.0, we can start by creating a simple YAML configuration with our datasets. This is the easiest way to build the proper `opts` `Namespace` that will be used to create the vocabulary(ies).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"yaml_config = \\\"\\\"\\\"\\n\",\n    \"## Where the vocab(s) will be written\\n\",\n    \"save_data: toy-ende/run/example\\n\",\n    \"src_vocab: toy-ende/run/example.vocab.src\\n\",\n    \"tgt_vocab: toy-ende/run/example.vocab.tgt\\n\",\n    \"# Corpus opts:\\n\",\n    \"data:\\n\",\n    \"    corpus:\\n\",\n    \"        path_src: toy-ende/src-train.txt\\n\",\n    \"        path_tgt: toy-ende/tgt-train.txt\\n\",\n    \"        transforms: []\\n\",\n    \"        weight: 1\\n\",\n    \"    valid:\\n\",\n    \"        path_src: toy-ende/src-val.txt\\n\",\n    \"        path_tgt: toy-ende/tgt-val.txt\\n\",\n    \"        transforms: []\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"config = yaml.safe_load(yaml_config)\\n\",\n    \"with open(\\\"toy-ende/config.yaml\\\", \\\"w\\\") as f:\\n\",\n    \"    f.write(yaml_config)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from onmt.utils.parse import ArgumentParser\\n\",\n    \"parser = ArgumentParser(description='build_vocab.py')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from onmt.opts import dynamic_prepare_opts\\n\",\n    \"dynamic_prepare_opts(parser, build_vocab_only=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"base_args = ([\\\"-config\\\", \\\"toy-ende/config.yaml\\\", \\\"-n_sample\\\", \\\"10000\\\"])\\n\",\n    \"opts, unknown = parser.parse_known_args(base_args)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Namespace(config='toy-ende/config.yaml', data=\\\"{'corpus': {'path_src': 'toy-ende/src-train.txt', 'path_tgt': 'toy-ende/tgt-train.txt', 'transforms': [], 'weight': 1}, 'valid': {'path_src': 'toy-ende/src-val.txt', 'path_tgt': 'toy-ende/tgt-val.txt', 'transforms': []}}\\\", insert_ratio=0.0, mask_length='subword', mask_ratio=0.0, n_sample=10000, src_onmttok_kwargs=\\\"{'mode': 'none'}\\\", tgt_onmttok_kwargs=\\\"{'mode': 'none'}\\\", overwrite=False, permute_sent_ratio=0.0, poisson_lambda=0.0, random_ratio=0.0, replace_length=-1, rotate_ratio=0.5, save_config=None, save_data='toy-ende/run/example', seed=-1, share_vocab=False, skip_empty_level='warning', src_seq_length=200, src_subword_model=None, src_subword_type='none', src_vocab=None, subword_alpha=0, subword_nbest=1, switchout_temperature=1.0, tgt_seq_length=200, tgt_subword_model=None, tgt_subword_type='none', tgt_vocab=None, tokendrop_temperature=1.0, tokenmask_temperature=1.0, transforms=[])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"opts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2020-09-25 15:28:08,068 INFO] Parsed 2 corpora from -data.\\n\",\n      \"[2020-09-25 15:28:08,069 INFO] Counter vocab from 10000 samples.\\n\",\n      \"[2020-09-25 15:28:08,070 INFO] Save 10000 transformed example/corpus.\\n\",\n      \"[2020-09-25 15:28:08,070 INFO] corpus's transforms: TransformPipe()\\n\",\n      \"[2020-09-25 15:28:08,101 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:28:08,320 INFO] Just finished the first loop\\n\",\n      \"[2020-09-25 15:28:08,320 INFO] Counters src:24995\\n\",\n      \"[2020-09-25 15:28:08,321 INFO] Counters tgt:35816\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from onmt.bin.build_vocab import build_vocab_main\\n\",\n    \"build_vocab_main(opts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"example.vocab.src  example.vocab.tgt  \\u001b[0m\\u001b[01;34msample\\u001b[0m/\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ls toy-ende/run\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We just created our source and target vocabularies, respectively `toy-ende/run/example.vocab.src` and `toy-ende/run/example.vocab.tgt`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Build fields\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can build the fields from the text files that were just created.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_vocab_path = \\\"toy-ende/run/example.vocab.src\\\"\\n\",\n    \"tgt_vocab_path = \\\"toy-ende/run/example.vocab.tgt\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2020-09-25 15:28:08,495 INFO] Loading src vocabulary from toy-ende/run/example.vocab.src\\n\",\n      \"[2020-09-25 15:28:08,554 INFO] Loaded src vocab has 24995 tokens.\\n\",\n      \"[2020-09-25 15:28:08,562 INFO] Loading tgt vocabulary from toy-ende/run/example.vocab.tgt\\n\",\n      \"[2020-09-25 15:28:08,617 INFO] Loaded tgt vocab has 35816 tokens.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# initialize the frequency counter\\n\",\n    \"counters = defaultdict(Counter)\\n\",\n    \"# load source vocab\\n\",\n    \"_src_vocab, _src_vocab_size = _load_vocab(\\n\",\n    \"    src_vocab_path,\\n\",\n    \"    'src',\\n\",\n    \"    counters)\\n\",\n    \"# load target vocab\\n\",\n    \"_tgt_vocab, _tgt_vocab_size = _load_vocab(\\n\",\n    \"    tgt_vocab_path,\\n\",\n    \"    'tgt',\\n\",\n    \"    counters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# initialize fields\\n\",\n    \"src_nfeats, tgt_nfeats = 0, 0 # do not support word features for now\\n\",\n    \"fields = get_fields(\\n\",\n    \"    'text', src_nfeats, tgt_nfeats)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'src': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802c50>,\\n\",\n       \" 'tgt': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802f60>,\\n\",\n       \" 'indices': <torchtext.data.field.Field at 0x7fca93802940>}\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"fields\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2020-09-25 15:28:08,699 INFO]  * tgt vocab size: 30004.\\n\",\n      \"[2020-09-25 15:28:08,749 INFO]  * src vocab size: 24997.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# build fields vocab\\n\",\n    \"share_vocab = False\\n\",\n    \"vocab_size_multiple = 1\\n\",\n    \"src_vocab_size = 30000\\n\",\n    \"tgt_vocab_size = 30000\\n\",\n    \"src_words_min_frequency = 1\\n\",\n    \"tgt_words_min_frequency = 1\\n\",\n    \"vocab_fields = _build_fields_vocab(\\n\",\n    \"    fields, counters, 'text', share_vocab,\\n\",\n    \"    vocab_size_multiple,\\n\",\n    \"    src_vocab_size, src_words_min_frequency,\\n\",\n    \"    tgt_vocab_size, tgt_words_min_frequency)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"An alternative way of creating these fields is to run `onmt_train` without actually training, to just output the necessary files.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Prepare for training: model and optimizer creation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's get a few fields/vocab related variables to simplify the model creation a bit:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_text_field = vocab_fields[\\\"src\\\"].base_field\\n\",\n    \"src_vocab = src_text_field.vocab\\n\",\n    \"src_padding = src_vocab.stoi[src_text_field.pad_token]\\n\",\n    \"\\n\",\n    \"tgt_text_field = vocab_fields['tgt'].base_field\\n\",\n    \"tgt_vocab = tgt_text_field.vocab\\n\",\n    \"tgt_padding = tgt_vocab.stoi[tgt_text_field.pad_token]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next we specify the core model itself. Here we will build a small model with an encoder and an attention based input feeding decoder. Both models will be RNNs and the encoder will be bidirectional\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"emb_size = 100\\n\",\n    \"rnn_size = 500\\n\",\n    \"# Specify the core model.\\n\",\n    \"\\n\",\n    \"encoder_embeddings = onmt.modules.Embeddings(emb_size, len(src_vocab),\\n\",\n    \"                                             word_padding_idx=src_padding)\\n\",\n    \"\\n\",\n    \"encoder = onmt.encoders.RNNEncoder(hidden_size=rnn_size, num_layers=1,\\n\",\n    \"                                   rnn_type=\\\"LSTM\\\", bidirectional=True,\\n\",\n    \"                                   embeddings=encoder_embeddings)\\n\",\n    \"\\n\",\n    \"decoder_embeddings = onmt.modules.Embeddings(emb_size, len(tgt_vocab),\\n\",\n    \"                                             word_padding_idx=tgt_padding)\\n\",\n    \"decoder = onmt.decoders.decoder.InputFeedRNNDecoder(\\n\",\n    \"    hidden_size=rnn_size, num_layers=1, bidirectional_encoder=True, \\n\",\n    \"    rnn_type=\\\"LSTM\\\", embeddings=decoder_embeddings)\\n\",\n    \"\\n\",\n    \"device = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\",\n    \"model = onmt.models.model.NMTModel(encoder, decoder)\\n\",\n    \"model.to(device)\\n\",\n    \"\\n\",\n    \"# Specify the tgt word generator and loss computation module\\n\",\n    \"model.generator = nn.Sequential(\\n\",\n    \"    nn.Linear(rnn_size, len(tgt_vocab)),\\n\",\n    \"    nn.LogSoftmax(dim=-1)).to(device)\\n\",\n    \"\\n\",\n    \"loss = onmt.utils.loss.NMTLossCompute(\\n\",\n    \"    criterion=nn.NLLLoss(ignore_index=tgt_padding, reduction=\\\"sum\\\"),\\n\",\n    \"    generator=model.generator)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we set up the optimizer. This could be a core torch optim class, or our wrapper which handles learning rate updates and gradient normalization automatically.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lr = 1\\n\",\n    \"torch_optimizer = torch.optim.SGD(model.parameters(), lr=lr)\\n\",\n    \"optim = onmt.utils.optimizers.Optimizer(\\n\",\n    \"    torch_optimizer, learning_rate=lr, max_grad_norm=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Create the training and validation data iterators\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need to create the dynamic dataset iterator.\\n\",\n    \"\\n\",\n    \"This is not very 'library-friendly' for now because of the way the `DynamicDatasetIter` constructor is defined. It may evolve in the future.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_train = \\\"toy-ende/src-train.txt\\\"\\n\",\n    \"tgt_train = \\\"toy-ende/tgt-train.txt\\\"\\n\",\n    \"src_val = \\\"toy-ende/src-val.txt\\\"\\n\",\n    \"tgt_val = \\\"toy-ende/tgt-val.txt\\\"\\n\",\n    \"\\n\",\n    \"# build the ParallelCorpus\\n\",\n    \"corpus = ParallelCorpus(\\\"corpus\\\", src_train, tgt_train)\\n\",\n    \"valid = ParallelCorpus(\\\"valid\\\", src_val, tgt_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# build the training iterator\\n\",\n    \"train_iter = DynamicDatasetIter(\\n\",\n    \"    corpora={\\\"corpus\\\": corpus},\\n\",\n    \"    corpora_info={\\\"corpus\\\": {\\\"weight\\\": 1}},\\n\",\n    \"    transforms={},\\n\",\n    \"    fields=vocab_fields,\\n\",\n    \"    is_train=True,\\n\",\n    \"    batch_type=\\\"tokens\\\",\\n\",\n    \"    batch_size=4096,\\n\",\n    \"    batch_size_multiple=1,\\n\",\n    \"    data_type=\\\"text\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# make sure the iteration happens on GPU 0 (-1 for CPU, N for GPU N)\\n\",\n    \"train_iter = iter(IterOnDevice(train_iter, 0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# build the validation iterator\\n\",\n    \"valid_iter = DynamicDatasetIter(\\n\",\n    \"    corpora={\\\"valid\\\": valid},\\n\",\n    \"    corpora_info={\\\"valid\\\": {\\\"weight\\\": 1}},\\n\",\n    \"    transforms={},\\n\",\n    \"    fields=vocab_fields,\\n\",\n    \"    is_train=False,\\n\",\n    \"    batch_type=\\\"sents\\\",\\n\",\n    \"    batch_size=8,\\n\",\n    \"    batch_size_multiple=1,\\n\",\n    \"    data_type=\\\"text\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"valid_iter = IterOnDevice(valid_iter, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally we train.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2020-09-25 15:28:15,184 INFO] Start training loop and validate every 500 steps...\\n\",\n      \"[2020-09-25 15:28:15,185 INFO] corpus's transforms: TransformPipe()\\n\",\n      \"[2020-09-25 15:28:15,187 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:28:21,140 INFO] Step 50/ 1000; acc:   7.52; ppl: 8832.29; xent: 9.09; lr: 1.00000; 18916/18871 tok/s;      6 sec\\n\",\n      \"[2020-09-25 15:28:24,869 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:28:27,121 INFO] Step 100/ 1000; acc:   9.34; ppl: 1840.06; xent: 7.52; lr: 1.00000; 18911/18785 tok/s;     12 sec\\n\",\n      \"[2020-09-25 15:28:33,048 INFO] Step 150/ 1000; acc:  10.35; ppl: 1419.18; xent: 7.26; lr: 1.00000; 19062/19017 tok/s;     18 sec\\n\",\n      \"[2020-09-25 15:28:37,019 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:28:39,022 INFO] Step 200/ 1000; acc:  11.14; ppl: 1127.44; xent: 7.03; lr: 1.00000; 19084/18911 tok/s;     24 sec\\n\",\n      \"[2020-09-25 15:28:45,073 INFO] Step 250/ 1000; acc:  12.46; ppl: 912.13; xent: 6.82; lr: 1.00000; 18575/18570 tok/s;     30 sec\\n\",\n      \"[2020-09-25 15:28:49,301 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:28:51,151 INFO] Step 300/ 1000; acc:  13.04; ppl: 779.50; xent: 6.66; lr: 1.00000; 18394/18307 tok/s;     36 sec\\n\",\n      \"[2020-09-25 15:28:57,316 INFO] Step 350/ 1000; acc:  14.04; ppl: 685.48; xent: 6.53; lr: 1.00000; 18339/18173 tok/s;     42 sec\\n\",\n      \"[2020-09-25 15:29:02,117 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:29:03,576 INFO] Step 400/ 1000; acc:  14.99; ppl: 590.20; xent: 6.38; lr: 1.00000; 18090/18029 tok/s;     48 sec\\n\",\n      \"[2020-09-25 15:29:09,546 INFO] Step 450/ 1000; acc:  16.00; ppl: 524.51; xent: 6.26; lr: 1.00000; 18726/18536 tok/s;     54 sec\\n\",\n      \"[2020-09-25 15:29:14,585 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:29:15,596 INFO] Step 500/ 1000; acc:  16.78; ppl: 453.38; xent: 6.12; lr: 1.00000; 17877/17980 tok/s;     60 sec\\n\",\n      \"[2020-09-25 15:29:15,597 INFO] valid's transforms: TransformPipe()\\n\",\n      \"[2020-09-25 15:29:15,599 INFO] Loading ParallelCorpus(toy-ende/src-val.txt, toy-ende/tgt-val.txt, align=None)...\\n\",\n      \"[2020-09-25 15:29:24,528 INFO] Validation perplexity: 295.1\\n\",\n      \"[2020-09-25 15:29:24,529 INFO] Validation accuracy: 17.6533\\n\",\n      \"[2020-09-25 15:29:30,592 INFO] Step 550/ 1000; acc:  17.47; ppl: 421.26; xent: 6.04; lr: 1.00000; 7726/7610 tok/s;     75 sec\\n\",\n      \"[2020-09-25 15:29:36,055 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:29:36,695 INFO] Step 600/ 1000; acc:  18.95; ppl: 354.44; xent: 5.87; lr: 1.00000; 17470/17598 tok/s;     82 sec\\n\",\n      \"[2020-09-25 15:29:42,794 INFO] Step 650/ 1000; acc:  19.60; ppl: 328.47; xent: 5.79; lr: 1.00000; 18994/18793 tok/s;     88 sec\\n\",\n      \"[2020-09-25 15:29:48,635 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:29:48,924 INFO] Step 700/ 1000; acc:  20.57; ppl: 285.48; xent: 5.65; lr: 1.00000; 17856/17788 tok/s;     94 sec\\n\",\n      \"[2020-09-25 15:29:54,898 INFO] Step 750/ 1000; acc:  21.97; ppl: 249.06; xent: 5.52; lr: 1.00000; 19030/18924 tok/s;    100 sec\\n\",\n      \"[2020-09-25 15:30:01,233 INFO] Step 800/ 1000; acc:  22.66; ppl: 228.54; xent: 5.43; lr: 1.00000; 17571/17471 tok/s;    106 sec\\n\",\n      \"[2020-09-25 15:30:01,357 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:30:07,345 INFO] Step 850/ 1000; acc:  24.32; ppl: 193.65; xent: 5.27; lr: 1.00000; 18344/18313 tok/s;    112 sec\\n\",\n      \"[2020-09-25 15:30:11,363 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:30:13,487 INFO] Step 900/ 1000; acc:  24.93; ppl: 177.65; xent: 5.18; lr: 1.00000; 18293/18105 tok/s;    118 sec\\n\",\n      \"[2020-09-25 15:30:19,670 INFO] Step 950/ 1000; acc:  26.33; ppl: 157.10; xent: 5.06; lr: 1.00000; 17791/17746 tok/s;    124 sec\\n\",\n      \"[2020-09-25 15:30:24,072 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\\n\",\n      \"[2020-09-25 15:30:25,820 INFO] Step 1000/ 1000; acc:  27.47; ppl: 137.64; xent: 4.92; lr: 1.00000; 17942/17962 tok/s;    131 sec\\n\",\n      \"[2020-09-25 15:30:25,822 INFO] Loading ParallelCorpus(toy-ende/src-val.txt, toy-ende/tgt-val.txt, align=None)...\\n\",\n      \"[2020-09-25 15:30:34,665 INFO] Validation perplexity: 241.801\\n\",\n      \"[2020-09-25 15:30:34,666 INFO] Validation accuracy: 20.2837\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<onmt.utils.statistics.Statistics at 0x7fca934e8e80>\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"report_manager = onmt.utils.ReportMgr(\\n\",\n    \"    report_every=50, start_time=None, tensorboard_writer=None)\\n\",\n    \"\\n\",\n    \"trainer = onmt.Trainer(model=model,\\n\",\n    \"                       train_loss=loss,\\n\",\n    \"                       valid_loss=loss,\\n\",\n    \"                       optim=optim,\\n\",\n    \"                       report_manager=report_manager,\\n\",\n    \"                       dropout=[0.1])\\n\",\n    \"\\n\",\n    \"trainer.train(train_iter=train_iter,\\n\",\n    \"              train_steps=1000,\\n\",\n    \"              valid_iter=valid_iter,\\n\",\n    \"              valid_steps=500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Translate\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For translation, we can build a \\\"traditional\\\" (as opposed to dynamic) dataset for now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_data = {\\\"reader\\\": onmt.inputters.str2reader[\\\"text\\\"](), \\\"data\\\": src_val}\\n\",\n    \"tgt_data = {\\\"reader\\\": onmt.inputters.str2reader[\\\"text\\\"](), \\\"data\\\": tgt_val}\\n\",\n    \"_readers, _data = onmt.inputters.Dataset.config(\\n\",\n    \"    [('src', src_data), ('tgt', tgt_data)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = onmt.inputters.Dataset(\\n\",\n    \"    vocab_fields, readers=_readers, data=_data,\\n\",\n    \"    sort_key=onmt.inputters.str2sortkey[\\\"text\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"data_iter = onmt.inputters.OrderedIterator(\\n\",\n    \"            dataset=dataset,\\n\",\n    \"            device=\\\"cuda\\\",\\n\",\n    \"            batch_size=10,\\n\",\n    \"            train=False,\\n\",\n    \"            sort=False,\\n\",\n    \"            sort_within_batch=True,\\n\",\n    \"            shuffle=False\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_reader = onmt.inputters.str2reader[\\\"text\\\"]\\n\",\n    \"tgt_reader = onmt.inputters.str2reader[\\\"text\\\"]\\n\",\n    \"scorer = GNMTGlobalScorer(alpha=0.7, \\n\",\n    \"                          beta=0., \\n\",\n    \"                          length_penalty=\\\"avg\\\", \\n\",\n    \"                          coverage_penalty=\\\"none\\\")\\n\",\n    \"gpu = 0 if torch.cuda.is_available() else -1\\n\",\n    \"translator = Translator(model=model, \\n\",\n    \"                        fields=vocab_fields, \\n\",\n    \"                        src_reader=src_reader, \\n\",\n    \"                        tgt_reader=tgt_reader, \\n\",\n    \"                        global_scorer=scorer,\\n\",\n    \"                        gpu=gpu)\\n\",\n    \"builder = onmt.translate.TranslationBuilder(data=dataset, \\n\",\n    \"                                            fields=vocab_fields)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note**: translations will be very poor, because of the very low quantity of data, the absence of proper tokenization, and the brevity of the training.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"SENT 0: ['Parliament', 'Does', 'Not', 'Support', 'Amendment', 'Freeing', 'Tymoshenko']\\n\",\n      \"PRED 0: Parlament das Parlament über die Europäische Parlament , die sich in der Lage in der Lage ist , die es in der Lage sind .\\n\",\n      \"PRED SCORE: -1.5935\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['Today', ',', 'the', 'Ukraine', 'parliament', 'dismissed', ',', 'within', 'the', 'Code', 'of', 'Criminal', 'Procedure', 'amendment', ',', 'the', 'motion', 'to', 'revoke', 'an', 'article', 'based', 'on', 'which', 'the', 'opposition', 'leader', ',', 'Yulia', 'Tymoshenko', ',', 'was', 'sentenced', '.']\\n\",\n      \"PRED 0: In der Nähe des Hotels , die in der Lage , die sich in der Lage ist , in der Lage , die in der Lage , die in der Lage ist .\\n\",\n      \"PRED SCORE: -1.7173\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['The', 'amendment', 'that', 'would', 'lead', 'to', 'freeing', 'the', 'imprisoned', 'former', 'Prime', 'Minister', 'was', 'revoked', 'during', 'second', 'reading', 'of', 'the', 'proposal', 'for', 'mitigation', 'of', 'sentences', 'for', 'economic', 'offences', '.']\\n\",\n      \"PRED 0: Die Tatsache , die sich in der Lage in der Lage ist , die für eine Antwort der Entwicklung für die Entwicklung von Präsident .\\n\",\n      \"PRED SCORE: -1.6834\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['In', 'October', ',', 'Tymoshenko', 'was', 'sentenced', 'to', 'seven', 'years', 'in', 'prison', 'for', 'entering', 'into', 'what', 'was', 'reported', 'to', 'be', 'a', 'disadvantageous', 'gas', 'deal', 'with', 'Russia', '.']\\n\",\n      \"PRED 0: In der Nähe wurde die Menschen in der Lage ist , die sich in der Lage <unk> .\\n\",\n      \"PRED SCORE: -1.5765\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['The', 'verdict', 'is', 'not', 'yet', 'final;', 'the', 'court', 'will', 'hear', 'Tymoshenko', '&apos;s', 'appeal', 'in', 'December', '.']\\n\",\n      \"PRED 0: Es ist nicht der Fall , die in der Lage in der Lage sind .\\n\",\n      \"PRED SCORE: -1.3287\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['Tymoshenko', 'claims', 'the', 'verdict', 'is', 'a', 'political', 'revenge', 'of', 'the', 'regime;', 'in', 'the', 'West', ',', 'the', 'trial', 'has', 'also', 'evoked', 'suspicion', 'of', 'being', 'biased', '.']\\n\",\n      \"PRED 0: Um in der Lage ist auch eine Lösung Rolle .\\n\",\n      \"PRED SCORE: -1.3975\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['The', 'proposal', 'to', 'remove', 'Article', '365', 'from', 'the', 'Code', 'of', 'Criminal', 'Procedure', ',', 'upon', 'which', 'the', 'former', 'Prime', 'Minister', 'was', 'sentenced', ',', 'was', 'supported', 'by', '147', 'members', 'of', 'parliament', '.']\\n\",\n      \"PRED 0: Der Vorschlag , die in der Lage , die in der Lage , die in der Lage ist , war er von der Fall <unk> wurde .\\n\",\n      \"PRED SCORE: -1.6062\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['Its', 'ratification', 'would', 'require', '226', 'votes', '.']\\n\",\n      \"PRED 0: Es wäre noch einmal noch einmal <unk> .\\n\",\n      \"PRED SCORE: -1.8001\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['Libya', '&apos;s', 'Victory']\\n\",\n      \"PRED 0: In der Nähe des Hotels befindet sich in der Nähe des Hotels in der Lage .\\n\",\n      \"PRED SCORE: -1.7097\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"SENT 0: ['The', 'story', 'of', 'Libya', '&apos;s', 'liberation', ',', 'or', 'rebellion', ',', 'already', 'has', 'its', 'defeated', '.']\\n\",\n      \"PRED 0: In der Nähe des Hotels in der Lage ist in der Lage .\\n\",\n      \"PRED SCORE: -1.7885\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for batch in data_iter:\\n\",\n    \"    trans_batch = translator.translate_batch(\\n\",\n    \"        batch=batch, src_vocabs=[src_vocab],\\n\",\n    \"        attn_debug=False)\\n\",\n    \"    translations = builder.from_batch(trans_batch)\\n\",\n    \"    for trans in translations:\\n\",\n    \"        print(trans.log(0))\\n\",\n    \"    break\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/source/examples/Library.md",
    "content": "\n# Library\n\nThe example notebook (available [here](https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/examples/Library.ipynb)) should be able to run as a standalone execution, provided `onmt` is in the path (installed via `pip` for instance).\n\nSome parts may not be 100% 'library-friendly' but it's mostly workable.\n\n### Import a few modules and functions that will be necessary\n\n\n```python\nimport yaml\nimport torch\nimport torch.nn as nn\nfrom argparse import Namespace\nfrom collections import defaultdict, Counter\n```\n\n\n```python\nimport onmt\nfrom onmt.inputters.inputter import _load_vocab, _build_fields_vocab, get_fields, IterOnDevice\nfrom onmt.inputters.corpus import ParallelCorpus\nfrom onmt.inputters.dynamic_iterator import DynamicDatasetIter\nfrom onmt.translate import GNMTGlobalScorer, Translator, TranslationBuilder\nfrom onmt.utils.misc import set_random_seed\n```\n\n### Enable logging\n\n\n```python\n# enable logging\nfrom onmt.utils.logging import init_logger, logger\ninit_logger()\n```\n\n\n\n\n    <RootLogger root (INFO)>\n\n\n\n### Set random seed\n\n\n```python\nis_cuda = torch.cuda.is_available()\nset_random_seed(1111, is_cuda)\n```\n\n### Retrieve data\n\nTo make a proper example, we will need some data, as well as some vocabulary(ies).\n\nLet's take the same data as in the [quickstart](https://opennmt.net/OpenNMT-py/quickstart.html):\n\n\n```python\n!wget https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz\n```\n\n    --2020-09-25 15:28:05--  https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz\n    Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.18.38\n    Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.18.38|:443... connected.\n    HTTP request sent, awaiting response... 200 OK\n    Length: 1662081 (1,6M) [application/x-gzip]\n    Saving to: ‘toy-ende.tar.gz.5’\n    \n    toy-ende.tar.gz.5   100%[===================>]   1,58M  2,33MB/s    in 0,7s    \n    \n    2020-09-25 15:28:07 (2,33 MB/s) - ‘toy-ende.tar.gz.5’ saved [1662081/1662081]\n    \n\n\n\n```python\n!tar xf toy-ende.tar.gz\n```\n\n\n```python\nls toy-ende\n```\n\n    config.yaml  src-test.txt   src-val.txt   tgt-train.txt\n    \u001b[0m\u001b[01;34mrun\u001b[0m/         src-train.txt  tgt-test.txt  tgt-val.txt\n\n\n### Prepare data and vocab\n\nAs for any use case of OpenNMT-py 2.0, we can start by creating a simple YAML configuration with our datasets. This is the easiest way to build the proper `opts` `Namespace` that will be used to create the vocabulary(ies).\n\n\n```python\nyaml_config = \"\"\"\n## Where the samples will be written\nsave_data: toy-ende/run/example\n## Where the vocab(s) will be written\nsrc_vocab: toy-ende/run/example.vocab.src\ntgt_vocab: toy-ende/run/example.vocab.tgt\n# Corpus opts:\ndata:\n    corpus:\n        path_src: toy-ende/src-train.txt\n        path_tgt: toy-ende/tgt-train.txt\n        transforms: []\n        weight: 1\n    valid:\n        path_src: toy-ende/src-val.txt\n        path_tgt: toy-ende/tgt-val.txt\n        transforms: []\n\"\"\"\nconfig = yaml.safe_load(yaml_config)\nwith open(\"toy-ende/config.yaml\", \"w\") as f:\n    f.write(yaml_config)\n```\n\n\n```python\nfrom onmt.utils.parse import ArgumentParser\nparser = DynamicArgumentParser(description='build_vocab.py')\n```\n\n\n```python\nfrom onmt.opts import dynamic_prepare_opts\ndynamic_prepare_opts(parser, build_vocab_only=True)\n```\n\n\n```python\nbase_args = ([\"-config\", \"toy-ende/config.yaml\", \"-n_sample\", \"10000\"])\nopts, unknown = parser.parse_known_args(base_args)\n```\n\n\n```python\nopts\n```\n\n\n\n\n    Namespace(config='toy-ende/config.yaml', data=\"{'corpus': {'path_src': 'toy-ende/src-train.txt', 'path_tgt': 'toy-ende/tgt-train.txt', 'transforms': [], 'weight': 1}, 'valid': {'path_src': 'toy-ende/src-val.txt', 'path_tgt': 'toy-ende/tgt-val.txt', 'transforms': []}}\", insert_ratio=0.0, mask_length='subword', mask_ratio=0.0, n_sample=10000, src_onmttok_kwargs=\"{'mode': 'none'}\", tgt_onmttok_kwargs=\"{'mode': 'none'}\", overwrite=False, permute_sent_ratio=0.0, poisson_lambda=0.0, random_ratio=0.0, replace_length=-1, rotate_ratio=0.5, save_config=None, save_data='toy-ende/run/example', seed=-1, share_vocab=False, skip_empty_level='warning', src_seq_length=200, src_subword_model=None, src_subword_type='none', src_vocab=None, subword_alpha=0, subword_nbest=1, switchout_temperature=1.0, tgt_seq_length=200, tgt_subword_model=None, tgt_subword_type='none', tgt_vocab=None, tokendrop_temperature=1.0, tokenmask_temperature=1.0, transforms=[])\n\n\n\n\n```python\nfrom onmt.bin.build_vocab import build_vocab_main\nbuild_vocab_main(opts)\n```\n\n    [2020-09-25 15:28:08,068 INFO] Parsed 2 corpora from -data.\n    [2020-09-25 15:28:08,069 INFO] Counter vocab from 10000 samples.\n    [2020-09-25 15:28:08,070 INFO] Save 10000 transformed example/corpus.\n    [2020-09-25 15:28:08,070 INFO] corpus's transforms: TransformPipe()\n    [2020-09-25 15:28:08,101 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:28:08,320 INFO] Just finished the first loop\n    [2020-09-25 15:28:08,320 INFO] Counters src:24995\n    [2020-09-25 15:28:08,321 INFO] Counters tgt:35816\n\n\n\n```python\nls toy-ende/run\n```\n\n    example.vocab.src  example.vocab.tgt  \u001b[0m\u001b[01;34msample\u001b[0m/\n\n\nWe just created our source and target vocabularies, respectively `toy-ende/run/example.vocab.src` and `toy-ende/run/example.vocab.tgt`.\n\n### Build fields\n\nWe can build the fields from the text files that were just created.\n\n\n```python\nsrc_vocab_path = \"toy-ende/run/example.vocab.src\"\ntgt_vocab_path = \"toy-ende/run/example.vocab.tgt\"\n```\n\n\n```python\n# initialize the frequency counter\ncounters = defaultdict(Counter)\n# load source vocab\n_src_vocab, _src_vocab_size = _load_vocab(\n    src_vocab_path,\n    'src',\n    counters)\n# load target vocab\n_tgt_vocab, _tgt_vocab_size = _load_vocab(\n    tgt_vocab_path,\n    'tgt',\n    counters)\n```\n\n    [2020-09-25 15:28:08,495 INFO] Loading src vocabulary from toy-ende/run/example.vocab.src\n    [2020-09-25 15:28:08,554 INFO] Loaded src vocab has 24995 tokens.\n    [2020-09-25 15:28:08,562 INFO] Loading tgt vocabulary from toy-ende/run/example.vocab.tgt\n    [2020-09-25 15:28:08,617 INFO] Loaded tgt vocab has 35816 tokens.\n\n\n\n```python\n# initialize fields\nsrc_nfeats, tgt_nfeats = 0, 0 # do not support word features for now\nfields = get_fields(\n    'text', src_nfeats, tgt_nfeats)\n```\n\n\n```python\nfields\n```\n\n\n\n\n    {'src': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802c50>,\n     'tgt': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802f60>,\n     'indices': <torchtext.data.field.Field at 0x7fca93802940>}\n\n\n\n\n```python\n# build fields vocab\nshare_vocab = False\nvocab_size_multiple = 1\nsrc_vocab_size = 30000\ntgt_vocab_size = 30000\nsrc_words_min_frequency = 1\ntgt_words_min_frequency = 1\nvocab_fields = _build_fields_vocab(\n    fields, counters, 'text', share_vocab,\n    vocab_size_multiple,\n    src_vocab_size, src_words_min_frequency,\n    tgt_vocab_size, tgt_words_min_frequency)\n```\n\n    [2020-09-25 15:28:08,699 INFO]  * tgt vocab size: 30004.\n    [2020-09-25 15:28:08,749 INFO]  * src vocab size: 24997.\n\n\nAn alternative way of creating these fields is to run `onmt_train` without actually training, to just output the necessary files.\n\n### Prepare for training: model and optimizer creation\n\nLet's get a few fields/vocab related variables to simplify the model creation a bit:\n\n\n```python\nsrc_text_field = vocab_fields[\"src\"].base_field\nsrc_vocab = src_text_field.vocab\nsrc_padding = src_vocab.stoi[src_text_field.pad_token]\n\ntgt_text_field = vocab_fields['tgt'].base_field\ntgt_vocab = tgt_text_field.vocab\ntgt_padding = tgt_vocab.stoi[tgt_text_field.pad_token]\n```\n\nNext we specify the core model itself. Here we will build a small model with an encoder and an attention based input feeding decoder. Both models will be RNNs and the encoder will be bidirectional\n\n\n```python\nemb_size = 100\nrnn_size = 500\n# Specify the core model.\n\nencoder_embeddings = onmt.modules.Embeddings(emb_size, len(src_vocab),\n                                             word_padding_idx=src_padding)\n\nencoder = onmt.encoders.RNNEncoder(hidden_size=rnn_size, num_layers=1,\n                                   rnn_type=\"LSTM\", bidirectional=True,\n                                   embeddings=encoder_embeddings)\n\ndecoder_embeddings = onmt.modules.Embeddings(emb_size, len(tgt_vocab),\n                                             word_padding_idx=tgt_padding)\ndecoder = onmt.decoders.decoder.InputFeedRNNDecoder(\n    hidden_size=rnn_size, num_layers=1, bidirectional_encoder=True, \n    rnn_type=\"LSTM\", embeddings=decoder_embeddings)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel = onmt.models.model.NMTModel(encoder, decoder)\nmodel.to(device)\n\n# Specify the tgt word generator and loss computation module\nmodel.generator = nn.Sequential(\n    nn.Linear(rnn_size, len(tgt_vocab)),\n    nn.LogSoftmax(dim=-1)).to(device)\n\nloss = onmt.utils.loss.NMTLossCompute(\n    criterion=nn.NLLLoss(ignore_index=tgt_padding, reduction=\"sum\"),\n    generator=model.generator)\n```\n\nNow we set up the optimizer. This could be a core torch optim class, or our wrapper which handles learning rate updates and gradient normalization automatically.\n\n\n```python\nlr = 1\ntorch_optimizer = torch.optim.SGD(model.parameters(), lr=lr)\noptim = onmt.utils.optimizers.Optimizer(\n    torch_optimizer, learning_rate=lr, max_grad_norm=2)\n```\n\n### Create the training and validation data iterators\n\nNow we need to create the dynamic dataset iterator.\n\nThis is not very 'library-friendly' for now because of the way the `DynamicDatasetIter` constructor is defined. It may evolve in the future.\n\n\n```python\nsrc_train = \"toy-ende/src-train.txt\"\ntgt_train = \"toy-ende/tgt-train.txt\"\nsrc_val = \"toy-ende/src-val.txt\"\ntgt_val = \"toy-ende/tgt-val.txt\"\n\n# build the ParallelCorpus\ncorpus = ParallelCorpus(\"corpus\", src_train, tgt_train)\nvalid = ParallelCorpus(\"valid\", src_val, tgt_val)\n```\n\n\n```python\n# build the training iterator\ntrain_iter = DynamicDatasetIter(\n    corpora={\"corpus\": corpus},\n    corpora_info={\"corpus\": {\"weight\": 1}},\n    transforms={},\n    fields=vocab_fields,\n    is_train=True,\n    batch_type=\"tokens\",\n    batch_size=4096,\n    batch_size_multiple=1,\n    data_type=\"text\")\n```\n\n\n```python\n# make sure the iteration happens on GPU 0 (-1 for CPU, N for GPU N)\ntrain_iter = iter(IterOnDevice(train_iter, 0))\n```\n\n\n```python\n# build the validation iterator\nvalid_iter = DynamicDatasetIter(\n    corpora={\"valid\": valid},\n    corpora_info={\"valid\": {\"weight\": 1}},\n    transforms={},\n    fields=vocab_fields,\n    is_train=False,\n    batch_type=\"sents\",\n    batch_size=8,\n    batch_size_multiple=1,\n    data_type=\"text\")\n```\n\n\n```python\nvalid_iter = IterOnDevice(valid_iter, 0)\n```\n\n### Training\n\nFinally we train.\n\n\n```python\nreport_manager = onmt.utils.ReportMgr(\n    report_every=50, start_time=None, tensorboard_writer=None)\n\ntrainer = onmt.Trainer(model=model,\n                       train_loss=loss,\n                       valid_loss=loss,\n                       optim=optim,\n                       report_manager=report_manager,\n                       dropout=[0.1])\n\ntrainer.train(train_iter=train_iter,\n              train_steps=1000,\n              valid_iter=valid_iter,\n              valid_steps=500)\n```\n\n    [2020-09-25 15:28:15,184 INFO] Start training loop and validate every 500 steps...\n    [2020-09-25 15:28:15,185 INFO] corpus's transforms: TransformPipe()\n    [2020-09-25 15:28:15,187 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:28:21,140 INFO] Step 50/ 1000; acc:   7.52; ppl: 8832.29; xent: 9.09; lr: 1.00000; 18916/18871 tok/s;      6 sec\n    [2020-09-25 15:28:24,869 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:28:27,121 INFO] Step 100/ 1000; acc:   9.34; ppl: 1840.06; xent: 7.52; lr: 1.00000; 18911/18785 tok/s;     12 sec\n    [2020-09-25 15:28:33,048 INFO] Step 150/ 1000; acc:  10.35; ppl: 1419.18; xent: 7.26; lr: 1.00000; 19062/19017 tok/s;     18 sec\n    [2020-09-25 15:28:37,019 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:28:39,022 INFO] Step 200/ 1000; acc:  11.14; ppl: 1127.44; xent: 7.03; lr: 1.00000; 19084/18911 tok/s;     24 sec\n    [2020-09-25 15:28:45,073 INFO] Step 250/ 1000; acc:  12.46; ppl: 912.13; xent: 6.82; lr: 1.00000; 18575/18570 tok/s;     30 sec\n    [2020-09-25 15:28:49,301 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:28:51,151 INFO] Step 300/ 1000; acc:  13.04; ppl: 779.50; xent: 6.66; lr: 1.00000; 18394/18307 tok/s;     36 sec\n    [2020-09-25 15:28:57,316 INFO] Step 350/ 1000; acc:  14.04; ppl: 685.48; xent: 6.53; lr: 1.00000; 18339/18173 tok/s;     42 sec\n    [2020-09-25 15:29:02,117 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:29:03,576 INFO] Step 400/ 1000; acc:  14.99; ppl: 590.20; xent: 6.38; lr: 1.00000; 18090/18029 tok/s;     48 sec\n    [2020-09-25 15:29:09,546 INFO] Step 450/ 1000; acc:  16.00; ppl: 524.51; xent: 6.26; lr: 1.00000; 18726/18536 tok/s;     54 sec\n    [2020-09-25 15:29:14,585 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:29:15,596 INFO] Step 500/ 1000; acc:  16.78; ppl: 453.38; xent: 6.12; lr: 1.00000; 17877/17980 tok/s;     60 sec\n    [2020-09-25 15:29:15,597 INFO] valid's transforms: TransformPipe()\n    [2020-09-25 15:29:15,599 INFO] Loading ParallelCorpus(toy-ende/src-val.txt, toy-ende/tgt-val.txt, align=None)...\n    [2020-09-25 15:29:24,528 INFO] Validation perplexity: 295.1\n    [2020-09-25 15:29:24,529 INFO] Validation accuracy: 17.6533\n    [2020-09-25 15:29:30,592 INFO] Step 550/ 1000; acc:  17.47; ppl: 421.26; xent: 6.04; lr: 1.00000; 7726/7610 tok/s;     75 sec\n    [2020-09-25 15:29:36,055 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:29:36,695 INFO] Step 600/ 1000; acc:  18.95; ppl: 354.44; xent: 5.87; lr: 1.00000; 17470/17598 tok/s;     82 sec\n    [2020-09-25 15:29:42,794 INFO] Step 650/ 1000; acc:  19.60; ppl: 328.47; xent: 5.79; lr: 1.00000; 18994/18793 tok/s;     88 sec\n    [2020-09-25 15:29:48,635 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:29:48,924 INFO] Step 700/ 1000; acc:  20.57; ppl: 285.48; xent: 5.65; lr: 1.00000; 17856/17788 tok/s;     94 sec\n    [2020-09-25 15:29:54,898 INFO] Step 750/ 1000; acc:  21.97; ppl: 249.06; xent: 5.52; lr: 1.00000; 19030/18924 tok/s;    100 sec\n    [2020-09-25 15:30:01,233 INFO] Step 800/ 1000; acc:  22.66; ppl: 228.54; xent: 5.43; lr: 1.00000; 17571/17471 tok/s;    106 sec\n    [2020-09-25 15:30:01,357 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:30:07,345 INFO] Step 850/ 1000; acc:  24.32; ppl: 193.65; xent: 5.27; lr: 1.00000; 18344/18313 tok/s;    112 sec\n    [2020-09-25 15:30:11,363 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:30:13,487 INFO] Step 900/ 1000; acc:  24.93; ppl: 177.65; xent: 5.18; lr: 1.00000; 18293/18105 tok/s;    118 sec\n    [2020-09-25 15:30:19,670 INFO] Step 950/ 1000; acc:  26.33; ppl: 157.10; xent: 5.06; lr: 1.00000; 17791/17746 tok/s;    124 sec\n    [2020-09-25 15:30:24,072 INFO] Loading ParallelCorpus(toy-ende/src-train.txt, toy-ende/tgt-train.txt, align=None)...\n    [2020-09-25 15:30:25,820 INFO] Step 1000/ 1000; acc:  27.47; ppl: 137.64; xent: 4.92; lr: 1.00000; 17942/17962 tok/s;    131 sec\n    [2020-09-25 15:30:25,822 INFO] Loading ParallelCorpus(toy-ende/src-val.txt, toy-ende/tgt-val.txt, align=None)...\n    [2020-09-25 15:30:34,665 INFO] Validation perplexity: 241.801\n    [2020-09-25 15:30:34,666 INFO] Validation accuracy: 20.2837\n\n\n\n\n\n    <onmt.utils.statistics.Statistics at 0x7fca934e8e80>\n\n\n\n### Translate\n\nFor translation, we can build a \"traditional\" (as opposed to dynamic) dataset for now.\n\n\n```python\nsrc_data = {\"reader\": onmt.inputters.str2reader[\"text\"](), \"data\": src_val}\ntgt_data = {\"reader\": onmt.inputters.str2reader[\"text\"](), \"data\": tgt_val}\n_readers, _data = onmt.inputters.Dataset.config(\n    [('src', src_data), ('tgt', tgt_data)])\n```\n\n\n```python\ndataset = onmt.inputters.Dataset(\n    vocab_fields, readers=_readers, data=_data,\n    sort_key=onmt.inputters.str2sortkey[\"text\"])\n```\n\n\n```python\ndata_iter = onmt.inputters.OrderedIterator(\n            dataset=dataset,\n            device=\"cuda\",\n            batch_size=10,\n            train=False,\n            sort=False,\n            sort_within_batch=True,\n            shuffle=False\n        )\n```\n\n\n```python\nsrc_reader = onmt.inputters.str2reader[\"text\"]\ntgt_reader = onmt.inputters.str2reader[\"text\"]\nscorer = GNMTGlobalScorer(alpha=0.7, \n                          beta=0., \n                          length_penalty=\"avg\", \n                          coverage_penalty=\"none\")\ngpu = 0 if torch.cuda.is_available() else -1\ntranslator = Translator(model=model, \n                        fields=vocab_fields, \n                        src_reader=src_reader, \n                        tgt_reader=tgt_reader, \n                        global_scorer=scorer,\n                        gpu=gpu)\nbuilder = onmt.translate.TranslationBuilder(data=dataset, \n                                            fields=vocab_fields)\n```\n\n**Note**: translations will be very poor, because of the very low quantity of data, the absence of proper tokenization, and the brevity of the training.\n\n\n```python\nfor batch in data_iter:\n    trans_batch = translator.translate_batch(\n        batch=batch, src_vocabs=[src_vocab],\n        attn_debug=False)\n    translations = builder.from_batch(trans_batch)\n    for trans in translations:\n        print(trans.log(0))\n    break\n```\n\n    \n    SENT 0: ['Parliament', 'Does', 'Not', 'Support', 'Amendment', 'Freeing', 'Tymoshenko']\n    PRED 0: Parlament das Parlament über die Europäische Parlament , die sich in der Lage in der Lage ist , die es in der Lage sind .\n    PRED SCORE: -1.5935\n    \n    \n    SENT 0: ['Today', ',', 'the', 'Ukraine', 'parliament', 'dismissed', ',', 'within', 'the', 'Code', 'of', 'Criminal', 'Procedure', 'amendment', ',', 'the', 'motion', 'to', 'revoke', 'an', 'article', 'based', 'on', 'which', 'the', 'opposition', 'leader', ',', 'Yulia', 'Tymoshenko', ',', 'was', 'sentenced', '.']\n    PRED 0: In der Nähe des Hotels , die in der Lage , die sich in der Lage ist , in der Lage , die in der Lage , die in der Lage ist .\n    PRED SCORE: -1.7173\n    \n    \n    SENT 0: ['The', 'amendment', 'that', 'would', 'lead', 'to', 'freeing', 'the', 'imprisoned', 'former', 'Prime', 'Minister', 'was', 'revoked', 'during', 'second', 'reading', 'of', 'the', 'proposal', 'for', 'mitigation', 'of', 'sentences', 'for', 'economic', 'offences', '.']\n    PRED 0: Die Tatsache , die sich in der Lage in der Lage ist , die für eine Antwort der Entwicklung für die Entwicklung von Präsident .\n    PRED SCORE: -1.6834\n    \n    \n    SENT 0: ['In', 'October', ',', 'Tymoshenko', 'was', 'sentenced', 'to', 'seven', 'years', 'in', 'prison', 'for', 'entering', 'into', 'what', 'was', 'reported', 'to', 'be', 'a', 'disadvantageous', 'gas', 'deal', 'with', 'Russia', '.']\n    PRED 0: In der Nähe wurde die Menschen in der Lage ist , die sich in der Lage <unk> .\n    PRED SCORE: -1.5765\n    \n    \n    SENT 0: ['The', 'verdict', 'is', 'not', 'yet', 'final;', 'the', 'court', 'will', 'hear', 'Tymoshenko', '&apos;s', 'appeal', 'in', 'December', '.']\n    PRED 0: Es ist nicht der Fall , die in der Lage in der Lage sind .\n    PRED SCORE: -1.3287\n    \n    \n    SENT 0: ['Tymoshenko', 'claims', 'the', 'verdict', 'is', 'a', 'political', 'revenge', 'of', 'the', 'regime;', 'in', 'the', 'West', ',', 'the', 'trial', 'has', 'also', 'evoked', 'suspicion', 'of', 'being', 'biased', '.']\n    PRED 0: Um in der Lage ist auch eine Lösung Rolle .\n    PRED SCORE: -1.3975\n    \n    \n    SENT 0: ['The', 'proposal', 'to', 'remove', 'Article', '365', 'from', 'the', 'Code', 'of', 'Criminal', 'Procedure', ',', 'upon', 'which', 'the', 'former', 'Prime', 'Minister', 'was', 'sentenced', ',', 'was', 'supported', 'by', '147', 'members', 'of', 'parliament', '.']\n    PRED 0: Der Vorschlag , die in der Lage , die in der Lage , die in der Lage ist , war er von der Fall <unk> wurde .\n    PRED SCORE: -1.6062\n    \n    \n    SENT 0: ['Its', 'ratification', 'would', 'require', '226', 'votes', '.']\n    PRED 0: Es wäre noch einmal noch einmal <unk> .\n    PRED SCORE: -1.8001\n    \n    \n    SENT 0: ['Libya', '&apos;s', 'Victory']\n    PRED 0: In der Nähe des Hotels befindet sich in der Nähe des Hotels in der Lage .\n    PRED SCORE: -1.7097\n    \n    \n    SENT 0: ['The', 'story', 'of', 'Libya', '&apos;s', 'liberation', ',', 'or', 'rebellion', ',', 'already', 'has', 'its', 'defeated', '.']\n    PRED 0: In der Nähe des Hotels in der Lage ist in der Lage .\n    PRED SCORE: -1.7885\n"
  },
  {
    "path": "docs/source/examples/Summarization.md",
    "content": "# Summarization\n\nNote: The process and results below are presented in the paper `Bottom-Up Abstractive Summarization`. Please consider citing it if you follow these instructions.\n\n```\n@inproceedings{gehrmann2018bottom,\n  title={Bottom-Up Abstractive Summarization},\n  author={Gehrmann, Sebastian and Deng, Yuntian and Rush, Alexander},\n  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},\n  pages={4098--4109},\n  year={2018}\n}\n```\n\n\nThis document describes how to replicate summarization experiments on the CNN-DM and gigaword datasets using OpenNMT-py.\nIn the following, we assume access to a tokenized form of the corpus split into train/valid/test set. You can find the data [here](https://github.com/harvardnlp/sent-summary).\n\nAn example article-title pair from Gigaword should look like this:\n\n**Input**\n*australia 's current account deficit shrunk by a record #.## billion dollars -lrb- #.## billion us -rrb- in the june quarter due to soaring commodity prices , figures released monday showed .*\n\n**Output**\n*australian current account deficit narrows sharply*\n\n\n### Preparing the data and vocab\n\nFor CNN-DM we follow See et al. [2] and additionally truncate the source length at 400 tokens and the target at 100. We also note that in CNN-DM, we found models to work better if the target surrounds sentences with tags such that a sentence looks like `<t> w1 w2 w3 . </t>`. If you use this formatting, you can remove the tags after the inference step with the commands `sed -i 's/ <\\/t>//g' FILE.txt` and `sed -i 's/<t> //g' FILE.txt`.\n\n**YAML Configuration**:\n\n```yaml\n# cnndm.yaml\n\n## Where the samples will be written\nsave_data: cnndm/run/example\n## Where the vocab(s) will be written\nsrc_vocab: cnndm/run/example.vocab.src\ntgt_vocab: cnndm/run/example.vocab.tgt\n# Prevent overwriting existing files in the folder\noverwrite: False\n\n# truncate examples\nsrc_seq_length_trunc: 400\ntgt_seq_length_trunc: 100\n\n# common vocabulary for source and target\nshare_vocab: True\n\n# Corpus opts:\ndata:\n    cnndm:\n        path_src: cnndm/train.txt.src\n        path_tgt: cnndm/train.txt.tgt.tagged\n    valid:\n        path_src: cnndm/val.txt.src\n        path_tgt: cnndm/val.txt.tgt.tagged\n...\n```\n\nLet's compute the vocab over the full dataset (`-n_sample -1`):\n```bash\nonmt_build_vocab -config cnndm.yaml -n_sample -1\n```\n\nThis command will have written source and target vocabulary to `cnndm/run/example.vocab.src` and `cnndm/run/example.vocab.tgt`. These two files should be the same, as `share_vocab` is set.\n\n### Training\n\nThe training procedure described in this section for the most part follows parameter choices and implementation similar to that of See et al. [2].\n\nMost significant options are:\n\n- `copy_attn`: This is the most important option, since it allows the model to copy words from the source.\n- `global_attention mlp`: This makes the model use the  attention mechanism introduced by Bahdanau et al. [3] instead of that by Luong et al. [4] (`global_attention dot`).\n- `share_embeddings`: This shares the word embeddings between encoder and decoder. This option drastically decreases the number of parameters a model has to learn. We did not find this option to helpful, but you can try it out by adding it to the command below.\n-  `reuse_copy_attn`: This option reuses the standard attention as copy attention. Without this, the model learns an additional attention that is only used for copying.\n-  `copy_loss_by_seqlength`: This modifies the loss to divide the loss of a sequence by the number of tokens in it. In practice, we found this to generate longer sequences during inference. However, this effect can also be achieved by using penalties during decoding.\n-  `bridge`: This is an additional layer that uses the final hidden state of the encoder as input and computes an initial hidden state for the decoder. Without this, the decoder is initialized with the final hidden state of the encoder directly.\n-  `optim adagrad`: Adagrad outperforms SGD when coupled with the following option.\n-  `adagrad_accumulator_init 0.1`: PyTorch does not initialize the accumulator in adagrad with any values. To match the optimization algorithm with the Tensorflow version, this option needs to be added.\n\nNote: Since we are using copy-attention [1] in the model, additional fields will be computed so that source and target are aligned and use the same dictionary. Previously achieved with the `-dynamic_dict` preprocessing flag in the legacy version, this is now automatically handled when `-copy_attn` is enabled.\n\nWe are using using a 128-dimensional word-embedding, and 512-dimensional 1 layer LSTM. On the encoder side, we use a bidirectional LSTM (`brnn`), which means that the 512 dimensions are split into 256 dimensions per direction.\n\nWe additionally set the maximum norm of the gradient to 2, and renormalize if the gradient norm exceeds this value and do not use any dropout.\n\n**Configurations**:\n\n(1) CNN-DM\n\nThe basic RNN configuration is defined by these parameters:\n\n```yaml\n# maximum vocab size\nsrc_vocab_size: 50000\ntgt_vocab_size: 50000\n\nsrc_vocab: cnndm/run/example.vocab.src\ntgt_vocab: cnndm/run/example.vocab.tgt\n\nsave_model: cnndm/run/model\ncopy_attn: true\nglobal_attention: mlp\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\nencoder_type: brnn\ntrain_steps: 200000\nmax_grad_norm: 2\ndropout: 0\nbatch_size: 16\nvalid_batch_size: 16\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nreuse_copy_attn: true\ncopy_loss_by_seqlength: true\nbridge: true\nseed: 777\nworld_size: 2\ngpu_ranks: [0, 1]\n```\n\n(2) CNN-DM Transformer\n\nTransformer configuration is the following:\n\n```yaml\nsrc_vocab_size: 50000\ntgt_vocab_size: 50000\n\nsrc_vocab: cnndm/run/example.vocab.src\ntgt_vocab: cnndm/run/example.vocab.tgt\n\nsave_model: cnndm/run/model_transformer\nlayers: 4\nrnn_size: 512\nword_vec_size: 512\nmax_grad_norm: 0\noptim: adam\nencoder_type: transformer\ndecoder_type: transformer\nposition_encoding: true\ndropout: 0.2\nattention_dropout: 0.2\nparam_init: 0\nwarmup_steps: 8000\nlearning_rate: 2\ndecay_method: noam\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nbatch_size: 4096\nbatch_type: tokens\nnormalization: tokens\ntrain_steps: 200000\naccum_count: 4\nshare_embeddings: true\ncopy_attn: true\nparam_init_glorot: true\nworld_size: 2\ngpu_ranks: [0, 1]\n```\n\n(3) Gigaword\n\nGigaword can be trained equivalently. You just need to adapt the `data` part of the YAML configuration. \n\n```yaml\n# gigaword.yaml\n\n## Where the vocab(s) will be written\nsave_data: gigaword/run/example\n# Prevent overwriting existing files in the folder\noverwrite: False\n\n# prevent filtering of long examples\nsrc_seq_length: 10000\ntgt_seq_length: 10000\n\n# common vocabulary for source and target\nshare_vocab: True\n\n# Corpus opts:\ndata:\n    cnndm:\n        path_src: gigaword/train.article.txt\n        path_tgt: gigaword/train.title.txt\n        transforms: [filtertoolong]\n        weight: 1\n    valid:\n        path_src: gigaword/valid.article.txt\n        path_tgt: gigaword/valid.title.txt\n        transforms: [filtertoolong]\n...\n```\n\n\n### Inference\n\nDuring inference, we use beam-search with a beam-size of 10. We also added specific penalties that we can use during decoding, described in the following.\n\n- `stepwise_penalty`: Applies penalty at every step\n- `coverage_penalty summary`: Uses a penalty that prevents repeated attention to the same source word\n- `beta 5`: Parameter for the Coverage Penalty\n- `length_penalty wu`: Uses the Length Penalty by Wu et al.\n- `alpha 0.8`: Parameter for the Length Penalty.\n- `block_ngram_repeat 3`: Prevent the model from repeating trigrams.\n- `ignore_when_blocking \".\" \"</t>\" \"<t>\"`: Allow the model to repeat trigrams with the sentence boundary tokens.\n\n**Commands used**:\n\n(1) CNN-DM\n\n```\nonmt_translate -gpu X \\\n               -batch_size 20 \\\n               -beam_size 10 \\\n               -model cnndm/run/... \\\n               -src cnndm/test.txt.src \\\n               -output testout/cnndm.out \\\n               -min_length 35 \\\n               -verbose \\\n               -stepwise_penalty \\\n               -coverage_penalty summary \\\n               -beta 5 \\\n               -length_penalty wu \\\n               -alpha 0.9 \\\n               -verbose \\\n               -block_ngram_repeat 3 \\\n               -ignore_when_blocking \".\" \"</t>\" \"<t>\"\n```\n\n\n### Evaluation\n\n#### CNN-DM\n\nTo evaluate the ROUGE scores on CNN-DM, we extended the `pyrouge` wrapper with additional evaluations such as the amount of repeated n-grams (typically found in models with copy attention), found [here](https://github.com/sebastianGehrmann/rouge-baselines). The repository includes a sub-repo called pyrouge. Make sure to clone the code with the `git clone --recurse-submodules https://github.com/sebastianGehrmann/rouge-baselines` command to check this out as well and follow the installation instructions on the pyrouge repository before calling this script.\nThe installation instructions can be found [here](https://github.com/falcondai/pyrouge/tree/9cdbfbda8b8d96e7c2646ffd048743ddcf417ed9#installation). Note that on MacOS, we found that the pointer to your perl installation in line 1 of `pyrouge/RELEASE-1.5.5/ROUGE-1.5.5.pl` might be different from the one you have installed. A simple fix is to change this line to `#!/usr/local/bin/perl -w` if it fails.\n\nIt can be run with the following command:\n\n```\npython baseline.py -s testout/cnndm.out -t data/cnndm/test.txt.tgt.tagged -m sent_tag_verbatim -r\n```\n\nThe `sent_tag_verbatim` option strips `<t>` and `</t>` tags around sentences - when a sentence previously was `<t> w w w w . </t>`, it becomes `w w w w .`.\n\n#### Gigaword\n\nFor evaluation of large test sets such as Gigaword, we use the a parallel python wrapper around ROUGE, found [here](https://github.com/pltrdy/files2rouge).\n\n**Command used**:\n`files2rouge giga.out test.title.txt --verbose`\n\n### Scores and Models\n\n#### CNN-DM\n\n| Model Type    | Model    | R1 R  | R1 P  | R1 F  | R2 R  | R2 P  | R2 F  | RL R  | RL P  | RL F  |\n| ------------- |  -------- | -----:| -----:| -----:|------:| -----:| -----:|-----: | -----:| -----:|\n| Pointer-Generator + Coverage [2]     | [link](https://github.com/abisee/pointer-generator)  | 39.05 |\t43.02 |\t39.53 |\t17.16 | 18.77 | 17.28  | 35.98 | 39.56 | 36.38 |\n| Pointer-Generator [2]  |  [link](https://github.com/abisee/pointer-generator)  | 37.76 | 37.60| 36.44| 16.31| 16.12| 15.66| 34.66| 34.46| 33.42 |\n| OpenNMT BRNN  (1 layer, emb 128, hid 512)  |  [link](https://s3.amazonaws.com/opennmt-models/Summary/ada6_bridge_oldcopy_tagged_acc_54.17_ppl_11.17_e20.pt)     | 40.90| 40.20| \t39.02| \t17.91| \t17.99| \t17.25| \t37.76\t| 37.18| \t36.05 |\n| OpenNMT BRNN  (1 layer, emb 128, hid 512, shared embeddings)  |  [link](https://s3.amazonaws.com/opennmt-models/Summary/ada6_bridge_oldcopy_tagged_share_acc_54.50_ppl_10.89_e20.pt)     | 38.59\t| 40.60\t| 37.97\t| 16.75\t| 17.93\t| 16.59\t| 35.67\t| 37.60\t| 35.13 |\n| OpenNMT BRNN (2 layer, emb 256, hid 1024)   |  [link](https://s3.amazonaws.com/opennmt-models/Summary/ada6_bridge_oldcopy_tagged_larger_acc_54.84_ppl_10.58_e17.pt)     | 40.41\t| 40.94 | 39.12 | 17.76 | 18.38 | 17.35 | 37.27 | 37.83 | 36.12 |\n| OpenNMT Transformer  |  [link](https://s3.amazonaws.com/opennmt-models/sum_transformer_model_acc_57.25_ppl_9.22_e16.pt)  | 40.31\t| 41.09\t| 39.25\t| 17.97\t| 18.46\t| 17.54\t| 37.41\t| 38.18\t| 36.45 |\n\n\n#### Gigaword\n\n| Model Type    | Model    | R1 R  | R1 P  | R1 F  | R2 R  | R2 P  | R2 F  | RL R  | RL P  | RL F  |\n| ------------- |  -------- | -----:| -----:| -----:|------:| -----:| -----:|-----: | -----:| -----:|\n| OpenNMT, no penalties | [link](https://s3.amazonaws.com/opennmt-models/gigaword_copy_acc_51.78_ppl_11.71_e20.pt)  | ? |\t? |\t35.51 |\t? | ? | 17.35  | ? | ? | 33.17 |\n\n\n\n### References\n\n[1] Vinyals, O., Fortunato, M. and Jaitly, N., 2015. Pointer Network. NIPS\n\n[2] See, A., Liu, P.J. and Manning, C.D., 2017. Get To The Point: Summarization with Pointer-Generator Networks. ACL\n\n[3] Bahdanau, D., Cho, K. and Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. ICLR\n\n[4] Luong, M.T., Pham, H. and Manning, C.D., 2015. Effective approaches to attention-based neural machine translation. EMNLP\n"
  },
  {
    "path": "docs/source/examples/Translation.md",
    "content": "\n# Translation\n\nThis example is for training for the [WMT'14 English to German news translation task](https://www.statmt.org/wmt14/translation-task.html). It will use on the fly tokenization with [sentencepiece](https://github.com/google/sentencepiece) and [sacrebleu](https://github.com/mjpost/sacrebleu) for evaluation.\n\n\n## Step 0: Download the data and prepare the subwords model\n\nPreliminary steps are defined in the [`examples/scripts/prepare_wmt_data.sh`](https://github.com/OpenNMT/OpenNMT-py/tree/master/examples/scripts/prepare_wmt_data.sh). The following command will download the necessary datasets, and prepare a sentencepiece model:\n```bash\nchmod u+x prepare_wmt_data.sh\n./prepare_wmt_data.sh\n```\n\nNote: you should have installed [sentencepiece](https://github.com/google/sentencepiece) binaries before running this script.\n\n## Step 1. Build the vocabulary.\n\nWe need to setup the desired configuration with 1. the data 2. the tokenization options:\n\n```yaml\n# wmt14_en_de.yaml\nsave_data: data/wmt/run/example\n## Where the vocab(s) will be written\nsrc_vocab: data/wmt/run/example.vocab.src\ntgt_vocab: data/wmt/run/example.vocab.tgt\n\n# Corpus opts:\ndata:\n    commoncrawl:\n        path_src: data/wmt/commoncrawl.de-en.en\n        path_tgt: data/wmt/commoncrawl.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 23\n    europarl:\n        path_src: data/wmt/europarl-v7.de-en.en\n        path_tgt: data/wmt/europarl-v7.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 19\n    news_commentary:\n        path_src: data/wmt/news-commentary-v11.de-en.en\n        path_tgt: data/wmt/news-commentary-v11.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 3\n    valid:\n        path_src: data/wmt/valid.en\n        path_tgt: data/wmt/valid.de\n        transforms: [sentencepiece]\n\n### Transform related opts:\n#### Subword\nsrc_subword_model: data/wmt/wmtende.model\ntgt_subword_model: data/wmt/wmtende.model\nsrc_subword_nbest: 1\nsrc_subword_alpha: 0.0\ntgt_subword_nbest: 1\ntgt_subword_alpha: 0.0\n#### Filter\nsrc_seq_length: 150\ntgt_seq_length: 150\n\n# silently ignore empty lines in the data\nskip_empty_level: silent\n\n```\n\nThen we can execute the vocabulary building script. Let's set `-n_sample` to `-1` to compute the vocabulary over the whole corpora:\n\n```bash\nonmt_build_vocab -config wmt14_en_de.yaml -n_sample -1\n```\n\n## Step 2: Train the model\n\nWe need to add the following parameters to the YAML configuration:\n\n```yaml\n...\n\n# General opts\nsave_model: data/wmt/run/model\nkeep_checkpoint: 50\nsave_checkpoint_steps: 5000\naverage_decay: 0.0005\nseed: 1234\nreport_every: 100\ntrain_steps: 100000\nvalid_steps: 5000\n\n# Batching\nqueue_size: 10000\nbucket_size: 32768\nworld_size: 2\ngpu_ranks: [0, 1]\nbatch_type: \"tokens\"\nbatch_size: 4096\nvalid_batch_size: 16\nbatch_size_multiple: 1\nmax_generator_batches: 0\naccum_count: [3]\naccum_steps: [0]\n\n# Optimization\nmodel_dtype: \"fp32\"\noptim: \"adam\"\nlearning_rate: 2\nwarmup_steps: 8000\ndecay_method: \"noam\"\nadam_beta2: 0.998\nmax_grad_norm: 0\nlabel_smoothing: 0.1\nparam_init: 0\nparam_init_glorot: true\nnormalization: \"tokens\"\n\n# Model\nencoder_type: transformer\ndecoder_type: transformer\nenc_layers: 6\ndec_layers: 6\nheads: 8\nrnn_size: 512\nword_vec_size: 512\ntransformer_ff: 2048\ndropout_steps: [0]\ndropout: [0.1]\nattention_dropout: [0.1]\nshare_decoder_embeddings: true\nshare_embeddings: true\n```\n\n## Step 3: Translate and evaluate\n\nWe need to tokenize the testset with the same sentencepiece model as used in training:\n\n```bash\nspm_encode --model=data/wmt/wmtende.model \\\n    < data/wmt/test.en \\\n    > data/wmt/test.en.sp\nspm_encode --model=data/wmt/wmtende.model \\\n    < data/wmt/test.de \\\n    > data/wmt/test.de.sp\n```\n\nWe can translate the testset with the following command:\n\n```bash\nfor checkpoint in data/wmt/run/model_step*.pt; do\n    echo \"# Translating with checkpoint $checkpoint\"\n    base=$(basename $checkpoint)\n    onmt_translate \\\n        -gpu 0 \\\n        -batch_size 16384 -batch_type tokens \\\n        -beam_size 5 \\\n        -model $checkpoint \\\n        -src data/wmt/test.en.sp \\\n        -tgt data/wmt/test.de.sp \\\n        -output data/wmt/test.de.hyp_${base%.*}.sp\ndone\n```\n\nPrior to evaluation, we need to detokenize the hypothesis:\n\n```bash\nfor checkpoint in data/wmt/run/model_step*.pt; do\n    base=$(basename $checkpoint)\n    spm_decode \\\n        -model=data/wmt/wmtende.model \\\n        -input_format=piece \\\n        < data/wmt/test.de.hyp_${base%.*}.sp \\\n        > data/wmt/test.de.hyp_${base%.*}\ndone\n```\n\n\nFinally, we can compute detokenized BLEU with `sacrebleu`:\n\n```bash\nfor checkpoint in data/wmt/run/model_step*.pt; do\n    echo \"$checkpoint\"\n    base=$(basename $checkpoint)\n    sacrebleu data/wmt/test.de < data/wmt/test.de.hyp_${base%.*}\ndone\n```\n"
  },
  {
    "path": "docs/source/index.rst",
    "content": "Contents\n--------\n\n.. toctree::\n      :caption: Getting Started\n      :maxdepth: 2\n\n      main.md\n      quickstart.md\n      CONTRIBUTING.md\n      ref.rst\n\n.. toctree::\n      :caption: FAQ\n      :maxdepth: 2\n\n      FAQ.md\n\n\n.. toctree::\n      :caption: Examples\n      :maxdepth: 2\n\n      examples/Library.md\n      examples/Translation.md\n      examples/Summarization.md\n      examples/LanguageModelGeneration.md\n      examples/GGNN.md\n\n\n.. toctree::\n      :caption: Scripts\n      :maxdepth: 2\n\n      options/build_vocab.rst\n      options/train.rst\n      options/translate.rst\n      options/server.rst\n\n\n.. toctree::\n      :caption: API\n      :maxdepth: 2\n\n      onmt.rst\n      onmt.modules.rst\n      onmt.translation.rst\n      onmt.translate.translation_server.rst\n      onmt.inputters.rst\n\n\n.. toctree::\n      :caption: Legacy\n      :maxdepth: 2\n\n      legacy/FAQ.md\n      legacy/im2text.md\n      legacy/speech2text.md\n      legacy/vid2text.rst\n"
  },
  {
    "path": "docs/source/legacy/FAQ.md",
    "content": "# FAQ (Legacy version)\n\nThis is the FAQ for the legacy version of OpenNMT-py (prior to OpenNMT-py v2.0 release).\n\n## How do I use Pretrained embeddings (e.g. GloVe)?\n\nUsing vocabularies from OpenNMT-py preprocessing outputs, `embeddings_to_torch.py` to generate encoder and decoder embeddings initialized with GloVe's values.\n\nthe script is a slightly modified version of ylhsieh's one2.\n\nUsage:\n\n```shell\nembeddings_to_torch.py [-h] [-emb_file_both EMB_FILE_BOTH]\n                       [-emb_file_enc EMB_FILE_ENC]\n                       [-emb_file_dec EMB_FILE_DEC] -output_file\n                       OUTPUT_FILE -dict_file DICT_FILE [-verbose]\n                       [-skip_lines SKIP_LINES]\n                       [-type {GloVe,word2vec}]\n```\n\nRun embeddings_to_torch.py -h for more usagecomplete info.\n\n### Example\n\n1. Get GloVe files:\n\n    ```shell\n    mkdir \"glove_dir\"\n    wget http://nlp.stanford.edu/data/glove.6B.zip\n    unzip glove.6B.zip -d \"glove_dir\"\n    ```\n\n2. Prepare data:\n\n    ```shell\n    onmt_preprocess \\\n    -train_src data/train.src.txt \\\n    -train_tgt data/train.tgt.txt \\\n    -valid_src data/valid.src.txt \\\n    -valid_tgt data/valid.tgt.txt \\\n    -save_data data/data\n    ```\n\n3. Prepare embeddings:\n\n    ```shell\n    ./tools/embeddings_to_torch.py -emb_file_both \"glove_dir/glove.6B.100d.txt\" \\\n    -dict_file \"data/data.vocab.pt\" \\\n    -output_file \"data/embeddings\"\n    ```\n\n4. Train using pre-trained embeddings:\n\n    ```shell\n    onmt_train -save_model data/model \\\n               -batch_size 64 \\\n               -layers 2 \\\n               -rnn_size 200 \\\n               -word_vec_size 100 \\\n               -pre_word_vecs_enc \"data/embeddings.enc.pt\" \\\n               -pre_word_vecs_dec \"data/embeddings.dec.pt\" \\\n               -data data/data\n    ```\n\n## How do I use the Transformer model?\n\nThe transformer model is very sensitive to hyperparameters. To run it\neffectively you need to set a bunch of different options that mimic the Google\nsetup. We have confirmed the following command can replicate their WMT results.\n\n```shell\npython  train.py -data /tmp/de2/data -save_model /tmp/extra \\\n        -layers 6 -rnn_size 512 -word_vec_size 512 -transformer_ff 2048 -heads 8  \\\n        -encoder_type transformer -decoder_type transformer -position_encoding \\\n        -train_steps 200000  -max_generator_batches 2 -dropout 0.1 \\\n        -batch_size 4096 -batch_type tokens -normalization tokens  -accum_count 2 \\\n        -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 \\\n        -max_grad_norm 0 -param_init 0  -param_init_glorot \\\n        -label_smoothing 0.1 -valid_steps 10000 -save_checkpoint_steps 10000 \\\n        -world_size 4 -gpu_ranks 0 1 2 3\n```\n\nHere are what each of the parameters mean:\n\n* `param_init_glorot` `-param_init 0`: correct initialization of parameters\n* `position_encoding`: add sinusoidal position encoding to each embedding\n* `optim adam`, `decay_method noam`, `warmup_steps 8000`: use special learning rate.\n* `batch_type tokens`, `normalization tokens`, `accum_count 4`: batch and normalize based on number of tokens and not sentences. Compute gradients based on four batches.\n* `label_smoothing 0.1`: use label smoothing loss.\n\n## Do you support multi-gpu?\n\nFirst you need to make sure you `export CUDA_VISIBLE_DEVICES=0,1,2,3`.\n\nIf you want to use GPU id 1 and 3 of your OS, you will need to `export CUDA_VISIBLE_DEVICES=1,3`\n\nBoth `-world_size` and `-gpu_ranks` need to be set. E.g. `-world_size 4 -gpu_ranks 0 1 2 3` will use 4 GPU on this node only.\n\nIf you want to use 2 nodes with 2 GPU each, you need to set `-master_ip` and `-master_port`, and\n\n* `-world_size 4 -gpu_ranks 0 1`: on the first node\n* `-world_size 4 -gpu_ranks 2 3`: on the second node\n* `-accum_count 2`: This will accumulate over 2 batches before updating parameters.\n\nif you use a regular network card (1 Gbps) then we suggest to use a higher `-accum_count` to minimize the inter-node communication.\n\n**Note:**\n\nWhen training on several GPUs, you can't have them in 'Exclusive' compute mode (`nvidia-smi -c 3`).\n\nThe multi-gpu setup relies on a Producer/Consumer setup. This setup means there will be `2<n_gpu> + 1` processes spawned, with 2 processes per GPU, one for model training and one (Consumer) that hosts a `Queue` of batches that will be processed next. The additional process is the Producer, creating batches and sending them to the Consumers. This setup is beneficial for both wall time and memory, since it loads data shards 'in advance', and does not require to load it for each GPU process.\n\n## How can I ensemble Models at inference?\n\nYou can specify several models in the translate.py command line: -model model1_seed1 model2_seed2\nBear in mind that your models must share the same target vocabulary.\n\n## How can I weight different corpora at training?\n\n### Preprocessing\n\nWe introduced `-train_ids` which is a list of IDs that will be given to the preprocessed shards.\n\nE.g. we have two corpora : `parallel.en` and  `parallel.de` + `from_backtranslation.en` `from_backtranslation.de`, we can pass the following in the `preprocess.py` command:\n\n```shell\n...\n-train_src parallel.en from_backtranslation.en \\\n-train_tgt parallel.de from_backtranslation.de \\\n-train_ids A B \\\n-save_data my_data \\\n...\n```\n\nand it will dump `my_data.train_A.X.pt` based on `parallel.en`//`parallel.de` and `my_data.train_B.X.pt` based on `from_backtranslation.en`//`from_backtranslation.de`.\n\n### Training\n\nWe introduced `-data_ids` based on the same principle as above, as well as `-data_weights`, which is the list of the weight each corpus should have.\nE.g.\n\n```shell\n...\n-data my_data \\\n-data_ids A B \\\n-data_weights 1 7 \\\n...\n```\n\nwill mean that we'll look for `my_data.train_A.*.pt` and `my_data.train_B.*.pt`, and that when building batches, we'll take 1 example from corpus A, then 7 examples from corpus B, and so on.\n\n**Warning**: This means that we'll load as many shards as we have `-data_ids`, in order to produce batches containing data from every corpus. It may be a good idea to reduce the `-shard_size` at preprocessing.\n\n## Can I get word alignment while translating?\n\n### Raw alignments from averaging Transformer attention heads\n\nCurrently, we support producing word alignment while translating for Transformer based models. Using `-report_align` when calling `translate.py` will output the inferred alignments in Pharaoh format. Those alignments are computed from an argmax on the average of the attention heads of the *second to last* decoder layer. The resulting alignment src-tgt (Pharaoh) will be pasted to the translation sentence, separated by ` ||| `.\nNote: The *second to last* default behaviour was empirically determined. It is not the same as the paper (they take the *penultimate* layer), probably because of light differences in the architecture.\n\n* alignments use the standard \"Pharaoh format\", where a pair `i-j` indicates the i<sub>th</sub> word of source language is aligned to j<sub>th</sub> word of target language.\n* Example: {'src': 'das stimmt nicht !'; 'output': 'that is not true ! ||| 0-0 0-1 1-2 2-3 1-4 1-5 3-6'}\n* Using the`-tgt` option when calling `translate.py`, we output alignments between the source and the gold target rather than the inferred target, assuming we're doing evaluation.\n* To convert subword alignments to word alignments, or symetrize bidirectional alignments, please refer to the [lilt scripts](https://github.com/lilt/alignment-scripts).\n\n### Supervised learning on a specific head\n\nThe quality of output alignments can be further improved by providing reference alignments while training. This will invoke multi-task learning on translation and alignment. This is an implementation based on the paper [Jointly Learning to Align and Translate with Transformer Models](https://arxiv.org/abs/1909.02074).\n\nThe data need to be preprocessed with the reference alignments in order to learn the supervised task.\n\nWhen calling `preprocess.py`, add:\n\n* `--train_align <path>`: path(s) to the training alignments in Pharaoh format\n* `--valid_align <path>`: path to the validation set alignments in Pharaoh format (optional).\nThe reference alignment file(s) could be generated by [GIZA++](https://github.com/moses-smt/mgiza/) or [fast_align](https://github.com/clab/fast_align).\n\nNote: There should be no blank lines in the alignment files provided.\n\nOptions to learn such alignments are:\n\n* `-lambda_align`: set the value > 0.0 to enable joint align training, the paper suggests 0.05;\n* `-alignment_layer`: indicate the index of the decoder layer;\n* `-alignment_heads`:  number of alignment heads for the alignment task - should be set to 1 for the supervised task, and preferably kept to default (or same as `num_heads`) for the average task;\n* `-full_context_alignment`: do full context decoder pass (no future mask) when computing alignments. This will slow down the training (~12% in terms of tok/s) but will be beneficial to generate better alignment.\n"
  },
  {
    "path": "docs/source/legacy/im2text.md",
    "content": "# Image to Text\n\n---------\n\n**WARNING**: This example is based on the [legacy version of OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py/tree/legacy)!\n\n---------\n\nA deep learning-based approach to learning the image-to-text conversion, built on top of the <a href=\"http://opennmt.net/\">OpenNMT</a> system. It is completely data-driven, hence can be used for a variety of image-to-text problems, such as image captioning, optical character recognition and LaTeX decompilation. \n\nTake LaTeX decompilation as an example, given a formula image:\n\n<p align=\"center\"><img src=\"http://lstm.seas.harvard.edu/latex/results/website/images/119b93a445-orig.png\"></p>\n\nThe goal is to infer the LaTeX source that can be compiled to such an image:\n\n```\n d s _ { 1 1 } ^ { 2 } = d x ^ { + } d x ^ { - } + l _ { p } ^ { 9 } \\frac { p _ { - } } { r ^ { 7 } } \\delta ( x ^ { - } ) d x ^ { - } d x ^ { - } + d x _ { 1 } ^ { 2 } + \\; \\cdots \\; + d x _ { 9 } ^ { 2 } \n```\n\nThe paper [[What You Get Is What You See: A Visual Markup Decompiler]](https://arxiv.org/pdf/1609.04938.pdf) provides more technical details of this model.\n\n### Dependencies\n\n* `torchvision`: `conda install torchvision`\n* `Pillow`: `pip install Pillow`\n\n### Quick Start\n\nTo get started, we provide a toy Math-to-LaTex example. We assume that the working directory is `OpenNMT-py` throughout this document.\n\nIm2Text consists of four commands:\n\n0) Download the data.\n\n```bash\nwget -O data/im2text.tgz http://lstm.seas.harvard.edu/latex/im2text_small.tgz; tar zxf data/im2text.tgz -C data/\n```\n\n1) Preprocess the data.\n\n```bash\nonmt_preprocess -data_type img \\\n                -src_dir data/im2text/images/ \\\n                -train_src data/im2text/src-train.txt \\\n                -train_tgt data/im2text/tgt-train.txt -valid_src data/im2text/src-val.txt \\\n                -valid_tgt data/im2text/tgt-val.txt -save_data data/im2text/demo \\\n                -tgt_seq_length 150 \\\n                -tgt_words_min_frequency 2 \\\n                -shard_size 500 \\\n                -image_channel_size 1\n```\n\n2) Train the model.\n\n```bash\nonmt_train -model_type img \\\n           -data data/im2text/demo \\\n           -save_model demo-model \\\n           -gpu_ranks 0 \\\n           -batch_size 20 \\\n           -max_grad_norm 20 \\\n           -learning_rate 0.1 \\\n           -word_vec_size 80 \\\n           -encoder_type brnn \\\n           -image_channel_size 1\n```\n\n3) Translate the images.\n\n```bash\nonmt_translate -data_type img \\\n               -model demo-model_acc_x_ppl_x_e13.pt \\\n               -src_dir data/im2text/images \\\n               -src data/im2text/src-test.txt \\\n               -output pred.txt \\\n               -max_length 150 \\\n               -beam_size 5 \\\n               -gpu 0 \\\n               -verbose\n```\n\nThe above dataset is sampled from the [im2latex-100k-dataset](http://lstm.seas.harvard.edu/latex/im2text.tgz). We provide a trained model [[link]](http://lstm.seas.harvard.edu/latex/py-model.pt) on this dataset.\n\n### Options\n\n* `-src_dir`: The directory containing the images.\n\n* `-train_tgt`: The file storing the tokenized labels, one label per line. It shall look like:\n```\n<label0_token0> <label0_token1> ... <label0_tokenN0>\n<label1_token0> <label1_token1> ... <label1_tokenN1>\n<label2_token0> <label2_token1> ... <label2_tokenN2>\n...\n```\n\n* `-train_src`: The file storing the paths of the images (relative to `src_dir`).\n```\n<image0_path>\n<image1_path>\n<image2_path>\n...\n```\n"
  },
  {
    "path": "docs/source/legacy/speech2text.md",
    "content": "# Speech to Text\n\n---------\n\n**WARNING**: This example is based on the [legacy version of OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py/tree/legacy)!\n\n---------\n\n\nA deep learning-based approach to learning the speech-to-text conversion, built on top of the <a href=\"http://opennmt.net/\">OpenNMT</a> system.\n\nGiven raw audio, we first apply short-time Fourier transform (STFT), then apply Convolutional Neural Networks to get the source features. Based on this source representation, we use an LSTM decoder with attention to produce the text character by character.\n\n### Dependencies\n\n* `torchaudio`: `sudo apt-get install -y sox libsox-dev libsox-fmt-all; pip install git+https://github.com/pytorch/audio`\n* `librosa`: `pip install librosa`\n\n### Quick Start\n\nTo get started, we provide a toy speech-to-text example. We assume that the working directory is `OpenNMT-py` throughout this document.\n\n0) Download the data.\n\n```\nwget -O data/speech.tgz http://lstm.seas.harvard.edu/latex/speech.tgz; tar zxf data/speech.tgz -C data/\n```\n\n\n1) Preprocess the data.\n\n```\nonmt_preprocess -data_type audio -src_dir data/speech/an4_dataset -train_src data/speech/src-train.txt -train_tgt data/speech/tgt-train.txt -valid_src data/speech/src-val.txt -valid_tgt data/speech/tgt-val.txt -shard_size 300 -save_data data/speech/demo\n```\n\n2) Train the model.\n\n```\nonmt_train -model_type audio -enc_rnn_size 512 -dec_rnn_size 512 -audio_enc_pooling 1,1,2,2 -dropout 0 -enc_layers 4 -dec_layers 1 -rnn_type LSTM -data data/speech/demo -save_model demo-model -global_attention mlp -gpu_ranks 0 -batch_size 8 -optim adam -max_grad_norm 100 -learning_rate 0.0003 -learning_rate_decay 0.8 -train_steps 100000\n```\n\n3) Translate the speechs.\n\n```\nonmt_translate -data_type audio -model demo-model_acc_x_ppl_x_e13.pt -src_dir data/speech/an4_dataset -src data/speech/src-val.txt -output pred.txt -gpu 0 -verbose\n```\n\n\n### Options\n\n* `-src_dir`: The directory containing the audio files.\n\n* `-train_tgt`: The file storing the tokenized labels, one label per line. It shall look like:\n```\n<label0_token0> <label0_token1> ... <label0_tokenN0>\n<label1_token0> <label1_token1> ... <label1_tokenN1>\n<label2_token0> <label2_token1> ... <label2_tokenN2>\n...\n```\n\n* `-train_src`: The file storing the paths of the audio files (relative to `src_dir`).\n```\n<speech0_path>\n<speech1_path>\n<speech2_path>\n...\n```\n\n* `sample_rate`: Sample rate. Default: 16000.\n* `window_size`: Window size for spectrogram in seconds. Default: 0.02.\n* `window_stride`: Window stride for spectrogram in seconds. Default: 0.01.\n* `window`: Window type for spectrogram generation. Default: hamming.\n\n### Acknowledgement\n\nOur preprocessing and CNN encoder is adapted from [deepspeech.pytorch](https://github.com/SeanNaren/deepspeech.pytorch).\n"
  },
  {
    "path": "docs/source/legacy/vid2text.rst",
    "content": "Video to Text\n=============\n\n---------\n\n**WARNING**: This example is based on the\n`legacy version of OpenNMT-py <https://github.com/OpenNMT/OpenNMT-py/tree/legacy>`_\n!\n\n---------\n\n\nRecurrent\n---------\n\nThis tutorial shows how to replicate the results from\n`\"Describing Videos by Exploiting Temporal Structure\" <https://arxiv.org/pdf/1502.08029.pdf>`_\n[`code <https://github.com/yaoli/arctic-capgen-vid>`_]\nusing OpenNMT-py.\n\nGet `YouTubeClips.tar` from `here <http://www.cs.utexas.edu/users/ml/clamp/videoDescription/>`_.\nUse ``tar -xvf YouTubeClips.tar`` to decompress the archive.\n\nNow, visit `this repo <https://github.com/yaoli/arctic-capgen-vid>`_.\nFollow the \"preprocessed YouTube2Text download link.\"\nWe'll be throwing away the Googlenet features. We just need the captions.\nUse ``unzip youtube2text_iccv15.zip`` to decompress the files.\n\nGet to the following directory structure: ::\n\n    yt2t\n    |-YouTubeClips\n    |-youtube2text_iccv15\n\nChange directories to `yt2t`. We'll rename the videos to follow the \"vid#.avi\" format:\n\n.. code-block:: python\n\n    import pickle\n    import os\n\n\n    YT = \"youtube2text_iccv15\"\n    YTC = \"YouTubeClips\"\n\n    # load the YouTube hash -> vid### map.\n    with open(os.path.join(YT, \"dict_youtube_mapping.pkl\"), \"rb\") as f:\n        yt2vid = pickle.load(f, encoding=\"latin-1\")\n\n    for f in os.listdir(YTC):\n        hashy, ext = os.path.splitext(f)\n        vid = yt2vid[hashy]\n        fpath_old = os.path.join(YTC, f)\n        f_new = vid + ext\n        fpath_new = os.path.join(YTC, f_new)\n        os.rename(fpath_old, fpath_new)\n\nMake sure all the videos have the same (low) framerate by changing to the YouTubeClips directory and using\n\n.. code-block:: bash\n\n    for fi in $( ls ); do ffmpeg -y -i $fi -r 2 $fi; done\n\nNow we want to convert the frames into sequences of CNN feature vectors.\n(We'll use the environment variable ``Y2T2`` to refer to the `yt2t` directory, so change directories back and use)\n\n.. code-block:: bash\n\n    export YT2T=`pwd`\n\nThen change directories back to the `OpenNMT-py` directory.\nUse `tools/img_feature_extractor.py`.\nSet the ``--world_size`` argument to the number of GPUs you have available\n(You can use the environment variable ``CUDA_VISIBLE_DEVICES`` to restrict the GPUs used).\n\n.. code-block:: bash\n\n    PYTHONPATH=$PWD:$PYTHONPATH python tools/vid_feature_extractor.py --root_dir $YT2T/YouTubeClips --out_dir $YT2T/r152\n\nEnsure the count is equal to 1970.\nYou can use ``ls -1 $YT2T/r152 | wc -l``.\nIf not, rerun the script. It will only process on the missing feature vectors.\n(Note this is unexpected behavior and consider opening an issue.)\n\nNow we turn our attention to the annotations. Each video has multiple associated captions. We want to\ntrain the model on each video + single caption pair. We'll collect all the captions per video, then we'll\nflatten them into files listing the feature vector sequence filenames (repeating for each caption) and the\nannotations. We skip the test videos since they are handled separately at translation time.\n\nChange directories back to ``YT2T``:\n\n.. code-block:: bash\n\n    cd $YT2T\n\n.. code-block:: python\n\n    import pickle\n    import os\n    from random import shuffle\n\n\n    YT = \"youtube2text_iccv15\"\n    SHUFFLE = True\n\n    with open(os.path.join(YT, \"CAP.pkl\"), \"rb\") as f:\n        ann = pickle.load(f, encoding=\"latin-1\")\n\n    vid2anns = {}\n    for vid_name, data in ann.items():\n        for d in data:\n            try:\n                vid2anns[vid_name].append(d[\"tokenized\"])\n            except KeyError:\n                vid2anns[vid_name] = [d[\"tokenized\"]]\n\n    with open(os.path.join(YT, \"train.pkl\"), \"rb\") as f:\n        train = pickle.load(f, encoding=\"latin-1\")\n\n    with open(os.path.join(YT, \"valid.pkl\"), \"rb\") as f:\n        val = pickle.load(f, encoding=\"latin-1\")\n\n    with open(os.path.join(YT, \"test.pkl\"), \"rb\") as f:\n        test = pickle.load(f, encoding=\"latin-1\")\n\n    train_files = open(\"yt2t_train_files.txt\", \"w\")\n    val_files = open(\"yt2t_val_files.txt\", \"w\")\n    val_folded = open(\"yt2t_val_folded_files.txt\", \"w\")\n    test_files = open(\"yt2t_test_files.txt\", \"w\")\n\n    train_cap = open(\"yt2t_train_cap.txt\", \"w\")\n    val_cap = open(\"yt2t_val_cap.txt\", \"w\")\n\n    vid_names = vid2anns.keys()\n    if SHUFFLE:\n        vid_names = list(vid_names)\n        shuffle(vid_names)\n\n\n    for vid_name in vid_names:\n        anns = vid2anns[vid_name]\n        vid_path = vid_name + \".npy\"\n        for i, an in enumerate(anns):\n            an = an.replace(\"\\n\", \" \")  # some caps have newlines\n            split_name = vid_name + \"_\" + str(i)\n            if split_name in train:\n                train_files.write(vid_path + \"\\n\")\n                train_cap.write(an + \"\\n\")\n            elif split_name in val:\n                if i == 0:\n                    val_folded.write(vid_path + \"\\n\")\n                val_files.write(vid_path + \"\\n\")\n                val_cap.write(an + \"\\n\")\n            else:\n                # Don't need to save out the test captions,\n                # just the files. And, don't need to repeat\n                # it for each caption\n                assert split_name in test\n                if i == 0:\n                    test_files.write(vid_path + \"\\n\")\n\nReturn to the `OpenNMT-py` directory. Now we preprocess the data for training.\nWe preprocess with a small shard size of 1000. This keeps the amount of data in memory (RAM) to a\nmanageable 10 G. If you have more RAM, you can increase the shard size.\n\nPreprocess the data with\n\n.. code-block:: bash\n\n    onmt_preprocess -data_type vec -train_src $YT2T/yt2t_train_files.txt -src_dir $YT2T/r152/ -train_tgt $YT2T/yt2t_train_cap.txt -valid_src $YT2T/yt2t_val_files.txt -valid_tgt $YT2T/yt2t_val_cap.txt -save_data data/yt2t --shard_size 1000\n\nTrain with\n\n.. code-block:: bash\n\n    onmt_train -data data/yt2t -save_model yt2t-model -world_size 2 -gpu_ranks 0 1 -model_type vec -batch_size 64 -train_steps 10000 -valid_steps 500 -save_checkpoint_steps 500 -encoder_type brnn -optim adam -learning_rate .0001 -feat_vec_size 2048\n\nTranslate with\n\n.. code-block::\n\n    onmt_translate -model yt2t-model_step_7200.pt -src $YT2T/yt2t_test_files.txt -output pred.txt -verbose -data_type vec -src_dir $YT2T/r152 -gpu 0 -batch_size 10\n\n.. note::\n\n    Generally, you want to keep the model that has the lowest validation perplexity. That turned out to be\n    at step 7200, but choosing a different validation frequency or random seed could result in different results.\n\n\nThen you can use `coco-caption <https://github.com/tylin/coco-caption/tree/master/pycocoevalcap>`_ to evaluate the predictions.\n(Note that the fork `flauted <https://github.com/flauted/coco-caption>`_ can be used for Python 3 compatibility).\nInstall the git repository with pip using\n\n\n.. code-block:: bash\n\n    pip install git+<clone URL>\n\nThen use the following Python code to evaluate:\n\n.. code-block:: python\n\n    import os\n    from pprint import pprint\n    from pycocoevalcap.bleu.bleu import Bleu\n    from pycocoevalcap.meteor.meteor import Meteor\n    from pycocoevalcap.rouge.rouge import Rouge\n    from pycocoevalcap.cider.cider import Cider\n    from pycocoevalcap.spice.spice import Spice\n\n\n    if __name__ == \"__main__\":\n        pred = open(\"pred.txt\")\n\n        import pickle\n        import os\n\n        YT = os.path.join(os.environ[\"YT2T\"], \"youtube2text_iccv15\")\n\n        with open(os.path.join(YT, \"CAP.pkl\"), \"rb\") as f:\n            ann = pickle.load(f, encoding=\"latin-1\")\n\n        vid2anns = {}\n        for vid_name, data in ann.items():\n            for d in data:\n                try:\n                    vid2anns[vid_name].append(d[\"tokenized\"])\n                except KeyError:\n                    vid2anns[vid_name] = [d[\"tokenized\"]]\n\n        test_files = open(os.path.join(os.environ[\"YT2T\"], \"yt2t_test_files.txt\"))\n\n        scorers = {\n            \"Bleu\": Bleu(4),\n            \"Meteor\": Meteor(),\n            \"Rouge\": Rouge(),\n            \"Cider\": Cider(),\n            \"Spice\": Spice()\n        }\n\n        gts = {}\n        res = {}\n        for outp, filename in zip(pred, test_files):\n            filename = filename.strip(\"\\n\")\n            outp = outp.strip(\"\\n\")\n            vid_id = os.path.splitext(filename)[0]\n            anns = vid2anns[vid_id]\n            gts[vid_id] = anns\n            res[vid_id] = [outp]\n\n        scores = {}\n        for name, scorer in scorers.items():\n            score, all_scores = scorer.compute_score(gts, res)\n            if isinstance(score, list):\n                for i, sc in enumerate(score, 1):\n                    scores[name + str(i)] = sc\n            else:\n                scores[name] = score\n        pprint(scores)\n\nHere are our results ::\n\n    {'Bleu1': 0.7888553878084233,\n     'Bleu2': 0.6729376621109295,\n     'Bleu3': 0.5778428507344473,\n     'Bleu4': 0.47633625833397897,\n     'Cider': 0.7122415518428051,\n     'Meteor': 0.31829562714082704,\n     'Rouge': 0.6811305229481235,\n     'Spice': 0.044147089472463576}\n\n\nSo how does this stack up against the paper? These results should be compared to the \"Global (Temporal Attention)\"\nrow in Table 1. The authors report BLEU4 0.4028, METEOR 0.2900, and CIDEr 0.4801. So, our results are a significant\nimprovement. Our architecture follows the general encoder + attentional decoder described in the paper, but the\nactual attention implementation is slightly different. The paper downsamples by choosing 26 equally spaced frames from\nthe first 240, while we downsample the video to 2 fps. Also, we use ResNet features instead of GoogLeNet, and we\nlowercase while the paper does not, so some improvement is expected.\n\nTransformer\n-----------\n\nNow we will try to replicate the baseline transformer results from\n`\"TVT: Two-View Transformer Network for Video Captioning\" <http://proceedings.mlr.press/v95/chen18b.html>`_\non the MSVD (YouTube2Text) dataset. See Table 3, Base model(R).\n\nIn Section 4.3, the authors report most of their preprocessing and hyperparameters.\n\nCreate a folder called *yt2t_2*. Copy *youtube2text_iccv15* directory and *YouTubeClips.tar* into\nthe new directory and untar *YouTubeClips*. Rerun the renaming code. Subssample at 5 FPS using\n\n..  code-block:: bash\n\n    for fi in $( ls ); do ffmpeg -y -i $fi -r 5 $fi; done\n\nSet the environment variable ``$YT2T`` to this new directory and change to the repo directory.\nRun the feature extraction command again to extract ResNet features on the frames.\nThen use this reprocessing code. Note that it shuffles the data differently, and it performs\ntokenization similar to what the authors report.\n\n.. code-block:: python\n\n    import pickle\n    import os\n    import random\n    import string\n\n    seed = 2345\n    random.seed(seed)\n\n\n    YT = \"youtube2text_iccv15\"\n    SHUFFLE = True\n\n    with open(os.path.join(YT, \"CAP.pkl\"), \"rb\") as f:\n        ann = pickle.load(f, encoding=\"latin-1\")\n\n    def clean(caption):\n        caption = caption.lower()\n        caption = caption.replace(\"\\n\", \" \").replace(\"\\t\", \" \").replace(\"\\r\", \" \")\n        # remove punctuation\n        caption = caption.translate(str.maketrans(\"\", \"\", string.punctuation))\n        # multiple whitespace\n        caption = \" \".join(caption.split())\n        return caption\n\n\n    with open(os.path.join(YT, \"train.pkl\"), \"rb\") as f:\n        train = pickle.load(f, encoding=\"latin-1\")\n\n    with open(os.path.join(YT, \"valid.pkl\"), \"rb\") as f:\n        val = pickle.load(f, encoding=\"latin-1\")\n\n    with open(os.path.join(YT, \"test.pkl\"), \"rb\") as f:\n        test = pickle.load(f, encoding=\"latin-1\")\n\n    train_data = []\n    val_data = []\n    test_data = []\n    for vid_name, data in ann.items():\n        vid_path = vid_name + \".npy\"\n        for i, d in enumerate(data):\n            split_name = vid_name + \"_\" + str(i)\n            datum = (vid_path, i, clean(d[\"caption\"]))\n            if split_name in train:\n                train_data.append(datum)\n            elif split_name in val:\n                val_data.append(datum)\n            elif split_name in test:\n                test_data.append(datum)\n            else:\n                assert False\n\n    if SHUFFLE:\n        random.shuffle(train_data)\n\n    train_files = open(\"yt2t_train_files.txt\", \"w\")\n    train_cap = open(\"yt2t_train_cap.txt\", \"w\")\n\n    for vid_path, _, an in train_data:\n        train_files.write(vid_path + \"\\n\")\n        train_cap.write(an + \"\\n\")\n\n    train_files.close()\n    train_cap.close()\n\n    val_files = open(\"yt2t_val_files.txt\", \"w\")\n    val_folded = open(\"yt2t_val_folded_files.txt\", \"w\")\n    val_cap = open(\"yt2t_val_cap.txt\", \"w\")\n\n    for vid_path, i, an in val_data:\n        if i == 0:\n            val_folded.write(vid_path + \"\\n\")\n        val_files.write(vid_path + \"\\n\")\n        val_cap.write(an + \"\\n\")\n\n    val_files.close()\n    val_folded.close()\n    val_cap.close()\n\n    test_files = open(\"yt2t_test_files.txt\", \"w\")\n\n    for vid_path, i, an in test_data:\n        # Don't need to save out the test captions,\n        # just the files. And, don't need to repeat\n        # it for each caption\n        if i == 0:\n            test_files.write(vid_path + \"\\n\")\n\n    test_files.close()\n\nThen preprocess the data with max-length filtering. (Note you will be prompted to remove the\nold data. Do this, i.e. ``rm data/yt2t.*.pt.``)\n\n.. code-block:: bash\n\n    onmt_preprocess -data_type vec -train_src $YT2T/yt2t_train_files.txt -src_dir $YT2T/r152/ -train_tgt $YT2T/yt2t_train_cap.txt -valid_src $YT2T/yt2t_val_files.txt -valid_tgt $YT2T/yt2t_val_cap.txt -save_data data/yt2t --shard_size 1000 --src_seq_length 50 --tgt_seq_length 20\n\nDelete the old checkpoints and train a transformer model on this data.\n\n.. code-block:: bash\n\n    rm -r yt2t-model_step_*.pt; onmt_train -data data/yt2t -save_model yt2t-model -world_size 2 -gpu_ranks 0 1 -model_type vec -batch_size 64 -train_steps 8000 -valid_steps 400 -save_checkpoint_steps 400 -optim adam -learning_rate .0001 -feat_vec_size 2048 -layers 4 -rnn_size 512 -word_vec_size 512 -transformer_ff 2048 -heads 8 -encoder_type transformer -decoder_type transformer -position_encoding -dropout 0.3 -param_init 0 -param_init_glorot -report_every 400 --share_decoder_embedding --seed 7000\n\nNote we use the hyperparameters described in the paper.\nWe estimate the length of 20 epochs with ``-train_steps``. Note that this depends on\nusing a world size of 2. If you use a different world size, scale the ``-train_steps`` (and\n``-save_checkpoint_steps``, along with other parameters) accordingly.\n\nThe batch size is not specified in the paper, so we assume one checkpoint\nper our estimated epoch. And, sharing\nthe decoder embeddings is not mentioned, although we find this helps performance. Like the paper, we perform\n\"early-stopping\" with the COCO scores. We use beam search on the early stopping,\nalthough this too is not mentioned. You can reproduce our early-stops with these scripts\n(namely, running `find_val_stops.sh` and then `test_early_stops.sh` -\n`process_results.py` is a dependency of `find_val_stops.sh`):\n\n.. code-block:: python\n    :caption: `process_results.py`\n\n    import argparse\n\n    from collections import defaultdict\n    import pandas as pd\n\n\n    def load_results(fname=\"results.txt\"):\n        index = []\n        data = []\n        with open(fname, \"r\") as f:\n            while True:\n                try:\n                    filename = next(f).strip()\n                except:\n                    break\n                step = int(filename.split(\"_\")[-1].split(\".\")[0])\n                next(f)  # blank\n                next(f)  # spice junk\n                next(f)  # length stats\n                next(f)  # ratios\n                scores = {}\n                while True:\n                    score_line = next(f).strip().strip(\"{\").strip(\",\")\n                    metric, score = score_line.split(\": \")\n                    metric = metric.strip(\"'\")\n                    score_num = float(score.strip(\"}\").strip(\",\"))\n                    scores[metric] = float(score_num)\n                    if score.endswith(\"}\"):\n                        break\n                next(f)  # blank\n                next(f)  # blank\n                next(f)  # blank\n                index.append(step)\n                data.append(scores)\n        df = pd.DataFrame(data, index=index)\n        return df\n\n\n    def find_absolute_stops(df):\n        return df.idxmax()\n\n\n    def find_early_stops(df, stop_count):\n        maxes = defaultdict(lambda: 0)\n        argmaxes = {}\n        count_since_max = {}\n        ended_metrics = set()\n        for index, row in df.iterrows():\n            for metric, score in row.items():\n                if metric in ended_metrics:\n                    continue\n                if score >= maxes[metric]:\n                    maxes[metric] = score\n                    argmaxes[metric] = index\n                    count_since_max[metric] = 0\n                else:\n                    count_since_max[metric] += 1\n                    if count_since_max[metric] == stop_count:\n                        ended_metrics.add(metric)\n                        if len(ended_metrics) == len(row):\n                            break\n        return pd.Series(argmaxes)\n\n\n    def find_stops(df, stop_count):\n        if stop_count > 0:\n            return find_early_stops(df, stop_count)\n        else:\n            return find_absolute_stops(df)\n\n\n    if __name__ == \"__main__\":\n        parser = argparse.ArgumentParser(\"Find locations of best scores\")\n        parser.add_argument(\n            \"-s\", \"--stop_count\", type=int, default=0,\n            help=\"Stop after this many scores worse than running max (0 to disable).\")\n        args = parser.parse_args()\n        df = load_results()\n        maxes = find_stops(df, args.stop_count)\n        for metric, idx in maxes.iteritems():\n            print(f\"{metric} maxed @ {idx}\")\n            print(df.loc[idx])\n            print()\n\n\n.. code-block:: bash\n    :caption: `find_val_stops.sh`\n\n    rm results.txt\n    touch results.txt\n    for file in $( ls -1v yt2t-model_step*.pt )\n    do\n        echo $file\n        onmt_translate -model $file -src $YT2T/yt2t_val_folded_files.txt -output pred.txt -verbose -data_type vec -src_dir $YT2T/r152 -gpu 0 -batch_size 16 -max_length 20 >/dev/null 2>/dev/null\n        echo -e \"$file\\n\" >> results.txt\n        python coco.py -s val >> results.txt\n        echo -e \"\\n\\n\" >> results.txt\n    done\n    python process_results.py -s 10 > val_stops.txt\n\n.. code-block:: bash\n    :caption: `test_early_stops.sh`\n\n    rm test_results.txt\n    touch test_results.txt\n    while IFS='' read -r line || [[ -n \"$line\" ]]; do\n        if [[ $line == *\"maxed\"* ]]; then\n            metric=$(echo $line | awk '{print $1}')\n        step=$(echo $line | awk '{print $NF}')\n        echo $metric early stopped @ $step | tee -a test_results.txt\n        onmt_translate -model \"yt2t-model_step_${step}.pt\" -src $YT2T/yt2t_test_files.txt -output pred.txt -data_type vec -src_dir $YT2T/r152 -gpu 0 -batch_size 16 -max_length 20 >/dev/null 2>/dev/null\n        python coco.py -s 'test' >> test_results.txt\n        echo -e \"\\n\\n\" >> test_results.txt\n        fi\n    done < val_stops.txt\n    cat test_results.txt\n\nThus we test the checkpoint at step 2000 and find the following scores::\n\n    Meteor early stopped @ 2000\n    SPICE evaluation took: 2.522 s\n    {'testlen': 3410, 'reflen': 3417, 'guess': [3410, 2740, 2070, 1400], 'correct': [2664, 1562, 887, 386]}\n    ratio: 0.9979514193734276\n    {'Bleu1': 0.7796296150773093,\n     'Bleu2': 0.6659837622637965,\n     'Bleu3': 0.5745524496015597,\n     'Bleu4': 0.4779574102543823,\n     'Cider': 0.7541600090591118,\n     'Meteor': 0.3259497476899707,\n     'Rouge': 0.6800279518634998,\n     'Spice': 0.046435637924854}\n\n\nNote our scores are an improvement over the recurrent approach.\n\nThe paper reports\nBLEU4 50.25, CIDEr 72.11, METEOR 33.41, ROUGE 70.16.\n\nThe CIDEr score is higher than the paper (but, considering the sensitivity of this\nmetric, not by much), while the other metrics are slightly lower.\nThis could be indicative of an implementation difference. Note that Table 5 reports\n24M parameters for a 2-layer transformer with ResNet inputs, while we find a few M less. This\ncould be due to generator or embedding differences, or perhaps linear layers on the\nresidual connections. Alternatively, the difference could be the initial tokenization.\nThe paper reports 9861 tokens, while we find fewer.\n\nPart of this could be due to using\nthe annotations from the other repository, where perhaps some annotations have been\nstripped. We also do not know the batch size or checkpoint frequency from the original\nwork.\n\nDifferent random initializations could account for some of the difference, although\nour random seed gives good results.\n\nOverall, however, the scores are nearly reproduced\nand the scores are favorable.\n"
  },
  {
    "path": "docs/source/main.md",
    "content": "# Overview\n\n\nThis portal provides a detailed documentation of the OpenNMT-py toolkit. It describes how to use the PyTorch project and how it works.\n\n\n\n## Installation\nInstall `OpenNMT-py` from `pip`:\n```bash\npip install OpenNMT-py\n```\n\nor from the sources:\n```bash\ngit clone https://github.com/OpenNMT/OpenNMT-py.git\ncd OpenNMT-py\npython setup.py install\n```\n\n*(Optional)* some advanced features (e.g. working pretrained models or specific transforms) requires extra packages, you can install it with:\n```bash\npip install -r requirements.opt.txt\n```\n\nAnd you are ready to go!\n\nTake a look at the [quickstart](quickstart) to familiarize yourself with the main training workflow.\n\n## Citation\n\nWhen using OpenNMT-py for research please cite our\n[OpenNMT technical report](https://doi.org/10.18653/v1/P17-4012)\n\n```\n@inproceedings{opennmt,\n  author    = {Guillaume Klein and\n               Yoon Kim and\n               Yuntian Deng and\n               Jean Senellart and\n               Alexander M. Rush},\n  title     = {OpenNMT: Open-Source Toolkit for Neural Machine Translation},\n  booktitle = {Proc. ACL},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-4012},\n  doi       = {10.18653/v1/P17-4012}\n}\n```\n\n## Additional resources\n\nYou can find additional help or tutorials in the following resources:\n\n* [Forum](http://forum.opennmt.net/)\n\n* [Gitter channel](https://gitter.im/OpenNMT/openmt-py)\n"
  },
  {
    "path": "docs/source/onmt.inputters.rst",
    "content": "Data Loaders\n=================\n\nData Readers\n-------------\n\n.. autoexception:: onmt.inputters.datareader_base.MissingDependencyException\n\n.. autoclass:: onmt.inputters.DataReaderBase\n    :members:\n\n.. autoclass:: onmt.inputters.TextDataReader\n    :members:\n\n\nDataset\n--------\n\n.. autoclass:: onmt.inputters.Dataset\n    :members:\n"
  },
  {
    "path": "docs/source/onmt.modules.rst",
    "content": "Modules\n=============\n\nCore Modules\n------------\n\n.. autoclass:: onmt.modules.Embeddings\n    :members:\n\n\nEncoders\n---------\n\n.. autoclass:: onmt.encoders.EncoderBase\n    :members:\n\n.. autoclass:: onmt.encoders.MeanEncoder\n    :members:\n\n.. autoclass:: onmt.encoders.RNNEncoder\n    :members:\n\n\nDecoders\n---------\n\n\n.. autoclass:: onmt.decoders.DecoderBase\n    :members:\n    \n.. autoclass:: onmt.decoders.decoder.RNNDecoderBase\n    :members:\n\n.. autoclass:: onmt.decoders.StdRNNDecoder\n    :members:\n\n.. autoclass:: onmt.decoders.InputFeedRNNDecoder\n    :members:\n\nAttention\n----------\n\n.. autoclass:: onmt.modules.AverageAttention\n    :members:\n\n.. autoclass:: onmt.modules.GlobalAttention\n    :members:\n\n\n\nArchitecture: Transformer\n----------------------------\n\n.. autoclass:: onmt.modules.PositionalEncoding\n    :members:\n\n.. autoclass:: onmt.modules.position_ffn.PositionwiseFeedForward\n    :members:\n\n.. autoclass:: onmt.encoders.TransformerEncoder\n    :members:\n\n.. autoclass:: onmt.decoders.TransformerDecoder\n    :members:\n\n.. autoclass:: onmt.modules.MultiHeadedAttention\n    :members:\n    :undoc-members:\n\n\nArchitecture: Conv2Conv\n----------------------------\n\n(These methods are from a user contribution\nand have not been thoroughly tested.)\n\n\n.. autoclass:: onmt.encoders.CNNEncoder\n    :members:\n\n\n.. autoclass:: onmt.decoders.CNNDecoder\n    :members:\n\n.. autoclass:: onmt.modules.ConvMultiStepAttention\n    :members:\n\n.. autoclass:: onmt.modules.WeightNormConv2d\n    :members:\n\nArchitecture: SRU\n----------------------------\n\n.. autoclass:: onmt.models.sru.SRU\n    :members:\n\n\nCopy Attention\n--------------\n\n.. autoclass:: onmt.modules.CopyGenerator\n    :members:\n\n\nStructured Attention\n-------------------------------------------\n\n.. autoclass:: onmt.modules.structured_attention.MatrixTree\n    :members:\n"
  },
  {
    "path": "docs/source/onmt.rst",
    "content": "Framework\n=================\n\nModel\n-----\n\n.. autoclass:: onmt.models.NMTModel\n    :members:\n\nTrainer\n-------\n\n.. autoclass:: onmt.Trainer\n    :members:\n\n\n.. autoclass:: onmt.utils.Statistics\n    :members:\n\nLoss\n----\n\n\n.. autoclass:: onmt.utils.loss.LossComputeBase\n    :members:\n\n\nOptimizer\n---------\n\n.. autoclass:: onmt.utils.Optimizer\n    :members:\n"
  },
  {
    "path": "docs/source/onmt.translate.translation_server.rst",
    "content": "Server\n======\n\n\nModels\n-------------\n\n.. autoclass:: onmt.translate.translation_server.ServerModel\n    :members:\n\n\nCore Server\n------------\n\n.. autoexception:: onmt.translate.translation_server.ServerModelError\n\n.. autoclass:: onmt.translate.translation_server.Timer\n    :members:\n\n.. autoclass:: onmt.translate.translation_server.TranslationServer\n    :members:\n"
  },
  {
    "path": "docs/source/onmt.translation.rst",
    "content": "Translation\n==================\n\nTranslations\n-------------\n\n.. autoclass:: onmt.translate.Translation\n    :members:\n\nTranslator Class\n-----------------\n\n.. autoclass:: onmt.translate.Translator\n    :members:\n\n.. autoclass:: onmt.translate.TranslationBuilder\n    :members:\n\n\nDecoding Strategies\n--------------------\n.. autoclass:: onmt.translate.DecodeStrategy\n    :members:\n\n.. autoclass:: onmt.translate.BeamSearch\n    :members:\n\n.. autofunction:: onmt.translate.greedy_search.sample_with_temperature\n\n.. autoclass:: onmt.translate.GreedySearch\n    :members:\n\nScoring\n--------\n.. autoclass:: onmt.translate.penalties.PenaltyBuilder\n    :members:\n\n.. autoclass:: onmt.translate.GNMTGlobalScorer\n    :members:\n"
  },
  {
    "path": "docs/source/options/build_vocab.rst",
    "content": "Build Vocab\n===========\n\n.. argparse::\n    :filename: ../onmt/bin/build_vocab.py\n    :func: _get_parser\n    :prog: build_vocab.py\n\n    Transform/BART : @before\n        .. Caution:: This transform will not take effect when building vocabulary.\n\n    Transform/SwitchOut : @before\n        .. Caution:: This transform will not take effect when building vocabulary.\n"
  },
  {
    "path": "docs/source/options/server.rst",
    "content": "Server\n=========\n\n.. argparse::\n    :filename: ../onmt/bin/server.py\n    :func: _get_parser\n    :prog: server.py"
  },
  {
    "path": "docs/source/options/train.rst",
    "content": "Train\n=====\n\n.. argparse::\n    :filename: ../onmt/bin/train.py\n    :func: _get_parser\n    :prog: train.py"
  },
  {
    "path": "docs/source/options/translate.rst",
    "content": "Translate\n=========\n\n.. argparse::\n    :filename: ../onmt/bin/translate.py\n    :func: _get_parser\n    :prog: translate.py"
  },
  {
    "path": "docs/source/quickstart.md",
    "content": "\n\n# Quickstart\n\n### Step 0: Install OpenNMT-py\n\n```bash\npip install --upgrade pip\npip install OpenNMT-py\n```\n\n### Step 1: Prepare the data\n\nTo get started, we propose to download a toy English-German dataset for machine translation containing 10k tokenized sentences:\n\n```bash\nwget https://s3.amazonaws.com/opennmt-trainingdata/toy-ende.tar.gz\ntar xf toy-ende.tar.gz\ncd toy-ende\n```\n\nThe data consists of parallel source (`src`) and target (`tgt`) data containing one sentence per line with tokens separated by a space:\n\n* `src-train.txt`\n* `tgt-train.txt`\n* `src-val.txt`\n* `tgt-val.txt`\n\nValidation files are used to evaluate the convergence of the training. It usually contains no more than 5k sentences.\n\n```text\n$ head -n 3 toy_ende/src-train.txt\nIt is not acceptable that , with the help of the national bureaucracies , Parliament &apos;s legislative prerogative should be made null and void by means of implementing provisions whose content , purpose and extent are not laid down in advance .\nFederal Master Trainer and Senior Instructor of the Italian Federation of Aerobic Fitness , Group Fitness , Postural Gym , Stretching and Pilates; from 2004 , he has been collaborating with Antiche Terme as personal Trainer and Instructor of Stretching , Pilates and Postural Gym .\n&quot; Two soldiers came up to me and told me that if I refuse to sleep with them , they will kill me . They beat me and ripped my clothes .\n```\n\nWe need to build a **YAML configuration file** to specify the data that will be used:\n\n```yaml\n# toy_en_de.yaml\n\n## Where the samples will be written\nsave_data: toy-ende/run/example\n## Where the vocab(s) will be written\nsrc_vocab: toy-ende/run/example.vocab.src\ntgt_vocab: toy-ende/run/example.vocab.tgt\n# Prevent overwriting existing files in the folder\noverwrite: False\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: toy-ende/src-train.txt\n        path_tgt: toy-ende/tgt-train.txt\n    valid:\n        path_src: toy-ende/src-val.txt\n        path_tgt: toy-ende/tgt-val.txt\n...\n\n```\n\nFrom this configuration, we can build the vocab(s), that will be necessary to train the model:\n\n```bash\nonmt_build_vocab -config toy_en_de.yaml -n_sample 10000\n```\n\n**Notes**:\n- `-n_sample` is required here -- it represents the number of lines sampled from each corpus to build the vocab.\n- This configuration is the simplest possible, without any tokenization or other *transforms*. See [other example configurations](https://github.com/OpenNMT/OpenNMT-py/tree/master/config) for more complex pipelines.\n\n### Step 2: Train the model\n\nTo train a model, we need to **add the following to the YAML configuration file**:\n- the vocabulary path(s) that will be used: can be that generated by onmt_build_vocab;\n- training specific parameters.\n\n```yaml\n# toy_en_de.yaml\n\n...\n\n# Vocabulary files that were just created\nsrc_vocab: toy-ende/run/example.vocab.src\ntgt_vocab: toy-ende/run/example.vocab.tgt\n\n# Train on a single GPU\nworld_size: 1\ngpu_ranks: [0]\n\n# Where to save the checkpoints\nsave_model: toy-ende/run/model\nsave_checkpoint_steps: 500\ntrain_steps: 1000\nvalid_steps: 500\n\n```\n\nThen you can simply run:\n\n```bash\nonmt_train -config toy_en_de.yaml\n```\n\nThis configuration will run the default model, which consists of a 2-layer LSTM with 500 hidden units on both the encoder and decoder. It will run on a single GPU (`world_size 1` & `gpu_ranks [0]`).\n\nBefore the training process actually starts, the `*.vocab.pt` together with `*.transforms.pt` can be dumped to `-save_data` with configurations specified in `-config` yaml file by enabling the `-dump_fields` and `-dump_transforms` flags. It is also possible to generate transformed samples to simplify any potentially required visual inspection. The number of sample lines to dump per corpus is set with the `-n_sample` flag.\n\nFor more advanded models and parameters, see [other example configurations](https://github.com/OpenNMT/OpenNMT-py/tree/master/config) or the [FAQ](FAQ).\n\n### Step 3: Translate\n\n```bash\nonmt_translate -model toy-ende/run/model_step_1000.pt -src toy-ende/src-test.txt -output toy-ende/pred_1000.txt -gpu 0 -verbose\n```\n\nNow you have a model which you can use to predict on new data. We do this by running beam search. This will output predictions into `toy-ende/pred_1000.txt`.\n\n**Note**:\n\nThe predictions are going to be quite terrible, as the demo dataset is small. Try running on some larger datasets! For example you can download millions of parallel sentences for [translation](http://www.statmt.org/wmt16/translation-task.html) or [summarization](https://github.com/harvardnlp/sent-summary).\n"
  },
  {
    "path": "docs/source/ref.rst",
    "content": "==========\nReferences\n==========\n\n\n\nReferences\n            \n.. bibliography:: refs.bib\n   \n"
  },
  {
    "path": "docs/source/refs.bib",
    "content": "@article{DBLP:journals/corr/LiuL17d,\n  author    = {Yang Liu and\n                 Mirella Lapata},\n                   title     = {Learning Structured Text Representations},\n                     journal   = {CoRR},\n                       volume    = {abs/1705.09207},\n                         year      = {2017},\n                           url       = {http://arxiv.org/abs/1705.09207},\n                             archivePrefix = {arXiv},\n                               eprint    = {1705.09207},\n                                 timestamp = {Wed, 07 Jun 2017 14:41:46 +0200},\n                                   biburl    = {http://dblp.org/rec/bib/journals/corr/LiuL17d},\n                                     bibsource = {dblp computer science bibliography, http://dblp.org}\n                                     }\n\n@article{sennrich2016linguistic,\n  title={Linguistic Input Features Improve Neural Machine Translation},\n    author={Sennrich, Rico and Haddow, Barry},\n      journal={arXiv preprint arXiv:1606.02892},\n        year={2016}\n        }\n\n@inproceedings{Li2016\n  author    = {Yujia Li and\n               Daniel Tarlow and\n               Marc Brockschmidt and\n               Richard S. Zemel},\n  title     = {Gated Graph Sequence Neural Networks},\n  booktitle = {4th International Conference on Learning Representations, {ICLR} 2016,\n               San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings},\n  year      = {2016},\n  crossref  = {DBLP:conf/iclr/2016},\n  url       = {http://arxiv.org/abs/1511.05493},\n  timestamp = {Thu, 25 Jul 2019 14:25:40 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/LiTBZ15.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@inproceedings{Bahdanau2015,\narchivePrefix = {arXiv},\narxivId = {1409.0473},\nauthor = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},\nbooktitle = {ICLR},\ndoi = {10.1146/annurev.neuro.26.041002.131047},\neprint = {1409.0473},\nisbn = {0147-006X (Print)},\nissn = {0147-006X},\nkeywords = {Neural machine translation is a recently proposed,Unlike the traditional statistical machine transla,a source sentence into a fixed-length vector from,and propose to extend this by allowing a model to,bottleneck in improving the performance of this ba,for parts of a source sentence that are relevant t,having to form these parts as a hard segment expli,machine translation often belong to a family of en,maximize the translation performance. The models p,phrase-based system on the task of English-to-Fren,qualitative analysis reveals that the (soft-)align,the neural machine,translation aims at building a single neural netwo,translation. In this paper,we achieve a translation performance comparable to,we conjecture that the use of a fixed-length vecto,well with our intuition,without},\npages = {1--15},\npmid = {14527267},\ntitle = {{Neural Machine Translation By Jointly Learning To Align and Translate}},\nurl = {http://arxiv.org/abs/1409.0473 http://arxiv.org/abs/1409.0473v3},\nyear = {2014}\n}\n\n@inproceedings{sutskever14sequence,\nabstract = {Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.},\narchivePrefix = {arXiv},\narxivId = {1409.3215},\nauthor = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.},\nbooktitle = {NIPS},\neprint = {1409.3215},\nisbn = {1409.3215},\npages = {9},\npmid = {2079951},\ntitle = {{Sequence to Sequence Learning with Neural Networks}},\nurl = {http://arxiv.org/abs/1409.3215},\nyear = {2014}\n}\n\n@article{Xu2015,\nabstract = {Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.},\narchivePrefix = {arXiv},\narxivId = {1502.03044},\nauthor = {Xu, Kelvin and Ba, Jimmy and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard and Bengio, Yoshua},\neprint = {1502.03044},\nfile = {:home/srush/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Xu et al. - 2015 - Show, Attend and Tell Neural Image Caption Generation with Visual Attention(2).pdf:pdf},\njournal = {ICML},\nmonth = {feb},\ntitle = {{Show, Attend and Tell: Neural Image Caption Generation with Visual Attention}},\nurl = {http://arxiv.org/abs/1502.03044},\nyear = {2015}\n}\n@article{systran,\n  title={SYSTRAN's Pure Neural Machine Translation System},\n    author={Josep Crego and Jungi Kim and Jean Senellart},\n      journal={arXiv preprint arXiv:1602.06023},\n        year={2016}\n        }\n@InProceedings{Cho2014,\n  title     = {{L}earning {P}hrase {R}epresentations using {RNN} {E}ncoder-{D}ecoder for {S}tatistical {M}achine {T}ranslation},\n  author    = {Kyunghyun Cho and Bart van Merrienboer and Caglar Gulcehre and Dzmitry Bahdanau and Fethi Bougares and Holger Schwenk and Yoshua Bengio},\n  booktitle = {Proc of EMNLP},\n  year      = {2014}\n}\n\n@InProceedings{Luong2015,\n  title     = {{E}ffective {A}pproaches to {A}ttention-based {N}eural {M}achine {T}ranslation},\n  author    = {Minh-Thang Luong and Hieu Pham and Christopher D. Manning},\n  booktitle = {Proc of EMNLP},\n  year      = {2015}\n}\n\n@InProceedings{Luong2015b,\n  title     = {{A}ddressing the {R}are {W}ord {P}roblem in {N}eural {M}achine {T}ranslation},\n  author    = {Minh-Thang Luong and Ilya Sutskever and Quoc Le and Oriol Vinyals and Wojciech Zaremba},\n  booktitle = {Proc of ACL},\n  year      = {2015}\n}\n\n@article{wu2016google,\n  title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n    author={Wu, Yonghui and Schuster, Mike and Chen, Zhifeng and Le, Quoc V and Norouzi, Mohammad and Macherey, Wolfgang and Krikun, Maxim and Cao, Yuan and Gao, Qin and Macherey, Klaus and others},\n      journal={arXiv preprint arXiv:1609.08144},\n        year={2016}\n        }\n\n\n@inproceedings{dean2012large,\n  title={Large scale distributed deep networks},\n  author={Dean, Jeffrey and Corrado, Greg and Monga, Rajat and Chen, Kai and Devin, Matthieu and Mao, Mark and Senior, Andrew and Tucker, Paul and Yang, Ke and Le, Quoc V and others},\n  booktitle={Advances in neural information processing systems},\n  pages={1223--1231},\n  year={2012}\n}\n@inproceedings{koehn2007moses,\n  title={Moses: Open source toolkit for statistical machine translation},\n    author={Koehn, Philipp and Hoang, Hieu and Birch, Alexandra and Callison-Burch, Chris and Federico, Marcello and Bertoldi, Nicola and Cowan, Brooke and Shen, Wade and Moran, Christine and Zens, Richard and others},\n      booktitle={Proc ACL},\n        pages={177--180},\n          year={2007},\n            organization={Association for Computational Linguistics}\n            }\n\n@inproceedings{dyer2010cdec,\n  title={cdec: A decoder, alignment, and learning framework for finite-state and context-free translation models},\n    author={Dyer, Chris and Weese, Jonathan and Setiawan, Hendra and Lopez, Adam and Ture, Ferhan and Eidelman, Vladimir and Ganitkevitch, Juri and Blunsom, Phil and Resnik, Philip},\n      booktitle={Proc  ACL},\n        pages={7--12},\n          year={2010},\n            organization={Association for Computational Linguistics}\n            }\n\n@article{hochreiter1997long,\n  title={Long short-term memory},\n  author={Hochreiter, Sepp and Schmidhuber, J{\\\"u}rgen},\n  journal={Neural computation},\n  volume={9},\n  number={8},\n  pages={1735--1780},\n  year={1997},\n  publisher={MIT Press}\n}\n\n\n@article{chung2014empirical,\n  title={Empirical evaluation of gated recurrent neural networks on sequence modeling},\n    author={Chung, Junyoung and Gulcehre, Caglar and Cho, KyungHyun and Bengio, Yoshua},\n      journal={arXiv preprint arXiv:1412.3555},\n        year={2014}\n        }\n\n\n@inproceedings{yang2016hierarchical,\n  title={Hierarchical attention networks for document classification},\n    author={Yang, Zichao and Yang, Diyi and Dyer, Chris and He, Xiaodong and Smola, Alex and Hovy, Eduard},\n      booktitle={Proc ACL},\n        year={2016}\n        }\n\n@article{martins2016softmax,\n  title={From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification},\n    author={Martins, Andr{\\'e} FT and Astudillo, Ram{\\'o}n Fernandez},\n      journal={arXiv preprint arXiv:1602.02068},\n        year={2016}\n        }\n\n@article{DBLP:journals/corr/LeonardWW15,\n  author    = {Nicholas L{\\'{e}}onard and\n                 Sagar Waghmare and\n                                Yang Wang and\n                                               Jin{-}Hwa Kim},\n                                                 title     = {rnn : Recurrent Library for Torch},\n                                                   journal   = {CoRR},\n                                                     volume    = {abs/1511.07889},\n                                                       year      = {2015},\n                                                         url       = {http://arxiv.org/abs/1511.07889},\n                                                           timestamp = {Wed, 23 Dec 2015 08:46:28 +0100},\n                                                             biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/LeonardWW15},\n                                                               bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                               }\n\n\n@inproceedings{DBLP:conf/conll/BowmanVVDJB16,\n  author    = {Samuel R. Bowman and\n                 Luke Vilnis and\n                                Oriol Vinyals and\n                                               Andrew M. Dai and\n                                                              Rafal J{\\'{o}}zefowicz and\n                                                                             Samy Bengio},\n                                                                               title     = {Generating Sentences from a Continuous Space},\n                                                                                 booktitle = {Proceedings of the 20th {SIGNLL} Conference on Computational Natural\n                                                                                                Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016},\n                                                                                                  pages     = {10--21},\n                                                                                                    year      = {2016},\n                                                                                                      crossref  = {DBLP:conf/conll/2016},\n                                                                                                        url       = {http://aclweb.org/anthology/K/K16/K16-1002.pdf},\n                                                                                                          timestamp = {Sun, 04 Sep 2016 10:01:12 +0200},\n                                                                                                            biburl    = {http://dblp.uni-trier.de/rec/bib/conf/conll/BowmanVVDJB16},\n                                                                                                              bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                                                                              }\n\n@inproceedings{DBLP:conf/nips/VinyalsBLKW16,\n  author    = {Oriol Vinyals and\n                 Charles Blundell and\n                                Tim Lillicrap and\n                                               Koray Kavukcuoglu and\n                                                              Daan Wierstra},\n                                                                title     = {Matching Networks for One Shot Learning},\n                                                                  booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference\n                                                                                 on Neural Information Processing Systems 2016, December 5-10, 2016,\n                                                                                                Barcelona, Spain},\n                                                                                                  pages     = {3630--3638},\n                                                                                                    year      = {2016},\n                                                                                                      crossref  = {DBLP:conf/nips/2016},\n                                                                                                        url       = {http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning},\n                                                                                                          timestamp = {Fri, 16 Dec 2016 19:45:58 +0100},\n                                                                                                            biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nips/VinyalsBLKW16},\n                                                                                                              bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                                                                              }\n\n\n@article{DBLP:journals/corr/WestonCB14,\n  author    = {Jason Weston and\n                 Sumit Chopra and\n                                Antoine Bordes},\n                                  title     = {Memory Networks},\n                                    journal   = {CoRR},\n                                      volume    = {abs/1410.3916},\n                                        year      = {2014},\n                                          url       = {http://arxiv.org/abs/1410.3916},\n                                            timestamp = {Sun, 02 Nov 2014 11:25:59 +0100},\n                                              biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/WestonCB14},\n                                                bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                }\n\n@article{DBLP:journals/corr/XuBKCCSZB15,\n  author    = {Kelvin Xu and\n                 Jimmy Ba and\n                                Ryan Kiros and\n                                               Kyunghyun Cho and\n                                                              Aaron C. Courville and\n                                                                             Ruslan Salakhutdinov and\n                                                                                            Richard S. Zemel and\n                                                                                                           Yoshua Bengio},\n                                                                                                             title     = {Show, Attend and Tell: Neural Image Caption Generation with Visual\n                                                                                                                            Attention},\n                                                                                                                              journal   = {CoRR},\n                                                                                                                                volume    = {abs/1502.03044},\n                                                                                                                                  year      = {2015},\n                                                                                                                                    url       = {http://arxiv.org/abs/1502.03044},\n                                                                                                                                      timestamp = {Mon, 02 Mar 2015 14:17:34 +0100},\n                                                                                                                                        biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/XuBKCCSZB15},\n                                                                                                                                          bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                                                                                                          }\n\n\n\n@article{DBLP:journals/corr/DengKR16,\n  author    = {Yuntian Deng and\n                 Anssi Kanervisto and\n                                Alexander M. Rush},\n                                  title     = {What You Get Is What You See: {A} Visual Markup Decompiler},\n                                    journal   = {CoRR},\n                                      volume    = {abs/1609.04938},\n                                        year      = {2016},\n                                          url       = {http://arxiv.org/abs/1609.04938},\n                                            timestamp = {Mon, 03 Oct 2016 17:51:10 +0200},\n                                              biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/DengKR16},\n                                                bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                }\n\n\n@article{DBLP:journals/corr/ChanJLV15,\n  author    = {William Chan and\n                 Navdeep Jaitly and\n                                Quoc V. Le and\n                                               Oriol Vinyals},\n                                                 title     = {Listen, Attend and Spell},\n                                                   journal   = {CoRR},\n                                                     volume    = {abs/1508.01211},\n                                                       year      = {2015},\n                                                         url       = {http://arxiv.org/abs/1508.01211},\n                                                           timestamp = {Tue, 01 Sep 2015 14:42:40 +0200},\n                                                             biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/ChanJLV15},\n                                                               bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                               }\n\n@article{DBLP:journals/corr/SennrichHB15,\n  author    = {Rico Sennrich and\n                 Barry Haddow and\n                                Alexandra Birch},\n                                  title     = {Neural Machine Translation of Rare Words with Subword Units},\n                                    journal   = {CoRR},\n                                      volume    = {abs/1508.07909},\n                                        year      = {2015},\n                                          url       = {http://arxiv.org/abs/1508.07909},\n                                            timestamp = {Tue, 01 Sep 2015 14:42:40 +0200},\n                                              biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/SennrichHB15},\n                                                bibsource = {dblp computer science bibliography, http://dblp.org}\n                                                }\n\n\n@article{chopra2016abstractive,\n  title={Abstractive sentence summarization with attentive recurrent neural networks},\n    author={Chopra, Sumit and Auli, Michael and Rush, Alexander M and Harvard, SEAS},\n      journal={Proceedings of NAACL-HLT16},\n        pages={93--98},\n          year={2016}\n          }\n\n@article{vinyals2015neural,\n  title={A neural conversational model},\n    author={Vinyals, Oriol and Le, Quoc},\n      journal={arXiv preprint arXiv:1506.05869},\n        year={2015}\n        }\n\n@inproceedings{neubig13travatar,\n  title = {Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers},\n    author = {Graham Neubig},\n      booktitle = {Proc ACL },\n        address = {Sofia, Bulgaria},\n          month = {August},\n            year = {2013}\n            }\n\n@ARTICLE{2017arXiv170301619N,\n   author = {{Neubig}, G.},\n       title = \"{Neural Machine Translation and Sequence-to-sequence Models: A Tutorial}\",\n         journal = {ArXiv e-prints},\n         archivePrefix = \"arXiv\",\n            eprint = {1703.01619},\n             primaryClass = \"cs.CL\",\n              keywords = {Computer Science - Computation and Language, Computer Science - Learning, Statistics - Machine Learning},\n                   year = 2017,\n                       month = mar,\n                          adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170301619N},\n                            adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n                            }\n\n@article{DBLP:journals/corr/VaswaniSPUJGKP17,\n  author    = {Ashish Vaswani and\n               Noam Shazeer and\n               Niki Parmar and\n               Jakob Uszkoreit and\n               Llion Jones and\n               Aidan N. Gomez and\n               Lukasz Kaiser and\n               Illia Polosukhin},\n  title     = {Attention Is All You Need},\n  journal   = {CoRR},\n  volume    = {abs/1706.03762},\n  year      = {2017},\n  url       = {http://arxiv.org/abs/1706.03762},\n  archivePrefix = {arXiv},\n  eprint    = {1706.03762},\n  timestamp = {Mon, 13 Aug 2018 16:48:37 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/VaswaniSPUJGKP17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@article{DBLP:journals/corr/GehringAGYD17,\n  author    = {Jonas Gehring and\n               Michael Auli and\n               David Grangier and\n               Denis Yarats and\n               Yann N. Dauphin},\n  title     = {Convolutional Sequence to Sequence Learning},\n  journal   = {CoRR},\n  volume    = {abs/1705.03122},\n  year      = {2017},\n  url       = {http://arxiv.org/abs/1705.03122},\n  archivePrefix = {arXiv},\n  eprint    = {1705.03122},\n  timestamp = {Mon, 13 Aug 2018 16:48:03 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/GehringAGYD17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@article{DBLP:journals/corr/abs-1709-02755,\n  author    = {Tao Lei and\n               Yu Zhang and\n               Yoav Artzi},\n  title     = {Training RNNs as Fast as CNNs},\n  journal   = {CoRR},\n  volume    = {abs/1709.02755},\n  year      = {2017},\n  url       = {http://arxiv.org/abs/1709.02755},\n  archivePrefix = {arXiv},\n  eprint    = {1709.02755},\n  timestamp = {Mon, 13 Aug 2018 16:46:29 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1709-02755},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@article{DBLP:journals/corr/SeeLM17,\n  author    = {Abigail See and\n               Peter J. Liu and\n               Christopher D. Manning},\n  title     = {Get To The Point: Summarization with Pointer-Generator Networks},\n  journal   = {CoRR},\n  volume    = {abs/1704.04368},\n  year      = {2017},\n  url       = {http://arxiv.org/abs/1704.04368},\n  archivePrefix = {arXiv},\n  eprint    = {1704.04368},\n  timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/SeeLM17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@article{DBLP:journals/corr/abs-1805-00631,\n  author    = {Biao Zhang and\n               Deyi Xiong and\n               Jinsong Su},\n  title     = {Accelerating Neural Transformer via an Average Attention Network},\n  journal   = {CoRR},\n  volume    = {abs/1805.00631},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1805.00631},\n  archivePrefix = {arXiv},\n  eprint    = {1805.00631},\n  timestamp = {Mon, 13 Aug 2018 16:46:01 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1805-00631},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@article{DBLP:journals/corr/MartinsA16,\n  author    = {Andr{\\'{e}} F. T. Martins and\n               Ram{\\'{o}}n Fern{\\'{a}}ndez Astudillo},\n  title     = {From Softmax to Sparsemax: {A} Sparse Model of Attention and Multi-Label\n               Classification},\n  journal   = {CoRR},\n  volume    = {abs/1602.02068},\n  year      = {2016},\n  url       = {http://arxiv.org/abs/1602.02068},\n  archivePrefix = {arXiv},\n  eprint    = {1602.02068},\n  timestamp = {Mon, 13 Aug 2018 16:49:13 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/MartinsA16},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n@inproceedings{garg2019jointly,\n  title = {Jointly Learning to Align and Translate with Transformer Models},\n  author = {Garg, Sarthak and Peitz, Stephan and Nallasamy, Udhyakumar and Paulik, Matthias},\n  booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},\n  address = {Hong Kong},\n  month = {November},\n  url = {https://arxiv.org/abs/1909.02074},\n  year = {2019},\n}\n\n@inproceedings{DeeperTransformer,\n    title = \"Learning Deep Transformer Models for Machine Translation\",\n    author = \"Wang, Qiang  and\n      Li, Bei  and\n      Xiao, Tong  and\n      Zhu, Jingbo  and\n      Li, Changliang  and\n      Wong, Derek F.  and\n      Chao, Lidia S.\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1176\",\n    doi = \"10.18653/v1/P19-1176\",\n    pages = \"1810--1822\",\n    abstract = \"Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT{'}16 English-German and NIST OpenMT{'}12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.\",\n}\n\n@article{DBLP:journals/corr/abs-1808-07512,\n  author    = {Xinyi Wang and\n               Hieu Pham and\n               Zihang Dai and\n               Graham Neubig},\n  title     = {SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine\n               Translation},\n  journal   = {CoRR},\n  volume    = {abs/1808.07512},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1808.07512},\n  archivePrefix = {arXiv},\n  eprint    = {1808.07512},\n  timestamp = {Sun, 02 Sep 2018 15:01:54 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-1808-07512.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n"
  },
  {
    "path": "examples/README.md",
    "content": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/memray/OpenNMT-kpg-release/blob/master/examples/wikibart_inference.ipynb)\n"
  },
  {
    "path": "examples/cnndm.yaml",
    "content": "## Where the vocab(s) will be written\nsave_data: cnndm/run/example\n# Prevent overwriting existing files in the folder\noverwrite: False\n\n# filter long examples\nsrc_seq_length: 10000\ntgt_seq_length: 10000\nsrc_seq_length_trunc: 400\ntgt_seq_length_trunc: 100\n\n# common vocabulary for source and target\nshare_vocab: True\n\n# Corpus opts:\ndata:\n    cnndm:\n        path_src: cnndm/train.txt.src\n        path_tgt: cnndm/train.txt.tgt.tagged\n        transforms: []\n        weight: 1\n    valid:\n        path_src: cnndm/val.txt.src\n        path_tgt: cnndm/val.txt.tgt.tagged\n        transforms: []\n\nsrc_vocab_size: 50000\ntgt_vocab_size: 50000\n\nsrc_vocab: cnndm/run/example.vocab.src\ntgt_vocab: cnndm/run/example.vocab.tgt\n\nsave_model: cnndm/run/model\ncopy_attn: true\nglobal_attention: mlp\nword_vec_size: 128\nrnn_size: 512\nlayers: 1\nencoder_type: brnn\ntrain_steps: 200000\nmax_grad_norm: 2\ndropout: 0\nbatch_size: 16\nvalid_batch_size: 16\noptim: adagrad\nlearning_rate: 0.15\nadagrad_accumulator_init: 0.1\nreuse_copy_attn: true\ncopy_loss_by_seqlength: true\nbridge: true\nseed: 777\nworld_size: 2\ngpu_ranks: [0, 1]"
  },
  {
    "path": "examples/ggnn.yaml",
    "content": "## Where the necessary objects will be written\nsave_data: OpenNMT-py-ggnn-example/run/example\n\n# Filter long examples\nsrc_seq_length: 1000\ntgt_seq_length: 30\n\n# Data definition\ndata:\n    cnndm:\n        path_src: OpenNMT-py-ggnn-example/src-train.txt\n        path_tgt: OpenNMT-py-ggnn-example/tgt-train.txt\n        transforms: [filtertoolong]\n        weight: 1\n    valid:\n        path_src: OpenNMT-py-ggnn-example/src-val.txt\n        path_tgt: OpenNMT-py-ggnn-example/tgt-val.txt\n\nsrc_vocab: OpenNMT-py-ggnn-example/srcvocab.txt\ntgt_vocab: OpenNMT-py-ggnn-example/tgtvocab.txt\n\nsave_model: OpenNMT-py-ggnn-example/run/model\n\n# Model options\ntrain_steps: 10000\nsave_checkpoint_steps: 5000\nencoder_type: ggnn\nlayers: 2\ndecoder_type: rnn\nrnn_size: 256\nlearning_rate: 0.1\nstart_decay_steps: 5000\nlearning_rate_decay: 0.8\nglobal_attention: general\nbatch_size: 32\nword_vec_size: 256\nbridge: true\ngpu_ranks: 0\nn_edge_types: 9\nstate_dim: 256\nn_steps: 10\nn_node: 64"
  },
  {
    "path": "examples/onmt.train.fp16.transformer.yaml",
    "content": "# Meta opts:\n## IO\nsave_data: generated/dynamic.ex0\noverwrite: False\n\n### vocab:\nsrc_vocab: data/vocab-train.src\ntgt_vocab: data/vocab-train.tgt\nsrc_vocab_size: 32000\ntgt_vocab_size: 32000\nvocab_size_multiple: 8\nsrc_words_min_frequency: 10\ntgt_words_min_frequency: 10\nshare_vocab: True\n\n### Transform related opts:\n#### Subword\nsrc_subword_model: examples/subword.spm.model\ntgt_subword_model: examples/subword.spm.model\nsrc_subword_nbest: 1\ntgt_subword_nbest: 1\nsrc_subword_alpha: 0.0\ntgt_subword_alpha: 0.0\nsrc_subword_type: sentencepiece\ntgt_subword_type: sentencepiece\nsrc_onmttok_kwargs: \"{'mode': 'aggressive', 'spacer_annotate': True}\"\ntgt_onmttok_kwargs: \"{'mode': 'aggressive', 'spacer_annotate': True}\"\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter\nsrc_seq_length: 300\ntgt_seq_length: 300\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: data/src-train.txt\n        path_tgt: data/tgt-train.txt\n        transforms: [tokenmask, tokendrop, onmt_tokenize, filtertoolong]\n    valid:\n        path_src: data/src-val.txt\n        path_tgt: data/tgt-val.txt\n        transforms: [onmt_tokenize]\n\n# Model configuration\nsave_model: foo\nkeep_checkpoint: 50\nsave_checkpoint_steps: 4000\naverage_decay: 0.0001\nseed: 2345\nreport_every: 100\ntrain_steps: 100000\nvalid_steps: 4000\n\nqueue_size: 10000\nbucket_size: 32768\nworld_size: 2\ngpu_ranks: [0, 1]\nbatch_type: \"tokens\"\nbatch_size: 4096\nvalid_batch_size: 8\nbatch_size_multiple: 1\nmax_generator_batches: 0\naccum_count: [3]\naccum_steps: [0]\n\nmodel_dtype: \"fp16\"\noptim: \"fusedadam\"\nlearning_rate: 2\nwarmup_steps: 6000\ndecay_method: \"noam\"\nadam_beta2: 0.998\nmax_grad_norm: 0\nlabel_smoothing: 0.1\nparam_init: 0\nparam_init_glorot: true\nnormalization: \"tokens\"\n\nencoder_type: transformer\ndecoder_type: transformer\nenc_layers: 6\ndec_layers: 6\nheads: 8\nrnn_size: 512\nword_vec_size: 512\ntransformer_ff: 2048\ndropout_steps: [0]\ndropout: [0.1]\nattention_dropout: [0.1]\nshare_decoder_embeddings: true\nshare_embeddings: true\n"
  },
  {
    "path": "examples/scripts/prepare_wikitext-103_data.sh",
    "content": "#!/bin/bash\n\n##################################################################################\n# This script will download wikitext-103-raw and will do basic data preparation\n# for BPE and training\n##################################################################################\n\n# provide script usage instructions\nif [ $# -eq 0 ]\nthen\n    echo \"usage: $0 <data_dir>\"\n    exit 1\nfi\n\nget_seeded_random()\n{\n  seed=\"$1\"\n  openssl enc -aes-256-ctr -pass pass:\"$seed\" -nosalt \\\n    </dev/zero 2>/dev/null\n}\n\n# set relevant paths\nSP_PATH=/usr/local/bin\nDATA_PATH=$1\nTEST_PATH=$DATA_PATH/test\n\nCUR_DIR=$(pwd)\n\n# Download the default datasets into the $DATA_PATH; mkdir if it doesn't exist\nmkdir -p $DATA_PATH\ncd $DATA_PATH\n\necho \"Downloading and extracting WikiText-103 (183 MB) for training and inference...\"\nwget --trust-server-names https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip\nunzip wikitext-103-raw-v1.zip\nrm wikitext-103-raw-v1.zip\ncd wikitext-103-raw\n\necho \"Removing empty lines and shuffling training data\"\nsed -r '/^\\s*$/d' -i wiki.train.raw\nsed -r '/^\\s*$/d' -i wiki.valid.raw\nsed -r '/^\\s*$/d' -i wiki.test.raw\nsort --random-source=<(get_seeded_random 42) -R -o wiki.train.raw wiki.train.raw\n"
  },
  {
    "path": "examples/scripts/prepare_wmt_data.sh",
    "content": "#!/bin/bash\n\n##################################################################################\n# The default script downloads the commoncrawl, europarl and newstest2014 and\n# newstest2017 datasets. Files that are not English or German are removed in\n# this script for tidyness.You may switch datasets out depending on task.\n# (Note that commoncrawl europarl-v7 are the same for all tasks).\n# http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n# http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz\n#\n# WMT14 http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz\n# WMT15 http://www.statmt.org/wmt15/training-parallel-nc-v10.tgz\n# WMT16 http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz\n# WMT17 http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz\n# Note : there are very little difference, but each year added a few sentences\n# new WMT17 http://data.statmt.org/wmt17/translation-task/rapid2016.tgz\n#\n# For WMT16 Rico Sennrich released some News back translation\n# http://data.statmt.org/rsennrich/wmt16_backtranslations/en-de/\n#\n# Tests sets: http://data.statmt.org/wmt17/translation-task/test.tgz\n##################################################################################\n\n# provide script usage instructions\nif [ $# -eq 0 ]\nthen\n    echo \"usage: $0 <data_dir>\"\n    exit 1\nfi\n\n# set relevant paths\nSP_PATH=/usr/local/bin\nDATA_PATH=$1\nTEST_PATH=$DATA_PATH/test\n\nCUR_DIR=$(pwd)\n\n# set vocabulary size and source and target languages\nvocab_size=32000\nsl=en\ntl=de\n\n# Download the default datasets into the $DATA_PATH; mkdir if it doesn't exist\nmkdir -p $DATA_PATH\ncd $DATA_PATH\n\necho \"Downloading and extracting Commoncrawl data (919 MB) for training...\"\nwget --trust-server-names http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\ntar zxvf training-parallel-commoncrawl.tgz\nls | grep -v 'commoncrawl.de-en.[de,en]' | xargs rm\n\necho \"Downloading and extracting Europarl data (658 MB) for training...\"\nwget --trust-server-names http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz\ntar zxvf training-parallel-europarl-v7.tgz\ncd training && ls | grep -v 'europarl-v7.de-en.[de,en]' | xargs rm\ncd .. && mv training/europarl* . && rm -r training training-parallel-europarl-v7.tgz\n\necho \"Downloading and extracting News Commentary data (76 MB) for training...\"\nwget --trust-server-names http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz\ntar zxvf training-parallel-nc-v11.tgz\ncd training-parallel-nc-v11 && ls | grep -v news-commentary-v11.de-en.[de,en] | xargs rm\ncd .. && mv training-parallel-nc-v11/* . && rm -r training-parallel-nc-v11 training-parallel-nc-v11.tgz\n\n# Validation and test data are put into the $DATA_PATH/test folder\necho \"Downloading and extracting newstest2014 data (4 MB) for validation...\"\nwget --trust-server-names http://www.statmt.org/wmt14/test-filtered.tgz\necho \"Downloading and extracting newstest2017 data (5 MB) for testing...\"\nwget --trust-server-names http://data.statmt.org/wmt17/translation-task/test.tgz\ntar zxvf test-filtered.tgz && tar zxvf test.tgz\ncd test && ls | grep -v '.*deen\\|.*ende' | xargs rm\ncd .. && rm test-filtered.tgz test.tgz && cd ..\n\n# set training, validation, and test corpuses\ncorpus[1]=commoncrawl.de-en\ncorpus[2]=europarl-v7.de-en\ncorpus[3]=news-commentary-v11.de-en\n#corpus[3]=news-commentary-v12.de-en\n#corpus[4]=news.bt.en-de\n#corpus[5]=rapid2016.de-en\n\nvalidset=newstest2014-deen\ntestset=newstest2017-ende\n\ncd $CUR_DIR\n\n# retrieve file preparation from Moses repository\nwget -nc \\\n    https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/ems/support/input-from-sgm.perl \\\n    -O $TEST_PATH/input-from-sgm.perl\n\n##################################################################################\n# Starting from here, original files are supposed to be in $DATA_PATH\n# a data folder will be created in scripts/wmt\n##################################################################################\n\nexport PATH=$SP_PATH:$PATH\n\n# Data preparation using SentencePiece\n# First we concat all the datasets to train the SP model\nif false; then\n echo \"$0: Training sentencepiece model\"\n rm -f $DATA_PATH/train.txt\n for ((i=1; i<= ${#corpus[@]}; i++))\n do\n  for f in $DATA_PATH/${corpus[$i]}.$sl $DATA_PATH/${corpus[$i]}.$tl\n   do\n    cat $f >> $DATA_PATH/train.txt\n   done\n done\n spm_train --input=$DATA_PATH/train.txt --model_prefix=$DATA_PATH/wmt$sl$tl \\\n           --vocab_size=$vocab_size --character_coverage=1\n rm $DATA_PATH/train.txt\nfi\n\n# Second we use the trained model to tokenize all the files\n# This is not necessary, as it can be done on the fly in OpenNMT-py 2.0\n# if false; then\n#  echo \"$0: Tokenizing with sentencepiece model\"\n#  rm -f $DATA_PATH/train.txt\n#  for ((i=1; i<= ${#corpus[@]}; i++))\n#  do\n#   for f in $DATA_PATH/${corpus[$i]}.$sl $DATA_PATH/${corpus[$i]}.$tl\n#    do\n#     file=$(basename $f)\n#     spm_encode --model=$DATA_PATH/wmt$sl$tl.model < $f > $DATA_PATH/$file.sp\n#    done\n#  done\n# fi\n\n# We concat the training sets into two (src/tgt) tokenized files\n# if false; then\n#  cat $DATA_PATH/*.$sl.sp > $DATA_PATH/train.$sl\n#  cat $DATA_PATH/*.$tl.sp > $DATA_PATH/train.$tl\n# fi\n\n#  We use the same tokenization method for a valid set (and test set)\n# if true; then\n#  perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$validset-src.$sl.sgm \\\n#     | spm_encode --model=$DATA_PATH/wmt$sl$tl.model > $DATA_PATH/valid.$sl.sp\n#  perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$validset-ref.$tl.sgm \\\n#     | spm_encode --model=$DATA_PATH/wmt$sl$tl.model > $DATA_PATH/valid.$tl.sp\n#  perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$testset-src.$sl.sgm \\\n#     | spm_encode --model=$DATA_PATH/wmt$sl$tl.model > $DATA_PATH/test.$sl.sp\n#  perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$testset-ref.$tl.sgm \\\n#     | spm_encode --model=$DATA_PATH/wmt$sl$tl.model > $DATA_PATH/test.$tl.sp\n# fi\n\n# Parse the valid and test sets\nif true; then\n perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$validset-src.$sl.sgm \\\n    > $DATA_PATH/valid.$sl\n perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$validset-ref.$tl.sgm \\\n    > $DATA_PATH/valid.$tl\n perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$testset-src.$sl.sgm \\\n    > $DATA_PATH/test.$sl\n perl $TEST_PATH/input-from-sgm.perl < $TEST_PATH/$testset-ref.$tl.sgm \\\n    > $DATA_PATH/test.$tl\nfi\n"
  },
  {
    "path": "examples/wiki_103.yaml",
    "content": "\nseed: 42\nshare_vocab: true\nsave_data: data/wikitext-103-raw/run/example\n## Where the vocab(s) will be written\nsrc_vocab: data/wikitext-103-raw/run/example.vocab.src\nsrc_vocab_size: 60000\ntgt_vocab_size: 60000\nsrc_subword_type: bpe\nsrc_subword_model: data/wikitext-103-raw/subwords.bpe\nsrc_onmttok_kwargs: '{\"mode\": \"aggressive\", \"joiner_annotate\": True, \"preserve_placeholders\":\n  True, \"case_markup\": True, \"soft_case_regions\": True, \"preserve_segmented_tokens\":\n  True}'\ntransforms: [onmt_tokenize, filtertoolong]\nsrc_seq_length: 512\ntgt_seq_length: 512\n\n# Prevent overwriting existing files in the folder\noverwrite: True\n\n# Corpus opts:\ndata:\n    corpus_1:\n        path_src: data/wikitext-103-raw/wiki.train.raw\n    valid:\n        path_src: data/wikitext-103-raw/wiki.valid.raw\n\n\n# Vocabulary files that were just created\nsrc_vocab: data/wikitext-103-raw/run/example.vocab.src\n\n# Train on a single GPU\nworld_size: 1\ngpu_ranks: [0]\n\n# Where to save the checkpoints\nsave_model: data/wikitext-103-raw/run/model-lm\nsave_checkpoint_steps: 50000\ntrain_steps: 1000000\nvalid_steps: 500\nreport_every: 100\ntensorboard: true\ntensorboard_log_dir: data/wikitext-103-raw/run/tensorboard\n\n# Model\nmodel_task: lm\nencoder_type: transformer_lm\ndecoder_type: transformer_lm\nposition_encoding: true\ndec_layers: 6\nheads: 8\nrnn_size: 512\nword_vec_size: 512\ntransformer_ff: 2048\ndropout_steps: [0]\ndropout: [0.1]\nattention_dropout: [0.1]\nbatch_size: 2048\nbatch_type: tokens\n\nmodel_dtype: \"fp32\"\noptim: \"adam\"\nlearning_rate: 2\nwarmup_steps: 8000\ndecay_method: \"noam\"\nadam_beta2: 0.998\nmax_grad_norm: 0\nlabel_smoothing: 0.1\nparam_init: 0\nparam_init_glorot: true\nnormalization: \"tokens\"\n"
  },
  {
    "path": "examples/wikibart_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2022-03-06 00:55:47.260454: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import json\\n\",\n    \"import logging\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"from dataclasses import dataclass, field\\n\",\n    \"from typing import Optional\\n\",\n    \"\\n\",\n    \"import datasets\\n\",\n    \"import nltk  # Here to have a nice missing dependency error message early on\\n\",\n    \"from nltk.stem import *\\n\",\n    \"import numpy as np\\n\",\n    \"from datasets import load_dataset, load_metric\\n\",\n    \"\\n\",\n    \"from filelock import FileLock\\n\",\n    \"from transformers import (\\n\",\n    \"    AutoConfig,\\n\",\n    \"    AutoModelForSeq2SeqLM,\\n\",\n    \"    AutoTokenizer,\\n\",\n    \"    DataCollatorForSeq2Seq,\\n\",\n    \"    HfArgumentParser,\\n\",\n    \"    Seq2SeqTrainer,\\n\",\n    \"    Seq2SeqTrainingArguments,\\n\",\n    \"    set_seed, TrainingArguments, TrainerState, TrainerControl,\\n\",\n    \"    TrainerCallback\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"logger = logging.getLogger(__name__)\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    nltk.data.find(\\\"tokenizers/punkt\\\")\\n\",\n    \"except (LookupError, OSError):\\n\",\n    \"    if is_offline_mode():\\n\",\n    \"        raise LookupError(\\n\",\n    \"            \\\"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files\\\"\\n\",\n    \"        )\\n\",\n    \"    with FileLock(\\\".lock\\\") as lock:\\n\",\n    \"        nltk.download(\\\"punkt\\\", quiet=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"@dataclass\\n\",\n    \"class ModelArguments:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    model_name_or_path: str = field(\\n\",\n    \"        metadata={\\\"help\\\": \\\"Path to pretrained model or model identifier from huggingface.co/models\\\"}\\n\",\n    \"    )\\n\",\n    \"    config_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"Pretrained config name or path if not the same as model_name\\\"}\\n\",\n    \"    )\\n\",\n    \"    tokenizer_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"Pretrained tokenizer name or path if not the same as model_name\\\"}\\n\",\n    \"    )\\n\",\n    \"    cache_dir: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"Where to store the pretrained models downloaded from huggingface.co\\\"},\\n\",\n    \"    )\\n\",\n    \"    use_fast_tokenizer: bool = field(\\n\",\n    \"        default=True,\\n\",\n    \"        metadata={\\\"help\\\": \\\"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\\\"},\\n\",\n    \"    )\\n\",\n    \"    model_revision: str = field(\\n\",\n    \"        default=\\\"main\\\",\\n\",\n    \"        metadata={\\\"help\\\": \\\"The specific model version to use (can be a branch name, tag name or commit id).\\\"},\\n\",\n    \"    )\\n\",\n    \"    use_auth_token: bool = field(\\n\",\n    \"        default=False,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Will use the token generated when running `transformers-cli login` (necessary to use this script \\\"\\n\",\n    \"            \\\"with private models).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    resize_position_embeddings: Optional[bool] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to automatically resize the position embeddings if `max_source_length` exceeds \\\"\\n\",\n    \"            \\\"the model's position embeddings.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"@dataclass\\n\",\n    \"class DataTrainingArguments:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Arguments pertaining to what data we are going to input our model for training and eval.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    lang: str = field(default=None, metadata={\\\"help\\\": \\\"Language id for summarization.\\\"})\\n\",\n    \"\\n\",\n    \"    dataset_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The name of the dataset to use (via the datasets library).\\\"}\\n\",\n    \"    )\\n\",\n    \"    dataset_config_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The configuration name of the dataset to use (via the datasets library).\\\"}\\n\",\n    \"    )\\n\",\n    \"    text_column: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The name of the column in the datasets containing the full texts (for summarization).\\\"},\\n\",\n    \"    )\\n\",\n    \"    keyphrase_column: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The name of the column in the datasets containing the summaries (for summarization).\\\"},\\n\",\n    \"    )\\n\",\n    \"    train_file: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The input training data file (a jsonlines or csv file).\\\"}\\n\",\n    \"    )\\n\",\n    \"    validation_file: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"An optional input evaluation data file to evaluate the metrics (rouge) on \\\"\\n\",\n    \"            \\\"(a jsonlines or csv file).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    test_file: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"An optional input test data file to evaluate the metrics (rouge) on \\\" \\\"(a jsonlines or csv file).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    overwrite_cache: bool = field(\\n\",\n    \"        default=False, metadata={\\\"help\\\": \\\"Overwrite the cached training and evaluation sets\\\"}\\n\",\n    \"    )\\n\",\n    \"    preprocessing_num_workers: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The number of processes to use for the preprocessing.\\\"},\\n\",\n    \"    )\\n\",\n    \"    max_source_length: Optional[int] = field(\\n\",\n    \"        default=1024,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total input sequence length after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_target_length: Optional[int] = field(\\n\",\n    \"        default=128,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total sequence length for target text after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    val_max_target_length: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total sequence length for validation target text after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`.\\\"\\n\",\n    \"            \\\"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used \\\"\\n\",\n    \"            \\\"during ``evaluate`` and ``predict``.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    pad_to_max_length: bool = field(\\n\",\n    \"        default=False,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to pad all samples to model maximum sentence length. \\\"\\n\",\n    \"            \\\"If False, will pad the samples dynamically when batching to the maximum length in the batch. More \\\"\\n\",\n    \"            \\\"efficient on GPU but very bad for TPU.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_train_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of training examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_eval_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of evaluation examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_predict_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of prediction examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    num_beams: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, \\\"\\n\",\n    \"            \\\"which is used during ``evaluate`` and ``predict``.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    ignore_pad_token_for_loss: bool = field(\\n\",\n    \"        default=True,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to ignore the tokens corresponding to padded labels in the loss computation or not.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    source_prefix: Optional[str] = field(\\n\",\n    \"        default=\\\"\\\", metadata={\\\"help\\\": \\\"A prefix to add before every source text (useful for T5 models).\\\"}\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    forced_bos_token: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The token to force as the first generated token after the decoder_start_token_id.\\\"\\n\",\n    \"            \\\"Useful for multilingual models like mBART where the first generated token\\\"\\n\",\n    \"            \\\"needs to be the target language token (Usually it is the target language token)\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    def __post_init__(self):\\n\",\n    \"        if self.dataset_name is None and self.train_file is None and self.validation_file is None:\\n\",\n    \"            raise ValueError(\\\"Need either a dataset name or a training/validation file.\\\")\\n\",\n    \"        else:\\n\",\n    \"            if self.train_file is not None:\\n\",\n    \"                extension = self.train_file.split(\\\".\\\")[-1]\\n\",\n    \"                assert extension in [\\\"csv\\\", \\\"json\\\"], \\\"`train_file` should be a csv or a json file.\\\"\\n\",\n    \"            if self.validation_file is not None:\\n\",\n    \"                extension = self.validation_file.split(\\\".\\\")[-1]\\n\",\n    \"                assert extension in [\\\"csv\\\", \\\"json\\\"], \\\"`validation_file` should be a csv or a json file.\\\"\\n\",\n    \"        if self.val_max_target_length is None:\\n\",\n    \"            self.val_max_target_length = self.max_target_length\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_name_or_path = 'memray/bart_wikikp'\\n\",\n    \"cache_dir = './hf_cache'\\n\",\n    \"dataset_name='midas/duc2001'\\n\",\n    \"num_beams=1\\n\",\n    \"max_length=128\\n\",\n    \"max_target_length=128\\n\",\n    \"padding='max_length'\\n\",\n    \"prefix='<present>10<header>5<category>5<seealso>2<infill>0<s>'\\n\",\n    \"# Get the column names for input/target.\\n\",\n    \"text_column = 'document'\\n\",\n    \"keyphrase_column = 'extractive_keyphrases'\\n\",\n    \"\\n\",\n    \"training_args = Seq2SeqTrainingArguments(per_device_eval_batch_size=8, output_dir=cache_dir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PyTorch: setting up devices\\n\",\n      \"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['wikibart_inference.ipynb', '--config_name', 'memray/bart_wikikp', '--model_name_or_path', 'memray/bart_wikikp', '--tokenizer_name', 'memray/bart_wikikp', '--dataset_name', 'midas/duc2001', '--do_predict', '--output_dir', 'kp_output/duc2001/', '--overwrite_output_dir', '--per_device_eval_batch_size', '32', '--predict_with_generate', '--text_column', 'document', '--keyphrase_column', 'extractive_keyphrases', '--source_prefix', '<present>10<header>5<category>5<seealso>2<infill>0<s>', '--num_beams', '1', '--generation_max_length', '60']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sys.argv = ['wikibart_inference.ipynb'] +\\\\\\n\",\n    \"('--config_name memray/bart_wikikp --model_name_or_path memray/bart_wikikp --tokenizer_name memray/bart_wikikp --dataset_name midas/duc2001 --do_predict --output_dir kp_output/duc2001/ --overwrite_output_dir --per_device_eval_batch_size 32 --predict_with_generate --text_column document --keyphrase_column extractive_keyphrases --source_prefix <present>10<header>5<category>5<seealso>2<infill>0<s> --num_beams 1 --generation_max_length 60'.split())\\n\",\n    \"print(sys.argv)\\n\",\n    \"\\n\",\n    \"parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\\n\",\n    \"model_args, data_args, training_args = parser.parse_args_into_dataclasses()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"config = AutoConfig.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    use_fast=model_args.use_fast_tokenizer,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"model = AutoModelForSeq2SeqLM.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    from_tf=bool(\\\".ckpt\\\" in model_args.model_name_or_path),\\n\",\n    \"    config=config,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.resize_token_embeddings(len(tokenizer))\\n\",\n    \"\\n\",\n    \"if (\\n\",\n    \"        hasattr(model.config, \\\"max_position_embeddings\\\")\\n\",\n    \"        and model.config.max_position_embeddings < data_args.max_source_length\\n\",\n    \"    ):\\n\",\n    \"        if model_args.resize_position_embeddings is None:\\n\",\n    \"            logger.warning(\\n\",\n    \"                f\\\"Increasing the model's number of position embedding vectors from {model.config.max_position_embeddings} \\\"\\n\",\n    \"                f\\\"to {data_args.max_source_length}.\\\"\\n\",\n    \"            )\\n\",\n    \"            model.resize_position_embeddings(data_args.max_source_length)\\n\",\n    \"        elif model_args.resize_position_embeddings:\\n\",\n    \"            model.resize_position_embeddings(data_args.max_source_length)\\n\",\n    \"        else:\\n\",\n    \"            raise ValueError(\\n\",\n    \"                f\\\"`--max_source_length` is set to {data_args.max_source_length}, but the model only has {model.config.max_position_embeddings}\\\"\\n\",\n    \"                f\\\" position encodings. Consider either reducing `--max_source_length` to {model.config.max_position_embeddings} or to automatically \\\"\\n\",\n    \"                \\\"resize the model's position encodings by passing `--resize_position_embeddings`.\\\"\\n\",\n    \"            )\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess_function(examples):\\n\",\n    \"    # remove pairs where at least one record is None\\n\",\n    \"    inputs, targets = [], []\\n\",\n    \"    for i in range(len(examples[text_column])):\\n\",\n    \"        if examples[text_column][i] is not None and examples[keyphrase_column][i] is not None:\\n\",\n    \"            inputs.append(examples[text_column][i])\\n\",\n    \"            targets.append(examples[keyphrase_column][i])\\n\",\n    \"\\n\",\n    \"    inputs = examples[text_column]\\n\",\n    \"    targets = ['<sep>'.join(kps) for kps in examples[keyphrase_column]]\\n\",\n    \"    inputs = [prefix + ' '.join(inp) for inp in inputs]\\n\",\n    \"    model_inputs = tokenizer(inputs, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # Setup the tokenizer for targets\\n\",\n    \"    with tokenizer.as_target_tokenizer():\\n\",\n    \"        labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\\n\",\n    \"    # padding in the loss.\\n\",\n    \"    labels[\\\"input_ids\\\"] = [\\n\",\n    \"        [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\\\"input_ids\\\"]\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"    model_inputs[\\\"labels\\\"] = labels[\\\"input_ids\\\"]\\n\",\n    \"    return model_inputs\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Metric\\n\",\n    \"def postprocess_text(preds, labels, sep_token):\\n\",\n    \"    stemmer = PorterStemmer()\\n\",\n    \"    preds = [pred.lower().replace('</s>', '').replace('<pad>', '').split(sep_token) for pred in preds]\\n\",\n    \"    labels = [label.lower().replace('<s>', '').replace('<pad>', '').split(sep_token) for label in labels]\\n\",\n    \"    preds = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in pred] for pred in preds]\\n\",\n    \"    labels = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in label] for label in labels]\\n\",\n    \"    preds = [[p.strip() for p in pred if len(p.strip()) > 0] for pred in preds]\\n\",\n    \"    labels = [[p.strip() for p in label if len(p.strip()) > 0] for label in labels]\\n\",\n    \"\\n\",\n    \"    return preds, labels\\n\",\n    \"\\n\",\n    \"def compute_metrics(eval_preds):\\n\",\n    \"    preds = eval_preds.predictions\\n\",\n    \"    labels = eval_preds.label_ids\\n\",\n    \"    if isinstance(preds, tuple):\\n\",\n    \"        preds = preds[0]\\n\",\n    \"    print(preds.shape)\\n\",\n    \"    if len(preds.shape) == 3:\\n\",\n    \"        preds = preds.argmax(axis=-1)\\n\",\n    \"    \\n\",\n    \"    raw_decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=False)\\n\",\n    \"    # Replace -100 in the labels as we can't decode them.\\n\",\n    \"    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\\n\",\n    \"    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)\\n\",\n    \"\\n\",\n    \"    # Some simple post-processing\\n\",\n    \"    decoded_preds, decoded_labels = postprocess_text(raw_decoded_preds, decoded_labels, tokenizer.sep_token)\\n\",\n    \"\\n\",\n    \"    precs, recalls, f_scores = [], [], []\\n\",\n    \"    num_match, num_pred, num_gold = [], [], []\\n\",\n    \"    for raw_pred, pred, label in zip(raw_decoded_preds, decoded_preds, decoded_labels):\\n\",\n    \"        pred_set = set(pred)\\n\",\n    \"        label_set = set(label)\\n\",\n    \"        match_set = label_set.intersection(pred_set)\\n\",\n    \"        p = float(len(match_set)) / float(len(pred_set)) if len(pred_set) > 0 else 0.0\\n\",\n    \"        r = float(len(match_set)) / float(len(label_set)) if len(label_set) > 0 else 0.0\\n\",\n    \"        f1 = float(2 * (p * r)) / (p + r) if (p + r) > 0 else 0.0\\n\",\n    \"        precs.append(p)\\n\",\n    \"        recalls.append(r)\\n\",\n    \"        f_scores.append(f1)\\n\",\n    \"        num_match.append(len(match_set))\\n\",\n    \"        num_pred.append(len(pred_set))\\n\",\n    \"        num_gold.append(len(label_set))\\n\",\n    \"        \\n\",\n    \"#         print(f'raw_PRED: {raw_pred}')\\n\",\n    \"        print(f'PRED: num={len(pred_set)} - {pred_set}')\\n\",\n    \"        print(f'GT: num={len(label_set)} - {label_set}')\\n\",\n    \"        print(f'p={p}, r={r}, f1={f1}')\\n\",\n    \"        print('-' * 20)\\n\",\n    \"\\n\",\n    \"    result = {\\n\",\n    \"        'precision@M': np.mean(precs) * 100.0,\\n\",\n    \"        'recall@M': np.mean(recalls) * 100.0,\\n\",\n    \"        'fscore@M': np.mean(f_scores) * 100.0,\\n\",\n    \"        'num_match': np.mean(num_match),\\n\",\n    \"        'num_pred': np.mean(num_pred),\\n\",\n    \"        'num_gold': np.mean(num_gold),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    result = {k: round(v, 4) for k, v in result.items()}\\n\",\n    \"    return result\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Reusing dataset duc2001 (./hf_cache/midas___duc2001/raw/0.0.1/7888b46165d8a58f49f00e28410b46b1f22fabfd72a9e89f3e80a4e2d27e4a9b)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"46779f64551348a5ab215c46ee563137\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"08f9f508c1a74ef499b54ea3ee67c023\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Running tokenizer on prediction dataset:   0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set seed before initializing model.\\n\",\n    \"set_seed(training_args.seed)\\n\",\n    \"raw_datasets = load_dataset(\\n\",\n    \"    dataset_name, \\\"raw\\\", cache_dir=cache_dir\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"column_names = raw_datasets[\\\"test\\\"].column_names\\n\",\n    \"max_target_length = data_args.val_max_target_length\\n\",\n    \"padding = \\\"max_length\\\" if data_args.pad_to_max_length else False\\n\",\n    \"prefix = data_args.source_prefix if data_args.source_prefix is not None else \\\"\\\"\\n\",\n    \"predict_dataset = raw_datasets[\\\"test\\\"]\\n\",\n    \"\\n\",\n    \"with training_args.main_process_first(desc=\\\"prediction dataset map pre-processing\\\"):\\n\",\n    \"    predict_dataset = predict_dataset.map(\\n\",\n    \"        preprocess_function,\\n\",\n    \"        batched=True,\\n\",\n    \"        num_proc=data_args.preprocessing_num_workers,\\n\",\n    \"        remove_columns=column_names,\\n\",\n    \"        load_from_cache_file=not data_args.overwrite_cache,\\n\",\n    \"        desc=\\\"Running tokenizer on prediction dataset\\\",\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"# Data collator\\n\",\n    \"label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id\\n\",\n    \"data_collator = DataCollatorForSeq2Seq(\\n\",\n    \"    tokenizer,\\n\",\n    \"    model=model,\\n\",\n    \"    label_pad_token_id=label_pad_token_id,\\n\",\n    \"    pad_to_multiple_of=8 if training_args.fp16 else None,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"# print(torch.cuda.is_available())\\n\",\n    \"# print(torch.cuda.get_device_name())\\n\",\n    \"# # device = torch.device(\\\"cuda:2\\\")\\n\",\n    \"# torch.cuda.set_device(1)\\n\",\n    \"# device = torch.device()\\n\",\n    \"# print(device)\\n\",\n    \"print(torch.cuda.device_count())\\n\",\n    \"\\n\",\n    \"model = model.to('cuda:0')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"***** Running Prediction *****\\n\",\n      \"  Num examples = 308\\n\",\n      \"  Batch size = 32\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"    <div>\\n\",\n       \"      \\n\",\n       \"      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\\n\",\n       \"      [10/10 00:26]\\n\",\n       \"    </div>\\n\",\n       \"    \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(308, 128)\\n\",\n      \"PRED: num=26 - {'panamerican world airway', 'aardvark', 'and the follow develop today:', 'list of aircraft', 'articl contain video clip', 'and', 'lockerbi', 'scotland, scotland', 'scotland', 'aviation-rel death', 'histori of aviat', 'aerojet aircraft', 'airbu a380', 'aeroship aircraft', 'pan american world airway flight 103', 'pan american', 'histori', 'scotland,', 'larnockerbi flight 103 flight 103 (wednesday night)', 'aerial aircraft', 'aircraft accid', 'panamer', 'lancasterbi', 'aerospac accid', 'airlin', 'aero'}\\n\",\n      \"GT: num=3 - {'pan american world airway flight 103', 'lockerbi', 'crash'}\\n\",\n      \"p=0.07692307692307693, r=0.6666666666666666, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'cultur of the southern unit state', 'bunki', 'buddi mcintyr', 'texa', 'tunnel alley', 'american geographi', 'histori of the unit state (1825–1921)', 'historyof the unit state (1921–present)', 'tropic storm', 'and a', 'american weather', 'and', 'histori', 't tornado alley', 'geographi of the western unit state and the caribbean', 'tornado alley', 'and the univers of texa at austin,', 'nation weather servic'}\\n\",\n      \"GT: num=8 - {'tornado season', 'tornado watch', 'texa', 'tornado warn', 'tornado', 'spring thunderstorm', 'disast research', 'properti damag'}\\n\",\n      \"p=0.05555555555555555, r=0.125, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'forest and', 'the aircraft were', 'the u.s. air forc', 'the two aircraft', 'mountain and hill rang of germani and austria', '20th centuri', 'the air forc', 'blackforest', 'black forest', 'the', 'f-16', 'baden-soellingen', 'unit state', 'hahn air base', 'forest and woodland of germani (state)', 'black', 'ramstein air base and spangdahlen air base.', 'histori', 'marxzell-burbach', 'mainz', 'region of germani'}\\n\",\n      \"GT: num=5 - {'pilot', 'bodenheim', 'crash', 'train mission', 'aircraft'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the forest servic', 'fire manag plan', 'wild yellowston nation park', 'forestri', 'articl contain video clip', 'the nation park servic', 'fire in the unit kingdom', 'western', 'fire-fight equip', 'the', 'western fire season', 'unit state', 'fire season', 'charl philpot', 'forest fire polici', 'forestri manag', 'forest servic', 'forest', 'forest fire', 'in the unit state', 'sustain forest manag', 'and the possibl of multipl fires. the report said that', 'nation park servic'}\\n\",\n      \"GT: num=7 - {'nation forest', 'natur fire', 'forest fire polici', 'panel', 'western fire season', 'fire manag plan', 'recommend halt'}\\n\",\n      \"p=0.13043478260869565, r=0.42857142857142855, f1=0.2\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'arson', 'arsen and old lace', 'is a', 'mark twain nation forest', 'mark twain', 'and the', 'the', 'natur disast', 'is the', 'rolla', 'ozark hillbilli', 'missouri', 'the forest servic is', 'is', 'terror tactic', 'forestri crime', 'forest fire', 'mark t. twain nation forest', 'in the unit state', 'ron mcdonald', 'dale smallwood', 'the forest is a lot more'}\\n\",\n      \"GT: num=7 - {'pass truck', 'arson problem', 'missouri', 'crimin investig', 'arsonist', 'investig', 'forest fire'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'reagan administr', 'labor day weekend radio address', 'american societi', '20th centuri', 'peopl with disabl', 'the', 'american worker', 'labor depart', 'thoma j.-n.y.', 'histori', 'vietnam war', \\\"in fact thing have gotten worse. '' the presid said that\\\", 'american citizen', 'vetto pen', 'peopl of the american revolut', 'american peopl', 'thoma j. downey', 'the presid', 'peopl from west virginia'}\\n\",\n      \"GT: num=7 - {'welfar program', 'unemploy', 'presid reagan', 'welfar legisl', 'welfar reform', 'american', 'work requir'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'fountain hill', 'qujet', 'marquett gener hospit', 'succasunna', 'boe b-52 stratolaunch', 'and debruzzi were list in stabl condit at', 'aircraft first flown in 1940', 'quadruple-aircraft ballist missil system', 'stephenson', 'oper histori', 'unit state', 'unit kingdom', 'aerial bomb of citi', 'quadjet', 'the base hospital.', 'mulberri', 'k.i. sawyer air forc base'}\\n\",\n      \"GT: num=4 - {'crew member', 'train flight', 'crash', 'pilot'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'anti–trust case', 'central sell organ', 'anti-trust', 'unit kingdom', 'of the', 'offic of fair trade', 'de beer', 'unit state', 'the offic of fair', 'anti –trust law in the unit kingdom', 'zair', 'world trade organ member economi', 'of de beers.', 'diamond cartel', 'unit nation gener assembl observ', 'the complaint wa file by consolid gold field plc, a british mine concern', 'of', 'mineralco sa', 'soviet union', 'anti -trust law'}\\n\",\n      \"GT: num=7 - {'fair trade', 'central sell organ', 'south africa', 'world diamond trade', 'investig', 'de beer diamond organ', 'defens move'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'crisi in the unit state', 'senat', '1988 in agricultur', 'corn', 'ag agricultur depart', 'a $ 3.9 billion', 'unit nation econom commiss for africa', 'crop product', 'agroecolog', 'patrick j. leahi', 'and the', 'and', 'unit state', 'the unit states. the report came hour after presid reagan sign a $', 'senat agricultur committe', 'and $3.9', 'agricultur depart', '1988 event', 'ewen m. wilson', 'agronomi'}\\n\",\n      \"GT: num=9 - {'annual agricultur depart survey', 'presid reagan', 'food product', 'sharp reduct', 'deaden drought', 'higher retail food price', 'food price increas', 'disast relief bill', 'fall corn harvest'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'canadian sprinter of iranian descent', 'johnson', 'controversi', '1946 birth', 'southeast asia', 'toronto star', 'she and johnson', 'that she and johnson took steroids. she also said she had been', 'stanozolol', 'canadian peopl of iranian-jewish descent', 'she', 'canadian olymp sprinter', 'canadian male sprinter', 'that', 'mazda optimist track club', 'ben johnson', 'steroid', 'canadian olympian', 'johnson and', 'angella issajenko', 'world championship'}\\n\",\n      \"GT: num=6 - {'canadian olymp sprinter', 'angella issajenko', 'olymp gold medal', 'illeg drug', 'steroid stanozolol', 'ben johnson'}\\n\",\n      \"p=0.14285714285714285, r=0.5, f1=0.22222222222222224\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'and that the', 'that the engin had been shut down', 'b 737-200', 'feder aviat administr', 'belfast', 'unit kingdom', 'the airplan', 'bird of prey', 'aircraft first flown in 1981', 'collis', 'the', 'daili star', 'civil aviat author', 'b boe 737', 'east midland airport', 'accid and incid', '1980 unit state airlin', 'cfm56', 'the aircraft', 'the plane', 'boe 737'}\\n\",\n      \"GT: num=7 - {'wrong engin', 'injur pilot', 'crash', 'undamag right engin', 'engin monitor system', 'boe 737', 'left engin'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'by countri', 'asia-pacif', 'observ', 'of the', 'kuala lumpur', 'astro- eclips', 'list of eclips and partial eclips of the sun', \\\"the event. in the citi of davao, the city' mayor\\\", 'the', 'kurukshetra', 'asia', 'solar eclips', 'bolivia', 'bengal', \\\"the city'\\\", 'cultur aspect of death', 'boulder, colo.', 'astronom event', 'baguio citi', 'celesti cartographi', 'of', 'the town of'}\\n\",\n      \"GT: num=8 - {'sun', 'wit', 'moon', 'total eclips', 'tourist', 'wide area', 'partial eclips', 'solar eclips'}\\n\",\n      \"p=0.045454545454545456, r=0.125, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the pollut', 'exxon ship co.', 'the oil', 'of the oil', 'north slope', 'blight reef', 'bligh reef', 'blanchard island', 'blachford island', 'princ william sound', 'oil slick', 'blind area', 'and the', 'the', 'fujian province, republ of china', 'exxon baton roug', 'the oil pollut', 'denni kelso', 'richard golob', 'blow-up island', 'oil spill', 'effect'}\\n\",\n      \"GT: num=6 - {'major environment catastroph', 'oil spill', 'alaska', 'cleanup effort', 'crude oil', 'cleanup equip'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cantor fitzgerald', 'north florida junior colleg', 'tampa', 'madison, florida, unit state', '1885 establish in florida', 'north florida', 'tornado', 'the', 'tallahassee, florida', 'tornado,', 'madison counti memori hospit', 'and other util worker were on their way to the town', 'to help with the', 'histori', 'the cleanup,', 'the tornado,', 'tallahasse', 'citi in madison county, florida (u.s. state)', '1901-present', 'cultur of the central florida', 'coral springs, florida,'}\\n\",\n      \"GT: num=5 - {'thunderstorm', 'tornado', 'death', 'madison', 'destruct'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=15 - {'armenian earthquak', 'in 1988, the strongest earthquak in the unit state wa a magnitud of 7.5 on the richter scale.', 'eartholog scienc', 'richter scale', '1988', 'gulf of alaska', 'artifici extinct', 'articl contain video clip', 'soviet central asia', 'china', 'histori', 'earth quak', 'earthquak', 'modern era', 'u.s. geolog survey'}\\n\",\n      \"GT: num=7 - {'armenian earthquak', 'deadli earthquak', 'earthquak death', 'properti damag', 'offshor earthquak', 'earthquak death toll', 'chines earthquak'}\\n\",\n      \"p=0.06666666666666667, r=0.14285714285714285, f1=0.09090909090909091\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'american diabet associ', 'in the develop of', 'is a major factor in the', 'phenylalanin ammonia lyas', 'e-numb addit', 'and', 'diabet', 'amino acid', 'diabetics,', 'oxford univers', 'new zealand', 'garcia', 'amlin', 'of diabetes,', 'garth cooper', 'pancreat hormon', 'ammylin', \\\"st. luke' - roosevelt hospit center\\\", 'amphenyl compound', 'medic use', 'intern diabet feder', 'diabetes,'}\\n\",\n      \"GT: num=10 - {'insulin secret', 'pancreat hormon', 'amylin', 'blood sugar level', 'obes', 'diabet', 'american diabet associ', 'new treatment', 'diseas process', 'hormon research'}\\n\",\n      \"p=0.13636363636363635, r=0.3, f1=0.18749999999999997\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'kika de la garza', 'forest fire suppress', '20th centuri', 'articl contain video clip', 'and the u-s.,', 'the', 'u-s. depart of agricultur', '1990 farm bill', 'american wildfir', 'u.s. forest servic', 'histori', 'the u.s., the u.s. forest', 'forestry-rel death', 'the unit states.', 'forest fire', '1871 establish in the unit state', '1988', 'u-.s.', 'hous interior committe'}\\n\",\n      \"GT: num=7 - {'fire fight polici', 'summer fire', 'joint hear', 'hous agricultur committe', 'yellowston nation park', 'stubborn wildfir', 'forest fire practic'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'stanislau nation forest in wyom', 'ami vanderbilt', 'the largest fire', 'current situat', 'control monday after the blaze consum more than 1,100 acres.', 'the', 'americanwildfir', 'y yosemit nation park in california', 'shoshon nation forest', 'western unit state', 'yoseph', 'yosemit nation park', 'cultur of the pacif northwest', 'michigan', 'in the', 'american cultur', 'american wildfir', 'histori of the rocki mountain', 'the unit states.', '1871 establish in new york (state)', '1851 establish in montana', 'wyom', 'the fire wa', 'north america'}\\n\",\n      \"GT: num=7 - {'wildfir', 'wyom', 'firefight', 'blaze', 'illeg firework', 'steadi rain', 'forest fire'}\\n\",\n      \"p=0.041666666666666664, r=0.14285714285714285, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'latin american and caribbean american', 'u-s. district court', 'u.s. constitut', 'dan stein', 'censu', 'census', 'sampl (statistical)', 'upscal', '1990 censu', 'the', 'sampl (statistics)', 'unit state', 'that the question should be ad to the', 'histori', 'the u.', 'tom ridg', 'sampl (statistic)', 'jan meyer'}\\n\",\n      \"GT: num=5 - {'hous apportion', 'american immigr reform', 'censu bureau', 'nation head count', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'aircraft first flown in 1975', 'aerospac aircraft', 'airlin of the unit state', 'twin piston-engin aircraft', 'the thunderbolt ii is the safest plane in the air forc', 'militari equip introduc in the 1970', 'raf alconburi', 'rafa bentwat', 'a-10 thunderbolt ii', 'oper histori', \\\"tohono o'odham indian reserv\\\", 'tuscon', 'unit state', 'unit kingdom', 'traci', 'aerial bomb of citi', 'in the world.'}\\n\",\n      \"GT: num=5 - {'pilot', 'second crash', 'routin train flight', 'britain', 'suspens'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'poverti reduct', 'roberto de abreau sodr', 'west berlin', 'organ base in new york', 'obe y. asamoah', 'brazil', 'meet', 'unitedn', 'gro harlem brundtland', 'third world', 'unit nation', 'the world are not do enough to help the third world.', 'unit state', 'unit nation gener assembl observ', 'susana ruiz cerutti', 'gener assembl'}\\n\",\n      \"GT: num=8 - {'42nd gener assembl', 'third world countri', 'foreign loan', 'brazil', 'intern econom relat', 'debt restructur', 'industri nation', 'foreign debt'}\\n\",\n      \"p=0.0625, r=0.125, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'a-10 thunderbolt ii', 'the citi of remscheid.', 'train flight were order in the citi of', 'athlet sport', 'unit kingdom', 'aerial warfar', 'dieter wellershoff', 'militari personnel kill in action', 'czechoslovakia', 'and the', 'the', 'terror', 'unit state', 'ramstein', 'aircraft hijack', 'u.s. air forc', 'histori', 'terror tactic', 'welt am sonntag', 'airlin'}\\n\",\n      \"GT: num=7 - {'militari aircraft', 'pilot', 'fatal crash', 'deadli accid', 'remscheid', 'fieri crash', 'west german'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'biographi', '1946 birth', 'olymp career', 'toronto', 'the boston globe', 'american sprinter from new york', 'american olymp medalist', 'american peopl of english descent', 'canadian sprinter', 'return to canada', 'of peopl who had been wait for him to arriv at the airport', 'saskatchewan', 'saskatoon', 'ben johnson', 'stonewal', 'american male sprinter of english-languag descent', 'sydney', 'to hi', 'to', 'to the'}\\n\",\n      \"GT: num=8 - {'disappoint', 'homecom', 'canadian', 'drug test', 'sprinter', 'olymp gold medal', 'ben johnson', 'illeg steroid stanzolol'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'fighter squadron 143', 'north carolina', 'the airplan', 'virginia beach', 'f-16a', 'the', 'unit state', 'aircraft accid in the unit state', 'the jet over a hangar at gillespi field,', 'the plane wa', 'unit airlin flight 11', 'f-14', 'unit nation air forc', 'airlin that reenter the atlant ocean', 'virginia', 'hill air forc base', 'accid', 'the aircraft', 'aerospac accid in california', 'american airlin flight 175'}\\n\",\n      \"GT: num=6 - {'injuri', 'pilot', 'atlant ocean', 'navi aircraft', 'navi aviat', 'atlant accid'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'canadian provinc', 'cultur', 'john furedi', 'member state of the unit nation', 'the countri wa on a roller coaster ride to the bottom, and then', 'g20 nation', 'canadian', 'sport', 'countri in north america', 'the', 'the olymp', 'univers of toronto of toronto and the globe and mail', 'john faughti', 'canada', 'pat reid', 'globe and mail of toronto', 'wayn gretzki', 'olymp medal'}\\n\",\n      \"GT: num=7 - {'strip', 'stanzolol', 'canadian', 'sprinter', 'olymp gold medal', 'ben johnson', 'drug scandal'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'polic', 'a', 'counti of california', 'polic brutality.', 'thoma j. clark', 'popul coastal place in california', \\\"the city' polic chief said the incid wa\\\", '1871 establish in california territori', 'long beach, california', 'polic brutal', 'curt livesay', 'erni kell', 'nation associ for the advanc of color peopl', 'a polic', 'burbank, california,', 'long beach citi manag', 'histori', 'recent histori', 'counti seat in california (u.s. state)', 'a result of', 'today', 'long island'}\\n\",\n      \"GT: num=8 - {'racism complaint', 'polic racism', 'lo angel area', 'white policeman', 'seriou injuri', 'long beach polic', 'polic brutal', 'black policeman'}\\n\",\n      \"p=0.045454545454545456, r=0.125, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the blaze wa', 'wildfir', 'bridger-teton nation forest and two other major fire in wyom', 'summer firework in the philippin', 'zion nation park', 'summer event in the netherland', 'hudson valley', 'the fire wa about', 'hiawatha nation forest', 'event', 'the weekend, but on tuesday, the fire wa', 'the', 'unit state', 'wa about', 'yosemit nation park and wyom', 'wa', 'u.s. forest servic', 'fourth of juli', 'summer in the czech republ', 'public holiday in the unit state', 'fourthofjuli'}\\n\",\n      \"GT: num=6 - {'forest fire', 'brush fire', 'firefight', 'utah', 'zion nation park', 'fire line'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'prepar', 'wildfir', 'custer nation forest', 'the largest fire', 'zion nation park', 'event', 'day of the year', 'the', 'upper peninsula', 'hiawatha nation park and wilder', 'in the', 'u.s. forest servic', 'yosemit nation park<infill>august 1', 'day', 'daysof the year (august)', 'the wilder area.', 'august (period)', 'august', \\\"the fire' size. the fire wa\\\"}\\n\",\n      \"GT: num=6 - {'nation forest', 'wildfir', 'fire', 'firefight', 'blaze', 'illeg firework'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'forestfir', 'westernst', 'in the western states:', 'list of environment issu', 'cultur geographi', 'forestri', 'western', 'climat chang', 'climat', 'climat scienc', 'citi', 'western unit state', 'climatechang', 'forest', 'climat of the unit state (census)', 'western state :', 'climatolog', 'climat engin', 'forest fire', 'fire develop', 'forest and grass fire', 'western state', 'climat evolut', 'forest fire develop', 'climat impact', 'climat resili'}\\n\",\n      \"GT: num=2 - {'forest fire develop', 'western state'}\\n\",\n      \"p=0.07692307692307693, r=1.0, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'u.s. air base at misawa', 'modern histori', 'athlet aircraft', 'aerial hijack', 'the', 'f-16', 'unit state', 'iwat prefectur', 'aircraft hijack', 'the unit', 'jane', 'sakhalin', 'the u.s.', 'aerospac accid and incid', 'terror tactic', 'ishat', 'militari equip introduc in the 1970', 'airlin', 'yuzhno sakhalinsk', 'the u.s.-japan militari base at misawasawa is about'}\\n\",\n      \"GT: num=5 - {'pilot', 'militari jet', 'crash', 'misawa', 'northern japan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'tunnel', 'ed ferguson', 'nation sever storm forecast center', '20th centuri', \\\"mother 's day\\\", 'tornado', 'and the', \\\"mother' day\\\", 't tornado season', 'the', 'american weather', 'index of american-rel articl', 'histori', 'state of the unit kingdom', '1871 establish in the unit state', 'the tornado', 'the air to form thunderclouds. the warm weather', 'state and territori establish in 1871', 'nation climat data center', 'the nation.', 'north america', 'nation weather servic'}\\n\",\n      \"GT: num=7 - {'tornado season', 'tornado death', 'tornado outbreak', 'storm', 'tornado', 'public awar', 'fatal'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'carl lewi', 'post-cold war', 'peopl of the cold war', 'canada', 'do', 'canadalcan peopl', 'ken read', 'wayn gretzki', 'did not do', 'did', 'to do drugs. he did not do drugs. he', 'olymp medalist', 'north american indigen peopl', 'histori', 'rexdal', 'canadian peopl', 'brian mulroney', 'canadian', 'do drugs.', 'jean charest'}\\n\",\n      \"GT: num=8 - {'american carl lewi', 'canadian', 'disappoint nation', 'sprinter', 'olymp gold medal', 'anabol steroid', 'ben johnson', 'drug scandal'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'civil war', 'histori and scienc of south america', 'the rebel are', 'are not', 'and the fact that the govern', 'anda', 'histori of peru', 'peru', 'coloni peru', 'ayacucho provinc', 'spanish colon of the america', 'the', 'and', 'histori (polit and scientific)', 'ayacuchso', 'the shine path', 'the guerrilla are', 'historyof peru', 'histori', 'independ', 'andean report', 'is not', 'shine path', 'are', 'spanish-speak countri and territori', 'the andes,'}\\n\",\n      \"GT: num=8 - {'huaycan', 'rebel movement', 'shantytown', 'shine path guerrilla', 'central highway', 'polit forc', 'public support', 'polit violenc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'a report', 'scienc magazin', 'a', 'climat chang commun and research program', 'grant w. branstat', 'a studi', 'temperatur pattern', 'a book', 'that the studi wa a', 'nation ocean and atmospher administr', 'climat chang', 'climat', 'scientif evid', 'climat scienc', 'boulder', 'climatolog', 'climat engin', 'camp spring', 'nation center for atmospher research', 'a paper', 'drought in the midwest'}\\n\",\n      \"GT: num=8 - {'new comput studi', 'ocean temperatur abnorm', 'temperatur abnorm', 'drought', 'pacif ocean temperatur', 'atmospher research', 'weather pattern', 'midwest'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'memberst of the european union', 'index of peru-rel articl', 'ayacucho', 'the govern', 'peruvian-speak countri and territori', 'peru', 'the', '1960', 'that ha kill', 'the govern say the death squad is a death squad', 'countri in south america', 'andean', 'member state of the african union', 'osman morot', 'govern', 'pucallpa', 'histori', 'colombia', 'maoist', 'sh shine path', 'member state of the unit nation', 'govern and', 'war in the andean region', 'shine path', 'death squad'}\\n\",\n      \"GT: num=8 - {'district attorney carlo escobar', 'shine path guerrilla', 'osman morot', 'polic post', 'rodrigo franco command', 'properti damag', 'death squad', 'rebel raid'}\\n\",\n      \"p=0.08, r=0.25, f1=0.12121212121212122\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'ocean', 'atlant current', 'antarct', 'landform of the atlant ocean', 'a', 'nation hurrican center', 'that wa', 'bob', 'havana', 'bob case', 'landscap of the pacif ocean', 'that', 'wa', 'atlant ocean', 'bob sheet', 'histori', 'dominican republ', 'wa a', 'the next day. it wa then hit by a storm', '1988', 'atlant hurrican'}\\n\",\n      \"GT: num=8 - {'first tropic depress', 'hurrican', 'typic atlant hurrican season', 'atlant storm season', 'coastal counti', 'hurrican coastal flood model', 'forecast', 'nation hurrican center'}\\n\",\n      \"p=0.047619047619047616, r=0.125, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a', 'hurrican gilbert', 'nation hurrican center', 'weather in the unit state', 'the media, he is not a reporter. he is', 'hurrican elena', 'hurri gilbert', 'hurricana', 'weather organ', 'a director', 'special event', 'director', 'labor day hurrican of 1935', 'weather extrem', 'weather servic', 'hurrican camil', 'director of the nation hurrican', 'weather station in theunit state', 'nation weather servic'}\\n\",\n      \"GT: num=8 - {'barometr pressur', 'intens hurrican', 'categori 5 hurrican', 'catastroph damag', 'western hemispher', 'hurrican gilbert', 'destruct gilbert', 'nation hurrican center'}\\n\",\n      \"p=0.10526315789473684, r=0.25, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'cook city, montana', '1895 establish in the unit state', 'co cook city, mont.', '1885 establish in montana', 'citi in the yellowston nation park and preserv', 'the', 'north platt', 'popul place establish in 1885', 'codi', 'the fire is gone and the town is', 'a ghost town. the', 'last year', 'u.s. forest servic', 'histori', 'north platm', 'the fire', 'bill', 'yellowston nation park', 'the town'}\\n\",\n      \"GT: num=6 - {'unpreced fire season', 'firefight effort', 'yellowston nation park', 'yellowston ecosystem', 'new wildfir season', 'forest fire'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'air accid', 'tuesday crash', 'the plane,', 'nation transport safeti board', 'feder aviat administr', 'the wreckage,', 'citi in new mexican', 'al albuquerqu', 'event', 'popul place establish in 1876', 'coronado airport', 'and the', 'albuquerque, new mexico', 'the', '1876 establish in new mexicali', 'the crash site. the plane wa', 'air accid in new mexico in 1979', 'plane,', 'offic of the medic investig', 'the airplane,', 'albany, new mexicano', 'cessna p210', 'single-engin airplan'}\\n\",\n      \"GT: num=7 - {'albuquerqu', 'crash site', 'wit', 'crumpl airplan', 'victim', 'investig', 'rescu crew'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'mid-atlant state', 'event', 'august-rel list', 'drought', 'month', 'august event', 'and the', 'west virginia', \\\"the agency' long-rang forecast through next monday,\\\", 'and', 'unit state', 'mississippi valley', 'august', 'august of thi year', 'ohio valley', 'great lake', 'august and the unit state', 'nation weather servic'}\\n\",\n      \"GT: num=9 - {'dri weather', 'western great lake region', 'drought region', 'extrem drought', 'water shortag', 'northern great plain', 'agricultur depart', 'littl relief', 'nation weather servic'}\\n\",\n      \"p=0.05555555555555555, r=0.1111111111111111, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'bolivar', 'hurolog advisori', 'caribbean', 'hurrican', 'to the surface.', 'san andr', 'hurolog', 'carmen de bolivar, colombia', 'track', 'huron shower', 'northwest caribbean', 'hurrican joan', 'bogota', 'nation hurrican center', 'bolivia', 'hurican'}\\n\",\n      \"GT: num=8 - {'hurrican forc', 'hurrican joan', 'tropic storm', 'open caribbean', 'panama', 'hurrican watch', 'colombia', 'atlant hurrican season'}\\n\",\n      \"p=0.0625, r=0.125, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'unit nation food and agricultur organ', 'agricorn', 'agricultur stabil and conserv servic', 'agenc', 'salli michael', 'respons', 'usda background', 'peter c. myer', 'richard e. lyng', 'american drought', 'unit state', 'ag agricultur depart', '1962 establish in the unit state', '1961 establish in north america', 'natur disast', 'drought in the carolina', 'to help feed livestock in drought counties. _ june 9.'}\\n\",\n      \"GT: num=9 - {'usda drought task forc', 'meat purchas', 'emerg relief measur', 'congression drought relief task forc', 'drought aid', 'agricultur depart', 'action list', 'conserv reserv program', 'drought panel'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'barometr pressur', 'historyof the unit state', 'hurrican gilbert', 'hurrican debbi', 'histori (1801–present)', 'hurricaneolog', '1901 to present', 'the', 'the wind is', 'history(s)', 'of heat and moisture, and', 'is', 'wind is', 'histori', 'hurrican camil', 'wind', '191900 hurrican', '1935 labor day hurrican', 'histori of the unit state', 'histori and technolog of the u.s.', 'huronicad'}\\n\",\n      \"GT: num=8 - {'barometr pressur', 'intens hurrican', 'storm surg', 'western hemispher', 'hurrican gilbert', 'hurrican fatal', 'nation hurrican center', 'tropic storm forc wind'}\\n\",\n      \"p=0.09523809523809523, r=0.25, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'albert g. bustament', 'william f. goodl', 'background', 'thoma j. ridg', 'mervyn m. dymal', 'censu', 'census', 'sampl (statistical)', '1990 censu', 'don edward', \\\"and rep. john m. o'donnell, r-calif., said they are\\\", 'not sure whether the bill are constitutional.', 'sampl (statistics)', 'unit state', 'bill to count illeg alien', 'popul', 'citizenship studi'}\\n\",\n      \"GT: num=5 - {'lawmak', 'censu bureau', 'hous seat', 'repres', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'memberst of the european union', 'member state of the mercosur', 'peru', 'wa captured. he', 'the capital. he wa', 'minoist', 'countri in south america', 'peruvian communist parti', 'moonshin', 'southern cone countri', 'osman morot', 'wa', 'histori', 'perú', 'maoist', 'mao tse-tung', 'wa arrest', 'member state of the unit nation', 'the leadership of the shine path. morot wa arrest in', 'shine path', 'peruvian revolut'}\\n\",\n      \"GT: num=7 - {'top militari leader', 'counterinsurg polic', 'osman morot', 'radic leader', 'maoist rebel group', 'peru', 'shine path movement'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'rail transport in the british isl', 'london-pari', 'eurotunnel', 'railway in the netherland', 'railway tunnel in franc', 'railport in the channel tunnel', '1992', 'rail transport in franc and germani', 'the', 'rail tunnel in the unit kingdom', 'london station', 'will', 'construct', 'to be carri on the train. the', 'channel tunnel', 'the tunnel will', 'histori', 'will be', 'dover', 'waterloo station', 'london'}\\n\",\n      \"GT: num=8 - {'british fear', 'english channel', 'tunnel speed', 'tunnel builder', 'tunnel traffic', 'french coast', 'freight', 'channel train'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the hous of representatives. on the other hand, the', 'census', 'by countri', 'latin american and caribbean american histori', 'two seat', 'citizenship studi', 'hous of repres', 'censu bureau', 'unit state', 'hous of congress', 'state', 'popul', 'one seat', 'survey methodolog', 'two', 'popul refer bureau', 'popul expert', 'would lose', 'one', 'hous', 'one hous seat', '1990 censu', \\\"william o'har\\\"}\\n\",\n      \"GT: num=5 - {'censu bureau', '1990 censu', 'hous seat', 'nation head count', 'illeg alien'}\\n\",\n      \"p=0.08695652173913043, r=0.4, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'the govern', 's slovenian', 'thegovernment.', 'mladina', 'to take action against', 's slovenia', 'countri in europ', 'bosnia and herzegovina', 'yugoslavia', 'milan kucan', 'state and territori establish in 1991', 'the', 'slovenian', 'militari action against dissid', 'svetozar visnjic', 'militari', 'slojian communist', 'southeastern european countri', 'state of europ', 'state with multipl capit', 'balkan countri', 'that the govern wa not plan', 'polit', 'the state', 'srijani', 'the countri'}\\n\",\n      \"GT: num=8 - {'polit dissid', 'yugoslavia', 'dissid tendenc', 'slovenia polic', 'ministri statement', 'communist govern', 'slovenian communist', 'feder militari command'}\\n\",\n      \"p=0.038461538461538464, r=0.125, f1=0.058823529411764705\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'govern agenc establish in 1935', 'hurrican camil', 'nationalrican center', 'a major', 'hurricana', 'weather organ', 'to be in the middl of a hurricane, and', \\\"it' a\\\", 'a', 'bob sheet', 'hurrican gilbert', '1959 establish in the unit state', 'special program', 'hurrican elena', 'weather servic', 'nation hurrican center', 'nation weather servic'}\\n\",\n      \"GT: num=7 - {'hurrican headquart', 'destruct hurrican gilbert', 'catastroph damag', 'western caribbean', 'bob sheet', 'nation hurrican center', 'atlant hurrican season'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'colorado state univers', 'landform of the atlant ocean', 'articl contain video clip', 'oceanographi', 'the', 'climat', 'than the', 'atlant', 'more intens than', 'hurrican season', 'the last', 'west', 'atlant ocean', 'nation hurrican confer', 'el nino', 'atlant hurrican', 'west africa', 'forecast', 'oceanograph model', 'that the storm system will be'}\\n\",\n      \"GT: num=8 - {'storm', 'annual hurrican forecast', 'atlant hurrican', 'atlant ocean', 'hurrican expert', 'turbul summer', 'william gray', 'hurrican season'}\\n\",\n      \"p=0.15, r=0.375, f1=0.21428571428571425\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'organ of the palestinian peopl', 'critic', 'tuni', 'richard murphi', 'organ design as terrorist by iran', 'wa not approv by the unit states.', 'khalil wazir', 'organis of the arab leagu', 'charl redman', 'unit state', 'assassin of khalil wazir', 'arafat', 'tunisi', 'palestin liber organ', 'organ crime', 'organis design as terrorist by china'}\\n\",\n      \"GT: num=12 - {'palestinian guerrilla', 'accus', 'american target', 'khalil wazir', 'isra offici', 'isra squad', 'plo leader yasser arafat', 'terrorist attack', 'unit state', 'polit assassin', 'plo offici', 'possibl plo attack'}\\n\",\n      \"p=0.125, r=0.16666666666666666, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'and canadian presid john baird said that johnson had been', 'summer olymp sport', 'controversi', 'carl lewi', 'biathlon at the olymp', 'that johnson', 'sarasparilla', \\\"canada' chief of mission\\\", 'canada', 'that he', \\\"canada' nation team\\\", 'that hi', 'canada in the summer olymp', 'olymp dope', 'that', 'summer paralymp sport', 'slovenia', 'summer olymp', 'sierra leon', 'opinion', 'drug', 'bi olymp', 'that the', 'omnisport', 'biathlet', 'and that'}\\n\",\n      \"GT: num=7 - {'canadian ben johnson', 'american carl lewi', 'olymp', 'sprinter', 'illeg anabol steroid', 'gold medal', 'drug use'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'[[nation velvet]]', 'actress elizabeth taylor', '[[nationalelvet (film)|nat velvet]].', 'she ha been treat for pneumonia at st. john', 'hospit in lo', 'american film actress', 'american actress of english-languag descent', 'elizabeth taylor', 'and the', '[[ butterfield 8 (film)]]', 'the', 'and', '1961 birth', 'american peopl of english descent', 'nation velvet', 'list of highest-gross film', 'health problem', '[[butterfield 8]]', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=5 - {'miss taylor', 'sinu infect', 'actress elizabeth taylor', 'health problem', 'pneumonia'}\\n\",\n      \"p=0.14285714285714285, r=0.6, f1=0.23076923076923073\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'boycott exxon', 'environment issu', 'the cleanup will be', 'nation press club', 'alcohol beverag control act of 1988', 'environment disast', 'samuel skinner', 'alaska', 'consum backlash', 'alaskan oil spill', 'a major environment disaster.', 'environment backlash', 'ralph nader', 'aftermath', 'environment impact of the energi industri', 'coast guard', 'al alaska'}\\n\",\n      \"GT: num=10 - {'boycott exxon', 'signific environment disast', 'oil industri', 'ga price', 'fish industri', 'cleanup strategi', 'alaskan oil spill', 'tanker exxon valdez', 'exxon valdez accid', 'oil compani'}\\n\",\n      \"p=0.11764705882352941, r=0.2, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of the', 'hurrican dean', 'of antiguan and', 'stuart', 'cultur of antigua & barbuda (1936–1953)', 'island, which is about the size of', 'hurrican', 'caribbean', 'hur franc', 'antigua and barbuda', 'hur hurrican', 'of', 'list of hurrican', 'antigen and', 'antarctica', 'antiguan', 'citi in antigua', 'st. thoma', 'hurrah'}\\n\",\n      \"GT: num=10 - {'second hurrican', 'atlant season', 'hurrican advisori', 'eastern caribbean', 'emerg suppli', 'hurrican watch', 'forecast', 'hurrican dean', 'hurrican warn', 'puerto rico'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'elizabethtaylor', 'to her', 'univers of southern california', '[[nation velvet (1945 film)|nat velvet]]', 'american film actress', 'american actress of english-languag descent', 'elizabeth taylor', '1941 birth', 'american peopl of english descent', 'nation velvet', 'list of highest-gross film', 'health problem', 'in a video messag to', '[[butterfield 8]]', \\\"[[who' afraid of virginia woolf]]\\\", 'to', 'american women in journal', 'pneumonia', 'hospit', 'to the'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'sinu infect', 'health problem', 'lung biopsi', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.15, r=0.5, f1=0.23076923076923075\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'mountain and foothil of the appalachian mountain', 'mine town in maine<infill>c of the central main', 'main suprem court', 'cumberland counti superior court', 'the right to keep and bear arm', 'perkin wrote that the court had no basi to dismiss the gun possess charge. he also', 'the', '19th centuri', 'cape and island', 'histori', 'municip and state constitut amend', 'cumberland', 'cedar falls, main', 'that the', 'stephen l. perkin', 'marijuana', 'jame e. tierney', 'index of maine-rel articl', 'citi in main'}\\n\",\n      \"GT: num=10 - {'firearm', 'gun', 'felon', 'constitut amend', 'restrict', 'gun possess charg', 'main constitut', 'right', 'crimin threaten', 'arm'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'depart of interior', 'exxon ship co.', 'the oil', 'steve cowper', 'unit fishermen of alaska', 'gregori cousin', 'river of alaska (u.s. state)', 'princ william sound', 'in the histori of the', '1980', 'princewilliam sound', 'alaska', 'princewil sound', 'the', \\\"the nation' largest oil spill\\\", 'oil spill in', 'expo valdez', 'histori', 'of', 'geographi of the pacif northwest', 'the histori', 'oil spill'}\\n\",\n      \"GT: num=7 - {'captain', 'environment damag', 'joseph hazelwood', 'full investig', 'feder regul', 'proper pilot', 'oil spill'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'tumor', 'be test for', 'for tb.', '1920 establish in theunit state', 'prevent', 'american civil liberti organ', 'tuberculosi', 'aids-viru', 'canadian civil liberti associ', 'organ establish in 1920', 'prison', 'that all inmat', 'treatment', 'center for diseas control', 'in prison be', 'drug treatment', 'american civil liberti union', 'the cdc said, note that the cdc ha recommend', 'tuberculosi in the unit state', 'be', 'tculosi in u.s. prison'}\\n\",\n      \"GT: num=4 - {'tuberculosi case', 'tuberculosi rate', 'airborn transmiss', 'cdc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'citi in the unit state', 'the genet suscept and you can do', 'cultur tourism in texa', 'american diabet associ', 'citi in greater san antonio', 'mexican-american', 'do more', 'univers of texa health scienc center', 'do', 'latin american and latino american', 'san antonio', 'health', 'diabet', 'yale univers', 'popul place establish in 1836', 'type i diabet', 'demograph', 'list of peopl from san antonio and the surround area', 'type ii diabet'}\\n\",\n      \"GT: num=10 - {'obes diabet', 'diabet patient', 'hispan diabet', 'minor', 'insulin', 'diabet studi', 'diabet test', 'american diabet associ', 'type ii diabet', 'hispan'}\\n\",\n      \"p=0.10526315789473684, r=0.2, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'controversi', 'foreign polici of slovenia and the european union', 's slovenian conflict', 's krupska', 'josima dukaj', 'srukom,', 'foreign relat of slovenia', 's drukom', 'slukom,', 'foreign affair of slovenia<infill>foreign relationsof yugoslavia', 'southeast european countri', 'slovenia', 'aleksandar prlja', 'foreignrel of croatia', 'serbia', 'josip broz tito', 'janez drnovsek', 's slovenia, a slovene, said that the', 'ljubljana', 'srijani'}\\n\",\n      \"GT: num=7 - {'slovenian presid', 'slovenian serbian conflict', 'yugoslav feder', 'region autonomi', 'econom contact', 'econom boycott', 'serbian action'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'unit kingdom', 'europ', 'cultur depict of cattl', 'govern ha', 'the', 'cattledog', 'martin raff', 'not', 'cow in the unit state', 'ha not been', 'cattl diseas', 'crisi in cattl farm', 'ha', 'the u.s. depart of agricultur ha', 'the govern ha', 'nation farmer union', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'ha been', 'not been', 'been', 'univers college, london', 'c cattl diseas', 'scraie'}\\n\",\n      \"GT: num=8 - {'sheep diseas', 'govern', 'scrapi', 'british cattl', 'mad cow diseas', 'bse', 'immun system', 'export'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'nation air forc', \\\"the pattern of the '40s, '50 and '60 when we had a tremend number of landfal\\\", 'prepar', 'histori', 'atlant hurrican', 'nation ocean and atmospher administr', 'atlant ocean', 'climat chang', '1930', 'of landfal hurricanes.', 'hurrican histori', 'max mayfield', 'north palm beach, fla.', 'climat scienc', 'climat resili', 'nation hurrican center', 'nation weather servic', 'atlant hurrican season'}\\n\",\n      \"GT: num=8 - {'hurrican hunter', 'weather satellit', 'coastal popul', 'hurrican activ', 'hurrican reconnaiss flight', 'air forc', 'forecast', 'atlant hurrican season'}\\n\",\n      \"p=0.05555555555555555, r=0.125, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'smithfield meat market', 'to the', 'diamond', 'african nation congress', 'south african', 'central sell organ', 'chemic element', 'south african have been pressur the organ to increas price', 'south africa', 'product', 'diamond industri', 'to', 'to $ 4.09 billion,', 'product by countri', 'articl contain video clip', 'peter miller', 'argyl diamond'}\\n\",\n      \"GT: num=7 - {'central sell organ', 'de beer diamond empir', 'rough diamond sale', 'diamond busi', 'world diamond industri', 'south african interest', 'diamond produc'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'of a', 'lui carlo galan', 'the countri is in a state of', 'carlo pizarro', 'virgilio barco', 'of the', 'of polit', 'antonio navarro', 'april 19 movement', 'spanish colon of the america', 'april 27 elect', 'coloni era', 'countri in south america', 'bernardo jaramillo', 'counti seat in colombia', 'republ', 'histori', 'colombia', 'of', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=10 - {'assassin', 'drug traffick', 'suicid mission', 'presidenti elect', 'gunman', 'drug baron', 'terrorist campaign', 'polit chao', 'candid carlo pizarro', 'colombia'}\\n\",\n      \"p=0.05, r=0.1, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'exxon valdez oil spill', 'exxonvaldez oil spill', 'cleanup', 'exxon exxon valdel oil spill, it cleanup', 'exxon valdez', 'coordin drill', '1962', 'coast', 'oil spill of exxon valdaz', 'major event', 'exxon valdess', 'environment event', 'exxon', 'cooper develop', 'cooper', 'occurr', 'exxon valdezoil spill', 'transport in alaska', 'chronolog', '1970', 'coastal geographi', 'ch chronolog', 'coal mine', 'chromolog', 'chronolog of the exxon valdz oil spill (1962)', 'exxonaldez'}\\n\",\n      \"GT: num=4 - {'exxon valdez oil spill', 'develop', 'chronolog', 'cleanup'}\\n\",\n      \"p=0.11538461538461539, r=0.75, f1=0.19999999999999998\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'tunnel', 'ill.', 'tornado fact', 'tropic cyclon', 'tornado', 'north', 'natur disast', 'natur hazard', 'to the northeast,', 'insur inform institut', 'indiana', 'missouri', 'the nation weather servic ha a tornado scale of', 'histori', 'list of tornado', 'southwest, northeast, northeast', 't tornado fact', 'illinoi', 'northwest,', 'nation weather servic'}\\n\",\n      \"GT: num=3 - {'tornado', 'thunderstorm', 'tornado fact'}\\n\",\n      \"p=0.1, r=0.6666666666666666, f1=0.1739130434782609\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'a predict of', 'cultur of the southern unit state', 'hurrican hugo', 'suitland', 'hurrican', 'massachussett institut of technolog', 'colin mcadi', 'weather forecast', 'nation ocean and atmospher administr', 'cultur histori of the unit state (1865–1953)', 'the weather is a', 'a', 'gil clark', 'american weather', 'a forecast', 'climatolog', 'histori', '20th centuri'}\\n\",\n      \"GT: num=10 - {'hurrican forecast', 'hurrican hugo', 'real forecast problem', 'forecast abil', 'landfal', 'supercomput predict', 'south carolina coast', 'satellit data', 'destruct path', 'satellit pictur'}\\n\",\n      \"p=0.05555555555555555, r=0.1, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'state and territori establish in 1822', \\\"the solomons. a 6 read is a `` minor '' earthquak ; a 7 read is\\\", 'golden', 'former dutch coloni', 'island countri', 'recent histori', 'a quak', 'solomon', 'pacif tsunami warn center', 'a', 'san francisco bay', 'pacific-wid tsunami', 'state of the pacif', 'san cristob', 'histori', 'earthquak', 'solomon island', 'u.s. geolog survey'}\\n\",\n      \"GT: num=7 - {'earthquak monitor', 'richter scale', 'major earthquak', 'widespread heavi damag', 'largest earthquak', 'epicent', 'solomon island'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'corazon aquino', 'assassin', 'rafael ileto', 'to protect the peopl and to protect the', \\\"new people' armi\\\", 'brig. gen. alexand aguirr', 'attack by firearm', 'assault on govern offici', 'the govern and', 'the', 'asia', 'philippin capit', 'terror in the unit state', 'kill by firearm in the philippin', 'govern', 'govern and', 'the philippin', 'nation secur advis', 'philippin'}\\n\",\n      \"GT: num=9 - {'gunmen', 'assasin attempt', 'makati', 'street violenc', 'communist rebel', 'polit assassin', 'rebel assassin', 'philippin', 'polic chief'}\\n\",\n      \"p=0.05263157894736842, r=0.1111111111111111, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'and her family. she wa also in the intens care unit', 'elizabethtaylor', 'michael wild', \\\"[[who' afraid of virginia woolf?]]\\\", '1946 birth', \\\"[[who' afraid?]]\\\", 'american film actress', 'elizabeth taylor', '[[ butterfield 8 (film)|butterfield 8]] and', 'and the', 'and', 'american peopl of english descent', 'hollywood actress', 'list of highest-gross film', 'health problem', '[[butterfield 8]]', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=6 - {'viral pneumonia', 'sinu infect', 'bacteri pneumonia', 'intraven therapi', 'yeast infect', 'elizabeth taylor'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'scientif opinion', 'vaccin', 'rttb', 'to continu to suppress the tuberculosi bacteria, which would have', 'dana-farb cancer institut', 'be', 'world health organ essenti medicin', 'stanford univers', 'nation institut of allergi and infecti diseas', 'be abl to', 'rtb drug', 'epidem', 'drug for aid', 'societi and cultur', 'william haseltin', 'rttem', 'sten vermund', 'to', 'american associ for the advanc of scienc', 'aid', 'been abl to'}\\n\",\n      \"GT: num=6 - {'aid epidem', 'remiss', 'tuberculosi bacteria', 'aid vaccin', 'aid infect', 'drug'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american', 'american societi', '18th-centuri introduct', 'health', 'unit state', 'that', 'tuberculosi', 'american cultur', 'the cdc said that', 'tculosi', 'acquir immun defici syndrom', 'peopl of african descent', 'center for diseas control', 'in 1988, the cdc said. in 1988, there were', 'that the', 'ethnic group in the unit state', 'u.s. tuberculosi', 'american peopl', 'aid'}\\n\",\n      \"GT: num=5 - {'complet statist', 'tuberculosi case', 'steadi declin', 'aid case', 'diseas control'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'western unit state', 'idlewildfir command post', 'wildfir', 'citi in the pacif northwest', \\\"'' it' a veri dri\\\", 'boe', 'western cultur', 'idealist', '1880 establish in the unit states<infill>western unit state', 'yellowston nation park', 'american weather', 'climat', 'the weather is not consist', 'and dri', 'idaho', 'nation weather servic', 'cultur of the unit kingdom'}\\n\",\n      \"GT: num=6 - {'fire danger', 'dri weather', 'western wildfir', 'firefight', 'blaze', 'contigu unit state'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'univers of medicin and dentistri', 'atlanta', 'american societi', 'newark, n.j', 'tuber tuberculosi', 'tubulatru', 'tubbularculosi', 'health', 'tuberculosi', 'american peopl', 'tusbularculosis.', 'aid viru', 'american cultur', 'the center for diseas control and prevention. the center for', 'tubularculosi', 'north american societi', 'tculosi', 'american lung associ', 'tubi', 'cultur', 'tub', 'treatment'}\\n\",\n      \"GT: num=6 - {'new health threat', 'lung diseas', 'tuberculosi', 'aid viru', 'tb case', 'diseas control'}\\n\",\n      \"p=0.09090909090909091, r=0.3333333333333333, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'henri waxman', 'he', 'carl lewi', '20th centuri', \\\"he'\\\", '19th-centuri american male writer and editor', \\\"that he' here to compet with me. '' he said he\\\", 'benjamin gilman', 'mel levin', 'benjamingilman', 'american male of english descent', \\\"i'm here to tell the peopl of thi countri\\\", 'histori', \\\"he' here to\\\", 'ben johnson', 'steroid', \\\"american men' basketbal player\\\", 'american non-fict writer', 'american male writer', 'to', 'american peopl'}\\n\",\n      \"GT: num=6 - {'control substanc', 'canadian', 'seoul', 'gold medal', 'anabol steroid', 'ben johnson'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'cultur view of the moon', 'san', 'san francisco', 'at 11:07 a.m. mst in san francisco ; and', 'a partial eclips', 'list of eclips and partial eclips by countri', 'in san', 'mazatlan', 'partial solar eclips', 'solar event', 'welder', 'observ', 'pasadena', 'unit state', 'edmonton', 'north america', 'solar eclips'}\\n\",\n      \"GT: num=7 - {'solar telescop', 'stun view', 'partial solar', 'eye injuri', 'eye damag', 'north america', 'solar eclips'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'of a', 'tracer fire', 'august and septemb', 'of an', 'of.', 'gila river basin', 'event', 'day of the year', 'big sur', 'fort collin', 'western unit state', 'day', 'of', 'gila nation forest', 'precipit', 'mescalero fish hatcheri', 'august (period)', 'august', 'of the black tiger fire in the roosevelt nation forest,', 'august 1'}\\n\",\n      \"GT: num=8 - {'arson fire', 'new mexico fire', 'fire', 'western state', 'forest', 'firefight', 'fire season', 'blaze'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'aterian-era record', 'a total solar eclips', 'a', 'astrobiolog', \\\"the tablet wa found in 1948 in the ruin of ugarit, an ancient citi near syria'\\\", 'ancient histori', 'british museum', 'griffith observatori', 'egyptian-styl calendar', 'syria', 'ugarit', 'asteroid', 'athropolog object', 'astronom object discov in 1948', 'histori', 'a solar', 'aten asteroid', 'astrophys', 'total solar eclips', 'natur', 'the tablet is'}\\n\",\n      \"GT: num=6 - {'syria', 'ancient observ', 'dutch scientist', 'clay tablet', 'reliabl record', 'solar eclips'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'1815 establish in the unit kingdom', ':', 'histori and cultur of the america', 'tornado and sever thunderstorm that have kill at least 27 peopl', 'north american', 'articl contain video clip', 'geolog of theamerican state', 'tornado', 'state-by-st', 'southern unit state (1815–1921)', 'unit state', 'north america', 'geographi of the american state', 'american weather', 'sever thunderstorm', 'histori', 'american geographi', 'sever', 'north american weather', 'geograph midpoint of the earth', 'tornad', 'histori of the unit state'}\\n\",\n      \"GT: num=2 - {'tornado', 'sever thunderstorm'}\\n\",\n      \"p=0.09090909090909091, r=1.0, f1=0.16666666666666669\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'cleanup', 'paul a. yost jr.', 'sacramento', 'environment disast', 'plan', 'will be treat and', 'alyeska pipelin servic co.', 'exxon', 'alcohol beverag control act', 'and', 'environment issu in alaska', 'alaskan oil spill of march 2010', 'and the oil-lac wastewat', 'exxxon', 'alakan oil industri', 'paul a., yost', 'alaska oil spill', 'bill lamoreaux', 'gulf of alaska', 'and oil-taint'}\\n\",\n      \"GT: num=9 - {'exxon tanker', 'crude oil price', 'valdez spill', 'pollut area', 'oil coastlin', 'exxon offici', 'alaskan coastlin', 'oil spill', 'cleanup plan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'census', 'by countri', 'to be', 'unit kingdom', 'latin american and caribbean american histori', 'suprem court', 'citizenship studi', 'robert mosbach', 'censu bureau', 'the', 'sampl (statistics)', 'unit state', 'popul', 'richard shelbi', 'crimin law of the unit state', 'sun belt', 'the hous of repres to be redistrict', '1990 censu', 'to', 'to the'}\\n\",\n      \"GT: num=7 - {'illeg alien', '1990 censu', 'censu bureau', 'hous seat', 'nation head count', 'congression reapportion', 'illeg resid'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'citi in the unit state', 'list of peopl from minneapoli', 'sayl belton', 'minneapoli', '1950', '20th centuri', 'hubert h. humphrey', 'capit in the american south', 'that are', 'citi in minnesota', 'are the', 'are not the onli one who are', 'popul place establish in 1836', 'univers of minnesota', 'john h. laux-', 'histori', 'john laux', 'u.s. senat', 'are', 'racial tension'}\\n\",\n      \"GT: num=6 - {'polic racism', 'polic misconduct', 'civil right', 'drug raid', 'brutal', 'racial harmoni'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {\\\"[[who' af afraid of virginia woolf?\\\", 'elizabethtaylor', 'to the hospital.', \\\"[[who' afraid of virginia woolf?]]\\\", \\\"who '' afraid\\\", 'american film actress', 'elizabeth taylor', 'list of anim right activist', '1913 birth', 'american peopl of english descent', 'health problem', \\\"she wa move to st. john' hospit and health center on april 23. she wa then move to\\\", 'butterfield 8', 'viral pneumonia', '[[butterfield 8]]', '19th-centuri american actress', 'to', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'viral pneumonia', 'bacteri pneumonia', 'yeast infect', 'recoveri', 'elizabeth taylor'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'survey methodolog', 'censu', 'to be taken up in the', 'census', '1990 censu', 'don edward', 'mervyn dymal', 'the issu of the censu', 'sampl (statistics)', 'samuel a. teller', 'unit state', 'histori', 'the senate.', 'tom ridg', 'popul', 'don'}\\n\",\n      \"GT: num=7 - {'censu number', '1990 censu', 'censu bureau', 'hous seat', 'nation head count', 'hous reapportion', 'illeg alien'}\\n\",\n      \"p=0.0625, r=0.14285714285714285, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'alp', 'the oil', 'exxon valdez', '2010', 'of the oil that wa', 'al alaska', 'geolog of the arctic', 'princ william sound', 'the', 'princewilliam sound', 'bald eagl', 'al arctic', 'alaska depart of environment conserv', 'histori', 'geographi of the pacif northwest', 'alaskan arctic', 'exxon corp.', 'oil spill', 'alcohol beverag control act of 2004'}\\n\",\n      \"GT: num=7 - {'tanker exxon valdez', 'wildlif popul', 'civil lawsuit', 'crude oil', 'oil spill', 'environment conserv', 'crimin indict'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'st. john hospit and health center', 'maria burton-carson', '20th-centuri american actress', 'american actress of english-jewish descent', 'american film actress', 'elizabeth taylor', \\\"in the hospital' intens care\\\", 'intens care', 'in', 'american peopl of english descent', 'nation velvet', 'st- john hospit', 'list of highest-gross film', 'elizabeth taylor (1928–present)', 'health problem', 'tracheotomi', 'intens', 'she wa taken off the ventil and wa in good spirits. she wa', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=3 - {'miss taylor', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.09523809523809523, r=0.6666666666666666, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'geographi of the american west', 'casper', 'wildfir', '1881 establish in north america', 'okefenok swamp', 'cheyenn', 'okefenskoot', 'the', 'climat', 'american west', 'region of the unit kingdom', 'west', 'the largest fire in the west', 'fire', 'ashley nation forest', 'fire.', 'were report contain saturday. the', '1871 establish in the unit state', 'the fire', 'western unit state and canada', 'diamond peak fire'}\\n\",\n      \"GT: num=6 - {'feder firefight effort', 'forest fire', 'fire season', 'firefight', 'fire crew', 'fire line'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clarenc thoma', 'yale law school', 'alan simpson', 'sup court of the unit kingdom', 'that he', 'supremaci in the unit state', 'supran court nomine', 'upscal', 'unit state', 'suprem court nomin', 'he wa a lawyer at the law firm of', 'unit nation suprem court nomine', 'that', 'unit state district court nomine', \\\"unit kingdom' highest court\\\", 'john melcher', 'u.s. circuit court of appeal', 'howard m. metzenbaum', 'that thoma'}\\n\",\n      \"GT: num=6 - {'clarenc thoma', 'senat', 'feder appeal judg', 'columbia', 'nomin', 'black offici'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'geographi of the southern unit state', 'hurrican hugo', 'louisiana', 'the u.s. coast', 'hurrican', 'florida key', 'histori (unit states)', 'the', 'american weather', 'had hit the u.s. coast, but it would have been much more deadli if it had hit almost anywher else, say bob sheets, director of the nation hurrican center', 'histori and scienc of the caribbean', 'historyof the unit kingdom', 'histori of the unit state (1876–1953)', 'histori', 'missisippi', '20th centuri', 'franci marion nation forest'}\\n\",\n      \"GT: num=8 - {'hurrican hugo', 'deadli storm', 'forecast', 'south carolina', 'damag', 'william gray', 'popul densiti', 'atlant hurrican season'}\\n\",\n      \"p=0.058823529411764705, r=0.125, f1=0.07999999999999999\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'to the gulf of alaska. the exxon', 'golob oil pollut bulletin', 'steve cowper', 'of the', 'blind spot', 'blight reef', 'bligh reef', 'blach reef', 'princ william sound', 'ed wieliczkiewicz', 'fujian province, republ of china', 'in alaska', 'of oil', 'depart of environment conserv', 'blanchard reef', 'ed wielech', 'of', 'seabe', 'oil spill', 'effect'}\\n\",\n      \"GT: num=6 - {'oil slick', 'environment disast', 'crude oil', 'oil spill', 'environment conserv', 'bligh reef'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'the studi also found that the drug isoniazidine, which is a', 'drug abus', 'new england journal of medicin', 'rttb', 'tumor', 'rtt', 'aid viuru', 'prevent', 'isoniazid', 'a drug that', 'tuberculosi', 'aid viru', 'is use', 'is', 'acquir immun defici syndrom', 'rtb', 'rttem', 'wikipedia medicin articl readi to translat', 'aid', 'treatment'}\\n\",\n      \"GT: num=6 - {'tuberculosi infect', 'drug abus', 'aid viru', 'drug addict', 'tuberculosi bacteria', 'medicin'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american civil war', 'war of independ', 'disastr relief', '1988 drought', '19th-centuri conflict', 'drought', '1914 in the american civil war (1865–1896)', 'and the', 'unit state', 'war involv the unit state', 'the disast relief', 'aftermath', 'disast relief', 'associ press', '1988', 'disast', 'post-war', 'war in the unit nation', 'intern war of the unit kingdom'}\\n\",\n      \"GT: num=4 - {'disast relief measur', 'drought', 'america', 'associ press'}\\n\",\n      \"p=0.10526315789473684, r=0.5, f1=0.17391304347826086\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'citi in the unit state', 'american diabet associ', 'citi in greater san antonio', 'mexican-american', 'san angelo, texa', 'univers of texa health scienc center', 'popul place establish in 1835', 'san antonio', 'health', 'diabet', 'yale univers', 'demograph', 'cultur tourism in texa and the unit kingdom', '1835 establish in texa', 'that she is go to have diabetes. she is go', 'to have', 'san andrea fault', 'to', 'mexican'}\\n\",\n      \"GT: num=7 - {'healthi diet', 'diabet patient', 'hispan diabet', 'minor', 'diabet studi', 'diabet test', 'american diabet associ'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'geographi', 'index of california-rel articl', 'california', 'probabl of a major earthquak in northern california is about 30 percent. the probabl of a', 'san francisco bay', 'the', 'state and territori establish in 1850', 'california in the unit state', 'u.s. geolog survey', 'a major earthquak', 'geolog', 'in the', 'the san', 'state of the unit kingdom', 'earthquak', 'u-s. earthquak center', 'california and the unit nation', 'california institu of technolog', 'whittier quak', 'san'}\\n\",\n      \"GT: num=8 - {'richter scale', 'larg earthquak', 'high probabl', 'strong quak', 'major earthquak', 'widespread heavi damag', 'earthquak center', 'northern california'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american farm organ', 'organ base in bismarck, north dakota', 'north dakota.', 'drought of1988', 'drain of 1988 hit hardest in the upper midwest', 'the', 'american food industri', 'unit state', 'american feed associ', 'drought of 1988', \\\"cash in onth drought, ''\\\", 'organis base in chicago', 'the third stori in a four-part series, `` cash in', 'american farm bureau feder', 'histori', 'american public health associ', 'north dakota', 'cash in on the drought', 'north america'}\\n\",\n      \"GT: num=6 - {'north dakota', 'drought', 'upper midwest', 'farmer', 'disast relief aid', 'disast aid program'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'the center, and the center is', 'joe blow', 'histori of the pacif northwest', 'lagrand', 'biolog museum in idaho', 'reed jarvi', 'lightn', 'bibliographi of the bois interag fire center', 'the', 'nation fire hall of fame', 'bureau of land manag', '1940 and 1950', 'arnold hartigan', 'firefight', 'histori', 'lynn findley', 'fire hall', \\\"the country, ''\\\", 'the most danger place in the country,'}\\n\",\n      \"GT: num=6 - {'bois interag fire center', 'wildfir battl', 'worst blaze', 'firefight', 'wildfir command post', 'fire line'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'southern u.s. state', 'a', 'index of alabama-rel articl', 'alabama', 'deep south', 'a tornado', 'tornado', 'jefferson counti', 'been a', 'sever weather', '1836 establish in alabama', 'state of the unit state (1836–1921)', 'histori', 'alorton, ill.', 'and build were demolished. the tornado', 'greenwood, s.c.', 'wa report to have', 'southern unit state', 'state and territori establish in 1836', 'nation weather servic', 'nation guardsmen'}\\n\",\n      \"GT: num=9 - {'tornado watch', 'rescu', 'disast', 'tornado', 'huntsvil', 'victim', 'properti damag', 'sever thunderstorm', 'destruct'}\\n\",\n      \"p=0.047619047619047616, r=0.1111111111111111, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cultur tourism in chicago', 'richard j. daley', '1901 establish in illinoi', 'illinoi popul place on lake michigan', \\\"chicago. the city' mayor\\\", 'chicago', 'northeastern illinoi univers', 'harold washington', '20th centuri', 'the', 'citi', 'ha been a vocal critic of', 'cultur of chicago', 'richard m. daley', \\\"city'\\\", 'machin polit', \\\"the city'\\\", '1991 mayor elect', 'histori', 'citi in illinoi (u.s. state)', 'racial tension'}\\n\",\n      \"GT: num=7 - {'race relat', 'racism', 'new mayor', 'black', 'polic brutal', 'racial issu', 'chicago'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'environment issu', 'citizen scienc', 'exxon', 'princ william sound', 'exxon ship co.', 'darrel buttic', 'exxon valdez', 'blind carbon copi', 'black', 'unit state', 'the environment. the valdez is expect to be', 'to be repaired.', 'oil spill', 'artifici intellig', 'neil goldschmidt', 'articl contain video clip', 'blacksmith'}\\n\",\n      \"GT: num=6 - {'crimin charg', 'exxon crew', 'joseph hazelwood', 'tanker exxon valdez', 'annual her industri', 'crude oil'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'paul feeley', 'histori of peru', 'the countryside, the shine path ha set down', 'aguayaco', 'coloni peru', 'spanish colon of the america', 'histori and scienc of peru (1901–present)', 'the shine path', 'upper huallaga river valley', 'aucayacu', 'the revolut', 'historyof peru', 'histori', 'of law and order.', 'maoist', 'a system of', 'raul aranda', 'upris against the militari', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=7 - {'maoist shine path guerrilla', 'corrupt local offici', 'revolutionari justic', 'rebel', 'puritan honesti', 'peru', 'coca product'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'he also promot villag banking.', 'foundat of thenon-profit', 'he', 'bootstamp', 'sustain', 'villag', 'bootstrap', 'english word', 'john hatch, founder of the non-profit foundat for intern commun assist', 'poverti reduct', 'sustain develop goal', 'econom ideolog', 'other use', 'foundat', 'foundat for internationalcommun assist', 'bootstrap econom', 'applic', 'commun', 'foundationfor intern commun assist', 'john hatch', 'third-world', 'practic econom', 'third world', 'villag bank'}\\n\",\n      \"GT: num=5 - {'third world', 'privat enterpris', 'john hatch', 'villag bank', 'world poverti'}\\n\",\n      \"p=0.125, r=0.6, f1=0.20689655172413793\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'latin american and caribbean american', 'georg comstock', 'black peopl', 'black (human racial classification)', 'new england journal of medicin', 'is a', 'black women', 'georg curlin', 'health', 'is the', 'the disease, which is', 'peopl from african-american commun', 'tuberculosi', 'black and latino american', 'is', 'john hopkin univers', 'arkansa depart of health', 'peopl of african descent', 'been blame on the', 'cultur', 'nation institut of allergi and infecti diseas'}\\n\",\n      \"GT: num=6 - {'infecti diseas', 'tuberculosi', 'black american', 'racial differ', 'white', 'tb bacteria'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'drug traffick', 'm-19', 'lui carlo galan', 'carlo pizarro', 'outlin of colombia', 'to continu the fight against', 'medellin drug cartel', 'state and territori establish in 1810', 'spanish colon of the america', 'countri in south america', 'cesar', 'republ', 'histori', 'against the drug traffickers.', 'colombia', 'independ', 'cesar gaviria', 'spanish-speak countri and territori', 'the govern ha vow to'}\\n\",\n      \"GT: num=8 - {'assassin', 'drug traffick', 'medellin drug cartel', 'elect', 'terrorist act', 'cocain cartel', 'presidenti candid carlo pizarro', 'colombia'}\\n\",\n      \"p=0.15789473684210525, r=0.375, f1=0.22222222222222218\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'aircraft manufactur of south korea and the unit state', 'to testifi on behalf of the victims. the prosecut is', 'to the', 'aerospac industri in south korea', 'aerial bomb of the soviet union', 'no tae-u', 'choe kyu-ha', 'republ of korea (1949–present)', 'azerbaijan', 'aero- ballist missil of the unit kingdom', 'to', 'chong sung-hwa', 'aterom', 'histori', 'cho kyu', 'chon tu-hwan', 'chong kyong-sik', 'changwon and the khmer roug'}\\n\",\n      \"GT: num=5 - {'assassin', 'crimin evid', 'militari prosecut', 'assassin kim', 'concret evid'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'hurrican andrew', 'unit kingdom', 'hurci', 'wallac stickney', 'new york', 'unit state', 'a major contribut to the', 'hurvey', 'louisiana', \\\"the president' re-elect campaign.\\\", 'hurrican', 'harri poll', 'new hampshir', 'aftermath', 'storm', 'in the end', 'georg bush', 'homestead air forc base', 'huronicad'}\\n\",\n      \"GT: num=6 - {'louisiana', 'presid georg bush', 'disast', 'emerg relief', 'hurrican andrew', 'florida'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the compani ha a branch in', 'the area.', 'hurrican andrew', 'nelson robertson', 'tropic cyclon', 'n nelson robertson', 'the', 'storm involv the unit kingdom', 'unit state', 'hurican andrew (song)', 'gener accid', 'hurrican', 'in the', 'orlando', 'aftermath', 'hur hurrican', 'orlando.', 'lord airli', 'the loss adjust are', 'lord', 'are', 'insur'}\\n\",\n      \"GT: num=4 - {'insur', 'loss', 'hurrican andrew', 'insur claim'}\\n\",\n      \"p=0.09090909090909091, r=0.5, f1=0.15384615384615385\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the ground and bloodi him with nightsticks. the rev.', 'citi in missouri', 'cultur tourism in missouri<infill>c of the missouri territori', 'andi brez', 'roman cathol', \\\"the polic department'\\\", \\\"officer'\\\", 'joseph okoy', 'the', \\\"the officer'\\\", 'polic brutal', '1872 establish in missouri territori (u.s. state)', 'steven bishop', \\\"polic department'\\\", 'histori', \\\"officers'\\\", 'terri d. barn', 'recent histori', 'kansa city, missouri', \\\"officials'\\\", 'popul place establish in 1872', 'excess forc'}\\n\",\n      \"GT: num=6 - {'polic forc', 'accident shoot death', 'racism', 'black citizen', 'brutal', 'excess forc'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'content', \\\"na'ib al - ma'ayitah\\\", 'lebanon', 'na', 'abu-nid group', 'al jazeera', 'and the fra', 'the frc and the frc', \\\"na-'ib al-'ma `\\\", 'and', 'al-watan', 'alitalia', 'investig', 'and hi', 'arabic-languag media', \\\"al-'watan\\\", \\\"na'-ib al- ma'ayitah assassin\\\", \\\"na'ib al-ma ` ayitah in beirut\\\", \\\"naʼib al‐ma'aytah\\\", 'na-ib al', 'arabist'}\\n\",\n      \"GT: num=7 - {'intern terror', 'investig report', 'imyo', 'jordanian author', 'ayitah assassin', 'jordanian fundamentalist extremist group', 'lebanes secur author'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'siemen', 'to the', 'economi of slovenia', 'economist', 'to find new market in the former yugoslavia, and are look for', 'world trade', 'servic', 'sector', 'renault', 'slovenia', 'world trade organ member economi', 'trade', 'to export to', 'european union member economi of slovenia and croatia', 'sloven', 'economi of europ by countri', 'comecon', 'iskra', 'to', 'econom of europ'}\\n\",\n      \"GT: num=10 - {'independ', 'sloven enterpris', 'sloven export', 'foreign invest', 'capit inflow', 'slovenia', 'trade link', 'sloven offici', 'european commun countri', 'former yugoslavia'}\\n\",\n      \"p=0.05, r=0.1, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'hurrican hugo', 'the year', 'hurrican andrew', 'oakland fire disast', 'in the us in', 'the', 'unit state', 'insur loss', 'cost', 'hurvey', 'dade counti', 'gulf hurrican', 'hurrican', 'hurkel', 'lo angel riot', 'hur hurrican', 'georg bush', 'american insur servic group', 'year', 'hurrah', 'the us'}\\n\",\n      \"GT: num=6 - {'insur claim', 'insur industri', 'florida loss', 'hurrican andrew', 'properti claim', 'insur loss'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'are not', 'in a popul area, but are not consid', 'geographi', 'tonopah, nev.', 'iben brown', 'new caledonia', 'colorado', 'golden', '1871 establish in colorado territori', 'cottonwood, colorado,', 'colorado springs, colorado', 'earth quak', 'not', 'in popul areas,', 'earthquak', 'new marid', 'new madrid fault', 'citi in el paso county, colorado (u.s. state)', 'are', 'cactusville, colorado,'}\\n\",\n      \"GT: num=8 - {'richter scale', 'moder earthquak', 'major earthquak', 'monitor equip', 'widespread heavi damag', 'earthquak emerg kit', 'countless earthquak predict', 'earthquak center'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'hurrican hugo', 'the caribbean, the', 'hurrican andrew', 'holiday inn', 'the', 'cajun countri', 'unit state', 'the state of florida, had', 'in', 'state', 'index of unit states-rel articl', '1848 establish in new orlean', 'louisiana', 'cuba', 'state and territori establish in 1847', 'hurrican', 'the bahamas,', 'the unit', '1847 establish in the unit state', 'histori', 'state of the unit kingdom', 'lloyd', 'in the caribbean,'}\\n\",\n      \"GT: num=7 - {'louisiana', 'uninsur loss', 'tropic storm', 'hurrican andrew', 'landfal', 'new orlean', 'sever damag'}\\n\",\n      \"p=0.08695652173913043, r=0.2857142857142857, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'barber conabl', 'the world bank', 'a state of', 'world develop report', 'john charl flickner', 'world bank', 'intern organ base in the unit state', 'robert mcnamara', 'intern monetari fund', \\\"the bank' new presid\\\", 'in', 'is still in the', 'in a state of', 'j p morgan', 'unit nation gener assembl observ', 'histori', 'john williamson', 'unit state intern monetari fund ( imf)', 'develop', 'in the 1980s, but the bank', 'intern bank', 'presidenti failur', 'develop in the third world'}\\n\",\n      \"GT: num=7 - {'poverti allevi', 'lewi preston', 'presid', 'world bank', 'third world', 'washington', 'loan condit'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'iowa', 'gerald w. whittak', 'great plain', 'minnesota', 'averag', 'agroecolog', 'agenc establish in 1876', 'agricultur', 'the', 'unit state', 'agrica', 'agri-food and drug administr', 'agribusi', 'drought of 1988', 'the averag', 'average.', 'wisconsin', 'histori', 'in 1988. in 1987, the', 'agronomi', 'illinoi'}\\n\",\n      \"GT: num=9 - {'feder disast relief', 'agricultur depart analysi', 'emerg drought aid', 'favor financi posit', 'commod market price', 'solvenc posit', 'sever drought region', '1988 drought', 'feder payment'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'hosni mubarak', 'the country.', 'the govern', 'cairo', 'dougla hurd', 'modern histori', 'citi in egypt and the arab world', 'the', 'egypt', 'anwar sadat', 'hassan abu-basha', 'rifaat el-mahgoub', 'mediev citi', '21st centuri', 'abu nidal', 'histori', 'abdul-halim moussa', 'the state', 'popularli own enterpris in egypt<infill>outlin of egypt', 'the perpetr were in the area. the', 'cultur tourism in egypt', 'popul place in cairo'}\\n\",\n      \"GT: num=8 - {'assassin', 'iraqi agent', 'egyptian politician', 'terrorist activ', 'funer', 'egyptian moslem fundamentalist', 'death toll', 'islam extremist'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'economi of europ', 'sri lanka', 'sindh', 'econom of the european union', 'poverti', 'bishop of oxford', 'economi of the unit kingdom', 'tigr', 'the world. the world ha chang sinc the industri revolution, and', 'the', 'sudanes govern', 'unit', 'economist of asia', 'unit states.', 'the unit', 'misus of statist', 'oecd member economi of the world', 'world trade organ member economi', 'critic', 'bishop', 'third world', 'econom of the british empir', 'civil conflict'}\\n\",\n      \"GT: num=6 - {'real factor', 'third world poverti', 'jame skinner', 'african debt', 'debt burden', 'govern polici'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'osman morot barrionuevo', 'abimael guzman', 'the govern', 'histori of peru', 'and the govern', 'the', 'and', 'la republica', 'osman morot', 'histori and cultur of peru (1918–1953)', 'polit repress', 'historyof peru', 'histori', 'spanish empir', 'sinc the end of the war', 'elena iparraguirr revoredo', 'rosa angelica sala de la cruz', 'member state of the unit nation', 'shine path', 'polit develop', 'spanish-speak countri and territori', 'the presid'}\\n\",\n      \"GT: num=8 - {'imprison shine path leader abimael guzman', 'peac propos', 'polit defeat', 'shine path peac strategi document', 'peac talk', 'peac agreement', 'govern repres', 'genuin shine path statement'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'to take precaut to prevent accidents. he said the', 'u.s. central command', 'saudi-american relat', 'were fli', 'f-15', 'pentagon', 'fli', 'saudi arabian civil war', 'saudi citi', 'were', 'u.s. air forc', 'outlin of saudi arabian arabia', 'saudi arabia', 'histori', 'vietnam war', 'citi in the kingdom of saudi arabia', 'aircraft were', 'saudi town and citi', 'fighter-bomb', 'modern era'}\\n\",\n      \"GT: num=3 - {'regular train flight', 'oper desert shield', 'fatal crash'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'slogan', 'serbian-speak countri and territori', 'and the sloven govern', 'serb-speak nation and territori of europ', 'former countri in europ', 'milan kucan', 'croatia', 'the', 'verica rudar', 'foreign polici', 'belgrad', 'the sloven government.', 'serbia', 'serbo-croatian', 'foreign relat', 'sloven govern', 's sloven', 'belgium–serbia relat', 'balkan war', 'srijani', 'cultur', 'to the'}\\n\",\n      \"GT: num=10 - {'sloven politician', 'possibl polit contact', 'sloven independ', 'offici sloven deleg', 'feder republ', 'sloven attitud', 'normal', 'offici belgrad', 'yugoslav diplomat', 'sloven govern'}\\n\",\n      \"p=0.045454545454545456, r=0.1, f1=0.06250000000000001\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'royal exchang', 'georg lloyd-robert', 'hur hurrican', 'hurrican', 'the gulf', 'list of hurrican', 'warburg secur', 'hurrican andrew', 'hur franc', 'rainbow hurrican', 'the', 'georg lloyd', 'unit state', 'the us gulf coast. the', 'hurrah', 'sulfur acid hurrican', 'royal insur'}\\n\",\n      \"GT: num=7 - {'hurrican andrew', 'southern florida', 'new orlean', 'florida', 'damag claim', 'insur industri loss', 'sever properti damag'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'econom effici', 'post-cold war', 'the world bank', 'the world', 'polici', 'intern organ base in london', 'poverti reduct', 'develop in europ', 'poverti allevi', 'world bank', 'the', \\\"of the bank' new polici directives,\\\", 'intern organis base in the unit kingdom', 'develop bank', 'polici to fight poverti', 'possibl', 'unit nation gener assembl observ', 'histori', 'lewi preston', 'pessim', \\\"the bank'\\\"}\\n\",\n      \"GT: num=8 - {'poverti allevi', 'poverti assess', 'loan volum', 'bank polici', 'world bank', 'third world', 'poverti relief', 'poverti reduct object'}\\n\",\n      \"p=0.09523809523809523, r=0.25, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'aircraft crash in japan in april', 'athlet accid and fatal in japan by type', 'the dfaa ha', 'okinawa', '2010', 'aerospac accid and death in japan (2010)', 'f-15 fighter', 'the', 'u yokota', '44th fighter squadron', 'u.s. forc in japan', 'aerial accid and fire', 'airlin that reenter the atmospher', 'defens facil administr agenc', 'kadena air base', 'militari accid and incid', 'aerosafeti', 'the defens', 'the dod', 'the u.'}\\n\",\n      \"GT: num=4 - {'investig', 'routin train', 'crash', 'okinawa'}\\n\",\n      \"p=0.05, r=0.25, f1=0.08333333333333334\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'finnair', 'the moon will be between the earth and the', 'astrobiolog', 'astrolog', 'total eclips in finland', 'the sun,', 'the', 'astrolog sub-disciplin', 'the eclipse,', 'soviet border', 'solar eclips', 'astronom event in 2019', 'joensuu', 'origin of the solar eclips (geometry)', 'special event', 'falkland meteorolog servic', 'astrophys', 'total', 'total solar eclips', 'finland', 'a total'}\\n\",\n      \"GT: num=7 - {'watcher', 'total solar eclips', 'observ', 'finland', 'special eyeglass', 'solar eclips', 'total phase'}\\n\",\n      \"p=0.14285714285714285, r=0.42857142857142855, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'anglo american corpor', 'russia', 'angolan american corpor compani', 'komdragmet', 'the diamond industry.', 'the russian', 'diamantair', 'oversea compani', 'of the', 'konrad', 'dollar', 'the', 'compani base in johannesburg', 'the diamond of the russian feder and the', 'world trade organ member economi', 'african compani establish in 1847', 'diamond industri', 'yukutia', 'diamond', 'rosalmazzoloto', 'the industry.', 'angola'}\\n\",\n      \"GT: num=12 - {'rough diamond', 'exclus sale agreement', 'russian diamond', 'de beer', 'beleagu diamond industri', 'unoffici export', 'south africa', 'russian feder', 'state diamond centr', 'russian diamond industri', 'yakut govern', 'harri oppenheim'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'the unit state', 'hurrican hugo', 'hurricana', 'climat of the atlant ocean', 'hurrican', 'and the nation hurrican center, which is base on', 'atlant hurrican', 'nation ocean and atmospher administr', 'atlant ocean', '1990 atlant hurrican season and forecast', 'climat chang', 'the', 'atlant cyclon', 'hurrican gilbert', 'histori', 'nation hurrican center', 'atlant hurrican season'}\\n\",\n      \"GT: num=9 - {'destruct storm', 'caribbean', 'predict', 'coastal resid', 'hurrican activ', 'hugo', 'hurrican gilbert', 'hurrican emerg', 'atlant hurrican season'}\\n\",\n      \"p=0.11764705882352941, r=0.2222222222222222, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'assassin', 'hamburg dpa', 'of the', 'carlo salina de gortari', 'havana', 'the', 'unit state', 'assassin of lui donaldo colosio murrieta', 'colosio.', 'assalt peopl', 'the assassin of', 'polit murder in mexico and central america', 'baja california', 'ass murder in mexico', 'polit crime', 'baja', 'hondura', 'intern', 'of', 'reaction', 'habana', 'dpa in spanish', 'mexican', 'of colosio.', 'list of assassin by firearm'}\\n\",\n      \"GT: num=9 - {'assassin', 'pri', 'mexican govern', 'reaction', 'presidenti candid lui donaldo colosio', 'antidemocrat forc', 'colombian govern', 'mexican presid carlo salina', 'sympathi'}\\n\",\n      \"p=0.08, r=0.2222222222222222, f1=0.11764705882352941\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'kelli air forc base', 'strateg bomber', 'airlin of the unit state', 'c-5a transport', 'the', 'unit state', 'iraq', 'hahn air base and england air base in louisiana', 'richard swope', 'militari equip introduc in 1991 and 1992', 'oper histori', 'mcchord air base', 'air', 'c- 5a stratolaunch', 'the air force. the four were hospit and report in satisfactori condition,', 'the air', 'aircraft first flown in 1991', 'militari aircraft of the cold war', 'richard w. chase'}\\n\",\n      \"GT: num=7 - {'ramstein air base', 'major accid', 'reservist', 'massiv aircraft', 'crash', 'victim', 'west germani'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'latin american raini season', 'astronom sub-disciplin', 'mexican astronom societi', 'the state', 'mexico and the unit state', 'mexico', 'mexico citi', 'total solar eclips', 'astrophys', 'articl contain video clip', 'astrobiolog', 'astrolog', 'special event', 'the day of the eclipse. the state ha alreadi book', 'the eclipse,', 'the', 'solar eclips', 'branch of biolog'}\\n\",\n      \"GT: num=7 - {'slar eclips', 'mexico', 'total solar eclips', 'moon', 'tourist', 'eclips path', 'partial eclips'}\\n\",\n      \"p=0.1111111111111111, r=0.2857142857142857, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'rttb: forum research', 'latvia', 'most of the', 'the most', 'the', 'ogr rayon', 'tuberculosi', 'wikipedia tuberculosi articl', 'in the', 'societi and cultur', 'tculosi', 'most', 'rttem', 'anda mikelson', 'the most common type of tuberculosi is', 'inta pavlovska', 'wikipedia medicin articl readi to translat', 'cso', 'epidemiolog', 'state tuberculosi and lung diseas center', 'riga', 'treatment'}\\n\",\n      \"GT: num=5 - {'tuberculosi hospit', 'mortal', 'tuberculosi case', 'tuberculosi morbid', 'mandatori treatment'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cuango', 'southern african countri', 'angolan diamond industri', 'de beer', 'the', 'to keep it tight grip on the market. it is', 'countri in africa', 'economi', 'the diamond', 'diamond industri', 'republ', 'index of angola-rel articl', 'endiama', 'north american industri', 'diamond', 'member state of the unit nation', 'thediamonds.', 'that the', 'antwerp', 'angola', 'northwest territori'}\\n\",\n      \"GT: num=10 - {'first diamond mine', 'diamond hunter', 'rough diamond product', 'diamond cartel', 'world diamond busi', 'greedi rush', 'canada', 'diamond market', 'diamond price', 'diamond sale'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'koper', 'kanavank road tunnel', 'b balkan countri', 'b balkan', 'region of europ', 'most of the', 'the most', 'balkan', 'the', 'and it own, ha', 'the world.', 'bibliographi of bosnia and herzegovina', 'slovenia', 'bosnia and herzeg', 'bakal', 'and the balkans. slovenia, with it own', 'indep - endenc', 'kapel', 'most', 'polit', 'koper - burg', 'balograd', 'ljubljana', 'bavarian languag'}\\n\",\n      \"GT: num=9 - {'independ', 'sloven enterpris', 'slovenian economi', 'former feder', 'sloven border', 'normal econom tie', 'privatis polici', 'ration market reform', 'former yugoslavia'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'hurrican andrew', 'huron storm', 'rebuild in', 'joe her', 'andrew card', 'hurolog', 'the', 'unit state', 'hurrican irma', 'state', 'andrew', 'state,', \\\"the state' oil industri wa unaffect by the storm. the state\\\", 'georg bush last night', 'hurci andrew', 'andrew andrew', 'louisiana', 'hurrican', 'andrew day', 'wa expect to', 'aftermath', 'the state,', 'georg bush', 'huricayn', 'the us'}\\n\",\n      \"GT: num=6 - {'widespread destruct', 'louisiana', 'feder emerg', 'hurrican andrew', 'florida', 'massiv rebuild effort'}\\n\",\n      \"p=0.08, r=0.3333333333333333, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'econom statu', 'in the world.', 'economi of slovenia', 'foreign exchang', 'nation bank of yugoslavia', 'world bank', 'yugoslavia', 'european union member economi of slovenia and croatia', 'marko kranjec', 'intern monetari fund', 'economi of europ by countri', 'world trade organ member economi', 'economist of slovenia, or central bank, is the largest', 'ljuba', 'ljubljanska bank', 'foreign trade, direct invest and aid', 'econom of europ'}\\n\",\n      \"GT: num=10 - {'independ', 'feder govern', 'negoti', 'nation bank of yugoslavia', 'foreign exchang deposit', 'yugoslavia', 'sloven bank', 'unalloc feder debt', 'foreign creditor', 'sloven citizen'}\\n\",\n      \"p=0.11764705882352941, r=0.2, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the movement', 'lima america channel 4 televis network', 'abimael guzman', 'the govern', 'luzon', 'the govern ha not', 'peruvian civil war', 'state and territori establish in 1825', 'outlin of the peruvian arm forc', 'lrb', 'the agreement. the', 'the', 'state', 'luna', 'the shine path', 'lima', 'peac agreement', 'republ', 'histori', 'chiapa', 'state of the and', 'shine path', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=7 - {'shine path member', 'guerrilla armi', 'gener amnesti', 'econom support', 'peac agreement', 'peruvian govern', 'popular war'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'controversi', 'trade agreement of canada', 'north american free trade agreement', 'free trade agreement', 'treati of mexico', 'california gubernatori hope', 'california', 'anti-nafta', 'the', 'california and texa', 'unit state', 'california governor', 'california hous of repres', 'anti nafta', 'san francisco', 'the opposit ha been stalk by demonstrators, who', 'nafta', 'free-trad agreement', 'tort reform', 'the us'}\\n\",\n      \"GT: num=11 - {'california', 'nafta oppon', 'massiv campaign', 'side agreement', 'north american free trade agreement', 'nafta foe', 'free trade pact', 'lead propon', 'fair trade campaign', 'environment degrad', 'american public'}\\n\",\n      \"p=0.1, r=0.18181818181818182, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'airlin that crash', 'uh-1 huey', 'a total of', 'in the unit state', 'u.s. air forc', 'of', 'militari equip crash', 'the f-111 is a twin-engin aircraft that can carri up', 'aerospac accid and incid', \\\"iraq' aug. 2 takeov of kuwait\\\", 'f-4', 'aircraft crash', 'unit state', 'saudi arabia', 'athlet aircraft', 'iraq', 'aerial warfar', 'articl contain video clip'}\\n\",\n      \"GT: num=4 - {'crew member', 'crash', 'saudi arabia', 'oper desert shield'}\\n\",\n      \"p=0.05555555555555555, r=0.25, f1=0.0909090909090909\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'and texas, and', 'hurrican andrew', 'of the', 'allstat', 'hurican', '1901–1910', 'sear roebuck', 'unit state', 'allstat insur', 'hurricana', 'hurrican', 'and the nation associ of mutual', 'of texas,', 'of louisiana,', 'hur hurrican', 'of', 'british petroleum', 'rican', 'rican of the atlant ocean', 'hurrah', 'histori of the unit state'}\\n\",\n      \"GT: num=6 - {'emerg servic', 'disast loss', 'insur claim', 'hurrican andrew', 'new orlean', 'properti damag'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'new front', 'peruvian-speak countri', 'index of peru-rel articl', 'alberto fujimori', 'for more than a decade.', 'peru', 'peruvian civil war', 'been in the countri', 'member countri of the mercosur', 'mario varga llosa', 'countri in south america', 'southern cone countri', 'histori', 'maoist', 'sh shine path', 'the shine path ha', 'member state of the unit nation', 'shine path', 'alan garcia', 'the capital. the presid said the'}\\n\",\n      \"GT: num=11 - {'coastal urban area', 'presid alan garcia', 'indigen peopl', 'rebel attack', 'presidenti runoff', 'shine path rebel', 'power car bomb', 'lima', 'shine path leader abimael guzman', 'defens revolutionari movement', 'polit violenc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'british rail', 'britain', 'chinnel', 'british', 'modern histori', 'railway in franc &amp; germani', 'the chunnel, a project', 'rail transport in franc', 'the', 'rail tunnel in the unit kingdom', 'andr benard', 'franc &amp, unit kingdom rail transport', 'british public', 'albert mathieu', 'channel tunnel', 'the english', 'histori', 'that will', 'railway tunnel in franc and england', 'the french', 'the british', 'chunnel', 'the project', 'british island'}\\n\",\n      \"GT: num=8 - {'rail tunnel', 'easi conduit', 'english channel', 'budget', 'contractor disput', 'chunnel train', 'chunnel project', 'invest money'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'henri waxman', 'in the house, and the senat', 'anti-[nuclear] prolifer', 'a', 'anti-nuclear terror', 'north american free trade agreement', 'a trade', 'background', 'richard w. bush', 'ross perot', 'unit state', 'anti–nuclear terror in the unit state', 'been a', 'richard bush', 'unit nation gener assembl observ', 'trade', 'bill clinton', 'anti-(nuclear) prolifer', 'ha been', 'polit', 'been', 'nafta', 'trade agreement.', 'richard gephardt'}\\n\",\n      \"GT: num=13 - {'environment issu', 'congression democrat', 'mr ross perot', 'governor bill clinton', 'public disaffect', 'us elect campaign', 'republican establish', 'food safeti provis', 'north american', 'free trade agreement', 'nafta pact', 'hispan american', 'presid bush'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'develop organ base on contin', 'develop in africa', 'worldbank', 'clinton administr', 'polici', 'intern organ base in london', 'poverti reduct', 'clinton', 'world bank', 'and', 'world bank presid', 'the philippines,', 'lloyd bentsen', 'intern organis base in pari', 'unit nation gener assembl observ', 'histori', 'the unit states, japan and south korea, and', 'lewi preston', 'l lloyd bentson'}\\n\",\n      \"GT: num=8 - {'poverti reduct', 'lewi preston', 'develop countri', 'world bank', 'third world', 'adjust loan', 'poverti relief', 'bank lend'}\\n\",\n      \"p=0.15789473684210525, r=0.375, f1=0.22222222222222218\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'france–unit kingdom border', 'railway in the netherland', 'britain', 'railway tunnel in franc', 'the first time we have air pass between the two', 'rail transport in franc and germani', 'the', 'rail tunnel in the unit kingdom', 'the two', 'ice age', 'calai', 'two inch', 'construct', 'sangatt', 'channel tunnel', 'two', 'two inch of', 'daili express', 'two-inch probe', 'dover', 'chunnel', 'linkup'}\\n\",\n      \"GT: num=10 - {'histor linkup', 'continent europ', 'english channel', 'britain', 'chunnel', 'traffic congest', 'channel tunnel project', 'franc', 'cost overrun', 'transmanch link'}\\n\",\n      \"p=0.09090909090909091, r=0.2, f1=0.12500000000000003\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'jj pickl', 'trade agreement of canada', 'north american free trade agreement', 'free trade agreement', 'anti-nafta sentiment', 'unit nation gener assembl', 'background', 'ross perot', \\\"ford'\\\", 'anti–fre trade agreement in the unit state', 'the', 'unit state', 'have been work to build support for the pact. they have', 'north america free trade pact', 'carlo salina', 'in the', 'the unit', 'carter', 'opposit', 'ford', 'been', 'anti free trade agreement', 'guerrilla tactic', 'the us'}\\n\",\n      \"GT: num=7 - {'widespread public hostil', 'hous hear', 'environment protect', 'environment activist', 'impoverish immigr', 'north american free trade agreement', 'presid clinton'}\\n\",\n      \"p=0.041666666666666664, r=0.14285714285714285, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'the american', 'unit food and commerci worker intern union', 'american and', 'american', 'free trade agreement', 'north american develop bank', 'background', 'latin american and latino american', 'the', 'unit state', 'would be', 'southwest voter registr project', 'anti-nuclear movement', 'the unit', 'northamerican trade agreement', 'world trade organ member economi', 'and the us', 'abel guerra', 'trade', 'richard lopez', 'negoti', 'nafta', 'trade agreement', 'univers of california in lo angel', 'north america', 'the us'}\\n\",\n      \"GT: num=8 - {'trilater north american develop bank', 'neg impact', 'key hispan group', 'worker right', 'north american trade agreement', 'nafta', 'stringent side agreement', 'hispan commun'}\\n\",\n      \"p=0.038461538461538464, r=0.125, f1=0.058823529411764705\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the govern', 'unit kingdom', 'of the', 'western gener hospit', 'peter warhurst', 'europ', 'the', 'bse', 'transmiss spongial encephalopathi', 'scotland', 'rare diseas', 'the public', 'cjd', 'cattl diseas', 'infecti diseas', 'bovin spongiform encephalopathi', 'depart of health', 'creutzfeld-jacob diseas', 'wikipedia medicin articl readi to translat', 'prion', 'rare infecti diseas', 'cjd is not known to have caus the diseas in humans. the'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'causal link', 'bse case', 'cjd case', 'public anxieti', 'infecti protein', 'scientif evid'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'a', 'srijan', 'constitut', 's slovenia', 'milan kucan', 'sourc', 'sourc of law', 'constitut of yugoslavia', 'slobodan milosev', 'slovenia', 'yugoslav republ', 's slovenian presid', 'sourc for constitut document', 'histori', 'spartan', 'modern constitut', \\\"constitution, '' tanjug said.\\\", 'the new constitut will be', 'constitu assembl', 'constitut document'}\\n\",\n      \"GT: num=9 - {'slovenian presid', 'feder goal', 'yugoslav feder', 'milan kucan', 'serbia', 'new yugoslav confeder', 'full sovereignti', 'separatist tendenc', 'feder control'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {\\\"the state' own\\\", 'ljetnica', 'lubljan', 'countri in europ', 'outlin of slovenia', 'the', 'lojz peterl', 'the slovenian', 'northern republ', 'southeastern european countri', 'declar of sovereignti', 'slovenia', 'the slovenia', 'ljubljana nightli televis news', 'polit', 'lljubbana', 'ljze peterl', 'lithuania', 'would take preced over feder law and would take over', 'state and territori establish in 1918', 'srijani', 's slovenian-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'feder author', 'independ legal system', 'yugoslav republ', 'slovenian declar', 'slovenian control', 'full sovereignti', 'troubl yugoslav feder', 'loos confeder'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'sale are made through the central sell organis (cso ).', 'diamond industri associ', 'miner polici committe', 'the world', 'articl contain video clip', 'de beer', 'the', \\\"the country'\\\", 'central sell organis', 'gemston', 'debeer', 'diamond industri', 'intern cartel', 'intern', 'botswana', 'nichola oppenheim', 'diamond', 'product', 'sculptur'}\\n\",\n      \"GT: num=11 - {'de beer', 'individu diamond produc', 'south african group', 'rough diamond output', 'rough diamond sale', 'contract negoti', 'diamond trade', 'botswana diamond', 'central sell organis', 'exclus sale contract', 'botswana politician'}\\n\",\n      \"p=0.10526315789473684, r=0.18181818181818182, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=28 - {'enriqu bernal', 'chile', 'chicano', 'to be', 'is tri to', 'carlo tapia', 'conflict', 'endirecto', 'spanish-speak countri and territori', 'the shine path,', 'the', 'to becom a', 'drug war (1910–present)', 'histori of mexico', 'tobecom a', 'is', 'en directo', 'histori', 'spanish languag', 'is attempt to', 'the govern is', 'chiapa', 'enriqu', 'sh shine path', 'spanish word and phrase', 'cox said that the shine path is', 'to', 'state and territori establish in 1824'}\\n\",\n      \"GT: num=7 - {'peac negoti', 'peac propos', 'shine path faction', 'nation reconcili', 'second parti congress', 'govern favor', 'imprison shine path member'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'frederick ostbi', 'geographi of the unit state', 'historyof the unitedst', 'histori of the midwest', 'tornado', 'kentucki', 'west coast', 'to a', '1990', 'missouri', 'geolog of the american midwest', 'american weather', 'to predict the strength of storm and', 'histori', 'kansa', 'american geographi', 'to', 'north america', 'to the'}\\n\",\n      \"GT: num=7 - {'storm', 'tornado', 'death', 'flood', 'studi', 'tornado trend', 'damag'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'counti seat in california', 'the public and the police. the', 'citi of lo angel', 'daryl gate', 'of the', '20th centuri', 'citi in lo angel (u.s. state)', 'georg georg', 'georg w. bush', 'cultur tourism in california and the pacif northwest', 'the', 'the polic', 'nation guard', 'lo angel', 'histori', 'warren christoph', 'the department.', 'cultur of lo angel (upper lo angeles)', 'carotid choke hold', 'citi colleg of lo angel (l.a.', 'georg bush', 'the lapd'}\\n\",\n      \"GT: num=6 - {'violenc', 'brutal complaint', 'polic brutal', 'daryl gate', 'brutal polic forc', 'investig'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'creutzfieldt- jakob', 'john macgregor', 'unit kingdom', 'kent', 'creep cow', 'unit nation food and agricultur organ', 'the', 'singl market', 'unit state', 'biopesticid', 'transmiss spongial encephalopathi', 'to protect', 'bioviru', 'creutzfeldt-jakob diseas', 'the unit', 'rare diseas', 'histori', 'a new food polici', 'bovin spongiform encephalopathi', 'the british', 'the uk', 'creep cow mad', 'anim virolog', 'rare infecti diseas', 'the us'}\\n\",\n      \"GT: num=6 - {'diseas sheep', 'diseas scrapi', 'british beef import', 'british agricultur', 'infect feed', 'matern transmiss'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'davidmaclean', 'the govern said that the diseas wa not', 'greater kudu', 'to be', 'cattledom', 'be', 'ron davies, a labour agricultur spokesman', 'ron davi', 'arabian oryx', 'cattl diseas', 'spongiform', 'crisi in cattl farm', 'spongiform encephalopathi', 'cultur of the unit state', 'infecti diseas', 'in the unit kingdom', 'david maclean', 'c cattl diseas', 'to', 'cow', 'insect-born diseas', 'in sheep', 'to the'}\\n\",\n      \"GT: num=5 - {'antelop popul', 'scrapi', 'mad cow diseas', 'sheep encephalopathi', 'spongiform encephalopathi'}\\n\",\n      \"p=0.043478260869565216, r=0.2, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'hurrican hugo', 'landform of the atlant ocean', 'landscap of theatlant ocean (geography)', 'hurrican gilbert', 'articl contain video clip', 'atlantic,', 'and are given a name, if they reach a sustain wind of', 'the', 'atlant', 'coral gabl', 'hurrican diana', 'hurricana', 'hurrican', 'atlant ocean', 'histori', 'atlant sea', 'atlant hurrican', 'of the atlantic,', 'modern era', '1990 atlant hurrican season'}\\n\",\n      \"GT: num=5 - {'intens hurrican', 'devast storm', 'forc hurrican', 'forecast', 'atlant hurrican season'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'construct', 'the tunnel is now complete. the tunnel machin on the', 'francoi mitterrand', 'channel tunnel', 'the tunnel', 'rail transport in england', 'french presid', 'the french side.', 'daili express', 'margaret thatcher', 'chunnel', 'france–unit kingdom border cross', 'france-unit kingdom sport rivalri', 'the', 'railway tunnel in franc', 'histori', 'ice age', 'french side.'}\\n\",\n      \"GT: num=13 - {'first land link', 'histor linkup', 'channel tunnel', 'grow unif', 'english channel', 'chunnel', 'britain', 'continent ill', 'tunnel construct', 'tunnel train', 'franc', 'cost overrun', 'transmanch link'}\\n\",\n      \"p=0.1111111111111111, r=0.15384615384615385, f1=0.12903225806451615\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'transmiss', 'the zoo', 'transmiss spongic diseas', '[[mad cow disease]]', 'biolog and pharmacolog', \\\"[[ mad cow diseas ''\\\", 'anim diseas', \\\"[[zoo antelop catch mad cow disease]] ''\\\", 'the', 'biopesticid', 'the diseas may have been pass on to other animals.', 'zoo diseas', \\\"[[zoo antelop catch mad cow diseas ''\\\", 'rare diseas', 'drug for neglect diseas', 'london zoo', 'bovin spongiform encephalopathi', 'biolog caus', 'mad cow diseas', 'the kudu', 'rare anim diseas'}\\n\",\n      \"GT: num=6 - {'endang speci', 'mad cow diseas', 'kudu herd', 'similar transmiss', 'affect anim', 'london zoo'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'ayacucho', 'histori and mytholog of peru (1825–18 pacifism)', 'histori of peru', 'coloni peru', 'the meet wa held in', 'the', 'history, literatur and cultur of peru.', 'the shine path', 'razuhuillca', 'rafirmars en la base de unidad partidaria y construir la conquesta del power', 'nation counterterror director', 'polit repress', 'historyof peru', 'histori', 'peruvian civil war (1925–1937)', 'oscar ramirez durand', 'shine path', 'ayacucha', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'shine path central committe', 'black group', 'abimael guzman reinoso', 'shine path meet', 'peac accord', 'revolutionari violenc', 'shine path congress', 'arrest leader'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'famili plan', 'a presid', 'a', 'donna shalala', 'new jersey', '1946 birth', 'presidenti transit', 'be', 'new york', 'welfar', 'and the presid said he would let state experi with such programmes, even when he disagre with them', 'unit state', 'clinton dynasti', 'social safeti net', 'arkansa', 'famili support act', '1956 birth', 'michigan', 'clinton famili', 'welfar reform', 'clinton school of public servic', 'apresid', 'he said he', 'would'}\\n\",\n      \"GT: num=4 - {'bill clinton', 'famili support act', 'welfar reform', 'social safeti net'}\\n\",\n      \"p=0.125, r=0.75, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'biographi', 'person', 'a', 'carl lewi', 'olymp career', 'st.', 'toronto star', 'st. kitt', 'stanozolol', 'american sprinter from new jersey', 'canadian male weightlift', 'american peopl of maltes descent', 'canadian sprinter', 'a person physician and', 'american bodybuild', 'ben johnson', 'benjohnson', 'american male sprinter of world war ii', 'person physician', 'he said he had been', 's korean game'}\\n\",\n      \"GT: num=9 - {'ban steroid', 'stanozolol use', 'seoul olymp', 'jami astaphan', 'disgrac olymp sprinter', 'olymp gold medal', 'johnson scandal', 'person physician', 'ben johnson'}\\n\",\n      \"p=0.09523809523809523, r=0.2222222222222222, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=27 - {'yellow rose trail', 'the 1988 blaze', 'spring bring rebirth', 'spring', 'the aftermath', 'where', 'the park is', 'a place where', 'yellowston park', 'the', 'bald eagl', 'spring ha come to yellowston', 'cultur of the yellowston park', 'in the', 'citi in the yellowston nation park', 'where the', 'is', 'yellowston', 'histori', 'rhode island', 'much unscath', 'is a sign that the forest is', 'henri shovic', 'yelloweston park', 'museum in yellowston county, montana', 'the great fire', 'is still'}\\n\",\n      \"GT: num=8 - {'yellowston park fire', 'fire ecolog', 'natur fire', 'lodgepol pine', 'firefight effort', 'firefight crew', 'destruct blaze', 'lightn fire'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'1885 establish in california', 'list of american marathon record', 'marathon cours in california (u.s. state)', \\\"the men' field is led by\\\", 'race cours', 'san diego half marathon', 'san josé', 'trib 10k', 'the', 'san jose', 'race in san diego', 'alphonc swai', 'in the', '1880 establish in the unit state', 'san francisco marathon', 'race day', 'sammi rotich', 'american marathon', 'who place second in the'}\\n\",\n      \"GT: num=6 - {'runner', 'alphonc swai', 'race', 'challeng cours', 'homef half marathon', 'comeback bid'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'tokai region', 'richter scale', 'tokaido', 'of protest and', 'tokai', 'and', 'geograph region of japan and the pacif ocean', 'philippin sea plate', 'geolog', 'geolog region', 'the tokai region ha been the subject of a seri of', 'eurasian plate', 'of', 'geographi of japan', 'geograph of asia (disambiguation)', 'geograph region of asia', 'list of extrem point', 'geographi of japan (1912–present)', 'suruga trough'}\\n\",\n      \"GT: num=8 - {'richter scale', 'japan', 'tokai earthquak', 'seismic activ', 'earthquak warn', 'immin earthquak', 'gener earthquak predict research', 'coastal tokai region'}\\n\",\n      \"p=0.05263157894736842, r=0.125, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'boston marathon', 'aebb bido', 'abeb mekonnen', 'the world record on', 'abe mekonné', 'the course.', 'abeyb biko', 'list of boston marathon medal', \\\"the women' race in 2:08:39. in the women '' race on monday,\\\", 'gulf coast', 'marathon', '1872 establish in the unit state', 'olymp sport', 'abe bikila', 'histori', 'juma ikangaa', 'boston', 'ethiopia', 'recent histori', 'joan benoit samuelson', 'recent winner'}\\n\",\n      \"GT: num=8 - {'ethiopia', 'ingrid kristiansen', 'abeb bikila', 'race', 'african runner', 'domin group', 'marathon runner', 'consecut olymp gold medal'}\\n\",\n      \"p=0.047619047619047616, r=0.125, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'wildfir', \\\"smoker' paradox\\\", 'in the unit', 'health effect', 'mark linan', 'john hopkin univers school of hygien and public health', 'smog', 'carbon monoxide, which can caus', 'unit', 'unit states.', 'hydrocarbon (gas)', 'the unit', 'yosemit fire', 'u.s. forest servic', 'california depart of health servic', 'carbon monoxid', 'in the unit state', 'carboxyl acid', 'to be the deadliest', 'toxicolog', 'johnsburg'}\\n\",\n      \"GT: num=12 - {'carbon monoxid', 'hazard chemic', 'lung function', 'health servic', 'respiratori diseas', 'poison stew', 'unseen hazard', 'wildfir', 'fatal heart attack', 'wildfir smoke', 'california wildland firefight', 'respiratori protect'}\\n\",\n      \"p=0.09523809523809523, r=0.16666666666666666, f1=0.12121212121212123\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'western unit state', 'outlin of alaska and the arctic', '1901-present', 'juneau', 'princ william sound', 'valdez', 'exxon valdez', 'alumni of the univers of alaska', 'alaska', 'alcohol beverag control act', 'to move the ship to a new port', 'alaskan oil spill', 'in the unit states.', 'histori', 'oil spill', 'expo valdez spill', 'coast guard', 'al alaska'}\\n\",\n      \"GT: num=10 - {'devast oil spill', 'spill damag', 'complet cleanup', 'spill cost', 'alaska', 'exxon valdez spill', 'tanker exxon valdez', 'crude oil', 'fresh oil sheen', 'spill conting plan'}\\n\",\n      \"p=0.05555555555555555, r=0.1, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of the', 'african union member economi', 'de beer', 'global market', 'the', 'the fact that', 'oversea diamond industri', 'central sell organis', 'venetia', 'zimbabw', 'diamond industri', 'the lack of', 'botswana', 'of', 'diamond', 'south africa', 'to do their job. thi is a result of the', 'venetu', 'angola'}\\n\",\n      \"GT: num=9 - {'venetia', 'global diamond market', 'rough gem diamond output', 'south african diamond mine', 'de beer', 'russian export', 'turbul', 'diamond cartel', 'central sell organis'}\\n\",\n      \"p=0.15789473684210525, r=0.3333333333333333, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'athanasiu univers', 'seoul olymp', 'ace (racing)', 'ollan cassel', 'carl lewi', 'lewi', 'articl contain video clip', 'intern amateur athlet feder', 'aerob exercis', 'unit state', 'that johnson should be strip of hi world record. lewi ha the second-fastest legal time in history, 9.92, in finish second to', 'the athlet congress', 'ha', 'histori', 'aesthet', 'ben johnson', 'athlet', 'ha been', 'aerial sport', 'world championship'}\\n\",\n      \"GT: num=6 - {'world championship', 'canadian inquiri', 'ben johnson', 'carl lewi', 'world record', 'steroid use'}\\n\",\n      \"p=0.15, r=0.5, f1=0.23076923076923075\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'assault weapon', 'stockton', 'weapon and ammunit introduc in 1947', 'militari equip introduc in 1945', 'the nra ha becom a more effect advoc for gun-control', 'and the', 'rifl', 'use in crimin justic', 'unit state', 'gun-control measur', 'and', 'assault', 'stockton schoolyard', 'ak-47', 'nation rifl assn.', 'weapon of mass destruct', 'denni deconcini'}\\n\",\n      \"GT: num=7 - {'deconcini', 'firearm', 'gun lobbi', 'nra', 'constitut right', 'gun ban', 'gun control'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'bioterror', 'the govern', 'unit kingdom', 'the health', 'western gener hospit', 'richard lacey', 'keith meldrum', 'creutzfield-jacob diseas', 'biolog and human', 'the', 'bse', 'leed univers', 'have die of cjd in the uk sinc 1990.', 'biopesticid', 'transmiss spongial encephalopathi', 'medic', 'the public', 'is', 'infect', 'transvers myeliti', 'biovasculopathi', 'hematolog', 'bovin spongiform encephalopathi', 'the diseas', 'creutzfeld-jacob diseas'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'human brain diseas', 'mad cow diseas', 'public concern', 'matern transmiss', 'bse', 'epidem'}\\n\",\n      \"p=0.08, r=0.2857142857142857, f1=0.125\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'counti in africa by gdp', 'debswana', 'in the world.', 'de beer', 'cape provinc', 'member state of the unit nation', 'countri in africa', 'member countri of the african union', 'member state of the commonwealth', 'which wa the lowest', 'economi', 'outlin of botswana', 'diamond industri', 'jwaneng', 'central sell organis', 'kalahari desert', 'botswana'}\\n\",\n      \"GT: num=10 - {'jwaneng mine', 'de beer', 'south african group', 'diamond mine', 'russian export', 'underground mine', 'diamond output', 'world diamond product', 'central sell organis', 'botswana'}\\n\",\n      \"p=0.17647058823529413, r=0.3, f1=0.22222222222222224\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'cat', 'health concern', 'in the unit kingdom, and', 'british cuisin', 'european commun', 'british beef', 'the', 'health', 'european', 'the diseas is', 'is', 'cattl', \\\"mad cow '' diseas\\\", 'british brand', 'british product', 'british food and drink', 'not known whether the', 'bovin spongiform encephalopathi', 'is not known', 'scrapi', 'british cattl', 'mad cow diseas'}\\n\",\n      \"GT: num=6 - {'bovin spongiform encephalopathi', 'british beef import', 'mad cow diseas', 'health fear', 'trade friction', 'germani import ban'}\\n\",\n      \"p=0.09090909090909091, r=0.3333333333333333, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'make', 'controversi', 'oper rescu', 'nonpartisan organ in theunit state', 'william armstrong', 'the new law, the aclu ha', 'nazi germani', 'lo angel polic offic', 'freedom rider', '1920 establish in the unit state', 'organ establish in 1920', 'american civil right organ', 'lo angel', 'that would', 'polic state', 'american civil liberti union', 'institut for justic', 'would make', 'a new law that would', 'would', 'pain-compli law'}\\n\",\n      \"GT: num=4 - {'nonviol protest', 'polic brutal charg', 'polic abus', 'lo angel polic offic'}\\n\",\n      \"p=0.047619047619047616, r=0.25, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'oper', 'alyska pipelin servic co.', 'of the oil', 'steve cowper', 'transport', 'aleska pipelin safeti plan', 'of oil', 'transport in alaska', 'oil', 'oil-spil cleanup.', 'autopilot', 'to be readi for a spill of thi magnitude. the', 'aleyeska pipelin (company)', 'of', 'alisha', 'pipelin in alaska and the pacif ocean', 'ayleska pipelin', 'alyeska marin termin', 'bp america', 'aileska pipelin system', 'oil spill', 'exxon valdez oil spill disast', 'trans-alaska oil pipelin'}\\n\",\n      \"GT: num=7 - {'cleanup respons plan', 'cleanup measur', 'improv safeti', 'oil spill', 'exxon valdez oil spill disast', 'minim environment effect', 'oil compani'}\\n\",\n      \"p=0.08695652173913043, r=0.2857142857142857, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'north america free trade act', 'for an', 'controversi', 'north american free trade agreement', 'free trade agreement', 'ross perot', 'trade agreement of the unit kingdom', 'unit state', \\\"veterans'day\\\", 'to vote for', 'for a', 'vice presid', 'outsid the unit state', 'david bonior', 'unit nation gener assembl observ', 'outsid washington', 'treati of canada', 'for the', 'vote for nafta. the administr is also tri to get member to', 'for', 'nafta', 'arthur andersen & compani', 'al gore'}\\n\",\n      \"GT: num=8 - {'mexican congress', 'hous vote', 'trade pact', 'public opinion', 'congression district', 'debat victori', 'trade campaign', 'north american free trade agreement'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'sting', 'long beach', 'popul place establish in 1846', 'california', 'law enforc', 'california state capit', 'law and govern', 'polic misconduct', 'polic brutal', 'that he wa', 'long-term disabl', 'that', 'wa', 'that you', 'long beach polic offic', '1846 establish in california<infill>list of peopl from long beach', 'citi in california', 'and you were so angri that you were', 'he wa', 'counti seat in california (u.s. state)', 'longer-term'}\\n\",\n      \"GT: num=6 - {'racism', 'polic offic', 'don jackson', 'polic brutal', 'dickey', 'arrest'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'alaskan oil spill', '1959 establish in new york (state)', 'oil compani establish in 1959', 'tuberculosi treatment in new', 'lawrenc g. rawl', \\\"''valdez'' cleanup\\\", 'princ william sound', 'exxon', 'the cleanup cost and the chang in account', '1980 in the unit state', 'drexel burnham lambert inc.', 'compani base in new jersey', '1990', 'to $ 1.38 billion,', 'to.', 'histori', 'exxon valdez accid', 'to', 'exxon corp.'}\\n\",\n      \"GT: num=11 - {'cleanup charg', 'lower net incom', 'expens environment disast', 'valdez spill', 'tanker exxon valdez', 'total cleanup cost', 'financi effect', 'massiv alaskan oil spill', 'valdez cleanup', 'exxon valdez accid', 'revenu'}\\n\",\n      \"p=0.05263157894736842, r=0.09090909090909091, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clean water act', 'exxon valdez', 'environment disast', 'exxon salvag crew', 'princ william sound', 'outcom', 'alcohol beverag control act', 'unit state', 'georg miller', 'the oil spill.', 'of the hous of repres on wednesday', 'the cleanup of the', 'hiroshima', 'environment issu', 'alaska oil spill', 'gulf of alaska', 'alaskan oil spill of march 24', 'respons', 'long island'}\\n\",\n      \"GT: num=8 - {'crimin charg', 'cleanup', 'joseph hazelwood', 'alaska', 'stricken tanker exxon valdez', 'massiv oil spill', 'wildlif death', 'exxon salvag crew'}\\n\",\n      \"p=0.05263157894736842, r=0.125, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'a major', 'american peopl of irish descent', 'american male non-fict writer', 'a', 'polit career', '19th-centuri american politician', 'richard m daley', 'chicago', 'american polit parti founder', 'richard daley.', 'richard j daley.', 'washington', 'north america free trade agreement', 'bill daley (politician)', 'a key', 'a leader in', 'been a', 'list of american polit parti chairmen', 'bill clinton', 'richard m', 'american nonprofit chief execut', 'nafta', 'georg bush', \\\"mr daley ha been a key support of the president' agenda and ha\\\"}\\n\",\n      \"GT: num=8 - {'grassroot opposit', 'side agreement', 'presid bill clinton', 'congression support', 'controversi battl', 'north america free trade agreement', 'william daley', 'free trade pact'}\\n\",\n      \"p=0.041666666666666664, r=0.125, f1=0.0625\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'the white hous ha said that the plan will', 'post-cold war', 'wisconsin and georgia', 'schoolfar', 'winni mae', 'sustain develop goal', 'welfar', 'spite (social policy)', 'the', 'and', 'unit state', 'georgia', 'the unit state', 'wisconsin', 'florida', 'welfar reform', 'histori', 'vermont', 'bill clinton', 'develop assist program', 'be a pilot', 'the state', 'intergovernment organ establish in 1945', 'the us', 'tommi thompson'}\\n\",\n      \"GT: num=5 - {'food stamp', 'wisconsin', 'presid bill clinton', 'welfar reform', 'georgia'}\\n\",\n      \"p=0.12, r=0.6, f1=0.19999999999999998\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'with the', \\\"st. john' hospit and medic center\\\", 'and the star ha a cover photograph of liz taylor', 'american women in film', '20th-centuri american actress', 'american film actress', 'malcolm forb', 'career', 'american peopl of maltes descent', 'marin del rey hospit', 'list of highest-gross film', 'with', 'nation enquir', 'liz taylor', 'tropic diseas', 'stereo', 'marina del rey', 'with a', 'aid'}\\n\",\n      \"GT: num=5 - {'pneumonia', 'rumor', 'celebr', 'addict', 'liz taylor'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'longbeach, california, unit state', 'beachcomb', 'counti seat in california, california (state)', 'the runner', 'popul coastal place in california', 'viktor gural', '1854 establish in california (u.s. state)', 'the race.', 'rex wilson', 'long beach, california', 'the', 'marathon', 'long-dist run', 'the heat did not help. the', 'ric sayr', 'beje', 'diann rodger', 'sport', 'bejing, china', 'long island'}\\n\",\n      \"GT: num=7 - {'patienc', 'winner', 'cours record', 'rex wilson', 'race', 'wen yanmin', 'long beach marathon'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the survivor and the bodi of the', 'unit airlin', 'the corps of', 'crash', 'unit flight 232', 'feder aviat administr', 'of the', 'aircraft first flown in 1981', 'unit airlin flight 22', 'flight', 'cabl news network', 'unit state air forc aircraft', 'the', 'airlin base in denver', 'tail engin explod', 'american airlin flight 232 (unit airlines)', 'aerospac accid and incid', 'of', 'terri e. branstad', 'the bodi of', 'unit air line flight 21'}\\n\",\n      \"GT: num=9 - {'emerg land', 'explos', 'crash land', 'crash victim', 'air crash', 'complet hydraul failur', 'tail engin', 'flight crew', 'violent crash'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'elizabethtaylor', \\\"sutton' law in medicin\\\", '20th-centuri american actress', 'santa monica hospit', 'american film actress', 'elizabeth taylor', 'liza todd-tivey', 'american peopl of english descent', 'that', 'list of highest-gross film', 'lung', 'health problem', 'list', 'that she', 'that the', '19th- centuri american actress and produc', 'santa monica', 'willi -lrb- the actor', 'pneumonia', 'of the biopsy, he said he wa not sure'}\\n\",\n      \"GT: num=7 - {'ventil', 'surgeri', 'actress', 'health problem', 'seriou condit', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.15, r=0.42857142857142855, f1=0.2222222222222222\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'counti seat in new york (state)', 'fbi', 'long beach', 'a', 'modern era', 'long island, new york', 'lo angel counti district attorney', 'lo angel counti districtattorney', 'today show', 'video', 'counti in new mexico', '1871 establish in new jersey', 'video of the incid', 'long beach citi council', 'histori', 'vacat citi in the unit state', 'jeff hill', 'borough of new york citi', 'that the video wa', 'long island', 'a video'}\\n\",\n      \"GT: num=7 - {'polic forc', 'civil right violat', 'lo angel', 'polic brutal', 'jackson', 'investig', 'dickey'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'western cultur', 'reynold', 'cultur geographi', \\\"reggi democrat '\\\", 'welfar', 'western', 'and the', 'the', 'of the famili in afdc need afdc are abl to take low-paid job', 'western philosophi', 'unit state', 'franklin roosevelt', 'the unit', 'american cultur', 'american cultur tradit', 'bill clinton', 'american folklor', 'georg bush', 'reagan democrat', 'reagan', 'polit aspect', 'the us'}\\n\",\n      \"GT: num=6 - {'deficit reduct', 'welfar benefit', 'presid bill clinton', 'welfar reform', 'healthcar reform', 'feder assist'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'is a member of the hous arm servic committee. he says:', '17th-centuri introduct', 'state of the unit nation', 'north american free trade agreement', 'ross perot', 'the', 'unit state', 'virginia and the unit state', 'unit', 'norton si whiski', 'the unit', 'norman sisiski', 'american cultur', 'virginia', 'state and territori establish in 1788', 'n ralph dombrow', 'american polit', 'that the', 'north american fta', 'outlin of the american peopl', 'florida state univers', 'nation polit', 'north america'}\\n\",\n      \"GT: num=8 - {'demonstr', 'judici system', 'foreign polici implic', 'north american free trade agreement', 'presid bill clinton', 'passion opposit', 'virginia', 'opposit organis'}\\n\",\n      \"p=0.08695652173913043, r=0.25, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'unit aircraft corpor', 'desoto nation forest', 'air accid', 'hattiesburg', 'caus', 'nation forest.', 'and wa report miss at about 9:30 p.m.', 'in the desoto', 'aircraft accid in mississippi', 'mickey leland', 'american airlin flight 11', 'gulfport', 'dixi youth world seri', 'unit state air forc', 'unit airlin flight 21', 'unit air line flight 11 (d-l)', 'investig', 'marlin fitzwat'}\\n\",\n      \"GT: num=6 - {'mississippi', 'aviat accid', 'light plane crash', 'airplan crash', 'freshman congressman larkin smith', 'wreckag'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'caría', 'countri in north america', 'carib basin', 'list of caribbean countri', 'north american free trade agreement', 'alexand watson, assist secretari of state', 'caricom', 'the', 'and', 'theu', 'carlo salinas, prime minist of beliz', 'the unit state', 'watson ha been brief on the propos', 'carlo salina', 'caribbean', 'economi', 'and the us', 'trade', 'bill clinton', 'man manuel esquivl', 'member state of the unit nation', 'alexand watson', 'manuel esquivel', 'the us'}\\n\",\n      \"GT: num=9 - {'competit mexico', 'central american', 'econom disloc', 'presid bill clinton', 'north american free trade agreement', 'pariti propos', 'possibl divers', 'nafta market', 'caribbean basin countri'}\\n\",\n      \"p=0.041666666666666664, r=0.1111111111111111, f1=0.06060606060606061\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'economi of the european union', 'econom of europ (disambiguation)', 'world health organ', 'organis for econom co-oper and develop', 'welfar', 'the cost of the welfar state. the', 'the', 'economist of europe, a group of', 'cost', 'olymp game', 'oecd', 'world trade organ member economi', 'key issu', 'welfar state', 'otto van der walt', 'economi of europ by countri', 'the welfar state', 'oecd countri', 'econom of europ'}\\n\",\n      \"GT: num=5 - {'nation welfar system', 'welfar cost', 'healthcar reform', 'presid clinton', 'budget deficit'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the us,', 'blair hous', 'domest polici', 'the treaty, and he ha been abl to', 'north american free trade agreement', 'presid', 'asian pacif nation', 'the', '2016 elector', 'american rhode scholar', 'the unit', 'seattl', '2016 unit state presidenti elector', 'uruguay round', 'trade', 'bill clinton', 'clinton school of public servic', 'uganda round', 'nafta', 'american polit career', 'to get the treati through', 'american peopl', 'gatt'}\\n\",\n      \"GT: num=8 - {'trade pact', 'investor', 'intern protection', 'open trade environ', 'econom benefit', 'north american free trade agreement', 'presid clinton', 'global inflat'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'build and structur destroy in 1991 and 1992', 'lo angel counti fire depart', 'precaut', 'the beetl are abl to burrow into the bark and', 'california', 'southern california', 'california wildfir', 'drought', 'and the', 'the', 'depart of forestri', 'build and structur demolish in 1991', 'build and structur disestablish in 1992', 'bark beetl', 'is', 'u.s. forest servic', 'list of wildfir in california', 'fungi', 'the fire season is'}\\n\",\n      \"GT: num=11 - {'wildfir season', 'southern california neighborhood', 'fire offici', 'wildfir danger', 'fuel sourc', 'fire prevent regul', 'rainfal season', 'vulner fire zone', 'vulner foothil', 'prolong drought', 'high fire hazard'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'the govern', 'the militia', 'gun ownership', 'social philosophi', 'gun violenc', 'parad magazin', 'don kate', 'the', 'that the militia ha changed, the', 'michigan law review', 'the unit state', 'gun right', 'michigan', 'histori', 'gun control', 'gun cultur', 'gun safeti', 'the state', 'warren burger', 'modern time', 'the countri', 'cultur', \\\"warren burger'\\\", 'second amend'}\\n\",\n      \"GT: num=8 - {'handgun purchas', 'right', 'law enforc', 'gun ownership', 'kate', 'constitut', 'gun control', 'second amend'}\\n\",\n      \"p=0.125, r=0.375, f1=0.1875\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'1853 establish in california<infill>oxnard', 'polic', 'ventur counti public defend', 'the three', 'popul place establish in 1853', 'oxnard, california', 'of the five', 'the four', 'ventura counti', 'the', 'law and govern', 'canton capit of california', 'polic brutal', 'were arrest on june 15. the', 'ventura counti district attorney', 'william kadi', 'citi in california', 'edward brodi', 'of', 'beyond a reason doubt', 'vento counti', 'the five'}\\n\",\n      \"GT: num=6 - {'polic offic', 'forcibl brutal', 'polic brutal', 'victim', 'excess forc', 'arrest report'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'prepar', 'wa also expect to', 'to be', 'and food. the coast guard', 'august and septemb', 'nation hurrican center', 'event', 'day of the year', 'morgan citi', \\\"port o'connor\\\", 'freeport', 'day', 'nation hurrican servic', 'hurrican chantal', 'chevron corp.', 'august (period)', 'august', 'to', 'august 1', 'be'}\\n\",\n      \"GT: num=9 - {'hurrican stapl', 'hurrican chantal', 'oil drill work vessel', 'coastal resid', 'coast guard', 'rescu diver', 'crew member', 'hurrican warn', 'hurrican season'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'venetia', 'diamond', 'de beer', 'carr kitcatt & aitken', 'the largest diamond mine in the world,', 'robin baxter-brown', 'diamond mine', 'global market', 'diamond industri associ', 'zim', 'and', 'zimbabw ministri of mine', 'diamond industri', 'toronto', 'zealand', 'and the second, cost', 'african union member economi'}\\n\",\n      \"GT: num=11 - {'diamond deposit', 'river ranch', 'diamond explor experi', 'diamond mine', 'south african group', 'zimbabw govern', 'diamond busi', 'exclus explor right', 'diamond trade', 'joint ventur', 'central sell organis'}\\n\",\n      \"p=0.058823529411764705, r=0.09090909090909091, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a hous in franc', 'franc and the british', 'properti market', 'french.', 'railway in franc & germani', 'hesdin', 'montreuil', 'the', 'and', 'calai', 'channel tunnel', 'mitterrand', 'franc & the unit kingdom', 'histori', 'the french', 'railway tunnel in franceand the unit state', 'hovercraft', 'hover craft', 'hepdin', 'modern time', 'rail transport in franc and the unit kingdom', 'french', 'and the bank are not happi with the fact that the tunnel is'}\\n\",\n      \"GT: num=10 - {'channel tunnel', 'start signal', 'britain', 'uk market', 'prospect buyer', 'price competit', 'recoveri', 'franc', 'properti market', 'french properti'}\\n\",\n      \"p=0.08695652173913043, r=0.2, f1=0.12121212121212122\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'person life and career', 'carllewi', 'the olympics.', 'american sportspeopl', 'the', 'carl lewi', 'stanozolol', 'toronto', 'drug use', 'american male film actor', 'american film produc', 'american race track and field coach', 'the use of the ban steroid furazabol in', 'furazabol', 'intern amateur athlet feder', 's korean olymp', 'american peopl of maltes descent'}\\n\",\n      \"GT: num=7 - {'seoul olymp', 'anabol steroid stanozolol', 'steroid furazabol', 'drug test', 'carl lewi', 'drug use', 'ben johnson'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'wikipedia medicin articl readyto translat', 'hiv/aid', 'of the', 'elizabeth taylor', 'pulmonari diseas', 'candida albican', 'bacteri pneumonia', 'the', 'biolog', 'candida', 'y yeast', 'bacteria', 'infecti diseas', 'bacteriolyt pneumonia', 'of', 'rttem', 'the infect is not relat to the viral pneumonia. the', 'santa monica', 'wikipedia medicin articl readi to translat', 'wikipedia: medicin articl contain video clip', 'pneumonia'}\\n\",\n      \"GT: num=7 - {'miss taylor', 'viral pneumonia', 'actress elizabeth taylor', 'bacteri pneumonia', 'intraven therapi', 'yeast infect', 'hospit'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'california', 'cultur tourism in california', '1901-present', 'lake elsinor', 'by late friday afternoon, author said.', 'casa del sol', 'cultur of the southern california', 'citi in riversid county, california', 'counti seat in california (u.s. state)', 'cleveland nation forest', 'california in popular cultur', 'histori', 'antelop valley', 'ortega highway', 'firefight were abl to extinguish the blaze', 'riversid counti', 'san mateo wilder'}\\n\",\n      \"GT: num=7 - {'brush fire', 'cleveland nation forest', 'firefight', 'blaze', 'damag', 'investig', 'fire crew'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'to the white hous', 'clinton administr', 'alfr r. emerson', 'north american develop bank', 'the', 'presid of bill clinton', '1946 establish in the unit state', 'the white house.', 'the white hous ha been', 'afl-cio', 'clinton famili', 'foreign relat', 'northamerican free trade agreement', 'trade', 'effort to impeach bill clinton (illustr in thi video)', 'bill clinton', 'nafta', 'presid of the unit nation', 'the administration.', 'to', 'esteban torr', 'north america'}\\n\",\n      \"GT: num=7 - {'hous vote', 'undecid congressman', 'presid bill clinton', 'clinton administr', 'north american free trade agreement', 'north american develop bank', 'job loss'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of cattl in', 'unit kingdom', 'europ', 'cultur depict of cattl', 'in', 'cattledog', 'martin raff', 'in the', 'cow in the unit state', 'cattl diseas', 'crisi in cattl farm', 'nation farmer union', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'univers college, london', 'c cattl diseas', 'scraie', 'the unit state ha ban the use of'}\\n\",\n      \"GT: num=9 - {'bovin spongiform encephalopathi', 'sheep diseas', 'cattl feed', 'british cattl', 'mad cow diseas', 'import', 'ban', 'bse', 'export'}\\n\",\n      \"p=0.05263157894736842, r=0.1111111111111111, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'european singl market', 'brussel', 'the uk.', 'to take action against', 'intern trade', 'europ', 'british cuisin', 'bivin spongeiform enceopathi', 'british beef', 'bse', 'horst seehof', 'european commiss', 'british and european livestock', 'creutzfeld-jakob diseas', 'bovin diseas', 'the uk is also hope to persuad the european commiss', 'cattl', 'creutzfield-jakob diseas and creutzfeldt-jakov diseas', 'british food and drink', 'bovin spongiform encephalopathi', 'anim virolog'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'germani', 'british export', 'mad cow diseas', 'british beef export', 'restrict', 'unilater ban'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'organiz allianc', 'amzigh cultur associ', 'of all', 'ronald l. dellum', 'ronald v. dellum', 'of the', 'organ of the amazigh peopl', 'u.s. agenc for intern develop', 'u-2', 'organ base in addi ababa', 'the leland plane', 'u-.s. militari', 'histori', \\\"the plane' disappear wa widespread. the\\\", 'of', 'ethiopia', 'amazigh cultur associ', 'organis base in ethiopia', 'u.-2'}\\n\",\n      \"GT: num=7 - {'crash site', 'texa congressman mickey leland', 'rescu oper', 'american helicopt', 'fugnido refuge camp', 'wreckag', 'heavi weather'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'canadian steroid abus', 'controversi', 'carl lewi', 'huey said that franci had told her', 'stanozolol', 'that he wa', 'that franci', 'charli franci', 'canadian olymp medalist', 'canadian coach charli franci', 'angella taylor issajenko', 'olymp dope', 'canadian male sprinter', 'that', 'canadian sprinter', 'ben johnson', 'canadian femal sprinter (age 18–24)', 'nbc-tv', 'biotechnolog', 'biogenesi'}\\n\",\n      \"GT: num=8 - {'urin sampl', 'canadian coach charli franci', 'ban steroid', 'seoul olymp', 'lynda huey', 'canadian inquiri', 'drug use', 'sprinter ben johnson'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'prepar', 'france–unit kingdom border cross', 'railway tunnel in franc', 'of the', 'wallonia', 'organis for econom co-oper and develop', 'nord-pa de calai', 'of belgium.', 'belgium', 'e40 european motorway', 'construct', 'channel tunnel', 'france-uk border', 'economi', 'and the tunnel will be', 'of', 'brussel region', 'rail transport in franc and the unit kingdom', 'bruge', 'the centr of'}\\n\",\n      \"GT: num=11 - {'develop', 'channel tunnel', 'chunnel', 'traffic load', 'western flander', 'freight carrier', 'belgium', 'rapid increas', 'european metropolitan area', 'offici open', 'holidaymak'}\\n\",\n      \"p=0.1, r=0.18181818181818182, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'govern of colombia (1918–present)', 'mexico', 'a', 'polit of colombia', 'polit in colombia', 'polit by countri', 'polit parti', 'the assassin wa', 'jose franciscoruiz massi', \\\"mexico' congress\\\", 'politicsof colombia', 'coloni in colombia (1824–1948)', 'institut revolutionari parti', 'the two allegedli hire the gunman, and other accomplices, accord to testimoni', 'mexico and the unit state', 'colombia', 'polit', 'list of presid of colombia and the america', 'jose lui donaldo colosio', 'carlo fuent', 'jose francisco ruiz massieu'}\\n\",\n      \"GT: num=7 - {'assassin', 'mexico', 'crimin justic system', 'gulf drug cartel', 'violent resist', 'jose francisco ruiz massieu', 'alleg allianc'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'cincinnati red', 'are not', 'black belt', 'crimin justic', 'oakland', 'who are', 'black-american cultur', 'to stop peopl who are', 'unit state', 'and we have to use them', 'lo angel intern airport', 'civil liberti', 'black belt in the unit state', 'joe morgan', 'civil right organ', 'drug courier', 'the polic are not', 'american civil liberti union', 'major leagu basebal', 'are'}\\n\",\n      \"GT: num=7 - {'racism', 'polic offic', 'public affair', 'lo angel', 'joe morgan', 'clayton searl', 'drug'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a major', 'a', 'lo diego', 'state with multipl time zone', 'california', 'to make the state inelig for feder aid. the state', 'lo francisco', 'state and territori establish in 1850', 'california in the unit state', 'been a', 'censu', 'govern', 'lo angel', 'ha', 'peter chacon', 'state of the unit kingdom', 'california and the unit nation', \\\"major impact on the state'\\\", 'lo angel counti', 'ha been', 'lo angel citi atty.', 'polit', 'been'}\\n\",\n      \"GT: num=7 - {'california', 'feder aid', 'congression seat', 'censu count', 'censu bureau', '1990 censu', 'illeg alien'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cat', 'gerstman', 'cat in the unit state', 'europ', 'gottingen univers', 'the', 'creutzfeldt jakob diseas', 'gerstmann straussler syndrom', 'germani', 'europ and the unit kingdom', 'cattl diseas', 'crisi in cattl farm', 'cattl', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'c cattl diseas', 'the countri', 'the german govern said that there had been no known case in germany. in the unit', 'cow', 'creutfeldt'}\\n\",\n      \"GT: num=6 - {'bovin spongiform encephalopathi', 'british beef import', 'possibl connect', 'mad cow diseas', 'cattl diseas', 'bse'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'evergreen currant', 'california and the pacif ocean', 'chaparr countri', 'geographi', 'california', '1850 establish in california', 'pacif coast iri', 'southern california', 'the', 'outlin of california', 'than the', 'state and territori establish in 1850', 'state', 'geograph region', 'theciti', 'the citi', 'are more', 'jade', 'of the chaparral. the chaparr are', 'evergreen', 'ever green', 'state of the unit state', 'monkeyflow', 'manzanita'}\\n\",\n      \"GT: num=8 - {'firescap demonstr garden', 'maximum fire protect', 'four plant zone', 'wildfir', 'southern californian', 'firescap garden', 'ice plant', 'firescap'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'list of american women in music', 'american femal screenwrit', '20th centuri', 'american film actress', 'elizabeth taylor', 'in the treatment of', 'lo angel counti district attorney', 'the report said the doctor had been', '20', '1980', 'american peopl of english descent', 'american women in the art', 'betti ford clinic', 'peopl from san gabriel valley', 'lo angel', 'histori', 'of elizabeth taylor.', 'rancho mirag', 'american actress', 'ranvo mirag'}\\n\",\n      \"GT: num=6 - {'physician', 'actress elizabeth taylor', 'medic practic', 'rehabilit clinic', 'painkil', 'investig'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'state of the unit state (18alsh)', 'exxon ship co.', 'u.-s.', 'long beach', '20th centuri', 'be spill into', 'princ william sound', 'alaska', 'state and territori establish in 1959', 'alcohol beverag control', 'wa about. the oil wa', 'u-s. coast guard', 'western unit state', 'histori', 'the oil wa be', 'u.s. fish and wildlif servic', 'alaskan arctic', 'former russian coloni', 'oil spill', 'be', 'into the water.'}\\n\",\n      \"GT: num=5 - {'wildlif hurt', 'exxon valdez', 'alaskan oil spill', 'crude oil', 'cleanup equip'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'tunnel', 'take', 'list oftornado and tornado by countri', 'univers of chicago', 'to the point where they', 't tornado alley', 'taker', 'denver', 't take', 'tornado', 'geophys scienc', 'unit state', 'natur hazard', 'took', 't tornado', 'histori', 'weather hazard', 'tetsuya theodor fujita', 'taken', 'univers', 'tune in', 'have been'}\\n\",\n      \"GT: num=7 - {'violent storm', 'small twister', 'thunderstorm', 'natur tornado', 'tornado', 'fujita', 'scientist'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a major', 'a', 'martin luther king jr', 'a signific', 'gun legisl', 'gun violenc', 'jame bradi', 'ronald reagan', 'unit state', 'the abil to kill a target with a singl bullet. thi is', 'social conservat', 'gun right', 'black talon', 'gun control in the unit state', 'social theori', 'gun control', 'robert kennedi', 'jame brady, the former white hous press secretari', 'current debat'}\\n\",\n      \"GT: num=7 - {'gun control law', 'bradi bill', 'gun purchas', 'complet ban', 'us congress', 'gun ownership', 'restrict'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'exxon ship co.', 'long beach', 'exxon valdez', 'steve cowper', 'the accident.', 'gregori cousin', 'exxonmobil accid', 'pilot', 'princ william sound', 'shipwreck in alaska', 'transport accid in alaska (1980s)', 'the', 'cousin wa not charged. the coast guard is', '1980 unit state oil spill', 'accid', 'ship', 'caus', 'killer whale', 'still investig the accid and', 'list of exxon vald'}\\n\",\n      \"GT: num=11 - {'unqualifi mate', 'separ accid', 'oil contamin', 'coast guard regul', 'pilot', 'feder disast', 'exxon valdez spill', 'cleanup effort', 'investig', 'oil spill', 'environment conserv'}\\n\",\n      \"p=0.05, r=0.09090909090909091, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'antiwar movement', 'jim kolb', 'the us,', 'bill daley', 'n fta', 'jim jontz', 'jim daley, draft from hi chicago district', 'theus,', 'anti–nuclear weapon', 'the', 'unit state', 'tafta/tafta treati', 'anti-(nuclear) weapon in', 'unit nation', 'lori wallach', 'the unit states, is the', 'anti-nuclear weapon in the unit state', 'histori', 'treati of canada', 'jim kelleh', 'nafta'}\\n\",\n      \"GT: num=9 - {'global trade structur', 'side agreement', 'north american free trade agreement', 'presid bill clinton', 'jim jontz', 'polit battl', 'fair trade campaign', 'intens retail polit war', 'democrat coalit'}\\n\",\n      \"p=0.047619047619047616, r=0.1111111111111111, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'american on welfar', 'american societi', 'of the', 'hous of repres', 'aid to famili with depend children', '1980', 'the', 'demograph of the american peopl', 'unit state', 'ha increas in real terms, the', 'democraci', 'been a', 'welfar reform', 'histori', 'thoma downey', 'latino-american cultur', 'ha', 'ha been', 'been', 'demograph histori of the unit state', 'anthoni beilenson', 'trojan hors', 'the system ha', 'famili welfar reform act of 1987'}\\n\",\n      \"GT: num=9 - {'welfar program', 'welfar benefit', 'welfar depend', 'invest', 'welfar system', 'welfar reform', 'welfar recipi', 'american taxpay', 'hous democrat'}\\n\",\n      \"p=0.041666666666666664, r=0.1111111111111111, f1=0.06060606060606061\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'bradi bill', 'gun ownership', 'gari kleck', 'social philosophi', 'gun violenc', 'nra ha', 'unit state', 'not enact gun- control law', 'gun right', 'not', 'jame d. wright', 'nation rifl associ', 'gun-control debat', 'social theori', 'gun control', 'frank t. iorio mamaroneck', 'persian gulf war', 'laws.', 'gun cultur', 'to enact gun-control law in the unit states. the', 'gun-control', 'issu'}\\n\",\n      \"GT: num=8 - {'firearm', 'bradi bill', 'crimin', 'violent crime', 'ban', 'protect', 'gun control', 'prison survey'}\\n\",\n      \"p=0.09090909090909091, r=0.25, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'teotihuacan', 'jean-michel jarr', \\\"the sun' rays. ; the eclips will be the\\\", 'the world', 'kailua', 'mexico', 'articl contain video clip', 'asteroid', 'the', 'baja california', 'astrobiolog', 'occurr', 'astronom object', 'alan dyer', 'new age', 'solar eclips', 'athropogen radioact', 'the first time'}\\n\",\n      \"GT: num=7 - {'visitor', 'mexico', 'sun', 'tourism', 'eclips fan', 'total eclips', 'hawaii'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'howard metzenbaum', 'peopl with bipolar disord', 'equal employ opportun commiss', 'clarenc thoma', 'background', \\\"of thomas' record on abort and other issues. ;\\\", 'arlen specter', 'initi reaction', 'unit state suprem court justic', 'sup court of the unit state', 'juliu l. chamber', 'democrat', 'unit nation gener assembl observ', 'orrin hatch', 'cb thi morn', 'suprem court nomin', 'peopl from west virginia'}\\n\",\n      \"GT: num=7 - {'clarenc thoma', 'senat judiciari committe', 'conserv major', 'confirm hear', 'black justic', 'circuit court', 'columbia'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'coke war', 'peru', 'use in differ countri', 'cultur of the southern unit state', 'in the upper huallagas, the shine', 'the', 'path. the', 'upperhuallaga valley', 'upper huallga', 'the shine path.', 'coffe cultur', 'carcinolog', 'cocain', 'coca-cola and the shine path', 'u.s.', 'state of emerg', 'upper huallaga', 'shine path', 'cannabi'}\\n\",\n      \"GT: num=7 - {'coca farmer', 'upper huallaga valley', 'drug traffick', 'coca grower', 'guerrilla', 'shine path', 'emerg control'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'histori and scienc of south america', 'alberto fujimori', 'histori of peru', 'the government, and', 'the army.', 'peru', 'fernando belaund', 'the', 'the militari', 'the shine path', 'santiago garcia', 'senderlogo', 'gener elect after 12 year of a militari dictatorship', 'polit histori', 'historyof peru', 'histori', 'gener elect', 'historyof south america (1901–present)', 'histori (1925–present), the shine path is', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'next presid', 'arm forc', 'presid garcia', 'alberto fujimori', 'shine path', 'democrat legal', 'peru', 'emerg law'}\\n\",\n      \"p=0.1, r=0.25, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'index of california-rel articl', 'morgan hill', 'california', '1850 establish in california', 'south bay', 'the', 'climat', 'and san francisco,', 'fire season', 'state and territori establish in 1850', 'east bay', 'the citi of', 'u.s. forest servic', 'state of the unit kingdom', 'stand kindl', 'santa clara counti', 'california and the unit state', 'of san', 'forest and forest', 'and the east bay, and the south bay,'}\\n\",\n      \"GT: num=9 - {'nation forest', 'california depart', 'fire prevent unit', 'fire protect', 'heavi fuel', 'firefight effort', 'fire season', 'fire threat', 'wildfir precaut'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'organ of petroleum export countri', 'world trade organ member compani', 'to increas sale of diamond jewelry, and to promot', 'diamond', 'debeer', 'n.w. ayer & son', 'j. walter thompson co.', 'diamond mine', 'd', 'the', 'de beer consolid mine ltd.', 'diamond industri', 'histori', 'd diamond', 'modern era', 'chemic compani establish in 1891', 'tin cartel', 'harri oppenheim'}\\n\",\n      \"GT: num=8 - {'south african concern', 'promot campaign', 'diamond trade', 'diamond jewelri', 'world diamond market', 'world diamond product', 'de beer campaign', 'diamond produc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'joseph mcnamara', 'counti seat in california', 'polic', 'a', 'daryl gate', \\\"and the city' mayor, a democrat, ha\\\", 'citi in lo angel', 'american civil liberti union of southern california', 'stanford univers', 'law and govern', 'popul place establish in 1932', 'a model for', 'been a', 'lo angel', '1898 establish in california<infill>list of peopl from lo angel counti', 'hoover institut', 'christoph commiss', 'polic reform', 'counti in california (u.s. state)'}\\n\",\n      \"GT: num=5 - {'inadequ disciplin', 'racism', 'polic reform', 'polic brutal', 'investig'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'intern organ base in franc', 'sub-saharan africa', 'intern financ institut', 'debt servic', 'south america', 'to pay off their debts.', 'south korea', 'develop in europ', 'event', 'world bank', 'latin america', 'have', 'east asia', 'last year', 'unit nation gener assembl observ', 'middl east', 'have been abl to maintain their', 'and have', 'unit state intern monetari fund', 'been abl to'}\\n\",\n      \"GT: num=6 - {'creditor', 'third world countri', 'new loan', 'global debt burden', 'third world debtor', 'cash drain'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'santa clara counti', 'lrb-408-rrb- 287-3785', 'counti seat in california', 'countri in california (\\\"eastern unit states\\\")', 'and exercise. the diabet societi of santa clara counti also offer a', 'a', 'diabet societi', 'health', 'counticut', 'diabet', 'educ', \\\"alexian brother' hospit\\\", 'counti', 'counti in california (u.s. state)', 'program', 'a program', 'polyunsatur oil', '1854 establish in california territori'}\\n\",\n      \"GT: num=7 - {'hispan diabet', 'health profession', 'diabet societi', 'good nutrit', 'educ director', 'hispan lifestyl', 'hispan diet'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'marin sport', 'marathon in san diego', 'new york citi marathon', 'glendal', 'women', 'of glendora.', 'list of sport event in sandiego', 'result', 'san diego intern marathon', 'san gabriel valley', 'mindi ireland', 'marathon event in california', '1885 establish in california (u.s. state)', 'glendora', 'lake forest', 'tijuana', 'race result'}\\n\",\n      \"GT: num=6 - {'runner', 'marathon cours', 'san diego intern marathon', 'mexico citi', 'ernesto beatriz martinez', 'mari rollin'}\\n\",\n      \"p=0.058823529411764705, r=0.16666666666666666, f1=0.08695652173913045\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'pete wilson', 'census', 'a', 'thad cochran', 'the censu is', 'is a', 'daniel patrick moynihan', 'senate-hous confer committe', 'background', 'unit state senat', 'moynihan -lrb- r-n.y.. -rrb- argu that the', 'sampl (statistics)', 'unit state', 'popul', 'a censu', 'censu', 'samuel a.uelson', 'bob dole', 'robert a. mosbach'}\\n\",\n      \"GT: num=10 - {'feder aid', '1990 popul count', 'appropri bill', 'congression seat', 'censu bureau', 'hous seat', 'illeg immigr', 'voic vote', 'prohibit', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'1910 in the thirteen coloni', 'nation governor associ', 'historyof the unit state (1872–1919)', '20th centuri', 'l rb', 'histori of the unit state (1876–1922)', 'lrb', '19th centuri in the unit state', 'work and', 'bill clinton of arkansa', 'american cultur', 'welfar reform', 'histori', 'bill clinton', 'and the govern would be abl to', 'american public life', 'rrb', 'to', 'to work and to'}\\n\",\n      \"GT: num=4 - {'reform program', 'welfar reform', 'welfar recipi', 'welfar system'}\\n\",\n      \"p=0.05263157894736842, r=0.25, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'bush administr', 'in the city.', 'citi in california (u.s. state)', \\\"california. the city' mayor, john m. o'malley, said that the\\\", 'voic vote', 'california', 'be', '1850 establish in california', 'senate-hous confer committe', 'the citi would', 'popul', 'miguel a. pulido', 'demograph', 'immigr', 'california and the unit state', '1990 censu', 'californiaian societi', 'demograph statist', 'robert a. mosbach'}\\n\",\n      \"GT: num=11 - {'feder aid', 'illeg alien', '1990 popul count', 'senat', 'appropri bill', 'congression seat', 'censu bureau', 'hous seat', 'illeg immigr', 'voic vote', 'larg immigr popul'}\\n\",\n      \"p=0.05263157894736842, r=0.09090909090909091, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'census', 'of the', 'hous of repres', 'the censu bureau to count illeg immigr in the', 'the', 'sampl (statistics)', 'unit state', 'sampl (statistic)', 'the popul of', 'richard shelbi', 'censu', 'popul refer bureau', 'histori', 'feder for american immigr reform', 'of', 'sampl (statistical)', '1990 censu', 'immigr and natur servic', 'hous debat'}\\n\",\n      \"GT: num=7 - {'congression campaign', 'congression seat', 'censu bureau', '1990 censu', 'congression reapportion', 'illeg immigr', 'illeg alien'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'crime', 'current practic', 'joseph macnamara', 'criminolog', 'code of silenc', 'to report', 'briberi', 'california', 'the', 'unit state', 'the polic', 'code', 'human right', 'the polic are not requir to', 'crude languag', 'intern affair depart', 'santa clara counti', 'rape', 'san jose polic chief', 'to the'}\\n\",\n      \"GT: num=6 - {'injuri', 'brutal complaint', 'polic abus', 'polic misconduct', 'polic brutal', 'santa clara'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'cancer', 'univers of wisconsin', 'switzerland', 'rttem', 'the diseas is a', '20th centuri', \\\"alzheimer' diseas\\\", 'outlin of neurosci', 'a diseas that is', 'a', '2030', 'univers', 'd. carleton gajdusek', 'wikipedia medicin articl readi to translat', 'histori', 'brain diseas', 'a mysteri that', 'spongiform encephalopathi'}\\n\",\n      \"GT: num=8 - {'sheep diseas', 'cjd victim', 'alter protein', 'cjd case', 'british cattl', 'mad cow diseas', 'infect', 'infect sheep'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bay area', 'ernest kraul', 'california depart of forestri and fire protect', 'geographi', 'is a fire chief in the santa cruz counti fire depart', 'santa cruz counti', 'california', 'and the', '1891 establish in california', 'the', 'lo alto hill', 'popul place establish in 1891', 'bayarea', 'hill and canyon', 'fire', 'citi in alameda county, california', 'california depart', 'the fire', 'list of peopl from the bay area', 'the fire department.'}\\n\",\n      \"GT: num=9 - {'fire prevent', 'deadli firestorm', 'fire protect', 'fire hazard', 'fire offici', 'saratoga foothil', 'brush fire', 'oakland firefight', 'damag wildfir'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bermuda', 'to run a marathon, but to take a', 'a', 'fred lebow', 'articl contain video clip', 'tour, a', 'london marathon', 'a marathon vacation,', 'marathon', 'distanc run', 'outdoor recreat', 'a vacation, a', 'histori', 'bermudian', 'new york marathon', 'tour', 'tourism', \\\"newyork roadrunner' club\\\", 'modern era', 'road run'}\\n\",\n      \"GT: num=4 - {'restrict entri race', 'marathon tour', 'marathon vacat', 'run vacat'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'counti seat in california', 'counts.', 'count', 'countries.', 'santa ana', \\\"county'\\\", 'feder immigr reform act', \\\"the county' population. the counti\\\", 'popul', 'demograph', 'house-sen confer', 'georg frank', 'orang county, california', 'u.s. senat', 'citi in orang county,california', 'counti', 'hous', 'countri in california (\\\"orang county\\\")', 'immigr', 'orang counti', 'counti in california (u.s. state)'}\\n\",\n      \"GT: num=7 - {'orang counti resid', 'feder revenu', 'censu bureau', '1990 censu', 'repres', 'complet count', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'american indian', 'santa clara counti', 'demograph', 'counti seat in california', 'st. louis health center', 'the clinic', 'the', 'health', 'diabet', 'countri in california (\\\"san jose mercuri news\\\")', 'st- louis drive', 'counti in california (u.s. state)', 'list of peopl from santa clara county, california', 'the clinic is free and open to the public. ; the', 'santa cruz counti', 'morgan hill', 'morganhil', '1854 establish in california territori'}\\n\",\n      \"GT: num=5 - {'exercis', 'diabet manag program', 'diabet societi', 'contract diabet', 'hispan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'central sell organ', 'shearson lehman secur', 'market', 'diamond fabric', 'diamond jewelri', 'far east', 'west germani', 'articl contain video clip', \\\"japan, the world' second biggest jewelri market, ha\\\", 'demand', 'the', 'been boost by the', 'de beer consolid mine ltd.', 'diamond industri', 'the rise in', 'diamond', 'diamond mine', 'world trade organ', 'antwerp'}\\n\",\n      \"GT: num=8 - {'south african concern', 'diamond dealer', 'diamond price increas', 'world diamond market', 'world diamond product', 'diamond jewelri market', 'diamond price', 'diamond sale'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'build and structur destroy in fire', 'of the u. forest servic in', 'pendleton', 'the region', 'california', 'the area is', 'list of wildfir', 'the', 'build and structur demolish in 1991', 'umatilla nation forest', 'upper cascad', 'precipit and fire season', 'persian gulf', 'fire corridor', 'american wildfir', 'citi in california', 'u.s. forest servic', 'histori', 'builder and structur lost in fire'}\\n\",\n      \"GT: num=10 - {'california', 'aggress approach', 'wildfir', 'prevent measur', 'brush fire', 'natur fire corridor', 'firefight', 'fire threat', 'fire season', 'sever fire spread'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'popul in miami county, florida', 'cleo', 'galveston', 'geographi', 'nation hurrican center', 'citi in miami (city)', 'the', 'climat', 'slam', 'hurrican season', 'big quak', 'lash', 'the miami', 'popul place establish in 1796', 'the citi', 'counti seat in florida', 'miami', 'list of peopl from miami', 'that the season is go to be a quiet one, the'}\\n\",\n      \"GT: num=6 - {'miami', 'tropic storm', 'hurrican specialist', 'tropic depress', 'hurrican season', 'nation hurrican center'}\\n\",\n      \"p=0.15789473684210525, r=0.5, f1=0.23999999999999996\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'colorado state univers', 'hurrican histori', 'of the', 'nation hurrican center', 'articl contain video clip', 'a few', 'the', 'huron', 'hurrican', 'atlant ocean', 'jamaica', 'histori', 'the caribbean, there have been', 'saffir-simpson scale', 'storm', 'huron outbreak', 'yucatan', 'some of the', 'south carolina', 'hurrah'}\\n\",\n      \"GT: num=6 - {'intens hurrican', 'feroci hurrican', 'hurrican pattern', 'catastroph storm', 'atlant region', 'atmospher condit'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'gambia', 'ocean', 'histori', 'wa a', 'atlant hurrican', 'wa', 'atlant ocean', 'land surfac effect of the sun', 'gloria', '1990', 'landform of the atlant ocean', 'a', 'scienc', 'atlant oceanographi', 'mauritania', 'hurrican season', 'the sahel region in the 1990s. he said the sahel', 'mali'}\\n\",\n      \"GT: num=9 - {'rainfal level', 'atlant hurrican', 'coastal resid', 'hurrican pattern', 'global hurrican activ', 'tropic storm season', 'west african sahel region', 'weather research', 'nation hurrican center'}\\n\",\n      \"p=0.05555555555555555, r=0.1111111111111111, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'second fastest', 'gerardo alcala', 'el-mostafa nechchadi', 'race cours', 'old style/chicago marathon', 'list of sport event in chicago<infill>th second-fastest time in the histori of the chicago marathon. he finish in 2:28:15, a pace of', 'paul davies-hal', 'the', 'chicago marathon and half-marathon', 'sunday, august 27', '1885 establish in illinoi', 'race in chicago', 'olymp sport', 'issaquah, wash.', 'race result', 'carlo montero', 'the second fastest time in', 'don janicki', 'chicago marathon'}\\n\",\n      \"GT: num=5 - {'race condit', 'steadi runner', 'patient', 'windi weather', 'distanc runner'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'vote system', 'electorally, the system is not', 'larri pressler', 'elect', 'be abl to vote', 'elect system', 'term limit', 'michael kinsley', 'ronald reagan', 'unit state', 'new republ', 'to vote', 'david pryor', 'elector system', 'histori', 'gerrymand', 'vote', 'polit system', 'electron vote', 'to be abl to', 'to'}\\n\",\n      \"GT: num=10 - {'unfair campaign financ law', 'congression career', 'legisl arrog', 'opinion poll', 'voluntari servic limit', 'limit congression term', 'unfair advantag', 'term limit', 'constitut amend', 'hous incumb'}\\n\",\n      \"p=0.047619047619047616, r=0.1, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'battleship', 'world war ii', 'the wisconsin is about 1,000 feet long, and the', 'wisconsin', 'marin corp marathon', 'the', 'washington', 'maritim warfar', 'ship type', 'unit state', 'is about', 'middl eastern', 'middl east', 'histori', 'shipbuild in the middl east', 'shipwreck in the unit state', 'marin', 'shipyard in washington'}\\n\",\n      \"GT: num=5 - {'battleship wisconsin', 'marathon run', 'marin corp marathon', 'washington', 'train'}\\n\",\n      \"p=0.1111111111111111, r=0.4, f1=0.1739130434782609\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'construct', 'champlain', 'francoi mitterrand', 'channel tunnel', 'french presid', 'chalet', 'france–unit kingdom border cross', 'rail transport in franc and england', 'france-unit kingdom sport rivalri', 'the same', 'same.', 'railway tunnel in franc', 'histori', 'the channel tunnel', 'ice age', 'folkeston', 'same', 'and shook hands. the two countri have been'}\\n\",\n      \"GT: num=7 - {'rail tunnel', 'channel tunnel', 'continent europ', 'english channel', 'symbol mileston', 'brilliant sign', 'engin project'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'air accid', \\\"mcdonnel dougla corp.'\\\", 'the airplan', 'airlin', 'northwest airlin flight 255', 'the', 'aircraft which explod in flame', 'airbu md-82', 'the accident, the ntsb said that the', 'unit state air forc one', 'aviat accid', 'the airlin', 'accid', 'unit aviat administr', 'caus', \\\"the aircraft'\\\", 'unit technolog corp.', 'the plane', 'aerospac accid', 'airport safeti institut'}\\n\",\n      \"GT: num=8 - {'emerg land', 'aviat safeti issu', 'safeti investig', 'crash victim', 'northwest airlin jet', 'detroit', 'heavi load', 'feder crash inspector'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'newyork citi marathon (running)', 'list of new york citi marathon record', 'race. it wa the first time he had won the race.', 'race cours', 'wanda panfil', \\\"gerri o'hara\\\", 'ken martin', 'ken wakiihuri', 'result', '1894 establish in new york (state)', 'new york citi marathon', 'sunday, april 9', 'wa', 'kenyan', 'marathon record', 'race result', 'wa the', 'he wa', 'organ establish in 1894', 'miki gorman'}\\n\",\n      \"GT: num=6 - {'winner', 'wanda panfil', 'new york citi marathon', 'dougla wakiihuri', 'third consecut marathon victori', 'foreign'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'european rail link ltd.', 'construct', 'channel tunnel', 'confeder of british industri', 'railway in the unit kingdom', 'railport in the british isl', 'british rail', 'france–unit kingdom border cross', 'hous of common', 'rail transport in franc and germani', 'railway tunnel in franc', 'rail tunnel in the netherland', 'cecil parkinson', 'leed univers', 'histori', 'and the govern will have to pay for it.', '1992'}\\n\",\n      \"GT: num=6 - {'channel tunnel', 'refus', 'singl market', 'fast rail servic', 'total benefit', 'ad cost'}\\n\",\n      \"p=0.058823529411764705, r=0.16666666666666666, f1=0.08695652173913045\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'american review of respiratori diseas', 'boston', 'bioterror', 'caus', 'boston depart of public health', 'air pollut', 'boston public health depart', 'airbourn', 'iarc group 3 carcinogen', 'evolutionari biolog', 'air ventil', 'the same time as the other end.', '[[ sick build syndrome]]', 'air diseas', 'boston medic school', 'airborn diseas'}\\n\",\n      \"GT: num=5 - {'tuberculosi studi', 'poor air ventil', 'ventil system', 'infecti air', 'airborn infect'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'prepar', 'railway in the unit kingdom', 'is a', 'tunnel in franc', 'farthinglo villag', 'franc', 'the', 'rail tunnel in the netherland', 'rolls-royc', 'rail transport in franc & germani', 'the tunnel is', 'construct', 'channel tunnel', 'work on the tunnel', 'is', 'the largest is the one for', 'railway tunnel in franc and england', 'franc & germani (france)', 'canterburi', 'dover', 'margaret thatcher'}\\n\",\n      \"GT: num=7 - {'full benefit', 'british rail', 'tunnel treati', 'british industri', 'bore machin', 'british termin', 'domin trade link'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'franc', 'the', 'avion accid', 'terror', \\\"n'djamena\\\", 'aviat accid in africa', 'lebanon crisi', 'airlin accid', 'aviat accid', 'brazzavil', 'air accid in franc', 'niger', 'aircraft accid', 'the french', 'francoi mitterrand', 'caus', 'french government.', 'the attack on', 'the caller also claim respons for the', 'french'}\\n\",\n      \"GT: num=6 - {'rescu mission', 'terrorist bomb', 'lebanon crisi', 'crash site', 'terrorist attack', 'niger'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'california', 'saratoga', 'bay area', 'cultur', 'list of peopl from the bay area', 'popul place establish in 1876', 'are the best place to watch the eclipse. ;', 'citi in alameda counti', 'scienc', 'de anza colleg', 'cupertino', 'baja', 'orion telescop', 'bayarea', 'solar eclips', 'hawaii'}\\n\",\n      \"GT: num=6 - {'mexico', 'eclips viewer', 'californian', 'partial eclips', 'solar eclips', 'hawaii'}\\n\",\n      \"p=0.125, r=0.3333333333333333, f1=0.18181818181818182\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'hamilton spectat', 'seoul olymp', 'johnson', 'johnson \\\"a', '1946 birth', 'canadian sprinter of english-languag descent', 'johnson\\\"', 'olymp sprinter', 'return to competit', 'castelfranco veneto', 'career', 'american peopl of english descent', 'charli franci', 'ben johnson', 'canadian male sprinter (age 18–24)', 'hamilton spectat indoor game', 'anabol steroid', 'american sprinter who have been disqualifi', 'that johnson wa \\\"a littl bit more confident\\\" in', 'suspens'}\\n\",\n      \"GT: num=8 - {'news confer', 'seoul olymp', 'drug test', 'expect', 'hamilton spectat indoor game', 'anabol steroid', 'ben johnson', 'suspens'}\\n\",\n      \"p=0.25, r=0.625, f1=0.35714285714285715\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'person life', 'american rhode scholar', 'tom bradley', 'bill gate', 'the incident. ;', 'american civil liberti union', 'controversi', 'american nonprofit chief execut', 'apocalyps now', 'lo angel time', '1946 birth', 'american technolog chief execut and chief execut of compani', 'racial discrimin', 'ride of the valkyri', 'list of wealthiest histor figur', 'the lapd ha been order to investig the', 'rodney g. king'}\\n\",\n      \"GT: num=6 - {'excess forc', 'racism', 'minor suspect', 'polic brutal', 'investig', 'white polic offic'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'mountain peak', 'astrobiolog', 'articl contain video clip', 'the sun,', 'california institut of technolog', 'and the', 'new moon', 'the', 'solar eclips in the unit state', 'astronom object', 'athropogen element', 'occurr', 'chabot colleg', 'solar eclips', 'on the mountain,', 'asteroid', 'the sun', 'aten asteroid', 'the moon', 'solar and lunar eclips by countri', 'alan dyer'}\\n\",\n      \"GT: num=7 - {'solar scientist', 'shadow', 'mexico citi', 'sun', 'total eclips', 'hawaii', 'solar eclips'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'affirm action', 'controversi', 'virginia thoma', 'a group that', 'polit career', '1946 birth', 'of the nation council of churches, which is', 'american lawyer', 'suprem court nomin', 'american peopl of english descent', 'american legal scholar', 'that', 'u.s. chamber of congress', 'lifespr', 'howard univers', 'american women lawyer', 'that her', 'critic', 'that she', 'nation council of church', 'dean kelley'}\\n\",\n      \"GT: num=9 - {'critic', 'clarenc thoma', 'affirm action', 'virginia thoma', 'senat confirm hear', 'conserv viewpoint', 'suprem court', 'support spous', 'nomin'}\\n\",\n      \"p=0.14285714285714285, r=0.3333333333333333, f1=0.2\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'assassin', '1947 birth', 'the govern', 'sri lanka', 'india', 'indian politician kill in action', 'elect', 't.n. seshan', 'indian minist of financ', 'the', 'jawaharl nehru', 'the election. ;', 'indian polit leader', 'abdul rashid gandhi', 'indian male writer', 'indira gandhi', 'death', 'the blast, the govern announc that', 'boe 737'}\\n\",\n      \"GT: num=10 - {'assassin scene', 'indian polit', 'nation parliamentari elect', 'polit dynasti', 'india', 'gandhi', 'new delhi', 'congress parti', 'time bomb', 'blast'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'daron council', 'to the podium. ;', 'bradley johnson', 'american male weightlift', 'american marathon', 'drug test', 'return to the sport', 'alachua counti', '1988 summer olymp', 'the start of the race, johnson wa the first athlet to', 'alachua county, fla.', 'performance-enhanc steroid', 'sportspeopl from jacksonville, florida', 'american sprinter', 'career', 'american peopl of english descent', 'alchua counti'}\\n\",\n      \"GT: num=7 - {'slow start', 'johnson', 'record crowd', 'drug test', 'first race', 'daron council', 'first indoor loss'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'san francisco amateur astronom', 'a', 'is a', 'san mateo astronom societi', 'astrobiolog', 'astrolog', 'branch of biolog', 'california', 'astronom sub-disciplin', 'outlin of astrobiolog', 'tuxpan', 'san jose', 'solar eclips', 'to make reservations. ; the', 'special event', 'campbel', 'baja', 'astrophys', 'the eclips is'}\\n\",\n      \"GT: num=4 - {'special mylar viewer', 'hawaii', 'solar eclips', 'mexico citi'}\\n\",\n      \"p=0.05263157894736842, r=0.25, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'suprem court of the unit state', 'unit state constitut law', 'sup court of the netherland', 'anti-defam leagu', 'thoma', 'a strong', 'william brennan', 'to affirm action. ;', 'are like to be more conserv than those of justic clarenc thomas, who ha', 'roman cathol', 'right to privaci', 'strong opposit to', 'sup court of canada', 'supremaci in the unit kingdom', 'david souter', \\\"unit kingdom' highest court\\\", 'histori', 'unit nation gener assembl observ', 'justic of the suprem court', 'right'}\\n\",\n      \"GT: num=5 - {'clarenc thoma', 'racial prefer', 'affirm action', 'suprem court', 'nomin'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bank in the unit state', 'banker are', 'financi servic compani establish in 1837', 'chicago', 'new york', 'bankruptci', 'list of bank', 'bank reserv', 'keef bruyett & wood inc.', 'san francisco', 'merril lynch & co.', 'bank of boston', 'histori', 'recent histori', 'the move is like to increas the cost of loan to develop countries. the', 'bank base in boston', 'bond of the unit kingdom', 'merril lynch', 'to', 'to the'}\\n\",\n      \"GT: num=4 - {'region bank', 'boston corp', 'third world loan', 'develop countri'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'sport medicin', 'a coach', 'champion athlet', 'a', \\\"st. martin'\\\", 'furazabol', 'and hi athlet to use them, he is', 'stanozolol', 'st.martin', 'sport scienc', 'olymp sport', 'chadwick, ontario', 'dr. frankenstein', 'histori', 'dianabol', 'champ', '1988', 'a trainer', 'the franci system', 'championship'}\\n\",\n      \"GT: num=10 - {'charli franci', 'steroid combin', 'canadian nation sprint coach', 'gold medal', 'toronto', 'world record', 'illeg steroid use', 'ben johnson', 'steroid', 'feder inquiri'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'i wa rais to be independ and to be', 'american peopl of irish descent', 'clarenc thoma', 'pinpoint', 'to be', 'savannah', 'peopl from savannah, georgia', 'earli childhood', 'welfar', 'washington post', 'american lawyer', 'and to', 'unit state suprem court justic', 'unit nation suprem court nomine', 'heritag foundat', 'earli year', 'earli life', 'booker t. washington', 'to'}\\n\",\n      \"GT: num=3 - {'grandpar', 'clarenc thoma', 'black power'}\\n\",\n      \"p=0.05263157894736842, r=0.3333333333333333, f1=0.09090909090909091\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'water', 'sustain', 'american', 'poverti', 'state of the union', 'domest polici council', 'welfar', 'sustain capit', 'the', 'unit state', 'primari', 'state', 'social respons capit', 'state ofth union', \\\"have been reluct to go along with the president' welfar reform proposals.\\\", 'current issu', 'the public', 'social', 'primari respons', 'welfar reform', 'in the unit state', 'the american public', 'the presid'}\\n\",\n      \"GT: num=6 - {'welfar program', 'feder govern', 'welfar depend', 'presid reagan', 'american', 'welfar reform'}\\n\",\n      \"p=0.08695652173913043, r=0.3333333333333333, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'shipwreck in the gulf of mexico', 'pensaola', 'world war ii', 'the crash occur on the flight deck of the aircraft carrier, the navi said. the aircraft carrier is the onli aircraft carrier', 'a aircraft carrier', 'pentagon', 't-2 buckey', 'pensacola', 'aircraft carrier', 'unit state', 'ship type', 'histori', 'shipbuild in the unit state', 'aerospac accid', 'airlin', 'articl contain video clip', 'meridian'}\\n\",\n      \"GT: num=6 - {'major damag', 'crash', 'flight deck', 'victim', 'aircraft carrier lexington', 'jet trainer'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the highest concentr of tb wa in those age 25-', 'new jersey', 'health and safeti', 'health depart', 'counti seat in new jersey', 'alcohol', 'hiv', 'unit state depart of health and human resourc', 'health', 'hiv infect', 'alcohol abus', '18th-centuri establish in new york citi (state)', 'tuberculosi', 'the greatest concentr of tuberculosi wa in the 25-to-44 age group, account for 57 %', 'tuberculosi prevent and control', 'treatment of tuberculosi', 'drug', 'alcohol abus and neglect', 'new york citi', '1810 establish in the unit state', 'new yorker'}\\n\",\n      \"GT: num=5 - {'control', 'activ tuberculosi diseas', 'health depart', 'tuberculosi prevent', 'tuberculosi test'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'of the year. the', 'event', 'drought', 'day of the year', 'august (period)', 'august and septemb', 'special trade repres', 'unit state', 'august', 'dow jone industri averag', 'the year.', 'marin fitzwat (white house)', 'marlin fitzwat', 'august 1', 'richard lyng', 'day'}\\n\",\n      \"GT: num=9 - {'reagan administr', 'export enhanc program', 'worsen drought', 'drought damag', 'farm export', 'food price', 'farm economi', 'export enhanc fund', 'disast relief'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'the debt problem.', 'american rhode scholar', 'barber conabl', 'tom foley', '1947 birth', 'and the world bank ha been slow to take action on', 'world bank', 'american non-fict writer', 'american nonprofit chief execut', 'john lafalc', 'polit career', 'bill bradley', 'third world debt', 'barb conabl (politician)', 'american peopl of english descent', 'jame baker', 'debt problem'}\\n\",\n      \"GT: num=7 - {'barber conabl', 'critic', 'third world debtor', 'third world debt', 'debt burden', 'world bank presid', 'debt crisi'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'is an', 'is a', 'tuber tuberculosi', 'world health organ', 'world public health organ essenti medicin', 'hiv', 'world health disast', 'global', 'hiroshi nakajima', 'tuberculosi', 'aid epidem', 'worldculosi', 'the diseas is', 'societi and cultur', 'is', 'acquir immun defici syndrom', 'rttem', 'wikipedia medicin articl readi to translat', 'in the develop world, where they are', 'treatment'}\\n\",\n      \"GT: num=7 - {'parallel epidem', 'aid epidem', 'deadliest infecti diseas', 'global tuberculosi', 'tuberculosi case', 'human immunodefici viru', 'world health organ'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'bay area', 'missoula, missouri', 'the midwest,', 'mississippi river', 'geographi', '1836 establish in missouri', 'the big one is', 'arnold', 'the', 'missourinet', 'geolog', 'missourian-speak countri and territori', 'in the', 'cultur tourism in missouri and arkansa', 'missouri', 'paragould', 'earthquak', 'region,', 'citi in the central unit state', 'the region,', 'new madrid'}\\n\",\n      \"GT: num=7 - {'predict method', 'earthquak surviv class', 'feder emerg help', 'earthquak expert', 'new madrid fault', 'earthquak prepared', 'quak insur'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'american peopl of irish descent', 'clarenc thoma', 'the stori of a man who ha been', 'who is', 'peopl from savannah, georgia', 'polit and philosoph transform', 'a man', 'malcolm x', 'who', 'senat judiciari committe', 'american lawyer', 'robert frost', 'earli life and educ', 'unit state suprem court justic', 'earli polit and philosoph influenc', 'unit nation suprem court nomine', 'nina simon', \\\"robert frost' poetic recollect\\\", 'william barclay allen', 'who ha'}\\n\",\n      \"GT: num=7 - {'clarenc thoma', 'senat judiciari committe', 'thoma sowel', 'polit chang', 'affirm action program', 'suprem court nomin', 'racial minor'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a', 'you can look at the eclipse. ; q can i look at', 'astrobiolog', 'astrolog', 'cupertino', 'articl contain video clip', 'california', 'mylar', 'astrolog sub-disciplin', 'san jose', 'orion telescop center', 'solar eclips', 'special event', 'baja', 'hawaii', 'astronom event', 'aborigin stone', 'a. ; a', 'astrophys'}\\n\",\n      \"GT: num=7 - {'shadow', 'sun', 'eyeshad', 'eye damag', 'eclips', 'san jose', 'sunglass'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'ethnic group', 'counti seat in california', 'health fair', 'citi in lo angel', 'american indian', 'list of peopl from lo angel counti', 'health', 'diabet', 'latino', 'cultur tourism in lo angel', 'olvera street plaza', 'demograph', 'lo angel', 'upjohn co.', 'american diabet assn.', '1833 establish in california (u.s. state)', 'and the american diabet assn. held a', 'popul place establish in 1833', 'silent killer', 'a diabet'}\\n\",\n      \"GT: num=7 - {'american indian', 'latino diabet', 'major diabet risk factor', 'blood pressur', 'uncontrol diabet', 'diabet complic', 'educ effort'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'boston marathon', 'the race in', 'race cours', 'cours', 'the', 'marathon', 'in', 'the runner-up in', 'olymp sport', 'abe mekonnen', 'boston', 'john treaci', '1872 establish in massachusett', 'gelindo bordin', 'list of boston marathon entrant', 'the boston marathon in 2017 ; and', 'john kelley', 'racecours', 'ed eyeston'}\\n\",\n      \"GT: num=7 - {'60th boston marathon', 'boston marathon', 'respect entrant', 'checkpoint record', 'race run', 'alarm rate', 'stori histori'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'dwighter d.hower', 'colorado', 'polit cultur', 'california', 'polit parti', 'and the', 'term limit', 'the', 'and', 'and are not paid for their services,', 'dwight d. eisenhow', '17th-centuri establish', 'american cultur', 'polit histori', 'histori', 'american polit histori', 'american polit', 'franklin d. roosevelt', 'the state legislatur', 'the legislatur', 'modern era', '22nd amend'}\\n\",\n      \"GT: num=7 - {'state lawmak', 'american polit', 'legisl reform', 'polit institut', 'term limit', 'state legislatur', 'oppos polit coalit'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'represent democraci', 'congress', 'constut', 'state legisl', 'constitut', 'thesam way. the', 'who', 'constitution', 'who are', 'term limit', 'the', 'elector allianc', 'repres', 'constit', 'sourc of law', 'the term limit', 'the term of', 'key issu', 'repres govern', 'represent govern', 'constitu state', 'that the', 'the same way.', 'the legisl who', 'apostol constitut'}\\n\",\n      \"GT: num=7 - {'repres democraci', 'interest group', 'term limit', 'elector allianc', 'state legisl', 'constitut restrict', 'constitut chang'}\\n\",\n      \"p=0.12, r=0.42857142857142855, f1=0.1875\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'second', 'constitut law', 'of the', 'second 10 amend', 'u-rrb', 'the', 'unit state', 'the second amend is a', 'right of the', 'amend right', 'u.s. vs. cruikshank', 'histori', 'a constitut', 'u.s. suprem court', 'william neali studio citi', 'nation', 'apostol constitut', 'feder', 'second amend'}\\n\",\n      \"GT: num=6 - {'gun control law', 'feder govern', 'arm', 'constitut law', 'second amend', 'secur'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'myer anderson', 'clarenc thoma', 'american peopl of irish descent', 'leola william', 'peopl from savannah, georgia', 'father', 'father and', 'the time he wa a young boy, and hi', 'hi', 'baby-boom', 'georgia', 'unit state feder judg', 'unit state suprem court', 'cathol school uniform', 'unit nation suprem court justic', 'earli year', 'earli life', 'american prosecutor', 'hi father wa', 'thurgood marshal'}\\n\",\n      \"GT: num=6 - {'clarenc thoma', 'senat confirm', 'thoma sowel', 'affirm action program', 'black conserv', 'suprem court nomine'}\\n\",\n      \"p=0.05, r=0.16666666666666666, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'be san francisco', 'chile', 'geographi', 'the quak', '1824 establish in south america', 'nazca plate', 'the', 'the ship', 'state and territori establish in 1818', 'geolog', '1818 establish in chile', 'ring of fire', 'the earthquak', 'earthquak', 'index of chile-rel articl', 'member state of the unit nation', 'san francisco earthquak in 1891, the captain of', 'univers of chile', 'santiago'}\\n\",\n      \"GT: num=10 - {'earth movement', 'chilean earthquak', 'chilean build', 'seismic countri', 'chilean seismolog', 'chile', 'consider damag', 'major earthquak', 'san francisco earthquak', 'massiv plate'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'mahanama beach', 'astrobiolog', 'astrolog', 'branch of biolog', 'mauna kea', 'mauna loa', 'and the', 'mountain view', 'the', 'the eclipse. ; the eclips wa visibl', 'astrolog sub-disciplin', 'solar eclips', 'astronom event in california', 'the sun.', 'special event', 'baja', 'mauna kea and mauna lani', 'the moon', 'astrophys', 'pacif', 'sun.'}\\n\",\n      \"GT: num=6 - {'mexico', 'sun', 'eclipsefest', 'eclips', 'moon obscur', 'hawaii'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clarenc thoma', \\\"i 'd tell her to pray, '' she replied. ; the crowd roar with\\\", 'unit airlin', 'the crowd', 'atlanta', 'supremaci in the unit state', 'unit state suprem court nomine', 'background', 'the', 'unit nation clarenc thoma suprem court nomin (2005)', 'unit state suprem court justic', 'suprem court nomin', 'sweet field of eden baptist church', 'anita hill', 'abbey famble, deacon', 'background of the nomin', '23rd psalm', 'abraham fambl', 'pinpoint georgia'}\\n\",\n      \"GT: num=6 - {'birthplac', 'clarenc thoma', 'hometown crowd', 'georgia', 'senat vote', 'william'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'nov. 6 ballot', 'index of california-rel articl', 'propos on the nov. 6, 1996 ballot', 'richard l. mountjoy', 'california', 'robert presley', 'would be more', 'term limit', 'state and territori establish in 1850', 'state', 'to the public.', 'monrovia', 'riversid counti sheriff', 'govern', 'more', 'more respons to the', 'proposit 140', 'california and the unit nation', 'state of the unit state', 'and the california state senat -lrb-', 'legisl branch', 'i think that the legislatur would'}\\n\",\n      \"GT: num=9 - {'california', 'neg impact', 'special interest', 'polit process', 'term limit', 'polit chang', 'statewid officehold', 'state legisl', 'profession occup'}\\n\",\n      \"p=0.09090909090909091, r=0.2222222222222222, f1=0.1290322580645161\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'polic', 'controversi', 'use', 'martin luther king jr.', 'unit kingdom', 'wa arrested, he said that the', 'use of forc', 'west hartford', 'nunchaku', 'gandhi', 'unit state', 'lo angel polic beat of motorist rodney king', 'the polic', 'polic use', 'human right', 'human reproduct', 'rttem', 'abort', 'lo angel polic depart', 'wikipedia medicin articl readi to translat'}\\n\",\n      \"GT: num=6 - {'demonstr', 'seriou injuri', 'polic abus', 'polic brutal', 'excess forc', 'civil right demonstr'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'firestorm', 'the park.', 'the forest', 'donald despain', 'the', 'mountain and hill rang of yellowston counti', 'reconstruct', 'firewe', 'wildflow', 'citi in yellowston county, wyom', 'the number of tree that will be', 'histori', 'grant villag', 'yellowston nation park and preserv', 'wild geranium', 'yellowston nation park', 'popul place establish in 1872', 'yelloweston park', 'the great fire'}\\n\",\n      \"GT: num=6 - {'fire damag', 'scorch hillsid', 'firestorm', 'tini seed', 'worst fire', 'rebirth'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'european union', 'caus of world war i', 'causal in intern relat', 'ant markov', 'cease-fir', 'european commun', 'caus of world war', 'milan kucan', 'outcom', 'the', 'the slovenian', 'yugoslav presid', 'slovenia', 'government. ;', 'caus by the bosnian war', 'balkan war', 'stipe mesic', '1910 in europ', \\\"the army' militari and polic forces. ; the slovenian govern\\\"}\\n\",\n      \"GT: num=12 - {'feder armi oper', 'feder troop', 'slovenian defens forc', 'breakaway republ', 'slovenia', 'croatia', 'yugoslav republ', 'serbia', 'slovenian independ', 'yugoslav armi', 'independ declar', 'slovenian fear'}\\n\",\n      \"p=0.05263157894736842, r=0.08333333333333333, f1=0.06451612903225808\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'polit corrupt', 'milton friedman', 'public and', 'constitut', 'edward h. crane', 'polit ideolog', 'constitution', \\\"citizens' republ\\\", 'term limit', 'the', 'public', 'unit state', 'polit institut', 'cato institut', 'and the public', 'the public', 'histori', 'polit terminolog', 'ralph nader', 'croni capit', 'term limit in the unit state'}\\n\",\n      \"GT: num=7 - {'corrupt influenc', 'true citizen congress', 'term limit', 'impervi congress', 'congression term limit', 'congression deleg', 'legisl influenc'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a key part of', 'polit corrupt', 'a major', 'shirley chisholm', 'a', 'is a', 'polit reform', 'of the', 'harri truman', 'madelein kunin', 'constitution', 'democrat parti', 'term limit', 'unit state', 'term limit propos', 'richardlamm', 'and the democrat party. the democrat parti', 'histori', 'polit terminolog', 'john f. kennedi', 'of', 'term-limit propos', 'jerri brown'}\\n\",\n      \"GT: num=8 - {'american polit', 'polit system', 'liber democrat', 'california initi', 'term limit', 'term limit measur', 'democrat officehold', 'career incumb'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a fight with', 'a', 'a battl', 'reuter news servic', 'background', 'janez jansa and the militia', 'battl countri', 'bogataj vejko', 'fight in slovenia', 'battl involv croatia', 'jansa', 'slovenia', 'jajce, who wa in the middl of', 'u.s. civil war', 'balkan war', 'battl of jajc', '1962 in europ', 'ljubljana', 'bibliographi of the bosnian war'}\\n\",\n      \"GT: num=8 - {'croatian independ', 'civil war', 'yugoslavia', 'breakaway republ', 'feder soldier', 'strong central control', 'independ declar', 'independ grab'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'key featur', 'a', 'sourc (law)', 'constitut', 'the court found that the', 'larri smith', 'california suprem court', 'sources(law) that are not base on constitut law', 'dillon v. fiorina', 'term-limit initi', 'term limit', 'unit state', 'sourc', 'for a', 'bullock v. carter', 'sourc of law', 'first amend', 'a candid', 'right to vote for', 'constitut document', 'clement v. fash'}\\n\",\n      \"GT: num=7 - {'congression regul', 'term limit', 'constitut', 'congression officehold', 'suprem court', 'hous incumb', 'fair elect system'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'the gate commiss', 'of polic', 'polic agenc establish in 1933', 'citi in lo angel', 'new york polic depart', 'polic chief around the countri', 'polic depart establish in 1932', 'list of lapd district', 'john a. arguel', 'knapp commiss', 'of the offic who were found to have', 'hubert william', 'polic academi', 'lo angel polic depart ( lapd)', 'a cultur of', 'lo angel', 'histori', 'of', 'lapd', 'recent histori'}\\n\",\n      \"GT: num=7 - {'racial bia', 'brutal complaint', 'lapd offic', 'rodney king incid', 'polic brutal', 'lo angel', 'excess forc'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'of a', 'the issu of gun ownership and use. the second amend wa a', 'a', 'select servic', 'polit ideolog', 'nation firearm associ', 'nation guardsman', 'jame madison', '19th centuri', 'a handgun.', 'nation guard', 'a gun', 'unit state feder', 'nation rifl associ', 'a firearm.', 'elbridg gerri', 'elbrida gerri', 'histori', 'second congress', 'of the public', 'of', 'reaction', 'nation', 'second amend'}\\n\",\n      \"GT: num=6 - {'nation guard', 'gun ownership', 'american citizen', 'gun control', 'undeni right', 'second amend'}\\n\",\n      \"p=0.08333333333333333, r=0.3333333333333333, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'boston marathon', 'and he is the oldest person to', 'mafia man', 'the race.', 'list of boston marathon medal', 'bill rodger', '1891 establish in massachusett (u.s. state)', 'john adelbert kelley', 'lesli pawson', 'to finish the', 'marathon cours in massachusett', 'to run the', 'organ establish in 1901', 'histori', 'boston', '1901-present', 'organ of the boston marathon', 'hopkinton, mass.', 'john kelley', 'to', 'peter foley'}\\n\",\n      \"GT: num=5 - {'runner', '60th boston marathon', 'oldest competitor', 'john kelley', 'train'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'amend to the second amend', 'roger pilon', 'i will give up my right to speak when they have cut out my tongu ; i will give', 'the', 'ohio militia', 'unit state', 'when they have', 'nation guard', 'unit state constitut', 'cato institut', 'have', 'histori', 'nation firearm act of 1934', 'ohio', 'nation', 'thoma jefferson', 'have the', 'feder', 'second amend'}\\n\",\n      \"GT: num=5 - {'nation guard', 'right', 'bear arm', 'gun control', 'second amend'}\\n\",\n      \"p=0.10526315789473684, r=0.4, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'to keep and bear arms, but', 'to be', 'key featur', 'constitut', 'amend claus', 'to the', 'first congress of the unit state', 'firstcongress', 'jame madison', \\\"to render militari servic in person ''. the second amend wa\\\", 'sourc', 'sourc for law', 'bill of right', 'sourc of law', 'jame madison of virginia', 'to a', 'unit state constitut', 'first amend', 'to an', 'apostol constitut', 'to', 'second amend'}\\n\",\n      \"GT: num=7 - {'privat right', 'congress', 'arm', 'constitut amend', 'gun owner', 'gun control', 'second amend'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'polit corrupt', 'constitut term limit', 'robert livingston', 'daniel webster', 'term limit', 'and the peopl are the best judg who ought to repres them.', 'polit theori', 'unit state', 'henri clay', 'everett dirksen', 'feder', 'histori', 'term-limit propos', 'constitut', 't.k. wetherel', 'claud pepper'}\\n\",\n      \"GT: num=4 - {'first congress', 'constitut convent', 'term limit', 'natur right'}\\n\",\n      \"p=0.0625, r=0.25, f1=0.1\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'constitut term limit', 'rcb--', 'the govern', 'the congress', 'polit law', 'constitut', 'federalist', 'the legislatur is not in session, the', 'presid', 'term limit', 'the', 'unit state', 'the hous', 'alexand hamilton', 'lrc-', 'federalist 72', 'citizen congress', 'the senat', 'histori', 'polit terminolog', 'lrb-', 'term limit in the unit state', 'rrb-'}\\n\",\n      \"GT: num=9 - {'citizen congress', 'polit process', 'legisl term limit', 'constitut amend', 'elector control process', 'civil right amend', 'execut term limit', 'officehold', 'polit repres'}\\n\",\n      \"p=0.043478260869565216, r=0.1111111111111111, f1=0.0625\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'tuberculosi treatment in colorado spring', 'airlin accid', 'the faa report that the aircraft wa', 'unit boe 747', 'recent histori', 'unit airlin flight 11', 'colorado rocki', 'unit dc-10', 'citi in el paso county, colorado<infill>1871 establish in colorado territori', 'counti seat in colorado', 'feder aviat administr', 'colorado springs, colorado', 'a major area of investigation.', 'histori', 'sioux citi', 'persian gulf war', 'nation weather servic'}\\n\",\n      \"GT: num=5 - {'third crash', 'colorado spring', 'travel industri', 'unit flight', 'first high wind warn'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'jess helm', 'clarenc thoma', 'american peopl of irish descent', 'polit career', 'race', 'and he', 'lrb', 'and the', 'and', 'unit state suprem court justic', 'suprem court nomin', 'afdc', 'jessehel', 'peopl from east harlem', 'cathol school', 'peopl of african descent', 'the right way. he did it becaus he wa love', 'american prosecutor', 'rrb'}\\n\",\n      \"GT: num=7 - {'triumph', 'clarenc thoma', 'affirm action', 'conserv jurist', 'judg thoma', 'conserv black', 'thoma hear'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'borrow', 'that they', 'free- market', 'financi servic industri', 'bank', 'and', 'green revolut', 'microloan', 'bond-bas lend', 'that', 'monterrey', 'bank organ establish in 1947', 'financi market', 'free-market econom', 'accion intern', 'and the repay rate wa', 'bond', 'trickl up', 'and that'}\\n\",\n      \"GT: num=5 - {'tini third world busi', 'repay perform', 'poverti lend', 'microloan', 'favor credit rate'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'lo angel superior court', 'controversi', 'a', 'to file for', '1941 birth', 'g.p. group inc.', 'list of american woman model', 'the enquir', 'settlement of lawsuit', '1990', 'american woman', 'star tabloid', 'women in the art', 'nation enquir', \\\"st. john' hospit\\\", '1940 birth', 'initi public offer', 'lantana', 'the enquir also announc that it is plan to', 'american women in journal', 'a lawsuit against the'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'actress', 'enquir', 'lawsuit', 'medic condit', 'pneumonia'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'el salvadoran', \\\"the polic chief' statement wa a\\\", 'rodney king beat', 'polit career', '1946 birth', 'american technolog chief execut', 'polic chief', 'that he', 'public servic', 'rambo', 'list of wealthiest histor figur', 'commun polic', 'swat', 'american peopl of english descent', 'american rhode scholar', 'that', 'bill gate', 'a statement that', 'that the', 'polic corp program'}\\n\",\n      \"GT: num=5 - {'lapd brutal', 'rodney king incid', 'polic brutal', 'lo angel', 'excess forc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'robert bork', 'that i', 'american peopl of irish descent', 'court of appeal for the district of columbia circuit', 'clarenc thoma', 'of the kind of judici philosophi that he ha', 'unit kingdom', 'appel legal career', 'american judg of the unit state', 'that he', 'equal employ opportun commiss', 'american lawyer', 'ronald reagan', 'equal', 'american legal scholar', 'that', 'legal career', 'david souter', 'unit nation gener assembl observ', 'unit state suprem court nomin'}\\n\",\n      \"GT: num=6 - {'judici conservat', 'clarenc thoma', 'columbia circuit', 'minor', 'suprem court nomine', 'judg'}\\n\",\n      \"p=0.05, r=0.16666666666666666, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {\\\"to do so. ''\\\", 'croat', 'and we are not go to be abl to', 'croato-macedonian war', 'countri in europ', 'european commun', 'bosnia and herzegovina', 'balkan', 'southeastern european countri', 'outlin of bosnia and herzeg', 'slovenia', 'southeast european countri and territori', 'croatia', 'modern histori', 'croatian', 'histori', 'mikhail gorbachev', 'state and territori establish in 1992'}\\n\",\n      \"GT: num=7 - {'sloven risk', 'european commun', 'nation ident', 'yugoslavia', 'slovenia bond', 'independ declar', 'industri output'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'elector histori of the unit state', 'world war ii', 'alan baron', 'of the unit', '1990 elect', 'elect', 'hart-bailey report', 'focu group', 'the voter', 'the', 'histori of the republican parti', 'the elector', 'elector signific document', '1990', 'the public', 'elector', 'electron histori of polit', 'histori', 'unit state in world war ii (1947–1953)', '[[hart- bailey report]]', 'elect histori of unit state govern'}\\n\",\n      \"GT: num=7 - {'washington politician', 'congression elect', 'limit committe assign', 'elector system', 'term limit', 'averag victori margin', 'hous incumb'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'american civil war', 'war of independ', 'to keep and', 'john sanford', \\\"taney' rule\\\", '19th-centuri conflict', 'conflict in 1865', 'the right to keep and bear arm', 'keep and', 'missouri compromis', 'outcom', 'reconstruct', 'intern war of the unit state', 'john taney', 'dred scott', 'the civil war, when the', 'to', 'racial tension', 'second amend', 'war involv the unit kingdom'}\\n\",\n      \"GT: num=6 - {'black peopl', 'racial paranoia', 'infam rule', 'gun control', 'second amend', 'dred scott'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"***** predict metrics *****\\n\",\n      \"  predict_fscore@M           =      7.751\\n\",\n      \"  predict_loss               =     4.6972\\n\",\n      \"  predict_num_gold           =     7.1266\\n\",\n      \"  predict_num_match          =     1.0617\\n\",\n      \"  predict_num_pred           =     20.289\\n\",\n      \"  predict_precision@M        =     5.2456\\n\",\n      \"  predict_recall@M           =    16.3283\\n\",\n      \"  predict_runtime            = 0:00:29.82\\n\",\n      \"  predict_samples            =        308\\n\",\n      \"  predict_samples_per_second =     10.325\\n\",\n      \"  predict_steps_per_second   =      0.335\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Initialize our Trainer\\n\",\n    \"trainer = Seq2SeqTrainer(\\n\",\n    \"    model=model,\\n\",\n    \"    args=training_args,\\n\",\n    \"    train_dataset=train_dataset if training_args.do_train else None,\\n\",\n    \"    eval_dataset=eval_dataset if training_args.do_eval else None,\\n\",\n    \"    tokenizer=tokenizer,\\n\",\n    \"    data_collator=data_collator,\\n\",\n    \"    compute_metrics=compute_metrics if training_args.predict_with_generate else None,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Evaluation\\n\",\n    \"results = {}\\n\",\n    \"predict_results = trainer.predict(\\n\",\n    \"    predict_dataset, metric_key_prefix=\\\"predict\\\", max_length=max_length, num_beams=num_beams,\\n\",\n    \")\\n\",\n    \"metrics = predict_results.metrics\\n\",\n    \"max_predict_samples = len(predict_dataset)\\n\",\n    \"metrics[\\\"predict_samples\\\"] = min(max_predict_samples, len(predict_dataset))\\n\",\n    \"\\n\",\n    \"trainer.log_metrics(\\\"predict\\\", metrics)\\n\",\n    \"trainer.save_metrics(\\\"predict\\\", metrics)\\n\",\n    \"\\n\",\n    \"if trainer.is_world_process_zero():\\n\",\n    \"    if training_args.predict_with_generate:\\n\",\n    \"        predictions = tokenizer.batch_decode(predict_results.predictions)\\n\",\n    \"        predictions = [pred.lower().replace('</s>', '').replace('<pad>', '').strip().split(tokenizer.sep_token) for pred in predictions]\\n\",\n    \"        output_prediction_file = os.path.join(training_args.output_dir, \\\"generated_predictions.txt\\\")\\n\",\n    \"        with open(output_prediction_file, \\\"w\\\") as writer:\\n\",\n    \"            writer.write(\\\"\\\\n\\\".join([json.dumps(pred) for pred in predictions]))\\n\",\n    \"\\n\",\n    \"kwargs = {\\\"finetuned_from\\\": model_name_or_path, \\\"tasks\\\": \\\"keyphrasification\\\"}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'4.17.0'\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import transformers\\n\",\n    \"transformers.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "examples/wmt14_en_de.yaml",
    "content": "# wmt14_en_de.yaml\nsave_data: data/wmt/run/example\n\n# Corpus opts:\ndata:\n    commoncrawl:\n        path_src: data/wmt/commoncrawl.de-en.en\n        path_tgt: data/wmt/commoncrawl.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 23\n    europarl:\n        path_src: data/wmt/europarl-v7.de-en.en\n        path_tgt: data/wmt/europarl-v7.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 19\n    news_commentary:\n        path_src: data/wmt/news-commentary-v11.de-en.en\n        path_tgt: data/wmt/news-commentary-v11.de-en.de\n        transforms: [sentencepiece, filtertoolong]\n        weight: 3\n    valid:\n        path_src: data/wmt/valid.en\n        path_tgt: data/wmt/valid.de\n        transforms: [sentencepiece]\n\n### Transform related opts:\n#### Subword\nsrc_subword_model: data/wmt/wmtende.model\ntgt_subword_model: data/wmt/wmtende.model\n# src_subword_type: sentencepiece\n# tgt_subword_type: sentencepiece\n# onmttok_kwargs: \"{'mode': 'none', 'spacer_annotate': True}\"\n\nsubword_nbest: 1\nsubword_alpha: 0.0\n#### Filter\nsrc_seq_length: 150\ntgt_seq_length: 150\n\n# silently ignore empty lines in the data\nskip_empty_level: silent\n\n\n# # Vocab opts\n# ### vocab:\nsrc_vocab: data/wmt/run/example.vocab.src\ntgt_vocab: data/wmt/run/example.vocab.tgt\nsrc_vocab_size: 32000\ntgt_vocab_size: 32000\nvocab_size_multiple: 8\nsrc_words_min_frequency: 1\ntgt_words_min_frequency: 1\nshare_vocab: True\n\n# # Model training parameters\n\n# General opts\nsave_model: data/wmt/run/model\nkeep_checkpoint: 50\nsave_checkpoint_steps: 5000\naverage_decay: 0.0005\nseed: 1234\nreport_every: 100\ntrain_steps: 100000\nvalid_steps: 5000\n\n# Batching\nqueue_size: 1024\nbucket_size: 32768\npool_factor: 8192\nworld_size: 2\ngpu_ranks: [0, 1]\nbatch_type: \"tokens\"\nbatch_size: 4096\nvalid_batch_size: 16\nbatch_size_multiple: 1\nmax_generator_batches: 0\naccum_count: [3]\naccum_steps: [0]\n\n# Optimization\nmodel_dtype: \"fp32\"\noptim: \"adam\"\nlearning_rate: 2\nwarmup_steps: 6000\ndecay_method: \"noam\"\nadam_beta2: 0.998\nmax_grad_norm: 0\nlabel_smoothing: 0.1\nparam_init: 0\nparam_init_glorot: true\nnormalization: \"tokens\"\n\n# Model\nencoder_type: transformer\ndecoder_type: transformer\nenc_layers: 6\ndec_layers: 6\nheads: 8\nrnn_size: 512\nword_vec_size: 512\ntransformer_ff: 2048\ndropout_steps: [0]\ndropout: [0.1]\nattention_dropout: [0.1]\nshare_decoder_embeddings: true\nshare_embeddings: true\nposition_encoding: true\n"
  },
  {
    "path": "floyd.yml",
    "content": "env: pytorch-0.4\nmachine: cpu\n"
  },
  {
    "path": "floyd_requirements.txt",
    "content": "git+https://github.com/pytorch/text\n"
  },
  {
    "path": "kp_convert.sh",
    "content": "#!/usr/bin/env bash\nBASE_DATA_DIR=\"data/keyphrase/json\"\nTOKENIZER=\"meng17\"\nOUTPUT_DIR=\"data/keyphrase/$TOKENIZER\"\n\ndeclare -a train_sets=(\"kp20k\" \"magkp\" \"kp20k_small\")\ndeclare -a valid_sets=(\"kp20k\" \"inspec\" \"krapivin\" \"semeval\")\ndeclare -a test_sets=(\"kp20k\" \"kp20k_valid2k\" \"kp20k_valid500\" \"kp20k_small\" \"duc\" \"inspec\" \"krapivin\" \"nus\" \"semeval\")\n#declare -a train_sets=(\"stackexchange\")\n#declare -a valid_sets=(\"stackexchange\")\n#declare -a test_sets=(\"stackexchange\")\n\ntrain_param=\"-filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -lower -shuffle -tokenizer $TOKENIZER -replace_digit\"\nfor train in \"${train_sets[@]}\"\ndo\n    echo 'Processing train' $train\n    cmd='python kp_data_converter.py -src_file '\"$BASE_DATA_DIR\"'/'\"$train\"'/'\"$train\"'_train.json -output_path '\"$OUTPUT_DIR\"'/'\"$train\"'/'\"$train\"'_train '\"$train_param\"\n    echo $cmd\n    eval $cmd\ndone\n\nvalid_param=\"-lower -include_original -tokenizer $TOKENIZER -replace_digit\"\nfor valid in \"${valid_sets[@]}\"\ndo\n    echo 'Processing valid' $valid\n    cmd='python kp_data_converter.py -src_file '\"$BASE_DATA_DIR\"'/'\"$valid\"'/'\"$valid\"'_valid.json -output_path '\"$OUTPUT_DIR\"'/'\"$valid\"'/'\"$valid\"'_valid '\"$valid_param\"\n    echo $cmd\n    eval $cmd\ndone\n\n# only test part is well tested\ntest_param=\"-lower -include_original -tokenizer $TOKENIZER -replace_digit\"\nfor test in \"${test_sets[@]}\"\ndo\n    echo 'Processing test' $test\n    cmd='python kp_data_converter.py -src_file '\"$BASE_DATA_DIR\"'/'\"$test\"'/'\"$test\"'_test.json -output_path '\"$OUTPUT_DIR\"'/'\"$test\"'/'\"$test\"'_test '\"$test_param\"\n    echo $cmd\n    eval $cmd\ndone\n\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_validation.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/kp20k/kp20k_valid -lower -filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -target_type one2many -include_original\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_testing.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/kp20k/kp20k_test -lower -include_original -tokenizer meng17 -replace_digit\n\n# a small KP20k for debugging\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_training_nodup.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/kp20k/kp20k_train_small -lower -filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -shuffle -target_type one2many\n\n# for MAG\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/magkp/magkp_training.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/magkp/magkp_train -lower -filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -shuffle\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_validation.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/magkp/kp20k_valid -lower -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -include_original\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_testing.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/magkp/kp20k_test -lower -include_original -tokenizer meng17 -replace_digit\n\n# for StackExchange\n#python -m kp_data_converter -src_file ~/project/kp/OpenNMT-kpg/data/keyphrase/json/stackexchange/stackexchange_train.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/meng17/stackexchange/stackexchange_train -filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -lower -shuffle -tokenizer meng17 -replace_digit -is_stack\n#python -m kp_data_converter -src_file ~/project/keyphrase/OpenNMT-kpg/data/kp/json/stackexchange/stackexchange_valid.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/meng17/stackexchange/stackexchange_valid -filter -max_src_seq_length 1000 -min_src_seq_length 10 -max_tgt_seq_length 8 -min_src_seq_length 1 -lower -include_original -tokenizer meng17 -replace_digit -is_stack\n#python -m kp_data_converter -src_file ~/project/keyphrase/OpenNMT-kpg/data/kp/json/stackexchange/stackexchange_test.json -output_path ~/project/kp/OpenNMT-kpg/data/keyphrase/meng17/stackexchange/stackexchange_test -lower -include_original -tokenizer meng17 -replace_digit -is_stack\n"
  },
  {
    "path": "kp_data_converter.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nOriginal corpus is in JSON format. But OpenNMT separates data to two files by source/target (src/tgt).\nAlso OpenNMT preprocess is not flexible dealing with one-to-one/one-to-many data format.\nTherefore this script means to do two things:\n1. Separate a JSON data file to source/target files.\n2. Output to one-to-one/one-to-many format.\n3. Other specified preprocessing (lowercase, shuffle, filtering etc.)\n\"\"\"\nimport argparse\nimport json\nimport os\nimport random\nimport re\n\nimport onmt.inputters.keyphrase_dataset as keyphrase_dataset\nfrom onmt.keyphrase import utils\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\n\ndef heuristic_filter(src_token, tgts_token, tgts_str, opt):\n    '''\n    tokenize and truncate data, filter examples that exceed the length limit\n    :param src_tgts_pairs:\n    :param tokenize:\n    :return:\n    '''\n    print('*' * 50)\n    print('len(src)=%d, len(tgt)=%d' % (len(src_token), len(tgts_token)))\n    print('src: %s' % str(src_token))\n    print('tgt: %s' % str(tgts_token))\n    print('*' * 50)\n\n    # SOURCE FILTER: if length of src is over/under the given length limit, discard\n    if opt.max_src_seq_length and len(src_token) > opt.max_src_seq_length:\n        print(\"INVALID: source is too long [len=%d]: \\n%s\" % (len(src_token), str(src_token)))\n        return False, None, None\n    if opt.min_src_seq_length and len(src_token) < opt.min_src_seq_length:\n        print(\"INVALID: source is too short [len=%d]: \\n%s\" % (len(src_token), str(src_token)))\n        return False, None, None\n\n    filtered_tgts_str = []\n    filtered_tgts_token = []\n\n    # Go over each keyphrase and check its validity\n    for tgt_token, tgt_str in zip(tgts_token, tgts_str):\n        tgt_token_for_filter = utils.meng17_tokenize(tgt_str)\n\n        # FILTER 1: if length of tgt exceeds limit, discard\n        if opt.max_tgt_seq_length and len(tgt_token_for_filter) > opt.max_tgt_seq_length:\n            print(\"\\tInvalid Target: target is too long: %s (originally %s)\" % (str(tgt_token), tgt_str))\n            continue\n        if opt.min_tgt_seq_length and len(tgt_token_for_filter) < opt.min_tgt_seq_length:\n            print(\"\\tInvalid Target: target is too short: %s (originally %s)\" % (str(tgt_token), tgt_str))\n            continue\n\n        # FILTER 2: ingore all the keyphrases that contains strange punctuations, very DIRTY data!\n        punc_flag = False\n        puncts = re.findall(r'[,_\\\"<>\\(\\){}\\[\\]\\?~`!@$%\\^=]', tgt_str)\n        if len(puncts) > 0:\n            print('-' * 50)\n            print('Find punctuations in keyword: %s' % tgt_str)\n            print('- tokens: %s' % str(tgt_token))\n            punc_flag = True\n\n\n        # FILTER 3: check the quality of long keyphrases (>5 words) with a heuristic rule, repeating meaningless words\n        heuristic_rule_flag = False\n        if len(tgt_token_for_filter) > 5:\n            tgt_set = set(tgt_token_for_filter)\n            if len(tgt_set) * 2 < len(tgt_token_for_filter):\n                print('\\t Invalid Target: heuristic_rule on long keyphrases (>5 words)')\n                heuristic_rule_flag = True\n\n        # FILTER 4: filter keywords like primary 75v05;secondary 76m10;65n30\n        if (len(tgt_token_for_filter) > 0 and re.match(r'\\d\\d[a-zA-Z\\-]\\d\\d', tgt_token_for_filter[0].strip())) \\\n                or (len(tgt_token_for_filter) > 1 and re.match(r'\\d\\d\\w\\d\\d', tgt_token_for_filter[1].strip())):\n            print('\\tInvalid Target: matching template \\d\\d[a-z]\\d\\d: %s' % tgt_str)\n            continue\n\n        if (punc_flag or heuristic_rule_flag):\n            if heuristic_rule_flag:\n                print('\\t Invalid Target: heuristic_rule on long keyphrases (>5 words)')\n            if punc_flag:\n                print('\\t Invalid Target: found punctuation in keyphrases')\n            continue\n\n        filtered_tgts_str.append(tgt_str)\n        filtered_tgts_token.append(tgt_token)\n\n    # ignore the examples that have zero valid targets, for training they are no helpful\n    if len(filtered_tgts_str) == 0:\n        print('INVALID: found no valid targets')\n        return False, None, None\n\n    return True, filtered_tgts_token, filtered_tgts_str\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    # Input/output options\n    parser.add_argument('--src_file', '-src_file', required=True,\n              help=\"Source JSON file of keyphrase dataset.\")\n    parser.add_argument('--output_path', '-output_path', required=True,\n              help=\"The prefix for output files after preprocessing\")\n\n    # Data processing options\n    parser.add_argument('--is_stack', '-is_stack', action='store_true', help='StackExchange data')\n    parser.add_argument('--lower', '-lower', action='store_true', help='lowercase data')\n    parser.add_argument('--filter', '-filter', action='store_true',\n              help='Filter data by heuristics or not')\n    parser.add_argument('--max_src_seq_length', '-max_src_seq_length', type=int, default=None,\n              help=\"Max source text length\")\n    parser.add_argument('--min_src_seq_length', '-min_src_seq_length', type=int, default=None,\n              help=\"Min source text length\")\n    parser.add_argument('--max_tgt_seq_length', '-max_tgt_seq_length', type=int, default=None,\n              help=\"Max keyword length\")\n    parser.add_argument('--min_tgt_seq_length', '-min_tgt_seq_length', type=int, default=None,\n              help=\"Min keyword length\")\n    parser.add_argument('--shuffle', '-shuffle', action='store_true', help=\"Shuffle data\")\n    parser.add_argument('--seed', '-seed', type=int, default=3435,\n              help=\"Random seed\")\n\n    # Option relevant to keyphrase\n    parser.add_argument('--tokenizer', '-tokenizer', type=str,\n                        required=True, choices=['str', 'en_word', 'meng17', 'en_subword', 'en_retain_punc'],\n                        help=\"Type of tokenization. \"\n                             \"No matter which tokenizer is applied, the output is a string concatenated by whitespace.\"\n                             \"en_word: simply tokenized by whitespace\"\n                             \"meng17: use the tokenization in Meng et al 2017\"\n                             \"en_subword: use BPE\"\n                             \"str: input string will be left as is\")\n\n    parser.add_argument('--replace_digit', '-replace_digit', action='store_true',\n                        help=\"Whether replace all numbers to a special token <DIGIT>\")\n    parser.add_argument('--target_type', '-target_type', default='one2many',\n              help=\"\"\"Type of the target phrases.\n                       Options are [one2one|one2many].\n                       one2one means each pair of data contains only one target phrase; \n                       one2many means each pair of data contains multiple target phrases, \n                       which are concatenated in one string.\"\"\")\n    parser.add_argument('--include_original', '-include_original', action='store_true',\n                        help='Export all the original data as well')\n\n    parser.add_argument('--report_every', '-report_every', type=int, default=10000,\n              help=\"Report status every this many sentences\")\n\n    opt = parser.parse_args()\n\n    print('*' * 50)\n    print(\"Processing %s, type=%s\" % (opt.src_file, opt.target_type))\n\n    examples = []\n    trg_count = 0\n    valid_trg_count = 0\n\n    for line_id, line in enumerate(open(opt.src_file, 'r')):\n        if line_id + 1 % opt.report_every == 0:\n            print(\"Processing %d\" % line_id)\n\n        json_dict = json.loads(line)\n        if opt.is_stack:\n            json_dict['abstract'] = json_dict['question']\n            json_dict['keywords'] = json_dict['tags']\n            del json_dict['question']\n            del json_dict['tags']\n\n        # may add more fields in the future, say dataset_name, keyword-specific features\n        if 'id' in json_dict:\n            id = json_dict['id']\n        else:\n            id = str(line_id)\n        title = json_dict['title']\n        abstract = json_dict['abstract']\n        keywords = json_dict['keywords']\n\n        # process strings\n        # keywords may be a string concatenated by ';', make sure the output is a list of strings\n        if isinstance(keywords, str):\n            keywords = keywords.split(';')\n            json_dict['keywords'] = keywords\n\n        # remove all the abbreviations/acronyms in parentheses in keyphrases\n        keywords = [re.sub(r'\\(.*?\\)|\\[.*?\\]|\\{.*?\\}', '', kw) for kw in keywords]\n\n        if opt.lower:\n            title = title.lower()\n            abstract = abstract.lower()\n            keywords = [k.lower() for k in keywords]\n\n        if opt.tokenizer == \"str\":\n            title_token = [title]\n            abstract_token = [abstract]\n            keywords_token = keywords\n        elif opt.tokenizer == \"en_word\":\n            title_token = title.split(' ')\n            abstract_token = abstract.split(' ')\n            keywords_token = [kw.split(' ') for kw in keywords]\n        elif opt.tokenizer == \"meng17\":\n            title_token = utils.meng17_tokenize(title)\n            abstract_token = utils.meng17_tokenize(abstract)\n            keywords_token = [utils.meng17_tokenize(kw) for kw in keywords]\n        elif opt.tokenizer == \"en_retain_punc\":\n            title_token = utils.retain_punc_tokenize(title)\n            abstract_token = utils.retain_punc_tokenize(abstract)\n            keywords_token = [utils.retain_punc_tokenize(kw) for kw in keywords]\n        elif opt.tokenizer == \"en_subword\":\n            raise NotImplementedError\n        else:\n            raise NotImplementedError\n\n        if opt.replace_digit:\n            title_token = utils.replace_numbers_to_DIGIT(title_token, k=2)\n            abstract_token = utils.replace_numbers_to_DIGIT(abstract_token, k=2)\n            keywords_token = [utils.replace_numbers_to_DIGIT(kw, k=2) for kw in keywords_token]\n\n        src_token = title_token+[\".\"]+abstract_token\n        tgts_token = keywords_token\n\n        # validate keywords\n        if opt.filter:\n            valid_flag, filtered_tgts_token, _ = heuristic_filter(src_token=src_token,\n                                                             tgts_token=tgts_token,\n                                                             tgts_str=keywords,\n                                                             opt=opt)\n            if not valid_flag:\n                continue\n            tgts_token = filtered_tgts_token\n\n        trg_count += len(json_dict['keywords'])\n        valid_trg_count += len(tgts_token)\n\n        new_ex_list = []\n        if opt.target_type == 'one2one':\n            for tgt_token in tgts_token:\n                ex = json_dict if opt.include_original else {}\n                ex.update({\n                    'id': id,\n                    'src': ' '.join(src_token),\n                    'tgt': ' '.join(tgt_token),\n                })\n                new_ex_list.append(ex)\n        else:\n            ex = json_dict if opt.include_original else {}\n            ex.update({\n                'id': id,\n                'src': ' '.join(src_token),\n                'tgt': [' '.join(tgt) for tgt in tgts_token] if opt.tokenizer!='str' else tgts_token,\n            })\n            new_ex_list.append(ex)\n\n        examples.extend(new_ex_list)\n\n    if opt.shuffle:\n        random.seed(opt.seed)\n        random.shuffle(examples)\n\n    output_dir = opt.output_path[: opt.output_path.rfind('/')]\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    # filename = '.' + (opt.tokenizer + ('.lower' if opt.lower else ''))\n    filename = ''\n    src_file= open(opt.output_path+filename+'.src', 'w')\n    tgt_file= open(opt.output_path+filename+'.tgt', 'w')\n\n    src_fields = ['id', 'title', 'abstract', 'src']\n    tgt_fields = ['id', 'keywords', 'tgt']\n\n    for ex_dict in examples:\n        src_file.write(json.dumps({k: v for k, v in ex_dict.items() if k in src_fields})+'\\n')\n        tgt_file.write(json.dumps({k: v for k, v in ex_dict.items() if k in tgt_fields})+'\\n')\n\n    src_file.close()\n    tgt_file.close()\n\n    print(\"Process done\")\n    print(\"#(valid examples)=%d/%d\" % (len(examples), line_id+1))\n    print(\"#(valid trgs)=%d/%d\" % (valid_trg_count, trg_count))\n    print('*' * 50)"
  },
  {
    "path": "kp_evaluate.py",
    "content": "\nimport argparse\nimport json\nimport os\nimport re\nimport time\n\nimport tqdm\nimport numpy as np\nimport pandas as pd\n\nfrom onmt.inputters.keyphrase_dataset import infer_dataset_type, KP_DATASET_FIELDS, parse_src_fn\nfrom onmt.keyphrase.eval import compute_match_scores, run_classic_metrics, run_advanced_metrics\nfrom onmt.keyphrase.utils import if_present_duplicate_phrases, validate_phrases, print_predeval_result, gather_scores\nfrom onmt.utils.logging import init_logger\n\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\n\ndef evaluate(src_list, tgt_list, pred_list,\n             unk_token,\n             logger=None, verbose=False,\n             report_path=None, tokenizer=None):\n    if report_path:\n        report_file = open(report_path, 'w+')\n    else:\n        report_file = None\n    # 'k' means the number of phrases in ground-truth, add 1,3 for openkp\n    topk_range = [5, 10, 'k', 'M', 1, 3]\n    absent_topk_range = [10, 50, 'k', 'M']\n    # 'precision_hard' and 'f_score_hard' mean that precision is calculated with denominator strictly as K (say 5 or 10), won't be lessened even number of preds is smaller\n    metric_names = ['correct', 'precision', 'recall', 'f_score', 'precision_hard', 'f_score_hard']\n\n    individual_score_dicts = []  # {'precision@5':[],'recall@5':[],'f1score@5':[], 'precision@10':[],'recall@10':[],'f1score@10':[]}\n    gathered_score_dict = {}  # {'precision@5':[],'recall@5':[],'f1score@5':[], 'precision@10':[],'recall@10':[],'f1score@10':[]}\n\n    # for i, (src_dict, tgt_dict, pred_dict) in tqdm.tqdm(enumerate(zip(src_list, tgt_list, pred_list))):\n    for i, (src_dict, tgt_dict, pred_dict) in tqdm.tqdm(enumerate(zip(src_list, tgt_list, pred_list))):\n        \"\"\"\n        1. Process each data example and predictions\n        \"\"\"\n        pred_seqs = pred_dict[\"pred_sents\"]\n        if len(pred_seqs) > 0 and isinstance(pred_seqs[0], str):\n            pred_seqs = [p.split() for p in pred_seqs]\n        pred_idxs = pred_dict[\"preds\"] if \"preds\" in pred_dict else None\n        pred_scores = pred_dict[\"pred_scores\"] if \"pred_scores\" in pred_dict else None\n        copied_flags = pred_dict[\"copied_flags\"] if \"copied_flags\" in pred_dict else None\n\n        # @memray 20200410 add split_nopunc tokenization, spacy runs very slow\n        if tokenizer == 'spacy':\n            src_seq = [t.text for t in spacy_nlp(src_dict[\"src\"], disable=[\"textcat\"])]\n            tgt_seqs = [[t.text for t in spacy_nlp(p, disable=[\"textcat\"])] for p in tgt_dict[\"tgt\"]]\n            if len(pred_seqs) > 0 and isinstance(pred_seqs[0], str):\n                pred_seqs = [[t.text for t in spacy_nlp(p, disable=[\"textcat\"])] for p in pred_seqs]\n            else:\n                pred_seqs = [[t.text for t in spacy_nlp(' '.join(p), disable=[\"textcat\"])] for p in pred_seqs]\n            unk_token = 'unk'\n        elif tokenizer == 'split':\n            src_seq = src_dict[\"src\"].split()\n            tgt_seqs = [t.split() for t in tgt_dict[\"tgt\"]]\n            pred_seqs = pred_seqs\n        elif tokenizer == 'split_nopunc':\n            src_seq = [t for t in re.split(r'\\W', src_dict[\"src\"]) if len(t) > 0]\n            tgt_seqs = [[t for t in re.split(r'\\W', p) if len(t) > 0] for p in tgt_dict[\"tgt\"]]\n            pred_seqs = [[t for t in re.split(r'\\W', ' '.join(p)) if len(t) > 0] for p in pred_seqs]\n            unk_token = 'unk'\n        else:\n            raise Exception('Unset or unsupported tokenizer for evaluation: %s' % str(tokenizer))\n\n        # 1st filtering, ignore phrases having <unk> and puncs\n        valid_pred_flags = validate_phrases(pred_seqs, unk_token)\n        # 2nd filtering: filter out phrases that don't appear in text, and keep unique ones after stemming\n        present_pred_flags, _, duplicate_flags = if_present_duplicate_phrases(src_seq, pred_seqs, stemming=True, lowercase=True)\n        # treat duplicates as invalid\n        valid_pred_flags = valid_pred_flags * ~duplicate_flags if len(valid_pred_flags) > 0 else []\n        valid_and_present_flags = valid_pred_flags * present_pred_flags if len(valid_pred_flags) > 0 else []\n        valid_and_absent_flags = valid_pred_flags * ~present_pred_flags if len(valid_pred_flags) > 0 else []\n\n        # compute match scores (exact, partial and mixed), for exact it's a list otherwise matrix\n        match_scores_exact = compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs, do_lower=True, do_stem=True, type='exact')\n        match_scores_partial = compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs, do_lower=True, do_stem=True, type='ngram')\n        # simply add full-text to n-grams might not be good as its contribution is not clear\n        # match_scores_mixed = compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs, type='mixed')\n\n        # split tgts by present/absent\n        present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs, stemming=True, lowercase=True)\n        present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\n        absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\n\n        # filter out results of invalid preds\n        valid_preds = [seq for seq, valid in zip(pred_seqs, valid_pred_flags) if valid]\n        valid_present_pred_flags = present_pred_flags[valid_pred_flags]\n\n        valid_match_scores_exact = match_scores_exact[valid_pred_flags]\n        valid_match_scores_partial = match_scores_partial[valid_pred_flags]\n        # match_scores_mixed = match_scores_mixed[valid_pred_flags]\n\n        # split preds by present/absent and exact/partial/mixed\n        valid_present_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if present]\n        valid_absent_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if ~present]\n        if len(valid_present_pred_flags) > 0:\n            present_exact_match_scores = valid_match_scores_exact[valid_present_pred_flags]\n            present_partial_match_scores = valid_match_scores_partial[valid_present_pred_flags][:, present_tgt_flags]\n            # present_mixed_match_scores = match_scores_mixed[present_pred_flags][:, present_tgt_flags]\n            absent_exact_match_scores = valid_match_scores_exact[~valid_present_pred_flags]\n            absent_partial_match_scores = valid_match_scores_partial[~valid_present_pred_flags][:, ~present_tgt_flags]\n            # absent_mixed_match_scores = match_scores_mixed[~present_pred_flags][:, ~present_tgt_flags]\n        else:\n            present_exact_match_scores = []\n            present_partial_match_scores = []\n            # present_mixed_match_scores = []\n            absent_exact_match_scores = []\n            absent_partial_match_scores = []\n            # absent_mixed_match_scores = []\n\n        # assert len(valid_pred_seqs) == len(match_scores_exact) == len(present_pred_flags)\n        # assert len(present_preds) == len(present_exact_match_scores) == len(present_partial_match_scores) == len(present_mixed_match_scores)\n        # assert present_partial_match_scores.shape == present_mixed_match_scores.shape\n        # assert len(absent_preds) == len(absent_exact_match_scores) == len(absent_partial_match_scores) == len(absent_mixed_match_scores)\n        # assert absent_partial_match_scores.shape == absent_mixed_match_scores.shape\n\n\n        \"\"\"\n        2. Compute metrics\n        \"\"\"\n        # get the scores on different scores (for absent results, only recall matters)\n        all_exact_results = run_classic_metrics(valid_match_scores_exact, valid_preds, tgt_seqs, metric_names, topk_range)\n        present_exact_results = run_classic_metrics(present_exact_match_scores, valid_present_preds, present_tgts, metric_names, topk_range)\n        absent_exact_results = run_classic_metrics(absent_exact_match_scores, valid_absent_preds, absent_tgts, metric_names, absent_topk_range)\n\n        all_partial_results = run_classic_metrics(valid_match_scores_partial, valid_preds, tgt_seqs, metric_names, topk_range, type='partial')\n        present_partial_results = run_classic_metrics(present_partial_match_scores, valid_present_preds, present_tgts, metric_names, topk_range, type='partial')\n        absent_partial_results = run_classic_metrics(absent_partial_match_scores, valid_absent_preds, absent_tgts, metric_names, absent_topk_range, type='partial')\n        # present_mixed_results = run_metrics(present_mixed_match_scores, present_preds, present_tgts, metric_names, topk_range, type='partial')\n        # absent_mixed_results = run_metrics(absent_mixed_match_scores, absent_preds, absent_tgts, metric_names, absent_topk_range, type='partial')\n\n        all_exact_advanced_results = run_advanced_metrics(valid_match_scores_exact, valid_preds, tgt_seqs)\n        present_exact_advanced_results = run_advanced_metrics(present_exact_match_scores, valid_present_preds, present_tgts)\n        absent_exact_advanced_results = run_advanced_metrics(absent_exact_match_scores, valid_absent_preds, absent_tgts)\n        # print(advanced_present_exact_results)\n        # print(advanced_absent_exact_results)\n\n        \"\"\"\n        3. Gather scores\n        \"\"\"\n        eval_results_names = [\n            'all_exact', 'all_partial',\n            'present_exact', 'absent_exact',\n            'present_partial', 'absent_partial',\n            # 'present_mixed', 'absent_mixed'\n            'all_exact_advanced', 'present_exact_advanced', 'absent_exact_advanced',\n            ]\n        eval_results_list = [all_exact_results, all_partial_results,\n                             present_exact_results, absent_exact_results,\n                             present_partial_results, absent_partial_results,\n                             # present_mixed_results, absent_mixed_results\n                             all_exact_advanced_results, present_exact_advanced_results, absent_exact_advanced_results\n                            ]\n        # update score_dict, appending new scores (results_list) to it\n        individual_score_dict = {result_name: results for result_name, results in zip(eval_results_names, eval_results_list)}\n        gathered_score_dict = gather_scores(gathered_score_dict, eval_results_names, eval_results_list)\n\n        # add tgt/pred count for computing average performance on non-empty items\n        stats_results_names = ['present_tgt_num', 'absent_tgt_num', 'present_pred_num', 'absent_pred_num', 'unique_pred_num', 'dup_pred_num', 'beam_num', 'beamstep_num']\n        stats_results_list = [\n                        {'present_tgt_num': len(present_tgts)},\n                        {'absent_tgt_num': len(absent_tgts)},\n                        {'present_pred_num': len(valid_present_preds)},\n                        {'absent_pred_num': len(valid_absent_preds)},\n                        # TODO some stat should be calculated here since exhaustive/self-terminating makes difference\n                        {'unique_pred_num': pred_dict['unique_pred_num'] if 'unique_pred_num' in pred_dict else 0},\n                        {'dup_pred_num': pred_dict['dup_pred_num'] if 'dup_pred_num' in pred_dict else 0},\n                        {'beam_num': pred_dict['beam_num'] if 'beam_num' in pred_dict else 0},\n                        {'beamstep_num': pred_dict['beamstep_num'] if 'beamstep_num' in pred_dict else 0},\n                        ]\n        for result_name, result_dict in zip(stats_results_names, stats_results_list):\n            individual_score_dict[result_name] = result_dict[result_name]\n        gathered_score_dict = gather_scores(gathered_score_dict, stats_results_names, stats_results_list)\n        # individual_score_dicts.append(individual_score_dict)\n\n        \"\"\"\n        4. Print results if necessary\n        \"\"\"\n        if verbose or report_file:\n            print_out = print_predeval_result(i, ' '.join(src_seq),\n                                              tgt_seqs, present_tgt_flags,\n                                              pred_seqs, pred_scores, pred_idxs, copied_flags,\n                                              present_pred_flags, valid_pred_flags,\n                                              valid_and_present_flags, valid_and_absent_flags,\n                                              match_scores_exact, match_scores_partial,\n                                              eval_results_names, eval_results_list, gathered_score_dict)\n\n            if verbose:\n                if logger:\n                    logger.info(print_out)\n                else:\n                    print(print_out)\n\n            if report_file:\n                report_file.write(print_out)\n\n    # for k, v in score_dict.items():\n    #     print('%s, num=%d, mean=%f' % (k, len(v), np.average(v)))\n\n    if report_file:\n        report_file.close()\n\n    return gathered_score_dict\n\n\ndef kp_results_to_str(results_dict):\n    \"\"\"\n    return \">> ROUGE(1/2/3/L/SU4): {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}\".format(\n        results_dict[\"rouge_1_f_score\"] * 100,\n        results_dict[\"rouge_2_f_score\"] * 100,\n        results_dict[\"rouge_3_f_score\"] * 100,\n        results_dict[\"rouge_l_f_score\"] * 100,\n        results_dict[\"rouge_su*_f_score\"] * 100)\n    \"\"\"\n    summary_dict = {}\n    for k,v in results_dict.items():\n        summary_dict[k] = np.average(v)\n\n    return json.dumps(summary_dict)\n\n\ndef baseline_pred_loader(pred_path, model_name):\n    pred_dict_list = []\n\n    if model_name in ['tfidf', 'textrank', 'singlerank', 'expandrank', 'maui', 'kea']:\n        doc_list = [file_name for file_name in os.listdir(pred_path) if file_name.endswith('txt.phrases')]\n        doc_list = sorted(doc_list, key=lambda k: int(k[:k.index('.txt.phrases')]))\n        for doc_name in doc_list:\n            doc_path = os.path.join(pred_path, doc_name)\n            pred_dict = {}\n            pred_dict['pred_sents'] = []\n\n            for l in open(doc_path, 'r').readlines():\n                pred_dict['pred_sents'].append(l.lower().split())\n            pred_dict_list.append(pred_dict)\n    else:\n        raise NotImplementedError\n\n    return pred_dict_list\n\n\ndef keyphrase_eval(datasplit_name, src_path, tgt_path, pred_path,\n                   unk_token='<unk>', verbose=False, logger=None,\n                   report_path=None, model_name='nn',\n                   tokenizer=None):\n    # change data loader to iterator, otherwise it consumes more than 64gb RAM\n    # check line numbers first\n    dataset_name = '_'.join(datasplit_name.split('_')[: -1])\n    split_name = datasplit_name.split('_')[-1]\n    dataset_name = dataset_name.strip().lower()\n    src_line_number = sum([1 for _ in open(src_path, \"r\")])\n    tgt_line_number = sum([1 for _ in open(tgt_path, \"r\")])\n    if model_name == 'nn':\n        pred_line_number = sum([1 for _ in open(pred_path, \"r\")])\n    else:\n        pred_line_number = len(baseline_pred_loader(pred_path, model_name))\n\n    logger.info(\"pred file=%s\" % (pred_path))\n    logger.info(\"#(src)=%d, #(tgt)=%d, #(pred)=%d\" % (src_line_number, tgt_line_number, pred_line_number))\n    if src_line_number == tgt_line_number == pred_line_number:\n        src_data = [json.loads(l) for l in open(src_path, \"r\")]\n        tgt_data = [json.loads(l) for l in open(tgt_path, \"r\")]\n\n        # Load from the json-format raw data, preprocess the src and tgt\n        if src_path.endswith('json') or src_path.endswith('jsonl'):\n            assert src_path == tgt_path, \\\n                'src and tgt should be from the same raw file: \\n\\tsrc_path: %s \\n\\ttgt_path: %s' % (src_path, tgt_path)\n            dataset_type = infer_dataset_type(src_path)\n            title_field, text_field, keyword_field, _ = KP_DATASET_FIELDS[dataset_type]\n\n            for src_ex, tgt_ex in zip(src_data, tgt_data):\n                src_str = parse_src_fn(src_ex, title_field, text_field)\n                if isinstance(tgt_ex[keyword_field], str):\n                    tgt_kps = tgt_ex[keyword_field].split(';')\n                else:\n                    tgt_kps = tgt_ex[keyword_field]\n\n                src_ex['src'] = src_str\n                tgt_ex['tgt'] = tgt_kps\n        else:\n            raise Exception('Currently only support json/jsonl data format: %s' % src_path)\n\n        if model_name == 'nn':\n            pred_data = [json.loads(l) for l in open(pred_path, \"r\")]\n        else:\n            pred_data = baseline_pred_loader(pred_path, model_name)\n        start_time = time.time()\n        results_dict = evaluate(src_data, tgt_data, pred_data,\n                                unk_token=unk_token,\n                                logger=logger, verbose=verbose,\n                                report_path=report_path,\n                                tokenizer=tokenizer)\n        total_time = time.time() - start_time\n        logger.info(\"Total evaluation time (s): %f\" % total_time)\n\n        return results_dict\n    else:\n        logger.error(\"\")\n        return None\n\n\ndef summarize_scores(score_dict, ckpt_name,\n                     exp_name=None, pred_name=None, dataset_name=None,\n                     eval_file_path=None, pred_file_path=None, step=None):\n    avg_dict = {}\n    avg_dict['checkpoint_name'] = ckpt_name\n    avg_dict['exp_name'] = exp_name\n    avg_dict['pred_name'] = pred_name\n    avg_dict['test_dataset'] = dataset_name\n    avg_dict['eval_file_path'] = eval_file_path\n    avg_dict['pred_file_path'] = pred_file_path\n    if step is not None:\n        avg_dict['step'] = step\n    elif ckpt_name.find('_') > 0:\n        avg_dict['step'] = ckpt_name.rsplit('_')[-1]\n    else:\n        avg_dict['step'] = ''\n\n    # doc stat\n    avg_dict['#doc'] = len(score_dict['present_tgt_num'])\n    avg_dict['#pre_doc'] = len([x for x in score_dict['present_tgt_num'] if x > 0])\n    avg_dict['#ab_doc'] = len([x for x in score_dict['absent_tgt_num'] if x > 0])\n\n    # tgt stat\n    if 'present_tgt_num' in score_dict and 'absent_tgt_num' in score_dict:\n        avg_dict['#tgt'] = np.average(score_dict['present_tgt_num']) + np.average(score_dict['absent_tgt_num'])\n        avg_dict['#pre_tgt'] = np.average(score_dict['present_tgt_num'])\n        avg_dict['#ab_tgt'] = np.average(score_dict['absent_tgt_num'])\n    else:\n        avg_dict['#tgt'] = 0\n        avg_dict['#pre_tgt'] = 0\n        avg_dict['#ab_tgt'] = 0\n\n    # pred stat\n    if 'present_pred_num' in score_dict and 'absent_pred_num' in score_dict:\n        avg_dict['#pred'] = np.average(score_dict['present_pred_num']) + np.average(score_dict['absent_pred_num'])\n        avg_dict['#pre_pred'] = np.average(score_dict['present_pred_num'])\n        avg_dict['#ab_pred'] = np.average(score_dict['absent_pred_num'])\n    else:\n        avg_dict['#pred'] = 0\n        avg_dict['#pre_pred'] = 0\n        avg_dict['#ab_pred'] = 0\n\n    avg_dict['#uni_pred'] = np.average(score_dict['unique_pred_num']) if 'unique_pred_num' in score_dict else 0\n    avg_dict['#dup_pred'] = np.average(score_dict['dup_pred_num']) if 'dup_pred_num' in score_dict else 0\n    avg_dict['#beam'] = np.average(score_dict['beam_num']) if 'beam_num' in score_dict else 0\n    avg_dict['#beamstep'] = np.average(score_dict['beamstep_num']) if 'beamstep_num' in score_dict else 0\n\n    # remove meta stats from score_dict\n    if 'unique_pred_num' in score_dict: del score_dict['present_tgt_num']\n    if 'absent_tgt_num' in score_dict: del score_dict['absent_tgt_num']\n    if 'present_pred_num' in score_dict: del score_dict['present_pred_num']\n    if 'absent_pred_num' in score_dict: del score_dict['absent_pred_num']\n    if 'unique_pred_num' in score_dict: del score_dict['unique_pred_num']\n    if 'dup_pred_num' in score_dict: del score_dict['dup_pred_num']\n    if 'beam_num' in score_dict: del score_dict['beam_num']\n    if 'beamstep_num' in score_dict: del score_dict['beamstep_num']\n\n    # average scores of each metric\n    for score_name, score_list in score_dict.items():\n        # number of correct phrases\n        if score_name.find('correct') > 0:\n            # only keep exact results (partial count is trivial)\n            if score_name.find('exact') > 0:\n                avg_dict[score_name] = np.sum(score_list)\n            continue\n\n        # various scores (precision, recall, f-score)\n        # NOTE! here can be tricky, we can average over all data examples or just valid examples\n        #  in empirical paper, we use the former, to keep it consistent and simple\n        '''\n        if score_name.startswith('all') or score_name.startswith('present'):\n            tmp_scores = [score for score, num in zip(score_list, present_tgt_num) if num > 0]\n            avg_dict[score_name] = np.average(tmp_scores)\n        elif score_name.startswith('absent'):\n            tmp_scores = [score for score, num in zip(score_list, absent_tgt_num) if num > 0]\n            avg_dict[score_name] = np.average(tmp_scores)\n        else:\n            logger.error(\"NotImplementedError: found key %s\" % score_name)\n            raise NotImplementedError\n        '''\n        avg_dict[score_name] = np.average(score_list)\n\n    columns = list(avg_dict.keys())\n    # print(columns)\n    summary_df = pd.DataFrame.from_dict(avg_dict, orient='index').transpose()[columns]\n    # print('\\n')\n    # print(list(summary_df.columns))\n    # input()\n\n    return summary_df\n\n\ndef gather_eval_results(eval_root_dir, report_csv_dir=None, tokenizer=None, empirical_result=False):\n    dataset_scores_dict = {}\n    assert tokenizer is not None\n    evals_to_skip = set()\n    if report_csv_dir:\n        # load previous reports\n        for report_csv_file in os.listdir(report_csv_dir):\n            if not report_csv_file.endswith('.%s.csv' % tokenizer): continue\n            dataset_name = report_csv_file.split('.')[0] # truncate 'tokenizer.csv'\n            prev_df = pd.read_csv(os.path.join(report_csv_dir, report_csv_file))\n            prev_df = prev_df.loc[:, ~prev_df.columns.str.contains('^Unnamed')]\n\n            dataset_scores_dict[dataset_name] = prev_df\n            for eval_path in prev_df.eval_file_path:\n                evals_to_skip.add(eval_path)\n\n    eval_suffix = '.%s.eval' % tokenizer\n    total_file_num = len([file for subdir, dirs, files in os.walk(eval_root_dir)\n                          for file in files if file.endswith(eval_suffix)])\n    file_count = 0\n\n    for subdir, dirs, files in os.walk(eval_root_dir):\n        for file in files:\n            if not file.endswith(eval_suffix): continue\n            file_count += 1\n            if file_count % 10 == 0: print(\"file_count/file_num=%d/%d\" % (file_count, total_file_num))\n\n            eval_file_path = os.path.join(subdir, file)\n            pred_file_path = eval_file_path[: -len(eval_suffix)]+'.pred' # might be a very bad way\n            if eval_file_path in evals_to_skip: continue\n            if not os.path.exists(pred_file_path):\n                # only count ones that both pred/eval exist, and remove some leftover files\n                if os.path.exists(eval_file_path): os.remove(eval_file_path)\n                report_file_path = eval_file_path[:-4]+'report'\n                if os.path.exists(report_file_path): os.remove(report_file_path)\n                continue\n\n            if empirical_result:\n                # legacy result\n                exp_step_name = subdir.strip('/')[subdir.strip('/').rfind('/') + 1:]\n                exp_name, step = exp_step_name.split('_step_')\n                dataset_name = file[: file.find(eval_suffix)]\n                ckpt_name = 'checkpoint_step_%s' % step\n                pred_name = 'meng17-one2seq-beam50-maxlen40'  # very hard-coded\n            else:\n                file_name = file[: file.find(eval_suffix)]\n                ckpt_name = file_name[: file.rfind('-')] if file.find('-') > 0 else file_name\n                # exp_dirname = re.search('.*/(.*?)/outputs', subdir).group(1)\n                # exp_name = exp_dirname.split('/')[1]\n                exp_name = re.search('.*/(.*?)/outputs', subdir).group(1)\n                pred_name = re.search('outputs/(.*?)/pred', subdir).group(1) # very hard-coded\n                dataset_name = file_name[file.rfind('-') + 1: ]\n                dataset_name = dataset_name[5:] if dataset_name.startswith('data_') else dataset_name\n                step = None\n\n            # key is dataset name, value is a dict whose key is metric name and value is a list of floats\n            try:\n                score_dict = json.load(open(eval_file_path, 'r'))\n            except:\n                print('error while loading %s' % eval_file_path)\n                continue\n            # ignore scores where no tgts available and return the average\n            score_df = summarize_scores(score_dict,\n                                        ckpt_name, exp_name, pred_name, dataset_name,\n                                        eval_file_path, pred_file_path, step=step)\n\n            # print(df_key)\n            if dataset_name in dataset_scores_dict:\n                dataset_scores_dict[dataset_name] = dataset_scores_dict[dataset_name].append(score_df)\n            else:\n                dataset_scores_dict[dataset_name] = score_df\n\n        #     if file_count > 20:\n        #         break\n        #\n        # if file_count > 20:\n        #     break\n\n    if report_csv_dir:\n        for dataset_name, score_df in dataset_scores_dict.items():\n            report_csv_path = os.path.join(report_csv_dir, dataset_name + '.%s.csv' % tokenizer)\n            print(\"Writing summary to: %s\" % (report_csv_path))\n            score_df = score_df.sort_values(by=['exp_name', 'step'])\n            score_df.to_csv(report_csv_path, index=False)\n            # print(score_df.to_csv(index=False))\n\n    return dataset_scores_dict\n\ndef init_opt():\n\n    parser = argparse.ArgumentParser()\n    # Input/output options\n    parser.add_argument('--data', '-data', required=True,\n                        help=\"Path to the source/target file of groundtruth data.\")\n    parser.add_argument('--pred_dir', '-pred_dir', required=True,\n                        help=\"Directory to pred folders, each folder contains .pred files, each line is a JSON dict about predicted keyphrases.\")\n    parser.add_argument('--output_dir', '-output_dir',\n                        help=\"Path to output log/results.\")\n    parser.add_argument('--unk_token', '-unk_token', default=\"<unk>\",\n                        help=\".\")\n    parser.add_argument('--verbose', '-v', action='store_true',\n                        help=\".\")\n    parser.add_argument('-testsets', nargs='+', type=str, default=[\"inspec\", \"krapivin\", \"nus\", \"semeval\", \"duc\"], help='Specify datasets to test on')\n\n    opt = parser.parse_args()\n\n    return opt\n\n\nif __name__ == '__main__':\n    opt = init_opt()\n    score_dicts = {}\n\n    for ckpt_name in os.listdir(opt.pred_dir):\n        if not os.path.isdir(os.path.join(opt.pred_dir, ckpt_name)):\n            continue\n\n        for dataname in opt.testsets:\n            src_path = os.path.join(opt.data, dataname, \"%s_test.src\" % dataname)\n            tgt_path = os.path.join(opt.data, dataname, \"%s_test.tgt\" % dataname)\n            pred_path = os.path.join(opt.pred_dir, ckpt_name, \"%s.pred\" % dataname)\n\n            if not os.path.exists(opt.output_dir):\n                os.makedirs(opt.output_dir)\n            if not os.path.exists(os.path.join(opt.output_dir, 'pred', ckpt_name)):\n                os.makedirs(os.path.join(opt.output_dir, 'pred', ckpt_name))\n            if not os.path.exists(os.path.join(opt.output_dir, 'eval')):\n                os.makedirs(os.path.join(opt.output_dir, 'eval'))\n\n            logger = init_logger(opt.output_dir + \"kp_evaluate.%s.eval.log\" % dataname)\n            report_path = os.path.join(opt.output_dir, 'pred', ckpt_name, '%s.report.txt' % dataname)\n            score_path = os.path.join(opt.output_dir, 'eval', ckpt_name + '-%s.eval' % dataname)\n\n            logger.info(\"Evaluating %s\" % dataname)\n\n            if not os.path.exists(score_path):\n                score_dict = keyphrase_eval(src_path=src_path,\n                                              tgt_path=tgt_path,\n                                              pred_path=pred_path,\n                                              unk_token = '<unk>',\n                                              verbose = opt.verbose,\n                                              logger = logger,\n                                              report_path = report_path\n                                            )\n                logger.info(kp_results_to_str(score_dict))\n\n                with open(score_path, 'w') as output_json:\n                    output_json.write(json.dumps(score_dict))\n\n                score_dicts[dataname] = score_dict\n\n        gather_eval_results(eval_root_dir=os.path.join(opt.output_dir, 'eval'),\n                            report_csv_dir=os.path.join(opt.output_dir, 'summary_%s.csv' % ('%s')))\n\n        logger.info(\"Done!\")"
  },
  {
    "path": "kp_gen_eval.py",
    "content": "# -*- encoding: utf-8 -*-\nimport codecs\nimport json\nimport random\nimport shutil\n\nfrom onmt.translate.translator import build_translator\nfrom onmt.utils.parse import ArgumentParser\nimport os\n\nimport datetime\nimport time\nimport numpy as np\n\nimport kp_evaluate\nfrom onmt.utils import split_corpus\nfrom onmt.utils.logging import init_logger\n\nimport onmt.opts as opts\n\n\ndef scan_new_checkpoints(ckpt_dir):\n    ckpts = {}\n    for subdir, dirs, files in os.walk(ckpt_dir):\n        for file in files:\n            if file.endswith('.pt'):\n                ckpt_name = file[: file.find('.pt')]\n                ckpts[ckpt_name] = os.path.join(subdir, file)\n\n    return ckpts\n\n\ndef _get_parser():\n    parser = ArgumentParser(description='run_kp_eval.py')\n\n    opts.config_opts(parser)\n    opts.translate_opts(parser)\n\n    return parser\n\n\nif __name__ == \"__main__\":\n    parser = _get_parser()\n\n    parser.add_argument('--tasks', '-tasks', nargs='+', type=str,\n                        required=True,\n                        choices=['pred', 'eval', 'report'],\n                        help='Specify process to run, generation or evaluation')\n    parser.add_argument('-ckpt_dir', type=str, required=True, help='Directory to all checkpoints')\n    parser.add_argument('--step_base', '-step_base', type=int, default=1,\n                        help='the base of step to be evaluated, only if ckpt_step % step_base==0 we evaluate it,  '\n                             '1 means evaluate everything.')\n    parser.add_argument('-output_dir', type=str, required=True, help='Directory to output results')\n    parser.add_argument('-data_dir', type=str, required=True, help='Directory to datasets (ground-truth)')\n    parser.add_argument('-test_interval', type=int, default=600, help='Minimum time interval the job should wait if a .pred file is not updated by another job (imply another job failed).')\n    parser.add_argument('-testsets', nargs='+', type=str, default=[\"nus\", \"semeval\"], help='Specify datasets to test on')\n    # parser.add_argument('-testsets', nargs='+', type=str, default=[\"kp20k\", \"duc\", \"inspec\", \"krapivin\", \"nus\", \"semeval\"], help='Specify datasets to test on')\n    parser.add_argument('--onepass', '-onepass', action='store_true', help='If true, it only scans and generates once, otherwise an infinite loop scanning new available ckpts.')\n    parser.add_argument('--wait_patience', '-wait_patience', type=int, default=1, help='Terminates evaluation after scan this number of times.')\n    parser.add_argument('--wait_time', '-wait_time', type=int, default=120, help='.')\n    parser.add_argument('--sleep_time', '-sleep_time', type=int, default=600, help='.')\n    parser.add_argument('--ignore_existing', '-ignore_existing', action='store_true', help='If true, it ignores previous generated results.')\n    parser.add_argument('--eval_topbeam', '-eval_topbeam',action=\"store_true\", help='Evaluate with top beam only (self-terminating) or all beams (full search)')\n\n    opt = parser.parse_args()\n\n    # np.random.seed()\n    wait_time = np.random.randint(opt.wait_time)\n    current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # \"%Y-%m-%d_%H:%M:%S\"\n    logger = init_logger(opt.output_dir + '/autoeval_%s_%s.log'\n                         % ('-'.join(opt.testsets), current_time))\n    if not opt.onepass:\n        logger.info('Sleep for %d sec to avoid conflicting with other threads' % wait_time)\n        time.sleep(wait_time)\n\n    if not os.path.exists(opt.output_dir):\n        os.makedirs(opt.output_dir)\n    if not os.path.exists(os.path.join(opt.output_dir, 'eval')):\n        os.makedirs(os.path.join(opt.output_dir, 'eval'))\n    if not os.path.exists(os.path.join(opt.output_dir, 'pred')):\n        os.makedirs(os.path.join(opt.output_dir, 'pred'))\n\n    # shutil.copy2(opt.config, opt.output_dir)\n    logger.info(opt)\n\n    testset_path_dict = {}\n    for testset in opt.testsets:\n        src_shard = split_corpus(opt.data_dir + '/%s/%s_test.src' % (testset, testset), shard_size=-1)\n        tgt_shard = split_corpus(opt.data_dir + '/%s/%s_test.tgt' % (testset, testset), shard_size=-1)\n        src_shard, tgt_shard = list(zip(src_shard, tgt_shard))[0]\n        logger.info(\"Loaded data from %s: #src=%d, #tgt=%d\" % (testset, len(src_shard), len(tgt_shard)))\n        testset_path_dict[testset] = (opt.data_dir + '/%s/%s_test.src' % (testset, testset),\n                                      opt.data_dir + '/%s/%s_test.tgt' % (testset, testset),\n                                      src_shard, tgt_shard)\n\n    current_patience = opt.wait_patience\n    pred_linecount_dict = {}\n    eval_linecount_dict = {}\n    while True:\n        new_ckpts = scan_new_checkpoints(opt.ckpt_dir)\n        new_ckpts_items = sorted(new_ckpts.items(), key=lambda x:int(x[0][x[0].rfind('step_')+5:]))\n        random.shuffle(new_ckpts_items)\n        logger.info('Found %d checkpoints from %s!' % (len(new_ckpts), opt.ckpt_dir))\n\n        if opt.step_base is not None and opt.step_base > 1:\n            logger.warn('-step_base is set, filtering some ckpts')\n            new_ckpts_items = [(ckpt_name, ckpt_path) for ckpt_name, ckpt_path in new_ckpts_items if int(ckpt_name[ckpt_name.rfind('step_')+5:]) % opt.step_base == 0 and int(ckpt_name[ckpt_name.rfind('step_')+5:]) // opt.step_base > 0]\n            logger.info('After filtering non opt.step_base ckpts, found %d checkpoints!' % (len(new_ckpts_items)))\n\n        job_done = False # a flag indicating if any real pred/eval job is done\n\n        for ckpt_id, (ckpt_name, ckpt_path) in enumerate(new_ckpts_items):\n            logger.info(\"[%d/%d] Checking checkpoint: %s\" % (ckpt_id, len(new_ckpts_items), ckpt_path))\n            setattr(opt, 'models', [ckpt_path])\n\n            translator = None\n\n            score_dicts = {}\n            for dataname, dataset in testset_path_dict.items():\n                src_path, tgt_path, src_shard, tgt_shard = dataset\n\n                pred_path = os.path.join(opt.output_dir, 'pred', ckpt_name, '%s.pred' % dataname)\n                printout_path = os.path.join(opt.output_dir, 'pred', ckpt_name, '%s.report.txt' % dataname)\n                eval_dir = os.path.join(opt.output_dir, 'eval')\n                eval_path = os.path.join(eval_dir, ckpt_name + '-%s-%s.json'\n                                         % (dataname, 'selfterminating' if opt.eval_topbeam else 'exhaustive'))\n                report_path = os.path.join(eval_dir, '%s_summary_%s.csv' % (current_time, '%s'))\n\n                # create dirs\n                if not os.path.exists(os.path.join(opt.output_dir, 'pred', ckpt_name)):\n                    os.makedirs(os.path.join(opt.output_dir, 'pred', ckpt_name))\n                if not os.path.exists(eval_dir):\n                    os.makedirs(eval_dir)\n\n                # do translation\n                # skip translation for this dataset if previous pred exists\n                do_trans_flag = True\n                if os.path.exists(pred_path):\n                    elapsed_time = time.time() - os.stat(pred_path).st_mtime\n                    if pred_path in pred_linecount_dict and pred_linecount_dict[pred_path] == len(src_shard):\n                        # if it's already in pred_linecount_dict, means it's done and counted\n                        do_trans_flag = False\n                    elif elapsed_time < opt.test_interval:\n                        do_trans_flag = False\n                        logger.info(\"Skip translating because previous PRED file was generated only %d sec ago (<%d sec).\" % (elapsed_time, opt.test_interval))\n                    else:\n                        # count line numbers of long-done files to check if this is a done pred\n                        try:\n                            pred_linecount_dict[pred_path] = len([1 for i in open(pred_path, 'r').readlines()])\n                            # count is same means it's done\n                            if pred_linecount_dict[pred_path] == len(src_shard):\n                                do_trans_flag = False\n                                logger.info(\"Skip translating because previous PRED is complete.\")\n                            else:\n                                # if file is modified less than opt.test_interval min, it might be being processed by another job. Otherwise it's a bad result and delete it\n                                if elapsed_time < opt.test_interval:\n                                    do_trans_flag = False\n                                else:\n                                    os.remove(pred_path)\n                                    logger.info('Removed a bad PRED file, #(line)=%d, #(elapsed_time)=%ds: %s'\n                                                % (pred_linecount_dict[pred_path], int(elapsed_time), pred_path))\n                        except Exception as e:\n                            logger.exception('Error while validating or deleting PRED file: %s' % pred_path)\n\n                if 'pred' in opt.tasks:\n                    try:\n                        if do_trans_flag or opt.ignore_existing:\n                            if translator is None:\n                                translator = build_translator(opt, report_score=opt.verbose, logger=logger)\n                            # create an empty file to indicate that the translator is working on it\n                            codecs.open(pred_path, 'w+', 'utf-8').close()\n                            # set output_file for each dataset (instead of outputting to opt.output)\n                            translator.out_file = codecs.open(pred_path, 'w+', 'utf-8')\n                            logger.info(\"Start translating [%s] for %s.\" % (dataname, ckpt_name))\n                            logger.info(\"\\t exporting PRED result to %s.\" % (pred_path))\n                            _, _ = translator.translate(\n                                src=src_shard,\n                                tgt=tgt_shard,\n                                src_dir=opt.src_dir,\n                                batch_size=opt.batch_size,\n                                attn_debug=opt.attn_debug,\n                                opt=opt\n                            )\n                            job_done = True\n                        else:\n                            logger.info(\"Skip translating [%s] for %s.\" % (dataname, ckpt_name))\n                    except Exception as e:\n                        logger.exception('Error while translating')\n\n                # do evaluation\n                do_eval_flag = True\n                if not os.path.exists(pred_path):\n                    do_eval_flag = False\n                    # logger.info(\"Skip evaluating because no available pred file.\")\n                else:\n                    try:\n                        if not pred_path in pred_linecount_dict:\n                            pred_linecount_dict[pred_path] = len([1 for i in open(pred_path, 'r').readlines()])\n                        num_pred = pred_linecount_dict[pred_path]\n                        if num_pred != len(src_shard):\n                            do_eval_flag = False\n                            logger.info(\"Skip evaluating because current PRED file is not complete, #(line)=%d.\" % (num_pred))\n                            elapsed_time = time.time() - os.stat(pred_path).st_mtime\n                            if elapsed_time > opt.test_interval:\n                                os.remove(pred_path)\n                                logger.warn('Removed a bad PRED file, #(line)=%d, #(elapsed_time)=%ds: %s' % (num_pred, int(elapsed_time), pred_path))\n                        else:\n                            # if pred is good, check if re-eval is necessary\n                            if os.path.exists(eval_path):\n                                elapsed_time = time.time() - os.stat(eval_path).st_mtime\n                                if eval_path in eval_linecount_dict and eval_linecount_dict[eval_path] == len(src_shard):\n                                    do_eval_flag = False\n                                elif elapsed_time < opt.test_interval:\n                                    # if file is modified less than opt.test_interval min, it might be being processed by another job.\n                                    do_eval_flag = False\n                                    logger.info(\"Skip evaluating because previous EVAL file was generated only %d sec ago (<%d sec).\" % (elapsed_time, opt.test_interval))\n                                else:\n                                    score_dict = json.load(open(eval_path, 'r'))\n                                    if 'present_exact_correct@5' in score_dict:\n                                        num_eval = len(score_dict['present_exact_correct@5'])\n                                    else:\n                                        num_eval = 0\n                                    eval_linecount_dict[eval_path] = num_eval\n                                    if num_eval == len(src_shard):\n                                        do_eval_flag = False\n                                        logger.info(\"Skip evaluating because existing eval file is complete.\")\n                                    else:\n                                        # it's a bad result and delete it\n                                        os.remove(eval_path)\n                                        logger.info('Removed a bad eval file, #(pred)=%d, #(eval)=%d, #(elapsed_time)=%ds: %s' % (num_pred, num_eval, int(elapsed_time), eval_path))\n                    except Exception as e:\n                        logger.exception('Error while validating or deleting EVAL file: %s' % eval_path)\n\n                if 'eval' in opt.tasks:\n                    try:\n                        if do_eval_flag or opt.ignore_existing:\n                            logger.info(\"Start evaluating [%s] for %s\" % (dataname, ckpt_name))\n                            logger.info(\"\\t will export eval result to %s.\" % (eval_path))\n                            score_dict = kp_evaluate.keyphrase_eval(src_path, tgt_path,\n                                                                    pred_path=pred_path, logger=logger,\n                                                                    verbose=opt.verbose,\n                                                                    report_path=printout_path,\n                                                                    eval_topbeam=opt.eval_topbeam\n                                                                    )\n                            if score_dict is not None:\n                                score_dicts[dataname] = score_dict\n                                with open(eval_path, 'w') as output_json:\n                                    output_json.write(json.dumps(score_dict)+'\\n')\n                            job_done = True\n                        else:\n                            logger.info(\"Skip evaluating [%s] for %s.\" % (dataname, ckpt_name))\n                    except Exception as e:\n                        logger.exception('Error while evaluating')\n\n                # do generate summarized report\n                if 'report' in opt.tasks:\n                    kp_evaluate.gather_eval_results(eval_root_dir=eval_dir, report_csv_dir=report_path)\n\n        if job_done: # reset current_patience if no real job is done in the current iteration\n            current_patience = opt.wait_patience\n        else:\n            current_patience -= 1\n\n        if opt.onepass:\n            break\n\n        if current_patience <= 0:\n            break\n        else:\n            # scan again for every 10min\n            sleep_time = opt.sleep_time\n            logger.info('Sleep for %d sec, current_patience=%d' % (sleep_time, current_patience))\n            logger.info('*' * 50)\n            time.sleep(sleep_time)\n"
  },
  {
    "path": "kp_gen_eval_transfer.py",
    "content": "# -*- encoding: utf-8 -*-\nimport codecs\nimport json\nimport random\nimport shutil\n\nfrom onmt.constants import ModelTask\nfrom onmt.translate.translator import build_translator\nfrom onmt.utils.parse import ArgumentParser\nimport os\n\nimport datetime\nimport time\nimport numpy as np\n\nimport kp_evaluate\nfrom onmt.utils import split_corpus\nfrom onmt.utils.logging import init_logger\n\nimport onmt.opts as opts\n\ntrain_test_mappings = {\n    'kp20k': ['kp20k', 'kp20k_valid2k', 'inspec', 'krapivin', 'semeval', 'nus', 'duc'],\n    'openkp': ['openkp', 'openkp_valid2k', 'jptimes', 'duc'],\n    'kptimes': ['kptimes', 'kptimes_valid2k', 'jptimes', 'duc'],\n    'stackex': ['stackex', 'stackex_valid2k', 'duc'],\n}\n\ndef scan_new_checkpoints(ckpt_dir):\n    ckpts = []\n    for subdir, dirs, files in os.walk(ckpt_dir):\n        for file in files:\n            if file.endswith('.pt'):\n                ckpt_name = file[: file.find('.pt')]\n                if not 'step' in ckpt_name:\n                    # skip the last ckpt of fairseq jobs such as 'checkpoint_last.pt' and 'checkpoint54.pt'\n                    continue\n                ckpt_path = os.path.join(subdir, file)\n                exp_dir = os.path.dirname(subdir)\n                exp_name = exp_dir[exp_dir.rfind('/') + 1: ]\n                ckpt_dict = {\n                    'exp_dir': exp_dir,\n                    'exp_name': exp_name,\n                    'ckpt_path': ckpt_path,\n                    'ckpt_name': ckpt_name,\n                    'step': int(ckpt_name[ckpt_name.rfind('step_')+5:])\n                }\n                ckpts.append(ckpt_dict)\n\n    return ckpts\n\ndef scan_predictions(exp_root_dir):\n    preds = []\n    for subdir, dirs, files in os.walk(exp_root_dir):\n        for file in files:\n            if file.endswith('.pred'):\n                try:\n                    pred_name = file[: -5]\n                    step, data = pred_name.split('-')\n                    step = int(step[step.rfind('step_') + 5:])\n                    data = data[5:]\n                    pred = {\n                        'ckpt_name': pred_name,\n                        'pred_path': os.path.join(subdir, file),\n                        'dataset': data,\n                        'step': step\n                    }\n                    preds.append(pred)\n                except:\n                    print('invalid pred name %s' % file)\n\n    return preds\n\ndef _get_parser():\n    parser = ArgumentParser(description='run_kp_eval_transfer.py')\n\n    opts.translate_opts(parser)\n    # opts.train_opts(parser)\n    opts.model_opts(parser)\n    opts.dynamic_prepare_opts(parser, build_vocab_only=False)\n\n    return parser\n\n\nif __name__ == \"__main__\":\n    parser = _get_parser()\n\n    parser.add_argument('--tasks', '-tasks', nargs='+', type=str,\n                        required=True,\n                        choices=['pred', 'eval', 'report'],\n                        help='Specify process to run, generation or evaluation')\n    parser.add_argument('-exp_root_dir', type=str, required=True, help='Directory to all checkpoints')\n    parser.add_argument('-data_dir', type=str, required=True, help='Directory to datasets (ground-truth)')\n    parser.add_argument('--step_base', '-step_base', type=int, default=1,\n                        help='the base of step to be evaluated, only if ckpt_step % step_base==0 we evaluate it,  '\n                             '1 means evaluate everything.')\n    parser.add_argument('-test_interval', type=int, default=600, help='Minimum time interval the job should wait if a .pred file is not updated by another job (imply another job failed).')\n    parser.add_argument('-testsets', nargs='+', type=str, default=['kp20k', 'openkp', 'stackex', 'kptimes', 'jptimes'], help='Specify datasets to test on')\n    parser.add_argument('--pred_trained_only', '-pred_trained_only', action='store_true', help='If true, it only runs inference of testsets that the job has been trained with.')\n    parser.add_argument('--ignore_existing', '-ignore_existing', action='store_true', help='If true, it ignores previous generated results.')\n    parser.add_argument('--empirical_result', '-empirical_result', action='store_true', help='For export empirical results to CSV.')\n\n    parser.add_argument('-splits', nargs='+', type=str, default=['train', 'test', 'valid'], help='Specify datasets to test on')\n    parser.add_argument('-tokenizer', type=str, default='split_nopunc', choices=['spacy', 'split', 'split_nopunc'],\n                        help='Specify what tokenizer used in evaluation')\n\n    parser.add_argument('--onepass', '-onepass', action='store_true', help='If true, it only scans and generates once, otherwise an infinite loop scanning new available ckpts.')\n    parser.add_argument('--wait_patience', '-wait_patience', type=int, default=2, help='Terminates evaluation after scan this number of times.')\n    parser.add_argument('--wait_time', '-wait_time', type=int, default=120, help='.')\n    parser.add_argument('--sleep_time', '-sleep_time', type=int, default=600, help='.')\n\n    opt = parser.parse_args()\n    if isinstance(opt.data, str):\n        setattr(opt, 'data', json.loads(opt.data.replace('\\'', '\"')))\n    setattr(opt, 'data_task', ModelTask.SEQ2SEQ)\n    if opt.data:\n        ArgumentParser._get_all_transform(opt)\n        ArgumentParser._validate_transforms_opts(opt)\n        ArgumentParser._validate_fields_opts(opt)\n\n    opt.__setattr__('valid_batch_size', opt.batch_size)\n    opt.__setattr__('batch_size_multiple', 1)\n    opt.__setattr__('bucket_size', 128)\n    opt.__setattr__('pool_factor', 256)\n\n    # np.random.seed()\n    wait_time = np.random.randint(opt.wait_time) if opt.wait_time > 0 else 0\n    current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # \"%Y-%m-%d_%H:%M:%S\"\n    if not os.path.exists(os.path.join(opt.exp_root_dir, 'logs')):\n        os.makedirs(os.path.join(opt.exp_root_dir, 'logs'))\n    logger = init_logger(opt.exp_root_dir + '/logs/autoeval_%s.log'\n                         % (current_time))\n    if not opt.onepass:\n        logger.info('Sleep for %d sec to avoid conflicting with other threads' % wait_time)\n        time.sleep(wait_time)\n\n    # do generate summarized report\n    if 'report' in opt.tasks:\n        report_dir = os.path.join(opt.exp_root_dir, 'report')\n        if not os.path.exists(report_dir): os.makedirs(report_dir)\n        kp_evaluate.gather_eval_results(eval_root_dir=opt.exp_root_dir,\n                                        report_csv_dir=report_dir,\n                                        tokenizer=opt.tokenizer,\n                                        empirical_result=opt.empirical_result)\n        logger.warning('Report accomplished, exit!')\n        exit(0)\n\n    logger.info(opt)\n    testset_path_dict = {}\n    for testset in opt.testsets:\n        for split in opt.splits:\n            if not os.path.exists(opt.data_dir + '/%s/%s.json' % (testset, split)):\n                logger.info(\"Data does not exist, skip: %s-%s\" % (testset, split))\n                continue\n            src_shard = split_corpus(opt.data_dir + '/%s/%s.json' % (testset, split), shard_size=-1)\n            tgt_shard = split_corpus(opt.data_dir + '/%s/%s.json' % (testset, split), shard_size=-1)\n            src_shard, tgt_shard = list(zip(src_shard, tgt_shard))[0]\n            src_shard = [json.loads(l) for l in src_shard]\n            tgt_shard = [json.loads(l) for l in tgt_shard]\n            logger.info(\"Loaded data from %s-%s: #src=%d, #tgt=%d\" % (testset, split, len(src_shard), len(tgt_shard)))\n            testset_path_dict[testset+'_'+split] = (opt.data_dir + '/%s/%s.json' % (testset, split),\n                                          opt.data_dir + '/%s/%s.json' % (testset, split),\n                                          src_shard, tgt_shard)\n\n    current_patience = opt.wait_patience\n\n    while True:\n        pred_linecount_dict = {}\n        eval_linecount_dict = {}\n        job_done = False # a flag indicating if any real pred/eval job is done\n\n        if 'pred' in opt.tasks:\n            ckpts = scan_new_checkpoints(opt.exp_root_dir)\n            ckpts = sorted(ckpts, key=lambda x:x['step'])\n            random.shuffle(ckpts)\n            logger.info('Found %d checkpoints from %s!' % (len(ckpts), opt.exp_root_dir))\n\n            if opt.step_base is not None and opt.step_base > 1:\n                num_ckpts = len(ckpts)\n                ckpts = [ckpt for ckpt in ckpts if ckpt['step'] % opt.step_base == 0 and ckpt['step'] // opt.step_base > 0]\n                logger.info('After filtering non opt.step_base=%d ckpts, found %d/%d checkpoints!' %\n                            (opt.step_base, len(ckpts), num_ckpts))\n\n            # iterate each ckpt and start predicting/evaluating\n            for ckpt_id, ckpt in enumerate(ckpts):\n                ckpt_path = ckpt['ckpt_path']\n                ckpt_name = ckpt['ckpt_name']\n                exp_dir = ckpt['exp_dir']\n                exp_name = ckpt['exp_name']\n                logger.info(\"[%d/%d] Checking checkpoint: %s\" % (ckpt_id, len(ckpts), ckpt_path))\n                setattr(opt, 'models', [ckpt['ckpt_path']])\n\n                for datasplit_name, dataset in testset_path_dict.items():\n                    # ignore current testdata-split if this data is not used in training\n                    if opt.pred_trained_only:\n                        pass_flag = False\n                        cur_testname = datasplit_name[: datasplit_name.index('_')] if '_' in datasplit_name else datasplit_name\n                        for trainname, testnames in train_test_mappings.items():\n                            # training dataset name appears in exp_name\n                            if trainname in exp_name and cur_testname in testnames:\n                                pass_flag = True\n                                break\n                        if not pass_flag:\n                            print('Skip predict/evaluate test=[%s] for ckpt=[%s] due to train/test mismatch.' % (datasplit_name, exp_name))\n                            continue\n\n                    src_path, tgt_path, src_shard, tgt_shard = dataset\n\n                    decoding_method = 'beamsearch-width_%d-maxlen_%d' % (opt.beam_size, opt.max_length)\n                    pred_dir = os.path.join(exp_dir, 'outputs', decoding_method, 'pred')\n                    if not os.path.exists(pred_dir): os.makedirs(pred_dir)\n                    pred_file = '%s-data_%s.pred' % (ckpt_name, datasplit_name)\n                    pred_path = os.path.join(pred_dir, pred_file)\n\n                    # skip translation for this dataset if previous pred exists\n                    do_trans_flag = True\n                    if os.path.exists(pred_path):\n                        elapsed_time = time.time() - os.stat(pred_path).st_mtime\n                        if pred_path in pred_linecount_dict and pred_linecount_dict[pred_path] == len(src_shard):\n                            # if it's already in pred_linecount_dict, means it's done and counted\n                            do_trans_flag = False\n                        elif elapsed_time < opt.test_interval:\n                            do_trans_flag = False\n                            logger.info(\"Skip translating because previous PRED file was generated only %d sec ago (<%d sec). PRED file: %s\"\n                                        % (elapsed_time, opt.test_interval, pred_path))\n                        else:\n                            # count line numbers of long-done files to check if this is a done pred\n                            try:\n                                pred_linecount_dict[pred_path] = len([1 for i in open(pred_path, 'r').readlines()])\n                                # count is same means it's done\n                                if pred_linecount_dict[pred_path] == len(src_shard):\n                                    do_trans_flag = False\n                                    logger.info(\"Skip translating because previous PRED is complete. PRED file: %s\" % (pred_path))\n                                else:\n                                    # if file is modified less than opt.test_interval min, it might be being processed by another job. Otherwise it's a bad result and delete it\n                                    if elapsed_time < opt.test_interval:\n                                        do_trans_flag = False\n                                    else:\n                                        os.remove(pred_path)\n                                        logger.info('Removed a bad PRED file, #(line)=%d, #(elapsed_time)=%ds. PRED file: %s'\n                                                    % (pred_linecount_dict[pred_path], int(elapsed_time), pred_path))\n                            except Exception as e:\n                                logger.exception('Error while validating or deleting PRED file: %s' % pred_path)\n\n                    opt.data['valid']['path_src'] = src_path\n                    opt.data['valid']['path_tgt'] = src_path\n                    opt.data['valid']['path_align'] = None\n\n                    # do translation\n                    try:\n                        if do_trans_flag or opt.ignore_existing:\n                            logger.info(\"*\" * 50)\n                            logger.info(\"Start translating [data=%s] for [exp=%s]-[ckpt=%s].\" % (datasplit_name, exp_name, ckpt_name))\n                            logger.info(\"\\t exporting PRED result to %s.\" % (pred_path))\n                            logger.info(\"*\" * 50)\n\n                            # if it's BART model, OpenNMT has to do something additional\n                            if 'bart' in exp_name.lower() and not exp_name.lower().startswith('transformer'):\n                                opt.__setattr__('fairseq_model', True)\n                                opt.__setattr__('encoder_type', 'bart')\n                                opt.__setattr__('decoder_type', 'bart')\n                                opt.__setattr__('pretrained_tokenizer', True)\n                                opt.__setattr__('copy_attn', False)\n                                opt.__setattr__('model_dtype', 'fp16')\n                            else:\n                                opt.__setattr__('fairseq_model', False)\n\n                            translator = build_translator(opt, report_score=opt.verbose, logger=logger)\n                            # create an empty file to indicate that the translator is working on it\n                            codecs.open(pred_path, 'w+', 'utf-8').close()\n                            # set output_file for each dataset (instead of outputting to opt.output)\n                            translator.out_file = codecs.open(pred_path, 'w+', 'utf-8')\n                            _, _ = translator.translate(\n                                src=src_shard,\n                                batch_size=opt.batch_size,\n                                attn_debug=opt.attn_debug,\n                                opt=opt\n                            )\n                            job_done = True\n                            logger.info(\"Complete translating [%s], PRED file: %s.\" % (datasplit_name, pred_path))\n                        else:\n                            logger.info(\"Skip translating [%s] for %s, PRED file: %s.\" % (datasplit_name, ckpt_name, pred_path))\n                    except Exception as e:\n                        logger.exception('Error while translating [%s], PRED file: %s.' % (datasplit_name, pred_path))\n\n        # do evaluation\n        if 'eval' in opt.tasks:\n            new_preds = scan_predictions(opt.exp_root_dir)\n            new_preds = sorted(new_preds, key=lambda x: x['step'])\n            random.shuffle(new_preds)\n            logger.info('Found %d predictions from %s!' % (len(new_preds), opt.exp_root_dir))\n\n            for pred_id, pred in enumerate(new_preds):\n                ckpt_name = pred['ckpt_name']\n                pred_path = pred['pred_path']\n                datasplit_name = pred['dataset']\n\n                if datasplit_name not in testset_path_dict: continue\n\n                logger.info(\"[%d/%d] Checking prediction: %s\" % (pred_id, len(new_preds), pred_path))\n                eval_path = pred_path[: -5] + '.%s.eval' % opt.tokenizer\n                printout_path = pred_path[: -5] + '.%s.report' % opt.tokenizer\n\n                do_eval_flag = True\n                try:\n                    src_path, tgt_path, src_shard, tgt_shard = testset_path_dict[datasplit_name]\n                    if not pred_path in pred_linecount_dict: # may be out of date\n                        pred_linecount_dict[pred_path] = len([1 for i in open(pred_path, 'r').readlines()])\n                    num_pred = pred_linecount_dict[pred_path]\n                    if num_pred != len(src_shard):\n                        do_eval_flag = False\n                        logger.info(\"Skip evaluating because current PRED file is not complete, #(line)=%d. PRED file: %s\"\n                                    % (num_pred, pred_path))\n                        elapsed_time = time.time() - os.stat(pred_path).st_mtime\n                        if elapsed_time > opt.test_interval:\n                            os.remove(pred_path)\n                            logger.warning('Removed a bad PRED file, #(line)=%d, #(elapsed_time)=%ds. PRED file: %s'\n                                        % (num_pred, int(elapsed_time), pred_path))\n                            del pred_linecount_dict[pred_path]\n                    else:\n                        # if pred is good, check if re-eval is necessary\n                        if os.path.exists(eval_path):\n                            elapsed_time = time.time() - os.stat(eval_path).st_mtime\n                            if eval_path in eval_linecount_dict and eval_linecount_dict[eval_path] == len(src_shard):\n                                do_eval_flag = False\n                            elif elapsed_time < opt.test_interval:\n                                # if file is modified less than opt.test_interval min, it might be being processed by another job.\n                                do_eval_flag = False\n                                logger.info(\"Skip evaluating because previous EVAL file was generated only %d sec ago (<%d sec). EVAL file: %s\"\n                                            % (elapsed_time, opt.test_interval, eval_path))\n                            else:\n                                try:\n                                    score_dict = json.load(open(eval_path, 'r'))\n                                    if 'present_exact_correct@5' in score_dict:\n                                        num_eval = len(score_dict['present_exact_correct@5'])\n                                    else:\n                                        num_eval = 0\n                                    eval_linecount_dict[eval_path] = num_eval\n                                    if num_eval == len(src_shard):\n                                        do_eval_flag = False\n                                        logger.info(\"Skip evaluating because existing eval file is complete.\")\n                                    else:\n                                        # it's a bad result and delete it\n                                        os.remove(eval_path)\n                                        logger.info('Removed a bad eval file, #(pred)=%d, #(eval)=%d, #(elapsed_time)=%ds: %s' % (num_pred, num_eval, int(elapsed_time), eval_path))\n                                except:\n                                    os.remove(eval_path)\n                                    logger.info('Removed a bad eval file: %s' % (eval_path))\n                except Exception as e:\n                    logger.exception('Error while validating or deleting EVAL file: %s' % eval_path)\n\n                try:\n                    if do_eval_flag or opt.ignore_existing:\n                        logger.info(\"*\" * 50)\n                        logger.info(\"Start evaluating [%s] for %s\" % (datasplit_name, ckpt_name))\n                        logger.info(\"\\t will export eval result to %s.\" % (eval_path))\n                        logger.info(\"*\" * 50)\n                        codecs.open(eval_path, 'w+', 'utf-8').close()\n\n                        score_dict = kp_evaluate.keyphrase_eval(datasplit_name,\n                                                                src_path, tgt_path,\n                                                                pred_path=pred_path, logger=logger,\n                                                                verbose=opt.verbose,\n                                                                report_path=printout_path,\n                                                                tokenizer=opt.tokenizer\n                                                                )\n                        if score_dict is not None:\n                            with open(eval_path, 'w') as output_json:\n                                output_json.write(json.dumps(score_dict)+'\\n')\n                        job_done = True\n                    else:\n                        logger.info(\"Skip evaluating [%s] for %s, EVAL file: %s.\" % (datasplit_name, ckpt_name, eval_path))\n                except Exception as e:\n                    logger.exception('Error while evaluating')\n\n        if job_done: # reset current_patience if no real job is done in the current iteration\n            current_patience = opt.wait_patience\n        else:\n            current_patience -= 1\n\n        if opt.onepass:\n            break\n\n        if current_patience <= 0:\n            break\n        else:\n            # scan again for every 10min\n            sleep_time = opt.sleep_time\n            logger.info('Sleep for %d sec, current_patience=%d' % (sleep_time, current_patience))\n            logger.info('*' * 50)\n            time.sleep(sleep_time)\n"
  },
  {
    "path": "kp_gen_magkp_transfer_labelling.py",
    "content": "# -*- encoding: utf-8 -*-\nimport codecs\nimport json\nimport random\nimport re\nimport shutil\n\nfrom onmt.constants import ModelTask\nfrom onmt.translate.translator import build_translator\nfrom onmt.utils.parse import ArgumentParser\nimport os\n\nimport datetime\nimport time\nimport numpy as np\n\nimport kp_evaluate\nfrom onmt.utils import split_corpus\nfrom onmt.utils.logging import init_logger\n\nimport onmt.opts as opts\n\ndef scan_new_checkpoints(ckpt_dir):\n    ckpts = []\n    for subdir, dirs, files in os.walk(ckpt_dir):\n        for file in files:\n            if file.endswith('.pt'):\n                ckpt_name = file[: file.find('.pt')]\n                if not 'step' in ckpt_name:\n                    # skip the last ckpt of fairseq jobs such as 'checkpoint_last.pt' and 'checkpoint54.pt'\n                    continue\n                ckpt_path = os.path.join(subdir, file)\n                exp_dir = os.path.dirname(subdir)\n                exp_name = exp_dir[exp_dir.rfind('/') + 1: ]\n                ckpt_dict = {\n                    'exp_dir': exp_dir,\n                    'exp_name': exp_name,\n                    'ckpt_path': ckpt_path,\n                    'ckpt_name': ckpt_name,\n                    'step': int(ckpt_name[ckpt_name.rfind('step_')+5:])\n                }\n                ckpts.append(ckpt_dict)\n\n    return ckpts\n\ndef scan_predictions(exp_root_dir):\n    preds = []\n    for subdir, dirs, files in os.walk(exp_root_dir):\n        for file in files:\n            if file.endswith('.pred'):\n                try:\n                    pred_name = file[: -5]\n                    step, data = pred_name.split('-')\n                    step = int(step[step.rfind('step_') + 5:])\n                    data = data[5:]\n                    pred = {\n                        'ckpt_name': pred_name,\n                        'pred_path': os.path.join(subdir, file),\n                        'dataset': data,\n                        'step': step\n                    }\n                    preds.append(pred)\n                except:\n                    print('invalid pred name %s' % file)\n\n    return preds\n\ndef _get_parser():\n    parser = ArgumentParser(description='run_kp_eval_transfer.py')\n\n    opts.translate_opts(parser)\n    # opts.train_opts(parser)\n    opts.model_opts(parser)\n    opts.dynamic_prepare_opts(parser, build_vocab_only=False)\n\n    return parser\n\n\nif __name__ == \"__main__\":\n    parser = _get_parser()\n\n    parser.add_argument('--tasks', '-tasks', nargs='+', type=str,\n                        required=True,\n                        choices=['pred', 'eval', 'report'],\n                        help='Specify process to run, generation or evaluation')\n    parser.add_argument('-exp_root_dir', type=str, required=True, help='Directory to all checkpoints')\n    parser.add_argument('-output_dir', type=str, required=True, help='Directory to outputs')\n    parser.add_argument('-data_dir', type=str, required=True, help='Directory to datasets (ground-truth)')\n    parser.add_argument('--wait_patience', '-wait_patience', type=int, default=2, help='Terminates evaluation after scan this number of times.')\n    parser.add_argument('--wait_time', '-wait_time', type=int, default=120, help='.')\n    parser.add_argument('--sleep_time', '-sleep_time', type=int, default=600, help='.')\n    parser.add_argument('-test_interval', type=int, default=600,\n                        help='Minimum time interval the job should wait if a .pred file is not updated by another job (imply another job failed).')\n\n    opt = parser.parse_args()\n    if isinstance(opt.data, str):\n        setattr(opt, 'data', json.loads(opt.data.replace('\\'', '\"')))\n    setattr(opt, 'data_task', ModelTask.SEQ2SEQ)\n    if opt.data: ArgumentParser._get_all_transform(opt)\n\n    opt.__setattr__('valid_batch_size', opt.batch_size)\n    opt.__setattr__('batch_size_multiple', 1)\n    opt.__setattr__('bucket_size', 128)\n    opt.__setattr__('pool_factor', 256)\n\n    # np.random.seed()\n    wait_time = np.random.randint(opt.wait_time) if opt.wait_time > 0 else 0\n    current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # \"%Y-%m-%d_%H:%M:%S\"\n    if not os.path.exists(os.path.join(opt.exp_root_dir, 'logs')):\n        os.makedirs(os.path.join(opt.exp_root_dir, 'logs'))\n    if not os.path.exists(opt.output_dir):\n        os.makedirs(opt.output_dir)\n    logger = init_logger(opt.exp_root_dir + '/logs/autoeval_%s.log'\n                         % (current_time))\n\n    logger.info(opt)\n\n    current_patience = opt.wait_patience\n    pred_linecount_dict = {}\n    eval_linecount_dict = {}\n\n    while True:\n        job_done = False # a flag indicating if any real pred/eval job is done\n\n        ckpts = scan_new_checkpoints(opt.exp_root_dir)\n        ckpts = sorted(ckpts, key=lambda x:x['step'])\n        random.shuffle(ckpts)\n        logger.info('Found %d checkpoints from %s!' % (len(ckpts), opt.exp_root_dir))\n\n        # iterate each ckpt and start predicting/evaluating\n        for ckpt_id, ckpt in enumerate(ckpts):\n            ckpt_path = ckpt['ckpt_path']\n            ckpt_name = ckpt['ckpt_name']\n            exp_dir = ckpt['exp_dir']\n            exp_name = ckpt['exp_name']\n            logger.info(\"[%d/%d] Checking checkpoint: %s\" % (ckpt_id, len(ckpts), ckpt_path))\n            setattr(opt, 'models', [ckpt['ckpt_path']])\n\n            file_list = os.listdir(opt.data_dir)\n            random.shuffle(file_list)\n            for src_file in file_list:\n                # ignore current testdata-split if this data is not used in training\n                if not re.match('train_\\d+.json', src_file):\n                    continue\n                src_path = os.path.join(opt.data_dir, src_file)\n                pred_path = os.path.join(opt.output_dir, src_file)\n                data_line_number = 58583 if src_file == 'train_120.json' else 100000\n\n                # skip translation for this dataset if previous pred exists\n                do_trans_flag = True\n                if os.path.exists(pred_path):\n                    elapsed_time = time.time() - os.stat(pred_path).st_mtime\n                    if pred_path in pred_linecount_dict and pred_linecount_dict[pred_path] == data_line_number:\n                        # if it's already in pred_linecount_dict, means it's done and counted\n                        do_trans_flag = False\n                    elif elapsed_time < opt.test_interval:\n                        do_trans_flag = False\n                        logger.info(\"Skip translating because previous PRED file was generated only %d sec ago (<%d sec). PRED file: %s\"\n                                    % (elapsed_time, opt.test_interval, pred_path))\n                    else:\n                        # count line numbers of long-done files to check if this is a done pred\n                        try:\n                            pred_linecount_dict[pred_path] = len([1 for i in open(pred_path, 'r').readlines()])\n                            # counts are same means it's done\n                            if pred_linecount_dict[pred_path] == data_line_number:\n                                do_trans_flag = False\n                                logger.info(\"Skip translating because previous PRED is complete. PRED file: %s\" % (pred_path))\n                            else:\n                                # if file is modified less than opt.test_interval min, it might be being processed by another job. Otherwise it's a bad result and delete it\n                                if elapsed_time < opt.test_interval:\n                                    do_trans_flag = False\n                                else:\n                                    os.remove(pred_path)\n                                    logger.info('Removed a bad PRED file, #(line)=%d, #(elapsed_time)=%ds. PRED file: %s'\n                                                % (pred_linecount_dict[pred_path], int(elapsed_time), pred_path))\n                        except Exception as e:\n                            logger.exception('Error while validating or deleting PRED file: %s' % pred_path)\n\n                opt.data['valid']['path_src'] = src_path\n                opt.data['valid']['path_tgt'] = src_path\n                opt.data['valid']['path_align'] = None\n\n                # do translation\n                try:\n                    if do_trans_flag:\n                        logger.info(\"*\" * 50)\n                        logger.info(\"Start translating [data=%s] for [exp=%s]-[ckpt=%s].\" % (src_file, exp_name, ckpt_name))\n                        logger.info(\"\\t exporting PRED result to %s.\" % (pred_path))\n                        logger.info(\"*\" * 50)\n\n                        # if it's BART model, OpenNMT has to do something additional\n                        if 'bart' in exp_name:\n                            opt.__setattr__('fairseq_model', True)\n                            opt.__setattr__('encoder_type', 'bart')\n                            opt.__setattr__('decoder_type', 'bart')\n                            opt.__setattr__('pretrained_tokenizer', True)\n                            opt.__setattr__('copy_attn', False)\n                            opt.__setattr__('model_dtype', 'fp16')\n                        else:\n                            opt.__setattr__('fairseq_model', False)\n\n                        translator = build_translator(opt, report_score=opt.verbose, logger=logger)\n                        # create an empty file to indicate that the translator is working on it\n                        codecs.open(pred_path, 'w+', 'utf-8').close()\n                        # set output_file for each dataset (instead of outputting to opt.output)\n                        translator.out_file = codecs.open(pred_path, 'w+', 'utf-8')\n                        src_shard = [json.loads(l) for l in open(src_path, 'r')]\n                        _, _ = translator.translate(\n                            src=src_shard,\n                            batch_size=opt.batch_size,\n                            attn_debug=opt.attn_debug,\n                            opt=opt\n                        )\n                        job_done = True\n                        logger.info(\"Complete translating [%s], PRED file: %s.\" % (src_file, pred_path))\n                    else:\n                        logger.info(\"Skip translating [%s] for %s, PRED file: %s.\" % (src_file, ckpt_name, pred_path))\n                except Exception as e:\n                    logger.exception('Error while translating [%s], PRED file: %s.' % (src_file, pred_path))\n\n        if job_done: # reset current_patience if no real job is done in the current iteration\n            current_patience = opt.wait_patience\n        else:\n            current_patience -= 1\n\n        if current_patience <= 0:\n            break\n        else:\n            # scan again for every 10min\n            sleep_time = opt.sleep_time\n            logger.info('Sleep for %d sec, current_patience=%d' % (sleep_time, current_patience))\n            logger.info('*' * 50)\n            time.sleep(sleep_time)\n"
  },
  {
    "path": "kp_generate.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom itertools import repeat\n\nfrom onmt.utils.logging import init_logger\nfrom onmt.utils.misc import split_corpus\nfrom onmt.translate.translator import build_translator\n\nimport onmt.opts as opts\nfrom onmt.utils.parse import ArgumentParser\n\n\ndef _get_parser():\n    parser = ArgumentParser(description='kp_generate.py')\n\n    opts.config_opts(parser)\n    opts.translate_opts(parser)\n    return parser\n\n\nif __name__ == \"__main__\":\n    parser = _get_parser()\n\n    opt = parser.parse_args()\n    logger = init_logger(opt.log_file)\n    translator = build_translator(opt, logger)\n\n    src_shards = split_corpus(opt.src, opt.shard_size)\n    tgt_shards = split_corpus(opt.tgt, opt.shard_size) \\\n        if opt.tgt is not None else repeat(None)\n    shard_pairs = zip(src_shards, tgt_shards)\n\n    for i, (src_shard, tgt_shard) in enumerate(shard_pairs):\n        logger.info(\"Translating shard %d.\" % i)\n        translator.translate(\n            src=src_shard,\n            tgt=tgt_shard,\n            src_dir=opt.src_dir,\n            batch_size=opt.batch_size,\n            attn_debug=opt.attn_debug,\n            opt=opt\n        )\n"
  },
  {
    "path": "kp_report.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\n\nimport os\n\nimport kp_evaluate\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    eval_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/'\n    dataset_scores_dict = kp_evaluate.gather_eval_results(eval_root_dir=eval_dir)\n\n    print(dataset_scores_dict)\n"
  },
  {
    "path": "notebook/PRauc_example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"import math\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from itertools import cycle\\n\",\n    \"\\n\",\n    \"from sklearn import svm, datasets\\n\",\n    \"from sklearn.metrics import roc_curve, auc\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import label_binarize\\n\",\n    \"from sklearn.multiclass import OneVsRestClassifier\\n\",\n    \"from scipy import interp\\n\",\n    \"\\n\",\n    \"# Import some data to play with\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris.data\\n\",\n    \"y = iris.target\\n\",\n    \"\\n\",\n    \"# Binarize the output\\n\",\n    \"y = label_binarize(y, classes=[0, 1, 2])\\n\",\n    \"n_classes = y.shape[1]\\n\",\n    \"\\n\",\n    \"# Add noisy features to make the problem harder\\n\",\n    \"random_state = np.random.RandomState(0)\\n\",\n    \"n_samples, n_features = X.shape\\n\",\n    \"X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\\n\",\n    \"\\n\",\n    \"# shuffle and split training and test sets\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\\n\",\n    \"                                                    random_state=0)\\n\",\n    \"\\n\",\n    \"# Learn to predict each class against the other\\n\",\n    \"classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\\n\",\n    \"                                 random_state=random_state))\\n\",\n    \"y_score = classifier.fit(X_train, y_train).decision_function(X_test)\\n\",\n    \"\\n\",\n    \"# Compute ROC curve and ROC area for each class\\n\",\n    \"fpr = dict()\\n\",\n    \"tpr = dict()\\n\",\n    \"roc_auc = dict()\\n\",\n    \"for i in range(n_classes):\\n\",\n    \"    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\\n\",\n    \"    roc_auc[i] = auc(fpr[i], tpr[i])\\n\",\n    \"\\n\",\n    \"# Compute micro-average ROC curve and ROC area\\n\",\n    \"fpr[\\\"micro\\\"], tpr[\\\"micro\\\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\\n\",\n    \"roc_auc[\\\"micro\\\"] = auc(fpr[\\\"micro\\\"], tpr[\\\"micro\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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3GmTu3ItdeWD3Q4mbJEYYyv7OE5cwESE5N5/fUV7NhxhAkTbgagZctK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\\nxqmHGwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"lw = 2\\n\",\n    \"plt.plot(fpr[2], tpr[2], color='darkorange',\\n\",\n    \"         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])\\n\",\n    \"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\\n\",\n    \"plt.xlim([0.0, 1.0])\\n\",\n    \"plt.ylim([0.0, 1.05])\\n\",\n    \"plt.xlabel('False Positive Rate')\\n\",\n    \"plt.ylabel('True Positive Rate')\\n\",\n    \"plt.title('Receiver operating characteristic example')\\n\",\n    \"plt.legend(loc=\\\"lower right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## PR Curve\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn import svm, datasets\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"iris = datasets.load_iris()\\n\",\n    \"X = iris.data\\n\",\n    \"y = iris.target\\n\",\n    \"\\n\",\n    \"# Add noisy features\\n\",\n    \"random_state = np.random.RandomState(0)\\n\",\n    \"n_samples, n_features = X.shape\\n\",\n    \"X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\\n\",\n    \"\\n\",\n    \"# Limit to the two first classes, and split into training and test\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X[y < 2], y[y < 2],\\n\",\n    \"                                                    test_size=.5,\\n\",\n    \"                                                    random_state=random_state)\\n\",\n    \"\\n\",\n    \"# Create a simple classifier\\n\",\n    \"classifier = svm.LinearSVC(random_state=random_state)\\n\",\n    \"classifier.fit(X_train, y_train)\\n\",\n    \"y_score = classifier.decision_function(X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute the average precision score\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Average precision-recall score: 0.88\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import average_precision_score\\n\",\n    \"average_precision = average_precision_score(y_test, y_score)\\n\",\n    \"\\n\",\n    \"print('Average precision-recall score: {0:0.2f}'.format(\\n\",\n    \"      average_precision))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot the Precision-Recall curve\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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G1oZs3KLQizJmpqi2DYHj0ZU158lRmzts8tCLOt0JQWgVl75QRhnd5T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\\neGyavwJNywAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import precision_recall_curve\\n\",\n    \"from sklearn.metrics import plot_precision_recall_curve\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"disp = plot_precision_recall_curve(classifier, X_test, y_test)\\n\",\n    \"disp.ax_.set_title('2-class Precision-Recall curve: '\\n\",\n    \"                   'AP={0:0.2f}'.format(average_precision))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### PR Curve Example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.61904762 0.6097561  0.625      0.64102564 0.63157895 0.64864865\\n\",\n      \" 0.66666667 0.68571429 0.67647059 0.66666667 0.6875     0.70967742\\n\",\n      \" 0.73333333 0.75862069 0.75       0.77777778 0.76923077 0.76\\n\",\n      \" 0.75       0.7826087  0.77272727 0.80952381 0.8        0.84210526\\n\",\n      \" 0.88888889 0.88235294 0.875      0.93333333 0.92857143 0.92307692\\n\",\n      \" 0.91666667 1.         1.         1.         1.         1.\\n\",\n      \" 1.         1.         1.         1.         1.         1.\\n\",\n      \" 1.        ]\\n\",\n      \"[1.         0.96153846 0.96153846 0.96153846 0.92307692 0.92307692\\n\",\n      \" 0.92307692 0.92307692 0.88461538 0.84615385 0.84615385 0.84615385\\n\",\n      \" 0.84615385 0.84615385 0.80769231 0.80769231 0.76923077 0.73076923\\n\",\n      \" 0.69230769 0.69230769 0.65384615 0.65384615 0.61538462 0.61538462\\n\",\n      \" 0.61538462 0.57692308 0.53846154 0.53846154 0.5        0.46153846\\n\",\n      \" 0.42307692 0.42307692 0.38461538 0.34615385 0.30769231 0.26923077\\n\",\n      \" 0.23076923 0.19230769 0.15384615 0.11538462 0.07692308 0.03846154\\n\",\n      \" 0.        ]\\n\",\n      \"[-0.20078777 -0.18028896 -0.15043766 -0.14958104 -0.127333   -0.11429083\\n\",\n      \" -0.09281708 -0.08794325 -0.08257976 -0.07007126 -0.06385956 -0.04914672\\n\",\n      \" -0.0453569  -0.03990992  0.00329292  0.01213704  0.02619553  0.031461\\n\",\n      \"  0.04871914  0.05156366  0.06494036  0.06502325  0.09898447  0.11549715\\n\",\n      \"  0.12179294  0.12585524  0.16106568  0.19062428  0.20018893  0.20106318\\n\",\n      \"  0.22408891  0.22931613  0.24693203  0.26336977  0.27524034  0.30424058\\n\",\n      \"  0.31364864  0.39424863  0.401491    0.42411777  0.42593626  0.49589744]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"precision, recall, thresholds = precision_recall_curve(y_test, y_score)\\n\",\n    \"print(precision)\\n\",\n    \"print(recall)\\n\",\n    \"print(thresholds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pr_auc = metrics.auc(recall, precision)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EgIj4KwKNi5Avb+4yQdUyGtXr2LoUM/Y/FipyPtHj1OJSUlnZo1i6/7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\\nmPnLAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"lw = 2\\n\",\n    \"plt.plot(recall, precision, color='darkorange',\\n\",\n    \"         lw=lw, label='PR curve (AUC=%0.4f, AP=%.4f)' % (pr_auc, average_precision))\\n\",\n    \"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\\n\",\n    \"plt.xlim([0.0, 1.0])\\n\",\n    \"plt.ylim([0.0, 1.05])\\n\",\n    \"plt.xlabel('False Positive Rate')\\n\",\n    \"plt.ylabel('True Positive Rate')\\n\",\n    \"plt.title('Receiver operating characteristic example')\\n\",\n    \"plt.legend(loc=\\\"lower right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Integrete w/ kp eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/memray/Project/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n      \"  from ._conv import register_converters as _register_converters\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import numpy as np\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"from kp_evaluate import run_metrics\\n\",\n    \"from onmt.keyphrase.utils import if_present_duplicate_phrases\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load KP data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"src_path = '../data/keyphrase/meng17/inspec/inspec_test.src'\\n\",\n    \"tgt_path = '../data/keyphrase/meng17/inspec/inspec_test.tgt'\\n\",\n    \"pred_path = '../output/keyphrase/meng17-one2seq-fullbeam-local/pred/kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue_step_90000/inspec.pred'\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"500 500 500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"src_data = [json.loads(l) for l in open(src_path, \\\"r\\\")]\\n\",\n    \"tgt_data = [json.loads(l) for l in open(tgt_path, \\\"r\\\")]\\n\",\n    \"pred_data = [json.loads(l) for l in open(pred_path, \\\"r\\\")]\\n\",\n    \"\\n\",\n    \"print(len(src_data), len(tgt_data), len(pred_data))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"[Title]: Accelerated simulation of the steady-state availability of non-Markovian \\n\",\n      \"\\n\",\n      \"[Abstract]: systems A general accelerated simulation method for evaluation of the steady-state availability of non-Markovian systems is proposed. It is applied to the investigation of a class of systems with repair. Numerical examples are given \\n\",\n      \"\\n\",\n      \"GT[#=5]: [['numerical', 'examples'], ['general', 'accelerated', 'simulation', 'method'], ['steady', 'state', 'availability'], ['accelerated', 'simulation'], ['non', 'markovian', 'systems']]\\n\",\n      \"PRESENT GT[#=5]: [['numerical', 'examples'], ['general', 'accelerated', 'simulation', 'method'], ['steady', 'state', 'availability'], ['accelerated', 'simulation'], ['non', 'markovian', 'systems']]\\n\",\n      \"ABSENT GT[#=0]: []\\n\",\n      \"PRED[#=16]: [['accelerated', 'simulation'], ['markovian', 'availability'], ['non', 'markovian', 'availability'], ['steady', 'markovian', 'availability'], ['steady', 'state', 'availability'], ['state', 'availability'], ['markovian', 'systems'], ['accelerated', 'systems'], ['non', 'state', 'availability'], ['it', 'systems'], ['non', 'markovian', 'systems'], ['steady', 'availability'], [], ['markovian'], ['steady'], ['steady', 'state']]\\n\",\n      \"valid_pred_flags = [1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1]\\n\",\n      \"present_pred_flags = [1 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1]\\n\",\n      \"duplicate_flags = [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\\n\",\n      \"valid_and_present_flags = [1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1]\\n\",\n      \"PRESENT PRED[#=8]: [['accelerated', 'simulation'], ['steady', 'state', 'availability'], ['state', 'availability'], ['markovian', 'systems'], ['non', 'markovian', 'systems'], ['markovian'], ['steady'], ['steady', 'state']]\\n\",\n      \"ABSENT PRED[#=7]: [['markovian', 'availability'], ['non', 'markovian', 'availability'], ['steady', 'markovian', 'availability'], ['accelerated', 'systems'], ['non', 'state', 'availability'], ['it', 'systems'], ['steady', 'availability']]\\n\",\n      \"\\n\",\n      \"PRED[#=16]: ['accelerated', 'simulation'], 1\\n\",\n      \"['markovian', 'availability'], 1\\n\",\n      \"['non', 'markovian', 'availability'], 1\\n\",\n      \"['steady', 'markovian', 'availability'], 1\\n\",\n      \"['steady', 'state', 'availability'], 1\\n\",\n      \"['state', 'availability'], 1\\n\",\n      \"['markovian', 'systems'], 1\\n\",\n      \"['accelerated', 'systems'], 1\\n\",\n      \"['non', 'state', 'availability'], 1\\n\",\n      \"['it', 'systems'], 1\\n\",\n      \"['non', 'markovian', 'systems'], 1\\n\",\n      \"['steady', 'availability'], 1\\n\",\n      \"[], 0\\n\",\n      \"['markovian'], 1\\n\",\n      \"['steady'], 1\\n\",\n      \"['steady', 'state'], 1\\n\",\n      \"MATCH[#=8]: [1. 1. 0. 0. 1. 0. 0. 0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"doc_id = 4\\n\",\n    \"src_dict = src_data[doc_id]\\n\",\n    \"tgt_dict = tgt_data[doc_id]\\n\",\n    \"pred_dict = pred_data[doc_id]\\n\",\n    \"\\n\",\n    \"src_seq = src_dict[\\\"src\\\"].split()\\n\",\n    \"tgt_seqs =[t.split() for t in tgt_dict[\\\"tgt\\\"]]\\n\",\n    \"pred_seqs = pred_dict[\\\"pred_sents\\\"]\\n\",\n    \"\\n\",\n    \"# split tgts by present/absent\\n\",\n    \"present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs)\\n\",\n    \"present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\\n\",\n    \"absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\\n\",\n    \"\\n\",\n    \"# filter out results of invalid preds\\n\",\n    \"# 1st filtering, ignore phrases having <unk> and puncs\\n\",\n    \"valid_pred_flags = kp_evaluate.process_predseqs(pred_seqs, '<unk>')\\n\",\n    \"# 2nd filtering: if filter out phrases that don't appear in text, and keep unique ones after stemming\\n\",\n    \"present_pred_flags, _, duplicate_flags = if_present_duplicate_phrases(src_seq, pred_seqs)\\n\",\n    \"# treat duplicates as invalid\\n\",\n    \"valid_pred_flags = valid_pred_flags * ~duplicate_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"valid_and_present_flags = valid_pred_flags * present_pred_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"valid_and_absent_flags = valid_pred_flags * ~present_pred_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"\\n\",\n    \"# split preds by present/absent and exact/partial/mixed\\n\",\n    \"present_preds = [seq for seq, valid in zip(pred_seqs, valid_and_present_flags) if valid]\\n\",\n    \"absent_preds = [seq for seq, valid in zip(pred_seqs, valid_and_absent_flags) if valid]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print('\\\\n[Title]: %s \\\\n' % (src_dict[\\\"title\\\"]))\\n\",\n    \"print('[Abstract]: %s \\\\n' % (src_dict[\\\"abstract\\\"]))\\n\",\n    \"print('GT[#=%d]: %s' % (len(tgt_seqs), str(tgt_seqs)))\\n\",\n    \"print('PRESENT GT[#=%d]: %s' % (len(present_tgts), str(present_tgts)))\\n\",\n    \"print('ABSENT GT[#=%d]: %s' % (len(absent_tgts), str(absent_tgts)))\\n\",\n    \"\\n\",\n    \"print('PRED[#=%d]: %s' % (len(pred_seqs), str(pred_seqs)))\\n\",\n    \"print('valid_pred_flags = %s' % str(valid_pred_flags.astype(int)))\\n\",\n    \"print('present_pred_flags = %s' % str(present_pred_flags.astype(int)))\\n\",\n    \"print('duplicate_flags = %s' % str(duplicate_flags.astype(int)))\\n\",\n    \"print('valid_and_present_flags = %s' % str(valid_and_present_flags.astype(int)))\\n\",\n    \"print('PRESENT PRED[#=%d]: %s' % (sum(valid_and_present_flags), str(present_preds)))\\n\",\n    \"print('ABSENT PRED[#=%d]: %s' % (sum(valid_and_absent_flags), str(absent_preds)))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print()\\n\",\n    \"print('PRED[#=%d]: %s' % (len(pred_seqs), \\n\",\n    \"                          '\\\\n'.join(['%s, %d' % (s, f) for s,f in list(zip(pred_seqs, valid_pred_flags.astype(int)))]\\n\",\n    \"                             )))\\n\",\n    \"\\n\",\n    \"match_scores_exact = kp_evaluate.compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs, type='exact')\\n\",\n    \"match_scores_exact = match_scores_exact[valid_and_present_flags]\\n\",\n    \"print('MATCH[#=%d]: %s' % (len(match_scores_exact), str(match_scores_exact)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# deprecated data example\\n\",\n    \"# present_tgts  = [['performance'], ['analytical', 'modeling'], ['pending', 'hit'], ['data', 'prefetching'], ['miss', 'status', 'holding', 'register']]\\n\",\n    \"# present_preds = [['analytical', 'model'], ['data', 'prefetching'], ['superscalar', 'microprocessors'], ['moving', 'average'], ['hardware', 'prefetching'], ['hybrid', 'analytical', 'modeling'], ['memory', 'access']]    \\n\",\n    \"# exact_match_scores = [1., 1., 0., 0., 0., 0., 0.]\\n\",\n    \"\\n\",\n    \"metric_names = ['correct', 'precision', 'recall', 'f_score', 'precision_hard', 'f_score_hard']\\n\",\n    \"topk_range = [5, 10, 'k', 'M']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"present_exact_results = kp_evaluate.run_metrics(match_scores_exact, present_preds, present_tgts, metric_names, topk_range)\\n\",\n    \"\\n\",\n    \"for k,v in present_exact_results.items():\\n\",\n    \"    print('%s = %s' % (str(k), str(v)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print('Match=' + str(match_scores_exact))\\n\",\n    \"corrects, precisions, recalls, fscores = compute_PRF1(match_scores_exact, present_preds, present_tgts)\\n\",\n    \"print('Accum Corrects=' + str(corrects))\\n\",\n    \"print('P@x=' + str(precisions))\\n\",\n    \"print('R@x=' + str(recalls))\\n\",\n    \"print('F-score@x=' + str(fscores))\\n\",\n    \"\\n\",\n    \"print('F-score@5=%f' % fscores[4])\\n\",\n    \"print('F-score@10=%f' % (fscores[9] if len(fscores) > 9 else present_exact_results['precision@10']))\\n\",\n    \"print('F-score@O=%f' % fscores[len(present_tgts) - 1])\\n\",\n    \"print('F-score@M=%f' % fscores[len(match_scores_exact) - 1])\\n\",\n    \"auc = compute_PR_AUC(precisions, recalls)\\n\",\n    \"print('AUC=%f' % auc)\\n\",\n    \"ap = compute_AP(match_scores_exact, precisions)\\n\",\n    \"print('AP=%f' % ap)\\n\",\n    \"mrr = compute_MRR(match_scores_exact)\\n\",\n    \"print('MRR=%f' % mrr)\\n\",\n    \"sadr = compute_SizeAdjustedDiscountedRecall(match_scores_exact, present_tgts)\\n\",\n    \"print('SADR=%f' % sadr)\\n\",\n    \"\\n\",\n    \"ndcg = compute_NormalizedDiscountedCumulativeGain(match_scores_exact, present_tgts)\\n\",\n    \"print('nDCG=%f' % ndcg)\\n\",\n    \"alpha_ndcg = compute_alphaNormalizedDiscountedCumulativeGain(present_preds, present_tgts, k=10, alpha=0.5)\\n\",\n    \"print('α-nDCG=%f' % alpha_ndcg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure()\\n\",\n    \"lw = 1.0\\n\",\n    \"plt.plot(recalls, precisions, color='darkorange',\\n\",\n    \"         lw=lw, label='PR curve (AUC=%0.4f, AP=%.4f)' % (pr_auc, 0.0))\\n\",\n    \"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\\n\",\n    \"plt.xlim([0.0, 1.0])\\n\",\n    \"plt.ylim([0.0, 1.05])\\n\",\n    \"plt.xlabel('False Positive Rate')\\n\",\n    \"plt.ylabel('True Positive Rate')\\n\",\n    \"plt.title('Receiver operating characteristic example')\\n\",\n    \"plt.legend(loc=\\\"lower right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebook/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\n\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    pass"
  },
  {
    "path": "notebook/beam_stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Beam statistics?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pred_dir = \\\"/Users/memray/Project/keyphrase/OpenNMT-kpg/output/meng17-one2many-beam10-maxlen40/pred\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ckpt_list = [\\\"kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\\", \\\"kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\\", \\n\",\n    \"\\\"kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_70000\\\", \\n\",\n    \"\\\"kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\\", \\n\",\n    \"\\\"kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_65000\\\", \\n\",\n    \"\\\"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_50000\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"datasets = [\\\"kp20k_valid500\\\", \\\"duc\\\", \\\"inspec\\\", \\\"krapivin\\\", \\\"nus\\\", \\\"semeval\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\n\",\n      \"44297\\n\",\n      \"80496\\n\",\n      \"126943\\n\",\n      \"166049\\n\",\n      \"186091\\n\",\n      \"194660\\n\",\n      \"kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\n\",\n      \"88390\\n\",\n      \"163454\\n\",\n      \"243031\\n\",\n      \"306603\\n\",\n      \"337194\\n\",\n      \"350751\\n\",\n      \"kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_70000\\n\",\n      \"80127\\n\",\n      \"148773\\n\",\n      \"244356\\n\",\n      \"311582\\n\",\n      \"339008\\n\",\n      \"351476\\n\",\n      \"kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_90000\\n\",\n      \"96323\\n\",\n      \"194150\\n\",\n      \"291413\\n\",\n      \"368299\\n\",\n      \"403866\\n\",\n      \"419570\\n\",\n      \"kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_65000\\n\",\n      \"78480\\n\",\n      \"140857\\n\",\n      \"208477\\n\",\n      \"265226\\n\",\n      \"295064\\n\",\n      \"309370\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_50000\\n\",\n      \"200996\\n\",\n      \"326339\\n\",\n      \"540482\\n\",\n      \"723039\\n\",\n      \"810702\\n\",\n      \"849983\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"for ckpt in ckpt_list:\\n\",\n    \"    print(ckpt)\\n\",\n    \"    \\n\",\n    \"    beam_num = []\\n\",\n    \"    beam_len = []\\n\",\n    \"    for dataset in datasets:\\n\",\n    \"        # print(dataset)\\n\",\n    \"        \\n\",\n    \"        pred_json_path = os.path.join(pred_dir, ckpt, dataset + '.pred')\\n\",\n    \"        for jsonl in open(pred_json_path, 'r'):\\n\",\n    \"            pred = json.loads(jsonl)\\n\",\n    \"            beams = pred[\\\"ori_pred_sents\\\"]\\n\",\n    \"            beam_num.append(len(beams))\\n\",\n    \"            beam_len.extend([len(b) for b in beams])\\n\",\n    \"        \\n\",\n    \"        # print(\\\"beam number: total=%d, avg=%f\\\" % (sum(beam_num), sum(beam_num)/len(beam_num)))\\n\",\n    \"        # print(\\\"beam length: total=%d, avg=%f\\\" % (sum(beam_len), sum(beam_len)/len(beam_len)))\\n\",\n    \"        # print(\\\"%d\\\\t%f\\\\t%d\\\\t%f\\\" % (sum(beam_num), sum(beam_num)/len(beam_num), sum(beam_len), sum(beam_len)/len(beam_len)))\\n\",\n    \"        print(sum(beam_len))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'/Users/memray/Project/keyphrase/OpenNMT-kpg/output/meng17-one2many-beam10-maxlen40/pred'\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"os.path.abspath(pred_dir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"os.path.exists(pred_dir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebook/dataset_stat.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/memray/Project/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n      \"  from ._conv import register_converters as _register_converters\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"inspec\\n\",\n      \"krapivin\\n\",\n      \"nus\\n\",\n      \"semeval\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/memray/Project/anaconda3/lib/python3.6/site-packages/seaborn/distributions.py:215: MatplotlibDeprecationWarning: \\n\",\n      \"The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\\n\",\n      \"  color=hist_color, **hist_kws)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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v8aN26sRx55RBs2bPBFbQAAoArc7rl/+umnrp+NMfruu+9UXFzs1aIA\\nAEDVuQ33WbNmuX622WyKiorStGnTvFoUAACoukqdcz969KiuuOIKnTp1Srm5uWrcuLEvagMAAFXg\\n9px7enq6HnnkEUlSXl6eHnvsMS1dutTrhQEAgKpxG+5Lly7VW2+9JenXi+tWrlyphQsXer0wAABQ\\nNW4Py58+fVrBwcGu12ffgvZ8HA6HkpOTtXfvXtlsNk2YMEEhISFKTEyUzWZTs2bNNG7cOAUEBGjO\\nnDnatGmT7Ha7xowZo+uvv1779u0rty8AADg/t+Heo0cPPfTQQ7rzzjslSevXr1f37t3dLvjjjz+W\\nJC1ZskSZmZl68cUXZYzRiBEj1K5dO6WkpGjjxo2Kjo7W9u3btXz5ch08eFDx8fFasWKFpk6dek7f\\n22+/vZrTBQDA+tyG+7PPPqsPPvhAn376qYKCgvTggw+qR48ebhfco0cPdenSRZKUk5OjiIgIbd26\\nVbGxsZKkTp06acuWLYqJiVGHDh1ks9kUHR0th8OhvLw87dy585y+hDsAAO65DXdJuvzyyxUUFCSH\\nw3FhC7fblZCQoA8//FCzZs3Sli1bXLeyDQsLU35+vgoKChQZGen6zJl2Y8w5fc8nKipUdnvgBdVX\\nU9WtW8ury7fbAxQcUqlVX202m63CsTxdw/nG8jS7PaDS68nb67OmuBTmeSnMUWKeVuD2L+GCBQu0\\nfv169enTR8YYzZs3T99//70ee+yxSg2QmpqqUaNG6b777itz85vCwkJFREQoPDxchYWFZdpr1apV\\n5vz6mb7nc+xYUaXqqenq1q2lw4fPvyFTXaWlTpUUl3p1jDOMMeWOFRxi93gNFY3lDaWlzkqtJ1+s\\nz5rgUpjnpTBHiXlebCraQHF7hdqaNWuUnp6uBx98UA899JDS09O1evVqtwO+8847evXVVyX9uudv\\ns9l03XXXKTMzU5KUkZGhtm3b6sYbb9TmzZvldDqVk5Mjp9OpOnXqqFWrVuf0BQAA7rndczfG6LLL\\nLnO9DgkJkd3u/tDnHXfcodGjR2vw4MEqLS3VmDFj1LRpU40dO1ZpaWlq0qSJevbsqcDAQLVt21Zx\\ncXFyOp1KSUmRJCUkJJzTFwAAuOc2pdu3b6/4+Hj1799f0q975O3atXO74NDQUM2cOfOc9vK+Ix8f\\nH6/4+PgybTExMXyfHgCAKnAb7klJSVq8eLHeeecdGWPUvn17xcXF+aI2AABQBRWGe05OjuvnLl26\\nuL7WJkm5ubmKjo72amEAAKBqKgz3Bx54QDabTcXFxTp69KgaNmyogIAA7d+/Xw0bNtS6det8WScA\\nAKikCsP9o48+kiQ9/fTTGjx4sOtq9R07dui1117zTXUAAOCCuf0q3J49e8p8De3666/X3r17vVoU\\nAACoOrcX1NWrV08zZ85U79695XQ6tWbNGv3+97/3QWkAAKAq3O65z5gxQydPntQzzzyjUaNGyeFw\\naOrUqb6oDQAAVIHbPffatWtr7NixvqgFAAB4AA9IBwDAYioM96IiazyIBQCAS02F4T5kyBBJ0vjx\\n431VCwAA8IAKz7kXFRVp1KhR+ve//13mUa1ncFEdAAA1U4Xh/vrrryszM1OfffaZYmNjfVkTAACo\\nhgrDvX79+rr77rvVsmVLNW3aVHv37pXD4VCzZs0q9chXwMr27dur7nf3dtvPbg9QaamzWmM1rFdf\\nb8z7e7WWAeDS4jalT58+rZ49eyoyMlJOp1NHjhzRyy+/rNatW/uiPqBGKnE4VH/Yo277BYfYVVJc\\nWq2xshfMr9bnAVx63Ib75MmT9eKLL7rCPCsrSxMnTtTbb7/t9eIAAMCFc/s996KiojJ76W3atCn3\\nAjsAAFAzuA332rVra8OGDa7XGzZsUGRkpFeLAgAAVef2sPzEiRP17LPPKikpSZLUsGFDzZgxw+uF\\nAQCAqnEb7r///e+1fPlyFRUVyel0Kjw83Bd1AQCAKqr0d9pCQ0O9WQcAAPAQHhwDAIDFuA33xYsX\\n+6IOAADgIW7D/a233vJFHQAAwEPcnnOvV6+eHnzwQbVu3VohISGu9ieffNKrhQEAgKpxG+5t2rTx\\nRR0AAMBD3Ib7k08+qaKiIu3fv1/NmzfXL7/8wpXzAADUYG7PuX/yySfq16+f/vrXv+rIkSPq1q2b\\nNm/e7IvaAABAFbgN97S0NC1atEgRERG66qqrtHDhQk2fPt0XtQEAgCpwG+5Op1N169Z1vb7mmmu8\\nWhAAAKieSl0t//HHH8tms+nkyZN66623FB0d7YvaAABAFbjdc3/++ee1du1aHTx4UD169NCuXbv0\\n/PPP+6I2AABQBW733K+44gqlpaWpoKBAdrtdl112mS/qAgAAVeQ23L/55hslJiYqJydHktSkSROl\\npqaqUaNGXi8OAABcOLeH5ceNG6cRI0YoMzNTmZmZevjhhzVmzBhf1AYAAKrAbbgXFxerc+fOrte3\\n3367CgoKvFoUAACougrDPScnRzk5OWrZsqXmz5+vvLw8nThxQgsXLlTbtm19WSMAALgAFZ5zf+CB\\nB2Sz2WSMUWZmppYsWeJ6z2azKTk52ScFAgCAC1NhuH/00Ue+rAMAAHiI26vlf/jhBy1btkwnTpwo\\n0z516lSvFQUAAKquUk+F6927t1q0aOGLegAAQDW5DfeIiAg9+eSTvqgFAAB4gNtw79+/v1588UW1\\nb99edvv/d7/55pu9WhgAAKgat+G+fft2/fe//9Xnn3/uarPZbHrzzTe9WhgAAKgat+H+1Vdfaf36\\n9b6oBQAAeIDbO9Q1b95cu3fv9kUtAADAA9zuuWdnZ6t///6qW7eugoKCZIyRzWbTxo0bfVEfAAC4\\nQG7D/eWXX/ZFHQAAwEPchvunn35abvvVV1/t8WIAAED1uQ33zMxM18+nT5/WZ599prZt2+ruu+/2\\namEAAKBq3Ib7b28ze/z4cT399NPn/czp06c1ZswY/fTTTyopKdHjjz+ua665RomJibLZbGrWrJnG\\njRungIAAzZkzR5s2bZLdbteYMWN0/fXXa9++feX2BQAA7l1wYoaGhuqnn346b581a9YoMjJSixYt\\n0muvvaaJEydq6tSpGjFihBYtWiRjjDZu3KidO3dq+/btWr58udLS0jRhwgRJKrcvAACoHLd77kOG\\nDJHNZpMkGWN04MABde7c+byf6dWrl3r27On6TGBgoHbu3KnY2FhJUqdOnbRlyxbFxMSoQ4cOstls\\nio6OlsPhUF5eXrl9b7/99mpNFACAS4XbcI+Pj3f9bLPZFBUVpWuuuea8nwkLC5MkFRQUaPjw4Rox\\nYoRSU1NdGwlhYWHKz89XQUGBIiMjy3wuPz/f9XW7s9vciYoKld0e6LbfxaBu3VpeXb7dHqDgELer\\n3iNsNluFY3m6hvON5WkXMlZ1a7LbA7z+O+EJF0ON1XUpzFFinlZQ4V+dnJwcSVKDBg3KfS86Ovq8\\nCz548KCeeOIJDRo0SH369NGMGTNc7xUWFioiIkLh4eEqLCws016rVq0y59fP9HXn2LEit30uBnXr\\n1tLhw+43ZqqjtNSpkuJSr45xhjGm3LGCQ+wer6GisbyhsmN5Yp6lpU6v/05Uly9+b/3tUpijxDwv\\nNhVtoFQY7g888IBsNpuMMa42m82m3NxclZaWateuXRUOduTIET388MNKSUnRLbfcIklq1aqVMjMz\\n1a5dO2VkZKh9+/Zq1KiRZsyYoaFDh+rQoUNyOp2qU6dOuX0BAEDlVBjuH330UZnXhYWFSk1N1ebN\\nmzVx4sTzLnTevHk6efKk5s6dq7lz50qSkpKSNGnSJKWlpalJkybq2bOnAgMD1bZtW8XFxcnpdCol\\nJUWSlJCQoLFjx5bpCwAAKqdSJwM/+eQTJScn67bbbtOaNWsUHh5+3v7JyclKTk4+p33hwoXntMXH\\nx5c5ry9JMTEx5fYFAADunTfci4qKNG3aNNfe+m233earugAAQBVV+D33Tz75RH369JEkrV27lmAH\\nAOAiUeGe+5///GfZ7XZt3rxZW7ZscbXzVDgAAGq2CsOd8AYA4OJUYbjz1DcAAC5OPI0FAACLIdwB\\nALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCw\\nGMIdAACLIdwBALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh0AAIsh3AEAsBjC\\nHQAAiyHcAQCwGMIdAACLIdwBALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh0A\\nAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACL\\nIdwBALAYwh0AAIvxarh/+eWXGjJkiCRp3759uv/++zVo0CCNGzdOTqdTkjRnzhwNGDBAAwcO1I4d\\nO87bFwAAuOe1cF+wYIGSk5NVXFwsSZo6dapGjBihRYsWyRijjRs3aufOndq+fbuWL1+utLQ0TZgw\\nocK+AACgcrwW7o0aNdLs2bNdr3fu3KnY2FhJUqdOnbR161Z99tln6tChg2w2m6Kjo+VwOJSXl1du\\nXwAAUDl2by24Z8+eOnDggOu1MUY2m02SFBYWpvz8fBUUFCgyMtLV50x7eX3diYoKld0e6OFZ+Efd\\nurW8uny7PUDBIV5b9WXYbLYKx/J0Decby9MuZKzq1mS3B3j9d8ITLoYaq+tSmKPEPK3AN38JJQUE\\n/P9BgsLCQkVERCg8PFyFhYVl2mvVqlVuX3eOHSvybMF+UrduLR0+7H5jpjpKS50qKS716hhnGGPK\\nHSs4xO7xGioayxsqO5Yn5rlnzx5df1vHai2jshrWq6835v39gj/ni99bf7sU5igxz4tNRRsoPgv3\\nVq1aKTMzU+3atVNGRobat2+vRo0aacaMGRo6dKgOHTokp9OpOnXqlNsXuFSVOByqP+xRn4yVvWC+\\nT8YB4F0+C/eEhASNHTtWaWlpatKkiXr27KnAwEC1bdtWcXFxcjqdSklJqbAvAACoHK+Ge4MGDbRs\\n2TJJUkxMjBYuXHhOn/j4eMXHx5dpq6gvAABwj5vYAABgMYQ7AAAWQ7gDAGAxhDsAABZDuAMAYDGE\\nOwAAFkO4AwBgMYQ7AAAWQ7gDAGAxhDsAABZDuAMAYDGEOwAAFkO4AwBgMYQ7AAAWQ7gDAGAxhDsA\\nABZDuAMAYDGEOwAAFkO4AwBgMYQ7AAAWQ7gDAGAxdn8XgF/96bGhyj50UHZ7gEpLnV4da1/2PtX3\\n6ggAAH8i3GuI7EMHVX/YowoOsaukuNSrY303+jmvLh8A4F8clgcAwGLYcwfgsm/fXnW/u/cFf64q\\np5Ma1quvN+b9/YLHAuAe4Q7ApcThUP1hj17w56pyOil7wfwLHgdA5XBYHgAAiyHcAQCwGMIdAACL\\nIdwBALAYwh0AAIsh3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh0AAIsh3AEAsBgeHAPAL6r6\\nBLqq4Al0uNQQ7gD8oqpPoKsKnkCHSw2H5QEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYrpYHYHme\\n+Nqd3R6g0lKn23587Q41AeEOwPI88bW74BC7SopL3fbja3eoCTgsDwCAxbDnfh5/emyosg8d9MlY\\n+7L3qb5PRgLgTdx5DzUB4X4e2YcO+uwOWt+Nfs4n4wDwLl/eeW9z8miPb0hUdG0BGxIXlxob7k6n\\nU+PHj9c333yj4OBgTZo0SY0bN/Z3WQBQY3hjQ6Kiawu8sSFRETYkqq/GhvuGDRtUUlKipUuXKisr\\nS9OmTdMrr7zi77IA4JJ0sR+R+K0zRyhyDx3UVfV8c1LUlxstNTbcP/vsM3Xs2FGS1KZNG3311Vd+\\nrggA4Au+2JA4c4Tiu9HPqbUFH2BkM8YYn412AZKSknTHHXeoc+fOkqQuXbpow4YNsttr7PYIAAA1\\nQo39Klx4eLgKCwtdr51OJ8EOAEAl1Nhwv/HGG5WRkSFJysrKUvPmzf1cEQAAF4cae1j+zNXy3377\\nrYwxmjJlipo2bervsgAAqPFqbLgDAICqqbGH5QEAQNUQ7gAAWAzhXgOcPn1azz77rAYNGqQBAwZo\\n48aN/i7Jq44eParOnTtrz549/i7Fa1599VXFxcXpnnvu0fLly/1djlecPn1aI0eO1MCBAzVo0CBL\\nrs8vv/xSQ4YMkSTt27dP999/vwYNGqRx48bJ6XT/+NeLxdnz3LVrlwYNGqQhQ4Zo6NChOnLkiJ+r\\n85yz53nG2rVrFRcX56eKvL6WohwAAAz8SURBVIdwrwHWrFmjyMhILVq0SK+99pomTpzo75K85vTp\\n00pJSdFll13m71K8JjMzU1988YUWL16s9PR0HTp0yN8lecW//vUvlZaWasmSJXriiSf00ksv+bsk\\nj1qwYIGSk5NVXFwsSZo6dapGjBihRYsWyRhjmY3w385z8uTJGjt2rNLT03X77bdrwYIFfq7QM347\\nT0n6+uuv9fbbb8uKl54R7jVAr1699NRTT0mSjDEKDAz0c0Xek5qaqoEDB+qqq67ydyles3nzZjVv\\n3lxPPPGEHnvsMXXp0sXfJXlFTEyMHA6HnE6nCgoKLHcfikaNGmn27Nmu1zt37lRsbKwkqVOnTtq6\\ndau/SvOo384zLS1N1157rSTJ4XAoJCTEX6V51G/neezYMaWlpWnMmDF+rMp7rPV/40UqLCxMklRQ\\nUKDhw4drxIgRfq7IO1auXKk6deqoY8eOmj/fd7dh9LVjx44pJydH8+bN04EDB/T444/rgw8+kM1m\\n83dpHhUaGqqffvpJd955p44dO6Z58+b5uySP6tmzpw4cOOB6bYxxrcOwsDDl5+f7qzSP+u08z2x4\\nf/7551q4cKHeeustf5XmUWfP0+FwKCkpSaNHj7bMxstvsedeQxw8eFAPPvig+vXrpz59+vi7HK9Y\\nsWKFtm7dqiFDhmjXrl1KSEjQ4cOH/V2Wx0VGRqpDhw4KDg5WkyZNFBISory8PH+X5XFvvPGGOnTo\\noHXr1mn16tVKTEwsc8jTagIC/v/PZWFhoSIiIvxYjXe9//77GjdunObPn686der4uxyP27lzp/bt\\n26fx48frmWee0ffff6/Jkyf7uyyPYs+9Bjhy5IgefvhhpaSk6JZbbvF3OV5z9h7AkCFDNH78eNWt\\nW9ePFXnHTTfdpDfffFN//vOflZubq1OnTikyMtLfZXlcRESEgoKCJEm1a9dWaWmpHA6Hn6vynlat\\nWikzM1Pt2rVTRkaG2rdv7++SvGL16tVaunSp0tPTLfl7K0nXX3+93nvvPUnSgQMH9MwzzygpKcnP\\nVXkW4V4DzJs3TydPntTcuXM1d+5cSb9e/GHli86srGvXrvr00081YMAAGWOUkpJiyeso/vSnP2nM\\nmDEaNGiQTp8+raefflqhoaH+LstrEhISNHbsWKWlpalJkybq2bOnv0vyOIfDocmTJ6t+/fqKj4+X\\nJN18880aPny4nyvDheIOdQAAWAzn3AEAsBjCHQAAiyHcAQCwGMIdAACLIdwBALAYwh2ohAMHDqhF\\nixbasmVLmfZu3bqVubtXVXlqOeeTk5OjXr166Z577lFBQYGrffbs2WVuy+lJmZmZ5zyow582btyo\\nmTNnVumzBw4cULdu3TxcEeAdhDtQSUFBQRo7dmyZYLyYbN++Xf/zP/+jlStXKjw83N/l+EX37t1d\\nz3EArIyb2ACVdNVVV+nWW29VamrqOU/uy8zM1Jw5c5Seni5JSkxMVGxsrGJjY/XEE0+oYcOG+vbb\\nb3XdddcpNjZWq1at0okTJ/Tyyy+radOmkqQ5c+Zo9+7dCgkJ0YQJE9SyZUsdOXJEKSkpOnTokGw2\\nm0aOHKlbb71Vs2fPVlZWlg4ePKjBgwdr8ODBrlr27t2rlJQUHT9+XKGhoUpKSlJQUJBeeuklFRUV\\nKSUlRc8///w583M4HHr66afVoEEDPffcc8rIyNCsWbNUWlqqBg0aaOLEidq9e7dmzpypJUuWSJJW\\nrVqlrKwstW7dWuvXr9eJEyd09OhRde3aVYmJiZKkvLw8DRs2TPv371dMTIxmzZql3NxcPfLII4qK\\nilJISIjmzJmjMWPG6Oeff1Zubq7atm2r6dOn6+eff9aoUaNUVFSkgIAAJScnq02bNtqxY4emTp2q\\nX375RVFRUZowYYIaNmyof/zjH1q1apUCAgJ0/fXXnzPPlStXavv27Zo2bZq6deumvn37avPmzTp1\\n6pRSU1N13XXXlen/9ddfu+5c1rJlS1f7kSNHlJSUpJycHNntdj399NPq1KmTjh8/rqSkJP3www8K\\nDg5WYmKipe86iRrMAHArOzvbdO3a1eTn55suXbqYzZs3G2OM6dq1q8nOzjbbtm0zDzzwgKt/QkKC\\nWbFihcnOzjYtWrQwO3fuNA6Hw/To0cO88MILxhhjZs+ebSZPnuxazty5c40xxmzatMn069fPGGPM\\niBEjzIYNG4wxxvz888+me/fuJj8/38yaNavMeGf74x//aNatW2eMMeaLL74wXbp0McXFxWbFihUm\\nISHhnP6zZs0yM2fONImJia56jh49avr27WuOHz9ujDFm8eLFZsyYMcbpdJpu3bqZffv2GWOMGTJk\\niMnKyjIrVqwwt912mzl8+LApLi42cXFxZt26dWbbtm2mTZs2Zv/+/cbhcJg//vGP5uOPPzbZ2dmm\\nefPmJjs72xhjzNq1a13zLy4uNj169DD//e9/zezZs82CBQuMMcZs27bNvPbaa6a4uNj06dPH/PTT\\nT8YYYzIyMsxDDz1kTp8+bdq1a2dKSkqMw+EwKSkp5tChQ2Xmeva/QdeuXc0//vEPY4wxb775pnny\\nySfP+be56667zJYtW4wxxsyZM8d07drVGGPM8OHDzeuvv26MMWb//v2uuY8fP95MmzbNGGPM7t27\\nzX333VfuOgK8jT134AKEh4dr4sSJGjt2rNasWVOpz1x55ZVq1aqVJKlevXquPbno6Ogy59nvvfde\\nSVLnzp317LPP6uTJk9q6dat++OEHzZo1S5JUWlqq7OxsSb/eH/u3CgsLtX//ft1xxx2SpDZt2qh2\\n7dr64YcfzlvjkiVLlJ+f73pG+Zdfful6mJEkOZ1O1a5dWzabTf3799eaNWt0zz336OjRo2rdurX2\\n7Nmjbt266corr5Qk9e7dW9u2bVPPnj3VsmVLNWzYUJLUtGlTHTt2TJJ0xRVXqEGDBpKku+66Szt2\\n7NAbb7yhH374QcePH1dRUZFuueUWxcfHa9euXercubMeeOAB/fjjj8rOztbjjz/uqv/MI2dvuOEG\\nDRgwQN27d9fgwYP1u9/97rzz7tixoySpWbNmWr9+fZn38vLylJubq1tvvVWSdM8992jFihWSpG3b\\ntmnSpEmSpIYNG6p169b68ssv9emnn+qFF16QJLVo0UJLly497/iAtxDuwAXq0KGD6/D8GTabTeas\\nOzmfPn3a9XNwcHCZz1d0n/nftgcFBcnpdOqf//yn6wEeP//8s6688kpt2LCh3GcPGGPK1HGmzd0D\\nXW644Qa1atVKkyZN0qxZs+RwOHTjjTe6HuNaXFyswsJCSVL//v31yCOPKDg4WP369Su3fqfT6Xp9\\n9nPez/53Orv+9PR0rVu3Tvfdd59uvfVWffvttzLG6KabbtJ7772nTZs26f3339eqVauUkJCgBg0a\\naPXq1ZJ+PZ1w5MgRSdLcuXOVlZWljIwMPfLII3rhhRdcz2Avz5nHfZb3ON7frtOz51fRv/Fvn2m/\\nZ88excTElHmiHOAL/MYBVZCYmKjNmzcrNzdXkhQVFaXs7GwVFxfr+PHj+uyzzy54mWvXrpUkffjh\\nh2rSpIkuv/xytW/fXosWLZIkff/99+rbt69OnTpV4TLCw8PVsGFD115oVlaWjhw5ombNmp137JYt\\nW2rYsGH67rvv9PHHH6t169bKysrS3r17Jf0amtOnT5ckXX311apXr56WLFlSJtwzMjKUn5+v4uJi\\nvffee+rUqVOl575lyxbFxcWpb9++stls2r17t5xOp6ZPn67Vq1erf//+SklJ0ddff60mTZroxIkT\\n+s9//iPp10cJjxo1Snl5ebrzzjvVvHlzPfXUU7rtttv0zTffVLqG34qKilJ0dLQ2bdokSXr33Xdd\\n77Vv315vv/22JCk7O1uff/652rRpo7Zt2+r999+X9GuwDxs2rNwNB8Db2HMHquDM4fmhQ4dK+vWw\\nbufOnfW///u/uvrqq3XTTTdd8DJ//PFH9evXT2FhYZo2bZokKTk5WSkpKerTp48kafr06W6vdJ8x\\nY4bGjx+v2bNnKygoSLNnzz7n6EF5goODNX78eCUmJurdd9/VlClTNGLECDmdTv3ud7/TjBkzXH17\\n9+6t9evXlznsfcUVV2jYsGE6duyY+vXrp44dOyozM7NSc3/ooYc0fvx4vf766woLC9MNN9ygAwcO\\naMiQIRo5cqRWrVqlwMBAjRs3TsHBwZo5c6YmT56s4uJihYeHKzU1VXXq1NHAgQM1YMAAXX755apf\\nv7769+9fqfErMmPGDI0ePVovvfSS2rRp42pPSkpSSkqKVq5cKUmaNGmSrrrqKg0fPlzJycnq27ev\\n7Ha7pk+fTrjDL3gqHIALUlpaqueee069evVynds/+yp0AP7HYXkAlWaMUceOHWWz2dSjRw9/lwOg\\nAuy5AwBgMey5AwBgMYQ7AAAWQ7gDAGAxhDsAABZDuAMAYDGEOwAAFvN/6oUrNskUpe4AAAAASUVO\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['inspec', 'krapivin', 'nus', 'semeval', 'kp20k', 'duc', 'stackexchange']\\n\",\n    \"dataset_names = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval']\\n\",\n    \"\\n\",\n    \"json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"tgt_nums = {}\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    tgt_nums[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s_test.json' % dataset_name)\\n\",\n    \"    output_json_path = os.path.join(json_base_dir, dataset_name, '%s_test_meng17token.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"#     with open(input_json_path, 'r') as input_json, open(output_json_path, 'w') as output_json:\\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                json_dict['abstract'] = json_dict['question']\\n\",\n    \"                json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"                del json_dict['question']\\n\",\n    \"                del json_dict['tags']\\n\",\n    \"\\n\",\n    \"            title = json_dict['title']\\n\",\n    \"            abstract = json_dict['abstract']\\n\",\n    \"            fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                json_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"            if len(keywords) < 16:\\n\",\n    \"                tgt_nums[dataset_name].append(len(keywords))\\n\",\n    \"    \\n\",\n    \"#     sns.distplot(np.asarray(tgt_nums, dtype=int), bins=15, color=\\\"r\\\", kde=False, rug=False);\\n\",\n    \"    \\n\",\n    \"    # Plot a simple histogram with binsize determined automatically\\n\",\n    \"#     sns.distplot(tgt_nums, kde=False, color=\\\"b\\\", ax=ax)\\n\",\n    \"\\n\",\n    \"#     # Plot a kernel density estimate and rug plot\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, rug=True, color=\\\"r\\\")\\n\",\n    \"\\n\",\n    \"#     # Plot a filled kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, color=\\\"g\\\", kde_kws={\\\"shade\\\": True})\\n\",\n    \"\\n\",\n    \"#     # Plot a histogram and kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=True, color=\\\"m\\\", ax=ax)\\n\",\n    \"    \\n\",\n    \"# sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"skyblue\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"teal\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"inspec\\\"] , color=\\\"red\\\", label=\\\"Inspec\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"krapivin\\\"] , color=\\\"olive\\\", label=\\\"Krapivin\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"nus\\\"] , color=\\\"gold\\\", label=\\\"NUS\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"semeval\\\"] , color=\\\"teal\\\", label=\\\"Semeval\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"\\n\",\n    \"ax.set(xlabel='Number of keyphrases in doc', ylabel='Number of documents')\\n\",\n    \"sns.plt.legend()\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebook/empirical_analysis_v2_lite.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load packages\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:17:20.769558Z\",\n     \"start_time\": \"2020-11-26T05:17:19.079496Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import fnmatch\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"import tqdm\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"# !pip install -q adjustText\\n\",\n    \"# from adjustText import adjust_text # not quite useful\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def find_dirs(target_name, root_path):\\n\",\n    \"    match_files = []\\n\",\n    \"    for root, dirs, files in os.walk(root_path):\\n\",\n    \"        for dir_name in dirs:\\n\",\n    \"            if target_name == dir_name:\\n\",\n    \"                match_files.append(os.path.join(root, dir_name))\\n\",\n    \"    return match_files\\n\",\n    \"\\n\",\n    \"def find_files(pattern, root_path, recursive=False):\\n\",\n    \"    match_files = []\\n\",\n    \"    if recursive:\\n\",\n    \"        for root, dirs, files in os.walk(root_path):\\n\",\n    \"            for file_name in files:\\n\",\n    \"                if fnmatch.fnmatch(file_name, pattern):\\n\",\n    \"                    match_files.append(os.path.join(root, file_name))\\n\",\n    \"    else:\\n\",\n    \"        # list all subfiles\\n\",\n    \"        for file_name in os.listdir(root_path):\\n\",\n    \"            if fnmatch.fnmatch(file_name, pattern):\\n\",\n    \"                match_files.append(os.path.join(root_path, file_name))\\n\",\n    \"\\n\",\n    \"    return match_files\\n\",\n    \"\\n\",\n    \"def load_eval_results(eval_file_dict, previous_eval_dict_list=[]):\\n\",\n    \"    '''\\n\",\n    \"    Load eval results from json files and return a dict with averaged scores.\\n\",\n    \"    input eval_file_dict is a dict in which key is a dir path and value is a list of eval files.\\n\",\n    \"    '''\\n\",\n    \"    eval_dict_list = previous_eval_dict_list\\n\",\n    \"    loaded_evals_set = set(eval_dict['path'] for eval_dict in previous_eval_dict_list)\\n\",\n    \"    new_evals_set = set()\\n\",\n    \"    \\n\",\n    \"    exp_set =set()\\n\",\n    \"    for eval_dir_name, eval_file_list in tqdm.tqdm(eval_file_dict.items(), desc='Processing eval dir'):\\n\",\n    \"        for eval_file in tqdm.tqdm(eval_file_list, desc='Processing eval dir %s' % eval_dir_name, miniters=100):\\n\",\n    \"#             print('*' * 50)\\n\",\n    \"#             print(eval_dir_name)\\n\",\n    \"#             print(eval_file)\\n\",\n    \"            new_evals_set.add(eval_file)\\n\",\n    \"            if eval_file in loaded_evals_set:\\n\",\n    \"                continue\\n\",\n    \"                \\n\",\n    \"            name_fields = eval_file.split('/')\\n\",\n    \"\\n\",\n    \"            eval_dict = {}\\n\",\n    \"            \\n\",\n    \"\\n\",\n    \"            eval_dict['path'] = eval_file\\n\",\n    \"            eval_dict['exp_group'] = name_fields[-6]\\n\",\n    \"\\n\",\n    \"            exp_name = name_fields[-2]\\n\",\n    \"            exp_fields = exp_name.split('-')\\n\",\n    \"            step = int(exp_fields[-1][exp_fields[-1].rfind('_')+1:])\\n\",\n    \"            exp_name = exp_name[: exp_name.rfind('_step')]\\n\",\n    \"            eval_dict['exp_name'] = exp_name\\n\",\n    \"            \\n\",\n    \"#             if eval_dict['exp_name'] in exp_set:\\n\",\n    \"#                 continue\\n\",\n    \"#             exp_set.add(eval_dict['exp_name'])\\n\",\n    \"#             for i, k in enumerate(name_fields):\\n\",\n    \"#                 print('%d: %s' % (i, k))\\n\",\n    \"#             for i, k in enumerate(exp_fields):\\n\",\n    \"#                 print('%d: %s' % (i, k))\\n\",\n    \"\\n\",\n    \"            if '-v3' in eval_file:\\n\",\n    \"                eval_dict['test_name'] = name_fields[-1][: name_fields[-1].rfind('.')]\\n\",\n    \"                eval_dict['tokenization'], eval_dict['train_mode'], _ = name_fields[-5].split('-')\\n\",\n    \"\\n\",\n    \"                eval_dict['model_base'] = exp_fields[4]\\n\",\n    \"                eval_dict['order'] = exp_fields[3]\\n\",\n    \"                eval_dict['train_dataset'] = exp_fields[2]\\n\",\n    \"                eval_dict['step'] = step\\n\",\n    \"\\n\",\n    \"                eval_dict['test_dataset'] = name_fields[-1][:name_fields[-1].find('.')]\\n\",\n    \"                eval_dict['decoding_method'] = 'exhaustive'\\n\",\n    \"                eval_dict['decoding_terminate'] = name_fields[-5].split('-')[-1]\\n\",\n    \"                eval_dict['beam_width'] = name_fields[-4].split('-')[-2][4:]\\n\",\n    \"                eval_dict['max_length'] = name_fields[-4].split('-')[-1][6:]\\n\",\n    \"            else:\\n\",\n    \"                eval_dict['test_name'] = name_fields[-1][: name_fields[-1].rfind('.')]\\n\",\n    \"                eval_dict['tokenization'], eval_dict['train_mode'], _ = name_fields[-5].split('-')\\n\",\n    \"\\n\",\n    \"                eval_dict['model_base'] = exp_fields[3]\\n\",\n    \"                eval_dict['order'] = exp_fields[2]\\n\",\n    \"                eval_dict['train_dataset'] = exp_fields[0]\\n\",\n    \"                eval_dict['step'] = step\\n\",\n    \"\\n\",\n    \"                eval_dict['test_dataset'] = name_fields[-1][:name_fields[-1].find('.')]\\n\",\n    \"                eval_dict['decoding_method'] = 'exhaustive'\\n\",\n    \"                eval_dict['decoding_terminate'] = name_fields[-5].split('-')[-1]\\n\",\n    \"                eval_dict['beam_width'] = name_fields[-4].split('-')[-2][4:]\\n\",\n    \"                eval_dict['max_length'] = name_fields[-4].split('-')[-1][6:]\\n\",\n    \"        \\n\",\n    \"            try:\\n\",\n    \"                eval_full_dict = json.load(open(eval_file, 'r'))\\n\",\n    \"            except:\\n\",\n    \"                print('Error when loading %s' % eval_file)\\n\",\n    \"                continue\\n\",\n    \"            \\n\",\n    \"            eval_avg_dict = {k: np.average(v) for k,v in eval_full_dict.items()}\\n\",\n    \"\\n\",\n    \"            \\n\",\n    \"#             for k,v in eval_dict.items():\\n\",\n    \"#                 print('\\\\t%s : %s' % (k,v))\\n\",\n    \"#             for k, v in eval_avg_dict.items():\\n\",\n    \"#                 if k.endswith('_num'):\\n\",\n    \"#                     eval_dict[k] = v\\n\",\n    \"                    \\n\",\n    \"            eval_dict.update(eval_avg_dict)\\n\",\n    \"            eval_dict_list.append(eval_dict)\\n\",\n    \"\\n\",\n    \"#             break\\n\",\n    \"#         continue\\n\",\n    \"    \\n\",\n    \"    # some results are actually removed from disk.\\n\",\n    \"    eval_dict_list = [eval_dict for eval_dict in eval_dict_list if eval_dict['path'] in new_evals_set]\\n\",\n    \"            \\n\",\n    \"    return eval_dict_list\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def peak_index(group, x_index, y_index):\\n\",\n    \"    max_x, max_y = 0, -1\\n\",\n    \"\\n\",\n    \"    for id_label, _ in group.iterrows():\\n\",\n    \"        if group.at[id_label , y_index] > max_y:\\n\",\n    \"            max_y = group.at[id_label , y_index]\\n\",\n    \"            max_x = group.at[id_label , x_index]\\n\",\n    \"            \\n\",\n    \"    return max_x, max_y\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def get_max_change(group, y_index, skip_first=5):\\n\",\n    \"    max_change = 0.0\\n\",\n    \"    max_change_rate = 0.0\\n\",\n    \"\\n\",\n    \"    for idx, (id_label, _) in enumerate(group.iterrows()):\\n\",\n    \"        if idx < skip_first:\\n\",\n    \"            continue\\n\",\n    \"        if idx == skip_first:\\n\",\n    \"            prev_id_label = id_label\\n\",\n    \"            continue\\n\",\n    \"        if abs(group.at[id_label, y_index] - group.at[prev_id_label, y_index]) > max_change:\\n\",\n    \"            max_change = abs(group.at[id_label, y_index] - group.at[prev_id_label, y_index])\\n\",\n    \"            max_change_rate = max_change / group.at[prev_id_label, y_index]\\n\",\n    \"            \\n\",\n    \"    return max_change, max_change_rate\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def plot_testing_curve(df, y_index, title='', plot_valid_peak=True):\\n\",\n    \"    fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"\\n\",\n    \"    if plot_valid_peak:\\n\",\n    \"        valid_peak_x, valid_peak_y = peak_index(df[df.test_dataset.str.endswith('kp20k_valid2k')], x_index='step', y_index=y_index)\\n\",\n    \"    #     print(valid_peak_x, valid_peak_y)\\n\",\n    \"        valid_box_props = dict(facecolor='w', alpha=0.5)\\n\",\n    \"    \\n\",\n    \"    peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"    \\n\",\n    \"    for key, grp in df.groupby(['test_dataset']):\\n\",\n    \"#         print(key)\\n\",\n    \"#         print(grp.shape)\\n\",\n    \"#         display(grp)\\n\",\n    \"        if key.endswith('kp20k_valid2k'):\\n\",\n    \"            ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', linestyle='dashed', markersize=8.0, linewidth=4)\\n\",\n    \"        else:\\n\",\n    \"            ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', markersize=8.0, linewidth=4)\\n\",\n    \"\\n\",\n    \"        peak_x, peak_y = peak_index(grp, x_index='step', y_index=y_index)\\n\",\n    \"        variance = grp[y_index].var()\\n\",\n    \"        max_change, max_change_rate = get_max_change(grp, y_index=y_index)\\n\",\n    \"\\n\",\n    \"        if not plot_valid_peak or peak_x == valid_peak_x:\\n\",\n    \"            ax.annotate('%s peak=%.3f (@step=%d)' % (key, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#             ax.annotate('peak=%.3f (@step=%d), var=%.3f, ckpt\\\\u2195=%.3f(%.1f%%)' % (peak_y, peak_x, variance, max_change, max_change_rate * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"        else:\\n\",\n    \"            valid_peak_y = grp[grp.step == valid_peak_x][y_index].item()\\n\",\n    \"            ax.annotate('%s peak=%.3f (@step=%d), valid\\\\u2193=%.3f(%.1f%%), ' % (key, peak_y, peak_x, valid_peak_y, (peak_y-valid_peak_y)/valid_peak_y * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#             ax.annotate('peak=%.3f (@step=%d), var=%.3f, ckpt\\\\u2195=%.3f(%.1f%%), valid\\\\u2193=%.3f(%.1f%%), ' % (peak_y, peak_x, variance, max_change, max_change_rate * 100, valid_peak_y, (peak_y-valid_peak_y)/valid_peak_y * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"\\n\",\n    \"    if plot_valid_peak:\\n\",\n    \"        plt.axvline(x=valid_peak_x, color='k', linestyle='--')\\n\",\n    \"    plt.legend(loc='best')\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"def brief_eval_results(df, base_metric, metrics='all', ignore_valid=False):\\n\",\n    \"    '''\\n\",\n    \"    Given complete results of a set of training experiments (performance of all ckpts on each dataset),\\n\",\n    \"    return best-test/best-valid/last-ckpt score of each dataset\\n\",\n    \"    '''\\n\",\n    \"    assert base_metric is not None, \\\"base metric must be given!\\\"\\n\",\n    \"    if isinstance(metrics, str):\\n\",\n    \"        if metrics == 'present':\\n\",\n    \"            metrics = ['present_exact_precision@5', 'present_exact_recall@5', 'present_exact_f_score@5', 'present_exact_precision_hard@5', 'present_exact_f_score_hard@5', 'present_exact_precision@10', 'present_exact_recall@10', 'present_exact_f_score@10', 'present_exact_precision_hard@10', 'present_exact_f_score_hard@10', 'present_exact_precision@k', 'present_exact_recall@k', 'present_exact_f_score@k', 'present_exact_precision_hard@k', 'present_exact_f_score_hard@k', 'present_exact_precision@M', 'present_exact_recall@M', 'present_exact_f_score@M', 'present_exact_precision_hard@M', 'present_exact_f_score_hard@M', 'present_exact_advanced_auc', 'present_exact_advanced_ap', 'present_exact_advanced_mrr', 'present_exact_advanced_sadr', 'present_exact_advanced_ndcg', 'present_exact_advanced_alpha_ndcg@5', 'present_exact_advanced_alpha_ndcg@10']\\n\",\n    \"        elif metrics == 'absent':\\n\",\n    \"            metrics = ['absent_exact_precision@10', 'absent_exact_recall@10', 'absent_exact_f_score@10', 'absent_exact_precision_hard@10', 'absent_exact_f_score_hard@10', 'absent_exact_precision@50', 'absent_exact_recall@50', 'absent_exact_f_score@50', 'absent_exact_precision_hard@50', 'absent_exact_f_score_hard@50', 'absent_exact_precision@M', 'absent_exact_recall@M', 'absent_exact_f_score@M', 'absent_exact_precision_hard@M', 'absent_exact_f_score_hard@M', 'absent_exact_advanced_auc', 'absent_exact_advanced_ap', 'absent_exact_advanced_mrr', 'absent_exact_advanced_sadr', 'absent_exact_advanced_ndcg', 'absent_exact_advanced_alpha_ndcg@5', 'absent_exact_advanced_alpha_ndcg@10']\\n\",\n    \"        else:\\n\",\n    \"            metrics = ['present_exact_precision@5', 'present_exact_recall@5', 'present_exact_f_score@5', 'present_exact_precision_hard@5', 'present_exact_f_score_hard@5', 'present_exact_precision@10', 'present_exact_recall@10', 'present_exact_f_score@10', 'present_exact_precision_hard@10', 'present_exact_f_score_hard@10', 'present_exact_precision@k', 'present_exact_recall@k', 'present_exact_f_score@k', 'present_exact_precision_hard@k', 'present_exact_f_score_hard@k', 'present_exact_precision@M', 'present_exact_recall@M', 'present_exact_f_score@M', 'present_exact_precision_hard@M', 'present_exact_f_score_hard@M', 'present_exact_advanced_auc', 'present_exact_advanced_ap', 'present_exact_advanced_mrr', 'present_exact_advanced_sadr', 'present_exact_advanced_ndcg', 'present_exact_advanced_alpha_ndcg@5', 'present_exact_advanced_alpha_ndcg@10', 'absent_exact_precision@10', 'absent_exact_recall@10', 'absent_exact_f_score@10', 'absent_exact_precision_hard@10', 'absent_exact_f_score_hard@10', 'absent_exact_precision@50', 'absent_exact_recall@50', 'absent_exact_f_score@50', 'absent_exact_precision_hard@50', 'absent_exact_f_score_hard@50', 'absent_exact_precision@M', 'absent_exact_recall@M', 'absent_exact_f_score@M', 'absent_exact_precision_hard@M', 'absent_exact_f_score_hard@M', 'absent_exact_advanced_auc', 'absent_exact_advanced_ap', 'absent_exact_advanced_mrr', 'absent_exact_advanced_sadr', 'absent_exact_advanced_ndcg', 'absent_exact_advanced_alpha_ndcg@5', 'absent_exact_advanced_alpha_ndcg@10']\\n\",\n    \"    else:\\n\",\n    \"        assert isinstance(metrics, list)\\n\",\n    \"\\n\",\n    \"    str_rows = []\\n\",\n    \"    self_peak_df = pd.DataFrame()\\n\",\n    \"    valid_peak_df = pd.DataFrame()\\n\",\n    \"    last_ckpt_df = pd.DataFrame()\\n\",\n    \"    \\n\",\n    \"    # group rows by those values and return the ones with best valid scores\\n\",\n    \"    for exp_name, exp_grp in df.groupby(['exp_name', 'beam_width', 'decoding_method', 'decoding_terminate']):\\n\",\n    \"#         print(exp_name)\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='test_dataset', ascending=True)\\n\",\n    \"        \\n\",\n    \"        if not ignore_valid:\\n\",\n    \"            valid_peak_step, _ = peak_index(exp_grp[exp_grp.test_dataset.str.endswith('kp20k_valid2k')], x_index='step', y_index=base_metric)\\n\",\n    \"\\n\",\n    \"        for testset, testset_grp in exp_grp.groupby(['test_dataset']):            \\n\",\n    \"            self_peak_step, _ = peak_index(testset_grp, x_index='step', y_index=base_metric)\\n\",\n    \"            last_step = testset_grp['step'].max()\\n\",\n    \"            \\n\",\n    \"            try:\\n\",\n    \"                train_mode = testset_grp.loc[testset_grp.step == self_peak_step, 'train_mode'].values[0]\\n\",\n    \"            except Exception as e:\\n\",\n    \"                print('Error while locating best test score!')\\n\",\n    \"                print('self_peak_step=%s' % self_peak_step)\\n\",\n    \"                print(testset_grp.iloc[0]['path'])\\n\",\n    \"                print(testset_grp.shape)\\n\",\n    \"                display(testset_grp)\\n\",\n    \"                raise e\\n\",\n    \"\\n\",\n    \"            order = testset_grp[testset_grp.step==self_peak_step].order.values[0]\\n\",\n    \"            decoding_terminate = testset_grp[testset_grp.step==self_peak_step].decoding_terminate.values[0]\\n\",\n    \"            str_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step}\\n\",\n    \"#             self_peak_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step, 'valid_peak_step': valid_peak_step}\\n\",\n    \"#             valid_peak_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step, 'valid_peak_step': valid_peak_step}\\n\",\n    \"            if not ignore_valid:\\n\",\n    \"                str_row['valid_peak_step'] = valid_peak_step\\n\",\n    \"\\n\",\n    \"            for metric in metrics:\\n\",\n    \"                self_peak_value = testset_grp[testset_grp.step==self_peak_step][metric].values[0]\\n\",\n    \"\\n\",\n    \"                if not ignore_valid:\\n\",\n    \"                    try:\\n\",\n    \"                        valid_peak_value = testset_grp[testset_grp.step==valid_peak_step][metric].values[0]\\n\",\n    \"                    except Exception:\\n\",\n    \"                        print('Error while locating best valid score!')\\n\",\n    \"                        print('metric=', metric)\\n\",\n    \"                        print('valid_peak_step=', valid_peak_step)\\n\",\n    \"                        print('len(testset_grp)=', len(testset_grp))\\n\",\n    \"                        print(next(iter(testset_grp.path), 'no match'))\\n\",\n    \"                        display(testset_grp)\\n\",\n    \"                        display(testset_grp[testset_grp.step==valid_peak_step])\\n\",\n    \"                        display(testset_grp[testset_grp.step==valid_peak_step][metric])\\n\",\n    \"                        return\\n\",\n    \"                else: \\n\",\n    \"                    valid_peak_value = 0.0\\n\",\n    \"\\n\",\n    \"                str_row[metric] = '%.4f (%.4f)' % (self_peak_value, valid_peak_value)\\n\",\n    \"#                 print('%s@%s - %s = %.4f' % (testset, self_peak_step, metric, testset_grp[testset_grp.step==self_peak_step][metric].item()))\\n\",\n    \"#                 display(testset_grp[testset_grp.step==self_peak_step][metric])\\n\",\n    \"            self_peak_row = testset_grp[testset_grp.step==self_peak_step]\\n\",\n    \"            valid_peak_row = testset_grp[testset_grp.step==valid_peak_step] if not ignore_valid else self_peak_row\\n\",\n    \"            last_step_row = testset_grp[testset_grp.step==last_step]\\n\",\n    \"            \\n\",\n    \"            str_rows.append(str_row)\\n\",\n    \"            self_peak_df = self_peak_df.append(self_peak_row)\\n\",\n    \"            last_ckpt_df = last_ckpt_df.append(last_step_row)\\n\",\n    \"            if not ignore_valid:\\n\",\n    \"                valid_peak_df = valid_peak_df.append(valid_peak_row)\\n\",\n    \"    \\n\",\n    \"    str_summary_df = pd.DataFrame(str_rows).sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    self_peak_df = self_peak_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    last_ckpt_df = last_ckpt_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    \\n\",\n    \"    if not ignore_valid:\\n\",\n    \"        valid_peak_df = valid_peak_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"  \\n\",\n    \"    return str_summary_df, self_peak_df, valid_peak_df, last_ckpt_df\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:26:17.547680Z\",\n     \"start_time\": \"2020-11-26T05:26:12.405349Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"28\\n\",\n      \"28\\n\",\n      \"28\\n\",\n      \"28\\n\",\n      \"43718\\n\",\n      \"(15443, 236)\\n\",\n      \"(28275, 236)\\n\",\n      \"(43718, 236)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pickle\\n\",\n    \"# load from pickle\\n\",\n    \"with open('empirical_analysis_v2_splitnopunk_eval.pkl', 'rb') as tmp_pkl:\\n\",\n    \"    eval_dirs, eval_file_dict, pred_file_dict, kp20k_eval_file_dict, all_splitnopunk_eval_result_dicts, one2one_eval_df, one2seq_eval_df, all_eval_df = pickle.load(tmp_pkl)\\n\",\n    \"\\n\",\n    \"print(len(eval_dirs))\\n\",\n    \"print(len(eval_file_dict))\\n\",\n    \"print(len(pred_file_dict))\\n\",\n    \"print(len(kp20k_eval_file_dict))\\n\",\n    \"print(len(all_splitnopunk_eval_result_dicts))\\n\",\n    \"print(one2one_eval_df.shape)\\n\",\n    \"print(one2seq_eval_df.shape)\\n\",\n    \"print(all_eval_df.shape)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## #1 Plot the testing curve\\n\",\n    \"Models to plot:\\n\",\n    \" - One2one: kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1\\n\",\n    \" - One2seq: \\n\",\n    \"     - kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     \\n\",\n    \"Messages:\\n\",\n    \" - One2one converges much faster than One2seq\\n\",\n    \" - If we check the testing curve close\\n\",\n    \"     - The optimal time-step on transfered datasets is very different from train/valid dataset, which can lead to huge gap between valid-peak with real-peak (up to 16.6% for one2one, 7.2% for one2seq).\\n\",\n    \"     - One2seq is less rough than one2one. Which means for the top 10 results, the one2seq is more stable with respect to ranking, partly because it takes into account the correlation between phrases in a sequence.\\n\",\n    \"     - The more distance between the training and testing dataset, the larger the performance gap between real/valid peak. A few-shot learning might be useful for this domain adaptation.\\n\",\n    \"     \\n\",\n    \"TODO:\\n\",\n    \" - Run pred&eval of step=5000 for seq2seq\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Summary (used in paper Table 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Present  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:16:35.080712Z\",\n     \"start_time\": \"2020-11-23T19:16:34.351927Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"# BaseRNN-one2one\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"# Transformer-one2one\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# Transformer-one2one, MagKP20k\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# BigRNN-one2one\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"# BaseRNN-one2seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# Transformer-one2seq\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# Transformer-one2seq, MagKP20k\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# BigRNN-one2seq\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"     kp20k_one2one_df.at[index_label, 'test_dataset'] = 'one2one - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2seq_df.iterrows():\\n\",\n    \"     kp20k_one2seq_df.at[index_label, 'test_dataset'] = 'one2seq - ' + kp20k_one2seq_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"# combine both and plot\\n\",\n    \"combined_kp20k_df = kp20k_one2one_df.append(kp20k_one2seq_df, ignore_index=True)\\n\",\n    \"combined_df = one2one_df.append(one2seq_df, ignore_index=True)\\n\",\n    \"\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False, title='Present: One2One vs. One2Seq')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:16:38.271556Z\",\n     \"start_time\": \"2020-11-23T19:16:37.947528Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False, title='Absent: One2One vs. One2Seq')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Valid-peak\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:20:01.983221Z\",\n     \"start_time\": \"2020-11-23T19:19:57.386911Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Present F-score@O\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Present F-score@10\\n\",\n    \"# change to use F@O as anchor metric for present KPG\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score@k')\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"ax.legend(loc='center left')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"     \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"# Absent results\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Phrase number\\n\",\n    \"\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Copy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Sum up (used in paper)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T06:57:34.248312Z\",\n     \"start_time\": \"2020-11-22T06:57:25.987702Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(700, 236)\\n\",\n      \"absent valid_kp_df\\n\",\n      \"(28, 236)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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p6tENtTMwYbd++VdnZWSopKdHGjev14IO1AdG4cRO1efNflJKySSkpmzR+/CQNHPiAkpNX299fWVmpiooKSVJ1dbUqKip09erVphnoLSAwAgAAaANu9Skwvr6++lnfTtr3ZbGh5qPsYnm5m9U/yLhk/29nS9TNz0OD7/KVu9mk6f06KfviFX35pWMwBQBAS5CQsESVlRUaO3akli9/TgkJSxUeHqG8vDyNHDlYeXl5kqQBAwYqPv4RLVjwhCZPHqugoGDNmjVXktSuXTv5+wfYvzw9PeXu7qEOHTrYrxMfP0kjRjyggoJ8PfPMfI0Y8YDy8nKbZcz1wS1pAAAAbcCNngJz5MhnDrXZ2VkaPDjS/jq8QztdvFKtkivV8mnnqrJKm1KP5Ctp1F3afeai4b1niysU1uG7VUft3EwK9nZXZmZmI4wKAIDb5+Pjq6SkZIf2oKAg7d17wNA2bdp0TZs2/abnvBYkXW/Llm233slmQGAEAADQBtzOU2Dau5tr26tr5CPp7SP5GtWjgwLbuzu+t9omXw/jr5jt3cyGTUIBAGhqlbYqBQZ63/Z5rlRVqrS4wgk9avnqFRhlZ2dryZIlKi4ulp+fn1atWqW77rrLUGOz2bRixQodOHBALi4umjNnjqZMmWKoycrK0kMPPaT4+Hj96le/ctogAAAA8P1u5ykw5ZW22nZXk74ssurz3MtaHRtR93VczSqvqjG0lVfZ1L59e6m4zrcAANDo3M1umrr5yds+z7txa1SqthEY1WsPoxdeeEHx8fHavXu34uPjtWzZMoeabdu26ezZs9qzZ482b96sV199VefOnbMft9lseuGFFxQVFeW83gMAAKBern8KzDU3ewrMNVkXr6hDO1f5tHPVsbwyXSir1M/fO634d0/qvROF+tvZEs1Pr73lLNTPQ9kXr9jfe6WqRrmllerevXsjjg4AADjbTQOjwsJCnThxQrGxtTt/x8bG6sSJEyoqKjLU7dixQ1OmTJHJZFLHjh0VFRWlXbt22Y+/+eabGjp0qMPKJADA/2fvzuNruvM/jr+yy56GkMSapZYu6K9K1ZKKLZaIpcooU0NqGaVVqmipPVK01aKtMkqZVqk1BDVKaQnaqul0LJEgSGwRke1G7s3vj4zb3N6oLYng/Xw8OpP7PZ/zPd/vPce5537O93uuiEjJu91fgbl8+TJf/vs8rYK8AGhX05t/dKnJnPAg5oQH0b7mQzxV2Z0prWoA8Ew1D46n5bDrxGVyjSaWHTxHjYfKERRU9IgkERERKZtumDBKTk6mUqVK2NkVzF23s7OjYsWKJCcnW8X5+/ubX/v5+ZmfJH7o0CF27dpF3759i7HpIiIiInIrbudXYFq0aEFFVwd6168IQDl7W7ydHcz/Odvb4mhng1e5gicdeJWz562Qaiz++Szdv/wvhy9kMaZ51bvWZxEREbk9Jf7Q66tXrzJu3DiioqLMSafbUb68242DHkDF8dAueTDoWJHSpmNOboWOl5KRe9WIo8Pv118+Pu4sWDDfKs7Hx50DBw5YlA0dOpihQwue9ZAwtdt1t9G7fiWrsif83fi0c83bbfaf0rEit0LHy4NB+1lK24NyzN0wYeTn58fZs2cxGo3Y2dlhNBo5d+4cfn5+VnFnzpyhbt26wO8jjs6fP8/JkycZMGAAAOnp6eTn55ORkcHkyZNvuqEXL2ZgMuXfSt/uez4+7pw/f+VuN0NKWHGdjHSslG3344eOjrmScT8eK6DjpaT4+LgTPmLtHdWxflZEMbWmeOhYKRk6t0hpK85jTvv5/lfWzlH3yzFna2vzp4NzbpgwKl++PHXq1CEmJoaIiAhiYmKoU6cO3t7eFnFhYWGsWLGCNm3akJaWxtatW1m2bBn+/v7ExcWZ4z788EOysrL0K2ki97D09MtERU1m3749eHp6MXDgy7RpE1Zk7PLly1i2bAkGQw4hIaGMHDkGR8eCn2GeNGkcP/64l+zsHLy9y/PCC38lPLwzAFu2xDJjxjRzPSaTCYPBwIIFn1O7dp2S76SIiIiI3JJbuUb87LPP+PirQxiMJppU8+Dlp/1xtCt4Yso7O5M4kJJJTp4J73L2PPdYBcIeLvj+uS0hjQ/3nPm9oq/qkZOTo2tEkRJwU1PSJkyYwOjRo5k3bx4eHh5ER0cD8NJLLzFs2DAef/xxIiIi+OWXX2jTpg0AQ4YMoWpVzVcXuR/NmhWNg4MD69Zt4ejRI4wa9QrBwQ8TGGj5QNO4uN0sXbqY2bM/okIFH8aOHcnChZ8wePBQAHr37svo0eNwdHTkxInjDB06kIcfrkXt2nVo06Ydbdq0M9e1ceN6PvtsAbVq1S7VvoqIiIjIzbmVa8T58+cT1aYG3s4OTN5+gqUHztHvSV8Aejzuw6vPVMbRzpakywbe2JxIkLczD5d3JjTQi9BAL3NdB+q8wIcfztE1okgJuKmEUVBQECtWrLAq//TTT81/29nZMXHixBvWNXTo0FtonoiUNdnZ2ezYsY0lS5bj4uJCvXr1adq0OZs3bzQngq6JjY2hY8cI80VC376RTJo0zhxX+OLBxqbgv9OnTxV5dyg2NoawsA7Y2NiUYO9ERERE5Hbc6jXic889R/Xs7wH4S92KzNh5ypwwqu5Vzhxr87//Sb6Sy8Plna22u3r16iKvEUtjRDxATk4Oc+a8z7fffkNeXh7BwTWZO/fTIrcjcq8p8Ydei8j9JSnpBLa2dlSrVt1cFhRUkwMHfrKKTUxMoFmzEPPr4OCapKZe5PLlNDw9C+4MzZw5ndjY9RgMBmrWrEXjxk2s6klJSeaXX35mzJjxJdAjEREREblTt3qN2KFDGPxckDAKfKgcl3LySM/Jw+N/v7g4Z88Zth67hMGYT5B3OZ6qbP2clbMZuezf/xsjR75ptaw0RsQDvPPOVIzGPJYuXYmHhwdHjx65zXfwwVMaUxgBcvJMLNifwveNGpGefQVnXzeC+z9ZKn2819ne7QaIyL0lOzsbNzfLD2w3NzeysjKLiM3C1dXNIg4gKyvLXDZy5Gi2bPmOuXMX0Lx5C/PdnMI2bdpA3br18fevXFzdEBEREZFidKvXiIVjXR0Lfs0xO89kLnv5aX++/ssjzGgbQJNqHjjYWX91/VdCGg0aNLC6Rrw22ikycpDVaKc/Kjwi3sPDg759I4mNjTEvDwwMMl+fFh4RD3Dy5HF27fqOUaPe5KGHHsLOzk7PUboFhZN648dPYdasKBISjlnFFZ7C+FnXWqRk5LL0wDnz8h6P+/BZ15qs+ssjvB1anSU/n+PoxWzz8g92n+ZKrpHY2FgeG9Mc/3YPl0r/7gdKGInILXF2diYzM8OiLDMzExcX1yJiXcjMzCwUV7Cei4uLRZydnR316tXn/PlzrF690qqeTZs20K5dx+JovojcA9LTLzNmzEhatWpKt24d2bJl03Vjly9fRqdObWnbNoRp0yaSm5trXjZp0jgiItrSpk0IPXt2Zf36NeZlyclnaNq0Aa1bNzP/99lnC0q0XyIi97NbvUbMyPg9NivXWFBub/n11M7WhscquXIh6yobDqda1fOvY2l07tzZqvx6o50SExOsYhMTEwgO/j2BUHhE/DUzZ06nZcsm9Or1HOXLVzCPiP/Pf37F19eXhQs/oUOHlvz1rz3Yvv1fVtsQa7ea1Hvuueeo7lUOdyc7/lK3IluP/b5/qnuVM482KjyFEeDUZQN7Tl1h2NP+eHt7Y2Nrg4u/R2l08b6gKWkickuqVq2O0WgkKekkVatWAyA+/ggBAYFWsQEBgcTHH6Fly9b/izuKt3d583S0PzIajeY7NtccPHiACxfO06JFy2LuiYiUVaU1jQAgNvZb7O11OSQicqdu9Rrx8OHDXHtMdcKlHB4qZ2+ejvZHRtPvCYBr/nMuk4vZV2nbti3Z2fkWy4prRPy1a9aRI0czfPjr/ODay8YAACAASURBVPrrv/n55/3mEUfnz58jIeEYISGhrFmziV9/PcioUa9So0YgNWoEXO+tEkpvCuOhC1lUcnVg6S/n2N6oETmOV6nUIgCvRyuWQi/vfRphJCK3xNnZmZCQFixY8DHZ2dkcPHiAXbt20LZte6vYsLAObNiwjsTEBNLT01m8eKF5pNClS6ls3bqZrKwsjEYjcXG72bp1M08+2cCijtjYDYSEhBZ5d0pE7j+lNY1ARESK161eI65cuZITaTlcMRj58t/naRVUkJxJy85je2Ia2VeNGE35/Hj6CtuPp1HP1/JacOuxNJpW87RKDF1rS2mMiHdycsLe3p4XX+yPg4MDTzzxJE880YC9e/fczFv2QCutKYwXsq5yPM2Aq4MtO3fupHKHmiSt/i855623I9aUMBKRWzZixGhycw2Eh7dmwoQ3GTFiDIGBQaSkpNC6dTNSUlIAePrpZ+jVqw/Dhg3iuefC8fX1o3//gf+rxYY1a76ma9f2tGsXyty57zNs2AiaNXvWvB2DwcC3336j6WgiD5DSmkZwzXPPhdOlS3umTZtIWloaIiJy+27lGjEyMpLRWxJ5cdVhKro60Lv+/0Z82MCGI6n0WXmY7sv/y4IfUxjYwI/G1X6fRpRrNLHz+GVaBhU9ar3waKdrbjQi/ve4mx8RHxSkZ+HcrtKawuhkZ4u9rQ1/qVsRR0dH3AIewq2GF1firac4ijWNwRaRm/aQuyP25Zzw8XFnwYL5Vst9fNw5cOCARdnQoYMZOnSw+XVejoFLV3J56KGHmDPHuo7CnJyc2LRpe7G0XUTuDaU1jcDT04sFC5YQHFyT9PTLvPtuNJMmvcW7784pqa6JiNw33D2cKedk/VXyVq4R//a3vxFyJsYq1qucPTPaWid2CnO0s2XlXx657vLCo51Gjx7H0aOH2bVrBx999A+r2LCwDkybNpE2bdpRvnwFqxHxP/64j2eeaYaTkxP79+9l69bNvP32FADq1/8/KlXyZenSz+jduy+//fYrP//8I0OGvPKn7ZfSm8JY46FyJdL+B4USRiJy0+zLOfF9RLc7qqPJ2q/hD3PQRUSuKclpBFu2bGT16pV0794TFxcXatcu+LLh7V2e4cNHERERRmZmhkUSSkRErJVzsid8xNo7qmP9rIhiak3RRowYTVTUJMLDW+Ph4Wkx2qlPn+58/vkKfH19LUbEGwwGnn021GpE/MyZUZhM+fj6+lqMiLe3tycqahbR0VNYuvQzfH39eOutiVSvXqNE+3Y/uNWk3vTpk6j/jBfezg5WUxgPpGTQqIo7jna2HEjOYPvxNN5oWhWAxyu54uPqwPJfzzM6L4/ME2lkHE/Dr21wqfb3XqWEkYiIiJQZpf1g/WtsbGwAyM8vcrGIiJRRucar+Pi4W5Xfyminlwb1p2fP3laxNzMiPjAwiE8+WXSLrRa4taReZGQko9+fgcGYT9NqHlZTGOfsOYMJqOTqYDGF0d7WhrdbVOP9H07ToEEDTG42VOv6COV89HzUm6GEkYiIiJQZpTWN4D//+RV3dzeqVKnGlSvpvP/+TJ544skiH54qIiJll6OdA88vH3zjwD/xVY+PuIKhmFokhV1v+iKU3hTG6l7leK99EIFvfn3Hx8qDRgkjERERKVNKYxrBmTOnmD9/HpcupeLq6kqDBo2YMGHqXeuz3L709MtERU1m3749eHp6MXDgy7RpE1Zk7PLly1i2bAkGQw4hIaGMHDnG/FyrSZPG8eOPe8nOzsHbuzwvvPBXwsM7AwUPWJ8y5W3zCLVaterw6qsjixz5JiIivyuO6YtQ8lMYpWhKGImIiEiZ4uHhSVTULKtyX19fvvlmp0VZz569b2saQevWYbRuXXRSQe4ts2ZF4+DgwLp1Wzh69AijRr1CcPDDBAYGWcTFxe1m6dLFzJ79ERUq+DB27EgWLvyEwYOHAtC7d19Gjx6Ho6MjJ04cZ+jQgTz8cC1q165DhQo+TJkSja+vHyaTiVWrVjBhwlgWL/7ybnRZRESkVChhJCIiJao07v7/+uu/WbDgIw4fPoSdnS316z/Jq6++ToUKFUqtn3L7TLm5RT5/4lZc+wVGebBkZ2ezY8c2lixZjouLC/Xq1adp0+Zs3rzRnAi6JjY2ho4dI8yJpL59I5k0aZw5rnCCycam4L/Tp09Ru3Yd3N3dcXcvOEbz8/OxtbXl1KmkUuqliIjI3aGEkYiIlKjSuPt/5Uo6nTp1pVGjp7Gzs+fdd6OZNm0i77774d3ostwiW0dH/QKj3JakpBPY2tpRrVp1c1lQUE0OHPjJKjYxMYFmzULMr4ODa5KaepHLl9PMD0qfOXM6sbHrMRgM1KxZi8aNm1jUERb2LNnZ2ZhMpkLTH0VERO5Ptne7ASIicv+6dvc/MnKQ1d3/Pyp899/Dw4O+fSOJjf394YaBgUHm0UaF7/4DNG7chNDQVri6ulGuXDm6devBv//9S+l0UkTumuzsbKsHlbu5uZGVlVlEbBaurm4WcQBZWVnmspEjR7Nly3fMnbuA5s1bmM8512zatJ1Nm7YzfPgoatasXZxdERERKXOUMBIRkRJzvbv/iYkJVrGJiQkEBz9sfl347v81M2dOp2XLJvTq9Rzly1ewuvt/zS+//KSH0Yo8AJydncnMzLAoy8zMxMXF+ueSnZ1dyMzMLBRXsJ6Li4tFnJ2dHfXq1ef8+XOsXr2yyG127tyNKVPe5tKl1OLohoiISJmkhJGIiJSY0r77DxAff5RFixYwZMgrxdUNESmjqlatjtFoJCnppLksPv5IkQnjgIBA4uOPFIo7ird3efN0tD8yGo3mUYx/ZDKZyMnJ4fz5c3fYAxERkbJLCSMRESkxpX33/9SpJEaOHMYrr4ygXr0niqsbIlJGOTs7ExLSggULPiY7O5uDBw+wa9cO2rZtbxUbFtaBDRvWkZiYQHp6OosXL6Rdu44AXLqUytatm8nKysJoNBIXt5utWzfz5JMNANi3bw9HjhzCaDSSmZnBnDnv4e7uTvXqAaXaXxERkdKkh16LiEiJKXz3v2rVasCN7/63bNn6f3G3dvc/JSWZV1/9O3379icsrEMJ9EZEyqIRI0YTFTWJ8PDWeHh4MmLEGAIDg0hJSaFPn+58/vkKfH19efrpZ+jVqw/Dhg3CYDDw7LOhhR5cbcOaNV8zc2YUJlM+vr6+DBs2gmbNngXgypUM3ntvBufPn8PJyYnatR9h1qwPcXJyumv9FhERKWlKGImISIkpfPd/9OhxHD16mF27dvDRR/+wig0L68C0aRNp06Yd5ctXsLr7/+OP+3jmmWY4OTmxf/9etm7dzNtvTwHg/PlzDBs2iK5du9O583Ol2kcRubs8PDyJipplVe7r68s33+y0KOvZszc9e/a2in3ooYeYM2f+dbcRGtqK0NBWd95YERGRe4gSRiIiUqJK4+7/+vVrOHPmNIsWfcqiRZ+at/3HL4sicm8z5ebi4+N+x/Xk5Ri4dCW3GFokIiJy/1LCSERESlRp3P3v128A/foNuPPGikiZZuvoyPcR3e64niZrvwYljERERP6UEkYiIlLsNApARETKovT0y0RFTWbfvj14enoxcODLtGkTVmTs8uXLWLZsCQZDDiEhoYwcOQZHR0dyc3OZNWs6+/fvJT09nSpVqjBgwBAaN25iXnf9+jUsXfoZqakXefzx+owdO54KFXxKq5siIsVCCSMRESl2GgUgIiJl0axZ0Tg4OLBu3RaOHj3CqFGvEBz8MIGBQRZxcXG7Wbp0MbNnf0SFCj6MHTuShQs/YfDgoRiNRipWrMScOfOpVMmX3bu/Z/z4MSxZ8iV+fv78/POPfPLJXD744GOqVq3G7NkzmTDhzT8dKSsiUhbZ3u0GiIiIiIiIlLTs7Gx27NhGZOQgXFxcqFevPk2bNmfz5o1WsbGxMXTsGEFgYBAeHh707RtJbGwMUPCDDv37D8TPzx9bW1uaNGmGv78/hw//F4Dvv99JixatCAwMwsHBgb59Izlw4CeLX/YUEbkXKGEkIiIiIiL3vaSkE9ja2lGtWnVzWVBQTRITE6xiExMTCA5+2Pw6OLgmqakXuXw5zSo2NfUiSUknCQgoGKWUn59Pfn6+efm1vxMS4outLyIipUEJIxERERERue9lZ2fj5uZmUebm5kZWVmYRsVm4urpZxAFkZWVZxOXl5TFx4jjCwjpQvXoNABo3bsK3335DfPxRDIYcFi36FBsbG3Jycoq5RyIiJUvPMBIRERERkfues7MzmZkZFmWZmZm4uLgWEetCZmZmobiC9VxcXMxlJpOJyZPH4eBgz2uvvWEub9CgIf36DeStt0aRkZFBjx69cHFxoWLFSsXdJRGREqURRiIiIiIict+rWrU6RqORpKST5rL4+CMEBARaxQYEBBIff6RQ3FG8vcvj6ekFFEwzmz59MqmpqUyd+g729pb34bt1e54vv1xNTMw3hISEYjQazVPWRETuFUoYiYiIiIjIfc/Z2ZmQkBYsWPAx2dnZHDx4gF27dtC2bXur2LCwDmzYsI7ExATS09NZvHgh7dp1NC+fOTOK48cTiY5+DyenchbrGgwGEhLiyc/PJyUlhXfemUr37n/Bw8OjxPsoIlKcNCVNREREREQeCCNGjCYqahLh4a3x8PBkxIgxBAYGkZKSQp8+3fn88xX4+vry9NPP0KtXH4YNG4TBYODZZ0Pp338gACkpyaxduwpHR0ciItqa63799bG0adOO3NxcJk58i9OnT+Hi4kr79uFERg66W10WEbltShiJiIiIiMgDwcPDk6ioWVblvr6+fPPNTouynj1707Nn7yJi/di1a/91t+Hu7s7ixV/eeWNFRO4yJYxEREREROS+Y8rNxcfH/Y7qyMsxcOlKbjG1SETk3qKEkYiIiIiI3HdsHR35PqLbHdXRZO3XoISRiDyg9NBrERERERERERGxoISRiIiIiIiIiIhYUMJIREREREREREQsKGEkIiIiIiIiIiIW9NBrkVKQnn6ZqKjJ7Nu3B09PLwYOfJk2bcKKjF2+fBnLli3BYMghJCSU6OhpAOQaTcyNO8OB5EyuGIz4uTvS9/8q8VRl61//WPbLOZb+co5prWrwhL9bifZNRERERETkQXcn3/lGjhyDo6Mjubm5zJo1nf3795Kenk6VKlUYMGAIjRs3ASA5+Qzdu3fC2dnZXNcLL7xI376RJdInJYxESsGsWdE4ODiwbt0Wjh49wqhRrxAc/DCBgUEWcXFxu1m6dDGzZ39EhQo+jB07kg8++ICugMkEPi4OvNM2AB9XB/advkLUjiQ+6hRMJTdHcx1nrhjYdeIy3s765y0iIiIiIlIa7uQ738KFnzB48FCMRiMVK1Zizpz5VKrky+7d3zN+/BiWLPkSPz9/cx2xsd9ib1/y3/c0JU2khGVnZ7NjxzYiIwfh4uJCvXr1adq0OZs3b7SKjY2NoWPHCAIDg/Dw8KBv30hWr14NQDkHW3rXr0QlN0dsbWxoVMWDSm4OHL2YbVHHR3HJ/O3/fLG3tSmV/omIiIiIiDzI7vQ7X2xsDADOzs707z8QPz9/bG1tadKkGf7+/hw+/N/S7hKghJFIiUtKOoGtrR3VqlU3lwUF1SQxMcEqNjExgeDgh82vg4NrcuHCBdJz8qxiL2XncTo9l+pe5cxlO49fxt7WhoZVrKepiYiIiIiISPG70+98qakXuXw5zSo2NfUiSUknCQiwHKX03HPhdOnSnmnTJpKWZr1ecVHCSKSEZWdn4+Zm+RwhNzc3srIyi4jNwtXVzSIOIDvPZBGXZ8rnnZ1JtAryoqqnU0HMVSOf/XyWgU/5FXcXRERERERE5DqK4ztfVlaWRVxeXh4TJ44jLKwD1avXAMDT04sFC5awcuV6Fi78nKysTCZNequYe/M7JYxESpizszOZmRkWZZmZmbi4uBYR60JmZmahuIL1nO1//6dqys9nxq5T2NvZ8PdGv89jXfrLOUIDvfB1//15RiIiIiIiIlKyiuM7n4uLi7nMZDIxefI4HBzsee21N8zlLi4u1K79CPb29nh7l2f48FHs3bvHatvFRQkjkRJWtWp1jEYjSUknzWXx8UcICAi0ig0ICCQ+/kihuKNUqFABj3IFDzTLz8/n/R9Ok5adx1sh1SyeU3QgOZN1hy7S66tD9PrqEBeyrjLtuyS++vV8CfZORERERETkwXan3/m8vcvj6ekFFHznmz59MqmpqUyd+s6fPtzaxsbmf+sUV08sKWEkUsKcnZ0JCWnBggUfk52dzcGDB9i1awdt27a3ig0L68CGDetITEwgPT2dxYsX0qVLF/PyOXFnOHnZwITQajjZW/7zjWpdg486BTMnPIg54UF4O9sz7Gl/wmt5l3gfRUREREREHlR3+p2vXbuO5uUzZ0Zx/Hgi0dHv4eRUzmLd//znV06ePI7JZOLy5TTef38mTzzxpNV0uOKi390WKQUjRowmKmoS4eGt8fDwZMSIMQQGBpGSkkKfPt35/PMV+Pr68vTTz9CrVx+GDRuEwWDg2WdDGTZsGKdm7OZsRi4bj1zCwdaGXisOm+se+rQ/oYFe5lFI19ja2ODmaIezg11pd1dEREREROSBciff+fr3HwhASkoya9euwtHRkYiItua6X399LG3atOPMmVPMnz+PS5dScXV1pUGDRkyYMLXE+qSEkUgp8PDwJCpqllW5r68v33yz06KsZ8/e9OzZ2/za0bHgmUSV3ByJ/etjN73Nxd1q3WZrRUREREREpCim3Fx8fKx/ldrHx50FC+YXWX7gwAGLssEv9bP4zneNr68fu3btv+62W7cOo3XrsNto9e1RwkikhLh7OFPOSf/ERERERERE7he2jo58H9HtjuposvZruJJbTC0qOfo2K1JCyjnZEz5i7R3Xs35WRDG0RkREREREROTm6aHXIiIiIiIiIiJiQSOMRB4w6emXiYqazL59e/D09GLgwJdp06boebDLly9j2bIlGAw5hISEEh09DYCrJhOfnz3DfzMzyTAZqeTgSFefStR1K5jLm5dv4pMzpzienc3FvKuMqlqD2q4l8+R+ERERERERKX4aYSTygJk1KxoHBwfWrdvC+PFTmDUrioSEY1ZxcXG7Wbp0Me+/P48VK9Zz5sxpPvjgAwBMgLe9A29UC2Duw3Xo7FORj84kcSH393m4Dzu78JJ/FTztlJcWERERERG51yhhJPIAyc7OZseObURGDsLFxYV69erTtGlzNm/eaBUbGxtDx44RBAYG4eHhQd++kaxevRoAJ1tbOvtUooKjI7Y2NtR388DHwYHjOdkA2NvY0sa7AjVdXLG1KdUuioiIiIiISDFQwkjkAZKUdAJbWzuqVatuLgsKqkliYoJVbGJiAsHBD5tfBwfX5MKFC2QY86xiL+flkZKbS2WnciXTcBERERERESlVShiJPECys7Nxc7N8lpCbmxtZWZlFxGbhWui5Q9fWyzGaLOLy8vOZfyaJJp5e+Dk5lUCrRUREREREpLQpYSTyAHF2diYzM8OiLDMzExcX1yJiXcjMzCwUV7BeObvfTxum/HwWnDmFvY0NL1TyL6FWi4iIiIiISGlTwkjkAVK1anWMRiNJSSfNZfHxRwgICLSKDQgIJD7+SKG4o1SoUAG3/z3EOj8/n0Upp0k35jGkcjXsbfSwIhERERERkfuFEkYiDxBnZ2dCQlqwYMHHZGdnc/DgAXbt2kHbtu2tYsPCOrBhwzoSExNIT09n8eKFdOnSxbz887NnSDYYGFalGo621qeSqyYTV00F09fyyOeqyUR+fn7JdU5ERERERESKzU393nViYiKjR48mLS0NLy8voqOjqVGjhkWM0WhkypQp7Ny5ExsbGwYMGED37t0BmDt3Lhs3bsTOzg57e3uGDx9Os2bNir0zInJjI0aMJipqEuHhrfHw8GTEiDEEBgaRkpJCnz7d+fzzFfj6+vL008/Qq1cfhg0bhMFg4NlnQxk2bBj7du7mwtVctqddwt7GhuFHD5vr/quvP409vQAYm3CUi3lXAXg36QQA7wTWLP0Oi4iIiIiIyC27qYTR22+/Ta9evYiIiGDt2rWMHz+eJUuWWMSsX7+ekydPsmXLFtLS0ujcuTONGzemSpUq1K1bl379+uHs7MyhQ4fo3bs3u3btolw5/aKSSGnINV7Fx8cdAB8fdxYsmG8V4+PjzoEDByzKhg4dzNChg61iKzg48o/aj/3pNmcE17qDFouIiIiIiMjddMOE0cWLF/ntt99YtGgRAB07dmTy5Mmkpqbi7e1tjtu4cSPdu3fH1tYWb29vWrVqxaZNm4iMjLQYTVSrVi3y8/NJS0vD19e3BLokIn/kaOfA88utEz+36qseHxVDa0RERERERKSsu2HCKDk5mUqVKmFnZweAnZ0dFStWJDk52SJhlJycjL//77+S5OfnR0pKilV9a9asoVq1akoWiYiIiIiISJmVnn6ZqKjJ7Nu3B09PLwYOfJk2bcKKjF2+fBnLli3BYMghJCSUkSPH4OjoCMDXXy9n48YYEhLiadWqLW++OcG83pYtscyYMc382mQyYTAYWLDgc2rXrlOi/RO5kZuaklZc9u7dy+zZs/nHP/5xy+uWL+9WAi26912bZiRyL9FxK7dCx4vcLB0rcit0vMjN0rHyYChqP0dFvY2bmzM//PAD//3vfxk4cCANG9bn4YcftojbuXMn//znEpYsWUzFihV5+eWX+eKLRYwcORKAwMBqDBv2Mjt37sRgMFhs64UXnueFF543v161ahXz5s2jadOnsNGvEN/X7oVzyw0TRn5+fpw9exaj0YidnR1Go5Fz587h5+dnFXfmzBnq1q0LWI84+vnnn3n99deZN28egYHWP+F9IxcvZmAy6ReWCvPxcef8+St3uxlyHffCCeBu0XFrTcfL9el4saRj5fp0rFjT8XJ9Ol4s6Vi5Ph0r1u7H4+WP+zk7O5vNmzezZMlysrJMVK9eiyZNmvHFFysYPHioReyXX66gfftOeHn5kpsLvXr1ZdKkcbz44kAAnniiMQBxcT9y+XLGnx5TX321ktat23HhQkYx9/DuuB+PleJSFs4ttrY2fzo4x/q3sP+gfPny1KlTh5iYGABiYmKoU6eOxXQ0gLCwMFasWIHJZCI1NZWtW7fStm1bAA4ePMjw4cP54IMPePTRR++kPyIiIiIiIiIlKinpBLa2dlSrVt1cFhRUk8TEBKvYxMQEgoN/H3UUHFyT1NSLXL6cdkvbTElJ5pdffiYsrMPtN1ykGN3UlLQJEyYwevRo5s2bh4eHB9HR0QC89NJLDBs2jMcff5yIiAh++eUX2rRpA8CQIUOoWrUqABMnTiQnJ4fx48eb63znnXeoVUu/oiQiIiIiIiJlS3Z2Nm5uliMv3NzcyMrKLCI2C1dXN4s4gKysLDw9vW56m5s2baBu3fr4+1e+zVaLFK+bShgFBQWxYsUKq/JPP/3U/LednR0TJ04scv2vv/76NpsnIiIiIiIiUrqcnZ3JzLScFpaZmYmLi2sRsS5kZmYWiitYz8XF5Za2uWnTBvr0+dtttFakZNxwSpqIiIiIiIjIg6Rq1eoYjUaSkk6ay+LjjxAQYP083oCAQOLjjxSKO4q3d/lbGl108OABLlw4T4sWLe+s4SLFSAkjERERERERkUKcnZ0JCWnBggUfk52dzcGDB9i1awdt27a3ig0L68CGDetITEwgPT2dxYsX0q5dR/PyvLw8DAYDJpMJk8mIwWAgLy/Poo7Y2A2EhIQWOYJJ5G5RwkhERERERETkD0aMGE1uroHw8NZMmPAmI0aMITAwiJSUFFq3bkZKSgoATz/9DL169WHYsEE891w4vr5+9O8/0FzP4sULadmyCUuXfsbmzbG0bNmExYsXmpcbDAa+/fYbiySTSFlwU88wEhEREREREXmQeHh4EhU1y6rc19eXb77ZaVHWs2dvevbsXWQ9/fsPtEgg/ZGTkxObNm2/o7aKlAQljEREREREROSBZcrNxcfH/Y7rycsxcOlKbjG0SKRsUMJIREREREREHli2jo58H9HtjutpsvZrUMJI7iN6hpGIiIiIiIiIiFhQwkhERERERERERCwoYSQiIiIiIiIiIhaUMBIREREREREREQtKGImIiIiIiIiIiAUljERERERERERExIISRiIiIiIiIiIiYkEJIxERERERERERsaCEkYiIiIiIiIiIWFDCSERERERERERELChhJCIiIiIiIiIiFpQwEhERERERERERC0oYiYiIiIiIiIiIBSWMRERERERERETEghJGIiIiIiIiIiJiQQkjERERERERERGxoISRiIiIiIiIiIhYUMJIREREREREREQsKGEkIiIiIiIiIiIWlDASERERERERERELShiJiIiIiIiIiIgFJYxERERERERERMSCEkYiIiIiIiIiImJBCSMREREREREREbGghJGIiIiIiIiIiFhQwkhERERERERERCwoYSQiIiIiIiIiIhaUMBIREREREREREQtKGImIiIiIiIiIiAUljERERERERERExIISRiIiIiIiIiIiYkEJIxERERERERERsaCEkYiIiIiIiIiIWFDCSERERERERERELChhJCIiIiIiIiIiFpQwEhERERERERERC0oYiYiIiIiIiIiIBSWMRERERERERETEghJGIiIiIiIiIiJiQQkjERERERERERGxoISRiIiIiIiIiIhYUMJIREREREREREQsKGEkIiIiIiIiIiIWlDASERERERERERELShiJiIiIiIiIiIgFJYxERERERERERMSCEkYiIiIiIiIiImLB/m434F6Vnn6ZqKjJ7Nu3B09PLwYOfJk2bcKKjF2+fBnLli3BYMghJCSUkSPH4OjoSHr6ZSZPHk9c3G5MJhMeHp68+urrFvXs37+Xd9+N5vTpU9jY2ODo6Mizz7Zk5Mgx5pjTp5P4+99f4uLF1ZEk0AAAGH9JREFUC9ja2hIR0Y0RI96wquPs2RQqVPAhMzODq1evFmtbHB0db+p9KdyWRx55jDffnICvr1+x7RcRERERERERuXMaYXSbZs2KxsHBgXXrtjB+/BRmzYoiIeGYVVxc3G6WLl3M++/PY8WK9Zw5c5qFCz8x13HsWDxNm4bw7rtzyM3NZcaMqeZ60tLSePPN1wkJaYm7uwdt2rSjatXqFnUADBs2GDs7O9au3cQrr7zO6tUr+O677RZ1REYOZuLEKC5cuIC3d4USa8ufvS+F27Jx4zZq136E8eN/T3yJiIiIiIiISNmghNFtyM7OZseObURGDsLFxYV69erTtGlzNm/eaBUbGxtDx44RBAYG4eHhQd++kcTGxpjruHjxAoMGvUzDhk8TEvIsvr5+5np27NhGQEAQycmnCQ/vzPDho0hMTKBdu47ExsYAkJqaytmzKbzxxluUL1+Bbt264+fnz9Kln1nUERraiq1bN9Oly3OcOpXEpUupxd6WG70vhdvi5OREv34DiI8/yokTx0t4j4mIiIiIiIjIrVDC6DYkJZ3A1taOatWqm8uCgmqSmJhgFZuYmEBw8MPm18HBNUlNvch///srNjY22NnZm+sJCqqJyZRvrufautf+39nZmcqVKwOQmnqRS5cu8eOPewFo1KixeRs1agSSnHzGavuJiQnUqfMIlStXJjHxWLG25fLltBu+L398L67VkZhoPTJLRERERERERO4eJYxuQ3Z2Nm5ubhZlbm5uZGVlFhGbhaurm0UcFEzPKlfO2aIeNzc3TCajuZ5r6xauoyDGBEBmZibp6ZexsbGx2KaHhwe5ublW27/2d0Fbs4q1LVlZWTd8X/74Xvy+PMvqfRMRERERERGRu+emEkaJiYn06NGDtm3b0qNHD44fP24VYzQamThxIq1ataJ169asWLHippbdi5ydncnMzLAoy8zMxMXFtYhYFzIzMwvFFazn5eWFwZBjUU9mZiZ2dnbmeq6tW7iOgpiC3ebq6oqHhyf5+fkW27xy5Yr5IdSF1732d0FbXYq1LS4uLjd8X/74Xvy+3MXqfRMRERERERGRu+emEkZvv/02vXr1YvPmzfTq1Yvx48dbxaxfv56TJ0+yZcsWli9fzocffsipU6duuOxeVLVqdYxGI0lJJ81l8fFHCAgItIoNCAgkPv5IobijeHuXp06dxzCZTOTl5ZnriY8/go2NjbmegIBAjh07Yq4jOzub06dPATZ4e5fnoYce4sknGwKwd+8e8zaOH0/Az8/foo5rfx869BunT58iICCoWNvi6el1w/elcFsAcx0BAUG3tyNEREREREREpETcMGF08eJFfvvtNzp27AhAx44d+e2330hNTbWI27hxI927d8fW1hZvb29atWrFpk2bbrjsXuTs7ExISAsWLPiY7OxsDh48wK5dO2jbtr1VbFhYBzZsWEdiYgLp6eksXryQdu06muuoUKECn3wyh3374tix41tSUpLN9TRv3oKEhGP4+1cmJmYt778/gxo1Atm0aQPt2hXsD29vbypWrER09BRSU1NZvXoFZ86cpnfvvhZ1bN/+L1q2bMPq1SuoUqUqDz3kXextudH7UrgtBoOBRYs+JSjoYapXr1HCe0xEREREREREboX9jQKSk5OpVKkSdnZ2ANjZ2VGxYkWSk5Px9va2iPP39ze/9vPzIyUl5YbLbpatrc2Ng0rR66+PYerUiYSHt8bT05PXXx9DcHAwKSnJvPBCd5YtW4Gvrx/PPNOEF174K8OGDcJgMPDss6G89NIgbG1teP31MUycOI7vvtvO9u3b8PDw4LXXRuHm5krr1s1YtmwF06bNYNasaNLTL7N580YcHBxo0aIVL700CCh4X+bO/YRBg/rTqVMbbG1t6dq1O88+28LcllGj3mT+/HmkpKTg4+PDhQsXeO658GJty7X9c733BaB8eW9zHZMmjefRRx9j8uSoMrdvi1PFh5yLpR57T587rsPHxfvGQTfBqeKdt+V+3ud3ojiOl+I4VqB4jpfiOFZAx0tRdG4pmo6VouncUjQdL9Z0bimajpWi6dxSNB0v1nRuKVpZOFZu1Aab/D8+AOcPfv31V9544w02bNhgLmvfvj0zZszg0UcfNZeFh4czdepU6tatC8Cnn37K2bNneeutt/50mYiIiIiIiIiIlC03nJLm5+fH2bNnMRqNQMEDrM+dO4efn59V3JkzZ8yvk5OT8fX1veEyEREREREREREpW26YMCpfvjx16tQhJiYGgJiYGOrUqWMxHQ0gLCyMFStWYDKZSE1NZevWrbRt2/aGy0REREREREREpGy54ZQ0gGPHjjF69GjS09Px8PAgOjqawMBAXnrpJYYNG8bjjz+O0Whk0qRJfP/99wC89NJL9OjRA+BPl4mIiIiIiIiISNlyUwkjERERERERERF5cNxwSpqIiIiIiIiIiDxYlDASERERERERERELShiJiIiIiIiIiIgFJYxERERERERERMSCEkYiIiIiIiIiImJBCaN7RK1atcjMzLzbzZC7ICIigpycnLvdDLmH3M75Yu7cuXTo0IFOnTrRtWtXdu7caV5mNBqZOHEirVq1onXr1qxYscK8bPTo0SxdurTY2i4iZUNJX3f8+9//ZsSIETeMmz17Nhs3biyxdoiI/NGHH35IdHT03W6GSJmghJFIGbd27VrKlSt3t5sh97m6deuycuVK1q1bx7Rp0xg+fLg5Ubl+/XpOnjzJli1bWL58OR9++CGnTp26yy1+sNxvScDCF+P/+te/rnthHhcXR9euXQH46aef6NmzJ+3bt6d9+/ZER0djMpms4qT05OXl3fa6jz/+OLNmzbph3CuvvEL79u1vezsiIiKF3cln14PI/m43QIq2ZcsW3n33Xby8vGjevDkAp0+fpk+fPsTFxQFw6tQpunXrZn797bff8uGHH5KXl4etrS3Tp0+ndu3ad60PUjxq1arFTz/9hKurK6GhoURERPDDDz9w/vx5+vXrR+/evTGZTEyaNIk9e/bg6OiIi4sLX375pfkY6dq1K/v27cNgMPD222/ToEEDAHbs2MFHH31Ebm4uDg4OjBkzhvr16wOwcuVKlixZAoCDgwOffPIJFSpUuGvvg9w6k8nE9OnTuXDhAtOnT2f8+PHY29tz6tQpkpOTeeqppxg/fjyOjo40a9bMvF6tWrXIz88nLS0NX19fNm7cSPfu3bG1tcXb25tWrVqxadMmIiMjLba3Z88epk6dyqxZs6hZs2Zpd1f+oG7duvTr1w9nZ2cOHTpE79692bVrF+XKlbNIAqalpdG5c2caN25MlSpVSr2dLVu2pGXLljeMc3NzY/r06dSoUYPc3FxefPFF1q1bR+fOnUuhlQ+uos4jrq6uHD9+nEuXLrFq1SpGjBhBYmIiV69epVq1akybNg1PT0/i4uKYOnUqjz76KIcOHcLOzo7p06cTHBxMXFwc0dHRrFq1irFjx1KrVi1efPFFAI4cOcLgwYPZunUrY8aM4bHHHqN37958+OGHJCYmcuXKFZKSkqhWrRqzZ8/G2dn5Lr9Lcqtq1arF8OHD+eabb0hLS2PUqFG0bdvW6tq28OuLFy8yYsQILl68CEDjxo0ZO3bs3eyG3ILs7GzeeOMN4uPjsbe3JyAggNmzZ7N69Wr++c9/YjQacXNzY8KECQQGBrJq1SpiYmJwd3fn8OHDVKpUiXHjxvHOO+9w4sQJHnvsMWbOnImNjQ0ZGRlERUVx+PBhDAYDjRo1YsyYMfz8889MmTKFNWvWmNvRtWtXRo8eTUBAAK+99hqZmZkYDAZCQkIYNWrUXXyH5I+K+mwZNmwYffr0oVWrVgBs27aNRYsW8fnnn3Pu3DmmTJnCmTNnMBgMdOjQgUGDBgEQGhpKt27d2LNnD1WrVmX48OHX3f9Xrlxh7NixHD16lEqVKlGpUiXKly/PG2+8QW5uLu+99x779u3j6tWr1KxZkwkTJuDq6nrX3qeSpoRRGXTx4kXGjRvHF198QWBgIJ9++ukN10lMTOStt95i2bJl5ovp3NzcUmitlLacnByWL1/OqVOnCA8Pp0uXLpw4cYLdu3cTGxuLra0tly9fNsenpaVRq1Yt3njjDfbu3ctrr73G1q1bSUlJYd68eSxcuBA3NzeOHj3KSy+9xPbt24mLi+OTTz7hn//8Jz4+PmRmZmJvr9PFvcRgMDBmzBgqV67MrFmzsLGxAeCXX37hyy+/xMnJiQEDBvDVV1/Ru3dvi3XXrFlDtWrV8PX1BSA5ORl/f3/zcj8/P1JSUizWWbduHYsXL2bBggVUqlSphHv34CprScD9+/ff8cX4qlWr2L59Ox988AEA7733Hhs3bqRSpUo8/vjj5rjC23d0dOSRRx7hzJkzVvWlp6fz8ssvExoaSt++fW/+zRUr1zuP/PzzzyxduhQXFxcA3nzzTby9vYGC/ffpp58ycuRIAA4fPsxbb71Fw4YNWb16NaNGjWLVqlUW2+natStTp041J4xWrVpFly5dzNsr7Ndff2XlypW4u7vTv39/1q9fz/PPP19i74GUHDc3N77++mt+/PFHXn31Vdq2bfun8evXr8ff35/PPvsMwOJaR8q+Xbt2kZ6ebp5ievnyZfbv309sbCzLli3D0dGRHTt2MHbsWL788kugYOrq+vXr8fX1ZeDAgYwYMYKlS5fi7OxMly5d2L17N8888wxRUVE89dRTTJ06FZPJxMiRI/n66695/vnnycrK4tChQ9SuXZsjR46Qnp7OU089RW5uLh9//DGurq5cvXqV/v37891335lv1MvdV9RnS5cuXVizZo05YbR69Wq6desGwBtvvMHf//538/7t27cvjz/+OE2aNAHg/PnzfP7550DB59v19v/cuXPx8PBg06ZNpKWl0bVrV/P5acGCBbi7u7Ny5UoAZsyYwfz58xk+fHipvjelSd8Ay6ADBw7wyCOPEBgYCECPHj2YOXPmn67zww8/0Lx5c2rUqAEUXEw7OjqWdFPlLrg2NL9KlSp4eHiQkpJC1apVMRqNvPnmmzRq1IgWLVqY4x0cHOjUqRMADRs2pFy5ciQkJPDjjz9y8uRJXnjhBXNsXl4eFy5cYPv27URERODj4wNwX2fN71eRkZF06NCB/v37W5S3b9/evD87d+7Mli1bLBJGe/fuZfbs2fzjH/+46W2tWrUKJycnFi9ejJubW/F0QKyUxSRggwYNivVifNu2bWzbto01a9ZQrlw5hgwZUmTcxYsX2bx5M/Pnz7coP336NEOHDmXAgAGEhYVddztyc653HgkLCzMni6Bg6vT69eu5evUqWVlZ5msRgOrVq9OwYUOg4Jl848aNIyMjw6K+Bg0akJmZyaFDhwgODiYmJobly5cX2aamTZvi4eEBFIyiO3nyZHF0Ve6Ca9cz9evX59y5cxgMhj+Nr1evHosWLSI6OpqGDRvStGnT0mimFJPatWuTkJDAxIkTadiwIc8++yzbtm3j0KFDdO/eHYD8/HzS09PN6/zf//2f+XOrTp06VK5cGXd3d3N9J06c4JlnnmHbtm0cPHiQRYsWAQU3V699bkVERLB69WrGjBljkYw2Go288847/Pzzz+Tn53PhwgUOHTqkhFEZUtRny5AhQ4iKiiI1NRUbGxv27t1LdHQ0WVlZ7N27l9TUVPP6mZmZHDt2zJwwKjwi+c/2f1xcHG+99RYAXl5e5uQUFFynZGRksHnzZgByc3Pv+xk9ShiVQfn5+UWWe3h4WCwr/MF6vXXk/uPk5GT+287ODqPRiLu7Oxs2bCAuLo7du3czc+ZMVq9eXeT6+fn55i+azZo145133imVdkvpatSoETt37uQvf/mLxRe7wgofC1AwauD1119n3rx55oQ1FCQTzpw5Q926dQHrZEOtWrXYv38/8fHx5imNUvzKahKwOC/G4+LiLPrz3HPPMW/ePIuYjIwMBg8eTL9+/XjkkUfM5efPn+evf/0r0dHR5mm3cmeudx4p/Pf+/fv54osv+PLLL/H29mb9+vV89dVXt7ytiIgI1qxZQ8OGDQkKCqJy5cpFxv3xM/BGSQYpu67tSzs7O6DgppW9vf11r3WfeOIJ1qxZww8//MDatWuZP38+X3zxRek2Wm5b1apV2bhxI3v27OG7777jvffeo2XLlnTr1o1XXnmlyHX++O+9qGtgKLiemTdvHlWrVrWqo0uXLjz//PO89tprFsnoRYsWkZ6ezooVK3BycmLcuHE6n5Qh1/tscXZ2pmXLlmzYsAEomNbu4uJCRkYGNjY2rFy5EgcHhyLrLPzZ9Wf7/4/Xx4Xl5+fz9ttv07hx42Lucdmlh16XQU888QS//fYbx48fBzA/jNTd3Z2rV69y4sQJAGJiYszrNG3alO+++868Tm5urtUdPLl/paamkpOTQ/PmzRk5ciTu7u4kJSUBcPXqVdavXw8UnHwNBgMBAQE0adKEnTt3cvToUXM9Bw8eBKBFixasXbuWCxcuAAUZek1xvLe8/PLLPPPMM0RGRlqcCzZt2kRWVhZ5eXmsW7eORo0aAQX7fvjw4XzwwQc8+uijFnWFhYWxYsUKTCYTqampbN261WLqwKOPPsqcOXN4/fXX2bt3b+l08AF07ct7VlbWdWOulwScO3dukUnAa5KTk813caEgCXjhwgXi4+Nv2K4uXbqwYcMGDAYDMTEx5jt4hS/G1q9fT6tWrW54MX6jmx/Z2dkMGjSIJk2a0K9fP4tlnp6eBAQE8N13392wzXJzrnceKSw9PR03Nze8vLzIzc3l66+/tlh+4sQJ9u/fDxRMKapZs2aRScguXboQExPDihUr9ADzB1iFChWue62blJSEm5sbHTp0YMyYMfznP/8xP/heyr6UlBTs7Oxo1aoVY8aMITU1ldDQUNauXWse4Wo0Gvn1119vue7Q0FDmz59vTiClpqaar4P9/f0JCgpiypQpBAcHm5PRV65cwcfHBycnJ86ePcu//vWvYuqpFIc/+2zp2rUrq1evZvXq1ebPCzc3N5588kmLkcfJycmcP3++yPr/bP83atTIPNX+8uXLFstCQ0P57LPPzD8Mk5GRwbFjx4qv42WQEkZlUPny5Zk8eTKDBg2iZ8+e5jsvUDCX829/+xt9+vSxKK9RowaTJ09m+PDhdOrUiR49enD69Om70Xy5C5KTk/nb3/5Gp06d6NSpE82bNzeP9PDy8uLEiRN0796diRMn8u677+Lo6EiNGjWYMWMGb775Jp06daJdu3bmuy4NGzZkwIAB5jpffPFFiyHCcm+4Ni2nb9++pKWlAfDUU08xZMgQOnTogJ+fn/nZHxMnTiQnJ4fx48cTERFBREQEhw8fBgru/FepUoU2bdrw/PPPM2TIEKu7eLVq1eLjjz/mrbfesvg1Lik+ZTUJWJwX440bNyY2NpasrCyMRqPFBaLBYGDQoEHUq1evyLvRjo6OzJs3j2PHjjFlyhSNvC0mRZ1HCmvevDnVqlWjXbt2REZGWoz6goJpJDExMXTt2pXPP//8uqNa/f39CQ4OZu/evbRp06ZE+iJln729/XWvdffu3UuXLl2IiIggMjKSiRMnYmurrzL3isOHD9OjRw86depE9+7dGTBgAE899RSvvvoqgwcPplOnTnTs2PG2Ejdjx47F1taWiIgIwsPDiYyM5OzZs+blXbt25auvvqJLly7msj59+vDTTz/RuXPnB27EyL3gzz5bGjRoQEZGBhkZGRYjimfOnMmxY8cIDw8nPDyc4cOHX/f7y5/t/yFDhvD/7d2vqyJhGMXxs8kwZjHaNFsEi1VQMSiIdoUBRRD8FSxikAkWq0mwClqMYvDPmKhtmgji6N2wcPG9u2y7uLrfT5z0wDth5rxwHs/zlMlk1G63FY/HPy86arWaYrGYisWicrmcKpXK2wdGPz74ogLe1tdtI/i/9Xq9z21DeC2P2xLn87nW67Vms5nG47GCwaBc19XxeDRKrwuFgg6Hg9E/5DiOotGobrebhsOh9vu9JKlarapUKkky3xPXdWXbtgaDgVGi/dVqtVKn05HjOMrn85J+9Qk1m035vq9wOCzLshSJRNRoNDSdTnU+n9Xtdv9Yer3ZbBQKhZRIJLTdbrVcLrVYLDQajYzy63Q6Ldu2jY1bvu+r3W7LsiwNh0N+KJ/o8VwAAHgF1+tV9/tdgUBAp9NJ5XJZ/X5fyWTy2aM9BYER8MYIjPCIwOj9cKb4lxEYAQBejed5qlarut1uulwuymazqtfrzx7raQiMAAB4UQRGAAAA+C4ERgAA4K92u50mk8lvz1utllKp1BMmAgAAwHcjMAIAAAAAAICBJkgAAAAAAAAYCIwAAAAAAABgIDACAAAAAACAgcAIAAAAAAAABgIjAAAAAAAAGH4CeeghXe0lve8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = {\\n\",\n    \"        'one2one': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"        'one2one+copy': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"        'one2seq': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"        'one2seq+copy': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# kp_exps = {\\n\",\n    \"#         'one2one': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"#         'one2one+copy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#         'one2seq': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"#         'one2seq+copy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# }\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps.values())]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"############## present F@10\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## present F@O\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Phrase number\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plot average scores (Appendix C1, Fig 7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T20:30:52.362678Z\",\n     \"start_time\": \"2020-11-23T20:30:41.926548Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>w/o copy</th>\\n\",\n       \"      <th>w/ copy</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>RNN-O2O</th>\\n\",\n       \"      <td>0.035713</td>\\n\",\n       \"      <td>0.087383</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-O2O</th>\\n\",\n       \"      <td>0.154474</td>\\n\",\n       \"      <td>0.140182</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>RNN-O2S</th>\\n\",\n       \"      <td>0.041472</td>\\n\",\n       \"      <td>0.024597</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-O2S</th>\\n\",\n       \"      <td>0.110343</td>\\n\",\n       \"      <td>0.098487</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         w/o copy   w/ copy\\n\",\n       \"RNN-O2O  0.035713  0.087383\\n\",\n       \"TF-O2O   0.154474  0.140182\\n\",\n       \"RNN-O2S  0.041472  0.024597\\n\",\n       \"TF-O2S   0.110343  0.098487\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = {\\n\",\n    \"        'RNN-O2O': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"        'RNN-O2O+copy': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"        'RNN-O2S': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"        'RNN-O2S+copy': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"        'TF-O2O': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"        'TF-O2O+copy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"        'TF-O2S': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"        'TF-O2S+copy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps.values())]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"############## present F@10\\n\",\n    \"# change to use F@O as anchor metric for present KPG\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"f10_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# display(f10_df)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## present F@O\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"fo_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# display(fo_df)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"r50_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"display(r50_df)\\n\",\n    \"\\n\",\n    \"f10_df = f10_df * 100.0\\n\",\n    \"fo_df = fo_df * 100.0\\n\",\n    \"r50_df = r50_df * 100.0\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T20:30:52.724086Z\",\n     \"start_time\": \"2020-11-23T20:30:52.363839Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7fc3428ea5d0>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x576 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"sns.set_palette(\\\"Set2\\\")\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\\n\",\n    \"# f10_df.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"# for p in axes[0].patches:\\n\",\n    \"#     axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.05))  \\n\",\n    \"# axes[0].set_ylabel(\\\"Present F@10\\\")\\n\",\n    \"\\n\",\n    \"fo_df.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.05))    \\n\",\n    \"axes[0].set_ylabel(\\\"Present F@O\\\") \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"r50_df.plot.bar(ax=axes[1], legend=True, rot=0)\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.04, p.get_height() + 0.05))    \\n\",\n    \"axes[1].set_ylabel(\\\"Absent R@50\\\") \\n\",\n    \"axes[1].legend(loc='upper left')\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Phrase Order on One2Seq Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Figures in paper are generated by onmt/keyphrase/order_heatmaps.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq - Present Phrase Prediction\\n\",\n    \"\\n\",\n    \"Terminology:\\n\",\n    \" - present_pred_num means valid phrases (no <unk>, doesn't contain ,/.) that can be found in the source text.\\n\",\n    \" - verbatim_prepend means (absent_phrases+present_phrases)\\n\",\n    \" - verbatim_append means (present_phrases+absent_phrases)\\n\",\n    \"    \\n\",\n    \"**verbatim_prepend** outputs significantly more present_phrases and **verbatim_append/alphabetic** outputs significantly more absent_phrases, which means the more noisy hidden state lead to more diverse following predictions.\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-16T20:43:01.112594Z\",\n     \"start_time\": \"2020-11-16T20:42:51.562467Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  after removing the cwd from sys.path.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"one2seq_exps = ['kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_f_score@10')\\n\",\n    \"ax.legend(loc='lower left')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"'''\\n\",\n    \"# SADR\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_advanced_sadr')\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_sadr')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"# AUC\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_advanced_auc')\\n\",\n    \"metric_names = ['present_exact_advanced_auc']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_auc')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"'''    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"metric_names = ['beam_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='beam_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='unique_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['present_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='absent_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Beam Width\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.318714</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>0.323840</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0.323963</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>64</td>\\n\",\n       \"      <td>0.324130</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>0.327513</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0  8     0.318714  RNN \\n\",\n       \"1  16    0.323840  RNN \\n\",\n       \"2  32    0.323963  RNN \\n\",\n       \"3  64    0.324130  RNN \\n\",\n       \"4  200   0.327513  RNN \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'F@O': [0.26681409223739766, 0.3129089058965166, 0.3187650134663627, 0.32321889951660443], 'model': ['RNN', 'RNN', 'RNN', 'RNN']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.266814</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.312909</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0.318765</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0.323219</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0  1     0.266814  RNN \\n\",\n       \"1  10    0.312909  RNN \\n\",\n       \"2  25    0.318765  RNN \\n\",\n       \"3  50    0.323219  RNN \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"rnn_one2one_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"# '''\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"rnn_one2seq_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=rnn_one2one_present_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=rnn_one2seq_present_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:53: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'R@50': [0.002497695838612458, 0.01140273249601975, 0.019286495093150988, 0.02459737124635035], 'model': ['RNN', 'RNN', 'RNN', 'RNN']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"rnn_one2one_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"rnn_one2seq_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=rnn_one2one_absent_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=rnn_one2seq_absent_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.303891</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>0.311420</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0.311312</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>64</td>\\n\",\n       \"      <td>0.311648</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>0.311682</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0  8     0.303891  TF  \\n\",\n       \"1  16    0.311420  TF  \\n\",\n       \"2  32    0.311312  TF  \\n\",\n       \"3  64    0.311648  TF  \\n\",\n       \"4  200   0.311682  TF  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:51: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1295, 236)\\n\",\n      \"{'Beam': [1, 10, 25, 50], 'F@O': [0.2740236653246884, 0.3060720943414134, 0.3280425324930268, 0.3292538448836406], 'model': ['TF', 'TF', 'TF', 'TF']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.274024</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.306072</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0.328043</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0.329254</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0  1     0.274024  TF  \\n\",\n       \"1  10    0.306072  TF  \\n\",\n       \"2  25    0.328043  TF  \\n\",\n       \"3  50    0.329254  TF  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"tf_one2one_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"# one2seq_df = one2seq_df.loc[(one2seq_df.step >= 100000) | (one2seq_df.beam_width == 50) | (one2seq_df.beam_width == 1)] # a workaround, some kp20k results are missing\\n\",\n    \"\\n\",\n    \"print(one2seq_df.shape)\\n\",\n    \"# a workaround, some kp20k results are missing\\n\",\n    \"# beam_step_TBR = []\\n\",\n    \"# for beam_width in one2seq_df['beam_width'].unique():\\n\",\n    \"# #     print('beam_width=', beam_width)\\n\",\n    \"#     beam_df = one2seq_df.loc[one2seq_df.beam_width == beam_width]\\n\",\n    \"#     for step in one2seq_df['step'].unique():\\n\",\n    \"#         step_df = one2seq_df.loc[one2seq_df.step == step]\\n\",\n    \"#         print('beam=%d, step=%d, #(dataset)=%d, step_df.shape=%s'\\n\",\n    \"#               % (beam_width, step, len(step_df.test_dataset.unique()), step_df.shape))\\n\",\n    \"        \\n\",\n    \"#         if len(step_df.test_dataset.unique()) < 7:\\n\",\n    \"#             print('remove! ', (beam_width, step, step_df.shape[0]))\\n\",\n    \"#             beam_step_TBR.append((beam_width, step, step_df.shape[0]))\\n\",\n    \"\\n\",\n    \"# # some rows cannot be detected by df_TBR, don't know why!\\n\",\n    \"# for beam_width, step, num_dp in beam_step_TBR:\\n\",\n    \"#     df_TBR = one2seq_df.loc[(one2seq_df['beam_width'] == beam_width) & (one2seq_df['step'] == step)]\\n\",\n    \"#     print('Removing: ', (beam_width, step, step_df.shape[0]), ' \\\\t Found:', df_TBR.shape[0])\\n\",\n    \"#     one2seq_df.drop(df_TBR.index, inplace = True) \\n\",\n    \"#     print(one2seq_df.shape)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"tf_one2seq_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=tf_one2one_present_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=tf_one2seq_present_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'R@50': [0.013290150897522368, 0.05982099588811556, 0.07951483220487852, 0.09848689000693868], 'model': ['TF', 'TF', 'TF', 'TF']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"tf_one2one_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"tf_one2seq_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=tf_one2one_absent_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=tf_one2seq_absent_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Combine all (Appendix C2, Figure 8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:26.640766Z\",\n     \"start_time\": \"2020-11-26T07:23:25.847352Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 16,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 16,\\n\",\n    \"                             \\\"legend.fontsize\\\": 16})\\n\",\n    \"\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, 2, figsize=(10, 6), sharex=False, sharey=False)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"model_names = ['RNN', 'TF']\\n\",\n    \"one2one_beams = [8, 16, 32, 64, 200]\\n\",\n    \"one2seq_beams = [1, 10, 25, 50]\\n\",\n    \"\\n\",\n    \"# one2one_present\\n\",\n    \"concat_df = pd.concat([rnn_one2one_present_df, tf_one2one_present_df], ignore_index=True, sort=False)\\n\",\n    \"# display(one2one_present_df)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2one_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2one_beams.index(row_series.Beam)] = float(row_series['F@O'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2one_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"# display(one2one_present_df)\\n\",\n    \"g = concat_df.plot.bar(ax=axes[0][0], legend=True, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[0][0].patches:\\n\",\n    \"    axes[0][0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.005), rotation=0)   \\n\",\n    \"axes[0][0].set_ylabel(\\\"Present (F@O)\\\")\\n\",\n    \"axes[0][0].legend(loc='lower left')\\n\",\n    \"g.set_ylim(25, 35)\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# one2seq_present\\n\",\n    \"concat_df = pd.concat([rnn_one2seq_present_df, tf_one2seq_present_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2seq_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2seq_beams.index(row_series.Beam)] = float(row_series['F@O'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2seq_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[0][1], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[0][1].patches:\\n\",\n    \"    axes[0][1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.01), rotation=0)   \\n\",\n    \"g.set_ylim(0, 35)\\n\",\n    \"g.set(yticklabels=[])\\n\",\n    \"# g.set(yticks=[])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"g.set(ylabel=None)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"# one2one_absent\\n\",\n    \"concat_df = pd.concat([rnn_one2one_absent_df, tf_one2one_absent_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2one_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2one_beams.index(row_series.Beam)] = float(row_series['R@50'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2one_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[1][0], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[1][0].patches:\\n\",\n    \"    axes[1][0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.005), rotation=0)   \\n\",\n    \"axes[1][0].set_ylabel(\\\"Absent (R@50)\\\")\\n\",\n    \"axes[1][0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"axes[1][0].legend(loc='upper left')\\n\",\n    \"g.set_ylim(0, 15)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# one2seq_absent\\n\",\n    \"concat_df = pd.concat([rnn_one2seq_absent_df, tf_one2seq_absent_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2seq_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2seq_beams.index(row_series.Beam)] = float(row_series['R@50'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2seq_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[1][1], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[1][1].patches:\\n\",\n    \"    axes[1][1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.01), rotation=0)   \\n\",\n    \"# axes[1][1].set_ylabel(\\\"One2Seq Absent (R@50)\\\")\\n\",\n    \"axes[1][1].set_xlabel(\\\"One2Seq\\\")\\n\",\n    \"g.set(yticklabels=[])\\n\",\n    \"# g.set(yticks=[])\\n\",\n    \"# g.set(ylabel=None)\\n\",\n    \"g.set_ylim(0, 15)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from matplotlib.ticker import MaxNLocator\\n\",\n    \"axes[1][0].yaxis.set_major_locator(MaxNLocator(integer=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - [7]['kp20k_valid2k' 'nus' 'semeval' 'kp20k' 'inspec' 'duc' 'krapivin']\\n\",\n      \"(280, 121)\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - [7]['semeval' 'kp20k_valid2k' 'krapivin' 'inspec' 'duc' 'nus' 'kp20k']\\n\",\n      \"(911, 121)\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10': [0.00032467532467532473, 0.04039430353823858, 0.06842784980073414, 0.06873645721741518, 0.04685716433495115, 0.10031317913118709, 0.07364274609978945, 0.05695662506385585], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10': [0.1240018880821837, 0.32429694315801766, 0.278800915528659, 0.28287264555675606, 0.26333446401667293, 0.365868622238561, 0.35177721248479754, 0.28442181300937824]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50': [0.0, 0.023323088023088023, 0.058231797832538304, 0.05360714285714285, 0.03986628709454796, 0.055115389442821835, 0.03658567821067821, 0.03810419763725959], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50': [0.0, 0.0929945165945166, 0.13248352632452295, 0.13661190476190474, 0.13927457807892588, 0.11269831924825102, 0.06120905483405483, 0.09646741426316799]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2one_exps = [\\n\",\n    \"#                 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n    \"#                 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"#                 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"\\n\",\n    \"#                 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"# #                 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n    \"#                 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"    \\n\",\n    \"# This is baseline\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1'\\n\",\n    \"    \\n\",\n    \"'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"    \\n\",\n    \"'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse-Covfalse-Contboth-IF1', # does not exist\\n\",\n    \"'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D128-E128-DO0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue'\\n\",\n    \"               ]\\n\",\n    \"\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2one_exps)]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for exp_name, exp_group in one2one_df.groupby('exp_name'):\\n\",\n    \"    datasets = exp_group.test_dataset.unique()\\n\",\n    \"    print('%s - [%d]%s' % (exp_name, len(datasets), datasets))\\n\",\n    \"    print(exp_group.shape)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"# remove kp20k because we didn't run it for other exps\\n\",\n    \"# one2one_df = one2one_df.loc[one2one_df.test_dataset != 'kp20k']\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = '%s' % (one2one_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2one_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"    \\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(datasets)\\n\",\n    \"print(bar_values)\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"###### Absent \\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2one_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"    \\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(datasets)\\n\",\n    \"print(bar_values)\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2Seq\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = [\\n\",\n    \"# this is baseline setting\\n\",\n    \"# 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1'\\n\",\n    \"    \\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n    \"   \\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'topbeamends'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# for exp_name, exp_group in one2seq_df.groupby('exp_name'):\\n\",\n    \"#     datasets = exp_group.test_dataset.unique()\\n\",\n    \"#     print('%s - [%d]%s' % (exp_name, len(datasets), datasets))\\n\",\n    \"#     print(exp_group.shape)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"###### absent\\n\",\n    \"\\n\",\n    \"one2seq_exps = [\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"\\n\",\n    \"# 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'topbeamends'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(valid_one2seq_df.shape)\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of More Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### For One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - shape=(7, 121) - testset: [7]['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval']\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10': [0.16205516155350783, 0.3626685189604522, 0.29477669245242893, 0.29697447382777, 0.29157708760405887, 0.3935247633808257, 0.3625451904047105, 0.3091602697405363], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10': [0.1240018880821837, 0.32429694315801766, 0.278800915528659, 0.28287264555675606, 0.26333446401667293, 0.365868622238561, 0.35177721248479754, 0.28442181300937824], 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10': [0.09345181028682024, 0.3249416723640013, 0.29010839528504384, 0.28992104217216985, 0.26345420721603946, 0.37432637016625475, 0.327735086903121, 0.2805626549133501], 'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10': [0.17267552855475668, 0.37145815367388, 0.2784982081508915, 0.27943677714172077, 0.28187106988771, 0.38135997274062267, 0.3579011020541206, 0.30331440174338603], 'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - present_exact_f_score_hard@10': [0.16410561345820762, 0.3778170874257921, 0.27442938224755525, 0.2748487651349996, 0.2824476631027105, 0.3820310164124178, 0.354002074344644, 0.3013830860180467], 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10': [0.11885245637400234, 0.3329251952788952, 0.2877766979746826, 0.2892135536420742, 0.26928056289597574, 0.37943724950387075, 0.338128752061492, 0.2879449239615704]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - absent_exact_recall@50 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 7\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50 = 7\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - absent_exact_recall@50 = 7\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - absent_exact_recall@50 = 7\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - absent_exact_recall@50': [0.007575757575757575, 0.12978571428571428, 0.2051774627811552, 0.19953354978354979, 0.2265425214881737, 0.18486357170409964, 0.09804707792207791, 0.15021795079150402], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50': [0.0, 0.0929945165945166, 0.13248352632452295, 0.13661190476190474, 0.13927457807892588, 0.11269831924825102, 0.06120905483405483, 0.09646741426316799], 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50': [0.0010822510822510823, 0.10933261183261184, 0.2230514533636563, 0.21897142857142857, 0.22510289227680533, 0.20468179850099943, 0.09516215728715728, 0.15391208470213], 'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - absent_exact_recall@50': [0.003246753246753247, 0.12364444444444445, 0.16710126393733238, 0.16049107142857144, 0.20856233138841834, 0.15119664734718366, 0.08230501443001442, 0.12807821803181688], 'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - absent_exact_recall@50': [0.0010822510822510823, 0.10635714285714286, 0.13270487364961273, 0.1309202380952381, 0.1628703180877094, 0.13822370332252507, 0.05239989177489177, 0.10350834555276729], 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50': [0.0021645021645021645, 0.1325961038961039, 0.21745590011462826, 0.21385138888888888, 0.23891578831796223, 0.2023558943769353, 0.10105339105339105, 0.1583418526874874]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue - absent_exact_recall@50 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 8\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50 = 8\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue - absent_exact_recall@50 = 8\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue - absent_exact_recall@50 = 8\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue - absent_exact_recall@50 = 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2one_exps = [\\n\",\n    \"# This is baseline\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1'\\n\",\n    \"\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"# 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"# 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"# 'magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"# 'magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.1-Copytrue',\\n\",\n    \"# 'magkp-meng17-one2one-BS128-OPTadagrad-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copyfalse-Covfalse-Contextboth-IF1',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covfalse-Contextboth-IF1',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"# still running'magkp-meng17-one2one-transformer-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"\\n\",\n    \"# 'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue',\\n\",\n    \"# 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"# no kp20k\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.5-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"# 'magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2one_exps)]\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"# remove kp20k because we didn't run it for other exps\\n\",\n    \"# one2one_df = one2one_df.loc[one2one_df.test_dataset != 'kp20k']\\n\",\n    \"\\n\",\n    \"# we have previous .eval files that @M is NaN\\n\",\n    \"# one2one_df.drop_duplicates(keep=False,inplace=True) \\n\",\n    \"one2one_df = one2one_df.loc[one2one_df['present_exact_f_score@M'].notna()]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# for exp_name, exp_group in one2one_df.groupby('exp_name'):\\n\",\n    \"#     datasets = exp_group.test_dataset.unique()\\n\",\n    \"#     print('%s - [%d]%s' % (exp_name, len(datasets), datasets))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = '%s' % (one2one_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"for exp_name, exp_group in valid_one2one_df.groupby('exp_name'):\\n\",\n    \"    datasets = exp_group.test_dataset.unique()\\n\",\n    \"    print('%s - shape=%s - testset: [%d]%s' % (exp_name, exp_group.shape, len(datasets), datasets))\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2one_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"# print(datasets)\\n\",\n    \"# print(exp_names)\\n\",\n    \"# display(peak_one2one_df.loc[peak_one2one_df['exp_name'] == 'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1'])\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"    \\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(datasets)\\n\",\n    \"print(bar_values)\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"###### Absent \\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2one_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"    \\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(datasets)\\n\",\n    \"print(bar_values)\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### For One2Seq\\n\",\n    \"\\n\",\n    \"    - On MagKP, all performance drops by large margin.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 - present_exact_f_score_hard@10': [0.1588704033327202, 0.3805737542893092, 0.25809123198233547, 0.2631219492400964, 0.2645375121906429, 0.36239447108961603, 0.3440150840301769, 0.29022920087927107], 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue - present_exact_f_score_hard@10': [0.1461993715412771, 0.3800099736352761, 0.2549325955591649, 0.259008986554861, 0.26556922602007665, 0.3583856523439583, 0.33478769168567923, 0.2855562139057562], 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue - present_exact_f_score_hard@10': [0.09803425424208011, 0.33880775149473047, 0.2844657990997523, 0.2832275922932068, 0.27662319706870436, 0.36634924133641855, 0.3333306747088481, 0.28297693003482005], 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue - present_exact_f_score_hard@10': [0.18301739844901474, 0.40178411497234606, 0.23783838308466132, 0.24429261998394225, 0.2577347184601979, 0.3331474810906511, 0.3315445831859919, 0.28419418560382936], 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10': [0.18541076404143308, 0.41127966787381437, 0.23620578577931375, 0.24074832898684445, 0.2564134998145047, 0.3403451791560325, 0.3373751920164459, 0.28682548823834125], 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10': [0.18689342060847725, 0.39355965591381226, 0.30118549363784447, 0.2999177614083131, 0.30103004628571733, 0.3934065650549947, 0.36128498273312504, 0.31961113223461207]}\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10 = 8\\n\",\n      \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue - present_exact_f_score_hard@10 = 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Compare baseline One2Seq RNN model on different training data\\n\",\n    \"\\n\",\n    \"one2seq_exps = [\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"#                 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"#                 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"#                 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"#                 'kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                \\n\",\n    \"                'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"                'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"               ]\\n\",\n    \"'''\\n\",\n    \"                # kp20k\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                # N/A 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"\\n\",\n    \"                'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"                'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"                # 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n    \"#                 'magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue']\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"# print(one2seq_df.shape)\\n\",\n    \"# for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"#     one2seq_df.at[index_label , 'exp_name'] = '%s-%s' % (one2seq_df.at[index_label , 'decoding_terminate'], one2seq_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# for exp_name, exp_group in valid_one2seq_df.groupby('exp_name'):\\n\",\n    \"#     datasets = exp_group.test_dataset.unique()\\n\",\n    \"#     print('%s - shape=%s - testset: [%d]%s' % (exp_name, exp_group.shape, len(datasets), datasets))\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"# print(valid_one2seq_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"train_datasets = valid_one2seq_df.train_dataset.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"    \\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print(datasets)\\n\",\n    \"print(bar_values)\\n\",\n    \"for k,v in bar_values.items():\\n\",\n    \"    print('%s = %d' % (k, len(v)))\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"######### Absent prediction\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"train_datasets = valid_one2seq_df.train_dataset.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"######### pred numbers\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"train_datasets = valid_one2seq_df.train_dataset.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"\\n\",\n    \"one2seq_exps = [\\n\",\n    \"    \\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"               \\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"# 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n    \"'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"# 'magkp-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n    \"'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'topbeamends'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## absent\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50', ignore_valid=True)\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_one2seq_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## V3: more Transformers+MagKP exps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 151,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T21:50:18.552654Z\",\n     \"start_time\": \"2020-11-22T21:50:18.534641Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n       \" 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n       \" 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n       \" 'kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \" 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\"\n      ]\n     },\n     \"execution_count\": 151,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"[name for name in all_eval_df['exp_name'].unique() if name.startswith('kpgen-')]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-04T03:22:23.910362Z\",\n     \"start_time\": \"2020-11-04T03:22:23.892228Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(7634, 121)\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1                  911\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse          420\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            420\\n\",\n      \"kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         420\\n\",\n      \"kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            420\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1                 351\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1                 313\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1                         312\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1                         301\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1                        301\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue                                                     280\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse                                              280\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                        280\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1      217\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covfalse-Contextboth-IF1     214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1      214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copyfalse-Covfalse-Contextboth-IF1    214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.5-Copytrue-Covtrue-Contextboth-IF1      185\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Covtrue-Contextboth-IF1      185\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1      184\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1                        156\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.1-Copytrue                                                     140\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue                                                     140\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                     140\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue                                                   140\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.0-Copytrue                                                     140\\n\",\n      \"magkp-meng17-one2one-BS128-OPTadagrad-LR0.05-L1-H-D150-E100-DO0.0-Copytrue                                           140\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue                                        70 \\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue                                        70 \\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D128-E128-DO0.1-Copytrue                                        70 \\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                        6  \\n\",\n      \"Name: exp_name, dtype: int64\\n\",\n      \"meng17-one2one-kp20k              3252\\n\",\n      \"meng17-one2one-kp20k-v2           1896\\n\",\n      \"meng17-one2one-kp20k-v3           1680\\n\",\n      \"meng17-one2one-kp20k-topmodels    806 \\n\",\n      \"Name: exp_group, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"one2one_eval_df = all_eval_df.loc[all_eval_df['train_mode'] == 'one2one']\\n\",\n    \"print(one2one_eval_df.shape)\\n\",\n    \"print(one2one_eval_df.exp_name.value_counts())\\n\",\n    \"print(one2one_eval_df.exp_group.value_counts())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 131,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T21:04:45.318520Z\",\n     \"start_time\": \"2020-11-22T21:04:42.184233Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(420, 121)\\n\",\n      \"present valid_kp_df\\n\",\n      \"(21, 121)\\n\",\n      \"All data\\n\",\n      \"(420, 121)\\n\",\n      \"absent valid_kp_df\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:61: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(21, 121)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:117: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"kp_exps = [\\n\",\n    \"    'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"    'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"    'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"    \\n\",\n    \"#     'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"#     'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# prepare one2one data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.beam_width == '200']\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', 100, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"#     display(tmp_df)\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', 100, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq Transformer Order Matters?\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-17T23:28:34.138655Z\",\n     \"start_time\": \"2020-11-17T23:28:34.020853Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(50277, 121)\\n\",\n      \"kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue              3187\\n\",\n      \"kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                    3186\\n\",\n      \"kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                   3183\\n\",\n      \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                   2535\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue                                      1680\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue                                      1680\\n\",\n      \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                           1680\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue                                      1680\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue                                    1120\\n\",\n      \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                    1120\\n\",\n      \"kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                1120\\n\",\n      \"kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                 1120\\n\",\n      \"kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                 1120\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue                                    1120\\n\",\n      \"kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1           1120\\n\",\n      \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                    1120\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse                                   1120\\n\",\n      \"kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1       1120\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1        1120\\n\",\n      \"kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue                                               1098\\n\",\n      \"kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue                                                     1092\\n\",\n      \"kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue                                                    1079\\n\",\n      \"kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                            939\\n\",\n      \"kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                             933\\n\",\n      \"kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                             933\\n\",\n      \"kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                       932\\n\",\n      \"kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                   928\\n\",\n      \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse                  921\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue                                       645\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue                                       582\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue                                       575\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse                                       560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue                                    560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                               560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue                                        560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse                                       560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                     560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                               560\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                     560\\n\",\n      \"kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue             394\\n\",\n      \"kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                    384\\n\",\n      \"kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue             380\\n\",\n      \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 372\\n\",\n      \"kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 369\\n\",\n      \"kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           362\\n\",\n      \"kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       351\\n\",\n      \"kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       349\\n\",\n      \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse               348\\n\",\n      \"kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            140\\n\",\n      \"kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue     140\\n\",\n      \"kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         140\\n\",\n      \"kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue     140\\n\",\n      \"kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         140\\n\",\n      \"Name: exp_name, dtype: int64\\n\",\n      \"meng17-one2seq-kp20k-v3           24955\\n\",\n      \"meng17-one2seq-kp20k-v2           18602\\n\",\n      \"meng17-one2seq-kp20k-topmodels     6720\\n\",\n      \"Name: exp_group, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"one2seq_eval_df = all_eval_df.loc[all_eval_df['train_mode'] == 'one2seq']\\n\",\n    \"print(one2seq_eval_df.shape)\\n\",\n    \"print(one2seq_eval_df.exp_name.value_counts())\\n\",\n    \"print(one2seq_eval_df.exp_group.value_counts())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-17T23:29:02.652022Z\",\n     \"start_time\": \"2020-11-17T23:28:48.040388Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(3254, 236)\\n\",\n      \"present valid_kp_df\\n\",\n      \"(63, 236)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>all_exact_correct@5</th>\\n\",\n       \"      <th>all_exact_precision@5</th>\\n\",\n       \"      <th>all_exact_recall@5</th>\\n\",\n       \"      <th>all_exact_f_score@5</th>\\n\",\n       \"      <th>all_exact_precision_hard@5</th>\\n\",\n       \"      <th>all_exact_f_score_hard@5</th>\\n\",\n       \"      <th>all_exact_correct@10</th>\\n\",\n       \"      <th>all_exact_precision@10</th>\\n\",\n       \"      <th>all_exact_recall@10</th>\\n\",\n       \"      <th>all_exact_f_score@10</th>\\n\",\n       \"      <th>all_exact_precision_hard@10</th>\\n\",\n       \"      <th>all_exact_f_score_hard@10</th>\\n\",\n       \"      <th>all_exact_correct@k</th>\\n\",\n       \"      <th>all_exact_precision@k</th>\\n\",\n       \"      <th>all_exact_recall@k</th>\\n\",\n       \"      <th>all_exact_f_score@k</th>\\n\",\n       \"      <th>all_exact_precision_hard@k</th>\\n\",\n       \"      <th>all_exact_f_score_hard@k</th>\\n\",\n       \"      <th>all_exact_correct@M</th>\\n\",\n       \"      <th>all_exact_precision@M</th>\\n\",\n       \"      <th>all_exact_recall@M</th>\\n\",\n       \"      <th>all_exact_f_score@M</th>\\n\",\n       \"      <th>all_exact_precision_hard@M</th>\\n\",\n       \"      <th>all_exact_f_score_hard@M</th>\\n\",\n       \"      <th>all_exact_correct@1</th>\\n\",\n       \"      <th>all_exact_precision@1</th>\\n\",\n       \"      <th>all_exact_recall@1</th>\\n\",\n       \"      <th>all_exact_f_score@1</th>\\n\",\n       \"      <th>all_exact_precision_hard@1</th>\\n\",\n       \"      <th>all_exact_f_score_hard@1</th>\\n\",\n       \"      <th>all_exact_correct@3</th>\\n\",\n       \"      <th>all_exact_precision@3</th>\\n\",\n       \"      <th>all_exact_recall@3</th>\\n\",\n       \"      <th>all_exact_f_score@3</th>\\n\",\n       \"      <th>all_exact_precision_hard@3</th>\\n\",\n       \"      <th>all_exact_f_score_hard@3</th>\\n\",\n       \"      <th>all_partial_correct@5</th>\\n\",\n       \"      <th>all_partial_precision@5</th>\\n\",\n       \"      <th>all_partial_recall@5</th>\\n\",\n       \"      <th>all_partial_f_score@5</th>\\n\",\n       \"      <th>all_partial_precision_hard@5</th>\\n\",\n       \"      <th>all_partial_f_score_hard@5</th>\\n\",\n       \"      <th>all_partial_correct@10</th>\\n\",\n       \"      <th>all_partial_precision@10</th>\\n\",\n       \"      <th>all_partial_recall@10</th>\\n\",\n       \"      <th>all_partial_f_score@10</th>\\n\",\n       \"      <th>all_partial_precision_hard@10</th>\\n\",\n       \"      <th>all_partial_f_score_hard@10</th>\\n\",\n       \"      <th>all_partial_correct@k</th>\\n\",\n       \"      <th>all_partial_precision@k</th>\\n\",\n       \"      <th>all_partial_recall@k</th>\\n\",\n       \"      <th>all_partial_f_score@k</th>\\n\",\n       \"      <th>all_partial_precision_hard@k</th>\\n\",\n       \"      <th>all_partial_f_score_hard@k</th>\\n\",\n       \"      <th>all_partial_correct@M</th>\\n\",\n       \"      <th>all_partial_precision@M</th>\\n\",\n       \"      <th>all_partial_recall@M</th>\\n\",\n       \"      <th>all_partial_f_score@M</th>\\n\",\n       \"      <th>all_partial_precision_hard@M</th>\\n\",\n       \"      <th>all_partial_f_score_hard@M</th>\\n\",\n       \"      <th>all_partial_correct@1</th>\\n\",\n       \"      <th>all_partial_precision@1</th>\\n\",\n       \"      <th>all_partial_recall@1</th>\\n\",\n       \"      <th>all_partial_f_score@1</th>\\n\",\n       \"      <th>all_partial_precision_hard@1</th>\\n\",\n       \"      <th>all_partial_f_score_hard@1</th>\\n\",\n       \"      <th>all_partial_correct@3</th>\\n\",\n       \"      <th>all_partial_precision@3</th>\\n\",\n       \"      <th>all_partial_recall@3</th>\\n\",\n       \"      <th>all_partial_f_score@3</th>\\n\",\n       \"      <th>all_partial_precision_hard@3</th>\\n\",\n       \"      <th>all_partial_f_score_hard@3</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_correct@1</th>\\n\",\n       \"      <th>present_exact_precision@1</th>\\n\",\n       \"      <th>present_exact_recall@1</th>\\n\",\n       \"      <th>present_exact_f_score@1</th>\\n\",\n       \"      <th>present_exact_precision_hard@1</th>\\n\",\n       \"      <th>present_exact_f_score_hard@1</th>\\n\",\n       \"      <th>present_exact_correct@3</th>\\n\",\n       \"      <th>present_exact_precision@3</th>\\n\",\n       \"      <th>present_exact_recall@3</th>\\n\",\n       \"      <th>present_exact_f_score@3</th>\\n\",\n       \"      <th>present_exact_precision_hard@3</th>\\n\",\n       \"      <th>present_exact_f_score_hard@3</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@k</th>\\n\",\n       \"      <th>absent_exact_precision@k</th>\\n\",\n       \"      <th>absent_exact_recall@k</th>\\n\",\n       \"      <th>absent_exact_f_score@k</th>\\n\",\n       \"      <th>absent_exact_precision_hard@k</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@k</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@1</th>\\n\",\n       \"      <th>present_partial_precision@1</th>\\n\",\n       \"      <th>present_partial_recall@1</th>\\n\",\n       \"      <th>present_partial_f_score@1</th>\\n\",\n       \"      <th>present_partial_precision_hard@1</th>\\n\",\n       \"      <th>present_partial_f_score_hard@1</th>\\n\",\n       \"      <th>present_partial_correct@3</th>\\n\",\n       \"      <th>present_partial_precision@3</th>\\n\",\n       \"      <th>present_partial_recall@3</th>\\n\",\n       \"      <th>present_partial_f_score@3</th>\\n\",\n       \"      <th>present_partial_precision_hard@3</th>\\n\",\n       \"      <th>present_partial_f_score_hard@3</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@k</th>\\n\",\n       \"      <th>absent_partial_precision@k</th>\\n\",\n       \"      <th>absent_partial_recall@k</th>\\n\",\n       \"      <th>absent_partial_f_score@k</th>\\n\",\n       \"      <th>absent_partial_precision_hard@k</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@k</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>all_exact_advanced_auc</th>\\n\",\n       \"      <th>all_exact_advanced_ap</th>\\n\",\n       \"      <th>all_exact_advanced_mrr</th>\\n\",\n       \"      <th>all_exact_advanced_sadr</th>\\n\",\n       \"      <th>all_exact_advanced_ndcg</th>\\n\",\n       \"      <th>all_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>all_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15880</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.529221</td>\\n\",\n       \"      <td>0.105844</td>\\n\",\n       \"      <td>0.069845</td>\\n\",\n       \"      <td>0.082995</td>\\n\",\n       \"      <td>0.105844</td>\\n\",\n       \"      <td>0.082995</td>\\n\",\n       \"      <td>0.805195</td>\\n\",\n       \"      <td>0.080519</td>\\n\",\n       \"      <td>0.106542</td>\\n\",\n       \"      <td>0.090510</td>\\n\",\n       \"      <td>0.080519</td>\\n\",\n       \"      <td>0.090510</td>\\n\",\n       \"      <td>0.698052</td>\\n\",\n       \"      <td>0.090007</td>\\n\",\n       \"      <td>0.090007</td>\\n\",\n       \"      <td>0.090007</td>\\n\",\n       \"      <td>0.090007</td>\\n\",\n       \"      <td>0.090007</td>\\n\",\n       \"      <td>1.295455</td>\\n\",\n       \"      <td>0.023826</td>\\n\",\n       \"      <td>0.171131</td>\\n\",\n       \"      <td>0.040440</td>\\n\",\n       \"      <td>0.023826</td>\\n\",\n       \"      <td>0.040440</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.012181</td>\\n\",\n       \"      <td>0.021352</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.021352</td>\\n\",\n       \"      <td>0.334416</td>\\n\",\n       \"      <td>0.111472</td>\\n\",\n       \"      <td>0.044722</td>\\n\",\n       \"      <td>0.063044</td>\\n\",\n       \"      <td>0.111472</td>\\n\",\n       \"      <td>0.063044</td>\\n\",\n       \"      <td>0.577922</td>\\n\",\n       \"      <td>0.182602</td>\\n\",\n       \"      <td>0.119342</td>\\n\",\n       \"      <td>0.142365</td>\\n\",\n       \"      <td>0.182569</td>\\n\",\n       \"      <td>0.142359</td>\\n\",\n       \"      <td>0.967532</td>\\n\",\n       \"      <td>0.136018</td>\\n\",\n       \"      <td>0.176502</td>\\n\",\n       \"      <td>0.151501</td>\\n\",\n       \"      <td>0.135806</td>\\n\",\n       \"      <td>0.151407</td>\\n\",\n       \"      <td>0.824675</td>\\n\",\n       \"      <td>0.153434</td>\\n\",\n       \"      <td>0.153305</td>\\n\",\n       \"      <td>0.153348</td>\\n\",\n       \"      <td>0.153305</td>\\n\",\n       \"      <td>0.153305</td>\\n\",\n       \"      <td>1.782468</td>\\n\",\n       \"      <td>0.041645</td>\\n\",\n       \"      <td>0.288982</td>\\n\",\n       \"      <td>0.069829</td>\\n\",\n       \"      <td>0.041645</td>\\n\",\n       \"      <td>0.069829</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.169005</td>\\n\",\n       \"      <td>0.022100</td>\\n\",\n       \"      <td>0.038840</td>\\n\",\n       \"      <td>0.169005</td>\\n\",\n       \"      <td>0.038840</td>\\n\",\n       \"      <td>0.347403</td>\\n\",\n       \"      <td>0.198752</td>\\n\",\n       \"      <td>0.078321</td>\\n\",\n       \"      <td>0.110984</td>\\n\",\n       \"      <td>0.198752</td>\\n\",\n       \"      <td>0.110984</td>\\n\",\n       \"      <td>0.610390</td>\\n\",\n       \"      <td>0.122240</td>\\n\",\n       \"      <td>0.085890</td>\\n\",\n       \"      <td>0.098449</td>\\n\",\n       \"      <td>0.122078</td>\\n\",\n       \"      <td>0.098407</td>\\n\",\n       \"      <td>0.967532</td>\\n\",\n       \"      <td>0.100037</td>\\n\",\n       \"      <td>0.133169</td>\\n\",\n       \"      <td>0.111597</td>\\n\",\n       \"      <td>0.096753</td>\\n\",\n       \"      <td>0.109804</td>\\n\",\n       \"      <td>0.818182</td>\\n\",\n       \"      <td>0.110022</td>\\n\",\n       \"      <td>0.108095</td>\\n\",\n       \"      <td>0.108818</td>\\n\",\n       \"      <td>0.108095</td>\\n\",\n       \"      <td>0.108095</td>\\n\",\n       \"      <td>1.288961</td>\\n\",\n       \"      <td>0.085915</td>\\n\",\n       \"      <td>0.177815</td>\\n\",\n       \"      <td>0.110981</td>\\n\",\n       \"      <td>0.085915</td>\\n\",\n       \"      <td>0.110981</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.017428</td>\\n\",\n       \"      <td>0.030183</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.030183</td>\\n\",\n       \"      <td>0.399351</td>\\n\",\n       \"      <td>0.133117</td>\\n\",\n       \"      <td>0.057218</td>\\n\",\n       \"      <td>0.078092</td>\\n\",\n       \"      <td>0.133117</td>\\n\",\n       \"      <td>0.078092</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000649</td>\\n\",\n       \"      <td>0.002165</td>\\n\",\n       \"      <td>0.000999</td>\\n\",\n       \"      <td>0.000649</td>\\n\",\n       \"      <td>0.000999</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000382</td>\\n\",\n       \"      <td>0.002165</td>\\n\",\n       \"      <td>0.000649</td>\\n\",\n       \"      <td>0.000130</td>\\n\",\n       \"      <td>0.000245</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000382</td>\\n\",\n       \"      <td>0.002165</td>\\n\",\n       \"      <td>0.000649</td>\\n\",\n       \"      <td>0.000382</td>\\n\",\n       \"      <td>0.000649</td>\\n\",\n       \"      <td>0.668831</td>\\n\",\n       \"      <td>0.206782</td>\\n\",\n       \"      <td>0.141208</td>\\n\",\n       \"      <td>0.164149</td>\\n\",\n       \"      <td>0.205714</td>\\n\",\n       \"      <td>0.163574</td>\\n\",\n       \"      <td>1.181818</td>\\n\",\n       \"      <td>0.166052</td>\\n\",\n       \"      <td>0.213560</td>\\n\",\n       \"      <td>0.182309</td>\\n\",\n       \"      <td>0.158415</td>\\n\",\n       \"      <td>0.178156</td>\\n\",\n       \"      <td>0.996753</td>\\n\",\n       \"      <td>0.183356</td>\\n\",\n       \"      <td>0.178916</td>\\n\",\n       \"      <td>0.180606</td>\\n\",\n       \"      <td>0.178916</td>\\n\",\n       \"      <td>0.178916</td>\\n\",\n       \"      <td>1.616883</td>\\n\",\n       \"      <td>0.139805</td>\\n\",\n       \"      <td>0.273228</td>\\n\",\n       \"      <td>0.176132</td>\\n\",\n       \"      <td>0.139805</td>\\n\",\n       \"      <td>0.176132</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.221956</td>\\n\",\n       \"      <td>0.030366</td>\\n\",\n       \"      <td>0.052793</td>\\n\",\n       \"      <td>0.221956</td>\\n\",\n       \"      <td>0.052793</td>\\n\",\n       \"      <td>0.418831</td>\\n\",\n       \"      <td>0.230701</td>\\n\",\n       \"      <td>0.096004</td>\\n\",\n       \"      <td>0.132561</td>\\n\",\n       \"      <td>0.230376</td>\\n\",\n       \"      <td>0.132366</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.001341</td>\\n\",\n       \"      <td>0.007752</td>\\n\",\n       \"      <td>0.002217</td>\\n\",\n       \"      <td>0.001341</td>\\n\",\n       \"      <td>0.002217</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000637</td>\\n\",\n       \"      <td>0.009010</td>\\n\",\n       \"      <td>0.001137</td>\\n\",\n       \"      <td>0.000309</td>\\n\",\n       \"      <td>0.000594</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001314</td>\\n\",\n       \"      <td>0.001314</td>\\n\",\n       \"      <td>0.001314</td>\\n\",\n       \"      <td>0.001314</td>\\n\",\n       \"      <td>0.001314</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000619</td>\\n\",\n       \"      <td>0.009630</td>\\n\",\n       \"      <td>0.001101</td>\\n\",\n       \"      <td>0.000619</td>\\n\",\n       \"      <td>0.001101</td>\\n\",\n       \"      <td>0.038296</td>\\n\",\n       \"      <td>0.051100</td>\\n\",\n       \"      <td>0.165073</td>\\n\",\n       \"      <td>0.117306</td>\\n\",\n       \"      <td>0.133756</td>\\n\",\n       \"      <td>0.225359</td>\\n\",\n       \"      <td>0.262494</td>\\n\",\n       \"      <td>0.050630</td>\\n\",\n       \"      <td>0.065119</td>\\n\",\n       \"      <td>0.198821</td>\\n\",\n       \"      <td>0.136682</td>\\n\",\n       \"      <td>0.151283</td>\\n\",\n       \"      <td>0.266606</td>\\n\",\n       \"      <td>0.311655</td>\\n\",\n       \"      <td>0.001295</td>\\n\",\n       \"      <td>0.001353</td>\\n\",\n       \"      <td>0.001826</td>\\n\",\n       \"      <td>0.001468</td>\\n\",\n       \"      <td>0.002004</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>15.402597</td>\\n\",\n       \"      <td>56.288961</td>\\n\",\n       \"      <td>81.353896</td>\\n\",\n       \"      <td>3786.366883</td>\\n\",\n       \"      <td>447.863636</td>\\n\",\n       \"      <td>11661.003247</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20240</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.733766</td>\\n\",\n       \"      <td>0.146753</td>\\n\",\n       \"      <td>0.095473</td>\\n\",\n       \"      <td>0.114233</td>\\n\",\n       \"      <td>0.146753</td>\\n\",\n       \"      <td>0.114233</td>\\n\",\n       \"      <td>1.120130</td>\\n\",\n       \"      <td>0.113157</td>\\n\",\n       \"      <td>0.145205</td>\\n\",\n       \"      <td>0.125659</td>\\n\",\n       \"      <td>0.112013</td>\\n\",\n       \"      <td>0.124847</td>\\n\",\n       \"      <td>0.967532</td>\\n\",\n       \"      <td>0.121305</td>\\n\",\n       \"      <td>0.120919</td>\\n\",\n       \"      <td>0.121081</td>\\n\",\n       \"      <td>0.120919</td>\\n\",\n       \"      <td>0.120919</td>\\n\",\n       \"      <td>1.772727</td>\\n\",\n       \"      <td>0.048374</td>\\n\",\n       \"      <td>0.227893</td>\\n\",\n       \"      <td>0.075937</td>\\n\",\n       \"      <td>0.048374</td>\\n\",\n       \"      <td>0.075937</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.030574</td>\\n\",\n       \"      <td>0.053634</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.053634</td>\\n\",\n       \"      <td>0.487013</td>\\n\",\n       \"      <td>0.162338</td>\\n\",\n       \"      <td>0.063131</td>\\n\",\n       \"      <td>0.090053</td>\\n\",\n       \"      <td>0.162338</td>\\n\",\n       \"      <td>0.090053</td>\\n\",\n       \"      <td>0.837662</td>\\n\",\n       \"      <td>0.242969</td>\\n\",\n       \"      <td>0.157478</td>\\n\",\n       \"      <td>0.188618</td>\\n\",\n       \"      <td>0.242969</td>\\n\",\n       \"      <td>0.188618</td>\\n\",\n       \"      <td>1.405844</td>\\n\",\n       \"      <td>0.185242</td>\\n\",\n       \"      <td>0.236737</td>\\n\",\n       \"      <td>0.205206</td>\\n\",\n       \"      <td>0.183780</td>\\n\",\n       \"      <td>0.204157</td>\\n\",\n       \"      <td>1.211039</td>\\n\",\n       \"      <td>0.203697</td>\\n\",\n       \"      <td>0.203227</td>\\n\",\n       \"      <td>0.203424</td>\\n\",\n       \"      <td>0.203227</td>\\n\",\n       \"      <td>0.203227</td>\\n\",\n       \"      <td>2.399351</td>\\n\",\n       \"      <td>0.077279</td>\\n\",\n       \"      <td>0.361758</td>\\n\",\n       \"      <td>0.121264</td>\\n\",\n       \"      <td>0.077279</td>\\n\",\n       \"      <td>0.121264</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.352715</td>\\n\",\n       \"      <td>0.047658</td>\\n\",\n       \"      <td>0.083446</td>\\n\",\n       \"      <td>0.352715</td>\\n\",\n       \"      <td>0.083446</td>\\n\",\n       \"      <td>0.525974</td>\\n\",\n       \"      <td>0.276333</td>\\n\",\n       \"      <td>0.107169</td>\\n\",\n       \"      <td>0.152810</td>\\n\",\n       \"      <td>0.276333</td>\\n\",\n       \"      <td>0.152810</td>\\n\",\n       \"      <td>0.740260</td>\\n\",\n       \"      <td>0.148214</td>\\n\",\n       \"      <td>0.101372</td>\\n\",\n       \"      <td>0.117720</td>\\n\",\n       \"      <td>0.148052</td>\\n\",\n       \"      <td>0.117565</td>\\n\",\n       \"      <td>1.181818</td>\\n\",\n       \"      <td>0.120376</td>\\n\",\n       \"      <td>0.159815</td>\\n\",\n       \"      <td>0.134929</td>\\n\",\n       \"      <td>0.118182</td>\\n\",\n       \"      <td>0.133124</td>\\n\",\n       \"      <td>1.012987</td>\\n\",\n       \"      <td>0.129740</td>\\n\",\n       \"      <td>0.129412</td>\\n\",\n       \"      <td>0.129553</td>\\n\",\n       \"      <td>0.129412</td>\\n\",\n       \"      <td>0.129412</td>\\n\",\n       \"      <td>1.772727</td>\\n\",\n       \"      <td>0.091469</td>\\n\",\n       \"      <td>0.236609</td>\\n\",\n       \"      <td>0.127492</td>\\n\",\n       \"      <td>0.091469</td>\\n\",\n       \"      <td>0.127492</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.032663</td>\\n\",\n       \"      <td>0.056304</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.056304</td>\\n\",\n       \"      <td>0.503247</td>\\n\",\n       \"      <td>0.167749</td>\\n\",\n       \"      <td>0.069635</td>\\n\",\n       \"      <td>0.096086</td>\\n\",\n       \"      <td>0.167749</td>\\n\",\n       \"      <td>0.096086</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.847403</td>\\n\",\n       \"      <td>0.244165</td>\\n\",\n       \"      <td>0.165434</td>\\n\",\n       \"      <td>0.193027</td>\\n\",\n       \"      <td>0.243905</td>\\n\",\n       \"      <td>0.192780</td>\\n\",\n       \"      <td>1.454545</td>\\n\",\n       \"      <td>0.190849</td>\\n\",\n       \"      <td>0.252000</td>\\n\",\n       \"      <td>0.213384</td>\\n\",\n       \"      <td>0.188023</td>\\n\",\n       \"      <td>0.211010</td>\\n\",\n       \"      <td>1.237013</td>\\n\",\n       \"      <td>0.210013</td>\\n\",\n       \"      <td>0.209605</td>\\n\",\n       \"      <td>0.209782</td>\\n\",\n       \"      <td>0.209605</td>\\n\",\n       \"      <td>0.209605</td>\\n\",\n       \"      <td>2.266234</td>\\n\",\n       \"      <td>0.139757</td>\\n\",\n       \"      <td>0.354896</td>\\n\",\n       \"      <td>0.193866</td>\\n\",\n       \"      <td>0.139757</td>\\n\",\n       \"      <td>0.193866</td>\\n\",\n       \"      <td>0.227273</td>\\n\",\n       \"      <td>0.351065</td>\\n\",\n       \"      <td>0.050424</td>\\n\",\n       \"      <td>0.086870</td>\\n\",\n       \"      <td>0.351065</td>\\n\",\n       \"      <td>0.086870</td>\\n\",\n       \"      <td>0.538961</td>\\n\",\n       \"      <td>0.280942</td>\\n\",\n       \"      <td>0.115308</td>\\n\",\n       \"      <td>0.159801</td>\\n\",\n       \"      <td>0.280942</td>\\n\",\n       \"      <td>0.159801</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001503</td>\\n\",\n       \"      <td>0.007943</td>\\n\",\n       \"      <td>0.002432</td>\\n\",\n       \"      <td>0.001180</td>\\n\",\n       \"      <td>0.002018</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000953</td>\\n\",\n       \"      <td>0.009354</td>\\n\",\n       \"      <td>0.001569</td>\\n\",\n       \"      <td>0.000279</td>\\n\",\n       \"      <td>0.000538</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000939</td>\\n\",\n       \"      <td>0.009354</td>\\n\",\n       \"      <td>0.001543</td>\\n\",\n       \"      <td>0.000939</td>\\n\",\n       \"      <td>0.001543</td>\\n\",\n       \"      <td>0.067856</td>\\n\",\n       \"      <td>0.081617</td>\\n\",\n       \"      <td>0.241161</td>\\n\",\n       \"      <td>0.159086</td>\\n\",\n       \"      <td>0.194062</td>\\n\",\n       \"      <td>0.314216</td>\\n\",\n       \"      <td>0.364432</td>\\n\",\n       \"      <td>0.073612</td>\\n\",\n       \"      <td>0.088588</td>\\n\",\n       \"      <td>0.248960</td>\\n\",\n       \"      <td>0.169905</td>\\n\",\n       \"      <td>0.202607</td>\\n\",\n       \"      <td>0.325369</td>\\n\",\n       \"      <td>0.376033</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>20.912338</td>\\n\",\n       \"      <td>26.587662</td>\\n\",\n       \"      <td>56.889610</td>\\n\",\n       \"      <td>3801.233766</td>\\n\",\n       \"      <td>473.775974</td>\\n\",\n       \"      <td>11638.435065</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16579</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.340909</td>\\n\",\n       \"      <td>0.069156</td>\\n\",\n       \"      <td>0.044977</td>\\n\",\n       \"      <td>0.053545</td>\\n\",\n       \"      <td>0.068182</td>\\n\",\n       \"      <td>0.053364</td>\\n\",\n       \"      <td>0.525974</td>\\n\",\n       \"      <td>0.053896</td>\\n\",\n       \"      <td>0.069508</td>\\n\",\n       \"      <td>0.059381</td>\\n\",\n       \"      <td>0.052597</td>\\n\",\n       \"      <td>0.059042</td>\\n\",\n       \"      <td>0.441558</td>\\n\",\n       \"      <td>0.057790</td>\\n\",\n       \"      <td>0.056630</td>\\n\",\n       \"      <td>0.056888</td>\\n\",\n       \"      <td>0.056630</td>\\n\",\n       \"      <td>0.056630</td>\\n\",\n       \"      <td>0.766234</td>\\n\",\n       \"      <td>0.007226</td>\\n\",\n       \"      <td>0.099783</td>\\n\",\n       \"      <td>0.010708</td>\\n\",\n       \"      <td>0.007226</td>\\n\",\n       \"      <td>0.010708</td>\\n\",\n       \"      <td>0.097403</td>\\n\",\n       \"      <td>0.097403</td>\\n\",\n       \"      <td>0.013012</td>\\n\",\n       \"      <td>0.022746</td>\\n\",\n       \"      <td>0.097403</td>\\n\",\n       \"      <td>0.022746</td>\\n\",\n       \"      <td>0.262987</td>\\n\",\n       \"      <td>0.088203</td>\\n\",\n       \"      <td>0.034441</td>\\n\",\n       \"      <td>0.048840</td>\\n\",\n       \"      <td>0.087662</td>\\n\",\n       \"      <td>0.048767</td>\\n\",\n       \"      <td>0.383117</td>\\n\",\n       \"      <td>0.132822</td>\\n\",\n       \"      <td>0.085223</td>\\n\",\n       \"      <td>0.102154</td>\\n\",\n       \"      <td>0.131848</td>\\n\",\n       \"      <td>0.101973</td>\\n\",\n       \"      <td>0.629870</td>\\n\",\n       \"      <td>0.099860</td>\\n\",\n       \"      <td>0.127803</td>\\n\",\n       \"      <td>0.109982</td>\\n\",\n       \"      <td>0.098491</td>\\n\",\n       \"      <td>0.109608</td>\\n\",\n       \"      <td>0.522727</td>\\n\",\n       \"      <td>0.109802</td>\\n\",\n       \"      <td>0.108603</td>\\n\",\n       \"      <td>0.108879</td>\\n\",\n       \"      <td>0.108603</td>\\n\",\n       \"      <td>0.108603</td>\\n\",\n       \"      <td>1.211039</td>\\n\",\n       \"      <td>0.013365</td>\\n\",\n       \"      <td>0.212286</td>\\n\",\n       \"      <td>0.021589</td>\\n\",\n       \"      <td>0.013365</td>\\n\",\n       \"      <td>0.021589</td>\\n\",\n       \"      <td>0.097403</td>\\n\",\n       \"      <td>0.168254</td>\\n\",\n       \"      <td>0.022032</td>\\n\",\n       \"      <td>0.038667</td>\\n\",\n       \"      <td>0.168254</td>\\n\",\n       \"      <td>0.038667</td>\\n\",\n       \"      <td>0.292208</td>\\n\",\n       \"      <td>0.160013</td>\\n\",\n       \"      <td>0.062121</td>\\n\",\n       \"      <td>0.088252</td>\\n\",\n       \"      <td>0.159472</td>\\n\",\n       \"      <td>0.088180</td>\\n\",\n       \"      <td>0.509740</td>\\n\",\n       \"      <td>0.112500</td>\\n\",\n       \"      <td>0.070689</td>\\n\",\n       \"      <td>0.083691</td>\\n\",\n       \"      <td>0.101948</td>\\n\",\n       \"      <td>0.081343</td>\\n\",\n       \"      <td>0.720779</td>\\n\",\n       \"      <td>0.108648</td>\\n\",\n       \"      <td>0.098447</td>\\n\",\n       \"      <td>0.097583</td>\\n\",\n       \"      <td>0.072078</td>\\n\",\n       \"      <td>0.081371</td>\\n\",\n       \"      <td>0.652597</td>\\n\",\n       \"      <td>0.108373</td>\\n\",\n       \"      <td>0.085286</td>\\n\",\n       \"      <td>0.092317</td>\\n\",\n       \"      <td>0.085286</td>\\n\",\n       \"      <td>0.085286</td>\\n\",\n       \"      <td>0.766234</td>\\n\",\n       \"      <td>0.108706</td>\\n\",\n       \"      <td>0.105791</td>\\n\",\n       \"      <td>0.099742</td>\\n\",\n       \"      <td>0.108706</td>\\n\",\n       \"      <td>0.099742</td>\\n\",\n       \"      <td>0.146104</td>\\n\",\n       \"      <td>0.146104</td>\\n\",\n       \"      <td>0.019714</td>\\n\",\n       \"      <td>0.034387</td>\\n\",\n       \"      <td>0.146104</td>\\n\",\n       \"      <td>0.034387</td>\\n\",\n       \"      <td>0.340909</td>\\n\",\n       \"      <td>0.117424</td>\\n\",\n       \"      <td>0.048399</td>\\n\",\n       \"      <td>0.066269</td>\\n\",\n       \"      <td>0.113636</td>\\n\",\n       \"      <td>0.065994</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0.194771</td>\\n\",\n       \"      <td>0.120940</td>\\n\",\n       \"      <td>0.144720</td>\\n\",\n       \"      <td>0.179562</td>\\n\",\n       \"      <td>0.141166</td>\\n\",\n       \"      <td>0.863636</td>\\n\",\n       \"      <td>0.178659</td>\\n\",\n       \"      <td>0.161275</td>\\n\",\n       \"      <td>0.160621</td>\\n\",\n       \"      <td>0.120840</td>\\n\",\n       \"      <td>0.135305</td>\\n\",\n       \"      <td>0.762987</td>\\n\",\n       \"      <td>0.182398</td>\\n\",\n       \"      <td>0.143959</td>\\n\",\n       \"      <td>0.156138</td>\\n\",\n       \"      <td>0.143959</td>\\n\",\n       \"      <td>0.143959</td>\\n\",\n       \"      <td>0.918831</td>\\n\",\n       \"      <td>0.176762</td>\\n\",\n       \"      <td>0.170419</td>\\n\",\n       \"      <td>0.162001</td>\\n\",\n       \"      <td>0.176762</td>\\n\",\n       \"      <td>0.162001</td>\\n\",\n       \"      <td>0.146104</td>\\n\",\n       \"      <td>0.238600</td>\\n\",\n       \"      <td>0.032257</td>\\n\",\n       \"      <td>0.056090</td>\\n\",\n       \"      <td>0.238600</td>\\n\",\n       \"      <td>0.056090</td>\\n\",\n       \"      <td>0.370130</td>\\n\",\n       \"      <td>0.210705</td>\\n\",\n       \"      <td>0.084573</td>\\n\",\n       \"      <td>0.117386</td>\\n\",\n       \"      <td>0.205794</td>\\n\",\n       \"      <td>0.116978</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000735</td>\\n\",\n       \"      <td>0.005328</td>\\n\",\n       \"      <td>0.001263</td>\\n\",\n       \"      <td>0.000735</td>\\n\",\n       \"      <td>0.001263</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000293</td>\\n\",\n       \"      <td>0.009263</td>\\n\",\n       \"      <td>0.000562</td>\\n\",\n       \"      <td>0.000293</td>\\n\",\n       \"      <td>0.000562</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001979</td>\\n\",\n       \"      <td>0.001979</td>\\n\",\n       \"      <td>0.001979</td>\\n\",\n       \"      <td>0.001979</td>\\n\",\n       \"      <td>0.001979</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000070</td>\\n\",\n       \"      <td>0.009928</td>\\n\",\n       \"      <td>0.000138</td>\\n\",\n       \"      <td>0.000070</td>\\n\",\n       \"      <td>0.000138</td>\\n\",\n       \"      <td>0.027337</td>\\n\",\n       \"      <td>0.034537</td>\\n\",\n       \"      <td>0.161769</td>\\n\",\n       \"      <td>0.072601</td>\\n\",\n       \"      <td>0.088172</td>\\n\",\n       \"      <td>0.164482</td>\\n\",\n       \"      <td>0.190235</td>\\n\",\n       \"      <td>0.039526</td>\\n\",\n       \"      <td>0.048672</td>\\n\",\n       \"      <td>0.225114</td>\\n\",\n       \"      <td>0.094964</td>\\n\",\n       \"      <td>0.108833</td>\\n\",\n       \"      <td>0.241096</td>\\n\",\n       \"      <td>0.267379</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>7.961039</td>\\n\",\n       \"      <td>201.379870</td>\\n\",\n       \"      <td>234.685065</td>\\n\",\n       \"      <td>3533.779221</td>\\n\",\n       \"      <td>456.996753</td>\\n\",\n       \"      <td>12471.379870</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15963</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.558442</td>\\n\",\n       \"      <td>0.111688</td>\\n\",\n       \"      <td>0.072751</td>\\n\",\n       \"      <td>0.086882</td>\\n\",\n       \"      <td>0.111688</td>\\n\",\n       \"      <td>0.086882</td>\\n\",\n       \"      <td>0.857143</td>\\n\",\n       \"      <td>0.086800</td>\\n\",\n       \"      <td>0.111587</td>\\n\",\n       \"      <td>0.096264</td>\\n\",\n       \"      <td>0.085714</td>\\n\",\n       \"      <td>0.095754</td>\\n\",\n       \"      <td>0.746753</td>\\n\",\n       \"      <td>0.095192</td>\\n\",\n       \"      <td>0.094476</td>\\n\",\n       \"      <td>0.094745</td>\\n\",\n       \"      <td>0.094476</td>\\n\",\n       \"      <td>0.094476</td>\\n\",\n       \"      <td>1.353896</td>\\n\",\n       \"      <td>0.031018</td>\\n\",\n       \"      <td>0.176396</td>\\n\",\n       \"      <td>0.046575</td>\\n\",\n       \"      <td>0.031018</td>\\n\",\n       \"      <td>0.046575</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.017154</td>\\n\",\n       \"      <td>0.030049</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.030049</td>\\n\",\n       \"      <td>0.379870</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.049760</td>\\n\",\n       \"      <td>0.070579</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.070579</td>\\n\",\n       \"      <td>0.655844</td>\\n\",\n       \"      <td>0.195582</td>\\n\",\n       \"      <td>0.127142</td>\\n\",\n       \"      <td>0.151932</td>\\n\",\n       \"      <td>0.195582</td>\\n\",\n       \"      <td>0.151932</td>\\n\",\n       \"      <td>1.074675</td>\\n\",\n       \"      <td>0.149885</td>\\n\",\n       \"      <td>0.190488</td>\\n\",\n       \"      <td>0.165313</td>\\n\",\n       \"      <td>0.147726</td>\\n\",\n       \"      <td>0.164208</td>\\n\",\n       \"      <td>0.922078</td>\\n\",\n       \"      <td>0.166773</td>\\n\",\n       \"      <td>0.165525</td>\\n\",\n       \"      <td>0.165991</td>\\n\",\n       \"      <td>0.165525</td>\\n\",\n       \"      <td>0.165525</td>\\n\",\n       \"      <td>1.844156</td>\\n\",\n       \"      <td>0.053985</td>\\n\",\n       \"      <td>0.296501</td>\\n\",\n       \"      <td>0.080257</td>\\n\",\n       \"      <td>0.053985</td>\\n\",\n       \"      <td>0.080257</td>\\n\",\n       \"      <td>0.126623</td>\\n\",\n       \"      <td>0.244263</td>\\n\",\n       \"      <td>0.032920</td>\\n\",\n       \"      <td>0.057607</td>\\n\",\n       \"      <td>0.244263</td>\\n\",\n       \"      <td>0.057607</td>\\n\",\n       \"      <td>0.425325</td>\\n\",\n       \"      <td>0.227950</td>\\n\",\n       \"      <td>0.089417</td>\\n\",\n       \"      <td>0.126859</td>\\n\",\n       \"      <td>0.227950</td>\\n\",\n       \"      <td>0.126859</td>\\n\",\n       \"      <td>0.629870</td>\\n\",\n       \"      <td>0.128409</td>\\n\",\n       \"      <td>0.086883</td>\\n\",\n       \"      <td>0.100952</td>\\n\",\n       \"      <td>0.125974</td>\\n\",\n       \"      <td>0.100354</td>\\n\",\n       \"      <td>0.967532</td>\\n\",\n       \"      <td>0.104261</td>\\n\",\n       \"      <td>0.132736</td>\\n\",\n       \"      <td>0.113339</td>\\n\",\n       \"      <td>0.096753</td>\\n\",\n       \"      <td>0.109710</td>\\n\",\n       \"      <td>0.824675</td>\\n\",\n       \"      <td>0.114358</td>\\n\",\n       \"      <td>0.109358</td>\\n\",\n       \"      <td>0.110825</td>\\n\",\n       \"      <td>0.109358</td>\\n\",\n       \"      <td>0.109358</td>\\n\",\n       \"      <td>1.350649</td>\\n\",\n       \"      <td>0.089301</td>\\n\",\n       \"      <td>0.183420</td>\\n\",\n       \"      <td>0.113019</td>\\n\",\n       \"      <td>0.089301</td>\\n\",\n       \"      <td>0.113019</td>\\n\",\n       \"      <td>0.155844</td>\\n\",\n       \"      <td>0.155844</td>\\n\",\n       \"      <td>0.022636</td>\\n\",\n       \"      <td>0.039062</td>\\n\",\n       \"      <td>0.155844</td>\\n\",\n       \"      <td>0.039062</td>\\n\",\n       \"      <td>0.422078</td>\\n\",\n       \"      <td>0.141234</td>\\n\",\n       \"      <td>0.058670</td>\\n\",\n       \"      <td>0.080767</td>\\n\",\n       \"      <td>0.140693</td>\\n\",\n       \"      <td>0.080677</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000058</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000110</td>\\n\",\n       \"      <td>0.000058</td>\\n\",\n       \"      <td>0.000110</td>\\n\",\n       \"      <td>0.707792</td>\\n\",\n       \"      <td>0.211086</td>\\n\",\n       \"      <td>0.142212</td>\\n\",\n       \"      <td>0.165907</td>\\n\",\n       \"      <td>0.208098</td>\\n\",\n       \"      <td>0.164991</td>\\n\",\n       \"      <td>1.194805</td>\\n\",\n       \"      <td>0.169659</td>\\n\",\n       \"      <td>0.214228</td>\\n\",\n       \"      <td>0.184252</td>\\n\",\n       \"      <td>0.158497</td>\\n\",\n       \"      <td>0.178503</td>\\n\",\n       \"      <td>1.006494</td>\\n\",\n       \"      <td>0.186900</td>\\n\",\n       \"      <td>0.179906</td>\\n\",\n       \"      <td>0.182085</td>\\n\",\n       \"      <td>0.179906</td>\\n\",\n       \"      <td>0.179906</td>\\n\",\n       \"      <td>1.681818</td>\\n\",\n       \"      <td>0.139717</td>\\n\",\n       \"      <td>0.281648</td>\\n\",\n       \"      <td>0.176099</td>\\n\",\n       \"      <td>0.139717</td>\\n\",\n       \"      <td>0.176099</td>\\n\",\n       \"      <td>0.155844</td>\\n\",\n       \"      <td>0.269942</td>\\n\",\n       \"      <td>0.038538</td>\\n\",\n       \"      <td>0.066520</td>\\n\",\n       \"      <td>0.269942</td>\\n\",\n       \"      <td>0.066520</td>\\n\",\n       \"      <td>0.461039</td>\\n\",\n       \"      <td>0.239784</td>\\n\",\n       \"      <td>0.098515</td>\\n\",\n       \"      <td>0.136400</td>\\n\",\n       \"      <td>0.239242</td>\\n\",\n       \"      <td>0.136310</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.002173</td>\\n\",\n       \"      <td>0.009374</td>\\n\",\n       \"      <td>0.003331</td>\\n\",\n       \"      <td>0.001681</td>\\n\",\n       \"      <td>0.002766</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.001249</td>\\n\",\n       \"      <td>0.013611</td>\\n\",\n       \"      <td>0.001952</td>\\n\",\n       \"      <td>0.000491</td>\\n\",\n       \"      <td>0.000940</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.002487</td>\\n\",\n       \"      <td>0.002487</td>\\n\",\n       \"      <td>0.002487</td>\\n\",\n       \"      <td>0.002487</td>\\n\",\n       \"      <td>0.002487</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.001087</td>\\n\",\n       \"      <td>0.013611</td>\\n\",\n       \"      <td>0.001650</td>\\n\",\n       \"      <td>0.001087</td>\\n\",\n       \"      <td>0.001650</td>\\n\",\n       \"      <td>0.045350</td>\\n\",\n       \"      <td>0.057300</td>\\n\",\n       \"      <td>0.187435</td>\\n\",\n       \"      <td>0.121634</td>\\n\",\n       \"      <td>0.143167</td>\\n\",\n       \"      <td>0.234883</td>\\n\",\n       \"      <td>0.273612</td>\\n\",\n       \"      <td>0.056503</td>\\n\",\n       \"      <td>0.069403</td>\\n\",\n       \"      <td>0.212344</td>\\n\",\n       \"      <td>0.137225</td>\\n\",\n       \"      <td>0.158349</td>\\n\",\n       \"      <td>0.256096</td>\\n\",\n       \"      <td>0.296792</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>16.422078</td>\\n\",\n       \"      <td>68.798701</td>\\n\",\n       \"      <td>110.707792</td>\\n\",\n       \"      <td>3269.951299</td>\\n\",\n       \"      <td>405.662338</td>\\n\",\n       \"      <td>10885.237013</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20419</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.327922</td>\\n\",\n       \"      <td>0.065584</td>\\n\",\n       \"      <td>0.044145</td>\\n\",\n       \"      <td>0.052217</td>\\n\",\n       \"      <td>0.065584</td>\\n\",\n       \"      <td>0.052217</td>\\n\",\n       \"      <td>0.490260</td>\\n\",\n       \"      <td>0.049165</td>\\n\",\n       \"      <td>0.064798</td>\\n\",\n       \"      <td>0.055253</td>\\n\",\n       \"      <td>0.049026</td>\\n\",\n       \"      <td>0.055171</td>\\n\",\n       \"      <td>0.464286</td>\\n\",\n       \"      <td>0.060057</td>\\n\",\n       \"      <td>0.060057</td>\\n\",\n       \"      <td>0.060057</td>\\n\",\n       \"      <td>0.060057</td>\\n\",\n       \"      <td>0.060057</td>\\n\",\n       \"      <td>0.970779</td>\\n\",\n       \"      <td>0.010515</td>\\n\",\n       \"      <td>0.127110</td>\\n\",\n       \"      <td>0.018231</td>\\n\",\n       \"      <td>0.010515</td>\\n\",\n       \"      <td>0.018231</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.013001</td>\\n\",\n       \"      <td>0.022599</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.022599</td>\\n\",\n       \"      <td>0.217532</td>\\n\",\n       \"      <td>0.072511</td>\\n\",\n       \"      <td>0.029767</td>\\n\",\n       \"      <td>0.041821</td>\\n\",\n       \"      <td>0.072511</td>\\n\",\n       \"      <td>0.041821</td>\\n\",\n       \"      <td>0.389610</td>\\n\",\n       \"      <td>0.135763</td>\\n\",\n       \"      <td>0.088881</td>\\n\",\n       \"      <td>0.106134</td>\\n\",\n       \"      <td>0.135763</td>\\n\",\n       \"      <td>0.106134</td>\\n\",\n       \"      <td>0.620130</td>\\n\",\n       \"      <td>0.098219</td>\\n\",\n       \"      <td>0.126771</td>\\n\",\n       \"      <td>0.109236</td>\\n\",\n       \"      <td>0.097824</td>\\n\",\n       \"      <td>0.109008</td>\\n\",\n       \"      <td>0.577922</td>\\n\",\n       \"      <td>0.115826</td>\\n\",\n       \"      <td>0.115691</td>\\n\",\n       \"      <td>0.115745</td>\\n\",\n       \"      <td>0.115691</td>\\n\",\n       \"      <td>0.115691</td>\\n\",\n       \"      <td>1.412338</td>\\n\",\n       \"      <td>0.020512</td>\\n\",\n       \"      <td>0.242259</td>\\n\",\n       \"      <td>0.035184</td>\\n\",\n       \"      <td>0.020512</td>\\n\",\n       \"      <td>0.035184</td>\\n\",\n       \"      <td>0.090909</td>\\n\",\n       \"      <td>0.186445</td>\\n\",\n       \"      <td>0.025313</td>\\n\",\n       \"      <td>0.044285</td>\\n\",\n       \"      <td>0.186445</td>\\n\",\n       \"      <td>0.044285</td>\\n\",\n       \"      <td>0.243506</td>\\n\",\n       \"      <td>0.155859</td>\\n\",\n       \"      <td>0.061454</td>\\n\",\n       \"      <td>0.087249</td>\\n\",\n       \"      <td>0.155859</td>\\n\",\n       \"      <td>0.087249</td>\\n\",\n       \"      <td>0.496753</td>\\n\",\n       \"      <td>0.102922</td>\\n\",\n       \"      <td>0.068144</td>\\n\",\n       \"      <td>0.080311</td>\\n\",\n       \"      <td>0.099351</td>\\n\",\n       \"      <td>0.079243</td>\\n\",\n       \"      <td>0.795455</td>\\n\",\n       \"      <td>0.093570</td>\\n\",\n       \"      <td>0.107774</td>\\n\",\n       \"      <td>0.096699</td>\\n\",\n       \"      <td>0.079545</td>\\n\",\n       \"      <td>0.089823</td>\\n\",\n       \"      <td>0.678571</td>\\n\",\n       \"      <td>0.096857</td>\\n\",\n       \"      <td>0.088814</td>\\n\",\n       \"      <td>0.091654</td>\\n\",\n       \"      <td>0.088814</td>\\n\",\n       \"      <td>0.088814</td>\\n\",\n       \"      <td>0.967532</td>\\n\",\n       \"      <td>0.089625</td>\\n\",\n       \"      <td>0.131660</td>\\n\",\n       \"      <td>0.099908</td>\\n\",\n       \"      <td>0.089625</td>\\n\",\n       \"      <td>0.099908</td>\\n\",\n       \"      <td>0.142857</td>\\n\",\n       \"      <td>0.142857</td>\\n\",\n       \"      <td>0.019769</td>\\n\",\n       \"      <td>0.034478</td>\\n\",\n       \"      <td>0.142857</td>\\n\",\n       \"      <td>0.034478</td>\\n\",\n       \"      <td>0.334416</td>\\n\",\n       \"      <td>0.112554</td>\\n\",\n       \"      <td>0.048104</td>\\n\",\n       \"      <td>0.066089</td>\\n\",\n       \"      <td>0.111472</td>\\n\",\n       \"      <td>0.065780</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000032</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.000032</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.577922</td>\\n\",\n       \"      <td>0.185519</td>\\n\",\n       \"      <td>0.122234</td>\\n\",\n       \"      <td>0.143970</td>\\n\",\n       \"      <td>0.180207</td>\\n\",\n       \"      <td>0.142508</td>\\n\",\n       \"      <td>1.006494</td>\\n\",\n       \"      <td>0.160503</td>\\n\",\n       \"      <td>0.182055</td>\\n\",\n       \"      <td>0.164809</td>\\n\",\n       \"      <td>0.137225</td>\\n\",\n       \"      <td>0.153469</td>\\n\",\n       \"      <td>0.870130</td>\\n\",\n       \"      <td>0.170513</td>\\n\",\n       \"      <td>0.156861</td>\\n\",\n       \"      <td>0.161669</td>\\n\",\n       \"      <td>0.156861</td>\\n\",\n       \"      <td>0.156861</td>\\n\",\n       \"      <td>1.204545</td>\\n\",\n       \"      <td>0.148702</td>\\n\",\n       \"      <td>0.211780</td>\\n\",\n       \"      <td>0.163875</td>\\n\",\n       \"      <td>0.148702</td>\\n\",\n       \"      <td>0.163875</td>\\n\",\n       \"      <td>0.142857</td>\\n\",\n       \"      <td>0.265625</td>\\n\",\n       \"      <td>0.036122</td>\\n\",\n       \"      <td>0.063052</td>\\n\",\n       \"      <td>0.265625</td>\\n\",\n       \"      <td>0.063052</td>\\n\",\n       \"      <td>0.363636</td>\\n\",\n       \"      <td>0.211542</td>\\n\",\n       \"      <td>0.087398</td>\\n\",\n       \"      <td>0.121219</td>\\n\",\n       \"      <td>0.210027</td>\\n\",\n       \"      <td>0.120856</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000940</td>\\n\",\n       \"      <td>0.005707</td>\\n\",\n       \"      <td>0.001572</td>\\n\",\n       \"      <td>0.000940</td>\\n\",\n       \"      <td>0.001572</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000392</td>\\n\",\n       \"      <td>0.010966</td>\\n\",\n       \"      <td>0.000748</td>\\n\",\n       \"      <td>0.000373</td>\\n\",\n       \"      <td>0.000713</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.003780</td>\\n\",\n       \"      <td>0.003780</td>\\n\",\n       \"      <td>0.003780</td>\\n\",\n       \"      <td>0.003780</td>\\n\",\n       \"      <td>0.003780</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000208</td>\\n\",\n       \"      <td>0.011533</td>\\n\",\n       \"      <td>0.000404</td>\\n\",\n       \"      <td>0.000208</td>\\n\",\n       \"      <td>0.000404</td>\\n\",\n       \"      <td>0.025675</td>\\n\",\n       \"      <td>0.033209</td>\\n\",\n       \"      <td>0.143434</td>\\n\",\n       \"      <td>0.078720</td>\\n\",\n       \"      <td>0.095757</td>\\n\",\n       \"      <td>0.178156</td>\\n\",\n       \"      <td>0.206053</td>\\n\",\n       \"      <td>0.040631</td>\\n\",\n       \"      <td>0.051126</td>\\n\",\n       \"      <td>0.213502</td>\\n\",\n       \"      <td>0.106516</td>\\n\",\n       \"      <td>0.122136</td>\\n\",\n       \"      <td>0.253754</td>\\n\",\n       \"      <td>0.294877</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>11.353896</td>\\n\",\n       \"      <td>123.496753</td>\\n\",\n       \"      <td>148.159091</td>\\n\",\n       \"      <td>3582.892857</td>\\n\",\n       \"      <td>457.750000</td>\\n\",\n       \"      <td>12021.103896</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18552</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.396104</td>\\n\",\n       \"      <td>0.079221</td>\\n\",\n       \"      <td>0.052715</td>\\n\",\n       \"      <td>0.062526</td>\\n\",\n       \"      <td>0.079221</td>\\n\",\n       \"      <td>0.062526</td>\\n\",\n       \"      <td>0.571429</td>\\n\",\n       \"      <td>0.057318</td>\\n\",\n       \"      <td>0.074222</td>\\n\",\n       \"      <td>0.063882</td>\\n\",\n       \"      <td>0.057143</td>\\n\",\n       \"      <td>0.063791</td>\\n\",\n       \"      <td>0.509740</td>\\n\",\n       \"      <td>0.065368</td>\\n\",\n       \"      <td>0.065267</td>\\n\",\n       \"      <td>0.065315</td>\\n\",\n       \"      <td>0.065267</td>\\n\",\n       \"      <td>0.065267</td>\\n\",\n       \"      <td>0.990260</td>\\n\",\n       \"      <td>0.017710</td>\\n\",\n       \"      <td>0.127735</td>\\n\",\n       \"      <td>0.028588</td>\\n\",\n       \"      <td>0.017710</td>\\n\",\n       \"      <td>0.028588</td>\\n\",\n       \"      <td>0.074675</td>\\n\",\n       \"      <td>0.074675</td>\\n\",\n       \"      <td>0.010263</td>\\n\",\n       \"      <td>0.017999</td>\\n\",\n       \"      <td>0.074675</td>\\n\",\n       \"      <td>0.017999</td>\\n\",\n       \"      <td>0.240260</td>\\n\",\n       \"      <td>0.080087</td>\\n\",\n       \"      <td>0.032440</td>\\n\",\n       \"      <td>0.045725</td>\\n\",\n       \"      <td>0.080087</td>\\n\",\n       \"      <td>0.045725</td>\\n\",\n       \"      <td>0.457792</td>\\n\",\n       \"      <td>0.151762</td>\\n\",\n       \"      <td>0.098355</td>\\n\",\n       \"      <td>0.117777</td>\\n\",\n       \"      <td>0.151762</td>\\n\",\n       \"      <td>0.117777</td>\\n\",\n       \"      <td>0.714286</td>\\n\",\n       \"      <td>0.112081</td>\\n\",\n       \"      <td>0.142568</td>\\n\",\n       \"      <td>0.123806</td>\\n\",\n       \"      <td>0.111600</td>\\n\",\n       \"      <td>0.123543</td>\\n\",\n       \"      <td>0.626623</td>\\n\",\n       \"      <td>0.123487</td>\\n\",\n       \"      <td>0.123249</td>\\n\",\n       \"      <td>0.123359</td>\\n\",\n       \"      <td>0.123249</td>\\n\",\n       \"      <td>0.123249</td>\\n\",\n       \"      <td>1.431818</td>\\n\",\n       \"      <td>0.034537</td>\\n\",\n       \"      <td>0.240592</td>\\n\",\n       \"      <td>0.055531</td>\\n\",\n       \"      <td>0.034537</td>\\n\",\n       \"      <td>0.055531</td>\\n\",\n       \"      <td>0.074675</td>\\n\",\n       \"      <td>0.158488</td>\\n\",\n       \"      <td>0.020862</td>\\n\",\n       \"      <td>0.036696</td>\\n\",\n       \"      <td>0.158488</td>\\n\",\n       \"      <td>0.036696</td>\\n\",\n       \"      <td>0.256494</td>\\n\",\n       \"      <td>0.165606</td>\\n\",\n       \"      <td>0.064966</td>\\n\",\n       \"      <td>0.092325</td>\\n\",\n       \"      <td>0.165606</td>\\n\",\n       \"      <td>0.092325</td>\\n\",\n       \"      <td>0.490260</td>\\n\",\n       \"      <td>0.099621</td>\\n\",\n       \"      <td>0.067812</td>\\n\",\n       \"      <td>0.078828</td>\\n\",\n       \"      <td>0.098052</td>\\n\",\n       \"      <td>0.078284</td>\\n\",\n       \"      <td>0.762987</td>\\n\",\n       \"      <td>0.086617</td>\\n\",\n       \"      <td>0.103414</td>\\n\",\n       \"      <td>0.091321</td>\\n\",\n       \"      <td>0.076299</td>\\n\",\n       \"      <td>0.086081</td>\\n\",\n       \"      <td>0.668831</td>\\n\",\n       \"      <td>0.092653</td>\\n\",\n       \"      <td>0.086589</td>\\n\",\n       \"      <td>0.088779</td>\\n\",\n       \"      <td>0.086589</td>\\n\",\n       \"      <td>0.086589</td>\\n\",\n       \"      <td>0.990260</td>\\n\",\n       \"      <td>0.085301</td>\\n\",\n       \"      <td>0.133198</td>\\n\",\n       \"      <td>0.097990</td>\\n\",\n       \"      <td>0.085301</td>\\n\",\n       \"      <td>0.097990</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.014668</td>\\n\",\n       \"      <td>0.025624</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.025624</td>\\n\",\n       \"      <td>0.298701</td>\\n\",\n       \"      <td>0.099567</td>\\n\",\n       \"      <td>0.041969</td>\\n\",\n       \"      <td>0.057950</td>\\n\",\n       \"      <td>0.099567</td>\\n\",\n       \"      <td>0.057950</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.558442</td>\\n\",\n       \"      <td>0.180520</td>\\n\",\n       \"      <td>0.120230</td>\\n\",\n       \"      <td>0.141217</td>\\n\",\n       \"      <td>0.178195</td>\\n\",\n       \"      <td>0.140406</td>\\n\",\n       \"      <td>0.961039</td>\\n\",\n       \"      <td>0.151651</td>\\n\",\n       \"      <td>0.178312</td>\\n\",\n       \"      <td>0.159037</td>\\n\",\n       \"      <td>0.134231</td>\\n\",\n       \"      <td>0.150077</td>\\n\",\n       \"      <td>0.834416</td>\\n\",\n       \"      <td>0.164089</td>\\n\",\n       \"      <td>0.154034</td>\\n\",\n       \"      <td>0.157803</td>\\n\",\n       \"      <td>0.154034</td>\\n\",\n       \"      <td>0.154034</td>\\n\",\n       \"      <td>1.233766</td>\\n\",\n       \"      <td>0.144176</td>\\n\",\n       \"      <td>0.217467</td>\\n\",\n       \"      <td>0.163672</td>\\n\",\n       \"      <td>0.144176</td>\\n\",\n       \"      <td>0.163672</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.199111</td>\\n\",\n       \"      <td>0.027641</td>\\n\",\n       \"      <td>0.048039</td>\\n\",\n       \"      <td>0.199111</td>\\n\",\n       \"      <td>0.048039</td>\\n\",\n       \"      <td>0.318182</td>\\n\",\n       \"      <td>0.191309</td>\\n\",\n       \"      <td>0.078682</td>\\n\",\n       \"      <td>0.109194</td>\\n\",\n       \"      <td>0.191309</td>\\n\",\n       \"      <td>0.109194</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001229</td>\\n\",\n       \"      <td>0.005953</td>\\n\",\n       \"      <td>0.001719</td>\\n\",\n       \"      <td>0.000957</td>\\n\",\n       \"      <td>0.001603</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000715</td>\\n\",\n       \"      <td>0.008254</td>\\n\",\n       \"      <td>0.000901</td>\\n\",\n       \"      <td>0.000257</td>\\n\",\n       \"      <td>0.000496</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001562</td>\\n\",\n       \"      <td>0.001367</td>\\n\",\n       \"      <td>0.001422</td>\\n\",\n       \"      <td>0.001367</td>\\n\",\n       \"      <td>0.001367</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000667</td>\\n\",\n       \"      <td>0.008404</td>\\n\",\n       \"      <td>0.000811</td>\\n\",\n       \"      <td>0.000667</td>\\n\",\n       \"      <td>0.000811</td>\\n\",\n       \"      <td>0.027581</td>\\n\",\n       \"      <td>0.036519</td>\\n\",\n       \"      <td>0.141536</td>\\n\",\n       \"      <td>0.085298</td>\\n\",\n       \"      <td>0.099971</td>\\n\",\n       \"      <td>0.197257</td>\\n\",\n       \"      <td>0.224648</td>\\n\",\n       \"      <td>0.037485</td>\\n\",\n       \"      <td>0.048140</td>\\n\",\n       \"      <td>0.180384</td>\\n\",\n       \"      <td>0.105843</td>\\n\",\n       \"      <td>0.116337</td>\\n\",\n       \"      <td>0.245719</td>\\n\",\n       \"      <td>0.286987</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>11.756494</td>\\n\",\n       \"      <td>74.120130</td>\\n\",\n       \"      <td>104.837662</td>\\n\",\n       \"      <td>3787.483766</td>\\n\",\n       \"      <td>468.448052</td>\\n\",\n       \"      <td>11840.282468</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20748</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.370130</td>\\n\",\n       \"      <td>0.076461</td>\\n\",\n       \"      <td>0.050426</td>\\n\",\n       \"      <td>0.059493</td>\\n\",\n       \"      <td>0.074026</td>\\n\",\n       \"      <td>0.059005</td>\\n\",\n       \"      <td>0.483766</td>\\n\",\n       \"      <td>0.054248</td>\\n\",\n       \"      <td>0.065121</td>\\n\",\n       \"      <td>0.056747</td>\\n\",\n       \"      <td>0.048377</td>\\n\",\n       \"      <td>0.054647</td>\\n\",\n       \"      <td>0.431818</td>\\n\",\n       \"      <td>0.060975</td>\\n\",\n       \"      <td>0.056356</td>\\n\",\n       \"      <td>0.057694</td>\\n\",\n       \"      <td>0.056356</td>\\n\",\n       \"      <td>0.056356</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0.020798</td>\\n\",\n       \"      <td>0.099819</td>\\n\",\n       \"      <td>0.024598</td>\\n\",\n       \"      <td>0.020798</td>\\n\",\n       \"      <td>0.024598</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.013815</td>\\n\",\n       \"      <td>0.024258</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.024258</td>\\n\",\n       \"      <td>0.269481</td>\\n\",\n       \"      <td>0.090368</td>\\n\",\n       \"      <td>0.036499</td>\\n\",\n       \"      <td>0.051378</td>\\n\",\n       \"      <td>0.089827</td>\\n\",\n       \"      <td>0.051306</td>\\n\",\n       \"      <td>0.428571</td>\\n\",\n       \"      <td>0.143395</td>\\n\",\n       \"      <td>0.093451</td>\\n\",\n       \"      <td>0.111091</td>\\n\",\n       \"      <td>0.140646</td>\\n\",\n       \"      <td>0.110524</td>\\n\",\n       \"      <td>0.623377</td>\\n\",\n       \"      <td>0.104458</td>\\n\",\n       \"      <td>0.128786</td>\\n\",\n       \"      <td>0.111929</td>\\n\",\n       \"      <td>0.097730</td>\\n\",\n       \"      <td>0.109492</td>\\n\",\n       \"      <td>0.545455</td>\\n\",\n       \"      <td>0.117279</td>\\n\",\n       \"      <td>0.112011</td>\\n\",\n       \"      <td>0.113558</td>\\n\",\n       \"      <td>0.112011</td>\\n\",\n       \"      <td>0.112011</td>\\n\",\n       \"      <td>1.100649</td>\\n\",\n       \"      <td>0.032742</td>\\n\",\n       \"      <td>0.199471</td>\\n\",\n       \"      <td>0.043003</td>\\n\",\n       \"      <td>0.032742</td>\\n\",\n       \"      <td>0.043003</td>\\n\",\n       \"      <td>0.103896</td>\\n\",\n       \"      <td>0.190844</td>\\n\",\n       \"      <td>0.025001</td>\\n\",\n       \"      <td>0.043960</td>\\n\",\n       \"      <td>0.190844</td>\\n\",\n       \"      <td>0.043960</td>\\n\",\n       \"      <td>0.288961</td>\\n\",\n       \"      <td>0.172371</td>\\n\",\n       \"      <td>0.068354</td>\\n\",\n       \"      <td>0.096624</td>\\n\",\n       \"      <td>0.171830</td>\\n\",\n       \"      <td>0.096552</td>\\n\",\n       \"      <td>0.496753</td>\\n\",\n       \"      <td>0.113636</td>\\n\",\n       \"      <td>0.070593</td>\\n\",\n       \"      <td>0.082838</td>\\n\",\n       \"      <td>0.099351</td>\\n\",\n       \"      <td>0.080445</td>\\n\",\n       \"      <td>0.688312</td>\\n\",\n       \"      <td>0.102949</td>\\n\",\n       \"      <td>0.096335</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.068831</td>\\n\",\n       \"      <td>0.078420</td>\\n\",\n       \"      <td>0.607143</td>\\n\",\n       \"      <td>0.106739</td>\\n\",\n       \"      <td>0.081781</td>\\n\",\n       \"      <td>0.088217</td>\\n\",\n       \"      <td>0.081781</td>\\n\",\n       \"      <td>0.081781</td>\\n\",\n       \"      <td>0.746753</td>\\n\",\n       \"      <td>0.101676</td>\\n\",\n       \"      <td>0.104559</td>\\n\",\n       \"      <td>0.092719</td>\\n\",\n       \"      <td>0.101676</td>\\n\",\n       \"      <td>0.092719</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.020534</td>\\n\",\n       \"      <td>0.035957</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.035957</td>\\n\",\n       \"      <td>0.370130</td>\\n\",\n       \"      <td>0.130411</td>\\n\",\n       \"      <td>0.053113</td>\\n\",\n       \"      <td>0.073078</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.072531</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.000325</td>\\n\",\n       \"      <td>0.000500</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.000065</td>\\n\",\n       \"      <td>0.000123</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000027</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000053</td>\\n\",\n       \"      <td>0.000027</td>\\n\",\n       \"      <td>0.000053</td>\\n\",\n       \"      <td>0.574675</td>\\n\",\n       \"      <td>0.194935</td>\\n\",\n       \"      <td>0.120252</td>\\n\",\n       \"      <td>0.141956</td>\\n\",\n       \"      <td>0.173404</td>\\n\",\n       \"      <td>0.138384</td>\\n\",\n       \"      <td>0.837662</td>\\n\",\n       \"      <td>0.172158</td>\\n\",\n       \"      <td>0.160860</td>\\n\",\n       \"      <td>0.153441</td>\\n\",\n       \"      <td>0.117423</td>\\n\",\n       \"      <td>0.132629</td>\\n\",\n       \"      <td>0.740260</td>\\n\",\n       \"      <td>0.180954</td>\\n\",\n       \"      <td>0.140513</td>\\n\",\n       \"      <td>0.151288</td>\\n\",\n       \"      <td>0.140513</td>\\n\",\n       \"      <td>0.140513</td>\\n\",\n       \"      <td>0.918831</td>\\n\",\n       \"      <td>0.167757</td>\\n\",\n       \"      <td>0.170840</td>\\n\",\n       \"      <td>0.153065</td>\\n\",\n       \"      <td>0.167757</td>\\n\",\n       \"      <td>0.153065</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.258069</td>\\n\",\n       \"      <td>0.035343</td>\\n\",\n       \"      <td>0.061403</td>\\n\",\n       \"      <td>0.258069</td>\\n\",\n       \"      <td>0.061403</td>\\n\",\n       \"      <td>0.396104</td>\\n\",\n       \"      <td>0.219527</td>\\n\",\n       \"      <td>0.088185</td>\\n\",\n       \"      <td>0.121715</td>\\n\",\n       \"      <td>0.208581</td>\\n\",\n       \"      <td>0.120892</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.001079</td>\\n\",\n       \"      <td>0.005792</td>\\n\",\n       \"      <td>0.001731</td>\\n\",\n       \"      <td>0.001079</td>\\n\",\n       \"      <td>0.001731</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000333</td>\\n\",\n       \"      <td>0.008313</td>\\n\",\n       \"      <td>0.000629</td>\\n\",\n       \"      <td>0.000302</td>\\n\",\n       \"      <td>0.000575</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.002507</td>\\n\",\n       \"      <td>0.002507</td>\\n\",\n       \"      <td>0.002507</td>\\n\",\n       \"      <td>0.002507</td>\\n\",\n       \"      <td>0.002507</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000233</td>\\n\",\n       \"      <td>0.008878</td>\\n\",\n       \"      <td>0.000441</td>\\n\",\n       \"      <td>0.000233</td>\\n\",\n       \"      <td>0.000441</td>\\n\",\n       \"      <td>0.026436</td>\\n\",\n       \"      <td>0.034426</td>\\n\",\n       \"      <td>0.169859</td>\\n\",\n       \"      <td>0.071470</td>\\n\",\n       \"      <td>0.089262</td>\\n\",\n       \"      <td>0.183034</td>\\n\",\n       \"      <td>0.207975</td>\\n\",\n       \"      <td>0.038667</td>\\n\",\n       \"      <td>0.048909</td>\\n\",\n       \"      <td>0.231509</td>\\n\",\n       \"      <td>0.092255</td>\\n\",\n       \"      <td>0.109764</td>\\n\",\n       \"      <td>0.242981</td>\\n\",\n       \"      <td>0.272287</td>\\n\",\n       \"      <td>0.000180</td>\\n\",\n       \"      <td>0.000361</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.001082</td>\\n\",\n       \"      <td>0.000762</td>\\n\",\n       \"      <td>0.000762</td>\\n\",\n       \"      <td>0.000762</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>7.977273</td>\\n\",\n       \"      <td>103.051948</td>\\n\",\n       \"      <td>133.188312</td>\\n\",\n       \"      <td>3429.607143</td>\\n\",\n       \"      <td>434.775974</td>\\n\",\n       \"      <td>11398.892857</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19592</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.480519</td>\\n\",\n       \"      <td>0.096104</td>\\n\",\n       \"      <td>0.062244</td>\\n\",\n       \"      <td>0.074550</td>\\n\",\n       \"      <td>0.096104</td>\\n\",\n       \"      <td>0.074550</td>\\n\",\n       \"      <td>0.688312</td>\\n\",\n       \"      <td>0.069191</td>\\n\",\n       \"      <td>0.089638</td>\\n\",\n       \"      <td>0.076891</td>\\n\",\n       \"      <td>0.068831</td>\\n\",\n       \"      <td>0.076761</td>\\n\",\n       \"      <td>0.629870</td>\\n\",\n       \"      <td>0.080039</td>\\n\",\n       \"      <td>0.079611</td>\\n\",\n       \"      <td>0.079761</td>\\n\",\n       \"      <td>0.079611</td>\\n\",\n       \"      <td>0.079611</td>\\n\",\n       \"      <td>0.899351</td>\\n\",\n       \"      <td>0.017719</td>\\n\",\n       \"      <td>0.116450</td>\\n\",\n       \"      <td>0.027100</td>\\n\",\n       \"      <td>0.017719</td>\\n\",\n       \"      <td>0.027100</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.020013</td>\\n\",\n       \"      <td>0.035100</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.035100</td>\\n\",\n       \"      <td>0.370130</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.047404</td>\\n\",\n       \"      <td>0.067693</td>\\n\",\n       \"      <td>0.123377</td>\\n\",\n       \"      <td>0.067693</td>\\n\",\n       \"      <td>0.558442</td>\\n\",\n       \"      <td>0.176492</td>\\n\",\n       \"      <td>0.114174</td>\\n\",\n       \"      <td>0.136724</td>\\n\",\n       \"      <td>0.176492</td>\\n\",\n       \"      <td>0.136724</td>\\n\",\n       \"      <td>0.840909</td>\\n\",\n       \"      <td>0.124529</td>\\n\",\n       \"      <td>0.160386</td>\\n\",\n       \"      <td>0.138100</td>\\n\",\n       \"      <td>0.124053</td>\\n\",\n       \"      <td>0.137913</td>\\n\",\n       \"      <td>0.762987</td>\\n\",\n       \"      <td>0.145062</td>\\n\",\n       \"      <td>0.144512</td>\\n\",\n       \"      <td>0.144705</td>\\n\",\n       \"      <td>0.144512</td>\\n\",\n       \"      <td>0.144512</td>\\n\",\n       \"      <td>1.279221</td>\\n\",\n       \"      <td>0.032511</td>\\n\",\n       \"      <td>0.223324</td>\\n\",\n       \"      <td>0.050631</td>\\n\",\n       \"      <td>0.032511</td>\\n\",\n       \"      <td>0.050631</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.285503</td>\\n\",\n       \"      <td>0.037757</td>\\n\",\n       \"      <td>0.066154</td>\\n\",\n       \"      <td>0.285503</td>\\n\",\n       \"      <td>0.066154</td>\\n\",\n       \"      <td>0.386364</td>\\n\",\n       \"      <td>0.221946</td>\\n\",\n       \"      <td>0.086561</td>\\n\",\n       \"      <td>0.123007</td>\\n\",\n       \"      <td>0.221946</td>\\n\",\n       \"      <td>0.123007</td>\\n\",\n       \"      <td>0.581169</td>\\n\",\n       \"      <td>0.117803</td>\\n\",\n       \"      <td>0.080044</td>\\n\",\n       \"      <td>0.092764</td>\\n\",\n       \"      <td>0.116234</td>\\n\",\n       \"      <td>0.092248</td>\\n\",\n       \"      <td>0.792208</td>\\n\",\n       \"      <td>0.095214</td>\\n\",\n       \"      <td>0.107718</td>\\n\",\n       \"      <td>0.096936</td>\\n\",\n       \"      <td>0.079221</td>\\n\",\n       \"      <td>0.089230</td>\\n\",\n       \"      <td>0.701299</td>\\n\",\n       \"      <td>0.101457</td>\\n\",\n       \"      <td>0.090451</td>\\n\",\n       \"      <td>0.094544</td>\\n\",\n       \"      <td>0.090451</td>\\n\",\n       \"      <td>0.090451</td>\\n\",\n       \"      <td>0.899351</td>\\n\",\n       \"      <td>0.091106</td>\\n\",\n       \"      <td>0.121827</td>\\n\",\n       \"      <td>0.097104</td>\\n\",\n       \"      <td>0.091106</td>\\n\",\n       \"      <td>0.097104</td>\\n\",\n       \"      <td>0.159091</td>\\n\",\n       \"      <td>0.159091</td>\\n\",\n       \"      <td>0.021427</td>\\n\",\n       \"      <td>0.037432</td>\\n\",\n       \"      <td>0.159091</td>\\n\",\n       \"      <td>0.037432</td>\\n\",\n       \"      <td>0.412338</td>\\n\",\n       \"      <td>0.137446</td>\\n\",\n       \"      <td>0.055363</td>\\n\",\n       \"      <td>0.077218</td>\\n\",\n       \"      <td>0.137446</td>\\n\",\n       \"      <td>0.077218</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.672078</td>\\n\",\n       \"      <td>0.204022</td>\\n\",\n       \"      <td>0.136799</td>\\n\",\n       \"      <td>0.159776</td>\\n\",\n       \"      <td>0.201049</td>\\n\",\n       \"      <td>0.158721</td>\\n\",\n       \"      <td>0.948052</td>\\n\",\n       \"      <td>0.162090</td>\\n\",\n       \"      <td>0.181071</td>\\n\",\n       \"      <td>0.164499</td>\\n\",\n       \"      <td>0.134593</td>\\n\",\n       \"      <td>0.151074</td>\\n\",\n       \"      <td>0.834416</td>\\n\",\n       \"      <td>0.176535</td>\\n\",\n       \"      <td>0.158332</td>\\n\",\n       \"      <td>0.165137</td>\\n\",\n       \"      <td>0.158332</td>\\n\",\n       \"      <td>0.158332</td>\\n\",\n       \"      <td>1.090909</td>\\n\",\n       \"      <td>0.152383</td>\\n\",\n       \"      <td>0.201046</td>\\n\",\n       \"      <td>0.161939</td>\\n\",\n       \"      <td>0.152383</td>\\n\",\n       \"      <td>0.161939</td>\\n\",\n       \"      <td>0.159091</td>\\n\",\n       \"      <td>0.301511</td>\\n\",\n       \"      <td>0.042017</td>\\n\",\n       \"      <td>0.072721</td>\\n\",\n       \"      <td>0.301511</td>\\n\",\n       \"      <td>0.072721</td>\\n\",\n       \"      <td>0.431818</td>\\n\",\n       \"      <td>0.241249</td>\\n\",\n       \"      <td>0.098881</td>\\n\",\n       \"      <td>0.137121</td>\\n\",\n       \"      <td>0.241069</td>\\n\",\n       \"      <td>0.137082</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000525</td>\\n\",\n       \"      <td>0.003310</td>\\n\",\n       \"      <td>0.000869</td>\\n\",\n       \"      <td>0.000525</td>\\n\",\n       \"      <td>0.000869</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000324</td>\\n\",\n       \"      <td>0.008288</td>\\n\",\n       \"      <td>0.000612</td>\\n\",\n       \"      <td>0.000276</td>\\n\",\n       \"      <td>0.000527</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000595</td>\\n\",\n       \"      <td>0.000595</td>\\n\",\n       \"      <td>0.000595</td>\\n\",\n       \"      <td>0.000595</td>\\n\",\n       \"      <td>0.000595</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000194</td>\\n\",\n       \"      <td>0.008325</td>\\n\",\n       \"      <td>0.000369</td>\\n\",\n       \"      <td>0.000194</td>\\n\",\n       \"      <td>0.000369</td>\\n\",\n       \"      <td>0.038987</td>\\n\",\n       \"      <td>0.047296</td>\\n\",\n       \"      <td>0.209089</td>\\n\",\n       \"      <td>0.092261</td>\\n\",\n       \"      <td>0.112293</td>\\n\",\n       \"      <td>0.264917</td>\\n\",\n       \"      <td>0.299624</td>\\n\",\n       \"      <td>0.044918</td>\\n\",\n       \"      <td>0.055190</td>\\n\",\n       \"      <td>0.234240</td>\\n\",\n       \"      <td>0.104708</td>\\n\",\n       \"      <td>0.123071</td>\\n\",\n       \"      <td>0.303057</td>\\n\",\n       \"      <td>0.336799</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000961</td>\\n\",\n       \"      <td>0.000961</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>10.724026</td>\\n\",\n       \"      <td>87.172078</td>\\n\",\n       \"      <td>128.685065</td>\\n\",\n       \"      <td>3691.931818</td>\\n\",\n       \"      <td>437.032468</td>\\n\",\n       \"      <td>11593.006494</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19154</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>0.532468</td>\\n\",\n       \"      <td>0.106494</td>\\n\",\n       \"      <td>0.069696</td>\\n\",\n       \"      <td>0.083228</td>\\n\",\n       \"      <td>0.106494</td>\\n\",\n       \"      <td>0.083228</td>\\n\",\n       \"      <td>0.876623</td>\\n\",\n       \"      <td>0.087662</td>\\n\",\n       \"      <td>0.113887</td>\\n\",\n       \"      <td>0.097710</td>\\n\",\n       \"      <td>0.087662</td>\\n\",\n       \"      <td>0.097710</td>\\n\",\n       \"      <td>0.756494</td>\\n\",\n       \"      <td>0.093991</td>\\n\",\n       \"      <td>0.093991</td>\\n\",\n       \"      <td>0.093991</td>\\n\",\n       \"      <td>0.093991</td>\\n\",\n       \"      <td>0.093991</td>\\n\",\n       \"      <td>1.389610</td>\\n\",\n       \"      <td>0.018478</td>\\n\",\n       \"      <td>0.180493</td>\\n\",\n       \"      <td>0.032349</td>\\n\",\n       \"      <td>0.018478</td>\\n\",\n       \"      <td>0.032349</td>\\n\",\n       \"      <td>0.165584</td>\\n\",\n       \"      <td>0.165584</td>\\n\",\n       \"      <td>0.020890</td>\\n\",\n       \"      <td>0.036931</td>\\n\",\n       \"      <td>0.165584</td>\\n\",\n       \"      <td>0.036931</td>\\n\",\n       \"      <td>0.363636</td>\\n\",\n       \"      <td>0.121212</td>\\n\",\n       \"      <td>0.047059</td>\\n\",\n       \"      <td>0.067227</td>\\n\",\n       \"      <td>0.121212</td>\\n\",\n       \"      <td>0.067227</td>\\n\",\n       \"      <td>0.607143</td>\\n\",\n       \"      <td>0.186477</td>\\n\",\n       \"      <td>0.121246</td>\\n\",\n       \"      <td>0.144926</td>\\n\",\n       \"      <td>0.186477</td>\\n\",\n       \"      <td>0.144926</td>\\n\",\n       \"      <td>1.068182</td>\\n\",\n       \"      <td>0.150242</td>\\n\",\n       \"      <td>0.192199</td>\\n\",\n       \"      <td>0.166159</td>\\n\",\n       \"      <td>0.149839</td>\\n\",\n       \"      <td>0.166027</td>\\n\",\n       \"      <td>0.925325</td>\\n\",\n       \"      <td>0.165289</td>\\n\",\n       \"      <td>0.164882</td>\\n\",\n       \"      <td>0.165002</td>\\n\",\n       \"      <td>0.164882</td>\\n\",\n       \"      <td>0.164882</td>\\n\",\n       \"      <td>1.948052</td>\\n\",\n       \"      <td>0.032836</td>\\n\",\n       \"      <td>0.308361</td>\\n\",\n       \"      <td>0.056255</td>\\n\",\n       \"      <td>0.032836</td>\\n\",\n       \"      <td>0.056255</td>\\n\",\n       \"      <td>0.165584</td>\\n\",\n       \"      <td>0.291343</td>\\n\",\n       \"      <td>0.037886</td>\\n\",\n       \"      <td>0.066616</td>\\n\",\n       \"      <td>0.291343</td>\\n\",\n       \"      <td>0.066616</td>\\n\",\n       \"      <td>0.386364</td>\\n\",\n       \"      <td>0.220052</td>\\n\",\n       \"      <td>0.085667</td>\\n\",\n       \"      <td>0.122016</td>\\n\",\n       \"      <td>0.220052</td>\\n\",\n       \"      <td>0.122016</td>\\n\",\n       \"      <td>0.590909</td>\\n\",\n       \"      <td>0.118182</td>\\n\",\n       \"      <td>0.081515</td>\\n\",\n       \"      <td>0.094415</td>\\n\",\n       \"      <td>0.118182</td>\\n\",\n       \"      <td>0.094415</td>\\n\",\n       \"      <td>0.990260</td>\\n\",\n       \"      <td>0.099957</td>\\n\",\n       \"      <td>0.136674</td>\\n\",\n       \"      <td>0.112545</td>\\n\",\n       \"      <td>0.099026</td>\\n\",\n       \"      <td>0.112073</td>\\n\",\n       \"      <td>0.814935</td>\\n\",\n       \"      <td>0.105261</td>\\n\",\n       \"      <td>0.104379</td>\\n\",\n       \"      <td>0.104737</td>\\n\",\n       \"      <td>0.104379</td>\\n\",\n       \"      <td>0.104379</td>\\n\",\n       \"      <td>1.389610</td>\\n\",\n       \"      <td>0.074732</td>\\n\",\n       \"      <td>0.188949</td>\\n\",\n       \"      <td>0.102942</td>\\n\",\n       \"      <td>0.074732</td>\\n\",\n       \"      <td>0.102942</td>\\n\",\n       \"      <td>0.181818</td>\\n\",\n       \"      <td>0.181818</td>\\n\",\n       \"      <td>0.024240</td>\\n\",\n       \"      <td>0.042386</td>\\n\",\n       \"      <td>0.181818</td>\\n\",\n       \"      <td>0.042386</td>\\n\",\n       \"      <td>0.383117</td>\\n\",\n       \"      <td>0.127706</td>\\n\",\n       \"      <td>0.052788</td>\\n\",\n       \"      <td>0.073171</td>\\n\",\n       \"      <td>0.127706</td>\\n\",\n       \"      <td>0.073171</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.665584</td>\\n\",\n       \"      <td>0.200243</td>\\n\",\n       \"      <td>0.136710</td>\\n\",\n       \"      <td>0.158548</td>\\n\",\n       \"      <td>0.199777</td>\\n\",\n       \"      <td>0.158494</td>\\n\",\n       \"      <td>1.194805</td>\\n\",\n       \"      <td>0.163114</td>\\n\",\n       \"      <td>0.217470</td>\\n\",\n       \"      <td>0.181569</td>\\n\",\n       \"      <td>0.160857</td>\\n\",\n       \"      <td>0.180678</td>\\n\",\n       \"      <td>0.996753</td>\\n\",\n       \"      <td>0.177968</td>\\n\",\n       \"      <td>0.175718</td>\\n\",\n       \"      <td>0.176447</td>\\n\",\n       \"      <td>0.175718</td>\\n\",\n       \"      <td>0.175718</td>\\n\",\n       \"      <td>1.759740</td>\\n\",\n       \"      <td>0.119564</td>\\n\",\n       \"      <td>0.293355</td>\\n\",\n       \"      <td>0.162591</td>\\n\",\n       \"      <td>0.119564</td>\\n\",\n       \"      <td>0.162591</td>\\n\",\n       \"      <td>0.181818</td>\\n\",\n       \"      <td>0.305814</td>\\n\",\n       \"      <td>0.041957</td>\\n\",\n       \"      <td>0.072893</td>\\n\",\n       \"      <td>0.305814</td>\\n\",\n       \"      <td>0.072893</td>\\n\",\n       \"      <td>0.405844</td>\\n\",\n       \"      <td>0.225196</td>\\n\",\n       \"      <td>0.092140</td>\\n\",\n       \"      <td>0.128124</td>\\n\",\n       \"      <td>0.225196</td>\\n\",\n       \"      <td>0.128124</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001052</td>\\n\",\n       \"      <td>0.007068</td>\\n\",\n       \"      <td>0.001778</td>\\n\",\n       \"      <td>0.001052</td>\\n\",\n       \"      <td>0.001778</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000330</td>\\n\",\n       \"      <td>0.009192</td>\\n\",\n       \"      <td>0.000629</td>\\n\",\n       \"      <td>0.000279</td>\\n\",\n       \"      <td>0.000537</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001840</td>\\n\",\n       \"      <td>0.001840</td>\\n\",\n       \"      <td>0.001840</td>\\n\",\n       \"      <td>0.001840</td>\\n\",\n       \"      <td>0.001840</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000217</td>\\n\",\n       \"      <td>0.009308</td>\\n\",\n       \"      <td>0.000416</td>\\n\",\n       \"      <td>0.000217</td>\\n\",\n       \"      <td>0.000416</td>\\n\",\n       \"      <td>0.047311</td>\\n\",\n       \"      <td>0.058467</td>\\n\",\n       \"      <td>0.195248</td>\\n\",\n       \"      <td>0.123702</td>\\n\",\n       \"      <td>0.147617</td>\\n\",\n       \"      <td>0.262402</td>\\n\",\n       \"      <td>0.310480</td>\\n\",\n       \"      <td>0.055377</td>\\n\",\n       \"      <td>0.067957</td>\\n\",\n       \"      <td>0.215512</td>\\n\",\n       \"      <td>0.136965</td>\\n\",\n       \"      <td>0.159842</td>\\n\",\n       \"      <td>0.282537</td>\\n\",\n       \"      <td>0.329692</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>19.350649</td>\\n\",\n       \"      <td>85.801948</td>\\n\",\n       \"      <td>109.129870</td>\\n\",\n       \"      <td>3622.568182</td>\\n\",\n       \"      <td>471.587662</td>\\n\",\n       \"      <td>11201.649351</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15881</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.666000</td>\\n\",\n       \"      <td>0.333200</td>\\n\",\n       \"      <td>0.210013</td>\\n\",\n       \"      <td>0.243642</td>\\n\",\n       \"      <td>0.333200</td>\\n\",\n       \"      <td>0.243642</td>\\n\",\n       \"      <td>2.618000</td>\\n\",\n       \"      <td>0.261850</td>\\n\",\n       \"      <td>0.317071</td>\\n\",\n       \"      <td>0.270850</td>\\n\",\n       \"      <td>0.261800</td>\\n\",\n       \"      <td>0.270818</td>\\n\",\n       \"      <td>2.706000</td>\\n\",\n       \"      <td>0.286475</td>\\n\",\n       \"      <td>0.286458</td>\\n\",\n       \"      <td>0.286466</td>\\n\",\n       \"      <td>0.286458</td>\\n\",\n       \"      <td>0.286458</td>\\n\",\n       \"      <td>4.040000</td>\\n\",\n       \"      <td>0.104126</td>\\n\",\n       \"      <td>0.461503</td>\\n\",\n       \"      <td>0.159934</td>\\n\",\n       \"      <td>0.104126</td>\\n\",\n       \"      <td>0.159934</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.054940</td>\\n\",\n       \"      <td>0.094491</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.094491</td>\\n\",\n       \"      <td>1.146000</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.146971</td>\\n\",\n       \"      <td>0.201964</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.201964</td>\\n\",\n       \"      <td>1.964000</td>\\n\",\n       \"      <td>0.478807</td>\\n\",\n       \"      <td>0.293108</td>\\n\",\n       \"      <td>0.342894</td>\\n\",\n       \"      <td>0.478807</td>\\n\",\n       \"      <td>0.342894</td>\\n\",\n       \"      <td>3.170000</td>\\n\",\n       \"      <td>0.362325</td>\\n\",\n       \"      <td>0.425149</td>\\n\",\n       \"      <td>0.368693</td>\\n\",\n       \"      <td>0.362235</td>\\n\",\n       \"      <td>0.368637</td>\\n\",\n       \"      <td>3.336000</td>\\n\",\n       \"      <td>0.394070</td>\\n\",\n       \"      <td>0.394040</td>\\n\",\n       \"      <td>0.394055</td>\\n\",\n       \"      <td>0.394040</td>\\n\",\n       \"      <td>0.394040</td>\\n\",\n       \"      <td>4.876000</td>\\n\",\n       \"      <td>0.135852</td>\\n\",\n       \"      <td>0.590894</td>\\n\",\n       \"      <td>0.207608</td>\\n\",\n       \"      <td>0.135852</td>\\n\",\n       \"      <td>0.207608</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.623179</td>\\n\",\n       \"      <td>0.079988</td>\\n\",\n       \"      <td>0.137318</td>\\n\",\n       \"      <td>0.623179</td>\\n\",\n       \"      <td>0.137318</td>\\n\",\n       \"      <td>1.298000</td>\\n\",\n       \"      <td>0.559063</td>\\n\",\n       \"      <td>0.210321</td>\\n\",\n       \"      <td>0.289980</td>\\n\",\n       \"      <td>0.559063</td>\\n\",\n       \"      <td>0.289980</td>\\n\",\n       \"      <td>1.862000</td>\\n\",\n       \"      <td>0.372600</td>\\n\",\n       \"      <td>0.296673</td>\\n\",\n       \"      <td>0.309041</td>\\n\",\n       \"      <td>0.372400</td>\\n\",\n       \"      <td>0.308898</td>\\n\",\n       \"      <td>2.992000</td>\\n\",\n       \"      <td>0.305492</td>\\n\",\n       \"      <td>0.448695</td>\\n\",\n       \"      <td>0.342835</td>\\n\",\n       \"      <td>0.299200</td>\\n\",\n       \"      <td>0.337349</td>\\n\",\n       \"      <td>2.726000</td>\\n\",\n       \"      <td>0.351139</td>\\n\",\n       \"      <td>0.348425</td>\\n\",\n       \"      <td>0.349578</td>\\n\",\n       \"      <td>0.348425</td>\\n\",\n       \"      <td>0.348425</td>\\n\",\n       \"      <td>3.900000</td>\\n\",\n       \"      <td>0.253410</td>\\n\",\n       \"      <td>0.554303</td>\\n\",\n       \"      <td>0.330950</td>\\n\",\n       \"      <td>0.253410</td>\\n\",\n       \"      <td>0.330950</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.072606</td>\\n\",\n       \"      <td>0.120982</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.120982</td>\\n\",\n       \"      <td>1.218000</td>\\n\",\n       \"      <td>0.406000</td>\\n\",\n       \"      <td>0.195174</td>\\n\",\n       \"      <td>0.248771</td>\\n\",\n       \"      <td>0.406000</td>\\n\",\n       \"      <td>0.248771</td>\\n\",\n       \"      <td>0.112000</td>\\n\",\n       \"      <td>0.011829</td>\\n\",\n       \"      <td>0.049394</td>\\n\",\n       \"      <td>0.017875</td>\\n\",\n       \"      <td>0.011200</td>\\n\",\n       \"      <td>0.017098</td>\\n\",\n       \"      <td>0.140000</td>\\n\",\n       \"      <td>0.007735</td>\\n\",\n       \"      <td>0.059060</td>\\n\",\n       \"      <td>0.012705</td>\\n\",\n       \"      <td>0.002800</td>\\n\",\n       \"      <td>0.005255</td>\\n\",\n       \"      <td>0.070000</td>\\n\",\n       \"      <td>0.026827</td>\\n\",\n       \"      <td>0.026827</td>\\n\",\n       \"      <td>0.026827</td>\\n\",\n       \"      <td>0.026827</td>\\n\",\n       \"      <td>0.026827</td>\\n\",\n       \"      <td>0.140000</td>\\n\",\n       \"      <td>0.007633</td>\\n\",\n       \"      <td>0.059060</td>\\n\",\n       \"      <td>0.012528</td>\\n\",\n       \"      <td>0.007633</td>\\n\",\n       \"      <td>0.012528</td>\\n\",\n       \"      <td>2.086000</td>\\n\",\n       \"      <td>0.489705</td>\\n\",\n       \"      <td>0.377280</td>\\n\",\n       \"      <td>0.398031</td>\\n\",\n       \"      <td>0.488945</td>\\n\",\n       \"      <td>0.397871</td>\\n\",\n       \"      <td>3.350000</td>\\n\",\n       \"      <td>0.382255</td>\\n\",\n       \"      <td>0.545321</td>\\n\",\n       \"      <td>0.422623</td>\\n\",\n       \"      <td>0.373919</td>\\n\",\n       \"      <td>0.416181</td>\\n\",\n       \"      <td>3.096000</td>\\n\",\n       \"      <td>0.449883</td>\\n\",\n       \"      <td>0.445373</td>\\n\",\n       \"      <td>0.446987</td>\\n\",\n       \"      <td>0.445373</td>\\n\",\n       \"      <td>0.445373</td>\\n\",\n       \"      <td>4.276000</td>\\n\",\n       \"      <td>0.302534</td>\\n\",\n       \"      <td>0.648429</td>\\n\",\n       \"      <td>0.391413</td>\\n\",\n       \"      <td>0.302534</td>\\n\",\n       \"      <td>0.391413</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.625759</td>\\n\",\n       \"      <td>0.100774</td>\\n\",\n       \"      <td>0.166742</td>\\n\",\n       \"      <td>0.625759</td>\\n\",\n       \"      <td>0.166742</td>\\n\",\n       \"      <td>1.328000</td>\\n\",\n       \"      <td>0.558123</td>\\n\",\n       \"      <td>0.264029</td>\\n\",\n       \"      <td>0.336506</td>\\n\",\n       \"      <td>0.557812</td>\\n\",\n       \"      <td>0.336500</td>\\n\",\n       \"      <td>0.124000</td>\\n\",\n       \"      <td>0.030748</td>\\n\",\n       \"      <td>0.123569</td>\\n\",\n       \"      <td>0.045950</td>\\n\",\n       \"      <td>0.029373</td>\\n\",\n       \"      <td>0.044513</td>\\n\",\n       \"      <td>0.172000</td>\\n\",\n       \"      <td>0.018935</td>\\n\",\n       \"      <td>0.152870</td>\\n\",\n       \"      <td>0.030860</td>\\n\",\n       \"      <td>0.007464</td>\\n\",\n       \"      <td>0.013954</td>\\n\",\n       \"      <td>0.076000</td>\\n\",\n       \"      <td>0.059404</td>\\n\",\n       \"      <td>0.059367</td>\\n\",\n       \"      <td>0.059384</td>\\n\",\n       \"      <td>0.059367</td>\\n\",\n       \"      <td>0.059367</td>\\n\",\n       \"      <td>0.174000</td>\\n\",\n       \"      <td>0.018649</td>\\n\",\n       \"      <td>0.153430</td>\\n\",\n       \"      <td>0.030369</td>\\n\",\n       \"      <td>0.018649</td>\\n\",\n       \"      <td>0.030369</td>\\n\",\n       \"      <td>0.207744</td>\\n\",\n       \"      <td>0.230875</td>\\n\",\n       \"      <td>0.264893</td>\\n\",\n       \"      <td>0.351455</td>\\n\",\n       \"      <td>0.405804</td>\\n\",\n       \"      <td>0.485206</td>\\n\",\n       \"      <td>0.568567</td>\\n\",\n       \"      <td>0.277817</td>\\n\",\n       \"      <td>0.308156</td>\\n\",\n       \"      <td>0.298649</td>\\n\",\n       \"      <td>0.436319</td>\\n\",\n       \"      <td>0.478774</td>\\n\",\n       \"      <td>0.547265</td>\\n\",\n       \"      <td>0.626950</td>\\n\",\n       \"      <td>0.024390</td>\\n\",\n       \"      <td>0.028695</td>\\n\",\n       \"      <td>0.051734</td>\\n\",\n       \"      <td>0.039348</td>\\n\",\n       \"      <td>0.042308</td>\\n\",\n       \"      <td>0.044964</td>\\n\",\n       \"      <td>0.045140</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>15.890000</td>\\n\",\n       \"      <td>31.746000</td>\\n\",\n       \"      <td>53.452000</td>\\n\",\n       \"      <td>3405.790000</td>\\n\",\n       \"      <td>450.332000</td>\\n\",\n       \"      <td>11872.876000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20241</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.740000</td>\\n\",\n       \"      <td>0.348000</td>\\n\",\n       \"      <td>0.219346</td>\\n\",\n       \"      <td>0.254103</td>\\n\",\n       \"      <td>0.348000</td>\\n\",\n       \"      <td>0.254103</td>\\n\",\n       \"      <td>2.872000</td>\\n\",\n       \"      <td>0.287200</td>\\n\",\n       \"      <td>0.345004</td>\\n\",\n       \"      <td>0.296207</td>\\n\",\n       \"      <td>0.287200</td>\\n\",\n       \"      <td>0.296207</td>\\n\",\n       \"      <td>2.916000</td>\\n\",\n       \"      <td>0.304113</td>\\n\",\n       \"      <td>0.303762</td>\\n\",\n       \"      <td>0.303914</td>\\n\",\n       \"      <td>0.303762</td>\\n\",\n       \"      <td>0.303762</td>\\n\",\n       \"      <td>4.214000</td>\\n\",\n       \"      <td>0.134493</td>\\n\",\n       \"      <td>0.477555</td>\\n\",\n       \"      <td>0.197995</td>\\n\",\n       \"      <td>0.134493</td>\\n\",\n       \"      <td>0.197995</td>\\n\",\n       \"      <td>0.438000</td>\\n\",\n       \"      <td>0.438000</td>\\n\",\n       \"      <td>0.058691</td>\\n\",\n       \"      <td>0.100849</td>\\n\",\n       \"      <td>0.438000</td>\\n\",\n       \"      <td>0.100849</td>\\n\",\n       \"      <td>1.194000</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.153583</td>\\n\",\n       \"      <td>0.210499</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.210499</td>\\n\",\n       \"      <td>2.078000</td>\\n\",\n       \"      <td>0.500348</td>\\n\",\n       \"      <td>0.305256</td>\\n\",\n       \"      <td>0.357357</td>\\n\",\n       \"      <td>0.500348</td>\\n\",\n       \"      <td>0.357357</td>\\n\",\n       \"      <td>3.452000</td>\\n\",\n       \"      <td>0.388488</td>\\n\",\n       \"      <td>0.453772</td>\\n\",\n       \"      <td>0.394773</td>\\n\",\n       \"      <td>0.388471</td>\\n\",\n       \"      <td>0.394759</td>\\n\",\n       \"      <td>3.556000</td>\\n\",\n       \"      <td>0.415448</td>\\n\",\n       \"      <td>0.415018</td>\\n\",\n       \"      <td>0.415205</td>\\n\",\n       \"      <td>0.415018</td>\\n\",\n       \"      <td>0.415018</td>\\n\",\n       \"      <td>4.928000</td>\\n\",\n       \"      <td>0.170605</td>\\n\",\n       \"      <td>0.597657</td>\\n\",\n       \"      <td>0.249919</td>\\n\",\n       \"      <td>0.170605</td>\\n\",\n       \"      <td>0.249919</td>\\n\",\n       \"      <td>0.438000</td>\\n\",\n       \"      <td>0.650587</td>\\n\",\n       \"      <td>0.086261</td>\\n\",\n       \"      <td>0.147501</td>\\n\",\n       \"      <td>0.650587</td>\\n\",\n       \"      <td>0.147501</td>\\n\",\n       \"      <td>1.360000</td>\\n\",\n       \"      <td>0.579652</td>\\n\",\n       \"      <td>0.217965</td>\\n\",\n       \"      <td>0.300319</td>\\n\",\n       \"      <td>0.579652</td>\\n\",\n       \"      <td>0.300319</td>\\n\",\n       \"      <td>1.836000</td>\\n\",\n       \"      <td>0.367200</td>\\n\",\n       \"      <td>0.289703</td>\\n\",\n       \"      <td>0.302983</td>\\n\",\n       \"      <td>0.367200</td>\\n\",\n       \"      <td>0.302983</td>\\n\",\n       \"      <td>3.170000</td>\\n\",\n       \"      <td>0.322277</td>\\n\",\n       \"      <td>0.474886</td>\\n\",\n       \"      <td>0.360113</td>\\n\",\n       \"      <td>0.317000</td>\\n\",\n       \"      <td>0.356684</td>\\n\",\n       \"      <td>2.800000</td>\\n\",\n       \"      <td>0.360440</td>\\n\",\n       \"      <td>0.354010</td>\\n\",\n       \"      <td>0.356384</td>\\n\",\n       \"      <td>0.354010</td>\\n\",\n       \"      <td>0.354010</td>\\n\",\n       \"      <td>4.114000</td>\\n\",\n       \"      <td>0.260525</td>\\n\",\n       \"      <td>0.581948</td>\\n\",\n       \"      <td>0.339713</td>\\n\",\n       \"      <td>0.260525</td>\\n\",\n       \"      <td>0.339713</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.078179</td>\\n\",\n       \"      <td>0.127991</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.127991</td>\\n\",\n       \"      <td>1.242000</td>\\n\",\n       \"      <td>0.414000</td>\\n\",\n       \"      <td>0.200712</td>\\n\",\n       \"      <td>0.253334</td>\\n\",\n       \"      <td>0.414000</td>\\n\",\n       \"      <td>0.253334</td>\\n\",\n       \"      <td>0.086000</td>\\n\",\n       \"      <td>0.010230</td>\\n\",\n       \"      <td>0.040686</td>\\n\",\n       \"      <td>0.015501</td>\\n\",\n       \"      <td>0.008600</td>\\n\",\n       \"      <td>0.013484</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.008346</td>\\n\",\n       \"      <td>0.046252</td>\\n\",\n       \"      <td>0.013128</td>\\n\",\n       \"      <td>0.002000</td>\\n\",\n       \"      <td>0.003782</td>\\n\",\n       \"      <td>0.050000</td>\\n\",\n       \"      <td>0.019519</td>\\n\",\n       \"      <td>0.019519</td>\\n\",\n       \"      <td>0.019519</td>\\n\",\n       \"      <td>0.019519</td>\\n\",\n       \"      <td>0.019519</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.008342</td>\\n\",\n       \"      <td>0.046252</td>\\n\",\n       \"      <td>0.013122</td>\\n\",\n       \"      <td>0.008342</td>\\n\",\n       \"      <td>0.013122</td>\\n\",\n       \"      <td>2.086000</td>\\n\",\n       \"      <td>0.492592</td>\\n\",\n       \"      <td>0.375771</td>\\n\",\n       \"      <td>0.398300</td>\\n\",\n       \"      <td>0.492059</td>\\n\",\n       \"      <td>0.398291</td>\\n\",\n       \"      <td>3.516000</td>\\n\",\n       \"      <td>0.395181</td>\\n\",\n       \"      <td>0.567337</td>\\n\",\n       \"      <td>0.436339</td>\\n\",\n       \"      <td>0.388623</td>\\n\",\n       \"      <td>0.432495</td>\\n\",\n       \"      <td>3.154000</td>\\n\",\n       \"      <td>0.459128</td>\\n\",\n       \"      <td>0.450766</td>\\n\",\n       \"      <td>0.453667</td>\\n\",\n       \"      <td>0.450766</td>\\n\",\n       \"      <td>0.450766</td>\\n\",\n       \"      <td>4.462000</td>\\n\",\n       \"      <td>0.303251</td>\\n\",\n       \"      <td>0.668272</td>\\n\",\n       \"      <td>0.392935</td>\\n\",\n       \"      <td>0.303251</td>\\n\",\n       \"      <td>0.392935</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.642257</td>\\n\",\n       \"      <td>0.107927</td>\\n\",\n       \"      <td>0.176685</td>\\n\",\n       \"      <td>0.642257</td>\\n\",\n       \"      <td>0.176685</td>\\n\",\n       \"      <td>1.362000</td>\\n\",\n       \"      <td>0.566677</td>\\n\",\n       \"      <td>0.267154</td>\\n\",\n       \"      <td>0.340370</td>\\n\",\n       \"      <td>0.566232</td>\\n\",\n       \"      <td>0.340366</td>\\n\",\n       \"      <td>0.098000</td>\\n\",\n       \"      <td>0.028949</td>\\n\",\n       \"      <td>0.104550</td>\\n\",\n       \"      <td>0.041607</td>\\n\",\n       \"      <td>0.024532</td>\\n\",\n       \"      <td>0.037372</td>\\n\",\n       \"      <td>0.122000</td>\\n\",\n       \"      <td>0.022308</td>\\n\",\n       \"      <td>0.123166</td>\\n\",\n       \"      <td>0.033347</td>\\n\",\n       \"      <td>0.005788</td>\\n\",\n       \"      <td>0.010858</td>\\n\",\n       \"      <td>0.054000</td>\\n\",\n       \"      <td>0.053640</td>\\n\",\n       \"      <td>0.052873</td>\\n\",\n       \"      <td>0.053064</td>\\n\",\n       \"      <td>0.052873</td>\\n\",\n       \"      <td>0.052873</td>\\n\",\n       \"      <td>0.122000</td>\\n\",\n       \"      <td>0.022253</td>\\n\",\n       \"      <td>0.123227</td>\\n\",\n       \"      <td>0.033249</td>\\n\",\n       \"      <td>0.022253</td>\\n\",\n       \"      <td>0.033249</td>\\n\",\n       \"      <td>0.224407</td>\\n\",\n       \"      <td>0.248337</td>\\n\",\n       \"      <td>0.275272</td>\\n\",\n       \"      <td>0.372616</td>\\n\",\n       \"      <td>0.425980</td>\\n\",\n       \"      <td>0.508248</td>\\n\",\n       \"      <td>0.599941</td>\\n\",\n       \"      <td>0.292379</td>\\n\",\n       \"      <td>0.323239</td>\\n\",\n       \"      <td>0.297168</td>\\n\",\n       \"      <td>0.452468</td>\\n\",\n       \"      <td>0.497978</td>\\n\",\n       \"      <td>0.552804</td>\\n\",\n       \"      <td>0.634871</td>\\n\",\n       \"      <td>0.018263</td>\\n\",\n       \"      <td>0.022985</td>\\n\",\n       \"      <td>0.039957</td>\\n\",\n       \"      <td>0.031902</td>\\n\",\n       \"      <td>0.033424</td>\\n\",\n       \"      <td>0.038879</td>\\n\",\n       \"      <td>0.039389</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>16.442000</td>\\n\",\n       \"      <td>21.064000</td>\\n\",\n       \"      <td>42.146000</td>\\n\",\n       \"      <td>3437.558000</td>\\n\",\n       \"      <td>462.630000</td>\\n\",\n       \"      <td>11975.882000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16573</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.376000</td>\\n\",\n       \"      <td>0.275200</td>\\n\",\n       \"      <td>0.168542</td>\\n\",\n       \"      <td>0.197807</td>\\n\",\n       \"      <td>0.275200</td>\\n\",\n       \"      <td>0.197807</td>\\n\",\n       \"      <td>2.132000</td>\\n\",\n       \"      <td>0.213300</td>\\n\",\n       \"      <td>0.251095</td>\\n\",\n       \"      <td>0.218603</td>\\n\",\n       \"      <td>0.213200</td>\\n\",\n       \"      <td>0.218540</td>\\n\",\n       \"      <td>2.136000</td>\\n\",\n       \"      <td>0.228391</td>\\n\",\n       \"      <td>0.228391</td>\\n\",\n       \"      <td>0.228391</td>\\n\",\n       \"      <td>0.228391</td>\\n\",\n       \"      <td>0.228391</td>\\n\",\n       \"      <td>3.168000</td>\\n\",\n       \"      <td>0.033370</td>\\n\",\n       \"      <td>0.368761</td>\\n\",\n       \"      <td>0.057544</td>\\n\",\n       \"      <td>0.033370</td>\\n\",\n       \"      <td>0.057544</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.038265</td>\\n\",\n       \"      <td>0.065975</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.065975</td>\\n\",\n       \"      <td>0.980000</td>\\n\",\n       \"      <td>0.326667</td>\\n\",\n       \"      <td>0.122821</td>\\n\",\n       \"      <td>0.169928</td>\\n\",\n       \"      <td>0.326667</td>\\n\",\n       \"      <td>0.169928</td>\\n\",\n       \"      <td>1.666000</td>\\n\",\n       \"      <td>0.421806</td>\\n\",\n       \"      <td>0.254773</td>\\n\",\n       \"      <td>0.299822</td>\\n\",\n       \"      <td>0.421552</td>\\n\",\n       \"      <td>0.299746</td>\\n\",\n       \"      <td>2.648000</td>\\n\",\n       \"      <td>0.310936</td>\\n\",\n       \"      <td>0.359882</td>\\n\",\n       \"      <td>0.314899</td>\\n\",\n       \"      <td>0.310370</td>\\n\",\n       \"      <td>0.314613</td>\\n\",\n       \"      <td>2.708000</td>\\n\",\n       \"      <td>0.333795</td>\\n\",\n       \"      <td>0.333572</td>\\n\",\n       \"      <td>0.333628</td>\\n\",\n       \"      <td>0.333572</td>\\n\",\n       \"      <td>0.333572</td>\\n\",\n       \"      <td>4.052000</td>\\n\",\n       \"      <td>0.048655</td>\\n\",\n       \"      <td>0.505576</td>\\n\",\n       \"      <td>0.082829</td>\\n\",\n       \"      <td>0.048655</td>\\n\",\n       \"      <td>0.082829</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.477448</td>\\n\",\n       \"      <td>0.061992</td>\\n\",\n       \"      <td>0.106063</td>\\n\",\n       \"      <td>0.477448</td>\\n\",\n       \"      <td>0.106063</td>\\n\",\n       \"      <td>1.108000</td>\\n\",\n       \"      <td>0.500272</td>\\n\",\n       \"      <td>0.186157</td>\\n\",\n       \"      <td>0.257384</td>\\n\",\n       \"      <td>0.500161</td>\\n\",\n       \"      <td>0.257365</td>\\n\",\n       \"      <td>1.676000</td>\\n\",\n       \"      <td>0.343200</td>\\n\",\n       \"      <td>0.261355</td>\\n\",\n       \"      <td>0.276910</td>\\n\",\n       \"      <td>0.335200</td>\\n\",\n       \"      <td>0.273863</td>\\n\",\n       \"      <td>2.676000</td>\\n\",\n       \"      <td>0.306457</td>\\n\",\n       \"      <td>0.406060</td>\\n\",\n       \"      <td>0.326263</td>\\n\",\n       \"      <td>0.267600</td>\\n\",\n       \"      <td>0.302091</td>\\n\",\n       \"      <td>2.350000</td>\\n\",\n       \"      <td>0.329622</td>\\n\",\n       \"      <td>0.306942</td>\\n\",\n       \"      <td>0.314609</td>\\n\",\n       \"      <td>0.306942</td>\\n\",\n       \"      <td>0.306942</td>\\n\",\n       \"      <td>3.046000</td>\\n\",\n       \"      <td>0.281587</td>\\n\",\n       \"      <td>0.447459</td>\\n\",\n       \"      <td>0.320838</td>\\n\",\n       \"      <td>0.281587</td>\\n\",\n       \"      <td>0.320838</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.057729</td>\\n\",\n       \"      <td>0.094772</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.094772</td>\\n\",\n       \"      <td>1.120000</td>\\n\",\n       \"      <td>0.375667</td>\\n\",\n       \"      <td>0.179244</td>\\n\",\n       \"      <td>0.227094</td>\\n\",\n       \"      <td>0.373333</td>\\n\",\n       \"      <td>0.226554</td>\\n\",\n       \"      <td>0.098000</td>\\n\",\n       \"      <td>0.009800</td>\\n\",\n       \"      <td>0.046941</td>\\n\",\n       \"      <td>0.015314</td>\\n\",\n       \"      <td>0.009800</td>\\n\",\n       \"      <td>0.015314</td>\\n\",\n       \"      <td>0.120000</td>\\n\",\n       \"      <td>0.002493</td>\\n\",\n       \"      <td>0.055413</td>\\n\",\n       \"      <td>0.004675</td>\\n\",\n       \"      <td>0.002400</td>\\n\",\n       \"      <td>0.004524</td>\\n\",\n       \"      <td>0.054000</td>\\n\",\n       \"      <td>0.019775</td>\\n\",\n       \"      <td>0.019775</td>\\n\",\n       \"      <td>0.019775</td>\\n\",\n       \"      <td>0.019775</td>\\n\",\n       \"      <td>0.019775</td>\\n\",\n       \"      <td>0.122000</td>\\n\",\n       \"      <td>0.001398</td>\\n\",\n       \"      <td>0.057413</td>\\n\",\n       \"      <td>0.002657</td>\\n\",\n       \"      <td>0.001398</td>\\n\",\n       \"      <td>0.002657</td>\\n\",\n       \"      <td>1.890000</td>\\n\",\n       \"      <td>0.464976</td>\\n\",\n       \"      <td>0.348676</td>\\n\",\n       \"      <td>0.372222</td>\\n\",\n       \"      <td>0.455602</td>\\n\",\n       \"      <td>0.368554</td>\\n\",\n       \"      <td>2.988000</td>\\n\",\n       \"      <td>0.383240</td>\\n\",\n       \"      <td>0.495397</td>\\n\",\n       \"      <td>0.403932</td>\\n\",\n       \"      <td>0.336402</td>\\n\",\n       \"      <td>0.375135</td>\\n\",\n       \"      <td>2.660000</td>\\n\",\n       \"      <td>0.431467</td>\\n\",\n       \"      <td>0.402232</td>\\n\",\n       \"      <td>0.412296</td>\\n\",\n       \"      <td>0.402232</td>\\n\",\n       \"      <td>0.402232</td>\\n\",\n       \"      <td>3.334000</td>\\n\",\n       \"      <td>0.342339</td>\\n\",\n       \"      <td>0.531724</td>\\n\",\n       \"      <td>0.386598</td>\\n\",\n       \"      <td>0.342339</td>\\n\",\n       \"      <td>0.386598</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.515448</td>\\n\",\n       \"      <td>0.086559</td>\\n\",\n       \"      <td>0.141577</td>\\n\",\n       \"      <td>0.515448</td>\\n\",\n       \"      <td>0.141577</td>\\n\",\n       \"      <td>1.216000</td>\\n\",\n       \"      <td>0.526289</td>\\n\",\n       \"      <td>0.248023</td>\\n\",\n       \"      <td>0.315856</td>\\n\",\n       \"      <td>0.523702</td>\\n\",\n       \"      <td>0.315207</td>\\n\",\n       \"      <td>0.108000</td>\\n\",\n       \"      <td>0.026366</td>\\n\",\n       \"      <td>0.111484</td>\\n\",\n       \"      <td>0.039778</td>\\n\",\n       \"      <td>0.025766</td>\\n\",\n       \"      <td>0.039232</td>\\n\",\n       \"      <td>0.156000</td>\\n\",\n       \"      <td>0.008663</td>\\n\",\n       \"      <td>0.157116</td>\\n\",\n       \"      <td>0.015515</td>\\n\",\n       \"      <td>0.007535</td>\\n\",\n       \"      <td>0.014091</td>\\n\",\n       \"      <td>0.062000</td>\\n\",\n       \"      <td>0.048648</td>\\n\",\n       \"      <td>0.048648</td>\\n\",\n       \"      <td>0.048648</td>\\n\",\n       \"      <td>0.048648</td>\\n\",\n       \"      <td>0.048648</td>\\n\",\n       \"      <td>0.168000</td>\\n\",\n       \"      <td>0.005488</td>\\n\",\n       \"      <td>0.165742</td>\\n\",\n       \"      <td>0.009745</td>\\n\",\n       \"      <td>0.005488</td>\\n\",\n       \"      <td>0.009745</td>\\n\",\n       \"      <td>0.145012</td>\\n\",\n       \"      <td>0.166550</td>\\n\",\n       \"      <td>0.257002</td>\\n\",\n       \"      <td>0.276394</td>\\n\",\n       \"      <td>0.319189</td>\\n\",\n       \"      <td>0.434928</td>\\n\",\n       \"      <td>0.501636</td>\\n\",\n       \"      <td>0.216803</td>\\n\",\n       \"      <td>0.246558</td>\\n\",\n       \"      <td>0.318229</td>\\n\",\n       \"      <td>0.370755</td>\\n\",\n       \"      <td>0.397626</td>\\n\",\n       \"      <td>0.523511</td>\\n\",\n       \"      <td>0.592846</td>\\n\",\n       \"      <td>0.018018</td>\\n\",\n       \"      <td>0.022741</td>\\n\",\n       \"      <td>0.040653</td>\\n\",\n       \"      <td>0.034471</td>\\n\",\n       \"      <td>0.035978</td>\\n\",\n       \"      <td>0.033113</td>\\n\",\n       \"      <td>0.033671</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>11.396000</td>\\n\",\n       \"      <td>117.424000</td>\\n\",\n       \"      <td>144.256000</td>\\n\",\n       \"      <td>3131.078000</td>\\n\",\n       \"      <td>448.714000</td>\\n\",\n       \"      <td>11956.116000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15964</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.648000</td>\\n\",\n       \"      <td>0.329600</td>\\n\",\n       \"      <td>0.206437</td>\\n\",\n       \"      <td>0.239518</td>\\n\",\n       \"      <td>0.329600</td>\\n\",\n       \"      <td>0.239518</td>\\n\",\n       \"      <td>2.688000</td>\\n\",\n       \"      <td>0.268800</td>\\n\",\n       \"      <td>0.321256</td>\\n\",\n       \"      <td>0.276233</td>\\n\",\n       \"      <td>0.268800</td>\\n\",\n       \"      <td>0.276233</td>\\n\",\n       \"      <td>2.716000</td>\\n\",\n       \"      <td>0.287630</td>\\n\",\n       \"      <td>0.287483</td>\\n\",\n       \"      <td>0.287544</td>\\n\",\n       \"      <td>0.287483</td>\\n\",\n       \"      <td>0.287483</td>\\n\",\n       \"      <td>4.120000</td>\\n\",\n       \"      <td>0.096841</td>\\n\",\n       \"      <td>0.466564</td>\\n\",\n       \"      <td>0.146109</td>\\n\",\n       \"      <td>0.096841</td>\\n\",\n       \"      <td>0.146109</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.061949</td>\\n\",\n       \"      <td>0.106548</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.106548</td>\\n\",\n       \"      <td>1.108000</td>\\n\",\n       \"      <td>0.369333</td>\\n\",\n       \"      <td>0.142356</td>\\n\",\n       \"      <td>0.195170</td>\\n\",\n       \"      <td>0.369333</td>\\n\",\n       \"      <td>0.195170</td>\\n\",\n       \"      <td>1.976000</td>\\n\",\n       \"      <td>0.479350</td>\\n\",\n       \"      <td>0.292696</td>\\n\",\n       \"      <td>0.342117</td>\\n\",\n       \"      <td>0.479350</td>\\n\",\n       \"      <td>0.342117</td>\\n\",\n       \"      <td>3.208000</td>\\n\",\n       \"      <td>0.366052</td>\\n\",\n       \"      <td>0.426914</td>\\n\",\n       \"      <td>0.371372</td>\\n\",\n       \"      <td>0.366052</td>\\n\",\n       \"      <td>0.371372</td>\\n\",\n       \"      <td>3.320000</td>\\n\",\n       \"      <td>0.395502</td>\\n\",\n       \"      <td>0.395251</td>\\n\",\n       \"      <td>0.395355</td>\\n\",\n       \"      <td>0.395251</td>\\n\",\n       \"      <td>0.395251</td>\\n\",\n       \"      <td>4.930000</td>\\n\",\n       \"      <td>0.124244</td>\\n\",\n       \"      <td>0.594004</td>\\n\",\n       \"      <td>0.187272</td>\\n\",\n       \"      <td>0.124244</td>\\n\",\n       \"      <td>0.187272</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.688084</td>\\n\",\n       \"      <td>0.089124</td>\\n\",\n       \"      <td>0.152772</td>\\n\",\n       \"      <td>0.688084</td>\\n\",\n       \"      <td>0.152772</td>\\n\",\n       \"      <td>1.280000</td>\\n\",\n       \"      <td>0.553252</td>\\n\",\n       \"      <td>0.208325</td>\\n\",\n       \"      <td>0.286656</td>\\n\",\n       \"      <td>0.553252</td>\\n\",\n       \"      <td>0.286656</td>\\n\",\n       \"      <td>1.790000</td>\\n\",\n       \"      <td>0.358500</td>\\n\",\n       \"      <td>0.279304</td>\\n\",\n       \"      <td>0.293077</td>\\n\",\n       \"      <td>0.358000</td>\\n\",\n       \"      <td>0.292934</td>\\n\",\n       \"      <td>3.014000</td>\\n\",\n       \"      <td>0.310487</td>\\n\",\n       \"      <td>0.447573</td>\\n\",\n       \"      <td>0.344755</td>\\n\",\n       \"      <td>0.301400</td>\\n\",\n       \"      <td>0.338323</td>\\n\",\n       \"      <td>2.726000</td>\\n\",\n       \"      <td>0.355538</td>\\n\",\n       \"      <td>0.350402</td>\\n\",\n       \"      <td>0.352470</td>\\n\",\n       \"      <td>0.350402</td>\\n\",\n       \"      <td>0.350402</td>\\n\",\n       \"      <td>3.990000</td>\\n\",\n       \"      <td>0.257773</td>\\n\",\n       \"      <td>0.560871</td>\\n\",\n       \"      <td>0.335210</td>\\n\",\n       \"      <td>0.257773</td>\\n\",\n       \"      <td>0.335210</td>\\n\",\n       \"      <td>0.498000</td>\\n\",\n       \"      <td>0.498000</td>\\n\",\n       \"      <td>0.082044</td>\\n\",\n       \"      <td>0.135153</td>\\n\",\n       \"      <td>0.498000</td>\\n\",\n       \"      <td>0.135153</td>\\n\",\n       \"      <td>1.198000</td>\\n\",\n       \"      <td>0.399333</td>\\n\",\n       \"      <td>0.193183</td>\\n\",\n       \"      <td>0.244078</td>\\n\",\n       \"      <td>0.399333</td>\\n\",\n       \"      <td>0.244078</td>\\n\",\n       \"      <td>0.102000</td>\\n\",\n       \"      <td>0.010466</td>\\n\",\n       \"      <td>0.045575</td>\\n\",\n       \"      <td>0.015864</td>\\n\",\n       \"      <td>0.010200</td>\\n\",\n       \"      <td>0.015571</td>\\n\",\n       \"      <td>0.130000</td>\\n\",\n       \"      <td>0.004991</td>\\n\",\n       \"      <td>0.058075</td>\\n\",\n       \"      <td>0.008517</td>\\n\",\n       \"      <td>0.002600</td>\\n\",\n       \"      <td>0.004887</td>\\n\",\n       \"      <td>0.066000</td>\\n\",\n       \"      <td>0.027009</td>\\n\",\n       \"      <td>0.027009</td>\\n\",\n       \"      <td>0.027009</td>\\n\",\n       \"      <td>0.027009</td>\\n\",\n       \"      <td>0.027009</td>\\n\",\n       \"      <td>0.130000</td>\\n\",\n       \"      <td>0.004585</td>\\n\",\n       \"      <td>0.058075</td>\\n\",\n       \"      <td>0.007776</td>\\n\",\n       \"      <td>0.004585</td>\\n\",\n       \"      <td>0.007776</td>\\n\",\n       \"      <td>2.030000</td>\\n\",\n       \"      <td>0.478601</td>\\n\",\n       \"      <td>0.363972</td>\\n\",\n       \"      <td>0.385589</td>\\n\",\n       \"      <td>0.477687</td>\\n\",\n       \"      <td>0.385430</td>\\n\",\n       \"      <td>3.320000</td>\\n\",\n       \"      <td>0.382209</td>\\n\",\n       \"      <td>0.540449</td>\\n\",\n       \"      <td>0.419975</td>\\n\",\n       \"      <td>0.371404</td>\\n\",\n       \"      <td>0.412719</td>\\n\",\n       \"      <td>3.090000</td>\\n\",\n       \"      <td>0.451141</td>\\n\",\n       \"      <td>0.444146</td>\\n\",\n       \"      <td>0.446752</td>\\n\",\n       \"      <td>0.444146</td>\\n\",\n       \"      <td>0.444146</td>\\n\",\n       \"      <td>4.370000</td>\\n\",\n       \"      <td>0.302967</td>\\n\",\n       \"      <td>0.653051</td>\\n\",\n       \"      <td>0.391869</td>\\n\",\n       \"      <td>0.302967</td>\\n\",\n       \"      <td>0.391869</td>\\n\",\n       \"      <td>0.498000</td>\\n\",\n       \"      <td>0.672564</td>\\n\",\n       \"      <td>0.110505</td>\\n\",\n       \"      <td>0.181698</td>\\n\",\n       \"      <td>0.672564</td>\\n\",\n       \"      <td>0.181698</td>\\n\",\n       \"      <td>1.306000</td>\\n\",\n       \"      <td>0.550593</td>\\n\",\n       \"      <td>0.259724</td>\\n\",\n       \"      <td>0.330279</td>\\n\",\n       \"      <td>0.550371</td>\\n\",\n       \"      <td>0.330275</td>\\n\",\n       \"      <td>0.112000</td>\\n\",\n       \"      <td>0.028980</td>\\n\",\n       \"      <td>0.110357</td>\\n\",\n       \"      <td>0.042475</td>\\n\",\n       \"      <td>0.026370</td>\\n\",\n       \"      <td>0.039885</td>\\n\",\n       \"      <td>0.166000</td>\\n\",\n       \"      <td>0.016694</td>\\n\",\n       \"      <td>0.146541</td>\\n\",\n       \"      <td>0.026051</td>\\n\",\n       \"      <td>0.007065</td>\\n\",\n       \"      <td>0.013222</td>\\n\",\n       \"      <td>0.072000</td>\\n\",\n       \"      <td>0.055539</td>\\n\",\n       \"      <td>0.055268</td>\\n\",\n       \"      <td>0.055348</td>\\n\",\n       \"      <td>0.055268</td>\\n\",\n       \"      <td>0.055268</td>\\n\",\n       \"      <td>0.168000</td>\\n\",\n       \"      <td>0.015589</td>\\n\",\n       \"      <td>0.149837</td>\\n\",\n       \"      <td>0.024053</td>\\n\",\n       \"      <td>0.015589</td>\\n\",\n       \"      <td>0.024053</td>\\n\",\n       \"      <td>0.210842</td>\\n\",\n       \"      <td>0.232162</td>\\n\",\n       \"      <td>0.278860</td>\\n\",\n       \"      <td>0.352051</td>\\n\",\n       \"      <td>0.412672</td>\\n\",\n       \"      <td>0.491592</td>\\n\",\n       \"      <td>0.576239</td>\\n\",\n       \"      <td>0.282227</td>\\n\",\n       \"      <td>0.310379</td>\\n\",\n       \"      <td>0.310439</td>\\n\",\n       \"      <td>0.437409</td>\\n\",\n       \"      <td>0.487074</td>\\n\",\n       \"      <td>0.545016</td>\\n\",\n       \"      <td>0.625309</td>\\n\",\n       \"      <td>0.023558</td>\\n\",\n       \"      <td>0.026983</td>\\n\",\n       \"      <td>0.049435</td>\\n\",\n       \"      <td>0.037418</td>\\n\",\n       \"      <td>0.039883</td>\\n\",\n       \"      <td>0.045613</td>\\n\",\n       \"      <td>0.046254</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>16.232000</td>\\n\",\n       \"      <td>52.562000</td>\\n\",\n       \"      <td>76.120000</td>\\n\",\n       \"      <td>3354.018000</td>\\n\",\n       \"      <td>446.178000</td>\\n\",\n       \"      <td>11801.188000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20420</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.448000</td>\\n\",\n       \"      <td>0.289600</td>\\n\",\n       \"      <td>0.178376</td>\\n\",\n       \"      <td>0.208952</td>\\n\",\n       \"      <td>0.289600</td>\\n\",\n       \"      <td>0.208952</td>\\n\",\n       \"      <td>2.096000</td>\\n\",\n       \"      <td>0.209600</td>\\n\",\n       \"      <td>0.250371</td>\\n\",\n       \"      <td>0.216245</td>\\n\",\n       \"      <td>0.209600</td>\\n\",\n       \"      <td>0.216245</td>\\n\",\n       \"      <td>2.138000</td>\\n\",\n       \"      <td>0.231749</td>\\n\",\n       \"      <td>0.231749</td>\\n\",\n       \"      <td>0.231749</td>\\n\",\n       \"      <td>0.231749</td>\\n\",\n       \"      <td>0.231749</td>\\n\",\n       \"      <td>3.636000</td>\\n\",\n       \"      <td>0.051404</td>\\n\",\n       \"      <td>0.412016</td>\\n\",\n       \"      <td>0.087131</td>\\n\",\n       \"      <td>0.051404</td>\\n\",\n       \"      <td>0.087131</td>\\n\",\n       \"      <td>0.358000</td>\\n\",\n       \"      <td>0.358000</td>\\n\",\n       \"      <td>0.046484</td>\\n\",\n       \"      <td>0.079731</td>\\n\",\n       \"      <td>0.358000</td>\\n\",\n       \"      <td>0.079731</td>\\n\",\n       \"      <td>1.006000</td>\\n\",\n       \"      <td>0.335333</td>\\n\",\n       \"      <td>0.125992</td>\\n\",\n       \"      <td>0.174001</td>\\n\",\n       \"      <td>0.335333</td>\\n\",\n       \"      <td>0.174001</td>\\n\",\n       \"      <td>1.712000</td>\\n\",\n       \"      <td>0.428855</td>\\n\",\n       \"      <td>0.259884</td>\\n\",\n       \"      <td>0.305426</td>\\n\",\n       \"      <td>0.428802</td>\\n\",\n       \"      <td>0.305412</td>\\n\",\n       \"      <td>2.622000</td>\\n\",\n       \"      <td>0.307131</td>\\n\",\n       \"      <td>0.358755</td>\\n\",\n       \"      <td>0.312288</td>\\n\",\n       \"      <td>0.307038</td>\\n\",\n       \"      <td>0.312255</td>\\n\",\n       \"      <td>2.712000</td>\\n\",\n       \"      <td>0.334973</td>\\n\",\n       \"      <td>0.334897</td>\\n\",\n       \"      <td>0.334920</td>\\n\",\n       \"      <td>0.334897</td>\\n\",\n       \"      <td>0.334897</td>\\n\",\n       \"      <td>4.490000</td>\\n\",\n       \"      <td>0.069781</td>\\n\",\n       \"      <td>0.546947</td>\\n\",\n       \"      <td>0.117770</td>\\n\",\n       \"      <td>0.069781</td>\\n\",\n       \"      <td>0.117770</td>\\n\",\n       \"      <td>0.358000</td>\\n\",\n       \"      <td>0.547104</td>\\n\",\n       \"      <td>0.070711</td>\\n\",\n       \"      <td>0.121270</td>\\n\",\n       \"      <td>0.547104</td>\\n\",\n       \"      <td>0.121270</td>\\n\",\n       \"      <td>1.140000</td>\\n\",\n       \"      <td>0.504558</td>\\n\",\n       \"      <td>0.186942</td>\\n\",\n       \"      <td>0.259100</td>\\n\",\n       \"      <td>0.504558</td>\\n\",\n       \"      <td>0.259100</td>\\n\",\n       \"      <td>1.730000</td>\\n\",\n       \"      <td>0.349967</td>\\n\",\n       \"      <td>0.269167</td>\\n\",\n       \"      <td>0.285558</td>\\n\",\n       \"      <td>0.346000</td>\\n\",\n       \"      <td>0.283464</td>\\n\",\n       \"      <td>2.790000</td>\\n\",\n       \"      <td>0.301349</td>\\n\",\n       \"      <td>0.414481</td>\\n\",\n       \"      <td>0.329457</td>\\n\",\n       \"      <td>0.279000</td>\\n\",\n       \"      <td>0.313501</td>\\n\",\n       \"      <td>2.492000</td>\\n\",\n       \"      <td>0.333977</td>\\n\",\n       \"      <td>0.323430</td>\\n\",\n       \"      <td>0.327417</td>\\n\",\n       \"      <td>0.323430</td>\\n\",\n       \"      <td>0.323430</td>\\n\",\n       \"      <td>3.470000</td>\\n\",\n       \"      <td>0.263641</td>\\n\",\n       \"      <td>0.490802</td>\\n\",\n       \"      <td>0.323459</td>\\n\",\n       \"      <td>0.263641</td>\\n\",\n       \"      <td>0.323459</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.068197</td>\\n\",\n       \"      <td>0.111129</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.111129</td>\\n\",\n       \"      <td>1.148000</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.181329</td>\\n\",\n       \"      <td>0.231196</td>\\n\",\n       \"      <td>0.382667</td>\\n\",\n       \"      <td>0.230710</td>\\n\",\n       \"      <td>0.114000</td>\\n\",\n       \"      <td>0.011486</td>\\n\",\n       \"      <td>0.051429</td>\\n\",\n       \"      <td>0.017750</td>\\n\",\n       \"      <td>0.011400</td>\\n\",\n       \"      <td>0.017639</td>\\n\",\n       \"      <td>0.162000</td>\\n\",\n       \"      <td>0.004336</td>\\n\",\n       \"      <td>0.074095</td>\\n\",\n       \"      <td>0.007946</td>\\n\",\n       \"      <td>0.003240</td>\\n\",\n       \"      <td>0.006108</td>\\n\",\n       \"      <td>0.058000</td>\\n\",\n       \"      <td>0.021962</td>\\n\",\n       \"      <td>0.021962</td>\\n\",\n       \"      <td>0.021962</td>\\n\",\n       \"      <td>0.021962</td>\\n\",\n       \"      <td>0.021962</td>\\n\",\n       \"      <td>0.166000</td>\\n\",\n       \"      <td>0.003668</td>\\n\",\n       \"      <td>0.075095</td>\\n\",\n       \"      <td>0.006728</td>\\n\",\n       \"      <td>0.003668</td>\\n\",\n       \"      <td>0.006728</td>\\n\",\n       \"      <td>1.932000</td>\\n\",\n       \"      <td>0.471306</td>\\n\",\n       \"      <td>0.358146</td>\\n\",\n       \"      <td>0.381062</td>\\n\",\n       \"      <td>0.466228</td>\\n\",\n       \"      <td>0.378601</td>\\n\",\n       \"      <td>3.126000</td>\\n\",\n       \"      <td>0.378543</td>\\n\",\n       \"      <td>0.509913</td>\\n\",\n       \"      <td>0.409313</td>\\n\",\n       \"      <td>0.350742</td>\\n\",\n       \"      <td>0.390212</td>\\n\",\n       \"      <td>2.840000</td>\\n\",\n       \"      <td>0.435203</td>\\n\",\n       \"      <td>0.420307</td>\\n\",\n       \"      <td>0.425771</td>\\n\",\n       \"      <td>0.420307</td>\\n\",\n       \"      <td>0.420307</td>\\n\",\n       \"      <td>3.804000</td>\\n\",\n       \"      <td>0.319325</td>\\n\",\n       \"      <td>0.583395</td>\\n\",\n       \"      <td>0.387936</td>\\n\",\n       \"      <td>0.319325</td>\\n\",\n       \"      <td>0.387936</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.589647</td>\\n\",\n       \"      <td>0.097511</td>\\n\",\n       \"      <td>0.159337</td>\\n\",\n       \"      <td>0.589647</td>\\n\",\n       \"      <td>0.159337</td>\\n\",\n       \"      <td>1.254000</td>\\n\",\n       \"      <td>0.533413</td>\\n\",\n       \"      <td>0.249689</td>\\n\",\n       \"      <td>0.319075</td>\\n\",\n       \"      <td>0.531769</td>\\n\",\n       \"      <td>0.318514</td>\\n\",\n       \"      <td>0.126000</td>\\n\",\n       \"      <td>0.027225</td>\\n\",\n       \"      <td>0.118075</td>\\n\",\n       \"      <td>0.041510</td>\\n\",\n       \"      <td>0.027129</td>\\n\",\n       \"      <td>0.041386</td>\\n\",\n       \"      <td>0.200000</td>\\n\",\n       \"      <td>0.010508</td>\\n\",\n       \"      <td>0.174280</td>\\n\",\n       \"      <td>0.019135</td>\\n\",\n       \"      <td>0.008281</td>\\n\",\n       \"      <td>0.015512</td>\\n\",\n       \"      <td>0.064000</td>\\n\",\n       \"      <td>0.053995</td>\\n\",\n       \"      <td>0.053995</td>\\n\",\n       \"      <td>0.053995</td>\\n\",\n       \"      <td>0.053995</td>\\n\",\n       \"      <td>0.053995</td>\\n\",\n       \"      <td>0.204000</td>\\n\",\n       \"      <td>0.008696</td>\\n\",\n       \"      <td>0.181264</td>\\n\",\n       \"      <td>0.015888</td>\\n\",\n       \"      <td>0.008696</td>\\n\",\n       \"      <td>0.015888</td>\\n\",\n       \"      <td>0.160056</td>\\n\",\n       \"      <td>0.180548</td>\\n\",\n       \"      <td>0.244553</td>\\n\",\n       \"      <td>0.288906</td>\\n\",\n       \"      <td>0.346509</td>\\n\",\n       \"      <td>0.439874</td>\\n\",\n       \"      <td>0.505829</td>\\n\",\n       \"      <td>0.244312</td>\\n\",\n       \"      <td>0.272758</td>\\n\",\n       \"      <td>0.303988</td>\\n\",\n       \"      <td>0.396496</td>\\n\",\n       \"      <td>0.432315</td>\\n\",\n       \"      <td>0.537820</td>\\n\",\n       \"      <td>0.614173</td>\\n\",\n       \"      <td>0.022928</td>\\n\",\n       \"      <td>0.028275</td>\\n\",\n       \"      <td>0.047608</td>\\n\",\n       \"      <td>0.041222</td>\\n\",\n       \"      <td>0.045348</td>\\n\",\n       \"      <td>0.050594</td>\\n\",\n       \"      <td>0.051120</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>13.728000</td>\\n\",\n       \"      <td>74.622000</td>\\n\",\n       \"      <td>96.760000</td>\\n\",\n       \"      <td>3339.868000</td>\\n\",\n       \"      <td>463.460000</td>\\n\",\n       \"      <td>12096.522000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18546</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.564000</td>\\n\",\n       \"      <td>0.312800</td>\\n\",\n       \"      <td>0.194621</td>\\n\",\n       \"      <td>0.226384</td>\\n\",\n       \"      <td>0.312800</td>\\n\",\n       \"      <td>0.226384</td>\\n\",\n       \"      <td>2.402000</td>\\n\",\n       \"      <td>0.240311</td>\\n\",\n       \"      <td>0.290784</td>\\n\",\n       \"      <td>0.248448</td>\\n\",\n       \"      <td>0.240200</td>\\n\",\n       \"      <td>0.248370</td>\\n\",\n       \"      <td>2.398000</td>\\n\",\n       \"      <td>0.258560</td>\\n\",\n       \"      <td>0.258378</td>\\n\",\n       \"      <td>0.258460</td>\\n\",\n       \"      <td>0.258378</td>\\n\",\n       \"      <td>0.258378</td>\\n\",\n       \"      <td>3.628000</td>\\n\",\n       \"      <td>0.080954</td>\\n\",\n       \"      <td>0.421334</td>\\n\",\n       \"      <td>0.127594</td>\\n\",\n       \"      <td>0.080954</td>\\n\",\n       \"      <td>0.127594</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.055496</td>\\n\",\n       \"      <td>0.095283</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.095283</td>\\n\",\n       \"      <td>1.106000</td>\\n\",\n       \"      <td>0.368667</td>\\n\",\n       \"      <td>0.140075</td>\\n\",\n       \"      <td>0.193469</td>\\n\",\n       \"      <td>0.368667</td>\\n\",\n       \"      <td>0.193469</td>\\n\",\n       \"      <td>1.866000</td>\\n\",\n       \"      <td>0.458283</td>\\n\",\n       \"      <td>0.278307</td>\\n\",\n       \"      <td>0.326213</td>\\n\",\n       \"      <td>0.458283</td>\\n\",\n       \"      <td>0.326213</td>\\n\",\n       \"      <td>2.980000</td>\\n\",\n       \"      <td>0.340476</td>\\n\",\n       \"      <td>0.398855</td>\\n\",\n       \"      <td>0.346040</td>\\n\",\n       \"      <td>0.340349</td>\\n\",\n       \"      <td>0.345952</td>\\n\",\n       \"      <td>3.026000</td>\\n\",\n       \"      <td>0.367082</td>\\n\",\n       \"      <td>0.366853</td>\\n\",\n       \"      <td>0.366957</td>\\n\",\n       \"      <td>0.366853</td>\\n\",\n       \"      <td>0.366853</td>\\n\",\n       \"      <td>4.420000</td>\\n\",\n       \"      <td>0.106797</td>\\n\",\n       \"      <td>0.545241</td>\\n\",\n       \"      <td>0.167721</td>\\n\",\n       \"      <td>0.106797</td>\\n\",\n       \"      <td>0.167721</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.599123</td>\\n\",\n       \"      <td>0.078843</td>\\n\",\n       \"      <td>0.134906</td>\\n\",\n       \"      <td>0.599123</td>\\n\",\n       \"      <td>0.134906</td>\\n\",\n       \"      <td>1.256000</td>\\n\",\n       \"      <td>0.543605</td>\\n\",\n       \"      <td>0.203721</td>\\n\",\n       \"      <td>0.281233</td>\\n\",\n       \"      <td>0.543605</td>\\n\",\n       \"      <td>0.281233</td>\\n\",\n       \"      <td>1.780000</td>\\n\",\n       \"      <td>0.357833</td>\\n\",\n       \"      <td>0.276258</td>\\n\",\n       \"      <td>0.292360</td>\\n\",\n       \"      <td>0.356000</td>\\n\",\n       \"      <td>0.291288</td>\\n\",\n       \"      <td>2.836000</td>\\n\",\n       \"      <td>0.300864</td>\\n\",\n       \"      <td>0.420080</td>\\n\",\n       \"      <td>0.331021</td>\\n\",\n       \"      <td>0.283600</td>\\n\",\n       \"      <td>0.318427</td>\\n\",\n       \"      <td>2.542000</td>\\n\",\n       \"      <td>0.337469</td>\\n\",\n       \"      <td>0.329063</td>\\n\",\n       \"      <td>0.332248</td>\\n\",\n       \"      <td>0.329063</td>\\n\",\n       \"      <td>0.329063</td>\\n\",\n       \"      <td>3.484000</td>\\n\",\n       \"      <td>0.260409</td>\\n\",\n       \"      <td>0.500848</td>\\n\",\n       \"      <td>0.322993</td>\\n\",\n       \"      <td>0.260409</td>\\n\",\n       \"      <td>0.322993</td>\\n\",\n       \"      <td>0.454000</td>\\n\",\n       \"      <td>0.454000</td>\\n\",\n       \"      <td>0.074266</td>\\n\",\n       \"      <td>0.123773</td>\\n\",\n       \"      <td>0.454000</td>\\n\",\n       \"      <td>0.123773</td>\\n\",\n       \"      <td>1.198000</td>\\n\",\n       \"      <td>0.399333</td>\\n\",\n       \"      <td>0.190352</td>\\n\",\n       \"      <td>0.242958</td>\\n\",\n       \"      <td>0.399333</td>\\n\",\n       \"      <td>0.242958</td>\\n\",\n       \"      <td>0.114000</td>\\n\",\n       \"      <td>0.012330</td>\\n\",\n       \"      <td>0.057410</td>\\n\",\n       \"      <td>0.018716</td>\\n\",\n       \"      <td>0.011400</td>\\n\",\n       \"      <td>0.017908</td>\\n\",\n       \"      <td>0.144000</td>\\n\",\n       \"      <td>0.006543</td>\\n\",\n       \"      <td>0.070976</td>\\n\",\n       \"      <td>0.010508</td>\\n\",\n       \"      <td>0.002880</td>\\n\",\n       \"      <td>0.005448</td>\\n\",\n       \"      <td>0.064000</td>\\n\",\n       \"      <td>0.029990</td>\\n\",\n       \"      <td>0.029990</td>\\n\",\n       \"      <td>0.029990</td>\\n\",\n       \"      <td>0.029990</td>\\n\",\n       \"      <td>0.029990</td>\\n\",\n       \"      <td>0.144000</td>\\n\",\n       \"      <td>0.006394</td>\\n\",\n       \"      <td>0.070976</td>\\n\",\n       \"      <td>0.010238</td>\\n\",\n       \"      <td>0.006394</td>\\n\",\n       \"      <td>0.010238</td>\\n\",\n       \"      <td>1.988000</td>\\n\",\n       \"      <td>0.473907</td>\\n\",\n       \"      <td>0.359479</td>\\n\",\n       \"      <td>0.382446</td>\\n\",\n       \"      <td>0.471693</td>\\n\",\n       \"      <td>0.381279</td>\\n\",\n       \"      <td>3.162000</td>\\n\",\n       \"      <td>0.376147</td>\\n\",\n       \"      <td>0.516934</td>\\n\",\n       \"      <td>0.410240</td>\\n\",\n       \"      <td>0.355828</td>\\n\",\n       \"      <td>0.395546</td>\\n\",\n       \"      <td>2.884000</td>\\n\",\n       \"      <td>0.435037</td>\\n\",\n       \"      <td>0.424696</td>\\n\",\n       \"      <td>0.428560</td>\\n\",\n       \"      <td>0.424696</td>\\n\",\n       \"      <td>0.424696</td>\\n\",\n       \"      <td>3.822000</td>\\n\",\n       \"      <td>0.310355</td>\\n\",\n       \"      <td>0.587953</td>\\n\",\n       \"      <td>0.382670</td>\\n\",\n       \"      <td>0.310355</td>\\n\",\n       \"      <td>0.382670</td>\\n\",\n       \"      <td>0.454000</td>\\n\",\n       \"      <td>0.606243</td>\\n\",\n       \"      <td>0.100246</td>\\n\",\n       \"      <td>0.165739</td>\\n\",\n       \"      <td>0.606243</td>\\n\",\n       \"      <td>0.165739</td>\\n\",\n       \"      <td>1.300000</td>\\n\",\n       \"      <td>0.548674</td>\\n\",\n       \"      <td>0.260015</td>\\n\",\n       \"      <td>0.330910</td>\\n\",\n       \"      <td>0.548563</td>\\n\",\n       \"      <td>0.330907</td>\\n\",\n       \"      <td>0.128000</td>\\n\",\n       \"      <td>0.030893</td>\\n\",\n       \"      <td>0.131358</td>\\n\",\n       \"      <td>0.046410</td>\\n\",\n       \"      <td>0.029453</td>\\n\",\n       \"      <td>0.045047</td>\\n\",\n       \"      <td>0.190000</td>\\n\",\n       \"      <td>0.015934</td>\\n\",\n       \"      <td>0.172767</td>\\n\",\n       \"      <td>0.026272</td>\\n\",\n       \"      <td>0.007925</td>\\n\",\n       \"      <td>0.014865</td>\\n\",\n       \"      <td>0.072000</td>\\n\",\n       \"      <td>0.066324</td>\\n\",\n       \"      <td>0.066301</td>\\n\",\n       \"      <td>0.066311</td>\\n\",\n       \"      <td>0.066301</td>\\n\",\n       \"      <td>0.066301</td>\\n\",\n       \"      <td>0.192000</td>\\n\",\n       \"      <td>0.015287</td>\\n\",\n       \"      <td>0.174323</td>\\n\",\n       \"      <td>0.025124</td>\\n\",\n       \"      <td>0.015287</td>\\n\",\n       \"      <td>0.025124</td>\\n\",\n       \"      <td>0.186255</td>\\n\",\n       \"      <td>0.207093</td>\\n\",\n       \"      <td>0.286310</td>\\n\",\n       \"      <td>0.315023</td>\\n\",\n       \"      <td>0.374756</td>\\n\",\n       \"      <td>0.461722</td>\\n\",\n       \"      <td>0.538970</td>\\n\",\n       \"      <td>0.254492</td>\\n\",\n       \"      <td>0.282576</td>\\n\",\n       \"      <td>0.328528</td>\\n\",\n       \"      <td>0.403226</td>\\n\",\n       \"      <td>0.446749</td>\\n\",\n       \"      <td>0.531579</td>\\n\",\n       \"      <td>0.608713</td>\\n\",\n       \"      <td>0.029606</td>\\n\",\n       \"      <td>0.034991</td>\\n\",\n       \"      <td>0.056360</td>\\n\",\n       \"      <td>0.046584</td>\\n\",\n       \"      <td>0.049330</td>\\n\",\n       \"      <td>0.063306</td>\\n\",\n       \"      <td>0.063661</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>13.972000</td>\\n\",\n       \"      <td>41.762000</td>\\n\",\n       \"      <td>61.630000</td>\\n\",\n       \"      <td>3372.120000</td>\\n\",\n       \"      <td>470.756000</td>\\n\",\n       \"      <td>12042.998000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20750</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.530000</td>\\n\",\n       \"      <td>0.306200</td>\\n\",\n       \"      <td>0.189564</td>\\n\",\n       \"      <td>0.221504</td>\\n\",\n       \"      <td>0.306000</td>\\n\",\n       \"      <td>0.221431</td>\\n\",\n       \"      <td>2.226000</td>\\n\",\n       \"      <td>0.223711</td>\\n\",\n       \"      <td>0.267734</td>\\n\",\n       \"      <td>0.230316</td>\\n\",\n       \"      <td>0.222600</td>\\n\",\n       \"      <td>0.229823</td>\\n\",\n       \"      <td>2.226000</td>\\n\",\n       \"      <td>0.245848</td>\\n\",\n       \"      <td>0.244688</td>\\n\",\n       \"      <td>0.245121</td>\\n\",\n       \"      <td>0.244688</td>\\n\",\n       \"      <td>0.244688</td>\\n\",\n       \"      <td>3.160000</td>\\n\",\n       \"      <td>0.056322</td>\\n\",\n       \"      <td>0.366465</td>\\n\",\n       \"      <td>0.089404</td>\\n\",\n       \"      <td>0.056322</td>\\n\",\n       \"      <td>0.089404</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.052186</td>\\n\",\n       \"      <td>0.089393</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.089393</td>\\n\",\n       \"      <td>1.080000</td>\\n\",\n       \"      <td>0.360000</td>\\n\",\n       \"      <td>0.135251</td>\\n\",\n       \"      <td>0.187309</td>\\n\",\n       \"      <td>0.360000</td>\\n\",\n       \"      <td>0.187309</td>\\n\",\n       \"      <td>1.820000</td>\\n\",\n       \"      <td>0.447048</td>\\n\",\n       \"      <td>0.270190</td>\\n\",\n       \"      <td>0.317879</td>\\n\",\n       \"      <td>0.446815</td>\\n\",\n       \"      <td>0.317794</td>\\n\",\n       \"      <td>2.750000</td>\\n\",\n       \"      <td>0.319154</td>\\n\",\n       \"      <td>0.371882</td>\\n\",\n       \"      <td>0.323771</td>\\n\",\n       \"      <td>0.317887</td>\\n\",\n       \"      <td>0.323202</td>\\n\",\n       \"      <td>2.792000</td>\\n\",\n       \"      <td>0.348095</td>\\n\",\n       \"      <td>0.346625</td>\\n\",\n       \"      <td>0.347185</td>\\n\",\n       \"      <td>0.346625</td>\\n\",\n       \"      <td>0.346625</td>\\n\",\n       \"      <td>3.964000</td>\\n\",\n       \"      <td>0.079305</td>\\n\",\n       \"      <td>0.497077</td>\\n\",\n       \"      <td>0.125343</td>\\n\",\n       \"      <td>0.079305</td>\\n\",\n       \"      <td>0.125343</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.592509</td>\\n\",\n       \"      <td>0.077663</td>\\n\",\n       \"      <td>0.132752</td>\\n\",\n       \"      <td>0.592509</td>\\n\",\n       \"      <td>0.132752</td>\\n\",\n       \"      <td>1.212000</td>\\n\",\n       \"      <td>0.532475</td>\\n\",\n       \"      <td>0.197815</td>\\n\",\n       \"      <td>0.274028</td>\\n\",\n       \"      <td>0.532475</td>\\n\",\n       \"      <td>0.274028</td>\\n\",\n       \"      <td>1.806000</td>\\n\",\n       \"      <td>0.370367</td>\\n\",\n       \"      <td>0.283551</td>\\n\",\n       \"      <td>0.300473</td>\\n\",\n       \"      <td>0.361200</td>\\n\",\n       \"      <td>0.296635</td>\\n\",\n       \"      <td>2.692000</td>\\n\",\n       \"      <td>0.321767</td>\\n\",\n       \"      <td>0.399083</td>\\n\",\n       \"      <td>0.333151</td>\\n\",\n       \"      <td>0.269200</td>\\n\",\n       \"      <td>0.302293</td>\\n\",\n       \"      <td>2.430000</td>\\n\",\n       \"      <td>0.353912</td>\\n\",\n       \"      <td>0.324004</td>\\n\",\n       \"      <td>0.334135</td>\\n\",\n       \"      <td>0.324004</td>\\n\",\n       \"      <td>0.324004</td>\\n\",\n       \"      <td>3.048000</td>\\n\",\n       \"      <td>0.293764</td>\\n\",\n       \"      <td>0.442110</td>\\n\",\n       \"      <td>0.325367</td>\\n\",\n       \"      <td>0.293764</td>\\n\",\n       \"      <td>0.325367</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.075998</td>\\n\",\n       \"      <td>0.123670</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.123670</td>\\n\",\n       \"      <td>1.232000</td>\\n\",\n       \"      <td>0.414000</td>\\n\",\n       \"      <td>0.196077</td>\\n\",\n       \"      <td>0.249412</td>\\n\",\n       \"      <td>0.410667</td>\\n\",\n       \"      <td>0.248422</td>\\n\",\n       \"      <td>0.068000</td>\\n\",\n       \"      <td>0.007317</td>\\n\",\n       \"      <td>0.030533</td>\\n\",\n       \"      <td>0.011273</td>\\n\",\n       \"      <td>0.006800</td>\\n\",\n       \"      <td>0.010556</td>\\n\",\n       \"      <td>0.108000</td>\\n\",\n       \"      <td>0.003506</td>\\n\",\n       \"      <td>0.048171</td>\\n\",\n       \"      <td>0.006194</td>\\n\",\n       \"      <td>0.002160</td>\\n\",\n       \"      <td>0.004072</td>\\n\",\n       \"      <td>0.048000</td>\\n\",\n       \"      <td>0.018967</td>\\n\",\n       \"      <td>0.018967</td>\\n\",\n       \"      <td>0.018967</td>\\n\",\n       \"      <td>0.018967</td>\\n\",\n       \"      <td>0.018967</td>\\n\",\n       \"      <td>0.112000</td>\\n\",\n       \"      <td>0.003089</td>\\n\",\n       \"      <td>0.049171</td>\\n\",\n       \"      <td>0.005426</td>\\n\",\n       \"      <td>0.003089</td>\\n\",\n       \"      <td>0.005426</td>\\n\",\n       \"      <td>2.004000</td>\\n\",\n       \"      <td>0.488626</td>\\n\",\n       \"      <td>0.366317</td>\\n\",\n       \"      <td>0.391316</td>\\n\",\n       \"      <td>0.477524</td>\\n\",\n       \"      <td>0.386894</td>\\n\",\n       \"      <td>2.984000</td>\\n\",\n       \"      <td>0.403379</td>\\n\",\n       \"      <td>0.490755</td>\\n\",\n       \"      <td>0.412960</td>\\n\",\n       \"      <td>0.338045</td>\\n\",\n       \"      <td>0.375556</td>\\n\",\n       \"      <td>2.754000</td>\\n\",\n       \"      <td>0.456107</td>\\n\",\n       \"      <td>0.415813</td>\\n\",\n       \"      <td>0.429341</td>\\n\",\n       \"      <td>0.415813</td>\\n\",\n       \"      <td>0.415813</td>\\n\",\n       \"      <td>3.360000</td>\\n\",\n       \"      <td>0.360731</td>\\n\",\n       \"      <td>0.528297</td>\\n\",\n       \"      <td>0.394148</td>\\n\",\n       \"      <td>0.360731</td>\\n\",\n       \"      <td>0.394148</td>\\n\",\n       \"      <td>0.456000</td>\\n\",\n       \"      <td>0.629768</td>\\n\",\n       \"      <td>0.105463</td>\\n\",\n       \"      <td>0.171810</td>\\n\",\n       \"      <td>0.629768</td>\\n\",\n       \"      <td>0.171810</td>\\n\",\n       \"      <td>1.338000</td>\\n\",\n       \"      <td>0.563593</td>\\n\",\n       \"      <td>0.263251</td>\\n\",\n       \"      <td>0.336326</td>\\n\",\n       \"      <td>0.559837</td>\\n\",\n       \"      <td>0.335237</td>\\n\",\n       \"      <td>0.084000</td>\\n\",\n       \"      <td>0.025209</td>\\n\",\n       \"      <td>0.101847</td>\\n\",\n       \"      <td>0.037888</td>\\n\",\n       \"      <td>0.024391</td>\\n\",\n       \"      <td>0.036914</td>\\n\",\n       \"      <td>0.152000</td>\\n\",\n       \"      <td>0.010448</td>\\n\",\n       \"      <td>0.144859</td>\\n\",\n       \"      <td>0.018283</td>\\n\",\n       \"      <td>0.007083</td>\\n\",\n       \"      <td>0.013242</td>\\n\",\n       \"      <td>0.050000</td>\\n\",\n       \"      <td>0.051853</td>\\n\",\n       \"      <td>0.051853</td>\\n\",\n       \"      <td>0.051853</td>\\n\",\n       \"      <td>0.051853</td>\\n\",\n       \"      <td>0.051853</td>\\n\",\n       \"      <td>0.158000</td>\\n\",\n       \"      <td>0.009011</td>\\n\",\n       \"      <td>0.148451</td>\\n\",\n       \"      <td>0.015683</td>\\n\",\n       \"      <td>0.009011</td>\\n\",\n       \"      <td>0.015683</td>\\n\",\n       \"      <td>0.165620</td>\\n\",\n       \"      <td>0.185557</td>\\n\",\n       \"      <td>0.305462</td>\\n\",\n       \"      <td>0.286117</td>\\n\",\n       \"      <td>0.339814</td>\\n\",\n       \"      <td>0.447400</td>\\n\",\n       \"      <td>0.514134</td>\\n\",\n       \"      <td>0.238979</td>\\n\",\n       \"      <td>0.265890</td>\\n\",\n       \"      <td>0.365091</td>\\n\",\n       \"      <td>0.377680</td>\\n\",\n       \"      <td>0.417896</td>\\n\",\n       \"      <td>0.536644</td>\\n\",\n       \"      <td>0.604292</td>\\n\",\n       \"      <td>0.016320</td>\\n\",\n       \"      <td>0.020294</td>\\n\",\n       \"      <td>0.034467</td>\\n\",\n       \"      <td>0.029412</td>\\n\",\n       \"      <td>0.031286</td>\\n\",\n       \"      <td>0.038905</td>\\n\",\n       \"      <td>0.038959</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>11.160000</td>\\n\",\n       \"      <td>68.212000</td>\\n\",\n       \"      <td>86.802000</td>\\n\",\n       \"      <td>3287.000000</td>\\n\",\n       \"      <td>464.730000</td>\\n\",\n       \"      <td>11935.956000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19593</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.598000</td>\\n\",\n       \"      <td>0.319600</td>\\n\",\n       \"      <td>0.198977</td>\\n\",\n       \"      <td>0.231883</td>\\n\",\n       \"      <td>0.319600</td>\\n\",\n       \"      <td>0.231883</td>\\n\",\n       \"      <td>2.416000</td>\\n\",\n       \"      <td>0.241600</td>\\n\",\n       \"      <td>0.287326</td>\\n\",\n       \"      <td>0.248131</td>\\n\",\n       \"      <td>0.241600</td>\\n\",\n       \"      <td>0.248131</td>\\n\",\n       \"      <td>2.504000</td>\\n\",\n       \"      <td>0.265237</td>\\n\",\n       \"      <td>0.265219</td>\\n\",\n       \"      <td>0.265228</td>\\n\",\n       \"      <td>0.265219</td>\\n\",\n       \"      <td>0.265219</td>\\n\",\n       \"      <td>3.946000</td>\\n\",\n       \"      <td>0.058601</td>\\n\",\n       \"      <td>0.443652</td>\\n\",\n       \"      <td>0.097756</td>\\n\",\n       \"      <td>0.058601</td>\\n\",\n       \"      <td>0.097756</td>\\n\",\n       \"      <td>0.374000</td>\\n\",\n       \"      <td>0.374000</td>\\n\",\n       \"      <td>0.049407</td>\\n\",\n       \"      <td>0.084712</td>\\n\",\n       \"      <td>0.374000</td>\\n\",\n       \"      <td>0.084712</td>\\n\",\n       \"      <td>1.114000</td>\\n\",\n       \"      <td>0.371333</td>\\n\",\n       \"      <td>0.141622</td>\\n\",\n       \"      <td>0.195156</td>\\n\",\n       \"      <td>0.371333</td>\\n\",\n       \"      <td>0.195156</td>\\n\",\n       \"      <td>1.920000</td>\\n\",\n       \"      <td>0.466363</td>\\n\",\n       \"      <td>0.283654</td>\\n\",\n       \"      <td>0.332660</td>\\n\",\n       \"      <td>0.466363</td>\\n\",\n       \"      <td>0.332660</td>\\n\",\n       \"      <td>2.976000</td>\\n\",\n       \"      <td>0.340075</td>\\n\",\n       \"      <td>0.395479</td>\\n\",\n       \"      <td>0.344705</td>\\n\",\n       \"      <td>0.340075</td>\\n\",\n       \"      <td>0.344705</td>\\n\",\n       \"      <td>3.100000</td>\\n\",\n       \"      <td>0.371996</td>\\n\",\n       \"      <td>0.371973</td>\\n\",\n       \"      <td>0.371984</td>\\n\",\n       \"      <td>0.371973</td>\\n\",\n       \"      <td>0.371973</td>\\n\",\n       \"      <td>4.790000</td>\\n\",\n       \"      <td>0.078325</td>\\n\",\n       \"      <td>0.573896</td>\\n\",\n       \"      <td>0.129928</td>\\n\",\n       \"      <td>0.078325</td>\\n\",\n       \"      <td>0.129928</td>\\n\",\n       \"      <td>0.374000</td>\\n\",\n       \"      <td>0.591726</td>\\n\",\n       \"      <td>0.076638</td>\\n\",\n       \"      <td>0.131480</td>\\n\",\n       \"      <td>0.591726</td>\\n\",\n       \"      <td>0.131480</td>\\n\",\n       \"      <td>1.262000</td>\\n\",\n       \"      <td>0.546788</td>\\n\",\n       \"      <td>0.205368</td>\\n\",\n       \"      <td>0.283172</td>\\n\",\n       \"      <td>0.546788</td>\\n\",\n       \"      <td>0.283172</td>\\n\",\n       \"      <td>1.754000</td>\\n\",\n       \"      <td>0.354367</td>\\n\",\n       \"      <td>0.270989</td>\\n\",\n       \"      <td>0.288578</td>\\n\",\n       \"      <td>0.350800</td>\\n\",\n       \"      <td>0.286772</td>\\n\",\n       \"      <td>2.858000</td>\\n\",\n       \"      <td>0.304745</td>\\n\",\n       \"      <td>0.428702</td>\\n\",\n       \"      <td>0.335010</td>\\n\",\n       \"      <td>0.285800</td>\\n\",\n       \"      <td>0.321785</td>\\n\",\n       \"      <td>2.594000</td>\\n\",\n       \"      <td>0.339351</td>\\n\",\n       \"      <td>0.331778</td>\\n\",\n       \"      <td>0.334227</td>\\n\",\n       \"      <td>0.331778</td>\\n\",\n       \"      <td>0.331778</td>\\n\",\n       \"      <td>3.792000</td>\\n\",\n       \"      <td>0.257251</td>\\n\",\n       \"      <td>0.532850</td>\\n\",\n       \"      <td>0.326068</td>\\n\",\n       \"      <td>0.257251</td>\\n\",\n       \"      <td>0.326068</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.062279</td>\\n\",\n       \"      <td>0.103306</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.103306</td>\\n\",\n       \"      <td>1.196000</td>\\n\",\n       \"      <td>0.399000</td>\\n\",\n       \"      <td>0.190416</td>\\n\",\n       \"      <td>0.242672</td>\\n\",\n       \"      <td>0.398667</td>\\n\",\n       \"      <td>0.242472</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.010133</td>\\n\",\n       \"      <td>0.044639</td>\\n\",\n       \"      <td>0.015507</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.015423</td>\\n\",\n       \"      <td>0.148000</td>\\n\",\n       \"      <td>0.004036</td>\\n\",\n       \"      <td>0.062447</td>\\n\",\n       \"      <td>0.007122</td>\\n\",\n       \"      <td>0.002960</td>\\n\",\n       \"      <td>0.005552</td>\\n\",\n       \"      <td>0.054000</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.154000</td>\\n\",\n       \"      <td>0.003276</td>\\n\",\n       \"      <td>0.065513</td>\\n\",\n       \"      <td>0.005745</td>\\n\",\n       \"      <td>0.003276</td>\\n\",\n       \"      <td>0.005745</td>\\n\",\n       \"      <td>1.970000</td>\\n\",\n       \"      <td>0.473193</td>\\n\",\n       \"      <td>0.356562</td>\\n\",\n       \"      <td>0.380658</td>\\n\",\n       \"      <td>0.469394</td>\\n\",\n       \"      <td>0.378729</td>\\n\",\n       \"      <td>3.198000</td>\\n\",\n       \"      <td>0.379796</td>\\n\",\n       \"      <td>0.521058</td>\\n\",\n       \"      <td>0.413221</td>\\n\",\n       \"      <td>0.357421</td>\\n\",\n       \"      <td>0.397734</td>\\n\",\n       \"      <td>2.950000</td>\\n\",\n       \"      <td>0.434384</td>\\n\",\n       \"      <td>0.424704</td>\\n\",\n       \"      <td>0.427948</td>\\n\",\n       \"      <td>0.424704</td>\\n\",\n       \"      <td>0.424704</td>\\n\",\n       \"      <td>4.132000</td>\\n\",\n       \"      <td>0.306187</td>\\n\",\n       \"      <td>0.621745</td>\\n\",\n       \"      <td>0.385345</td>\\n\",\n       \"      <td>0.306187</td>\\n\",\n       \"      <td>0.385345</td>\\n\",\n       \"      <td>0.382000</td>\\n\",\n       \"      <td>0.575249</td>\\n\",\n       \"      <td>0.093933</td>\\n\",\n       \"      <td>0.155113</td>\\n\",\n       \"      <td>0.575249</td>\\n\",\n       \"      <td>0.155113</td>\\n\",\n       \"      <td>1.298000</td>\\n\",\n       \"      <td>0.547112</td>\\n\",\n       \"      <td>0.258167</td>\\n\",\n       \"      <td>0.328942</td>\\n\",\n       \"      <td>0.546779</td>\\n\",\n       \"      <td>0.328742</td>\\n\",\n       \"      <td>0.116000</td>\\n\",\n       \"      <td>0.026752</td>\\n\",\n       \"      <td>0.108380</td>\\n\",\n       \"      <td>0.040100</td>\\n\",\n       \"      <td>0.026287</td>\\n\",\n       \"      <td>0.039665</td>\\n\",\n       \"      <td>0.188000</td>\\n\",\n       \"      <td>0.011153</td>\\n\",\n       \"      <td>0.164802</td>\\n\",\n       \"      <td>0.019659</td>\\n\",\n       \"      <td>0.008001</td>\\n\",\n       \"      <td>0.014962</td>\\n\",\n       \"      <td>0.058000</td>\\n\",\n       \"      <td>0.042186</td>\\n\",\n       \"      <td>0.042186</td>\\n\",\n       \"      <td>0.042186</td>\\n\",\n       \"      <td>0.042186</td>\\n\",\n       \"      <td>0.042186</td>\\n\",\n       \"      <td>0.194000</td>\\n\",\n       \"      <td>0.009278</td>\\n\",\n       \"      <td>0.169128</td>\\n\",\n       \"      <td>0.016277</td>\\n\",\n       \"      <td>0.009278</td>\\n\",\n       \"      <td>0.016277</td>\\n\",\n       \"      <td>0.188621</td>\\n\",\n       \"      <td>0.211138</td>\\n\",\n       \"      <td>0.262824</td>\\n\",\n       \"      <td>0.327248</td>\\n\",\n       \"      <td>0.382558</td>\\n\",\n       \"      <td>0.490353</td>\\n\",\n       \"      <td>0.564088</td>\\n\",\n       \"      <td>0.254249</td>\\n\",\n       \"      <td>0.285401</td>\\n\",\n       \"      <td>0.296996</td>\\n\",\n       \"      <td>0.416598</td>\\n\",\n       \"      <td>0.452579</td>\\n\",\n       \"      <td>0.540057</td>\\n\",\n       \"      <td>0.615019</td>\\n\",\n       \"      <td>0.017388</td>\\n\",\n       \"      <td>0.022074</td>\\n\",\n       \"      <td>0.042559</td>\\n\",\n       \"      <td>0.034749</td>\\n\",\n       \"      <td>0.037965</td>\\n\",\n       \"      <td>0.031535</td>\\n\",\n       \"      <td>0.031814</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>15.574000</td>\\n\",\n       \"      <td>69.900000</td>\\n\",\n       \"      <td>95.020000</td>\\n\",\n       \"      <td>3333.850000</td>\\n\",\n       \"      <td>433.622000</td>\\n\",\n       \"      <td>11919.248000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19155</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.782000</td>\\n\",\n       \"      <td>0.356400</td>\\n\",\n       \"      <td>0.219340</td>\\n\",\n       \"      <td>0.257241</td>\\n\",\n       \"      <td>0.356400</td>\\n\",\n       \"      <td>0.257241</td>\\n\",\n       \"      <td>2.730000</td>\\n\",\n       \"      <td>0.273050</td>\\n\",\n       \"      <td>0.329392</td>\\n\",\n       \"      <td>0.281847</td>\\n\",\n       \"      <td>0.273000</td>\\n\",\n       \"      <td>0.281815</td>\\n\",\n       \"      <td>2.898000</td>\\n\",\n       \"      <td>0.296899</td>\\n\",\n       \"      <td>0.296899</td>\\n\",\n       \"      <td>0.296899</td>\\n\",\n       \"      <td>0.296899</td>\\n\",\n       \"      <td>0.296899</td>\\n\",\n       \"      <td>5.044000</td>\\n\",\n       \"      <td>0.109332</td>\\n\",\n       \"      <td>0.563146</td>\\n\",\n       \"      <td>0.174393</td>\\n\",\n       \"      <td>0.109332</td>\\n\",\n       \"      <td>0.174393</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.049076</td>\\n\",\n       \"      <td>0.084510</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.084510</td>\\n\",\n       \"      <td>1.220000</td>\\n\",\n       \"      <td>0.406667</td>\\n\",\n       \"      <td>0.154213</td>\\n\",\n       \"      <td>0.213324</td>\\n\",\n       \"      <td>0.406667</td>\\n\",\n       \"      <td>0.213324</td>\\n\",\n       \"      <td>2.062000</td>\\n\",\n       \"      <td>0.500806</td>\\n\",\n       \"      <td>0.305237</td>\\n\",\n       \"      <td>0.357850</td>\\n\",\n       \"      <td>0.500806</td>\\n\",\n       \"      <td>0.357850</td>\\n\",\n       \"      <td>3.258000</td>\\n\",\n       \"      <td>0.370549</td>\\n\",\n       \"      <td>0.435504</td>\\n\",\n       \"      <td>0.377248</td>\\n\",\n       \"      <td>0.370459</td>\\n\",\n       \"      <td>0.377192</td>\\n\",\n       \"      <td>3.502000</td>\\n\",\n       \"      <td>0.404835</td>\\n\",\n       \"      <td>0.404835</td>\\n\",\n       \"      <td>0.404835</td>\\n\",\n       \"      <td>0.404835</td>\\n\",\n       \"      <td>0.404835</td>\\n\",\n       \"      <td>5.776000</td>\\n\",\n       \"      <td>0.133132</td>\\n\",\n       \"      <td>0.673575</td>\\n\",\n       \"      <td>0.211548</td>\\n\",\n       \"      <td>0.133132</td>\\n\",\n       \"      <td>0.211548</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.596611</td>\\n\",\n       \"      <td>0.078287</td>\\n\",\n       \"      <td>0.134133</td>\\n\",\n       \"      <td>0.596611</td>\\n\",\n       \"      <td>0.134133</td>\\n\",\n       \"      <td>1.386000</td>\\n\",\n       \"      <td>0.580891</td>\\n\",\n       \"      <td>0.217817</td>\\n\",\n       \"      <td>0.301103</td>\\n\",\n       \"      <td>0.580891</td>\\n\",\n       \"      <td>0.301103</td>\\n\",\n       \"      <td>1.868000</td>\\n\",\n       \"      <td>0.373600</td>\\n\",\n       \"      <td>0.289511</td>\\n\",\n       \"      <td>0.305781</td>\\n\",\n       \"      <td>0.373600</td>\\n\",\n       \"      <td>0.305781</td>\\n\",\n       \"      <td>3.078000</td>\\n\",\n       \"      <td>0.309053</td>\\n\",\n       \"      <td>0.463270</td>\\n\",\n       \"      <td>0.348434</td>\\n\",\n       \"      <td>0.307800</td>\\n\",\n       \"      <td>0.347360</td>\\n\",\n       \"      <td>2.780000</td>\\n\",\n       \"      <td>0.348045</td>\\n\",\n       \"      <td>0.347117</td>\\n\",\n       \"      <td>0.347466</td>\\n\",\n       \"      <td>0.347117</td>\\n\",\n       \"      <td>0.347117</td>\\n\",\n       \"      <td>4.950000</td>\\n\",\n       \"      <td>0.225865</td>\\n\",\n       \"      <td>0.686583</td>\\n\",\n       \"      <td>0.324365</td>\\n\",\n       \"      <td>0.225865</td>\\n\",\n       \"      <td>0.324365</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.067614</td>\\n\",\n       \"      <td>0.111564</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.111564</td>\\n\",\n       \"      <td>1.238000</td>\\n\",\n       \"      <td>0.412667</td>\\n\",\n       \"      <td>0.194451</td>\\n\",\n       \"      <td>0.249151</td>\\n\",\n       \"      <td>0.412667</td>\\n\",\n       \"      <td>0.249151</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.008350</td>\\n\",\n       \"      <td>0.041325</td>\\n\",\n       \"      <td>0.013078</td>\\n\",\n       \"      <td>0.008000</td>\\n\",\n       \"      <td>0.012678</td>\\n\",\n       \"      <td>0.094000</td>\\n\",\n       \"      <td>0.004439</td>\\n\",\n       \"      <td>0.047058</td>\\n\",\n       \"      <td>0.007509</td>\\n\",\n       \"      <td>0.001880</td>\\n\",\n       \"      <td>0.003558</td>\\n\",\n       \"      <td>0.058000</td>\\n\",\n       \"      <td>0.028091</td>\\n\",\n       \"      <td>0.028091</td>\\n\",\n       \"      <td>0.028091</td>\\n\",\n       \"      <td>0.028091</td>\\n\",\n       \"      <td>0.028091</td>\\n\",\n       \"      <td>0.094000</td>\\n\",\n       \"      <td>0.004280</td>\\n\",\n       \"      <td>0.047058</td>\\n\",\n       \"      <td>0.007215</td>\\n\",\n       \"      <td>0.004280</td>\\n\",\n       \"      <td>0.007215</td>\\n\",\n       \"      <td>2.054000</td>\\n\",\n       \"      <td>0.488566</td>\\n\",\n       \"      <td>0.373851</td>\\n\",\n       \"      <td>0.395960</td>\\n\",\n       \"      <td>0.488566</td>\\n\",\n       \"      <td>0.395960</td>\\n\",\n       \"      <td>3.388000</td>\\n\",\n       \"      <td>0.377901</td>\\n\",\n       \"      <td>0.552990</td>\\n\",\n       \"      <td>0.421285</td>\\n\",\n       \"      <td>0.376529</td>\\n\",\n       \"      <td>0.420128</td>\\n\",\n       \"      <td>3.132000</td>\\n\",\n       \"      <td>0.445149</td>\\n\",\n       \"      <td>0.443871</td>\\n\",\n       \"      <td>0.444332</td>\\n\",\n       \"      <td>0.443871</td>\\n\",\n       \"      <td>0.443871</td>\\n\",\n       \"      <td>5.244000</td>\\n\",\n       \"      <td>0.252692</td>\\n\",\n       \"      <td>0.757664</td>\\n\",\n       \"      <td>0.361263</td>\\n\",\n       \"      <td>0.252692</td>\\n\",\n       \"      <td>0.361263</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.606680</td>\\n\",\n       \"      <td>0.100124</td>\\n\",\n       \"      <td>0.164865</td>\\n\",\n       \"      <td>0.606680</td>\\n\",\n       \"      <td>0.164865</td>\\n\",\n       \"      <td>1.344000</td>\\n\",\n       \"      <td>0.561907</td>\\n\",\n       \"      <td>0.264289</td>\\n\",\n       \"      <td>0.337355</td>\\n\",\n       \"      <td>0.561907</td>\\n\",\n       \"      <td>0.337355</td>\\n\",\n       \"      <td>0.092000</td>\\n\",\n       \"      <td>0.026838</td>\\n\",\n       \"      <td>0.111309</td>\\n\",\n       \"      <td>0.040440</td>\\n\",\n       \"      <td>0.025684</td>\\n\",\n       \"      <td>0.039120</td>\\n\",\n       \"      <td>0.126000</td>\\n\",\n       \"      <td>0.015099</td>\\n\",\n       \"      <td>0.132273</td>\\n\",\n       \"      <td>0.024759</td>\\n\",\n       \"      <td>0.006193</td>\\n\",\n       \"      <td>0.011600</td>\\n\",\n       \"      <td>0.064000</td>\\n\",\n       \"      <td>0.059912</td>\\n\",\n       \"      <td>0.059892</td>\\n\",\n       \"      <td>0.059900</td>\\n\",\n       \"      <td>0.059892</td>\\n\",\n       \"      <td>0.059892</td>\\n\",\n       \"      <td>0.126000</td>\\n\",\n       \"      <td>0.014707</td>\\n\",\n       \"      <td>0.132686</td>\\n\",\n       \"      <td>0.024052</td>\\n\",\n       \"      <td>0.014707</td>\\n\",\n       \"      <td>0.024052</td>\\n\",\n       \"      <td>0.231911</td>\\n\",\n       \"      <td>0.259039</td>\\n\",\n       \"      <td>0.231714</td>\\n\",\n       \"      <td>0.388808</td>\\n\",\n       \"      <td>0.456142</td>\\n\",\n       \"      <td>0.511239</td>\\n\",\n       \"      <td>0.594618</td>\\n\",\n       \"      <td>0.308452</td>\\n\",\n       \"      <td>0.342923</td>\\n\",\n       \"      <td>0.252308</td>\\n\",\n       \"      <td>0.477432</td>\\n\",\n       \"      <td>0.537041</td>\\n\",\n       \"      <td>0.553746</td>\\n\",\n       \"      <td>0.631410</td>\\n\",\n       \"      <td>0.026972</td>\\n\",\n       \"      <td>0.029718</td>\\n\",\n       \"      <td>0.045020</td>\\n\",\n       \"      <td>0.035963</td>\\n\",\n       \"      <td>0.038149</td>\\n\",\n       \"      <td>0.050239</td>\\n\",\n       \"      <td>0.050627</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>22.748000</td>\\n\",\n       \"      <td>31.816000</td>\\n\",\n       \"      <td>57.432000</td>\\n\",\n       \"      <td>3260.374000</td>\\n\",\n       \"      <td>482.488000</td>\\n\",\n       \"      <td>11623.312000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15886</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.439936</td>\\n\",\n       \"      <td>0.288002</td>\\n\",\n       \"      <td>0.311385</td>\\n\",\n       \"      <td>0.288303</td>\\n\",\n       \"      <td>0.287987</td>\\n\",\n       \"      <td>0.288296</td>\\n\",\n       \"      <td>1.990344</td>\\n\",\n       \"      <td>0.199186</td>\\n\",\n       \"      <td>0.420234</td>\\n\",\n       \"      <td>0.259843</td>\\n\",\n       \"      <td>0.199034</td>\\n\",\n       \"      <td>0.259775</td>\\n\",\n       \"      <td>1.549957</td>\\n\",\n       \"      <td>0.296912</td>\\n\",\n       \"      <td>0.296350</td>\\n\",\n       \"      <td>0.296551</td>\\n\",\n       \"      <td>0.296350</td>\\n\",\n       \"      <td>0.296350</td>\\n\",\n       \"      <td>2.571421</td>\\n\",\n       \"      <td>0.070670</td>\\n\",\n       \"      <td>0.523737</td>\\n\",\n       \"      <td>0.118971</td>\\n\",\n       \"      <td>0.070670</td>\\n\",\n       \"      <td>0.118971</td>\\n\",\n       \"      <td>0.370341</td>\\n\",\n       \"      <td>0.370341</td>\\n\",\n       \"      <td>0.081887</td>\\n\",\n       \"      <td>0.131250</td>\\n\",\n       \"      <td>0.370341</td>\\n\",\n       \"      <td>0.131250</td>\\n\",\n       \"      <td>1.050233</td>\\n\",\n       \"      <td>0.350086</td>\\n\",\n       \"      <td>0.230319</td>\\n\",\n       \"      <td>0.268675</td>\\n\",\n       \"      <td>0.350078</td>\\n\",\n       \"      <td>0.268671</td>\\n\",\n       \"      <td>1.515035</td>\\n\",\n       \"      <td>0.366303</td>\\n\",\n       \"      <td>0.396032</td>\\n\",\n       \"      <td>0.366775</td>\\n\",\n       \"      <td>0.366285</td>\\n\",\n       \"      <td>0.366766</td>\\n\",\n       \"      <td>2.104118</td>\\n\",\n       \"      <td>0.244086</td>\\n\",\n       \"      <td>0.513422</td>\\n\",\n       \"      <td>0.318063</td>\\n\",\n       \"      <td>0.243923</td>\\n\",\n       \"      <td>0.317989</td>\\n\",\n       \"      <td>1.655376</td>\\n\",\n       \"      <td>0.380685</td>\\n\",\n       \"      <td>0.380061</td>\\n\",\n       \"      <td>0.380286</td>\\n\",\n       \"      <td>0.380061</td>\\n\",\n       \"      <td>0.380061</td>\\n\",\n       \"      <td>2.727623</td>\\n\",\n       \"      <td>0.084295</td>\\n\",\n       \"      <td>0.623254</td>\\n\",\n       \"      <td>0.141853</td>\\n\",\n       \"      <td>0.084295</td>\\n\",\n       \"      <td>0.141853</td>\\n\",\n       \"      <td>0.370341</td>\\n\",\n       \"      <td>0.490662</td>\\n\",\n       \"      <td>0.109192</td>\\n\",\n       \"      <td>0.174839</td>\\n\",\n       \"      <td>0.490662</td>\\n\",\n       \"      <td>0.174839</td>\\n\",\n       \"      <td>1.089358</td>\\n\",\n       \"      <td>0.454554</td>\\n\",\n       \"      <td>0.300108</td>\\n\",\n       \"      <td>0.349715</td>\\n\",\n       \"      <td>0.454544</td>\\n\",\n       \"      <td>0.349711</td>\\n\",\n       \"      <td>1.466853</td>\\n\",\n       \"      <td>0.293541</td>\\n\",\n       \"      <td>0.509155</td>\\n\",\n       \"      <td>0.347090</td>\\n\",\n       \"      <td>0.293371</td>\\n\",\n       \"      <td>0.346953</td>\\n\",\n       \"      <td>2.009456</td>\\n\",\n       \"      <td>0.203461</td>\\n\",\n       \"      <td>0.669059</td>\\n\",\n       \"      <td>0.292192</td>\\n\",\n       \"      <td>0.200946</td>\\n\",\n       \"      <td>0.289543</td>\\n\",\n       \"      <td>1.295442</td>\\n\",\n       \"      <td>0.353874</td>\\n\",\n       \"      <td>0.350674</td>\\n\",\n       \"      <td>0.351790</td>\\n\",\n       \"      <td>0.350674</td>\\n\",\n       \"      <td>0.350674</td>\\n\",\n       \"      <td>2.314104</td>\\n\",\n       \"      <td>0.145541</td>\\n\",\n       \"      <td>0.741827</td>\\n\",\n       \"      <td>0.227579</td>\\n\",\n       \"      <td>0.145541</td>\\n\",\n       \"      <td>0.227579</td>\\n\",\n       \"      <td>0.387902</td>\\n\",\n       \"      <td>0.387902</td>\\n\",\n       \"      <td>0.143277</td>\\n\",\n       \"      <td>0.196908</td>\\n\",\n       \"      <td>0.387902</td>\\n\",\n       \"      <td>0.196908</td>\\n\",\n       \"      <td>1.071396</td>\\n\",\n       \"      <td>0.357174</td>\\n\",\n       \"      <td>0.383186</td>\\n\",\n       \"      <td>0.344101</td>\\n\",\n       \"      <td>0.357132</td>\\n\",\n       \"      <td>0.344075</td>\\n\",\n       \"      <td>0.232001</td>\\n\",\n       \"      <td>0.024601</td>\\n\",\n       \"      <td>0.108995</td>\\n\",\n       \"      <td>0.038013</td>\\n\",\n       \"      <td>0.023200</td>\\n\",\n       \"      <td>0.036614</td>\\n\",\n       \"      <td>0.257117</td>\\n\",\n       \"      <td>0.014079</td>\\n\",\n       \"      <td>0.119425</td>\\n\",\n       \"      <td>0.023199</td>\\n\",\n       \"      <td>0.005142</td>\\n\",\n       \"      <td>0.009735</td>\\n\",\n       \"      <td>0.137489</td>\\n\",\n       \"      <td>0.057158</td>\\n\",\n       \"      <td>0.056999</td>\\n\",\n       \"      <td>0.057048</td>\\n\",\n       \"      <td>0.056999</td>\\n\",\n       \"      <td>0.056999</td>\\n\",\n       \"      <td>0.257317</td>\\n\",\n       \"      <td>0.013969</td>\\n\",\n       \"      <td>0.119485</td>\\n\",\n       \"      <td>0.022999</td>\\n\",\n       \"      <td>0.013969</td>\\n\",\n       \"      <td>0.022999</td>\\n\",\n       \"      <td>1.495622</td>\\n\",\n       \"      <td>0.336905</td>\\n\",\n       \"      <td>0.582617</td>\\n\",\n       \"      <td>0.397674</td>\\n\",\n       \"      <td>0.336724</td>\\n\",\n       \"      <td>0.397528</td>\\n\",\n       \"      <td>2.041577</td>\\n\",\n       \"      <td>0.222579</td>\\n\",\n       \"      <td>0.726473</td>\\n\",\n       \"      <td>0.318590</td>\\n\",\n       \"      <td>0.219890</td>\\n\",\n       \"      <td>0.315767</td>\\n\",\n       \"      <td>1.332816</td>\\n\",\n       \"      <td>0.420237</td>\\n\",\n       \"      <td>0.416591</td>\\n\",\n       \"      <td>0.417859</td>\\n\",\n       \"      <td>0.416591</td>\\n\",\n       \"      <td>0.416591</td>\\n\",\n       \"      <td>2.345475</td>\\n\",\n       \"      <td>0.155879</td>\\n\",\n       \"      <td>0.789316</td>\\n\",\n       \"      <td>0.243073</td>\\n\",\n       \"      <td>0.155879</td>\\n\",\n       \"      <td>0.243073</td>\\n\",\n       \"      <td>0.387902</td>\\n\",\n       \"      <td>0.471608</td>\\n\",\n       \"      <td>0.176018</td>\\n\",\n       \"      <td>0.241146</td>\\n\",\n       \"      <td>0.471608</td>\\n\",\n       \"      <td>0.241146</td>\\n\",\n       \"      <td>1.089308</td>\\n\",\n       \"      <td>0.423596</td>\\n\",\n       \"      <td>0.455241</td>\\n\",\n       \"      <td>0.408357</td>\\n\",\n       \"      <td>0.423554</td>\\n\",\n       \"      <td>0.408331</td>\\n\",\n       \"      <td>0.239356</td>\\n\",\n       \"      <td>0.041275</td>\\n\",\n       \"      <td>0.178178</td>\\n\",\n       \"      <td>0.063302</td>\\n\",\n       \"      <td>0.038717</td>\\n\",\n       \"      <td>0.060786</td>\\n\",\n       \"      <td>0.271176</td>\\n\",\n       \"      <td>0.024483</td>\\n\",\n       \"      <td>0.202064</td>\\n\",\n       \"      <td>0.040046</td>\\n\",\n       \"      <td>0.008899</td>\\n\",\n       \"      <td>0.016818</td>\\n\",\n       \"      <td>0.140291</td>\\n\",\n       \"      <td>0.091553</td>\\n\",\n       \"      <td>0.091228</td>\\n\",\n       \"      <td>0.091331</td>\\n\",\n       \"      <td>0.091228</td>\\n\",\n       \"      <td>0.091228</td>\\n\",\n       \"      <td>0.271677</td>\\n\",\n       \"      <td>0.024290</td>\\n\",\n       \"      <td>0.202400</td>\\n\",\n       \"      <td>0.039699</td>\\n\",\n       \"      <td>0.024290</td>\\n\",\n       \"      <td>0.039699</td>\\n\",\n       \"      <td>0.236145</td>\\n\",\n       \"      <td>0.270485</td>\\n\",\n       \"      <td>0.330120</td>\\n\",\n       \"      <td>0.384596</td>\\n\",\n       \"      <td>0.425191</td>\\n\",\n       \"      <td>0.477369</td>\\n\",\n       \"      <td>0.491745</td>\\n\",\n       \"      <td>0.355014</td>\\n\",\n       \"      <td>0.410471</td>\\n\",\n       \"      <td>0.364954</td>\\n\",\n       \"      <td>0.521975</td>\\n\",\n       \"      <td>0.548159</td>\\n\",\n       \"      <td>0.497429</td>\\n\",\n       \"      <td>0.503571</td>\\n\",\n       \"      <td>0.052466</td>\\n\",\n       \"      <td>0.062885</td>\\n\",\n       \"      <td>0.108269</td>\\n\",\n       \"      <td>0.084436</td>\\n\",\n       \"      <td>0.090136</td>\\n\",\n       \"      <td>0.082443</td>\\n\",\n       \"      <td>0.082734</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>16.779457</td>\\n\",\n       \"      <td>26.082354</td>\\n\",\n       \"      <td>48.238455</td>\\n\",\n       \"      <td>3420.827688</td>\\n\",\n       \"      <td>456.603842</td>\\n\",\n       \"      <td>12126.808926</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20238</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.449542</td>\\n\",\n       \"      <td>0.289915</td>\\n\",\n       \"      <td>0.315257</td>\\n\",\n       \"      <td>0.291303</td>\\n\",\n       \"      <td>0.289908</td>\\n\",\n       \"      <td>0.291303</td>\\n\",\n       \"      <td>1.988643</td>\\n\",\n       \"      <td>0.198924</td>\\n\",\n       \"      <td>0.422114</td>\\n\",\n       \"      <td>0.260395</td>\\n\",\n       \"      <td>0.198864</td>\\n\",\n       \"      <td>0.260370</td>\\n\",\n       \"      <td>1.563516</td>\\n\",\n       \"      <td>0.301253</td>\\n\",\n       \"      <td>0.300911</td>\\n\",\n       \"      <td>0.301040</td>\\n\",\n       \"      <td>0.300911</td>\\n\",\n       \"      <td>0.300911</td>\\n\",\n       \"      <td>2.538000</td>\\n\",\n       \"      <td>0.079284</td>\\n\",\n       \"      <td>0.517029</td>\\n\",\n       \"      <td>0.131874</td>\\n\",\n       \"      <td>0.079284</td>\\n\",\n       \"      <td>0.131874</td>\\n\",\n       \"      <td>0.378246</td>\\n\",\n       \"      <td>0.378246</td>\\n\",\n       \"      <td>0.084675</td>\\n\",\n       \"      <td>0.135362</td>\\n\",\n       \"      <td>0.378246</td>\\n\",\n       \"      <td>0.135362</td>\\n\",\n       \"      <td>1.069645</td>\\n\",\n       \"      <td>0.356548</td>\\n\",\n       \"      <td>0.236130</td>\\n\",\n       \"      <td>0.274888</td>\\n\",\n       \"      <td>0.356548</td>\\n\",\n       \"      <td>0.274888</td>\\n\",\n       \"      <td>1.526242</td>\\n\",\n       \"      <td>0.368637</td>\\n\",\n       \"      <td>0.399783</td>\\n\",\n       \"      <td>0.369859</td>\\n\",\n       \"      <td>0.368625</td>\\n\",\n       \"      <td>0.369858</td>\\n\",\n       \"      <td>2.107270</td>\\n\",\n       \"      <td>0.244236</td>\\n\",\n       \"      <td>0.515325</td>\\n\",\n       \"      <td>0.318873</td>\\n\",\n       \"      <td>0.244162</td>\\n\",\n       \"      <td>0.318842</td>\\n\",\n       \"      <td>1.670136</td>\\n\",\n       \"      <td>0.385090</td>\\n\",\n       \"      <td>0.384673</td>\\n\",\n       \"      <td>0.384830</td>\\n\",\n       \"      <td>0.384673</td>\\n\",\n       \"      <td>0.384673</td>\\n\",\n       \"      <td>2.693301</td>\\n\",\n       \"      <td>0.094638</td>\\n\",\n       \"      <td>0.616027</td>\\n\",\n       \"      <td>0.157275</td>\\n\",\n       \"      <td>0.094638</td>\\n\",\n       \"      <td>0.157275</td>\\n\",\n       \"      <td>0.378246</td>\\n\",\n       \"      <td>0.512857</td>\\n\",\n       \"      <td>0.115057</td>\\n\",\n       \"      <td>0.183909</td>\\n\",\n       \"      <td>0.512857</td>\\n\",\n       \"      <td>0.183909</td>\\n\",\n       \"      <td>1.111272</td>\\n\",\n       \"      <td>0.464250</td>\\n\",\n       \"      <td>0.307533</td>\\n\",\n       \"      <td>0.358015</td>\\n\",\n       \"      <td>0.464250</td>\\n\",\n       \"      <td>0.358015</td>\\n\",\n       \"      <td>1.462150</td>\\n\",\n       \"      <td>0.292525</td>\\n\",\n       \"      <td>0.511019</td>\\n\",\n       \"      <td>0.347445</td>\\n\",\n       \"      <td>0.292430</td>\\n\",\n       \"      <td>0.347365</td>\\n\",\n       \"      <td>1.994897</td>\\n\",\n       \"      <td>0.200924</td>\\n\",\n       \"      <td>0.667825</td>\\n\",\n       \"      <td>0.290033</td>\\n\",\n       \"      <td>0.199490</td>\\n\",\n       \"      <td>0.288484</td>\\n\",\n       \"      <td>1.300145</td>\\n\",\n       \"      <td>0.356239</td>\\n\",\n       \"      <td>0.353672</td>\\n\",\n       \"      <td>0.354612</td>\\n\",\n       \"      <td>0.353672</td>\\n\",\n       \"      <td>0.353672</td>\\n\",\n       \"      <td>2.333467</td>\\n\",\n       \"      <td>0.137905</td>\\n\",\n       \"      <td>0.748755</td>\\n\",\n       \"      <td>0.218289</td>\\n\",\n       \"      <td>0.137905</td>\\n\",\n       \"      <td>0.218289</td>\\n\",\n       \"      <td>0.385801</td>\\n\",\n       \"      <td>0.385801</td>\\n\",\n       \"      <td>0.142417</td>\\n\",\n       \"      <td>0.196249</td>\\n\",\n       \"      <td>0.385801</td>\\n\",\n       \"      <td>0.196249</td>\\n\",\n       \"      <td>1.076350</td>\\n\",\n       \"      <td>0.358808</td>\\n\",\n       \"      <td>0.388114</td>\\n\",\n       \"      <td>0.347357</td>\\n\",\n       \"      <td>0.358783</td>\\n\",\n       \"      <td>0.347339</td>\\n\",\n       \"      <td>0.187622</td>\\n\",\n       \"      <td>0.021335</td>\\n\",\n       \"      <td>0.088541</td>\\n\",\n       \"      <td>0.032273</td>\\n\",\n       \"      <td>0.018762</td>\\n\",\n       \"      <td>0.029624</td>\\n\",\n       \"      <td>0.204483</td>\\n\",\n       \"      <td>0.014904</td>\\n\",\n       \"      <td>0.095338</td>\\n\",\n       \"      <td>0.023383</td>\\n\",\n       \"      <td>0.004090</td>\\n\",\n       \"      <td>0.007742</td>\\n\",\n       \"      <td>0.117927</td>\\n\",\n       \"      <td>0.049158</td>\\n\",\n       \"      <td>0.048945</td>\\n\",\n       \"      <td>0.049018</td>\\n\",\n       \"      <td>0.048945</td>\\n\",\n       \"      <td>0.048945</td>\\n\",\n       \"      <td>0.204533</td>\\n\",\n       \"      <td>0.014865</td>\\n\",\n       \"      <td>0.095363</td>\\n\",\n       \"      <td>0.023313</td>\\n\",\n       \"      <td>0.014865</td>\\n\",\n       \"      <td>0.023313</td>\\n\",\n       \"      <td>1.490819</td>\\n\",\n       \"      <td>0.335842</td>\\n\",\n       \"      <td>0.583485</td>\\n\",\n       \"      <td>0.397538</td>\\n\",\n       \"      <td>0.335715</td>\\n\",\n       \"      <td>0.397437</td>\\n\",\n       \"      <td>2.030870</td>\\n\",\n       \"      <td>0.220567</td>\\n\",\n       \"      <td>0.726239</td>\\n\",\n       \"      <td>0.316919</td>\\n\",\n       \"      <td>0.218980</td>\\n\",\n       \"      <td>0.315230</td>\\n\",\n       \"      <td>1.340872</td>\\n\",\n       \"      <td>0.425713</td>\\n\",\n       \"      <td>0.422704</td>\\n\",\n       \"      <td>0.423801</td>\\n\",\n       \"      <td>0.422704</td>\\n\",\n       \"      <td>0.422704</td>\\n\",\n       \"      <td>2.368389</td>\\n\",\n       \"      <td>0.147637</td>\\n\",\n       \"      <td>0.796131</td>\\n\",\n       \"      <td>0.232905</td>\\n\",\n       \"      <td>0.147637</td>\\n\",\n       \"      <td>0.232905</td>\\n\",\n       \"      <td>0.385801</td>\\n\",\n       \"      <td>0.481961</td>\\n\",\n       \"      <td>0.179697</td>\\n\",\n       \"      <td>0.246680</td>\\n\",\n       \"      <td>0.481961</td>\\n\",\n       \"      <td>0.246680</td>\\n\",\n       \"      <td>1.094962</td>\\n\",\n       \"      <td>0.427058</td>\\n\",\n       \"      <td>0.461467</td>\\n\",\n       \"      <td>0.413012</td>\\n\",\n       \"      <td>0.427024</td>\\n\",\n       \"      <td>0.412988</td>\\n\",\n       \"      <td>0.194476</td>\\n\",\n       \"      <td>0.039036</td>\\n\",\n       \"      <td>0.156194</td>\\n\",\n       \"      <td>0.058334</td>\\n\",\n       \"      <td>0.033959</td>\\n\",\n       \"      <td>0.053286</td>\\n\",\n       \"      <td>0.215340</td>\\n\",\n       \"      <td>0.028018</td>\\n\",\n       \"      <td>0.171130</td>\\n\",\n       \"      <td>0.043375</td>\\n\",\n       \"      <td>0.007535</td>\\n\",\n       \"      <td>0.014238</td>\\n\",\n       \"      <td>0.120478</td>\\n\",\n       \"      <td>0.085838</td>\\n\",\n       \"      <td>0.085289</td>\\n\",\n       \"      <td>0.085468</td>\\n\",\n       \"      <td>0.085289</td>\\n\",\n       \"      <td>0.085289</td>\\n\",\n       \"      <td>0.215440</td>\\n\",\n       \"      <td>0.027951</td>\\n\",\n       \"      <td>0.171300</td>\\n\",\n       \"      <td>0.043255</td>\\n\",\n       \"      <td>0.027951</td>\\n\",\n       \"      <td>0.043255</td>\\n\",\n       \"      <td>0.240050</td>\\n\",\n       \"      <td>0.274052</td>\\n\",\n       \"      <td>0.339334</td>\\n\",\n       \"      <td>0.386011</td>\\n\",\n       \"      <td>0.427112</td>\\n\",\n       \"      <td>0.490760</td>\\n\",\n       \"      <td>0.505148</td>\\n\",\n       \"      <td>0.356837</td>\\n\",\n       \"      <td>0.413211</td>\\n\",\n       \"      <td>0.363329</td>\\n\",\n       \"      <td>0.525624</td>\\n\",\n       \"      <td>0.551931</td>\\n\",\n       \"      <td>0.510731</td>\\n\",\n       \"      <td>0.516926</td>\\n\",\n       \"      <td>0.044350</td>\\n\",\n       \"      <td>0.052918</td>\\n\",\n       \"      <td>0.095065</td>\\n\",\n       \"      <td>0.069945</td>\\n\",\n       \"      <td>0.074918</td>\\n\",\n       \"      <td>0.075180</td>\\n\",\n       \"      <td>0.075361</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>17.917396</td>\\n\",\n       \"      <td>18.781108</td>\\n\",\n       \"      <td>41.134187</td>\\n\",\n       \"      <td>3369.358283</td>\\n\",\n       \"      <td>462.757292</td>\\n\",\n       \"      <td>12155.847101</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16578</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.313654</td>\\n\",\n       \"      <td>0.262776</td>\\n\",\n       \"      <td>0.286682</td>\\n\",\n       \"      <td>0.264573</td>\\n\",\n       \"      <td>0.262731</td>\\n\",\n       \"      <td>0.264567</td>\\n\",\n       \"      <td>1.788112</td>\\n\",\n       \"      <td>0.179097</td>\\n\",\n       \"      <td>0.383883</td>\\n\",\n       \"      <td>0.235680</td>\\n\",\n       \"      <td>0.178811</td>\\n\",\n       \"      <td>0.235584</td>\\n\",\n       \"      <td>1.340822</td>\\n\",\n       \"      <td>0.269624</td>\\n\",\n       \"      <td>0.269237</td>\\n\",\n       \"      <td>0.269332</td>\\n\",\n       \"      <td>0.269237</td>\\n\",\n       \"      <td>0.269237</td>\\n\",\n       \"      <td>2.217892</td>\\n\",\n       \"      <td>0.024639</td>\\n\",\n       \"      <td>0.469246</td>\\n\",\n       \"      <td>0.044533</td>\\n\",\n       \"      <td>0.024639</td>\\n\",\n       \"      <td>0.044533</td>\\n\",\n       \"      <td>0.362736</td>\\n\",\n       \"      <td>0.362736</td>\\n\",\n       \"      <td>0.080287</td>\\n\",\n       \"      <td>0.128584</td>\\n\",\n       \"      <td>0.362736</td>\\n\",\n       \"      <td>0.128584</td>\\n\",\n       \"      <td>0.957672</td>\\n\",\n       \"      <td>0.319224</td>\\n\",\n       \"      <td>0.211885</td>\\n\",\n       \"      <td>0.246390</td>\\n\",\n       \"      <td>0.319224</td>\\n\",\n       \"      <td>0.246390</td>\\n\",\n       \"      <td>1.388653</td>\\n\",\n       \"      <td>0.342618</td>\\n\",\n       \"      <td>0.372511</td>\\n\",\n       \"      <td>0.344255</td>\\n\",\n       \"      <td>0.342571</td>\\n\",\n       \"      <td>0.344248</td>\\n\",\n       \"      <td>1.909091</td>\\n\",\n       \"      <td>0.225763</td>\\n\",\n       \"      <td>0.480236</td>\\n\",\n       \"      <td>0.295967</td>\\n\",\n       \"      <td>0.225446</td>\\n\",\n       \"      <td>0.295857</td>\\n\",\n       \"      <td>1.452744</td>\\n\",\n       \"      <td>0.354115</td>\\n\",\n       \"      <td>0.353686</td>\\n\",\n       \"      <td>0.353792</td>\\n\",\n       \"      <td>0.353686</td>\\n\",\n       \"      <td>0.353686</td>\\n\",\n       \"      <td>2.426277</td>\\n\",\n       \"      <td>0.030995</td>\\n\",\n       \"      <td>0.583606</td>\\n\",\n       \"      <td>0.056045</td>\\n\",\n       \"      <td>0.030995</td>\\n\",\n       \"      <td>0.056045</td>\\n\",\n       \"      <td>0.362736</td>\\n\",\n       \"      <td>0.459859</td>\\n\",\n       \"      <td>0.102519</td>\\n\",\n       \"      <td>0.164015</td>\\n\",\n       \"      <td>0.459859</td>\\n\",\n       \"      <td>0.164015</td>\\n\",\n       \"      <td>0.993646</td>\\n\",\n       \"      <td>0.423850</td>\\n\",\n       \"      <td>0.281183</td>\\n\",\n       \"      <td>0.327099</td>\\n\",\n       \"      <td>0.423850</td>\\n\",\n       \"      <td>0.327099</td>\\n\",\n       \"      <td>1.378696</td>\\n\",\n       \"      <td>0.277279</td>\\n\",\n       \"      <td>0.483234</td>\\n\",\n       \"      <td>0.328757</td>\\n\",\n       \"      <td>0.275739</td>\\n\",\n       \"      <td>0.327707</td>\\n\",\n       \"      <td>1.833041</td>\\n\",\n       \"      <td>0.197746</td>\\n\",\n       \"      <td>0.622387</td>\\n\",\n       \"      <td>0.281363</td>\\n\",\n       \"      <td>0.183304</td>\\n\",\n       \"      <td>0.266962</td>\\n\",\n       \"      <td>1.121729</td>\\n\",\n       \"      <td>0.331753</td>\\n\",\n       \"      <td>0.326610</td>\\n\",\n       \"      <td>0.328123</td>\\n\",\n       \"      <td>0.326610</td>\\n\",\n       \"      <td>0.326610</td>\\n\",\n       \"      <td>1.957122</td>\\n\",\n       \"      <td>0.162770</td>\\n\",\n       \"      <td>0.657909</td>\\n\",\n       \"      <td>0.242381</td>\\n\",\n       \"      <td>0.162770</td>\\n\",\n       \"      <td>0.242381</td>\\n\",\n       \"      <td>0.384500</td>\\n\",\n       \"      <td>0.384500</td>\\n\",\n       \"      <td>0.141222</td>\\n\",\n       \"      <td>0.194288</td>\\n\",\n       \"      <td>0.384500</td>\\n\",\n       \"      <td>0.194288</td>\\n\",\n       \"      <td>1.002752</td>\\n\",\n       \"      <td>0.334551</td>\\n\",\n       \"      <td>0.360275</td>\\n\",\n       \"      <td>0.322882</td>\\n\",\n       \"      <td>0.334251</td>\\n\",\n       \"      <td>0.322759</td>\\n\",\n       \"      <td>0.224796</td>\\n\",\n       \"      <td>0.022580</td>\\n\",\n       \"      <td>0.105580</td>\\n\",\n       \"      <td>0.035521</td>\\n\",\n       \"      <td>0.022480</td>\\n\",\n       \"      <td>0.035448</td>\\n\",\n       \"      <td>0.257718</td>\\n\",\n       \"      <td>0.005826</td>\\n\",\n       \"      <td>0.119692</td>\\n\",\n       \"      <td>0.010779</td>\\n\",\n       \"      <td>0.005154</td>\\n\",\n       \"      <td>0.009756</td>\\n\",\n       \"      <td>0.129734</td>\\n\",\n       \"      <td>0.052779</td>\\n\",\n       \"      <td>0.052732</td>\\n\",\n       \"      <td>0.052742</td>\\n\",\n       \"      <td>0.052732</td>\\n\",\n       \"      <td>0.052732</td>\\n\",\n       \"      <td>0.260770</td>\\n\",\n       \"      <td>0.003569</td>\\n\",\n       \"      <td>0.120982</td>\\n\",\n       \"      <td>0.006601</td>\\n\",\n       \"      <td>0.003569</td>\\n\",\n       \"      <td>0.006601</td>\\n\",\n       \"      <td>1.407765</td>\\n\",\n       \"      <td>0.322742</td>\\n\",\n       \"      <td>0.559043</td>\\n\",\n       \"      <td>0.381391</td>\\n\",\n       \"      <td>0.321015</td>\\n\",\n       \"      <td>0.380226</td>\\n\",\n       \"      <td>1.873017</td>\\n\",\n       \"      <td>0.220835</td>\\n\",\n       \"      <td>0.688005</td>\\n\",\n       \"      <td>0.312598</td>\\n\",\n       \"      <td>0.204876</td>\\n\",\n       \"      <td>0.296839</td>\\n\",\n       \"      <td>1.162606</td>\\n\",\n       \"      <td>0.398135</td>\\n\",\n       \"      <td>0.391800</td>\\n\",\n       \"      <td>0.393668</td>\\n\",\n       \"      <td>0.391800</td>\\n\",\n       \"      <td>0.391800</td>\\n\",\n       \"      <td>1.999550</td>\\n\",\n       \"      <td>0.179964</td>\\n\",\n       \"      <td>0.718848</td>\\n\",\n       \"      <td>0.266461</td>\\n\",\n       \"      <td>0.179964</td>\\n\",\n       \"      <td>0.266461</td>\\n\",\n       \"      <td>0.384500</td>\\n\",\n       \"      <td>0.453119</td>\\n\",\n       \"      <td>0.168407</td>\\n\",\n       \"      <td>0.230874</td>\\n\",\n       \"      <td>0.453119</td>\\n\",\n       \"      <td>0.230874</td>\\n\",\n       \"      <td>1.019212</td>\\n\",\n       \"      <td>0.401367</td>\\n\",\n       \"      <td>0.432446</td>\\n\",\n       \"      <td>0.387295</td>\\n\",\n       \"      <td>0.401041</td>\\n\",\n       \"      <td>0.387163</td>\\n\",\n       \"      <td>0.233552</td>\\n\",\n       \"      <td>0.038844</td>\\n\",\n       \"      <td>0.177636</td>\\n\",\n       \"      <td>0.060777</td>\\n\",\n       \"      <td>0.038678</td>\\n\",\n       \"      <td>0.060661</td>\\n\",\n       \"      <td>0.278731</td>\\n\",\n       \"      <td>0.010898</td>\\n\",\n       \"      <td>0.217564</td>\\n\",\n       \"      <td>0.020127</td>\\n\",\n       \"      <td>0.009639</td>\\n\",\n       \"      <td>0.018205</td>\\n\",\n       \"      <td>0.133237</td>\\n\",\n       \"      <td>0.087343</td>\\n\",\n       \"      <td>0.087270</td>\\n\",\n       \"      <td>0.087285</td>\\n\",\n       \"      <td>0.087270</td>\\n\",\n       \"      <td>0.087270</td>\\n\",\n       \"      <td>0.285536</td>\\n\",\n       \"      <td>0.006771</td>\\n\",\n       \"      <td>0.224105</td>\\n\",\n       \"      <td>0.012518</td>\\n\",\n       \"      <td>0.006771</td>\\n\",\n       \"      <td>0.012518</td>\\n\",\n       \"      <td>0.210552</td>\\n\",\n       \"      <td>0.241665</td>\\n\",\n       \"      <td>0.336226</td>\\n\",\n       \"      <td>0.347355</td>\\n\",\n       \"      <td>0.386985</td>\\n\",\n       \"      <td>0.455121</td>\\n\",\n       \"      <td>0.467533</td>\\n\",\n       \"      <td>0.324335</td>\\n\",\n       \"      <td>0.375235</td>\\n\",\n       \"      <td>0.381029</td>\\n\",\n       \"      <td>0.477542</td>\\n\",\n       \"      <td>0.503000</td>\\n\",\n       \"      <td>0.482593</td>\\n\",\n       \"      <td>0.487446</td>\\n\",\n       \"      <td>0.048363</td>\\n\",\n       \"      <td>0.059002</td>\\n\",\n       \"      <td>0.102016</td>\\n\",\n       \"      <td>0.081471</td>\\n\",\n       \"      <td>0.086854</td>\\n\",\n       \"      <td>0.078595</td>\\n\",\n       \"      <td>0.078842</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>13.125682</td>\\n\",\n       \"      <td>112.295092</td>\\n\",\n       \"      <td>139.650723</td>\\n\",\n       \"      <td>3077.361785</td>\\n\",\n       \"      <td>444.802322</td>\\n\",\n       \"      <td>12007.063942</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15967</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.429529</td>\\n\",\n       \"      <td>0.286003</td>\\n\",\n       \"      <td>0.311196</td>\\n\",\n       \"      <td>0.287474</td>\\n\",\n       \"      <td>0.285906</td>\\n\",\n       \"      <td>0.287460</td>\\n\",\n       \"      <td>1.961175</td>\\n\",\n       \"      <td>0.196429</td>\\n\",\n       \"      <td>0.418276</td>\\n\",\n       \"      <td>0.257706</td>\\n\",\n       \"      <td>0.196117</td>\\n\",\n       \"      <td>0.257551</td>\\n\",\n       \"      <td>1.510932</td>\\n\",\n       \"      <td>0.294485</td>\\n\",\n       \"      <td>0.294039</td>\\n\",\n       \"      <td>0.294181</td>\\n\",\n       \"      <td>0.294039</td>\\n\",\n       \"      <td>0.294039</td>\\n\",\n       \"      <td>2.632161</td>\\n\",\n       \"      <td>0.063620</td>\\n\",\n       \"      <td>0.537857</td>\\n\",\n       \"      <td>0.106636</td>\\n\",\n       \"      <td>0.063620</td>\\n\",\n       \"      <td>0.106636</td>\\n\",\n       \"      <td>0.350278</td>\\n\",\n       \"      <td>0.350278</td>\\n\",\n       \"      <td>0.079140</td>\\n\",\n       \"      <td>0.126368</td>\\n\",\n       \"      <td>0.350278</td>\\n\",\n       \"      <td>0.126368</td>\\n\",\n       \"      <td>1.042027</td>\\n\",\n       \"      <td>0.347392</td>\\n\",\n       \"      <td>0.229921</td>\\n\",\n       \"      <td>0.267745</td>\\n\",\n       \"      <td>0.347342</td>\\n\",\n       \"      <td>0.267740</td>\\n\",\n       \"      <td>1.505579</td>\\n\",\n       \"      <td>0.364431</td>\\n\",\n       \"      <td>0.395706</td>\\n\",\n       \"      <td>0.365901</td>\\n\",\n       \"      <td>0.364331</td>\\n\",\n       \"      <td>0.365885</td>\\n\",\n       \"      <td>2.080953</td>\\n\",\n       \"      <td>0.242088</td>\\n\",\n       \"      <td>0.512342</td>\\n\",\n       \"      <td>0.316659</td>\\n\",\n       \"      <td>0.241741</td>\\n\",\n       \"      <td>0.316478</td>\\n\",\n       \"      <td>1.618402</td>\\n\",\n       \"      <td>0.379016</td>\\n\",\n       \"      <td>0.378492</td>\\n\",\n       \"      <td>0.378662</td>\\n\",\n       \"      <td>0.378492</td>\\n\",\n       \"      <td>0.378492</td>\\n\",\n       \"      <td>2.797618</td>\\n\",\n       \"      <td>0.076091</td>\\n\",\n       \"      <td>0.638330</td>\\n\",\n       \"      <td>0.127377</td>\\n\",\n       \"      <td>0.076091</td>\\n\",\n       \"      <td>0.127377</td>\\n\",\n       \"      <td>0.350328</td>\\n\",\n       \"      <td>0.495927</td>\\n\",\n       \"      <td>0.111814</td>\\n\",\n       \"      <td>0.178619</td>\\n\",\n       \"      <td>0.495927</td>\\n\",\n       \"      <td>0.178619</td>\\n\",\n       \"      <td>1.083554</td>\\n\",\n       \"      <td>0.454629</td>\\n\",\n       \"      <td>0.301385</td>\\n\",\n       \"      <td>0.350767</td>\\n\",\n       \"      <td>0.454578</td>\\n\",\n       \"      <td>0.350762</td>\\n\",\n       \"      <td>1.450993</td>\\n\",\n       \"      <td>0.290427</td>\\n\",\n       \"      <td>0.509034</td>\\n\",\n       \"      <td>0.345189</td>\\n\",\n       \"      <td>0.290199</td>\\n\",\n       \"      <td>0.345058</td>\\n\",\n       \"      <td>1.977485</td>\\n\",\n       \"      <td>0.200706</td>\\n\",\n       \"      <td>0.665832</td>\\n\",\n       \"      <td>0.289818</td>\\n\",\n       \"      <td>0.197749</td>\\n\",\n       \"      <td>0.286801</td>\\n\",\n       \"      <td>1.271476</td>\\n\",\n       \"      <td>0.351660</td>\\n\",\n       \"      <td>0.348474</td>\\n\",\n       \"      <td>0.349565</td>\\n\",\n       \"      <td>0.348474</td>\\n\",\n       \"      <td>0.348474</td>\\n\",\n       \"      <td>2.367789</td>\\n\",\n       \"      <td>0.143706</td>\\n\",\n       \"      <td>0.761764</td>\\n\",\n       \"      <td>0.225728</td>\\n\",\n       \"      <td>0.143706</td>\\n\",\n       \"      <td>0.225728</td>\\n\",\n       \"      <td>0.367789</td>\\n\",\n       \"      <td>0.367789</td>\\n\",\n       \"      <td>0.138106</td>\\n\",\n       \"      <td>0.189219</td>\\n\",\n       \"      <td>0.367789</td>\\n\",\n       \"      <td>0.189219</td>\\n\",\n       \"      <td>1.063641</td>\\n\",\n       \"      <td>0.354614</td>\\n\",\n       \"      <td>0.382798</td>\\n\",\n       \"      <td>0.342756</td>\\n\",\n       \"      <td>0.354547</td>\\n\",\n       \"      <td>0.342734</td>\\n\",\n       \"      <td>0.220543</td>\\n\",\n       \"      <td>0.025570</td>\\n\",\n       \"      <td>0.102751</td>\\n\",\n       \"      <td>0.038090</td>\\n\",\n       \"      <td>0.022054</td>\\n\",\n       \"      <td>0.034733</td>\\n\",\n       \"      <td>0.260469</td>\\n\",\n       \"      <td>0.014850</td>\\n\",\n       \"      <td>0.120002</td>\\n\",\n       \"      <td>0.022799</td>\\n\",\n       \"      <td>0.005209</td>\\n\",\n       \"      <td>0.009857</td>\\n\",\n       \"      <td>0.135238</td>\\n\",\n       \"      <td>0.056107</td>\\n\",\n       \"      <td>0.055619</td>\\n\",\n       \"      <td>0.055755</td>\\n\",\n       \"      <td>0.055619</td>\\n\",\n       \"      <td>0.055619</td>\\n\",\n       \"      <td>0.264372</td>\\n\",\n       \"      <td>0.014109</td>\\n\",\n       \"      <td>0.121745</td>\\n\",\n       \"      <td>0.021443</td>\\n\",\n       \"      <td>0.014109</td>\\n\",\n       \"      <td>0.021443</td>\\n\",\n       \"      <td>1.481563</td>\\n\",\n       \"      <td>0.334278</td>\\n\",\n       \"      <td>0.582072</td>\\n\",\n       \"      <td>0.395985</td>\\n\",\n       \"      <td>0.334036</td>\\n\",\n       \"      <td>0.395846</td>\\n\",\n       \"      <td>2.015410</td>\\n\",\n       \"      <td>0.220895</td>\\n\",\n       \"      <td>0.725578</td>\\n\",\n       \"      <td>0.317476</td>\\n\",\n       \"      <td>0.217755</td>\\n\",\n       \"      <td>0.314288</td>\\n\",\n       \"      <td>1.311302</td>\\n\",\n       \"      <td>0.423023</td>\\n\",\n       \"      <td>0.419363</td>\\n\",\n       \"      <td>0.420615</td>\\n\",\n       \"      <td>0.419363</td>\\n\",\n       \"      <td>0.419363</td>\\n\",\n       \"      <td>2.398809</td>\\n\",\n       \"      <td>0.153516</td>\\n\",\n       \"      <td>0.807983</td>\\n\",\n       \"      <td>0.240370</td>\\n\",\n       \"      <td>0.153516</td>\\n\",\n       \"      <td>0.240370</td>\\n\",\n       \"      <td>0.367789</td>\\n\",\n       \"      <td>0.473724</td>\\n\",\n       \"      <td>0.178876</td>\\n\",\n       \"      <td>0.244486</td>\\n\",\n       \"      <td>0.473724</td>\\n\",\n       \"      <td>0.244486</td>\\n\",\n       \"      <td>1.083104</td>\\n\",\n       \"      <td>0.423744</td>\\n\",\n       \"      <td>0.457273</td>\\n\",\n       \"      <td>0.409398</td>\\n\",\n       \"      <td>0.423673</td>\\n\",\n       \"      <td>0.409375</td>\\n\",\n       \"      <td>0.227698</td>\\n\",\n       \"      <td>0.042646</td>\\n\",\n       \"      <td>0.168400</td>\\n\",\n       \"      <td>0.063083</td>\\n\",\n       \"      <td>0.036670</td>\\n\",\n       \"      <td>0.057532</td>\\n\",\n       \"      <td>0.275279</td>\\n\",\n       \"      <td>0.025281</td>\\n\",\n       \"      <td>0.203515</td>\\n\",\n       \"      <td>0.038604</td>\\n\",\n       \"      <td>0.008984</td>\\n\",\n       \"      <td>0.016976</td>\\n\",\n       \"      <td>0.138490</td>\\n\",\n       \"      <td>0.089132</td>\\n\",\n       \"      <td>0.088179</td>\\n\",\n       \"      <td>0.088451</td>\\n\",\n       \"      <td>0.088179</td>\\n\",\n       \"      <td>0.088179</td>\\n\",\n       \"      <td>0.280783</td>\\n\",\n       \"      <td>0.023962</td>\\n\",\n       \"      <td>0.206848</td>\\n\",\n       \"      <td>0.036195</td>\\n\",\n       \"      <td>0.023962</td>\\n\",\n       \"      <td>0.036195</td>\\n\",\n       \"      <td>0.233643</td>\\n\",\n       \"      <td>0.268815</td>\\n\",\n       \"      <td>0.322354</td>\\n\",\n       \"      <td>0.385799</td>\\n\",\n       \"      <td>0.426983</td>\\n\",\n       \"      <td>0.480651</td>\\n\",\n       \"      <td>0.494769</td>\\n\",\n       \"      <td>0.351799</td>\\n\",\n       \"      <td>0.409281</td>\\n\",\n       \"      <td>0.356126</td>\\n\",\n       \"      <td>0.525392</td>\\n\",\n       \"      <td>0.551388</td>\\n\",\n       \"      <td>0.504166</td>\\n\",\n       \"      <td>0.509991</td>\\n\",\n       \"      <td>0.050511</td>\\n\",\n       \"      <td>0.060760</td>\\n\",\n       \"      <td>0.104933</td>\\n\",\n       \"      <td>0.082760</td>\\n\",\n       \"      <td>0.088492</td>\\n\",\n       \"      <td>0.082380</td>\\n\",\n       \"      <td>0.082630</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>17.719217</td>\\n\",\n       \"      <td>46.197428</td>\\n\",\n       \"      <td>71.841397</td>\\n\",\n       \"      <td>3387.699555</td>\\n\",\n       \"      <td>448.358583</td>\\n\",\n       \"      <td>11918.865463</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42862</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.303347</td>\\n\",\n       \"      <td>0.260779</td>\\n\",\n       \"      <td>0.283327</td>\\n\",\n       \"      <td>0.261662</td>\\n\",\n       \"      <td>0.260669</td>\\n\",\n       \"      <td>0.261643</td>\\n\",\n       \"      <td>1.785060</td>\\n\",\n       \"      <td>0.178921</td>\\n\",\n       \"      <td>0.379804</td>\\n\",\n       \"      <td>0.233942</td>\\n\",\n       \"      <td>0.178506</td>\\n\",\n       \"      <td>0.233808</td>\\n\",\n       \"      <td>1.370441</td>\\n\",\n       \"      <td>0.267862</td>\\n\",\n       \"      <td>0.267337</td>\\n\",\n       \"      <td>0.267483</td>\\n\",\n       \"      <td>0.267337</td>\\n\",\n       \"      <td>0.267337</td>\\n\",\n       \"      <td>2.484265</td>\\n\",\n       \"      <td>0.037427</td>\\n\",\n       \"      <td>0.511971</td>\\n\",\n       \"      <td>0.066408</td>\\n\",\n       \"      <td>0.037427</td>\\n\",\n       \"      <td>0.066408</td>\\n\",\n       \"      <td>0.375094</td>\\n\",\n       \"      <td>0.375094</td>\\n\",\n       \"      <td>0.084232</td>\\n\",\n       \"      <td>0.134586</td>\\n\",\n       \"      <td>0.375094</td>\\n\",\n       \"      <td>0.134586</td>\\n\",\n       \"      <td>0.954570</td>\\n\",\n       \"      <td>0.318207</td>\\n\",\n       \"      <td>0.209969</td>\\n\",\n       \"      <td>0.244605</td>\\n\",\n       \"      <td>0.318190</td>\\n\",\n       \"      <td>0.244605</td>\\n\",\n       \"      <td>1.377996</td>\\n\",\n       \"      <td>0.340371</td>\\n\",\n       \"      <td>0.368996</td>\\n\",\n       \"      <td>0.341223</td>\\n\",\n       \"      <td>0.340253</td>\\n\",\n       \"      <td>0.341200</td>\\n\",\n       \"      <td>1.903787</td>\\n\",\n       \"      <td>0.225083</td>\\n\",\n       \"      <td>0.475471</td>\\n\",\n       \"      <td>0.293720</td>\\n\",\n       \"      <td>0.224633</td>\\n\",\n       \"      <td>0.293563</td>\\n\",\n       \"      <td>1.478211</td>\\n\",\n       \"      <td>0.353620</td>\\n\",\n       \"      <td>0.353050</td>\\n\",\n       \"      <td>0.353210</td>\\n\",\n       \"      <td>0.353050</td>\\n\",\n       \"      <td>0.353050</td>\\n\",\n       \"      <td>2.667984</td>\\n\",\n       \"      <td>0.045540</td>\\n\",\n       \"      <td>0.618540</td>\\n\",\n       \"      <td>0.080798</td>\\n\",\n       \"      <td>0.045540</td>\\n\",\n       \"      <td>0.080798</td>\\n\",\n       \"      <td>0.375094</td>\\n\",\n       \"      <td>0.508277</td>\\n\",\n       \"      <td>0.114603</td>\\n\",\n       \"      <td>0.183006</td>\\n\",\n       \"      <td>0.508277</td>\\n\",\n       \"      <td>0.183006</td>\\n\",\n       \"      <td>0.994847</td>\\n\",\n       \"      <td>0.426557</td>\\n\",\n       \"      <td>0.282085</td>\\n\",\n       \"      <td>0.328493</td>\\n\",\n       \"      <td>0.426536</td>\\n\",\n       \"      <td>0.328491</td>\\n\",\n       \"      <td>1.376545</td>\\n\",\n       \"      <td>0.276107</td>\\n\",\n       \"      <td>0.478362</td>\\n\",\n       \"      <td>0.325994</td>\\n\",\n       \"      <td>0.275309</td>\\n\",\n       \"      <td>0.325460</td>\\n\",\n       \"      <td>1.891529</td>\\n\",\n       \"      <td>0.197645</td>\\n\",\n       \"      <td>0.632839</td>\\n\",\n       \"      <td>0.281687</td>\\n\",\n       \"      <td>0.189153</td>\\n\",\n       \"      <td>0.273165</td>\\n\",\n       \"      <td>1.202281</td>\\n\",\n       \"      <td>0.341331</td>\\n\",\n       \"      <td>0.337171</td>\\n\",\n       \"      <td>0.338489</td>\\n\",\n       \"      <td>0.337171</td>\\n\",\n       \"      <td>0.337171</td>\\n\",\n       \"      <td>2.166658</td>\\n\",\n       \"      <td>0.152173</td>\\n\",\n       \"      <td>0.702815</td>\\n\",\n       \"      <td>0.232659</td>\\n\",\n       \"      <td>0.152173</td>\\n\",\n       \"      <td>0.232659</td>\\n\",\n       \"      <td>0.405564</td>\\n\",\n       \"      <td>0.405564</td>\\n\",\n       \"      <td>0.152763</td>\\n\",\n       \"      <td>0.208574</td>\\n\",\n       \"      <td>0.405564</td>\\n\",\n       \"      <td>0.208574</td>\\n\",\n       \"      <td>1.004653</td>\\n\",\n       \"      <td>0.335018</td>\\n\",\n       \"      <td>0.358740</td>\\n\",\n       \"      <td>0.322126</td>\\n\",\n       \"      <td>0.334884</td>\\n\",\n       \"      <td>0.322067</td>\\n\",\n       \"      <td>0.235203</td>\\n\",\n       \"      <td>0.023658</td>\\n\",\n       \"      <td>0.110199</td>\\n\",\n       \"      <td>0.037242</td>\\n\",\n       \"      <td>0.023520</td>\\n\",\n       \"      <td>0.037111</td>\\n\",\n       \"      <td>0.310702</td>\\n\",\n       \"      <td>0.007843</td>\\n\",\n       \"      <td>0.142896</td>\\n\",\n       \"      <td>0.014382</td>\\n\",\n       \"      <td>0.006214</td>\\n\",\n       \"      <td>0.011758</td>\\n\",\n       \"      <td>0.129784</td>\\n\",\n       \"      <td>0.053094</td>\\n\",\n       \"      <td>0.053087</td>\\n\",\n       \"      <td>0.053090</td>\\n\",\n       \"      <td>0.053087</td>\\n\",\n       \"      <td>0.053087</td>\\n\",\n       \"      <td>0.317606</td>\\n\",\n       \"      <td>0.006406</td>\\n\",\n       \"      <td>0.146117</td>\\n\",\n       \"      <td>0.011757</td>\\n\",\n       \"      <td>0.006406</td>\\n\",\n       \"      <td>0.011757</td>\\n\",\n       \"      <td>1.405564</td>\\n\",\n       \"      <td>0.321071</td>\\n\",\n       \"      <td>0.554268</td>\\n\",\n       \"      <td>0.378381</td>\\n\",\n       \"      <td>0.320181</td>\\n\",\n       \"      <td>0.377781</td>\\n\",\n       \"      <td>1.926802</td>\\n\",\n       \"      <td>0.219077</td>\\n\",\n       \"      <td>0.696329</td>\\n\",\n       \"      <td>0.311185</td>\\n\",\n       \"      <td>0.209781</td>\\n\",\n       \"      <td>0.301918</td>\\n\",\n       \"      <td>1.241507</td>\\n\",\n       \"      <td>0.413138</td>\\n\",\n       \"      <td>0.408212</td>\\n\",\n       \"      <td>0.409770</td>\\n\",\n       \"      <td>0.408212</td>\\n\",\n       \"      <td>0.408212</td>\\n\",\n       \"      <td>2.200580</td>\\n\",\n       \"      <td>0.165297</td>\\n\",\n       \"      <td>0.757194</td>\\n\",\n       \"      <td>0.251727</td>\\n\",\n       \"      <td>0.165297</td>\\n\",\n       \"      <td>0.251727</td>\\n\",\n       \"      <td>0.405564</td>\\n\",\n       \"      <td>0.501872</td>\\n\",\n       \"      <td>0.190966</td>\\n\",\n       \"      <td>0.259990</td>\\n\",\n       \"      <td>0.501872</td>\\n\",\n       \"      <td>0.259990</td>\\n\",\n       \"      <td>1.023916</td>\\n\",\n       \"      <td>0.404469</td>\\n\",\n       \"      <td>0.434125</td>\\n\",\n       \"      <td>0.389362</td>\\n\",\n       \"      <td>0.404322</td>\\n\",\n       \"      <td>0.389296</td>\\n\",\n       \"      <td>0.242157</td>\\n\",\n       \"      <td>0.038943</td>\\n\",\n       \"      <td>0.178276</td>\\n\",\n       \"      <td>0.061015</td>\\n\",\n       \"      <td>0.038699</td>\\n\",\n       \"      <td>0.060779</td>\\n\",\n       \"      <td>0.331215</td>\\n\",\n       \"      <td>0.013407</td>\\n\",\n       \"      <td>0.238148</td>\\n\",\n       \"      <td>0.024508</td>\\n\",\n       \"      <td>0.010572</td>\\n\",\n       \"      <td>0.019966</td>\\n\",\n       \"      <td>0.132586</td>\\n\",\n       \"      <td>0.086054</td>\\n\",\n       \"      <td>0.086034</td>\\n\",\n       \"      <td>0.086040</td>\\n\",\n       \"      <td>0.086034</td>\\n\",\n       \"      <td>0.086034</td>\\n\",\n       \"      <td>0.340922</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.244711</td>\\n\",\n       \"      <td>0.020105</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.020105</td>\\n\",\n       \"      <td>0.218190</td>\\n\",\n       \"      <td>0.248444</td>\\n\",\n       \"      <td>0.318053</td>\\n\",\n       \"      <td>0.354803</td>\\n\",\n       \"      <td>0.403446</td>\\n\",\n       \"      <td>0.458379</td>\\n\",\n       \"      <td>0.470848</td>\\n\",\n       \"      <td>0.343571</td>\\n\",\n       \"      <td>0.392526</td>\\n\",\n       \"      <td>0.371930</td>\\n\",\n       \"      <td>0.494405</td>\\n\",\n       \"      <td>0.526690</td>\\n\",\n       \"      <td>0.505188</td>\\n\",\n       \"      <td>0.510686</td>\\n\",\n       \"      <td>0.050253</td>\\n\",\n       \"      <td>0.061269</td>\\n\",\n       \"      <td>0.102328</td>\\n\",\n       \"      <td>0.087671</td>\\n\",\n       \"      <td>0.094594</td>\\n\",\n       \"      <td>0.078634</td>\\n\",\n       \"      <td>0.078886</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>15.420723</td>\\n\",\n       \"      <td>68.863561</td>\\n\",\n       \"      <td>93.807925</td>\\n\",\n       \"      <td>3371.902487</td>\\n\",\n       \"      <td>467.865613</td>\\n\",\n       \"      <td>12367.344824</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18547</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.385651</td>\\n\",\n       \"      <td>0.277210</td>\\n\",\n       \"      <td>0.302579</td>\\n\",\n       \"      <td>0.279326</td>\\n\",\n       \"      <td>0.277130</td>\\n\",\n       \"      <td>0.279311</td>\\n\",\n       \"      <td>1.886526</td>\\n\",\n       \"      <td>0.188930</td>\\n\",\n       \"      <td>0.404753</td>\\n\",\n       \"      <td>0.248638</td>\\n\",\n       \"      <td>0.188653</td>\\n\",\n       \"      <td>0.248518</td>\\n\",\n       \"      <td>1.432681</td>\\n\",\n       \"      <td>0.283353</td>\\n\",\n       \"      <td>0.282969</td>\\n\",\n       \"      <td>0.283093</td>\\n\",\n       \"      <td>0.282969</td>\\n\",\n       \"      <td>0.282969</td>\\n\",\n       \"      <td>2.481163</td>\\n\",\n       \"      <td>0.060232</td>\\n\",\n       \"      <td>0.517150</td>\\n\",\n       \"      <td>0.103191</td>\\n\",\n       \"      <td>0.060232</td>\\n\",\n       \"      <td>0.103191</td>\\n\",\n       \"      <td>0.313704</td>\\n\",\n       \"      <td>0.313704</td>\\n\",\n       \"      <td>0.069526</td>\\n\",\n       \"      <td>0.111355</td>\\n\",\n       \"      <td>0.313704</td>\\n\",\n       \"      <td>0.111355</td>\\n\",\n       \"      <td>0.996047</td>\\n\",\n       \"      <td>0.332032</td>\\n\",\n       \"      <td>0.220203</td>\\n\",\n       \"      <td>0.256322</td>\\n\",\n       \"      <td>0.332016</td>\\n\",\n       \"      <td>0.256321</td>\\n\",\n       \"      <td>1.459048</td>\\n\",\n       \"      <td>0.354840</td>\\n\",\n       \"      <td>0.385915</td>\\n\",\n       \"      <td>0.356742</td>\\n\",\n       \"      <td>0.354760</td>\\n\",\n       \"      <td>0.356728</td>\\n\",\n       \"      <td>2.003752</td>\\n\",\n       \"      <td>0.234114</td>\\n\",\n       \"      <td>0.497866</td>\\n\",\n       \"      <td>0.306948</td>\\n\",\n       \"      <td>0.233813</td>\\n\",\n       \"      <td>0.306811</td>\\n\",\n       \"      <td>1.537750</td>\\n\",\n       \"      <td>0.366451</td>\\n\",\n       \"      <td>0.366008</td>\\n\",\n       \"      <td>0.366154</td>\\n\",\n       \"      <td>0.366008</td>\\n\",\n       \"      <td>0.366008</td>\\n\",\n       \"      <td>2.638315</td>\\n\",\n       \"      <td>0.072005</td>\\n\",\n       \"      <td>0.613886</td>\\n\",\n       \"      <td>0.123126</td>\\n\",\n       \"      <td>0.072005</td>\\n\",\n       \"      <td>0.123126</td>\\n\",\n       \"      <td>0.313704</td>\\n\",\n       \"      <td>0.423120</td>\\n\",\n       \"      <td>0.093803</td>\\n\",\n       \"      <td>0.150236</td>\\n\",\n       \"      <td>0.423120</td>\\n\",\n       \"      <td>0.150236</td>\\n\",\n       \"      <td>1.035423</td>\\n\",\n       \"      <td>0.437514</td>\\n\",\n       \"      <td>0.290220</td>\\n\",\n       \"      <td>0.337750</td>\\n\",\n       \"      <td>0.437497</td>\\n\",\n       \"      <td>0.337749</td>\\n\",\n       \"      <td>1.424476</td>\\n\",\n       \"      <td>0.285605</td>\\n\",\n       \"      <td>0.500582</td>\\n\",\n       \"      <td>0.339797</td>\\n\",\n       \"      <td>0.284895</td>\\n\",\n       \"      <td>0.339315</td>\\n\",\n       \"      <td>1.924351</td>\\n\",\n       \"      <td>0.200704</td>\\n\",\n       \"      <td>0.651233</td>\\n\",\n       \"      <td>0.288096</td>\\n\",\n       \"      <td>0.192435</td>\\n\",\n       \"      <td>0.279654</td>\\n\",\n       \"      <td>1.184720</td>\\n\",\n       \"      <td>0.333998</td>\\n\",\n       \"      <td>0.329754</td>\\n\",\n       \"      <td>0.331106</td>\\n\",\n       \"      <td>0.329754</td>\\n\",\n       \"      <td>0.329754</td>\\n\",\n       \"      <td>2.173213</td>\\n\",\n       \"      <td>0.155402</td>\\n\",\n       \"      <td>0.716243</td>\\n\",\n       \"      <td>0.237955</td>\\n\",\n       \"      <td>0.155402</td>\\n\",\n       \"      <td>0.237955</td>\\n\",\n       \"      <td>0.334267</td>\\n\",\n       \"      <td>0.334267</td>\\n\",\n       \"      <td>0.123469</td>\\n\",\n       \"      <td>0.170023</td>\\n\",\n       \"      <td>0.334267</td>\\n\",\n       \"      <td>0.170023</td>\\n\",\n       \"      <td>1.024616</td>\\n\",\n       \"      <td>0.341714</td>\\n\",\n       \"      <td>0.369477</td>\\n\",\n       \"      <td>0.330884</td>\\n\",\n       \"      <td>0.341539</td>\\n\",\n       \"      <td>0.330789</td>\\n\",\n       \"      <td>0.255066</td>\\n\",\n       \"      <td>0.026317</td>\\n\",\n       \"      <td>0.119309</td>\\n\",\n       \"      <td>0.041055</td>\\n\",\n       \"      <td>0.025507</td>\\n\",\n       \"      <td>0.040238</td>\\n\",\n       \"      <td>0.305899</td>\\n\",\n       \"      <td>0.012805</td>\\n\",\n       \"      <td>0.141699</td>\\n\",\n       \"      <td>0.021973</td>\\n\",\n       \"      <td>0.006118</td>\\n\",\n       \"      <td>0.011580</td>\\n\",\n       \"      <td>0.143793</td>\\n\",\n       \"      <td>0.059694</td>\\n\",\n       \"      <td>0.059587</td>\\n\",\n       \"      <td>0.059615</td>\\n\",\n       \"      <td>0.059587</td>\\n\",\n       \"      <td>0.059587</td>\\n\",\n       \"      <td>0.307950</td>\\n\",\n       \"      <td>0.012460</td>\\n\",\n       \"      <td>0.142622</td>\\n\",\n       \"      <td>0.021351</td>\\n\",\n       \"      <td>0.012460</td>\\n\",\n       \"      <td>0.021351</td>\\n\",\n       \"      <td>1.453095</td>\\n\",\n       \"      <td>0.328700</td>\\n\",\n       \"      <td>0.572366</td>\\n\",\n       \"      <td>0.389596</td>\\n\",\n       \"      <td>0.327922</td>\\n\",\n       \"      <td>0.389071</td>\\n\",\n       \"      <td>1.957622</td>\\n\",\n       \"      <td>0.220777</td>\\n\",\n       \"      <td>0.709188</td>\\n\",\n       \"      <td>0.315319</td>\\n\",\n       \"      <td>0.211847</td>\\n\",\n       \"      <td>0.306301</td>\\n\",\n       \"      <td>1.222795</td>\\n\",\n       \"      <td>0.399601</td>\\n\",\n       \"      <td>0.394548</td>\\n\",\n       \"      <td>0.396156</td>\\n\",\n       \"      <td>0.394548</td>\\n\",\n       \"      <td>0.394548</td>\\n\",\n       \"      <td>2.201181</td>\\n\",\n       \"      <td>0.167037</td>\\n\",\n       \"      <td>0.762243</td>\\n\",\n       \"      <td>0.254522</td>\\n\",\n       \"      <td>0.167037</td>\\n\",\n       \"      <td>0.254522</td>\\n\",\n       \"      <td>0.334267</td>\\n\",\n       \"      <td>0.411228</td>\\n\",\n       \"      <td>0.152306</td>\\n\",\n       \"      <td>0.209282</td>\\n\",\n       \"      <td>0.411228</td>\\n\",\n       \"      <td>0.209282</td>\\n\",\n       \"      <td>1.042878</td>\\n\",\n       \"      <td>0.408979</td>\\n\",\n       \"      <td>0.441689</td>\\n\",\n       \"      <td>0.395522</td>\\n\",\n       \"      <td>0.408790</td>\\n\",\n       \"      <td>0.395419</td>\\n\",\n       \"      <td>0.263771</td>\\n\",\n       \"      <td>0.042781</td>\\n\",\n       \"      <td>0.190101</td>\\n\",\n       \"      <td>0.066363</td>\\n\",\n       \"      <td>0.041433</td>\\n\",\n       \"      <td>0.065016</td>\\n\",\n       \"      <td>0.323610</td>\\n\",\n       \"      <td>0.021212</td>\\n\",\n       \"      <td>0.229263</td>\\n\",\n       \"      <td>0.036247</td>\\n\",\n       \"      <td>0.010140</td>\\n\",\n       \"      <td>0.019157</td>\\n\",\n       \"      <td>0.147046</td>\\n\",\n       \"      <td>0.095183</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.095051</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.326412</td>\\n\",\n       \"      <td>0.020642</td>\\n\",\n       \"      <td>0.230613</td>\\n\",\n       \"      <td>0.035220</td>\\n\",\n       \"      <td>0.020642</td>\\n\",\n       \"      <td>0.035220</td>\\n\",\n       \"      <td>0.214730</td>\\n\",\n       \"      <td>0.250578</td>\\n\",\n       \"      <td>0.311319</td>\\n\",\n       \"      <td>0.371370</td>\\n\",\n       \"      <td>0.404592</td>\\n\",\n       \"      <td>0.452137</td>\\n\",\n       \"      <td>0.465135</td>\\n\",\n       \"      <td>0.324875</td>\\n\",\n       \"      <td>0.382946</td>\\n\",\n       \"      <td>0.354083</td>\\n\",\n       \"      <td>0.501106</td>\\n\",\n       \"      <td>0.519823</td>\\n\",\n       \"      <td>0.466873</td>\\n\",\n       \"      <td>0.472176</td>\\n\",\n       \"      <td>0.055143</td>\\n\",\n       \"      <td>0.066979</td>\\n\",\n       \"      <td>0.113525</td>\\n\",\n       \"      <td>0.093100</td>\\n\",\n       \"      <td>0.099506</td>\\n\",\n       \"      <td>0.085904</td>\\n\",\n       \"      <td>0.086246</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>15.234402</td>\\n\",\n       \"      <td>35.191575</td>\\n\",\n       \"      <td>56.722620</td>\\n\",\n       \"      <td>3385.852354</td>\\n\",\n       \"      <td>471.806274</td>\\n\",\n       \"      <td>12230.480662</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20753</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.340471</td>\\n\",\n       \"      <td>0.268318</td>\\n\",\n       \"      <td>0.292625</td>\\n\",\n       \"      <td>0.269878</td>\\n\",\n       \"      <td>0.268094</td>\\n\",\n       \"      <td>0.269821</td>\\n\",\n       \"      <td>1.777405</td>\\n\",\n       \"      <td>0.179004</td>\\n\",\n       \"      <td>0.380955</td>\\n\",\n       \"      <td>0.234397</td>\\n\",\n       \"      <td>0.177741</td>\\n\",\n       \"      <td>0.233677</td>\\n\",\n       \"      <td>1.385401</td>\\n\",\n       \"      <td>0.276310</td>\\n\",\n       \"      <td>0.275474</td>\\n\",\n       \"      <td>0.275720</td>\\n\",\n       \"      <td>0.275474</td>\\n\",\n       \"      <td>0.275474</td>\\n\",\n       \"      <td>2.259519</td>\\n\",\n       \"      <td>0.046079</td>\\n\",\n       \"      <td>0.471528</td>\\n\",\n       \"      <td>0.078092</td>\\n\",\n       \"      <td>0.046079</td>\\n\",\n       \"      <td>0.078092</td>\\n\",\n       \"      <td>0.357682</td>\\n\",\n       \"      <td>0.357682</td>\\n\",\n       \"      <td>0.079931</td>\\n\",\n       \"      <td>0.127857</td>\\n\",\n       \"      <td>0.357682</td>\\n\",\n       \"      <td>0.127857</td>\\n\",\n       \"      <td>0.986741</td>\\n\",\n       \"      <td>0.328939</td>\\n\",\n       \"      <td>0.218372</td>\\n\",\n       \"      <td>0.253870</td>\\n\",\n       \"      <td>0.328914</td>\\n\",\n       \"      <td>0.253863</td>\\n\",\n       \"      <td>1.419122</td>\\n\",\n       \"      <td>0.348563</td>\\n\",\n       \"      <td>0.378727</td>\\n\",\n       \"      <td>0.349930</td>\\n\",\n       \"      <td>0.348321</td>\\n\",\n       \"      <td>0.349865</td>\\n\",\n       \"      <td>1.904788</td>\\n\",\n       \"      <td>0.226630</td>\\n\",\n       \"      <td>0.478738</td>\\n\",\n       \"      <td>0.295832</td>\\n\",\n       \"      <td>0.225191</td>\\n\",\n       \"      <td>0.294991</td>\\n\",\n       \"      <td>1.498824</td>\\n\",\n       \"      <td>0.362202</td>\\n\",\n       \"      <td>0.361264</td>\\n\",\n       \"      <td>0.361545</td>\\n\",\n       \"      <td>0.361264</td>\\n\",\n       \"      <td>0.361264</td>\\n\",\n       \"      <td>2.434633</td>\\n\",\n       \"      <td>0.056781</td>\\n\",\n       \"      <td>0.577668</td>\\n\",\n       \"      <td>0.096244</td>\\n\",\n       \"      <td>0.056781</td>\\n\",\n       \"      <td>0.096244</td>\\n\",\n       \"      <td>0.357733</td>\\n\",\n       \"      <td>0.486150</td>\\n\",\n       \"      <td>0.109174</td>\\n\",\n       \"      <td>0.174510</td>\\n\",\n       \"      <td>0.486150</td>\\n\",\n       \"      <td>0.174510</td>\\n\",\n       \"      <td>1.028368</td>\\n\",\n       \"      <td>0.438281</td>\\n\",\n       \"      <td>0.290764</td>\\n\",\n       \"      <td>0.338180</td>\\n\",\n       \"      <td>0.438256</td>\\n\",\n       \"      <td>0.338173</td>\\n\",\n       \"      <td>1.394356</td>\\n\",\n       \"      <td>0.281490</td>\\n\",\n       \"      <td>0.489330</td>\\n\",\n       \"      <td>0.332971</td>\\n\",\n       \"      <td>0.278871</td>\\n\",\n       \"      <td>0.331221</td>\\n\",\n       \"      <td>1.822034</td>\\n\",\n       \"      <td>0.202969</td>\\n\",\n       \"      <td>0.613204</td>\\n\",\n       \"      <td>0.284068</td>\\n\",\n       \"      <td>0.182203</td>\\n\",\n       \"      <td>0.263862</td>\\n\",\n       \"      <td>1.182619</td>\\n\",\n       \"      <td>0.343768</td>\\n\",\n       \"      <td>0.337785</td>\\n\",\n       \"      <td>0.339553</td>\\n\",\n       \"      <td>0.337785</td>\\n\",\n       \"      <td>0.337785</td>\\n\",\n       \"      <td>1.999500</td>\\n\",\n       \"      <td>0.172278</td>\\n\",\n       \"      <td>0.658896</td>\\n\",\n       \"      <td>0.251282</td>\\n\",\n       \"      <td>0.172278</td>\\n\",\n       \"      <td>0.251282</td>\\n\",\n       \"      <td>0.382599</td>\\n\",\n       \"      <td>0.382599</td>\\n\",\n       \"      <td>0.143371</td>\\n\",\n       \"      <td>0.196188</td>\\n\",\n       \"      <td>0.382599</td>\\n\",\n       \"      <td>0.196188</td>\\n\",\n       \"      <td>1.025366</td>\\n\",\n       \"      <td>0.342272</td>\\n\",\n       \"      <td>0.370059</td>\\n\",\n       \"      <td>0.330847</td>\\n\",\n       \"      <td>0.341789</td>\\n\",\n       \"      <td>0.330607</td>\\n\",\n       \"      <td>0.210037</td>\\n\",\n       \"      <td>0.022004</td>\\n\",\n       \"      <td>0.098186</td>\\n\",\n       \"      <td>0.033919</td>\\n\",\n       \"      <td>0.021004</td>\\n\",\n       \"      <td>0.033110</td>\\n\",\n       \"      <td>0.256167</td>\\n\",\n       \"      <td>0.008772</td>\\n\",\n       \"      <td>0.118737</td>\\n\",\n       \"      <td>0.014833</td>\\n\",\n       \"      <td>0.005123</td>\\n\",\n       \"      <td>0.009697</td>\\n\",\n       \"      <td>0.129534</td>\\n\",\n       \"      <td>0.054067</td>\\n\",\n       \"      <td>0.053821</td>\\n\",\n       \"      <td>0.053885</td>\\n\",\n       \"      <td>0.053821</td>\\n\",\n       \"      <td>0.053821</td>\\n\",\n       \"      <td>0.260019</td>\\n\",\n       \"      <td>0.007860</td>\\n\",\n       \"      <td>0.120529</td>\\n\",\n       \"      <td>0.013169</td>\\n\",\n       \"      <td>0.007860</td>\\n\",\n       \"      <td>0.013169</td>\\n\",\n       \"      <td>1.425627</td>\\n\",\n       \"      <td>0.326685</td>\\n\",\n       \"      <td>0.564071</td>\\n\",\n       \"      <td>0.385186</td>\\n\",\n       \"      <td>0.323826</td>\\n\",\n       \"      <td>0.383275</td>\\n\",\n       \"      <td>1.859559</td>\\n\",\n       \"      <td>0.226336</td>\\n\",\n       \"      <td>0.679501</td>\\n\",\n       \"      <td>0.315734</td>\\n\",\n       \"      <td>0.203551</td>\\n\",\n       \"      <td>0.293646</td>\\n\",\n       \"      <td>1.221344</td>\\n\",\n       \"      <td>0.414748</td>\\n\",\n       \"      <td>0.407732</td>\\n\",\n       \"      <td>0.409810</td>\\n\",\n       \"      <td>0.407732</td>\\n\",\n       \"      <td>0.407732</td>\\n\",\n       \"      <td>2.033672</td>\\n\",\n       \"      <td>0.189100</td>\\n\",\n       \"      <td>0.717006</td>\\n\",\n       \"      <td>0.274753</td>\\n\",\n       \"      <td>0.189100</td>\\n\",\n       \"      <td>0.274753</td>\\n\",\n       \"      <td>0.382599</td>\\n\",\n       \"      <td>0.474410</td>\\n\",\n       \"      <td>0.179026</td>\\n\",\n       \"      <td>0.244367</td>\\n\",\n       \"      <td>0.474410</td>\\n\",\n       \"      <td>0.244367</td>\\n\",\n       \"      <td>1.044879</td>\\n\",\n       \"      <td>0.411915</td>\\n\",\n       \"      <td>0.444250</td>\\n\",\n       \"      <td>0.397637</td>\\n\",\n       \"      <td>0.411413</td>\\n\",\n       \"      <td>0.397390</td>\\n\",\n       \"      <td>0.217942</td>\\n\",\n       \"      <td>0.038301</td>\\n\",\n       \"      <td>0.169196</td>\\n\",\n       \"      <td>0.059046</td>\\n\",\n       \"      <td>0.036866</td>\\n\",\n       \"      <td>0.057838</td>\\n\",\n       \"      <td>0.272777</td>\\n\",\n       \"      <td>0.015446</td>\\n\",\n       \"      <td>0.210152</td>\\n\",\n       \"      <td>0.026441</td>\\n\",\n       \"      <td>0.009282</td>\\n\",\n       \"      <td>0.017536</td>\\n\",\n       \"      <td>0.132686</td>\\n\",\n       \"      <td>0.089970</td>\\n\",\n       \"      <td>0.089658</td>\\n\",\n       \"      <td>0.089740</td>\\n\",\n       \"      <td>0.089658</td>\\n\",\n       \"      <td>0.089658</td>\\n\",\n       \"      <td>0.278081</td>\\n\",\n       \"      <td>0.013815</td>\\n\",\n       \"      <td>0.214057</td>\\n\",\n       \"      <td>0.023475</td>\\n\",\n       \"      <td>0.013815</td>\\n\",\n       \"      <td>0.023475</td>\\n\",\n       \"      <td>0.212109</td>\\n\",\n       \"      <td>0.243622</td>\\n\",\n       \"      <td>0.338183</td>\\n\",\n       \"      <td>0.349895</td>\\n\",\n       \"      <td>0.389039</td>\\n\",\n       \"      <td>0.463211</td>\\n\",\n       \"      <td>0.475634</td>\\n\",\n       \"      <td>0.330021</td>\\n\",\n       \"      <td>0.380997</td>\\n\",\n       \"      <td>0.382675</td>\\n\",\n       \"      <td>0.483571</td>\\n\",\n       \"      <td>0.507394</td>\\n\",\n       \"      <td>0.498063</td>\\n\",\n       \"      <td>0.503122</td>\\n\",\n       \"      <td>0.049451</td>\\n\",\n       \"      <td>0.058902</td>\\n\",\n       \"      <td>0.102338</td>\\n\",\n       \"      <td>0.080097</td>\\n\",\n       \"      <td>0.086251</td>\\n\",\n       \"      <td>0.081099</td>\\n\",\n       \"      <td>0.081348</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>12.844749</td>\\n\",\n       \"      <td>57.081453</td>\\n\",\n       \"      <td>77.429179</td>\\n\",\n       \"      <td>3273.308100</td>\\n\",\n       \"      <td>463.474508</td>\\n\",\n       \"      <td>12165.802522</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19595</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.405664</td>\\n\",\n       \"      <td>0.281133</td>\\n\",\n       \"      <td>0.304067</td>\\n\",\n       \"      <td>0.281538</td>\\n\",\n       \"      <td>0.281133</td>\\n\",\n       \"      <td>0.281538</td>\\n\",\n       \"      <td>1.919748</td>\\n\",\n       \"      <td>0.192053</td>\\n\",\n       \"      <td>0.406481</td>\\n\",\n       \"      <td>0.251011</td>\\n\",\n       \"      <td>0.191975</td>\\n\",\n       \"      <td>0.250984</td>\\n\",\n       \"      <td>1.494371</td>\\n\",\n       \"      <td>0.289101</td>\\n\",\n       \"      <td>0.288834</td>\\n\",\n       \"      <td>0.288915</td>\\n\",\n       \"      <td>0.288834</td>\\n\",\n       \"      <td>0.288834</td>\\n\",\n       \"      <td>2.602742</td>\\n\",\n       \"      <td>0.040297</td>\\n\",\n       \"      <td>0.531113</td>\\n\",\n       \"      <td>0.071689</td>\\n\",\n       \"      <td>0.040297</td>\\n\",\n       \"      <td>0.071689</td>\\n\",\n       \"      <td>0.408415</td>\\n\",\n       \"      <td>0.408415</td>\\n\",\n       \"      <td>0.091084</td>\\n\",\n       \"      <td>0.145865</td>\\n\",\n       \"      <td>0.408415</td>\\n\",\n       \"      <td>0.145865</td>\\n\",\n       \"      <td>1.033322</td>\\n\",\n       \"      <td>0.344441</td>\\n\",\n       \"      <td>0.227080</td>\\n\",\n       \"      <td>0.264894</td>\\n\",\n       \"      <td>0.344441</td>\\n\",\n       \"      <td>0.264894</td>\\n\",\n       \"      <td>1.482163</td>\\n\",\n       \"      <td>0.359802</td>\\n\",\n       \"      <td>0.389333</td>\\n\",\n       \"      <td>0.360488</td>\\n\",\n       \"      <td>0.359802</td>\\n\",\n       \"      <td>0.360488</td>\\n\",\n       \"      <td>2.034723</td>\\n\",\n       \"      <td>0.237445</td>\\n\",\n       \"      <td>0.500912</td>\\n\",\n       \"      <td>0.309917</td>\\n\",\n       \"      <td>0.237360</td>\\n\",\n       \"      <td>0.309885</td>\\n\",\n       \"      <td>1.602592</td>\\n\",\n       \"      <td>0.374451</td>\\n\",\n       \"      <td>0.374171</td>\\n\",\n       \"      <td>0.374257</td>\\n\",\n       \"      <td>0.374171</td>\\n\",\n       \"      <td>0.374171</td>\\n\",\n       \"      <td>2.775854</td>\\n\",\n       \"      <td>0.048565</td>\\n\",\n       \"      <td>0.633016</td>\\n\",\n       \"      <td>0.086324</td>\\n\",\n       \"      <td>0.048565</td>\\n\",\n       \"      <td>0.086324</td>\\n\",\n       \"      <td>0.408415</td>\\n\",\n       \"      <td>0.539250</td>\\n\",\n       \"      <td>0.120940</td>\\n\",\n       \"      <td>0.193459</td>\\n\",\n       \"      <td>0.539250</td>\\n\",\n       \"      <td>0.193459</td>\\n\",\n       \"      <td>1.074498</td>\\n\",\n       \"      <td>0.453438</td>\\n\",\n       \"      <td>0.299959</td>\\n\",\n       \"      <td>0.349497</td>\\n\",\n       \"      <td>0.453438</td>\\n\",\n       \"      <td>0.349497</td>\\n\",\n       \"      <td>1.436133</td>\\n\",\n       \"      <td>0.287489</td>\\n\",\n       \"      <td>0.499722</td>\\n\",\n       \"      <td>0.340148</td>\\n\",\n       \"      <td>0.287227</td>\\n\",\n       \"      <td>0.339935</td>\\n\",\n       \"      <td>1.953920</td>\\n\",\n       \"      <td>0.200636</td>\\n\",\n       \"      <td>0.650998</td>\\n\",\n       \"      <td>0.287255</td>\\n\",\n       \"      <td>0.195392</td>\\n\",\n       \"      <td>0.281694</td>\\n\",\n       \"      <td>1.280582</td>\\n\",\n       \"      <td>0.354728</td>\\n\",\n       \"      <td>0.351574</td>\\n\",\n       \"      <td>0.352628</td>\\n\",\n       \"      <td>0.351574</td>\\n\",\n       \"      <td>0.351574</td>\\n\",\n       \"      <td>2.277180</td>\\n\",\n       \"      <td>0.144787</td>\\n\",\n       \"      <td>0.728972</td>\\n\",\n       \"      <td>0.224767</td>\\n\",\n       \"      <td>0.144787</td>\\n\",\n       \"      <td>0.224767</td>\\n\",\n       \"      <td>0.411568</td>\\n\",\n       \"      <td>0.411568</td>\\n\",\n       \"      <td>0.153419</td>\\n\",\n       \"      <td>0.210510</td>\\n\",\n       \"      <td>0.411568</td>\\n\",\n       \"      <td>0.210510</td>\\n\",\n       \"      <td>1.051133</td>\\n\",\n       \"      <td>0.350411</td>\\n\",\n       \"      <td>0.375372</td>\\n\",\n       \"      <td>0.337499</td>\\n\",\n       \"      <td>0.350378</td>\\n\",\n       \"      <td>0.337476</td>\\n\",\n       \"      <td>0.242358</td>\\n\",\n       \"      <td>0.024569</td>\\n\",\n       \"      <td>0.115012</td>\\n\",\n       \"      <td>0.038657</td>\\n\",\n       \"      <td>0.024236</td>\\n\",\n       \"      <td>0.038316</td>\\n\",\n       \"      <td>0.318757</td>\\n\",\n       \"      <td>0.009126</td>\\n\",\n       \"      <td>0.149801</td>\\n\",\n       \"      <td>0.016366</td>\\n\",\n       \"      <td>0.006375</td>\\n\",\n       \"      <td>0.012074</td>\\n\",\n       \"      <td>0.131786</td>\\n\",\n       \"      <td>0.054140</td>\\n\",\n       \"      <td>0.054137</td>\\n\",\n       \"      <td>0.054138</td>\\n\",\n       \"      <td>0.054137</td>\\n\",\n       \"      <td>0.054137</td>\\n\",\n       \"      <td>0.325562</td>\\n\",\n       \"      <td>0.007722</td>\\n\",\n       \"      <td>0.152746</td>\\n\",\n       \"      <td>0.013791</td>\\n\",\n       \"      <td>0.007722</td>\\n\",\n       \"      <td>0.013791</td>\\n\",\n       \"      <td>1.464002</td>\\n\",\n       \"      <td>0.331143</td>\\n\",\n       \"      <td>0.573330</td>\\n\",\n       \"      <td>0.391035</td>\\n\",\n       \"      <td>0.330831</td>\\n\",\n       \"      <td>0.390785</td>\\n\",\n       \"      <td>1.984240</td>\\n\",\n       \"      <td>0.220587</td>\\n\",\n       \"      <td>0.711298</td>\\n\",\n       \"      <td>0.314835</td>\\n\",\n       \"      <td>0.214892</td>\\n\",\n       \"      <td>0.308846</td>\\n\",\n       \"      <td>1.318257</td>\\n\",\n       \"      <td>0.425308</td>\\n\",\n       \"      <td>0.421644</td>\\n\",\n       \"      <td>0.422866</td>\\n\",\n       \"      <td>0.421644</td>\\n\",\n       \"      <td>0.421644</td>\\n\",\n       \"      <td>2.306649</td>\\n\",\n       \"      <td>0.155651</td>\\n\",\n       \"      <td>0.778074</td>\\n\",\n       \"      <td>0.240875</td>\\n\",\n       \"      <td>0.155651</td>\\n\",\n       \"      <td>0.240875</td>\\n\",\n       \"      <td>0.411568</td>\\n\",\n       \"      <td>0.505084</td>\\n\",\n       \"      <td>0.189931</td>\\n\",\n       \"      <td>0.259854</td>\\n\",\n       \"      <td>0.505084</td>\\n\",\n       \"      <td>0.259854</td>\\n\",\n       \"      <td>1.070246</td>\\n\",\n       \"      <td>0.420308</td>\\n\",\n       \"      <td>0.451290</td>\\n\",\n       \"      <td>0.405215</td>\\n\",\n       \"      <td>0.420255</td>\\n\",\n       \"      <td>0.405180</td>\\n\",\n       \"      <td>0.250113</td>\\n\",\n       \"      <td>0.040305</td>\\n\",\n       \"      <td>0.183933</td>\\n\",\n       \"      <td>0.062996</td>\\n\",\n       \"      <td>0.039664</td>\\n\",\n       \"      <td>0.062356</td>\\n\",\n       \"      <td>0.338970</td>\\n\",\n       \"      <td>0.015362</td>\\n\",\n       \"      <td>0.240910</td>\\n\",\n       \"      <td>0.027336</td>\\n\",\n       \"      <td>0.010558</td>\\n\",\n       \"      <td>0.019953</td>\\n\",\n       \"      <td>0.134938</td>\\n\",\n       \"      <td>0.087431</td>\\n\",\n       \"      <td>0.087389</td>\\n\",\n       \"      <td>0.087402</td>\\n\",\n       \"      <td>0.087389</td>\\n\",\n       \"      <td>0.087389</td>\\n\",\n       \"      <td>0.346875</td>\\n\",\n       \"      <td>0.013051</td>\\n\",\n       \"      <td>0.246540</td>\\n\",\n       \"      <td>0.023113</td>\\n\",\n       \"      <td>0.013051</td>\\n\",\n       \"      <td>0.023113</td>\\n\",\n       \"      <td>0.241348</td>\\n\",\n       \"      <td>0.272225</td>\\n\",\n       \"      <td>0.333148</td>\\n\",\n       \"      <td>0.377756</td>\\n\",\n       \"      <td>0.429229</td>\\n\",\n       \"      <td>0.491812</td>\\n\",\n       \"      <td>0.505279</td>\\n\",\n       \"      <td>0.359335</td>\\n\",\n       \"      <td>0.410309</td>\\n\",\n       \"      <td>0.372595</td>\\n\",\n       \"      <td>0.514206</td>\\n\",\n       \"      <td>0.545572</td>\\n\",\n       \"      <td>0.517368</td>\\n\",\n       \"      <td>0.523265</td>\\n\",\n       \"      <td>0.051141</td>\\n\",\n       \"      <td>0.062906</td>\\n\",\n       \"      <td>0.105510</td>\\n\",\n       \"      <td>0.090992</td>\\n\",\n       \"      <td>0.097764</td>\\n\",\n       \"      <td>0.080872</td>\\n\",\n       \"      <td>0.081097</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>17.096213</td>\\n\",\n       \"      <td>66.068945</td>\\n\",\n       \"      <td>93.091810</td>\\n\",\n       \"      <td>3356.086856</td>\\n\",\n       \"      <td>439.706359</td>\\n\",\n       \"      <td>12178.431030</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19151</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.433882</td>\\n\",\n       \"      <td>0.286776</td>\\n\",\n       \"      <td>0.311597</td>\\n\",\n       \"      <td>0.287907</td>\\n\",\n       \"      <td>0.286776</td>\\n\",\n       \"      <td>0.287907</td>\\n\",\n       \"      <td>1.988292</td>\\n\",\n       \"      <td>0.198852</td>\\n\",\n       \"      <td>0.422140</td>\\n\",\n       \"      <td>0.260404</td>\\n\",\n       \"      <td>0.198829</td>\\n\",\n       \"      <td>0.260395</td>\\n\",\n       \"      <td>1.547956</td>\\n\",\n       \"      <td>0.297848</td>\\n\",\n       \"      <td>0.297730</td>\\n\",\n       \"      <td>0.297772</td>\\n\",\n       \"      <td>0.297730</td>\\n\",\n       \"      <td>0.297730</td>\\n\",\n       \"      <td>2.706209</td>\\n\",\n       \"      <td>0.058528</td>\\n\",\n       \"      <td>0.546641</td>\\n\",\n       \"      <td>0.101707</td>\\n\",\n       \"      <td>0.058528</td>\\n\",\n       \"      <td>0.101707</td>\\n\",\n       \"      <td>0.400610</td>\\n\",\n       \"      <td>0.400610</td>\\n\",\n       \"      <td>0.089938</td>\\n\",\n       \"      <td>0.143828</td>\\n\",\n       \"      <td>0.400610</td>\\n\",\n       \"      <td>0.143828</td>\\n\",\n       \"      <td>1.053535</td>\\n\",\n       \"      <td>0.351178</td>\\n\",\n       \"      <td>0.232363</td>\\n\",\n       \"      <td>0.270578</td>\\n\",\n       \"      <td>0.351178</td>\\n\",\n       \"      <td>0.270578</td>\\n\",\n       \"      <td>1.508781</td>\\n\",\n       \"      <td>0.365475</td>\\n\",\n       \"      <td>0.396569</td>\\n\",\n       \"      <td>0.366763</td>\\n\",\n       \"      <td>0.365475</td>\\n\",\n       \"      <td>0.366763</td>\\n\",\n       \"      <td>2.100215</td>\\n\",\n       \"      <td>0.243506</td>\\n\",\n       \"      <td>0.514317</td>\\n\",\n       \"      <td>0.318135</td>\\n\",\n       \"      <td>0.243482</td>\\n\",\n       \"      <td>0.318125</td>\\n\",\n       \"      <td>1.659028</td>\\n\",\n       \"      <td>0.383300</td>\\n\",\n       \"      <td>0.383167</td>\\n\",\n       \"      <td>0.383215</td>\\n\",\n       \"      <td>0.383167</td>\\n\",\n       \"      <td>0.383167</td>\\n\",\n       \"      <td>2.862311</td>\\n\",\n       \"      <td>0.069198</td>\\n\",\n       \"      <td>0.642530</td>\\n\",\n       \"      <td>0.120118</td>\\n\",\n       \"      <td>0.069198</td>\\n\",\n       \"      <td>0.120118</td>\\n\",\n       \"      <td>0.400610</td>\\n\",\n       \"      <td>0.532188</td>\\n\",\n       \"      <td>0.119727</td>\\n\",\n       \"      <td>0.191396</td>\\n\",\n       \"      <td>0.532188</td>\\n\",\n       \"      <td>0.191396</td>\\n\",\n       \"      <td>1.097914</td>\\n\",\n       \"      <td>0.460624</td>\\n\",\n       \"      <td>0.305265</td>\\n\",\n       \"      <td>0.355373</td>\\n\",\n       \"      <td>0.460624</td>\\n\",\n       \"      <td>0.355373</td>\\n\",\n       \"      <td>1.444339</td>\\n\",\n       \"      <td>0.288887</td>\\n\",\n       \"      <td>0.505599</td>\\n\",\n       \"      <td>0.343022</td>\\n\",\n       \"      <td>0.288868</td>\\n\",\n       \"      <td>0.343009</td>\\n\",\n       \"      <td>1.993496</td>\\n\",\n       \"      <td>0.199656</td>\\n\",\n       \"      <td>0.671003</td>\\n\",\n       \"      <td>0.289310</td>\\n\",\n       \"      <td>0.199350</td>\\n\",\n       \"      <td>0.289002</td>\\n\",\n       \"      <td>1.289538</td>\\n\",\n       \"      <td>0.354128</td>\\n\",\n       \"      <td>0.353367</td>\\n\",\n       \"      <td>0.353653</td>\\n\",\n       \"      <td>0.353367</td>\\n\",\n       \"      <td>0.353367</td>\\n\",\n       \"      <td>2.505729</td>\\n\",\n       \"      <td>0.107536</td>\\n\",\n       \"      <td>0.795612</td>\\n\",\n       \"      <td>0.179321</td>\\n\",\n       \"      <td>0.107536</td>\\n\",\n       \"      <td>0.179321</td>\\n\",\n       \"      <td>0.413018</td>\\n\",\n       \"      <td>0.413018</td>\\n\",\n       \"      <td>0.154065</td>\\n\",\n       \"      <td>0.211489</td>\\n\",\n       \"      <td>0.413018</td>\\n\",\n       \"      <td>0.211489</td>\\n\",\n       \"      <td>1.060890</td>\\n\",\n       \"      <td>0.353630</td>\\n\",\n       \"      <td>0.380568</td>\\n\",\n       \"      <td>0.341487</td>\\n\",\n       \"      <td>0.353630</td>\\n\",\n       \"      <td>0.341487</td>\\n\",\n       \"      <td>0.193876</td>\\n\",\n       \"      <td>0.021322</td>\\n\",\n       \"      <td>0.092397</td>\\n\",\n       \"      <td>0.032734</td>\\n\",\n       \"      <td>0.019388</td>\\n\",\n       \"      <td>0.030720</td>\\n\",\n       \"      <td>0.200380</td>\\n\",\n       \"      <td>0.012067</td>\\n\",\n       \"      <td>0.095241</td>\\n\",\n       \"      <td>0.019334</td>\\n\",\n       \"      <td>0.004008</td>\\n\",\n       \"      <td>0.007599</td>\\n\",\n       \"      <td>0.128934</td>\\n\",\n       \"      <td>0.054434</td>\\n\",\n       \"      <td>0.054290</td>\\n\",\n       \"      <td>0.054345</td>\\n\",\n       \"      <td>0.054290</td>\\n\",\n       \"      <td>0.054290</td>\\n\",\n       \"      <td>0.200480</td>\\n\",\n       \"      <td>0.011876</td>\\n\",\n       \"      <td>0.095308</td>\\n\",\n       \"      <td>0.018983</td>\\n\",\n       \"      <td>0.011876</td>\\n\",\n       \"      <td>0.018983</td>\\n\",\n       \"      <td>1.471056</td>\\n\",\n       \"      <td>0.332656</td>\\n\",\n       \"      <td>0.579097</td>\\n\",\n       \"      <td>0.393905</td>\\n\",\n       \"      <td>0.332633</td>\\n\",\n       \"      <td>0.393888</td>\\n\",\n       \"      <td>2.027018</td>\\n\",\n       \"      <td>0.218813</td>\\n\",\n       \"      <td>0.727951</td>\\n\",\n       \"      <td>0.315546</td>\\n\",\n       \"      <td>0.218480</td>\\n\",\n       \"      <td>0.315214</td>\\n\",\n       \"      <td>1.339371</td>\\n\",\n       \"      <td>0.426160</td>\\n\",\n       \"      <td>0.425244</td>\\n\",\n       \"      <td>0.425589</td>\\n\",\n       \"      <td>0.425244</td>\\n\",\n       \"      <td>0.425244</td>\\n\",\n       \"      <td>2.544404</td>\\n\",\n       \"      <td>0.114064</td>\\n\",\n       \"      <td>0.835241</td>\\n\",\n       \"      <td>0.189435</td>\\n\",\n       \"      <td>0.114064</td>\\n\",\n       \"      <td>0.189435</td>\\n\",\n       \"      <td>0.413018</td>\\n\",\n       \"      <td>0.506115</td>\\n\",\n       \"      <td>0.190320</td>\\n\",\n       \"      <td>0.260473</td>\\n\",\n       \"      <td>0.506115</td>\\n\",\n       \"      <td>0.260473</td>\\n\",\n       \"      <td>1.080502</td>\\n\",\n       \"      <td>0.425022</td>\\n\",\n       \"      <td>0.458131</td>\\n\",\n       \"      <td>0.410646</td>\\n\",\n       \"      <td>0.425022</td>\\n\",\n       \"      <td>0.410646</td>\\n\",\n       \"      <td>0.200130</td>\\n\",\n       \"      <td>0.037703</td>\\n\",\n       \"      <td>0.157909</td>\\n\",\n       \"      <td>0.057229</td>\\n\",\n       \"      <td>0.034044</td>\\n\",\n       \"      <td>0.053561</td>\\n\",\n       \"      <td>0.209436</td>\\n\",\n       \"      <td>0.022128</td>\\n\",\n       \"      <td>0.168432</td>\\n\",\n       \"      <td>0.035057</td>\\n\",\n       \"      <td>0.007320</td>\\n\",\n       \"      <td>0.013845</td>\\n\",\n       \"      <td>0.131736</td>\\n\",\n       \"      <td>0.089512</td>\\n\",\n       \"      <td>0.089132</td>\\n\",\n       \"      <td>0.089267</td>\\n\",\n       \"      <td>0.089132</td>\\n\",\n       \"      <td>0.089132</td>\\n\",\n       \"      <td>0.209636</td>\\n\",\n       \"      <td>0.021770</td>\\n\",\n       \"      <td>0.168683</td>\\n\",\n       \"      <td>0.034404</td>\\n\",\n       \"      <td>0.021770</td>\\n\",\n       \"      <td>0.034404</td>\\n\",\n       \"      <td>0.248030</td>\\n\",\n       \"      <td>0.280909</td>\\n\",\n       \"      <td>0.332315</td>\\n\",\n       \"      <td>0.391699</td>\\n\",\n       \"      <td>0.441045</td>\\n\",\n       \"      <td>0.495364</td>\\n\",\n       \"      <td>0.510198</td>\\n\",\n       \"      <td>0.371130</td>\\n\",\n       \"      <td>0.425017</td>\\n\",\n       \"      <td>0.356658</td>\\n\",\n       \"      <td>0.536002</td>\\n\",\n       \"      <td>0.572149</td>\\n\",\n       \"      <td>0.518501</td>\\n\",\n       \"      <td>0.524330</td>\\n\",\n       \"      <td>0.048364</td>\\n\",\n       \"      <td>0.057215</td>\\n\",\n       \"      <td>0.102916</td>\\n\",\n       \"      <td>0.074007</td>\\n\",\n       \"      <td>0.078787</td>\\n\",\n       \"      <td>0.080706</td>\\n\",\n       \"      <td>0.080839</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>24.584480</td>\\n\",\n       \"      <td>30.873218</td>\\n\",\n       \"      <td>59.228198</td>\\n\",\n       \"      <td>3301.570421</td>\\n\",\n       \"      <td>478.233802</td>\\n\",\n       \"      <td>12010.344874</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15882</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.428500</td>\\n\",\n       \"      <td>0.285700</td>\\n\",\n       \"      <td>0.308249</td>\\n\",\n       \"      <td>0.285874</td>\\n\",\n       \"      <td>0.285700</td>\\n\",\n       \"      <td>0.285874</td>\\n\",\n       \"      <td>1.997500</td>\\n\",\n       \"      <td>0.199849</td>\\n\",\n       \"      <td>0.420406</td>\\n\",\n       \"      <td>0.261114</td>\\n\",\n       \"      <td>0.199750</td>\\n\",\n       \"      <td>0.261026</td>\\n\",\n       \"      <td>1.538500</td>\\n\",\n       \"      <td>0.294509</td>\\n\",\n       \"      <td>0.294346</td>\\n\",\n       \"      <td>0.294412</td>\\n\",\n       \"      <td>0.294346</td>\\n\",\n       \"      <td>0.294346</td>\\n\",\n       \"      <td>2.565000</td>\\n\",\n       \"      <td>0.069475</td>\\n\",\n       \"      <td>0.521574</td>\\n\",\n       \"      <td>0.117794</td>\\n\",\n       \"      <td>0.069475</td>\\n\",\n       \"      <td>0.117794</td>\\n\",\n       \"      <td>0.379000</td>\\n\",\n       \"      <td>0.379000</td>\\n\",\n       \"      <td>0.084054</td>\\n\",\n       \"      <td>0.134520</td>\\n\",\n       \"      <td>0.379000</td>\\n\",\n       \"      <td>0.134520</td>\\n\",\n       \"      <td>1.045500</td>\\n\",\n       \"      <td>0.348500</td>\\n\",\n       \"      <td>0.229419</td>\\n\",\n       \"      <td>0.267296</td>\\n\",\n       \"      <td>0.348500</td>\\n\",\n       \"      <td>0.267296</td>\\n\",\n       \"      <td>1.507500</td>\\n\",\n       \"      <td>0.365402</td>\\n\",\n       \"      <td>0.395395</td>\\n\",\n       \"      <td>0.366257</td>\\n\",\n       \"      <td>0.365402</td>\\n\",\n       \"      <td>0.366257</td>\\n\",\n       \"      <td>2.107500</td>\\n\",\n       \"      <td>0.245119</td>\\n\",\n       \"      <td>0.514465</td>\\n\",\n       \"      <td>0.319729</td>\\n\",\n       \"      <td>0.245007</td>\\n\",\n       \"      <td>0.319627</td>\\n\",\n       \"      <td>1.652500</td>\\n\",\n       \"      <td>0.379619</td>\\n\",\n       \"      <td>0.379396</td>\\n\",\n       \"      <td>0.379486</td>\\n\",\n       \"      <td>0.379396</td>\\n\",\n       \"      <td>0.379396</td>\\n\",\n       \"      <td>2.729000</td>\\n\",\n       \"      <td>0.083324</td>\\n\",\n       \"      <td>0.624020</td>\\n\",\n       \"      <td>0.141068</td>\\n\",\n       \"      <td>0.083324</td>\\n\",\n       \"      <td>0.141068</td>\\n\",\n       \"      <td>0.379000</td>\\n\",\n       \"      <td>0.497795</td>\\n\",\n       \"      <td>0.111160</td>\\n\",\n       \"      <td>0.177622</td>\\n\",\n       \"      <td>0.497795</td>\\n\",\n       \"      <td>0.177622</td>\\n\",\n       \"      <td>1.086500</td>\\n\",\n       \"      <td>0.454361</td>\\n\",\n       \"      <td>0.300387</td>\\n\",\n       \"      <td>0.349681</td>\\n\",\n       \"      <td>0.454361</td>\\n\",\n       \"      <td>0.349681</td>\\n\",\n       \"      <td>1.444500</td>\\n\",\n       \"      <td>0.288950</td>\\n\",\n       \"      <td>0.505452</td>\\n\",\n       \"      <td>0.342523</td>\\n\",\n       \"      <td>0.288900</td>\\n\",\n       \"      <td>0.342476</td>\\n\",\n       \"      <td>1.999000</td>\\n\",\n       \"      <td>0.201939</td>\\n\",\n       \"      <td>0.669581</td>\\n\",\n       \"      <td>0.290916</td>\\n\",\n       \"      <td>0.199900</td>\\n\",\n       \"      <td>0.288741</td>\\n\",\n       \"      <td>1.280000</td>\\n\",\n       \"      <td>0.352489</td>\\n\",\n       \"      <td>0.349323</td>\\n\",\n       \"      <td>0.350447</td>\\n\",\n       \"      <td>0.349323</td>\\n\",\n       \"      <td>0.349323</td>\\n\",\n       \"      <td>2.312500</td>\\n\",\n       \"      <td>0.143491</td>\\n\",\n       \"      <td>0.745841</td>\\n\",\n       \"      <td>0.225489</td>\\n\",\n       \"      <td>0.143491</td>\\n\",\n       \"      <td>0.225489</td>\\n\",\n       \"      <td>0.391000</td>\\n\",\n       \"      <td>0.391000</td>\\n\",\n       \"      <td>0.145587</td>\\n\",\n       \"      <td>0.199184</td>\\n\",\n       \"      <td>0.391000</td>\\n\",\n       \"      <td>0.199184</td>\\n\",\n       \"      <td>1.061000</td>\\n\",\n       \"      <td>0.353667</td>\\n\",\n       \"      <td>0.380926</td>\\n\",\n       \"      <td>0.340925</td>\\n\",\n       \"      <td>0.353667</td>\\n\",\n       \"      <td>0.340925</td>\\n\",\n       \"      <td>0.232000</td>\\n\",\n       \"      <td>0.024618</td>\\n\",\n       \"      <td>0.103581</td>\\n\",\n       \"      <td>0.038007</td>\\n\",\n       \"      <td>0.023200</td>\\n\",\n       \"      <td>0.036428</td>\\n\",\n       \"      <td>0.251500</td>\\n\",\n       \"      <td>0.013130</td>\\n\",\n       \"      <td>0.111977</td>\\n\",\n       \"      <td>0.021774</td>\\n\",\n       \"      <td>0.005030</td>\\n\",\n       \"      <td>0.009519</td>\\n\",\n       \"      <td>0.137500</td>\\n\",\n       \"      <td>0.051156</td>\\n\",\n       \"      <td>0.051156</td>\\n\",\n       \"      <td>0.051156</td>\\n\",\n       \"      <td>0.051156</td>\\n\",\n       \"      <td>0.051156</td>\\n\",\n       \"      <td>0.252500</td>\\n\",\n       \"      <td>0.013025</td>\\n\",\n       \"      <td>0.112394</td>\\n\",\n       \"      <td>0.021583</td>\\n\",\n       \"      <td>0.013025</td>\\n\",\n       \"      <td>0.021583</td>\\n\",\n       \"      <td>1.474000</td>\\n\",\n       \"      <td>0.332548</td>\\n\",\n       \"      <td>0.578348</td>\\n\",\n       \"      <td>0.393197</td>\\n\",\n       \"      <td>0.332498</td>\\n\",\n       \"      <td>0.393149</td>\\n\",\n       \"      <td>2.035000</td>\\n\",\n       \"      <td>0.221237</td>\\n\",\n       \"      <td>0.725614</td>\\n\",\n       \"      <td>0.317310</td>\\n\",\n       \"      <td>0.219062</td>\\n\",\n       \"      <td>0.315002</td>\\n\",\n       \"      <td>1.321500</td>\\n\",\n       \"      <td>0.419532</td>\\n\",\n       \"      <td>0.415809</td>\\n\",\n       \"      <td>0.417121</td>\\n\",\n       \"      <td>0.415809</td>\\n\",\n       \"      <td>0.415809</td>\\n\",\n       \"      <td>2.348500</td>\\n\",\n       \"      <td>0.153634</td>\\n\",\n       \"      <td>0.791604</td>\\n\",\n       \"      <td>0.240556</td>\\n\",\n       \"      <td>0.153634</td>\\n\",\n       \"      <td>0.240556</td>\\n\",\n       \"      <td>0.391000</td>\\n\",\n       \"      <td>0.471054</td>\\n\",\n       \"      <td>0.177259</td>\\n\",\n       \"      <td>0.241651</td>\\n\",\n       \"      <td>0.471054</td>\\n\",\n       \"      <td>0.241651</td>\\n\",\n       \"      <td>1.080500</td>\\n\",\n       \"      <td>0.420061</td>\\n\",\n       \"      <td>0.452184</td>\\n\",\n       \"      <td>0.404939</td>\\n\",\n       \"      <td>0.420061</td>\\n\",\n       \"      <td>0.404939</td>\\n\",\n       \"      <td>0.239000</td>\\n\",\n       \"      <td>0.041881</td>\\n\",\n       \"      <td>0.175844</td>\\n\",\n       \"      <td>0.064143</td>\\n\",\n       \"      <td>0.039126</td>\\n\",\n       \"      <td>0.061353</td>\\n\",\n       \"      <td>0.268500</td>\\n\",\n       \"      <td>0.023954</td>\\n\",\n       \"      <td>0.199347</td>\\n\",\n       \"      <td>0.039318</td>\\n\",\n       \"      <td>0.008975</td>\\n\",\n       \"      <td>0.016965</td>\\n\",\n       \"      <td>0.139500</td>\\n\",\n       \"      <td>0.088344</td>\\n\",\n       \"      <td>0.088122</td>\\n\",\n       \"      <td>0.088184</td>\\n\",\n       \"      <td>0.088122</td>\\n\",\n       \"      <td>0.088122</td>\\n\",\n       \"      <td>0.269500</td>\\n\",\n       \"      <td>0.023755</td>\\n\",\n       \"      <td>0.199868</td>\\n\",\n       \"      <td>0.038959</td>\\n\",\n       \"      <td>0.023755</td>\\n\",\n       \"      <td>0.038959</td>\\n\",\n       \"      <td>0.236409</td>\\n\",\n       \"      <td>0.270086</td>\\n\",\n       \"      <td>0.333435</td>\\n\",\n       \"      <td>0.383152</td>\\n\",\n       \"      <td>0.425400</td>\\n\",\n       \"      <td>0.477452</td>\\n\",\n       \"      <td>0.492088</td>\\n\",\n       \"      <td>0.354705</td>\\n\",\n       \"      <td>0.410280</td>\\n\",\n       \"      <td>0.365572</td>\\n\",\n       \"      <td>0.521598</td>\\n\",\n       \"      <td>0.549621</td>\\n\",\n       \"      <td>0.495484</td>\\n\",\n       \"      <td>0.501693</td>\\n\",\n       \"      <td>0.045570</td>\\n\",\n       \"      <td>0.056570</td>\\n\",\n       \"      <td>0.101817</td>\\n\",\n       \"      <td>0.078562</td>\\n\",\n       \"      <td>0.083960</td>\\n\",\n       \"      <td>0.076981</td>\\n\",\n       \"      <td>0.077185</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>16.971000</td>\\n\",\n       \"      <td>25.935000</td>\\n\",\n       \"      <td>48.269500</td>\\n\",\n       \"      <td>3423.342000</td>\\n\",\n       \"      <td>457.334000</td>\\n\",\n       \"      <td>12147.611500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20242</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.449500</td>\\n\",\n       \"      <td>0.289900</td>\\n\",\n       \"      <td>0.313741</td>\\n\",\n       \"      <td>0.290713</td>\\n\",\n       \"      <td>0.289900</td>\\n\",\n       \"      <td>0.290713</td>\\n\",\n       \"      <td>1.983500</td>\\n\",\n       \"      <td>0.198377</td>\\n\",\n       \"      <td>0.420245</td>\\n\",\n       \"      <td>0.260055</td>\\n\",\n       \"      <td>0.198350</td>\\n\",\n       \"      <td>0.260040</td>\\n\",\n       \"      <td>1.532000</td>\\n\",\n       \"      <td>0.295983</td>\\n\",\n       \"      <td>0.295658</td>\\n\",\n       \"      <td>0.295780</td>\\n\",\n       \"      <td>0.295658</td>\\n\",\n       \"      <td>0.295658</td>\\n\",\n       \"      <td>2.512000</td>\\n\",\n       \"      <td>0.078463</td>\\n\",\n       \"      <td>0.514364</td>\\n\",\n       \"      <td>0.130757</td>\\n\",\n       \"      <td>0.078463</td>\\n\",\n       \"      <td>0.130757</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.086215</td>\\n\",\n       \"      <td>0.138160</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.138160</td>\\n\",\n       \"      <td>1.060500</td>\\n\",\n       \"      <td>0.353500</td>\\n\",\n       \"      <td>0.231001</td>\\n\",\n       \"      <td>0.270150</td>\\n\",\n       \"      <td>0.353500</td>\\n\",\n       \"      <td>0.270150</td>\\n\",\n       \"      <td>1.524500</td>\\n\",\n       \"      <td>0.368045</td>\\n\",\n       \"      <td>0.398531</td>\\n\",\n       \"      <td>0.369025</td>\\n\",\n       \"      <td>0.368045</td>\\n\",\n       \"      <td>0.369025</td>\\n\",\n       \"      <td>2.099000</td>\\n\",\n       \"      <td>0.244117</td>\\n\",\n       \"      <td>0.515366</td>\\n\",\n       \"      <td>0.319219</td>\\n\",\n       \"      <td>0.244070</td>\\n\",\n       \"      <td>0.319190</td>\\n\",\n       \"      <td>1.636500</td>\\n\",\n       \"      <td>0.379042</td>\\n\",\n       \"      <td>0.378558</td>\\n\",\n       \"      <td>0.378737</td>\\n\",\n       \"      <td>0.378558</td>\\n\",\n       \"      <td>0.378558</td>\\n\",\n       \"      <td>2.675500</td>\\n\",\n       \"      <td>0.094397</td>\\n\",\n       \"      <td>0.615673</td>\\n\",\n       \"      <td>0.156890</td>\\n\",\n       \"      <td>0.094397</td>\\n\",\n       \"      <td>0.156890</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.522311</td>\\n\",\n       \"      <td>0.116464</td>\\n\",\n       \"      <td>0.186300</td>\\n\",\n       \"      <td>0.522311</td>\\n\",\n       \"      <td>0.186300</td>\\n\",\n       \"      <td>1.106000</td>\\n\",\n       \"      <td>0.460945</td>\\n\",\n       \"      <td>0.303055</td>\\n\",\n       \"      <td>0.353703</td>\\n\",\n       \"      <td>0.460945</td>\\n\",\n       \"      <td>0.353703</td>\\n\",\n       \"      <td>1.454000</td>\\n\",\n       \"      <td>0.290917</td>\\n\",\n       \"      <td>0.513133</td>\\n\",\n       \"      <td>0.346251</td>\\n\",\n       \"      <td>0.290800</td>\\n\",\n       \"      <td>0.346165</td>\\n\",\n       \"      <td>1.988500</td>\\n\",\n       \"      <td>0.200724</td>\\n\",\n       \"      <td>0.670995</td>\\n\",\n       \"      <td>0.290072</td>\\n\",\n       \"      <td>0.198850</td>\\n\",\n       \"      <td>0.288198</td>\\n\",\n       \"      <td>1.268500</td>\\n\",\n       \"      <td>0.352923</td>\\n\",\n       \"      <td>0.350067</td>\\n\",\n       \"      <td>0.351043</td>\\n\",\n       \"      <td>0.350067</td>\\n\",\n       \"      <td>0.350067</td>\\n\",\n       \"      <td>2.319000</td>\\n\",\n       \"      <td>0.137452</td>\\n\",\n       \"      <td>0.748998</td>\\n\",\n       \"      <td>0.217533</td>\\n\",\n       \"      <td>0.137452</td>\\n\",\n       \"      <td>0.217533</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.147817</td>\\n\",\n       \"      <td>0.202631</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.202631</td>\\n\",\n       \"      <td>1.065500</td>\\n\",\n       \"      <td>0.355167</td>\\n\",\n       \"      <td>0.383861</td>\\n\",\n       \"      <td>0.342837</td>\\n\",\n       \"      <td>0.355167</td>\\n\",\n       \"      <td>0.342837</td>\\n\",\n       \"      <td>0.183000</td>\\n\",\n       \"      <td>0.020530</td>\\n\",\n       \"      <td>0.081844</td>\\n\",\n       \"      <td>0.030775</td>\\n\",\n       \"      <td>0.018300</td>\\n\",\n       \"      <td>0.028771</td>\\n\",\n       \"      <td>0.193000</td>\\n\",\n       \"      <td>0.013597</td>\\n\",\n       \"      <td>0.086611</td>\\n\",\n       \"      <td>0.021225</td>\\n\",\n       \"      <td>0.003860</td>\\n\",\n       \"      <td>0.007309</td>\\n\",\n       \"      <td>0.115500</td>\\n\",\n       \"      <td>0.046586</td>\\n\",\n       \"      <td>0.046211</td>\\n\",\n       \"      <td>0.046286</td>\\n\",\n       \"      <td>0.046211</td>\\n\",\n       \"      <td>0.046211</td>\\n\",\n       \"      <td>0.193000</td>\\n\",\n       \"      <td>0.013563</td>\\n\",\n       \"      <td>0.086611</td>\\n\",\n       \"      <td>0.021165</td>\\n\",\n       \"      <td>0.013563</td>\\n\",\n       \"      <td>0.021165</td>\\n\",\n       \"      <td>1.481500</td>\\n\",\n       \"      <td>0.333272</td>\\n\",\n       \"      <td>0.581800</td>\\n\",\n       \"      <td>0.394779</td>\\n\",\n       \"      <td>0.333119</td>\\n\",\n       \"      <td>0.394668</td>\\n\",\n       \"      <td>2.020000</td>\\n\",\n       \"      <td>0.220049</td>\\n\",\n       \"      <td>0.726723</td>\\n\",\n       \"      <td>0.316383</td>\\n\",\n       \"      <td>0.218001</td>\\n\",\n       \"      <td>0.314359</td>\\n\",\n       \"      <td>1.309500</td>\\n\",\n       \"      <td>0.417908</td>\\n\",\n       \"      <td>0.414510</td>\\n\",\n       \"      <td>0.415668</td>\\n\",\n       \"      <td>0.414510</td>\\n\",\n       \"      <td>0.414510</td>\\n\",\n       \"      <td>2.350000</td>\\n\",\n       \"      <td>0.146960</td>\\n\",\n       \"      <td>0.793580</td>\\n\",\n       \"      <td>0.231652</td>\\n\",\n       \"      <td>0.146960</td>\\n\",\n       \"      <td>0.231652</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.490693</td>\\n\",\n       \"      <td>0.182521</td>\\n\",\n       \"      <td>0.250516</td>\\n\",\n       \"      <td>0.490693</td>\\n\",\n       \"      <td>0.250516</td>\\n\",\n       \"      <td>1.091500</td>\\n\",\n       \"      <td>0.422306</td>\\n\",\n       \"      <td>0.453442</td>\\n\",\n       \"      <td>0.406579</td>\\n\",\n       \"      <td>0.422306</td>\\n\",\n       \"      <td>0.406579</td>\\n\",\n       \"      <td>0.189500</td>\\n\",\n       \"      <td>0.038673</td>\\n\",\n       \"      <td>0.150208</td>\\n\",\n       \"      <td>0.057394</td>\\n\",\n       \"      <td>0.033652</td>\\n\",\n       \"      <td>0.052758</td>\\n\",\n       \"      <td>0.203500</td>\\n\",\n       \"      <td>0.027435</td>\\n\",\n       \"      <td>0.165104</td>\\n\",\n       \"      <td>0.042198</td>\\n\",\n       \"      <td>0.007405</td>\\n\",\n       \"      <td>0.014005</td>\\n\",\n       \"      <td>0.117500</td>\\n\",\n       \"      <td>0.084764</td>\\n\",\n       \"      <td>0.084039</td>\\n\",\n       \"      <td>0.084246</td>\\n\",\n       \"      <td>0.084039</td>\\n\",\n       \"      <td>0.084039</td>\\n\",\n       \"      <td>0.203500</td>\\n\",\n       \"      <td>0.027367</td>\\n\",\n       \"      <td>0.165162</td>\\n\",\n       \"      <td>0.042074</td>\\n\",\n       \"      <td>0.027367</td>\\n\",\n       \"      <td>0.042074</td>\\n\",\n       \"      <td>0.238691</td>\\n\",\n       \"      <td>0.271949</td>\\n\",\n       \"      <td>0.346519</td>\\n\",\n       \"      <td>0.382285</td>\\n\",\n       \"      <td>0.426072</td>\\n\",\n       \"      <td>0.487097</td>\\n\",\n       \"      <td>0.500793</td>\\n\",\n       \"      <td>0.358184</td>\\n\",\n       \"      <td>0.413828</td>\\n\",\n       \"      <td>0.369259</td>\\n\",\n       \"      <td>0.523908</td>\\n\",\n       \"      <td>0.553735</td>\\n\",\n       \"      <td>0.500780</td>\\n\",\n       \"      <td>0.506654</td>\\n\",\n       \"      <td>0.040418</td>\\n\",\n       \"      <td>0.048045</td>\\n\",\n       \"      <td>0.091654</td>\\n\",\n       \"      <td>0.064169</td>\\n\",\n       \"      <td>0.069207</td>\\n\",\n       \"      <td>0.069688</td>\\n\",\n       \"      <td>0.069742</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>17.901000</td>\\n\",\n       \"      <td>18.703500</td>\\n\",\n       \"      <td>41.178000</td>\\n\",\n       \"      <td>3364.475500</td>\\n\",\n       \"      <td>461.883500</td>\\n\",\n       \"      <td>12158.770000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16576</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.303500</td>\\n\",\n       \"      <td>0.261000</td>\\n\",\n       \"      <td>0.283812</td>\\n\",\n       \"      <td>0.261920</td>\\n\",\n       \"      <td>0.260700</td>\\n\",\n       \"      <td>0.261887</td>\\n\",\n       \"      <td>1.796000</td>\\n\",\n       \"      <td>0.180210</td>\\n\",\n       \"      <td>0.383017</td>\\n\",\n       \"      <td>0.236240</td>\\n\",\n       \"      <td>0.179600</td>\\n\",\n       \"      <td>0.236084</td>\\n\",\n       \"      <td>1.349000</td>\\n\",\n       \"      <td>0.270489</td>\\n\",\n       \"      <td>0.269872</td>\\n\",\n       \"      <td>0.270018</td>\\n\",\n       \"      <td>0.269872</td>\\n\",\n       \"      <td>0.269872</td>\\n\",\n       \"      <td>2.217000</td>\\n\",\n       \"      <td>0.025331</td>\\n\",\n       \"      <td>0.468041</td>\\n\",\n       \"      <td>0.045207</td>\\n\",\n       \"      <td>0.025331</td>\\n\",\n       \"      <td>0.045207</td>\\n\",\n       \"      <td>0.367000</td>\\n\",\n       \"      <td>0.367000</td>\\n\",\n       \"      <td>0.081866</td>\\n\",\n       \"      <td>0.130565</td>\\n\",\n       \"      <td>0.367000</td>\\n\",\n       \"      <td>0.130565</td>\\n\",\n       \"      <td>0.952000</td>\\n\",\n       \"      <td>0.317500</td>\\n\",\n       \"      <td>0.209556</td>\\n\",\n       \"      <td>0.243586</td>\\n\",\n       \"      <td>0.317333</td>\\n\",\n       \"      <td>0.243573</td>\\n\",\n       \"      <td>1.379500</td>\\n\",\n       \"      <td>0.341335</td>\\n\",\n       \"      <td>0.370548</td>\\n\",\n       \"      <td>0.342407</td>\\n\",\n       \"      <td>0.341013</td>\\n\",\n       \"      <td>0.342365</td>\\n\",\n       \"      <td>1.924500</td>\\n\",\n       \"      <td>0.227653</td>\\n\",\n       \"      <td>0.481680</td>\\n\",\n       \"      <td>0.297725</td>\\n\",\n       \"      <td>0.226985</td>\\n\",\n       \"      <td>0.297534</td>\\n\",\n       \"      <td>1.461500</td>\\n\",\n       \"      <td>0.355681</td>\\n\",\n       \"      <td>0.355039</td>\\n\",\n       \"      <td>0.355195</td>\\n\",\n       \"      <td>0.355039</td>\\n\",\n       \"      <td>0.355039</td>\\n\",\n       \"      <td>2.431500</td>\\n\",\n       \"      <td>0.031675</td>\\n\",\n       \"      <td>0.584043</td>\\n\",\n       \"      <td>0.056728</td>\\n\",\n       \"      <td>0.031675</td>\\n\",\n       \"      <td>0.056728</td>\\n\",\n       \"      <td>0.367000</td>\\n\",\n       \"      <td>0.464387</td>\\n\",\n       \"      <td>0.103987</td>\\n\",\n       \"      <td>0.165923</td>\\n\",\n       \"      <td>0.464387</td>\\n\",\n       \"      <td>0.165923</td>\\n\",\n       \"      <td>0.990500</td>\\n\",\n       \"      <td>0.422923</td>\\n\",\n       \"      <td>0.280602</td>\\n\",\n       \"      <td>0.325895</td>\\n\",\n       \"      <td>0.422757</td>\\n\",\n       \"      <td>0.325883</td>\\n\",\n       \"      <td>1.355000</td>\\n\",\n       \"      <td>0.272933</td>\\n\",\n       \"      <td>0.477681</td>\\n\",\n       \"      <td>0.323437</td>\\n\",\n       \"      <td>0.271000</td>\\n\",\n       \"      <td>0.322149</td>\\n\",\n       \"      <td>1.851500</td>\\n\",\n       \"      <td>0.199566</td>\\n\",\n       \"      <td>0.626785</td>\\n\",\n       \"      <td>0.283697</td>\\n\",\n       \"      <td>0.185150</td>\\n\",\n       \"      <td>0.269204</td>\\n\",\n       \"      <td>1.120500</td>\\n\",\n       \"      <td>0.331888</td>\\n\",\n       \"      <td>0.326896</td>\\n\",\n       \"      <td>0.328325</td>\\n\",\n       \"      <td>0.326896</td>\\n\",\n       \"      <td>0.326896</td>\\n\",\n       \"      <td>1.969500</td>\\n\",\n       \"      <td>0.162999</td>\\n\",\n       \"      <td>0.661149</td>\\n\",\n       \"      <td>0.243103</td>\\n\",\n       \"      <td>0.162999</td>\\n\",\n       \"      <td>0.243103</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.145633</td>\\n\",\n       \"      <td>0.198087</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.198087</td>\\n\",\n       \"      <td>0.993500</td>\\n\",\n       \"      <td>0.331750</td>\\n\",\n       \"      <td>0.358071</td>\\n\",\n       \"      <td>0.319447</td>\\n\",\n       \"      <td>0.331167</td>\\n\",\n       \"      <td>0.319151</td>\\n\",\n       \"      <td>0.213500</td>\\n\",\n       \"      <td>0.021500</td>\\n\",\n       \"      <td>0.095614</td>\\n\",\n       \"      <td>0.033694</td>\\n\",\n       \"      <td>0.021350</td>\\n\",\n       \"      <td>0.033563</td>\\n\",\n       \"      <td>0.244500</td>\\n\",\n       \"      <td>0.005552</td>\\n\",\n       \"      <td>0.109881</td>\\n\",\n       \"      <td>0.010214</td>\\n\",\n       \"      <td>0.004890</td>\\n\",\n       \"      <td>0.009258</td>\\n\",\n       \"      <td>0.128500</td>\\n\",\n       \"      <td>0.051848</td>\\n\",\n       \"      <td>0.051848</td>\\n\",\n       \"      <td>0.051848</td>\\n\",\n       \"      <td>0.051848</td>\\n\",\n       \"      <td>0.051848</td>\\n\",\n       \"      <td>0.247500</td>\\n\",\n       \"      <td>0.003339</td>\\n\",\n       \"      <td>0.111298</td>\\n\",\n       \"      <td>0.006125</td>\\n\",\n       \"      <td>0.003339</td>\\n\",\n       \"      <td>0.006125</td>\\n\",\n       \"      <td>1.386000</td>\\n\",\n       \"      <td>0.318144</td>\\n\",\n       \"      <td>0.551778</td>\\n\",\n       \"      <td>0.375654</td>\\n\",\n       \"      <td>0.316161</td>\\n\",\n       \"      <td>0.374339</td>\\n\",\n       \"      <td>1.895500</td>\\n\",\n       \"      <td>0.222069</td>\\n\",\n       \"      <td>0.690533</td>\\n\",\n       \"      <td>0.314191</td>\\n\",\n       \"      <td>0.206417</td>\\n\",\n       \"      <td>0.298504</td>\\n\",\n       \"      <td>1.168000</td>\\n\",\n       \"      <td>0.397177</td>\\n\",\n       \"      <td>0.391036</td>\\n\",\n       \"      <td>0.392818</td>\\n\",\n       \"      <td>0.391036</td>\\n\",\n       \"      <td>0.391036</td>\\n\",\n       \"      <td>2.015500</td>\\n\",\n       \"      <td>0.180044</td>\\n\",\n       \"      <td>0.720174</td>\\n\",\n       \"      <td>0.266961</td>\\n\",\n       \"      <td>0.180044</td>\\n\",\n       \"      <td>0.266961</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.446925</td>\\n\",\n       \"      <td>0.169090</td>\\n\",\n       \"      <td>0.230426</td>\\n\",\n       \"      <td>0.446925</td>\\n\",\n       \"      <td>0.230426</td>\\n\",\n       \"      <td>1.011000</td>\\n\",\n       \"      <td>0.398922</td>\\n\",\n       \"      <td>0.430777</td>\\n\",\n       \"      <td>0.384569</td>\\n\",\n       \"      <td>0.398338</td>\\n\",\n       \"      <td>0.384273</td>\\n\",\n       \"      <td>0.219500</td>\\n\",\n       \"      <td>0.038362</td>\\n\",\n       \"      <td>0.171237</td>\\n\",\n       \"      <td>0.060044</td>\\n\",\n       \"      <td>0.038200</td>\\n\",\n       \"      <td>0.059894</td>\\n\",\n       \"      <td>0.268500</td>\\n\",\n       \"      <td>0.010608</td>\\n\",\n       \"      <td>0.211057</td>\\n\",\n       \"      <td>0.019660</td>\\n\",\n       \"      <td>0.009492</td>\\n\",\n       \"      <td>0.017937</td>\\n\",\n       \"      <td>0.130500</td>\\n\",\n       \"      <td>0.086743</td>\\n\",\n       \"      <td>0.086743</td>\\n\",\n       \"      <td>0.086743</td>\\n\",\n       \"      <td>0.086743</td>\\n\",\n       \"      <td>0.086743</td>\\n\",\n       \"      <td>0.274500</td>\\n\",\n       \"      <td>0.006448</td>\\n\",\n       \"      <td>0.217850</td>\\n\",\n       \"      <td>0.011999</td>\\n\",\n       \"      <td>0.006448</td>\\n\",\n       \"      <td>0.011999</td>\\n\",\n       \"      <td>0.210007</td>\\n\",\n       \"      <td>0.241103</td>\\n\",\n       \"      <td>0.343520</td>\\n\",\n       \"      <td>0.347239</td>\\n\",\n       \"      <td>0.387166</td>\\n\",\n       \"      <td>0.453054</td>\\n\",\n       \"      <td>0.466446</td>\\n\",\n       \"      <td>0.325944</td>\\n\",\n       \"      <td>0.376409</td>\\n\",\n       \"      <td>0.382225</td>\\n\",\n       \"      <td>0.478340</td>\\n\",\n       \"      <td>0.504796</td>\\n\",\n       \"      <td>0.472436</td>\\n\",\n       \"      <td>0.477689</td>\\n\",\n       \"      <td>0.044986</td>\\n\",\n       \"      <td>0.054317</td>\\n\",\n       \"      <td>0.093101</td>\\n\",\n       \"      <td>0.075665</td>\\n\",\n       \"      <td>0.080104</td>\\n\",\n       \"      <td>0.076289</td>\\n\",\n       \"      <td>0.076406</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>13.148500</td>\\n\",\n       \"      <td>113.461500</td>\\n\",\n       \"      <td>140.564000</td>\\n\",\n       \"      <td>3060.686000</td>\\n\",\n       \"      <td>444.335500</td>\\n\",\n       \"      <td>11976.134500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15969</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.423500</td>\\n\",\n       \"      <td>0.284700</td>\\n\",\n       \"      <td>0.308897</td>\\n\",\n       \"      <td>0.285624</td>\\n\",\n       \"      <td>0.284700</td>\\n\",\n       \"      <td>0.285624</td>\\n\",\n       \"      <td>1.970500</td>\\n\",\n       \"      <td>0.197114</td>\\n\",\n       \"      <td>0.419283</td>\\n\",\n       \"      <td>0.258689</td>\\n\",\n       \"      <td>0.197050</td>\\n\",\n       \"      <td>0.258631</td>\\n\",\n       \"      <td>1.490500</td>\\n\",\n       \"      <td>0.287936</td>\\n\",\n       \"      <td>0.287687</td>\\n\",\n       \"      <td>0.287782</td>\\n\",\n       \"      <td>0.287687</td>\\n\",\n       \"      <td>0.287687</td>\\n\",\n       \"      <td>2.637500</td>\\n\",\n       \"      <td>0.063566</td>\\n\",\n       \"      <td>0.538349</td>\\n\",\n       \"      <td>0.107298</td>\\n\",\n       \"      <td>0.063566</td>\\n\",\n       \"      <td>0.107298</td>\\n\",\n       \"      <td>0.347000</td>\\n\",\n       \"      <td>0.347000</td>\\n\",\n       \"      <td>0.076234</td>\\n\",\n       \"      <td>0.122490</td>\\n\",\n       \"      <td>0.347000</td>\\n\",\n       \"      <td>0.122490</td>\\n\",\n       \"      <td>1.030500</td>\\n\",\n       \"      <td>0.343500</td>\\n\",\n       \"      <td>0.224856</td>\\n\",\n       \"      <td>0.262601</td>\\n\",\n       \"      <td>0.343500</td>\\n\",\n       \"      <td>0.262601</td>\\n\",\n       \"      <td>1.502000</td>\\n\",\n       \"      <td>0.366029</td>\\n\",\n       \"      <td>0.396721</td>\\n\",\n       \"      <td>0.367248</td>\\n\",\n       \"      <td>0.366029</td>\\n\",\n       \"      <td>0.367248</td>\\n\",\n       \"      <td>2.087000</td>\\n\",\n       \"      <td>0.243814</td>\\n\",\n       \"      <td>0.516283</td>\\n\",\n       \"      <td>0.319238</td>\\n\",\n       \"      <td>0.243739</td>\\n\",\n       \"      <td>0.319171</td>\\n\",\n       \"      <td>1.604500</td>\\n\",\n       \"      <td>0.376504</td>\\n\",\n       \"      <td>0.376167</td>\\n\",\n       \"      <td>0.376293</td>\\n\",\n       \"      <td>0.376167</td>\\n\",\n       \"      <td>0.376167</td>\\n\",\n       \"      <td>2.800500</td>\\n\",\n       \"      <td>0.076071</td>\\n\",\n       \"      <td>0.640154</td>\\n\",\n       \"      <td>0.128196</td>\\n\",\n       \"      <td>0.076071</td>\\n\",\n       \"      <td>0.128196</td>\\n\",\n       \"      <td>0.347000</td>\\n\",\n       \"      <td>0.493927</td>\\n\",\n       \"      <td>0.108998</td>\\n\",\n       \"      <td>0.175179</td>\\n\",\n       \"      <td>0.493927</td>\\n\",\n       \"      <td>0.175179</td>\\n\",\n       \"      <td>1.073500</td>\\n\",\n       \"      <td>0.455828</td>\\n\",\n       \"      <td>0.299882</td>\\n\",\n       \"      <td>0.349964</td>\\n\",\n       \"      <td>0.455828</td>\\n\",\n       \"      <td>0.349964</td>\\n\",\n       \"      <td>1.426000</td>\\n\",\n       \"      <td>0.285375</td>\\n\",\n       \"      <td>0.501923</td>\\n\",\n       \"      <td>0.338949</td>\\n\",\n       \"      <td>0.285200</td>\\n\",\n       \"      <td>0.338800</td>\\n\",\n       \"      <td>1.973000</td>\\n\",\n       \"      <td>0.199872</td>\\n\",\n       \"      <td>0.663963</td>\\n\",\n       \"      <td>0.288606</td>\\n\",\n       \"      <td>0.197300</td>\\n\",\n       \"      <td>0.285854</td>\\n\",\n       \"      <td>1.238500</td>\\n\",\n       \"      <td>0.345704</td>\\n\",\n       \"      <td>0.342384</td>\\n\",\n       \"      <td>0.343553</td>\\n\",\n       \"      <td>0.342384</td>\\n\",\n       \"      <td>0.342384</td>\\n\",\n       \"      <td>2.361500</td>\\n\",\n       \"      <td>0.142535</td>\\n\",\n       \"      <td>0.760569</td>\\n\",\n       \"      <td>0.224557</td>\\n\",\n       \"      <td>0.142535</td>\\n\",\n       \"      <td>0.224557</td>\\n\",\n       \"      <td>0.363000</td>\\n\",\n       \"      <td>0.363000</td>\\n\",\n       \"      <td>0.139657</td>\\n\",\n       \"      <td>0.188782</td>\\n\",\n       \"      <td>0.363000</td>\\n\",\n       \"      <td>0.188782</td>\\n\",\n       \"      <td>1.028000</td>\\n\",\n       \"      <td>0.342667</td>\\n\",\n       \"      <td>0.370913</td>\\n\",\n       \"      <td>0.330757</td>\\n\",\n       \"      <td>0.342667</td>\\n\",\n       \"      <td>0.330757</td>\\n\",\n       \"      <td>0.237500</td>\\n\",\n       \"      <td>0.027197</td>\\n\",\n       \"      <td>0.107503</td>\\n\",\n       \"      <td>0.040498</td>\\n\",\n       \"      <td>0.023750</td>\\n\",\n       \"      <td>0.037440</td>\\n\",\n       \"      <td>0.272000</td>\\n\",\n       \"      <td>0.015096</td>\\n\",\n       \"      <td>0.122324</td>\\n\",\n       \"      <td>0.023169</td>\\n\",\n       \"      <td>0.005440</td>\\n\",\n       \"      <td>0.010300</td>\\n\",\n       \"      <td>0.141000</td>\\n\",\n       \"      <td>0.057893</td>\\n\",\n       \"      <td>0.057177</td>\\n\",\n       \"      <td>0.057393</td>\\n\",\n       \"      <td>0.057177</td>\\n\",\n       \"      <td>0.057177</td>\\n\",\n       \"      <td>0.276000</td>\\n\",\n       \"      <td>0.014331</td>\\n\",\n       \"      <td>0.124033</td>\\n\",\n       \"      <td>0.021765</td>\\n\",\n       \"      <td>0.014331</td>\\n\",\n       \"      <td>0.021765</td>\\n\",\n       \"      <td>1.456500</td>\\n\",\n       \"      <td>0.331720</td>\\n\",\n       \"      <td>0.578304</td>\\n\",\n       \"      <td>0.392666</td>\\n\",\n       \"      <td>0.331545</td>\\n\",\n       \"      <td>0.392517</td>\\n\",\n       \"      <td>2.005500</td>\\n\",\n       \"      <td>0.220289</td>\\n\",\n       \"      <td>0.724342</td>\\n\",\n       \"      <td>0.316622</td>\\n\",\n       \"      <td>0.217555</td>\\n\",\n       \"      <td>0.313717</td>\\n\",\n       \"      <td>1.285000</td>\\n\",\n       \"      <td>0.418904</td>\\n\",\n       \"      <td>0.415040</td>\\n\",\n       \"      <td>0.416397</td>\\n\",\n       \"      <td>0.415040</td>\\n\",\n       \"      <td>0.415040</td>\\n\",\n       \"      <td>2.391500</td>\\n\",\n       \"      <td>0.152434</td>\\n\",\n       \"      <td>0.805587</td>\\n\",\n       \"      <td>0.239272</td>\\n\",\n       \"      <td>0.152434</td>\\n\",\n       \"      <td>0.239272</td>\\n\",\n       \"      <td>0.363000</td>\\n\",\n       \"      <td>0.469235</td>\\n\",\n       \"      <td>0.179269</td>\\n\",\n       \"      <td>0.243164</td>\\n\",\n       \"      <td>0.469235</td>\\n\",\n       \"      <td>0.243164</td>\\n\",\n       \"      <td>1.047500</td>\\n\",\n       \"      <td>0.414880</td>\\n\",\n       \"      <td>0.448060</td>\\n\",\n       \"      <td>0.400350</td>\\n\",\n       \"      <td>0.414880</td>\\n\",\n       \"      <td>0.400350</td>\\n\",\n       \"      <td>0.242500</td>\\n\",\n       \"      <td>0.045242</td>\\n\",\n       \"      <td>0.175633</td>\\n\",\n       \"      <td>0.066768</td>\\n\",\n       \"      <td>0.038972</td>\\n\",\n       \"      <td>0.061197</td>\\n\",\n       \"      <td>0.286500</td>\\n\",\n       \"      <td>0.026080</td>\\n\",\n       \"      <td>0.207930</td>\\n\",\n       \"      <td>0.039664</td>\\n\",\n       \"      <td>0.009333</td>\\n\",\n       \"      <td>0.017643</td>\\n\",\n       \"      <td>0.143000</td>\\n\",\n       \"      <td>0.092008</td>\\n\",\n       \"      <td>0.090856</td>\\n\",\n       \"      <td>0.091212</td>\\n\",\n       \"      <td>0.090856</td>\\n\",\n       \"      <td>0.090856</td>\\n\",\n       \"      <td>0.290000</td>\\n\",\n       \"      <td>0.024702</td>\\n\",\n       \"      <td>0.210671</td>\\n\",\n       \"      <td>0.037144</td>\\n\",\n       \"      <td>0.024702</td>\\n\",\n       \"      <td>0.037144</td>\\n\",\n       \"      <td>0.228797</td>\\n\",\n       \"      <td>0.264650</td>\\n\",\n       \"      <td>0.320567</td>\\n\",\n       \"      <td>0.382886</td>\\n\",\n       \"      <td>0.424568</td>\\n\",\n       \"      <td>0.478464</td>\\n\",\n       \"      <td>0.492242</td>\\n\",\n       \"      <td>0.347467</td>\\n\",\n       \"      <td>0.404380</td>\\n\",\n       \"      <td>0.353796</td>\\n\",\n       \"      <td>0.521092</td>\\n\",\n       \"      <td>0.547379</td>\\n\",\n       \"      <td>0.497241</td>\\n\",\n       \"      <td>0.502927</td>\\n\",\n       \"      <td>0.052157</td>\\n\",\n       \"      <td>0.062350</td>\\n\",\n       \"      <td>0.109813</td>\\n\",\n       \"      <td>0.084289</td>\\n\",\n       \"      <td>0.091460</td>\\n\",\n       \"      <td>0.085036</td>\\n\",\n       \"      <td>0.085103</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>17.758000</td>\\n\",\n       \"      <td>45.973000</td>\\n\",\n       \"      <td>71.672000</td>\\n\",\n       \"      <td>3408.626000</td>\\n\",\n       \"      <td>450.868500</td>\\n\",\n       \"      <td>11994.931500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20416</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.264500</td>\\n\",\n       \"      <td>0.252900</td>\\n\",\n       \"      <td>0.271925</td>\\n\",\n       \"      <td>0.252945</td>\\n\",\n       \"      <td>0.252900</td>\\n\",\n       \"      <td>0.252945</td>\\n\",\n       \"      <td>1.762000</td>\\n\",\n       \"      <td>0.176330</td>\\n\",\n       \"      <td>0.374511</td>\\n\",\n       \"      <td>0.231559</td>\\n\",\n       \"      <td>0.176200</td>\\n\",\n       \"      <td>0.231467</td>\\n\",\n       \"      <td>1.323000</td>\\n\",\n       \"      <td>0.258367</td>\\n\",\n       \"      <td>0.258215</td>\\n\",\n       \"      <td>0.258280</td>\\n\",\n       \"      <td>0.258215</td>\\n\",\n       \"      <td>0.258215</td>\\n\",\n       \"      <td>2.456000</td>\\n\",\n       \"      <td>0.037178</td>\\n\",\n       \"      <td>0.508569</td>\\n\",\n       \"      <td>0.066468</td>\\n\",\n       \"      <td>0.037178</td>\\n\",\n       \"      <td>0.066468</td>\\n\",\n       \"      <td>0.359000</td>\\n\",\n       \"      <td>0.359000</td>\\n\",\n       \"      <td>0.080044</td>\\n\",\n       \"      <td>0.128266</td>\\n\",\n       \"      <td>0.359000</td>\\n\",\n       \"      <td>0.128266</td>\\n\",\n       \"      <td>0.920000</td>\\n\",\n       \"      <td>0.306667</td>\\n\",\n       \"      <td>0.199958</td>\\n\",\n       \"      <td>0.234278</td>\\n\",\n       \"      <td>0.306667</td>\\n\",\n       \"      <td>0.234278</td>\\n\",\n       \"      <td>1.347500</td>\\n\",\n       \"      <td>0.335858</td>\\n\",\n       \"      <td>0.363081</td>\\n\",\n       \"      <td>0.336529</td>\\n\",\n       \"      <td>0.335858</td>\\n\",\n       \"      <td>0.336529</td>\\n\",\n       \"      <td>1.887000</td>\\n\",\n       \"      <td>0.223930</td>\\n\",\n       \"      <td>0.474309</td>\\n\",\n       \"      <td>0.293326</td>\\n\",\n       \"      <td>0.223791</td>\\n\",\n       \"      <td>0.293226</td>\\n\",\n       \"      <td>1.440500</td>\\n\",\n       \"      <td>0.347041</td>\\n\",\n       \"      <td>0.346855</td>\\n\",\n       \"      <td>0.346934</td>\\n\",\n       \"      <td>0.346855</td>\\n\",\n       \"      <td>0.346855</td>\\n\",\n       \"      <td>2.653000</td>\\n\",\n       \"      <td>0.045635</td>\\n\",\n       \"      <td>0.619302</td>\\n\",\n       \"      <td>0.081427</td>\\n\",\n       \"      <td>0.045635</td>\\n\",\n       \"      <td>0.081427</td>\\n\",\n       \"      <td>0.359000</td>\\n\",\n       \"      <td>0.499238</td>\\n\",\n       \"      <td>0.112406</td>\\n\",\n       \"      <td>0.179750</td>\\n\",\n       \"      <td>0.499238</td>\\n\",\n       \"      <td>0.179750</td>\\n\",\n       \"      <td>0.969500</td>\\n\",\n       \"      <td>0.421039</td>\\n\",\n       \"      <td>0.277662</td>\\n\",\n       \"      <td>0.323693</td>\\n\",\n       \"      <td>0.421039</td>\\n\",\n       \"      <td>0.323693</td>\\n\",\n       \"      <td>1.337000</td>\\n\",\n       \"      <td>0.268017</td>\\n\",\n       \"      <td>0.468546</td>\\n\",\n       \"      <td>0.317832</td>\\n\",\n       \"      <td>0.267400</td>\\n\",\n       \"      <td>0.317241</td>\\n\",\n       \"      <td>1.887000</td>\\n\",\n       \"      <td>0.197044</td>\\n\",\n       \"      <td>0.638073</td>\\n\",\n       \"      <td>0.282817</td>\\n\",\n       \"      <td>0.188700</td>\\n\",\n       \"      <td>0.273867</td>\\n\",\n       \"      <td>1.144500</td>\\n\",\n       \"      <td>0.329595</td>\\n\",\n       \"      <td>0.325697</td>\\n\",\n       \"      <td>0.326933</td>\\n\",\n       \"      <td>0.325697</td>\\n\",\n       \"      <td>0.325697</td>\\n\",\n       \"      <td>2.142500</td>\\n\",\n       \"      <td>0.152217</td>\\n\",\n       \"      <td>0.701727</td>\\n\",\n       \"      <td>0.233717</td>\\n\",\n       \"      <td>0.152217</td>\\n\",\n       \"      <td>0.233717</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.148333</td>\\n\",\n       \"      <td>0.202078</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.202078</td>\\n\",\n       \"      <td>0.964500</td>\\n\",\n       \"      <td>0.321500</td>\\n\",\n       \"      <td>0.347308</td>\\n\",\n       \"      <td>0.310340</td>\\n\",\n       \"      <td>0.321500</td>\\n\",\n       \"      <td>0.310340</td>\\n\",\n       \"      <td>0.238500</td>\\n\",\n       \"      <td>0.023973</td>\\n\",\n       \"      <td>0.110015</td>\\n\",\n       \"      <td>0.037741</td>\\n\",\n       \"      <td>0.023850</td>\\n\",\n       \"      <td>0.037588</td>\\n\",\n       \"      <td>0.302500</td>\\n\",\n       \"      <td>0.007737</td>\\n\",\n       \"      <td>0.136840</td>\\n\",\n       \"      <td>0.014243</td>\\n\",\n       \"      <td>0.006050</td>\\n\",\n       \"      <td>0.011453</td>\\n\",\n       \"      <td>0.133000</td>\\n\",\n       \"      <td>0.052590</td>\\n\",\n       \"      <td>0.052590</td>\\n\",\n       \"      <td>0.052590</td>\\n\",\n       \"      <td>0.052590</td>\\n\",\n       \"      <td>0.052590</td>\\n\",\n       \"      <td>0.313500</td>\\n\",\n       \"      <td>0.006406</td>\\n\",\n       \"      <td>0.142540</td>\\n\",\n       \"      <td>0.011816</td>\\n\",\n       \"      <td>0.006406</td>\\n\",\n       \"      <td>0.011816</td>\\n\",\n       \"      <td>1.370000</td>\\n\",\n       \"      <td>0.316783</td>\\n\",\n       \"      <td>0.549232</td>\\n\",\n       \"      <td>0.374334</td>\\n\",\n       \"      <td>0.315932</td>\\n\",\n       \"      <td>0.373604</td>\\n\",\n       \"      <td>1.926500</td>\\n\",\n       \"      <td>0.219142</td>\\n\",\n       \"      <td>0.700786</td>\\n\",\n       \"      <td>0.312658</td>\\n\",\n       \"      <td>0.209817</td>\\n\",\n       \"      <td>0.302844</td>\\n\",\n       \"      <td>1.193000</td>\\n\",\n       \"      <td>0.405196</td>\\n\",\n       \"      <td>0.400284</td>\\n\",\n       \"      <td>0.401832</td>\\n\",\n       \"      <td>0.400284</td>\\n\",\n       \"      <td>0.400284</td>\\n\",\n       \"      <td>2.183500</td>\\n\",\n       \"      <td>0.166125</td>\\n\",\n       \"      <td>0.757117</td>\\n\",\n       \"      <td>0.253605</td>\\n\",\n       \"      <td>0.166125</td>\\n\",\n       \"      <td>0.253605</td>\\n\",\n       \"      <td>0.391500</td>\\n\",\n       \"      <td>0.488452</td>\\n\",\n       \"      <td>0.186533</td>\\n\",\n       \"      <td>0.253696</td>\\n\",\n       \"      <td>0.488452</td>\\n\",\n       \"      <td>0.253696</td>\\n\",\n       \"      <td>0.990000</td>\\n\",\n       \"      <td>0.396764</td>\\n\",\n       \"      <td>0.428011</td>\\n\",\n       \"      <td>0.382940</td>\\n\",\n       \"      <td>0.396653</td>\\n\",\n       \"      <td>0.382896</td>\\n\",\n       \"      <td>0.248500</td>\\n\",\n       \"      <td>0.039789</td>\\n\",\n       \"      <td>0.181404</td>\\n\",\n       \"      <td>0.062503</td>\\n\",\n       \"      <td>0.039568</td>\\n\",\n       \"      <td>0.062227</td>\\n\",\n       \"      <td>0.322500</td>\\n\",\n       \"      <td>0.013715</td>\\n\",\n       \"      <td>0.239551</td>\\n\",\n       \"      <td>0.025197</td>\\n\",\n       \"      <td>0.010675</td>\\n\",\n       \"      <td>0.020184</td>\\n\",\n       \"      <td>0.137000</td>\\n\",\n       \"      <td>0.085709</td>\\n\",\n       \"      <td>0.085709</td>\\n\",\n       \"      <td>0.085709</td>\\n\",\n       \"      <td>0.085709</td>\\n\",\n       \"      <td>0.085709</td>\\n\",\n       \"      <td>0.336000</td>\\n\",\n       \"      <td>0.011316</td>\\n\",\n       \"      <td>0.247236</td>\\n\",\n       \"      <td>0.020828</td>\\n\",\n       \"      <td>0.011316</td>\\n\",\n       \"      <td>0.020828</td>\\n\",\n       \"      <td>0.209053</td>\\n\",\n       \"      <td>0.239531</td>\\n\",\n       \"      <td>0.313326</td>\\n\",\n       \"      <td>0.347306</td>\\n\",\n       \"      <td>0.395801</td>\\n\",\n       \"      <td>0.454936</td>\\n\",\n       \"      <td>0.466994</td>\\n\",\n       \"      <td>0.334510</td>\\n\",\n       \"      <td>0.384048</td>\\n\",\n       \"      <td>0.364991</td>\\n\",\n       \"      <td>0.487280</td>\\n\",\n       \"      <td>0.520044</td>\\n\",\n       \"      <td>0.496335</td>\\n\",\n       \"      <td>0.501635</td>\\n\",\n       \"      <td>0.048781</td>\\n\",\n       \"      <td>0.060083</td>\\n\",\n       \"      <td>0.098324</td>\\n\",\n       \"      <td>0.086539</td>\\n\",\n       \"      <td>0.092722</td>\\n\",\n       \"      <td>0.077544</td>\\n\",\n       \"      <td>0.077844</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>15.239000</td>\\n\",\n       \"      <td>68.735500</td>\\n\",\n       \"      <td>93.612000</td>\\n\",\n       \"      <td>3338.641000</td>\\n\",\n       \"      <td>465.702000</td>\\n\",\n       \"      <td>12349.972500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18549</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.356000</td>\\n\",\n       \"      <td>0.271350</td>\\n\",\n       \"      <td>0.296349</td>\\n\",\n       \"      <td>0.273125</td>\\n\",\n       \"      <td>0.271200</td>\\n\",\n       \"      <td>0.273102</td>\\n\",\n       \"      <td>1.860000</td>\\n\",\n       \"      <td>0.186367</td>\\n\",\n       \"      <td>0.399976</td>\\n\",\n       \"      <td>0.245578</td>\\n\",\n       \"      <td>0.186000</td>\\n\",\n       \"      <td>0.245461</td>\\n\",\n       \"      <td>1.391500</td>\\n\",\n       \"      <td>0.276028</td>\\n\",\n       \"      <td>0.275550</td>\\n\",\n       \"      <td>0.275701</td>\\n\",\n       \"      <td>0.275550</td>\\n\",\n       \"      <td>0.275550</td>\\n\",\n       \"      <td>2.480500</td>\\n\",\n       \"      <td>0.060536</td>\\n\",\n       \"      <td>0.519746</td>\\n\",\n       \"      <td>0.103789</td>\\n\",\n       \"      <td>0.060536</td>\\n\",\n       \"      <td>0.103789</td>\\n\",\n       \"      <td>0.306000</td>\\n\",\n       \"      <td>0.306000</td>\\n\",\n       \"      <td>0.068879</td>\\n\",\n       \"      <td>0.109766</td>\\n\",\n       \"      <td>0.306000</td>\\n\",\n       \"      <td>0.109766</td>\\n\",\n       \"      <td>0.971500</td>\\n\",\n       \"      <td>0.323917</td>\\n\",\n       \"      <td>0.213372</td>\\n\",\n       \"      <td>0.248512</td>\\n\",\n       \"      <td>0.323833</td>\\n\",\n       \"      <td>0.248503</td>\\n\",\n       \"      <td>1.432500</td>\\n\",\n       \"      <td>0.350807</td>\\n\",\n       \"      <td>0.381863</td>\\n\",\n       \"      <td>0.352549</td>\\n\",\n       \"      <td>0.350630</td>\\n\",\n       \"      <td>0.352522</td>\\n\",\n       \"      <td>1.974500</td>\\n\",\n       \"      <td>0.232742</td>\\n\",\n       \"      <td>0.495629</td>\\n\",\n       \"      <td>0.305336</td>\\n\",\n       \"      <td>0.232324</td>\\n\",\n       \"      <td>0.305202</td>\\n\",\n       \"      <td>1.501500</td>\\n\",\n       \"      <td>0.361011</td>\\n\",\n       \"      <td>0.360414</td>\\n\",\n       \"      <td>0.360606</td>\\n\",\n       \"      <td>0.360414</td>\\n\",\n       \"      <td>0.360414</td>\\n\",\n       \"      <td>2.637000</td>\\n\",\n       \"      <td>0.072732</td>\\n\",\n       \"      <td>0.617847</td>\\n\",\n       \"      <td>0.124287</td>\\n\",\n       \"      <td>0.072732</td>\\n\",\n       \"      <td>0.124287</td>\\n\",\n       \"      <td>0.306000</td>\\n\",\n       \"      <td>0.409594</td>\\n\",\n       \"      <td>0.092152</td>\\n\",\n       \"      <td>0.146925</td>\\n\",\n       \"      <td>0.409594</td>\\n\",\n       \"      <td>0.146925</td>\\n\",\n       \"      <td>1.008000</td>\\n\",\n       \"      <td>0.428107</td>\\n\",\n       \"      <td>0.283396</td>\\n\",\n       \"      <td>0.329700</td>\\n\",\n       \"      <td>0.428012</td>\\n\",\n       \"      <td>0.329689</td>\\n\",\n       \"      <td>1.390500</td>\\n\",\n       \"      <td>0.279058</td>\\n\",\n       \"      <td>0.495309</td>\\n\",\n       \"      <td>0.332236</td>\\n\",\n       \"      <td>0.278100</td>\\n\",\n       \"      <td>0.331590</td>\\n\",\n       \"      <td>1.928500</td>\\n\",\n       \"      <td>0.202214</td>\\n\",\n       \"      <td>0.654364</td>\\n\",\n       \"      <td>0.289838</td>\\n\",\n       \"      <td>0.192850</td>\\n\",\n       \"      <td>0.280240</td>\\n\",\n       \"      <td>1.151000</td>\\n\",\n       \"      <td>0.323042</td>\\n\",\n       \"      <td>0.318908</td>\\n\",\n       \"      <td>0.320253</td>\\n\",\n       \"      <td>0.318908</td>\\n\",\n       \"      <td>0.318908</td>\\n\",\n       \"      <td>2.182000</td>\\n\",\n       \"      <td>0.156151</td>\\n\",\n       \"      <td>0.723010</td>\\n\",\n       \"      <td>0.239716</td>\\n\",\n       \"      <td>0.156151</td>\\n\",\n       \"      <td>0.239716</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.117507</td>\\n\",\n       \"      <td>0.161675</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.161675</td>\\n\",\n       \"      <td>0.998500</td>\\n\",\n       \"      <td>0.332917</td>\\n\",\n       \"      <td>0.360917</td>\\n\",\n       \"      <td>0.320917</td>\\n\",\n       \"      <td>0.332833</td>\\n\",\n       \"      <td>0.320893</td>\\n\",\n       \"      <td>0.248000</td>\\n\",\n       \"      <td>0.025574</td>\\n\",\n       \"      <td>0.114564</td>\\n\",\n       \"      <td>0.039994</td>\\n\",\n       \"      <td>0.024800</td>\\n\",\n       \"      <td>0.039132</td>\\n\",\n       \"      <td>0.298500</td>\\n\",\n       \"      <td>0.012220</td>\\n\",\n       \"      <td>0.135145</td>\\n\",\n       \"      <td>0.021177</td>\\n\",\n       \"      <td>0.005970</td>\\n\",\n       \"      <td>0.011303</td>\\n\",\n       \"      <td>0.130000</td>\\n\",\n       \"      <td>0.052027</td>\\n\",\n       \"      <td>0.051985</td>\\n\",\n       \"      <td>0.052003</td>\\n\",\n       \"      <td>0.051985</td>\\n\",\n       \"      <td>0.051985</td>\\n\",\n       \"      <td>0.298500</td>\\n\",\n       \"      <td>0.011876</td>\\n\",\n       \"      <td>0.135145</td>\\n\",\n       \"      <td>0.020554</td>\\n\",\n       \"      <td>0.011876</td>\\n\",\n       \"      <td>0.020554</td>\\n\",\n       \"      <td>1.424000</td>\\n\",\n       \"      <td>0.323932</td>\\n\",\n       \"      <td>0.568747</td>\\n\",\n       \"      <td>0.384025</td>\\n\",\n       \"      <td>0.322885</td>\\n\",\n       \"      <td>0.383336</td>\\n\",\n       \"      <td>1.963000</td>\\n\",\n       \"      <td>0.221772</td>\\n\",\n       \"      <td>0.709286</td>\\n\",\n       \"      <td>0.316154</td>\\n\",\n       \"      <td>0.211826</td>\\n\",\n       \"      <td>0.306064</td>\\n\",\n       \"      <td>1.195500</td>\\n\",\n       \"      <td>0.387859</td>\\n\",\n       \"      <td>0.382785</td>\\n\",\n       \"      <td>0.384430</td>\\n\",\n       \"      <td>0.382785</td>\\n\",\n       \"      <td>0.382785</td>\\n\",\n       \"      <td>2.217500</td>\\n\",\n       \"      <td>0.167699</td>\\n\",\n       \"      <td>0.765985</td>\\n\",\n       \"      <td>0.255812</td>\\n\",\n       \"      <td>0.167699</td>\\n\",\n       \"      <td>0.255812</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.392447</td>\\n\",\n       \"      <td>0.146126</td>\\n\",\n       \"      <td>0.200070</td>\\n\",\n       \"      <td>0.392447</td>\\n\",\n       \"      <td>0.200070</td>\\n\",\n       \"      <td>1.015500</td>\\n\",\n       \"      <td>0.397878</td>\\n\",\n       \"      <td>0.430504</td>\\n\",\n       \"      <td>0.383434</td>\\n\",\n       \"      <td>0.397783</td>\\n\",\n       \"      <td>0.383406</td>\\n\",\n       \"      <td>0.254000</td>\\n\",\n       \"      <td>0.042522</td>\\n\",\n       \"      <td>0.187252</td>\\n\",\n       \"      <td>0.066138</td>\\n\",\n       \"      <td>0.041017</td>\\n\",\n       \"      <td>0.064491</td>\\n\",\n       \"      <td>0.312000</td>\\n\",\n       \"      <td>0.021125</td>\\n\",\n       \"      <td>0.226438</td>\\n\",\n       \"      <td>0.036312</td>\\n\",\n       \"      <td>0.010084</td>\\n\",\n       \"      <td>0.019066</td>\\n\",\n       \"      <td>0.133000</td>\\n\",\n       \"      <td>0.089001</td>\\n\",\n       \"      <td>0.088910</td>\\n\",\n       \"      <td>0.088940</td>\\n\",\n       \"      <td>0.088910</td>\\n\",\n       \"      <td>0.088910</td>\\n\",\n       \"      <td>0.312000</td>\\n\",\n       \"      <td>0.020557</td>\\n\",\n       \"      <td>0.227174</td>\\n\",\n       \"      <td>0.035285</td>\\n\",\n       \"      <td>0.020557</td>\\n\",\n       \"      <td>0.035285</td>\\n\",\n       \"      <td>0.211207</td>\\n\",\n       \"      <td>0.246907</td>\\n\",\n       \"      <td>0.304964</td>\\n\",\n       \"      <td>0.367598</td>\\n\",\n       \"      <td>0.401878</td>\\n\",\n       \"      <td>0.442791</td>\\n\",\n       \"      <td>0.455859</td>\\n\",\n       \"      <td>0.317053</td>\\n\",\n       \"      <td>0.376703</td>\\n\",\n       \"      <td>0.342053</td>\\n\",\n       \"      <td>0.496301</td>\\n\",\n       \"      <td>0.515905</td>\\n\",\n       \"      <td>0.449441</td>\\n\",\n       \"      <td>0.454925</td>\\n\",\n       \"      <td>0.049082</td>\\n\",\n       \"      <td>0.061112</td>\\n\",\n       \"      <td>0.106736</td>\\n\",\n       \"      <td>0.086695</td>\\n\",\n       \"      <td>0.093107</td>\\n\",\n       \"      <td>0.079030</td>\\n\",\n       \"      <td>0.079167</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>15.219500</td>\\n\",\n       \"      <td>35.354000</td>\\n\",\n       \"      <td>56.765500</td>\\n\",\n       \"      <td>3396.998000</td>\\n\",\n       \"      <td>471.833500</td>\\n\",\n       \"      <td>12246.869000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20752</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.332000</td>\\n\",\n       \"      <td>0.266400</td>\\n\",\n       \"      <td>0.290192</td>\\n\",\n       \"      <td>0.268098</td>\\n\",\n       \"      <td>0.266400</td>\\n\",\n       \"      <td>0.268098</td>\\n\",\n       \"      <td>1.773500</td>\\n\",\n       \"      <td>0.178254</td>\\n\",\n       \"      <td>0.380147</td>\\n\",\n       \"      <td>0.234159</td>\\n\",\n       \"      <td>0.177350</td>\\n\",\n       \"      <td>0.233679</td>\\n\",\n       \"      <td>1.342500</td>\\n\",\n       \"      <td>0.268588</td>\\n\",\n       \"      <td>0.267730</td>\\n\",\n       \"      <td>0.267978</td>\\n\",\n       \"      <td>0.267730</td>\\n\",\n       \"      <td>0.267730</td>\\n\",\n       \"      <td>2.257000</td>\\n\",\n       \"      <td>0.045589</td>\\n\",\n       \"      <td>0.474661</td>\\n\",\n       \"      <td>0.077594</td>\\n\",\n       \"      <td>0.045589</td>\\n\",\n       \"      <td>0.077594</td>\\n\",\n       \"      <td>0.356500</td>\\n\",\n       \"      <td>0.356500</td>\\n\",\n       \"      <td>0.079276</td>\\n\",\n       \"      <td>0.126914</td>\\n\",\n       \"      <td>0.356500</td>\\n\",\n       \"      <td>0.126914</td>\\n\",\n       \"      <td>0.976500</td>\\n\",\n       \"      <td>0.325500</td>\\n\",\n       \"      <td>0.213877</td>\\n\",\n       \"      <td>0.249915</td>\\n\",\n       \"      <td>0.325500</td>\\n\",\n       \"      <td>0.249915</td>\\n\",\n       \"      <td>1.408000</td>\\n\",\n       \"      <td>0.346743</td>\\n\",\n       \"      <td>0.377025</td>\\n\",\n       \"      <td>0.348361</td>\\n\",\n       \"      <td>0.346743</td>\\n\",\n       \"      <td>0.348361</td>\\n\",\n       \"      <td>1.894000</td>\\n\",\n       \"      <td>0.226351</td>\\n\",\n       \"      <td>0.480347</td>\\n\",\n       \"      <td>0.296334</td>\\n\",\n       \"      <td>0.225305</td>\\n\",\n       \"      <td>0.295766</td>\\n\",\n       \"      <td>1.447500</td>\\n\",\n       \"      <td>0.355541</td>\\n\",\n       \"      <td>0.354499</td>\\n\",\n       \"      <td>0.354800</td>\\n\",\n       \"      <td>0.354499</td>\\n\",\n       \"      <td>0.354499</td>\\n\",\n       \"      <td>2.433500</td>\\n\",\n       \"      <td>0.056475</td>\\n\",\n       \"      <td>0.583429</td>\\n\",\n       \"      <td>0.096001</td>\\n\",\n       \"      <td>0.056475</td>\\n\",\n       \"      <td>0.096001</td>\\n\",\n       \"      <td>0.356500</td>\\n\",\n       \"      <td>0.489436</td>\\n\",\n       \"      <td>0.109826</td>\\n\",\n       \"      <td>0.175568</td>\\n\",\n       \"      <td>0.489436</td>\\n\",\n       \"      <td>0.175568</td>\\n\",\n       \"      <td>1.021000</td>\\n\",\n       \"      <td>0.437702</td>\\n\",\n       \"      <td>0.289421</td>\\n\",\n       \"      <td>0.337229</td>\\n\",\n       \"      <td>0.437702</td>\\n\",\n       \"      <td>0.337229</td>\\n\",\n       \"      <td>1.380500</td>\\n\",\n       \"      <td>0.279283</td>\\n\",\n       \"      <td>0.483513</td>\\n\",\n       \"      <td>0.330264</td>\\n\",\n       \"      <td>0.276100</td>\\n\",\n       \"      <td>0.328009</td>\\n\",\n       \"      <td>1.809500</td>\\n\",\n       \"      <td>0.202163</td>\\n\",\n       \"      <td>0.615644</td>\\n\",\n       \"      <td>0.283708</td>\\n\",\n       \"      <td>0.180950</td>\\n\",\n       \"      <td>0.263389</td>\\n\",\n       \"      <td>1.145500</td>\\n\",\n       \"      <td>0.337388</td>\\n\",\n       \"      <td>0.331392</td>\\n\",\n       \"      <td>0.333152</td>\\n\",\n       \"      <td>0.331392</td>\\n\",\n       \"      <td>0.331392</td>\\n\",\n       \"      <td>1.990500</td>\\n\",\n       \"      <td>0.172337</td>\\n\",\n       \"      <td>0.664448</td>\\n\",\n       \"      <td>0.251910</td>\\n\",\n       \"      <td>0.172337</td>\\n\",\n       \"      <td>0.251910</td>\\n\",\n       \"      <td>0.382500</td>\\n\",\n       \"      <td>0.382500</td>\\n\",\n       \"      <td>0.142017</td>\\n\",\n       \"      <td>0.194732</td>\\n\",\n       \"      <td>0.382500</td>\\n\",\n       \"      <td>0.194732</td>\\n\",\n       \"      <td>1.017500</td>\\n\",\n       \"      <td>0.339833</td>\\n\",\n       \"      <td>0.365170</td>\\n\",\n       \"      <td>0.326957</td>\\n\",\n       \"      <td>0.339167</td>\\n\",\n       \"      <td>0.326657</td>\\n\",\n       \"      <td>0.213500</td>\\n\",\n       \"      <td>0.021435</td>\\n\",\n       \"      <td>0.096531</td>\\n\",\n       \"      <td>0.033698</td>\\n\",\n       \"      <td>0.021350</td>\\n\",\n       \"      <td>0.033612</td>\\n\",\n       \"      <td>0.258500</td>\\n\",\n       \"      <td>0.007527</td>\\n\",\n       \"      <td>0.116265</td>\\n\",\n       \"      <td>0.013643</td>\\n\",\n       \"      <td>0.005170</td>\\n\",\n       \"      <td>0.009784</td>\\n\",\n       \"      <td>0.125500</td>\\n\",\n       \"      <td>0.050476</td>\\n\",\n       \"      <td>0.050476</td>\\n\",\n       \"      <td>0.050476</td>\\n\",\n       \"      <td>0.050476</td>\\n\",\n       \"      <td>0.050476</td>\\n\",\n       \"      <td>0.266500</td>\\n\",\n       \"      <td>0.006603</td>\\n\",\n       \"      <td>0.120132</td>\\n\",\n       \"      <td>0.011967</td>\\n\",\n       \"      <td>0.006603</td>\\n\",\n       \"      <td>0.011967</td>\\n\",\n       \"      <td>1.407500</td>\\n\",\n       \"      <td>0.324149</td>\\n\",\n       \"      <td>0.559380</td>\\n\",\n       \"      <td>0.382138</td>\\n\",\n       \"      <td>0.320740</td>\\n\",\n       \"      <td>0.379781</td>\\n\",\n       \"      <td>1.843500</td>\\n\",\n       \"      <td>0.225645</td>\\n\",\n       \"      <td>0.681261</td>\\n\",\n       \"      <td>0.315109</td>\\n\",\n       \"      <td>0.202506</td>\\n\",\n       \"      <td>0.293190</td>\\n\",\n       \"      <td>1.184500</td>\\n\",\n       \"      <td>0.409796</td>\\n\",\n       \"      <td>0.402368</td>\\n\",\n       \"      <td>0.404514</td>\\n\",\n       \"      <td>0.402368</td>\\n\",\n       \"      <td>0.402368</td>\\n\",\n       \"      <td>2.026000</td>\\n\",\n       \"      <td>0.189243</td>\\n\",\n       \"      <td>0.721125</td>\\n\",\n       \"      <td>0.275007</td>\\n\",\n       \"      <td>0.189243</td>\\n\",\n       \"      <td>0.275007</td>\\n\",\n       \"      <td>0.382500</td>\\n\",\n       \"      <td>0.475844</td>\\n\",\n       \"      <td>0.179263</td>\\n\",\n       \"      <td>0.244733</td>\\n\",\n       \"      <td>0.475844</td>\\n\",\n       \"      <td>0.244733</td>\\n\",\n       \"      <td>1.036000</td>\\n\",\n       \"      <td>0.410394</td>\\n\",\n       \"      <td>0.441468</td>\\n\",\n       \"      <td>0.395201</td>\\n\",\n       \"      <td>0.409727</td>\\n\",\n       \"      <td>0.394901</td>\\n\",\n       \"      <td>0.218500</td>\\n\",\n       \"      <td>0.038054</td>\\n\",\n       \"      <td>0.172208</td>\\n\",\n       \"      <td>0.059632</td>\\n\",\n       \"      <td>0.037702</td>\\n\",\n       \"      <td>0.059328</td>\\n\",\n       \"      <td>0.275500</td>\\n\",\n       \"      <td>0.014180</td>\\n\",\n       \"      <td>0.212965</td>\\n\",\n       \"      <td>0.025408</td>\\n\",\n       \"      <td>0.009471</td>\\n\",\n       \"      <td>0.017909</td>\\n\",\n       \"      <td>0.126500</td>\\n\",\n       \"      <td>0.086689</td>\\n\",\n       \"      <td>0.086587</td>\\n\",\n       \"      <td>0.086615</td>\\n\",\n       \"      <td>0.086587</td>\\n\",\n       \"      <td>0.086587</td>\\n\",\n       \"      <td>0.284000</td>\\n\",\n       \"      <td>0.012484</td>\\n\",\n       \"      <td>0.218035</td>\\n\",\n       \"      <td>0.022323</td>\\n\",\n       \"      <td>0.012484</td>\\n\",\n       \"      <td>0.022323</td>\\n\",\n       \"      <td>0.210948</td>\\n\",\n       \"      <td>0.242067</td>\\n\",\n       \"      <td>0.335252</td>\\n\",\n       \"      <td>0.346862</td>\\n\",\n       \"      <td>0.388391</td>\\n\",\n       \"      <td>0.457895</td>\\n\",\n       \"      <td>0.469312</td>\\n\",\n       \"      <td>0.328443</td>\\n\",\n       \"      <td>0.379174</td>\\n\",\n       \"      <td>0.380737</td>\\n\",\n       \"      <td>0.480691</td>\\n\",\n       \"      <td>0.507037</td>\\n\",\n       \"      <td>0.493155</td>\\n\",\n       \"      <td>0.497805</td>\\n\",\n       \"      <td>0.046241</td>\\n\",\n       \"      <td>0.055671</td>\\n\",\n       \"      <td>0.100878</td>\\n\",\n       \"      <td>0.077251</td>\\n\",\n       \"      <td>0.084235</td>\\n\",\n       \"      <td>0.078739</td>\\n\",\n       \"      <td>0.078816</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>12.835000</td>\\n\",\n       \"      <td>56.678500</td>\\n\",\n       \"      <td>77.238000</td>\\n\",\n       \"      <td>3271.568000</td>\\n\",\n       \"      <td>464.186000</td>\\n\",\n       \"      <td>12207.972000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42853</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.370000</td>\\n\",\n       \"      <td>0.274000</td>\\n\",\n       \"      <td>0.298272</td>\\n\",\n       \"      <td>0.275155</td>\\n\",\n       \"      <td>0.274000</td>\\n\",\n       \"      <td>0.275155</td>\\n\",\n       \"      <td>1.911000</td>\\n\",\n       \"      <td>0.191133</td>\\n\",\n       \"      <td>0.407051</td>\\n\",\n       \"      <td>0.250746</td>\\n\",\n       \"      <td>0.191100</td>\\n\",\n       \"      <td>0.250732</td>\\n\",\n       \"      <td>1.447000</td>\\n\",\n       \"      <td>0.281060</td>\\n\",\n       \"      <td>0.280957</td>\\n\",\n       \"      <td>0.281004</td>\\n\",\n       \"      <td>0.280957</td>\\n\",\n       \"      <td>0.280957</td>\\n\",\n       \"      <td>2.632500</td>\\n\",\n       \"      <td>0.041552</td>\\n\",\n       \"      <td>0.539348</td>\\n\",\n       \"      <td>0.073996</td>\\n\",\n       \"      <td>0.041552</td>\\n\",\n       \"      <td>0.073996</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.086948</td>\\n\",\n       \"      <td>0.139024</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.139024</td>\\n\",\n       \"      <td>1.002000</td>\\n\",\n       \"      <td>0.334000</td>\\n\",\n       \"      <td>0.219287</td>\\n\",\n       \"      <td>0.255718</td>\\n\",\n       \"      <td>0.334000</td>\\n\",\n       \"      <td>0.255718</td>\\n\",\n       \"      <td>1.455500</td>\\n\",\n       \"      <td>0.356751</td>\\n\",\n       \"      <td>0.387711</td>\\n\",\n       \"      <td>0.358089</td>\\n\",\n       \"      <td>0.356751</td>\\n\",\n       \"      <td>0.358089</td>\\n\",\n       \"      <td>2.026500</td>\\n\",\n       \"      <td>0.237338</td>\\n\",\n       \"      <td>0.503006</td>\\n\",\n       \"      <td>0.310557</td>\\n\",\n       \"      <td>0.237304</td>\\n\",\n       \"      <td>0.310542</td>\\n\",\n       \"      <td>1.564000</td>\\n\",\n       \"      <td>0.370804</td>\\n\",\n       \"      <td>0.370684</td>\\n\",\n       \"      <td>0.370738</td>\\n\",\n       \"      <td>0.370684</td>\\n\",\n       \"      <td>0.370684</td>\\n\",\n       \"      <td>2.803500</td>\\n\",\n       \"      <td>0.049845</td>\\n\",\n       \"      <td>0.640942</td>\\n\",\n       \"      <td>0.088650</td>\\n\",\n       \"      <td>0.049845</td>\\n\",\n       \"      <td>0.088650</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.524971</td>\\n\",\n       \"      <td>0.118312</td>\\n\",\n       \"      <td>0.188733</td>\\n\",\n       \"      <td>0.524971</td>\\n\",\n       \"      <td>0.188733</td>\\n\",\n       \"      <td>1.040500</td>\\n\",\n       \"      <td>0.443913</td>\\n\",\n       \"      <td>0.293511</td>\\n\",\n       \"      <td>0.341576</td>\\n\",\n       \"      <td>0.443913</td>\\n\",\n       \"      <td>0.341576</td>\\n\",\n       \"      <td>1.400500</td>\\n\",\n       \"      <td>0.280342</td>\\n\",\n       \"      <td>0.491290</td>\\n\",\n       \"      <td>0.332503</td>\\n\",\n       \"      <td>0.280100</td>\\n\",\n       \"      <td>0.332311</td>\\n\",\n       \"      <td>1.960500</td>\\n\",\n       \"      <td>0.200882</td>\\n\",\n       \"      <td>0.658079</td>\\n\",\n       \"      <td>0.288522</td>\\n\",\n       \"      <td>0.196050</td>\\n\",\n       \"      <td>0.283261</td>\\n\",\n       \"      <td>1.230500</td>\\n\",\n       \"      <td>0.339938</td>\\n\",\n       \"      <td>0.336936</td>\\n\",\n       \"      <td>0.337934</td>\\n\",\n       \"      <td>0.336936</td>\\n\",\n       \"      <td>0.336936</td>\\n\",\n       \"      <td>2.298000</td>\\n\",\n       \"      <td>0.144580</td>\\n\",\n       \"      <td>0.738844</td>\\n\",\n       \"      <td>0.226084</td>\\n\",\n       \"      <td>0.144580</td>\\n\",\n       \"      <td>0.226084</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.145922</td>\\n\",\n       \"      <td>0.199710</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.199710</td>\\n\",\n       \"      <td>1.011500</td>\\n\",\n       \"      <td>0.337167</td>\\n\",\n       \"      <td>0.362904</td>\\n\",\n       \"      <td>0.324792</td>\\n\",\n       \"      <td>0.337167</td>\\n\",\n       \"      <td>0.324792</td>\\n\",\n       \"      <td>0.261000</td>\\n\",\n       \"      <td>0.026378</td>\\n\",\n       \"      <td>0.117182</td>\\n\",\n       \"      <td>0.041348</td>\\n\",\n       \"      <td>0.026100</td>\\n\",\n       \"      <td>0.041064</td>\\n\",\n       \"      <td>0.325000</td>\\n\",\n       \"      <td>0.009042</td>\\n\",\n       \"      <td>0.148519</td>\\n\",\n       \"      <td>0.016362</td>\\n\",\n       \"      <td>0.006500</td>\\n\",\n       \"      <td>0.012311</td>\\n\",\n       \"      <td>0.137000</td>\\n\",\n       \"      <td>0.055278</td>\\n\",\n       \"      <td>0.055278</td>\\n\",\n       \"      <td>0.055278</td>\\n\",\n       \"      <td>0.055278</td>\\n\",\n       \"      <td>0.055278</td>\\n\",\n       \"      <td>0.334500</td>\\n\",\n       \"      <td>0.007615</td>\\n\",\n       \"      <td>0.153024</td>\\n\",\n       \"      <td>0.013751</td>\\n\",\n       \"      <td>0.007615</td>\\n\",\n       \"      <td>0.013751</td>\\n\",\n       \"      <td>1.432000</td>\\n\",\n       \"      <td>0.327003</td>\\n\",\n       \"      <td>0.568221</td>\\n\",\n       \"      <td>0.386543</td>\\n\",\n       \"      <td>0.326669</td>\\n\",\n       \"      <td>0.386287</td>\\n\",\n       \"      <td>1.997000</td>\\n\",\n       \"      <td>0.220642</td>\\n\",\n       \"      <td>0.715277</td>\\n\",\n       \"      <td>0.315474</td>\\n\",\n       \"      <td>0.215366</td>\\n\",\n       \"      <td>0.309770</td>\\n\",\n       \"      <td>1.280500</td>\\n\",\n       \"      <td>0.412775</td>\\n\",\n       \"      <td>0.409234</td>\\n\",\n       \"      <td>0.410406</td>\\n\",\n       \"      <td>0.409234</td>\\n\",\n       \"      <td>0.409234</td>\\n\",\n       \"      <td>2.329500</td>\\n\",\n       \"      <td>0.154658</td>\\n\",\n       \"      <td>0.783236</td>\\n\",\n       \"      <td>0.240828</td>\\n\",\n       \"      <td>0.154658</td>\\n\",\n       \"      <td>0.240828</td>\\n\",\n       \"      <td>0.389500</td>\\n\",\n       \"      <td>0.486677</td>\\n\",\n       \"      <td>0.182330</td>\\n\",\n       \"      <td>0.249640</td>\\n\",\n       \"      <td>0.486677</td>\\n\",\n       \"      <td>0.249640</td>\\n\",\n       \"      <td>1.039000</td>\\n\",\n       \"      <td>0.409645</td>\\n\",\n       \"      <td>0.440451</td>\\n\",\n       \"      <td>0.394780</td>\\n\",\n       \"      <td>0.409612</td>\\n\",\n       \"      <td>0.394760</td>\\n\",\n       \"      <td>0.268500</td>\\n\",\n       \"      <td>0.042272</td>\\n\",\n       \"      <td>0.187765</td>\\n\",\n       \"      <td>0.066050</td>\\n\",\n       \"      <td>0.041701</td>\\n\",\n       \"      <td>0.065476</td>\\n\",\n       \"      <td>0.345500</td>\\n\",\n       \"      <td>0.015319</td>\\n\",\n       \"      <td>0.242604</td>\\n\",\n       \"      <td>0.027516</td>\\n\",\n       \"      <td>0.010827</td>\\n\",\n       \"      <td>0.020473</td>\\n\",\n       \"      <td>0.139500</td>\\n\",\n       \"      <td>0.089087</td>\\n\",\n       \"      <td>0.089059</td>\\n\",\n       \"      <td>0.089069</td>\\n\",\n       \"      <td>0.089059</td>\\n\",\n       \"      <td>0.089059</td>\\n\",\n       \"      <td>0.355500</td>\\n\",\n       \"      <td>0.012968</td>\\n\",\n       \"      <td>0.249229</td>\\n\",\n       \"      <td>0.023221</td>\\n\",\n       \"      <td>0.012968</td>\\n\",\n       \"      <td>0.023221</td>\\n\",\n       \"      <td>0.234871</td>\\n\",\n       \"      <td>0.266891</td>\\n\",\n       \"      <td>0.322358</td>\\n\",\n       \"      <td>0.376192</td>\\n\",\n       \"      <td>0.426610</td>\\n\",\n       \"      <td>0.485484</td>\\n\",\n       \"      <td>0.499334</td>\\n\",\n       \"      <td>0.348518</td>\\n\",\n       \"      <td>0.401577</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.509435</td>\\n\",\n       \"      <td>0.540643</td>\\n\",\n       \"      <td>0.502800</td>\\n\",\n       \"      <td>0.508864</td>\\n\",\n       \"      <td>0.051866</td>\\n\",\n       \"      <td>0.064641</td>\\n\",\n       \"      <td>0.108721</td>\\n\",\n       \"      <td>0.093498</td>\\n\",\n       \"      <td>0.100031</td>\\n\",\n       \"      <td>0.080583</td>\\n\",\n       \"      <td>0.080690</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>17.162500</td>\\n\",\n       \"      <td>64.949500</td>\\n\",\n       \"      <td>92.169500</td>\\n\",\n       \"      <td>3345.185000</td>\\n\",\n       \"      <td>439.194500</td>\\n\",\n       \"      <td>12184.255500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19153</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.427000</td>\\n\",\n       \"      <td>0.285400</td>\\n\",\n       \"      <td>0.308361</td>\\n\",\n       \"      <td>0.285729</td>\\n\",\n       \"      <td>0.285400</td>\\n\",\n       \"      <td>0.285729</td>\\n\",\n       \"      <td>1.982000</td>\\n\",\n       \"      <td>0.198200</td>\\n\",\n       \"      <td>0.420821</td>\\n\",\n       \"      <td>0.259691</td>\\n\",\n       \"      <td>0.198200</td>\\n\",\n       \"      <td>0.259691</td>\\n\",\n       \"      <td>1.516000</td>\\n\",\n       \"      <td>0.290802</td>\\n\",\n       \"      <td>0.290723</td>\\n\",\n       \"      <td>0.290757</td>\\n\",\n       \"      <td>0.290723</td>\\n\",\n       \"      <td>0.290723</td>\\n\",\n       \"      <td>2.682000</td>\\n\",\n       \"      <td>0.059712</td>\\n\",\n       \"      <td>0.545239</td>\\n\",\n       \"      <td>0.103531</td>\\n\",\n       \"      <td>0.059712</td>\\n\",\n       \"      <td>0.103531</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.088586</td>\\n\",\n       \"      <td>0.142324</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.142324</td>\\n\",\n       \"      <td>1.035500</td>\\n\",\n       \"      <td>0.345167</td>\\n\",\n       \"      <td>0.226099</td>\\n\",\n       \"      <td>0.264096</td>\\n\",\n       \"      <td>0.345167</td>\\n\",\n       \"      <td>0.264096</td>\\n\",\n       \"      <td>1.505000</td>\\n\",\n       \"      <td>0.366312</td>\\n\",\n       \"      <td>0.396280</td>\\n\",\n       \"      <td>0.367025</td>\\n\",\n       \"      <td>0.366312</td>\\n\",\n       \"      <td>0.367025</td>\\n\",\n       \"      <td>2.097500</td>\\n\",\n       \"      <td>0.243983</td>\\n\",\n       \"      <td>0.515781</td>\\n\",\n       \"      <td>0.318987</td>\\n\",\n       \"      <td>0.243983</td>\\n\",\n       \"      <td>0.318987</td>\\n\",\n       \"      <td>1.632500</td>\\n\",\n       \"      <td>0.379458</td>\\n\",\n       \"      <td>0.379345</td>\\n\",\n       \"      <td>0.379394</td>\\n\",\n       \"      <td>0.379345</td>\\n\",\n       \"      <td>0.379345</td>\\n\",\n       \"      <td>2.847000</td>\\n\",\n       \"      <td>0.070893</td>\\n\",\n       \"      <td>0.643791</td>\\n\",\n       \"      <td>0.122678</td>\\n\",\n       \"      <td>0.070893</td>\\n\",\n       \"      <td>0.122678</td>\\n\",\n       \"      <td>0.402000</td>\\n\",\n       \"      <td>0.531348</td>\\n\",\n       \"      <td>0.118834</td>\\n\",\n       \"      <td>0.190101</td>\\n\",\n       \"      <td>0.531348</td>\\n\",\n       \"      <td>0.190101</td>\\n\",\n       \"      <td>1.077500</td>\\n\",\n       \"      <td>0.457745</td>\\n\",\n       \"      <td>0.301768</td>\\n\",\n       \"      <td>0.351841</td>\\n\",\n       \"      <td>0.457745</td>\\n\",\n       \"      <td>0.351841</td>\\n\",\n       \"      <td>1.433500</td>\\n\",\n       \"      <td>0.286700</td>\\n\",\n       \"      <td>0.502124</td>\\n\",\n       \"      <td>0.340413</td>\\n\",\n       \"      <td>0.286700</td>\\n\",\n       \"      <td>0.340413</td>\\n\",\n       \"      <td>1.970000</td>\\n\",\n       \"      <td>0.197141</td>\\n\",\n       \"      <td>0.666321</td>\\n\",\n       \"      <td>0.286112</td>\\n\",\n       \"      <td>0.197000</td>\\n\",\n       \"      <td>0.285922</td>\\n\",\n       \"      <td>1.256500</td>\\n\",\n       \"      <td>0.347959</td>\\n\",\n       \"      <td>0.347389</td>\\n\",\n       \"      <td>0.347617</td>\\n\",\n       \"      <td>0.347389</td>\\n\",\n       \"      <td>0.347389</td>\\n\",\n       \"      <td>2.466500</td>\\n\",\n       \"      <td>0.106008</td>\\n\",\n       \"      <td>0.789384</td>\\n\",\n       \"      <td>0.177566</td>\\n\",\n       \"      <td>0.106008</td>\\n\",\n       \"      <td>0.177566</td>\\n\",\n       \"      <td>0.398500</td>\\n\",\n       \"      <td>0.398500</td>\\n\",\n       \"      <td>0.150514</td>\\n\",\n       \"      <td>0.205309</td>\\n\",\n       \"      <td>0.398500</td>\\n\",\n       \"      <td>0.205309</td>\\n\",\n       \"      <td>1.035500</td>\\n\",\n       \"      <td>0.345167</td>\\n\",\n       \"      <td>0.372187</td>\\n\",\n       \"      <td>0.332999</td>\\n\",\n       \"      <td>0.345167</td>\\n\",\n       \"      <td>0.332999</td>\\n\",\n       \"      <td>0.207500</td>\\n\",\n       \"      <td>0.023229</td>\\n\",\n       \"      <td>0.097003</td>\\n\",\n       \"      <td>0.035129</td>\\n\",\n       \"      <td>0.020750</td>\\n\",\n       \"      <td>0.032872</td>\\n\",\n       \"      <td>0.215500</td>\\n\",\n       \"      <td>0.013593</td>\\n\",\n       \"      <td>0.099653</td>\\n\",\n       \"      <td>0.021280</td>\\n\",\n       \"      <td>0.004310</td>\\n\",\n       \"      <td>0.008171</td>\\n\",\n       \"      <td>0.136000</td>\\n\",\n       \"      <td>0.055920</td>\\n\",\n       \"      <td>0.055478</td>\\n\",\n       \"      <td>0.055563</td>\\n\",\n       \"      <td>0.055478</td>\\n\",\n       \"      <td>0.055478</td>\\n\",\n       \"      <td>0.215500</td>\\n\",\n       \"      <td>0.013386</td>\\n\",\n       \"      <td>0.099653</td>\\n\",\n       \"      <td>0.020903</td>\\n\",\n       \"      <td>0.013386</td>\\n\",\n       \"      <td>0.020903</td>\\n\",\n       \"      <td>1.459000</td>\\n\",\n       \"      <td>0.330557</td>\\n\",\n       \"      <td>0.575537</td>\\n\",\n       \"      <td>0.391255</td>\\n\",\n       \"      <td>0.330557</td>\\n\",\n       \"      <td>0.391255</td>\\n\",\n       \"      <td>2.005000</td>\\n\",\n       \"      <td>0.216770</td>\\n\",\n       \"      <td>0.723623</td>\\n\",\n       \"      <td>0.312923</td>\\n\",\n       \"      <td>0.216626</td>\\n\",\n       \"      <td>0.312730</td>\\n\",\n       \"      <td>1.311500</td>\\n\",\n       \"      <td>0.418872</td>\\n\",\n       \"      <td>0.418069</td>\\n\",\n       \"      <td>0.418387</td>\\n\",\n       \"      <td>0.418069</td>\\n\",\n       \"      <td>0.418069</td>\\n\",\n       \"      <td>2.514500</td>\\n\",\n       \"      <td>0.112772</td>\\n\",\n       \"      <td>0.828989</td>\\n\",\n       \"      <td>0.187885</td>\\n\",\n       \"      <td>0.112772</td>\\n\",\n       \"      <td>0.187885</td>\\n\",\n       \"      <td>0.398500</td>\\n\",\n       \"      <td>0.490027</td>\\n\",\n       \"      <td>0.185041</td>\\n\",\n       \"      <td>0.252363</td>\\n\",\n       \"      <td>0.490027</td>\\n\",\n       \"      <td>0.252363</td>\\n\",\n       \"      <td>1.052500</td>\\n\",\n       \"      <td>0.417611</td>\\n\",\n       \"      <td>0.448953</td>\\n\",\n       \"      <td>0.402314</td>\\n\",\n       \"      <td>0.417611</td>\\n\",\n       \"      <td>0.402314</td>\\n\",\n       \"      <td>0.212000</td>\\n\",\n       \"      <td>0.039814</td>\\n\",\n       \"      <td>0.164892</td>\\n\",\n       \"      <td>0.060201</td>\\n\",\n       \"      <td>0.035918</td>\\n\",\n       \"      <td>0.056570</td>\\n\",\n       \"      <td>0.224000</td>\\n\",\n       \"      <td>0.023642</td>\\n\",\n       \"      <td>0.176105</td>\\n\",\n       \"      <td>0.037308</td>\\n\",\n       \"      <td>0.007756</td>\\n\",\n       \"      <td>0.014676</td>\\n\",\n       \"      <td>0.139000</td>\\n\",\n       \"      <td>0.092640</td>\\n\",\n       \"      <td>0.092139</td>\\n\",\n       \"      <td>0.092248</td>\\n\",\n       \"      <td>0.092139</td>\\n\",\n       \"      <td>0.092139</td>\\n\",\n       \"      <td>0.224000</td>\\n\",\n       \"      <td>0.023238</td>\\n\",\n       \"      <td>0.176228</td>\\n\",\n       \"      <td>0.036574</td>\\n\",\n       \"      <td>0.023238</td>\\n\",\n       \"      <td>0.036574</td>\\n\",\n       \"      <td>0.244555</td>\\n\",\n       \"      <td>0.277072</td>\\n\",\n       \"      <td>0.332901</td>\\n\",\n       \"      <td>0.386949</td>\\n\",\n       \"      <td>0.437472</td>\\n\",\n       \"      <td>0.494691</td>\\n\",\n       \"      <td>0.509122</td>\\n\",\n       \"      <td>0.362936</td>\\n\",\n       \"      <td>0.416994</td>\\n\",\n       \"      <td>0.353086</td>\\n\",\n       \"      <td>0.529715</td>\\n\",\n       \"      <td>0.563858</td>\\n\",\n       \"      <td>0.504926</td>\\n\",\n       \"      <td>0.510325</td>\\n\",\n       \"      <td>0.050602</td>\\n\",\n       \"      <td>0.060350</td>\\n\",\n       \"      <td>0.109468</td>\\n\",\n       \"      <td>0.077684</td>\\n\",\n       \"      <td>0.083656</td>\\n\",\n       \"      <td>0.085095</td>\\n\",\n       \"      <td>0.085237</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>24.486500</td>\\n\",\n       \"      <td>29.746500</td>\\n\",\n       \"      <td>57.974000</td>\\n\",\n       \"      <td>3308.951000</td>\\n\",\n       \"      <td>478.443500</td>\\n\",\n       \"      <td>12029.724500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15884</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.395652</td>\\n\",\n       \"      <td>0.279130</td>\\n\",\n       \"      <td>0.289710</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>0.279130</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>1.891304</td>\\n\",\n       \"      <td>0.189130</td>\\n\",\n       \"      <td>0.384865</td>\\n\",\n       \"      <td>0.240809</td>\\n\",\n       \"      <td>0.189130</td>\\n\",\n       \"      <td>0.240809</td>\\n\",\n       \"      <td>1.534783</td>\\n\",\n       \"      <td>0.275040</td>\\n\",\n       \"      <td>0.275040</td>\\n\",\n       \"      <td>0.275040</td>\\n\",\n       \"      <td>0.275040</td>\\n\",\n       \"      <td>0.275040</td>\\n\",\n       \"      <td>2.545652</td>\\n\",\n       \"      <td>0.067112</td>\\n\",\n       \"      <td>0.488857</td>\\n\",\n       \"      <td>0.112859</td>\\n\",\n       \"      <td>0.067112</td>\\n\",\n       \"      <td>0.112859</td>\\n\",\n       \"      <td>0.352174</td>\\n\",\n       \"      <td>0.352174</td>\\n\",\n       \"      <td>0.079250</td>\\n\",\n       \"      <td>0.125361</td>\\n\",\n       \"      <td>0.352174</td>\\n\",\n       \"      <td>0.125361</td>\\n\",\n       \"      <td>0.978261</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.210013</td>\\n\",\n       \"      <td>0.243207</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.243207</td>\\n\",\n       \"      <td>1.471739</td>\\n\",\n       \"      <td>0.360601</td>\\n\",\n       \"      <td>0.367212</td>\\n\",\n       \"      <td>0.343762</td>\\n\",\n       \"      <td>0.360601</td>\\n\",\n       \"      <td>0.343762</td>\\n\",\n       \"      <td>2.065217</td>\\n\",\n       \"      <td>0.241937</td>\\n\",\n       \"      <td>0.476513</td>\\n\",\n       \"      <td>0.303628</td>\\n\",\n       \"      <td>0.241937</td>\\n\",\n       \"      <td>0.303628</td>\\n\",\n       \"      <td>1.702174</td>\\n\",\n       \"      <td>0.355786</td>\\n\",\n       \"      <td>0.355786</td>\\n\",\n       \"      <td>0.355786</td>\\n\",\n       \"      <td>0.355786</td>\\n\",\n       \"      <td>0.355786</td>\\n\",\n       \"      <td>2.843478</td>\\n\",\n       \"      <td>0.083976</td>\\n\",\n       \"      <td>0.596113</td>\\n\",\n       \"      <td>0.140271</td>\\n\",\n       \"      <td>0.083976</td>\\n\",\n       \"      <td>0.140271</td>\\n\",\n       \"      <td>0.352174</td>\\n\",\n       \"      <td>0.478290</td>\\n\",\n       \"      <td>0.103756</td>\\n\",\n       \"      <td>0.164698</td>\\n\",\n       \"      <td>0.478290</td>\\n\",\n       \"      <td>0.164698</td>\\n\",\n       \"      <td>1.004348</td>\\n\",\n       \"      <td>0.434227</td>\\n\",\n       \"      <td>0.272946</td>\\n\",\n       \"      <td>0.318046</td>\\n\",\n       \"      <td>0.434227</td>\\n\",\n       \"      <td>0.318046</td>\\n\",\n       \"      <td>1.402174</td>\\n\",\n       \"      <td>0.280435</td>\\n\",\n       \"      <td>0.503402</td>\\n\",\n       \"      <td>0.331913</td>\\n\",\n       \"      <td>0.280435</td>\\n\",\n       \"      <td>0.331913</td>\\n\",\n       \"      <td>1.900000</td>\\n\",\n       \"      <td>0.191429</td>\\n\",\n       \"      <td>0.644148</td>\\n\",\n       \"      <td>0.275119</td>\\n\",\n       \"      <td>0.190000</td>\\n\",\n       \"      <td>0.273341</td>\\n\",\n       \"      <td>1.226087</td>\\n\",\n       \"      <td>0.341756</td>\\n\",\n       \"      <td>0.340916</td>\\n\",\n       \"      <td>0.341284</td>\\n\",\n       \"      <td>0.340916</td>\\n\",\n       \"      <td>0.340916</td>\\n\",\n       \"      <td>2.195652</td>\\n\",\n       \"      <td>0.135892</td>\\n\",\n       \"      <td>0.717157</td>\\n\",\n       \"      <td>0.214282</td>\\n\",\n       \"      <td>0.135892</td>\\n\",\n       \"      <td>0.214282</td>\\n\",\n       \"      <td>0.378261</td>\\n\",\n       \"      <td>0.378261</td>\\n\",\n       \"      <td>0.148989</td>\\n\",\n       \"      <td>0.199616</td>\\n\",\n       \"      <td>0.378261</td>\\n\",\n       \"      <td>0.199616</td>\\n\",\n       \"      <td>1.010870</td>\\n\",\n       \"      <td>0.336957</td>\\n\",\n       \"      <td>0.382241</td>\\n\",\n       \"      <td>0.329282</td>\\n\",\n       \"      <td>0.336957</td>\\n\",\n       \"      <td>0.329282</td>\\n\",\n       \"      <td>0.295652</td>\\n\",\n       \"      <td>0.030211</td>\\n\",\n       \"      <td>0.112944</td>\\n\",\n       \"      <td>0.044925</td>\\n\",\n       \"      <td>0.029565</td>\\n\",\n       \"      <td>0.044239</td>\\n\",\n       \"      <td>0.347826</td>\\n\",\n       \"      <td>0.016406</td>\\n\",\n       \"      <td>0.135843</td>\\n\",\n       \"      <td>0.027262</td>\\n\",\n       \"      <td>0.006957</td>\\n\",\n       \"      <td>0.012983</td>\\n\",\n       \"      <td>0.206522</td>\\n\",\n       \"      <td>0.072830</td>\\n\",\n       \"      <td>0.072799</td>\\n\",\n       \"      <td>0.072814</td>\\n\",\n       \"      <td>0.072799</td>\\n\",\n       \"      <td>0.072799</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.016128</td>\\n\",\n       \"      <td>0.136386</td>\\n\",\n       \"      <td>0.026782</td>\\n\",\n       \"      <td>0.016128</td>\\n\",\n       \"      <td>0.026782</td>\\n\",\n       \"      <td>1.432609</td>\\n\",\n       \"      <td>0.325242</td>\\n\",\n       \"      <td>0.572728</td>\\n\",\n       \"      <td>0.380369</td>\\n\",\n       \"      <td>0.325242</td>\\n\",\n       \"      <td>0.380369</td>\\n\",\n       \"      <td>1.965217</td>\\n\",\n       \"      <td>0.214815</td>\\n\",\n       \"      <td>0.702803</td>\\n\",\n       \"      <td>0.305262</td>\\n\",\n       \"      <td>0.213253</td>\\n\",\n       \"      <td>0.303342</td>\\n\",\n       \"      <td>1.304348</td>\\n\",\n       \"      <td>0.408348</td>\\n\",\n       \"      <td>0.407364</td>\\n\",\n       \"      <td>0.407797</td>\\n\",\n       \"      <td>0.407364</td>\\n\",\n       \"      <td>0.407364</td>\\n\",\n       \"      <td>2.276087</td>\\n\",\n       \"      <td>0.150441</td>\\n\",\n       \"      <td>0.771767</td>\\n\",\n       \"      <td>0.235208</td>\\n\",\n       \"      <td>0.150441</td>\\n\",\n       \"      <td>0.235208</td>\\n\",\n       \"      <td>0.378261</td>\\n\",\n       \"      <td>0.462386</td>\\n\",\n       \"      <td>0.181482</td>\\n\",\n       \"      <td>0.241374</td>\\n\",\n       \"      <td>0.462386</td>\\n\",\n       \"      <td>0.241374</td>\\n\",\n       \"      <td>1.021739</td>\\n\",\n       \"      <td>0.404013</td>\\n\",\n       \"      <td>0.450144</td>\\n\",\n       \"      <td>0.389284</td>\\n\",\n       \"      <td>0.404013</td>\\n\",\n       \"      <td>0.389284</td>\\n\",\n       \"      <td>0.306522</td>\\n\",\n       \"      <td>0.052963</td>\\n\",\n       \"      <td>0.191779</td>\\n\",\n       \"      <td>0.077179</td>\\n\",\n       \"      <td>0.050698</td>\\n\",\n       \"      <td>0.075503</td>\\n\",\n       \"      <td>0.373913</td>\\n\",\n       \"      <td>0.029371</td>\\n\",\n       \"      <td>0.228947</td>\\n\",\n       \"      <td>0.047198</td>\\n\",\n       \"      <td>0.012077</td>\\n\",\n       \"      <td>0.022494</td>\\n\",\n       \"      <td>0.210870</td>\\n\",\n       \"      <td>0.115298</td>\\n\",\n       \"      <td>0.114562</td>\\n\",\n       \"      <td>0.114683</td>\\n\",\n       \"      <td>0.114562</td>\\n\",\n       \"      <td>0.114562</td>\\n\",\n       \"      <td>0.376087</td>\\n\",\n       \"      <td>0.028854</td>\\n\",\n       \"      <td>0.229588</td>\\n\",\n       \"      <td>0.046308</td>\\n\",\n       \"      <td>0.028854</td>\\n\",\n       \"      <td>0.046308</td>\\n\",\n       \"      <td>0.212684</td>\\n\",\n       \"      <td>0.244722</td>\\n\",\n       \"      <td>0.327862</td>\\n\",\n       \"      <td>0.357773</td>\\n\",\n       \"      <td>0.399457</td>\\n\",\n       \"      <td>0.435728</td>\\n\",\n       \"      <td>0.458196</td>\\n\",\n       \"      <td>0.343390</td>\\n\",\n       \"      <td>0.398324</td>\\n\",\n       \"      <td>0.369707</td>\\n\",\n       \"      <td>0.508382</td>\\n\",\n       \"      <td>0.533233</td>\\n\",\n       \"      <td>0.474885</td>\\n\",\n       \"      <td>0.487756</td>\\n\",\n       \"      <td>0.056376</td>\\n\",\n       \"      <td>0.067318</td>\\n\",\n       \"      <td>0.129540</td>\\n\",\n       \"      <td>0.096980</td>\\n\",\n       \"      <td>0.102299</td>\\n\",\n       \"      <td>0.100911</td>\\n\",\n       \"      <td>0.101483</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>16.882609</td>\\n\",\n       \"      <td>28.667391</td>\\n\",\n       \"      <td>51.589130</td>\\n\",\n       \"      <td>3682.267391</td>\\n\",\n       \"      <td>459.256522</td>\\n\",\n       \"      <td>12259.767391</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20237</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.393478</td>\\n\",\n       \"      <td>0.278696</td>\\n\",\n       \"      <td>0.293146</td>\\n\",\n       \"      <td>0.270963</td>\\n\",\n       \"      <td>0.278696</td>\\n\",\n       \"      <td>0.270963</td>\\n\",\n       \"      <td>1.904348</td>\\n\",\n       \"      <td>0.190435</td>\\n\",\n       \"      <td>0.381141</td>\\n\",\n       \"      <td>0.240992</td>\\n\",\n       \"      <td>0.190435</td>\\n\",\n       \"      <td>0.240992</td>\\n\",\n       \"      <td>1.591304</td>\\n\",\n       \"      <td>0.288258</td>\\n\",\n       \"      <td>0.288258</td>\\n\",\n       \"      <td>0.288258</td>\\n\",\n       \"      <td>0.288258</td>\\n\",\n       \"      <td>0.288258</td>\\n\",\n       \"      <td>2.491304</td>\\n\",\n       \"      <td>0.075852</td>\\n\",\n       \"      <td>0.474038</td>\\n\",\n       \"      <td>0.125170</td>\\n\",\n       \"      <td>0.075852</td>\\n\",\n       \"      <td>0.125170</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.088204</td>\\n\",\n       \"      <td>0.140511</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.140511</td>\\n\",\n       \"      <td>1.043478</td>\\n\",\n       \"      <td>0.347826</td>\\n\",\n       \"      <td>0.225637</td>\\n\",\n       \"      <td>0.261270</td>\\n\",\n       \"      <td>0.347826</td>\\n\",\n       \"      <td>0.261270</td>\\n\",\n       \"      <td>1.495652</td>\\n\",\n       \"      <td>0.365146</td>\\n\",\n       \"      <td>0.373280</td>\\n\",\n       \"      <td>0.348538</td>\\n\",\n       \"      <td>0.365146</td>\\n\",\n       \"      <td>0.348538</td>\\n\",\n       \"      <td>2.104348</td>\\n\",\n       \"      <td>0.244797</td>\\n\",\n       \"      <td>0.475488</td>\\n\",\n       \"      <td>0.305315</td>\\n\",\n       \"      <td>0.244784</td>\\n\",\n       \"      <td>0.305302</td>\\n\",\n       \"      <td>1.786957</td>\\n\",\n       \"      <td>0.373877</td>\\n\",\n       \"      <td>0.373877</td>\\n\",\n       \"      <td>0.373877</td>\\n\",\n       \"      <td>0.373877</td>\\n\",\n       \"      <td>0.373877</td>\\n\",\n       \"      <td>2.789130</td>\\n\",\n       \"      <td>0.095453</td>\\n\",\n       \"      <td>0.584117</td>\\n\",\n       \"      <td>0.156348</td>\\n\",\n       \"      <td>0.095453</td>\\n\",\n       \"      <td>0.156348</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.543633</td>\\n\",\n       \"      <td>0.118195</td>\\n\",\n       \"      <td>0.187558</td>\\n\",\n       \"      <td>0.543633</td>\\n\",\n       \"      <td>0.187558</td>\\n\",\n       \"      <td>1.076087</td>\\n\",\n       \"      <td>0.458776</td>\\n\",\n       \"      <td>0.289678</td>\\n\",\n       \"      <td>0.337308</td>\\n\",\n       \"      <td>0.458776</td>\\n\",\n       \"      <td>0.337308</td>\\n\",\n       \"      <td>1.378261</td>\\n\",\n       \"      <td>0.275652</td>\\n\",\n       \"      <td>0.495642</td>\\n\",\n       \"      <td>0.327046</td>\\n\",\n       \"      <td>0.275652</td>\\n\",\n       \"      <td>0.327046</td>\\n\",\n       \"      <td>1.893478</td>\\n\",\n       \"      <td>0.190316</td>\\n\",\n       \"      <td>0.649659</td>\\n\",\n       \"      <td>0.274256</td>\\n\",\n       \"      <td>0.189348</td>\\n\",\n       \"      <td>0.273095</td>\\n\",\n       \"      <td>1.263043</td>\\n\",\n       \"      <td>0.364159</td>\\n\",\n       \"      <td>0.364050</td>\\n\",\n       \"      <td>0.364099</td>\\n\",\n       \"      <td>0.364050</td>\\n\",\n       \"      <td>0.364050</td>\\n\",\n       \"      <td>2.247826</td>\\n\",\n       \"      <td>0.135553</td>\\n\",\n       \"      <td>0.743375</td>\\n\",\n       \"      <td>0.216449</td>\\n\",\n       \"      <td>0.135553</td>\\n\",\n       \"      <td>0.216449</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.164415</td>\\n\",\n       \"      <td>0.217201</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.217201</td>\\n\",\n       \"      <td>1.050000</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.398597</td>\\n\",\n       \"      <td>0.344039</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.344039</td>\\n\",\n       \"      <td>0.223913</td>\\n\",\n       \"      <td>0.026621</td>\\n\",\n       \"      <td>0.084213</td>\\n\",\n       \"      <td>0.036166</td>\\n\",\n       \"      <td>0.022391</td>\\n\",\n       \"      <td>0.033589</td>\\n\",\n       \"      <td>0.243478</td>\\n\",\n       \"      <td>0.017972</td>\\n\",\n       \"      <td>0.093179</td>\\n\",\n       \"      <td>0.024955</td>\\n\",\n       \"      <td>0.004870</td>\\n\",\n       \"      <td>0.009101</td>\\n\",\n       \"      <td>0.156522</td>\\n\",\n       \"      <td>0.057145</td>\\n\",\n       \"      <td>0.055026</td>\\n\",\n       \"      <td>0.055455</td>\\n\",\n       \"      <td>0.055026</td>\\n\",\n       \"      <td>0.055026</td>\\n\",\n       \"      <td>0.243478</td>\\n\",\n       \"      <td>0.017850</td>\\n\",\n       \"      <td>0.093179</td>\\n\",\n       \"      <td>0.024741</td>\\n\",\n       \"      <td>0.017850</td>\\n\",\n       \"      <td>0.024741</td>\\n\",\n       \"      <td>1.423913</td>\\n\",\n       \"      <td>0.324017</td>\\n\",\n       \"      <td>0.569065</td>\\n\",\n       \"      <td>0.378375</td>\\n\",\n       \"      <td>0.324017</td>\\n\",\n       \"      <td>0.378375</td>\\n\",\n       \"      <td>1.958696</td>\\n\",\n       \"      <td>0.214672</td>\\n\",\n       \"      <td>0.710637</td>\\n\",\n       \"      <td>0.305508</td>\\n\",\n       \"      <td>0.213623</td>\\n\",\n       \"      <td>0.304259</td>\\n\",\n       \"      <td>1.336957</td>\\n\",\n       \"      <td>0.432867</td>\\n\",\n       \"      <td>0.432697</td>\\n\",\n       \"      <td>0.432774</td>\\n\",\n       \"      <td>0.432697</td>\\n\",\n       \"      <td>0.432697</td>\\n\",\n       \"      <td>2.321739</td>\\n\",\n       \"      <td>0.148096</td>\\n\",\n       \"      <td>0.790404</td>\\n\",\n       \"      <td>0.234416</td>\\n\",\n       \"      <td>0.148096</td>\\n\",\n       \"      <td>0.234416</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.503498</td>\\n\",\n       \"      <td>0.204190</td>\\n\",\n       \"      <td>0.268826</td>\\n\",\n       \"      <td>0.503498</td>\\n\",\n       \"      <td>0.268826</td>\\n\",\n       \"      <td>1.065217</td>\\n\",\n       \"      <td>0.419536</td>\\n\",\n       \"      <td>0.469231</td>\\n\",\n       \"      <td>0.405687</td>\\n\",\n       \"      <td>0.419536</td>\\n\",\n       \"      <td>0.405687</td>\\n\",\n       \"      <td>0.234783</td>\\n\",\n       \"      <td>0.049874</td>\\n\",\n       \"      <td>0.161402</td>\\n\",\n       \"      <td>0.069320</td>\\n\",\n       \"      <td>0.043266</td>\\n\",\n       \"      <td>0.064465</td>\\n\",\n       \"      <td>0.267391</td>\\n\",\n       \"      <td>0.034600</td>\\n\",\n       \"      <td>0.183895</td>\\n\",\n       \"      <td>0.050168</td>\\n\",\n       \"      <td>0.009892</td>\\n\",\n       \"      <td>0.018432</td>\\n\",\n       \"      <td>0.167391</td>\\n\",\n       \"      <td>0.096449</td>\\n\",\n       \"      <td>0.094084</td>\\n\",\n       \"      <td>0.094600</td>\\n\",\n       \"      <td>0.094084</td>\\n\",\n       \"      <td>0.094084</td>\\n\",\n       \"      <td>0.267391</td>\\n\",\n       \"      <td>0.034362</td>\\n\",\n       \"      <td>0.184028</td>\\n\",\n       \"      <td>0.049747</td>\\n\",\n       \"      <td>0.034362</td>\\n\",\n       \"      <td>0.049747</td>\\n\",\n       \"      <td>0.219571</td>\\n\",\n       \"      <td>0.250830</td>\\n\",\n       \"      <td>0.352861</td>\\n\",\n       \"      <td>0.359876</td>\\n\",\n       \"      <td>0.404645</td>\\n\",\n       \"      <td>0.456433</td>\\n\",\n       \"      <td>0.481405</td>\\n\",\n       \"      <td>0.358619</td>\\n\",\n       \"      <td>0.412908</td>\\n\",\n       \"      <td>0.377501</td>\\n\",\n       \"      <td>0.524112</td>\\n\",\n       \"      <td>0.551218</td>\\n\",\n       \"      <td>0.509473</td>\\n\",\n       \"      <td>0.521344</td>\\n\",\n       \"      <td>0.048586</td>\\n\",\n       \"      <td>0.055584</td>\\n\",\n       \"      <td>0.123804</td>\\n\",\n       \"      <td>0.071696</td>\\n\",\n       \"      <td>0.081008</td>\\n\",\n       \"      <td>0.077773</td>\\n\",\n       \"      <td>0.078284</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>17.254348</td>\\n\",\n       \"      <td>21.489130</td>\\n\",\n       \"      <td>43.819565</td>\\n\",\n       \"      <td>3674.576087</td>\\n\",\n       \"      <td>471.521739</td>\\n\",\n       \"      <td>12270.245652</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16574</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.263043</td>\\n\",\n       \"      <td>0.252609</td>\\n\",\n       \"      <td>0.262107</td>\\n\",\n       \"      <td>0.244179</td>\\n\",\n       \"      <td>0.252609</td>\\n\",\n       \"      <td>0.244179</td>\\n\",\n       \"      <td>1.780435</td>\\n\",\n       \"      <td>0.178611</td>\\n\",\n       \"      <td>0.358211</td>\\n\",\n       \"      <td>0.226484</td>\\n\",\n       \"      <td>0.178043</td>\\n\",\n       \"      <td>0.225943</td>\\n\",\n       \"      <td>1.441304</td>\\n\",\n       \"      <td>0.261572</td>\\n\",\n       \"      <td>0.261572</td>\\n\",\n       \"      <td>0.261572</td>\\n\",\n       \"      <td>0.261572</td>\\n\",\n       \"      <td>0.261572</td>\\n\",\n       \"      <td>2.289130</td>\\n\",\n       \"      <td>0.023875</td>\\n\",\n       \"      <td>0.442072</td>\\n\",\n       \"      <td>0.042822</td>\\n\",\n       \"      <td>0.023875</td>\\n\",\n       \"      <td>0.042822</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.069998</td>\\n\",\n       \"      <td>0.111887</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.111887</td>\\n\",\n       \"      <td>0.934783</td>\\n\",\n       \"      <td>0.311594</td>\\n\",\n       \"      <td>0.200224</td>\\n\",\n       \"      <td>0.232457</td>\\n\",\n       \"      <td>0.311594</td>\\n\",\n       \"      <td>0.232457</td>\\n\",\n       \"      <td>1.347826</td>\\n\",\n       \"      <td>0.341263</td>\\n\",\n       \"      <td>0.347394</td>\\n\",\n       \"      <td>0.325622</td>\\n\",\n       \"      <td>0.341168</td>\\n\",\n       \"      <td>0.325594</td>\\n\",\n       \"      <td>1.932609</td>\\n\",\n       \"      <td>0.232770</td>\\n\",\n       \"      <td>0.454718</td>\\n\",\n       \"      <td>0.291418</td>\\n\",\n       \"      <td>0.231862</td>\\n\",\n       \"      <td>0.290701</td>\\n\",\n       \"      <td>1.595652</td>\\n\",\n       \"      <td>0.346197</td>\\n\",\n       \"      <td>0.345995</td>\\n\",\n       \"      <td>0.346069</td>\\n\",\n       \"      <td>0.345995</td>\\n\",\n       \"      <td>0.345995</td>\\n\",\n       \"      <td>2.584783</td>\\n\",\n       \"      <td>0.031122</td>\\n\",\n       \"      <td>0.557804</td>\\n\",\n       \"      <td>0.055379</td>\\n\",\n       \"      <td>0.031122</td>\\n\",\n       \"      <td>0.055379</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.429051</td>\\n\",\n       \"      <td>0.091034</td>\\n\",\n       \"      <td>0.145850</td>\\n\",\n       \"      <td>0.429051</td>\\n\",\n       \"      <td>0.145850</td>\\n\",\n       \"      <td>0.960870</td>\\n\",\n       \"      <td>0.423600</td>\\n\",\n       \"      <td>0.266561</td>\\n\",\n       \"      <td>0.311024</td>\\n\",\n       \"      <td>0.423600</td>\\n\",\n       \"      <td>0.311024</td>\\n\",\n       \"      <td>1.323913</td>\\n\",\n       \"      <td>0.266268</td>\\n\",\n       \"      <td>0.480703</td>\\n\",\n       \"      <td>0.315414</td>\\n\",\n       \"      <td>0.264783</td>\\n\",\n       \"      <td>0.314293</td>\\n\",\n       \"      <td>1.810870</td>\\n\",\n       \"      <td>0.198967</td>\\n\",\n       \"      <td>0.616566</td>\\n\",\n       \"      <td>0.279186</td>\\n\",\n       \"      <td>0.181087</td>\\n\",\n       \"      <td>0.260571</td>\\n\",\n       \"      <td>1.167391</td>\\n\",\n       \"      <td>0.328166</td>\\n\",\n       \"      <td>0.326109</td>\\n\",\n       \"      <td>0.326983</td>\\n\",\n       \"      <td>0.326109</td>\\n\",\n       \"      <td>0.326109</td>\\n\",\n       \"      <td>1.991304</td>\\n\",\n       \"      <td>0.175953</td>\\n\",\n       \"      <td>0.657262</td>\\n\",\n       \"      <td>0.257118</td>\\n\",\n       \"      <td>0.175953</td>\\n\",\n       \"      <td>0.257118</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.139898</td>\\n\",\n       \"      <td>0.187583</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.187583</td>\\n\",\n       \"      <td>0.982609</td>\\n\",\n       \"      <td>0.327899</td>\\n\",\n       \"      <td>0.368707</td>\\n\",\n       \"      <td>0.319646</td>\\n\",\n       \"      <td>0.327536</td>\\n\",\n       \"      <td>0.319429</td>\\n\",\n       \"      <td>0.258696</td>\\n\",\n       \"      <td>0.025894</td>\\n\",\n       \"      <td>0.104815</td>\\n\",\n       \"      <td>0.039520</td>\\n\",\n       \"      <td>0.025870</td>\\n\",\n       \"      <td>0.039480</td>\\n\",\n       \"      <td>0.295652</td>\\n\",\n       \"      <td>0.006315</td>\\n\",\n       \"      <td>0.114277</td>\\n\",\n       \"      <td>0.011747</td>\\n\",\n       \"      <td>0.005913</td>\\n\",\n       \"      <td>0.011067</td>\\n\",\n       \"      <td>0.167391</td>\\n\",\n       \"      <td>0.063728</td>\\n\",\n       \"      <td>0.063728</td>\\n\",\n       \"      <td>0.063728</td>\\n\",\n       \"      <td>0.063728</td>\\n\",\n       \"      <td>0.063728</td>\\n\",\n       \"      <td>0.297826</td>\\n\",\n       \"      <td>0.003302</td>\\n\",\n       \"      <td>0.115364</td>\\n\",\n       \"      <td>0.006243</td>\\n\",\n       \"      <td>0.003302</td>\\n\",\n       \"      <td>0.006243</td>\\n\",\n       \"      <td>1.365217</td>\\n\",\n       \"      <td>0.314933</td>\\n\",\n       \"      <td>0.559564</td>\\n\",\n       \"      <td>0.369010</td>\\n\",\n       \"      <td>0.313363</td>\\n\",\n       \"      <td>0.367811</td>\\n\",\n       \"      <td>1.884783</td>\\n\",\n       \"      <td>0.224402</td>\\n\",\n       \"      <td>0.680697</td>\\n\",\n       \"      <td>0.311623</td>\\n\",\n       \"      <td>0.204792</td>\\n\",\n       \"      <td>0.291546</td>\\n\",\n       \"      <td>1.232609</td>\\n\",\n       \"      <td>0.391240</td>\\n\",\n       \"      <td>0.388378</td>\\n\",\n       \"      <td>0.389557</td>\\n\",\n       \"      <td>0.388378</td>\\n\",\n       \"      <td>0.388378</td>\\n\",\n       \"      <td>2.032609</td>\\n\",\n       \"      <td>0.194172</td>\\n\",\n       \"      <td>0.714649</td>\\n\",\n       \"      <td>0.281545</td>\\n\",\n       \"      <td>0.194172</td>\\n\",\n       \"      <td>0.281545</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.426859</td>\\n\",\n       \"      <td>0.168494</td>\\n\",\n       \"      <td>0.224906</td>\\n\",\n       \"      <td>0.426859</td>\\n\",\n       \"      <td>0.224906</td>\\n\",\n       \"      <td>0.993478</td>\\n\",\n       \"      <td>0.394860</td>\\n\",\n       \"      <td>0.440788</td>\\n\",\n       \"      <td>0.380945</td>\\n\",\n       \"      <td>0.394497</td>\\n\",\n       \"      <td>0.380727</td>\\n\",\n       \"      <td>0.276087</td>\\n\",\n       \"      <td>0.047179</td>\\n\",\n       \"      <td>0.184922</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.046592</td>\\n\",\n       \"      <td>0.070315</td>\\n\",\n       \"      <td>0.336957</td>\\n\",\n       \"      <td>0.013172</td>\\n\",\n       \"      <td>0.224457</td>\\n\",\n       \"      <td>0.023778</td>\\n\",\n       \"      <td>0.011802</td>\\n\",\n       \"      <td>0.022016</td>\\n\",\n       \"      <td>0.178261</td>\\n\",\n       \"      <td>0.107476</td>\\n\",\n       \"      <td>0.107320</td>\\n\",\n       \"      <td>0.107372</td>\\n\",\n       \"      <td>0.107320</td>\\n\",\n       \"      <td>0.107320</td>\\n\",\n       \"      <td>0.347826</td>\\n\",\n       \"      <td>0.007302</td>\\n\",\n       \"      <td>0.231010</td>\\n\",\n       \"      <td>0.013119</td>\\n\",\n       \"      <td>0.007302</td>\\n\",\n       \"      <td>0.013119</td>\\n\",\n       \"      <td>0.186897</td>\\n\",\n       \"      <td>0.218882</td>\\n\",\n       \"      <td>0.335039</td>\\n\",\n       \"      <td>0.330643</td>\\n\",\n       \"      <td>0.365902</td>\\n\",\n       \"      <td>0.419372</td>\\n\",\n       \"      <td>0.439389</td>\\n\",\n       \"      <td>0.315360</td>\\n\",\n       \"      <td>0.369778</td>\\n\",\n       \"      <td>0.381062</td>\\n\",\n       \"      <td>0.478599</td>\\n\",\n       \"      <td>0.499000</td>\\n\",\n       \"      <td>0.454279</td>\\n\",\n       \"      <td>0.465742</td>\\n\",\n       \"      <td>0.057027</td>\\n\",\n       \"      <td>0.065805</td>\\n\",\n       \"      <td>0.131233</td>\\n\",\n       \"      <td>0.085743</td>\\n\",\n       \"      <td>0.095316</td>\\n\",\n       \"      <td>0.091677</td>\\n\",\n       \"      <td>0.092186</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>12.130435</td>\\n\",\n       \"      <td>124.906522</td>\\n\",\n       \"      <td>153.013043</td>\\n\",\n       \"      <td>3240.113043</td>\\n\",\n       \"      <td>452.704348</td>\\n\",\n       \"      <td>12215.660870</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15966</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.367391</td>\\n\",\n       \"      <td>0.273478</td>\\n\",\n       \"      <td>0.283478</td>\\n\",\n       \"      <td>0.264282</td>\\n\",\n       \"      <td>0.273478</td>\\n\",\n       \"      <td>0.264282</td>\\n\",\n       \"      <td>1.965217</td>\\n\",\n       \"      <td>0.196739</td>\\n\",\n       \"      <td>0.394894</td>\\n\",\n       \"      <td>0.249329</td>\\n\",\n       \"      <td>0.196522</td>\\n\",\n       \"      <td>0.249134</td>\\n\",\n       \"      <td>1.510870</td>\\n\",\n       \"      <td>0.272216</td>\\n\",\n       \"      <td>0.272171</td>\\n\",\n       \"      <td>0.272191</td>\\n\",\n       \"      <td>0.272171</td>\\n\",\n       \"      <td>0.272171</td>\\n\",\n       \"      <td>2.630435</td>\\n\",\n       \"      <td>0.052939</td>\\n\",\n       \"      <td>0.503788</td>\\n\",\n       \"      <td>0.089019</td>\\n\",\n       \"      <td>0.052939</td>\\n\",\n       \"      <td>0.089019</td>\\n\",\n       \"      <td>0.356522</td>\\n\",\n       \"      <td>0.356522</td>\\n\",\n       \"      <td>0.075818</td>\\n\",\n       \"      <td>0.121094</td>\\n\",\n       \"      <td>0.356522</td>\\n\",\n       \"      <td>0.121094</td>\\n\",\n       \"      <td>0.991304</td>\\n\",\n       \"      <td>0.330435</td>\\n\",\n       \"      <td>0.211266</td>\\n\",\n       \"      <td>0.245467</td>\\n\",\n       \"      <td>0.330435</td>\\n\",\n       \"      <td>0.245467</td>\\n\",\n       \"      <td>1.436957</td>\\n\",\n       \"      <td>0.355902</td>\\n\",\n       \"      <td>0.361573</td>\\n\",\n       \"      <td>0.338773</td>\\n\",\n       \"      <td>0.355902</td>\\n\",\n       \"      <td>0.338773</td>\\n\",\n       \"      <td>2.130435</td>\\n\",\n       \"      <td>0.248781</td>\\n\",\n       \"      <td>0.486270</td>\\n\",\n       \"      <td>0.311299</td>\\n\",\n       \"      <td>0.248543</td>\\n\",\n       \"      <td>0.311085</td>\\n\",\n       \"      <td>1.669565</td>\\n\",\n       \"      <td>0.353807</td>\\n\",\n       \"      <td>0.353653</td>\\n\",\n       \"      <td>0.353719</td>\\n\",\n       \"      <td>0.353653</td>\\n\",\n       \"      <td>0.353653</td>\\n\",\n       \"      <td>2.963043</td>\\n\",\n       \"      <td>0.065357</td>\\n\",\n       \"      <td>0.611197</td>\\n\",\n       \"      <td>0.109582</td>\\n\",\n       \"      <td>0.065357</td>\\n\",\n       \"      <td>0.109582</td>\\n\",\n       \"      <td>0.356522</td>\\n\",\n       \"      <td>0.501291</td>\\n\",\n       \"      <td>0.105561</td>\\n\",\n       \"      <td>0.168643</td>\\n\",\n       \"      <td>0.501291</td>\\n\",\n       \"      <td>0.168643</td>\\n\",\n       \"      <td>1.021739</td>\\n\",\n       \"      <td>0.435734</td>\\n\",\n       \"      <td>0.272923</td>\\n\",\n       \"      <td>0.318615</td>\\n\",\n       \"      <td>0.435734</td>\\n\",\n       \"      <td>0.318615</td>\\n\",\n       \"      <td>1.410870</td>\\n\",\n       \"      <td>0.282174</td>\\n\",\n       \"      <td>0.512198</td>\\n\",\n       \"      <td>0.335666</td>\\n\",\n       \"      <td>0.282174</td>\\n\",\n       \"      <td>0.335666</td>\\n\",\n       \"      <td>1.973913</td>\\n\",\n       \"      <td>0.200600</td>\\n\",\n       \"      <td>0.675733</td>\\n\",\n       \"      <td>0.288713</td>\\n\",\n       \"      <td>0.197391</td>\\n\",\n       \"      <td>0.284892</td>\\n\",\n       \"      <td>1.180435</td>\\n\",\n       \"      <td>0.327544</td>\\n\",\n       \"      <td>0.326685</td>\\n\",\n       \"      <td>0.327041</td>\\n\",\n       \"      <td>0.326685</td>\\n\",\n       \"      <td>0.326685</td>\\n\",\n       \"      <td>2.310870</td>\\n\",\n       \"      <td>0.143963</td>\\n\",\n       \"      <td>0.758657</td>\\n\",\n       \"      <td>0.226948</td>\\n\",\n       \"      <td>0.143963</td>\\n\",\n       \"      <td>0.226948</td>\\n\",\n       \"      <td>0.376087</td>\\n\",\n       \"      <td>0.376087</td>\\n\",\n       \"      <td>0.140778</td>\\n\",\n       \"      <td>0.192307</td>\\n\",\n       \"      <td>0.376087</td>\\n\",\n       \"      <td>0.192307</td>\\n\",\n       \"      <td>1.010870</td>\\n\",\n       \"      <td>0.336957</td>\\n\",\n       \"      <td>0.384085</td>\\n\",\n       \"      <td>0.330013</td>\\n\",\n       \"      <td>0.336957</td>\\n\",\n       \"      <td>0.330013</td>\\n\",\n       \"      <td>0.258696</td>\\n\",\n       \"      <td>0.026790</td>\\n\",\n       \"      <td>0.096325</td>\\n\",\n       \"      <td>0.039259</td>\\n\",\n       \"      <td>0.025870</td>\\n\",\n       \"      <td>0.038703</td>\\n\",\n       \"      <td>0.313043</td>\\n\",\n       \"      <td>0.012342</td>\\n\",\n       \"      <td>0.122050</td>\\n\",\n       \"      <td>0.019680</td>\\n\",\n       \"      <td>0.006261</td>\\n\",\n       \"      <td>0.011714</td>\\n\",\n       \"      <td>0.173913</td>\\n\",\n       \"      <td>0.059982</td>\\n\",\n       \"      <td>0.059493</td>\\n\",\n       \"      <td>0.059681</td>\\n\",\n       \"      <td>0.059493</td>\\n\",\n       \"      <td>0.059493</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.011044</td>\\n\",\n       \"      <td>0.124296</td>\\n\",\n       \"      <td>0.017311</td>\\n\",\n       \"      <td>0.011044</td>\\n\",\n       \"      <td>0.017311</td>\\n\",\n       \"      <td>1.439130</td>\\n\",\n       \"      <td>0.327534</td>\\n\",\n       \"      <td>0.582721</td>\\n\",\n       \"      <td>0.384466</td>\\n\",\n       \"      <td>0.327534</td>\\n\",\n       \"      <td>0.384466</td>\\n\",\n       \"      <td>2.039130</td>\\n\",\n       \"      <td>0.224002</td>\\n\",\n       \"      <td>0.734338</td>\\n\",\n       \"      <td>0.318451</td>\\n\",\n       \"      <td>0.220482</td>\\n\",\n       \"      <td>0.314470</td>\\n\",\n       \"      <td>1.254348</td>\\n\",\n       \"      <td>0.395212</td>\\n\",\n       \"      <td>0.393781</td>\\n\",\n       \"      <td>0.394340</td>\\n\",\n       \"      <td>0.393781</td>\\n\",\n       \"      <td>0.393781</td>\\n\",\n       \"      <td>2.384783</td>\\n\",\n       \"      <td>0.157481</td>\\n\",\n       \"      <td>0.808159</td>\\n\",\n       \"      <td>0.245777</td>\\n\",\n       \"      <td>0.157481</td>\\n\",\n       \"      <td>0.245777</td>\\n\",\n       \"      <td>0.376087</td>\\n\",\n       \"      <td>0.482847</td>\\n\",\n       \"      <td>0.179631</td>\\n\",\n       \"      <td>0.244430</td>\\n\",\n       \"      <td>0.482847</td>\\n\",\n       \"      <td>0.244430</td>\\n\",\n       \"      <td>1.028261</td>\\n\",\n       \"      <td>0.405937</td>\\n\",\n       \"      <td>0.454858</td>\\n\",\n       \"      <td>0.392239</td>\\n\",\n       \"      <td>0.405937</td>\\n\",\n       \"      <td>0.392239</td>\\n\",\n       \"      <td>0.265217</td>\\n\",\n       \"      <td>0.047557</td>\\n\",\n       \"      <td>0.169794</td>\\n\",\n       \"      <td>0.069487</td>\\n\",\n       \"      <td>0.045615</td>\\n\",\n       \"      <td>0.067882</td>\\n\",\n       \"      <td>0.354348</td>\\n\",\n       \"      <td>0.022163</td>\\n\",\n       \"      <td>0.219551</td>\\n\",\n       \"      <td>0.035884</td>\\n\",\n       \"      <td>0.011742</td>\\n\",\n       \"      <td>0.021866</td>\\n\",\n       \"      <td>0.178261</td>\\n\",\n       \"      <td>0.102298</td>\\n\",\n       \"      <td>0.101726</td>\\n\",\n       \"      <td>0.101942</td>\\n\",\n       \"      <td>0.101726</td>\\n\",\n       \"      <td>0.101726</td>\\n\",\n       \"      <td>0.371739</td>\\n\",\n       \"      <td>0.019621</td>\\n\",\n       \"      <td>0.224311</td>\\n\",\n       \"      <td>0.031309</td>\\n\",\n       \"      <td>0.019621</td>\\n\",\n       \"      <td>0.031309</td>\\n\",\n       \"      <td>0.207686</td>\\n\",\n       \"      <td>0.241963</td>\\n\",\n       \"      <td>0.330960</td>\\n\",\n       \"      <td>0.360693</td>\\n\",\n       \"      <td>0.404364</td>\\n\",\n       \"      <td>0.435411</td>\\n\",\n       \"      <td>0.456531</td>\\n\",\n       \"      <td>0.341917</td>\\n\",\n       \"      <td>0.403845</td>\\n\",\n       \"      <td>0.364700</td>\\n\",\n       \"      <td>0.518935</td>\\n\",\n       \"      <td>0.549278</td>\\n\",\n       \"      <td>0.467921</td>\\n\",\n       \"      <td>0.479196</td>\\n\",\n       \"      <td>0.051525</td>\\n\",\n       \"      <td>0.060251</td>\\n\",\n       \"      <td>0.130940</td>\\n\",\n       \"      <td>0.083779</td>\\n\",\n       \"      <td>0.093955</td>\\n\",\n       \"      <td>0.094239</td>\\n\",\n       \"      <td>0.095667</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>17.052174</td>\\n\",\n       \"      <td>62.245652</td>\\n\",\n       \"      <td>88.960870</td>\\n\",\n       \"      <td>3524.852174</td>\\n\",\n       \"      <td>456.723913</td>\\n\",\n       \"      <td>12030.665217</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20417</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.230435</td>\\n\",\n       \"      <td>0.246087</td>\\n\",\n       \"      <td>0.253608</td>\\n\",\n       \"      <td>0.236781</td>\\n\",\n       \"      <td>0.246087</td>\\n\",\n       \"      <td>0.236781</td>\\n\",\n       \"      <td>1.706522</td>\\n\",\n       \"      <td>0.170652</td>\\n\",\n       \"      <td>0.345732</td>\\n\",\n       \"      <td>0.217724</td>\\n\",\n       \"      <td>0.170652</td>\\n\",\n       \"      <td>0.217724</td>\\n\",\n       \"      <td>1.378261</td>\\n\",\n       \"      <td>0.254592</td>\\n\",\n       \"      <td>0.254592</td>\\n\",\n       \"      <td>0.254592</td>\\n\",\n       \"      <td>0.254592</td>\\n\",\n       \"      <td>0.254592</td>\\n\",\n       \"      <td>2.491304</td>\\n\",\n       \"      <td>0.035769</td>\\n\",\n       \"      <td>0.479741</td>\\n\",\n       \"      <td>0.063675</td>\\n\",\n       \"      <td>0.035769</td>\\n\",\n       \"      <td>0.063675</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.072841</td>\\n\",\n       \"      <td>0.115411</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.115411</td>\\n\",\n       \"      <td>0.908696</td>\\n\",\n       \"      <td>0.302899</td>\\n\",\n       \"      <td>0.193360</td>\\n\",\n       \"      <td>0.224755</td>\\n\",\n       \"      <td>0.302899</td>\\n\",\n       \"      <td>0.224755</td>\\n\",\n       \"      <td>1.323913</td>\\n\",\n       \"      <td>0.334607</td>\\n\",\n       \"      <td>0.338707</td>\\n\",\n       \"      <td>0.317812</td>\\n\",\n       \"      <td>0.334293</td>\\n\",\n       \"      <td>0.317742</td>\\n\",\n       \"      <td>1.897826</td>\\n\",\n       \"      <td>0.225866</td>\\n\",\n       \"      <td>0.443900</td>\\n\",\n       \"      <td>0.283324</td>\\n\",\n       \"      <td>0.225407</td>\\n\",\n       \"      <td>0.283175</td>\\n\",\n       \"      <td>1.567391</td>\\n\",\n       \"      <td>0.340371</td>\\n\",\n       \"      <td>0.340002</td>\\n\",\n       \"      <td>0.340093</td>\\n\",\n       \"      <td>0.340002</td>\\n\",\n       \"      <td>0.340002</td>\\n\",\n       \"      <td>2.808696</td>\\n\",\n       \"      <td>0.046161</td>\\n\",\n       \"      <td>0.593543</td>\\n\",\n       \"      <td>0.080975</td>\\n\",\n       \"      <td>0.046161</td>\\n\",\n       \"      <td>0.080975</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.463666</td>\\n\",\n       \"      <td>0.101551</td>\\n\",\n       \"      <td>0.160711</td>\\n\",\n       \"      <td>0.463666</td>\\n\",\n       \"      <td>0.160711</td>\\n\",\n       \"      <td>0.945652</td>\\n\",\n       \"      <td>0.413483</td>\\n\",\n       \"      <td>0.259405</td>\\n\",\n       \"      <td>0.302171</td>\\n\",\n       \"      <td>0.413362</td>\\n\",\n       \"      <td>0.302158</td>\\n\",\n       \"      <td>1.295652</td>\\n\",\n       \"      <td>0.260254</td>\\n\",\n       \"      <td>0.472587</td>\\n\",\n       \"      <td>0.309172</td>\\n\",\n       \"      <td>0.259130</td>\\n\",\n       \"      <td>0.308781</td>\\n\",\n       \"      <td>1.745652</td>\\n\",\n       \"      <td>0.183538</td>\\n\",\n       \"      <td>0.602777</td>\\n\",\n       \"      <td>0.260960</td>\\n\",\n       \"      <td>0.174565</td>\\n\",\n       \"      <td>0.252659</td>\\n\",\n       \"      <td>1.130435</td>\\n\",\n       \"      <td>0.326001</td>\\n\",\n       \"      <td>0.323991</td>\\n\",\n       \"      <td>0.324624</td>\\n\",\n       \"      <td>0.323991</td>\\n\",\n       \"      <td>0.323991</td>\\n\",\n       \"      <td>2.063043</td>\\n\",\n       \"      <td>0.151703</td>\\n\",\n       \"      <td>0.681483</td>\\n\",\n       \"      <td>0.230146</td>\\n\",\n       \"      <td>0.151703</td>\\n\",\n       \"      <td>0.230146</td>\\n\",\n       \"      <td>0.380435</td>\\n\",\n       \"      <td>0.380435</td>\\n\",\n       \"      <td>0.154975</td>\\n\",\n       \"      <td>0.205308</td>\\n\",\n       \"      <td>0.380435</td>\\n\",\n       \"      <td>0.205308</td>\\n\",\n       \"      <td>0.950000</td>\\n\",\n       \"      <td>0.316667</td>\\n\",\n       \"      <td>0.364095</td>\\n\",\n       \"      <td>0.309685</td>\\n\",\n       \"      <td>0.316667</td>\\n\",\n       \"      <td>0.309685</td>\\n\",\n       \"      <td>0.300000</td>\\n\",\n       \"      <td>0.030000</td>\\n\",\n       \"      <td>0.125536</td>\\n\",\n       \"      <td>0.046009</td>\\n\",\n       \"      <td>0.030000</td>\\n\",\n       \"      <td>0.046009</td>\\n\",\n       \"      <td>0.415217</td>\\n\",\n       \"      <td>0.009838</td>\\n\",\n       \"      <td>0.172278</td>\\n\",\n       \"      <td>0.018186</td>\\n\",\n       \"      <td>0.008304</td>\\n\",\n       \"      <td>0.015602</td>\\n\",\n       \"      <td>0.184783</td>\\n\",\n       \"      <td>0.066530</td>\\n\",\n       \"      <td>0.066530</td>\\n\",\n       \"      <td>0.066530</td>\\n\",\n       \"      <td>0.066530</td>\\n\",\n       \"      <td>0.066530</td>\\n\",\n       \"      <td>0.428261</td>\\n\",\n       \"      <td>0.007804</td>\\n\",\n       \"      <td>0.177097</td>\\n\",\n       \"      <td>0.014536</td>\\n\",\n       \"      <td>0.007804</td>\\n\",\n       \"      <td>0.014536</td>\\n\",\n       \"      <td>1.347826</td>\\n\",\n       \"      <td>0.313069</td>\\n\",\n       \"      <td>0.553423</td>\\n\",\n       \"      <td>0.365863</td>\\n\",\n       \"      <td>0.311632</td>\\n\",\n       \"      <td>0.365302</td>\\n\",\n       \"      <td>1.834783</td>\\n\",\n       \"      <td>0.211966</td>\\n\",\n       \"      <td>0.673976</td>\\n\",\n       \"      <td>0.297296</td>\\n\",\n       \"      <td>0.201613</td>\\n\",\n       \"      <td>0.287900</td>\\n\",\n       \"      <td>1.204348</td>\\n\",\n       \"      <td>0.401210</td>\\n\",\n       \"      <td>0.398647</td>\\n\",\n       \"      <td>0.399465</td>\\n\",\n       \"      <td>0.398647</td>\\n\",\n       \"      <td>0.398647</td>\\n\",\n       \"      <td>2.134783</td>\\n\",\n       \"      <td>0.169225</td>\\n\",\n       \"      <td>0.742162</td>\\n\",\n       \"      <td>0.254362</td>\\n\",\n       \"      <td>0.169225</td>\\n\",\n       \"      <td>0.254362</td>\\n\",\n       \"      <td>0.380435</td>\\n\",\n       \"      <td>0.481081</td>\\n\",\n       \"      <td>0.196942</td>\\n\",\n       \"      <td>0.258868</td>\\n\",\n       \"      <td>0.481081</td>\\n\",\n       \"      <td>0.258868</td>\\n\",\n       \"      <td>0.971739</td>\\n\",\n       \"      <td>0.386919</td>\\n\",\n       \"      <td>0.435188</td>\\n\",\n       \"      <td>0.372605</td>\\n\",\n       \"      <td>0.386799</td>\\n\",\n       \"      <td>0.372532</td>\\n\",\n       \"      <td>0.317391</td>\\n\",\n       \"      <td>0.051691</td>\\n\",\n       \"      <td>0.206072</td>\\n\",\n       \"      <td>0.078006</td>\\n\",\n       \"      <td>0.051470</td>\\n\",\n       \"      <td>0.077813</td>\\n\",\n       \"      <td>0.454348</td>\\n\",\n       \"      <td>0.017407</td>\\n\",\n       \"      <td>0.282818</td>\\n\",\n       \"      <td>0.031602</td>\\n\",\n       \"      <td>0.014281</td>\\n\",\n       \"      <td>0.026698</td>\\n\",\n       \"      <td>0.193478</td>\\n\",\n       \"      <td>0.105799</td>\\n\",\n       \"      <td>0.105799</td>\\n\",\n       \"      <td>0.105799</td>\\n\",\n       \"      <td>0.105799</td>\\n\",\n       \"      <td>0.105799</td>\\n\",\n       \"      <td>0.476087</td>\\n\",\n       \"      <td>0.013711</td>\\n\",\n       \"      <td>0.290117</td>\\n\",\n       \"      <td>0.024997</td>\\n\",\n       \"      <td>0.013711</td>\\n\",\n       \"      <td>0.024997</td>\\n\",\n       \"      <td>0.192873</td>\\n\",\n       \"      <td>0.222762</td>\\n\",\n       \"      <td>0.302936</td>\\n\",\n       \"      <td>0.331940</td>\\n\",\n       \"      <td>0.374835</td>\\n\",\n       \"      <td>0.414851</td>\\n\",\n       \"      <td>0.435327</td>\\n\",\n       \"      <td>0.327671</td>\\n\",\n       \"      <td>0.377393</td>\\n\",\n       \"      <td>0.375279</td>\\n\",\n       \"      <td>0.480473</td>\\n\",\n       \"      <td>0.509140</td>\\n\",\n       \"      <td>0.483033</td>\\n\",\n       \"      <td>0.494557</td>\\n\",\n       \"      <td>0.061273</td>\\n\",\n       \"      <td>0.072742</td>\\n\",\n       \"      <td>0.129561</td>\\n\",\n       \"      <td>0.104693</td>\\n\",\n       \"      <td>0.115693</td>\\n\",\n       \"      <td>0.096528</td>\\n\",\n       \"      <td>0.097513</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>14.545652</td>\\n\",\n       \"      <td>75.341304</td>\\n\",\n       \"      <td>101.960870</td>\\n\",\n       \"      <td>3530.776087</td>\\n\",\n       \"      <td>469.758696</td>\\n\",\n       \"      <td>12490.252174</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18548</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.332609</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.275329</td>\\n\",\n       \"      <td>0.257042</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.257042</td>\\n\",\n       \"      <td>1.852174</td>\\n\",\n       \"      <td>0.185320</td>\\n\",\n       \"      <td>0.375193</td>\\n\",\n       \"      <td>0.235839</td>\\n\",\n       \"      <td>0.185217</td>\\n\",\n       \"      <td>0.235723</td>\\n\",\n       \"      <td>1.502174</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>0.269348</td>\\n\",\n       \"      <td>2.491304</td>\\n\",\n       \"      <td>0.056697</td>\\n\",\n       \"      <td>0.481023</td>\\n\",\n       \"      <td>0.096688</td>\\n\",\n       \"      <td>0.056697</td>\\n\",\n       \"      <td>0.096688</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.067361</td>\\n\",\n       \"      <td>0.107764</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.107764</td>\\n\",\n       \"      <td>0.947826</td>\\n\",\n       \"      <td>0.315942</td>\\n\",\n       \"      <td>0.200508</td>\\n\",\n       \"      <td>0.233600</td>\\n\",\n       \"      <td>0.315942</td>\\n\",\n       \"      <td>0.233600</td>\\n\",\n       \"      <td>1.417391</td>\\n\",\n       \"      <td>0.348747</td>\\n\",\n       \"      <td>0.352029</td>\\n\",\n       \"      <td>0.330990</td>\\n\",\n       \"      <td>0.348747</td>\\n\",\n       \"      <td>0.330990</td>\\n\",\n       \"      <td>2.032609</td>\\n\",\n       \"      <td>0.239071</td>\\n\",\n       \"      <td>0.466842</td>\\n\",\n       \"      <td>0.298852</td>\\n\",\n       \"      <td>0.238668</td>\\n\",\n       \"      <td>0.298584</td>\\n\",\n       \"      <td>1.680435</td>\\n\",\n       \"      <td>0.347557</td>\\n\",\n       \"      <td>0.347357</td>\\n\",\n       \"      <td>0.347436</td>\\n\",\n       \"      <td>0.347357</td>\\n\",\n       \"      <td>0.347357</td>\\n\",\n       \"      <td>2.800000</td>\\n\",\n       \"      <td>0.071924</td>\\n\",\n       \"      <td>0.589017</td>\\n\",\n       \"      <td>0.121428</td>\\n\",\n       \"      <td>0.071924</td>\\n\",\n       \"      <td>0.121428</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.436310</td>\\n\",\n       \"      <td>0.091779</td>\\n\",\n       \"      <td>0.146118</td>\\n\",\n       \"      <td>0.436310</td>\\n\",\n       \"      <td>0.146118</td>\\n\",\n       \"      <td>0.986957</td>\\n\",\n       \"      <td>0.420320</td>\\n\",\n       \"      <td>0.261173</td>\\n\",\n       \"      <td>0.305597</td>\\n\",\n       \"      <td>0.420320</td>\\n\",\n       \"      <td>0.305597</td>\\n\",\n       \"      <td>1.384783</td>\\n\",\n       \"      <td>0.279239</td>\\n\",\n       \"      <td>0.494273</td>\\n\",\n       \"      <td>0.328947</td>\\n\",\n       \"      <td>0.276957</td>\\n\",\n       \"      <td>0.326938</td>\\n\",\n       \"      <td>1.873913</td>\\n\",\n       \"      <td>0.199415</td>\\n\",\n       \"      <td>0.639712</td>\\n\",\n       \"      <td>0.283617</td>\\n\",\n       \"      <td>0.187391</td>\\n\",\n       \"      <td>0.270281</td>\\n\",\n       \"      <td>1.202174</td>\\n\",\n       \"      <td>0.338384</td>\\n\",\n       \"      <td>0.337488</td>\\n\",\n       \"      <td>0.337876</td>\\n\",\n       \"      <td>0.337488</td>\\n\",\n       \"      <td>0.337488</td>\\n\",\n       \"      <td>2.097826</td>\\n\",\n       \"      <td>0.156265</td>\\n\",\n       \"      <td>0.696846</td>\\n\",\n       \"      <td>0.238110</td>\\n\",\n       \"      <td>0.156265</td>\\n\",\n       \"      <td>0.238110</td>\\n\",\n       \"      <td>0.354348</td>\\n\",\n       \"      <td>0.354348</td>\\n\",\n       \"      <td>0.141198</td>\\n\",\n       \"      <td>0.186834</td>\\n\",\n       \"      <td>0.354348</td>\\n\",\n       \"      <td>0.186834</td>\\n\",\n       \"      <td>0.980435</td>\\n\",\n       \"      <td>0.326812</td>\\n\",\n       \"      <td>0.365078</td>\\n\",\n       \"      <td>0.316231</td>\\n\",\n       \"      <td>0.326812</td>\\n\",\n       \"      <td>0.316231</td>\\n\",\n       \"      <td>0.328261</td>\\n\",\n       \"      <td>0.033285</td>\\n\",\n       \"      <td>0.136620</td>\\n\",\n       \"      <td>0.050491</td>\\n\",\n       \"      <td>0.032826</td>\\n\",\n       \"      <td>0.050182</td>\\n\",\n       \"      <td>0.391304</td>\\n\",\n       \"      <td>0.013082</td>\\n\",\n       \"      <td>0.159303</td>\\n\",\n       \"      <td>0.022636</td>\\n\",\n       \"      <td>0.007826</td>\\n\",\n       \"      <td>0.014660</td>\\n\",\n       \"      <td>0.210870</td>\\n\",\n       \"      <td>0.072610</td>\\n\",\n       \"      <td>0.072417</td>\\n\",\n       \"      <td>0.072486</td>\\n\",\n       \"      <td>0.072417</td>\\n\",\n       \"      <td>0.072417</td>\\n\",\n       \"      <td>0.393478</td>\\n\",\n       \"      <td>0.012374</td>\\n\",\n       \"      <td>0.159575</td>\\n\",\n       \"      <td>0.021370</td>\\n\",\n       \"      <td>0.012374</td>\\n\",\n       \"      <td>0.021370</td>\\n\",\n       \"      <td>1.430435</td>\\n\",\n       \"      <td>0.323811</td>\\n\",\n       \"      <td>0.562992</td>\\n\",\n       \"      <td>0.376591</td>\\n\",\n       \"      <td>0.321476</td>\\n\",\n       \"      <td>0.374547</td>\\n\",\n       \"      <td>1.950000</td>\\n\",\n       \"      <td>0.223593</td>\\n\",\n       \"      <td>0.699807</td>\\n\",\n       \"      <td>0.314533</td>\\n\",\n       \"      <td>0.210711</td>\\n\",\n       \"      <td>0.300246</td>\\n\",\n       \"      <td>1.286957</td>\\n\",\n       \"      <td>0.401439</td>\\n\",\n       \"      <td>0.400252</td>\\n\",\n       \"      <td>0.400769</td>\\n\",\n       \"      <td>0.400252</td>\\n\",\n       \"      <td>0.400252</td>\\n\",\n       \"      <td>2.171739</td>\\n\",\n       \"      <td>0.173002</td>\\n\",\n       \"      <td>0.749870</td>\\n\",\n       \"      <td>0.260954</td>\\n\",\n       \"      <td>0.173002</td>\\n\",\n       \"      <td>0.260954</td>\\n\",\n       \"      <td>0.354348</td>\\n\",\n       \"      <td>0.438214</td>\\n\",\n       \"      <td>0.172169</td>\\n\",\n       \"      <td>0.228541</td>\\n\",\n       \"      <td>0.438214</td>\\n\",\n       \"      <td>0.228541</td>\\n\",\n       \"      <td>1.006522</td>\\n\",\n       \"      <td>0.393099</td>\\n\",\n       \"      <td>0.431490</td>\\n\",\n       \"      <td>0.375351</td>\\n\",\n       \"      <td>0.393099</td>\\n\",\n       \"      <td>0.375351</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.055778</td>\\n\",\n       \"      <td>0.216700</td>\\n\",\n       \"      <td>0.082664</td>\\n\",\n       \"      <td>0.054154</td>\\n\",\n       \"      <td>0.081685</td>\\n\",\n       \"      <td>0.441304</td>\\n\",\n       \"      <td>0.024476</td>\\n\",\n       \"      <td>0.263475</td>\\n\",\n       \"      <td>0.040890</td>\\n\",\n       \"      <td>0.013669</td>\\n\",\n       \"      <td>0.025492</td>\\n\",\n       \"      <td>0.221739</td>\\n\",\n       \"      <td>0.113556</td>\\n\",\n       \"      <td>0.112699</td>\\n\",\n       \"      <td>0.112861</td>\\n\",\n       \"      <td>0.112699</td>\\n\",\n       \"      <td>0.112699</td>\\n\",\n       \"      <td>0.445652</td>\\n\",\n       \"      <td>0.023328</td>\\n\",\n       \"      <td>0.265277</td>\\n\",\n       \"      <td>0.038857</td>\\n\",\n       \"      <td>0.023328</td>\\n\",\n       \"      <td>0.038857</td>\\n\",\n       \"      <td>0.194397</td>\\n\",\n       \"      <td>0.228572</td>\\n\",\n       \"      <td>0.320178</td>\\n\",\n       \"      <td>0.348968</td>\\n\",\n       \"      <td>0.383363</td>\\n\",\n       \"      <td>0.403613</td>\\n\",\n       \"      <td>0.427234</td>\\n\",\n       \"      <td>0.325393</td>\\n\",\n       \"      <td>0.381326</td>\\n\",\n       \"      <td>0.371673</td>\\n\",\n       \"      <td>0.496726</td>\\n\",\n       \"      <td>0.515590</td>\\n\",\n       \"      <td>0.454797</td>\\n\",\n       \"      <td>0.467676</td>\\n\",\n       \"      <td>0.061558</td>\\n\",\n       \"      <td>0.075191</td>\\n\",\n       \"      <td>0.132986</td>\\n\",\n       \"      <td>0.107242</td>\\n\",\n       \"      <td>0.114522</td>\\n\",\n       \"      <td>0.104049</td>\\n\",\n       \"      <td>0.105513</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>14.402174</td>\\n\",\n       \"      <td>40.097826</td>\\n\",\n       \"      <td>61.019565</td>\\n\",\n       \"      <td>3500.791304</td>\\n\",\n       \"      <td>471.541304</td>\\n\",\n       \"      <td>12128.019565</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20754</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.306522</td>\\n\",\n       \"      <td>0.261304</td>\\n\",\n       \"      <td>0.270471</td>\\n\",\n       \"      <td>0.251048</td>\\n\",\n       \"      <td>0.261304</td>\\n\",\n       \"      <td>0.251048</td>\\n\",\n       \"      <td>1.782609</td>\\n\",\n       \"      <td>0.179627</td>\\n\",\n       \"      <td>0.357412</td>\\n\",\n       \"      <td>0.226187</td>\\n\",\n       \"      <td>0.178261</td>\\n\",\n       \"      <td>0.225067</td>\\n\",\n       \"      <td>1.460870</td>\\n\",\n       \"      <td>0.266411</td>\\n\",\n       \"      <td>0.266202</td>\\n\",\n       \"      <td>0.266282</td>\\n\",\n       \"      <td>0.266202</td>\\n\",\n       \"      <td>0.266202</td>\\n\",\n       \"      <td>2.345652</td>\\n\",\n       \"      <td>0.044875</td>\\n\",\n       \"      <td>0.447164</td>\\n\",\n       \"      <td>0.075211</td>\\n\",\n       \"      <td>0.044875</td>\\n\",\n       \"      <td>0.075211</td>\\n\",\n       \"      <td>0.382609</td>\\n\",\n       \"      <td>0.382609</td>\\n\",\n       \"      <td>0.082738</td>\\n\",\n       \"      <td>0.131965</td>\\n\",\n       \"      <td>0.382609</td>\\n\",\n       \"      <td>0.131965</td>\\n\",\n       \"      <td>0.978261</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.207893</td>\\n\",\n       \"      <td>0.240747</td>\\n\",\n       \"      <td>0.326087</td>\\n\",\n       \"      <td>0.240747</td>\\n\",\n       \"      <td>1.376087</td>\\n\",\n       \"      <td>0.347615</td>\\n\",\n       \"      <td>0.351798</td>\\n\",\n       \"      <td>0.329676</td>\\n\",\n       \"      <td>0.347615</td>\\n\",\n       \"      <td>0.329676</td>\\n\",\n       \"      <td>1.950000</td>\\n\",\n       \"      <td>0.234661</td>\\n\",\n       \"      <td>0.453251</td>\\n\",\n       \"      <td>0.291777</td>\\n\",\n       \"      <td>0.232769</td>\\n\",\n       \"      <td>0.290245</td>\\n\",\n       \"      <td>1.619565</td>\\n\",\n       \"      <td>0.351603</td>\\n\",\n       \"      <td>0.351273</td>\\n\",\n       \"      <td>0.351399</td>\\n\",\n       \"      <td>0.351273</td>\\n\",\n       \"      <td>0.351273</td>\\n\",\n       \"      <td>2.636957</td>\\n\",\n       \"      <td>0.058407</td>\\n\",\n       \"      <td>0.558638</td>\\n\",\n       \"      <td>0.096906</td>\\n\",\n       \"      <td>0.058407</td>\\n\",\n       \"      <td>0.096906</td>\\n\",\n       \"      <td>0.382609</td>\\n\",\n       \"      <td>0.515622</td>\\n\",\n       \"      <td>0.109909</td>\\n\",\n       \"      <td>0.174967</td>\\n\",\n       \"      <td>0.515622</td>\\n\",\n       \"      <td>0.174967</td>\\n\",\n       \"      <td>1.021739</td>\\n\",\n       \"      <td>0.437592</td>\\n\",\n       \"      <td>0.272844</td>\\n\",\n       \"      <td>0.318091</td>\\n\",\n       \"      <td>0.437592</td>\\n\",\n       \"      <td>0.318091</td>\\n\",\n       \"      <td>1.347826</td>\\n\",\n       \"      <td>0.272138</td>\\n\",\n       \"      <td>0.485660</td>\\n\",\n       \"      <td>0.319622</td>\\n\",\n       \"      <td>0.269565</td>\\n\",\n       \"      <td>0.317674</td>\\n\",\n       \"      <td>1.804348</td>\\n\",\n       \"      <td>0.202296</td>\\n\",\n       \"      <td>0.605883</td>\\n\",\n       \"      <td>0.280568</td>\\n\",\n       \"      <td>0.180435</td>\\n\",\n       \"      <td>0.258007</td>\\n\",\n       \"      <td>1.204348</td>\\n\",\n       \"      <td>0.351628</td>\\n\",\n       \"      <td>0.349761</td>\\n\",\n       \"      <td>0.350525</td>\\n\",\n       \"      <td>0.349761</td>\\n\",\n       \"      <td>0.349761</td>\\n\",\n       \"      <td>1.989130</td>\\n\",\n       \"      <td>0.174421</td>\\n\",\n       \"      <td>0.651960</td>\\n\",\n       \"      <td>0.254662</td>\\n\",\n       \"      <td>0.174421</td>\\n\",\n       \"      <td>0.254662</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.168837</td>\\n\",\n       \"      <td>0.218965</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.218965</td>\\n\",\n       \"      <td>1.019565</td>\\n\",\n       \"      <td>0.340580</td>\\n\",\n       \"      <td>0.384739</td>\\n\",\n       \"      <td>0.329836</td>\\n\",\n       \"      <td>0.339855</td>\\n\",\n       \"      <td>0.329401</td>\\n\",\n       \"      <td>0.260870</td>\\n\",\n       \"      <td>0.026329</td>\\n\",\n       \"      <td>0.102930</td>\\n\",\n       \"      <td>0.039710</td>\\n\",\n       \"      <td>0.026087</td>\\n\",\n       \"      <td>0.039418</td>\\n\",\n       \"      <td>0.341304</td>\\n\",\n       \"      <td>0.008993</td>\\n\",\n       \"      <td>0.131924</td>\\n\",\n       \"      <td>0.016176</td>\\n\",\n       \"      <td>0.006826</td>\\n\",\n       \"      <td>0.012762</td>\\n\",\n       \"      <td>0.182609</td>\\n\",\n       \"      <td>0.066191</td>\\n\",\n       \"      <td>0.066191</td>\\n\",\n       \"      <td>0.066191</td>\\n\",\n       \"      <td>0.066191</td>\\n\",\n       \"      <td>0.066191</td>\\n\",\n       \"      <td>0.356522</td>\\n\",\n       \"      <td>0.007279</td>\\n\",\n       \"      <td>0.136422</td>\\n\",\n       \"      <td>0.013115</td>\\n\",\n       \"      <td>0.007279</td>\\n\",\n       \"      <td>0.013115</td>\\n\",\n       \"      <td>1.389130</td>\\n\",\n       \"      <td>0.319046</td>\\n\",\n       \"      <td>0.555790</td>\\n\",\n       \"      <td>0.370100</td>\\n\",\n       \"      <td>0.316422</td>\\n\",\n       \"      <td>0.368123</td>\\n\",\n       \"      <td>1.871739</td>\\n\",\n       \"      <td>0.229069</td>\\n\",\n       \"      <td>0.670121</td>\\n\",\n       \"      <td>0.314560</td>\\n\",\n       \"      <td>0.205061</td>\\n\",\n       \"      <td>0.290154</td>\\n\",\n       \"      <td>1.271739</td>\\n\",\n       \"      <td>0.424410</td>\\n\",\n       \"      <td>0.421729</td>\\n\",\n       \"      <td>0.422778</td>\\n\",\n       \"      <td>0.421729</td>\\n\",\n       \"      <td>0.421729</td>\\n\",\n       \"      <td>2.060870</td>\\n\",\n       \"      <td>0.193998</td>\\n\",\n       \"      <td>0.710534</td>\\n\",\n       \"      <td>0.280979</td>\\n\",\n       \"      <td>0.193998</td>\\n\",\n       \"      <td>0.280979</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.496558</td>\\n\",\n       \"      <td>0.208789</td>\\n\",\n       \"      <td>0.270075</td>\\n\",\n       \"      <td>0.496558</td>\\n\",\n       \"      <td>0.270075</td>\\n\",\n       \"      <td>1.050000</td>\\n\",\n       \"      <td>0.405802</td>\\n\",\n       \"      <td>0.446722</td>\\n\",\n       \"      <td>0.387179</td>\\n\",\n       \"      <td>0.405077</td>\\n\",\n       \"      <td>0.386744</td>\\n\",\n       \"      <td>0.280435</td>\\n\",\n       \"      <td>0.048380</td>\\n\",\n       \"      <td>0.183012</td>\\n\",\n       \"      <td>0.071808</td>\\n\",\n       \"      <td>0.047142</td>\\n\",\n       \"      <td>0.070526</td>\\n\",\n       \"      <td>0.386957</td>\\n\",\n       \"      <td>0.018046</td>\\n\",\n       \"      <td>0.236318</td>\\n\",\n       \"      <td>0.031211</td>\\n\",\n       \"      <td>0.012427</td>\\n\",\n       \"      <td>0.023167</td>\\n\",\n       \"      <td>0.193478</td>\\n\",\n       \"      <td>0.104763</td>\\n\",\n       \"      <td>0.104705</td>\\n\",\n       \"      <td>0.104727</td>\\n\",\n       \"      <td>0.104705</td>\\n\",\n       \"      <td>0.104705</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.015079</td>\\n\",\n       \"      <td>0.243870</td>\\n\",\n       \"      <td>0.025927</td>\\n\",\n       \"      <td>0.015079</td>\\n\",\n       \"      <td>0.025927</td>\\n\",\n       \"      <td>0.199182</td>\\n\",\n       \"      <td>0.227884</td>\\n\",\n       \"      <td>0.341685</td>\\n\",\n       \"      <td>0.333074</td>\\n\",\n       \"      <td>0.376478</td>\\n\",\n       \"      <td>0.428900</td>\\n\",\n       \"      <td>0.449866</td>\\n\",\n       \"      <td>0.339638</td>\\n\",\n       \"      <td>0.386557</td>\\n\",\n       \"      <td>0.393398</td>\\n\",\n       \"      <td>0.486404</td>\\n\",\n       \"      <td>0.510631</td>\\n\",\n       \"      <td>0.503750</td>\\n\",\n       \"      <td>0.516174</td>\\n\",\n       \"      <td>0.057091</td>\\n\",\n       \"      <td>0.066706</td>\\n\",\n       \"      <td>0.123194</td>\\n\",\n       \"      <td>0.091994</td>\\n\",\n       \"      <td>0.100732</td>\\n\",\n       \"      <td>0.089114</td>\\n\",\n       \"      <td>0.089556</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>12.436957</td>\\n\",\n       \"      <td>65.300000</td>\\n\",\n       \"      <td>86.934783</td>\\n\",\n       \"      <td>3365.826087</td>\\n\",\n       \"      <td>458.002174</td>\\n\",\n       \"      <td>12080.713043</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19591</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.341304</td>\\n\",\n       \"      <td>0.268261</td>\\n\",\n       \"      <td>0.282144</td>\\n\",\n       \"      <td>0.261159</td>\\n\",\n       \"      <td>0.268261</td>\\n\",\n       \"      <td>0.261159</td>\\n\",\n       \"      <td>1.854348</td>\\n\",\n       \"      <td>0.185459</td>\\n\",\n       \"      <td>0.375091</td>\\n\",\n       \"      <td>0.236170</td>\\n\",\n       \"      <td>0.185435</td>\\n\",\n       \"      <td>0.236161</td>\\n\",\n       \"      <td>1.513043</td>\\n\",\n       \"      <td>0.280033</td>\\n\",\n       \"      <td>0.279973</td>\\n\",\n       \"      <td>0.279999</td>\\n\",\n       \"      <td>0.279973</td>\\n\",\n       \"      <td>0.279973</td>\\n\",\n       \"      <td>2.589130</td>\\n\",\n       \"      <td>0.036468</td>\\n\",\n       \"      <td>0.499860</td>\\n\",\n       \"      <td>0.065230</td>\\n\",\n       \"      <td>0.036468</td>\\n\",\n       \"      <td>0.065230</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.085494</td>\\n\",\n       \"      <td>0.136523</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.136523</td>\\n\",\n       \"      <td>0.982609</td>\\n\",\n       \"      <td>0.327536</td>\\n\",\n       \"      <td>0.210828</td>\\n\",\n       \"      <td>0.243128</td>\\n\",\n       \"      <td>0.327536</td>\\n\",\n       \"      <td>0.243128</td>\\n\",\n       \"      <td>1.450000</td>\\n\",\n       \"      <td>0.354045</td>\\n\",\n       \"      <td>0.361474</td>\\n\",\n       \"      <td>0.338116</td>\\n\",\n       \"      <td>0.354045</td>\\n\",\n       \"      <td>0.338116</td>\\n\",\n       \"      <td>2.045652</td>\\n\",\n       \"      <td>0.239795</td>\\n\",\n       \"      <td>0.470618</td>\\n\",\n       \"      <td>0.300945</td>\\n\",\n       \"      <td>0.239745</td>\\n\",\n       \"      <td>0.300925</td>\\n\",\n       \"      <td>1.693478</td>\\n\",\n       \"      <td>0.361220</td>\\n\",\n       \"      <td>0.361095</td>\\n\",\n       \"      <td>0.361148</td>\\n\",\n       \"      <td>0.361095</td>\\n\",\n       \"      <td>0.361095</td>\\n\",\n       \"      <td>2.926087</td>\\n\",\n       \"      <td>0.046063</td>\\n\",\n       \"      <td>0.611950</td>\\n\",\n       \"      <td>0.081892</td>\\n\",\n       \"      <td>0.046063</td>\\n\",\n       \"      <td>0.081892</td>\\n\",\n       \"      <td>0.397826</td>\\n\",\n       \"      <td>0.540820</td>\\n\",\n       \"      <td>0.114064</td>\\n\",\n       \"      <td>0.182494</td>\\n\",\n       \"      <td>0.540820</td>\\n\",\n       \"      <td>0.182494</td>\\n\",\n       \"      <td>1.030435</td>\\n\",\n       \"      <td>0.441894</td>\\n\",\n       \"      <td>0.278378</td>\\n\",\n       \"      <td>0.323472</td>\\n\",\n       \"      <td>0.441894</td>\\n\",\n       \"      <td>0.323472</td>\\n\",\n       \"      <td>1.345652</td>\\n\",\n       \"      <td>0.269674</td>\\n\",\n       \"      <td>0.488823</td>\\n\",\n       \"      <td>0.321205</td>\\n\",\n       \"      <td>0.269130</td>\\n\",\n       \"      <td>0.320742</td>\\n\",\n       \"      <td>1.884783</td>\\n\",\n       \"      <td>0.194152</td>\\n\",\n       \"      <td>0.646410</td>\\n\",\n       \"      <td>0.279455</td>\\n\",\n       \"      <td>0.188478</td>\\n\",\n       \"      <td>0.273231</td>\\n\",\n       \"      <td>1.180435</td>\\n\",\n       \"      <td>0.340083</td>\\n\",\n       \"      <td>0.339355</td>\\n\",\n       \"      <td>0.339638</td>\\n\",\n       \"      <td>0.339355</td>\\n\",\n       \"      <td>0.339355</td>\\n\",\n       \"      <td>2.182609</td>\\n\",\n       \"      <td>0.141247</td>\\n\",\n       \"      <td>0.725231</td>\\n\",\n       \"      <td>0.222029</td>\\n\",\n       \"      <td>0.141247</td>\\n\",\n       \"      <td>0.222029</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.162913</td>\\n\",\n       \"      <td>0.215559</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.215559</td>\\n\",\n       \"      <td>0.993478</td>\\n\",\n       \"      <td>0.331159</td>\\n\",\n       \"      <td>0.372813</td>\\n\",\n       \"      <td>0.321424</td>\\n\",\n       \"      <td>0.331159</td>\\n\",\n       \"      <td>0.321424</td>\\n\",\n       \"      <td>0.284783</td>\\n\",\n       \"      <td>0.028988</td>\\n\",\n       \"      <td>0.114064</td>\\n\",\n       \"      <td>0.043713</td>\\n\",\n       \"      <td>0.028478</td>\\n\",\n       \"      <td>0.043194</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.010257</td>\\n\",\n       \"      <td>0.158367</td>\\n\",\n       \"      <td>0.018276</td>\\n\",\n       \"      <td>0.008087</td>\\n\",\n       \"      <td>0.015108</td>\\n\",\n       \"      <td>0.180435</td>\\n\",\n       \"      <td>0.062216</td>\\n\",\n       \"      <td>0.062216</td>\\n\",\n       \"      <td>0.062216</td>\\n\",\n       \"      <td>0.062216</td>\\n\",\n       \"      <td>0.062216</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.007505</td>\\n\",\n       \"      <td>0.158911</td>\\n\",\n       \"      <td>0.013327</td>\\n\",\n       \"      <td>0.007505</td>\\n\",\n       \"      <td>0.013327</td>\\n\",\n       \"      <td>1.391304</td>\\n\",\n       \"      <td>0.319429</td>\\n\",\n       \"      <td>0.563441</td>\\n\",\n       \"      <td>0.374098</td>\\n\",\n       \"      <td>0.318798</td>\\n\",\n       \"      <td>0.373609</td>\\n\",\n       \"      <td>1.963043</td>\\n\",\n       \"      <td>0.219679</td>\\n\",\n       \"      <td>0.710394</td>\\n\",\n       \"      <td>0.311804</td>\\n\",\n       \"      <td>0.213063</td>\\n\",\n       \"      <td>0.304780</td>\\n\",\n       \"      <td>1.260870</td>\\n\",\n       \"      <td>0.411797</td>\\n\",\n       \"      <td>0.410681</td>\\n\",\n       \"      <td>0.411108</td>\\n\",\n       \"      <td>0.410681</td>\\n\",\n       \"      <td>0.410681</td>\\n\",\n       \"      <td>2.280435</td>\\n\",\n       \"      <td>0.156516</td>\\n\",\n       \"      <td>0.779044</td>\\n\",\n       \"      <td>0.243496</td>\\n\",\n       \"      <td>0.156516</td>\\n\",\n       \"      <td>0.243496</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.506183</td>\\n\",\n       \"      <td>0.201715</td>\\n\",\n       \"      <td>0.267741</td>\\n\",\n       \"      <td>0.506183</td>\\n\",\n       \"      <td>0.267741</td>\\n\",\n       \"      <td>1.021739</td>\\n\",\n       \"      <td>0.403202</td>\\n\",\n       \"      <td>0.442968</td>\\n\",\n       \"      <td>0.385401</td>\\n\",\n       \"      <td>0.403202</td>\\n\",\n       \"      <td>0.385401</td>\\n\",\n       \"      <td>0.300000</td>\\n\",\n       \"      <td>0.051918</td>\\n\",\n       \"      <td>0.200218</td>\\n\",\n       \"      <td>0.077662</td>\\n\",\n       \"      <td>0.051198</td>\\n\",\n       \"      <td>0.076914</td>\\n\",\n       \"      <td>0.471739</td>\\n\",\n       \"      <td>0.018137</td>\\n\",\n       \"      <td>0.267915</td>\\n\",\n       \"      <td>0.032172</td>\\n\",\n       \"      <td>0.014155</td>\\n\",\n       \"      <td>0.026369</td>\\n\",\n       \"      <td>0.189130</td>\\n\",\n       \"      <td>0.102499</td>\\n\",\n       \"      <td>0.102499</td>\\n\",\n       \"      <td>0.102499</td>\\n\",\n       \"      <td>0.102499</td>\\n\",\n       \"      <td>0.102499</td>\\n\",\n       \"      <td>0.482609</td>\\n\",\n       \"      <td>0.013429</td>\\n\",\n       \"      <td>0.271236</td>\\n\",\n       \"      <td>0.023751</td>\\n\",\n       \"      <td>0.013429</td>\\n\",\n       \"      <td>0.023751</td>\\n\",\n       \"      <td>0.216810</td>\\n\",\n       \"      <td>0.246940</td>\\n\",\n       \"      <td>0.330232</td>\\n\",\n       \"      <td>0.358620</td>\\n\",\n       \"      <td>0.405071</td>\\n\",\n       \"      <td>0.459719</td>\\n\",\n       \"      <td>0.481585</td>\\n\",\n       \"      <td>0.350432</td>\\n\",\n       \"      <td>0.401690</td>\\n\",\n       \"      <td>0.372284</td>\\n\",\n       \"      <td>0.506520</td>\\n\",\n       \"      <td>0.536893</td>\\n\",\n       \"      <td>0.504426</td>\\n\",\n       \"      <td>0.516278</td>\\n\",\n       \"      <td>0.052768</td>\\n\",\n       \"      <td>0.064566</td>\\n\",\n       \"      <td>0.113187</td>\\n\",\n       \"      <td>0.096469</td>\\n\",\n       \"      <td>0.104293</td>\\n\",\n       \"      <td>0.088646</td>\\n\",\n       \"      <td>0.089481</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>16.600000</td>\\n\",\n       \"      <td>77.628261</td>\\n\",\n       \"      <td>104.693478</td>\\n\",\n       \"      <td>3466.678261</td>\\n\",\n       \"      <td>440.126087</td>\\n\",\n       \"      <td>12255.560870</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19149</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.380435</td>\\n\",\n       \"      <td>0.276087</td>\\n\",\n       \"      <td>0.289428</td>\\n\",\n       \"      <td>0.268656</td>\\n\",\n       \"      <td>0.276087</td>\\n\",\n       \"      <td>0.268656</td>\\n\",\n       \"      <td>1.986957</td>\\n\",\n       \"      <td>0.198696</td>\\n\",\n       \"      <td>0.397545</td>\\n\",\n       \"      <td>0.251735</td>\\n\",\n       \"      <td>0.198696</td>\\n\",\n       \"      <td>0.251735</td>\\n\",\n       \"      <td>1.610870</td>\\n\",\n       \"      <td>0.288413</td>\\n\",\n       \"      <td>0.288413</td>\\n\",\n       \"      <td>0.288413</td>\\n\",\n       \"      <td>0.288413</td>\\n\",\n       \"      <td>0.288413</td>\\n\",\n       \"      <td>2.854348</td>\\n\",\n       \"      <td>0.058520</td>\\n\",\n       \"      <td>0.525772</td>\\n\",\n       \"      <td>0.100719</td>\\n\",\n       \"      <td>0.058520</td>\\n\",\n       \"      <td>0.100719</td>\\n\",\n       \"      <td>0.393478</td>\\n\",\n       \"      <td>0.393478</td>\\n\",\n       \"      <td>0.087100</td>\\n\",\n       \"      <td>0.138354</td>\\n\",\n       \"      <td>0.393478</td>\\n\",\n       \"      <td>0.138354</td>\\n\",\n       \"      <td>1.013043</td>\\n\",\n       \"      <td>0.337681</td>\\n\",\n       \"      <td>0.219995</td>\\n\",\n       \"      <td>0.253829</td>\\n\",\n       \"      <td>0.337681</td>\\n\",\n       \"      <td>0.253829</td>\\n\",\n       \"      <td>1.478261</td>\\n\",\n       \"      <td>0.362256</td>\\n\",\n       \"      <td>0.369193</td>\\n\",\n       \"      <td>0.345729</td>\\n\",\n       \"      <td>0.362256</td>\\n\",\n       \"      <td>0.345729</td>\\n\",\n       \"      <td>2.180435</td>\\n\",\n       \"      <td>0.253575</td>\\n\",\n       \"      <td>0.493220</td>\\n\",\n       \"      <td>0.316753</td>\\n\",\n       \"      <td>0.253575</td>\\n\",\n       \"      <td>0.316753</td>\\n\",\n       \"      <td>1.791304</td>\\n\",\n       \"      <td>0.370760</td>\\n\",\n       \"      <td>0.370760</td>\\n\",\n       \"      <td>0.370760</td>\\n\",\n       \"      <td>0.370760</td>\\n\",\n       \"      <td>0.370760</td>\\n\",\n       \"      <td>3.097826</td>\\n\",\n       \"      <td>0.071215</td>\\n\",\n       \"      <td>0.628415</td>\\n\",\n       \"      <td>0.122157</td>\\n\",\n       \"      <td>0.071215</td>\\n\",\n       \"      <td>0.122157</td>\\n\",\n       \"      <td>0.393478</td>\\n\",\n       \"      <td>0.528948</td>\\n\",\n       \"      <td>0.113076</td>\\n\",\n       \"      <td>0.180504</td>\\n\",\n       \"      <td>0.528948</td>\\n\",\n       \"      <td>0.180504</td>\\n\",\n       \"      <td>1.052174</td>\\n\",\n       \"      <td>0.447138</td>\\n\",\n       \"      <td>0.282344</td>\\n\",\n       \"      <td>0.328632</td>\\n\",\n       \"      <td>0.447138</td>\\n\",\n       \"      <td>0.328632</td>\\n\",\n       \"      <td>1.363043</td>\\n\",\n       \"      <td>0.272609</td>\\n\",\n       \"      <td>0.491922</td>\\n\",\n       \"      <td>0.324800</td>\\n\",\n       \"      <td>0.272609</td>\\n\",\n       \"      <td>0.324800</td>\\n\",\n       \"      <td>1.939130</td>\\n\",\n       \"      <td>0.193913</td>\\n\",\n       \"      <td>0.657576</td>\\n\",\n       \"      <td>0.279683</td>\\n\",\n       \"      <td>0.193913</td>\\n\",\n       \"      <td>0.279683</td>\\n\",\n       \"      <td>1.245652</td>\\n\",\n       \"      <td>0.358957</td>\\n\",\n       \"      <td>0.358957</td>\\n\",\n       \"      <td>0.358957</td>\\n\",\n       \"      <td>0.358957</td>\\n\",\n       \"      <td>0.358957</td>\\n\",\n       \"      <td>2.560870</td>\\n\",\n       \"      <td>0.106496</td>\\n\",\n       \"      <td>0.804771</td>\\n\",\n       \"      <td>0.179086</td>\\n\",\n       \"      <td>0.106496</td>\\n\",\n       \"      <td>0.179086</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.169789</td>\\n\",\n       \"      <td>0.223516</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.223516</td>\\n\",\n       \"      <td>1.026087</td>\\n\",\n       \"      <td>0.342029</td>\\n\",\n       \"      <td>0.392531</td>\\n\",\n       \"      <td>0.336038</td>\\n\",\n       \"      <td>0.342029</td>\\n\",\n       \"      <td>0.336038</td>\\n\",\n       \"      <td>0.282609</td>\\n\",\n       \"      <td>0.030072</td>\\n\",\n       \"      <td>0.111058</td>\\n\",\n       \"      <td>0.044568</td>\\n\",\n       \"      <td>0.028261</td>\\n\",\n       \"      <td>0.042579</td>\\n\",\n       \"      <td>0.293478</td>\\n\",\n       \"      <td>0.015000</td>\\n\",\n       \"      <td>0.114030</td>\\n\",\n       \"      <td>0.024277</td>\\n\",\n       \"      <td>0.005870</td>\\n\",\n       \"      <td>0.010974</td>\\n\",\n       \"      <td>0.210870</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.070769</td>\\n\",\n       \"      <td>0.293478</td>\\n\",\n       \"      <td>0.014584</td>\\n\",\n       \"      <td>0.114030</td>\\n\",\n       \"      <td>0.023523</td>\\n\",\n       \"      <td>0.014584</td>\\n\",\n       \"      <td>0.023523</td>\\n\",\n       \"      <td>1.417391</td>\\n\",\n       \"      <td>0.322484</td>\\n\",\n       \"      <td>0.569207</td>\\n\",\n       \"      <td>0.378201</td>\\n\",\n       \"      <td>0.322484</td>\\n\",\n       \"      <td>0.378201</td>\\n\",\n       \"      <td>2.021739</td>\\n\",\n       \"      <td>0.219099</td>\\n\",\n       \"      <td>0.724711</td>\\n\",\n       \"      <td>0.312397</td>\\n\",\n       \"      <td>0.219099</td>\\n\",\n       \"      <td>0.312397</td>\\n\",\n       \"      <td>1.326087</td>\\n\",\n       \"      <td>0.424761</td>\\n\",\n       \"      <td>0.424761</td>\\n\",\n       \"      <td>0.424761</td>\\n\",\n       \"      <td>0.424761</td>\\n\",\n       \"      <td>0.424761</td>\\n\",\n       \"      <td>2.615217</td>\\n\",\n       \"      <td>0.114379</td>\\n\",\n       \"      <td>0.848030</td>\\n\",\n       \"      <td>0.191439</td>\\n\",\n       \"      <td>0.114379</td>\\n\",\n       \"      <td>0.191439</td>\\n\",\n       \"      <td>0.406522</td>\\n\",\n       \"      <td>0.501324</td>\\n\",\n       \"      <td>0.203061</td>\\n\",\n       \"      <td>0.268276</td>\\n\",\n       \"      <td>0.501324</td>\\n\",\n       \"      <td>0.268276</td>\\n\",\n       \"      <td>1.050000</td>\\n\",\n       \"      <td>0.412921</td>\\n\",\n       \"      <td>0.463033</td>\\n\",\n       \"      <td>0.398945</td>\\n\",\n       \"      <td>0.412921</td>\\n\",\n       \"      <td>0.398945</td>\\n\",\n       \"      <td>0.289130</td>\\n\",\n       \"      <td>0.051557</td>\\n\",\n       \"      <td>0.185549</td>\\n\",\n       \"      <td>0.075573</td>\\n\",\n       \"      <td>0.048087</td>\\n\",\n       \"      <td>0.072101</td>\\n\",\n       \"      <td>0.306522</td>\\n\",\n       \"      <td>0.026984</td>\\n\",\n       \"      <td>0.201869</td>\\n\",\n       \"      <td>0.042867</td>\\n\",\n       \"      <td>0.010469</td>\\n\",\n       \"      <td>0.019549</td>\\n\",\n       \"      <td>0.213043</td>\\n\",\n       \"      <td>0.116655</td>\\n\",\n       \"      <td>0.116456</td>\\n\",\n       \"      <td>0.116531</td>\\n\",\n       \"      <td>0.116456</td>\\n\",\n       \"      <td>0.116456</td>\\n\",\n       \"      <td>0.306522</td>\\n\",\n       \"      <td>0.026242</td>\\n\",\n       \"      <td>0.202238</td>\\n\",\n       \"      <td>0.041543</td>\\n\",\n       \"      <td>0.026242</td>\\n\",\n       \"      <td>0.041543</td>\\n\",\n       \"      <td>0.230182</td>\\n\",\n       \"      <td>0.262611</td>\\n\",\n       \"      <td>0.336550</td>\\n\",\n       \"      <td>0.379023</td>\\n\",\n       \"      <td>0.427383</td>\\n\",\n       \"      <td>0.452842</td>\\n\",\n       \"      <td>0.478436</td>\\n\",\n       \"      <td>0.373385</td>\\n\",\n       \"      <td>0.427582</td>\\n\",\n       \"      <td>0.366626</td>\\n\",\n       \"      <td>0.541457</td>\\n\",\n       \"      <td>0.577068</td>\\n\",\n       \"      <td>0.502387</td>\\n\",\n       \"      <td>0.514789</td>\\n\",\n       \"      <td>0.058269</td>\\n\",\n       \"      <td>0.070562</td>\\n\",\n       \"      <td>0.141219</td>\\n\",\n       \"      <td>0.093560</td>\\n\",\n       \"      <td>0.100882</td>\\n\",\n       \"      <td>0.105392</td>\\n\",\n       \"      <td>0.105861</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>24.510870</td>\\n\",\n       \"      <td>34.800000</td>\\n\",\n       \"      <td>64.134783</td>\\n\",\n       \"      <td>3463.456522</td>\\n\",\n       \"      <td>487.454348</td>\\n\",\n       \"      <td>11897.319565</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15885</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>2.075829</td>\\n\",\n       \"      <td>0.415166</td>\\n\",\n       \"      <td>0.236202</td>\\n\",\n       \"      <td>0.273684</td>\\n\",\n       \"      <td>0.415166</td>\\n\",\n       \"      <td>0.273684</td>\\n\",\n       \"      <td>2.900474</td>\\n\",\n       \"      <td>0.290047</td>\\n\",\n       \"      <td>0.325551</td>\\n\",\n       \"      <td>0.278171</td>\\n\",\n       \"      <td>0.290047</td>\\n\",\n       \"      <td>0.278171</td>\\n\",\n       \"      <td>3.113744</td>\\n\",\n       \"      <td>0.297896</td>\\n\",\n       \"      <td>0.296947</td>\\n\",\n       \"      <td>0.297299</td>\\n\",\n       \"      <td>0.296947</td>\\n\",\n       \"      <td>0.296947</td>\\n\",\n       \"      <td>3.914692</td>\\n\",\n       \"      <td>0.105389</td>\\n\",\n       \"      <td>0.408693</td>\\n\",\n       \"      <td>0.155600</td>\\n\",\n       \"      <td>0.105389</td>\\n\",\n       \"      <td>0.155600</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.067998</td>\\n\",\n       \"      <td>0.115777</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.115777</td>\\n\",\n       \"      <td>1.559242</td>\\n\",\n       \"      <td>0.519747</td>\\n\",\n       \"      <td>0.179313</td>\\n\",\n       \"      <td>0.244772</td>\\n\",\n       \"      <td>0.519747</td>\\n\",\n       \"      <td>0.244772</td>\\n\",\n       \"      <td>2.203791</td>\\n\",\n       \"      <td>0.519239</td>\\n\",\n       \"      <td>0.295058</td>\\n\",\n       \"      <td>0.341064</td>\\n\",\n       \"      <td>0.519239</td>\\n\",\n       \"      <td>0.341064</td>\\n\",\n       \"      <td>3.331754</td>\\n\",\n       \"      <td>0.375646</td>\\n\",\n       \"      <td>0.407352</td>\\n\",\n       \"      <td>0.354492</td>\\n\",\n       \"      <td>0.375646</td>\\n\",\n       \"      <td>0.354492</td>\\n\",\n       \"      <td>3.763033</td>\\n\",\n       \"      <td>0.390114</td>\\n\",\n       \"      <td>0.388653</td>\\n\",\n       \"      <td>0.389191</td>\\n\",\n       \"      <td>0.388653</td>\\n\",\n       \"      <td>0.388653</td>\\n\",\n       \"      <td>4.805687</td>\\n\",\n       \"      <td>0.141042</td>\\n\",\n       \"      <td>0.521063</td>\\n\",\n       \"      <td>0.205754</td>\\n\",\n       \"      <td>0.141042</td>\\n\",\n       \"      <td>0.205754</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.672064</td>\\n\",\n       \"      <td>0.082445</td>\\n\",\n       \"      <td>0.139223</td>\\n\",\n       \"      <td>0.672064</td>\\n\",\n       \"      <td>0.139223</td>\\n\",\n       \"      <td>1.611374</td>\\n\",\n       \"      <td>0.627058</td>\\n\",\n       \"      <td>0.221331</td>\\n\",\n       \"      <td>0.298876</td>\\n\",\n       \"      <td>0.627058</td>\\n\",\n       \"      <td>0.298876</td>\\n\",\n       \"      <td>2.184834</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.426318</td>\\n\",\n       \"      <td>0.397940</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.397940</td>\\n\",\n       \"      <td>3.028436</td>\\n\",\n       \"      <td>0.309236</td>\\n\",\n       \"      <td>0.575048</td>\\n\",\n       \"      <td>0.372295</td>\\n\",\n       \"      <td>0.302844</td>\\n\",\n       \"      <td>0.367975</td>\\n\",\n       \"      <td>2.592417</td>\\n\",\n       \"      <td>0.421379</td>\\n\",\n       \"      <td>0.417039</td>\\n\",\n       \"      <td>0.418743</td>\\n\",\n       \"      <td>0.417039</td>\\n\",\n       \"      <td>0.417039</td>\\n\",\n       \"      <td>3.483412</td>\\n\",\n       \"      <td>0.239961</td>\\n\",\n       \"      <td>0.635611</td>\\n\",\n       \"      <td>0.322701</td>\\n\",\n       \"      <td>0.239961</td>\\n\",\n       \"      <td>0.322701</td>\\n\",\n       \"      <td>0.620853</td>\\n\",\n       \"      <td>0.620853</td>\\n\",\n       \"      <td>0.121761</td>\\n\",\n       \"      <td>0.195807</td>\\n\",\n       \"      <td>0.620853</td>\\n\",\n       \"      <td>0.195807</td>\\n\",\n       \"      <td>1.592417</td>\\n\",\n       \"      <td>0.530806</td>\\n\",\n       \"      <td>0.312501</td>\\n\",\n       \"      <td>0.364618</td>\\n\",\n       \"      <td>0.530806</td>\\n\",\n       \"      <td>0.364618</td>\\n\",\n       \"      <td>0.379147</td>\\n\",\n       \"      <td>0.039310</td>\\n\",\n       \"      <td>0.089139</td>\\n\",\n       \"      <td>0.048106</td>\\n\",\n       \"      <td>0.037915</td>\\n\",\n       \"      <td>0.046573</td>\\n\",\n       \"      <td>0.431280</td>\\n\",\n       \"      <td>0.019981</td>\\n\",\n       \"      <td>0.097071</td>\\n\",\n       \"      <td>0.029881</td>\\n\",\n       \"      <td>0.008626</td>\\n\",\n       \"      <td>0.015012</td>\\n\",\n       \"      <td>0.336493</td>\\n\",\n       \"      <td>0.072618</td>\\n\",\n       \"      <td>0.072470</td>\\n\",\n       \"      <td>0.072534</td>\\n\",\n       \"      <td>0.072470</td>\\n\",\n       \"      <td>0.072470</td>\\n\",\n       \"      <td>0.431280</td>\\n\",\n       \"      <td>0.019816</td>\\n\",\n       \"      <td>0.097071</td>\\n\",\n       \"      <td>0.029628</td>\\n\",\n       \"      <td>0.019816</td>\\n\",\n       \"      <td>0.029628</td>\\n\",\n       \"      <td>2.218009</td>\\n\",\n       \"      <td>0.498171</td>\\n\",\n       \"      <td>0.490385</td>\\n\",\n       \"      <td>0.453971</td>\\n\",\n       \"      <td>0.498171</td>\\n\",\n       \"      <td>0.453971</td>\\n\",\n       \"      <td>3.142180</td>\\n\",\n       \"      <td>0.352892</td>\\n\",\n       \"      <td>0.645880</td>\\n\",\n       \"      <td>0.422942</td>\\n\",\n       \"      <td>0.346038</td>\\n\",\n       \"      <td>0.418285</td>\\n\",\n       \"      <td>2.710900</td>\\n\",\n       \"      <td>0.491744</td>\\n\",\n       \"      <td>0.486865</td>\\n\",\n       \"      <td>0.488781</td>\\n\",\n       \"      <td>0.486865</td>\\n\",\n       \"      <td>0.486865</td>\\n\",\n       \"      <td>3.654028</td>\\n\",\n       \"      <td>0.270990</td>\\n\",\n       \"      <td>0.707183</td>\\n\",\n       \"      <td>0.363256</td>\\n\",\n       \"      <td>0.270990</td>\\n\",\n       \"      <td>0.363256</td>\\n\",\n       \"      <td>0.620853</td>\\n\",\n       \"      <td>0.690600</td>\\n\",\n       \"      <td>0.143780</td>\\n\",\n       \"      <td>0.226442</td>\\n\",\n       \"      <td>0.690600</td>\\n\",\n       \"      <td>0.226442</td>\\n\",\n       \"      <td>1.601896</td>\\n\",\n       \"      <td>0.604302</td>\\n\",\n       \"      <td>0.367879</td>\\n\",\n       \"      <td>0.420904</td>\\n\",\n       \"      <td>0.604302</td>\\n\",\n       \"      <td>0.420904</td>\\n\",\n       \"      <td>0.450237</td>\\n\",\n       \"      <td>0.080030</td>\\n\",\n       \"      <td>0.163873</td>\\n\",\n       \"      <td>0.094270</td>\\n\",\n       \"      <td>0.077276</td>\\n\",\n       \"      <td>0.091465</td>\\n\",\n       \"      <td>0.625592</td>\\n\",\n       \"      <td>0.044845</td>\\n\",\n       \"      <td>0.190337</td>\\n\",\n       \"      <td>0.064734</td>\\n\",\n       \"      <td>0.019134</td>\\n\",\n       \"      <td>0.032752</td>\\n\",\n       \"      <td>0.440758</td>\\n\",\n       \"      <td>0.121035</td>\\n\",\n       \"      <td>0.120102</td>\\n\",\n       \"      <td>0.120447</td>\\n\",\n       \"      <td>0.120102</td>\\n\",\n       \"      <td>0.120102</td>\\n\",\n       \"      <td>0.625592</td>\\n\",\n       \"      <td>0.044498</td>\\n\",\n       \"      <td>0.190645</td>\\n\",\n       \"      <td>0.064216</td>\\n\",\n       \"      <td>0.044498</td>\\n\",\n       \"      <td>0.064216</td>\\n\",\n       \"      <td>0.221641</td>\\n\",\n       \"      <td>0.241683</td>\\n\",\n       \"      <td>0.334461</td>\\n\",\n       \"      <td>0.339945</td>\\n\",\n       \"      <td>0.412209</td>\\n\",\n       \"      <td>0.403678</td>\\n\",\n       \"      <td>0.464161</td>\\n\",\n       \"      <td>0.377364</td>\\n\",\n       \"      <td>0.410233</td>\\n\",\n       \"      <td>0.384929</td>\\n\",\n       \"      <td>0.513514</td>\\n\",\n       \"      <td>0.572105</td>\\n\",\n       \"      <td>0.528286</td>\\n\",\n       \"      <td>0.571887</td>\\n\",\n       \"      <td>0.053043</td>\\n\",\n       \"      <td>0.058057</td>\\n\",\n       \"      <td>0.154921</td>\\n\",\n       \"      <td>0.081848</td>\\n\",\n       \"      <td>0.093069</td>\\n\",\n       \"      <td>0.109770</td>\\n\",\n       \"      <td>0.119410</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>15.488152</td>\\n\",\n       \"      <td>26.322275</td>\\n\",\n       \"      <td>46.587678</td>\\n\",\n       \"      <td>3641.772512</td>\\n\",\n       \"      <td>475.360190</td>\\n\",\n       \"      <td>12192.909953</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20243</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>2.161137</td>\\n\",\n       \"      <td>0.432227</td>\\n\",\n       \"      <td>0.246344</td>\\n\",\n       \"      <td>0.285526</td>\\n\",\n       \"      <td>0.432227</td>\\n\",\n       \"      <td>0.285526</td>\\n\",\n       \"      <td>3.033175</td>\\n\",\n       \"      <td>0.303318</td>\\n\",\n       \"      <td>0.330595</td>\\n\",\n       \"      <td>0.286220</td>\\n\",\n       \"      <td>0.303318</td>\\n\",\n       \"      <td>0.286220</td>\\n\",\n       \"      <td>3.194313</td>\\n\",\n       \"      <td>0.301743</td>\\n\",\n       \"      <td>0.300580</td>\\n\",\n       \"      <td>0.301065</td>\\n\",\n       \"      <td>0.300580</td>\\n\",\n       \"      <td>0.300580</td>\\n\",\n       \"      <td>3.876777</td>\\n\",\n       \"      <td>0.117622</td>\\n\",\n       \"      <td>0.407894</td>\\n\",\n       \"      <td>0.167875</td>\\n\",\n       \"      <td>0.117622</td>\\n\",\n       \"      <td>0.167875</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.074917</td>\\n\",\n       \"      <td>0.126743</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.126743</td>\\n\",\n       \"      <td>1.549763</td>\\n\",\n       \"      <td>0.516588</td>\\n\",\n       \"      <td>0.177461</td>\\n\",\n       \"      <td>0.243807</td>\\n\",\n       \"      <td>0.516588</td>\\n\",\n       \"      <td>0.243807</td>\\n\",\n       \"      <td>2.317536</td>\\n\",\n       \"      <td>0.534377</td>\\n\",\n       \"      <td>0.304268</td>\\n\",\n       \"      <td>0.351388</td>\\n\",\n       \"      <td>0.534377</td>\\n\",\n       \"      <td>0.351388</td>\\n\",\n       \"      <td>3.459716</td>\\n\",\n       \"      <td>0.387752</td>\\n\",\n       \"      <td>0.412643</td>\\n\",\n       \"      <td>0.361988</td>\\n\",\n       \"      <td>0.387752</td>\\n\",\n       \"      <td>0.361988</td>\\n\",\n       \"      <td>3.890995</td>\\n\",\n       \"      <td>0.392671</td>\\n\",\n       \"      <td>0.390883</td>\\n\",\n       \"      <td>0.391633</td>\\n\",\n       \"      <td>0.390883</td>\\n\",\n       \"      <td>0.390883</td>\\n\",\n       \"      <td>4.848341</td>\\n\",\n       \"      <td>0.158478</td>\\n\",\n       \"      <td>0.523250</td>\\n\",\n       \"      <td>0.223772</td>\\n\",\n       \"      <td>0.158478</td>\\n\",\n       \"      <td>0.223772</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.728815</td>\\n\",\n       \"      <td>0.092851</td>\\n\",\n       \"      <td>0.156189</td>\\n\",\n       \"      <td>0.728815</td>\\n\",\n       \"      <td>0.156189</td>\\n\",\n       \"      <td>1.611374</td>\\n\",\n       \"      <td>0.629347</td>\\n\",\n       \"      <td>0.221815</td>\\n\",\n       \"      <td>0.300461</td>\\n\",\n       \"      <td>0.629347</td>\\n\",\n       \"      <td>0.300461</td>\\n\",\n       \"      <td>2.218009</td>\\n\",\n       \"      <td>0.443602</td>\\n\",\n       \"      <td>0.433562</td>\\n\",\n       \"      <td>0.403012</td>\\n\",\n       \"      <td>0.443602</td>\\n\",\n       \"      <td>0.403012</td>\\n\",\n       \"      <td>3.066351</td>\\n\",\n       \"      <td>0.308698</td>\\n\",\n       \"      <td>0.591638</td>\\n\",\n       \"      <td>0.374882</td>\\n\",\n       \"      <td>0.306635</td>\\n\",\n       \"      <td>0.373162</td>\\n\",\n       \"      <td>2.663507</td>\\n\",\n       \"      <td>0.445702</td>\\n\",\n       \"      <td>0.445099</td>\\n\",\n       \"      <td>0.445383</td>\\n\",\n       \"      <td>0.445099</td>\\n\",\n       \"      <td>0.445099</td>\\n\",\n       \"      <td>3.497630</td>\\n\",\n       \"      <td>0.226759</td>\\n\",\n       \"      <td>0.653385</td>\\n\",\n       \"      <td>0.313475</td>\\n\",\n       \"      <td>0.226759</td>\\n\",\n       \"      <td>0.313475</td>\\n\",\n       \"      <td>0.616114</td>\\n\",\n       \"      <td>0.616114</td>\\n\",\n       \"      <td>0.138758</td>\\n\",\n       \"      <td>0.210463</td>\\n\",\n       \"      <td>0.616114</td>\\n\",\n       \"      <td>0.210463</td>\\n\",\n       \"      <td>1.582938</td>\\n\",\n       \"      <td>0.527646</td>\\n\",\n       \"      <td>0.320215</td>\\n\",\n       \"      <td>0.367657</td>\\n\",\n       \"      <td>0.527646</td>\\n\",\n       \"      <td>0.367657</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.032987</td>\\n\",\n       \"      <td>0.079495</td>\\n\",\n       \"      <td>0.039714</td>\\n\",\n       \"      <td>0.031280</td>\\n\",\n       \"      <td>0.038138</td>\\n\",\n       \"      <td>0.374408</td>\\n\",\n       \"      <td>0.022152</td>\\n\",\n       \"      <td>0.086081</td>\\n\",\n       \"      <td>0.030621</td>\\n\",\n       \"      <td>0.007488</td>\\n\",\n       \"      <td>0.012891</td>\\n\",\n       \"      <td>0.284360</td>\\n\",\n       \"      <td>0.060884</td>\\n\",\n       \"      <td>0.060100</td>\\n\",\n       \"      <td>0.060361</td>\\n\",\n       \"      <td>0.060100</td>\\n\",\n       \"      <td>0.060100</td>\\n\",\n       \"      <td>0.379147</td>\\n\",\n       \"      <td>0.022176</td>\\n\",\n       \"      <td>0.086512</td>\\n\",\n       \"      <td>0.030673</td>\\n\",\n       \"      <td>0.022176</td>\\n\",\n       \"      <td>0.030673</td>\\n\",\n       \"      <td>2.270142</td>\\n\",\n       \"      <td>0.508627</td>\\n\",\n       \"      <td>0.498532</td>\\n\",\n       \"      <td>0.462071</td>\\n\",\n       \"      <td>0.508627</td>\\n\",\n       \"      <td>0.462071</td>\\n\",\n       \"      <td>3.199052</td>\\n\",\n       \"      <td>0.355178</td>\\n\",\n       \"      <td>0.664834</td>\\n\",\n       \"      <td>0.428667</td>\\n\",\n       \"      <td>0.352839</td>\\n\",\n       \"      <td>0.426733</td>\\n\",\n       \"      <td>2.796209</td>\\n\",\n       \"      <td>0.512231</td>\\n\",\n       \"      <td>0.511479</td>\\n\",\n       \"      <td>0.511833</td>\\n\",\n       \"      <td>0.511479</td>\\n\",\n       \"      <td>0.511479</td>\\n\",\n       \"      <td>3.715640</td>\\n\",\n       \"      <td>0.260357</td>\\n\",\n       \"      <td>0.730011</td>\\n\",\n       \"      <td>0.357598</td>\\n\",\n       \"      <td>0.260357</td>\\n\",\n       \"      <td>0.357598</td>\\n\",\n       \"      <td>0.616114</td>\\n\",\n       \"      <td>0.712074</td>\\n\",\n       \"      <td>0.158256</td>\\n\",\n       \"      <td>0.240966</td>\\n\",\n       \"      <td>0.712074</td>\\n\",\n       \"      <td>0.240966</td>\\n\",\n       \"      <td>1.606635</td>\\n\",\n       \"      <td>0.607334</td>\\n\",\n       \"      <td>0.373483</td>\\n\",\n       \"      <td>0.425370</td>\\n\",\n       \"      <td>0.607334</td>\\n\",\n       \"      <td>0.425370</td>\\n\",\n       \"      <td>0.417062</td>\\n\",\n       \"      <td>0.075737</td>\\n\",\n       \"      <td>0.150265</td>\\n\",\n       \"      <td>0.086208</td>\\n\",\n       \"      <td>0.070891</td>\\n\",\n       \"      <td>0.082958</td>\\n\",\n       \"      <td>0.549763</td>\\n\",\n       \"      <td>0.052113</td>\\n\",\n       \"      <td>0.167959</td>\\n\",\n       \"      <td>0.067824</td>\\n\",\n       \"      <td>0.017163</td>\\n\",\n       \"      <td>0.029196</td>\\n\",\n       \"      <td>0.398104</td>\\n\",\n       \"      <td>0.115507</td>\\n\",\n       \"      <td>0.111517</td>\\n\",\n       \"      <td>0.112848</td>\\n\",\n       \"      <td>0.111517</td>\\n\",\n       \"      <td>0.111517</td>\\n\",\n       \"      <td>0.554502</td>\\n\",\n       \"      <td>0.051962</td>\\n\",\n       \"      <td>0.168308</td>\\n\",\n       \"      <td>0.067611</td>\\n\",\n       \"      <td>0.051962</td>\\n\",\n       \"      <td>0.067611</td>\\n\",\n       \"      <td>0.232377</td>\\n\",\n       \"      <td>0.249998</td>\\n\",\n       \"      <td>0.355100</td>\\n\",\n       \"      <td>0.341608</td>\\n\",\n       \"      <td>0.419408</td>\\n\",\n       \"      <td>0.419990</td>\\n\",\n       \"      <td>0.484790</td>\\n\",\n       \"      <td>0.395293</td>\\n\",\n       \"      <td>0.425608</td>\\n\",\n       \"      <td>0.390179</td>\\n\",\n       \"      <td>0.530620</td>\\n\",\n       \"      <td>0.585925</td>\\n\",\n       \"      <td>0.554391</td>\\n\",\n       \"      <td>0.596053</td>\\n\",\n       \"      <td>0.045018</td>\\n\",\n       \"      <td>0.050922</td>\\n\",\n       \"      <td>0.136970</td>\\n\",\n       \"      <td>0.071346</td>\\n\",\n       \"      <td>0.080853</td>\\n\",\n       \"      <td>0.094792</td>\\n\",\n       \"      <td>0.102540</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>16.459716</td>\\n\",\n       \"      <td>21.341232</td>\\n\",\n       \"      <td>42.080569</td>\\n\",\n       \"      <td>3599.331754</td>\\n\",\n       \"      <td>476.601896</td>\\n\",\n       \"      <td>11974.620853</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16577</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.900474</td>\\n\",\n       \"      <td>0.380095</td>\\n\",\n       \"      <td>0.218194</td>\\n\",\n       \"      <td>0.252484</td>\\n\",\n       \"      <td>0.380095</td>\\n\",\n       \"      <td>0.252484</td>\\n\",\n       \"      <td>2.658768</td>\\n\",\n       \"      <td>0.265877</td>\\n\",\n       \"      <td>0.303088</td>\\n\",\n       \"      <td>0.256067</td>\\n\",\n       \"      <td>0.265877</td>\\n\",\n       \"      <td>0.256067</td>\\n\",\n       \"      <td>2.672986</td>\\n\",\n       \"      <td>0.258456</td>\\n\",\n       \"      <td>0.258456</td>\\n\",\n       \"      <td>0.258456</td>\\n\",\n       \"      <td>0.258456</td>\\n\",\n       \"      <td>0.258456</td>\\n\",\n       \"      <td>3.383886</td>\\n\",\n       \"      <td>0.029858</td>\\n\",\n       \"      <td>0.370373</td>\\n\",\n       \"      <td>0.052357</td>\\n\",\n       \"      <td>0.029858</td>\\n\",\n       \"      <td>0.052357</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.066921</td>\\n\",\n       \"      <td>0.114048</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.114048</td>\\n\",\n       \"      <td>1.379147</td>\\n\",\n       \"      <td>0.459716</td>\\n\",\n       \"      <td>0.158644</td>\\n\",\n       \"      <td>0.216468</td>\\n\",\n       \"      <td>0.459716</td>\\n\",\n       \"      <td>0.216468</td>\\n\",\n       \"      <td>2.056872</td>\\n\",\n       \"      <td>0.482495</td>\\n\",\n       \"      <td>0.277433</td>\\n\",\n       \"      <td>0.318873</td>\\n\",\n       \"      <td>0.482495</td>\\n\",\n       \"      <td>0.318873</td>\\n\",\n       \"      <td>3.090047</td>\\n\",\n       \"      <td>0.349177</td>\\n\",\n       \"      <td>0.382187</td>\\n\",\n       \"      <td>0.329825</td>\\n\",\n       \"      <td>0.349177</td>\\n\",\n       \"      <td>0.329825</td>\\n\",\n       \"      <td>3.303318</td>\\n\",\n       \"      <td>0.346409</td>\\n\",\n       \"      <td>0.346409</td>\\n\",\n       \"      <td>0.346409</td>\\n\",\n       \"      <td>0.346409</td>\\n\",\n       \"      <td>0.346409</td>\\n\",\n       \"      <td>4.483412</td>\\n\",\n       \"      <td>0.044196</td>\\n\",\n       \"      <td>0.496253</td>\\n\",\n       \"      <td>0.076438</td>\\n\",\n       \"      <td>0.044196</td>\\n\",\n       \"      <td>0.076438</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.657289</td>\\n\",\n       \"      <td>0.078794</td>\\n\",\n       \"      <td>0.133514</td>\\n\",\n       \"      <td>0.657289</td>\\n\",\n       \"      <td>0.133514</td>\\n\",\n       \"      <td>1.412322</td>\\n\",\n       \"      <td>0.568418</td>\\n\",\n       \"      <td>0.200694</td>\\n\",\n       \"      <td>0.270643</td>\\n\",\n       \"      <td>0.568418</td>\\n\",\n       \"      <td>0.270643</td>\\n\",\n       \"      <td>2.080569</td>\\n\",\n       \"      <td>0.419273</td>\\n\",\n       \"      <td>0.407542</td>\\n\",\n       \"      <td>0.381322</td>\\n\",\n       \"      <td>0.416114</td>\\n\",\n       \"      <td>0.379941</td>\\n\",\n       \"      <td>2.739336</td>\\n\",\n       \"      <td>0.308796</td>\\n\",\n       \"      <td>0.531540</td>\\n\",\n       \"      <td>0.357438</td>\\n\",\n       \"      <td>0.273934</td>\\n\",\n       \"      <td>0.334891</td>\\n\",\n       \"      <td>2.298578</td>\\n\",\n       \"      <td>0.401548</td>\\n\",\n       \"      <td>0.386473</td>\\n\",\n       \"      <td>0.392692</td>\\n\",\n       \"      <td>0.386473</td>\\n\",\n       \"      <td>0.386473</td>\\n\",\n       \"      <td>2.919431</td>\\n\",\n       \"      <td>0.269362</td>\\n\",\n       \"      <td>0.557818</td>\\n\",\n       \"      <td>0.328466</td>\\n\",\n       \"      <td>0.269362</td>\\n\",\n       \"      <td>0.328466</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.122989</td>\\n\",\n       \"      <td>0.190737</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.190737</td>\\n\",\n       \"      <td>1.464455</td>\\n\",\n       \"      <td>0.488152</td>\\n\",\n       \"      <td>0.290384</td>\\n\",\n       \"      <td>0.337703</td>\\n\",\n       \"      <td>0.488152</td>\\n\",\n       \"      <td>0.337703</td>\\n\",\n       \"      <td>0.364929</td>\\n\",\n       \"      <td>0.036493</td>\\n\",\n       \"      <td>0.100255</td>\\n\",\n       \"      <td>0.045807</td>\\n\",\n       \"      <td>0.036493</td>\\n\",\n       \"      <td>0.045807</td>\\n\",\n       \"      <td>0.450237</td>\\n\",\n       \"      <td>0.009265</td>\\n\",\n       \"      <td>0.113555</td>\\n\",\n       \"      <td>0.016120</td>\\n\",\n       \"      <td>0.009005</td>\\n\",\n       \"      <td>0.015684</td>\\n\",\n       \"      <td>0.322275</td>\\n\",\n       \"      <td>0.073014</td>\\n\",\n       \"      <td>0.073014</td>\\n\",\n       \"      <td>0.073014</td>\\n\",\n       \"      <td>0.073014</td>\\n\",\n       \"      <td>0.073014</td>\\n\",\n       \"      <td>0.464455</td>\\n\",\n       \"      <td>0.004566</td>\\n\",\n       \"      <td>0.115273</td>\\n\",\n       \"      <td>0.008423</td>\\n\",\n       \"      <td>0.004566</td>\\n\",\n       \"      <td>0.008423</td>\\n\",\n       \"      <td>2.127962</td>\\n\",\n       \"      <td>0.483450</td>\\n\",\n       \"      <td>0.473372</td>\\n\",\n       \"      <td>0.439287</td>\\n\",\n       \"      <td>0.479911</td>\\n\",\n       \"      <td>0.437727</td>\\n\",\n       \"      <td>2.890995</td>\\n\",\n       \"      <td>0.359328</td>\\n\",\n       \"      <td>0.605652</td>\\n\",\n       \"      <td>0.412767</td>\\n\",\n       \"      <td>0.319356</td>\\n\",\n       \"      <td>0.386934</td>\\n\",\n       \"      <td>2.454976</td>\\n\",\n       \"      <td>0.478760</td>\\n\",\n       \"      <td>0.460716</td>\\n\",\n       \"      <td>0.468158</td>\\n\",\n       \"      <td>0.460716</td>\\n\",\n       \"      <td>0.460716</td>\\n\",\n       \"      <td>3.118483</td>\\n\",\n       \"      <td>0.313202</td>\\n\",\n       \"      <td>0.634596</td>\\n\",\n       \"      <td>0.379283</td>\\n\",\n       \"      <td>0.313202</td>\\n\",\n       \"      <td>0.379283</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.647965</td>\\n\",\n       \"      <td>0.139431</td>\\n\",\n       \"      <td>0.214287</td>\\n\",\n       \"      <td>0.647965</td>\\n\",\n       \"      <td>0.214287</td>\\n\",\n       \"      <td>1.488152</td>\\n\",\n       \"      <td>0.564994</td>\\n\",\n       \"      <td>0.343075</td>\\n\",\n       \"      <td>0.393663</td>\\n\",\n       \"      <td>0.564994</td>\\n\",\n       \"      <td>0.393663</td>\\n\",\n       \"      <td>0.450237</td>\\n\",\n       \"      <td>0.074203</td>\\n\",\n       \"      <td>0.167027</td>\\n\",\n       \"      <td>0.087939</td>\\n\",\n       \"      <td>0.074203</td>\\n\",\n       \"      <td>0.087939</td>\\n\",\n       \"      <td>0.734597</td>\\n\",\n       \"      <td>0.022260</td>\\n\",\n       \"      <td>0.217886</td>\\n\",\n       \"      <td>0.037841</td>\\n\",\n       \"      <td>0.021636</td>\\n\",\n       \"      <td>0.036901</td>\\n\",\n       \"      <td>0.450237</td>\\n\",\n       \"      <td>0.120352</td>\\n\",\n       \"      <td>0.120352</td>\\n\",\n       \"      <td>0.120352</td>\\n\",\n       \"      <td>0.120352</td>\\n\",\n       \"      <td>0.120352</td>\\n\",\n       \"      <td>0.777251</td>\\n\",\n       \"      <td>0.011179</td>\\n\",\n       \"      <td>0.224996</td>\\n\",\n       \"      <td>0.020185</td>\\n\",\n       \"      <td>0.011179</td>\\n\",\n       \"      <td>0.020185</td>\\n\",\n       \"      <td>0.195867</td>\\n\",\n       \"      <td>0.214036</td>\\n\",\n       \"      <td>0.352565</td>\\n\",\n       \"      <td>0.302136</td>\\n\",\n       \"      <td>0.374525</td>\\n\",\n       \"      <td>0.374335</td>\\n\",\n       \"      <td>0.431086</td>\\n\",\n       \"      <td>0.337575</td>\\n\",\n       \"      <td>0.368302</td>\\n\",\n       \"      <td>0.412933</td>\\n\",\n       \"      <td>0.464273</td>\\n\",\n       \"      <td>0.518239</td>\\n\",\n       \"      <td>0.515114</td>\\n\",\n       \"      <td>0.549683</td>\\n\",\n       \"      <td>0.050333</td>\\n\",\n       \"      <td>0.059669</td>\\n\",\n       \"      <td>0.146523</td>\\n\",\n       \"      <td>0.088247</td>\\n\",\n       \"      <td>0.097419</td>\\n\",\n       \"      <td>0.104929</td>\\n\",\n       \"      <td>0.111944</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>11.890995</td>\\n\",\n       \"      <td>131.308057</td>\\n\",\n       \"      <td>155.497630</td>\\n\",\n       \"      <td>3279.786730</td>\\n\",\n       \"      <td>460.668246</td>\\n\",\n       \"      <td>12254.459716</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15965</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>2.099526</td>\\n\",\n       \"      <td>0.419905</td>\\n\",\n       \"      <td>0.245218</td>\\n\",\n       \"      <td>0.280465</td>\\n\",\n       \"      <td>0.419905</td>\\n\",\n       \"      <td>0.280465</td>\\n\",\n       \"      <td>2.985782</td>\\n\",\n       \"      <td>0.298578</td>\\n\",\n       \"      <td>0.331445</td>\\n\",\n       \"      <td>0.283926</td>\\n\",\n       \"      <td>0.298578</td>\\n\",\n       \"      <td>0.283926</td>\\n\",\n       \"      <td>3.189573</td>\\n\",\n       \"      <td>0.306454</td>\\n\",\n       \"      <td>0.305601</td>\\n\",\n       \"      <td>0.305973</td>\\n\",\n       \"      <td>0.305601</td>\\n\",\n       \"      <td>0.305601</td>\\n\",\n       \"      <td>4.189573</td>\\n\",\n       \"      <td>0.082169</td>\\n\",\n       \"      <td>0.435929</td>\\n\",\n       \"      <td>0.122360</td>\\n\",\n       \"      <td>0.082169</td>\\n\",\n       \"      <td>0.122360</td>\\n\",\n       \"      <td>0.535545</td>\\n\",\n       \"      <td>0.535545</td>\\n\",\n       \"      <td>0.068445</td>\\n\",\n       \"      <td>0.114750</td>\\n\",\n       \"      <td>0.535545</td>\\n\",\n       \"      <td>0.114750</td>\\n\",\n       \"      <td>1.488152</td>\\n\",\n       \"      <td>0.496051</td>\\n\",\n       \"      <td>0.178251</td>\\n\",\n       \"      <td>0.239589</td>\\n\",\n       \"      <td>0.496051</td>\\n\",\n       \"      <td>0.239589</td>\\n\",\n       \"      <td>2.189573</td>\\n\",\n       \"      <td>0.513981</td>\\n\",\n       \"      <td>0.295686</td>\\n\",\n       \"      <td>0.339589</td>\\n\",\n       \"      <td>0.513981</td>\\n\",\n       \"      <td>0.339589</td>\\n\",\n       \"      <td>3.383886</td>\\n\",\n       \"      <td>0.380691</td>\\n\",\n       \"      <td>0.409464</td>\\n\",\n       \"      <td>0.357324</td>\\n\",\n       \"      <td>0.380691</td>\\n\",\n       \"      <td>0.357324</td>\\n\",\n       \"      <td>3.810427</td>\\n\",\n       \"      <td>0.392044</td>\\n\",\n       \"      <td>0.391089</td>\\n\",\n       \"      <td>0.391508</td>\\n\",\n       \"      <td>0.391089</td>\\n\",\n       \"      <td>0.391089</td>\\n\",\n       \"      <td>5.203791</td>\\n\",\n       \"      <td>0.108726</td>\\n\",\n       \"      <td>0.554822</td>\\n\",\n       \"      <td>0.161163</td>\\n\",\n       \"      <td>0.108726</td>\\n\",\n       \"      <td>0.161163</td>\\n\",\n       \"      <td>0.535545</td>\\n\",\n       \"      <td>0.660502</td>\\n\",\n       \"      <td>0.085254</td>\\n\",\n       \"      <td>0.142612</td>\\n\",\n       \"      <td>0.660502</td>\\n\",\n       \"      <td>0.142612</td>\\n\",\n       \"      <td>1.526066</td>\\n\",\n       \"      <td>0.597286</td>\\n\",\n       \"      <td>0.215338</td>\\n\",\n       \"      <td>0.288578</td>\\n\",\n       \"      <td>0.597286</td>\\n\",\n       \"      <td>0.288578</td>\\n\",\n       \"      <td>2.175355</td>\\n\",\n       \"      <td>0.435071</td>\\n\",\n       \"      <td>0.439912</td>\\n\",\n       \"      <td>0.400072</td>\\n\",\n       \"      <td>0.435071</td>\\n\",\n       \"      <td>0.400072</td>\\n\",\n       \"      <td>3.109005</td>\\n\",\n       \"      <td>0.315666</td>\\n\",\n       \"      <td>0.595993</td>\\n\",\n       \"      <td>0.382008</td>\\n\",\n       \"      <td>0.310900</td>\\n\",\n       \"      <td>0.378249</td>\\n\",\n       \"      <td>2.606635</td>\\n\",\n       \"      <td>0.439262</td>\\n\",\n       \"      <td>0.435978</td>\\n\",\n       \"      <td>0.437318</td>\\n\",\n       \"      <td>0.435978</td>\\n\",\n       \"      <td>0.435978</td>\\n\",\n       \"      <td>3.696682</td>\\n\",\n       \"      <td>0.234006</td>\\n\",\n       \"      <td>0.677230</td>\\n\",\n       \"      <td>0.320866</td>\\n\",\n       \"      <td>0.234006</td>\\n\",\n       \"      <td>0.320866</td>\\n\",\n       \"      <td>0.568720</td>\\n\",\n       \"      <td>0.568720</td>\\n\",\n       \"      <td>0.130225</td>\\n\",\n       \"      <td>0.196472</td>\\n\",\n       \"      <td>0.568720</td>\\n\",\n       \"      <td>0.196472</td>\\n\",\n       \"      <td>1.545024</td>\\n\",\n       \"      <td>0.515008</td>\\n\",\n       \"      <td>0.325563</td>\\n\",\n       \"      <td>0.363380</td>\\n\",\n       \"      <td>0.515008</td>\\n\",\n       \"      <td>0.363380</td>\\n\",\n       \"      <td>0.336493</td>\\n\",\n       \"      <td>0.034853</td>\\n\",\n       \"      <td>0.080522</td>\\n\",\n       \"      <td>0.040423</td>\\n\",\n       \"      <td>0.033649</td>\\n\",\n       \"      <td>0.039281</td>\\n\",\n       \"      <td>0.492891</td>\\n\",\n       \"      <td>0.015417</td>\\n\",\n       \"      <td>0.101885</td>\\n\",\n       \"      <td>0.023268</td>\\n\",\n       \"      <td>0.009858</td>\\n\",\n       \"      <td>0.016514</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.056993</td>\\n\",\n       \"      <td>0.056634</td>\\n\",\n       \"      <td>0.056761</td>\\n\",\n       \"      <td>0.056634</td>\\n\",\n       \"      <td>0.056634</td>\\n\",\n       \"      <td>0.492891</td>\\n\",\n       \"      <td>0.012708</td>\\n\",\n       \"      <td>0.101885</td>\\n\",\n       \"      <td>0.019141</td>\\n\",\n       \"      <td>0.012708</td>\\n\",\n       \"      <td>0.019141</td>\\n\",\n       \"      <td>2.213270</td>\\n\",\n       \"      <td>0.492577</td>\\n\",\n       \"      <td>0.491572</td>\\n\",\n       \"      <td>0.450339</td>\\n\",\n       \"      <td>0.492577</td>\\n\",\n       \"      <td>0.450339</td>\\n\",\n       \"      <td>3.270142</td>\\n\",\n       \"      <td>0.359960</td>\\n\",\n       \"      <td>0.664509</td>\\n\",\n       \"      <td>0.432654</td>\\n\",\n       \"      <td>0.354919</td>\\n\",\n       \"      <td>0.428662</td>\\n\",\n       \"      <td>2.744076</td>\\n\",\n       \"      <td>0.502375</td>\\n\",\n       \"      <td>0.498349</td>\\n\",\n       \"      <td>0.499992</td>\\n\",\n       \"      <td>0.498349</td>\\n\",\n       \"      <td>0.498349</td>\\n\",\n       \"      <td>3.876777</td>\\n\",\n       \"      <td>0.263929</td>\\n\",\n       \"      <td>0.750186</td>\\n\",\n       \"      <td>0.360628</td>\\n\",\n       \"      <td>0.263929</td>\\n\",\n       \"      <td>0.360628</td>\\n\",\n       \"      <td>0.568720</td>\\n\",\n       \"      <td>0.651948</td>\\n\",\n       \"      <td>0.148296</td>\\n\",\n       \"      <td>0.224311</td>\\n\",\n       \"      <td>0.651948</td>\\n\",\n       \"      <td>0.224311</td>\\n\",\n       \"      <td>1.549763</td>\\n\",\n       \"      <td>0.583830</td>\\n\",\n       \"      <td>0.367845</td>\\n\",\n       \"      <td>0.411165</td>\\n\",\n       \"      <td>0.583830</td>\\n\",\n       \"      <td>0.411165</td>\\n\",\n       \"      <td>0.398104</td>\\n\",\n       \"      <td>0.071050</td>\\n\",\n       \"      <td>0.142106</td>\\n\",\n       \"      <td>0.080877</td>\\n\",\n       \"      <td>0.068844</td>\\n\",\n       \"      <td>0.079165</td>\\n\",\n       \"      <td>0.729858</td>\\n\",\n       \"      <td>0.033891</td>\\n\",\n       \"      <td>0.193894</td>\\n\",\n       \"      <td>0.049719</td>\\n\",\n       \"      <td>0.021191</td>\\n\",\n       \"      <td>0.035600</td>\\n\",\n       \"      <td>0.431280</td>\\n\",\n       \"      <td>0.103439</td>\\n\",\n       \"      <td>0.102348</td>\\n\",\n       \"      <td>0.102733</td>\\n\",\n       \"      <td>0.102348</td>\\n\",\n       \"      <td>0.102348</td>\\n\",\n       \"      <td>0.758294</td>\\n\",\n       \"      <td>0.028552</td>\\n\",\n       \"      <td>0.198528</td>\\n\",\n       \"      <td>0.041572</td>\\n\",\n       \"      <td>0.028552</td>\\n\",\n       \"      <td>0.041572</td>\\n\",\n       \"      <td>0.224174</td>\\n\",\n       \"      <td>0.246631</td>\\n\",\n       \"      <td>0.338336</td>\\n\",\n       \"      <td>0.351571</td>\\n\",\n       \"      <td>0.424614</td>\\n\",\n       \"      <td>0.404334</td>\\n\",\n       \"      <td>0.469069</td>\\n\",\n       \"      <td>0.392972</td>\\n\",\n       \"      <td>0.430376</td>\\n\",\n       \"      <td>0.382393</td>\\n\",\n       \"      <td>0.540071</td>\\n\",\n       \"      <td>0.594893</td>\\n\",\n       \"      <td>0.527697</td>\\n\",\n       \"      <td>0.570448</td>\\n\",\n       \"      <td>0.037247</td>\\n\",\n       \"      <td>0.046973</td>\\n\",\n       \"      <td>0.137074</td>\\n\",\n       \"      <td>0.074509</td>\\n\",\n       \"      <td>0.084071</td>\\n\",\n       \"      <td>0.083115</td>\\n\",\n       \"      <td>0.090884</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>17.199052</td>\\n\",\n       \"      <td>63.701422</td>\\n\",\n       \"      <td>89.800948</td>\\n\",\n       \"      <td>3507.440758</td>\\n\",\n       \"      <td>461.905213</td>\\n\",\n       \"      <td>11827.218009</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20418</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.786730</td>\\n\",\n       \"      <td>0.357346</td>\\n\",\n       \"      <td>0.203655</td>\\n\",\n       \"      <td>0.235470</td>\\n\",\n       \"      <td>0.357346</td>\\n\",\n       \"      <td>0.235470</td>\\n\",\n       \"      <td>2.554502</td>\\n\",\n       \"      <td>0.255661</td>\\n\",\n       \"      <td>0.282525</td>\\n\",\n       \"      <td>0.243897</td>\\n\",\n       \"      <td>0.255450</td>\\n\",\n       \"      <td>0.243797</td>\\n\",\n       \"      <td>2.753555</td>\\n\",\n       \"      <td>0.257047</td>\\n\",\n       \"      <td>0.256767</td>\\n\",\n       \"      <td>0.256900</td>\\n\",\n       \"      <td>0.256767</td>\\n\",\n       \"      <td>0.256767</td>\\n\",\n       \"      <td>3.843602</td>\\n\",\n       \"      <td>0.053151</td>\\n\",\n       \"      <td>0.401223</td>\\n\",\n       \"      <td>0.086804</td>\\n\",\n       \"      <td>0.053151</td>\\n\",\n       \"      <td>0.086804</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.051375</td>\\n\",\n       \"      <td>0.088839</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.088839</td>\\n\",\n       \"      <td>1.312796</td>\\n\",\n       \"      <td>0.437599</td>\\n\",\n       \"      <td>0.151337</td>\\n\",\n       \"      <td>0.206420</td>\\n\",\n       \"      <td>0.437599</td>\\n\",\n       \"      <td>0.206420</td>\\n\",\n       \"      <td>1.895735</td>\\n\",\n       \"      <td>0.461935</td>\\n\",\n       \"      <td>0.265495</td>\\n\",\n       \"      <td>0.305289</td>\\n\",\n       \"      <td>0.461935</td>\\n\",\n       \"      <td>0.305289</td>\\n\",\n       \"      <td>2.971564</td>\\n\",\n       \"      <td>0.339187</td>\\n\",\n       \"      <td>0.366888</td>\\n\",\n       \"      <td>0.319654</td>\\n\",\n       \"      <td>0.338794</td>\\n\",\n       \"      <td>0.319504</td>\\n\",\n       \"      <td>3.426540</td>\\n\",\n       \"      <td>0.351444</td>\\n\",\n       \"      <td>0.350911</td>\\n\",\n       \"      <td>0.351126</td>\\n\",\n       \"      <td>0.350911</td>\\n\",\n       \"      <td>0.350911</td>\\n\",\n       \"      <td>4.928910</td>\\n\",\n       \"      <td>0.073492</td>\\n\",\n       \"      <td>0.526218</td>\\n\",\n       \"      <td>0.119014</td>\\n\",\n       \"      <td>0.073492</td>\\n\",\n       \"      <td>0.119014</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.617191</td>\\n\",\n       \"      <td>0.073951</td>\\n\",\n       \"      <td>0.125472</td>\\n\",\n       \"      <td>0.617191</td>\\n\",\n       \"      <td>0.125472</td>\\n\",\n       \"      <td>1.369668</td>\\n\",\n       \"      <td>0.559493</td>\\n\",\n       \"      <td>0.200396</td>\\n\",\n       \"      <td>0.269387</td>\\n\",\n       \"      <td>0.559493</td>\\n\",\n       \"      <td>0.269387</td>\\n\",\n       \"      <td>2.028436</td>\\n\",\n       \"      <td>0.409953</td>\\n\",\n       \"      <td>0.394035</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.405687</td>\\n\",\n       \"      <td>0.369014</td>\\n\",\n       \"      <td>2.876777</td>\\n\",\n       \"      <td>0.304863</td>\\n\",\n       \"      <td>0.538367</td>\\n\",\n       \"      <td>0.359224</td>\\n\",\n       \"      <td>0.287678</td>\\n\",\n       \"      <td>0.348722</td>\\n\",\n       \"      <td>2.393365</td>\\n\",\n       \"      <td>0.397135</td>\\n\",\n       \"      <td>0.388204</td>\\n\",\n       \"      <td>0.391009</td>\\n\",\n       \"      <td>0.388204</td>\\n\",\n       \"      <td>0.388204</td>\\n\",\n       \"      <td>3.274882</td>\\n\",\n       \"      <td>0.251583</td>\\n\",\n       \"      <td>0.597892</td>\\n\",\n       \"      <td>0.323563</td>\\n\",\n       \"      <td>0.251583</td>\\n\",\n       \"      <td>0.323563</td>\\n\",\n       \"      <td>0.545024</td>\\n\",\n       \"      <td>0.545024</td>\\n\",\n       \"      <td>0.111447</td>\\n\",\n       \"      <td>0.174271</td>\\n\",\n       \"      <td>0.545024</td>\\n\",\n       \"      <td>0.174271</td>\\n\",\n       \"      <td>1.426540</td>\\n\",\n       \"      <td>0.478673</td>\\n\",\n       \"      <td>0.284898</td>\\n\",\n       \"      <td>0.330281</td>\\n\",\n       \"      <td>0.475513</td>\\n\",\n       \"      <td>0.329740</td>\\n\",\n       \"      <td>0.374408</td>\\n\",\n       \"      <td>0.038863</td>\\n\",\n       \"      <td>0.089106</td>\\n\",\n       \"      <td>0.046542</td>\\n\",\n       \"      <td>0.037441</td>\\n\",\n       \"      <td>0.045934</td>\\n\",\n       \"      <td>0.554502</td>\\n\",\n       \"      <td>0.014447</td>\\n\",\n       \"      <td>0.119576</td>\\n\",\n       \"      <td>0.022252</td>\\n\",\n       \"      <td>0.011090</td>\\n\",\n       \"      <td>0.019194</td>\\n\",\n       \"      <td>0.293839</td>\\n\",\n       \"      <td>0.045047</td>\\n\",\n       \"      <td>0.044019</td>\\n\",\n       \"      <td>0.044394</td>\\n\",\n       \"      <td>0.044019</td>\\n\",\n       \"      <td>0.044019</td>\\n\",\n       \"      <td>0.568720</td>\\n\",\n       \"      <td>0.011648</td>\\n\",\n       \"      <td>0.121814</td>\\n\",\n       \"      <td>0.017807</td>\\n\",\n       \"      <td>0.011648</td>\\n\",\n       \"      <td>0.017807</td>\\n\",\n       \"      <td>2.080569</td>\\n\",\n       \"      <td>0.478654</td>\\n\",\n       \"      <td>0.466916</td>\\n\",\n       \"      <td>0.433444</td>\\n\",\n       \"      <td>0.474341</td>\\n\",\n       \"      <td>0.432443</td>\\n\",\n       \"      <td>2.995261</td>\\n\",\n       \"      <td>0.350659</td>\\n\",\n       \"      <td>0.616864</td>\\n\",\n       \"      <td>0.412199</td>\\n\",\n       \"      <td>0.332010</td>\\n\",\n       \"      <td>0.400657</td>\\n\",\n       \"      <td>2.526066</td>\\n\",\n       \"      <td>0.476692</td>\\n\",\n       \"      <td>0.466592</td>\\n\",\n       \"      <td>0.469859</td>\\n\",\n       \"      <td>0.466592</td>\\n\",\n       \"      <td>0.466592</td>\\n\",\n       \"      <td>3.431280</td>\\n\",\n       \"      <td>0.287652</td>\\n\",\n       \"      <td>0.674428</td>\\n\",\n       \"      <td>0.369004</td>\\n\",\n       \"      <td>0.287652</td>\\n\",\n       \"      <td>0.369004</td>\\n\",\n       \"      <td>0.545024</td>\\n\",\n       \"      <td>0.652368</td>\\n\",\n       \"      <td>0.143771</td>\\n\",\n       \"      <td>0.219069</td>\\n\",\n       \"      <td>0.652368</td>\\n\",\n       \"      <td>0.219069</td>\\n\",\n       \"      <td>1.445498</td>\\n\",\n       \"      <td>0.558853</td>\\n\",\n       \"      <td>0.343940</td>\\n\",\n       \"      <td>0.390592</td>\\n\",\n       \"      <td>0.555693</td>\\n\",\n       \"      <td>0.390050</td>\\n\",\n       \"      <td>0.483412</td>\\n\",\n       \"      <td>0.078338</td>\\n\",\n       \"      <td>0.161909</td>\\n\",\n       \"      <td>0.091366</td>\\n\",\n       \"      <td>0.076792</td>\\n\",\n       \"      <td>0.090703</td>\\n\",\n       \"      <td>0.819905</td>\\n\",\n       \"      <td>0.028333</td>\\n\",\n       \"      <td>0.221880</td>\\n\",\n       \"      <td>0.044890</td>\\n\",\n       \"      <td>0.023268</td>\\n\",\n       \"      <td>0.039708</td>\\n\",\n       \"      <td>0.398104</td>\\n\",\n       \"      <td>0.093029</td>\\n\",\n       \"      <td>0.091905</td>\\n\",\n       \"      <td>0.092318</td>\\n\",\n       \"      <td>0.091905</td>\\n\",\n       \"      <td>0.091905</td>\\n\",\n       \"      <td>0.857820</td>\\n\",\n       \"      <td>0.022394</td>\\n\",\n       \"      <td>0.229624</td>\\n\",\n       \"      <td>0.035724</td>\\n\",\n       \"      <td>0.022394</td>\\n\",\n       \"      <td>0.035724</td>\\n\",\n       \"      <td>0.177303</td>\\n\",\n       \"      <td>0.199469</td>\\n\",\n       \"      <td>0.298370</td>\\n\",\n       \"      <td>0.306411</td>\\n\",\n       \"      <td>0.371937</td>\\n\",\n       \"      <td>0.374796</td>\\n\",\n       \"      <td>0.428296</td>\\n\",\n       \"      <td>0.338120</td>\\n\",\n       \"      <td>0.371345</td>\\n\",\n       \"      <td>0.373078</td>\\n\",\n       \"      <td>0.480191</td>\\n\",\n       \"      <td>0.531138</td>\\n\",\n       \"      <td>0.517288</td>\\n\",\n       \"      <td>0.555827</td>\\n\",\n       \"      <td>0.027307</td>\\n\",\n       \"      <td>0.038406</td>\\n\",\n       \"      <td>0.097735</td>\\n\",\n       \"      <td>0.075169</td>\\n\",\n       \"      <td>0.080896</td>\\n\",\n       \"      <td>0.069498</td>\\n\",\n       \"      <td>0.078206</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>14.113744</td>\\n\",\n       \"      <td>76.985782</td>\\n\",\n       \"      <td>99.502370</td>\\n\",\n       \"      <td>3533.298578</td>\\n\",\n       \"      <td>480.521327</td>\\n\",\n       \"      <td>12429.412322</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18551</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.990521</td>\\n\",\n       \"      <td>0.398104</td>\\n\",\n       \"      <td>0.227730</td>\\n\",\n       \"      <td>0.263043</td>\\n\",\n       \"      <td>0.398104</td>\\n\",\n       \"      <td>0.263043</td>\\n\",\n       \"      <td>2.767773</td>\\n\",\n       \"      <td>0.276777</td>\\n\",\n       \"      <td>0.309773</td>\\n\",\n       \"      <td>0.264381</td>\\n\",\n       \"      <td>0.276777</td>\\n\",\n       \"      <td>0.264381</td>\\n\",\n       \"      <td>2.900474</td>\\n\",\n       \"      <td>0.277477</td>\\n\",\n       \"      <td>0.277220</td>\\n\",\n       \"      <td>0.277334</td>\\n\",\n       \"      <td>0.277220</td>\\n\",\n       \"      <td>0.277220</td>\\n\",\n       \"      <td>3.763033</td>\\n\",\n       \"      <td>0.082236</td>\\n\",\n       \"      <td>0.400329</td>\\n\",\n       \"      <td>0.126005</td>\\n\",\n       \"      <td>0.082236</td>\\n\",\n       \"      <td>0.126005</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.061642</td>\\n\",\n       \"      <td>0.104782</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.104782</td>\\n\",\n       \"      <td>1.454976</td>\\n\",\n       \"      <td>0.484992</td>\\n\",\n       \"      <td>0.168267</td>\\n\",\n       \"      <td>0.228984</td>\\n\",\n       \"      <td>0.484992</td>\\n\",\n       \"      <td>0.228984</td>\\n\",\n       \"      <td>2.123223</td>\\n\",\n       \"      <td>0.493977</td>\\n\",\n       \"      <td>0.281764</td>\\n\",\n       \"      <td>0.325003</td>\\n\",\n       \"      <td>0.493977</td>\\n\",\n       \"      <td>0.325003</td>\\n\",\n       \"      <td>3.194313</td>\\n\",\n       \"      <td>0.361672</td>\\n\",\n       \"      <td>0.393679</td>\\n\",\n       \"      <td>0.341386</td>\\n\",\n       \"      <td>0.361672</td>\\n\",\n       \"      <td>0.341386</td>\\n\",\n       \"      <td>3.592417</td>\\n\",\n       \"      <td>0.368861</td>\\n\",\n       \"      <td>0.368537</td>\\n\",\n       \"      <td>0.368681</td>\\n\",\n       \"      <td>0.368537</td>\\n\",\n       \"      <td>0.368537</td>\\n\",\n       \"      <td>4.725118</td>\\n\",\n       \"      <td>0.113138</td>\\n\",\n       \"      <td>0.517908</td>\\n\",\n       \"      <td>0.170628</td>\\n\",\n       \"      <td>0.113138</td>\\n\",\n       \"      <td>0.170628</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.633108</td>\\n\",\n       \"      <td>0.074623</td>\\n\",\n       \"      <td>0.126861</td>\\n\",\n       \"      <td>0.633108</td>\\n\",\n       \"      <td>0.126861</td>\\n\",\n       \"      <td>1.497630</td>\\n\",\n       \"      <td>0.586640</td>\\n\",\n       \"      <td>0.207197</td>\\n\",\n       \"      <td>0.279735</td>\\n\",\n       \"      <td>0.586640</td>\\n\",\n       \"      <td>0.279735</td>\\n\",\n       \"      <td>2.113744</td>\\n\",\n       \"      <td>0.423223</td>\\n\",\n       \"      <td>0.415818</td>\\n\",\n       \"      <td>0.384696</td>\\n\",\n       \"      <td>0.422749</td>\\n\",\n       \"      <td>0.384432</td>\\n\",\n       \"      <td>2.933649</td>\\n\",\n       \"      <td>0.317210</td>\\n\",\n       \"      <td>0.566169</td>\\n\",\n       \"      <td>0.373303</td>\\n\",\n       \"      <td>0.293365</td>\\n\",\n       \"      <td>0.357487</td>\\n\",\n       \"      <td>2.383886</td>\\n\",\n       \"      <td>0.399126</td>\\n\",\n       \"      <td>0.389511</td>\\n\",\n       \"      <td>0.392942</td>\\n\",\n       \"      <td>0.389511</td>\\n\",\n       \"      <td>0.389511</td>\\n\",\n       \"      <td>3.241706</td>\\n\",\n       \"      <td>0.265737</td>\\n\",\n       \"      <td>0.609994</td>\\n\",\n       \"      <td>0.336570</td>\\n\",\n       \"      <td>0.265737</td>\\n\",\n       \"      <td>0.336570</td>\\n\",\n       \"      <td>0.563981</td>\\n\",\n       \"      <td>0.563981</td>\\n\",\n       \"      <td>0.114894</td>\\n\",\n       \"      <td>0.181233</td>\\n\",\n       \"      <td>0.563981</td>\\n\",\n       \"      <td>0.181233</td>\\n\",\n       \"      <td>1.521327</td>\\n\",\n       \"      <td>0.507109</td>\\n\",\n       \"      <td>0.308195</td>\\n\",\n       \"      <td>0.351405</td>\\n\",\n       \"      <td>0.507109</td>\\n\",\n       \"      <td>0.351405</td>\\n\",\n       \"      <td>0.393365</td>\\n\",\n       \"      <td>0.039389</td>\\n\",\n       \"      <td>0.089651</td>\\n\",\n       \"      <td>0.045724</td>\\n\",\n       \"      <td>0.039336</td>\\n\",\n       \"      <td>0.045638</td>\\n\",\n       \"      <td>0.516588</td>\\n\",\n       \"      <td>0.017154</td>\\n\",\n       \"      <td>0.114849</td>\\n\",\n       \"      <td>0.026326</td>\\n\",\n       \"      <td>0.010332</td>\\n\",\n       \"      <td>0.017683</td>\\n\",\n       \"      <td>0.345972</td>\\n\",\n       \"      <td>0.061148</td>\\n\",\n       \"      <td>0.061013</td>\\n\",\n       \"      <td>0.061074</td>\\n\",\n       \"      <td>0.061013</td>\\n\",\n       \"      <td>0.061013</td>\\n\",\n       \"      <td>0.521327</td>\\n\",\n       \"      <td>0.016310</td>\\n\",\n       \"      <td>0.115322</td>\\n\",\n       \"      <td>0.025002</td>\\n\",\n       \"      <td>0.016310</td>\\n\",\n       \"      <td>0.025002</td>\\n\",\n       \"      <td>2.189573</td>\\n\",\n       \"      <td>0.490895</td>\\n\",\n       \"      <td>0.482838</td>\\n\",\n       \"      <td>0.445990</td>\\n\",\n       \"      <td>0.490421</td>\\n\",\n       \"      <td>0.445727</td>\\n\",\n       \"      <td>3.090047</td>\\n\",\n       \"      <td>0.364488</td>\\n\",\n       \"      <td>0.636082</td>\\n\",\n       \"      <td>0.426119</td>\\n\",\n       \"      <td>0.338105</td>\\n\",\n       \"      <td>0.408771</td>\\n\",\n       \"      <td>2.535545</td>\\n\",\n       \"      <td>0.476532</td>\\n\",\n       \"      <td>0.465381</td>\\n\",\n       \"      <td>0.469400</td>\\n\",\n       \"      <td>0.465381</td>\\n\",\n       \"      <td>0.465381</td>\\n\",\n       \"      <td>3.436019</td>\\n\",\n       \"      <td>0.303928</td>\\n\",\n       \"      <td>0.683365</td>\\n\",\n       \"      <td>0.383333</td>\\n\",\n       \"      <td>0.303928</td>\\n\",\n       \"      <td>0.383333</td>\\n\",\n       \"      <td>0.563981</td>\\n\",\n       \"      <td>0.656148</td>\\n\",\n       \"      <td>0.135899</td>\\n\",\n       \"      <td>0.213246</td>\\n\",\n       \"      <td>0.656148</td>\\n\",\n       \"      <td>0.213246</td>\\n\",\n       \"      <td>1.549763</td>\\n\",\n       \"      <td>0.584325</td>\\n\",\n       \"      <td>0.361693</td>\\n\",\n       \"      <td>0.408456</td>\\n\",\n       \"      <td>0.584325</td>\\n\",\n       \"      <td>0.408456</td>\\n\",\n       \"      <td>0.469194</td>\\n\",\n       \"      <td>0.077223</td>\\n\",\n       \"      <td>0.153058</td>\\n\",\n       \"      <td>0.086575</td>\\n\",\n       \"      <td>0.074898</td>\\n\",\n       \"      <td>0.085701</td>\\n\",\n       \"      <td>0.748815</td>\\n\",\n       \"      <td>0.038082</td>\\n\",\n       \"      <td>0.210970</td>\\n\",\n       \"      <td>0.055167</td>\\n\",\n       \"      <td>0.022049</td>\\n\",\n       \"      <td>0.037350</td>\\n\",\n       \"      <td>0.454976</td>\\n\",\n       \"      <td>0.109915</td>\\n\",\n       \"      <td>0.107888</td>\\n\",\n       \"      <td>0.108435</td>\\n\",\n       \"      <td>0.107888</td>\\n\",\n       \"      <td>0.107888</td>\\n\",\n       \"      <td>0.758294</td>\\n\",\n       \"      <td>0.036113</td>\\n\",\n       \"      <td>0.211727</td>\\n\",\n       \"      <td>0.052163</td>\\n\",\n       \"      <td>0.036113</td>\\n\",\n       \"      <td>0.052163</td>\\n\",\n       \"      <td>0.203436</td>\\n\",\n       \"      <td>0.223779</td>\\n\",\n       \"      <td>0.332697</td>\\n\",\n       \"      <td>0.322328</td>\\n\",\n       \"      <td>0.392731</td>\\n\",\n       \"      <td>0.382614</td>\\n\",\n       \"      <td>0.442020</td>\\n\",\n       \"      <td>0.352287</td>\\n\",\n       \"      <td>0.386760</td>\\n\",\n       \"      <td>0.391645</td>\\n\",\n       \"      <td>0.488649</td>\\n\",\n       \"      <td>0.545153</td>\\n\",\n       \"      <td>0.522944</td>\\n\",\n       \"      <td>0.562108</td>\\n\",\n       \"      <td>0.045275</td>\\n\",\n       \"      <td>0.054524</td>\\n\",\n       \"      <td>0.151221</td>\\n\",\n       \"      <td>0.082836</td>\\n\",\n       \"      <td>0.095441</td>\\n\",\n       \"      <td>0.086584</td>\\n\",\n       \"      <td>0.094718</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>13.445498</td>\\n\",\n       \"      <td>41.900474</td>\\n\",\n       \"      <td>61.118483</td>\\n\",\n       \"      <td>3591.137441</td>\\n\",\n       \"      <td>483.838863</td>\\n\",\n       \"      <td>12143.298578</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20751</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.919431</td>\\n\",\n       \"      <td>0.384518</td>\\n\",\n       \"      <td>0.225842</td>\\n\",\n       \"      <td>0.257387</td>\\n\",\n       \"      <td>0.383886</td>\\n\",\n       \"      <td>0.257329</td>\\n\",\n       \"      <td>2.530806</td>\\n\",\n       \"      <td>0.255977</td>\\n\",\n       \"      <td>0.296128</td>\\n\",\n       \"      <td>0.247833</td>\\n\",\n       \"      <td>0.253081</td>\\n\",\n       \"      <td>0.246462</td>\\n\",\n       \"      <td>2.573460</td>\\n\",\n       \"      <td>0.257879</td>\\n\",\n       \"      <td>0.254849</td>\\n\",\n       \"      <td>0.255760</td>\\n\",\n       \"      <td>0.254849</td>\\n\",\n       \"      <td>0.254849</td>\\n\",\n       \"      <td>3.336493</td>\\n\",\n       \"      <td>0.065324</td>\\n\",\n       \"      <td>0.367928</td>\\n\",\n       \"      <td>0.097158</td>\\n\",\n       \"      <td>0.065324</td>\\n\",\n       \"      <td>0.097158</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.076657</td>\\n\",\n       \"      <td>0.127249</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.127249</td>\\n\",\n       \"      <td>1.421801</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.169194</td>\\n\",\n       \"      <td>0.228810</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.228810</td>\\n\",\n       \"      <td>2.080569</td>\\n\",\n       \"      <td>0.494424</td>\\n\",\n       \"      <td>0.286588</td>\\n\",\n       \"      <td>0.327561</td>\\n\",\n       \"      <td>0.493665</td>\\n\",\n       \"      <td>0.327491</td>\\n\",\n       \"      <td>2.957346</td>\\n\",\n       \"      <td>0.343820</td>\\n\",\n       \"      <td>0.378017</td>\\n\",\n       \"      <td>0.325291</td>\\n\",\n       \"      <td>0.340167</td>\\n\",\n       \"      <td>0.323704</td>\\n\",\n       \"      <td>3.255924</td>\\n\",\n       \"      <td>0.353917</td>\\n\",\n       \"      <td>0.349765</td>\\n\",\n       \"      <td>0.351051</td>\\n\",\n       \"      <td>0.349765</td>\\n\",\n       \"      <td>0.349765</td>\\n\",\n       \"      <td>4.327014</td>\\n\",\n       \"      <td>0.091590</td>\\n\",\n       \"      <td>0.483922</td>\\n\",\n       \"      <td>0.135197</td>\\n\",\n       \"      <td>0.091590</td>\\n\",\n       \"      <td>0.135197</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.695940</td>\\n\",\n       \"      <td>0.094827</td>\\n\",\n       \"      <td>0.157297</td>\\n\",\n       \"      <td>0.695940</td>\\n\",\n       \"      <td>0.157297</td>\\n\",\n       \"      <td>1.473934</td>\\n\",\n       \"      <td>0.593370</td>\\n\",\n       \"      <td>0.212982</td>\\n\",\n       \"      <td>0.286053</td>\\n\",\n       \"      <td>0.593370</td>\\n\",\n       \"      <td>0.286053</td>\\n\",\n       \"      <td>2.047393</td>\\n\",\n       \"      <td>0.419036</td>\\n\",\n       \"      <td>0.403510</td>\\n\",\n       \"      <td>0.378483</td>\\n\",\n       \"      <td>0.409479</td>\\n\",\n       \"      <td>0.374778</td>\\n\",\n       \"      <td>2.668246</td>\\n\",\n       \"      <td>0.313701</td>\\n\",\n       \"      <td>0.507187</td>\\n\",\n       \"      <td>0.354980</td>\\n\",\n       \"      <td>0.266825</td>\\n\",\n       \"      <td>0.324989</td>\\n\",\n       \"      <td>2.331754</td>\\n\",\n       \"      <td>0.413376</td>\\n\",\n       \"      <td>0.393297</td>\\n\",\n       \"      <td>0.400245</td>\\n\",\n       \"      <td>0.393297</td>\\n\",\n       \"      <td>0.393297</td>\\n\",\n       \"      <td>2.933649</td>\\n\",\n       \"      <td>0.285524</td>\\n\",\n       \"      <td>0.554653</td>\\n\",\n       \"      <td>0.337768</td>\\n\",\n       \"      <td>0.285524</td>\\n\",\n       \"      <td>0.337768</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.129019</td>\\n\",\n       \"      <td>0.198050</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.198050</td>\\n\",\n       \"      <td>1.492891</td>\\n\",\n       \"      <td>0.502370</td>\\n\",\n       \"      <td>0.303017</td>\\n\",\n       \"      <td>0.347347</td>\\n\",\n       \"      <td>0.497630</td>\\n\",\n       \"      <td>0.346354</td>\\n\",\n       \"      <td>0.293839</td>\\n\",\n       \"      <td>0.030806</td>\\n\",\n       \"      <td>0.090565</td>\\n\",\n       \"      <td>0.038324</td>\\n\",\n       \"      <td>0.029384</td>\\n\",\n       \"      <td>0.037716</td>\\n\",\n       \"      <td>0.388626</td>\\n\",\n       \"      <td>0.011348</td>\\n\",\n       \"      <td>0.102311</td>\\n\",\n       \"      <td>0.016843</td>\\n\",\n       \"      <td>0.007773</td>\\n\",\n       \"      <td>0.013468</td>\\n\",\n       \"      <td>0.265403</td>\\n\",\n       \"      <td>0.066443</td>\\n\",\n       \"      <td>0.065427</td>\\n\",\n       \"      <td>0.065796</td>\\n\",\n       \"      <td>0.065427</td>\\n\",\n       \"      <td>0.065427</td>\\n\",\n       \"      <td>0.402844</td>\\n\",\n       \"      <td>0.009511</td>\\n\",\n       \"      <td>0.103963</td>\\n\",\n       \"      <td>0.013944</td>\\n\",\n       \"      <td>0.009511</td>\\n\",\n       \"      <td>0.013944</td>\\n\",\n       \"      <td>2.099526</td>\\n\",\n       \"      <td>0.484669</td>\\n\",\n       \"      <td>0.470709</td>\\n\",\n       \"      <td>0.438037</td>\\n\",\n       \"      <td>0.474880</td>\\n\",\n       \"      <td>0.434194</td>\\n\",\n       \"      <td>2.800948</td>\\n\",\n       \"      <td>0.364523</td>\\n\",\n       \"      <td>0.584741</td>\\n\",\n       \"      <td>0.411509</td>\\n\",\n       \"      <td>0.312573</td>\\n\",\n       \"      <td>0.378309</td>\\n\",\n       \"      <td>2.469194</td>\\n\",\n       \"      <td>0.488746</td>\\n\",\n       \"      <td>0.465795</td>\\n\",\n       \"      <td>0.473802</td>\\n\",\n       \"      <td>0.465795</td>\\n\",\n       \"      <td>0.465795</td>\\n\",\n       \"      <td>3.085308</td>\\n\",\n       \"      <td>0.328428</td>\\n\",\n       \"      <td>0.628378</td>\\n\",\n       \"      <td>0.387229</td>\\n\",\n       \"      <td>0.328428</td>\\n\",\n       \"      <td>0.387229</td>\\n\",\n       \"      <td>0.587678</td>\\n\",\n       \"      <td>0.678460</td>\\n\",\n       \"      <td>0.151918</td>\\n\",\n       \"      <td>0.231405</td>\\n\",\n       \"      <td>0.678460</td>\\n\",\n       \"      <td>0.231405</td>\\n\",\n       \"      <td>1.511848</td>\\n\",\n       \"      <td>0.582125</td>\\n\",\n       \"      <td>0.357579</td>\\n\",\n       \"      <td>0.405101</td>\\n\",\n       \"      <td>0.577386</td>\\n\",\n       \"      <td>0.404108</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.069172</td>\\n\",\n       \"      <td>0.154610</td>\\n\",\n       \"      <td>0.080214</td>\\n\",\n       \"      <td>0.066626</td>\\n\",\n       \"      <td>0.079134</td>\\n\",\n       \"      <td>0.620853</td>\\n\",\n       \"      <td>0.027670</td>\\n\",\n       \"      <td>0.193874</td>\\n\",\n       \"      <td>0.040997</td>\\n\",\n       \"      <td>0.019101</td>\\n\",\n       \"      <td>0.032471</td>\\n\",\n       \"      <td>0.402844</td>\\n\",\n       \"      <td>0.114527</td>\\n\",\n       \"      <td>0.112622</td>\\n\",\n       \"      <td>0.113264</td>\\n\",\n       \"      <td>0.112622</td>\\n\",\n       \"      <td>0.112622</td>\\n\",\n       \"      <td>0.654028</td>\\n\",\n       \"      <td>0.023274</td>\\n\",\n       \"      <td>0.198314</td>\\n\",\n       \"      <td>0.034288</td>\\n\",\n       \"      <td>0.023274</td>\\n\",\n       \"      <td>0.034288</td>\\n\",\n       \"      <td>0.201780</td>\\n\",\n       \"      <td>0.218762</td>\\n\",\n       \"      <td>0.372643</td>\\n\",\n       \"      <td>0.297620</td>\\n\",\n       \"      <td>0.379007</td>\\n\",\n       \"      <td>0.397196</td>\\n\",\n       \"      <td>0.445924</td>\\n\",\n       \"      <td>0.341926</td>\\n\",\n       \"      <td>0.371128</td>\\n\",\n       \"      <td>0.432711</td>\\n\",\n       \"      <td>0.463909</td>\\n\",\n       \"      <td>0.520995</td>\\n\",\n       \"      <td>0.520350</td>\\n\",\n       \"      <td>0.556307</td>\\n\",\n       \"      <td>0.046908</td>\\n\",\n       \"      <td>0.055406</td>\\n\",\n       \"      <td>0.137669</td>\\n\",\n       \"      <td>0.080340</td>\\n\",\n       \"      <td>0.087278</td>\\n\",\n       \"      <td>0.089470</td>\\n\",\n       \"      <td>0.095103</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>11.592417</td>\\n\",\n       \"      <td>62.218009</td>\\n\",\n       \"      <td>80.611374</td>\\n\",\n       \"      <td>3376.568720</td>\\n\",\n       \"      <td>471.374408</td>\\n\",\n       \"      <td>12008.763033</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19594</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>2.023697</td>\\n\",\n       \"      <td>0.404739</td>\\n\",\n       \"      <td>0.232169</td>\\n\",\n       \"      <td>0.266463</td>\\n\",\n       \"      <td>0.404739</td>\\n\",\n       \"      <td>0.266463</td>\\n\",\n       \"      <td>2.848341</td>\\n\",\n       \"      <td>0.285045</td>\\n\",\n       \"      <td>0.314165</td>\\n\",\n       \"      <td>0.270357</td>\\n\",\n       \"      <td>0.284834</td>\\n\",\n       \"      <td>0.270258</td>\\n\",\n       \"      <td>3.023697</td>\\n\",\n       \"      <td>0.284184</td>\\n\",\n       \"      <td>0.283946</td>\\n\",\n       \"      <td>0.284059</td>\\n\",\n       \"      <td>0.283946</td>\\n\",\n       \"      <td>0.283946</td>\\n\",\n       \"      <td>3.990521</td>\\n\",\n       \"      <td>0.056287</td>\\n\",\n       \"      <td>0.421316</td>\\n\",\n       \"      <td>0.090835</td>\\n\",\n       \"      <td>0.056287</td>\\n\",\n       \"      <td>0.090835</td>\\n\",\n       \"      <td>0.578199</td>\\n\",\n       \"      <td>0.578199</td>\\n\",\n       \"      <td>0.068911</td>\\n\",\n       \"      <td>0.117214</td>\\n\",\n       \"      <td>0.578199</td>\\n\",\n       \"      <td>0.117214</td>\\n\",\n       \"      <td>1.469194</td>\\n\",\n       \"      <td>0.489731</td>\\n\",\n       \"      <td>0.166670</td>\\n\",\n       \"      <td>0.228300</td>\\n\",\n       \"      <td>0.489731</td>\\n\",\n       \"      <td>0.228300</td>\\n\",\n       \"      <td>2.180095</td>\\n\",\n       \"      <td>0.511331</td>\\n\",\n       \"      <td>0.292767</td>\\n\",\n       \"      <td>0.336146</td>\\n\",\n       \"      <td>0.511331</td>\\n\",\n       \"      <td>0.336146</td>\\n\",\n       \"      <td>3.227488</td>\\n\",\n       \"      <td>0.367731</td>\\n\",\n       \"      <td>0.396379</td>\\n\",\n       \"      <td>0.345523</td>\\n\",\n       \"      <td>0.367484</td>\\n\",\n       \"      <td>0.345407</td>\\n\",\n       \"      <td>3.682464</td>\\n\",\n       \"      <td>0.377788</td>\\n\",\n       \"      <td>0.377509</td>\\n\",\n       \"      <td>0.377642</td>\\n\",\n       \"      <td>0.377509</td>\\n\",\n       \"      <td>0.377509</td>\\n\",\n       \"      <td>5.061611</td>\\n\",\n       \"      <td>0.077822</td>\\n\",\n       \"      <td>0.542039</td>\\n\",\n       \"      <td>0.124296</td>\\n\",\n       \"      <td>0.077822</td>\\n\",\n       \"      <td>0.124296</td>\\n\",\n       \"      <td>0.578199</td>\\n\",\n       \"      <td>0.702767</td>\\n\",\n       \"      <td>0.086858</td>\\n\",\n       \"      <td>0.146418</td>\\n\",\n       \"      <td>0.702767</td>\\n\",\n       \"      <td>0.146418</td>\\n\",\n       \"      <td>1.530806</td>\\n\",\n       \"      <td>0.605400</td>\\n\",\n       \"      <td>0.213456</td>\\n\",\n       \"      <td>0.288005</td>\\n\",\n       \"      <td>0.605400</td>\\n\",\n       \"      <td>0.288005</td>\\n\",\n       \"      <td>2.170616</td>\\n\",\n       \"      <td>0.434123</td>\\n\",\n       \"      <td>0.438456</td>\\n\",\n       \"      <td>0.398725</td>\\n\",\n       \"      <td>0.434123</td>\\n\",\n       \"      <td>0.398725</td>\\n\",\n       \"      <td>2.924171</td>\\n\",\n       \"      <td>0.304279</td>\\n\",\n       \"      <td>0.551068</td>\\n\",\n       \"      <td>0.363175</td>\\n\",\n       \"      <td>0.292417</td>\\n\",\n       \"      <td>0.354195</td>\\n\",\n       \"      <td>2.521327</td>\\n\",\n       \"      <td>0.419279</td>\\n\",\n       \"      <td>0.415829</td>\\n\",\n       \"      <td>0.417122</td>\\n\",\n       \"      <td>0.415829</td>\\n\",\n       \"      <td>0.415829</td>\\n\",\n       \"      <td>3.398104</td>\\n\",\n       \"      <td>0.231057</td>\\n\",\n       \"      <td>0.629717</td>\\n\",\n       \"      <td>0.309661</td>\\n\",\n       \"      <td>0.231057</td>\\n\",\n       \"      <td>0.309661</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.128103</td>\\n\",\n       \"      <td>0.198238</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.198238</td>\\n\",\n       \"      <td>1.521327</td>\\n\",\n       \"      <td>0.507109</td>\\n\",\n       \"      <td>0.307584</td>\\n\",\n       \"      <td>0.352417</td>\\n\",\n       \"      <td>0.507109</td>\\n\",\n       \"      <td>0.352417</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.042180</td>\\n\",\n       \"      <td>0.094478</td>\\n\",\n       \"      <td>0.049037</td>\\n\",\n       \"      <td>0.040758</td>\\n\",\n       \"      <td>0.048429</td>\\n\",\n       \"      <td>0.582938</td>\\n\",\n       \"      <td>0.015178</td>\\n\",\n       \"      <td>0.122398</td>\\n\",\n       \"      <td>0.023109</td>\\n\",\n       \"      <td>0.011659</td>\\n\",\n       \"      <td>0.019986</td>\\n\",\n       \"      <td>0.374408</td>\\n\",\n       \"      <td>0.064759</td>\\n\",\n       \"      <td>0.063743</td>\\n\",\n       \"      <td>0.064113</td>\\n\",\n       \"      <td>0.063743</td>\\n\",\n       \"      <td>0.063743</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.012110</td>\\n\",\n       \"      <td>0.123642</td>\\n\",\n       \"      <td>0.018254</td>\\n\",\n       \"      <td>0.012110</td>\\n\",\n       \"      <td>0.018254</td>\\n\",\n       \"      <td>2.232227</td>\\n\",\n       \"      <td>0.498554</td>\\n\",\n       \"      <td>0.500712</td>\\n\",\n       \"      <td>0.457062</td>\\n\",\n       \"      <td>0.498554</td>\\n\",\n       \"      <td>0.457062</td>\\n\",\n       \"      <td>3.061611</td>\\n\",\n       \"      <td>0.351922</td>\\n\",\n       \"      <td>0.630166</td>\\n\",\n       \"      <td>0.418741</td>\\n\",\n       \"      <td>0.338267</td>\\n\",\n       \"      <td>0.408328</td>\\n\",\n       \"      <td>2.625592</td>\\n\",\n       \"      <td>0.487235</td>\\n\",\n       \"      <td>0.483365</td>\\n\",\n       \"      <td>0.484816</td>\\n\",\n       \"      <td>0.483365</td>\\n\",\n       \"      <td>0.483365</td>\\n\",\n       \"      <td>3.578199</td>\\n\",\n       \"      <td>0.263065</td>\\n\",\n       \"      <td>0.704823</td>\\n\",\n       \"      <td>0.351918</td>\\n\",\n       \"      <td>0.263065</td>\\n\",\n       \"      <td>0.351918</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.687377</td>\\n\",\n       \"      <td>0.147843</td>\\n\",\n       \"      <td>0.229130</td>\\n\",\n       \"      <td>0.687377</td>\\n\",\n       \"      <td>0.229130</td>\\n\",\n       \"      <td>1.540284</td>\\n\",\n       \"      <td>0.586753</td>\\n\",\n       \"      <td>0.364325</td>\\n\",\n       \"      <td>0.411139</td>\\n\",\n       \"      <td>0.586753</td>\\n\",\n       \"      <td>0.411139</td>\\n\",\n       \"      <td>0.464455</td>\\n\",\n       \"      <td>0.082859</td>\\n\",\n       \"      <td>0.166488</td>\\n\",\n       \"      <td>0.093653</td>\\n\",\n       \"      <td>0.079489</td>\\n\",\n       \"      <td>0.092548</td>\\n\",\n       \"      <td>0.824645</td>\\n\",\n       \"      <td>0.032371</td>\\n\",\n       \"      <td>0.229177</td>\\n\",\n       \"      <td>0.047753</td>\\n\",\n       \"      <td>0.023821</td>\\n\",\n       \"      <td>0.040509</td>\\n\",\n       \"      <td>0.478673</td>\\n\",\n       \"      <td>0.115840</td>\\n\",\n       \"      <td>0.112918</td>\\n\",\n       \"      <td>0.113749</td>\\n\",\n       \"      <td>0.112918</td>\\n\",\n       \"      <td>0.112918</td>\\n\",\n       \"      <td>0.867299</td>\\n\",\n       \"      <td>0.026250</td>\\n\",\n       \"      <td>0.232653</td>\\n\",\n       \"      <td>0.038309</td>\\n\",\n       \"      <td>0.026250</td>\\n\",\n       \"      <td>0.038309</td>\\n\",\n       \"      <td>0.216750</td>\\n\",\n       \"      <td>0.236206</td>\\n\",\n       \"      <td>0.335318</td>\\n\",\n       \"      <td>0.332650</td>\\n\",\n       \"      <td>0.411956</td>\\n\",\n       \"      <td>0.411547</td>\\n\",\n       \"      <td>0.475803</td>\\n\",\n       \"      <td>0.375740</td>\\n\",\n       \"      <td>0.407650</td>\\n\",\n       \"      <td>0.390659</td>\\n\",\n       \"      <td>0.507204</td>\\n\",\n       \"      <td>0.566347</td>\\n\",\n       \"      <td>0.535610</td>\\n\",\n       \"      <td>0.576973</td>\\n\",\n       \"      <td>0.039300</td>\\n\",\n       \"      <td>0.050450</td>\\n\",\n       \"      <td>0.160534</td>\\n\",\n       \"      <td>0.086138</td>\\n\",\n       \"      <td>0.097994</td>\\n\",\n       \"      <td>0.101404</td>\\n\",\n       \"      <td>0.107722</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>16.402844</td>\\n\",\n       \"      <td>74.331754</td>\\n\",\n       \"      <td>98.995261</td>\\n\",\n       \"      <td>3550.052133</td>\\n\",\n       \"      <td>453.876777</td>\\n\",\n       \"      <td>12369.369668</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19152</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>2.042654</td>\\n\",\n       \"      <td>0.408531</td>\\n\",\n       \"      <td>0.230146</td>\\n\",\n       \"      <td>0.268390</td>\\n\",\n       \"      <td>0.408531</td>\\n\",\n       \"      <td>0.268390</td>\\n\",\n       \"      <td>3.018957</td>\\n\",\n       \"      <td>0.301896</td>\\n\",\n       \"      <td>0.330977</td>\\n\",\n       \"      <td>0.285706</td>\\n\",\n       \"      <td>0.301896</td>\\n\",\n       \"      <td>0.285706</td>\\n\",\n       \"      <td>3.236967</td>\\n\",\n       \"      <td>0.295810</td>\\n\",\n       \"      <td>0.294997</td>\\n\",\n       \"      <td>0.295342</td>\\n\",\n       \"      <td>0.294997</td>\\n\",\n       \"      <td>0.294997</td>\\n\",\n       \"      <td>4.355450</td>\\n\",\n       \"      <td>0.091562</td>\\n\",\n       \"      <td>0.453922</td>\\n\",\n       \"      <td>0.141474</td>\\n\",\n       \"      <td>0.091562</td>\\n\",\n       \"      <td>0.141474</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.064196</td>\\n\",\n       \"      <td>0.110396</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.110396</td>\\n\",\n       \"      <td>1.540284</td>\\n\",\n       \"      <td>0.513428</td>\\n\",\n       \"      <td>0.171288</td>\\n\",\n       \"      <td>0.238219</td>\\n\",\n       \"      <td>0.513428</td>\\n\",\n       \"      <td>0.238219</td>\\n\",\n       \"      <td>2.194313</td>\\n\",\n       \"      <td>0.513181</td>\\n\",\n       \"      <td>0.291380</td>\\n\",\n       \"      <td>0.336718</td>\\n\",\n       \"      <td>0.513181</td>\\n\",\n       \"      <td>0.336718</td>\\n\",\n       \"      <td>3.402844</td>\\n\",\n       \"      <td>0.383329</td>\\n\",\n       \"      <td>0.411929</td>\\n\",\n       \"      <td>0.359404</td>\\n\",\n       \"      <td>0.383329</td>\\n\",\n       \"      <td>0.359404</td>\\n\",\n       \"      <td>3.872038</td>\\n\",\n       \"      <td>0.386243</td>\\n\",\n       \"      <td>0.385075</td>\\n\",\n       \"      <td>0.385573</td>\\n\",\n       \"      <td>0.385075</td>\\n\",\n       \"      <td>0.385075</td>\\n\",\n       \"      <td>5.213270</td>\\n\",\n       \"      <td>0.118371</td>\\n\",\n       \"      <td>0.558772</td>\\n\",\n       \"      <td>0.180823</td>\\n\",\n       \"      <td>0.118371</td>\\n\",\n       \"      <td>0.180823</td>\\n\",\n       \"      <td>0.559242</td>\\n\",\n       \"      <td>0.683891</td>\\n\",\n       \"      <td>0.082840</td>\\n\",\n       \"      <td>0.140842</td>\\n\",\n       \"      <td>0.683891</td>\\n\",\n       \"      <td>0.140842</td>\\n\",\n       \"      <td>1.597156</td>\\n\",\n       \"      <td>0.626108</td>\\n\",\n       \"      <td>0.217940</td>\\n\",\n       \"      <td>0.296852</td>\\n\",\n       \"      <td>0.626108</td>\\n\",\n       \"      <td>0.296852</td>\\n\",\n       \"      <td>2.109005</td>\\n\",\n       \"      <td>0.421801</td>\\n\",\n       \"      <td>0.418254</td>\\n\",\n       \"      <td>0.385038</td>\\n\",\n       \"      <td>0.421801</td>\\n\",\n       \"      <td>0.385038</td>\\n\",\n       \"      <td>3.161137</td>\\n\",\n       \"      <td>0.316324</td>\\n\",\n       \"      <td>0.596365</td>\\n\",\n       \"      <td>0.382725</td>\\n\",\n       \"      <td>0.316114</td>\\n\",\n       \"      <td>0.382517</td>\\n\",\n       \"      <td>2.563981</td>\\n\",\n       \"      <td>0.408198</td>\\n\",\n       \"      <td>0.408198</td>\\n\",\n       \"      <td>0.408198</td>\\n\",\n       \"      <td>0.408198</td>\\n\",\n       \"      <td>0.408198</td>\\n\",\n       \"      <td>4.042654</td>\\n\",\n       \"      <td>0.176361</td>\\n\",\n       \"      <td>0.734413</td>\\n\",\n       \"      <td>0.268863</td>\\n\",\n       \"      <td>0.176361</td>\\n\",\n       \"      <td>0.268863</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.123268</td>\\n\",\n       \"      <td>0.193204</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.193204</td>\\n\",\n       \"      <td>1.578199</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.309054</td>\\n\",\n       \"      <td>0.360377</td>\\n\",\n       \"      <td>0.526066</td>\\n\",\n       \"      <td>0.360377</td>\\n\",\n       \"      <td>0.293839</td>\\n\",\n       \"      <td>0.030625</td>\\n\",\n       \"      <td>0.084979</td>\\n\",\n       \"      <td>0.038151</td>\\n\",\n       \"      <td>0.029384</td>\\n\",\n       \"      <td>0.036535</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.016581</td>\\n\",\n       \"      <td>0.086637</td>\\n\",\n       \"      <td>0.023551</td>\\n\",\n       \"      <td>0.006256</td>\\n\",\n       \"      <td>0.010857</td>\\n\",\n       \"      <td>0.251185</td>\\n\",\n       \"      <td>0.056744</td>\\n\",\n       \"      <td>0.055911</td>\\n\",\n       \"      <td>0.056219</td>\\n\",\n       \"      <td>0.055911</td>\\n\",\n       \"      <td>0.055911</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.016433</td>\\n\",\n       \"      <td>0.086637</td>\\n\",\n       \"      <td>0.023303</td>\\n\",\n       \"      <td>0.016433</td>\\n\",\n       \"      <td>0.023303</td>\\n\",\n       \"      <td>2.170616</td>\\n\",\n       \"      <td>0.486438</td>\\n\",\n       \"      <td>0.485011</td>\\n\",\n       \"      <td>0.443620</td>\\n\",\n       \"      <td>0.486438</td>\\n\",\n       \"      <td>0.443620</td>\\n\",\n       \"      <td>3.270142</td>\\n\",\n       \"      <td>0.356554</td>\\n\",\n       \"      <td>0.663214</td>\\n\",\n       \"      <td>0.429534</td>\\n\",\n       \"      <td>0.356298</td>\\n\",\n       \"      <td>0.429280</td>\\n\",\n       \"      <td>2.687204</td>\\n\",\n       \"      <td>0.475930</td>\\n\",\n       \"      <td>0.475930</td>\\n\",\n       \"      <td>0.475930</td>\\n\",\n       \"      <td>0.475930</td>\\n\",\n       \"      <td>0.475930</td>\\n\",\n       \"      <td>4.199052</td>\\n\",\n       \"      <td>0.195353</td>\\n\",\n       \"      <td>0.795989</td>\\n\",\n       \"      <td>0.296610</td>\\n\",\n       \"      <td>0.195353</td>\\n\",\n       \"      <td>0.296610</td>\\n\",\n       \"      <td>0.592417</td>\\n\",\n       \"      <td>0.682916</td>\\n\",\n       \"      <td>0.145513</td>\\n\",\n       \"      <td>0.226669</td>\\n\",\n       \"      <td>0.682916</td>\\n\",\n       \"      <td>0.226669</td>\\n\",\n       \"      <td>1.592417</td>\\n\",\n       \"      <td>0.600302</td>\\n\",\n       \"      <td>0.367440</td>\\n\",\n       \"      <td>0.417535</td>\\n\",\n       \"      <td>0.600302</td>\\n\",\n       \"      <td>0.417535</td>\\n\",\n       \"      <td>0.388626</td>\\n\",\n       \"      <td>0.069168</td>\\n\",\n       \"      <td>0.146485</td>\\n\",\n       \"      <td>0.079916</td>\\n\",\n       \"      <td>0.066171</td>\\n\",\n       \"      <td>0.077413</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.039089</td>\\n\",\n       \"      <td>0.161479</td>\\n\",\n       \"      <td>0.052487</td>\\n\",\n       \"      <td>0.015790</td>\\n\",\n       \"      <td>0.026884</td>\\n\",\n       \"      <td>0.350711</td>\\n\",\n       \"      <td>0.105637</td>\\n\",\n       \"      <td>0.102570</td>\\n\",\n       \"      <td>0.103576</td>\\n\",\n       \"      <td>0.102570</td>\\n\",\n       \"      <td>0.102570</td>\\n\",\n       \"      <td>0.473934</td>\\n\",\n       \"      <td>0.038456</td>\\n\",\n       \"      <td>0.162066</td>\\n\",\n       \"      <td>0.051518</td>\\n\",\n       \"      <td>0.038456</td>\\n\",\n       \"      <td>0.051518</td>\\n\",\n       \"      <td>0.227396</td>\\n\",\n       \"      <td>0.248570</td>\\n\",\n       \"      <td>0.317193</td>\\n\",\n       \"      <td>0.352437</td>\\n\",\n       \"      <td>0.431444</td>\\n\",\n       \"      <td>0.412366</td>\\n\",\n       \"      <td>0.476504</td>\\n\",\n       \"      <td>0.395399</td>\\n\",\n       \"      <td>0.430733</td>\\n\",\n       \"      <td>0.348542</td>\\n\",\n       \"      <td>0.536694</td>\\n\",\n       \"      <td>0.610715</td>\\n\",\n       \"      <td>0.526793</td>\\n\",\n       \"      <td>0.573681</td>\\n\",\n       \"      <td>0.042531</td>\\n\",\n       \"      <td>0.051139</td>\\n\",\n       \"      <td>0.146366</td>\\n\",\n       \"      <td>0.071534</td>\\n\",\n       \"      <td>0.079631</td>\\n\",\n       \"      <td>0.094398</td>\\n\",\n       \"      <td>0.100518</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>24.099526</td>\\n\",\n       \"      <td>30.744076</td>\\n\",\n       \"      <td>58.848341</td>\\n\",\n       \"      <td>3385.260664</td>\\n\",\n       \"      <td>488.587678</td>\\n\",\n       \"      <td>11639.303318</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15883</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.900000</td>\\n\",\n       \"      <td>0.380000</td>\\n\",\n       \"      <td>0.129851</td>\\n\",\n       \"      <td>0.192226</td>\\n\",\n       \"      <td>0.380000</td>\\n\",\n       \"      <td>0.192226</td>\\n\",\n       \"      <td>2.810000</td>\\n\",\n       \"      <td>0.281000</td>\\n\",\n       \"      <td>0.189829</td>\\n\",\n       \"      <td>0.224608</td>\\n\",\n       \"      <td>0.281000</td>\\n\",\n       \"      <td>0.224608</td>\\n\",\n       \"      <td>3.250000</td>\\n\",\n       \"      <td>0.218710</td>\\n\",\n       \"      <td>0.218149</td>\\n\",\n       \"      <td>0.218396</td>\\n\",\n       \"      <td>0.218149</td>\\n\",\n       \"      <td>0.218149</td>\\n\",\n       \"      <td>3.850000</td>\\n\",\n       \"      <td>0.099362</td>\\n\",\n       \"      <td>0.261349</td>\\n\",\n       \"      <td>0.138755</td>\\n\",\n       \"      <td>0.099362</td>\\n\",\n       \"      <td>0.138755</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.031805</td>\\n\",\n       \"      <td>0.059223</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.059223</td>\\n\",\n       \"      <td>1.340000</td>\\n\",\n       \"      <td>0.446667</td>\\n\",\n       \"      <td>0.092290</td>\\n\",\n       \"      <td>0.152263</td>\\n\",\n       \"      <td>0.446667</td>\\n\",\n       \"      <td>0.152263</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>0.507639</td>\\n\",\n       \"      <td>0.173110</td>\\n\",\n       \"      <td>0.256255</td>\\n\",\n       \"      <td>0.507639</td>\\n\",\n       \"      <td>0.256255</td>\\n\",\n       \"      <td>3.370000</td>\\n\",\n       \"      <td>0.384116</td>\\n\",\n       \"      <td>0.259649</td>\\n\",\n       \"      <td>0.306886</td>\\n\",\n       \"      <td>0.384116</td>\\n\",\n       \"      <td>0.306886</td>\\n\",\n       \"      <td>4.130000</td>\\n\",\n       \"      <td>0.305153</td>\\n\",\n       \"      <td>0.304344</td>\\n\",\n       \"      <td>0.304701</td>\\n\",\n       \"      <td>0.304344</td>\\n\",\n       \"      <td>0.304344</td>\\n\",\n       \"      <td>5.050000</td>\\n\",\n       \"      <td>0.142206</td>\\n\",\n       \"      <td>0.373060</td>\\n\",\n       \"      <td>0.198199</td>\\n\",\n       \"      <td>0.142206</td>\\n\",\n       \"      <td>0.198199</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.604595</td>\\n\",\n       \"      <td>0.042502</td>\\n\",\n       \"      <td>0.079244</td>\\n\",\n       \"      <td>0.604595</td>\\n\",\n       \"      <td>0.079244</td>\\n\",\n       \"      <td>1.400000</td>\\n\",\n       \"      <td>0.584104</td>\\n\",\n       \"      <td>0.120403</td>\\n\",\n       \"      <td>0.198640</td>\\n\",\n       \"      <td>0.584104</td>\\n\",\n       \"      <td>0.198640</td>\\n\",\n       \"      <td>1.980000</td>\\n\",\n       \"      <td>0.396000</td>\\n\",\n       \"      <td>0.327635</td>\\n\",\n       \"      <td>0.337321</td>\\n\",\n       \"      <td>0.396000</td>\\n\",\n       \"      <td>0.337321</td>\\n\",\n       \"      <td>2.900000</td>\\n\",\n       \"      <td>0.291750</td>\\n\",\n       \"      <td>0.465048</td>\\n\",\n       \"      <td>0.339042</td>\\n\",\n       \"      <td>0.290000</td>\\n\",\n       \"      <td>0.337734</td>\\n\",\n       \"      <td>2.400000</td>\\n\",\n       \"      <td>0.341444</td>\\n\",\n       \"      <td>0.337888</td>\\n\",\n       \"      <td>0.339435</td>\\n\",\n       \"      <td>0.337888</td>\\n\",\n       \"      <td>0.337888</td>\\n\",\n       \"      <td>3.410000</td>\\n\",\n       \"      <td>0.229161</td>\\n\",\n       \"      <td>0.538694</td>\\n\",\n       \"      <td>0.304920</td>\\n\",\n       \"      <td>0.229161</td>\\n\",\n       \"      <td>0.304920</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.077521</td>\\n\",\n       \"      <td>0.129530</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.129530</td>\\n\",\n       \"      <td>1.380000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.228660</td>\\n\",\n       \"      <td>0.289925</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.289925</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.036000</td>\\n\",\n       \"      <td>0.047284</td>\\n\",\n       \"      <td>0.039656</td>\\n\",\n       \"      <td>0.035000</td>\\n\",\n       \"      <td>0.038863</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.017731</td>\\n\",\n       \"      <td>0.058086</td>\\n\",\n       \"      <td>0.025614</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.015056</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.048713</td>\\n\",\n       \"      <td>0.048713</td>\\n\",\n       \"      <td>0.048713</td>\\n\",\n       \"      <td>0.048713</td>\\n\",\n       \"      <td>0.048713</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.017535</td>\\n\",\n       \"      <td>0.058086</td>\\n\",\n       \"      <td>0.025310</td>\\n\",\n       \"      <td>0.017535</td>\\n\",\n       \"      <td>0.025310</td>\\n\",\n       \"      <td>2.050000</td>\\n\",\n       \"      <td>0.477740</td>\\n\",\n       \"      <td>0.392942</td>\\n\",\n       \"      <td>0.406438</td>\\n\",\n       \"      <td>0.477740</td>\\n\",\n       \"      <td>0.406438</td>\\n\",\n       \"      <td>3.070000</td>\\n\",\n       \"      <td>0.346695</td>\\n\",\n       \"      <td>0.549491</td>\\n\",\n       \"      <td>0.402062</td>\\n\",\n       \"      <td>0.344544</td>\\n\",\n       \"      <td>0.400464</td>\\n\",\n       \"      <td>2.550000</td>\\n\",\n       \"      <td>0.416309</td>\\n\",\n       \"      <td>0.411800</td>\\n\",\n       \"      <td>0.413764</td>\\n\",\n       \"      <td>0.411800</td>\\n\",\n       \"      <td>0.411800</td>\\n\",\n       \"      <td>3.590000</td>\\n\",\n       \"      <td>0.266128</td>\\n\",\n       \"      <td>0.618229</td>\\n\",\n       \"      <td>0.352466</td>\\n\",\n       \"      <td>0.266128</td>\\n\",\n       \"      <td>0.352466</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.586444</td>\\n\",\n       \"      <td>0.097221</td>\\n\",\n       \"      <td>0.162246</td>\\n\",\n       \"      <td>0.586444</td>\\n\",\n       \"      <td>0.162246</td>\\n\",\n       \"      <td>1.420000</td>\\n\",\n       \"      <td>0.556062</td>\\n\",\n       \"      <td>0.274574</td>\\n\",\n       \"      <td>0.348721</td>\\n\",\n       \"      <td>0.556062</td>\\n\",\n       \"      <td>0.348721</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.088112</td>\\n\",\n       \"      <td>0.107859</td>\\n\",\n       \"      <td>0.093812</td>\\n\",\n       \"      <td>0.085262</td>\\n\",\n       \"      <td>0.092026</td>\\n\",\n       \"      <td>0.690000</td>\\n\",\n       \"      <td>0.048773</td>\\n\",\n       \"      <td>0.142775</td>\\n\",\n       \"      <td>0.067094</td>\\n\",\n       \"      <td>0.022760</td>\\n\",\n       \"      <td>0.038608</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.102620</td>\\n\",\n       \"      <td>0.101311</td>\\n\",\n       \"      <td>0.101786</td>\\n\",\n       \"      <td>0.101311</td>\\n\",\n       \"      <td>0.101311</td>\\n\",\n       \"      <td>0.690000</td>\\n\",\n       \"      <td>0.048187</td>\\n\",\n       \"      <td>0.142800</td>\\n\",\n       \"      <td>0.066204</td>\\n\",\n       \"      <td>0.048187</td>\\n\",\n       \"      <td>0.066204</td>\\n\",\n       \"      <td>0.129299</td>\\n\",\n       \"      <td>0.141772</td>\\n\",\n       \"      <td>0.299896</td>\\n\",\n       \"      <td>0.233262</td>\\n\",\n       \"      <td>0.296341</td>\\n\",\n       \"      <td>0.330373</td>\\n\",\n       \"      <td>0.410507</td>\\n\",\n       \"      <td>0.282019</td>\\n\",\n       \"      <td>0.313478</td>\\n\",\n       \"      <td>0.345733</td>\\n\",\n       \"      <td>0.425725</td>\\n\",\n       \"      <td>0.477870</td>\\n\",\n       \"      <td>0.508668</td>\\n\",\n       \"      <td>0.553986</td>\\n\",\n       \"      <td>0.025518</td>\\n\",\n       \"      <td>0.029867</td>\\n\",\n       \"      <td>0.151463</td>\\n\",\n       \"      <td>0.051564</td>\\n\",\n       \"      <td>0.065844</td>\\n\",\n       \"      <td>0.082020</td>\\n\",\n       \"      <td>0.094061</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>15.080000</td>\\n\",\n       \"      <td>29.760000</td>\\n\",\n       \"      <td>49.900000</td>\\n\",\n       \"      <td>3607.160000</td>\\n\",\n       \"      <td>487.130000</td>\\n\",\n       \"      <td>12120.350000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20239</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_25000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>alphabetical_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>25000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.860000</td>\\n\",\n       \"      <td>0.372000</td>\\n\",\n       \"      <td>0.127396</td>\\n\",\n       \"      <td>0.188490</td>\\n\",\n       \"      <td>0.372000</td>\\n\",\n       \"      <td>0.188490</td>\\n\",\n       \"      <td>2.880000</td>\\n\",\n       \"      <td>0.288000</td>\\n\",\n       \"      <td>0.193820</td>\\n\",\n       \"      <td>0.229459</td>\\n\",\n       \"      <td>0.288000</td>\\n\",\n       \"      <td>0.229459</td>\\n\",\n       \"      <td>3.440000</td>\\n\",\n       \"      <td>0.228295</td>\\n\",\n       \"      <td>0.228065</td>\\n\",\n       \"      <td>0.228176</td>\\n\",\n       \"      <td>0.228065</td>\\n\",\n       \"      <td>0.228065</td>\\n\",\n       \"      <td>4.100000</td>\\n\",\n       \"      <td>0.119364</td>\\n\",\n       \"      <td>0.274790</td>\\n\",\n       \"      <td>0.159202</td>\\n\",\n       \"      <td>0.119364</td>\\n\",\n       \"      <td>0.159202</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.039358</td>\\n\",\n       \"      <td>0.073445</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.073445</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.092003</td>\\n\",\n       \"      <td>0.151579</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.151579</td>\\n\",\n       \"      <td>2.160000</td>\\n\",\n       \"      <td>0.514456</td>\\n\",\n       \"      <td>0.174854</td>\\n\",\n       \"      <td>0.259010</td>\\n\",\n       \"      <td>0.514456</td>\\n\",\n       \"      <td>0.259010</td>\\n\",\n       \"      <td>3.470000</td>\\n\",\n       \"      <td>0.398021</td>\\n\",\n       \"      <td>0.267667</td>\\n\",\n       \"      <td>0.316767</td>\\n\",\n       \"      <td>0.398021</td>\\n\",\n       \"      <td>0.316767</td>\\n\",\n       \"      <td>4.310000</td>\\n\",\n       \"      <td>0.315507</td>\\n\",\n       \"      <td>0.315223</td>\\n\",\n       \"      <td>0.315360</td>\\n\",\n       \"      <td>0.315223</td>\\n\",\n       \"      <td>0.315223</td>\\n\",\n       \"      <td>5.250000</td>\\n\",\n       \"      <td>0.163234</td>\\n\",\n       \"      <td>0.382544</td>\\n\",\n       \"      <td>0.218782</td>\\n\",\n       \"      <td>0.163234</td>\\n\",\n       \"      <td>0.218782</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.719095</td>\\n\",\n       \"      <td>0.049455</td>\\n\",\n       \"      <td>0.092313</td>\\n\",\n       \"      <td>0.719095</td>\\n\",\n       \"      <td>0.092313</td>\\n\",\n       \"      <td>1.460000</td>\\n\",\n       \"      <td>0.600186</td>\\n\",\n       \"      <td>0.123601</td>\\n\",\n       \"      <td>0.203834</td>\\n\",\n       \"      <td>0.600186</td>\\n\",\n       \"      <td>0.203834</td>\\n\",\n       \"      <td>1.860000</td>\\n\",\n       \"      <td>0.372000</td>\\n\",\n       \"      <td>0.304118</td>\\n\",\n       \"      <td>0.315208</td>\\n\",\n       \"      <td>0.372000</td>\\n\",\n       \"      <td>0.315208</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>0.300667</td>\\n\",\n       \"      <td>0.477682</td>\\n\",\n       \"      <td>0.348646</td>\\n\",\n       \"      <td>0.300000</td>\\n\",\n       \"      <td>0.348075</td>\\n\",\n       \"      <td>2.410000</td>\\n\",\n       \"      <td>0.343236</td>\\n\",\n       \"      <td>0.338896</td>\\n\",\n       \"      <td>0.340750</td>\\n\",\n       \"      <td>0.338896</td>\\n\",\n       \"      <td>0.338896</td>\\n\",\n       \"      <td>3.750000</td>\\n\",\n       \"      <td>0.224714</td>\\n\",\n       \"      <td>0.591673</td>\\n\",\n       \"      <td>0.306544</td>\\n\",\n       \"      <td>0.224714</td>\\n\",\n       \"      <td>0.306544</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.096513</td>\\n\",\n       \"      <td>0.159909</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.159909</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.218588</td>\\n\",\n       \"      <td>0.276951</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.276951</td>\\n\",\n       \"      <td>0.330000</td>\\n\",\n       \"      <td>0.034873</td>\\n\",\n       \"      <td>0.040498</td>\\n\",\n       \"      <td>0.035852</td>\\n\",\n       \"      <td>0.033000</td>\\n\",\n       \"      <td>0.035215</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.020162</td>\\n\",\n       \"      <td>0.044109</td>\\n\",\n       \"      <td>0.024446</td>\\n\",\n       \"      <td>0.007000</td>\\n\",\n       \"      <td>0.011885</td>\\n\",\n       \"      <td>0.330000</td>\\n\",\n       \"      <td>0.042886</td>\\n\",\n       \"      <td>0.040498</td>\\n\",\n       \"      <td>0.041318</td>\\n\",\n       \"      <td>0.040498</td>\\n\",\n       \"      <td>0.040498</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.020162</td>\\n\",\n       \"      <td>0.044109</td>\\n\",\n       \"      <td>0.024446</td>\\n\",\n       \"      <td>0.020162</td>\\n\",\n       \"      <td>0.024446</td>\\n\",\n       \"      <td>1.970000</td>\\n\",\n       \"      <td>0.460010</td>\\n\",\n       \"      <td>0.373859</td>\\n\",\n       \"      <td>0.388816</td>\\n\",\n       \"      <td>0.460010</td>\\n\",\n       \"      <td>0.388816</td>\\n\",\n       \"      <td>3.170000</td>\\n\",\n       \"      <td>0.355399</td>\\n\",\n       \"      <td>0.562614</td>\\n\",\n       \"      <td>0.411674</td>\\n\",\n       \"      <td>0.354592</td>\\n\",\n       \"      <td>0.410982</td>\\n\",\n       \"      <td>2.580000</td>\\n\",\n       \"      <td>0.424338</td>\\n\",\n       \"      <td>0.419417</td>\\n\",\n       \"      <td>0.421521</td>\\n\",\n       \"      <td>0.419417</td>\\n\",\n       \"      <td>0.419417</td>\\n\",\n       \"      <td>3.940000</td>\\n\",\n       \"      <td>0.256687</td>\\n\",\n       \"      <td>0.668648</td>\\n\",\n       \"      <td>0.349271</td>\\n\",\n       \"      <td>0.256687</td>\\n\",\n       \"      <td>0.349271</td>\\n\",\n       \"      <td>0.570000</td>\\n\",\n       \"      <td>0.682468</td>\\n\",\n       \"      <td>0.119601</td>\\n\",\n       \"      <td>0.196300</td>\\n\",\n       \"      <td>0.682468</td>\\n\",\n       \"      <td>0.196300</td>\\n\",\n       \"      <td>1.400000</td>\\n\",\n       \"      <td>0.546411</td>\\n\",\n       \"      <td>0.270305</td>\\n\",\n       \"      <td>0.341866</td>\\n\",\n       \"      <td>0.546411</td>\\n\",\n       \"      <td>0.341866</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.086412</td>\\n\",\n       \"      <td>0.101539</td>\\n\",\n       \"      <td>0.088720</td>\\n\",\n       \"      <td>0.081276</td>\\n\",\n       \"      <td>0.086784</td>\\n\",\n       \"      <td>0.560000</td>\\n\",\n       \"      <td>0.055388</td>\\n\",\n       \"      <td>0.124841</td>\\n\",\n       \"      <td>0.068051</td>\\n\",\n       \"      <td>0.020063</td>\\n\",\n       \"      <td>0.033936</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.100318</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.096851</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.095001</td>\\n\",\n       \"      <td>0.560000</td>\\n\",\n       \"      <td>0.054953</td>\\n\",\n       \"      <td>0.125208</td>\\n\",\n       \"      <td>0.067425</td>\\n\",\n       \"      <td>0.054953</td>\\n\",\n       \"      <td>0.067425</td>\\n\",\n       \"      <td>0.139938</td>\\n\",\n       \"      <td>0.150929</td>\\n\",\n       \"      <td>0.300181</td>\\n\",\n       \"      <td>0.244827</td>\\n\",\n       \"      <td>0.317315</td>\\n\",\n       \"      <td>0.345038</td>\\n\",\n       \"      <td>0.425617</td>\\n\",\n       \"      <td>0.307327</td>\\n\",\n       \"      <td>0.335646</td>\\n\",\n       \"      <td>0.333588</td>\\n\",\n       \"      <td>0.444754</td>\\n\",\n       \"      <td>0.516941</td>\\n\",\n       \"      <td>0.517476</td>\\n\",\n       \"      <td>0.564942</td>\\n\",\n       \"      <td>0.022847</td>\\n\",\n       \"      <td>0.025727</td>\\n\",\n       \"      <td>0.133987</td>\\n\",\n       \"      <td>0.041487</td>\\n\",\n       \"      <td>0.055245</td>\\n\",\n       \"      <td>0.071170</td>\\n\",\n       \"      <td>0.082541</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>17.590000</td>\\n\",\n       \"      <td>23.540000</td>\\n\",\n       \"      <td>46.010000</td>\\n\",\n       \"      <td>3566.150000</td>\\n\",\n       \"      <td>487.450000</td>\\n\",\n       \"      <td>12300.010000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16575</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.760000</td>\\n\",\n       \"      <td>0.352000</td>\\n\",\n       \"      <td>0.119758</td>\\n\",\n       \"      <td>0.177461</td>\\n\",\n       \"      <td>0.352000</td>\\n\",\n       \"      <td>0.177461</td>\\n\",\n       \"      <td>2.510000</td>\\n\",\n       \"      <td>0.251000</td>\\n\",\n       \"      <td>0.170784</td>\\n\",\n       \"      <td>0.201384</td>\\n\",\n       \"      <td>0.251000</td>\\n\",\n       \"      <td>0.201384</td>\\n\",\n       \"      <td>3.030000</td>\\n\",\n       \"      <td>0.206055</td>\\n\",\n       \"      <td>0.206055</td>\\n\",\n       \"      <td>0.206055</td>\\n\",\n       \"      <td>0.206055</td>\\n\",\n       \"      <td>0.206055</td>\\n\",\n       \"      <td>3.640000</td>\\n\",\n       \"      <td>0.031830</td>\\n\",\n       \"      <td>0.247687</td>\\n\",\n       \"      <td>0.054987</td>\\n\",\n       \"      <td>0.031830</td>\\n\",\n       \"      <td>0.054987</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.032599</td>\\n\",\n       \"      <td>0.060779</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.060779</td>\\n\",\n       \"      <td>1.320000</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.089999</td>\\n\",\n       \"      <td>0.148656</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.148656</td>\\n\",\n       \"      <td>1.940000</td>\\n\",\n       \"      <td>0.472572</td>\\n\",\n       \"      <td>0.160610</td>\\n\",\n       \"      <td>0.237892</td>\\n\",\n       \"      <td>0.472572</td>\\n\",\n       \"      <td>0.237892</td>\\n\",\n       \"      <td>3.080000</td>\\n\",\n       \"      <td>0.351524</td>\\n\",\n       \"      <td>0.238222</td>\\n\",\n       \"      <td>0.281176</td>\\n\",\n       \"      <td>0.351524</td>\\n\",\n       \"      <td>0.281176</td>\\n\",\n       \"      <td>3.810000</td>\\n\",\n       \"      <td>0.287727</td>\\n\",\n       \"      <td>0.287727</td>\\n\",\n       \"      <td>0.287727</td>\\n\",\n       \"      <td>0.287727</td>\\n\",\n       \"      <td>0.287727</td>\\n\",\n       \"      <td>4.930000</td>\\n\",\n       \"      <td>0.047247</td>\\n\",\n       \"      <td>0.363971</td>\\n\",\n       \"      <td>0.081522</td>\\n\",\n       \"      <td>0.047247</td>\\n\",\n       \"      <td>0.081522</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.588988</td>\\n\",\n       \"      <td>0.040683</td>\\n\",\n       \"      <td>0.075929</td>\\n\",\n       \"      <td>0.588988</td>\\n\",\n       \"      <td>0.075929</td>\\n\",\n       \"      <td>1.390000</td>\\n\",\n       \"      <td>0.569921</td>\\n\",\n       \"      <td>0.116705</td>\\n\",\n       \"      <td>0.192631</td>\\n\",\n       \"      <td>0.569921</td>\\n\",\n       \"      <td>0.192631</td>\\n\",\n       \"      <td>1.880000</td>\\n\",\n       \"      <td>0.378000</td>\\n\",\n       \"      <td>0.316294</td>\\n\",\n       \"      <td>0.322481</td>\\n\",\n       \"      <td>0.376000</td>\\n\",\n       \"      <td>0.321780</td>\\n\",\n       \"      <td>2.790000</td>\\n\",\n       \"      <td>0.302730</td>\\n\",\n       \"      <td>0.451142</td>\\n\",\n       \"      <td>0.339725</td>\\n\",\n       \"      <td>0.279000</td>\\n\",\n       \"      <td>0.326478</td>\\n\",\n       \"      <td>2.330000</td>\\n\",\n       \"      <td>0.352199</td>\\n\",\n       \"      <td>0.334404</td>\\n\",\n       \"      <td>0.340094</td>\\n\",\n       \"      <td>0.334404</td>\\n\",\n       \"      <td>0.334404</td>\\n\",\n       \"      <td>3.160000</td>\\n\",\n       \"      <td>0.268197</td>\\n\",\n       \"      <td>0.502560</td>\\n\",\n       \"      <td>0.324419</td>\\n\",\n       \"      <td>0.268197</td>\\n\",\n       \"      <td>0.324419</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.080481</td>\\n\",\n       \"      <td>0.133085</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.133085</td>\\n\",\n       \"      <td>1.350000</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.226646</td>\\n\",\n       \"      <td>0.283473</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.283473</td>\\n\",\n       \"      <td>0.420000</td>\\n\",\n       \"      <td>0.042000</td>\\n\",\n       \"      <td>0.054768</td>\\n\",\n       \"      <td>0.045553</td>\\n\",\n       \"      <td>0.042000</td>\\n\",\n       \"      <td>0.045553</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.009643</td>\\n\",\n       \"      <td>0.061125</td>\\n\",\n       \"      <td>0.016274</td>\\n\",\n       \"      <td>0.009400</td>\\n\",\n       \"      <td>0.015983</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.048518</td>\\n\",\n       \"      <td>0.048518</td>\\n\",\n       \"      <td>0.048518</td>\\n\",\n       \"      <td>0.048518</td>\\n\",\n       \"      <td>0.048518</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.004733</td>\\n\",\n       \"      <td>0.062034</td>\\n\",\n       \"      <td>0.008452</td>\\n\",\n       \"      <td>0.004733</td>\\n\",\n       \"      <td>0.008452</td>\\n\",\n       \"      <td>1.970000</td>\\n\",\n       \"      <td>0.459220</td>\\n\",\n       \"      <td>0.382968</td>\\n\",\n       \"      <td>0.391399</td>\\n\",\n       \"      <td>0.457053</td>\\n\",\n       \"      <td>0.390655</td>\\n\",\n       \"      <td>2.970000</td>\\n\",\n       \"      <td>0.357340</td>\\n\",\n       \"      <td>0.531591</td>\\n\",\n       \"      <td>0.401365</td>\\n\",\n       \"      <td>0.330749</td>\\n\",\n       \"      <td>0.385956</td>\\n\",\n       \"      <td>2.460000</td>\\n\",\n       \"      <td>0.425355</td>\\n\",\n       \"      <td>0.405025</td>\\n\",\n       \"      <td>0.411687</td>\\n\",\n       \"      <td>0.405025</td>\\n\",\n       \"      <td>0.405025</td>\\n\",\n       \"      <td>3.340000</td>\\n\",\n       \"      <td>0.310635</td>\\n\",\n       \"      <td>0.578507</td>\\n\",\n       \"      <td>0.375973</td>\\n\",\n       \"      <td>0.310635</td>\\n\",\n       \"      <td>0.375973</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.563940</td>\\n\",\n       \"      <td>0.095668</td>\\n\",\n       \"      <td>0.158143</td>\\n\",\n       \"      <td>0.563940</td>\\n\",\n       \"      <td>0.158143</td>\\n\",\n       \"      <td>1.380000</td>\\n\",\n       \"      <td>0.543279</td>\\n\",\n       \"      <td>0.275673</td>\\n\",\n       \"      <td>0.344100</td>\\n\",\n       \"      <td>0.543279</td>\\n\",\n       \"      <td>0.344100</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.088303</td>\\n\",\n       \"      <td>0.111916</td>\\n\",\n       \"      <td>0.094959</td>\\n\",\n       \"      <td>0.088303</td>\\n\",\n       \"      <td>0.094959</td>\\n\",\n       \"      <td>0.770000</td>\\n\",\n       \"      <td>0.025686</td>\\n\",\n       \"      <td>0.156695</td>\\n\",\n       \"      <td>0.043192</td>\\n\",\n       \"      <td>0.025193</td>\\n\",\n       \"      <td>0.042591</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.097317</td>\\n\",\n       \"      <td>0.097317</td>\\n\",\n       \"      <td>0.097317</td>\\n\",\n       \"      <td>0.097317</td>\\n\",\n       \"      <td>0.097317</td>\\n\",\n       \"      <td>0.820000</td>\\n\",\n       \"      <td>0.013165</td>\\n\",\n       \"      <td>0.167490</td>\\n\",\n       \"      <td>0.023614</td>\\n\",\n       \"      <td>0.013165</td>\\n\",\n       \"      <td>0.023614</td>\\n\",\n       \"      <td>0.117071</td>\\n\",\n       \"      <td>0.128683</td>\\n\",\n       \"      <td>0.291034</td>\\n\",\n       \"      <td>0.219555</td>\\n\",\n       \"      <td>0.282536</td>\\n\",\n       \"      <td>0.309698</td>\\n\",\n       \"      <td>0.389332</td>\\n\",\n       \"      <td>0.267683</td>\\n\",\n       \"      <td>0.298516</td>\\n\",\n       \"      <td>0.350812</td>\\n\",\n       \"      <td>0.412512</td>\\n\",\n       \"      <td>0.461251</td>\\n\",\n       \"      <td>0.496706</td>\\n\",\n       \"      <td>0.547521</td>\\n\",\n       \"      <td>0.022388</td>\\n\",\n       \"      <td>0.027338</td>\\n\",\n       \"      <td>0.160677</td>\\n\",\n       \"      <td>0.053414</td>\\n\",\n       \"      <td>0.065606</td>\\n\",\n       \"      <td>0.073172</td>\\n\",\n       \"      <td>0.088561</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>12.440000</td>\\n\",\n       \"      <td>122.910000</td>\\n\",\n       \"      <td>147.860000</td>\\n\",\n       \"      <td>3257.280000</td>\\n\",\n       \"      <td>472.300000</td>\\n\",\n       \"      <td>12356.920000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15968</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>length_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.770000</td>\\n\",\n       \"      <td>0.354000</td>\\n\",\n       \"      <td>0.120971</td>\\n\",\n       \"      <td>0.179071</td>\\n\",\n       \"      <td>0.354000</td>\\n\",\n       \"      <td>0.179071</td>\\n\",\n       \"      <td>2.750000</td>\\n\",\n       \"      <td>0.275000</td>\\n\",\n       \"      <td>0.186167</td>\\n\",\n       \"      <td>0.219930</td>\\n\",\n       \"      <td>0.275000</td>\\n\",\n       \"      <td>0.219930</td>\\n\",\n       \"      <td>3.180000</td>\\n\",\n       \"      <td>0.217881</td>\\n\",\n       \"      <td>0.214036</td>\\n\",\n       \"      <td>0.215471</td>\\n\",\n       \"      <td>0.214036</td>\\n\",\n       \"      <td>0.214036</td>\\n\",\n       \"      <td>4.210000</td>\\n\",\n       \"      <td>0.074018</td>\\n\",\n       \"      <td>0.285521</td>\\n\",\n       \"      <td>0.106636</td>\\n\",\n       \"      <td>0.074018</td>\\n\",\n       \"      <td>0.106636</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.029140</td>\\n\",\n       \"      <td>0.054473</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.054473</td>\\n\",\n       \"      <td>1.300000</td>\\n\",\n       \"      <td>0.433333</td>\\n\",\n       \"      <td>0.088872</td>\\n\",\n       \"      <td>0.146678</td>\\n\",\n       \"      <td>0.433333</td>\\n\",\n       \"      <td>0.146678</td>\\n\",\n       \"      <td>1.960000</td>\\n\",\n       \"      <td>0.477711</td>\\n\",\n       \"      <td>0.162626</td>\\n\",\n       \"      <td>0.240799</td>\\n\",\n       \"      <td>0.477711</td>\\n\",\n       \"      <td>0.240799</td>\\n\",\n       \"      <td>3.150000</td>\\n\",\n       \"      <td>0.369669</td>\\n\",\n       \"      <td>0.250168</td>\\n\",\n       \"      <td>0.295397</td>\\n\",\n       \"      <td>0.369549</td>\\n\",\n       \"      <td>0.295357</td>\\n\",\n       \"      <td>3.860000</td>\\n\",\n       \"      <td>0.298572</td>\\n\",\n       \"      <td>0.294012</td>\\n\",\n       \"      <td>0.295703</td>\\n\",\n       \"      <td>0.294012</td>\\n\",\n       \"      <td>0.294012</td>\\n\",\n       \"      <td>5.410000</td>\\n\",\n       \"      <td>0.100777</td>\\n\",\n       \"      <td>0.394982</td>\\n\",\n       \"      <td>0.146567</td>\\n\",\n       \"      <td>0.100777</td>\\n\",\n       \"      <td>0.146567</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.616143</td>\\n\",\n       \"      <td>0.041870</td>\\n\",\n       \"      <td>0.078231</td>\\n\",\n       \"      <td>0.616143</td>\\n\",\n       \"      <td>0.078231</td>\\n\",\n       \"      <td>1.340000</td>\\n\",\n       \"      <td>0.562609</td>\\n\",\n       \"      <td>0.114946</td>\\n\",\n       \"      <td>0.189781</td>\\n\",\n       \"      <td>0.562609</td>\\n\",\n       \"      <td>0.189781</td>\\n\",\n       \"      <td>1.890000</td>\\n\",\n       \"      <td>0.378000</td>\\n\",\n       \"      <td>0.308780</td>\\n\",\n       \"      <td>0.319867</td>\\n\",\n       \"      <td>0.378000</td>\\n\",\n       \"      <td>0.319867</td>\\n\",\n       \"      <td>2.880000</td>\\n\",\n       \"      <td>0.293222</td>\\n\",\n       \"      <td>0.467740</td>\\n\",\n       \"      <td>0.341110</td>\\n\",\n       \"      <td>0.288000</td>\\n\",\n       \"      <td>0.337005</td>\\n\",\n       \"      <td>2.440000</td>\\n\",\n       \"      <td>0.354103</td>\\n\",\n       \"      <td>0.349683</td>\\n\",\n       \"      <td>0.351318</td>\\n\",\n       \"      <td>0.349683</td>\\n\",\n       \"      <td>0.349683</td>\\n\",\n       \"      <td>3.740000</td>\\n\",\n       \"      <td>0.233780</td>\\n\",\n       \"      <td>0.588589</td>\\n\",\n       \"      <td>0.317659</td>\\n\",\n       \"      <td>0.233780</td>\\n\",\n       \"      <td>0.317659</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.081922</td>\\n\",\n       \"      <td>0.133676</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.133676</td>\\n\",\n       \"      <td>1.270000</td>\\n\",\n       \"      <td>0.423333</td>\\n\",\n       \"      <td>0.207102</td>\\n\",\n       \"      <td>0.262339</td>\\n\",\n       \"      <td>0.423333</td>\\n\",\n       \"      <td>0.262339</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.038000</td>\\n\",\n       \"      <td>0.046508</td>\\n\",\n       \"      <td>0.039943</td>\\n\",\n       \"      <td>0.035000</td>\\n\",\n       \"      <td>0.038832</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.018552</td>\\n\",\n       \"      <td>0.061177</td>\\n\",\n       \"      <td>0.024658</td>\\n\",\n       \"      <td>0.009400</td>\\n\",\n       \"      <td>0.016020</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.047633</td>\\n\",\n       \"      <td>0.045133</td>\\n\",\n       \"      <td>0.045966</td>\\n\",\n       \"      <td>0.045133</td>\\n\",\n       \"      <td>0.045133</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.016505</td>\\n\",\n       \"      <td>0.061177</td>\\n\",\n       \"      <td>0.021576</td>\\n\",\n       \"      <td>0.016505</td>\\n\",\n       \"      <td>0.021576</td>\\n\",\n       \"      <td>1.970000</td>\\n\",\n       \"      <td>0.453805</td>\\n\",\n       \"      <td>0.371003</td>\\n\",\n       \"      <td>0.384623</td>\\n\",\n       \"      <td>0.453805</td>\\n\",\n       \"      <td>0.384623</td>\\n\",\n       \"      <td>3.020000</td>\\n\",\n       \"      <td>0.343024</td>\\n\",\n       \"      <td>0.542257</td>\\n\",\n       \"      <td>0.397597</td>\\n\",\n       \"      <td>0.337278</td>\\n\",\n       \"      <td>0.393185</td>\\n\",\n       \"      <td>2.590000</td>\\n\",\n       \"      <td>0.428207</td>\\n\",\n       \"      <td>0.423219</td>\\n\",\n       \"      <td>0.425062</td>\\n\",\n       \"      <td>0.423219</td>\\n\",\n       \"      <td>0.423219</td>\\n\",\n       \"      <td>3.930000</td>\\n\",\n       \"      <td>0.266437</td>\\n\",\n       \"      <td>0.665304</td>\\n\",\n       \"      <td>0.360921</td>\\n\",\n       \"      <td>0.266437</td>\\n\",\n       \"      <td>0.360921</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.597089</td>\\n\",\n       \"      <td>0.106510</td>\\n\",\n       \"      <td>0.173349</td>\\n\",\n       \"      <td>0.597089</td>\\n\",\n       \"      <td>0.173349</td>\\n\",\n       \"      <td>1.290000</td>\\n\",\n       \"      <td>0.511702</td>\\n\",\n       \"      <td>0.254347</td>\\n\",\n       \"      <td>0.320440</td>\\n\",\n       \"      <td>0.511702</td>\\n\",\n       \"      <td>0.320440</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.082559</td>\\n\",\n       \"      <td>0.100823</td>\\n\",\n       \"      <td>0.087119</td>\\n\",\n       \"      <td>0.079050</td>\\n\",\n       \"      <td>0.085807</td>\\n\",\n       \"      <td>0.740000</td>\\n\",\n       \"      <td>0.037208</td>\\n\",\n       \"      <td>0.143891</td>\\n\",\n       \"      <td>0.052789</td>\\n\",\n       \"      <td>0.023065</td>\\n\",\n       \"      <td>0.039082</td>\\n\",\n       \"      <td>0.430000</td>\\n\",\n       \"      <td>0.094238</td>\\n\",\n       \"      <td>0.091075</td>\\n\",\n       \"      <td>0.092140</td>\\n\",\n       \"      <td>0.091075</td>\\n\",\n       \"      <td>0.091075</td>\\n\",\n       \"      <td>0.760000</td>\\n\",\n       \"      <td>0.031946</td>\\n\",\n       \"      <td>0.148684</td>\\n\",\n       \"      <td>0.044938</td>\\n\",\n       \"      <td>0.031946</td>\\n\",\n       \"      <td>0.044938</td>\\n\",\n       \"      <td>0.127509</td>\\n\",\n       \"      <td>0.141079</td>\\n\",\n       \"      <td>0.272232</td>\\n\",\n       \"      <td>0.237259</td>\\n\",\n       \"      <td>0.306084</td>\\n\",\n       \"      <td>0.316382</td>\\n\",\n       \"      <td>0.400004</td>\\n\",\n       \"      <td>0.292704</td>\\n\",\n       \"      <td>0.325422</td>\\n\",\n       \"      <td>0.321637</td>\\n\",\n       \"      <td>0.445230</td>\\n\",\n       \"      <td>0.501580</td>\\n\",\n       \"      <td>0.501753</td>\\n\",\n       \"      <td>0.554109</td>\\n\",\n       \"      <td>0.025206</td>\\n\",\n       \"      <td>0.028551</td>\\n\",\n       \"      <td>0.144386</td>\\n\",\n       \"      <td>0.049863</td>\\n\",\n       \"      <td>0.065265</td>\\n\",\n       \"      <td>0.085675</td>\\n\",\n       \"      <td>0.100537</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>16.780000</td>\\n\",\n       \"      <td>63.120000</td>\\n\",\n       \"      <td>90.000000</td>\\n\",\n       \"      <td>3493.620000</td>\\n\",\n       \"      <td>462.640000</td>\\n\",\n       \"      <td>11628.280000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20421</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>155000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.750000</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.119874</td>\\n\",\n       \"      <td>0.177301</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.177301</td>\\n\",\n       \"      <td>2.350000</td>\\n\",\n       \"      <td>0.235000</td>\\n\",\n       \"      <td>0.158789</td>\\n\",\n       \"      <td>0.187703</td>\\n\",\n       \"      <td>0.235000</td>\\n\",\n       \"      <td>0.187703</td>\\n\",\n       \"      <td>2.800000</td>\\n\",\n       \"      <td>0.187304</td>\\n\",\n       \"      <td>0.187304</td>\\n\",\n       \"      <td>0.187304</td>\\n\",\n       \"      <td>0.187304</td>\\n\",\n       \"      <td>0.187304</td>\\n\",\n       \"      <td>3.700000</td>\\n\",\n       \"      <td>0.051892</td>\\n\",\n       \"      <td>0.250364</td>\\n\",\n       \"      <td>0.082984</td>\\n\",\n       \"      <td>0.051892</td>\\n\",\n       \"      <td>0.082984</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.033112</td>\\n\",\n       \"      <td>0.061640</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.061640</td>\\n\",\n       \"      <td>1.250000</td>\\n\",\n       \"      <td>0.416667</td>\\n\",\n       \"      <td>0.086080</td>\\n\",\n       \"      <td>0.141936</td>\\n\",\n       \"      <td>0.416667</td>\\n\",\n       \"      <td>0.141936</td>\\n\",\n       \"      <td>1.940000</td>\\n\",\n       \"      <td>0.470034</td>\\n\",\n       \"      <td>0.159750</td>\\n\",\n       \"      <td>0.236614</td>\\n\",\n       \"      <td>0.470034</td>\\n\",\n       \"      <td>0.236614</td>\\n\",\n       \"      <td>2.880000</td>\\n\",\n       \"      <td>0.336919</td>\\n\",\n       \"      <td>0.227021</td>\\n\",\n       \"      <td>0.268492</td>\\n\",\n       \"      <td>0.336919</td>\\n\",\n       \"      <td>0.268492</td>\\n\",\n       \"      <td>3.660000</td>\\n\",\n       \"      <td>0.271580</td>\\n\",\n       \"      <td>0.271580</td>\\n\",\n       \"      <td>0.271580</td>\\n\",\n       \"      <td>0.271580</td>\\n\",\n       \"      <td>0.271580</td>\\n\",\n       \"      <td>5.030000</td>\\n\",\n       \"      <td>0.075657</td>\\n\",\n       \"      <td>0.370775</td>\\n\",\n       \"      <td>0.121487</td>\\n\",\n       \"      <td>0.075657</td>\\n\",\n       \"      <td>0.121487</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.614205</td>\\n\",\n       \"      <td>0.042704</td>\\n\",\n       \"      <td>0.079646</td>\\n\",\n       \"      <td>0.614205</td>\\n\",\n       \"      <td>0.079646</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.543806</td>\\n\",\n       \"      <td>0.111357</td>\\n\",\n       \"      <td>0.183791</td>\\n\",\n       \"      <td>0.543806</td>\\n\",\n       \"      <td>0.183791</td>\\n\",\n       \"      <td>1.880000</td>\\n\",\n       \"      <td>0.376500</td>\\n\",\n       \"      <td>0.305752</td>\\n\",\n       \"      <td>0.317973</td>\\n\",\n       \"      <td>0.376000</td>\\n\",\n       \"      <td>0.317844</td>\\n\",\n       \"      <td>2.680000</td>\\n\",\n       \"      <td>0.282730</td>\\n\",\n       \"      <td>0.426769</td>\\n\",\n       \"      <td>0.321331</td>\\n\",\n       \"      <td>0.268000</td>\\n\",\n       \"      <td>0.311377</td>\\n\",\n       \"      <td>2.300000</td>\\n\",\n       \"      <td>0.340683</td>\\n\",\n       \"      <td>0.330673</td>\\n\",\n       \"      <td>0.334043</td>\\n\",\n       \"      <td>0.330673</td>\\n\",\n       \"      <td>0.330673</td>\\n\",\n       \"      <td>3.150000</td>\\n\",\n       \"      <td>0.233621</td>\\n\",\n       \"      <td>0.489711</td>\\n\",\n       \"      <td>0.295608</td>\\n\",\n       \"      <td>0.233621</td>\\n\",\n       \"      <td>0.295608</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.095346</td>\\n\",\n       \"      <td>0.155307</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.155307</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.222699</td>\\n\",\n       \"      <td>0.278964</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.278964</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.040000</td>\\n\",\n       \"      <td>0.053961</td>\\n\",\n       \"      <td>0.044416</td>\\n\",\n       \"      <td>0.040000</td>\\n\",\n       \"      <td>0.044416</td>\\n\",\n       \"      <td>0.540000</td>\\n\",\n       \"      <td>0.011797</td>\\n\",\n       \"      <td>0.067914</td>\\n\",\n       \"      <td>0.019682</td>\\n\",\n       \"      <td>0.010800</td>\\n\",\n       \"      <td>0.018323</td>\\n\",\n       \"      <td>0.360000</td>\\n\",\n       \"      <td>0.045568</td>\\n\",\n       \"      <td>0.045568</td>\\n\",\n       \"      <td>0.045568</td>\\n\",\n       \"      <td>0.045568</td>\\n\",\n       \"      <td>0.045568</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.008751</td>\\n\",\n       \"      <td>0.069342</td>\\n\",\n       \"      <td>0.014997</td>\\n\",\n       \"      <td>0.008751</td>\\n\",\n       \"      <td>0.014997</td>\\n\",\n       \"      <td>1.990000</td>\\n\",\n       \"      <td>0.456133</td>\\n\",\n       \"      <td>0.369490</td>\\n\",\n       \"      <td>0.384696</td>\\n\",\n       \"      <td>0.455633</td>\\n\",\n       \"      <td>0.384568</td>\\n\",\n       \"      <td>2.870000</td>\\n\",\n       \"      <td>0.340370</td>\\n\",\n       \"      <td>0.515794</td>\\n\",\n       \"      <td>0.387328</td>\\n\",\n       \"      <td>0.324488</td>\\n\",\n       \"      <td>0.376630</td>\\n\",\n       \"      <td>2.440000</td>\\n\",\n       \"      <td>0.415255</td>\\n\",\n       \"      <td>0.403433</td>\\n\",\n       \"      <td>0.407426</td>\\n\",\n       \"      <td>0.403433</td>\\n\",\n       \"      <td>0.403433</td>\\n\",\n       \"      <td>3.360000</td>\\n\",\n       \"      <td>0.275666</td>\\n\",\n       \"      <td>0.580418</td>\\n\",\n       \"      <td>0.349229</td>\\n\",\n       \"      <td>0.275666</td>\\n\",\n       \"      <td>0.349229</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.637522</td>\\n\",\n       \"      <td>0.113865</td>\\n\",\n       \"      <td>0.185907</td>\\n\",\n       \"      <td>0.637522</td>\\n\",\n       \"      <td>0.185907</td>\\n\",\n       \"      <td>1.350000</td>\\n\",\n       \"      <td>0.529896</td>\\n\",\n       \"      <td>0.266708</td>\\n\",\n       \"      <td>0.333805</td>\\n\",\n       \"      <td>0.529896</td>\\n\",\n       \"      <td>0.333805</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.085682</td>\\n\",\n       \"      <td>0.109752</td>\\n\",\n       \"      <td>0.092704</td>\\n\",\n       \"      <td>0.085682</td>\\n\",\n       \"      <td>0.092704</td>\\n\",\n       \"      <td>0.860000</td>\\n\",\n       \"      <td>0.029850</td>\\n\",\n       \"      <td>0.162336</td>\\n\",\n       \"      <td>0.048979</td>\\n\",\n       \"      <td>0.026592</td>\\n\",\n       \"      <td>0.044915</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.094988</td>\\n\",\n       \"      <td>0.094988</td>\\n\",\n       \"      <td>0.094988</td>\\n\",\n       \"      <td>0.094988</td>\\n\",\n       \"      <td>0.094988</td>\\n\",\n       \"      <td>0.940000</td>\\n\",\n       \"      <td>0.022896</td>\\n\",\n       \"      <td>0.170290</td>\\n\",\n       \"      <td>0.038539</td>\\n\",\n       \"      <td>0.022896</td>\\n\",\n       \"      <td>0.038539</td>\\n\",\n       \"      <td>0.116068</td>\\n\",\n       \"      <td>0.126857</td>\\n\",\n       \"      <td>0.287747</td>\\n\",\n       \"      <td>0.207046</td>\\n\",\n       \"      <td>0.278768</td>\\n\",\n       \"      <td>0.305168</td>\\n\",\n       \"      <td>0.373827</td>\\n\",\n       \"      <td>0.275262</td>\\n\",\n       \"      <td>0.300274</td>\\n\",\n       \"      <td>0.369758</td>\\n\",\n       \"      <td>0.399248</td>\\n\",\n       \"      <td>0.458735</td>\\n\",\n       \"      <td>0.506921</td>\\n\",\n       \"      <td>0.555828</td>\\n\",\n       \"      <td>0.022055</td>\\n\",\n       \"      <td>0.026842</td>\\n\",\n       \"      <td>0.141197</td>\\n\",\n       \"      <td>0.053918</td>\\n\",\n       \"      <td>0.065734</td>\\n\",\n       \"      <td>0.071944</td>\\n\",\n       \"      <td>0.085384</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>14.100000</td>\\n\",\n       \"      <td>71.890000</td>\\n\",\n       \"      <td>93.080000</td>\\n\",\n       \"      <td>3430.890000</td>\\n\",\n       \"      <td>480.710000</td>\\n\",\n       \"      <td>12244.390000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18550</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>no_sort_reverse</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.820000</td>\\n\",\n       \"      <td>0.364000</td>\\n\",\n       \"      <td>0.123565</td>\\n\",\n       \"      <td>0.183155</td>\\n\",\n       \"      <td>0.364000</td>\\n\",\n       \"      <td>0.183155</td>\\n\",\n       \"      <td>2.590000</td>\\n\",\n       \"      <td>0.259750</td>\\n\",\n       \"      <td>0.174538</td>\\n\",\n       \"      <td>0.206663</td>\\n\",\n       \"      <td>0.259000</td>\\n\",\n       \"      <td>0.206538</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>0.204013</td>\\n\",\n       \"      <td>0.201404</td>\\n\",\n       \"      <td>0.202141</td>\\n\",\n       \"      <td>0.201404</td>\\n\",\n       \"      <td>0.201404</td>\\n\",\n       \"      <td>3.710000</td>\\n\",\n       \"      <td>0.081824</td>\\n\",\n       \"      <td>0.252670</td>\\n\",\n       \"      <td>0.115734</td>\\n\",\n       \"      <td>0.081824</td>\\n\",\n       \"      <td>0.115734</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.030472</td>\\n\",\n       \"      <td>0.056949</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.056949</td>\\n\",\n       \"      <td>1.320000</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.090547</td>\\n\",\n       \"      <td>0.149320</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.149320</td>\\n\",\n       \"      <td>2.020000</td>\\n\",\n       \"      <td>0.495514</td>\\n\",\n       \"      <td>0.168193</td>\\n\",\n       \"      <td>0.249207</td>\\n\",\n       \"      <td>0.495514</td>\\n\",\n       \"      <td>0.249207</td>\\n\",\n       \"      <td>3.170000</td>\\n\",\n       \"      <td>0.366943</td>\\n\",\n       \"      <td>0.246497</td>\\n\",\n       \"      <td>0.291762</td>\\n\",\n       \"      <td>0.365918</td>\\n\",\n       \"      <td>0.291592</td>\\n\",\n       \"      <td>3.760000</td>\\n\",\n       \"      <td>0.288225</td>\\n\",\n       \"      <td>0.284545</td>\\n\",\n       \"      <td>0.285605</td>\\n\",\n       \"      <td>0.284545</td>\\n\",\n       \"      <td>0.284545</td>\\n\",\n       \"      <td>4.880000</td>\\n\",\n       \"      <td>0.118493</td>\\n\",\n       \"      <td>0.363627</td>\\n\",\n       \"      <td>0.167036</td>\\n\",\n       \"      <td>0.118493</td>\\n\",\n       \"      <td>0.167036</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.593024</td>\\n\",\n       \"      <td>0.040379</td>\\n\",\n       \"      <td>0.075436</td>\\n\",\n       \"      <td>0.593024</td>\\n\",\n       \"      <td>0.075436</td>\\n\",\n       \"      <td>1.390000</td>\\n\",\n       \"      <td>0.581215</td>\\n\",\n       \"      <td>0.119498</td>\\n\",\n       \"      <td>0.197087</td>\\n\",\n       \"      <td>0.581215</td>\\n\",\n       \"      <td>0.197087</td>\\n\",\n       \"      <td>1.900000</td>\\n\",\n       \"      <td>0.381000</td>\\n\",\n       \"      <td>0.314000</td>\\n\",\n       \"      <td>0.323253</td>\\n\",\n       \"      <td>0.380000</td>\\n\",\n       \"      <td>0.322809</td>\\n\",\n       \"      <td>2.860000</td>\\n\",\n       \"      <td>0.303679</td>\\n\",\n       \"      <td>0.458006</td>\\n\",\n       \"      <td>0.345550</td>\\n\",\n       \"      <td>0.286000</td>\\n\",\n       \"      <td>0.333252</td>\\n\",\n       \"      <td>2.400000</td>\\n\",\n       \"      <td>0.362376</td>\\n\",\n       \"      <td>0.351064</td>\\n\",\n       \"      <td>0.355481</td>\\n\",\n       \"      <td>0.351064</td>\\n\",\n       \"      <td>0.351064</td>\\n\",\n       \"      <td>3.260000</td>\\n\",\n       \"      <td>0.244920</td>\\n\",\n       \"      <td>0.510295</td>\\n\",\n       \"      <td>0.308436</td>\\n\",\n       \"      <td>0.244920</td>\\n\",\n       \"      <td>0.308436</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.070640</td>\\n\",\n       \"      <td>0.118437</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.118437</td>\\n\",\n       \"      <td>1.350000</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.231473</td>\\n\",\n       \"      <td>0.286691</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.286691</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.031361</td>\\n\",\n       \"      <td>0.044203</td>\\n\",\n       \"      <td>0.035968</td>\\n\",\n       \"      <td>0.031000</td>\\n\",\n       \"      <td>0.035620</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.015167</td>\\n\",\n       \"      <td>0.061544</td>\\n\",\n       \"      <td>0.023635</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.015236</td>\\n\",\n       \"      <td>0.290000</td>\\n\",\n       \"      <td>0.041346</td>\\n\",\n       \"      <td>0.041346</td>\\n\",\n       \"      <td>0.041346</td>\\n\",\n       \"      <td>0.041346</td>\\n\",\n       \"      <td>0.041346</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.014210</td>\\n\",\n       \"      <td>0.062794</td>\\n\",\n       \"      <td>0.022211</td>\\n\",\n       \"      <td>0.014210</td>\\n\",\n       \"      <td>0.022211</td>\\n\",\n       \"      <td>1.980000</td>\\n\",\n       \"      <td>0.462796</td>\\n\",\n       \"      <td>0.379397</td>\\n\",\n       \"      <td>0.392485</td>\\n\",\n       \"      <td>0.461629</td>\\n\",\n       \"      <td>0.391967</td>\\n\",\n       \"      <td>3.060000</td>\\n\",\n       \"      <td>0.358025</td>\\n\",\n       \"      <td>0.537230</td>\\n\",\n       \"      <td>0.407049</td>\\n\",\n       \"      <td>0.338088</td>\\n\",\n       \"      <td>0.392891</td>\\n\",\n       \"      <td>2.600000</td>\\n\",\n       \"      <td>0.442239</td>\\n\",\n       \"      <td>0.429108</td>\\n\",\n       \"      <td>0.434269</td>\\n\",\n       \"      <td>0.429108</td>\\n\",\n       \"      <td>0.429108</td>\\n\",\n       \"      <td>3.480000</td>\\n\",\n       \"      <td>0.285168</td>\\n\",\n       \"      <td>0.587295</td>\\n\",\n       \"      <td>0.358352</td>\\n\",\n       \"      <td>0.285168</td>\\n\",\n       \"      <td>0.358352</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.556227</td>\\n\",\n       \"      <td>0.094648</td>\\n\",\n       \"      <td>0.156061</td>\\n\",\n       \"      <td>0.556227</td>\\n\",\n       \"      <td>0.156061</td>\\n\",\n       \"      <td>1.400000</td>\\n\",\n       \"      <td>0.546902</td>\\n\",\n       \"      <td>0.278626</td>\\n\",\n       \"      <td>0.347340</td>\\n\",\n       \"      <td>0.546902</td>\\n\",\n       \"      <td>0.347340</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.079910</td>\\n\",\n       \"      <td>0.101102</td>\\n\",\n       \"      <td>0.086071</td>\\n\",\n       \"      <td>0.078041</td>\\n\",\n       \"      <td>0.085206</td>\\n\",\n       \"      <td>0.760000</td>\\n\",\n       \"      <td>0.039958</td>\\n\",\n       \"      <td>0.152328</td>\\n\",\n       \"      <td>0.058678</td>\\n\",\n       \"      <td>0.023654</td>\\n\",\n       \"      <td>0.040281</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.094101</td>\\n\",\n       \"      <td>0.092475</td>\\n\",\n       \"      <td>0.092907</td>\\n\",\n       \"      <td>0.092475</td>\\n\",\n       \"      <td>0.092475</td>\\n\",\n       \"      <td>0.760000</td>\\n\",\n       \"      <td>0.037463</td>\\n\",\n       \"      <td>0.154409</td>\\n\",\n       \"      <td>0.054989</td>\\n\",\n       \"      <td>0.037463</td>\\n\",\n       \"      <td>0.054989</td>\\n\",\n       \"      <td>0.119028</td>\\n\",\n       \"      <td>0.131886</td>\\n\",\n       \"      <td>0.305003</td>\\n\",\n       \"      <td>0.218023</td>\\n\",\n       \"      <td>0.286678</td>\\n\",\n       \"      <td>0.326002</td>\\n\",\n       \"      <td>0.399241</td>\\n\",\n       \"      <td>0.262614</td>\\n\",\n       \"      <td>0.297904</td>\\n\",\n       \"      <td>0.350359</td>\\n\",\n       \"      <td>0.420214</td>\\n\",\n       \"      <td>0.462683</td>\\n\",\n       \"      <td>0.514779</td>\\n\",\n       \"      <td>0.564423</td>\\n\",\n       \"      <td>0.025261</td>\\n\",\n       \"      <td>0.028569</td>\\n\",\n       \"      <td>0.124276</td>\\n\",\n       \"      <td>0.048362</td>\\n\",\n       \"      <td>0.062944</td>\\n\",\n       \"      <td>0.082205</td>\\n\",\n       \"      <td>0.090114</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>14.120000</td>\\n\",\n       \"      <td>42.690000</td>\\n\",\n       \"      <td>62.880000</td>\\n\",\n       \"      <td>3505.720000</td>\\n\",\n       \"      <td>479.840000</td>\\n\",\n       \"      <td>11996.540000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20749</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>random</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>115000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.710000</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.117027</td>\\n\",\n       \"      <td>0.173137</td>\\n\",\n       \"      <td>0.342000</td>\\n\",\n       \"      <td>0.173137</td>\\n\",\n       \"      <td>2.450000</td>\\n\",\n       \"      <td>0.246222</td>\\n\",\n       \"      <td>0.167464</td>\\n\",\n       \"      <td>0.197635</td>\\n\",\n       \"      <td>0.245000</td>\\n\",\n       \"      <td>0.197224</td>\\n\",\n       \"      <td>2.730000</td>\\n\",\n       \"      <td>0.188370</td>\\n\",\n       \"      <td>0.185263</td>\\n\",\n       \"      <td>0.186498</td>\\n\",\n       \"      <td>0.185263</td>\\n\",\n       \"      <td>0.185263</td>\\n\",\n       \"      <td>3.380000</td>\\n\",\n       \"      <td>0.066926</td>\\n\",\n       \"      <td>0.231347</td>\\n\",\n       \"      <td>0.094018</td>\\n\",\n       \"      <td>0.066926</td>\\n\",\n       \"      <td>0.094018</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.033288</td>\\n\",\n       \"      <td>0.062208</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.062208</td>\\n\",\n       \"      <td>1.180000</td>\\n\",\n       \"      <td>0.393333</td>\\n\",\n       \"      <td>0.083029</td>\\n\",\n       \"      <td>0.136428</td>\\n\",\n       \"      <td>0.393333</td>\\n\",\n       \"      <td>0.136428</td>\\n\",\n       \"      <td>1.960000</td>\\n\",\n       \"      <td>0.472301</td>\\n\",\n       \"      <td>0.160306</td>\\n\",\n       \"      <td>0.237570</td>\\n\",\n       \"      <td>0.472301</td>\\n\",\n       \"      <td>0.237570</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>0.348684</td>\\n\",\n       \"      <td>0.235208</td>\\n\",\n       \"      <td>0.278196</td>\\n\",\n       \"      <td>0.347296</td>\\n\",\n       \"      <td>0.277730</td>\\n\",\n       \"      <td>3.510000</td>\\n\",\n       \"      <td>0.272224</td>\\n\",\n       \"      <td>0.268598</td>\\n\",\n       \"      <td>0.270043</td>\\n\",\n       \"      <td>0.268598</td>\\n\",\n       \"      <td>0.268598</td>\\n\",\n       \"      <td>4.630000</td>\\n\",\n       \"      <td>0.096940</td>\\n\",\n       \"      <td>0.341090</td>\\n\",\n       \"      <td>0.138126</td>\\n\",\n       \"      <td>0.096940</td>\\n\",\n       \"      <td>0.138126</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.642008</td>\\n\",\n       \"      <td>0.043835</td>\\n\",\n       \"      <td>0.081885</td>\\n\",\n       \"      <td>0.642008</td>\\n\",\n       \"      <td>0.081885</td>\\n\",\n       \"      <td>1.290000</td>\\n\",\n       \"      <td>0.552358</td>\\n\",\n       \"      <td>0.114275</td>\\n\",\n       \"      <td>0.188311</td>\\n\",\n       \"      <td>0.552358</td>\\n\",\n       \"      <td>0.188311</td>\\n\",\n       \"      <td>1.840000</td>\\n\",\n       \"      <td>0.382333</td>\\n\",\n       \"      <td>0.300093</td>\\n\",\n       \"      <td>0.316564</td>\\n\",\n       \"      <td>0.368000</td>\\n\",\n       \"      <td>0.312626</td>\\n\",\n       \"      <td>2.560000</td>\\n\",\n       \"      <td>0.311980</td>\\n\",\n       \"      <td>0.410961</td>\\n\",\n       \"      <td>0.326802</td>\\n\",\n       \"      <td>0.256000</td>\\n\",\n       \"      <td>0.300088</td>\\n\",\n       \"      <td>2.200000</td>\\n\",\n       \"      <td>0.368918</td>\\n\",\n       \"      <td>0.327183</td>\\n\",\n       \"      <td>0.339751</td>\\n\",\n       \"      <td>0.327183</td>\\n\",\n       \"      <td>0.327183</td>\\n\",\n       \"      <td>2.980000</td>\\n\",\n       \"      <td>0.286066</td>\\n\",\n       \"      <td>0.473367</td>\\n\",\n       \"      <td>0.318737</td>\\n\",\n       \"      <td>0.286066</td>\\n\",\n       \"      <td>0.318737</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.090388</td>\\n\",\n       \"      <td>0.148304</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.148304</td>\\n\",\n       \"      <td>1.270000</td>\\n\",\n       \"      <td>0.426667</td>\\n\",\n       \"      <td>0.212052</td>\\n\",\n       \"      <td>0.268898</td>\\n\",\n       \"      <td>0.423333</td>\\n\",\n       \"      <td>0.268595</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.033750</td>\\n\",\n       \"      <td>0.043761</td>\\n\",\n       \"      <td>0.036626</td>\\n\",\n       \"      <td>0.032000</td>\\n\",\n       \"      <td>0.035827</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.013970</td>\\n\",\n       \"      <td>0.054115</td>\\n\",\n       \"      <td>0.019651</td>\\n\",\n       \"      <td>0.008000</td>\\n\",\n       \"      <td>0.013732</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.042832</td>\\n\",\n       \"      <td>0.041761</td>\\n\",\n       \"      <td>0.042151</td>\\n\",\n       \"      <td>0.041761</td>\\n\",\n       \"      <td>0.041761</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.012057</td>\\n\",\n       \"      <td>0.054115</td>\\n\",\n       \"      <td>0.016672</td>\\n\",\n       \"      <td>0.012057</td>\\n\",\n       \"      <td>0.016672</td>\\n\",\n       \"      <td>1.940000</td>\\n\",\n       \"      <td>0.466131</td>\\n\",\n       \"      <td>0.366955</td>\\n\",\n       \"      <td>0.386099</td>\\n\",\n       \"      <td>0.451117</td>\\n\",\n       \"      <td>0.381878</td>\\n\",\n       \"      <td>2.720000</td>\\n\",\n       \"      <td>0.371159</td>\\n\",\n       \"      <td>0.493678</td>\\n\",\n       \"      <td>0.391021</td>\\n\",\n       \"      <td>0.307376</td>\\n\",\n       \"      <td>0.359583</td>\\n\",\n       \"      <td>2.340000</td>\\n\",\n       \"      <td>0.444789</td>\\n\",\n       \"      <td>0.397438</td>\\n\",\n       \"      <td>0.411902</td>\\n\",\n       \"      <td>0.397438</td>\\n\",\n       \"      <td>0.397438</td>\\n\",\n       \"      <td>3.130000</td>\\n\",\n       \"      <td>0.334635</td>\\n\",\n       \"      <td>0.552657</td>\\n\",\n       \"      <td>0.373975</td>\\n\",\n       \"      <td>0.334635</td>\\n\",\n       \"      <td>0.373975</td>\\n\",\n       \"      <td>0.520000</td>\\n\",\n       \"      <td>0.637377</td>\\n\",\n       \"      <td>0.112012</td>\\n\",\n       \"      <td>0.183348</td>\\n\",\n       \"      <td>0.637377</td>\\n\",\n       \"      <td>0.183348</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.537803</td>\\n\",\n       \"      <td>0.268241</td>\\n\",\n       \"      <td>0.338778</td>\\n\",\n       \"      <td>0.534470</td>\\n\",\n       \"      <td>0.338475</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.082547</td>\\n\",\n       \"      <td>0.102432</td>\\n\",\n       \"      <td>0.087917</td>\\n\",\n       \"      <td>0.080735</td>\\n\",\n       \"      <td>0.087063</td>\\n\",\n       \"      <td>0.680000</td>\\n\",\n       \"      <td>0.033633</td>\\n\",\n       \"      <td>0.139161</td>\\n\",\n       \"      <td>0.050179</td>\\n\",\n       \"      <td>0.022363</td>\\n\",\n       \"      <td>0.037854</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.094151</td>\\n\",\n       \"      <td>0.093079</td>\\n\",\n       \"      <td>0.093469</td>\\n\",\n       \"      <td>0.093079</td>\\n\",\n       \"      <td>0.093079</td>\\n\",\n       \"      <td>0.690000</td>\\n\",\n       \"      <td>0.028662</td>\\n\",\n       \"      <td>0.143215</td>\\n\",\n       \"      <td>0.042609</td>\\n\",\n       \"      <td>0.028662</td>\\n\",\n       \"      <td>0.042609</td>\\n\",\n       \"      <td>0.108777</td>\\n\",\n       \"      <td>0.120239</td>\\n\",\n       \"      <td>0.321813</td>\\n\",\n       \"      <td>0.200130</td>\\n\",\n       \"      <td>0.270503</td>\\n\",\n       \"      <td>0.313016</td>\\n\",\n       \"      <td>0.381323</td>\\n\",\n       \"      <td>0.262716</td>\\n\",\n       \"      <td>0.288836</td>\\n\",\n       \"      <td>0.385090</td>\\n\",\n       \"      <td>0.389207</td>\\n\",\n       \"      <td>0.447269</td>\\n\",\n       \"      <td>0.504259</td>\\n\",\n       \"      <td>0.542692</td>\\n\",\n       \"      <td>0.028345</td>\\n\",\n       \"      <td>0.031543</td>\\n\",\n       \"      <td>0.157393</td>\\n\",\n       \"      <td>0.046325</td>\\n\",\n       \"      <td>0.065663</td>\\n\",\n       \"      <td>0.073281</td>\\n\",\n       \"      <td>0.081985</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>11.690000</td>\\n\",\n       \"      <td>62.400000</td>\\n\",\n       \"      <td>80.880000</td>\\n\",\n       \"      <td>3329.960000</td>\\n\",\n       \"      <td>475.300000</td>\\n\",\n       \"      <td>11862.080000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19596</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.820000</td>\\n\",\n       \"      <td>0.364000</td>\\n\",\n       \"      <td>0.122982</td>\\n\",\n       \"      <td>0.182395</td>\\n\",\n       \"      <td>0.364000</td>\\n\",\n       \"      <td>0.182395</td>\\n\",\n       \"      <td>2.590000</td>\\n\",\n       \"      <td>0.259000</td>\\n\",\n       \"      <td>0.174538</td>\\n\",\n       \"      <td>0.206366</td>\\n\",\n       \"      <td>0.259000</td>\\n\",\n       \"      <td>0.206366</td>\\n\",\n       \"      <td>3.130000</td>\\n\",\n       \"      <td>0.207928</td>\\n\",\n       \"      <td>0.207928</td>\\n\",\n       \"      <td>0.207928</td>\\n\",\n       \"      <td>0.207928</td>\\n\",\n       \"      <td>0.207928</td>\\n\",\n       \"      <td>4.050000</td>\\n\",\n       \"      <td>0.051164</td>\\n\",\n       \"      <td>0.272854</td>\\n\",\n       \"      <td>0.083703</td>\\n\",\n       \"      <td>0.051164</td>\\n\",\n       \"      <td>0.083703</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.031622</td>\\n\",\n       \"      <td>0.059091</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.059091</td>\\n\",\n       \"      <td>1.230000</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.083609</td>\\n\",\n       \"      <td>0.138127</td>\\n\",\n       \"      <td>0.410000</td>\\n\",\n       \"      <td>0.138127</td>\\n\",\n       \"      <td>2.070000</td>\\n\",\n       \"      <td>0.486023</td>\\n\",\n       \"      <td>0.164403</td>\\n\",\n       \"      <td>0.243713</td>\\n\",\n       \"      <td>0.486023</td>\\n\",\n       \"      <td>0.243713</td>\\n\",\n       \"      <td>3.060000</td>\\n\",\n       \"      <td>0.355494</td>\\n\",\n       \"      <td>0.239537</td>\\n\",\n       \"      <td>0.283171</td>\\n\",\n       \"      <td>0.355494</td>\\n\",\n       \"      <td>0.283171</td>\\n\",\n       \"      <td>3.860000</td>\\n\",\n       \"      <td>0.288227</td>\\n\",\n       \"      <td>0.288227</td>\\n\",\n       \"      <td>0.288227</td>\\n\",\n       \"      <td>0.288227</td>\\n\",\n       \"      <td>0.288227</td>\\n\",\n       \"      <td>5.350000</td>\\n\",\n       \"      <td>0.072535</td>\\n\",\n       \"      <td>0.388629</td>\\n\",\n       \"      <td>0.118625</td>\\n\",\n       \"      <td>0.072535</td>\\n\",\n       \"      <td>0.118625</td>\\n\",\n       \"      <td>0.470000</td>\\n\",\n       \"      <td>0.631048</td>\\n\",\n       \"      <td>0.043038</td>\\n\",\n       \"      <td>0.080369</td>\\n\",\n       \"      <td>0.631048</td>\\n\",\n       \"      <td>0.080369</td>\\n\",\n       \"      <td>1.340000</td>\\n\",\n       \"      <td>0.556063</td>\\n\",\n       \"      <td>0.113672</td>\\n\",\n       \"      <td>0.187682</td>\\n\",\n       \"      <td>0.556063</td>\\n\",\n       \"      <td>0.187682</td>\\n\",\n       \"      <td>1.890000</td>\\n\",\n       \"      <td>0.378000</td>\\n\",\n       \"      <td>0.297355</td>\\n\",\n       \"      <td>0.314427</td>\\n\",\n       \"      <td>0.378000</td>\\n\",\n       \"      <td>0.314427</td>\\n\",\n       \"      <td>2.860000</td>\\n\",\n       \"      <td>0.294333</td>\\n\",\n       \"      <td>0.454842</td>\\n\",\n       \"      <td>0.337997</td>\\n\",\n       \"      <td>0.286000</td>\\n\",\n       \"      <td>0.331892</td>\\n\",\n       \"      <td>2.420000</td>\\n\",\n       \"      <td>0.346333</td>\\n\",\n       \"      <td>0.341128</td>\\n\",\n       \"      <td>0.343224</td>\\n\",\n       \"      <td>0.341128</td>\\n\",\n       \"      <td>0.341128</td>\\n\",\n       \"      <td>3.440000</td>\\n\",\n       \"      <td>0.241528</td>\\n\",\n       \"      <td>0.538662</td>\\n\",\n       \"      <td>0.312895</td>\\n\",\n       \"      <td>0.241528</td>\\n\",\n       \"      <td>0.312895</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.074396</td>\\n\",\n       \"      <td>0.124423</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.124423</td>\\n\",\n       \"      <td>1.280000</td>\\n\",\n       \"      <td>0.426667</td>\\n\",\n       \"      <td>0.208817</td>\\n\",\n       \"      <td>0.264046</td>\\n\",\n       \"      <td>0.426667</td>\\n\",\n       \"      <td>0.264046</td>\\n\",\n       \"      <td>0.370000</td>\\n\",\n       \"      <td>0.037000</td>\\n\",\n       \"      <td>0.052197</td>\\n\",\n       \"      <td>0.041465</td>\\n\",\n       \"      <td>0.037000</td>\\n\",\n       \"      <td>0.041465</td>\\n\",\n       \"      <td>0.600000</td>\\n\",\n       \"      <td>0.012695</td>\\n\",\n       \"      <td>0.078804</td>\\n\",\n       \"      <td>0.021467</td>\\n\",\n       \"      <td>0.012000</td>\\n\",\n       \"      <td>0.020458</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.046209</td>\\n\",\n       \"      <td>0.046209</td>\\n\",\n       \"      <td>0.046209</td>\\n\",\n       \"      <td>0.046209</td>\\n\",\n       \"      <td>0.046209</td>\\n\",\n       \"      <td>0.610000</td>\\n\",\n       \"      <td>0.008927</td>\\n\",\n       \"      <td>0.080233</td>\\n\",\n       \"      <td>0.015641</td>\\n\",\n       \"      <td>0.008927</td>\\n\",\n       \"      <td>0.015641</td>\\n\",\n       \"      <td>1.990000</td>\\n\",\n       \"      <td>0.453297</td>\\n\",\n       \"      <td>0.362583</td>\\n\",\n       \"      <td>0.380844</td>\\n\",\n       \"      <td>0.453297</td>\\n\",\n       \"      <td>0.380844</td>\\n\",\n       \"      <td>2.960000</td>\\n\",\n       \"      <td>0.341511</td>\\n\",\n       \"      <td>0.528476</td>\\n\",\n       \"      <td>0.392318</td>\\n\",\n       \"      <td>0.332009</td>\\n\",\n       \"      <td>0.385264</td>\\n\",\n       \"      <td>2.530000</td>\\n\",\n       \"      <td>0.414980</td>\\n\",\n       \"      <td>0.409224</td>\\n\",\n       \"      <td>0.411544</td>\\n\",\n       \"      <td>0.409224</td>\\n\",\n       \"      <td>0.409224</td>\\n\",\n       \"      <td>3.570000</td>\\n\",\n       \"      <td>0.276718</td>\\n\",\n       \"      <td>0.616331</td>\\n\",\n       \"      <td>0.358830</td>\\n\",\n       \"      <td>0.276718</td>\\n\",\n       \"      <td>0.358830</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.582119</td>\\n\",\n       \"      <td>0.099616</td>\\n\",\n       \"      <td>0.164095</td>\\n\",\n       \"      <td>0.582119</td>\\n\",\n       \"      <td>0.164095</td>\\n\",\n       \"      <td>1.340000</td>\\n\",\n       \"      <td>0.531885</td>\\n\",\n       \"      <td>0.262094</td>\\n\",\n       \"      <td>0.331620</td>\\n\",\n       \"      <td>0.531885</td>\\n\",\n       \"      <td>0.331620</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.085787</td>\\n\",\n       \"      <td>0.112900</td>\\n\",\n       \"      <td>0.093319</td>\\n\",\n       \"      <td>0.085787</td>\\n\",\n       \"      <td>0.093319</td>\\n\",\n       \"      <td>0.940000</td>\\n\",\n       \"      <td>0.030981</td>\\n\",\n       \"      <td>0.178741</td>\\n\",\n       \"      <td>0.051598</td>\\n\",\n       \"      <td>0.028320</td>\\n\",\n       \"      <td>0.047902</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.097145</td>\\n\",\n       \"      <td>0.097145</td>\\n\",\n       \"      <td>0.097145</td>\\n\",\n       \"      <td>0.097145</td>\\n\",\n       \"      <td>0.097145</td>\\n\",\n       \"      <td>1.010000</td>\\n\",\n       \"      <td>0.022727</td>\\n\",\n       \"      <td>0.187658</td>\\n\",\n       \"      <td>0.039021</td>\\n\",\n       \"      <td>0.022727</td>\\n\",\n       \"      <td>0.039021</td>\\n\",\n       \"      <td>0.122616</td>\\n\",\n       \"      <td>0.134836</td>\\n\",\n       \"      <td>0.263915</td>\\n\",\n       \"      <td>0.227990</td>\\n\",\n       \"      <td>0.295382</td>\\n\",\n       \"      <td>0.323113</td>\\n\",\n       \"      <td>0.402687</td>\\n\",\n       \"      <td>0.267874</td>\\n\",\n       \"      <td>0.299774</td>\\n\",\n       \"      <td>0.321906</td>\\n\",\n       \"      <td>0.420668</td>\\n\",\n       \"      <td>0.470478</td>\\n\",\n       \"      <td>0.499213</td>\\n\",\n       \"      <td>0.552022</td>\\n\",\n       \"      <td>0.028523</td>\\n\",\n       \"      <td>0.032548</td>\\n\",\n       \"      <td>0.135946</td>\\n\",\n       \"      <td>0.057616</td>\\n\",\n       \"      <td>0.074324</td>\\n\",\n       \"      <td>0.073305</td>\\n\",\n       \"      <td>0.083136</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>15.120000</td>\\n\",\n       \"      <td>81.070000</td>\\n\",\n       \"      <td>105.300000</td>\\n\",\n       \"      <td>3593.590000</td>\\n\",\n       \"      <td>470.870000</td>\\n\",\n       \"      <td>12564.770000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19150</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-v3</td>\\n\",\n       \"      <td>kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>transformer</td>\\n\",\n       \"      <td>verbatim_prepend</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>1.940000</td>\\n\",\n       \"      <td>0.388000</td>\\n\",\n       \"      <td>0.132194</td>\\n\",\n       \"      <td>0.195839</td>\\n\",\n       \"      <td>0.388000</td>\\n\",\n       \"      <td>0.195839</td>\\n\",\n       \"      <td>2.820000</td>\\n\",\n       \"      <td>0.282000</td>\\n\",\n       \"      <td>0.190986</td>\\n\",\n       \"      <td>0.225703</td>\\n\",\n       \"      <td>0.282000</td>\\n\",\n       \"      <td>0.225703</td>\\n\",\n       \"      <td>3.550000</td>\\n\",\n       \"      <td>0.233854</td>\\n\",\n       \"      <td>0.233854</td>\\n\",\n       \"      <td>0.233854</td>\\n\",\n       \"      <td>0.233854</td>\\n\",\n       \"      <td>0.233854</td>\\n\",\n       \"      <td>4.640000</td>\\n\",\n       \"      <td>0.095101</td>\\n\",\n       \"      <td>0.307389</td>\\n\",\n       \"      <td>0.140154</td>\\n\",\n       \"      <td>0.095101</td>\\n\",\n       \"      <td>0.140154</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.032868</td>\\n\",\n       \"      <td>0.061376</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.061376</td>\\n\",\n       \"      <td>1.330000</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.090628</td>\\n\",\n       \"      <td>0.149667</td>\\n\",\n       \"      <td>0.443333</td>\\n\",\n       \"      <td>0.149667</td>\\n\",\n       \"      <td>2.150000</td>\\n\",\n       \"      <td>0.510177</td>\\n\",\n       \"      <td>0.173143</td>\\n\",\n       \"      <td>0.256606</td>\\n\",\n       \"      <td>0.510177</td>\\n\",\n       \"      <td>0.256606</td>\\n\",\n       \"      <td>3.340000</td>\\n\",\n       \"      <td>0.382158</td>\\n\",\n       \"      <td>0.258692</td>\\n\",\n       \"      <td>0.305445</td>\\n\",\n       \"      <td>0.382158</td>\\n\",\n       \"      <td>0.305445</td>\\n\",\n       \"      <td>4.300000</td>\\n\",\n       \"      <td>0.315421</td>\\n\",\n       \"      <td>0.315421</td>\\n\",\n       \"      <td>0.315421</td>\\n\",\n       \"      <td>0.315421</td>\\n\",\n       \"      <td>0.315421</td>\\n\",\n       \"      <td>5.710000</td>\\n\",\n       \"      <td>0.125748</td>\\n\",\n       \"      <td>0.410648</td>\\n\",\n       \"      <td>0.185906</td>\\n\",\n       \"      <td>0.125748</td>\\n\",\n       \"      <td>0.185906</td>\\n\",\n       \"      <td>0.480000</td>\\n\",\n       \"      <td>0.646381</td>\\n\",\n       \"      <td>0.044208</td>\\n\",\n       \"      <td>0.082552</td>\\n\",\n       \"      <td>0.646381</td>\\n\",\n       \"      <td>0.082552</td>\\n\",\n       \"      <td>1.420000</td>\\n\",\n       \"      <td>0.585585</td>\\n\",\n       \"      <td>0.119463</td>\\n\",\n       \"      <td>0.197316</td>\\n\",\n       \"      <td>0.585585</td>\\n\",\n       \"      <td>0.197316</td>\\n\",\n       \"      <td>1.950000</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.319265</td>\\n\",\n       \"      <td>0.330216</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.330216</td>\\n\",\n       \"      <td>2.960000</td>\\n\",\n       \"      <td>0.297000</td>\\n\",\n       \"      <td>0.473546</td>\\n\",\n       \"      <td>0.345492</td>\\n\",\n       \"      <td>0.296000</td>\\n\",\n       \"      <td>0.344672</td>\\n\",\n       \"      <td>2.540000</td>\\n\",\n       \"      <td>0.344903</td>\\n\",\n       \"      <td>0.344903</td>\\n\",\n       \"      <td>0.344903</td>\\n\",\n       \"      <td>0.344903</td>\\n\",\n       \"      <td>0.344903</td>\\n\",\n       \"      <td>4.250000</td>\\n\",\n       \"      <td>0.190448</td>\\n\",\n       \"      <td>0.652836</td>\\n\",\n       \"      <td>0.280388</td>\\n\",\n       \"      <td>0.190448</td>\\n\",\n       \"      <td>0.280388</td>\\n\",\n       \"      <td>0.510000</td>\\n\",\n       \"      <td>0.510000</td>\\n\",\n       \"      <td>0.082404</td>\\n\",\n       \"      <td>0.138197</td>\\n\",\n       \"      <td>0.510000</td>\\n\",\n       \"      <td>0.138197</td>\\n\",\n       \"      <td>1.340000</td>\\n\",\n       \"      <td>0.446667</td>\\n\",\n       \"      <td>0.216613</td>\\n\",\n       \"      <td>0.275888</td>\\n\",\n       \"      <td>0.446667</td>\\n\",\n       \"      <td>0.275888</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.039611</td>\\n\",\n       \"      <td>0.052586</td>\\n\",\n       \"      <td>0.043772</td>\\n\",\n       \"      <td>0.039000</td>\\n\",\n       \"      <td>0.043186</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.021049</td>\\n\",\n       \"      <td>0.052586</td>\\n\",\n       \"      <td>0.029113</td>\\n\",\n       \"      <td>0.007800</td>\\n\",\n       \"      <td>0.013356</td>\\n\",\n       \"      <td>0.350000</td>\\n\",\n       \"      <td>0.045979</td>\\n\",\n       \"      <td>0.045979</td>\\n\",\n       \"      <td>0.045979</td>\\n\",\n       \"      <td>0.045979</td>\\n\",\n       \"      <td>0.045979</td>\\n\",\n       \"      <td>0.390000</td>\\n\",\n       \"      <td>0.020798</td>\\n\",\n       \"      <td>0.052586</td>\\n\",\n       \"      <td>0.028729</td>\\n\",\n       \"      <td>0.020798</td>\\n\",\n       \"      <td>0.028729</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>0.460612</td>\\n\",\n       \"      <td>0.377082</td>\\n\",\n       \"      <td>0.390647</td>\\n\",\n       \"      <td>0.460612</td>\\n\",\n       \"      <td>0.390647</td>\\n\",\n       \"      <td>3.090000</td>\\n\",\n       \"      <td>0.348051</td>\\n\",\n       \"      <td>0.553319</td>\\n\",\n       \"      <td>0.404408</td>\\n\",\n       \"      <td>0.346967</td>\\n\",\n       \"      <td>0.403519</td>\\n\",\n       \"      <td>2.680000</td>\\n\",\n       \"      <td>0.416846</td>\\n\",\n       \"      <td>0.416846</td>\\n\",\n       \"      <td>0.416846</td>\\n\",\n       \"      <td>0.416846</td>\\n\",\n       \"      <td>0.416846</td>\\n\",\n       \"      <td>4.410000</td>\\n\",\n       \"      <td>0.211801</td>\\n\",\n       \"      <td>0.724550</td>\\n\",\n       \"      <td>0.311953</td>\\n\",\n       \"      <td>0.211801</td>\\n\",\n       \"      <td>0.311953</td>\\n\",\n       \"      <td>0.510000</td>\\n\",\n       \"      <td>0.621905</td>\\n\",\n       \"      <td>0.107586</td>\\n\",\n       \"      <td>0.177305</td>\\n\",\n       \"      <td>0.621905</td>\\n\",\n       \"      <td>0.177305</td>\\n\",\n       \"      <td>1.380000</td>\\n\",\n       \"      <td>0.537250</td>\\n\",\n       \"      <td>0.265915</td>\\n\",\n       \"      <td>0.335960</td>\\n\",\n       \"      <td>0.537250</td>\\n\",\n       \"      <td>0.335960</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.090968</td>\\n\",\n       \"      <td>0.116162</td>\\n\",\n       \"      <td>0.098098</td>\\n\",\n       \"      <td>0.089676</td>\\n\",\n       \"      <td>0.097162</td>\\n\",\n       \"      <td>0.600000</td>\\n\",\n       \"      <td>0.048626</td>\\n\",\n       \"      <td>0.130225</td>\\n\",\n       \"      <td>0.066088</td>\\n\",\n       \"      <td>0.020469</td>\\n\",\n       \"      <td>0.034663</td>\\n\",\n       \"      <td>0.450000</td>\\n\",\n       \"      <td>0.098088</td>\\n\",\n       \"      <td>0.097562</td>\\n\",\n       \"      <td>0.097783</td>\\n\",\n       \"      <td>0.097562</td>\\n\",\n       \"      <td>0.097562</td>\\n\",\n       \"      <td>0.600000</td>\\n\",\n       \"      <td>0.047803</td>\\n\",\n       \"      <td>0.130441</td>\\n\",\n       \"      <td>0.064784</td>\\n\",\n       \"      <td>0.047803</td>\\n\",\n       \"      <td>0.064784</td>\\n\",\n       \"      <td>0.144172</td>\\n\",\n       \"      <td>0.157583</td>\\n\",\n       \"      <td>0.261817</td>\\n\",\n       \"      <td>0.260514</td>\\n\",\n       \"      <td>0.329868</td>\\n\",\n       \"      <td>0.341187</td>\\n\",\n       \"      <td>0.423840</td>\\n\",\n       \"      <td>0.313334</td>\\n\",\n       \"      <td>0.346375</td>\\n\",\n       \"      <td>0.291617</td>\\n\",\n       \"      <td>0.465584</td>\\n\",\n       \"      <td>0.535353</td>\\n\",\n       \"      <td>0.510777</td>\\n\",\n       \"      <td>0.560282</td>\\n\",\n       \"      <td>0.029349</td>\\n\",\n       \"      <td>0.033308</td>\\n\",\n       \"      <td>0.185012</td>\\n\",\n       \"      <td>0.049960</td>\\n\",\n       \"      <td>0.069465</td>\\n\",\n       \"      <td>0.095735</td>\\n\",\n       \"      <td>0.103617</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>23.690000</td>\\n\",\n       \"      <td>33.010000</td>\\n\",\n       \"      <td>60.380000</td>\\n\",\n       \"      <td>3319.710000</td>\\n\",\n       \"      <td>490.770000</td>\\n\",\n       \"      <td>11428.150000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                                                                                                                                                                                                                                                                                path  \\\\\\n\",\n       \"15880  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       \\n\",\n       \"15963  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/duc.split_nopunc.eval                   \\n\",\n       \"20419  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/duc.split_nopunc.eval                         \\n\",\n       \"18552  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       \\n\",\n       \"16574  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_115000/krapivin.split_nopunc.eval                     \\n\",\n       \"15966  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/krapivin.split_nopunc.eval              \\n\",\n       \"20417  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       \\n\",\n       \"19591  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/krapivin.split_nopunc.eval             \\n\",\n       \"19149  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/krapivin.split_nopunc.eval            \\n\",\n       \"15885  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       \\n\",\n       \"15965  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/nus.split_nopunc.eval                   \\n\",\n       \"20418  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/nus.split_nopunc.eval                         \\n\",\n       \"18551  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       \\n\",\n       \"19152  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/nus.split_nopunc.eval                 \\n\",\n       \"15883  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_50000/semeval.split_nopunc.eval                 \\n\",\n       \"20239  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       \\n\",\n       \"20421  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_155000/semeval.split_nopunc.eval                     \\n\",\n       \"18550  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_80000/semeval.split_nopunc.eval              \\n\",\n       \"20749  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       \\n\",\n       \"\\n\",\n       \"                     exp_group  \\\\\\n\",\n       \"15880  meng17-one2seq-kp20k-v3   \\n\",\n       \"20240  meng17-one2seq-kp20k-v3   \\n\",\n       \"16579  meng17-one2seq-kp20k-v3   \\n\",\n       \"15963  meng17-one2seq-kp20k-v3   \\n\",\n       \"20419  meng17-one2seq-kp20k-v3   \\n\",\n       \"18552  meng17-one2seq-kp20k-v3   \\n\",\n       \"20748  meng17-one2seq-kp20k-v3   \\n\",\n       \"19592  meng17-one2seq-kp20k-v3   \\n\",\n       \"19154  meng17-one2seq-kp20k-v3   \\n\",\n       \"15881  meng17-one2seq-kp20k-v3   \\n\",\n       \"20241  meng17-one2seq-kp20k-v3   \\n\",\n       \"16573  meng17-one2seq-kp20k-v3   \\n\",\n       \"15964  meng17-one2seq-kp20k-v3   \\n\",\n       \"20420  meng17-one2seq-kp20k-v3   \\n\",\n       \"18546  meng17-one2seq-kp20k-v3   \\n\",\n       \"20750  meng17-one2seq-kp20k-v3   \\n\",\n       \"19593  meng17-one2seq-kp20k-v3   \\n\",\n       \"19155  meng17-one2seq-kp20k-v3   \\n\",\n       \"15886  meng17-one2seq-kp20k-v3   \\n\",\n       \"20238  meng17-one2seq-kp20k-v3   \\n\",\n       \"16578  meng17-one2seq-kp20k-v3   \\n\",\n       \"15967  meng17-one2seq-kp20k-v3   \\n\",\n       \"42862  meng17-one2seq-kp20k-v3   \\n\",\n       \"18547  meng17-one2seq-kp20k-v3   \\n\",\n       \"20753  meng17-one2seq-kp20k-v3   \\n\",\n       \"19595  meng17-one2seq-kp20k-v3   \\n\",\n       \"19151  meng17-one2seq-kp20k-v3   \\n\",\n       \"15882  meng17-one2seq-kp20k-v3   \\n\",\n       \"20242  meng17-one2seq-kp20k-v3   \\n\",\n       \"16576  meng17-one2seq-kp20k-v3   \\n\",\n       \"15969  meng17-one2seq-kp20k-v3   \\n\",\n       \"20416  meng17-one2seq-kp20k-v3   \\n\",\n       \"18549  meng17-one2seq-kp20k-v3   \\n\",\n       \"20752  meng17-one2seq-kp20k-v3   \\n\",\n       \"42853  meng17-one2seq-kp20k-v3   \\n\",\n       \"19153  meng17-one2seq-kp20k-v3   \\n\",\n       \"15884  meng17-one2seq-kp20k-v3   \\n\",\n       \"20237  meng17-one2seq-kp20k-v3   \\n\",\n       \"16574  meng17-one2seq-kp20k-v3   \\n\",\n       \"15966  meng17-one2seq-kp20k-v3   \\n\",\n       \"20417  meng17-one2seq-kp20k-v3   \\n\",\n       \"18548  meng17-one2seq-kp20k-v3   \\n\",\n       \"20754  meng17-one2seq-kp20k-v3   \\n\",\n       \"19591  meng17-one2seq-kp20k-v3   \\n\",\n       \"19149  meng17-one2seq-kp20k-v3   \\n\",\n       \"15885  meng17-one2seq-kp20k-v3   \\n\",\n       \"20243  meng17-one2seq-kp20k-v3   \\n\",\n       \"16577  meng17-one2seq-kp20k-v3   \\n\",\n       \"15965  meng17-one2seq-kp20k-v3   \\n\",\n       \"20418  meng17-one2seq-kp20k-v3   \\n\",\n       \"18551  meng17-one2seq-kp20k-v3   \\n\",\n       \"20751  meng17-one2seq-kp20k-v3   \\n\",\n       \"19594  meng17-one2seq-kp20k-v3   \\n\",\n       \"19152  meng17-one2seq-kp20k-v3   \\n\",\n       \"15883  meng17-one2seq-kp20k-v3   \\n\",\n       \"20239  meng17-one2seq-kp20k-v3   \\n\",\n       \"16575  meng17-one2seq-kp20k-v3   \\n\",\n       \"15968  meng17-one2seq-kp20k-v3   \\n\",\n       \"20421  meng17-one2seq-kp20k-v3   \\n\",\n       \"18550  meng17-one2seq-kp20k-v3   \\n\",\n       \"20749  meng17-one2seq-kp20k-v3   \\n\",\n       \"19596  meng17-one2seq-kp20k-v3   \\n\",\n       \"19150  meng17-one2seq-kp20k-v3   \\n\",\n       \"\\n\",\n       \"                                                                                                                     exp_name  \\\\\\n\",\n       \"15880  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20240  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16579  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15963  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20419  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18552  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20748  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19592  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19154  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15881  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20241  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16573  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15964  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20420  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18546  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20750  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19593  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19155  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15886  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20238  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16578  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15967  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"42862  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18547  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20753  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19595  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19151  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15882  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20242  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16576  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15969  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20416  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18549  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20752  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"42853  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19153  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15884  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20237  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16574  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15966  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20417  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18548  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20754  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19591  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19149  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15885  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20243  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16577  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15965  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20418  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18551  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20751  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19594  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19152  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"15883  kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           \\n\",\n       \"20239  kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue   \\n\",\n       \"16575  kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"15968  kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         \\n\",\n       \"20421  kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                \\n\",\n       \"18550  kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"20749  kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 \\n\",\n       \"19596  kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue        \\n\",\n       \"19150  kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       \\n\",\n       \"\\n\",\n       \"                        test_name tokenization train_mode   model_base  \\\\\\n\",\n       \"15880  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20240  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"16579  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"15963  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20419  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"18552  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20748  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"19592  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"19154  duc.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"15881  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"20241  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"16573  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"15964  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"20420  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"18546  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"20750  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"19593  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"19155  inspec.split_nopunc         meng17       one2seq    transformer   \\n\",\n       \"15886  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"20238  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"16578  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"15967  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"42862  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"18547  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"20753  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"19595  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"19151  kp20k.split_nopunc          meng17       one2seq    transformer   \\n\",\n       \"15882  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"20242  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"16576  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"15969  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"20416  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"18549  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"20752  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"42853  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"19153  kp20k_valid2k.split_nopunc  meng17       one2seq    transformer   \\n\",\n       \"15884  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"20237  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"16574  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"15966  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"20417  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"18548  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"20754  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"19591  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"19149  krapivin.split_nopunc       meng17       one2seq    transformer   \\n\",\n       \"15885  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20243  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"16577  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"15965  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20418  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"18551  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"20751  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"19594  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"19152  nus.split_nopunc            meng17       one2seq    transformer   \\n\",\n       \"15883  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"20239  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"16575  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"15968  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"20421  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"18550  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"20749  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"19596  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"19150  semeval.split_nopunc        meng17       one2seq    transformer   \\n\",\n       \"\\n\",\n       \"                      order train_dataset    step   test_dataset  \\\\\\n\",\n       \"15880  alphabetical          kp20k         50000   duc             \\n\",\n       \"20240  alphabetical_reverse  kp20k         25000   duc             \\n\",\n       \"16579  length                kp20k         115000  duc             \\n\",\n       \"15963  length_reverse        kp20k         50000   duc             \\n\",\n       \"20419  no_sort               kp20k         155000  duc             \\n\",\n       \"18552  no_sort_reverse       kp20k         80000   duc             \\n\",\n       \"20748  random                kp20k         115000  duc             \\n\",\n       \"19592  verbatim_append       kp20k         80000   duc             \\n\",\n       \"19154  verbatim_prepend      kp20k         50000   duc             \\n\",\n       \"15881  alphabetical          kp20k         50000   inspec          \\n\",\n       \"20241  alphabetical_reverse  kp20k         25000   inspec          \\n\",\n       \"16573  length                kp20k         115000  inspec          \\n\",\n       \"15964  length_reverse        kp20k         50000   inspec          \\n\",\n       \"20420  no_sort               kp20k         155000  inspec          \\n\",\n       \"18546  no_sort_reverse       kp20k         80000   inspec          \\n\",\n       \"20750  random                kp20k         115000  inspec          \\n\",\n       \"19593  verbatim_append       kp20k         80000   inspec          \\n\",\n       \"19155  verbatim_prepend      kp20k         50000   inspec          \\n\",\n       \"15886  alphabetical          kp20k         50000   kp20k           \\n\",\n       \"20238  alphabetical_reverse  kp20k         25000   kp20k           \\n\",\n       \"16578  length                kp20k         115000  kp20k           \\n\",\n       \"15967  length_reverse        kp20k         50000   kp20k           \\n\",\n       \"42862  no_sort               kp20k         155000  kp20k           \\n\",\n       \"18547  no_sort_reverse       kp20k         80000   kp20k           \\n\",\n       \"20753  random                kp20k         115000  kp20k           \\n\",\n       \"19595  verbatim_append       kp20k         80000   kp20k           \\n\",\n       \"19151  verbatim_prepend      kp20k         50000   kp20k           \\n\",\n       \"15882  alphabetical          kp20k         50000   kp20k_valid2k   \\n\",\n       \"20242  alphabetical_reverse  kp20k         25000   kp20k_valid2k   \\n\",\n       \"16576  length                kp20k         115000  kp20k_valid2k   \\n\",\n       \"15969  length_reverse        kp20k         50000   kp20k_valid2k   \\n\",\n       \"20416  no_sort               kp20k         155000  kp20k_valid2k   \\n\",\n       \"18549  no_sort_reverse       kp20k         80000   kp20k_valid2k   \\n\",\n       \"20752  random                kp20k         115000  kp20k_valid2k   \\n\",\n       \"42853  verbatim_append       kp20k         80000   kp20k_valid2k   \\n\",\n       \"19153  verbatim_prepend      kp20k         50000   kp20k_valid2k   \\n\",\n       \"15884  alphabetical          kp20k         50000   krapivin        \\n\",\n       \"20237  alphabetical_reverse  kp20k         25000   krapivin        \\n\",\n       \"16574  length                kp20k         115000  krapivin        \\n\",\n       \"15966  length_reverse        kp20k         50000   krapivin        \\n\",\n       \"20417  no_sort               kp20k         155000  krapivin        \\n\",\n       \"18548  no_sort_reverse       kp20k         80000   krapivin        \\n\",\n       \"20754  random                kp20k         115000  krapivin        \\n\",\n       \"19591  verbatim_append       kp20k         80000   krapivin        \\n\",\n       \"19149  verbatim_prepend      kp20k         50000   krapivin        \\n\",\n       \"15885  alphabetical          kp20k         50000   nus             \\n\",\n       \"20243  alphabetical_reverse  kp20k         25000   nus             \\n\",\n       \"16577  length                kp20k         115000  nus             \\n\",\n       \"15965  length_reverse        kp20k         50000   nus             \\n\",\n       \"20418  no_sort               kp20k         155000  nus             \\n\",\n       \"18551  no_sort_reverse       kp20k         80000   nus             \\n\",\n       \"20751  random                kp20k         115000  nus             \\n\",\n       \"19594  verbatim_append       kp20k         80000   nus             \\n\",\n       \"19152  verbatim_prepend      kp20k         50000   nus             \\n\",\n       \"15883  alphabetical          kp20k         50000   semeval         \\n\",\n       \"20239  alphabetical_reverse  kp20k         25000   semeval         \\n\",\n       \"16575  length                kp20k         115000  semeval         \\n\",\n       \"15968  length_reverse        kp20k         50000   semeval         \\n\",\n       \"20421  no_sort               kp20k         155000  semeval         \\n\",\n       \"18550  no_sort_reverse       kp20k         80000   semeval         \\n\",\n       \"20749  random                kp20k         115000  semeval         \\n\",\n       \"19596  verbatim_append       kp20k         80000   semeval         \\n\",\n       \"19150  verbatim_prepend      kp20k         50000   semeval         \\n\",\n       \"\\n\",\n       \"      decoding_method decoding_terminate beam_width max_length  \\\\\\n\",\n       \"15880  exhaustive      fullbeam           50         40          \\n\",\n       \"20240  exhaustive      fullbeam           50         40          \\n\",\n       \"16579  exhaustive      fullbeam           50         40          \\n\",\n       \"15963  exhaustive      fullbeam           50         40          \\n\",\n       \"20419  exhaustive      fullbeam           50         40          \\n\",\n       \"18552  exhaustive      fullbeam           50         40          \\n\",\n       \"20748  exhaustive      fullbeam           50         40          \\n\",\n       \"19592  exhaustive      fullbeam           50         40          \\n\",\n       \"19154  exhaustive      fullbeam           50         40          \\n\",\n       \"15881  exhaustive      fullbeam           50         40          \\n\",\n       \"20241  exhaustive      fullbeam           50         40          \\n\",\n       \"16573  exhaustive      fullbeam           50         40          \\n\",\n       \"15964  exhaustive      fullbeam           50         40          \\n\",\n       \"20420  exhaustive      fullbeam           50         40          \\n\",\n       \"18546  exhaustive      fullbeam           50         40          \\n\",\n       \"20750  exhaustive      fullbeam           50         40          \\n\",\n       \"19593  exhaustive      fullbeam           50         40          \\n\",\n       \"19155  exhaustive      fullbeam           50         40          \\n\",\n       \"15886  exhaustive      fullbeam           50         40          \\n\",\n       \"20238  exhaustive      fullbeam           50         40          \\n\",\n       \"16578  exhaustive      fullbeam           50         40          \\n\",\n       \"15967  exhaustive      fullbeam           50         40          \\n\",\n       \"42862  exhaustive      fullbeam           50         40          \\n\",\n       \"18547  exhaustive      fullbeam           50         40          \\n\",\n       \"20753  exhaustive      fullbeam           50         40          \\n\",\n       \"19595  exhaustive      fullbeam           50         40          \\n\",\n       \"19151  exhaustive      fullbeam           50         40          \\n\",\n       \"15882  exhaustive      fullbeam           50         40          \\n\",\n       \"20242  exhaustive      fullbeam           50         40          \\n\",\n       \"16576  exhaustive      fullbeam           50         40          \\n\",\n       \"15969  exhaustive      fullbeam           50         40          \\n\",\n       \"20416  exhaustive      fullbeam           50         40          \\n\",\n       \"18549  exhaustive      fullbeam           50         40          \\n\",\n       \"20752  exhaustive      fullbeam           50         40          \\n\",\n       \"42853  exhaustive      fullbeam           50         40          \\n\",\n       \"19153  exhaustive      fullbeam           50         40          \\n\",\n       \"15884  exhaustive      fullbeam           50         40          \\n\",\n       \"20237  exhaustive      fullbeam           50         40          \\n\",\n       \"16574  exhaustive      fullbeam           50         40          \\n\",\n       \"15966  exhaustive      fullbeam           50         40          \\n\",\n       \"20417  exhaustive      fullbeam           50         40          \\n\",\n       \"18548  exhaustive      fullbeam           50         40          \\n\",\n       \"20754  exhaustive      fullbeam           50         40          \\n\",\n       \"19591  exhaustive      fullbeam           50         40          \\n\",\n       \"19149  exhaustive      fullbeam           50         40          \\n\",\n       \"15885  exhaustive      fullbeam           50         40          \\n\",\n       \"20243  exhaustive      fullbeam           50         40          \\n\",\n       \"16577  exhaustive      fullbeam           50         40          \\n\",\n       \"15965  exhaustive      fullbeam           50         40          \\n\",\n       \"20418  exhaustive      fullbeam           50         40          \\n\",\n       \"18551  exhaustive      fullbeam           50         40          \\n\",\n       \"20751  exhaustive      fullbeam           50         40          \\n\",\n       \"19594  exhaustive      fullbeam           50         40          \\n\",\n       \"19152  exhaustive      fullbeam           50         40          \\n\",\n       \"15883  exhaustive      fullbeam           50         40          \\n\",\n       \"20239  exhaustive      fullbeam           50         40          \\n\",\n       \"16575  exhaustive      fullbeam           50         40          \\n\",\n       \"15968  exhaustive      fullbeam           50         40          \\n\",\n       \"20421  exhaustive      fullbeam           50         40          \\n\",\n       \"18550  exhaustive      fullbeam           50         40          \\n\",\n       \"20749  exhaustive      fullbeam           50         40          \\n\",\n       \"19596  exhaustive      fullbeam           50         40          \\n\",\n       \"19150  exhaustive      fullbeam           50         40          \\n\",\n       \"\\n\",\n       \"       all_exact_correct@5  all_exact_precision@5  all_exact_recall@5  \\\\\\n\",\n       \"15880  0.529221             0.105844               0.069845             \\n\",\n       \"20240  0.733766             0.146753               0.095473             \\n\",\n       \"16579  0.340909             0.069156               0.044977             \\n\",\n       \"15963  0.558442             0.111688               0.072751             \\n\",\n       \"20419  0.327922             0.065584               0.044145             \\n\",\n       \"18552  0.396104             0.079221               0.052715             \\n\",\n       \"20748  0.370130             0.076461               0.050426             \\n\",\n       \"19592  0.480519             0.096104               0.062244             \\n\",\n       \"19154  0.532468             0.106494               0.069696             \\n\",\n       \"15881  1.666000             0.333200               0.210013             \\n\",\n       \"20241  1.740000             0.348000               0.219346             \\n\",\n       \"16573  1.376000             0.275200               0.168542             \\n\",\n       \"15964  1.648000             0.329600               0.206437             \\n\",\n       \"20420  1.448000             0.289600               0.178376             \\n\",\n       \"18546  1.564000             0.312800               0.194621             \\n\",\n       \"20750  1.530000             0.306200               0.189564             \\n\",\n       \"19593  1.598000             0.319600               0.198977             \\n\",\n       \"19155  1.782000             0.356400               0.219340             \\n\",\n       \"15886  1.439936             0.288002               0.311385             \\n\",\n       \"20238  1.449542             0.289915               0.315257             \\n\",\n       \"16578  1.313654             0.262776               0.286682             \\n\",\n       \"15967  1.429529             0.286003               0.311196             \\n\",\n       \"42862  1.303347             0.260779               0.283327             \\n\",\n       \"18547  1.385651             0.277210               0.302579             \\n\",\n       \"20753  1.340471             0.268318               0.292625             \\n\",\n       \"19595  1.405664             0.281133               0.304067             \\n\",\n       \"19151  1.433882             0.286776               0.311597             \\n\",\n       \"15882  1.428500             0.285700               0.308249             \\n\",\n       \"20242  1.449500             0.289900               0.313741             \\n\",\n       \"16576  1.303500             0.261000               0.283812             \\n\",\n       \"15969  1.423500             0.284700               0.308897             \\n\",\n       \"20416  1.264500             0.252900               0.271925             \\n\",\n       \"18549  1.356000             0.271350               0.296349             \\n\",\n       \"20752  1.332000             0.266400               0.290192             \\n\",\n       \"42853  1.370000             0.274000               0.298272             \\n\",\n       \"19153  1.427000             0.285400               0.308361             \\n\",\n       \"15884  1.395652             0.279130               0.289710             \\n\",\n       \"20237  1.393478             0.278696               0.293146             \\n\",\n       \"16574  1.263043             0.252609               0.262107             \\n\",\n       \"15966  1.367391             0.273478               0.283478             \\n\",\n       \"20417  1.230435             0.246087               0.253608             \\n\",\n       \"18548  1.332609             0.266522               0.275329             \\n\",\n       \"20754  1.306522             0.261304               0.270471             \\n\",\n       \"19591  1.341304             0.268261               0.282144             \\n\",\n       \"19149  1.380435             0.276087               0.289428             \\n\",\n       \"15885  2.075829             0.415166               0.236202             \\n\",\n       \"20243  2.161137             0.432227               0.246344             \\n\",\n       \"16577  1.900474             0.380095               0.218194             \\n\",\n       \"15965  2.099526             0.419905               0.245218             \\n\",\n       \"20418  1.786730             0.357346               0.203655             \\n\",\n       \"18551  1.990521             0.398104               0.227730             \\n\",\n       \"20751  1.919431             0.384518               0.225842             \\n\",\n       \"19594  2.023697             0.404739               0.232169             \\n\",\n       \"19152  2.042654             0.408531               0.230146             \\n\",\n       \"15883  1.900000             0.380000               0.129851             \\n\",\n       \"20239  1.860000             0.372000               0.127396             \\n\",\n       \"16575  1.760000             0.352000               0.119758             \\n\",\n       \"15968  1.770000             0.354000               0.120971             \\n\",\n       \"20421  1.750000             0.350000               0.119874             \\n\",\n       \"18550  1.820000             0.364000               0.123565             \\n\",\n       \"20749  1.710000             0.342000               0.117027             \\n\",\n       \"19596  1.820000             0.364000               0.122982             \\n\",\n       \"19150  1.940000             0.388000               0.132194             \\n\",\n       \"\\n\",\n       \"       all_exact_f_score@5  all_exact_precision_hard@5  \\\\\\n\",\n       \"15880  0.082995             0.105844                     \\n\",\n       \"20240  0.114233             0.146753                     \\n\",\n       \"16579  0.053545             0.068182                     \\n\",\n       \"15963  0.086882             0.111688                     \\n\",\n       \"20419  0.052217             0.065584                     \\n\",\n       \"18552  0.062526             0.079221                     \\n\",\n       \"20748  0.059493             0.074026                     \\n\",\n       \"19592  0.074550             0.096104                     \\n\",\n       \"19154  0.083228             0.106494                     \\n\",\n       \"15881  0.243642             0.333200                     \\n\",\n       \"20241  0.254103             0.348000                     \\n\",\n       \"16573  0.197807             0.275200                     \\n\",\n       \"15964  0.239518             0.329600                     \\n\",\n       \"20420  0.208952             0.289600                     \\n\",\n       \"18546  0.226384             0.312800                     \\n\",\n       \"20750  0.221504             0.306000                     \\n\",\n       \"19593  0.231883             0.319600                     \\n\",\n       \"19155  0.257241             0.356400                     \\n\",\n       \"15886  0.288303             0.287987                     \\n\",\n       \"20238  0.291303             0.289908                     \\n\",\n       \"16578  0.264573             0.262731                     \\n\",\n       \"15967  0.287474             0.285906                     \\n\",\n       \"42862  0.261662             0.260669                     \\n\",\n       \"18547  0.279326             0.277130                     \\n\",\n       \"20753  0.269878             0.268094                     \\n\",\n       \"19595  0.281538             0.281133                     \\n\",\n       \"19151  0.287907             0.286776                     \\n\",\n       \"15882  0.285874             0.285700                     \\n\",\n       \"20242  0.290713             0.289900                     \\n\",\n       \"16576  0.261920             0.260700                     \\n\",\n       \"15969  0.285624             0.284700                     \\n\",\n       \"20416  0.252945             0.252900                     \\n\",\n       \"18549  0.273125             0.271200                     \\n\",\n       \"20752  0.268098             0.266400                     \\n\",\n       \"42853  0.275155             0.274000                     \\n\",\n       \"19153  0.285729             0.285400                     \\n\",\n       \"15884  0.269348             0.279130                     \\n\",\n       \"20237  0.270963             0.278696                     \\n\",\n       \"16574  0.244179             0.252609                     \\n\",\n       \"15966  0.264282             0.273478                     \\n\",\n       \"20417  0.236781             0.246087                     \\n\",\n       \"18548  0.257042             0.266522                     \\n\",\n       \"20754  0.251048             0.261304                     \\n\",\n       \"19591  0.261159             0.268261                     \\n\",\n       \"19149  0.268656             0.276087                     \\n\",\n       \"15885  0.273684             0.415166                     \\n\",\n       \"20243  0.285526             0.432227                     \\n\",\n       \"16577  0.252484             0.380095                     \\n\",\n       \"15965  0.280465             0.419905                     \\n\",\n       \"20418  0.235470             0.357346                     \\n\",\n       \"18551  0.263043             0.398104                     \\n\",\n       \"20751  0.257387             0.383886                     \\n\",\n       \"19594  0.266463             0.404739                     \\n\",\n       \"19152  0.268390             0.408531                     \\n\",\n       \"15883  0.192226             0.380000                     \\n\",\n       \"20239  0.188490             0.372000                     \\n\",\n       \"16575  0.177461             0.352000                     \\n\",\n       \"15968  0.179071             0.354000                     \\n\",\n       \"20421  0.177301             0.350000                     \\n\",\n       \"18550  0.183155             0.364000                     \\n\",\n       \"20749  0.173137             0.342000                     \\n\",\n       \"19596  0.182395             0.364000                     \\n\",\n       \"19150  0.195839             0.388000                     \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@5  all_exact_correct@10  all_exact_precision@10  \\\\\\n\",\n       \"15880  0.082995                  0.805195              0.080519                 \\n\",\n       \"20240  0.114233                  1.120130              0.113157                 \\n\",\n       \"16579  0.053364                  0.525974              0.053896                 \\n\",\n       \"15963  0.086882                  0.857143              0.086800                 \\n\",\n       \"20419  0.052217                  0.490260              0.049165                 \\n\",\n       \"18552  0.062526                  0.571429              0.057318                 \\n\",\n       \"20748  0.059005                  0.483766              0.054248                 \\n\",\n       \"19592  0.074550                  0.688312              0.069191                 \\n\",\n       \"19154  0.083228                  0.876623              0.087662                 \\n\",\n       \"15881  0.243642                  2.618000              0.261850                 \\n\",\n       \"20241  0.254103                  2.872000              0.287200                 \\n\",\n       \"16573  0.197807                  2.132000              0.213300                 \\n\",\n       \"15964  0.239518                  2.688000              0.268800                 \\n\",\n       \"20420  0.208952                  2.096000              0.209600                 \\n\",\n       \"18546  0.226384                  2.402000              0.240311                 \\n\",\n       \"20750  0.221431                  2.226000              0.223711                 \\n\",\n       \"19593  0.231883                  2.416000              0.241600                 \\n\",\n       \"19155  0.257241                  2.730000              0.273050                 \\n\",\n       \"15886  0.288296                  1.990344              0.199186                 \\n\",\n       \"20238  0.291303                  1.988643              0.198924                 \\n\",\n       \"16578  0.264567                  1.788112              0.179097                 \\n\",\n       \"15967  0.287460                  1.961175              0.196429                 \\n\",\n       \"42862  0.261643                  1.785060              0.178921                 \\n\",\n       \"18547  0.279311                  1.886526              0.188930                 \\n\",\n       \"20753  0.269821                  1.777405              0.179004                 \\n\",\n       \"19595  0.281538                  1.919748              0.192053                 \\n\",\n       \"19151  0.287907                  1.988292              0.198852                 \\n\",\n       \"15882  0.285874                  1.997500              0.199849                 \\n\",\n       \"20242  0.290713                  1.983500              0.198377                 \\n\",\n       \"16576  0.261887                  1.796000              0.180210                 \\n\",\n       \"15969  0.285624                  1.970500              0.197114                 \\n\",\n       \"20416  0.252945                  1.762000              0.176330                 \\n\",\n       \"18549  0.273102                  1.860000              0.186367                 \\n\",\n       \"20752  0.268098                  1.773500              0.178254                 \\n\",\n       \"42853  0.275155                  1.911000              0.191133                 \\n\",\n       \"19153  0.285729                  1.982000              0.198200                 \\n\",\n       \"15884  0.269348                  1.891304              0.189130                 \\n\",\n       \"20237  0.270963                  1.904348              0.190435                 \\n\",\n       \"16574  0.244179                  1.780435              0.178611                 \\n\",\n       \"15966  0.264282                  1.965217              0.196739                 \\n\",\n       \"20417  0.236781                  1.706522              0.170652                 \\n\",\n       \"18548  0.257042                  1.852174              0.185320                 \\n\",\n       \"20754  0.251048                  1.782609              0.179627                 \\n\",\n       \"19591  0.261159                  1.854348              0.185459                 \\n\",\n       \"19149  0.268656                  1.986957              0.198696                 \\n\",\n       \"15885  0.273684                  2.900474              0.290047                 \\n\",\n       \"20243  0.285526                  3.033175              0.303318                 \\n\",\n       \"16577  0.252484                  2.658768              0.265877                 \\n\",\n       \"15965  0.280465                  2.985782              0.298578                 \\n\",\n       \"20418  0.235470                  2.554502              0.255661                 \\n\",\n       \"18551  0.263043                  2.767773              0.276777                 \\n\",\n       \"20751  0.257329                  2.530806              0.255977                 \\n\",\n       \"19594  0.266463                  2.848341              0.285045                 \\n\",\n       \"19152  0.268390                  3.018957              0.301896                 \\n\",\n       \"15883  0.192226                  2.810000              0.281000                 \\n\",\n       \"20239  0.188490                  2.880000              0.288000                 \\n\",\n       \"16575  0.177461                  2.510000              0.251000                 \\n\",\n       \"15968  0.179071                  2.750000              0.275000                 \\n\",\n       \"20421  0.177301                  2.350000              0.235000                 \\n\",\n       \"18550  0.183155                  2.590000              0.259750                 \\n\",\n       \"20749  0.173137                  2.450000              0.246222                 \\n\",\n       \"19596  0.182395                  2.590000              0.259000                 \\n\",\n       \"19150  0.195839                  2.820000              0.282000                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@10  all_exact_f_score@10  all_exact_precision_hard@10  \\\\\\n\",\n       \"15880  0.106542             0.090510              0.080519                      \\n\",\n       \"20240  0.145205             0.125659              0.112013                      \\n\",\n       \"16579  0.069508             0.059381              0.052597                      \\n\",\n       \"15963  0.111587             0.096264              0.085714                      \\n\",\n       \"20419  0.064798             0.055253              0.049026                      \\n\",\n       \"18552  0.074222             0.063882              0.057143                      \\n\",\n       \"20748  0.065121             0.056747              0.048377                      \\n\",\n       \"19592  0.089638             0.076891              0.068831                      \\n\",\n       \"19154  0.113887             0.097710              0.087662                      \\n\",\n       \"15881  0.317071             0.270850              0.261800                      \\n\",\n       \"20241  0.345004             0.296207              0.287200                      \\n\",\n       \"16573  0.251095             0.218603              0.213200                      \\n\",\n       \"15964  0.321256             0.276233              0.268800                      \\n\",\n       \"20420  0.250371             0.216245              0.209600                      \\n\",\n       \"18546  0.290784             0.248448              0.240200                      \\n\",\n       \"20750  0.267734             0.230316              0.222600                      \\n\",\n       \"19593  0.287326             0.248131              0.241600                      \\n\",\n       \"19155  0.329392             0.281847              0.273000                      \\n\",\n       \"15886  0.420234             0.259843              0.199034                      \\n\",\n       \"20238  0.422114             0.260395              0.198864                      \\n\",\n       \"16578  0.383883             0.235680              0.178811                      \\n\",\n       \"15967  0.418276             0.257706              0.196117                      \\n\",\n       \"42862  0.379804             0.233942              0.178506                      \\n\",\n       \"18547  0.404753             0.248638              0.188653                      \\n\",\n       \"20753  0.380955             0.234397              0.177741                      \\n\",\n       \"19595  0.406481             0.251011              0.191975                      \\n\",\n       \"19151  0.422140             0.260404              0.198829                      \\n\",\n       \"15882  0.420406             0.261114              0.199750                      \\n\",\n       \"20242  0.420245             0.260055              0.198350                      \\n\",\n       \"16576  0.383017             0.236240              0.179600                      \\n\",\n       \"15969  0.419283             0.258689              0.197050                      \\n\",\n       \"20416  0.374511             0.231559              0.176200                      \\n\",\n       \"18549  0.399976             0.245578              0.186000                      \\n\",\n       \"20752  0.380147             0.234159              0.177350                      \\n\",\n       \"42853  0.407051             0.250746              0.191100                      \\n\",\n       \"19153  0.420821             0.259691              0.198200                      \\n\",\n       \"15884  0.384865             0.240809              0.189130                      \\n\",\n       \"20237  0.381141             0.240992              0.190435                      \\n\",\n       \"16574  0.358211             0.226484              0.178043                      \\n\",\n       \"15966  0.394894             0.249329              0.196522                      \\n\",\n       \"20417  0.345732             0.217724              0.170652                      \\n\",\n       \"18548  0.375193             0.235839              0.185217                      \\n\",\n       \"20754  0.357412             0.226187              0.178261                      \\n\",\n       \"19591  0.375091             0.236170              0.185435                      \\n\",\n       \"19149  0.397545             0.251735              0.198696                      \\n\",\n       \"15885  0.325551             0.278171              0.290047                      \\n\",\n       \"20243  0.330595             0.286220              0.303318                      \\n\",\n       \"16577  0.303088             0.256067              0.265877                      \\n\",\n       \"15965  0.331445             0.283926              0.298578                      \\n\",\n       \"20418  0.282525             0.243897              0.255450                      \\n\",\n       \"18551  0.309773             0.264381              0.276777                      \\n\",\n       \"20751  0.296128             0.247833              0.253081                      \\n\",\n       \"19594  0.314165             0.270357              0.284834                      \\n\",\n       \"19152  0.330977             0.285706              0.301896                      \\n\",\n       \"15883  0.189829             0.224608              0.281000                      \\n\",\n       \"20239  0.193820             0.229459              0.288000                      \\n\",\n       \"16575  0.170784             0.201384              0.251000                      \\n\",\n       \"15968  0.186167             0.219930              0.275000                      \\n\",\n       \"20421  0.158789             0.187703              0.235000                      \\n\",\n       \"18550  0.174538             0.206663              0.259000                      \\n\",\n       \"20749  0.167464             0.197635              0.245000                      \\n\",\n       \"19596  0.174538             0.206366              0.259000                      \\n\",\n       \"19150  0.190986             0.225703              0.282000                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@10  all_exact_correct@k  all_exact_precision@k  \\\\\\n\",\n       \"15880  0.090510                   0.698052             0.090007                \\n\",\n       \"20240  0.124847                   0.967532             0.121305                \\n\",\n       \"16579  0.059042                   0.441558             0.057790                \\n\",\n       \"15963  0.095754                   0.746753             0.095192                \\n\",\n       \"20419  0.055171                   0.464286             0.060057                \\n\",\n       \"18552  0.063791                   0.509740             0.065368                \\n\",\n       \"20748  0.054647                   0.431818             0.060975                \\n\",\n       \"19592  0.076761                   0.629870             0.080039                \\n\",\n       \"19154  0.097710                   0.756494             0.093991                \\n\",\n       \"15881  0.270818                   2.706000             0.286475                \\n\",\n       \"20241  0.296207                   2.916000             0.304113                \\n\",\n       \"16573  0.218540                   2.136000             0.228391                \\n\",\n       \"15964  0.276233                   2.716000             0.287630                \\n\",\n       \"20420  0.216245                   2.138000             0.231749                \\n\",\n       \"18546  0.248370                   2.398000             0.258560                \\n\",\n       \"20750  0.229823                   2.226000             0.245848                \\n\",\n       \"19593  0.248131                   2.504000             0.265237                \\n\",\n       \"19155  0.281815                   2.898000             0.296899                \\n\",\n       \"15886  0.259775                   1.549957             0.296912                \\n\",\n       \"20238  0.260370                   1.563516             0.301253                \\n\",\n       \"16578  0.235584                   1.340822             0.269624                \\n\",\n       \"15967  0.257551                   1.510932             0.294485                \\n\",\n       \"42862  0.233808                   1.370441             0.267862                \\n\",\n       \"18547  0.248518                   1.432681             0.283353                \\n\",\n       \"20753  0.233677                   1.385401             0.276310                \\n\",\n       \"19595  0.250984                   1.494371             0.289101                \\n\",\n       \"19151  0.260395                   1.547956             0.297848                \\n\",\n       \"15882  0.261026                   1.538500             0.294509                \\n\",\n       \"20242  0.260040                   1.532000             0.295983                \\n\",\n       \"16576  0.236084                   1.349000             0.270489                \\n\",\n       \"15969  0.258631                   1.490500             0.287936                \\n\",\n       \"20416  0.231467                   1.323000             0.258367                \\n\",\n       \"18549  0.245461                   1.391500             0.276028                \\n\",\n       \"20752  0.233679                   1.342500             0.268588                \\n\",\n       \"42853  0.250732                   1.447000             0.281060                \\n\",\n       \"19153  0.259691                   1.516000             0.290802                \\n\",\n       \"15884  0.240809                   1.534783             0.275040                \\n\",\n       \"20237  0.240992                   1.591304             0.288258                \\n\",\n       \"16574  0.225943                   1.441304             0.261572                \\n\",\n       \"15966  0.249134                   1.510870             0.272216                \\n\",\n       \"20417  0.217724                   1.378261             0.254592                \\n\",\n       \"18548  0.235723                   1.502174             0.269348                \\n\",\n       \"20754  0.225067                   1.460870             0.266411                \\n\",\n       \"19591  0.236161                   1.513043             0.280033                \\n\",\n       \"19149  0.251735                   1.610870             0.288413                \\n\",\n       \"15885  0.278171                   3.113744             0.297896                \\n\",\n       \"20243  0.286220                   3.194313             0.301743                \\n\",\n       \"16577  0.256067                   2.672986             0.258456                \\n\",\n       \"15965  0.283926                   3.189573             0.306454                \\n\",\n       \"20418  0.243797                   2.753555             0.257047                \\n\",\n       \"18551  0.264381                   2.900474             0.277477                \\n\",\n       \"20751  0.246462                   2.573460             0.257879                \\n\",\n       \"19594  0.270258                   3.023697             0.284184                \\n\",\n       \"19152  0.285706                   3.236967             0.295810                \\n\",\n       \"15883  0.224608                   3.250000             0.218710                \\n\",\n       \"20239  0.229459                   3.440000             0.228295                \\n\",\n       \"16575  0.201384                   3.030000             0.206055                \\n\",\n       \"15968  0.219930                   3.180000             0.217881                \\n\",\n       \"20421  0.187703                   2.800000             0.187304                \\n\",\n       \"18550  0.206538                   3.000000             0.204013                \\n\",\n       \"20749  0.197224                   2.730000             0.188370                \\n\",\n       \"19596  0.206366                   3.130000             0.207928                \\n\",\n       \"19150  0.225703                   3.550000             0.233854                \\n\",\n       \"\\n\",\n       \"       all_exact_recall@k  all_exact_f_score@k  all_exact_precision_hard@k  \\\\\\n\",\n       \"15880  0.090007            0.090007             0.090007                     \\n\",\n       \"20240  0.120919            0.121081             0.120919                     \\n\",\n       \"16579  0.056630            0.056888             0.056630                     \\n\",\n       \"15963  0.094476            0.094745             0.094476                     \\n\",\n       \"20419  0.060057            0.060057             0.060057                     \\n\",\n       \"18552  0.065267            0.065315             0.065267                     \\n\",\n       \"20748  0.056356            0.057694             0.056356                     \\n\",\n       \"19592  0.079611            0.079761             0.079611                     \\n\",\n       \"19154  0.093991            0.093991             0.093991                     \\n\",\n       \"15881  0.286458            0.286466             0.286458                     \\n\",\n       \"20241  0.303762            0.303914             0.303762                     \\n\",\n       \"16573  0.228391            0.228391             0.228391                     \\n\",\n       \"15964  0.287483            0.287544             0.287483                     \\n\",\n       \"20420  0.231749            0.231749             0.231749                     \\n\",\n       \"18546  0.258378            0.258460             0.258378                     \\n\",\n       \"20750  0.244688            0.245121             0.244688                     \\n\",\n       \"19593  0.265219            0.265228             0.265219                     \\n\",\n       \"19155  0.296899            0.296899             0.296899                     \\n\",\n       \"15886  0.296350            0.296551             0.296350                     \\n\",\n       \"20238  0.300911            0.301040             0.300911                     \\n\",\n       \"16578  0.269237            0.269332             0.269237                     \\n\",\n       \"15967  0.294039            0.294181             0.294039                     \\n\",\n       \"42862  0.267337            0.267483             0.267337                     \\n\",\n       \"18547  0.282969            0.283093             0.282969                     \\n\",\n       \"20753  0.275474            0.275720             0.275474                     \\n\",\n       \"19595  0.288834            0.288915             0.288834                     \\n\",\n       \"19151  0.297730            0.297772             0.297730                     \\n\",\n       \"15882  0.294346            0.294412             0.294346                     \\n\",\n       \"20242  0.295658            0.295780             0.295658                     \\n\",\n       \"16576  0.269872            0.270018             0.269872                     \\n\",\n       \"15969  0.287687            0.287782             0.287687                     \\n\",\n       \"20416  0.258215            0.258280             0.258215                     \\n\",\n       \"18549  0.275550            0.275701             0.275550                     \\n\",\n       \"20752  0.267730            0.267978             0.267730                     \\n\",\n       \"42853  0.280957            0.281004             0.280957                     \\n\",\n       \"19153  0.290723            0.290757             0.290723                     \\n\",\n       \"15884  0.275040            0.275040             0.275040                     \\n\",\n       \"20237  0.288258            0.288258             0.288258                     \\n\",\n       \"16574  0.261572            0.261572             0.261572                     \\n\",\n       \"15966  0.272171            0.272191             0.272171                     \\n\",\n       \"20417  0.254592            0.254592             0.254592                     \\n\",\n       \"18548  0.269348            0.269348             0.269348                     \\n\",\n       \"20754  0.266202            0.266282             0.266202                     \\n\",\n       \"19591  0.279973            0.279999             0.279973                     \\n\",\n       \"19149  0.288413            0.288413             0.288413                     \\n\",\n       \"15885  0.296947            0.297299             0.296947                     \\n\",\n       \"20243  0.300580            0.301065             0.300580                     \\n\",\n       \"16577  0.258456            0.258456             0.258456                     \\n\",\n       \"15965  0.305601            0.305973             0.305601                     \\n\",\n       \"20418  0.256767            0.256900             0.256767                     \\n\",\n       \"18551  0.277220            0.277334             0.277220                     \\n\",\n       \"20751  0.254849            0.255760             0.254849                     \\n\",\n       \"19594  0.283946            0.284059             0.283946                     \\n\",\n       \"19152  0.294997            0.295342             0.294997                     \\n\",\n       \"15883  0.218149            0.218396             0.218149                     \\n\",\n       \"20239  0.228065            0.228176             0.228065                     \\n\",\n       \"16575  0.206055            0.206055             0.206055                     \\n\",\n       \"15968  0.214036            0.215471             0.214036                     \\n\",\n       \"20421  0.187304            0.187304             0.187304                     \\n\",\n       \"18550  0.201404            0.202141             0.201404                     \\n\",\n       \"20749  0.185263            0.186498             0.185263                     \\n\",\n       \"19596  0.207928            0.207928             0.207928                     \\n\",\n       \"19150  0.233854            0.233854             0.233854                     \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@k  all_exact_correct@M  all_exact_precision@M  \\\\\\n\",\n       \"15880  0.090007                  1.295455             0.023826                \\n\",\n       \"20240  0.120919                  1.772727             0.048374                \\n\",\n       \"16579  0.056630                  0.766234             0.007226                \\n\",\n       \"15963  0.094476                  1.353896             0.031018                \\n\",\n       \"20419  0.060057                  0.970779             0.010515                \\n\",\n       \"18552  0.065267                  0.990260             0.017710                \\n\",\n       \"20748  0.056356                  0.750000             0.020798                \\n\",\n       \"19592  0.079611                  0.899351             0.017719                \\n\",\n       \"19154  0.093991                  1.389610             0.018478                \\n\",\n       \"15881  0.286458                  4.040000             0.104126                \\n\",\n       \"20241  0.303762                  4.214000             0.134493                \\n\",\n       \"16573  0.228391                  3.168000             0.033370                \\n\",\n       \"15964  0.287483                  4.120000             0.096841                \\n\",\n       \"20420  0.231749                  3.636000             0.051404                \\n\",\n       \"18546  0.258378                  3.628000             0.080954                \\n\",\n       \"20750  0.244688                  3.160000             0.056322                \\n\",\n       \"19593  0.265219                  3.946000             0.058601                \\n\",\n       \"19155  0.296899                  5.044000             0.109332                \\n\",\n       \"15886  0.296350                  2.571421             0.070670                \\n\",\n       \"20238  0.300911                  2.538000             0.079284                \\n\",\n       \"16578  0.269237                  2.217892             0.024639                \\n\",\n       \"15967  0.294039                  2.632161             0.063620                \\n\",\n       \"42862  0.267337                  2.484265             0.037427                \\n\",\n       \"18547  0.282969                  2.481163             0.060232                \\n\",\n       \"20753  0.275474                  2.259519             0.046079                \\n\",\n       \"19595  0.288834                  2.602742             0.040297                \\n\",\n       \"19151  0.297730                  2.706209             0.058528                \\n\",\n       \"15882  0.294346                  2.565000             0.069475                \\n\",\n       \"20242  0.295658                  2.512000             0.078463                \\n\",\n       \"16576  0.269872                  2.217000             0.025331                \\n\",\n       \"15969  0.287687                  2.637500             0.063566                \\n\",\n       \"20416  0.258215                  2.456000             0.037178                \\n\",\n       \"18549  0.275550                  2.480500             0.060536                \\n\",\n       \"20752  0.267730                  2.257000             0.045589                \\n\",\n       \"42853  0.280957                  2.632500             0.041552                \\n\",\n       \"19153  0.290723                  2.682000             0.059712                \\n\",\n       \"15884  0.275040                  2.545652             0.067112                \\n\",\n       \"20237  0.288258                  2.491304             0.075852                \\n\",\n       \"16574  0.261572                  2.289130             0.023875                \\n\",\n       \"15966  0.272171                  2.630435             0.052939                \\n\",\n       \"20417  0.254592                  2.491304             0.035769                \\n\",\n       \"18548  0.269348                  2.491304             0.056697                \\n\",\n       \"20754  0.266202                  2.345652             0.044875                \\n\",\n       \"19591  0.279973                  2.589130             0.036468                \\n\",\n       \"19149  0.288413                  2.854348             0.058520                \\n\",\n       \"15885  0.296947                  3.914692             0.105389                \\n\",\n       \"20243  0.300580                  3.876777             0.117622                \\n\",\n       \"16577  0.258456                  3.383886             0.029858                \\n\",\n       \"15965  0.305601                  4.189573             0.082169                \\n\",\n       \"20418  0.256767                  3.843602             0.053151                \\n\",\n       \"18551  0.277220                  3.763033             0.082236                \\n\",\n       \"20751  0.254849                  3.336493             0.065324                \\n\",\n       \"19594  0.283946                  3.990521             0.056287                \\n\",\n       \"19152  0.294997                  4.355450             0.091562                \\n\",\n       \"15883  0.218149                  3.850000             0.099362                \\n\",\n       \"20239  0.228065                  4.100000             0.119364                \\n\",\n       \"16575  0.206055                  3.640000             0.031830                \\n\",\n       \"15968  0.214036                  4.210000             0.074018                \\n\",\n       \"20421  0.187304                  3.700000             0.051892                \\n\",\n       \"18550  0.201404                  3.710000             0.081824                \\n\",\n       \"20749  0.185263                  3.380000             0.066926                \\n\",\n       \"19596  0.207928                  4.050000             0.051164                \\n\",\n       \"19150  0.233854                  4.640000             0.095101                \\n\",\n       \"\\n\",\n       \"       all_exact_recall@M  all_exact_f_score@M  all_exact_precision_hard@M  \\\\\\n\",\n       \"15880  0.171131            0.040440             0.023826                     \\n\",\n       \"20240  0.227893            0.075937             0.048374                     \\n\",\n       \"16579  0.099783            0.010708             0.007226                     \\n\",\n       \"15963  0.176396            0.046575             0.031018                     \\n\",\n       \"20419  0.127110            0.018231             0.010515                     \\n\",\n       \"18552  0.127735            0.028588             0.017710                     \\n\",\n       \"20748  0.099819            0.024598             0.020798                     \\n\",\n       \"19592  0.116450            0.027100             0.017719                     \\n\",\n       \"19154  0.180493            0.032349             0.018478                     \\n\",\n       \"15881  0.461503            0.159934             0.104126                     \\n\",\n       \"20241  0.477555            0.197995             0.134493                     \\n\",\n       \"16573  0.368761            0.057544             0.033370                     \\n\",\n       \"15964  0.466564            0.146109             0.096841                     \\n\",\n       \"20420  0.412016            0.087131             0.051404                     \\n\",\n       \"18546  0.421334            0.127594             0.080954                     \\n\",\n       \"20750  0.366465            0.089404             0.056322                     \\n\",\n       \"19593  0.443652            0.097756             0.058601                     \\n\",\n       \"19155  0.563146            0.174393             0.109332                     \\n\",\n       \"15886  0.523737            0.118971             0.070670                     \\n\",\n       \"20238  0.517029            0.131874             0.079284                     \\n\",\n       \"16578  0.469246            0.044533             0.024639                     \\n\",\n       \"15967  0.537857            0.106636             0.063620                     \\n\",\n       \"42862  0.511971            0.066408             0.037427                     \\n\",\n       \"18547  0.517150            0.103191             0.060232                     \\n\",\n       \"20753  0.471528            0.078092             0.046079                     \\n\",\n       \"19595  0.531113            0.071689             0.040297                     \\n\",\n       \"19151  0.546641            0.101707             0.058528                     \\n\",\n       \"15882  0.521574            0.117794             0.069475                     \\n\",\n       \"20242  0.514364            0.130757             0.078463                     \\n\",\n       \"16576  0.468041            0.045207             0.025331                     \\n\",\n       \"15969  0.538349            0.107298             0.063566                     \\n\",\n       \"20416  0.508569            0.066468             0.037178                     \\n\",\n       \"18549  0.519746            0.103789             0.060536                     \\n\",\n       \"20752  0.474661            0.077594             0.045589                     \\n\",\n       \"42853  0.539348            0.073996             0.041552                     \\n\",\n       \"19153  0.545239            0.103531             0.059712                     \\n\",\n       \"15884  0.488857            0.112859             0.067112                     \\n\",\n       \"20237  0.474038            0.125170             0.075852                     \\n\",\n       \"16574  0.442072            0.042822             0.023875                     \\n\",\n       \"15966  0.503788            0.089019             0.052939                     \\n\",\n       \"20417  0.479741            0.063675             0.035769                     \\n\",\n       \"18548  0.481023            0.096688             0.056697                     \\n\",\n       \"20754  0.447164            0.075211             0.044875                     \\n\",\n       \"19591  0.499860            0.065230             0.036468                     \\n\",\n       \"19149  0.525772            0.100719             0.058520                     \\n\",\n       \"15885  0.408693            0.155600             0.105389                     \\n\",\n       \"20243  0.407894            0.167875             0.117622                     \\n\",\n       \"16577  0.370373            0.052357             0.029858                     \\n\",\n       \"15965  0.435929            0.122360             0.082169                     \\n\",\n       \"20418  0.401223            0.086804             0.053151                     \\n\",\n       \"18551  0.400329            0.126005             0.082236                     \\n\",\n       \"20751  0.367928            0.097158             0.065324                     \\n\",\n       \"19594  0.421316            0.090835             0.056287                     \\n\",\n       \"19152  0.453922            0.141474             0.091562                     \\n\",\n       \"15883  0.261349            0.138755             0.099362                     \\n\",\n       \"20239  0.274790            0.159202             0.119364                     \\n\",\n       \"16575  0.247687            0.054987             0.031830                     \\n\",\n       \"15968  0.285521            0.106636             0.074018                     \\n\",\n       \"20421  0.250364            0.082984             0.051892                     \\n\",\n       \"18550  0.252670            0.115734             0.081824                     \\n\",\n       \"20749  0.231347            0.094018             0.066926                     \\n\",\n       \"19596  0.272854            0.083703             0.051164                     \\n\",\n       \"19150  0.307389            0.140154             0.095101                     \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@M  all_exact_correct@1  all_exact_precision@1  \\\\\\n\",\n       \"15880  0.040440                  0.090909             0.090909                \\n\",\n       \"20240  0.075937                  0.227273             0.227273                \\n\",\n       \"16579  0.010708                  0.097403             0.097403                \\n\",\n       \"15963  0.046575                  0.126623             0.126623                \\n\",\n       \"20419  0.018231                  0.090909             0.090909                \\n\",\n       \"18552  0.028588                  0.074675             0.074675                \\n\",\n       \"20748  0.024598                  0.103896             0.103896                \\n\",\n       \"19592  0.027100                  0.152597             0.152597                \\n\",\n       \"19154  0.032349                  0.165584             0.165584                \\n\",\n       \"15881  0.159934                  0.430000             0.430000                \\n\",\n       \"20241  0.197995                  0.438000             0.438000                \\n\",\n       \"16573  0.057544                  0.298000             0.298000                \\n\",\n       \"15964  0.146109                  0.480000             0.480000                \\n\",\n       \"20420  0.087131                  0.358000             0.358000                \\n\",\n       \"18546  0.127594                  0.420000             0.420000                \\n\",\n       \"20750  0.089404                  0.402000             0.402000                \\n\",\n       \"19593  0.097756                  0.374000             0.374000                \\n\",\n       \"19155  0.174393                  0.370000             0.370000                \\n\",\n       \"15886  0.118971                  0.370341             0.370341                \\n\",\n       \"20238  0.131874                  0.378246             0.378246                \\n\",\n       \"16578  0.044533                  0.362736             0.362736                \\n\",\n       \"15967  0.106636                  0.350278             0.350278                \\n\",\n       \"42862  0.066408                  0.375094             0.375094                \\n\",\n       \"18547  0.103191                  0.313704             0.313704                \\n\",\n       \"20753  0.078092                  0.357682             0.357682                \\n\",\n       \"19595  0.071689                  0.408415             0.408415                \\n\",\n       \"19151  0.101707                  0.400610             0.400610                \\n\",\n       \"15882  0.117794                  0.379000             0.379000                \\n\",\n       \"20242  0.130757                  0.391500             0.391500                \\n\",\n       \"16576  0.045207                  0.367000             0.367000                \\n\",\n       \"15969  0.107298                  0.347000             0.347000                \\n\",\n       \"20416  0.066468                  0.359000             0.359000                \\n\",\n       \"18549  0.103789                  0.306000             0.306000                \\n\",\n       \"20752  0.077594                  0.356500             0.356500                \\n\",\n       \"42853  0.073996                  0.389500             0.389500                \\n\",\n       \"19153  0.103531                  0.402000             0.402000                \\n\",\n       \"15884  0.112859                  0.352174             0.352174                \\n\",\n       \"20237  0.125170                  0.404348             0.404348                \\n\",\n       \"16574  0.042822                  0.319565             0.319565                \\n\",\n       \"15966  0.089019                  0.356522             0.356522                \\n\",\n       \"20417  0.063675                  0.326087             0.326087                \\n\",\n       \"18548  0.096688                  0.319565             0.319565                \\n\",\n       \"20754  0.075211                  0.382609             0.382609                \\n\",\n       \"19591  0.065230                  0.397826             0.397826                \\n\",\n       \"19149  0.100719                  0.393478             0.393478                \\n\",\n       \"15885  0.155600                  0.573460             0.573460                \\n\",\n       \"20243  0.167875                  0.592417             0.592417                \\n\",\n       \"16577  0.052357                  0.573460             0.573460                \\n\",\n       \"15965  0.122360                  0.535545             0.535545                \\n\",\n       \"20418  0.086804                  0.473934             0.473934                \\n\",\n       \"18551  0.126005                  0.526066             0.526066                \\n\",\n       \"20751  0.097158                  0.559242             0.559242                \\n\",\n       \"19594  0.090835                  0.578199             0.578199                \\n\",\n       \"19152  0.141474                  0.559242             0.559242                \\n\",\n       \"15883  0.138755                  0.440000             0.440000                \\n\",\n       \"20239  0.159202                  0.570000             0.570000                \\n\",\n       \"16575  0.054987                  0.460000             0.460000                \\n\",\n       \"15968  0.106636                  0.430000             0.430000                \\n\",\n       \"20421  0.082984                  0.460000             0.460000                \\n\",\n       \"18550  0.115734                  0.450000             0.450000                \\n\",\n       \"20749  0.094018                  0.490000             0.490000                \\n\",\n       \"19596  0.083703                  0.470000             0.470000                \\n\",\n       \"19150  0.140154                  0.480000             0.480000                \\n\",\n       \"\\n\",\n       \"       all_exact_recall@1  all_exact_f_score@1  all_exact_precision_hard@1  \\\\\\n\",\n       \"15880  0.012181            0.021352             0.090909                     \\n\",\n       \"20240  0.030574            0.053634             0.227273                     \\n\",\n       \"16579  0.013012            0.022746             0.097403                     \\n\",\n       \"15963  0.017154            0.030049             0.126623                     \\n\",\n       \"20419  0.013001            0.022599             0.090909                     \\n\",\n       \"18552  0.010263            0.017999             0.074675                     \\n\",\n       \"20748  0.013815            0.024258             0.103896                     \\n\",\n       \"19592  0.020013            0.035100             0.152597                     \\n\",\n       \"19154  0.020890            0.036931             0.165584                     \\n\",\n       \"15881  0.054940            0.094491             0.430000                     \\n\",\n       \"20241  0.058691            0.100849             0.438000                     \\n\",\n       \"16573  0.038265            0.065975             0.298000                     \\n\",\n       \"15964  0.061949            0.106548             0.480000                     \\n\",\n       \"20420  0.046484            0.079731             0.358000                     \\n\",\n       \"18546  0.055496            0.095283             0.420000                     \\n\",\n       \"20750  0.052186            0.089393             0.402000                     \\n\",\n       \"19593  0.049407            0.084712             0.374000                     \\n\",\n       \"19155  0.049076            0.084510             0.370000                     \\n\",\n       \"15886  0.081887            0.131250             0.370341                     \\n\",\n       \"20238  0.084675            0.135362             0.378246                     \\n\",\n       \"16578  0.080287            0.128584             0.362736                     \\n\",\n       \"15967  0.079140            0.126368             0.350278                     \\n\",\n       \"42862  0.084232            0.134586             0.375094                     \\n\",\n       \"18547  0.069526            0.111355             0.313704                     \\n\",\n       \"20753  0.079931            0.127857             0.357682                     \\n\",\n       \"19595  0.091084            0.145865             0.408415                     \\n\",\n       \"19151  0.089938            0.143828             0.400610                     \\n\",\n       \"15882  0.084054            0.134520             0.379000                     \\n\",\n       \"20242  0.086215            0.138160             0.391500                     \\n\",\n       \"16576  0.081866            0.130565             0.367000                     \\n\",\n       \"15969  0.076234            0.122490             0.347000                     \\n\",\n       \"20416  0.080044            0.128266             0.359000                     \\n\",\n       \"18549  0.068879            0.109766             0.306000                     \\n\",\n       \"20752  0.079276            0.126914             0.356500                     \\n\",\n       \"42853  0.086948            0.139024             0.389500                     \\n\",\n       \"19153  0.088586            0.142324             0.402000                     \\n\",\n       \"15884  0.079250            0.125361             0.352174                     \\n\",\n       \"20237  0.088204            0.140511             0.404348                     \\n\",\n       \"16574  0.069998            0.111887             0.319565                     \\n\",\n       \"15966  0.075818            0.121094             0.356522                     \\n\",\n       \"20417  0.072841            0.115411             0.326087                     \\n\",\n       \"18548  0.067361            0.107764             0.319565                     \\n\",\n       \"20754  0.082738            0.131965             0.382609                     \\n\",\n       \"19591  0.085494            0.136523             0.397826                     \\n\",\n       \"19149  0.087100            0.138354             0.393478                     \\n\",\n       \"15885  0.067998            0.115777             0.573460                     \\n\",\n       \"20243  0.074917            0.126743             0.592417                     \\n\",\n       \"16577  0.066921            0.114048             0.573460                     \\n\",\n       \"15965  0.068445            0.114750             0.535545                     \\n\",\n       \"20418  0.051375            0.088839             0.473934                     \\n\",\n       \"18551  0.061642            0.104782             0.526066                     \\n\",\n       \"20751  0.076657            0.127249             0.559242                     \\n\",\n       \"19594  0.068911            0.117214             0.578199                     \\n\",\n       \"19152  0.064196            0.110396             0.559242                     \\n\",\n       \"15883  0.031805            0.059223             0.440000                     \\n\",\n       \"20239  0.039358            0.073445             0.570000                     \\n\",\n       \"16575  0.032599            0.060779             0.460000                     \\n\",\n       \"15968  0.029140            0.054473             0.430000                     \\n\",\n       \"20421  0.033112            0.061640             0.460000                     \\n\",\n       \"18550  0.030472            0.056949             0.450000                     \\n\",\n       \"20749  0.033288            0.062208             0.490000                     \\n\",\n       \"19596  0.031622            0.059091             0.470000                     \\n\",\n       \"19150  0.032868            0.061376             0.480000                     \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@1  all_exact_correct@3  all_exact_precision@3  \\\\\\n\",\n       \"15880  0.021352                  0.334416             0.111472                \\n\",\n       \"20240  0.053634                  0.487013             0.162338                \\n\",\n       \"16579  0.022746                  0.262987             0.088203                \\n\",\n       \"15963  0.030049                  0.379870             0.126623                \\n\",\n       \"20419  0.022599                  0.217532             0.072511                \\n\",\n       \"18552  0.017999                  0.240260             0.080087                \\n\",\n       \"20748  0.024258                  0.269481             0.090368                \\n\",\n       \"19592  0.035100                  0.370130             0.123377                \\n\",\n       \"19154  0.036931                  0.363636             0.121212                \\n\",\n       \"15881  0.094491                  1.146000             0.382000                \\n\",\n       \"20241  0.100849                  1.194000             0.398000                \\n\",\n       \"16573  0.065975                  0.980000             0.326667                \\n\",\n       \"15964  0.106548                  1.108000             0.369333                \\n\",\n       \"20420  0.079731                  1.006000             0.335333                \\n\",\n       \"18546  0.095283                  1.106000             0.368667                \\n\",\n       \"20750  0.089393                  1.080000             0.360000                \\n\",\n       \"19593  0.084712                  1.114000             0.371333                \\n\",\n       \"19155  0.084510                  1.220000             0.406667                \\n\",\n       \"15886  0.131250                  1.050233             0.350086                \\n\",\n       \"20238  0.135362                  1.069645             0.356548                \\n\",\n       \"16578  0.128584                  0.957672             0.319224                \\n\",\n       \"15967  0.126368                  1.042027             0.347392                \\n\",\n       \"42862  0.134586                  0.954570             0.318207                \\n\",\n       \"18547  0.111355                  0.996047             0.332032                \\n\",\n       \"20753  0.127857                  0.986741             0.328939                \\n\",\n       \"19595  0.145865                  1.033322             0.344441                \\n\",\n       \"19151  0.143828                  1.053535             0.351178                \\n\",\n       \"15882  0.134520                  1.045500             0.348500                \\n\",\n       \"20242  0.138160                  1.060500             0.353500                \\n\",\n       \"16576  0.130565                  0.952000             0.317500                \\n\",\n       \"15969  0.122490                  1.030500             0.343500                \\n\",\n       \"20416  0.128266                  0.920000             0.306667                \\n\",\n       \"18549  0.109766                  0.971500             0.323917                \\n\",\n       \"20752  0.126914                  0.976500             0.325500                \\n\",\n       \"42853  0.139024                  1.002000             0.334000                \\n\",\n       \"19153  0.142324                  1.035500             0.345167                \\n\",\n       \"15884  0.125361                  0.978261             0.326087                \\n\",\n       \"20237  0.140511                  1.043478             0.347826                \\n\",\n       \"16574  0.111887                  0.934783             0.311594                \\n\",\n       \"15966  0.121094                  0.991304             0.330435                \\n\",\n       \"20417  0.115411                  0.908696             0.302899                \\n\",\n       \"18548  0.107764                  0.947826             0.315942                \\n\",\n       \"20754  0.131965                  0.978261             0.326087                \\n\",\n       \"19591  0.136523                  0.982609             0.327536                \\n\",\n       \"19149  0.138354                  1.013043             0.337681                \\n\",\n       \"15885  0.115777                  1.559242             0.519747                \\n\",\n       \"20243  0.126743                  1.549763             0.516588                \\n\",\n       \"16577  0.114048                  1.379147             0.459716                \\n\",\n       \"15965  0.114750                  1.488152             0.496051                \\n\",\n       \"20418  0.088839                  1.312796             0.437599                \\n\",\n       \"18551  0.104782                  1.454976             0.484992                \\n\",\n       \"20751  0.127249                  1.421801             0.473934                \\n\",\n       \"19594  0.117214                  1.469194             0.489731                \\n\",\n       \"19152  0.110396                  1.540284             0.513428                \\n\",\n       \"15883  0.059223                  1.340000             0.446667                \\n\",\n       \"20239  0.073445                  1.330000             0.443333                \\n\",\n       \"16575  0.060779                  1.320000             0.440000                \\n\",\n       \"15968  0.054473                  1.300000             0.433333                \\n\",\n       \"20421  0.061640                  1.250000             0.416667                \\n\",\n       \"18550  0.056949                  1.320000             0.440000                \\n\",\n       \"20749  0.062208                  1.180000             0.393333                \\n\",\n       \"19596  0.059091                  1.230000             0.410000                \\n\",\n       \"19150  0.061376                  1.330000             0.443333                \\n\",\n       \"\\n\",\n       \"       all_exact_recall@3  all_exact_f_score@3  all_exact_precision_hard@3  \\\\\\n\",\n       \"15880  0.044722            0.063044             0.111472                     \\n\",\n       \"20240  0.063131            0.090053             0.162338                     \\n\",\n       \"16579  0.034441            0.048840             0.087662                     \\n\",\n       \"15963  0.049760            0.070579             0.126623                     \\n\",\n       \"20419  0.029767            0.041821             0.072511                     \\n\",\n       \"18552  0.032440            0.045725             0.080087                     \\n\",\n       \"20748  0.036499            0.051378             0.089827                     \\n\",\n       \"19592  0.047404            0.067693             0.123377                     \\n\",\n       \"19154  0.047059            0.067227             0.121212                     \\n\",\n       \"15881  0.146971            0.201964             0.382000                     \\n\",\n       \"20241  0.153583            0.210499             0.398000                     \\n\",\n       \"16573  0.122821            0.169928             0.326667                     \\n\",\n       \"15964  0.142356            0.195170             0.369333                     \\n\",\n       \"20420  0.125992            0.174001             0.335333                     \\n\",\n       \"18546  0.140075            0.193469             0.368667                     \\n\",\n       \"20750  0.135251            0.187309             0.360000                     \\n\",\n       \"19593  0.141622            0.195156             0.371333                     \\n\",\n       \"19155  0.154213            0.213324             0.406667                     \\n\",\n       \"15886  0.230319            0.268675             0.350078                     \\n\",\n       \"20238  0.236130            0.274888             0.356548                     \\n\",\n       \"16578  0.211885            0.246390             0.319224                     \\n\",\n       \"15967  0.229921            0.267745             0.347342                     \\n\",\n       \"42862  0.209969            0.244605             0.318190                     \\n\",\n       \"18547  0.220203            0.256322             0.332016                     \\n\",\n       \"20753  0.218372            0.253870             0.328914                     \\n\",\n       \"19595  0.227080            0.264894             0.344441                     \\n\",\n       \"19151  0.232363            0.270578             0.351178                     \\n\",\n       \"15882  0.229419            0.267296             0.348500                     \\n\",\n       \"20242  0.231001            0.270150             0.353500                     \\n\",\n       \"16576  0.209556            0.243586             0.317333                     \\n\",\n       \"15969  0.224856            0.262601             0.343500                     \\n\",\n       \"20416  0.199958            0.234278             0.306667                     \\n\",\n       \"18549  0.213372            0.248512             0.323833                     \\n\",\n       \"20752  0.213877            0.249915             0.325500                     \\n\",\n       \"42853  0.219287            0.255718             0.334000                     \\n\",\n       \"19153  0.226099            0.264096             0.345167                     \\n\",\n       \"15884  0.210013            0.243207             0.326087                     \\n\",\n       \"20237  0.225637            0.261270             0.347826                     \\n\",\n       \"16574  0.200224            0.232457             0.311594                     \\n\",\n       \"15966  0.211266            0.245467             0.330435                     \\n\",\n       \"20417  0.193360            0.224755             0.302899                     \\n\",\n       \"18548  0.200508            0.233600             0.315942                     \\n\",\n       \"20754  0.207893            0.240747             0.326087                     \\n\",\n       \"19591  0.210828            0.243128             0.327536                     \\n\",\n       \"19149  0.219995            0.253829             0.337681                     \\n\",\n       \"15885  0.179313            0.244772             0.519747                     \\n\",\n       \"20243  0.177461            0.243807             0.516588                     \\n\",\n       \"16577  0.158644            0.216468             0.459716                     \\n\",\n       \"15965  0.178251            0.239589             0.496051                     \\n\",\n       \"20418  0.151337            0.206420             0.437599                     \\n\",\n       \"18551  0.168267            0.228984             0.484992                     \\n\",\n       \"20751  0.169194            0.228810             0.473934                     \\n\",\n       \"19594  0.166670            0.228300             0.489731                     \\n\",\n       \"19152  0.171288            0.238219             0.513428                     \\n\",\n       \"15883  0.092290            0.152263             0.446667                     \\n\",\n       \"20239  0.092003            0.151579             0.443333                     \\n\",\n       \"16575  0.089999            0.148656             0.440000                     \\n\",\n       \"15968  0.088872            0.146678             0.433333                     \\n\",\n       \"20421  0.086080            0.141936             0.416667                     \\n\",\n       \"18550  0.090547            0.149320             0.440000                     \\n\",\n       \"20749  0.083029            0.136428             0.393333                     \\n\",\n       \"19596  0.083609            0.138127             0.410000                     \\n\",\n       \"19150  0.090628            0.149667             0.443333                     \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@3  all_partial_correct@5  \\\\\\n\",\n       \"15880  0.063044                  0.577922                \\n\",\n       \"20240  0.090053                  0.837662                \\n\",\n       \"16579  0.048767                  0.383117                \\n\",\n       \"15963  0.070579                  0.655844                \\n\",\n       \"20419  0.041821                  0.389610                \\n\",\n       \"18552  0.045725                  0.457792                \\n\",\n       \"20748  0.051306                  0.428571                \\n\",\n       \"19592  0.067693                  0.558442                \\n\",\n       \"19154  0.067227                  0.607143                \\n\",\n       \"15881  0.201964                  1.964000                \\n\",\n       \"20241  0.210499                  2.078000                \\n\",\n       \"16573  0.169928                  1.666000                \\n\",\n       \"15964  0.195170                  1.976000                \\n\",\n       \"20420  0.174001                  1.712000                \\n\",\n       \"18546  0.193469                  1.866000                \\n\",\n       \"20750  0.187309                  1.820000                \\n\",\n       \"19593  0.195156                  1.920000                \\n\",\n       \"19155  0.213324                  2.062000                \\n\",\n       \"15886  0.268671                  1.515035                \\n\",\n       \"20238  0.274888                  1.526242                \\n\",\n       \"16578  0.246390                  1.388653                \\n\",\n       \"15967  0.267740                  1.505579                \\n\",\n       \"42862  0.244605                  1.377996                \\n\",\n       \"18547  0.256321                  1.459048                \\n\",\n       \"20753  0.253863                  1.419122                \\n\",\n       \"19595  0.264894                  1.482163                \\n\",\n       \"19151  0.270578                  1.508781                \\n\",\n       \"15882  0.267296                  1.507500                \\n\",\n       \"20242  0.270150                  1.524500                \\n\",\n       \"16576  0.243573                  1.379500                \\n\",\n       \"15969  0.262601                  1.502000                \\n\",\n       \"20416  0.234278                  1.347500                \\n\",\n       \"18549  0.248503                  1.432500                \\n\",\n       \"20752  0.249915                  1.408000                \\n\",\n       \"42853  0.255718                  1.455500                \\n\",\n       \"19153  0.264096                  1.505000                \\n\",\n       \"15884  0.243207                  1.471739                \\n\",\n       \"20237  0.261270                  1.495652                \\n\",\n       \"16574  0.232457                  1.347826                \\n\",\n       \"15966  0.245467                  1.436957                \\n\",\n       \"20417  0.224755                  1.323913                \\n\",\n       \"18548  0.233600                  1.417391                \\n\",\n       \"20754  0.240747                  1.376087                \\n\",\n       \"19591  0.243128                  1.450000                \\n\",\n       \"19149  0.253829                  1.478261                \\n\",\n       \"15885  0.244772                  2.203791                \\n\",\n       \"20243  0.243807                  2.317536                \\n\",\n       \"16577  0.216468                  2.056872                \\n\",\n       \"15965  0.239589                  2.189573                \\n\",\n       \"20418  0.206420                  1.895735                \\n\",\n       \"18551  0.228984                  2.123223                \\n\",\n       \"20751  0.228810                  2.080569                \\n\",\n       \"19594  0.228300                  2.180095                \\n\",\n       \"19152  0.238219                  2.194313                \\n\",\n       \"15883  0.152263                  2.110000                \\n\",\n       \"20239  0.151579                  2.160000                \\n\",\n       \"16575  0.148656                  1.940000                \\n\",\n       \"15968  0.146678                  1.960000                \\n\",\n       \"20421  0.141936                  1.940000                \\n\",\n       \"18550  0.149320                  2.020000                \\n\",\n       \"20749  0.136428                  1.960000                \\n\",\n       \"19596  0.138127                  2.070000                \\n\",\n       \"19150  0.149667                  2.150000                \\n\",\n       \"\\n\",\n       \"       all_partial_precision@5  all_partial_recall@5  all_partial_f_score@5  \\\\\\n\",\n       \"15880  0.182602                 0.119342              0.142365                \\n\",\n       \"20240  0.242969                 0.157478              0.188618                \\n\",\n       \"16579  0.132822                 0.085223              0.102154                \\n\",\n       \"15963  0.195582                 0.127142              0.151932                \\n\",\n       \"20419  0.135763                 0.088881              0.106134                \\n\",\n       \"18552  0.151762                 0.098355              0.117777                \\n\",\n       \"20748  0.143395                 0.093451              0.111091                \\n\",\n       \"19592  0.176492                 0.114174              0.136724                \\n\",\n       \"19154  0.186477                 0.121246              0.144926                \\n\",\n       \"15881  0.478807                 0.293108              0.342894                \\n\",\n       \"20241  0.500348                 0.305256              0.357357                \\n\",\n       \"16573  0.421806                 0.254773              0.299822                \\n\",\n       \"15964  0.479350                 0.292696              0.342117                \\n\",\n       \"20420  0.428855                 0.259884              0.305426                \\n\",\n       \"18546  0.458283                 0.278307              0.326213                \\n\",\n       \"20750  0.447048                 0.270190              0.317879                \\n\",\n       \"19593  0.466363                 0.283654              0.332660                \\n\",\n       \"19155  0.500806                 0.305237              0.357850                \\n\",\n       \"15886  0.366303                 0.396032              0.366775                \\n\",\n       \"20238  0.368637                 0.399783              0.369859                \\n\",\n       \"16578  0.342618                 0.372511              0.344255                \\n\",\n       \"15967  0.364431                 0.395706              0.365901                \\n\",\n       \"42862  0.340371                 0.368996              0.341223                \\n\",\n       \"18547  0.354840                 0.385915              0.356742                \\n\",\n       \"20753  0.348563                 0.378727              0.349930                \\n\",\n       \"19595  0.359802                 0.389333              0.360488                \\n\",\n       \"19151  0.365475                 0.396569              0.366763                \\n\",\n       \"15882  0.365402                 0.395395              0.366257                \\n\",\n       \"20242  0.368045                 0.398531              0.369025                \\n\",\n       \"16576  0.341335                 0.370548              0.342407                \\n\",\n       \"15969  0.366029                 0.396721              0.367248                \\n\",\n       \"20416  0.335858                 0.363081              0.336529                \\n\",\n       \"18549  0.350807                 0.381863              0.352549                \\n\",\n       \"20752  0.346743                 0.377025              0.348361                \\n\",\n       \"42853  0.356751                 0.387711              0.358089                \\n\",\n       \"19153  0.366312                 0.396280              0.367025                \\n\",\n       \"15884  0.360601                 0.367212              0.343762                \\n\",\n       \"20237  0.365146                 0.373280              0.348538                \\n\",\n       \"16574  0.341263                 0.347394              0.325622                \\n\",\n       \"15966  0.355902                 0.361573              0.338773                \\n\",\n       \"20417  0.334607                 0.338707              0.317812                \\n\",\n       \"18548  0.348747                 0.352029              0.330990                \\n\",\n       \"20754  0.347615                 0.351798              0.329676                \\n\",\n       \"19591  0.354045                 0.361474              0.338116                \\n\",\n       \"19149  0.362256                 0.369193              0.345729                \\n\",\n       \"15885  0.519239                 0.295058              0.341064                \\n\",\n       \"20243  0.534377                 0.304268              0.351388                \\n\",\n       \"16577  0.482495                 0.277433              0.318873                \\n\",\n       \"15965  0.513981                 0.295686              0.339589                \\n\",\n       \"20418  0.461935                 0.265495              0.305289                \\n\",\n       \"18551  0.493977                 0.281764              0.325003                \\n\",\n       \"20751  0.494424                 0.286588              0.327561                \\n\",\n       \"19594  0.511331                 0.292767              0.336146                \\n\",\n       \"19152  0.513181                 0.291380              0.336718                \\n\",\n       \"15883  0.507639                 0.173110              0.256255                \\n\",\n       \"20239  0.514456                 0.174854              0.259010                \\n\",\n       \"16575  0.472572                 0.160610              0.237892                \\n\",\n       \"15968  0.477711                 0.162626              0.240799                \\n\",\n       \"20421  0.470034                 0.159750              0.236614                \\n\",\n       \"18550  0.495514                 0.168193              0.249207                \\n\",\n       \"20749  0.472301                 0.160306              0.237570                \\n\",\n       \"19596  0.486023                 0.164403              0.243713                \\n\",\n       \"19150  0.510177                 0.173143              0.256606                \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@5  all_partial_f_score_hard@5  \\\\\\n\",\n       \"15880  0.182569                      0.142359                     \\n\",\n       \"20240  0.242969                      0.188618                     \\n\",\n       \"16579  0.131848                      0.101973                     \\n\",\n       \"15963  0.195582                      0.151932                     \\n\",\n       \"20419  0.135763                      0.106134                     \\n\",\n       \"18552  0.151762                      0.117777                     \\n\",\n       \"20748  0.140646                      0.110524                     \\n\",\n       \"19592  0.176492                      0.136724                     \\n\",\n       \"19154  0.186477                      0.144926                     \\n\",\n       \"15881  0.478807                      0.342894                     \\n\",\n       \"20241  0.500348                      0.357357                     \\n\",\n       \"16573  0.421552                      0.299746                     \\n\",\n       \"15964  0.479350                      0.342117                     \\n\",\n       \"20420  0.428802                      0.305412                     \\n\",\n       \"18546  0.458283                      0.326213                     \\n\",\n       \"20750  0.446815                      0.317794                     \\n\",\n       \"19593  0.466363                      0.332660                     \\n\",\n       \"19155  0.500806                      0.357850                     \\n\",\n       \"15886  0.366285                      0.366766                     \\n\",\n       \"20238  0.368625                      0.369858                     \\n\",\n       \"16578  0.342571                      0.344248                     \\n\",\n       \"15967  0.364331                      0.365885                     \\n\",\n       \"42862  0.340253                      0.341200                     \\n\",\n       \"18547  0.354760                      0.356728                     \\n\",\n       \"20753  0.348321                      0.349865                     \\n\",\n       \"19595  0.359802                      0.360488                     \\n\",\n       \"19151  0.365475                      0.366763                     \\n\",\n       \"15882  0.365402                      0.366257                     \\n\",\n       \"20242  0.368045                      0.369025                     \\n\",\n       \"16576  0.341013                      0.342365                     \\n\",\n       \"15969  0.366029                      0.367248                     \\n\",\n       \"20416  0.335858                      0.336529                     \\n\",\n       \"18549  0.350630                      0.352522                     \\n\",\n       \"20752  0.346743                      0.348361                     \\n\",\n       \"42853  0.356751                      0.358089                     \\n\",\n       \"19153  0.366312                      0.367025                     \\n\",\n       \"15884  0.360601                      0.343762                     \\n\",\n       \"20237  0.365146                      0.348538                     \\n\",\n       \"16574  0.341168                      0.325594                     \\n\",\n       \"15966  0.355902                      0.338773                     \\n\",\n       \"20417  0.334293                      0.317742                     \\n\",\n       \"18548  0.348747                      0.330990                     \\n\",\n       \"20754  0.347615                      0.329676                     \\n\",\n       \"19591  0.354045                      0.338116                     \\n\",\n       \"19149  0.362256                      0.345729                     \\n\",\n       \"15885  0.519239                      0.341064                     \\n\",\n       \"20243  0.534377                      0.351388                     \\n\",\n       \"16577  0.482495                      0.318873                     \\n\",\n       \"15965  0.513981                      0.339589                     \\n\",\n       \"20418  0.461935                      0.305289                     \\n\",\n       \"18551  0.493977                      0.325003                     \\n\",\n       \"20751  0.493665                      0.327491                     \\n\",\n       \"19594  0.511331                      0.336146                     \\n\",\n       \"19152  0.513181                      0.336718                     \\n\",\n       \"15883  0.507639                      0.256255                     \\n\",\n       \"20239  0.514456                      0.259010                     \\n\",\n       \"16575  0.472572                      0.237892                     \\n\",\n       \"15968  0.477711                      0.240799                     \\n\",\n       \"20421  0.470034                      0.236614                     \\n\",\n       \"18550  0.495514                      0.249207                     \\n\",\n       \"20749  0.472301                      0.237570                     \\n\",\n       \"19596  0.486023                      0.243713                     \\n\",\n       \"19150  0.510177                      0.256606                     \\n\",\n       \"\\n\",\n       \"       all_partial_correct@10  all_partial_precision@10  \\\\\\n\",\n       \"15880  0.967532                0.136018                   \\n\",\n       \"20240  1.405844                0.185242                   \\n\",\n       \"16579  0.629870                0.099860                   \\n\",\n       \"15963  1.074675                0.149885                   \\n\",\n       \"20419  0.620130                0.098219                   \\n\",\n       \"18552  0.714286                0.112081                   \\n\",\n       \"20748  0.623377                0.104458                   \\n\",\n       \"19592  0.840909                0.124529                   \\n\",\n       \"19154  1.068182                0.150242                   \\n\",\n       \"15881  3.170000                0.362325                   \\n\",\n       \"20241  3.452000                0.388488                   \\n\",\n       \"16573  2.648000                0.310936                   \\n\",\n       \"15964  3.208000                0.366052                   \\n\",\n       \"20420  2.622000                0.307131                   \\n\",\n       \"18546  2.980000                0.340476                   \\n\",\n       \"20750  2.750000                0.319154                   \\n\",\n       \"19593  2.976000                0.340075                   \\n\",\n       \"19155  3.258000                0.370549                   \\n\",\n       \"15886  2.104118                0.244086                   \\n\",\n       \"20238  2.107270                0.244236                   \\n\",\n       \"16578  1.909091                0.225763                   \\n\",\n       \"15967  2.080953                0.242088                   \\n\",\n       \"42862  1.903787                0.225083                   \\n\",\n       \"18547  2.003752                0.234114                   \\n\",\n       \"20753  1.904788                0.226630                   \\n\",\n       \"19595  2.034723                0.237445                   \\n\",\n       \"19151  2.100215                0.243506                   \\n\",\n       \"15882  2.107500                0.245119                   \\n\",\n       \"20242  2.099000                0.244117                   \\n\",\n       \"16576  1.924500                0.227653                   \\n\",\n       \"15969  2.087000                0.243814                   \\n\",\n       \"20416  1.887000                0.223930                   \\n\",\n       \"18549  1.974500                0.232742                   \\n\",\n       \"20752  1.894000                0.226351                   \\n\",\n       \"42853  2.026500                0.237338                   \\n\",\n       \"19153  2.097500                0.243983                   \\n\",\n       \"15884  2.065217                0.241937                   \\n\",\n       \"20237  2.104348                0.244797                   \\n\",\n       \"16574  1.932609                0.232770                   \\n\",\n       \"15966  2.130435                0.248781                   \\n\",\n       \"20417  1.897826                0.225866                   \\n\",\n       \"18548  2.032609                0.239071                   \\n\",\n       \"20754  1.950000                0.234661                   \\n\",\n       \"19591  2.045652                0.239795                   \\n\",\n       \"19149  2.180435                0.253575                   \\n\",\n       \"15885  3.331754                0.375646                   \\n\",\n       \"20243  3.459716                0.387752                   \\n\",\n       \"16577  3.090047                0.349177                   \\n\",\n       \"15965  3.383886                0.380691                   \\n\",\n       \"20418  2.971564                0.339187                   \\n\",\n       \"18551  3.194313                0.361672                   \\n\",\n       \"20751  2.957346                0.343820                   \\n\",\n       \"19594  3.227488                0.367731                   \\n\",\n       \"19152  3.402844                0.383329                   \\n\",\n       \"15883  3.370000                0.384116                   \\n\",\n       \"20239  3.470000                0.398021                   \\n\",\n       \"16575  3.080000                0.351524                   \\n\",\n       \"15968  3.150000                0.369669                   \\n\",\n       \"20421  2.880000                0.336919                   \\n\",\n       \"18550  3.170000                0.366943                   \\n\",\n       \"20749  3.000000                0.348684                   \\n\",\n       \"19596  3.060000                0.355494                   \\n\",\n       \"19150  3.340000                0.382158                   \\n\",\n       \"\\n\",\n       \"       all_partial_recall@10  all_partial_f_score@10  \\\\\\n\",\n       \"15880  0.176502               0.151501                 \\n\",\n       \"20240  0.236737               0.205206                 \\n\",\n       \"16579  0.127803               0.109982                 \\n\",\n       \"15963  0.190488               0.165313                 \\n\",\n       \"20419  0.126771               0.109236                 \\n\",\n       \"18552  0.142568               0.123806                 \\n\",\n       \"20748  0.128786               0.111929                 \\n\",\n       \"19592  0.160386               0.138100                 \\n\",\n       \"19154  0.192199               0.166159                 \\n\",\n       \"15881  0.425149               0.368693                 \\n\",\n       \"20241  0.453772               0.394773                 \\n\",\n       \"16573  0.359882               0.314899                 \\n\",\n       \"15964  0.426914               0.371372                 \\n\",\n       \"20420  0.358755               0.312288                 \\n\",\n       \"18546  0.398855               0.346040                 \\n\",\n       \"20750  0.371882               0.323771                 \\n\",\n       \"19593  0.395479               0.344705                 \\n\",\n       \"19155  0.435504               0.377248                 \\n\",\n       \"15886  0.513422               0.318063                 \\n\",\n       \"20238  0.515325               0.318873                 \\n\",\n       \"16578  0.480236               0.295967                 \\n\",\n       \"15967  0.512342               0.316659                 \\n\",\n       \"42862  0.475471               0.293720                 \\n\",\n       \"18547  0.497866               0.306948                 \\n\",\n       \"20753  0.478738               0.295832                 \\n\",\n       \"19595  0.500912               0.309917                 \\n\",\n       \"19151  0.514317               0.318135                 \\n\",\n       \"15882  0.514465               0.319729                 \\n\",\n       \"20242  0.515366               0.319219                 \\n\",\n       \"16576  0.481680               0.297725                 \\n\",\n       \"15969  0.516283               0.319238                 \\n\",\n       \"20416  0.474309               0.293326                 \\n\",\n       \"18549  0.495629               0.305336                 \\n\",\n       \"20752  0.480347               0.296334                 \\n\",\n       \"42853  0.503006               0.310557                 \\n\",\n       \"19153  0.515781               0.318987                 \\n\",\n       \"15884  0.476513               0.303628                 \\n\",\n       \"20237  0.475488               0.305315                 \\n\",\n       \"16574  0.454718               0.291418                 \\n\",\n       \"15966  0.486270               0.311299                 \\n\",\n       \"20417  0.443900               0.283324                 \\n\",\n       \"18548  0.466842               0.298852                 \\n\",\n       \"20754  0.453251               0.291777                 \\n\",\n       \"19591  0.470618               0.300945                 \\n\",\n       \"19149  0.493220               0.316753                 \\n\",\n       \"15885  0.407352               0.354492                 \\n\",\n       \"20243  0.412643               0.361988                 \\n\",\n       \"16577  0.382187               0.329825                 \\n\",\n       \"15965  0.409464               0.357324                 \\n\",\n       \"20418  0.366888               0.319654                 \\n\",\n       \"18551  0.393679               0.341386                 \\n\",\n       \"20751  0.378017               0.325291                 \\n\",\n       \"19594  0.396379               0.345523                 \\n\",\n       \"19152  0.411929               0.359404                 \\n\",\n       \"15883  0.259649               0.306886                 \\n\",\n       \"20239  0.267667               0.316767                 \\n\",\n       \"16575  0.238222               0.281176                 \\n\",\n       \"15968  0.250168               0.295397                 \\n\",\n       \"20421  0.227021               0.268492                 \\n\",\n       \"18550  0.246497               0.291762                 \\n\",\n       \"20749  0.235208               0.278196                 \\n\",\n       \"19596  0.239537               0.283171                 \\n\",\n       \"19150  0.258692               0.305445                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@10  all_partial_f_score_hard@10  \\\\\\n\",\n       \"15880  0.135806                       0.151407                      \\n\",\n       \"20240  0.183780                       0.204157                      \\n\",\n       \"16579  0.098491                       0.109608                      \\n\",\n       \"15963  0.147726                       0.164208                      \\n\",\n       \"20419  0.097824                       0.109008                      \\n\",\n       \"18552  0.111600                       0.123543                      \\n\",\n       \"20748  0.097730                       0.109492                      \\n\",\n       \"19592  0.124053                       0.137913                      \\n\",\n       \"19154  0.149839                       0.166027                      \\n\",\n       \"15881  0.362235                       0.368637                      \\n\",\n       \"20241  0.388471                       0.394759                      \\n\",\n       \"16573  0.310370                       0.314613                      \\n\",\n       \"15964  0.366052                       0.371372                      \\n\",\n       \"20420  0.307038                       0.312255                      \\n\",\n       \"18546  0.340349                       0.345952                      \\n\",\n       \"20750  0.317887                       0.323202                      \\n\",\n       \"19593  0.340075                       0.344705                      \\n\",\n       \"19155  0.370459                       0.377192                      \\n\",\n       \"15886  0.243923                       0.317989                      \\n\",\n       \"20238  0.244162                       0.318842                      \\n\",\n       \"16578  0.225446                       0.295857                      \\n\",\n       \"15967  0.241741                       0.316478                      \\n\",\n       \"42862  0.224633                       0.293563                      \\n\",\n       \"18547  0.233813                       0.306811                      \\n\",\n       \"20753  0.225191                       0.294991                      \\n\",\n       \"19595  0.237360                       0.309885                      \\n\",\n       \"19151  0.243482                       0.318125                      \\n\",\n       \"15882  0.245007                       0.319627                      \\n\",\n       \"20242  0.244070                       0.319190                      \\n\",\n       \"16576  0.226985                       0.297534                      \\n\",\n       \"15969  0.243739                       0.319171                      \\n\",\n       \"20416  0.223791                       0.293226                      \\n\",\n       \"18549  0.232324                       0.305202                      \\n\",\n       \"20752  0.225305                       0.295766                      \\n\",\n       \"42853  0.237304                       0.310542                      \\n\",\n       \"19153  0.243983                       0.318987                      \\n\",\n       \"15884  0.241937                       0.303628                      \\n\",\n       \"20237  0.244784                       0.305302                      \\n\",\n       \"16574  0.231862                       0.290701                      \\n\",\n       \"15966  0.248543                       0.311085                      \\n\",\n       \"20417  0.225407                       0.283175                      \\n\",\n       \"18548  0.238668                       0.298584                      \\n\",\n       \"20754  0.232769                       0.290245                      \\n\",\n       \"19591  0.239745                       0.300925                      \\n\",\n       \"19149  0.253575                       0.316753                      \\n\",\n       \"15885  0.375646                       0.354492                      \\n\",\n       \"20243  0.387752                       0.361988                      \\n\",\n       \"16577  0.349177                       0.329825                      \\n\",\n       \"15965  0.380691                       0.357324                      \\n\",\n       \"20418  0.338794                       0.319504                      \\n\",\n       \"18551  0.361672                       0.341386                      \\n\",\n       \"20751  0.340167                       0.323704                      \\n\",\n       \"19594  0.367484                       0.345407                      \\n\",\n       \"19152  0.383329                       0.359404                      \\n\",\n       \"15883  0.384116                       0.306886                      \\n\",\n       \"20239  0.398021                       0.316767                      \\n\",\n       \"16575  0.351524                       0.281176                      \\n\",\n       \"15968  0.369549                       0.295357                      \\n\",\n       \"20421  0.336919                       0.268492                      \\n\",\n       \"18550  0.365918                       0.291592                      \\n\",\n       \"20749  0.347296                       0.277730                      \\n\",\n       \"19596  0.355494                       0.283171                      \\n\",\n       \"19150  0.382158                       0.305445                      \\n\",\n       \"\\n\",\n       \"       all_partial_correct@k  all_partial_precision@k  all_partial_recall@k  \\\\\\n\",\n       \"15880  0.824675               0.153434                 0.153305               \\n\",\n       \"20240  1.211039               0.203697                 0.203227               \\n\",\n       \"16579  0.522727               0.109802                 0.108603               \\n\",\n       \"15963  0.922078               0.166773                 0.165525               \\n\",\n       \"20419  0.577922               0.115826                 0.115691               \\n\",\n       \"18552  0.626623               0.123487                 0.123249               \\n\",\n       \"20748  0.545455               0.117279                 0.112011               \\n\",\n       \"19592  0.762987               0.145062                 0.144512               \\n\",\n       \"19154  0.925325               0.165289                 0.164882               \\n\",\n       \"15881  3.336000               0.394070                 0.394040               \\n\",\n       \"20241  3.556000               0.415448                 0.415018               \\n\",\n       \"16573  2.708000               0.333795                 0.333572               \\n\",\n       \"15964  3.320000               0.395502                 0.395251               \\n\",\n       \"20420  2.712000               0.334973                 0.334897               \\n\",\n       \"18546  3.026000               0.367082                 0.366853               \\n\",\n       \"20750  2.792000               0.348095                 0.346625               \\n\",\n       \"19593  3.100000               0.371996                 0.371973               \\n\",\n       \"19155  3.502000               0.404835                 0.404835               \\n\",\n       \"15886  1.655376               0.380685                 0.380061               \\n\",\n       \"20238  1.670136               0.385090                 0.384673               \\n\",\n       \"16578  1.452744               0.354115                 0.353686               \\n\",\n       \"15967  1.618402               0.379016                 0.378492               \\n\",\n       \"42862  1.478211               0.353620                 0.353050               \\n\",\n       \"18547  1.537750               0.366451                 0.366008               \\n\",\n       \"20753  1.498824               0.362202                 0.361264               \\n\",\n       \"19595  1.602592               0.374451                 0.374171               \\n\",\n       \"19151  1.659028               0.383300                 0.383167               \\n\",\n       \"15882  1.652500               0.379619                 0.379396               \\n\",\n       \"20242  1.636500               0.379042                 0.378558               \\n\",\n       \"16576  1.461500               0.355681                 0.355039               \\n\",\n       \"15969  1.604500               0.376504                 0.376167               \\n\",\n       \"20416  1.440500               0.347041                 0.346855               \\n\",\n       \"18549  1.501500               0.361011                 0.360414               \\n\",\n       \"20752  1.447500               0.355541                 0.354499               \\n\",\n       \"42853  1.564000               0.370804                 0.370684               \\n\",\n       \"19153  1.632500               0.379458                 0.379345               \\n\",\n       \"15884  1.702174               0.355786                 0.355786               \\n\",\n       \"20237  1.786957               0.373877                 0.373877               \\n\",\n       \"16574  1.595652               0.346197                 0.345995               \\n\",\n       \"15966  1.669565               0.353807                 0.353653               \\n\",\n       \"20417  1.567391               0.340371                 0.340002               \\n\",\n       \"18548  1.680435               0.347557                 0.347357               \\n\",\n       \"20754  1.619565               0.351603                 0.351273               \\n\",\n       \"19591  1.693478               0.361220                 0.361095               \\n\",\n       \"19149  1.791304               0.370760                 0.370760               \\n\",\n       \"15885  3.763033               0.390114                 0.388653               \\n\",\n       \"20243  3.890995               0.392671                 0.390883               \\n\",\n       \"16577  3.303318               0.346409                 0.346409               \\n\",\n       \"15965  3.810427               0.392044                 0.391089               \\n\",\n       \"20418  3.426540               0.351444                 0.350911               \\n\",\n       \"18551  3.592417               0.368861                 0.368537               \\n\",\n       \"20751  3.255924               0.353917                 0.349765               \\n\",\n       \"19594  3.682464               0.377788                 0.377509               \\n\",\n       \"19152  3.872038               0.386243                 0.385075               \\n\",\n       \"15883  4.130000               0.305153                 0.304344               \\n\",\n       \"20239  4.310000               0.315507                 0.315223               \\n\",\n       \"16575  3.810000               0.287727                 0.287727               \\n\",\n       \"15968  3.860000               0.298572                 0.294012               \\n\",\n       \"20421  3.660000               0.271580                 0.271580               \\n\",\n       \"18550  3.760000               0.288225                 0.284545               \\n\",\n       \"20749  3.510000               0.272224                 0.268598               \\n\",\n       \"19596  3.860000               0.288227                 0.288227               \\n\",\n       \"19150  4.300000               0.315421                 0.315421               \\n\",\n       \"\\n\",\n       \"       all_partial_f_score@k  all_partial_precision_hard@k  \\\\\\n\",\n       \"15880  0.153348               0.153305                       \\n\",\n       \"20240  0.203424               0.203227                       \\n\",\n       \"16579  0.108879               0.108603                       \\n\",\n       \"15963  0.165991               0.165525                       \\n\",\n       \"20419  0.115745               0.115691                       \\n\",\n       \"18552  0.123359               0.123249                       \\n\",\n       \"20748  0.113558               0.112011                       \\n\",\n       \"19592  0.144705               0.144512                       \\n\",\n       \"19154  0.165002               0.164882                       \\n\",\n       \"15881  0.394055               0.394040                       \\n\",\n       \"20241  0.415205               0.415018                       \\n\",\n       \"16573  0.333628               0.333572                       \\n\",\n       \"15964  0.395355               0.395251                       \\n\",\n       \"20420  0.334920               0.334897                       \\n\",\n       \"18546  0.366957               0.366853                       \\n\",\n       \"20750  0.347185               0.346625                       \\n\",\n       \"19593  0.371984               0.371973                       \\n\",\n       \"19155  0.404835               0.404835                       \\n\",\n       \"15886  0.380286               0.380061                       \\n\",\n       \"20238  0.384830               0.384673                       \\n\",\n       \"16578  0.353792               0.353686                       \\n\",\n       \"15967  0.378662               0.378492                       \\n\",\n       \"42862  0.353210               0.353050                       \\n\",\n       \"18547  0.366154               0.366008                       \\n\",\n       \"20753  0.361545               0.361264                       \\n\",\n       \"19595  0.374257               0.374171                       \\n\",\n       \"19151  0.383215               0.383167                       \\n\",\n       \"15882  0.379486               0.379396                       \\n\",\n       \"20242  0.378737               0.378558                       \\n\",\n       \"16576  0.355195               0.355039                       \\n\",\n       \"15969  0.376293               0.376167                       \\n\",\n       \"20416  0.346934               0.346855                       \\n\",\n       \"18549  0.360606               0.360414                       \\n\",\n       \"20752  0.354800               0.354499                       \\n\",\n       \"42853  0.370738               0.370684                       \\n\",\n       \"19153  0.379394               0.379345                       \\n\",\n       \"15884  0.355786               0.355786                       \\n\",\n       \"20237  0.373877               0.373877                       \\n\",\n       \"16574  0.346069               0.345995                       \\n\",\n       \"15966  0.353719               0.353653                       \\n\",\n       \"20417  0.340093               0.340002                       \\n\",\n       \"18548  0.347436               0.347357                       \\n\",\n       \"20754  0.351399               0.351273                       \\n\",\n       \"19591  0.361148               0.361095                       \\n\",\n       \"19149  0.370760               0.370760                       \\n\",\n       \"15885  0.389191               0.388653                       \\n\",\n       \"20243  0.391633               0.390883                       \\n\",\n       \"16577  0.346409               0.346409                       \\n\",\n       \"15965  0.391508               0.391089                       \\n\",\n       \"20418  0.351126               0.350911                       \\n\",\n       \"18551  0.368681               0.368537                       \\n\",\n       \"20751  0.351051               0.349765                       \\n\",\n       \"19594  0.377642               0.377509                       \\n\",\n       \"19152  0.385573               0.385075                       \\n\",\n       \"15883  0.304701               0.304344                       \\n\",\n       \"20239  0.315360               0.315223                       \\n\",\n       \"16575  0.287727               0.287727                       \\n\",\n       \"15968  0.295703               0.294012                       \\n\",\n       \"20421  0.271580               0.271580                       \\n\",\n       \"18550  0.285605               0.284545                       \\n\",\n       \"20749  0.270043               0.268598                       \\n\",\n       \"19596  0.288227               0.288227                       \\n\",\n       \"19150  0.315421               0.315421                       \\n\",\n       \"\\n\",\n       \"       all_partial_f_score_hard@k  all_partial_correct@M  \\\\\\n\",\n       \"15880  0.153305                    1.782468                \\n\",\n       \"20240  0.203227                    2.399351                \\n\",\n       \"16579  0.108603                    1.211039                \\n\",\n       \"15963  0.165525                    1.844156                \\n\",\n       \"20419  0.115691                    1.412338                \\n\",\n       \"18552  0.123249                    1.431818                \\n\",\n       \"20748  0.112011                    1.100649                \\n\",\n       \"19592  0.144512                    1.279221                \\n\",\n       \"19154  0.164882                    1.948052                \\n\",\n       \"15881  0.394040                    4.876000                \\n\",\n       \"20241  0.415018                    4.928000                \\n\",\n       \"16573  0.333572                    4.052000                \\n\",\n       \"15964  0.395251                    4.930000                \\n\",\n       \"20420  0.334897                    4.490000                \\n\",\n       \"18546  0.366853                    4.420000                \\n\",\n       \"20750  0.346625                    3.964000                \\n\",\n       \"19593  0.371973                    4.790000                \\n\",\n       \"19155  0.404835                    5.776000                \\n\",\n       \"15886  0.380061                    2.727623                \\n\",\n       \"20238  0.384673                    2.693301                \\n\",\n       \"16578  0.353686                    2.426277                \\n\",\n       \"15967  0.378492                    2.797618                \\n\",\n       \"42862  0.353050                    2.667984                \\n\",\n       \"18547  0.366008                    2.638315                \\n\",\n       \"20753  0.361264                    2.434633                \\n\",\n       \"19595  0.374171                    2.775854                \\n\",\n       \"19151  0.383167                    2.862311                \\n\",\n       \"15882  0.379396                    2.729000                \\n\",\n       \"20242  0.378558                    2.675500                \\n\",\n       \"16576  0.355039                    2.431500                \\n\",\n       \"15969  0.376167                    2.800500                \\n\",\n       \"20416  0.346855                    2.653000                \\n\",\n       \"18549  0.360414                    2.637000                \\n\",\n       \"20752  0.354499                    2.433500                \\n\",\n       \"42853  0.370684                    2.803500                \\n\",\n       \"19153  0.379345                    2.847000                \\n\",\n       \"15884  0.355786                    2.843478                \\n\",\n       \"20237  0.373877                    2.789130                \\n\",\n       \"16574  0.345995                    2.584783                \\n\",\n       \"15966  0.353653                    2.963043                \\n\",\n       \"20417  0.340002                    2.808696                \\n\",\n       \"18548  0.347357                    2.800000                \\n\",\n       \"20754  0.351273                    2.636957                \\n\",\n       \"19591  0.361095                    2.926087                \\n\",\n       \"19149  0.370760                    3.097826                \\n\",\n       \"15885  0.388653                    4.805687                \\n\",\n       \"20243  0.390883                    4.848341                \\n\",\n       \"16577  0.346409                    4.483412                \\n\",\n       \"15965  0.391089                    5.203791                \\n\",\n       \"20418  0.350911                    4.928910                \\n\",\n       \"18551  0.368537                    4.725118                \\n\",\n       \"20751  0.349765                    4.327014                \\n\",\n       \"19594  0.377509                    5.061611                \\n\",\n       \"19152  0.385075                    5.213270                \\n\",\n       \"15883  0.304344                    5.050000                \\n\",\n       \"20239  0.315223                    5.250000                \\n\",\n       \"16575  0.287727                    4.930000                \\n\",\n       \"15968  0.294012                    5.410000                \\n\",\n       \"20421  0.271580                    5.030000                \\n\",\n       \"18550  0.284545                    4.880000                \\n\",\n       \"20749  0.268598                    4.630000                \\n\",\n       \"19596  0.288227                    5.350000                \\n\",\n       \"19150  0.315421                    5.710000                \\n\",\n       \"\\n\",\n       \"       all_partial_precision@M  all_partial_recall@M  all_partial_f_score@M  \\\\\\n\",\n       \"15880  0.041645                 0.288982              0.069829                \\n\",\n       \"20240  0.077279                 0.361758              0.121264                \\n\",\n       \"16579  0.013365                 0.212286              0.021589                \\n\",\n       \"15963  0.053985                 0.296501              0.080257                \\n\",\n       \"20419  0.020512                 0.242259              0.035184                \\n\",\n       \"18552  0.034537                 0.240592              0.055531                \\n\",\n       \"20748  0.032742                 0.199471              0.043003                \\n\",\n       \"19592  0.032511                 0.223324              0.050631                \\n\",\n       \"19154  0.032836                 0.308361              0.056255                \\n\",\n       \"15881  0.135852                 0.590894              0.207608                \\n\",\n       \"20241  0.170605                 0.597657              0.249919                \\n\",\n       \"16573  0.048655                 0.505576              0.082829                \\n\",\n       \"15964  0.124244                 0.594004              0.187272                \\n\",\n       \"20420  0.069781                 0.546947              0.117770                \\n\",\n       \"18546  0.106797                 0.545241              0.167721                \\n\",\n       \"20750  0.079305                 0.497077              0.125343                \\n\",\n       \"19593  0.078325                 0.573896              0.129928                \\n\",\n       \"19155  0.133132                 0.673575              0.211548                \\n\",\n       \"15886  0.084295                 0.623254              0.141853                \\n\",\n       \"20238  0.094638                 0.616027              0.157275                \\n\",\n       \"16578  0.030995                 0.583606              0.056045                \\n\",\n       \"15967  0.076091                 0.638330              0.127377                \\n\",\n       \"42862  0.045540                 0.618540              0.080798                \\n\",\n       \"18547  0.072005                 0.613886              0.123126                \\n\",\n       \"20753  0.056781                 0.577668              0.096244                \\n\",\n       \"19595  0.048565                 0.633016              0.086324                \\n\",\n       \"19151  0.069198                 0.642530              0.120118                \\n\",\n       \"15882  0.083324                 0.624020              0.141068                \\n\",\n       \"20242  0.094397                 0.615673              0.156890                \\n\",\n       \"16576  0.031675                 0.584043              0.056728                \\n\",\n       \"15969  0.076071                 0.640154              0.128196                \\n\",\n       \"20416  0.045635                 0.619302              0.081427                \\n\",\n       \"18549  0.072732                 0.617847              0.124287                \\n\",\n       \"20752  0.056475                 0.583429              0.096001                \\n\",\n       \"42853  0.049845                 0.640942              0.088650                \\n\",\n       \"19153  0.070893                 0.643791              0.122678                \\n\",\n       \"15884  0.083976                 0.596113              0.140271                \\n\",\n       \"20237  0.095453                 0.584117              0.156348                \\n\",\n       \"16574  0.031122                 0.557804              0.055379                \\n\",\n       \"15966  0.065357                 0.611197              0.109582                \\n\",\n       \"20417  0.046161                 0.593543              0.080975                \\n\",\n       \"18548  0.071924                 0.589017              0.121428                \\n\",\n       \"20754  0.058407                 0.558638              0.096906                \\n\",\n       \"19591  0.046063                 0.611950              0.081892                \\n\",\n       \"19149  0.071215                 0.628415              0.122157                \\n\",\n       \"15885  0.141042                 0.521063              0.205754                \\n\",\n       \"20243  0.158478                 0.523250              0.223772                \\n\",\n       \"16577  0.044196                 0.496253              0.076438                \\n\",\n       \"15965  0.108726                 0.554822              0.161163                \\n\",\n       \"20418  0.073492                 0.526218              0.119014                \\n\",\n       \"18551  0.113138                 0.517908              0.170628                \\n\",\n       \"20751  0.091590                 0.483922              0.135197                \\n\",\n       \"19594  0.077822                 0.542039              0.124296                \\n\",\n       \"19152  0.118371                 0.558772              0.180823                \\n\",\n       \"15883  0.142206                 0.373060              0.198199                \\n\",\n       \"20239  0.163234                 0.382544              0.218782                \\n\",\n       \"16575  0.047247                 0.363971              0.081522                \\n\",\n       \"15968  0.100777                 0.394982              0.146567                \\n\",\n       \"20421  0.075657                 0.370775              0.121487                \\n\",\n       \"18550  0.118493                 0.363627              0.167036                \\n\",\n       \"20749  0.096940                 0.341090              0.138126                \\n\",\n       \"19596  0.072535                 0.388629              0.118625                \\n\",\n       \"19150  0.125748                 0.410648              0.185906                \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@M  all_partial_f_score_hard@M  \\\\\\n\",\n       \"15880  0.041645                      0.069829                     \\n\",\n       \"20240  0.077279                      0.121264                     \\n\",\n       \"16579  0.013365                      0.021589                     \\n\",\n       \"15963  0.053985                      0.080257                     \\n\",\n       \"20419  0.020512                      0.035184                     \\n\",\n       \"18552  0.034537                      0.055531                     \\n\",\n       \"20748  0.032742                      0.043003                     \\n\",\n       \"19592  0.032511                      0.050631                     \\n\",\n       \"19154  0.032836                      0.056255                     \\n\",\n       \"15881  0.135852                      0.207608                     \\n\",\n       \"20241  0.170605                      0.249919                     \\n\",\n       \"16573  0.048655                      0.082829                     \\n\",\n       \"15964  0.124244                      0.187272                     \\n\",\n       \"20420  0.069781                      0.117770                     \\n\",\n       \"18546  0.106797                      0.167721                     \\n\",\n       \"20750  0.079305                      0.125343                     \\n\",\n       \"19593  0.078325                      0.129928                     \\n\",\n       \"19155  0.133132                      0.211548                     \\n\",\n       \"15886  0.084295                      0.141853                     \\n\",\n       \"20238  0.094638                      0.157275                     \\n\",\n       \"16578  0.030995                      0.056045                     \\n\",\n       \"15967  0.076091                      0.127377                     \\n\",\n       \"42862  0.045540                      0.080798                     \\n\",\n       \"18547  0.072005                      0.123126                     \\n\",\n       \"20753  0.056781                      0.096244                     \\n\",\n       \"19595  0.048565                      0.086324                     \\n\",\n       \"19151  0.069198                      0.120118                     \\n\",\n       \"15882  0.083324                      0.141068                     \\n\",\n       \"20242  0.094397                      0.156890                     \\n\",\n       \"16576  0.031675                      0.056728                     \\n\",\n       \"15969  0.076071                      0.128196                     \\n\",\n       \"20416  0.045635                      0.081427                     \\n\",\n       \"18549  0.072732                      0.124287                     \\n\",\n       \"20752  0.056475                      0.096001                     \\n\",\n       \"42853  0.049845                      0.088650                     \\n\",\n       \"19153  0.070893                      0.122678                     \\n\",\n       \"15884  0.083976                      0.140271                     \\n\",\n       \"20237  0.095453                      0.156348                     \\n\",\n       \"16574  0.031122                      0.055379                     \\n\",\n       \"15966  0.065357                      0.109582                     \\n\",\n       \"20417  0.046161                      0.080975                     \\n\",\n       \"18548  0.071924                      0.121428                     \\n\",\n       \"20754  0.058407                      0.096906                     \\n\",\n       \"19591  0.046063                      0.081892                     \\n\",\n       \"19149  0.071215                      0.122157                     \\n\",\n       \"15885  0.141042                      0.205754                     \\n\",\n       \"20243  0.158478                      0.223772                     \\n\",\n       \"16577  0.044196                      0.076438                     \\n\",\n       \"15965  0.108726                      0.161163                     \\n\",\n       \"20418  0.073492                      0.119014                     \\n\",\n       \"18551  0.113138                      0.170628                     \\n\",\n       \"20751  0.091590                      0.135197                     \\n\",\n       \"19594  0.077822                      0.124296                     \\n\",\n       \"19152  0.118371                      0.180823                     \\n\",\n       \"15883  0.142206                      0.198199                     \\n\",\n       \"20239  0.163234                      0.218782                     \\n\",\n       \"16575  0.047247                      0.081522                     \\n\",\n       \"15968  0.100777                      0.146567                     \\n\",\n       \"20421  0.075657                      0.121487                     \\n\",\n       \"18550  0.118493                      0.167036                     \\n\",\n       \"20749  0.096940                      0.138126                     \\n\",\n       \"19596  0.072535                      0.118625                     \\n\",\n       \"19150  0.125748                      0.185906                     \\n\",\n       \"\\n\",\n       \"       all_partial_correct@1  all_partial_precision@1  all_partial_recall@1  \\\\\\n\",\n       \"15880  0.090909               0.169005                 0.022100               \\n\",\n       \"20240  0.227273               0.352715                 0.047658               \\n\",\n       \"16579  0.097403               0.168254                 0.022032               \\n\",\n       \"15963  0.126623               0.244263                 0.032920               \\n\",\n       \"20419  0.090909               0.186445                 0.025313               \\n\",\n       \"18552  0.074675               0.158488                 0.020862               \\n\",\n       \"20748  0.103896               0.190844                 0.025001               \\n\",\n       \"19592  0.152597               0.285503                 0.037757               \\n\",\n       \"19154  0.165584               0.291343                 0.037886               \\n\",\n       \"15881  0.430000               0.623179                 0.079988               \\n\",\n       \"20241  0.438000               0.650587                 0.086261               \\n\",\n       \"16573  0.298000               0.477448                 0.061992               \\n\",\n       \"15964  0.480000               0.688084                 0.089124               \\n\",\n       \"20420  0.358000               0.547104                 0.070711               \\n\",\n       \"18546  0.420000               0.599123                 0.078843               \\n\",\n       \"20750  0.402000               0.592509                 0.077663               \\n\",\n       \"19593  0.374000               0.591726                 0.076638               \\n\",\n       \"19155  0.370000               0.596611                 0.078287               \\n\",\n       \"15886  0.370341               0.490662                 0.109192               \\n\",\n       \"20238  0.378246               0.512857                 0.115057               \\n\",\n       \"16578  0.362736               0.459859                 0.102519               \\n\",\n       \"15967  0.350328               0.495927                 0.111814               \\n\",\n       \"42862  0.375094               0.508277                 0.114603               \\n\",\n       \"18547  0.313704               0.423120                 0.093803               \\n\",\n       \"20753  0.357733               0.486150                 0.109174               \\n\",\n       \"19595  0.408415               0.539250                 0.120940               \\n\",\n       \"19151  0.400610               0.532188                 0.119727               \\n\",\n       \"15882  0.379000               0.497795                 0.111160               \\n\",\n       \"20242  0.391500               0.522311                 0.116464               \\n\",\n       \"16576  0.367000               0.464387                 0.103987               \\n\",\n       \"15969  0.347000               0.493927                 0.108998               \\n\",\n       \"20416  0.359000               0.499238                 0.112406               \\n\",\n       \"18549  0.306000               0.409594                 0.092152               \\n\",\n       \"20752  0.356500               0.489436                 0.109826               \\n\",\n       \"42853  0.389500               0.524971                 0.118312               \\n\",\n       \"19153  0.402000               0.531348                 0.118834               \\n\",\n       \"15884  0.352174               0.478290                 0.103756               \\n\",\n       \"20237  0.404348               0.543633                 0.118195               \\n\",\n       \"16574  0.319565               0.429051                 0.091034               \\n\",\n       \"15966  0.356522               0.501291                 0.105561               \\n\",\n       \"20417  0.326087               0.463666                 0.101551               \\n\",\n       \"18548  0.319565               0.436310                 0.091779               \\n\",\n       \"20754  0.382609               0.515622                 0.109909               \\n\",\n       \"19591  0.397826               0.540820                 0.114064               \\n\",\n       \"19149  0.393478               0.528948                 0.113076               \\n\",\n       \"15885  0.573460               0.672064                 0.082445               \\n\",\n       \"20243  0.592417               0.728815                 0.092851               \\n\",\n       \"16577  0.573460               0.657289                 0.078794               \\n\",\n       \"15965  0.535545               0.660502                 0.085254               \\n\",\n       \"20418  0.473934               0.617191                 0.073951               \\n\",\n       \"18551  0.526066               0.633108                 0.074623               \\n\",\n       \"20751  0.559242               0.695940                 0.094827               \\n\",\n       \"19594  0.578199               0.702767                 0.086858               \\n\",\n       \"19152  0.559242               0.683891                 0.082840               \\n\",\n       \"15883  0.440000               0.604595                 0.042502               \\n\",\n       \"20239  0.570000               0.719095                 0.049455               \\n\",\n       \"16575  0.460000               0.588988                 0.040683               \\n\",\n       \"15968  0.430000               0.616143                 0.041870               \\n\",\n       \"20421  0.460000               0.614205                 0.042704               \\n\",\n       \"18550  0.450000               0.593024                 0.040379               \\n\",\n       \"20749  0.490000               0.642008                 0.043835               \\n\",\n       \"19596  0.470000               0.631048                 0.043038               \\n\",\n       \"19150  0.480000               0.646381                 0.044208               \\n\",\n       \"\\n\",\n       \"       all_partial_f_score@1  all_partial_precision_hard@1  \\\\\\n\",\n       \"15880  0.038840               0.169005                       \\n\",\n       \"20240  0.083446               0.352715                       \\n\",\n       \"16579  0.038667               0.168254                       \\n\",\n       \"15963  0.057607               0.244263                       \\n\",\n       \"20419  0.044285               0.186445                       \\n\",\n       \"18552  0.036696               0.158488                       \\n\",\n       \"20748  0.043960               0.190844                       \\n\",\n       \"19592  0.066154               0.285503                       \\n\",\n       \"19154  0.066616               0.291343                       \\n\",\n       \"15881  0.137318               0.623179                       \\n\",\n       \"20241  0.147501               0.650587                       \\n\",\n       \"16573  0.106063               0.477448                       \\n\",\n       \"15964  0.152772               0.688084                       \\n\",\n       \"20420  0.121270               0.547104                       \\n\",\n       \"18546  0.134906               0.599123                       \\n\",\n       \"20750  0.132752               0.592509                       \\n\",\n       \"19593  0.131480               0.591726                       \\n\",\n       \"19155  0.134133               0.596611                       \\n\",\n       \"15886  0.174839               0.490662                       \\n\",\n       \"20238  0.183909               0.512857                       \\n\",\n       \"16578  0.164015               0.459859                       \\n\",\n       \"15967  0.178619               0.495927                       \\n\",\n       \"42862  0.183006               0.508277                       \\n\",\n       \"18547  0.150236               0.423120                       \\n\",\n       \"20753  0.174510               0.486150                       \\n\",\n       \"19595  0.193459               0.539250                       \\n\",\n       \"19151  0.191396               0.532188                       \\n\",\n       \"15882  0.177622               0.497795                       \\n\",\n       \"20242  0.186300               0.522311                       \\n\",\n       \"16576  0.165923               0.464387                       \\n\",\n       \"15969  0.175179               0.493927                       \\n\",\n       \"20416  0.179750               0.499238                       \\n\",\n       \"18549  0.146925               0.409594                       \\n\",\n       \"20752  0.175568               0.489436                       \\n\",\n       \"42853  0.188733               0.524971                       \\n\",\n       \"19153  0.190101               0.531348                       \\n\",\n       \"15884  0.164698               0.478290                       \\n\",\n       \"20237  0.187558               0.543633                       \\n\",\n       \"16574  0.145850               0.429051                       \\n\",\n       \"15966  0.168643               0.501291                       \\n\",\n       \"20417  0.160711               0.463666                       \\n\",\n       \"18548  0.146118               0.436310                       \\n\",\n       \"20754  0.174967               0.515622                       \\n\",\n       \"19591  0.182494               0.540820                       \\n\",\n       \"19149  0.180504               0.528948                       \\n\",\n       \"15885  0.139223               0.672064                       \\n\",\n       \"20243  0.156189               0.728815                       \\n\",\n       \"16577  0.133514               0.657289                       \\n\",\n       \"15965  0.142612               0.660502                       \\n\",\n       \"20418  0.125472               0.617191                       \\n\",\n       \"18551  0.126861               0.633108                       \\n\",\n       \"20751  0.157297               0.695940                       \\n\",\n       \"19594  0.146418               0.702767                       \\n\",\n       \"19152  0.140842               0.683891                       \\n\",\n       \"15883  0.079244               0.604595                       \\n\",\n       \"20239  0.092313               0.719095                       \\n\",\n       \"16575  0.075929               0.588988                       \\n\",\n       \"15968  0.078231               0.616143                       \\n\",\n       \"20421  0.079646               0.614205                       \\n\",\n       \"18550  0.075436               0.593024                       \\n\",\n       \"20749  0.081885               0.642008                       \\n\",\n       \"19596  0.080369               0.631048                       \\n\",\n       \"19150  0.082552               0.646381                       \\n\",\n       \"\\n\",\n       \"       all_partial_f_score_hard@1  all_partial_correct@3  \\\\\\n\",\n       \"15880  0.038840                    0.347403                \\n\",\n       \"20240  0.083446                    0.525974                \\n\",\n       \"16579  0.038667                    0.292208                \\n\",\n       \"15963  0.057607                    0.425325                \\n\",\n       \"20419  0.044285                    0.243506                \\n\",\n       \"18552  0.036696                    0.256494                \\n\",\n       \"20748  0.043960                    0.288961                \\n\",\n       \"19592  0.066154                    0.386364                \\n\",\n       \"19154  0.066616                    0.386364                \\n\",\n       \"15881  0.137318                    1.298000                \\n\",\n       \"20241  0.147501                    1.360000                \\n\",\n       \"16573  0.106063                    1.108000                \\n\",\n       \"15964  0.152772                    1.280000                \\n\",\n       \"20420  0.121270                    1.140000                \\n\",\n       \"18546  0.134906                    1.256000                \\n\",\n       \"20750  0.132752                    1.212000                \\n\",\n       \"19593  0.131480                    1.262000                \\n\",\n       \"19155  0.134133                    1.386000                \\n\",\n       \"15886  0.174839                    1.089358                \\n\",\n       \"20238  0.183909                    1.111272                \\n\",\n       \"16578  0.164015                    0.993646                \\n\",\n       \"15967  0.178619                    1.083554                \\n\",\n       \"42862  0.183006                    0.994847                \\n\",\n       \"18547  0.150236                    1.035423                \\n\",\n       \"20753  0.174510                    1.028368                \\n\",\n       \"19595  0.193459                    1.074498                \\n\",\n       \"19151  0.191396                    1.097914                \\n\",\n       \"15882  0.177622                    1.086500                \\n\",\n       \"20242  0.186300                    1.106000                \\n\",\n       \"16576  0.165923                    0.990500                \\n\",\n       \"15969  0.175179                    1.073500                \\n\",\n       \"20416  0.179750                    0.969500                \\n\",\n       \"18549  0.146925                    1.008000                \\n\",\n       \"20752  0.175568                    1.021000                \\n\",\n       \"42853  0.188733                    1.040500                \\n\",\n       \"19153  0.190101                    1.077500                \\n\",\n       \"15884  0.164698                    1.004348                \\n\",\n       \"20237  0.187558                    1.076087                \\n\",\n       \"16574  0.145850                    0.960870                \\n\",\n       \"15966  0.168643                    1.021739                \\n\",\n       \"20417  0.160711                    0.945652                \\n\",\n       \"18548  0.146118                    0.986957                \\n\",\n       \"20754  0.174967                    1.021739                \\n\",\n       \"19591  0.182494                    1.030435                \\n\",\n       \"19149  0.180504                    1.052174                \\n\",\n       \"15885  0.139223                    1.611374                \\n\",\n       \"20243  0.156189                    1.611374                \\n\",\n       \"16577  0.133514                    1.412322                \\n\",\n       \"15965  0.142612                    1.526066                \\n\",\n       \"20418  0.125472                    1.369668                \\n\",\n       \"18551  0.126861                    1.497630                \\n\",\n       \"20751  0.157297                    1.473934                \\n\",\n       \"19594  0.146418                    1.530806                \\n\",\n       \"19152  0.140842                    1.597156                \\n\",\n       \"15883  0.079244                    1.400000                \\n\",\n       \"20239  0.092313                    1.460000                \\n\",\n       \"16575  0.075929                    1.390000                \\n\",\n       \"15968  0.078231                    1.340000                \\n\",\n       \"20421  0.079646                    1.330000                \\n\",\n       \"18550  0.075436                    1.390000                \\n\",\n       \"20749  0.081885                    1.290000                \\n\",\n       \"19596  0.080369                    1.340000                \\n\",\n       \"19150  0.082552                    1.420000                \\n\",\n       \"\\n\",\n       \"       all_partial_precision@3  all_partial_recall@3  all_partial_f_score@3  \\\\\\n\",\n       \"15880  0.198752                 0.078321              0.110984                \\n\",\n       \"20240  0.276333                 0.107169              0.152810                \\n\",\n       \"16579  0.160013                 0.062121              0.088252                \\n\",\n       \"15963  0.227950                 0.089417              0.126859                \\n\",\n       \"20419  0.155859                 0.061454              0.087249                \\n\",\n       \"18552  0.165606                 0.064966              0.092325                \\n\",\n       \"20748  0.172371                 0.068354              0.096624                \\n\",\n       \"19592  0.221946                 0.086561              0.123007                \\n\",\n       \"19154  0.220052                 0.085667              0.122016                \\n\",\n       \"15881  0.559063                 0.210321              0.289980                \\n\",\n       \"20241  0.579652                 0.217965              0.300319                \\n\",\n       \"16573  0.500272                 0.186157              0.257384                \\n\",\n       \"15964  0.553252                 0.208325              0.286656                \\n\",\n       \"20420  0.504558                 0.186942              0.259100                \\n\",\n       \"18546  0.543605                 0.203721              0.281233                \\n\",\n       \"20750  0.532475                 0.197815              0.274028                \\n\",\n       \"19593  0.546788                 0.205368              0.283172                \\n\",\n       \"19155  0.580891                 0.217817              0.301103                \\n\",\n       \"15886  0.454554                 0.300108              0.349715                \\n\",\n       \"20238  0.464250                 0.307533              0.358015                \\n\",\n       \"16578  0.423850                 0.281183              0.327099                \\n\",\n       \"15967  0.454629                 0.301385              0.350767                \\n\",\n       \"42862  0.426557                 0.282085              0.328493                \\n\",\n       \"18547  0.437514                 0.290220              0.337750                \\n\",\n       \"20753  0.438281                 0.290764              0.338180                \\n\",\n       \"19595  0.453438                 0.299959              0.349497                \\n\",\n       \"19151  0.460624                 0.305265              0.355373                \\n\",\n       \"15882  0.454361                 0.300387              0.349681                \\n\",\n       \"20242  0.460945                 0.303055              0.353703                \\n\",\n       \"16576  0.422923                 0.280602              0.325895                \\n\",\n       \"15969  0.455828                 0.299882              0.349964                \\n\",\n       \"20416  0.421039                 0.277662              0.323693                \\n\",\n       \"18549  0.428107                 0.283396              0.329700                \\n\",\n       \"20752  0.437702                 0.289421              0.337229                \\n\",\n       \"42853  0.443913                 0.293511              0.341576                \\n\",\n       \"19153  0.457745                 0.301768              0.351841                \\n\",\n       \"15884  0.434227                 0.272946              0.318046                \\n\",\n       \"20237  0.458776                 0.289678              0.337308                \\n\",\n       \"16574  0.423600                 0.266561              0.311024                \\n\",\n       \"15966  0.435734                 0.272923              0.318615                \\n\",\n       \"20417  0.413483                 0.259405              0.302171                \\n\",\n       \"18548  0.420320                 0.261173              0.305597                \\n\",\n       \"20754  0.437592                 0.272844              0.318091                \\n\",\n       \"19591  0.441894                 0.278378              0.323472                \\n\",\n       \"19149  0.447138                 0.282344              0.328632                \\n\",\n       \"15885  0.627058                 0.221331              0.298876                \\n\",\n       \"20243  0.629347                 0.221815              0.300461                \\n\",\n       \"16577  0.568418                 0.200694              0.270643                \\n\",\n       \"15965  0.597286                 0.215338              0.288578                \\n\",\n       \"20418  0.559493                 0.200396              0.269387                \\n\",\n       \"18551  0.586640                 0.207197              0.279735                \\n\",\n       \"20751  0.593370                 0.212982              0.286053                \\n\",\n       \"19594  0.605400                 0.213456              0.288005                \\n\",\n       \"19152  0.626108                 0.217940              0.296852                \\n\",\n       \"15883  0.584104                 0.120403              0.198640                \\n\",\n       \"20239  0.600186                 0.123601              0.203834                \\n\",\n       \"16575  0.569921                 0.116705              0.192631                \\n\",\n       \"15968  0.562609                 0.114946              0.189781                \\n\",\n       \"20421  0.543806                 0.111357              0.183791                \\n\",\n       \"18550  0.581215                 0.119498              0.197087                \\n\",\n       \"20749  0.552358                 0.114275              0.188311                \\n\",\n       \"19596  0.556063                 0.113672              0.187682                \\n\",\n       \"19150  0.585585                 0.119463              0.197316                \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@3  all_partial_f_score_hard@3  \\\\\\n\",\n       \"15880  0.198752                      0.110984                     \\n\",\n       \"20240  0.276333                      0.152810                     \\n\",\n       \"16579  0.159472                      0.088180                     \\n\",\n       \"15963  0.227950                      0.126859                     \\n\",\n       \"20419  0.155859                      0.087249                     \\n\",\n       \"18552  0.165606                      0.092325                     \\n\",\n       \"20748  0.171830                      0.096552                     \\n\",\n       \"19592  0.221946                      0.123007                     \\n\",\n       \"19154  0.220052                      0.122016                     \\n\",\n       \"15881  0.559063                      0.289980                     \\n\",\n       \"20241  0.579652                      0.300319                     \\n\",\n       \"16573  0.500161                      0.257365                     \\n\",\n       \"15964  0.553252                      0.286656                     \\n\",\n       \"20420  0.504558                      0.259100                     \\n\",\n       \"18546  0.543605                      0.281233                     \\n\",\n       \"20750  0.532475                      0.274028                     \\n\",\n       \"19593  0.546788                      0.283172                     \\n\",\n       \"19155  0.580891                      0.301103                     \\n\",\n       \"15886  0.454544                      0.349711                     \\n\",\n       \"20238  0.464250                      0.358015                     \\n\",\n       \"16578  0.423850                      0.327099                     \\n\",\n       \"15967  0.454578                      0.350762                     \\n\",\n       \"42862  0.426536                      0.328491                     \\n\",\n       \"18547  0.437497                      0.337749                     \\n\",\n       \"20753  0.438256                      0.338173                     \\n\",\n       \"19595  0.453438                      0.349497                     \\n\",\n       \"19151  0.460624                      0.355373                     \\n\",\n       \"15882  0.454361                      0.349681                     \\n\",\n       \"20242  0.460945                      0.353703                     \\n\",\n       \"16576  0.422757                      0.325883                     \\n\",\n       \"15969  0.455828                      0.349964                     \\n\",\n       \"20416  0.421039                      0.323693                     \\n\",\n       \"18549  0.428012                      0.329689                     \\n\",\n       \"20752  0.437702                      0.337229                     \\n\",\n       \"42853  0.443913                      0.341576                     \\n\",\n       \"19153  0.457745                      0.351841                     \\n\",\n       \"15884  0.434227                      0.318046                     \\n\",\n       \"20237  0.458776                      0.337308                     \\n\",\n       \"16574  0.423600                      0.311024                     \\n\",\n       \"15966  0.435734                      0.318615                     \\n\",\n       \"20417  0.413362                      0.302158                     \\n\",\n       \"18548  0.420320                      0.305597                     \\n\",\n       \"20754  0.437592                      0.318091                     \\n\",\n       \"19591  0.441894                      0.323472                     \\n\",\n       \"19149  0.447138                      0.328632                     \\n\",\n       \"15885  0.627058                      0.298876                     \\n\",\n       \"20243  0.629347                      0.300461                     \\n\",\n       \"16577  0.568418                      0.270643                     \\n\",\n       \"15965  0.597286                      0.288578                     \\n\",\n       \"20418  0.559493                      0.269387                     \\n\",\n       \"18551  0.586640                      0.279735                     \\n\",\n       \"20751  0.593370                      0.286053                     \\n\",\n       \"19594  0.605400                      0.288005                     \\n\",\n       \"19152  0.626108                      0.296852                     \\n\",\n       \"15883  0.584104                      0.198640                     \\n\",\n       \"20239  0.600186                      0.203834                     \\n\",\n       \"16575  0.569921                      0.192631                     \\n\",\n       \"15968  0.562609                      0.189781                     \\n\",\n       \"20421  0.543806                      0.183791                     \\n\",\n       \"18550  0.581215                      0.197087                     \\n\",\n       \"20749  0.552358                      0.188311                     \\n\",\n       \"19596  0.556063                      0.187682                     \\n\",\n       \"19150  0.585585                      0.197316                     \\n\",\n       \"\\n\",\n       \"       present_exact_correct@5  present_exact_precision@5  \\\\\\n\",\n       \"15880  0.610390                 0.122240                    \\n\",\n       \"20240  0.740260                 0.148214                    \\n\",\n       \"16579  0.509740                 0.112500                    \\n\",\n       \"15963  0.629870                 0.128409                    \\n\",\n       \"20419  0.496753                 0.102922                    \\n\",\n       \"18552  0.490260                 0.099621                    \\n\",\n       \"20748  0.496753                 0.113636                    \\n\",\n       \"19592  0.581169                 0.117803                    \\n\",\n       \"19154  0.590909                 0.118182                    \\n\",\n       \"15881  1.862000                 0.372600                    \\n\",\n       \"20241  1.836000                 0.367200                    \\n\",\n       \"16573  1.676000                 0.343200                    \\n\",\n       \"15964  1.790000                 0.358500                    \\n\",\n       \"20420  1.730000                 0.349967                    \\n\",\n       \"18546  1.780000                 0.357833                    \\n\",\n       \"20750  1.806000                 0.370367                    \\n\",\n       \"19593  1.754000                 0.354367                    \\n\",\n       \"19155  1.868000                 0.373600                    \\n\",\n       \"15886  1.466853                 0.293541                    \\n\",\n       \"20238  1.462150                 0.292525                    \\n\",\n       \"16578  1.378696                 0.277279                    \\n\",\n       \"15967  1.450993                 0.290427                    \\n\",\n       \"42862  1.376545                 0.276107                    \\n\",\n       \"18547  1.424476                 0.285605                    \\n\",\n       \"20753  1.394356                 0.281490                    \\n\",\n       \"19595  1.436133                 0.287489                    \\n\",\n       \"19151  1.444339                 0.288887                    \\n\",\n       \"15882  1.444500                 0.288950                    \\n\",\n       \"20242  1.454000                 0.290917                    \\n\",\n       \"16576  1.355000                 0.272933                    \\n\",\n       \"15969  1.426000                 0.285375                    \\n\",\n       \"20416  1.337000                 0.268017                    \\n\",\n       \"18549  1.390500                 0.279058                    \\n\",\n       \"20752  1.380500                 0.279283                    \\n\",\n       \"42853  1.400500                 0.280342                    \\n\",\n       \"19153  1.433500                 0.286700                    \\n\",\n       \"15884  1.402174                 0.280435                    \\n\",\n       \"20237  1.378261                 0.275652                    \\n\",\n       \"16574  1.323913                 0.266268                    \\n\",\n       \"15966  1.410870                 0.282174                    \\n\",\n       \"20417  1.295652                 0.260254                    \\n\",\n       \"18548  1.384783                 0.279239                    \\n\",\n       \"20754  1.347826                 0.272138                    \\n\",\n       \"19591  1.345652                 0.269674                    \\n\",\n       \"19149  1.363043                 0.272609                    \\n\",\n       \"15885  2.184834                 0.436967                    \\n\",\n       \"20243  2.218009                 0.443602                    \\n\",\n       \"16577  2.080569                 0.419273                    \\n\",\n       \"15965  2.175355                 0.435071                    \\n\",\n       \"20418  2.028436                 0.409953                    \\n\",\n       \"18551  2.113744                 0.423223                    \\n\",\n       \"20751  2.047393                 0.419036                    \\n\",\n       \"19594  2.170616                 0.434123                    \\n\",\n       \"19152  2.109005                 0.421801                    \\n\",\n       \"15883  1.980000                 0.396000                    \\n\",\n       \"20239  1.860000                 0.372000                    \\n\",\n       \"16575  1.880000                 0.378000                    \\n\",\n       \"15968  1.890000                 0.378000                    \\n\",\n       \"20421  1.880000                 0.376500                    \\n\",\n       \"18550  1.900000                 0.381000                    \\n\",\n       \"20749  1.840000                 0.382333                    \\n\",\n       \"19596  1.890000                 0.378000                    \\n\",\n       \"19150  1.950000                 0.390000                    \\n\",\n       \"\\n\",\n       \"       present_exact_recall@5  present_exact_f_score@5  \\\\\\n\",\n       \"15880  0.085890                0.098449                  \\n\",\n       \"20240  0.101372                0.117720                  \\n\",\n       \"16579  0.070689                0.083691                  \\n\",\n       \"15963  0.086883                0.100952                  \\n\",\n       \"20419  0.068144                0.080311                  \\n\",\n       \"18552  0.067812                0.078828                  \\n\",\n       \"20748  0.070593                0.082838                  \\n\",\n       \"19592  0.080044                0.092764                  \\n\",\n       \"19154  0.081515                0.094415                  \\n\",\n       \"15881  0.296673                0.309041                  \\n\",\n       \"20241  0.289703                0.302983                  \\n\",\n       \"16573  0.261355                0.276910                  \\n\",\n       \"15964  0.279304                0.293077                  \\n\",\n       \"20420  0.269167                0.285558                  \\n\",\n       \"18546  0.276258                0.292360                  \\n\",\n       \"20750  0.283551                0.300473                  \\n\",\n       \"19593  0.270989                0.288578                  \\n\",\n       \"19155  0.289511                0.305781                  \\n\",\n       \"15886  0.509155                0.347090                  \\n\",\n       \"20238  0.511019                0.347445                  \\n\",\n       \"16578  0.483234                0.328757                  \\n\",\n       \"15967  0.509034                0.345189                  \\n\",\n       \"42862  0.478362                0.325994                  \\n\",\n       \"18547  0.500582                0.339797                  \\n\",\n       \"20753  0.489330                0.332971                  \\n\",\n       \"19595  0.499722                0.340148                  \\n\",\n       \"19151  0.505599                0.343022                  \\n\",\n       \"15882  0.505452                0.342523                  \\n\",\n       \"20242  0.513133                0.346251                  \\n\",\n       \"16576  0.477681                0.323437                  \\n\",\n       \"15969  0.501923                0.338949                  \\n\",\n       \"20416  0.468546                0.317832                  \\n\",\n       \"18549  0.495309                0.332236                  \\n\",\n       \"20752  0.483513                0.330264                  \\n\",\n       \"42853  0.491290                0.332503                  \\n\",\n       \"19153  0.502124                0.340413                  \\n\",\n       \"15884  0.503402                0.331913                  \\n\",\n       \"20237  0.495642                0.327046                  \\n\",\n       \"16574  0.480703                0.315414                  \\n\",\n       \"15966  0.512198                0.335666                  \\n\",\n       \"20417  0.472587                0.309172                  \\n\",\n       \"18548  0.494273                0.328947                  \\n\",\n       \"20754  0.485660                0.319622                  \\n\",\n       \"19591  0.488823                0.321205                  \\n\",\n       \"19149  0.491922                0.324800                  \\n\",\n       \"15885  0.426318                0.397940                  \\n\",\n       \"20243  0.433562                0.403012                  \\n\",\n       \"16577  0.407542                0.381322                  \\n\",\n       \"15965  0.439912                0.400072                  \\n\",\n       \"20418  0.394035                0.370000                  \\n\",\n       \"18551  0.415818                0.384696                  \\n\",\n       \"20751  0.403510                0.378483                  \\n\",\n       \"19594  0.438456                0.398725                  \\n\",\n       \"19152  0.418254                0.385038                  \\n\",\n       \"15883  0.327635                0.337321                  \\n\",\n       \"20239  0.304118                0.315208                  \\n\",\n       \"16575  0.316294                0.322481                  \\n\",\n       \"15968  0.308780                0.319867                  \\n\",\n       \"20421  0.305752                0.317973                  \\n\",\n       \"18550  0.314000                0.323253                  \\n\",\n       \"20749  0.300093                0.316564                  \\n\",\n       \"19596  0.297355                0.314427                  \\n\",\n       \"19150  0.319265                0.330216                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@5  present_exact_f_score_hard@5  \\\\\\n\",\n       \"15880  0.122078                        0.098407                       \\n\",\n       \"20240  0.148052                        0.117565                       \\n\",\n       \"16579  0.101948                        0.081343                       \\n\",\n       \"15963  0.125974                        0.100354                       \\n\",\n       \"20419  0.099351                        0.079243                       \\n\",\n       \"18552  0.098052                        0.078284                       \\n\",\n       \"20748  0.099351                        0.080445                       \\n\",\n       \"19592  0.116234                        0.092248                       \\n\",\n       \"19154  0.118182                        0.094415                       \\n\",\n       \"15881  0.372400                        0.308898                       \\n\",\n       \"20241  0.367200                        0.302983                       \\n\",\n       \"16573  0.335200                        0.273863                       \\n\",\n       \"15964  0.358000                        0.292934                       \\n\",\n       \"20420  0.346000                        0.283464                       \\n\",\n       \"18546  0.356000                        0.291288                       \\n\",\n       \"20750  0.361200                        0.296635                       \\n\",\n       \"19593  0.350800                        0.286772                       \\n\",\n       \"19155  0.373600                        0.305781                       \\n\",\n       \"15886  0.293371                        0.346953                       \\n\",\n       \"20238  0.292430                        0.347365                       \\n\",\n       \"16578  0.275739                        0.327707                       \\n\",\n       \"15967  0.290199                        0.345058                       \\n\",\n       \"42862  0.275309                        0.325460                       \\n\",\n       \"18547  0.284895                        0.339315                       \\n\",\n       \"20753  0.278871                        0.331221                       \\n\",\n       \"19595  0.287227                        0.339935                       \\n\",\n       \"19151  0.288868                        0.343009                       \\n\",\n       \"15882  0.288900                        0.342476                       \\n\",\n       \"20242  0.290800                        0.346165                       \\n\",\n       \"16576  0.271000                        0.322149                       \\n\",\n       \"15969  0.285200                        0.338800                       \\n\",\n       \"20416  0.267400                        0.317241                       \\n\",\n       \"18549  0.278100                        0.331590                       \\n\",\n       \"20752  0.276100                        0.328009                       \\n\",\n       \"42853  0.280100                        0.332311                       \\n\",\n       \"19153  0.286700                        0.340413                       \\n\",\n       \"15884  0.280435                        0.331913                       \\n\",\n       \"20237  0.275652                        0.327046                       \\n\",\n       \"16574  0.264783                        0.314293                       \\n\",\n       \"15966  0.282174                        0.335666                       \\n\",\n       \"20417  0.259130                        0.308781                       \\n\",\n       \"18548  0.276957                        0.326938                       \\n\",\n       \"20754  0.269565                        0.317674                       \\n\",\n       \"19591  0.269130                        0.320742                       \\n\",\n       \"19149  0.272609                        0.324800                       \\n\",\n       \"15885  0.436967                        0.397940                       \\n\",\n       \"20243  0.443602                        0.403012                       \\n\",\n       \"16577  0.416114                        0.379941                       \\n\",\n       \"15965  0.435071                        0.400072                       \\n\",\n       \"20418  0.405687                        0.369014                       \\n\",\n       \"18551  0.422749                        0.384432                       \\n\",\n       \"20751  0.409479                        0.374778                       \\n\",\n       \"19594  0.434123                        0.398725                       \\n\",\n       \"19152  0.421801                        0.385038                       \\n\",\n       \"15883  0.396000                        0.337321                       \\n\",\n       \"20239  0.372000                        0.315208                       \\n\",\n       \"16575  0.376000                        0.321780                       \\n\",\n       \"15968  0.378000                        0.319867                       \\n\",\n       \"20421  0.376000                        0.317844                       \\n\",\n       \"18550  0.380000                        0.322809                       \\n\",\n       \"20749  0.368000                        0.312626                       \\n\",\n       \"19596  0.378000                        0.314427                       \\n\",\n       \"19150  0.390000                        0.330216                       \\n\",\n       \"\\n\",\n       \"       present_exact_correct@10  present_exact_precision@10  \\\\\\n\",\n       \"15880  0.967532                  0.100037                     \\n\",\n       \"20240  1.181818                  0.120376                     \\n\",\n       \"16579  0.720779                  0.108648                     \\n\",\n       \"15963  0.967532                  0.104261                     \\n\",\n       \"20419  0.795455                  0.093570                     \\n\",\n       \"18552  0.762987                  0.086617                     \\n\",\n       \"20748  0.688312                  0.102949                     \\n\",\n       \"19592  0.792208                  0.095214                     \\n\",\n       \"19154  0.990260                  0.099957                     \\n\",\n       \"15881  2.992000                  0.305492                     \\n\",\n       \"20241  3.170000                  0.322277                     \\n\",\n       \"16573  2.676000                  0.306457                     \\n\",\n       \"15964  3.014000                  0.310487                     \\n\",\n       \"20420  2.790000                  0.301349                     \\n\",\n       \"18546  2.836000                  0.300864                     \\n\",\n       \"20750  2.692000                  0.321767                     \\n\",\n       \"19593  2.858000                  0.304745                     \\n\",\n       \"19155  3.078000                  0.309053                     \\n\",\n       \"15886  2.009456                  0.203461                     \\n\",\n       \"20238  1.994897                  0.200924                     \\n\",\n       \"16578  1.833041                  0.197746                     \\n\",\n       \"15967  1.977485                  0.200706                     \\n\",\n       \"42862  1.891529                  0.197645                     \\n\",\n       \"18547  1.924351                  0.200704                     \\n\",\n       \"20753  1.822034                  0.202969                     \\n\",\n       \"19595  1.953920                  0.200636                     \\n\",\n       \"19151  1.993496                  0.199656                     \\n\",\n       \"15882  1.999000                  0.201939                     \\n\",\n       \"20242  1.988500                  0.200724                     \\n\",\n       \"16576  1.851500                  0.199566                     \\n\",\n       \"15969  1.973000                  0.199872                     \\n\",\n       \"20416  1.887000                  0.197044                     \\n\",\n       \"18549  1.928500                  0.202214                     \\n\",\n       \"20752  1.809500                  0.202163                     \\n\",\n       \"42853  1.960500                  0.200882                     \\n\",\n       \"19153  1.970000                  0.197141                     \\n\",\n       \"15884  1.900000                  0.191429                     \\n\",\n       \"20237  1.893478                  0.190316                     \\n\",\n       \"16574  1.810870                  0.198967                     \\n\",\n       \"15966  1.973913                  0.200600                     \\n\",\n       \"20417  1.745652                  0.183538                     \\n\",\n       \"18548  1.873913                  0.199415                     \\n\",\n       \"20754  1.804348                  0.202296                     \\n\",\n       \"19591  1.884783                  0.194152                     \\n\",\n       \"19149  1.939130                  0.193913                     \\n\",\n       \"15885  3.028436                  0.309236                     \\n\",\n       \"20243  3.066351                  0.308698                     \\n\",\n       \"16577  2.739336                  0.308796                     \\n\",\n       \"15965  3.109005                  0.315666                     \\n\",\n       \"20418  2.876777                  0.304863                     \\n\",\n       \"18551  2.933649                  0.317210                     \\n\",\n       \"20751  2.668246                  0.313701                     \\n\",\n       \"19594  2.924171                  0.304279                     \\n\",\n       \"19152  3.161137                  0.316324                     \\n\",\n       \"15883  2.900000                  0.291750                     \\n\",\n       \"20239  3.000000                  0.300667                     \\n\",\n       \"16575  2.790000                  0.302730                     \\n\",\n       \"15968  2.880000                  0.293222                     \\n\",\n       \"20421  2.680000                  0.282730                     \\n\",\n       \"18550  2.860000                  0.303679                     \\n\",\n       \"20749  2.560000                  0.311980                     \\n\",\n       \"19596  2.860000                  0.294333                     \\n\",\n       \"19150  2.960000                  0.297000                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@10  present_exact_f_score@10  \\\\\\n\",\n       \"15880  0.133169                 0.111597                   \\n\",\n       \"20240  0.159815                 0.134929                   \\n\",\n       \"16579  0.098447                 0.097583                   \\n\",\n       \"15963  0.132736                 0.113339                   \\n\",\n       \"20419  0.107774                 0.096699                   \\n\",\n       \"18552  0.103414                 0.091321                   \\n\",\n       \"20748  0.096335                 0.091304                   \\n\",\n       \"19592  0.107718                 0.096936                   \\n\",\n       \"19154  0.136674                 0.112545                   \\n\",\n       \"15881  0.448695                 0.342835                   \\n\",\n       \"20241  0.474886                 0.360113                   \\n\",\n       \"16573  0.406060                 0.326263                   \\n\",\n       \"15964  0.447573                 0.344755                   \\n\",\n       \"20420  0.414481                 0.329457                   \\n\",\n       \"18546  0.420080                 0.331021                   \\n\",\n       \"20750  0.399083                 0.333151                   \\n\",\n       \"19593  0.428702                 0.335010                   \\n\",\n       \"19155  0.463270                 0.348434                   \\n\",\n       \"15886  0.669059                 0.292192                   \\n\",\n       \"20238  0.667825                 0.290033                   \\n\",\n       \"16578  0.622387                 0.281363                   \\n\",\n       \"15967  0.665832                 0.289818                   \\n\",\n       \"42862  0.632839                 0.281687                   \\n\",\n       \"18547  0.651233                 0.288096                   \\n\",\n       \"20753  0.613204                 0.284068                   \\n\",\n       \"19595  0.650998                 0.287255                   \\n\",\n       \"19151  0.671003                 0.289310                   \\n\",\n       \"15882  0.669581                 0.290916                   \\n\",\n       \"20242  0.670995                 0.290072                   \\n\",\n       \"16576  0.626785                 0.283697                   \\n\",\n       \"15969  0.663963                 0.288606                   \\n\",\n       \"20416  0.638073                 0.282817                   \\n\",\n       \"18549  0.654364                 0.289838                   \\n\",\n       \"20752  0.615644                 0.283708                   \\n\",\n       \"42853  0.658079                 0.288522                   \\n\",\n       \"19153  0.666321                 0.286112                   \\n\",\n       \"15884  0.644148                 0.275119                   \\n\",\n       \"20237  0.649659                 0.274256                   \\n\",\n       \"16574  0.616566                 0.279186                   \\n\",\n       \"15966  0.675733                 0.288713                   \\n\",\n       \"20417  0.602777                 0.260960                   \\n\",\n       \"18548  0.639712                 0.283617                   \\n\",\n       \"20754  0.605883                 0.280568                   \\n\",\n       \"19591  0.646410                 0.279455                   \\n\",\n       \"19149  0.657576                 0.279683                   \\n\",\n       \"15885  0.575048                 0.372295                   \\n\",\n       \"20243  0.591638                 0.374882                   \\n\",\n       \"16577  0.531540                 0.357438                   \\n\",\n       \"15965  0.595993                 0.382008                   \\n\",\n       \"20418  0.538367                 0.359224                   \\n\",\n       \"18551  0.566169                 0.373303                   \\n\",\n       \"20751  0.507187                 0.354980                   \\n\",\n       \"19594  0.551068                 0.363175                   \\n\",\n       \"19152  0.596365                 0.382725                   \\n\",\n       \"15883  0.465048                 0.339042                   \\n\",\n       \"20239  0.477682                 0.348646                   \\n\",\n       \"16575  0.451142                 0.339725                   \\n\",\n       \"15968  0.467740                 0.341110                   \\n\",\n       \"20421  0.426769                 0.321331                   \\n\",\n       \"18550  0.458006                 0.345550                   \\n\",\n       \"20749  0.410961                 0.326802                   \\n\",\n       \"19596  0.454842                 0.337997                   \\n\",\n       \"19150  0.473546                 0.345492                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@10  present_exact_f_score_hard@10  \\\\\\n\",\n       \"15880  0.096753                         0.109804                        \\n\",\n       \"20240  0.118182                         0.133124                        \\n\",\n       \"16579  0.072078                         0.081371                        \\n\",\n       \"15963  0.096753                         0.109710                        \\n\",\n       \"20419  0.079545                         0.089823                        \\n\",\n       \"18552  0.076299                         0.086081                        \\n\",\n       \"20748  0.068831                         0.078420                        \\n\",\n       \"19592  0.079221                         0.089230                        \\n\",\n       \"19154  0.099026                         0.112073                        \\n\",\n       \"15881  0.299200                         0.337349                        \\n\",\n       \"20241  0.317000                         0.356684                        \\n\",\n       \"16573  0.267600                         0.302091                        \\n\",\n       \"15964  0.301400                         0.338323                        \\n\",\n       \"20420  0.279000                         0.313501                        \\n\",\n       \"18546  0.283600                         0.318427                        \\n\",\n       \"20750  0.269200                         0.302293                        \\n\",\n       \"19593  0.285800                         0.321785                        \\n\",\n       \"19155  0.307800                         0.347360                        \\n\",\n       \"15886  0.200946                         0.289543                        \\n\",\n       \"20238  0.199490                         0.288484                        \\n\",\n       \"16578  0.183304                         0.266962                        \\n\",\n       \"15967  0.197749                         0.286801                        \\n\",\n       \"42862  0.189153                         0.273165                        \\n\",\n       \"18547  0.192435                         0.279654                        \\n\",\n       \"20753  0.182203                         0.263862                        \\n\",\n       \"19595  0.195392                         0.281694                        \\n\",\n       \"19151  0.199350                         0.289002                        \\n\",\n       \"15882  0.199900                         0.288741                        \\n\",\n       \"20242  0.198850                         0.288198                        \\n\",\n       \"16576  0.185150                         0.269204                        \\n\",\n       \"15969  0.197300                         0.285854                        \\n\",\n       \"20416  0.188700                         0.273867                        \\n\",\n       \"18549  0.192850                         0.280240                        \\n\",\n       \"20752  0.180950                         0.263389                        \\n\",\n       \"42853  0.196050                         0.283261                        \\n\",\n       \"19153  0.197000                         0.285922                        \\n\",\n       \"15884  0.190000                         0.273341                        \\n\",\n       \"20237  0.189348                         0.273095                        \\n\",\n       \"16574  0.181087                         0.260571                        \\n\",\n       \"15966  0.197391                         0.284892                        \\n\",\n       \"20417  0.174565                         0.252659                        \\n\",\n       \"18548  0.187391                         0.270281                        \\n\",\n       \"20754  0.180435                         0.258007                        \\n\",\n       \"19591  0.188478                         0.273231                        \\n\",\n       \"19149  0.193913                         0.279683                        \\n\",\n       \"15885  0.302844                         0.367975                        \\n\",\n       \"20243  0.306635                         0.373162                        \\n\",\n       \"16577  0.273934                         0.334891                        \\n\",\n       \"15965  0.310900                         0.378249                        \\n\",\n       \"20418  0.287678                         0.348722                        \\n\",\n       \"18551  0.293365                         0.357487                        \\n\",\n       \"20751  0.266825                         0.324989                        \\n\",\n       \"19594  0.292417                         0.354195                        \\n\",\n       \"19152  0.316114                         0.382517                        \\n\",\n       \"15883  0.290000                         0.337734                        \\n\",\n       \"20239  0.300000                         0.348075                        \\n\",\n       \"16575  0.279000                         0.326478                        \\n\",\n       \"15968  0.288000                         0.337005                        \\n\",\n       \"20421  0.268000                         0.311377                        \\n\",\n       \"18550  0.286000                         0.333252                        \\n\",\n       \"20749  0.256000                         0.300088                        \\n\",\n       \"19596  0.286000                         0.331892                        \\n\",\n       \"19150  0.296000                         0.344672                        \\n\",\n       \"\\n\",\n       \"       present_exact_correct@k  present_exact_precision@k  \\\\\\n\",\n       \"15880  0.818182                 0.110022                    \\n\",\n       \"20240  1.012987                 0.129740                    \\n\",\n       \"16579  0.652597                 0.108373                    \\n\",\n       \"15963  0.824675                 0.114358                    \\n\",\n       \"20419  0.678571                 0.096857                    \\n\",\n       \"18552  0.668831                 0.092653                    \\n\",\n       \"20748  0.607143                 0.106739                    \\n\",\n       \"19592  0.701299                 0.101457                    \\n\",\n       \"19154  0.814935                 0.105261                    \\n\",\n       \"15881  2.726000                 0.351139                    \\n\",\n       \"20241  2.800000                 0.360440                    \\n\",\n       \"16573  2.350000                 0.329622                    \\n\",\n       \"15964  2.726000                 0.355538                    \\n\",\n       \"20420  2.492000                 0.333977                    \\n\",\n       \"18546  2.542000                 0.337469                    \\n\",\n       \"20750  2.430000                 0.353912                    \\n\",\n       \"19593  2.594000                 0.339351                    \\n\",\n       \"19155  2.780000                 0.348045                    \\n\",\n       \"15886  1.295442                 0.353874                    \\n\",\n       \"20238  1.300145                 0.356239                    \\n\",\n       \"16578  1.121729                 0.331753                    \\n\",\n       \"15967  1.271476                 0.351660                    \\n\",\n       \"42862  1.202281                 0.341331                    \\n\",\n       \"18547  1.184720                 0.333998                    \\n\",\n       \"20753  1.182619                 0.343768                    \\n\",\n       \"19595  1.280582                 0.354728                    \\n\",\n       \"19151  1.289538                 0.354128                    \\n\",\n       \"15882  1.280000                 0.352489                    \\n\",\n       \"20242  1.268500                 0.352923                    \\n\",\n       \"16576  1.120500                 0.331888                    \\n\",\n       \"15969  1.238500                 0.345704                    \\n\",\n       \"20416  1.144500                 0.329595                    \\n\",\n       \"18549  1.151000                 0.323042                    \\n\",\n       \"20752  1.145500                 0.337388                    \\n\",\n       \"42853  1.230500                 0.339938                    \\n\",\n       \"19153  1.256500                 0.347959                    \\n\",\n       \"15884  1.226087                 0.341756                    \\n\",\n       \"20237  1.263043                 0.364159                    \\n\",\n       \"16574  1.167391                 0.328166                    \\n\",\n       \"15966  1.180435                 0.327544                    \\n\",\n       \"20417  1.130435                 0.326001                    \\n\",\n       \"18548  1.202174                 0.338384                    \\n\",\n       \"20754  1.204348                 0.351628                    \\n\",\n       \"19591  1.180435                 0.340083                    \\n\",\n       \"19149  1.245652                 0.358957                    \\n\",\n       \"15885  2.592417                 0.421379                    \\n\",\n       \"20243  2.663507                 0.445702                    \\n\",\n       \"16577  2.298578                 0.401548                    \\n\",\n       \"15965  2.606635                 0.439262                    \\n\",\n       \"20418  2.393365                 0.397135                    \\n\",\n       \"18551  2.383886                 0.399126                    \\n\",\n       \"20751  2.331754                 0.413376                    \\n\",\n       \"19594  2.521327                 0.419279                    \\n\",\n       \"19152  2.563981                 0.408198                    \\n\",\n       \"15883  2.400000                 0.341444                    \\n\",\n       \"20239  2.410000                 0.343236                    \\n\",\n       \"16575  2.330000                 0.352199                    \\n\",\n       \"15968  2.440000                 0.354103                    \\n\",\n       \"20421  2.300000                 0.340683                    \\n\",\n       \"18550  2.400000                 0.362376                    \\n\",\n       \"20749  2.200000                 0.368918                    \\n\",\n       \"19596  2.420000                 0.346333                    \\n\",\n       \"19150  2.540000                 0.344903                    \\n\",\n       \"\\n\",\n       \"       present_exact_recall@k  present_exact_f_score@k  \\\\\\n\",\n       \"15880  0.108095                0.108818                  \\n\",\n       \"20240  0.129412                0.129553                  \\n\",\n       \"16579  0.085286                0.092317                  \\n\",\n       \"15963  0.109358                0.110825                  \\n\",\n       \"20419  0.088814                0.091654                  \\n\",\n       \"18552  0.086589                0.088779                  \\n\",\n       \"20748  0.081781                0.088217                  \\n\",\n       \"19592  0.090451                0.094544                  \\n\",\n       \"19154  0.104379                0.104737                  \\n\",\n       \"15881  0.348425                0.349578                  \\n\",\n       \"20241  0.354010                0.356384                  \\n\",\n       \"16573  0.306942                0.314609                  \\n\",\n       \"15964  0.350402                0.352470                  \\n\",\n       \"20420  0.323430                0.327417                  \\n\",\n       \"18546  0.329063                0.332248                  \\n\",\n       \"20750  0.324004                0.334135                  \\n\",\n       \"19593  0.331778                0.334227                  \\n\",\n       \"19155  0.347117                0.347466                  \\n\",\n       \"15886  0.350674                0.351790                  \\n\",\n       \"20238  0.353672                0.354612                  \\n\",\n       \"16578  0.326610                0.328123                  \\n\",\n       \"15967  0.348474                0.349565                  \\n\",\n       \"42862  0.337171                0.338489                  \\n\",\n       \"18547  0.329754                0.331106                  \\n\",\n       \"20753  0.337785                0.339553                  \\n\",\n       \"19595  0.351574                0.352628                  \\n\",\n       \"19151  0.353367                0.353653                  \\n\",\n       \"15882  0.349323                0.350447                  \\n\",\n       \"20242  0.350067                0.351043                  \\n\",\n       \"16576  0.326896                0.328325                  \\n\",\n       \"15969  0.342384                0.343553                  \\n\",\n       \"20416  0.325697                0.326933                  \\n\",\n       \"18549  0.318908                0.320253                  \\n\",\n       \"20752  0.331392                0.333152                  \\n\",\n       \"42853  0.336936                0.337934                  \\n\",\n       \"19153  0.347389                0.347617                  \\n\",\n       \"15884  0.340916                0.341284                  \\n\",\n       \"20237  0.364050                0.364099                  \\n\",\n       \"16574  0.326109                0.326983                  \\n\",\n       \"15966  0.326685                0.327041                  \\n\",\n       \"20417  0.323991                0.324624                  \\n\",\n       \"18548  0.337488                0.337876                  \\n\",\n       \"20754  0.349761                0.350525                  \\n\",\n       \"19591  0.339355                0.339638                  \\n\",\n       \"19149  0.358957                0.358957                  \\n\",\n       \"15885  0.417039                0.418743                  \\n\",\n       \"20243  0.445099                0.445383                  \\n\",\n       \"16577  0.386473                0.392692                  \\n\",\n       \"15965  0.435978                0.437318                  \\n\",\n       \"20418  0.388204                0.391009                  \\n\",\n       \"18551  0.389511                0.392942                  \\n\",\n       \"20751  0.393297                0.400245                  \\n\",\n       \"19594  0.415829                0.417122                  \\n\",\n       \"19152  0.408198                0.408198                  \\n\",\n       \"15883  0.337888                0.339435                  \\n\",\n       \"20239  0.338896                0.340750                  \\n\",\n       \"16575  0.334404                0.340094                  \\n\",\n       \"15968  0.349683                0.351318                  \\n\",\n       \"20421  0.330673                0.334043                  \\n\",\n       \"18550  0.351064                0.355481                  \\n\",\n       \"20749  0.327183                0.339751                  \\n\",\n       \"19596  0.341128                0.343224                  \\n\",\n       \"19150  0.344903                0.344903                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@k  present_exact_f_score_hard@k  \\\\\\n\",\n       \"15880  0.108095                        0.108095                       \\n\",\n       \"20240  0.129412                        0.129412                       \\n\",\n       \"16579  0.085286                        0.085286                       \\n\",\n       \"15963  0.109358                        0.109358                       \\n\",\n       \"20419  0.088814                        0.088814                       \\n\",\n       \"18552  0.086589                        0.086589                       \\n\",\n       \"20748  0.081781                        0.081781                       \\n\",\n       \"19592  0.090451                        0.090451                       \\n\",\n       \"19154  0.104379                        0.104379                       \\n\",\n       \"15881  0.348425                        0.348425                       \\n\",\n       \"20241  0.354010                        0.354010                       \\n\",\n       \"16573  0.306942                        0.306942                       \\n\",\n       \"15964  0.350402                        0.350402                       \\n\",\n       \"20420  0.323430                        0.323430                       \\n\",\n       \"18546  0.329063                        0.329063                       \\n\",\n       \"20750  0.324004                        0.324004                       \\n\",\n       \"19593  0.331778                        0.331778                       \\n\",\n       \"19155  0.347117                        0.347117                       \\n\",\n       \"15886  0.350674                        0.350674                       \\n\",\n       \"20238  0.353672                        0.353672                       \\n\",\n       \"16578  0.326610                        0.326610                       \\n\",\n       \"15967  0.348474                        0.348474                       \\n\",\n       \"42862  0.337171                        0.337171                       \\n\",\n       \"18547  0.329754                        0.329754                       \\n\",\n       \"20753  0.337785                        0.337785                       \\n\",\n       \"19595  0.351574                        0.351574                       \\n\",\n       \"19151  0.353367                        0.353367                       \\n\",\n       \"15882  0.349323                        0.349323                       \\n\",\n       \"20242  0.350067                        0.350067                       \\n\",\n       \"16576  0.326896                        0.326896                       \\n\",\n       \"15969  0.342384                        0.342384                       \\n\",\n       \"20416  0.325697                        0.325697                       \\n\",\n       \"18549  0.318908                        0.318908                       \\n\",\n       \"20752  0.331392                        0.331392                       \\n\",\n       \"42853  0.336936                        0.336936                       \\n\",\n       \"19153  0.347389                        0.347389                       \\n\",\n       \"15884  0.340916                        0.340916                       \\n\",\n       \"20237  0.364050                        0.364050                       \\n\",\n       \"16574  0.326109                        0.326109                       \\n\",\n       \"15966  0.326685                        0.326685                       \\n\",\n       \"20417  0.323991                        0.323991                       \\n\",\n       \"18548  0.337488                        0.337488                       \\n\",\n       \"20754  0.349761                        0.349761                       \\n\",\n       \"19591  0.339355                        0.339355                       \\n\",\n       \"19149  0.358957                        0.358957                       \\n\",\n       \"15885  0.417039                        0.417039                       \\n\",\n       \"20243  0.445099                        0.445099                       \\n\",\n       \"16577  0.386473                        0.386473                       \\n\",\n       \"15965  0.435978                        0.435978                       \\n\",\n       \"20418  0.388204                        0.388204                       \\n\",\n       \"18551  0.389511                        0.389511                       \\n\",\n       \"20751  0.393297                        0.393297                       \\n\",\n       \"19594  0.415829                        0.415829                       \\n\",\n       \"19152  0.408198                        0.408198                       \\n\",\n       \"15883  0.337888                        0.337888                       \\n\",\n       \"20239  0.338896                        0.338896                       \\n\",\n       \"16575  0.334404                        0.334404                       \\n\",\n       \"15968  0.349683                        0.349683                       \\n\",\n       \"20421  0.330673                        0.330673                       \\n\",\n       \"18550  0.351064                        0.351064                       \\n\",\n       \"20749  0.327183                        0.327183                       \\n\",\n       \"19596  0.341128                        0.341128                       \\n\",\n       \"19150  0.344903                        0.344903                       \\n\",\n       \"\\n\",\n       \"       present_exact_correct@M  present_exact_precision@M  \\\\\\n\",\n       \"15880  1.288961                 0.085915                    \\n\",\n       \"20240  1.772727                 0.091469                    \\n\",\n       \"16579  0.766234                 0.108706                    \\n\",\n       \"15963  1.350649                 0.089301                    \\n\",\n       \"20419  0.967532                 0.089625                    \\n\",\n       \"18552  0.990260                 0.085301                    \\n\",\n       \"20748  0.746753                 0.101676                    \\n\",\n       \"19592  0.899351                 0.091106                    \\n\",\n       \"19154  1.389610                 0.074732                    \\n\",\n       \"15881  3.900000                 0.253410                    \\n\",\n       \"20241  4.114000                 0.260525                    \\n\",\n       \"16573  3.046000                 0.281587                    \\n\",\n       \"15964  3.990000                 0.257773                    \\n\",\n       \"20420  3.470000                 0.263641                    \\n\",\n       \"18546  3.484000                 0.260409                    \\n\",\n       \"20750  3.048000                 0.293764                    \\n\",\n       \"19593  3.792000                 0.257251                    \\n\",\n       \"19155  4.950000                 0.225865                    \\n\",\n       \"15886  2.314104                 0.145541                    \\n\",\n       \"20238  2.333467                 0.137905                    \\n\",\n       \"16578  1.957122                 0.162770                    \\n\",\n       \"15967  2.367789                 0.143706                    \\n\",\n       \"42862  2.166658                 0.152173                    \\n\",\n       \"18547  2.173213                 0.155402                    \\n\",\n       \"20753  1.999500                 0.172278                    \\n\",\n       \"19595  2.277180                 0.144787                    \\n\",\n       \"19151  2.505729                 0.107536                    \\n\",\n       \"15882  2.312500                 0.143491                    \\n\",\n       \"20242  2.319000                 0.137452                    \\n\",\n       \"16576  1.969500                 0.162999                    \\n\",\n       \"15969  2.361500                 0.142535                    \\n\",\n       \"20416  2.142500                 0.152217                    \\n\",\n       \"18549  2.182000                 0.156151                    \\n\",\n       \"20752  1.990500                 0.172337                    \\n\",\n       \"42853  2.298000                 0.144580                    \\n\",\n       \"19153  2.466500                 0.106008                    \\n\",\n       \"15884  2.195652                 0.135892                    \\n\",\n       \"20237  2.247826                 0.135553                    \\n\",\n       \"16574  1.991304                 0.175953                    \\n\",\n       \"15966  2.310870                 0.143963                    \\n\",\n       \"20417  2.063043                 0.151703                    \\n\",\n       \"18548  2.097826                 0.156265                    \\n\",\n       \"20754  1.989130                 0.174421                    \\n\",\n       \"19591  2.182609                 0.141247                    \\n\",\n       \"19149  2.560870                 0.106496                    \\n\",\n       \"15885  3.483412                 0.239961                    \\n\",\n       \"20243  3.497630                 0.226759                    \\n\",\n       \"16577  2.919431                 0.269362                    \\n\",\n       \"15965  3.696682                 0.234006                    \\n\",\n       \"20418  3.274882                 0.251583                    \\n\",\n       \"18551  3.241706                 0.265737                    \\n\",\n       \"20751  2.933649                 0.285524                    \\n\",\n       \"19594  3.398104                 0.231057                    \\n\",\n       \"19152  4.042654                 0.176361                    \\n\",\n       \"15883  3.410000                 0.229161                    \\n\",\n       \"20239  3.750000                 0.224714                    \\n\",\n       \"16575  3.160000                 0.268197                    \\n\",\n       \"15968  3.740000                 0.233780                    \\n\",\n       \"20421  3.150000                 0.233621                    \\n\",\n       \"18550  3.260000                 0.244920                    \\n\",\n       \"20749  2.980000                 0.286066                    \\n\",\n       \"19596  3.440000                 0.241528                    \\n\",\n       \"19150  4.250000                 0.190448                    \\n\",\n       \"\\n\",\n       \"       present_exact_recall@M  present_exact_f_score@M  \\\\\\n\",\n       \"15880  0.177815                0.110981                  \\n\",\n       \"20240  0.236609                0.127492                  \\n\",\n       \"16579  0.105791                0.099742                  \\n\",\n       \"15963  0.183420                0.113019                  \\n\",\n       \"20419  0.131660                0.099908                  \\n\",\n       \"18552  0.133198                0.097990                  \\n\",\n       \"20748  0.104559                0.092719                  \\n\",\n       \"19592  0.121827                0.097104                  \\n\",\n       \"19154  0.188949                0.102942                  \\n\",\n       \"15881  0.554303                0.330950                  \\n\",\n       \"20241  0.581948                0.339713                  \\n\",\n       \"16573  0.447459                0.320838                  \\n\",\n       \"15964  0.560871                0.335210                  \\n\",\n       \"20420  0.490802                0.323459                  \\n\",\n       \"18546  0.500848                0.322993                  \\n\",\n       \"20750  0.442110                0.325367                  \\n\",\n       \"19593  0.532850                0.326068                  \\n\",\n       \"19155  0.686583                0.324365                  \\n\",\n       \"15886  0.741827                0.227579                  \\n\",\n       \"20238  0.748755                0.218289                  \\n\",\n       \"16578  0.657909                0.242381                  \\n\",\n       \"15967  0.761764                0.225728                  \\n\",\n       \"42862  0.702815                0.232659                  \\n\",\n       \"18547  0.716243                0.237955                  \\n\",\n       \"20753  0.658896                0.251282                  \\n\",\n       \"19595  0.728972                0.224767                  \\n\",\n       \"19151  0.795612                0.179321                  \\n\",\n       \"15882  0.745841                0.225489                  \\n\",\n       \"20242  0.748998                0.217533                  \\n\",\n       \"16576  0.661149                0.243103                  \\n\",\n       \"15969  0.760569                0.224557                  \\n\",\n       \"20416  0.701727                0.233717                  \\n\",\n       \"18549  0.723010                0.239716                  \\n\",\n       \"20752  0.664448                0.251910                  \\n\",\n       \"42853  0.738844                0.226084                  \\n\",\n       \"19153  0.789384                0.177566                  \\n\",\n       \"15884  0.717157                0.214282                  \\n\",\n       \"20237  0.743375                0.216449                  \\n\",\n       \"16574  0.657262                0.257118                  \\n\",\n       \"15966  0.758657                0.226948                  \\n\",\n       \"20417  0.681483                0.230146                  \\n\",\n       \"18548  0.696846                0.238110                  \\n\",\n       \"20754  0.651960                0.254662                  \\n\",\n       \"19591  0.725231                0.222029                  \\n\",\n       \"19149  0.804771                0.179086                  \\n\",\n       \"15885  0.635611                0.322701                  \\n\",\n       \"20243  0.653385                0.313475                  \\n\",\n       \"16577  0.557818                0.328466                  \\n\",\n       \"15965  0.677230                0.320866                  \\n\",\n       \"20418  0.597892                0.323563                  \\n\",\n       \"18551  0.609994                0.336570                  \\n\",\n       \"20751  0.554653                0.337768                  \\n\",\n       \"19594  0.629717                0.309661                  \\n\",\n       \"19152  0.734413                0.268863                  \\n\",\n       \"15883  0.538694                0.304920                  \\n\",\n       \"20239  0.591673                0.306544                  \\n\",\n       \"16575  0.502560                0.324419                  \\n\",\n       \"15968  0.588589                0.317659                  \\n\",\n       \"20421  0.489711                0.295608                  \\n\",\n       \"18550  0.510295                0.308436                  \\n\",\n       \"20749  0.473367                0.318737                  \\n\",\n       \"19596  0.538662                0.312895                  \\n\",\n       \"19150  0.652836                0.280388                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@M  present_exact_f_score_hard@M  \\\\\\n\",\n       \"15880  0.085915                        0.110981                       \\n\",\n       \"20240  0.091469                        0.127492                       \\n\",\n       \"16579  0.108706                        0.099742                       \\n\",\n       \"15963  0.089301                        0.113019                       \\n\",\n       \"20419  0.089625                        0.099908                       \\n\",\n       \"18552  0.085301                        0.097990                       \\n\",\n       \"20748  0.101676                        0.092719                       \\n\",\n       \"19592  0.091106                        0.097104                       \\n\",\n       \"19154  0.074732                        0.102942                       \\n\",\n       \"15881  0.253410                        0.330950                       \\n\",\n       \"20241  0.260525                        0.339713                       \\n\",\n       \"16573  0.281587                        0.320838                       \\n\",\n       \"15964  0.257773                        0.335210                       \\n\",\n       \"20420  0.263641                        0.323459                       \\n\",\n       \"18546  0.260409                        0.322993                       \\n\",\n       \"20750  0.293764                        0.325367                       \\n\",\n       \"19593  0.257251                        0.326068                       \\n\",\n       \"19155  0.225865                        0.324365                       \\n\",\n       \"15886  0.145541                        0.227579                       \\n\",\n       \"20238  0.137905                        0.218289                       \\n\",\n       \"16578  0.162770                        0.242381                       \\n\",\n       \"15967  0.143706                        0.225728                       \\n\",\n       \"42862  0.152173                        0.232659                       \\n\",\n       \"18547  0.155402                        0.237955                       \\n\",\n       \"20753  0.172278                        0.251282                       \\n\",\n       \"19595  0.144787                        0.224767                       \\n\",\n       \"19151  0.107536                        0.179321                       \\n\",\n       \"15882  0.143491                        0.225489                       \\n\",\n       \"20242  0.137452                        0.217533                       \\n\",\n       \"16576  0.162999                        0.243103                       \\n\",\n       \"15969  0.142535                        0.224557                       \\n\",\n       \"20416  0.152217                        0.233717                       \\n\",\n       \"18549  0.156151                        0.239716                       \\n\",\n       \"20752  0.172337                        0.251910                       \\n\",\n       \"42853  0.144580                        0.226084                       \\n\",\n       \"19153  0.106008                        0.177566                       \\n\",\n       \"15884  0.135892                        0.214282                       \\n\",\n       \"20237  0.135553                        0.216449                       \\n\",\n       \"16574  0.175953                        0.257118                       \\n\",\n       \"15966  0.143963                        0.226948                       \\n\",\n       \"20417  0.151703                        0.230146                       \\n\",\n       \"18548  0.156265                        0.238110                       \\n\",\n       \"20754  0.174421                        0.254662                       \\n\",\n       \"19591  0.141247                        0.222029                       \\n\",\n       \"19149  0.106496                        0.179086                       \\n\",\n       \"15885  0.239961                        0.322701                       \\n\",\n       \"20243  0.226759                        0.313475                       \\n\",\n       \"16577  0.269362                        0.328466                       \\n\",\n       \"15965  0.234006                        0.320866                       \\n\",\n       \"20418  0.251583                        0.323563                       \\n\",\n       \"18551  0.265737                        0.336570                       \\n\",\n       \"20751  0.285524                        0.337768                       \\n\",\n       \"19594  0.231057                        0.309661                       \\n\",\n       \"19152  0.176361                        0.268863                       \\n\",\n       \"15883  0.229161                        0.304920                       \\n\",\n       \"20239  0.224714                        0.306544                       \\n\",\n       \"16575  0.268197                        0.324419                       \\n\",\n       \"15968  0.233780                        0.317659                       \\n\",\n       \"20421  0.233621                        0.295608                       \\n\",\n       \"18550  0.244920                        0.308436                       \\n\",\n       \"20749  0.286066                        0.318737                       \\n\",\n       \"19596  0.241528                        0.312895                       \\n\",\n       \"19150  0.190448                        0.280388                       \\n\",\n       \"\\n\",\n       \"       present_exact_correct@1  present_exact_precision@1  \\\\\\n\",\n       \"15880  0.123377                 0.123377                    \\n\",\n       \"20240  0.227273                 0.227273                    \\n\",\n       \"16579  0.146104                 0.146104                    \\n\",\n       \"15963  0.155844                 0.155844                    \\n\",\n       \"20419  0.142857                 0.142857                    \\n\",\n       \"18552  0.103896                 0.103896                    \\n\",\n       \"20748  0.152597                 0.152597                    \\n\",\n       \"19592  0.159091                 0.159091                    \\n\",\n       \"19154  0.181818                 0.181818                    \\n\",\n       \"15881  0.460000                 0.460000                    \\n\",\n       \"20241  0.456000                 0.456000                    \\n\",\n       \"16573  0.342000                 0.342000                    \\n\",\n       \"15964  0.498000                 0.498000                    \\n\",\n       \"20420  0.410000                 0.410000                    \\n\",\n       \"18546  0.454000                 0.454000                    \\n\",\n       \"20750  0.456000                 0.456000                    \\n\",\n       \"19593  0.382000                 0.382000                    \\n\",\n       \"19155  0.404000                 0.404000                    \\n\",\n       \"15886  0.387902                 0.387902                    \\n\",\n       \"20238  0.385801                 0.385801                    \\n\",\n       \"16578  0.384500                 0.384500                    \\n\",\n       \"15967  0.367789                 0.367789                    \\n\",\n       \"42862  0.405564                 0.405564                    \\n\",\n       \"18547  0.334267                 0.334267                    \\n\",\n       \"20753  0.382599                 0.382599                    \\n\",\n       \"19595  0.411568                 0.411568                    \\n\",\n       \"19151  0.413018                 0.413018                    \\n\",\n       \"15882  0.391000                 0.391000                    \\n\",\n       \"20242  0.398000                 0.398000                    \\n\",\n       \"16576  0.384000                 0.384000                    \\n\",\n       \"15969  0.363000                 0.363000                    \\n\",\n       \"20416  0.391500                 0.391500                    \\n\",\n       \"18549  0.320000                 0.320000                    \\n\",\n       \"20752  0.382500                 0.382500                    \\n\",\n       \"42853  0.389500                 0.389500                    \\n\",\n       \"19153  0.398500                 0.398500                    \\n\",\n       \"15884  0.378261                 0.378261                    \\n\",\n       \"20237  0.404348                 0.404348                    \\n\",\n       \"16574  0.350000                 0.350000                    \\n\",\n       \"15966  0.376087                 0.376087                    \\n\",\n       \"20417  0.380435                 0.380435                    \\n\",\n       \"18548  0.354348                 0.354348                    \\n\",\n       \"20754  0.397826                 0.397826                    \\n\",\n       \"19591  0.400000                 0.400000                    \\n\",\n       \"19149  0.406522                 0.406522                    \\n\",\n       \"15885  0.620853                 0.620853                    \\n\",\n       \"20243  0.616114                 0.616114                    \\n\",\n       \"16577  0.587678                 0.587678                    \\n\",\n       \"15965  0.568720                 0.568720                    \\n\",\n       \"20418  0.545024                 0.545024                    \\n\",\n       \"18551  0.563981                 0.563981                    \\n\",\n       \"20751  0.587678                 0.587678                    \\n\",\n       \"19594  0.592417                 0.592417                    \\n\",\n       \"19152  0.592417                 0.592417                    \\n\",\n       \"15883  0.460000                 0.460000                    \\n\",\n       \"20239  0.570000                 0.570000                    \\n\",\n       \"16575  0.470000                 0.470000                    \\n\",\n       \"15968  0.470000                 0.470000                    \\n\",\n       \"20421  0.520000                 0.520000                    \\n\",\n       \"18550  0.440000                 0.440000                    \\n\",\n       \"20749  0.520000                 0.520000                    \\n\",\n       \"19596  0.460000                 0.460000                    \\n\",\n       \"19150  0.510000                 0.510000                    \\n\",\n       \"\\n\",\n       \"       present_exact_recall@1  present_exact_f_score@1  \\\\\\n\",\n       \"15880  0.017428                0.030183                  \\n\",\n       \"20240  0.032663                0.056304                  \\n\",\n       \"16579  0.019714                0.034387                  \\n\",\n       \"15963  0.022636                0.039062                  \\n\",\n       \"20419  0.019769                0.034478                  \\n\",\n       \"18552  0.014668                0.025624                  \\n\",\n       \"20748  0.020534                0.035957                  \\n\",\n       \"19592  0.021427                0.037432                  \\n\",\n       \"19154  0.024240                0.042386                  \\n\",\n       \"15881  0.072606                0.120982                  \\n\",\n       \"20241  0.078179                0.127991                  \\n\",\n       \"16573  0.057729                0.094772                  \\n\",\n       \"15964  0.082044                0.135153                  \\n\",\n       \"20420  0.068197                0.111129                  \\n\",\n       \"18546  0.074266                0.123773                  \\n\",\n       \"20750  0.075998                0.123670                  \\n\",\n       \"19593  0.062279                0.103306                  \\n\",\n       \"19155  0.067614                0.111564                  \\n\",\n       \"15886  0.143277                0.196908                  \\n\",\n       \"20238  0.142417                0.196249                  \\n\",\n       \"16578  0.141222                0.194288                  \\n\",\n       \"15967  0.138106                0.189219                  \\n\",\n       \"42862  0.152763                0.208574                  \\n\",\n       \"18547  0.123469                0.170023                  \\n\",\n       \"20753  0.143371                0.196188                  \\n\",\n       \"19595  0.153419                0.210510                  \\n\",\n       \"19151  0.154065                0.211489                  \\n\",\n       \"15882  0.145587                0.199184                  \\n\",\n       \"20242  0.147817                0.202631                  \\n\",\n       \"16576  0.145633                0.198087                  \\n\",\n       \"15969  0.139657                0.188782                  \\n\",\n       \"20416  0.148333                0.202078                  \\n\",\n       \"18549  0.117507                0.161675                  \\n\",\n       \"20752  0.142017                0.194732                  \\n\",\n       \"42853  0.145922                0.199710                  \\n\",\n       \"19153  0.150514                0.205309                  \\n\",\n       \"15884  0.148989                0.199616                  \\n\",\n       \"20237  0.164415                0.217201                  \\n\",\n       \"16574  0.139898                0.187583                  \\n\",\n       \"15966  0.140778                0.192307                  \\n\",\n       \"20417  0.154975                0.205308                  \\n\",\n       \"18548  0.141198                0.186834                  \\n\",\n       \"20754  0.168837                0.218965                  \\n\",\n       \"19591  0.162913                0.215559                  \\n\",\n       \"19149  0.169789                0.223516                  \\n\",\n       \"15885  0.121761                0.195807                  \\n\",\n       \"20243  0.138758                0.210463                  \\n\",\n       \"16577  0.122989                0.190737                  \\n\",\n       \"15965  0.130225                0.196472                  \\n\",\n       \"20418  0.111447                0.174271                  \\n\",\n       \"18551  0.114894                0.181233                  \\n\",\n       \"20751  0.129019                0.198050                  \\n\",\n       \"19594  0.128103                0.198238                  \\n\",\n       \"19152  0.123268                0.193204                  \\n\",\n       \"15883  0.077521                0.129530                  \\n\",\n       \"20239  0.096513                0.159909                  \\n\",\n       \"16575  0.080481                0.133085                  \\n\",\n       \"15968  0.081922                0.133676                  \\n\",\n       \"20421  0.095346                0.155307                  \\n\",\n       \"18550  0.070640                0.118437                  \\n\",\n       \"20749  0.090388                0.148304                  \\n\",\n       \"19596  0.074396                0.124423                  \\n\",\n       \"19150  0.082404                0.138197                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@1  present_exact_f_score_hard@1  \\\\\\n\",\n       \"15880  0.123377                        0.030183                       \\n\",\n       \"20240  0.227273                        0.056304                       \\n\",\n       \"16579  0.146104                        0.034387                       \\n\",\n       \"15963  0.155844                        0.039062                       \\n\",\n       \"20419  0.142857                        0.034478                       \\n\",\n       \"18552  0.103896                        0.025624                       \\n\",\n       \"20748  0.152597                        0.035957                       \\n\",\n       \"19592  0.159091                        0.037432                       \\n\",\n       \"19154  0.181818                        0.042386                       \\n\",\n       \"15881  0.460000                        0.120982                       \\n\",\n       \"20241  0.456000                        0.127991                       \\n\",\n       \"16573  0.342000                        0.094772                       \\n\",\n       \"15964  0.498000                        0.135153                       \\n\",\n       \"20420  0.410000                        0.111129                       \\n\",\n       \"18546  0.454000                        0.123773                       \\n\",\n       \"20750  0.456000                        0.123670                       \\n\",\n       \"19593  0.382000                        0.103306                       \\n\",\n       \"19155  0.404000                        0.111564                       \\n\",\n       \"15886  0.387902                        0.196908                       \\n\",\n       \"20238  0.385801                        0.196249                       \\n\",\n       \"16578  0.384500                        0.194288                       \\n\",\n       \"15967  0.367789                        0.189219                       \\n\",\n       \"42862  0.405564                        0.208574                       \\n\",\n       \"18547  0.334267                        0.170023                       \\n\",\n       \"20753  0.382599                        0.196188                       \\n\",\n       \"19595  0.411568                        0.210510                       \\n\",\n       \"19151  0.413018                        0.211489                       \\n\",\n       \"15882  0.391000                        0.199184                       \\n\",\n       \"20242  0.398000                        0.202631                       \\n\",\n       \"16576  0.384000                        0.198087                       \\n\",\n       \"15969  0.363000                        0.188782                       \\n\",\n       \"20416  0.391500                        0.202078                       \\n\",\n       \"18549  0.320000                        0.161675                       \\n\",\n       \"20752  0.382500                        0.194732                       \\n\",\n       \"42853  0.389500                        0.199710                       \\n\",\n       \"19153  0.398500                        0.205309                       \\n\",\n       \"15884  0.378261                        0.199616                       \\n\",\n       \"20237  0.404348                        0.217201                       \\n\",\n       \"16574  0.350000                        0.187583                       \\n\",\n       \"15966  0.376087                        0.192307                       \\n\",\n       \"20417  0.380435                        0.205308                       \\n\",\n       \"18548  0.354348                        0.186834                       \\n\",\n       \"20754  0.397826                        0.218965                       \\n\",\n       \"19591  0.400000                        0.215559                       \\n\",\n       \"19149  0.406522                        0.223516                       \\n\",\n       \"15885  0.620853                        0.195807                       \\n\",\n       \"20243  0.616114                        0.210463                       \\n\",\n       \"16577  0.587678                        0.190737                       \\n\",\n       \"15965  0.568720                        0.196472                       \\n\",\n       \"20418  0.545024                        0.174271                       \\n\",\n       \"18551  0.563981                        0.181233                       \\n\",\n       \"20751  0.587678                        0.198050                       \\n\",\n       \"19594  0.592417                        0.198238                       \\n\",\n       \"19152  0.592417                        0.193204                       \\n\",\n       \"15883  0.460000                        0.129530                       \\n\",\n       \"20239  0.570000                        0.159909                       \\n\",\n       \"16575  0.470000                        0.133085                       \\n\",\n       \"15968  0.470000                        0.133676                       \\n\",\n       \"20421  0.520000                        0.155307                       \\n\",\n       \"18550  0.440000                        0.118437                       \\n\",\n       \"20749  0.520000                        0.148304                       \\n\",\n       \"19596  0.460000                        0.124423                       \\n\",\n       \"19150  0.510000                        0.138197                       \\n\",\n       \"\\n\",\n       \"       present_exact_correct@3  present_exact_precision@3  \\\\\\n\",\n       \"15880  0.399351                 0.133117                    \\n\",\n       \"20240  0.503247                 0.167749                    \\n\",\n       \"16579  0.340909                 0.117424                    \\n\",\n       \"15963  0.422078                 0.141234                    \\n\",\n       \"20419  0.334416                 0.112554                    \\n\",\n       \"18552  0.298701                 0.099567                    \\n\",\n       \"20748  0.370130                 0.130411                    \\n\",\n       \"19592  0.412338                 0.137446                    \\n\",\n       \"19154  0.383117                 0.127706                    \\n\",\n       \"15881  1.218000                 0.406000                    \\n\",\n       \"20241  1.242000                 0.414000                    \\n\",\n       \"16573  1.120000                 0.375667                    \\n\",\n       \"15964  1.198000                 0.399333                    \\n\",\n       \"20420  1.148000                 0.384000                    \\n\",\n       \"18546  1.198000                 0.399333                    \\n\",\n       \"20750  1.232000                 0.414000                    \\n\",\n       \"19593  1.196000                 0.399000                    \\n\",\n       \"19155  1.238000                 0.412667                    \\n\",\n       \"15886  1.071396                 0.357174                    \\n\",\n       \"20238  1.076350                 0.358808                    \\n\",\n       \"16578  1.002752                 0.334551                    \\n\",\n       \"15967  1.063641                 0.354614                    \\n\",\n       \"42862  1.004653                 0.335018                    \\n\",\n       \"18547  1.024616                 0.341714                    \\n\",\n       \"20753  1.025366                 0.342272                    \\n\",\n       \"19595  1.051133                 0.350411                    \\n\",\n       \"19151  1.060890                 0.353630                    \\n\",\n       \"15882  1.061000                 0.353667                    \\n\",\n       \"20242  1.065500                 0.355167                    \\n\",\n       \"16576  0.993500                 0.331750                    \\n\",\n       \"15969  1.028000                 0.342667                    \\n\",\n       \"20416  0.964500                 0.321500                    \\n\",\n       \"18549  0.998500                 0.332917                    \\n\",\n       \"20752  1.017500                 0.339833                    \\n\",\n       \"42853  1.011500                 0.337167                    \\n\",\n       \"19153  1.035500                 0.345167                    \\n\",\n       \"15884  1.010870                 0.336957                    \\n\",\n       \"20237  1.050000                 0.350000                    \\n\",\n       \"16574  0.982609                 0.327899                    \\n\",\n       \"15966  1.010870                 0.336957                    \\n\",\n       \"20417  0.950000                 0.316667                    \\n\",\n       \"18548  0.980435                 0.326812                    \\n\",\n       \"20754  1.019565                 0.340580                    \\n\",\n       \"19591  0.993478                 0.331159                    \\n\",\n       \"19149  1.026087                 0.342029                    \\n\",\n       \"15885  1.592417                 0.530806                    \\n\",\n       \"20243  1.582938                 0.527646                    \\n\",\n       \"16577  1.464455                 0.488152                    \\n\",\n       \"15965  1.545024                 0.515008                    \\n\",\n       \"20418  1.426540                 0.478673                    \\n\",\n       \"18551  1.521327                 0.507109                    \\n\",\n       \"20751  1.492891                 0.502370                    \\n\",\n       \"19594  1.521327                 0.507109                    \\n\",\n       \"19152  1.578199                 0.526066                    \\n\",\n       \"15883  1.380000                 0.460000                    \\n\",\n       \"20239  1.330000                 0.443333                    \\n\",\n       \"16575  1.350000                 0.450000                    \\n\",\n       \"15968  1.270000                 0.423333                    \\n\",\n       \"20421  1.330000                 0.443333                    \\n\",\n       \"18550  1.350000                 0.450000                    \\n\",\n       \"20749  1.270000                 0.426667                    \\n\",\n       \"19596  1.280000                 0.426667                    \\n\",\n       \"19150  1.340000                 0.446667                    \\n\",\n       \"\\n\",\n       \"       present_exact_recall@3  present_exact_f_score@3  \\\\\\n\",\n       \"15880  0.057218                0.078092                  \\n\",\n       \"20240  0.069635                0.096086                  \\n\",\n       \"16579  0.048399                0.066269                  \\n\",\n       \"15963  0.058670                0.080767                  \\n\",\n       \"20419  0.048104                0.066089                  \\n\",\n       \"18552  0.041969                0.057950                  \\n\",\n       \"20748  0.053113                0.073078                  \\n\",\n       \"19592  0.055363                0.077218                  \\n\",\n       \"19154  0.052788                0.073171                  \\n\",\n       \"15881  0.195174                0.248771                  \\n\",\n       \"20241  0.200712                0.253334                  \\n\",\n       \"16573  0.179244                0.227094                  \\n\",\n       \"15964  0.193183                0.244078                  \\n\",\n       \"20420  0.181329                0.231196                  \\n\",\n       \"18546  0.190352                0.242958                  \\n\",\n       \"20750  0.196077                0.249412                  \\n\",\n       \"19593  0.190416                0.242672                  \\n\",\n       \"19155  0.194451                0.249151                  \\n\",\n       \"15886  0.383186                0.344101                  \\n\",\n       \"20238  0.388114                0.347357                  \\n\",\n       \"16578  0.360275                0.322882                  \\n\",\n       \"15967  0.382798                0.342756                  \\n\",\n       \"42862  0.358740                0.322126                  \\n\",\n       \"18547  0.369477                0.330884                  \\n\",\n       \"20753  0.370059                0.330847                  \\n\",\n       \"19595  0.375372                0.337499                  \\n\",\n       \"19151  0.380568                0.341487                  \\n\",\n       \"15882  0.380926                0.340925                  \\n\",\n       \"20242  0.383861                0.342837                  \\n\",\n       \"16576  0.358071                0.319447                  \\n\",\n       \"15969  0.370913                0.330757                  \\n\",\n       \"20416  0.347308                0.310340                  \\n\",\n       \"18549  0.360917                0.320917                  \\n\",\n       \"20752  0.365170                0.326957                  \\n\",\n       \"42853  0.362904                0.324792                  \\n\",\n       \"19153  0.372187                0.332999                  \\n\",\n       \"15884  0.382241                0.329282                  \\n\",\n       \"20237  0.398597                0.344039                  \\n\",\n       \"16574  0.368707                0.319646                  \\n\",\n       \"15966  0.384085                0.330013                  \\n\",\n       \"20417  0.364095                0.309685                  \\n\",\n       \"18548  0.365078                0.316231                  \\n\",\n       \"20754  0.384739                0.329836                  \\n\",\n       \"19591  0.372813                0.321424                  \\n\",\n       \"19149  0.392531                0.336038                  \\n\",\n       \"15885  0.312501                0.364618                  \\n\",\n       \"20243  0.320215                0.367657                  \\n\",\n       \"16577  0.290384                0.337703                  \\n\",\n       \"15965  0.325563                0.363380                  \\n\",\n       \"20418  0.284898                0.330281                  \\n\",\n       \"18551  0.308195                0.351405                  \\n\",\n       \"20751  0.303017                0.347347                  \\n\",\n       \"19594  0.307584                0.352417                  \\n\",\n       \"19152  0.309054                0.360377                  \\n\",\n       \"15883  0.228660                0.289925                  \\n\",\n       \"20239  0.218588                0.276951                  \\n\",\n       \"16575  0.226646                0.283473                  \\n\",\n       \"15968  0.207102                0.262339                  \\n\",\n       \"20421  0.222699                0.278964                  \\n\",\n       \"18550  0.231473                0.286691                  \\n\",\n       \"20749  0.212052                0.268898                  \\n\",\n       \"19596  0.208817                0.264046                  \\n\",\n       \"19150  0.216613                0.275888                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@3  present_exact_f_score_hard@3  \\\\\\n\",\n       \"15880  0.133117                        0.078092                       \\n\",\n       \"20240  0.167749                        0.096086                       \\n\",\n       \"16579  0.113636                        0.065994                       \\n\",\n       \"15963  0.140693                        0.080677                       \\n\",\n       \"20419  0.111472                        0.065780                       \\n\",\n       \"18552  0.099567                        0.057950                       \\n\",\n       \"20748  0.123377                        0.072531                       \\n\",\n       \"19592  0.137446                        0.077218                       \\n\",\n       \"19154  0.127706                        0.073171                       \\n\",\n       \"15881  0.406000                        0.248771                       \\n\",\n       \"20241  0.414000                        0.253334                       \\n\",\n       \"16573  0.373333                        0.226554                       \\n\",\n       \"15964  0.399333                        0.244078                       \\n\",\n       \"20420  0.382667                        0.230710                       \\n\",\n       \"18546  0.399333                        0.242958                       \\n\",\n       \"20750  0.410667                        0.248422                       \\n\",\n       \"19593  0.398667                        0.242472                       \\n\",\n       \"19155  0.412667                        0.249151                       \\n\",\n       \"15886  0.357132                        0.344075                       \\n\",\n       \"20238  0.358783                        0.347339                       \\n\",\n       \"16578  0.334251                        0.322759                       \\n\",\n       \"15967  0.354547                        0.342734                       \\n\",\n       \"42862  0.334884                        0.322067                       \\n\",\n       \"18547  0.341539                        0.330789                       \\n\",\n       \"20753  0.341789                        0.330607                       \\n\",\n       \"19595  0.350378                        0.337476                       \\n\",\n       \"19151  0.353630                        0.341487                       \\n\",\n       \"15882  0.353667                        0.340925                       \\n\",\n       \"20242  0.355167                        0.342837                       \\n\",\n       \"16576  0.331167                        0.319151                       \\n\",\n       \"15969  0.342667                        0.330757                       \\n\",\n       \"20416  0.321500                        0.310340                       \\n\",\n       \"18549  0.332833                        0.320893                       \\n\",\n       \"20752  0.339167                        0.326657                       \\n\",\n       \"42853  0.337167                        0.324792                       \\n\",\n       \"19153  0.345167                        0.332999                       \\n\",\n       \"15884  0.336957                        0.329282                       \\n\",\n       \"20237  0.350000                        0.344039                       \\n\",\n       \"16574  0.327536                        0.319429                       \\n\",\n       \"15966  0.336957                        0.330013                       \\n\",\n       \"20417  0.316667                        0.309685                       \\n\",\n       \"18548  0.326812                        0.316231                       \\n\",\n       \"20754  0.339855                        0.329401                       \\n\",\n       \"19591  0.331159                        0.321424                       \\n\",\n       \"19149  0.342029                        0.336038                       \\n\",\n       \"15885  0.530806                        0.364618                       \\n\",\n       \"20243  0.527646                        0.367657                       \\n\",\n       \"16577  0.488152                        0.337703                       \\n\",\n       \"15965  0.515008                        0.363380                       \\n\",\n       \"20418  0.475513                        0.329740                       \\n\",\n       \"18551  0.507109                        0.351405                       \\n\",\n       \"20751  0.497630                        0.346354                       \\n\",\n       \"19594  0.507109                        0.352417                       \\n\",\n       \"19152  0.526066                        0.360377                       \\n\",\n       \"15883  0.460000                        0.289925                       \\n\",\n       \"20239  0.443333                        0.276951                       \\n\",\n       \"16575  0.450000                        0.283473                       \\n\",\n       \"15968  0.423333                        0.262339                       \\n\",\n       \"20421  0.443333                        0.278964                       \\n\",\n       \"18550  0.450000                        0.286691                       \\n\",\n       \"20749  0.423333                        0.268595                       \\n\",\n       \"19596  0.426667                        0.264046                       \\n\",\n       \"19150  0.446667                        0.275888                       \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@10  absent_exact_precision@10  \\\\\\n\",\n       \"15880  0.006494                 0.000649                    \\n\",\n       \"20240  0.000000                 0.000000                    \\n\",\n       \"16579  0.000000                 0.000000                    \\n\",\n       \"15963  0.003247                 0.000325                    \\n\",\n       \"20419  0.003247                 0.000325                    \\n\",\n       \"18552  0.000000                 0.000000                    \\n\",\n       \"20748  0.003247                 0.000325                    \\n\",\n       \"19592  0.000000                 0.000000                    \\n\",\n       \"19154  0.000000                 0.000000                    \\n\",\n       \"15881  0.112000                 0.011829                    \\n\",\n       \"20241  0.086000                 0.010230                    \\n\",\n       \"16573  0.098000                 0.009800                    \\n\",\n       \"15964  0.102000                 0.010466                    \\n\",\n       \"20420  0.114000                 0.011486                    \\n\",\n       \"18546  0.114000                 0.012330                    \\n\",\n       \"20750  0.068000                 0.007317                    \\n\",\n       \"19593  0.100000                 0.010133                    \\n\",\n       \"19155  0.080000                 0.008350                    \\n\",\n       \"15886  0.232001                 0.024601                    \\n\",\n       \"20238  0.187622                 0.021335                    \\n\",\n       \"16578  0.224796                 0.022580                    \\n\",\n       \"15967  0.220543                 0.025570                    \\n\",\n       \"42862  0.235203                 0.023658                    \\n\",\n       \"18547  0.255066                 0.026317                    \\n\",\n       \"20753  0.210037                 0.022004                    \\n\",\n       \"19595  0.242358                 0.024569                    \\n\",\n       \"19151  0.193876                 0.021322                    \\n\",\n       \"15882  0.232000                 0.024618                    \\n\",\n       \"20242  0.183000                 0.020530                    \\n\",\n       \"16576  0.213500                 0.021500                    \\n\",\n       \"15969  0.237500                 0.027197                    \\n\",\n       \"20416  0.238500                 0.023973                    \\n\",\n       \"18549  0.248000                 0.025574                    \\n\",\n       \"20752  0.213500                 0.021435                    \\n\",\n       \"42853  0.261000                 0.026378                    \\n\",\n       \"19153  0.207500                 0.023229                    \\n\",\n       \"15884  0.295652                 0.030211                    \\n\",\n       \"20237  0.223913                 0.026621                    \\n\",\n       \"16574  0.258696                 0.025894                    \\n\",\n       \"15966  0.258696                 0.026790                    \\n\",\n       \"20417  0.300000                 0.030000                    \\n\",\n       \"18548  0.328261                 0.033285                    \\n\",\n       \"20754  0.260870                 0.026329                    \\n\",\n       \"19591  0.284783                 0.028988                    \\n\",\n       \"19149  0.282609                 0.030072                    \\n\",\n       \"15885  0.379147                 0.039310                    \\n\",\n       \"20243  0.312796                 0.032987                    \\n\",\n       \"16577  0.364929                 0.036493                    \\n\",\n       \"15965  0.336493                 0.034853                    \\n\",\n       \"20418  0.374408                 0.038863                    \\n\",\n       \"18551  0.393365                 0.039389                    \\n\",\n       \"20751  0.293839                 0.030806                    \\n\",\n       \"19594  0.407583                 0.042180                    \\n\",\n       \"19152  0.293839                 0.030625                    \\n\",\n       \"15883  0.350000                 0.036000                    \\n\",\n       \"20239  0.330000                 0.034873                    \\n\",\n       \"16575  0.420000                 0.042000                    \\n\",\n       \"15968  0.350000                 0.038000                    \\n\",\n       \"20421  0.400000                 0.040000                    \\n\",\n       \"18550  0.310000                 0.031361                    \\n\",\n       \"20749  0.320000                 0.033750                    \\n\",\n       \"19596  0.370000                 0.037000                    \\n\",\n       \"19150  0.390000                 0.039611                    \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@10  absent_exact_f_score@10  \\\\\\n\",\n       \"15880  0.002165                0.000999                  \\n\",\n       \"20240  0.000000                0.000000                  \\n\",\n       \"16579  0.000000                0.000000                  \\n\",\n       \"15963  0.001082                0.000500                  \\n\",\n       \"20419  0.001082                0.000500                  \\n\",\n       \"18552  0.000000                0.000000                  \\n\",\n       \"20748  0.001082                0.000500                  \\n\",\n       \"19592  0.000000                0.000000                  \\n\",\n       \"19154  0.000000                0.000000                  \\n\",\n       \"15881  0.049394                0.017875                  \\n\",\n       \"20241  0.040686                0.015501                  \\n\",\n       \"16573  0.046941                0.015314                  \\n\",\n       \"15964  0.045575                0.015864                  \\n\",\n       \"20420  0.051429                0.017750                  \\n\",\n       \"18546  0.057410                0.018716                  \\n\",\n       \"20750  0.030533                0.011273                  \\n\",\n       \"19593  0.044639                0.015507                  \\n\",\n       \"19155  0.041325                0.013078                  \\n\",\n       \"15886  0.108995                0.038013                  \\n\",\n       \"20238  0.088541                0.032273                  \\n\",\n       \"16578  0.105580                0.035521                  \\n\",\n       \"15967  0.102751                0.038090                  \\n\",\n       \"42862  0.110199                0.037242                  \\n\",\n       \"18547  0.119309                0.041055                  \\n\",\n       \"20753  0.098186                0.033919                  \\n\",\n       \"19595  0.115012                0.038657                  \\n\",\n       \"19151  0.092397                0.032734                  \\n\",\n       \"15882  0.103581                0.038007                  \\n\",\n       \"20242  0.081844                0.030775                  \\n\",\n       \"16576  0.095614                0.033694                  \\n\",\n       \"15969  0.107503                0.040498                  \\n\",\n       \"20416  0.110015                0.037741                  \\n\",\n       \"18549  0.114564                0.039994                  \\n\",\n       \"20752  0.096531                0.033698                  \\n\",\n       \"42853  0.117182                0.041348                  \\n\",\n       \"19153  0.097003                0.035129                  \\n\",\n       \"15884  0.112944                0.044925                  \\n\",\n       \"20237  0.084213                0.036166                  \\n\",\n       \"16574  0.104815                0.039520                  \\n\",\n       \"15966  0.096325                0.039259                  \\n\",\n       \"20417  0.125536                0.046009                  \\n\",\n       \"18548  0.136620                0.050491                  \\n\",\n       \"20754  0.102930                0.039710                  \\n\",\n       \"19591  0.114064                0.043713                  \\n\",\n       \"19149  0.111058                0.044568                  \\n\",\n       \"15885  0.089139                0.048106                  \\n\",\n       \"20243  0.079495                0.039714                  \\n\",\n       \"16577  0.100255                0.045807                  \\n\",\n       \"15965  0.080522                0.040423                  \\n\",\n       \"20418  0.089106                0.046542                  \\n\",\n       \"18551  0.089651                0.045724                  \\n\",\n       \"20751  0.090565                0.038324                  \\n\",\n       \"19594  0.094478                0.049037                  \\n\",\n       \"19152  0.084979                0.038151                  \\n\",\n       \"15883  0.047284                0.039656                  \\n\",\n       \"20239  0.040498                0.035852                  \\n\",\n       \"16575  0.054768                0.045553                  \\n\",\n       \"15968  0.046508                0.039943                  \\n\",\n       \"20421  0.053961                0.044416                  \\n\",\n       \"18550  0.044203                0.035968                  \\n\",\n       \"20749  0.043761                0.036626                  \\n\",\n       \"19596  0.052197                0.041465                  \\n\",\n       \"19150  0.052586                0.043772                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@10  absent_exact_f_score_hard@10  \\\\\\n\",\n       \"15880  0.000649                        0.000999                       \\n\",\n       \"20240  0.000000                        0.000000                       \\n\",\n       \"16579  0.000000                        0.000000                       \\n\",\n       \"15963  0.000325                        0.000500                       \\n\",\n       \"20419  0.000325                        0.000500                       \\n\",\n       \"18552  0.000000                        0.000000                       \\n\",\n       \"20748  0.000325                        0.000500                       \\n\",\n       \"19592  0.000000                        0.000000                       \\n\",\n       \"19154  0.000000                        0.000000                       \\n\",\n       \"15881  0.011200                        0.017098                       \\n\",\n       \"20241  0.008600                        0.013484                       \\n\",\n       \"16573  0.009800                        0.015314                       \\n\",\n       \"15964  0.010200                        0.015571                       \\n\",\n       \"20420  0.011400                        0.017639                       \\n\",\n       \"18546  0.011400                        0.017908                       \\n\",\n       \"20750  0.006800                        0.010556                       \\n\",\n       \"19593  0.010000                        0.015423                       \\n\",\n       \"19155  0.008000                        0.012678                       \\n\",\n       \"15886  0.023200                        0.036614                       \\n\",\n       \"20238  0.018762                        0.029624                       \\n\",\n       \"16578  0.022480                        0.035448                       \\n\",\n       \"15967  0.022054                        0.034733                       \\n\",\n       \"42862  0.023520                        0.037111                       \\n\",\n       \"18547  0.025507                        0.040238                       \\n\",\n       \"20753  0.021004                        0.033110                       \\n\",\n       \"19595  0.024236                        0.038316                       \\n\",\n       \"19151  0.019388                        0.030720                       \\n\",\n       \"15882  0.023200                        0.036428                       \\n\",\n       \"20242  0.018300                        0.028771                       \\n\",\n       \"16576  0.021350                        0.033563                       \\n\",\n       \"15969  0.023750                        0.037440                       \\n\",\n       \"20416  0.023850                        0.037588                       \\n\",\n       \"18549  0.024800                        0.039132                       \\n\",\n       \"20752  0.021350                        0.033612                       \\n\",\n       \"42853  0.026100                        0.041064                       \\n\",\n       \"19153  0.020750                        0.032872                       \\n\",\n       \"15884  0.029565                        0.044239                       \\n\",\n       \"20237  0.022391                        0.033589                       \\n\",\n       \"16574  0.025870                        0.039480                       \\n\",\n       \"15966  0.025870                        0.038703                       \\n\",\n       \"20417  0.030000                        0.046009                       \\n\",\n       \"18548  0.032826                        0.050182                       \\n\",\n       \"20754  0.026087                        0.039418                       \\n\",\n       \"19591  0.028478                        0.043194                       \\n\",\n       \"19149  0.028261                        0.042579                       \\n\",\n       \"15885  0.037915                        0.046573                       \\n\",\n       \"20243  0.031280                        0.038138                       \\n\",\n       \"16577  0.036493                        0.045807                       \\n\",\n       \"15965  0.033649                        0.039281                       \\n\",\n       \"20418  0.037441                        0.045934                       \\n\",\n       \"18551  0.039336                        0.045638                       \\n\",\n       \"20751  0.029384                        0.037716                       \\n\",\n       \"19594  0.040758                        0.048429                       \\n\",\n       \"19152  0.029384                        0.036535                       \\n\",\n       \"15883  0.035000                        0.038863                       \\n\",\n       \"20239  0.033000                        0.035215                       \\n\",\n       \"16575  0.042000                        0.045553                       \\n\",\n       \"15968  0.035000                        0.038832                       \\n\",\n       \"20421  0.040000                        0.044416                       \\n\",\n       \"18550  0.031000                        0.035620                       \\n\",\n       \"20749  0.032000                        0.035827                       \\n\",\n       \"19596  0.037000                        0.041465                       \\n\",\n       \"19150  0.039000                        0.043186                       \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@50  absent_exact_precision@50  \\\\\\n\",\n       \"15880  0.006494                 0.000382                    \\n\",\n       \"20240  0.000000                 0.000000                    \\n\",\n       \"16579  0.000000                 0.000000                    \\n\",\n       \"15963  0.003247                 0.000065                    \\n\",\n       \"20419  0.003247                 0.000065                    \\n\",\n       \"18552  0.000000                 0.000000                    \\n\",\n       \"20748  0.003247                 0.000065                    \\n\",\n       \"19592  0.000000                 0.000000                    \\n\",\n       \"19154  0.000000                 0.000000                    \\n\",\n       \"15881  0.140000                 0.007735                    \\n\",\n       \"20241  0.100000                 0.008346                    \\n\",\n       \"16573  0.120000                 0.002493                    \\n\",\n       \"15964  0.130000                 0.004991                    \\n\",\n       \"20420  0.162000                 0.004336                    \\n\",\n       \"18546  0.144000                 0.006543                    \\n\",\n       \"20750  0.108000                 0.003506                    \\n\",\n       \"19593  0.148000                 0.004036                    \\n\",\n       \"19155  0.094000                 0.004439                    \\n\",\n       \"15886  0.257117                 0.014079                    \\n\",\n       \"20238  0.204483                 0.014904                    \\n\",\n       \"16578  0.257718                 0.005826                    \\n\",\n       \"15967  0.260469                 0.014850                    \\n\",\n       \"42862  0.310702                 0.007843                    \\n\",\n       \"18547  0.305899                 0.012805                    \\n\",\n       \"20753  0.256167                 0.008772                    \\n\",\n       \"19595  0.318757                 0.009126                    \\n\",\n       \"19151  0.200380                 0.012067                    \\n\",\n       \"15882  0.251500                 0.013130                    \\n\",\n       \"20242  0.193000                 0.013597                    \\n\",\n       \"16576  0.244500                 0.005552                    \\n\",\n       \"15969  0.272000                 0.015096                    \\n\",\n       \"20416  0.302500                 0.007737                    \\n\",\n       \"18549  0.298500                 0.012220                    \\n\",\n       \"20752  0.258500                 0.007527                    \\n\",\n       \"42853  0.325000                 0.009042                    \\n\",\n       \"19153  0.215500                 0.013593                    \\n\",\n       \"15884  0.347826                 0.016406                    \\n\",\n       \"20237  0.243478                 0.017972                    \\n\",\n       \"16574  0.295652                 0.006315                    \\n\",\n       \"15966  0.313043                 0.012342                    \\n\",\n       \"20417  0.415217                 0.009838                    \\n\",\n       \"18548  0.391304                 0.013082                    \\n\",\n       \"20754  0.341304                 0.008993                    \\n\",\n       \"19591  0.404348                 0.010257                    \\n\",\n       \"19149  0.293478                 0.015000                    \\n\",\n       \"15885  0.431280                 0.019981                    \\n\",\n       \"20243  0.374408                 0.022152                    \\n\",\n       \"16577  0.450237                 0.009265                    \\n\",\n       \"15965  0.492891                 0.015417                    \\n\",\n       \"20418  0.554502                 0.014447                    \\n\",\n       \"18551  0.516588                 0.017154                    \\n\",\n       \"20751  0.388626                 0.011348                    \\n\",\n       \"19594  0.582938                 0.015178                    \\n\",\n       \"19152  0.312796                 0.016581                    \\n\",\n       \"15883  0.440000                 0.017731                    \\n\",\n       \"20239  0.350000                 0.020162                    \\n\",\n       \"16575  0.470000                 0.009643                    \\n\",\n       \"15968  0.470000                 0.018552                    \\n\",\n       \"20421  0.540000                 0.011797                    \\n\",\n       \"18550  0.440000                 0.015167                    \\n\",\n       \"20749  0.400000                 0.013970                    \\n\",\n       \"19596  0.600000                 0.012695                    \\n\",\n       \"19150  0.390000                 0.021049                    \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@50  absent_exact_f_score@50  \\\\\\n\",\n       \"15880  0.002165                0.000649                  \\n\",\n       \"20240  0.000000                0.000000                  \\n\",\n       \"16579  0.000000                0.000000                  \\n\",\n       \"15963  0.001082                0.000123                  \\n\",\n       \"20419  0.001082                0.000123                  \\n\",\n       \"18552  0.000000                0.000000                  \\n\",\n       \"20748  0.001082                0.000123                  \\n\",\n       \"19592  0.000000                0.000000                  \\n\",\n       \"19154  0.000000                0.000000                  \\n\",\n       \"15881  0.059060                0.012705                  \\n\",\n       \"20241  0.046252                0.013128                  \\n\",\n       \"16573  0.055413                0.004675                  \\n\",\n       \"15964  0.058075                0.008517                  \\n\",\n       \"20420  0.074095                0.007946                  \\n\",\n       \"18546  0.070976                0.010508                  \\n\",\n       \"20750  0.048171                0.006194                  \\n\",\n       \"19593  0.062447                0.007122                  \\n\",\n       \"19155  0.047058                0.007509                  \\n\",\n       \"15886  0.119425                0.023199                  \\n\",\n       \"20238  0.095338                0.023383                  \\n\",\n       \"16578  0.119692                0.010779                  \\n\",\n       \"15967  0.120002                0.022799                  \\n\",\n       \"42862  0.142896                0.014382                  \\n\",\n       \"18547  0.141699                0.021973                  \\n\",\n       \"20753  0.118737                0.014833                  \\n\",\n       \"19595  0.149801                0.016366                  \\n\",\n       \"19151  0.095241                0.019334                  \\n\",\n       \"15882  0.111977                0.021774                  \\n\",\n       \"20242  0.086611                0.021225                  \\n\",\n       \"16576  0.109881                0.010214                  \\n\",\n       \"15969  0.122324                0.023169                  \\n\",\n       \"20416  0.136840                0.014243                  \\n\",\n       \"18549  0.135145                0.021177                  \\n\",\n       \"20752  0.116265                0.013643                  \\n\",\n       \"42853  0.148519                0.016362                  \\n\",\n       \"19153  0.099653                0.021280                  \\n\",\n       \"15884  0.135843                0.027262                  \\n\",\n       \"20237  0.093179                0.024955                  \\n\",\n       \"16574  0.114277                0.011747                  \\n\",\n       \"15966  0.122050                0.019680                  \\n\",\n       \"20417  0.172278                0.018186                  \\n\",\n       \"18548  0.159303                0.022636                  \\n\",\n       \"20754  0.131924                0.016176                  \\n\",\n       \"19591  0.158367                0.018276                  \\n\",\n       \"19149  0.114030                0.024277                  \\n\",\n       \"15885  0.097071                0.029881                  \\n\",\n       \"20243  0.086081                0.030621                  \\n\",\n       \"16577  0.113555                0.016120                  \\n\",\n       \"15965  0.101885                0.023268                  \\n\",\n       \"20418  0.119576                0.022252                  \\n\",\n       \"18551  0.114849                0.026326                  \\n\",\n       \"20751  0.102311                0.016843                  \\n\",\n       \"19594  0.122398                0.023109                  \\n\",\n       \"19152  0.086637                0.023551                  \\n\",\n       \"15883  0.058086                0.025614                  \\n\",\n       \"20239  0.044109                0.024446                  \\n\",\n       \"16575  0.061125                0.016274                  \\n\",\n       \"15968  0.061177                0.024658                  \\n\",\n       \"20421  0.067914                0.019682                  \\n\",\n       \"18550  0.061544                0.023635                  \\n\",\n       \"20749  0.054115                0.019651                  \\n\",\n       \"19596  0.078804                0.021467                  \\n\",\n       \"19150  0.052586                0.029113                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@50  absent_exact_f_score_hard@50  \\\\\\n\",\n       \"15880  0.000130                        0.000245                       \\n\",\n       \"20240  0.000000                        0.000000                       \\n\",\n       \"16579  0.000000                        0.000000                       \\n\",\n       \"15963  0.000065                        0.000123                       \\n\",\n       \"20419  0.000065                        0.000123                       \\n\",\n       \"18552  0.000000                        0.000000                       \\n\",\n       \"20748  0.000065                        0.000123                       \\n\",\n       \"19592  0.000000                        0.000000                       \\n\",\n       \"19154  0.000000                        0.000000                       \\n\",\n       \"15881  0.002800                        0.005255                       \\n\",\n       \"20241  0.002000                        0.003782                       \\n\",\n       \"16573  0.002400                        0.004524                       \\n\",\n       \"15964  0.002600                        0.004887                       \\n\",\n       \"20420  0.003240                        0.006108                       \\n\",\n       \"18546  0.002880                        0.005448                       \\n\",\n       \"20750  0.002160                        0.004072                       \\n\",\n       \"19593  0.002960                        0.005552                       \\n\",\n       \"19155  0.001880                        0.003558                       \\n\",\n       \"15886  0.005142                        0.009735                       \\n\",\n       \"20238  0.004090                        0.007742                       \\n\",\n       \"16578  0.005154                        0.009756                       \\n\",\n       \"15967  0.005209                        0.009857                       \\n\",\n       \"42862  0.006214                        0.011758                       \\n\",\n       \"18547  0.006118                        0.011580                       \\n\",\n       \"20753  0.005123                        0.009697                       \\n\",\n       \"19595  0.006375                        0.012074                       \\n\",\n       \"19151  0.004008                        0.007599                       \\n\",\n       \"15882  0.005030                        0.009519                       \\n\",\n       \"20242  0.003860                        0.007309                       \\n\",\n       \"16576  0.004890                        0.009258                       \\n\",\n       \"15969  0.005440                        0.010300                       \\n\",\n       \"20416  0.006050                        0.011453                       \\n\",\n       \"18549  0.005970                        0.011303                       \\n\",\n       \"20752  0.005170                        0.009784                       \\n\",\n       \"42853  0.006500                        0.012311                       \\n\",\n       \"19153  0.004310                        0.008171                       \\n\",\n       \"15884  0.006957                        0.012983                       \\n\",\n       \"20237  0.004870                        0.009101                       \\n\",\n       \"16574  0.005913                        0.011067                       \\n\",\n       \"15966  0.006261                        0.011714                       \\n\",\n       \"20417  0.008304                        0.015602                       \\n\",\n       \"18548  0.007826                        0.014660                       \\n\",\n       \"20754  0.006826                        0.012762                       \\n\",\n       \"19591  0.008087                        0.015108                       \\n\",\n       \"19149  0.005870                        0.010974                       \\n\",\n       \"15885  0.008626                        0.015012                       \\n\",\n       \"20243  0.007488                        0.012891                       \\n\",\n       \"16577  0.009005                        0.015684                       \\n\",\n       \"15965  0.009858                        0.016514                       \\n\",\n       \"20418  0.011090                        0.019194                       \\n\",\n       \"18551  0.010332                        0.017683                       \\n\",\n       \"20751  0.007773                        0.013468                       \\n\",\n       \"19594  0.011659                        0.019986                       \\n\",\n       \"19152  0.006256                        0.010857                       \\n\",\n       \"15883  0.008800                        0.015056                       \\n\",\n       \"20239  0.007000                        0.011885                       \\n\",\n       \"16575  0.009400                        0.015983                       \\n\",\n       \"15968  0.009400                        0.016020                       \\n\",\n       \"20421  0.010800                        0.018323                       \\n\",\n       \"18550  0.008800                        0.015236                       \\n\",\n       \"20749  0.008000                        0.013732                       \\n\",\n       \"19596  0.012000                        0.020458                       \\n\",\n       \"19150  0.007800                        0.013356                       \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@k  absent_exact_precision@k  \\\\\\n\",\n       \"15880  0.003247                0.001082                   \\n\",\n       \"20240  0.000000                0.000000                   \\n\",\n       \"16579  0.000000                0.000000                   \\n\",\n       \"15963  0.003247                0.001082                   \\n\",\n       \"20419  0.003247                0.001082                   \\n\",\n       \"18552  0.000000                0.000000                   \\n\",\n       \"20748  0.003247                0.001082                   \\n\",\n       \"19592  0.000000                0.000000                   \\n\",\n       \"19154  0.000000                0.000000                   \\n\",\n       \"15881  0.070000                0.026827                   \\n\",\n       \"20241  0.050000                0.019519                   \\n\",\n       \"16573  0.054000                0.019775                   \\n\",\n       \"15964  0.066000                0.027009                   \\n\",\n       \"20420  0.058000                0.021962                   \\n\",\n       \"18546  0.064000                0.029990                   \\n\",\n       \"20750  0.048000                0.018967                   \\n\",\n       \"19593  0.054000                0.018672                   \\n\",\n       \"19155  0.058000                0.028091                   \\n\",\n       \"15886  0.137489                0.057158                   \\n\",\n       \"20238  0.117927                0.049158                   \\n\",\n       \"16578  0.129734                0.052779                   \\n\",\n       \"15967  0.135238                0.056107                   \\n\",\n       \"42862  0.129784                0.053094                   \\n\",\n       \"18547  0.143793                0.059694                   \\n\",\n       \"20753  0.129534                0.054067                   \\n\",\n       \"19595  0.131786                0.054140                   \\n\",\n       \"19151  0.128934                0.054434                   \\n\",\n       \"15882  0.137500                0.051156                   \\n\",\n       \"20242  0.115500                0.046586                   \\n\",\n       \"16576  0.128500                0.051848                   \\n\",\n       \"15969  0.141000                0.057893                   \\n\",\n       \"20416  0.133000                0.052590                   \\n\",\n       \"18549  0.130000                0.052027                   \\n\",\n       \"20752  0.125500                0.050476                   \\n\",\n       \"42853  0.137000                0.055278                   \\n\",\n       \"19153  0.136000                0.055920                   \\n\",\n       \"15884  0.206522                0.072830                   \\n\",\n       \"20237  0.156522                0.057145                   \\n\",\n       \"16574  0.167391                0.063728                   \\n\",\n       \"15966  0.173913                0.059982                   \\n\",\n       \"20417  0.184783                0.066530                   \\n\",\n       \"18548  0.210870                0.072610                   \\n\",\n       \"20754  0.182609                0.066191                   \\n\",\n       \"19591  0.180435                0.062216                   \\n\",\n       \"19149  0.210870                0.070769                   \\n\",\n       \"15885  0.336493                0.072618                   \\n\",\n       \"20243  0.284360                0.060884                   \\n\",\n       \"16577  0.322275                0.073014                   \\n\",\n       \"15965  0.312796                0.056993                   \\n\",\n       \"20418  0.293839                0.045047                   \\n\",\n       \"18551  0.345972                0.061148                   \\n\",\n       \"20751  0.265403                0.066443                   \\n\",\n       \"19594  0.374408                0.064759                   \\n\",\n       \"19152  0.251185                0.056744                   \\n\",\n       \"15883  0.370000                0.048713                   \\n\",\n       \"20239  0.330000                0.042886                   \\n\",\n       \"16575  0.390000                0.048518                   \\n\",\n       \"15968  0.350000                0.047633                   \\n\",\n       \"20421  0.360000                0.045568                   \\n\",\n       \"18550  0.290000                0.041346                   \\n\",\n       \"20749  0.310000                0.042832                   \\n\",\n       \"19596  0.350000                0.046209                   \\n\",\n       \"19150  0.350000                0.045979                   \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@k  absent_exact_f_score@k  \\\\\\n\",\n       \"15880  0.001082               0.001082                 \\n\",\n       \"20240  0.000000               0.000000                 \\n\",\n       \"16579  0.000000               0.000000                 \\n\",\n       \"15963  0.001082               0.001082                 \\n\",\n       \"20419  0.001082               0.001082                 \\n\",\n       \"18552  0.000000               0.000000                 \\n\",\n       \"20748  0.001082               0.001082                 \\n\",\n       \"19592  0.000000               0.000000                 \\n\",\n       \"19154  0.000000               0.000000                 \\n\",\n       \"15881  0.026827               0.026827                 \\n\",\n       \"20241  0.019519               0.019519                 \\n\",\n       \"16573  0.019775               0.019775                 \\n\",\n       \"15964  0.027009               0.027009                 \\n\",\n       \"20420  0.021962               0.021962                 \\n\",\n       \"18546  0.029990               0.029990                 \\n\",\n       \"20750  0.018967               0.018967                 \\n\",\n       \"19593  0.018672               0.018672                 \\n\",\n       \"19155  0.028091               0.028091                 \\n\",\n       \"15886  0.056999               0.057048                 \\n\",\n       \"20238  0.048945               0.049018                 \\n\",\n       \"16578  0.052732               0.052742                 \\n\",\n       \"15967  0.055619               0.055755                 \\n\",\n       \"42862  0.053087               0.053090                 \\n\",\n       \"18547  0.059587               0.059615                 \\n\",\n       \"20753  0.053821               0.053885                 \\n\",\n       \"19595  0.054137               0.054138                 \\n\",\n       \"19151  0.054290               0.054345                 \\n\",\n       \"15882  0.051156               0.051156                 \\n\",\n       \"20242  0.046211               0.046286                 \\n\",\n       \"16576  0.051848               0.051848                 \\n\",\n       \"15969  0.057177               0.057393                 \\n\",\n       \"20416  0.052590               0.052590                 \\n\",\n       \"18549  0.051985               0.052003                 \\n\",\n       \"20752  0.050476               0.050476                 \\n\",\n       \"42853  0.055278               0.055278                 \\n\",\n       \"19153  0.055478               0.055563                 \\n\",\n       \"15884  0.072799               0.072814                 \\n\",\n       \"20237  0.055026               0.055455                 \\n\",\n       \"16574  0.063728               0.063728                 \\n\",\n       \"15966  0.059493               0.059681                 \\n\",\n       \"20417  0.066530               0.066530                 \\n\",\n       \"18548  0.072417               0.072486                 \\n\",\n       \"20754  0.066191               0.066191                 \\n\",\n       \"19591  0.062216               0.062216                 \\n\",\n       \"19149  0.070769               0.070769                 \\n\",\n       \"15885  0.072470               0.072534                 \\n\",\n       \"20243  0.060100               0.060361                 \\n\",\n       \"16577  0.073014               0.073014                 \\n\",\n       \"15965  0.056634               0.056761                 \\n\",\n       \"20418  0.044019               0.044394                 \\n\",\n       \"18551  0.061013               0.061074                 \\n\",\n       \"20751  0.065427               0.065796                 \\n\",\n       \"19594  0.063743               0.064113                 \\n\",\n       \"19152  0.055911               0.056219                 \\n\",\n       \"15883  0.048713               0.048713                 \\n\",\n       \"20239  0.040498               0.041318                 \\n\",\n       \"16575  0.048518               0.048518                 \\n\",\n       \"15968  0.045133               0.045966                 \\n\",\n       \"20421  0.045568               0.045568                 \\n\",\n       \"18550  0.041346               0.041346                 \\n\",\n       \"20749  0.041761               0.042151                 \\n\",\n       \"19596  0.046209               0.046209                 \\n\",\n       \"19150  0.045979               0.045979                 \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@k  absent_exact_f_score_hard@k  \\\\\\n\",\n       \"15880  0.001082                       0.001082                      \\n\",\n       \"20240  0.000000                       0.000000                      \\n\",\n       \"16579  0.000000                       0.000000                      \\n\",\n       \"15963  0.001082                       0.001082                      \\n\",\n       \"20419  0.001082                       0.001082                      \\n\",\n       \"18552  0.000000                       0.000000                      \\n\",\n       \"20748  0.001082                       0.001082                      \\n\",\n       \"19592  0.000000                       0.000000                      \\n\",\n       \"19154  0.000000                       0.000000                      \\n\",\n       \"15881  0.026827                       0.026827                      \\n\",\n       \"20241  0.019519                       0.019519                      \\n\",\n       \"16573  0.019775                       0.019775                      \\n\",\n       \"15964  0.027009                       0.027009                      \\n\",\n       \"20420  0.021962                       0.021962                      \\n\",\n       \"18546  0.029990                       0.029990                      \\n\",\n       \"20750  0.018967                       0.018967                      \\n\",\n       \"19593  0.018672                       0.018672                      \\n\",\n       \"19155  0.028091                       0.028091                      \\n\",\n       \"15886  0.056999                       0.056999                      \\n\",\n       \"20238  0.048945                       0.048945                      \\n\",\n       \"16578  0.052732                       0.052732                      \\n\",\n       \"15967  0.055619                       0.055619                      \\n\",\n       \"42862  0.053087                       0.053087                      \\n\",\n       \"18547  0.059587                       0.059587                      \\n\",\n       \"20753  0.053821                       0.053821                      \\n\",\n       \"19595  0.054137                       0.054137                      \\n\",\n       \"19151  0.054290                       0.054290                      \\n\",\n       \"15882  0.051156                       0.051156                      \\n\",\n       \"20242  0.046211                       0.046211                      \\n\",\n       \"16576  0.051848                       0.051848                      \\n\",\n       \"15969  0.057177                       0.057177                      \\n\",\n       \"20416  0.052590                       0.052590                      \\n\",\n       \"18549  0.051985                       0.051985                      \\n\",\n       \"20752  0.050476                       0.050476                      \\n\",\n       \"42853  0.055278                       0.055278                      \\n\",\n       \"19153  0.055478                       0.055478                      \\n\",\n       \"15884  0.072799                       0.072799                      \\n\",\n       \"20237  0.055026                       0.055026                      \\n\",\n       \"16574  0.063728                       0.063728                      \\n\",\n       \"15966  0.059493                       0.059493                      \\n\",\n       \"20417  0.066530                       0.066530                      \\n\",\n       \"18548  0.072417                       0.072417                      \\n\",\n       \"20754  0.066191                       0.066191                      \\n\",\n       \"19591  0.062216                       0.062216                      \\n\",\n       \"19149  0.070769                       0.070769                      \\n\",\n       \"15885  0.072470                       0.072470                      \\n\",\n       \"20243  0.060100                       0.060100                      \\n\",\n       \"16577  0.073014                       0.073014                      \\n\",\n       \"15965  0.056634                       0.056634                      \\n\",\n       \"20418  0.044019                       0.044019                      \\n\",\n       \"18551  0.061013                       0.061013                      \\n\",\n       \"20751  0.065427                       0.065427                      \\n\",\n       \"19594  0.063743                       0.063743                      \\n\",\n       \"19152  0.055911                       0.055911                      \\n\",\n       \"15883  0.048713                       0.048713                      \\n\",\n       \"20239  0.040498                       0.040498                      \\n\",\n       \"16575  0.048518                       0.048518                      \\n\",\n       \"15968  0.045133                       0.045133                      \\n\",\n       \"20421  0.045568                       0.045568                      \\n\",\n       \"18550  0.041346                       0.041346                      \\n\",\n       \"20749  0.041761                       0.041761                      \\n\",\n       \"19596  0.046209                       0.046209                      \\n\",\n       \"19150  0.045979                       0.045979                      \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@M  absent_exact_precision@M  \\\\\\n\",\n       \"15880  0.006494                0.000382                   \\n\",\n       \"20240  0.000000                0.000000                   \\n\",\n       \"16579  0.000000                0.000000                   \\n\",\n       \"15963  0.003247                0.000058                   \\n\",\n       \"20419  0.003247                0.000032                   \\n\",\n       \"18552  0.000000                0.000000                   \\n\",\n       \"20748  0.003247                0.000027                   \\n\",\n       \"19592  0.000000                0.000000                   \\n\",\n       \"19154  0.000000                0.000000                   \\n\",\n       \"15881  0.140000                0.007633                   \\n\",\n       \"20241  0.100000                0.008342                   \\n\",\n       \"16573  0.122000                0.001398                   \\n\",\n       \"15964  0.130000                0.004585                   \\n\",\n       \"20420  0.166000                0.003668                   \\n\",\n       \"18546  0.144000                0.006394                   \\n\",\n       \"20750  0.112000                0.003089                   \\n\",\n       \"19593  0.154000                0.003276                   \\n\",\n       \"19155  0.094000                0.004280                   \\n\",\n       \"15886  0.257317                0.013969                   \\n\",\n       \"20238  0.204533                0.014865                   \\n\",\n       \"16578  0.260770                0.003569                   \\n\",\n       \"15967  0.264372                0.014109                   \\n\",\n       \"42862  0.317606                0.006406                   \\n\",\n       \"18547  0.307950                0.012460                   \\n\",\n       \"20753  0.260019                0.007860                   \\n\",\n       \"19595  0.325562                0.007722                   \\n\",\n       \"19151  0.200480                0.011876                   \\n\",\n       \"15882  0.252500                0.013025                   \\n\",\n       \"20242  0.193000                0.013563                   \\n\",\n       \"16576  0.247500                0.003339                   \\n\",\n       \"15969  0.276000                0.014331                   \\n\",\n       \"20416  0.313500                0.006406                   \\n\",\n       \"18549  0.298500                0.011876                   \\n\",\n       \"20752  0.266500                0.006603                   \\n\",\n       \"42853  0.334500                0.007615                   \\n\",\n       \"19153  0.215500                0.013386                   \\n\",\n       \"15884  0.350000                0.016128                   \\n\",\n       \"20237  0.243478                0.017850                   \\n\",\n       \"16574  0.297826                0.003302                   \\n\",\n       \"15966  0.319565                0.011044                   \\n\",\n       \"20417  0.428261                0.007804                   \\n\",\n       \"18548  0.393478                0.012374                   \\n\",\n       \"20754  0.356522                0.007279                   \\n\",\n       \"19591  0.406522                0.007505                   \\n\",\n       \"19149  0.293478                0.014584                   \\n\",\n       \"15885  0.431280                0.019816                   \\n\",\n       \"20243  0.379147                0.022176                   \\n\",\n       \"16577  0.464455                0.004566                   \\n\",\n       \"15965  0.492891                0.012708                   \\n\",\n       \"20418  0.568720                0.011648                   \\n\",\n       \"18551  0.521327                0.016310                   \\n\",\n       \"20751  0.402844                0.009511                   \\n\",\n       \"19594  0.592417                0.012110                   \\n\",\n       \"19152  0.312796                0.016433                   \\n\",\n       \"15883  0.440000                0.017535                   \\n\",\n       \"20239  0.350000                0.020162                   \\n\",\n       \"16575  0.480000                0.004733                   \\n\",\n       \"15968  0.470000                0.016505                   \\n\",\n       \"20421  0.550000                0.008751                   \\n\",\n       \"18550  0.450000                0.014210                   \\n\",\n       \"20749  0.400000                0.012057                   \\n\",\n       \"19596  0.610000                0.008927                   \\n\",\n       \"19150  0.390000                0.020798                   \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@M  absent_exact_f_score@M  \\\\\\n\",\n       \"15880  0.002165               0.000649                 \\n\",\n       \"20240  0.000000               0.000000                 \\n\",\n       \"16579  0.000000               0.000000                 \\n\",\n       \"15963  0.001082               0.000110                 \\n\",\n       \"20419  0.001082               0.000062                 \\n\",\n       \"18552  0.000000               0.000000                 \\n\",\n       \"20748  0.001082               0.000053                 \\n\",\n       \"19592  0.000000               0.000000                 \\n\",\n       \"19154  0.000000               0.000000                 \\n\",\n       \"15881  0.059060               0.012528                 \\n\",\n       \"20241  0.046252               0.013122                 \\n\",\n       \"16573  0.057413               0.002657                 \\n\",\n       \"15964  0.058075               0.007776                 \\n\",\n       \"20420  0.075095               0.006728                 \\n\",\n       \"18546  0.070976               0.010238                 \\n\",\n       \"20750  0.049171               0.005426                 \\n\",\n       \"19593  0.065513               0.005745                 \\n\",\n       \"19155  0.047058               0.007215                 \\n\",\n       \"15886  0.119485               0.022999                 \\n\",\n       \"20238  0.095363               0.023313                 \\n\",\n       \"16578  0.120982               0.006601                 \\n\",\n       \"15967  0.121745               0.021443                 \\n\",\n       \"42862  0.146117               0.011757                 \\n\",\n       \"18547  0.142622               0.021351                 \\n\",\n       \"20753  0.120529               0.013169                 \\n\",\n       \"19595  0.152746               0.013791                 \\n\",\n       \"19151  0.095308               0.018983                 \\n\",\n       \"15882  0.112394               0.021583                 \\n\",\n       \"20242  0.086611               0.021165                 \\n\",\n       \"16576  0.111298               0.006125                 \\n\",\n       \"15969  0.124033               0.021765                 \\n\",\n       \"20416  0.142540               0.011816                 \\n\",\n       \"18549  0.135145               0.020554                 \\n\",\n       \"20752  0.120132               0.011967                 \\n\",\n       \"42853  0.153024               0.013751                 \\n\",\n       \"19153  0.099653               0.020903                 \\n\",\n       \"15884  0.136386               0.026782                 \\n\",\n       \"20237  0.093179               0.024741                 \\n\",\n       \"16574  0.115364               0.006243                 \\n\",\n       \"15966  0.124296               0.017311                 \\n\",\n       \"20417  0.177097               0.014536                 \\n\",\n       \"18548  0.159575               0.021370                 \\n\",\n       \"20754  0.136422               0.013115                 \\n\",\n       \"19591  0.158911               0.013327                 \\n\",\n       \"19149  0.114030               0.023523                 \\n\",\n       \"15885  0.097071               0.029628                 \\n\",\n       \"20243  0.086512               0.030673                 \\n\",\n       \"16577  0.115273               0.008423                 \\n\",\n       \"15965  0.101885               0.019141                 \\n\",\n       \"20418  0.121814               0.017807                 \\n\",\n       \"18551  0.115322               0.025002                 \\n\",\n       \"20751  0.103963               0.013944                 \\n\",\n       \"19594  0.123642               0.018254                 \\n\",\n       \"19152  0.086637               0.023303                 \\n\",\n       \"15883  0.058086               0.025310                 \\n\",\n       \"20239  0.044109               0.024446                 \\n\",\n       \"16575  0.062034               0.008452                 \\n\",\n       \"15968  0.061177               0.021576                 \\n\",\n       \"20421  0.069342               0.014997                 \\n\",\n       \"18550  0.062794               0.022211                 \\n\",\n       \"20749  0.054115               0.016672                 \\n\",\n       \"19596  0.080233               0.015641                 \\n\",\n       \"19150  0.052586               0.028729                 \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@M  absent_exact_f_score_hard@M  \\\\\\n\",\n       \"15880  0.000382                       0.000649                      \\n\",\n       \"20240  0.000000                       0.000000                      \\n\",\n       \"16579  0.000000                       0.000000                      \\n\",\n       \"15963  0.000058                       0.000110                      \\n\",\n       \"20419  0.000032                       0.000062                      \\n\",\n       \"18552  0.000000                       0.000000                      \\n\",\n       \"20748  0.000027                       0.000053                      \\n\",\n       \"19592  0.000000                       0.000000                      \\n\",\n       \"19154  0.000000                       0.000000                      \\n\",\n       \"15881  0.007633                       0.012528                      \\n\",\n       \"20241  0.008342                       0.013122                      \\n\",\n       \"16573  0.001398                       0.002657                      \\n\",\n       \"15964  0.004585                       0.007776                      \\n\",\n       \"20420  0.003668                       0.006728                      \\n\",\n       \"18546  0.006394                       0.010238                      \\n\",\n       \"20750  0.003089                       0.005426                      \\n\",\n       \"19593  0.003276                       0.005745                      \\n\",\n       \"19155  0.004280                       0.007215                      \\n\",\n       \"15886  0.013969                       0.022999                      \\n\",\n       \"20238  0.014865                       0.023313                      \\n\",\n       \"16578  0.003569                       0.006601                      \\n\",\n       \"15967  0.014109                       0.021443                      \\n\",\n       \"42862  0.006406                       0.011757                      \\n\",\n       \"18547  0.012460                       0.021351                      \\n\",\n       \"20753  0.007860                       0.013169                      \\n\",\n       \"19595  0.007722                       0.013791                      \\n\",\n       \"19151  0.011876                       0.018983                      \\n\",\n       \"15882  0.013025                       0.021583                      \\n\",\n       \"20242  0.013563                       0.021165                      \\n\",\n       \"16576  0.003339                       0.006125                      \\n\",\n       \"15969  0.014331                       0.021765                      \\n\",\n       \"20416  0.006406                       0.011816                      \\n\",\n       \"18549  0.011876                       0.020554                      \\n\",\n       \"20752  0.006603                       0.011967                      \\n\",\n       \"42853  0.007615                       0.013751                      \\n\",\n       \"19153  0.013386                       0.020903                      \\n\",\n       \"15884  0.016128                       0.026782                      \\n\",\n       \"20237  0.017850                       0.024741                      \\n\",\n       \"16574  0.003302                       0.006243                      \\n\",\n       \"15966  0.011044                       0.017311                      \\n\",\n       \"20417  0.007804                       0.014536                      \\n\",\n       \"18548  0.012374                       0.021370                      \\n\",\n       \"20754  0.007279                       0.013115                      \\n\",\n       \"19591  0.007505                       0.013327                      \\n\",\n       \"19149  0.014584                       0.023523                      \\n\",\n       \"15885  0.019816                       0.029628                      \\n\",\n       \"20243  0.022176                       0.030673                      \\n\",\n       \"16577  0.004566                       0.008423                      \\n\",\n       \"15965  0.012708                       0.019141                      \\n\",\n       \"20418  0.011648                       0.017807                      \\n\",\n       \"18551  0.016310                       0.025002                      \\n\",\n       \"20751  0.009511                       0.013944                      \\n\",\n       \"19594  0.012110                       0.018254                      \\n\",\n       \"19152  0.016433                       0.023303                      \\n\",\n       \"15883  0.017535                       0.025310                      \\n\",\n       \"20239  0.020162                       0.024446                      \\n\",\n       \"16575  0.004733                       0.008452                      \\n\",\n       \"15968  0.016505                       0.021576                      \\n\",\n       \"20421  0.008751                       0.014997                      \\n\",\n       \"18550  0.014210                       0.022211                      \\n\",\n       \"20749  0.012057                       0.016672                      \\n\",\n       \"19596  0.008927                       0.015641                      \\n\",\n       \"19150  0.020798                       0.028729                      \\n\",\n       \"\\n\",\n       \"       present_partial_correct@5  present_partial_precision@5  \\\\\\n\",\n       \"15880  0.668831                   0.206782                      \\n\",\n       \"20240  0.847403                   0.244165                      \\n\",\n       \"16579  0.571429                   0.194771                      \\n\",\n       \"15963  0.707792                   0.211086                      \\n\",\n       \"20419  0.577922                   0.185519                      \\n\",\n       \"18552  0.558442                   0.180520                      \\n\",\n       \"20748  0.574675                   0.194935                      \\n\",\n       \"19592  0.672078                   0.204022                      \\n\",\n       \"19154  0.665584                   0.200243                      \\n\",\n       \"15881  2.086000                   0.489705                      \\n\",\n       \"20241  2.086000                   0.492592                      \\n\",\n       \"16573  1.890000                   0.464976                      \\n\",\n       \"15964  2.030000                   0.478601                      \\n\",\n       \"20420  1.932000                   0.471306                      \\n\",\n       \"18546  1.988000                   0.473907                      \\n\",\n       \"20750  2.004000                   0.488626                      \\n\",\n       \"19593  1.970000                   0.473193                      \\n\",\n       \"19155  2.054000                   0.488566                      \\n\",\n       \"15886  1.495622                   0.336905                      \\n\",\n       \"20238  1.490819                   0.335842                      \\n\",\n       \"16578  1.407765                   0.322742                      \\n\",\n       \"15967  1.481563                   0.334278                      \\n\",\n       \"42862  1.405564                   0.321071                      \\n\",\n       \"18547  1.453095                   0.328700                      \\n\",\n       \"20753  1.425627                   0.326685                      \\n\",\n       \"19595  1.464002                   0.331143                      \\n\",\n       \"19151  1.471056                   0.332656                      \\n\",\n       \"15882  1.474000                   0.332548                      \\n\",\n       \"20242  1.481500                   0.333272                      \\n\",\n       \"16576  1.386000                   0.318144                      \\n\",\n       \"15969  1.456500                   0.331720                      \\n\",\n       \"20416  1.370000                   0.316783                      \\n\",\n       \"18549  1.424000                   0.323932                      \\n\",\n       \"20752  1.407500                   0.324149                      \\n\",\n       \"42853  1.432000                   0.327003                      \\n\",\n       \"19153  1.459000                   0.330557                      \\n\",\n       \"15884  1.432609                   0.325242                      \\n\",\n       \"20237  1.423913                   0.324017                      \\n\",\n       \"16574  1.365217                   0.314933                      \\n\",\n       \"15966  1.439130                   0.327534                      \\n\",\n       \"20417  1.347826                   0.313069                      \\n\",\n       \"18548  1.430435                   0.323811                      \\n\",\n       \"20754  1.389130                   0.319046                      \\n\",\n       \"19591  1.391304                   0.319429                      \\n\",\n       \"19149  1.417391                   0.322484                      \\n\",\n       \"15885  2.218009                   0.498171                      \\n\",\n       \"20243  2.270142                   0.508627                      \\n\",\n       \"16577  2.127962                   0.483450                      \\n\",\n       \"15965  2.213270                   0.492577                      \\n\",\n       \"20418  2.080569                   0.478654                      \\n\",\n       \"18551  2.189573                   0.490895                      \\n\",\n       \"20751  2.099526                   0.484669                      \\n\",\n       \"19594  2.232227                   0.498554                      \\n\",\n       \"19152  2.170616                   0.486438                      \\n\",\n       \"15883  2.050000                   0.477740                      \\n\",\n       \"20239  1.970000                   0.460010                      \\n\",\n       \"16575  1.970000                   0.459220                      \\n\",\n       \"15968  1.970000                   0.453805                      \\n\",\n       \"20421  1.990000                   0.456133                      \\n\",\n       \"18550  1.980000                   0.462796                      \\n\",\n       \"20749  1.940000                   0.466131                      \\n\",\n       \"19596  1.990000                   0.453297                      \\n\",\n       \"19150  2.000000                   0.460612                      \\n\",\n       \"\\n\",\n       \"       present_partial_recall@5  present_partial_f_score@5  \\\\\\n\",\n       \"15880  0.141208                  0.164149                    \\n\",\n       \"20240  0.165434                  0.193027                    \\n\",\n       \"16579  0.120940                  0.144720                    \\n\",\n       \"15963  0.142212                  0.165907                    \\n\",\n       \"20419  0.122234                  0.143970                    \\n\",\n       \"18552  0.120230                  0.141217                    \\n\",\n       \"20748  0.120252                  0.141956                    \\n\",\n       \"19592  0.136799                  0.159776                    \\n\",\n       \"19154  0.136710                  0.158548                    \\n\",\n       \"15881  0.377280                  0.398031                    \\n\",\n       \"20241  0.375771                  0.398300                    \\n\",\n       \"16573  0.348676                  0.372222                    \\n\",\n       \"15964  0.363972                  0.385589                    \\n\",\n       \"20420  0.358146                  0.381062                    \\n\",\n       \"18546  0.359479                  0.382446                    \\n\",\n       \"20750  0.366317                  0.391316                    \\n\",\n       \"19593  0.356562                  0.380658                    \\n\",\n       \"19155  0.373851                  0.395960                    \\n\",\n       \"15886  0.582617                  0.397674                    \\n\",\n       \"20238  0.583485                  0.397538                    \\n\",\n       \"16578  0.559043                  0.381391                    \\n\",\n       \"15967  0.582072                  0.395985                    \\n\",\n       \"42862  0.554268                  0.378381                    \\n\",\n       \"18547  0.572366                  0.389596                    \\n\",\n       \"20753  0.564071                  0.385186                    \\n\",\n       \"19595  0.573330                  0.391035                    \\n\",\n       \"19151  0.579097                  0.393905                    \\n\",\n       \"15882  0.578348                  0.393197                    \\n\",\n       \"20242  0.581800                  0.394779                    \\n\",\n       \"16576  0.551778                  0.375654                    \\n\",\n       \"15969  0.578304                  0.392666                    \\n\",\n       \"20416  0.549232                  0.374334                    \\n\",\n       \"18549  0.568747                  0.384025                    \\n\",\n       \"20752  0.559380                  0.382138                    \\n\",\n       \"42853  0.568221                  0.386543                    \\n\",\n       \"19153  0.575537                  0.391255                    \\n\",\n       \"15884  0.572728                  0.380369                    \\n\",\n       \"20237  0.569065                  0.378375                    \\n\",\n       \"16574  0.559564                  0.369010                    \\n\",\n       \"15966  0.582721                  0.384466                    \\n\",\n       \"20417  0.553423                  0.365863                    \\n\",\n       \"18548  0.562992                  0.376591                    \\n\",\n       \"20754  0.555790                  0.370100                    \\n\",\n       \"19591  0.563441                  0.374098                    \\n\",\n       \"19149  0.569207                  0.378201                    \\n\",\n       \"15885  0.490385                  0.453971                    \\n\",\n       \"20243  0.498532                  0.462071                    \\n\",\n       \"16577  0.473372                  0.439287                    \\n\",\n       \"15965  0.491572                  0.450339                    \\n\",\n       \"20418  0.466916                  0.433444                    \\n\",\n       \"18551  0.482838                  0.445990                    \\n\",\n       \"20751  0.470709                  0.438037                    \\n\",\n       \"19594  0.500712                  0.457062                    \\n\",\n       \"19152  0.485011                  0.443620                    \\n\",\n       \"15883  0.392942                  0.406438                    \\n\",\n       \"20239  0.373859                  0.388816                    \\n\",\n       \"16575  0.382968                  0.391399                    \\n\",\n       \"15968  0.371003                  0.384623                    \\n\",\n       \"20421  0.369490                  0.384696                    \\n\",\n       \"18550  0.379397                  0.392485                    \\n\",\n       \"20749  0.366955                  0.386099                    \\n\",\n       \"19596  0.362583                  0.380844                    \\n\",\n       \"19150  0.377082                  0.390647                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@5  present_partial_f_score_hard@5  \\\\\\n\",\n       \"15880  0.205714                          0.163574                         \\n\",\n       \"20240  0.243905                          0.192780                         \\n\",\n       \"16579  0.179562                          0.141166                         \\n\",\n       \"15963  0.208098                          0.164991                         \\n\",\n       \"20419  0.180207                          0.142508                         \\n\",\n       \"18552  0.178195                          0.140406                         \\n\",\n       \"20748  0.173404                          0.138384                         \\n\",\n       \"19592  0.201049                          0.158721                         \\n\",\n       \"19154  0.199777                          0.158494                         \\n\",\n       \"15881  0.488945                          0.397871                         \\n\",\n       \"20241  0.492059                          0.398291                         \\n\",\n       \"16573  0.455602                          0.368554                         \\n\",\n       \"15964  0.477687                          0.385430                         \\n\",\n       \"20420  0.466228                          0.378601                         \\n\",\n       \"18546  0.471693                          0.381279                         \\n\",\n       \"20750  0.477524                          0.386894                         \\n\",\n       \"19593  0.469394                          0.378729                         \\n\",\n       \"19155  0.488566                          0.395960                         \\n\",\n       \"15886  0.336724                          0.397528                         \\n\",\n       \"20238  0.335715                          0.397437                         \\n\",\n       \"16578  0.321015                          0.380226                         \\n\",\n       \"15967  0.334036                          0.395846                         \\n\",\n       \"42862  0.320181                          0.377781                         \\n\",\n       \"18547  0.327922                          0.389071                         \\n\",\n       \"20753  0.323826                          0.383275                         \\n\",\n       \"19595  0.330831                          0.390785                         \\n\",\n       \"19151  0.332633                          0.393888                         \\n\",\n       \"15882  0.332498                          0.393149                         \\n\",\n       \"20242  0.333119                          0.394668                         \\n\",\n       \"16576  0.316161                          0.374339                         \\n\",\n       \"15969  0.331545                          0.392517                         \\n\",\n       \"20416  0.315932                          0.373604                         \\n\",\n       \"18549  0.322885                          0.383336                         \\n\",\n       \"20752  0.320740                          0.379781                         \\n\",\n       \"42853  0.326669                          0.386287                         \\n\",\n       \"19153  0.330557                          0.391255                         \\n\",\n       \"15884  0.325242                          0.380369                         \\n\",\n       \"20237  0.324017                          0.378375                         \\n\",\n       \"16574  0.313363                          0.367811                         \\n\",\n       \"15966  0.327534                          0.384466                         \\n\",\n       \"20417  0.311632                          0.365302                         \\n\",\n       \"18548  0.321476                          0.374547                         \\n\",\n       \"20754  0.316422                          0.368123                         \\n\",\n       \"19591  0.318798                          0.373609                         \\n\",\n       \"19149  0.322484                          0.378201                         \\n\",\n       \"15885  0.498171                          0.453971                         \\n\",\n       \"20243  0.508627                          0.462071                         \\n\",\n       \"16577  0.479911                          0.437727                         \\n\",\n       \"15965  0.492577                          0.450339                         \\n\",\n       \"20418  0.474341                          0.432443                         \\n\",\n       \"18551  0.490421                          0.445727                         \\n\",\n       \"20751  0.474880                          0.434194                         \\n\",\n       \"19594  0.498554                          0.457062                         \\n\",\n       \"19152  0.486438                          0.443620                         \\n\",\n       \"15883  0.477740                          0.406438                         \\n\",\n       \"20239  0.460010                          0.388816                         \\n\",\n       \"16575  0.457053                          0.390655                         \\n\",\n       \"15968  0.453805                          0.384623                         \\n\",\n       \"20421  0.455633                          0.384568                         \\n\",\n       \"18550  0.461629                          0.391967                         \\n\",\n       \"20749  0.451117                          0.381878                         \\n\",\n       \"19596  0.453297                          0.380844                         \\n\",\n       \"19150  0.460612                          0.390647                         \\n\",\n       \"\\n\",\n       \"       present_partial_correct@10  present_partial_precision@10  \\\\\\n\",\n       \"15880  1.181818                    0.166052                       \\n\",\n       \"20240  1.454545                    0.190849                       \\n\",\n       \"16579  0.863636                    0.178659                       \\n\",\n       \"15963  1.194805                    0.169659                       \\n\",\n       \"20419  1.006494                    0.160503                       \\n\",\n       \"18552  0.961039                    0.151651                       \\n\",\n       \"20748  0.837662                    0.172158                       \\n\",\n       \"19592  0.948052                    0.162090                       \\n\",\n       \"19154  1.194805                    0.163114                       \\n\",\n       \"15881  3.350000                    0.382255                       \\n\",\n       \"20241  3.516000                    0.395181                       \\n\",\n       \"16573  2.988000                    0.383240                       \\n\",\n       \"15964  3.320000                    0.382209                       \\n\",\n       \"20420  3.126000                    0.378543                       \\n\",\n       \"18546  3.162000                    0.376147                       \\n\",\n       \"20750  2.984000                    0.403379                       \\n\",\n       \"19593  3.198000                    0.379796                       \\n\",\n       \"19155  3.388000                    0.377901                       \\n\",\n       \"15886  2.041577                    0.222579                       \\n\",\n       \"20238  2.030870                    0.220567                       \\n\",\n       \"16578  1.873017                    0.220835                       \\n\",\n       \"15967  2.015410                    0.220895                       \\n\",\n       \"42862  1.926802                    0.219077                       \\n\",\n       \"18547  1.957622                    0.220777                       \\n\",\n       \"20753  1.859559                    0.226336                       \\n\",\n       \"19595  1.984240                    0.220587                       \\n\",\n       \"19151  2.027018                    0.218813                       \\n\",\n       \"15882  2.035000                    0.221237                       \\n\",\n       \"20242  2.020000                    0.220049                       \\n\",\n       \"16576  1.895500                    0.222069                       \\n\",\n       \"15969  2.005500                    0.220289                       \\n\",\n       \"20416  1.926500                    0.219142                       \\n\",\n       \"18549  1.963000                    0.221772                       \\n\",\n       \"20752  1.843500                    0.225645                       \\n\",\n       \"42853  1.997000                    0.220642                       \\n\",\n       \"19153  2.005000                    0.216770                       \\n\",\n       \"15884  1.965217                    0.214815                       \\n\",\n       \"20237  1.958696                    0.214672                       \\n\",\n       \"16574  1.884783                    0.224402                       \\n\",\n       \"15966  2.039130                    0.224002                       \\n\",\n       \"20417  1.834783                    0.211966                       \\n\",\n       \"18548  1.950000                    0.223593                       \\n\",\n       \"20754  1.871739                    0.229069                       \\n\",\n       \"19591  1.963043                    0.219679                       \\n\",\n       \"19149  2.021739                    0.219099                       \\n\",\n       \"15885  3.142180                    0.352892                       \\n\",\n       \"20243  3.199052                    0.355178                       \\n\",\n       \"16577  2.890995                    0.359328                       \\n\",\n       \"15965  3.270142                    0.359960                       \\n\",\n       \"20418  2.995261                    0.350659                       \\n\",\n       \"18551  3.090047                    0.364488                       \\n\",\n       \"20751  2.800948                    0.364523                       \\n\",\n       \"19594  3.061611                    0.351922                       \\n\",\n       \"19152  3.270142                    0.356554                       \\n\",\n       \"15883  3.070000                    0.346695                       \\n\",\n       \"20239  3.170000                    0.355399                       \\n\",\n       \"16575  2.970000                    0.357340                       \\n\",\n       \"15968  3.020000                    0.343024                       \\n\",\n       \"20421  2.870000                    0.340370                       \\n\",\n       \"18550  3.060000                    0.358025                       \\n\",\n       \"20749  2.720000                    0.371159                       \\n\",\n       \"19596  2.960000                    0.341511                       \\n\",\n       \"19150  3.090000                    0.348051                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@10  present_partial_f_score@10  \\\\\\n\",\n       \"15880  0.213560                   0.182309                     \\n\",\n       \"20240  0.252000                   0.213384                     \\n\",\n       \"16579  0.161275                   0.160621                     \\n\",\n       \"15963  0.214228                   0.184252                     \\n\",\n       \"20419  0.182055                   0.164809                     \\n\",\n       \"18552  0.178312                   0.159037                     \\n\",\n       \"20748  0.160860                   0.153441                     \\n\",\n       \"19592  0.181071                   0.164499                     \\n\",\n       \"19154  0.217470                   0.181569                     \\n\",\n       \"15881  0.545321                   0.422623                     \\n\",\n       \"20241  0.567337                   0.436339                     \\n\",\n       \"16573  0.495397                   0.403932                     \\n\",\n       \"15964  0.540449                   0.419975                     \\n\",\n       \"20420  0.509913                   0.409313                     \\n\",\n       \"18546  0.516934                   0.410240                     \\n\",\n       \"20750  0.490755                   0.412960                     \\n\",\n       \"19593  0.521058                   0.413221                     \\n\",\n       \"19155  0.552990                   0.421285                     \\n\",\n       \"15886  0.726473                   0.318590                     \\n\",\n       \"20238  0.726239                   0.316919                     \\n\",\n       \"16578  0.688005                   0.312598                     \\n\",\n       \"15967  0.725578                   0.317476                     \\n\",\n       \"42862  0.696329                   0.311185                     \\n\",\n       \"18547  0.709188                   0.315319                     \\n\",\n       \"20753  0.679501                   0.315734                     \\n\",\n       \"19595  0.711298                   0.314835                     \\n\",\n       \"19151  0.727951                   0.315546                     \\n\",\n       \"15882  0.725614                   0.317310                     \\n\",\n       \"20242  0.726723                   0.316383                     \\n\",\n       \"16576  0.690533                   0.314191                     \\n\",\n       \"15969  0.724342                   0.316622                     \\n\",\n       \"20416  0.700786                   0.312658                     \\n\",\n       \"18549  0.709286                   0.316154                     \\n\",\n       \"20752  0.681261                   0.315109                     \\n\",\n       \"42853  0.715277                   0.315474                     \\n\",\n       \"19153  0.723623                   0.312923                     \\n\",\n       \"15884  0.702803                   0.305262                     \\n\",\n       \"20237  0.710637                   0.305508                     \\n\",\n       \"16574  0.680697                   0.311623                     \\n\",\n       \"15966  0.734338                   0.318451                     \\n\",\n       \"20417  0.673976                   0.297296                     \\n\",\n       \"18548  0.699807                   0.314533                     \\n\",\n       \"20754  0.670121                   0.314560                     \\n\",\n       \"19591  0.710394                   0.311804                     \\n\",\n       \"19149  0.724711                   0.312397                     \\n\",\n       \"15885  0.645880                   0.422942                     \\n\",\n       \"20243  0.664834                   0.428667                     \\n\",\n       \"16577  0.605652                   0.412767                     \\n\",\n       \"15965  0.664509                   0.432654                     \\n\",\n       \"20418  0.616864                   0.412199                     \\n\",\n       \"18551  0.636082                   0.426119                     \\n\",\n       \"20751  0.584741                   0.411509                     \\n\",\n       \"19594  0.630166                   0.418741                     \\n\",\n       \"19152  0.663214                   0.429534                     \\n\",\n       \"15883  0.549491                   0.402062                     \\n\",\n       \"20239  0.562614                   0.411674                     \\n\",\n       \"16575  0.531591                   0.401365                     \\n\",\n       \"15968  0.542257                   0.397597                     \\n\",\n       \"20421  0.515794                   0.387328                     \\n\",\n       \"18550  0.537230                   0.407049                     \\n\",\n       \"20749  0.493678                   0.391021                     \\n\",\n       \"19596  0.528476                   0.392318                     \\n\",\n       \"19150  0.553319                   0.404408                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@10  present_partial_f_score_hard@10  \\\\\\n\",\n       \"15880  0.158415                           0.178156                          \\n\",\n       \"20240  0.188023                           0.211010                          \\n\",\n       \"16579  0.120840                           0.135305                          \\n\",\n       \"15963  0.158497                           0.178503                          \\n\",\n       \"20419  0.137225                           0.153469                          \\n\",\n       \"18552  0.134231                           0.150077                          \\n\",\n       \"20748  0.117423                           0.132629                          \\n\",\n       \"19592  0.134593                           0.151074                          \\n\",\n       \"19154  0.160857                           0.180678                          \\n\",\n       \"15881  0.373919                           0.416181                          \\n\",\n       \"20241  0.388623                           0.432495                          \\n\",\n       \"16573  0.336402                           0.375135                          \\n\",\n       \"15964  0.371404                           0.412719                          \\n\",\n       \"20420  0.350742                           0.390212                          \\n\",\n       \"18546  0.355828                           0.395546                          \\n\",\n       \"20750  0.338045                           0.375556                          \\n\",\n       \"19593  0.357421                           0.397734                          \\n\",\n       \"19155  0.376529                           0.420128                          \\n\",\n       \"15886  0.219890                           0.315767                          \\n\",\n       \"20238  0.218980                           0.315230                          \\n\",\n       \"16578  0.204876                           0.296839                          \\n\",\n       \"15967  0.217755                           0.314288                          \\n\",\n       \"42862  0.209781                           0.301918                          \\n\",\n       \"18547  0.211847                           0.306301                          \\n\",\n       \"20753  0.203551                           0.293646                          \\n\",\n       \"19595  0.214892                           0.308846                          \\n\",\n       \"19151  0.218480                           0.315214                          \\n\",\n       \"15882  0.219062                           0.315002                          \\n\",\n       \"20242  0.218001                           0.314359                          \\n\",\n       \"16576  0.206417                           0.298504                          \\n\",\n       \"15969  0.217555                           0.313717                          \\n\",\n       \"20416  0.209817                           0.302844                          \\n\",\n       \"18549  0.211826                           0.306064                          \\n\",\n       \"20752  0.202506                           0.293190                          \\n\",\n       \"42853  0.215366                           0.309770                          \\n\",\n       \"19153  0.216626                           0.312730                          \\n\",\n       \"15884  0.213253                           0.303342                          \\n\",\n       \"20237  0.213623                           0.304259                          \\n\",\n       \"16574  0.204792                           0.291546                          \\n\",\n       \"15966  0.220482                           0.314470                          \\n\",\n       \"20417  0.201613                           0.287900                          \\n\",\n       \"18548  0.210711                           0.300246                          \\n\",\n       \"20754  0.205061                           0.290154                          \\n\",\n       \"19591  0.213063                           0.304780                          \\n\",\n       \"19149  0.219099                           0.312397                          \\n\",\n       \"15885  0.346038                           0.418285                          \\n\",\n       \"20243  0.352839                           0.426733                          \\n\",\n       \"16577  0.319356                           0.386934                          \\n\",\n       \"15965  0.354919                           0.428662                          \\n\",\n       \"20418  0.332010                           0.400657                          \\n\",\n       \"18551  0.338105                           0.408771                          \\n\",\n       \"20751  0.312573                           0.378309                          \\n\",\n       \"19594  0.338267                           0.408328                          \\n\",\n       \"19152  0.356298                           0.429280                          \\n\",\n       \"15883  0.344544                           0.400464                          \\n\",\n       \"20239  0.354592                           0.410982                          \\n\",\n       \"16575  0.330749                           0.385956                          \\n\",\n       \"15968  0.337278                           0.393185                          \\n\",\n       \"20421  0.324488                           0.376630                          \\n\",\n       \"18550  0.338088                           0.392891                          \\n\",\n       \"20749  0.307376                           0.359583                          \\n\",\n       \"19596  0.332009                           0.385264                          \\n\",\n       \"19150  0.346967                           0.403519                          \\n\",\n       \"\\n\",\n       \"       present_partial_correct@k  present_partial_precision@k  \\\\\\n\",\n       \"15880  0.996753                   0.183356                      \\n\",\n       \"20240  1.237013                   0.210013                      \\n\",\n       \"16579  0.762987                   0.182398                      \\n\",\n       \"15963  1.006494                   0.186900                      \\n\",\n       \"20419  0.870130                   0.170513                      \\n\",\n       \"18552  0.834416                   0.164089                      \\n\",\n       \"20748  0.740260                   0.180954                      \\n\",\n       \"19592  0.834416                   0.176535                      \\n\",\n       \"19154  0.996753                   0.177968                      \\n\",\n       \"15881  3.096000                   0.449883                      \\n\",\n       \"20241  3.154000                   0.459128                      \\n\",\n       \"16573  2.660000                   0.431467                      \\n\",\n       \"15964  3.090000                   0.451141                      \\n\",\n       \"20420  2.840000                   0.435203                      \\n\",\n       \"18546  2.884000                   0.435037                      \\n\",\n       \"20750  2.754000                   0.456107                      \\n\",\n       \"19593  2.950000                   0.434384                      \\n\",\n       \"19155  3.132000                   0.445149                      \\n\",\n       \"15886  1.332816                   0.420237                      \\n\",\n       \"20238  1.340872                   0.425713                      \\n\",\n       \"16578  1.162606                   0.398135                      \\n\",\n       \"15967  1.311302                   0.423023                      \\n\",\n       \"42862  1.241507                   0.413138                      \\n\",\n       \"18547  1.222795                   0.399601                      \\n\",\n       \"20753  1.221344                   0.414748                      \\n\",\n       \"19595  1.318257                   0.425308                      \\n\",\n       \"19151  1.339371                   0.426160                      \\n\",\n       \"15882  1.321500                   0.419532                      \\n\",\n       \"20242  1.309500                   0.417908                      \\n\",\n       \"16576  1.168000                   0.397177                      \\n\",\n       \"15969  1.285000                   0.418904                      \\n\",\n       \"20416  1.193000                   0.405196                      \\n\",\n       \"18549  1.195500                   0.387859                      \\n\",\n       \"20752  1.184500                   0.409796                      \\n\",\n       \"42853  1.280500                   0.412775                      \\n\",\n       \"19153  1.311500                   0.418872                      \\n\",\n       \"15884  1.304348                   0.408348                      \\n\",\n       \"20237  1.336957                   0.432867                      \\n\",\n       \"16574  1.232609                   0.391240                      \\n\",\n       \"15966  1.254348                   0.395212                      \\n\",\n       \"20417  1.204348                   0.401210                      \\n\",\n       \"18548  1.286957                   0.401439                      \\n\",\n       \"20754  1.271739                   0.424410                      \\n\",\n       \"19591  1.260870                   0.411797                      \\n\",\n       \"19149  1.326087                   0.424761                      \\n\",\n       \"15885  2.710900                   0.491744                      \\n\",\n       \"20243  2.796209                   0.512231                      \\n\",\n       \"16577  2.454976                   0.478760                      \\n\",\n       \"15965  2.744076                   0.502375                      \\n\",\n       \"20418  2.526066                   0.476692                      \\n\",\n       \"18551  2.535545                   0.476532                      \\n\",\n       \"20751  2.469194                   0.488746                      \\n\",\n       \"19594  2.625592                   0.487235                      \\n\",\n       \"19152  2.687204                   0.475930                      \\n\",\n       \"15883  2.550000                   0.416309                      \\n\",\n       \"20239  2.580000                   0.424338                      \\n\",\n       \"16575  2.460000                   0.425355                      \\n\",\n       \"15968  2.590000                   0.428207                      \\n\",\n       \"20421  2.440000                   0.415255                      \\n\",\n       \"18550  2.600000                   0.442239                      \\n\",\n       \"20749  2.340000                   0.444789                      \\n\",\n       \"19596  2.530000                   0.414980                      \\n\",\n       \"19150  2.680000                   0.416846                      \\n\",\n       \"\\n\",\n       \"       present_partial_recall@k  present_partial_f_score@k  \\\\\\n\",\n       \"15880  0.178916                  0.180606                    \\n\",\n       \"20240  0.209605                  0.209782                    \\n\",\n       \"16579  0.143959                  0.156138                    \\n\",\n       \"15963  0.179906                  0.182085                    \\n\",\n       \"20419  0.156861                  0.161669                    \\n\",\n       \"18552  0.154034                  0.157803                    \\n\",\n       \"20748  0.140513                  0.151288                    \\n\",\n       \"19592  0.158332                  0.165137                    \\n\",\n       \"19154  0.175718                  0.176447                    \\n\",\n       \"15881  0.445373                  0.446987                    \\n\",\n       \"20241  0.450766                  0.453667                    \\n\",\n       \"16573  0.402232                  0.412296                    \\n\",\n       \"15964  0.444146                  0.446752                    \\n\",\n       \"20420  0.420307                  0.425771                    \\n\",\n       \"18546  0.424696                  0.428560                    \\n\",\n       \"20750  0.415813                  0.429341                    \\n\",\n       \"19593  0.424704                  0.427948                    \\n\",\n       \"19155  0.443871                  0.444332                    \\n\",\n       \"15886  0.416591                  0.417859                    \\n\",\n       \"20238  0.422704                  0.423801                    \\n\",\n       \"16578  0.391800                  0.393668                    \\n\",\n       \"15967  0.419363                  0.420615                    \\n\",\n       \"42862  0.408212                  0.409770                    \\n\",\n       \"18547  0.394548                  0.396156                    \\n\",\n       \"20753  0.407732                  0.409810                    \\n\",\n       \"19595  0.421644                  0.422866                    \\n\",\n       \"19151  0.425244                  0.425589                    \\n\",\n       \"15882  0.415809                  0.417121                    \\n\",\n       \"20242  0.414510                  0.415668                    \\n\",\n       \"16576  0.391036                  0.392818                    \\n\",\n       \"15969  0.415040                  0.416397                    \\n\",\n       \"20416  0.400284                  0.401832                    \\n\",\n       \"18549  0.382785                  0.384430                    \\n\",\n       \"20752  0.402368                  0.404514                    \\n\",\n       \"42853  0.409234                  0.410406                    \\n\",\n       \"19153  0.418069                  0.418387                    \\n\",\n       \"15884  0.407364                  0.407797                    \\n\",\n       \"20237  0.432697                  0.432774                    \\n\",\n       \"16574  0.388378                  0.389557                    \\n\",\n       \"15966  0.393781                  0.394340                    \\n\",\n       \"20417  0.398647                  0.399465                    \\n\",\n       \"18548  0.400252                  0.400769                    \\n\",\n       \"20754  0.421729                  0.422778                    \\n\",\n       \"19591  0.410681                  0.411108                    \\n\",\n       \"19149  0.424761                  0.424761                    \\n\",\n       \"15885  0.486865                  0.488781                    \\n\",\n       \"20243  0.511479                  0.511833                    \\n\",\n       \"16577  0.460716                  0.468158                    \\n\",\n       \"15965  0.498349                  0.499992                    \\n\",\n       \"20418  0.466592                  0.469859                    \\n\",\n       \"18551  0.465381                  0.469400                    \\n\",\n       \"20751  0.465795                  0.473802                    \\n\",\n       \"19594  0.483365                  0.484816                    \\n\",\n       \"19152  0.475930                  0.475930                    \\n\",\n       \"15883  0.411800                  0.413764                    \\n\",\n       \"20239  0.419417                  0.421521                    \\n\",\n       \"16575  0.405025                  0.411687                    \\n\",\n       \"15968  0.423219                  0.425062                    \\n\",\n       \"20421  0.403433                  0.407426                    \\n\",\n       \"18550  0.429108                  0.434269                    \\n\",\n       \"20749  0.397438                  0.411902                    \\n\",\n       \"19596  0.409224                  0.411544                    \\n\",\n       \"19150  0.416846                  0.416846                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@k  present_partial_f_score_hard@k  \\\\\\n\",\n       \"15880  0.178916                          0.178916                         \\n\",\n       \"20240  0.209605                          0.209605                         \\n\",\n       \"16579  0.143959                          0.143959                         \\n\",\n       \"15963  0.179906                          0.179906                         \\n\",\n       \"20419  0.156861                          0.156861                         \\n\",\n       \"18552  0.154034                          0.154034                         \\n\",\n       \"20748  0.140513                          0.140513                         \\n\",\n       \"19592  0.158332                          0.158332                         \\n\",\n       \"19154  0.175718                          0.175718                         \\n\",\n       \"15881  0.445373                          0.445373                         \\n\",\n       \"20241  0.450766                          0.450766                         \\n\",\n       \"16573  0.402232                          0.402232                         \\n\",\n       \"15964  0.444146                          0.444146                         \\n\",\n       \"20420  0.420307                          0.420307                         \\n\",\n       \"18546  0.424696                          0.424696                         \\n\",\n       \"20750  0.415813                          0.415813                         \\n\",\n       \"19593  0.424704                          0.424704                         \\n\",\n       \"19155  0.443871                          0.443871                         \\n\",\n       \"15886  0.416591                          0.416591                         \\n\",\n       \"20238  0.422704                          0.422704                         \\n\",\n       \"16578  0.391800                          0.391800                         \\n\",\n       \"15967  0.419363                          0.419363                         \\n\",\n       \"42862  0.408212                          0.408212                         \\n\",\n       \"18547  0.394548                          0.394548                         \\n\",\n       \"20753  0.407732                          0.407732                         \\n\",\n       \"19595  0.421644                          0.421644                         \\n\",\n       \"19151  0.425244                          0.425244                         \\n\",\n       \"15882  0.415809                          0.415809                         \\n\",\n       \"20242  0.414510                          0.414510                         \\n\",\n       \"16576  0.391036                          0.391036                         \\n\",\n       \"15969  0.415040                          0.415040                         \\n\",\n       \"20416  0.400284                          0.400284                         \\n\",\n       \"18549  0.382785                          0.382785                         \\n\",\n       \"20752  0.402368                          0.402368                         \\n\",\n       \"42853  0.409234                          0.409234                         \\n\",\n       \"19153  0.418069                          0.418069                         \\n\",\n       \"15884  0.407364                          0.407364                         \\n\",\n       \"20237  0.432697                          0.432697                         \\n\",\n       \"16574  0.388378                          0.388378                         \\n\",\n       \"15966  0.393781                          0.393781                         \\n\",\n       \"20417  0.398647                          0.398647                         \\n\",\n       \"18548  0.400252                          0.400252                         \\n\",\n       \"20754  0.421729                          0.421729                         \\n\",\n       \"19591  0.410681                          0.410681                         \\n\",\n       \"19149  0.424761                          0.424761                         \\n\",\n       \"15885  0.486865                          0.486865                         \\n\",\n       \"20243  0.511479                          0.511479                         \\n\",\n       \"16577  0.460716                          0.460716                         \\n\",\n       \"15965  0.498349                          0.498349                         \\n\",\n       \"20418  0.466592                          0.466592                         \\n\",\n       \"18551  0.465381                          0.465381                         \\n\",\n       \"20751  0.465795                          0.465795                         \\n\",\n       \"19594  0.483365                          0.483365                         \\n\",\n       \"19152  0.475930                          0.475930                         \\n\",\n       \"15883  0.411800                          0.411800                         \\n\",\n       \"20239  0.419417                          0.419417                         \\n\",\n       \"16575  0.405025                          0.405025                         \\n\",\n       \"15968  0.423219                          0.423219                         \\n\",\n       \"20421  0.403433                          0.403433                         \\n\",\n       \"18550  0.429108                          0.429108                         \\n\",\n       \"20749  0.397438                          0.397438                         \\n\",\n       \"19596  0.409224                          0.409224                         \\n\",\n       \"19150  0.416846                          0.416846                         \\n\",\n       \"\\n\",\n       \"       present_partial_correct@M  present_partial_precision@M  \\\\\\n\",\n       \"15880  1.616883                   0.139805                      \\n\",\n       \"20240  2.266234                   0.139757                      \\n\",\n       \"16579  0.918831                   0.176762                      \\n\",\n       \"15963  1.681818                   0.139717                      \\n\",\n       \"20419  1.204545                   0.148702                      \\n\",\n       \"18552  1.233766                   0.144176                      \\n\",\n       \"20748  0.918831                   0.167757                      \\n\",\n       \"19592  1.090909                   0.152383                      \\n\",\n       \"19154  1.759740                   0.119564                      \\n\",\n       \"15881  4.276000                   0.302534                      \\n\",\n       \"20241  4.462000                   0.303251                      \\n\",\n       \"16573  3.334000                   0.342339                      \\n\",\n       \"15964  4.370000                   0.302967                      \\n\",\n       \"20420  3.804000                   0.319325                      \\n\",\n       \"18546  3.822000                   0.310355                      \\n\",\n       \"20750  3.360000                   0.360731                      \\n\",\n       \"19593  4.132000                   0.306187                      \\n\",\n       \"19155  5.244000                   0.252692                      \\n\",\n       \"15886  2.345475                   0.155879                      \\n\",\n       \"20238  2.368389                   0.147637                      \\n\",\n       \"16578  1.999550                   0.179964                      \\n\",\n       \"15967  2.398809                   0.153516                      \\n\",\n       \"42862  2.200580                   0.165297                      \\n\",\n       \"18547  2.201181                   0.167037                      \\n\",\n       \"20753  2.033672                   0.189100                      \\n\",\n       \"19595  2.306649                   0.155651                      \\n\",\n       \"19151  2.544404                   0.114064                      \\n\",\n       \"15882  2.348500                   0.153634                      \\n\",\n       \"20242  2.350000                   0.146960                      \\n\",\n       \"16576  2.015500                   0.180044                      \\n\",\n       \"15969  2.391500                   0.152434                      \\n\",\n       \"20416  2.183500                   0.166125                      \\n\",\n       \"18549  2.217500                   0.167699                      \\n\",\n       \"20752  2.026000                   0.189243                      \\n\",\n       \"42853  2.329500                   0.154658                      \\n\",\n       \"19153  2.514500                   0.112772                      \\n\",\n       \"15884  2.276087                   0.150441                      \\n\",\n       \"20237  2.321739                   0.148096                      \\n\",\n       \"16574  2.032609                   0.194172                      \\n\",\n       \"15966  2.384783                   0.157481                      \\n\",\n       \"20417  2.134783                   0.169225                      \\n\",\n       \"18548  2.171739                   0.173002                      \\n\",\n       \"20754  2.060870                   0.193998                      \\n\",\n       \"19591  2.280435                   0.156516                      \\n\",\n       \"19149  2.615217                   0.114379                      \\n\",\n       \"15885  3.654028                   0.270990                      \\n\",\n       \"20243  3.715640                   0.260357                      \\n\",\n       \"16577  3.118483                   0.313202                      \\n\",\n       \"15965  3.876777                   0.263929                      \\n\",\n       \"20418  3.431280                   0.287652                      \\n\",\n       \"18551  3.436019                   0.303928                      \\n\",\n       \"20751  3.085308                   0.328428                      \\n\",\n       \"19594  3.578199                   0.263065                      \\n\",\n       \"19152  4.199052                   0.195353                      \\n\",\n       \"15883  3.590000                   0.266128                      \\n\",\n       \"20239  3.940000                   0.256687                      \\n\",\n       \"16575  3.340000                   0.310635                      \\n\",\n       \"15968  3.930000                   0.266437                      \\n\",\n       \"20421  3.360000                   0.275666                      \\n\",\n       \"18550  3.480000                   0.285168                      \\n\",\n       \"20749  3.130000                   0.334635                      \\n\",\n       \"19596  3.570000                   0.276718                      \\n\",\n       \"19150  4.410000                   0.211801                      \\n\",\n       \"\\n\",\n       \"       present_partial_recall@M  present_partial_f_score@M  \\\\\\n\",\n       \"15880  0.273228                  0.176132                    \\n\",\n       \"20240  0.354896                  0.193866                    \\n\",\n       \"16579  0.170419                  0.162001                    \\n\",\n       \"15963  0.281648                  0.176099                    \\n\",\n       \"20419  0.211780                  0.163875                    \\n\",\n       \"18552  0.217467                  0.163672                    \\n\",\n       \"20748  0.170840                  0.153065                    \\n\",\n       \"19592  0.201046                  0.161939                    \\n\",\n       \"19154  0.293355                  0.162591                    \\n\",\n       \"15881  0.648429                  0.391413                    \\n\",\n       \"20241  0.668272                  0.392935                    \\n\",\n       \"16573  0.531724                  0.386598                    \\n\",\n       \"15964  0.653051                  0.391869                    \\n\",\n       \"20420  0.583395                  0.387936                    \\n\",\n       \"18546  0.587953                  0.382670                    \\n\",\n       \"20750  0.528297                  0.394148                    \\n\",\n       \"19593  0.621745                  0.385345                    \\n\",\n       \"19155  0.757664                  0.361263                    \\n\",\n       \"15886  0.789316                  0.243073                    \\n\",\n       \"20238  0.796131                  0.232905                    \\n\",\n       \"16578  0.718848                  0.266461                    \\n\",\n       \"15967  0.807983                  0.240370                    \\n\",\n       \"42862  0.757194                  0.251727                    \\n\",\n       \"18547  0.762243                  0.254522                    \\n\",\n       \"20753  0.717006                  0.274753                    \\n\",\n       \"19595  0.778074                  0.240875                    \\n\",\n       \"19151  0.835241                  0.189435                    \\n\",\n       \"15882  0.791604                  0.240556                    \\n\",\n       \"20242  0.793580                  0.231652                    \\n\",\n       \"16576  0.720174                  0.266961                    \\n\",\n       \"15969  0.805587                  0.239272                    \\n\",\n       \"20416  0.757117                  0.253605                    \\n\",\n       \"18549  0.765985                  0.255812                    \\n\",\n       \"20752  0.721125                  0.275007                    \\n\",\n       \"42853  0.783236                  0.240828                    \\n\",\n       \"19153  0.828989                  0.187885                    \\n\",\n       \"15884  0.771767                  0.235208                    \\n\",\n       \"20237  0.790404                  0.234416                    \\n\",\n       \"16574  0.714649                  0.281545                    \\n\",\n       \"15966  0.808159                  0.245777                    \\n\",\n       \"20417  0.742162                  0.254362                    \\n\",\n       \"18548  0.749870                  0.260954                    \\n\",\n       \"20754  0.710534                  0.280979                    \\n\",\n       \"19591  0.779044                  0.243496                    \\n\",\n       \"19149  0.848030                  0.191439                    \\n\",\n       \"15885  0.707183                  0.363256                    \\n\",\n       \"20243  0.730011                  0.357598                    \\n\",\n       \"16577  0.634596                  0.379283                    \\n\",\n       \"15965  0.750186                  0.360628                    \\n\",\n       \"20418  0.674428                  0.369004                    \\n\",\n       \"18551  0.683365                  0.383333                    \\n\",\n       \"20751  0.628378                  0.387229                    \\n\",\n       \"19594  0.704823                  0.351918                    \\n\",\n       \"19152  0.795989                  0.296610                    \\n\",\n       \"15883  0.618229                  0.352466                    \\n\",\n       \"20239  0.668648                  0.349271                    \\n\",\n       \"16575  0.578507                  0.375973                    \\n\",\n       \"15968  0.665304                  0.360921                    \\n\",\n       \"20421  0.580418                  0.349229                    \\n\",\n       \"18550  0.587295                  0.358352                    \\n\",\n       \"20749  0.552657                  0.373975                    \\n\",\n       \"19596  0.616331                  0.358830                    \\n\",\n       \"19150  0.724550                  0.311953                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@M  present_partial_f_score_hard@M  \\\\\\n\",\n       \"15880  0.139805                          0.176132                         \\n\",\n       \"20240  0.139757                          0.193866                         \\n\",\n       \"16579  0.176762                          0.162001                         \\n\",\n       \"15963  0.139717                          0.176099                         \\n\",\n       \"20419  0.148702                          0.163875                         \\n\",\n       \"18552  0.144176                          0.163672                         \\n\",\n       \"20748  0.167757                          0.153065                         \\n\",\n       \"19592  0.152383                          0.161939                         \\n\",\n       \"19154  0.119564                          0.162591                         \\n\",\n       \"15881  0.302534                          0.391413                         \\n\",\n       \"20241  0.303251                          0.392935                         \\n\",\n       \"16573  0.342339                          0.386598                         \\n\",\n       \"15964  0.302967                          0.391869                         \\n\",\n       \"20420  0.319325                          0.387936                         \\n\",\n       \"18546  0.310355                          0.382670                         \\n\",\n       \"20750  0.360731                          0.394148                         \\n\",\n       \"19593  0.306187                          0.385345                         \\n\",\n       \"19155  0.252692                          0.361263                         \\n\",\n       \"15886  0.155879                          0.243073                         \\n\",\n       \"20238  0.147637                          0.232905                         \\n\",\n       \"16578  0.179964                          0.266461                         \\n\",\n       \"15967  0.153516                          0.240370                         \\n\",\n       \"42862  0.165297                          0.251727                         \\n\",\n       \"18547  0.167037                          0.254522                         \\n\",\n       \"20753  0.189100                          0.274753                         \\n\",\n       \"19595  0.155651                          0.240875                         \\n\",\n       \"19151  0.114064                          0.189435                         \\n\",\n       \"15882  0.153634                          0.240556                         \\n\",\n       \"20242  0.146960                          0.231652                         \\n\",\n       \"16576  0.180044                          0.266961                         \\n\",\n       \"15969  0.152434                          0.239272                         \\n\",\n       \"20416  0.166125                          0.253605                         \\n\",\n       \"18549  0.167699                          0.255812                         \\n\",\n       \"20752  0.189243                          0.275007                         \\n\",\n       \"42853  0.154658                          0.240828                         \\n\",\n       \"19153  0.112772                          0.187885                         \\n\",\n       \"15884  0.150441                          0.235208                         \\n\",\n       \"20237  0.148096                          0.234416                         \\n\",\n       \"16574  0.194172                          0.281545                         \\n\",\n       \"15966  0.157481                          0.245777                         \\n\",\n       \"20417  0.169225                          0.254362                         \\n\",\n       \"18548  0.173002                          0.260954                         \\n\",\n       \"20754  0.193998                          0.280979                         \\n\",\n       \"19591  0.156516                          0.243496                         \\n\",\n       \"19149  0.114379                          0.191439                         \\n\",\n       \"15885  0.270990                          0.363256                         \\n\",\n       \"20243  0.260357                          0.357598                         \\n\",\n       \"16577  0.313202                          0.379283                         \\n\",\n       \"15965  0.263929                          0.360628                         \\n\",\n       \"20418  0.287652                          0.369004                         \\n\",\n       \"18551  0.303928                          0.383333                         \\n\",\n       \"20751  0.328428                          0.387229                         \\n\",\n       \"19594  0.263065                          0.351918                         \\n\",\n       \"19152  0.195353                          0.296610                         \\n\",\n       \"15883  0.266128                          0.352466                         \\n\",\n       \"20239  0.256687                          0.349271                         \\n\",\n       \"16575  0.310635                          0.375973                         \\n\",\n       \"15968  0.266437                          0.360921                         \\n\",\n       \"20421  0.275666                          0.349229                         \\n\",\n       \"18550  0.285168                          0.358352                         \\n\",\n       \"20749  0.334635                          0.373975                         \\n\",\n       \"19596  0.276718                          0.358830                         \\n\",\n       \"19150  0.211801                          0.311953                         \\n\",\n       \"\\n\",\n       \"       present_partial_correct@1  present_partial_precision@1  \\\\\\n\",\n       \"15880  0.123377                   0.221956                      \\n\",\n       \"20240  0.227273                   0.351065                      \\n\",\n       \"16579  0.146104                   0.238600                      \\n\",\n       \"15963  0.155844                   0.269942                      \\n\",\n       \"20419  0.142857                   0.265625                      \\n\",\n       \"18552  0.103896                   0.199111                      \\n\",\n       \"20748  0.152597                   0.258069                      \\n\",\n       \"19592  0.159091                   0.301511                      \\n\",\n       \"19154  0.181818                   0.305814                      \\n\",\n       \"15881  0.460000                   0.625759                      \\n\",\n       \"20241  0.456000                   0.642257                      \\n\",\n       \"16573  0.342000                   0.515448                      \\n\",\n       \"15964  0.498000                   0.672564                      \\n\",\n       \"20420  0.410000                   0.589647                      \\n\",\n       \"18546  0.454000                   0.606243                      \\n\",\n       \"20750  0.456000                   0.629768                      \\n\",\n       \"19593  0.382000                   0.575249                      \\n\",\n       \"19155  0.404000                   0.606680                      \\n\",\n       \"15886  0.387902                   0.471608                      \\n\",\n       \"20238  0.385801                   0.481961                      \\n\",\n       \"16578  0.384500                   0.453119                      \\n\",\n       \"15967  0.367789                   0.473724                      \\n\",\n       \"42862  0.405564                   0.501872                      \\n\",\n       \"18547  0.334267                   0.411228                      \\n\",\n       \"20753  0.382599                   0.474410                      \\n\",\n       \"19595  0.411568                   0.505084                      \\n\",\n       \"19151  0.413018                   0.506115                      \\n\",\n       \"15882  0.391000                   0.471054                      \\n\",\n       \"20242  0.398000                   0.490693                      \\n\",\n       \"16576  0.384000                   0.446925                      \\n\",\n       \"15969  0.363000                   0.469235                      \\n\",\n       \"20416  0.391500                   0.488452                      \\n\",\n       \"18549  0.320000                   0.392447                      \\n\",\n       \"20752  0.382500                   0.475844                      \\n\",\n       \"42853  0.389500                   0.486677                      \\n\",\n       \"19153  0.398500                   0.490027                      \\n\",\n       \"15884  0.378261                   0.462386                      \\n\",\n       \"20237  0.404348                   0.503498                      \\n\",\n       \"16574  0.350000                   0.426859                      \\n\",\n       \"15966  0.376087                   0.482847                      \\n\",\n       \"20417  0.380435                   0.481081                      \\n\",\n       \"18548  0.354348                   0.438214                      \\n\",\n       \"20754  0.397826                   0.496558                      \\n\",\n       \"19591  0.400000                   0.506183                      \\n\",\n       \"19149  0.406522                   0.501324                      \\n\",\n       \"15885  0.620853                   0.690600                      \\n\",\n       \"20243  0.616114                   0.712074                      \\n\",\n       \"16577  0.587678                   0.647965                      \\n\",\n       \"15965  0.568720                   0.651948                      \\n\",\n       \"20418  0.545024                   0.652368                      \\n\",\n       \"18551  0.563981                   0.656148                      \\n\",\n       \"20751  0.587678                   0.678460                      \\n\",\n       \"19594  0.592417                   0.687377                      \\n\",\n       \"19152  0.592417                   0.682916                      \\n\",\n       \"15883  0.460000                   0.586444                      \\n\",\n       \"20239  0.570000                   0.682468                      \\n\",\n       \"16575  0.470000                   0.563940                      \\n\",\n       \"15968  0.470000                   0.597089                      \\n\",\n       \"20421  0.520000                   0.637522                      \\n\",\n       \"18550  0.440000                   0.556227                      \\n\",\n       \"20749  0.520000                   0.637377                      \\n\",\n       \"19596  0.460000                   0.582119                      \\n\",\n       \"19150  0.510000                   0.621905                      \\n\",\n       \"\\n\",\n       \"       present_partial_recall@1  present_partial_f_score@1  \\\\\\n\",\n       \"15880  0.030366                  0.052793                    \\n\",\n       \"20240  0.050424                  0.086870                    \\n\",\n       \"16579  0.032257                  0.056090                    \\n\",\n       \"15963  0.038538                  0.066520                    \\n\",\n       \"20419  0.036122                  0.063052                    \\n\",\n       \"18552  0.027641                  0.048039                    \\n\",\n       \"20748  0.035343                  0.061403                    \\n\",\n       \"19592  0.042017                  0.072721                    \\n\",\n       \"19154  0.041957                  0.072893                    \\n\",\n       \"15881  0.100774                  0.166742                    \\n\",\n       \"20241  0.107927                  0.176685                    \\n\",\n       \"16573  0.086559                  0.141577                    \\n\",\n       \"15964  0.110505                  0.181698                    \\n\",\n       \"20420  0.097511                  0.159337                    \\n\",\n       \"18546  0.100246                  0.165739                    \\n\",\n       \"20750  0.105463                  0.171810                    \\n\",\n       \"19593  0.093933                  0.155113                    \\n\",\n       \"19155  0.100124                  0.164865                    \\n\",\n       \"15886  0.176018                  0.241146                    \\n\",\n       \"20238  0.179697                  0.246680                    \\n\",\n       \"16578  0.168407                  0.230874                    \\n\",\n       \"15967  0.178876                  0.244486                    \\n\",\n       \"42862  0.190966                  0.259990                    \\n\",\n       \"18547  0.152306                  0.209282                    \\n\",\n       \"20753  0.179026                  0.244367                    \\n\",\n       \"19595  0.189931                  0.259854                    \\n\",\n       \"19151  0.190320                  0.260473                    \\n\",\n       \"15882  0.177259                  0.241651                    \\n\",\n       \"20242  0.182521                  0.250516                    \\n\",\n       \"16576  0.169090                  0.230426                    \\n\",\n       \"15969  0.179269                  0.243164                    \\n\",\n       \"20416  0.186533                  0.253696                    \\n\",\n       \"18549  0.146126                  0.200070                    \\n\",\n       \"20752  0.179263                  0.244733                    \\n\",\n       \"42853  0.182330                  0.249640                    \\n\",\n       \"19153  0.185041                  0.252363                    \\n\",\n       \"15884  0.181482                  0.241374                    \\n\",\n       \"20237  0.204190                  0.268826                    \\n\",\n       \"16574  0.168494                  0.224906                    \\n\",\n       \"15966  0.179631                  0.244430                    \\n\",\n       \"20417  0.196942                  0.258868                    \\n\",\n       \"18548  0.172169                  0.228541                    \\n\",\n       \"20754  0.208789                  0.270075                    \\n\",\n       \"19591  0.201715                  0.267741                    \\n\",\n       \"19149  0.203061                  0.268276                    \\n\",\n       \"15885  0.143780                  0.226442                    \\n\",\n       \"20243  0.158256                  0.240966                    \\n\",\n       \"16577  0.139431                  0.214287                    \\n\",\n       \"15965  0.148296                  0.224311                    \\n\",\n       \"20418  0.143771                  0.219069                    \\n\",\n       \"18551  0.135899                  0.213246                    \\n\",\n       \"20751  0.151918                  0.231405                    \\n\",\n       \"19594  0.147843                  0.229130                    \\n\",\n       \"19152  0.145513                  0.226669                    \\n\",\n       \"15883  0.097221                  0.162246                    \\n\",\n       \"20239  0.119601                  0.196300                    \\n\",\n       \"16575  0.095668                  0.158143                    \\n\",\n       \"15968  0.106510                  0.173349                    \\n\",\n       \"20421  0.113865                  0.185907                    \\n\",\n       \"18550  0.094648                  0.156061                    \\n\",\n       \"20749  0.112012                  0.183348                    \\n\",\n       \"19596  0.099616                  0.164095                    \\n\",\n       \"19150  0.107586                  0.177305                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@1  present_partial_f_score_hard@1  \\\\\\n\",\n       \"15880  0.221956                          0.052793                         \\n\",\n       \"20240  0.351065                          0.086870                         \\n\",\n       \"16579  0.238600                          0.056090                         \\n\",\n       \"15963  0.269942                          0.066520                         \\n\",\n       \"20419  0.265625                          0.063052                         \\n\",\n       \"18552  0.199111                          0.048039                         \\n\",\n       \"20748  0.258069                          0.061403                         \\n\",\n       \"19592  0.301511                          0.072721                         \\n\",\n       \"19154  0.305814                          0.072893                         \\n\",\n       \"15881  0.625759                          0.166742                         \\n\",\n       \"20241  0.642257                          0.176685                         \\n\",\n       \"16573  0.515448                          0.141577                         \\n\",\n       \"15964  0.672564                          0.181698                         \\n\",\n       \"20420  0.589647                          0.159337                         \\n\",\n       \"18546  0.606243                          0.165739                         \\n\",\n       \"20750  0.629768                          0.171810                         \\n\",\n       \"19593  0.575249                          0.155113                         \\n\",\n       \"19155  0.606680                          0.164865                         \\n\",\n       \"15886  0.471608                          0.241146                         \\n\",\n       \"20238  0.481961                          0.246680                         \\n\",\n       \"16578  0.453119                          0.230874                         \\n\",\n       \"15967  0.473724                          0.244486                         \\n\",\n       \"42862  0.501872                          0.259990                         \\n\",\n       \"18547  0.411228                          0.209282                         \\n\",\n       \"20753  0.474410                          0.244367                         \\n\",\n       \"19595  0.505084                          0.259854                         \\n\",\n       \"19151  0.506115                          0.260473                         \\n\",\n       \"15882  0.471054                          0.241651                         \\n\",\n       \"20242  0.490693                          0.250516                         \\n\",\n       \"16576  0.446925                          0.230426                         \\n\",\n       \"15969  0.469235                          0.243164                         \\n\",\n       \"20416  0.488452                          0.253696                         \\n\",\n       \"18549  0.392447                          0.200070                         \\n\",\n       \"20752  0.475844                          0.244733                         \\n\",\n       \"42853  0.486677                          0.249640                         \\n\",\n       \"19153  0.490027                          0.252363                         \\n\",\n       \"15884  0.462386                          0.241374                         \\n\",\n       \"20237  0.503498                          0.268826                         \\n\",\n       \"16574  0.426859                          0.224906                         \\n\",\n       \"15966  0.482847                          0.244430                         \\n\",\n       \"20417  0.481081                          0.258868                         \\n\",\n       \"18548  0.438214                          0.228541                         \\n\",\n       \"20754  0.496558                          0.270075                         \\n\",\n       \"19591  0.506183                          0.267741                         \\n\",\n       \"19149  0.501324                          0.268276                         \\n\",\n       \"15885  0.690600                          0.226442                         \\n\",\n       \"20243  0.712074                          0.240966                         \\n\",\n       \"16577  0.647965                          0.214287                         \\n\",\n       \"15965  0.651948                          0.224311                         \\n\",\n       \"20418  0.652368                          0.219069                         \\n\",\n       \"18551  0.656148                          0.213246                         \\n\",\n       \"20751  0.678460                          0.231405                         \\n\",\n       \"19594  0.687377                          0.229130                         \\n\",\n       \"19152  0.682916                          0.226669                         \\n\",\n       \"15883  0.586444                          0.162246                         \\n\",\n       \"20239  0.682468                          0.196300                         \\n\",\n       \"16575  0.563940                          0.158143                         \\n\",\n       \"15968  0.597089                          0.173349                         \\n\",\n       \"20421  0.637522                          0.185907                         \\n\",\n       \"18550  0.556227                          0.156061                         \\n\",\n       \"20749  0.637377                          0.183348                         \\n\",\n       \"19596  0.582119                          0.164095                         \\n\",\n       \"19150  0.621905                          0.177305                         \\n\",\n       \"\\n\",\n       \"       present_partial_correct@3  present_partial_precision@3  \\\\\\n\",\n       \"15880  0.418831                   0.230701                      \\n\",\n       \"20240  0.538961                   0.280942                      \\n\",\n       \"16579  0.370130                   0.210705                      \\n\",\n       \"15963  0.461039                   0.239784                      \\n\",\n       \"20419  0.363636                   0.211542                      \\n\",\n       \"18552  0.318182                   0.191309                      \\n\",\n       \"20748  0.396104                   0.219527                      \\n\",\n       \"19592  0.431818                   0.241249                      \\n\",\n       \"19154  0.405844                   0.225196                      \\n\",\n       \"15881  1.328000                   0.558123                      \\n\",\n       \"20241  1.362000                   0.566677                      \\n\",\n       \"16573  1.216000                   0.526289                      \\n\",\n       \"15964  1.306000                   0.550593                      \\n\",\n       \"20420  1.254000                   0.533413                      \\n\",\n       \"18546  1.300000                   0.548674                      \\n\",\n       \"20750  1.338000                   0.563593                      \\n\",\n       \"19593  1.298000                   0.547112                      \\n\",\n       \"19155  1.344000                   0.561907                      \\n\",\n       \"15886  1.089308                   0.423596                      \\n\",\n       \"20238  1.094962                   0.427058                      \\n\",\n       \"16578  1.019212                   0.401367                      \\n\",\n       \"15967  1.083104                   0.423744                      \\n\",\n       \"42862  1.023916                   0.404469                      \\n\",\n       \"18547  1.042878                   0.408979                      \\n\",\n       \"20753  1.044879                   0.411915                      \\n\",\n       \"19595  1.070246                   0.420308                      \\n\",\n       \"19151  1.080502                   0.425022                      \\n\",\n       \"15882  1.080500                   0.420061                      \\n\",\n       \"20242  1.091500                   0.422306                      \\n\",\n       \"16576  1.011000                   0.398922                      \\n\",\n       \"15969  1.047500                   0.414880                      \\n\",\n       \"20416  0.990000                   0.396764                      \\n\",\n       \"18549  1.015500                   0.397878                      \\n\",\n       \"20752  1.036000                   0.410394                      \\n\",\n       \"42853  1.039000                   0.409645                      \\n\",\n       \"19153  1.052500                   0.417611                      \\n\",\n       \"15884  1.021739                   0.404013                      \\n\",\n       \"20237  1.065217                   0.419536                      \\n\",\n       \"16574  0.993478                   0.394860                      \\n\",\n       \"15966  1.028261                   0.405937                      \\n\",\n       \"20417  0.971739                   0.386919                      \\n\",\n       \"18548  1.006522                   0.393099                      \\n\",\n       \"20754  1.050000                   0.405802                      \\n\",\n       \"19591  1.021739                   0.403202                      \\n\",\n       \"19149  1.050000                   0.412921                      \\n\",\n       \"15885  1.601896                   0.604302                      \\n\",\n       \"20243  1.606635                   0.607334                      \\n\",\n       \"16577  1.488152                   0.564994                      \\n\",\n       \"15965  1.549763                   0.583830                      \\n\",\n       \"20418  1.445498                   0.558853                      \\n\",\n       \"18551  1.549763                   0.584325                      \\n\",\n       \"20751  1.511848                   0.582125                      \\n\",\n       \"19594  1.540284                   0.586753                      \\n\",\n       \"19152  1.592417                   0.600302                      \\n\",\n       \"15883  1.420000                   0.556062                      \\n\",\n       \"20239  1.400000                   0.546411                      \\n\",\n       \"16575  1.380000                   0.543279                      \\n\",\n       \"15968  1.290000                   0.511702                      \\n\",\n       \"20421  1.350000                   0.529896                      \\n\",\n       \"18550  1.400000                   0.546902                      \\n\",\n       \"20749  1.330000                   0.537803                      \\n\",\n       \"19596  1.340000                   0.531885                      \\n\",\n       \"19150  1.380000                   0.537250                      \\n\",\n       \"\\n\",\n       \"       present_partial_recall@3  present_partial_f_score@3  \\\\\\n\",\n       \"15880  0.096004                  0.132561                    \\n\",\n       \"20240  0.115308                  0.159801                    \\n\",\n       \"16579  0.084573                  0.117386                    \\n\",\n       \"15963  0.098515                  0.136400                    \\n\",\n       \"20419  0.087398                  0.121219                    \\n\",\n       \"18552  0.078682                  0.109194                    \\n\",\n       \"20748  0.088185                  0.121715                    \\n\",\n       \"19592  0.098881                  0.137121                    \\n\",\n       \"19154  0.092140                  0.128124                    \\n\",\n       \"15881  0.264029                  0.336506                    \\n\",\n       \"20241  0.267154                  0.340370                    \\n\",\n       \"16573  0.248023                  0.315856                    \\n\",\n       \"15964  0.259724                  0.330279                    \\n\",\n       \"20420  0.249689                  0.319075                    \\n\",\n       \"18546  0.260015                  0.330910                    \\n\",\n       \"20750  0.263251                  0.336326                    \\n\",\n       \"19593  0.258167                  0.328942                    \\n\",\n       \"19155  0.264289                  0.337355                    \\n\",\n       \"15886  0.455241                  0.408357                    \\n\",\n       \"20238  0.461467                  0.413012                    \\n\",\n       \"16578  0.432446                  0.387295                    \\n\",\n       \"15967  0.457273                  0.409398                    \\n\",\n       \"42862  0.434125                  0.389362                    \\n\",\n       \"18547  0.441689                  0.395522                    \\n\",\n       \"20753  0.444250                  0.397637                    \\n\",\n       \"19595  0.451290                  0.405215                    \\n\",\n       \"19151  0.458131                  0.410646                    \\n\",\n       \"15882  0.452184                  0.404939                    \\n\",\n       \"20242  0.453442                  0.406579                    \\n\",\n       \"16576  0.430777                  0.384569                    \\n\",\n       \"15969  0.448060                  0.400350                    \\n\",\n       \"20416  0.428011                  0.382940                    \\n\",\n       \"18549  0.430504                  0.383434                    \\n\",\n       \"20752  0.441468                  0.395201                    \\n\",\n       \"42853  0.440451                  0.394780                    \\n\",\n       \"19153  0.448953                  0.402314                    \\n\",\n       \"15884  0.450144                  0.389284                    \\n\",\n       \"20237  0.469231                  0.405687                    \\n\",\n       \"16574  0.440788                  0.380945                    \\n\",\n       \"15966  0.454858                  0.392239                    \\n\",\n       \"20417  0.435188                  0.372605                    \\n\",\n       \"18548  0.431490                  0.375351                    \\n\",\n       \"20754  0.446722                  0.387179                    \\n\",\n       \"19591  0.442968                  0.385401                    \\n\",\n       \"19149  0.463033                  0.398945                    \\n\",\n       \"15885  0.367879                  0.420904                    \\n\",\n       \"20243  0.373483                  0.425370                    \\n\",\n       \"16577  0.343075                  0.393663                    \\n\",\n       \"15965  0.367845                  0.411165                    \\n\",\n       \"20418  0.343940                  0.390592                    \\n\",\n       \"18551  0.361693                  0.408456                    \\n\",\n       \"20751  0.357579                  0.405101                    \\n\",\n       \"19594  0.364325                  0.411139                    \\n\",\n       \"19152  0.367440                  0.417535                    \\n\",\n       \"15883  0.274574                  0.348721                    \\n\",\n       \"20239  0.270305                  0.341866                    \\n\",\n       \"16575  0.275673                  0.344100                    \\n\",\n       \"15968  0.254347                  0.320440                    \\n\",\n       \"20421  0.266708                  0.333805                    \\n\",\n       \"18550  0.278626                  0.347340                    \\n\",\n       \"20749  0.268241                  0.338778                    \\n\",\n       \"19596  0.262094                  0.331620                    \\n\",\n       \"19150  0.265915                  0.335960                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@3  present_partial_f_score_hard@3  \\\\\\n\",\n       \"15880  0.230376                          0.132366                         \\n\",\n       \"20240  0.280942                          0.159801                         \\n\",\n       \"16579  0.205794                          0.116978                         \\n\",\n       \"15963  0.239242                          0.136310                         \\n\",\n       \"20419  0.210027                          0.120856                         \\n\",\n       \"18552  0.191309                          0.109194                         \\n\",\n       \"20748  0.208581                          0.120892                         \\n\",\n       \"19592  0.241069                          0.137082                         \\n\",\n       \"19154  0.225196                          0.128124                         \\n\",\n       \"15881  0.557812                          0.336500                         \\n\",\n       \"20241  0.566232                          0.340366                         \\n\",\n       \"16573  0.523702                          0.315207                         \\n\",\n       \"15964  0.550371                          0.330275                         \\n\",\n       \"20420  0.531769                          0.318514                         \\n\",\n       \"18546  0.548563                          0.330907                         \\n\",\n       \"20750  0.559837                          0.335237                         \\n\",\n       \"19593  0.546779                          0.328742                         \\n\",\n       \"19155  0.561907                          0.337355                         \\n\",\n       \"15886  0.423554                          0.408331                         \\n\",\n       \"20238  0.427024                          0.412988                         \\n\",\n       \"16578  0.401041                          0.387163                         \\n\",\n       \"15967  0.423673                          0.409375                         \\n\",\n       \"42862  0.404322                          0.389296                         \\n\",\n       \"18547  0.408790                          0.395419                         \\n\",\n       \"20753  0.411413                          0.397390                         \\n\",\n       \"19595  0.420255                          0.405180                         \\n\",\n       \"19151  0.425022                          0.410646                         \\n\",\n       \"15882  0.420061                          0.404939                         \\n\",\n       \"20242  0.422306                          0.406579                         \\n\",\n       \"16576  0.398338                          0.384273                         \\n\",\n       \"15969  0.414880                          0.400350                         \\n\",\n       \"20416  0.396653                          0.382896                         \\n\",\n       \"18549  0.397783                          0.383406                         \\n\",\n       \"20752  0.409727                          0.394901                         \\n\",\n       \"42853  0.409612                          0.394760                         \\n\",\n       \"19153  0.417611                          0.402314                         \\n\",\n       \"15884  0.404013                          0.389284                         \\n\",\n       \"20237  0.419536                          0.405687                         \\n\",\n       \"16574  0.394497                          0.380727                         \\n\",\n       \"15966  0.405937                          0.392239                         \\n\",\n       \"20417  0.386799                          0.372532                         \\n\",\n       \"18548  0.393099                          0.375351                         \\n\",\n       \"20754  0.405077                          0.386744                         \\n\",\n       \"19591  0.403202                          0.385401                         \\n\",\n       \"19149  0.412921                          0.398945                         \\n\",\n       \"15885  0.604302                          0.420904                         \\n\",\n       \"20243  0.607334                          0.425370                         \\n\",\n       \"16577  0.564994                          0.393663                         \\n\",\n       \"15965  0.583830                          0.411165                         \\n\",\n       \"20418  0.555693                          0.390050                         \\n\",\n       \"18551  0.584325                          0.408456                         \\n\",\n       \"20751  0.577386                          0.404108                         \\n\",\n       \"19594  0.586753                          0.411139                         \\n\",\n       \"19152  0.600302                          0.417535                         \\n\",\n       \"15883  0.556062                          0.348721                         \\n\",\n       \"20239  0.546411                          0.341866                         \\n\",\n       \"16575  0.543279                          0.344100                         \\n\",\n       \"15968  0.511702                          0.320440                         \\n\",\n       \"20421  0.529896                          0.333805                         \\n\",\n       \"18550  0.546902                          0.347340                         \\n\",\n       \"20749  0.534470                          0.338475                         \\n\",\n       \"19596  0.531885                          0.331620                         \\n\",\n       \"19150  0.537250                          0.335960                         \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@10  absent_partial_precision@10  \\\\\\n\",\n       \"15880  0.006494                   0.001341                      \\n\",\n       \"20240  0.000000                   0.001503                      \\n\",\n       \"16579  0.000000                   0.000735                      \\n\",\n       \"15963  0.003247                   0.002173                      \\n\",\n       \"20419  0.003247                   0.000940                      \\n\",\n       \"18552  0.000000                   0.001229                      \\n\",\n       \"20748  0.003247                   0.001079                      \\n\",\n       \"19592  0.000000                   0.000525                      \\n\",\n       \"19154  0.000000                   0.001052                      \\n\",\n       \"15881  0.124000                   0.030748                      \\n\",\n       \"20241  0.098000                   0.028949                      \\n\",\n       \"16573  0.108000                   0.026366                      \\n\",\n       \"15964  0.112000                   0.028980                      \\n\",\n       \"20420  0.126000                   0.027225                      \\n\",\n       \"18546  0.128000                   0.030893                      \\n\",\n       \"20750  0.084000                   0.025209                      \\n\",\n       \"19593  0.116000                   0.026752                      \\n\",\n       \"19155  0.092000                   0.026838                      \\n\",\n       \"15886  0.239356                   0.041275                      \\n\",\n       \"20238  0.194476                   0.039036                      \\n\",\n       \"16578  0.233552                   0.038844                      \\n\",\n       \"15967  0.227698                   0.042646                      \\n\",\n       \"42862  0.242157                   0.038943                      \\n\",\n       \"18547  0.263771                   0.042781                      \\n\",\n       \"20753  0.217942                   0.038301                      \\n\",\n       \"19595  0.250113                   0.040305                      \\n\",\n       \"19151  0.200130                   0.037703                      \\n\",\n       \"15882  0.239000                   0.041881                      \\n\",\n       \"20242  0.189500                   0.038673                      \\n\",\n       \"16576  0.219500                   0.038362                      \\n\",\n       \"15969  0.242500                   0.045242                      \\n\",\n       \"20416  0.248500                   0.039789                      \\n\",\n       \"18549  0.254000                   0.042522                      \\n\",\n       \"20752  0.218500                   0.038054                      \\n\",\n       \"42853  0.268500                   0.042272                      \\n\",\n       \"19153  0.212000                   0.039814                      \\n\",\n       \"15884  0.306522                   0.052963                      \\n\",\n       \"20237  0.234783                   0.049874                      \\n\",\n       \"16574  0.276087                   0.047179                      \\n\",\n       \"15966  0.265217                   0.047557                      \\n\",\n       \"20417  0.317391                   0.051691                      \\n\",\n       \"18548  0.350000                   0.055778                      \\n\",\n       \"20754  0.280435                   0.048380                      \\n\",\n       \"19591  0.300000                   0.051918                      \\n\",\n       \"19149  0.289130                   0.051557                      \\n\",\n       \"15885  0.450237                   0.080030                      \\n\",\n       \"20243  0.417062                   0.075737                      \\n\",\n       \"16577  0.450237                   0.074203                      \\n\",\n       \"15965  0.398104                   0.071050                      \\n\",\n       \"20418  0.483412                   0.078338                      \\n\",\n       \"18551  0.469194                   0.077223                      \\n\",\n       \"20751  0.407583                   0.069172                      \\n\",\n       \"19594  0.464455                   0.082859                      \\n\",\n       \"19152  0.388626                   0.069168                      \\n\",\n       \"15883  0.460000                   0.088112                      \\n\",\n       \"20239  0.440000                   0.086412                      \\n\",\n       \"16575  0.490000                   0.088303                      \\n\",\n       \"15968  0.430000                   0.082559                      \\n\",\n       \"20421  0.490000                   0.085682                      \\n\",\n       \"18550  0.390000                   0.079910                      \\n\",\n       \"20749  0.400000                   0.082547                      \\n\",\n       \"19596  0.490000                   0.085787                      \\n\",\n       \"19150  0.490000                   0.090968                      \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@10  absent_partial_f_score@10  \\\\\\n\",\n       \"15880  0.007752                  0.002217                    \\n\",\n       \"20240  0.007943                  0.002432                    \\n\",\n       \"16579  0.005328                  0.001263                    \\n\",\n       \"15963  0.009374                  0.003331                    \\n\",\n       \"20419  0.005707                  0.001572                    \\n\",\n       \"18552  0.005953                  0.001719                    \\n\",\n       \"20748  0.005792                  0.001731                    \\n\",\n       \"19592  0.003310                  0.000869                    \\n\",\n       \"19154  0.007068                  0.001778                    \\n\",\n       \"15881  0.123569                  0.045950                    \\n\",\n       \"20241  0.104550                  0.041607                    \\n\",\n       \"16573  0.111484                  0.039778                    \\n\",\n       \"15964  0.110357                  0.042475                    \\n\",\n       \"20420  0.118075                  0.041510                    \\n\",\n       \"18546  0.131358                  0.046410                    \\n\",\n       \"20750  0.101847                  0.037888                    \\n\",\n       \"19593  0.108380                  0.040100                    \\n\",\n       \"19155  0.111309                  0.040440                    \\n\",\n       \"15886  0.178178                  0.063302                    \\n\",\n       \"20238  0.156194                  0.058334                    \\n\",\n       \"16578  0.177636                  0.060777                    \\n\",\n       \"15967  0.168400                  0.063083                    \\n\",\n       \"42862  0.178276                  0.061015                    \\n\",\n       \"18547  0.190101                  0.066363                    \\n\",\n       \"20753  0.169196                  0.059046                    \\n\",\n       \"19595  0.183933                  0.062996                    \\n\",\n       \"19151  0.157909                  0.057229                    \\n\",\n       \"15882  0.175844                  0.064143                    \\n\",\n       \"20242  0.150208                  0.057394                    \\n\",\n       \"16576  0.171237                  0.060044                    \\n\",\n       \"15969  0.175633                  0.066768                    \\n\",\n       \"20416  0.181404                  0.062503                    \\n\",\n       \"18549  0.187252                  0.066138                    \\n\",\n       \"20752  0.172208                  0.059632                    \\n\",\n       \"42853  0.187765                  0.066050                    \\n\",\n       \"19153  0.164892                  0.060201                    \\n\",\n       \"15884  0.191779                  0.077179                    \\n\",\n       \"20237  0.161402                  0.069320                    \\n\",\n       \"16574  0.184922                  0.070769                    \\n\",\n       \"15966  0.169794                  0.069487                    \\n\",\n       \"20417  0.206072                  0.078006                    \\n\",\n       \"18548  0.216700                  0.082664                    \\n\",\n       \"20754  0.183012                  0.071808                    \\n\",\n       \"19591  0.200218                  0.077662                    \\n\",\n       \"19149  0.185549                  0.075573                    \\n\",\n       \"15885  0.163873                  0.094270                    \\n\",\n       \"20243  0.150265                  0.086208                    \\n\",\n       \"16577  0.167027                  0.087939                    \\n\",\n       \"15965  0.142106                  0.080877                    \\n\",\n       \"20418  0.161909                  0.091366                    \\n\",\n       \"18551  0.153058                  0.086575                    \\n\",\n       \"20751  0.154610                  0.080214                    \\n\",\n       \"19594  0.166488                  0.093653                    \\n\",\n       \"19152  0.146485                  0.079916                    \\n\",\n       \"15883  0.107859                  0.093812                    \\n\",\n       \"20239  0.101539                  0.088720                    \\n\",\n       \"16575  0.111916                  0.094959                    \\n\",\n       \"15968  0.100823                  0.087119                    \\n\",\n       \"20421  0.109752                  0.092704                    \\n\",\n       \"18550  0.101102                  0.086071                    \\n\",\n       \"20749  0.102432                  0.087917                    \\n\",\n       \"19596  0.112900                  0.093319                    \\n\",\n       \"19150  0.116162                  0.098098                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@10  absent_partial_f_score_hard@10  \\\\\\n\",\n       \"15880  0.001341                          0.002217                         \\n\",\n       \"20240  0.001180                          0.002018                         \\n\",\n       \"16579  0.000735                          0.001263                         \\n\",\n       \"15963  0.001681                          0.002766                         \\n\",\n       \"20419  0.000940                          0.001572                         \\n\",\n       \"18552  0.000957                          0.001603                         \\n\",\n       \"20748  0.001079                          0.001731                         \\n\",\n       \"19592  0.000525                          0.000869                         \\n\",\n       \"19154  0.001052                          0.001778                         \\n\",\n       \"15881  0.029373                          0.044513                         \\n\",\n       \"20241  0.024532                          0.037372                         \\n\",\n       \"16573  0.025766                          0.039232                         \\n\",\n       \"15964  0.026370                          0.039885                         \\n\",\n       \"20420  0.027129                          0.041386                         \\n\",\n       \"18546  0.029453                          0.045047                         \\n\",\n       \"20750  0.024391                          0.036914                         \\n\",\n       \"19593  0.026287                          0.039665                         \\n\",\n       \"19155  0.025684                          0.039120                         \\n\",\n       \"15886  0.038717                          0.060786                         \\n\",\n       \"20238  0.033959                          0.053286                         \\n\",\n       \"16578  0.038678                          0.060661                         \\n\",\n       \"15967  0.036670                          0.057532                         \\n\",\n       \"42862  0.038699                          0.060779                         \\n\",\n       \"18547  0.041433                          0.065016                         \\n\",\n       \"20753  0.036866                          0.057838                         \\n\",\n       \"19595  0.039664                          0.062356                         \\n\",\n       \"19151  0.034044                          0.053561                         \\n\",\n       \"15882  0.039126                          0.061353                         \\n\",\n       \"20242  0.033652                          0.052758                         \\n\",\n       \"16576  0.038200                          0.059894                         \\n\",\n       \"15969  0.038972                          0.061197                         \\n\",\n       \"20416  0.039568                          0.062227                         \\n\",\n       \"18549  0.041017                          0.064491                         \\n\",\n       \"20752  0.037702                          0.059328                         \\n\",\n       \"42853  0.041701                          0.065476                         \\n\",\n       \"19153  0.035918                          0.056570                         \\n\",\n       \"15884  0.050698                          0.075503                         \\n\",\n       \"20237  0.043266                          0.064465                         \\n\",\n       \"16574  0.046592                          0.070315                         \\n\",\n       \"15966  0.045615                          0.067882                         \\n\",\n       \"20417  0.051470                          0.077813                         \\n\",\n       \"18548  0.054154                          0.081685                         \\n\",\n       \"20754  0.047142                          0.070526                         \\n\",\n       \"19591  0.051198                          0.076914                         \\n\",\n       \"19149  0.048087                          0.072101                         \\n\",\n       \"15885  0.077276                          0.091465                         \\n\",\n       \"20243  0.070891                          0.082958                         \\n\",\n       \"16577  0.074203                          0.087939                         \\n\",\n       \"15965  0.068844                          0.079165                         \\n\",\n       \"20418  0.076792                          0.090703                         \\n\",\n       \"18551  0.074898                          0.085701                         \\n\",\n       \"20751  0.066626                          0.079134                         \\n\",\n       \"19594  0.079489                          0.092548                         \\n\",\n       \"19152  0.066171                          0.077413                         \\n\",\n       \"15883  0.085262                          0.092026                         \\n\",\n       \"20239  0.081276                          0.086784                         \\n\",\n       \"16575  0.088303                          0.094959                         \\n\",\n       \"15968  0.079050                          0.085807                         \\n\",\n       \"20421  0.085682                          0.092704                         \\n\",\n       \"18550  0.078041                          0.085206                         \\n\",\n       \"20749  0.080735                          0.087063                         \\n\",\n       \"19596  0.085787                          0.093319                         \\n\",\n       \"19150  0.089676                          0.097162                         \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@50  absent_partial_precision@50  \\\\\\n\",\n       \"15880  0.006494                   0.000637                      \\n\",\n       \"20240  0.000000                   0.000953                      \\n\",\n       \"16579  0.000000                   0.000293                      \\n\",\n       \"15963  0.006494                   0.001249                      \\n\",\n       \"20419  0.003247                   0.000392                      \\n\",\n       \"18552  0.000000                   0.000715                      \\n\",\n       \"20748  0.003247                   0.000333                      \\n\",\n       \"19592  0.000000                   0.000324                      \\n\",\n       \"19154  0.000000                   0.000330                      \\n\",\n       \"15881  0.172000                   0.018935                      \\n\",\n       \"20241  0.122000                   0.022308                      \\n\",\n       \"16573  0.156000                   0.008663                      \\n\",\n       \"15964  0.166000                   0.016694                      \\n\",\n       \"20420  0.200000                   0.010508                      \\n\",\n       \"18546  0.190000                   0.015934                      \\n\",\n       \"20750  0.152000                   0.010448                      \\n\",\n       \"19593  0.188000                   0.011153                      \\n\",\n       \"19155  0.126000                   0.015099                      \\n\",\n       \"15886  0.271176                   0.024483                      \\n\",\n       \"20238  0.215340                   0.028018                      \\n\",\n       \"16578  0.278731                   0.010898                      \\n\",\n       \"15967  0.275279                   0.025281                      \\n\",\n       \"42862  0.331215                   0.013407                      \\n\",\n       \"18547  0.323610                   0.021212                      \\n\",\n       \"20753  0.272777                   0.015446                      \\n\",\n       \"19595  0.338970                   0.015362                      \\n\",\n       \"19151  0.209436                   0.022128                      \\n\",\n       \"15882  0.268500                   0.023954                      \\n\",\n       \"20242  0.203500                   0.027435                      \\n\",\n       \"16576  0.268500                   0.010608                      \\n\",\n       \"15969  0.286500                   0.026080                      \\n\",\n       \"20416  0.322500                   0.013715                      \\n\",\n       \"18549  0.312000                   0.021125                      \\n\",\n       \"20752  0.275500                   0.014180                      \\n\",\n       \"42853  0.345500                   0.015319                      \\n\",\n       \"19153  0.224000                   0.023642                      \\n\",\n       \"15884  0.373913                   0.029371                      \\n\",\n       \"20237  0.267391                   0.034600                      \\n\",\n       \"16574  0.336957                   0.013172                      \\n\",\n       \"15966  0.354348                   0.022163                      \\n\",\n       \"20417  0.454348                   0.017407                      \\n\",\n       \"18548  0.441304                   0.024476                      \\n\",\n       \"20754  0.386957                   0.018046                      \\n\",\n       \"19591  0.471739                   0.018137                      \\n\",\n       \"19149  0.306522                   0.026984                      \\n\",\n       \"15885  0.625592                   0.044845                      \\n\",\n       \"20243  0.549763                   0.052113                      \\n\",\n       \"16577  0.734597                   0.022260                      \\n\",\n       \"15965  0.729858                   0.033891                      \\n\",\n       \"20418  0.819905                   0.028333                      \\n\",\n       \"18551  0.748815                   0.038082                      \\n\",\n       \"20751  0.620853                   0.027670                      \\n\",\n       \"19594  0.824645                   0.032371                      \\n\",\n       \"19152  0.473934                   0.039089                      \\n\",\n       \"15883  0.690000                   0.048773                      \\n\",\n       \"20239  0.560000                   0.055388                      \\n\",\n       \"16575  0.770000                   0.025686                      \\n\",\n       \"15968  0.740000                   0.037208                      \\n\",\n       \"20421  0.860000                   0.029850                      \\n\",\n       \"18550  0.760000                   0.039958                      \\n\",\n       \"20749  0.680000                   0.033633                      \\n\",\n       \"19596  0.940000                   0.030981                      \\n\",\n       \"19150  0.600000                   0.048626                      \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@50  absent_partial_f_score@50  \\\\\\n\",\n       \"15880  0.009010                  0.001137                    \\n\",\n       \"20240  0.009354                  0.001569                    \\n\",\n       \"16579  0.009263                  0.000562                    \\n\",\n       \"15963  0.013611                  0.001952                    \\n\",\n       \"20419  0.010966                  0.000748                    \\n\",\n       \"18552  0.008254                  0.000901                    \\n\",\n       \"20748  0.008313                  0.000629                    \\n\",\n       \"19592  0.008288                  0.000612                    \\n\",\n       \"19154  0.009192                  0.000629                    \\n\",\n       \"15881  0.152870                  0.030860                    \\n\",\n       \"20241  0.123166                  0.033347                    \\n\",\n       \"16573  0.157116                  0.015515                    \\n\",\n       \"15964  0.146541                  0.026051                    \\n\",\n       \"20420  0.174280                  0.019135                    \\n\",\n       \"18546  0.172767                  0.026272                    \\n\",\n       \"20750  0.144859                  0.018283                    \\n\",\n       \"19593  0.164802                  0.019659                    \\n\",\n       \"19155  0.132273                  0.024759                    \\n\",\n       \"15886  0.202064                  0.040046                    \\n\",\n       \"20238  0.171130                  0.043375                    \\n\",\n       \"16578  0.217564                  0.020127                    \\n\",\n       \"15967  0.203515                  0.038604                    \\n\",\n       \"42862  0.238148                  0.024508                    \\n\",\n       \"18547  0.229263                  0.036247                    \\n\",\n       \"20753  0.210152                  0.026441                    \\n\",\n       \"19595  0.240910                  0.027336                    \\n\",\n       \"19151  0.168432                  0.035057                    \\n\",\n       \"15882  0.199347                  0.039318                    \\n\",\n       \"20242  0.165104                  0.042198                    \\n\",\n       \"16576  0.211057                  0.019660                    \\n\",\n       \"15969  0.207930                  0.039664                    \\n\",\n       \"20416  0.239551                  0.025197                    \\n\",\n       \"18549  0.226438                  0.036312                    \\n\",\n       \"20752  0.212965                  0.025408                    \\n\",\n       \"42853  0.242604                  0.027516                    \\n\",\n       \"19153  0.176105                  0.037308                    \\n\",\n       \"15884  0.228947                  0.047198                    \\n\",\n       \"20237  0.183895                  0.050168                    \\n\",\n       \"16574  0.224457                  0.023778                    \\n\",\n       \"15966  0.219551                  0.035884                    \\n\",\n       \"20417  0.282818                  0.031602                    \\n\",\n       \"18548  0.263475                  0.040890                    \\n\",\n       \"20754  0.236318                  0.031211                    \\n\",\n       \"19591  0.267915                  0.032172                    \\n\",\n       \"19149  0.201869                  0.042867                    \\n\",\n       \"15885  0.190337                  0.064734                    \\n\",\n       \"20243  0.167959                  0.067824                    \\n\",\n       \"16577  0.217886                  0.037841                    \\n\",\n       \"15965  0.193894                  0.049719                    \\n\",\n       \"20418  0.221880                  0.044890                    \\n\",\n       \"18551  0.210970                  0.055167                    \\n\",\n       \"20751  0.193874                  0.040997                    \\n\",\n       \"19594  0.229177                  0.047753                    \\n\",\n       \"19152  0.161479                  0.052487                    \\n\",\n       \"15883  0.142775                  0.067094                    \\n\",\n       \"20239  0.124841                  0.068051                    \\n\",\n       \"16575  0.156695                  0.043192                    \\n\",\n       \"15968  0.143891                  0.052789                    \\n\",\n       \"20421  0.162336                  0.048979                    \\n\",\n       \"18550  0.152328                  0.058678                    \\n\",\n       \"20749  0.139161                  0.050179                    \\n\",\n       \"19596  0.178741                  0.051598                    \\n\",\n       \"19150  0.130225                  0.066088                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@50  absent_partial_f_score_hard@50  \\\\\\n\",\n       \"15880  0.000309                          0.000594                         \\n\",\n       \"20240  0.000279                          0.000538                         \\n\",\n       \"16579  0.000293                          0.000562                         \\n\",\n       \"15963  0.000491                          0.000940                         \\n\",\n       \"20419  0.000373                          0.000713                         \\n\",\n       \"18552  0.000257                          0.000496                         \\n\",\n       \"20748  0.000302                          0.000575                         \\n\",\n       \"19592  0.000276                          0.000527                         \\n\",\n       \"19154  0.000279                          0.000537                         \\n\",\n       \"15881  0.007464                          0.013954                         \\n\",\n       \"20241  0.005788                          0.010858                         \\n\",\n       \"16573  0.007535                          0.014091                         \\n\",\n       \"15964  0.007065                          0.013222                         \\n\",\n       \"20420  0.008281                          0.015512                         \\n\",\n       \"18546  0.007925                          0.014865                         \\n\",\n       \"20750  0.007083                          0.013242                         \\n\",\n       \"19593  0.008001                          0.014962                         \\n\",\n       \"19155  0.006193                          0.011600                         \\n\",\n       \"15886  0.008899                          0.016818                         \\n\",\n       \"20238  0.007535                          0.014238                         \\n\",\n       \"16578  0.009639                          0.018205                         \\n\",\n       \"15967  0.008984                          0.016976                         \\n\",\n       \"42862  0.010572                          0.019966                         \\n\",\n       \"18547  0.010140                          0.019157                         \\n\",\n       \"20753  0.009282                          0.017536                         \\n\",\n       \"19595  0.010558                          0.019953                         \\n\",\n       \"19151  0.007320                          0.013845                         \\n\",\n       \"15882  0.008975                          0.016965                         \\n\",\n       \"20242  0.007405                          0.014005                         \\n\",\n       \"16576  0.009492                          0.017937                         \\n\",\n       \"15969  0.009333                          0.017643                         \\n\",\n       \"20416  0.010675                          0.020184                         \\n\",\n       \"18549  0.010084                          0.019066                         \\n\",\n       \"20752  0.009471                          0.017909                         \\n\",\n       \"42853  0.010827                          0.020473                         \\n\",\n       \"19153  0.007756                          0.014676                         \\n\",\n       \"15884  0.012077                          0.022494                         \\n\",\n       \"20237  0.009892                          0.018432                         \\n\",\n       \"16574  0.011802                          0.022016                         \\n\",\n       \"15966  0.011742                          0.021866                         \\n\",\n       \"20417  0.014281                          0.026698                         \\n\",\n       \"18548  0.013669                          0.025492                         \\n\",\n       \"20754  0.012427                          0.023167                         \\n\",\n       \"19591  0.014155                          0.026369                         \\n\",\n       \"19149  0.010469                          0.019549                         \\n\",\n       \"15885  0.019134                          0.032752                         \\n\",\n       \"20243  0.017163                          0.029196                         \\n\",\n       \"16577  0.021636                          0.036901                         \\n\",\n       \"15965  0.021191                          0.035600                         \\n\",\n       \"20418  0.023268                          0.039708                         \\n\",\n       \"18551  0.022049                          0.037350                         \\n\",\n       \"20751  0.019101                          0.032471                         \\n\",\n       \"19594  0.023821                          0.040509                         \\n\",\n       \"19152  0.015790                          0.026884                         \\n\",\n       \"15883  0.022760                          0.038608                         \\n\",\n       \"20239  0.020063                          0.033936                         \\n\",\n       \"16575  0.025193                          0.042591                         \\n\",\n       \"15968  0.023065                          0.039082                         \\n\",\n       \"20421  0.026592                          0.044915                         \\n\",\n       \"18550  0.023654                          0.040281                         \\n\",\n       \"20749  0.022363                          0.037854                         \\n\",\n       \"19596  0.028320                          0.047902                         \\n\",\n       \"19150  0.020469                          0.034663                         \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@k  absent_partial_precision@k  \\\\\\n\",\n       \"15880  0.003247                  0.001314                     \\n\",\n       \"20240  0.000000                  0.001916                     \\n\",\n       \"16579  0.000000                  0.001979                     \\n\",\n       \"15963  0.003247                  0.002487                     \\n\",\n       \"20419  0.003247                  0.003780                     \\n\",\n       \"18552  0.000000                  0.001562                     \\n\",\n       \"20748  0.003247                  0.002507                     \\n\",\n       \"19592  0.000000                  0.000595                     \\n\",\n       \"19154  0.000000                  0.001840                     \\n\",\n       \"15881  0.076000                  0.059404                     \\n\",\n       \"20241  0.054000                  0.053640                     \\n\",\n       \"16573  0.062000                  0.048648                     \\n\",\n       \"15964  0.072000                  0.055539                     \\n\",\n       \"20420  0.064000                  0.053995                     \\n\",\n       \"18546  0.072000                  0.066324                     \\n\",\n       \"20750  0.050000                  0.051853                     \\n\",\n       \"19593  0.058000                  0.042186                     \\n\",\n       \"19155  0.064000                  0.059912                     \\n\",\n       \"15886  0.140291                  0.091553                     \\n\",\n       \"20238  0.120478                  0.085838                     \\n\",\n       \"16578  0.133237                  0.087343                     \\n\",\n       \"15967  0.138490                  0.089132                     \\n\",\n       \"42862  0.132586                  0.086054                     \\n\",\n       \"18547  0.147046                  0.095183                     \\n\",\n       \"20753  0.132686                  0.089970                     \\n\",\n       \"19595  0.134938                  0.087431                     \\n\",\n       \"19151  0.131736                  0.089512                     \\n\",\n       \"15882  0.139500                  0.088344                     \\n\",\n       \"20242  0.117500                  0.084764                     \\n\",\n       \"16576  0.130500                  0.086743                     \\n\",\n       \"15969  0.143000                  0.092008                     \\n\",\n       \"20416  0.137000                  0.085709                     \\n\",\n       \"18549  0.133000                  0.089001                     \\n\",\n       \"20752  0.126500                  0.086689                     \\n\",\n       \"42853  0.139500                  0.089087                     \\n\",\n       \"19153  0.139000                  0.092640                     \\n\",\n       \"15884  0.210870                  0.115298                     \\n\",\n       \"20237  0.167391                  0.096449                     \\n\",\n       \"16574  0.178261                  0.107476                     \\n\",\n       \"15966  0.178261                  0.102298                     \\n\",\n       \"20417  0.193478                  0.105799                     \\n\",\n       \"18548  0.221739                  0.113556                     \\n\",\n       \"20754  0.193478                  0.104763                     \\n\",\n       \"19591  0.189130                  0.102499                     \\n\",\n       \"19149  0.213043                  0.116655                     \\n\",\n       \"15885  0.440758                  0.121035                     \\n\",\n       \"20243  0.398104                  0.115507                     \\n\",\n       \"16577  0.450237                  0.120352                     \\n\",\n       \"15965  0.431280                  0.103439                     \\n\",\n       \"20418  0.398104                  0.093029                     \\n\",\n       \"18551  0.454976                  0.109915                     \\n\",\n       \"20751  0.402844                  0.114527                     \\n\",\n       \"19594  0.478673                  0.115840                     \\n\",\n       \"19152  0.350711                  0.105637                     \\n\",\n       \"15883  0.470000                  0.102620                     \\n\",\n       \"20239  0.420000                  0.100318                     \\n\",\n       \"16575  0.460000                  0.097317                     \\n\",\n       \"15968  0.430000                  0.094238                     \\n\",\n       \"20421  0.460000                  0.094988                     \\n\",\n       \"18550  0.400000                  0.094101                     \\n\",\n       \"20749  0.410000                  0.094151                     \\n\",\n       \"19596  0.450000                  0.097145                     \\n\",\n       \"19150  0.450000                  0.098088                     \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@k  absent_partial_f_score@k  \\\\\\n\",\n       \"15880  0.001314                 0.001314                   \\n\",\n       \"20240  0.001916                 0.001916                   \\n\",\n       \"16579  0.001979                 0.001979                   \\n\",\n       \"15963  0.002487                 0.002487                   \\n\",\n       \"20419  0.003780                 0.003780                   \\n\",\n       \"18552  0.001367                 0.001422                   \\n\",\n       \"20748  0.002507                 0.002507                   \\n\",\n       \"19592  0.000595                 0.000595                   \\n\",\n       \"19154  0.001840                 0.001840                   \\n\",\n       \"15881  0.059367                 0.059384                   \\n\",\n       \"20241  0.052873                 0.053064                   \\n\",\n       \"16573  0.048648                 0.048648                   \\n\",\n       \"15964  0.055268                 0.055348                   \\n\",\n       \"20420  0.053995                 0.053995                   \\n\",\n       \"18546  0.066301                 0.066311                   \\n\",\n       \"20750  0.051853                 0.051853                   \\n\",\n       \"19593  0.042186                 0.042186                   \\n\",\n       \"19155  0.059892                 0.059900                   \\n\",\n       \"15886  0.091228                 0.091331                   \\n\",\n       \"20238  0.085289                 0.085468                   \\n\",\n       \"16578  0.087270                 0.087285                   \\n\",\n       \"15967  0.088179                 0.088451                   \\n\",\n       \"42862  0.086034                 0.086040                   \\n\",\n       \"18547  0.095001                 0.095051                   \\n\",\n       \"20753  0.089658                 0.089740                   \\n\",\n       \"19595  0.087389                 0.087402                   \\n\",\n       \"19151  0.089132                 0.089267                   \\n\",\n       \"15882  0.088122                 0.088184                   \\n\",\n       \"20242  0.084039                 0.084246                   \\n\",\n       \"16576  0.086743                 0.086743                   \\n\",\n       \"15969  0.090856                 0.091212                   \\n\",\n       \"20416  0.085709                 0.085709                   \\n\",\n       \"18549  0.088910                 0.088940                   \\n\",\n       \"20752  0.086587                 0.086615                   \\n\",\n       \"42853  0.089059                 0.089069                   \\n\",\n       \"19153  0.092139                 0.092248                   \\n\",\n       \"15884  0.114562                 0.114683                   \\n\",\n       \"20237  0.094084                 0.094600                   \\n\",\n       \"16574  0.107320                 0.107372                   \\n\",\n       \"15966  0.101726                 0.101942                   \\n\",\n       \"20417  0.105799                 0.105799                   \\n\",\n       \"18548  0.112699                 0.112861                   \\n\",\n       \"20754  0.104705                 0.104727                   \\n\",\n       \"19591  0.102499                 0.102499                   \\n\",\n       \"19149  0.116456                 0.116531                   \\n\",\n       \"15885  0.120102                 0.120447                   \\n\",\n       \"20243  0.111517                 0.112848                   \\n\",\n       \"16577  0.120352                 0.120352                   \\n\",\n       \"15965  0.102348                 0.102733                   \\n\",\n       \"20418  0.091905                 0.092318                   \\n\",\n       \"18551  0.107888                 0.108435                   \\n\",\n       \"20751  0.112622                 0.113264                   \\n\",\n       \"19594  0.112918                 0.113749                   \\n\",\n       \"19152  0.102570                 0.103576                   \\n\",\n       \"15883  0.101311                 0.101786                   \\n\",\n       \"20239  0.095001                 0.096851                   \\n\",\n       \"16575  0.097317                 0.097317                   \\n\",\n       \"15968  0.091075                 0.092140                   \\n\",\n       \"20421  0.094988                 0.094988                   \\n\",\n       \"18550  0.092475                 0.092907                   \\n\",\n       \"20749  0.093079                 0.093469                   \\n\",\n       \"19596  0.097145                 0.097145                   \\n\",\n       \"19150  0.097562                 0.097783                   \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@k  absent_partial_f_score_hard@k  \\\\\\n\",\n       \"15880  0.001314                         0.001314                        \\n\",\n       \"20240  0.001916                         0.001916                        \\n\",\n       \"16579  0.001979                         0.001979                        \\n\",\n       \"15963  0.002487                         0.002487                        \\n\",\n       \"20419  0.003780                         0.003780                        \\n\",\n       \"18552  0.001367                         0.001367                        \\n\",\n       \"20748  0.002507                         0.002507                        \\n\",\n       \"19592  0.000595                         0.000595                        \\n\",\n       \"19154  0.001840                         0.001840                        \\n\",\n       \"15881  0.059367                         0.059367                        \\n\",\n       \"20241  0.052873                         0.052873                        \\n\",\n       \"16573  0.048648                         0.048648                        \\n\",\n       \"15964  0.055268                         0.055268                        \\n\",\n       \"20420  0.053995                         0.053995                        \\n\",\n       \"18546  0.066301                         0.066301                        \\n\",\n       \"20750  0.051853                         0.051853                        \\n\",\n       \"19593  0.042186                         0.042186                        \\n\",\n       \"19155  0.059892                         0.059892                        \\n\",\n       \"15886  0.091228                         0.091228                        \\n\",\n       \"20238  0.085289                         0.085289                        \\n\",\n       \"16578  0.087270                         0.087270                        \\n\",\n       \"15967  0.088179                         0.088179                        \\n\",\n       \"42862  0.086034                         0.086034                        \\n\",\n       \"18547  0.095001                         0.095001                        \\n\",\n       \"20753  0.089658                         0.089658                        \\n\",\n       \"19595  0.087389                         0.087389                        \\n\",\n       \"19151  0.089132                         0.089132                        \\n\",\n       \"15882  0.088122                         0.088122                        \\n\",\n       \"20242  0.084039                         0.084039                        \\n\",\n       \"16576  0.086743                         0.086743                        \\n\",\n       \"15969  0.090856                         0.090856                        \\n\",\n       \"20416  0.085709                         0.085709                        \\n\",\n       \"18549  0.088910                         0.088910                        \\n\",\n       \"20752  0.086587                         0.086587                        \\n\",\n       \"42853  0.089059                         0.089059                        \\n\",\n       \"19153  0.092139                         0.092139                        \\n\",\n       \"15884  0.114562                         0.114562                        \\n\",\n       \"20237  0.094084                         0.094084                        \\n\",\n       \"16574  0.107320                         0.107320                        \\n\",\n       \"15966  0.101726                         0.101726                        \\n\",\n       \"20417  0.105799                         0.105799                        \\n\",\n       \"18548  0.112699                         0.112699                        \\n\",\n       \"20754  0.104705                         0.104705                        \\n\",\n       \"19591  0.102499                         0.102499                        \\n\",\n       \"19149  0.116456                         0.116456                        \\n\",\n       \"15885  0.120102                         0.120102                        \\n\",\n       \"20243  0.111517                         0.111517                        \\n\",\n       \"16577  0.120352                         0.120352                        \\n\",\n       \"15965  0.102348                         0.102348                        \\n\",\n       \"20418  0.091905                         0.091905                        \\n\",\n       \"18551  0.107888                         0.107888                        \\n\",\n       \"20751  0.112622                         0.112622                        \\n\",\n       \"19594  0.112918                         0.112918                        \\n\",\n       \"19152  0.102570                         0.102570                        \\n\",\n       \"15883  0.101311                         0.101311                        \\n\",\n       \"20239  0.095001                         0.095001                        \\n\",\n       \"16575  0.097317                         0.097317                        \\n\",\n       \"15968  0.091075                         0.091075                        \\n\",\n       \"20421  0.094988                         0.094988                        \\n\",\n       \"18550  0.092475                         0.092475                        \\n\",\n       \"20749  0.093079                         0.093079                        \\n\",\n       \"19596  0.097145                         0.097145                        \\n\",\n       \"19150  0.097562                         0.097562                        \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@M  absent_partial_precision@M  \\\\\\n\",\n       \"15880  0.006494                  0.000619                     \\n\",\n       \"20240  0.000000                  0.000939                     \\n\",\n       \"16579  0.000000                  0.000070                     \\n\",\n       \"15963  0.006494                  0.001087                     \\n\",\n       \"20419  0.003247                  0.000208                     \\n\",\n       \"18552  0.000000                  0.000667                     \\n\",\n       \"20748  0.003247                  0.000233                     \\n\",\n       \"19592  0.000000                  0.000194                     \\n\",\n       \"19154  0.000000                  0.000217                     \\n\",\n       \"15881  0.174000                  0.018649                     \\n\",\n       \"20241  0.122000                  0.022253                     \\n\",\n       \"16573  0.168000                  0.005488                     \\n\",\n       \"15964  0.168000                  0.015589                     \\n\",\n       \"20420  0.204000                  0.008696                     \\n\",\n       \"18546  0.192000                  0.015287                     \\n\",\n       \"20750  0.158000                  0.009011                     \\n\",\n       \"19593  0.194000                  0.009278                     \\n\",\n       \"19155  0.126000                  0.014707                     \\n\",\n       \"15886  0.271677                  0.024290                     \\n\",\n       \"20238  0.215440                  0.027951                     \\n\",\n       \"16578  0.285536                  0.006771                     \\n\",\n       \"15967  0.280783                  0.023962                     \\n\",\n       \"42862  0.340922                  0.010988                     \\n\",\n       \"18547  0.326412                  0.020642                     \\n\",\n       \"20753  0.278081                  0.013815                     \\n\",\n       \"19595  0.346875                  0.013051                     \\n\",\n       \"19151  0.209636                  0.021770                     \\n\",\n       \"15882  0.269500                  0.023755                     \\n\",\n       \"20242  0.203500                  0.027367                     \\n\",\n       \"16576  0.274500                  0.006448                     \\n\",\n       \"15969  0.290000                  0.024702                     \\n\",\n       \"20416  0.336000                  0.011316                     \\n\",\n       \"18549  0.312000                  0.020557                     \\n\",\n       \"20752  0.284000                  0.012484                     \\n\",\n       \"42853  0.355500                  0.012968                     \\n\",\n       \"19153  0.224000                  0.023238                     \\n\",\n       \"15884  0.376087                  0.028854                     \\n\",\n       \"20237  0.267391                  0.034362                     \\n\",\n       \"16574  0.347826                  0.007302                     \\n\",\n       \"15966  0.371739                  0.019621                     \\n\",\n       \"20417  0.476087                  0.013711                     \\n\",\n       \"18548  0.445652                  0.023328                     \\n\",\n       \"20754  0.406522                  0.015079                     \\n\",\n       \"19591  0.482609                  0.013429                     \\n\",\n       \"19149  0.306522                  0.026242                     \\n\",\n       \"15885  0.625592                  0.044498                     \\n\",\n       \"20243  0.554502                  0.051962                     \\n\",\n       \"16577  0.777251                  0.011179                     \\n\",\n       \"15965  0.758294                  0.028552                     \\n\",\n       \"20418  0.857820                  0.022394                     \\n\",\n       \"18551  0.758294                  0.036113                     \\n\",\n       \"20751  0.654028                  0.023274                     \\n\",\n       \"19594  0.867299                  0.026250                     \\n\",\n       \"19152  0.473934                  0.038456                     \\n\",\n       \"15883  0.690000                  0.048187                     \\n\",\n       \"20239  0.560000                  0.054953                     \\n\",\n       \"16575  0.820000                  0.013165                     \\n\",\n       \"15968  0.760000                  0.031946                     \\n\",\n       \"20421  0.940000                  0.022896                     \\n\",\n       \"18550  0.760000                  0.037463                     \\n\",\n       \"20749  0.690000                  0.028662                     \\n\",\n       \"19596  1.010000                  0.022727                     \\n\",\n       \"19150  0.600000                  0.047803                     \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@M  absent_partial_f_score@M  \\\\\\n\",\n       \"15880  0.009630                 0.001101                   \\n\",\n       \"20240  0.009354                 0.001543                   \\n\",\n       \"16579  0.009928                 0.000138                   \\n\",\n       \"15963  0.013611                 0.001650                   \\n\",\n       \"20419  0.011533                 0.000404                   \\n\",\n       \"18552  0.008404                 0.000811                   \\n\",\n       \"20748  0.008878                 0.000441                   \\n\",\n       \"19592  0.008325                 0.000369                   \\n\",\n       \"19154  0.009308                 0.000416                   \\n\",\n       \"15881  0.153430                 0.030369                   \\n\",\n       \"20241  0.123227                 0.033249                   \\n\",\n       \"16573  0.165742                 0.009745                   \\n\",\n       \"15964  0.149837                 0.024053                   \\n\",\n       \"20420  0.181264                 0.015888                   \\n\",\n       \"18546  0.174323                 0.025124                   \\n\",\n       \"20750  0.148451                 0.015683                   \\n\",\n       \"19593  0.169128                 0.016277                   \\n\",\n       \"19155  0.132686                 0.024052                   \\n\",\n       \"15886  0.202400                 0.039699                   \\n\",\n       \"20238  0.171300                 0.043255                   \\n\",\n       \"16578  0.224105                 0.012518                   \\n\",\n       \"15967  0.206848                 0.036195                   \\n\",\n       \"42862  0.244711                 0.020105                   \\n\",\n       \"18547  0.230613                 0.035220                   \\n\",\n       \"20753  0.214057                 0.023475                   \\n\",\n       \"19595  0.246540                 0.023113                   \\n\",\n       \"19151  0.168683                 0.034404                   \\n\",\n       \"15882  0.199868                 0.038959                   \\n\",\n       \"20242  0.165162                 0.042074                   \\n\",\n       \"16576  0.217850                 0.011999                   \\n\",\n       \"15969  0.210671                 0.037144                   \\n\",\n       \"20416  0.247236                 0.020828                   \\n\",\n       \"18549  0.227174                 0.035285                   \\n\",\n       \"20752  0.218035                 0.022323                   \\n\",\n       \"42853  0.249229                 0.023221                   \\n\",\n       \"19153  0.176228                 0.036574                   \\n\",\n       \"15884  0.229588                 0.046308                   \\n\",\n       \"20237  0.184028                 0.049747                   \\n\",\n       \"16574  0.231010                 0.013119                   \\n\",\n       \"15966  0.224311                 0.031309                   \\n\",\n       \"20417  0.290117                 0.024997                   \\n\",\n       \"18548  0.265277                 0.038857                   \\n\",\n       \"20754  0.243870                 0.025927                   \\n\",\n       \"19591  0.271236                 0.023751                   \\n\",\n       \"19149  0.202238                 0.041543                   \\n\",\n       \"15885  0.190645                 0.064216                   \\n\",\n       \"20243  0.168308                 0.067611                   \\n\",\n       \"16577  0.224996                 0.020185                   \\n\",\n       \"15965  0.198528                 0.041572                   \\n\",\n       \"20418  0.229624                 0.035724                   \\n\",\n       \"18551  0.211727                 0.052163                   \\n\",\n       \"20751  0.198314                 0.034288                   \\n\",\n       \"19594  0.232653                 0.038309                   \\n\",\n       \"19152  0.162066                 0.051518                   \\n\",\n       \"15883  0.142800                 0.066204                   \\n\",\n       \"20239  0.125208                 0.067425                   \\n\",\n       \"16575  0.167490                 0.023614                   \\n\",\n       \"15968  0.148684                 0.044938                   \\n\",\n       \"20421  0.170290                 0.038539                   \\n\",\n       \"18550  0.154409                 0.054989                   \\n\",\n       \"20749  0.143215                 0.042609                   \\n\",\n       \"19596  0.187658                 0.039021                   \\n\",\n       \"19150  0.130441                 0.064784                   \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@M  absent_partial_f_score_hard@M  \\\\\\n\",\n       \"15880  0.000619                         0.001101                        \\n\",\n       \"20240  0.000939                         0.001543                        \\n\",\n       \"16579  0.000070                         0.000138                        \\n\",\n       \"15963  0.001087                         0.001650                        \\n\",\n       \"20419  0.000208                         0.000404                        \\n\",\n       \"18552  0.000667                         0.000811                        \\n\",\n       \"20748  0.000233                         0.000441                        \\n\",\n       \"19592  0.000194                         0.000369                        \\n\",\n       \"19154  0.000217                         0.000416                        \\n\",\n       \"15881  0.018649                         0.030369                        \\n\",\n       \"20241  0.022253                         0.033249                        \\n\",\n       \"16573  0.005488                         0.009745                        \\n\",\n       \"15964  0.015589                         0.024053                        \\n\",\n       \"20420  0.008696                         0.015888                        \\n\",\n       \"18546  0.015287                         0.025124                        \\n\",\n       \"20750  0.009011                         0.015683                        \\n\",\n       \"19593  0.009278                         0.016277                        \\n\",\n       \"19155  0.014707                         0.024052                        \\n\",\n       \"15886  0.024290                         0.039699                        \\n\",\n       \"20238  0.027951                         0.043255                        \\n\",\n       \"16578  0.006771                         0.012518                        \\n\",\n       \"15967  0.023962                         0.036195                        \\n\",\n       \"42862  0.010988                         0.020105                        \\n\",\n       \"18547  0.020642                         0.035220                        \\n\",\n       \"20753  0.013815                         0.023475                        \\n\",\n       \"19595  0.013051                         0.023113                        \\n\",\n       \"19151  0.021770                         0.034404                        \\n\",\n       \"15882  0.023755                         0.038959                        \\n\",\n       \"20242  0.027367                         0.042074                        \\n\",\n       \"16576  0.006448                         0.011999                        \\n\",\n       \"15969  0.024702                         0.037144                        \\n\",\n       \"20416  0.011316                         0.020828                        \\n\",\n       \"18549  0.020557                         0.035285                        \\n\",\n       \"20752  0.012484                         0.022323                        \\n\",\n       \"42853  0.012968                         0.023221                        \\n\",\n       \"19153  0.023238                         0.036574                        \\n\",\n       \"15884  0.028854                         0.046308                        \\n\",\n       \"20237  0.034362                         0.049747                        \\n\",\n       \"16574  0.007302                         0.013119                        \\n\",\n       \"15966  0.019621                         0.031309                        \\n\",\n       \"20417  0.013711                         0.024997                        \\n\",\n       \"18548  0.023328                         0.038857                        \\n\",\n       \"20754  0.015079                         0.025927                        \\n\",\n       \"19591  0.013429                         0.023751                        \\n\",\n       \"19149  0.026242                         0.041543                        \\n\",\n       \"15885  0.044498                         0.064216                        \\n\",\n       \"20243  0.051962                         0.067611                        \\n\",\n       \"16577  0.011179                         0.020185                        \\n\",\n       \"15965  0.028552                         0.041572                        \\n\",\n       \"20418  0.022394                         0.035724                        \\n\",\n       \"18551  0.036113                         0.052163                        \\n\",\n       \"20751  0.023274                         0.034288                        \\n\",\n       \"19594  0.026250                         0.038309                        \\n\",\n       \"19152  0.038456                         0.051518                        \\n\",\n       \"15883  0.048187                         0.066204                        \\n\",\n       \"20239  0.054953                         0.067425                        \\n\",\n       \"16575  0.013165                         0.023614                        \\n\",\n       \"15968  0.031946                         0.044938                        \\n\",\n       \"20421  0.022896                         0.038539                        \\n\",\n       \"18550  0.037463                         0.054989                        \\n\",\n       \"20749  0.028662                         0.042609                        \\n\",\n       \"19596  0.022727                         0.039021                        \\n\",\n       \"19150  0.047803                         0.064784                        \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_auc  all_exact_advanced_ap  all_exact_advanced_mrr  \\\\\\n\",\n       \"15880  0.038296                0.051100               0.165073                 \\n\",\n       \"20240  0.067856                0.081617               0.241161                 \\n\",\n       \"16579  0.027337                0.034537               0.161769                 \\n\",\n       \"15963  0.045350                0.057300               0.187435                 \\n\",\n       \"20419  0.025675                0.033209               0.143434                 \\n\",\n       \"18552  0.027581                0.036519               0.141536                 \\n\",\n       \"20748  0.026436                0.034426               0.169859                 \\n\",\n       \"19592  0.038987                0.047296               0.209089                 \\n\",\n       \"19154  0.047311                0.058467               0.195248                 \\n\",\n       \"15881  0.207744                0.230875               0.264893                 \\n\",\n       \"20241  0.224407                0.248337               0.275272                 \\n\",\n       \"16573  0.145012                0.166550               0.257002                 \\n\",\n       \"15964  0.210842                0.232162               0.278860                 \\n\",\n       \"20420  0.160056                0.180548               0.244553                 \\n\",\n       \"18546  0.186255                0.207093               0.286310                 \\n\",\n       \"20750  0.165620                0.185557               0.305462                 \\n\",\n       \"19593  0.188621                0.211138               0.262824                 \\n\",\n       \"19155  0.231911                0.259039               0.231714                 \\n\",\n       \"15886  0.236145                0.270485               0.330120                 \\n\",\n       \"20238  0.240050                0.274052               0.339334                 \\n\",\n       \"16578  0.210552                0.241665               0.336226                 \\n\",\n       \"15967  0.233643                0.268815               0.322354                 \\n\",\n       \"42862  0.218190                0.248444               0.318053                 \\n\",\n       \"18547  0.214730                0.250578               0.311319                 \\n\",\n       \"20753  0.212109                0.243622               0.338183                 \\n\",\n       \"19595  0.241348                0.272225               0.333148                 \\n\",\n       \"19151  0.248030                0.280909               0.332315                 \\n\",\n       \"15882  0.236409                0.270086               0.333435                 \\n\",\n       \"20242  0.238691                0.271949               0.346519                 \\n\",\n       \"16576  0.210007                0.241103               0.343520                 \\n\",\n       \"15969  0.228797                0.264650               0.320567                 \\n\",\n       \"20416  0.209053                0.239531               0.313326                 \\n\",\n       \"18549  0.211207                0.246907               0.304964                 \\n\",\n       \"20752  0.210948                0.242067               0.335252                 \\n\",\n       \"42853  0.234871                0.266891               0.322358                 \\n\",\n       \"19153  0.244555                0.277072               0.332901                 \\n\",\n       \"15884  0.212684                0.244722               0.327862                 \\n\",\n       \"20237  0.219571                0.250830               0.352861                 \\n\",\n       \"16574  0.186897                0.218882               0.335039                 \\n\",\n       \"15966  0.207686                0.241963               0.330960                 \\n\",\n       \"20417  0.192873                0.222762               0.302936                 \\n\",\n       \"18548  0.194397                0.228572               0.320178                 \\n\",\n       \"20754  0.199182                0.227884               0.341685                 \\n\",\n       \"19591  0.216810                0.246940               0.330232                 \\n\",\n       \"19149  0.230182                0.262611               0.336550                 \\n\",\n       \"15885  0.221641                0.241683               0.334461                 \\n\",\n       \"20243  0.232377                0.249998               0.355100                 \\n\",\n       \"16577  0.195867                0.214036               0.352565                 \\n\",\n       \"15965  0.224174                0.246631               0.338336                 \\n\",\n       \"20418  0.177303                0.199469               0.298370                 \\n\",\n       \"18551  0.203436                0.223779               0.332697                 \\n\",\n       \"20751  0.201780                0.218762               0.372643                 \\n\",\n       \"19594  0.216750                0.236206               0.335318                 \\n\",\n       \"19152  0.227396                0.248570               0.317193                 \\n\",\n       \"15883  0.129299                0.141772               0.299896                 \\n\",\n       \"20239  0.139938                0.150929               0.300181                 \\n\",\n       \"16575  0.117071                0.128683               0.291034                 \\n\",\n       \"15968  0.127509                0.141079               0.272232                 \\n\",\n       \"20421  0.116068                0.126857               0.287747                 \\n\",\n       \"18550  0.119028                0.131886               0.305003                 \\n\",\n       \"20749  0.108777                0.120239               0.321813                 \\n\",\n       \"19596  0.122616                0.134836               0.263915                 \\n\",\n       \"19150  0.144172                0.157583               0.261817                 \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_sadr  all_exact_advanced_ndcg  \\\\\\n\",\n       \"15880  0.117306                 0.133756                  \\n\",\n       \"20240  0.159086                 0.194062                  \\n\",\n       \"16579  0.072601                 0.088172                  \\n\",\n       \"15963  0.121634                 0.143167                  \\n\",\n       \"20419  0.078720                 0.095757                  \\n\",\n       \"18552  0.085298                 0.099971                  \\n\",\n       \"20748  0.071470                 0.089262                  \\n\",\n       \"19592  0.092261                 0.112293                  \\n\",\n       \"19154  0.123702                 0.147617                  \\n\",\n       \"15881  0.351455                 0.405804                  \\n\",\n       \"20241  0.372616                 0.425980                  \\n\",\n       \"16573  0.276394                 0.319189                  \\n\",\n       \"15964  0.352051                 0.412672                  \\n\",\n       \"20420  0.288906                 0.346509                  \\n\",\n       \"18546  0.315023                 0.374756                  \\n\",\n       \"20750  0.286117                 0.339814                  \\n\",\n       \"19593  0.327248                 0.382558                  \\n\",\n       \"19155  0.388808                 0.456142                  \\n\",\n       \"15886  0.384596                 0.425191                  \\n\",\n       \"20238  0.386011                 0.427112                  \\n\",\n       \"16578  0.347355                 0.386985                  \\n\",\n       \"15967  0.385799                 0.426983                  \\n\",\n       \"42862  0.354803                 0.403446                  \\n\",\n       \"18547  0.371370                 0.404592                  \\n\",\n       \"20753  0.349895                 0.389039                  \\n\",\n       \"19595  0.377756                 0.429229                  \\n\",\n       \"19151  0.391699                 0.441045                  \\n\",\n       \"15882  0.383152                 0.425400                  \\n\",\n       \"20242  0.382285                 0.426072                  \\n\",\n       \"16576  0.347239                 0.387166                  \\n\",\n       \"15969  0.382886                 0.424568                  \\n\",\n       \"20416  0.347306                 0.395801                  \\n\",\n       \"18549  0.367598                 0.401878                  \\n\",\n       \"20752  0.346862                 0.388391                  \\n\",\n       \"42853  0.376192                 0.426610                  \\n\",\n       \"19153  0.386949                 0.437472                  \\n\",\n       \"15884  0.357773                 0.399457                  \\n\",\n       \"20237  0.359876                 0.404645                  \\n\",\n       \"16574  0.330643                 0.365902                  \\n\",\n       \"15966  0.360693                 0.404364                  \\n\",\n       \"20417  0.331940                 0.374835                  \\n\",\n       \"18548  0.348968                 0.383363                  \\n\",\n       \"20754  0.333074                 0.376478                  \\n\",\n       \"19591  0.358620                 0.405071                  \\n\",\n       \"19149  0.379023                 0.427383                  \\n\",\n       \"15885  0.339945                 0.412209                  \\n\",\n       \"20243  0.341608                 0.419408                  \\n\",\n       \"16577  0.302136                 0.374525                  \\n\",\n       \"15965  0.351571                 0.424614                  \\n\",\n       \"20418  0.306411                 0.371937                  \\n\",\n       \"18551  0.322328                 0.392731                  \\n\",\n       \"20751  0.297620                 0.379007                  \\n\",\n       \"19594  0.332650                 0.411956                  \\n\",\n       \"19152  0.352437                 0.431444                  \\n\",\n       \"15883  0.233262                 0.296341                  \\n\",\n       \"20239  0.244827                 0.317315                  \\n\",\n       \"16575  0.219555                 0.282536                  \\n\",\n       \"15968  0.237259                 0.306084                  \\n\",\n       \"20421  0.207046                 0.278768                  \\n\",\n       \"18550  0.218023                 0.286678                  \\n\",\n       \"20749  0.200130                 0.270503                  \\n\",\n       \"19596  0.227990                 0.295382                  \\n\",\n       \"19150  0.260514                 0.329868                  \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_alpha_ndcg@5  all_exact_advanced_alpha_ndcg@10  \\\\\\n\",\n       \"15880  0.225359                         0.262494                           \\n\",\n       \"20240  0.314216                         0.364432                           \\n\",\n       \"16579  0.164482                         0.190235                           \\n\",\n       \"15963  0.234883                         0.273612                           \\n\",\n       \"20419  0.178156                         0.206053                           \\n\",\n       \"18552  0.197257                         0.224648                           \\n\",\n       \"20748  0.183034                         0.207975                           \\n\",\n       \"19592  0.264917                         0.299624                           \\n\",\n       \"19154  0.262402                         0.310480                           \\n\",\n       \"15881  0.485206                         0.568567                           \\n\",\n       \"20241  0.508248                         0.599941                           \\n\",\n       \"16573  0.434928                         0.501636                           \\n\",\n       \"15964  0.491592                         0.576239                           \\n\",\n       \"20420  0.439874                         0.505829                           \\n\",\n       \"18546  0.461722                         0.538970                           \\n\",\n       \"20750  0.447400                         0.514134                           \\n\",\n       \"19593  0.490353                         0.564088                           \\n\",\n       \"19155  0.511239                         0.594618                           \\n\",\n       \"15886  0.477369                         0.491745                           \\n\",\n       \"20238  0.490760                         0.505148                           \\n\",\n       \"16578  0.455121                         0.467533                           \\n\",\n       \"15967  0.480651                         0.494769                           \\n\",\n       \"42862  0.458379                         0.470848                           \\n\",\n       \"18547  0.452137                         0.465135                           \\n\",\n       \"20753  0.463211                         0.475634                           \\n\",\n       \"19595  0.491812                         0.505279                           \\n\",\n       \"19151  0.495364                         0.510198                           \\n\",\n       \"15882  0.477452                         0.492088                           \\n\",\n       \"20242  0.487097                         0.500793                           \\n\",\n       \"16576  0.453054                         0.466446                           \\n\",\n       \"15969  0.478464                         0.492242                           \\n\",\n       \"20416  0.454936                         0.466994                           \\n\",\n       \"18549  0.442791                         0.455859                           \\n\",\n       \"20752  0.457895                         0.469312                           \\n\",\n       \"42853  0.485484                         0.499334                           \\n\",\n       \"19153  0.494691                         0.509122                           \\n\",\n       \"15884  0.435728                         0.458196                           \\n\",\n       \"20237  0.456433                         0.481405                           \\n\",\n       \"16574  0.419372                         0.439389                           \\n\",\n       \"15966  0.435411                         0.456531                           \\n\",\n       \"20417  0.414851                         0.435327                           \\n\",\n       \"18548  0.403613                         0.427234                           \\n\",\n       \"20754  0.428900                         0.449866                           \\n\",\n       \"19591  0.459719                         0.481585                           \\n\",\n       \"19149  0.452842                         0.478436                           \\n\",\n       \"15885  0.403678                         0.464161                           \\n\",\n       \"20243  0.419990                         0.484790                           \\n\",\n       \"16577  0.374335                         0.431086                           \\n\",\n       \"15965  0.404334                         0.469069                           \\n\",\n       \"20418  0.374796                         0.428296                           \\n\",\n       \"18551  0.382614                         0.442020                           \\n\",\n       \"20751  0.397196                         0.445924                           \\n\",\n       \"19594  0.411547                         0.475803                           \\n\",\n       \"19152  0.412366                         0.476504                           \\n\",\n       \"15883  0.330373                         0.410507                           \\n\",\n       \"20239  0.345038                         0.425617                           \\n\",\n       \"16575  0.309698                         0.389332                           \\n\",\n       \"15968  0.316382                         0.400004                           \\n\",\n       \"20421  0.305168                         0.373827                           \\n\",\n       \"18550  0.326002                         0.399241                           \\n\",\n       \"20749  0.313016                         0.381323                           \\n\",\n       \"19596  0.323113                         0.402687                           \\n\",\n       \"19150  0.341187                         0.423840                           \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_auc  present_exact_advanced_ap  \\\\\\n\",\n       \"15880  0.050630                    0.065119                    \\n\",\n       \"20240  0.073612                    0.088588                    \\n\",\n       \"16579  0.039526                    0.048672                    \\n\",\n       \"15963  0.056503                    0.069403                    \\n\",\n       \"20419  0.040631                    0.051126                    \\n\",\n       \"18552  0.037485                    0.048140                    \\n\",\n       \"20748  0.038667                    0.048909                    \\n\",\n       \"19592  0.044918                    0.055190                    \\n\",\n       \"19154  0.055377                    0.067957                    \\n\",\n       \"15881  0.277817                    0.308156                    \\n\",\n       \"20241  0.292379                    0.323239                    \\n\",\n       \"16573  0.216803                    0.246558                    \\n\",\n       \"15964  0.282227                    0.310379                    \\n\",\n       \"20420  0.244312                    0.272758                    \\n\",\n       \"18546  0.254492                    0.282576                    \\n\",\n       \"20750  0.238979                    0.265890                    \\n\",\n       \"19593  0.254249                    0.285401                    \\n\",\n       \"19155  0.308452                    0.342923                    \\n\",\n       \"15886  0.355014                    0.410471                    \\n\",\n       \"20238  0.356837                    0.413211                    \\n\",\n       \"16578  0.324335                    0.375235                    \\n\",\n       \"15967  0.351799                    0.409281                    \\n\",\n       \"42862  0.343571                    0.392526                    \\n\",\n       \"18547  0.324875                    0.382946                    \\n\",\n       \"20753  0.330021                    0.380997                    \\n\",\n       \"19595  0.359335                    0.410309                    \\n\",\n       \"19151  0.371130                    0.425017                    \\n\",\n       \"15882  0.354705                    0.410280                    \\n\",\n       \"20242  0.358184                    0.413828                    \\n\",\n       \"16576  0.325944                    0.376409                    \\n\",\n       \"15969  0.347467                    0.404380                    \\n\",\n       \"20416  0.334510                    0.384048                    \\n\",\n       \"18549  0.317053                    0.376703                    \\n\",\n       \"20752  0.328443                    0.379174                    \\n\",\n       \"42853  0.348518                    0.401577                    \\n\",\n       \"19153  0.362936                    0.416994                    \\n\",\n       \"15884  0.343390                    0.398324                    \\n\",\n       \"20237  0.358619                    0.412908                    \\n\",\n       \"16574  0.315360                    0.369778                    \\n\",\n       \"15966  0.341917                    0.403845                    \\n\",\n       \"20417  0.327671                    0.377393                    \\n\",\n       \"18548  0.325393                    0.381326                    \\n\",\n       \"20754  0.339638                    0.386557                    \\n\",\n       \"19591  0.350432                    0.401690                    \\n\",\n       \"19149  0.373385                    0.427582                    \\n\",\n       \"15885  0.377364                    0.410233                    \\n\",\n       \"20243  0.395293                    0.425608                    \\n\",\n       \"16577  0.337575                    0.368302                    \\n\",\n       \"15965  0.392972                    0.430376                    \\n\",\n       \"20418  0.338120                    0.371345                    \\n\",\n       \"18551  0.352287                    0.386760                    \\n\",\n       \"20751  0.341926                    0.371128                    \\n\",\n       \"19594  0.375740                    0.407650                    \\n\",\n       \"19152  0.395399                    0.430733                    \\n\",\n       \"15883  0.282019                    0.313478                    \\n\",\n       \"20239  0.307327                    0.335646                    \\n\",\n       \"16575  0.267683                    0.298516                    \\n\",\n       \"15968  0.292704                    0.325422                    \\n\",\n       \"20421  0.275262                    0.300274                    \\n\",\n       \"18550  0.262614                    0.297904                    \\n\",\n       \"20749  0.262716                    0.288836                    \\n\",\n       \"19596  0.267874                    0.299774                    \\n\",\n       \"19150  0.313334                    0.346375                    \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_mrr  present_exact_advanced_sadr  \\\\\\n\",\n       \"15880  0.198821                    0.136682                      \\n\",\n       \"20240  0.248960                    0.169905                      \\n\",\n       \"16579  0.225114                    0.094964                      \\n\",\n       \"15963  0.212344                    0.137225                      \\n\",\n       \"20419  0.213502                    0.106516                      \\n\",\n       \"18552  0.180384                    0.105843                      \\n\",\n       \"20748  0.231509                    0.092255                      \\n\",\n       \"19592  0.234240                    0.104708                      \\n\",\n       \"19154  0.215512                    0.136965                      \\n\",\n       \"15881  0.298649                    0.436319                      \\n\",\n       \"20241  0.297168                    0.452468                      \\n\",\n       \"16573  0.318229                    0.370755                      \\n\",\n       \"15964  0.310439                    0.437409                      \\n\",\n       \"20420  0.303988                    0.396496                      \\n\",\n       \"18546  0.328528                    0.403226                      \\n\",\n       \"20750  0.365091                    0.377680                      \\n\",\n       \"19593  0.296996                    0.416598                      \\n\",\n       \"19155  0.252308                    0.477432                      \\n\",\n       \"15886  0.364954                    0.521975                      \\n\",\n       \"20238  0.363329                    0.525624                      \\n\",\n       \"16578  0.381029                    0.477542                      \\n\",\n       \"15967  0.356126                    0.525392                      \\n\",\n       \"42862  0.371930                    0.494405                      \\n\",\n       \"18547  0.354083                    0.501106                      \\n\",\n       \"20753  0.382675                    0.483571                      \\n\",\n       \"19595  0.372595                    0.514206                      \\n\",\n       \"19151  0.356658                    0.536002                      \\n\",\n       \"15882  0.365572                    0.521598                      \\n\",\n       \"20242  0.369259                    0.523908                      \\n\",\n       \"16576  0.382225                    0.478340                      \\n\",\n       \"15969  0.353796                    0.521092                      \\n\",\n       \"20416  0.364991                    0.487280                      \\n\",\n       \"18549  0.342053                    0.496301                      \\n\",\n       \"20752  0.380737                    0.480691                      \\n\",\n       \"42853  0.358846                    0.509435                      \\n\",\n       \"19153  0.353086                    0.529715                      \\n\",\n       \"15884  0.369707                    0.508382                      \\n\",\n       \"20237  0.377501                    0.524112                      \\n\",\n       \"16574  0.381062                    0.478599                      \\n\",\n       \"15966  0.364700                    0.518935                      \\n\",\n       \"20417  0.375279                    0.480473                      \\n\",\n       \"18548  0.371673                    0.496726                      \\n\",\n       \"20754  0.393398                    0.486404                      \\n\",\n       \"19591  0.372284                    0.506520                      \\n\",\n       \"19149  0.366626                    0.541457                      \\n\",\n       \"15885  0.384929                    0.513514                      \\n\",\n       \"20243  0.390179                    0.530620                      \\n\",\n       \"16577  0.412933                    0.464273                      \\n\",\n       \"15965  0.382393                    0.540071                      \\n\",\n       \"20418  0.373078                    0.480191                      \\n\",\n       \"18551  0.391645                    0.488649                      \\n\",\n       \"20751  0.432711                    0.463909                      \\n\",\n       \"19594  0.390659                    0.507204                      \\n\",\n       \"19152  0.348542                    0.536694                      \\n\",\n       \"15883  0.345733                    0.425725                      \\n\",\n       \"20239  0.333588                    0.444754                      \\n\",\n       \"16575  0.350812                    0.412512                      \\n\",\n       \"15968  0.321637                    0.445230                      \\n\",\n       \"20421  0.369758                    0.399248                      \\n\",\n       \"18550  0.350359                    0.420214                      \\n\",\n       \"20749  0.385090                    0.389207                      \\n\",\n       \"19596  0.321906                    0.420668                      \\n\",\n       \"19150  0.291617                    0.465584                      \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_ndcg  present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"15880  0.151283                     0.266606                              \\n\",\n       \"20240  0.202607                     0.325369                              \\n\",\n       \"16579  0.108833                     0.241096                              \\n\",\n       \"15963  0.158349                     0.256096                              \\n\",\n       \"20419  0.122136                     0.253754                              \\n\",\n       \"18552  0.116337                     0.245719                              \\n\",\n       \"20748  0.109764                     0.242981                              \\n\",\n       \"19592  0.123071                     0.303057                              \\n\",\n       \"19154  0.159842                     0.282537                              \\n\",\n       \"15881  0.478774                     0.547265                              \\n\",\n       \"20241  0.497978                     0.552804                              \\n\",\n       \"16573  0.397626                     0.523511                              \\n\",\n       \"15964  0.487074                     0.545016                              \\n\",\n       \"20420  0.432315                     0.537820                              \\n\",\n       \"18546  0.446749                     0.531579                              \\n\",\n       \"20750  0.417896                     0.536644                              \\n\",\n       \"19593  0.452579                     0.540057                              \\n\",\n       \"19155  0.537041                     0.553746                              \\n\",\n       \"15886  0.548159                     0.497429                              \\n\",\n       \"20238  0.551931                     0.510731                              \\n\",\n       \"16578  0.503000                     0.482593                              \\n\",\n       \"15967  0.551388                     0.504166                              \\n\",\n       \"42862  0.526690                     0.505188                              \\n\",\n       \"18547  0.519823                     0.466873                              \\n\",\n       \"20753  0.507394                     0.498063                              \\n\",\n       \"19595  0.545572                     0.517368                              \\n\",\n       \"19151  0.572149                     0.518501                              \\n\",\n       \"15882  0.549621                     0.495484                              \\n\",\n       \"20242  0.553735                     0.500780                              \\n\",\n       \"16576  0.504796                     0.472436                              \\n\",\n       \"15969  0.547379                     0.497241                              \\n\",\n       \"20416  0.520044                     0.496335                              \\n\",\n       \"18549  0.515905                     0.449441                              \\n\",\n       \"20752  0.507037                     0.493155                              \\n\",\n       \"42853  0.540643                     0.502800                              \\n\",\n       \"19153  0.563858                     0.504926                              \\n\",\n       \"15884  0.533233                     0.474885                              \\n\",\n       \"20237  0.551218                     0.509473                              \\n\",\n       \"16574  0.499000                     0.454279                              \\n\",\n       \"15966  0.549278                     0.467921                              \\n\",\n       \"20417  0.509140                     0.483033                              \\n\",\n       \"18548  0.515590                     0.454797                              \\n\",\n       \"20754  0.510631                     0.503750                              \\n\",\n       \"19591  0.536893                     0.504426                              \\n\",\n       \"19149  0.577068                     0.502387                              \\n\",\n       \"15885  0.572105                     0.528286                              \\n\",\n       \"20243  0.585925                     0.554391                              \\n\",\n       \"16577  0.518239                     0.515114                              \\n\",\n       \"15965  0.594893                     0.527697                              \\n\",\n       \"20418  0.531138                     0.517288                              \\n\",\n       \"18551  0.545153                     0.522944                              \\n\",\n       \"20751  0.520995                     0.520350                              \\n\",\n       \"19594  0.566347                     0.535610                              \\n\",\n       \"19152  0.610715                     0.526793                              \\n\",\n       \"15883  0.477870                     0.508668                              \\n\",\n       \"20239  0.516941                     0.517476                              \\n\",\n       \"16575  0.461251                     0.496706                              \\n\",\n       \"15968  0.501580                     0.501753                              \\n\",\n       \"20421  0.458735                     0.506921                              \\n\",\n       \"18550  0.462683                     0.514779                              \\n\",\n       \"20749  0.447269                     0.504259                              \\n\",\n       \"19596  0.470478                     0.499213                              \\n\",\n       \"19150  0.535353                     0.510777                              \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"15880  0.311655                              0.001295                    \\n\",\n       \"20240  0.376033                              0.000000                    \\n\",\n       \"16579  0.267379                              0.000000                    \\n\",\n       \"15963  0.296792                              0.001082                    \\n\",\n       \"20419  0.294877                              0.001082                    \\n\",\n       \"18552  0.286987                              0.000000                    \\n\",\n       \"20748  0.272287                              0.000180                    \\n\",\n       \"19592  0.336799                              0.000000                    \\n\",\n       \"19154  0.329692                              0.000000                    \\n\",\n       \"15881  0.626950                              0.024390                    \\n\",\n       \"20241  0.634871                              0.018263                    \\n\",\n       \"16573  0.592846                              0.018018                    \\n\",\n       \"15964  0.625309                              0.023558                    \\n\",\n       \"20420  0.614173                              0.022928                    \\n\",\n       \"18546  0.608713                              0.029606                    \\n\",\n       \"20750  0.604292                              0.016320                    \\n\",\n       \"19593  0.615019                              0.017388                    \\n\",\n       \"19155  0.631410                              0.026972                    \\n\",\n       \"15886  0.503571                              0.052466                    \\n\",\n       \"20238  0.516926                              0.044350                    \\n\",\n       \"16578  0.487446                              0.048363                    \\n\",\n       \"15967  0.509991                              0.050511                    \\n\",\n       \"42862  0.510686                              0.050253                    \\n\",\n       \"18547  0.472176                              0.055143                    \\n\",\n       \"20753  0.503122                              0.049451                    \\n\",\n       \"19595  0.523265                              0.051141                    \\n\",\n       \"19151  0.524330                              0.048364                    \\n\",\n       \"15882  0.501693                              0.045570                    \\n\",\n       \"20242  0.506654                              0.040418                    \\n\",\n       \"16576  0.477689                              0.044986                    \\n\",\n       \"15969  0.502927                              0.052157                    \\n\",\n       \"20416  0.501635                              0.048781                    \\n\",\n       \"18549  0.454925                              0.049082                    \\n\",\n       \"20752  0.497805                              0.046241                    \\n\",\n       \"42853  0.508864                              0.051866                    \\n\",\n       \"19153  0.510325                              0.050602                    \\n\",\n       \"15884  0.487756                              0.056376                    \\n\",\n       \"20237  0.521344                              0.048586                    \\n\",\n       \"16574  0.465742                              0.057027                    \\n\",\n       \"15966  0.479196                              0.051525                    \\n\",\n       \"20417  0.494557                              0.061273                    \\n\",\n       \"18548  0.467676                              0.061558                    \\n\",\n       \"20754  0.516174                              0.057091                    \\n\",\n       \"19591  0.516278                              0.052768                    \\n\",\n       \"19149  0.514789                              0.058269                    \\n\",\n       \"15885  0.571887                              0.053043                    \\n\",\n       \"20243  0.596053                              0.045018                    \\n\",\n       \"16577  0.549683                              0.050333                    \\n\",\n       \"15965  0.570448                              0.037247                    \\n\",\n       \"20418  0.555827                              0.027307                    \\n\",\n       \"18551  0.562108                              0.045275                    \\n\",\n       \"20751  0.556307                              0.046908                    \\n\",\n       \"19594  0.576973                              0.039300                    \\n\",\n       \"19152  0.573681                              0.042531                    \\n\",\n       \"15883  0.553986                              0.025518                    \\n\",\n       \"20239  0.564942                              0.022847                    \\n\",\n       \"16575  0.547521                              0.022388                    \\n\",\n       \"15968  0.554109                              0.025206                    \\n\",\n       \"20421  0.555828                              0.022055                    \\n\",\n       \"18550  0.564423                              0.025261                    \\n\",\n       \"20749  0.542692                              0.028345                    \\n\",\n       \"19596  0.552022                              0.028523                    \\n\",\n       \"19150  0.560282                              0.029349                    \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"15880  0.001353                  0.001826                    \\n\",\n       \"20240  0.000000                  0.000000                    \\n\",\n       \"16579  0.000000                  0.000000                    \\n\",\n       \"15963  0.001082                  0.003247                    \\n\",\n       \"20419  0.001082                  0.003247                    \\n\",\n       \"18552  0.000000                  0.000000                    \\n\",\n       \"20748  0.000361                  0.001082                    \\n\",\n       \"19592  0.000000                  0.000000                    \\n\",\n       \"19154  0.000000                  0.000000                    \\n\",\n       \"15881  0.028695                  0.051734                    \\n\",\n       \"20241  0.022985                  0.039957                    \\n\",\n       \"16573  0.022741                  0.040653                    \\n\",\n       \"15964  0.026983                  0.049435                    \\n\",\n       \"20420  0.028275                  0.047608                    \\n\",\n       \"18546  0.034991                  0.056360                    \\n\",\n       \"20750  0.020294                  0.034467                    \\n\",\n       \"19593  0.022074                  0.042559                    \\n\",\n       \"19155  0.029718                  0.045020                    \\n\",\n       \"15886  0.062885                  0.108269                    \\n\",\n       \"20238  0.052918                  0.095065                    \\n\",\n       \"16578  0.059002                  0.102016                    \\n\",\n       \"15967  0.060760                  0.104933                    \\n\",\n       \"42862  0.061269                  0.102328                    \\n\",\n       \"18547  0.066979                  0.113525                    \\n\",\n       \"20753  0.058902                  0.102338                    \\n\",\n       \"19595  0.062906                  0.105510                    \\n\",\n       \"19151  0.057215                  0.102916                    \\n\",\n       \"15882  0.056570                  0.101817                    \\n\",\n       \"20242  0.048045                  0.091654                    \\n\",\n       \"16576  0.054317                  0.093101                    \\n\",\n       \"15969  0.062350                  0.109813                    \\n\",\n       \"20416  0.060083                  0.098324                    \\n\",\n       \"18549  0.061112                  0.106736                    \\n\",\n       \"20752  0.055671                  0.100878                    \\n\",\n       \"42853  0.064641                  0.108721                    \\n\",\n       \"19153  0.060350                  0.109468                    \\n\",\n       \"15884  0.067318                  0.129540                    \\n\",\n       \"20237  0.055584                  0.123804                    \\n\",\n       \"16574  0.065805                  0.131233                    \\n\",\n       \"15966  0.060251                  0.130940                    \\n\",\n       \"20417  0.072742                  0.129561                    \\n\",\n       \"18548  0.075191                  0.132986                    \\n\",\n       \"20754  0.066706                  0.123194                    \\n\",\n       \"19591  0.064566                  0.113187                    \\n\",\n       \"19149  0.070562                  0.141219                    \\n\",\n       \"15885  0.058057                  0.154921                    \\n\",\n       \"20243  0.050922                  0.136970                    \\n\",\n       \"16577  0.059669                  0.146523                    \\n\",\n       \"15965  0.046973                  0.137074                    \\n\",\n       \"20418  0.038406                  0.097735                    \\n\",\n       \"18551  0.054524                  0.151221                    \\n\",\n       \"20751  0.055406                  0.137669                    \\n\",\n       \"19594  0.050450                  0.160534                    \\n\",\n       \"19152  0.051139                  0.146366                    \\n\",\n       \"15883  0.029867                  0.151463                    \\n\",\n       \"20239  0.025727                  0.133987                    \\n\",\n       \"16575  0.027338                  0.160677                    \\n\",\n       \"15968  0.028551                  0.144386                    \\n\",\n       \"20421  0.026842                  0.141197                    \\n\",\n       \"18550  0.028569                  0.124276                    \\n\",\n       \"20749  0.031543                  0.157393                    \\n\",\n       \"19596  0.032548                  0.135946                    \\n\",\n       \"19150  0.033308                  0.185012                    \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"15880  0.001468                    0.002004                     \\n\",\n       \"20240  0.000000                    0.000000                     \\n\",\n       \"16579  0.000000                    0.000000                     \\n\",\n       \"15963  0.001082                    0.001524                     \\n\",\n       \"20419  0.001082                    0.001524                     \\n\",\n       \"18552  0.000000                    0.000000                     \\n\",\n       \"20748  0.001082                    0.000762                     \\n\",\n       \"19592  0.000000                    0.000000                     \\n\",\n       \"19154  0.000000                    0.000000                     \\n\",\n       \"15881  0.039348                    0.042308                     \\n\",\n       \"20241  0.031902                    0.033424                     \\n\",\n       \"16573  0.034471                    0.035978                     \\n\",\n       \"15964  0.037418                    0.039883                     \\n\",\n       \"20420  0.041222                    0.045348                     \\n\",\n       \"18546  0.046584                    0.049330                     \\n\",\n       \"20750  0.029412                    0.031286                     \\n\",\n       \"19593  0.034749                    0.037965                     \\n\",\n       \"19155  0.035963                    0.038149                     \\n\",\n       \"15886  0.084436                    0.090136                     \\n\",\n       \"20238  0.069945                    0.074918                     \\n\",\n       \"16578  0.081471                    0.086854                     \\n\",\n       \"15967  0.082760                    0.088492                     \\n\",\n       \"42862  0.087671                    0.094594                     \\n\",\n       \"18547  0.093100                    0.099506                     \\n\",\n       \"20753  0.080097                    0.086251                     \\n\",\n       \"19595  0.090992                    0.097764                     \\n\",\n       \"19151  0.074007                    0.078787                     \\n\",\n       \"15882  0.078562                    0.083960                     \\n\",\n       \"20242  0.064169                    0.069207                     \\n\",\n       \"16576  0.075665                    0.080104                     \\n\",\n       \"15969  0.084289                    0.091460                     \\n\",\n       \"20416  0.086539                    0.092722                     \\n\",\n       \"18549  0.086695                    0.093107                     \\n\",\n       \"20752  0.077251                    0.084235                     \\n\",\n       \"42853  0.093498                    0.100031                     \\n\",\n       \"19153  0.077684                    0.083656                     \\n\",\n       \"15884  0.096980                    0.102299                     \\n\",\n       \"20237  0.071696                    0.081008                     \\n\",\n       \"16574  0.085743                    0.095316                     \\n\",\n       \"15966  0.083779                    0.093955                     \\n\",\n       \"20417  0.104693                    0.115693                     \\n\",\n       \"18548  0.107242                    0.114522                     \\n\",\n       \"20754  0.091994                    0.100732                     \\n\",\n       \"19591  0.096469                    0.104293                     \\n\",\n       \"19149  0.093560                    0.100882                     \\n\",\n       \"15885  0.081848                    0.093069                     \\n\",\n       \"20243  0.071346                    0.080853                     \\n\",\n       \"16577  0.088247                    0.097419                     \\n\",\n       \"15965  0.074509                    0.084071                     \\n\",\n       \"20418  0.075169                    0.080896                     \\n\",\n       \"18551  0.082836                    0.095441                     \\n\",\n       \"20751  0.080340                    0.087278                     \\n\",\n       \"19594  0.086138                    0.097994                     \\n\",\n       \"19152  0.071534                    0.079631                     \\n\",\n       \"15883  0.051564                    0.065844                     \\n\",\n       \"20239  0.041487                    0.055245                     \\n\",\n       \"16575  0.053414                    0.065606                     \\n\",\n       \"15968  0.049863                    0.065265                     \\n\",\n       \"20421  0.053918                    0.065734                     \\n\",\n       \"18550  0.048362                    0.062944                     \\n\",\n       \"20749  0.046325                    0.065663                     \\n\",\n       \"19596  0.057616                    0.074324                     \\n\",\n       \"19150  0.049960                    0.069465                     \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"15880  0.001524                             \\n\",\n       \"20240  0.001524                             \\n\",\n       \"16579  0.000000                             \\n\",\n       \"15963  0.001524                             \\n\",\n       \"20419  0.001524                             \\n\",\n       \"18552  0.000000                             \\n\",\n       \"20748  0.000762                             \\n\",\n       \"19592  0.000961                             \\n\",\n       \"19154  0.000000                             \\n\",\n       \"15881  0.044964                             \\n\",\n       \"20241  0.038879                             \\n\",\n       \"16573  0.033113                             \\n\",\n       \"15964  0.045613                             \\n\",\n       \"20420  0.050594                             \\n\",\n       \"18546  0.063306                             \\n\",\n       \"20750  0.038905                             \\n\",\n       \"19593  0.031535                             \\n\",\n       \"19155  0.050239                             \\n\",\n       \"15886  0.082443                             \\n\",\n       \"20238  0.075180                             \\n\",\n       \"16578  0.078595                             \\n\",\n       \"15967  0.082380                             \\n\",\n       \"42862  0.078634                             \\n\",\n       \"18547  0.085904                             \\n\",\n       \"20753  0.081099                             \\n\",\n       \"19595  0.080872                             \\n\",\n       \"19151  0.080706                             \\n\",\n       \"15882  0.076981                             \\n\",\n       \"20242  0.069688                             \\n\",\n       \"16576  0.076289                             \\n\",\n       \"15969  0.085036                             \\n\",\n       \"20416  0.077544                             \\n\",\n       \"18549  0.079030                             \\n\",\n       \"20752  0.078739                             \\n\",\n       \"42853  0.080583                             \\n\",\n       \"19153  0.085095                             \\n\",\n       \"15884  0.100911                             \\n\",\n       \"20237  0.077773                             \\n\",\n       \"16574  0.091677                             \\n\",\n       \"15966  0.094239                             \\n\",\n       \"20417  0.096528                             \\n\",\n       \"18548  0.104049                             \\n\",\n       \"20754  0.089114                             \\n\",\n       \"19591  0.088646                             \\n\",\n       \"19149  0.105392                             \\n\",\n       \"15885  0.109770                             \\n\",\n       \"20243  0.094792                             \\n\",\n       \"16577  0.104929                             \\n\",\n       \"15965  0.083115                             \\n\",\n       \"20418  0.069498                             \\n\",\n       \"18551  0.086584                             \\n\",\n       \"20751  0.089470                             \\n\",\n       \"19594  0.101404                             \\n\",\n       \"19152  0.094398                             \\n\",\n       \"15883  0.082020                             \\n\",\n       \"20239  0.071170                             \\n\",\n       \"16575  0.073172                             \\n\",\n       \"15968  0.085675                             \\n\",\n       \"20421  0.071944                             \\n\",\n       \"18550  0.082205                             \\n\",\n       \"20749  0.073281                             \\n\",\n       \"19596  0.073305                             \\n\",\n       \"19150  0.095735                             \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_alpha_ndcg@10  present_tgt_num  absent_tgt_num  \\\\\\n\",\n       \"15880  0.001524                             7.860390         0.204545         \\n\",\n       \"20240  0.001524                             7.860390         0.204545         \\n\",\n       \"16579  0.000000                             7.860390         0.204545         \\n\",\n       \"15963  0.001524                             7.860390         0.204545         \\n\",\n       \"20419  0.001524                             7.860390         0.204545         \\n\",\n       \"18552  0.000000                             7.860390         0.204545         \\n\",\n       \"20748  0.000762                             7.860390         0.204545         \\n\",\n       \"19592  0.000961                             7.860390         0.204545         \\n\",\n       \"19154  0.000000                             7.860390         0.204545         \\n\",\n       \"15881  0.045140                             7.842000         1.984000         \\n\",\n       \"20241  0.039389                             7.842000         1.984000         \\n\",\n       \"16573  0.033671                             7.842000         1.984000         \\n\",\n       \"15964  0.046254                             7.842000         1.984000         \\n\",\n       \"20420  0.051120                             7.842000         1.984000         \\n\",\n       \"18546  0.063661                             7.842000         1.984000         \\n\",\n       \"20750  0.038959                             7.842000         1.984000         \\n\",\n       \"19593  0.031814                             7.842000         1.984000         \\n\",\n       \"19155  0.050627                             7.842000         1.984000         \\n\",\n       \"15886  0.082734                             3.339471         1.923000         \\n\",\n       \"20238  0.075361                             3.339471         1.923000         \\n\",\n       \"16578  0.078842                             3.339471         1.923000         \\n\",\n       \"15967  0.082630                             3.339471         1.923000         \\n\",\n       \"42862  0.078886                             3.339471         1.923000         \\n\",\n       \"18547  0.086246                             3.339471         1.923000         \\n\",\n       \"20753  0.081348                             3.339471         1.923000         \\n\",\n       \"19595  0.081097                             3.339471         1.923000         \\n\",\n       \"19151  0.080839                             3.339471         1.923000         \\n\",\n       \"15882  0.077185                             3.337000         1.928000         \\n\",\n       \"20242  0.069742                             3.337000         1.928000         \\n\",\n       \"16576  0.076406                             3.337000         1.928000         \\n\",\n       \"15969  0.085103                             3.337000         1.928000         \\n\",\n       \"20416  0.077844                             3.337000         1.928000         \\n\",\n       \"18549  0.079167                             3.337000         1.928000         \\n\",\n       \"20752  0.078816                             3.337000         1.928000         \\n\",\n       \"42853  0.080690                             3.337000         1.928000         \\n\",\n       \"19153  0.085237                             3.337000         1.928000         \\n\",\n       \"15884  0.101483                             3.243478         2.497826         \\n\",\n       \"20237  0.078284                             3.243478         2.497826         \\n\",\n       \"16574  0.092186                             3.243478         2.497826         \\n\",\n       \"15966  0.095667                             3.243478         2.497826         \\n\",\n       \"20417  0.097513                             3.243478         2.497826         \\n\",\n       \"18548  0.105513                             3.243478         2.497826         \\n\",\n       \"20754  0.089556                             3.243478         2.497826         \\n\",\n       \"19591  0.089481                             3.243478         2.497826         \\n\",\n       \"19149  0.105861                             3.243478         2.497826         \\n\",\n       \"15885  0.119410                             5.971564         5.691943         \\n\",\n       \"20243  0.102540                             5.971564         5.691943         \\n\",\n       \"16577  0.111944                             5.971564         5.691943         \\n\",\n       \"15965  0.090884                             5.971564         5.691943         \\n\",\n       \"20418  0.078206                             5.971564         5.691943         \\n\",\n       \"18551  0.094718                             5.971564         5.691943         \\n\",\n       \"20751  0.095103                             5.971564         5.691943         \\n\",\n       \"19594  0.107722                             5.971564         5.691943         \\n\",\n       \"19152  0.100518                             5.971564         5.691943         \\n\",\n       \"15883  0.094061                             6.730000         8.340000         \\n\",\n       \"20239  0.082541                             6.730000         8.340000         \\n\",\n       \"16575  0.088561                             6.730000         8.340000         \\n\",\n       \"15968  0.100537                             6.730000         8.340000         \\n\",\n       \"20421  0.085384                             6.730000         8.340000         \\n\",\n       \"18550  0.090114                             6.730000         8.340000         \\n\",\n       \"20749  0.081985                             6.730000         8.340000         \\n\",\n       \"19596  0.083136                             6.730000         8.340000         \\n\",\n       \"19150  0.103617                             6.730000         8.340000         \\n\",\n       \"\\n\",\n       \"       present_pred_num  absent_pred_num  unique_pred_num  dup_pred_num  \\\\\\n\",\n       \"15880  15.402597         56.288961        81.353896        3786.366883    \\n\",\n       \"20240  20.912338         26.587662        56.889610        3801.233766    \\n\",\n       \"16579  7.961039          201.379870       234.685065       3533.779221    \\n\",\n       \"15963  16.422078         68.798701        110.707792       3269.951299    \\n\",\n       \"20419  11.353896         123.496753       148.159091       3582.892857    \\n\",\n       \"18552  11.756494         74.120130        104.837662       3787.483766    \\n\",\n       \"20748  7.977273          103.051948       133.188312       3429.607143    \\n\",\n       \"19592  10.724026         87.172078        128.685065       3691.931818    \\n\",\n       \"19154  19.350649         85.801948        109.129870       3622.568182    \\n\",\n       \"15881  15.890000         31.746000        53.452000        3405.790000    \\n\",\n       \"20241  16.442000         21.064000        42.146000        3437.558000    \\n\",\n       \"16573  11.396000         117.424000       144.256000       3131.078000    \\n\",\n       \"15964  16.232000         52.562000        76.120000        3354.018000    \\n\",\n       \"20420  13.728000         74.622000        96.760000        3339.868000    \\n\",\n       \"18546  13.972000         41.762000        61.630000        3372.120000    \\n\",\n       \"20750  11.160000         68.212000        86.802000        3287.000000    \\n\",\n       \"19593  15.574000         69.900000        95.020000        3333.850000    \\n\",\n       \"19155  22.748000         31.816000        57.432000        3260.374000    \\n\",\n       \"15886  16.779457         26.082354        48.238455        3420.827688    \\n\",\n       \"20238  17.917396         18.781108        41.134187        3369.358283    \\n\",\n       \"16578  13.125682         112.295092       139.650723       3077.361785    \\n\",\n       \"15967  17.719217         46.197428        71.841397        3387.699555    \\n\",\n       \"42862  15.420723         68.863561        93.807925        3371.902487    \\n\",\n       \"18547  15.234402         35.191575        56.722620        3385.852354    \\n\",\n       \"20753  12.844749         57.081453        77.429179        3273.308100    \\n\",\n       \"19595  17.096213         66.068945        93.091810        3356.086856    \\n\",\n       \"19151  24.584480         30.873218        59.228198        3301.570421    \\n\",\n       \"15882  16.971000         25.935000        48.269500        3423.342000    \\n\",\n       \"20242  17.901000         18.703500        41.178000        3364.475500    \\n\",\n       \"16576  13.148500         113.461500       140.564000       3060.686000    \\n\",\n       \"15969  17.758000         45.973000        71.672000        3408.626000    \\n\",\n       \"20416  15.239000         68.735500        93.612000        3338.641000    \\n\",\n       \"18549  15.219500         35.354000        56.765500        3396.998000    \\n\",\n       \"20752  12.835000         56.678500        77.238000        3271.568000    \\n\",\n       \"42853  17.162500         64.949500        92.169500        3345.185000    \\n\",\n       \"19153  24.486500         29.746500        57.974000        3308.951000    \\n\",\n       \"15884  16.882609         28.667391        51.589130        3682.267391    \\n\",\n       \"20237  17.254348         21.489130        43.819565        3674.576087    \\n\",\n       \"16574  12.130435         124.906522       153.013043       3240.113043    \\n\",\n       \"15966  17.052174         62.245652        88.960870        3524.852174    \\n\",\n       \"20417  14.545652         75.341304        101.960870       3530.776087    \\n\",\n       \"18548  14.402174         40.097826        61.019565        3500.791304    \\n\",\n       \"20754  12.436957         65.300000        86.934783        3365.826087    \\n\",\n       \"19591  16.600000         77.628261        104.693478       3466.678261    \\n\",\n       \"19149  24.510870         34.800000        64.134783        3463.456522    \\n\",\n       \"15885  15.488152         26.322275        46.587678        3641.772512    \\n\",\n       \"20243  16.459716         21.341232        42.080569        3599.331754    \\n\",\n       \"16577  11.890995         131.308057       155.497630       3279.786730    \\n\",\n       \"15965  17.199052         63.701422        89.800948        3507.440758    \\n\",\n       \"20418  14.113744         76.985782        99.502370        3533.298578    \\n\",\n       \"18551  13.445498         41.900474        61.118483        3591.137441    \\n\",\n       \"20751  11.592417         62.218009        80.611374        3376.568720    \\n\",\n       \"19594  16.402844         74.331754        98.995261        3550.052133    \\n\",\n       \"19152  24.099526         30.744076        58.848341        3385.260664    \\n\",\n       \"15883  15.080000         29.760000        49.900000        3607.160000    \\n\",\n       \"20239  17.590000         23.540000        46.010000        3566.150000    \\n\",\n       \"16575  12.440000         122.910000       147.860000       3257.280000    \\n\",\n       \"15968  16.780000         63.120000        90.000000        3493.620000    \\n\",\n       \"20421  14.100000         71.890000        93.080000        3430.890000    \\n\",\n       \"18550  14.120000         42.690000        62.880000        3505.720000    \\n\",\n       \"20749  11.690000         62.400000        80.880000        3329.960000    \\n\",\n       \"19596  15.120000         81.070000        105.300000       3593.590000    \\n\",\n       \"19150  23.690000         33.010000        60.380000        3319.710000    \\n\",\n       \"\\n\",\n       \"         beam_num  beamstep_num  \\n\",\n       \"15880  447.863636  11661.003247  \\n\",\n       \"20240  473.775974  11638.435065  \\n\",\n       \"16579  456.996753  12471.379870  \\n\",\n       \"15963  405.662338  10885.237013  \\n\",\n       \"20419  457.750000  12021.103896  \\n\",\n       \"18552  468.448052  11840.282468  \\n\",\n       \"20748  434.775974  11398.892857  \\n\",\n       \"19592  437.032468  11593.006494  \\n\",\n       \"19154  471.587662  11201.649351  \\n\",\n       \"15881  450.332000  11872.876000  \\n\",\n       \"20241  462.630000  11975.882000  \\n\",\n       \"16573  448.714000  11956.116000  \\n\",\n       \"15964  446.178000  11801.188000  \\n\",\n       \"20420  463.460000  12096.522000  \\n\",\n       \"18546  470.756000  12042.998000  \\n\",\n       \"20750  464.730000  11935.956000  \\n\",\n       \"19593  433.622000  11919.248000  \\n\",\n       \"19155  482.488000  11623.312000  \\n\",\n       \"15886  456.603842  12126.808926  \\n\",\n       \"20238  462.757292  12155.847101  \\n\",\n       \"16578  444.802322  12007.063942  \\n\",\n       \"15967  448.358583  11918.865463  \\n\",\n       \"42862  467.865613  12367.344824  \\n\",\n       \"18547  471.806274  12230.480662  \\n\",\n       \"20753  463.474508  12165.802522  \\n\",\n       \"19595  439.706359  12178.431030  \\n\",\n       \"19151  478.233802  12010.344874  \\n\",\n       \"15882  457.334000  12147.611500  \\n\",\n       \"20242  461.883500  12158.770000  \\n\",\n       \"16576  444.335500  11976.134500  \\n\",\n       \"15969  450.868500  11994.931500  \\n\",\n       \"20416  465.702000  12349.972500  \\n\",\n       \"18549  471.833500  12246.869000  \\n\",\n       \"20752  464.186000  12207.972000  \\n\",\n       \"42853  439.194500  12184.255500  \\n\",\n       \"19153  478.443500  12029.724500  \\n\",\n       \"15884  459.256522  12259.767391  \\n\",\n       \"20237  471.521739  12270.245652  \\n\",\n       \"16574  452.704348  12215.660870  \\n\",\n       \"15966  456.723913  12030.665217  \\n\",\n       \"20417  469.758696  12490.252174  \\n\",\n       \"18548  471.541304  12128.019565  \\n\",\n       \"20754  458.002174  12080.713043  \\n\",\n       \"19591  440.126087  12255.560870  \\n\",\n       \"19149  487.454348  11897.319565  \\n\",\n       \"15885  475.360190  12192.909953  \\n\",\n       \"20243  476.601896  11974.620853  \\n\",\n       \"16577  460.668246  12254.459716  \\n\",\n       \"15965  461.905213  11827.218009  \\n\",\n       \"20418  480.521327  12429.412322  \\n\",\n       \"18551  483.838863  12143.298578  \\n\",\n       \"20751  471.374408  12008.763033  \\n\",\n       \"19594  453.876777  12369.369668  \\n\",\n       \"19152  488.587678  11639.303318  \\n\",\n       \"15883  487.130000  12120.350000  \\n\",\n       \"20239  487.450000  12300.010000  \\n\",\n       \"16575  472.300000  12356.920000  \\n\",\n       \"15968  462.640000  11628.280000  \\n\",\n       \"20421  480.710000  12244.390000  \\n\",\n       \"18550  479.840000  11996.540000  \\n\",\n       \"20749  475.300000  11862.080000  \\n\",\n       \"19596  470.870000  12564.770000  \\n\",\n       \"19150  490.770000  11428.150000  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:116: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(3254, 236)\\n\",\n      \"absent valid_kp_df\\n\",\n      \"(63, 236)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:226: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.options.display.max_colwidth = -1\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = [\\n\",\n    \"    'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',    \\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"#     'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"]    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps)]\\n\",\n    \"kp_df = kp_df.loc[kp_df.step % 5000 == 0] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.beam_width == '50']\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_df_present, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_df_present.shape)\\n\",\n    \"display(valid_df_present)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"datasets = valid_df_present.test_dataset.unique()\\n\",\n    \"exp_names = valid_df_present.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_df_present.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_f_score@10')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"'''\\n\",\n    \"# SADR\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_advanced_sadr')\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"orders = valid_kp_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_sadr')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"    \\n\",\n    \"# AUC\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_advanced_auc')\\n\",\n    \"metric_names = ['present_exact_advanced_auc']\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"orders = valid_kp_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_auc')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"'''    \\n\",\n    \"    \\n\",\n    \"# export to Latex\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"    tmp_df = valid_df_present[['exp_name', 'model_base', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"#     display(tmp_df)\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_df_absent, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_df_absent.shape)\\n\",\n    \"# display(valid_df_absent)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_df_absent.test_dataset.unique()\\n\",\n    \"exp_names = valid_df_absent.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_df_absent.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# SADR\\n\",\n    \"'''\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='absent_exact_advanced_sadr')\\n\",\n    \"metric_names = ['absent_exact_advanced_sadr']\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"orders = valid_kp_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='absent_exact_advanced_sadr')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"    \\n\",\n    \"# AUC\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='absent_exact_advanced_auc')\\n\",\n    \"metric_names = ['absent_exact_advanced_auc']\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"orders = valid_kp_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='absent_exact_advanced_auc')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"    tmp_df = valid_df_absent[['exp_name', 'model_base', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### beam_width=1 (greedy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-18T21:27:57.270201Z\",\n     \"start_time\": \"2020-11-18T21:27:49.667068Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - present_exact_f_score@10',\\n\",\n      \"       'alphabetical_reverse - present_exact_f_score@10',\\n\",\n      \"       'length - present_exact_f_score@10',\\n\",\n      \"       'length_reverse - present_exact_f_score@10',\\n\",\n      \"       'no_sort - present_exact_f_score@10',\\n\",\n      \"       'no_sort_reverse - present_exact_f_score@10',\\n\",\n      \"       'random - present_exact_f_score@10',\\n\",\n      \"       'verbatim_append - present_exact_f_score@10',\\n\",\n      \"       'verbatim_prepend - present_exact_f_score@10'],\\n\",\n      \"      dtype='object')\\n\",\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - absent_exact_recall@50',\\n\",\n      \"       'alphabetical_reverse - absent_exact_recall@50',\\n\",\n      \"       'length - absent_exact_recall@50',\\n\",\n      \"       'length_reverse - absent_exact_recall@50',\\n\",\n      \"       'no_sort - absent_exact_recall@50',\\n\",\n      \"       'no_sort_reverse - absent_exact_recall@50',\\n\",\n      \"       'random - absent_exact_recall@50',\\n\",\n      \"       'verbatim_append - absent_exact_recall@50',\\n\",\n      \"       'verbatim_prepend - absent_exact_recall@50'],\\n\",\n      \"      dtype='object')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7f7860da2350>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = [\\n\",\n    \"    'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',    \\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"import copy\\n\",\n    \"\\n\",\n    \"########### Present #############\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '1']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_50 = copy.copy(df)\\n\",\n    \"df_f1_50 = df_f1_50.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_50.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"    'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"    'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"    'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"    'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"    'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"    'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"    'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"    'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"    'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_50 = df_f1_50.div(df_f1_50.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_50.columns)\\n\",\n    \"normalized_df_f1_50.columns = [column_name_map[item] for item in normalized_df_f1_50.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 18,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"f, axes = plt.subplots(figsize=(12, 8))\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.2, ax=axes, cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"########### Absent #############\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_50 = copy.copy(df)\\n\",\n    \"df_f1_50 = df_f1_50.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_50.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"    'random - absent_exact_recall@50': \\\"random\\\",\\n\",\n    \"    'alphabetical - absent_exact_recall@50': \\\"alpha\\\",\\n\",\n    \"    'alphabetical_reverse - absent_exact_recall@50': \\\"alpha-reverse\\\",\\n\",\n    \"    'length - absent_exact_recall@50': \\\"length\\\",\\n\",\n    \"    'length_reverse - absent_exact_recall@50': \\\"length-reverse\\\",\\n\",\n    \"    'no_sort - absent_exact_recall@50': \\\"no-sort\\\",\\n\",\n    \"    'no_sort_reverse - absent_exact_recall@50': \\\"no-sort-reverse\\\",\\n\",\n    \"    'verbatim_append - absent_exact_recall@50': \\\"pres-abs\\\",\\n\",\n    \"    'verbatim_prepend - absent_exact_recall@50': \\\"abs-pres\\\"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_50 = df_f1_50.div(df_f1_50.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_50.columns)\\n\",\n    \"normalized_df_f1_50.columns = [column_name_map[item] for item in normalized_df_f1_50.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 18,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"f, axes = plt.subplots(figsize=(12, 8))\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.2, ax=axes, cmap=cmap, fmt='.3f', cbar=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### beam_width=50\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-16T21:04:29.056690Z\",\n     \"start_time\": \"2020-11-16T21:04:22.892429Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - present_exact_f_score@10',\\n\",\n      \"       'alphabetical_reverse - present_exact_f_score@10',\\n\",\n      \"       'length - present_exact_f_score@10',\\n\",\n      \"       'length_reverse - present_exact_f_score@10',\\n\",\n      \"       'no_sort - present_exact_f_score@10',\\n\",\n      \"       'no_sort_reverse - present_exact_f_score@10',\\n\",\n      \"       'random - present_exact_f_score@10',\\n\",\n      \"       'verbatim_append - present_exact_f_score@10',\\n\",\n      \"       'verbatim_prepend - present_exact_f_score@10'],\\n\",\n      \"      dtype='object')\\n\",\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - absent_exact_recall@50',\\n\",\n      \"       'alphabetical_reverse - absent_exact_recall@50',\\n\",\n      \"       'length - absent_exact_recall@50',\\n\",\n      \"       'length_reverse - absent_exact_recall@50',\\n\",\n      \"       'no_sort - absent_exact_recall@50',\\n\",\n      \"       'no_sort_reverse - absent_exact_recall@50',\\n\",\n      \"       'random - absent_exact_recall@50',\\n\",\n      \"       'verbatim_append - absent_exact_recall@50',\\n\",\n      \"       'verbatim_prepend - absent_exact_recall@50'],\\n\",\n      \"      dtype='object')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7f76e8aa5bd0>\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+2PeOw6RRJxcHD6DXVRttvhqTMLPR6Pd2CDTtUnQN9sDQ342R6w+I1OytLXOxtMTdrOPf0vEJKK9VEB3hjcUXw6K6yx9/NmVOZ2eia+c08awsL+ncMpkKt4Uhqw+Lx4ooqMvKL8HJW4eHoUJ+uADoH+FCj09U3wrdCwvl0dHo9gyING6A+Ye2xsjDnYHLDgnmVjTUejg4G9+TspVyKyyvpHRaIpXlDuo+LIx083TmcktHsvbKzsmRMtyhKK9XsSjpfn25tYY6pj1iknyfBnm6cysxGW3NrptsNGDQEhULBssWLDNLXrV6FuqqKwcPuqE/Lz88j7cIFgzmk0TFdcHF15Y81q6i8Yv1R8rmzHDtymP4DBzX72reS4mJmz/oeR0cnRo4Z12RegBVLFpOacp7x99yL9S18xFy6eXttO3Xv3QbpqtEjatupDVvq08xcXbDw97u+dmrTttp2asxV7dS9d9e2U1u216dpL2VTeewE1hFhWIVeUeeVShzHjECv1VJx4NA1n0tr9Bk4CIVCweplSwzSN65djVpdRf/BQ+rTCvLzyUhLQ31FnYqM7oyziyub/lhjMIc4JTmZk8eO0qv/gGbrVGlJMb/O/hGVoyPDR41p9pzXLF9KWmoKo8ZPuGV1qlP3XigUCuI3rDNIP7h9C9UaNZ179qlPKy0qJPdSJporOpLtwyJwcHLi4I6tBvfvUtoFUk4lEtWtJ2bN3KeKslI2LlmArb0D3QcNNZnnyO6d1NTUENt/0LVc5g2x/1waOr2eYdGGwWP/iGCsLMzZe+ZCfZqjrTWeTg4G7faZSzkUlVfU5r/invi6OhHm3Y6D59ONnqpfzc7KkvE9oimtrGJbYkMH28bSwmSfKdrfmw5e7iRmZN2y9nxon74oFAoWrDZ8ur1i00aq1GqG9+tfn5ZXWEBqRgZV1xGc3NG3H1VqNcs3bjBIX7B6FWZmZgzt3aeRkrfO/9wUID8/P1JTU6mpqTF4CpCTk0PpVaPRjo61o73FxcaP8a9++09AQAAKhYJEEyMvN5Mm5QLFy9fgNH40Xu+9Rfm+A1j6++E0YQwVR44ZBABuTz2C6s6hZLz0OpVHGqaaWEdHYtO5duTGKqz2TQ9Od4+mpqwMgMK5C+vzFq9ej2rEMNyffQILz3Zo0tKx6xGHff/e5M/5zeBxvE2XaNz/8iRl8fvQXspCX1ODdXgoDsMGoS0qJvebmTfz1hjIKyknISWD2CA/xnWP5nx2Hq4OdsQG+ZGWV0hiRkMAMCAyhE7+3szfdYj0utFsnV7P5uOnGRvXicn9unH0QiaW5mbEBftTodaw69R5g+MFebjSPSSA1NwCyqs0ONpaEx3gjbWFBUv2HaXyqsBn47HTTO4Xy319unLofDqVmmoifDzwdnFk96nzlFbemlEQgIuFJexMTGZAZAhPDOnFyfRLeDqpGBgZwtlLuQYBwJhuUfQMDeSrNds5WzedRKfXs3jvER4d3JO/jhpI/OkUrC0sGBTVgbIqNWsSDEe8O/p6MrRTKKcu5lBSUYWLvS29w9pja2XB9xvjDUb0Q73bMb57NMfTL5FfWo5OpyfA3Zm44ABKK9Us2XvrXm3ZPjiY0ePGs3LZEt77x5vE9exF2oVUVixZTHRMDIOGDqvPO3vm92z8Yx2ffvk1nbt0BcDc3Jz/e/5FPnr3bV55/llGjBpNRUU5S39fhKOTEw89+rjB8fbv3cPvv82na7c4XFxcyM7O5o81qygrLeWdjz7B0cnJIP8/Xv0bnt7eBAQEgkJBwoH9xO/aSfdevXngIcN3Xd9smvOpFC9dhdPEsXh9OJXyPQewDPTHaeJYKg4fpXRjwxQvt6cfRXXXHWQ8/3cqDx+rT7fuHIVNTO1TC6vwunZqwpiGduqXhqkRxSvXobrrDtyffxoLL080qWnY9YrDfkBf8n/+Fe0lw6kMuf/5Ft/pn+Pz5ScULV5OTXEJDkMGYN0xnPyf5qHNbv1UqWsR0D6IO8eMZd2K5fzr3X/StXsPMtLSWLt8KZHRnel3RQDw608/sHXjet779xdEda4dgTY3N+fxvzzL5x++zz9efpGhI0ZSWVHOqqVLUDk6cv8Uw3/3Q/v3suL3RUR3jcXZ2YXcnGw2rVtDWVkZb7z7ASpHwyeeH7z1Oh5eXvj6B6JQwJFDB9kfv5vYHj2ZOOnBm3+D6nj6+dNj8DD2bt7A/GlfEBodQ+6lTPZsWk9gWATRVwQAGxYv4PDuHTz22lSCwmuf0JuZmzNy0sMsnPE1P3z8Dt0GDKaqspL4DWuxc1Ax5O6JBsc7ffQwu/5YTXBkJxxUjhTl53Fwx1aqKsqZ/MLfsHMwPXvg0K5tmFtYENOr7827Gc3ILChm64mzDOkUyl+G9+V42kW8nBwZ0imUU5nZ7DubWp93Qo/O9AkP4tMVm+unb9bo9Py2K4Gn7+jD6+OGsiPpHNaWFgyLDqe0Ss2Kq6atdvL34s6YCE5mZFFSUYWrgy39woOxtbJk2rodlFU1tOfh3u24r09XjqZmkltSRo1OT3sPV3p1CKC0sooFu69vylRrhAQEMOHOESxet5bXPv2E3l1jSc3IYNHaNXSJjDQIAL6dN4+127Yy/d33iY1qeOp1+ORJDifV9gGTkmufTi1etxb7ut+aeGxiw1ugxg4dxuqtW/jq59lcyskh0NeX+IRDbN+3j0cn3oP3VW8Ias2+b5T/uQBgyJAhzJw5k+XLl9cvAgaYNWuWUV57e3vc3d3Zu7f2EfvlSDU9PZ1NmwznsDk5OdG/f3+2b99OfHw8vXv3Nth+ZfkbLfebmVRnZeM46k5se8ahKy6maOkq8mfPg2ZGWgFsu3bG9ZHJBmnO9zX82MuVAQBaLZmvvIXr4w/hMGQASpWK6ouXyPlqBsXLVhvsQ5OWSdWZc9j16o65ixOYmaPNzaN45ToKfl1ETd6tG9UG2HzsNMUVlXQO8CHYw41KjYaE8+nsTDrffGHg9MUcluw9Su+w9gyK7ECNTseF3AK2nTxHWZVhB724oooanY7YID9sLC2oUFdzIa+APadTTE4VyikuZd6Og/SLCKZbsB/mSiX5pRWsSTjJibSWPTK9kRbvO0J+WTl9woKI9POkvErD9sRzrD50kuZrFBxOzaR6YzzDY8K5u3s02hodpy/msOLAcaP5+QVl5Wh1OgZ2DMHWypLyKjWnL+Xwx5EkcorLDPJmF5WSll9ElJ8XKhtrlEoFReWV7DqVzPqjp1o99/96PfP8C3h4ebJu1Ur2792DytGRseMnMuWxx1u07qf/oMFYWlnx29xfmDVjOhYWFsTEduPxp/8PN3d3g7wenp5YWFqwYuliSktKUDk60qVrNx6Y8jB+/sZThSIio9i+dTMb/6gdJfX3D+C5l17mrjFjjaZA3gq5X39X206NGYFtr+7oiksoWryC/B/ntKydio3B9bGHDNKcH2joqF0ZAKDVkvnS67g++QgOQweiVDlQnXmJnC+mU7zU+Ad91GeTSf+/v+L65MM43XM3CktLNBfSyPrwM0rXbbz2i74Gjz3zLO08PNm4djWH9u9DpVJx19i7uf/hR1tUp3r3H4ilpRW/z5/HL7O+w8LCgk4xXZnyxJO4uhnWqXYenphbWLB2+VLKSktxUDkS3aULEyc9iI+J6WehEZHs3r6VrRvWA+DjH8CTz73IHSNH3fI6ddekh3Fyc+fg9i2cPnYYW3sHeg4ZzpC772nRfYqK64m5hSXbVi3jj4W/YmZuQXDHSO64ZxIqZ8Ppj85u7pibm7N34x9Ulpdh6+BAUEQUA0ffjbuXt8n9p509Q+7FTKJ79sHG7s9dcP/b7gTySssZ0DGY6ABvyirVbDlxhuX7j7eoPT94Ph3Nuh2Mio3knl5d0Op0JGVksXjvUYrKDefn55WWU12jY2inUOysLCmrUpOUkc2qhJNkFxkOsmYVlXIht4DoAG9UttaYKZUUllWwLfEcaxISjfZ9s/310cfwateOFRs3EH/oEE4qFfeMuIun7n+gRXXq4Inj/LhooUHa/JUr6v/7yk66hYUF095+l+/n/8rGXTspLi3Fx9OTVx5/kokjDJ9ctnbfN4pCf/W8mP9yxcXFjBs3juzsbO6//35CQkLYv39//Tt+O3TowNy5c+vzz5gxgy+//JK+ffsydOhQcnJyWLBgAT4+Phw/fpzNmzfjWze3Nj09nQceeIDCwkLGjRtHZGQkarWao0eP4uPjw9///vdmz6+1i4DbomtZBNxWXcsi4LboWhYBt1XXsgi4LbqWRcBt0bUsAm6rrmURcFt0LYuA26qmFgH/zz0BcHR05Ndff+WTTz5h+fLl6PV6evTowZw5c3jkkUeM8j/55JOUlpaycuVK9u/fT0hICB9++CEnT540+qU2Pz8/lixZwvTp09mxYwcrVqxApVIRHh7Offe1/A0XQgghhBBC/Fn+5wIAAG9vb77++muj9C1bthilmZub8+qrr/Lqq4avvRw8eDDPP/+8UX4PDw/ee++9G3eyQgghhBBC3EL/k28BEkIIIYQQQpgmAYAQQgghhBBtiAQAQgghhBBCtCESAAghhBBCCNGGSAAghBBCCCFEGyIBgBBCCCGEEG2IBABCCCGEEEK0IRIACCGEEEII0YZIACCEEEIIIUQbIgGAEEIIIYQQbYgEAEIIIYQQQrQhEgAIIYQQQgjRhkgAIIQQQgghRBsiAYAQQgghhBBtiAQAQgghhBBCtCESAAghhBBCCNGGSAAghBBCCCFEGyIBgBBCCCGEEG2IBABCCCGEEEK0IRIACCGEEEII0YZIACCEEEIIIUQbIgGAEEIIIYQQbYgEAEIIIYQQQrQhEgAIIYQQQgjRhkgAIIQQQgghRBsiAYAQQgghhBBtiAQAQgghhBBCtCESAAghhBBCCNGGKPR6vf7PPgkhhBBCCCHErWH+Z59AW5NdUPRnn8Jtz8PFiQ+XrP+zT+O/wlsThlOSefHPPo3bnsrHmwW7Dv3Zp/Ff4f6+saS98OqffRq3Pf+vP6Xo1Jk/+zRue07hoRw4k/Jnn8Z/hbjQ9jzx3YI/+zRuez88cz+Xvp/9Z5/GfwWvpx9tdJtMARJCCCGEEKINkQBACCGEEEKINkQCACGEEEIIIdoQCQCEEEIIIYRoQyQAEEIIIYQQog2RAEAIIYQQQog2RAIAIYQQQggh2hAJAIQQQgghhGhDJAAQQgghhBCiDZEAQAghhBBCiDZEAgAhhBBCCCHaEAkAhBBCCCGEaEMkABBCCCGEEKINkQBACCGEEEKINkQCACGEEEIIIdoQCQCEEEIIIYRoQyQAEEIIIYQQog2RAEAIIYQQQog2RAIAIYQQQggh2hAJAIQQQgghhGhDJAAQQgghhBCiDZEAQAghhBBCiDZEAgAhhBBCCCHaEAkAhBBCCCGEaEMkABBCCCGEEKINkQBACCGEEEKINkQCACGEEEIIIdoQ8z/7BETzdDodixcuZOXyZWRlXcLRyYlBQ4by+JNPYWNj02TZ0pIS/li3lj3xu7mQmkpxUTEenh7EdOnClEcfx8PDw6hMdlYWc36eTcLBg+Tm5qJSqQgNC+P+yQ8S06VLfb7DCYd48dm/NHn86d/NpFPnztd24dege0gAXYJ8cbK1oUKtITEjmx2J56iuqWmynLWFOZ0CvAnxdMfNwQ4bK0tKKqpIyytgZ9J5SiurDPI/2D+OAHeXRvd3PjuP33YdAsDfzZmHBnRv8vi/bNtHRn5RC6/y+ul0OhYsWcLS1au4lJWFk5MTQwcO5JlHHm22TpWUlrJmw3p2791HStoFiouL8WjnQdfO0Tz+0BQ827UzKpOVnc1Pv87jQEICuXl5qBxUhHXowEP33UfXq+qHVqtl7sIFrN24kcxLl7C1saFr58785fEnCPT3v6H3oTk6nY69m/7g0PbNFOXlYevgQGRcTwaPm4illXWTZSvLyzgSv5Ozx46QeymTirJSHF3cCAwLZ8Do8Ti6uBqVKcrPY8ea5aQknaSksAAbO3u8AgLpM3wUgWERRvnPHDvMjtXLyUpPw9zcnPYRkdxxzySc3Y3/DW46hQKHAX2x79MDcxdnasrKqTh8jOK169Frqpsuq1TiPHEcVgG+mDk7o7S2oqa4BPWFdEo2baU64w4CFbUAACAASURBVKLx4aytcRo5HJvOUZjZ2VKdl0/ZznjKdu01eQjrjuE4Dh+Mhbc3eq0W9ZlzFK5YQ01B4Y24+hbT6XQsXLWSZev/4FJODk4qR4b27ctTkyZjY910nSopK2Pt1i3sPniA1PQMiktL8HBzp0tUFI/fex8e7u5GZU4ln+OHBQs4mpRIVVUVvl5ejBl2B/eOHIWZmZlR/t0HDzJ70ULOpqZgaWFBt+jOPP/II3h7eN6we9ASOp2O9SuXs+WPteTlZOPg6EiPvv2ZMHkK1s3cp/KyUnZu2cyRA/u5mJFGaUkJru7uRERFM+6+SbiauE95OTmsWPQbJ48doTA/H3t7BwKDQxg5fiLhUZ2aPN7Xn3zI/t078fUP4JPp31/XdV8LBTA0OpT+ESG4OdhRWqXmYHIayw8cR6Nt+rvP1tKCXmHtifb3xstZhb21JQVlFZy+mMvqQycpLK8wyP/3MYMJ8268fUnMyOKL1dsa3f70sN7EBfuTWVDE24v+aM1lXjedXs+ShAOsPHaErJJinGxsGRQWzqO9+2FjYdlk2dKqKtYnHmdvSjIX8vMprqzEQ6Wis68fU3r2oZ2DyqjMmewsftm7i+OZGVRWV+Pj5MzITp0ZHxOLmdJ4/F2r07HiSAJ/JB4nvaAAM6USbycnRkfHMCa6i1H+62X2zjvvvHPD99qIffv2MWTIEHx8fIiIMP4yawvKr+pItsTXX37BLz/9SHRMFybeey/Ozi4s+X0Rx48fY/idI1AoFI2WPZKQwMcfvI+3jw+Dhw5j4KDB2Nvbs3b1alatWE6ffv1wcnauz5+Xm8vjj0zh/Plkho+4izvuHIF/gD9798SzdPHvhIWH41fXCbOysiKkQwf6Dxxo8Nezdx/2xO/GycmJZ198CaWJit4UextrdiYlt/o+DescTv+OIaTlFXLgXBoVGg1xIf74uTpxPM24A3Elf3cXxnSLorCikqSMbE5lZqOu1tI5wJeu7X05eymHiis6MaWVas5n53E6M9vgz0ypwNXBjv3nLnCxsBgAbY2O7KJSo7znsnIJ8XKnXK1h87HT6Ft9xdC/Ywjq0tJWl/t8+jf8MHcOXaI7c9/48bg4O7Fw2TKOnTjJXcOGNVmnDh09ynv/+gQfby+GDRrMkAH9sbezZ9Uff7Bs9Sr69+6Ns5NTff7cvDwefPopklNSuGvYHdw1bBgB/n7E79/HouXLiAgNxd/XDwC9Xs/Lb73F0tWr6BwVxcSxYwlpH8TWnTtZumql0b5bykrlwIm0S60ut+63OWxftYyA0Ah6DBmOvUrFvi0bSD93huhefZu8T6mnk1j+03c4u7cjKq4XHbv1wNrWliO7t3No+xbCYrpid8WXRklhId+9+yY5mel07tWP6F59cfP04tzxo+zbvB7vgCBcPb3q8yce2s+C6f/Bxs6efneNwSuwPScP7uXIru1Ede+NVTOBXGOi/L0pXrex1eWcx4/BccQw1MnnKd2+G11ZGQ4D+mAVFEj5gYQmyyoszFHdMQRNSiqVJxKpOHqCmoJCbKIiUA3sh/p8qmFH3cwMjxeexiaqI+X7DlC+PwEzWxtUQwYCoD533mD/NtFRuD8xBV1FBSWbtlGdkYltl87Y9Yyj4tAR9Gp1q6/XccQwqvLyW13uix9m8ePCBXSJjOLe0aNxcXRk0ZrVHEtKYsTAQU3WqYQTx3n/qy/x8fBkWL9+DO7dB3tbW1Zv3sTyDevpF9cdZ0fH+vyHT57gL2+9SVFJCfeOHMXAnr0oKy9n4aqV5BUW0q+74cDE1j3xvPrxhzg6qJgyYSLhISFs2rWL1Zs3c0e//tjZ2rb6eq3dXLl4DYMbc2d9x/IF8wmPimL46HGoHJ3YuHolZ5MS6TNoSJP3Ken4cWZ++RntPD3p2W8A3fv0xdbWnh2bNrB1/Vq6du+JyrGhHSnMz+cfLz1H+oVU+g4aSt9Bg/Hy9eXowQNsWLOS9iEhePn4mjzW4f37WDJ/LpYWltjZ2zN05OhWX+tlPq7OrDx4otXl7u/ThTHdOnH2Ug6bT5yhtLKKwVGhhHi5s/dMapNlQ73b8digHuSWlnEgOY1D5zOo1FTTJ6w9AzoGc+RCJmVVDZ+PovIKTqZnkZCSYfBnplTi6aRi07HTpOQUmDxWtL834+Ki0GhrqNBo2HbyXKuvFWBMtyjKDh1pdblvtm3il73xdPb1Y0KXbjjb2rH0yCFOXMzgjo5RTfel0tP4ZP0avB2dGRQWwYDQMOysrFh38jirjh+hT3AHnK74fBzNSOOl3+dTXFnJ3TGx9A8Jo0xdxZLDBykoL6N3cAeD/VfX1PDm8sWsPnGUbgGBjOzUmS5+AdhaWlJVrSXWP7DV1wvg0K3xwEGeANzmUs6fZ+nvv9N/4EA++Phf9ele3t589cXnbN64kWHDhzda3j8wgHkLFuHja9h49erdh5dffJ4fZ83k/Y8+qU//Y+0aiouK+PBfn9Kv/4D69CHD7mDSvRNZtXIFvfr0BcDFxZU77hxhdMxNG9aj0+kYPuIuzM1vTRVzc7AjLtifU5nZLNnb0DAUlVcyPCaCSD8vTqY33gHMLy1jxoZdFJVXGqSfy8plcr84+ncMYem+o/XpKTmmv/j7RAShrakx6GyWqzWcMHHsjr6eKBUKjqddRKe/lu7/tUlOSWHRsmUM6tePT999rz7d29OLz76ZxoatW7hzyNBGywf6+7P4lzn4+vgYpPfp2ZPn/v43vv95Nv9659369NUb1lNUXMxn77/PgLq6AzB88BDGP/Qgy9esoW/PXgBs372b+P37uHvUKN58+ZX6vCOGDeP+xx/js2+m8e1nn1/3PWiJnMwM9m/ZQETXOO5/9q/16U7u7Vg3/xdO7N9DdM8+jZZ38/Lm+Q8/x6Wd4VO20OgY5nz+MVuXL+a+v7xUn34kfgcVZaU88NzLhHfpVp/eqXtvvn7zZQ7t2EJo59rGvEarZe38X1A5u/LY629jVTci2qFTZ75/7y22rVzMmIefvCH3oSUsPD2w79+biiPHyftpbn26Nr8Al4njsO3amYomvrD1mmqyP/vaKL1091583n0T1eAB5J5tGBSw79UdqwB/ChYvp2xHPADle/bj9thDqIYNomzvAWoK6zqdSiXOE8dSU1RM9pcz0Gs0AFQmnsLz7y/iOGIYBQuX3Ijb0KzzaRf4fc1qBvbqxb9ef7M+3dvDg89nzWTjzh0MHzCw0fIBPr4s+vY7fL28DNL7dIvj+benMnP+r3zy+hv16Z/PmolCoeDHT/+Nj2ftCP7Eu0by8bffsHz9eu4aNIiYjpFA7ZO3z2d+j4ebG99//Am2dQFk766xPPzKX5m14DfefPa5G3UrmpRxIZWNq1cS16sPL745tT69nYcnc2bOYO+O7fQeOKjR8t6+vvz7ux/w8PI2SI+Ji+OTqW+y+Ne5vPjGP+rTd27ZSGlJMX99621i69oigF79B/G3px9j2/o/6BLXw+g4VZWV/DzjG4bdNYqE/aafPN1s3s4qBkeFcuh8OjM27K5PzystZ1LfWOJCAth/7kKj5bOKSvnHgrXklpQZpB+7cJFXRg9ibLdOfLexYb+JGdkm9zOyayTV2hr2njV9LCtzcyb3i2XryXN0DvQxmedmSsnLZenhQ/QPCeW9MePr070cHfl66ya2nEpkaERko+X9XVyY++hT+Dg5G6T3bB/C35Ys4Kf4nbw3+u769K+3bkKpUDD9/il41w1ajYvpyucb/2DV8SPc0TGKaB+/+vxz9u7mUFoqn0+4ny7+ATfqspt0S9cAxMXFcezYMcaOHXsrD/tfbdPGDej1eu65736D9FFjxmJtbc2G9euaLO/l5W3U+Qfo1r07KpWKlGTDkbLy8nIA3NwMH5G6uLqiVCqxtm5+VHH1ypX153irRPp5oVAo2H821SD9cEoGGq2WKH8v0wXrFFdUGXX+AVJzCqhQa3B3tG/2HPxcnXBzsOf0xRyqqpuZ8gDEtK/9dzmSktFs3htpw5Yt6PV6Hpgw0SB93KhRWFtbs27jpibLe3t6GnX+AXrExuKoUpGckmKQXl73CNnN1c0g3dXFpa5ONTzOP3jkMACjrwosfb296dKpEwcSEsjKNv0FdKMd3xePXq+n1zDDc4ntPwgLSyuO7d3VZHlnN3ejzj9AcMdO2NjZk5OZbpCurqqtfw5XfcHYOzqhUCiwsLKqT0s9k0RpUSFd+w+s7/wDePkHEhjWkRMH9lKj1bbsQm8A29gYFEolpdt2GqSXxe9Hp9Zg163rNe1XV1qGvlqL0taw3bGNjUGn1lAWv98gvXTbThTm5th2bZhWZhUShLmTI2V79td3/gGqMy+hPpuMbddoaOVTymu1YccO9Ho99482bBvH3jEcaysr1m3f1mR5bw8Po84/QPeYGFQODpxPa+h8lZSVcTYlhS6RUfWd/8tGDa4N8Fdv3lyflnDiBLkFBYwZdkd95x8gNCiIrlFRbNq1E+0tqlN7dmxDr9czfOzdBukDh4/AysqK3du2NFne3cPTqPMPEBXTFXsHBzIupBqkV1bUtlFOLobTOp2cnVEolQafsSstmvszNboaJj70cHOXdNN0DwlAqVCw6dgZg/QdScmoq7X0Cm26M5lfWm7U+QdIysymrEqNj4ujiVKGOni64+WsIiElg3K1xmSeu3t0wkypZNn+Y83u72bYfDoJPTCxa5xB+shOMVibW7Ax6WST5b0cnYw6/wDdAgJRWVuTkpdbn1ZaVUVybg7RPn71nf/L7oysnU72x4nj9WmV1RqWHD5I3+AOdPEPQK/XU6Fp/VPJ1rqlAYBSqcTKysrkvENh2qmkRJRKJREdDSPT2uk3oZxKSrqm/ZaVlVFRUYHzVQ1e9549AfjPZ59yJCGB3JwckhITee+fU7GxseG+SZOa3O/Fixc5nHCI6M6d8Q+4NVEsgJeLIzq9vn7azWU1utrpN17OzTdipliZm2NlYU55lelG7UqdA1veoXe0tSHQ3YW0vEIKyiqazX8jJZ4+hVKpJDI83CDdytKS0OBgEk+fuqb9lpWVUV5RgYuzYSPZK662wf3XV19y6OgRcnJzOXnqFG+9/z42NjY8eM+99Xmr6wIn6ys6u5dZ1825P3GNdb61MlOTUSgU+LQPNki3sLDE0z+AzJTzjZRsWlVFBZqqSuxUhnUyJDIagNXzZpN6OomSwgIyU5JZPHMaltbW9B4+suHc6o7td9VjZADf4BDUlZXkZ2dd0/ldC0t/X/Q6Heo0w6AGrZbqzItYBpiePmFEoUBpZ4vSwR5Lf19cH56E0tqKysRTBnks/XzQZGTCVR1SdVo6ep0OK/+GkTWrgNr/VqcYj0yqU9NQ2thg0c54TvjNkHj2bO1nLzTUIN3K0pLQ9kEknT17TfstKy+norISlys6G019lqzq0k5c8VlPPFd77E5h4Ub5o0LDKK+oIO1i5jWdX2udP3sGhVJJ8FX3ydLSEv+gYM6fPdNIyaZVlJdTWVmJ41UduU5dYwH4ecY3JB0/RkF+HslnTjP9359gbW3NiHHjjfaVfOY0G9es4sEnnsHW1u6azudGCGzngk6nM3oqra3RkZ5fSGATa9WaYmNpgbWFOSUtmLbcNyIIgJ2nTLeJ7du5MDiyAwviE6iqvnUDE1c6nXUJpUJBuKdhAG1lbk5Iu3acym79FFGAMnUVFRoNLlfUAU1N7TVaW1gY5beyqJ0VkZjVMC35WEYGFRoNoR6eTNu6kbu++Q93ffMfxs74ilm7tqPV6a7p3JpzS6cA7du3jylTpvDxxx8zfvx4g//X6/X89NNPXLhwAXd3dyZNmsSTTxo+wk5ISODbb78lKSmJkpISnJycCA8P59lnnyUmJgaAadOm8c0337B69WoWLlzIunXrKC0tJSwsjJdffplevXoZnVd8fDw//PADx44dQ61WExgYyKRJk3jggQeM8iYmJvLdd99x8OBBSkpKcHV1JTY2lpdeegn/m7BAMS83D0dHRywtjReouLu7c+L4Maqrq7EwUdGaMmf2T2i1Wu686y6D9C5dY/nr3/7OT7Nm8sKz/1ef7uvnx4wffiQwsH2T+127aiV6vZ6Ro2/tUx4Haysq1BpqdMZTaUqr1Pi5OaNUKFo91aZPRBBmSiXHLzS9hsDS3IwIXw8KyytIzTU9//FKMYE+KBSKWz76D5Cbn49TI3WqnZsbx06evKY69eO8eWi1WkZdNSUtNiaGV198ke9nz+aZvzZMpfH39WX2N9Npf0WgGBQQCMCBw4fpENzQ8a6qquLEqdqOf3ZuTqvO61qVFhVh6+CAuYn7oHJyJv3cGbRabaunuW1fvYyamhpievc3SG8f3pGRkx9l64rfmf3p+/Xprh6ePPnme7h7Nzx1KS0qrDsP4y/3y2klhQW0a2Te8o1m5qhCV1YOJhYcaouLsQoKBDMzaGYxvoVnO7zeaJj6pauopHjDFko2bq1PU9rYoLS0pKa4xHgH2hp05RWYOTWsrTBT1f63qfyX08ycVFRn3fwnS3mFBTg6qLA0UafcXV04dirpmj57Py1aiFar5a7BQ+rTXJyccFKpOHH6NFVqtUEgcOh47Shsdl5ew7kVFNSdh/Hi9MtpOfn5BN2C6QmFBQU4qFRYmFiY6eziytmkRLTV1SY/m01ZvnA+NVot/a6a4tixU2cefuZZlvw6lw/ffLU+3dPbh3c++xIfP8Pv9pqaGn6c9iWdYrrSs5/h5/hWc7K1oaxKY7KTWFheSYinO2ZKJTWt7ESO7NoRczMz4k+nNJnP2sKcbkF+5JaUcSrT+DOkVCiY0j+OkxlZHExON7GHWyOvrAxHGxssTbTXbvYOnLiYSXVNDRatHKCeuzcerU7H8Mio+jQXWzscbWxIvHQRdXU1VlfU08PpaQDklDa0R+mFtcHb4oSDmJuZ8Uz/gaisbdh06iS/7t9DXlkpb9w5qlXn1RK3xRqABQsWkJeXx8SJE1GpVKxcuZLPPvsMT09PRo+uXVBz/vx5HnvsMdzc3JgyZQqurq7k5eWRkJDAqVOn6gOAy1577TWUSiVPPvkkZWVlLFy4kCeeeIJZs2bRu3fv+nwLFy7k7bffJiYmhmeeeQYbGxvi4+N55513SEtL47XXXqvPu3XrVp5//nlsbW2ZOHEiAQEB5ObmsmvXLs6cOXNTAgC1ugoLEx01oL4DV1VV1aovjG1bNrPwt/l079GTu0YZL1hycnImLDyC2Lg4/Pz9SU9LY8Gv83jtlZf5+tvvTL45CGobxXVr12BnZ8egIUNM5rlZLMzMGm3gtHWdDgtzM9StGH0I9/GgZ4dAkrPyOHqh6ZGvSD8vLM3Nm20sofaNDdEB3lRVV5OUeetGaS+rqlI3Wl/q65S68TymbN6+nV9/X0TPuDij6TsAzo5ORISF0b1rLP6+vqRlZDBv0UJeevMNvv/Pl/VvDhoxbBg//TqPmT/Pxsbamu6xsRQVFzPz558pKi6uP/9boVqjxtzc9D243PGozdPyZvTkwX3s2bCWkKhouvQdYLTdzsEB78AggiKicPX0Ij/rErvXr+bXrz7l0df+Wf/moOq6qSxmFsbHvvLcbhWFpSX6xqaH1H3mFJYW6CubDgC0+QVkfzMThbk55m6u2MV1QWljjcLcDL1GV7+f2symj6evrkZxRcfxcn5T56fX1o6SK5p5A8iNUqVWY2ni3wzA0uIaP3u7dzN/xXJ6dunK6Cs6tgqFggfGjGXGvLm8/slHPDVpMk4qFfuPHmXWb/MxMzNDfcXi56q6/zYVnFxuF9TXsFj6WmjUaiwa+exdeS6tCQD2797JuuVL6dQ1lv5D7zDarnJ0pH2HDkR17oKnjw9ZmZmsWbaYz979J//4+N8Gbw5as/R3si5e5KW3/tnKK7vxLM3NG33LXXVdQG5pbkalpuUBQGyQL3d0DudE2iV2N/Od1j0kACsLc3YdNj36PzwmnHaODkxf3/SUyZtNra3GwqyRz15dp7+qurpVAcC2M6dYdGg/cQHtGVH3BBdqP3v3dI3jh907mLpqGY/17oejjQ2HLqTyc/wuzJRKg2nCFXXteUlVJbMffoKAunZ+UFgELy2az/rEEzwQ15PAq6bRXq/bIgC4ePEia9euRVU3UjNhwgQGDRrEvHnz6gOAXbt2UVlZyRdffEF0dHRTuwPAzMyMX3/9tb6xmDhxIiNGjOD9999n3braefM5OTl88MEHjBw5ks8/b1hYOHnyZD744AN+/vlnHnjgAfz9/amsrOSNN97AwcGB5cuXG3SCn3vuOXQ36RGNlZU1lRWmR5Q1dZWmuVeiXWlP/G7ef+dtwsLDeffDD41Wva9asZwv/v0pP/4yl6ArRmC79+jJE49MYeaMb5l6xQLPK+3ft5fcnBzGjLu7Ved0I1TX1GBrbvpL3LzuA13dzOvQrhTs6cbYuGguFZawdF/zbxuICfRFp9NxLLX5R+RBnm6obG1IOJ+Otubm1JumWFtbUVhkvN4BrqhTJqYNNGb33r1M/ehDwkND+fifbxvVqWWrV/Ovr75k3sxZhLRveILUKy6OB59+iuk/zOL9N98CQOXgwPR/f8bbn3zMR180fCa7REcz5f4H+GneXOzsWv8mkmthYWlFeWmxyW3ausbbwrLl9+nMscMsmTUdr4D23PPMC0b36eD2Laz5dTbP/PMjPHwbprCEREXz3XtvsWnJAiY8+WzdcWvreo2JgPZazu166TUalPaNrJOp6/A2+yrQujzqMw1vBinfewDPV1/E7fEp5M740XA/jQReCgsL9NUN/26X8ytM5FfUdTL11c1P8bsRrK2sKGhkSoWm+ho+ewcP8vYXnxEeHMyHr75mVKemTJhIlVrN/BXLefRvtU9WbK1tePHxx/lu3lxqrug4Xj6uxsT6pcvtglUrzu16WFpZUVLVdBvVmnM5cnA/3372KYHBITz/2ptG92nr+nX8POMbPvhqOn51TyEBorvG8o+XnmPhnJ/4yyu1g4FZFy+ybMF8xt37AO08m15bditotFpUNqa/by3MzerytPy7r5O/F08M6cWF3AKDxb+N6RcRRI1OZzJQaKeyZ3RsJGsSEskrLW/xOdwMVuYWVFaaPgdN3efA1JSdxuw9n8yH61YR6uHJO6PGGdWpSd17UaXVsujgfp6Z/wsANhaWPDtgMD/s3mEwYGlV1zZ19PKp7/xfNrxjFEcy0jiakfa/GQBMmDChvvMPYGNjQ0xMDIcPH65Pc3BwAGDz5s2EhYU1++F/5JFHDKY4XH6asHDhQpKTkwkODmb9+vVoNBomTpxIQYFhJ3vw4MHMnTuXPXv24O/vz65duygsLOSVV14xOQLe2lddtpSbuxsXUlPQaDRGUzZyc3NxdHJq8WjRvj17mPrG6wS2D+LzL7/Gzs74C3venF/wDwg06PwDBIeE4B8QyJHDjb/Ob82qW7/497LSKjVuKnvMlAqjaUAO1laUqzUtnv4T5OHGxJ4x5JWU8duug802nu4qe7xdHDl7KYfSFoxOx7RircDN4O7qSsqFCybrVE5eHk6Oji2uU/H79/Pq2/8kKCCQbz79N/Z2xnNhf/5tPoH+/gadf4CQoCAC/f1JOHrUKP3XmbNIz8wkNy8Pdzc3/Hx8+Pr77wAI9Ls1vwXg4ORE7sUMk1MNSooKsbV3aPHo/9njR1k4/Uvaefsy5eXXsbYxDmJ2rV2Bm6e3QecfwMPXHzdPb1JPN6x9uLxQuKSowGBq0OU0AJXztc39vRY1xSVYeHqAuZnRNCBzR0dqysqanf5jil6joeLoCRyHDcLczQVtXgG6ykp0Gg1mjsbv3cbcDKWdLTXnGh6v15TUTfNxVKHNNpw+dnkfNUUmphPdBG7OLqSkp6OprjYaac/NL8BJpWrxZ29PwiFe/+Qjgvz9+frd97E38YpOpVLJMw8+xMMT7+HchVTQQ4f2gej18Mm304kKDWs4t7r1YLn5+bT3M6yDufm1UxTamZgedDM4u7iQmZ5GdbXGaBpQYUE+DirHFo/+Hz10kK8+eh8ff39ef/8jk/P1V/6+EC9fP4POP4BfYHu8fP04dcWCzfk/zcTe3oFuvXqTdbFhamhNjQ6tVkvWxYtYWVvhbOJ3Pm6GoopKvJ1VmCuVRtOAnO1sKK2savH0n0g/T/5yR18uFhTzn9Xbmp2v7+PiSPt2rhy9kGnyJRr39IqhXK0hISWDdqqG/oZSocBMqaSdyh61VktxRetfj95abvb2XCjIQ6PVGk0DyisrxdHGpsWj//tSzjN11VICXd34bML92JnojyoVCp7o05/J3XtyPrd2gXCwezv06Pl80x909Gpot93rXgftYuL706Wun1ZadePv0W3xS8C+Jt5S4+TkRFFRw7uDR44cSe/evfnuu+/o3r07U6ZMYebMmWRmmh5xDb6qA3tlWnp67Ty05OTa18o98sgj9OrVy+Dv0UcfBSCvbo5kamoqAB07drzGq7w24REd0el0JCUarlBXq9WcO3uG8PCW/Z7C/r17eev11/APCOA/X0/DQWXiy5Pa3wHQ6Ux/UdfU1BiMGF2psKCA+F27CA4JIfxP+I2HSwXFKBUKvK9a7GumVOLh5EBWoemR3KsFebgysVcM+aXl/LrzYIsWLNW/zacFo/+2VpZ08HInu6iUS7eo03G1jmHh6HQ6Tp4yXOyr1mg4k5xMRFhYIyUN7Tmwn1f/OZUAf3+mf/YZqrog/Wq5eXmNPiFrqk75+fjQtXNn/OreOBS/fz92dnZ0jooymf9G8wkMRq/Xk5li+JsU1dUastIu4B0Y1KL9nDtxlAXTv8DNy5spr7yJjYnAG2qDCn0j90mnqzG4hz7ta4+dnmy8aDQj+RxWNja43sIfbtKkZdS+LcXfsOOIuTkWPt5o0q492FXWdfSUlzu4ej2a9EwsfX1qA44rWPn7oVAqUac3HE99oba9t2pvPHfdKtAfXWUl1Tm5Rttuho4dOtR+9s4YuKzfHgAAIABJREFULmJVazScSTlPREhIi/azNyGB1z7+iABfX6a99wGqxp6+1LGxtqZTWDidwsOxtrIm/tAh9Ho9vbs1vG62Y0jtgvLjJl4CcOLMaexsbfH3vjWvbwzqEIpepyP5qvuk0WhIO59M+xDjxe+mHEs4yJcfvYeXrx9vfPAJdvam26jC/PxG2yjdVW1UXk4OhQX5vPbs0/zt6cfq/wrz88i6mMnfnn6MH6d91cIrvX6pOQUolUratzMMOMzNlPi5OrdoTRpApK8nzw7vy6WiEr5Yvc3gd28a0+/y4t8k09N/XB3scLaz5f377uKjSaPq/1zsbfF0UvHRpFFMGRBnsuyNFubphU6v51SW4WJftVbLuZwcwjxa9jRnf+p5pq5cir+LK59PuB+HZmY72FhYEuntQ6S3D9YWFuxLOY8e6NG+4fsjou5JUq6J3/TJLatNc7oJC81viwCgJW8FsrS0ZPbs2fz+++889dRTmJmZ8fXXXzNixAg2bmzZj9borxoBvvz///rXv5g9e7bJvzFjxhjkbeqHIm6GwUOGolAo+H3hAoP01StXUFVVZfAbAHl5eVxITaXqqkhx/769vPnaq/j5+/GfadNROTb+RpzAwPakp6Vx8ooRD4ATx4+TkZ5GeITpAOiPdWtrF4D+CaP/UPvrg3q9nu4dAg3Su7T3xdLc3OC9/PbWlrg62GFuZlj927dzZWKvLhSUVtR1/ptvAM2UCqL8vCirUv8/e/cdV3X1P3D8xRBU9p4yVVBxACoKbnHPTG2opZllmanlzpW5K3NrmfotrdwT916ACzcOlCUKiaLsIeP3B0Mu9wJqjvrd9/Px8NGjcz+fzz33cO655/35nEFYbPmdiNoOtmhpanIh8s3c/Qdo0zJ/s6G/Nm1USN8aEEBGRobCHgAPHj4kMjpaqU4FnznDqIkTcbC3Z8kPP2JUSkAJ4OzoSNSdO1wODVVIv3T1KtExMdRUsepISes2b+Z2RATvv92z3J2KXxaPho3Q0NAgaL/iUrvnjh3mSVamwh4AyY8fER97l6wSY6RvXbnEX4vmYmZlw4cjx1O5jI6ahY0dD+LuKXXq79y6ycO4WOyKBRxO1WtgYGRMyLEjZBb728TdiSLyRii16vug9Zr24ABIC7lIXm4uBi2aKqTr+zZEU1eH1LNPn+ZqGhqgbWmBRrE7uJr6eqCibdU00KeSZx1yMzJ5Evt0gmFayAU0dXXQ91Vcm92gRVPycnJIC3n6VCnzVjjZiUnoN26IRrEnXhVsbdCt5krahcvwioZwluTfpCkaGhqs3bFNIX3bvr1kZGYq7AHwICGByJg7ZGSW+O6dD2H0zOlUsbVl8dRpGJUSeJcmMSmJpWt+x9jQkB7t2xele3l4YG5iyvb9+0hLf3o392ZEBCFXrtDa1++17evSqGlzNDQ02Ltti0L6kb27yczMVNgD4FHCQ+7duaPwPQC4HHKOn6ZPxcbWjnHTZqFfRjnZOTgQezeGW9cVVxgLux5K7L27uFR7uhrR+x99zJdjv1H6Z2hkhJm5BV+O/Yauvd75Jx//uZy5HU1uXh7+dRRXTGpWwxXdCtqcKrYuv1HlilgbG6BTInCuaW/NkPZN+PtxMj/uOFzqUp7FaWtq4lPVicS0dC6VskjGhqALLN13QulfUnoGD5NTWbrvBLvPv55V3VpVd0cD2BhyRiF95+ULZGQ/wd/9ad/mYUoKUQkPlfoAZyIjmLBtM1VMTJjb8z0Mn/O3KDE9nV9PHMWoUiW61n26QZeNkTEetvZcj7vHzWKrt+Xk5rLz8gW0NDVpUOLp1MvwrxgC9Dzq1KlTNAcgNjaW7t27M2/ePNq0aaNw3O3bt3EvscxheHjB0nkFjzednJwAMDExUZgYrIqLS/6Pb2hoKH5+pW/+87K5Vq3KW2/3ZPPGDXwzdgyNfX2JjIxk0/p11PP0wr/t0wDgl6VL2LNrJ/MXL8GzYFmz69euMX70aCCPDp06cyooUOk9im/mNWDQICaMHcNXw76kW/e3sK9ShZg7d9i2ZTPa2hUYMHCgynzuCtiBjo4ubdu1V/n6qxaflMLZ29E0qOrI243qcTsuHjMDfRpUdSAqPkFhI64WtapT18mO1UdPE/0gfzUVG2NDevl6ogFcjLqLq5XyWDtVm3lVt7Wisq4OgTcilAJMVeo62fEkJ4cr5exM/CpVdXGhV7furN+6hVGTJuHn40NEdBTrNm/Gq25d2hebwL3o1+Xs3LuXZXN/wrtgon3ojRuMnDiBvLw8OrfvQODp00rv0bHY9/GTD/szevIkvhg1kh5dulLF3o47MXfZtH0bFbS1GfThBwrnDhs7FjsbG5ydHNFAg1Nnz3Lk5AmaNGrER337vqJSUWZl70CDlm04fWgfaxf/RLXa9YiPvcupg3txcqtBbZ+nbcaBTeu4EHiM/qMm4FzwQ3I3Mpy/Fv0IeeDZpDlhly8qvUfdxk83RmvZvSdrF83l9x9nUr9Fa8ysrHn4dxxnjxxAS1ubFsU2r9HS1qbDex+w4eeFrJz1Ld7NWpGZkU7Q/l3oGRjSsltPpfd6lZ7ExpFyPAiD5n6YD+xHeugNKlhZYtDcj4yw2wqbgBl36YC+T33+XrCsaMdevfqeGDRvQtqlq2Q/TICcHLQtzdFr6I1mpUokrN1IXrEf45TA0+j5NMDkrS5om5ry5O/7VKrpRuW6tUncc0Bx1+DcXB5t2oZ5/z5YDf+MlMDTaFbUxaBFU3JTUkncte+1lVNVJyd6duzEhp0BjJk5A19vbyJjYlgXsAMvDw/aFdt8ccnq39h56BBLps3Au3b+2uHXwsIYPWN6/nevtT+BIeeU3qNDsc7xybNnWbNlMz716mFqYkLc/fts37+PpJQUfvhmIsbFlqLV1tbmq0GD+Ob7OXw6bizd2rYlNS2Nv7Zvx9jQkEHv93mFJaOoipMz/p26sD9gO/NmTKWudwPuxdxh345tuHvUxrf508+4/rdVHD90gPEzZlOzdv7+D+FhN5k7/VvIy6OZf1sunjuj9B5NWj5t53q835d5M75j1qTxtGrfEWtbO+Lu3eXg7p1oa2vT472n7Y5HPdV7Wvy5cjkVK1aioV9Tla+/KncTEjl8JYzWtavzeVs/LkfHYlOwOdiNe/cVAoAePnXxc3Pm++2HuHEvfzico4UJX7RvggYanLwRQW0Ve+ao2tzL09kOg0q67D5/rdThtddUrAoE0KuxJ5lPnnAu/PXdCHOxsKR7PS+2XAhh4vbN+Di7Ep3wgE3nz1HXvorCJmC/nDjC3tAr/NTrPTyr5D85vB4XyzfbNpFHHu096nCqxJNhgLY1nz6dDg6/zdqzp6jv6ISpnh5/JyWx8/JFkjMzmN6tJ8YlhoEOa+XP0HV/8PXGtfTw9MaoUiUO3bjOtbhYPmzkh5Xhiy1lXpb/TACQkJCAaYk1662trTE1NSUxUXl4x//+9z/atGlTNMY5Li6OHTt24OzsXDQUqEOHDsydO5eFCxfi4+OjNHE1OTkZXV1ddHR08PPzw8TEhFWrVtGtWzcsC1YtKZSXl/fKng4MHT4CaxsbdmzbSnDgSYyMjHm7V28+GvRJuXMPIsJvk1WwGsii+fNUHlM8AGjStBk/zl/I2j/WsCtgB6mpqegbGNDAx4cPBwykWol1mQEuX7pEVGQk/m3blTq06HXYf/E6iWnpeDpXoaq1BelZWZy9Hc3RZ9hu3MJIv2j8X9u6qu9IqwoA6hXsaHjxGe7o25kaY2Goz5Xoe29sLeRCXw0Zgo21NVsCAjh5KhhjQyPeeestPh3wUbl16nZEBJkFE/F+WrJY5THFA4Dmfn4smvM9q9evY8ee3aSkpGBgYECjBg0Y2O8D3EoMe6hdqyb7Dx8mYO8eAJwcHRk9bBg9Ond57XuIdHjvA4zNLTh39BA3L52nsr4BPq3a0rJ7r3LL6X7MnaIJuXvWrlZ5TPEAwL2eNx98PZ6TewI4f+IomelpVKysh2utOjTv8hY2JbaCr9WgEdo6OhwL2Mq+DX+gpa2NSw0P2vR877WO/y/0aPN2shMeoe/rQ6VaNchJSSX52EkSd+6DcoLjjNsR6DhUoZJHDbQMDdDQ0iInOYWMG7dIPnqCrJJr+OfkcH/xLxh3akdl73po6VUm+8FDEjZsJeW48k2O9AuXiV/+G0btWmHcvRNkZ5Nx8xaPt+9SvZzoKzRi4MfYWFqyde9eTp49g7GhIb07deaT9/uU/92Ljir67s1b8avKY4oHADaWluhUqMC6gB0kpaRgbGBI/bp1+KjXOziqGHrb2q8Jujo6rFy/ngWrVqJToQL169Tliw/7v7bx/4X6ffwpFpZWHNq7iwtnzmBgaEibzl3p2eeDcsspJiqyaKWsNb/+rPKY4gGAt09jxk6dwc7NGzl2YB9pqano6RtQx9Ob7u++j6OL8pDif5O1ged5mJxKs5qu1Ha0JSUjk0NXbrLtzBXKuy1lZ2pcNCb+XT/VwY2qAKCJe36ZnChl7f9/oy9a+GNtaETA5YsER9zGqGIletTzZoBvUzTL6b9FPIwvWt9/8ZGDKo8pHgBYGxlRQUuLTefPkZyRjlGlyng5ONLPxxcHFfNDqllas/jdfqw4eYyN58+SlZ2No6kZY9p1VFhh6GXSyHuW25YvSVn7APToobjRxtixY9myZQs3btwAYPr06Zw8eZIWLVpgb29PXl4ehw8f5vjx43z88ceMGjUKeLoPQK1atdDS0qJTp06kpqaydu1aHj58yC+//EKTJk9/dDdt2sSECROwsbGha9eu2NnZkZCQwM2bNzlw4AA7d+4smqNw8OBBhg0bhp6eXtEyoAkJCZw4cYL+/fvj76+4trAqfyc8LvcYdWdlasz0TXvfdDb+E755ux1Jd9/c04T/CkM7W9aeUL5bKpS928Sb6C9Hl3+gmnNYMIfH119sQyp1YuxenTM3y18eWUCD6s58vGxt+QequV8Hv0vsz6vedDb+E2w+HVDqa/+ZJwD+/v7Ex8ezZ88eHjx4QMWKFXF0dGTatGn07Kn8uHv27NmsXbuW5cuXk5SUhJubG7NmzVIavvP222/j5OTEypUrWbduHcnJyRgbG+Ps7MywYcOwKLb2b+vWrfnzzz9ZtmwZGzduJDU1FXNzc7y9vXF7xomTQgghhBBCvEmvNQDw8fEpuqOv6v+LmzVrFrNmzVI41sfHR+WxqlSqVImJEycyceLEco/19vbG29v7ma5bp04dlixZ8sz5EEIIIYQQ4t/kX7EKkBBCCCGEEOL1kABACCGEEEIINSIBgBBCCCGEEGrk/10AMHToUG7cuKFyd2EhhBBCCCHU3f+7AEAIIYQQQghROgkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQjLy8v701nQgghhBBCCPF6aL/pDKib2AcJbzoL/3o25qYcvXLzTWfjP6G5R3USgs+86Wz865k2asCuc1fedDb+Ezp6e5AYfedNZ+Nfz8ihCo+vSztVHmP36sTv2vems/GfYNGxLY+uhL7pbPzrmXjUpPOs5W86G/8JAWMHlfqaDAESQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1ov2mMyDKl5uby6b169i+bStxcXEYGxvTslVrBnw8iEqVKpV5bnJSEnv37CY4MJCoqEgSHz/Gysqaup6efNB/AJZWVirPi4yIYPVvqzgfEkJyUhJGxsa416jBV6PGYGpq+o+u/ark5uZycOd2ju3bw8P4+xgYGlHftwld3+2DbsWKZZ6bmpJC8JFDXAo5Q1xMDCnJSZiaW1C9pgeder2DqbmFwvE3rlzmx8njVV6rtnd9ho6frJCWnZ3Nvm2bCT56mAd/x6FbsRLVa3nQ/f1+2NhX+Wcf/AXk5uaybt9eth45RNyDBxgbGNC6oQ+DerxNJd2yyyopNZXdJ48TeOECkbH3eJycjLWZGZ5uNRjQrTtWZmYqz4u4e5dV27cSci2UpNRUjA0MqOHswpj+H2FqZARAyLVQhsyaUeb7L/tmEnWrV3+xD/6ccnNzObZnJ0EH95HwIB59A0PqNfKlfc93y61TaSkpnDl+hNALIdy/G0NqcjLG5ua4utekbY9emJiZKxx/K/QKi6dNVnmtmp7eDBqlWN/y8vIICTzBiX27iY+9R3b2E4zNzPFs7Efz9p2pWLnyP/vwzyk3N5e1WzazZedOYgvaKf9mzfn0ww/LbaeSkpPZtX8/J0+dIuJONImJiVhZWuJVpw4D+/TFytJS5XnhUVGs/GMN5y5eJCk5GRMjI2q4uTF22HDMTEyKjhv89VeEXLqk8hr/W7SYmm5uL/7Bn1Nubi7rdmxny949xN6/j7GhEf5NmvDJ+32oVE6dSkpJYdfhQ5w8e4bIOzEkJidhZW6Bp4cHA3u/g5WFhcrzwqOjWbVhHecuXyYpORljIyNqVq3GmM8/x8zYROHY7JwcNu3ayc5DB4m6exctLS3sra3p3q49Pdp3eGnlUJ7c3Fw2HDvCtqCTxCUkYKyvT8t6nnzcvhOVdHXLPDcpLY09Z04TFHqVqPtxPE5NxcrYhHquVenftj1WJiYqz4uIi+W3/Xs5HxZGUloaxvr6uDs4MKrXO5gaGBYdl52Tw5+HDrD37BnuPXxIJV0dPKtW45OOnXG0sn6p5fAscnNzWbczgK379hEbfx9jQ0Na+/rxybvvPVOd2n3kMCdDzhEZE0NicjJW5uZ41qzFR716Y2VurvK8iDt3WLVxA+euXCEpJRljQyNqVq3K6E8HY2ZsrHBsdk4Om/bsZufhw0Tfy69TdlbWvNW2LW+1bffSyqE8GkDXBh60r1cDKyN9EtMyOHE9nDXHz5H5JLvMc/V0dWhduxr1XR2oYmaMYaWKxCelcOVOLGtPnudBcmqZ5ztZmDKv/1toa2kyc8sBTt6IUDqmeU1XOnvVwtbUiApamsQnpXL8+m22nblCetaTf/LRVXpjAcDmzZsZN24cv//+Oz4+Pm8qG89s4cKFLFq0iIMHD2Jvb/9a33vxgvls2rCeps2a88577xMVGcmmDesJu3mTH+cvQFOz9Ac5oaFXWbJoId7e9Xnr7Z4YGRkRER7Ojm1bOXzoIIuX/YKTs7PCOadPBTNh7Bhs7ex5u2cvTExNefToEaFXrpCWmloUALzItV+l9at+5dCuHXj6NKZt17eIjbnDwV07iI4IZ8Tk78osp4iwG2z4bQXutevSskMn9A0MuXsnimP79nA28ARjZszBtoqD0nlN27SjWo1aCmklO3Z5eXksmTWNK+fPUbeBD606diY5KYkje3Yxa9yoUq/9Ks3/cw3r9++juXd93m/fkch7d1m/fx83o6JYMHpsmWV19fYtFv71J/Vr1qKnfxuM9A0Ij4lh65FDHDxzil8mTMbZzk7hnODLlxgz/yfsLa3o1aYdpkZGPEpK5MqtW6SmpxcFAE62dkz+ZLDSe2ZlZzN71QqMDAyo5eLycgujDFtXr+L43l3UbuBDi05d+ftuDMf27iImMoLPxk8us5yiboex/Y/fqFarNk3adkDPwIDYmDsEHdzHhVOBDJsyA2sVwV/jVm1wca+hkGZsqhxU7Vr/Jwe2baZardq0fbs3Wlpa3A69yp6N67h2IYRh385EQ0PjnxfCM/pp6VLWbd1CC78m9OnZk4joaNZt3cKN27dYPHtO2XXq+jXm/7yM+p6e9OraDWMjI25HRrBl504OHD3Kr/MX4OLoqHBO0JkzjJ4yGTtbW97p3h1TExMePX7M5dBQUlNTFQIAAGMjI0YM/kzpve1sbF5OATyjn1b8yvqAHbRo1Jj3u79F5J07rAvYwY3wcBZNLbudunrzBgtWrqB+nbr06tQJY0NDbkdFsWXvHg6ePMHyWXNwcVBsS4JDQhg9czp21tb07twFU2Pj/HK6cYPUtHSFAODJkyeMnD6Nc5cv0a55C95q34GcnBzu3LtHXHz8KysTVRZs3czG40dpVrsO77ZoRdTff7Px2FHCYmKY99kXZf/uRUWyePsWvKtVp0eTZhjr6RMee49tQSc5fOE8S4eNwNla8e9+6vo1xq1cjp2ZOT2bNcfEwIBHyclcjYokNSOjKADIy8tj7IpfCL4WSlOP2rzdtBmPU1LYcvIEn86bq/Lar9q8VStZv2snzX18eK9rVyJjYli/ayc3I8JZOPnbsutU2E0W/PY/6teuQ88OHTE2MCT8TjRb9u3lYOBJls+YhXMVxXYq+Px5xsyZhZ2VFb07dcpvzxMTuXzzBqlpaQoBwJMnTxg1aybnrlymXbNm9GjbjuzcHGJiY197nRrk35iu9T0IvBHB1tOXqGJmQhdvD1yszJnw107yyjjXzdaSga0acTHyHgHnQklKz8DRwoT29WrQxN2FUau3c+fhY5XnagBDOzQlKzsbbS0dlcf0a1afd3w9uRh5l79OhpCTk0ttBxv6Nq1PfZcqjFy9/Z8XQAn/r54AXL16lYCAAIKDg4mJiQHAwcGBHj160Lt3bypUqKB0zsWLF/npp5+4ePEiGhoaeHp6MnLkSGrUqKF07JsQER7O5o0baNa8BVNnzCxKt7GxZcG8uRw6sB//MiJoB0cnVv+5FrsSQUsjX19GDh/Gyl+XM3X60zuujx4lMG3KZOp5eTFj9vdoa5deRZ732q/SvegoDu8OwNOnMZ+Nfnqn1NzKirUrfuHMyWP4NG1R6vnWdvZMXbgMyxINd22vBsybOpHta/9g8KhxSue5VnenUfOWZebtwulgrpw/R9M27eg3+Iui9EbNWjJlxBDWrviFr6ZMe8ZP+s+Fx8Sw4cB+WtRvwMyhw4rSbS0smbvmd/afCqZdY99Sz3eysWXtrO+xL/GEx7dePYbNmcXyzRuZUey6CUmJTF66BC/3Gnw//Ksy65SpkRHt/Zoope8LCiQ3L48Ofk3KPP9lio2J5sS+3dRp4MOAEaOf5tHSii2/reB80Em8/ZqWer6VrR3jflyIeYk7gjXrebFs5lR2b1zLgOGjlM5zqlad+k2al5m3nJwcju3Zib2TC4PHTSr6gffzb4emlhbnTh7jXlQkdk6vJwC/HRnJ+m1badmkCbMnTylKt7W25sfFi9l35DDtW7Uu9XzHKg5sWPU/7G1tFdKb+PjwxZgx/PLb/5g16enTkYRHj5g4cwZedevy49TvnqlOVKpYkQ7+/s//4V6i8OgoNuwMoEXjxswe+7SdsrWy4sflv7D/+DHaNW9R6vmOdvasX7IM+xJBi1/9BgydPJFf/vyDWWOftlMJjx8zce4PeHl48MM3E8stpxXr13Hm4gUWfPsd9evUebEP+RKEx8ay6cQxmtepy/QBHxel25iaMW/LRg6cD6Gtd/1Sz3e0suLPcROwK/HktnHNWoxYtpgVu3cxbcDAovRHycl8u/p/eLpWZfbHn6KtpVXqtY9fuUTwtVC6NvZjdO93i9Lb1W/IB3NmMG/zRuZ/PvRFPvYLCY+OZsPuXbTwacSs0WOK0m2trJi74lf2nzxBu6bNSj3f0c6edQsXYV/it8/Xy5svp07hl7V/MXPU0/YvIfExk+bNxbNWLX4YO77cOrVy4wbOXLrIgklT8K5d+wU/5T/nYG5CZ+9anLwRwcwtB4rS4xKTGdzGl2Y1XTkaervU82MSHvPpL+uJe5yskH7mVjTT3+tE36bezNx6UOW5XerXwsHchE2nLtK3qXK91dTQoFt9D27FxTNh7a6iQGT3hWvk5ObS0qMazpamRNxPeP4PXob/V3MAfv31VzZv3kzNmjUZMWIEw4YNw9jYmKlTpzJ48GDy8hTjuwsXLtC3b19iYmIYNmwYX375JVFRUbz//vvcuHHjDX0KRQcP7CcvL4+evd9RSO/UtSsVK1Zk/969ZZ5vY2Oj1EEHqN+gIYaGhkSEK1b47Vu2kJSUxODPh6CtrU1GRgbZ2aofjT3vtV+l0yeOkZeXh3/nbgrpTf3boaOry6mjR8o839zSSqnzD1Czbj309A24Gx1V6rmZGRk8ycoq9fUbVy4D4NdKsfNhYW1NtRq1uH75Ig/j75eZv5dpf3AQeXl5vFMicOzavAUVdXTZG3iyzPNtLCyUOv8ADWt5YKinz+27MQrpWw4dIik1hSHvvJdfpzIzS61Tpdl+7EhRHl+X84EnyMvLo1mHzgrpjVv6o6Ory7kTR8s839TCUqnzD+BWuy6V9fWJuxNd6rnl1amcnGyeZGVhYGysdHfPsODOt045wyRepn2HD5GXl8e7Pd5WSO/esRMVK1ZkzwHVP4yFbK2tlTr/AA29vDE0MOB2ZKRC+uaAAJKSkxk6aFC57VRxubm5pKSmKv0WvC77juW3U+92UWynurVtR0VdXXaX007ZWlkpdf4BGtarh6GBAeEl2qnNe3aTlJzfiDc4AAAgAElEQVTMFx8OKPjulV5O6RkZrNuxnaY+PtSvU4e8vDxS09Ke7wO+JAfOnyMvL4/ezVoopHdp7EtFHR32nTtT5vk2pmZKnX+ABm7uGFauTHjcPYX0rYEnSEpL4/Mu3dHW0iIjK4vsnByV1w4JCwOgY0PFkQt25ubUdXHlXNhN4h693I5aWfadOJ5fpzp3UUjv5t+Girq67Dladjtla2mp1PkHaFi3Lob6+oRHK7ZTW/buJSklhS/6fVhue56ekcG6nQE0bdAQ79q18+tUevpzfsKXo1kNVzQ1NNh+5opC+t4L18nIekLLWlXLPP9+YopS5x/gYtQ9ktIzcLAwVXmeuYEefZvW588T54hPSlF5jLaWJjoVtHmUkq70FCIhJf87WN4QpRfx/+oJQN++fZk1axa6xX74+vbty8iRI9mxYwdHjhyhZcund2unTZtGhQoV+OOPP7Aq6NB06NCBDh06MHv2bFauXPnaP0NJN65dQ1NTE/eaNRXSdXV1qVqtGtevX3uh66akpJCWloZzieEUwUFB6OnpkZKcwsAPP+D2rTA0NTWp5VGbIV9+iXuNmqVcsfxrv0qRt8LQ0NTEqZri2PAKOjpUcXIh8nbYC103LTWVjIx0bB1UD9FZu2o5/1s8HwBLG1tatu9Eq05dFIZeZGfnj93T0VHukBV20iLCbmJmoXqc88t2LSIcTQ0Narq4KqTr6uhQzcGBaxHhL3TdlLQ00jLScSkRFAZduoBepUqkpKXywcTxhEVHo6mhQe1q1fjyvT5K+SjpXvx9Qq5do2716jjaKHcSX5Xo27fQ0NDE0bWaQnoFHR1sHZ2IfsEANz0tlcz0DGzsVdepLb+v4q+fFwNgYW2DX5v2NGvfSaFO6ejo4uJek+sXL3Bw+xbqNGyElpYWt0KvcHL/XrybNMPiNZZV6I0b+e1EibH0ujo6VHdxJfTmi91QSUlNIS09HdcSTzICT59Cr3JlklNS6fPpp4SF30ZTU5PaNWsyYvBgarq5K13r/oMHNO/ahczMTCpWrEgj7/p8/tFHOJXy3X4VQsMK2tMSc1h0dXSo7uzCtbAXa6dSUlPzy6nEZwk8dy6/nFJT6Tv8S8IiIvLLyd2d4R8NpGax9vJC6FXS0tOp4VqVH5f/QsCBA6RlpGNsaEi3tu345P0+Zd4Zf5muR0ehqaFBjRLDvnQrVKCarR3Xo0sPnsuSkp5OWmYmLiW+G0HXQtGrWJGU9DT6fz+LW/fuoqmhgYeTM0O796CGw9N8PCno7FbUUR7KoVuQFhoVhbWJ6g7hy3bt1i00NTWpWU2xndLV0aGakzPXbt96oeumpKaSlpGhXKdCQtCrXJmU1FT6fT2CsMjI/Drl5saw/gOoWfVpPi5cCyUtPR13V1fmrviVgEMHScvIyK9T/m0Y9O57r61OVbexICc3lxuxijfbnuTkEH7/IdVsVM+fKU9l3QpU0qlAVPwjla9/3s6PvxOT2XbmCi09VAcZWdk5XL0Ti5eLPW/71CXwRgQ5eflDgDp61eTQlTDuPUp6ofyV5V/3BGDp0qW4ubnx3XffkZubi5ubG2PHjiUwMJDevXtTt25d/Pz8mDZtGmkl7k54e3srdP4LdezYEYCwYo1rVFQUly9fpn379kWdfwArKyvat29PYGAg8eWMT8vJyWHSpEm4u7uzfPnyf/KxS/XgQTxGRkboqGhszC0sSHz8mCdPnn9yyOr/rSI7O5t2HToqpN+5E01OTg6jvx5B1WrV+HbaDD79fAgREeEM/2IIEeHldw5Lu/ar9PhRAvoGhiqHeRmbmpKSlET2C5TTro3ryMnOpnELxeELWtpa1G3gw9v9+jNk7ET6fPo5lfX0WLdqOb8VBASFCsf3X7+iOAkxMzODiLCbADx68OC58/ai4h8/wsjAAB0VZWVhYsLj5OSiH7nnsWr7VrJzcujYRHFYTHRsLDk5uYz44XuqOTgy44svGfLOe4THxDBk5gzCY2JKuWK+HceOkpeXR5fXePcfIOnxI/QMDNBWUU5GJqakJicVBXfPY/+WjeTkZNOgxN1NTS1tPLwb0OX9fgz8eiy9Bn5Kpcp6bF29irUFAUFxfYcMo2rNWgSsXcOMr77gu2GfsfaXJTTv0Jk+n3353Pn6Jx48fIixoaHKdsrC3IzHiYkv1E6t/OMPsrOz6dS2jUJ6VEwMObm5DBs/juqursyaNImhHw8iPDKSwSNHKj0xsLW2oV/vd5g0ciQzJ06iZ5cuBJ05zYChX3DrBQPeF/HgUQJGBoaqv3tmpjxOSnqxclq/juzsbDqWGGYVfTeGnJwchn87merOzswcM5YvPuxPeFQUn30zXuGJQdTduwCs3bGdw0GBfNG/P9NHjaa2ew1+27iB6QsXPHe+XtSDpESM9PTR0VYuJ3MjYx6nprxQG/Xb/r1k5+TQvkFDhfTo+3+Tk5vL178spZqdHdP6D+SzLt0Ij4tl6OIFhMfGFh1bOL4/pKDtLpSRlUVoVCQA9x+r7gy+Cvl1SnV7bmn64nVq1cYN+XWqheIQ1+h7d/Pr1LSpVHNyZsbI0Qzp9wHh0dF8PmmiwhOD6II6tS5gB4eDgxnS70OmfTWS2m5u/LZ5EzOWLHrufL0oU/3KJKVnkJ2Tq/Taw+Q0jCpXQruMuRKlecfXkwpaWhy6clPptabuLtR3dWDxnhPklvPU8Yfth7kcHcuAlg1ZPvgdVn72HsM6NmfbmcvMDTjy3Pl6Fv+aJwC5ublMnTqVv/76i6+//ppPPvmk6LWrV6+yd+9eevXqRbdu3Th16hSrV68mLCyMVatWlTnBBSAuLg4As2Krk1y+nD8sw9PTU+n4evXqsWnTJq5evUqLFi1UXjMjI4OvvvqKY8eOMXv2bLp166byuH8qMyOTCip+VIGiH9uMjAyVHd/SHDl8iPVr/6KBjw8dOikOb0hLSyM3Jwf/tu0YN2FiUXp1N3dGDB3C76tWMvm70serl3XtVykrMxPtCqqrc2H5ZWVlquzMleZc0En279hKrXpeSsN3qrrXpOpYxachTf3bsXD6twQePohf6zZFk4N9mrVk58b1bF/7B7q6FalRpy4pyUlsX/snKclJRXl7XTIzs1T+sALoVCioU5mZVHiOsfaHzpzmrz278aldm84lxpumZWSQk5tLu8a+TBz0aVG6u5MTQ2bNYOW2LUwbonrMbE5uLrtOHEevUiVaN3i9iwXk1ynV5VShoJyeZGahXUpZqnLhVBBHdu3AvU49GjZvpfCai5s7Lm5jFdIatfRn+ZzpnD52GJ8WrRUmB2trV8DM0gojUzNq1K0HaHDpdDD7t26kgk4F2nTv+cz5+qcyMjNLbYOK2qkyjlHl4LFj/LFxI43q16dLu/YKr6WlpZGTm0v71q2ZPPrp+GT36tX4bORIVqxZzYxi7dekUYpzLVo3a0bTxr58NvJr5i1bxqLZc545X/9ERmYmOqW0Uwrfvecpp5Mn+XPbVhp5etGltWI7lZaenl9OzVswadiIonR316p8PmE8K9auZXrBuPG0gqEZScnJ/LlwEU4FE9T9mzTls2/Gs+vwIfr1eFtpkvGrkJH1pNT2p7D8Mp5kPVcbdfjCedYeOURD9xp0athI4bX0zExycnNp612fb97vV5TuVsWBLxcv4H/7djP1w48AaFu/Ab/t38uvu3dRUUeH+tXdSExNZcWeXSSmphbkv/They9bfp0q57uXlfVcdepQUCB/7thOo3qedC4RVBbWqXbNmjFp6NMbDe4urgyZPJEVG9Yz/euRAKRmZAD5Kw398dN8nAqeDvv7+fH5pInsOnKEft17KE0yfhV0K2jzJFu58w/5TwEKj8nOfPa/nZ+bM281rMO58Dvsv6QYAOjp6jDIvzF7L1zn+r3yh/g+yckh7nESBy+nci48BsjD182Zd/28yMrOYX3QhWfO17P6VzwByMjIYOjQoWzcuJHZs2crdP4Bbt68yffff8/48ePp06cPCxYsoF+/fgQHB7N79+4yr52amsqKFSswMDCgdeunFfn+/fw/iKWK5eUKnwj8/fffKq/5+PFj+vfvT1BQEMuWLXtlnX8A3Yq6pY4FzipIr1jOMl/FBQcGMv3bKVR3c2fKd9OVVgnRLRim0r6j4t17Ty8vrKysuXA+5IWv/Srp6OqSXcoYucLyUzUEpzSXz51lxbwfcHBx5ZOvxzzTZ9HU1KR9j/xO15WQc0Xpevr6jJj8HRbWNqxetojxnw9ixpivyczMoH33/DHTFctZJvFl0tXVIauUO9dZTwrq1HOMHw+8eIEpy5bg7uTE9CFDVdSp/B+hjiUCA68aNbE2MyOkjGFspy5f4n5CAm0aNX6uPL0M+XVKdTk9KSinCrqqg3NVQs+fY83iedg7u/Dhl18/c51q3a0HANcuPP3uZWVmsmDKeDLT0+nz2VC8fJvi5duE/sNH4tnIj90b1nH/3t1nzts/VVFXt9S7jEXt1HP8/U6eOsWkWTNxr1aNGRMmKtepgmt1bttWId27bj2sLS0JuXix3PfwrF0bz9q1OXfhAhmZrycAr6irS1Yp7dSLfPdOnj3L5Lk/4O7qyvTRyu1UYXveqUQnzrt2bawtLDh35UqxY/Prci03t6LOf6GOLfOD1fNXFcdPvyoVdSqUeoe/sPwqVnj2715Q6FWmrvkdN/sqfPfhAKVyKuxAdyhxk8GrajWsTEw4f+vpMBrDypWZ99kX2JmbM2f9WnpP+5ZBP/1ARlYWfQpuFOk9x2/yP5Vfp8r57pVyE1GVwHPnmDzvJ9xdXJn+9chS2/NOLRRvYHh7eGBtbkHIVRV1qnr1os5/oY4FN1dDrl595rz9E5lPsqmgrbrLW6FgGNLzjLOv71KFkV1acivuAbNUTP79qJUPGhrwvyOny72WrrYW3/frRmUdHX7aeZRj125z7Fo4s7Ye5Fjobfo09cbO1OiZ8/as3ngAkJiYyIABAwgMDGTp0qV0795d6RhnZ2f8S6zeUBgk7N+/v9Rr5+TkMGrUKGJiYpgyZQrGxZamSi+426HqkXVhWrqKySr37t3jvffe486dO6xZs4YmTZRXLHmZzM0tSExMLPoiF/cgPh4jY+NnjuxPBQcx8ZtxODk788NP89DT01M6xsIyfxycqYq13E3NzEhOVp4E86zXfpWMTUxJSVb9qPNxQgL6hobPfPf/yvlzLP1+BjZVHBg+6TsqPcd66uYW+cFjSpLieD17Rycm/jCfaYt+ZuTUmUxb9DOjvptVlF9ru9e3tKyFsQmJyckqfzTiHz3C2MDgme+sBV26yLiF83G2s2PeqLHoVVIuK4uCsbBmRsoNmJmxMcmppa+fvONY/gS21zn5t5ChsQmpyckqg4DERwnoGRg+893/axfPs2re91jbV2Hw2EnPtUZ/4R4UhU+LAC6eCiI+Lpa6jRorHV+3UWPy8nIJv/Fi84NehLmZGY+TklS2U/EPHmJsZPTM7VTQmdOM+XYKLo6OLJw1G30VbYllQZmYqRhnbW5qSlKK6sl2JdlYWZOTm1tqu/aymZuYkpicpPq79zABY0PVwxhVCQo5x9hZM3BxcGDBt9+hr6JOWZrnt+Mll0TNTzMlOfVpOVkWLF9ccl8AAHPT/LTkZyzXf8rc0IjE1BSVNyoeJD7GWE//mduo4GuhfLPqV5ytrZk7eAh6FZVvtlga5fcNzAwNlV4zMzQiOV1xuLGrrS2rRo5h7fhJLPpiWNF/C4MWR8vXtwdOfp1S3Z7fT3jOOnU+hLHfz8a5igPzJ01GT0WdsjArrFPGSq+ZmZgotOeWhceqqFOFdbJ4HXyVElLSMKxUEW0t5W6vmUFlEtPSyc5V/YSgJC9ne8b38CfqwSMmrt2ltEa/q5UZbeq4sfNcKIaVKmJjbIiNsSHGlfPrnoleJWyMDYvy4ufugp2pESduKA9HPHEjHC1NTWrav/z9Jd54ADB27FjOnz/P8uXLadpU9ZJ6rq7KkwQtLS0xNDTkzp07Ks/Jzc1l/PjxHDx4kBEjRtC5s+JwlMKNaVT9YBWmqdq8ZvDgwdy7d48///yTWrVqKb3+srnVqEFubi7XQ0MV0jMzM7kVFoabu/JkN1VOnwpm4rixODg48uP8BRioaOgAahRM8o2/r/zIKj7+PsYqfkie9dqvklPVauTl5hJZYlzmk6ws7kSG4+ha9gz/QlfPh7B0zgys7ez5avI09PT1nysff8fmry5haKzcOEL+ROHqtTywLJiEduX8OSpWrkxV9/InV78sNZxdyM3LI7TEJNbMrCzCoqNxf8alI4MvX2Lsgnk42tiwYPQ4DEsJ+moWTAa/n6C8Msb9hARMSqkvCUmJnDgfQtUqDtRwfn0Tygs5uFYlLy+XqBITyJ9kZXEvKpIq5UxeLnT94nlWzZ2Dpa0dn42fTOXnrFPxcfnjjw2MntapxIJVRnJV/GDlFoxxVfXaq1LTzY3c3Fyullg9LTMri5vht6nxjBu35a/tPwXHKg4smj0HQwMD1e/nnj/Z+P4D5Xla9x88wLSU719Jd+7GoKWlheFrarNqVquWX043FdupzKwsbkaEU6Pqs7VTwSEhjJk5A0d7exZOnYZhKXWqcJLv/YfKc4zuP3yASbGgvFb1agXpD5WPLZijZKIiiH8V3B0cyc3L41qU4qpGmU+eEHbvLm7POGTk1PVrjF/1Kw6WVsz77AsMSwm8Cyf53n+svI57/OPHmOirrof2FhbUc62KfcEGbMHX8ycT136N7VWNqlXJzc0ltMQE8sysLMIiI3BX0X9SJfj8ecbOmY2jnR0LJ08ptU7VqlZGPXn4EBPDYnWqatnHwuurUzdj49HS1MTNRnHURwUtLVwszQiLfbZ5eF7O9nzTow0xDxOZsHYXqSqGDFkY6qOpoUHfZvVZPvidon8DWuY/YRrc1o/lg9/BqWDlIDP9/HqpqaHcJdcqSNPSfPkjKt54ANCxY0c0NTVZsmQJGQXjxUoq7VF5aUu55eXl8c0337B161a++OILBg9W3liocOjPfRUd3cKhP1Yqljrs3LkzGRkZLFmy5LX8wLZq3RoNDQ02rl+nkL5z+3YyMjIU9gB4+OABUVGRSuV45tQpJowdQxUHB+YuWIihYelfuDbt88fabt+6VSE98MRxHsTH06jE+vDPc+1Xqb5fUzQ0NDgQsE0h/fiBvWRlZirsAfD4UQKxMXfIzFQsp6sXQlgyZzpWNrZ8NWUaeqV0PkDxbmyhJ0+esGP9nwDUqd9Q6fWSDu3awb3oKPw7dyt3V9mXqbVPIzQ0NFi3T3EJ2e1Hj5CRlamwB8CDx4+IvHdPaYjEqcuXGTP/JxysbVg4ZhxGZXRqC9f133r4kEL68fMhxD96hG+deirP233iBNk5OW/k7j9AvUZ+aGhocGx3gEJ60OEDZGVmKuwBkPjoEX/fjSGrRDldv3SBlXPnYGFjw+fjp6BXSkcCIFXFXejsJ0/Yu2k9ALW8nq4fbVXwxOhMwfKoxZ05dhiAKi7P1pl8Gdq0aIGGhgZrN29SSN+6aycZGRkKewA8ePiQyOhopXYq+OxZRk+ZTBV7exZ/PwejMjrlHQueCG8KUPzbHA8K4v6DB/g2fPr9S0lNIUfFko4nTgVz8epVfLy8i4YqvGr+TfLbqbU7FNupbfv2kpGZqbAHwIOEBCJj7pBRop0KPp+/sVcVW1sWT52GURntVIeCCZyb9ygOlT1++jTxDx/iW2wtfVsra+rUqEFo2E2uF1s5Jicnh2379qGlpYWPijlzr0Lrel5oaGiwvkT93hEUSEZWFm29GxSlPUhMJOrvOKVx96cLNvaqYmHB/M+HlnqDAqBd/fzrbQs8oZB+4spl4hMf0+gZVr/beOwo4bGx9G7estydil8mf78m+XUqYIdC+rYD+/PrVLGhlw8eJRAZE6Pcnl+4wJg5s6hiY8uiKd+WWafaFyxesKXEEuTHz5whPuEhvl5eRWm2VlbUcXcn9FYY14vdcMrJyWHbgf35daqu6vb/ZTt+/Ta5eXl0beChkN6unjsVdSpwJPRpnTfRq4S9qRG62oorFHk62fFNjzbce5TIN3/tJCVD9dDBm7HxzNxyQOlfwLn84U6bT11i5pYDxBas7BNdsIFYa49qStdqXTs/LSz25W+a9sYnAXfp0oXGjRszevRoPv30U5YtW6Z05/3WLeVlrO7fv09ycjJVStwJKOz8b968mc8++4yhQ1VPLqxdsCHF+fPn6dWrl8JrFy5cQENDQ+Ud/k8++QRHR0fmzJlDdnY2c+bMQesVLmPl4lqV7j3eZsumjUwcNxafxr5ER+XvBFzX0xP/Nk/HwP6ybCl7d+/ip4WL8Sz4El6/do1vxo4mD2jfsROngoOU3qNtsQl29Rs0pHWbNhzcv58xX39FYz8//o6LY/PGDZiZmdP/o6ebpzzvtV8le0cnWrTvxOHdASydMwMPL2/iYmI4uGsH1Wt50LDp042Vtqz5jaAjh/j62xm4eeTXg8hbYSyZPZ28vDx8W/krjOEvVHzDr/nfTcbY1AxHF1eMTE1JTEgg+NgR7sfeo1XHzjiXWI50wbQpmFtZY1ulCqBB6MXzXDgdTG3v+nR8u/crKZPSVK1Shbdb+7PxwH7GLpiHb526RMbeY/3+fXi6u9O2WACwdMN6dp04zuKx4/Eq+BG8FhHO6PlzAejUtBlBKsZbF9/Mq2EtD9o0asz+4CC++vF7/OrVI+7BQzYc2Ie5sTED3+qhMp8Bx4+iU6EC7Xz9XubHf2a2Do74tWnPiX27WfnTHGrW8yraCdi1Ri28fJ8GADvXreHMsSMMmfAtVWvm/8BEh99i5Y+zySOPhs1bce2i8vyZ4ht+/Tz7O4xMTLF3dsHIxJTERwmcO3GM+LhYmrbriGOx5fVqeXnj4FqNaxdCWDh1AnUa5E9qvHQmmPDr16jr05gqr/EuZFVnF3p27cqGbdsYPWUKvg0bElmwE7BXnTq0a/V0vPDiFSvYuX8fS3/4Ae+CH//QGzcYNXlS/mpP7doRdFp5nffim3g19PKmbcuW7Dt8mOHjx9OkkQ+xf99n/batmJuaMajfB0XHnr1wkXnLltK0UWPsbGzQ0tLi6o3r7Dl4MH934M8/f4Ulo6iqkxM9O3Ziw84Axsycga+3N5ExMawL2IGXhwftmj2tD0tW/8bOQ4dYMm1G0QZK18LCGD0jv53q3NqfQBXtVIdiq7Y0rFePts2ase/YMYZPnUKT+g2Ii49nfcAOzE1MGfTuewrnjvzkUz4dN5ahkybSu3MXjAwM2H/iOFfDbjLwnXexfk1LFbva2tLDrymbThxj/MrlNK5Zi8i/49h47Cj1XKvSxsu76Nifd25n95nTLBjyJV4F35Hr0dGMXbkc8vLo1LARwddCld6jsNMP+fsD+Ht5cyDkHCN/WYpvzVrEPXrEpuNHMTM0ZGB7xTlxI39Ziq2ZGU5W1mhoaHD6xnWOX76Eb81afNim9I05X4Wqjo683b4DG3fvYsycWfh6eRftBOxZq5ZCALBkzRp2HTnM4m+/w9sjv526dusWo2fPzK9TrVoRGKLcTnUoFpg2rFuXtk2asu/EcUZM+w6/+vWJi49nw66dmJuY8PE77yqc+/XAQQye+A1ffjuFXh07YWRgwIGTJwgNC2Ngr95YW7zY8pvPKyr+ETtDQuniXYvxb/lz9vYdqpgb08Xbg8vR9zh69Wk/88MWDfGvXZ1xfwZwOTr/CWxVa3MmvN0WDQ3Yf+km3q7KT6GOFFwjISWNkzcilF6vqJPf5b5x777C62duRXPj3n0aVHVgVp/OBN6IADTwdXPCo4oNx6+Fc/tv5aco/9QbDwAAOnXqhJaWFiNHjmTQoEH8/PPPCmPIIyIiOHDggMI8gMJlN4un5eXlMWHCBDZt2sTgwYMZPnx4qe/p6OiIh4cHe/bsYdiwYQoTf/fs2UOjRo2wKKViDhw4EG1tbWbMmEFOTg4//PDDK92d9Ithw7G2sSFg2zaCgwIxMjKiR89eDPh4ULkrIEWEhxcNaVq8YL7KY0p20sdNmIRr1WrsDghg0fx56Osb0LxlSz7+ZDDmxcrkRa79Kr0z4GPMLC05vn8vl8+dQd/QkFYdOtP13T7lltO96KiiycLrV/2q8pjiAYB3Yz8unA7m0O4A0lJT0dWtSBVnF7q+875CsFHIxc2dsyePE3Qkf7KQtZ097w8aTLM27dF8TesgFze8Tz9szC3YduQQgRcvYKRvQC//Ngzq0bPcsgqPiSkabzr/zzUqjym5m++kTwZTzcGBgGNHmffHGgwq69GyfkMG9+yFhYphZZfCbhJ57x5tG/uWeefuVXvrgwGYWlgSdGg/oefPoW9gSNO2HejQ693yVx+7E100WXjr6lUqjykeANRt2JjLZ09zfO9u0tNS0dHVxd7JmfY931EINgA0NbX4bPxkDm7bzKUzwez4azUaGhpYWNvQ+b1+tOjYpeRbvXJfffY5tlbWbNm1k5OnT2FsaEjv7t359MP+5depyEgyC75/Py1dqvKYkrv4ThkzlmouruzYu4e5S5dioK9Pq6ZN+XzAR1iYmxcd52hvj3u16pw4FUzCo0dk5+RgaW5Oj06d6f/++1gWO/Z1GDHwY2wsLdm6dy8nz57JL6dOnfnk/fLbqdvRUUXlNG+F6naqQ4llGycP/4pqTs7sOHCAn1b8ioGeHq18/fisb7+i8dyF3FxcWT57Dj+vWcPaHdvJysrCyb4KE78cRucSKwy9al++9TbWpqZsDwokKDQUI309ejZtzsAOncqvT3H3itqoBVs3qzymeAAAMOH9flS1tWPnqWAWbN2MfqVKtKhbj086dsG8xDAVDydnDp4PYffpUwA4Wlnz1du96ObbBK0XWErynxox4CNsLC3Ztn8fgefOYWxoSK8OHfnk3feeoU5FP61Tq1TvfdShxFPYSV8Oo6qTEwGHDjJv1UoMKlemVWNfBr/fBwtTxXk5bi4u/IMJFjgAACAASURBVDJ9Jj//9SfrAnaQ9eQJTvb2TBgylM6tFCcSv2rLDwRxPzGZdnXdaeDqQFJ6BgHnrrLm+FmlDbhKcrQwQbdgBapP/JXnXsHTAOB55eblMWHtLno1qktjN2cGtPAhjzzuJSSx6vAptpy+/ELXLY9G3hvaEnHz5s2MGzeO33//HR+f/HFRBw4cYPjw4Xh4ePDrr7+ir6+Pm5sb1atXJyYmhl69euHo6MipU6fYu3cvDRs25Lfffiuq4IWbd7m7u/PRRx8pvaeDg4PCsp8hISF88MEHWFtb07dvXwDWrFnDw4cP+euvv3AvNr5+4cKFLFq0iIMHD2JfMJv9jz/+4LvvvqNNmzbMnTv3mSbaxD54fTsE/lfZmJtyVMWaukJZc4/qJASXvSumANNGDdh17vWsYPJf19Hbg8Ro1XOrxFNGDlV4fF3aqfIYu1cnfte+N52N/wSLjm15dEX5aYVQZOJRk86zXs3eS//fBIwdVOpr/4onAIX8/f1ZtGgRQ4cO5aOPPuLXX/PvcNSqVYtx48bx008/sXbtWvT19enbty8jRoxQiG6vFCxpdv36dUYXWxu60FtvvaUQAHh5ebF69WrmzZvH/Pnzi9Lmz5+v0PkvTZ8+fahQoQKTJk3iyy+/ZP78+SpXFRJCCCGEEOLf4o0FAD169KBHD+Wxvy1atCjapKs4X19ffH19ldKLW7169XPnw9PTk99++63c44YOHapyPkHv3r3p3fv1juEWQgghhBDiRb3xVYCEEEIIIYQQr48EAEIIIYQQQqgRCQCEEEIIIYRQI/+qScCq3Cixs6QQQgghhBDixckTACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQCACGEEEIIIdSIBABCCCGEEEKoEQkAhBBCCCGEUCMSAAghhBBCCKFGJAAQQgghhBBCjUgAIIQQQgghhBqRAEAIIYQQQgg1IgGAEEIIIYQQakQjLy8v701nQgghhBBCCPF6aL/pDKib9+b9/qaz8K/31/AP2BAY8qaz8Z/Qy9eLO6Mmvuls/OtV+f47kuLi3nQ2/hMMra1JCDr9prPxr2fauCF7Qq6+6Wz867X3qkXUJ8PfdDb+Exx/mcejkAtvOhv/eiZe9WgxZdGbzsZ/wpEpX5T6mgwBEkIIIYQQQo1IACCEEEIIIYQakQBACCGEEEIINSIBgBBCCCGEEGpEAgAhhBBCCCHUiAQAQgghhBBCqBEJAIQQQgghhFAjEgAIIYQQQgihRiQAEEIIIYQQQo1IACCEEEIIIYQakQBACCGEEEIINSIBgBBCCCGEEGpEAgAhhBBCCCHUiAQAQgghhBBCqBEJAIQQQgghhFAjEgAIIYQQQgihRiQAEEIIIYQQQo1IACCEEEIIIYQakQBACCGEEEIINSIBgBBCCCGEEGpEAgAhhBBCCCHUiAQAQgghhBBCqBEJAIQQQgghhFAjEgAIIYQQQgihRiQAEEIIIYQQQo1IACCEEEIIIYQakQBACCGEEEIINaL9pjMgyqcBtPesQeva1bEw1Cc5PYPgm5FsCLpIZnZ2mefq6erQtIYLns722JkaYVBJlwdJqVy7+zebT10iISVN4XhPJzta166Og4UJhpUq8iQnh/ikFI5fC+fApRs8yclVeg9NDQ3a1HWjeU1XbEwMyc3N4+/EZA5evsnBy2EvsyjKlJubS9D+PZw5cpDHD+KpbGBA7YaNaP1WL3R0K5Z5bnpqCudPHufG/7F332FRHfvjx9+U3aX3LgKC0psNUOwNG3ZjTDU9N4nppnlT7k1uyk1iTDR6Y4pGk9h7xd5FY0MQEQVEQbr0pW35/QEi6y5VMPn+mNfz8Dxxdmb27Mkp85mZM+fCOfJuZiIvK8XK1g4PHz+GRE/Byta2yfLZN9JZ9K+5qJRKHnzhVQL7htd/plQo2Pb7MjLTUikqyKOqshJzK2tcu3kxaNwEXNy7tcfPbx09PcwGRGAW0RdDayuU5XIq4hIojtmHuqam6aLGRpj2DsXYzwdDB3v0TU1QFhZTlZpGyd6DKItLNPLLPD1w+MdTOuuqSLxM/tLfNBP19TEfMgDTXqEY2lqjqqqmKjWN4p17UeTl39PPbi2VSsWqdevYsHUrWdnZWFlaMmLoUJ5/8kmMjY2bLFtSWsr2mBiOnThBWno6xcXFODo60iskhKcefxwnBweN/GfOneP5V1/VWdeAfv345vPPtdKPxcby8/LlXElJQSqR0LdXL2b/4x90cXZu+49uI5VKxeo9MWw6cIDs/HysLMwZ3jecZ6ZMwbiZ86+kvJydx45yPO4817JuUlRaipOtLT19fHliwiQc7zr/zl66xItffKqzrv4hoXz92httrrujqVQqDu3azvF9u7mVl4uZuQWhEf0ZO30mMqOm95O8rIxTRw6SeO4MOZkZlJeWYm1nh5dfAFFTpmNta9dk+cz0a3w1dw4qpZInXn2T0PD+TeZfOv8rzp88jpNrV9798ttW/9Z7oqeH+fBBmA/qj6GtDcrSMuSnz1O0ZSfq6uomi+qbGGMa0RfjYH8kTo7om5mivFVEZfJVirfvRllYpLOcxNkRy7GjkPl2x8DEFGVZGdXXrlPw2xpUpWUaeY0C/bAcNwqpqwtqhYLKpCsUrduCouBWu+2CllKpVKzetZNN+/aSlZeHlbkFwyMieHb6Axg3c0yVlJWx88hhjp07x7XMTIpLS3C0s6Onnz9PTpmC413H1JnEi7z48b911hXZsxdfv/V2m+vuaHp6MDU8hAl9AnGyMqeovIIDF6+y9MBJKmuabkuZGcmICvEhwtsDdztrLE2MySkuJS79JssP/UleiebxEdHDneg+gXg52mJlakyNQklWUQm74y6z5XQC1Qplk9/34fQohgb0IC23gCcWrbzn365LqwOADRs28O6777J8+XLCw8ObL3CfPfroo2RmZrJ///42lffx8WHy5Ml8ruNm+1d5dHBfxvT049TVdHacTcTFxpKoUD88HGz4z/o9qJso293JjkcG9SHhRjYxcUmUVlTR1daK4UHeRHh78OHqnWTeKq7P39XOGpVazYGEKxSVVyA1NMS3iwOPDe5LT48ufLpxr0b9Bvr6zJkwFH9XJ45dTmPvhWQM9PVxsjLHztysg/aIbjtXruDE3l349+pL5Oix5N28yYm9MdxMv8YTc+air9/4gNeN1KvsWv0bnn6BRAyPwsTcnNzMG/x5cB8Jp2J5du6/cOjiqrOsSqVi07IfMZRIqFZqn9RKpYLMa6m49fAmtP8ApEbGFBfkc/boIX74+H0ee/0dvPwD220/tIRV9BjMB/ZDHp9I6aFjSBztMRsQgaSLM3lLloG68aNK5uaK1fjRVF5NpezYSVTl5UicHDGN6INJSCA5C39EkZunVa4s9k+q0tI10pRFxVr57GY9jLGfN/KES5Qdi0XfzBSzfmE4zn620bo7yryFC1m9fj1DBg7k4Qce4Fp6OqvXryf5yhW+nzevyWMqITGRbxctom+vXjwwZQpWlpakpKWxYcsW9h48yM/ff4+nh4dWucnR0YQGB2ukOdrba+Xbf/gw73zwAT28vHj5+ecpKy9n1bp1PP3iiyxfsgR7u/t7c/125e+s2bObwb378NDoMVzLusmavbtJvn6N7+a80+S+upiSwoJVf9DHP4Bpw0diaWZGamYGmw4eYN+pUyz55wd069JFq9zEIUMJ9fbRSHOwtmmXujvKxhVLObxrO8F9wxk6dgI5NzM4HLODzGtpvDD3oyb3U/rVZDb/tgzvwGAGRo3B1NyCrBvXOb5vN+djj/Hqvz7DybWrzrIqlYrVPy5GIpFQpeM6dbeEs6eJOxWLRCpt82+9F9YPTMJi+GDkZ+Mo2X0AibMT5sMHIXXrQs43i5u8Rkm7uWM9fSKVSVcoPXAUVVkZki7OmA3qj2mfnmR/MZ+arByNMkb+vti/8CSKvAJK9x1BWVqKgbkZMk8P9I2NNAIA457B2D83i5qMmxSu24K+sTEWIwbj+PYrZP/na61OkI42f8Vy1uzayeC+fZk5djzXbmayJmYXydeusWDuP5s+965e5bvfVtAnMJBpUVFYmZuTeuMGG/ftZV/sCX7818d0c9W+900aPpwQHz+NNAfbu869NtbdUV6MGsi0iBAOX0ph9fFzuNvbMDU8mB7O9ryxfFNThxT+ro68EDWAM6kZbDwVT7G8gm4OtkT3CWBoQHde/Hkd6XmF9fk9HW1RqVRsP5vIrTI5UkNDgt2deWn0QCJ6uPPmii2Nflc/bw8G+XlR2Uxn3L0SIwB/c642lkSF+nLySjrztx+qT88rLmPW0DD6+XTj+OW0RstnFhbz+q+byC3WjE7PpWUyd+pIpvcL1ah3y+kErTpi4pIoqahkVIgvXo62pOQU1H82JTyYQDdnPt2wh8SMHK2y90tO5g1i98Xg3zuMh156rT7d2t6e7b//SvzJE4T0i2y0vL1zF175bB62Do4a6d7BPVn21afs27SWmS++prNs7N4YcjMzGDAmmv2b1ml9LpUZ8cKH2r2VYUNH8OWbszm2a/t9DQAMHR0wiwxHHn+RguWr6tMVtwqxnjQek5Ag5OcvNFq+JjefrC+/RVlQqJFekXQZh2efwDJqOAUrVmmVq0q/gfxsXJPbZhzgh7GfN2Wxf1K4/s4FsvzMeZzeeAnrSeNqA5T7ICUtjTUbNjB00CD++/HH9ekuzs589d137N63j9EjRzZa3sPNjXUrVuB6V+MyMiKCl954gx9++YUv/q3dkxYUEMDYUaOa3DaFQsFX336Lo4MDPy5YgImJCQD9w8N57NlnWbJ0KXPnzGnNz70nqZkZrN27hyG9+/DZ7Ffq013s7Jn3+wr2nIwlql/jvc0ezs6s+vy/uN51/vUPCeWVL7/gx43r+fSll7XKBXl1Z3T/xs/re6m7I2TduM6RmB0Eh0Xw1Gtv1afb2juw/tefOXviKH0iBzVa3qGLK3PnLcTO0UkjPaBnbxZ9+i92rF3Jkw3qbehwzA6yMm4wPHoSO9dpn58NVVVWsPaXJQwcNZqEM3+24he2D4mzE+ZDB1J+No78/y2tT1fkF2AzcyomfXsiP3W20fI12bnc/OBTFHkFGukV8Yk4vvYClhPGkP/Dsvp0fXMz7J5+lKrkFHK//xF0jHTXM9DH5sEpKAuLyP7yO9RVtaMRFQmXcP7nG1hGj+bWb2va9sPbIPXGDdbG7GJIWBifNxj5crG3Z96vy9hz4jhRkQMaLe/exYXV877B9a5jqn/Pnrz86X9YsnYNn732ula5wB7ejBk4sMlta2vdHcHD3oYp4cEcSkzhwzU769OzCkt4ZewghgV6sy8+udHy1/MLeXTBb9ws1AzuYq9c4+vHJvHk0HA+XLOrPv2Po9rH58ZTFygqr2ByWDC+XRxIyszVymMslfDquMFs+jOeSJ+OnR3w/90zAD///DO7du1qPmMjLly4wMcNbvZ/tf4+3dDX02PnuUsa6fsTkqmsqWGAb9MHSH5JuVbjHyDhRhalFVW42lq1aDvyS8oBMDWS1afJDA0ZHerL6ZQb9Y1/I8lfE1NeiD2OWq2m/6gxGul9Bg9DIpURd+Jok+Wt7ey1Gv8A3QOCMDY1IycjQ2e5ooIC9m5cw7CJ07Bq5XCmqYUlhhIJFfLyVpW7VyahQejp61N65IRGetnJM6iqqzHpFdJkeWVhkVbjH6DqSirKcjkSJwcdpWrpSSRg2PgxIvOqPZ7L/9S8eCpvFVKVlo5RDy8MrCyb3L72snvfPtRqNTOnTdNInzR+PEZGRuzcs6fJ8i7OzlqNf4DwPn2wtLAgJa3xwL2iooKqqqpGPz9z/jx5+flMHDeuvvEP4NOjB71CQ9lz4ACKZqYHtqc9sSdQq9XMGDVaI33C4CEYSaXEnDjWZHlne3utBjpAWEAgFqZmpDRy/gFUVFVS1cSUkHupu72dPX4UtVrNkDHjNdL7DRuJVCbj9NHDTZa3tXfQavwD+ASFYGJmRlbGDZ3lCgvy2bHmD8ZMewDrFowMbVv9ByqlknEPPNRs3o5gGtar9hq195BGeumRE6iqqjAL79NkeWXBLa3GP0DlpWSUZeVIu2hOkTMfFImBmWltp4NShZ5UAga6m0dG3t0xtLai7GhsfeMfoCYjk8rLVzHt27PRsh1h9/FjqNVqHhwzViN94rDhGMlk7Dp6pMnyLvYOWg10gLCgYCzMzEht5JgCqKhs+ty7l7rb2/CgHujr6bEu9rxG+vazF6mormFksHeT5bOLSrUa/wBnUjMollfSzaFlUwlziksBMG9katZTwyIw0Nfn5/2xLarvXtzX1lpZWRlmZh07LUR6j8OVMpms+Uz3kaeTHSqVipQczbnPNUoV6XmFeDm2bZjfWCrBWGpIRkGFzs+NJIZIDAwwlkrwdnEguk8gpRWVXM2+M/3Ct4sDJjIpabkFPDa4L0MCumMslVAir2R/whXWnjiPqqkxtXaUmZaKnp4ert28NNIlEinObu5kpKW0qd5KuZzqygocGxmm3LriF2zsHeg3akyzQYZKpaKivAyVSkVxQQFHd22jurIS7+DQNm1bW0m7dkGtUlF9/a6Gj0JBzc0spF3bNh1Cz0iGvkxKTbbukSDrCWOxnTEFgJq8fMqOn6TsqOZFTs/QAEDncwi306RurlTomDrU3hKTktDX1yfAT3OYWyaT4d29O4lJSW2qt6ysjHK5HM9uuoP3rxcs4N91UxDdXF2ZNnkyD06dip6ensa2Qe1owd2C/P05ffYs6Tdu4NXId7S3S2lp6Ovp4e/pqZEuk0rp4ebOpSaCnaaUyeXIKyvwdNV9TH7zx2988vOPAHR1dGLq8BE8MHKUxr5qa90d4XrqVfT09HH36qGRLpFK6eLuwfWUq22qt0JeTlVFJc6ubjo/X/vLEmwdHBk8JprTRw/pzHNb+tUrHInZyeOzX8OoQXB5P0k93FCrVFRd05wyiEJB9Y1MpB66f2dz9IyN0DeSUXMzSyPdOMgPVUUF+sbGOL8/p/4aWZWSRuGaTVSn32moSt1rv7sq9ZpW/dVp1zD280bi4EBNVnabtrG1LqWm1J57Xt010mVSKT3c3bmU0rZ7X5lcjryiAq9GppR98+syPvnfYgC6OjkzbdQoHhg9puXnXhN1dwQfF0eUKhVJmZr3p2qFkqvZ+fi6aHcStISpTIqJTEJarnbACbVtLamhASYyKYFdnZkZ2ZtieQWXMrWPD98uDkwOC+Lj9buRV3Xs9B9oxwBg8eLFzJ8/n0ceeYS5c+fi5+fH5MmTmThxIt999x1JSUkEBgayYsUKcnJyWLp0KSdOnODmzZtUVlbStWtXJk2axFNPPYWBgUF9vbefOVi6dClnzpxhw4YN5OXl0a1bN55//nnGjRunsR13PwPw6quvsnfvXg4fPoyNjeb8tNTUVMaMGcNjjz3G3LlzAd3PANxOmzFjBl9//TUJCQnIZDJGjBjBe++9h6mpaXvtRi3WpsaUVlSh0DEkWVgmx8fFAQN9fZSqJoYsdZgcFoyhgQGHE1N1fv78qEjCe7jX//tKVh5LD5zUOCidrS0AGNPTD4VSxR9Hz1BWUUWkryeTwoKwMTNh8e6me/7aS2lRISbm5hhKJFqfWVhbc/1qMgqFAsMmep91Obh1I0qlkp79tYfl40+eIPnCOZ557yONY7YxeTczWfD+neF5I2MTBo2byKBxE1u1TffKwMICVbkcdD2vUFyKzMMdDAx0ft4Ui+FD0DM0RH5Gs4dFrVJRcfESFUnJKItLMbA0x7Rvb6wnjkPq4sytNRvr89bk1A6Jyrw8Nebo6kkkyLrWBmH3awQgLz8fK0tLnZ0KDnZ2XEhIoKamBomOY64pP69YgUKhYHxUlEa6oaEhgyIjiYyIwM7WlvyCAjZv3868BQtIvnKFD999tz5vfn5+/Xbc7fbc/7z8/PsWAOQVFWJpbo5Ux76wt7Ym/uoVahQKJK08/5Zu2YxCqWRspOZUAwNDAwb27EW/4BDsrKzILypi6+FDzP/jN65cT+efTz/b5ro7UnHhLUwbuU5ZWtuSlnwZhaIGQ8PWHVO7N65DqVQQNmio1mdnTxwl8dwZXvno02avU0qlklU/LsI3OISeTUyZ7GgGVhaoyspBx4OSyqJijLp7tukaZTl2FHqGhpSd0JzWZOjoAPr6OLzyHPIzcRRvj8HA1gbLsaNwfPMlsj/9pr5Bb2BVe99TFGp3QtxOM7C2vG8BQH5hIZbmFjrPPQdrG+KTk9t27m3cUHt+DBqskW5oYMDA3n3oHxqKnbUN+YW32HrwAN8s/5Xk9Gu8//wLba67I9mZm1Isr9S5kEl+aRlBbs4YGujrbGs15dFBfZAYGBATp7tD6J1Jwxnsfyc4S8zIZv72Q5RVao6cGOjr8Wb0ME6n3ODgxbZ1BLTWPQcAKpWKf//736xcuZI33niDZ5+9c+FNSEggJiaGBx54gMmTJ9enX758md27dzNy5Ejc3NyoqanhyJEjfP3112RkZPBvHfNiv/rqK+RyOTNnzgRqA4PXX3+dqqoqpkyZ0uj2TZ48mZ07d7Jjxw4eeeQRjc82b95cn6c5ly5d4vnnn2fKlCmMHz+eU6dOsW7dOvT19Tt0ypDM0FDnAQvUP0Uukxggr2r5QRvW3Y1xvf2Ju5bJwUTdB9r62Dj2XkjGwkSGv6sTbnbWmBlpjo4YS2svOGZGMt5asaV+eCz2Sjr/nDqKQf5ebDmdoPGQcUeprq5q9KZpKKltwNVUV7UqAEj48yTHYrbTIzCYXgOHaHxWIS9n+8rl9B40DLfuTQ8d3mZtb8+sN99DqVBwKzeH8yeOUlUhR6moaVEA0V70pRLUjUwPuZ2uJ5GgbsXN1TgoAPNB/am4fEVr+k71tevkL/tDI6385BnsnnoU0769KDt1hupr12vTz8ZhMXwIllHDUFdXU3klBQNTUyxGDUPftLY3Ur+VDe62qqyqarRxfzsoqKysbFUAsO/gQX5fvZqIsDCix2oO2YcEBfF1UJBG2qTx43nl7bfZtmsXE8eNq384uLJuepCuBzRlDbbtfqmqqkbayPl3u2FSWVXVqkbI/j9PsTJmJ+GBQYwfqBmAh/TwJuQVzfNu4uAhvD7vK7YfPUL0oMGE3PVwcEvr7kjVVVU6G/8AkrrraXVVdasCgPMnj3Ng+xZ8g0MJHzJM4zN5eTkbl/9Cv2Ej6NbE/rht/9ZN5GVn8dTrbzebtyPpSaWNX6PqVmvRk0pRV+gewdbFpFcIFiOHUJFwifJjJzU+0zeSoWdgQFnsaQoaXKuq0zNwevMlLMdHkf/jr7V5b59zOrav/vp5Hx+crqyqRtrI1FuptI3n3slY/ti+jYjgEMYPGaLxWYiPLyE+vhppE4cN5/UvPmf7oUNEDxlGqK/m5y2tuyPJJIbUNHJPu92WMpIYUqZseoWphgb7e/FA/56cupquNU37tmUH/2TL6QQsTYzp2c0VL0dbLEy0p//M6N8LV1tL3l+9o8Xff6/uKQCorKzkjTfe4NChQ3zxxRdMmjRJ4/MrV66wdOlS+vfXfPgrLCyMffv2aQwVzZo1izlz5rB27VpeeuklHO5aIq+wsJAtW7Zgbm4OwMyZM5kwYQKff/45Y8eOxaiR+VQDBgzA3t6eTZs2aQQAarWaLVu24O3tjb+/f7O/9fLly6xatYrQ0NrpGg8++CBlZWVs2LCBd955p8NGAaoUCiyNdf82ad10iaqaljfUQj268NLogaTlFPDtjsbnm94oKOJGQe1SaccvX2N4UA/emTScf62NITmrdhrQ7ZPmSla+1ty4I5dSCOjqhJ+r430JAKRSGWWlur9HUVN7QkukLZ/edTnuHGuXLMTFvRszXnhFa1hz16rfUatVRE1/sOXbKDOie8CdBl6vgUNY9NG7FCz4hllvvttEyfalqq7BwEz38apXd5NobinQhox8e2D70DRqMm9SsGJ1ywqp1ZTuP4yxTw+Mfb3rAwB1RSV5S5Zh8+BUbKbfuZ5UpqRRcvAoliOGoKpsfG58ezKSyShspIFRXTfvtbHrji7HYmN5/5NP8PX25rOPPmrRULm+vj6zHn6Y2FOnOBYbWx8AGNVNVazRMf+2qg3bdq9kMinyEt0rn1TXHUtGrZheeTzuPB/9sBhfDw/+8+LsFu+rx8ZHczIhnuMX4hoNANpSd3uRymSUFuu+TtVU101xk7W88Xjx3BmWL5yPazdPnnjlTa3fsvn3ZahUaqIffLTZuvKys4jZsJZRk6fpfM7gflJXV6Nfd6+/m15dY7e5pUAbMgr0w+6pR6m+nqFzEQF1TQ16BgaUnzilkV6VfBVFwS2MfO704Kpuf6+OBnX99bMV23avjGRSbhXrDvarq9tw7p07x4cLF+DbrRv/eeXVlp97EycReyGOE+fPNRoAtKXu9lJVo8BYqnvp5tttqeaWAm0ovIc7c6eMIvlmLh+tbfy507TcAtLqnvXdn3CF6N4BfPFwNK8s3UDCjdpRoi42ljw+uC8rDv9Jlo7nDDpKm59UKS4u5oknnuD48eMsXrxYq/EP4Ovrq9X4h9ob0+3/8dXV1RQVFXHr1i0GDBiASqUiIUF7JZqZM2fWN/4BzM3NefDBBykuLubkyZNa+W8zMDAgOjqa+Ph4UhrMhTt58iQ3b95sUe8/QGhoaH3j/7aIiAgUCgWZmZktqqMtCssrMDeWYajjoSJrMxNK5JUtnv4T4u7Ca+OHkHGriM827qWiuuWNvCOXaqcKjWjwoExB3TsEiuXajaSi8to001bczO6FBQ+fQAAAIABJREFUuZU18tJSFDoariWFhZiYmbe49z85/jwrF36Dg4srs958FyNjzXmwN6+lcfboQSKGRyEvK6MgJ5uCnGzKS2pv7GXFRRTkZOvcloZkRkb49w7j6sULFOTevxWUlCUltb3pOkYdDCzNUZaVt3ho3cinO3aPzaQmO5fcH39F3cSDq3dT3Kp9kPh2z/5tNdk55MxfRNbn35C76CeyPv+GvP/9Uv98QE3e/VkG1N7OjqLi4vrGfkO5ddODWtr7f/zkSd56/308PTxY+PXXmLWiw8DFqbYxVtSg4WhXN80nN1/7vQh5dWn3cxlQeytriktL6xv7GttTWIiVuXmLeyBPXLjAuwu+o1uXLsx/821Mm3nfQkPOdrXLpRaXlrZ73e3B0tqG8kauU8WFBZiaW7S49//S+bP88s1/cXbtygvvfqg1X/9GWgonD+5nUNQYystKycvOIi87i7K646ikqIi87Kz6bdn02zJMzMwI7htenzcvOwulSolSoSAvO4viwvuzxr2yqAR9M1Mw1HGNsrJEWVrW8mtUgC8O/3iS6qwscucvRq2jA0FZN3VH1/KdyuIS9E3uHCfKoto8htbaUxFvpyl1TA/qKHbW1hSXlug893ILb7Xu3Dt/nne++Zpurq58++5cTFvxDIhz3VLFRY2de/dQd3vILy3H0sQIiY62lJ25GUXlFS2e/hPW3Y2PZ4zhWl4Bb67Y0qr5+rsvXAZgQp87K//9Y1QkJRWVHElKpYuNZf2fgb4+hgYGdLGxxMas/fdXm0cA3nnnHeRyOb/99ht9+uh+It9DxxrXULuE3ZIlS9i8eTPp6emo73pQtERHT5LnXQ+XAXh51T7wmdHMKg6TJk3il19+YfPmzbz+eu2SU5s3b64PDlqia1fth1WsrGpX0Ckq0v1SkfaQmp1PiLsLXo52XL55Z8koiYE+7vbWOpeR0iXY3YXXo4dws7CY/6zfQ3lV63ooJAYG6Ovra0wDSsmubWjoOjBvp5XI7880hC7dPLl68QIZaSl4eN/pfaipqSbrejoePo0PSTZ0JT6OPxbMw87ZhSfmzMXYVPuh9aJb+ajVavZtXMu+jWu1Pt/2+zIA/vHBJ3S566HkuynqGpcVZWWgY6WSjlB9IxNjnx5I3Vypbrguv6EhEhdnnQ+36WLk3R3bxx+iJi+fvCXLUFe07v+1oX3tqgnKMt2rICkKbmm8VMfIxxtVRSXVaddb9T1t5e/rS+yff3Lx0iV6htxZGamqqorkq1fpedda/Y05ceoUb/3zn7i7ufH9vHlYNNKz2Zjrdde3hs8w+df1sMVfvEj4Xdff+MRETE1Ncddxzeooft26cTIhnsTUVEJ97vS8V1VXc+V6OqEtPP9i4y/wzoL5uDs7892cd7Bo5cjqjZzaHjVrC+3G2b3W3R7cPLuTdOE86SlX8PK9M/JcU11NZvo1jbSmXIo7x8/z/oujSxdemPsRJjoW1yjMr71O7Vi7ih1rtZf9XL/sJwDe+OS/uHl1pzA/j+LCW3w25xWtvACfvPYi/j1789xbc1u0jfei+tp1jAN8kXm4U3W1wXNqhoZIu3ah6oruZ9fuZuTvi/0/nqztoJi3GJWOziqAqrR0JM6OGFhbUXNTc+6+gbVVbcBxe9vSa68/Mk8PKi9pLhsp7eaBqqKCmtyW3Zfbg5+nFycvXCAx5SqhvncWLKiqruZKerpGWlNi487zzryvcHdxYcHc97Fo5YItN7JrH6y2sdRx7t1j3e3h8s0cwrq74dvFkfjrdx4Clxoa0N3JjgvpN1tUT18vNz6eMZbr+YW8sXwzZa0ckZYYGGCgr495g5kdTlbm2FuY8euLD+ss8/vLj3Ii+Rrv/rGtVd/VnDYHAGPHjmXDhg0sWrSIRYsW6RxubuxNmZ9//jkrVqxg7NixPP/889jY2CCRSLh48SJfffUVKh092vcyVOTj44Ofnx9btmzhtddeo7KykpiYGCIjI7HX8YIdXZqao313ANOeTiRfY2JYEGN6+mkEAMMCvTGSSDiWdOdCaGVijIlMQn5pucZb5oLcnHkjeghZhSXNNv4tTYwo1tFoHx1aewO/knWn9zWvpIykzFy8XezxsLfhWl5tY01PT49hQT1QKFVcuJ6lVVdHCArrx+Htmzm+e6dGAHD60H5qqqsIibjzQFtpUSGVFXIsbeyQNhgavZJwgd8XfI2dkzNPvjVX500VwLWbFw++oP3G1rSkRE7u301k1Di6evXApq5BX15SgrGZmdbLWEqLi0g4HYvUyKjRl4x1hIq4BCyGDcJ8YD8KGgQAZuG90ZdKkZ+78w4AfXMz9I2MUBYVa0wLknl7YTvrIRR5BeT9sBRVE3Nx9U2MtW+8BgZYjqydr1yZ2PxqOmaR4UidHSnevb9V05PuxcihQ1n622+sXLdOIwDYtG0blZWVGu8AyC8ooKysDCdHR41rYeyffzJn7lzcunZl0bx5WFpYNPp9RcXFWN1186yurubHZcsAGNRgNLV3aCh2trZs3r6dh6ZPr18KNPnqVc6eP0/0mDGtfuD9XgwPi+DXbVtZvXuXRgCw5dBBKqurNd4BkF9URJlcjpOtrcbUhJMJ8bz93XzcnJxY8PY7WDbRSCguK8XSTDOQqq6p4edNtQ+UD+jZU+Oz1tTdkXr2i2TP5vUc3LlNo7F/Yv8eqquq6N3gHQDFhbeolMuxtrPXuE4lXTjPz19/gb2zMy/O/RemZroDSnevHjzx6pta6VcTL3Jk906GjpuARw/v+uk+Ex9+XOeSxGt/WYKhRMrkR2dhYWXd5t/eGuWnz2ExZgTmIwZrBADmA/uhL5NRfvJ0fZqBpQV6xkYobxWibjCqbeTvU/tir5w8cuZ9j0qu+dZ7je87eRqz/mGYD46k8uKd65FxcACG1laUHj5en1aZfBVFUTFmAyIo2XuwfilQiasLRj7dKTt2sun3CLSzEf368+vmTazauUOjsb95/z4qq6o03gGQX1hYe+7Z2WmeexfiePvrr+jq7MzCue83fe6VlmJprn3u/bS+9h04A3r11visNXV3pP0JV3l4YB+mRYRqBADjegVgLJWwN/5yfZqNmQlmRlJyisuoajAtqI9XVz55cCw3Cop4/dfNlFY03vi3MTPhVpn2MTc1vLbjKDHjTqC5ePcxrWcsAV4dN5hqhZJFMUcpKG3/5cLbfIeIjo6mX79+vPXWWzz33HP873//a7TBf7fNmzfTt29fvvnmG4309PT0RkpASkoKw4cP10oDcG3Bm+QmTZrEZ599RmxsLHl5eZSXl7d4+s9f6UZBEXviLhMV6str4wdzPi2TLnVvAk7MyOZY0p3l9R4c0JPB/t3597oYLtWty+/pYMubE4YCehxKTCHUw0XrO442qOPLRyeQdDOXa7m3uFUmx9xYRpCbC0FuzlzPK9R60OXXg6f4cHoUc6eOZNf5JMoqqojw9qC7kz3rY+M65KDVxamrG+HDRhK7bzd/LJiHd3AoeVmZnNgbg4ePH8ENAoDd61Zx7thhnnz7fTzrbsKZaSn8/t1XoIZeAwaTfOG81neE9q9dLcTC2obAvtpvwa6uqg2cunr10Pg8LvYox3fvxL93X6ztHDAwNCA/O5tzxw5TKS9n0qxnNG7wHa0mO4ey46cwHxABj82kMikZiUPtm4ArU9I0AgCrsSMx7dOL3MU/148MSFxdsJv1MHrUrtdv5Kv9EHTDF37ZP/0YypJSqjNuoiwpxcDCHJNeIUjs7Sg9eoLqG5pT6OyefBTFrVsocvJQo8bIuzsmgf5UJF6mZF/TSxi2p+5eXkyfNIk1Gzcy55//JDIigrS6NwH3Cg1l9IgR9XkXLlnC9l27+N/8+fSua3wmJiXx5nvvoQbGjxnDcR1TFRu+8OvlOXOwt7PD19sbezs78vLz2bVnD9czMpgxZYrGcqSGhoa8MXs27/3rXzwzezaTxo+nXC5n5dq1WFlZ8ewTT3TcjtGhe9euTB02gnX79vDOgm/pHxzCtZu1bwLu6ePLqIh+9XkXr13NjmNH+f7t9+hV95supaXy1rffgBrGDRjEiQvaL6Jr+MKv177+Ejsra3w8PLC3siavqJCY48e5kZPN9BEjCfC8M/LW2ro7koubOwNGjubI7p38PO8L/EN7kZ1Z+ybg7n4B9G6wItG2Vb9z6vABXnr/3/Soe1Hg9ZSr/PTV56hREz54GInntV821Hdg7coqljY2hIZrT8Gtqns43KOHt8bnPkG63/+x+bdfkRoZ6ayro9RkZlF68CgWwwah9/wTVCRcQuLkiPnwQVRevkp5g5eAWU0ej1n/MLK/WkhVcu2iFlL3rti/8BR6enqUHT+FcaB2L3j5yTP1/115KZnyk2cwDe+Nw+xnkV+4iKGtDebDBqIoKqZ4a4M53koVhas3YPfM4zjNeZnSIyfQNzbCYsQQVKVlmnnvg+5ubkwdOYp1u2N4e95X9A/tybXM2jcB9/TzJyryzrG9aNVKdhw+xPfvf0Bv/9olhC+lpPDWV1/WXqcGD+H4ee17X8MXfr36+afYWdvg260bdtbW5BcWsuvoUW5kZzE9ajQB3e88L9HaujtSWm4Bm07FMyU8mH/PGMPJK+m42VkzNTyY89cy2dvgJWDPjujH6FA/Xl22kfPXau9PPi4O/OfBcejpwa7zlwjvob0U7Z4Ld+pY+sJM4q9nkZyVR35JGZYmxvTx6kpvz66k5OSzLvbOPfJMqu5ZLP8YFUlFdQ2HEtu2lGtz7qmLaNy4cRgYGPDmm2/yzDPP8MMPP7ToYVh9fX2tXnO5XM6yup4uXVauXKnxHEBpaSmrVq3CwsKCsLCwZr8zOjqaL7/8ks2bN5OXl4e5ublWQPF39euhP8krKWNYYA96erhSWllFTFwSa0+cp7mxB1c7K6R1PYGPDe6rM0/DAGDX+SSC3FwYFeyDqZGMaoWCrMISVh07y65zSVTdtfLBtbxbfLhmJw/068mYnn5IDAy4eauYxbuPcbiDDtrGjH3ocazs7Dl9aD+XL5zDxMyciOFRDJ88vclXoQPkZGbUz4XdsXKFzjy3A4DWcvf2JSMtlaTzZykrLkKpUGBqYYmXfyD9R47BrUfLVhFqT0VbdqAsLMQ0vC/Gft4oy+WUHYulOGY/Tb4PHZA4OdavxGM9cazOPA0DAPmFRIwDfTGLjEDf2Ah1dTXVmVmU7N6P/Hy8Vtnq6zcwCQnEoE9tQ1qRm0fhhq2Uxf7Z7La1t9dnz8bZ2ZmNW7dyLDYWK0tLZkyZwnNPPtnsMZWSllb/QO43CxfqzNMwABg+ZAiHjhxhzYYNlJaVYWxsjE/37jz7xBNENQg2bhsxdCgymYxfVqzg28WLkUok9O3dm9nPPYdDC0c229OrDz+Cs50dmw8d4HjceSzNzJk+YiTPTJ7a7L5Kzcion8P87crfdeZp2Egf2ieMw2fPsG7vHkrlcoylMrzd3Xl68hSNYKMtdXe0KY8/iY29Ayf27+HiuTOYmVswKGosY6c/2Ox+ysq4Tk3dogYbVyzVmed2APB/XeHqjSgKbmE+sD/GQQEoy8oo3X+Eoi07mr9GuTjXr9ZjM0N3R1/DAAAgf+nvVGfcxCwyHJsZk1HJK5CfiaNo03atZwPkZ+LIq/4Jy3GjsJ4+EXWNgsqkZIo2bEV5H95RcrfXHp+Fs709m/fv4/i5c1iZmzM9ajTPTn+g+etUxg2q6s6P+SuW68zTsJE+NDyCw6f/ZG3MrtpzTybD28ODZ6ZNZ1Sk5nnU2ro72sJdR8guKmF87wAienhQLK9gw6l4lh442eytpZuDDbK6B9BfGq17mxsGAOtPXqCvV1cm9Q3CwlhGlULJjfxCluw9wYaTca164Lij6KlbOX/l9rr8y5cvJzy8tpdz7969vPrqqwQGBvLTTz9hZmamcz392z744ANWr17NmDFj6N+/P/n5+axfvx4rKysSEhL47LPP6pf2vP19AQEByOVypk6dilqtZsOGDaSlpfHJJ58wffr0+rrvfg9AQ88//zwnT56kqqqKadOm6VxutKn3ANz9W3Tti+bMnK/7JBDuWPnqY6w93vhr3oU7pvfvxY057//Vm/G31/XLjynJvj/rcv9fZ+HkxK27VkMRtNn0C2PX2Yt/9Wb87Y3uFUD6s9pTJgVt7kvmU3hWu5dc0GTdK5QhH+nuVBE0HfzopUY/a5dJoiNGjGDhwoXMnj2bJ598kp9++qnJ/O+++y6mpqbs2rWLffv24ezszIwZMwgKCmLWrFk6y7z55pucPn2a33//nfz8fDw8PPjqq69a/BAv1K73f+DAAQAmTry/L18SBEEQBEEQhL+DVgcAU6ZM0fnirSFDhhAff2c4//Lly1p5bjM2Nubtt9/m7be1XzbSWDkDAwNefvllXn755Sa3b8UK3dM3AKKioprcrsa+v7Eyje0LQRAEQRAEQfi7avN7AARBEARBEARB+L9HBACCIAiCIAiC0ImIAEAQBEEQBEEQOpH796aYNhLz7AVBEARBEASh/YgRAEEQBEEQBEHoREQAIAiCIAiCIAidiAgABEEQBEEQBKETEQGAIAiCIAiCIHQiIgAQBEEQBEEQhE5EBACCIAiCIAiC0ImIAEAQBEEQBEEQOhERAAiCIAiCIAhCJyICAEEQBEEQBEHoREQAIAiCIAiCIAidiAgABEEQBEEQBKETEQGAIAiCIAiCIHQiIgAQBEEQBEEQhE5EBACCIAiCIAiC0ImIAEAQBEEQBEEQOhERAAiCIAiCIAhCJyICAEEQBEEQBEHoREQAIAiCIAiCIAidiAgABEEQBEEQBKETEQGAIAiCIAiCIHQiIgAQBEEQBEEQhE5EBACCIAiCIAiC0ImIAEAQBEEQBEEQOhE9tVqt/qs3QhAEQRAEQRCE+8Pwr96AzuY/62P+6k3425s7NYrYy6l/9Wb8nxDh40nOH2v/6s3423N8aDr5MXv/6s34P8EuagTFGRl/9Wb87Vm6upKek/dXb8bfnrujPVcys//qzfg/oUcXJ85eTf+rN+Nvr1d3d/5MTvurN+P/hL7e3Rr9TEwBEgRBEARBEIRORAQAgiAIgiAIgtCJiABAEARBEARBEDoREQAIgiAIgiAIQiciAgBBEARBEARB6EREACAIgiAIgiAInYgIAARBEARBEAShExEBgCAIgiAIgiB0IiIAEARBEARBEIRORAQAgiAIgiAIgtCJiABAEARBEARBEDoREQAIgiAIgiAIQiciAgBBEARBEARB6EREACAIgiAIgiAInYgIAARBEARBEAShExEBgCAIgiAIgiB0IiIAEARBEARBEIRORAQAgiAIgiAIgtCJiABAEARBEARBEDoREQAIgiAIgiAIQiciAgBBEARBEARB6EREACAIgiAIgiAInYgIAARBEARBEAShExEBgCAIgiAIgiB0IiIAEARBEARBEIRORAQAgiAIgiAIgtCJiABAEARBEARBEDoRw796A4SWCevuTk9PV6xMjJFXVZOYkcPhxKvUKJVNltPX0yMq1A9nawssTYyRGhpSVlnJzVvFHL+cRk5xqUZ+MyMpfbzccLKyxMnaAlOZlLhrmWw7k9Bo/RHeHgS5uWBlakK1QsH1/EIOXrxCQWl5u/3+llCpVOzeupmDu3aQn5uDuaUlYZGDmPLwo8iMjJosq1Ao+G3JYlKvJFOQm0tlhRwrG1s8vX0YP3U67l7dNfInJVzg1NEjXL6YQH5uDhKJBEcXV0aMiyZi0GD09PTq816Kv8Dnc99u8vvnfv4V3v4Bbf/xraRSq1gXe4ItZ/4ku6gIS1MThvoH8dTQ4RhLpU2WVSiVzN+5jaSbmeQUFSGvrsLW3By/Lq48HDkIb2eXJsun5GTz9JJFKFUq/j39QYb4B2rlOXHlMssPHyQlJxuJgSG9PT15fkQULtY29/S7W0ulUrHm0EE2HztK9q0CrMzMGNazF0+PHY+xTNZkWYVSybx1a0hKTye78BbyyirsLC3xc3fn0RGj8O7aVSP/8YsJbDp2lJSbmRSWliI1NMTZ1o7RfcOYNGAgMomkPu/ZK8nMXvBtk9+/+NXXCfb0avuPbyWVSsWqDRvYuG0bWdnZWFlZMWLwYJ6bNQtjY+MmyyoUCr5csIBLly+TlZODvKICO1tbAnx9efzBB/Hp0UMj/9HYWDZu28bV1FRuFRUhlUhwcXJi7KhRTImORtbgGG5t3R1NpVKxcd1atm/ZTE52NpaWVgweOpTHnnq6Rfvp+/nfcDnpErk5OVTI5djY2uHr58eMhx+hu7e3Rv4L589xaP9+4uPiyMnOQiqV0aVrVyZOmcKQ4SM0rlO361+78g/2xsSQnXUTI2NjQkJ7MuuZZ3Fzd2/3fdEUlUrFlvXr2LVta+1+srJkwJChPDLrSYxasJ/+t+BbriQlkZeTg7xCjq2tLd6+fkyb+RBePTT3U3zceY4ePEDChThys7ORSKV0ce3K+EmTGTRsuM79tGH1Svbv2U12VhbGxsYEhYTy6FNP09Xt/u4nqN1XuzZvZN+u7eTl1N77IgYOZvojj2Fk1Py+Wva/70lNvkxeXi6V8gqsbW3w8vZlwvQZdLvr3pcYf4HYI4dISognLzcHiUSKc5cujBo/kf6Dh2jsq8QLcXz87pwmv/+jL7/B5z7d+1QqFTFbNrG/QRshfMAgpj78GEYtaCMs/2ERqVeSyc/NpbKiAmsbGzy9fYie9gAed+2nS/EXOHn0MEn1bQQpTl26MHJcNP0G3bWf4uP49L2m2wgffPF1u7cRDD766KOP2rVGoUlHLqW0uszIEF8G+Xfnen4hf169jry6mr7d3ehqa0X89ZtNljU0MCDS15OMgiKSs/K4fDOH4vIKerg4EN7DgxsFRRTLK+rzO1lZML5PEAb6euQUl2JjZkpOUSnJWbk6638gshe9Pd24UVDI2dQb5JWU4dPFkV7dunIlKxd5dU2rf+8g/+5kFBS2utzvP/3A5lV/4BMQyMjoiVhYWbF32xauXEqk/9BhWhfxhmqqq9m6djXefv70Co+gd79I7BwdOX/qJDFbN9HD1x97J6f6/Iv++xmpV5IJ7tWH/kOG4eXjR2Z6Gnu2babwVgE9wyLq80qlUrp286R3v0iNv5A+YZw/fQoLC0tmPvUM+vqtH5BztbOmPD6x1eW+27WDXw8fINjdg2nhEVibmrH+VCzxN9KJCgltcl9VKRT8duQQQV3diPT1Y5CvP85W1hxPvsza2BMEurk12lBXqVW8t+p3SisrUCiVDA0IxMPeQSPPoUsXmbvqDyxNTHh4wCB8nF3YfzGenefPMSIoGJNmGt66mAUFIE9JbXW5bzesY+munYR4dWf64KFYm5mz7vBB4tNSGd03rOn9VFPD8t0xBHl6MjAomMEhoTjb2nD8YgKrDx4g2NMTF1u7+vwH485TUFJMP/9AhvXsRbBXdyqqKll1cD8X069pfJ9MIqFHF1cGh4Rq/PULCOT4xQSszM2ZPXlqm44pk+6eVJWUtLrcvO+/5+cVK+gZHMyMKVOwtrJizaZNxF28yNiRI5veV1VVLP3jD4IDAxkcGcmQAQNwcXLiSGwsK9evJzgwkC7OzvX5Dxw5Ql5BAZHh4QwfPJjQoCDkFRX8sW4dFy9dYkyD72tt3S1lZGFBcbm81eUWf/ctv/+6jKDgECZNm4aVtTWb16/jYnw8I6Kimt1PK39bTkBQMP0jBxI5aDBOzs7EHj/GxrVr8A8MwtnlTgD+n48+JDkpib7hEQyPisI3IIBrqSlsWreOgvx8+kUOqM+rVqv54J232b5lMwFBwURPnkw3T0+OHj7E9i2b6RcZiZWVdat/r5WZKbdKy1pdbsn3C1i14lcCgoOJnjIVS2trtm3cwKWEBIaOHNXkfqquqmLN77/hHxhIROQA+g8chKOTE6dOHGfz+nX4BQTh1OD/+X8//hfJSUn0Dgtj2KhR+Pj5k56WxpYN6ynIzyO8f2R9XrVazb/nvsuubVvxDwxi3MRJuHfz5PiRw+zaupXw/pFYWlm1+vcC2FqYkXWruNXlli9ZzIaVv+MbEETUhElYWlkRs3UzlxMvMnCYdqDXUHV1FZtWr8TbP4A+Ef0J6x+JvaMTZ0/FsnPzRrz9/XFwurOvvvviP6QmJxPSuy8Dhw2nh48vN9KvsWvLJm4VFNA7vF99XolUhns3T/r2j9T469k3jLN/nsLC0pJHnn6u1dcpZxsrbhYUtXo/rfjxf2xa9Qe+gYFERU/CwtKKPXVthMih2oGe5n6qZsvaVXj7+dM7oh99+vXH3tGJc6dOsmvLJrz9/HFo0EZY+GVtGyGkdx8GDB1O97r9tHvrZgoLCugV3rCNIMOtmyd9+kVq/IX2CePc6dr99NBTz7bpet7FtvFzVowA/M3ZmZvS18uNpMwc1seer08vKq8gKtSPgK7OXLyR1Wj5GqWSX/bHaqWfTbvBS2MGE+HtQXrerfr07KISvtm6H3l1DcZSCa9HD2u0bm8XB7o72XM29QY7z91phMZfv8mzIyIZFerHH0dOt/Ynt0nG9XT2bttCn36RzH73n/Xp9o6O/Lbkf5w8coh+g4c2Wl5mZMS/5n2nlT5s9Dhef+oxdm5aj39IaH36A7OexNsvAH0Dg/q0UdET+Xzu2xzavYtR0RNxdfcAwNLamsih2vvxxKGDqFUqIocNx9Dw/p2Kabk5bDgVyyA/fz554KH6dGcra77dtZ19CfGMDApptLyxVMqPz76glT6hTxjT53/JquPH6N1Nd8/z+pOxXMvNZWbkAH45uF/rc4VSybc7t+FgacGCJ57GRFrb2A/v4c0zSxax9OB+5kRPau1PbpPUrJusO3yIwSGhfPrUM/Xpzra2zF+/lr1nzzCqT99GyxvLZPwyR7tXZ1LkQKZ8+E/+2L+X3t4+9emPjhyllXf64CFYrVnNhqOHuXQ9Hf+6Y8rGwoKovmFa+fecOY1KrWZ03zAMGxy97DNaAAAgAElEQVSbHS3l2jXWbNrE0IED+aJBn5KLszNfL1zI7gMHGD18eKPljY2NWb54sVb6lOhoomfO5Pc1a+jbs2d9+uMzZ2rlnTF5Mv/99lvWbdlC4uXLBPj6tqnujnQtLZXNG9YzYNBgPvjkP/XpTs7OLPp2Pgf37WWYjuPgNmNjY77/8Wet9HETJvLI9KmsW7WSnr1716c//fw/CAgKxqDBsTB52nTmvPIyO7dtZdK06XTz9ATg+NEj/HkylrHRE3h1zlv1+UdERfHs44+x6Nv5fPFN06NO7SU9LY1tGzfQf+Ag3vvXx/XpTk7O/LDwOw4f2MeQ4SMbLW9kbMz8/y3RSh8TPZEnHpzOhjWrCOnVqz591rPP4R8YpLGfJkydxnuvv8ruHduZMHUaHt1q91PssaOcOXWS0eOjeen1N+vzDxs5ihefmsUPC7/jP1/Nu5ef3yo30q8Rs3UzYf0H8NrcD+rT7R2d+PWHRZw4fJDIIY3fx42MjPn02++10keMHcfsWY+wfcM6AkPunB8zn3gaX3/Ne9/oiZP5+N05HIjZyZgJk+jq0Q0AK2trBg4boVX3sYMHUKtUDBo24r7d+zLSr7Fn2xb69ovklffer093cHRi+ZLFxB4+RP8hjbcRjIyM+PibBVrpw8aM5dUnH2PHxvUENGgjPPj4k/jctZ+iJkzi0/fe5uDuXURNmETXBm2EAUO1r4/HD9XupwFDO6aNIJ4B+JsL6OqMnp4ep65c00g/l5ZBtUJBoFvre64AyiurUShVGDWYVgBQrVC2uNfe3b62lzcuPVMjvai8gusFhXRzsMXCuOlhtfYSe/ggarWaURM0G4eDR41BKpNxXEdjsyUsLC2RSKWUl2n2YPkGBmuc2AD6+vr0retRy0i/1mzdh/fsqt3GkaPbtG1ttTfhAmrUTA/vr5E+vncfjCQSdl+Ia1O91qamdVPMKnR+nlNcxE8H9jJryDAcLXX3kJ1PTyO/tJTxPfvUN/4Bejg5E+rRjf0X41E0M+2tvew9cwa1Ws0Dd90UJvSPxEgqJeb0qTbVa21ujlQioVSuez/dzcmm9jwrlTff27z1xDEAovtFNpOzfe3evx+1Ws2DU6ZopE8aNw4jIyN27d3bpnqtrayQSaWUlJY2nxlwcnQEaFH+1tbdHg7s3YtarWby9Ac00seOj0ZmZMS+3bvbVK+VtTVSqZSyMs3fEhzaU6NRC7XXqYFDhgC1AcltcWfPAhA1dqxGfmeXLgQGh3DuzBlyc7LbtH2tdXj/PtRqNROmTtNIjxo/HpmREQf27GlTvZZWVjr3U1BIqM79FDloMFAbkNx24fw5AEaMHqOR38nFBf+gYOLOniE3J6dN29cWxw8dQK1WM2biZI30YaPHIpPJOHpgX5vqtbS00nnv8w/Sfe8LjxwI1AYkzTmweycAQ6PGNJOz/ZyoayNE3bWfhkSNQSaTcayNbYQ7+0nzmPJrZD+FtaKNcHB3TO02juqYNoIYAfibc7axRKVWc7NQc1hQqVKRU1SKs7Vli+rRA4ykEvT19LAwMSKihwcyiSEp2Xlt3jbDuuEohUK7QXY7zcXGkpLMyjZ/R0ulXUlGT18fz7vmwEqlUty6eZF2JblF9aiUSsrLy1AqldzKz2Pnxg1UVlQQ0rvxnt6GbuXnA2DZzFB5XnY2l+Iv4O0fgLOra4vqbi9JNzPR19PDr4vm98oMJXR3cibpZkaL6lGqVJRWVqBUqcgtLmbViaNUVFcT0d1bZ/5vdmzFxdqG6RH92NNIkJGUWRtMBtw1Px4goEtXzqalcqMgn24Oji3axntx6Xo6+np6+N81p/f29Juk9OstqkepUlEql6NUKcktLOKP/XupqKqiXyPzOcsrK6lRKCivrCQ+NYXf9u7B0tS0vve/MTcL8jl75QrBnl64O3b8/mko8fJl9PX163vdb5NJpXh7eZF4+XKL6lEqlZSWlaFQKsnNzeW3tWuRV1QQGR6uM3+5XE5NTQ3l5eXEXbzI8lWrsLSwINDP757r7gjJSUno6+vjc9f2SWUyvLr3IDkpqUX1KJVKykpLUSqV5OXmsm7VSioqKugb0a/5wkB+bu2UTusGU/Vqamo7fnQ9LyUzqg3GkxITcXB00vq8vSVfrttPvnftJ6kMT6/uXLnciv1UVoqqbj9tXLOaiooK+jSYftGU/Pza+6O19Z3ref1+kunYT3XTE5MvJeJwn87B1Lp7n5ePj0a6VCrF3dOLlOSW3/vKyspQKZUU5OexbcM6KisqCG1ilLOhlt77crOzSLwQh09AIC6u2tf5jlK/n3S1ETy9SG1NG6GsDKVKSUFeHjs2rq9tI7R0PxW0dD9lcyk+Dm//gA7bT/9fBgAbNmzg3XffZdmyZSQmJrJy5Uqys7Pp0qULzz//PJMn10aAGRkZDB8+nJdeeonZs2dr1LFgwQIWLlzIvn37cK1roGVlZfHdd98RGxtLXl4e5ubmuLu7M2PGjPo625u5kQx5VTVKlVrrs9LKKrraWaOvp4dKrf15Q3YWZjw78k6vYGV1DceSUjl2Oa2JUk3LK6ntGXB3sCW35E4vgaGBPi42tYGJhcn9GQEounULc3MLJBLtB1itbW25mpSIoqYGw7tGPO52M+MGc2f/o/7fJqamjJ82g/HTZzS7DYUFBRyM2Ym9kxM9mnlY5/De3ajVagaPjGq23vaWX1qKpYkJUh1DivbmFiTcuE6NUoHEoOnLQ3p+HrMW3xkSNZMZ8ciAQTw8cJBW3n0J8ZxITub7J5/BUL/xqSn5db2xduYWWp/ZWdSm5ZWW3JcAIL+4GEszM6Q6jhl7S0vi01KpUSiQNDM0m56dzaOf35nuYWZszKMjR+mc8gPw6e8rOBh3Z7qfv7sHb0yfgbmJSZPfsy32BGq1muh+/ZvM1xHyCwqwsrBAquMBcns7Oy5cvEhNTQ2SZs6/a9evM/Ppp+v/bWZqyqyZM3n8oYd05v/4v/9l/5Ej9f8O9PPj/7F332FRHokDx7/U3aWDIE2pKqJSrLEr9i7WGBMTY5LL3aVc2pmYakw0XU3P5WJMM7H3ir0j9gaKoHQV6UvZxu7vj4WFZZdmQPM75vM8Pgnj+747OzLzTp9/P/88jg4Of/rZzSE3JwcnZ2ez6eTu4U78pYsNSqe01FSenv2o4Wd7BwdmPDKLhx5+pEFx2LZlM94+PnQJDzeE+wfqp22cO32GoGoLGhUKBVfi9VM8s7PNrwVranm5OYbR15paubuTcPlSg9IpPS2VZ5943PCzvb0D02Y+zPSZD9cbh9ycHHZu2YKXt75nv5J/RUP8wtkzBAZXTXVUKBQkXkkA4M6de5NOoH/vODrV9u5zJzGhYe++zPQ05j7ztOFnO3t7Jk6fwcTpptPtasrLzWXvzm209vImpLPppg7VHdi9C51OR1Qz9WrXJj8vr/Z0cmvFtYamU0Y68579u+FnO3t7xk97kAnTZtQfh9xc9u3cTmsv73oX9B7co0+n5ur9h//RBkClJUuWoFAoePDBB7G1teWPP/7gtddew8/Pj+7V5kk2hEaj4fHHH+f27dvMnDmTgIAAiouLuXr1KqdOnWq2BoCNlRXlWq35OFVMhbCxtkKp1tT5nIKSMlYcPomVpSWu9naE+fkgsbHG2tKy3p2EanMpLYt+HYMY1Kkdak05N7JzsZPYMLBTO+wktob43wtKpaLWjFv5ElEqlfVmbg9PL+YuWIRGo+b2zZscO7CPstISNGq1yRBxzc//4oP3UCoUvPDm/Drn62nLyzmybzcyOzt69h/QgG/XtJRqVa2V+8pGgUKtrrcB4O3iyuJZs1GXl5OZl0fMhXMUK5SoNeVY21allVxRxpe7tjGuW3e6tPWr85kKtcooHubiplQ3fmH53VCoVGbjARgaBQqVqt4GgHerVix95jnUGg2ZOXfYdfIkJWX6Xn5z8/TnjB5DdP8BFBQXc+ZaIkmZmRSW1L2jVrlWy44TsdhLpQzp2q3Oa5uDQqEwW1kDDJVdhVJZb4XNx8uLrz7+GLVGQ0ZmJjv27KG4pAS1SoW1mZ1fnnz0USaPH09+YSGnz50j6fp1CmtZwNzYZzcHpVJRaxpUppNSUfs1lby8vflw8RI0ag2ZmRnsi4mhpLgYlVqNrI7fR4VCwfw35qEoK2PBBx8ZlVNDR4zg919+5pcff0Aqk9K1ew+KCgv55cdlFBUWGuJ2LygVtf+uVJXnDUgnL2/e/+Qz1GoNN7My2L97N6UlJahVaqxkdafTwrffRKEo462Fi4zSKWr4CFat+JXffvoRiVRKZPceFBUWsOKn5dXSSdnYr3zXlHXkK9vGvPu8vHj9/Q/RaDTcvpnJkf379GmlVmFlVXv+UCoULH5/PkqFgn+/vaDed9/BPTHI7Ozo3d+0o6g5qZRKbKybIJ08vXjtvUX6dMrK4uiBfZSVlKCur46gULB00QKUCgUvvfVuvel0eK++jlA5tao5/E83AFQqFWvXrjX8444aNYqhQ4eyYsWKRjcAkpKSuHHjBq+88gpPPfVU/Tc0EXV5OXbW5l+slZUHtZkpOOaek5Jdtdj3fEomTwztw5Q+kaw8cvqu4qZQa/j98Ckm9AxjbPeq1mzqnTyOX71B/9DgehsmTUUikVJUZn5XALVKVXFN/bvHSKRSOkdWLXgaOGwEb7/4HLc/eI9/v7vQ7D0qlYrPFy7gRtI1nvrXy/X2gFw8e5q8nByiRo0xO4zc3CQ2tpSpzO/KodLo/71qrg0xR2ZrS4+gqp7CMV278eR/vuHN1bl89shsQ/g3MTvR6XT8fVj9ox3Sit6ZyniYi5ukAXFrClJbW/KLzc8PV1U0QqT1bJkK+sXAPUOqpsaM7d2HOR9/xOvL/suSfz5rcn2wjy+V/YrDu/dg49HDvPLd13z9rxdr3dbzREI82QUFTOzXv0FxampSqZT8fPM7d6kq8p+0AflPJpPRq1rZPH70aGY9/TTp8+fz5UcfmVzfrmIBK8DIIUNYv2ULL8ybx3+WLCGii3E+bOyzm4NEIqWsrO50qm/LYtB/l27VphyMGjOWfz45hwVvvsEHn5lfgKpSKpn/+jyuXb3KK6+/QViE8UJ/R0cnPlq8lI8Xvc/STz42hIdFRDJ95sP8/svP2Nnb1xu3piCRSigsML9Gpqo8rz+dpDIZkd17GH4ePnoM/3r6KbLeeYv3Pv7U7D0qlZKFb71BUuJVXnx1Hl3CjdPJwdGR9z/5jMUfLuKrxVXP6BIewZQZM1n12y/Y2dc9WteUJBIJhYXm00rViHefVCojrFrnweDho5j3/D9ZsnAB8977oNbnf/b+fK4nXeMfL75Cxy5hdX7G+TP6d9/Q0WMb9HvelGwlEopqWZ/WuHSS0iWyKp0GDR/Jmy88y+eLFvDqgkW1Pn/JwgVcT7rG0y+8TMd66ggXKuoIQ0aNadZ0+p9eBDxz5kyjoVZPT08CAwNJSUlp9LMcHR0BOHHiBLm5uU0VxXrJFUrsJLZYWZpuT+UolVCiVNU7/cccdXk5V7NuE+zpjov93fd+3SkqZtne43yz8zC/HIzjm52H+e2QfqQBuGdnAbi4uSGXF6Gu6EGurnKItL6WvTlSmYweffpy6ewZbt803XJVpVLxxcIFxJ8/x5xnnje7209Nh3brF/rdj+k/AO6OjhSWlpqtZN+RF+FsZ1dv7785drYSBoZ24mRyEpl5+jxy9WYW28+eYXLP3hSWlZKRl0tGXi75FT3aucXFZOTlGuLiXpHPcuSmvbg5FT27HmamBzUHd2dnCit6VWu6U1iIi71Dvb3/5thJpAyKiCDuSgIZd+pfgzOqp36O+sYjh2u9ZmvscYD7Mv0HwL1VKwqKigwv0uru5OTg4uxcb2+tOXYyGVEDBnDi1Ckysure8hhgzHD9zjDrt2xp8mc3hVbu7hQVFppNp5w7OfoFhXeRTjI7O/oPHMTpk3FkZWaa/L1KqWT+G/M4e/oUL/x7LsNGmC97AoOD+XbZcpb/vpJPv/iK5b+v5LMvvzLMe79Xe9y7tdKnk9pMOlVOo7qrdJLZ0bf/QM6eOslNc+mkUvL+W29y7sxpnn35FaJqmaYXEBTMF98v4/tfV/Dhki/0/136heH906btvTsLwLVVK+RFtb37cnB0cr7rd1+vvv25cOZ0re++z96bz6VzZ3nquRfM7vZT04EY/cYX93LxbyVXN7fa0ykv90+lU48+/bhYRx1hycJ3uXz+LE8++y+zu/3UdLCZF/9W+p9uALQ1s5DQxcWFgoLG7x9buX7g6NGj9O/fn8mTJ/Pxxx9z4cKFpohqrW7mFWJpYYFPjcW+VpaWeLo4ciu/8XsGV6ocQZDZ/vke1fySUtJz8smv2Bc72MsdhVpNxl3s1Xs3Att3QKfVcr3GgieVSkXajWQCalmY2hCVL+uaq/zVahVfLHqPS+fOMPuZ5xnYgAp9UUEBZ0+eoG1AIIHt7z5Of0ZHH1+0Oh0JmcaLfZUaNUm3btLRx/eun1054lNUpu9pyS4sQIeOZQf2MvPLJYY/3+3RF3Cf79jKzC+XcD1bv2tGR1/9Z19OTzd59uXMdOwlEtpW2zu/OYX6+aPV6YhPSzUKV6rVXMvMoKNf3dOZ6lI5jakhO/uoNBq0Oh1FtVybL5dz9NJF2vn4EnofDiEC6BQSglar5XKNRaxKlYrE5GRCO9z977pSqZ9OUdSAswlUajVarbbBO/s05tlNoUPHjmi1Wq4mJBiFq5RKkpOu0b5jSC131q/yu8hrfBeVSsX8N1/n9MmTvPDvuYwaO67eZ/m2aUN4ZCS+FevfTsbGYmdvT+ewunt4m0qHkIp0ulIjnVRKricn0T7kT6STqiKdanQyqFUqFr79JmdPneTZl15hxOix9T7Lx7cNXSIi8KnYUOF03Ans7O3p1KXuHt6mFFTx7kuusdBepVKRej2ZoD9x0J2qIq2K5abvvsXvz+fi2dM8+dwLDZrPX1iQz+m4WPwCg0wOYrsXDOlkro5wPZnAds2TTksXLuDS2TPMeeb5BnX6FRYUcObkCfwCAglq5nT6n24A1HdoQl2HPmjM9I6++OKLxMTE8Prrr9O2bVvWrl3LtGnT+OSTT/50XGsTn3ELnU5Hr/YBRuFdA9tga23NpbSqMwAcpLa0crTH2qrqe9vVUrm3l9gS6uuJUq0xLOZtKj2C/Wjt7EjctdS7Xl/QWA/0H4iFhQUxmzcahR+M2YFKqTQ6A6AgL4+sjHSUyqr5rEWFBWjNrLUoyM/j5NHDSGUyfKtVrtRqFZ8vfI9LZ0/z2D+ebXBL/cj+PZRrNAyqpQfuXhjSOQwLLFhz4phR+NbTp1Co1UZnAOTI5aTm3DHMzQcoKClBqzNNq9xiOQfiLyGztSWwtf5wr1DfNiyYNsPkz6SKXu0H+/RjwbQZ+FbsRhLpH0grB0e2nj1FqapqHm3SrZucS7nB4E5d7tn+9kO7dcPCwoLVB/YbhW8+dhSFSmV0BkBOYSGpt2+hqNZjmS+Xm/2dyi0qZN/Zs8gkEgKrHUaUW2S+Mb/2oP7zO1fsrV3TjrgTaMrLGXefev8Bhg/Wn2y5cv16o/CN27ahUCiMzgDIyc0lJS0NRbX55PkF5vNfTl4eew8dwk4mIyggwCjcnFUbNgAY7QLU2Gc3p8EVp8puWLPaKHz71i0oFQqjMwByc3JIS001SqeCgnyz3yUvN5dDB/Yjk8kMi3mhovL/xjxOx8Xx/MuvMHrc+EbHeeO6taTcuM7kadPrPam4qQyIisLCwoLN69Yahe/auhWlQmF0BkBebi7pacbpVFjLv3l+Xi5HDx5AJpPhVy0/qVUq3n/7Dc6cPMkzL77EyAY0kmrasn4dqTduMHHKtHpPKm5KlafK7ti0wSh8387tKJVKozMA8vNyyUxPM1rLUeu7Ly+P2COHkMpktKnx7vvsvflcOHOaJ555niEN7M0/vFf/7rvXi38r9R4wCAsLC3bVSKcDu3agVCqNzgDIz8slKz29YemUn0dcRR2hZjoteX8BF8+e5vF/PtfgUY8j+/Tp1Ny9//A/vgagPs7O+l71wkLTF29GhvmtENu2bcusWbOYNWsWSqWSJ554gh9++IE5c+bQqlWrJo/jnaJiTiWn0bOdP1N6R5J86w6tHB3o2c6P1Dt5XKp2CNjgzh2ICPDl14NxpOXo55l29vOhVzt/rmbdpqCkDK1Wi5ujPWF+Pshsbdh2+jKacuNf6n4d9fNqKxfwejo7GsLScvJJz6maw/pgv24UlJSRU1SMDghq3YoQX0+u3czm6JXGn7x6t9oGBDJ0zDj2bNvCF4veI7xHT26mp7N76yY6dgmjz6DBhmvX/LKcI/v28NrCjwit2N3h+IH9xGzZSLfe+tP9rK2tuZWVyZF9eygtLmbOs/8ymnP63Wcfc/HMKTpHdMVWIuXo/n0m8fELNK2wHd4Tg42tLX3rOJiluQV7ejGp5wOsPxnLG6t+p0/7DqTcucO6uONE+gcwrNqOF9/vjWHn+bN8/tgcugbofwdiLp5nbewxBnTshLerKzZWVqTn5rDz/FnkZQrmTog2zOV3d3RicCfT3rCyiopy5zZtjf7e2sqK50eNZf7aVTy3/AfGdetBiVLBmthjuNjZM2dw/cOnTSXYx5fJAway7tBB5v3wPX06dSb19i3WHDxA13btGV5tbvF3WzaxI+4EXz73L7pV9NrEnDrJ6oP7GRgegU+rVlhbWZOenc2OuBPIy0p5bcZMo/n6sz5YSHhQMB3atMXDxYXC4mJOXr3CqcSrBPv4mJxHUGlb7HFsbWzMHgx2r7QLCmLqxIms2biRue+8Q99evUhJS2PVhg10i4hgZLUGwNc//MC2mBi+/ewzukfqD87ZuXcvK9etY1D//vh6eWFtY0NaejrbYmKQFxfzxssvI602F/ahJ54goksXOrZvj4e7OwWFhcSdOcPJM2doFxjIjClTDNc29tnNKTA4mAmTJrNp/TrefeN1evXpQ1pKChvXrSU8MpIhw6oqtj9+/x9279zBJ59/QUTF3Ox9MbvZsHY1fQcMxMvbGxsbGzLS09m9cwfFcjkvzn3V6Lt8+N4CTp04QdcePZBIpeypmFpQKSg42GjHnzf+/QrePj74BQRgYWHB6ZNxHDt8mAf69GXmo481c+pUCQgKZuzEaLZu3MDCt9+kxwO9SU9LZcv6dXSJiGTQ0KrpJj//8D17d+1k0eKlhFes3zqwZzeb1q2lT/8BeHp7YW1tQ2ZGOvtidlEsl/PcK/82SqdPFr3P6bg4Irt3RyKRsn93jEl8qu/4885rc/Hy9sEvwB8LLDhz6iSxR4/Qs3cfHnxkVjOnjjG/gECGj51AzNZNLH7/XSJ79iIzPY1dmzcSGhZu1ABY+dOPHNq7m7c++IROFWsbjuzfx45NG+jZpy+tvbywsrbhVmYGh/bupqS4mKeef9FoHvpXn3zI+dOn6BLZFVuJhMP7jM/48AsMwj8wiJoO7N6Fja1tg6bANIe2AYEMGzue3Vs3s3TRAiK69yQrI52YLfo6Qt9qnYSrf17O4X17eH3RR3Sq6BA7dmA/OzdvoEfvfnh4emJtY8PNzAyO7NtDSXExTz73glE6ffPpx1w4U5VONc9j8AsIxM9MOh2sqCM0ZDrxn9WiGwAODg54eHgQGxuLTqczjAikp6ezp8bBNXK5HKlUajTvUCKREBQUxMmTJyksLGyWBgDA7vNXKCwto2tgW9p5eVCmUnEqOY2Dl5PqvTc9Jx8fVyfae7fGQWqLlaUlJQolKdm5xCWlkZlnOkVncGfjoTAvVye8XPXzrg/FJxk1ADJzCwht40W4v/74+ZyiEnaejefM9XQavzLhz3n4yadxb+3JgZgdnD8Vh4OTM8PGTWDyzFn1jgZ16NyFG0mJnIs7QWFBPhqNBmcXFzpHdGXE+Im0D+1kdH1K0jUALp8/y+XzZ02eFz3jYZMGwLWEeLLS0+kzaDD2Do5/8tv+Oc+NGoOXiwtbzpwi9tpVnO3smNKrN3OihmJpUXdaRfj5cyUrg2OJV8grLkZdXo6rgz3dg4KZ+kBfwurZ6ac+UZ27ILGx5pdDB/gmZic21lZ0Dwzm78NG4OF0b+b/V/rX5Kl4u7mx6dhRjl++jLODPVMHDubJsWPr/Z2KCG5HQloqRy9dJK+oCHV5OW6OjvQICWH6oCjCgowL/6kDB3PyagLrjxyiqKQEiY0tfp6teXrcBKYNGozMzAK1i9evk3L7FsO798Cpnm1Cm9tL//wnPp6ebNi2jaMnTuDi5MT06GiefvzxetMqMiyM+KtXOXL8OLl5eag1GtxcXenVvTszJk8mvLPxlnkPTprEidOnWbtpE4VyOVKJBL+2bfnnE0/w4KRJRj3VjX12c/v7c8/j6eXF9i2biYs9jpOzMxOnTOWxOU/Um05dIsK5eiWBE8eOkpeXh0atxsXVjW7dexA9dZrJFJ3K/fLPnjrF2VOmp7I/MvtxowZAaJcuHNy3l5id+oOa/Pz9efbFlxg7YWKdO5w0h6eeeY7WXt7s2rqFkydicXJyZtykyTzy+Jx606lzeDiJV68Qd/wY+Xl5aDRqXFxdiezWnQmTpxJaY4pOUsX0mXOnT3PutOmmGA89OtuoAdCxc2cO79/P3l36Oe1t/f34x79eYNS4Cfc8nQAe+9vf8fD0ZN/O7Zw9GYejsxMjx09k2iOP1ZtWHTt34fq1q5yJO0FBfp7h3dclshujJ0SbbFd5/Zr+3Xfp3FkunTN9902Z+YhJAyAx/jKZ6Wn0GxyFg+P9e/fNevJpPFp7sm/Xds6dPImjkxPDx01g6sOP1ptOIZ27cP1aImdOxlKYb1xHGDkhmg416gg3kvRTjWpLp0kPPWzSAEhMiCcrPY2+g6LuSR3BQqe7ixWkf3GV5wD88ssvPFDjkJdZs2aRmZnJvn36Httvv/2WpUuX0r9/f4YNG0Z2djYrV67E19eXixcvGs4B2LFkJNMAACAASURBVLNnD2+99RYjRowgMDAQe3t7Ll26xJo1a+jSpQurV682FxUTC9ftqv+iFu6NKSOJvXrvRg/+P+sdEsTt39fc72j85XnOnEbOrrs7jbalcR85jMJaRkCFKs5t2pB6++4PUmwp/D09uJZ5b04Q/v+uva8XZ5JS67+whevWzp+TiXd/hlFL0rOD+amj0MJHAACeeuop5HI5mzdvJi4ujnbt2rFw4UIuX77MxYsXDdeFhIQwfPhw4uLi2LJlC1qtFm9vb55++mnmzJlzH7+BIAiCIAiCIDTc/+QIwF+ZGAGonxgBaDgxAtAwYgSg4cQIQMOIEYCGESMADSdGABpGjAA0XF0jAP/TuwAJgiAIgiAIgmBMNAAEQRAEQRAEoQURDQBBEARBEARBaEFEA0AQBEEQBEEQWhDRABAEQRAEQRCEFkQ0AARBEARBEAShBRENAEEQBEEQBEFoQUQDQBAEQRAEQRBaENEAEARBEARBEIQWRDQABEEQBEEQBKEFEQ0AQRAEQRAEQWhBRANAEARBEARBEFoQ0QAQBEEQBEEQhBZENAAEQRAEQRAEoQURDQBBEARBEARBaEFEA0AQBEEQBEEQWhDRABAEQRAEQRCEFkQ0AARBEARBEAShBRENAEEQBEEQBEFoQUQDQBAEQRAEQRBaENEAEARBEARBEIQWRDQABEEQBEEQBKEFEQ0AQRAEQRAEQWhBRANAEARBEARBEFoQ0QAQBEEQBEEQhBZENAAEQRAEQRAEoQURDQBBEARBEARBaEFEA0AQBEEQBEEQWhALnU6nu9+REARBEARBEATh3rC+3xFoaXILi+53FP7yWjk7kZadc7+j8f+CX2t3rg0Yfb+j8ZfX/vAOshZ/fb+j8f+Cz0vPcDMn735H4y/P292NO/mF9zsaf3kers78djDufkfj/4VHBvVixtKf73c0/vJWvvAY81Zsud/R+H/hg4fH1/p3YgqQIAiCIAiCILQgogEgCIIgCIIgCC2IaAAIgiAIgiAIQgsiGgCCIAiCIAiC0IKIBoAgCIIgCIIgtCCiASAIgiAIgiAILYhoAAiCIAiCIAhCCyIaAIIgCIIgCILQgogGgCAIgiAIgiC0IKIBIAiCIAiCIAgtiGgACIIgCIIgCEILIhoAgiAIgiAIgtCCiAaAIAiCIAiCILQgogEgCIIgCIIgCC2IaAAIgiAIgiAIQgsiGgCCIAiCIAiC0IKIBoAgCIIgCIIgtCCiASAIgiAIgiAILYhoAAiCIAiCIAhCCyIaAIIgCIIgCILQgogGgCAIgiAIgiC0IKIBIAiCIAiCIAgtiGgACIIgCIIgCEILIhoAgiAIgiAIgtCCiAaAIAiCIAiCILQgogEgCIIgCIIgCC2IaAAIgiAIgiAIQgtifb8jINRPq9WyeuVKNm5Yz62bN3FxcWHIsGE89fTfkclkdd6r0WhY/MknJCTEc+vmTUpLS3F396BT50488thsQkJCTO4pLi7m+2+/5cCB/RQVFuLr68uUadOZNGUKFhYWhuuKiorYsX0bx44eJfXGDQoKC/Hy9CSyWzcef+IJPD29mjwt6qLVatmwZjXbNm/i1q1buLi4MDBqCI898WSD0umrpYtJTLjC7du3KCstpZW7OyGhocx4eBbtOnQwuaekuJjl//2eI4cOUlRUhI+PDxMnT2VcdLRROgEc3LeXuNhYkhITSU25QXl5Ob+uXouXt3eTpkGDWVjgMm0izhPGYO3lSXlBIcX7D5G77Fd0CmXd91pZ0fqFfyAJ7YCNZ2ss7Owoz8lFkXCV/BVrUF5LNrpcFhmGQ9QAZBFdsPH2RKtSo07PoGDdFor3HjT7EXa9e+L26Awk7YLQqdWUnj5HzrfL0Ny83VQp0GD23SKwC++MtZMj2rIyyq4mIz92Ap1GU/eNlpY4Rw3Axqs1Vk6OWNrYUl5SgvrWbeRxZ9DcyTG+3N4O+8gwbDw9sGndGis7GaWXEyjYtc/k0RYSCXadQpAE+mPTyhVLmYzyIjnKjCzksafQFhc3ZRI0iFarZd3qVWzetNGQ/6KGDOXxJ59qUP77fPFnXL2SwK1bVfkvNLQTM2fNon0H8+XUsu//w+GDBygsKsLXx5dJU6cyIXqSSf7bv3cPJ2JjuZZ4lZQb+vz3x9r1eN+H/KfValmzaiWbNm4wlOdRQ4fx5N+eblA6LfnsExLiE7h9q7I8dye0U2ceefQxOtRSnv/3u285ePAARYWF+Pj6MmXqNKInm5bnO3ds5/jRo6Sm6MtzT09PIrt2Y/acJ/D09GzytKiLTqvlxN5dnDm0n4LcHOwdHenU4wEGTZiMrURa571lJSVciD1C0oVz5NzKorRYjpNbK/w7dGTA2Gic3VqZ3HMzNYVDWzeQnpSISqnErXVruvYfTM8hI7C0rLuvdO1/viThdBwePr78ff6Hf+p73w0LYHTXTgwN64CHkwPyMgXHE1NYc/wcynrKKXuJLQNDg+ka2AZfN2ccZRJyikpIyLzN+hPnyS0uNbmnlaM9k3qF0aWtN24OdhQrVNzIzmXL6ctcyawqozu18eTtqaPq/Py3V20n8eadu/rejWUB9O0YRK92/rg6yChRqLiYlsXu81dRl5fXea/U1oZugW3o6OuJh5MD9hJbCkrLuHE7l32XEiksVZjc4+PqzNCwDgS0dsPG2opceQmnktI4lngDnc70M0J8WhPVpT3erk5oyrUk38phx9l48kvKmigFjFnNnz9/frM8uQVYv3490dHR9OrVizZt2jTonjJlPZUrM5Yu/ozly34gsmtXpj34IK6ubqxdvYoL5y8waswYk5dddUqlkl+WLycsPIKBgwYyaHAU3j7eHD18mFV//EFYeAQ+vr6G69VqNc/+4+8cPXKYceMnMHrsGORyOX+sWAFAt+7dDdeePXOahQsW4OPjy9DhwxkyZCgODg5s27KFTRs20H/AQFxdXRv9fe2kEgpLTAud+nzzxef89tNywiIimDRlGi6urmxct5b4SxcZNnJUven0x6+/0DksjL79B9Bv4CA8vbyJPXqM9WtW0TksHG8fH8P1arWaV/71HLHHjjJq7DiGjxyNvFjO2pV/ABDRtZvR879cvJhTcSdwb+2BVCqlqLCQydOm4+Do2OjvWZ2zvR15y1c0+j7355+m1eOPUHb+EgXrNlNeUIDL1InIwjohN1PhrM5CYovbozNQXEqg5EgsJYeOor6VjX2/B3CdHk3ZxXijirrX/HlIQ0MojTtN0a59KOOvYBsUiOv0aKw9WlFy9ITR8+0H9sVn0duUF8nJX7EaZWISjkMG4jxmBPI9B9CVNb4wbDXnEeTHTzb6PqfB/XHq0wtVRhYl5y6iLS3DvmsYtr7elMVfrfNeCysrHB7ogermLZRJN1AkX6e8SI4kKACH7hGosm5SXiQ3XG/j6YHryKFgaYXmTg7Wri6o7+SgSL5h8mxJW19cRg2lvLAIRWISimvJaJUq7LqEYh/eGUXyDbRlpi+khnDs04vi0san8VefL+Xn5T8SERHJlGnTcXV1Zf3aNVy6eJERo+rPf7/98jNdwsLpN2AAAwYNwtvbm2PHjrJ21Sq6hJvmvxefe4bjR48yZtx4Ro4ejVwuZ9UfvwPQtZtx/lu6+FNOnojFw6M1EqmEosJCpk5/EMc/kf8c7WSU1tdYNuPzJYv56cdlRER2Zer0B3F1dWXtmtVcvHCekaMbUJ7/tJzw8HAGDBzEoMGD8fbx4eiRw6xe+Qdh4eH4+BiX588/8w+OHjnCuHHjGTV6LHK5nJW/myvPz7DovQX4+PowZNhwooYMwcHBge1bt7B54wb6DRhwV+W5vUzKhdTMRt8Xs+o3Dm/biF+HjvQaOgJ7RydO7ttNRnIi4b371ZlOqYlX2PzT97h6tKZTz96Edu+JVGbHuaOHOXN4Px0iumHv6GR0/S+fLaKsWE6PqGGEduuBorSUuL27KC4soENEt1o/K/HCWQ5uXoe1jS0ye3t6DB7W6O9aKTzAl7Wx5xt932ODejK1dyRXMm+z81wCRWUKRkWGEuLbmsMJyXXe26mNJ/8Y2Z/sIjnHE1M4cS2VMpWawZ3bMTQ8hNPX05GXVf2eu9rL+HDmeNq6u3I44TpHrlznZn4RXQN8GRXZkeu3c7lVoC/XVJpyUu/kE5eUZvTn7I0MugW2obBUwW+HT5mtDNdlau9I9l5MbHQ6jevemWHhIdzIzuX41RuUKJX0DQkkwMONszcy6rw3qHUrpvaJJK+4lAtpWVxKv4lCpaF7sB+92vuTkHGLEqXKcH1AazeeGt4Xe4mE2MQU4tNvIrO1oX9oMI5SqVFDCaBzWy8eGdSTMqWag5eTyMwvJNzflx7BfpxPzUSlqbuBUpth4aadApXECMBf3PXkZNauXs3gqCgWffSxIdzHx4cln33KnpgYRoyqvYUtk8n48ZdfTMKjJ09h0vhx/LHiN3r07GkI37xpIwnx8bz48itMe/BBACZGT+L1V+fyy0/LGTt+vKHXzN8/gD/WrDVp/PTt349/Pfss//3+Pyz68KM/9f0bKuXGdTatW0v/QYN45/1FhnAvb2++/nwpB/buYcjwEbXeL5PJ+OaHH03Cx02M5uGpk1mz8ne6VntZ7ti6hasJCTzzrxeInjoNgDETJvDum6/zx6+/MHLMWDy9qkZAXn3zTVq1csfK2povl3xGelpaU3ztu2Ib4IfLlAkUHzjCzbcWGsLVN2/T+oV/4Dh0EPI9B2q9X6dQkv7Uv0zCCzdtI3DtL7jOmELZmaqXWO53P1J24TJotYawgjWb8P3iI5zHj6ZgzSZUN1L1f1ExuqDJvkPGs6+gq6jElsSewu+HL2g15xGyP/niT6ZAw1i3csO+azhl15LJ37LTEF5eWITzkIHIOran7Mq1Wu/XaTTk/L7GJLzkwiU8n3wUhx5dyUuvqhipb9/h1rfL0JYpsJRK8frnE7U+W5OXT/byFZQXFhmFK26k4j51Io59e5G/dVdjvu6fcuP6ddavXcPAQYNZsOgDQ7i3tw9fLF3Mvj27GTZiZK33y2Qyvv9xuUn4hOhJTJ8czarff6db9x6G8G1bNnMlIYHnX3iJydP0+W/chIm8/fo8VvzyM6PHjsXLq6p3//U336aVuzvW1tYs/ezT+5b/rl9PZt2a1QwaHMXCamWjt48PSxd/xp7dMYwYWXd5vuwn0/J84qTJTJk4nj9WrKB7j6ryfMumTSTEx/PCSy8zdbq+PJ8QHc0br73Krz//xNhx4w2jkP7+/vy+ag2+NcrzPn378+Lzz7Ls++95/4N707udnZVB3P7ddOzag2n/qCprXNw92LXyVy6djCXsgb613u/u5c0/F3yMW2vjUYt2YZGsWPoRBzavY9rfnzeE71r5KxYWFjz+2ju4erQGoMfgYWz79UfOHN5PeO/++LU3rUipFAp2rPiZHlHDSDx/9s9+7bvSxs2FkZGhnLiWypJtBwzh2YXFPB71AH1DAjl61bQToVJWfiEv/byR24Vyo/AzNzJ4c8oIpveJZMm2qpHagZ2CcbKT8snmfZy+nm4IP3r1Bp8/PpkhYR04m6Iv1wpLFRy5ct3kM/uGBGJpacnhhGTKtY2s/d+l1s4O9AkJ5FLaTVYcPmUIzysuZULPMMIDfDmfUntDNbuomMVb9pNXY0TkStZtnhzah2ERIfx++LQhfHz3Luh08G3MEfIr7om9lkp0r3AeaO/PmRsZpN7JA8DSwoLxPbpQWFrGf3YfNVT2E7OyeXbUQIaFhbAh7kKTpUUlsQbgL253TAw6nY7pMx4yCp8QHY1UKmXXzh139VxXV1ckEglFRcaZfveuXUilUiZERxuFT5/xEBqNhr27dxvCvH18zI589Oz1AE5OzlxPrrvnoSnt37MHnU7H5GnTjcLHjJ+AVCplT8zdVYZcXF2xtbWlWG6cTvt270YqlTJm/ASj8MnTpqPRaDiwd69ReGtPL6ys/xrtbcdhg7GwtCR/zUaj8KItO9CWKXAcMeSunlueX4hOpcLS0cEovOzcRaPKPwA6HcUHjgBgGxRgCJZFhmHt4U7R1l2Gyj+AKuk6Zecu4jBkIFhZ3VX8GksW0h4LCwtKzhj3yJVcjEerViMLrb1npS7a0jJ05eVYSiRG4Tq1usG99uVFcpPKP4AqLQNtmQJrd9MpDs1p757d6HQ6QyWz0tgJ+vy3e9efyX8S5DXy357dMUilUsZOMM5/U6c/iEajYX+N/Ofp5YX1XyD/7TGU5zOMwsdP1JfnMTt31nJn3VxdXbGVSJDLjX8ndsfoy/PxE2uW5zP05fke4/K8ZuUfoGevXjg5OXH9+r0rzy/HHQedjgeGGTeGug0YjI2tLZdOHK3zfhd3D5PKP0BQpy7I7B24k1nV21tWUsLtjDT8O4QYKv+VIvoOAOD8sUNmP2f/xjVoteVETZzWoO/VHPqGBGJpYcGOs/FG4fsuJaJQq+nfMajO++8UlZhU/gEupd9EXqagTSvjUR+ZrS0A+TVG6QtKy9BqtSjV9UyNBIZ0bq+P4+XaO1CaWkSAL5YWFhyt0SA5mZSGSqOha4BvLXfqFZSUmVT+AZJv5VCqVOHpXDWiJLW1wcfNmZTsXEPlv9KZikZT96C2hrBAz1Y428k4lZRm1NN/M7+I69k5hPv7YFnHiNfduv8lolCnhPh4LC0t6dS5s1G4RCKhfYcOJMTH13KnsfLycuRyOeUaDbezb/PHb79RWlpK335VvSharZarV64Q0rEjkhqVk06dO2NpadmgzysuLqa0tISg4LoLnqZ0NSEBS0tLQkI7GYXbSiQEtWtPYsKVBj2nvLycYrmc8vJy7mRns2bl75SVldGrdx/DNVqtlqTEq7TrEIJtjXQKCe2EpaUlV68k/Pkv1UwkoR3QlZejTDAeQtWp1CiTkpGEmq53MMvSEktHByysrLBu7YHrjMlY2tlRGtuwqTbWHu4AlOflG8KkFZ9ddsk0/RSXr2DXPRLbtr6oUpq/B9fGqzU6rRbVrRrrDsrL0WTnYOPZ2vyNNVlY6Cv7lpZYOTrg0CMSS1tbFJWjHk3IwtYWC1sbtLm5Tf7sulTmv46djPOfRCKhXfv2XGlgfjCUU+Xl3Mm+zarff6esrJTefYzz37WrV2kfEmJSTnXspM9/VxIaVi7ea1cS9OV5aCcz5Xn7Dg2Od1U6aci+nc0fK36jrLSUPn37Ga7RarUkXr1ChxDT8jy0U0V53oDP05fnpQQFBzcobk0hK+UGFhYW+AQYv0OsbWzxbOtPVkrtPdp1UZSWolSU4VFtmlS5Rq1/tq3E5HrrispuhpnGT+aNZE7u382kp55BUs/ajeYU7NVK/066bbymSF2uJfVOPsGe7nf1XJmtDTJbG9JzC4zCL6RmEt0zjCeievPb4VPcKpDj6iBjygMRKNQatp6+XOdzPZwc6NTWiyuZt7mZb9qJ0VzauLmg1epMvo9GqyUrvwjfVi539VyJjTW21tYUK6oaUdYVa0bMTdupDPNzr2pYtan47NScfJPr03MKaOflgbuTPdmFTbu2q0ENAKVSyffff8/WrVu5desWNjY2eHt7079/f1599VXDdceOHeOHH37gwoULKJVKAgICmDlzJg89ZNx7PWTIEHx9fXnjjTf48MMPOX/+PFKplOjoaF5++WXKy8tZunQpW7dupaCggPDwcBYsWEBwjQJIpVLx448/smXLFtLS0pBIJPTo0YPnn3+eThUvouTkZMaMGcPs2bOZN2+eyXd76aWXiImJ4dChQ7i5uZGcnMyvv/7KyZMnycrKQqvVEhwczIwZM5g+fbrJ/c0tJ+cOzi4u2FYURNV5eLTm4oULqNVqbGxs6nxOSsoNZlX7d3BwcODR2bOZ9dhsQ5i8qAilUomHh4fJ/ba2tjg5O3PnTna9cf7px2VoNBrGjB1X77VNJTc3BydnZ7Pp5O7hQfyliw1Kp7TUVP722CzDz/YODsx4ZBYPPVIVViyXo1QqcfcwLVhtbW1xcnIiN+feLGq6G9at3CgvLEKnVpv8neZOLrKwzmBtDfUsHrP1b4v/L98Zfi6XF5P360ryfltVbxysWrnhPGE0qsyb+ulBlXGr6LnW5JhWYCsXzVp7uN+TBoCVg72+R75ca/J35cUl2Pp6g6Wl6ehGDdZurrR+rCrvaRVK5CdOUxx3uo677o5j7x5YWFlRernu9QlNLSfnDs515L9LFxuW/1JTU5gz6xHDz/YODjw861FmznrUECavyH8e7rWUU07O5Nz5a+a/nJwcnJ3Nl+furT24eLFh5XlqSgqPPmxcns96bDaPPPqYIUwur788b0g6/bz8RzQaDaPGjK332qYiL8jHzsERazPp4OjiSkbyNco1mkaPqh7ZvglteTkRfQYYwuydnLFzcCTzehJqlQqbav82qVf1DdeifOPySFteztZflxHUKYzOPR5oVByamqu9HUVlSjRmyqm84lJCfFpjZWlJeT3lVE2Te4VjbWXFoXjjxk98xm2W7YtlWp9I3plWNUJzM7+QN1duJyu/sM7nRnVuh6WFBfsu3bvefwAnOyklSpXZdCgqVRDg4YaVpUWjpyQN6dIeaytLQ88+QLFCSbFCiZ+7K9ZWlkb/NsGe+necs33VQnYnmdQQD9O46ddjOctk96cB8O6777Ju3Tqio6OJjIxEq9WSkpLCiRNVi/dWrVrFO++8Q2RkJH//u353mmPHjjF//nzS0tKMGgoAt27d4vHHH2fMmDGMHDmSo0eP8uOPP2JpaUlSUhIKhYK//e1v5Ofn8+OPP/LPf/6THTt2GFbjq9VqnnjiCc6ePcvEiRN5+OGHKS4uZvXq1Tz00EP89ttvhIWFERwcTFhYGFu3bmXu3LlYVZs+UFxczN69exkwYABubm4AxMXFcerUKQYPHkybNm0oKytj586dvPXWW+Tn5/P000//6URvDIVCgW0tL4PKl4hCoaj3heHj48vnX32FWq0hIyOdXTt2UFxcjFqtNgyNK5T6Xz4bG9OXE4DE1halou4pCvv27uWPFSt4oHdvxo4fX+e1TUmpUNQa78p0UjYgnby8vfloyVLUajVZmZnsjdlFSUkJKrUaWWU6KepOJxtbieGavyILqdRs5R9Ap9IvYrKUStAW190AUN+8RcYL87CwscHG1xunEUOwtLfHwsYGXXntiyMtJBJ8Fr2FpUxK1mvzodruCxYVPZU6lWn8KsMspKY9dc3BwtoaXS07Q+jK9WljYWONrtrCL3PKC4vIWbtJP1Li4owstAOWElssrKzq30moEaTtg7HvHokiJZWyy/d2BEqpUBpVnKprTDnl7e3Dp0s/R6PWkJmZwe5dOykpMS6nKssgG9taykWJLYq7WJx7LygUitrj3Zh08vFhyRdfodGoyUjPIGaXmfK8Ig3q+rz6yqn9+/ay8vcV9Ordm7Hj7l15rlGpsLI2H+/KRoFapWxUAyD+dBzHd+8guHMYEf0GGsItLCx4YNgo9m9cw5pvP2fwxCnIHBy4kXCZg5vXY2lphVplnMePxWwj7/Ztpv/jhbv4dk1LYm2NppZySl3R2yyxsaJU2fAGwAPt/BnbvTPnUjI5EJ9k8vdFZQqu387lUtpNbhYU4e3ixLjunXk1eigL1uw0u3MQ6NN6UKd2lCpVxF5LaXB8moKNlVWtjaDK9NNf0/AyuUtbb/qHBpOYlW20HgLg6JXrjIwM5ZGBPdlz/golShXtvDwYFh5CuVaLTbW6aOX/a8zET13ReLCxbvqprw3KPXv27GHgwIF89JH5BZ3Z2dm8//77jB07ls8++8wQ/vDDD/P+++/z008/8dBDD+Hn52f4u7S0NJYuXcro0aMBeOihh5g8eTLLli0jKiqKn376ybDK38XFhYULF3L06FEGDNC33FesWEFcXBw//PCDIQxg5syZjBs3jo8//phff/0VgEmTJrFgwQKOHDnCoEGDDNfu2LEDhULBpEmTDGETJ040GbGYPXs2jz32GN9//z1z5sypt3BuSlKplPx802Eh0I+AVF5TH5lMRs9eVT0V48ZP4PFZs5iXPpelX36pf07F1mpqtfkKjVKlQlLHZx07epR3336LkI4deX/RB3Xu0tDUJFIpZfWkU11xrySTyehWbRHdqDFj+ccTc3g343U+XLwEqErv2tJJrVIilZr2uv1V6BQKLF3ND3daVFRCtA2oQOkUSspOnzP8XLQ9Br9lX+Ld5i2yXn6zlufb4L3obSQh7bm96DMUF4yHi3UVu2RZmKm0VIbVu01pE9FpNFjWMrRvYaUvOnUNmO+q02hQpennHCuB0ksJeDwyHdcJo8lbv6VJ4ioJ9Md19HDUt7Pv6eJfw+dLJZTlm3/pN7ac6tGzl+Hn0WPH8bc5s8l8fR6fLFla8VkV+c9MIxFApVQh9bg3jcTGkkql5Oc1VXlelU5jx49nzmOzeOO1dBZ/XlGeVzSUa00nlarOzzp+7CgL3nmbkI4deW/hontanlvb2qKSm58eoqnovLAxM2WnNtcunmPjsm/x9gtgyt+eM/ku/UaNQ61SErt7B8sWvQOArUTK8Okz9fP8q/Xg5mXf5vDWjfQfM9FkzcD9oNRoDD3INVVWGpXqhu8gExngy7OjBnDjdi6fbzfdpnlIl/bMierNa79vIaPadJrzqZl8MHM8M/p14+tdR8w+O8Lfh1aO9uy+cPWud7W5W+rycmxraTBaV1TA69sKtLoQn9Y82K8rWXmFRot/Kx28nISNtRUDOgbzzGh9g1Op1rDt9GVGRHY0mtNf+bnWZrabtbGq6PRuhvRq0CJgBwcHkpKSSEw0v+3Srl27UKlUTJ06lby8PKM/Q4YMQavVcvz4caN7PD09DZX/St26dUOn0zFr1iyjDNqjh373h9TUqjmzmzdvJigoiM6dOxt9nkqlom/fvpw+fdrQuzF27FhsbGzYuNF40eOmTZtwcXFh8ODBhjA7OzvD/yuVSvLzm+t12AAAIABJREFU8ykoKKBfv34UFxdz/brpivbm5O7uQWFBgeHlUN2dO9m4uLjcVYPEzs6OQVGDiTsRS0aGvnLi6OSERCLhjplhYZVKRVFhIR61FHixx4/x+qtzCQwKYumXX2Hv4GD2uubSqpU7RYWFZtMp584dnJ3vLp1kdnb0HzSI0yfjyKpYOObg6IhEIiGnxj7uUJFORUW0MjM94a9Ck5uHlbMTFmbSw9qjFZqCwnqn/5ijK1NQfPAY9r26Y+Njur96ZeXfrkck2R9/gTxmv2ncKqb+mFvEWrlmoOb++c2lvLgES5kUrEyLSSsHe8pLy+qd/mOOTq2m7Foy0gA/rKotHLtbkgA/3MaPQp2bR+66LWZHT5qbu7sHhXXlvz9RTg0YNJiTcSfIrCynKvLfHTPT7PT5rxB3M9Ne/grc3d0pLDRfnudk3/lz5fngKOJOVE+n+svz2tIp9vhx3njtVQIDg1j8+ZfY29/b8tzRxZXSYrmhsl9d5fSghvb+J126wJpvv8DD25eHX3jV7Hx9C0tLoqKn8fLib3j8tXd4/NW3eenTr+jSqw+lxcW4V9tRavea35HZO9Cxaw/ysm8b/mjLyynXlJOXfRt5QYHJZzSX/JJSnGQSrM2UU24OdhSVKho8/SfC34eXxkWRkVfAog27KTNTlkzsGUZWfqFR5R8gPbeArPxCOrWp/fyfqIrFv/vv8fQf0E+vsZfYYmWmku1kJ6VYoWzw9J8O3h48PLAHtwuLWbYv1uxZCzpg9/mrvLd2F9/sPMy3u46wcF0M51IysZPYcqeoajpPUcXmD052pg05Jzv972vhXWx/XZ8G5aDXX3+duXPnMn78eNq2bcsDDzxAVFQUQ4YMwdLSkuSK3V5mz55d6zNycoxf2uZ2j3F2djb7d05O+pdkQbVMlZycjEKhoE+1xWE15efn4+3tbajk7927F7lcjqOjIxkZGZw6dYqZM2cazccsKSnhq6++YseOHdy8edPkmUVF927RCkBop07EnYgl/vJlIrt2NYQrlUquJSYahTWWsqK3taioEGijX0TbsSOJV6+iUqmM0iX+8mW0Wi0dQ0NNnhN7/DivzZ2Lv78/X3z1teHf614KCQ3l9Mk4ribEExYRaQhXKZVcT7pmFNZYlekkL5KDL1haWtKuQwjJ1xJN0ulqQjxarZYOHTve/ZdpZsqEROx7dUcS2sGoB97C1gZJu2DKzl+862dbSPRpYenkCFlV+cfCxgbvhW9h17Mb2Z98QdH2GLP3KyoWJsu6hBqNLgBIO3ekvLgEVXrtW7U1JfWtbKQBfth6eaLKrFYWWFlh3dodVUbWXT/boqLyYimVmt3Np6Ek/m1xmzAaTV4Bues2GUZQ7rWQ0FBOxp3gSnw84ZFVeU2pVJJ07ZpRWGOpKvNfRY+wpaUl7UNCSEo0zX9X4vX5L6SjaTn1V9AxtBNxJ06QEH+ZiMga5fm1RKOwxqoqz4vwRZ9OHUI6ci3RtDxPiK8oz82k04nY47z+2lz8/P1Z+uVX96U89wkI5Hr8RbJSrhttv6lRq7idnopf+4aVr8mXL7Dm26W4e3nzyEuvIbO3r/N6W4mUNkHtDD/Hn44DnY52YRGGsMLcHOQF+Xw3/zWzz/j6zVdoHxbJjOdeblAc/6zkW7lE+PvSztOdK1lVa/RsrCzx93A12W++NuH+Prw8Poqs/ELeXxdjtKd9dW72dmZ3DQKwsrTA0tL8SJGTTEr3oDak3snjeva93aQAICOvgA4+rWnbyoWUiu03Qd/r7uPqxI3svDrurtLe24NHBvbkTmExy/YeR1FPh4u6vNxo4XGXtt5YWlhwtdq/VWVjyt/dleRbxnXltu4uKFRqcopKGhS/xmjQCMCwYcPYt28fH3/8Mb179+b48eM888wzzJo1C5VKha7iFIePPvqI5cuXm/0zocZ2bVZ1bOVX26l7umqnReh0Ojp06FDr5y1fvtwwrx8gOjoapVLJzopt1jZt2oROpyO6xnaXL7/8MsuXL2fgwIF8+umn/Pe//2X58uWGxo32Lnr8/oxhw4djYWHB6ooDpipt3rgRhUJhdAZATk4OKSkpRvM68/PzzcY5NyeH/Xv3YmdnR1BQ1eLq4SNGoFAo2LRhg9H1q1f+gZWVFUOHGR9yciI2ltfm/hu/tn588fU3OFU04u61wUOGYmFhwfo1q43Ct2/ZjEKhMDoDIDcnh7TUVKN0KqglnfJyczm0fx8ymQz/wEBDeNSwYSgUCrZv3mR0/fo1q7GysmJQ1N1tpXkvyPcdQqfV4jrN+HffafxoLGVS5LureuatWrli49fGMDcfwMrFGcxMB7Byc8UxagDa0tKqff2pqPwvegu7Xt3J/vQriuqYolJ27iKanFycxo3Eotqwtm1wILLIMIoPHDZaM9CcyhKvodPpsO8WYRRuH9YJSxsbyq5UjYha2tth7epiqNgD+tEDMyzt7JB1aIdWpUKT27CXjjkS/7a4TRyDJr+AnLUb79nUKHOGDNXnv7WrjReAb9usz3/VzwDIzckhNTWlQfkvNzeXA/v2IZPZERBYtSPM0GHDUSgUbN1knP/Wrl6FlZUVUUOGNtVXa1JDh1WW5yuNwrdsqijPRxqX56kNLc9zc9i/by8yOzsCg6rSaVhFeb55Y83yfCVWVlYMqVGex52IZd6rc2nbti2ff/X1fSvPO/XoDRYWnNhjvC3qmcMHUKtUdKl2BoC8oICcm1moazR+ky9fZPU3S3Hz9Kqo/DduFKO0WM7+DWuwc3Ck+8Cq36dhUx9iytPPmfyxc3TEybUVU55+jn6j7916ieOJN9DqdIzuarwD15AuHZDa2HDkStWOSS52MnxcnbCtMZ883M+HV8ZHcTO/qM7KP+gr0j6uTrTzMt4Eo723B94uTiTfMl+5HxgajLWV1T1f/FvpQmoWWp2OfjW2Re3Zzg9ba2vOVTsIzFEqwcPJwWiePkB7Lw9mDexJjlxf+Tc3QlIXO1sbRkZ2pFih5MS1qnfkjdu5FJUq6NHOz+jfxsvFiaDW7lxM08e9qTV4BY2LiwsTJ05k4sSJ6HQ6Pv30U3744Qf27t1LQEAAoN+LuG/f2g/naEr+/v7k5+fTu3fveo/pBhg0aBBubm5s3LiRadOmGaYQhYeHG64pKiriwIEDTJw4kQULFhjdf+zYsSb/Dg0R3K4dU6ZOY+2a1cyb+2/69O1HSsoN1qxaRddu3YxeGN99/RXbt23jq2+/M5zwGLNzB6tWrmTQoMF4+/pgY21DWloaO7ZtQy4vYt4bbxrNA50QPYltW7byxdIl3LyZRUBAIMePHeXggQPMnjPH6NTghPh4Xv33K6DTMXb8OI4fN02jUaPHNGPqVAkMDmbCpMlsWr+O+W/Mo1fvPqSlprJx7RrCI7syZPhww7XL/vMdu3fu4NMvvjSc2Ltvdwzr16ym34CBePn4YG1tTWZ6OjE7d1Asl/PSq68ZpdOY8RPYtX0b3331Jbdu3cLP35+42OMcPXSIhx97zOjUUoAL585x8by+Rzvxin5L0k3r1+FQMVXq4Wq7MTU31fUUCjdsxWXKBLzff5OS2JPY+rfFZepESs9eQL77gOFa96cfx2n0cDKem6vfzx9wHB6Fy7Roig8fQ511CzQabNr64jRqGJaODmR/9LlRT7Tn23Ox792T0pNn0CkVOI6IMoqPMvkGquQU/Q/l5dz5/Du83p1Hm68+pWjLDizt7XCZPonygkJyl/3W3MljoMnJo+TcRRy6hsP4UShvpGLt5oZ91zCU6ZmUVdtG1al/b+w6h5KzeoNhZEDWsQP23SJQJOlPANaVl2Pt6oJdp45YSCUUxOw3WQTs8IA+31pULIC0dnc3hKkysgwjETaeHrhN1Oet0ksJSAP9TeJfltD4kzLvVlBwO6InT2HDurW8Ne81HujTl7TUFNatWU1E164Mq9YA//67b9m1YztLvvzacGLv7phdrF29igEDB+Fdkf8y0tPZtWM7crmcf782zyj/jZswkR3btvH1l59z69ZN/PwDOHH8GIcPHWTWY7NN8t/5c2c5f06f/65W5L8N69bg4KA/CfjR2Y83a/pUCm7XjslTprJu7Rpef3Uuffr2JSUlhbWrVxHZtRvDR1Y1lP7zzdfs2L6NL77+1lCe7961k9UrVzJw8CC8vX2xsbEmPS2NHdu3IZfLefX1N4zL84nRbN+6hS8/X8rNmzcJCAjg+LFjHDp4gMcen2N0avCVhHhem/tvfXk+bjyxx4yn7QKMrDFtt7l4tmlLz8HDOLl/N6u//Zz2XSLIuZVF3N4Y/Dt0JKxX1cj/vg2ruHD8CLNefp2AEP2IRlbKdVZ/swSdDiL7DiTpkukhSuG9q7ZMvXbxHMd3bSeoUxccnJwpyMvh3JGDlJWU8OAzL2JX7cTooE5dzMZ5z9o/sJVI6NS9l9m/by7puQXEnL/CqMhQXho3mLP/x959R0Vxtg0c/oE0pYuAFAUsgBVsIIi9d8SGMYk10diNyZtEvyRvEqPR115i7Bq7giAqNmyoYAkWVDQoKApiQQWkt/3+QDYuuyBY2DX7XOfknDAt9zyZnZl7nnYnAZuqxnRzrUdU/EOZce+HeDWlbf06/Ox3kKj4wpqBWhZmfNWnPaDBiajbuCoYD//Vybz8zl5hWq92zPDpQkjk3zxMTqW6iRGdGzuRV1CA/7nLcvsDtGtQh5y8PIUTg1WER8kvOBt9F08nB4a2bs7fDx5jYWyAp5MDsY+SZCYB6+paj2a1a7DqSBh3XtZW2FQ15pO2LUADImLu42gt3xz68ivHcLK2oHW92tx++IQXmdmY6lemeZ2aVNbR4c+T58l4JckqkEjYG3GNIV7NGNO5FRdux6GrrU0r51qkZ2cTEvl+7uWvTQDy8/NJT0+XqQbU0NCQDrOZkpJC9+7dWbBgAUuXLsXd3V2uY9GLFy/Q1dVVOPTZm/L29mbu3LmsX7+eUaPkZ8xMSkqiWrV/MlRtbW169uzJ5s2b2bt3L3fv3mXaNNkquqJEQlIs03r8+DG7dsnP6FlRJn/5JdWtrQgKCCDszBmMTUwYMGgwn40Z89rkx8W1CTeiojh9+hTPnj4lNzeXqlWr0sKtBYN8fWnUWPbrpra2NouXL2fVHysIOXyYlJQUbGxt+fKrr+k/UHayk9jYGGn1/OKFCxX+9ysqAQD4YtJkLK2sCA7aw/nwcIyMjfHuP4Bho0a/tpwaurjw980bnA07w7Nnz8jLzcW0alWaNm9OvwGDaNCokcz22trazF24mPVrVnE85AgvUlOxsrZh/JSp9PXpL3f8yxcj2LRedqZhv1dqdSoyAQB4smQluYmPMO7TnSoebhSkpJDsH8TTtZt43bzsmVeuoevsiL6nO1pVTdHQ1iLvWTIZf10i2W8PWcXG8NdzKmz3WaVFU6q0aCp3vKfrNvOsKAGAwhmKv/sJ00+HUG38aCS5uWREXOHpinXkKxge9H1KPXGa/NQX6Deqj56DPQVZmaRfvsqLsPOv3TcnIRHt6pbo1XZAs0oVNCppUpCRQfa9+6RdjCQ38aHcPkatWsr8rWNpjo5lYTvtF+HnpQmAlpmZtLbBuH1rFKnIBABgwuQpVLeyYt+ePZwND8PY2BifAQMZMfqz1/7+Gru48veNG4SdOS3z+2vWvAX9Bw2iYaPGMttra2szf/ES1q5aydEjR0hNTcHaxoZJU7+kX/8Bcse/GBHBxnVrZZbt3PbP76+iEgCASVO/pLqVNUF7AggPe3k/HziIUZ+//n7e2NWVG1FRnDl9WuZ+3ryFGwMH+9KosXw5LVq6nNUr/yDkyGFSU1KwsbFl6rSv8BlQ7H4eEyu9ny9ZpPh+XlEJAECXwR9jbFaNi6eOc/vqZaoYGNKiQ2fa9emPxmvK6cmDeGn/gcM7tyjc5tUEwMTMHC1tLc4fO0xmehpVDAxxcG6AV8++Mu3/VdXGkxd4kppGx4aONLG35UVWNoeu3GBn+GVe9924RjUTaefYYW0VJy+vvrRHxN7n191H6N28Ae0a1KGKrg7pWTlciXvA7vNXiHsi38nd0cocWzMTTt+MLbV24X3bF3GN52kZuNW1w9nGgvTsHML/vsORyL9fW06WJobSTtW9mitOAl9NAJ6nZZBfUICnkwOVdXTIyM4h5lESx65Gk/RCvjnPtXuJbDp5nvYNHenetAH5+fncfpTEwUs3pH0E3jUNSfG33WJSU1Px8vKiQ4cO1K9fn6pVqxIfH8+2bduQSCTs3bsXS0tL/P39+b//+z+srKzo06cPNjY2PHv2jOjoaEJCQti/f7+0bX/RPABFo/QUWbp0KcuWLePo0aMy/QDi4+Pp2LEjEyZMYOLEiUDhMKBjx47l9OnTtGnThpYtW2JgYMCDBw84e/YsOjo6cse/fv06Pj4+GBgYkJGRwfHjx6leXbbDyqhRozhz5gyDBg2iUaNGJCQksGPHDqytrbl27Rp//vkn7u6Fo+ns3r2b7777TmbZ6zx9i/a+6sLM2Ih7jyumo+eHrqZFNW61rriH8oeq7qkDPFiwXNlhfBCsvxxPYtKbN01SF1bVqvLkNWOeC2Buaszmk69PmAX4uK0bvos2KjsMlbd9yjC+2/JuRlD7t5s9tOTmaK+tAdDT02PYsGGEh4cTHh5Oeno6FhYWdOjQgTFjxmBpWTjddv/+/bG3t2fdunXs2LGDFy9eYGJigoODA5MnT1Y4Gcnb0NbWZuXKlWzdupU9e/aw9OVQlhYWFjRq1EhmaM8iDRo0wNHRkejoaDw9PeVe/gH+97//MX/+fI4dO0ZAQAD29vZMnToVLS0thROJCYIgCIIgCMKH5LU1AMK7JWoAXk/UAJSdqAEoG1EDUHaiBqBsRA1A2YgagLITNQBlI2oAyq60GoAyjQIkCIIgCIIgCMK/g0gABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSIhkQikSg7CEEQBEEQBEEQKoaWsgNQN+f+vqPsEFSeu5MDBy9eV3YYH4RuTRvweMduZYeh8iwG+5D65Imyw/ggGJmb8/RUmLLDUHlmrT158jxF2WGoPHNTY16kpio7jA+CoZERQecjlR2Gyuvj1pidZyKUHcYHYVCrZiWuE02ABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY2IBEAQBEEQBEEQ1IhIAARBEARBEARBjYgEQBAEQRAEQRDUiEgABEEQBEEQBEGNiARAEARBEARBENSISAAEQRAEQRAEQY1oKTsA4fUKCgo4vDeQ4weDSXr8CENjY9xataH/0E/R1dMrdd/0tBecPnaUK3+d50H8PV6kpmJmbo5zg8b0HfwRZubmMtvfuHqF2TO+UXgsl+ZuTPvh5zc+9vtWUFDAyYP7CTt6mGdPHmNgaIRrS096DBzy2nLKSEvj/KkTRF2K4FFCPOkvXmBarRq16zWgq89ATM2qyWx/K+oay375QeGx6jdpxpj/zJBbfv1SBIcD/Hhw7y5aWto4NmxEn48+xczC8s1P+g0VFBSw62wYQX+d52Hyc0yq6NO+YSNGdehMZR2dUvd9kZnJwcsXCY++SdyTJyRnpGNpbIKrvQPD2nXA0thE4X53Hj/iz5PHuXgnlheZGZjo6+NsY8tXvb2pamD4Vsd+XwoKCti+axe79+wh8eFDTExM6NS+PWNHj6Zy5cql7puamsr+gwc5Ex7Onbg4UpKTsbS0pKmrK6OGD6e6pez/94iLFxk7aZLCY3l5erJw7ly55Xl5efgFBLDvwAHi7t2jUqVK2NrY4NOnDz7e3m9+4m+goKCAnSFHCAw9wcOkJEwMDenQ3I3PvPtRWVe31H1T09M5EH6GsMhI4hIfkJyWRvWqZrg6OTGiV28sq5rJbH/x5k0mzJuj8FiejV2YN2mKzLK8vDy2HDrIwfAwHiQ9obKuLk2dnPm8X3/sraze7sTLqaCggF07trMnMICHiYmYmJjQvmMnRn8+pkzX1MEDwYSfOUPc3Tskp6RgaWmJa5OmDB85Csti19TFiAgmjf9C4bE8W7Vi7vyFMssmfDGWy5cuKtx+zfoNONerX44zfTsFBQVs276d3bt3k5iYiKmJCZ06dWLs2LFl++3t38/pM2e4e+efcmrWtCmjRo2ievXqMtv/FRHB2LFjFR7Ly8uLRQtly+nzMWO4eFFxOf25cSP161dcOUFhWZ0+FMzZ40d4nvQEfUMjXNw96OozGJ3XPfvS04g4fZIbly/y+EEC6S9SMTWrRi3n+nTyHoBJsWdfzI3r/DHrvwqPVc+1KSOnfSe3/Mblixzd48+De3FoaWtRt34jevp+TNUKfvYVFBRwNuQgF04cJTkpiSqGhjRs0ZKO/Qago1t6OWWmp3E57BR/R17myYMEMtJeYFy1Gg5OzrTr44NxsXtUcQ/v32PFzzMoyM9n8LjJNGzuLrM+Py+P0wf3cTn8NM+fPEZHVw8H53p08hmEuZXNW5+7IiIB+ABsXbuSw3v30KylJ928+/Mg/h5H9u0hLjaGb36ZjaZmyRU5MX//zbZ1q6jv4kqnnn0wNDQi/l4cxw8Gc/5MKN/PWYBNTTu5/dp37Y5j/YYyy6pWK3YjeMNjvy8Bm9YTenA/jVu4075HHx49iCf0UDAJd+8wbsZ/Sy2nuNvR7Nm8AceGjWndtTv6hkYk3r9H2NHDXD57hik/zaa6bQ25/Tw7dqaWk+zN3sRM/kZw5fxZ1i/6H9Y17enz0adkZWRw4sA+Fv04na9+/R/GVau+fQGUw9KD+/E7G0abeg0Y7OlF3JPH+J0N41biAxYOG1VqWUXF32P5oWCaOtTGx90D4ypViH38iKC/znP8+lV+Hz0Wh2I39nO3opm+bRM2Vc0Y0NKTqgYGPE9L43r8PdKzs6UJwJsc+31asGQJO/z8aNemDUN9fbkbF8cOPz+ib91i+aJFpZbTtagoFi9fTotmzRjk44OJiQkxsbHs3rOHkOPHWbtiBbUcHOT269enD64uLjLLLBUk07m5uXz5zTdEXLpEt86d8enbl/z8fO7Hx5P46NHbn3w5Ld6xjV1HQ2jbpClDunTjbuIDdh0LIfp+HEu+/Lr0a+pOLMt27qBZvXr079AREwNDYhMSCAw9wbEL51n53QwcrOUfgn3btMWlrqPMMgtT2d+SRCLhm2VLCL92ldauTRjQsRPJL1LZffw4n8/6pcRjvy9LFi3Eb+cO2rRth++QocTdvYPfzh3civ6bRUuXl15O16+zfMlimjVvjs+AgZiYmBAbG8OegACOHw1hxeo1ODjUktuvj3c/XFxcZZaZW1go/G+YmJgwcfJUueXWFVhGAAsWLGD7jh20b9eOj4cO5c7du2zfsYO/o6P5fXnp5XTt2jUWLV5MixYtGDhoUOFvLyaG3bt3cyQkhHVr11Krlnw59evXjyausuVkYan4fmNiYsKXU+XLycamYssJYO+WDZw+fICGzd1o2703jx7Ec/rwARLu3uHzb38otazuxdxi39Y/qdOgEa06d0PfwJCH8fc5e/wIV86HM+GHmVjayD/73Nt3wsGpnswyEwUvwVcvnGPT0vlY1bSj15BPyMrI4NSh/Sz/5Xsm/fwbxqYV9+w7sH0TZ0MOUa9pC1p17cmTxATOHj1E4r27DP9qeqnlFB8bw8EdW6hVrwHuHbugb2DIo4R4/jp5lGsXzvHZ9P9iYWOrcN+CggL2bFiNlpY2Ofn5cuslEglbls7n1tUrODdpRsuOXUl/kcr540dYNfPHUo/9NsqdAKSkpODl5UVOTg5z586lb9++7zwo4R/x9+5yZF8QzT1aMem776XLzS2rs3nVCs6eOoln2/Yl7m9la8ucFWuwtLKWWe7SvAVzf5jO7q2bmPjt/8ntV8e5Hq3adyw1tjc99vuQeP8epw4F09itJaOm/ke63MzcAv+Na7kYfprmrdqUuL+FjS0zFiyjmqXsl6EGTZrx+6yfCN61jZGvHLeIfV0nWrRuW2ps+Xl5+G9Yg4mZGZP/OxNdvcKvV/VcmzJv+tcc8N+B72eKv9K9D3ceP8L/XDht6zdgpu/H0uVWplVZHLyXo9ci6dzYtcT9a1azYMukL7EpdrP3dHRm6sa1rD0WwkzfodLlz9PS+NlvO03sa/Hb0E/RqlTpnR37fYqJjWWnvz/t27Zl7q+/SpdbW1kxb9EiDoeE0K1LlxL3t7ezw2/rVmyLvRC08vBgwtSprFy7ljkzZ8rt16hhQ3p07fra+NZs2MCFiAiWLVxI86ZNy3Fm715sQgJ+x47SrmkzZo2bIF1uXc2chdu2EHLhHF3cPUrc3666Fdtmzsa22EupZ+PGTF4wj9V7ApnZ6L2iAAAgAElEQVT1xXi5/RrWrkM3D89SYwu9fInwa1fp26Yt33w6XLq8m4cnH//4PQu3bWXJtK/LeKZvJzY2Bv9dO2nbrj2//vZPDYaVtTWLFswn5MhhunTtVuL+dnZ2bN2xCxtb2ZcBD08vpk6awNpVq5g5+ze5/Ro2bETX7t3LFKOeXuUyb/u+xMTEsGPnTtq3b8//Xqn5sra2Zt68eRw+fJhu3UouJ3t7e/z9/LAtVk5erVoxfsIE/li5krlz5GuQGjdqRI8ePcoUY+XKlcu87fv0MP4+Z44cpGFzd4ZN/kq6vKq5JXs2rePK2TM08Wxd4v4WVjZ8PXex3LPP2bUpq+f8wiH/HXw66Su5/ezqONKslGcqFD77AjetxbiqGeP+72fps8/JxZXF33/Dkd07GTBKcc3Lu/YoIZ5zRw9Tv1kLhoz/J3EzrWbB/q0buXo+HJeWrUrcv5qVNZNnzZertXBq7MqG+bM5GujHkPFTFO577ughHj+Ix6t7b44F+smtv3HpL25dvULzth3oO2y0dLmrhxfLfviG/Vs3MuJr+VYFb6vcfQD27t1Lbm4utra2+PnJn4jwbp0NPYFEIqFrn34yy9t16Y6Ori5hJ46Vur+5ZXW5F3SAhq5N0Tc0JD7ubon7ZmdlkZOT816O/a5dDDuNRCKhXfdeMss9OnRGR1eXv06Hlrq/mbmF3A0QwKmRC1UMDEiMv1/ivtlZWeSWUk63b1wn5fkzPNp3kt4AAWztHahTvwGXws+Qn5dXanzvUkjkFSQSCQOL3ex6N2uBnrY2h69cKnV/K1NTuRd0gOa162BUuTJ3Hst+fQ68cI7UzEy+6NodrUqVyMrJIU/BV5A3Ofb7dDgkBIlEwpBBg2SWe/fujZ6eHgcOHy51f2srK7mXfwD3Fi0wNjIiJja2xH0zMzPJzs4udf2OXbto4+VF86ZNkUgkpGdkvOaM3p8j588ikUgY1Ek2IerTpi16OjocCg8vdX+ratXkXv4BWtRvgJG+PrEJ8SXum5mdTXZubonrL968AUDPVrIvQTbmFrjUrctfN6J4+PRpqfG9KyGHDxeWk6+vzPLefb3R09Pj8MGDpe5vZW0t9/IP0MLNDSMjI2JjY0rc93XX1KsKCgpIT09DIpGUaft37dDLcvpoyBCZ5f28C8sp+MCBUve3traWe/kHcHd3x9jYmJiYd1dOaWnKKyeAy+GFz77W3XrKLHdv1xFtHV0unjlV6v5VS3j2OTZsTBUDAx6W8uzLec2zL+ZmFKnPn+PWrqPMs8/GzoHa9Rpw5VxYhT37rp4LQyKR4NFZNrlt1rY92jq6XAk/Xer+ptXMFTZZqt2gEZX1DXicoLicUp49JWT3Ltr37V9iM6E7N6MAaOol+zGxqoUldnWdiL1xneSnSaXG9ybKXQPg5+eHu7s7HTt2ZNasWdy7d4+aNWu+88DKQiKRkJGRgb6+vlL++xUh9lY0Gpqa1HKUrebW0dHBzqE2d25Fv9FxM9LTycrMxLamvcL1m1f/werFCwCwtLahU4/edOndFw0Njbc+9vtwL/Y2Ghqa2NWuK7NcW0cHGzt77sXcfqPjZmakk52ZhZWt4mt898Z1bP1jGQDm1a3w6tKdtt16ypRT0X/bvq6T3P72dR25df0qjxMfYFWjYn5HNxLi0dTQoF6xJk262trUqW7NjYSENzpuWlYWGTk5OFgYyCw/e+tv9HV1ScvMZMTvS7j9MBFNDQ0a1rBjQvce1FNQvVzWY79PUTdvoqmpSYN6stXcurq6ONatS9TNm2903LS0NNIzMhQ2QQCYv3gxP8+aBUBNW1sG+PjgO3CgzDV16coV0jMyqOfkxLxFi9i7fz8ZmZmYmJjg3bs3Y0aNQkur4lp43rh7B00NDeoXa9Kkq61N3Ro1uXH3zhsdNy0jg4ysLGqVUP29aPtWfl2/FoAalpb4tO/AoI6dZcoq9+ULhp6Cvi16OoV9E6LuxFJdQdO9d+3mjSg0NTWpV7+BzHJdXV3q1nXk5o2oNzpuWloaGRkZ1KpdW+H6xQvnM2tmYf8t2xo18BkwkIGDBiu8nz958pjO7duSnZ2Nnp4ebu4tGfPFOOzs7d8otjcRFVVYTg0ayJeTo6MjUVFvXk7p6enULuG3N2/+fH76ubCcatasycABA/D19VVYTo8fP6Z1mzbScvJo2ZLx48djX4HlBHD/TgwaGhrUrFVHZrm2jg7Wdvbcv/M2z75Mqpdwfw7avJ6dq38HoFp1Kzw7dcWrSw+Zsop/mZDa1XGU279mnbrcjrrGk4eJCpvXvmsJL8vJ1kH2N6KtrYNVTTsS7pT8QaY0WRkZ5GRlYlnCPWrvpnWYmlvg0bl7iUlGURKkrSPfV6poWXzsbbn+GG+rXE+I69evc+PGDebMmUPbtm2ZO3cu/v7+TH3ZDi4vL4+2bdtiZWWlsHZgy5Yt/Pzzz/zxxx+0b1/YbCU7O5u1a9eyb98+7t+/j56eHs2aNWPKlCk4OztL9w0LC2PEiBHMmTOHFy9esHXrVu7fv8+4ceMYN24cly9fZtu2bVy6dIlHjx4V3mTr1WPUqFF07CjflCU8PJyFCxdy8+ZNjIyM6NGjBz4+PvTt25fJkyczbtw46bYFBQVs3boVf39/YmNj0dTUpHHjxowfPx43N7fyFGG5JT97hqGhEdra8g8vUzMzbt2MIi83Fy1t7XIdd8/OreTn5eHVoZPM8kqVtGji1hKX5i0wrWrG82dPCT1yiC1r/uDenRg+mzztjY/9PqU8f4a+oaHCcjA2NeNO9N/k5eWipVW+cjoc4Ed+fh5ubWSbWVWqVImGzVpQ37UpxqZVSXn+jLMnjhLw5zoS4u4wdOxEmdgAhdl/UfvHlOfPKiwBePoiFeMq+ugoeEE0NzLi2v04cvPy0C7nC+TGk8fIy8+nexPZ5ij3kp6QX1DAV5vW065BI4a17cDD5OdsPHmMSetWs2rM+Ne26y/p2O/Tk6QkTIyN0VHw4mhRrRqRV6+Sm5uLdjl/e2s3biQvL49exZowaGlp0cbLi1YtW1KtWjWSkpLYs38/C5YsIfr2bX6cPl26bdy9ewBs27kTbW1tJo4bh7GREQePHGHDpk08efKE//5fxTS/A0hKTsbYwBAdBWVhbmrK1Zjbb3RNbdi/l7z8fHp4yjbz0apUCS9XVzwbNaaasSlJKc/Ze+oUi7dv49a9+/zfyFHSbYva90fcvEGdGv+8aGRlZxP1shbm0bNn5YrrTSUlJWFsbKLwmqpmYc7Vq5FvdE1tXL+OvLw8uvWQ/QqspaWFV+s2tPT0pFo1c5KSnrB/bxBLFi7gdnQ007+XHcjA2tqaRo0bU6dOXTQraRJ1/Tr+fruI+OsCv69cTe06si+Z78uTJ08wMVFcThYWFkRGvlk5rV27lry8PHr2kq0p1tLSok2bNrRq1QrzatV4kpTEnj17mL9gAdHR0fz4448y29tYW+Pi4kLdOnXQrFSJ69eusXPXLs5fuMDaNWuoU0HlBJD6/Bn6hkYlPPuqEnfrzZ59R/f4k5+fT7PW7WSWa1aqRP2mzXF2aYqxiSkpyc+5cPIoQZs38CDuLoM//6epXmryM2kc8rEVPg9Tnj+rkAQgNTmZKiW8IxiamHLvdjR5eXnl/nByYl8A+fn5uCpoDnX1fDjRkZcZ/d2PVCql6auFdWHyEHvjOtVfeQ/Iyc4mPrYwgUt59u5rKct1pn5+flSpUoUuXbpQpUoV2rVrR2BgIJMnT0ZTUxMtLS169erFhg0biImJoXaxrxGBgYGYmZnRunVhVWxOTg4jR47kypUreHt788knn5CamsrOnTvx9fVl69atcr3p169fT2pqKv3798fc3Bxr68ImKIcOHeLu3bv06NEDa2trnj9/TkBAAOPGjWPhwoUybfXOnTvHZ599homJCZ9//jkGBgYcOHCAv/76S+F5f/XVVxw4cIDu3bszYMAAsrKyCAoKYvjw4fz++++0a9euPMVYLjnZ2SW+3Gu/vDlml7KNIufPnOJg4G4aNWlGm2JV9o71G+BY7OtUuy7dmf/T95w6eoQ2nbviVKxzcFmP/T6VXk7aL7fJKddN8PK5MI7vD8K5sSvu7TrIrKvlVI9axTpAeXTozMo5Mzl/8jgt23WitnPh+qJmVIpuLEWJXU4Zq5zfhazcXLS1FN+MipKCwm3Kfns4fv0qO8JO41anLj2aNJNZl5mTQ35BAZ0buzLDZ6B0uZO1DZPWr2bDiaP8NOijNzr2+5SVlVXiC0bRi0lp2yhy9PhxtmzfTks3N3r3lH1Zc2ncmPmNG8ss8+7Th8lffcW+4GD69uwp7Ryc8bK5T+qLF2z/80/s7Qo723fu2JGxEyey/+BBPh06VGEn4/chKycHHW3F10tRUpCVk1Oua+rYXxfYdvgQ7g0ayjXfaVy3LnPrTpZZ1qd1W6YtXkhw2Gl6t24t7RzctaUHG/btZfWeAPR0dWlRrz7JaS9YuyeQ5LQXL2OrmN9fVlaW9H5U3JteU8ePHWX71i24tWxJz169ZdY1dnGhcbEO5X36evPVl1MI3r+Pnr374PJKp9fiCUH7Dh3xat2aieO+YOniRSxauqzMcb2N9/HbCzl6lM1btuDRsiV9esuWk6uLC67z58ss6+ftzeTJk9m7bx99+/bF9ZVyKp4QdOrYkTZt2jBm7FgWLFzI78uXlzmut5WTk4NWCb+9omdibjmffZHnwwk9sA/HRi60KPbxy8HRGQdHZ5ll7u06snbeLP46dQK3th2knYOLnmuKns3/xFYxv73cnOwSy0AaS052uRKAa3+dI+xQMHUaNpZrvpOZkU7wtk00a9OemgpqQF7l4tGKE/sCOBboh46uLrXrNyQ97QXHAv3IeHmPKq2p1Zsqcx+A7Oxs9u/fT9euXalSpQoA3t7ePHz4kFOn/mlj1q9fYVv1PXv2yOwfGxtLZGQkvXv3lhbwn3/+SUREBKtWrWLmzJkMGTKEMWPGEBAQgJGREXMVDHv38OFD/P39mTBhAoMHD5YmExMnTmTHjh1MmTKFQYMGMWbMGHbv3o2dnR0rVqyQOcZvv/2GpqYmO3bsYMKECQwfPpzNmzcr/B9/4MAB9u/fzy+//MKCBQsYOnQoo0aNws/PD0dHR2a9rKp/X3R0dckroX1r0QWh+5oh9l515a/z/DF/Lva16zDhm+llatKjqalJ74GDAYj868I7Pfa7Uno55b7cpvThLV91/VIEfy5bhK1DLUZM/qrM5dS5b38Aoi5H/BPbywdWnoK2jrm5OdL4K4qetja5eYrb4OcUNZcox4M1PPomv/jtwMnKmp8HfSRXVkVJRfGX9yYOtbA0NuHSnZKbh7zu2O+Tnp4euSVcU0VJnd5rhth71ZnwcL7/+WecnZyY/csvZb6mhn/ySeH+Z89Klxf95hvWry99+S/S42XNwsXLl8sc29vS09EhJ1dxW96cl2WoqAlOScIir/DTmlU42dkxc+y4MpfVpy+/gIdfjZQuN9LXZ8m0r7Axt2DOnxsY8N1/GP3rL2Tm5PBx98IPQ/p6pQ8r+a7o6elJ70fFvck1FR52hp9//AEnZ2d++XVWmcvpk5edoc+Gh712exfXJri4NuHSxQiys7LKHNvbeNe/vdNnzvD9999Tz9mZ2bNnl/23N3w4AGfOnHnt9k2aNKFJkyZERESQVUHlBIXPl7wSfntFz0Ttcjz7bly+yNYVS7Cxr8UnE78sc1l16F347nfzlT5kRc81Rc/mf2KrmGefto4ueXmKrylpLAqa4JQkOvISfquWY23nwOAvJsmV06EdW5BICugywLeEI/yjsr4Bw7+ajqmFBXs2rmHBN1NY+cv35GZn49W9MFnVfQ/3qDKnOocPHyYlJQXvV8aWbteuHWZmZvj7+9O2bWH24+zsjLOzM0FBQUydOlVaKEUJQVGCABAUFETdunVxdnbmWbEqWA8PD/bt20dOTo5MNaCPjw9VFQyZWJSUQGEnnqysLCQSCW5ubvj5+ZGRkUGVKlV49OgRUVFR9OrVS2a4Lm1tbT755BOuXLkic9ygoCCMjIzo0KGDXIzt2rVjxYoV3L9/nxo1ylaF5e5Uvi9y9jVsCbt/jyYONnLVoQsz0jA1NaVVw9KzyyKhoaEs/W0mjo512bBhA8bGxmWOw9ZAh1lAZU2JwnN4m2Mr0q1pg9dv9IpddjUJCwujQ8O6cuW0IScTU1NTermVPLLNq0JDQ9mw6H84OTqW+1wSLE1Y+gtU1dWSnkPchXqcOnwARzNDPIud142ThR1Je7Zyo27dunLHKwuLwT7l2t768H7uhoVh0q+XXFklB+zE1NQUm6GDSthbVmhoKP83cxt1nZxKLCurDauJjY2lzgBvLIr1Zanuv52oqCiF51CWY5eHUTnnpbCytubO3bvoKWgG9DQ5GVNTU8ys5TvBKxIaGsp/Zsygbt3y/z4cX7aDzsjOlp6D/csmBtWtreXOq+bL9s05BQXlPuciZq1LH1mnOGsHB+6GhWHo3lyurJ4vX4KpqSnV25c+YkiR0NBQpv/xO3Xf4PdXr5Yd/G8OWQb6MudgBuwbMpi4uDgeP36MhYUFdnZ20o9MjTp3xMyrfOcMYG5avmvS2sqKu3fuYKxfWf639+wZpqamWFuUrZ1vaGgoM7795o2uqQbOhb/D7MyMMp2Dg70dly5GoK1Z/nMGMDQyKtf2VlZW3LlzB109Pfnf3tOnmJqaUrWMfTZCQ0P5z3/+80blVPfl/So9I6NM52BnZ0dERAQFEkm5z7lIH7fGr9/oFXvs7QgLC6Obq7NcWW3NzcLU1BQfz7LVnIaGhrJ56fw3e/bZmPHHLKhWWVt6DomX6nPmyEEaWJrgWey8Ys4cBcC7Tcs3evYNalW+2uBDDoXl5N2ikVw57V6ag6mpKR+1dS9hb1mhoaH88vviEsvp+vXr/HD6JBMnTqSdk710eUrVwuGuHc2McLethpWV1SuxNGPS4H4K71Engf6d2uJVznN+nTLXAPj5+VG1alWqV69OXFwccXFxJCQk4OnpybFjx2Rejr29vUlMTOTsyy9WEomEvXv34uTkJNOuPzY2lujoaDw8POT+CQwMJC8vj+TkZJk4Supg8+TJE2bMmIGHhweurq60bNkSDw8Pdu3ahUQi4cWLwmqU+/cLe2o7KKgaV7QsNjaW1NRUhTEW1SwkJb373tlFGjZsSEFBAZGRkTLLs7OzuXnzJg0bltwc51WnTp1iwoQJ1KpVi/Xr15f7ZSouLg4AMwU33bc99rugyuXUqFEjAC5dkh9d5/LlyxgYGFRox7GKLqvGL5u1PHz4UG7dw4cPFSb04pr6R2nXlKIyffRyDgBFv9X3RZXL6lV2dna0aNECu5e1JqdOncLAwICmFTSM6odSTsXdvXsXLS0tTEwqZiI+UU5lp8plpUrPvoosp8TERCQSCUuWLKFLly7Sf+bNmwfAL7/8QpcuXfj777/l9q3Ie1SZEoD79+9z7tw5nj17RteuXWVOqGhY0KCgIOn2Rc18ir76nzt3joSEBJmv/1CYGNSrV4/169eX+E/xH5Kiar+CggJGjhxJUFAQPj4+LFy4kDVr1rB+/Xpp2/83HaZLIpFgbm5eaozvs8NPjx6Fveo3btwos3znzp1kZmbS+5W2jI8fPyYmJobMzEyZbU+fPi0dnWDDhg2l3pyeP38utywnJ4elS5cC0KGDbFv48hz7fVLlcmrRogXm5ub4+fmRnp4uXX7z5k3Onz9Pt27dyt2Z7W1UdFkVzRWyfft2meXHjh3j0aNH0trDNzn2+6TK11SNGjVo2rQpkZGRXL9+Xbo8Pz+fnTt3oqWlRatWJY9p/a6pclmVZNOmTURHRzN8+HCZGuT3SZXL6cWLF+QrGJ73xIkTXLx4EU9Pz3I1N30bopzKTpXLSpWefRVZTo0aNWLx4sVy/wwdWjiHzciRI1m8ePFrR9B83/eoMjUB2r17NxKJhJkzZ2JoaCi3ftGiRfj7+0vby1WrVg0vLy8OHTrEjz/+yJ49e9DS0pIpYCjMdJ49e4aHh8dbte2NiooiOjqaSZMmMX687GQxxV86isYGvqOg3bGiZXZ2hdVGTZo0ee304++Dk5MTQ4cOZfPmzUyYMIG2bdsSExPDpk2bcHNzkynTBQsWEBAQwJ9//om7e2FV1tWrVxk3bhwSiQQfHx9CQ+XHw391MrfRo0djYWFBgwYNsLS05NGjR+zdu5e7d+/yySefSL/mvsmx3ydVLidtbW1mzJjB1KlTGTp0KAMHDiQ9PZ0NGzZQtWpVJk2a9B5LRl5Fl5Wnpye9evVi3759fPbZZ7Rr144HDx6wefNmzM3NmTDhn4mjxDVVtmsK4Pvvv2fo0KGMGDGCTz75BBMTE4KDg4mMjGT8+PHSARIqgqqX1WeffUaNGjWoXbs2GhoanDlzhpCQENq1a8fYsRUzERGodjmdO3eO2bNn0759e2rUqIGWlhaRkZEEBQVhamrK9FdGoXrfRDmVnSqXlSo9+yqynCwtLRVOVFc0eIOLi4vcemXco16bABQUFBAQEICjoyMDBw5UuM3t27dZunQpkZGR0v/53t7enDhxgqCgIA4dOoSXlxfVqsm2bfT29mb+/Pls3LhRmjy8KikpSW4fRYqGVyr+lf/mzZscOyY7UVb16tWpV68eR44cISEhQdoPIDc3l02bNskd29vbm9DQUBYuXKjwh13WGN/G9OnTsbGxYceOHZw4cQJTU1M+/vhjJk2aVOrU1QC3bt2STmoye/Zshdu8+uPu2rUrR48eZfPmzbx48YLKlStTr149Jk6cSK9iQ6eV99jvm6qWE0D37t3R09NjxYoVzJ07Fx0dHTw8PPjqq6+wLGGq+fepIssKYM6cOTg5OeHv78/s2bMxNDSka9euTJ06Veb8xTVV9muqfv36bNu2jUWLFrFx40ays7OpXbs2s2fPxsenfP1C3gVVLitXV1cOHDhAQEAAALVq1eKHH37A19e31OH53gdVLScHBwcaNGjAiRMnePr0Kbm5uVSvXh1fX1/Gjh1b4fcpUU5lp6plBar17Kvo5155KOMepSF5TduY0NBQPvvsMyZOnCjzpe5V0dHR9O7dm8GDB/Pzy0k0cnJy8PLyIj8/n7S0NBYtWkT3YtOL5+Tk8PnnnxMeHk67du1wc3NDX1+fxMREwsPD0dfXZ/369cA/8wDMnTtXrpBzc3Pp06cPCQkJDB06FAcHB2JjY9mxYwcODg5cv36dkydPUr164Wx34eHhjB49GlNTU3x9fTE0NCQ4OJi8vDyuXbvGlClT+OKLL6TH/+abbwgMDKRZs2a0bdsWU1NTHj58yMWLF0lMTOTQoUPlLHZBEARBEARBUI7X1gAUTejVuXPnErdxdHTE3t6e4OBgpk+fjt7Lnvvdu3dn+/btGBkZKZyMS0dHhzVr1rB582aCgoKkbcgsLCxwcXGR6zNQEm1tbVatWsXcuXMJCAggMzMTR0dH5s2bJ9dGFgpHGFq9ejULFy5k5cqVGBkZ0bNnT7p168aQIUPk+hnMmTOHli1bsnPnTlauXEleXh7VqlWjYcOG+Pq+fognQRAEQRAEQVAVr60BUCfBwcFMnTqVxYsXK2y/JQiCIAiCIAgfujIPA/pvUlBQIJ1MpEhOTg4bNmxAW1sbNzc3JUUmCIIgCIIgCO9X2ec8/hfJzMykS5cu9O7dG3t7e5KTk9m/fz/R0dGMHTtW4bjkgiAIgiAIgvBvoJYJgI6ODm3atCEkJIQnT54gkUioVasW//3vfxkyZIiywxMEQRAEQRCE90b0ARAEQRBUSmBgIM2bN5fO21JcfHw8f/31F97e3hUcmSCor5ycHHR0dJQdhsr7UMpJJACC8Bo5OTk8f/4cU1PTD+JHLXwY4uLiSEpKwtHRUeEEi+qsXr16zJ07V27yyCLBwcFMmzaNGzduVHBkqunChQucPn2ap0+fMmLECGrXrk16ejpRUVE4OTlhZGSk7BBVxt27d4mLi1M4oy2g9knlyZMniYyMZOLEidJlW7ZsYf78+WRlZdG9e3d+++23Cp29XhX9G8pJLZsAqbu8vDxCQkK4cuUKqampFBQUyKzX0NBg1qxZSopOdVy/fp05c+Zw8eJF8vPzWbduHR4eHjx9+pQvv/ySMWPG4OnpqewwVcKlS5fYvHkzcXFxJCcny03Kp6GhQUhIiJKiUy3Hjx/n119/JSEhAUDmuvL19WXatGlqPwrZ675L5ebmvnbiHnWQn5/PtGnTOHToEBKJBA0NDXr27Ent2rXR0tJi/PjxjBw5skJnO1ZVjx8/5ttvvyU8PBxQfI1paGiofQKwdu1azMzMpH/HxMQwa9YsatSoga2tLcHBwTRq1Ejh5K3q5N9QTiIBUDPJycl8+umn3Lp1S/rAKLoRFv27SADgxo0bDB06FFNTU/r27cvu3bul68zMzMjOziYgIEAkABQ21/juu+/Q0tLC3t4eKysrZYekss6dO8eECRNwdnbG29ubZcuWSdeZmZlRs2ZNgoOD1T4BgML7kSKpqamcPHkSc3PzCo5I9axevZrDhw/z7bff0rp1a3r06CFdp6urS6dOnTh58qRIAIAffviBc+fOMWzYMJo3by5qRUoQGxtL27ZtpX8HBwejq6uLn58fBgYGTJs2jcDAQJV+sa0I/4ZyEgmAmlm0aBGxsbHMnDkTNzc3OnfuzNq1a7GysuL3338nLi6OtWvXKjtMpVu8eDEWFhYEBASQnZ2Nv7+/zPqWLVty4MABJUWnWlasWIGDgwPr16+v8KndPzTLly/HycmJXbt2kZKSIpMAQOF08IGBgUqKTrmWLVvG8uXLgcKX/6+//pqvv/66xO1HjBhRUaGprMDAQPr27cuwYcMUNmmpXbs2oaGhSohM9Zw9e5ZPP/2Ub775RtmhqLSUlBRMTU2lf4eFhdGyZUsMDAwAcHNz4+TJk8oKT2X8G8pJJABq5rZdvOIAACAASURBVOTJk3h7e9O/f3/pA0NTU5NatWoxb948PvnkE+bPn89PP/2k5EiVKyIigs8//xx9fX25OSMArK2tefz4sRIiUz0PHjzgP//5j3j5L4Nr164xadKkEpuvVK9enaSkpAqOSjUU1YpIJBJpJ+AaNWrIbaevr4+Liwu9evVSQpSqJSEhgZEjR5a43sjIiJSUlAqMSHVVqVKFmjVrKjsMlWdqasqDBw8ASEtL4+rVq0ydOlW6Pi8vj/z8fGWFpzL+DeUkEgA18+TJExo1agSAllbh//5XX3A7duzI2rVr1T4ByM7OLrVjZlpaWgVGo9qqV6+uMEkS5BUUFJTaKez58+cq3WnsferUqROdOnUCCl9sx40bh4eHh5KjUm36+vokJyeXuD4uLk7Ma/NSu3btCA8PF0N9v4arqyvbt2+nTp06hIaGkp+fL9PUJS4uDgsLCyVGqBr+DeUkelGpGRMTEzIzM4HCh4eWlhaJiYnS9dra2qSmpiorPJVRs2ZNrl+/XuL6s2fPUqdOnQqMSHX5+vqyd+9elf/aoQpq1apFREREieuPHz+Os7NzBUaketLT07G1tS31xVYo1KxZM/bu3auwQ2tKSgr+/v64u7srITLV8+233xIfH8+sWbO4f//+azuaq6tJkyZRUFDAlClT2L17N97e3tJnnUQiISQkhKZNmyo5SuX7N5STqAFQM/b29ty+fRsobPpTv359AgIC8PHxIT8/n8DAQIXV7uqmV69e/P7773Tv3p169eoB/3RKXLduHadOnWLGjBnKDFFpLly4IPN3w4YNOXz4MAMHDuSjjz7C1taWSpUqye3XokWLigpRZQ0YMIBff/2VXbt20bFjR6DwusrMzGT+/PlcvnyZOXPmKDlK5dLX1yc4OFjlH56qYOzYsXz00Ud8+umn+Pj4APD3338TFxfHqlWryMzM5PPPP1dylKrByMgIb29vZs+ezaZNmxRuo6GhQVRUVAVHplrq1KlDcHAwFy9exNDQUOa+nZqayrBhw0RSyb+jnMQ8AGpmxYoVrFu3jjNnzqCjo0NwcDBffvklenp6aGhokJWVxc8//8zAgQOVHapS5eTkMGrUKP766y9q1apFbGwsjo6OPHv2jKSkJDw9PVm9erVaDkXo7OwsN0LLq7cRRes0NDTEmO0vffXVV+zbtw8DAwPS09OpWrUqycnJ5Ofn4+Pjo/YjcAH4+PjQpk0bpkyZouxQVN7JkyeZMWOGtO9I0WhuZmZmzJkzBy8vLyVHqBpWr17NggULMDMzo3HjxhgbGyvcbvbs2RUcmSAoh0gA1IxEIiE3N1dmQqvDhw8TFBSEpqYm3bp1kxlKTp3l5eWxefNmgoKCiI2NRSKRYGdnh7e3N59++qm0D4W6CQgIeKP9+vXr944j+XAdOXJE4XXVtWtXZYemEoKDg/npp5/Yvn07Dg4Oyg5H5eXk5HDmzBliYmKQSCTY29vj5eVF5cqVlR2aymjbti329vasWbNGbfvZlMejR484fvw49+/fB6BGjRq0b99eDPZQzIc8CZ9IAARBEASVsmzZMkJCQrh9+zbt27fHzs4OPT09mW00NDQYP368kiIUPjSurq58++23+Pr6KjsUlbd8+XJWrFhBXl6ezHItLS3Gjh3LhAkTlBSZ6lA0CV/RpI7Z2dm0bt1a5SfhU89PmILwFnJycmRqUNTdd999h6+vLy4uLgrXR0ZGsm3bNlG1Xopnz56RmpqKvb29skNRCa/Oj3DkyBGF24gEoPAlJCcnR+ZLf2pqKn5+fqSkpNCjRw+cnJyUGKHqcHZ2lhnwQlBs8+bNLF26VDqLbe3atQG4ffs2GzZsYPny5ZiYmPDxxx8rOVLl+jdMwicSADWUkZHBvn37uHv3LsnJyXKjIYiZgAvb1UZGRjJx4kTpsi1btjB//nyysrLo3r07v/32m6hKBumMyCUlAPHx8QQGBooEgMKJmyIiIvjll1+ky+bNmyedfM/FxYU1a9ZIJ5NRV0ePHlV2CB+EH374gStXrrBv3z4AcnNzGTJkCDExMQCsX7+eHTt2SAcyUGdTpkxhypQpdOrUSToUtiBv06ZNNG7cmK1bt8o0c3V2dqZr164MGTKETZs2qX0C8G+YhE8kAGomMjKSzz//vNQh9kQCAGvXrsXMzEz6d0xMDLNmzaJGjRrY2toSHBws/UIilC4jI0Nt+0sUV7xN+9WrV1mzZg0tWrTAwcEBf39/NmzYoPZV7DY2NsoO4YMQERFBly5dpH8fOnSImJgYfvjhB+rXr8+XX37JqlWrWLhwoRKjVA179uzB0tKSwYMH4+rqSo0aNeQGcRDPPkhMTOSjjz5SeM/W1tamd+/ezJ8/XwmRqZZ/wyR84qmsZmbPnk1eXh6LFi2iZcuWmJiYKDsklRQbGyszqUdwcDC6urr4+flhYGDAtGnTCAwMVNsE4MGDByQkJEj/jo2NlRseFArHIt+2bRt2dnYVGZ7KunfvHt26dZP+ffDgQYyNjVm7di06OjpoaGhw4MABtU8AXvX8+XPi4+MBsLW1xdTUVMkRqY4nT55ga2sr/fvEiRPUrVuXjz76CIBBgwaxY8cOZYWnUl4dvODixYtcvHhRbhuRAICVlRXp6eklrk9PT8fKyqoCI1JN/4ZJ+EQCoGauX7/OmDFjZF5CBHkpKSkyLxphYWG0bNlS2jTDzc2NkydPKis8pdu9ezfLli1DQ0MDDQ0N/vjjD/744w+57SQSCZqammr/UC3y4sULmRmmw8PD8fT0lPYpadiwIUFBQcoKT6XcvHmTmTNnyk2c1rx5c2bMmKH2E6ZB4e/r1Qn4zp8/L1MjYG5uztOnT5URmsq5efOmskP4IHz88cesWbOGAQMGyM1k++jRI7Zv3y7mluCfSfg+++wzuXVFk/C1bt1aCZGVnUgA1IyBgYH46l8GpqamPHjwAIC0tDSuXr3K1KlTpevz8vLUeubbTp06YWNjg0QiYfr06QwaNIgmTZrIbKOhoUGVKlVo1KiR+GL0krm5OXFxcUBhx9+bN2/Sv39/6fqMjAyFk6ipm+joaIYMGUJOTg4dOnSgbt26QGFHxOPHjzN06FC2b98uXa6ubG1tOX36NEOGDCEiIoInT57ITD70+PFjmYRTEIoLDAyU+dvQ0BAzMzO6d+9Onz59qFWrFhoaGty+fZu9e/dib2+v9n2U4N8xCZ9IANRM586dOX36NEOHDlV2KCrN1dWV7du3U6dOHUJDQ8nPz5dpEhQXFyf3dUSdODs7S7/APnjwgC5duuDo6KjkqFSfu7s7W7ZswdjYmHPnzqGhoSFzXd25c0eMsw0sWbIEbW1ttm/fLjeKTXR0NB9//DFLlixh6dKlSopQNfj4+PDbb7/Rq1cvHj16hJmZmczEX1euXKFWrVpKjFD1SCQSoqKiZMa3r1+/vtwEhuri22+/lU4eV9y2bdvkll2/fp3vvvsOb2/vighPZTVq1Ihly5YxY8YMvvvuOwDmzJkjnYRv2bJl1KlTR8lRlk7MA6Bm0tLSGDVqFA0bNmTYsGHUqFFDbW98pbl16xbDhg3j2bNnQOEkVkWj2EgkEjp27Ii7u7sY2UYol4cPHzJ8+HDu3r0LwBdffMHkyZOBwlqlNm3a0KVLF/773/8qL0gV4O7uzpAhQ0qcCXjhwoVs376dc+fOVXBkqmf58uUcPXoUAwMDvvzyS1xdXYHCvhM9evRg5MiRCpspqKPQ0FB++uknae1uERsbG3788UeVb7LxPpw/f/6N9nNzc3vHkXyYPuRJ+EQC8C/n7Ows94JfNGlFSTQ0NIiKinrfoam85ORkLl68iKGhIS1atJAuT0lJITAwEHd3d9EOGfkqZEX09PSwtramfv36aj8iUH5+Prdv38bQ0BBra2vp8rS0NM6ePYuzs7NMx0511LhxY7755psSayq3bNnCnDlziIyMrODIhA9VREQEw4YNo3LlyvTr10+mWVlAQAAZGRn8+eefNG3aVMmRCqouKyuLgwcP4uDgUOLw1x8CkQD8yxVV75WXOn/ZzsjIYN26dbi4uKjlF6HyKp5kFt1Sii/T0NDAxMSEqVOnMmjQoAqPU9nS09OZOXMmbdq0oXv37soOR6X17NkTKysr1qxZo3D96NGjSUxMZP/+/RUcmepIT0+nefPmTJw4kXHjxik7HJU3atQoYmJi2Llzp1zzzcePHzNo0CBq164tnZNDEEpSUFBA48aNmTFjBkOGDFF2OG9MvT/FqYHffvtN2SF8cKpUqcLKlSv54YcflB3KB2H9+vXMmzePlJQUfH19pePcx8bGsmPHDkxNTRkzZgz37t1jy5Yt/PjjjxgbG9O1a1clR16x9PX1CQ4OFl8Yy6Bv374sWLCAadOmMXbsWGk79piYGFauXMmZM2eYNm2akqNULn19fYyMjFR+qEFVceXKFUaOHKmw75aFhQUDBw5k/fr1SohMNV29epXIyEhSUlIoKCiQWafus3BrampiZWVFWlqaskN5KyIBEAQFatasyZMnT5QdxgchIiKCnJwc9u7dK9PusWPHjnz00Uf4+voSHR3NuHHjGDx4MH379mX9+vVqlwBA4eyQr86fICg2atQooqKi2L9/P8HBwdIJmwoKCpBIJHTv3r3USXjUhbu7OxcuXMDX11fZoai83Nxc9PX1S1xvYGBAbm5uBUakmrKyspgwYQJnzpyR1ty+WqtbtEydEwAAb29vgoKCGDZsmHQY5w9Npf+qe28zNRUZGcnmzZvZuXMnBw8e5Pr16+jr61O9enVlh6YyNm/eTN++fT+IzjzK9M033zB48GCFncJ0dHTIyspi+/bt0htlamoqISEhjBkzRgnRKpehoSHLli2jU6dOYkKrUmhqatKtWzeaNGlClSpVMDY2xtbWFi8vL6ZOncpnn30mBi+gcN6I5cuXk5qaSoMGDT7YF5GKcOTIEWJjY+nXr5/cDMB5eXnMnTsXY2NjBg8erKQIVcPixYvZs2cPY8eOZdKkSQQEBPDbb78xePBgEhMTMTMzY/369Wp//8rPzycsLIzNmzcjkUhISUnh8ePHPHjwQOYfVZ7VXNQAqJn8/Hy+//57AgIC5Ib9WrNmDd7e3sycOVPtxyLX19fH2NiYbt260a9fP+zs7BQmAuo+FBrA06dPS50TIS8vj6SkJOnfFhYWajuHQmxsLFZWVvTu3Zv27dtjZ2eHnp6ezDbi69o/WrVqRatWrZQdhsoaPnw42dnZrFixghUrVlC1alWF11NISIiSIlQdQ4YM4fvvv2f48OGMHj2a2rVrA4WdgNeuXcuVK1f4+eeflRyl8h06dIhu3boxefJknj9/DoClpSUeHh54eHgwYMAAAgIC1L4J3ogRI6T//uuvv5Y42MqNGzcqOrQyEwmAmlmxYgW7d++mU6dOjB49WjpO7a1bt1izZg2BgYHY2NgwYcIEJUeqXN9++6303zds2KBwGw0NDZEAAPb29vj5+TFkyBC5CWJevHiBv7+/tF8AQHx8PGZmZhUdpkpYtmyZ9N+PHDmicBuRAJTs2rVrpKSk0Lx5c3R1dZUdjtK9OoqUULqBAwdy9+5d1q1bJze7NBQ2Oxs4cKASIlMtiYmJDB8+HED6IbCoaZSWlhY9e/Zk27Ztap8A/BsGShEJgJrx9/enVatWMi8iAE2aNGH58uWMGDECf39/tU8A/vzzT2WH8MEYP348U6ZMoVu3bvj4+GBvbw8UTmoVEBDA06dPWbhwIVDYhnv//v1yswari6NHjyo7hA/C2rVruXDhAn/88Yd02bRp0wgODgYKJ2/aunUr1apVU1aIKmHTpk3KDuGD8vXXXzNgwACOHj1KfHw8EomEmjVr0qFDB5mPFOpMX19fWkOrr6+PpqYmjx8/lq43NDSUqdFVV/369VN2CG9NJABq5unTp4wePbrE9Z06dWLOnDkVGJFqEpOclF3Xrl2ZP38+s2fPZtWqVTLrzM3N+d///ke3bt2AwiZoq1evVtuRS1S5Pagq2b9/v8z42uHh4ezfv5+ePXvi5OTEihUrWLNmjUxNnSCUhYODQ6nPQHVXs2ZN6USFlSpVok6dOhw6dIgBAwYgkUg4cuQIVlZWyg1SeCdEAqBm7O3tSx3d5vHjx9IvuEKhnJwcnj9/jqmpqehkV4IePXrQtWtXrl+/Lv2yZmtrS8OGDWX6k2hra0uHdFR3cXFxJCUl4ejoiKGhobLDUSkJCQkyX9iOHj2Kubk58+bNQ0NDg+fPn3Ps2DGRALx04cIFTp8+zdOnTxkxYgS1a9cmPT2dqKgonJycMDIyUnaIwgfCw8MDf39/pk+f/v/t3Xtcznf/B/DXtxO3ktIBqaRQznYPE5qlWhE6CAnJYdhy28jmtAM20mhsk903qjncyWldt0O0Sg7LYZtDIbcJucs5nZRO6vr90Vw/l66Sbfp+L9fr+Xjs8dD1qcfj9djjkuv9/Xw+7ze0tbUxZswYfP7553B1dYUgCMjJycHs2bPFjikJ5eXl2Lx5M5KSkpCdnQ2gZnfS1dUVEyZMqHUfR2pYAGiY6dOnY8mSJfDw8Kg1xTYjIwPbtm0DG0PVuHjxIsLCwnDmzBlUVVUhKioKjo6OePDgAebMmYPp06ejf//+YseUDG1tbfTo0QM9evQQO4qkpaSkYNmyZYp2oE+/r/z9/RESEqLYMdFUpaWlSv94njx5Ev3791dctLOzs8O2bdvEiicZVVVVCAkJQUJCguLSoaenJ+zs7KCjo4Pg4GBMnjwZM2bMEDtqowsMDHzhnxEEAZs2bXoJadTHtGnT4OXlpWgSMm7cOFRUVGDPnj3Q0tJSdOHSdHl5eZg4cSKuXLkCAwMDWFlZQS6X4+rVq0hLS8N//vMfbN68WdK73SwANMz169dhaWmJkSNHYsCAAbC1tYUgCMjMzMTx48dhb2+Pa9euKd0R0MRLiZcuXcK4ceNgbGwMLy8v/PDDD4o1ExMTlJeXIy4ujgXAM0pLS1FQUFCrwxTAC4sAcOrUKcycORMODg7w9vZW+ntmYmICa2trxMfHa3wB0KpVK1y+fBlAzW5AZmam4mIiABQVFXE3DsCGDRvw448/Yv78+XBycsLQoUMVa02aNIGrqyuOHDmikQVATk5OrddKS0sVnW0MDQ0hl8vx8OFDAICxsTGaNWvWqBmlSF9fv9Yu7aRJk5S63hDw5ZdfIjMzE/Pnz0dAQIDi91FFRQViYmIQFhaGL7/8UtLDWFkAaJinP3AcPXoUR48eVVrPyMhARkaG0muaWAB8/fXXMDc3R1xcHMrLy7F7926l9X79+uHAgQMipZOW6upqbNy4EVu2bKn3cpiU26E1loiICNjb22Pnzp0oLCysdRm/V69ekMlkIqWTDmdnZ8TExKC6uhppaWnQ09PDW2+9pVi/cuUK71MAkMlk8PLywsSJExUfbJ9mZ2dX63e8pjh06JDS19nZ2QgMDERgYCDeeecdmJmZAQDu37+P9evXIzk5uc6Ob5qsuroad+7cgampKYvup6SkpMDPz0/pwQRQM/smKCgIV65ckXz7XRYAGoZdSBrm9OnTmDZtGvT19VFRUVFr3cLCQqkzgiZbtWoVoqKi0LFjR7i7u8PIyEjsSJJ14cIFzJo1q9Ygoidat27NDhuo6Sx1+fJlxMTEQE9PDwsXLlR0/CkrK0NiYiL8/PxETim+mzdv1jsR2dDQEIWFhY2YSLqWL1+O1157DQsXLlR63czMDIsWLUJubi5CQ0Oxbt06kRJKU15eHlxcXBRHFalGRUUFunTpUud6t27dFF3LpIoFgIbhU7OGKS8vr/diZnFxcSOmkbY9e/bAyckJGzZsEDuK5FVXV0NXV7fO9fz8/HrXNUWLFi2wadMmFBcXo0mTJrX+n2zdupVTy1FzXKOgoKDO9Rs3bkj6DHJj+vnnnzF37tw61/v27YtVq1Y1YiL1oepIp6br3r17rdMST7t48aLk78OpfgxFpOGsra1x8eLFOtdPnjypGKKm6YqKiuDi4iJ2DLVga2urcgjREykpKbUu52syAwODWh/+mzZtCgcHB+40AXj99dexd+9elR/QCgsLsXv3brzxxhsiJJMeQRBw9erVOtczMzNrTXMlqsv8+fORkJCALVu2KAalATWT7zdt2oTExETJdynjDsAr7tkzxg2hiWf+nzVs2DCsW7cOQ4YMQefOnQFA8Y9DVFQUjh07hkWLFokZUTI6depUb2tZ+n9+fn5YtmwZdu7cqSiaBEFAaWkpwsPDce7cOc7hUCE3NxdOTk48hvCMGTNmICAgAIGBgfD19QUAXL58GTdu3MD69etRWlqKadOmiZxSGgYMGIDY2Fh069YNXl5eit/ncrkcMpkM27dv54MMarAVK1bAyMgIy5cvxzfffAMrKysANXdNiouLYW1tXWtasNS6TAly7u280v7I00RBEDT+wmZFRQWmTJmCX3/9Fba2trh27Ro6deqEvLw85Obmon///tiwYUOdZ7k1yeHDh7Fo0SLs2rWLA2IaYO7cudi3bx8MDAxQUlKCli1boqCgAFVVVfD19cXy5cvFjig5ubm5GDhwIKKjo1kAPOPIkSOKM+xAze9vuVwOExMThIWFYeDAgSInlIY7d+4gICAAt2/fhomJCWxsbCAIAq5fv44HDx6gTZs2iImJ4dGyZxQXF2PZsmWYOnUq7OzsxI4jGYMHD/5DP/fs5XQxsQB4xT3pNf6ieFegZitv69at2LNnD65duwa5XI527drB29sbgYGB0NHhBhpQs8t05MgRZGZmws3NDZaWlrUKI+4qKUtMTFT5vnJ3dxc7miSxAKhfRUUFUlNTcfXqVcjlctjY2GDgwIH429/+JnY0SXn48CE2bNiA5ORkpcFNLi4umDp1KgemkUZhAUBEf0pDdpm4q0R/BgsAosZRVVWFiooKpeKxqKgIu3btQmFhIYYOHQp7e3sRE9JfhY8wNdT58+eRnp6OwsJCVFdXK63xaW1Nu9S33noL2traYkeRPLaWbbjNmzdj2LBh7MzygnR1ddGnTx+0aNFC7CiS4uPjAx8fH76n6C/z6aefIi0tDfv27QMAVFZWYuzYsYoL1NHR0di+fbvibhzVUMejUtwB0DBlZWWYOXMmUlNTFaPjn7wFnvyZT2trnmq3bNkSw4YNg7e3d739fokaysHBATo6Ohg0aBB8fHwwaNAgtv2kP+zNN9/EvXv3oKOjgzfffBM+Pj546623+J6qg1wux/Hjx5GVlaVyYjkffgEeHh54++23MWfOHADAvn37MHfuXHz66afo0qUL5syZg549e2L16tUiJ5UWdWxUwB0ADRMREYHU1FTMmDEDjo6OCAwMxIoVK2BiYoL169ejrKyMXUgALF68GDKZDJs3b8aWLVvQoUMH+Pj4YPjw4YoJklTbjRs3kJubi06dOtU7R0FTbdiwATKZDIcOHcKhQ4dgaGiIYcOGwcvLS/I9o8Ugl8uRkZGhdF67S5cubNf4uyNHjuD48eOQyWRISkpCSkoKDA0N4enpCW9vb76nnpKVlYXg4GDFvRtVWADUTEa2tLRUfH348GF07NgRAQEBAIDRo0dj+/btYsWTNHV7ns4CQMMkJCTAw8MD77//vmJ0fKtWreDo6AhHR0f4+fkhLi4OISEhIicVl7+/P/z9/ZGdnY24uDjs3bsXX375JcLDwzFgwAB4e3vD1dWVo9F/l5KSgmXLlikunT95CvLgwQP4+/sjJCQEHh4eIqcUn5OTE5ycnFBSUoKDBw9CJpMhJiYGMTExaN++PXx8fDBixAi0atVK7KiiO3r0KJYsWYJbt24pvd62bVt89tlncHJyEimZdAiCgAEDBmDAgAF49OgREhISIJPJEBsbi23btsHGxgY+Pj5sBQrg888/x//+9z/MnTsX/fr14xyJOsjlclRVVSm+/vnnn/H2228rvjYzM8ODBw/EiCZ56vZggj0MNczt27fRp08fAFCcb38yxEJHRweenp7Yv3+/aPmkxsrKCrNmzUJiYiK2bt0KHx8fnD17FiEhIWyv97tTp05h5syZaNGiBYKDg5WegpiYmMDa2lryI9Ebm76+PkaOHIktW7YgOTkZs2bNglwux1dffcVe5ABOnz6N9957D0VFRZgwYQKWLl2KpUuXIjAwEEVFRXj33Xdx5swZsWNKSrNmzeDj44NNmzYhJSUFH3zwAe7fv481a9aIHU0Szpw5g4kTJ2LKlCno2rUr2rZtq/I/TWdpaYmffvoJQM3fw/v37ysNk7t37x53d+vAHQCSNH19fUV1r6+vDy0tLdy7d0+x3rx5c0U/aVLWu3dvdO3aFd26dcOqVavw8OFDsSNJQkREBOzt7bFz504UFhbWGj7Xq1cvyGQykdJJn4WFBYYPH46qqip8//33KCkpETuS6NatWwdTU1Ps2LED5ubmSmtTpkzB6NGjERERgcjISJESSld2djZkMhn27NmD4uJitiv+na6urtLRFlLN19cXK1aswLBhw3D37l2YmJgoPexKS0uDra2tiAmlqWXLlkhOTlarI8L8zaBhrK2tkZWVBaBmB6BDhw5ISEiAn58f5HI5EhMTOcxJhSfnbBMTE1FWVoYWLVpg3LhxYseShAsXLmDWrFl1DkVr3bo1i0oViouLceDAAchkMsXT7I4dO8LHx0fkZOJLS0vD5MmTa334BwBzc3OMGjUK0dHRIiSTpocPHyI+Ph4ymQznzp2DXC6Hvb095s+fj+HDh4sdTxIGDhyIM2fOwN/fX+wokhYUFISSkhIkJyejc+fOmDNnjqIlaH5+vuLvJinT0tJS7CBVVFSoxfFgFgAaxtHREbt378bChQuhra2NMWPG4PPPP4erqysEQUBOTg5mz54tdkxJyMzMhEwmw969e3Hv3j1oa2uze4sK1dXV9f6/yM/P5/+r31VXV+PYsWOKi8Dl5eVo2bIlJkyYAB8fH7bW+11lZSX09fXrXDcwVJUsTwAAH1NJREFUMFAcXdRkKSkpkMlkOHz4MMrLy2FiYoLAwED4+Pj8oSnwr7L58+dj/PjxiIqKwvjx49XiA5pYgoODVV6GNjY2xokTJ0RIJD1HjhxBeno6/vGPfyhe+/e//43w8HCUlZVhyJAhWLFihaT/7WMBoGGmTZsGLy8vxVm1cePGoaKiAnv27IGWlhZmz56Nd955R+SU4vP19cWlS5cgl8vRtWtXTJ06FcOGDYOxsbHY0STH1tYWp0+frnNHJCUlhR9Gfufk5IS8vDzo6OjA2dkZ3t7eGDRoEOdNPMPOzg7x8fEYN25crSMsjx8/xoEDB9Sm1/bL9O6770JPTw/Ozs7w8fGBk5MT30t1GDt2LEpLS7Fy5UqEh4fD3Nxc5cTypKQkkRJKT0VFBfLz82FsbMyC6RmRkZEwMTFRfH316lUsX74cVlZWsLS0RHx8PLp3746goCDxQj4HCwANo6+vX+v83qRJkzBp0iSREklTbm4uJk+eDB8fH3To0EHsOJLm5+eHZcuWYefOnYoLrIIgoLS0FOHh4Th37hxby/7OwsICwcHB8PT05FCreowdOxaffPIJgoKClAbrZGZmIjIyEmlpaVi6dKnIKcX32WefwdPTE4aGhmJHkTwLCwuxI6iNixcvIiwsDGfOnEFVVZVSV7c5c+Zg+vTp6N+/v9gxRXXt2jUMGjRI8XV8fDyaNGmCXbt2wcDAACEhIZDJZCwAiNTN4cOH6zzTTsoCAgJw5swZfPLJJwgLC4MgCAgJCUFBQQGqqqrg6+uLESNGiB1TEnbu3Cl2BLUwatQoZGVlISoqCqdPn661PmXKFIwaNUqEZNIyduxYsSOojS1btogdQS1cunQJ48aNg7GxMby8vPDDDz8o1kxMTFBeXo64uDiNLwAKCwuVTgQcP34c/fr1g4GBAQCgb9++OHLkiFjxGoQFAJEKTz78P3r0COfOnUNubi769+8PU1NTkZNJ06pVq+Du7o49e/YoBu306NED3t7ecHd3Fzue5GRnZ+PkyZPIzc3F8OHDYWlpiYqKCuTm5sLU1JTb7QA+/PBD+Pn5ISkpCTdv3oRcLoe1tTUGDx6M9u3bix1PMoqLi/H9998jNTUVDx48QFhYGF577TXk5eUhJiYGQ4YM4XEparCvv/4a5ubmiIuLQ3l5OXbv3q203q9fPxw4cECkdNJhbGysmFFSXFyM8+fPK92ffPz4sdI8BSliAUBUh5iYGHz11VcoLi6GIAiIioqCqakp8vLyMGjQIHz88ccYM2aM2DElw83NDW5ubmLHkLyVK1fi+++/R1VVFQRBQK9evRQFgKenJ95//31Jbxs3pvbt2/NOUj3y8vIwduxY5OTkwNraGtnZ2SgrKwNQ05ZQJpPh4cOHWLBggchJSV2cPn0a06ZNg76+PioqKmqtW1hYKLUO11S9evVCbGwsOnTogKNHj6KqqkrpSNCNGzdUdjGTEhYARCokJCRg6dKlcHFxgbOzMz7++GPFWsuWLeHk5ITk5GSNLAD+aE9/b2/vvziJ+omNjUVkZCQmTJgAZ2dnpXZ6BgYGGDx4MFJSUlgAADh79iy2bt2KGzduoKCgoNaQHV7YBNasWYPc3Fzs2LEDbdq0qXUsw8XFhV1bnnL69GmsX78eaWlpKCoqUvmeysjIECmdNJSXl9c76Ku4uLgR00jXrFmzEBgYiA8++AAAlO4LyuVyJCUlKQ1QkyIWAEQqREZG4o033kBERATy8/OVCgAA6Natm8ae554/fz4EQXihqYeCILAAQM2ukpubGxYtWoT8/Pxa6/b29vjll19ESCYtMpkMCxYsgI6ODmxsbDibpA4pKSkICAhA165dVb6frKysEBcXJ0Iy6fnll18wadIkGBgYoGfPnjhy5Aj69euHR48eIT09HZ06dULXrl3Fjik6a2trXLx4sc71kydPsjEGgA4dOiA+Ph5nzpxB8+bN0adPH8VaUVERJk6cyAKASB399ttvmDt3bp3rZmZmePDgQSMmko7NmzeLHUFtZWVl1Xtx09jYWOUHOU3z3XffoX379oiOjkarVq3EjiNZ+fn5sLa2rnNdEASUl5c3YiLp+uc//wkzMzPFmfb+/ftj+vTpcHR0xE8//YRZs2bhs88+Ezml+IYNG4Z169ZhyJAhirkkgiAAAKKionDs2DEsWrRIzIiSYWRkhMGDB9d6vUWLFpg4caIIiV4MCwAiFbS0tFBdXV3n+r179xTTETVN3759xY6gtpo0aYLS0tI612/dusWWjqj5//DRRx/xw/9zmJmZITs7u871S5cucffkd+np6QgKCkLLli1RUFAAAIpdzIEDB8LLywtff/21xj/gmDx5MlJTUzFlyhTY2tpCEASEhoYiLy9P0QwjICBA7JiScffuXaSkpCj+HlpZWcHZ2VktfnexzyGRCg4ODvjpp59UrlVXV+PgwYPo3r17I6ciddejRw8kJiaqXCsvL8d//vMf/P3vf2/kVNLTunVrlRcQSdmbb76JXbt2qbyUmZaWBplMppjNoekqKioUH8qedNkqKSlRrHfu3Lneoy+aQk9PD9HR0Zg3bx6aNGmCJk2aICsrC8bGxvjwww/xr3/9iy2yfxcREQEXFxcsXrwYkZGRiIyMxOLFi+Hi4oK1a9eKHe+5uANApML48eMxZ84crFmzRnF2XS6X49q1a1i9ejUyMzPrPSJEpMqUKVMwZcoUfPjhhxg5ciSAmqFzx44dw7fffou7d+8iPDxc5JTi8/f3x969exEUFMTJtvWYOXMmDh06BB8fHwwePBiCIEAmk2Hnzp348ccfYW5uzi5KvzMzM8OdO3cAAM2aNYOhoSF+++03ReeyO3fu1Jo6ral0dHQQFBTEZgT12Lp1K7799lvFtN+nhxV+//33iIiIgJGREcaPHy9y0roJ8he5yUekQVavXq142lFdXQ0tLS3I5XLI5XL84x//QHBwsNgRSQ1t374dy5YtQ2VlJeRyueJ8ra6uLhYvXgxfX1+RE4rv5MmTWL16NSorKxEQEABLS0uVhcDTF+801e3bt7F06VIcOXJEcWxREAQMGjQIixcvRuvWrUVOKA2zZ89GUVERIiMjFV+npqZi4cKFqK6uRlhYGHr06IENGzaInFQ8JSUl8PLywvjx4/nh/znc3d3RokULxMTE1CocKysrMXbsWDx8+BAJCQkiJXw+FgBE9bh48SL27t2rGG7Vrl07eHl58fgP/Sn379/HwYMHFe8rGxsbDBkyRC3OjTYGBwcHpa+fFElPPCmcLl261JixJK24uBjXrl0DUNPJxcjISORE0pKamooffvgBy5YtQ9OmTZGdnY2AgADcv38fAGBqaoqoqCh06tRJ5KTi6t27N+bNm8dJ28/Ro0cPhISE1HnZd9OmTQgPD0d6enojJ2s47ncRPaOqqgp3795Fs2bN0LVrV7aGo79ERUUF0tLSYGZmBhsbG0yYMEHsSJIVGhoqdgTJKykpwRdffIE333wTQ4YMgYGBAXr06CF2LEkqKyvD/fv3ERgYiKZNmwKouayZkJCAEydOQFtbG6+//nq9/e81Rc+ePXH+/HkWAM/Rpk0bpTskzyopKZH8BXwWAETPePz4MVxdXTFnzhxMnTpV7Dj0itDS0kJQUBDmzZsHGxsbseNImo+Pj9gRJE9fXx/x8fG8NN4Aenp6+Pjjj7Fo0SL07NlT8XqzZs14SfoZc+fOxcSJE9GzZ0/4+vrW2n2jGuPHj8fGjRvh5+dXa+Lv3bt3ERsbi2nTpomUrmFYABA9o0mTJjA2NtbYNp/0cujo6MDU1PSFBqgR1cfOzg43b94UO4bkaWlpoU2bNpxi2wChoaEwNDTExx9/jJUrV8La2lqxa/KEIAjYtGmTSAnFIZPJlL5u3rw5TExMMGTIEIwYMULRMjUzMxN79+6FjY0NDAwMRErbMLwDQKTCggULkJubq9EXwuivFxoairS0NMTExLCVHv1p8fHxWLJkCWJjY9G+fXux40haREQEDh48iN27dyvagFJtqgZbqXLo0KGXnERaHBwcIAjCCz3Akfo9JRYARCrk5eVh8uTJsLe3x+TJk2FjY4MmTZqIHYvU3JP2sU8mRbZr107lTpOFhYUI6UjdrF27FklJScjMzISzszPatWun8mktO5YBJ06cQFhYGMrLyxEQEFDn3z12liJVfv755z/0c1IenMkCgEiFp6v9us5ACoKAjIyMRk5G6qwh7ysAkn5qRNLxbLckVaT+FLKxsLPUi6moqMCpU6cUE26tra3Rp08fPgh7hfAOAJEK3t7evPxEf7ng4GC+r+gvk5ycLHYEtcHOUg0nk8kQGhqKoqIixZEXQRBgaGiIefPmcVbJU86fP4/09HQUFhYq5nA8IfXdN+4AEBERERHi4+MxZ84cWFhYwN/fH3Z2dpDL5bh69SpiY2Nx584dhIeHY+jQoWJHFVVZWRlmzpyJ1NRUxe7R08WSOuwosQAgIiJ6BZSXl+PAgQMYOHAgTE1NxY5DamjEiBF4/PgxduzYUauLzcOHDzFq1Cjo6upi7969IiWUhvDwcGzcuBEzZsyAo6MjAgMDsWLFCpiYmGD9+vUoKytDWFgYbG1txY5aJ7ahIGqAvLw8uLi44OzZs2JHoVdIbm4uOnfujBMnTogdhV4BDx8+xIIFC3DlyhWxo5Caun79Onx9fVW2sGzevDl8fX1x48YNEZJJS0JCAjw8PPD++++jY8eOAIBWrVrByckJ0dHRqKysRFxcnMgp68cCgKgBqqurcfPmTZSVlYkdhV4x3ISlvxLfT/RnmJmZ1fse0tLS4u4SgNu3bys6RmlrawMAKisrAdTMfPH09MT+/ftFy9cQLACIiIiICD4+PoiLi0NJSUmtteLiYuzevZuXgFEzibuqqkrxZy0tLdy7d0+x3rx5c+Tm5ooVr0HYBYiIiIiI0Lt3b6SkpGD48OEICAhQmnC7bds2GBsb4/XXX8cvv/yi9HOaNj/B2toaWVlZAGp2ADp06ICEhAT4+flBLpcjMTERbdq0ETfkc7AAIGoALS0tWFhY1BqyQ/Rn6Orqok+fPmjRooXYUegV0Lx5c4SGhirOJBO9qEmTJin+vGrVKkXb4ifHgm7duoXJkycrvkcdut28DI6Ojti9ezcWLlwIbW1tjBkzBp9//jlcXV0hCAJycnIwe/ZssWPWi12AiIiI1JBMJkPv3r1haWmpcj0nJwe//vorvL29GzkZqas/enHVx8fnL04ibSUlJbh79y6sra2ho1PzLD06Ohp79uyBlpYW3N3d8c4770h67gsLACIiIjXUuXNnfPnllxg+fLjK9fj4eISEhGjc01kiej4eASKqw+PHj5GUlIS0tDQUFRWpnPK3fPlykdKRujp79iy2bt2KGzduoKCgoFbHDUEQkJSUJFI6UifPe35XWVkJLS32+iCi2lgAEKlQUFCAwMBAXLlypd4pfywA6EXIZDIsWLAAOjo6sLGxkfwlMZK+uo4YFBUV4ciRIzAzM2vkRESkDngEiEiFxYsXY9euXViyZAn69u0LNzc3REZGok2bNli3bh1u3LiByMhIGBoaih2V1Ii7uzu0tbURHR2NVq1aiR2H1NDatWsRERHR4O+fNGkSPvroo5eYiIjUEXcAiFQ4cuQIvL29MXLkSOTn5wOo6QRka2uLVatWYcKECQgPD8eSJUtETkrq5NatW/joo4/44Z/+MAcHB3h7e0MulysuAVtZWdX6Pn19ffTs2RPDhg0TISURSR0LACIV7t+/j+7duwOA4oZ/RUWFYt3FxQWRkZEsAOiFtG7dWul9RPSiXF1d4erqCgC4efMm3nvvPTg6OoqciojUDW8HEalgZGSE0tJSADVP0nR0dHD79m3Fuq6uLoqKisSKR2rK398fe/fuVUyQJPqjSkpKYGlpiYKCArGjEJEa4g4AkQo2NjbIzMwEUHP0p0uXLoiLi4Ovry+qqqogk8lUbrsTPe3ZaZndunXDjz/+iFGjRiEgIACWlpbQ1tau9XOaNlWTXpy+vj7i4+Px97//XewoRKSGWAAQqTBgwABERUXh008/hZ6eHoKCgjBnzhz07dsXgiCgrKwMS5cuFTsmSdyECRNqdWl50nfh448/VrmmiVM16Y+xs7PDzZs3xY5BRGqIXYCIVJDL5aisrISenp7itR9//FEx5c/DwwNDhw4VMSGpA07VpJcpPj4eS5YsQWxsLNq3by92HCJSIywAiIiI1NDatWuRlJSEzMxMODs7o127dmjatKnS9wiCgODgYJESEpFUsQAgImokCxYsgL+/P3r27KlyPT09Hdu2bUNoaGgjJyN15ODg8Nzv4ZEyIlKFdwCI6vDo0SPs27cPWVlZKCgowLO1MicB04uKi4tD//796ywAcnJyIJPJWABQgyQnJ4sdgYjUFAsAIhXS09Mxbdq0elvssQCgv9qjR48UcyeInqdt27ZiRyAiNcV/aYhUCA0NxePHj7FmzRr069cPRkZGYkciNXXr1i2lTi3Xrl2r1R4UAAoLC7Ft2za0a9euMePRKyI/Px85OTkAAEtLSxgbG4uciIikjHcAiFTo0aMHpk+fzstz9KetXbsWa9eurdXy81lyuRxaWlpYvnw5vL29Gykdqbv//ve/+OKLL3D69Gml13v37o1FixY16J4AEWke7gAQqWBgYMCn/vSXcHV1Rdu2bSGXy7Fw4UKMHj0ar732mtL3CIKAZs2aoXv37mjTpo1ISUnd/Pbbbxg7diwqKiowePBgdOzYEQCQmZmJlJQUjBs3DrGxsYrXiYieYAFApIKbmxt++uknjBs3TuwopOYcHBwUT2Fv3bqFt99+G506dRI5Fb0KvvnmG+jq6iI2Nhb29vZKa7/99hvGjx+Pb775Bt9++61ICYlIqngEiEiF4uJiTJkyBd26dcPEiRNhZWX13CMcRESN6Y033sDYsWPxwQcfqFxfvXo1YmNjcerUqUZORkRSxx0AItQ8pX32A75cLkd6ejpiYmJU/owgCMjIyGiMePSKkMlkz/2epk2bwsLCAl26dGFHIKpXaWkpzMzM6lw3NzdHaWlpIyYiInXBf12IAHh7e/MJP7108+fPV3qfPdmAffY1QRBgZGSE2bNnY/To0Y2ek9SDlZWV4qy/KikpKbCysmrkVESkDngEiIiokZw4cQKrVq1CYWEh/P390b59ewA1rUG3b98OY2NjTJ8+Hf/73//w73//G7du3cKaNWvg7u4ucnKSovXr1+Orr76Cp6cnZsyYAVtbWwDA1atX8a9//Qvx8fEICQnB1KlTRU5KRFLDAoCIqJGsXbsWCQkJ2LFjB/72t78prZWUlMDf3x9DhgzBe++9h5KSEnh5ecHU1BSxsbEiJSYpq6qqQkhICA4ePAhBEKClpQUAqK6uhlwux5AhQxAeHq54nYjoCRYARPVIT09HYmIisrOzAdRsubu6uqJnz54iJyN15OzsjAkTJmDy5Mkq16OiorB161YcOnQIQE3BEBUVhTNnzjRmTFIzqampSEpKQk5ODuRyOaytreHq6or+/fuLHY2IJIp3AIhUqKqqwieffIK4uDg8WyNv3LgR3t7e+OKLL6CtrS1SQlJHDx48QFVVVZ3rjx8/Rm5uruJrc3Pzer+fCAAGDBiAAQMGiB2DiNQI9wWJVPjuu+/www8/wMXFBbGxsfj111/x66+/Ytu2bRg8eDBkMhm+++47sWOSmrGxscGuXbtQXFxca+3hw4fYvXu34l4AAOTk5MDExKQxI9Ir4MKFC0hNTUV5ebnYUYhIongEiEgFZ2dn2NraIjIyUuX6pEmTkJWVhZSUlEZORuosISEBH3zwAUxMTODr6wsbGxsAwPXr1xEXF4cHDx5g9erV8PDwQHV1Ndzc3NCrVy+Eh4eLG5wkKTIyEr/88gv++c9/Kl4LCQlBfHw8gJojizExMTA1NRUrIhFJFI8AEanw4MGDejtnuLq6IiwsrBET0avA3d0d4eHhCA0Nxfr165XWzMzMsHLlSnh4eACoOYa2YcMGtGzZUoyopAb279+vdB/pxIkT2L9/Pzw9PWFvb4/vvvsOGzduxPz580VMSURSxAKASAUbGxvcv3+/zvV79+4pnt4SvYihQ4fC3d0dFy9eVFzatLS0RLdu3ZTulOjq6iraOhKpcvPmTfj4+Ci+Tk5OhpmZGVatWgVBEJCfn49Dhw6xACCiWngHgEiF6dOnIyYmBv/9739rrWVkZGDbtm2YMWOGCMnoVaCtrY0ePXpg6NCh8PT0RM+ePXmhnF5YaWkpmjZtqvj65MmT6N+/v2KwnJ2dHe7evStWPCKSMO4AEKlw/fp1WFpaYuTIkRgwYABsbW0hCAIyMzNx/Phx2Nvb49q1a1i7dq3iZwRBQHBwsIipSZ2UlpaioKCgVpcpALCwsBAhEambVq1a4fLlywBqdgMyMzMRFBSkWC8qKoKenp5I6YhIylgAEKnw9Af7o0eP4ujRo0rrGRkZyMjIUHqNBQA9T3V1NTZu3IgtW7Yotft81qVLlxoxFakrZ2dnxMTEoLq6GmlpadDT08Nbb72lWL9y5Qratm0rXkAikiwWAEQqJCcnix2BXkGrVq1CVFQUOnbsCHd3dxgZGYkdidRYcHAwLl++jJiYGOjp6WHhwoWKjj9lZWVITEyEn5+fyCmJSIrYBpSIqJEMHDgQnTt3xoYNG8SOQq+Q4uJiNGnSBLq6uorXysrKkJWVhdatW7PQJKJauANARNRIioqK4OLiInYMesUYGBjUeq1p06ZwcHAQIQ0RqQMWAERQPvPfUDzzTy+qU6dO9baXJfozcnNz4eTkhKioKDg6Ooodh4gkjAUAEVgAUOOYOXMmFi1aBD8/P7Rp00bsOPQK4qleImoIFgBE4KVfahwXLlyAhYUFhg4dCjc3N1haWkJLS3kcCwtLIiJ62XgJmIiokTTkTLYgCGwDSn9Ibm4uBg4ciOjoaB4BIqJ6cQeAqB7nz59Heno6CgsLUV1drbTGJ7X0orjTRC+Trq4u+vTpgxYtWogdhYgkjjsARCqUlZVh5syZSE1NhVwuhyAIirO1T/7MJ7VERESkjrgDQKRCREQEUlNTMWPGDDg6OiIwMBArVqyAiYkJ1q9fj7KyMoSFhYkdk9TYjRs3kJubi06dOqF58+ZixyE1JpfLkZGRgezsbACAlZUVunTpAkEQRE5GRFKl9fxvIdI8CQkJ8PDwwPvvv4+OHTsCAFq1agUnJydER0ejsrIScXFxIqckdZSSkgJXV1d4eHhg/PjxuHDhAgDgwYMHcHNzw8GDB0VOSOrk6NGjcHV1hZ+fH2bPno3Zs2fDz88Pbm5uOHbsmNjxiEiiWAAQqXD79m306dMHAKCtrQ0AqKysBADo6OjA09MT+/fvFy0fqadTp05h5syZaNGiBYKDg5VaNpqYmMDa2hrx8fEiJiR1cvr0abz33nsoKirChAkTsHTpUixduhSBgYEoKirCu+++izNnzogdk4gkiEeAiFTQ19dHVVWV4s9aWlq4d++eYr158+bIzc0VKx6pqYiICNjb22Pnzp0oLCysNX+iV69ekMlkIqUjdbNu3TqYmppix44dMDc3V1qbMmUKRo8ejYiICERGRoqUkIikijsARCpYW1sjKysLQM0OQIcOHZCQkACg5rxtYmIiBznRC7tw4QJGjBhRq/f/E61bt2ZhSQ2WlpaG0aNH1/rwDwDm5uYYNWoU0tLSREhGRFLHAoBIBUdHRyQkJCh2AcaMGYNjx47B1dUVb7/9No4fP46RI0eKnJLUTXV1NXR1detcz8/Pr3ed6GmVlZXQ19evc93AwEBxdJGI6GksAIhUmDZtGjZv3qw4oz1u3DjMmzcPzZs3h6GhIWbPno133nlH5JSkbmxtbXH69Ok611NSUho0LIwIAOzs7BAfH4/Hjx/XWnv8+DEOHDgAOzs7EZIRkdSxACBSQV9fH7a2ttDR+f9rMpMmTUJcXBx2796NadOmscUevTA/Pz8kJCRg586dSnMlSktL8cUXX+DcuXMYPXq0yClJXYwdOxZpaWkICgrC4cOHkZ2djezsbKSkpCAoKAhpaWkYO3as2DGJSII4CIyIqBHNnTsX+/btg4GBAUpKStCyZUsUFBSgqqoKvr6+WL58udgRSY2sXLkSUVFRKtemTJmCuXPnNnIiIlIHLACIiBpZYmIi9uzZg2vXrkEul6Ndu3bw9vaGu7u72NFIDV2/fh1JSUm4efMm5HI5rK2tMXjwYLRv317saEQkUSwAiIiIiIg0COcAEBG9JH+0p7+3t/dfnIReVWfPnsXWrVtx48YNFBQU4NlneoIgICkpSaR0RCRV3AEgInpJHBwcIAhCrQ9l9REEAZcuXXqJqehVIZPJsGDBAujo6MDGxgZGRkYqv2/Lli2NnIyIpI4FABHRS/Lzzz//oZ/r27fvX5yEXkXu7u7Q1tZGdHQ0WrVqJXYcIlIjPAJERPSS8IM8vUy3bt3CRx99xA//RPTCOAeAiIhIDbVu3RoVFRVixyAiNcQCgIiISA35+/tj7969qKqqEjsKEakZ3gEgIiJSQydPnsTq1atRWVmJgIAAWFpaQltbu9b39enTR4R0RCRlLACIiIjUkIODg9LXgiAofS2Xy9lViohU4iVgIiIiNRQaGip2BCJSU9wBICIiIiLSILwETERERESkQVgAEBERERFpEBYAREREREQahAUAEREREZEGYQFARERERKRB/g/Q5X/eWxpsXAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = [\\n\",\n    \"    'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',    \\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"import copy\\n\",\n    \"\\n\",\n    \"########### Present #############\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, last_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = last_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = last_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in last_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_50 = copy.copy(df)\\n\",\n    \"df_f1_50 = df_f1_50.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_50.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"    'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"    'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"    'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"    'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"    'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"    'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"    'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"    'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"    'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_50 = df_f1_50.div(df_f1_50.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_50.columns)\\n\",\n    \"normalized_df_f1_50.columns = [column_name_map[item] for item in normalized_df_f1_50.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 18,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"f, axes = plt.subplots(figsize=(12, 8))\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.2, ax=axes, cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"########### Absent #############\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = last_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = last_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in last_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_50 = copy.copy(df)\\n\",\n    \"df_f1_50 = df_f1_50.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_50.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"    'random - absent_exact_recall@50': \\\"random\\\",\\n\",\n    \"    'alphabetical - absent_exact_recall@50': \\\"alpha\\\",\\n\",\n    \"    'alphabetical_reverse - absent_exact_recall@50': \\\"alpha-reverse\\\",\\n\",\n    \"    'length - absent_exact_recall@50': \\\"length\\\",\\n\",\n    \"    'length_reverse - absent_exact_recall@50': \\\"length-reverse\\\",\\n\",\n    \"    'no_sort - absent_exact_recall@50': \\\"no-sort\\\",\\n\",\n    \"    'no_sort_reverse - absent_exact_recall@50': \\\"no-sort-reverse\\\",\\n\",\n    \"    'verbatim_append - absent_exact_recall@50': \\\"pres-abs\\\",\\n\",\n    \"    'verbatim_prepend - absent_exact_recall@50': \\\"abs-pres\\\"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_50 = df_f1_50.div(df_f1_50.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_50.columns)\\n\",\n    \"normalized_df_f1_50.columns = [column_name_map[item] for item in normalized_df_f1_50.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 18,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"f, axes = plt.subplots(figsize=(12, 8))\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.2, ax=axes, cmap=cmap, fmt='.3f', cbar=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-18T21:48:14.786698Z\",\n     \"start_time\": \"2020-11-18T21:48:12.112751Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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OHj2bz5gBsbMrnePyhQ9+jUCgMCv6Ehp7i//7vI9zdG/Lpp18YFGASQgghRMkz6htGy5YtGTJkiNY2c3PzQh2QEEIIIYTIWVpaGsuX+9GuXXsqVqxIdHQUADEx94Gs5WLR0VHY2dlToUKFHPupW9eNjRu3Eh0dRWxsDE5Ozri6VmfVqq+BrGpn+ty4cYOrV6/g5dUBZ+dKuY719OkQ5s59n9q167BkyYpcg1BCCCGEKF2MChhVr16dvn37FtVYhBBCCCFEHlJTU4mLe0RIyClCQk7p7D96NIijR4OYMuVthg0bmWd/rq7VcXWtrvn59OkQbGxseOmlpnrb79mzG4DevXNPdv3bb6HMmTOLGjVqsXTpKmxtbfMcixBCCCFKD6PnMKelpZGeno6NjU1RjEcIIYQQQuTCysqKTz9doLM9Li6OxYsX0KaNF71796Fu3XoAxMbGkpSUSOXKVfRWRXvW7t3b+euvW4wZMx4rKyud/WlpaRw6dBBHx4p4eXXIsZ8zZ07z4YfvUb16Db7+ehW2tnZGnqUQQgghSppRAaOjR4+yf/9+lEoljo6O9OzZk3feeSfX6c5CCCGEEKLwmJmZ0alTF53t//57BwAXFxet/f7+KwgKOsiyZWto0aKlZvt7702nWjUXatWqg4mJCWfOnOaXX37Cy6sDo0eP1fvav/zyE3FxcQwbNirH3EnXrl3hgw/eBVT06uXN6dMhOm26detpzCkLIYQQogQYHDBq0qQJ3bt3p2bNmiQmJnLy5Em2bNnCmTNn2L59u8w4EkIIIYQoQzw8mnDs2A8EBR0EoGbN2sycOZu+fQegUCj0HnPw4PcA9O6dc4qCv/66RVpaKgDLli3R20YCRkIIIUTpZ6JSqVT5PXjNmjX4+fnxzjvvMHnyZIOOSU9X5vflnlsKhSlKZWZJD0OUAXKtCGPI9SIMJdeKMIZcL8JQcq0IY8j1Igwl14p+5ub6H/YURIECRunp6TRv3hwPDw+2b99u0DExMQn5fbnnlr29NXFxySU9DFEGyLUijCHXizCUXCvCGHK9CEPJtSKMIdeLMJRcK/o5Oxd+qiCjk14/y9zcnEqVKvHo0aPCGo8QQgghhMhGYWGGMjPnZ3wKUxOUaRnFOCLxPEtJSWHkyCH8++8dBgwYzMyZs/M8RqVSERx8lO++20lUVARpaelUq1aVV1/twpAhb2BjU17TNjY2lj17dnD9+jVu3LhKXFwcPXr0Zu7c/+bYf2joKTZv3sDNmzcwN7fA07MVU6Zk5eESQghRNAoUMEpNTeXevXs0baq/7KoQQgghhCg4ZaaKN+cH57h/w7yuxTga8bxbv34NcXFxRh2zdu0qAgM34unZijFjxmNmZsalSxcICPAnNPRX/P03YmJiAkBk5G0CAzdSqVJlGjRopDcx+rNOnjzOvHmzcXOrx5Qpb5OUlMjOnduYPHksAQGBODk55/tchRBC5MyggNGjR49wcHDQ2b506VIyMjLo1KlToQ9MCCGEEEIIUbyuX7/Grl3bmDx5GitWLDXomIyMDHbt2kb9+g3w81uJqakpAL6+o8jMhB9+COLmzRvUq+cOQIMGDTlwIBgHBwfi4uLo3Vu36t+zffv5fUmlSpVZuXI91tbWALRt68XYsSMJCFjL7NlzC3jWQggh9DEoYLR69WouXLhAmzZtqFq1KsnJyZw8eZLffvuNpk2bMnLkyKIepxBCCCGEEKIIKZVKFi6cT5s27XjlldcMDhgplRmkpqZSsWJFTbBIzcnJCYBy5aw026ytbbC2NqzCcnh4GLGxMYwbN0kTLAKoV8+d5s09OX78B959dzZmZgVaOCGEEEIPgz5ZW7duza1bt9i7dy9xcXEoFApq1qzJjBkzGDNmDJaWlkU9TiGEEEIIIUQR2rFjK5GRt/nss0VGHWdpWY6mTZvz22+hbNmyiVdf7YxCoeCnny6yd+9uunXrQfXqNfI1pmvXrgDQuPFLOvsaNfIgLOwskZER1KlTN1/9CyGEyJlBAaMuXbrQpUvOU0WFEEIIIYQQZdedO/+wYYM/vr7jqFq1Gv/+e8eo4z/+eD7z5/+XNWtWsGbNCgBMTEwYNepNxo2blO9xxcbGAODsXElnn3pbbGyMBIyEEKIIyNxNIYQQQgghXjDZK6FFR0dTtWo1fHxG6G1/7tzvTJ+ee+CnffuX6d69F23atMPExISQkJNs3hyAhYUF7du/zNixI1AqlXz66QI6ddJ+GJ2Zmck332zg6NHD3LnzD1ZW1jRv3gLISpRtbm6u83oWFhaacxFCCFH4JGAkhBBCCCHEC+bZSmiRkRGEhZ1lxYp1OeYCqlWrNh999H8629PT01i48DNMTEywsLBg3rxPNPsGDerP22+/TUCAP8eOBWNhYcmTJ8l6+w8LO8vRo4fp2PEVBg4cSlzcI/bu3U1iYsL/Xidd55i0tDQAypUrZ9zJCyGEMIgEjIQQQgghhHiBZK+EdvHiBdq1a0/FihWJjo4CICbmPgBJSYlER0dhZ2dPt249dfoKDj6CSqVCpVLRuXNXnf2dOnXh2LFgoqIiGDXqTQIC/PWOKTY2hj59+vP++08rnnXr1pNhwwZqxlOrVm2tY9RjdHJyzse7IIQQIi+meTcRQgghhBBCPA+yV0IDSE1NJSTkFD4+/TX/TZs2EYCjR4Pw8enPgQP79PZ38OD3z/SdqbP/4cMHAHh5daRy5Sq5jq1nzz5aP7u4uOLmVh+A06dDdNpfuXIJGxsbatSomWu/Qggh8kdmGAkhhBBCCPGC0FcJrU2bdvTu3VerXVxcHIsXL6BNGy969+5D3br1AIiNjSUpKZHKlavw8OEDzp37nZo1axMR8TdHjhzUmWW0YcM6AIYPH8Xt23/nOjZ9S8ucnJy5fv0qR44cYuzYiVhbWwPw5583CA8Po2dP7xyX0QkhhCgY+XQVQgghhBClVvbkzDNnzjbouIyMDPbu3U1Q0AEiIyNQKBS4uLjSp88A+vUbqNX2+vVrbNy4lj/+uEBKyhNcXFzx9u7HwIFDUSgUWn36+S3i6tUr3Lv3L8nJyTg5OdOwYWNGjBhN/foNCvXcC1tOldBcXFx1klA/3eeitc/ffwVBQQdZtmwNYWFnUalUDBs2kn379hAa+itTp47/38wlFQcO7CMu7hEtWrSkUSMPrYDRpk3rgazZTWpr1izHw6MJzZq1oFmzFqSkpHDt2hUAHj+OY+rUcXh79ycpKYmdO7dib+/A2LETi+S9EkIIIQEjIYQQokwqzJvoGjVq0KtXP52b6Lt37/LNNwGEhZ0lJiYGW1tb6tdvwLBhI2nWrIVBfed0gy6EoZ5Nzmyo9PR0Zs+eSXj473Tt2p2+fQeiVCqJjo7k3r27Wm3Pnz/HzJlvYWNTnkGDhmJv78DZs7+xbNkS/v77b2bPnqvV77VrV2nSpCnVqvXE2tqae/fucejQfiZM8GXx4uV4erYqlPMuCl99tSDXSmjGyMzMJCjoIDY2NnTu/DqdOnUmMHATJ08eZ/XqZZo2DRs2ZsmSFTrHr1+/Rmfb6dMhnD4dwqBBPlhaWhIQ4M/jx1m/+9df70F0dBQrVy7F3NyCli1bMXnydJydKxX4XIQQQugnASMhhBCiDCrMm+iYmH91bqJjY2MYN24EGRlK+vYdQPXq1YmNjWX//r1Mnz6JBQuW4OXVIc++9d2gC2Go7MmZDbVp03rCws7g57eSFi1a5tp26dKvMDExYc2aDbi4uAIwYMBgFi36jP3799K9ey+aNm0GgJWVFQEBgTp99O07kIEDe7FtW2CpDRgdPXqYs2dP51oJ7VlVq1bj1KnfdbbPnftf5s79L6Ghp7h//x59+w7QLCWbOHEqEydOBWDBgk8JCTnF4sXL9b5e9r5v3brJ/Pn/4c8/b7B793Z2795Os2YtGD58NJs3B9CwYWP+859P83PqQggh8kkCRkIIIUQZU9g30fb21sTFaZe6Dgo6SFxcHF988RUdO76q2d6lS7f/JcDdqxUwMuYGXQhDZE/ObOi1/uTJE3bt2k6HDi/TokVLVCoVT54kY21to9M2Pj6emzdv0Lp1O02wSK1nT2/279/L4cP7NQGjnDg4OGBhYUlCQoLhJ1iM0tLSWL7cz6BKaBUqVDCoT3Wya2/vfjr7rl+/xqFD+5k69S3i4x8TH/8YgEePHgJZibCjo6OoVKkyFhYWANSt68bGjVuJjo4iNjYGJydnXF2rs2rV1wDUrFkr/2+AEEKIfJGAkRBCCFGGFMdNNEBSUhKgW67a0bEipqamlCtnle++RfEr6iWM5879zvTpk/T24eXVgUWLlhrUVm3VqvVcunRRJzmzIS5cCCc5OQl394YsXfoVhw7t58mTZOzt7fH27s/YsRM1M17S09MA/cmWLS2ztl2+fElnn1KpJCEhAaUyg/v377Ft2xaePEmmXbv2Ro21uKSmphIX94iQkFOEhJzS2X/0aBBHjwbx7vSpjBo5ioRUhZ5ennr06CG//voLdevWo0GDRjr77927i0qlYsWK5axYsVxnv5/flwCsX/+NzvGurtVxda2u+fn06RBsbGx46aWmBp2rEEKIwiMBIyGEEKIM0VfhyBC53UQPGjSYESPGai0bad26LVu2bGLx4oVMnfo2rq7ViY2NYePG9VhZWWnlQDHmBl2UjKJewqjWp09/mjZtrrUte46ZWrVq89FH/6fn9dJYtOhz7OzscXBw1Juc2RCRkREA7Ny5DXNzc6ZMmY6trR3BwUEEBm4kJuY+8+Z9AmQFQO3t7bl8+SKpqSmaIBFAeHjWkqn79+/pvEZExN+MGuWj+bl8+fKMHDmGESN8DR5ncbKysuLTTxcAYG2WwYMj/gA8Tslg5W//0rJaeV6v50DtyB8wVQ0jNvaRphKavmBaUNAhMjIy8Pbuq7MPoFGjxnz66QJsbCxJSnqa1Do8PIzvvtuFj88IGjf2oFo1V73Hq+3evZ2//rrFmDHjsbKyyrWtEEKIwiff3oQQQogyIqcKR4bI7SZ6/fp1REff0dxEA7Ro0ZKZM2cTELCGadOeViFyda2Bv/8matWqbVDf2W/QRfErjiWMah4eTejWrWeu/To6VtTbJjj4CJmZmXTv3gs/vy/znZw5OTlrdlxCQjzffLNDs5Spc+euTJs2kSNHDjF8+Ghq166DiYkJQ4YMY+3aVcyZ8z7jxk3Ezs6e338/Q0CAPwqFgtTUFJ3XqFrVBT+/lWRkpBMdHc0PPxwmMTGR9PT0UhkcNTMz01Q6s7NIJfrqdgDuJaYB/1K1ggUda9pp2j9bCU3fEtPDh/djYWHJ66/r/107OTnTqVMXnWvlyZMnADRu7KFTle2996ZTrZoLtWpl/V7OnDnNL7/8hJdXB0aPHlug8xdCCJE/pe8vmhBCCCH0KkiFo9xuomfMmKJ1E61mb++Au3sjWrZsTY0aNYiMjGTbtkBmzXqHFSv8qVy5Sp59Z79BF8WruJYwZj/W1NQUS0tLo8aqzonj4OBgVHLm7NSzhBo18tDJe9O9ey/Cw8M4f/6c5nocMcKXlJQUtm//lvHjRwNgZWXNtGkzWLt2FUqlUuc1rKysaNWqjebnXr368OabI5g7d5beimDPk4sXL3D79t907dodW1vbQuvXw6MJx479QFDQQQBq1qzNzJmz6dt3AApF7kvkhBBCFA0JGAkhRClRmDlGXFxc8fHx4fXXvTXtYmNj2bNnB9evX+PGjavExcXRo0dv5s79r95+33prAufPn9O7T1/eCVG0jK1wlF1uN9F9+vTh7NkzWjfR+/fvZfHiBWzc+C116rhp2rZp04433xyOv/9KTcUiY2/QRfEpriWMal9//RWff541m8zVtQYDBgxm8GAfTExMcn29O3f+4dy53/HwaMLWrYEFSs5cqVLWEriKFSvq7KtY0QnICm6qmZqaMmHCFEaOHMOtWzcBFW5u9VGpVHz55ec0bvxSHu8WWFtb88ornfj2283880+0TgLt0qpyeQuCRnnobFdXQtPnpZea6q2eZoiePb3p2dNb7z5f33H4+o7LV79CCCGKhgSMhBCilCjMHCPR0ZHcuaO9XCky8jaBgRupVKkyDRo04vTpkDz7t7e3Z9q0mTrbq1VzMWqcomAKo8JRbjfRzq54OE8AACAASURBVM5Zia2fvYkODNxEzZq1tIJFkFXJqGbNWlrBRGNv0EXxKM4ljGZmZnTo8DJt27bHycmZ2NgYDh36nmXLFnPz5g3mzPk419c7dGg/KpWKbt16sHjxwjyTM0+Z8jbDho3U21fDho0BuH//vs4+9f8zDg6OOvusrKzw8HgaHDpx4kdUKhVt23rlOna11NSsXD3x8Y/LTMBICCGEyI0EjIQQohQo7BwjoJtnpEGDhhw4EIyDgwNxcXH07t0le3c6ypWzyjMfCeRvdpSxM5iuX7/Gxo1r+eOPC6SkPMHFxRVv734MHDhUZ7nCsWPB/PZbCNevX+P27b9QKpXs2rWfqlWr5Tmu0sjQCkf5vYm+ezcrqe+zN9GxsfdzvOlVKpUolRkG9Z3bDbooWsW5hLFJk2Y0aaJder5Pn/68997bHD58gF69+uZYml6pVBIUdBAbGxu6du2Bvb2DTpu4uDgWL15AmzZe9O7dh7p16wFZMyezJ2euVs2Fl15qyqVLf3D9+jXc3RtoXmf//r0oFApatWqb6/k/fhyHv/8q7O3t6ddvkGb7o0ePsLOzw9TUVKv9gwexnDjxI1ZW1tSuXTfXvouawsIMZaYqlxa5z/YSQggh1CRgJIQQJay4coxYW9vkq8x5Zmampt+clpXkZ3YUGD6D6fz5c8yc+RY2NuUZNGgo9vYOnD37G8uWLeHvv/9m9uy5Wu337t3FlSuXcXOrh4uLq2a2RFn1bIWjZxXWTfSePbt0bqJr1arNrVs3uXTpotasi0uX/iAqKlKrfLixN+hFHWDMrS1Ay5atWbp0leZnlUpFcPBRvvtuJ1FREaSlpVO5chU6d+7KkCFvYGNTPs/xlTbFvYRRH1NTU0aO9OXMmVBOn/41x4DRmTOh3L9/j759B1C+fHmdZMiAZnaUi4uL1v6ckjPPmDGLqVPH8847Uxg0aCh2dnYcOxbM1auXGTNmPFWqVNG0DQ09xdatgbRq1QZHx4rcu3eXAwf2kZAQz4IFS7C3t9e0DQ4OYufObbz88qtUreqCubkZUVGRBAUdIiEhntmz5+mtKlaclJkq3pwfnOP+Pf/pUIyjEUIIUZZJwEgIIUpYUeQY8fbuz7vvzijw2GJi7tO1a0dSU1MpV64crVu3Y+LEqVo3kPmdHQWGz2BauvQrTExMWLNmg2bWy4ABg1m06DP2799L9+69tG5G5837P5ycnDAzM2PJkoVlPmD0bIWjZxXWTfTFixd1bqLHjp3InDmzmDFjKv36DcTVtTrR0VHs27cbMzNzxoyZoDUWY27QizrAOHr0m3h799Npd+xYMCEhv9C+/cta29euXUVg4EY8PVsxZsx4zMzMCA8PIyDAn9DQX/H335hnDp7SpCSWMOZEPasvt9+3Otm1vt9ZftWv34DVqzewbt0qdu3aRlpaGjVr1mLOnI91cuhUqVINc3MLdu/eQXz8Y+zs7GnZshWjR4+lRo1aWm2bNGnO1atX+PXXX3j48AHp6ek4OlakZcvWDB7sw0svNS20cxBCCCFKmgSMhBCiBBVVjpHAwI08fvyQ99//KN9jU88acXOrh6mpKVeuXGbPnp2EhZ1h1aoA6tZ1y/fsqGflNYMpPj6emzdv0Lp1O50lUj17erN//14OH96vFTB6NjghnsrpJnr+/M949dVuWm07dHgFP7+VbN0ayKFD+0lKSqRChQq0bt0WX99x1KvnblDf2W/QiyPAmNNyo82bA7CwsKBbtx6abRkZGezatY369Rvg57dSs9SoX79BKBRm/PBDEDdv3tA539KsJJYw5iQqKhIAR0f9bR89esivv/5C3br1ck2kX7VqNb2JlnNLzuzmVo+FC/3yHGPt2nVYsmR5nu0ga2nvxx/PN6itEEIIUdZJwEgIIUpQUeUYmTZtIvv3f8/gwcPzXZkqe5LaTp260KHDy0ybNpHly5ewdOmqfM+OUjNkBlN6ehqA3mUe6mUzly9fytfrl3WFdROdPd+VmqdnKzw9Wxk8nrxu0IsjwJiTCxfCiYyMoHPn17G1tXtmTBmkpqZSsWJFnbw0Tk5ZCbvLlbMyepwlqSSWMD5+HIednb3W66WlpbFhw1oA2rfvqHesQUGHyMjIwNu7b8FPXAghhBCFSgJGQghRQooyx0hRlTJv2rQ5TZs2Jzw8jNu3/8r37CgwbAYTgKNjRezt7bl8+SKpqSma8wYID88Klty/f6/QzlEUneIIMOYkp2VPlpblaNq0Ob/9FsqWLZt49dXOKBQKwsPD2Lt3N9269aB69Rr5Gm9JKYkljO++Ox0nJyfc3RtqqqQdPRpEdHQkgwYNpVEj3dLtAEFB+7G0tMTbuweWlrqJmk1MFKSkZOb7vRBCCCFE/knASAghSkBR5xgpylLmVatWIzw8jMWLF+Z7dhQYNoMJwMTEhCFDhrF27SrmzHmfceMmYmdnz++/nyEgwB+FQkFqakqBz6u0qWCpxFSVkeP+TBMzElIVOe4vbQqy/BIMDzDqk5SUyIkTP1K1qoveGVMffzyf+fP/y5o1K1izZgWQdd2NGvUm48ZNMmqczxNjljC++upr/PLLSfbs2UFCQgJWVlbUq+fO2LET6Nq1u97+L168wN9//42bmxs7duzQ22b8+PFIVS8hhBCiZEjASAghSkBR5xgpylLmUVGRmJiYcP78uXzPjsrJszOYnp1NNGKELykpKWzf/i3jx48GwMrKmmnTZrB27SqUSmWhjaG0MFVlEL1iYo77Xd/yB8pOwKggyy/B8ACjPsHBR0lJSaFXL2+9y9jMzS2oVs0FZ+detGnTDhMTE3766bgm59Ho0WPzNebSpiiXMI4Y4cuIEb5Gjeell5py9uxZ1q1bZ9RxQgghhCgeEjASogCKujQ0QGJiIuvWreLkyRPExz+mWjVXBg4cQr9+A/PM3/HRRx9w4sSP1K5dh8DAnYafmChyRZ1jZP/+vZiZmeWY/DcviYmJWFlZoVBoByRCQk5x8eIFzMzMaN26bb5nR+VGPYMpISFBEzAyNTVlwoQpjBw5hlu3bgIq3Nzqo1Kp+PLLz2nc+KXcOxUlqqDLL3OSU4Axu0OHvkehUNCrVx+dfSkpKUya9Cbu7u588skXmu1dunTj448/JCDAn06dOutUyxJCCCHEi6c03v9lZGSwd+9ugoMP8/fff6NQKKhRowZDhw7Fx8cnfyf6PxIwEqIAiro0dHp6OjNmTOHGjesMGjSUhg3dOX78JxYvXsDDhw8YOzbn2Qe//voLJ08ex9LS0ujxiaJX1DlGrl69zKRJk3WqhW3atB7ImuEEcOvWn5ptzZq1oFmzFgCcO/c7y5f70b59R6pVc0GhUHD16mV++CEIW1tb4uPjCzQ7KjdRUZEoFAoqVLDV2WdlZYWHx9Pg0IkTP6JSqWjb1svo1xHFozCWX+ZGX4DxWbdu3eTq1St4eXXA2bmSzv4TJ34kOjqSSZOm6uzr1KkLx44F88cf50t9wKiCWTqmpul695W15YtCCCGeb6Ut6HLsWDC//RbC9evXuH37L5RKJbt27adq1Wp6Xqt47/9q1qzN6dMhOd7/paenM3v2TMLDf8fb2xsfHx8yMjKIiIjgzh3jlv/rIwEjIfKpOEpDHziwj6tXr/DOO+8xaJAP9vbWdOnSi7lzZxEYuJFevfpQpUpVneOSk5NZvHgBAwYM5tSpn40aG+TvQzy7vGY33b17l2++CSAs7CwxMTHY2tpSv34Dhg0bqQlaqKlUKoKDj/LddzuJioogLS2dypWr0LlzV4YMeQMbm/JGj+95kVsp82HDhuosG1m/fo3WzzduXOfGjesAjBkzXvPe16hRE3f3BoSE/MKjRw/JyMjA2bkSffsOZNiwkVy7dkVnLMbMjsprBlPbtl55BjsfP47D338V9vb29Os3yIh3TRSnwlh+mZvcAoyQ9TkK0Lt3P737Y2NjAFAqdRMrq5c6ZmSU/iWPppk5L2Esa8sXhRBCPN8KGnSxtrYgOTlNs72gQZe9e3dx5cpl3Nzq4eLiSmRkhN7XL4n7P4A+ffrneP+3adN6wsLO4Oe3km7dOhk1JkNIwEiIfCiu0tDBwUcoV64c3t79tbYPGTKMkydPcOzYDwwfPlrnOHVOl/HjJ+crYJTfD3G1vGY3xcbGMG7cCDIylPTtO4Dq1asTGxvL/v17mT59EgsWLMHLq4PW+QQGbsTTsxVjxozHzMyM8PAwAgL8CQ39FX//jUaV1y7NCivHSE709a1PrVq1mT9/Ya7jzM6Y2VG5zWCyt7dn+vR3tfoODT3F1q2BtGrVBkfHity7d5cDB/aRkBDPggVLsLfXLud9/vw5zVOoa9euArBnz07Kl88KLvr6jjPofRAFVxjLL/MbYExLSyM4OAhHx4panynPqlWrNgBHjhykc+euWvuCgg4CT/OFCSGEEKJgCiPooi+X3rOMDbrMm/d/ODk5YWZmxpIlC/UGjErj/d+TJ0/YtWs7HTq8TIsWLVGpVCQlJWm+7xYGCRgJkQ/FURo6MzOTGzeuUb9+A50boYYNG2NqasrVq7qzPK5cucR33+3k448/y9fMm4J8iINhs5uCgg4SFxfHF198RceOr2q2d+nSDR+f/hw4sFdzc5eRkcGuXduoX78Bfn4rMTU1BaBfv0EoFGb88EMQN2/eoF49d6PHKkpObjOYRo0ao7N0qEqVapibW7B79w7i4x9jZ2dPy5atGD16rN6lQmFhZ9m4UTuR7vbtWzT/loBR8SmM5ZfGBhjVfvnlJx4/fsywYaNyzJ3k5dWRhg0bExr6K1OnjueVV14DVJw8eYILF8Lp1KmLJj+YEMIwxTFTGeDvv/9i8+YAwsN/Jz4+Hnt7Bxo0aMSsWR/i6KhbQdSYvoUQha+wgi6JiYmoVCaF9tA9ewoHfUrj/d+FC+EkJyfh7t6QpUu/4vDh/SQnJ+Pg4MCQIUOYPn16gXNHSsBICCMVV2nohIR4UlNT9ebcsLCwwNbWTrOUQi0jI4OFCz+jVau2Ok/KDVEYH+KGzG5KSkoCwMnJWWu7o2NFTE1NKVfO6pkxZZCamkrFihU1wSI1J6es0vHPti/tciuVXtbzjBgzOyqvGUzZ1a5dhyVLlhvcfuzYibnm+BJli7EBRrWDB78HoHfvvjn2rVAo+PrrVQQGbuLkyeOsXr0MExMTXF2rM3nyNIYOHV4k5yTE86yoZyoD/PZbKB9++B4uLi4MGuSDg4Mjjx494vLlP0hKSsoxYCQ5HvOnqIOA5879zvTpk/Qe5+XVgUWLdL+ThoaeYvPmDdy8eQNzcws8PVsxZcp0neVJovjldL0YE3TRd708G3TJSbt27XWCLtnzH61evZzVq7O+V+rLf5Rdab3/U8+E2rlzG+bm5syaNQt7e3sOHDiAv78/9+7dY+FCw79v6yMBoxdEUX/Ih4Sc4vvv93Dr1k0ePXr4vxLF1ejevRd9+w7U+aNclnPSFFdp6JSUFADMzc319mNhYaFpo7ZtWyDR0ZF8/vmX+RpbQSPnhs5uat26LVu2bGLx4oVMnfo2rq7ViY2NYePG9VhZWWm9t5aW5WjatDm//RbKli2bePXVzigUCsLDw9i7dzfduvWgevUa+RpvScitVLrkGREvkqIMMKr5+a00qJ21tQ0TJ05l4kTdxNdCCOMUx0zlR48e8skn82je3JOFC5cY/AS9oDkeX2TFEQSErGVDTZs219qm7+b55MnjzJs3Gze3ekyZ8jZJSYns3LmNyZPHEhAQqPNQUhQvfdeLMUEXfdfLs0GXe/f+YdWqrJUH5ubmjB49jsqVKwNgbW1NaOivOteNOv/R4sULcXR0ZMyY8Zp+81Ja7/+Sk7MewickxPPNNzto2TKrMEzPnj0ZOXIk+/btY/z48bi5ueVr3CABoxdGUX/I37p1E1NTBb1796ViRSdSU1O4cOE8y5YtISTkFH5+K7WmDJbVnDTFWRpanbsjPV1/1Zu0tDRNG4Do6Cg2blzP6NFv4uLiavQYCho5N2Z2U4sWLZk5czYBAWuYNu1p8MTVtQb+/ps0OUXUPv54PvPn/5c1a1awZs0KAExMTBg16k3GjdP/NEoUrnLlTFGpck78a2KiICVFN2mwKL2efZDwxhvDmDpVt3JHXgrzQQI8LQsbFHSAyMgIFAoFLi6u9OkzgH79Bub7XIUQxaO4Zirv27eH+PjHTJmStdwiJSUFMzOzPL+bFTTH44uqOIKAah4eTfJMDJyRkYGf35dUqlSZlSvXY21tDUDbtl6MHTuSgIC1zJ491+hxisKR0/ViaNAlp+vl2aDLjRuXABg2bBRbt37DuXNnNUGXe/fuArpBF3X+I3//lQYnoIbSff+nrg7bqJGH1tI2gH79+nHmzBnOnDkjASORu+L4kB850ldn26BBPixevJC9e3dx9eplGjXyAMpuTpriLg1doYItlpaWmv6zjyU+/jFOTk+ria1Y4YetrS0vv9xJMzbI+vKWnp5BdHQU5cpZaZZxZVfQyLmxs5vs7R1wd29Ey5atqVGjBpGRkWzbFsisWe+wYoU/lSs/XUucdaPpgrNzL9q0aYeJiQk//XSczZsDsLCwYPTosfkaszCcSqVk3bp1Oe4fP348UPqCvCJnRfkgoVw5UyIi/sTcXEHfvn1wcnIiNTWV8+dzfpDwbFnYrl2707fvQJRKJdHRkZovf0KI0q24ZiqHhv6KjY0NiYkJ+PoO4+bNG5iamuLh0YRp02boTVRf0ByPL6riCgI+68mTJ5iamub4oDo8POx/BVQmaYJFAPXqudO8uSfHj//Au+/OLtSbe2GYnK4XY4IuxlwvNWrU5KWXmnLunGFBl8zMzP/lEDJsSWppv/+rVClrFlXFirpLcJ2ds2bZxcfHGz2uZ8n/Rc+5kviQf5Y6gVhCQsIzYyqbOWmKuzS0qakp9es34M8/r5OWloaFhYWm7dWrl8nMzKRBg4aabXfv3iU2NoaRI4fo7d/Hp3+Oa8ALGjk3dnbT/v17Wbx4ARs3fkudOk8j3m3atOPNN4fj77+S//znUyBrFsSkSW/i7u7OJ598oWnbpUs3Pv74QwIC/OnUqbPexMdCCP2K+kGCSqUkPT2d+vXro1KpiInJWm9fp04dBg0axO7du7UeJIB2WVh1smshRNlRnDOVIyMjUCqVvPvuNDp16oKv71j+/fdfNm8OYNq0iaxdu5k6dermq2+hrbiCgGpff/0Vn3/+CZA183zAgMEMHuyj9YDh2rWspL+NG7+kc3yjRh6EhZ0lMjJC6xoQxUPf9aJUKg0OukRFRRh9vahzo44YMYTBg99g4MAheoMuMTH36dKlA2lpaSQkxDNnziydpNPZlfb7P3Vw/P593QDTvXv3AP3BJGNIwOg5V9wf8snJSaSlpZOUlMjFixf49ttvsLOz07opKKs5aUqiNHSXLt24ePEC+/d/pykJCbBz51YUCgWvvfb0S8/UqW+TmJhAdosXL8TCwoJp02ZQsaLu7KLCiJwbO7spMHATNWvW0goWAdSt60bNmrW0ktKdOPEj0dGRTJqkm1ukU6cuHDsWzB9/nJeAkRAGKukHCVWrZpWxffZBgr6ysOqys0KIsqE4Zyo/eZKMUqnk9dd7aOU8c3dvwPTpk9i0aT3/939PHzIVNMfji6o4g4BZFTU74enZFicnZ2JjYzh06HuWLVvMzZs3tJYjqZP+6sttpN4WGxsjAaNiltP1olQqDQq6TJo0jeDgIwZfLx06vEzbtu3ZtWs7UVER2Nraaq6X7EEXdf4jc3MzNmxYx0svNeX06RCdpNPZlfb7P/V5Xbr0B9evX8PZuZXmPd+5cydmZma0b98+1/cyLxIweo4V54e82ueff8JPPx3X/NyokQczZ87WCTSUxZw0JVEauk+f/hw+fIDly/34999/adjQnePHT/DzzycYPXqsVpK2Vq3a6B33ypVfY2VlpXfsUDiRc2NnN8XG3s9xJpJSqUSpfFpFTP2lQKnUzY+jVGbl1MnIyDm3jhBCW3E/SEhLSyMzM5O0tDTu3r1LeHi4zoOE7GVhDx3az5Mnydjb2+Pt3Z+xYyfK0gIhSrHinqlsYWHJkyfJ9OjRW2t7ixYtqVy5CuHhYfnuWzxVnEHAJk2a8fLLXsTFJWu29enTn/fee5vDhw/Qq1dfmjZtBuSeGFg9IyN7YRhR9HK6XszMzAwKuly6dJHo6EhmzfqQiIjbWukpsgddWrRowYIFWUvcIyL+pm1bLxYtWqq5XoYMGaYVdFEHHOfOnYVCoWDu3P8SE3NfJ+l0dqXh/q9WrdqEhv6q9/4PYMaMWUydOp533pnC6NGjsLe35/Dhw/zxxx9MnTqVatWq6f+FGUi+fT3HijsnDcCYMRPo23cgcXGPOHcujFu3/iQ+/rFOuxc1J42xpaHNzc1ZunQV69at4scfs6rKubi4MmPGLAYM0B+cMVZhRM6Nnd1Uq1Ztbt26yaVLF/HweDqd+NKlP4iKiqRdu/ZabQGOHDmoE7gMCjoIoDdXgRBCV0k8SDh58iR///235mcPDw/eeUf7QUL2srBTpkzH1taO4OAgAgM3EhNzn3nzPjFqrEKI4lESM5UrVapERMRtvUstKlZ04saNa/nuW2Qp7iCgPqampowc6cuZM6GcPv2rJmCUW46atLQ0rTZ5MbaSdFbC7UVcvXqFe/f+JTk5GScnZxo2bMyIEaOpX7+BzjF3797lm28CCAs7S0xMDLa2ttSv34Bhw0bSrFkLnfahoafYvHkDN2/ewNzcAk/PVkyZMt2gal4lJbfrxdTUNM+gS7167nz66ceMHv0m33//nSboopY96FKhghVhYeFaQZdnrxczMzPc3RvmGnSpVs1Fk3T67NnTXL6clUj72rWrAOzZs5Py5bMejPn6jsv3e1PQ+7/4+Me53v/Vr9+A1as3sG7dKjZv3kxqaip169bliy++YMCAAfket5oEjJ5TJfUhX7eum2ZKX9eu3dm3bw/vvTedFSvW0qTJ06cCz1NOmqIuDV2hQgVmzpzNzJmzsbe31nryYojduw/kur8wIufGzm4aO3Yic+bMYsaMqfTrNxBX1+pER0exb99uzMzMGTNmgqatl1dHGjZsTGjor0ydOp5XXnkNUHHy5AkuXAinU6cuuLvr/nF+0RTHFx6AR48ece7cOe7cuUNqaipWVlY4OzszYMAAypd30up/69ZvOHr0MHfu/IOVlTXNm7dgwoTc14qLolUSDxI8PT1p2LAhKSkp3LmT9bmS/UFC9rKw6mukc+euTJs2kSNHDjF8+Ghq166Tr3ELIYpOScxUbtiwMRERt7l//77O8vb79++RmZlJhw4tGTBgsEF9V65cBTs7e4P+Hr711gStpfPZtWzZWme2QlkLAJREEDAnVatmzY54tkiDk5OzZjzZK+uqx6hukxdjC0Ckp6dz7dpVmjRpSrVqPbG2tubevXscOrSfCRN8Wbx4OZ6erTTts5JzjyAjQ0nfvgOoXr06sbGx7N+/l+nTJ7FgwRK8vDpo2p88eZx582bj5laPKVPeJikpkZ07tzF58lgCAgINPq/iVNjXy7p1q/93fIzmerG0tKB27dp5Bl3U10tCQoJBQRd10unTp0PZseNbrTFt375F8+/sAaPivP8zhJtbPRYu9MPZ2fjE23mRgNFzqDR9yHfv3oslSxby/fd7NAEjyUkjOnR4BT+/lWzdGsihQ/tJSkqkQoUKtG7dFl/fcVoV8hQKBV9/vYrAwE2cPHmc1auXYWJigqtrdSZPnsbQocNL8ExKj6L+wgMQFRXFDz/8gK2tLR4eHlhZWZGSksK9e/dISkrSBIxUKhUffDCT06dD6NjxFQYOHEpc3CP27t3NxIm+rF69QW78S0BJPUhwdHTE0dERADc3NypWrKjzICG3srDdu/ciPDyM8+fPyXUjRClUEjOVu3XryZEjh/j++z20beul2X7q1M/ExsagUJgBmQb1PXHiVDZvDjD47+Ho0W/i7d1Pp79jx4IJCfmF9u1f1tpeFgMAJREEzElUVCQAJ04Ec+DAXgYMGEzHjq8CcPnyRZ2HlleuXMLa2ppdu7Zz/frVXIOA2QtAxMfH89//ziU8/Hfi4+Oxt3egQYNGzJr1IY6OWbPZrKysCAgI1AkCeng04cGDWLZtC9S6XoKCDhIXF8cXX3ylGTdkPSj38enPgQN7NQGjrId5X1KpUmVWrlyvqQDXtq0XY8eOJCBgLbNnz831/SoJ+b1eng26+PoO07lePv30I82/3313Ol5eHdi583uAHB+iq68XR0dHg4Iu6qTTEyZMYdq0GUae+YtBAkbPodL0Ia/OXfFsOb+ylpNGYWGGMlOV835TE5RpGTnuL6uMiZznJLfZTZ6erXQCEjmxtrZh4sSpTJyoG2QU+at4pf7Ck13fvgMZOLCXzheehw8fcvz4capVq0a3bt10KhzWqFGD1NSsf//yy0lOnw6hT5/+vP/+0y823br1ZNSooSxd+hVff61/rbgoGnk9SEhISCi2Bwk9e/Zk4ULtBwm5lYVV3yQmJBSsLKwQomiUxEzlVq3a0KVLN3788SjvvTcdL6+O3L37L7t2bQNg1KgxbNy4zqC+u3XrSbduPXX25/T3sFWrtnr7U6dV6Nath2ZbWQ0AlEQQ8PHjOOztrbXapqWlsWHDWgDS059+127e3JOKFZ04cGAfQ4YM07yvf/55g/DwMF5/vQc3blzLNQjYrFkLnQIQx48HU7NmLQYN8sHBwZFHjx5x+fIfJCUlaQJGoD8IuGPHVpTKTB49eqh1DuoKXtkDg46OWdWin60MHR4e9r8ZSZM05wRQr547zZt7cvz4D7z77uxSl9NPfb1YmWdionp6f/foURzzF31F+3ZtGdCnN27/eyBcGNdL1kNSC622z14v7dt31Gw3JOm0nZ0VKpX++08TEwUpKbr3rS+K0nW1iUJREh/yDx7E6q3AtXv3dkC77GVZy0mjzFTx5vzg37vmTAAAIABJREFUHPdvmCflWUXJKYyKV89ycHDAwsJSq4IVwJ49e0hNTaVNmzaYmpqSkZGBqampTuAIstaZA/Ts2Udru4uLK02bNufMmdPcvXuXKlWq6BwrikZeDxIOHjzAwYMHSuxBQm5lYdVBLQcHx1z7FUK8WObN+wQ3t3ocOrSfZcsWY2NTHisrK5o0aUbPnt6agFF+5fT3UJ8LF8KJjIygc+fXsbW102wvqwGAkggCvvvudKpWrUydOvU1VdKOHg0iOjoSExMTxo+fpPmOY2Zmxttvv8fHH3/I1Knj8PbuT1JSEjt3bsXe3oGJE6fqraD2bBDw+vVrmgIQjx9nzdB2dq7Exo1bc/19qIOAzs6V+Pzzr7CwsOD+/XucO/c7Z8/+BphotW/dui1btmxi8eKFTJ36Nq6u1YmNjWHjxvVYWVlpLRG/du0KoH3fpNaokQdhYWeJjIwoddXf1NeLnUUq0SsmarbfS8zKJ+X48AaNrm7HtbM/j9MK53qZOHECDg6OuLs31LleBg0aqlVYw5Ck0yqVknXr9H9mjB8/nuy/1xdJ6fp0EoWiJD7kR40aSpMmzahfvwHOzs7ExcVx9uwZwsLOULeuG0OGvKFpKzlpSo9y5Uwlml7GFbTilVKpJCEhAaUyg/v377Ft2xaePEnWSjwOEBISgrm5OampqezZs4cHDx5gYmJC5cqVadtW+2lrenrOCSfVS4+uXLkkAaNilNeDhA4dOtCtW+9CfZCQnJysdYOktmPHDkD7C3H2srDqvwFKpZL9+/eiUChyfKovhCidqlatxqVLV3SWjRTWTGUzMzNGjPBlxAhfALZuDWTDBn8++OCjHI/JrW9D/x7qc/Bg1jKZ7EvVymoAoCS8+uprhIb+Qnj4DhISErCyssLNrT5KZQa1a9fReSj22mtdsLS0ZPPmAFauXIq5uQUtW7Zi8uTpeoNF8DQI+ODBA60CELt2ZT3g9vBogpmZGSkpKZiZmekNHKmDgAMGDGHw4KcPxsqXL0+VKlWJioogIyNDc2yLFi2ZOXM2AQFrmDbtaTDF1bUG/v6btHIwqVdh6Bu/eltsbIxcL8Drr7/ODz8Es2fP0+ulXj13xo6dQNeu3bXaGpZ0OufVJC86CRiJQjFokA9nzpzmu+92ER//GEvLctSoUZOJE6cyaJAPVlZPp1u+SDlpCjsRcevW2pUUYmNj2bNnB9evX+PGjavExcXRo0dvg7+IffDB+/z44484ODgwePBgrX0vejS9LChoxSuAiIi/GTXKR/Nz+fLlGTlyjOYL+NN2EahUKoKCgqhTpw7NmzcnMTGRc+fOcfDgQXx8fHB1zUo8qs4zc+7cWdzc6mn6SElJ4cqVrAoU9+/fNXqsIv/yepBQvXqNQn+QsGvXLqpUqYKTkxM2NjakpKTwzz//8M8//+g8SADtsrCDBg3Fzs6OY8eCuXr1MmPGjJcAoxAiR8X59zC7pKRETpz4kapVXXSW2j9vAYCiTFcwYoQvb701RSvAqA4CPlst61nt23fUWnqUXU5BwCdPkrUKQKhnRqenp+PrO4ybN29gamqKh0cTpk2bobXyQR0EbN26LR07vkJGRjrR0dH88MNhlMpM7t79VycIaG/vgLt7I1q2bE2NGjWIjIxk27ZAZs16hxUr/DXl41NSUoCsKlnZWVhYaLUpCyqXtyBolIfO9sK4XsaOHcfAgcMM6iM/SafFUxIweoEU5Yf8mDHjGTNmvMFjeVFy0hR2ImJ//7W4uzfRtI+MvE3g/7N3ngFRXG0bvoClI12QbkHBLiJiL9FYQOztNUYxGDUazWuMMZbEN4lfokksMdFEI1FiosYSY8FeYixgA6OCgkBoNkDpfRe+H+suLLtLE1Bkrl84c+bM2XVnzsxznue+t2/FysoaV9c2BAdfqvS5Ll48z5kzZ5TqeQXqD8/reAVgY2PH2rUbFB54srKyKCwsVFhZy8nJQSKR4OzsTL9+/eTbLS0tOXz4MFu2bOF//5NmsAwa5EVAwM9s2bIJPT19unTpSnp6Gv7+m+Rp3/XpgUegerRr147ExETCw8Plq7WmpqbMnj2bUaP+o7CQAIq2sHv27KSgoAAnp6YsWbIcLy+fF/QpBAQEymKkJQGJGu1GLRFZkrp/rqjL+bAsJ08eJy8vD29vHzQ0FBfaXrUAQF1SW0HAnj37cOnSeQUDiAcP7gNw8eLfDBo0FF9fPx4+fEhAgD9z585k8+YAeQBIFgS0t3dQyA7y9h7O+PEj5G1k7Q8e3M/q1SvZuvU3BUc/T8/uvPXWG2zatIFPPvkcKMnMLiwsVPosBQXqs7cFBGoTIWBUhqpmhGRkZHDsWCBBQReIi4slLS0Na2trOnVyx9fXTx4xLk1ExF22bt3MzZv/kJeXi6OjI15ewxkzZoLSy7tgTV1/qQ0h4m3btvLll2vl211dW3Po0EnMzMxIS0tj2DDlDAJV5OTksHr1SsaNG0dgYGDlPpDAS8XzOl7J0NfXV8gc8fYezltvTWbp0oWsWfO9fLuuri45OTm0atVK4XhbW1uMjIwICSmxGDY2Nmbduo2sWPEJX331f/LtnTp15o03phIQ4I+hoVG1xyxQc8gWEsq6jSxY8BE3boQwb96sCufCvXsPkZGRwe7dO+VzYXp6Gnp6etjY2DBgwACMjEr+v6dNm0Z+vobSXGhnZ4+Pz0i++OIbpbnw9OmTXL58iYiIu8TGxiCRSNiz56DcPldAQKAOkYgJmT5T5a7OWzYBdRswquv5sCyBgQfQ0tLC23u40r76EAB4Wc1daiMIeOzYYa5evUzXrt0UDCDy8nIBsLKyZupUP7kBhIuLK/PmzWLbti189tmXz9qqDgIaGBjg6tqa4OBL3L+fKN++ffs2nJyaKgSLAFq0cMbJqSk3bpQ8P8mEsZOTkxSCUbJtpdu8CCr6rQiVCa8mQsCoDFXNCAkPv82GDetwd/dg9OhxmJqaEhMTzYEDf3D27Ekl++gbN0J4//13MTQ0YuzYCZiamnHjxjXWr1/Dv//+q+CUUB1r6vI0aUDQpakrakuIuLRILEgztQwMDKvc3+bNG5FIJMyaNUsIGNVDKnK8ys7OIjExAU1NG6p6mzcwMKBv3/789lsA9+8nyu3TraysiI2NValJY2BgwNOniq4gLVo4s3XrDhITE0hJScbSsjH29g5s3PgtgBDwrgOeJwvgeedCS0tT/vzzT+7cuUNMTAwjRozAzMxM3l7VXHj16mWVcyHA/v17CA8Pw9m5JXZ29sTHx1V6bAICAq8ulZ0Py3OAVIe6+bA00dFR3LkTTo8evVSWnb3sAQB4Oc1daisI2Ldvf0aP9uby5SAmThyl1P7hwwdMnDhKbgDRuXMXrK2bEBp6Xd6mvCBg/jO7WJnrM0BKSpLK346snaTUPO3q2gaAsLBbSmXg4eG3MTQ0xNHRqcLPXVtU9FvZ90mvOhyNQF0hBIxKUZ2MECenpuzYsU/pRtC9ey/mz5+Dv/+PrFhRIka7bt03aGho8OOPP8uPeeutqSxZspSDB/czZIg3HTtKbYarY01dnsI7CLo0dUVtCRH37t3nuccWHn6bP/7YzfLl/6ew6i9Qf6jI8er48aMcP36UBQs+YNSoiSp6qLh/gIyMdPl9qm3btsTGxj6zllV0q1K1TYa9vQP29g7yfwcHX8LQ0JD27TtWeVwCVaSaWQA1MRfq6haTlJSEo6MjR44c4dq1a7z+eslLh6q5cPTocXz11f8pzYUAy5Z9hqWlJSKRiDVrVgkBIwEBAaDy82F5DpAV9Q+K82FpDh36E4Bhw0Yq7YOXPwDwMlKbQUBzcwt69+7L+fPnmD//Q/mzy9q1X/H06VM6dOjIuHH/UTCAMDJqRGxsjLwPWYZ02SDgkycpKkXOmzZtRnR0FLdv36Jdu5Ltt2/fJCEhXkFU3c3NHQsLSw4d+pPx4yfJF+nu3YskNPQ6Xl4+L52j3osgLy+PwYNHcP/+/VqpCDp06BAPHz6U/3vz5s0K+7t06cq6dYrv4EFBFwgI+JmoqEi0tXVwd/dg9ux52NraPeenffEIv7hnVDcjRF06vIeHJ8bGJsTERMu3ZWRkEBUVSdeu3ZUmHS8vHw4e3M+RIwflD8mCNXX9pDaFF6dPf5usZxaV1UEsFrNq1f/h4dGNAQNeR3AEqJ9U5Hjl6dmDYcOG4+Ym1btS5XiVmpqKiYkJmpqaCn08eZLC2bOn0Nc3oFmzEsHGoUOHEhgYSHh4OA4OJQGguLg4srOzFYIB6ti7dxcxMdFMm/a2kn6NwMtBTc+F9vb26OrqKmSgVXUuBIR5rh5Q3ZL+y5cvcPv2bfLy8jAyMsLGxobOnTurXdD4998YAgL8CQ29RkZGBqamZri6tmHhwsWYm1vI2wlljA2Dys6H5TlAVnU+lFFQUMDJk0cxN7egRw/VmRVCAKDq1GYQUCQSYW1tA0CbNm3lYtYnTx7n77/PYmRkrGQAER19DxMTU/m2jIx0APz9fyQ29l+0tUUkJMRz9Gggubm56Ojo0LJlSfm+n99MlixZyPz5cxg5cgz29g4kJibw5597EYm0mTZthsL43nvvA5YvX8ycOdPx8RlFdnY2u3fvwNTUDD8/1YtADY0tW34kNTW10u0rUxHk6loS/HNzc8PVtcSxu1+/fojFGpw+fZJLl87Ts6fiAv65c2dYtmwRzs4tmT37PbKzs9i9eyfvvOOHv//2F55F+LwId6hnPG9GSFmysrLIyclWUMivjNV0WNjtKrUXrKlfPmqq5nrVqrWsXPk5qalP0dbWJisrS17vrgqZ3fWVK8GMHu2tMnq+c+d2EhPj+eKLrwkJuca8ebPkx5eOnjs6Oj7LRlPPxx9/xNmzp2jWrDnbt++u9mcVqDoVOV7Z2dnRv/9AuS6NKserkyePsnv3Tvr06YeNjZ3CA09mZgaLFi1TuPd4enrSokULoqOjOXr0KI6OjmRlZXH79m0MDAyYMWOGwlg++EC6qtK0aXM0NDS4ciWY8+f/okePXkyd6leL347A81DTc2FBQQGFhYUKGWjl6XaomgsF6gfVLWP08PCgbdu26Onp8fTpU7VljACXLwexePEH2NnZMXbsRMzMzElNTSUs7OazTMeSgJFQxtgwqOx8KKMm5kMZ58//RXp6OpMmTVEb9BECAFXnRQQB+/btz99/n+Xff6MV2j9+/BiANm1KnL6GDvXhyJHD3LkTTlRUFGJxIebmFri4tObatcsMGjRU4ffQq1df1q7dwI4d2wkMPEh2dhaNGjWia9du+PpOp2VLF4VzvvbaQHR1dQkI8GfDhnVoa+vQpYsH77wzT2XZY0NDlgX9/vsL+Prryj2rVKYi6OuvS1zU7O3LLmZ5kZ+vQUCAPzo6OgwePFS+T+pw/TVWVtZs2LBFHhTu1q0Hfn5v4u+/WanMvr4hBIyomYyQsgQE+CMWixkyxFu+zdzcAlNTU8LCbpGfnyd/MAYIDZVmEyUlPZZvE6yp6x81WXMdGnpdLqzXu3c/rl69zH//+x5fffWtymMiIu4AoKGhoTJ6/r//fcHWrVuYOvUt7OzsefxY+tsRiUTo6Ojg6VmSKm1oWL4u0sWL5zl37gy6urrV/owCL5YOHdy4cyecixfP8/Tpk2cv9RZ06dKVceMmqiwZ69+/PxYWFkRERBAUFISOjg7NmzfHw8ODxo0b8yxzH4B27Tpw+vQJjh49DICTUzPef38RI0aMFpz5XlJqYy4MCQmhqKiIli1L5jALi6rNhQIvP89Txti8uZ1CKb26MsbU1Kd8+uky3NzcWbVqTYVzrFDGKFBZqjMfAhw+fACAYcNGlNt/QwkA1IRxkK2tDe3bu6ksEyobBIyKukffvp5yvSBVQcCWLVsRFXWP5OQkNDQ0KS4uQiKRKAUBBw/2IijoIqdOHeeDD+bRo0dvHj16SFjYTSwsLBVe+Nu2bceyZZ+yfPliHBwcFIKAZmbmKoOA7u4euLt7VPq77NmzNz179q50+4ZC6SzogQNfr3TAqCoVQer4559Q4uPjGDBgEMbGJvLtoaHXSUlJZvr0WQo6ny1buuDm5s6ZMydYsGBRvc4krL8jr0FqIiOkNGfPnmLXrl/p2rW7gmOChoYG48dPYvPmjSxZ8iHTp8/ExMSUU6du4O+/CS0tLfLzS6w1BWvq+kVN1lyXffgWiUQVCi/a2zsC0pvf5Mm+8u2y6PmXX36GsbExffr0JzExgeRkqS2opqYm2traWFlZoa2trVLUuDQyh7XRo8dx4cLflf5+BGofmeNVWZYu/R9Ll/5PYZura2uWL19Rpf41NTXp1KkTnTp1qrCtr+90fH2nV6l/gRdLTc+FMTEx3Lx5E3t7e1xcSlZQ1c2F165dUTkXCrzcPH8Zo2JptKoyRoA//9xHRkY6s2fPQyQSkZeXh0gkUvsQLmRfN2xqez4EWLt2Q6XbNoQAQE0YB92/H8/u3b+rNA4qTVFREatWrUBHR5fc3Byl/R06uHHu3BnOnz+HhoYGGhoa6OvrUVBQQKNGhnTr1kPpmGXLPsXZuSWBgQdZv341RkaN6NdvADNmzFYqKWooQcCXjbqoCFKHLEDs46OoV6ZKt0pGmzbtuH79KvHxcZU6x8tKgw8Y1VRGiIygoAt89tnHuLi05vPPv0RDQ1FgevJkX/Ly8ti16zfefnsqIHVhePfd+XLnKhmCNXX9oqZqrtU9fFckvFh2JUaGLHqekZFOYWEhb745XmF/QUEBBQUF/P777zg6OjJkyJByP6fsd/r22+8IASMBgVeEQ4cO1uhcePHiRc6cOUPjxo0ZOHBgpeZCfX0D5s5VngsFXm7qoowRICjoIoaGhmRlZeLrO4moqEg0NTVp164Dc+fOl2uRCNQPaiIbpTzR2pSUFPbt+52IiLtERt4hLS2NoUOHKQWLZBQXF3Py5HH++GM3CQlxFBQUYm3dhAEDXmf8+P8Iz9sVUFPGQaamBnTu7KnSOKh0EHD37p3ExsYwadIU/P038fnnK+XZRQDOzi158OA+TZo04ffff5cvhkZERDBlyhS2bdvEsmWfKDhHi0QiJk/2VVh0LY+GEAR8mSibBZ2dXXkNI3WoqghSRVZWFmfPnsLGxk4pUywlRboArypQKNuWkpIsBIzqKzWtwh8cfImlSz+kWbPmrFnzvcrJRVNTkxkzZvPmm9OIjo4CinF370haWg5ff/2FUnRSsKauP9RUzfWRI4eUHr7z8vK4fDkIAwPVwovlIYueOzk1Vcj4+PffaH7+WdFR78mTJ9y6dYt27dqV7QZQdFgTHp7qnnJt0qFCq3QBAVUUFBTw9ddf1ehcuGTJh5iZmeHl5YWOjo5SG1VzobNzK4qLi1XOhQIvJ3VVxggQHx+HRCJhwYK59O8/EF9fPx4+fEhAgD9z585k8+aAev1A3tB4Hs2r8eOl2SjR0dH88Ye07N7f358WLVrKAwDx8bFs374VKytrXF3bEBx8qdz+N2/eyPbtW3F392DatLcRiUSEhl7H338TQUEX2bRpq1LgW0BKXRgHlebx40f89NMPTJs2A1NTU5VtQkOvk5ycTJcuXfjtt98U9jVp0oTAwEAWLVoEaFdqrAIvnrqqCFLFiRMnyMvLw9vbR+k+IKv20dZW/i3Jnn/qe0VQgw4Y1aQK/+XLQSxZshBHx6asW7cRY2Pjctvr6+vLrRX19fU5duwkxcXFKlMkQbCmrg/UhPDi3r27+OWXn2nXrgMXLvxNTk4WIHVvEIsL+fTTz5SEF7dt2wKUZCBFR9+Tb+vUqTMXL55HLBYzbtx/FM5vYWFJVFQEly9fRiQS4ebmJtemefLkiZKIsbLDmkCdU45NOpRvlS4goI78/HyePn1ao3Ohk5MT3bt3r1DnrPRcCNIHuPLmQoGXi7oqYwTIzc1BIpEwaNBQhSwRFxdX5s2bxbZtW/jssy9rZBwCtUtNaF4lJSXRqFEjBgwYwJEjR/joo4/YvXs3IH2Zc3VtzaFDJzEzMyMtLY1hw5Sfz2SIxWL27NlJq1aurF27QS6UPHLkWLS0RJw4cZSoqEglcWIBKc+TZairI6K4qKQsNTengIK8HHJysmnRogW6OiLyCxQXylavXoWtrR3jx/+HEyeOquxXViZkZaWc9WFlZcWDBw+Ii4vD3t65ymMWqHvqpiJIvXP0gQMH0NLSUhlYkr2XFRYWKu0rz+SjPtGgA0Y1kRECUleqxYs/wMHBkW+/3agghFUZ0tLS2LRpI6ampowcObbC9oI19avLtWtXMDAw5MmTFDZv3iC/0VhZWbNs2f/o3bs7aWmKtdpbtvyo8O/IyAgiIyMA6N9/AH/9dUZl9LxDh054eHSkf//+aGtr06ZNG1q3bs3Ro0eJjIzkxo0btG7tJm9f2mFNQEDg1UFfX581a9aSnZ2vsP155sKNGzeyZ8+eKo0jPb1qc6HAi6WmH+Dj4+PLLWOUaZUMHTpMYXvnzl2wtm5CaOj15x6DQO1TV5pXBgaGGBiUb+BRMiYx+fn5WFhYKLlqWVpaAqCnJzxvq+J5swyLi4r5dsUZhW0h4YcQi8XoSpwVgkkAp0+fICjoAj/84F/ufUdWJqTKxEW2LTk5WQgY1QPUVQTl5mYAtVMRVJqnT58SHh5Ojx69VJadyfStkpOTaNq0mcI+WaZ2WQ2s+kaDDhhVNSNk48Zv5ZFsWb313bvhfPTRAqAYb28fpZTXsLDb5OXlEhFxl9jYGCQSCW3atKNXrz6Ym1vw+PEjAgMPkJ6ezsqVa8jLy+Orr/6P69evkpycTHFxMRYWFvTp0x8HB0e5NXXXrt0wNDRi/vw5CrXc7u6dMTAwwMhIKBd6Wais8OLx40cID7/N99//RMeOUlHhhw8fMG7ccDw9u6nNJlPVN0ij50uWLFSrpyVj0qRJ8r81NDTo1KkTiYmJXLx4UR4wSkxMUHBYExAQeHUQiUQMGjRYKRhdlezIsnPhpUuXuHfvnkJ/pUuMgoIusGPHdjw8POVz4aFDf5KZmcHKlWuUygxu3Ajhxo2QZ+eSOkLu27dbPtcJAut1S02X9CckJHDy5MlyyxitrKyIi4vFwsJCaZ+FhSWRkXef81MJ1AV1pXlVFXR19ejY0Y3Ll4P49ddt9Os3AC0tLUJDr7N//14GDx6Kg4NjjYz3VaOmswzjH/zDnZhz2DR2oblDV4V9mZmZrF+/Gh+fkbRr16HcfmQlQKpcWWXb6nuZUEPhRVYEAdy9K51bhg0bqXK/q2sbAMLCbuHh4amwLzz8NoaGhjg6OlV4npeZBh0wqir37kUobYuJiaagQLoqu379GpXH6ejo4uzcEjs7e+Lj49DW1mbv3t/JyEjHxMSU7t27M2nSVAwMDPH1/Q9isYQRI0bj4ODAyZPHuXEjhN27dzyzsHbm/fcX0aSJDYsXL1BwFpBZqBcXFzNixAjMzMxq9fsQqDkq+/CtqWlDZS7bqkbPSyN7uC+tLfD992sVHNZkSCQSCgvFJCYmoKenL1+JExAQaFhUZi4sHTBq0sQWbW0dhbmwSxcPpk71w9GxqdKx169fZetWRc21Xbt+lf8tBIzqlry8vBp7gA8KCuLEiROYmpri7e2ttoyxdeu2xMXFkpSURPPmilkByclJmJlVP2AgUDfUpeZVVVm+fAUrVvyPH3/8nh9//B6QLqJNmfIW06fPeu5xvorUdJbh/cd3uHjjN8xN7OndeYrSQueGDesoKipm1qy5FfYly35VZaAg21bfy4QaCuoqgvLzc1ix4rNarQiSSCTcu3cPc3NzevTopbKNm5s7FhaWHDr0J+PHT5ILrN+7F0lo6HW8vHxq5Pp4kdTv0dcSqjJCIiLuEh8fx7vv/lchfdbLywcvLx+1fT169AhLS0tEIhFr1qwiPj6OZcs+VRB6MzU1IC0th+3bt5KWlsaXX35D7979AGk0MzExgYkTR+Hp2Z0vv1wNSFd+yzoLAPTp05N3332Xa9eu8frrgs5MXdBIVIimpnLdqowiDRGZ+eXrylQ2er5gwQeMGjWx3L6qEz0vTXp6OoDCat2jR49ISUlWcliTMXHiKHr06MVXX1UutVxAQODlpyq21GXnQl3dYn766SfUIQ1mf1fpsfj5zcTPT71+l0DtU9rVasKEiZUq6U9KSuKLLz4lLOw2CQlxFBUVsWfPQfkzkPQBfiEmJia4u7vzyy+/KPW5efNmevToxbhx/+HYsUAOHNiHjo4O8+Ypv8T36lXikrRx4xY6dOik9vOcPn2Sy5cvKWSAlx6bKv79N4aAAH9CQ6+RkZGBqakZrq5tWLhwMebmFs/Vd0OgLjWvqoq2tg62tnY0buyNp2d3NDQ0+OuvMwQE+KOjo8PUqX41MuZXhZrOMnyQdJe/r2/DxKgJr3nOQFtbMZgTEXGXwMCD+PnNJCMjnYwM6bNqaqq0FPHp0yckJiZgZWWNjo6OvAQoOztbaQE9OzsbgMaN63eZUENBXUWQzCVNXRa0ubkFT58+YfTocXh5+ZRbEQSgry/ir7/+IiUlhdTUVIqLi+nRowf5+flMmDBBIegTEnJN5Rw0aFAfmjVrwaBBQ9m9ewempmZ4enZXmJtUUdF89aJpsAEjLR0RkiL14lZamhpInomsVbfeGqRK/JVFdgMrW+dobi6tqS5dP63uocPT01NlLbdA7aFZJCbxe/UvMvbvVixEXFk9LTc3aQpuTUTP09PTsLJS3C+RSLh+XaoD0adPH/n2OXPeIysrU6mP1atXoaOjw9y587GwELKLBAQEBF5VSrtaaWpqVqqk/913ZxAeHoa+vj5FRUUKbUuXMbq4uJCUJH3RtLGxkZckNmnShH79+mFmZk3nzl0YOHAwp04dJydHqmWUlpbG1auXkSUiyERHzczMKSwslBtAqCpjvHbtCuHhYQoZ4OVx+XIQixd/gJ2dHWPHTsTMzJzU1FTCwm6SnZ2tEDDav39PlfpuCNS15lVVyMvLY9ast3BxceEihtyXAAAgAElEQVTTT0uE0wcOHMzy5Yvx999E//4DVGY/NlRqskzoQdJd/r62FRMjKwZ0m4WujoFSm8ePH1FcXMyWLT8qaXcCrF0r1dfcsuUXXF3byMuEkpKSsLdXXFxPSkpCW1sbJycnVCQgCbwiZGdnyf+uTBZ0586diY6OxsLCAmNjY9LT04mOljr1DR+u2kVt+PBRdOwole+IjIzg/PlzJCbG89tvAXTp4sE778xDX1+fjz/+TOnYwsICvvrqC0xMTGnTRrU79ctCgw0YSYqKeWvFSbX7f15Wkp1T0/XW6ujatRu//rqN1atXMWfOe9jbO5CSkszWrVvQ19ev1IpMVlbWc9dyC9Q9ldXTkmWjVUZDRFX0fPBgL/nfCxbMw8rKkuzsbAwNDcnOziYqKor09HTatm1L27ZteWa8plSTK2PDhm/R19dXOXYBAYGXlyIdA3RKvb/n5hSgIyoJbGtoaig50wg0XKrjagWwbNlnWFpasmrVCo4ePaywr/QDfFBQkHz7w4cPefjwIQC9e/fGy8uL/HyNZ/19irNzSwIDD/LPP6EYGTXCw6Mrly8HMXv2PPnYhgzx5saNkHLLGPfuPayUAa6O1NSnfPrpMtzc3Fm1ak2FAQ/Z565M3w2BF6F5VRXOnj1FYmI8s2bNUdrXv/9ATp8+yc2bN4SAUSlqyjgo+HIQf1/bSiPDxmqDRQBt2rRVeb7Q0Ov88cceJk6cTNu27bC1lQaH3NzcsbS05O7du7Rv315uef7kyRMePnxIq1atEIlEQsCoHmNnZ6cyC3rs2ImcOHGUt99+Rz4nVFQRBJCa+pADBw6gqanJhQsXSE9P57XXXqNRo0Y4OTnJ34lK065dB/m71eDBXsydO19l36Xfv2ScPHmMoqIihgzxfulL1l7u0b0E1Ea9tTo6d+7C++8vwt//R+bOLclYsbd3ZNOmbUrK66rw9/evkVpugfpHZaLnpW9Y/fq9xsWL57h37x75+floa2tjYWGBu7s7zs6Ca0RtkJeXx4Rx47j/8CE+LubM9qy4PKF0aUNcbAxiiYSvmrfCUsUD8rXQUALPnOOff0J4+PAhenq62Ns7MHr0eAYOHKxyBVYsFrN//16OHj1EfHwcWlpa2NnZM3z4aEaOHFMjn1vg5UNShJIzTWneW/ZaHY5G4GWmKlnWZcsYZVnWS5f+D319ff74o8Q5T/YALytffPDgAYcPH6Zv3740b95c5f1KJBIxebIvkyf7ysf29ttT6dath8LYfHxG4OjYtMbKGP/8cx8ZGenMnj0PkUhEXl4eIpFI7UN+VbLLGwI1mY1SWc2rqiBz1JJIipT2yfRuxGIhslCaqhoHqVvoXPjhfIqBFg5deZCkSrheOhdZWjZWeb7c3FwA2rZtp7BfJBKxYMECFi9ezMGDB3F1daWwsJBbt26hp6dHly7llwgJ1E+etyqorEtiZcjNzUVTU7PK96LDhw8A0vnqZUcIGFVATddbV4SpqRkuLm3o0qUrjo6OxMfHs3PndhYu/C/ff78Ja2v1DyFnz57it99+q5FaboGXg+fREKmIyZN98fObWq7OSEXs3Xuo2sc2RLZs+ZHU9LSKG5aidGmDvZ0dsfHxatt++8NGHiWn0KdPP8aMcUZDQ8Lhw4F8+ukyQkKusWjRMoX2hYWFLFr0PqGh13j99SGMGDEGiURCYmI8jx8/qtZnFBAQeLWoqyxrGZcuXeLcuXMAmJiYYGRkxKhR/1EZQCo9Ntk9y8LCssYzQYKCLmJoaEhWVia+vpOIiopEU1OTdu06MHfufFq3bluj53vVqKlslNKaV97e3jUmWixbkD127DADBijqf8oy44T/45onJiaa/GdpG9fDD6hptVDt8adPn5S/dC9fvoSPP/5IQSts4MCB/PXXX4SGhsqzGHV1dSkqKuK3334jLS2NxYs/Vdt/UNAFAgJ+JioqEm1tHdzdPZg9ex62tnbV+LQClcFISwISNdnNWiKyJOVLfNT1fPXtt9/wxRfS35C9vSOjR49j3LiJFZbIPnhwn5CQa3To0KleZC4KAaNyqOl664o4eHA/q1evZOvW3xQcQDw9u/PWW2+wadMGPvnkc5XHBgVd4LPPPsbV1RVPT8/nquUWEBCoeWQlHfPfnc0331Ze7Ld0acN3a74oN2D03juzadnBQ24Za2pqwLBhY5g3bxaHDv3JuHETFe4t27Zt4fr1K6xdu0G+4icgICAgQ5ZlPd1vJk4ODjx4IM0eEIslNV7CqKmpiZOTEw4ODvIy6YiICNasWcPdu/dYsmS5yrHJMsBl5WaVycauKvHxcUgkEhYsmEv//gPx9fXj4cOHBAT4M3fuTDZvDqB58xY1ft5XhZrKRimteZWQkKDUX1lkGlayoER09D35tk6dOtOpU2cAevToTevWbQkKusicOW/Tt+9rQDHnzp3ln39C6d9/IC4urtX+/A2Jqi50jhw+km9XnCEk/CD34oIRS/Jp5dQTj/ajKzzX/v17uHv3Dm3atCMrK1Nl6aeTkxNOTk7yDEZNTU0sLS0r/P2cO3eGZcsW4ezcktmz3yM7O4vdu3fyzjt++PtvV9KbFaghJGJCpqvODO28pXxN2LqsChKJRPTq1Ydu3XpiadmYlJRkAgMPsH79aqKiIpXmq7IEBh6kuLgYH5+RtTbGmkQIGKmhpuutQVr6ATBu3HBGjx7H++8vUti/ffs2nJyaKtnFJiU9QiQSceLEUYKCLipFuEtbqM+bN4+VK1fy6NEjxGIxxsbGuLq60rZtW4U0O3Xq7qV52RXbXxWeN5ou8PJTOkV2YP++VQoYVaW0oYubG1llJlNNTU369XuNGzdCiImJlt9fcnNz2bNnF7169aFz5y4UFxeTm5uDgYFhpc8nICDwaiPLsp70nzf4dsUZsnKkhhr/XElUKGmsiRLGJk2aKN3vWrduza1btzhy5BDe3iPo2LHkmaR0BrhEIuGvv6Tjsbd3eO6xlCU3NweJRMKgQUMVXnpdXFyZN28W27Zt4bPPvlTfgcBzo07zqjzKiiNHRkYQGRkBwLRpb8sDRlpaWnz77Ua2b9/GuXNn+OGH9WhoaGBv78A778xlwoQ3avCTCJTlaXoid/89j1vrYYSEH6z0cVXRCrO0tOTNN99EX1+fvLw8lY6MMsRiMWvXfo2VlTUbNmyR26R369YDP7838fffzKJFSyv/AQXqhLqsCurQoZPSO/Lw4aP44IP3VM5XpZFIJBw9ehhDQ8N6owErBIzUUJP11jLu3Akrd39KShJ2dopK/rIIt0ikjaGhIZMmvakQ4Y6OjpJbqPv5zeK9995DS0uLdu3aoaenx/379wkKCiI1NVXB9app02b1XrH9laGa0fTSFseqApDqKJ1iq6Ojg4WFBZ6enhgbGyu1ffToEb/84s/161dJTk7G2NiYVq1cmTTpTfmDFkBGRgbHjgUSFHSBuLhY0tLSsLa2plMnd3x9/cotpWwIKKbIFtT5+WXuQ6VdfP75J5ScnGxcXFqzbt03BAYeJDc3B1NTU3x8RuHnN/OlF+F71cnNy8P3jwgeZxVWWvMKFK9xrYJCWhsaMq5xExqr0L3Kzk3l9r1TPEq5R25eOjraBpib2NM71IR27RUfdt59dwY3boSoPKfMmUbg1UExy1r7hYxBQ0MDX19fgoODCQ6+KH8AL5sBHhR0gSdPUgDkGZY1iY6OLrm5Ume20nTu3AVr6yaEhl6v8XM2BKpTdi/TvKoMqvpWh4GBITNnzmHmTGXh64ZOI1EhmpqFKvcVaYjIzK/+NSeRSLh8cw82jV1waNK+SgGjqiyoVUUYPTT0OikpyUyfPkseLAJo2dIFNzd3zpw5wYIFi4RnpJeIuq4KUoWmpiZvvunLlStBCvNVWa5cCSIp6TEjRoyusbLa2kb4patBX1+vRuqtZURE3CUq6l6552zatBnR0VHcvn2Ldu3ayyPcUuvWp3Tt6smbb06TR7hXrvyckJDrcgv1996bjYaGBiNGjJC//Ldt25a///6bu3fv0qpVK/m5zM0t6r1ie0OntMVxZSmbYpuXl8nWrVs5ePAgo0aNwtCwJLskOTmZ6dMnIxZLGDFiNA4ODqSkpHDw4H7mzZvFypVr6NGjFwDh4bfZsGEd7u4ejB49DlNTU2Jiojlw4A/Onj3JDz/8TLNmzWv089cXyqbIZqXE1un5U1KSOXjwD2xt7RRWQ2Qrcbt370RbW5vZs+dhbGzCyZNH2b59K8nJSSxbpr62X6D22bBpCxl5VRNaLX2Nz589h8jtv3LyaQpfxMfwiVMLzLRLXvxz8tI5en4txcVFODt2o5FhY3LzMoiKD2b2nBkK17gMU1NT5s59X+m8gqbDq0XZLOuEhHgys1PIyUuX7hfnkZmdgq6OITra+rU6FltbaaBUNt+pygDfs2envH11MsArwsrKiri4WCwsLJT2WVhYEhmpSqxXQODVQLNITOL3qhc27d8tv0yoInbu+o30rCR6u0+tdh81zd274QC0bdteaV+bNu24fv0q8fFxQhnqS0JtVAVVF5l+VnnvZyVi1/WjHA2EgJFaRCLt5663BrhxI4SQkGscOPAHhoZGZGVlAtKb0bZtW/D1nS5v6+c3kyVLFjJ//hxGjhyDWCwmJSUZkUiEtrYO06bNAKQR7latXAkOvoS2tjbe3j6cPXuaqKhInJ2defz4MY8fP5Y7pbm4uHD37l0iIiIq/Nz1SbG9IVMdi2NVKba6usU8evSI/fv3c/36dYUstMOHD5OWlsaXX35D79795NsHDhzMxImjOHRov/xl0smpKTt27FPKkOvevRfz58/B3/9HVqyoGwG6l426Fs4vTW5uLosXf0Bubi6rVq1VCALn5GQDkJmZwS+//I6TU1MABgx4nblzZ3LsWCBvvDG1wQb6XjQREXf5dddu3nKz4qdrlRMgL3uNWxloEXLkOO0Njfg0NpoDKUn42pQEdmISr5FfkE2fLtNwaFKSUdrUzo2DZ79UuMZl6Onpq1xsEHi1qCjLOvb+dWLvX8et9TDatOhfq2OJf6bbZm5uXqmxVScDvCJat25LXFwsSUlJSrIByclJmJmZ18h5XgWKdAzQKWU2lptTUON6VwKvBg8e3GfzTz/QvuXrGBmYy0teXzQy17zGja2U9sm2paQkCwGjl4QXMSeoIyFBcb4qS2rqUy5ePE+LFi3rVVa2EDCqZa5fv8rWrcqps+HhYYSHhykEjLp08cTU1IynT5+wd+/viMXS9M82bdoyf/6HtGxZ4nwm+yEWFhYqWKhHRUURFRUFIA8YyV4SZWUpJW3v4ecnrf3//POVuLi0VlBsF4vF7NjxC8ePH+HBg/vo6xvg5taZGTPmyF8uBeqe6lpGqkuxtbS0xMbGhujoaHr16iXXusrOzn62X1HYz9zcAk1NTfT0SlaVZRH1snh4eGJsbEJMTHSVPuOrwotMkc3Pz2fhwoVERNxh6dL/0bGjm8J+XV1pJmSbNu2UruchQ7wJDb3OjRshQsDoBSC7xnt286SndUqlA0bK17hU78NRTx9XA0OuZqYzuYktomemCIWFeQAY6CmWo+rpNlK6xktTVFQk17sSDBZeTcq6Wom0NDmy9zZ5Bdlcvb0Pm8auODt0xdRYeu9PSUkmNT1DbZZ1ZcjLy1M6ViKRyMuPevbsrXJsFy78zfHjR+jffyBnz56qcgZ4ZRg82ItjxwI5cGAf3br1UDh3cnISPj6jqtXvq4ikCAV9q7LUhN6VwKvBN9+sxNbWjtbN+77ooSiQlyedG7W1lUtxZaVtsjYCL56acmGsCunpaZiYmCpsKygo4OefNwMl81VZjh4NRCwW17vEDCFgVEWqUm8NMHToMHbu3I6v73QmT/bl4cMHakWvt2z5kdzcXECa4aOhocG+fbv58MNlSq4f3br15NKlC/j6TicgwB9n55YkJiagoaFBUVERmpqaZGdnY2hoKHc1ycrKkh9fVFTEqlUr5HX5oKjYXlxczEcfvU9w8CV69+7LmDETSEtLZf/+vcyc6dugS4xeNNW1jCwvxdbKyooHDx6QlpYmD0Z2796dgIAAVq9exZw572Fv70BKSjJbt25BX1+/UhkzWVlZ5ORkN8hVGHUpsjlp0pf/7EIJDzLyMdYTYaRTs5ob+fn5LF78AVevBrNo0TKVGSFWVtJVMnUlFiDNPhKoe2TX+PqvVvDolyWVPq68a7y5vgF3crJ5XJCP3bNgoW1jF8Kjz3Dl1h90buNDIwNLcvPTuRV5En19A5XXeHJyEq+/3pv8/Hz09PTo2rU7M2cKiwivGmVdrXREWty+pCXPAGhkYIGjbUf5/g0bvyPwyCGVWdYy3au7d+8AsG/fboyMjAAUFs2OHj2KgYEBlpaWcpe0qKgo0tPTGTt2glxXsezY/P03oaOjy5Qpb3H27KkqZYBXdmweHp4MHDiYU6eO88EH8+jRozePHj1k377fsbCwxM9vhsL3V5W+BQQaIrIFtU0/+HPucOqLHo4CsiBCYaGyblNBQYFCG4EXT024MAKEhIQQEiK9b6ekSDXxwsLC0NHRwd/fn8mTS+7bCxbMw9LSEheX1nKXtOPHj5KYGK8wX5XlyJGD6OjoMmhQ/crUFgJGaqgpgbfKlqOoKjGqTIT7jz92y8sP9u7dxebNG7G2tubx48cEBQVhZ2fHtWvX0NDQkLu0Aezd+zuxsTFMmjQFf/9NFBUVKSi2nz9/juDgSwwfPooPPyxxAhg82IspUyawbt03fPvtxkp9BwI1R2JiYrUtI8tLsZVpF+Xk5MgDRu7u7rz//iL8/X9k7tyS2nV7e0c2bdpWKevigAB/xGIxQ4Z4V3qcrwoVpcieiUnnTEw6fu5NGNvWssbOW1BQwJIlC7l6NZjlyz9l4EDVk1Lr1m0B5cxDKKn7Fsos6p7Smld2tjZULrdISnnXuNmzDLdUcaE8YGRt6YxHu9HcjDjGqaCS+3kjQ0t+3hKAnYOTQh+2tna0b98RZ+eWaGpqEh4exr59u7l+/QobN/rTooViqY6AgKos6127fpX/XTpw0qxZM2JjYwkLCyM/Px9tbW0sLCxYuHAh/foNUdn/rVv/EBv7L6+/PkQejKmNsQEsW/Ypzs4tCQw8yPr1qzEyakS/fgOYMWO2UiZuVfsWEGhIlF1Qy8yWaryq0kh7Eciu5+TkJKVnXdnzUdlrXqD+c+3aNa5dU0wKuXnzpnxf6YBRv36vcf78Ofbt+53MzEz09fVp2dIFP78ZvP56xfOVKqOhlxkhYKSGmhB4q2w5iroSo8pEuDMyMhg/fhIGBgZMnuyLWJxHQEAAADExMcTHx9O9e3euXr1KcXExAI8fP+Knn35g2rQZmJpK0+mioiIVFNtDQqQXjJfXcIXz2tnZ07GjG1euBPPo0aMqORQIPD+ff/5ZtfVwygtAylxlSgcVAUxNzXBxaUOXLl1xdHQkPj6enTu3s3Dhf/n++03lup+dPXuKXbt+pWvX7nh7D1fb7lVFXYpsfmYKK776hi62RgxqaUYzM+l1/jSnkILYOAzNHau9clVQUMDixUu5ciWIDz5YzNixY0lLy1HZVvbyf/v2TSIi7uLi4gpI70cHD+5HS0sLD49u1RqHQPVRXGSomuB1ede4toa01LSgqFhhu66OEeamDjSxbImxYWMyspO5E/0X8xfM47vvFK/xJUuWKxzbv/9AevXqw9y5M/nuuzWsWycsIrzqGBmY88aw1Urbl3/yGYvL/D5Aqs3o56f6WaosnTp1olMnZVeZwYMHk5+v+pj27TsqZH1XJQO8KmMD6Sr25Mm+TJ7sW2HbqvYtINCQqIpGGtT9gqNMWyYs7BYeHp4K+8LDb2NoaIijo5OqQwVeIqpaFTRjxgy1ZfZvv/22wjxU2bmgNGXnq/qEEDCqJaqi2L5//y6VJUaViXBDSfmBpqYm77zzDsXFxVy4cIF79+7h4+ODqakp58+fx9raGoDVq1dha2vH+PH/4cSJo4B0NQxKFNsLC9WnXMq0T8LDbwsBozrk+PEjXLp0sdp6OOUFICUS6Ytp6X7379/PqlWr2Lr1NwWRT0/P7rz11hts2rSBTz75XOW5goIu8NlnH+Pi0prPP/+yQeqcqEuRlbmk2TTSobeTiXz71tDHnNo7qdyyifC7Uiee02lPMNCUBvl8LEuySZZ89imXL1+iS5eu6OnpcejQQXJyCuT7W7RoibNzS/m/589fyJw5b/Pf/85m7NgJmJiYcPr0Se7cCWPatLeF67uOUV5kqFrAqLxrvLBYqkKro1lyLUbFBXPl9j68er+PqbGNfLtNY1eOX1xX7jUuo2NHNzp2dCM09Dr5+Xny+UFAoKbQ1dZAR6T+WihGi6zsOhyQgIDAc1F6QU2mjwaUo5H2/JozVcHNzR0LC0sOHfpTvigPcO9eJKGh1/Hy8hGcpAUaFMKvvZaorGL75Mm+7Nv3u8oSo4oi3CKRCLFYrFR+oK2tjZWVFffu3SM/P5+EBGmwysHBgRMnThAUdIEffvBXuNndvXtHQbFdpk8UEnJV4QUzLy+P8HDpjT0pqSrFEgLPgywA2bt3n2pbRpYXgJQJXJcWw962bRtOTk2VHGFatHDGyampPIhRluDgSyxd+iHNmjVnzZrvMTSsWpmAgCKqShuOP30i/7t0wCjsWUDp2rUrXLt2RamvadPeVrieW7Vy5YcffuannzayZ89OCgoKcHJqypIly/Hy8qnpjyJQDqoWGdK1C3icJQ3+VEbzqrxrPPVZ9qCZqCT7KCz6NMZGVgrBIgAzYxuaNlV/jZfFxsaW0NDrZGZmCgGjeoqRlgQk6p2rCjFQu686xMfHsnXrFiIj75KSkkxeXh5GRkY4OjrSsWNHhbkIJIRd+D+lPsSSIg6fiuJqWB6JiRUbc0RE3GXr1s3cvPkPeXm52NnZ4+MzkjFjJsizbEHmNvgVd+6E8/jxQ3JycrC0bEzr1m2ZPHkqrVq51uh3ISDQ0Ci9oCbTRwPUaqRVV4ds5kw/hfPK9Glk2fRRUVFs27YFgE6dOtOpU2f5+N577wOWL1/MnDnT8fEZRXZ2Nrt378DU1EzIHnxBCA6MLw4hYFRLVFaxfc+eXdjZ2TFw4BDi4mLlZWNQcYTbzs6ehIT4ckuMcnJyCA0NRU9PD2dnZ9asWYOPz0jateug0L6oqEhBsX3QIC8CAn5my5ZN6Onp06VLV9LT0/D330R6ehogOATUJbIA5N9/n+Pvv88p7a+MZWR5AcikpCS0tbXlJYoAycnJ2NnZq+xLIpEgUfFycflyEEuWLMTRsSnr1m2sdzW6dYGdrQ1HpyiL4S3oac/anYGkF+gqbC9d2mBEPiHT1T+oHN27jyxKjjc1NVBbkibD2bklq1atrcpHEKgFakLzqrxrPCY3B31NTax1Sn4fOXnpGBmo7ksiVn2NqyIhIR4tLS0aNRKu93qLRFzuvaX9tu01erqkpCSePEmhT5/+2Ng0JigoiKdPn3Lnzh2io6MZM2YM+vqqXfoAiouLWb35Cv+EJ9G/Xx9GjSrfmOPGjRDef/9dDA2NGDt2AqamZly9epn169fw77//smhRiVZjYWEhd+/eoUOHjtjaemFgYMDjx48JDDzIjBm+rF79He7uHjX6fQgICFSdirTCygaMyurTREREEBERAUgX1GQBI4DXXhuIrq4uAQH+bNiwDm1tHbp08eCdd+ap1AkUqH0EB8YXhxAwqiUqo9heUFDArVv/EBDwC/7+P3L06GGWL1+h0Ed5Ee727TuSkBAvLz8ICrrArl3b0dTUJDU19dm2IMRiMYMHDyY0NJTi4mJmzZqrcrylFduNjY1Zt24jK1Z8wldflazsderUmTfemEpAgL+QOVKHyAKQhoa6ZGeXFNFWxTJSXQDyyZMnPHz4kFatWqGpqSnvu1mzZkRFRXH79i3atStxXbp9+yYJCfF0795TYYxXrgSzePEHODg48u23GzE2NkFAQKByqFpkMBCJiflzAxsuP6xQ8yo+PpbAwANoaWnh77+J7du3YmNtTavMbNobGnE3J5teJmaISpWHmhhZk5bxkJTUOCzNnCgqkhAefZao+GCyc1MRiUQsXbqQGTPmYGFhib6+vnwx4t13ZyhlIA0YUHJP6Nq1q1yPJj8/n8jISOLj40lLSyMgIAArK2s6dXLH19evXC00gVeTLl260qVLVwB0dYtJS5MuRNnY2HDq1CkiIiJU6hnJuHbzEf+EJ/FaTyfWrv2azGzp71KdMce6dd+goaHBjz/+LF8IGT16HF999X8cPLifIUO86dhRej59fX38/ZUDZCNGjGHMGG927twuBIwEBGoBdRpp1dchU9TsmzFD0c1QqkujXjKhZ8/eau3RBQQaEkLA6AUhkUjk5QeWlpbykqCnT6XpmLISIw8PT1auXKMywn3mzEmgpPygSRNbtLW1+eeff8jNzQWk1tndu3dHLBYTERHBzJkzychIJyND6kRw504YINU2ychIR09PT+7A1qKFM1u37iAxMYGUlGQsLRtjb+/Axo3fAgg2ynWILABZNmOkKpaRqgKQ+flZHDlyBD09Pbp06aJwzhkzZvDhhx8yf/4cRo4cg729A4mJCfz5515EIm2mTSuZeO/eDeejjxYAxXh7+xAcfEnpM6iydn8V0dIRISkjLKxIw9NzEqgYVYsMJjr5XL/6C/CwQs2rpKQknj59So8evTl//i9MTEywMDfndHwCJ1OfYKipxUhLxVXRDq0G8/e1bZwO3oSzYzceJkeQnvUIDTQQiUR4eflw/vw5Zs70xc9vFrt376Rnz97Y2trRvLkzRUUSbt26ib6+Pr6+07GwsOT06ZNcunSe3r17k5mZCUizSYKDg7Gzs6Nt27YMGjSIyMhoDhz4g7NnTyplgwg0XGSlJDJjD3WE35NaHvf1dFDYrsqYIyMjg6ioSLp27a6UNevl5cPBg/s5cuSgPGCkDjMzM3R0dOW/awEBARuODccAACAASURBVAEBgYaAEDCqY2SK7ZmZmRw48AeXLl3Ay2uofP93360BlEuMVEW4y5YfNGvWnO+++46ffvqJw4cPk5yczODBg9HU1CQ2NhaATZs2sWnTJqW+IiLuMHHiKLZs+UXerwx7ewfs7UseyoKDL2FoaEj79h3LdiPwklM2xVZHR4cmTZrg6emJoaGifWnfvn1Zu3YDO3ZsJzDwINnZWTRq1IiuXbvh6zudli1d5G1jYqIpKJBmPq1fv0bluRtKwEhSVMxbK06q3b/vk151OBqBhkLpjI2LF88TEOBP2J07aGmApBh6m5piVqZ82b5JO17rNpPw6L+Iig9CLClAU1OEnVVrvvxqKU2bO/PGG1OZMmUCp04dx8XFlUuXzpOa+lSunzdy5FimTJkmT9EPCPBHR0eHoUOHsnv3bgBMTU2ZMGGCvER1xIgR5Odr0L17L+bPn4O//4+sWKFo+iDQMMjPzyc3N4esrCzS0tK4fPkyINVcLA+xWCpkoatCz6usMUdlTDzCwm4r7ZNIJGRmZiKRiElKeszOnb+Sm5ujlF0rICCgnrrWRxMQEKh5hIBRLVDRzREtEeJS5Qely4xqq8SocePGDBw4kIEDB1LaQCc09Dp//LGHiRMn07ZtO2xtVWvWyNi7dxcxMdFMm/Z2ufoCAnVDVS0jQTHFVle3mJ9++kllOwB3d49Kpd57efkIIskCArWAtZFOlTSvoOQaNyKfvW+8yYq4GDTUZLY1sWxJE8uWXLv9JxGx53m9+2wszZxo1cqFArFEIWNj797D5Trn/fNPKPHxcQwYMAgTk5JsKHVC/B4enhgbmxATE13R1yDwinL48J+sXfu1/N+NGjWif//+2NjYlHMU2NtIf1NhkSkMLbVdlTGHubkFpqamhIXdUnLyCw299qztY6VzxMX9y5QpE+X/NjIy4s03p1XZSllAoEFTx/poAgICNY8QMKoNKrg5dt6yCZFIV15+ULrMqLZKjAwNDWnevDkDBgxQqNeVla61bdtOqRzigw/mYWtrR9OmzdHQ0ODKlWDOn/+LHj16MXWqopCcgICAgEDVqI3yRWnGRi4FBfncjI3gl0fSOaWDkeqgjQxJkXSRQ0tL2UShbMaGOg4fPgCAj8/ISo01KyuLnJxsmjdvUan2Aq8evXv3o0ULJw4ePEhKSgpxcXGVMtTo6WHP/uOR7D0SQbO2f9KufTe1xhwaGhqMHz+JzZs3smTJh0yfPhMTE1OuXbuCv/8mtLS0yM9XPqeNjR1r125ALC4kMTGREyeOkJWVRWFhoWCpLSAgICDQYBBmvHpOVUqMqkq7dh04ffoER48eBsDJqRnvv7+IESNGK1jQCtQegoWkgMCrS22UL5bN2LDU1uZtG3taGZQ/H5g2sgbgcUoUZsa28u2qMjZUkZ2dxdmzp7Cxsau0IHBAgD9isZghQ7wr1V7g1cPKyhoHBytu3rxJ06ZNadasGfv370csFuPm5qb2OCMDHZa824Mftofw2edfyrerM+aYPNmXvLw8du36jbffngqAvr4Bc+fOZ/PmjUgkEqVz6OvrK7gNensP5623JrN06ULWrPm+Jj6+gICAgIDAS48QMHrJqO0So7KUV0rk6zsdX9/ple5LoOapKQvJ+PhYtm7dQmTkXVJSkhGLxVhbN6FXr55oamrKyxnLQywWs2PHLxw/foQHD+6jr2+Am1tnZsyYoySAnpKSwr59vxMRcZfIyDukpaUxdOgwlb/hjIwMjh0LJCjoAnFxsaSlpWFtLTgoCQhUh969++Ho2JTc3Fxi74VxbMdOMssrkX5GUzt3bt87xT+RxxBp6XD/gQspT56ozNhQxcmTx8nLy8Pb2wcNDQ3KutOU5ezZU+za9Stdu3bH23t4lT6jwKuLhYUFlpaWhIeHlxswAnC0NebLRf1o5DiFuISn5RpzaGpqMmPGbN58cxrR0VFAMc7OrSguLubrr7+gbdv2qk9SCgMDA/r27c9vvwVw/36ikoC2gICAgIDAq0i1Aka5ubl4e3tz//593njjDT755JOaHpeAgEANkpSUxJMnKfTp05/Gja3Q0tIiJiaK/fv3o6GhwZgxY8rVpCouLuajj94nOPgSvXv3ZcyYCaSlpbJ//15mzvRVcjmKj49l+/atWFlZ4+raRqVrmozw8Nts2LAOd3cPRo8eh6mpKTExgoOSgEB1sLKyxspKmi3k1ac7dmfP83lcNIXFxXhbNFZ7nK6OAa91m0XQjR1cvrWHUaP3AOozNsoSGHgALS2tSgV/Ll68yGeffYyLS2s+//zLZwEmAQEpYrGY/Pz8Srd3dHTAzKKp/N/lGXPo6+vTrl1JcOjs2VMUFxfTrVuPSp1LNq6MjHQhYFRPULdg1r17T/7znylYWlpW2EdVFsxAWm77008bOXfuLBkZ6dja2jNmzHhGjhyj8n4XFHSBgICfiYqKRFtbB3d3D2bPlspCCAgICLxoqhUwWr9+PampqTU9lgaDUGYkUNeUdlBS3O7G4sWLiYiIoFMn9ZbC58+fIzj4EsOHj+LDD5fKtw8e7MWUKRNYt+4bvv12o3y7q2trDh06iZmZGWlpaQwbNlBVt4B0FXjHjn1KD9+Cg5KAwPPjoKeHo64eZ1KflBswAjAztsGrzwIys1PwGt8CUzMLtRkbpYmOjuLOnXB69Ogld0tTR0JCAtu2baNZs+asWfN9uUEogVeXJ09SsLBQflF/8OABqampCqLXyckp3H+UiaW5Pro65T+2VsWYIz09jU2bNmJqasrIkWPl21NTUzExMZGbhpQe89mzp9DXN6BZM0F3q76gbsHs4MH9nDp1gm3bdmBmZq72+KoumBUWFjJ//mwiIyMYO3YCTk7NCA6+xOrVK3n69Al+fooap+fOnWHZskU4O7dk9uz3yM7OYvfunbzzjh/+/tuxtCz/vi0gICBQ21Q5YBQWFkZAQAALFy5k5cqVtTGmV56aKjMSEHheZA/lBQUF5bYLCZGWSXp5KWYPlHZQevTokVwQ18DAEIMKNFNKxmCrcrvgoCQgUDMUFBeTrUKjRR2NDC3p7OZOgVh6THkZGwCHDv0JwLBh5YtdJyQkcOLECZo3b866dRsxNjau9JgEXi2++WYlT56k4O7ugb19E27dukVKSgrR0dFoa2vTrVs3edtv12/k4KGzLJvXgzYtS4JMq34IxsrCAI8He8kv0CrXmCMo6AI7dmzHw8MTc3MLHj9+xKFDf5KZmcHKlWswNTWVtz158ii7d++kT59+2NjYoa0tIiEhnqNHA8nMzGDRomVyt1qBlx91C2YdO3bmk08+4siRQ7zxxlS1x1d1wezQoT+5cyec//73A8aOlbrsDR8+iqVLF7J9+1a8vYfTpIn02UssFrN27ddYWVmzYcMWuTxAt2498PN7E3//zSxatBQBAQGBF0mVAkYSiYSPP/6Y3r178/rrrwsBIwGBekZpB6XY2H/58cf1ADg4OJR7XGGhNKCk6iG5sg5KVUVwUBIQqDzqMjbuZGdxPz8P11IB3DRxIbGx/yKWFCDS0im334oyNgoKCjh58ijm5hb06KFepFvqMnUCExMTNm7ciL6+SRU+ncCrxsCBgzl27DDHjx8hLS0ViUSCkZERrVu3pmPHjhgZVZx51rKZGcEhD7hwTSpAXZ4xR5Mmtmhr67B37+9kZKRjYmJKly4eTJ3qh6NjU4W2HTq4cedOOBcvnufp0ycUFhZibm5Bly5dGTduotrAqUD9Qva8kpmZWW67qi6YnTx5DD09PXx8Rim0Hz9+EufOneX06RPyAFVo6HVSUpKZPn2WgpZky5YuuLm5c+bMCRYsWCS48gkICLxQqnQH2rZtGzExMaxfv762xiMgIFCLlHVQsrW1pX///grp/6qQpVuHhFzF2bmlfHtlHZSqg+CgJCBQeUpnbFhbN6GgoICYiDCOJsShp6nJBKuSYO6+pMdcnDiagd3ewdrSWb797OWfMDKwwKSRNXv3pRAUHKQ2Y0PG+fN/kZ6ezqRJU9S+1CQnJ3P8+HEAXFxcuHTpEmKxoo7H4MFez/sVCNQjBgx4nQEDXgcqNutY8fknTByk7Mw6eogLo4e40LbXUjKzy3dulZZAflepsbm6tmb58hWVaitQfyi7YPbDD9J3me7de5Z7XFUWzIqKioiMvEurVq7o6uoqtG3dui2amprcuRMu33b3rvRvVaLrbdq04/r1q8THxwkLZwICAi+USgeMEhIS+O6775g9ezb29vYkJiZW64SmphW7MdUFGTnll+CUh4aGRrmfoyij+n2DVMOout9TXl5O+X1XMHYBZZ7ntwK1+3up6m/F23sobdq4kJOTw507dzhz5rRa56PS4x43bgy//LIVf/9NmJub0K1bd1JTU9m48Xu5g5KGRpHKsRQXS0VCdXRElR7riRPH2bXrV3r27MUbb0ysV6K4L/LeoqmpgalxyfFaWpo1dr0L95aapybvLSNG+HDgwAFOnDhKaupTNDQ0sLG2pp+pGUMsLLHQLj+TCMDSrClxD28Qk3iV29FHad68BcuWfcy4ceOVMjZkHDt2GIBJkyYq/P+X/r08ffpUblseFBREUFCQUj8TJoxV2iagSE3eW/79919+/HEj4eF3SE5OQlxYiOn/s3feAU2d6x//kLCHICBbRAVBHDjQonWA4t6jjrZaLXXUVnvb2uu1+qu19d7bcatW6xat2mmtA8U960BRQavIEFSGIEOmjISE/P5IEwhJWI46zucvec973nMCx5Pnfcb3UShob2HFQDt7bAyNal1ToVBwJz2ahDtn2Dfgc8rLpTg7OzNw4CAmTZqskSUUGRnJm29O0blOjx498PX1Vf8skUhISEggJSWF/Px8tmzZgq21IT6e9owe2Aq7xpqZbqKHsJmeV54nu6U6NX0X1XTfP/20i//859/qn11dXfniiy/p3btmh5Gvrw979kBs7FX8/Suzy0pLS4mLiwGgqCgXGxtz8vPzkUgkuLg467gPc2xsbMjLu68+VlSk1INt0cJda36zZm5/XafwsT/fwp5IoK4I75YXkzo7jD799FPc3NyYOnXqQ10wP7/m/7xPDMOao1E1oVAoKKjhc1jW0k641vUrFA3+PZmY1HxthaLha7+wPMSzAo/3eanvs2Jq2ojWrZXi1p07d6dXr568/vrryGQyrRbGms+KEcuWrWLJkk/49NNF6jlVOyiJRMY676WgoBQAqVRWp3uNiDjDxx//E2/v1nzyyRL1+c8Mf+O7paJCQeFf56ek3OHHHzdz/XpMgzvDKBQKjhw5xM6d20lNTaakpARLS0tatGhBu3btMDaudEKcPn2aHTt2kpSUSF5eLkZGxri4uDBw4BBGjBijFW19993pXLkSpfO6GzduxcfHV+ex54pH+G4JCOhNQEBvjeOWSIh6a4bWeSEubiw/fILl1bT02rXqR7tWyqyP9xb2UWsYFRXp71j19deVGcdV/39X/S7y9vbG29tb/fO0adOQSDSdwML3Uh14hO+WpKRkMjIy6dGjN02aOGAuVhCxYSOn8vO4UFTAYg9PGtVSBnM1/gAxicdwtPPkjanTMRCJiI6+zKpV33HixEnWrduMgYEBVqZGKP5q5DF21Bg6deiksY6tox1RUZXvgqysLM6fP4+rqytt2rRh8KB+XDj1M8fOJnMh+i6fvt8TN2cr9fyKCgVFRcLzo8FzZLdUpyY7tyYb19+/O8uWraK0tJSbN+M5c+YU6emZtd5Lz57BrF27lpUrV6JQiPH370pBQT6hoevUDYDy8grJzy8hMzP3r7NEOtc1NDSiuLhEfayg4AEAZWVyrfmyv3rf3L9f8Pjfj8KeSKCuCO+Wp54mTaxqn1RP6uQw2rNnD2fPnuWHH37AyKj2qNOLgr5Wnb26BdBJVl7vCF3Rg2zkFXLOxbjSp08/xo2bqNFBJjr6MsePH+Xq1SgyMjIwMTGmaVN3Ro8eR3DwAK0sjL1795KRkaExtn79euAF2pAJ1IiXlxf29vbcuHFDy2FUnZYtPdm8+SfS0lLJycnG3r5JnToo1Yfz58+xYME/hQ5Kj4CsrCyysxveGQZg/frVbNu2mc6duzBt2jTOnz9Peno6ly9fJjU1lREjRqjfO4mJiYhEYoYOHYGdnT0SSRlXr15hxYqlnDt3hmXLVmm9o2xsbJg9+wOt6wqthAUEHi/VhYAtkdBi735amVuwJj2VswV5DKqhq15FhZy426extXajb8AMxo8LRiqTM3LkWMRiQw4fPkBiYgJeXt4YKCBzbzwAbrmNaJ+mqXXnNNhPw2FkY2PD+PHj1aLoo0cNp5X9NTq0ceS/qyLYsT+Of4R0eZS/DoEXAAcHRxwcHAHo1SuQ3r37MG3aZCQSCZMm6Q+GN2rUiOXLV7NkySd89VVlhlLVgJnKVlGVrZWXl+tcSyqVapS21TRf1YxEEFgXEBD4u6nVYSSVSvniiy/o3bs3TZo0ITk5GYDMzExAKRaXnJxM48aNX7iOJ/padf4eFsbe8vJ6R+jateqPgUiMjWMRoaHriIg4q47QAaxZs5Ls7Cx69QpkzBhPyspKOXbsCIsXLyQq6hLz5i3UWt/U1JRu3bqpfw4MDEQmMxA2ZAJqZDIZEon+DILquLk1xc2tUiS7tg5KdeXChQg+/vgj3N09hA5KjwB//64EBwdqRUTq2hlGJpPx228/06qVD8uWrcLMzIDCwkJ8fX05fvw4iYmJ3L9/X52pNGXKFK2skbFjJ/DNN1+ya9dvxMbG4OvbVuO4qamZoF3zEJQZGWEgluk9bqC72lRAQC92fwUFa+uqV6GoQC4vx9TECgMDzfbzqneCqanutvZl5RJEBiKM9QTVrKx0R0fb+TTB0tyI1PSaRYoFBOqCp6cXXl7e7Nq1o0aHEdQ9YGZl1QgTExOys7O01pBKpRQWFmBvX5lhZ2+vdMpmZ2fh4dFcY75qDdUcAQEBgb+LWh1GZWVl5ObmcvLkSU6ePKl1PCwsjLCwMP75z38SEqJbFPN5RV+rzpf82vHPT/6v3hE6ldH13sI+GIjEGhE6gLffnk379h00tCReeWUic+bMZO/e3bzyygRat9YUxjM0NMTLq1KkePDgwVqbOoHnH30dlC5dukReXp6G6HVJSQlSqZSysjIMDHQb/Cpq66BUVyIjzzN//lyaNnXn229X06iR0EHpcVHXzjByudKRaGdnh0gkgippwqpuLnXJOK3tehUVFZSWlmBubvFMaVU9DZQr5MwK199yeUvfz5/g3Qg8i6iEgIulRVwvLmJHljIY2N6y5pR2Q7ERDrYtyMiKIybxOKmpnlRgQHT0ZXbt2sGAAYNo2tRd67z1535h+anNALg0cmRImyCmK9rX6V5LSssplchwcxGCCQKPBolEQmFhQZ3n1xYwE4lEtGrlw82b8UilUo2y7djYGCoqKvDxaa0eU2X6x8Rco0uXlzSudePGdSwsLHB3b9agzyYgIPBk0Fdx9DASEFJpOY6OTvTtq11xBMpO0hs2rObUqRMUFhbg4uLGmDHjGDlyjMa88vJyPv/8c65fv87du3cpLi7GwcGB9u3bM336dA39wJqo1WFkZmbGt99+qzWem5vL4sWL6dmzJ2PHjtXQJ3jRcf5rg/SoI3QdO3bWWkMkEhEY2IcrV6K4dStJy2EEyoewvLxcvbl72Ac7KuoSc+bMrHHO6tUbad++g/rn+Pg4Nm9ez59/XqWsrBRXVzeGDRvJmDHjtcRUBW2TR4+uDkrx8bEcO3YYIyMjAgIC1HMjIyNJSEigT58+tG3rrx6fO3cOLi6ueHi0wMDAgMjI8zV2UPr++40A6uylpKSb6rEOHTrR4S8di7i4G/zrXx8CCoYMGcb58+e01hKyUBpOQzvDmJiY4ufXkQsXIvjhh+/p378PRUVFpKenc+PGDTw9PbG21nbslZQUI5WWU1z8gGvXrvLjj1uxtrbWyi4CZQS1X7+eSCQSTE1N6dq1GzNmvPNIyhsFBARqp3rnTHsjI6Y5u9HK3KLWc1/u+BoRV3/mSlw4Y14JB5TCoJMnv8lbb2naCIYiMS8164B/03bYmtuQW5LP4fgzbIj4hXufFePsUnOnToBdhxKQyxX06tq01rkCAir0Bcyioi5x+3aShm2dk5NDcfEDHB2dai0F0xcwCw4ewLVrVwkL28nYsRPU49u3/4RYLKZPn37qsY4dO2NnZ8/evbsZN+5VdTDm5s0EoqMvM3jwML3dJwUEnidUe9PEhFjupaYiVyiwNTKqcyOGzJxEugZ8WOOc2vambm5uODo60qZNm78CpZUcOXKEM2ciiI+P486dW8jlcn77LQxnZxe9FUcNlYCYOnUahoaGREdf1llxVF5ezvvvzyIhIZ6xY8fTrFlzzp8/xzfffEFu7n3+9a+56nXLy8u5fv06nTp1Yvjw4VhYWJCRkcHOnTsZN24cGzZs0KhE0ketbyEjIyMGDhyoNa7qkubu7q7z+ItE9Q3ZujVKB1t9I3Tuzu0xMBCxb19YjRG66mRlKdNWbW3ttI4VFxezadMm5HI5hoaG3Lx5k27dej3Ug+3h0Zz/+7/PtMbLy6V89dV/sLa20dgcXrkSxQcfvIuFhSVjx47HxqYxFy9eYMWKpdy+fZt587Qj5IK2yaMlOHgABw/u49Ch/eTn5wEGODk5MXr0aAwNDTW62eijbdv2HDt2mAMHlF2RmjVrzgcfzGPEiNE6Oyht3LhW4+eEhHgSEpQ6FlOnTlM7jG7dSkIqVTqVVqxYqvPagsOo4VTfEDo7u/DJJ5/j51ezZhXAokVLWLLkU9au/Y61a79Tj3fs2BF/f3+d5/znP4s5ebJSTNnXty0ffDBPq8zExcWVdu388PT0QiQSceNGDL//vp3LlyNZvTqUli09qy8tICDwiOnZMxB3dw8UpYWc/GYpVx4UUSTXX+ZYFZFYjKW5Hc3drHlz+mjkFQpOnjzOli2hGBsbawQSfJ288HXy0jh/gE8vPj34LXv37WX48OHqbERdXIhOZ//xJNq3bkLvAMFhJFB3agqYmZub8+67/1DPXbfuOw4c2MeKFWvp1KlhAbPhw0exf/9eVq5cRkZGBh4ezYmIOMsff5zgjTdCNOxYQ0ND3ntvLosWzeedd95i2LBRFBcXs337T9jYNCYkRLtpgYDA84jK6RLUqxfSw0cRGRhwt6xMoxFDTTSycmTxoiXIKio0xuuzN42KusDJkyfJy8ujV69eGuvs2LGD69dj8PT0wtXVjZSUZPUxfRVHDZWAUDmrdGkCAuzdu5vY2Bv84x9z1U7p4cNHsWDBR2zbtplJkybi6qp8z5ibm7Nz506ta06YMIGgoCA2bdr0aBxGArVTfUPm4uzcoAjdlThlhC7shO4InS5ycrIJC9uJi4urhtcUlDoAjo6O2NnZYWBgQFZWFhEREVy8eFHnhqyuD7atrZ3ODfyRIwepqKhg4MAhGhGR5cv/h4GBAWvXbsLVVdkmdPToV/jqq38TFraLgQOH4Oenee+CtsmjpW/ffvTt209r3MREwYYNGzTGAgMDCQwMpHPnzlSVNpoy5S2mTHmrztc8c+ZSneYNHjyMwYOH1Xldgfqh2hBW7QyjdBrWjrLTmStNmgyhR49uHD9+nNu3bxMdHY1YLKZTp05a50ydOp0RI8aQn59HVNRlkpJu6kz5//jjRRo/BwUF06NHL2bPnsHKlUtZvnx1wz6wgIBAnVEJAVsiwWbrj3S2subz5CTKFQqG1FBSL5NLOXx2JbbWbvToNIkB/ZVd9YKDB7Bo0XxCQ9cRFNQXd3cPvWuIDES80mEwUWnXSUlJ0eswOn36LKu2RtG8qQ1zpvoLpasC9UJfwGzEiNFMnDi5RkelivoEzIyMjFi+fDUbNqzm6NFDFBYW4Orqxvvvf8To0eO01u7TJxgTExO2bAll1arlGBkZ4+/fhbffnkOTJg6P5HcgIPC0o3K6WCIh6mK0erxqI4aa8uLNTKwYNKiyu6uK+uxNJ04cy6RJk4iLi6NVq1Ya74bFixdjZdUEQ0NDli79UsNhpI+GS0BUoqvi6MiRg5iamjJs2CiNuePGvcqpUyfYv38/06ZNq/GadnZ2GBsbU1hYWOvngIdwGLm5uREfH9/Q058rqm/Izp05SVFB3UQZq0boXJp4AwaYNL6nM0JXnbKyMubPn0tpaSlffrnsr/8IlTojgYGBGvNbtGjBu+++y8yZM3VuyOr6YOtj3749AAwbNkI9VlhYSGJiAl27dlP/h1QxePAwwsJ2sX9/mJbDCJ4/bZOH7aqXmZPI0fNrNMZ+3Kc5p3q6JcDt27fYsiWU6OhLFBYWYmPTGB8fXz76aD7OzpWZZAqFgqSkJGJiYsjPz+eHH37AwUF//azAs0FDO8OUlZUxc+abeHt7s3jxfzExUZCSkoKnpydHjx7l8uXLtGjRAhsbG43zWrb0VDuj+/UbyO7dvzN37hy++2691rNZHT+/jvj5dSQ6+jISSRkmJkJ3GAGBJ0lTU1PcTUw5nne/RodRSsafFBXn0MFniNaxoKBgjh07wp9/XqnRYQTgaKnMjC4r063Qnpqayvfff4+bkxX/eicAczOhU69A/dAXMNPFggWfsmDBp1rj9Q2YWVlZ8cEH8/jgg3l1mv/yyz15+eWedV7/WeBRlBgdPb9Gy86tSl1sXmNjY5o0aUKPHj3UJX8qzp49y8aNm0hMTMDIyJjOnbswa9YcoZrhKaKujRj0Ud+9qbe3N3FxccTHx2s4jJycnGrVAH5UEhCBgX0Ri8U6NQErKipISIijVSsfTExMNNZp3VpZSnft2jWta8jlcgoKCpDL5WRkZLBp0yZKSkq0Mqn0IWQYPQKqb8gG9e7Bq1On1DtCp+K9hX2Yv+CfNUboJBIJ8+d/SHx8LAsWfFqn8hJQlpKoNmRFRQXI5Yp6P9i6SE+/S1TUJdq376Bxv+Xl+tuCqjaDMTHXtY49j9omD9tV7CeF8gAAIABJREFUr5GVI907vKoxNmCEL6WSMp3plgB/Rkfy/tz3cXN147Xxr2FnZ0tubi5Xr/2JQbkEAypffhcvXuTKlSu4uLjQuXNnevXqxcWLuutnBZ5d6toZ5sSJo6SlpTBz5jtax1q0aMGtW7e4d++elsOoOgMHDmHp0i/Zs+f3Wh1GoCyZU76figSHkYDA34BUoajVOC8pU2YNKhQVWsfkf50rk9Vu4KcXKkvqdTVNSE1N5fDhw3i2bMGHb7bE0txYa46AgMDTSV1KjOpi8w4Y4atRZqSvxAiU3Xbnz5+Lq6srY8dOwMHBlkOHDpGZmUl5ebnG3Nu3b7NhwwY8Pb2YNes9iosfsH37z7z9dgifffZfdu/+/YlpvdY0v3v3Hnz11XKt8YiIM2zZ8vw5uyQSCUUyGeUKBenSsjo3YtBFQ/amqiwkldxLfXjUEhC6NAGLigqRSCQ6sw+NjY1p1Mha570nJSUxbFhlNYeVlRUzZsxgxoy6lb0KDqPHQCtPz8caoVM6i+Zy6VIk8+YtrHfplmpDtnPnDjZsWKMxXtcHuzrh4WEoFAqGDRupMW5ra4eNjQ0xMde0Mgaio5UlS1l/vQxUPK/aJg/bVc/MxIrmbprC54MG9SH8QLjOdMu8vFzmL5xP2yZe/F/QbAzLDeEeQFP6efvBwWyY7gIoPdbXr1/H3t6eIUOGYGBgwJgxYxg6VHf9rMCzTV06w+TkZAMgl2tvCBUKZSZjRYX2sepIpVIqKirqnPaampqCWCzGykrohCQg8LjQJwQcW/yAu5IyfKqU1OfLyrlz5zYyuRRDsdJpY22pDJLdSrtEMxdNR7CqbKd16zbqscKyBzQy1cxSLZeX89NlZfS3WTPNTlBpaWkcPnwYa2trNqxfRdq17xAQqA0TIwOMDfU7KhWIeVD8BG/oBaYuJUZ1sXmrlxnpKzHKy8tl8eKFdOzYmS+/XIqhoSEmJgoyMzO11q6oqODs2bM4OjqyatVGdeZRQEB3QkImsW3b90ilkiem9api+PBRWnswXY6BU6eOs3DhPJ3OrtDQbdjb6/+9Pu3s2reXLxLj1D/XpxFDdRqyN01PTweUXcjqy6OSgHjppW4YGBjo1ARUZePq61JsbGxMaWmp1ribmxubN2+mvLyc5ORkwsLCKCoqQiqV1klYX3AYPSYeV4ROKpXy8ccfcfHief75zwUMHTpC69zaUG3I+vbtj69v2wY92NXv9cCBfVhYWBAUFKxxzMDAgHHjXmX9+tV8/PE/eeutGVhb23DpUiShoesQi8VIJJqp6C+atkldu+rpQ1e6JcDu3b9TUFDA1OAPMBQZUiaTYCgSYyjS/m9fUVGBTCbDzMxMK4tIV/2swNPP/fs52Nhoi+bXtTOMh0dzAA4e3KeVzp+QkACAg4ODxhpWVtpGyo4dvwDQpk079diDBw8wMzPT0n44d+4M165dJSCgu1aqrYCAwKOjuhCwgbSEM+lpRBYWYCoSMd6hMhX/96xMzk4YTXDA2zjaKwM2ro6+2Nm4k54Vy+Fzq3D89R4yuZxTp05w9Wo0QUHBeHv7qNdYdGAZthY2eNo3U3dJO3HzPOmFmYwfP16j42J2djaHDh0ClOUBZ86eIy0+VeP+e3QRhK8FdCEn5sy/9R5t02MBoN2kQ+DJ8ThKjEBp8xYWFjBr1hwMDQ0pKyvT2ZAFlE6BkpISJk2apFGm5uXlTceOnbl27Qrh4ce0NtKPS+tVRdu27WtNApDJZCxb9jUODrqdXaGh63U2E3pWCOrZi/Idu5BUVJBSVlqvRgxVacje9MqVC1y6dAkDAwNksvpf81FJQKjQpQmostGrZ8ypkEqlOjN2zc3N6d69u/rnMWPGMHr0aGbPnk1oaGitn01wGD0E+iJ0F6Mu64zQlcorsK3iEaxvhE4qlTJ//lwiIyOYO3e+lse0KiqPYXXxrDNnKjdkbm5NcXNTGl31ebCrExkZQVZWJiNGjNaZ3vf661MoKyvjl19+ZNo05QvWzMyc2bPfZ/361WrnWE08T9omDe2qp4u7etItASIizmJpYckDSQmzf/+U27mpiAwM8HHw5K1u42nVpLl6rqGhIc7OzqSlpXHlyhWaN29Oeno6Fy5E1atjn8DTw//+9wUFBbn4+XVuUGeY7t170rp1GyIizvLOO9Po2zeIa9eucfv2be7du0eLFi000rInTJhA+/YdaNXKhyZNmpCfn8/Fi5FcvhxJy5aejBs3UT03KuoSK1cu4+WXe+Li4opYLCY2NobDhw9gY2PDnDk1t0YVEBB4OKoLARsAjYFAm8YMtLPHzqjm8i+RgYi+ATOISTxOasafrPxuOQYGBri5NeXtt2czfvxrGvNfbt6ZiORo9sYco1hSiqmRMS3s3Hmt8wjGz53O+g3r1XNzc3PVdkFERAQRERFa1xccRk+eqjqM93OykZaUPFZNmujoyxw/fpSrV6O4e/cuFRVytU0rk8mwtLTE3d0dPz8/9ab59OmzLF96mpS7hRgaimjTyp5XR/iSk1fKkhXngLB6XzsjIwMTE2OaNnVn9OhxBAcP0Fme/7yWCD0sT6LECJQ2r4WFBQ8eFDFlyqskJiYgEolwcHAgICBAI8CVna3MoG7Xrh3V8fVty+XLF0lJSaZFi5Yaxx6H1mt1SktLEYlEeoNm0dGXycnJ5q23Zup0dh0/fpgPP5xXp6yRpxFHBwfa/KWZ2smqkUYjBu2/ln4asjc1NzcnICCAixcvqjPpH4ZHIQFRveLIyqoRJiYmZGdrl51JpVIKCws0nnV9WFhY0K9fPzZs2EBKSgru7jXv8Z7Np+kpoaZWnTojdIX5/LOpB6pGndUjdO5Oyv8K02f+wJUr2hG6zz5byIUL5/D374qpqSmHDu3XuJ+WLb1o00YZ/UtPTyciIoJmzZphZWWFSCQiKyuLjRs36t2Q1fXBrk7lC1C3A0skEjF9+iwmTZpKUlIioMDTsxUKhYKvv/6PRuZBTTwv2iYP01WvOnv37taZbgmQkpKMTC5j0cFl9Gjuz4ROQ8ksus+v0fuYv+8rlo5YiBN+6vl9+vTh5MmTREZGEhkZya+//qqzflbg2SA4eABHjx5ocGcYsVjMt9+uZtu27zl16jgrV66koqKCRo0a0bVrV9q3b68xf/z48UREXGDnzt8oLCzAxMQUd/dmzJjxDmPHTtCIeLi7N8Pb24dz506Tl5eLTCajSRMHRowYw+TJU4XOMAICj5nqQsCWSIh6S7eWQYiLG8sPn2D5kuMa40aGpnTwGUwHn8G8t1C7O01VxnYYzNgOdSuf9/b2xtu7svz53VkhXD+tP2tE4MlQVYfRrYktd3/6+bFq0qxZs5Ls7Cx69QrE378r586dobS0lPv3c3BycsLOzo7Y2FiSkpIYM2YM9+7dY8OGDbi7WvHqCF9KymQcOJnEp8vOMG9WALMmd8S11QjKJKJ6XXvMGE/Kyko5duwIixcvJCrqEvPmLdT4bM9zidDD8iRKjEBp88rlcj78cDZBQcFMmRJCdnY6a9asYd++fYwcORJbW2UZWUlJCYDOjbXK/sjJycbV1a1BIsa6qMnZpeLbb//Hf/6zGAA3N3dGj36FV16ZoOGgjIu7AaBz71STs+tZpWojhn/V47yG7E3btPFi06ZNnD59GkdHx4e/eR5eAqJ6xZFIJKJVKx9u3oxHKpVibFwZ3ImNjaGiooK2bbXLHXWhKm/Lz88XHEaPE32tOseOGEHnK9fqHaGLjtsHGNC8uYfOCF1cXCwAly5FculSpNZ6U6dOUzuMrK2tsbe3Jzk5mdLSUioqKrCwsGD06NG89tqbejdkdXmwq5KXl8vZs6dp2dILHx/fGueamZnRtm3lC+7EiaMoFAoCArrXcFYlz4u2ycN01atKhaKCfeF7daZbApSWliCXywn0DOD9wMpue572zfg4/Gt+id7LS1Qa8MrfrRVeXl40bdqUvn37cuSIdv2swLNB3779GDNmBPn5JbXO1dcZxtzcghkz3mHGjHcwMVGwYcMGvWtMmzaNyZOn1+nePDyas2TJl3WaKyAg8PCUGRlhINafYm+gu0mZgICaqjqMlkiI2q8sG3xcmjRvvz2b9u07qMuK5sz5kIqKCt57bwbR0dH06NEDZ2dnjh49SlxcHDExMTg5ObLoH50wNVGu4+frwIKvTnH4jztMm+hHmx6DKCoW1/vaAK+8MpE5c2ayd+9uXnllAi1aKO3t571E6GF5EiVGUGnz9u8/SG3PmJgou//u27ePqKgogoOV56nKjXTpwKg24GVlZQ8lYlydmpxdhoaG9OjRi4CAl7G3b0JOTjbh4XtYseIbEhMTNKQ6VM4FXfu4qs6u58VhBHWTealKQ/emJiYKUlOV5c9Nm9Y9i1VfxdGjkIDQVXEUHDyAa9euEha2k7FjJ6jHt2//CbFYzKBBg9Rjubm52NjYaFUcZWdnc/DgQczNzfHy8qr1MwoOo4dAX6tOXZG6EBc3QlzctOZWjdCp0Bep27Fjbx3uSplC17hxY/r10763adOmkZ5+X+eZdX2wq3LgQDgymazG9EpdFBTks27damxsbBg5cqx6/EXQNmloV73qZGTF15huaWxsQmlpCcGtNCMh7V18aGJpy7WMePWYTCZjz5492NnZqb9Q+/fvT+/e2vWzAgICAgLPFuUKObPC9W9at/T9/AnejcDzxOPSpKlqi6oQiUT07duX6OhocnNzadRIGUC8f/8+JSUlTJ3yOqYm99TzPdys8fWy53zUXaaO087IqO+1AwP7cOVKFLduJakdRs97idDDUlOJUX1s3tpKjFQ276BBQzXGXVxcsLS0JCMjQz2m+lvo0oGRSis7aD2MiHFVanN2tW/fQauL7PDho5g79z3279/LkCEj8PNTHq9J9Liqs+tZoz6NGErLCpHKSrEwa6xuxFCVhu5NlVIKFzE1NcXXt2ZHU1VqqjhqiARE7959AIVeTcDhw0exf/9eVq5cRkZGBh4ezYmIOMsff5zgjTdCNJxdYWFhbN26leDgYNzc3DAyMuLOnTvs3r2bgoIClixZolPzqDov5tvrBedhH+yq7N8fhrGxCf376081j4g4w08/baNLl5ewtbUjM/Mee/fupqiokC++WKrRlvtF1Dapa1e96iSlXgD0p1s6ODiQnHwHGzPtjCxbcxuScpLVP9+6dYuCggK6dOmiNbemjn0CTxcVxuYYV8loLS2RYmxY6Xw1EBkgkdY/sldfqmpdNKQlrQqZTMauXTs4cGAvKSnJiMViXF3dGD58NCNHjlHPe/fd6Vy5EqV3HX//rhpi+QqFgiNHDrFz53ZSU5ORSstxdHSib99+jBs3EQsLS71rCQgICLzISCQSykvzyS0vf6yaNPqunZKSAkBxcTFXr14FKjfKfu3bQfk9jXM8PRoTk5DDvawHVYrw639tqGy1bWtrpx570UqEHpaqJUb1sXlrKzFS2bx2dnZax8zNzcnJydH4GZR/TxeXFhpzVdow9vZNGixiXJ3anF26EIlETJo0hcjICM6fP6t2GNUkelzV2fWsodqbBnTuiCwvl3JFBXfKynQ2YrgSF86ttEsajRiq0tC96b59u3nw4AEDBgzQ+h1GRUVx8a9uf6qKn99/346lpSVGRoZYW1s/MgmINWtW1KgJaGRkxPLlq9mwYTVHjx6isLAAV1c33n//I0aPHqcx19/fn2vXrnHixAlycnIoLy/Hzs6Obt26MXnyZDp16lTjvakQHEYN4FlP7dZXSlfXB1vFtWtXuXPnNv36DVRHeXTh5OSCkZExO3b8SmFhAdbWNvj7d+GNN0K0vqRfVG2T+qZblkmKuJt5A09P/emWrVu3ITn5DveL8/Cw1cxuyynOw7qKI6m4WNlnVpfIm6p+ds+enfz88w9PpQMABMFJAHkFfFtNZ6Qq7y3s80Tuo6rWRUNa0oLSGJo37wOioy/Rr99ARowYg1wuJy0thcxMzQ3BG2+8qdOIPHbsCOfOnebll3tpjK9fv5pt2zbTuXMXpk6dhqGhIdHRlwkNXUdExFnWrdusU9RU4PHwpB2MKm7fvsWWLaFER1+isLAQG5vG+Pj48tFH89UbQmW5yVfExt4gMzODkpIS7O2b0Lp1G15//Q1atfLRWldA4HmmepnO49Kk0cUvv/zA9u3bAbhw4QJWVlYEBQWpHTkODk0ouat5jq21cuOXm69pnNf32jk52YSF7cTFxVUjG+RFLBF6WB5HiZHK5s3KysLQ0JDNmzdy82Ycd+/epby8HAMDAyIiIvDz86NJE6Wj6tq1a3ToEKCxzo0b17GwsMDdvZnO7xSxWMxPP23VcBjVZrPa2DQGNJ1ddbFZnZ1dAGXmiwqVHlZ2dpa6lElFVWfXs4Zqbxp+6BD3s7MxQJm9WNdGDCoeZm/atWsXrKysNBIZVFy6dImNGzVlGX755Qf1v8+cuVSn+6uLBERdsLKy4oMP5vHBB/NqnNe2bVu++eabOq1ZE4LDqAE866nd+krpdKHvwQZo186vTv9BmjdvwdKlK+t0vedZ26QhXfW8ykp1rnUr7RIVCjkjho/Se70BAwZz8GA4B2JP0rlpZeTrQvIV7hfnMcCnchPduLHyyywhIYGWLTWNGlX9rELBU+sAEAQnny6qal1Upa4taQG+/34jly9HsmzZKp3ZjVXp0iVA57hKf2vAgMp6bplMxm+//UyrVj4sW7ZKXdc9cuRYxGJDDh8+QGJiAl5e3jrXFHj0PGkHI8CFCxHMnz8XV1dXxo6dQOPGtuTl5RET8yfFxcVqh1F5eTlxcbG0b++Hi8tgzM3NyczMJDw8jOnTp/DNNyvp3Fk7M1NA4HmlZ89AvN1duf6/pY9Vk6Y6ZWVlnDhxDJFIRKdOnaioqCA5OZmysjK1Jo2xsTHVVfuMjJRZthJppYOiIdeeP38upaWlfPnlMo3ysue1ROhhedIlRiqbd8+e3xkzZjz37+cQGBjIhQsXiI2NxdraWi2SPmrUKMzNzdmzZw9jxrymzji6eTOB6OjLDB48DIVCofM7JTR0HQ8ePNC4dm02a2FhgYazq642a2qqMptOJdYNqNeIiblGly4vaVyvqrPrWUO1N62pAYOKbh0m0q3DRJ3HHmZvWpNW5/Tp03njjZrv63lGcBgJCDwhGtJVb21MjM61klIvIhYZMmjgEL3X69LlJQYNGMSBQwdYdHA5Xd3bk1V0n30xx7E1t+bVTsPVc93d3WnSpAmpqamEhYXRvHlzfvrpJ44fP6mun/388y+0rvG0OAAEwclng7q2pC0tLeW3336hR49edOrkj0KhoLS0BPN6RLCvXo0mJSWZvn3706iRtXpcLpchkUiws7PTEgFUZbKYmtZezy3w6HjSDsa8vFwWL15Ix46d+fLLpTVqi5iZmREauk1rfMSIMYwZM4Sff94mOIwEXigcHBxp4WCDsVWjx6pJUxWJRML8+R9y61YiixYt4u5dZRpR8+bN2bVrl/rdrSrJqUp5udJRZGJcWZ7dkGvHx8eyYMGnWoLHz2uJ0MPypEuMunR56a/usIcoLy8nMLAvOTkZJCQkYG5uzpAhQ8jMzOTo0aPcvHmT7t27c+zYMd555y2GDRtFcXEx27f/hI1NY0aPfkXnd0pU1CWKigq1tF4dHJxo166D1t95y5ZQxGIxcrlc7ezSZbMWFORr2axSqZRNm9YD8PLLPdVrduzYGTs7e/bu3c24ca/qdHa9qHpZjxMTIwOMDfVnxSkQ86D4Cd7QE0Z4ol4wXvQH/u/kYbvqqcjOvU3hg0w8XDrSqFGjGlsZ//vTJTjmmnMk4QwbIn7Bwtic7s07M7nLaOwsGqvniUQihgwZwpUrV7h9+zYXLlzg8uXLeutnVTwNDoDnVXBSV5mOs6MjrYqKGWhnj42hdjRTFxUVchKSz3Er9SK/By6otUxHRWLiTUJCXkcul/P5518wcGBfjeNJSUmkpqaSk5NDXl4e69ev57ffwtQp1KA0tBvSkvbq1WhKSorx9m7N8uX/Izw8jNLSEmxsbBg2bBQhITNq/Xvq0zswMTHFz68jFy5E8MMP3xMY2BexWEx09GV27drBgAGDaNq05vaiAk+Gx/V+2b37dwoLC5g1aw6GhoaUlZVhaGhYr3dE48aNMTY2qfXeBASedx6XJo0KpcNmLpcuRTJv3kIGDx6szgKws7PD3t6evDylEHFWVjbVFehyC5TZPbY2lZv5hl57wABth8XzWiL0sDyqEqM//7xSpxIjgIULF+Pp6UV4eBgrVnyDlZUVLVq0oEuXLlhYWGBpqXw6pFIpLVq0YPDgwWzcuIlVq5ZjZGSMv38X3n57DitWfENExFlcXd1ISUkmMTGBGzeuc/Lk8TprvapsVnNzC2QymdrZpctm/fDDOdjb2+Po6MShQ+HY29tz5Mgh0tJSGDt2PL6+lW3SDQ0Nee+9uSxaNF+nsysk5OnOgnl2ZV3kxJz5t96jbXosAMR6jz/rPFs7KIFHwIv9wP+dNKSrXrvO/pw+oKlJ08S2Oa8NrVs9qqGhIWM7DGZsB/1RGRXGxsZ07dqVrl2Vkf5p06YhkWjquDyNDoDnVXBSV5lO6q14du3cxYWiAhZ7eNKolt+ZvELGqYubyLyfiIdrJ2a+NhWJtFxvmY6KiooKvvxyibrriC5u3LhBVlYWdnZ2NGrUiIKCAq05DW1Jm5KiFGTfvv1njIyMmDVrDo0aWXPkyAG2bdtMdnYWCxcu1nt+cfEDTpw4irOzq87sj0WLlrBkyaesXfsda9d+B4CBgQGTJ7/JW2/NrPHeBB4fT+r9EhFxFgsLCx48KGLKlFdJTExAJBLRtm17Zs9+X6N9rQq5XE5RURFyuYysrEx+/vkHSktLar03AYEXgcfV9loqlfLxxx9x8eJ5/vnPBQwdOgJVN2AVMpmMigplt4erf17j5daaayTeycPM1BAnB8tHcG1tntcSoYflUZUYtW/foc76MIaGhrz++hRef33KXyNlrF+/HplMRlpaGhcuKJvFqLpI9ezZk65de2mt07x5S86ePU1u7n2WLv1S/XwZGxvTv/8gmjev3Z5U2awlJcUazi5dNmtgYB9Onz5Fbu59pFIpv/76I97erQkJmU6/fgO11u7TJxgTExO2bAnVcnY97Vqvz7qsy4uK4DASEHjMPLvedG2eRgfA8yo4qatMxxIJjSMiWZOeytmCPAbVEs29fvMI93Ju0uelGTjZezJqZJ8aM9JU7NjxK3fu3OLVVycTGrpO55ygoCDMzc0RiUScOXNGp8OooS1pS0qUaY5FRYVs3forzZp5AEoDdPbsGRw8GM5rr71B8+YtdJ5/5MghysrKGDJkmE7xaiMjY1xcXGnSZAgvvdQNAwMDTp48ri55fOONkFrvUeDR86TeLykpycjlcj78cDZBQcFMmRJCRkYGW7aEMnv2DNav36L1vkhOvs3kyRPUP1taWjJp0tQqGxMBgeebJ61JI5VKmT9/LpGREcycOVtnNlB6ejp5eXk4OzuTn5/Pzl1htLT3RS5XYG9rxr2sYm7czKF3gDuGYlGDrj137vwaM5GEEqGnlz179rB161b1zyqRdGdn5xrPU2nYGRkZY25uweTJb6q/U3bu/I2SkhL1d4ourdeqNuv27bs17BBdNqvKybVz528sXfolS5Z8RdeuuqUZVLz8ck+NUjUBgceJ8AYTEHjMPE/e9KfRAfCiCU7a/fU5a4vmymQS4m6fxs2xDU72nigUCoqLizEyqVlLITPzHhs2rGHq1Ok6O0WoUKV210RDW9Ka/HWPvr5t1c+KioEDhxAdfZkrV6L0Pi/h4XsQi8UMGTJc61hZWRkzZ76Jt7c3ixf/Vz0eHDyARYvmExq6jqCgvnVusyzw6HhS75fS0hLkcjn9+w/SMPS9vX2YM2cm33+/kc8++6/GNZydXVm2bBUyWTlpaWkcPryfBw8eUF5eLmwGBV4IquowNnO0Jyk357Fq0nz22UIuXDiHv39Xjh8/QljYTjw8mtO4sTVxcXFIpVLS0tIwMjIiICCAwsJCjh07xv/97z7FJeUEdXPn8vV7NLI0Yexg7wZf29TUlEOH9mscb9nSC09PL+DZLxF6WJ7moGjv3r2JiYlBJpORk5OjFkmvDcFmFRDQRLByBAQE6szT6AB43gUnq5bp/Hknnq330gFob2lV43lZubeRySTYWrtx6fpuklIj+SlcUmsZ4DfffImLiyvjxk3k8OEDj/SzeHp64eXlza5dO2p8XhwclJE3Ozs7rWOqCHdRUaHOc5OSEomNvUH37j10Zp2dOHGUtLQUZs7Ubl0aFBTMsWNH+PPPK4LD6G/gSb1fVKWWgwYN1ZjbqZM/jo5OREdf1rqGmZmZRrnJkCHDefPN11mw4COWLv2uQZ9XQOBZoroOo6K8/LFq0sTFxQJw6VKkeuzu3TT1v01MTGjdujV+fn5YWlpib2/PiOGD+Hj+fADOXb6Ln68DE4f7YmujbGRw5cqf9b521eurmDp1mtphBM92idDD8jQHRR0dHXFzcwPAw8NDLZIuk8no2LGjXl3XRlbKZ7ldO8FmFRAAwWEkICDwEDwNDoDnXXCyepmOvZER05zdaFWLYHjhA+Vnj7t9GpFITMfWQxk5oSsHDoazbdtmjh49hLGxsVpQ29HRCVdXNyIizrJ27aZasyb27t1LRkaGxtgrryiNo40bt2ppQ9y7d4+tW0NJSIhHJitn5MhBtGrlw6uvTqJDh04ac1UaMhcunCc4uAdGRsZ07tyFWbPmqP+m+tqs7927G4ChQ3WXEKjSweXyCq1j8r+ytmR1KNsTePw8rveLg4MDycl39M5PSIir9d7Mzc3p3TuIH3/cwt27abi6utV6joDAs0xVHcbadGkehSbNjh17dY7X1Pq6d68erPtCW/NFRYcO7R/q2jUhlAg9/ahE0m/cuEHHjh3Rp+tanqfswmdvp21nCDarwIuI4DASEBDNuStzAAAgAElEQVR4KCQSCYWF2vo1VVE5ALKysrSOPawD4HkXnKxapnPnZgwHf/qZIrn+9G8V5XIJANLyEob0nou1pSP9gvvQO7AvU6ZMJDHxJsOGjcTLyxuxWExcXCx79+7CxMS0zptfU1NTunXrRnx8POnp6cye/T42No212tIrO4K8TlmZBLlcRrNmHvTrN5CwsF3Mnj2DDz/8FwMHDlFH1W7ejAeUpUOvvfYGlpaWbN/+MzNnvomdnT1isZguXbTr+6VSKUeOHMDW1o7u3XvovGeVgXbw4D4tEfoDB/YB6BQ9ftHR1bXP0dGJbt1eZuLEyeq21jWhcjKuX79e65guJyMoO6Tl5OQwYsQACgsLsbFpjI+PLx99NF+tM6H6e924EcNrr40lPf0uZmbmdOzYCS8vZSlK1fdL69ZtSE6+Q1ZWFi1aaJbLZGdn6X0XVUciUf4fKywsEBxGAgICAs8AMplM/e7WR8tmynL8TMFmfSFQ2Tc3b8Zx9+5dKioqsLS0xN3dHT8/P40OzPoIeettLl2O0nmsun3z7rvTuXJF91xQapguX75aYywi4gxbtmwiMTFBI4jq4uJax0/5cAgOIwEBgVrRJ3YZFXWJ27eT6Nixs3osJyeH4uIHODo6qR0ALi6utGvnx/XrfxIfH4e3tw+gzOgIC9v1UA6A511wsmqZzuBe3XA9cZrPk5MoVyhqbGFsKFLWx9vbuGNt6ahx7JVXJvLf/35Gq1Y+jBo1FoDY2M+xsLCkuPgB+/fv5bXX3qj13gwNDfHy8iIzM5P09HR69QrC2dmF+fPnqrUuHB2dOHfuNPn5+YjFYiwsLPj003/j5eVNcPAAJkwYxf/+91/c3ZvRqZM/MpmMZcu+xtbWjpKSEsLCdjF27HgGDx7Kjz9uJTf3PlOnTlO3XK/K6dMnKSgo4NVXJ+v9m3fv3pPWrdsQEXGWd96ZRu/efQAFp06d4OrVaIKCgtXPp0Alurr23bqVSFjYLo4ePcz33/9UJ0eLqakp8+fPRyar1HV48KBIp9Hz449bSE1NxtTUlLFjJ9C4sS1paWncuHGN3NxctcPI2dkFKysrsrIy6dixE2PGjCc/P4+dO3/j9OlTWu+XAQMGc/BgOHv2/E5AQHf1+Jkzf5CdncWwYaPUY3l5eVhbWyMSiTTu7f79HE6cOIqZmXmdOuYICDxL/J26NFamRhgo9B+Xol3KIyBQFX02a1WRdBV5BWWUlJZjb2uGibHSbnCwt6BVC1uuX78h2KwvACr7JjAwkJs3byISicjNzSU2NpakpCTGjBlTp3VsbGyYPfsDrfHq9s0bb7ypU0j/2LEjnDt3mpdf1uzcd+rUcRYunIenpxezZr1HcfEDtm//mbffDiE0dNsTyUgTnkgBAYFaqSp26ejohFQqJT4+lmPHDmNubs677/5DPXfduu84cGAfK1aspVMnf/X4++9/xDvvTOMf/5jF2LHjsba25tixI8TGxjyUA+BFE5xsamqKu4kpx/Pu1+gwMjOzBsDURFunoXpKdXx8HOHhYQwbNpKwsF2kp98lLS2VvLxcAHJz75OamopcLkcsFmuspVAo1KVcKqprXaiO9+oVxDvv/EP9t1Zt+KsSHX35r4ykmfTo0ZsNG1bz228/I5VKMTMzRy6X6e1ipmpjq6/9MYBYLObbb1ezbdv3nDp1nDVrVmBgYICbW1Pefns248e/pvfcFxldXfsA/Pw68ckn/6qXk3Hw4MFIJJUOo/nz53Lo0AGN98u1a1c5ceIoYrGYlSvX07q1Mjr3739/SlTUJY2sxtOnT1FUVIRYLCYxMZEOHTpjbW2No6MT+fl5ODu7aLxfunR5ieDgARw9eoi5c+fQvXtP7t3L4Pfff8XOzp6QkOnquUeOHGD79p/p1SsQZ2dXjIwMSU1N4cCBcIqKCpk3b6GgN6GDqhlp93NyKC+T0sTSls5N2zHGbyC25voF9VXUlJG25KNetHDXv0Zi4k1CQl5HLpfz+edfEBQUrDVHJpOxa9cODhzYS0pKMmKxGFdXN4YPH83IkXXbIDyv/J26NAYKSFmvv2zMabrfY7u2wPOBymbt2tWfxMRE5HI5OTk5JCUlqUXSVfwaFssfkaksnNMdX69KJ9OUse34fGWkYLO+AKjsm+rlrs7Ozhw9epT4+Pg6rWNqasaAAfrF9FXocjYC6k69AwYMUo+pgqgODo6sWrVR7WAMCOhOSMgkQkPXM2+e/nf1o0JwGAkICNRKdQcAGODk5MSIEaOZOHGyzi/O6rRq5cOaNZs0HADNmnnw8ceLGDx4mM5z6uIAgBdPcFKqUNTaJc3exh2AkrJ8rWOqlGorq0bk5+eTkBCHQqEgLGwXAHv27GTPnp3q+cuWfc2yZTBq1CiaNKl0UhUXF7Np0ya1Q+jrr//De+/N1dC6AGUm2pw5M8nISCcj4y5isYicnGw2b96IhYUFK1asU0fw4uJuANCmTTs8Pb348stl6nXWrVvFtm2bSUlJ1mp7rrzPVTX+TlSYm1swY8Y7zJihLXwtUD9U//eLiorqfE5FRQXFxaWYm1tgYGCg8/1iZqYsa/z6629p3dqXsrIyvQZ4VJRyczl//iJOnDii8X5p3rwFt2/f4t69exrvqYULF+Pp6UV4eBgrVnyDpaUVgYF9mT59lka0rn37jsTG3uDs2dPk5t6nvLwcW1s7/P278sorE2jXTti86qJqRlpTZ2cKz6VxJzeNQ3F/cPpWJCtGf4qNmX7RYRWmpqZ88n//Ii1+j8a4g53+EoGKigq+/HKJWtxcF+Xl5cyb9wHR0Zfo128gI0aMQS6Xk5aWQmbmvfp9WAEBgacK1XfK/v37uX//PqDs7FpVJL02PJpas/X7DSxfsU6wWV9QVM+JSoy8LlRUVFBaWqK2b+rK1avRpKQk07dvfxo1slaPVw2iVi2N8/LypmPHzhw/fpgPP5z32LPSBIeRgIBArVR3ANTEggWfarSqrkp1B0Bt1NUBAM+f4KS+lOrY4gfclZThU0X0Ol9WTqm8Aq+yUvWYpbkdTRp7kJ2XTG5BGrbWSo2VqinV+fl5DB1aGXm3sWlMcHB//Pw6Asovqp07f2PChNfp0KEtsbGx6rlWVlY4OjpiZ2dHfHw8aWlpREdHMX36G6xeHUrLlpXaMJ06+fPBB/MIDV3L7NmV0TM3N3fWrfteQ/hRJUqty2hSjeXkZOt0GAk8fqp27btz5zZr1qwAoFu3l+t0fnFxMb169UIikWBqakrXrt2YMeMdvv76W41506dPQSYrx8TEhClTXiUxMQGRSETbtu3ZsGGLhs5UebnSmPP09GLgQM3o3vz5c7l9+xY3blzXcBgZGhry+utTeP31KTXer49PaxYtWlKnzyZQSdWMtEYmRqTkKZ16bZ1b8cWxtRxNOMtYv0E1LQEo/05DhwzieiP9eg/V2bHjV+7cucWrr04mNHSdzjnff7+Ry5cjWbZslUYmrICAwLOPymatSSBdxcxJHZk5qaPOY61aCTbri4REIqGsrAyZTEZ+fj4XLlwAoGnTpnU6Pzs7i379emrZN9U77elC5WysXqpWNYhaHV/ftly+fFFvEPVRIjiMBAQEdKKv3agKBWIeFD/BG3rB0FUGeCs+hgOpyZiKRIx3qNz8/p6VydnCfNbGxGis4d92FEfOreLY+bV4e/Tk1+2ZHD5ySJ1SPWTIcNq2bU9paSk3b8Zz5swpXFxc1eUbpaVKB1SbNm3p27cvt27dUq/t7e1NRkYGBQUF6uhLn6BeHD5yjAULPiR8706Ne3Fyaoy3ty/+/l1xd3cnJSWFn3/exkcf/YPvvluHo6Py85SVKcUxjIyMtH4nxsbGGnMEnjzVu/Y5O7vwySefq52MNaFyMr7yyitUVIi5cSOG33/fzuXLkVpOxpSUZORyOR9+OJugoGCmTAkhIyODLVtCmT17BuvXb1EbSKrWxlFRFzVaXZeVlXHjxnUAsrKErJGngSaWyjLUBxLdmT+6qKiooKS0HDNTw1ojtpmZ99iwYQ1Tp07HxkZ3yVppaSm//fYLPXr0olMnfxQKhToiLCAg8Owg6F0JPEr27NnD1q1b1T9bWVkRFBSkoXmlDxdXZ1r7+uHp6YVIJKrRvqlOcfEDTpw4irOzK507d9E49rQEUQWHkYCAgB50txtV0abHAkCs97jAw6GrTMfFyZFAm8YMtLPHzsi41jVsrd3o//JsrsYfIO72H8R/d1IrpVolqN2rVyC9e/dh2rTJSCSSGtuYA9y9e5eoKM2o/6HDRwFIS7tL9PHPMDZWPh/Hzybz/Y7rbNr0o0ZXqpde6sabb77GunWr+OQTpSaGSg+mvFzb0FM5pgTNmL+Pql37VE5G5fNZO4GBgQAMGzIAhULO8KFB9A9+mZBps1izeinr1q4ElM7o0tIS5HI5/fsP0shY9Pb2Yc6cmXz//UY+++y/APTvP5gtWzaxceM6TE3N8PfvSkFBPqGh6ygoUJZkCk7GvweJREJe6QNyHuSSkp/O95E7APBvqh0t1UVxcTEB3QMpK5NgYiymvU8Txg1rjauTlc7533zzJS4urowbN5HDhw/onHP1ajQlJcV4e7dm+fL/ER4eRmlpCTY2NgwbNoqQkBmC6KyAwDOAoHcl8Cjp3bs3MTExyGQycnJySE5OrrPt8PniTygqrtwTBQUF06NHL2bPnsHKlUu1up5V5ciRQ5SVlTFkyDCtoMjTEkQVvhEFBAQEnkJ0lQFaIiHqLW1BxBAXN0Jc3GjX2Z/TB45rHGvcyIXALkqR6PcW9kEq05815unphZeXN7t27WDSpKkMHjysSq2+ZhjP398ff//KUo53Z4Vw/fS/WftDNH9cSKW4tFztMNpz5CYeHh5aLcxbtvSkWTMPjfaiKv2Y7OwsjVI11VjVOQJPnqpd++rrZKyk0hltAvi0bEzkxYtqJ2ObHgvU+jODBg3VOLNTJ38cHZ2Ijr6sHmvUqBHLl69myZJP+OqrSid3hw6deO21N9iyJRQLi9o1KwQePdUz0hwt7fkwaBptnVvVeq4qI+3ViWO5G7+bxOQ8Dv9xm+sJOSx6vwfuLpoaSMeOHSYi4gxr1oTW6PBJSUkGYPv2nzEyMmLWrDk0amTNkSMH2LZtM9nZWSxcuLiBn1hAQEBA4FnE0dERNzelfIOHhwfNmzdn165dyGT6O0bWhJ9fR/z8OhIdfRmJpAwTE93BzvDwPYjFYoYMGa517GkJogoOIwEBAYGnhL+zlbEKiUSi0YGqvtzLLkYsMsDCvDIakldQhlUj3Y4quVyOXF75mX18lN2wYmKu0aXLSxpzb9y4joWFBe7uzRp8fwKPlupOxoZgb2tOxc37Gk5GBwcHkpPvYGen3UnPzs6ehIQ4jbGWLT3ZvPkn0tJSycnJxt6+CW5uTVm9WqmNVBcNAYFHT8+egbRu6Unynj+5dT+FC8lXKCytm0C6KiNt4IB+XDeP5KWOLnRu68TnK87yw87rfPxud/XcoqIiVqz4hmHDRtK2bfsa1y0pKf7rnEK2bv1V/Wz07duP2bNncPBgOK+99oa61FFAQEBA4MXDzs4Oe3t7bty40eA1nJ1diI6+TFFRkU6HUVJSIrGxN+jevYfOsrOnJYgqOIwEBAQEnhKeVCtjfYLaUVGXuH07iY4dO6vHcnJyKC5+gLu7o3pMKpViaGiISCTSOD/6eiYJt3Lx83XA2KgyNdfVyZI7ySlcv36Ntm0rS1GuX/+T1NQUDcHkjh07Y2dnz969uxk37lV1V4ibNxOIjr7M4MHDhHKRp4zH4WRs3boNycl3yMrK0spMy87OonFjW51rubk1xc2tUqDy/PlzWFhYCN3M/iYcHBzxbOqGy1VDunl0ortHZ97fvQSJXMq4DkPqvZ6Ppx2tPe24cfM+Uqlc7WBctWo5FRUKZs6cXesaKqPd17etliNx4MAhREdf5sqVKMFhJCAgIPCCI5PJkEgkDT4/NTUFsViMlZXurqB79+4GYOjQkTqPPy1BVMHqFhAQEHjB0CWoHR8fy7FjhzE3N+fdd/+hnrtu3XccOLCPtWvXqsfS09OJiIigWbNmWFlZIRKJWLBwMfvCL2BlaczkMW01rjd2sA/LNl7i/fffYeTIMbi5NSUtLZXdu3dgaGjE1KnT1XMNDQ157725LFo0n3feeYthw0ZRXFzM9u0/YWPTmJAQ7ZI8gcdPfZ2M5eVFyGQytXOvvk7GAQMGc/BgOHv2/E5AQGUmyZkzf5CdncWwYaNqvecdO37h1q0kpk6dhpmZWb0/s8Cjp7ldU1rau7P/xokGOYxAOyMtNjaO8PAwQkJmUFhYoHZe5uXlApCbe5+0tFQcHBwxNjbGwUEZxdWXvQbK7CMBAQEBgecfffZNeno6eXl5GqLXeQVllJSWY29rhomx0r4pKS1HLpdTXdf13LkzXLt2lYCA7piYmGitL5VKOXLkALa2dnTv3kPnvT0tQVTBYSQgICDwgqFLUNvJyYkRI0YzceJkjfbjurC2tsbe3p7k5GRKS0upqKjAxcWZ4B4ejOjvha2N5ua8czsn1q5ZSeimHwkPD6O4+AFWVlZ07RrAlClv4eXlrTG/T59gTExM2LIllFWrlmNkZIy/fxfefnuOzpRdgcdPQ5yMQ4cOxcXFBdB0MtpYW5B15zZJyXmcuZim08nYpctLBAcP4OjRQ8ydO4fu3Xty714Gv//+K3Z29oSETNeYP3fuHFxcXPHwaIGBgQGRkec5ffok3bv34I03Qh7/L0igzkhkUookDW+xWT0jLeNeJgqFgo0b17Jx41qt+SoNpY0bt+Lj40vr1m0AyMrK0pqrSvHXl8EmICAgIPB8obJvunb1JzExEblcTk5ODklJSRgZGREQEKCe+2tYLH9EprJwTnd8vZROppiEHBZ9O5Zu3Xrh4uKKWCwmNjaGw4cPYGNjw5w5H+q87unTJykoKODVVyfrdfo8LUFUwWH0jJKScofNmzeSkBBHTk42MpkMR0cnevR4GZFIpPZA1odvN13iwuyXaN68Bdu2ba9xbmLiTUJCXkcul/P551+o23ADFBYWcvBgOBERZ0hOvkN+fj6Ojv/P3r0H1lw/fhx/7WYXs43d5xJyp5C53zYsGiLklnKXUJL6qnTXRSG+ueS2JJFUyDW35BahVO5zn7vdL3Y5O2f7/bHvDsfObNgYv+fjr533+/15f95n+3Sa194XX9WpU0/9+g00H58N4N6wtqF2bsaOfU9jx74nR8dM86loJUuWVEiI5fXZm17npmGDQNWo2TDX+hs1bdpcTZs2z3d7FK6CDBmnTpup9PQ0lfJwyjVklKS33npflSpV1urVK/TFF5Pk6lpCQUGtNWTIsBxr9mvVelSbNq3X2rWrJEkPPVRBr7wyRp06dZGdHac53m25/cX23wtHFBF7XrX8r4XEUVFRiouLk6ur623NSKtVs4bGjRuf41779v2ppUt/UM+efVSzZi0FBGRtZhoQUFqPPFJbBw78q6NHj6hq1WqSsvZTW7Fimezs7FS/fqMc/QEAHjzZv9+sWbNG0dHRkiRXV1dVr15dtWvXlqvrzQ/NCPB1VY3q1fT779sUGxsjo9Eob28fderUVc891z/XP3SuWvWzJKlDh0437b8o/BGVwOg+deXKFUVHR6lFi2B5e/vIzs5OJ08e17Jly2RjY6OuXbve0hT8vw5c0u6/L8jJKeeUuRtlZGTo008/NJ9ic6NDhw5o+vQpqlevvrp0eVoeHh46efKEfv55qTZv3qAvv/yKvQEA4D5yqyHjBx+8qzlz5pjLrg8Z8woXs9nb26tPn37q06dfnm379Rukfv0G5Wt8KHzXz0grX6a0Lh04ruORZ7Tt5G45OzhpUKMe5rbTpk3TqtW3PyPNx8fb4o9W2VJSUiRJNWvWylE/atRrGj58sF5+eZi6deshd3d3bdq0QYcPH1T//oPzDEABAA+G7N9vHB0zLX5vsWbos3U19Nm6FmWl/Upo4oSxSrx6a3+cmjx5er7b3us/ohIY3acCAxsoMLCBlfK6euONN3T06FHVqVMnX32lphk1b8l+Pd6igvaH530M048/fq/Tp0+qd+/nFBY2K0f9Qw+V16JFP6l06TIW5Y0bN9OoUcMVFjZTH374Wb7GBqDg2NsZZWNj/RQ2Gxs7pRpsrdYBwK3IMSPNlCkfV0+1q9ZSXWq3k49rzv2Drnc7M9JuRZUq1fTll19pzpwZ+uGH72QwGPTQQ+X15pvvKjS04x31DQDAg4TA6AGTvTGXwWDI9zXfrzwskylDT7evpv3hf9+07eXLlzRnzpfq33+IPDw8chlDgNXy+vUbys3NXSdPnsj32AAUnIwMo7751Ppa6ufGTJJEYIQsNwsXJQJG3Nz1M9LcHB0UMXtvrm3fe+89BZS2/L3hdmak3Sg0tONNw59KlSrr008n33K/AID7TwknB9lk5l5vUPrdG8x9hsCoiMptj6LGjZuqV6/n5OWVtTdAWlqaUlJSZDCk6fTpU5o58wtJUtmyZXP0uXHjRs2ePVtl/EvoszeDJUnHT8dq/dZTerFf1gk3cfHxSrtyRa1aNVFAQBl17dpdnTt3lY2NjSRp0qRPFRAQoLNnz+inn76XJL333ljNmPGFqlevqT59+qpKlWpW31NSUpKSk6+qYsWHC/abBQAoUDcLFyUCRgAAcP+wydRN/3jhN6T2XRzN/YXAqIjKbY+iFSuWaePG9fr660UqWbKUVq1abj4BRJICAgIUHBxscQSgJJ05c0anTp2y2KPIZMrQ3O/+0aPVfBT4qL/em7xdycnJcnNz19ChI7Rr1++aNGm8YmKiNXDg89q0ab127tyuKVNmaMaML1S2bDldvnxJHTt2VsmSpbR69QoNGdJPkyZNVb169XO8p/nzw2Q0GtWu3e0dpQsAAIqemy53lcNdHg0AAFmyJ2EcP3ZUl89fkinDJG/XUqpX9hF1rd1OpVysr5jJlpGRoXEfjteeP7YoKiZFqWlGlXRz0sMPeejJkMoqX9Y9xzVHjx7RvHmz9e+//yg1NUWlS5dRx46d1bVrD4uDOO6Xg6IIjIqo3PYoql37Mb3zzutas2alnnmmr5o3D1K5cuWVkpKiY8eOaseOLUpNtdyHKD09Xdu3b1eNGjUUGxMjKUmStGrTCV2KvKpRg+tr8+9ndDIiTm5ubvL09NSTTz6lJ598SmPHvqYFC+YpKKiVvvhikjp27Kx69eorLGyB1qxZqb17d6tevfoKDm6jTp26qmvX9vruuwU5AqPNmzdq8eJv1aBBY7Vv/2Shfd8AAMDddbMZaUPemHGXRwMAQJbsSRitglqp2LFU2dnY6XTMOa07slXbTu7WF13ek4ezW67Xm0wmHTx0WFUrllLz+i5ycrJXdEyKtvxxVm9P2qrXX2ikms2utf/777/0yisjVLy4q7p16yEPj5Las+cPffHF5zp16pTGjBlrbnu/HBRFYHSfyT65IzExUZLk4+MrHx9fSVKLFkEKCQlWnz59ZDQaVbdu1i7ue/bsUWZmpurXr6/169ZJki5FJmnpL0f1VNsq8vUqrh1/npdjMTsVd3GxuF/37r21ZctmffLJOGVkZGro0BdzHVvJkiVVrJijeWzZdu7crg8+eFtVq1bXuHGfmJe3AQAAAABQGLInYdy4n14t/yoav2mmNobvULfaT+R6vYODgxYvmp9jL73WzcrrpXc2aNWvJ9R94LXyKVMmysbGRjNnfmU+AKpLl6f12WcfacWKZWrXrr1q1846mOp+OSiKDQiKuLS0NMXFxenKlcvavXuXJkz4WJLUuHFTq+0rV64sLy8vHTp0SFJWqnrw4EE1btxYxYoVM7dbuOyQXF2KKfBRf124nKRTEXHy93WVKcOk9HSjzp07q6ioKFWvXlM2NjY6cuSQunbtroSEeJ07d1bnzp1VdHS0JOns2Qht2fKr3n33TaWkJFuMbdeu3zV27H9UoUJFff75NBUv7lpY3yoAAAAAAG7K+3+ndSalJd/W9e4lHOXgYKurydc2y05ISNDx4+GqXfuxHCFQ9iEMa9asMJf5+wfkaCcVvYOimGFUxN24R5G/f4DeeWecateum+s1RqNRaWlpysjI0NatW1WmTBk9/LDlRtNRMcmKjU/Vfz7ebC47fTbe/HXPnk+pSZNm+uyzKXJ2dlFy8lXNnTtTc+fOzHG/2bOzpps7Ozvr2Wf7q0+ffpKkP/7YqTfffE3lypXXlCkz5OaW+3Q/AAAAAAAKWlpamuJTE5VuTFdE3AV9vftHSVJg2UfydX1GRqaSkg3KyMhUdGyKVm86odQ0k+rU9DG3SU/POqXcyckpx/WOjlllBw8eyPNeRe2gKAKjIu7GPYq2b9+iuLhYRUdHydPTK0f7vXv3KjY2Vv7+/vrnn38UHx+v5s2bKy4uTq6u12b39O5cU8kpWYlo4lWDvvr+X1V7uJQux2TKwaGYXnxxlLl/Z2dnubm5afjwkRb32rNnt1asWKoWLYLl6uqq48fDlZSUpPT0dP3111698carKlu2nP773xlyc8u5IRgAAAAAAIVp6c9L9cmC8ebXvq5eGh08WLX8q+Tr+vOXEjXmk9/Mr12c7fVkSGV1CqlsLitVylMeHh46eHC/0tJSzSGRJO3bl7Uc7sqVy3neq6gdFEVgVMTduEdRy5atNHjwc1qxYplcXIqrXr368vX1k8Fg0NGjh7Vp03o5ODioVq1a2rRpk+rWravDhw8rPDxcHTp0MPf7SDVv89fZgZGHm5MSkjPl6OSs4OA25nqTySR//wCLMklKSUnRihVLFRLSVsHBbZScnKwBA/po5MgXdPz4MUmZat++o3bt+j3H+2rbNrSAv1MAAAAAAFgKbtlKxQ+mKyU9VSejI/THmb+VkJKY94X/4+3pojeGN5bRlKHLkVe1fc85paSmK92YYW5jY2Oj7t17a/bsGXrzzf9o0KDn5e7uob17dyssbJbs7ON5/tAAACAASURBVOyUlpZ6k7sUzYOiCIzuM5UqVVblylV17txZ+fsHaN26NYqLi5VkIz8/P3Xp0kX29vbasWOHHB0dVaFCBe3Zs0eSdPXqVRlNRtlmZuhSZJIci9mrpLuTijs7qJiDnWLiUyU5WtzPYDAoISFeXl6P5Tk2FxcXtWwZrIUL55vLvvjic6ttCYwAAAAAAIXNz9dXdUrXkCQ1Lv+YmpSvp1HLP1SayaDudfKeyePkaG8x4SKocTm9+ekWXYrco29bX2vXp08/paamavHihRo8uK8kydnZRS++OEqzZ8+QyWTK9R5F9aAoAqP7UFpamgyGNE2Y8N8cdY6OmZozZ87/1j4m64cffjDXbd58bb+iVz74VXVr+uq1oQ1la2uj8mXddeZcvLZv+01p6c7mdocPH1RGRoaqVaue416hoR3NG3hdPzZJmjNnvqpXr3nH7xUAAAAAgIJSwbOsHvYqpzWHNucrMLqRk6O96tf218qNx3X27Dl5lHpIkmRra6shQ4bp2Wf768SJ45IyValSFWVmZmrChI9Vs6b1PZOK8kFRBEZFVG57FP31116dOnVCdevWM5dFRUXp6tUk+fr6ydExa4ZQw4YNZTAYclz/559/ylap6vNUTXm4X5tN1KReaYWfjNGPPy1Xxyd7mcuXLFkkOzs7tWoVYi6LjY2Vu7u7bG0tD9mLjo7S5s0b5ezsogoVisYmXQAAAAAAXC/NaFBi2tXbvt6QnjVbKD4hQR6lLOucnZ1Vq9a1cGjz5o3KzMxUo0ZNcvRT1A+KIjAqoiZOHK/o6CirexQVdymuMaNelZujgyTpszkztGL1CoXNnKva9epIksqUyXlEnyQdOnhQtjKqYd0Ai/JWTR7Sll0RmvT5FJ0+c0nly1fQzp07tHXrZvXtO1ABAaXNbTdsWKslS75TixZB8vcvLQcHe509G6G1a1crMTFBY8a8ZXV3eAAAAAAA7obcJmH8e+GIImLPq5Z/VXNZcnKyDAaDXF1dZW+fFZOkpKQoIyMjx/VxCan6Y98FOTna6eGKFWXM2cQsPj5Os2bNkIeHhzp37mZRt3v3riJ/UBSBURHVpk1b/fLLqhx7FD3dpZtC7B6Ty5YkRWzJ2m39aniUJOnyyqPS/wKjW2Vvb6s3RzTRxr+Ka+PGdUpIiFfp0mU0atRr6tKlu0XbRx+tq8OHD2nHjm2KiYlWenq6SpXyVGBgAz39dE898kjtO3rvuHUREac1b95chYcfUVRUpIxGo3x9/dSyWXOF2NRVKRePm16flpam8PBwRUREKC4uTqmpqVq37hdVLG2jLu2qyLOkc45rLl68pBkzv9aff+5RZGSk3NzcVKVKNfXu/azq1Mm559XOnds1f/5XOn48XA4OxVSvXn0NG/aSRRgJAAAAAAUhexJG4wYN5XTcIIMpXccjz2jbyd1ydnDSoEY9zG13795tPigqICBrcsXx48cV2r6Lald1lreni+ztbXXxSpK2/XFWV1PSNbhXHTk7OynxfxOVdu7crkWLFqh+/YYqVcpTly9f0sqVy5WYmKDx4z+Xh8e1f5MdOXJIr78+WkX9oCgCoyKqdesQtW4dkqPczdFBEbP3WpSNChqoUUED89XvL2uX68C2j6zWFXdx0Juvv6YXX3z9pn1Uq1Zd7777Yb7uh7vjypUrio6OUosWwfL29pGdnZ1OnjyuH5f9pNU2K/VFl/fk4Zz79MYrV65o165dKl26tGrWrCknJyeVKxugJUt+0B/7zuu9Uc1Vxr+EuX1sfKqG9+6rdKNJnTp1UdmyZRUVFaUVK5bppZeGavz4z9WkSTNz+y1bftVbb41RpUqVNWzYSF29mqQlS77TCy8MVFjYAnl5eVsbFgAAAADcluxJGKvWrFJMdLRsZCMfV0+1q9ZSXWq3k4+r502v9/PzU0kPN/25Z5viEtJkNGXIvYSjalX1VrugiqpSsdQN7QPk4FBMP/74vRIS4uXu7qHAwPrq23egypUrb9H25MkTMhiy9v8tygdFERgVQfZ2RtnYGK3W2cjhLo8G94PAwAYKDGyQo7xxYH29+sZr2hi+Q91qP5Hr9R4eHurRo4fFmtkRwwaqrMcZfTJ9p35cc0QvD6xvrtv6x1nFxsXpk08mqnnzIHN5mzZt1bPnU1q5cpk5MDIajZo8eYJ8fHw1ffpcubi4SJIaNWqigQOfVVjYbI0ZM/ZOvwUAAAAAYJY9CcPapIsbBQUFKSgoyKLM29tbI4YNzHXCxY2yNq2emq+21g6QKooIjIqgjAyjvvl0tNW6IW/MuMujwf3M399fkpSUlnzTdiVKlLBa/kg1b7m6OOjshUSL8pTUdEnKMTOoVClP2draysnp2hK2ffv+VFRUpAYNGmoOiySpcuWqqlu3nn79db1Gjx5jXisMAAAAAPl1swkXEpMu7gT/QgMeIGlpaUpJSZHBkKbTp09p9syshDuwrPUjHPOSnJKulDSjygRYLmd7tLqPVmw4rkmTPtXw4SNVpkxZRUVFat68uXJ2dlbPnn3MbY8cOSRJVo+RrFGjlv78c48iIs6oYkVO1gMAAABwa2424UJi0sWdIDACHiCrVi3X5MkTzK8DAgI0OniwavlXua3+lq0Ll8mUqRYNylqU16jspTffeE3TZ8zWiy8+by4vU6acZs36WuXLVzCXRUVFSpK8vX1y9J9dFhUVSWAEAAAAAEUIgRHwAGnePEjlypVXSkqKjh07qp07tiohJTHvC61Yv2GT1vx6Qo9W91bLRmVz1JcsWVJVq9ZQYGADlStXThEREfruuwV67bWXNW3aLPn6+kmSUlNTJUkODjmnghYrVsyiDQAAAACgaCAwAh4gPj6+8vHxlSS1aBGk9iGPq2efXkozGdS9Tvt89xMREaF58+apQlkPvdQ/UDY2Nhb1v+44o69/XK2vvlqoihUrmcsbNmysAQOe0axZ0/XOO+MkSU5OTpKk9PT0HPcxGAwWbQAAAAAARQOBEfAAq1K5ih72Kqc1hzbnOzA6e/asNmzYoMqVK+mV/hXk4pxzZtDPG46pfPnyFmGRJD38cCU99FB5/f33X+ay7I2xIyOvWCxVyy67vg0AAPj/JSLitObNm6vw8COKjopSeqpB3q6lVK/sI+pau51KuXjk2ceJEyf09jsfaN/ebTp/KVGmjEz997028vZ0sdr+6NEjmjdvtv799x+lpqaodOky6tixs7p27SE7O7ub3uvtt1/X5s0bVaFCRS1YsOS23jMA3C8IjIAHXJrRoMS0q/lqe/bsWa1fv14eHh6aM2uqzv47zWq72PhUlXAzWa0zmUwyma6dUlCtWg1J0sGD+1W/fkOLtocOHVDx4sVVrtxD+RofAAB4sFy5ckXR0VFq0SJYZf39lfD7OZ2OOad1R7Zq28nd+qLLe/JwdrtpH4cOHVJMTIzK+rvIx6u4Ll5JyrXt33//pVdeGaHixV3VrVsPeXiU1J49f+iLLz7XqVOnNGbM2Fyv3bFjm7Zs+VWOjo63/X4B4H5CYAQ8AKKjo+Tp6ZWjfPfe3YqIPa9a/lXNZTHJcbpqSJG3aymLtufOndP69evl7u6u9u3by93dXWdzuV9pP1edPhOhAwf2q1ata6efHTjwr86ejVDjxk3NZXXr1pOnp5dWrlyu7t17y8Ul6699x46Fa9++PxUa2lH29nwUAQDw/1FgYAMFBjaQJLk5Oigidq8kqZZ/FY3fNFMbw3eoW+0nbtpHcHCwXn1luI7s/FTzlvx708BoypSJsrGx0cyZX6l06TKSpC5dntZnn32kFSuWqV279qpdu06O65KTkzVp0nh16fK0tm/fertvF3coe0ba8WNHdfn8JZkyTIU6I23EiCEWM+dvFBjYQFOmWJ7AtXPnds2f/5WOHw+Xg0Mx1atXX8OGvaSAgNK3/oaBe4x/pQEPgIkTxys6Okr16tWXr6+fDAaDjh49rE2b1svZwUmDGvUwt52/+ydtOva7Pm7/msor6xe0yMhIrVu3TpJUtWpVnT17VqtWr9W5o9cio2b1r2183S20mibP3atRo4arc+euKlOmrM6dO6vly3+Uvb2D+vcfYm5rb2+vkSNf1bvvvqHhwwepY8endPXqVS1ZskgeHiU1cOC1U9YAAAAkydvVU5KUlJacZ1tXV9d8/fEpISFBx4+Hq0GDxuawKFtoaEetWLFMa9assBoYzZ49QyaTSYMHv0BgdA9lz0hrFdRKxY6lys7GrlBnpPXtO0AdO3bOUb5p0wb9/vs2NW3awqJ8y5Zf9dZbY1SpUmUNGzZSV68macmS7/TCCwMVFraAbRhw3yEwAh4Abdq01S+/rNK6dWsUFxcryUZ+fn56uks3hdg9Jp///dKVm5iYGJlMWUvMdu7cKUnavHmzRZvrA6N6j/hp5pdTFfbVQq1evUJXryapRIkSatCgkfr1G6TKlataXNuqVRs5Ojpq/vwwTZ8+RQ4OxRQYWF8vvPCSvL19CuA7AOBeuX7/kaioSBmNRvn5+cnNzU21a9c2zyq8mV1/ndffh67o9Nl4XbiyWkajST/8sEL+/gF5Xnv8+DENHNhHJpNJ48aNV3BwG3Od0WjU5Mmf6fDhQ7p8+aKSk5Pl5eWt6tVrqk+fvqpSpdodvXcABSctLU2xKUmKSopRRNwFfb37R0lSYNlH8rgy/9LTcz9sw9Exq+zgwQM56g4dOqClS5fo3Xc/UvHirgU2Hty67Blpbo4Oipi911xeWDPS6tdvZLV8/vwwFStWTG3bXrtX1v9zJsjHx1fTp881//+vUaMmGjjwWYWFzb7pkkegKCIwAh4ArVuHqHXrkBzlN/7PVJJGBQ3UqKCBFmVVq1ZV1aqWIc+IYQN1YNtHud6zYYNA1ajZMNf6GzVt2lxNmzbPd3sA94fr9x/x9vaRnZ2dzpw5rqVLl+rEiRPq2rWrnJ2db9rHhm2ndfxMrB4q7a4yZcro9Okz+bp3RkaGPv30QxUr5qiUlJyzENLT03XkyGE9+mhtBQSEysXFRZcvX9bq1Ss0ZEg/TZo0VfXq1b+t9w2gYK1atVyTJ08wv/Z19dLo4MGq5V+lwO5RqpSnPDw8dPDgfqWlpZpDIknaty/r96UrVy5bXGM0GvXppx+pfv1GVn/XQtFQGDPScvPPP/sUEXFGrVs/Ljc3d3P5vn1/KioqUoMGDbX4Y0nlylVVt249/frreo0ePYatGHBf4WkF7mP2dkbZ2BhzrbdRzhPOAKAgXb//SDZHx0zFxMRo48aNOnr0qOrUybm843ovPFtXJd2dZGdnq5+3F8t3YPTjj9/r9OmT6t37OYWFzcpR7+zsrLCwBTnKO3Xqqq5d2+u77xYQGAFFRPPmQar+cCWd+flfnYyO0B9n/lZCSmKB3sPGxkbdu/fW7Nkz9Oab/9GgQc/L3d1De/fuVljYLNnZ2SktLdXimu++W6Bz5yL08ccTcukV90JaWpriUxOVbkwvtBlpuVm16mdJyrFU7ciRQ5KkmjVzjqFGjVr68889iog4o4oVHy70MQIFhcAIuI9lZBj1zaejc60f8saMXOuA3BT0EqOsDSVX5LrEKCoqSj/99L2OHj2i8PDDiouL0xNPdNDYse/laMsSo/uHq2vWsg2DwZBnW69SeT9TN7p8+ZLmzPlS/fsPkYdH3pucXq9kyZIqVsxRiYkF+49RALfPx8dXlcqWUcA/9mpc/jE1KV9Po5Z/qDSTQd3rtC+w+/Tp00+pqalavHihBg/uK0lydnbRiy+OMu9TlO3cubOaN2+u+vYdkGPPI9xbS39eqk8WjDe/LowZadZcvZqkzZs3yt+/dI4/OERFRUqS1e0WssuioiIJjHBfITACAFgo6CVGeW0oGRFxWgsWzJOPj6+qVauhXbt+z7UtS4yKrrS0NKWkpMhgSNP58ye1bds2SVLZsmXzuPL2TJr0qQICSqt7915av37tTduaTCYlJibKZDLqypXL+u67b5WSkmxxoiOAoqWCZ1k97FVOaw5tLtDAyNbWVkOGDNOzz/bXiRPHJWWqUqUqyszM1IQJH1vMDpk2bbLc3NzUokWwzp27dhCIyWRSerpR586dlZOTs7y8cp5Ui8IV3LKVih9MV0p6aqHNSLNmw4Z1Sk1NVfv2HWVjY2NRl5qaNTvNwSHnDP9ixYpZtAHuFwRGAAALBb3EKK8NJatVq66VKzeoZMmSiouLU4cObXJtyxKjouvG/UdKlCih4OBg+fv7F/i9Nm1ar507t+vLL8PytRfEmTOn9NxzPc2vXV1d9eyz/dWnT78CHxuAgpNmNCgx7Wqh9O3s7Kxata6FQ5s3b1RmZqYaNWpiLrt06ZKioiL17LPdrfbRs+dTatKkmT77bEqhjBG58/P1VZ3SNSSpUGek3Wj16p9lZ2en9u2fzFGXvZl6enp6jrrs2bbWNlwHijICIwBAvhTWEiMXl+JycSl+2+OSWGJUFDRvHqRy5corJSVFJ08e0bJlywrlL6mJiYn64otJ6tixs2rVejRf1/j7l9bkydNlNKbr3LlzWr9+jZKSkpSens7mo8A9Fh0dJU/PnDN0/r1wRBGx51XL/9qhHFFRUYqLi7vjTYtvFB8fp1mzZsjDw0OdO3czlw8fPlJJSTn/vzJp0qcqVqyYXnxxlNWx4+4rrBlp1ztx4rgOHz6kJk2aWV125uXlLUmKjLyi8uUrWNRFRl6xaAPcL/gtCQBg1d1eYnQrWGJU9Pj4+MrHx1eSFBLSUrGxsVq2bJmMRqPq1q1bYPeZPn2KMjIyNXToi/m+xtnZWfXrXzvVsX37JzVgQB+NHfuaPv98WoGNDcCtmzhxvKKjo1SvXn2VL1Nalw4c1/HIM9p2crecHZw0qFEPc9tp06Zp1epV6tChgwICru2Jd/HiRc2e85Uunw7XqbPxkqT1W0/JxTlradBT7a7ta7Nz53YtWrRA9es3VKlSnrp8+ZJWrlyuxMQEjR//ucWeaNd/blxv+vT/ytnZWcHBuc+Ixd1XmDPSJGnlyuWSpA4dOlutr1Yta8bTwYP7czw7hw4dUPHixVWu3EOFNj6gMBAYAQCsuptLjG4VS4yKPk9PT3l5eenQoUMFFhgdPXpEq1ev0MCBzyshIV4JCVn/MIyNjZEkxcRE69y5s/Lx8TXvF2GNi4uLWrYM1sKF83X+/Dk2swXuoTZt2uqXX1Zp3bo1iouLlUyZ8nH1VLtqLdWldjv5/O+49Js5f/68Vq5caVG2+tcT5q+vD4z8/ALk4FBMP/74vRIS4uXu7qHAwPrq23egypUrX2DvC4XjVmakJScny2AwFMiMNIPBoA0b1qpUKU81adLMapu6devJ09NLK1cuV/fuvc2HhBw7Fq59+/5UaGhHZrXivsMTCwCw6m4tMbodLDG6PxiNRqWlpRVYf5cvX1JmZqbmzp2puXNn5qjPDjjnzv3G/Jfe3GSPKyEhnsAIuIdatw5R69YhkiQ3RwdFzN6ba9v33ntPAaVznrYZGBior7/6Uge2fZTn/SpUqKjPP596+wOW9OOPK/NuhEKRPSOtcYOGcjpukMGUnuuMtN27dys8PPyOZqRl27btN8XHx6t37+dy/T3D3t5eI0e+qnfffUPDhw9Sx45P6erVq1qyZJE8PEpq4MDnC/JbAdwV/FYNALDqbi0xuh0sMSo6cvtr74ULFxQbG2sxIy05OVmnTp1WmsEox2K3/itIjRo1NW7c+Bzl+/b9qaVLf1DPnn1Us2YtBQRkBUCxsbFyd3eXra1tjjFv3rxRzs4uqlCB442Bu8XezigbG2Ou9TbKeboUcL3sGWmr1qxSTHS0bGRTqDPSsq1a9bMkqUOHTjftu1WrNnJ0dNT8+WGaPn2KHByKKTCwvl544SWr+x4BRR2BEQAgXwpjiVFBYYnRvXP9/iO+vn4yGAw6fvywfvnlFzk4OKhRo0bmtrt379a3336rt15qohqVr4VMh49H68jxaEnSkQg7SdJPPy0xb7Ter98gSVmbhVrbMyQlJUWSVLNmLYv6DRvWasmS79SiRZD8/UvLwcFeZ89GaO3a1UpMTNCYMW9xYg1wF2VkGPXNp6NzrR/yxoy7OBrcj7JnpOU1G02SgoKCFBQUlKP8VmakZZs8eXq+2zZt2lxNmzbPd3ugKCMwAgDkW0EvMSpILDG6N3LsPyIb+fv7qXr16qpdu7Y59LmZg+GRWro23KJs8eJvzV9nB0a36tFH6+rw4UPasWObYmKilZ6erlKlPBUY2EBPP91TjzxS+7b6BQAUvpvNSGM2GnB3EBgBAC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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HCpejatQdMTY2RkZELPz9/BAcPxKFDB+SCThVpm6peTQ5SFhQU4NatBLRr5wo7u74wNjZGSkoKoqIiMW7cKCxbthoeHl5VdmyIiIiISPMYdCIiqoVsbW0BANnZ2WXWy8vLw969v6JLl25wd/eEVCpFXl4ujI3rKK2fk5MDALC0tJIrNze3gLa2NgwNjSrdNlW9mhykNDIyQlhYhEIbgYGDMGhQP+zaFcGgExEREdFbhonEiYhqAaFQiIyMDKSmpuDSpVj8+OMCAICPT+cy17t+/Spyc3Pg4tISK1cuxXvvdcd773XHBx/4YePGtRCLxXL1vb07AgCWLVuMq1evICUlBQkJN/Htt7NhZGSE4ODhlW6bqs+bBClzc3NKrV+RIGVpzMzMoK9vUG7fiIiIiKj24UgnIqJa4PDhg1ix4kfZ5wYN7DB37vdwdXUrc73ExMcAgD17dkFPTw8TJ05B/fomOHHiCCIiwpGWloo5c76T1Xd398S0aTMQFrYBkyePl5U7ODTCxo1b0bhxk0q3TZojFAqRl5cHkUiIR48eYv36VQAqHqSMiopEXl4uTE1NERAwEKGh46Gr++qrg7d3R+zYsRXLli3GpEmfo1UrZzx4kIjw8M0KQcpiEokE2dnZkEjESE1Nwa5dO5CXl1tu34iIiIio9mHQiYioFujatQcaNWqMvLw83L17G+fOnUFGxoty1ysepZKdnYXt23fD0bExAKBXr96YPHk8jh6NwrBhI9GkSVPZOqamZnBxaQVPT2+0bOmEhIS72LUrAl999QXWrNkIGxvbSrdNmlETg5TFHj9+iBEjgmWf69ati5CQ0Rg+fFRld5eIiIiIaigGnYiIagFraxtYW9sAALp164Hu3Xti7NgREAqFCAkZXep6BgaGAIBWrdrIgkLF+vTph6tXr+DatThZYCgy8gCWLVuE8PBf0LRpc5iaGsPNrSM6dPDBmDHDsHHjWsyd+32l2ibNqYlBymINGthjxYq1EIsLkJycjOPHo/Hy5UsUFBTIjaIiIiIiotqP3+6IiGqh5s2d4OTkggMH9pUZdLK2tgYAWFhYKCyzsLAEUBRgKBYRsRWOjo3RtGlzubrNmjWHo2NjXLsWV+m2SXNqYpCymJGREby8Osg+9+vXH2PGDMfs2V9h+fI16th9IiIiIqohmEiciKiWEgqFyMrKLLNOy5atAQCpqakKy9LSisrMzMxlZQJBKgoLC5W2JZFIIJG8Sg5e0bap+pQMUpalKoOUpTE2Nkb37r64dCkW//6bXG59IiIiIqo9GHQiIqrB0tMFSsvj4i7j4cP7aN26raxMIBDg8eNHyM/Pl5XZ2dmjbVtXJCTcxO3bt2TlEokEkZEHoKOjAy+vjrLyxo2bIDHxMW7c+Eduezdu/I2kpES0aNGq0m1T9aruIGV5fQNQbv+IiIiIqHbh9Doiohps6dJFSE8XwMPDCzY2thCJRLh9OwExMcdhbGyMzz77QlZ348Y1OHLkMFat2gB3d09Z+dSpX2HSpLH44ouJGDz4I5iYmCAm5gQSEm5i9OixsLV9lXMnNHQ8Zs36ClOnTsKAAYPg7NwMd+7cx8GD+6Crq4fRo8fJ9a8ibVPVS08XyEYklVQcpHRz85CVCQQC5OS8hI2NLQwNi6bVFQcSb9z4G7dv34KLSwsAZQcp79+/hxs3/kGbNq8CoMVBypJvpHvx4gVMTEygrS3/vCs9XYDTp0/CyMgYTZo0U8+BICIiIqIaQUsqlUqruxMVlZaWXd1dqHFMTY2RkZFb3d2gWoLXS+0RE3MCR48exv379/6XCFoLtra28PLqgKFDR8gFdX744VulQScAuHfvLjZtWofr169CJBLB0bExhgwZir59AxS2eeXKX9i5MwIJCTeRk/MS9erVg6urG0aN+gROTi4K9SvSNlWtmTOnlxqkNDQ0xOrVG2XnsLTr5c6dW5g0aSx0dfXkAon//HMdo0ePRWjoq7fUnTt3BrNmfQUDA0OFIKVEUoh16zajRYuWAIA9e3Ziz55d6NatBxo0sIeeni6SkhJx5EgUsrOzMGPGHHzwQaBmDxhVG/4doorg9UKq4rVCFcHrRZGVVT21t8mg01uC/2CoIni9kKp4rdQuNTlIeetWAnbv/gXx8Tfw/Hk6CgoKYG5ugTZt2mHIkGC0betaZceFah7eW6gieL2QqnitUEXwelHEoNP/MOikiP9gqCJ4vZCqeK1QRfB6IVXxWqGK4PVCquK1QhXB60VRVQSdmNOJiKiG0dHXhaSw9OcBOtpakIhUS9BMRERERERUXRh0IiKqYSSFUoyZf6LU5Vvm9NZgb6imKytIyQAlEREREVUnBp2IiIhqsbKClAxQEhEREVF10i6/ChERERERERERUcUw6ERERERERERERGrHoBMREREREREREakdg05ERERERERERKR2DDoREREREREREZHaMehERERERERERERqx6ATERERERERERGpHYNORERERERERESkdrrV3QEiInr3JCY+Qnj4Zty5cwsCQRrEYjFsbGzh49MZQ4eOgKWlZYXb/Oabr3H69Ek0adIUERF75JZ99tk4XLsWV+q6np7eWLlyneyzWCzGzp3bcexYNJ48+RdGRsZwc3PHuHGT4OjYuMJ9IyLN0vQ9BgCePXuG7dvDcOXKX0hLS0P9+vXh7NwCH38cgvbt3WX14uIuY8qUCWVua926zWjXrn2F+0hERFTTMOhEREQal5qaivR0Abp184WVlTV0dHTw4ME9REYewMmTx7F1606YmZmr3N6ff57FmTOnYGBgoHT5yJFjEBAwQKE8JuYEzp8/i86du8nKpFIpvv56GmJjz6Nr1+4YNOgjZGS8wIED+zB+/CisX78FTZo0rfhOE5HGaPoeIxCk4ZNPhkMsliAwMAgNGzaEQCBAZOQBTJkyAYsWLUenTl0AAI0bN8E33/xXoY2CAhGWLFkAExNTtGrVpnI7TkREVMMw6ERERBrn6ekNT09vhXJXV3fMnfs1oqMPYdiwkSq1lZubi2XLFiEoaAjOnfs/pXW8vDoqLd+2LQz6+vrw939fVnb27BnExp5H//4D8Z//zJaV+/v3xYgRH2HlyqX46ad1ypojohpC0/eYI0cOIyMjAwsXLkXXrj1k5X5+/ggOHohDhw7Igk7m5hbw9++r0MaJE0dRWFiIPn36QVeXX9GJiOjtwJxORERUY9ja2gIAsrOzVV7n55/XQSKRYOzYTyu0revXryIx8TG6du2B+vVNZOVxcZcBAH379perb2/vAFdXN1y5cgnPnj2r0LaIqGaoqntMTk4OAMDS0kqu3NzcAtra2jA0NCp3O4cP/w4ACAgIVLlvRERENR0foxARUbURCoXIy8uDSCTEo0cPsX79KgCAj09nldaPj7+B/fv3YN68H1CnTt0KbfvVDzz5aXcFBSIAgKGhocI6BgaGsu0W/3ilqqfp/Dxl5dzp1KkLlixZqVB+4cI5bNu2Bffu3YGenj48PLwwceIU2NnZV7hvpD6ausd4e3fEjh1bsWzZYkya9DkcHBpCIEhDePhmGBkZITh4eJnbefLkX8TFXUa7du3RqFFjlfePiIiopmPQiYiIqs3hwwexYsWPss8NGthh7tzv4erqVu66YrEYixf/AC+vjujVq3eFtpuT8xKnT59Egwb28PDwkltWnK8pLu4vNG/uJCvPz89HfPwNAEBqKkc6aZKm8/MU699/oMK1aGVlrVDvzJlTmDNnBpo3d8LEiZ8jJ+cl9uzZhU8/DUVYWITC6BfSHE3dY9zdPTFt2gyEhW3A5MnjZeUODo2wceNWNG7cpMz1o6IiIZVKleaeIyIiqs0YdCIiomrTtWsPNGrUGHl5ebh79zbOnTuDjIwXKq27a1cEkpMTsWDBj+VXfs2JE8eQn5+Pfv0CoKWlJbfsvff6Ytu2Ldi8eSMMDY3g6emNzMwMhIVtRGZmBoCiABRpjqbz8xRr06ad0tw7JYnFYqxY8SOsrW2wdu1mGBsbAwA6duyE0NAQhIX9jBkzZpfZBlUdTd5jTE3N4OLSCp6e3mjUqBESExOxa1cEvvrqC6xZsxE2NspHR0okEhw5chh16tSBr6+fyvtGRERUG6gUdHrw4AHWrl2L+Ph4pKamQiwWo0GDBujevTtCQ0Nhba341O91ISEhuHTpktJl+/btQ9u2bSvWcyIiqvWsrW1gbW0DAOjWrQe6d++JsWNHQCgUIiRkdKnrJScnITx8M0aOHAN7e4cKbzcq6nfo6OigX7/+Csvq16+PlSvXYf78uViy5AdZefv27hg2bCS2bQur8FQ+qhpvmp+nvKATAOTl5UFbW7vUUVFXr17535vLJsgCTgDg5OQCNzcPnDp1HF9+OYOJoauJpu4xkZEHsGzZIoSH/4KmTZvLyjt08MGYMcOwceNazJ37vdJ1L126gNTUFAQGBimd1ktERFSbqfQNKCUlBWlpaejduzdsbGygq6uLO3fuYM+ePYiKisLvv/8OCwuLctsxMzPDzJkzFcobNmxY8Z4TEVGVSkx8hIULw3Hjxk2N5NERCASIiTkOfX0D/PzzOmzcuBbvv/8BZs/+VqGdNWtWoH79+ujcuRs2bVqPM2dO4cmTf1FQUABdXV2Eh29CQMBApX28f/8eEhLi0alTF6VTpQCgWbPmCA/fieTkJAgEabC0tIKDQ0OsW/cTAMDRsXGF953enKZzgP3001IsWPAdgKJpUkFBQzBkSLDc6Lhbt+IBAK1bKz48a9WqDa5c+QuJiY/RtGkzlfpIVat5cyc4ObngwIF9ZQadiu8x3br5Ijk5SVYukUhQUCBGcnISDA2NZPeYiIitcHRsLBdwAoruJY6OjXHtWlyp2yotvxwREdHbQKWgk4+PD3x8fBTKPT098cUXX2D//v0YO3Zsue0YGxsjMJBv5CAiqg1SU1ORlqa5PDqJiY8QEREOXV1daGlpQSqVltrWs2fPIBCkYdSooQrLCgoKEBa2EQkJN5UmfD506CAA4IMPyv+B5+DQEA4Orx6MxMaeR506ddC2rWu565L6aSo/j66uLrp06YaOHTvD0tIKAkEaoqJ+x6pVy3Dv3h3MmjVPVlcgSAOgPNdTcZlAkMagUw0iFAqRlZVZZp3ie0xIyIdKlwcHD5RLKi8QpJY6IkoikUAiEStd9uLFc/z551k0a+aEFi1aVWAviIiIaoc3Guttb1/0RpasrCyV1yksLERubi7q1KmjkEeDiIhqDk9Pb/j59UBGRq5cuTry6IjFij/AWrRoiR9++BHffDMDbdq44u+/rwIoGgGVk/MSNja2sqknkyZ9joMHf8PZs39g5MhQ2Q/6ZcsWQ19fH5MnT4WFheIoJ5FIhBMnjsDc3AKdOnWp0PHYt+9XPHhwH6NHj4WRUfmvPyf101R+nnbt2qNdu/ZyZf37D8T06Z8jOvoQ+vULhKtr0fLi/F56enoK7ejr68vVIc1JTxcovQfExV3Gw4f34ebmISsr7R7z8qXitM3S7jGNGzfB/fv3cOPGP2jT5tWotxs3/kZSUmKpo/GOHImCWCxGQAAfyhIR0dupQkEnoVCInJwciEQi3Lt3D0uXLgUAdO/eXaX1U1JS4Obmhvz8fBgZGaFLly6YOnUqmjXj0z8iotpCHXl00tMFGDduFDw8vGBjYwuRSITbtxMQE3McxsbG+OST8bJX1m/cuAZHjhzGqlUb4O7uCaAowfPs2f9B167dERo6HlKpFHl5uTA0NISRkVGpyXjPnv0DmZmZ+PjjEWXm2Jk+vehV940bN4WWlhYuXYrF2bN/oFOnLhg5MlTl/Sb1qq4cYACgra2NkJBRuHTpAmJj/5QFnYqDFAUFBQrriEQiuTqkOUuXLkJ6uqDUe8xnn30hq6vsHuPl1UFpu2vX/qT0HhMaOh6zZn2FqVMnYcCAQXBwaIjk5CQcPLgPurp6GD16nNL2oqMjoa9vgPfeKzthPRERUW1VoaDT3r178f33r5Ig2tvb48cff4Snp2e56zo4OMDd3R0uLi7Q1tbG9evX8csvv+DChQvYuXMnXFxcKt57IiKqclWRR6dOnbowMTHBsWPR/xupogVbW1sEBgZh6NAR5f5Iv379KnJzc+Di0hIrVy5FVFQk8vJyoRFrweIAACAASURBVKWlhfr1TSAWi5UGlYpzp3zwQdmjCtq0aYeYmOM4cuQwAMDRsQmmTZuBwMAg6OjoqLTfVPWqKj9PaRo0sAMAZGRkyMosLa0AAGlpqWjcuIlc/bS0VLk6pDl+fv44evRwqfeY4uC5unTp0h0rVqzFzp0RiIqKRE7OS9SrVw/e3h0xatQncHJS/J77zz/X8ejRQ/Tu3Qf169dXa3+IiIhqCi1pWUkzXvPs2TM8ePAAubm5iI+Px6lTpzBgwACMGjWqUhu/fPkyQkJC0LFjR4SHh6u8XkGBpFLbe5vp6GhDIims7m5QLcHrpWbLyhVh1H+Pl7p869z3UN9YXyN90dHRRkREBBYsePUWN3t7e0yePAUffBBQ7vpisRgffjgE1tZW2LDhZwDAe+/5wdjYGAcPRpa63osXL9C1a2cEBg7ADz8sUFgeERGBxYsXwtzcHHp6ehg7djxMTU0QFXUYp0+fRv/+gViwYGEl9rj2Ket60eS1AlTPvSUoaCCSkhLx119XSq0zaNBA3L59u8x2unXrjnXr1pdZ58KF8xg79hOMHz8BkydPkSv77LPJmDDhU7n6oaGjcfPmTZw9+6fS6XfvMv4doorg9UKq4rVCFcHrRZGenvofrlZopJOtra3syZCfnx/ee+89DB48GEKhEOPHj6/wxj09PeHp6YmLFy8iPz9f5eHnr+cXIcDU1JjHhVTG66WG0y37Zl9YKNXY+TM1NYanZyesWLFWLo/OkycpKvUhIiIcjx8/wvffL5bVLyyUQiIpLHP9zMw8AIBIJFZaLz39xf/qZWL79t2yt8l17NgdkyePR2Tk7xgyZBiaNGla0V2ufcq4XjR5rQBVd28pKz/PvXt34ebmIduusvw8EyZMUSk/T3EbmZkZMDExlasrEomwatVqAICHR0dZXSenNrCwsMTevXvRv/8QGBsbAwDu3r2Dv/76C337BiAnpwCA4vS7d5k6rxVDQ21IpaU/kNTS0kF+Pn9U1Gb83kKq4rVCFcHrRZGVVT21t/lGicRbtGiBVq1aYefOnZUKOgFF0+4uXbqEzMxM5jwgIqqB1JVHp/iHoZYWoKUFGBjID7StyA9DA4OivxetWrWRBZyK9enTD1evXsG1a3HvRtDpHaDp/DxffjkFlpaWcHFpKXt73bFjR5CcnIjBgz9Cq1ZtZHV1dXXx+efTMW/eTEya9AkCAgYiJycHe/bshKmpGUJDK/f9iFQnlUqwadOmUpcXvWGZL68hIiKqDm8UdAKK3siSmVn2a2fL8ujRI+jq6sLU1LT8ykREVO0qm0dHX1+K3bt3IysrC7m5uVi6dCn09PRkI0Mq8sPQ2rroVfQWFhYKy4pHxGRnq/5mVarZNJ2fp0ePnjh79gx++203srOzYWRkBCcnF4SGjkPv3n0U6vfs6QcDAwNs2xaGtWtXQk9PH56eXvj00ymwsrJWa9+IiIiIahOVgk5paWmwslJMghkbG4u7d+/C29tbVpaamors7GzY2dnJXimdnZ0NY2NjheSrf/zxB+Li4tCtWzcYGBi8yX4QEZEGCYVCZGWV/cDh2bNnEAjSEBLyodLlu3fvRqNGjdCnj+KP+PK0bNkaQNHfnNcVJ282MzOvcLtUM/Xq1Ru9evVWqe7s2d9i9uxvVaq7b98hpeXDh4/C8OGjVOxdkc6du6Jz564VWoeIiIjobadS0Onbb79FWloaOnbsCDs7OwiFQty8eRPR0dGoU6cOvv76a1nd5cuX48CBA9i+fTs6dCgazn7x4kUsXLgQvr6+aNiwIXR1dfH3338jMjISZmZmmDVrVtXsHRERVVp6ugCmpo0UyuPiLuPhw/twc/OQlSnLozNp0udyeXT09ICTJ0/i3Llz0NHRgY+Pj2yUU0XZ2dmjbVtX3LjxN27fvgUXlxYAit5GFhl5ADo6OvDy6liptomI6N2SmPgI4eGbcefOLQgEaRCLxbCxsYWPT2cMHTqi3DdbisVirFixBAkJ8UhJeYrc3FxYWlqhZcvWGD58JJydW5S5/r17dxEaOhwSiQTff79IYcpvTMwJXLx4Hrdv38KjRw8gkUiwd2+k7I2aREQ1mUpBp379+uHgwYP4/fff8fz5c2hpacHOzg4fffQRQkNDYWdX9g2vSZMmaN26Nf744w+kp6ejoKAAtra2CA4OxoQJE2BjY6OWnSEiIvVZunQRMjOfw9XVQy15dAwMpHjw4AFiY2Ohp6eHpk0V8y1t3boZQNFIKgC4f/+urKx9e3e0b+8uqzt16leYNGksvvhiIgYP/ggmJiaIiTmBhISbGD16rNqnXJFmMTk0EWlKamoq0tMF6NbNF1ZW1tDR0cGDB/cQGXkAJ08ex9atO8scPVtQUIBbtxLQrp0r7Oz6wtjYGCkpKYiKisS4caOwbNlqeHh4KV23sLAQixfPh76+AfLylCc0PnBgL+Ljb6J5cyfY2zsgMfGxWvabiEgTVAo69e3bF3379lWpwUWLFmHRokVyZc2aNcOqVasq3jsiIqo2fn7+OHnyiMby6ADA5s0b5D7fuXMbd+4Uvep+9OixckEnZ+cWWL9+CzZtWoe9e3dBJBLB0bExZs2ah759A9TeN9IsJocmIk3x9PSGp6e3Qrmrqzvmzv0a0dGHMGzYyFLXNzIyQlhYhEJ5YOAgDBrUD7t2RZQadNq3bzcePXqAjz8egbCwjUrrzJnzX1haWkJXVxfLly9m0ImoFtH0SMqrV6/g1KmTuH49Dk+fPoWBgT4aNmyEoKAP4efnDy0trTLbtra2Rrt27TBu3Di0atVKLcfgjROJExHR26lXr94YNChQpVfJViSPzscff1zqsnPnLqvaPQBFSc0XL15RoXWIiIhUUfxwJTs7u5yaypmZmUFf36DU9VNSnmHTpvUYPXpcmS9V4shdotpL0yMp169fjbS0VHTr1gODBjVHfn4eYmJO4Lvv5iAu7jJmzJhTZtvZ2S+wf/9+fPjhh9i0aRN8fHze+Bgw6ERERERERO88oVCIvLw8iERCPHr0EOvXF83U8PHprNL6EokE2dnZkEjESE1Nwa5dO5CXl1vq+suWLYadnT0+/HAojh8/orb9IKKaQ9MjKT/9dDLatWsv9xK3IUOGYsqUCTh06CCGDAlG06bNS23byqoegoOD4evriy1btjDoREQ1R3Uk4Xz58iU2bVqHM2dOIysrE3Z2Dhg06EMMGDBIbuioMt988zVOnz6JJk2aIiJizxvte01VmXNioK8LaaEUACAWF2DuN3MRH38TT589RW5uDqwsrdCikuekJKFQiDt37iAxMREZGRnIz8/H0aNH4erqgVGjQmFjw6e6RESkWYcPH8SKFT/KPjdoYIe5c7+Hq6ubSus/fvwQI0YEyz7XrVsXISGjlb4NMybmOC5cOIf168Ogq8ufZETvmqoaSVnyRT/FtLW10aNHT1y7FocHD+7Lgk6lsbCwgL6+PrKysirVt9fxDkdEaqHpoaMFBQWYOnUi7ty5jcGDP4KjYxPExp7HsmWL8Px5OkJDx5e6rT//PIszZ07BwMBArcegpqnMOZEWSvHT/FMAALFYiD8uxMLKrDGa2ztDV9cAbb1MEXno90qdk4kTx8n1LTY2Fvb29mjdujUMDQ1hb2+P/fv34/TpE1i/fguaNFFMNE5ERFRVunbtgUaNGiMvLw93797GuXNn/pfTUDUNGthjxYq1EIsLkJycjOPHo/Hy5UsUFBTIBZays7OxatUyBAQMQJs27apiV4iohtH0SMrXpaamAgDMzS3KbPvp04fYsmULcnNz0a1bNxX3rmwMOhGRWmh66OihQweRkBCPL76YjsGDi54q9u8/ELNnf4WIiHD069cftrYNFNrLzc3FsmWLEBQ0BOfO/V9ldrXWeNNzoqtrgPe7TpUrGze2Jz4ICKrUORk48FVyb1NTU3z00UeoX7++rGzs2LHw9u6CqVMnISxsA+bPX1LpfSciIqooa2sbWFsXvVW7W7ce6N69J8aOHQGhUIiQkNHlrm9kZCT35tZ+/fpjzJjhmD37KyxfvkZWvnbtShQWSjFhwmT17wQR1UiaHEn5OoEgDZGR+2FnZ4927dqX23a9evUwfvx4jB9f+kP8imDQiYiqVFUNHT1x4igMDQ0REDBQrvzDDz/GmTOnERNzXGlA5eef10EikWDs2E/f+qBTaarrnJw4cUJWVq9ePaVte3l1QP36Jnjw4H6l+kZERKQuzZs7wcnJBQcO7FMp6PQ6Y2NjdO/ui19+2YZ//02Gvb0Dbt++haioSISGjkdWViaysjIBAC9ePAcAPH+ejuTkJFhb20BfX1+t+0NE1UdTIylfl5+fj5kzpyMvLw+LF69QWrdk2xkZaYiMjER2djZEIpFapv8y6EREaqWJoaOFhYW4c+cWnJ1bKEyRa9myNbS1tZGQEK/Qdnz8Dezfvwfz5v2AOnXqvsFe1i5vek4KpYUQFeRCWliI+Pib2L5jW6XOSXx8PJo1a1bmtl6+fInc3Bw0bVp2PSIiIk0QCoWywFBl1weArKxM2Ns7ICXlGaRSKTZv3oDNmzco1C8eCbF583a0aKGe15UTUfXT1EjKkoRCIWbO/BK3bydg9uxvSx1VVbJtK6t6GDRoEIKCgjB58mSEhYVVdFcVMOhERGqliaGj2dlZEAqFsLKyVlhfX18f9eubQCBIkysXi8VYvPgHeHl1RK9evSu4V7Xbm56TrOwURP3fUgDA/pOVPyepqanlBp22bQuDWCxGnz79VOobERHRm0pPF8DCQvHlGnFxl/Hw4X25xLwCgQA5OS9hY2MLQ0NDAMCLFy9gYmICbW1thXZPnz4JIyNjNGlS9PevVavW+P77RQrbunr1Cvbv34vg4OFo3boN7Owc1LmLRFTDVMVIypKKAk7TcfnyJcyYMQf+/n1VbrtOnTro3bs3Nm3ahMTERDRq1KjC/SuJQSciUitNDB3Nz88HAOjp6SltQ19fX1an2K5dEUhOTsSCBT8qXedt9qbnpK6xOXp2GI/CQgk8upgh+miUWs7J62JiYvDrrzvg7e2Dfv36q9w/IiKiN7F06SKkpwvg4eEFGxtbiEQi3L6dgJiY4zA2NsZnn30hq7tx4xocOXIYq1ZtgLu7JwDgxIkj2LNnF7p164EGDeyhp6eLpKREHDkShezsLMyYMUcWoLK0tIKvr59CH/Ly8gAArVu3UVh+7Vocrl2LAwDcupUAAPjttz2oW7do1PaoUZ+o+YgQkSaoeyRlMZFIhFmzvsJff8XiP/+ZjQ8+CKxw28Xf2zMyMhh0IqKaRRNDR4u/uBUUFChtQyQSyeoAQHJyEsLDN2PkyDEKTwHeBW96TnR1DdDAyhkA8NFHPeH/fsAbn5PXJSYmIjw8HC4uLfH99wuhpaVVoX0kIiKqLD8/fxw9ehjHjkX/76GMFmxtbREYGIShQ0fIciGWpl07NyQkxOPPP8/i+fN0FBQUwNzcAp6e3hgyJBht27q+Uf+uXPkL4eGb5Mp+/XWH7P8ZdCKquTQ5khIo+s49c+Z0XLp0AdOnz0RAwIBS+1Za22lpaTh69CiMjY3h5ORUqf0uiUEnIqpSVTF0tF69+jAwMEBaWqpCfZFIhKysTFhausvK1qxZgfr166NbN18kJyfJyiUSCQoKxEhOToKhoREsLRX/ILyNquucWFu7K2kNSEpKwokTJ9C8eXMsX77mncq3RURE1a9Xr94qT72fPftbzJ79rVxZixYtMW/e/DfqQ9++AejbN0DpstDQ8QgNVc9bpIhIszQ5khIA/vvfObh48Tw8Pb1haGiIY8ei5frTrJkTmjd3KrVtgeAZDh48iMzMTMyfPx9GRkZvfAwYdCKiKqfuoaPa2tpwdm6Bu3dvQyQSyb3dJSHhJgoLC9GiRUtZ2bNnzyAQpCEk5EOl7QcHD0SnTl2wZMnKSvextqmOc9KyZUuIRCK5dpKSknD8+HGYmppi7dq1MDSsX+k+ERERERHVJJoeSVk8Bffy5Uu4fPmSQnujR4+VBZ2UtW1paQkfHx+MGDEC7u7KHxhXFINORKQWmh466ufnj3/+uY7IyP0YPPhV4vE9e3ZCR0cHPXu+emI5adLnePkyW6Fvy5Ythr6+PiZPnqq077VdZc5JQ3s7WVm+8CUM9I2hpaWec+Ln54fo6FdPW4pydh2HiYkJ+vXrBxMTE/wvlkVERFQlDA21IZVKSl2upaWD/PxCDfaIiN5mmh5JuW/fIZXrKmvbyqqeyuurikEnIlILTQ8d7d9/IKKjD2H16hV4+vQpGjduggsX/sT//d9pjBwZCjs7e1ndkjmiSlq79icYGRkpTej5NqjMOVm/9lXOiEf/xuHWw/9DQ9u2qGNsDm1tHaxYGYeo6MOVOif29q/OSVpaGo4dOwYAcHFxQVJSEqKjoyEWv8rlVJG3bBAREalCKpVg06ZNpS4fO3YsAOYVJCJSFwadiEgtND10VE9PDytXrsOmTetw8uQx2TSvqVO/QlCQ8ml075o3PSdW5k2QnpGI5JSbyBdmo7BQgscplmo5J8+fP4dEUvSk+cKFCwCA06dPy9Vh0ImIiIiIagOOoiydllQqlVZ3JyoqLU1xmsy7ztTUGBkZudXdDaoleL3UcLo6GDP/RKmLt8zpDYhL/6P2JvR1dfDT/FOlLv98Tk+IKrltAwNpuU+XhUI+Xa6wMq6XqrxWlFHnvYXXy9uN1wpVBK8XUhW/41JF8N6iqCqm12mXX4WIiIiIiIiIiKhiOL2OiN4Ih5LWPLrGUkhKOSc6WjoQ59b8pyxERERERFT7MehERG+ECTlrHolUgolRs5QuW9dvAXjrJyIiIiIiTeD0OiIiIiIiIiIiUjsGnYiIiIiIiIiISO04x4KIiIiIiIhIBYmJjxAevhl37tyCQJAGsVgMGxtb+Ph0xtChI2BpaVluGzExJ3Dx4nncvn0Ljx49gEQiwd69kWjQwE6hblzcZUyZMkFpO506dcGSJSsVyi9cOIdt27bg3r070NPTh4eHFyZOnAI7O/uK7zDRG2LQiYiIiIiIiEgFqampSE8XoFs3X1hZWUNHRwcPHtxDZOQBnDx5HFu37oSZmXmZbRw4sBfx8TfRvLkT7O0dkJj4uNzt9u8/EK6ubnJlVlbWCvXOnDmFOXNmoHlzJ0yc+Dlycl5iz55d+PTTUISFRcDS0qpiO0z0hhh0IiIiIiIiIlKBp6c3PD29FcpdXd0xd+7XiI4+hGHDRpbZxpw5/4WlpSV0dXWxfPlilYJObdq0g79/3zLriMVirFjxI6ytbbB27WYYGxsDADp27ITQ0BCEhf2MGTNml7stInVi0ImIiIjeWGLiIyxcGI4bN26qdbrB0KFDUa9ePYW6V65cwbFjMbh+PQ5Pnz6FgYE+GjZshKCgD+Hn5w8tLfm3ZkqlUpw4cQz79+9BUtJjiEQFsLGxRa9evfHhh0NRp05dtR0LIiJ699ja2gIAsrOzVa5bUXl5edDW1oaBgYHS5VevXoFAkIZPPpkgCzgBgJOTC9zcPHDq1HF8+eUM6OrWvDCApqctnj9/DtHRv+PWrVt48eI59PT0YWdnhz59+iEwcJDSY8xpi5VT8642IiIiqnVSU1ORlqa+6QYODg54/Lj0J79r1qxBSkoqunXrgUGDmiM/Pw8xMSfw3XdzEBd3GTNmzJGr//PP6xAREQ4PDy+MHj0Wurq6uHr1CsLCNuLChT+xcWO4QqCKiIioNEKhEHl5eRCJhHj06CHWr18FAPDx6Vwl2/vpp6VYsOA7AICDQyMEBQ3BkCHBcn+7bt2KBwC0bt1WYf1WrdrgypW/kJj4GE2bNquSPr4JTU9bvH//HrS1tfHBB4GwsLCEUJiP69evYdWq5Th//hxWrFgrd2zLm7Zob19+UOxdxaATERERvTFPT2/4+fVARkauXHllpxv89NPiMoNOn332GVq2dIOOjo6sbMiQoZgyZQIOHTqIIUOC0bRpcwBF0w327t0FZ+cWWLFiLbS1i17eO2DAYOjo6OL48SO4d+8OnJxcKrv7RET0jjl8+CBWrPhR9rlBAzvMnfu9Qt6lN6Wrq4suXbqhY8fOsLS0gkCQhqio37Fq1TLcu3cHs2bNk9UVCNIAKM/1VFwmEKTVyKCTpqcthoSMgqmpsdz3lsGDg7Fs2WIcOLAXCQk30apVGwCqTVucO3dWZXb7ncCgExER1TiaHmINAC9fvsSmTetw5sxpZGVlws7OAYMGfYgBAwa9NVO1quO4mpiYAAC2bt2MsLCNZR5XZdMNdu3aBTMzMwwZMkSu3MPDA0Kh/Pra2tro0aMnrl2Lw4MH92VBJ4lEDKFQCAsLC1nAqVjxPhsaGpW77++aqrxeTE2bK61fkX+Hrzt58iQePHig9HohIlK3rl17oFGjxsjLy8Pdu7dx7twZZGS8UPt22rVrj3bt2suV9e8/ENOnf47o6EPo1y8Qrq5Fy/Pz8wEAenp6Cu3o6+vL1aktNDFtsbztqTJtcdas/7zxtt9WDDoREVGNo+kh1gUFBZg6dSLu3LmNwYM/gqNjE8TGnseyZYvw/Hk6QkPHy9WvrVO1NHFcS043uHfvLr755msARdMNPD07lHlcS3ry5AkAKASJVNlHADA3t5CVGRgYwtXVDRcvXsCOHVvRo0cv6Ojo4OrVKzhwYB/8/d9Hw4aNKrSdd0FN/3dY0uPHj/Hw4UO5kW9ERFXJ2toG1tY2AIBu3Xqge/eeGDt2BIRCIUJCRlfptrW1tRESMgqXLl1AbOyfsqCToaEhgKL76etEIpFcnZpK09MWc3NzIBIVICfnJf755zp++WU7TExMZKOcANWmLZY1Ovtdx6ATERHVOJoeYn3o0EEkJMTjiy+mY/DgYABFTxFnz/4KERHh6NevP2xtGwCo3VO1NHFcX59uAAB9+vTFnDn/BVD6cS0pNzcXcXFxAFT7clw8Iich4SaSk5OgpaWFH39ciE6dXo3ImTdvPubP/xYbNqzBhg1rAABaWloYMWIMPvlkAoCKJQi9ffsWwsN/xt9/X0d+fh7s7R0QEDAAgwZ9JBf4KBqSvwQJCfFISXmK3NxcWFpaoWXL1hg+fCScnVuUu3/VpSb/OywpNzcX586dQ6tWrfiln4iqTfPmTnBycsGBA/uqPOgEQDbCOCMjQ1ZmaWkFAEhLS0Xjxk3k6qelpcrVqak0NW2x2IIF3+GPP07JPrdq1QbTps2Qe4mJKtMW09LSqqR/bwMGnYiIaqlSp7509UGBnRB69ZS/2eR1JX9o6+vrw7ROY7i1/AB1jS0U6j579hSbt2zClSt/IS0tDfXr14ezcwt8/HEI2rd3l9XLysrC0aNRuHDhHB4/foSMjAzY2trA2NgY7u7uqFu3ctPPqmqI9YkTR2FoaIiAgIFy5R9++DHOnDmNmJjjsh/Xb+NULXUe15LTDVau/BFpaamyaW7FlB3Xkn7+eR2kUqnK/S9KYp6KvLw8aGlpITh4OEQiodyInKK30tjDyqofOnTwgZaWFv744xS2bQuDvr4+GjduUmaC0JJf0q9di8O0aZ+hTp26GDz4I5iamuGvvy5i1arlePjwodzrqAsKCnDrVgLatXOFnV1fGBsbIyUlBVFRkRg3bhSWLVsNDw8vlfe1JqgJ/w5LWreu6Hrx8vJi0ImIqpVQKERWVqZGtpWUlAgAMDd/NeK0RYtWAICbN/+Bl1cHufrx8TdQp04dNGrkqJH+VZampi0WGz16HAIDByEj4wXi4q7g/v27CufwbZy2qEkMOhER1VKlTn3Z/zsKdCVw/tQbenX1y2zj9Tdx5OflYEvYVhz/cw36dP0CxoYmsrq5+ZkYMepjiMUSBAYGoWHDhhAIBIiMPIApUyZg0aLl6NSpC4CiLzZr166Eh4cXgoKGwNTUFI8f38eePXvw4MEDBAYGwszMrNx91MQQ68LCQty5cwvOzi0UXo/bsmVraGtrIyEhXlb2NkzVqsrjWjzdoLCwEN99NxvNmjlh06b1kEgksie/yo5rsfj4G9i/fw+8vb0RGxur0jbbtnWFnp4eXrx4jjlzvoO/f18Ar0bkREYewJEjUXBxccF33y2Urefn549582Zi8+YNMDU1KzNBaMlA0sqVS6GlpYUNG7bA3t4BABAUNARLlvyAyMgD6NOnn2yqg5GREcLCIhT6HBg4CIMG9cOuXRE1PuhUE/8dFouPv4G9e/fC19dX9sWfiKgqpacLYGGhmNMuLu4yHj68Dzc3D1mZQCBATs5L2NjYVnpaW2ZmBkxMTOXKRCIRtmz5GQDQuXNXWbmbmwcsLCxx6NBBfPjhx7K/Z3fv3sHVq1fQt28AdHVrdghA09MWmzVrjmbNih6O9e7dBwcP/obp06dgzZqfZbm03oZpi9WpZl9xRERUqtKmvrT3csU3X8/Gi6tPYd219KdZyt7Eoa+rg4c39HH07Ar8c+c4OrR7lYz3QfJlZGRkYOHCpejatYes3M/PH8HBA3Ho0AFZ0MnRsTF27vxN9oMcAAwMpMjMzER0dDQuX76M3r17l7uPmhhinZ2dBaFQqHTItL6+PurXN5ENqy6mylStmkyTx7VRI0fo6+vLTTco7biKxWIsXvwDvLw6omFDe5WCTkKhEDNnTsfly5cwY8YcWcAJeDXK5ubNf5CcnIgJEyYprO/r64eYmBN48eJ5mQlCv/xyBnR1dZGVlYV79+7A29tH7voGgL59AxAZeQDR0ZGyoFNpzMzMoK9voNJooepWU/8dFl8vHTp0QLNmNe9NTET0dlq6dBHS0wXw8PCCjY0tRCIRbt9OQEzMcRgbG+Ozz76Q1d24cQ2OHDmMVas2wN3dU1Z+7Vocrl0rmkZ+61YCAOC33/bIRoKPGvWJrO6XX06BpaUlXFxayt5ed+zYESQnJ2Lw4I/kcg/p6uri88+nY968mZg06RMEBAxETk4O9uzZCVNTszJz49VUmp622KdPPyxfvhi///6bLOikyrRFVkCoqQAAIABJREFUK6uaPW2xOjHoRET0lrFtUPRDW5IvLrNeaW/iMDexh7VFMzx+cg1ebYKgrV2Un6agoGjY8Ou5AMzNi6aZlZxSVtqbzBwcHGBgYIDnz5+rtC+aGGJd1pBpoOgH7+tDpsubqjVyZKha+6humj6uyqYbKDuuu3ZFIDk5EQsW/Ih9+34pdxsikQizZn2Fv/6KxX/+Mxu9e/dBRkaGwoic4mtWIilUaEMikcj+v6wEoYmJj9G0aTMUFJT+RNPAoKjs5s0bSreTnZ0NiUSM1NQU7Nq1A3l5uVWWGFWdauq/w+LrZenSJThy5Iha+0NEVBo/P38cPXoYx45F/+9eqAVbW1sEBgZh6NARKk0rvnLlL4SHb5Ir+/XXHbL/Lxl06tGjJ86ePYPfftuN7OxsGBkZwcnJBaGh49C7dx+Ftnv29IOBgQG2bQvD2rUroaenD09PL3z66RSlgf3aQJPTFkUiEQoLC5GVlSUrU2XaoqNjzZ62WJ0YdKJSqeNVyUDFErOWdO/eXYSGDodEIsH33y+Cr6+f3HKxWIydO7fj2LFoPHnyL4yMjOHm5o5x4ybB0bFxZXebqNZRmPqyseiHdj0nxZxMJZX1Jg5LM0ekpN9DVk4aTOsVfXmys3JB/P1TWLZsMSZN+hwODg0hEKQhPHwzjIyMEBw8vNy+ikQiFBQUyOUfKIsmhliXNWS6uM8lAwz5+fmYMGFMqVO1wsI2wte3Fxo1aqyW/lWFqjiu6ekCmJq+mlZYfMxSU1OUTjfIy8uTC2AmJychPHwzRo4cozCCSBmRSISZM7/CpUsXMH36TAQEDMBvv+1WOiLHyMgIkZEHcPToYfTqJT/C7siRw7L/LytBqECQhqZNm8Hc3AKmpqa4efMfCIX5skATAFy9elm2z697/PghRowIln2uW7cuQkJGY/jwUeXua3Wrif8OS14vDg7lXy9EROrSq1dvhb8lpZk9+1vMnv2tQnlo6HiVRx0NHz6qwn8rOnfuKjftrjbQ9LTF17+3FNu371cA8t+P34Zpi9WJR4ZKpY5XJb+eL6asxKwlFRYWYvHi+dDXN0Be3v+zd94BVZX/H3/dwZbtYrpQQXFvc+EWc6SimTlIDUfqt3LkKLOsnzY0t4iII7VcmOZemQoIIpqCCwdIiuBgiHIvXO7vj9s9cLkDVLLM8/pHOeO555z73Oc8z2e8P0/09qvVaj755COioiJo27Y9/fsPIiPjEeHh2wgKGsGKFWuoVq16mTwHEZF/O3qpL64uePavQ7mqDibOMl2JQ6vl9DQ3UzA6VSrvxdTJ0wkOWc6ECYUTJXd3T4KD1+qFGxvi7NmzFBQUULNmzZJvzAB/R4i1ra0dFhYWQnh0UZRKJVlZmZQvXyiSfuzY4RJTtf7449y/2uhUnLJ4rt99N4/MzIc0aNCESpUqo1AokEqlnDt3FhsbG510gxUrFpOdnYWZWaEGz9KlC7GysuThw4csXryAc+fOAZrvQKVSERERQcOGDYWJ3qeffsrp0xE0bdocS0tLDhzYS0FBAUOGDEepVKJUKkhIuEhGxiM6deqKj09dIiNPMX78aNq37wioOX78GOfPx+Hi4srdu3dKJRAqkUgYOPAdVq1azowZUxk1Kgh7ewfOnIkmNDQYmUyGQqEvJuri4sbChcvIz88jJSWFgwf38vjxY/Ly8v6xierzOpeK95eycC7NmfO1wd+hplrkT2RkPCIrK5Nu3drj5uaOSlWAnZ0d7dr5cfv2bTIzNR5wtVpNQUEBmZmZmJmZ6URxioiIiIj8e3nZaYvDhg2iSZMmVKtWkwoVKpCRkUFMTDSxsdHUqOHFwIGDhWP/i2mLLxPR6CRilBctlWxILwaMC7MWZdu2n7l16wbvvDOM0NBgvf0nThwnKiqC3r3fYurUwja6dfNn2LBB/PDDdyxatPxZb1lE5JVEL/Ul4jg5OYajBYpiKp1FJtW8HvJVSp3tDo6O1K5dh6ZNm+Pp6UlycjKbN29gypT/sXRpMJUqGQ8pP3LkCH/88Qfu7u7Url37WW5Rh7IOsZZKpdSq5c21a1dQKpU6YsSXLsVTUFCAt7ePsE1rrDOVqpWfr9Lb92/nRZ9r587dOHx4n066gZmZGfn5+YSErNcRV9emZnl6Fm5LTU0lMzOT7dt/1mk3Pz+f/Px8Ll68SFZWFt27a1IJLl3STCbPnInmzJlovesJDBzNzJlzhIicRYuWs2HDWo4fP8qKFYuRSCS4u3swduwEUlPvEh6+rdQCoe++O4Lc3Fx++mkjo0dr3oNWVtZMmPAhq1Yt10nZ02JlZaUTkt+zZ2/ee+9dZs6cwoIFS00/3L+JF3EuaftLWTmXDP0O8/LymDbtI2JjNd9vmzbtadq0OSkpyRw6dIBHjx4ydOhAg+3//PPPeHp6Cv1FRERE5FmxtJSiVht/n0skMnJz9ecCIs/Hy05bHDDgbc6ejSEubitZWZlYWFji6VmFoKDxDBjwNlZWupWIS05bLH3V3dcN0egk8syUtlSyMb0YQ8KsRbl3L5WQkBUEBr6Pg4PhSI2zZzUpDP7+vXW2u7m506BBI6Kjo0hNTX2mUs0iIq8qxVNfOnRrx4ghwyjIV1GpXVWj55lKZ1EVaPSg5LJCA0xiUhQ/7QsnLOxHqlf3Era3aNGK994bQnDwMj777EuDnxUZeZJPP/2UChUq0LlzZyQSicl7etkh1p07d+PChfPs2rWDAQMKU6C2bNmETCajY8fCMHptRJepVC0fn7rPdR1/N3/nc+3UqQv9+/chI6MwOnX79i0sXPgNp09H6BidLC0tkclkOlFV48dP4vHjwveKmRkcPnyYkydPIpPJaNWqlc67ZNeuXSgUpvsRoBORExQ0nqAg/Qi1DRvCANMCoUWNJ1KplPffH8fQoYFcv54IqPHyqoVarebbb782mLJaHGtra9q392PjxnX8+WdKqVIKy5qSnEvbtv3E6NHj9PZr+0vDho0F59IXX8yjoKCASpUql8q5tHHjRj3nUvHf4dq1q4mNjcbXtz4XL/7BBx/8T4ieatWqjdBftH0FMNpfRF5tbt68yeLFS8pE8mH9+lAuXbqETCbD1dWVFi1aYGdnp3NcXFwsR48e5vz5s9y9excLC3M8PDzp128gnTt303uHqdVqDh06wI4dW7h9OwmlMo9KlSrTqVMXBg4cjI1NuTJ7FiIvD7VaRUhIiNH9o0ePBkp+D4mUjpedthgYOJoPP5ykM28piVcxbfHfgGh0EimR5y2VbEovprgwa1G+/34+rq5uDBw4mIMHDQuDlkbINSHhomh0Enkt8apZEysXWx5E/2nS6GSqEseTXE3Ei9VfaXYA8dePULVqVR2DE2hKzVapUlUIZy5OVFQEM2dOpXr16rRs2bJUZc1fdoh1795vsXfvbpYsWcjdu3epWrUakZGn+P33YwwfPlInTah167YmU7X8/DpTu7Z3iff4T/B3P1crK3MGDRomHPssz7W4MKeFhZobN24QFRWFmZkZ1as/X8p0aSK4SiMQ6umpLxBqZWWFr2/hO+7YscOo1Wpatmxd6msDyMrK/EeMTsbQvjuPHj1MTEy00f7SsWNnvvtuHqNGjWH9+jU6/UXrXOrcuSsXLpwHCvvLhg1h7Nv3K40aNdWJjizaX27fvs3u3eE4Ojpy/nwcw4e/p+OIKvo9afsK8ML9ReTfyb17qWUm+VCzZk1atGiBUqnk4sWL7Nq1i7feegsbGxvh2BUrlpCenka7dh3o39+L3NynHDlyiDlzZnH27BmmTZul0/aqVcvZsCGMJk2aERg4GrlcTlxcLKGhwURGniI4OKxEZ4uIiIjIfxXR6CRSIs9bKtmUXkxxYVYtR44cJDLyJCtWhJrUuNDqNZ09G4OXV6E2TG5uLgkJmqpBaWmpJd2aiMh/loK8AlRPTafYmVpo33+UhJncEjubwuiOJ7mZFKjsMYRKpUKl0q+Wd/p0JDNmTMHTsyrLli1jy5Ytpbr+lx1ibWZmxg8/LCckZDmHDx8QjAAffjiFfv1003dkMpnJVK1Bg4aU6h7/CV7Gcx06RBO9JJFqFlilfa4vwotGcJWFQGhmZgbBwctxcHCgb98BwvZHjx5hb2+PVCrVu+Zjxw5jZWVNtWo1ijf3UjHmXOrSpTuXLsUb7S+HDmkcQ3Xr1uPPP1N02tQ6l44dO8LOndt09u3aFQ5AdHQknTt3FbYX/R0eOrQPpVJJfr6Khg0b8/PPm1m3bg0ODg706vUWI0cGiaKtL5mXXWDm/v37bN/+M1euXObatcs8evSISpUq60QrmpJ8+PTTTzh27DDVqlUnLGyTEJUXEhLCxo2a6pgeHh6Eh4cTGxtLu3bthHPHjp1A/foNkclkwraAgMFMnDiG3bt3EhDwtuCA0eiObaZWLW8WLlwm/Nb79h2ATCbn4MF9JCZepWbN508rFxH5LyEzl6MqMJyKJpNKUClNV18WefUQ39YiJfK8pZJN6cUUF2YFTbre4sXf06tXX3x965tsu2tXf9atW8Pq1cFYWlrRtGlzMjMzCA0NJjMzQ69tEZH/IsYW2rExseSmPaZcVUdhW162AlVuPrlPc7FAE+ljbKH9KOsOaQ+uU92jGVJp4YTbvlwlkpKTuHjxgk50x8WLf3D7drJe9GN0dBTTp0/Gw8OTRYuWY29v2GBliJcdYg1ga2vLRx9N46OPppV4rLW1jdFUrX8zZf1czeUyFs09qrNf+/ekWR2BZ3uuhnjnnXdKPOZFI7ieVSA0MvIkmzZtoFmzFjg5OXPvXiq7d+8kOzuLefMW6ETkHDq0jy1bNtOuXQdcXNwwM5Nz+3Yy+/btITs7i2nTZj13WmhZYcy51LVrD5PnFXUuFe8vWudSu3YdmDz5E2H7kSMH+fzzmWzYsJGqVWuxd+9unTa1/cXd3ZPFi78H4M8/Uxg/fhJ2dvYcOrSPDRvCSE9PY9asOXrXVJr+IvJ8lKQBNnfufHbs2GrSKFUaDTCtUerq1csolUosLCyoX78+MTExetekfa+sXbua0NBgXF3d6d9/IBUqVOT48aNYWFgAhZIPAO3bt9drJzExkTZt2ggGo6KGai1SqZQOHTpy7txZbty4LhidVKp8FAoFzs7OesZlrSHO0tJKrz0RkdcVVYGa9+YeMrhvzazSzVFEXi1Eo5NIiTxvqWRTejGGhFmXLfuBggI1Y8ZMKPGa7Ozs+OGH5cyd+xnffPOVsL1hw8YMGTKcdetCxfx5kf88phbaUgs5rt0L0+DuHrrOo3OpJPjG06iuJqrJ0EJbkfuEo1FhWFiUo34tXQHe+rW6ceLsOj78cDx9+/bH3d2DlJTb7Ny5DbncjMDA94VjL19O4JNPPgbU9OzZi6ioCORyNdeuXROOKV7BzsJMgrncuGCnGhmPc17kiYn8VymLCK6SBUILqVzZFTMzc7Zt+5msrEzs7R1o2rQZw4eP1KtaWL9+Iy5dSuDUqRM8fPiAvLw8nJycadq0OQEBb1OvXoMyfBLPxz/hXGrYsKFJHY0nT3L+OieL9et/pkqVqoDGcDphQhD79+9hyJDhYqXal0hpCsyYMkqtXr2+xAIzLVu2EoxS778/jqysLHbv3imkT6pUKjIyMlAqFSQmXuPTTzUGzVat3qBp0xZERUXw/ffzsLa2oV+/AE6e/B0olHywsbFh6tSp/Pbbb8L137hxg6SkJDIyMnByMp2il5am0XhzcnIWtllYWNKgQSNOn47kxx/X0qFDJ2QyGXFxsYSHb6Nbtx46mnYiIiL/PpKTkzh4cB/R0VHcuZOCQqHEzc0dP79ODBz4jp6ouCHy8/OJi4vj2rVrZGVlYWZmhouLC82bNzeoU3zlymXCwlbxxx/nyc19ipubO7169aV//0E6UZYAH3zwvlEZi9Wr1wvZC/9WRKOTyDNT2tLapvRiiguzXrlymT17djFyZBBZWZmC/sajRw8BePjwASkpt6lYsZIwka1Rw4uwsE2kpNzm/v10ypevgLu7B8uXLwIQJqgiIv9VjC60+/clwe0O5g4lR08UX2ibm5tTwak6jXx6Ym2lG5nkXtmXJYtXsH7DOvbs2UVOzmNsbW1p3rwlI0aM0kkduHHjOkqlRq9m8eIFBj+7uNEJVMSf/MrgsQB128wEZEb3i+hjK89DKjWeZlkgkZOtePWfaVlEcEHpBUKrVavOggVLSvV53t4+zJ49t1TH/lNonUu2FiqknVvRs3MbBo8YhSQ/h1EjCjW6iveXv9O5pNVnrFPHV+993r17T+LiYjl37qxodPoXoDXq2ts76GkdQaFRKjR0lckCM0eOHCAy8qSeUcrPrxMjRw4FICUlmTff7KzTfvfu/sya9QWg0QUbNKgvf/6ZQu/ebwlGJ22Uk5WVFf7+/vz555/C+UqlkqSkJJ48eWLS6HT/fjq7du3A1dWN+vUb6uybPXsuc+d+zsqVS1m5UlONUiKRMGzYe4waNabEZygiIvLPsmfPLnbs2EqbNu3o2rU7crmcs2djCQlZwdGjh1m1Kkx4LxlCrVbz8ccfExMTQ5UqVahbt+5fsi8J7Ny5kz59+ugcf+7cWT766ANsbMoxYMAgHBwciYk5zeLFC7h586bBIhwODg5MmPCR3vbiqcn/RkSjk8hzUdbCrPfupaJWq1m9eiWrV6/Ua0sb9m/Ikuvu7oG7u4fwd1RUBDY2Nv8K77GIyN+JsYW2xCqfcXtm6Gzz7FcHz351aNy0CeqnuscXXWgbSpcqSrOmzWnQUD/toDj+/r3w9++ls83CQm2yCoxI2SMtyCdlqfEUQ/cPgnlVDXmmIuPEqLjnQ6rW9BcboLq9nI1rQ+n++JSwv3h/eRHnUnKygqysXKPOpYoVNdFlzs7OFEebVpydnVU2Ny7yTDxrgRmtUSolJRkwXWDmyZMnBo1SDRs24uzZWCpUqMjCheN4+vQpP/zwLenpaTrFLRISLnLnjsagFBlZ2HcLI+4kFBQUoFQqMTMzQyKRCBEF+fnGdWRyc3OZPn0yT58+Zf78hXp6YmZm5ri6ulGhQk9atGiFRCLht9+Osm5dKObm5gwfPtJo2yIiIv88fn6dGDo0UCg2AxpdNnd3D9avX8Ovv/5C//6DjJ5/4sRxIiIi8Pb21tGHq1mzJlu3biUiIkLn+B9++A6JRMLKlWuEQiL9+gXwzTdfsWtXON2796RBA13jtqWlFd26+ZfF7b50RKOTiFFepjBrnTp1+fLLeXqfFRcXy44dW3n77XepW9cXV1fT1X22bfuJGzeuExg4ulRhkCIiIiIiryrGI+PEqLgXR6lSk600nu4Kf69zycenLlCYzlQUrUGrpGplIn8PJRWYMWaU0uovmSowA4aNUnXr1uXs2VhUqgKaNWtBQUEBc+bMpEaNmoSErEClUjF48FDmz/+Kpk1bEBsbzaVLCcL52rnpw4cPaNeuHQqFArlcjru7uxDdZEyYXqFQMH36x1y5comZMz/XK6STm5vLmDHvUbt2bebM+T9he+fO3Zg9ezqhocH4+XXSS70VERH592AsPa1Tp66sX7+GGzeumzz/7NkzANSurVswwM7ODhcXF1JSUkhNTcXR0YWsrCwSE6/SvHkrvcq1/v692LUrnL17d+kZnQAKCgp4+vQJ1tY2r1RFTNHoJGKUlynMWr58Bfz8Outdw9OnmpCMunV99fZPnqypdFK1anUkEgnR0VGcOPEbrVu3ET1KIv9pSkqZypZIje4TERER0WLMuXQ+9TFJGbnUq1RYQv7hkzyUt5KwcfIsE+eSjY0FOTkKo84lV1c36tVrwMWLf3DlymVq1/YGNJo+u3aFI5PJaNas5d/zYERMUpIGmDGjVGysRgjclAYYGDNKaSLmtBFL2dlZKBQKPD2rYG5uTni4pkJiSkoyX3/9LUFBgUJKHRRG3HXs2AU/v3YcO3aMtLQ04uPjSU7WRGAVja7SojE4TebMmWimTZtlMMrg2LHDpKQkM2aMfmEJP7/OHDlyiD/+OCcanV4y/4RGD8DNmzdYty6UuLgzZGVl4eDgiLd3HaZMma6jBQalr+Qo8s+RlnYPQO+7K05enial3JDxWrvt4sWLtG3rIhxrqIiINoUvPv6i3r709DS6dGmLQqHA0tKS5s1bERQ0/pWQlBGNTiJGednCrM+Kr299jhw5yL59vwJQpUo1PvpoGn369NMTXxMR+S9RUsqU/f/EFDYREZGSKe5ckhU84czJFI7fysRKLmV008L3fFjcPQ5ve6fMnEsODtZkZDwx6Vz68MMpjB8/mv/9bxwDBgzC3t6eI0cOcelSPIGBo0s1DxEpe0oqMGPMKFUaDTAwbZQqKNBE3xUVsVcoFGRmZhAWtprhw9/Dzc0dc3NzHRF7bRRD1arV6Nq1K0lJSVSvXp0qVaqwe/duJBKJnhFBqVQyY8YUYmKimDp1Jm++qavJokVr3FKpCvT2qVSa683PNx01KFL2vGyNHoDTpyOZPn0ybm5uDBjwNo6OTjx69Ij4+D/IycnRMVyUppKjyD+LSqVi7drVyGQyunTpZvJYrb7gnTt3dNLC8/PzhYjde/cKDVgODg7Ex19AocjV6YdxcZqIKa2xS4vWEePlVROpVEpCQjzbt28hNjaa5ctDqVHDi38zotFJxCgvW5jVEIZ0YbSMGDGKESNGPVe7IiIiIiIirzvFnUsSCVSwgB41HRlQtzwVy5mX2Mbf6VyqVcubFSvWEBKynK1bN6NUKqlSpSozZsw2OjcQefkULzBjzCillWUwpQEGho1Sjx5pIqmkUo1TUWvASku7x82b17G1tcPMzIx27fy4cOH8X4Uu7FCpVOTl5VOhQkUcHZ3YvXsnQ4cOFtrVGrPUajUFBQVIpZpIYaVSyfTpk4mOjmTy5On06tXX6P1r72X//l/15s1ax6g2XVTk5fGyNXoePXrInDmzaNSoCfPnLzCargkaQ0RJlRwNCUm/zpRV5NqmTes5cGAvd+78iZWVNc2bNycwMMhgtNDixd8TH3+BoKDxKJV5tG/fApVKxZdfztNzknTt2oOQkBVERUURE6OJ6rSyskIikQgGcO2/EomEgQPfYdWq5cyYMZVRo4Kwt3fgzJloQkODkclkKBS5Ou3PmDFb528/v860adOOCROCWLJkAT/8sLzUz/KfQDQ6iYi8ZP6uQbNRo8a8/75+iGVExEl++WU7168n8ujRQ8zMzPHwcKdLlx706dMfCwsLnePVajWHDh1gx44t3L6dhFKZR6VKlenUqQsDBw7GxqYcIiIiIiKvPsWdS/bmCqNRlB+/4c7CzXvIVFro7fu7nEugMWjMn7/wudoWeXmYKjCjNUpdvXoZMK4BZmZmRl5enkGj1N69ewBITb3Dzp3bUSgUSKVSzp07i42NDXZ2dty+nczQoQOFc65fvyb8/913A6hVy5tr164wevRoKlSoQF5eHhcuXEAmk6FSqQSdJ4AvvpjF6dMRNG3aHEtLSw4c2KtzPTVq1MTLS1OBtXXrtvj41CUy8hTjx4+mffuOgJrjx49x/nwcfn6dhfRQkZfHy9ToAdi5cztZWZmMGzcRuVxObm4ucrncoPEpLi7WZCXHo0cP8vHH00warl43yiJy7ZNPPiIqKoK2bdvTv/8gMjIesXPndqKiRrBixRqdaqghISvYvn0LvXu/xZAhwwkKCsTc3IKnT58YbH/z5h/JyclBLpcLRQmys7MBTerukydPsLEpTFl/990R5Obm8tNPGxk9ejgAVlbWTJjwIatWLReiJE3RoEEjGjRoRFxcrF7E1L+NUvXkGzdusGzZMhISEkhLSyM/Px8XFxfat2/PyJEjhQojJXH8+HFWrFjB5cuXMTc3p2XLlkyZMgUPD4+STxZ5aVhaSlGrjXd0iURGbq5+CLFI6fi7Bs3w8G0EBekPmtevJyKVynjzzT44O5dHocjl0qULLF68gIiIkyxcuExHiG7VquVs2BBGkybNCAwcjVwuJy4ultDQYCIjTxEcHPZKCdeJiIiIiGiQmctRFahNHCGO7SLGeZECMwqFgtzcXJMaYM2ateD06UiDRint4vv+/XQWL/4ekGBmZkZ+fj4hIetJTb3L48eaBd769Wu4du0qTZo04/r1RMzNzZkw4UOcncuTnZ3Nhg2hREVFIZPJcHNzIysri4cPH+o44S5fvgTAmTPRnDkTrXfPgYGjBaOTTCZj0aLlbNiwluPHj7JixWIkEgnu7h6MHTuBQYOGvMBT/+/ysiNXtE7YhASNVs7evb9y6VI83bv3NOiE1erunDhxgqysLGQyGa6urrRo0UJPowc01RJtbGx4/DibESPeITHxKlKpFF/f+kyY8KFOtNvlyxqRe1OVHJOTk6hevUYpn+Z/n7KIXIuKiqB377eYOrUwiiwgoD99+/bmhx++Y9EiTbRQaGgw69aF4u/fiylTZrB160/cunWDd94ZRmhosF7b+fn5bN26GW9vb9q2bUtWVhZPnjzB2tqa2NhYEhMTAahatapwjlQq5f33xzF0aCDXrycCary8aqFWq/n2268N9g1DuLi4EhcXS3Z29qtvdLp37x7p6el06dKFSpUqIZfLuXr1Klu2bGHPnj388ssvBkvaFuXgwYNMnDgRb29vpkyZwuPHj1m3bh2DBw9m+/btVKpUqUxuSOTFUatVJsuajx49GnFi+vz8XYNmt27+DBs2SGfQBBg6dIReGw4O72FtPZvw8K1cuhRPnTq+QOGgWauWNwsXLhPCzPv2HYBMJufgwX0kJl6lZs3aem2KiIiIiPy7URWoeW/uIaP7t3/W5qVdy61bt9i2bUeZLDgPHtxLcnKySZHfuLhYjh49zPnzZ7l79y4WFuZ4eHjSr99AOnfuVqIz5dNPP+HYscNUq1adDRu2vNC9v6qUVGCm6HyjaIEZQDBK9enTn9mzpxMUNIL27TsilcoID9+Kg4MjkydPZ8yY93SMUmvXrubBg/vcunULAE/PKrSHOL8yAAAgAElEQVRr5wdAZmYmW7du5vTpCAYMeFv47MOHD3DjxnWmTZvFxIljsLKyolmzllhZWSGTyejYsY0wz01OTmb//v14eHjoRJVs27b7mZ6NtbUNQUHjCQrSFxMXMczLjly5fj0RiUSKmZk5UqmUwYPf5fbtZKNOWG2kSW5uLi1atECpVHLx4kV++eUX4RitRg9ojGgqlYqPP56An19nRowYyd27d1m3LpQJE4JYtWqdYETS6oCZquR4/366aHQqQllFrvn799bZ7uHhQYMGjYiOjiI1NZW9e3cRFhZC9+49+eSTT0lLu0dIyAoCA983Kh6vUuWjUChwdnZGIpFgb28vVOvUGtfNzMxo0KCB3rlWVlb4+hYamI4dO4xaraZly9Ym70fL7dvJyGQybG3tSnX8P0WpjE6tWrWiVatWetubNm3K//73P3bs2PGXIcIweXl5fPnll7i4uLBx40YhtKxdu3b069ePpUuX8uWXXz7nLYiIvFqUNGju2bObAwf2GZ2EGxs03dzchUFz2bLFRET8bjL1TivA+vPPm8jOzubq1UtkZGQA4OzsLBictJQvr/FuWloWLgS0k3BHR0cCAgJe7MGIiIj8o5SF17mgQEXC9WPc/DOWLQc+MZn6e//+fbZv/5krVy4L40+PHm8a1AcsKCjg1KlTpKenk52dTV5eHr/+uhvPShJ6d6lJVQ/7MnoKIi+L8PAd/PzzpjJZcLZv3x43Nzc9kV9HR0fh+BUrlpCenka7dh3o39+L3NynHDlyiDlzZnH27BmmTZtl9LNOnTrB8eNH9SIhXjdKKjCzaNF3rFq1giZNmpGcnATAhg1hxMXFClWPa9asjYWFBV9/PYe1a1djaWlFy5atGDt2Ii4uroIw/QcfjKZv736sXr1S5xquXr3C1atXABg27D1q1/ZhyZKF3L17l6pVqxEZeYrffz/G8OEjdSqAnT17hiVLFvLGG23x9HQlPj6etLQ0EhMTsbS0NLjOEfl7eZmRK6Bxwi5c+A0nTx4nKGg8Q4cGAvD99/MNOmEjIk4ilUrJy8tDKpVSo0YNbGxs+O2334Q2i4rVP336BJVKRdeuPXTeY7VrezNx4hjWrl3NF1/8n855pkTzi7YtYpxnrS5nqmLcsmU/cOzYYbp182fGjNlIpVK+/34+rq5uDBw4mIMH9wGa6plJSbeESE4LC0saNGhEZGQkeXl5VKtWDalUyp07d7h4URNZV69ePaysrFAojF9jZmYGwcHLcXBwoG/fAcL2x48fC0bzokREnOTChfO0bNn6X/9+eqFEUTc3zWCelZVl8riYmBjS0tKYOHGiTi6jj48PzZs3Z+/evXz22WcGf3giIq8L2kHzxo3r+Pl1MjoJNzVomptrBpzNm9frpd69//4Ivv12Ec7Ozvz++2U2blyPjY0NR44cpGLFSnh71yEqKgJHRydOn47kxx/X0qFDJ2QyGXFxsYSHb6Nbtx54eHgC4iRcROS/Rll4nY/HrOFO+mXcK9VlzJiR3H/4wGjqb3LyLTZsCNMZf4yhUqlIT0+nUqVK1KxZEzMzM3y8a7Jt6yY+/f53Phnbkrq1xUo/rxJdunQlIODdMllwfvrpDCFypajIb8+ePYXjx46dQP36DXUm7QEBg5k4cQy7d+8kIOBtqlfXr/7z5MkTvv9+Hv36BXDy5O9lceuvLCUVmClulDI3t+DevVS9qsdvvNGW1q3bsG/fr3zzzUKhGiIUCtNvWL+G7xd8h7mZFZWca9LIpye2Nhrn16RZHVH+VQ1u8OChhIQs5/DhA2RlZeLm5s6HH06hXz+NtpM2YunWrZvUru1NRMQJ9ux5iEKhwMbGBh8fHxo1aqSzPhF5ObysyBVtvyuq0aM1OEGhE1arvwOayMiHDx8wYMAAjh07xokTJ4R95ubm5OfnU1BQoNNvtHo/PXq8qXM9jRs3pVKlysTFxQrbSlPJ0dA8X0SX56kud/ZsjJAaC/D06VMh5fLYscNUqlSZpk2bc+jQfuLjLxARcYLAwNFCVTmAQ4f28803X+tUc509ey4jRgwmOjqa6GjdlFwPDw8aN26ssy0y8iSbNm2gWbMWODk5c+9eKrt37yQ7O4t58xboRFUVNZq7urohk8m4dCmegwf34eDgwMSJHz/H03u5PJPRSaFQkJOTg1KpJDExke+++w6A9u3bmzzvwoULADRq1EhvX8OGDYmKiuLWrVvUrFlTb7+IyOuAdtCUSqWsXLkab+/CvO/ik3Bjg2Zubi7nz58FNKWn/+//vhf2devmz+DB/Rg3bqSwrU4dX8aNm0iVKtVwdHQkIyODN9/sTMOGjcnKymLlyqWsXLkU0FRZGDbsPUaNGgPoTsJPnfpd9MaIiPwHeFGvc8q9i9xJv4yXZ0ta1A9gwADNwtBY6q+3tw+7dx/SGX+MYWZmRr9+/XS2jR83kvqet5n42SF+PXpdNDq9Yvj6+pKRoS/I+qILzqIiv48fPxb6c1G9IS1SqZQOHTpy7txZbty4btDopBV0HT167GtndDKlASaTSlAp83W2vWjV4+LRlhKJFBsrR5zs3bCysDXYjq2tLR99NI2PPpoGFKZcvvtugF6099y58wGwsFALRkqlUklMTAw3b95EoVBgZ2eHo6Mjb745wGDKZWTkSdatW0Ni4lXMzMxp0qQZ48ZN1ImqEnkxyjJyJSHhIpUrV9bR6Bk/fhIZGRnk5DzmwoXzbNy4Hnt7eyHKCQo1lzp06ICTkxOZmZmCRs+VK1c4d+4coKvRU7FiRZKSbhmUnHF2Li8I6QOUL695X5mq5Kg9RsQ4RavLeXpWNXls167+rFu3htWrg7G0tKJp0+ZkZmawfv1qMjMzhOPu3Uvlq68+1zk3LCyEhg0bmyx6YWZmjouLC0+fPhVSM2UyGUqlkooVK+plkFSu7IqZmTnbtv1MVlYm9vYONG3ajOHDR+rdi6dnFcFo/ujRQ/LzNRU5+/Tpz7BhgS9cKfZl8ExGp61bt+qkwbm5ufHtt9/StGlTE2dBWprmx2NIt0krQn7v3j3R6CTy2lJ00CxqcNJSdBIeFPSBwUEzNDSYx48fA5qqKkVxc3OnTh1fLl78gxYtWnLt2lWuXLnExx9PwMOjCn5+nejWzR/QTMJdXd2oUKEnLVq0QiKR8NtvR1m3LhRzc3OGDXuPWbOm8vDhAw4e3Ed2djYSiYTY2Fjq1asnhAVruXz5MsHBIfzxx3lyc5/i5uZOr1596d9/kF6YaHFEDQ0RkZfHi3qd793X7K/h0Vxne9HU36JeZ2trG6ytXyy6wN7WAjMzKTlP9L3FIq8mZbHg1GrzpKWl6RhRDX9emtHPS0i4yI4dW5g9+6vXsnKrKQ2wrZ93QGpu/HdXIJGTrTD9ji9O0WjLHt39+f3Qde7dT+T8lX0k3TlHtzaTkMuMZ0U8a6EVlUrFnj17uH//Pr6+vjg4OHD79m3mz59PWtpDRo7UreR4/PhRZs2ahpdXTcaNm0ROzmO2bNnM2LEjCQ3dIBoJyoCyjlxJS0tlzZpVOho9n332Cb/9dlQ4vk4dXz76aBq2toWGTa3mknadWlSjRxvdJJfLdTR6fHzqkpR0i7S0ND0Ddnp6Go6OTsLf2vetsUqONjY2eHpWMXn/rzvGIteMYWdnxw8/LGfu3M/45puvhO1NmzZlyJDhrFsXyqRJkwkI0OjDzZv3JadOnWDjxm3Y2Wn0kvbu1URO9us3kCVLVglt5ObmMmbMe/j41KZdu3Y6n3v48GHOnj1LjRq6+lzVqlVnwYIlpbrXqlWrCUbzV5VnMjp17tyZ6tWr8+TJExISEjh69CgPHz4s8bynT58C6C1GASE151kiJRwcrEs+6DVDJpOW2XPJzTVcClKLRCIRv4MyZMmSxWzfvoWAgAAmTDAsQPnkicYC7+JSCU/PyoSGrmHGjE/0Bk1vbx8uXUqgfHknve+oYkXNZOjcuTi6dOlCfn4+Bw4cwMnJ8a/0Gc3EMirqFG3btuO77wojpQYMeIvJkz8mNDSYGzeuER0dhZeXFwEBA1m2bClPnz4lNjaW27dv06dPH8E7ePfuXUaOHEm5cra8++67ODk5ERERweLFC7hz5zaffz7H6HP57bffOH78KJaWlmXav18Fsp4oX+h8U3K4UqkEWxPP8mkJny2RPv/vv6SxpSSkL/DZ/2VepL+UZjwvOv4UP7Zof1EVaKIeZH8tCov2lXLlNJP0pKSreHtXpzhqtUbkwNxcLpxjqL8UFBSgVCopKCjg4sUElq6NJVehomFdXS+f2FcM88JjSxm+/w2N6yqVih9/XINcLqdfv74mP6tOHW9++QUuXTqPr2/hgjM/P18wJGkdMcauOy0tjd27w3F396Bt21Y6Mg/5+fl8993/0bp1a/r37wNo+tXr9D4y1V+kBfmkLA0yut9jwiocHAxHJxmjV6+efPDBOGxtbXn6REnq1SPUqtKac5f3EZ94mOvJp6ldrY3R99CRI4eJioogICCA2bML5xcBAf15660+LFu2gNWr1whjy+XLl0lPT6d169b4+mqiXHx8fLh27RobNoQxePBAIYIpLy+PRYu+o3LlymzcuFEwmHfp0pGBAwP48cc1Juc0IqXj66/nEh9/gUmT/kf9+oYdIVoCAvqzfn0YoaHBODnZ07JlKx49esTy5cuEyJWYmEhOnz5Nr169+eqrr5FKpUycOJEhQ97h0aNHREdHc/XqFR4+TOXhQ0dcXFywsrKi4K/3maG1a2pqKgCenp5YW1tjaanpi/37v8X+/XvYu3cn3bsXRu7+9tsx0tPTGDAgQOi3HTq0oUKFCuzZ8wvvvz9S6E+XL18mLi6Wvn3fonz5f7cw9ItgcmyRSrArYYxdtmwp69aF0rfvW3z55dxSV9Vu0qQ+4eE7SU5OIi0tnYoVK1CtWjW++eYbAOrUqY2DgzUJCQns2bOL8eM/ABRkZWmMkLm5mnfK06fZZGWlU7myC+bm5vzyyyFSUpKZMGE816/rOuiqV6/OjRs3SE1Nfa3X0M9kdKpcubLgoezcuTNdu3ZlwIABKBQKgoKMv3i04qPaHNWiKP5S03qWvFVD4divOw4O1mX2XCwsTJVT1niSxO+gbAgNDSYsLAR//15MnDjV4HNVqVQsXboMmUxGu3adyMh4QsWK7qxe/SMpKbe5fz+d8uUr4O7uwaRJYwF48CBDpy1N6t15QCO8OWnSBFJTH3Lw4EEcHZ0YNuw91q9fA0BOTg5t2nTQu5Y2bTqwf/8+jhw5jLW1DWvWbEIqlbJ2bRgymQxnZ2cSExN58OCBIDoeERGBRCJhxYpQ3NzcAejevQ/lyn3Ftm1b8fPrRoMGDfXu+cmTJ3zxxRxBQ0OlKni9+pz82bzDxTH1Cy4oMP37NS/hs9UlnG+KksaWkigoUJOd/Rr1g9LyAv1FrVaTaeL7NDT+FKVof3Gw1UQz37ufiKOdq9BXio4/N28mG+w/mZka55RSmS/sN9RfMjIy2LZtGwA//vgj1lZyenepSZ8uutGdYl8xwouOLSX0l2fB0Lxl4cJvOH/+PEFB43F0rGRyrGnbtjMrV65kyZIlyGRSsrKyyM3NJTY2VnBk5ufnC9ddvK3c3FwmTBjPkydPmDdvATk5eUBh5M6GDWEkJd3iyy/nC+cWFKhfr/fR3zi2GMLdvToqlWaeX3RsqeLakPjEw2Rkaxb7xt5DJ05odOE6dfLX2W9r60z9+ho5j8uXb1ClimasSkxMRC6X4+3trdPO4MGDOXbsGOHhuxgyZDgAMTGnSUtLY9SoMSiVEpRKTfuVK1ehUaMm7Nu3lw8++FinAp7IsxESsoJNmzbRu/dbBAS8W4rfmRkLFy5j7tzP+Pzz2cLWopErp0+fplKlytSv35itW3fonO3k5MT//jeNnTu3M3XqFABBo0cq1XyPc+bMQaFQ4OjoiEQiISUlRaikWKtWLZ2xxcenIZ07d+Pw4QOMGjWK1q3bkpp6l+3bf8bZuTzvvvuezj1NmPAxs2dPZ8iQIfTq9RY5OTls2bIJBwdHhg4d+d8eZ0yMLSXNU9esWcWaNavo3r0nH300XZg/lBYLcznlnSpT3klj03icncvvv/+OjU05fLw1ad+JibdQq9UsXbqEpUv1o5G+/lrj9F+9ej3e3nVITk4BCiseFkWtVv91XwWvzBq6QoVncxiUhhcaGb29valTpw6bNm0yaXQqmkJXPLTMVOqdiMh/meLhvsas9Kbyld3dPXB39xD+1oYDHzq0j9q1vXVS77ReH+3gp40WyMrK4p13hgtGJwCVqkDvOooOpLVre+vlJhctCQoag/KDBw9o2bKlYHDS4u/fi127wtm7d5dBo9PrrKEhIvJv4ln0Eqq6NeHitcOcv7ofucycP+/U5v6DBzrjT0lRzVevXuH990dw504KSqUSKysrqlWrRr169TAzM8PW1hZ/f38KCgpoUL8OW39aw9PcPPLyC5DJpKjVaiJi/2R+yChuJSWjVOZRqVJlOnXqwsCBgw2mSN28eYN160KJiztDVlYWDg6OeHvXYcqU6TopV1qtmAMH9pqsDCryfLxIqsTXX38tbHdxcaFBgwbExcUZjFIAzftp+vSPuXLlEjNnfk6DBrqaoykptwkLW83w4e/pvb9EXj5PcjXjh5WF6RTH0mr8VKlSCbVazf379ylfvryeoahu3bpIpVIuXUoQtmk1furWrUdx6tTxJTY2huTkJLHE/XNSVHNpypQZpT6vRg0vwsI26ThhfX1r8/XX/yccY0ijB6Bhw8Y0a9aS7t178t13/6ezT5sq6erqSkREBFevXgXA0dERd3d3UlJSDKbuzpo1By+vmuzZs4vFi7+nXDlbOnToxPvvj9NLv9SK5q9bF8qyZT9gZmZO06bNGDt24iuh0fNPEBYWwpo1q3Sqyxni/v375OQ8FqrLFUVdoGbR3ML0yis3T3D9eiL1anbF0sISZb6KOnXq8uWX8/TajYuLZceOrbz99rvUreuLq6vm/aDV5dqzZw916+rKpGj7jtYe8rrywub43NxcMjMzTR5Tr55mgI6Li6N169Y6+86dO0e5cuV0hNhERP7rlHbQXLToe7Zv30LPnr1LnIRv2/YTSUm36NOnPwkJF3RS7xo2bCx4fbSLrm3bfgI0EyithoaW/ft/1RMDDQ/fCmhexOfPxwnV7fLz81EoFCQkJODl5SXkvGuNVKYmf/HxF/X2ve4aGiIi/xae1QhgYW5Nx5ZjiDy3idMXtvJWP82YYWj8McatWzfo2LELXbt2x9JSzpYtWzhz5gw3btygb9++mJmZ4e6umeQNeWcQtSteZsb846SmxzB9fCu2/HqZXw5eo3nzpgQGjkYulxMXF0toaDCRkacIDg7TMfCfPh3J9OmTcXNzY8CAt3F0dOLRo0fEx/9BTk6OYHR6Vq0YkWfjRRecaWnJhIWFYW1tjb29PVFRUQA61X+0aAxOkzlzJppp02YJeoZFWbp0IXZ2drRr50dKym1hu0qlIi8vn5SU21haWglRvSJ/HwXqAi5ePYREIqWqW2OTx5oqtFJU4wc0/UClUhmsWmdubo6dnb3gyINCp54hY4B22/376aLR6TkorRPWFMWdsFFREdjY2LBz534h48YY2kycVq3eECqRaTWX3N3dCQgI0Dn+119/xczMzOD4IpfLeffdEbz77ohSXfcbb7TljTfalurY153t27cQGhqsU12uKE5OTjRr1hKA4OCl7Nv3q051OYDJkyfi4e7BrVt5gIS76VdIuXcR14o++NYsTIksX74Cfn76xU20kkF16/rq7G/dui0+PnU5deoU169fp1o1jRHq5s2bpKamUr169df+fVEqo1N6ejoVKuiL40VFRXHt2jWaNy8UDU1LSyM7OxtXV1fhR96sWTMqVKjAtm3bGDFihDDAX758mejoaPr166eTRy8i8l+mtINmaGgwW7duBqBr1x46x0yerKmUUrVqdSQSCdHRUZw48RutW7fhww+nIJfLBa/PJ598hK2trVDpJzHxKqNGvUdUVBQ1anjRv/9AJk+ehEwmQ6VSYW/vQGTkKcaPH0379h0BNcePH+P8+TjMzS349NMvCQlZrlPdDqBOnTo6FSqtrKywtLTk4sWLKBS5OqXWtWVHixu78vPzmT//K5o1a1nqCjgiIiJlz/MaARztXPBv9zHZOffxH1gDB0dn3N09WL58EUCJEUF+fp2ZPXsuoEmve/jwITExMcTFxXH58mVBd0WLpYWcZg1c2H04kTv3stn/2w2qetgTvGIJOU8184q+fQcgk8k5eHAfiYlXqVmzNgCPHj1kzpxZNGrUhPnzF5hMizlx4jhRURH07v0WU6fOFLYbq8wnUnrKYsHp4eGBi4uL8Pft27cxMzPTi6JXKpXMmDGFmJgopk6dyZtv9jHYXmpqKvfvpzN06ECD+99++y1at27DN9/88MzXKvJsxMbv5H5GEg28/bErZzpSwFh1KkPRltrUS2MFTczNzXUiM7X/N7Re0UbUiZV8n52yiFwpzsaNP3LjxnUCA0frGJwePLiPs7P+wr+oE1ZLo0ZNcHYuzy+//EKPHj2E7/3BgwfcvXuXWrVqGb1Wkb8HbbRhSZFrpvD1rc/Ro4dITtI4E+zKVaKZbz+8qrRCKnn+71Mmk7Fo0XI2bQojPDyc06dPI5FIsLOzo3nz5tSvX/+52/6vUCqj0+eff056ejotW7bE1dUVhUJBfHw8e/fuxcbGhk8++UQ4dsGCBYSHh7N+/XpatNCo8ZuZmTFz5kw+/PBDhgwZQkBAADk5OaxduxYnJycmTpz499ydiMi/kNIMmhcu/EFYWAhubu78+WeK3iTc17c+R44cZN++XwGoUqUaH300jT59+gkTKK3XZ9CgIURHRwlRRUePHqZ69eoEBY1nwIC3WblyCfHxFwRdp+bNW1K5sgvHjx9lxYrFSCQS3N09cHYuz4MH95k0aYzB+0pISODPP/9k0CBNSXWJREK9evWIiYlhxoypjBoVhL29A2fORBMaGoxMJkOh0J2gbd68gZSUZL7++tvnf8AiIiIvRFkYAWxtytO4UROU+ZqIR63XuV69BibPM7Sgq1GjBnFxcTx69MjgOco8zWdkZStQ5qlwsLXQWwxoPYyWloULkJ07t5OVlcm4cRORy+Xk5uYil8sNGp+0Rnt//946241V5hMpHX/HgvPixYs8evSIxo0b6/QnpVLJ9OmTiY6OZPLk6fTq1ddoG+PHT+Lx42y97d9/Px9zc3MmTPjQ4OJVpGw5f2UfV2+dwsuzJb5enUo83lh1KkPRltrfuSENFtD0l6J9Tfv/vDz9in3aSJln0acVKcPIFQ8PqlWpDhI4HR3F8ePHeOONtoweORq5XIZEKkGhzGfYsEHUr9+QWrW8qVChAhkZGcTERBMbG02NGl4MHDhYaFculzNp0mRmz57Orl278Pb2Ji8vjwsXLmBpaVli5XaRsmfmzM+ZOfPzFzp2xIhRvD8qSCe97lnw9++Fv38vg/usrW0YP3680bTu151SGZ169uzJzp07+eWXX3j48CESiQRXV1cGDRrEyJEjcXV1LbGNHj16YGlpyYoVK/jmm28wNzenVatWTJ48WdRzEnmtKGnQDAsLITQ0mG7d/Jk583ODk/ARI0bx5pt9SzUJDwwcja2tLRcunCcwcDQjRwYJAq5F02cGDnyH9es1VYOCgsYTFKRbSS8m5jQPHz5g+fLFuLi4MmjQOwAsWDAfpVJJuXLluHfvHhkZGULIccOGDalXrx4//riR0aM1YpxWVtZMmPChoNukRdTQEBH55zFlBEhOTuLgwX1ER0eRknIbpVKBu5sHNnIvvKu1RS63MNjmtm0/CV5nS0tLDh7cz44dW7h9O0nQXDKUXnDy5En2798vaD9euXKFtLQ0atWqhY+PDwAZWbmcjruDpYWMap4OeNdw5vylNNaErSfqdBynT0dQvnwFcnIe061bDzw8PIX2IyNPYWNjw+PH2YwY8Q6JiVeRSqX4+tZnwoQP8fEp1GUorVaMaHQqPZs3byqTBaerqxteXtVISEgQRH49PT1p3Fg3FeuLL2Zx+nQETZs2x9LSkgMH9ursr1GjppCSVbyEuZZlyxZhZWVlMO1CpGxZFbKSi9cOU92jGc3rDSj1eYY0fgxFW1pYWCCTycjJydFrQ6lUkpWVSfnyhX1Iq8eTnp4m6LdoSU9P0zlGpHSUZeRK+I5woDByxdOhFcvmnQBg0qyOAAwY8DbR0VHs2LGVrKxMLCws8fSsIjhhi6fhdezYmXLlzJk3bx5RUVHIZDLc3Nxo0aKFwbRMERER45TK6OTv74+/v37OuyHmzZvHvHn6wlsAfn5++Pn5lf7qREReM8rK62Ms9W748JHCcdr0mVq1alOhQkV+/nkjANevX2Pt2tWA5oXfsKFm0tWsWQv27fuVBw/u8+GHU+jQQeN11E7k6tWrx71790hNTRWMThKJhLFjxzJ4cCDXrycCary8NNU+vv32a51QZlFDQ0Tkn6Wk8efUqRNERp6iTZt2qNVqLl2Kx97entiz+0i6c45ubSZx4sxaylk7Y29biW3b7xMZFakz/qxatZwNG8Jo0qQZgYGjiYk5zd27d9i4cR2gSf/Vjj8PHtxDIpEI0ZsymYy8vDwiIyO5dOkSSsUTdoYfI+dpHqMHN8TCXM744Y1ZsSGORYuXCdd9/346w4ePZNQo3SjN5OQkVCoVH388AT+/zowYMZK7d++ybl0oEyYEsWrVOkGf5Vm0YkRKx8WLmuf2ogtObdRvXl4ejo6OvPHGG/j4+Og5bC5fvgTAmTPRnDkTrddWYOBone9W5J9DW52qmntTWtYfWKYaP9poS4lEQvny5Xnw4AEqlUonzS4+Pp6CggK8vX2EbVqNn/j4C3pGyYSEi9jY2ODpWeWZr/N15mVHrgQGjiYwcPQzXWPbtm25fPnyM50j8mLYyvOQSvUjCvFrAf4AACAASURBVLUUSORkK16sEqvIy0es6yki8pKQmctRFRguGS+TSlAp88vM61NS6t3y5cuE9Jn9+/dw9eoV4fyrV68IfwcGjhaMTlAopGmoul3RkqDFsbKywte30MB07Nhh1Go1LVsWFhYQNTT+WxSNirlzJwWFQom7uxv29vZCJbKS0FYiO/j7Te6m5VCgPkzFioYrkcXFxXL06GHOnz/L3bt3sbAwx8PDk379BtK5cze9RcuRI4c4fTqCK1cuc+vWDVQqFVu37sLFpeTI3f8qJY0/tWrVJjx8L+XKleOrrz7n0qV4Ro0MIjfLgfjEw1xPPk15x6ok3T3HjZQYLl7fpzP+qNVqtm7dTK1a3ixcuAypVMoPP3yn8xnXrl3l2jVNpZfRo0dTrlw5kpKSqFOnDkqlkrS0NCQSCRkZGezddwDf2hXo3qE6tao7ASCXS3F21OjJ1a/fkCtXLpOXl8e6daGYm5vrGN6fPn2CSqWia9ceOouZ2rW9mThxDGvXruaLLzQVjZ5FK0akdHz11ddMmTKrVMeaWnCOGDEKCws1ISEhJtvYtm3381xmmbchYhpttGWPHj1xlHZAYkRn5VlSLotGW2qiWTTzFS8vL+7du8elS5d09OI2b96MTCajY8dCbUmtxs/u3TsZOPAdoWLvtWtXiYuLxd+/l0ldOBERkdIhLcgnZWmQ0f3uHwQDotHpVUMcHUVEXhKqAjXvzT1kcN+aWZqJTVl5fUaMGGX0vKLpezNmzGbWrDkGj9NO6HJzc4UJnTakvGh1u127dhESEsK+ffuAkkuCZmZmEBy8HAcHB/r2LQyZFzU0Xj6GDUPuWEtrmEyXKoparebQoQN66VJ2dnZcu3aFNm3a07Vrd+RyOdHREfz++++cP38eqVSKQqGgVq1adOjQQa/dhGv3mbs4othWJbdu3SA0NJjQ0GCWLQsRSp2vWLGE9PQ02rXrQP/+XuTmPuXIkUPMmTOLs2fPMG2a7uI2PHwrCQnxeHnVxM3NneTkpOd9jP8Znmf8MZfLqOJ6hfjEw2Rkp9Ki/gDq1dKMDZNmdRQ0nQAUilwUCgXOzs5CFMrJkxqtpOXLF7Fp0wY2b94hpMCtWbOc+Ph4vL29adOmjdDOuXPniI6O5qu5n2OvOlLYvjKfzxecBMC2XDm+/HIew4cPxsnJierVaxAaGoyfXyc8PasCYG5uwdOnT+jR402de2vcuCmVKlUmLi5W2PYsWjEiIiLPR9Foy+bNWnBoV5zOfkuLcrhU0BQCeNFobwBvb2+uXLlCZGQk2dnZODo6kpyczK1btxg+fCSurm7CsUU1fsaPH0WvXm+Rk5PDli2bcHBwZORI44vk1xntPCP2TCQpKX+iUCrwcHOja6eOvDt4INZ/pbSZil4xNs/QOqDM/6qaDPA0N4srt07yMDOFh5kpKJQ5ZBT0YvqM2UavMTLyJOvWrSEx8SpmZuY0adKMceMm6nz/IiIiL4ZodBIReY3QTuhcXFyeK31PWxK0aHU7mayAXbt2GSwJmpyczJgxY2jSpAVOTs7cu5fK7t07yc7OYt68BTrlZkUNjZfPnj272LFjK23atBMMQ+fiznL4SGG6lFxmOiKpeLqUtkT9kSOHqF3bh88++1KIMvLyqsrvv/9OXl4eDg4OKBQKo+1WLm+NXC7F0c6CAT1rI5FIcKvVh+zH+cybp6luZm5eaBQbO3YC9es31EmRCAgYzMSJY9i9eycBAW9TvbqXsG/WrC8oX748crmcBQvmi0anF+BJribSx8rCtNHFwsKSBg0acfp0JD/+uJYOHTohk8mIi4slPHybjuZSaGgwYWFrqFWrFi1atCA3NxelUklqairnz5/HwsKCer51ST5faHQ6HXeX1HSNPss382foGIH8/Dpz5Mgh/vjjnGB0qlixIklJt3B2dta7Vmfn8ly9qptSUVqtGBHDWJjLUReJ9n36RIm5vPD3qhX7FXl9KRptOeeLz/T2V3SqIRidjFGaaG8tMpmMnj17EhMTw/Xr18nNzcXOzo4pU6bQu7d+1HXHjp2xsLBg3bpQli37ATMzc5o2bcbYsROpUMG0w+11RTvP8Gv3Bm9YPUQuteaP1DSWrFzFrz+vZ2GP6ljIpSajV4zNM0JDg4mMPEVY6Hrh2KzHacQnHsHa0gFnew/upJtOjTt+/CizZk3Dy6sm48ZNIifnMVu2bGbs2JGEhm7AzU10doqIlAWi0UlE5F/Ay8pf1k7o7t69+1zpe9qSoBs2rNWpbmdjY2OwJKitrS0ZGRls2/YzWVmZ2Ns70LRpM4YPHyks/ET+Ofz8OjF0aCDlyhUuzgcOGMTdpAIhXap2tTZGz8/Pz9dLlwLjJeq9vb158803+fXXX6lYsSIZGRlG2y5nY4FKVYBbZVvaNtcYIuq26cGOnYXRgkWvu1GjJnptSKVSOnToyLlzZ7lx47qO0UkUfNaQa2aGRGZ8oS+TyMh/YlxPRaVScfHqISQSKVXdGhs9Tsvs2XOZO/dzVq5cysqVSwGNtsqwYe8Jmkva6nk9e/bE1dWVw4cPc/PmTaGNihUr8sYbb2BnZ6vT9oNHTwGo4mZHt66dyS6iD6wtWpBfJPLKx6cuSUm3SEtL0+kboBEGdnR0MngPJWnFiBhGXaA2qbuiFfsVeX0pGm1pLpeZ7C/PG+1dHAsLC9q0aaMTTTlw4EAUCsPj3htvtDVY+EDEMNp5hpuTmZAy1bO2E65x9/jpQjoHEh/R21vf8K+lNPMMbUo2gJODO/27zMHSohy5ysdsP2g8wik/P5+FC7+lYsVKLFu2WkiZbNmyNSNHDiU0dBWffTajLB6DiMhrj2h0EhH5F/Cy8pe1kzRt9brSHFsca2sbnep2prQ0HB0dmTp1qtHJW2kQNTT+PrTCqMWp4tpQSJcyhUqVr5cupcVQiXobGxvy8/P/2m5ag8PcXCZUItt16BrNG7rgcOcOa9eGAtCmTTudSmTG0FY+c3IyPql9nclTqxi3Z6bR/ct7fo2pqcKChd9yPyOJBt7+2JUr2dNvZmaOq6sbFSr0pEWLVkgkEn777aiguVRQUCBUz/vss88IDQ2lSZMm+Pj4kJuby507d3j48CGZmZncvHkLhTIfC3PN9d1NewyAtbV+dJ426qFoRbpu3fzZv38Pv/yyXUdf7uTJ30lPT6NXr7dKvB99rRgRERERES2F8wzdyOZ2Ve356UI6SRmmtfBKN88onE+YyS0xK+XqNi4ulvv30xk1aoxgcAKoWbM2jRo14ejRg8yYMbV0jYmIiJhENDqJiLwGmEprEFMaRIrzd6RLgSba5OzZs0gkEqpVq8Yff/zx/+ydd1hUV/rHPzBDV0AEUUAFAUXsBVvUiH1RxBY1RY0aNeqatskviTFrsptmdlFjbICIZY2JsfdeErtSLIA0C2KhiIjSp/z+mMzIMDMwI2jUnM/z+ETuPffMvZMr55z3vO/3W2n/aieyn7cl8vO2REC1612vnivffhta5XPk5GSzbdsm3Nzcad26bZXtBaYREbGUXzf8gk+jLrT06VNl++LiYt5+eyLNmjXjyy+/1Rzv23cAc+Z8yvLly1AqlRr3vD179pCSkqJpZ2NjQ8+ePUlISODQoUMcOnSI2e90w9/XmTvZDzkVexMnR2sSU+4yYeJUuvfoQ0FBAffu3ePq1SsEBvalWTM/TX8BAZ3p23cABw7s5cMP36Fbtx7cuXObjRt/oW5dZyZNmqJ1/6ZoxQh0NeNKS0uxkjrSqEEbozTjYmLO8c47b1faZsmS5Vr/th8+fEhMTAy3bt2ioKAAKysrnJ2d6dChAy1aaGdD/v3vU4iLi9Hb7/Llqw0G5QU1Qy2JHOSG5x5l2Bo8J3j+ySlUZfc7Wle+FDVmnqFyDUwz+R7U2f/lnZTV+Pu3JDr6LNevi9J7gaAmEEEngeAvQGVlDaKkQVCeJ1EupWbevHlkZWUREBCAQznhT0NIpebUc7bDydGaNs3rkZBem0OHj5KVlcnq1SsqXegXFxfz6acfUlRUxNy584WrUA0TGRnGqlWRBA8OobbSuFKTw4cPkJGRzttvz9A5p9ZcAsPueQ0aNMDDw4OmTZty7NgxrXNrNydQ286K9yYFcPRUOpevZbFkyQ/IZDKkUgtee20cI0aM1ulz9uwv8fHxZefObSxcGEqtWrXp1asPU6ZMx9nZRautKVoxAl3NOCtLS9at2cn5JOM04zw9vfj883/pHC8rK+X777/BwcERf/9HjmMFBQVs2rQJhUJB8+bNcXBwoLCwkMTERKZNm8Z3382jWzftcmFHR0dmzvxA5zOEgPBTQC4j5i3DGd6tVq55ijcjeJrIFUp+Op+FxAwCvRyrbG/KPMMU1I7M+vS41Meys7Mfu3+BQPAIMQsXCAQCgYaaLpdSB4YiIpayfv16/Pz8aNeuXZX28monMs+GDrwzoSMKhZJf95zGzs6OgIAuOk5kWteWlPDpp/8gKSmRzz77QuNwJ6gZ1JpLAwcO4rNZ/+THb44YdZ16gi+XK3TOqTWXPvzwU4YOHQEYLt1Vt+3RXZXlBJCTW8i9+8X8M/R3nfYyWRk//bSaa9eu8P33C7TOSaVS3njjTd54480q799UrZi/OhU14yylEjIS6hJ3ebdRmnFOTnUZMCBI5/j+/XtQKBQMHDhIK5icnJxMcXEx/fv3x9PTU3Pc29ubX375he3bN+sEnaytbfR+hkAgeHKEnb3N5Zwi3mzniodD1S65Vc0zJlfISjUW9TzEwkI3+G1paanVRvDso8+R2d3dg8DAPowa9VqVJfCZOal06vKPSttUzK4FuHr1CqtWRRIbe478/HwsLS1xcXGhe/fumrLNW7dusWPHDsLDw03q+0VCBJ0EAoFAANR8uZQ6MLR//15VVkxwsNEC3monsjFDmgNwPjGLzMwsQkKGExDQmSNHDmo5kalRBZw+5Ny5M3z88WyxoKxhoqIiNJpLs2bN0dHYUFNUnM+1a1dxcq6n0dvw9PQCYM+eHfTp00+rvT7NpZycHL19X7p0CYDWrVsCKt2x14a2oLDokRlDQ7/hFJWYExo6F0tLS2bOfJ+6dYUL0dOkuppxhtixYysAwcEhWsfLylT//8trs6h/Njc319KXK49CoaCoqBBbWzuN06ZAIHgyrI7NZHtSLn/zrcPoVi5VtjdmntG/b//Huhf12KT+3VGe0tJSrTaCZx99jswxMdFERCzl0KEDhIdHYWVl+P+nfW1XvpzzFTKF9saYoexagNOnT/Lppx/i7u7OyJFjqFfPib1795KZman1Xjk6OhIYGEivXr2QycyM6vtFQwSdBAKBQPDEyqWWLFnIsWO/MXDgIGbPnk1kZKRRfd+7r9pdVPyhRXbkZDoAwcFDuXkzA9B2IgPVJHHWrI84e/YU//d/nzF4sPaiVFA9Nm5cT2RkmEZzaf/+PUjNzbmaodLFsLaqpbEzj7u8k01jvmThwmW0b98RgG7detC8eQtOnjzOjBmTGdC3H0qFkoNHDhITG0P/vv0JaP1IW2PQoDE4Ojri7OyMnZ0dxcXF3Lx5k5s3b+Lk5MQbr4/haowqc6mVn/bipUX3PjwokLB48Q/Y2NgQGNj3aXxFAiMwVjNOH7du3SQm5hytW7fVCTh7eHgQFxfH8ePH6dy5Mw4ODhQUFBATE4ONjQ1jxryh0192dhb9+vWgpKQEa2trOnXqytSpM2jc2FOnrUAgqB7/i8tk3cVs+nk7MrOLm1HXGDPPiDsfC1Rdsl8RdQl1dnaWZlNETXa2yoTExaXqwJjg2UCfI/PQoSPx8GjI6tUr2LFjq94yezU2VrX52996U1phbmkou/bevVy+/HI27dp1YO7ceUilUqyslGRmZur0bWtri6+vL0FBQVrmSob6fhF5sZ9OIBAIBFXyJMuljh37rVxWjP4sgsLCQkpLSykqepTG7l5fNWn47fQNmvs6E3PpDk19ffDz82f58mWAdlZMaWkpn376IWfOnOTDDz8lOHioUc8gMB616KohzaV6Tt6aoJM+JBIJP/ywhDVrVnL06CHmL5wPCiVu9q682Wkkwxr3Jz38nKb9mNFj2LJ1CwkJCRQXFyOVSnF0dCQgIICWLVtiZ2dX488oeLIolAqTNOMqsnPnNpRKpd5/325ubrz00kucO3eOHTt2aI47ODgQFRWFm1uTCu3dadWqDT4+vpibm5OQEM/GjeuJjj7DkiWReHv7mP6AAoFAL0sjVrD2QjZ9mjjyXjd3o7MKjZlnqP9rKupszPj4iwQEdNY6l5BwCTs7Oxo3bvxYfQuePoaya/v06c/q1Su4csV0sXkwnF27ZctG8vPvM336O0ilUoqLi03WdzTU94uICDpVk+rWj5rqznLixDG2bt1IWloq9+7l/lHn7MawYcPo3z8YK6vKa6M///wTDh8+gJdXE9asWW/awwr+dGqiXvnAqaVax9bu0G5jzPs2cOAgQkJG6H3fFAoFCQkJJCUlcf/+fdauXYu7uwdDhgzXaLUInh1MKZcqlRVRXFyEuVSldVBZuVRUlEqLx8mp7qOsGKlS40ZWfmA+c+YMycnJDOjXC/Ub3L5lfbwbOxKXkMU/Q39HLlfi0dCDGTMmc/58rI4T2b/+NZvTp0/QsWMnrK2t2bt3l9b9eHv74uPjq/k5Li5G41x1+XIioMrkUe+QCe0eXT777As+++wLrWOWUolek4KubV/l5w0ROjuGtrZ2TJ06g6lTZ2BvZaEVZKrI5MmTUaI0eN4YNmzYXq3rBTVLdPwWkzTjyiOXy9m9ewd2dnYGM9dsbGxwcXHB3d0dBwcH7t+/z4ULF3jvvff48cdwXF0flffOmjVH69rAwL50796TmTOn8uOP81iwYInpDygQCHSIioogMjKSPk0c+eAld8wNBJxycnIoKHiIq2t9k8qy/Zu34Eb8bZPvq127DtSt68z27VsYNeo1TWluSkoysbHRBAUFv/DZJ38FsrJUmUcSiYTly5dx7uxpUlKuIJfLqG1Xt1JH1fLZtTk5Obz22kidNuPHj9H83czMDFdXV7p06UJxcTEJCQnk5uZSVFSERCLh6NGjDBigWkPdvZujlbn7ojuqin9J1aS69aOmurOkpaVibi5h8OAQ6tZ1pqSkmPPn45g79zsOHjzE/PmLDe4eHD/+O0ePHqoyMCV4dqmJeuVubV/TOjYgxJ+ikmKT3reFC+dx4sQx5s9frNWXXC5n79693Lp1Cx8fH/z9/enatSvXrt0gM/Px9DsETw5Ty6WuZJxjULwvrf4Q5q5YLvXyy70BJUePHubatasA5Obe1ZsVo85SuXv3ruZYUnIKbRup/m5ubsasv3dj674Udh1KBeDYseN4eDRi2rSZjB79ulZ/6sDRuXNnOHfujM7nTZgwWSvoFB19VhMYU/Pzz//T/F0EnQSCmmVZ2GKSrx03WjOuImfOnCQrK5OQkOF6dVYSExM5duwYI0aMwMnJSXO8YcOGbN68mbCwxfzzn/+u9DPatGlHmzbtiI2NpqSkuNLxVCAQVI16ntGgvittG0g5cvW+1nlHaynt3VSbPWFhi9i9e0elZdnl5xnqDSg/v+bs5VHQ6WKKyglVLldp6qSmprBy5XIA2rZtT9u2qixLqVTKu+9+yJw5nzJjxlsEBw+joKCA9et/wtGxDpMmGXZXFDwfyOVyVq5cjkQioaysjF9++YmePV7GGm/MzCVk5qRqOapWpHx2bcU1+/fff4NCIUepVCKTybCzq8WECW8SHh7Ojh078PPzw9zcHD8/P2xtbZHJZNja2mrWUP7+LXUyd19kR1URdKom1a0fNdWdZezYN3Xajhw5hkWLQvn553UkJsbrFSIrLCwkNPQ7hg9/hWPHfjPxKQXPCjVRr+zl0UHr2N/+1pudu3ea9L6Fhs5l8+ZfSUyMp127RyVOMTEx3Lx5k0GDBuHmpqrXf+WVV7TqlwXPDjVdLrV06ULMzMzw8GioCQyVf5+srJQEBAQAKntz0A465ec/0OrfxlpK+5aubNufQreO7iyN2MSDAv2py6ZmtEyaNFVMKI3ATmYGlBg8X4atwXMCgZrIyDCiopbTpGEAnVrp7hQbw6MyBP2ls3FxcTg6OmoFnACcnJzw9PQ0uINckQYN3IiNjebBgwci6CQQVBP1POP2nUxC9ew9tnK11QSd9GHMPKMiF5L2aP2clHyZpOTLgGoDSh10Aujduy9WVlasWhXJ4sULsLCwpGPHAKZNewcXl3pQzWxbwZ/LwoWhxMdfZOrUGQQEdGbGjPdwcnTQZGg3bdxNy1EVBmiurZhda21trbVm/+abL5HL5bRq1YaLF88zZMgwxo8fz7Vr19ixYweFhYX0768tcj958mRq167D5s2/kpaWqpO5+yI7qoqgUzV52vWjhlAv8B88eKD3fHj4EuRyOZMnTxNBp+eYZ+V9UzuQlX/fysrKuHTpEo0bN8bNzQ2lUqnXEUTw7GBquVTXtq/SoUNHrZKp8uVSxjBlimFr4+nTJnHp96+1jjVt4sRPPw4xqm/BE0AmI+Ytw8G5VivXPMWbETyPqDXjgoIG42j28mM5xN27l8vx47/j7e1rcBwsKCjA3t5e7zm5XI5cLjPqs27cSEcikVC7tv6+BAKB8ajnGQ6WJWQsqnyjR9+cBEyfZ7w+OFTr53dn64pDl+ell3rw0kvGGagInh8iIpayceN6hgwZxtixEwy2M+SoWlV2raWlFUVFhRQXFwGP1lBubm7UqlWL27f1l3yq11D37uXq7ftFdVQVQacnhLp+1MmprsnXVubOoqawsIDS0jIKCh5y8eJ5IiOX4+DgoDfLKSHhEps2rWfOnK+xszPdLUbw7FOd9+3mY7xva9eu1nnf7ty5Q1lZGS4uLpw4cYKkpCTKysrYsmULwcHDmDRpqqiNfwZ40pkrta0tMDOwMViKCEIKBH8lymvG/XP2lwZNCoqK87l27SpOzvX0Tu53796JTCardGOkTp065ObmkpmZiaurq+Z4ZmYm6enpdO36kubYw4cPsbGx0RF9PXHiGBcvnqdLl25CikAgEAieU9SOzEFBwXz00axK2xpyVK0qu7ZevXpcv36N1NSUcmso1QTY1taWnJwcQGV0o1AoKC0tZdeuXaxduxqp1AKZrEyn7xfZUVWsAJ8A5etH+/UbUPUFFajMnUXNN998yZEjj7IRWrduzTvvfETt2rW12slkMubO/ZqAgC46AnyCF4Pqvm/bt28x+X3z92/JBx98/Mf7pvoFm5en+qV98eJFJBIJnTt3xsrKirKyMtasiSI7O4vZs780+f4ENcwTzlwxU2JQHLr+lDbV6lsgEDw/VNSM27t3t0YvDnQ14zaN+VJLy6U8u3Ztw9LSiv79DZcddOjQgf3797Nr1y6aN2+uERJPSEjAwsKCCRMeZVnGxJzjxx/n89JLPXBzc0cikZCYGM++fbtxdHTknXf+UYPfhEDw4iOxlCJXVFaK9uJkbAiebco7Mn/yyeeVZgsZclQ1Jru2efMWXL9+Te8aqqCgQGPudPToUa5evao516yZH6mpKTp9v+iOqiLo9AQoXz9qKHPEEMa4swBMmDCFkJAR5OXdIyYmmuvX08jPv6/Tbt26NWRkpPPNN/8x9TEEzwnVed8USgU7dm43+X1LS0vRed/UpXQlJSW88sorODo6Aqr65SlT3mbPnp28/vp4vLya6PQvEAgEgheL6mrGqbl48TzXrl2lX7+BBsvnADw9PQkKCuLChQskJSVRWlqKlZUVHh4efPXVV3h6PnK7bNSoMc2a+XHixO/cu5eLTCbDxaUeISEjGDduwh9aLgKBwFjkCiUTv9pv8PzGf3Z/incj+KtiqiPz5au/63VUNSa7tl+/gezZsxOJRKK1hrp+/ToFBQX4+anGnA4dOtC8eXOKi4uxt7fn5MlTyOVynb5fdEdVEXSqYYytHzVEVfWjary9fTQRz379BrJv33Y+/PAdFi0K19jdZ2TcICpqOePHT8Td3ePxHkjwTFPd9+12VtJjvW9btmzUvG8BAarsFXXpXL169TQBJzUDBw4iNjaauLgYEXQSCASCvwAV9VkM6cWBSjPu5w0RenVXWrVqw7Fj+rMnK+Lu7o67u67LT7NmzSgpV1Xs6enFV1/NNapPgUAgEDz76HNkLo+TkxMBAV2AR47MgF5HVWOyaxUK1Xgll8uZPfv/6NatBzk5tzlw4AC2trZ06NBB87lqg4vJkyczcOBAANzdG1X5TC+So6oIOtUgptSPGqKq+lFDBAcP4auv/s3WrRs1QadFi+Zjb29Pz56BZGTc0LSVy+WUlcnIyLiBtbUNzs7Oj3Wvgj+Xmnjf0m6cBkx/3wYOHMS8eXPZunWjJuhkZ2cHqOqYK1K3ruode/Ag/7HuUyAQCATPNrUkcqhErFs4HQoEAoHgSVFVdm3btu01Qafc+zcBaODip+Ooamx2rXrNPnz4K5w9e5qFC0OpXbs2TZo0ISAgQLMuKs/58+c1rs0HDuyma9duVT7Xi+KoKoJONYQp9aOGMKZ+1BBqkbL8/EeL+jt37pCTk83YsaP0XjNmzDC6devO998vMPleBX8uNfG+FZc84GZmAj4+NfO+ubi4AKo65opkZ2cBUKeOk845gUAgELwAyIXToUAgEAj+HAy5H1YkYnkYeQ9u4+XRka5tRuusoYzJri2/Zv/gg481x62slERERBi8rk2bNuzadYigoN5aa6jKeFEcVUXQqQYwtn40JyeHgoKHuLrWf2x3lrt3czRZI+VZu/Z/ALRo0UpzbMaMd3n48IFO29DQuVhaWjJz5vt6+xI82zzO+2ZZSzfafiXjHAqlnJAhwwx+lqH3bcOGnwHt983e3h5XV1cyMzPJycnRZNDJ5XK2bduMRCLR7DAIBAKBQCAQCASPS3r6dfbt2825s6dJSbmCXC6jtl1dGjVog59XD6RS4xwoZTIZmzdvYPfu7aSnX0cikeDuRB3aywAAIABJREFU7sGQIcMZPXq4Tvt79+4RExPDrVu3iIqKwtGxDn5+/nz00ac6LtInTx5j1aoVpKYmY2FhSYcOAUyf/g5ubrplwALjKLawwExiOKtWYiZBVqh/Mz4qKoLIyDC83Dv8EXB6cmv2wsJCvdUf+tZQfwVHVRF0qiam1I+GhS1i9+4d1XJnGTduNK1bt6VpUz9cXFzIy8vj7NkzREefwdvbh1GjXtW0DQjorLePxYt/wMbGplLhaMGzSVXvm0xWxu3btzlz5hQpKcmUlZXi7u5B8OAQZDJ3rQE47cZZJOZS/jZwkM7nqAfgH3+cB4BEIsXJyYlmzfwoKCjU+74BtGzZkszMTDZt2oSXlxcNGjRg6tSpJCbGM2HCZFxdXdm3bw+bNq3nxo3rlJaW4epanz59+jFq1KvY2dXSuReBQCAQCAQCgaA8O3duY9OmX+nZ42Ws8cbMXEJmTirnk3Zz/VYcA7q/i1RiUWkfZWVlfPzxB8TGnqNfv4GEhIxALpeTkZFOZuYdnfY3btxg37592Nvb07JlSwYOHEhW1j3i4y9QUFCgFXQ6evQQs2d/jI+PL9Onv0tBwUPWr1/HtGmTiIxcg7OzS41/J38FypRypu/8zOD5JYO+QV+IQ72Gql+/PvVdmnLtZqzWeWurWkBvoGbW7L/++iv169fH2dkZOzs7iouLmT59OmfPntVZQ/0VHFVF0KmamFI/WhnG1o+OHDmGM2dOsWnTr+Tn38fKyppGjRrz7rvvMXjwCI09o+DFpEo3oHquPHjwgO7de6JUKklMjKdePVeWhS3GsXYDzQCcnXuV/IeZeLq1w97eXku8tfwA7O3tS2FhAbm5uWRlZXL37l18fHyZOnUGI0eO+eN9U1nkKpVKLly4gFQqRSaTcePGDdLT0/H29mbWrDkEBQUTFraYNWui6NAhgAkTJiOVSomNjSYyMoyTJ48TFhb1WKWCAoFAIBAIBIK/DoGBfRg7dgJOjg4ak4KmjbsRd3k38akHSEs/TTOvyl3zVq5cTnT0GebPX6w3uKCe4wIUFRVx6NAh3NzcGDBgAObm5gwZMoSSEt15q0wmY/78/1CvniuLFy/XZLx06dKNSZPGEhkZzscfGw6cCGoe9Rrqzp073LmzTud8PSdvYFqV/Ri7Zm/ZsiUZGRkkJCRQXFyMVCrFx8enwhpKxV/BUVUEnaqJsfWjVbU11p1lwoTJTJgwWee4o6MteXmFRt3Hhg3bjWonePao6n27fDkBD49G1KqlnTEUuXwZUSuXawZgFycvXh8cqrePqgdg/Vy6dIl79+7Rpk0boqOj6dWrF02aNGHy5MmUlJghk8n49dd1NG3qx/z5izVlgUOHjkQikbJv325SU5Px9a3aQlsgEAgEAoFA8NfFkB5pY7e2xKceIO+BbqZSeYqKivj115/p3r0n7dt3RKlUUlRUiK2triQFQEJCAiUlJXTu3Blzc3NkMhkymQzQzaaKjY0mJyebt956W6vEyte3Ge3adeDQoX384x8fa5yfBU8e9RqqMifVim31YeyavUOHDhoHOzXqNVFF/gqOqvoLGQUCwTNFsYUFZjYyg3+ktqqdGD8/f52AE0C/vv0BHmsALizUFQavyMOHDzl37hwdOnTQ+/kAcrmMkpIS6tatq6NDpdZ/srYWmXoCgUAgEAgEgsejsDgPABuryiUbzp+PpbCwgGbNmrNgwX/p3/9l+vd/mcGD+xIWtviPgNIjbty4gYWFBSUlJWzcuJEVK1bw0ksvMX36WyQmxmu1VWfVlNftUePv35KCggLS069X5zEFgucKEV6tBhJLKXKF0vB5czPkpYaFzqqLWkDvzJlT3L59k+LiEtzdPQgM7MOoUa8ZXWqnT0DP2tqa5s2b4+//aBehsLCQS5cuceHCBS5fvkxeXh5/+9tgozO9Pv/8Ew4fPoCXVxPWrFn/OI/8l+Vx65fVZGVlAqYPwDt3bqOoqBBHR0eCg4cxadJUvbsyx44do3bt2rRq1YqUlBS9fVtZWdOmTTtOnz7J//63kl69+iCRSIiNjWbz5g0MGPA3GjZsVOn9CQQCgeDZQWseciuDwvx86llY0rG2Pf2dnLEyYHRREYVCzi+//MT2ndt0hHyHDh2haZeTk8PGjb+QlHSZ5OREzTzkX/+ao7ff7du3c/v2bcLDw3XOLV++2mTnVoFA8GyjUCq4lLwfMzNzPN3bV9pWHfRZv34dFhYWTJ/+Dvb2Duzfv5s1a6LIzs7i3//+QtM+Ly8PpVLJ7t27adKkCe3ataNFixZERq5g5syphIevokkTbwBycrIB9JZFqY/l5GRr2gtqDjuZGVBi8HwZuuLegiePCDpVA7lCycSv9hs8v2J2vyr7KD9hu3Urg5KSUqMDR2oBve7dexIcPJgzZ85x9uxpIiKWEhkZRpMm3gwdOlJrwgaqrJSIiCUcPXqY+/fzkEiklJWVMmBAECEhIzAzk7Fr1y7u3LnDsWPHKr3/e/dyq3xGgOPHf+fo0UPPvfL+84hcLmf5ivAaG4Bnz/5S65p9+/aRnp5OSEiIQSc9NXPmfMVXX33BsmWLWLZsEQBmZmaMGzeRt956+7GfUSAQCARPH615SP++3Fq3nsuFD9mck8XZB/nMbtwEyyrGBblCxtGzK8jZf6VKId/09GusWRNFvXqu+Pn5c+rUiSrv0dramk8//RSZTLukQbhHCQTPNo+zRoqO30JO3nXa+AVhX0sV3FHJO/yi407n4uIKwIMH+axe/Qt2drXYuPEXSkvLkEql7Nmzk8LCB/j5+QEqzVOlUomPjw+9evUCwMvLC2dnF65du8LEia/TvfvLTJ/+DsXFxQBYWOiW3llaWgJo2ghqGJmMmLemGjzdauWap3gzAjUi6PQnU37C1r//QKRSKTEx0URELOXQoQOEh0dhZaVr1QiPBPSsrKyYPfsjzpw5Tb9+A8nNvcupUyeoXdteZ8JWVlbG++9PJzk5iZEjR5Oefp2TJ08ASlxd6xMSMhwrKyV5eXkUFhbSsGFDzbUymQyFQkFgYCBff/01SqUSBwfHKp+xsLCQ0NDvGD78FY4d+61a35fAdBYuDOXSpYtaA7Ah1KV06gG4cWNPAPr06cfMmVPZs2cnr78+Hi+vJn+0e8C8efPw8/PD1dW1ynuxsLDEzc0dF5dBdO7cFTMzM44cOcSqVZFYWloyfvyk6j2sQCAQCJ4a6nlIrVq1qEUJMXsOEFjHCdfsTHbczeb3+/foU6dupX1cStnPnZwUFi9aRus2lW+M+Pk1Z/v2/dSpU4e8vDwGD67ahVcqlRIUFKRXR0MgEDy7mLpGOp+0m+Rrx/Fp1IWWPn0AVVD7gw/fJTr6rE5QOy0tFVCVuzVu7ElMzDlNULtRo8ZcuZJGbu6jzXWpVEpZWRlNmzYF4OrVq0RERODj40vt2vbIZGWcPx/LtGmT6Ny5K6Bad1WktLQUUAXEBYK/CiLo9CdTfsKmZujQkXh4NGT16hXs2LGVESNG671WnRYeEbGUU6dOaoSf09JSOXXqBI0aNWbq1Bla12zfvoXExATee+9DBg0KISRkIC+/3AuA1atX0Lt3X/z8VAEFW1tbfH19dT7X2toapVJVVlhVZgtAePgS5HI5kydPE0Gnp0xExFI2blzPsKEjsJV1q7K9evBWD8DlGThwELGx0cTFxWiCTosXL0CpVNK5c+cq+y4uLubttyfSrFkzvvzyW83xvn0HMGfOp0RGhhEY2IdGjTwNdyIQCASCZwZD5Wmdajuw4242N0sq38mXyUq4fPV3PFxb0LFDACVlskqFfG1t7QyeqwyFQkFBQRG2tnbCIVUgeE4wZY10IWkvl1IO0KRhAJ1ajdS0v5Syn8Qrp/Wa4xw8uI/o6LPUrasKjJcPah88uI85c2ZpBY3s7OzIy8vD1tYWhULB8ePHcXVVudO99950kpMvs2TJciZNGsuVK2kAZGdn4enppfW52dlZADg7u9TgtyUQPNuIoNOfjKEJW58+/Vm9eoXml5Yh1MLPgYG9NcLPN26oSqScnHR3F/fv34O1tTXBwcOIjY3W6PckJychl8sZO3Y0jo6OeHp60rFjR71BpS1bthj9fAkJl9i0aT1z5nyNnV3lekKCmiUyMoxVqyIJCgrmk48/Y+HXh6u8pl49VSaUegAuT926KrHvBw/yAUhKuszOnduYMmUKeXl5mjThoqIiQJXhdv/+/T92dKw4fPgAGRnpvP32DJ2+AwP7cvDgfi5ciBNBJ4FAIHjOyZWpFmr2ksqnmVm5V5HJSnBy8CB03vds277VKB1BUygoKKBnz56UlJRgbW1Np05dmTp1hs7GiuDJUxMaYPtPLGFtl3/oPadPp+vOnTusXh1JdPRZsrOzsbe3x8+vGXXq1KFBgwZabZVKJWlpaUycOJH09HRKS8twda1Pnz79GDXqVTGPfcoYu0aKWB7GxZR9eHl0pEvrUZrAsjqo3bPHy3rd6Zo3bwFAVpYqCFQ+qJ2TkwM8KoUDcHFxIS8vj4KCAgoLCyksLGTs2LHY2tqSnZ1FnTpOGne6+PiLAMTHXyQgQHtjNiHhEnZ2djRq1LhGvieB4HlABJ2eUdTCz/oCR+VRCz/7+/uzYMF/2bFjK8XFqkX/3bt3kclkmgmbQqEgOfkyTZv6YWVlpaXfI5VKMTMzo3nzFjg7O/Hbb79RUFBAYGCg1ufl5+cTHR2Nv39LEhIuVXpvMpmMuXO/JiCgC336VK1vJag5VqwIJyoqgoEDB/HJJ58bvbNbcQAuj3pnpk4dJwAyM++gVCoJCwvT29eJEyqtjaCgILy9W2hEFeVyhU5buVwOgEwmN+o+BQKBQPBsolAq2Z6ThQTobF95CX7+Q9W4cvnq79zOtTVKR9AUateujaurK6+88goKhYSEhHg2blxPdPQZliyJxNvb57H7FphOTWiAATg6OjJz5gc6xyvqdKks699AJpMTEjKchg0bkpOTw/btm8nKymLAgAE0avTIwOTs2bPExcXRsWNHJkyYjFQqJTY2msjIME6ePE5YWJTIlHsGKL9GioqKIDIyDC/3DnRtMxozs0fvjzqo7efnz7ff/psDB/ZSUlKsFdRu1aoNly5dICnpMs2aqbSb5HI5e/fuAlSBJjW+vr6kpKSQkJCg2aRt1aoVx479RnZ2FsHBwwBVtUB09FkcHeuwffsWRo16DVtblXh1SkoysbHRBAUFVzugLhA8T4i3/RlELpezcuVyJBIJ/foNqLStOnD0v/+tQSKR4ufXnLi4GBo39mTbtk2UlpZoJmwPHuRTUlKicU2oqN8zY8ZkzM3NCQ0NZciQIaSkpNC2bVvq1Kmj+bykpCSUSiUDBvytyqDTunVryMhI55tv/vPY34XAdKKiIlixIpwBA4KYNWuOwRLIouJ8SmVFFBcXYS5V7eS4ubkbHIC3bduMRCIhIKALAP7+Lfj3v7/DwgIOHDig6ffWrVskJCTQunVr6tWrh4eHB4AmvXjPnh06Qcjdu3cAj4JeAoFAIHg+WZd5m7TiIka4uNKgCvOQMrnKYai0rJBFP67GzUMVADCkI2gqarHf/v37U1JiRmBgX7p378nMmVP58cd5LFiw5LH6FTweNaEBBmBjbcOAAUFVttu9ewd5eXl8++1/6dGjl+Z4UFB/hg8fTmJioibopFAouHTpEs7OzixevJiyMgmgKueSSKTs27eb1NRkfH2bPd7DC2qE8mskpVJBZGQE9evXp75LU67djNVqm517FYCff1lLYWERJSXFvPnmZFJTkzRB7fff/4gZMybz3nvTGTlyNA4ODhw8uJ/k5MsAWmLlHh4eeHt7k5aWxt27dwGVmc7evXupW9eZSZOmAI/c6YYMGcaaNVHMmPEWwcHDKCgoYP36n3B0rMOkSYaFrgWCFxHjvGwFT5WFC0OJj7/IW2+9XWWpkTpwdP/+fV56qQdxcTEMGTKMtWs30K5dB/bs2cnVq1cAdJwUKur3WFpaatqoRfJu376t+SxVplQydnZ2dO/+cqX3lZFxg6io5YwbNxF3dw8TvwHB47Jx43oiI8Nwda1Px46d2L9/D3v37mL37p1czYjmdnaSpm3c5Z3sOPI98fHxWn28//5HWFtb895704mMDGPDhp/5+9+nkJgYz7hxE6lfvz6gqkUPDOxLnz59aNKkieaPeleoXr16NGnSBHt7ewC6detB8+YtOHnyODNmTGb9+nWsX/8TM2ZM5tSpEwQG9tUEuQQCgUDw/LEpO5ODebm87FiHQXWr1iuRmqvmI86OjXR0TwYOHARAXFxMjd5jmzbtaNOmHbGx0ZRUoTklqFn8/Py19HnUdKrtAFClBlh5VDpdDzUao/ooKFDNkStq59StWxczMzMtZzGFQoFMJsPGxkZns87ZWSUvYG1t2FFa8HQov0bKzFRlPN25c4eTces4EfeT1p9b2arAUX5+Pp06qTZM27fvwHffzdOskSwsLFm6dAWtW7fh11/XsWTJQoqLi3jvvY/0fn5gYCCdOnWipEQVMD9y5Ai9evUhPHyl5j1Tl+T5+fnz3XfzsLCwZPHiBaxdu4o2bdqydGmkJjAlEPxVEJlOzxhq4echQ4YxduyEKturA0cuLi5s2bKRoKBgPvpoFqAr/Kx2SVCL4lXU7yktLdW0UaeBqn+pAmRkZFBQUMDw4cMNOuqpWbRoPvb29vTsGUhGxg3NcblcTlmZjIyMG1hb22gGckHNcPlyAqAqffv66y90ztdz8qaBS+W7dE2b+rF06QoiIpbw66/rKC0tpXFjT2bNmkNQUPBj35tEIuGHH5awZs1Kjh49xNKlCzEzM8PDoyHTps1k9OjXH7tvgUAgEPy5LI2MZMfdbLo7ODLO1c2oa2xsVMEGayt7nXMVdQRrkgYN3IiNjebBgwdVzmcETx5jNcDUZGVn069fjyp1ujp16sL//reS0NC5zJjxLh4eDcnJyWbVqgikUimtW7fWtJVKpTRo0ICMjAxWrVpF9+59kEgkxMZGs3nzBgYM+BsNGzZC8Oehb4302WdfYCmV8MNXh3TaJ6YdISZxOy1btOLbb/+rda78GmnYsJHMnTtf63xeXh4LFuhWapibm9O2bVsKCgqIj49n+fLluLlpZ2KWd6fr1KkLL73Uo1rPLRC8CIig0zNEeeFndeCoKtSBozt37ujo91ScsNWubY+VlZVGm6e8fk9paSn5+fdxdlbZFat3h8qnlV6+rNoxCAkJqfK+7ty5Q05ONmPHjtJ7fsyYYXTr1p3vv19g1HMKjOOzz77gs8++0Dmub0Du2vZVurZ9lQ4dOlJaQUvJx8dXZwA2lmbNmtGsmf7Alq2tHVOnztBxVRQIBALB88uKFeGsiFpBN3tH3qzvbrTujbOjahFfWJync66ijmBNcuNGOhKJhNq1dYNdgqeLKRpgALVsnQgK7oVXE2/Mzc0r1elq374jH3zwMZGRy5g581E5U6NGjRg6dKiWfARA7969OXLkCIsWLWLRokUAmJmZMW7cRN566+2aeWCBydSSyFkaHsaqVSsICRrEFx/9AzMebYqXYav3OnVQ2xhzHFNRb85nZWXpBJ2EO51AoIsIOj0jPK7ws9odwdHRUUe/p/yELScnh4KCh/j4NCUlJYnS0lIt/Z69e3ehUCjw82uOXC4nMTHxjywUVWlcUVER6enpODk54e/vT2bm/Urva8aMd3n48IHO8dDQuVhaWjJz5vuaX/gCgUAgEAieT9Q6goMHDGTotRuYG5i/5ORkc/9hJnY2dZBKVOUntWzr4lLHk+x717l8OZEmPqrSfn06gqZSWlqKVCrVKZU6ceIYFy+ep0uXblhVoTklePKYogEG0LXtGKZP663ZLKtKp8vRsQ7NmvnTsWMnGjVqRHp6Oj//vIY9e/YQHBysVe6nCkTWZtCgQXTs2BUzMzOOHDnEqlWRWFpaMn78pJp9eIFRLIsIJ+yPoHZw2jViJ2sHAFutXKP3OnVQ2xhzHFNRS0lcvHiRtm21f0cJd7q/HmpnznPnTpGamopcLsfe3h4vLy9atWqlVcpriL//fYrBcvKKzpwnThxj69aNpKWlcu9eLhYWlri5uTFw4CBCQkbojG2m9P2kEEGnZwBjhZ/VgSNX1/pYW1uzceN61q9fh4WFBXl5eaxevYIGDVQp7QqFgp9+WqOZsIWFLWL37h2MHDmG+PiLbNu2iZEjx2gE9EJDv8PMzIySkhKmTp1KdnY27du31wzGycnJKBQK7OzsiIyMpKBAtcOQlpbCypXLAWjbtj1t26oypSrag6pZvPgHbGxsCAzsW6Pf4V8dO5kZlNv1qYihXSCBQCAQCB6X8jqCnTt25PQFbYMRe6mUFn/YzC9a8iM7j2ynb5dpuDo/ykbp2HIY+08s5u/vvM2IEY+EfBMT45kwYbJGR1CNes6hLv9PS0shMjKSmJgYGjRoQIMGDQCVqcXJkydp3LgxtWrVQqmUkpgYz759u3F0dOSdd/7xxL4XgXGYqgFmiIo6XeqSyW3bNhMa+h1RUWtp0uTRO9ejRxdee+01zpw5Q+/evQGV4/LWrVupW7cuX3zxBSUlquBp374DmDPnUyIjwwgM7FOl1qqgZomKiiByRSRd7R2Z2MDdYFBbbY6jL6gdn3CpSnMcU3Fzc8PW1patW7cyYsTrwp3uL47ambNnzx7Y29tjbm7OrVu3OHfuHFeuXGHo0KFGvQ/GOnOmpaVibi5h8OAQ6tZ1pqSkmPPn41i4cB4nThxj/vzFOgksxvb9pBD/Gv5k9Ak/l8fJyUnzC1EdOFq4cBnt23fU6PeoNZqWL1+m03/FCdtLL/Xg4sXz/PjjfG7fvo2npxfNm7ckJuYsFhaWrF27Ck9PT15++WWtEqmkpCQkEgk3btxg2bJHn5OcnERycpLms9RBJ8FTRiYj5i3DThiGdoEEAoFAIHhcyusIfv71Vzrnm9nYaoJOhnBy8KD/SzN5oDhrlI5gxblO+XlI+/btNUEnBwcHnJ2duX79OkuXLkUmk+HiUo+QkBGMGzdBCPn+yTyOBlhl6NPpWrNmJY0be2oFnAB8fHxwdHTUMsu5cuUK9+/fJyAgQKfvwMC+HDy4nwsX4kTQ6SmiXiM1cHXF31zK6XztKovyQe24yzu5knFOb1D7yLllOu50xga1U1NTNYLl5YPa5ubmdOvWjYMHDwp3OoHGmbNuXTsiIiIA8Pf35+zZs8TGxnL58mVatmxZZT/WRjpzjh37ps6xkSPHEBo6l82bfyUxMR5/f+3PM7bvJ4UIOv3JVCX83LZte4NR+PL6PZmZN5g3bx7nz8dqJmyvvPKqZsJWvq2fnz8REUs4cGAv+fn3cXf34P33P2L48FGYmZlhZaXU/IMBlT5TXl4ePj4+9O7dm8mTJ2t2gExlw4btj3WdQCAQCASCZ4vyc4talFS6+fHFP/+Fo3kvvefq2LvxxewfdPQF9XHs2DmdYxXnLQB16tShX79+ANWatwhqnsfVAKsMfTpdOTlZBh2UlUolCoVC87Nay1SfG55crnovZUa8n4KaQ71Gup2ZSaSe88YGtZeHr2TJskWPFdROSnrk+lw+qA3QpEkTgoKCWL58BYsXL8DCwpKOHQOYNu0dEdT+i/GoPE3794e3tzexsbHcu3fP6L4UCgVFRYXY2tqZ/LtRHUR98EBX4qa6fVcXEXT6kzEk/KwmPf06y5cv48yZU9y6lYGNjS0LF84jMLAPo0a9phH6btasmUHhZ1PrOB8+fMjx48e5evUqJSUl2Nvb0717d5o3b67VTiaTMX/+9yQmJpCZeZvCwkKcnV1o3rwFb7wxnqZN/Uz4JgTPOup6ZfW7WFJSiru7B/369aGsrKzG65VB9S5GRCzh6NHD5Offx83NgxEjRjF06Igqf1l+/vknHD58AC+vJqxZs974BxUIBAKBQPBEqI4GWGlZEVKpru6TIZ0uT08v0tJSuXTpIi1bttIcv3DhAvfv36dhw4aaY2pR8eTkZJ3+d+/eATwy4BE8HdRrpKoC2vDIHEcfvr5NjTbHqRjU1hfQLk+PHj3o1KmnUX0L/no8fPgQ0Dbmqozs7CyjnDnVFBYWUFpaRkHBQy5ePM/atatxcHDQyXJ6nL5rGhF0esZR14h2796T/v0HIpVKiYmJJiJiKYcPH2DF8tVYW1tTVFiKpVSiuc7M3IySUpnmZ2PrOMvKypgxYwaJiYm0bNkSR0dHbty4wbFjxygsLKRjx45abS9fTqR16za4uQVha2tLZmYmO3duY8qUNwkN/ZEOHXTTlAXPJ4bexaVLl+Lk5FTj9cplZWW8//50kpOTGDlyNI0be3Hq1AlCQ78jN/dupanLx4//ztGjh4RIrEAgENQgxRYWmElkes+ZFT/lmxE8d1RXAyzzbioxCdtQ2MXgWt8NiURSqU7XpElTmTXrI95/fwa9e/cjNzeH5OQk7t7NAeD+/fvExMTQqlUrGjVqhIuLCzdu3GDKlCn06NEbUHL06GHOn48lMLCvRhNITVWbW0lJl4mKCufChfMUFxfh7u5BcPBQRowYjUQi0WqrVCrZv38vmzat58aN65SWluHqWp8+ffoxatSr2FWR0fO886z9bnnawtBg+kbryZPHWLVqBampyVhYWNKhQwDTp7/z1DR6BJWjUCiIiYnBzMwMHx+fKturDb58fHyrdOZU8803X3LkyCN3cn//lnzwwcfUrl272n3XNCLo9IQxlB0SGNiH1958FUtrS73XScwkyArNNDWi5d01oqNVUfjU1BQ+mPE9zby6a12bez+DAmKIOx9HcXER5ubmWFhY0rfvAJ1BriJTp75JcnISNjY2dO3aFYDmzZuzb98+4uLitHSebGxsiIzU1QoKCRnBiBGDWLdujQg6vUDoexeHDh2Jp2dDVqxYUeP1ytu3byExMYH33vuQkSPHADBkyDA+++wj1qyJYtCgIdSv30DnusLCQkJDv2P48Fc4duw3E560J0QDAAAgAElEQVRQIBAIBJVRppQzfednes+t6vPvp3w3gueN6mqA2dvVw8nBg2PHfyc3926VOl3du7/M/PmL+emnNezfv4fS0hIsLCzw9fXFzs6O/Px8LaHfQYMGERcXR25uLkuXLvzDxbkh06bNZPTo17X6rmpzKy4uhg8++Dt2drUYOXI0jo51OHv2NAsXzuPq1at8/LH2v6Pw8CWsWRNFhw4BTJgwGalUSmxsNJGRYZw8eZywsKinXg7zNHnWfrc8bWFoUzdajx49xOzZH+Pj48v06e9SUPCQ9evXMW3aJCIj1+Ds/PjC/IKa4eTJk2RlZREQEICjo2OV7WfNmqP1c1XOnAATJkwhJGQEeXn3iImJJi0thfx8XYf5x+m7phFBpydMpZlKR/ZjM8YdcwvdQNCSQd8AUp0ouHqQs7CwpKyslLwHd7TOZ95N49DpcBwd7DWDXGTkMu7fv89//vMtM2e+Z7CO8/jx3zVinBUH0VatWnHt2jXS0tKqfOY6depgaWllsJ5U8HxiyE6zX79+rFixosbrlffv34O1tTXBwcO0jo8a9RpHjx7m4MF9vP76eJ3rwsOXIJfLmTx5mgg6CQQCgUDwjFBdDTCH2q706DCed2f3Nkr/C6BDhwA6dAjg8uUEPDwaUatWLa2SqYpCv506dapSA8yYza0FC/6LmZkZy5at0OhKDR/+Ct9//zXbtm1m4MBBtGnTFlDJVfz66zqaNvVj/vzFGhfroUNHIpFI2bdvN6mpyfj6NtP7WYKa52kLQ5uy0aqSN/kP9eq5snjxco1zXpcu3Zg0aSyRkeE6QU3B0+Xs2bPEx8fj5+dHu3btHrsfQ86cary9fTRZSv36DWTLlo18+OE7LFoUTuvWbavVd01j/kR7FxAY2IfNm3cxZ85XjBw5hqFDR/Kvf33LuHETSU1JJTfmdtWd/EH5Qa5WLTsAbKy0d4Si47dghhmRy1cxceIUhg9/BS8vb8zMzNixYwsDBvSiX78ezJr1EdevX9Pq+7///RaJRIKFhYVOIKBevXqYmZmRnZ2tc19yuZy8vDzu3s0hMTGeL7+cTVFRIV27vmTCNyV4XlG7ephar2zoXQRVUCo5+TK+vs10AqDNm7fA3NycxMQEnb4TEi6xadN63nnnHy98KrpAIBAIBM8qxRYWmNnIDP95ykk7fn7+Wpnaary9vQFM2jgrv7mlj/z8fFJTk2nTpr2OkLlavHrXrm2aY3K5jJKSEurWrasJOKlxdnYGVMELwdOjJt8XhUJBQcFDvSL1airbaJXJZBw8uE9zLDY2mpycbIKDh2oCTgC+vs1o164Dhw7tQybTX6ooePKcO3eO2NhYmjZtSo8ePardX4MGbsjlcqOSOQYOHATA1q0ba7zv6iIynZ4whrJD+vTpz+rVKyjOfGh0X+pBbuLEKWzdugkAT/f2mvMlpYXcy79FA5dmuLt7aHaB3NzcqV+/AXv27KR9+440a9Zcp44zPHwJMpkMuVyut05ZIpFgZWVFYWGhzrnr168ybtwYzc+1atVi7NgJvPHGm0Y/m+D5RC6XExkZWeP1yg8e5FNSUqLX/cPS0hJ7ewdycrQDoDKZjLlzvyYgoAt9+vSrmQcUCAQCgUBgMpWVS8GzU45pqtCvenNrzpyvDW5ulZWVAmBtrZs5oM4miI+/pHWsTZt2nD59kv/9byW9evVBIpEQGxvN5s0bGDDgbzRs2Mik5xI8GZ6EMLR6o7VpUz+jNlrVZaotWrSiIv7+LYmOPkt6+nWaNPE29fEE1SQiIoKYmBh8fX15+eWXn5gzpyFKS0tRKBTk5+fXeN/VRQSd/iSyslTZIdJa+jWdKlJ+kIuMDKOsrAwry1rY13q0KFcoVFFttcuHmlmz5pCSksyePTvJzc1lxox3teo4p0yZzqZN63nvvY+YN2+uwX8gEolEb+S8QQN35s9fjExWRkZGBvv27eLhw4eUlZUZVe8seH5ZuDCUixcv1ni9cnGxSjXSkFCjpaWlpo2adevWkJGRzjff/OdxHkUgEAgEAsFfCFOFfo3d3HJyqoujoyPx8Rd1ylZiY1W6rOp1gJo5c77iq6++YNmyRSxbtggAMzMzxo2byFtvvW36wwlqnCclDG3qRqv67/raq4/l5GSLoNNTJioqgsjIcHx9fenVq5fB9XROTg4FBQ9xda2vCUw/fPgQGxsbHe1lQ86cd+/mULeus07fGzb8DGgHJE3t+0khIgJ/AnK5nJUrlyORSHBs7Vpl+/KD3JUrqWzcuB5bG1sspNo7LNZWtbGytCPn3nWKi4sxlz5asFcc5NR1nDEx0Xz77b81A+i8eXMNpn/K5XK9QSQbGxsCAjprfh40aAgTJ77BZ599xLx5i6r+QgTPJRERS9m4cT3Dhg3DxeXxBQv11RSrfwmXlZXpvaa0tFRrBzEj4wZRUcsZP36iTiq7QCAQCAQCQUVMFfo1dnPLzMyMUaNeIzx8CbNm/R9vvTUVBwdHzp07Q2RkGBKJhJIS7Y0zCwtL3NzccXEZROfOXTEzM+PIkUOsWhWJpaUl48dPqtazCqrPkxKGNnWjtbL2lpaWWm0ETwe1M2f9+vVxd3cnNTVV67yNjQ0eHqr1SVjYInbv3sHChcto317lCh8Tc44ff5zPSy/1wM3NvUpnznHjRtO6dVuaNvXDxcWFvLw8zp49Q3T0Gby9fRg16lVNW1P7flKIoNOfwMKFocTHX2TqjGmcck6qsr16kGvXrj2rVkUSFBRMTPRZiipUupmZmeHn1ZPzSbv5+JN/MHFS5YNcgwZuxMZGc/PmDb77LpTate2xsrJCLtcVZ5TL5ZSUlGjVDhvC1taWl18OZO3aVdy8mSGCAC8gkZFhmnfx008/Zfny5dXqT/0uPnjwACsra827mJ2dpdO2tLSU/Pz7ODs/Ki1dtGg+9vb29OwZSEbGDc1xuVxOWZmMjIwbWFvbaLQRBAKBQCAQ/HUxVejX1M2tN954k+LiYn7+eS2TJ6tMT2xsbJk5832NXIaa4uJi3n57Is2aNePLL7/VHO/bdwBz5nxKZGQYgYF9aNTI0/QHFdQIT1IY2tSN1sral5YaLu0UPDnUJY937tzhzp07OucbNGigCTrpo1GjxjRr5seJE79z715ulc6cI0eO4cyZU2za9Cv5+fexsrKmUaPGTJ06g5Ejx2iVf5ra95PCqKDT1atX2bZtG8ePHyc9PZ2SkhIaNWrEwIEDGT9+vFGBiLFjx3LmzBm95zZs2ECrVrp1qS8i6uyQIUOGMW7ieE7tnFVpe/Ug17JlKzZs+IWBAwfxySefM3pUCKCbkdTCpzcyeRkxsb9zsopBLi0tBYDXXx+vGUCbNvXj0qULOtlOWVlZKJVKozNaSkpKAMjPvy+CTi8YK1aEExUVoXkXn0S9srm5OU2b+pGSkkRpaalm5wYgMTEehUKBn19zzbE7d+6Qk5PN2LGj9PY/ZswwunXrzvffL6j2vQoEAoHgzyc9/Tr79u3mzJlT3LqVQUFBAfb29nh5edGqVSuDWQOV8fnnn3D48AG8vJqwZs16rXMnThxj69aNpKWlcu9e7h+ZKW4MHDiIkJAROuUJMpmMn35azd69u7h16yY2Nra0a9eeKVO09VwET5/w8HCThX5N3dwyNzdnypTpjB07gbS0VECJj09TlEol//nPN1rlL4cPHyAjI523356h87mBgX05eHA/Fy7EiaDTn8STEIauzkars7NqLZadnYWnp5dWe3Uf6jaCp4PambO8M2ZVbcvj6enFV1/NNfrzJkyYzIQJk41qa2rfTwqjgk4bN25k7dq19O7dm+DgYKRSKadPn2bBggXs3r2b9evXGxVRrVOnDp9++qnO8YYNG5p+588h5bNDPvpoFlC13euiRfORSiXExJyjR49ejBs3kVu3biKXy1EoFDwoyEEqscTG2p6i4nxKZUW09O3Dkoh/kZiURFFRAf7+rTA3N9ca5E6cOEZS0mUsLCzp3bufZgDt2LETFy+ep7i4mPv372NhYYGtrS0XL17EzMyMJk2aaO7t3r17ODg46Dht3L2bw+HDB7CxscXLS9QTv0hERUWwYkU4AwYEMWvWnD/+3+sGPwsLCyktLdVy/jC1prhv3wFcvHiebds2aexjAdav/wmJRELv3o/0FGbMeJeHD3WdF0JD52JpacnMme/rrX0WCAQCwfPJzp3b2LTpV7p370lQ0EBOnTrFrVu3OHfuHFeuXGHo0KEm6UoeP/47R48eMqhtkZaWirm5hMGDQ6hb15mSkmLOn49j4cJ5nDhxjPnzF2s2YZRKJZ988gGnTp2gR4+XGTFiNHl599i8eQNTp77J0qUr8PJqovdzBE+WFSvCWbEiwmSh38fd3LKxsaFlS+0Ak1KppEuXbppjao0euVyh0696s1gmq3rNIKh5noYwtKkbrWqTqvj4i1ryJqDSALazs6NRo8bVvk+BoCYxajQeMGAAU6dOpXbt2ppjr776Ko0bN2bZsmVs2LCBN954o8p+bG1tCQkJefy7fY553OyQy5cvaxzjfv/9CL//fkTr/LbD3+JWrzmBnd4i7vJOrmSco2+XaZpB7rffjvDmm6/RuHFjlEol9vb2fPXVHPbt2425uYSyslK9A2hxcTG//PILTk5O2Nvbc+3aNdq1a4e9/SN1+/37d7N+/Tp69uxFgwbuWFhIuXEjnd27d/LgQT4ffzxbpHe+QKjrlV1d69OxYyf2798DgFSqJCUlRate+cyZMyQnJzN48GDN9abWFA8ZMoxdu7bz44/zuX37Np6eXpw8eZzffjvM+PGTcHNz17StOOiqWbz4B2xsbAgM7FvTX4dAIBAI/kQCA/swduwEatWqhZWVkvz8fPz9/Tl79iyxsbFcvnyZli1bGtVXYWEhoaHfMXz4Kxw79pveNmPHvqlzbOTIMYSGzmXz5l9JTIzH31/1eb//fpRTp04wZMgw/u//Hjm4DRgQxLhxo1mw4L/88MMS0x9aUC3UG2dBQUG4u7tXKvSbm1ugJfRbE5tb9+/nERa2BEdHR4YOHak5rs5W2bNnh45A+e7dOwCVi5ng6fI0haFN2Wht164Ddes6s337FkaNek1TcZSSkkxsbDRBQcHCyOkJUdvaAjP90scAlKK/RFJgZNDJUOlbUFAQy5YtIzk52egPVCgUFBYWYmdnVyPR4ucB/dkhupQ9KEFeLMPS4VGgxsvLi5wc3XRLNQ61XGnh08fg+UaNGtOkiTenTp0A4MyZ09Srp6rjbN26LVKpROea//73WwoKCjA3NycvLw+FQkG3bt1o0UJ7wGvduh2JiQkcP/47ubl3KSsrw8mpLh07duKVV8bQqlWbSr8XwfOFul45M/MOX3/9hc75mq5XtrCwYMGCJURELOHAgb2aUs333/+I4cP17zQKBAKB4K+Bere/It7e3sTGxnLv3j2j+1LLD0yePM1g0MkQ9evXB+DBg0cBiZgYlXlLUNAQrbbu7h60adOOM2dOcefOHc21gidP+Y2zTp06/T979x6X890/cPzV+USiUiqJUI4LZc40TIvkNGxzXGibH7vZzMZ2s4MN9xxGpFJJw5acwmxsMzfDQpgzY6ShA9JJqqt+f3R36XJdV10dnN/Px2OPx319vp/v5/pe+d5Xn97fz+f9Zs+ePSrHSz84CwoKYvv27SqJfiv6cOvAgX2sXRuNl9eL1KljTXLyDbZu3UxmZgZz5y5USUTdqVNXmjVrwYEDvzNx4ni6d38JKGLPnt0cP34Ub+9euLm5V+NPQ5TnUSeGrsiDVkNDQ959931mzfqIiRPH4ec3kOzsbGJi1mJlVZuAgMCH/NN5fukVQWLoYa3H7SfI377aVCkMWpIoS9fkvMnJybRp04bc3FzMzMzo0qULU6ZMwdX12d2CpW11CICesYLMv25Rs3EdAK7vusjtYzdwHXs/Qd2iRcs0jvvqED9ysovo132asq2jx2s41/PgxIWdRK4yoJZVbZKTb3Du3BkMDQ34+utvtP7SLG358m/Q19fn1VdfLbOfu3szZs36otzxxLNB0x5kQOP+5R49etCjRw+VtsrsKa5ZsyZTp05n6tTpFb1cAGJjt1bqPCGEEE+nrKwsAJVEqmU5ffokGzfGMGvWHCwsapTbPycnm7y8fLKzszhx4jhr1qymVq1aylVOAPn52pP5mpiYKt9Xgk6PTukHZ7Nnz1Y7Xt6Ds4qyt3fAyMiY2Njvyci4Q61aVnh6ejF6dIBabiYDAwO++WY50dGr2LPnV4KDl6Cnp4eTU33efnsSw4a9UW3XJXTzqBNDV/RB60sv9cLExISoqHCWLVuMkZExnp5evP325EeWGFqIiqh00EmhULB8+XIMDQ1VttBo4+TkRNu2bXFzc0NfX5/jx4+zZs0aDhw4wNq1a3Fzc6vspTzRylsdYuFipQw6VYca5rXR1zfk+5h15f6SE0IIIYR4VhQWFpKQkICenh6NGzcut39BQQHz5s3By6uD2rYmbb788lN+++1X5evmzVsydep0lRQUJfmaEhIO0bhxE2V7bm4up0+fBCAlRf0PWfHwlH5wVl6y39mzZ/PRR5/qNK62h1sNGzZi4cKlOl+fubkFgYETCQxUTyYuHr1HnRgaKv6gtXPnrnTuXPXE5kI8CpUOOn355ZccO3aMqVOnqiSX1uarr75See3j40PPnj0ZOXIkc+fOJTIyUuf3trIqv1reo5CRk1fmcX19Pf7zn/n85z/zNR7PzM/kra33E6s7D2qO86Di5eIWCj309bWPH7flBxZ//qtae62a9rz04gT+9e+emJkbazizfFu3biU0NFTrcT09vSfm3+BpUt79UpbyNqJWZaOqnn7Z/556+QqKFNo3MOdS9ueS+6XiqnKvQNn3Q1U3NZd3v5BbUMV30E6/vPd+Tj2t3y1yrzx6T/V3Sxlyc4tzXx44cICUlBS8vLxUti9p+z0UFhbKP/9cJSgoSHlcX18PAwN9rdcyefJk3njjdW7fvv2//IXnUChyVfq/+upgVq+OJDw8hDp1atGhQ0du377N8uVB3LmT/r9rKnzi79Gn9rulHCX3i9bxZd5SYU/zd0tZ81yZ4z4cT+13SxXnLc/z/VKpoNPixYv59ttvGTZsGIGBld836unpiaenJ3/88Qe5ubk6J51OTy/7l8UjoyEfUmkW+nkU5WRrPV5kqDm3EwD5BRwuY09uq1XRZb53UWFRpX9OJiZlZEijuCrLE/Nv8DQp534pS9n/IuUfL/Pccu4VSxOjKu1flvulEqpwr0DZ90NV7hXQ7X55WAoLi8jMlHtJzVP83fKwyL2ixVP83VIWE5MiDh06xKlTp3B3d6dNmzYqxzX9HkpKukpwcDCjR79JzZrWyuOFhUUoFIVar6VuXSfq1i3eVtOpkzebN2/g7bcDCQoKpXVrj//1MmLRomV88cW/mT17lvJcD4+2vPHGaKKiwtHXN37yfzc+pd8t5SX7LXd8mbdU3FP83VLWPFfmuA/JU/rdUtV5y9Nyv9ja1iy/UwVVOOi0dOlSgoODGTRoEJ9+qtvS07I4OTkRHx/PnTt3nrlKZ/qFBSQFaQ8c1fpX2cs1hRBCCCFE2UJDQzl69ChNmzala1fdtpsEBS3C0tKSbt28SUq6qmxXKBTk5xeQlHQVU1OzcvOW+vj0ZeHCeWzZsqFU0AlcXRsTGbmWpKSrpKWlYmNji5NTfZYv/waABg1cKv5BhU4k2a8QQjxZKhR0CgoKIigoiAEDBjBnzpxqqT53+fJlDA0NVZZBCyGEEEIIUZ6IiFAiIsJo0qQJ3bt313lueuPGDdLSUhk5UnM11OHDB9KpUxfmz19c5jh5eXkUFhaSkZGh8biTU32cnOorXx88uB8LCwup8CuEEOK5oXPQKSgoiKVLl+Lv789XX32Fvr7mrWEpKSlkZmbi4OCgrBySmZmJubk5BgaqS+l+++03EhIS6NatGyYmJlX4GEIIIYQQ4nkSGRlGREQovr6+ODo6ag04paWlcetWNnZ29spV9RMnvktWVqZa3wUL5mFsbMykSVOwtr6/yunmzTSV1yViY78DoEWLVuVeb2zsd1y6dJGxY8frXF1PCCGEeNrpFHRas2YNS5cuxcHBgU6dOrF1q2qlBhsbGzp37gzAwoUL2bRpE6tXr+bFF18E4I8//uCrr77C29ub+vXrY2hoyJ9//klcXBy1a9dmxowZ1fyxhBBCCCHEs2rDhhjCw0Ows7Onffv27NmzR+W4mZmZsqR5UFAQ27dvZ8mSFbRt6wmAl9eLGsddtuwbzMzM8PbupdI+atQwWrf2oGlTd2xtbUlPT+fQoXiOHInH1bUxQ4e+ptL//fcn4+DgiItLI/T09IiPP8jevb/RqVMXRo8OqK4fgxBCCPHE0ynodOLECQCuXbvG9OnqZRzbt2+vDDpp0rBhQ1q0aMFvv/3GzZs3yc/Px97enuHDh/PWW29hZ2dXycsXQgghhBDPm7NnTwOQnHyD2bNnqx2vV6+eMuhUHYYMGU58/EE2blxPRsYdTExMcXZuQGDgRIYMGa62cqlly9b88stOduzYBkCDBg2ZOnU6/v6D1Fb+CyGEEM8ynYJOc+fOZe7cuToNqKmvq6srS5YsqfjVCSGEeGIlJl5h584dxMcf5Nq1JO7dy8PZqT7tazXHv2VvTI3K3jZdWFjI77//TmpqKpmZmURGRmJV0wjXBlb0790El/q1VPr/d+8+vvt+Mxcv/sXt27cwMjLGwcEBH5+++PsPVtmmnZGRwY8/bufAgX1cuXKZ9PR07Ozs8PBox5gxAdjZ2T+Un4nQ7sH7JS8vDztTa7o08nwo98v+/fvYsmWDTvdLQsJhJk9+q8z3X758pUqiaPF4zZw5m5kzZwPF1evCwrQXZ5k9ezYffaRb8ZvY2K0a28eOHc/YseN1vr4xY8YxZsw4nfsLIYQQz6oKV68TQgghALZvj2PjxvV06dKNl1/2wdDQkBPHEoj+eRN7Lx1igf9MTAyNtZ6vUChITU3Fzs6OJk2a0Ofll/jzj/Xs+eMqnyz4Lx++3YEWbrbK/hcuXERf34B+/fyxtrbh3r1cjh8/xpIlC9m/fx+LFi1T5nQ5ffoky5Ytpl07LwYNehUrKysuXbrIli0b2b17F8HBETRs2Oih/4zEfQ/eLzVMTdgTs5Poww/nfrl48S+d7xcXl4Z88slnau+Zn5/H/PlfUquWFc2bt6z+H4rQWU1TI/S01MLOI//RXowQQgghdCZBJyGEEJXi7d2TkSPHUqNGDWXbqGGvYZVmxPfHtrPz3F78WvTUer6RkRGDBg1Svh40sD9NbU7Qs4sLk/+9i22/XlQJIgS8OZrMbNVtKUOGDGfBgnls2rSeM2dOKQMDDRq4sHbtBhwdVbfXdOzYhSlTJhIevoIvvphfpc8vKubB+8XSxIgONxvhYFn3odwvI0eOURtD2/1Sp441ffr4qvXftetHCgsL8fHpi6GhTJkeJ70iSAw9rPGY/QSpBCeEEEI8qTSXoBNCCCHK4e7eXCXgVKKra3sArtz6p1Lj1qppgpGRPtk5uq1esLcv3iqXmXm/ElW9eg5qAScoTh5saVmLS5cuVuraROU9yfeLNtu2bQHAz8+/UtcmhBBCCPG8k8d2QgghqlVa9m0Aaptb6tS/sLCQvLw80tJucvHKbbb/cpHcewo8WtTV2D8nJ5u8vHyys7M4ceI4a9asplatWjptf8rKyiInJ5tGjVx1/0DioXpS75dr1/4hIeEwrVt74OzsUqHPJIQQQgghiknQSQghRLVRKBSsS4jDQM+A7q4ddDonPT2d2NhYVq9eDYC5mSH9ezfBv3cTjf2//PJTfvvtV+Xr5s1bMnXqdGrWrFnue0VFhVNQUICPT1+drk08XIrCwif2ftm+PY6ioiL8/Abo+GmEEEIIIcSDJOgkhBCi2sxfOJ9zKZcY5TUIJyvdKsTVrFkTX19f+r7Siz9+i2DfoSTu5uaTX1CIgYH6LvCxYyfg7z+Y9PTbJCQc4eLFC2Rk3Cn3fXbv/pnvvvuW9u070rdv/wp/NlH9wg6seyLvF4VCwY4d27CwsMDbu1elPpsQQgghRHk0VYN2dHTC27snQ4e+jpmZWZnnV6Zi840bN1i9OpwjRw6RmpqKpaUlTZu68/rrI+ndu7uyX35+Pp9//jknT57kn3/+ITs7m7p169K6dWsmTJhA8+bNdfqMEnQSQghRLcLCglkX8x0+7t0Z6qH7SiIjIyOcnJzo2rUztfmNHh2dmTFvDzdSD/HRxI5q/V1dG+Pq2hiA3r192Lx5A++/P5mgoFCtJe0PHNjHZ599gptbMz7//Ctl1TLx+AQFB7Ht9K9P5P0SH3+AlJRk/P0HYWpqWrkPKIQQQghRDk3VoBMSjhAWFsyvv/5MaGgkJiba5yIVrdiclpbKuHEjKChQ4O8/iPr165OWlkZc3CYmT36L4OBgevToARQHnU6ePEnbtm3p378/FhYWXL9+nY0bNzJ06FDCwsLo2FF97vUgCToJIYSosvDwEKKiwhng50+AnV+VxjI1McTrhXps/fkvklOzsbO1KLO/j09fFi6cx5YtGzQGEQ4e3M/MmR/QsGEjFi4MwsJCPZm1eLTCw0OIjAyjV9POTOwyskpjVff9AqUTiMvWOiGEEEI8PJqqQQ8YMAQnp/qsXh3Btm1bGDx4mNbzK1qxeceObaSnp/PVV1/TtWsPZXuvXn0YPnwgMTExyqCTubk5GzduVHvP4cOH4+3tTUREhE5BJ6leJ4QQokoiIkKJjAzDx6cvsz+eXS2riPLyFQBk5eSV3zcvj8LCQjIyMtSO/fHHAWbMmIazswuLFy/H0lK3ZNXi4Sm5X/z6+jG525gn6n4BuH37Fr//vhdX1ya4u+u2bFwIIYQQojK0Vfft2fNlgHIrLle0YnN2djYANja2Ku116lijr69f7nY+AGtra4yNjbXOpR4kQSchhBCVFmPaFf0AACAASURBVBkZRkREKH36+DJjxiz09TX/WrmVk87ly5cpKChQtt29e5eioiK1vukZufxx9BqmJgY41buf7Dkt7abGsWNjvwOgRYtWKu3x8Qf56KP3qV/fmW++WY6lZa0Kfz5RvUrfL5//+zP09R7e/XLzZprGsbXdLyV27NhOQUEBfn7+On8uIYQQQojqlJKSDBQHgyqjpGLzg+e3b19cuGXBgnkcPXqE1NQUzpw5xezZMzEzM+PNN99UG0uhUHDr1i1SU1P5888/ee+998jJyaFbt246XYtsrxNCCFEpGzbEEB4egp2dPZ6e7dm160fMDA24eeFvAKzMLGnj1AKAqPgN/LJmP/369cPBwQGAv/76ixMnTuDi4oKlpSUx6zdyZP9J9v5xley7+Yx/zQMT4/u/pga/+hqtWnnQtKk7tra2pKenc+hQPEeOxOPq2pihQ19T9j179jQffvgeUETfvn4cPLhf7fr79PF9iD8d8aAH75cffvxBea9A9d8vo0YNo3Vr3e6X0n74IQ5jYxNeflnuDyGEEEI8egqFglWrVmJgYEDv3n0qNYa2is1t23oydep0wsNXMGlSoLLdycmZkJBVtGjRQm2sixcv4ud3P31GzZo1CQwMJDAwUK2vJhJ0EkIIUSlnz54GIDn5BnPmzFY73rKemzKIoIm9vT2pqakkJiaSk5NDfHw8ljUMaelmi0+PRjRtVEel/2vDh7Lv93g2blxPRsYdTExMcXZuQGDgRIYMGa6yHPjSpYvk5d0DYMmShRrfX4JOj9ajvl+GDBlOfPxBne6XEidOHOfy5b/p3dtHtmIKIYQQ4rFYsmQBp06dIDBwIs7OLhU+v7yKzVZWtXFza46nZ3ucnZ1JTExk3bpopk37F+vWraFevXoq/Z2cnIiMjCQ/P58rV64QFxdHZmYmeXl5GBqWH1KSoJMQQohKmTlzNjNnzlZpszQxIjH0sFrfKT0CmLd2CaFhoco2W1tbXnrpJeXr/3sngJN752h9v7cCx/HGCN2eqPj6+uHrW7WE5qJ6PXi/aLtXoHrul7FjxzN27PgKXWOrVi+wb5/maxJCCCGEeNjCwoLZsCGG/v0HMnLk2AqfX17F5ri4TSxYMJfIyDU0atRY2f7iix158803WLBgAV9//bXKOebm5nTq1En5evDgwQwaNIhJkyYRHh5e7jVJTichhBBCVDtDgwJMjPK1/lf19OFCCCGEEM+OkmrQvr5+TJs2o8Ln61KxOTp6FQ0auKgEnABcXRvToIELhw4dKvd9LCws6N27N/v27SMxMbHc/rLSSQghhM4MDQrQ0yvQelwPo0d4NeJJVlhYwOp572k9PuGj5Y/waoQQQgghKicx8Qp7fvmJ37b+zPWMFPIV+djXrEuXRp74t+yNqZFJmeffu3ePdevWsXfv71y5cpn09HTs7Ozw8GjHmDEB2NnZq1SDHjfubUJDl3Pu3FnOnz9Deno6r7zST22HQYmCggLmzfuCH3/cTlFREdevX2PevM+ZMGEiDRq4qPRNS0vRWO0OinNJKRQKnX4mubm5AKSnp+Ps7FxmXwk6CSGE0JkEEoQQj0Ji4hV27txBfPxBrl1LIi8vDztT6wpN8M+fP09iYiKbN28mPf2O2gT/QVlZWYSFLWfPnt1kZNzBwcGJwYOHMmDAYLXtCQ/65JMP2b37Zxo2bER0dEyVPrsQQogny/btcWzatB6veq3o4doBA30DTlw/S/ThTey9dIgF/jPLPD8lJYVvv/2Wdu28GDToVaysrLh06SJbtmxk9+5dvPyyLxs3xiirQR87lkB0dCR169rh7t5cWRAnLS2N7Ows7OzsMTU1BaCoqIi33nqTs2dPY2FhwahRAdy7l8umTbEEBo4hODiChg0bKa/FxaUhFy/+xcmTJ2jZ8n4l35Mn/+Tq1US6d++ubLt16xZWVlZq1alTU1P58ccfMTc3p0mTJuX+/CToJIQA1Cf49+7l4ejohE/vl+mR37zcCT4UVza4evUqaWlp3L59m6KiIvr27Yu1taPG/ufOnSUyMpQ//zxObu5dHB2d8PMbwODBwzAwMFDrX1BQwKZNsezYsZXExCsYGBjg6OhE//6DGDBgcJV/BkIIIZ4M27fHsXHjerp06cbLL/tQw9SEPTE7VSb4JobGWs9PSUnh4MGDODo6MnToUGrUUJ3gPzgJz8/PZ8qUdzh//hxDhgyjQYOGHDy4nwUL5nLr1k0CArTnk/v9973s2fMrJibl/54UQgjx9PH27sk748Zze+05ZZtv8x44WNbl+2Pb2XluL+Px0nq+lZUVsbGx2NrWV2nv2LELU6ZMZOPGGJVq0Pfu3WPq1OlYWFhgbGyiDDqFhASxY8c2lixZQdu2ngCsW/ctZ8+eRl9fn4CAQGrVsgLgjTdGsWJFEJ988iHffnv/YUhAQCAzZkxjypSJDBgwGCen+iQlXWXz5lgMDY34v//7P2XfuLg4Vq9eTa9evXBycsLIyIjLly+zefNm7ty5wxdffKGxMMuDJOgkhADUJ/iGhoYkJBwhKDiIbXWcyp3gA5w+fZqUlBSsra2xtLTkzp07WvseO5bA1Kn/h4VFDYYMGYaVVW0OHfqDJUsW8vfffzN9uuoTg/z8fKZPn8rRo4fp3dsHf//BKBQKkpISSU6+US0/AyGEEE8Gb++ejBw5lho1ivNRWJoY0eFmI5UJvl+LnlrPt7KyYtiwYVhaWjJ69Gju3SteqVQywQ8PX8EXX8xX9t+6dTNnzpzmX/96nyFDhgPQv/9AZs6cRnR0JH379sfevp7a++Tk5LBgwVwGDXqVffv+W50/AiGEEE8Id/fm1DQx4vYD7V1d2/P9se1cufVPmefXrFkTJycn7t1TbffyehEjIyPy8/O1V/dt2brMsePjDwBQWFiosWLz5cuXuHHjBvb2xSt8u3TpzqJFy1i7Nprt2+PIzs6iZs2atG/fgTFjxtGyZUvluZ6enpw4cYLdu3eTlpZGfn4+1tbWdOzYkVGjRtG2bdsyr62EBJ2EEID6BB9gwIAhuLq4EBYRVu4Ev3gMb8zNzdHX12ffvn1lBp0WL/4aPT09VqyIUO4rHjToVebPn0Nc3CZ8fPrywgseyv6rVq3kyJF4Fi1apozsCyGEeDa5uzfX2F6RCb4mXl4vYmlZi0uXLqq079r1I6ampvj5DVRpHzr0dfbs2c0vv+zkjTdGq40XGrochULB+PFvS9BJCCGeM2nZxWGo2uaWlTo/KyuLoqIiPDzaEhQUqrFPeno6/fr1AjRXjnZ0dOLw4XhWrVpH48aqW90++uh99u79jdOnTyqDTgDt2nnRrp32lVklWrZsyYIFCyr4qdRJ0EkIAWif4Pfp3YewiLByJ/iASsCqLBkZGfz113nat++olsjO19ePuLhN/PBDnDLodPfuXdav/44uXbrRtq0nRUVF3L2bg7m5hU7vJ4QQ4tlQHRP8nJxsGjVyVbYVFhZy/vxZmjZ1V9si16xZC/T19Tlz5rTaWKdPn2TjxhhmzZqjsUKQeHRKpwi4fu0fcrNzKpTkF+6nCNi9ezeXLl1CoVCwfn0c9eo5qPX9v/+bwLFjCVrH8vRsz+LFy3Xqv3Llaq1zMCHEk0tRWMi6hDgM9Azo7tqhUmNERYVTUFCAj0/fSl9HyVbxhIRDKkGn3NxcTp8+CUBKyuPdFSJBJyFEmZKTk4HKT/A1yc/PA1AmwCvNxKS47dSpk8q248ePkpOTjZtbMxYv/prt2+O4ezcHKysr/PwGEhAQiKGhfJ0JIcSz7GFN8DMzM7h37x62tnXV+hsbG2NpWYu0tFSV9uJKQXPw8upAz569K3UtovqUThHQ37cvGQeuqSX51TVFQLNmzXB0dCIx8YrWvqNHv4mf3wC19l9+2cX+/Xvp3Lmb2jErKysmTZqq1u7goDnvpRDiyRZ2YB3nUi4xymsQTlbqxSkeZGKkh7Hh/cpwu3b9wnfffUunTh0YNrQ/2TmVu46XX/YlKiqClStDMDU1w9OzPXfupBMeHsKdO+nA/Upzj4v8lSaE0EqhUBASHlKlCb4mdepYY2VlxalTJ7h3L1cZaAI4evQwACkpycq2kolfTMw6jIyMeOedyVha1mLXrh1ER0eSmprCxx9/Wm3XJ4QQ4slT0Qn+g3bv/pnvvvuW9u070rdvf2V7yWTcyMhI43nGxsZqE/Z166JJSkrkyy//U+HrENWvdIoASxMjEu8cVkvyq2uKgMDAQL76an6ZQScvL81zoqiocIyNjenT5xW1Y6amZvTp41uxDyaEeCJFH97EttO/4uPenaEeuq5SUnBq3xwAjp5KZtHKQzSsX4s3B9RGX68QUC+ipAtLS0sWL17OF1/8m/nz5yjbPTza8sYbo4mKCn/sq3El6PQM01aNzNu7J0OHvq5TpvmKVCNLSDjM5MlvaRynU6cuzJ+/WPm6oKCARYvmc+bMaZKTr5OTk4ONjS3NmrVgxIjRNG3qXvkPLqrNkiUL+PPEn5We4Gujp6fH0KGvExq6nBkzPmDcuOJKC4cPxxMeHoKBgQH37t2f4OfkZAPFT6NXr/6eBg1cAOjZszeTJgXy44/beeON0SqViIQQQjw7goKDKjHBv+/AgX189tknuLk14/PPv0JPT095rGTVbX5+vsZz8/LyVFbmJiVdJTJyJaNHv6m2RVw8HlXNAQa6pwjQ5vjxoyQmXqFnz5extKylsU9hYaEyPUDpe1AI8fRYc2QL3x/dRq+mnZnYZWSFzz9+OoXFKw/hZF+TDyd2wNxM8wOPinB1bUxk5FqSkq6SlpaKjY0tTk71Wb78GwDl306PiwSdnmHaqpGFhQXz668/ExoaqbLCRJOKVCMr0b//QF54oY1K24NL1vPz8zl79gytW7+Ag4Mv5ubmJCcns317HBMmjGHBgqU6JTcTD09YWDAbNsQwZOBghtpU/5O5ESPGkJuby3ffrWH8+OLkrGZm5kyaNEWZmLVEyX3avHlLtS9NH5++HD16hGPHEiToJIQQz6Dw8BAiI8MqPcHfv38/M2d+QMOGjVi4MEjtiW/NmpaYmJiQmpqidm5eXh4ZGXewsblfoScoaBGWlpZ06+ZNUtJVZbtCoSA/v4CkpKuYmpphY2NT4WsV1auqOcAqYtu2LQAat90BpKam0Lt3V+7du4epqSnt23ckMHDiY/9jUAihu7VHtrAuIY6XmnRicrcxFQ4eHz+TwsKV8TjY1WDG/3WkhnnZ234rysmpPk5O9ZWvDx7cj4WFBa1avVCt71NREnR6hmmrRubkVJ/VqyPYtm0LgwcPK2cM3auRlWjZsnW5y4fNzMwID49Wa/f3H8zgwX1Zty5agk6PUXh4CFFR4fj6+vHJR59wNexItb+Hvr4+Eya8w8iRY7l48S+giMaNm1JUVMR//vMlLVq0UvatW7c4aGltba02jrV18aQ+MzOj2q9RCCHE4xUREUpkZBh+ff0YX69/hSf4V69eJSoqCmdnFxYvXo6lpXrwQV9fn6ZN3blw4Rx5eXkYG9//I+DMmVMUFhbi7t5M2Xbjxg3S0lIZOXKoxvccPnyg2gpv8ehVRw4wXWVnZ7F798/Uq+eocf7q4OBIq1Yv0LhxE/T19Tl9+hQbNsRw5Eg8y5eH4+ra+KFen1BVXYnnP/30U86cOcfly2Unnn/QX39dICBgBAqFgs8/n4u3dy+1PgcO7CMqKoK//jqPkZEx7dp58c47kyUH2GO0ImwFaxPi8G7ckX91H4u+nr7Gfjk5OeTl5VGjRg2VnLP79x9kYVg89WxrMGNSJ2pYVG/A6UGxsd9x6dJFxo4dr9MOp4dJgk7PMG1LjXv2fJnVqyPUygVrUtmlxnfv3kVfX1+tCkx5ateujbGxCZmZmZV6X1F1JRN8H5++fPjhJw99+beZmRktW94PMO3e/TNFRUV06NBJ2dasWQsAUlLUn0KXPJmuXbvOQ71OIYQQj1ZkZBgREaH06ePL5//+jKSVmqt/paWlkZ6erjbBT0pKYufOnTRq1Oh/ASfNW54AevXqw4kTx4mL28iQIcOV7TExazEwMOCll+4nC5848V2ystTnKQsWzMPY2JhJk6YoH4iIx6eqOcBKKy9lxa5dP5Gbm0vfvn4a500zZszil1928ccf+zl37qwySJGfn8fSpQtVKt0B7N+/jy1bNnDx4l/cvn0LIyNjHBwc8PHpi7//YJX5taSsqLjqSjx/69YtXF2blJt4vrTCwkLmzfsCY2MT7t7VnDl6z55f+fjj6TRu3IR33nmX7OwsYmLW8fbbAYSHR2NjY1vhzyyqZsOGGJaHBmNbow4ejs3Z89cfKsetzCx5heLVRPHx8Zw/f55+/frh4FAchExNTeXdKR9AEXTvUJ/jp5NVzv/7zg669+in0rZq1UoA7t27B8DFixeUbR4ebfHwuL8C9/33iwOSLi6N0NPTIz7+IHv3/kanTl0YPTqgGn8SlSNBp+dQSYLmOnXUV41Uh2+++ZovvyxO6uzk5MygQa/y6qvDNf4SVigUZGZmolAUkJKSzLp133L3bg4dO3Z+KNcmylZ6gj9jxiz09TVH8G/lpJOddxfbGtUb6LlzJ52QkOVYWVkxYMAQZXvJE8KTJ//k3LmzuLkVT6AUCgVxcZswMDDQmtRTCCHE02fDhhjCw0Ows7PH07M9P/z4Azcv/K08bmVmSRun4gcSQUFBbNu+TW2C/9NPPwHQr18/Dh7cr/YepVdl9+8/kB9+2MrSpYu4fv06Li4NOXDgd/77392MHh2gsrrAy+tFjde8bNk3mJmZaVy1IB6tyiX51a68lBWGhgYYGBioJKh/0KZN6zl9+hSNG98PUjRr1oKjR4+oFVW5ePEv9PUN6NfPH2trG+7dy+X48WMsWbKQ/fv3sWjRMuW8WlJWVFx1JZ6fNGkSCoURCxfO0znoFBv7PZcvX+L110cRHh6idrw4iPgf6ta1Y9mylZibmwPQoUMnAgJGEh4eyvTpMyv+oUWVnD17GoDUrFss2hOudrxlPTde4XWt59+6dUsZPIreeEpDj6Ps26cadFq5coXK6/Pnz3H+/DkAxo4drxJ0atmyNb/8spMdO7YB0KBBQ6ZOnY6//yAMDCqXoLw6SdDpOaNQKFi1aiUGBga0bNmKlStXqD216d27J/n5+VqruJRW+qnN339fAuD110fh6tqEtLRUtm/fwpIlC/jrr/NMmDCRDRu+59y5s5w/f4b09HS6dOnGvn3/VY5Xo0YNRo4cy4gRY9Te65NPPmT37p9p2LAR0dEx1fYzEcUenODv2vUjAGaGBty88LfKBD8qfgO/XNjPl32n4UJ75RjXr1/n+vXrQPGTZ4CYmBjMzGoCMGbMOGXfAwf2sXZtNF5eL1KnjjXJyTfYunUzmZkZzJ27ECsrK5XrmzJlGhMnjudf/3qHIUOGUatWLX75ZRdnzpxi7Njx2NtXX6JzIYQQj1fJBD85+QZz5sxWO96ynpvyd5Imt27dUuYGXLRokcY+pYNORkZGLF68nLCw5fz8809kZNzB0dGJKVOmMWiQ5m104sm0PDS4Skl+NSkvZQUUF815MIdpaR9//Bk2NjYYGhoqgxR169px+vRJMjMzVYJOI0eOUTt/yJDhLFgwj02b1nPmzCmaN28JSMqKyqiuxPOGhoaUSkFaruTkG4SFBTN27AS1eW6Jo0ePkJaWyrhxbykDTgBNmrjRpk07fv11J++9N11lVad4+GbOnM28z+aQGHq43L49evSgR48eKm1ubm4s/WY+J/fO0XhOiy4zycxWbdu3r/z3KjFmzDiVv7OeNHK3PmeWLFnAqVMnCAycSELCEY1PbYKDg6lTpw4DBgwo9wut9FMbJ6fipzYvv/yKcj9z//4Def/9d/nhh600aeJGdHQkdeva4e7enIMH92NmZs6iRcsoKMj/3zL4H8jKyiI/P1/lvX//fS979vxa4e16QndVneAD/PPPPyQkqG5/WLNmjfJ/l/4ytLd3wMjImNjY78nIuEOtWlZ4enoxenQAzs4uamM3bepOcHAEYWHLWb9+HXl5eTRo4MKMGbPw9fWrwCcVQgjxpJs5czYzZ85WvrY0MdI62Z89ezYOjqp5VNzc3HBzcwNg/Pjx3LtX/lbxmjVrMnXqdKZOnV6pa46N3Vqp80T1iYgIJSIitNJJfrUpL2UFQL9+mhOIl9D0cOz69WsYGBhQs6Zuic5LxtAlDYWkrKi4h514fsGCeTg4ODJ06Gvs3LlDY5+S+Xjp3KYlmjdvyZEjh0hMvEKjRq4P5RqfZ4YGBejpFWg9rkfVq8w9ryTo9BwpqUbWv/9ARo4cy9mzpzU+tXFxqU9ERARnz56lZcuWZY6p6alNafr6+owcOYb4+AP/W8myi9q1a5Oenk6/fr0wNDRUWabet29/3nxzBDNnTmPhwiCgOBnbggVzGTToVZVVUaJ6PTjBL6Fpoj+lRwBTeqjvD/b09MTT01OlTdtkv7iK0NIKXWPjxk2YN0/zE2shhBBCPL9KUgT08+3HBAd/rUl+teUAq4xr14pXxJiamtGpUxeNfbKysjAzM9O4xeXcuTN06NBJ60PVnJxs8vLyyc7O4sSJ46xZs5patWopVzmVJikrquZhJ57/5ZedHDiwj+Dg8DLvu7S0VEC98nfptrS0VAk6PQSFhQWsnvee1uMTPlqu9ZgomwSdnhOlq5FNmzYD0P7Upnfv3kRERHD79u1yx9VlS1PJqqfs7Gxq165dZl9zc3O6d/dmzZoo/vknCUdHJ0JDl6NQKBg//m0JOgkhhBDPIHnCLKqidIqADu07sOc39SS/ZeUAg/spAsLDwzl79oxy3JKHsw9uXVEoFCxbVlyhsFevl7UGEhISDrN06SI6d+6Kg4MjBgYGHD4cDxSvrps8WfsfuV9++Sm//far8nXz5i2ZOnU6NWvWVOt75crfjBp1Pwl+WSkrhLrqTDz/oMzMTJYsWYCf3wBatmxdZt/c3FwAjWlOSiprlvQR4mkhQafnQEWrkSUnFycar67SilevJgJQp45uSadLkqxlZNzhzp10Nm6MYdasOVhYVK6SniibTPSFEEI8bvKEWVRF6RQBH8/+WO14RVIEHD58f3X3d999q/zfDwadlixZQFLSVaA4n6k2zs4NcHNzZ//+vdy+fYuCggKMjIqDB19/vQRn5wZazx07dgL+/oNJT79NQsIRLl68QEbGHY1969Vz1CllhVBX3YnnH7Rs2WIKC4t4661J5fY1NS3O7ZWfn692LC8vT6WPEE8L+QZ6xulajSwtLY3s7CxsbGwJDw9HT0+Pxo0bV+i9Sr4IH2yLiAgFoHPnrsr29PR0jWPcvJnG7t0/Y2ZmjrNzA955ZzxeXh3o2bO3xv6i6mSiL4QQQoinWekUAWXl/wLNOcDgfooAXXKAlU5Z8cEHZVcSc3FpyBdfzFNpW7hwHhs3ri+3krSra2NcXYvn4717+7B58wbef38yQUGhtG7todLXzMys3JQVQt3DSDxf2rlzZ9m+PY6AgEAyMu4og4a3b98C4NatmyQlXaVuXTuMjY2xsbEFIDU1BReXhipjpaamACj7CPG0kKDTM0xbNbISderUUZaZDwkJYseObXTr1oMTJ07g5eWFlZVVhaqRlWx9W7/+Oxo2bERaWio//bSDpKREhgwZprL/fM+eXwA4c+YU69d/h5GRIVevJrJjx3YyMzOYPv1jNm5cT1JSIl9++Z+H9BMSQgghhBBPk8e9QltTyopHxcenLwsXzmPLlg1qQacHaUpZIVQ9rMTzpSUn36CoqIiVK1ewcuUKteOLFhX/nbNy5Wrc3Zsr05+cOnVCJYgIcPr0SSwsLMpcHSfEk0iCTs+w8qqReXi0VQadSvz3v78xcOBAbG2LI+gVqUbm6OhEevptduzYSk5ODmZmZjRp4kZAwAR69/ZRGaN58+KKDCkpyYSGLiM/P586dazx9GzPq68Op3btOowaNZzRo9+UX5JCCCGEEAJ4vCu0K5qyorrl5eVRWFhIRkaGTv1Lp6yQ+bQqXRPP5+TkkJeXV+nE882bt+Dzz+eqtR89WlxFfPjwEbRo0RIHh+J/nzZt2mFtbcPWrZsZOvR1zM3NAbhw4TxHjx7B19dPtkuKp47csc8wbdXINLG3rweAr68fH330EStXrgQqVo3Mzc2dU6dOEBGxRpk8XJsmTZoC0L37Sxqv8cMPp2JpaUm3bt7K/fJQnLQxP7+ApKSrmJqaYWNjo9PnE0IIIYQQorIqmrLCzs6+0rl3bt5Mw9pafY4bG/sdAC1atFK23b59m1q1aqldT+mUFQ0bSqWz0iqSeD4+Pp7z589rTDwfHh5OQQFlJp63sbHF27uX2jXcvXsXgBYtWqocNzQ05N1332fWrI+YOHEcfn4Dyc7OJiZmLVZWtQkICKzGn4QQj4YEnZ5R5S491jMgN6/4l9PjfmqjyY0bN0hLS2XkyKEajw8fPpBOnbowf/7iR3xlQgghhBDieVKZlBVLlqygbdv7D26PHUvg2LHi3QPlVccbNWoYrVt70LSpO7a2tqSnp3PoUDxHjsTj6tqYoUNfU/bdtWsHMTHr6NatB/XqOWpMWSGJp1VVV+L5rVu3qrSVlXi+Il56qRcmJiZERYWzbNlijIyM8fT04u23J2NrW7fS4wrxuEjQ6RlV3tLjUdMXAPpantoUqfUvvbT0UZg48V2ysjLV2hcsmIexsTGTJk3R+ARICCGEEEKI6lSZlBUPOnLkEJGRYSpt2oIUQ4YMJz7+IBs3ricj4w4mJqY4OzcgMHAiQ4YMV6kw3bp1G86cOc3vv+/l1q2baikrWrV6oTIf+ZlWkcTzPXr0oEePHmrtnp6ehISElJt0XhtfXz98ff20Hu/cuatKESYhnmYSdHqOaXtqY2hYxIULFzAzM8PJqXh/cemlpaVV5KkNwKpVxdv2SvaYX7x4Qdnm4dEWD4+2AGqJ80osW/YNZmZmGpepDRBC4gAAIABJREFUCiGEEEIIUd0qkrJCW9+AgECdt0aNHTuesWPH69TX3b0Zs2Z9oVPf59HjTjwvhJCg03OtvKc29erVUwadtKnIUxtArWrD+fPnOH/+HFD8C7Yk6CSEEEIIIcSjZmKkh7GhQuOxIgzIyn7EFySq5HEmnhdCFJOg03NM25MYE5MiwsJUA0nalpZW5KkNwL592pev6iI2dmv5nYQQQgghtEhMvMLOnTuIjz/ItWtJ3LuXh6OjE97ePRk69HWVrUtlOXBgH1FREfz113mMjIxp186Ld96ZjIODo0q/tLQ0Nmz4nnPnznL+/BnS09N55ZV+GudgBQUFLFo0nzNnTpOcfJ2cnBxsbGxp1qwFI0aMpmlT9+r4EYgyKTi1b47GIy26zAQMHu3lCCHEU05z2QXxzDM1NMPSxEjjf3o8/kTiQgghhBAPw/btcXz//VocHZ0YM2YcEydOxtm5AWFhwbz11pvcu5db7hh79vzKBx9M4d69XN55511ef30kx48f5e23A0hLS1Xpm5h4mejoSC5fvoS7e/Myx83Pz+fs2TO0bv0Co0cH8N570/Hx6cvJk38yYcIYjhw5VKXPLoQQQjxqstLpeVWI1qR59hMk4aAQQgghnk3e3j0ZOXKsSnGUAQOG4ORUn9WrI9i2bQuDBw/Ten7xaqT/ULeuHcuWrcTc3ByADh06ERAwkvDwUKZPn6ns7+7ejK1bd1G7dm3S09Pp1097XkozMzPCw6PV2v39BzN4cF/WrYumXTuvynxsIYQQ4rGQlU5CCCGEEOK54e7eXGM13p49Xwbg0qWLZZ5/9OgR0tJS8fMboAw4ATRp4kabNu349dedFBTcT1xsbm5B7dq1q3TNtWvXxtjYhMxM9cq+QgghxJNMgk5CCCGEEOK5l5KSDECdOtZl9ispxNKiRSu1Y82btyQ7O5vExCtVuhaFQkF6ejo3b6Zx5swpPv30Y+7ezaFjx85VGlcIIYR41GR7nRBCCCGEeK4pFApWrVqJgYEBvXv3KbNvSc4mW9u6asdK2tLSUmnUyLXS13Plyt+MGjVc+bpGjRqMHDmWESPGVHpMIYQQ4nGQoJMQQgghhHiuLVmygFOnThAYOBFnZ5cy++bmFicaNzIyUjtmbGys0qey6tVzZNGiZRQU5JOUlMTOnT+QlZVFfn4+hoYyfRfiSRYdHcm5c2c5d+4s16//g719vQpX4C4oKGDt2tX89NMPXLv2D2Zm5rRp05YJEybSoIGLxnP+/vsSUVHhHD16mIyMDKysauPu3pxp0z5SW8Gpa/VNIaqD/NYSQgghhBDPrbCwYDZsiKF//4GMHDm23P6mpqZAcaW5B+Xl5an0qSwzMzO8vF5Uvu7btz9vvjmCmTOnsXBhUJXGFkI8XCEhy7C0rEXTpm5kZVU8D1tRUREffjiVgwf307VrdwYPHkZ6+m02bYolMHAMwcERNGzYSOWcP/44wEcfvY+joyNDhgyndu063L59m1On/iQ7O1sl6LRnz698/PF0GjduwjvvvEt2dhYxMet4++0AwsOjsbGxrfLPQIjSJOgkhBBCCCGeS+HhIURFhePr68e0aTN0OqfkD7LU1BRcXBqqHEtNTVHpU13Mzc3p3t2bNWui+OefJBwdnap1fCFE9fn++83K/4+OHDmUu3fvVuj8vXv3cPDgfvr3H8gHH9yvhNmnjy+jRg1j8eKv+eab5cr227dv8emnH9OmTTvmzVtY5mrIilbfFKI6SCJxIYQQQgjx3ImICCUyMgwfn758+OEn6Onp6XSeu3tzAE6dOqF27PTpk1hYWODs3KBarxXg3r17AGRk3Kn2sYUQ1aeqQeGEhMMA+Pr2Vxv3hRfacORIPDdu3FC2b968gYyMO7zzzmQMDQ3Jzc1VqaBZWkWrbwpRHXQKOv3999988803DB06lA4dOtCmTRv8/f0JDg4mJydH5zfbs2cPw4cPx8PDg/bt2zN58mSuXr1a6YsXQgghhBCioiIjw4iICKVPH19mzJiFvr7mKXFaWhpXrlxWydHUpk07rK1t2Lp1s8o8+MKF8xw9egRv716Vzrt0+/ZtCgsL1dpv3kxj9+6fMTMzp2HDyicoF0I8+fLztW/TNTEpbjt9+qSy7cCB37GwsCArK5MxY16nV68uvPRSJ955ZxxnzpxSOf9RVN8U4kE6/UbcsGEDa9as4aWXXsLPzw9DQ0P++OMPFi9ezI4dO4iJiSl37/rOnTuZPHky7u7uTJs2jaysLKKionjttdfYsGEDdnZ21fKBhBBCCCGE0GbDhhjCw0Ows7PH07M9u3b9qHK8Tp06eHl1ACAkJIgdO7axZMkK2rb1BMDQ0JB3332fWbM+YuLEcfj5DSQ7O5uYmLVYWdUmICBQ7T1XrVoJ3F+tdPHiBWWbh0dbPDzaArBr1w5iYtbRrVsP6tVzxMjIkKtXE9mxYzuZmRlMn/5xlfNFCSGebCX5mhISDtG4cRNle25urjLYlJJyf6VTYuIVFAoF7703CW/vXowZE8D169eJigpn0qRAQkOjlNU0H0X1TSEepFPQqU+fPgQGBlKzZk1l22uvvUaDBg1YsWIFsbGxjBgxQuv5+fn5fP7559SrV481a9ZgYWEBQLdu3Rg0aBBBQUF8/vnnVfwoQgghhBBClK3kSX9y8g3mzJmtdtzDo60y6KTNSy/1wsTEhKiocJYtW4yRkTGenl68/fZkjX/MrVy5QuX1+fPnOH/+HABjx45XBp1at27DmTOn+f33vdy6dZP8/Hzq1LHG07M9r746nFatXqjMRxZCPEVeftmXqKgIVq4MwdTUDE/P9ty5k054eAh37qQDqhUy797NQaFQ8PLLrzBz5mxlu5ubO5Mnv8WqVSv57LOvVM57mNU3hXiQTkGnVq3Ul98B+Pr6smLFCs6fP1/m+YcOHSIlJYXJkycrA04AzZo1o3379vzwww/8+9//1njzCyGEEEIIUV1mzpyt8odZZft27tyVzp276jTOvn2Hdern7t6MWbO+0KmvEOLZZGlpyeLFy/nii38zf/4cZbuHR1veeGM0UVHhWFjUULYbG5tw924Or7zST2Wctm09sbOz5+jRI8q2R1F9U4gHVal6XUkCMxsbmzL7nThRnGixTZs2asc8PDw4ePAgly9fpkmTJmrHhRBCCCGEqCwTIz2MDRVajxdhQFb2I7wgIYQoh6trYyIj15KUdJW0tFRsbGxxcqrP8uXfANCggYuyb926dbly5TLW1tZq41hb23D+/Fnl68dRfVOISgedFAoFy5cvx9DQkH79+pXZNyWl+AbWlLepbt3iJcjJyck6B52srMzL7/QIZOTkVen8smqk6FY/5WG9ezln6uk9Mf8GT5Oq3C/l/Ws93PulaqPL/VJxz+t3S3n09eVe0uR5/W4pi9wrmj2/3y0KTu2bo/Voq24fY2VVQ+vx55V8t6iT7xbNntfvFl3muAYG+lW6b6ys3AA35etDhw5So0YNunTpoKw+98ILL3DlymVycu6ovc/Nm6lYW1sr2z092xASAhcvnqV3b2+VvufPn6ZGjRq0auX+UHcgyXeLumf9u6XSQacvv/ySY8eOMXXqVBo1alRm37t37wL394mWZmJiAlRs72h6uu4V8x4qQ4MqnV5UyWPVo/LvUFRU9OT8GzxNqnC/lPev9XDvl6qNXnK/REdHcu7cWc6dO8v16/9gb1+P2NitFR5r166f2LgxhqtXr5CXl4+dnT09e/Zm6NDXVJYaQ/FqzNWrwzly5BCpqalYWlrStKk7r78+Upk/o7JjP1TP6XdLeQoLi8jMlO8eNc/pd0tZ5F7RQr5bNJL7RQv5blEj94oWz+l3iy5/EykUhRQWau+XlpZGdnYWdnb25W5ri439jgsXLjB27Hjy8iAvr3hMb++XiYvbwpo1a2nZsp2y/759/yU5ORk/v4HK92/SpCXW1jasX7+e/v1fVQauLlw4z6FDh/D19SM7Ox9Q335XbeS7Rc2T9N1ia1uz/E4VVKmg0+LFi/n2228ZNmwYgYHqFToeZGZmBtzfJ1paSRUP2TsqxLMtJGQZlpa1aNrUjayszEqNERq6nOjoSNq182Ls2PEYGhpy9OgRwsNDOHDgd0JCItHTK34KkZaWyrhxIygoUODvP4j69euTlpZGXNwmJk9+i7lzF9KpU5dKjS2EEEIIIYQmP/64nRs3rgOQnp5Ofn6+slqlvX09fHz6KvtqqpAJ8P77k3FwcMTFpRF6enrExx9k797f6NSpC6NHB6i8n5fXi/Tq1Yeff/6J99+fTKdOXblx4zobNnyPtbUNAQETlH0rU31TiKqqcNBp6dKlBAcHM2jQID799FOdzim9hc7VVbX8Yllb74QQz47vv9+Mo6MTACNHDlWugNRVQUEB69evo2lTdxYtWoa+vj4AAwYMwcDAkJ07d/DXX+dp0qR4CfKOHdtIT0/nq6++pmvXHspxevXqw/DhA9m6dZMy6FTRsYUQQgghhNBk27YtHDuWoNJWUsHSw6OtStBJm5YtW/PLLzvZsWMbAA0aNGTq1On4+w/CwEB9pdDHH39K48ZN2L49jiVLFlCjRk169OjJhAnvqOVoqmj1TSGqqkJBp6CgIIKCghgwYABz5szR+al/SfW7o0eP0qlTJ5Vjx44do0aNGri4uFTkUoQQT5mSgFNlKRQF3Lt3D2tra2VQqERJMQNTUzNlW3Z29v+Oqf6irVOn+PzSfSs6thBCCCGEEJoEBYXq3FdbhcwxY8YxZsw4nccxNDRkxIgxjBgxRqf+Fam+KURV6ZffpVhQUBBLly7F39+fr776Su0PsxIpKSlcvHhRZRWDl5cXtra2xMbGKv8QBDh79izx8fH4+Pg81GRlQoinn4mJKS+80IY//jjAt9+uIinpKtevX+OHH7ayaVMsffq8Qv36zsr+7dt3AGDBgnkcPXqE1NQUzpw5xezZMzEzM2P48BGVHlsIIYQQQjzfTIz0qGmh0PpfDYvHfYVCPBl0Wum0Zs0ali5dioODA506dWLrVtXkvzY2NnTu3BmAhQsXsmnTJlavXs2LL74IgJGRETNnzmTKlCm88cYbvPrqq2RnZ7Nq1Srq1KnD5MmTq/ljCSGeRbNmfcEXX8xmxYogVqwIAoorh4wa9Sbjxr2l0rdtW0+mTp1OePgKJk26vz/dycmZkJBVamViKzK2EEIIIYR43pVdGbNFl5lA1ZKsC/Es0CnodOLECQCuXbvG9OnT1Y63b99eGXTS5pVXXsHU1JTg4GDmz5+PsbExHTt25P3335d8TkIInRgZGePg4IitbV9efLEjenp6/Pbbr0RFhWNsbKyWWNHKqjZubs3x9GyPs7MziYmJrFsXzbRp/yIoKAQ7O/tKjy2EEEIIIYQQomw6BZ3mzp3L3LlzdRqwrL7e3t54e3vrfnVCCPE/ubm5vPXWm7i5ufHpp18p23v16sOsWR8RHh6Ct3dPnJ1dAIiL28SCBXOJjFxDo0aNlf1ffLEjb775BiEhy/j3vz+v1NhCCCGEEEIIIcqnc04nIYR4nHbv/pmkpES8vXupHfP27kVhYSF//nlM2RYdvYoGDVxUAk4Arq6NadDARaWqSEXHFkIIIYQQQghRPgk6CSGeCmlpqQAoFIVqxxQKBQAFBYpS/VMoLFTvW9JfoSio9NhCCCGEEEIIIconQSchxBMnLS2NK1cuk5ubq2wrSfz944/b1Prv2FHc1qxZC5X+iYlXOHnyhErfkyf/5OrVRNzdm1d6bCGEEEIIIYQQ5dMpp5MQQlTVjz9u58aN6wCkp6eTn5/PqlUrAbC3r4ePT19l35CQIHbs2MaSJSto29YTgE6dutKsWQsOHPidiRPH0737S0ARe/bs5vjxo3h798LNzV05RkBAIDNmTGPKlIkMGDAYJ6f6JCVdZfPmWAwNjRg7doKyb0XHFkIIIYQQQghRPgk6iUqLjo7k3LmznDt3luvX/8Hevh6xsVt1Pj8h4TCTJ5ddin758pW0bu2hfH3jxg1Wrw7nyJFDpKamYmlpSdOm7rz++kg8PNqqnFtQUMDatav56acfuHbtH8zMzGnTpi0TJkykQQOXCn1WUXXbtm1RyaMEsHLlCgA8PNqqBJ00MTAw4JtvlhMdvYo9e34lOHgJenp6ODnV5+23JzFs2Bsq/bt06c6iRctYuzaa7dvjyM7OombNmrRv34ExY8bRpIlbpccWQgghhBBCCFE+CTqJSgsJWYalZS2aNnUjKyuzwue7uDTkk08+U2vPz89j/vwvqVXLiubNWyrb09JSGTduBAUFCvz9B1G/fn3S0tKIi9vE5MlvMXfuQjp16gJAUVERH344lYMH99O1a3cGDx5GevptNm2KJTBwDMHBETRs2KjyH15UWFBQqM59Z86czcyZs9Xazc0tCAycSGDgRJ3GadfOi3btvHTqW9GxhRBCCCGEEEKUTYJOotK+/34zjo5OAIwcOZS7d+9W6Pw6dazp08dXrX3Xrh8pLCzEx6cvhob3b9EdO7aRnp7OV199TdeuPZTtvXr1YfjwgWzdukkZdNq7dw8HD+6nf/+BfPDBTGXfPn18GTVqGIsXf8033yyv0PWKyjEx0sPYUHMS7iIMyMp+xBckhBBCCCGEEOKRkKCTqLSSgFN127ZtCwB+fv4q7dnZxdEJGxtblfY6dazR19fH1NRM2ZaQcBgAX9/+Kn0dHZ144YU2xMcf5MaNG9jb21f79YsHKTi1b47GIy26zAQMHu3lCCGEEEIIIYR4JKR6nXiiXLv2DwkJh2nd2gNnZxeVY+3bdwBgwYJ5HD16hNTUFM6cOcXs2TMxMzNj+PARyr75+XkAmJqaqr2HiUlx2+nTJx/SpxBCCCGEEEIIIYSsdBJPlO3b4ygqKsLPb4DasbZtPZk6dTrh4SuYNClQ2e7k5ExIyCpl2XtAma8p4f/Zu+voqus/juPP3bvtbjDWRSPxAyWku0aObqRBWiWklBIDCZGULunuHDAaBEQkBFR+SteSHKx3f39ML+y3DQaMgfh6nOM59/v99Pfz2T2Ht9/P5x7/idy581juR0REWIJNQUEBL2sYIiIiIiIiIv96CjrJayM2NhY/v82kT58eH59qSeZxdnYhb953KF68JNmyZePKlSssW7aIAQM+ZsqUmXh5xW+Xq1GjNgsWfM+cOTOxs7OnePGS3L17h7lzZ3L37h0gPgAlIiIiIiIiIi+Hgk7y2jh69DBBQYE0aNA4yW1xGzeuY9y40cybt4ScOXNb7pcqVYaOHVszc+ZUhg0bDoCjoyMTJ07j66+HMWbMo/OEChcuSuvW7VmwYC7p0zu8/EGJiIiIiIiI/Esp6CSvjUcHiCfeWgewaNF8smfPkSDgBJArV26yZ8/ByZPHE92fN28p165dJSQkGHd3D7Jkycq0aZMAyJ49R+oPQkREREREREQABZ3kNXH79i1++OEAuXLlIV++d5LMExISlOwv5sXGxhIbG5NkWpYsWcmSJavl+siRQ6RPn56CBd998Y6LiIiIiIiISJL063WSJkJCQrh8+VKy5yj5+W0hJiaGevUaJFtHjhxvceXKZc6cOZ3g/pkzv3D16pVkg1WPW716ORcunKd581bY29s/2yBEREREREREJMX0ppM8t23bthAQcBOAO3fuEB0dzfz5cwDw9s6Ir28dS96ZM6fg57eZ776bQdGixRPVtXXrRmxtTdSoUTvZ9jp16sbgwQPo0+cjGjZsQpYsWbl27Srr16/G2tqG99/vmiB///69yJQpMzly5MTKyoqjR49w4MBeypYtT/v2nVLjEYiIiIiIiIhIMhR0kue2efOGROcozZkzA4g/sPvxoNOTnD59ikuXLlK9ui+Ojo7J5itfvhITJkxl6dJFbNmykQcPwsiQIQMlS5amQ4fO5MmTN0H+AgUKsWvXDvz8NgOQPftb9O37KQ0aNMZoND7LUEVERERERETkGSnoJM9typRZKc47ZMgXDBnyRZJpBQu+y8GDx1JUT7FiJShWrESK8nbo0JkOHTqntIsiIiIiIiIikooUdJJnZrKxwtY6Ntl0M0bCHqRhh0RERERERETktaOgkzyHWM4eHJFsav7yQwBtXxMRERERERH5N9Ov14mIiIiIiIiISKpT0ElERERERERERFKdgk4iIiIiIiIiIpLqFHQSEREREREREZFUp6CTiIiIiIiIiIikOgWdREREREREREQk1SnoJCIiIiIiIiIiqU5BJxERERERERERSXUKOomIiIiIiIiISKpT0ElERERERERERFKdgk4iIiIiIiIiIpLqFHQSEREREREREZFUp6CTiIiIiIiIiIikOgWdREREREREREQk1SnoJCIiIiIiIiIiqU5BJxERERERERERSXUKOomIiIiIiIiISKpT0ElERERERERERFKdgk4iIiIiIiIiIpLqFHQSEREREREREZFUp6CTiIiIiIiIiIikOgWdREREREREREQk1SnoJCIiIiIiIiIiqU5BJxERERERERERSXUKOomIiIiIiIiISKpT0ElERERERERERFKdgk4iIiIiIiIiIpLqFHQSEREREREREZFUp6CTiIiIiIiIiIikOuuUZJo5cyZnz57l7NmzXLt2jcyZM7N79+5naqht27YcPXo0ybTVq1dTsGDBZ6pPREREREREREReXykKOo0fPx5nZ2feeecd7t+//9yNubi4MGjQoET3s2bN+tx1ioiIiIiIiIjI6ydFQaedO3daAkN169bl4cOHz9VYunTpaNCgwXOVFRERERERERGRf44UnemUmm8ixcXFERYWhtlsTrU6RURERERERETk9ZKiN51SS2BgIEWKFCEiIgJ7e3vKly9Pnz59yJUrV1p2Q0REREREREREXrI0CzplyZKFokWLkjdvXgwGA6dOnWLJkiUcPnyYpUuXkjdv3rTqioiIiIiIiIiIvGRpFnQaNWpUgmtfX1+qVq1K27ZtGT16NPPmzUtxXc7O6VK7e8/l3sOoFypv9ZxpqePltWAwWL02c/Q6eZH18rTZernrRWslrem7JWlaL0nTd0tiWitJ03dL0rRekqbvlsS0VpKm75akab0kTd8tib3payVNt9f9v+LFi1O8eHF+/PFHIiIisLOzS1G5O3ee7yDzVGdtfKHiTzrV6uWfePXyWoiLM3P//msyR6+TF1gvT5utl7tetFbSnL5bkqT1kgx9tySitZIMfbckSeslGfpuSURrJRn6bkmS1ksy9N2SyOu0Vjw8MqR6nSk6SPxlypIlC7Gxsdy9e/dVd0VERERERERERFLJKw86Xbp0CWtra5ydnV91V0REREREREREJJWketApKCiI8+fPEx4ebrl3//59YmNjE+Xdu3cvx48fp2zZsphMptTuioiIiIiIiIiIvCIpOtNp/fr13LhxA4Bbt24RHR3NtGnTAMiUKRMNGza05B0/fjzr1q1j4cKFlCpVCoAff/yRUaNG4ePjQ9asWbG2tuaXX35h48aNuLi4MHjw4NQel4iIiIiIiIiIvEIpCjqtWbOGo0ePJrg3adIkAEqWLJkg6JSUt956i/z587N3715CQ0OJjo7G29ubFi1a0L17d7y8vJ6z+yIiIiIiIiIi8jpKUdBp0aJFKa5w9OjRjB49OsG9XLly8d133z1bz0RERERERERE5B/rlR8kLiIiIiIiIiIib54Uven0T2A2mwkPD+Phw7C0a9TKiu61MyebfPlmCJHlOyebHnU9mPdzNEky7WpwCOm6Jl/2ZuB1KtbOmGz6jYBgCldv+YS+XSOypEPSZW/epGTJUsmXvRqE0a12sulXrgWRxLnx8oT18iJrBV5svbzIWoEXWy9v8loxGq1xcnLDYFBsX0RERERE/p3emKDTvXu3sLKywtXVK03/kRcWlS7ZNDd3B6LiIpNNN7p5E2aMS7qsY0bsw6KSLWvv5k1MVPontJ0eomOST3fzJjLuYZJpNm7piItLfmm4ubmTzmROvm8O3sTFWSWb/m+W3Hp5kbUCL7ZeXmStwIutlzd5rYSHP+Du3VBcXDxedVdEREREREReiTfmf8FHR0eRIYOL3ioQkdeCvX16YmOTD+aJiIiIiIi86d6oCI2V1Zv5xoSIiIiIiIiIyD/NGxV0EhERERERERGR14OCTq+Bff67+LL/oBTl3b5/D72/Gvpc7Zw+c4b3n3DY9LNasmwlY8dPfqE6bt68QfnyxYmJ0TaktHDm7Ek6d2v2qrshIiIiIiIi/wJvdNDJaGsN1saX9p/R9o05h/2pTp/+hU6d2iS417plc/r37fmKevTPcfz4MRo1Sv7X/kRERERERETeRG901CQ2zkzHr/1fWv3fD63+0uqW1BcTE4O19Zuz5GNjYzEaja+6GyIiIiIiIiJJenP+Bf6aW7JmHZv9d3H77l083d3o3LolPg2yJ5m3de2GfNy7H8uXLuJheDg1K1amS4s2CX6Zb8qUiWzctJ706Rzo2uVjihYpBcCuPX6s37CcW7dCcMyQgSYNG+Jbo2aC+leuWc2mrVuws7Wnc5vOVK9UA4Co6CjmLJ7NviN7iYiIpHTpsnTs2BWz2czw4Z8RHR1NixaNsLKyYv7309iyZTvXb9xk8MB+AJw+8yuz5sznypVrpEuXns6du1O7dj0OHTrI7NnTuH79Og4ODtSpU59OnbqlynMdMeILbG1tuX79GmfPniFv3nwMHfol3t4ZAShfvjh9+nzCqlXLiI2NZdWqjVy+fIkJE8Zw7tzvODs707nzB1StGh9APHz4IFOnTiIwMJD06dPTvHkrWrVqC8APPxxg9uzpBATcIEeOnPTvP4jcufMA0LRpPRo3bs727VsICLhJqVJlGTLkC+Li4ujfvzfR0VFUr16BuDgzY79biIure5Ljmbd8JRevXMFgMHDk5xNkyZSRgT0+JK9X/Frp3aEL1erU4oc9+7h57Trfr1vBvTt3mf3NJE4cP4a9nR0yF/TvAAAgAElEQVRNfOvSqGb8m1WRUZFMmjebwyeO4+TkShUf3xQ9184fdKOOby1279tHSGgIJQuXZGDvwZhsTfjt8mPLzs1MGTXVkr9hw1pMnz6XjBkzMWnSOEwmE0FBgfz66xny5PkPw4b0Y9mK1ezw342LszNDBvcnT+5czzHjIiIiIiIi8k/xRm+ve51k8vZm8siv2LpkAR3ea8aIiZMJCQlJNv/+/XuZNnw007/+hkPHj7Ft3x5L2u/n/yRbtuzMn7uehg1aMHX6t5jNZgCcHJ0ZMnAk/v576P1RD+bMn8f5C+ctZW/fucP9+/fZuN6PQb0HM27aWK5cvwLAzAUzuHbjKvPnL2XGjLmEhoawcuVS7Ozs+Oyz4bi6urJ8+Tp27dqPu5tbgv4GBgUzaMiXNGpQh61b/Zk3bwl58vwHADs7O4YO/Ypt2/YwZsxE1q9fw/79e1Pr0bJjxzY6dOjMli07yZ37P3z5ZcIzrw4c2MusWfNZvHgl4eHh9OnzEdWr+7Jp0w6++GIk48eP5sJfz2j06OEMGDAYf//9LFy4gmLFSgBw7tzvjBr1FQMGDGbLll00aNCYgQP7EhUVZWlnzx5/xo2bzKpVGzl//g/8/DZhb2/P2LGTcHf3wN//AHMXb0024PS3g0ePUblsGTYvmke1CuUZMnpMgjOvDu3bz4AvhzJ71RKsrKwY++XX5M6dh+WTZzJm0Oes2baFn345CcDCtau4ERjIypXrGTZ0DHv2bU/xcz14+BBfDP2MNas3cv7yebbt3pbisj/8cIBWrdqxcOEKbGxs6dl7AHly52LtqsVUrFCO6TO/T3FdIiIiIiIi8s+koFMa8SlXBndXVwwGA1XKlyNLRm9++/VssvnbtG6Ho0MGvNw9aFyzDnsOH7Skebm5U79+I4xGIz6Va3L7dih37twGoHixMnh7Z8bKyooC+fNT5N3CnP3ttwR1t27REltbWwoXKEzpYmXYe3APZrOZLf6b+ahjTxwdnbC3T0fTpi04cGBfisa3a9deihZ5lyo+lbC2tsbJyZk8efICULRocXLlyo3BYCB37jxUq1aTkyd/ftZHmKyyZctRuHBRbG1t6dr1Q86ePU1gYIAlvW3b93F0dMJksuPQoQN4e2ekTp36WFtbkzdvPipVqsLevbsAMBqtuXjxAg8ehOHo6EjevPkA2LRpHQ0aNCZ//gIYjUZq1aqLjY0NZ8+etrTTtGkL3N09cHR0oly5Cvzxx3+fazx5c+WkctkyWFtb07x+XaKiozl75lE7NevXxc3DA1uTiQv//ZP7d+/R8f3O2FjbkMnTi9o+1dh7+AcA9v14mFYNGuPo6IS7uyd1ajVOcT/q1q6Dm6srTo5OlC1ejj8v/pHisqVLlyV37jzY2tpSqVJlbG1tqVG9CkajkcqVy/PnnxdS/kBERERERETkH0nb69LItj37WLVxEwFBwQCER0Rw9+6dZPN7enpaPnu5uxP6V1AJwMXZ2fLZZLIDICIiHIDjJ35kxaoFBARcJy42hsioKLJny2bJ7+DggJ2d3aO6Pb0IuR3Cnbt3iIiMoGu/LlgZIC7ODJiJi4tL0fiCg0PIlMk7ybSzZ88wY8ZkLl48T3R0NNHR0fj4VH1qnTt2+PHttyMBKFSoCOPGfZdkPk/PR+2mS5cOR0dHQkJC8PLy/ivdy5IeEHCTX389g69vZcu92NhYav61HW3EiDEsWDCXGTOmkDt3Hrp370GBAoUICLiJn99m1qxZYSkXHR1NSEiw5drV9dHbXyaT3RPfZHsSj8feIjMYDHi4uRESEoJzjvhxuLk/elMqJCiI26G3qOFbBfNfcxUXF0fBvG8DEHrnNp5uj/J7eDx6Fk+TcJ2ZCLmd8vE4/19ZF5fHrm1tCQ8PT3FdIiIiIiIi8s+koFMaCAgKZuy0GYz/chj58/4Ho9FIpz79LVvikhIUFIS3d6b4z6EhuDm7PLWd6Ogoxoz9nF49BlGvQQ3uBgcw4pvRCfKEhYURERHxqJ3gQN7KlhMnRydMtibmT15ApnzZCQ4OTlDOyurJbXt4uPP7uaTfhPnyyyE0adKcsWO/w2QyMWnSuCcG3P5Wo0YtatSo9dR8QUGP3mp6+PAh9+7dw/2xwIzVY5339PSicOGiTJw4Lcm63n47P6NHjycmJoY1a1YwbNgg1q7dgqenF+3adaR9+05P7c//s3raw/s/waGhls9xcXEEh4YmOx43D3c8vL1Yt3IDDy9dTlSXm7MzQaEhvP3XdUhI0LN1Pgn2dnZERj5aQ6GhzxdcExERERERkTebttelgYjICKysrHB2cgRg6649XLxy9YlllixbxP0HYQSFhrBu+1YqlS771HZiYmKIiY7GydEJo9HIz8ePc+LUyUT5lq5YTnR0NL+cPcXhY4epVK4yBoOBujXqMnXuFG7fvgXEBxNOnIjfBufs7ML9+/d58OBBkm1XrVqZ4ydOsXffQWJiYrh79w5//HEOiA8ExW9vM/Hrr2fw90/52UApcfjwIU6dOkl0dDRz5kznnXcKWN5y+n/lylXg6tUrbNu2Jf55xcTw229nuXTpItHR0ezY4UdYWBjW1takT5/ecnh7/fqN2LBhLWfPnsFsNhMeHs6hQwd5+DDp5/E4V1c37t69S1hYWIrGc+78BfYf/pGY2FhWbdqCrbUN+QsUTDJvrv/kwT6dPYsWLyAyKpLYuFguXr3C7+f/BKBSqbIs37SOe/fuERIazFa/dSnqw5PkypGbS1cu8ceFP4iMimTu3FkvXKeIiIiIiIi8ed7oN52MBiu+H1r9pdafEjmyZqV5/bp8OHAIBisDNSpXpEC+vE8sU6F8JT4Y+ikPwh9Ss0JlalWu8tR27O3T0aljD8ZO+IpvxkZTvGgxShUvkSCPi7MzDg4O1Gvgi8nGRN/u/cieJf6X0bq2687CFQvo2vV9bt++hZubO76+dShSpBhZsmSlQoVKdO/+PmazmbmzJyeo18vTg5FfD2PmrHmMmzAVBwcHunT5gDx58tKv36dMmTKR8ePHUKRIUapUqZbiAExKVK9ek3nzZnH27Gn+8598DBs2PNm86dKlZ8KEKUyePIEpUyYQF2cmd+489OzZB4Bt27YyfvwY4uLiyJYtO599Fl9Xvnzv8MknQ5gwYQzXrl3BZDJRsGBhChcu8tT+Zc+eg2rVatC8eQNiYmL4ZsK8Jx4mXr5kcXb/8AMjv5tC5ozeDP+0P9bWSf+pGoxG+n8+lPXzl9Nm2WKio2PIkjET7zdrAUDbRs2YOG8WzZrXx9nJjSo+vmzeuuapfX6SrJmz0u699vT7vC8mW1s++KgnGzasfaE6RURERERE5M3zRgedYqNinp7pRep/hrxd2rSiS5tWCe4ZvbJz+c4NKlWvSqXqCc84KlumLHVLlE5UT82KPtSs6JPg3tpVj37ZrpZvI2r5NsIrU3pCb15LkK9ggQLMmzUHgI969CUy+GGCdJOtiS5tu/Jh348Tba8D6NmzLwCenu6Eh92kfbuE4ylUMD9TJ4/F3iEjcXGPAnI+PtXw8amWqD6AjBkzcfDgsSTTUsrJyZkBAwYnmZZU3dmy5eDbbyclmX/8+MlJ3of4w7FLJ/PG2erVmxJcd+rULcH14MGfA3Dp5r1k6/+brY0tQ/v0SjJt0vzZie65uLny1ZdfJ7m9zs5kYmD3ntjneIvAm/cBaNigxVP7MGf6zATX77fsmOC6bbN2tG3WDgAbj3QULfoouNm7d78EeevXb0j1KqUs15kzZ8J/2/qn9kFERERERET+2bS9TkREREREREREUt0b/aaTyOtozIiBnPvtlwT3DAYrWjdumGZ9CA4OpHefDgBYGawSHGo/dcIkPDw80qwvIiIiIiIi8mZS0Ok1tGTrerI4Zkpyu5QkNGTIF6+6C8/skyGjE93LlcmBqKC0m28PDy+WLvYDSHIrpoiIiIiIiMiL0vY6ERERERERERFJdQo6iYiIiIiIiIhIqlPQSUREREREREREUp2CTiIiIiIiIiIikuoUdBIRERERERERkVSnoNNrYJ//Lr7sPyhFebfv30Pvr4Y+Vzunz5zh/a6dn6tsUpYsW8nY8ZNfqI6bN29QvnxxYmJiUqlX8m8yYsQXzJo17VV3Q0RERERERJJg/ao78DJlMMViML+8YEac1Rv9+BI4ffoXJk4cw9y5iy33Wrds/gp7JK/KqEkj8XD3oHPrLq+6KyIiIiIiIvIae6OjJgZzDNemdHtp9WfpMfOl1S3JM5vNmM1mDIaX/6JeTEwM1tav559JbGwsRqPxVXcj1bzOz1pERERERESenf6Fl0aWrFnHZv9d3L57F093Nzq3bolPg+xJ5m1duyEf9+7H8qWLeBgeTs2KlenSok2CIMuUKRPZuGk96dM50LXLxxQtUgqAXXv8WL9hObduheCYIQNNGjbEt0bNBPWvXLOaTVu3YGdrT+c2naleqQYAUdFRzFk8m31H9hIREUnp0mXp2LErZrOZ4cM/Izo6mhYtGmFlZcX876exZct2rt+4yeCB/QA4feZXZs2Zz5Ur10iXLj2dO3endu16HDp0kNmzp3H9+nUcHByoU6c+nTo9WzCwR4+uFCz4LidP/sy5c+dYuHA5zs4uTJ48niNHfsDKykDt2vXo1KkbsbGx1K9fg2nT5pAzZ24Abt++TZMmdVmzZhMuLq788MMBZs+eTkDADXLkyEn//oPInTsPAE2b1qNhwyb4+2/jypXL+PsfYPnyxaxevYIHDx7g7u5Ov34DKV68JHFxcSxZspBNm9YRFhZGsWIlGDBgEI6OTs80vr9VatSMnp3eZ/XmLTx8GE6tKpXp1q4NRuK3Ye7Z5k+uvHk4sHMP1erUonn71uzdsZPB6zYTGhxM3ly56dupG17uHpjNZqYvWcDuQweIjo3F3c2Lj3sPJXu2t4iOjmLJsrkcOrSX2LgYShUvTqcO72MymTh95gzjv5tI/br1WLt+HdY2NnRu1YVaVWuzaftGdu73x8rKitWbVlOkQBHGTvou2fF06dKeJk2asnXrJm7dukW5sqX5uNcH2NracvLUaUaP6UyTJs1ZuXIZJUqU5LPPhj9xbv77398ZPXo4V69epUyZclhZPf2ZHj9+jOHDh9G8eSuWLFmA0Wiga9ePqFOnvmVt1axZm3r1GgKwdesmNm1az/TpcwEoX744fft+ysqVSwkNDaV585bUrl2Pr776jIsXL1CqVBmGDRuOjY3Nc825iIiIiIjIm0pnOqWRTN7eTB75FVuXLKDDe80YMXEyISEhyebfv38v04aPZvrX33Do+DG27dtjSfv9/J9ky5ad+XPX07BBC6ZO/xaz2QyAk6MzQwaOxN9/D70/6sGc+fM4f+G8peztO3e4f/8+G9f7Maj3YMZNG8uV61cAmLlgBtduXGX+/KXMmDGX0NAQVq5cip2dHZ99NhxXV1eWL1/Hrl37cXdzS9DfwKBgBg35kkYN6rB1qz/z5i0hT57/AGBnZ8fQoV+xbdsexoyZyPr1a9i/f+8zP8Pt27cyYMAQduzYh7d3RkaM+Byj0cjy5euZN28JP/10hE2b1mNra0vFij74+2+3lN2925/ChYvi4uLKuXO/M2rUVwwYMJgtW3bRoEFjBg7sS1RUlCX/zp07GDNmItu27eHGjWusXbuKOXMW4u+/n/Hjp5AxYyYAVq9ezoEDe5kyZRbr1/uRIUMGxo375pnH9rgDPx5l1rejmT3uGw4ePcbWXY/m/vy5/+Lp7cX0ZQto2KIpxw4dYeOK1Ywa8Q1rps+lYN63GTF1IgDHTp/i9O+/Mv/b79i+bS/9+gwjQwZHABYunsWNG9cY9+1sVqxYQ+itWyxfvcrSzu07d3j48CHzZs1h8MDPmDhzAvfD7lOvZn2qVaxOi0Yt2bZ8O6OGjk7BvG3jm5FfsGj+LK5du87iJSstabduhXLv3j1Wr97EJ58MeeLcREdHM2hQf2rWrI2f3258fKqyd+/uFD3TW7dCefAgjPXr/Rg48DPGj/+Ge/fupagswI8/HmLu3EXMnDmPpUsXMmbMCIYNG87atVu4ePE8O3duf3olIiIiIiIi/zIKOqURn3JlcHd1xWAwUKV8ObJk9Oa3X88mm79N63Y4OmTAy92DxjXrsOfwQUual5s79es3wmg04lO5Jrdvh3Lnzm0Aihcrg7d3ZqysrCiQPz9F3i3M2d9+S1B36xYtsbW1pXCBwpQuVoa9B/dgNpvZ4r+Zjzr2xNHRCXv7dDRt2oIDB/alaHy7du2laJF3qeJTCWtra5ycnMmTJy8ARYsWJ1eu3BgMBnLnzkO1ajU5efLnZ32E1K5dj5w5c2Ftbc29e3c5cuQQvXv3w97eHhcXV5o3b8WuXTsAqF7dl507d1jK7ty5jerV49/42rRpHQ0aNCZ//gIYjUZq1aqLjY0NZ8+etuRv2vQ9vLy8MZnsMBiMREVFcfHiBWJiYsiYMROZM2cBYMOGtXTt+iGenl7Y2trSsWM39u7d9UIHo7dq1ADHDBnw8vCgab067DrwaO5d3FypWb8uRqMRW5OJXX47qN+8CTlyvIXRaKRV/Uacv3yJwJBgrI1GHkZEcOXmdcxmM1myZMfVxQ2z2czOXVvo2OFDMmRwJH369DRr3IQDPzxqx9ramhbNmmNtbU3ZsuWxt7e3BCefVdOmzfH09MDRMQOtWzVn9979ljQrKys6deqGra0tJpPdE+fm7NnTxMTE0Lx5K6ytrfHxqcbbb+dPUR+MRms6dOiMtbU1ZcqUx94+HVeuXE7xGFq3bk/69A7kzJmLt97KRYkSpcicOQsODg6UKlWW//733DM/FxERERERkTedttelkW179rFq4yYCgoIBCI+I4O7dO8nm9/T0tHz2cncn9K+gEoCLs7Pls8lkB0BERDgAx0/8yIpVCwgIuE5cbAyRUVFkz5bNkt/BwQE7O7tHdXt6EXI7hDt37xARGUHXfl2wMkBcnBkwExcXl6LxBQeHkCmTd5JpZ8+eYcaMyVy8eJ7o6Giio6Px8amaonof5+npZfkcEHCTmJgYGjTwtdyLizNb8hQrVoKoqAjOnj2Dm5sbf/zxXypV8rGU9fPbzJo1Kyxlo6OjCQkJtlx7eT1qK0uWrPTq1Y/vv5/113aq0vTs2Rd3dw8CAm4yePAADIZH+7yMRiO3b9/Cw+PRHAL069eLkyePA9CxW1/KVaiW9Djd3S2fvT3cCbn1aO5dH0sDCAkKYuHMuSybuwDzX3NlNpsJuXWLIvkL0rC6L5PnzyF44reUKFaO9u0+ICo6isjICPp/Gr/F0WCwIi4uLsFcZ3BwSHBelMnWjvC/1tizenzevDw9CQ29Zbl2dnbBZDJZrp80N1ZWVnh4eGL12J46L6+k19z/c3JySnBelJ2dHeHhD1M8BlfXR2/2mUymRNe3boWmuC4REREREZF/CwWd0kBAUDBjp81g/JfDyJ/3PxiNRjr16W/ZEpeUoKAgvL3jt3AFhYbg5uzy1Haio6MYM/ZzevUYRL0GNbgbHMCIbxJufwoLCyMiIuJRO8GBvJUtJ06OTphsTcyfvIBM+bITHBycoNzTzs7x8HDn93N/JJn25ZdDaNKkOWPHfofJZGLSpHFPDLgl5/Fgg6enNzY2tmzevDPJw6cNBgM+PtXZuXM7rq6ulC1bgXTp0v9V1ot27TrSvn2nJ7WW4KpGDV9q1PDlwYMwxowZyfTp3/HZZ8Px9PRi0KBhFCpU+Kn9HzfuOy7dfPqWrqCQEN7KlhWAwJAQ3F0fzb3V/02Em4c7DVs0o02Dljy8lPjNnUY1a9OoZm0inJz49NMBrN+4ghbNO2Bra2LS+Hm4uXnglSk9oTevPbVfyfXhqeMJCgRyxY8nKBg3N9dk63rS3Jw48TPBwUGYzWZLuaCgAMtbZ8/L3t4+wd9EaKgCSCIiIiIiIqlB2+vSQERkBFZWVjg7xZ+ns3XXHi5eufrEMkuWLeL+gzCCQkNYt30rlUqXfWo7MTExxERH4+TohNFo5Ofjxzlx6mSifEtXLCc6Oppfzp7i8LHDVCpXGYPBQN0adZk6dwq3b8e/iRIaGsKJE/Hb4JydXbh//z4PHjxIsu2qVStz/MQp9u47SExMDHfv3uGPP+K3HD18+BBHRydMJhO//noGf/9tTx3L07i7u1OyZCmmTJnIgwdhxMXFcf36NUt/IX6L3e7dO9ixw8+ytQ6gfv1GbNiwlrNnz2A2mwkPD+fQoYM8fJj02K5cucTPP/9EVFQUtrYmTCYTBkP8W0ANGzZh1qxpBATcBOIPLD9wYO8LjW35+o3cDwsjKCSENZu34lM++bmvWtuXjSvXcOGvc7vCHj5g34+Hgfizv3778w9iYmKws7PHxsYWg8GAwWCgetU6zFswlTt349+iCg0N5fjJEynqn4uzCzf/Gm9KrFmziuDgEO7du8/S5auoXKl8snmfNDcFChTCaDSyatVyYmJi2LdvN78+YYtqSuXO/R/27dtNREQE165dZcuWDS9cp4iIiIiIiLzhbzrFWVmTpcfMl1o/RD41X46sWWlevy4fDhyCwcpAjcoVKZAv7xPLVChfiQ+GfsqD8IfUrFCZWpWrPLUde/t0dOrYg7ETvuKbsdEUL1qMUsVLJMjj4uyMg4MD9Rr4YrIx0bd7P7Jnif8Vva7turNwxQK6dn2f27dv4ebmjq9vHYoUKUaWLFmpUKES3bu/j9lsZu7syQnq9fL0YOTXw5g5ax7jJkzFwcGBLl0+IE+evPTr9ylTpkxk/PgxFClSlCpVqhEWFvbU8TzN0KFfMWPGZNq0ac7Dhw/IlCkzrVu3t6Tnz18AOzt7QkJCKF26nOV+vnzv8MknQ5gwYQzXrl3BZDJRsGBhChcukmQ7UVHRzJgxmUuXLmFtbU3BgoX45JMhADRr1hKz2UyfPh8REhKCi4sLVavWoEKFys89rnIlS9Cl/6c8ePAQ3yqVqVM1+bkvUbY0EeHhDPtiKAE3bpA+XTqKFihEpVJleBgezvQl87kZFIjJzo5CBYvToN57ALRt042VqxcwaPBH3A+7h6uLM7Vq+FI0mWfwuDrV6vD5mM+p06o2hQsUZsyEiU/MX6NGTT4Z9DmhobcoW6YkbVq9l2zeJ82NjY0NI0d+yzfffM3s2dMpU6YclSo9/e/iad57rzW///4r9erVIHfu3FSv7suxY0dfuF4REREREZF/uzc66HQ/0ggYn5ovLXRp04oubVoluGf0ys7lOzeoVL0qlaonPOOobJmy1C1ROlE9NSv6ULOiT4J7a1c9+nWzWr6NqOXbKMktUwULFGDerDkAfNSjL5HBCc+0Mdma6NK2Kx/2/TjR9jqAnj37AuDp6U542E3at0s4nkIF8zN18ljsHTISF/do25SPTzV8fJI+vyhjxkwcPHgsybTHTZkyK9E9BwcH+vcfRP/+g5Itt2LF+iTvly5dltLJvD22evWmBNe5c+dh9uyFSeY1GAy0aNGGFi3aJNuHZ1W6WBGa1q2d6H5S6wSgQlUf2jVqnWh7XdECBZk9ahwA9jneIvDmfUuara0tbVp1oU2rLonWyuPr5G8rZj/6xbksmbIyd+L3KR7P22+/Q7PGvonuF363IBs2bOH/jw170tzky/cO8+YtTXHbEH+Q/bp1WxPce3yOnZ2dmTBhaoL0Tp26WT7///qcPn1uguuuXT98pv6IiIiIiIj8W2h7nYiIiIiIiIiIpLo3+k0n+WepXr1CkvfHjv2Od999+rYvSXuBwYF0aNkuyUPxJ09+eVtb/9/Chd+zaNG8RPcLFSrCuHHfpVk/RERERERE5BEFnV5DS7auJ4tjpiR/jexN5u9/4FV34ZXbt27Vq+7CM/Hy8GLnzgNJbscEmD17gWU75svUrl1H2rXr+FLbEBERERERkWej7XUiIiIiIiIiIpLqFHQSEREREREREZFUp6CTiIiIiIiIiIikOgWdREREREREREQk1SnoJCIiIiIiIiIiqU5BpzTyXtcPOXbqlzRvNzAoiPpNGxMbG5vmbUu88uWLc+3a1VfdDREREREREZE09UYHnazTmbGyj3lp/1mnM7/qISbS7cMWnPrl51fdjVdOgR4RERERERGRV8v6VXfgZYo1x/LhlsEvrf5pdUa+tLrfBGazGbPZjMHw5sQ2Y2NjMRqNr7obIiIiIiIiIq+9Nzro9LqKi4tj2boNbN69j3v375H/3YJ07PkBDhkyEBwYyMfvd2PokGHMnDGVyMgomtSqQ+sGTQCIjIpk4vezOXLyOI5OLlTx8WXL1rXMmbmKSd+NJCQkiFGjBzNmrJHmTZpSvmw5APYe2M+S5cuIjIykQd16fNSj73P1fdcuf/bu3UmunFnx99+Dq6sLvXp2p2iRdwHo238w7xYuwYkTP3Pu3DkWLlyOs7MLkyeP58iRH7CyMlC7dj06deqG0Wjk2rWrjB49nD/+OIe1tTXFipXkq69GAXD58iUmTBjDuXO/4+zsTOfOH1C1anUARoz4Ajs7ewICbnDy5Aly5HiLL74YQebMWfjooy4AdOjQEisrKwYO/IyqVWskOZ7jx48xfPgwGjVqyooVS7C3T0fXrh9So0YtSzsmk4mAgJucPHmcUaPG8e67RZg1axq7d/sTHR1NxYqV6dWrLyaTHQBLly5kxYolgBVdunyQouc6/OsvsTXHEBAcxKmzv5EjaxY+69ObzBm9uRkURItGzVi4aY0l4PX1p0Mo51MZH9/q7PPfxeid+8iTOSvb9+8hQ3oHBn3Qi2sBN5i/egXRMdF81LMvxQpXeq45FxEREREREXkeb84rKP8ga7b4cfDoT0yZNoMpi78nfQYH5k+blSDPqV9OMf/bSXw7aBiL163m8vVrACxcu4rAkCBWrtzA55+NZf/+nZYyvXsNxt3dk0EDR7Jz5z6aNGxkSfvtt9+YPmkyX3/+BctXreTSpYvP3f9ffz1LRm9v1q5eTPt2rTIvvgEAACAASURBVPjiy1Hcu3ffkr59+1YGDBjCjh378PbOyIgRn2M0Glm+fD3z5i3hp5+OsGnTegBmz55OiRKl8PPbw9q1W2nS5D0AwsPD6dPnI6pX92XTph188cVIxo8fzYUL5y3t7Ny5nfff74Kf326yZMnKrFlTAZg6dTYA8+cvw9//QLIBp7/duhXK3bt3WLfOjyFDvmDMmBFcuXLJku7vv4127TqyY8d+ChUqzPTpk7l69TLz5y9lxYp1BAcHM2/eHACOHDnEsmWLmTBhKsuXr+PYsaMpfq67Dh6kQ/NmbF48n8wZvZmzdFmKy/7661lyZs3O2hnfU6Vseb6eOoFzF86zYNxkBn7Qi/HjxxAeHp7i+kREREREREReVIqCTjNnzqRXr15UrVqVvHnzUqVKledqbN++fbRo0YLChQtTsmRJevXqxdWr/75zdzZu96dz65Z4enphY2ND49YtOHrwUILDvju93xmTrYlc2XOQM1t2Lly5DMC+Hw/Tsn5jHB0dcXfzoHbtxilqs0Xz5phMJt7K8RZv5cjBH3/+97n77+LiQpPG9bG2tsancgWyZM3Mj0ePWdJr165Lzpy5sLa25t69uxw5cojevfthb2+Pi4srzZu3YteuHQBYW1sTEBBASEgwJpOJd98tDMChQwfw9s5InTrx7eTNm49Klaqwd+8uSzuVKvnwzjsFsLa2pkYNX/744/nH1LnzB9ja2lKkSDHKlCnP7t2Pgnnly1eiUKHCGAwGbG1t2bRpHb169cPR0Yl06dLTrt37lvHs3u1P7dr1yJkzN/b29nTs2DXFfahYuhRv/ycP1kYj1StW4M+Ll1JcNmPGTPhW8sFoMFK5dFmCQ0Np26gptjY2FC/4LjY2NgQEXE9xfSIiIiIiIiIvKkXb68aPH4+zszPvvPMO9+/ff3qBJOzYsYNevXqRL18+BgwYQFhYGAsWLKBly5asWbMGLy+v56r3nygwOJiho7/F8O144sxxABgMBu7evmPJ4+rqRtTD+CCBydZEeEQEAKF3buPp5mbJ5+7mkaI2XZxdLJ9NtibCHyZ+6yUwOJD2PdthZRV/HtPy5euSrMvd3QMrKyvLtZenByGhtyzXnp6P5jIg4CYxMTE0aOBruRcXZ7bk+fDDXsyZM4MuXdqTIYMjLVq0pm7dBgQE3OTXX8/g61vZUi42NpaaNWtbrl1dHz0Hk8nuud/kyZAhA/b29pZrb++MhIQEJzmeO3duExERQadObSz3zGYzcXHx8xgSEkLevG8nqCulXJ2dLZ9NpkdznqKyLq6PytrYAuDilLC+iAi96SQiIiIiIiJpJ0VBp507d5I1a1YA6taty8OHD5+pkejoaIYPH07GjBlZsmQJ6dOnB6BixYo0btyYKVOmMHz48Gfs+j+Xp7sbn/b4kMKVa3L5zo0EacGBgU8s6+bsTPCtW+T76zokNDhBuhVWiQulkJeHF9uWb8fGIx3BwcHJ5gsJCcZsNlsCT0FBIZQtU+pRHx4LSHl6emNjY8vmzTuxtk683Nzc3Pn006EAnDp1kj59PqRw4aJ4enpRuHBRJk6c9tzjSan79+8THh5uCTwFBgaQM2cuS/rj43FycsZkMrFo0Uo8PDwT1eXu7k5Q0KM5DAwMeOH+2ZtMAERGRpIuXToA7jwWoBQRERERERF5HaVoe93fAafn9dNPPxEUFETTpk0tASeAt99+m5IlS7J161aio6NfqI1/kvo1azBnyTICbt4E4N7duxw7/GOKylYqVZZlG9dx7949QkOD8fNL+DaSk7MLgYE3kimdOm7fvs3a9ZuIiYlh3/6DXLl6lZIliyWZ193dnZIlSzFlykQePAgjLi6O69evceLEzwDs3r3TEqTJkCEDYIXBYKBcuQpcvXqFbdu2EBMTQ0xMDL/9djbFZ1G5urpx40bKt5PNnTuT6OhoTp06waFDB/DxqZZkPoPBQL16jfjuu/Hcvh3/dldwcBA//ngYAB+favj5bebixQtEREQwb97sFPchOc5OTnh4ePLD7r3Excayd8dOgm6+eDBLRERERERE5GVKk1+vO336NABFihRJlFa4cGGOHDnCpUuXyJMnT6q2a7QyMq3OyFSt8//rfx5N69bGbDbTp09PgoODcHRyonTF8hR/7G2h5LRp2JRJ82bRrHl9nJxcqVihGrv3bLOkN2nUijlzJ7N46SyaNWpM2TJln6uPT/LOO/m5fv0GjZu2wdnFmc+HDsTJ0THZ/EOHfsWMGZNp06Y5Dx8+IFOmzLRu3R6A338/y3ffjePBgzBcXFzp3bsfmTJlBmDChClMnjyBKVMmEBdnJnfuPPTs2SdFfezYsQsjRnxOZGQkAwYMsfzqXVJcXd3IkCEDDRv6YjLZ0b//ILJnz5Fs/g8+6Mn8+XPo2vV97t69g4eHBw0bNqVUqTKUKVOOZs1a0rv3B1hZxf963Y4dfinq85N8MnAwY8aMZOWCxVSqUY08b+d7eiERERERERGRVyhNgk5BQUEASZ7b5OkZv0UpMDAw1YNOMQ+teJlDjHmGvCtmPdomZjAYeK9BPVp17ZFoe52HlxdLtq7H2tqaqL/ujR/6pSXd3s6OgR/0wj7HWwTevM+27Rtwc3W3pJcsUZ6SJcrjlSk9oTfjf/Fu4+q1CdoY+dVwPDJmIzL42bZJ/s3KyopePbrTq0f3RGnjx47E3iEjfx1xBICDgwP9+w+if/9BifJ/+GFvPvywd5LtZMuWg2+/nZRk2pAhXyS4Llq0OOvWbbVcN2zYlIYNm6ZgNPHat+9E+/adntoOxJ+P1K3bR3Tr9lGSdbVt24G2bTtYruvWbfDU9j8b+jlRQZct10UK5Gf1nJmW6zJlyjJx3qykilKpelU6NGnLw0vx5TN7Z2Tn4lUJ8qxft5XAm893HpuIiIiIiIjI80iToNPfBzzb2tomSjP9dV5NxDMcmuzsnC7RvTt3DBiNKdotmGpiH4+spJHQ27e5GRxIsWzZuX79Chs3raRWrUbPUdPzn/2UkprTei6e19/9TIv+vor1kjq0Vp6XtbUhye+rp7n3MOrpmZ7gSTP28mbz5bdgMFg91/N8073IennabL3c9aK1ktb03ZI0rZek6bslMa2VpOm7JWlaL0nTd0tib/paSZOg098HNEdFJV5gkZGRANjZ2aW4vjt3Er+hExMTR2zsP/Uf9SkXExvDxO9nETBmBOnsHShXzgffGk9/kyYxc6r37fGa416juVi48HsWLZqX6H6hQkVo3bodQJqsnTZtmnPzZuLztjp260uu9xq+9Paf379nraS2mJi4JL+vnsr6+bbu/u1JM/byZvPltxAXZ+b+/ed7Q/ON9gLr5Wmz9XLXi9ZKmtN3S5K0XpKh75ZEtFaSoe+WJGm9JEPfLYm8TmvFwyNDqteZJkGnx7fQ5cqVK0Hak7beSWJe7h7MGT3esr0urVWtWp2WLVsSHnYzzdt+Xu3adaRdu47Jpj++Le9lWrx4JZdu3kuTtkRERERERERetTTZ11KwYEEATpw4kSjt5MmTODg4kCNHjrToioiIiIiIiIiIpIFUDzoFBQVx/vx5yzlOACVKlMDDw4PVq1fz4MEDy/3ff/+do0eP4uvri42NTWp3RUREREREREREXpEUba9bv349N27En0Vz69YtoqOjmTYt/tfYMmXKRMOGj86jGT9+POvWrWPhwoWUKlUKABsbG4YMGUKfPn1o3bo1zZo148GDB8yfPx9XV1d69eqV2uMSEREREREREZFXKEVBpzVr1nD06NEE9yZNiv8p+5IlSyYIOiWnVq1a2NnZMX36dMaMGYOtrS1lypShf//+Os9JREREREREROQNk6Kg06JFi1Jc4ejRoxk9enSSaT4+Pvj4+KS4LhERERERERER+WdKk4PEBd7r+iHHTv2S5u0GBgVRv2ljYmNj07xtefP47fKjx6CPXnU3RERERERE5B/gjQ46ORhjcSDy5f1nfP0COd0+bMGpX35+1d2QVOS3ew8fdO/yqrshIiIiIiIi8kxStL3uHys2huOdu7206ovOmfnS6n7dxMbGYjQa37i2nkVMTAzW1m/On8zr+pxFRERERETkzfDm/Av6HyQuLo5l6zawefc+7t2/R/53C9Kx5wc4ZMhAcGAgH7/fjaFDhjFzxlQiI6NoUqsOrRs0ASAyKpKJ38/myMnjODq5UMXHly1b1zJn5iomfTeSkJAgRo0ezJixRpo3aUr5suUA2HtgP0uWLyMyMpIGdevxUY++T+zjsmWLuXLlEra2thw9eoSOHbtStWoN1q1bza5dO7h//x5FixTi414f4uiYgYGDP6d0qRI0bFDXUkf79i3p2LELlSpV4fLlS0yYMIZz537H2dmZzp0/oGrV6gCMGPEFJpOJgICbnDx5nFGjxhETE83UqZMIDAwkffr0NG/eilat2gLwww8HmD17OgEBN8iRIyf9+w8id+48zzUXPXp0pUCBQvz881GuXLlMkSLFGDz4cxwdnbh58wbNmtVn4MChfP/9bDJmzMTUqbM5c+Y0U6ZM4NKlC3h5ZaR3734ULVocgK1bNzFv3hzu3LmNs7MzXbp8QI0atQDYvHkDCxct4M6dW+TKnY9O3fri7uENQJlyJenbrQsrNm7i7r37VKtQno+7duLyteuMnzGbmNhYOjZugdFoYPaqpcmOZ8zMKdja2HIjKIDf/vyDPDne4tPuPcmR4y0AGjfzoUunXmzesobY2FhmTFvG5cuX+Gb015y/cAEnR0dat2hpWTf37t9nzIQ+HD/+M9myZKNE4ZIpeq4NG9aie/cebNiwlrCw+/hUrkCvHt2wsrJiwcKlXL9xk8ED+wFYnvPevUewtramR4+uFCpUmOPHj3H+/B8UKVKcIUM+Z+LEsfzwwwGyZcvO8OGjyZgx03PNuYiIiIiIiKSNN3p73etqzRY/Dh79iSnTZjBl8fekz+DA/GmzEuQ59csp5n87iW8HDWPxutVcvn4NgIVrVxEYEsTKlRv4/LOx7N+/01Kmd6/BuLt7MmjgSHbu3EeTho0sab/99hvTJ03m68+/YPmqlVy6dPGp/Tx69AhlypRnyZLVVKzow+bNG/jxx0NMmzaTlcvm4+DgwHdTZgBQxacSu/fst5S9ePECgYE3KVOmPOHh4fTp8xHVq/uyadMOvvhiJOPHj+bChfOW/P7+22jXriM7duynUKHCjB49nAEDBuPvv5+FC1dQrFgJAM6d+51Ro75iwIDBbNmyiwYNGjNwYF+ioqKeYybibdu2hUGDhrF+/TaMRiMTJ45NkH7ixHGWLFnN+PGTCQ4O4pNPPqZ9+45s3bqbHj16M3Top9y+fZvw8HAmThzLuHGT8Pffz/Tp35M7938A2L9/L4sWzaN3/y+ZPncded8uyNSJXydo5/Cxn5n57Wi+n/Atew4d4uiJk+TImoW+3buQv0BBvl+7/IkBp7/tOnSANg2bsmb69+TKloOR0yYlSP/x6A+MHjmNSRPmExERzscf96BS+QosmjuP/h/3YfrsWVy5egWAmbNnYWsysWbeOj7pMRC/XVtT/FyPHTvKt99OYsGCpezbf5Cfjh1Pcdldu3bw2WdfsW6dHzduXKNbt47Url2PrVt3kT17DubNm53iukREREREROTVUNDpFdi43Z/OrVvi6emFjY0NjVu34OjBQwkO++70fmdMtiZyZc9BzmzZuXDlMgD7fjxMy/qNcXR0xN3Ng9q1G6eozRbNm2MymXgrx1u8lSMHf/z536eWyZv3bUqXLovBYMBkMrFjhx+tW7fH09MLW1sb2rdtyf4D8f0uX640589fJDAwCIAdO/yoWNEHW1tbDh06gLd3RurUqY+1tTV58+ajUqUq7N27y9JW+fKVKFSosKUto9Gaixcv8OBBGI6OjuTNmw+ATZvW0aBBY/LnL4DRaKRWrbrY2Nhw9uzpFD///1ezZm1y5syNvb09nTt/wO7d/gnmomPHrtjb22My2bF9+1bKlClLmTLlMRgMlChRmnz53ubIkR8AMBisuHDhPJGREbi7u5MzZy4ANmxYS9u2HcicJTtGo5H6jVpz+dJ5QoIDLO20atyQDOnT4+XhQZECBfjz4qXnGk+pwkUplO8dbG1s6Ni8Jb/98V8CAx+106RRKzJkcMRkMnHs58N4e2ekWpWqGI1GcuXMRdnSpfnh8GFiY2M59OMRunTujr2dPTmz56Smj2+K+9G4cTMcHBzw9vam8P/Yu++oKK63geNfdhcWEKSz2HsFrKjBgh0VY6+JEWPvGnvsGrvGXmNX7N1YUOxdU9RYY4zGLuwuTZG6sO8f/rJIAMGCJr7P55ycM3PbPHdmwjk+597Z0p7cuZNxovNvfn6NyJUrNzY2NlSqVJlcuXJToUIlVCoVNWvW4Y8/br3RPRFCCCGEEEII8eHJ9rqPIESnY9TUGShmzCLJmASAQqEgMjzC1MbR0Yn46McAqC3UxMTGAhAaEY6rk5OpnbOTS6au6WDvYDpWW6iJiY7JsI+zs3OKc61Wy9SpE5gxYzJGo9EUd1h4BC7OTlSq5MXR46foVKg0R44EMXToSACCg59y48Y16tevYRorMTGRevX8TOeurpoU15o0aTpr1qxgyZIFFC5chB49+uDhUYrg4KcEBu5l+/bNprYJCQno9bpU8a9du5KAgFUA+Po2YMiQEWnO89Vru7nlwGAwEBmZ/Cw0GjfTcXBwMMeOHeHMmVOmMoPBQNmyXlhZWTF+/BQ2bgxg6tQJeHqWpk+fAeTLl5+QkKfMnTsTM7PZpn5Go5GwML1pi52jg72pzlJtYXrmb8rFMfn9sLK0wtbGBr1ej7NjNgCcnF1N9Tp9CDduXOcL/69MZYlJSdTwqc6zZ89ITExE46qBqJfPW+OqgRuZi8PBwdF0rFariYnJ/HwcX5mDWq3G0fGfY0VneiwhhBBCCCGEEB+HJJ0+AldnJ4b16UWZGvW4H/EkRZ0uJOS1fZ3s7dGFhVH8f+f60JTJFjPM3lucZmYpx3J2dqZv3wFUr16dmKinqdrXquHD2nWbqFDRh7i4ONN3jlxdNZQpU445cxZl+lolSrgzdeosDAYD27dvZsyY4ezYsQ9XVw3+/p3o0KFzhvH7+3fC379Thu202uR7HhISjEqlws7OPkX53zQaDfXq+TFs2Kg0x6pUyZtKlbyJi4tl6dLFTJs2kUWLlpviLupZJcN4/ulNn6kuLNR0HBMbw/OoqJcJxKTU4zk7uVKmTDnGfPttqnH+/tB4iDaEHNYvE1VanfaN4/8nS0tL4uLiTOehoaGvaS2EEEIIIYQQ4r9Kttd9BI3r+bJ8/UaCn75M3DyLjOSXcxcy1bd6pcps/HEnz549IzRUR2DgzhT1dvYOhIQ8Saf3u6lfvyHr1q3h6f/ijoiI5MzZ86b6ihW9CNFqWb78B2rXrotC8fL1qlKlGg8fPuDAgX0YDAYMBgM3b15P97tSCQkJBAUFEhUVhUqlIlu2bKaxGjduxu7dO7h+/RpGo5GYmBjOnj1NdPSLt55XUFAgf/11l9jYWJYvX0KNGrXT/VU3X98GnDlzigsXXm4/i4uL4+LFX9BqQwgLC+X06RPExMRgbm6BtbW1Ke4mTVoQELCKRw9fzjn6RRQXzh3PVHwO9nbotFoMCQmZav/T5UtcvXWTBEMCq7ZtpnjhIilWa73Kq7w3Dx8+4NiJ46Znc/vP2zx89AilUol3pUqsWPEDsXGx3Ht4jwPHDmQqhtcpVKgAV65eJ0SrIyoqinXrVr3zmEIIIYQQQggh/n0+7ZVOShXllv+QpeNDXIbN/qnl534YjUYGDOiLTqclu50dn/lUxcu7UoZ9v2rakrmrltKqdWPs7BzxqVaHo68kAlo0+5LlK+azbsNSWjVrTmXvym8cX3o+/7zJ/+Lug06nw97ejhrVq1Kl8mcAWFiYU7WKNwcOHqZbt96mftbW2Zg9ewHz589mwYLZJCUZKVy4CH37Dkj3WgcO7GfWrOkkJSWRN28+Ro+eAEDx4iUZOnQks2dP59GjB6jVajw9y1CmTNm3nle9en5MnjyO+/fvU6ZMOQYPHp5uW43GjSlTZrJ48TzGjRuJUqmgRAl3Bg0aTlKSkU2b1jNhwhjMzMwoXLgogwa9XEFUvXpNYmKiWTB7Inp9CNbW2fAoVZ5K3jUyjK+cpwcFChSkV7uOmCnM+GFTwGvb16pclYCdW7lx+w+K5C/I8J790m1rZWXN7NnzmfX9FFasWY0xKYn8+fPTuUNHALp37sriFSto/nVT8ubOS4NaDbh07VKGMb+OV/my1Kheja7d+2Fv70C7dh04ffpkxh2FEEIIIYQQQvynfNJJp6hEJZD2ipX3IjHjJn/bvDR5a5lCoaBNk0Z82a1Pqu11LhoN6/fvQqVS8ffvsc0aNd5Ub2Vpybc9+2GVvwAhT59z4OBunByTv71UsUJVKlaoiiZnNkKfvvzFux+37UhxjcnfTcAlR17idOl/F+eLL75KVaZQKGjSpDldu3ZLc3sdwJBB/RgzdgpJSSm3hOXNm58ZM+am2WfkyHEpzs3NzZk1a366sX32WWU+++z9JdNy5cpNjx59UpXnyJGT06d/SVXu7u7BggVLU5UD6ZbDy5VixUtXS7Pu3JmfiNfeN50P75ccj7m5OTNmzk71rqQnu60t33Tqlmbdjq3HUpXly5ePMSPS3i5oZ2fHzBlzXvuupGXXrsAU58OGfJPivH/fHvTv2wMrmxwkJZnRuHHyLy3+8x5269YrxXmFCpXYvHnXG8UjhBBCCCGEEOLDk+11/zGh4eFc++N3kpKSePz4AT/u2UKlSmknMoQQQgghhBBCCCE+lk96pdOnyJBoYM7KpQRPn4S1lQ1VqtSkvm+Ttxpr6HdDuHLjSqpyf/+O+Pk1etdQRRYZ2qMvem3KD8grzMz45uuuHyyGK9d/Y+iEoQCYmWH6NUOATZt2ptdNCCGEEEIIIcT/I5J0+o/ROLuwfOos0/a6dzF9zIw0y81drNHpdGnWfWpetx3u32r6ktRbDwtkz0n0vfvUrvJhVr2Vci/NgU0Hgf9f74sQQgghhBBCiMyT7XVCCCGEEEIIIYQQ4r2TpJMQQgghhBBCCCGEeO8k6SSEEEIIIYQQQggh3jtJOgkhhBBCCCGEEEKI906STkIIIYQQQgghhBDivZOkkxAfgU4bzFetapGYmPixQxFCCCGEEEIIIbLEJ510UluosFAps+w/tYXqY0/xvQg8Ekif4b0/dhhZ7unTJ1St6oXBYPjYoQghhBBCCCGEEJ+8TyNrkg5jkpG5E49m2fj9R9XKsrE/FEPih0/AGAwGVKpP59UzGo0YjUYUik86hyuEEEIIIYQQQryRT+df/v9ybbr1oplffQ4eP0GITkfFsmUYNXG6qf7ogSD2bt1B1PMoirmXYOzwcWR7zXhGo5GVqxdy8tRhDAkJuLho+Kb/KPLlLcCLF1FMmDCds2fPoLawwLdOXVo1b4FCoeDIsaMcPHyI0qXLsX//Xsp6lOXcr+dJTDRQv209lAolQYeOv/H81qzdwF/3HqBUKvjp54vkzp2H4cPHUqRIUQBatmxE06YtOHToAA8e3OfQoVNERIQze/Z0fvvtElZW1rRu/SWtWrUF4MaNa8ycOY2HDx+gVqvx9a1P374DAbh27SoLFszm3r27aDQ56N9/EOXKeQHQp083Spcuy8WLP/Pnn3/i4eHJ2LGTsLe3p3fvrgA0aFATgNmzF+LhUSrN+ezfv4c9e3ZStGhxDhzYh5OTMwMHDsPLq6LpOp6epbl8+Vdu3brF2rWbsLd3YP78WZw/fwYzMwV+fo3o3Lk7SqWSxMREFi+ez959P2JllQ2/Rq0ydV/7jxpLqZIluPT7bW7/+QeFixejz9BB2Npl58aVqyyaMYcFAStM7dt904uBXXpQ3qMUa7Zv4f7jh5irzDl76VdcnDUMHTyecxdOsmfvNszNzendcwj1ctZ4w6cthBBCCCGEEEJkTJZmfEDHzpxlxpiRbFqykDv3HhC4fy8A1y9fYfPqAPoOH8LC9atwdnVhzNiRrx3rp5/Oc+PGbyycF0DAmj0MGjAGW9vsACxfOY+oqBcsXbiYyd9N5NiJ4xw+lrzi64/bt8mVMxc71+xm5IDRDOwxiJLF3Dmw6SD7Nux/6/mdPXcBn2pVOHDgCHXq1GfEiMEptrIdPhzE9OlzOHDgGAqFgqFDB1C4cFF27gxkzpxFbNmykQsXzgEwd+5MWrVqS1DQCTZv3kXNmnUB0Om0DB36DR06dGL//qP06dOfUaOGER4ebrrOoUMHGD58LHv3BpGQkMDGjQEALFy4DIDAwGMcOnQq3YTT327cuE7OnLnYt+8InTt3Z+TIITx7FmmqP3hwP0OGjCQo6ARubjmYNGksSqWSTZt2sWrVen7++Tx79uwCYM+enZw9e4pJ05cyYepifjp/MtP39fDJ0wwfOZrFG9aQaDCwb8euTPc9d+lX6lT14UDgUQoUKMx3E4diTDKy/IettG7pz5IfZmV6LCGEEEIIIYQQ4k1I0ukDatHQD2dHR7Lb2lK5Qnlu3/4DgDPHT1C9bm0KFC6Eubk5bb5uz7VrVwnWadMdS6VSERMbw6PHDzAajeTOnQ9HBycSExM5c/Y4PXr0wtrKCo2rK00bNeb4iROmvo6OjrRq1RaVUoVarX5v8ytapBDVfaqgUqlo27Yd8fFxXL9+1VTfsmUbNBo31GpLbt68QUREOB07dsXc3JxcuXLTuHFTjhwJMs3v8eNHREREYG1tjYeHJ/Ay0ePtXRlv76ooFAoqVPiM4sVLcP78GdN1/PwakTdvPtRqS2rVqmu6z2/K3t6B1q2/RKVSUbu2L3nz5uPs2dMprlOwYCFUKhXPnkVy/vxZ+vcfhJWVFQ4OjrRu/aVpPkePHqZ16y9wcnbFxjY7jZt9kek4GtSuSd68+bBQq6lUrQr37/6V6b6exYpTbz3lFgAAIABJREFUoVQZVCoVlb1r8OxZBM2afoFKpaJqlVpodcE8f/488zdFCCGEEEIIIYTIJNle9wE5Otibji3VasLCX/5jPzw0nPyFCiXXWVlhZ2eHPjwMNxfXNMcqX74CfvWbsmz5HPR6LZUqVqWDf0/i4+MwGBJwc8tBdEQoAC4uLoSGhZn6Ojs5vVHc169fY8KE0f8by5XNm7en2c7Fxdl0rFAocHHRoNfrTGUajcZ0HBz8lNBQPfXr1zCVJSYmUbp0GQC+/XY0y5cvoV27FuTIkYuOHbtSpUo1goODOXbsCGfOnDL1MxgMlC3rZTp3ckqOw9LSkpiY6Deab/J8XDEzM3sl/hwp5uPqmnI+BoOBJk3qm8qSkoymNnq9DldXt+QYnZP7ZsTRPvm9sVCriY2JzXRfh+yv9LVQY5vdDqVSaToHiImJRpnpEYUQQgghhBBCiMyRpNO/gIOTA3pt8qqm2NhYIiMjcXZwfG2/hn4taOjXgojIcGbOGs+uHzfTplUHVEoVwcFPyW5pAYBOr8fJMXmsVxMpL89fH5+7uwebNu3McB46nd50nJSUhE4XgrOzy6tXMh1pNBpy5MiZ7rh58uRl/PjJJCUlceLEUUaPHsa+fUfQaDTUq+fHsGGjMoznn/4574zodFqMRqOpX0hIMFWr+qQ5nqurG+bmFuzdezjNj6Q7OTmj1QaTs8DLLX2h+vRXsWWW2tKS+Lg403liUiKRz5+987hCCCGEEEIIIcT7INvr/gUq1/Dh5KGj3Ltzl4SEBLasXod7SY90VzkB3Lx5nT9u38BgMGCptsTc3AKFQoFSqaRy5RosXbqY6JgYtDotu/fsoYaPT7pjOdg7otPrSEhIeKd5/HH7DqdOn8VgMLBlywbMzS1wd/dMs22JEu5YW2dj3brVxMXFkpiYyN27f3Lz5nXg5Ta68PBwFAoFNja2wMvVU76+DThz5hQXLpwjMTGRuLg4Ll78Ba02JMP47O0dUCgUPHnyOFPziYgIZ+vWTRgMBo4ePcz9+/fw9q6SZltnZ2cqVqzEggVzePEiiqSkJB4/fsSlS78CUKtWXbZt20xoqI4XUc/Zs2tjpmJ4nRy5cpKQEM+ln37BYDCwftcO4t/xGQohhBBCCCGEEO/LJ73SyUxhRv9RtbJ0/PfBo0xpWrb/krmTpvEi6gVFShTnu/ETITr9bVQvXrxg8ZKZhIQ8wdzCgjKlK9CkURsAunTqx7qNi+jWuycW5ub41qlLnVq10x2rnGc5CuTNT/OOTTEzUxB44MhbzaOydyWOnzjN9O/nkStXbiZNmpHmqh8ApVLJ9OmzmT9/Nq1aNSE+Pp68efPRtWtPAC5cOMf8+bOJi4tFo8nBuHGTUavVaDRuTJkyk8WL5zFu3EiUSgUlSrgzaNDwDOOztLTE378TvXp1xmAw8P33803fikpLyZLuPHr0gIYNa+Pg4MTEidOws7NPt/2oUd+xZMl8vvqqNdHRL8iZMxft2nUAoFGjpjx8eJ+Rg7tiZWWNX+PW3Lh2KcOYX8c6Wza+7tWd5XMXsnzOQlo3+BwXxzfbOimEEEIIIYQQQmSVTzrpFBdvyLjRB7J56aIU5x3btkapycf9iCcA1GlYnzoNk78H5JpdQ/S9++mO5+VVkdkzV6RZZ2Njy9ix3xH69FGquto1a1G7ZspEnLm5OVNHT8/0XNJjYWHOiG8HYWWTg6SklAm5bdv2pGrv7OzC+PGT0xxrzJgJ6V7H3d2DBQuWpln3z3I/v0b4+TUynXfp0oMuXXqkO3ZKZgwcOIyBA4dleB0AGxsbBg8ezuDBqRNgKpWKfv0G0bhVV1NZ3fpNM4xg7sTxKc6r161N9bq1U50XyJ6T6Hv3ad2wsamuQ4vWKfqWLlWeHxZtMp0rlUp2bD2Gq2u2NN8VIYQQQgghhBDiXcj2OiGEEEIIIYQQQgjx3n3SK53+667+fpPhMyalWXfkyJkPHM2nZ8aMyQQFBaYq9/VtkO63qLJC56/8UCjMwJiUonza6JGULlnig8UhhBBCCCGEEEK8T5J0+hfzLF6CvSvWfewwMqWD/5cfO4Q3NmTICIYMGZFu/avb8rLSinX7KZTThnht+tsphRBCCCGEEEKI/xrZXieEEEIIIYQQQggh3jtJOgkhhBBCCCGEEEKI906STkIIIYQQQgghhBDivZOkkxBCCCGEEEIIIYR47yTpJIQQQgghhBBCCCHeO0k6CSHe2caN65g9e/rHDkMIIYQQQgghxL+I6mMHkJUsLZIwGhOzbHwzM2WWjf0hBR4JZN/hvSyYsvBjhyLewPZ1Gwl5GkyvIQM+dihCCCGEEEIIIUQqn3TSyWhMZO20QVk2vv+wmVk29odiSDS8U//ExESUyqxPvhmNRoxGIwrFv29xnsFgQKX6dP5X+lDPVAghhBBCCCHEp+3T+Zfyv1ybbr1o5lefg8dPEKLTUbFsGUZNTN6OdPRAEHu37iDqeRTF3Eswdvg4sr1mPKPRyMrVCzl56jCGhARcXDR8038U+fIW4MWLKCZMmM7Zs2dQW1jgW6curZq3QKFQcOTYUQ4ePkTp0uXYv38vZT3Kcu7X8yQmGqjfth5KhZKgQ8fTve7cuTOxt7fj8aN7XLlyje/Gj8TTw52VqwM4ceI08QkJ1KhRm759B6JWW9KuXUt69epPlSrVgJcJmiZN6jFr1kKKFSvOtWtXWbBgNvfu3UWjyUH//oMoV84LgD59uuHpWZrLl3/l1q1brF27iStXLrNq1XIiIsKxt7ena9ee+Po2AGDv3t1s3BhAaGgoJUu6M3ToSNzccrzV82rZshFNmjTn4MH96PV6fHxqMGjQt6jVai5e/IUJE8bQokVrtmzZSIUKFRk9egJnzpxi2bLFBAc/IX/+ggwePJzChYsAsG7darZt20xUVBT2Dk583fUbPDzLkZSUxN7dmzhzPJDnzyIpV8qTQT26kt3WlqdaLW2792Z4396s2LKN6JhoGjRtTNO2rfjtl4vs3rIdjEZ+OXeBPLlys2T8lHTn06dPNwrkL86Vqxd58uQh7u6l6dNrGLa22Xn69AktWzanT89ebNq6BVcXV6ZOmMjvf9xi5erVPHryGI2zhj6d+1HWsywAT0OeMHXeFP648wfuHp64umoyvKfBwSG08+/K0MH9Wb1mPbFxcbRt+xX+/p0BmDRpHC4urnTr1gvAdJ937txveibNm7fi4MH9PH78iNq1fenevTeTJo3nypXLlCzpwYQJU8mePftbPXMhhBBCCCGEEO/Xv2/ZyCfs2JmzzBgzkk1LFnLn3gMC9+8F4PrlK2xeHUDf4UNYuH4Vzq4ujBk78rVj/fTTeW7c+I2F8wIIWLOHQQPGYGv78h/by1fOIyrqBUsXLmbydxM5duI4h48dNfX94/ZtcuXMxc41uxk5YDQDewyiZDF3Dmw6yL4N+zOcR1DQAdp92Yo9uzfj6VGSZStW8+jRE35YPJeA1T+g02lZtWo5AHXq1OPw4YMp4razs6dYseLodFqGDv2GDh06sX//Ufr06c+oUcMIDw83tT94cD9DhowkKOgE9vYOzJnzPTNnzuXQoZMsXrySwoWLAnDy5HECAlYxadIM9u49RKlSZRg37vX3MON5BjJz5ny2bNnFw4f3WbNmhakuLCyUZ8+esW3bHoYOHcmtW78zZcp3DBkygn37jtCkSXO+/XYg8fHxPHhwjx07trJ8+VqWB+xj2KjpuLi8TNIEBe7g15/PsGjhEravXIqtTTZmL12RIo4rN39n46atjJj8HTs3bObxg4eU9ipHk9Yt+MynKit3bGLtmg0Zzuf4iSB69xrK8qVbUSqVrFg1P0X99evXWTRnHuNHjSY0NJQJkyfRumVLDgYepefXvRg7fTQRkREATJg1gaKFirE7YA8dO3bh6NHDmb6v167fZPXKxXw/bSKrVi3n3r2/Mt33+PGjzJ69kI0bd3DmzCkGD+5H9+692LfvMEZjEtu2bcr0WEIIIYQQQgghspYknT6gFg39cHZ0JLutLZUrlOf27T8AOHP8BNXr1qZA4UKYm5vT5uv2XLt2lWCdNt2xVCoVMbExPHr8AKPRSO7c+XB0cCIxMZEzZ4/To0cvrK2s0Li60rRRY46fOGHq6+joSKtWbVEpVajV6jeeR7Vq1fFwL4lCocDc3Jz9+4Po1aMz2bPbYm1tjb9/R44cCQKgbt36nD59ktjYWAAOHTpA3br1gZcJJW/vynh7V0WhUFChwmcUL16C8+fPmK7l59eIggULoVKpUCqVKBRm3L17h7i4WJydnSlYsBAAu3fvoH37r8mfvwAqlQp//07cvn2L4OCnbzy/v7Vo0RqNxo3s2e3w9++UInlmZmZG587dsbCwQK22ZM+enTRp0hx3dw+USiUNGnyOubk5169fRaFQEh8fz19/3cVgMODi6obGLRcARw/tpdUXnXF11WBhbk7HNq05ce48hsTkb5F93aYVarUl+QoWIG/BAjz4695bzae6T13y5S2ApaUVX7TpxNmzx0l85TpftGmDpaUlarWa4ydPUL5cebzKlUehUOBVpgLFChXj/K/nCNGF8Pvt3+n0ZWcszC0oU6YcFSpUynQc/l+1Ra1WU6hQAQoXLsKff/6R6b4tW7bB0dEJFxdXSpcuQ8mSHhQtWhwLCwt8fGpw+/atN7gjQgghhBBCCCGykmyv+4AcHexNx5ZqNWHhzwEIDw0nf6FCyXVWVtjZ2aEPD8PNxTXNscqXr4Bf/aYsWz4HvV5LpYpV6eDfk/j4OAyGBNzcchAdEQqAi4sLoWFhpr7OTk7vNI9Xt1JFREQSGxdHj94DU7RJSkoCIHfuPOTPn58zZ05SpYoPp0+fZNWq9QAEBwdz7NgRzpw5ZepnMBgoW9YrzWtZWVkxfvwUNm4MYOrUCXh6lqZPnwHky5efkJCnzJ07kwUL5pjaG42g02lTbbGbMWMyQUGBALRv3xF//07pzNPNdKzR5ECv15vO7e0dUiTsgoOfEhi4l+3bN5vKEhIS0Ot1lC1bnn79BrFy5VLu3L1DqdIVaNehJw6Ozuh1IcyZMYYFsxTwv3umVCgIj4gwjfPqe6NWWxAbE5tmvBlxdnYxHbu4aDAkGnj+PJLsDi+/3+Ts5Gyq1+p1nDl3lp9/+RkzhQJjEiQmGijjWRZ9mB5bGxusLK1euVeu6PW6TMXh6OhgOra0tCQmJibTc3BwcDQdq9WWqc6jozM/lhBCCCGEEEKIrCVJp38BBycH9NrkVU2xsbFERkbi/Mo/qNPS0K8FDf1aEBEZzsxZ49n142batOqASqkiOPgp2S0tANDp9Tg5Jo9lZmaWYpx/nGbo1f52dtlRqy1YsWwBLs4vk1lWNjlISkpu8/cWu6SkJPLnL0Du3HkA0Gg01Kvnx7BhozJ1LYBKlbypVMmbuLhYli5dzLRpE1m0aDmurhr8/TuZvu/0OkOGjGDIkBEZttNqg03HISHBODsnJ2X+Gdff1+/QoXOaY/n61sfXtz437jxl5dJZbFq3lJ79RuDo5EK3XkNpUOsz4rX3U/R5qk1/pdv/gshwDq96NSmk12tRKVXY2toBUanm5OzkTE2f6vTp2QuXHHmJ00Wb6oK1wTyPiiImNsaUeNLptKnuyZuytLQiLi45oRYWFvpO4wkhhBBCCCGE+Lhke92/QOUaPpw8dJR7d+6SkJDAltXrcC/pke4qJ4CbN6/zx+0bGAwGLNWWmJtboFAoUCqVVK5cg6VLFxMdE4NWp2X3nj3U8PFJdywHe0d0eh0JCQlvHLtCocCvQT0WL1lOePjL1Tk6nZYLF86Z2tSuXY+ffjrPrl3bTVvrAHx9G3DmzCkuXDhHYmIicXFxXLz4C1ptSJrXCgsL5fTpE8TExGBuboG1tbXp1+yaNGlBQMAq7t69A0BUVNQbfWcoLTt2bEWrDeHZs0gCAlZRu7Zvum0bN27G7t07uH79GkajkZiYGM6ePU109AsePLjHr7/+THx8PBbmFlhYqFEoXq4uqu3biC0bV/D0f9sAIyIjOX3h50zFZ2dvjy5Ea1pVlpETpw7x8OE94uJi2bh5Fd6f+aT7K3U1fKrz06+/cPHypZfPJj6OS1cvodVrcXN1o1jhYqzauJKEhAR+++0yP/98IVMxvE6RIkU5d+4Mz55FEhqqZ8uWje88phBCCCGEEEKIj+eTXulkZqbEf9jMLB3/ffAoU5qW7b9k7qRpvIh6QZESxflu/ESITn8b1YsXL1i8ZCYhIU8wt7CgTOkKNGnUBoAunfqxbuMiuvXuiYW5Ob516lKnVu10xyrnWY4CefPTvGNTzMwUBB448kbxd+vSgbXrNtG3/xAinz3DxUVD06YtqVTJGwBnZ2c8PEpx+fJFvvsu+RfWNBo3pkyZyeLF8xg3biRKpYISJdwZNGh4mtdJSjKyadN6JkwYg5mZGYULF2XQoG8BqF69JjEx0YwbN4Lg4GBsbGzw8qpIrVp13mgur6pbtz4DB/ZBr9dRtWr1dFcxARQvXpKhQ0cye/Z0Hj16gFqtxtOzDGXKlCU+PoElS+Zz7949FAolRYq507n7y+2I9fxaYDTCN9/0RafT4mBnR82qlalaqUKG8VWqVpkzx07QvW17cufMzaIxE1/bvoaPL/MXTuPxkwe4lyxN967D0m3r4uzMyKHfsnrdWmbOnYvCzIwSRUowoMcgAEYPHMOUuZNp1P5zPDw8qVmzDi9eRGUY8+vUq+fHL7/8RMuWjcmRIwd+fo3YtGn9O40phBBCCCGEEOLj+aSTTrHxCv4ti7k2L12U4rxj29YoNfm4H/EEgDoN61OnYfIqINfsGqLvpdxu9Sovr4rMnrkizTobG1vGjv2O0KePUtXVrlmL2jVrpSgzNzdn6ujpmZpH//6DcHV1JiYq+QPdFhYWdOnkT5dO/kDq7XUAc+cuTnM8d3cPFixYmmbdP8udnZ3TbQtQv35D6tdvmKl5ZEbx4iVp375jqvJy5bzYuTP1r/x99lllPvuscqrywoWLsGzZWgDuPX2Wok6hUODXqBV9u3dMtb0uh6srJ3ZuTVE2atok07Ft9uyM/f5lEq9A9pyvfV8ANG45md6ua6ryHDly8uO2HanKixUtypTvJqbaXgeQ0y0n86csAMDcxRqdLuPvObm5aTgS9GOKsoULfzC9K2q1OkVSEqBNm3am423b9qSoGzNmQorzRo2a0qhR0wzjEEIIIYQQQgjxYWQq6ZSUlMTatWvZtGkTjx8/xtHRkQYNGtCvXz+sra0z7N++fXt++umnNOu2bduGp6fnm0UthBBCCCGEEEIIIf7VMpV0mjx5MgEBAdStW5dOnTpx584dAgICuHHjBqtXrzZ9V+d1HBwcGD489bapPHnyvHnU/09c/f0mw2dMSrPuyJEzWXbddu1aExz8JFV5z559qV69Vho9xL/B552/SrN8ypCRHyyGEyeOsnjx/BRlZmZmuLo6s3LZwg8WhxBCCCGEEEKIjy/DpNPt27dZt24dvr6+zJ+f/I/J3LlzM3HiRPbt20ejRo0yvJC1tTVNmjR5t2j/n/EsXoK9K9Z98OuuX78lU9ulPmX/3Mr1X/C6d2VBfT9Cnj7P8hiqV6+VKjH5z+2YQgghhBBCCCH+f8hwidLevXsxGo106NAhRXnr1q2xsrLixx9/TKdnaklJSURFRWE0Gt88UiGEEEIIIYQQQgjxn5HhSqdr166hUCgoVapUinK1Wk3x4sW5evVqpi4UEhJC2bJliY2NxcrKiqpVqzJgwAAKFSr0dpELIYQQQgghhBBCiH+tDJNOWq0WBwcHLCwsUtVpNBouXbpEfHx8mvV/y507N+XKlaNYsWIoFAp+++031q9fz7lz59iwYQPFihV7t1kIIYQQQgghhBBCiH+VDJNOMTEx6SaU1Go1ALGxsa9NOk2ZkvJn0OvXr0/t2rVp3749U6dOZdWqVW8SM/b2qX8xLyJCgVKZ8QfN36fEpKQPer33yyxLR/7Qz+K/4L/7vsi78rZUKkWaf68y8iw6/p2u+7onlnVPM+uvoFCYvdX9/NS9y/uS0dPK2vdF3pUPTf62pE3el7TJ35bU5F1Jm/xtSZu8L2mTvy2pfervSoZJJysrK0JDQ9Osi4uLA8DS0vKNL+zl5YWXlxcXLlwgNjb2jcaIiIhOVWYwJJGY+F/9R/3HkHXf1TICSfIsPiHyrrwtgyEpzb9XGVIp3+m6r3tiWf9Fvay7QlKSkefP3+J+fure4X3J6Gll7fsi78oHJ39b0iTvSzrkb0sq8q6kQ/62pEnel3TI35ZU/k3viouL7XsfM8MlBq6uroSHhxMfnzojGRISku7Wu8zInTs3iYmJREZGvlV/kWzTltXMmTfpY4ch3sIPC6bxw9LFHzsMIYQQQgghhBDivcpwpZOHhwenT5/mypUreHl5mcrj4uL4/fffU5S9qXv37qFSqbC3t3/rMV7H1tIcsyxMdxqzfr2nyCKTJo3DxcWVbt16fexQhBBCCCGEEEKIT1KGSSc/Pz9++OEH1qxZkyLBtGXLFmJiYmjUqJGpTKvV8vz5c3LmzImVlRUAz58/x9raGqUy5TK648ePc/HiRXx8fEzfhnrfzIzwYOkvWTI2QN5ub5dwMyQmolK+2zLUT4XBYEClyvA1/M/41OYjhBBCCCGEEEK8rQz/dVysWDHatWvHunXr6NOnD9WrV+fOnTsEBARQsWLFFEmnWbNmsXPnTtauXUulSpUAuHDhAlOmTKFmzZrkyZMHlUrFlStX+PHHH3FwcGDEiBFZN7t/kTbdetGkvi+HTp7i4eMnHNi0jg1r17Bz1zaeRUbi6OxM6w5fUaHyZwDs27+XXds2U7JwUQKPH8UmmzX9vu5KxdJlAXjy5DFjx47m7l+3KVqkJLly5klxvVOnTrJwwVzCwkIpkL8APbt1J0/u3AB06dmdVq2+IHDfXp4EP6FW1Vp0ad+NqfOmcPXGFdw9PPnmm8HY2Lzdfs7KlSvQv/9gtmzZSHR0FH5+jenZsy8KhYL9+/ewZ89OSpRwJzBwH82ataRbt17s3bubjRsDCA0NpWRJd4YOHYmbWw6MRiPz588iKOgACQnxuLnlYOzYiRQsWJj4+HiWLl3E0aOHSEhIwMenBv36DUSttuTixV+YMGEMrVt/yfr1a1AqFXTr1puGDRuze/cOgoICMTMzY+vWjZQt68X06bPTnU/Llo1o0qQ5Bw/uR6/X4+NTg0GDvkWtVpuu06JFa7Zs2UiFChUZPXoCZ86cYtmyxQQHPyF//oIMHjycwoWLAHDvr9ssWzyDkKePKV22EmZmGS+Zu3jxV8aOHUWDZo3Yu3UnCoWC1h2+orpvbQB69+lBzfIV8av58vzgyWPsP36EuWMmAlClqhddu/Rn795thEeE8XnDltSqUY858ybz6PF9ypYuzcB+/TE3N3+rZy6EEEIIIYQQQvxTppZkjBgxgly5crF582aOHz+Og4MDX331Ff369UOheP1noQoUKIC7uzvHjx8nNDSUhIQE3NzcaNu2LT169ECj0byXifwXHDl1mmmjhmNna4tKqSRX7lyMmTEFOwd7Lpw6y+IZsym8YjEOjo4A/H7nT3yr1WD7khXsO3qYmcsWs2n+D5iZmTFu/CgKFSzG2NEz+OP2TSZNGU7FClUAePLkIePGjWL4kKF4unuwe+8eJkydzMLZc01JhePHj/L9+FkkJibSdWBnbv91m6F9hpEvT36+nfIte/f+SNu27d56ridPHmPFirVER8fwzTe9yJs3H40aNQXgxo3r1K7ty549QSQmGjh58jgBAauYNm02uXPnYd261YwbN5IlS1by00/nuXz5Ehs37sDGxob79++ZkmGLF8/nyZNHrF69AZVKxbhxo1i1ajk9evQBICwslBcvoti1K5Cffz7PqFHDqFatBk2aNOfatStvtL0uKCiQmTPnY2VlxbBhA1izZoWpb1hYKM+ePWPbtj0YjUncuvU7U6Z8x7RpsylevARBQYF8++1ANmzYjiEhgdnTR1O/YQvq1m/GxZ/PsHDuRIoXyZ9hDJHh4cS8iGZ+wAquXbrM3MnT8fKuRDZbm0zN4dKln5kx7Qf0oVoGD+3OrVvX+Kb/SAoVyUGXzv6cPHOa2jVqZmosIYQQQgghhBAiI5n6rXKlUkmnTp04ePAg165d49SpUwwfPpxs2bKlaDd16lRu3bplWuUEUKhQIebNm8fhw4e5dOkS165d4/Dhw4wdO/b/VcIJoHlDP1ydnU3bCWvVqoODkyMKhQLv6lXR5MrBnVu3Te01Ts40rFkHpUKJb7UahEaEEx4ZQYhex++/3+CLtp0wN7fAvWRpKpT3NvU7ffYYlStXoWzpMqhUKpo1bkJ8fDy/37platOqZRsc7R1xcXKhVIlSlChakiIFi2JhboGPTw3u3r3zTnNt164D2bPb4ebmRuvWX3D48EFTnZOTMy1btkWlUqFWW7J79w7at/+a/PkLoFKp8PfvxO3btwgOfopKpSI6Opr79+9hNBrJn78Azs7OGI1G9uzZSb9+g8ie3Q5r62z4+3fkyJEg03WUShVff90FlUqFt3dVrKysefDg/lvNp0WL1mg0bmTPboe/f6cU8zEzM6Nz5+5YWFigVluyZ89OmjRpjru7B0qlkgYNPsfc3Jzr16/y5+0bJCYmUr9hS1QqFRW9q1OwULFMxaBUqWj2ZRtUKhVlKnhhaWnFk8ePMz2HZk3bYm2djbx5CpA3T35Kl66AmyYnNjY2lCtblrt/3X3j+yKEEEIIIYQQQqRHPj7zAbk6O6U4DwzcR8D6NehDtADExsQS9eyZqd7hlQ+sW/4vURUTG0tk1HNsbW2xtLQy1bu4uKEPfTlOeFgoGk0OU51CocDZyYnQsNDksf+3mgrAQq3Gwc7BdK5WWxIbG5PmHL77bjQ3b17HaDQyoH8v6tSukfZcXZMTim5uOdDrdabzfyYbQ0KeMndkOnaOAAAgAElEQVTuTBYsmGMqMxpBp9NSvnwFWrRozaxZ09Bqg6lWrSZ9+vQnPj6e2NhYOnf+6pU+RpKSkkzndnZ2Kb6vZGlpSUzM2/0Upaur2yvx50Cv15vO7e0dUnyXLDj4KYGBe9m+fbOpLCEhAb1eR3hkLA6Ozim21Dm5ZC75amNrm+LbaBZqC+JiYjM9B3u7V565hRr7V5+5hZrwiPBMjyWEEEIIIYQQQmREkk4f0KuJhmCtjulTJzN88niKFC+GQqlkeJ9vMGbi1/ac7B14/vw5sbExpsSTTh9iGt/B0YmQkAem9kajEX1oKE6OTmmO9ybGjJmAq6szMVFPX9tOqw2hYMFCAISEBOPs7PJKbcpvGLm6avD374Svb4M0x2rVqi2tWrUlPDyM0aO/ZcOGADp37o5arSYgYAsuLq5vPI/MfEfpVVptsOn45Xyc0x3r7/l06NA51TiBh08RHqbHaDSa+oXqtVA4/xvF80+WVlbExceZzsMiIt5pPCGEEEIIIYQQ4l1lanudeP9i42IxMzPD1s4OgBNBR3h070EGvV7SOLtQrFgJNm1ZTUJCAjdvXuWXX8+Z6qt41+Ds2TP8duUKBoOBXT/uxlxlTvFimdvG9T5s3BjAs2fPCAkJZuvWjdSqVTfdtk2atCAgYJVpS19UVBRHjx4G4ObN61y/fg2DwYClpRUWFmoUCgUKhYJGjZoxb94swsPDgJcroy5cOJfudV7l4ODIkyeZ35q2Y8dWtNoQnj2LJCBgFbVr+6bbtnHjZuzevYPr169hNBqJiYnh7NnTREe/oEjRkigVSg7u30FiYiI/XzjJ3T9/z3Qc6SlSuAinfr5AbFwcj4OfcuDE0XceUwghhBBCCCGEeBef9Eonoxnk7eaVpeO/rfx58tCm7ZeMGzgMM4UZ1WrVpGjJEpnuP27sRMaOHU2Hjo0pWtSdGj6+vIiOAiBXrryMGTOexYvmExoWRoH8+Rk9fPgH/WWyqlWr07lze168iKJBg8/5/PMm6batXr0mMTHRjBs3guDgYGxsbPDyqkitWnV48eIF8+fP4smTx1hYWFCxojdffNEegJ49+7J69XK6detIZGQELi4uNG3akkqVvNO91t8+/7wJo0d/S/36NShbtjxTpsx8bfu6deszcGAf9HodVatWT3MV09+KFy/J0KEjmT17Oo8ePUCtVuPpWYYyZcqiMjen/5DxrFgyk22bVlK6bCW8KlbLMN6MtG3zJdcvXaRV7y4UzJOPWpWrcfH6lXceVwghhBBCCCGEeFufdNLpeWzCxw7BZPPSRanKuvfoRf22TdNs39Dvc2qW9ExRdnjdVtNxrly5mTRhXrrXq169Jh5Fi6RZt3zxD7jkyEuc7uX3jUYNGJ2ivnHjpnh7V0l37Mzw9q5Cq1ZtU5X7+TXCz69RqvL69RtSv37DVOVeXhVZs2ZTmtdQq9V0796b7t17p6orV86LnTv3pyjbtm2P6ThPnrysXr0hw3n8rXjxkrRv3zFT1wH47LPKfPZZ5dQDRT6jYKFiTJqxNEVxoZw2xGvT/8h5uXLlWRCwIkXZ3NXLTMf29vZM+zblc+zQorXp+MzpXwh5+tx0Pnni/BRtv/riy3SvLYQQQgghhBBCvA3ZXieEEEIIIYQQQggh3rtPeqWTEJkRHBxM+/at0qwLCNiaZnlWCNi2g/Xbd6Qq9yxRglkLl6bRQwghhBBCCCGE+PeSpJN4786e/ZmkpHf44NUH5ubmxqFDp9Ktf3VbXlZq37I57Vs2/yDXEkIIIYQQQgghsppsrxNCCCGEEEIIIYQQ750knYQQQgghhBBCCCHEeydJJyGEEEIIIYQQQgjx3knSSQghhBBCCCGEEEK8d5J0EkIIIYQQQgghhBDvnSSd/oMOnjxGz56d39t467YGMH3BtPc23n/J/v173uu9FK+3Zu0GJk+d+bHDEEIIIYQQQgjxAag+dgBZydJSgdGYmGXjm5kps2zsrHL12jVmzZvD3j1BprKvWrX/iBGJrLZm+xZCXkTRo+vQjx2KEEIIIYQQQoj/Rz7ppJPRmMiyZcuybPyuXbtm2djpSUzMuiSayFoGg+Fjh/BeGRINqJSf9J8QIYQQQgghhBDvQP7F+AGs376TW3fu8N3QwaayectXYmZlS7NOXxL94gXrlq3kt58vYqYww6dObQb3Ggi83Eq3/9gRihUqzKFTx2lUpx65NG4YMbJsxTxOnAjCwcGRrl36U8qzPABHjgWyd98WtCHBZM9uR4umTanvW4/Y2FjGT55IQkICtepUw2iEdYvWs+fgjzwOfsyoAaN5GvKUL5q2oW/fAWzYEEBsbCzt239NoUKFWbBgDjqdjgYN/OjV/fWrox4/fsS0aRP588/bmJlBxYreDBw4DFtbWwBatmxEkybNOXhwP3q9Hh+fGgwa9C1qtZqLF39hwoQxNGvWks2b12NlZU23br3w9W0AQHx8PEuXLuLo0UMkJCTg41ODfv0GolZbmvq2bv0l69evQalU0K1bbxo2bAxAZGQEkyeP59Kli+TLl4+KFb3f6dlmZh4tWrRmy5aNVKhQkfZdhnDp13Ns3bgSvS6YXLnz0bHbAArlLA3Ahh272L5vPy+iY3B2dGBA966UL+VJUlISP27ZzrEDQUS/iMa9tCed+vbExtYWXUgI7fyaMrRbb1Zt30RcXDwtGjSkXZMW/PTbJTb+uAMjcPLEMTRuOZn9/Yp05zNizGiKFSvGlatXePzkCR4l3fluwlQsMX/5bnRvw5DeQ1mzeTVurm7Mm7yAa9euMmvWdB4+fICrqyudO/fA07MUACEhwYwbN4Jbt36nRPGi5MmTK8N7+vTpE1q1aszIkeNYtmwxcXGxtG79JR06vNwGOWnSOFxcXOnWrReA6T7v3Lnf9EyaN2/FwYP7efz4EbVr+9K9e28mTRrPlSuXKVnSgwkTppI9e/Z3efRCCCGEEEIIITIg33T6AGpXq8r5Xy/xIjoaeLla6djZc9TxrQfAkplzUSqVzFyxmEnzZ3H10mX27Nlt6n/zzm1yuGrYtmgF7Zo0B+DGjeu4aXKweuUu2rT+mukzxvL8+TMA7LLbM336LDYFrKd/7z4sX72KO3fvYGlpydgRo3B0cODo4VMc2HQQZ0fnNGP+449bLF68gsGDv2XFih/YunUz48dPYf78JRw5cpjfrlx77ZyNRiPt23/Nrl2BrFu3Da02hJUrl6ZoExQUyMyZ89myZRcPH95nzZrkZEhYWCiRkRHs3BnIyJHjmD59Eg8e3ANg8eL5PHx4n9WrN7B58050Oh2rVi1P0ffFiyh27Qrk229HM2vWNJ49e3lvZs2ahoWFmt27DzB8+Bj27fsxw+eXkYzm8ezZM7Zt28PQoSP56+4fLFs0g07dB7Jk5S5q1m3ErGmjiI+P58Hjx+zYf4AfZkzlwMYAZowdhZuLCwDbtm7ml3MXGDV9EgvWrSSbrQ2rF6W8n1f/+J3VM+YyY/gY1u3cxv3Hj6hYuixfNG5O7dq+bFgX+NqE09+OnThOv169Wb10OUqlktmzv09R/9v131izIIAZY2eiC9UxZMg3tGrVlnXrtvD1112YPn0ikZERwMv7XaxYCXZsXUf7dm0IOnQs0/f1ypXLbNy4nTlzFrN69XLu3fsr032PHz/K7NkL2bhxB2fOnGLw4H50796LffsOYzQmsW3bpkyPJYQQQgghhBDi7UjS6QNwc3WhaMECnLrwMwAXr17D0kKNh4cnkeER/PbLRdp364ylpSV29vY0aNqIw0cOmfo7OTjQzLcBSqUStYUaAHt7Bz5v2BKVSkXVKrXImTMPv148D4BXeW9y586NmZkZHu7ulC1dhus3b75RzK1bf4GFhQVly5bH0tISH5/q2Nvb4+TkTJkyZfjzzzuv7Z87dx4qVPgMCwsLHBwcaNOmHZcv/5qiTYsWrdFo3Mie3Q5//04cPnwwRX2XLj1NMXh7V+Xo0cMYjUb27NlJv36DyJ7dDmvrbPj7d+TIkeRvVCmVKr7+ugsqlQpv76pYWVnz4MF9EhMTOX78KF269MDKyoqCBQvToMHnb3Rf0vK6eZiZmdG5c3csLCxQqy05fngfNet+TuEiJVAolfjUqIdKZc6169dQKBQkJCRw7+EjDAYDOVxdyZXDDYDdu3bSukM7nJydMTc3p3m7tvx0+myK7Zb+zVqhtlBTKF9+CubNx90H999qPjV9qpMvbz4sLS1p1/YLjhw9lOI6X7ftiJWlFWq1mkMngvD2royXV0UUCgVlypSjUKEi/Prrz+h0Wm7f/oNu3XpgYWFOqVIeeH9WIdNxdOzYFbXakiJFilK4cBH+/POPTPdt2bINjo5OuLi4Urp0GUqW9KBo0eJYWFjg41OD27dvvdE9EUIIIYQQQgjx5mR73QdS26caR06dpn7N6hw+dZo6PlUB0Gu1JCYm0vurjqa2SUlGcmjcTOeuaaxGcnFxxczM7JVzDWHhoQBcvHSBHWPX8eDBPYxJScTFx5Mvb943itfe3sF0bGFhgZ3dq+dqYmJiX9s/PDyMOXNm8Ntvl4mOjsZoTMLWNuV2JlfX5DlqNDnQ6/Wmc1tbW6ysrEznbm450Ot1RESEExsbS+fOX5nqjEYjSUlJpnM7OztUquRX29LSkpiYaCIiwklMTMTVVfPKdZNj+KcZMyYTFBQIQPv2HfH375Rmu9fNw97eAbVabTrX60M4dSKIQ4E7TWUGgwG9XodH6eL06fw1qzdt4a+Hj6hYtjS9O3bA2dGR4OCnzJ4wFYUi+ZkrFAoiwyNM54729qZjtYWamNjXP6P0ODslv2+uLi4YDAYin0cmlzm7mo5DtCEcO3aEU6dOmsoSEw14epYmLCwUGxsbrKysiIl6GafG1QWtLvn+vI6jo1PyfNSWxMTEZHoODg6OKfr+8zw6OvNjCSGEEEIIIYR4O5J0+kBqVv6MRavXoNWHcvr8TyycOgkARxdnVObmLNkUgFKZ/Gt4BbLnJPre/1aqmKUeT6fTYjQaTYknvT6ECl6VSUiIZ/r3YxkzZhwlCxdCpVIxadpUU79XE1VZacmSBYAZa9ZsxM7OnpMnjzN79vQUbbTaYNNxSEgwzs7JyY7nz58TExNjSjyFhARTsGAh7OzsUavVBARswcXFlTdhb++AUqlEqw0hX778pnHTM2TICIYMGZHhuK+bxz/vt6OTK02at6NJi69SlBfKaUO89j51fapR16caL6Kj+X7xUpasXceob/rhqtHQsW9PirmXSHV9XUjIa+N702euD01OCun0elQqFXa2dmjjtP8bMLmtq7Mr9er50blz91TjaLUhREVFpUgWabX6NN/nN2FpaUVcXHJCLSws9N0GFEIIIYQQQgiRJWR73Qdib2dHGXd3pi5YiJvGlfx5cgPg4OiIZ7kyrF+2iujoaJKSkgh5+pRLly6+dryIiHD27d+OwWDg7LnjPHr8gPLlKmEwGDAkJJgSLL9evMil3y4nx2Fvx/OoKKKiorJ0vtHR0VhbW2NjY4tOp2XjxrWp2uzYsRWtNoRnzyIJCFhF7dq+KepXrPiBhIQEfvvtEmfPnqJmzTooFAoaNWrGvHmzCA8PA14m4C5cOJdhTEqlkurVa/F/7d13VBXH28DxLx0VFRQFY28XFLA3BAsqSpGIBSuiYiPGaGzRaIzGkig/jRqNxt4woqIC9lhjwRpb1BgTjMaCCGIBpLPvH77ccL2XYm/P5xzP4c7uzM7eHXf3Pjszu2zZQpKTk/nnn6vs3Lnthfc1r/3IzqWVJ3t3b+Hvv/5AURSSk5M489sxEhMT+ffWLU6f/53UtDSMjYwwMTbGQP/Jf1Fv7w5sWBVETPSTwM+jhw85dfR4vupnUbQod6Jua/QGy82Bg7/y740bpKSksCZ4LS4uLTUCotm5NnPlyJFDnDnzGxkZGaSmpvL77+eJjY2hZEkrqlSpypIlT47j7xcucfT4iXzVITdVq6o4evQIjx495N69WNavX/vCZQohhBBCCCGEePne655OenoG9O/f/5WW/yxaNXXm2znzCPDT7OXyyYihBC9fzRcDPyM5KYmS1lb4+/XFxqJ4DiVB9ep2REXdore/N0XNLRg1YiKFCxcFoK//YMaPH0tqSjL169WnYb3/5tEpU7oMTZyc6ejTjoz0DFbO0w4GvQx9+vRnypQJuLk1p3TpsrRp48H69T9rrOPq6sbw4YOJjY3B2bmZ+u1k8GRoVeHChfH2dsPExJSRI79U90765JPPWLFiCQMG9OHhwweUKFECb+9ONGyY95vohg37gu+++4aPP25D+fLl8fDw4vTpUy+0r7ntx9MqVbah78ARrFz6A9FRNzEyNsHG1h73Fo6kpaWzcPUart+8haGhAfY2Nowc9KQHkU/nrsQ9fsj0ryZyPy6OIkWL0qipM/UcG+ZZv6YNHNl/6gS9/NtRsmQpZgYuynX95s2aM+fHudy8dQv76nYM/3wUpOlet2QJK6ZNm8mcOTOYOXMa+vr6VK1qQ0DAYACGDx/NggU/4N1xI9Wr2eDayoWEhMQ865ybNm08OHXqBJ06fUypUqXw8PAiOHjNC5UphBBCCCGEEOLle6+DTsnJmbzwWJ5c5a/nSJY2zZvRpnkzrfSChQrhPzgA/8EB6rSs4XVtmrrQpqmLZjlNXfD28yc6Kp7+/YZqlefu1p7e/r7ci7qpsx5DPx1MiVLlSIl58ja9Pt3+m6uolFUpjhw5RUxMjDpt6dIgjfwTJ04mKSEq132tVKkyy5Zp5uvWTTPYZmtbnZ49+5CTXr366gzgmJiYMHDgpwwc+KnWsjp16rF583aNtJCQLeq/LSwsCAycnWvdn1VO+6GrLgA1azegZu0GGmmFChWicoXyLPzfNK314cn8TR4d2uHRoZ3WshJWVkQcPvHfcEzg+6++Uf9dtHBhFixYSnRUfL72p5SVFb16/DcU0tzcnJSYx5SyKsWB0INa69vZ2TN16v90lmVtXYoFCxbn2V40tl/qIw4f1gwEzpv3X6DMxMSESZO+01jepUsP9d/ZjzfA119P1vjs5eWNl5d3vusjhBBCCCGEEOL5yPA6IYQQQgghhBBCCPHSvdc9ncSrNWvOfPbsPaCV7ubmwciReU/ALd6M7r7uGp/19PVQFIUJY796bXXYs/cAs+bM15rk3MqqFEFB619bPYQQQgghhBBCvDoSdBLPbdjQQQwbOkgrvYBZKfKas/rpIVDZ5TQs7W2U2368rX4O2qHx2eqjQuqhmN9Omqwry0vXqmVzWrVs/v9t5fW8UVEIIYQQQgghxOslw+uEEEIIIYQQQgghxEsnQSchhBBCCCGEEEII8dJJ0EkIIYQQQgghhBBCvHQSdBJCCCGEEEIIIYQQL50EnYQQQgghhBBCCCHESydBp3fQroP7+eSTvi+tvKANqwmcN/2llfeuW7p0IZMmjX/T1fhgTJ06kUWL5r/pagghhBBCCCGEeMkM33QFXiWzQqBHxisrX8HglZX9qvx+4QLf/zCbrVt+Uaf5+vR8gzUSb8LcedMoXrwE3bu9vOClEEIIIYQQQgiR3XsddNIjg4uHp76y8u2cx72ysnOSkfHqgmhviqIoKIqCvv6H1/EuIyMDA4N3L3iZk/dtf4QQQgghhBBCPL/3Ouj0tlizcTN/RkYy6YuR6rQflixDr0Bh2vt353FiIkGLl3Hu5Gn09PVo2qolIwcNB54Mpdu+fy82lauw+9ABvFq1obSVNQoKi5f+wK+//oKFRTH69xtKDYe6AOzdv4Ot29ZzN/oORYoUpaO3N26t25CcnMw3304hLS2NFq2aoCgQNH8NW3aFc+vOLb4aNp6o6Ci6eXfhs8+G8fPPq0lOTqZnz95UrlyFefNmExMTg7u7B4MG5t47avv2LWzZshmVypadO7dRvLglw4ePpl69BgAMHjwAB4eanD37G3/++SerVgVjbm7B3Lnfc+zYEfT09PHw8KJv34EYGBjkWV5CQkIeeUOxs3Ng69YwChcuzPDho3F0dALg9u1bfPvtN/z552Xs7OwpV678Cx1vZ+d6DB06kvXr1/L4cQIeHh/zySefoa+vz8H9O9m/dxuVq9hy6MAvtGrzMT7d+rJlazhBq5YT9+AB1apWYeQnA7EuWQJFUfhx+Up2H4ogJTUFy5Il+PSL4ZStUJ60tDTWrwzi+KEjZKZn4lS7Lp/49sLE2ISzly4ybcEPdHRvy7qtoRgYGdG1S19aurjzy+4tHDy8Bz302LothLr16jF62PAc96d9Ry+8Wnnxy4Fd3Lt/D+eGTRgWMBwTYxPO/H6Gb3+Yiru7J+HhodSsWZthw0Zx8uRx1qxZxd270VSqVIkhg/tRuVJFAP76O5IZ38/l1q0oGjd2AvTy/E5Pnz7F5Mlf07lzd9asWYmBgT4DBnyKp+fH6vbUpo0HXl7eAOpjvmDBUvUxGT58NOvX/8y9e/fo3LkbHh5eTJo0nn/+uUrDho58/fVkjIyMXujYCyGEEEIIIYT4z4fXteQNaNnEmWO/nSHx8WPgSW+Q/RFHadW6DQA/zZyDgYEBM5cuYOrc7/n9zFm2bAlT5/8j8i9KlbQiZP5SerTrAMClSxextirFimWhdOncm8D/TSA+/hEARYuYExj4PcGr1zD008EsWbGcyKuRmJqaMmHsVxSzsGDfnkPsDN6FZTFLnXW+cuVPFixYysiRY1i6dCEbNqzjm2++Y+7cn9i7dw/nzl/Ic78vXbrIRx+VZtu2vfTtO5Bx40bx6NFD9fJdu7YzatQ4fvnlV6ytSzF16gQMDAwIDg5l+fI1nDx5jC1bQvNVXt55L1CuXHm2bdtD9+5+TJs2GUVRAPjmm6+wsbFl27Y99O7djx07tuV9UPNw8OB+li5dxdKlazh06Fe2bQtXL4v86w9KWpVi/tJNtOvgy6kTh1m5agWTR48kbOVSalSvxqTvZwNw8uw5zl28xNp1ISzesIbPxozErEhhAIKXreTOrdt8O28W69dtJPZ+HKs3h6i3E/fwAYmPEwn+YSFjxoxn8ZI5JCTE09rVi6bOrfBu15Wfg3YQGPh9nvuz++Bu/jdhBj//FMzN2zdYvWHVf9uJu0d8fDyLF69g0KAhREb+zbx5sxg06DNWr16Ht3cHxk+YSmpqGmlpaXw98VtcW7oQunENLi6tOHBgX76+07i4eyQmJhAauoMxY8bz/ffTefToUb7yAhw/HsHSpatZuHA5P/+8isDAqXz99WQ2bdrGP/9EsmfPrnyXJYQQQgghhBAibxJ0eg2sS5ZAVakih46fBOD07xcwNTbB3t6Bh/cfcO7UaXoO6IupqSlFzc1x9/Ziz97d6vzFLSxo39odAwMDTIxNADA3t6CtZycMDQ1xdmrBRx+V5bfTxwCoV9eRMmXKoKenh72dHbVr1uLiH388U507d+6GsbExtWvXxdTUlKZNm2Fubk7x4pbUqlWLv/+OzLMMc3MLOnfujqGhIS1btqZcufJERBxWL/fw8KJSpcoYGhry6NFDjh2LYOjQERQoUAALi2J07tydvXt/ybO8uLh7eea1ti7Fxx+3x8DAAHf3tty7F0tc3D3u3LnD5cuX6NfvE4yNjalVqw5OTk2e6bvSpUePXhQpUhRra2s6d+6mEdCwsLCktXsHDAwMMDYxYd/urfj17EWFsmUwNDDAt2N7/v7nGnfuxmBoYMDjpGSuX7+GoiiULlcWi2LFUBSF/Tt34zvAH7PChSlUsBDdP27PgWNH1NsxNDCgZ3sfDA0NaezojKlpAW7dvvFc+9PeowMlS1hRpHARfDv1ZN/Bveplenp6dOvWEyMjY0xMTNi9ewetW3ugUtliYGCAh0dbjIyM+OPyn/zxx59kpGfQscPHGBoa0qJFS6pVs8tXHQwMDOndux+GhoY4OjpToEBB/v33er73oUePXhQqZEalSpWpWLEy9es3pHTpMpiZmdGwYWOuXPnzmb8XIYQQQgghhBA5k+F1r0nLpk3Ye+gwbi7N2HPoMK2aOgMQe/cuGRkZfOrbR71uZqZCKStr9eeSOnojlShREj09vWyfrYi7fw+A02eOs2lCEP/+ew0lM5OU1FTKlyv3TPU1N7dQ/21sbEzRotk/m5CUlJxnGU/X0cqqFLGxMf/tV0kr9d937kSRnp5Ou3Zu6rTMTEVjnZzKy0/eYsWKq/82NTUFICkpiQcPHlC4cGEKFCigXm5tXYq7d6N17tOIEUM4f/4MAKNGjaV1a3ed62XftrW15n4XsyyhsW5sTDSz53zP3Gz7pqAQGxdHnRoOtPdw4/uZ/yMq6jb1Gjeke78+pKWmkpKSwldDRgCgr6dPZkYGmZmZ6jKKmBXWmF/JxMSE5OQknfXNS0nLkuq/rUpaE3s/Vv3Z3NwCY2Nj9ee7d++yb99ede8ufX090tJSuXfvHqCHpWWxp47jf209N0WLFsXQ8L9TlqmpKUlJj/O9D9nbgImJidbnuLh7+S5LCCGEEEIIIUTeJOj0mrg0bsT8FSu5G3uPw8dO8OO0JxOcFythiaGRET8Fr9YIEFQs8hGPr/1/Lw4dU97ExNxFURT1j/fY2Gjq12tMWloqgTMm8PXXE6le5UkvoqnTp6nzZf+x/6o9Xcfo6Ds4OzfVWZeSJa0xMjJm69Y9GoGF/JSXn7w5sbS0JD4+nqSkJHXgKTr6To7f08yZP+Sr3CdzGVVWl2eZLdCk99QBLW5ZggF9++JSu5rOsjq19aBL3084f+0Sc7/7H9tCNtPRtxvGJsZMXzCXYpbFNdtLfjxjO7gbe/e/v2OisbT4LxD69HdlaVkCH58u+Ph0A6BkSUuSEqIAOHf+ArGxcRrH8e7dO5QuXeaZ6vO0AgUKkJz8XyD0SYBLCCGEEEIIIcSbJMPrXhPzokWpZWfHtHk/Ym1Vkgpln/zItihWDIc6tVizeDmPHz8mMzOT6Kgozpw5nWt5Dx7cZ9v2jaSnpxNx9AA3b/1L3WQWYvMAACAASURBVDoNSU9PJz0tDXNzCwwMDPjt9GnOnDv7Xz3MixKfkEBCQsIr3d+sOm7YEEx6ejr79u3h+vVr6sm7n2ZpaUmDBg2ZN282iYkJZGZmcuvWTc6c+S3P8vKTNyfW1qWwsanG0qULSUtL49y5sxw5cuiF933t2tU8evSI6Og7bNiwlhYtXHNct4WrF6tWr+Cff58MfUtITGT/kaMA/PHX31y68hfp6emYmJpiZGyMvr4++vr6uLRpTdCipTx88ACA2Lh7nDx/NsftZGde1ILo6Nv53p/Q7Zu4G3uXR/GPCAoJwsW5RY7rtm7txs6d27ly5TKKopCUlMSx4yd5/Pgx1avZYGCgz6bQLWRkZHDgwD4uXbqY73rkpEoVFb/+uo/k5GRu3rzBtm1heWcSQgghhBBCCPFKvdc9nRQMsHMe90rLfxatmjrz7Zx5BPj5aqR/MmIowctX88XAz0hOSqKktRX+fn2xsSieQ0lQvbodUVG36O3vTVFzC0aNmEjhwkUB6Os/mPHjx5Kakkz9evVpWK++Ol+Z0mVo4uRMR592ZKRnsHLeqpw28cKqV7fj5s1/8fRsiYVFcaZMmU7RouY5rv/VV5P46ae5+Pp25vHjRD76qDQ9evTKV3l55c3NhAlTmDp1Ih4eLbCzc8DNzeOFg3LOzs3o27cniYkJuLu3pW3bdjmuW79hE4oWUPhm5iyiY2IpVLAg9WrWwMXJkcdJScxbtoKoiZMxMDKkRp3aeHZ88oa2rv5+bP55HROGjybxUQKW5uZ4tWxN/Rq18qxfy5YezJg5Ed9ebalbty6jhn6e6/qtmroyauIIYuPu4dzQiZ6d/XJct0oVFZ9+OpRFi+Zz+/ZtChQwxa66DTUc7DAyMmLihC/5ftY8lq9YQ+PGTjRrlnMAK7+6dOnB5cuX8PJqTZUqVXB1dePUqRMvXK4QQgghhBBCiOf3XgedEhKBZwwMvUptmjejTfNmWukFCxXCf3AA/oMD1GlZw6XaNHWhTVMXzXKauuDt5090VDz9+w3VKs/drT29/X25F3VTZz2GfjqYEqXKkRLzZD6cPt381ctKWZXiyJFTxMT8NwfR0qVBGvknTpysHi6VOz2GDx/N8OGjtZbMm7dIK83MzIyRI79k5Mgvn7m83PJ6eHjh4eGlkXb48Cn136VLl2H+/CV57MuzcXR0wsenq1Z6Uxc3mrq4aaW7u3nQso72hNp1aziwfPZMDKzKc/2BZs8kY2NjuvTuSZfePbWG19Wqbkfw3IUa6y+cH6z++6NSZfh+xpN9tvqoUI5tJYtNVVt6dPLVSq/tUJvQ0O0a7QWgTp161KlTD9AcXgdgo6rKwgVzAChgVorMzLyH+tWpU4/Nm7drpIWEbFH/bW5uzqxZP2os79t3oPrv7McbYMGCpRqfBwwYlGcdhBBCCCGEEEI8GxleJ4QQQgghhBBCCCFeuve6p5N4tWbNmc+evQe00t3cPKhe3eH1V0g8lzt37tDDt7vOZT/OmvPa6rFq1TJWr16ulV6jRu18T+AuhBBCCCGEEOLtIUEn8dyGDR3EsKHaw5Kyhkw9PaTtRegaIve2enoo19vO2tqa9UE/57h888Yt6qGYr5Kfnz9+fv55ryiEEEIIIYQQ4p0gw+uEEEIIIYQQQgghxEv3XgWdFEV501UQQgghhBBCCCGEELxHQScjI2Pi4++TmZn5pqsihBAkJSViYCAjmIUQQgghhBAfrnz9IsrMzGTVqlUEBwdz69YtihUrhru7O0OGDKFgwYL52tCvv/7KggULuHz5MsbGxjRq1IhRo0ZRtmzZF9qBLEWKFCMpKYG4uOiXUl6+6OlxN+ZhjovNjQuTEnMrx+WGhibE3Ne9vEiqHol3c85bsHABYmIe5Vy2sRkxuWxb39iYlJhEncuM9AsSG3s3x7wGBuk8jr+dc91S9MjIyHHxhyuX9vIibQVerL28SFuBF2sv73NbMTAwpGjR4m+6GkIIIYQQQgjxxuQr6PTtt9+yevVqXF1d8ff3JzIyktWrV3Pp0iVWrFiBvn7uHaZ++eUXhgwZgq2tLaNGjSIhIYGVK1fSrVs3Nm7ciJWV1QvviJ6eHgULFqZgwcIvXFa+GRrwxeJLOS7e+LUzNzeOzXF50c8X8/WZ2TqXrWw5mdNjxuWYt/KK1axb/GeOy4eNb8zuldNyXD7gy/lc3/KbzmXWAyqzdeuWHPMOHtSXC4cW57i8XLVxxCca5Lj8g5VLe3mRtgIv1l5epK3Ai7UXaStCCCGEEEII8f7KM+j0119/ERQUROvWrZk7d646vUyZMkyZMoVt27bh5ZXzW8XS0tKYPHkypUqVYs2aNRQqVAiApk2b0qFDB+bNm8fkyZNfwq4IIYQQQgghhBBCiLdFnnM6bd26FUVR6NWrl0Z6586dKVCgAOHh4bnmP3nyJHfv3qVTp07qgBNAtWrVaNCgAdu3byctLe05qy+EEEIIIYQQQggh3kZ5Bp0uXLiAvr4+NWrU0Eg3MTHB1taW33//Pdf8Wctr166ttaxWrVokJCRw7dq1Z6iyEEIIIYQQQgghhHjb5Rl0unv3LhYWFhgbG2sts7Ky4v79+6SmpuaaP2vdp5UsWRKA6OjXOPm3EEIIIYQQQgghhHjl9BRFUXJboVWrVqSnp3PgwAGtZV988QVhYWGcPHmSIkWK6Mw/duxYNm7cyJ49e7TeVBcSEsK4ceP48ccfadWq1fPvhRBCCCGEEEIIIYR4q+TZ06lAgQI59mRKSUkBwNTUNNf8gM4y8pNfCCGEEEIIIYQQQrx78gw6lSxZMschdNHR0TkOvcueP2vdp+U29E4IIYQQQgghhBBCvLvyDDrZ29uTmZnJ+fPnNdJTUlK4fPky9vb2ueZ3cHAA4MyZM1rLzp49i5mZGRUqVHiGKgshhBBCCCGEEEKIt12eQScPDw/09PRYuXKlRvr69etJSkrCy8tLnXb37l0iIyNJSkpSp9WvX58SJUoQEhJCYmKiOv3y5cucOHECNzc3jIyMXsa+CCGEEEIIIYQQQoi3RJ4TiQNMnjyZoKAgXF1dadasGZGRkaxevZo6deqwcuVK9PWfxK7GjBnD5s2bWbVqFQ0bNlTn37FjB8OGDcPW1hYfHx8SExNZsWIFenp6bNq0SYbXCSGEEEIIIYQQQrxnDPOz0tixYyldujTr1q3jwIEDWFhY4Ovry5AhQ9QBp9y4u7tjamrKggULCAwMxNjYGEdHR0aOHCkBJyGEEEIIIYQQQoj3UL56OgkhhBBCCCGEEEII8Szy7qYkhBBCCCGEEEIIIcQzkqDTO6JFixb07NnzTVdDvCLHjx/HxsaGTZs2vemqiHfEpk2bsLGx4fjx42+6Kvkyd+5cbGxsuHnz5puuyislx+Xlu3nzJjY2NsydO1cj3cbGhjFjxuSrjBfdz5zq8K5529tnz549adGixXPnf5Y2IYQQr9Lbfr4V4nXK15xOQggh3n8XL15k69atHDt2TP3jvFy5cnTo0IHOnTvrfNPouXPnmDVrFufOnUNPT4/atWszcuRIqlWr9rqr/96S4/JqnDhxgh07dnDq1Clu3ryJiYkJFSpUwNfXF09PT/T09N50FYUQQoj30sOHD3F2diY1NZXAwEDatWv3pqskXiEJOgnxFqhfvz7nz5/H0FD+S4o3Z8mSJURERNCqVSt8fHzIzMxk//79TJo0iX379rFkyRKNH+Jnz56lZ8+eWFlZMXToUACCgoLo3r07wcHB2NjYvKldea/IcdHt/Pnz+XqZSU5mzJjBnTt3cHV1xdfXl6SkJLZv386IESM4duwYU6ZMeYm1FfmxdOnSF8r/om1CCCHE67FlyxbS0tIoU6YMISEhEnR6z8kvXCHeAvr6+piYmLzpaogPnK+vL9OmTdNoi76+vowcOZItW7Zw4MABXFxc1MumTJmCkZERa9asUb+J1N3dHXd3d6ZPn86yZcte+z68j+S46Pai58yRI0dSt25dDAwM1Gl+fn74+fmxYcMG/Pz8UKlUL1rN90ZCQgJmZmavdBvGxsYvlF+uo0II8W4ICQmhYcOGtGzZkm+//ZZ///2XcuXKvZG6KIrC48ePKVSo0BvZ/odAHge9ZaKiohg6dCh169alTp06BAQE8O+//2qtl9v8EjnNXRETE8OUKVNo2bIl9vb2ODo60qdPH44cOfLK9kfkz9NzOmX/vHHjRjw9PbG3t8fFxYXFixdr5T99+jT9+vXDyckJBwcHmjRpQv/+/Tl79qx6nax28ddffzFlyhScnJyoUaMGPj4+HD16VGe9IiIi8Pf3p169ejg4OODl5cXatWt1rnvp0iWGDBlC48aNsbe3p1mzZgwfPlxn+xWvzoIFC7CxsWHy5MlkZmaq5ziJiIigc+fO1KxZEycnJ6ZMmcLjx4818tatW1fnjzYPDw8A/vrrL3Xa9evX+f3333Fzc1MHNgCsrKxwc3MjIiKCmJiYXOuakZHB119/ja2trc52/T55349LRkYGTZo0oX379jqXZ/Ww2rNnD/AkgDFr1ix8fHxo2LAh9vb2uLq6MmPGDJKSkvK1TV3z92RmZrJw4UJatGihPmeFh4frzN+gQQONgBM8eQDQpk0bQPN7zcmhQ4eoXbs23bt35+HDh/mq99sop/Z59OhRunXrRu3atfnkk08AiI6OZtq0abRr14769evj4OCAh4cHixYtIiMjQ6PcrDlNIiIimDt3Li4uLtjb2+Pl5cW2bdu06vH0nE6ff/459vb2xMXFaa179epVbGxsmDp1qjpNV5vISjtz5gy+vr7UqlWLhg0bMm7cOBITE1/oexPPJqs9HD16lKVLl9KqVSvs7e1p06YNmzdvVq/3rPe4UVFRfPnll+r25ejoSNeuXTXKFK9OSkoKc+fOpU2bNtSsWZN69erh5eXF9OnTNdbL7z1l1jy2ly9fpnfv3tSuXRtHR0emT59Oeno6KSkpTJ8+nSZNmuDg4ECPHj2IjIzUKic1NZWffvoJT09PHBwcqFevHgEBAVy6dEm9TmRkJDY2Nnz33Xc692348OEa56DIyEgmTpyIp6cntWvXpmbNmnTo0IH169e/yFf4wbl48SJ//PEH7du3x8vLC0NDQzZu3Khenp6ejpOTE506ddKZf82aNdjY2LB//351WkpKCvPnz8fDwwMHBwfq169PQEAAly9f1sgbERGBjY0NoaGhrF69Gnd3dxwcHFi5ciXwpLf46NGjad26NTVr1lRf4/fu3auzLkePHqVz587UqFEDZ2dnvv32Wy5fvoyNjQ3z58/XWDczM5OgoCDat2+vLrtXr16cOHHiub7Hd4n0dHqLPHr0iB49enDnzh26du1K5cqVOXnyJH5+fiQnJ79Q2Tdv3qRbt27cu3ePdu3aYW9vT1JSEufOnSMiIgInJ6eXtBfiZQoODiY2NpZOnTpRpEgRwsPDmTFjBtbW1nh5eQFPbrz9/f2xtLTEz8+P4sWLExsby+nTp7l8+TK1atXSKHP06NHo6+vTv39/EhISWLduHf369WPx4sU0btxYvd66deuYMGECtWrVIiAggAIFChAREcHEiRP5999/GT16tHrd/fv389lnn1GwYEE6depE+fLliYmJ4fDhw1y5cuWNPbn4kGRmZjJp0iTWrl3LiBEjGDBggHrZxYsX2bVrFz4+PrRr147jx4+zevVq/vrrL5YvX57ncJQ7d+4AULx4cXXa77//DkDt2rW11q9VqxYbN27k4sWLNG/eXGeZycnJDB8+nIMHDzJ9+vT3tlv1h3JcDAwM8PLyYunSpVy5ckWrh1BoaCgWFhY0a9YMeBK4CAkJoXXr1rRt2xZDQ0NOnDjBkiVL+OOPP557mNV3333HqlWrqF+/Pr179+bevXtMmjSJsmXL5rsMXd+rLps3b+arr77CxcWFmTNnvpO9bHJrnxcuXGDXrl107txZI5j4559/8ssvv+Dq6kq5cuVIS0vj0KFDzJw5k5s3bzJp0iSt7cyYMYPHjx/TrVs34EnwYfjw4aSkpNChQ4cc69e+fXt27NjB9u3b8fX11VgWFhamXicvf/zxBwEBAXTo0IG2bdty4sQJQkJC0NfXZ/LkyXnmFy/XrFmzSE5OpkuXLhgbG7N27VrGjBlDuXLlqFu37jOVlZ6eTp8+fYiOjqZ79+5UqFCBhIQE/vzzT06dOpWv9iFezDfffMPGjRvx9vamVq1aZGZmcu3aNY0JtJ/lnhKenIf79OmDh4cHbdq04ciRIyxbtgx9fX3+/vtvkpOTGTBgAPfv32fZsmUMGjSIHTt2qK+baWlp9O3blzNnztCuXTt69OhBQkIC69evp1u3bgQFBeHg4EDlypVxcHBg69atfPHFFxoPIhISEti7dy9NmjShWLFiwJO5AE+dOkXz5s0pU6YMSUlJ7Ny5k/Hjx3P//n0GDhz4Gr7xd19ISAgFCxakdevWFCxYkObNmxMaGsrQoUPR19fH0NCQtm3bsmLFCiIjI6lcubJG/tDQUIoXL06TJk2AJwFGf39/zp07h7e3Nz179uTRo0esX7+erl278vPPP1O9enWNMpYvX86jR4/o2LEjJUqU4KOPPgJg165dXLt2DQ8PDz766CPu37/P5s2bGTRoELNmzVI/8IMnnQT69++Pubk5AwYMwMzMTD1XpC4jR45kx44duLu706lTJ5KTkwkPD6d3797Mnz8/x3uz94Ii3hozZ85UVCqVEhISopE+ZcoURaVSKb6+vuq0GzduKCqVSvnhhx+0yvnhhx8UlUql3LhxQ53Wr18/RaVSKQcPHtRaPyMj4yXuhXgex44dU1QqlbJx40aNz05OTsrDhw/V6z1+/Fhp2LCh0rlzZ3XaypUrFZVKpZw7dy7XbWS1i06dOikpKSnq9KioKKVWrVqKm5ubOi06Olqxt7dXhg8frlXO5MmTFVtbW+X69esadWrUqJFy584drfWlfb0aGzduVFQqlXLs2DElKSlJGTRokGJnZ6ds3rxZYz2VSqWoVCpl9+7dGumTJ09WVCqVsnXr1ly3k5CQoLRo0UKpW7eucv/+fXX60qVLFZVKpRw4cEArz4EDBxSVSqUEBwer07Kfl+7fv6906dJFqVWrlnLo0KHn2f231od8XK5cuaKoVCpl+vTpGunXr19XVCqVMnnyZHVaSkqKkpqaqlXGrFmztM5nOV3vVCqVMnr0aPXnyMhIxcbGRvHz81PS09PV6RcuXFBsbGy0rou63LlzR6lbt67SsmVLjfo9XYeFCxcqKpVKmTBhwjt1jnvW9nnkyBGtMpKSkpTMzEyt9JEjRyq2trZKdHS01vaaN2+uPHr0SJ3+6NEjpXnz5kr9+vWVpKQkdbqvr6/i4uKi/pyenq44OTkpHTt21NhWZmam0rx5c6Vt27Za9c7eJrLSbGxslDNnzmik9+/fX6levbqSkJCgtS/i1chqD+3atdO4D7lz545iZ2enDBs2TFGUZ7vH/eOPPxSVSqUsWrTo9eyE0FK/fn2lX79+OS5/lntKRVEUFxcXRaVSKdu3b9dYt3379oqNjY0SEBCgcQ7Kug/O/htn+fLlOn/3xMfHK82aNdP4TRUUFKTzurl+/XpFpVIpu3btUqclJiZq7UNGRobi6+ur1KlTR+O6kf18K/6TnJys1K9fX+NcvXv3bq1jkPV/e+bMmRr5IyMjFZVKpXz77bfqtMWLFys2NjZa16yHDx8qTZo0UXr16qVOO3LkiKJSqZQGDRoo9+7d06qfrmOcmJiouLq6al1zvL29FQcHB+XmzZvqtNTUVMXHx0dRqVTKjz/+qE7fvn27olKplA0bNmiUkZqaqrRr105xdXXV2u77RIbXvUX27NmDpaUl3t7eGun9+/d/oXIfPHjAoUOHaNKkiToinJ1Muvn26tixI0WKFFF/LlCgALVq1eLatWvqtMKFCwOwd+9eUlJS8iyzd+/eGvNmZPWaunr1qrp78q5du0hNTaVTp07ExcVp/GvRogWZmZnqIXmHDx/m/v379OnTR2M4TxZpX6/Ww4cP6dOnDxERESxYsEDr/AFQsWJFWrVqpZGW1aNh9+7dOZadkZHBqFGjuHnzJhMnTsTc3Fy9LGsIlK45WLLSdA2Tun37Nt26dePGjRsEBQXh7Oycj71893yIx6Vq1arY2dmxZcsWMjMz1emhoaEAGt+BsbGx+q176enpPHz4kLi4OHVvy3Pnzj3z9vfu3YuiKPTp00fjabWdnV2+evMmJSUxePBgkpKSmDZtms63Amb1DJo5cyZDhw5l4sSJ7+Q5Lj/t09bWVqP3axZTU1P1xPWpqak8ePCAuLg4nJ2dyczM5MKFC1p5unXrpr5WwZPrVteuXXn48GGurxPP6kH3+++/awyfOX78OLdv3853L5ZatWpp9fpt1KgR6enp3Lp1K19liJene/fuGucoKysrKlasqHFvk19Z7er48ePcu3fvZVVRPAMzMzP+/vtvrly5onP5s9xTZrGyssLd3V0jrU6dOiiKQs+ePTVenlGvXj3gyfDyLOHh4VSqVAk7OzuN7aWmptK4cWN+++039SgST09PjIyM1NeqLGFhYZibm2v0PilYsKD675SUFO7fv8+DBw9wcnIiISGBq1evPsM392H65ZdfePjwocZ1p3nz5hQvXlxjiJ2trS22traEh4ejKIo6XVcv1/DwcKpWrYqtra3G8U5PT8fR0ZGTJ0+SmpqqUY8OHTqoe7Bll/0YJyUlcf/+fZKTk2nQoAF//fWXegqE6OhoLl26hKurK6VLl1bnMTIyomfPnlrlhoeHU6RIEVq0aKFRx/j4eJo3b87169e5ceNGvr/Hd40Mr3uL3LhxAwcHB605JkqWLKkReHhW//77L4qiaHUrFG+/MmXKaKWZm5vz4MED9WdPT0/Cw8P56aefWLFiBTVr1sTZ2RlPT0+Nk2CWp7uoZk+7ceMGlStXVt/c9+7dO8e6xcbGAqhvEqV9vRljxozh8ePHBAUFqW+8nqbrmGedV3K6wGVmZjJ27Fj27t3LsGHDaNu2rcbyAgUKAGhdxLOnZa2TXUBAABkZGYSHh1O+fPncd+4d9qEeF29vb6ZOnUpERIQ6cJV1M2hvb6+x7po1awgODubvv//WCFIBzzU/UtZ3VqlSJa1llStX5vDhwznmTUlJ4dNPP+XChQtMmzYtx2O2cuVKEhMTGTZsGAEBAc9cx7dFftpnhQoVdKanp6ezaNEiwsLCuH79usaPAXgyVcDTcjomgNb8k0/z9vZm2bJlhIWFMXz4cODJj46sgFR+6BpemRWszX49Fa9HTsfjeQKApUuXJiAggEWLFuHs7Ey1atVo1KgRbm5u1KhR42VUV+Rh7NixfPHFF3h5eVG2bFkaNmyIi4sLLVq0QF9f/5nuKbPouv8tWrSozmVZv5Gy/1+OjIwkOTkZR0fHHLd5//59SpUqpQ4s7d27l/j4eAoXLszNmzc5deqUVoA0MTGRefPmsWPHDqKiorTK1HX+E5pCQkIoVqwY1tbWGoHCxo0bs3PnTuLi4tTBIG9vb6ZNm8axY8dwdHREURS2bNmCjY0Ntra26rxXr14lLS0t1+P94MEDSpYsqf6c0zUuJiaG2bNns2/fPp3zCcbHx1OwYEH1PUfFihW11tGVdvXqVR49epRrHWNjY59pOoB3iQSd3jLZI/fZPX1Tl9N68OSGUFfe3PKIt9PTAUhdjI2NWb58OefPn+fQoUOcOnWKH374gXnz5jFz5kxcXV3zLOPp9pX1efr06Ron6OyyTorSvt4sDw8PNm3axPz585k/fz6mpqZa6+T3vJI9fdy4cYSGhjJ48GCdP66z2sXdu3e1lkVHRwPo7PnWtm1b1q1bx/z58/nuu+/eyV4i+fGhHpe2bdsSGBhIaGgozs7OnDp1ihs3bjBy5EiN9ZYvX860adNwdnbGz8+PkiVLYmRkRHR0NGPGjMnxO8gPXd9rbuWlpKQwaNAgIiIimDJlSq7zWDk5OXHy5EnWrVuHp6fnO3tzmJ/2qSs4CTBt2jRWr16Nh4cHAQEBFCtWDCMjIy5evMiMGTO0AojwYtcHGxsbqlWrRnh4OMOGDSM5OZldu3bh5OREiRIl8lVGbtfSF2lr4vnkdX55lntcgGHDhtGpUycOHDjAqVOnCAkJYenSpfTr149Ro0a9cH1F7lq1asW+ffv49ddfOXnyJBEREYSEhFCvXj2WL1/+TPeUWXL7P5tT+8n+f1lRFFQqFV9++WWO5WTv5eLt7c3u3bvZuXMnPj4+hIWFoSiKVi/QESNGcODAATp37kz9+vUpWrQohoaG/Prrr6xYsULn+U/858aNGxw/fhxFUdQv7Xha1hxHAF5eXsyYMYOwsDAcHR05fvw4t27d0nphhKIoVKtWjS+++CLHbWfvFQ7ovO5lZmbi7+/PtWvX8PPzw87OjsKFC2NgYMCGDRvYvn37c18zFEWhRIkSBAYG5rhOlSpVnqvsd4EEnd4iZcuW5dq1a2RkZGicbO/evUt8fLzGulnRfl1Pg59+ali+fHn09PQ03tYg3j81atRQP9WLiorC29ub2bNnawWdIiMjNZ4OAOruwFkX/azov4WFhc7hFdllPcG+dOmSTEj/Bnh5eeHo6MgXX3zBwIED+emnn7R+LP79999a+bLOK0/f6GUFNjZt2sQnn3zCZ599pnO7Dg4OAJw5cwYfHx+NZWfPnkVPTw87OzutfAMGDKB8+fIEBgaSnp5OYGBgvoKr75oP9bgUK1aMpk2bsmfPHhITEwkNDUVfX5+PP/5YY72wsDBKly7N4sWLNX5AHDx48Jm3mSXrO4uMjNT6/nIa8pCamsqnn37KkSNHmDx5co5vysmiUqkYMmQIvXv3xtfXl5UrV+b4tPRtlp/2mZOwsDDq16/PrFmzNNKzP7F+WmRkJC1bttRKA909Gp7m7e3Nd999x7Fjx4iJiSExMVEmiH6PPcs9bpayZcvSLgawlgAACzFJREFUs2dPevbsSUpKCn379mXJkiX4+/vn+VIA8eLMzc1p164d7dq1Q1EUZsyYwZIlS9i7d+8z3VO+LOXLl+f+/fs0atQoXw9RmjVrRrFixQgNDcXHx0c9PC97b7lHjx5x4MAB2rVrp/XChIiIiJe+D++jTZs2oSgKU6ZM0RhynWX27Nls3LhRHXSytLTE2dmZXbt2MWHCBMLCwjA0NNTq5Vq+fHni4uJwdHR8oYccly5d4sqVKwwZMoRPP/1UY1lwcLDG56xr1z///KNVjq608uXLExERQe3atfN9vX2fvJ+PmN9RLVu2JDY2VmtMsa5XVpuZmVGiRAmOHTumEXG9ceOG+pXUWczNzWnatCkHDx7UeVKUp3zvNl1dP62trSlWrJjOG7YVK1ZoDL25c+cOW7ZsoWLFiurhDu7u7hgbGzN37lydb06Mj49Xl+Hk5ISFhQXLly/X2btC2ter5+npycyZM/ntt9/o37+/1mvA//nnH63zQtZ5JfucQoqi8NVXX7Fx40YCAgL4/PPPc9xm+fLlsbe3Z+fOneoeNPCkN83OnTtp1KhRjr0Q+vbty9ixY9m6dSsjRozQ+eT6ffChHpf27duTlJREeHg4O3fupHHjxlq9q/T19dHT09M4P6Snp+u83uVXixYt0NPTY/ny5WRkZKjTL168qPPal5qayqBBgzh8+DDffPONVpAuJ1WrVmXVqlVkZGTg6+ur81Xd74K82mdO9PX1tc7rjx8/ZsWKFTnmWbt2rcbDs/j4eIKDgylSpAgNGjTIc5tZr9QOCwsjLCyMwoULawWxxPvjWe5x4+PjSUtL00gzMTFRPxB7nqG6Iv8yMjK0hpTp6empp1x4+PDhM91Tvize3t7ExMSwfPlyncufHs5nZGSEp6cnv/32G1u2bOHatWtage2s4NXT57+7d++yYcOGl1j791NmZiabN29GpVLh4+ODm5ub1r+2bdty5coVzp8/r87n7e3N48ePCQ8PZ9euXTg7O2NpaalRtre3N9HR0axcuVLntp8+3jnJetj29DG+fPky+/bt00iztramWrVq7N69W2NocFpaGqtXr9Yq29vbm/T0dK0HNs9ax3eV9HR6i/Tr14+tW7cyfvx4Ll68SJUqVThx4gRnz57FwsJCa/0ePXowe/Zs+vXrR6tWrbh79y7BwcFUrVpV/drsLOPHj+fSpUv0798fb29v7OzsSElJ4dy5c5QuXVq6H7/DFixYwJEjR9Svb1UUhf3793P16lX69euntX5GRgY9evTA09OTxMREgoODSUlJ4auvvlKvY21tzcSJE/nqq6/w8PDg448/pnTp0sTFxXHlyhX27NnDtm3bKFOmDAUKFGDq1KkMHToULy8vOnXqpH7icPjwYXr37q01WbJ4+dzc3DA0NOTzzz9XP+E1MzMDnvTOGDVqFD4+PpQvX57jx4+za9cuGjRooPHq18DAQEJCQrC1taVSpUrqyRqzlCtXjtq1a6s/jxs3Dj8/P3r06KF+nXlQUBCKomh1fX5ar169MDQ0ZPLkyWRkZPD999/rnLj5XfchHpdmzZphbm7OjBkzSEhI0Nkjxc3NjZkzZ9K/f39cXV1JSEhg69atGBo+/21J5cqV6dGjB0FBQfTq1YvWrVtz79491qxZg62trVZv35EjR3Lo0CEaN26Mqamp1vf69JwRT28razt+fn6sWLGCqlWrPnfd35Tc2mdO2rRpw7p16/j8889p3LgxsbGxbNy4UWvoQnYWFhb4+PjQsWNHFEVh06ZN3L59mylTpuTriW/Wq7F37dpFSkoKnTp1wsTE5Jn3V7w78nuPe/z4ccaPH0/r1q2pWLEihQoV4sKFC4SEhFCzZk2d84mJlycxMRFnZ2datGhB9erVKVasGDdv3mTt2rUULVoUFxcXrKys8n1P+bL4+fkRERFBYGAgx44do1GjRpiZmXH79m2OHTuGsbGxVmCgffv2rF69Wv2CiKd76JqZmeHk5ER4eDimpqY4ODhw69Yt1q1bR5kyZWR+uDwcPnyYqKioXHsUt27dmrlz5xISEqLuZdayZUuKFi1KYGAgiYmJOl980bt3byIiIvjuu+84evQoDRo0oFChQkRFRXH06FEKFSqUYwAyuypVqlCpUiUWLVpEYmIiFStW5OrVq6xbtw6VSsXFixc11h89ejT9+vWjS5cudO3alcKFC7N9+3b1g6/sva48PT05ePAgK1eu5MKFCzRr1gwLCwvu3LnD6dOniYqKYteuXfn6Lt9FEnR6ixQtWpQ1a9Ywbdo0QkNDURSFhg0bsmrVKp2T7/Xv35/4+HjCw8M5ceIEVapUYerUqVy8eFEr6FS2bFk2btzIjz/+yMGDBwkLC6NIkSLY2trSpUuX17SH4lVo1aoVMTEx7Ny5k9jYWExNTSlfvjxTpkzReWKfPn06wcHBLF68mEePHmFjY8O0adO0hsZ17NiRChUqsGzZMtatW0d8fDzm5uZUrFiRoUOHavSWaNmyJT///DM//fQTISEhJCYmYmlpSd26dbGxsXnl34F4olWrVsybN4/PPvsMf39/lixZAjx5e9eXX37JrFmzCA4OxszMDF9fX4YNG6bR7TzrrVOXL1/WOS6+ffv2GsGNOnXqsHr1ambPns2cOXPUaXPmzMnxx3p2PXr0wMjIiK+//pohQ4YwZ84cnW9de9d9aMfF2NiYtm3bEhQUhJmZmc6gc9++fVEUhZCQEKZOnUqJEiVwd3enY8eOGgG3ZzVu3DgsLS1Zv349gYGBVKhQga+//prr169rBZ2yvteIiAidPaEGDx6c6/dVoUIFjcDT8uXL8/X9vm1yap85+fLLLylUqBA7d+5k7969lCpVii5duuDg4JDjRMEjR47k1KlTrFmzhtjYWCpUqMCMGTPyPRE4PGnn+/fvB8h13i3xfsjvPa6NjQ2urq6cOHFC/ebMUqVKMXDgQPz9/d/gHnwYTE1N6dWrF0ePHuXo0aMkJiZSsmRJWrRowcCBA9W9XJ/lnvJlMDIyYuHChfz888+EhYUxd+5c4Mm8hw4ODjofhtjZ2aFSqbhy5QqNGzfG2tpaa53//e9/zJw5k3379rF582YqVKjAsGHDMDQ0zHX+KPFkAnEg17lmVSoVFSpUYPv27YwdOxZTU1OMjY1xd3dX947V1cvV2NiYJUuWEBQURHh4uMbxrlmzZr6HYxsZGbFo0SICAwPZvHkzSUlJqFQqZsyYwfnz57WCTo6OjixevJhZs2axcOFCihQpgqenJ25ubnTr1k1r3qjp06fTqFEj1q9fz8KFC0lPT8fS0hJ7e3u6du2arzq+q/QUGfsixAdh7ty5zJs3j717977Up0ni7WZjY0P79u2ZNm3am66KyEaOi/gQbNq0iS+//JJVq1bRsGHDN10dIYQQ4pXbvn07w4YNY86cObi5ub3p6rwVZE4nIYQQQgghhBBCiHzKzMzUmo8sNTWVFStWYGRklK85Cz8UMrxOCCGEEDmKi4vTmJhbl4IFC1KoUKHXVCMhhBBCiDcrKSmJ1q1b4+XlRYUKFXjw4AHbtm3jypUrBAQEUKxYsTddxbeGBJ2EEEIIkaNOnTppvJlFl8GDB/PZZ5+9phoJIYQQQrxZxsbGNG3alD179hATE4OiKFSqVImJEyfSrVu3N129t4rM6SSEEEKIHP3222+kpKTkuk7ZsmUpW7bsa6qREEIIIYR4V0jQSQghhBBCCCGEEEK8dDKRuBBCCCGEEEIIIYR46SToJIQQQgghhBBCCCFeOgk6CSGEEEIIIYQQQoiXToJOQgghhBBCCCGEEOKlk6CTEEIIIYQQQgghhHjp/g+Knp2YNXWn2QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"metric_names = ['beam_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='beam_num')\\n\",\n    \"ax.legend(loc=\\\"lower left\\\")\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='unique_pred_num')\\n\",\n    \"ax.legend(loc=\\\"lower left\\\")\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['present_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_pred_num')\\n\",\n    \"ax.legend(loc=\\\"lower left\\\")\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='absent_pred_num')\\n\",\n    \"ax.legend(loc=\\\"lower left\\\")\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq MagKP Experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-26T01:56:04.655394Z\",\n     \"start_time\": \"2020-10-26T01:55:54.515446Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(3507, 121)\\n\",\n      \"present valid_kp_df\\n\",\n      \"(77, 121)\\n\",\n      \"All data\\n\",\n      \"(3507, 121)\\n\",\n      \"absent valid_kp_df\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:75: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(77, 121)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:133: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"# pd.options.display.max_colwidth = 100\\n\",\n    \"\\n\",\n    \"kp_exps = [\\n\",\n    \"    'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    \\n\",\n    \"    'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"    'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"]    \\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps)]\\n\",\n    \"kp_df = kp_df.loc[kp_df.step % 5000 == 0] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.beam_width == '50']\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"# kp_df = kp_df.loc[kp_df.test_dataset != 'kp20k']\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"#     display(tmp_df)\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"    \\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Fine-tune KP20k after MagKP\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 116,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T20:25:43.739785Z\",\n     \"start_time\": \"2020-11-22T20:25:41.396288Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(280, 121)\\n\",\n      \"['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1'\\n\",\n      \" 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue']\\n\",\n      \"present valid_kp_df\\n\",\n      \"(14, 121)\\n\",\n      \"All data\\n\",\n      \"(280, 121)\\n\",\n      \"absent valid_kp_df\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:82: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(14, 121)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:140: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = [\\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"# 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"\\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"# 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"]    \\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.beam_width == '50']\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print(kp_df.exp_name.unique())\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"# metric_names = ['present_exact_advanced_alpha_ndcg@5']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"#     display(tmp_df)\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"    \\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Summary (used in paper, Section 5 \\\"more data\\\", Figure 4/6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### MagKP results (alternate batching) (Paper Figure 4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T20:36:47.378228Z\",\n     \"start_time\": \"2020-11-23T20:36:39.798719Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"absent\\n\",\n      \"(56, 236)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, 'Absent (R@50)')\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x576 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"long2short = {\\n\",\n    \"'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1': 'KP20k - RNN-O2O',\\n\",\n    \"'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue': 'KP20k - TF-O2O',\\n\",\n    \"'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue': 'KP20k+MagKP - RNN-O2O',\\n\",\n    \"'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue': 'KP20k+MagKP - TF-O2O',\\n\",\n    \"        \\n\",\n    \"'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1': 'KP20k - RNN-O2S',\\n\",\n    \"'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue': 'KP20k - TF-O2S',\\n\",\n    \"'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue': 'KP20k+MagKP - RNN-O2S',\\n\",\n    \"'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue': 'KP20k+MagKP - TF-O2S',\\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"# for exp_name in kp_df['exp_name'].unique():\\n\",\n    \"#     print(exp_name, kp_df.loc[kp_df.exp_name == exp_name].shape)\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    kp_df.at[index_label , 'exp_name'] = long2short[kp_df.at[index_label , 'exp_name']]\\n\",\n    \"# for exp_name in kp_df['exp_name'].unique():\\n\",\n    \"#     print(exp_name, kp_df.loc[kp_df.exp_name == exp_name].shape)\\n\",\n    \"    \\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"# print('present')\\n\",\n    \"# print(valid_kp_df.shape)\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'KP20k': [0.0] * 4, 'KP20k+MagKP': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"# compute average scores over all datasets except for KP20k_valid2k\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for datamodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"#     print(datamodel_name)\\n\",\n    \"#     print(_v)\\n\",\n    \"    data_name, model_name = datamodel_name.split(' - ')\\n\",\n    \"    avg_bar_values[data_name][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"present_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# display(present_df)\\n\",\n    \"# ax = present_df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"# for p in ax.patches:\\n\",\n    \"#     ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"###### Absent \\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"print('absent')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'KP20k': [0.0] * 4, 'KP20k+MagKP': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"# compute average scores over all datasets except for KP20k_valid2k\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for datamodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    data_name, model_name = datamodel_name.split(' - ')\\n\",\n    \"    avg_bar_values[data_name][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"absent_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"# for p in ax.patches:\\n\",\n    \"#     ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"sns.set_palette(\\\"Set2\\\")\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"sns.set_palette(_set2[2:])\\n\",\n    \"\\n\",\n    \"present_df = present_df * 100.0\\n\",\n    \"absent_df = absent_df * 100.0\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\\n\",\n    \"present_df.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.5), rotation=0)  \\n\",\n    \"axes[0].set_ylabel(\\\"Present (F@O)\\\")\\n\",\n    \"\\n\",\n    \"absent_df.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.5), rotation=0)    \\n\",\n    \"axes[1].set_ylabel(\\\"Absent (R@50)\\\") \\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Transformer-One2Seq + finetuning (Paper Figure 6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T22:24:16.408767Z\",\n     \"start_time\": \"2020-11-23T22:24:03.706687Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'KP20k': [0.0, 0.3292538448836406, 0.0, 0.0], 'MagKP-LN': [0.0, 0.27605325562794386, 0.3440636217418103, 0.3463920799738904], 'MagKP-Nsmall': [0.0, 0.23339137320034456, 0.3354558662744714, 0.356921643462544], 'MagKP-Nlarge': [0.0, 0.23570759386304926, 0.34667951353463256, 0.3660799341111071], 'MagKP': [0.35859440972292217, 0.27331419923584616, 0.3494334040780327, 0.3631881687268496]}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>KP20k</th>\\n\",\n       \"      <th>MagKP-LN</th>\\n\",\n       \"      <th>MagKP-Nsmall</th>\\n\",\n       \"      <th>MagKP-Nlarge</th>\\n\",\n       \"      <th>MagKP</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>ALT</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Only</th>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>MX</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>FT</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"      KP20k  MagKP-LN  MagKP-Nsmall  MagKP-Nlarge  MagKP\\n\",\n       \"ALT  0.0    0.0       0.0           0.0           0.4   \\n\",\n       \"Only 0.3    0.3       0.2           0.2           0.3   \\n\",\n       \"MX   0.0    0.3       0.3           0.3           0.3   \\n\",\n       \"FT   0.0    0.3       0.4           0.4           0.4   \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"absent\\n\",\n      \"(98, 236)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 15)\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x576 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'MagKP - ALT': 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'KP20k - Only': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-LN - Only': 'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-LN - MX': 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-LN - FT': 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'MagKP-Nsmall - Only': 'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-Nsmall - MX': 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue', \\n\",\n    \"'MagKP-Nsmall - FT': 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'MagKP-Nlarge - Only': 'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-Nlarge - MX': 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-Nlarge - FT': 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'MagKP - Only': 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP - MX': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP - FT': 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"mode_names = ['ALT', 'Only', 'MX', 'FT']\\n\",\n    \"traindata_names = ['KP20k', 'MagKP-LN', 'MagKP-Nsmall', 'MagKP-Nlarge', 'MagKP']\\n\",\n    \"\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"# for exp_name in kp_df['exp_name'].unique():\\n\",\n    \"#     print(exp_name, kp_df.loc[kp_df.exp_name == exp_name].shape)\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    kp_df.at[index_label , 'exp_name'] = long2short[kp_df.at[index_label , 'exp_name']]\\n\",\n    \"# for exp_name in kp_df['exp_name'].unique():\\n\",\n    \"#     print(exp_name, kp_df.loc[kp_df.exp_name == exp_name].shape)\\n\",\n    \"    \\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"datamode_names = bar_values.keys()\\n\",\n    \"avg_bar_values = {dname: [0.0] * len(mode_names) for dname in traindata_names}\\n\",\n    \"\\n\",\n    \"# compute average scores over all datasets except for KP20k_valid2k\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for datamode_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"#     print(datamode_name)\\n\",\n    \"#     print(_v)\\n\",\n    \"    data_name, mode_name = datamode_name.split(' - ')\\n\",\n    \"    avg_bar_values[data_name][mode_names.index(mode_name)] = np.mean(_v)\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"present_df = pd.DataFrame(avg_bar_values, index=mode_names)\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"display(present_df)\\n\",\n    \"# ax = present_df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"# for p in ax.patches:\\n\",\n    \"#     ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"###### Absent \\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"print('absent')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"datamode_names = bar_values.keys()\\n\",\n    \"avg_bar_values = {dname: [0.0] * len(mode_names) for dname in traindata_names}\\n\",\n    \"\\n\",\n    \"# compute average scores over all datasets except for KP20k_valid2k\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for datamode_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    data_name, mode_name = datamode_name.split(' - ')\\n\",\n    \"    avg_bar_values[data_name][mode_names.index(mode_name)] = np.mean(_v)\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"absent_df = pd.DataFrame(avg_bar_values, index=mode_names)\\n\",\n    \"# ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"# for p in ax.patches:\\n\",\n    \"#     ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_set2 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"sns.set_palette(_set2[2:])\\n\",\n    \"\\n\",\n    \"present_df = present_df * 100.0\\n\",\n    \"absent_df = absent_df * 100.0\\n\",\n    \"# present_df = present_df.replace(0.0, np.NaN)\\n\",\n    \"# display(present_df)\\n\",\n    \"f, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\\n\",\n    \"g = present_df.plot.bar(ax=axes[0], legend=True, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.5), rotation=90)   \\n\",\n    \"axes[0].set_ylabel(\\\"Present (F@O)\\\")\\n\",\n    \"axes[0].legend(loc='lower left')\\n\",\n    \"g.set_ylim(22, 38)\\n\",\n    \"\\n\",\n    \"g = absent_df.plot.bar(ax=axes[1], legend=False, rot=0)\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.5), rotation=90)    \\n\",\n    \"axes[1].set_ylabel(\\\"Absent (R@50)\\\") \\n\",\n    \"g.set_ylim(3, 15)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Export to Latex format\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Export selected experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-24T07:04:23.981153Z\",\n     \"start_time\": \"2020-11-24T07:04:08.777381Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\\\begin{tabular}{lrrrrrrrrrrrrrrrrrrrrrrrrrrrr}\\n\",\n      \"\\\\toprule\\n\",\n      \"{} &  average - all\\\\_exact\\\\_f\\\\_score@5 &  average - all\\\\_exact\\\\_f\\\\_score@10 &  average - all\\\\_exact\\\\_f\\\\_score@k &  average - all\\\\_exact\\\\_f\\\\_score@M &  kp20k - all\\\\_exact\\\\_f\\\\_score@5 &  kp20k - all\\\\_exact\\\\_f\\\\_score@10 &  kp20k - all\\\\_exact\\\\_f\\\\_score@k &  kp20k - all\\\\_exact\\\\_f\\\\_score@M &  krapivin - all\\\\_exact\\\\_f\\\\_score@5 &  krapivin - all\\\\_exact\\\\_f\\\\_score@10 &  krapivin - all\\\\_exact\\\\_f\\\\_score@k &  krapivin - all\\\\_exact\\\\_f\\\\_score@M &  inspec - all\\\\_exact\\\\_f\\\\_score@5 &  inspec - all\\\\_exact\\\\_f\\\\_score@10 &  inspec - all\\\\_exact\\\\_f\\\\_score@k &  inspec - all\\\\_exact\\\\_f\\\\_score@M &  nus - all\\\\_exact\\\\_f\\\\_score@5 &  nus - all\\\\_exact\\\\_f\\\\_score@10 &  nus - all\\\\_exact\\\\_f\\\\_score@k &  nus - all\\\\_exact\\\\_f\\\\_score@M &  semeval - all\\\\_exact\\\\_f\\\\_score@5 &  semeval - all\\\\_exact\\\\_f\\\\_score@10 &  semeval - all\\\\_exact\\\\_f\\\\_score@k &  semeval - all\\\\_exact\\\\_f\\\\_score@M &  duc - all\\\\_exact\\\\_f\\\\_score@5 &  duc - all\\\\_exact\\\\_f\\\\_score@10 &  duc - all\\\\_exact\\\\_f\\\\_score@k &  duc - all\\\\_exact\\\\_f\\\\_score@M \\\\\\\\\\n\",\n      \"\\\\midrule\\n\",\n      \"rnn+random           & 13.9 & 13.9 & 14.0 & 13.9 & 19.3 & 19.3 & 19.6 & 19.3 & 17.5 & 17.4 & 17.6 & 17.4 & 13.9 & 13.9 & 13.9 & 13.9 & 17.5 & 17.5 & 17.5 & 17.5 & 10.3 & 10.3 & 10.3 & 10.3 & 4.9 & 4.9 & 4.9 & 4.9 \\\\\\\\\\n\",\n      \"rnn+alphab           & 17.0 & 16.5 & 16.9 & 16.5 & 21.3 & 20.4 & 21.6 & 20.4 & 20.1 & 19.6 & 20.2 & 19.6 & 18.2 & 17.7 & 18.0 & 17.7 & 20.1 & 19.4 & 19.8 & 19.4 & 14.9 & 14.5 & 14.5 & 14.5 & 7.5 & 7.5 & 7.5 & 7.5 \\\\\\\\\\n\",\n      \"rnn+alpharev         & 16.8 & 16.6 & 16.8 & 16.6 & 21.6 & 21.3 & 21.8 & 21.3 & 19.5 & 19.1 & 19.7 & 19.1 & 18.2 & 18.0 & 18.0 & 18.0 & 20.3 & 20.1 & 20.2 & 20.1 & 14.4 & 14.3 & 14.3 & 14.3 & 6.8 & 6.9 & 6.8 & 6.9 \\\\\\\\\\n\",\n      \"rnn+stol             & 15.7 & 15.7 & 15.8 & 15.7 & 22.1 & 22.1 & 22.3 & 22.1 & 19.7 & 19.7 & 20.1 & 19.7 & 15.6 & 15.6 & 15.4 & 15.6 & 19.1 & 19.1 & 19.4 & 19.1 & 12.6 & 12.6 & 12.6 & 12.6 & 5.1 & 5.1 & 5.1 & 5.1 \\\\\\\\\\n\",\n      \"rnn+ltos             & 15.9 & 15.6 & 16.0 & 15.6 & 19.9 & 19.6 & 20.4 & 19.6 & 18.3 & 18.0 & 18.6 & 18.0 & 17.7 & 17.5 & 17.7 & 17.5 & 16.8 & 16.4 & 17.0 & 16.4 & 13.0 & 12.9 & 12.9 & 12.9 & 9.6 & 9.4 & 9.4 & 9.4 \\\\\\\\\\n\",\n      \"rnn+ori              & 16.6 & 16.6 & 16.7 & 16.6 & 21.7 & 21.7 & 22.0 & 21.7 & 20.9 & 20.8 & 21.3 & 20.8 & 17.5 & 17.5 & 17.5 & 17.5 & 20.2 & 20.2 & 20.2 & 20.2 & 12.6 & 12.6 & 12.6 & 12.6 & 6.6 & 6.6 & 6.6 & 6.6 \\\\\\\\\\n\",\n      \"rnn+orirev           & 13.9 & 13.9 & 14.0 & 13.9 & 19.2 & 19.0 & 19.4 & 19.0 & 17.2 & 17.1 & 17.4 & 17.1 & 14.1 & 14.0 & 14.1 & 14.0 & 16.6 & 16.5 & 16.7 & 16.5 & 11.0 & 11.0 & 11.0 & 11.0 & 5.5 & 5.5 & 5.5 & 5.5 \\\\\\\\\\n\",\n      \"rnn+presabs          & 18.8 & 18.6 & 18.9 & 18.6 & 23.7 & 23.4 & 24.3 & 23.4 & 22.0 & 21.7 & 22.4 & 21.7 & 20.4 & 20.2 & 20.6 & 20.2 & 22.6 & 22.2 & 22.2 & 22.2 & 16.2 & 16.3 & 16.3 & 16.3 & 7.7 & 7.7 & 7.7 & 7.7 \\\\\\\\\\n\",\n      \"rnn+abspres          & 16.6 & 16.4 & 16.6 & 16.4 & 20.9 & 20.7 & 21.2 & 20.7 & 19.9 & 19.7 & 20.2 & 19.7 & 18.1 & 17.9 & 17.9 & 17.9 & 19.5 & 19.3 & 19.3 & 19.3 & 14.1 & 14.1 & 14.1 & 14.1 & 7.1 & 7.1 & 7.1 & 7.1 \\\\\\\\\\n\",\n      \"transformer+random   & 17.0 & 16.8 & 17.0 & 16.8 & 26.3 & 26.3 & 26.3 & 26.3 & 20.0 & 19.6 & 20.3 & 19.6 & 16.4 & 16.3 & 16.4 & 16.3 & 20.5 & 20.4 & 20.6 & 20.4 & 13.4 & 13.2 & 13.2 & 13.2 & 5.3 & 5.1 & 5.1 & 5.1 \\\\\\\\\\n\",\n      \"transformer+alphab   & 16.7 & 16.5 & 16.5 & 16.5 & 26.3 & 26.5 & 25.8 & 26.5 & 18.3 & 17.5 & 18.1 & 17.5 & 17.4 & 17.0 & 17.2 & 17.0 & 19.8 & 19.3 & 19.6 & 19.3 & 13.9 & 13.8 & 13.8 & 13.8 & 4.6 & 4.6 & 4.6 & 4.6 \\\\\\\\\\n\",\n      \"transformer+alpharev & 16.3 & 16.0 & 16.2 & 16.0 & 23.0 & 22.5 & 23.2 & 22.5 & 18.8 & 18.4 & 18.9 & 18.4 & 17.9 & 17.7 & 18.0 & 17.7 & 18.9 & 18.5 & 18.6 & 18.5 & 13.5 & 13.4 & 13.4 & 13.4 & 5.5 & 5.4 & 5.4 & 5.4 \\\\\\\\\\n\",\n      \"transformer+stol     & 16.4 & 16.3 & 16.1 & 16.3 & 25.4 & 25.4 & 25.2 & 25.4 & 19.4 & 19.1 & 18.6 & 19.1 & 15.5 & 15.4 & 15.2 & 15.4 & 19.8 & 19.6 & 19.5 & 19.6 & 13.7 & 13.7 & 13.7 & 13.7 & 4.4 & 4.4 & 4.4 & 4.4 \\\\\\\\\\n\",\n      \"transformer+ltos     & 16.6 & 16.4 & 16.7 & 16.4 & 22.3 & 21.9 & 22.8 & 21.9 & 19.0 & 18.7 & 19.2 & 18.7 & 18.3 & 18.1 & 18.2 & 18.1 & 20.6 & 20.4 & 21.1 & 20.4 & 13.5 & 13.3 & 13.3 & 13.3 & 5.8 & 5.7 & 5.7 & 5.7 \\\\\\\\\\n\",\n      \"transformer+ori      & 17.3 & 17.2 & 17.6 & 17.2 & 26.2 & 26.2 & 26.4 & 26.2 & 19.8 & 19.6 & 20.5 & 19.6 & 17.8 & 17.7 & 17.8 & 17.7 & 20.2 & 20.1 & 20.8 & 20.1 & 14.5 & 14.5 & 14.5 & 14.5 & 5.4 & 5.4 & 5.4 & 5.4 \\\\\\\\\\n\",\n      \"transformer+orirev   & 16.1 & 15.9 & 16.1 & 15.9 & 22.6 & 22.2 & 22.9 & 22.2 & 19.6 & 19.3 & 19.7 & 19.3 & 16.3 & 16.1 & 16.3 & 16.1 & 19.9 & 19.6 & 19.8 & 19.6 & 13.3 & 13.3 & 13.3 & 13.3 & 4.6 & 4.5 & 4.5 & 4.5 \\\\\\\\\\n\",\n      \"transformer+presabs  & 19.1 & 18.9 & 19.4 & 18.9 & 25.6 & 25.3 & 26.5 & 25.3 & 23.5 & 23.2 & 24.4 & 23.2 & 20.8 & 20.5 & 20.8 & 20.5 & 22.8 & 22.8 & 23.1 & 22.8 & 15.7 & 15.6 & 15.6 & 15.6 & 6.3 & 6.2 & 6.2 & 6.2 \\\\\\\\\\n\",\n      \"transformer+abspres  & 14.2 & 14.1 & 14.3 & 14.1 & 24.7 & 24.7 & 24.8 & 24.7 & 16.6 & 16.2 & 16.9 & 16.2 & 12.8 & 12.7 & 12.7 & 12.7 & 16.5 & 16.4 & 16.8 & 16.4 & 11.7 & 11.6 & 11.6 & 11.6 & 3.1 & 3.1 & 3.1 & 3.1 \\\\\\\\\\n\",\n      \"\\\\bottomrule\\n\",\n      \"\\\\end{tabular}\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"short2long = {\\n\",\n    \"# One2One\\n\",\n    \"# 'RNN-O2O-KP20k': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"# 'RNN-O2O-KP20k-nocopy': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"# 'RNN-O2O-KP20k+MagKP-ALT': 'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"\\n\",\n    \"# 'BIGRNN-O2O-KP20k': 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue',\\n\",\n    \"# 'BIGRNN-O2O-magkp20k-ALT': 'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"# 'TF-O2O-KP20k': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'TF-O2O-KP20k-nocopy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"# 'TF-O2O-KP20k+MagKP-ALT': 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# One2Seq\\n\",\n    \"# 'RNN-O2S-KP20k': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"# 'RNN-O2S-KP20k-nocopy': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"# 'RNN-O2S+KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'BIGRNN-O2S-KP20k': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"# 'BIGRNN-O2S-KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"# 'TF-O2S-KP20k': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'TF-O2S-KP20k-nocopy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"# 'TF-O2S-KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# +MagKP FT\\n\",\n    \"# 'MagKP-LN-Only': 'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nsmall-Only': 'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nlarge-Only': 'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Only': 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'MagKP-LN-MX': 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nsmall-MX': 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue', \\n\",\n    \"# 'MagKP-Nlarge-MX': 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-MX': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'MagKP-LN-FT': 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nsmall-FT': 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nlarge-FT': 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-FT': 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Order matters\\n\",\n    \"'rnn+random': 'kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"'rnn+alphab': 'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"'rnn+alpharev': 'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"'rnn+stol': 'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"'rnn+ltos': 'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"'rnn+ori': 'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"'rnn+orirev': 'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"'rnn+presabs': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"'rnn+abspres': 'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"'transformer+random': 'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',    \\n\",\n    \"'transformer+alphab': 'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+alpharev': 'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+stol': 'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+ltos': 'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+ori': 'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+orirev': 'kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'transformer+presabs': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"'transformer+abspres': 'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"# beam search\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"# greedy\\n\",\n    \"# kp_df = kp_df.loc[(kp_df.beam_width == '1')]\\n\",\n    \"# kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive']\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    kp_df.at[index_label , 'exp_name'] = long2short[kp_df.at[index_label , 'exp_name']]\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# present\\n\",\n    \"ordered_datasets = ['average', 'kp20k', 'krapivin', 'inspec', 'nus', 'semeval', 'duc']\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"df_dataset_list = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in short2long.keys() for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = df_dataset_list.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"df_dataset_list = np.append(df_dataset_list, 'average').tolist()\\n\",\n    \"\\n\",\n    \"transposed_dict = {'%s - %s' % (dataset_name, metric_name): [0.0] * len(short2long) for dataset_name in ordered_datasets for metric_name in metric_names}\\n\",\n    \"for exp_metric_name, scores in bar_values.items():\\n\",\n    \"    exp_shortname, metric_name = exp_metric_name.split(' - ')\\n\",\n    \"    for dataset_name in ordered_datasets:\\n\",\n    \"        dataset_idx_in_scores = df_dataset_list.index(dataset_name)\\n\",\n    \"        score = scores[dataset_idx_in_scores]\\n\",\n    \"        exp_idx = list(short2long.keys()).index(exp_shortname)\\n\",\n    \"        transposed_dict[dataset_name+' - '+metric_name][exp_idx] = score\\n\",\n    \"\\n\",\n    \"present_df = pd.DataFrame(transposed_dict, index=short2long.keys()) * 100.0       \\n\",\n    \"        \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    print(present_df.to_latex(index=True))\\n\",\n    \"#     display(present_df)\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## absent\\n\",\n    \"metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_recall@M']\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"df_dataset_list = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in short2long.keys() for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = df_dataset_list.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"df_dataset_list = np.append(df_dataset_list, 'average').tolist()\\n\",\n    \"\\n\",\n    \"transposed_dict = {'%s - %s' % (dataset_name, metric_name): [0.0] * len(short2long) for dataset_name in ordered_datasets for metric_name in metric_names}\\n\",\n    \"for exp_metric_name, scores in bar_values.items():\\n\",\n    \"    exp_shortname, metric_name = exp_metric_name.split(' - ')\\n\",\n    \"    for dataset_name in ordered_datasets:\\n\",\n    \"        dataset_idx_in_scores = df_dataset_list.index(dataset_name)\\n\",\n    \"        score = scores[dataset_idx_in_scores]\\n\",\n    \"        exp_idx = list(short2long.keys()).index(exp_shortname)\\n\",\n    \"        transposed_dict[dataset_name+' - '+metric_name][exp_idx] = score\\n\",\n    \"\\n\",\n    \"present_df = pd.DataFrame(transposed_dict, index=short2long.keys()) * 100.0       \\n\",\n    \"        \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    print(present_df.to_latex(index=True))\\n\",\n    \"#     display(present_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# '''    \\n\",\n    \"############## all\\n\",\n    \"metric_names = ['all_exact_f_score@5', 'all_exact_f_score@10', 'all_exact_f_score@k', 'all_exact_f_score@M']\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='all_exact_f_score@k')\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"df_dataset_list = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in short2long.keys() for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = df_dataset_list.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"df_dataset_list = np.append(df_dataset_list, 'average').tolist()\\n\",\n    \"\\n\",\n    \"transposed_dict = {'%s - %s' % (dataset_name, metric_name): [0.0] * len(short2long) for dataset_name in ordered_datasets for metric_name in metric_names}\\n\",\n    \"for exp_metric_name, scores in bar_values.items():\\n\",\n    \"    exp_shortname, metric_name = exp_metric_name.split(' - ')\\n\",\n    \"    for dataset_name in ordered_datasets:\\n\",\n    \"        dataset_idx_in_scores = df_dataset_list.index(dataset_name)\\n\",\n    \"        score = scores[dataset_idx_in_scores]\\n\",\n    \"        exp_idx = list(short2long.keys()).index(exp_shortname)\\n\",\n    \"        transposed_dict[dataset_name+' - '+metric_name][exp_idx] = score\\n\",\n    \"\\n\",\n    \"present_df = pd.DataFrame(transposed_dict, index=short2long.keys()) * 100.0       \\n\",\n    \"        \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    print(present_df.to_latex(index=True))\\n\",\n    \"#     display(present_df)\\n\",\n    \"\\n\",\n    \"# '''\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Export to CSV lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \",path,exp_group,exp_name,test_name,tokenization,train_mode,model_base,order,train_dataset,step,test_dataset,decoding_method,decoding_terminate,beam_width,max_length,all_exact_correct@5,all_exact_precision@5,all_exact_recall@5,all_exact_f_score@5,all_exact_precision_hard@5,all_exact_f_score_hard@5,all_exact_correct@10,all_exact_precision@10,all_exact_recall@10,all_exact_f_score@10,all_exact_precision_hard@10,all_exact_f_score_hard@10,all_exact_correct@k,all_exact_precision@k,all_exact_recall@k,all_exact_f_score@k,all_exact_precision_hard@k,all_exact_f_score_hard@k,all_exact_correct@M,all_exact_precision@M,all_exact_recall@M,all_exact_f_score@M,all_exact_precision_hard@M,all_exact_f_score_hard@M,all_exact_correct@1,all_exact_precision@1,all_exact_recall@1,all_exact_f_score@1,all_exact_precision_hard@1,all_exact_f_score_hard@1,all_exact_correct@3,all_exact_precision@3,all_exact_recall@3,all_exact_f_score@3,all_exact_precision_hard@3,all_exact_f_score_hard@3,all_partial_correct@5,all_partial_precision@5,all_partial_recall@5,all_partial_f_score@5,all_partial_precision_hard@5,all_partial_f_score_hard@5,all_partial_correct@10,all_partial_precision@10,all_partial_recall@10,all_partial_f_score@10,all_partial_precision_hard@10,all_partial_f_score_hard@10,all_partial_correct@k,all_partial_precision@k,all_partial_recall@k,all_partial_f_score@k,all_partial_precision_hard@k,all_partial_f_score_hard@k,all_partial_correct@M,all_partial_precision@M,all_partial_recall@M,all_partial_f_score@M,all_partial_precision_hard@M,all_partial_f_score_hard@M,all_partial_correct@1,all_partial_precision@1,all_partial_recall@1,all_partial_f_score@1,all_partial_precision_hard@1,all_partial_f_score_hard@1,all_partial_correct@3,all_partial_precision@3,all_partial_recall@3,all_partial_f_score@3,all_partial_precision_hard@3,all_partial_f_score_hard@3,present_exact_correct@5,present_exact_precision@5,present_exact_recall@5,present_exact_f_score@5,present_exact_precision_hard@5,present_exact_f_score_hard@5,present_exact_correct@10,present_exact_precision@10,present_exact_recall@10,present_exact_f_score@10,present_exact_precision_hard@10,present_exact_f_score_hard@10,present_exact_correct@k,present_exact_precision@k,present_exact_recall@k,present_exact_f_score@k,present_exact_precision_hard@k,present_exact_f_score_hard@k,present_exact_correct@M,present_exact_precision@M,present_exact_recall@M,present_exact_f_score@M,present_exact_precision_hard@M,present_exact_f_score_hard@M,present_exact_correct@1,present_exact_precision@1,present_exact_recall@1,present_exact_f_score@1,present_exact_precision_hard@1,present_exact_f_score_hard@1,present_exact_correct@3,present_exact_precision@3,present_exact_recall@3,present_exact_f_score@3,present_exact_precision_hard@3,present_exact_f_score_hard@3,absent_exact_correct@10,absent_exact_precision@10,absent_exact_recall@10,absent_exact_f_score@10,absent_exact_precision_hard@10,absent_exact_f_score_hard@10,absent_exact_correct@50,absent_exact_precision@50,absent_exact_recall@50,absent_exact_f_score@50,absent_exact_precision_hard@50,absent_exact_f_score_hard@50,absent_exact_correct@k,absent_exact_precision@k,absent_exact_recall@k,absent_exact_f_score@k,absent_exact_precision_hard@k,absent_exact_f_score_hard@k,absent_exact_correct@M,absent_exact_precision@M,absent_exact_recall@M,absent_exact_f_score@M,absent_exact_precision_hard@M,absent_exact_f_score_hard@M,present_partial_correct@5,present_partial_precision@5,present_partial_recall@5,present_partial_f_score@5,present_partial_precision_hard@5,present_partial_f_score_hard@5,present_partial_correct@10,present_partial_precision@10,present_partial_recall@10,present_partial_f_score@10,present_partial_precision_hard@10,present_partial_f_score_hard@10,present_partial_correct@k,present_partial_precision@k,present_partial_recall@k,present_partial_f_score@k,present_partial_precision_hard@k,present_partial_f_score_hard@k,present_partial_correct@M,present_partial_precision@M,present_partial_recall@M,present_partial_f_score@M,present_partial_precision_hard@M,present_partial_f_score_hard@M,present_partial_correct@1,present_partial_precision@1,present_partial_recall@1,present_partial_f_score@1,present_partial_precision_hard@1,present_partial_f_score_hard@1,present_partial_correct@3,present_partial_precision@3,present_partial_recall@3,present_partial_f_score@3,present_partial_precision_hard@3,present_partial_f_score_hard@3,absent_partial_correct@10,absent_partial_precision@10,absent_partial_recall@10,absent_partial_f_score@10,absent_partial_precision_hard@10,absent_partial_f_score_hard@10,absent_partial_correct@50,absent_partial_precision@50,absent_partial_recall@50,absent_partial_f_score@50,absent_partial_precision_hard@50,absent_partial_f_score_hard@50,absent_partial_correct@k,absent_partial_precision@k,absent_partial_recall@k,absent_partial_f_score@k,absent_partial_precision_hard@k,absent_partial_f_score_hard@k,absent_partial_correct@M,absent_partial_precision@M,absent_partial_r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     \"28797,/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse_step_100000/duc.split_nopunc.eval,meng17-one2one-kp20k-v2,RNN-O2O-KP20k-nocopy,duc.split_nopunc,meng17,one2one,rnn,one2one,kp20k,100000,duc,exhaustive,fullbeam,200,6,0.0,0.0,0.0,0.0,0.0,0.0,0.003246753246753247,0.0003246753246753247,0.0003246753246753247,0.00032467532467532473,0.0003246753246753247,0.00032467532467532473,0.003246753246753247,0.0003246753246753247,0.0003246753246753247,0.00032467532467532473,0.0003246753246753247,0.00032467532467532473,0.003246753246753247,2.193752193752194e-05,0.0003246753246753247,4.1098142363965145e-05,2.193752193752194e-05,4.1098142363965145e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0012554112870197792,0.0007147542392968258,0.0009057014642466652,0.0012554112870197792,0.0009057014642466652,0.003246753246753247,0.0018398268801438345,0.002314080873385634,0.0020218688670573044,0.0018398268801438345,0.0020218688670573044,0.003246753246753247,0.00140017993547371,0.00140017993547371,0.00140017993547371,0.00140017993547371,0.00140017993547371,0.022727272727272728,0.0009519648858663344,0.029467592208973152,0.0018357295385209842,0.0009519648858663344,0.0018357295385209842,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.003246753246753247,0.0006493506493506494,0.0003246753246753247,0.0004329004329004329,0.0006493506493506494,0.0004329004329004329,0.003246753246753247,0.00036075036075036075,0.0003246753246753247,0.0003417634996582365,0.0003246753246753247,0.00032467532467532473,0.003246753246753247,0.00036075036075036075,0.0003246753246753247,0.0003417634996582365,0.0003246753246753247,0.00032467532467532473,0.003246753246753247,0.00036075036075036075,0.0003246753246753247,0.0003417634996582365,0.00036075036075036075,0.0003417634996582365,0.003246753246753247,0.003246753246753247,0.0003246753246753247,0.0005903187721369539,0.003246753246753247,0.0005903187721369539,0.003246753246753247,0.0010822510822510823,0.0003246753246753247,0.0004995004995004994,0.0010822510822510823,0.0004995004995004994,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.003246753246753247,0.02512214041851235,0.014159343787469945,0.017594319323781502,0.021441971288098917,0.016657932836492587,0.00974025974025974,0.024508091765214957,0.019444112875939626,0.020274162636300618,0.014987116456999405,0.016579704990885877,0.00974025974025974,0.025324069542659488,0.018040020902674134,0.020267228095069954,0.018040020902674134,0.018040020902674134,0.00974025974025974,0.024192479713095766,0.02051266016105143,0.020324360116720473,0.024192479713095766,0.020324360116720473,0.003246753246753247,0.031045146092966006,0.004431544999728647,0.0075503947229674006,0.031045146092966006,0.0075503947229674006,0.003246753246753247,0.028583454331020258,0.010763441262457566,0.015374584509638694,0.02685185255748885,0.014987425962343169,0.0,0.00017316017735314059,0.000865800886765703,0.0002886002955885677,0.00017316017735314059,0.0002886002955885677,0.0,3.463203547062812e-05,0.000865800886765703,6.660006821274638e-05,3.463203547062812e-05,6.660006821274638e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.9069801896232132e-05,0.003695817253032288,3.7921708759780776e-05,1.9069801896232132e-05,3.7921708759780776e-05,2.0292207792207792e-05,4.0584415584415584e-05,0.00040584415584415587,0.0003246753246753247,0.00022542604524927685,0.0018106069058765814,0.002774516047437777,0.0003246753246753247,0.0003246753246753247,0.003246753246753247,0.0003246753246753247,0.0007145836568119487,0.0646937005664983,0.0747126492005536,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.8603896103896105,0.20454545454545456,6.386363636363637,248.33766233766235,500.0,500.0,500.0,2059.3084415584417\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"short2long = {\\n\",\n    \"# One2One\\n\",\n    \"'RNN-O2O-KP20k': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"'RNN-O2O-KP20k-nocopy': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"'RNN-O2O-KP20k+MagKP-ALT': 'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n    \"\\n\",\n    \"'BIGRNN-O2O-KP20k': 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue',\\n\",\n    \"'BIGRNN-O2O-magkp20k-ALT': 'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"'TF-O2O-KP20k': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'TF-O2O-KP20k-nocopy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"'TF-O2O-KP20k+MagKP-ALT': 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# One2Seq\\n\",\n    \"'RNN-O2S-KP20k': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"'RNN-O2S-KP20k-nocopy': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"'RNN-O2S+KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'BIGRNN-O2S-KP20k': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n    \"'BIGRNN-O2S-KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    \\n\",\n    \"'TF-O2S-KP20k': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'TF-O2S-KP20k-nocopy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"'TF-O2S-KP20k+MagKP-ALT': 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# +MagKP FT\\n\",\n    \"# 'MagKP-LN-Only': 'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nsmall-Only': 'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nlarge-Only': 'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Only': 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"# 'MagKP-LN-MX': 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-Nsmall-MX': 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue', \\n\",\n    \"# 'MagKP-Nlarge-MX': 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'MagKP-MX': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'MagKP-LN-FT': 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-Nsmall-FT': 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-Nlarge-FT': 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'MagKP-FT': 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Order matters\\n\",\n    \"# 'rnn+random': 'kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"# 'rnn+alphab': 'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"# 'rnn+alpharev': 'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"# 'rnn+stol': 'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"# 'rnn+ltos': 'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"# 'rnn+ori': 'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"# 'rnn+orirev': 'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"# 'rnn+presabs': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"# 'rnn+abspres': 'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# 'transformer+random': 'kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',    \\n\",\n    \"# 'transformer+alphab': 'kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+alpharev': 'kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+stol': 'kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+ltos': 'kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+ori': 'kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+orirev': 'kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'transformer+presabs': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"# 'transformer+abspres': 'kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"# beam search\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"# greedy\\n\",\n    \"# kp_df = kp_df.loc[(kp_df.beam_width == '1')]\\n\",\n    \"# kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive']\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    kp_df.at[index_label , 'exp_name'] = long2short[kp_df.at[index_label , 'exp_name']]\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# present\\n\",\n    \"ordered_datasets = ['kp20k', 'kp20k_valid2k', 'krapivin', 'inspec', 'nus', 'semeval', 'duc']\\n\",\n    \"ordered_datasets = ['duc']\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'present_exact_f_score@k'\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric=anchor_metric_name)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"df_dataset_list = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"for dataset in ordered_datasets:\\n\",\n    \"#     print('*' * 50)\\n\",\n    \"#     print(dataset)\\n\",\n    \"    selected_df = valid_kp_df.loc[valid_kp_df['test_dataset'] == dataset]\\n\",\n    \"#     print(len(selected_df))\\n\",\n    \"    print(selected_df.sort_values(by=anchor_metric_name, ascending=False).to_csv()) if selected_df is not None else print('Empty')\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Data Statistics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\\n\",\n      \"  \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>all_exact_correct@5</th>\\n\",\n       \"      <th>all_exact_precision@5</th>\\n\",\n       \"      <th>all_exact_recall@5</th>\\n\",\n       \"      <th>all_exact_f_score@5</th>\\n\",\n       \"      <th>all_exact_precision_hard@5</th>\\n\",\n       \"      <th>all_exact_f_score_hard@5</th>\\n\",\n       \"      <th>all_exact_correct@10</th>\\n\",\n       \"      <th>all_exact_precision@10</th>\\n\",\n       \"      <th>all_exact_recall@10</th>\\n\",\n       \"      <th>all_exact_f_score@10</th>\\n\",\n       \"      <th>all_exact_precision_hard@10</th>\\n\",\n       \"      <th>all_exact_f_score_hard@10</th>\\n\",\n       \"      <th>all_exact_correct@k</th>\\n\",\n       \"      <th>all_exact_precision@k</th>\\n\",\n       \"      <th>all_exact_recall@k</th>\\n\",\n       \"      <th>all_exact_f_score@k</th>\\n\",\n       \"      <th>all_exact_precision_hard@k</th>\\n\",\n       \"      <th>all_exact_f_score_hard@k</th>\\n\",\n       \"      <th>all_exact_correct@M</th>\\n\",\n       \"      <th>all_exact_precision@M</th>\\n\",\n       \"      <th>all_exact_recall@M</th>\\n\",\n       \"      <th>all_exact_f_score@M</th>\\n\",\n       \"      <th>all_exact_precision_hard@M</th>\\n\",\n       \"      <th>all_exact_f_score_hard@M</th>\\n\",\n       \"      <th>all_exact_correct@1</th>\\n\",\n       \"      <th>all_exact_precision@1</th>\\n\",\n       \"      <th>all_exact_recall@1</th>\\n\",\n       \"      <th>all_exact_f_score@1</th>\\n\",\n       \"      <th>all_exact_precision_hard@1</th>\\n\",\n       \"      <th>all_exact_f_score_hard@1</th>\\n\",\n       \"      <th>all_exact_correct@3</th>\\n\",\n       \"      <th>all_exact_precision@3</th>\\n\",\n       \"      <th>all_exact_recall@3</th>\\n\",\n       \"      <th>all_exact_f_score@3</th>\\n\",\n       \"      <th>all_exact_precision_hard@3</th>\\n\",\n       \"      <th>all_exact_f_score_hard@3</th>\\n\",\n       \"      <th>all_partial_correct@5</th>\\n\",\n       \"      <th>all_partial_precision@5</th>\\n\",\n       \"      <th>all_partial_recall@5</th>\\n\",\n       \"      <th>all_partial_f_score@5</th>\\n\",\n       \"      <th>all_partial_precision_hard@5</th>\\n\",\n       \"      <th>all_partial_f_score_hard@5</th>\\n\",\n       \"      <th>all_partial_correct@10</th>\\n\",\n       \"      <th>all_partial_precision@10</th>\\n\",\n       \"      <th>all_partial_recall@10</th>\\n\",\n       \"      <th>all_partial_f_score@10</th>\\n\",\n       \"      <th>all_partial_precision_hard@10</th>\\n\",\n       \"      <th>all_partial_f_score_hard@10</th>\\n\",\n       \"      <th>all_partial_correct@k</th>\\n\",\n       \"      <th>all_partial_precision@k</th>\\n\",\n       \"      <th>all_partial_recall@k</th>\\n\",\n       \"      <th>all_partial_f_score@k</th>\\n\",\n       \"      <th>all_partial_precision_hard@k</th>\\n\",\n       \"      <th>all_partial_f_score_hard@k</th>\\n\",\n       \"      <th>all_partial_correct@M</th>\\n\",\n       \"      <th>all_partial_precision@M</th>\\n\",\n       \"      <th>all_partial_recall@M</th>\\n\",\n       \"      <th>all_partial_f_score@M</th>\\n\",\n       \"      <th>all_partial_precision_hard@M</th>\\n\",\n       \"      <th>all_partial_f_score_hard@M</th>\\n\",\n       \"      <th>all_partial_correct@1</th>\\n\",\n       \"      <th>all_partial_precision@1</th>\\n\",\n       \"      <th>all_partial_recall@1</th>\\n\",\n       \"      <th>all_partial_f_score@1</th>\\n\",\n       \"      <th>all_partial_precision_hard@1</th>\\n\",\n       \"      <th>all_partial_f_score_hard@1</th>\\n\",\n       \"      <th>all_partial_correct@3</th>\\n\",\n       \"      <th>all_partial_precision@3</th>\\n\",\n       \"      <th>all_partial_recall@3</th>\\n\",\n       \"      <th>all_partial_f_score@3</th>\\n\",\n       \"      <th>all_partial_precision_hard@3</th>\\n\",\n       \"      <th>all_partial_f_score_hard@3</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_correct@1</th>\\n\",\n       \"      <th>present_exact_precision@1</th>\\n\",\n       \"      <th>present_exact_recall@1</th>\\n\",\n       \"      <th>present_exact_f_score@1</th>\\n\",\n       \"      <th>present_exact_precision_hard@1</th>\\n\",\n       \"      <th>present_exact_f_score_hard@1</th>\\n\",\n       \"      <th>present_exact_correct@3</th>\\n\",\n       \"      <th>present_exact_precision@3</th>\\n\",\n       \"      <th>present_exact_recall@3</th>\\n\",\n       \"      <th>present_exact_f_score@3</th>\\n\",\n       \"      <th>present_exact_precision_hard@3</th>\\n\",\n       \"      <th>present_exact_f_score_hard@3</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@k</th>\\n\",\n       \"      <th>absent_exact_precision@k</th>\\n\",\n       \"      <th>absent_exact_recall@k</th>\\n\",\n       \"      <th>absent_exact_f_score@k</th>\\n\",\n       \"      <th>absent_exact_precision_hard@k</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@k</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@1</th>\\n\",\n       \"      <th>present_partial_precision@1</th>\\n\",\n       \"      <th>present_partial_recall@1</th>\\n\",\n       \"      <th>present_partial_f_score@1</th>\\n\",\n       \"      <th>present_partial_precision_hard@1</th>\\n\",\n       \"      <th>present_partial_f_score_hard@1</th>\\n\",\n       \"      <th>present_partial_correct@3</th>\\n\",\n       \"      <th>present_partial_precision@3</th>\\n\",\n       \"      <th>present_partial_recall@3</th>\\n\",\n       \"      <th>present_partial_f_score@3</th>\\n\",\n       \"      <th>present_partial_precision_hard@3</th>\\n\",\n       \"      <th>present_partial_f_score_hard@3</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@k</th>\\n\",\n       \"      <th>absent_partial_precision@k</th>\\n\",\n       \"      <th>absent_partial_recall@k</th>\\n\",\n       \"      <th>absent_partial_f_score@k</th>\\n\",\n       \"      <th>absent_partial_precision_hard@k</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@k</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>all_exact_advanced_auc</th>\\n\",\n       \"      <th>all_exact_advanced_ap</th>\\n\",\n       \"      <th>all_exact_advanced_mrr</th>\\n\",\n       \"      <th>all_exact_advanced_sadr</th>\\n\",\n       \"      <th>all_exact_advanced_ndcg</th>\\n\",\n       \"      <th>all_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>all_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39540</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/duc.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>duc.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.701299</td>\\n\",\n       \"      <td>0.140260</td>\\n\",\n       \"      <td>0.091728</td>\\n\",\n       \"      <td>0.109333</td>\\n\",\n       \"      <td>0.140260</td>\\n\",\n       \"      <td>0.109333</td>\\n\",\n       \"      <td>1.165584</td>\\n\",\n       \"      <td>0.116558</td>\\n\",\n       \"      <td>0.151945</td>\\n\",\n       \"      <td>0.130069</td>\\n\",\n       \"      <td>0.116558</td>\\n\",\n       \"      <td>0.130069</td>\\n\",\n       \"      <td>1.025974</td>\\n\",\n       \"      <td>0.128720</td>\\n\",\n       \"      <td>0.128720</td>\\n\",\n       \"      <td>0.128720</td>\\n\",\n       \"      <td>0.128720</td>\\n\",\n       \"      <td>0.128720</td>\\n\",\n       \"      <td>3.762987</td>\\n\",\n       \"      <td>0.008688</td>\\n\",\n       \"      <td>0.479782</td>\\n\",\n       \"      <td>0.017048</td>\\n\",\n       \"      <td>0.008688</td>\\n\",\n       \"      <td>0.017048</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.027407</td>\\n\",\n       \"      <td>0.048353</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.048353</td>\\n\",\n       \"      <td>0.451299</td>\\n\",\n       \"      <td>0.150433</td>\\n\",\n       \"      <td>0.058992</td>\\n\",\n       \"      <td>0.083933</td>\\n\",\n       \"      <td>0.150433</td>\\n\",\n       \"      <td>0.083933</td>\\n\",\n       \"      <td>0.808442</td>\\n\",\n       \"      <td>0.238893</td>\\n\",\n       \"      <td>0.154901</td>\\n\",\n       \"      <td>0.185288</td>\\n\",\n       \"      <td>0.238893</td>\\n\",\n       \"      <td>0.185288</td>\\n\",\n       \"      <td>1.477273</td>\\n\",\n       \"      <td>0.192281</td>\\n\",\n       \"      <td>0.247432</td>\\n\",\n       \"      <td>0.213406</td>\\n\",\n       \"      <td>0.192281</td>\\n\",\n       \"      <td>0.213406</td>\\n\",\n       \"      <td>1.301948</td>\\n\",\n       \"      <td>0.213735</td>\\n\",\n       \"      <td>0.213735</td>\\n\",\n       \"      <td>0.213735</td>\\n\",\n       \"      <td>0.213735</td>\\n\",\n       \"      <td>0.213735</td>\\n\",\n       \"      <td>4.577922</td>\\n\",\n       \"      <td>0.011630</td>\\n\",\n       \"      <td>0.632043</td>\\n\",\n       \"      <td>0.022816</td>\\n\",\n       \"      <td>0.011630</td>\\n\",\n       \"      <td>0.022816</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.338932</td>\\n\",\n       \"      <td>0.044439</td>\\n\",\n       \"      <td>0.078135</td>\\n\",\n       \"      <td>0.338932</td>\\n\",\n       \"      <td>0.078135</td>\\n\",\n       \"      <td>0.470779</td>\\n\",\n       \"      <td>0.262569</td>\\n\",\n       \"      <td>0.102198</td>\\n\",\n       \"      <td>0.145536</td>\\n\",\n       \"      <td>0.262569</td>\\n\",\n       \"      <td>0.145536</td>\\n\",\n       \"      <td>0.717532</td>\\n\",\n       \"      <td>0.143506</td>\\n\",\n       \"      <td>0.098737</td>\\n\",\n       \"      <td>0.114036</td>\\n\",\n       \"      <td>0.143506</td>\\n\",\n       \"      <td>0.114036</td>\\n\",\n       \"      <td>1.214286</td>\\n\",\n       \"      <td>0.121429</td>\\n\",\n       \"      <td>0.166434</td>\\n\",\n       \"      <td>0.137279</td>\\n\",\n       \"      <td>0.121429</td>\\n\",\n       \"      <td>0.137279</td>\\n\",\n       \"      <td>1.042208</td>\\n\",\n       \"      <td>0.134033</td>\\n\",\n       \"      <td>0.134033</td>\\n\",\n       \"      <td>0.134033</td>\\n\",\n       \"      <td>0.134033</td>\\n\",\n       \"      <td>0.134033</td>\\n\",\n       \"      <td>3.756494</td>\\n\",\n       \"      <td>0.054140</td>\\n\",\n       \"      <td>0.494076</td>\\n\",\n       \"      <td>0.096856</td>\\n\",\n       \"      <td>0.054140</td>\\n\",\n       \"      <td>0.096856</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.028994</td>\\n\",\n       \"      <td>0.050561</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.050561</td>\\n\",\n       \"      <td>0.457792</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.063114</td>\\n\",\n       \"      <td>0.087580</td>\\n\",\n       \"      <td>0.152597</td>\\n\",\n       \"      <td>0.087580</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.006494</td>\\n\",\n       \"      <td>0.000019</td>\\n\",\n       \"      <td>0.002706</td>\\n\",\n       \"      <td>0.000037</td>\\n\",\n       \"      <td>0.000019</td>\\n\",\n       \"      <td>0.000037</td>\\n\",\n       \"      <td>0.814935</td>\\n\",\n       \"      <td>0.239459</td>\\n\",\n       \"      <td>0.162326</td>\\n\",\n       \"      <td>0.189020</td>\\n\",\n       \"      <td>0.239459</td>\\n\",\n       \"      <td>0.189020</td>\\n\",\n       \"      <td>1.512987</td>\\n\",\n       \"      <td>0.195414</td>\\n\",\n       \"      <td>0.261660</td>\\n\",\n       \"      <td>0.219188</td>\\n\",\n       \"      <td>0.195414</td>\\n\",\n       \"      <td>0.219188</td>\\n\",\n       \"      <td>1.311688</td>\\n\",\n       \"      <td>0.218568</td>\\n\",\n       \"      <td>0.218568</td>\\n\",\n       \"      <td>0.218568</td>\\n\",\n       \"      <td>0.218568</td>\\n\",\n       \"      <td>0.218568</td>\\n\",\n       \"      <td>4.399351</td>\\n\",\n       \"      <td>0.069471</td>\\n\",\n       \"      <td>0.630677</td>\\n\",\n       \"      <td>0.124232</td>\\n\",\n       \"      <td>0.069471</td>\\n\",\n       \"      <td>0.124232</td>\\n\",\n       \"      <td>0.211039</td>\\n\",\n       \"      <td>0.338005</td>\\n\",\n       \"      <td>0.046849</td>\\n\",\n       \"      <td>0.081298</td>\\n\",\n       \"      <td>0.338005</td>\\n\",\n       \"      <td>0.081298</td>\\n\",\n       \"      <td>0.477273</td>\\n\",\n       \"      <td>0.263156</td>\\n\",\n       \"      <td>0.107333</td>\\n\",\n       \"      <td>0.149529</td>\\n\",\n       \"      <td>0.263156</td>\\n\",\n       \"      <td>0.149529</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001784</td>\\n\",\n       \"      <td>0.011396</td>\\n\",\n       \"      <td>0.003001</td>\\n\",\n       \"      <td>0.001784</td>\\n\",\n       \"      <td>0.003001</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000544</td>\\n\",\n       \"      <td>0.017296</td>\\n\",\n       \"      <td>0.001045</td>\\n\",\n       \"      <td>0.000544</td>\\n\",\n       \"      <td>0.001045</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.002328</td>\\n\",\n       \"      <td>0.002328</td>\\n\",\n       \"      <td>0.002328</td>\\n\",\n       \"      <td>0.002328</td>\\n\",\n       \"      <td>0.002328</td>\\n\",\n       \"      <td>0.009740</td>\\n\",\n       \"      <td>0.000110</td>\\n\",\n       \"      <td>0.024927</td>\\n\",\n       \"      <td>0.000219</td>\\n\",\n       \"      <td>0.000110</td>\\n\",\n       \"      <td>0.000219</td>\\n\",\n       \"      <td>0.081467</td>\\n\",\n       \"      <td>0.098637</td>\\n\",\n       \"      <td>0.136927</td>\\n\",\n       \"      <td>0.210140</td>\\n\",\n       \"      <td>0.279386</td>\\n\",\n       \"      <td>0.312653</td>\\n\",\n       \"      <td>0.374509</td>\\n\",\n       \"      <td>0.093736</td>\\n\",\n       \"      <td>0.113044</td>\\n\",\n       \"      <td>0.144103</td>\\n\",\n       \"      <td>0.226051</td>\\n\",\n       \"      <td>0.297656</td>\\n\",\n       \"      <td>0.319047</td>\\n\",\n       \"      <td>0.382336</td>\\n\",\n       \"      <td>0.000016</td>\\n\",\n       \"      <td>0.000032</td>\\n\",\n       \"      <td>0.000077</td>\\n\",\n       \"      <td>0.000423</td>\\n\",\n       \"      <td>0.000547</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>0.001524</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>72.542208</td>\\n\",\n       \"      <td>361.094156</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>43530</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/inspec.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>inspec.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>1.714000</td>\\n\",\n       \"      <td>0.342800</td>\\n\",\n       \"      <td>0.211253</td>\\n\",\n       \"      <td>0.247074</td>\\n\",\n       \"      <td>0.342800</td>\\n\",\n       \"      <td>0.247074</td>\\n\",\n       \"      <td>2.706000</td>\\n\",\n       \"      <td>0.270600</td>\\n\",\n       \"      <td>0.319693</td>\\n\",\n       \"      <td>0.276529</td>\\n\",\n       \"      <td>0.270600</td>\\n\",\n       \"      <td>0.276529</td>\\n\",\n       \"      <td>2.848000</td>\\n\",\n       \"      <td>0.296557</td>\\n\",\n       \"      <td>0.296557</td>\\n\",\n       \"      <td>0.296557</td>\\n\",\n       \"      <td>0.296557</td>\\n\",\n       \"      <td>0.296557</td>\\n\",\n       \"      <td>6.732000</td>\\n\",\n       \"      <td>0.015159</td>\\n\",\n       \"      <td>0.706240</td>\\n\",\n       \"      <td>0.029529</td>\\n\",\n       \"      <td>0.015159</td>\\n\",\n       \"      <td>0.029529</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.056795</td>\\n\",\n       \"      <td>0.097180</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.097180</td>\\n\",\n       \"      <td>1.154000</td>\\n\",\n       \"      <td>0.384667</td>\\n\",\n       \"      <td>0.148068</td>\\n\",\n       \"      <td>0.203316</td>\\n\",\n       \"      <td>0.384667</td>\\n\",\n       \"      <td>0.203316</td>\\n\",\n       \"      <td>2.006000</td>\\n\",\n       \"      <td>0.486913</td>\\n\",\n       \"      <td>0.297090</td>\\n\",\n       \"      <td>0.347661</td>\\n\",\n       \"      <td>0.486913</td>\\n\",\n       \"      <td>0.347661</td>\\n\",\n       \"      <td>3.316000</td>\\n\",\n       \"      <td>0.376642</td>\\n\",\n       \"      <td>0.436810</td>\\n\",\n       \"      <td>0.380821</td>\\n\",\n       \"      <td>0.376642</td>\\n\",\n       \"      <td>0.380821</td>\\n\",\n       \"      <td>3.568000</td>\\n\",\n       \"      <td>0.410850</td>\\n\",\n       \"      <td>0.410850</td>\\n\",\n       \"      <td>0.410850</td>\\n\",\n       \"      <td>0.410850</td>\\n\",\n       \"      <td>0.410850</td>\\n\",\n       \"      <td>7.406000</td>\\n\",\n       \"      <td>0.017601</td>\\n\",\n       \"      <td>0.812324</td>\\n\",\n       \"      <td>0.034281</td>\\n\",\n       \"      <td>0.017601</td>\\n\",\n       \"      <td>0.034281</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.631623</td>\\n\",\n       \"      <td>0.082891</td>\\n\",\n       \"      <td>0.141776</td>\\n\",\n       \"      <td>0.631623</td>\\n\",\n       \"      <td>0.141776</td>\\n\",\n       \"      <td>1.272000</td>\\n\",\n       \"      <td>0.553650</td>\\n\",\n       \"      <td>0.209540</td>\\n\",\n       \"      <td>0.288114</td>\\n\",\n       \"      <td>0.553650</td>\\n\",\n       \"      <td>0.288114</td>\\n\",\n       \"      <td>1.746000</td>\\n\",\n       \"      <td>0.349200</td>\\n\",\n       \"      <td>0.270186</td>\\n\",\n       \"      <td>0.285199</td>\\n\",\n       \"      <td>0.349200</td>\\n\",\n       \"      <td>0.285199</td>\\n\",\n       \"      <td>2.896000</td>\\n\",\n       \"      <td>0.289600</td>\\n\",\n       \"      <td>0.433500</td>\\n\",\n       \"      <td>0.325433</td>\\n\",\n       \"      <td>0.289600</td>\\n\",\n       \"      <td>0.325433</td>\\n\",\n       \"      <td>2.678000</td>\\n\",\n       \"      <td>0.337068</td>\\n\",\n       \"      <td>0.337068</td>\\n\",\n       \"      <td>0.337068</td>\\n\",\n       \"      <td>0.337068</td>\\n\",\n       \"      <td>0.337068</td>\\n\",\n       \"      <td>6.412000</td>\\n\",\n       \"      <td>0.137513</td>\\n\",\n       \"      <td>0.836179</td>\\n\",\n       \"      <td>0.231272</td>\\n\",\n       \"      <td>0.137513</td>\\n\",\n       \"      <td>0.231272</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.071959</td>\\n\",\n       \"      <td>0.118048</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.118048</td>\\n\",\n       \"      <td>1.168000</td>\\n\",\n       \"      <td>0.389333</td>\\n\",\n       \"      <td>0.186159</td>\\n\",\n       \"      <td>0.237475</td>\\n\",\n       \"      <td>0.389333</td>\\n\",\n       \"      <td>0.237475</td>\\n\",\n       \"      <td>0.088000</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.040504</td>\\n\",\n       \"      <td>0.013514</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.013514</td>\\n\",\n       \"      <td>0.174000</td>\\n\",\n       \"      <td>0.003480</td>\\n\",\n       \"      <td>0.080937</td>\\n\",\n       \"      <td>0.006566</td>\\n\",\n       \"      <td>0.003480</td>\\n\",\n       \"      <td>0.006566</td>\\n\",\n       \"      <td>0.040000</td>\\n\",\n       \"      <td>0.011171</td>\\n\",\n       \"      <td>0.011171</td>\\n\",\n       \"      <td>0.011171</td>\\n\",\n       \"      <td>0.011171</td>\\n\",\n       \"      <td>0.011171</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.000811</td>\\n\",\n       \"      <td>0.131048</td>\\n\",\n       \"      <td>0.001608</td>\\n\",\n       \"      <td>0.000811</td>\\n\",\n       \"      <td>0.001608</td>\\n\",\n       \"      <td>1.948000</td>\\n\",\n       \"      <td>0.463709</td>\\n\",\n       \"      <td>0.354939</td>\\n\",\n       \"      <td>0.375253</td>\\n\",\n       \"      <td>0.463709</td>\\n\",\n       \"      <td>0.375253</td>\\n\",\n       \"      <td>3.288000</td>\\n\",\n       \"      <td>0.368808</td>\\n\",\n       \"      <td>0.538098</td>\\n\",\n       \"      <td>0.409561</td>\\n\",\n       \"      <td>0.368808</td>\\n\",\n       \"      <td>0.409561</td>\\n\",\n       \"      <td>3.096000</td>\\n\",\n       \"      <td>0.439068</td>\\n\",\n       \"      <td>0.439068</td>\\n\",\n       \"      <td>0.439068</td>\\n\",\n       \"      <td>0.439068</td>\\n\",\n       \"      <td>0.439068</td>\\n\",\n       \"      <td>6.638000</td>\\n\",\n       \"      <td>0.147509</td>\\n\",\n       \"      <td>0.894414</td>\\n\",\n       \"      <td>0.247918</td>\\n\",\n       \"      <td>0.147509</td>\\n\",\n       \"      <td>0.247918</td>\\n\",\n       \"      <td>0.428000</td>\\n\",\n       \"      <td>0.610255</td>\\n\",\n       \"      <td>0.102639</td>\\n\",\n       \"      <td>0.167536</td>\\n\",\n       \"      <td>0.610255</td>\\n\",\n       \"      <td>0.167536</td>\\n\",\n       \"      <td>1.240000</td>\\n\",\n       \"      <td>0.529538</td>\\n\",\n       \"      <td>0.251130</td>\\n\",\n       \"      <td>0.319574</td>\\n\",\n       \"      <td>0.529538</td>\\n\",\n       \"      <td>0.319574</td>\\n\",\n       \"      <td>0.098000</td>\\n\",\n       \"      <td>0.027277</td>\\n\",\n       \"      <td>0.115397</td>\\n\",\n       \"      <td>0.041278</td>\\n\",\n       \"      <td>0.027277</td>\\n\",\n       \"      <td>0.041278</td>\\n\",\n       \"      <td>0.230000</td>\\n\",\n       \"      <td>0.009479</td>\\n\",\n       \"      <td>0.197926</td>\\n\",\n       \"      <td>0.017727</td>\\n\",\n       \"      <td>0.009479</td>\\n\",\n       \"      <td>0.017727</td>\\n\",\n       \"      <td>0.046000</td>\\n\",\n       \"      <td>0.044370</td>\\n\",\n       \"      <td>0.044370</td>\\n\",\n       \"      <td>0.044370</td>\\n\",\n       \"      <td>0.044370</td>\\n\",\n       \"      <td>0.044370</td>\\n\",\n       \"      <td>0.442000</td>\\n\",\n       \"      <td>0.001829</td>\\n\",\n       \"      <td>0.284793</td>\\n\",\n       \"      <td>0.003623</td>\\n\",\n       \"      <td>0.001829</td>\\n\",\n       \"      <td>0.003623</td>\\n\",\n       \"      <td>0.232709</td>\\n\",\n       \"      <td>0.258530</td>\\n\",\n       \"      <td>0.191272</td>\\n\",\n       \"      <td>0.402676</td>\\n\",\n       \"      <td>0.500456</td>\\n\",\n       \"      <td>0.514255</td>\\n\",\n       \"      <td>0.610513</td>\\n\",\n       \"      <td>0.315749</td>\\n\",\n       \"      <td>0.350422</td>\\n\",\n       \"      <td>0.209305</td>\\n\",\n       \"      <td>0.492266</td>\\n\",\n       \"      <td>0.586410</td>\\n\",\n       \"      <td>0.543275</td>\\n\",\n       \"      <td>0.626779</td>\\n\",\n       \"      <td>0.011467</td>\\n\",\n       \"      <td>0.017885</td>\\n\",\n       \"      <td>0.027635</td>\\n\",\n       \"      <td>0.041653</td>\\n\",\n       \"      <td>0.046092</td>\\n\",\n       \"      <td>0.025783</td>\\n\",\n       \"      <td>0.026324</td>\\n\",\n       \"      <td>7.842000</td>\\n\",\n       \"      <td>1.984000</td>\\n\",\n       \"      <td>46.546000</td>\\n\",\n       \"      <td>396.912000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41211</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/kp20k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>kp20k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>1.380347</td>\\n\",\n       \"      <td>0.276069</td>\\n\",\n       \"      <td>0.298442</td>\\n\",\n       \"      <td>0.276320</td>\\n\",\n       \"      <td>0.276069</td>\\n\",\n       \"      <td>0.276320</td>\\n\",\n       \"      <td>1.894231</td>\\n\",\n       \"      <td>0.189423</td>\\n\",\n       \"      <td>0.398148</td>\\n\",\n       \"      <td>0.246720</td>\\n\",\n       \"      <td>0.189423</td>\\n\",\n       \"      <td>0.246720</td>\\n\",\n       \"      <td>1.523640</td>\\n\",\n       \"      <td>0.286520</td>\\n\",\n       \"      <td>0.286520</td>\\n\",\n       \"      <td>0.286520</td>\\n\",\n       \"      <td>0.286520</td>\\n\",\n       \"      <td>0.286520</td>\\n\",\n       \"      <td>3.483514</td>\\n\",\n       \"      <td>0.007894</td>\\n\",\n       \"      <td>0.667008</td>\\n\",\n       \"      <td>0.015513</td>\\n\",\n       \"      <td>0.007894</td>\\n\",\n       \"      <td>0.015513</td>\\n\",\n       \"      <td>0.453295</td>\\n\",\n       \"      <td>0.453295</td>\\n\",\n       \"      <td>0.101215</td>\\n\",\n       \"      <td>0.162035</td>\\n\",\n       \"      <td>0.453295</td>\\n\",\n       \"      <td>0.162035</td>\\n\",\n       \"      <td>1.028469</td>\\n\",\n       \"      <td>0.342823</td>\\n\",\n       \"      <td>0.225321</td>\\n\",\n       \"      <td>0.262950</td>\\n\",\n       \"      <td>0.342823</td>\\n\",\n       \"      <td>0.262950</td>\\n\",\n       \"      <td>1.451343</td>\\n\",\n       \"      <td>0.353728</td>\\n\",\n       \"      <td>0.382440</td>\\n\",\n       \"      <td>0.354119</td>\\n\",\n       \"      <td>0.353728</td>\\n\",\n       \"      <td>0.354119</td>\\n\",\n       \"      <td>2.020313</td>\\n\",\n       \"      <td>0.237018</td>\\n\",\n       \"      <td>0.497323</td>\\n\",\n       \"      <td>0.308567</td>\\n\",\n       \"      <td>0.237018</td>\\n\",\n       \"      <td>0.308567</td>\\n\",\n       \"      <td>1.624006</td>\\n\",\n       \"      <td>0.368083</td>\\n\",\n       \"      <td>0.368083</td>\\n\",\n       \"      <td>0.368083</td>\\n\",\n       \"      <td>0.368083</td>\\n\",\n       \"      <td>0.368083</td>\\n\",\n       \"      <td>3.622955</td>\\n\",\n       \"      <td>0.008913</td>\\n\",\n       \"      <td>0.755486</td>\\n\",\n       \"      <td>0.017520</td>\\n\",\n       \"      <td>0.008913</td>\\n\",\n       \"      <td>0.017520</td>\\n\",\n       \"      <td>0.453295</td>\\n\",\n       \"      <td>0.572172</td>\\n\",\n       \"      <td>0.128422</td>\\n\",\n       \"      <td>0.205387</td>\\n\",\n       \"      <td>0.572172</td>\\n\",\n       \"      <td>0.205387</td>\\n\",\n       \"      <td>1.059038</td>\\n\",\n       \"      <td>0.440666</td>\\n\",\n       \"      <td>0.290521</td>\\n\",\n       \"      <td>0.338701</td>\\n\",\n       \"      <td>0.440666</td>\\n\",\n       \"      <td>0.338701</td>\\n\",\n       \"      <td>1.395557</td>\\n\",\n       \"      <td>0.279111</td>\\n\",\n       \"      <td>0.487359</td>\\n\",\n       \"      <td>0.331136</td>\\n\",\n       \"      <td>0.279111</td>\\n\",\n       \"      <td>0.331136</td>\\n\",\n       \"      <td>1.927403</td>\\n\",\n       \"      <td>0.192741</td>\\n\",\n       \"      <td>0.644913</td>\\n\",\n       \"      <td>0.278559</td>\\n\",\n       \"      <td>0.192740</td>\\n\",\n       \"      <td>0.278557</td>\\n\",\n       \"      <td>1.336118</td>\\n\",\n       \"      <td>0.353287</td>\\n\",\n       \"      <td>0.353287</td>\\n\",\n       \"      <td>0.353287</td>\\n\",\n       \"      <td>0.353287</td>\\n\",\n       \"      <td>0.353287</td>\\n\",\n       \"      <td>3.050083</td>\\n\",\n       \"      <td>0.062343</td>\\n\",\n       \"      <td>0.901892</td>\\n\",\n       \"      <td>0.111292</td>\\n\",\n       \"      <td>0.062343</td>\\n\",\n       \"      <td>0.111292</td>\\n\",\n       \"      <td>0.454946</td>\\n\",\n       \"      <td>0.454946</td>\\n\",\n       \"      <td>0.169970</td>\\n\",\n       \"      <td>0.233278</td>\\n\",\n       \"      <td>0.454946</td>\\n\",\n       \"      <td>0.233278</td>\\n\",\n       \"      <td>1.035823</td>\\n\",\n       \"      <td>0.345274</td>\\n\",\n       \"      <td>0.370419</td>\\n\",\n       \"      <td>0.332978</td>\\n\",\n       \"      <td>0.345274</td>\\n\",\n       \"      <td>0.332978</td>\\n\",\n       \"      <td>0.144544</td>\\n\",\n       \"      <td>0.014454</td>\\n\",\n       \"      <td>0.068086</td>\\n\",\n       \"      <td>0.022835</td>\\n\",\n       \"      <td>0.014454</td>\\n\",\n       \"      <td>0.022835</td>\\n\",\n       \"      <td>0.286036</td>\\n\",\n       \"      <td>0.005721</td>\\n\",\n       \"      <td>0.132307</td>\\n\",\n       \"      <td>0.010822</td>\\n\",\n       \"      <td>0.005721</td>\\n\",\n       \"      <td>0.010822</td>\\n\",\n       \"      <td>0.071146</td>\\n\",\n       \"      <td>0.028639</td>\\n\",\n       \"      <td>0.028639</td>\\n\",\n       \"      <td>0.028639</td>\\n\",\n       \"      <td>0.028639</td>\\n\",\n       \"      <td>0.028639</td>\\n\",\n       \"      <td>0.433432</td>\\n\",\n       \"      <td>0.001112</td>\\n\",\n       \"      <td>0.198050</td>\\n\",\n       \"      <td>0.002208</td>\\n\",\n       \"      <td>0.001112</td>\\n\",\n       \"      <td>0.002208</td>\\n\",\n       \"      <td>1.424126</td>\\n\",\n       \"      <td>0.323370</td>\\n\",\n       \"      <td>0.561840</td>\\n\",\n       \"      <td>0.382626</td>\\n\",\n       \"      <td>0.323370</td>\\n\",\n       \"      <td>0.382626</td>\\n\",\n       \"      <td>1.965928</td>\\n\",\n       \"      <td>0.214684</td>\\n\",\n       \"      <td>0.712449</td>\\n\",\n       \"      <td>0.309130</td>\\n\",\n       \"      <td>0.214683</td>\\n\",\n       \"      <td>0.309128</td>\\n\",\n       \"      <td>1.374844</td>\\n\",\n       \"      <td>0.417764</td>\\n\",\n       \"      <td>0.417764</td>\\n\",\n       \"      <td>0.417764</td>\\n\",\n       \"      <td>0.417764</td>\\n\",\n       \"      <td>0.417764</td>\\n\",\n       \"      <td>3.062240</td>\\n\",\n       \"      <td>0.063641</td>\\n\",\n       \"      <td>0.917541</td>\\n\",\n       \"      <td>0.113513</td>\\n\",\n       \"      <td>0.063641</td>\\n\",\n       \"      <td>0.113513</td>\\n\",\n       \"      <td>0.454946</td>\\n\",\n       \"      <td>0.538957</td>\\n\",\n       \"      <td>0.203360</td>\\n\",\n       \"      <td>0.278133</td>\\n\",\n       \"      <td>0.538957</td>\\n\",\n       \"      <td>0.278133</td>\\n\",\n       \"      <td>1.049382</td>\\n\",\n       \"      <td>0.407171</td>\\n\",\n       \"      <td>0.437236</td>\\n\",\n       \"      <td>0.392563</td>\\n\",\n       \"      <td>0.407171</td>\\n\",\n       \"      <td>0.392563</td>\\n\",\n       \"      <td>0.151599</td>\\n\",\n       \"      <td>0.031760</td>\\n\",\n       \"      <td>0.145575</td>\\n\",\n       \"      <td>0.049819</td>\\n\",\n       \"      <td>0.031760</td>\\n\",\n       \"      <td>0.049819</td>\\n\",\n       \"      <td>0.312753</td>\\n\",\n       \"      <td>0.010701</td>\\n\",\n       \"      <td>0.240634</td>\\n\",\n       \"      <td>0.020203</td>\\n\",\n       \"      <td>0.010701</td>\\n\",\n       \"      <td>0.020203</td>\\n\",\n       \"      <td>0.073848</td>\\n\",\n       \"      <td>0.065676</td>\\n\",\n       \"      <td>0.065676</td>\\n\",\n       \"      <td>0.065676</td>\\n\",\n       \"      <td>0.065676</td>\\n\",\n       \"      <td>0.065676</td>\\n\",\n       \"      <td>0.489468</td>\\n\",\n       \"      <td>0.001885</td>\\n\",\n       \"      <td>0.326677</td>\\n\",\n       \"      <td>0.003740</td>\\n\",\n       \"      <td>0.001885</td>\\n\",\n       \"      <td>0.003740</td>\\n\",\n       \"      <td>0.252209</td>\\n\",\n       \"      <td>0.282558</td>\\n\",\n       \"      <td>0.287931</td>\\n\",\n       \"      <td>0.399323</td>\\n\",\n       \"      <td>0.474906</td>\\n\",\n       \"      <td>0.491475</td>\\n\",\n       \"      <td>0.505926</td>\\n\",\n       \"      <td>0.384584</td>\\n\",\n       \"      <td>0.435772</td>\\n\",\n       \"      <td>0.331484</td>\\n\",\n       \"      <td>0.552359</td>\\n\",\n       \"      <td>0.607514</td>\\n\",\n       \"      <td>0.518599</td>\\n\",\n       \"      <td>0.524472</td>\\n\",\n       \"      <td>0.029099</td>\\n\",\n       \"      <td>0.037118</td>\\n\",\n       \"      <td>0.060574</td>\\n\",\n       \"      <td>0.070328</td>\\n\",\n       \"      <td>0.079199</td>\\n\",\n       \"      <td>0.046830</td>\\n\",\n       \"      <td>0.047002</td>\\n\",\n       \"      <td>3.339471</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>51.233102</td>\\n\",\n       \"      <td>390.732126</td>\\n\",\n       \"      <td>497.355181</td>\\n\",\n       \"      <td>497.355181</td>\\n\",\n       \"      <td>497.355181</td>\\n\",\n       \"      <td>2,242.080052</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39537</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/kp20k_valid2k.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>kp20k_valid2k.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>1.399500</td>\\n\",\n       \"      <td>0.279900</td>\\n\",\n       \"      <td>0.301230</td>\\n\",\n       \"      <td>0.279613</td>\\n\",\n       \"      <td>0.279900</td>\\n\",\n       \"      <td>0.279613</td>\\n\",\n       \"      <td>1.922000</td>\\n\",\n       \"      <td>0.192200</td>\\n\",\n       \"      <td>0.406545</td>\\n\",\n       \"      <td>0.251214</td>\\n\",\n       \"      <td>0.192200</td>\\n\",\n       \"      <td>0.251214</td>\\n\",\n       \"      <td>1.538000</td>\\n\",\n       \"      <td>0.290517</td>\\n\",\n       \"      <td>0.290517</td>\\n\",\n       \"      <td>0.290517</td>\\n\",\n       \"      <td>0.290517</td>\\n\",\n       \"      <td>0.290517</td>\\n\",\n       \"      <td>3.487500</td>\\n\",\n       \"      <td>0.007916</td>\\n\",\n       \"      <td>0.667165</td>\\n\",\n       \"      <td>0.015554</td>\\n\",\n       \"      <td>0.007916</td>\\n\",\n       \"      <td>0.015554</td>\\n\",\n       \"      <td>0.460500</td>\\n\",\n       \"      <td>0.460500</td>\\n\",\n       \"      <td>0.103440</td>\\n\",\n       \"      <td>0.165148</td>\\n\",\n       \"      <td>0.460500</td>\\n\",\n       \"      <td>0.165148</td>\\n\",\n       \"      <td>1.046500</td>\\n\",\n       \"      <td>0.348833</td>\\n\",\n       \"      <td>0.228342</td>\\n\",\n       \"      <td>0.266807</td>\\n\",\n       \"      <td>0.348833</td>\\n\",\n       \"      <td>0.266807</td>\\n\",\n       \"      <td>1.460500</td>\\n\",\n       \"      <td>0.355454</td>\\n\",\n       \"      <td>0.383736</td>\\n\",\n       \"      <td>0.355678</td>\\n\",\n       \"      <td>0.355454</td>\\n\",\n       \"      <td>0.355678</td>\\n\",\n       \"      <td>2.048500</td>\\n\",\n       \"      <td>0.239674</td>\\n\",\n       \"      <td>0.505468</td>\\n\",\n       \"      <td>0.312847</td>\\n\",\n       \"      <td>0.239674</td>\\n\",\n       \"      <td>0.312847</td>\\n\",\n       \"      <td>1.635500</td>\\n\",\n       \"      <td>0.371101</td>\\n\",\n       \"      <td>0.371101</td>\\n\",\n       \"      <td>0.371101</td>\\n\",\n       \"      <td>0.371101</td>\\n\",\n       \"      <td>0.371101</td>\\n\",\n       \"      <td>3.643000</td>\\n\",\n       \"      <td>0.008967</td>\\n\",\n       \"      <td>0.758848</td>\\n\",\n       \"      <td>0.017624</td>\\n\",\n       \"      <td>0.008967</td>\\n\",\n       \"      <td>0.017624</td>\\n\",\n       \"      <td>0.460500</td>\\n\",\n       \"      <td>0.577391</td>\\n\",\n       \"      <td>0.130194</td>\\n\",\n       \"      <td>0.207798</td>\\n\",\n       \"      <td>0.577391</td>\\n\",\n       \"      <td>0.207798</td>\\n\",\n       \"      <td>1.076000</td>\\n\",\n       \"      <td>0.444412</td>\\n\",\n       \"      <td>0.292870</td>\\n\",\n       \"      <td>0.341433</td>\\n\",\n       \"      <td>0.444412</td>\\n\",\n       \"      <td>0.341433</td>\\n\",\n       \"      <td>1.402000</td>\\n\",\n       \"      <td>0.280400</td>\\n\",\n       \"      <td>0.495404</td>\\n\",\n       \"      <td>0.333310</td>\\n\",\n       \"      <td>0.280400</td>\\n\",\n       \"      <td>0.333310</td>\\n\",\n       \"      <td>1.953500</td>\\n\",\n       \"      <td>0.195350</td>\\n\",\n       \"      <td>0.663335</td>\\n\",\n       \"      <td>0.282860</td>\\n\",\n       \"      <td>0.195350</td>\\n\",\n       \"      <td>0.282860</td>\\n\",\n       \"      <td>1.352500</td>\\n\",\n       \"      <td>0.362472</td>\\n\",\n       \"      <td>0.362472</td>\\n\",\n       \"      <td>0.362472</td>\\n\",\n       \"      <td>0.362472</td>\\n\",\n       \"      <td>0.362472</td>\\n\",\n       \"      <td>3.055000</td>\\n\",\n       \"      <td>0.062428</td>\\n\",\n       \"      <td>0.902916</td>\\n\",\n       \"      <td>0.111265</td>\\n\",\n       \"      <td>0.062428</td>\\n\",\n       \"      <td>0.111265</td>\\n\",\n       \"      <td>0.461500</td>\\n\",\n       \"      <td>0.461500</td>\\n\",\n       \"      <td>0.179274</td>\\n\",\n       \"      <td>0.242168</td>\\n\",\n       \"      <td>0.461500</td>\\n\",\n       \"      <td>0.242168</td>\\n\",\n       \"      <td>1.049500</td>\\n\",\n       \"      <td>0.349833</td>\\n\",\n       \"      <td>0.380906</td>\\n\",\n       \"      <td>0.338184</td>\\n\",\n       \"      <td>0.349833</td>\\n\",\n       \"      <td>0.338184</td>\\n\",\n       \"      <td>0.146000</td>\\n\",\n       \"      <td>0.014600</td>\\n\",\n       \"      <td>0.066706</td>\\n\",\n       \"      <td>0.022980</td>\\n\",\n       \"      <td>0.014600</td>\\n\",\n       \"      <td>0.022980</td>\\n\",\n       \"      <td>0.286500</td>\\n\",\n       \"      <td>0.005730</td>\\n\",\n       \"      <td>0.133466</td>\\n\",\n       \"      <td>0.010861</td>\\n\",\n       \"      <td>0.005730</td>\\n\",\n       \"      <td>0.010861</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.031285</td>\\n\",\n       \"      <td>0.031285</td>\\n\",\n       \"      <td>0.031285</td>\\n\",\n       \"      <td>0.031285</td>\\n\",\n       \"      <td>0.031285</td>\\n\",\n       \"      <td>0.432500</td>\\n\",\n       \"      <td>0.001112</td>\\n\",\n       \"      <td>0.199031</td>\\n\",\n       \"      <td>0.002209</td>\\n\",\n       \"      <td>0.001112</td>\\n\",\n       \"      <td>0.002209</td>\\n\",\n       \"      <td>1.425000</td>\\n\",\n       \"      <td>0.322952</td>\\n\",\n       \"      <td>0.564210</td>\\n\",\n       \"      <td>0.382302</td>\\n\",\n       \"      <td>0.322952</td>\\n\",\n       \"      <td>0.382302</td>\\n\",\n       \"      <td>1.994000</td>\\n\",\n       \"      <td>0.216606</td>\\n\",\n       \"      <td>0.724392</td>\\n\",\n       \"      <td>0.312148</td>\\n\",\n       \"      <td>0.216606</td>\\n\",\n       \"      <td>0.312148</td>\\n\",\n       \"      <td>1.394500</td>\\n\",\n       \"      <td>0.426124</td>\\n\",\n       \"      <td>0.426124</td>\\n\",\n       \"      <td>0.426124</td>\\n\",\n       \"      <td>0.426124</td>\\n\",\n       \"      <td>0.426124</td>\\n\",\n       \"      <td>3.068000</td>\\n\",\n       \"      <td>0.063648</td>\\n\",\n       \"      <td>0.917881</td>\\n\",\n       \"      <td>0.113342</td>\\n\",\n       \"      <td>0.063648</td>\\n\",\n       \"      <td>0.113342</td>\\n\",\n       \"      <td>0.461500</td>\\n\",\n       \"      <td>0.545148</td>\\n\",\n       \"      <td>0.211517</td>\\n\",\n       \"      <td>0.285869</td>\\n\",\n       \"      <td>0.545148</td>\\n\",\n       \"      <td>0.285869</td>\\n\",\n       \"      <td>1.064500</td>\\n\",\n       \"      <td>0.409571</td>\\n\",\n       \"      <td>0.444043</td>\\n\",\n       \"      <td>0.395489</td>\\n\",\n       \"      <td>0.409571</td>\\n\",\n       \"      <td>0.395489</td>\\n\",\n       \"      <td>0.151500</td>\\n\",\n       \"      <td>0.032141</td>\\n\",\n       \"      <td>0.146838</td>\\n\",\n       \"      <td>0.050539</td>\\n\",\n       \"      <td>0.032141</td>\\n\",\n       \"      <td>0.050539</td>\\n\",\n       \"      <td>0.311000</td>\\n\",\n       \"      <td>0.010810</td>\\n\",\n       \"      <td>0.245387</td>\\n\",\n       \"      <td>0.020448</td>\\n\",\n       \"      <td>0.010810</td>\\n\",\n       \"      <td>0.020448</td>\\n\",\n       \"      <td>0.081000</td>\\n\",\n       \"      <td>0.068355</td>\\n\",\n       \"      <td>0.068355</td>\\n\",\n       \"      <td>0.068355</td>\\n\",\n       \"      <td>0.068355</td>\\n\",\n       \"      <td>0.068355</td>\\n\",\n       \"      <td>0.487500</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.335053</td>\\n\",\n       \"      <td>0.003804</td>\\n\",\n       \"      <td>0.001916</td>\\n\",\n       \"      <td>0.003804</td>\\n\",\n       \"      <td>0.256249</td>\\n\",\n       \"      <td>0.286813</td>\\n\",\n       \"      <td>0.293698</td>\\n\",\n       \"      <td>0.402698</td>\\n\",\n       \"      <td>0.479184</td>\\n\",\n       \"      <td>0.493073</td>\\n\",\n       \"      <td>0.507145</td>\\n\",\n       \"      <td>0.395229</td>\\n\",\n       \"      <td>0.446498</td>\\n\",\n       \"      <td>0.339469</td>\\n\",\n       \"      <td>0.560701</td>\\n\",\n       \"      <td>0.615740</td>\\n\",\n       \"      <td>0.522232</td>\\n\",\n       \"      <td>0.528234</td>\\n\",\n       \"      <td>0.031441</td>\\n\",\n       \"      <td>0.038770</td>\\n\",\n       \"      <td>0.062675</td>\\n\",\n       \"      <td>0.071403</td>\\n\",\n       \"      <td>0.080965</td>\\n\",\n       \"      <td>0.049142</td>\\n\",\n       \"      <td>0.049267</td>\\n\",\n       \"      <td>3.337000</td>\\n\",\n       \"      <td>1.928000</td>\\n\",\n       \"      <td>51.226500</td>\\n\",\n       \"      <td>390.050000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39538</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/krapivin.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>krapivin.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>1.352174</td>\\n\",\n       \"      <td>0.270435</td>\\n\",\n       \"      <td>0.280102</td>\\n\",\n       \"      <td>0.261053</td>\\n\",\n       \"      <td>0.270435</td>\\n\",\n       \"      <td>0.261053</td>\\n\",\n       \"      <td>1.873913</td>\\n\",\n       \"      <td>0.187391</td>\\n\",\n       \"      <td>0.367820</td>\\n\",\n       \"      <td>0.235310</td>\\n\",\n       \"      <td>0.187391</td>\\n\",\n       \"      <td>0.235310</td>\\n\",\n       \"      <td>1.567391</td>\\n\",\n       \"      <td>0.275213</td>\\n\",\n       \"      <td>0.275213</td>\\n\",\n       \"      <td>0.275213</td>\\n\",\n       \"      <td>0.275213</td>\\n\",\n       \"      <td>0.275213</td>\\n\",\n       \"      <td>3.521739</td>\\n\",\n       \"      <td>0.008066</td>\\n\",\n       \"      <td>0.625593</td>\\n\",\n       \"      <td>0.015857</td>\\n\",\n       \"      <td>0.008066</td>\\n\",\n       \"      <td>0.015857</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.095660</td>\\n\",\n       \"      <td>0.151809</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.151809</td>\\n\",\n       \"      <td>0.986957</td>\\n\",\n       \"      <td>0.328986</td>\\n\",\n       \"      <td>0.209221</td>\\n\",\n       \"      <td>0.243840</td>\\n\",\n       \"      <td>0.328986</td>\\n\",\n       \"      <td>0.243840</td>\\n\",\n       \"      <td>1.430435</td>\\n\",\n       \"      <td>0.356363</td>\\n\",\n       \"      <td>0.360137</td>\\n\",\n       \"      <td>0.338561</td>\\n\",\n       \"      <td>0.356363</td>\\n\",\n       \"      <td>0.338561</td>\\n\",\n       \"      <td>2.041304</td>\\n\",\n       \"      <td>0.242434</td>\\n\",\n       \"      <td>0.464621</td>\\n\",\n       \"      <td>0.300882</td>\\n\",\n       \"      <td>0.242434</td>\\n\",\n       \"      <td>0.300882</td>\\n\",\n       \"      <td>1.741304</td>\\n\",\n       \"      <td>0.360797</td>\\n\",\n       \"      <td>0.360797</td>\\n\",\n       \"      <td>0.360797</td>\\n\",\n       \"      <td>0.360797</td>\\n\",\n       \"      <td>0.360797</td>\\n\",\n       \"      <td>3.784783</td>\\n\",\n       \"      <td>0.009482</td>\\n\",\n       \"      <td>0.728281</td>\\n\",\n       \"      <td>0.018637</td>\\n\",\n       \"      <td>0.009482</td>\\n\",\n       \"      <td>0.018637</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.562613</td>\\n\",\n       \"      <td>0.123219</td>\\n\",\n       \"      <td>0.195280</td>\\n\",\n       \"      <td>0.562613</td>\\n\",\n       \"      <td>0.195280</td>\\n\",\n       \"      <td>1.019565</td>\\n\",\n       \"      <td>0.432742</td>\\n\",\n       \"      <td>0.269372</td>\\n\",\n       \"      <td>0.315247</td>\\n\",\n       \"      <td>0.432742</td>\\n\",\n       \"      <td>0.315247</td>\\n\",\n       \"      <td>1.350000</td>\\n\",\n       \"      <td>0.270000</td>\\n\",\n       \"      <td>0.482388</td>\\n\",\n       \"      <td>0.319698</td>\\n\",\n       \"      <td>0.270000</td>\\n\",\n       \"      <td>0.319698</td>\\n\",\n       \"      <td>1.880435</td>\\n\",\n       \"      <td>0.188043</td>\\n\",\n       \"      <td>0.636585</td>\\n\",\n       \"      <td>0.270322</td>\\n\",\n       \"      <td>0.188043</td>\\n\",\n       \"      <td>0.270322</td>\\n\",\n       \"      <td>1.245652</td>\\n\",\n       \"      <td>0.355393</td>\\n\",\n       \"      <td>0.355393</td>\\n\",\n       \"      <td>0.355393</td>\\n\",\n       \"      <td>0.355393</td>\\n\",\n       \"      <td>0.355393</td>\\n\",\n       \"      <td>2.963043</td>\\n\",\n       \"      <td>0.061795</td>\\n\",\n       \"      <td>0.896647</td>\\n\",\n       \"      <td>0.112224</td>\\n\",\n       \"      <td>0.061795</td>\\n\",\n       \"      <td>0.112224</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.176670</td>\\n\",\n       \"      <td>0.233906</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.233906</td>\\n\",\n       \"      <td>0.982609</td>\\n\",\n       \"      <td>0.327536</td>\\n\",\n       \"      <td>0.366359</td>\\n\",\n       \"      <td>0.318717</td>\\n\",\n       \"      <td>0.327536</td>\\n\",\n       \"      <td>0.318717</td>\\n\",\n       \"      <td>0.215217</td>\\n\",\n       \"      <td>0.021522</td>\\n\",\n       \"      <td>0.083059</td>\\n\",\n       \"      <td>0.032190</td>\\n\",\n       \"      <td>0.021522</td>\\n\",\n       \"      <td>0.032190</td>\\n\",\n       \"      <td>0.360870</td>\\n\",\n       \"      <td>0.007217</td>\\n\",\n       \"      <td>0.134764</td>\\n\",\n       \"      <td>0.013435</td>\\n\",\n       \"      <td>0.007217</td>\\n\",\n       \"      <td>0.013435</td>\\n\",\n       \"      <td>0.117391</td>\\n\",\n       \"      <td>0.037006</td>\\n\",\n       \"      <td>0.037006</td>\\n\",\n       \"      <td>0.037006</td>\\n\",\n       \"      <td>0.037006</td>\\n\",\n       \"      <td>0.037006</td>\\n\",\n       \"      <td>0.558696</td>\\n\",\n       \"      <td>0.001441</td>\\n\",\n       \"      <td>0.205719</td>\\n\",\n       \"      <td>0.002853</td>\\n\",\n       \"      <td>0.001441</td>\\n\",\n       \"      <td>0.002853</td>\\n\",\n       \"      <td>1.391304</td>\\n\",\n       \"      <td>0.318246</td>\\n\",\n       \"      <td>0.557157</td>\\n\",\n       \"      <td>0.371715</td>\\n\",\n       \"      <td>0.318246</td>\\n\",\n       \"      <td>0.371715</td>\\n\",\n       \"      <td>1.947826</td>\\n\",\n       \"      <td>0.213583</td>\\n\",\n       \"      <td>0.703996</td>\\n\",\n       \"      <td>0.303585</td>\\n\",\n       \"      <td>0.213583</td>\\n\",\n       \"      <td>0.303585</td>\\n\",\n       \"      <td>1.313043</td>\\n\",\n       \"      <td>0.418703</td>\\n\",\n       \"      <td>0.418703</td>\\n\",\n       \"      <td>0.418703</td>\\n\",\n       \"      <td>0.418703</td>\\n\",\n       \"      <td>0.418703</td>\\n\",\n       \"      <td>2.989130</td>\\n\",\n       \"      <td>0.063619</td>\\n\",\n       \"      <td>0.915720</td>\\n\",\n       \"      <td>0.115401</td>\\n\",\n       \"      <td>0.063619</td>\\n\",\n       \"      <td>0.115401</td>\\n\",\n       \"      <td>0.434783</td>\\n\",\n       \"      <td>0.520675</td>\\n\",\n       \"      <td>0.210437</td>\\n\",\n       \"      <td>0.277560</td>\\n\",\n       \"      <td>0.520675</td>\\n\",\n       \"      <td>0.277560</td>\\n\",\n       \"      <td>0.997826</td>\\n\",\n       \"      <td>0.392351</td>\\n\",\n       \"      <td>0.430838</td>\\n\",\n       \"      <td>0.376105</td>\\n\",\n       \"      <td>0.392351</td>\\n\",\n       \"      <td>0.376105</td>\\n\",\n       \"      <td>0.226087</td>\\n\",\n       \"      <td>0.042625</td>\\n\",\n       \"      <td>0.161839</td>\\n\",\n       \"      <td>0.063254</td>\\n\",\n       \"      <td>0.042625</td>\\n\",\n       \"      <td>0.063254</td>\\n\",\n       \"      <td>0.413043</td>\\n\",\n       \"      <td>0.014111</td>\\n\",\n       \"      <td>0.261438</td>\\n\",\n       \"      <td>0.026238</td>\\n\",\n       \"      <td>0.014111</td>\\n\",\n       \"      <td>0.026238</td>\\n\",\n       \"      <td>0.126087</td>\\n\",\n       \"      <td>0.075617</td>\\n\",\n       \"      <td>0.075617</td>\\n\",\n       \"      <td>0.075617</td>\\n\",\n       \"      <td>0.075617</td>\\n\",\n       \"      <td>0.075617</td>\\n\",\n       \"      <td>0.663043</td>\\n\",\n       \"      <td>0.002521</td>\\n\",\n       \"      <td>0.358958</td>\\n\",\n       \"      <td>0.004991</td>\\n\",\n       \"      <td>0.002521</td>\\n\",\n       \"      <td>0.004991</td>\\n\",\n       \"      <td>0.231903</td>\\n\",\n       \"      <td>0.260767</td>\\n\",\n       \"      <td>0.287466</td>\\n\",\n       \"      <td>0.378815</td>\\n\",\n       \"      <td>0.451300</td>\\n\",\n       \"      <td>0.467576</td>\\n\",\n       \"      <td>0.493646</td>\\n\",\n       \"      <td>0.378395</td>\\n\",\n       \"      <td>0.429610</td>\\n\",\n       \"      <td>0.336495</td>\\n\",\n       \"      <td>0.549921</td>\\n\",\n       \"      <td>0.600400</td>\\n\",\n       \"      <td>0.504450</td>\\n\",\n       \"      <td>0.517271</td>\\n\",\n       \"      <td>0.033968</td>\\n\",\n       \"      <td>0.042875</td>\\n\",\n       \"      <td>0.078378</td>\\n\",\n       \"      <td>0.079327</td>\\n\",\n       \"      <td>0.091876</td>\\n\",\n       \"      <td>0.059146</td>\\n\",\n       \"      <td>0.059801</td>\\n\",\n       \"      <td>3.243478</td>\\n\",\n       \"      <td>2.497826</td>\\n\",\n       \"      <td>48.671739</td>\\n\",\n       \"      <td>387.671739</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39541</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/nus.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>nus.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2.165877</td>\\n\",\n       \"      <td>0.433175</td>\\n\",\n       \"      <td>0.245394</td>\\n\",\n       \"      <td>0.285477</td>\\n\",\n       \"      <td>0.433175</td>\\n\",\n       \"      <td>0.285477</td>\\n\",\n       \"      <td>2.900474</td>\\n\",\n       \"      <td>0.290047</td>\\n\",\n       \"      <td>0.315136</td>\\n\",\n       \"      <td>0.273876</td>\\n\",\n       \"      <td>0.290047</td>\\n\",\n       \"      <td>0.273876</td>\\n\",\n       \"      <td>3.203791</td>\\n\",\n       \"      <td>0.300251</td>\\n\",\n       \"      <td>0.300251</td>\\n\",\n       \"      <td>0.300251</td>\\n\",\n       \"      <td>0.300251</td>\\n\",\n       \"      <td>0.300251</td>\\n\",\n       \"      <td>5.767773</td>\\n\",\n       \"      <td>0.012904</td>\\n\",\n       \"      <td>0.560455</td>\\n\",\n       \"      <td>0.024990</td>\\n\",\n       \"      <td>0.012904</td>\\n\",\n       \"      <td>0.024990</td>\\n\",\n       \"      <td>0.630332</td>\\n\",\n       \"      <td>0.630332</td>\\n\",\n       \"      <td>0.076844</td>\\n\",\n       \"      <td>0.130807</td>\\n\",\n       \"      <td>0.630332</td>\\n\",\n       \"      <td>0.130807</td>\\n\",\n       \"      <td>1.535545</td>\\n\",\n       \"      <td>0.511848</td>\\n\",\n       \"      <td>0.180122</td>\\n\",\n       \"      <td>0.245644</td>\\n\",\n       \"      <td>0.511848</td>\\n\",\n       \"      <td>0.245644</td>\\n\",\n       \"      <td>2.298578</td>\\n\",\n       \"      <td>0.537010</td>\\n\",\n       \"      <td>0.305021</td>\\n\",\n       \"      <td>0.352817</td>\\n\",\n       \"      <td>0.537010</td>\\n\",\n       \"      <td>0.352817</td>\\n\",\n       \"      <td>3.364929</td>\\n\",\n       \"      <td>0.381856</td>\\n\",\n       \"      <td>0.406378</td>\\n\",\n       \"      <td>0.356980</td>\\n\",\n       \"      <td>0.381856</td>\\n\",\n       \"      <td>0.356980</td>\\n\",\n       \"      <td>3.919431</td>\\n\",\n       \"      <td>0.396313</td>\\n\",\n       \"      <td>0.396313</td>\\n\",\n       \"      <td>0.396313</td>\\n\",\n       \"      <td>0.396313</td>\\n\",\n       \"      <td>0.396313</td>\\n\",\n       \"      <td>6.957346</td>\\n\",\n       \"      <td>0.016525</td>\\n\",\n       \"      <td>0.684537</td>\\n\",\n       \"      <td>0.031958</td>\\n\",\n       \"      <td>0.016525</td>\\n\",\n       \"      <td>0.031958</td>\\n\",\n       \"      <td>0.630332</td>\\n\",\n       \"      <td>0.735078</td>\\n\",\n       \"      <td>0.092250</td>\\n\",\n       \"      <td>0.155864</td>\\n\",\n       \"      <td>0.735078</td>\\n\",\n       \"      <td>0.155864</td>\\n\",\n       \"      <td>1.587678</td>\\n\",\n       \"      <td>0.622754</td>\\n\",\n       \"      <td>0.223385</td>\\n\",\n       \"      <td>0.300946</td>\\n\",\n       \"      <td>0.622754</td>\\n\",\n       \"      <td>0.300946</td>\\n\",\n       \"      <td>2.189573</td>\\n\",\n       \"      <td>0.437915</td>\\n\",\n       \"      <td>0.442020</td>\\n\",\n       \"      <td>0.401721</td>\\n\",\n       \"      <td>0.437915</td>\\n\",\n       \"      <td>0.401721</td>\\n\",\n       \"      <td>2.962085</td>\\n\",\n       \"      <td>0.296209</td>\\n\",\n       \"      <td>0.563851</td>\\n\",\n       \"      <td>0.359479</td>\\n\",\n       \"      <td>0.296209</td>\\n\",\n       \"      <td>0.359479</td>\\n\",\n       \"      <td>2.601896</td>\\n\",\n       \"      <td>0.433518</td>\\n\",\n       \"      <td>0.433518</td>\\n\",\n       \"      <td>0.433518</td>\\n\",\n       \"      <td>0.433518</td>\\n\",\n       \"      <td>0.433518</td>\\n\",\n       \"      <td>4.862559</td>\\n\",\n       \"      <td>0.102689</td>\\n\",\n       \"      <td>0.847547</td>\\n\",\n       \"      <td>0.176897</td>\\n\",\n       \"      <td>0.102689</td>\\n\",\n       \"      <td>0.176897</td>\\n\",\n       \"      <td>0.635071</td>\\n\",\n       \"      <td>0.635071</td>\\n\",\n       \"      <td>0.141707</td>\\n\",\n       \"      <td>0.215439</td>\\n\",\n       \"      <td>0.635071</td>\\n\",\n       \"      <td>0.215439</td>\\n\",\n       \"      <td>1.549763</td>\\n\",\n       \"      <td>0.516588</td>\\n\",\n       \"      <td>0.328648</td>\\n\",\n       \"      <td>0.365751</td>\\n\",\n       \"      <td>0.516588</td>\\n\",\n       \"      <td>0.365751</td>\\n\",\n       \"      <td>0.194313</td>\\n\",\n       \"      <td>0.019431</td>\\n\",\n       \"      <td>0.046084</td>\\n\",\n       \"      <td>0.022211</td>\\n\",\n       \"      <td>0.019431</td>\\n\",\n       \"      <td>0.022211</td>\\n\",\n       \"      <td>0.573460</td>\\n\",\n       \"      <td>0.011469</td>\\n\",\n       \"      <td>0.110261</td>\\n\",\n       \"      <td>0.019579</td>\\n\",\n       \"      <td>0.011469</td>\\n\",\n       \"      <td>0.019579</td>\\n\",\n       \"      <td>0.175355</td>\\n\",\n       \"      <td>0.021700</td>\\n\",\n       \"      <td>0.021700</td>\\n\",\n       \"      <td>0.021700</td>\\n\",\n       \"      <td>0.021700</td>\\n\",\n       \"      <td>0.021700</td>\\n\",\n       \"      <td>0.905213</td>\\n\",\n       \"      <td>0.002270</td>\\n\",\n       \"      <td>0.178371</td>\\n\",\n       \"      <td>0.004440</td>\\n\",\n       \"      <td>0.002270</td>\\n\",\n       \"      <td>0.004440</td>\\n\",\n       \"      <td>2.246445</td>\\n\",\n       \"      <td>0.505201</td>\\n\",\n       \"      <td>0.506380</td>\\n\",\n       \"      <td>0.462352</td>\\n\",\n       \"      <td>0.505201</td>\\n\",\n       \"      <td>0.462352</td>\\n\",\n       \"      <td>3.123223</td>\\n\",\n       \"      <td>0.345566</td>\\n\",\n       \"      <td>0.648436</td>\\n\",\n       \"      <td>0.417699</td>\\n\",\n       \"      <td>0.345566</td>\\n\",\n       \"      <td>0.417699</td>\\n\",\n       \"      <td>2.748815</td>\\n\",\n       \"      <td>0.507624</td>\\n\",\n       \"      <td>0.507624</td>\\n\",\n       \"      <td>0.507624</td>\\n\",\n       \"      <td>0.507624</td>\\n\",\n       \"      <td>0.507624</td>\\n\",\n       \"      <td>5.056872</td>\\n\",\n       \"      <td>0.111772</td>\\n\",\n       \"      <td>0.903140</td>\\n\",\n       \"      <td>0.192106</td>\\n\",\n       \"      <td>0.111772</td>\\n\",\n       \"      <td>0.192106</td>\\n\",\n       \"      <td>0.635071</td>\\n\",\n       \"      <td>0.724152</td>\\n\",\n       \"      <td>0.164747</td>\\n\",\n       \"      <td>0.249418</td>\\n\",\n       \"      <td>0.724152</td>\\n\",\n       \"      <td>0.249418</td>\\n\",\n       \"      <td>1.568720</td>\\n\",\n       \"      <td>0.596938</td>\\n\",\n       \"      <td>0.379181</td>\\n\",\n       \"      <td>0.422866</td>\\n\",\n       \"      <td>0.596938</td>\\n\",\n       \"      <td>0.422866</td>\\n\",\n       \"      <td>0.303318</td>\\n\",\n       \"      <td>0.061064</td>\\n\",\n       \"      <td>0.119035</td>\\n\",\n       \"      <td>0.068892</td>\\n\",\n       \"      <td>0.061064</td>\\n\",\n       \"      <td>0.068892</td>\\n\",\n       \"      <td>0.914692</td>\\n\",\n       \"      <td>0.025234</td>\\n\",\n       \"      <td>0.226438</td>\\n\",\n       \"      <td>0.042777</td>\\n\",\n       \"      <td>0.025234</td>\\n\",\n       \"      <td>0.042777</td>\\n\",\n       \"      <td>0.317536</td>\\n\",\n       \"      <td>0.077060</td>\\n\",\n       \"      <td>0.077060</td>\\n\",\n       \"      <td>0.077060</td>\\n\",\n       \"      <td>0.077060</td>\\n\",\n       \"      <td>0.077060</td>\\n\",\n       \"      <td>1.473934</td>\\n\",\n       \"      <td>0.004550</td>\\n\",\n       \"      <td>0.320352</td>\\n\",\n       \"      <td>0.008886</td>\\n\",\n       \"      <td>0.004550</td>\\n\",\n       \"      <td>0.008886</td>\\n\",\n       \"      <td>0.242825</td>\\n\",\n       \"      <td>0.262321</td>\\n\",\n       \"      <td>0.267252</td>\\n\",\n       \"      <td>0.368942</td>\\n\",\n       \"      <td>0.478799</td>\\n\",\n       \"      <td>0.435713</td>\\n\",\n       \"      <td>0.498921</td>\\n\",\n       \"      <td>0.428715</td>\\n\",\n       \"      <td>0.464357</td>\\n\",\n       \"      <td>0.319006</td>\\n\",\n       \"      <td>0.573981</td>\\n\",\n       \"      <td>0.664906</td>\\n\",\n       \"      <td>0.567121</td>\\n\",\n       \"      <td>0.610301</td>\\n\",\n       \"      <td>0.016146</td>\\n\",\n       \"      <td>0.023825</td>\\n\",\n       \"      <td>0.063280</td>\\n\",\n       \"      <td>0.059430</td>\\n\",\n       \"      <td>0.076505</td>\\n\",\n       \"      <td>0.045719</td>\\n\",\n       \"      <td>0.053390</td>\\n\",\n       \"      <td>5.971564</td>\\n\",\n       \"      <td>5.691943</td>\\n\",\n       \"      <td>49.829384</td>\\n\",\n       \"      <td>396.597156</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39539</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/semeval.split_nopunc.eval</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>RNN-O2O-KP20k</td>\\n\",\n       \"      <td>semeval.split_nopunc</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>76000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>2.020000</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.136254</td>\\n\",\n       \"      <td>0.202226</td>\\n\",\n       \"      <td>0.404000</td>\\n\",\n       \"      <td>0.202226</td>\\n\",\n       \"      <td>2.950000</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.197724</td>\\n\",\n       \"      <td>0.234394</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.234394</td>\\n\",\n       \"      <td>3.460000</td>\\n\",\n       \"      <td>0.227716</td>\\n\",\n       \"      <td>0.227716</td>\\n\",\n       \"      <td>0.227716</td>\\n\",\n       \"      <td>0.227716</td>\\n\",\n       \"      <td>0.227716</td>\\n\",\n       \"      <td>6.640000</td>\\n\",\n       \"      <td>0.014895</td>\\n\",\n       \"      <td>0.440094</td>\\n\",\n       \"      <td>0.028772</td>\\n\",\n       \"      <td>0.014895</td>\\n\",\n       \"      <td>0.028772</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.038393</td>\\n\",\n       \"      <td>0.071589</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.071589</td>\\n\",\n       \"      <td>1.470000</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.099253</td>\\n\",\n       \"      <td>0.164107</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.164107</td>\\n\",\n       \"      <td>2.220000</td>\\n\",\n       \"      <td>0.528949</td>\\n\",\n       \"      <td>0.179197</td>\\n\",\n       \"      <td>0.265591</td>\\n\",\n       \"      <td>0.528949</td>\\n\",\n       \"      <td>0.265591</td>\\n\",\n       \"      <td>3.600000</td>\\n\",\n       \"      <td>0.401281</td>\\n\",\n       \"      <td>0.269428</td>\\n\",\n       \"      <td>0.318989</td>\\n\",\n       \"      <td>0.401281</td>\\n\",\n       \"      <td>0.318989</td>\\n\",\n       \"      <td>4.420000</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>0.319565</td>\\n\",\n       \"      <td>8.000000</td>\\n\",\n       \"      <td>0.018977</td>\\n\",\n       \"      <td>0.558155</td>\\n\",\n       \"      <td>0.036648</td>\\n\",\n       \"      <td>0.018977</td>\\n\",\n       \"      <td>0.036648</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.680452</td>\\n\",\n       \"      <td>0.047169</td>\\n\",\n       \"      <td>0.087996</td>\\n\",\n       \"      <td>0.680452</td>\\n\",\n       \"      <td>0.087996</td>\\n\",\n       \"      <td>1.530000</td>\\n\",\n       \"      <td>0.610676</td>\\n\",\n       \"      <td>0.124113</td>\\n\",\n       \"      <td>0.205093</td>\\n\",\n       \"      <td>0.610676</td>\\n\",\n       \"      <td>0.205093</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.307830</td>\\n\",\n       \"      <td>0.328902</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"      <td>0.328902</td>\\n\",\n       \"      <td>2.980000</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.474956</td>\\n\",\n       \"      <td>0.345668</td>\\n\",\n       \"      <td>0.298000</td>\\n\",\n       \"      <td>0.345668</td>\\n\",\n       \"      <td>2.550000</td>\\n\",\n       \"      <td>0.351779</td>\\n\",\n       \"      <td>0.351779</td>\\n\",\n       \"      <td>0.351779</td>\\n\",\n       \"      <td>0.351779</td>\\n\",\n       \"      <td>0.351779</td>\\n\",\n       \"      <td>5.960000</td>\\n\",\n       \"      <td>0.092430</td>\\n\",\n       \"      <td>0.903935</td>\\n\",\n       \"      <td>0.164242</td>\\n\",\n       \"      <td>0.092430</td>\\n\",\n       \"      <td>0.164242</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.090745</td>\\n\",\n       \"      <td>0.150718</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.150718</td>\\n\",\n       \"      <td>1.470000</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.235178</td>\\n\",\n       \"      <td>0.300619</td>\\n\",\n       \"      <td>0.490000</td>\\n\",\n       \"      <td>0.300619</td>\\n\",\n       \"      <td>0.170000</td>\\n\",\n       \"      <td>0.017000</td>\\n\",\n       \"      <td>0.024651</td>\\n\",\n       \"      <td>0.019534</td>\\n\",\n       \"      <td>0.017000</td>\\n\",\n       \"      <td>0.019534</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.006200</td>\\n\",\n       \"      <td>0.042810</td>\\n\",\n       \"      <td>0.010684</td>\\n\",\n       \"      <td>0.006200</td>\\n\",\n       \"      <td>0.010684</td>\\n\",\n       \"      <td>0.140000</td>\\n\",\n       \"      <td>0.019234</td>\\n\",\n       \"      <td>0.019234</td>\\n\",\n       \"      <td>0.019234</td>\\n\",\n       \"      <td>0.019234</td>\\n\",\n       \"      <td>0.019234</td>\\n\",\n       \"      <td>0.680000</td>\\n\",\n       \"      <td>0.001814</td>\\n\",\n       \"      <td>0.084422</td>\\n\",\n       \"      <td>0.003544</td>\\n\",\n       \"      <td>0.001814</td>\\n\",\n       \"      <td>0.003544</td>\\n\",\n       \"      <td>2.060000</td>\\n\",\n       \"      <td>0.477565</td>\\n\",\n       \"      <td>0.377817</td>\\n\",\n       \"      <td>0.398012</td>\\n\",\n       \"      <td>0.477565</td>\\n\",\n       \"      <td>0.398012</td>\\n\",\n       \"      <td>3.160000</td>\\n\",\n       \"      <td>0.352758</td>\\n\",\n       \"      <td>0.561192</td>\\n\",\n       \"      <td>0.408963</td>\\n\",\n       \"      <td>0.352758</td>\\n\",\n       \"      <td>0.408963</td>\\n\",\n       \"      <td>2.730000</td>\\n\",\n       \"      <td>0.433077</td>\\n\",\n       \"      <td>0.433077</td>\\n\",\n       \"      <td>0.433077</td>\\n\",\n       \"      <td>0.433077</td>\\n\",\n       \"      <td>0.433077</td>\\n\",\n       \"      <td>6.070000</td>\\n\",\n       \"      <td>0.096314</td>\\n\",\n       \"      <td>0.935156</td>\\n\",\n       \"      <td>0.170902</td>\\n\",\n       \"      <td>0.096314</td>\\n\",\n       \"      <td>0.170902</td>\\n\",\n       \"      <td>0.550000</td>\\n\",\n       \"      <td>0.640881</td>\\n\",\n       \"      <td>0.109593</td>\\n\",\n       \"      <td>0.180088</td>\\n\",\n       \"      <td>0.640881</td>\\n\",\n       \"      <td>0.180088</td>\\n\",\n       \"      <td>1.500000</td>\\n\",\n       \"      <td>0.571606</td>\\n\",\n       \"      <td>0.279622</td>\\n\",\n       \"      <td>0.355059</td>\\n\",\n       \"      <td>0.571606</td>\\n\",\n       \"      <td>0.355059</td>\\n\",\n       \"      <td>0.300000</td>\\n\",\n       \"      <td>0.073624</td>\\n\",\n       \"      <td>0.095364</td>\\n\",\n       \"      <td>0.079940</td>\\n\",\n       \"      <td>0.073624</td>\\n\",\n       \"      <td>0.079940</td>\\n\",\n       \"      <td>0.750000</td>\\n\",\n       \"      <td>0.024536</td>\\n\",\n       \"      <td>0.154133</td>\\n\",\n       \"      <td>0.041586</td>\\n\",\n       \"      <td>0.024536</td>\\n\",\n       \"      <td>0.041586</td>\\n\",\n       \"      <td>0.290000</td>\\n\",\n       \"      <td>0.082642</td>\\n\",\n       \"      <td>0.082642</td>\\n\",\n       \"      <td>0.082642</td>\\n\",\n       \"      <td>0.082642</td>\\n\",\n       \"      <td>0.082642</td>\\n\",\n       \"      <td>1.400000</td>\\n\",\n       \"      <td>0.005017</td>\\n\",\n       \"      <td>0.231300</td>\\n\",\n       \"      <td>0.009791</td>\\n\",\n       \"      <td>0.005017</td>\\n\",\n       \"      <td>0.009791</td>\\n\",\n       \"      <td>0.164262</td>\\n\",\n       \"      <td>0.176712</td>\\n\",\n       \"      <td>0.198717</td>\\n\",\n       \"      <td>0.279747</td>\\n\",\n       \"      <td>0.392348</td>\\n\",\n       \"      <td>0.342606</td>\\n\",\n       \"      <td>0.442319</td>\\n\",\n       \"      <td>0.360125</td>\\n\",\n       \"      <td>0.393501</td>\\n\",\n       \"      <td>0.226980</td>\\n\",\n       \"      <td>0.514568</td>\\n\",\n       \"      <td>0.633782</td>\\n\",\n       \"      <td>0.519435</td>\\n\",\n       \"      <td>0.574970</td>\\n\",\n       \"      <td>0.012503</td>\\n\",\n       \"      <td>0.014684</td>\\n\",\n       \"      <td>0.077000</td>\\n\",\n       \"      <td>0.032604</td>\\n\",\n       \"      <td>0.049848</td>\\n\",\n       \"      <td>0.045558</td>\\n\",\n       \"      <td>0.052690</td>\\n\",\n       \"      <td>6.730000</td>\\n\",\n       \"      <td>8.340000</td>\\n\",\n       \"      <td>65.370000</td>\\n\",\n       \"      <td>380.420000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                                                                                                                                                                                                                                                            path  \\\\\\n\",\n       \"39540  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/duc.split_nopunc.eval               \\n\",\n       \"43530  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/inspec.split_nopunc.eval            \\n\",\n       \"41211  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/kp20k.split_nopunc.eval   \\n\",\n       \"39537  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/kp20k_valid2k.split_nopunc.eval     \\n\",\n       \"39538  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/krapivin.split_nopunc.eval          \\n\",\n       \"39541  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/nus.split_nopunc.eval               \\n\",\n       \"39539  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_76000/semeval.split_nopunc.eval           \\n\",\n       \"\\n\",\n       \"                            exp_group       exp_name  \\\\\\n\",\n       \"39540  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"43530  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"41211  meng17-one2one-kp20k-topmodels  RNN-O2O-KP20k   \\n\",\n       \"39537  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"39538  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"39541  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"39539  meng17-one2one-kp20k            RNN-O2O-KP20k   \\n\",\n       \"\\n\",\n       \"                        test_name tokenization train_mode model_base    order  \\\\\\n\",\n       \"39540  duc.split_nopunc            meng17       one2one    rnn        one2one   \\n\",\n       \"43530  inspec.split_nopunc         meng17       one2one    rnn        one2one   \\n\",\n       \"41211  kp20k.split_nopunc          meng17       one2one    rnn        one2one   \\n\",\n       \"39537  kp20k_valid2k.split_nopunc  meng17       one2one    rnn        one2one   \\n\",\n       \"39538  krapivin.split_nopunc       meng17       one2one    rnn        one2one   \\n\",\n       \"39541  nus.split_nopunc            meng17       one2one    rnn        one2one   \\n\",\n       \"39539  semeval.split_nopunc        meng17       one2one    rnn        one2one   \\n\",\n       \"\\n\",\n       \"      train_dataset   step   test_dataset decoding_method decoding_terminate  \\\\\\n\",\n       \"39540  kp20k         76000  duc            exhaustive      fullbeam            \\n\",\n       \"43530  kp20k         76000  inspec         exhaustive      fullbeam            \\n\",\n       \"41211  kp20k         76000  kp20k          exhaustive      fullbeam            \\n\",\n       \"39537  kp20k         76000  kp20k_valid2k  exhaustive      fullbeam            \\n\",\n       \"39538  kp20k         76000  krapivin       exhaustive      fullbeam            \\n\",\n       \"39541  kp20k         76000  nus            exhaustive      fullbeam            \\n\",\n       \"39539  kp20k         76000  semeval        exhaustive      fullbeam            \\n\",\n       \"\\n\",\n       \"      beam_width max_length  all_exact_correct@5  all_exact_precision@5  \\\\\\n\",\n       \"39540  200        6         0.701299             0.140260                 \\n\",\n       \"43530  200        6         1.714000             0.342800                 \\n\",\n       \"41211  200        6         1.380347             0.276069                 \\n\",\n       \"39537  200        6         1.399500             0.279900                 \\n\",\n       \"39538  200        6         1.352174             0.270435                 \\n\",\n       \"39541  200        6         2.165877             0.433175                 \\n\",\n       \"39539  200        6         2.020000             0.404000                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@5  all_exact_f_score@5  all_exact_precision_hard@5  \\\\\\n\",\n       \"39540 0.091728            0.109333             0.140260                      \\n\",\n       \"43530 0.211253            0.247074             0.342800                      \\n\",\n       \"41211 0.298442            0.276320             0.276069                      \\n\",\n       \"39537 0.301230            0.279613             0.279900                      \\n\",\n       \"39538 0.280102            0.261053             0.270435                      \\n\",\n       \"39541 0.245394            0.285477             0.433175                      \\n\",\n       \"39539 0.136254            0.202226             0.404000                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@5  all_exact_correct@10  all_exact_precision@10  \\\\\\n\",\n       \"39540 0.109333                  1.165584              0.116558                  \\n\",\n       \"43530 0.247074                  2.706000              0.270600                  \\n\",\n       \"41211 0.276320                  1.894231              0.189423                  \\n\",\n       \"39537 0.279613                  1.922000              0.192200                  \\n\",\n       \"39538 0.261053                  1.873913              0.187391                  \\n\",\n       \"39541 0.285477                  2.900474              0.290047                  \\n\",\n       \"39539 0.202226                  2.950000              0.295000                  \\n\",\n       \"\\n\",\n       \"       all_exact_recall@10  all_exact_f_score@10  all_exact_precision_hard@10  \\\\\\n\",\n       \"39540 0.151945             0.130069              0.116558                       \\n\",\n       \"43530 0.319693             0.276529              0.270600                       \\n\",\n       \"41211 0.398148             0.246720              0.189423                       \\n\",\n       \"39537 0.406545             0.251214              0.192200                       \\n\",\n       \"39538 0.367820             0.235310              0.187391                       \\n\",\n       \"39541 0.315136             0.273876              0.290047                       \\n\",\n       \"39539 0.197724             0.234394              0.295000                       \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@10  all_exact_correct@k  all_exact_precision@k  \\\\\\n\",\n       \"39540 0.130069                   1.025974             0.128720                 \\n\",\n       \"43530 0.276529                   2.848000             0.296557                 \\n\",\n       \"41211 0.246720                   1.523640             0.286520                 \\n\",\n       \"39537 0.251214                   1.538000             0.290517                 \\n\",\n       \"39538 0.235310                   1.567391             0.275213                 \\n\",\n       \"39541 0.273876                   3.203791             0.300251                 \\n\",\n       \"39539 0.234394                   3.460000             0.227716                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@k  all_exact_f_score@k  all_exact_precision_hard@k  \\\\\\n\",\n       \"39540 0.128720            0.128720             0.128720                      \\n\",\n       \"43530 0.296557            0.296557             0.296557                      \\n\",\n       \"41211 0.286520            0.286520             0.286520                      \\n\",\n       \"39537 0.290517            0.290517             0.290517                      \\n\",\n       \"39538 0.275213            0.275213             0.275213                      \\n\",\n       \"39541 0.300251            0.300251             0.300251                      \\n\",\n       \"39539 0.227716            0.227716             0.227716                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@k  all_exact_correct@M  all_exact_precision@M  \\\\\\n\",\n       \"39540 0.128720                  3.762987             0.008688                 \\n\",\n       \"43530 0.296557                  6.732000             0.015159                 \\n\",\n       \"41211 0.286520                  3.483514             0.007894                 \\n\",\n       \"39537 0.290517                  3.487500             0.007916                 \\n\",\n       \"39538 0.275213                  3.521739             0.008066                 \\n\",\n       \"39541 0.300251                  5.767773             0.012904                 \\n\",\n       \"39539 0.227716                  6.640000             0.014895                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@M  all_exact_f_score@M  all_exact_precision_hard@M  \\\\\\n\",\n       \"39540 0.479782            0.017048             0.008688                      \\n\",\n       \"43530 0.706240            0.029529             0.015159                      \\n\",\n       \"41211 0.667008            0.015513             0.007894                      \\n\",\n       \"39537 0.667165            0.015554             0.007916                      \\n\",\n       \"39538 0.625593            0.015857             0.008066                      \\n\",\n       \"39541 0.560455            0.024990             0.012904                      \\n\",\n       \"39539 0.440094            0.028772             0.014895                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@M  all_exact_correct@1  all_exact_precision@1  \\\\\\n\",\n       \"39540 0.017048                  0.211039             0.211039                 \\n\",\n       \"43530 0.029529                  0.428000             0.428000                 \\n\",\n       \"41211 0.015513                  0.453295             0.453295                 \\n\",\n       \"39537 0.015554                  0.460500             0.460500                 \\n\",\n       \"39538 0.015857                  0.434783             0.434783                 \\n\",\n       \"39541 0.024990                  0.630332             0.630332                 \\n\",\n       \"39539 0.028772                  0.550000             0.550000                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@1  all_exact_f_score@1  all_exact_precision_hard@1  \\\\\\n\",\n       \"39540 0.027407            0.048353             0.211039                      \\n\",\n       \"43530 0.056795            0.097180             0.428000                      \\n\",\n       \"41211 0.101215            0.162035             0.453295                      \\n\",\n       \"39537 0.103440            0.165148             0.460500                      \\n\",\n       \"39538 0.095660            0.151809             0.434783                      \\n\",\n       \"39541 0.076844            0.130807             0.630332                      \\n\",\n       \"39539 0.038393            0.071589             0.550000                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@1  all_exact_correct@3  all_exact_precision@3  \\\\\\n\",\n       \"39540 0.048353                  0.451299             0.150433                 \\n\",\n       \"43530 0.097180                  1.154000             0.384667                 \\n\",\n       \"41211 0.162035                  1.028469             0.342823                 \\n\",\n       \"39537 0.165148                  1.046500             0.348833                 \\n\",\n       \"39538 0.151809                  0.986957             0.328986                 \\n\",\n       \"39541 0.130807                  1.535545             0.511848                 \\n\",\n       \"39539 0.071589                  1.470000             0.490000                 \\n\",\n       \"\\n\",\n       \"       all_exact_recall@3  all_exact_f_score@3  all_exact_precision_hard@3  \\\\\\n\",\n       \"39540 0.058992            0.083933             0.150433                      \\n\",\n       \"43530 0.148068            0.203316             0.384667                      \\n\",\n       \"41211 0.225321            0.262950             0.342823                      \\n\",\n       \"39537 0.228342            0.266807             0.348833                      \\n\",\n       \"39538 0.209221            0.243840             0.328986                      \\n\",\n       \"39541 0.180122            0.245644             0.511848                      \\n\",\n       \"39539 0.099253            0.164107             0.490000                      \\n\",\n       \"\\n\",\n       \"       all_exact_f_score_hard@3  all_partial_correct@5  \\\\\\n\",\n       \"39540 0.083933                  0.808442                 \\n\",\n       \"43530 0.203316                  2.006000                 \\n\",\n       \"41211 0.262950                  1.451343                 \\n\",\n       \"39537 0.266807                  1.460500                 \\n\",\n       \"39538 0.243840                  1.430435                 \\n\",\n       \"39541 0.245644                  2.298578                 \\n\",\n       \"39539 0.164107                  2.220000                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision@5  all_partial_recall@5  all_partial_f_score@5  \\\\\\n\",\n       \"39540 0.238893                 0.154901              0.185288                 \\n\",\n       \"43530 0.486913                 0.297090              0.347661                 \\n\",\n       \"41211 0.353728                 0.382440              0.354119                 \\n\",\n       \"39537 0.355454                 0.383736              0.355678                 \\n\",\n       \"39538 0.356363                 0.360137              0.338561                 \\n\",\n       \"39541 0.537010                 0.305021              0.352817                 \\n\",\n       \"39539 0.528949                 0.179197              0.265591                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@5  all_partial_f_score_hard@5  \\\\\\n\",\n       \"39540 0.238893                      0.185288                      \\n\",\n       \"43530 0.486913                      0.347661                      \\n\",\n       \"41211 0.353728                      0.354119                      \\n\",\n       \"39537 0.355454                      0.355678                      \\n\",\n       \"39538 0.356363                      0.338561                      \\n\",\n       \"39541 0.537010                      0.352817                      \\n\",\n       \"39539 0.528949                      0.265591                      \\n\",\n       \"\\n\",\n       \"       all_partial_correct@10  all_partial_precision@10  \\\\\\n\",\n       \"39540 1.477273                0.192281                    \\n\",\n       \"43530 3.316000                0.376642                    \\n\",\n       \"41211 2.020313                0.237018                    \\n\",\n       \"39537 2.048500                0.239674                    \\n\",\n       \"39538 2.041304                0.242434                    \\n\",\n       \"39541 3.364929                0.381856                    \\n\",\n       \"39539 3.600000                0.401281                    \\n\",\n       \"\\n\",\n       \"       all_partial_recall@10  all_partial_f_score@10  \\\\\\n\",\n       \"39540 0.247432               0.213406                  \\n\",\n       \"43530 0.436810               0.380821                  \\n\",\n       \"41211 0.497323               0.308567                  \\n\",\n       \"39537 0.505468               0.312847                  \\n\",\n       \"39538 0.464621               0.300882                  \\n\",\n       \"39541 0.406378               0.356980                  \\n\",\n       \"39539 0.269428               0.318989                  \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@10  all_partial_f_score_hard@10  \\\\\\n\",\n       \"39540 0.192281                       0.213406                       \\n\",\n       \"43530 0.376642                       0.380821                       \\n\",\n       \"41211 0.237018                       0.308567                       \\n\",\n       \"39537 0.239674                       0.312847                       \\n\",\n       \"39538 0.242434                       0.300882                       \\n\",\n       \"39541 0.381856                       0.356980                       \\n\",\n       \"39539 0.401281                       0.318989                       \\n\",\n       \"\\n\",\n       \"       all_partial_correct@k  all_partial_precision@k  all_partial_recall@k  \\\\\\n\",\n       \"39540 1.301948               0.213735                 0.213735                \\n\",\n       \"43530 3.568000               0.410850                 0.410850                \\n\",\n       \"41211 1.624006               0.368083                 0.368083                \\n\",\n       \"39537 1.635500               0.371101                 0.371101                \\n\",\n       \"39538 1.741304               0.360797                 0.360797                \\n\",\n       \"39541 3.919431               0.396313                 0.396313                \\n\",\n       \"39539 4.420000               0.319565                 0.319565                \\n\",\n       \"\\n\",\n       \"       all_partial_f_score@k  all_partial_precision_hard@k  \\\\\\n\",\n       \"39540 0.213735               0.213735                        \\n\",\n       \"43530 0.410850               0.410850                        \\n\",\n       \"41211 0.368083               0.368083                        \\n\",\n       \"39537 0.371101               0.371101                        \\n\",\n       \"39538 0.360797               0.360797                        \\n\",\n       \"39541 0.396313               0.396313                        \\n\",\n       \"39539 0.319565               0.319565                        \\n\",\n       \"\\n\",\n       \"       all_partial_f_score_hard@k  all_partial_correct@M  \\\\\\n\",\n       \"39540 0.213735                    4.577922                 \\n\",\n       \"43530 0.410850                    7.406000                 \\n\",\n       \"41211 0.368083                    3.622955                 \\n\",\n       \"39537 0.371101                    3.643000                 \\n\",\n       \"39538 0.360797                    3.784783                 \\n\",\n       \"39541 0.396313                    6.957346                 \\n\",\n       \"39539 0.319565                    8.000000                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision@M  all_partial_recall@M  all_partial_f_score@M  \\\\\\n\",\n       \"39540 0.011630                 0.632043              0.022816                 \\n\",\n       \"43530 0.017601                 0.812324              0.034281                 \\n\",\n       \"41211 0.008913                 0.755486              0.017520                 \\n\",\n       \"39537 0.008967                 0.758848              0.017624                 \\n\",\n       \"39538 0.009482                 0.728281              0.018637                 \\n\",\n       \"39541 0.016525                 0.684537              0.031958                 \\n\",\n       \"39539 0.018977                 0.558155              0.036648                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@M  all_partial_f_score_hard@M  \\\\\\n\",\n       \"39540 0.011630                      0.022816                      \\n\",\n       \"43530 0.017601                      0.034281                      \\n\",\n       \"41211 0.008913                      0.017520                      \\n\",\n       \"39537 0.008967                      0.017624                      \\n\",\n       \"39538 0.009482                      0.018637                      \\n\",\n       \"39541 0.016525                      0.031958                      \\n\",\n       \"39539 0.018977                      0.036648                      \\n\",\n       \"\\n\",\n       \"       all_partial_correct@1  all_partial_precision@1  all_partial_recall@1  \\\\\\n\",\n       \"39540 0.211039               0.338932                 0.044439                \\n\",\n       \"43530 0.428000               0.631623                 0.082891                \\n\",\n       \"41211 0.453295               0.572172                 0.128422                \\n\",\n       \"39537 0.460500               0.577391                 0.130194                \\n\",\n       \"39538 0.434783               0.562613                 0.123219                \\n\",\n       \"39541 0.630332               0.735078                 0.092250                \\n\",\n       \"39539 0.550000               0.680452                 0.047169                \\n\",\n       \"\\n\",\n       \"       all_partial_f_score@1  all_partial_precision_hard@1  \\\\\\n\",\n       \"39540 0.078135               0.338932                        \\n\",\n       \"43530 0.141776               0.631623                        \\n\",\n       \"41211 0.205387               0.572172                        \\n\",\n       \"39537 0.207798               0.577391                        \\n\",\n       \"39538 0.195280               0.562613                        \\n\",\n       \"39541 0.155864               0.735078                        \\n\",\n       \"39539 0.087996               0.680452                        \\n\",\n       \"\\n\",\n       \"       all_partial_f_score_hard@1  all_partial_correct@3  \\\\\\n\",\n       \"39540 0.078135                    0.470779                 \\n\",\n       \"43530 0.141776                    1.272000                 \\n\",\n       \"41211 0.205387                    1.059038                 \\n\",\n       \"39537 0.207798                    1.076000                 \\n\",\n       \"39538 0.195280                    1.019565                 \\n\",\n       \"39541 0.155864                    1.587678                 \\n\",\n       \"39539 0.087996                    1.530000                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision@3  all_partial_recall@3  all_partial_f_score@3  \\\\\\n\",\n       \"39540 0.262569                 0.102198              0.145536                 \\n\",\n       \"43530 0.553650                 0.209540              0.288114                 \\n\",\n       \"41211 0.440666                 0.290521              0.338701                 \\n\",\n       \"39537 0.444412                 0.292870              0.341433                 \\n\",\n       \"39538 0.432742                 0.269372              0.315247                 \\n\",\n       \"39541 0.622754                 0.223385              0.300946                 \\n\",\n       \"39539 0.610676                 0.124113              0.205093                 \\n\",\n       \"\\n\",\n       \"       all_partial_precision_hard@3  all_partial_f_score_hard@3  \\\\\\n\",\n       \"39540 0.262569                      0.145536                      \\n\",\n       \"43530 0.553650                      0.288114                      \\n\",\n       \"41211 0.440666                      0.338701                      \\n\",\n       \"39537 0.444412                      0.341433                      \\n\",\n       \"39538 0.432742                      0.315247                      \\n\",\n       \"39541 0.622754                      0.300946                      \\n\",\n       \"39539 0.610676                      0.205093                      \\n\",\n       \"\\n\",\n       \"       present_exact_correct@5  present_exact_precision@5  \\\\\\n\",\n       \"39540 0.717532                 0.143506                     \\n\",\n       \"43530 1.746000                 0.349200                     \\n\",\n       \"41211 1.395557                 0.279111                     \\n\",\n       \"39537 1.402000                 0.280400                     \\n\",\n       \"39538 1.350000                 0.270000                     \\n\",\n       \"39541 2.189573                 0.437915                     \\n\",\n       \"39539 2.000000                 0.400000                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@5  present_exact_f_score@5  \\\\\\n\",\n       \"39540 0.098737                0.114036                   \\n\",\n       \"43530 0.270186                0.285199                   \\n\",\n       \"41211 0.487359                0.331136                   \\n\",\n       \"39537 0.495404                0.333310                   \\n\",\n       \"39538 0.482388                0.319698                   \\n\",\n       \"39541 0.442020                0.401721                   \\n\",\n       \"39539 0.307830                0.328902                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@5  present_exact_f_score_hard@5  \\\\\\n\",\n       \"39540 0.143506                        0.114036                        \\n\",\n       \"43530 0.349200                        0.285199                        \\n\",\n       \"41211 0.279111                        0.331136                        \\n\",\n       \"39537 0.280400                        0.333310                        \\n\",\n       \"39538 0.270000                        0.319698                        \\n\",\n       \"39541 0.437915                        0.401721                        \\n\",\n       \"39539 0.400000                        0.328902                        \\n\",\n       \"\\n\",\n       \"       present_exact_correct@10  present_exact_precision@10  \\\\\\n\",\n       \"39540 1.214286                  0.121429                      \\n\",\n       \"43530 2.896000                  0.289600                      \\n\",\n       \"41211 1.927403                  0.192741                      \\n\",\n       \"39537 1.953500                  0.195350                      \\n\",\n       \"39538 1.880435                  0.188043                      \\n\",\n       \"39541 2.962085                  0.296209                      \\n\",\n       \"39539 2.980000                  0.298000                      \\n\",\n       \"\\n\",\n       \"       present_exact_recall@10  present_exact_f_score@10  \\\\\\n\",\n       \"39540 0.166434                 0.137279                    \\n\",\n       \"43530 0.433500                 0.325433                    \\n\",\n       \"41211 0.644913                 0.278559                    \\n\",\n       \"39537 0.663335                 0.282860                    \\n\",\n       \"39538 0.636585                 0.270322                    \\n\",\n       \"39541 0.563851                 0.359479                    \\n\",\n       \"39539 0.474956                 0.345668                    \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@10  present_exact_f_score_hard@10  \\\\\\n\",\n       \"39540 0.121429                         0.137279                         \\n\",\n       \"43530 0.289600                         0.325433                         \\n\",\n       \"41211 0.192740                         0.278557                         \\n\",\n       \"39537 0.195350                         0.282860                         \\n\",\n       \"39538 0.188043                         0.270322                         \\n\",\n       \"39541 0.296209                         0.359479                         \\n\",\n       \"39539 0.298000                         0.345668                         \\n\",\n       \"\\n\",\n       \"       present_exact_correct@k  present_exact_precision@k  \\\\\\n\",\n       \"39540 1.042208                 0.134033                     \\n\",\n       \"43530 2.678000                 0.337068                     \\n\",\n       \"41211 1.336118                 0.353287                     \\n\",\n       \"39537 1.352500                 0.362472                     \\n\",\n       \"39538 1.245652                 0.355393                     \\n\",\n       \"39541 2.601896                 0.433518                     \\n\",\n       \"39539 2.550000                 0.351779                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@k  present_exact_f_score@k  \\\\\\n\",\n       \"39540 0.134033                0.134033                   \\n\",\n       \"43530 0.337068                0.337068                   \\n\",\n       \"41211 0.353287                0.353287                   \\n\",\n       \"39537 0.362472                0.362472                   \\n\",\n       \"39538 0.355393                0.355393                   \\n\",\n       \"39541 0.433518                0.433518                   \\n\",\n       \"39539 0.351779                0.351779                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@k  present_exact_f_score_hard@k  \\\\\\n\",\n       \"39540 0.134033                        0.134033                        \\n\",\n       \"43530 0.337068                        0.337068                        \\n\",\n       \"41211 0.353287                        0.353287                        \\n\",\n       \"39537 0.362472                        0.362472                        \\n\",\n       \"39538 0.355393                        0.355393                        \\n\",\n       \"39541 0.433518                        0.433518                        \\n\",\n       \"39539 0.351779                        0.351779                        \\n\",\n       \"\\n\",\n       \"       present_exact_correct@M  present_exact_precision@M  \\\\\\n\",\n       \"39540 3.756494                 0.054140                     \\n\",\n       \"43530 6.412000                 0.137513                     \\n\",\n       \"41211 3.050083                 0.062343                     \\n\",\n       \"39537 3.055000                 0.062428                     \\n\",\n       \"39538 2.963043                 0.061795                     \\n\",\n       \"39541 4.862559                 0.102689                     \\n\",\n       \"39539 5.960000                 0.092430                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@M  present_exact_f_score@M  \\\\\\n\",\n       \"39540 0.494076                0.096856                   \\n\",\n       \"43530 0.836179                0.231272                   \\n\",\n       \"41211 0.901892                0.111292                   \\n\",\n       \"39537 0.902916                0.111265                   \\n\",\n       \"39538 0.896647                0.112224                   \\n\",\n       \"39541 0.847547                0.176897                   \\n\",\n       \"39539 0.903935                0.164242                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@M  present_exact_f_score_hard@M  \\\\\\n\",\n       \"39540 0.054140                        0.096856                        \\n\",\n       \"43530 0.137513                        0.231272                        \\n\",\n       \"41211 0.062343                        0.111292                        \\n\",\n       \"39537 0.062428                        0.111265                        \\n\",\n       \"39538 0.061795                        0.112224                        \\n\",\n       \"39541 0.102689                        0.176897                        \\n\",\n       \"39539 0.092430                        0.164242                        \\n\",\n       \"\\n\",\n       \"       present_exact_correct@1  present_exact_precision@1  \\\\\\n\",\n       \"39540 0.211039                 0.211039                     \\n\",\n       \"43530 0.428000                 0.428000                     \\n\",\n       \"41211 0.454946                 0.454946                     \\n\",\n       \"39537 0.461500                 0.461500                     \\n\",\n       \"39538 0.434783                 0.434783                     \\n\",\n       \"39541 0.635071                 0.635071                     \\n\",\n       \"39539 0.550000                 0.550000                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@1  present_exact_f_score@1  \\\\\\n\",\n       \"39540 0.028994                0.050561                   \\n\",\n       \"43530 0.071959                0.118048                   \\n\",\n       \"41211 0.169970                0.233278                   \\n\",\n       \"39537 0.179274                0.242168                   \\n\",\n       \"39538 0.176670                0.233906                   \\n\",\n       \"39541 0.141707                0.215439                   \\n\",\n       \"39539 0.090745                0.150718                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@1  present_exact_f_score_hard@1  \\\\\\n\",\n       \"39540 0.211039                        0.050561                        \\n\",\n       \"43530 0.428000                        0.118048                        \\n\",\n       \"41211 0.454946                        0.233278                        \\n\",\n       \"39537 0.461500                        0.242168                        \\n\",\n       \"39538 0.434783                        0.233906                        \\n\",\n       \"39541 0.635071                        0.215439                        \\n\",\n       \"39539 0.550000                        0.150718                        \\n\",\n       \"\\n\",\n       \"       present_exact_correct@3  present_exact_precision@3  \\\\\\n\",\n       \"39540 0.457792                 0.152597                     \\n\",\n       \"43530 1.168000                 0.389333                     \\n\",\n       \"41211 1.035823                 0.345274                     \\n\",\n       \"39537 1.049500                 0.349833                     \\n\",\n       \"39538 0.982609                 0.327536                     \\n\",\n       \"39541 1.549763                 0.516588                     \\n\",\n       \"39539 1.470000                 0.490000                     \\n\",\n       \"\\n\",\n       \"       present_exact_recall@3  present_exact_f_score@3  \\\\\\n\",\n       \"39540 0.063114                0.087580                   \\n\",\n       \"43530 0.186159                0.237475                   \\n\",\n       \"41211 0.370419                0.332978                   \\n\",\n       \"39537 0.380906                0.338184                   \\n\",\n       \"39538 0.366359                0.318717                   \\n\",\n       \"39541 0.328648                0.365751                   \\n\",\n       \"39539 0.235178                0.300619                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision_hard@3  present_exact_f_score_hard@3  \\\\\\n\",\n       \"39540 0.152597                        0.087580                        \\n\",\n       \"43530 0.389333                        0.237475                        \\n\",\n       \"41211 0.345274                        0.332978                        \\n\",\n       \"39537 0.349833                        0.338184                        \\n\",\n       \"39538 0.327536                        0.318717                        \\n\",\n       \"39541 0.516588                        0.365751                        \\n\",\n       \"39539 0.490000                        0.300619                        \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@10  absent_exact_precision@10  \\\\\\n\",\n       \"39540 0.000000                 0.000000                     \\n\",\n       \"43530 0.088000                 0.008800                     \\n\",\n       \"41211 0.144544                 0.014454                     \\n\",\n       \"39537 0.146000                 0.014600                     \\n\",\n       \"39538 0.215217                 0.021522                     \\n\",\n       \"39541 0.194313                 0.019431                     \\n\",\n       \"39539 0.170000                 0.017000                     \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@10  absent_exact_f_score@10  \\\\\\n\",\n       \"39540 0.000000                0.000000                   \\n\",\n       \"43530 0.040504                0.013514                   \\n\",\n       \"41211 0.068086                0.022835                   \\n\",\n       \"39537 0.066706                0.022980                   \\n\",\n       \"39538 0.083059                0.032190                   \\n\",\n       \"39541 0.046084                0.022211                   \\n\",\n       \"39539 0.024651                0.019534                   \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@10  absent_exact_f_score_hard@10  \\\\\\n\",\n       \"39540 0.000000                        0.000000                        \\n\",\n       \"43530 0.008800                        0.013514                        \\n\",\n       \"41211 0.014454                        0.022835                        \\n\",\n       \"39537 0.014600                        0.022980                        \\n\",\n       \"39538 0.021522                        0.032190                        \\n\",\n       \"39541 0.019431                        0.022211                        \\n\",\n       \"39539 0.017000                        0.019534                        \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@50  absent_exact_precision@50  \\\\\\n\",\n       \"39540 0.000000                 0.000000                     \\n\",\n       \"43530 0.174000                 0.003480                     \\n\",\n       \"41211 0.286036                 0.005721                     \\n\",\n       \"39537 0.286500                 0.005730                     \\n\",\n       \"39538 0.360870                 0.007217                     \\n\",\n       \"39541 0.573460                 0.011469                     \\n\",\n       \"39539 0.310000                 0.006200                     \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@50  absent_exact_f_score@50  \\\\\\n\",\n       \"39540 0.000000                0.000000                   \\n\",\n       \"43530 0.080937                0.006566                   \\n\",\n       \"41211 0.132307                0.010822                   \\n\",\n       \"39537 0.133466                0.010861                   \\n\",\n       \"39538 0.134764                0.013435                   \\n\",\n       \"39541 0.110261                0.019579                   \\n\",\n       \"39539 0.042810                0.010684                   \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@50  absent_exact_f_score_hard@50  \\\\\\n\",\n       \"39540 0.000000                        0.000000                        \\n\",\n       \"43530 0.003480                        0.006566                        \\n\",\n       \"41211 0.005721                        0.010822                        \\n\",\n       \"39537 0.005730                        0.010861                        \\n\",\n       \"39538 0.007217                        0.013435                        \\n\",\n       \"39541 0.011469                        0.019579                        \\n\",\n       \"39539 0.006200                        0.010684                        \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@k  absent_exact_precision@k  \\\\\\n\",\n       \"39540 0.000000                0.000000                    \\n\",\n       \"43530 0.040000                0.011171                    \\n\",\n       \"41211 0.071146                0.028639                    \\n\",\n       \"39537 0.080000                0.031285                    \\n\",\n       \"39538 0.117391                0.037006                    \\n\",\n       \"39541 0.175355                0.021700                    \\n\",\n       \"39539 0.140000                0.019234                    \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@k  absent_exact_f_score@k  \\\\\\n\",\n       \"39540 0.000000               0.000000                  \\n\",\n       \"43530 0.011171               0.011171                  \\n\",\n       \"41211 0.028639               0.028639                  \\n\",\n       \"39537 0.031285               0.031285                  \\n\",\n       \"39538 0.037006               0.037006                  \\n\",\n       \"39541 0.021700               0.021700                  \\n\",\n       \"39539 0.019234               0.019234                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@k  absent_exact_f_score_hard@k  \\\\\\n\",\n       \"39540 0.000000                       0.000000                       \\n\",\n       \"43530 0.011171                       0.011171                       \\n\",\n       \"41211 0.028639                       0.028639                       \\n\",\n       \"39537 0.031285                       0.031285                       \\n\",\n       \"39538 0.037006                       0.037006                       \\n\",\n       \"39541 0.021700                       0.021700                       \\n\",\n       \"39539 0.019234                       0.019234                       \\n\",\n       \"\\n\",\n       \"       absent_exact_correct@M  absent_exact_precision@M  \\\\\\n\",\n       \"39540 0.006494                0.000019                    \\n\",\n       \"43530 0.320000                0.000811                    \\n\",\n       \"41211 0.433432                0.001112                    \\n\",\n       \"39537 0.432500                0.001112                    \\n\",\n       \"39538 0.558696                0.001441                    \\n\",\n       \"39541 0.905213                0.002270                    \\n\",\n       \"39539 0.680000                0.001814                    \\n\",\n       \"\\n\",\n       \"       absent_exact_recall@M  absent_exact_f_score@M  \\\\\\n\",\n       \"39540 0.002706               0.000037                  \\n\",\n       \"43530 0.131048               0.001608                  \\n\",\n       \"41211 0.198050               0.002208                  \\n\",\n       \"39537 0.199031               0.002209                  \\n\",\n       \"39538 0.205719               0.002853                  \\n\",\n       \"39541 0.178371               0.004440                  \\n\",\n       \"39539 0.084422               0.003544                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision_hard@M  absent_exact_f_score_hard@M  \\\\\\n\",\n       \"39540 0.000019                       0.000037                       \\n\",\n       \"43530 0.000811                       0.001608                       \\n\",\n       \"41211 0.001112                       0.002208                       \\n\",\n       \"39537 0.001112                       0.002209                       \\n\",\n       \"39538 0.001441                       0.002853                       \\n\",\n       \"39541 0.002270                       0.004440                       \\n\",\n       \"39539 0.001814                       0.003544                       \\n\",\n       \"\\n\",\n       \"       present_partial_correct@5  present_partial_precision@5  \\\\\\n\",\n       \"39540 0.814935                   0.239459                       \\n\",\n       \"43530 1.948000                   0.463709                       \\n\",\n       \"41211 1.424126                   0.323370                       \\n\",\n       \"39537 1.425000                   0.322952                       \\n\",\n       \"39538 1.391304                   0.318246                       \\n\",\n       \"39541 2.246445                   0.505201                       \\n\",\n       \"39539 2.060000                   0.477565                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@5  present_partial_f_score@5  \\\\\\n\",\n       \"39540 0.162326                  0.189020                     \\n\",\n       \"43530 0.354939                  0.375253                     \\n\",\n       \"41211 0.561840                  0.382626                     \\n\",\n       \"39537 0.564210                  0.382302                     \\n\",\n       \"39538 0.557157                  0.371715                     \\n\",\n       \"39541 0.506380                  0.462352                     \\n\",\n       \"39539 0.377817                  0.398012                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@5  present_partial_f_score_hard@5  \\\\\\n\",\n       \"39540 0.239459                          0.189020                          \\n\",\n       \"43530 0.463709                          0.375253                          \\n\",\n       \"41211 0.323370                          0.382626                          \\n\",\n       \"39537 0.322952                          0.382302                          \\n\",\n       \"39538 0.318246                          0.371715                          \\n\",\n       \"39541 0.505201                          0.462352                          \\n\",\n       \"39539 0.477565                          0.398012                          \\n\",\n       \"\\n\",\n       \"       present_partial_correct@10  present_partial_precision@10  \\\\\\n\",\n       \"39540 1.512987                    0.195414                        \\n\",\n       \"43530 3.288000                    0.368808                        \\n\",\n       \"41211 1.965928                    0.214684                        \\n\",\n       \"39537 1.994000                    0.216606                        \\n\",\n       \"39538 1.947826                    0.213583                        \\n\",\n       \"39541 3.123223                    0.345566                        \\n\",\n       \"39539 3.160000                    0.352758                        \\n\",\n       \"\\n\",\n       \"       present_partial_recall@10  present_partial_f_score@10  \\\\\\n\",\n       \"39540 0.261660                   0.219188                      \\n\",\n       \"43530 0.538098                   0.409561                      \\n\",\n       \"41211 0.712449                   0.309130                      \\n\",\n       \"39537 0.724392                   0.312148                      \\n\",\n       \"39538 0.703996                   0.303585                      \\n\",\n       \"39541 0.648436                   0.417699                      \\n\",\n       \"39539 0.561192                   0.408963                      \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@10  present_partial_f_score_hard@10  \\\\\\n\",\n       \"39540 0.195414                           0.219188                           \\n\",\n       \"43530 0.368808                           0.409561                           \\n\",\n       \"41211 0.214683                           0.309128                           \\n\",\n       \"39537 0.216606                           0.312148                           \\n\",\n       \"39538 0.213583                           0.303585                           \\n\",\n       \"39541 0.345566                           0.417699                           \\n\",\n       \"39539 0.352758                           0.408963                           \\n\",\n       \"\\n\",\n       \"       present_partial_correct@k  present_partial_precision@k  \\\\\\n\",\n       \"39540 1.311688                   0.218568                       \\n\",\n       \"43530 3.096000                   0.439068                       \\n\",\n       \"41211 1.374844                   0.417764                       \\n\",\n       \"39537 1.394500                   0.426124                       \\n\",\n       \"39538 1.313043                   0.418703                       \\n\",\n       \"39541 2.748815                   0.507624                       \\n\",\n       \"39539 2.730000                   0.433077                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@k  present_partial_f_score@k  \\\\\\n\",\n       \"39540 0.218568                  0.218568                     \\n\",\n       \"43530 0.439068                  0.439068                     \\n\",\n       \"41211 0.417764                  0.417764                     \\n\",\n       \"39537 0.426124                  0.426124                     \\n\",\n       \"39538 0.418703                  0.418703                     \\n\",\n       \"39541 0.507624                  0.507624                     \\n\",\n       \"39539 0.433077                  0.433077                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@k  present_partial_f_score_hard@k  \\\\\\n\",\n       \"39540 0.218568                          0.218568                          \\n\",\n       \"43530 0.439068                          0.439068                          \\n\",\n       \"41211 0.417764                          0.417764                          \\n\",\n       \"39537 0.426124                          0.426124                          \\n\",\n       \"39538 0.418703                          0.418703                          \\n\",\n       \"39541 0.507624                          0.507624                          \\n\",\n       \"39539 0.433077                          0.433077                          \\n\",\n       \"\\n\",\n       \"       present_partial_correct@M  present_partial_precision@M  \\\\\\n\",\n       \"39540 4.399351                   0.069471                       \\n\",\n       \"43530 6.638000                   0.147509                       \\n\",\n       \"41211 3.062240                   0.063641                       \\n\",\n       \"39537 3.068000                   0.063648                       \\n\",\n       \"39538 2.989130                   0.063619                       \\n\",\n       \"39541 5.056872                   0.111772                       \\n\",\n       \"39539 6.070000                   0.096314                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@M  present_partial_f_score@M  \\\\\\n\",\n       \"39540 0.630677                  0.124232                     \\n\",\n       \"43530 0.894414                  0.247918                     \\n\",\n       \"41211 0.917541                  0.113513                     \\n\",\n       \"39537 0.917881                  0.113342                     \\n\",\n       \"39538 0.915720                  0.115401                     \\n\",\n       \"39541 0.903140                  0.192106                     \\n\",\n       \"39539 0.935156                  0.170902                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@M  present_partial_f_score_hard@M  \\\\\\n\",\n       \"39540 0.069471                          0.124232                          \\n\",\n       \"43530 0.147509                          0.247918                          \\n\",\n       \"41211 0.063641                          0.113513                          \\n\",\n       \"39537 0.063648                          0.113342                          \\n\",\n       \"39538 0.063619                          0.115401                          \\n\",\n       \"39541 0.111772                          0.192106                          \\n\",\n       \"39539 0.096314                          0.170902                          \\n\",\n       \"\\n\",\n       \"       present_partial_correct@1  present_partial_precision@1  \\\\\\n\",\n       \"39540 0.211039                   0.338005                       \\n\",\n       \"43530 0.428000                   0.610255                       \\n\",\n       \"41211 0.454946                   0.538957                       \\n\",\n       \"39537 0.461500                   0.545148                       \\n\",\n       \"39538 0.434783                   0.520675                       \\n\",\n       \"39541 0.635071                   0.724152                       \\n\",\n       \"39539 0.550000                   0.640881                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@1  present_partial_f_score@1  \\\\\\n\",\n       \"39540 0.046849                  0.081298                     \\n\",\n       \"43530 0.102639                  0.167536                     \\n\",\n       \"41211 0.203360                  0.278133                     \\n\",\n       \"39537 0.211517                  0.285869                     \\n\",\n       \"39538 0.210437                  0.277560                     \\n\",\n       \"39541 0.164747                  0.249418                     \\n\",\n       \"39539 0.109593                  0.180088                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@1  present_partial_f_score_hard@1  \\\\\\n\",\n       \"39540 0.338005                          0.081298                          \\n\",\n       \"43530 0.610255                          0.167536                          \\n\",\n       \"41211 0.538957                          0.278133                          \\n\",\n       \"39537 0.545148                          0.285869                          \\n\",\n       \"39538 0.520675                          0.277560                          \\n\",\n       \"39541 0.724152                          0.249418                          \\n\",\n       \"39539 0.640881                          0.180088                          \\n\",\n       \"\\n\",\n       \"       present_partial_correct@3  present_partial_precision@3  \\\\\\n\",\n       \"39540 0.477273                   0.263156                       \\n\",\n       \"43530 1.240000                   0.529538                       \\n\",\n       \"41211 1.049382                   0.407171                       \\n\",\n       \"39537 1.064500                   0.409571                       \\n\",\n       \"39538 0.997826                   0.392351                       \\n\",\n       \"39541 1.568720                   0.596938                       \\n\",\n       \"39539 1.500000                   0.571606                       \\n\",\n       \"\\n\",\n       \"       present_partial_recall@3  present_partial_f_score@3  \\\\\\n\",\n       \"39540 0.107333                  0.149529                     \\n\",\n       \"43530 0.251130                  0.319574                     \\n\",\n       \"41211 0.437236                  0.392563                     \\n\",\n       \"39537 0.444043                  0.395489                     \\n\",\n       \"39538 0.430838                  0.376105                     \\n\",\n       \"39541 0.379181                  0.422866                     \\n\",\n       \"39539 0.279622                  0.355059                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision_hard@3  present_partial_f_score_hard@3  \\\\\\n\",\n       \"39540 0.263156                          0.149529                          \\n\",\n       \"43530 0.529538                          0.319574                          \\n\",\n       \"41211 0.407171                          0.392563                          \\n\",\n       \"39537 0.409571                          0.395489                          \\n\",\n       \"39538 0.392351                          0.376105                          \\n\",\n       \"39541 0.596938                          0.422866                          \\n\",\n       \"39539 0.571606                          0.355059                          \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@10  absent_partial_precision@10  \\\\\\n\",\n       \"39540 0.000000                   0.001784                       \\n\",\n       \"43530 0.098000                   0.027277                       \\n\",\n       \"41211 0.151599                   0.031760                       \\n\",\n       \"39537 0.151500                   0.032141                       \\n\",\n       \"39538 0.226087                   0.042625                       \\n\",\n       \"39541 0.303318                   0.061064                       \\n\",\n       \"39539 0.300000                   0.073624                       \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@10  absent_partial_f_score@10  \\\\\\n\",\n       \"39540 0.011396                  0.003001                     \\n\",\n       \"43530 0.115397                  0.041278                     \\n\",\n       \"41211 0.145575                  0.049819                     \\n\",\n       \"39537 0.146838                  0.050539                     \\n\",\n       \"39538 0.161839                  0.063254                     \\n\",\n       \"39541 0.119035                  0.068892                     \\n\",\n       \"39539 0.095364                  0.079940                     \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@10  absent_partial_f_score_hard@10  \\\\\\n\",\n       \"39540 0.001784                          0.003001                          \\n\",\n       \"43530 0.027277                          0.041278                          \\n\",\n       \"41211 0.031760                          0.049819                          \\n\",\n       \"39537 0.032141                          0.050539                          \\n\",\n       \"39538 0.042625                          0.063254                          \\n\",\n       \"39541 0.061064                          0.068892                          \\n\",\n       \"39539 0.073624                          0.079940                          \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@50  absent_partial_precision@50  \\\\\\n\",\n       \"39540 0.000000                   0.000544                       \\n\",\n       \"43530 0.230000                   0.009479                       \\n\",\n       \"41211 0.312753                   0.010701                       \\n\",\n       \"39537 0.311000                   0.010810                       \\n\",\n       \"39538 0.413043                   0.014111                       \\n\",\n       \"39541 0.914692                   0.025234                       \\n\",\n       \"39539 0.750000                   0.024536                       \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@50  absent_partial_f_score@50  \\\\\\n\",\n       \"39540 0.017296                  0.001045                     \\n\",\n       \"43530 0.197926                  0.017727                     \\n\",\n       \"41211 0.240634                  0.020203                     \\n\",\n       \"39537 0.245387                  0.020448                     \\n\",\n       \"39538 0.261438                  0.026238                     \\n\",\n       \"39541 0.226438                  0.042777                     \\n\",\n       \"39539 0.154133                  0.041586                     \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@50  absent_partial_f_score_hard@50  \\\\\\n\",\n       \"39540 0.000544                          0.001045                          \\n\",\n       \"43530 0.009479                          0.017727                          \\n\",\n       \"41211 0.010701                          0.020203                          \\n\",\n       \"39537 0.010810                          0.020448                          \\n\",\n       \"39538 0.014111                          0.026238                          \\n\",\n       \"39541 0.025234                          0.042777                          \\n\",\n       \"39539 0.024536                          0.041586                          \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@k  absent_partial_precision@k  \\\\\\n\",\n       \"39540 0.000000                  0.002328                      \\n\",\n       \"43530 0.046000                  0.044370                      \\n\",\n       \"41211 0.073848                  0.065676                      \\n\",\n       \"39537 0.081000                  0.068355                      \\n\",\n       \"39538 0.126087                  0.075617                      \\n\",\n       \"39541 0.317536                  0.077060                      \\n\",\n       \"39539 0.290000                  0.082642                      \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@k  absent_partial_f_score@k  \\\\\\n\",\n       \"39540 0.002328                 0.002328                    \\n\",\n       \"43530 0.044370                 0.044370                    \\n\",\n       \"41211 0.065676                 0.065676                    \\n\",\n       \"39537 0.068355                 0.068355                    \\n\",\n       \"39538 0.075617                 0.075617                    \\n\",\n       \"39541 0.077060                 0.077060                    \\n\",\n       \"39539 0.082642                 0.082642                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@k  absent_partial_f_score_hard@k  \\\\\\n\",\n       \"39540 0.002328                         0.002328                         \\n\",\n       \"43530 0.044370                         0.044370                         \\n\",\n       \"41211 0.065676                         0.065676                         \\n\",\n       \"39537 0.068355                         0.068355                         \\n\",\n       \"39538 0.075617                         0.075617                         \\n\",\n       \"39541 0.077060                         0.077060                         \\n\",\n       \"39539 0.082642                         0.082642                         \\n\",\n       \"\\n\",\n       \"       absent_partial_correct@M  absent_partial_precision@M  \\\\\\n\",\n       \"39540 0.009740                  0.000110                      \\n\",\n       \"43530 0.442000                  0.001829                      \\n\",\n       \"41211 0.489468                  0.001885                      \\n\",\n       \"39537 0.487500                  0.001916                      \\n\",\n       \"39538 0.663043                  0.002521                      \\n\",\n       \"39541 1.473934                  0.004550                      \\n\",\n       \"39539 1.400000                  0.005017                      \\n\",\n       \"\\n\",\n       \"       absent_partial_recall@M  absent_partial_f_score@M  \\\\\\n\",\n       \"39540 0.024927                 0.000219                    \\n\",\n       \"43530 0.284793                 0.003623                    \\n\",\n       \"41211 0.326677                 0.003740                    \\n\",\n       \"39537 0.335053                 0.003804                    \\n\",\n       \"39538 0.358958                 0.004991                    \\n\",\n       \"39541 0.320352                 0.008886                    \\n\",\n       \"39539 0.231300                 0.009791                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision_hard@M  absent_partial_f_score_hard@M  \\\\\\n\",\n       \"39540 0.000110                         0.000219                         \\n\",\n       \"43530 0.001829                         0.003623                         \\n\",\n       \"41211 0.001885                         0.003740                         \\n\",\n       \"39537 0.001916                         0.003804                         \\n\",\n       \"39538 0.002521                         0.004991                         \\n\",\n       \"39541 0.004550                         0.008886                         \\n\",\n       \"39539 0.005017                         0.009791                         \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_auc  all_exact_advanced_ap  all_exact_advanced_mrr  \\\\\\n\",\n       \"39540 0.081467                0.098637               0.136927                  \\n\",\n       \"43530 0.232709                0.258530               0.191272                  \\n\",\n       \"41211 0.252209                0.282558               0.287931                  \\n\",\n       \"39537 0.256249                0.286813               0.293698                  \\n\",\n       \"39538 0.231903                0.260767               0.287466                  \\n\",\n       \"39541 0.242825                0.262321               0.267252                  \\n\",\n       \"39539 0.164262                0.176712               0.198717                  \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_sadr  all_exact_advanced_ndcg  \\\\\\n\",\n       \"39540 0.210140                 0.279386                   \\n\",\n       \"43530 0.402676                 0.500456                   \\n\",\n       \"41211 0.399323                 0.474906                   \\n\",\n       \"39537 0.402698                 0.479184                   \\n\",\n       \"39538 0.378815                 0.451300                   \\n\",\n       \"39541 0.368942                 0.478799                   \\n\",\n       \"39539 0.279747                 0.392348                   \\n\",\n       \"\\n\",\n       \"       all_exact_advanced_alpha_ndcg@5  all_exact_advanced_alpha_ndcg@10  \\\\\\n\",\n       \"39540 0.312653                         0.374509                            \\n\",\n       \"43530 0.514255                         0.610513                            \\n\",\n       \"41211 0.491475                         0.505926                            \\n\",\n       \"39537 0.493073                         0.507145                            \\n\",\n       \"39538 0.467576                         0.493646                            \\n\",\n       \"39541 0.435713                         0.498921                            \\n\",\n       \"39539 0.342606                         0.442319                            \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_auc  present_exact_advanced_ap  \\\\\\n\",\n       \"39540 0.093736                    0.113044                     \\n\",\n       \"43530 0.315749                    0.350422                     \\n\",\n       \"41211 0.384584                    0.435772                     \\n\",\n       \"39537 0.395229                    0.446498                     \\n\",\n       \"39538 0.378395                    0.429610                     \\n\",\n       \"39541 0.428715                    0.464357                     \\n\",\n       \"39539 0.360125                    0.393501                     \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_mrr  present_exact_advanced_sadr  \\\\\\n\",\n       \"39540 0.144103                    0.226051                       \\n\",\n       \"43530 0.209305                    0.492266                       \\n\",\n       \"41211 0.331484                    0.552359                       \\n\",\n       \"39537 0.339469                    0.560701                       \\n\",\n       \"39538 0.336495                    0.549921                       \\n\",\n       \"39541 0.319006                    0.573981                       \\n\",\n       \"39539 0.226980                    0.514568                       \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_ndcg  present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"39540 0.297656                     0.319047                               \\n\",\n       \"43530 0.586410                     0.543275                               \\n\",\n       \"41211 0.607514                     0.518599                               \\n\",\n       \"39537 0.615740                     0.522232                               \\n\",\n       \"39538 0.600400                     0.504450                               \\n\",\n       \"39541 0.664906                     0.567121                               \\n\",\n       \"39539 0.633782                     0.519435                               \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"39540 0.382336                              0.000016                     \\n\",\n       \"43530 0.626779                              0.011467                     \\n\",\n       \"41211 0.524472                              0.029099                     \\n\",\n       \"39537 0.528234                              0.031441                     \\n\",\n       \"39538 0.517271                              0.033968                     \\n\",\n       \"39541 0.610301                              0.016146                     \\n\",\n       \"39539 0.574970                              0.012503                     \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"39540 0.000032                  0.000077                     \\n\",\n       \"43530 0.017885                  0.027635                     \\n\",\n       \"41211 0.037118                  0.060574                     \\n\",\n       \"39537 0.038770                  0.062675                     \\n\",\n       \"39538 0.042875                  0.078378                     \\n\",\n       \"39541 0.023825                  0.063280                     \\n\",\n       \"39539 0.014684                  0.077000                     \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"39540 0.000423                    0.000547                      \\n\",\n       \"43530 0.041653                    0.046092                      \\n\",\n       \"41211 0.070328                    0.079199                      \\n\",\n       \"39537 0.071403                    0.080965                      \\n\",\n       \"39538 0.079327                    0.091876                      \\n\",\n       \"39541 0.059430                    0.076505                      \\n\",\n       \"39539 0.032604                    0.049848                      \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"39540 0.001524                              \\n\",\n       \"43530 0.025783                              \\n\",\n       \"41211 0.046830                              \\n\",\n       \"39537 0.049142                              \\n\",\n       \"39538 0.059146                              \\n\",\n       \"39541 0.045719                              \\n\",\n       \"39539 0.045558                              \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_alpha_ndcg@10  present_tgt_num  absent_tgt_num  \\\\\\n\",\n       \"39540 0.001524                             7.860390         0.204545          \\n\",\n       \"43530 0.026324                             7.842000         1.984000          \\n\",\n       \"41211 0.047002                             3.339471         1.923000          \\n\",\n       \"39537 0.049267                             3.337000         1.928000          \\n\",\n       \"39538 0.059801                             3.243478         2.497826          \\n\",\n       \"39541 0.053390                             5.971564         5.691943          \\n\",\n       \"39539 0.052690                             6.730000         8.340000          \\n\",\n       \"\\n\",\n       \"       present_pred_num  absent_pred_num  unique_pred_num  dup_pred_num  \\\\\\n\",\n       \"39540 72.542208         361.094156       0.000000         0.000000        \\n\",\n       \"43530 46.546000         396.912000       0.000000         0.000000        \\n\",\n       \"41211 51.233102         390.732126       497.355181       497.355181      \\n\",\n       \"39537 51.226500         390.050000       0.000000         0.000000        \\n\",\n       \"39538 48.671739         387.671739       0.000000         0.000000        \\n\",\n       \"39541 49.829384         396.597156       0.000000         0.000000        \\n\",\n       \"39539 65.370000         380.420000       0.000000         0.000000        \\n\",\n       \"\\n\",\n       \"        beam_num  beamstep_num  \\n\",\n       \"39540 0.000000   0.000000       \\n\",\n       \"43530 0.000000   0.000000       \\n\",\n       \"41211 497.355181 2,242.080052   \\n\",\n       \"39537 0.000000   0.000000       \\n\",\n       \"39538 0.000000   0.000000       \\n\",\n       \"39541 0.000000   0.000000       \\n\",\n       \"39539 0.000000   0.000000       \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"duc\\n\",\n      \"#total=8.0649\\n\",\n      \"#present=7.8604 (97.46%)\\n\",\n      \"#absent=0.2045 (2.54%)\\n\",\n      \"inspec\\n\",\n      \"#total=9.8260\\n\",\n      \"#present=7.8420 (79.81%)\\n\",\n      \"#absent=1.9840 (20.19%)\\n\",\n      \"kp20k\\n\",\n      \"#total=5.2625\\n\",\n      \"#present=3.3395 (63.46%)\\n\",\n      \"#absent=1.9230 (36.54%)\\n\",\n      \"kp20k_valid2k\\n\",\n      \"#total=5.2650\\n\",\n      \"#present=3.3370 (63.38%)\\n\",\n      \"#absent=1.9280 (36.62%)\\n\",\n      \"krapivin\\n\",\n      \"#total=5.7413\\n\",\n      \"#present=3.2435 (56.49%)\\n\",\n      \"#absent=2.4978 (43.51%)\\n\",\n      \"nus\\n\",\n      \"#total=11.6635\\n\",\n      \"#present=5.9716 (51.20%)\\n\",\n      \"#absent=5.6919 (48.80%)\\n\",\n      \"semeval\\n\",\n      \"#total=15.0700\\n\",\n      \"#present=6.7300 (44.66%)\\n\",\n      \"#absent=8.3400 (55.34%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"pd.options.display.float_format = '{:,.6f}'.format\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['average', 'kp20k', 'krapivin', 'inspec', 'nus', 'semeval', 'duc']\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10']\\n\",\n    \"# metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"short2long = {\\n\",\n    \"# One2One\\n\",\n    \"'RNN-O2O-KP20k': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"# beam search\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"# greedy\\n\",\n    \"# kp_df = kp_df.loc[(kp_df.beam_width == '1')]\\n\",\n    \"# kp_df = kp_df.loc[kp_df.decoding_method == 'selfterminating'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    kp_df.at[index_label , 'exp_name'] = long2short[kp_df.at[index_label , 'exp_name']]\\n\",\n    \"\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"for index, row in valid_kp_df.iterrows():\\n\",\n    \"    print(row['test_dataset'])\\n\",\n    \"    total = row['present_tgt_num'] + row['absent_tgt_num']\\n\",\n    \"    print('#total=%.4f' % (total))\\n\",\n    \"    print('#present=%.4f (%.2f%%)' % (row['present_tgt_num'], row['present_tgt_num'] / total * 100.0))\\n\",\n    \"    print('#absent=%.4f (%.2f%%)' % (row['absent_tgt_num'], row['absent_tgt_num'] / total * 100.0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_keys(['all_exact_correct@5', 'all_exact_precision@5', 'all_exact_recall@5', 'all_exact_f_score@5', 'all_exact_precision_hard@5', 'all_exact_f_score_hard@5', 'all_exact_correct@10', 'all_exact_precision@10', 'all_exact_recall@10', 'all_exact_f_score@10', 'all_exact_precision_hard@10', 'all_exact_f_score_hard@10', 'all_exact_correct@k', 'all_exact_precision@k', 'all_exact_recall@k', 'all_exact_f_score@k', 'all_exact_precision_hard@k', 'all_exact_f_score_hard@k', 'all_exact_correct@M', 'all_exact_precision@M', 'all_exact_recall@M', 'all_exact_f_score@M', 'all_exact_precision_hard@M', 'all_exact_f_score_hard@M', 'all_exact_correct@1', 'all_exact_precision@1', 'all_exact_recall@1', 'all_exact_f_score@1', 'all_exact_precision_hard@1', 'all_exact_f_score_hard@1', 'all_exact_correct@3', 'all_exact_precision@3', 'all_exact_recall@3', 'all_exact_f_score@3', 'all_exact_precision_hard@3', 'all_exact_f_score_hard@3', 'all_partial_correct@5', 'all_partial_precision@5', 'all_partial_recall@5', 'all_partial_f_score@5', 'all_partial_precision_hard@5', 'all_partial_f_score_hard@5', 'all_partial_correct@10', 'all_partial_precision@10', 'all_partial_recall@10', 'all_partial_f_score@10', 'all_partial_precision_hard@10', 'all_partial_f_score_hard@10', 'all_partial_correct@k', 'all_partial_precision@k', 'all_partial_recall@k', 'all_partial_f_score@k', 'all_partial_precision_hard@k', 'all_partial_f_score_hard@k', 'all_partial_correct@M', 'all_partial_precision@M', 'all_partial_recall@M', 'all_partial_f_score@M', 'all_partial_precision_hard@M', 'all_partial_f_score_hard@M', 'all_partial_correct@1', 'all_partial_precision@1', 'all_partial_recall@1', 'all_partial_f_score@1', 'all_partial_precision_hard@1', 'all_partial_f_score_hard@1', 'all_partial_correct@3', 'all_partial_precision@3', 'all_partial_recall@3', 'all_partial_f_score@3', 'all_partial_precision_hard@3', 'all_partial_f_score_hard@3', 'present_exact_correct@5', 'present_exact_precision@5', 'present_exact_recall@5', 'present_exact_f_score@5', 'present_exact_precision_hard@5', 'present_exact_f_score_hard@5', 'present_exact_correct@10', 'present_exact_precision@10', 'present_exact_recall@10', 'present_exact_f_score@10', 'present_exact_precision_hard@10', 'present_exact_f_score_hard@10', 'present_exact_correct@k', 'present_exact_precision@k', 'present_exact_recall@k', 'present_exact_f_score@k', 'present_exact_precision_hard@k', 'present_exact_f_score_hard@k', 'present_exact_correct@M', 'present_exact_precision@M', 'present_exact_recall@M', 'present_exact_f_score@M', 'present_exact_precision_hard@M', 'present_exact_f_score_hard@M', 'present_exact_correct@1', 'present_exact_precision@1', 'present_exact_recall@1', 'present_exact_f_score@1', 'present_exact_precision_hard@1', 'present_exact_f_score_hard@1', 'present_exact_correct@3', 'present_exact_precision@3', 'present_exact_recall@3', 'present_exact_f_score@3', 'present_exact_precision_hard@3', 'present_exact_f_score_hard@3', 'absent_exact_correct@10', 'absent_exact_precision@10', 'absent_exact_recall@10', 'absent_exact_f_score@10', 'absent_exact_precision_hard@10', 'absent_exact_f_score_hard@10', 'absent_exact_correct@50', 'absent_exact_precision@50', 'absent_exact_recall@50', 'absent_exact_f_score@50', 'absent_exact_precision_hard@50', 'absent_exact_f_score_hard@50', 'absent_exact_correct@k', 'absent_exact_precision@k', 'absent_exact_recall@k', 'absent_exact_f_score@k', 'absent_exact_precision_hard@k', 'absent_exact_f_score_hard@k', 'absent_exact_correct@M', 'absent_exact_precision@M', 'absent_exact_recall@M', 'absent_exact_f_score@M', 'absent_exact_precision_hard@M', 'absent_exact_f_score_hard@M', 'present_partial_correct@5', 'present_partial_precision@5', 'present_partial_recall@5', 'present_partial_f_score@5', 'present_partial_precision_hard@5', 'present_partial_f_score_hard@5', 'present_partial_correct@10', 'present_partial_precision@10', 'present_partial_recall@10', 'present_partial_f_score@10', 'present_partial_precision_hard@10', 'present_partial_f_score_hard@10', 'present_partial_correct@k', 'present_partial_precision@k', 'present_partial_recall@k', 'present_partial_f_score@k', 'present_partial_precision_hard@k', 'present_partial_f_score_hard@k', 'present_partial_correct@M', 'present_partial_precision@M', 'present_partial_recall@M', 'present_partial_f_score@M', 'present_partial_precision_hard@M', 'present_partial_f_score_hard@M', 'present_partial_correct@1', 'present_partial_precision@1', 'present_partial_recall@1', 'present_partial_f_score@1', 'present_partial_precision_hard@1', 'present_partial_f_score_hard@1', 'present_partial_correct@3', 'present_partial_precision@3', 'present_partial_recall@3', 'present_partial_f_score@3', 'present_partial_precision_hard@3', 'present_partial_f_score_hard@3', 'absent_partial_correct@10', 'absent_partial_precision@10', 'absent_partial_recall@10', 'absent_partial_f_score@10', 'absent_partial_precision_hard@10', 'absent_partial_f_score_hard@10', 'absent_partial_correct@50', 'absent_partial_precision@50', 'absent_partial_recall@50', 'absent_partial_f_score@50', 'absent_partial_precision_hard@50', 'absent_partial_f_score_hard@50', 'absent_partial_correct@k', 'absent_partial_precision@k', 'absent_partial_recall@k', 'absent_partial_f_score@k', 'absent_partial_precision_hard@k', 'absent_partial_f_score_hard@k', 'absent_partial_correct@M', 'absent_partial_precision@M', 'absent_partial_recall@M', 'absent_partial_f_score@M', 'absent_partial_precision_hard@M', 'absent_partial_f_score_hard@M', 'all_exact_advanced_auc', 'all_exact_advanced_ap', 'all_exact_advanced_mrr', 'all_exact_advanced_sadr', 'all_exact_advanced_ndcg', 'all_exact_advanced_alpha_ndcg@5', 'all_exact_advanced_alpha_ndcg@10', 'present_exact_advanced_auc', 'present_exact_advanced_ap', 'present_exact_advanced_mrr', 'present_exact_advanced_sadr', 'present_exact_advanced_ndcg', 'present_exact_advanced_alpha_ndcg@5', 'present_exact_advanced_alpha_ndcg@10', 'absent_exact_advanced_auc', 'absent_exact_advanced_ap', 'absent_exact_advanced_mrr', 'absent_exact_advanced_sadr', 'absent_exact_advanced_ndcg', 'absent_exact_advanced_alpha_ndcg@5', 'absent_exact_advanced_alpha_ndcg@10', 'present_tgt_num', 'absent_tgt_num', 'present_pred_num', 'absent_pred_num', 'unique_pred_num', 'dup_pred_num', 'beam_num', 'beamstep_num'])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"eval_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/pred/kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue_step_95000/duc.split_nopunc.eval'\\n\",\n    \"\\n\",\n    \"eval_dict = json.load(open(eval_path, 'r'))\\n\",\n    \"\\n\",\n    \"print(eval_dict.keys())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2421.0\\n\",\n      \"63.00000000000001\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.mean(eval_dict['present_tgt_num'])*308)\\n\",\n    \"print(np.mean(eval_dict['absent_tgt_num'])*308)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.07171894928770407\\n\",\n      \"0.08274326564322508\\n\",\n      \"0.08310621557374805\\n\",\n      \"0.07029595794242598\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.mean(eval_dict['present_exact_f_score@5']))\\n\",\n    \"print(np.mean(eval_dict['present_exact_f_score@10']))\\n\",\n    \"print(np.mean(eval_dict['present_exact_f_score@k']))\\n\",\n    \"print(np.mean(eval_dict['present_exact_f_score@M']))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.0010822510822510823\\n\",\n      \"0.0021645021645021645\\n\",\n      \"0.0021645021645021645\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.mean(eval_dict['absent_exact_recall@10']))\\n\",\n    \"print(np.mean(eval_dict['absent_exact_recall@50']))\\n\",\n    \"print(np.mean(eval_dict['absent_exact_recall@M']))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.06284844569317449\\n\",\n      \"0.0695145268015736\\n\",\n      \"0.0689542600256886\\n\",\n      \"0.010727542468007297\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(np.mean(eval_dict['all_exact_f_score@5']))\\n\",\n    \"print(np.mean(eval_dict['all_exact_f_score@10']))\\n\",\n    \"print(np.mean(eval_dict['all_exact_f_score@k']))\\n\",\n    \"print(np.mean(eval_dict['all_exact_f_score@M']))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"349.091px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:31:24.890139Z\",\n     \"start_time\": \"2020-10-25T03:30:58.974266Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"exec('from __future__ import unicode_literals')\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import random\\n\",\n    \"import json\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('../'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"from itertools import repeat\\n\",\n    \"\\n\",\n    \"from onmt.utils.logging import init_logger\\n\",\n    \"from onmt.utils.misc import split_corpus\\n\",\n    \"import onmt.translate.translator as translator\\n\",\n    \"\\n\",\n    \"import onmt.opts as opts\\n\",\n    \"from onmt.utils.parse import ArgumentParser\\n\",\n    \"from kp_gen_eval import _get_parser\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from nltk.corpus import stopwords\\n\",\n    \"stoplist = stopwords.words('english')\\n\",\n    \"\\n\",\n    \"from string import punctuation\\n\",\n    \"import onmt.keyphrase.pke as pke\\n\",\n    \"from nltk.corpus import stopwords\\n\",\n    \"\\n\",\n    \"import onmt.keyphrase.kp_inference as kp_inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:31:24.912551Z\",\n     \"start_time\": \"2020-10-25T03:31:24.897625Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import importlib\\n\",\n    \"importlib.reload(kp_inference)\\n\",\n    \"importlib.reload(translator)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load a text (assume current directory is OpenNMT-kpg/notebook/)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:31:29.198694Z\",\n     \"start_time\": \"2020-10-25T03:31:29.025672Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_root_path = '../data/keyphrase/json/duc/duc_test.json'\\n\",\n    \"doc_dicts = []\\n\",\n    \"with open(data_root_path, 'r') as data_file:\\n\",\n    \"    doc_dicts = [json.loads(l) for l in data_file]\\n\",\n    \"        \\n\",\n    \"print('Loaded #(docs)=%d' % (len(doc_dicts)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### Sample a paragraph\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:31:32.200484Z\",\n     \"start_time\": \"2020-10-25T03:31:32.191236Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"doc_id = random.randint(0, len(doc_dicts))\\n\",\n    \"doc = doc_dicts[doc_id]\\n\",\n    \"print(doc.keys())\\n\",\n    \"text_to_extract = doc['abstract']\\n\",\n    \"print(doc_id)\\n\",\n    \"print(text_to_extract)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Supervised Deep Keyphrase Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:40:03.487500Z\",\n     \"start_time\": \"2020-10-25T03:40:01.483592Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/config/translate/config-rnn-keyphrase.yml\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"parser = _get_parser()\\n\",\n    \"config_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/config/translate/config-rnn-keyphrase.yml'\\n\",\n    \"print(os.path.abspath('../config/translate/config-rnn-keyphrase.yml'))\\n\",\n    \"print(os.path.exists(config_path))\\n\",\n    \"# one2one_ckpt_path = 'models/keyphrase/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_30000.pt'\\n\",\n    \"one2seq_ckpt_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_50000.pt'\\n\",\n    \"opt = parser.parse_args('-config %s' % (config_path))\\n\",\n    \"setattr(opt, 'models', [one2seq_ckpt_path])\\n\",\n    \"\\n\",\n    \"translator = translator.build_translator(opt, report_score=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T03:45:28.192887Z\",\n     \"start_time\": \"2020-10-25T03:45:27.564147Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Translating 10/1\\n\",\n      \"Total translation time (s): 0.613762\\n\",\n      \"Average translation time (s): 0.613762\\n\",\n      \"Tokens per second: 1.629296\\n\",\n      \"Paragraph:\\n\",\n      \"\\tElizabeth Taylor will remain in the hospital six more weeks due to complications in her fifth week of treatment for pneumonia, doctors said. The recovery of Miss Taylor, near death two weeks ago with viral pneumonia, was dealt a setback by bacterial pneumonia and a yeast infection, her doctors said Friday. ``This secondary bacterial pneumonia often follows viral pneumonia. Her condition is listed as stable and she is improving significantly,'' they said in a statement released by St. John's Hospital and Health Center. Earlier this week Miss Taylor's New York publicist, Chen Sam, had said the 58-year-old actress was improving and would be released from the hospital this week to recuperate at home. During a news conference last month, Miss Taylor's doctors revealed she was near death on April 22. The Oscar-winning star of ``Who's Afraid of Virginia Woolf?'' and ``Butterfield 8'' entered Daniel Freeman Marina Hospital on April 9 with a sinus infection, but her condition deteriorated and she was moved to St. John's for treatment of viral pneumonia.\\n\",\n      \"Top predictions:\\n\",\n      \"\\t1: elizabeth\\n\",\n      \"\\t2: taylor\\n\",\n      \"\\t3: pneumonia,\\n\",\n      \"\\t4: pneumonia\\n\",\n      \"\\t5: bacterial pneumonia\\n\",\n      \"\\t6: viral pneumonia.\\n\",\n      \"\\t7: miss\\n\",\n      \"\\t8: viral pneumonia,\\n\",\n      \"\\t9: doctors\\n\",\n      \"\\t10: hospital six\\n\",\n      \"\\t11: hospital six more weeks\\n\",\n      \"\\t12: viral\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scores, predictions = translator.translate(\\n\",\n    \"    src=[text_to_extract],\\n\",\n    \"    tgt=None,\\n\",\n    \"    src_dir=opt.src_dir,\\n\",\n    \"    batch_size=opt.batch_size,\\n\",\n    \"    attn_debug=opt.attn_debug,\\n\",\n    \"    opt=opt\\n\",\n    \")\\n\",\n    \"print('Paragraph:\\\\n\\\\t'+text_to_extract)\\n\",\n    \"print('Top predictions:')\\n\",\n    \"keyphrases = [kp.lower().strip() for kp in predictions[0] if (not kp.lower().strip() in stoplist) and (kp != '<unk>') and (len(kp.strip())) > 0]\\n\",\n    \"for kp_id, kp in enumerate(keyphrases[: min(len(keyphrases), 20)]):\\n\",\n    \"    print('\\\\t%d: %s' % (kp_id+1, kp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### PKE models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### TF-IDF\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset_name = 'test'\\n\",\n    \"dataset_path = '../data/%s/' % dataset_name\\n\",\n    \"_ = kp_inference.extract_pke(text_to_extract, method='tfidf' , dataset_path=dataset_path,\\n\",\n    \"            df_path=os.path.abspath(dataset_path + '../%s.df.tsv.gz' % dataset_name), top_k=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### YAKE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\t1: called business risk (0.0331)\\n\",\n      \"\\t2: financial goals (0.0332)\\n\",\n      \"\\t3: called business (0.0632)\\n\",\n      \"\\t4: business risk (0.0830)\\n\",\n      \"\\t5: risk (0.1075)\\n\",\n      \"\\t6: company (0.1209)\\n\",\n      \"\\t7: anything (0.1383)\\n\",\n      \"\\t8: ability (0.1383)\\n\",\n      \"\\t9: risks may come (0.1415)\\n\",\n      \"\\t10: business (0.1657)\\n\",\n      \"\\t11: may (0.1765)\\n\",\n      \"\\t12: threatens (0.1793)\\n\",\n      \"\\t13: meet (0.1793)\\n\",\n      \"\\t14: target (0.1793)\\n\",\n      \"\\t15: achieve (0.1793)\\n\",\n      \"\\t16: financial (0.1793)\\n\",\n      \"\\t17: goals (0.1793)\\n\",\n      \"\\t18: called (0.1793)\\n\",\n      \"\\t19: risk management strategy (0.1844)\\n\",\n      \"\\t20: anything that threatens (0.1851)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"_ = kp_inference.extract_pke(text_to_extract, method='yake', top_k=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### TextRank\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"WARNING:root:Candidates are generated using 0.33-top\\n\",\n      \"WARNING:root:Not enough candidates to choose from (10 requested, 6 given)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\t1: risk management (0.1901)\\n\",\n      \"\\t2: company head (0.1464)\\n\",\n      \"\\t3: financial goals (0.1464)\\n\",\n      \"\\t4: risk (0.0950)\\n\",\n      \"\\t5: sources (0.0732)\\n\",\n      \"\\t6: company (0.0732)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# define the set of valid Part-of-Speeches\\n\",\n    \"pos = {'NOUN', 'PROPN', 'ADJ'}\\n\",\n    \"\\n\",\n    \"# 1. create a TextRank extractor.\\n\",\n    \"extractor = pke.unsupervised.TextRank()\\n\",\n    \"\\n\",\n    \"# 2. load the content of the document.\\n\",\n    \"extractor.load_document(input=text_to_extract,\\n\",\n    \"                        language='en_core_web_sm',\\n\",\n    \"                        normalization=None)\\n\",\n    \"\\n\",\n    \"# 3. build the graph representation of the document and rank the words.\\n\",\n    \"#    Keyphrase candidates are composed from the 33-percent\\n\",\n    \"#    highest-ranked words.\\n\",\n    \"extractor.candidate_weighting(window=2,\\n\",\n    \"                              pos=pos,\\n\",\n    \"                              top_percent=0.33)\\n\",\n    \"\\n\",\n    \"# 4. get the 10-highest scored candidates as keyphrases\\n\",\n    \"keyphrases = extractor.get_n_best(n=10)\\n\",\n    \"for kp_id, kp in enumerate(keyphrases):\\n\",\n    \"    print('\\\\t%d: %s (%.4f)' % (kp_id+1, kp[0], kp[1]))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"pycharm\": {\n   \"stem_cell\": {\n    \"cell_type\": \"raw\",\n    \"metadata\": {\n     \"collapsed\": false\n    },\n    \"source\": []\n   }\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebook/json_process.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### About\\n\",\n    \"The goal of this script is to process a few common keyphrase datasets, including\\n\",\n    \" - **Tokenize**: by default using method from Meng et al. 2017, which fits more for academic text since it splits strings by hyphen etc. and makes tokens more fine-grained. \\n\",\n    \"     - keep [_<>,\\\\(\\\\)\\\\.\\\\'%]\\n\",\n    \"     - replace digits with < digit >\\n\",\n    \"     - split by [^a-zA-Z0-9_<>,#&\\\\+\\\\*\\\\(\\\\)\\\\.\\\\'%]\\n\",\n    \" - **Determine present/absent phrases**: determine whether a phrase appears verbatim in a text. This is believed a very important step for the evaluation of keyphrase-related tasks, since in general extraction methods cannot recall any phrases don't appear in the source text.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"inspec\\n\",\n      \"#doc=500, #present_doc=497, #absent_doc=381, #tgt=4913, #present=3858, #absent=1055\\n\",\n      \"krapivin\\n\",\n      \"#doc=460, #present_doc=437, #absent_doc=417, #tgt=2641, #present=1485, #absent=1156\\n\",\n      \"nus\\n\",\n      \"#doc=211, #present_doc=207, #absent_doc=195, #tgt=2461, #present=1263, #absent=1198\\n\",\n      \"semeval\\n\",\n      \"#doc=100, #present_doc=100, #absent_doc=99, #tgt=1507, #present=671, #absent=836\\n\",\n      \"kp20k\\n\",\n      \"#doc=19987, #present_doc=19048, #absent_doc=16357, #tgt=105181, #present=66595, #absent=38586\\n\",\n      \"duc\\n\",\n      \"#doc=308, #present_doc=308, #absent_doc=38, #tgt=2484, #present=2421, #absent=63\\n\",\n      \"stackexchange\\n\",\n      \"#doc=16000, #present_doc=13475, #absent_doc=10984, #tgt=43131, #present=24809, #absent=18322\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['inspec', 'krapivin', 'nus', 'semeval', 'kp20k', 'duc', 'stackexchange']\\n\",\n    \"\\n\",\n    \"json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    print(dataset_name)\\n\",\n    \"    \\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s_test.json' % dataset_name)\\n\",\n    \"    output_json_path = os.path.join(json_base_dir, dataset_name, '%s_test_meng17token.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"    doc_count, present_doc_count, absent_doc_count = 0, 0, 0\\n\",\n    \"    tgt_num, present_tgt_num, absent_tgt_num = [], [], []\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json, open(output_json_path, 'w') as output_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                json_dict['abstract'] = json_dict['question']\\n\",\n    \"                json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"                del json_dict['question']\\n\",\n    \"                del json_dict['tags']\\n\",\n    \"\\n\",\n    \"            title = json_dict['title']\\n\",\n    \"            abstract = json_dict['abstract']\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                json_dict['keywords'] = keywords\\n\",\n    \"            # remove all the abbreviations/acronyms in parentheses in keyphrases\\n\",\n    \"            keywords = [re.sub(r'\\\\(.*?\\\\)|\\\\[.*?\\\\]|\\\\{.*?\\\\}', '', kw) for kw in keywords]\\n\",\n    \"            \\n\",\n    \"            # tokenize text\\n\",\n    \"            title_token = utils.meng17_tokenize(title)\\n\",\n    \"            abstract_token = utils.meng17_tokenize(abstract)\\n\",\n    \"            keywords_token = [utils.meng17_tokenize(kw) for kw in keywords]\\n\",\n    \"\\n\",\n    \"            # replace numbers\\n\",\n    \"            title_token = utils.replace_numbers_to_DIGIT(title_token, k=2)\\n\",\n    \"            abstract_token = utils.replace_numbers_to_DIGIT(abstract_token, k=2)\\n\",\n    \"            keywords_token = [utils.replace_numbers_to_DIGIT(kw, k=2) for kw in keywords_token]                \\n\",\n    \"            \\n\",\n    \"            src_token = title_token+[\\\".\\\"]+abstract_token\\n\",\n    \"            tgts_token = keywords_token\\n\",\n    \"\\n\",\n    \"#             print(json_dict)\\n\",\n    \"#             print(src_token)\\n\",\n    \"#             print(tgts_token)\\n\",\n    \"\\n\",\n    \"            # split tgts by present/absent\\n\",\n    \"            src_seq = src_token\\n\",\n    \"            tgt_seqs = tgts_token\\n\",\n    \"            \\n\",\n    \"            present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs)\\n\",\n    \"            present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\\n\",\n    \"            absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\\n\",\n    \"            \\n\",\n    \"            doc_count += 1\\n\",\n    \"            present_doc_count = present_doc_count + 1 if len(present_tgts) > 0 else present_doc_count\\n\",\n    \"            absent_doc_count = absent_doc_count + 1 if len(absent_tgts) > 0 else absent_doc_count\\n\",\n    \"            \\n\",\n    \"            tgt_num.append(len(tgt_seqs))\\n\",\n    \"            present_tgt_num.append(len(present_tgts))\\n\",\n    \"            absent_tgt_num.append(len(absent_tgts))\\n\",\n    \"            \\n\",\n    \"            # write to output json\\n\",\n    \"            tokenized_dict = {'src': src_token, 'tgt': tgts_token, \\n\",\n    \"                              'present_tgt': present_tgts, 'absent_tgt': absent_tgts}\\n\",\n    \"            json_dict['meng17_tokenized'] = tokenized_dict\\n\",\n    \"            output_json.write(json.dumps(json_dict) + '\\\\n')\\n\",\n    \"\\n\",\n    \"    print('#doc=%d, #present_doc=%d, #absent_doc=%d, #tgt=%d, #present=%d, #absent=%d' \\n\",\n    \"          % (doc_count, present_doc_count, absent_doc_count, \\n\",\n    \"             sum(tgt_num), sum(present_tgt_num), sum(absent_tgt_num)))\\n\",\n    \"    \\n\",\n    \"    \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebook/kpeval.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import scipy\\n\",\n    \"\\n\",\n    \"from nltk.stem.porter import PorterStemmer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k_valid2k\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k_valid2k']\\n\",\n    \"# dataset_names = ['inspec', 'krapivin', 'nus', 'semeval', 'kp20k', 'duc', 'magkp', 'stackexchange']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/hdaqing/rum20/kp/data/kp/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_examples = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    dataset_examples[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, 'test.json')\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                ex_dict['abstract'] = ex_dict['question']\\n\",\n    \"                ex_dict['keywords'] = ex_dict['tags']            \\n\",\n    \"                del ex_dict['question']\\n\",\n    \"                del ex_dict['tags']\\n\",\n    \"\\n\",\n    \"            keywords = ex_dict['keywords']\\n\",\n    \"            ex_dict['fulltext'] = ''\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                ex_dict['keywords'] = keywords\\n\",\n    \"            keywords = [k.strip() for k in keywords]\\n\",\n    \"            ex_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"            dataset_examples[dataset_name].append(ex_dict)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"bart-o2s\\n\",\n      \"tf-o2s\\n\",\n      \"bart-o2o\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pred_paths = {\\n\",\n    \"    'bart-o2s': '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_kp20k_100k_rerun/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_45000-data_kp20k_valid2k_test.pred',\\n\",\n    \"    'tf-o2s': '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_95000-data_kp20k_valid2k_test.pred',\\n\",\n    \"    'bart-o2o': '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/bartFT_one2one_kp20k_100k/outputs/beamsearch-width_128-maxlen_8/pred/checkpoint_step_90000-data_kp20k_valid2k_test.pred',\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"pred_results = {}\\n\",\n    \"\\n\",\n    \"for exp_name, pred_path in pred_paths.items():\\n\",\n    \"    pred_results[exp_name] = []\\n\",\n    \"    print(exp_name)\\n\",\n    \"\\n\",\n    \"    with open(pred_path, 'r') as output_json:\\n\",\n    \"        for json_line in output_json:\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"            pred_results[exp_name].append(ex_dict)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"title: A structural approach to reversible computation\\n\",\n      \"abstract: Reversibility is a key issue in the interface between computation and physics, and of growing importance as miniaturization progresses towards its physical limits. Most foundational work on reversible computing to date has focussed on simulations of low-level machine models. By contrast, we develop a more structural approach. We show how high-level functional programs can be mapped compositionally (i.e. in a syntax-directed fashion) into a simple kind of automata which are immediately seen to be reversible. The size of the automaton is linear in the size of the functional term. In mathematical terms, we are building a concrete model of functional computation. This construction stems directly from ideas arising in Geometry of Interaction and Linear Logic-but can be understood without any knowledge of these topics. In fact, it serves as an excellent introduction to them. At the same time, an interesting logical delineation between reversible and irreversible forms of computation emerges from our analysis.  \\n\",\n      \"GT-keywords [5]\\n\",\n      \"\\t [0]  reversible computation\\n\",\n      \"\\t [1]  linear combinatory algebra\\n\",\n      \"\\t [2]  term-rewriting\\n\",\n      \"\\t [3]  automata\\n\",\n      \"\\t [4]  geometry of interaction\\n\",\n      \"\\n\",\n      \"PRED-keywords [15/61]\\n\",\n      \"\\t [0]  ['reversible', 'computation']\\n\",\n      \"\\t [1]  ['functional', 'programming']\\n\",\n      \"\\t [2]  ['reversible', 'computing']\\n\",\n      \"\\t [3]  ['compositionality']\\n\",\n      \"\\t [4]  ['syntax-directed', 'automata']\\n\",\n      \"\\t [5]  ['geometry', 'of', 'interaction']\\n\",\n      \"\\t [6]  ['linear', 'logic']\\n\",\n      \"\\t [7]  ['automata']\\n\",\n      \"\\t [8]  ['geometry', 'of', 'interactions']\\n\",\n      \"\\t [9]  ['reversibility']\\n\",\n      \"\\t [10]  ['geometry', 'ofinteraction']\\n\",\n      \"\\t [11]  ['syntax-directed', 'computation']\\n\",\n      \"\\t [12]  ['computation']\\n\",\n      \"\\t [13]  ['syntax-directed', 'programming']\\n\",\n      \"\\t [14]  ['symbolic', 'computation']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'kp20k_valid2k'\\n\",\n    \"exp_name = 'bart-o2s'\\n\",\n    \"num_pred = 15\\n\",\n    \"\\n\",\n    \"data_count = 0\\n\",\n    \"for ex_data, pred_data in zip(dataset_examples[dataset_name], pred_results[exp_name]):\\n\",\n    \"#     print(ex_data)\\n\",\n    \"#     print(pred_data)\\n\",\n    \"    data_count += 1\\n\",\n    \"    \\n\",\n    \"    if data_count < 12:\\n\",\n    \"        continue\\n\",\n    \"        \\n\",\n    \"    print('title:', ex_data['title'])\\n\",\n    \"    print('abstract:', ex_data['abstract'])\\n\",\n    \"    print('GT-keywords [%d]' % len(ex_data['keywords']))\\n\",\n    \"    for gt_id, gt in enumerate(ex_data['keywords']):\\n\",\n    \"        print('\\\\t [%d] ' % gt_id, gt)\\n\",\n    \"    \\n\",\n    \"    print()\\n\",\n    \"    print('PRED-keywords [%d/%d]' % (num_pred, len(pred_sents)))\\n\",\n    \"    pred_sents = pred_data['pred_sents']\\n\",\n    \"    for pred_id, pred in enumerate(pred_sents[: min(num_pred, len(pred_sents))]):\\n\",\n    \"        print('\\\\t [%d] ' % pred_id, pred)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/phrase_stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute the unique predictions for cat models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"import tqdm\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pred_path = \\\"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseq_pred/kp20k.pred\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"0it [00:00, ?it/s]\\u001b[A\\n\",\n      \"47it [00:00, 433.93it/s]\\u001b[A\\n\",\n      \"90it [00:00, 422.99it/s]\\u001b[A\\n\",\n      \"139it 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20.378996\\n\",\n      \"avg(dup_pred_num) = 13507853/19987 = 675.831941\\n\",\n      \"avg(pred_sents_local_count) = 407315/19987 = 20.378996\\n\",\n      \"avg(topseq_pred_num) = 43921/19987 = 2.197478\\n\",\n      \"avg(beam_num) = 1237891/19987 = 61.934808\\n\",\n      \"avg(beamstep_num) = 45347850/19987 = 2268.867264\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"keys = ['gold_num', \\n\",\n    \"        'unique_pred_num', 'dup_pred_num', 'pred_sents_local_count', 'topseq_pred_num',\\n\",\n    \"        'beam_num', 'beamstep_num']\\n\",\n    \"num_doc = 0\\n\",\n    \"stat_dict = {k:[] for k in keys}\\n\",\n    \"\\n\",\n    \"for l in tqdm.tqdm(open(pred_path, 'r')):\\n\",\n    \"    pred_dict = json.loads(l)\\n\",\n    \"#     print(pred_dict.keys())\\n\",\n    \"#     print(pred_dict['topseq_pred_sents']) # top beam, a sequence of words\\n\",\n    \"#     print(pred_dict['topseq_preds']) # a sequence of indices\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['pred_sents']) # unique phrases\\n\",\n    \"#     print(pred_dict['ori_pred_sents']) # beams, each is a list of words, seperated by <sep>\\n\",\n    \"#     print(pred_dict['ori_preds'])\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['unique_pred_num'])\\n\",\n    \"#     if num_doc > 10:\\n\",\n    \"#         break\\n\",\n    \"   \\n\",\n    \"    num_doc += 1\\n\",\n    \"    stat_dict['gold_num'].append(len(pred_dict['gold_sent']))\\n\",\n    \"    stat_dict['unique_pred_num'].append(pred_dict['unique_pred_num'])\\n\",\n    \"    stat_dict['dup_pred_num'].append(pred_dict['dup_pred_num'])\\n\",\n    \"    stat_dict['pred_sents_local_count'].append(len(pred_dict['pred_sents']))\\n\",\n    \"    stat_dict['beam_num'].append(pred_dict['beam_num'])\\n\",\n    \"    stat_dict['beamstep_num'].append(pred_dict['beamstep_num'])\\n\",\n    \"    stat_dict['topseq_pred_num'].append(len(pred_dict['topseq_pred_sents']))\\n\",\n    \"\\n\",\n    \"print('#(doc)=%d' % num_doc)\\n\",\n    \"for k, v in stat_dict.items():\\n\",\n    \"    print('avg(%s) = %d/%d = %f' % (k, np.sum(v), num_doc, np.mean(v)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pred_path = \\\"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseqd_pred/kp20k.pred\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"0it [00:00, ?it/s]\\u001b[A\\n\",\n      \"8it [00:00, 75.23it/s]\\u001b[A\\n\",\n      \"18it [00:00, 80.84it/s]\\u001b[A\\n\",\n      \"26it [00:00, 79.53it/s]\\u001b[A\\n\",\n      \"33it [00:00, 75.71it/s]\\u001b[A\\n\",\n      \"40it [00:00, 71.94it/s]\\u001b[A\\n\",\n      \"48it [00:00, 72.60it/s]\\u001b[A\\n\",\n      \"57it [00:00, 75.83it/s]\\u001b[A\\n\",\n      \"67it 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88.61it/s]\\u001b[A\\n\",\n      \"3098it [00:34, 89.62it/s]\\u001b[A\\n\",\n      \"3109it [00:34, 93.72it/s]\\u001b[A\\n\",\n      \"3119it [00:35, 92.82it/s]\\u001b[A\\n\",\n      \"3129it [00:35, 92.73it/s]\\u001b[A\\n\",\n      \"3139it [00:35, 89.59it/s]\\u001b[A\\n\",\n      \"3149it [00:35, 91.00it/s]\\u001b[A\\n\",\n      \"3159it [00:35, 91.40it/s]\\u001b[A\\n\",\n      \"3169it [00:35, 91.96it/s]\\u001b[A\\n\",\n      \"3179it [00:35, 90.46it/s]\\u001b[A\\n\",\n      \"3189it [00:35, 90.16it/s]\\u001b[A\\n\",\n      \"3200it [00:35, 92.47it/s]\\u001b[A\\n\",\n      \"3210it [00:36, 85.69it/s]\\u001b[A\\n\",\n      \"3220it [00:36, 87.84it/s]\\u001b[A\\n\",\n      \"3230it [00:36, 87.01it/s]\\u001b[A\\n\",\n      \"3241it [00:36, 87.64it/s]\\u001b[A\\n\",\n      \"3252it [00:36, 88.45it/s]\\u001b[A\\n\",\n      \"3262it [00:36, 87.15it/s]\\u001b[A\\n\",\n      \"3272it [00:36, 89.83it/s]\\u001b[A\\n\",\n      \"3282it [00:36, 90.96it/s]\\u001b[A\\n\",\n      \"3292it [00:36, 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99.51it/s]\\u001b[A\\n\",\n      \"3513it [00:39, 101.83it/s]\\u001b[A\\n\",\n      \"3524it [00:39, 97.62it/s] \\u001b[A\\n\",\n      \"3534it [00:39, 94.54it/s]\\u001b[A\\n\",\n      \"3544it [00:39, 92.43it/s]\\u001b[A\\n\",\n      \"3554it [00:39, 90.76it/s]\\u001b[A\\n\",\n      \"3564it [00:39, 90.38it/s]\\u001b[A\\n\",\n      \"3574it [00:39, 89.89it/s]\\u001b[A\\n\",\n      \"3584it [00:40, 91.50it/s]\\u001b[A\\n\",\n      \"3594it [00:40, 89.05it/s]\\u001b[A\\n\",\n      \"3604it [00:40, 88.07it/s]\\u001b[A\\n\",\n      \"3613it [00:40, 87.28it/s]\\u001b[A\\n\",\n      \"3623it [00:40, 89.76it/s]\\u001b[A\\n\",\n      \"3633it [00:40, 87.72it/s]\\u001b[A\\n\",\n      \"3642it [00:40, 83.76it/s]\\u001b[A\\n\",\n      \"3651it [00:40, 84.61it/s]\\u001b[A\\n\",\n      \"3660it [00:40, 81.97it/s]\\u001b[A\\n\",\n      \"3670it [00:41, 86.05it/s]\\u001b[A\\n\",\n      \"3680it [00:41, 88.45it/s]\\u001b[A\\n\",\n      \"3690it [00:41, 89.49it/s]\\u001b[A\\n\",\n      \"3700it 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88.06it/s]\\u001b[A\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(doc)=19987\\n\",\n      \"avg(gold_num) = 95233/19987 = 4.764747\\n\",\n      \"avg(unique_pred_num) = 1792899/19987 = 89.703257\\n\",\n      \"avg(dup_pred_num) = 47508000/19987 = 2376.945014\\n\",\n      \"avg(pred_sents_local_count) = 1792899/19987 = 89.703257\\n\",\n      \"avg(topseq_pred_num) = 39183/19987 = 1.960424\\n\",\n      \"avg(beam_num) = 8648021/19987 = 432.682293\\n\",\n      \"avg(beamstep_num) = 204738185/19987 = 10243.567569\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"keys = ['gold_num', \\n\",\n    \"        'unique_pred_num', 'dup_pred_num', 'pred_sents_local_count', 'topseq_pred_num',\\n\",\n    \"        'beam_num', 'beamstep_num']\\n\",\n    \"num_doc = 0\\n\",\n    \"stat_dict = {k:[] for k in keys}\\n\",\n    \"\\n\",\n    \"for l in tqdm.tqdm(open(pred_path, 'r')):\\n\",\n    \"    pred_dict = json.loads(l)\\n\",\n    \"#     print(pred_dict.keys())\\n\",\n    \"#     print(pred_dict['topseq_pred_sents']) # top beam, a sequence of words\\n\",\n    \"#     print(pred_dict['topseq_preds']) # a sequence of indices\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['pred_sents']) # unique phrases\\n\",\n    \"#     print(pred_dict['ori_pred_sents']) # beams, each is a list of words, seperated by <sep>\\n\",\n    \"#     print(pred_dict['ori_preds'])\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['unique_pred_num'])\\n\",\n    \"#     if num_doc > 10:\\n\",\n    \"#         break\\n\",\n    \"   \\n\",\n    \"    num_doc += 1\\n\",\n    \"    stat_dict['gold_num'].append(len(pred_dict['gold_sent']))\\n\",\n    \"    stat_dict['unique_pred_num'].append(pred_dict['unique_pred_num'])\\n\",\n    \"    stat_dict['dup_pred_num'].append(pred_dict['dup_pred_num'])\\n\",\n    \"    stat_dict['pred_sents_local_count'].append(len(pred_dict['pred_sents']))\\n\",\n    \"    stat_dict['beam_num'].append(pred_dict['beam_num'])\\n\",\n    \"    stat_dict['beamstep_num'].append(pred_dict['beamstep_num'])\\n\",\n    \"    stat_dict['topseq_pred_num'].append(len(pred_dict['topseq_pred_sents']))\\n\",\n    \"\\n\",\n    \"print('#(doc)=%d' % num_doc)\\n\",\n    \"for k, v in stat_dict.items():\\n\",\n    \"    print('avg(%s) = %d/%d = %f' % (k, np.sum(v), num_doc, np.mean(v)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": 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    "path": "notebook/pred_phrase_stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute the unique predictions for cat models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"import tqdm\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pred_path = \\\"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseq_pred/kp20k.pred\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"0it [00:00, ?it/s]\\u001b[A\\n\",\n      \"47it [00:00, 433.93it/s]\\u001b[A\\n\",\n      \"90it [00:00, 422.99it/s]\\u001b[A\\n\",\n      \"139it 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\"avg(gold_num) = 95233/19987 = 4.764747\\n\",\n      \"avg(unique_pred_num) = 407315/19987 = 20.378996\\n\",\n      \"avg(dup_pred_num) = 13507853/19987 = 675.831941\\n\",\n      \"avg(pred_sents_local_count) = 407315/19987 = 20.378996\\n\",\n      \"avg(topseq_pred_num) = 43921/19987 = 2.197478\\n\",\n      \"avg(beam_num) = 1237891/19987 = 61.934808\\n\",\n      \"avg(beamstep_num) = 45347850/19987 = 2268.867264\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"keys = ['gold_num', \\n\",\n    \"        'unique_pred_num', 'dup_pred_num', 'pred_sents_local_count', 'topseq_pred_num',\\n\",\n    \"        'beam_num', 'beamstep_num']\\n\",\n    \"num_doc = 0\\n\",\n    \"stat_dict = {k:[] for k in keys}\\n\",\n    \"\\n\",\n    \"for l in tqdm.tqdm(open(pred_path, 'r')):\\n\",\n    \"    pred_dict = json.loads(l)\\n\",\n    \"#     print(pred_dict.keys())\\n\",\n    \"#     print(pred_dict['topseq_pred_sents']) # top beam, a sequence of words\\n\",\n    \"#     print(pred_dict['topseq_preds']) # a sequence of indices\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['pred_sents']) # unique phrases\\n\",\n    \"#     print(pred_dict['ori_pred_sents']) # beams, each is a list of words, seperated by <sep>\\n\",\n    \"#     print(pred_dict['ori_preds'])\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['unique_pred_num'])\\n\",\n    \"#     if num_doc > 10:\\n\",\n    \"#         break\\n\",\n    \"   \\n\",\n    \"    num_doc += 1\\n\",\n    \"    stat_dict['gold_num'].append(len(pred_dict['gold_sent']))\\n\",\n    \"    stat_dict['unique_pred_num'].append(pred_dict['unique_pred_num'])\\n\",\n    \"    stat_dict['dup_pred_num'].append(pred_dict['dup_pred_num'])\\n\",\n    \"    stat_dict['pred_sents_local_count'].append(len(pred_dict['pred_sents']))\\n\",\n    \"    stat_dict['beam_num'].append(pred_dict['beam_num'])\\n\",\n    \"    stat_dict['beamstep_num'].append(pred_dict['beamstep_num'])\\n\",\n    \"    stat_dict['topseq_pred_num'].append(len(pred_dict['topseq_pred_sents']))\\n\",\n    \"\\n\",\n    \"print('#(doc)=%d' % num_doc)\\n\",\n    \"for k, v in stat_dict.items():\\n\",\n    \"    print('avg(%s) = %d/%d = %f' % (k, np.sum(v), num_doc, np.mean(v)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pred_path = \\\"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseqd_pred/kp20k.pred\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"0it [00:00, ?it/s]\\u001b[A\\n\",\n      \"8it [00:00, 75.23it/s]\\u001b[A\\n\",\n      \"18it [00:00, 80.84it/s]\\u001b[A\\n\",\n      \"26it [00:00, 79.53it/s]\\u001b[A\\n\",\n      \"33it [00:00, 75.71it/s]\\u001b[A\\n\",\n      \"40it [00:00, 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 \"19877it [03:46, 84.02it/s]\\u001b[A\\n\",\n      \"19886it [03:46, 84.39it/s]\\u001b[A\\n\",\n      \"19896it [03:47, 86.86it/s]\\u001b[A\\n\",\n      \"19905it [03:47, 85.50it/s]\\u001b[A\\n\",\n      \"19916it [03:47, 90.51it/s]\\u001b[A\\n\",\n      \"19926it [03:47, 88.95it/s]\\u001b[A\\n\",\n      \"19935it [03:47, 85.13it/s]\\u001b[A\\n\",\n      \"19946it [03:47, 89.98it/s]\\u001b[A\\n\",\n      \"19956it [03:47, 89.12it/s]\\u001b[A\\n\",\n      \"19966it [03:47, 87.76it/s]\\u001b[A\\n\",\n      \"19976it [03:47, 89.81it/s]\\u001b[A\\n\",\n      \"19987it [03:48, 88.06it/s]\\u001b[A\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(doc)=19987\\n\",\n      \"avg(gold_num) = 95233/19987 = 4.764747\\n\",\n      \"avg(unique_pred_num) = 1792899/19987 = 89.703257\\n\",\n      \"avg(dup_pred_num) = 47508000/19987 = 2376.945014\\n\",\n      \"avg(pred_sents_local_count) = 1792899/19987 = 89.703257\\n\",\n      \"avg(topseq_pred_num) = 39183/19987 = 1.960424\\n\",\n      \"avg(beam_num) = 8648021/19987 = 432.682293\\n\",\n      \"avg(beamstep_num) = 204738185/19987 = 10243.567569\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"keys = ['gold_num', \\n\",\n    \"        'unique_pred_num', 'dup_pred_num', 'pred_sents_local_count', 'topseq_pred_num',\\n\",\n    \"        'beam_num', 'beamstep_num']\\n\",\n    \"num_doc = 0\\n\",\n    \"stat_dict = {k:[] for k in keys}\\n\",\n    \"\\n\",\n    \"for l in tqdm.tqdm(open(pred_path, 'r')):\\n\",\n    \"    pred_dict = json.loads(l)\\n\",\n    \"#     print(pred_dict.keys())\\n\",\n    \"#     print(pred_dict['topseq_pred_sents']) # top beam, a sequence of words\\n\",\n    \"#     print(pred_dict['topseq_preds']) # a sequence of indices\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['pred_sents']) # unique phrases\\n\",\n    \"#     print(pred_dict['ori_pred_sents']) # beams, each is a list of words, seperated by <sep>\\n\",\n    \"#     print(pred_dict['ori_preds'])\\n\",\n    \"    \\n\",\n    \"#     print(pred_dict['unique_pred_num'])\\n\",\n    \"#     if num_doc > 10:\\n\",\n    \"#         break\\n\",\n    \"   \\n\",\n    \"    num_doc += 1\\n\",\n    \"    stat_dict['gold_num'].append(len(pred_dict['gold_sent']))\\n\",\n    \"    stat_dict['unique_pred_num'].append(pred_dict['unique_pred_num'])\\n\",\n    \"    stat_dict['dup_pred_num'].append(pred_dict['dup_pred_num'])\\n\",\n    \"    stat_dict['pred_sents_local_count'].append(len(pred_dict['pred_sents']))\\n\",\n    \"    stat_dict['beam_num'].append(pred_dict['beam_num'])\\n\",\n    \"    stat_dict['beamstep_num'].append(pred_dict['beamstep_num'])\\n\",\n    \"    stat_dict['topseq_pred_num'].append(len(pred_dict['topseq_pred_sents']))\\n\",\n    \"\\n\",\n    \"print('#(doc)=%d' % num_doc)\\n\",\n    \"for k, v in stat_dict.items():\\n\",\n    \"    print('avg(%s) = %d/%d = %f' % (k, np.sum(v), num_doc, np.mean(v)))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  },\n  \"pycharm\": {\n   \"stem_cell\": {\n    \"cell_type\": \"raw\",\n    \"metadata\": {\n     \"collapsed\": false\n    },\n    \"source\": []\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebook/scikp_dataset_stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T03:16:09.514882Z\",\n     \"start_time\": \"2020-11-22T03:15:31.760701Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import scipy\\n\",\n    \"\\n\",\n    \"from nltk.stem.porter import PorterStemmer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T05:39:07.881950Z\",\n     \"start_time\": \"2020-11-23T05:38:37.978143Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"DescribeResult(nobs=514154, minmax=(1, 110), mean=5.270924664594655, variance=14.141117540879774, skewness=5.39192287405869, kurtosis=40.41415445884668)\\n\",\n      \"magkp\\n\",\n      \"DescribeResult(nobs=2699094, minmax=(1, 438), mean=15.414788814320657, variance=168.752782332351, skewness=1.8635894274050995, kurtosis=7.294107031214651)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['inspec', 'krapivin', 'nus', 'semeval', 'kp20k', 'duc', 'stackexchange']\\n\",\n    \"dataset_names = ['kp20k', 'magkp']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"\\n\",\n    \"tgt_nums = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    tgt_nums[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                json_dict['abstract'] = json_dict['question']\\n\",\n    \"                json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"                del json_dict['question']\\n\",\n    \"                del json_dict['tags']\\n\",\n    \"\\n\",\n    \"            title = json_dict['title']\\n\",\n    \"            abstract = json_dict['abstract']\\n\",\n    \"            fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                json_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"            tgt_nums[dataset_name].append(len(keywords))\\n\",\n    \"\\n\",\n    \"    print(scipy.stats.describe(tgt_nums[dataset_name]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize histogram of two datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-10T04:06:47.180742Z\",\n     \"start_time\": \"2020-11-10T04:06:46.899160Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7fa94f6a28d0>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.__version__\\n\",\n    \"penguins = sns.load_dataset(\\\"penguins\\\")\\n\",\n    \"sns.histplot(data=penguins, x=\\\"flipper_length_mm\\\", hue=\\\"species\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-10T04:09:19.376341Z\",\n     \"start_time\": \"2020-11-10T04:09:18.196431Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k and MagKP')\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"Greens_r\\\", 8)[6], label=\\\"MagKP\\\", bins=np.arange(31) - 0.5, kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"w\\\", linewidth=0.2))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n >= 3 and n <= 6]\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-LN\\\", bins=np.arange(31)-0.5, \\n\",\n    \"             color=\\\"c\\\",\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"w\\\", linewidth=0.1))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10 and n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"Blues_r\\\", 8)[4], label=\\\"MagKP-N\\\", bins=np.arange(31)-0.5, kde=False, rug=False, hist_kws=dict(alpha=0.5, edgecolor=\\\"w\\\", linewidth=0.2))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"hls\\\", 8)[0], label=\\\"KP20k\\\", bins=np.arange(31) - 0.5, kde=False, rug=False, hist_kws=dict(alpha=0.6, edgecolor=\\\"k\\\", linewidth=1.5))\\n\",\n    \"\\n\",\n    \"plt.xlim([-1, 30])\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k and MagKP')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 203,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T21:06:33.627156Z\",\n     \"start_time\": \"2020-11-23T21:06:32.172778Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2391870\\n\",\n      \"521542\\n\",\n      \"1520307\\n\",\n      \"521542\\n\",\n      \"511653\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 0, '#(phrase) per paper')\"\n      ]\n     },\n     \"execution_count\": 203,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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t9rxRlRERkv2QyGUaOHImMjAx89dVX2L59uyHBKC0txcKFC5Gfnw+5XI6bN28iLy8Pjz32GM6fP4+RI0cCAB5//PF6G6EBtYnL8ePHcfLkyXpbtQPAf//7X0RFRQEA+vbti2nTpjUb47Rp0zBq1ChMnjy5yfc0FWufPn0A1O666uxc+6v7u+++Q3h4uKEn/rnnnsPu3bsB1G4bf+XKFbzwwguGc9fU1KC4uBidOnVqNk6xcKlwMsk7N28i7NMkw2tjJmDdOzv7J19/bHx2sgSREVFrFx0djfHjx2PAgAHw8vIyHE9KSkJERATWrVsHmUyGESNGoLq6GoIgQCaT1Vsm4X4DBgzA888/j5kzZ2LNmjX1koyBAwdizZo19d5/+PBhrFixAkBtMnDvlu0KhQIvvvhis7uxNhVrnXbt2hl+rou/KUOGDMHy5cubLJcaEwwySS+tFo/n5Vo7DCKiBrp164Y5c+bUewoCACoqKuDn5weZTIajR48iPz8fANChQwc89thj2Lt3L/7whz/gzJkzja42ff9W7ff3ZNxryJAhGDJkSJPlkydPxogRI5p86qapWBsTFhaGTZs2obS0FEqlEl9++aWhbNCgQVi3bh0uXLhgeNT31KlTCAl58PC0WJhgkFl0Li7QKBs+jtUU19JyyLVaCSMiImuy5C+u5sTExDQ4NnfuXLzzzjvYuHEjevToUW8YRK1WIyEhAdu2bUNQUBB69uzZ6ETO+5MMc7Vr1w7Tpk1DcnJyo+XNxXq/nj17YsqUKZg4cSI6deqEgQMHGmL39/fHBx98gD//+c+oqqqCVqtF3759Lfr3JBMEQbDYp9m5uueBxVoHo7GVKa9fv27U+VpS11x+fn5ILS7GQI0Gd3064/ozEUbX9U/dDec7d3G7TTvk+fqbNLRScf0icl1ckOjpKfk12iqu89A8tk/zpFgHQ6VSiXY+a7pz5w70ej3at2+PixcvIi4uDnv37rWbLdtv376N9u3bAwDWrl2L/Px8wxCNOe7/u23J7z32YJBFyLS1z2y3r7rDIRYishnff/893n//fcNchuTkZLtJLgDgww8/xPfffw+tVotu3bphyZIl1g7JgAkGWRSHVojIlgwePBhffPGFUb2qtigxMdHaITSJCQZZlEapMGloxW/vAbQt/EXCiIiISApWW8mTiIiIHBcTDCIiIhIdEwwiIiISHedgEBFRi7322muSnLe5HUrrtHS79qZYart2Ke3cuROHDh3CmjVrkJWVBbVabdgUTmrswSAiIrvX2rZrtwfswSAiIrtXt117eHh4o9u1r169GtXV1dDpdHj11Vfx7LPPAqjdrn3+/Pmorq5Gz549ceXKFbz22muGnoW67dpLSkqwefNmtGnTpsFn123XnpmZ2WR8U6dOxZo1axAbG2tIUOp88cUX+Otf/wpXV1fo9XqsXr0aAQEBiIiIwOjRo3Hs2DEUFhZi7ty5KCkpQXp6Om7evIn33nsP/fr1Q01NDaZNm4aysjJUV1cjJCQE77zzDlxdXcVqXrOwB4OIiOxeWFgYzp07V2+79jp1W6Dv2rULW7ZsgVqtxs2bNwEA8+bNQ0xMDNLT0zFp0iScPn263nlTUlJQWFiIjz/+uNHkAvh1u/bmVje9d7v2+y1fvhybN29GWloaduzYgS5dutQ79xdffIE1a9bg7bffhouLC/75z39izpw5+PDDDwEAcrkcK1aswM6dO5Geng6dTocdO3YY33gSYQ8GERHZPXverv23v/0tFixYgGHDhuGpp55Ct27dDGWjRo0CAAQFBeHu3buGWIODg3HlyhUAgF6vx+bNm/HNN99Ar9fj5s2bTSZDlsQeDCIicgjR0dFYs2YNAgMDG2zXPmDAAOzZswdpaWl46KGHTNqufeXKlZg5cyaOHz9er6xuDkZaWhoSExPRrl07HD58GFFRUYiKisKnn35a7/1Nbde+bt06vPnmm7h79y5eeumlekMtdft/1E1erXvt5OSEmpraLRj27NmD7OxsfP7559izZw9iY2Oh0WhMbT7RsQeDiIgcgj1u115TU4MbN24gJCQEISEhuHLlCs6ePYvw8HCjr7uiogJeXl5o3749KioqkJ6ejuDgYKPrS4UJBhEROQx7265dr9cjISEBFRUVkMlk8PX1xdy5c00659ixY/Gf//wHzz77LHx8fBAaGorq6mqzYxQLt2s3AbdrN3+79ke37YRcqzW5Xt1eJP91dcX4Tp24XTs1iu3TPG7X3jR7365dbNyunYiISAT2vl27LWOCQURErZa9b9duy/gUCREREYmOCQYRERGJjgkGERERiY5zMIiIqGVmzwZOnpTu/H36AKtXS3d+kgQTDCIiapmTJ4FmNvqi1okJBhERiULn4gKNUiHa+VxLyyHXah/4voiICGzYsAGBgYG4e/cu3njjDXh7e6OmpgZZWVnw8vJCVVUVnn76acTHxyMvLw+LFy/GL7/8AmdnZ/Ts2RNLly417N9x8uRJLF68GNXV1fDz88MHH3yAjh074tq1a3juueeQlZUl2jU6MiYYRK1QYwu1mau1Ln5GDWmUCpMW0nuQuoX2jFVRUYGpU6ciKCgIf/7zn7FgwQJMnToVL774IioqKhAVFYUnnngCPXr0wIIFC9CrVy/o9XrMnDkTmzZtwvTp0yEIAt566y3DVugpKSlYsWIF3nvvPdGuq7XgJE8iIrJ7JSUliIuLw29/+1ssWrSowQZmHTp0wOOPP468vDx07doVvXr1AlC7aVhwcDBu3LgBADh9+jTc3NzQr18/AMDEiROxd+/eBp+n0Wgwa9YsvPfee+CC2I1jDwZRK6ZWq82qN3/+fJEjIWqZ2bNnIzY2FrNmzWq0vLCwEN9//32DvUqqqqqQlpaG+Ph4AEBBQQG6dOliKFcqldDr9SgvLzccKy8vx4wZM/D73/8ekyZNkuBqHAMTDCIisnvh4eH46quv8Pzzz8Pb29tw/JNPPkFqairkcjmmTJmCgQMHGspqamowZ84c9O/fH8OGDTPqczQaDWJjYzFjxgyMHDlS9OtwJEwwiIjI7k2ZMgUHDx5EXFwcPvvsM0OSUTcH4346nQ7x8fHw9PTEvHnzDMd9fX0NwyUAUFpaCplMBoVCgdu3b8PFxQW9e/fGgQMHMHz4cMjlcukvzk5ZJMFQq9XYt28frl+/jj179iAwMBBlZWWYN28erly5AldXVzzyyCNYsmQJlEolACAvLw8JCQkoLy+HQqGAWq2Gv7+/ZGVErV1ISMgD33Pq1CkLREL2yrW0HH57D4h6PlNMmzYNgiAYkoym1G2RLpfL8e6776KqqspQFhwcjKqqKpw4cQL9+vXDP/7xj3o9FTKZDMuWLcOSJUswZ84cfPjhh3BxcTH94loBiyQYw4YNw0svvYQXXnjBcEwmk2HKlCkICwsDUJuErFixAsuWLQMAJCYmIjY2FlFRUUhLS8PixYuxdetWycpaCzGfHiAiupdcqzXpqQ8pvPrqq4Yko6n/77755hvs3r0bgYGBiI6Ohl6vR79+/ZCYmAgnJycsX74ciYmJ9R5TvZdMJkNiYiLUajWmT5+OtWvXmryVeWtgkQSjbjbuvRQKhSG5AIA+ffpg+/btAGpnA+fm5mLLli0AgMjISCQnJ6O0tBSCIIheVtdrQkREZujTx6rnP3Cgfq/Ja6+9htdee63J9z/11FM4d+6c4XVlZWW93VT79u2LPXv2NKjXtWvXemtgcLJz82xiDoZer8f27dsREVH7/HRBQQF8fHwMY1tyuRze3t4oKCiAIAiil5maYOTk5Jh1ndnZ2WbVk+L8T0w3b9ndDp8mAXm5ZtUVg9RtaMts9dptJS5bicNWidk+zs7OqKys/PXAu++Kdu4m3ft5kpxe2vPbC41GI9q9YhMJRnJyMtq1a9foRBxbFBwcbHJ3WHZ2NkJDQyWKqJbU57cFreEaG2OJ+8dcthCXLbePLRC7fc6ePVvvG7+9u78HozVzdXVF7969Da+rq6vN/lJt9QRDrVYjPz8fGzZsgJNT7bpfvr6+KCwshE6ng1wuh06nQ1FREXx9fSEIguhlRERkGkEQGixmRfZNr9eLej6rJhirVq1CTk4OPvnkE7i6uhqOd+zYESqVCunp6YiKikJ6ejpUKpVhKEOKstYspPeDnx4AgFM/8gkCIgLatGmDkpISdOzYkUmGAxAEAVqtFoWFhaL25FgkwVi6dCn279+P4uJivPzyy1AoFFi9ejU2bNgAf39/TJw4EUDtBJr169cDAJKSkpCQkICUlBR4eHjUW3FQijIiIjJO165dce3aNfzyi3WfGBGLRqOp9yW3NXJ2doanpyc6deok3jlFO1MzFi1ahEWLFjU4fu8s3vsFBAQgNTXVYmVERGQcFxcXPProo9YOQzTZ2dn15h2QOLjZGREREYmOCQYRERGJjgkGERERiY4JBhEREYmOCQYRERGJjgkGERERiY4JBhEREYnO6kuFE5miJdvNX79+XcRIiIioOezBICIiItGxB4PsQge/x/DElCST6/2wfrbosRAR0YOxB4OIiIhExwSDiIiIRMchErIr3FqeiMg+sAeDiIiIRMcEg4iIiETHBIOIiIhExzkYDqQli1ARERGJiT0YREREJDomGERERCQ6JhhEREQkOiYYREREJDpO8nQgxuwWyomgRERkCezBICIiItExwSAiIiLRMcEgIiIi0XEOBpGViTkvxph5OERElsAeDCIiIhIdEwwiIiISHRMMIiIiEh0TDCIiIhIdJ3kS2SAumkZE9s4iPRhqtRoRERHo0aMHzp8/bziel5eHmJgYjBgxAjExMbh8+bLVyoiIiEg8Fkkwhg0bhs8//7zBN67ExETExsZi3759iI2NxeLFi61WRkREROKxSILRr18/+Pr61jtWUlKC3NxcREZGAgAiIyORm5uL0tJSi5cROQo/Pz+j/hARSc1qczAKCgrg4+MDuVwOAJDL5fD29kZBQQEEQbBomVKptEILEBEROS5O8jRDTk6OWfWys7NFjoRMYe439927d4scibjUarVVP99W7mtbicNWsX2ax/YRn9USDF9fXxQWFkKn00Eul0On06GoqAi+vr4QBMGiZaYKDg6Gm5ubSXWys7MRGhpq8meR9fHvrXm20D7899U8tk/z2D5Nq66uNvtLtdUSjI4dO0KlUiE9PR1RUVFIT0+HSqUyDFdYuoyopTi3gYjoVxZJMJYuXYr9+/ejuLgYL7/8MhQKBTIyMpCUlISEhASkpKTAw8OjXlevpcuIHEFISIhR7zt16pTEkRBRa2eRBGPRokVYtGhRg+MBAQFITU1ttI6ly4iIiEg8nORJrYa5q2Ny6IOIyHRMMIiIRCJ2MmpMUkxkq5hgEEmIe4oQUWvFBIOISGQtmUA+f/58ESMhsh5u105ERESiYw8G0QMYOw7OoQ4iol8xwSAikhDXJqHWikMkREREJDomGERERCQ6o4ZIzpw5g0OHDuHcuXO4desWPDw80KNHDzz55JN4/PHHpY6RiIiI7EyzCcaRI0ewcuVKVFZWYsCAAejbty/c3d1RWVmJS5cuIT4+Hu7u7pgzZw6GDBliqZiJiIjIxjWbYHzxxRdISkpqdpLSqVOn8OmnnzLBICIiIoNmE4y1a9c+8AQhISFYs2aNaAERERGR/TN6kmdpaSkqKysBADqdDjt27MCuXbug1+slC46IiIjsk9EJxrRp05Cfnw8AWLVqFTZv3owtW7bg/ffflyw4IiIisk9GJxiXL1+GSqUCAOzevRsbN27E3/72N3z11VeSBUdERET2yeiVPJ2cnKDVapGXl4cOHTqgS5cu0Ov1hmETIiIiojpGJxhDhgzBrFmzUF5ejlGjRgEALl68CB8fH8mCI2m8c/Mmwj5NMrx2d3c3ql5lZSUeLbgsSUxERORYjE4wli1bhi+//BLOzs4YO3YsAKCsrAwzZsyQLDiSRi+tFo/n5Vo7DCIicmBGJRg6nQ6vvPIKNm3aBFdXV8PxsLAwyQIj6elcXKBRKkyq06aoGDJBkCgiIiJyFEYlGHK5HNeuXYPAXywORaNU4PozESbVeXTbTsi1WokiIiIiR2H0UyTTp09HYmIirl+/Dp1OB71eb/hDREREdC8ywEQAAB9wSURBVC+j52AsWrQIAJCWlmY4JggCZDIZzp49K35kREREZLeMTjD+85//SBkHERERORCjEww/Pz8AgF6vR3FxMby9vSULioiIiOyb0XMwbt26hblz5yIkJATDhw8HUNursWrVKsmCIyIiIvtkdIKRmJiI9u3b48CBA3BxcQEAPPHEE/j6668lC46IiIjsk9FDJN9++y0OHz4MFxcXyGQyAIBSqURJSYlkwREREZF9MroHo0OHDigrK6t37MaNG+jcubPoQREREZF9MzrBGD9+PGbOnIljx45Br9fjhx9+wPz58zFx4kQp4yMiIiI7ZPQQyZ/+9Ce4urpiyZIlqKmpwcKFCxETE4NJkyZJGR8RERHZIaMTDJlMhsmTJ2Py5MkShkNERESOwOgEA6id6JmRkYGioiJ4e3vj2Wefxe9+97sWB3Hw4EF89NFHEAQBer0eM2bMwPDhw5GXl4eEhASUl5dDoVBArVbD398fACQpIyIiInEYPQdjy5YtePPNN+Hp6Ynw8HAoFArMnTsXmzdvblEAgiBg3rx5WL58OdLS0vDBBx9g/vz50Ov1SExMRGxsLPbt24fY2FgsXrzYUE+KMiIiIhKH0QnG5s2b8be//Q1vvfUWXnjhBcTHx+Nvf/tbixMMAHByckJFRQUAoKKiAt7e3igrK0Nubi4iIyMBAJGRkcjNzUVpaSlKSkpELyMiIiLxmDRE8sgjj9R73a1bN8OaGOaSyWRYvXo1Xn/9dbRr1w6VlZX4y1/+goKCAvj4+EAulwOo3TLe29sbBQUFEARB9DKlUml0zDk5OWZda3Z2tln1iGyZrdzXthKHmMS8JkdsHzGxfcRndIIxY8YMLFy4EDNmzMBDDz2EgoICpKSkYObMmfW2bHdyMrpTBABQU1ODv/zlL0hJSUFoaCiys7MxZ84cLF++3KTzWFJwcDDc3NxMqpOdnY3Q0FCJIiKyHlu4rx3135dY1+So7SMWtk/Tqqurzf5SbXSCUTdXISMjAzKZDIIgAAD27NmDxYsXm711+9mzZ1FUVGT4yw0NDUXbtm3h5uaGwsJC6HQ6yOVy6HQ6FBUVwdfXF4IgiF5GRERE4rH6du0PPfQQfv75Z/z000/4zW9+g0uXLqG4uBiPPPIIVCoV0tPTERUVhfT0dKhUKsNQhhRlREREJA6Tt2sXW+fOnZGUlIRZs2YZ5nO89957UCgUSEpKQkJCAlJSUuDh4QG1Wm2oJ0UZERERicOkSZ7/+c9/cPz4cZSVlRmGSAC0eL7EmDFjMGbMmAbHAwICkJqa2mgdKcqIiIhIHEbPyFy3bh0SExOh1+uxd+9eKBQKHDlyBB4eHlLGR0RERHbI6ARjx44d2Lx5MxYuXAgXFxcsXLgQGzZswLVr16SMj4iIiOyQ0QnGrVu3EBgYCABwcXGBVqtFSEgIjh8/LllwREREZJ+MnoPx8MMP48KFC+jevTu6d++O7du3w8PDA56enlLGR0Q2riUTwK9fvy5iJERkS4xOMGbPno3y8nIAwNy5cxEfH487d+4gMTFRsuCIiIjIPhmdYISHhxt+7t27N/71r39JEhAR2b6WPN49f/58ESMhIltl0mOqly9fxtdff23Yrn3kyJHc6pyIiIgaMHqS5549ezBu3DicO3cObdu2xfnz5zFu3Djs2bNHyviIiIjIDhndg7F69Wp88skn6N+/v+HYiRMnMG/ePIwePVqS4IjI9oWEhBj1vlOnTkkcCRHZEqN7MCorK9GnT596x3r37o07d+6IHhQRERHZN6MTjJdffhkrV65EdXU1AKCqqgqrVq3Cyy+/LFlwREREZJ+MHiLZtm0biouL8dlnn8HDwwO3bt2CIAjo3Lkztm/fbnjfoUOHpIiTiByQuWtocP0MIttndILxwQcfSBkHERERORCjE4wBAwZIGQcRtRLmrqHB9TMa15KVVO/HniESU7NzMLZu3QqNRtPsCTQaDbZu3SpqUERERGTfmu3BKC4uxtNPP43w8HD0798fjz76KNzd3VFZWYnLly/ju+++wzfffIOoqChLxUtERI1gzxDZmmYTjDfffBOTJ0/Gl19+iX/+8584f/48Kioq4OHhgR49eiA8PBxz5syBl5eXpeKlVsal4jYA4NGCy3jv0yS4u7sbVa+yshIAUFFcjFwXFyRyUz6bZMwaGlw/g8g+PXAOhlKpxCuvvIJXXnkF165dQ9euXS0RFxEAQKatAQC0r7qDx/NyrRwNEREZy6S9SKKjo/Hdd99h69ateOmll6SKiagBnYsLNEqFSXVcS8sh12olioisiY+3No89Q2QLHphgREdHIygoCCqVCjqdDgCwbt06JhhkURqlAtefiTCpjt/eA2hb+ItEERERUXMemGB89NFHOHPmDHJyclBVVYVx48ZBo9Hg2LFjCAoKQocOHSwRJxG1cpzESGRfHrhUuCAIeOaZZxAfHw93d3ekpKRAEAT8/e9/R1RUFIYPH26JOImIiMiOPLAHY+7cuSgoKEBAQACqq6tx8+ZNuLm5Yd26dQCA8vJyyYMkIiIi+/LABCM1NRU1NTU4f/48YmNjkZycjMrKSiQmJiIoKAi9evWCQmHa5DsiopbgJEYi22fUbqrOzs7o1asXXFxc8Pnnn6Nt27YICwvD5cuXsWLFCqljJCIiIjtj0mOqCxYsAADIZDKMGjUKo0aNkiQoIiIism9G9WDUiY6OBgD8+9//liQYIiIicgwmJRh1PLnsMhERETXDrASDiIiIqDlMMIiIiEh0Jk3yJCKyZ+buYQK0nn1MiMTCHgwiIiISnU30YFRXV2PZsmX49ttv4ebmhj59+iA5ORl5eXlISEhAeXk5FAoF1Go1/P39AUCSMiJyPObuYQJwHxOilrCJHowPPvgAbm5u2LdvH/bs2YNZs2YBABITExEbG4t9+/YhNjYWixcvNtSRooyIiIjEYfUEo7KyErt27cKsWbMgk8kAAJ06dUJJSQlyc3MRGRkJAIiMjERubi5KS0slKSMiIiLxWH2I5OrVq1AoFFi3bh2ysrLg7u6OWbNmoU2bNvDx8YFcLgcAyOVyeHt7o6CgAIIgiF6mVCqNjjknJ8esa83OzjarHhGJw5g9TIDG9zGxxr9fS39ma/4/qjVfu1SsnmDU1NTg6tWr6NWrF+bPn48ff/wRr776Kj766CNrh9ak4OBguLm5mVQnOzsboaGhEkVERFKzxr9fS39ma/0/iv8/N626utrsL9VWTzC6dOkCZ2dnw7BF79694eXlhTZt2qCwsBA6nQ5yuRw6nQ5FRUXw9fWFIAiilxEREZF4rD4HQ6lUIiwsDEePHgVQ+5RHSUkJ/P39oVKpkJ6eDgBIT0+HSqWCUqlEx44dRS8jImqOn5/fA/8Q0a+s3oMBAO+88w4WLlwItVoNZ2dnLF++HB4eHkhKSkJCQgJSUlLg4eFR73EzKcqIiIhIHDaRYHTr1g2fffZZg+MBAQFITU1ttI4UZURE9+IXECLzWX2IhIiIiBwPEwwiIiISnU0MkRAR2Tpj1tBobP0M+pXYE2G5AZ1tYw8GERERiY49GERENspRH33lBnStA3swiIiISHTswSAisiF8NJYcBRMMIiKympZsQEe2jUMkREREJDr2YBAR2ShLPxrbkkmlfGSU7sceDCIiIhIdezCIiFoxPjJKUmGCQURELeaoa3aQ+ThEQkRERKJjDwYREQEw/ZFRrtlBzWGCQUREdsncYRk+8WIZHCIhIiIi0bEHg4iIWsxSa3aYOyzDJ14sjz0YREREJDomGERERCQ6DpEQEZFdsvRS6mQa9mAQERGR6JhgEBERkeg4REJERK0K18+wDPZgEBERkejYg0FERA6P62dYHnswiIiISHTswSAiIjJCS7akb43zN5hgEBFRq8L1MyyDCQYREVETWrIlfWufv8E5GERERCQ69mAQtWJHjx5t9OcHGTRokBThEJEDsakEY926dVi7di327NmDwMBA5OXlISEhAeXl5VAoFFCr1fD39wcAScqIiIiaYszcDYDzN+rYzBDJmTNncPLkSXTp0sVwLDExEbGxsdi3bx9iY2OxePFiScuIiIik4OfnZ9Yfe2YTCYZGo8GSJUuQmJgImUwGACgpKUFubi4iIyMBAJGRkcjNzUVpaakkZURERCQemxgi+eijjzBmzBh069bNcKygoAA+Pj6Qy+UAALlcDm9vbxQUFEAQBNHLlEql0fHm5OSYdZ3Z2dlm1SMiIvsjxuqh9vx7w+oJxg8//IDTp08jPj7e2qEYLTg4GG5ubibVyc7ORmhoqEQRERGRI7L2743q6mqzv1RbPcE4fvw4fvrpJwwbNgwA8PPPP+OVV17BggULUFhYCJ1OB7lcDp1Oh6KiIvj6+kIQBNHLiIiIbI09rx5q9QRj6tSpmDp1quF1REQENmzYgMDAQGzfvh3p6emIiopCeno6VCqVYShDpVKJXkZERCSF1rh6qNUTjOYkJSUhISEBKSkp8PDwqDeeJUUZERGRtTnK6qE2l2AcOHDA8HNAQABSU1MbfZ8UZURERCQOm3hMlYiIiByLzfVgEBERUa2WrB5q7gRRsSaHsgeDiIiIRMceDCIiIgchxuJeYmEPBhEREYmOCQYRERGJjkMkRHbu6NGjjf5MRK2btRf3Yg8GERERiY49GEREImtJr9KgQYPEDofIKtiDQURERKJjgkFERESiY4JBREREomOCQURERKJjgkFERESiY4JBREREomOCQURERKJjgkFERESiY4JBREREomOCQURERKJjgkFERESiY4JBREREomOCQURERKLjbqpEZDLuFkpED8IEgxyWS8VtAECQVovU4mLgqaceWCe1uNjwc66LCxI9PaUKj4jIoTHBIIcl09YAADwFAQM1GiAz84F1BkodFBFRK8EEgxyezsUFGqXC6Pe7lpZDrtVKGBERkeNjgkEOT6NU4PozEUa/32/vAbQt/EXCiFo3c+dvcO5G89iuZGv4FAkRERGJjgkGERERiY5DJERkFzgEQGRf2INBREREorN6glFWVoY//elPGDFiBEaPHo033ngDpaWlAIC8vDzExMRgxIgRiImJweXLlw31pCgjIiIicVh9iEQmk2HKlCkICwsDAKjVaqxYsQLLli1DYmIiYmNjERUVhbS0NCxevBhbt24FAEnKiMjxcNVRIuuweg+GQqEwJBcA0KdPH9y4cQMlJSXIzc1FZGQkACAyMhK5ubkoLS2VpIyIiIjEY/UejHvp9Xps374dERERKCgogI+PD+RyOQBALpfD29sbBQUFEARB9DKlUmmdiyYiInJANpVgJCcno127dnjxxReRm5tr7XCalJOTY1a97OxskSMhIim1ZHiFyF6J9bvKZhIMtVqN/Px8bNiwAU5OTvD19UVhYSF0Oh3kcjl0Oh2Kiorg6+sLQRBELzNFcHAw3NzcTKqTnZ2N0NBQk+oQERFZ2r2/q6qrq83+Um0TCcaqVauQk5ODTz75BK6urgCAjh07QqVSIT09HVFRUUhPT4dKpTIMZUhRRkRE0uPE29bB6gnGhQsXsGHDBvj7+2PixIkAgK5du2L9+vVISkpCQkICUlJS4OHhAbVabagnRRkRkbVxWIYchdUTjO7du+PcuXONlgUEBCA1NdViZURErQ17E0gqVn9MlYiIiByP1XswiIjIPlljOId70tgP9mAQERGR6NiDYcfeuXkTvbTaXw889dQD66QWFyPo3jpEREQSYIJhx3pptRio0fx6IDPzgXUGShgPEZGt4tCK5THBcAA6FxdolAqj39+mqBgyQZAwIiIix8CnbMzHBMMBaJQKXH8mwuj3P7ptJ+QcJiEiIglxkicRERGJjj0YRPdxqbgNAAjSapFaXGzU5FmgdgJtnVwXFyR6ekoRHhHZidY+74MJBtF9ZNoaAICnINROojVi8izACbREJA5HmffBBIOoCaZOngUA19Jyzm8hIgITDKImmTp5FgD89h5A28JfJIqIiKh5trRZHid5EhERkeiYYBAREZHomGAQERGR6JhgEBERkeiYYBAREZHomGAQERGR6JhgEBERkeiYYBAREZHomGAQERGR6LiSJ5GIzNkojZukEZEjYoJBJCJzNkrjJmlE5IiYYBBJwNSN0rhJGhE5GiYYRBIwdaM0bpJGRI6GkzyJiIhIdOzBILIBLZkc2iktDdc6dULqoEFShkhEZBImGEQ2oEWTQwsKpAuMiMhMTDCIbAgnhxKRo2CCQWRDODmUiBwFEwwre+fmTfS69xuoEWPvQO34exC/ubZ6dXM3upaUYE5amlnn4PwNIpICEwwr66XV1o651zFi7B3g4kxUq27uRjuNBoFmzsXoWlKCrvesJmosJiZE1JxWmWDk5eUhISEB5eXlUCgUUKvV8Pf3t2pMpo69A0CbomLIBEGiiMietOT+MTc5MScx6XzzJtxqalDt4oJfPDxM/kwmNUT2o1UmGImJiYiNjUVUVBTS0tKwePFibN26tcXn/fDDD7Fy5Uqz6po69g4Aj27byQl+BKBl94+pyUlLExOgtsfFq7LS5HqmJjUtSWjMrWtPn2lPsVrjM+0p1pbWjSspEX0vJJkgtK6vwCUlJRgxYgSysrIgl8uh0+kQFhaG/fv3Q6lUNlu3qqoKZ86cQWBgIFxdXRuUZ86Ygfb79pkUT2BNDToIAnTOztAqTLsh3ErKIDOjrrn1rPGZ9hSrNT7TmrESkWPJdnHBdKUSx44dMxzTaDQ4f/48goKC0KZNG5PO1+p6MAoKCuDj4wO5XA4AkMvl8Pb2RkFBwQMTDO3/egvOnz/faHnHadOAadNMiiffpHcTERFJww3ApwBycnIalGm1WiYYUnJ3d0dgYCBcXFwgk8msHQ4REZGkBEGAVquFu7u7yXVbXYLh6+uLwsJC6HQ6wxBJUVERfH19H1jXyckJHTp0sECUREREtsHUnos6rW6zs44dO0KlUiE9PR0AkJ6eDpVK9cDhESIiIjJeq5vkCQCXLl1CQkICbt26BQ8PD6jVavzmN7+xdlhEREQOo1UmGERERCStVjdEQkRERNJjgkFERESiY4JBREREomOCQURERKJjgkFERESia3ULbVmaLe7caksiIiLg6uoKNzc3AEB8fDyGDBli5aisR61WY9++fbh+/Tr27NmDwMBAALyP6jTVPryPapWVlWHevHm4cuUKXF1d8cgjj2DJkiVQKpW8h9B8+/AeqvX666/j2rVrcHJyQrt27fD2229DpVKZd/8IJKm4uDhh165dgiAIwq5du4S4uDgrR2Rbhg4dKpw7d87aYdiM48ePCzdu3GjQLryPajXVPryPapWVlQnHjh0zvH7//feFBQsWCILAe0gQmm8f3kO1bt26Zfj5X//6lzB27FhBEMy7fzhEIqGSkhLk5uYiMjISABAZGYnc3FyUlpZaOTKyVf369WuwbD3vo1811j70K4VCgbCwMMPrPn364MaNG7yH/qep9qFf3bsdxu3btyGTycy+fzhEIqGW7NzamsTHx0MQBISGhuLNN9+Eh4dp2447Ot5HxuF9VJ9er8f27dsRERHBe6gR97ZPHd5Dtf785z/j6NGjEAQBn376qdn3D3swyKo+//xz7N69Gzt27IAgCFiyZIm1QyI7xPuooeTkZLRr1w4vvviitUOxSfe3D++hX7377rs4dOgQ5syZg+XLl5t9HiYYErp351YAJu3c2lrUtYWrqytiY2Px/fffWzki28P76MF4H9WnVquRn5+P1atXw8nJiffQfe5vH4D3UGPGjh2LrKwsPPTQQ2bdP0wwJMSdW5t3584dVFRUAAAEQcBXX30FlUpl5ahsD++j5vE+qm/VqlXIycnB+vXr4erqCoD30L0aax/eQ7UqKytRUFBgeH3gwAF4enqaff9wszOJcefWpl29ehUzZsyATqeDXq9HQEAAFi1aBG9vb2uHZjVLly7F/v37UVxcDC8vLygUCmRkZPA++p/G2mfDhg28j/7nwoULiIyMhL+/P9q0aQMA6Nq1K9avX897CE23T0JCAu8hAMXFxXj99ddx9+5dODk5wdPTE/Pnz0dQUJBZ9w8TDCIiIhIdh0iIiIhIdEwwiIiISHRMMIiIiEh0TDCIiIhIdEwwiIiISHRMMIgc3Icffoi//vWvRr03ISEBq1atkjYgI7zxxhv45ptvrB0GEbUAEwwiO/eHP/wBly9fxtWrVzFu3Lh6ZaWlpdi1axcmTpxopejMM3XqVKxevdraYRBRCzDBILJjWq0WN27cwCOPPIKcnBz06tWrXvnOnTsRHh5uWFRITDU1NaKfs05ISAhu376N06dPS/YZdaS8jpay5diIHoQJBpEdu3DhAgICAiCTyRpNML755hv079/f8DorKwtPPvkkNmzYgLCwMERERGD37t316ty6dQtTp07FE088gfHjx+PKlSuGsh49euDzzz/H8OHDMXz4cAC1q2uGh4ejb9++iI6OxokTJwzvP3XqFKKjo9G3b18MHDgQ7733nqHs5MmTmDhxIvr164cxY8YgKyurXhwDBgxAZmZmo9f9oOvQaDRQq9V46qmnMHDgQCxevBhVVVX16n7yyScYNGgQFixY0OD8O3fuxMSJE5GcnIzQ0FA888wz+Pbbbw3lO3bswMiRI/HEE09g2LBh+Mc//mGx2IjshkBEduef//ynEBoaKoSEhAjBwcFCaGiooFKphD59+gihoaHClStXBEEQhLCwMOHHH3801Dt27JigUqmEZcuWCdXV1UJWVpbQu3dv4dKlS4IgCML8+fOF/v37Cz/++KOg1WqFN998U5g9e7ahfmBgoDB58mShrKxMuHv3riAIgrBr1y6htLRU0Gq1wqZNm4SBAwcKVVVVgiAIwoQJE4Qvv/xSEARBuH37tvDDDz8IgiAIP//8szBgwADh0KFDgk6nE44cOSIMGDBAKCkpMXzW5s2bhenTpzd6/Q+6jqVLlwrTpk0TysrKhIqKCmHatGnCihUr6tVdvny5UF1dbbiOe+3YsUNQqVTCli1bBI1GI2RkZAh9+/YVysrKBEEQhIMHDwr5+fmCXq8XsrKyhJCQECEnJ8cisRHZC/ZgENmh5557DidOnEBQUBD+7//+D7t370b37t3x/fff48SJE+jWrRsAoKKiAu7u7g3qz5o1C66urhgwYADCw8Px9ddfG8qefvpphISEwNnZGWPGjMHZs2fr1Z06dSoUCoVh2CUqKgpeXl5wdnbGH//4R2g0GuTl5QEAnJ2dceXKFZSWlsLd3R19+vQBAKSlpeHJJ59EeHg4nJycMGjQIAQHB9frsXB3d8etW7eabYfGrkMQBKSmpmLhwoVQKBRo3749pk2bhoyMDEM9JycnzJw5E66urk0OHymVSkyaNAkuLi4YNWoUHn30URw6dAgA8NRTT+Hhhx+GTCbDgAEDMGjQoHo9N1LHRmQPnK0dABGZpry8HL///e8hCALu3LmDuLg4aDQaAED//v3xxhtvYPLkyQAADw8PVFZW1qvv4eGBdu3aGV536dIFRUVFhtedOnUy/NymTRvcuXOnXv37t2jevHkzUlNTUVRUBJlMhtu3b6OsrAwA8O6772LNmjUYOXIkunbtijfeeANDhw7FjRs3sHfvXhw8eNBwnpqaGoSFhRleV1ZWwsPDo8l2aOo6SktLcffuXURHRxvKBEGAXq83vPby8oKbm1uT5wYAHx8fyGSyBucHgMzMTKxfvx6XL1+GXq9HVVUVAgMDLRYbkT1ggkFkZxQKBU6cOIGMjAxkZWVhyZIlmD59Ol544QUMHDiw3nt79OiBy5cvIyQkxHDs1q1buHPnjuEXYEFBAbp3727059/7S/fEiRPYuHEj/vrXv6J79+5wcnJC//79IfxvD0V/f3+sXLkSer0e+/fvx8yZM5GVlQVfX19ERUVh6dKlTX7OpUuX0LNnzybLm7oOLy8vtGnTBhkZGfDx8XngNTSlsLAQgiAY3ltQUICIiAhoNBrMnDkTarUaw4YNg4uLC15//XXDNVsiNiJ7wCESIjt176TOs2fPIigoqMF7wsPDcfz48QbH165dC41GgxMnTuDQoUN45plnzIqhsrIScrkcSqUSNTU1WLduHW7fvm0oT0tLQ2lpKZycnAy9EXK5HGPGjMHBgwdx+PBh6HQ6VFdXIysrCz///LOh7vHjx/Hkk082+/mNXYeTkxPGjx+PZcuWoaSkBEBtsnD48GGTrq20tBRbt26FVqvF119/jUuXLiE8PBwajQYajQZKpRLOzs7IzMzE0aNHLRobkT1gDwaRnTpz5gxGjhyJsrIyODk5wdPTs8F7oqKiMHbsWFRVVRnG8zt16gQPDw8MGTIEbdu2RVJSEgICAsyKYfDgwXjyyScxYsQItGvXDpMmTao3hHL48GG8//77qKqqQpcuXbBq1Sq4ubnB19cXKSkp+OCDDzB37lw4OTkhJCQESUlJAGqfPmnbtm29npf7NXcdb731FtavX48JEyagrKwMPj4+eP755zFkyBCjry0kJAT5+fn47W9/i06dOmHNmjXw8vICACxatAizZ8+GRqPB0KFDERERYdHYiOyBTLi3X4+IHM7KlSuhVCoxefJkZGVl4a233rL5VTJnzJiBP/zhDwgPD2+0XOrr2LlzJ1JTU7F9+3aT69pLGxNJjT0YRA7uzTfftHYIJlu7dq21QyCiFuIcDCIiIhIdh0iIiIhIdOzBICIiItExwSAiIiLRMcEgIiIi0THBICIiItExwSAiIiLR/X9t+iOe35LpcgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 30]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP\\\",\\n\",\n    \"             bins=np.arange(31) - 0.5, color=\\\"w\\\",\\n\",\n    \"             hist_kws=dict(alpha=1.0, edgecolor=\\\"k\\\", linewidth=5.0),\\n\",\n    \"             kde=False, kde_kws={\\\"color\\\": \\\"k\\\", \\\"lw\\\": 3, \\\"label\\\": \\\"KDE\\\"})\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n >= 3 and n <= 6]\\n\",\n    \"magkpln_tgt_nums = tmp_tgt_nums\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-LN\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Blues_r\\\", 8)[3],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.8, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-Nlarge\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Greys_r\\\", 8)[5],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.8, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10]\\n\",\n    \"tmp_tgt_nums = tmp_tgt_nums[: len(magkpln_tgt_nums)]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-Nsmall\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Greys_r\\\", 8)[2],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"KP20k\\\", \\n\",\n    \"             bins=np.arange(31) - 0.5, color=sns.color_palette(\\\"hls\\\", 8)[0], \\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"red\\\", linewidth=2.5))\\n\",\n    \"\\n\",\n    \"plt.xlim([-1, 30])\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"ax.set_ylabel('#(papers)')\\n\",\n    \"ax.set_xlabel('#(phrase) per paper')\\n\",\n    \"# ax.set_title('Histogram of #(kp per document) of KP20k and MagKP')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check #(unique_kp) in each dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T05:38:33.869419Z\",\n     \"start_time\": \"2020-11-23T05:38:31.184Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"magkp\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k', 'magkp']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_tgt_dict = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    dataset_tgt_dict[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                json_dict['abstract'] = json_dict['question']\\n\",\n    \"                json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"                del json_dict['question']\\n\",\n    \"                del json_dict['tags']\\n\",\n    \"\\n\",\n    \"            title = json_dict['title']\\n\",\n    \"            abstract = json_dict['abstract']\\n\",\n    \"            fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                json_dict['keywords'] = keywords\\n\",\n    \"            keywords = [k.lower().strip() for k in keywords]\\n\",\n    \"            \\n\",\n    \"            dataset_tgt_dict[dataset_name].append(keywords)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T00:22:10.562675Z\",\n     \"start_time\": \"2020-11-23T00:22:09.463568Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# prepare Magkp subsets\\n\",\n    \"dataset_tgt_dict['magkp_ln'] = [kps for kps in dataset_tgt_dict[\\\"magkp\\\"] if len(kps) >= 3 and len(kps) <= 6]\\n\",\n    \"dataset_tgt_dict['magkp_nlarge'] = [kps for kps in dataset_tgt_dict[\\\"magkp\\\"] if len(kps) > 10]\\n\",\n    \"dataset_tgt_dict['magkp_nsmall'] = dataset_tgt_dict['magkp_nlarge'][: len(dataset_tgt_dict['magkp_ln'])]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T21:27:32.323475Z\",\n     \"start_time\": \"2020-11-23T21:26:12.282108Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"**************************************************\\n\",\n      \"kp20k\\n\",\n      \"num_doc= 514154\\n\",\n      \"num_unique_kp= 699791\\n\",\n      \"num_kp= 2710067\\n\",\n      \"len_kp= 1.9230266262789812\\n\",\n      \"max_kp_in_doc= 110\\n\",\n      \"len_kp_list= 91\\n\",\n      \"**************************************************\\n\",\n      \"magkp\\n\",\n      \"num_doc= 2699094\\n\",\n      \"num_unique_kp= 6880853\\n\",\n      \"num_kp= 41605964\\n\",\n      \"len_kp= 3.4161944427005704\\n\",\n      \"max_kp_in_doc= 438\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_ln\\n\",\n      \"num_doc= 521542\\n\",\n      \"num_unique_kp= 579244\\n\",\n      \"num_kp= 2331072\\n\",\n      \"len_kp= 2.726639932185707\\n\",\n      \"max_kp_in_doc= 6\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_nlarge\\n\",\n      \"num_doc= 1520307\\n\",\n      \"num_unique_kp= 5784959\\n\",\n      \"num_kp= 35525765\\n\",\n      \"len_kp= 3.376301903702848\\n\",\n      \"max_kp_in_doc= 438\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_nsmall\\n\",\n      \"num_doc= 521542\\n\",\n      \"num_unique_kp= 2236091\\n\",\n      \"num_kp= 12193980\\n\",\n      \"len_kp= 3.3652286620119107\\n\",\n      \"max_kp_in_doc= 262\\n\",\n      \"len_kp_list= 100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"for dataset, kps_list in dataset_tgt_dict.items():\\n\",\n    \"    kp_set = set()\\n\",\n    \"    num_kp = 0\\n\",\n    \"    max_kp_in_doc = 0\\n\",\n    \"    max_len_kp = 0\\n\",\n    \"    len_kp_list = []\\n\",\n    \"    for kps in kps_list:\\n\",\n    \"        for kp in kps:\\n\",\n    \"            kp_set.add(kp)\\n\",\n    \"            num_kp += 1\\n\",\n    \"            num_word = len(kp.split())\\n\",\n    \"            len_kp_list.append(num_word)\\n\",\n    \"            if num_word > max_len_kp:\\n\",\n    \"                max_len_kp = num_word\\n\",\n    \"        if len(kps) > max_kp_in_doc:\\n\",\n    \"            max_kp_in_doc = len(kps)\\n\",\n    \"    num_unique_kp = len(kp_set)\\n\",\n    \"    print('*' * 50)\\n\",\n    \"    print(dataset)\\n\",\n    \"    print('num_doc=', len(kps_list))\\n\",\n    \"    print('num_unique_kp=', num_unique_kp)\\n\",\n    \"    print('num_kp=', num_kp)\\n\",\n    \"    print('len_kp=', np.mean(len_kp_list))\\n\",\n    \"    print('max_kp_in_doc=', max_kp_in_doc)\\n\",\n    \"    print('len_kp_list=', max_len_kp)\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### print num_paper binned by num_kp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T03:20:08.168149Z\",\n     \"start_time\": \"2020-11-22T03:20:08.113779Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"1884\\n\",\n      \"15433\\n\",\n      \"88651\\n\",\n      \"136732\\n\",\n      \"135117\\n\",\n      \"68930\\n\",\n      \"25108\\n\",\n      \"12786\\n\",\n      \"6306\\n\",\n      \"3871\\n\",\n      \"2054\\n\",\n      \"1517\\n\",\n      \"1088\\n\",\n      \"945\\n\",\n      \"859\\n\",\n      \"817\\n\",\n      \"844\\n\",\n      \"793\\n\",\n      \"866\\n\",\n      \"856\\n\",\n      \"807\\n\",\n      \"731\\n\",\n      \"743\\n\",\n      \"676\\n\",\n      \"657\\n\",\n      \"604\\n\",\n      \"546\\n\",\n      \"500\\n\",\n      \"492\\n\",\n      \"440\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"for bin_count in np.bincount(tmp_tgt_nums):\\n\",\n    \"    print(bin_count)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Hatch-filled histograms\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T04:33:08.778067Z\",\n     \"start_time\": \"2020-11-22T04:33:08.767431Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import itertools\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"from functools import partial\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import matplotlib.ticker as mticker\\n\",\n    \"from cycler import cycler\\n\",\n    \"from six.moves import zip\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def filled_hist(ax, edges, values, bottoms=None, orientation='v',\\n\",\n    \"                **kwargs):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Draw a histogram as a stepped patch.\\n\",\n    \"\\n\",\n    \"    Extra kwargs are passed through to `fill_between`\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    ax : Axes\\n\",\n    \"        The axes to plot to\\n\",\n    \"\\n\",\n    \"    edges : array\\n\",\n    \"        A length n+1 array giving the left edges of each bin and the\\n\",\n    \"        right edge of the last bin.\\n\",\n    \"\\n\",\n    \"    values : array\\n\",\n    \"        A length n array of bin counts or values\\n\",\n    \"\\n\",\n    \"    bottoms : scalar or array, optional\\n\",\n    \"        A length n array of the bottom of the bars.  If None, zero is used.\\n\",\n    \"\\n\",\n    \"    orientation : {'v', 'h'}\\n\",\n    \"       Orientation of the histogram.  'v' (default) has\\n\",\n    \"       the bars increasing in the positive y-direction.\\n\",\n    \"\\n\",\n    \"    Returns\\n\",\n    \"    -------\\n\",\n    \"    ret : PolyCollection\\n\",\n    \"        Artist added to the Axes\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    print(orientation)\\n\",\n    \"    if orientation not in set('hv'):\\n\",\n    \"        raise ValueError(\\\"orientation must be in {{'h', 'v'}} \\\"\\n\",\n    \"                         \\\"not {o}\\\".format(o=orientation))\\n\",\n    \"\\n\",\n    \"    kwargs.setdefault('step', 'post')\\n\",\n    \"    edges = np.asarray(edges)\\n\",\n    \"    values = np.asarray(values)\\n\",\n    \"    if len(edges) - 1 != len(values):\\n\",\n    \"        raise ValueError('Must provide one more bin edge than value not: '\\n\",\n    \"                         'len(edges): {lb} len(values): {lv}'.format(\\n\",\n    \"                             lb=len(edges), lv=len(values)))\\n\",\n    \"\\n\",\n    \"    if bottoms is None:\\n\",\n    \"        bottoms = np.zeros_like(values)\\n\",\n    \"    if np.isscalar(bottoms):\\n\",\n    \"        bottoms = np.ones_like(values) * bottoms\\n\",\n    \"\\n\",\n    \"    values = np.r_[values, values[-1]]\\n\",\n    \"    bottoms = np.r_[bottoms, bottoms[-1]]\\n\",\n    \"    if orientation == 'h':\\n\",\n    \"        return ax.fill_betweenx(edges, values, bottoms,\\n\",\n    \"                                **kwargs)\\n\",\n    \"    elif orientation == 'v':\\n\",\n    \"        return ax.fill_between(edges, values, bottoms,\\n\",\n    \"                               **kwargs)\\n\",\n    \"    else:\\n\",\n    \"        raise AssertionError(\\\"you should never be here\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def stack_hist(ax, stacked_data, sty_cycle, bottoms=None,\\n\",\n    \"               hist_func=None, labels=None,\\n\",\n    \"               plot_func=None, plot_kwargs=None):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    ax : axes.Axes\\n\",\n    \"        The axes to add artists too\\n\",\n    \"\\n\",\n    \"    stacked_data : array or Mapping\\n\",\n    \"        A (N, M) shaped array.  The first dimension will be iterated over to\\n\",\n    \"        compute histograms row-wise\\n\",\n    \"\\n\",\n    \"    sty_cycle : Cycler or operable of dict\\n\",\n    \"        Style to apply to each set\\n\",\n    \"\\n\",\n    \"    bottoms : array, optional\\n\",\n    \"        The initial positions of the bottoms, defaults to 0\\n\",\n    \"\\n\",\n    \"    hist_func : callable, optional\\n\",\n    \"        Must have signature `bin_vals, bin_edges = f(data)`.\\n\",\n    \"        `bin_edges` expected to be one longer than `bin_vals`\\n\",\n    \"\\n\",\n    \"    labels : list of str, optional\\n\",\n    \"        The label for each set.\\n\",\n    \"\\n\",\n    \"        If not given and stacked data is an array defaults to 'default set {n}'\\n\",\n    \"\\n\",\n    \"        If stacked_data is a mapping, and labels is None, default to the keys\\n\",\n    \"        (which may come out in a random order).\\n\",\n    \"\\n\",\n    \"        If stacked_data is a mapping and labels is given then only\\n\",\n    \"        the columns listed by be plotted.\\n\",\n    \"\\n\",\n    \"    plot_func : callable, optional\\n\",\n    \"        Function to call to draw the histogram must have signature:\\n\",\n    \"\\n\",\n    \"          ret = plot_func(ax, edges, top, bottoms=bottoms,\\n\",\n    \"                          label=label, **kwargs)\\n\",\n    \"\\n\",\n    \"    plot_kwargs : dict, optional\\n\",\n    \"        Any extra kwargs to pass through to the plotting function.  This\\n\",\n    \"        will be the same for all calls to the plotting function and will\\n\",\n    \"        over-ride the values in cycle.\\n\",\n    \"\\n\",\n    \"    Returns\\n\",\n    \"    -------\\n\",\n    \"    arts : dict\\n\",\n    \"        Dictionary of artists keyed on their labels\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # deal with default binning function\\n\",\n    \"    if hist_func is None:\\n\",\n    \"        hist_func = np.histogram\\n\",\n    \"\\n\",\n    \"    # deal with default plotting function\\n\",\n    \"    if plot_func is None:\\n\",\n    \"        plot_func = filled_hist\\n\",\n    \"\\n\",\n    \"    # deal with default\\n\",\n    \"    if plot_kwargs is None:\\n\",\n    \"        plot_kwargs = {}\\n\",\n    \"    print(plot_kwargs)\\n\",\n    \"    try:\\n\",\n    \"        l_keys = stacked_data.keys()\\n\",\n    \"        label_data = True\\n\",\n    \"        if labels is None:\\n\",\n    \"            labels = l_keys\\n\",\n    \"\\n\",\n    \"    except AttributeError:\\n\",\n    \"        label_data = False\\n\",\n    \"        if labels is None:\\n\",\n    \"            labels = itertools.repeat(None)\\n\",\n    \"\\n\",\n    \"    if label_data:\\n\",\n    \"        loop_iter = enumerate((stacked_data[lab], lab, s) for lab, s in\\n\",\n    \"                              zip(labels, sty_cycle))\\n\",\n    \"    else:\\n\",\n    \"        loop_iter = enumerate(zip(stacked_data, labels, sty_cycle))\\n\",\n    \"\\n\",\n    \"    arts = {}\\n\",\n    \"    for j, (data, label, sty) in loop_iter:\\n\",\n    \"        if label is None:\\n\",\n    \"            label = 'dflt set {n}'.format(n=j)\\n\",\n    \"        label = sty.pop('label', label)\\n\",\n    \"        vals, edges = hist_func(data)\\n\",\n    \"        if bottoms is None:\\n\",\n    \"            bottoms = np.zeros_like(vals)\\n\",\n    \"        top = bottoms + vals # stack\\n\",\n    \"        top = vals # non-stack\\n\",\n    \"        print(label)\\n\",\n    \"        print(sty)\\n\",\n    \"        sty.update(plot_kwargs)\\n\",\n    \"        print(sty)\\n\",\n    \"        ret = plot_func(ax, edges, top, bottoms=bottoms,\\n\",\n    \"                        label=label, **sty)\\n\",\n    \"        bottoms = top\\n\",\n    \"        arts[label] = ret\\n\",\n    \"    ax.legend(fontsize=10)\\n\",\n    \"    return arts\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T04:42:54.603568Z\",\n     \"start_time\": \"2020-11-22T04:42:54.133982Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"ename\": \"NameError\",\n     \"evalue\": \"name 'OrderedDict' is not defined\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mNameError\\u001b[0m                                 Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-8-2f46ed49d625>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mkp_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mOrderedDict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      2\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      3\\u001b[0m \\u001b[0mkp_data\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"MagKP\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mn\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mtgt_nums\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"magkp\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;34m<=\\u001b[0m \\u001b[0;36m10\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mn\\u001b[0m \\u001b[0;34m<\\u001b[0m \\u001b[0;36m3\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;34m>\\u001b[0m \\u001b[0;36m6\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      4\\u001b[0m \\u001b[0mkp_data\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"MagKP-SuperN\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mn\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mtgt_nums\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"magkp\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;34m>\\u001b[0m \\u001b[0;36m50\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m \\u001b[0mkp_data\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"MagKP-Nlarge\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0mn\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mtgt_nums\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m\\\"magkp\\\"\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;34m>\\u001b[0m \\u001b[0;36m10\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;34m<=\\u001b[0m \\u001b[0;36m30\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mNameError\\u001b[0m: name 'OrderedDict' is not defined\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"kp_data = OrderedDict()\\n\",\n    \"\\n\",\n    \"kp_data[\\\"MagKP\\\"] = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 10 and (n < 3 or n > 6)]\\n\",\n    \"kp_data[\\\"MagKP-Nlarge\\\"] = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10 and n <= 30]\\n\",\n    \"kp_data[\\\"MagKP-Nsmall\\\"] = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10 and n <= 30]\\n\",\n    \"kp_data[\\\"MagKP-Nsmall\\\"] = kp_data[\\\"MagKP-Nsmall\\\"][: len(kp_data[\\\"MagKP-Nsmall\\\"]) // 2]\\n\",\n    \"kp_data[\\\"MagKP-LN\\\"] = [n for n in tgt_nums[\\\"magkp\\\"] if n >= 3 and n <= 6]\\n\",\n    \"kp_data[\\\"KP20k\\\"] = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T04:44:01.391539Z\",\n     \"start_time\": \"2020-11-22T04:44:00.826352Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"odict_keys(['MagKP', 'MagKP-Nlarge', 'MagKP-Nsmall', 'MagKP-LN', 'KP20k'])\\n\",\n      \"{}\\n\",\n      \"MagKP\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': ' ', 'alpha': 0.6}\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': ' ', 'alpha': 0.6}\\n\",\n      \"v\\n\",\n      \"MagKP-Nlarge\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': '/', 'alpha': 0.6}\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': '/', 'alpha': 0.6}\\n\",\n      \"v\\n\",\n      \"MagKP-Nsmall\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': 'o', 'alpha': 0.5}\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': 'o', 'alpha': 0.5}\\n\",\n      \"v\\n\",\n      \"MagKP-LN\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': '+', 'alpha': 0.5}\\n\",\n      \"{'facecolor': (0.33999999999999997, 0.8287999999999999, 0.86), 'hatch': '+', 'alpha': 0.5}\\n\",\n      \"v\\n\",\n      \"KP20k\\n\",\n      \"{'facecolor': (0.86, 0.3712, 0.33999999999999997), 'hatch': ' ', 'alpha': 1.0}\\n\",\n      \"{'facecolor': (0.86, 0.3712, 0.33999999999999997), 'hatch': ' ', 'alpha': 1.0}\\n\",\n      \"v\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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29HbW1taivr0dPTw8WL16MmpqahNW2e3t78Yc//CFlte18xkBjEAwzRETyecJh/OHshayPDwQCsNls0Z+vH1GS0bo2ixYtwltvvYWbb74Ze/fuxeLFi6PVtt9//31s3LgxGng2btyIKVOmwOPxYO3atejo6MDUqVNx/Phx1NfX4+qrrwYAdHR0YM2aNdi0aROuueaaIc85cuRITJo0CSdOnEgYaO69915s3759SKD5+OOP8a//+q8IBAIIh8P46U9/CqvVivvuuw+zZ8/GoUOHUFlZidtvvx0/+clP0Nvbi8cffxzz5s1L+lqUwEtOBsAwQ0SkL7W1tdi3bx98Ph+OHDmCmTNnRrdNmTIFO3fuxO7du7F69Wo8/fTTAICXXnoJpaWl2LNnD+rq6vDhhx/GtVlXV4f6+nrMmTMn4XN2dXWhq6sLkyZNSrj92muvhd1ux5/+9Ke4x3/9619j5cqVaG5uxm9/+1uMGzcOAPDFF19g5cqVeO2119DZ2Yk9e/bg5Zdfxg9/+MNowclkr0UJnKHROVEQGGaIiHSmqqoKx44dQ0tLy5AZkb6+PqxZswaff/45BEFAIDBwSau1tRUrV64EAEybNm1IUcsFCxagqakJCxcujCvHsG/fPrS2tsJut+Oxxx5DWVlZ0n49+OCD2L59O37wgx9EH7v22muxY8cOnDx5Et/85jdxxRVXAAAmTJgQ7cOVV16JBQsWQBAEVFZW4sSJEylfixI4Q6NzdlFkmCEi0qGamhps2rQJixcvjnt827ZtmDdvHlpaWrB9+3b4/X4AQLrSi/X19QCARx99NO7x2tpaNDc3o6mpCTfeeCOAgctLy5YtwyOPPBK374IFC+Dz+XDo0KHoY0uXLsX27dvhdDpx77334uDBgwAQV3ZBFMXoz4IgRAtXJnstSmCg0Tl/OMwwQ0SkQ3fccQfq6uqGzLT09fVFbxJ+9dVXo4/Pnj0br7/+OgDgk08+wdGjR+OOEwQBW7ZsQWdnJ7Zt25byuX/xi1+gubkZTz755JBtDz74IJ577rnoz11dXZg4cSJWrlyJmpoaHDlyJOPXmOy1KIGBRufCLJZORJQ1uyBgZlEhJjnTF3mc6LRjZlFhxvunM27cONx9991DHr/vvvuwZcsW3HnnndGZDgBYsWIFzp49i6VLl+LZZ59FZWUliouL41+P3Y7Gxka8+eabePHFF7Pq1/XXX4/y8vLoz/v27cOSJUuwbNkydHR04NZbb824rWSvRQmClG4Oi6LSlS7XwucX3dh24kxWx/7LpHEY71T/daixUnBraytmz56d1fMYHccmOY5NYkYal48++gjV1dXRny8GQ/CFw7AIAsKQEExyRrQKgAgBoa9OmZH9vYEgrNZLt6Nm8rXtbIVCIQSDQTgcDnzxxRdYtWoVfve73+VlxW232w2XyzWsNgb/rtKdg3lTMBERmVaR1YKiDL5mnYzb74fLbku/Yw54PB6sXLkSwWAQkiRh/fr1eRlmtMJAQ0REpANFRUXYtWuX1t3IW7yHhoiIiHSPgYaIiIh0j4HGxERB0LoLREREOcFAY1KjbVbYGGiIiMggGGhMKFL7KcBv7BMRqa6yshIPP/xw9OdgMIj58+fj/vvvz7rNmpoa9Pb2AgAOHz6MmpoatLW1YdeuXZg/fz6WLVuG2tpavPLKKwmPv+uuu/DBBx/EPfbOO++gsrISb775ZvSx+++/H++8807W/VQSv+VkMrGFLK8qzI+1dIiItHIxGEJAkjJepDRyqT6yf1C0wOu/VJ/IIYooSrMOTWFhIT7++ONoBeoDBw5EV9Mdrvb2dqxevRpbt27F9OnT0d7ejtraWtTX16OnpweLFy9GTU1NwmrbiYwbNw47duxATU1NTvqnJAYaE2FVbiKieL5wGB9c7M/4b+Jo28BpM7J/IBCAzXZpHZrrR5RktK7NokWL8NZbb+Hmm2/G3r17sXjxYrS2tgIA3n//fWzcuDEaeDZu3IgpU6bA4/Fg7dq16OjowNSpU3H8+HHU19fj6quvBgB0dHRgzZo12LRpE6655pohzzly5EhMmjQJJ06cyDjQVFVVIRgM4sCBA7juuusyOkYrvORkEgwzRET5o7a2Fvv27YPP58ORI0cwc+bM6LYpU6Zg586d2L17N1avXo2nn34aAPDSSy+htLQUe/bsQV1dHT788MO4Nuvq6lBfX485c+YkfM6uri50dXVh0qRJsvoaqcCd7zhDYwIMM0RE+aWqqgrHjh1DS0sLrr/++rhtfX19WLNmDT7//HMIgoBAYOCSVmtrK1auXAkAmDZt2pCilgsWLEBTUxMWLlwIi+XSLNG+ffvQ2toKu92Oxx57DGVlZbL6GglIf/nLX2S/TjVxhsbgGGaIiPJTTU0NNm3ahMWLF8c9vm3bNsybNw8tLS3Yvn07/H4/ACBd6cX6+noAwKOPPhr3eG1tLZqbm9HU1IQbb7wRAHDvvfdi2bJleOSRRzLq6wMPPJD3szQMNAbGMENElL/uuOMO1NXVDZlp6evri94k/Oqrr0Yfnz17Nl5//XUAwCeffIKjR4/GHScIArZs2YLOzk5s27Yt5XP/4he/QHNzM5588smM+rpw4UJcuHAB7e3tGe2vBQYag2KYISLKb+PGjcPdd9895PH77rsPW7ZswZ133olQKBR9fMWKFTh79iyWLl2KZ599FpWVlSguLo471m63o7GxEW+++SZefPFFWf25//77sWjRIixatAirV68esv2BBx7AyZMnZbWpJt5DY0AMM0REmXGIIq4uKkQo/a4AEP3+UmT/YDAIq/XSqbRATD9P8N577w15bN68eZg3bx4AYNasWdi/f3902/e+972Bvjoc2Lx5MxwOB7744gusWrUKl112GQDErRVTXFyM5ubm6M+33XZb2j698MILCR+P9AkAbrjhBhw5ciRtW1phoDEYhhkioswVWS0Zfc06GXfAD5fdln7HHPB4PFi5ciWCwSAkScL69etht9tVeW49YKAxEIYZIiLjKioqwq5du7TuRt7iPTQGwTBDRERmpkqgaWhoQE1NDSorK4fclQ0AP/vZz4Zs6+zsxPLly3HTTTdh+fLl+OyzzxTdpmcMM0REZHaqBJobbrgBL774IsaPHz9k24cffoi//vWv0RubItavX48VK1Zg//79WLFiRfT79Upt0ytREBhmiIjI9FQJNHPmzEFFRcWQx/1+Px577DGsX78ewlcFvwCgp6cHbW1tWLJkCQBgyZIlaGtrQ29vryLb9MwuigwzRERkepreFLxt2zbccsstmDhxYtzj3d3dGDt2bHTpZovFgjFjxqC7uxuSJOV8W3l5uax+Hz58eLgvPWfGVFWjq+9iVscGg0G0tqr7WmbPnj2s4yPF23K9r9lwbJLj2CRmlHGxWq1wu905bVNue9dddx0OHDgAAHj77bexefNm7NixA83NzXj11VcxYsQIhEIhfOc738H111+PnTt34tVXX4XFYsGIESOwfv366FWNPXv24LnnngMwsH7N0qVLAQCLFy/Gzp07MWLEiBy+UnmGO85+v1/W+06zQPPee+/hgw8+wA9+8AOtupC1GTNmwOFwaN0NAMDnF91wOp1ZHWu1WocdMNSWaX9bW1t199rUwrFJjmOTmJHG5aOPPoLL5Yr+HPb7Iebwq8+ZtCcIAlwuFw4ePIjNmzfjl7/8JSZNmgS73Y577rkH9957Lz799FOsWLECBw8exMyZM7Fy5UoUFBTgpZdewjPPPIOtW7fi3LlzePbZZ/Hb3/4WgiDgtttuw9/93d+htLQUoiiisLAw7rWqye12D/u57XZ7XNFOn8+XckJBs0Dz7rvvoqOjAzfccAMA4OTJk7j33nvx1FNPobq6GqdOnUIoFILFYkEoFMLp06dRUVEBSZJyvo30IeTph6WgMKN9Y//4hoNBiFauUEBEQ4l2Oz5ZdWfO2rvy+V9ntN9f/vIXrFu3Dv/xH/+RsPr11KlTYbVacfbsWcyfPz/6+LXXXovXXnsNwMDsznXXXRctNnndddfhf//3f6O3VgCA1+vFt7/9bdx00034h3/4h+G8tLyn2V/5b33rW/jWt74V/bmmpgY7duzAtGnTAADV1dVoaWnBsmXL0NLSgurq6uilISW2Uf7rfPD/zeq4TP/AEBGpwe/3o66uDr/61a8wderUhPscOnQIgiAMOUf95je/waJFiwAAp06dwrhx46Lbxo4di1OnTkV/7u/vx/e//33ceuutuPXWWxV4JflFlUDzxBNP4I033sCZM2dwzz33oKysDHv37k15zIYNG7B27Vo0NjaipKQEDQ0Nim4jIiJSg9VqxaxZs/Cb3/wG69ati9v2/PPP47XXXoPL5cLWrVvjvjDT3NyMw4cPY+fOnQASV9+O3b+urg733XcfbrnlFoVeSX5RJdCsW7duyC9tsNg6FMDAdFtTU1PCfZXYRkREpAZRFLF161bcc8892LFjBx544IHotlWrVuHee+8dcswf//hH7NixAzt37oyWOxg3bhz+/Oc/R/c5deoU/uZv/ib689e+9jX8z//8D5YuXRoXdIyKKwUTERGprKCgADt27MCePXvSftBua2tDfX09tm/fjpEjR0YfX7hwId5++22cP38e58+fx9tvv42FCxdGt69evRplZWXYsGGDUi8jrzDQmJhogsRORJSvysrK8Nxzz2H79u34z//8z6T7bdq0Cf39/XjooYewbNmy6IxOWVkZ6urqcMcdd+COO+7At7/97egNwhGPPPII/H4/Nm3apOhryQf86odJjbZZYWOgISKTC/v9Of3iQCZf237vvfei/66oqIjecvGNb3wj4f7PP/980rYiYWaw2Ns4nnrqqZT9MQrO0JhQpPZTIMENZUREZjLcNWgGLx6XyzVtSB4GGpOJLWQZZqAhIiKD4CUnE8mXqtzZTu8GPf34LMu1aIiIyNgYaEwiX8IMADSd6on7ebRt4G2Yrl9/P3Zkyu1ERJmQJMkUX2PWs0Rr7KTDS04mkE9hhohIS06nEz09PVmdMEkdkiShp6dHdp1CztAYHMMMEdElEyZMwLFjx/Dll1/mpD2/3x9d6I4uGe64OJ1OTJgwQdYxDDQGxjBDRBTPZrNh8uTJOWuvtbU1riI0DdBiXHjJyaAYZoiIyEw4Q2NARg0z/kCQlbOJiCghBhqDMWqYAYDm3vMAMvtWVHd3NyoqKgDw21FERGbAS04GYuQwQ0RElAoDjUEwzBARkZnxkpMB6C3MRC4ZRX+22+Qdn8H+YpELI218exMRmQVnaHROFARdhRkiIiIl8COsztlFUXdhJllf5b6GlDcFX3QjXFwiqz0iItIvztDonD8c1lWYISIiUgIDjc6FWY+EiIiIgYaIiIj0j4GGiIiIdI+BhoiIiHSPgYaIiIh0j4GGiIiIdI+BxsREQdC6C0RERDnBQGNSo21W2BhoiIjIILhSsAlFaj8FNFrDhrWciIgo1zhDYzKxhSy5KB8RERkFP8KaSL5U5WYtJyIiyjXO0JhEvoQZIiIiJTDQmADDDBERGR0DjcExzBARkRkw0BgYwwwREZkFA41BMcwQEZGZMNAYEMMMERGZDQONwTDMEBGRGTHQGAjDDBERmRUDjUEwzBARkZlxpWAD0FuYYS0nIiLKNc7Q6JwoCLoKM0RERErgR1ids4ui7sIMazkREVGucYZG5/zhsK7CDBERkRIYaHQuLElad4GIiEhzDDRERESkeww0REREpHsMNERERKR7DDRERESkeww0REREpHsMNCYmCoLWXSAiIsoJBhqTGm2zwsZAQ0REBsGVgk0oUvspoNEaNqzlREREucYZGpOJLWTJRfmIiMgo+BHWRPKlKjdrORERUa4x0JhEvoQZMp5efwDucFj2cS5RRLnMy41ERMkw0JgAwwwpyR0O48C5Pkxw2gEAJ3x+vNfXn/KYmUWFuKG8VI3uEZFJMNAYHMMMKc0mCJjotKPD4wMATClwICwBhy4mDjWzigsx3mFHkPdwEVEO8aZgA2OYITVYBAEdHh/a3B60uT3o8PiiszWJXOYYCD8MNESUSww0BsUwQ/nMJgj840NEOcVLTgbEMENqCkkSphQ4oj9PKXDguM+fdP+TvgCqXAVcqZqIciul4KUAACAASURBVEqVD0kNDQ2oqalBZWUljh49CgA4e/Ys/umf/gk33XQTli5diu985zvo7e2NHtPZ2Ynly5fjpptuwvLly/HZZ58pus0oGGZIbQFJwnGfH5MLHJhc4ECXN/lNwQ5RxEl/AB0eH6wMNESUQ6oEmhtuuAEvvvgixo8fH31MEATcd9992L9/P/bs2YOJEyfixz/+cXT7+vXrsWLFCuzfvx8rVqxAfX29otuMgGGGtPJeXz/2njmHvWfOJb0Z2CGKGGWz4kwgiHcvXIQ3i696ExElo0qgmTNnDioqKuIeKysrw7x586I/X3vttThx4gQAoKenB21tbViyZAkAYMmSJWhra0Nvb68i24yAYYbyWWyY8THIEJEC8uIemnA4jJdffhk1NTUAgO7ubowdOxYWiwUAYLFYMGbMGHR3d0OSpJxvKy8v1+BV547ewgxrOZkLwwwRqSEv/uI//vjjKCwsxD/+4z9q3ZWMHD58WOsuRI2pqsbcogK8dfoMTvsDso4NBoNobZX/Wq695hpYbNmv8NozaFasoMg18PhFd0bHZ7p/9HnKS9Ha2iqzl8aWy/Eou6oS7v7El5kKLBaUFTjRdaEPnlAobpvfH0Drh/nzfymC75XEOC7JcWwSU3tcNA80DQ0N+Pzzz7Fjxw6I4sAVsIqKCpw6dQqhUAgWiwWhUAinT59GRUUFJEnK+Ta5ZsyYAYfDkX5HFZzyePHuRQ8uiBY4nRZZx1qtVsyePTur5/1k1Z1ZHXfl878eUmPJ89VMSrg4s/5nsn93d3fc7zbb12lEra2tOR2PLq8PrsLCIY/HzsyIDgdcg7bb7ba8+73kemyMguOSHMcmMSXGxefzpZxQ0HQpiKeffhqHDx/GM888A7v90kJcI0eORHV1NVpaWgAALS0tqK6uRnl5uSLb9MwfDuviMhOZCy8zEZHaVJmheeKJJ/DGG2/gzJkzuOeee1BWVoatW7dix44duOKKK3DnnQOf9idMmIBnnnkGALBhwwasXbsWjY2NKCkpQUNDQ7Q9JbbpVZirrVKeiQ0zVYVOWTWeiIiypUqgWbduHdatWzfk8SNHjiQ9ZurUqWhqalJtGxENX2yYme5yYrwj8xpPRETDofk9NERkDIMvM0VqNrW5PdF9Jhc4GGiISBEsp0JEw8Z7ZohIa5yhIaJhSxRmjnn9smo8ERENBwMNEQ2LCCScmTl0sR+iMHCZCQC6vH5ebiIixTDQmBirHVMuhIGkl5ne6+vnN5uISBW8h8akRtussDHQEBGRQXCGxoQitZ8CGq1hw1pORESUa5yhMZnYQpZclI+IiIyCH2FNJF+qcid7brl9SrV/90X3kJpRRERkXJyhMYl8CTNERERK4AyNCTDMkNJmFhXKqtk0s6gQTpGfp4godxhoDI5hhpRmEwRMdGZes2lWcSHGO+wI8h4uIsohfkQyMIYZUoNFEKI1m9rcHnR4fNHZmkQiNZ4YaIgolxhoDIphhvKZTRD4x4eIcoqXnAyIYYbUFJIkWTWbTvoCqHIVcKVqIsopBhqDYZghtQUkCcd9/oxqNjlEESf9AYiCgPGO5JeliIjkYqAxEIYZ0komNZscohityn3c58fcEpdKvSMiM+BlbINgmKF8FhtmkhWyJCIaDs7QGIDewgxrOZkLwwwRqYEzNDonCoKuwgyZC8MMEamFH2F1zi6KugszeqrlFA4GIVr53yQbDDNEpCb+pdY5fzisqzCjlU9W3ZnVcVc+/+sc98QcGGaISG0MNDoX5mqrlGdiw0xVoVNWjSciomwx0BBRzsSGmekuJ8Y7Mq/xREQ0HAw0RJQTgy8zRWo2tbk90X0mFzgYaIhIEfyWExENG++ZISKtcYaGiIYtUZg55vXLqvFERDQcDDRENCwikHBm5tDFfogCMqrxREQ0XAw0JsZqx5QLYSDpZaZMajwREeUC76ExqdE2K2wMNEREZBCcoTGhSO2ngEZr2LCWExER5RpnaEwmtpAlF+UjIiKj4EdYE8mXqtx6quVERET6wBkak8iXMENERKQEztCYAMMMKW1mUaGsmk0ziwrhFPl5iohyh4HG4BhmSGk2QcBEZ+Y1m2YVF2K8w44g7+EiohxioDGwdGHmfDAEeH2y2x3vdKTfiUzDIgiyajZFajxN4vuIiHKIgcagMpmZee7El1m1vWXa5cPpGhFsgsAb+IgopxhoDIiXmUhNIUmSVbPppC+AKlcBV6omopxioDEYpcPM4EXxiAKShOM+f0Y1mxyiiJP+AERBwHiHXc1uEpHB8exkIGqEmQWlxTlvl/Qvk5pNDlGMVuU+7vNjbolLpd4RkRnwMrZBqBVmDp7vy3nbZHyxYSZZIUsiouHgDI0BqBlmctE+azmZC8MMEamBMzQ6JwqCrsIMmQvDDBGphR9hdc4uiroLM6zlZA4MM0SkJs7Q6Jw/HNZVmCFzYJghIrVxhkbnwgosH88wQ8MRG2aqCp2yajwREWWLgYbiMMzQcMSGmekuJ8Y7Mq/xREQ0HAw0FMUwQ8Mx+DJTpGZTpjWeiIiGg/fQEACGGRoe3jNDRFrjDA0xzNCwJQozx7x+WTWeiIiGg4HG5BhmjOVcIAh3KBT9WRQE2AQBAUmK3kA+qrIKx72+uONcFgvKslyIUAQSzswcutgPUUBGNZ6IiIaLgcbEGGaMxx0K4f9+cRJA8t+v1+uF0+mMO+5fJo3LOtCEgaSXmTKp8URElAu8h8akGGaMjb9fIjIbztCYkNYnO9ZyUpbWv18iIi1whsZkeLIzNqVrexER5StzfoQ1qXwJM6zlpBybIGj++yUi0gJnaEwiX8IMKSsgSfz9EpEpcYbGBBhmzEOJ2l6ZmFlUKKtm08yiQjhFfp4iotxhoDE4hhlSmk0QMNGZec2mWcWFGO+wI6hR+CIiY1LlI1JDQwNqampQWVmJo0ePRh/v7OzE8uXLcdNNN2H58uX47LPPNNtmRAwzpAaLIERrNrW5Pejw+KKzNYlEajwx0BBRLqkSaG644Qa8+OKLGD9+fNzj69evx4oVK7B//36sWLEC9fX1mm0zGoYZymc2QeANfESUU6r8TZkzZw4qKiriHuvp6UFbWxuWLFkCAFiyZAna2trQ29ur+jajYZghNYUkCVMKHJjuKsB0VwGmFDhwIkXNppO+AKpcBRAFQcVeEpHRaXYPTXd3N8aOHQuLxQIAsFgsGDNmDLq7uyFJkqrbysvLNRgBZSgdZgYvikcUkCQc9/kzqtnkEEWc9AcgCgLGO5JfliIikotnpywcPnxY6y5EjaqsgtfrBQCMsdswt6gYb50+g9P+QM6fK9L+cHV3d8f9LBa5Bh6/6M7o+Ez3jz7P2JEyexivtbV1WMerKfb9kMrgfYLBIFpbs3tfl11VibdPnUm7X4HFgrICJ7ou9OFoKISZTju++OD9rJ5TSXr6fauJ45IcxyYxtcdFs0BTUVGBU6dOIRQKwWKxIBQK4fTp06ioqIAkSapuk2vGjBlwOBwKjIp8n190w+l0xs3MXBAtcDotOX2e2Pa/MXp4bQ0e80iJgkwXwstk/+7u7qx+t4nMnj07J+2o4bjXN6Tw5GCJilO67HZY/88MJC4xmdrZQBCuwsKU+zhEEaNsVpwJBCE6HHABsNtteTe2ra2tedenfMBxSY5jk5gS4+Lz+VJOKGgWaEaOHInq6mq0tLRg2bJlaGlpQXV1dfTyj9rb9EyNy0y5bJ+1nPLLaJsVAUnCsye+TFo1ezhiw4wS7RMRASoFmieeeAJvvPEGzpw5g3vuuQdlZWXYu3cvNmzYgLVr16KxsRElJSVoaGiIHqP2Nr1SunYPbzA2tsjvFwDDDBHpmiqBZt26dVi3bt2Qx6dOnYqmpqaEx6i9Ta/soqi7MKO3Wk7Hvb6Ej4uCAJsgICBJQ1bodVksKMvzGaLY32+lK/VlqmwwzBCRmvL7Ly6l5Q+HdRVm9Oj/fnFyyGPpxudfJo3L60Cj9O+XYYaI1Ja/f3EpI0rU7mGYSU3v4zPGbhvSf4cgYFZxYUY1mNLVbIoNM1WFTlk1noiIssVAQ3H0frJWmt7HZ7TNirlFQ/sfgoTxDntGNZhS1WyKDTPTXc60+xMR5QoDDUXp/WStNLXG51wgCHcolNWx54PJj4v0/63TZ3BBjP9af1ACOjw+TC5wJA0ckRpMbW5P9LHY/QdfZkq3PxFRLjHQEACGmXTUHB93KJTwvp3hiO3/aX8g5+sU8Z4ZItIaAw0xzKSh9/HJpP9WYeCS0PEUNZiOef2YUnBpQcnI/snCTLL9iYiUwEBjcno/WatBz+OT6e/XAiFlDSZg4N4XUUBczab2fm/SmZlE+/NyExEphYHGxBhmMqPX8ZHz+/VJUkZh472+/ug3lTK5zBS7PxGRkhhoTIphJnPZjM/5YAhIsiAfkHpRvlQ39maK68wQkdkw0JiQ1mHGDLWcnjvxZdJtequ9NRjDDBHlIwYak9E6zJgdwwzJ0esPwD1onEUgo6roLlFEucwPC0R6xkBjIvkSZvRWyylXGGZILnc4jMZjpwDIH/+6CWNRrnQHifKIqHUHSB35EmbMimGGhoPjT5QeZ2hMgGFGW3oMM7E1m077gzjh86c8mWZS42nw/k6Rn6fScYoi5pYU5Xz8iYyIgcbgGGa0pccwE1uzySYIqHIVICxJSRfFy6TGU6L9gwoUVjWaoCR9NZ7yx5/IbBhoDIxhRlt6DDPApZpNn3p8GGWzot3tGfj035d6/0xrNkX2n+R0JNxOl4QlKevxJzIbzvkaFMOMtvQaZiJsghC9ZyOgwEyKTRD4xycDYUCR8ScyIs7QGBDDjLb0HmZO+gKochWg3e1BqdWSdY2ndO2LgpDTfhuRVRCyHv/LOQNGJsNAYzBqnEwpOb2HGYco4qQ/AFEQojeZZlPjKdn+se2Pd9hz3n+jsQrZ1dg6dLEfC0qL1eomUV7g2clA1DqZUmJGCDORy0xyq2JnUrNpcPtzS1zD6a4peMNh2TW2iMyKl7ENQs2TKQ2l9zADQNF1TriOChEpjTM0BqC3k6nRajnpbfwTtQ+AYYaIdI0zNDonCoKuT6Z6Z4QwE7mMyDBDRHrGGRqds4ui7k6mRqnlZJQwc/B8Hypdzpy3zzBDRGriDI3O+cNhXZ5M9c5IYYa1n4zHIYr8406mwxkanQsrsOgWw0xqeg8bY+y2Ie07BAGzigszqsGUrmZQbJipKnSyxlAOyRl/xkgyGwYaisMwk5rew8xomxVzi4a2H4KE8Q57RjWYUtVsig0z011OWTWeKDW542/jwoVkMgw0FMUwk5oRwsyC0mK8dfoMLoiWuG1BCejw+DKqwZSsZtPgy0xyazxRanLH38JAQybDy6wEgGEmHaOEmYPn+3DaH8h5+7xnRlscfyLO0BAYZtIxUphJ1r5VSF+DKVnNoGQnU7k1nig1ueMfYlFLMhkGGpNjmElP67ChRvsWZFczqL3fm3RmQE6NJ0pP7vizSjeZDQONiTHMZEbrsKFG+z5Jkl0zKJPLHKwxlFtyx5/ITHgPjUkxzGRO67CRj+3zZKotjj/RUJyhMSGtw4zRajnJxTBDw8HxJ0osP//ik2JydbK78vlfZ3VcMGju2SCGGRoOjj9Rcgw0JpLLk13TqZ6UzwPktjaT3P3VqOUkF8MMDQfHnyg13kNjElpfZjI7hhkaDo4/UXqcoTEBhhlt6THMxNYMOu0P4oTPn/JkmkmNocH7O0V+nkrHKYqYW1KU8/EnMiIGGoNjmNGWHsNMbM0gmyCgylWAsCQlXRQvkxpDifYPcp2UtIKS9NV4yh9/IrNhoDEwhhlt6THMAJdqBn3q8WGUzYp2t2fg039f6v0zrdkU2X+S05FwO10SlqSsx5/IbDjna1AMM9rSa5iJsAlC9J4NJVactQkC//hkIAyu+EuUKc7QGBDDjLb0HmZO+gKochWg3e1BqdWSdY2ndO2LrAadllUQsh7/yzkDRibDQGMwapxMKTm9hxmHKOKkPwBREKI3mWZT4ynZ/rHtj3fYc95/o7EK2dXYOnSxHwtKi9XqJlFe4NnJQNQ6mVJiRggzkctMcqtiZ1KzaXD7c0tcw+muKXjDYdk1tojMipexDULNkykNpfcwA0DRdU64jgoRKY0zNAagxck01aUnJWozyd1fzVpOeg8zkd8lwwwR6RlnaHROFARdn0z1zghhJnIZkWGGiPSMMzQ6ZxdFTU6mmTyf0Ws5GSXMHDzfh0qXM+ftM8wQkZo4Q6Nz/nBYlydTvTNSmGHtJ+NxiCL/uJPpcIZG58IKLLrFMJOa3sPGGLttSPsOQcCs4sKMajClqxkUG2aqCp2sMZRDcsafMZLMhoGG4jDMpKb3MDPaZsXcoqHthyBhvMOeUQ2mVDWbYsPMdJdTVo0nSk3u+Nu4cCGZDAMNRTHMpGaEMLOgtBhvnT6DC6IlbltQAjo8voxqMCWr2TT4MpPcGk+UmtzxtzDQkMnwMisBYJhJxyhh5uD5Ppz2B3LePu+Z0RbHn4gzNASGmXSMFGaStW8V0tdgSlYzKNnJVG6NJ0pN7viHWNSSTIaBxuQYZtLTOmyo0b4F2dUMau/3Jp0ZkFPjidKTO/6s0k1mw0BjYgwzmdE6bKjRvk+SZNcMyuQyB2sM5Zbc8ScyE95DY1IMM5nTOmzkY/s8mWqL4080FGdoTCgXJzvWcsoewwwNB8efKDHO0JgMZ2a0xTBDw8HxJ0ouPz/CkiJyebJjLSf5GGZoODj+RKlxhsYkODOjLYYZGg6OP1F6eTFD89///d/Ytm0bJElCOBzGd7/7XXzzm99EZ2cn1q5di3PnzqGsrAwNDQ244oorAECRbUbFMKMtPYaZ2JpBp/1BnPD5U55MM6kxNHh/p8jPU+k4RRFzS4pyPv5ERqT5XxRJkvDDH/4QmzZtQnNzMzZv3ow1a9YgHA5j/fr1WLFiBfbv348VK1agvr4+epwS24yIYUZbegwzs4oLMdFpR6fHF13MbZzdlvRkGrt/p8eH8Q47ZhYVpm0/yHVS0gpKUs7Hn8ioNA80ACCKIvr6+gAAfX19GDNmDM6ePYu2tjYsWbIEALBkyRK0tbWht7cXPT09Od9mRAwz2tJjmAEu1Qz61OPDmUAQ7W4PxjmSf7MstsZQm9uDDo8vOluQan8GmvTCkpTz8ScyKs0vOQmCgK1bt6Kurg6FhYVwu934+c9/ju7ubowdOxYWy0ARPYvFgjFjxqC7uxuSJOV8W3l5ecZ9Pnz4cI5HIXujKqvg9XqHPD7GbsPcooFChErU7unu7k66TSxyDexz0Z1RW0rtH+3j2JEZtZtMovFNR+nxH277g19TOByGu3/gMkUgUIRwIIAySOi60AeHqwABixjdPlggUASvzxfd7rWKCFgtKfcPBwIIB0NobW2V3Xel5VOfRkyrRJ/Xm9X4+0URrR/m7m9VPo1LvuHYJKb2uGgeaILBIH7+85+jsbERs2fPRmtrK/75n/8ZmzZt0rprSc2YMQMOhyP9jir4/KIbTqcz7rHYT+4XRAucTkuSo7NXUVGRdFtk/ZdMv2WkxP7d3d0p+yjH4PFNR+nxH277Xq93yGsSRRGuwoHLFD1hCTNHlKLd7cHIAme0ZlBk+2CnQmFMLyuB86v/E+n2j7Rvt1kxe/ZsWX1XWmtra171qdvnx/SykqzG/0q7LWevJd/GJZ9wbBJTYlx8Pl/KCQXNA81HH32E06dPR1/47NmzUVBQAIfDgVOnTiEUCsFisSAUCuH06dOoqKiAJEk532YUalzmoOT0epkpwiGKOOkPQBSE6GWLbGo8Jds/tv3xDl4WSccqZFdj69DFfiwoLVarm0R5QfN7aMaNG4eTJ0+io6MDAPDpp5/izJkzuPzyy1FdXY2WlhYAQEtLC6qrq1FeXo6RI0fmfJsRqHUypcSMEGYiXw1+98JF7D1zDnvPnMu4xlO6/Qe37+XXj9PyhsM5G38io9P84/bo0aOxYcMGPPTQQxAEAQDw1FNPoaysDBs2bMDatWvR2NiIkpISNDQ0RI9TYpueqXky/dvy/FqwLh/oPcwAUHSdE66jQkRK0zzQAMAtt9yCW265ZcjjU6dORVNTU8JjlNimV1qcTFnL6RK9h5nI75Jhhoj0TPNLTjQ8oiDo+mSqd0YIM5HLiAwzRKRneTFDQ9mzi6ImJ1PWcjJOmDl4vg+VLnnf5MoEwwwRqYkzNDrnD4d1eTLVOyOFGdZ+Mh6HKPKPO5kOZ2h0LqzAaqsMM6npPWyMsduGtO8QBMwqLsyoBlO6mkGxYaaq0MkaQzkkZ/wZI8lsGGgoDsNManoPM6NtVswtGtp+CBLGO+wIS0j6td9ZxYUY/9Uy+8DAAm6D948NM9NdzrT7U+bkjr/tq2+NEplFxrOS7777bsLHI2u6kP4xzKRmhDCzoLQYb/ecG9J+UELGNZiS1QwafJmJNYZyS+74WxhoyGQyDjTf/e53sXnzZgQCA3VjLly4gO9973v46U9/qljnSD0MM6kZJcwcPN+nSG0p3jOjLY4/kYxA09zcjPb2dtxxxx1oamrC0qVLUVJSgldffVXJ/pEKGGZSM1KYSda+VRi4hHHC50/azjGvH1MKHJjuKsB0V0F0/2Qn02T7U3bkjn+I1czJZDIONGPHjsUzzzyDcDiM+vp6LFq0CI899hgKkxRMI31gmElP67ChRvsWDNQMSnXT7qGL/Tju82NygQOTCxzo8vrR5vYmnRlItD9vCs6e3PEPMNCQyWQcaD766CPcfvvtmDhxIhobG/GnP/0J3//+93HhwgUl+0cKYpjJjNZhQ432fZIku2ZQe3/yk2mi/Xkz8PDJHX8iM8k40KxatQqrVq1CY2Mjvv71r6O5uRlOpxNLly5Vsn+kEIaZzGkdNvKxfd6zoS2OP9FQGX9t+ze/+Q0mTpwY/bmwsBAbN27Ef/3XfynSMVJOLk52rOWUPYYZGg6OP1FiGc/QxIaZWDfccEPOOkPK48yMthhmaDg4/kTJ5edHWFJELk92rOUkH8NMvLMJ+igCGa1w6xJFlMuc2dM7hhmi1BhoTIIzM9pimBnq5VM9WbdfN2EsynPSC31gmCFKj4HGBBhmtKXHMBNbM+i0P4gTPn/Kk2kmNYZy2b5ZOEURc0uKcj7+REbEQGNwDDPa0mOYia0ZZBMEVLkKEJYkHE8SJDKpMZTr9s1SpygoSV+Np/zxJzIbBhoDY5jRlh7DDHCpZtCnHh9G2axod3sGPv33pd6/ze2JPja5wJE00OSi/WmFBVm/Pj0JS1LW409kNhl/y4n0hWFGW3oNMxE2QYjes6HEirNKt28UYXDFX6JMcYbGgBhmtKX3MHPSF0CVqwDtbg9KrRZMKXAkvdwBXKoxFJFu/1y0b5Y6RVZByHr8L3dylobMhYHGYNQ4mVJyeg8zDlHESX8AoiBEbzLt8vpTli04dLEfonDpMkeq/XPV/iSTnKytgpD1+C8oLVarm0R5gWcnA1HrZEqJGSHMRC4DpZoRSOS9vv6036zJZfvXlZnjfegNhzOuscVvNpHZ8R4ag1DzZEpD6T3MAFB0nROuo0JESuMMjQFocTJlLadL9B5mIr9Lhhki0jPO0OicKAi6PpnqnRHCTOQyIsMMEekZZ2h0zi6KmpxMWcvJOGHm4Pk+VLqcOW9fyTATqQOVae2nCDPWgCIyCwYanfOHw7o8meqdkcKMXmo/xdr15dms2jdLDSiHKHL6nUyHgUbnwgqsx8Ewk5rew8YYu21I+w5BwKziQlk1mJLVDIoNM1WFzqxrPCnRvt5LJsgZH17gI7NhoKE4DDOp6T3MjLZZMbdoaPshSBjvsGdcgwlIXLMpNmxMdzmzrvGkVPsWHQcaueOj9/BGJBcDDUUxzKRmhDCzoLQYb50+gwuiJW5bUAI6PL6MajAlq9k0+DJTtjWelGw/qOMVhuWOT7bhrdcfgDschlUQYBUEeMNhlF1ViS6vL+6eJacoIihJ0TG1CgIKRRGlXHyTNMJ3HgFgmEnHKGHm4Pk+nPYH4HRa0h8kg9L3zPDbUqnlcnzc4TAaj53CdFcBJhc4sPfMOYR9PkwsKY5rf/GoMnTGBKzprgLcPLJs2K+FKFsMNMQwk4aRwkyy9q1C+hpMyWoGJTuZyq3xpEb7lTqu0i13fLKtd2UVBEx3FcS1X1bgzGj8zVJji/ITA43JMcykp3XYUKN9C7KrGdTe7006MyCnxpNa7f/tCGW/yq8kueOTbZVuqyBgcoEjrv2uC30QHfH1s8xcY4vyEwONiZklzASDQVz5/K+zPl7rsKFG+z5Jkl0zKJPLHHJrDCndfrYn+Xwhd3yy4Q2HsffMubj2PaEQXGn6A5inxhblJwYakzJLmAGAty+4AWS+cF+kFMCXgSD+fuxIzcNGPrbPe2a0xfEnGoqBxoRycbLTUy0npfsjF8OMtu3rnRrjw/EnPeJikiZjppmZfMQwo237eqfG+IhQrlApkZI4Q2MiuTzZ6bGWk9L9SYdhRvv29fwJTq2wF4YyhUpJnyLrEmUidp2icVdNU6xPyTDQmARnZrTFMJMf7Z8NBhH2Jt8vsoBcIloWtuTMFWklsi5RKonen/eNUb9qGgONCTDMaEuPYSa2ZtBpfxAnfP6UJ9NMagzlQ/svnuxJuX8gEMSXHm/C9rUobOkURcwtKcr5+Mj1tbJiXFlSrFj7pC9y//+qhYHG4BhmtKXHMBNbM8gmCKhyFSAsSUkXrcukxpBe2vf6fJheVpKyfTUFJemr1yt/fHJlVnEhxogiOjMcfzI2uf+/1MRAY2AMM9rSY5gBLtUM+tTjwyibFe1uz8Cnsb7U+8ut2ZSP7bv7++F0OFK2r6awJGU9PrlymcOOj85dQEfw0qfvfBkfUp/c/19q0vM9cpQCw4y29BpmImyCEL0mrsRidHpvXy1h6H8xQDKefP3/xRkaA2KY0Zbew8xJXwBVrgK0uz0otVqyrsGkx/a9BcDdWQAAIABJREFUVjHt/mqyCkLW43N5jsoQHPP6cZWrEM5gKK59Mie5/7/UxEBjMGqcTCk5vYcZhyjipD8AURCiN/1lU4Mp2f753n7Aakm7v5qsQnY1tg5d7MeC0tyUITh0sR8BuxWTS4ri2ifzyeb/l5p4djIQtU6mlJgRwkxkGlnuJ65MairpoX13fz9chYWyjlWSNxyWXWNLCe+eu4A2f+r3nN7X+aHUhvP/Sy18/xmEmidTGkrvYQZQdrl7I6xjwz+WyUXGnyvkGJNe1kHiDI0BaHEyZS2nmP11HmYiv0s9hw012s/fP+Paih1/MiY9hBmAMzS6JwqCrk+memeEMBO5jKjnsKFG+zSUXj65U/b0VNuLMzQ6ZxdFTU6mrOVknDBz8HwfKl3OnLdvpDCjhz/mauP4mIOeantxhkbn/OGwLk+memekMMPaT+q3rzSl7/nR+/iQMXGGRufCCixqxDCTmt7Dxhi7bUj7DkHArOJCWTWSktX0iT3ZVRU6s67BlG/t2wQhZdtqkNP/bGOGUxSxeFRZVuNPxpPu/RAr9v1pF9WfL2GgoTgMM6npPcyMtlkxt2ho+yFIGO+wZ1wjCUhc0yf2ZDfd5cy6BpNW7RdYLEnbrywsSDGyypM7PtkGsKAkodPjy2r88yH0UW6lej/EGvz+nFqqfmV6BhqKYphJzQhhZkFpMd46fQYXREvctqAEdHh8GdVISlZTafBliGxrMGnZflmBM2n73ygvTdiuWuSOj2UYgSbyHHLHf5rGoY9yL9n7YbDB74fri9Vfz4n30BAAhpl0jBJmDp7vw2l/IOft6/2elkj73R6vLu8JUXp8Ymeu9Dg+ZA6coSGGmTSMFGaStW8V0tfoSVYzKNnJVG4Npnxo3xMKwZVkf6vGl1Pkjk8oy/vrrIKA6a6CuPZjZ65S9Sfb56T8Nfj9kMzg94ON99CQ2hhm0tM6bKjRvgXZ1Qxq7/cm/eQupwaTHtrX+mQtt//ZVkG2CgImFzji2u+60AfREV/sMlF/JuWoICblj9j3g5z/X8Gw+v9fGGhMjGEmM1qHDTXa90mS7JpBmVzmkFtjKJ/br5swNuPnUYrc/mfDGw5j75lzSWeukvUHAK4rY603o4m8HzIR+374p7HlSnYrIQYak2KYyZzWYSMf2zfKPTNy2j8rcxxFIPrVaZcoolxmSY1U8nF8iCIcogiLwEtOpIJcnOxYyyl7RgszK8eNgpji/hK7OLDNP2gKOixJ+NXJM2nbz7Vs23/5VE/W7ddNGItcfV5VI2wwzFC2Iu/PkKT+eycvAo3P58PGjRtx8OBBOBwOXHvttXj88cfR2dmJtWvX4ty5cygrK0NDQwOuuOIKAFBkmxlwZkZbRgszwEA9sT+cu5D0mCkFA2UVOjzeuMevLysZsm++hhmztA/oq3YP5ZfY96cWt5zlRaDZvHkzHA4H9u/fD0EQcObMwKe29evXY8WKFVi2bBmam5tRX1+PX/3qV4ptM7pcnuxYy0k+I4YZYODm086vFtNKZfA+/09p/F88vYcBvbcfoafaPZQ/8uEypebr0LjdbuzevRsPPfQQhK+mrUeNGoWenh60tbVhyZIlAIAlS5agra0Nvb29imwzOs7MaMuoYYbtG6N9ouHIl/en5jM0XV1dKCsrw89+9jO88847cLlceOihh+B0OjF27FhYLAMrmlosFowZMwbd3d2QJCnn28rL1b8jWy0MM9rSY5iJrcly2h/ECZ8/53+s7IKAxaPKMmo/kxpGw+l/ZP9AoAhfhsM5bz/bkgBOUcTckqKcj49cXysrxpUlxYq1T/qixt+HbGgeaILBILq6ujB9+nSsWbMGhw4dwgMPPIBt27Zp3bWkDh8+rHUXokZVVsHr9SbdPsZuw9yigeXuc7lCbHd3d9JtYtHAFzy7L7ozakup/SN9HFb7Y0emHN90lBr/4bY/+DWFw2G4+wdOUnPLSjDOIuKjcxdgF0VcU1oMr8+HoxcvJmxLjDk24XN9dS4fvE8gFMKnFy6mbT+2PwBwlasQlXYL/r9zfWn3z6T/arQvhUJobW1NuD2VMVXVmOywZdV/vz+A1g/l/60qu6oy7nc1t6wEEwucGY1Pts+pd9n8bvUi0fshs/d/uerjonmgueyyy2C1WqOXgWbOnIkRI0bA6XTi1KlTCIVCsFgsCIVCOH36NCoqKiBJUs63yTFjxgw4HPmxgNTnF91wOp0Jt8V+cr8gWuB0WhLul41UYxb5dtHge1jU3L+7uzvax+G2n2x801Fy/IfTvtfrHfKaRFGEq3Cg9srkkiJ0enw4HgZGWUQc8fgwqdiFD3yJA5PVYokem4jzq8XWXINuEhREEX1C+vYj/ekIDnz6cwZDuLKkGEf8oZT7Z9r/2Pbd/f1wOhw5bx8WC2bPnp1weyrHvT4c6evPanzsdltWz9nl9cX9PieXFOGjcxcyGv9sn1PPWltbDf2aE70fMn3/53pcfD5fygkFze+hKS8vx7x583DgwAEAA99C6unpwRVXXIHq6mq0tLQAAFpaWlBdXY3y8nKMHDky59uMhpeZtKXHy0yxbIIQvSae7YqzqThEEXZRVKx9pfuvdPsRYWS/4i+RUtR6/8ul+QwNADz66KP40Y9+hIaGBlitVmzatAklJSXYsGED1q5di8bGRpSUlKChoSF6jBLbjIJhRlt6DzMnfQFUuQrQ7vag1GpJW8NF7lXzS+tUSJha4Mi6BlOu+h/bvtcqKtJ+ZZZVqK2CkPX4XJ6jMgTHvH5c5SqEMxiKa5/MSe77X015EWgmTpyIF154YcjjU6dORVNTU8JjlNhmBGqcTCk5vYcZhyjipD8AURCiN/2lq+ESlPEJLfbbEAKQVY2YVPtn0//Y9gNWiyLt/+2IzC53DmYVsquxdehiPxaU5qYMwaGL/QjYrQOXzpD+9ZJxZfP+VxPPTgai1smUEjNCmImEDSU+cQ3+aqdfkrKqEZNJ+3L7H2nf3d+f9H6g4bSf7bS8NxyWXWNLCe+eu4A2f+r3nEMUtb+HgRSj9N+HXOD7zyDUPJnSUHoPM4Cyy93bBX2v05Iv62zkq8j4cGSMSS/vf87QGIAWJ1PWcorZX+dhJvK7zPaPlU0Qopc7EplWWIBCUUSb2xOt6xQ5LhcYZrQVOz5kTHp5/zPQ6JwoCLo+meqdEcJM5DKiUjMzhaKI/nAYfgWK1eklzKSq1B1blVvOcfmAYc/49FTbi4FG5+yiqMnJlLWcjBNmDp7vQ6Uru7V2gOS1nCInuza3B34pnLaWk1x6CTNA4krdeg8Deu8/ZUZPtb14D43O+cNhXZ5M9c5IYUbpG4zNPDOjZftK/nFnmKF8xBkanQsrsKgRw0xqeg8bY+y2Ie07BAGzigtl1TBKVtNnOCc7ue1XFTqzrvFk9PazjRlOUcTiUWVZ9Z+MJ937IVbs+9Muqj9fwkBDcRhmUtN7mBlts2Ju0dD2Q5Aw3mFHWELSrwnPKi7EeIcdHV9dOppS4EB4UJ4eTphJ1n5sf2Lbn+5ypt1fbvsFFoui7Svd/9j2s73pOvjVJcRs+p+rG70pf6R6P8Qa/P6cWirvyxW5wEBDUQwzqRkhzCwoHShkeUGMr/sUlIAOjw+TCxxJ/2Bd9tUfqza3J/pY7LebhnsZIln7kf4Mbj/d/tm0X1bgVLR9pfsf275lGIEm8hxy+z8tyxWRKX8lez8MNvj9cH1x8vpuSuE9NASAYSYdo4SZg+f7FKn6bYR7TkbZrOj2eHXdf6Xaj5254j0zlK84Q0MMM2kYKcwka98qpK/Rk6xmULnVmpOTXbL2k52s5dZ4yqR9TygEl4LtK93/2PZDWd5fZxUETHcVxLUfO3OVqj/ZPiflr8Hvh2QGvx9svIeG1MYwk57WYUON9i3IrmZQe78XXysuyskn92TtJwtLcmo8mbH9bMstWL9aKDG2/a4LfRAd8YsnJurPpBwVxKT8Eft+kPP+DA6+wU4FDDQmxjCTGa3Dhhrt+yRJds2gyMyAPxzO2WWIRO2nCktyaxiZrf1seMNh7D1zLunMVbL+AMB1ZerXeuv1B+BO8vpTLVoIAC5RRLnMlcHNJvJ+yETs++GfxpYr2a2EGGhMimEmc1qHjXxsP/Zkp8QdFXq/54Ttq8cdDqPx2Km4xzLtf92EsVD/tGt8DlGEReAlJ1JBLk52rOWUPSOFGV84nLaW05SCxKsQJ/uKr95P1npvH9BP7Z5E9BTGjCgy/iEFFtRMh99yMhnOzGjLaGGG7RurfUBftXsGY5jRVuz4a3F/OGdoTCSXJzvWcpLPqGEmWS2nwdLVctJ7GMi39rMpbCkCOBvUZxhgmNFWPow/A41JcGZGW0YNM2w/f9tPVBAzl+3nE733X+/yZfwZaEyAYUZbegwzsTVZTvuDOOHz5/yPlV0QsHhUWUbtZ1LDaDj9j+wfCBThy3BYsfaV7r9S7X+trBhXlhRn3L6abIKQ9mQqd3woNTX+PmSDgcbgGGa0pccwE1uTxSYIqHIVICxJSRfVsma5xH4YA4txpWs/kxpGw+l/7P5enw/Ty0oUa1/p/ivV/hhRjF4yTNe+2qyCgHF2W87Gh1KT+/5REwONgTHMaEuPYQa4VJPlU48Po2xWtLs9A5/G+hLvn+03C8KShDOBYNr2s615lGn/Y9t39/fD6XAo1r7S/Veq/Y/OXUBH8NKn71Ttqy0gSRjnsOVsfCg1ue8fNfFbTgbFMKMtvYaZiNhp/GxXnE3FIYqwi6Ji7Svdf7ZPZpav7x/O0BgQw4y29B5mTvoCqHIVoN3tQanVkraGi9yr5pfWqZAwtcCRdQ2jXPU/tn2vVVS0faX7r1T7V7kK4QyGMmpfbVZBwIkcjg+lJvf9oyYGGoNR42RKyek9zDhEESf9AYiCEL3pL10Nl6CMT2ix34YQgKxqxKTaP5v+x7YfsFoUbV/p/ivVfsBuxeSSooz2V1tIklLe5Cu3ZhYll837R008OxmIWidTSswIYSYSNpT4xDX4q51+Sfr/2zvXIDnK+9w/3T3X3dkLq9VKq0skWVy0QoXBAiukEjDyB1dgC7nKH+wScMJJAs7h2E6ZwrJcthEWcZ1sbthlI6uSOHaUouAUlYDDGhe2j0onlXApjLGNskeAQREyGmlZ3Xa1c93pPh92Z+jdne7p7um3u9+e5/cJaXp+89e7zbzPvt39/j31iHHid1t/3T9bKKC7q0uY344o+1++MI2Jiv05l1ZVXJqr4WSp9Z5EzfDaV8nJJQ+3PbPIckR/P/gBA01MCHIy/chAb+s3dBiyhxlA7Hb3KSV6+7TQ77//f0+e8+xnX6XoEpV9ZlrBQBMDwphM2cvJdLzkYab+s/T6ZdWql9OVXVl0qSomZotIqcqi9/lBXMIA/SSqyPLz5VNOkqMqitSTqezEIczULyOKWpnpUlUUdB0VAc3qZJ+s6SdRR6beXlyhkZyUqoYymbKXU3zCzAsXZ3BVd/OO2E6w6uVUn+wmZouoGHrLXk5ukX2ypj9c0qrK3+gdoEPMLzsiYKCRnIquSzmZyk6cwozoG4zNl5lE+GWcrOlfztJmmiqcbQngpQlnvX45pmniFAYaydEFbGrEMGOP7GFjKJVc5k8rCq7r6XLVA8iqJ047k51b/5aujOceRvRHy19vphlk/X7dxxVnMqqK2wb7XY9/Sg1+/YuBhiyCYcYe2cPMymQCN+Sa+6/NdeOaXLflvjIJRYEKQFuYBIaSSVyT68b0woZrQHthxknPHbN/a3fGcw8jq+OzmibUL7p++t35NQaalswtXFJ2O/6b+9w/gt8uDDSkAcOMPXEIMzf29eDI5BSmVW3RazUA38u/58hTf6Jp6T0x7V6GaNVzZ6nfaw8jO39/NiPUL7p++t353WwK2anMGUZjTN2M/809zfdzEgnviSIAGGZaEZcw88LFGUxWqr77Zbxno5k/XyxJXb8ov3nlSsb6Zb+BmTiDKzSEYaYFcQozom8AbmeysOq5Y+V326PHib9Yq6FboF90/aL85pUrGeu38m/KZJBfcCYUBTXDsNx5OKko0BQFc4aBjKLgMg+7GstIQlGwtTvrevyTvIeGBA3DTGtkDhuyhBmgec+dY4WSpd9tjx76vftPTs9ATadbHh/V+u38a9LzN7H+xkFPout6urAmncJVXVnb4+JEYmHjTLfjP6cHfzmPgaaDYZhxhqxhQ6YwU8fcc8eJ322PHvq9+c0rVzLW77d/wzrrnbHjRknXPfVcu2dV8I0sGGg6FIYZ58gYNkT72ZspGP8dqwaBJQ/iJBeeNlMUBRVdt95LxQAeOzPl6XNlGZ+w/MSetKpCU3jJiQSAH5Mdezl5J4phRjMtFbfCqjeTFR/INt+F2GoPENknOz/9SVXB/70wvejvzONv107i5n5vTWRlGp+w/Hyaxpr6+NcEtDppBX8uHQZXZsIlimHGDezNFK6f4x8NP9d8mmMe/zCeiOcKTQfh52THXk7uiXKYqRnL95VZSqveTK1o1cspLpOdn35znyy34++2V5aM4xOWnywnCpf5uELTIXBlJlyiHGacYP6y4spAuH6Of/z8shOV8eEKTQfAMBMucQozfn5ZpRQFtw32Y7Iyh1Pliq3fSY8eq+Pd+KvVHN7TdWF+t/WnFEXI+Lut/0P9Pbi8t8d1/U79Qf18Rfk7jaiODwNNzGGYCReGGWt0zO/9saU7C90wLDftctKjx+r4pKK48pfKZWzt7xXmd1v/ymTS9/H3Uv+QqjYucYkcfxn9nUaUx4eXnGIMw0y4MMzYoxsGpqpzODZbxOq09ZNl5h4xE7NFvF0sN347tDv+rWLZtf+16UtC/W7r1xTF9/H3Uv+bs4VAxl9Gf6cR5fFhoIkpDDPhwjDT2p9SVUxV5yy3mm+HpKI06pfZX9F1IeMfl/GR1S87UR0fXnKKIQwz4cIw48xfMwxszqY99+ix4nS5ii3dWRybLaIvobnylxKqUL/b+lWLvXrawUv9V3R3ITNXc12/6PGJgr/TiPL4MNDEjCAmU2INw4xzvwJ46hFjd3xaVXG6UoWqKI3LCm781YQm1O+2fr+X0L3WX00lsKk357p+p/6gfr5++c+b/t/rv+IqnC5XoC2ET7sGlwCQU1VpG1t6Gf8g4ewUI4KaTElzGGbc+SuG4alHjBO/298Y6/7ZQgHdXV3C/HY089+xetDV57j1O+XlC9OYqNifc2GMT1j+x8+cbfy33TnTzL9r5WW4zFnZkaKd8Q8K3kMTE4KcTMlyZA8zAITvcCvzPiH00++XX0aiss9MK7hCEwPCmEzZy8l0vORhZmUyAU0BcprWVm8mK6x6P1n1cnJLnCa7pf6koizrseV0/OvjG+fxoT8YZKmfKzSSoyqK1JOp7MQhzNQvI4rYgZa9h+inX44wYIUKSFM/V2gkJ6WqoUym7OUUnzDzwsUZXJ3Ltt2baSmteg+57TVk5Zd1MnLiN/dyWkqr8f/dPiP0+umXO8wA8xtgylI/V2gkp6LrUk6mshOnMMPeT/H01/f5kbV++olbuEIjObqATY0YZuyRPWwMpZKRfVrKSc8ds39LV8Zzjx4Z/U6o+yu6Hrn66S8seuR7KUlFsX38u1tVMRDwI98ZVcVtg/2uxyelBr9ewkBDFsEwY4/sYWZlMoEbctEMM0567pj9W7sznnv0WB2f1TSh/nbqd4LZryoK1meiU39c/c8X3PnNj3y38i/d4+W+dasw0PTd4phbuOzpdnw29wW/1w4DDWnAMGNPHMLMjX09ODI5hWlV893f7jK7uedOnU3ZdOMLdKm/1fFe/P3ZjFB/O/W3YqlfBSJVf1z9dYKoP4x7ROYMo1GTm/pv7mm9N4/f8B4aAoBhphVxCTMvXJzBZKXqu1/2exLq/nyxJHX9ovzmlSsZ64+Ln3fj2MMVGsIw04I4hZmoXWYyY9Vzx8rvtkePE3+xVkO3QH879Vth5dcXnH7Ub1658lp/FH6+IvxWYU9E/WGQUBRs7c66rj/Je2hI0DDMtEbmsCFLmAGa99w5VihZ+t306ImDvxl24z9nGHi3XPGl/pPTM1DT6ZbHx3n8rfzDTcJeEPUHRWJhc0e39c/pwXfhZqDpYBhmnCFr2JApzNQx99xx4nfSoydOfjNB1m9euRLhl2X8m/mbhb2g6g+Ckq576rl2z6qgb1+O2D003/72t3HVVVfhjTfeAAAcP34cn/zkJ/Gxj30Mn/zkJ/Ff//VfjWNFvNZJMMw4R8awIdrP3kzh+jn+0fEXazWhfhn3sUmrKjSlgy85/ed//id+8YtfYM2aNY2/27dvH3bv3o1du3bhBz/4AR588EEcOnRI2Gudgh+THXs5eSeKYUZTFj+xYYdVbyYrrHoPWfVyitNk166/WS8np+PvtVeWTONDf/So118TsKFmKyKxQlOpVLB//37s27cPysL/hGfPnsXExARGR0cBAKOjo5iYmMC5c+eEvNYpcGUmXKIYZtzA3kzh+jn+9EcZc/0C9nxtSSRWaL75zW/i9ttvx/r16xt/l8/nsWrVKmja/H4ZmqZhaGgI+XwehmH4/trAgPPrfUePHvXrn942g1dtQalUcnTsUCqJG3Lz+5C0++jur945afna2tz81fZ3L806cok6vv56O/6PDPQ5Ht9W+Dn+7fqX/pvmdANHz563fU9W0zCczeDVchllXcevZ53dG1Dqnt+PYunxO3JdmDVtSpbVNPRnMzg5PSNkGd+p31yTCL8bSnNzjZ+L2/FfOr6taFW/13Fx6m+XMP3tjk0rf533iiVUPHx3ZGDg9JtveKqr/4qrHP37ltU/2I9XXnnF02d6JfRA8+qrr+K1117DAw88EHYpjtm2bRvSFjeBBc2JS7PIZJov6Zsx/+Y+rWrIZNrbWG14eNjytfqlnqXNIYM8Pp/PN2ps1+9kfFvh9/i34y+VSsv+TYqioLvLeiMs829eXakkMgC6Hf4GlsnM/7+y9HhN0xqfuWiH23S66Q2o7eDUP1so2I5Du3631MfIy/ibx7cVrer3Oi5O/e0Spr/dsWnlN/Pk2Yue/PetW4Xt27d7eu/JUrnlv8+qfq+faUW5XLZdUAg90Lz88st4++238dGPfhQAcPr0afzRH/0RvvSlL+HMmTOo1WrQNA21Wg2Tk5MYHh6GYRi+vxZneJkpXGS/zCRqGTylKLhtsB+TlTmcKlds/U566Fgd78Zfrebwnq4L87utP6UoQsbfbf0f6u/B5b09rut36g/q59upfr/vLXFbf1CEfg/Nvffei3//93/H4cOHcfjwYaxevRrf/e53ceutt2JkZATj4+MAgPHxcYyMjGBgYAArVqzw/bW4wjATLgwz1uh4fzOu1amkpf+6ni6sz6RwvFjG8WIZa9MpfDBn/Ruj+Xi3/l/PFoT63dZvAL6Pv5f6N2azgYy/jP4b+nsjX7/m8QZxP+oPktBXaOx46KGHsHfvXhw4cAC9vb0YGxsT+lrcYJgJF4YZe3TDwFR1Dsdmi/O/7c00P85rT5y3imUMJhOu/LOFAjLptDC/2/pv7Ovxffy91P//Lkzj7bn3a4jK+ETBvy6biXz9a9Op5nIPuK0/SCIXaA4fPtz4782bN+PJJ59sepyI1+IEw0y4MMy09qdUFVPVOfQl/G+UmVSURv0y+yu6LmT84zI+9MfT75XIBRrSPgwz4cIw48xfMwxszqY997ix4nS5ii3dWRybLaIvobnylxKqUL/b+lUfLxU08zut/4ruLmTmaq7rFz0+UfCfKBZxxcL9RVGtv+bjM9Ru6w8SBpqYEcRkSqxhmHHuVwBPPWLsjk+rKk5XqlAVpXHToht/NaEJ9but3++bHL3WX00lsKk357p+p/6gfr4i/D+/MIOuhcuUUa3/tzL+PJXrpf4g4ewUI4KaTElzGGbc+SuG4alHjBO/298Y6367R3D98NvRzH/H6kFXn+PW75SXL0xjomJ/zoUxPvQ782/tzrr6PDPnF75r2qk/KBhoYkKQk+lHBpzt59JJyB5mAP+fpjHTrPdQoaY3nbDrW/ZX7ZbJDeCxM1ONP8q+gyv99Iv0P37mrFB/VGCgiQFhTKbs5WQ6XvIwszKZgKYAOU1rqzeTFVa9h35hsXNz3f920XqH5pv73w/VYU8WIv3Nejk5Hf96MIzz+NAvv99PQt+HhrSHqihST6ayE4cwU7+MKKI3EHsP0U8//UHBFRrJSalqKJOpk89zW5Ofx+cvzS5rdeB3PXEJMy9cnMHVufmN09zQ6vj6l+HEbBEVQ/fV/zt9hvRf5k78VcOwHIdW4/m7fUbo9dNPf5BwhUZyKrou5WQqO3EKM6JvMBaxMqNC7D0/sk8W5n1+ZK2f/vj6RcEVGsnRBfRoZ5ixR/awMZRKSv+0VH2y3tKV8dzjxup4c/1R8zuh7q/oeuTqp79z/Ck1+PUSBhqyCIYZe2QPMyuTCdyQkzvM1Cfrrd0ZrF3Yhh2Y33BMN2C5J8Z1PV0tj89qWqN+EX7z+Lj1uxmfqeocVEXB+kx06o+r//mC3PWL8m/uc/dwhR8w0JAGDDP2xCHM3NjXgyOTU5hW/d+uPMhl8Es1HdfmuqEDGEzOf3EaAFYmk9hm0bgvpShQAIyb9r4x98RJqyr6s5lG/V576Fgdv3R83PpbsdSvApGqP67+OrLWL8p/c491A01RMNAQAAwzrYhLmHnh4gwmK1VkMv4GmqCv6T/13rnGa/VJxclNx38wvNLWf3J6Bmran11Vm/lluOfhv60ehKooMDB/r9IdqwcBXUcmMb8yVrenFAWDySQ+vLCDsAEg6+Cxf9H10x89f1Aw0BCGmRbEKczIfJnJD7+K93dNrfegMfuLtRq6F45120PH6nir+t36rbDy6wtOt/WnVBX/5/zFRkBMqyr6YeAClKbjbw6UVoHR7F9aj1/jE5bffJlSxvpF+ZO8h4YEDcNMa2QOGwwzi9GxuCfOsULJ0u+mh47V8X76m2E3PnNYQ8dBAAAgAElEQVSGgXfLFdf1r0mllvmdrlzN2TykIHp8wvIPmy5Tyli/KP+c7v8DK61goOlgGGacIWvYYJhZzpypf5QTv5MeOlbHi/CbEVX/B3Pdy/zmlat2ED0+Yfjtwp4M9Yvy37NqwPH7/IKBpkNhmHGOjGFDtL9ZbyaZ/DKGMTMc/+j4/Qp7Vv6wx6d+T5VtbzUTSUWBCqDb4/1U7cBA04H4Mdmxl5N3ohhmNGXxExt2WPVmssKv3k+++A3g02uHkFRUFGo1LP0q12s6VG3xtf96DaWajkOnp9AKPyejZr2cnI5PvZeTW9Kqgq3d2UX+kgJkMs3PD/P4O/nMKE3W9Lf2ZzQVbxXLtr3VzNTPz7TGQEMEw5WZcIlimHGD6N5Mov0/m5nF1u4sCrqONwrFZa+XSuVlE3d9wl6fTi07filBrMyI7o2VVNh7i35vmM9PQ/N/a4hWMNB0EH5Oduzl5J4oh5maEW5vpqj4ZwsFdFusrK9usXInYrIw93JyOz6/0+fupsy6v1Cr4Y1CcZHfblzqHC+WbT9T9sm6U/0V3cDbxZLr/7/W9/h9Ia417OXUIXBlJlyiHGacILo3U5z8oicj0fWL2IUkqpM1/f77RZyfTuEKTQfAMBMucQozsn/Z+uU396yZrMzhVLli63fSQ6fZ8SlFiUT9ov1ex4f+cPxmorQpHwNNzGGYCReGmfj5zT1rkoqCLd1Z6At7vrQ6HnDXE2dlMhl6/aL97YxPFPw39Pd67pkVhfrd+s1EKcwADDSxhmEmXBhm4umv96x5q1jGYDKBY7PF+d9uZ+yPd9sT561iGTf29YRev2h/O+MTBf+6bEbq+r32FItamAF4D01sYZgJF4aZePuTitLwO92fw4u/outS109/PP1RDDMAV2hiCcNMuDDMxNt/ulzFlu4sjs0W0ZfQPPfQceJXPe4lY4fb+kX72xmfKPhPFIu4ordH2vrd+qMaZgAGmtgRxGRKrGGYib//dKUKVVEaN1F66aFjdfxSv99L6F7qd4MKBDo+UfD//MIMutJpaet3649qmAEYaGJFUJMpaQ7DTLz9JV3HnasHUdH1RY829+cS2Jbravy5vlvuoqV+A3jsjP0uw+b6678h37F60Lf6m/n9JK2qSKmqJ7+TnkHt1E+/v/4ohhmAgSY2BDmZfmSgt/UbOgzZwwwAqXsDBeF/8eIlR/76b7rmjchaBRPZw159/EXd8yP7+MjuF/3/l18w0MSAMCZT9nIyHS95mFmZTEBTgJymydebyWd/s55FfvjtehzZTUbNejk5HZ/6Z7qd7FKqsuwz7Ho5mcdHE3DPj+xhQH7/8t5edtTPnUQIzSn5lJPkqIoi9WQqO3EIM/XLiDL2ZpLdL/tkx/GJv19kby+/4QqN5KRUNZTJlL2c4hNmXrg4g6tz2Vj2ZnLrN/cs8tPfrMeRk8nI3MvJSf1mfrfP8K13T7NeTs3Gx23/KDviEAbi4G/W28sJmxLs5URcUtF1KSdT2YlTmGHvp3D9oiaj+g26MvbuiUsYiIM/+usy78MVGsnRBWyaxDBjj+xhYyiV5NNSEfFv6cp47qHTyl/Rddf+D+a6kGpxH0w74+OkZ1A740O///5W50OUYKAhi2CYsUf2MLMymcANOYYZK7KaJsRfqOm4Y/UgVMxfJq7oOlRFgQo0bqQdSiZxTa4bc6ZfUgo1dzWYx0dVFE89huw+sdX4J2wmPyc9g8z+rd0Zzz2MgvQ/X5C7/lZ+rtAQKWGYsScOYebGvh4cmZzCtKr57pc9zKRVFf3ZjBD/U++ds6y/2WPeXljqVwFPPYAGk82f8nMy/nb3MLTqGbTU77WHUdD+OrLW38pvdT5EEd5DQwAwzLQiLmHmhYszmKxUfffHIcwMJhPIF0tS1y/bylUd2ceH/mjAFRrCMNOCOIUZXmay9xdrNfj9bEZY46MDrnr01Hv6LL1s5Gblyu5Vq55BVvW77TEUlt8q7MlSfyu/3WXEqMFA0+EwzLRG5rDBMNO5/jnDwLvliqMePcD7PX3WpFLL/CenZ6Cmm2+st/QzW/nN9RwrlCzrd9pj6I5Vg8DCnJtQlEb9ZcPAZYkEPtSTW9auok5WVVCv2EvPo2OFEoYtwp6bHkkix6ddv/l8iDoMNB0Mw4wzZA0bDDP0O+nRs/T4D+a6l/n9Wrky1+Nb/Qrw2On3+2TVJ+xT5WpL/x2rB/HDqQtt1W8X9ryMv+/j06a/fj64JYyFHQaaDoVhxjkyhg3R/jj0Zoq7v/5kVdNmmTZUdUN4/bL3HorrZUq/SCkqNCX4W3QZaDoQPyY79nLyThTDjKZgWc8gK2TqzeTW36xnkUz1m/nFpdlF/reLJcf+pb177Ho5meu361lVx+/JdGm/Kzfj76TepcgeNvzo7WVHffzfv5gXHHzKqcPgyky4RDHMuEH23kn0h+tnb6l4+83jH3yc4QpNR+HnZMdeTu6JcpipGfHszeTW36xnkZ9+O6LstxsXs9+ul5OoybTe78rL+LjpPSV72PDqb9bby85fH//1PezlRATBlZlwiXKYcUKceifRH66fvaXi7Q+zKzdXaDoAhplwiVOYkf3Lln6x/vqNyImFtg51VEVpPDrd7CblhKIgqyo4Wa647kmUUhTfx8fsn6zM4VS5Yut30iMpTn4zUdqUj4Em5jDMhAvDDP2d5H/qvXMt/VatHv5geCWOF8uuehIlFQXr0ilfx2epf0t3FvrCnj7NuKG/11PPLKd+Jz2YgvSbiVKYARhoYg3DjD/8zZUbXL+nWK3iG7+ZZJihn36H/um5Gq7r6UZCUbAymcS2XBeA5Ss6KUWBDuCyRAIpVcVZn+uv9zB6q1jGYDKBY7PF+dWLmebHr8tmPPVUcur32rNJlL9O1MIMwEATWxhm/OPXd3/K9Xsu//4TDDP00+/Cv3R1p06zFR3R9SdNl7H6Ev43cpXdH8UwAzDQxBKGmWjAMEM//fL5T5er2NKdxbHZIvoSWsseSSeKRVzR29P4c6vj3frd9mwS7Y9qmAEYaGJHEPdsEGcwzNBPf7T9hZqOu4dXApi/pKUCSKkqdMPAhxYeO9YB9CUS2Nr9/iUw3TBwaKHdws8vzKArnXbUUymtqjhdqUJVlMZNuF56MIXpj2qYARhoYkVQN6CScGCYoZ9+f/1PvXfOVe8nYH7iv7l/8R5XTnoqmeu3WwFpRtT8UQwzAPehiQ1BPk1DgieIy4gy99ahn/4o+2UMe2ZEj49fcIUmBoTxaDB7OQVHED9fTQFymha73kxu/c16FslUvyi/XS8nGer32+/kuDqyh5m0qizr7WVHffwTLsbIL7hCIzmqokj9NA2xJ8jLiDL2BqKf/ij75Q8zKpKKuPHxG67QSE5KVUMJM+zlJJ4gV96uzmVj1XvIq9/cs0jG+kX5m/Vykql+v/3r06mWx8QhzAwmEyjUanijUHQ9/psSwfdyYqCRnIquc2UmhsRph2E3y/P00y+Dv6obuGP1IACgVqtB0xbv9VJ/Wqqi69CWtnowgMfOTPlWv+iwFP11mfdhoJEc3fC/STvDTLiIHv+hVJJPS9FPfxv+56dnGisWs4UCuru6LP1LNwasB6FmOOmpZPZv6cp47vHk1J9Sgr8XxisMNGQRDDPhEsTKzA05hhkrspomdf30y+t30lPJ7N/anfHc48mNnys0REoYZsIlqMtMRyanMK3Ktx16EP7+bEbq+umPvr/ekXwp9R5Vg8n5pywNYFFPKxXzTxz90+mzKOu65x5PVscvrb9+fL0eGWCgIQAYZsImyHtmJitVZDL+BpqoTBbt+k9Oz0BNN3882Q+/rOPDlSv//FY9q+rY9a7aeVlv6PVHmdADzfnz57Fnzx688847SKVS2LBhA/bv34+BgQEcP34ce/fuxYULF9Df34+xsTFs3LgRAIS81qkwzIRLnG4AlvHL1uwv1mrw+9mMOIwPV66sCTLsme1uezBZHW81PvXjExLdQxP6PjSKouCP//iP8dxzz+GZZ57B+vXr8Vd/9VcAgH379mH37t147rnnsHv3bjz44ION94l4rRNhmAkXhhn6ZfDniyWp6xfpHw4p7P3yUgHvlivYlJ3vI3WyZH9TcLPjJ2ZLLf2hhwQXhF5rf38/duzY0fjztddei1OnTuHs2bOYmJjA6OgoAGB0dBQTExM4d+6ckNc6EYaZcGGYoV8Wf7FWE+qXeXzCDHuvzhTww6kL+OHUBdsGlM2OP1awDjPm4ysen6QNY2En9EtOZnRdx+OPP46dO3cin89j1apVjef7NU3D0NAQ8vk8DMPw/bWBgQHHdR49etTnf7l3Bq/aglKp5Pp9Q6kkbsjN3yA6Wam6fn8+n7d8Tc3NL9rnL806cok6vl5jW/5VKxy9pxl2P5d2x78VrfxLazMMA7OF1l+IwPw2+GlVRT8MnJyeaTnZlRa+2GTxL/XIVr8ov9X7ZalfqN/mvX7WX6vVHHvsyGoa+rMZR+Oj13SUSmXX9WuKildeeaXtWt0QqUDz8MMPo6urC3feeScmJibCLseSbdu2IS3gxkEvnLg0i0zGXe8S82/u06rm6QbRa35rvbV/oRfSCocTtYjjz547hxULIVV0PVZY/Vz8GH87WvlLpdKy2hKqgm0rLnPkN/e+6XKwY2q9t4tVL6Ao+Uul8jKPTPWL8jcbFz/9dkTdbzc2fvjNaKqG/7l5w6K/q28MWNGtV1J0w8Ch0/Ob+ZlXftR0uuU9Y5mkhq2X9bmuX4GB7du3O3qPU8rlsu2CQmQCzdjYGE6cOIGDBw9CVVUMDw/jzJkzjV0Ya7UaJicnMTw8DMMwfH+tU+BlpnCR/TKT7L116KdfZv/z0zPL/q4emN4uWq8I39w/3wYmiK7c9foNzf+tIVoRiUDzyCOP4OjRo/jbv/1bpFLziXTFihUYGRnB+Pg4du3ahfHxcYyMjDQuDYl4Le74Odmxl5N7ohxmaobcvXX88jfrWeSn344o++3GxQ+/E6LqdzI27fidYnf87/QZnsNMRTfwdrHkuv71PR3Yy+nNN9/EwYMHsXHjRnzqU58CAKxbtw6PPvooHnroIezduxcHDhxAb28vxsbGGu8T8Vqc4cpMuEQ5zDgh6r116KeffmtKuo47Vw827y3VgqrNpaw6out3SuiB5oorrsDrr7/e9LXNmzfjySefDOy1uMIwEy5xCjMyP41CP/2d6n/x4iXL3lKt2JS1v3cmSpvyhf7YNhELw0y4MMzQTz/99AcDA02MYZgJF4YZ+umnn/7gYKCJKQwz4cMwQz/99NMfHAw0MYRhJhowzNBPP/30BwcDTcwI4jIHcQbDDP30009/cDDQxIig7tkg4cAwQz/99MfV7wcMNDEhyBtQSfAEcRlR9A6i9NNPP/0i4fWDGBDG0zR2l57qvZAc+wUcr+a6sWKhRtH1iCaIn6+mADlNc7QpVn2rdaeYe9NE3V9Slvfckal+Uf5m4+Kn346o++3Gxg9/K6LqT4SwwR5XaCRHVRSpn6Yh9gR5GTGOvW/op5/+6Pr9his0kpNS1VDCDHs5iSfIlberc9nY9NZpx2/uyyNj/aL8zfoVyVS/SL9VLydZ6hfl35TowF5OpD0quu55svv6pjXIJltfXvnIQG/LY4i/xGlTPhl739BPP/3h92ZyCwON5OgOG4w1I5tM4td3f8r1+y7//hOeP5O0RnTYGEol+bQU/fTTH4pfJAw0hESIIFZmbsgxzFiR1TSp66ef/ij7RcNAQ0hECOoy05HJKUyrmu9+2b9s06qK/mxG6vrppz+q/iDgU06ERIAg75mZrFR998v+ZVv354slqevnyhX9UfQHBQMNISETpxuAZfyyNfuLtZpQv6zjM8yVK0sY9qIDAw0hIcIwQ78Mfq5cWfsZ9qIDAw0hIcEwQ78sfq5cWfsZ9pqjhPC0NwNNh8Ku2eEie5iRvXcM/fT75RcR9uIwPpoSfLzgrNaB+NE1m72c7KkWCvibKze0PK7ZpoXFahVfPn7K82d7CTOaAmzKWvejMRPV3jF++Jv15ZGpflF+u35FMtQv0j9gdNn2cop6/aL8CrzvkeYVBpoOwzzZcQdgcZy47w89v7edjQuDWJmRuXcM/fTTH4zf0PzfGqIVDDQdhJ+THXs5RY92fr41Q/7eMX74rfry+OW3I8p+u3Hxw++EqPqdjE07fqdEzb++J/heTryHpkNg1+x4E+QNwCJ+s6OffvrpbxcGmg6AYSbe8Gkm+umnP65+NzDQxByGmXjDMEM//fTH1e8WBpoYwzATbxhm6Kef/rj6vcBAE1MYZuINwwz99NMfV79XGGhiCMNMvGGYoZ9++uPqbwcGmpjBMBNvGGbop5/+uPrbhYEmRjDMxBuGGfrppz+ufj9goIkJDDPxJoifr+y9Y+inn345/X7BnYJjQBhhhr2cgiOIRpaaAuQ0TereMX74m/Uskql+UX67Xk4y1C/Sbzc2fvhbEVV/wsGxfsMVGslRFYUrMzEmqK7cAKTvHUM//fTL5fcbrtBITkpVQwkz7OUknqDCzAsXZ3B1Lit97xg//Oa+PDLWL8rfrF+RTPWL9Fv1cpKlflH+TQn2ciIuqeh66BMv8Z8gwwx7P9FPP/1B+kXBQCM5uuGgzSuRDpFhYyiV5NNS9NNPfyh+kfCSEyERROTKzA05hhkrspomdf300x9lv2gYaAiJICIvMx2ZnMK0qvnul/3LNq2q6M9mpK6ffvqj6g8CXnIipAMw3zMzWan67pf9y7buzxdLUtfPlSv6o+gPCgYaQmIOdxh27i/WakL9so7PMFeuLGHYiw4MNITEGIYZ+v3wc+XK2s+wFx0YaAiJKQwz9Pvl58qVtZ9hrzlK8BsF86bgTsWudQEJl2qhgL+5coOn9xarVXz5+CnhYUb23jH00++Xv1irwe8t5OIwPpoS/HoJZ7UOxLzdfTsOy9fYy6ktTtz3h57fe/n3n/AUZjQF2JS17kdjJqq9Y/zwN+vLI1P9ovx2/YpkqF+kf8Dosu3lFPX6RfkVBL9HGi85dRjmyY7EE9ErMzL3jqGffvqD8Yex5StXaDoIPy9DsJdTdPHy860Z8veO8cNv1ZfHL78dUfbbjYsffidE1e9kbNrxOyVq/vU97OVEBCH6ngoSHdibiX766Y+T3ykMNB0AwwxpB9mftqCffvrl9buBgSbmMMyQdpD9y5B++umX1+8WBpoYwzBD2kH2L0P66adfXr8XeFNwTGGY6Uy87mGjzy0+R2T/MqSffvrl9XuFgSaGMMx0Ll73sLn8+080/lv2L0P66adfXn878JJTzGCYIe0g+5ch/fTTL6+/XRhoYgTDDGkH2b8M6aeffnn9fsBAExMYZki7yN47hn766ZfT7xe8hyYGhBFm2MspfuQ0TereMX74m/Uskql+UX67Xk4y1C/Sbzc2fvhbEVV/wsGxfsMVGslRFYUrM8QXZO8dQz/99Mvl9xuu0EjO2nQK67u78JGB3tYHL6FaKHj+XPZyihe1YhH3rl3l8l19AIBStYqvncgve1XG3jTmvjwy1i/K36xfkUz1i/Rb9XKSpX5R/k2J4Hs5MdBIjppI4Nd3fyrsMojkHP8f/93ze82PfNcx30DoZJnaLfTTT7+8flEw0BBC2qJaKOB/Xf5brt9ntbLTCtmf5qCf/k72i6QjA83x48exd+9eXLhwAf39/RgbG8PGjRvDLosQKfG6md+GA//gKQgB3sNQK7KaJvVkQT/9UfaLpiMDzb59+7B7927s2rULP/jBD/Dggw/i0KFDYZdFSEfhNQgB7YUhQuqUqlUcmjzni0v2sGHl/4OhAaST7p/8rM0Ffw9ixwWas2fPYmJiAt/73vcAAKOjo3j44Ydx7tw5DAwMhFwdIcQJ7YQhQupsOPAPHm6G7yzmigX8+p67XL+v2b11oum4QJPP57Fq1SpomgYA0DQNQ0NDyOfzDDSEENJBMBjHi44LNH5w9OhR353bt2/3/N6gk7A+N4eb+nKWr6vK/F3xutHkWcagju+7whe/PjcXym8ahBAiM/rcHF795S8D/cyOCzTDw8M4c+YMarUaNE1DrVbD5OQkhoeHHTu2bduGdNp6Z8ggeeWVV9oKQ15QEwmsSkT71AljXGSBY2MNx6Y5HBdrODbNefWXv/R9XMrlsu2CQsftFLxixQqMjIxgfHwcADA+Po6RkRFebiKEEEIkJtq/ZgvioYcewt69e3HgwAH09vZibGws7JIIIYQQ0gYdGWg2b96MJ598MuwyCCGEEOITHXfJiRBCCCHxg4GGEEIIIdLDQEMIIYQQ6WGgIYQQQoj0MNAQQgghRHoYaAghhBAiPQw0hBBCCJEeBhpCCCGESA8DDSGEEEKkh4GGEEIIIdLDQEMIIYQQ6WGgIYQQQoj0MNAQQgghRHoYaAghhBAiPQw0hBBCCJEeBhpCCCGESA8DDSGEEEKkh4GGEEIIIdLDQEMIIYQQ6WGgIYQQQoj0JMIuQCYMwwAAVCqVkCtZTLlcDruESMJxsYZjYw3HpjkcF2s4Ns3xe1zqc299Ll6KYli9QpYxMzODN954I+wyCCGEkI7lyiuvRE9Pz7K/Z6Bxga7rmJ2dRTKZhKIoYZdDCCGEdAyGYaBaraK7uxuquvyOGQYaQgghhEgPbwomhBBCiPQw0BBCCCFEehhoCCGEECI9DDSEEEIIkR4GGkIIIYRIDwMNIYQQQqSHgYYQQggh0sPWB5Jy/Phx7N27FxcuXEB/fz/GxsawcePGsMuKBDt37kQqlUI6nQYAPPDAA/i93/u9kKsKnrGxMTz33HN499138cwzz+DKK68EwHMHsB6bTj93zp8/jz179uCdd95BKpXChg0bsH//fgwMDHT8eWM3Np1+3tx33334zW9+A1VV0dXVha9+9asYGRkJ/pwxiJTcddddxtNPP20YhmE8/fTTxl133RVyRdHhlltuMV5//fWwywidl19+2Th16tSy8eC5Yz02nX7unD9/3njxxRcbf/7zP/9z40tf+pJhGDxv7Mam08+b6enpxn//5Cc/MT7+8Y8bhhH8OcNLThJy9uxZTExMYHR0FAAwOjqKiYkJnDt3LuTKSJS4/vrrMTw8vOjveO7M02xsCNDf348dO3Y0/nzttdfi1KlTPG9gPTYEi/oqXbp0CYqihHLO8JKThOTzeaxatQqapgEANE3D0NAQ8vk8BgYGQq4uGjzwwAMwDAPbt2/H/fffj97e3rBLigQ8d1rDc2ceXdfx+OOPY+fOnTxvlmAemzqdft58+ctfxn/8x3/AMAz8/d//fSjnDFdoSOx47LHH8K//+q/453/+ZxiGgf3794ddEpEEnjvv8/DDD6Orqwt33nln2KVEjqVjw/MG+PrXv44jR47g85//PP7iL/4ilBoYaCRkeHgYZ86cQa1WAwDUajVMTk5yCX2B+jikUins3r0bP//5z0OuKDrw3LGH5848Y2NjOHHiBL7xjW9AVVWeNyaWjg3A88bMxz/+cbz00ktYvXp14OcMA42ErFixAiMjIxgfHwcAjI+PY2RkpCOXfpdSKBQwMzMDYL7V/LPPPouRkZGQq4oOPHes4bkzzyOPPIKjR4/i0UcfRSqVAsDzpk6zsen082Z2dhb5fL7x58OHD6Ovry+Uc0YxDMMQZifCeOutt7B3715MT0+jt7cXY2Nj+MAHPhB2WaFz8uRJfPazn0WtVoOu69i8eTO+8pWvYGhoKOzSAufP/uzP8OMf/xhTU1O47LLL0N/fjx/+8Ic8d9B8bA4ePNjx586bb76J0dFRbNy4EZlMBgCwbt06PProox1/3liNzd69ezv6vJmamsJ9992HYrEIVVXR19eHL37xi7j66qsDP2cYaAghhBAiPbzkRAghhBDpYaAhhBBCiPQw0BBCCCFEehhoCCGEECI9DDSEEEIIkR4GGkIIIYRIDwMNIYQsYefOnXj++efDLoMQ4gIGGkIIIYRIDwMNISTy5PN5fOYzn8Fv//ZvY8eOHdi/fz90XceBAwdwyy234MYbb8SePXsaW9C/9NJLuOmmmxY5zKsu3/rWt/Cnf/qn2LNnD6677jrcdttteO211wAAX/jCF3Dq1Cn8yZ/8Ca677jr83d/9HcrlMh544AHs2LED119/PT7xiU9gamoq2EEghNjCQEMIiTS1Wg2f/vSnsWbNGhw+fBj/9m//hltvvRX/8i//gqeeegqHDh3CT3/6UxQKBVddjg8fPozbbrsNP/vZz7Bz5048/PDDAIC//Mu/xJo1a3Dw4EG8+uqruOeee/DUU0/h0qVLOHLkCF566SV87Wtfa2x/TwiJBgw0hJBI86tf/QqTk5PYs2cPurq6kE6ncf311+OZZ57B3XffjfXr16O7uxv3338/nn32WczNzTnybt++HTfffDM0TcOuXbtw7Ngxy2MTiQQuXLiAEydOQNM0bNu2Dblczq9/IiHEBxJhF0AIIXbk83msWbMGicTir6vJyUmsXbu28ee1a9dibm4OZ8+edeQdHBxs/Hcmk0G5XMbc3NyyzwGAXbt24fTp07j//vsxPT2N22+/HZ///OeRTCY9/qsIIX7DFRpCSKQZHh5GPp9ftvIyNDSEd999t/HnU6dOIZFIYMWKFchmsyiVSo3XarUazp0757mGZDKJz3zmM3j22WfxxBNP4MiRI3j66ac9+wgh/sNAQwiJNNdccw1WrlyJv/7rv0ahUEC5XMYrr7yC0dFR/OM//iNOnjyJ2dlZPPLII/j93/99JBIJbNq0CeVyGUeOHEG1WsV3vvMdVCoVx585ODiIkydPNv784osv4vXXX0etVkMul0MikYCmaSL+uYQQjzDQEEIijaZpOHjwIE6cOIFbbrkFN910E370ox/hE5/4BG6//Xbceeed+OhHP4pUKoWvfvWrAICenh7s27cPX/nKV3DTTTchm81i9erVjj/z3nvvxXe+8x1cf/31+O53v4upqSl87nOfw/N3p+0AAABwSURBVPbt23Hrrbfiwx/+MG6//XZR/2RCiAcUwzCMsIsghBBCCGkHrtAQQgghRHoYaAghhBAiPQw0hBBCCJEeBhpCCCGESA8DDSGEEEKkh4GGEEIIIdLDQEMIIYQQ6WGgIYQQQoj0MNAQQgghRHr+P+8i0CWtgbcDAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# set up histogram function to fixed bins\\n\",\n    \"edges = np.linspace(-1, 30, 31, endpoint=True)\\n\",\n    \"hist_func = partial(np.histogram, bins=edges)\\n\",\n    \"\\n\",\n    \"print(kp_data.keys())\\n\",\n    \"# set up style cycles\\n\",\n    \"color_cycle = cycler(facecolor=[sns.color_palette(\\\"hls\\\", 8)[4],\\n\",\n    \"                                sns.color_palette(\\\"hls\\\", 8)[4],\\n\",\n    \"                                sns.color_palette(\\\"hls\\\", 8)[4],\\n\",\n    \"                                sns.color_palette(\\\"hls\\\", 8)[4],\\n\",\n    \"                                sns.color_palette(\\\"hls\\\", 8)[0],\\n\",\n    \"                               ])\\n\",\n    \"hatch_cycle = cycler(hatch=[' ', '/', 'o', '+', ' '])\\n\",\n    \"# hatch_cycle = cycler(hatch=[' ', '/', 'o', '+', '|'])\\n\",\n    \"alpha_cycle = cycler(alpha=[0.6, 0.6, 0.5, 0.5, 1.0])\\n\",\n    \"# hist_kws=dict(alpha=0.5, edgecolor=\\\"w\\\", linewidth=0.2)\\n\",\n    \"\\n\",\n    \"# Fixing random state for reproducibility\\n\",\n    \"np.random.seed(19680801)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 9), tight_layout=True, sharey=True)\\n\",\n    \"\\n\",\n    \"arts = stack_hist(ax, kp_data,\\n\",\n    \"                  sty_cycle=color_cycle + hatch_cycle + alpha_cycle,\\n\",\n    \"                  labels=kp_data.keys(), hist_func=hist_func)\\n\",\n    \"\\n\",\n    \"ax.set_xlabel('counts')\\n\",\n    \"ax.set_ylabel('x')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Stats of KP20k\\n\",\n    \"\\n\",\n    \"##### w/o preprocessing\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(data examples)=514,154 \\n\",\n    \"- #(KP)=2,710,067\\n\",\n    \"- #(unique KP)=710,218\\n\",\n    \" \\n\",\n    \" \\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=19,336 (3.76%)\\n\",\n    \"- #(KP)=401,763 (14.82%)\\n\",\n    \"- #(unique KP)=52,176 (7.35%)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##### w/ preprocessing\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=514,154\\n\",\n    \"- #(KP)=2,710,067\\n\",\n    \"- #(unique KP)=625,058 (diff between w/&w/o preprocessing: 85,160)\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=19,336\\n\",\n    \"- #(KP)=401,763 (14.82%)\\n\",\n    \"- #(unique KP)=48,125 (7.70%, diff between w/&w/o preprocessing: 4,051)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count #kp per document\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DescribeResult(nobs=514154, minmax=(1, 110), mean=5.270924664594655, variance=14.141117540879774, skewness=5.39192287405869, kurtosis=40.41415445884668)\\n\",\n      \"Percentile@0 = 1.000000\\n\",\n      \"Percentile@1 = 2.000000\\n\",\n      \"Percentile@2 = 2.000000\\n\",\n      \"Percentile@3 = 2.000000\\n\",\n      \"Percentile@4 = 3.000000\\n\",\n      \"Percentile@5 = 3.000000\\n\",\n      \"Percentile@6 = 3.000000\\n\",\n      \"Percentile@7 = 3.000000\\n\",\n      \"Percentile@8 = 3.000000\\n\",\n      \"Percentile@9 = 3.000000\\n\",\n      \"Percentile@10 = 3.000000\\n\",\n      \"Percentile@11 = 3.000000\\n\",\n      \"Percentile@12 = 3.000000\\n\",\n      \"Percentile@13 = 3.000000\\n\",\n      \"Percentile@14 = 3.000000\\n\",\n      \"Percentile@15 = 3.000000\\n\",\n      \"Percentile@16 = 3.000000\\n\",\n      \"Percentile@17 = 3.000000\\n\",\n      \"Percentile@18 = 3.000000\\n\",\n      \"Percentile@19 = 3.000000\\n\",\n      \"Percentile@20 = 3.000000\\n\",\n      \"Percentile@21 = 4.000000\\n\",\n      \"Percentile@22 = 4.000000\\n\",\n      \"Percentile@23 = 4.000000\\n\",\n      \"Percentile@24 = 4.000000\\n\",\n      \"Percentile@25 = 4.000000\\n\",\n      \"Percentile@26 = 4.000000\\n\",\n      \"Percentile@27 = 4.000000\\n\",\n      \"Percentile@28 = 4.000000\\n\",\n      \"Percentile@29 = 4.000000\\n\",\n      \"Percentile@30 = 4.000000\\n\",\n      \"Percentile@31 = 4.000000\\n\",\n      \"Percentile@32 = 4.000000\\n\",\n      \"Percentile@33 = 4.000000\\n\",\n      \"Percentile@34 = 4.000000\\n\",\n      \"Percentile@35 = 4.000000\\n\",\n      \"Percentile@36 = 4.000000\\n\",\n      \"Percentile@37 = 4.000000\\n\",\n      \"Percentile@38 = 4.000000\\n\",\n      \"Percentile@39 = 4.000000\\n\",\n      \"Percentile@40 = 4.000000\\n\",\n      \"Percentile@41 = 4.000000\\n\",\n      \"Percentile@42 = 4.000000\\n\",\n      \"Percentile@43 = 4.000000\\n\",\n      \"Percentile@44 = 4.000000\\n\",\n      \"Percentile@45 = 4.000000\\n\",\n      \"Percentile@46 = 4.000000\\n\",\n      \"Percentile@47 = 4.000000\\n\",\n      \"Percentile@48 = 5.000000\\n\",\n      \"Percentile@49 = 5.000000\\n\",\n      \"Percentile@50 = 5.000000\\n\",\n      \"Percentile@51 = 5.000000\\n\",\n      \"Percentile@52 = 5.000000\\n\",\n      \"Percentile@53 = 5.000000\\n\",\n      \"Percentile@54 = 5.000000\\n\",\n      \"Percentile@55 = 5.000000\\n\",\n      \"Percentile@56 = 5.000000\\n\",\n      \"Percentile@57 = 5.000000\\n\",\n      \"Percentile@58 = 5.000000\\n\",\n      \"Percentile@59 = 5.000000\\n\",\n      \"Percentile@60 = 5.000000\\n\",\n      \"Percentile@61 = 5.000000\\n\",\n      \"Percentile@62 = 5.000000\\n\",\n      \"Percentile@63 = 5.000000\\n\",\n      \"Percentile@64 = 5.000000\\n\",\n      \"Percentile@65 = 5.000000\\n\",\n      \"Percentile@66 = 5.000000\\n\",\n      \"Percentile@67 = 5.000000\\n\",\n      \"Percentile@68 = 5.000000\\n\",\n      \"Percentile@69 = 5.000000\\n\",\n      \"Percentile@70 = 5.000000\\n\",\n      \"Percentile@71 = 5.000000\\n\",\n      \"Percentile@72 = 5.000000\\n\",\n      \"Percentile@73 = 5.000000\\n\",\n      \"Percentile@74 = 6.000000\\n\",\n      \"Percentile@75 = 6.000000\\n\",\n      \"Percentile@76 = 6.000000\\n\",\n      \"Percentile@77 = 6.000000\\n\",\n      \"Percentile@78 = 6.000000\\n\",\n      \"Percentile@79 = 6.000000\\n\",\n      \"Percentile@80 = 6.000000\\n\",\n      \"Percentile@81 = 6.000000\\n\",\n      \"Percentile@82 = 6.000000\\n\",\n      \"Percentile@83 = 6.000000\\n\",\n      \"Percentile@84 = 6.000000\\n\",\n      \"Percentile@85 = 6.000000\\n\",\n      \"Percentile@86 = 6.000000\\n\",\n      \"Percentile@87 = 7.000000\\n\",\n      \"Percentile@88 = 7.000000\\n\",\n      \"Percentile@89 = 7.000000\\n\",\n      \"Percentile@90 = 7.000000\\n\",\n      \"Percentile@91 = 7.000000\\n\",\n      \"Percentile@92 = 8.000000\\n\",\n      \"Percentile@93 = 8.000000\\n\",\n      \"Percentile@94 = 8.000000\\n\",\n      \"Percentile@95 = 9.000000\\n\",\n      \"Percentile@96 = 10.000000\\n\",\n      \"Percentile@97 = 13.000000\\n\",\n      \"Percentile@98 = 19.000000\\n\",\n      \"Percentile@99 = 25.000000\\n\",\n      \"Percentile@100 = 110.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data = tgt_nums[\\\"kp20k\\\"]\\n\",\n    \"print(scipy.stats.describe(data))\\n\",\n    \"\\n\",\n    \"for p in np.linspace(0, 100, 101):\\n\",\n    \"    percentile = np.percentile(data, p, interpolation='lower')\\n\",\n    \"    print('Percentile@%.0f = %.6f' % (p, percentile))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T04:00:35.137583Z\",\n     \"start_time\": \"2020-09-22T04:00:35.105311Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"429430/514154\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n >=3 and n <= 6]\\n\",\n    \"print('%d/%d' % (len(tmp_tgt_nums), len(tgt_nums[\\\"kp20k\\\"])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:57:34.284991Z\",\n     \"start_time\": \"2020-09-22T03:57:34.098594Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k (truncated at 10)')\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4bKH9JklvbtPdP/hxp+x3iK1v5LQM/Q0qSFUnubpcV811ZSZIWmrkc0V9M91vrUyQ5kO63ke8dKHsBsBw4tNX5YJLd2uTzgdPpfqTlkIFlngY8UlUH0/0G93ltWfsC5wAvpfvTg3My+i8lJUnSNGYN+qr6HN2fYgx7D90fSgz+4s4JwBVV9Xj77fRVwBFJng3sWVU3t19h+wjdH0BM1pn8A4urgWPa0f5xwA1V9XBVPQLcwIgdDkmSNL2tOkef5DeAdVU1/B+9E3T/RTxpbSubaLeHy6fUaX+vuYHuP9KnW5YkSZqjLf56XZKnAf8JOHbU5BFlNUP51tYZbtPpdKcFeO5znztqFkmSFqStOaJ/PnAQ8LX2F51LgC8n+Um6o+4DB+ZdQvfnKWvb7eFyBuskWQTsRXeqYLplPUlVXVBVy6pq2eLFI38vQJKkBWmLg76qbq2qA6pqaVUtpQvkw6vqG8B1wPI2kv4gukF3X6iqB4CNSY5s599PBa5ti7wOmBxRfxJwUzuP/yng2CT7tEF4x7YySZI0R7N23Se5HDga2D/JWuCcqrpw1LxVdVuSq4DbgU3AmVW1uU1+A90I/j2A69sF4ELg0iSr6I7kl7dlPZzkHcAX23x/WlWjBgVKkqRp9O5vapctW1b+BK4kaSFJ8qWqWjZqmr+MJ0lSjxn0kiT1WO/+vU7aGq865RTWrFs37mbMaOnEBNdcdtm4myFpF2PQS8CadeuYOOOMcTdjRms+9KFxN0HSLsiue0mSesyglySpx+y6l3YRq1ev5vCjjhp3M2bkOAJp52PQS7uIJzZvdhyBpC1m170kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9NmvQJ7koyUNJvj5Q9hdJ7kzyr0n+7yR7D0w7O8mqJHclOW6g/CVJbm3T3p8krXz3JFe28luSLB2osyLJ3e2yYluttCRJC8VcjugvBo4fKrsBOKyqXgj8d+BsgCQvAJYDh7Y6H0yyW6tzPnA6cEi7TC7zNOCRqjoYeA9wXlvWvsA5wEuBI4Bzkuyz5asoSdLCNWvQV9XngIeHyj5dVZva3X8BlrTbJwBXVNXjVXUPsAo4IsmzgT2r6uaqKuAjwIkDdS5pt68GjmlH+8cBN1TVw1X1CN3OxfAOhyRJmsG2OEf/H4Dr2+0J4L6BaWtb2US7PVw+pU7bedgA7DfDsp4kyelJViZZuX79+nmtjCRJfTKvoE/yn4BNwMcmi0bMVjOUb22dqYVVF1TVsqpatnjx4pkbLUnSArLVQd8Gx70CeE3rjofuqPvAgdmWAPe38iUjyqfUSbII2IvuVMF0y5IkSXO0VUGf5Hjgj4DfqKrvDky6DljeRtIfRDfo7gtV9QCwMcmR7fz7qcC1A3UmR9SfBNzUdhw+BRybZJ82CO/YViZJkuZo0WwzJLkcOBrYP8laupHwZwO7Aze0b8n9S1WdUVW3JbkKuJ2uS//MqtrcFvUGuhH8e9Cd0588r38hcGmSVXRH8ssBqurhJO8Avtjm+9OqmjIoUJIkzWzWoK+qV48ovnCG+c8Fzh1RvhI4bET594GTp1nWRcBFs7VRkiSN5i/jSZLUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPTZr0Ce5KMlDSb4+ULZvkhuS3N2u9xmYdnaSVUnuSnLcQPlLktzapr0/SVr57kmubOW3JFk6UGdFe4y7k6zYVistSdJCMZcj+ouB44fK3grcWFWHADe2+yR5AbAcOLTV+WCS3Vqd84HTgUPaZXKZpwGPVNXBwHuA89qy9gXOAV4KHAGcM7hDIUmSZjdr0FfV54CHh4pPAC5pty8BThwov6KqHq+qe4BVwBFJng3sWVU3V1UBHxmqM7msq4Fj2tH+ccANVfVwVT0C3MCTdzgkSdIMtvYc/bOq6gGAdn1AK58A7huYb20rm2i3h8un1KmqTcAGYL8ZliVJkuZoWw/Gy4iymqF8a+tMfdDk9CQrk6xcv379nBoqSdJCsLVB/2DrjqddP9TK1wIHDsy3BLi/lS8ZUT6lTpJFwF50pwqmW9aTVNUFVbWsqpYtXrx4K1dJkqT+2dqgvw6YHAW/Arh2oHx5G0l/EN2guy+07v2NSY5s599PHaozuayTgJvaefxPAccm2acNwju2lUmSpDlaNNsMSS4Hjgb2T7KWbiT8u4CrkpwG3AucDFBVtyW5Crgd2AScWVWb26LeQDeCfw/g+nYBuBC4NMkquiP55W1ZDyd5B/DFNt+fVtXwoEBJkjSDWYO+ql49zaRjppn/XODcEeUrgcNGlH+ftqMwYtpFwEWztVGSJI3mL+NJktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST22aNwN0MLwqlNOYc26deNuxrRWr1nDxLgbIUnbgUGvHWLNunVMnHHGuJsxrTvPOmvcTZCk7cKue0mSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6bF5Bn+TNSW5L8vUklyf5iST7Jrkhyd3tep+B+c9OsirJXUmOGyh/SZJb27T3J0kr3z3Jla38liRL59NeSZIWmq0O+iQTwJuAZVV1GLAbsBx4K3BjVR0C3Njuk+QFbfqhwPHAB5Ps1hZ3PnA6cEi7HN/KTwMeqaqDgfcA521teyVJWojm23W/CNgjySLgacD9wAnAJW36JcCJ7fYJwBVV9XhV3QOsAo5I8mxgz6q6uaoK+MhQncllXQ0cM3m0L0mSZrfVQV9V64C/BO4FHgA2VNWngWdV1QNtngeAA1qVCeC+gUWsbWUT7fZw+ZQ6VbUJ2ADsN9yWJKcnWZlk5fr167d2lSRJ6p35dN3vQ3fEfRDwHODpSV47U5URZTVD+Ux1phZUXVBVy6pq2eLFi2duuCRJC8h8uu5fBtxTVeur6gfANcAvAg+27nja9UNt/rXAgQP1l9B19a9tt4fLp9Rppwf2Ah6eR5slSVpQ5hP09wJHJnlaO29+DHAHcB2wos2zAri23b4OWN5G0h9EN+juC617f2OSI9tyTh2qM7msk4Cb2nl8SZI0B4u2tmJV3ZLkauDLwCbgK8AFwDOAq5KcRrczcHKb/7YkVwG3t/nPrKrNbXFvAC4G9gCubxeAC4FLk6yiO5JfvrXtlSRpIdrqoAeoqnOAc4aKH6c7uh81/7nAuSPKVwKHjSj/Pm1HQZIkbTl/GU+SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB5bNO4GSOqP1atXc/hRR427GdNaOjHBNZddNu5mSDuUQS9pm3li82Ymzjhj3M2Y1poPfWjcTZB2OLvuJUnqMYNekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeqxeQV9kr2TXJ3kziR3JPmFJPsmuSHJ3e16n4H5z06yKsldSY4bKH9JklvbtPcnSSvfPcmVrfyWJEvn015Jkhaa+R7Rvw/4x6r6WeBFwB3AW4Ebq+oQ4MZ2nyQvAJYDhwLHAx9MsltbzvnA6cAh7XJ8Kz8NeKSqDgbeA5w3z/ZKkrSgbHXQJ9kT+BXgQoCqeqKqHgVOAC5ps10CnNhunwBcUVWPV9U9wCrgiCTPBvasqpurqoCPDNWZXNbVwDGTR/uSJGl28zmi/ylgPfC3Sb6S5MNJng48q6oeAGjXB7T5J4D7BuqvbWUT7fZw+ZQ6VbUJ2ADsN9yQJKcnWZlk5fr16+exSpIk9ct8gn4RcDhwflW9GPgOrZt+GqOOxGuG8pnqTC2ouqCqllXVssWLF8/cakmSFpD5BP1aYG1V3dLuX00X/A+27nja9UMD8x84UH8JcH8rXzKifEqdJIuAvYCH59FmSZIWlK0O+qr6BnBfkp9pRccAtwPXASta2Qrg2nb7OmB5G0l/EN2guy+07v2NSY5s599PHaozuayTgJvaeXxJkjQHi+ZZ/43Ax5I8FVgN/DbdzsNVSU4D7gVOBqiq25JcRbczsAk4s6o2t+W8AbgY2AO4vl2gG+h3aZJVdEfyy+fZXkmSFpR5BX1VfRVYNmLSMdPMfy5w7ojylcBhI8q/T9tRkCRJW85fxpMkqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknps3kGfZLckX0nyiXZ/3yQ3JLm7Xe8zMO/ZSVYluSvJcQPlL0lya5v2/iRp5bsnubKV35Jk6XzbK0nSQrItjuh/H7hj4P5bgRur6hDgxnafJC8AlgOHAscDH0yyW6tzPnA6cEi7HN/KTwMeqaqDgfcA522D9kqStGDMK+iTLAFeDnx4oPgE4JJ2+xLgxIHyK6rq8aq6B1gFHJHk2cCeVXVzVRXwkaE6k8u6Gjhm8mhfkiTNbr5H9O8F3gL8cKDsWVX1AEC7PqCVTwD3Dcy3tpVNtNvD5VPqVNUmYAOw3zzbLEnSgrHVQZ/kFcBDVfWluVYZUVYzlM9UZ7gtpydZmWTl+vXr59gcSZL6bz5H9L8E/EaSNcAVwK8m+SjwYOuOp10/1OZfCxw4UH8JcH8rXzKifEqdJIuAvYCHhxtSVRdU1bKqWrZ48eJ5rJIkSf2y1UFfVWdX1ZKqWko3yO6mqnotcB2wos22Ari23b4OWN5G0h9EN+juC617f2OSI9v591OH6kwu66T2GE86opckSaMt2g7LfBdwVZLTgHuBkwGq6rYkVwG3A5uAM6tqc6vzBuBiYA/g+nYBuBC4NMkquiP55duhvZIk9dY2Cfqq+izw2Xb7W8Ax08x3LnDuiPKVwGEjyr9P21GQJElbzl/GkySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccWjbsBkrSjrF69msOPOmrczZjR0okJrrnssnE3Qz1i0EtaMJ7YvJmJM84YdzNmtOZDHxp3E9Qzdt1LktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ttVBn+TAJP+U5I4ktyX5/Va+b5IbktzdrvcZqHN2klVJ7kpy3ED5S5Lc2qa9P0la+e5JrmzltyRZuvWrKknSwjOfI/pNwFlV9XPAkXzJRbIAAAbBSURBVMCZSV4AvBW4saoOAW5s92nTlgOHAscDH0yyW1vW+cDpwCHtcnwrPw14pKoOBt4DnDeP9kqStOBsddBX1QNV9eV2eyNwBzABnABc0ma7BDix3T4BuKKqHq+qe4BVwBFJng3sWVU3V1UBHxmqM7msq4FjJo/2JUnS7LbJOfrWpf5i4BbgWVX1AHQ7A8ABbbYJ4L6Bamtb2US7PVw+pU5VbQI2APuNePzTk6xMsnL9+vXbYpUkSeqFeQd9kmcAfwf8QVV9e6ZZR5TVDOUz1ZlaUHVBVS2rqmWLFy+ercmSJC0Y8wr6JE+hC/mPVdU1rfjB1h1Pu36ola8FDhyovgS4v5UvGVE+pU6SRcBewMPzabMkSQvJfEbdB7gQuKOq3j0w6TpgRbu9Arh2oHx5G0l/EN2guy+07v2NSY5syzx1qM7ksk4Cbmrn8SVJ0hzM59/rfgl4HXBrkq+2srcB7wKuSnIacC9wMkBV3ZbkKuB2uhH7Z1bV5lbvDcDFwB7A9e0C3Y7EpUlW0R3JL59HeyVJWnC2Ouir6vOMPocOcMw0dc4Fzh1RvhI4bET592k7CpIkacv5y3iSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT12Hy+XqedxKtOOYU169aNuxkzWr1mzY9+11iStOMY9D2wZt06Js44Y9zNmNGdZ5017iZI0oJk170kST1m0EuS1GMGvSRJPWbQS5LUYw7Gk6SdyOrVqzn8qKPG3YxpLZ2Y4JrLLht3M7QFDHpJ2ok8sXnzTv0tmjUf+tC4m6AtZNe9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUY/6pjSRpznb2f9cD/2FvmEEvSZqznf3f9cB/2Btm170kST1m0EuS1GMGvSRJPWbQS5LUYw7GkyT1it8MmMqglyT1it8MmMque0mSesyglySpxwx6SZJ6bJc4R5/keOB9wG7Ah6vqXTvy8V91yimsWbduRz7kFlm9Zg0T426EJGmntNMHfZLdgL8Gfg1YC3wxyXVVdfuOasOadet26oEdd5511ribIEnaSe0KXfdHAKuqanVVPQFcAZww5jZJkrRL2BWCfgK4b+D+2lYmSZJmkaoadxtmlORk4Liq+p12/3XAEVX1xoF5TgdOb3d/Brhrhzd057M/8M1xN2IBcDvvGG7nHcPtvONs6239vKpaPGrCTn+Onu4I/sCB+0uA+wdnqKoLgAt2ZKN2dklWVtWycbej79zOO4bbecdwO+84O3Jb7wpd918EDklyUJKnAsuB68bcJkmSdgk7/RF9VW1K8nvAp+i+XndRVd025mZJkrRL2OmDHqCqPgl8ctzt2MV4KmPHcDvvGG7nHcPtvOPssG290w/GkyRJW29XOEcvSZK2kkHfI0kOTPJPSe5IcluS3x93m/osyW5JvpLkE+NuS58l2TvJ1UnubK/tXxh3m/ooyZvb58bXk1ye5CfG3aY+SHJRkoeSfH2gbN8kNyS5u13vsz3bYND3yybgrKr6OeBI4MwkLxhzm/rs94E7xt2IBeB9wD9W1c8CL8Jtvs0lmQDeBCyrqsPoBj4vH2+reuNi4PihsrcCN1bVIcCN7f52Y9D3SFU9UFVfbrc30n0g+iuC20GSJcDLgQ+Puy19lmRP4FeACwGq6omqenS8reqtRcAeSRYBT2Po90q0darqc8DDQ8UnAJe025cAJ27PNhj0PZVkKfBi4JbxtqS33gu8BfjhuBvScz8FrAf+tp0m+XCSp4+7UX1TVeuAvwTuBR4ANlTVp8fbql57VlU9AN0BGnDA9nwwg76HkjwD+DvgD6rq2+NuT98keQXwUFV9adxtWQAWAYcD51fVi4HvsJ27OReido74BOAg4DnA05O8dryt0rZi0PdMkqfQhfzHquqacbenp34J+I0ka+j+TfFXk3x0vE3qrbXA2qqa7Jm6mi74tW29DLinqtZX1Q+Aa4BfHHOb+uzBJM8GaNcPbc8HM+h7JEnozmXeUVXvHnd7+qqqzq6qJVW1lG7A0k1V5dHPdlBV3wDuS/IzregY4PYxNqmv7gWOTPK09jlyDA563J6uA1a02yuAa7fng+0Sv4ynOfsl4HXArUm+2sre1n5ZUNpVvRH4WPuvi9XAb4+5Pb1TVbckuRr4Mt23d76Cv5K3TSS5HDga2D/JWuAc4F3AVUlOo9vJOnm7tsFfxpMkqb/supckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSeuz/BywoQprfkjhRAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 10]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=10, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k (truncated at 10)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:17:46.526807Z\",\n     \"start_time\": \"2020-09-22T03:17:46.241163Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k')\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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NlNwCHVdVrgf8POBMgySHAKuDQVufcJLu0OucBa4Dl7TG+zlOBR6vqYOAjwNltXfsAZwGvB44Ezkqy9/PfREmSFq5pg76qvgg8MlT2+ara1ib/EVjSnh8PXFFVT1XVvcAG4MgkBwB7VtXNVVXAJ4ATBupc0p5fDRzTjvaPBW6oqkeq6lG6nYvhHQ5JkjSFHXGO/reA69vzMeD+gXmbW9lYez5cPqFO23l4HNh3inU9R5I1SdYlWbd169Y5bYwkSX0yp6BP8ofANuBT40UjFqspymdbZ2Jh1QVVtaKqVixevHjqRkuStIDMOujbxXFvBt7ehuOhO+peOrDYEuCBVr5kRPmEOkkWAXvRnSqYbF2SJGmGZhX0SY4D/h3wa1X1vYFZ1wGr2pX0B9FddHdrVT0IPJFkZTv/fgpw7UCd8SvqTwRuajsOnwPemGTvdhHeG1uZJEmaoUXTLZDkcuBoYL8km+muhD8T2BW4oX1K7h+r6rSqWp/kKuAOuiH906tqe1vVu+iu4N+N7pz++Hn9C4FLk2ygO5JfBVBVjyT5EPDlttwfV9WEiwIlSdLUpg36qnrbiOILp1h+LbB2RPk64LAR5U8CJ02yrouAi6ZroyRJGs0740mS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST02bdAnuSjJw0m+PlC2T5IbktzTfu49MO/MJBuS3J3k2IHyI5Lc3uadkyStfNckV7byW5IsG6izur3GPUlW76iNliRpoZjJEf3FwHFDZe8Hbqyq5cCNbZokhwCrgENbnXOT7NLqnAesAZa3x/g6TwUeraqDgY8AZ7d17QOcBbweOBI4a3CHQpIkTW/aoK+qLwKPDBUfD1zSnl8CnDBQfkVVPVVV9wIbgCOTHADsWVU3V1UBnxiqM76uq4Fj2tH+scANVfVIVT0K3MBzdzgkSdIUZnuO/lVV9SBA+7l/Kx8D7h9YbnMrG2vPh8sn1KmqbcDjwL5TrEuSJM3Qjr4YLyPKaory2daZ+KLJmiTrkqzbunXrjBoqSdJCMNugf6gNx9N+PtzKNwNLB5ZbAjzQypeMKJ9QJ8kiYC+6UwWTres5quqCqlpRVSsWL148y02SJKl/Zhv01wHjV8GvBq4dKF/VrqQ/iO6iu1vb8P4TSVa28++nDNUZX9eJwE3tPP7ngDcm2btdhPfGViZJkmZo0XQLJLkcOBrYL8lmuivhPwxcleRU4D7gJICqWp/kKuAOYBtwelVtb6t6F90V/LsB17cHwIXApUk20B3Jr2rreiTJh4Avt+X+uKqGLwqUJElTmDboq+ptk8w6ZpLl1wJrR5SvAw4bUf4kbUdhxLyLgIuma6MkSRrNO+NJktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST22aL4b8FL01pNPZtOWLc9Mb9y0ibF5bI8kSZMx6Gdh05YtjJ122jPTd51xxjy2RpKkyTl0L0lSjxn0kiT1mEEvSVKPGfSSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT1mEEvSVKPGfSSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT1mEEvSVKPzSnok7w3yfokX09yeZIfSbJPkhuS3NN+7j2w/JlJNiS5O8mxA+VHJLm9zTsnSVr5rkmubOW3JFk2l/ZKkrTQzDrok4wB7wFWVNVhwC7AKuD9wI1VtRy4sU2T5JA2/1DgOODcJLu01Z0HrAGWt8dxrfxU4NGqOhj4CHD2bNsrSdJCNNeh+0XAbkkWAbsDDwDHA5e0+ZcAJ7TnxwNXVNVTVXUvsAE4MskBwJ5VdXNVFfCJoTrj67oaOGb8aF+SJE1v1kFfVVuAPwfuAx4EHq+qzwOvqqoH2zIPAvu3KmPA/QOr2NzKxtrz4fIJdapqG/A4sO9wW5KsSbIuybqtW7fOdpMkSeqduQzd7013xH0QcCDwiiTvmKrKiLKaonyqOhMLqi6oqhVVtWLx4sVTN1ySpAVkLkP3bwDuraqtVfUD4BrgF4CH2nA87efDbfnNwNKB+kvohvo3t+fD5RPqtNMDewGPzKHNkiQtKHMJ+vuAlUl2b+fNjwHuBK4DVrdlVgPXtufXAavalfQH0V10d2sb3n8iycq2nlOG6oyv60TgpnYeX5IkzcCi2VasqluSXA18BdgGfBW4ANgDuCrJqXQ7Aye15dcnuQq4oy1/elVtb6t7F3AxsBtwfXsAXAhcmmQD3ZH8qtm2V5KkhWjWQQ9QVWcBZw0VP0V3dD9q+bXA2hHl64DDRpQ/SdtRkCRJz593xpMkqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccWzXcDFoKNGzdy+FFHPTO9bGyMay67bB5bJElaKAz6F8HT27czdtppz0xvOv/8eWyNJGkhcehekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB4z6CVJ6jGDXpKkHjPoJUnqMYNekqQeM+glSeoxg16SpB6bU9AneWWSq5PcleTOJD+fZJ8kNyS5p/3ce2D5M5NsSHJ3kmMHyo9Icnubd06StPJdk1zZym9Jsmwu7ZUkaaGZ6xH9R4G/raqfBn4WuBN4P3BjVS0HbmzTJDkEWAUcChwHnJtkl7ae84A1wPL2OK6Vnwo8WlUHAx8Bzp5jeyVJWlBmHfRJ9gT+NXAhQFU9XVWPAccDl7TFLgFOaM+PB66oqqeq6l5gA3BkkgOAPavq5qoq4BNDdcbXdTVwzPjRviRJmt5cjuh/HNgK/FWSryb5eJJXAK+qqgcB2s/92/JjwP0D9Te3srH2fLh8Qp2q2gY8Duw73JAka5KsS7Ju69atc9gkSZL6ZS5Bvwg4HDivql4HfJc2TD+JUUfiNUX5VHUmFlRdUFUrqmrF4sWLp261JEkLyFyCfjOwuapuadNX0wX/Q204nvbz4YHllw7UXwI80MqXjCifUCfJImAv4JE5tFmSpAVl1kFfVd8A7k/yU63oGOAO4DpgdStbDVzbnl8HrGpX0h9Ed9HdrW14/4kkK9v591OG6oyv60TgpnYeX5IkzcCiOdZ/N/CpJC8HNgK/SbfzcFWSU4H7gJMAqmp9kqvodga2AadX1fa2nncBFwO7Ade3B3QX+l2aZAPdkfyqObZXkqQFZU5BX1VfA1aMmHXMJMuvBdaOKF8HHDai/EnajoIkSXr+vDOeJEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUY3MO+iS7JPlqks+06X2S3JDknvZz74Flz0yyIcndSY4dKD8iye1t3jlJ0sp3TXJlK78lybK5tleSpIVkRxzR/x5w58D0+4Ebq2o5cGObJskhwCrgUOA44Nwku7Q65wFrgOXtcVwrPxV4tKoOBj4CnL0D2itJ0oIxp6BPsgR4E/DxgeLjgUva80uAEwbKr6iqp6rqXmADcGSSA4A9q+rmqirgE0N1xtd1NXDM+NG+JEma3lyP6P8SeB/ww4GyV1XVgwDt5/6tfAy4f2C5za1srD0fLp9Qp6q2AY8D+86xzZIkLRizDvokbwYerqrbZlplRFlNUT5VneG2rEmyLsm6rVu3zrA5kiT131yO6H8R+LUkm4ArgF9O8kngoTYcT/v5cFt+M7B0oP4S4IFWvmRE+YQ6SRYBewGPDDekqi6oqhVVtWLx4sVz2CRJkvpl1kFfVWdW1ZKqWkZ3kd1NVfUO4DpgdVtsNXBte34dsKpdSX8Q3UV3t7bh/SeSrGzn308ZqjO+rhPbazzniF6SJI226AVY54eBq5KcCtwHnARQVeuTXAXcAWwDTq+q7a3Ou4CLgd2A69sD4ELg0iQb6I7kV70A7ZUkqbd2SNBX1ReAL7Tn3wKOmWS5tcDaEeXrgMNGlD9J21GQJEnPn3fGkySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHDHpJknrMoJckqccWzXcDFqKNGzdy+FFHPTO9bGyMay67bB5bJEnqK4N+Hjy9fTtjp532zPSm88+fx9ZIkvrMoXtJknrMoJckqccMekmSesyglySpxwx6SZJ6zKCXJKnHZh30SZYm+fskdyZZn+T3Wvk+SW5Ick/7ufdAnTOTbEhyd5JjB8qPSHJ7m3dOkrTyXZNc2cpvSbJs9psqSdLCM5cj+m3AGVX1M8BK4PQkhwDvB26squXAjW2aNm8VcChwHHBukl3aus4D1gDL2+O4Vn4q8GhVHQx8BDh7Du2VJGnBmXXQV9WDVfWV9vwJ4E5gDDgeuKQtdglwQnt+PHBFVT1VVfcCG4AjkxwA7FlVN1dVAZ8YqjO+rquBY8aP9iVJ0vR2yDn6NqT+OuAW4FVV9SB0OwPA/m2xMeD+gWqbW9lYez5cPqFOVW0DHgf2HfH6a5KsS7Ju69atO2KTJEnqhTkHfZI9gL8Gfr+qvj3VoiPKaoryqepMLKi6oKpWVNWKxYsXT9dkSZIWjDkFfZKX0YX8p6rqmlb8UBuOp/18uJVvBpYOVF8CPNDKl4won1AnySJgL+CRubRZkqSFZC5X3Qe4ELizqv5iYNZ1wOr2fDVw7UD5qnYl/UF0F93d2ob3n0iysq3zlKE64+s6EbipnceXJEkzMJdvr/tF4DeA25N8rZV9APgwcFWSU4H7gJMAqmp9kquAO+iu2D+9qra3eu8CLgZ2A65vD+h2JC5NsoHuSH7VHNorSdKCM+ugr6ovMfocOsAxk9RZC6wdUb4OOGxE+ZO0HQVJkvT8+X30M/DWk09m05Ytz0xv3LTpmY8FSJK0MzPoZ2DTli2MnXbaM9N3nXHGPLZGkqSZ8173kiT1mEEvSVKPGfSSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT1mEEvSVKPGfSSJPWYd8bbCWzcuJHDjzrqmellY2Ncc9ll89giSVJfGPQ7gae3b59wi91N558/j62RJPWJQ/eSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT1mEEvSVKPGfSSJPWYQS9JUo8Z9JIk9ZhBL0lSjxn0kiT1mEEvSVKP+aU2OyG/zU6StKMY9Dshv81OkrSjOHQvSVKPGfSSJPWYQS9JUo8Z9JIk9ZgX470EeBW+JGm2DPqXAK/ClyTNlkP3kiT1mEEvSVKPOXT/EjTdOfu3nnwym7ZsmXS+JGnheEkEfZLjgI8CuwAfr6oPz3OT5tXwOfub3ve+CcG/cdMm/pcPP9tFntOXpIVrpw/6JLsA/xn4FWAz8OUk11XVHfPbsp3HcPDfdcYZ89ia2XEUQpJeGDt90ANHAhuqaiNAkiuA4wGDfoaGh/q/8cAD/NiBB05YZrjsxZ52FEKSXhgvhVuKwswAAARJSURBVKAfA+4fmN4MvH6e2vKSNOqI/4iB6VFl8zE9aLqdkxd6ejZ1HIWQtDNKVc13G6aU5CTg2Kr67Tb9G8CRVfXugWXWAGva5E8Bd8/xZfcDvjnHdSwE9tPM2VczYz/NnH01Mwuln15TVYtHzXgpHNFvBpYOTC8BHhhcoKouAC7YUS+YZF1VrdhR6+sr+2nm7KuZsZ9mzr6aGfvppfE5+i8Dy5MclOTlwCrgunlukyRJLwk7/RF9VW1L8rvA5+g+XndRVa2f52ZJkvSSsNMHPUBVfRb47Iv4kjvsNEDP2U8zZ1/NjP00c/bVzCz4ftrpL8aTJEmz91I4Ry9JkmbJoB+Q5LgkdyfZkOT9892enUmSpUn+PsmdSdYn+b1Wvk+SG5Lc037uPd9t3Rkk2SXJV5N8pk3bTyMkeWWSq5Pc1f62ft6+eq4k723/d19PcnmSH7GfOkkuSvJwkq8PlE3aN0nObO/xdyc5dn5a/eIy6JuBW+3+G+AQ4G1JDpnfVu1UtgFnVNXPACuB01v/vB+4saqWAze2acHvAXcOTNtPo30U+Nuq+mngZ+n6zL4akGQMeA+woqoOo7soeRX207iLgeOGykb2TXvPWgUc2uqc2977e82gf9Yzt9qtqqeB8VvtCqiqB6vqK+35E3RvyGN0fXRJW+wS4IT5aeHOI8kS4E3AxweK7achSfYE/jVwIUBVPV1Vj2FfjbII2C3JImB3unuJ2E9AVX0ReGSoeLK+OR64oqqeqqp7gQ107/29ZtA/a9StdsfmqS07tSTLgNcBtwCvqqoHodsZAPafv5btNP4SeB/ww4Ey++m5fhzYCvxVO83x8SSvwL6aoKq2AH8O3Ac8CDxeVZ/HfprKZH2zIN/nDfpnZUSZH0kYkmQP4K+B36+qb893e3Y2Sd4MPFxVt813W14CFgGHA+dV1euA77Jwh58n1c4vHw8cBBwIvCLJO+a3VS9ZC/J93qB/1rS32l3okryMLuQ/VVXXtOKHkhzQ5h8APDxf7dtJ/CLwa0k20Z3++eUkn8R+GmUzsLmqbmnTV9MFv3010RuAe6tqa1X9ALgG+AXsp6lM1jcL8n3eoH+Wt9qdQpLQnUu9s6r+YmDWdcDq9nw1cO2L3badSVWdWVVLqmoZ3d/QTVX1Duyn56iqbwD3J/mpVnQM3ddP21cT3QesTLJ7+z88hu4aGftpcpP1zXXAqiS7JjkIWA7cOg/te1F5w5wBSX6V7vzq+K12185zk3YaSX4J+O/A7Tx77vkDdOfprwJeTfeGdFJVDV8YsyAlORr4g6p6c5J9sZ+eI8nP0V20+HJgI/CbdAcg9tWAJB8Efp3u0y9fBX4b2AP7iSSXA0fTfUvdQ8BZwKeZpG+S/CHwW3R9+ftVdf08NPtFZdBLktRjDt1LktRjBr0kST1m0EuS1GMGvSRJPWbQS5LUYwa9JEk9ZtBLktRjBr0kST32/wN8KrBXdH3GnwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"]]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=100, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count unique phrases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### only count documents that #(kp)>10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:08:27.482077Z\",\n     \"start_time\": \"2020-08-12T06:08:13.670328Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=19336\\n\",\n      \"#(KP)=401763\\n\",\n      \"#(unique KP)=48125\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'kp20k'\\n\",\n    \"do_preprocess = True\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"        \\n\",\n    \"        if len(keywords) > 10:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = '_'.join(tokens)\\n\",\n    \"                    \\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#####  count all documents #(kp)>0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:37:34.579653Z\",\n     \"start_time\": \"2020-08-12T16:37:26.863175Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=514154\\n\",\n      \"#(KP)=2710067\\n\",\n      \"#(unique KP)=710218\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'kp20k'\\n\",\n    \"do_preprocess = False\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"kp_len_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"        \\n\",\n    \"        if len(keywords) > 0:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = ' '.join(tokens)\\n\",\n    \"                    \\n\",\n    \"                tokens = [t for t in keyword.split()]\\n\",\n    \"                kp_len_counter[len(tokens)] = kp_len_counter[len(tokens)] + 1\\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"start_time\": \"2020-09-22T00:02:32.633Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_kp_freqs = [v for k,v in unique_kp_counter.items() if v > 1000]\\n\",\n    \"sns.distplot(tmp_kp_freqs, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  KP length distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:40:25.845738Z\",\n     \"start_time\": \"2020-08-12T16:40:25.254505Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#kp_len=0, freq=63, accum/total=0.00%\\n\",\n      \"#kp_len=1, freq=907946, accum/total=33.51%\\n\",\n      \"#kp_len=2, freq=1267094, accum/total=80.26%\\n\",\n      \"#kp_len=3, freq=418621, accum/total=95.71%\\n\",\n      \"#kp_len=4, freq=87761, accum/total=98.95%\\n\",\n      \"#kp_len=5, freq=19873, accum/total=99.68%\\n\",\n      \"#kp_len=6, freq=5200, accum/total=99.87%\\n\",\n      \"#kp_len=7, freq=1625, accum/total=99.93%\\n\",\n      \"#kp_len=8, freq=670, accum/total=99.96%\\n\",\n      \"#kp_len=9, freq=268, accum/total=99.97%\\n\",\n      \"#kp_len=10, freq=147, accum/total=99.97%\\n\",\n      \"#kp_len=11, freq=219, accum/total=99.98%\\n\",\n      \"#kp_len=12, freq=234, accum/total=99.99%\\n\",\n      \"#kp_len=13, freq=98, accum/total=99.99%\\n\",\n      \"#kp_len=14, freq=38, accum/total=99.99%\\n\",\n      \"#kp_len=15, freq=41, accum/total=99.99%\\n\",\n      \"#kp_len=16, freq=30, accum/total=99.99%\\n\",\n      \"#kp_len=17, freq=19, accum/total=100.00%\\n\",\n      \"#kp_len=18, freq=9, accum/total=100.00%\\n\",\n      \"#kp_len=19, freq=21, accum/total=100.00%\\n\",\n      \"#kp_len=20, freq=14, accum/total=100.00%\\n\",\n      \"#kp_len=21, freq=12, accum/total=100.00%\\n\",\n      \"#kp_len=22, freq=5, accum/total=100.00%\\n\",\n      \"#kp_len=23, freq=17, accum/total=100.00%\\n\",\n      \"#kp_len=24, freq=11, accum/total=100.00%\\n\",\n      \"#kp_len=25, freq=6, accum/total=100.00%\\n\",\n      \"#kp_len=26, freq=4, accum/total=100.00%\\n\",\n      \"#kp_len=27, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=28, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=29, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=31, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=32, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=33, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=34, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=35, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=36, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=40, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=50, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=91, freq=1, accum/total=100.00%\\n\",\n      \"39\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(16, 12))\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"kp_lens = sorted([(kp_len, freq) for kp_len, freq in kp_len_counter.items()], key=lambda k:k[0])\\n\",\n    \"\\n\",\n    \"accum_kp_count = 0\\n\",\n    \"total_kp_count = sum(freq for _, freq in kp_lens)\\n\",\n    \"for kp_len, freq in kp_lens:\\n\",\n    \"    accum_kp_count += freq\\n\",\n    \"    print('#kp_len=%d, freq=%d, accum/total=%.2f%%' % (kp_len, freq, accum_kp_count / total_kp_count * 100))\\n\",\n    \"    \\n\",\n    \"print(len(kp_lens))\\n\",\n    \"kp_lens_df = pd.DataFrame(kp_lens, columns=['#kp_len', 'freq'])\\n\",\n    \"ax = sns.barplot(x=\\\"#kp_len\\\", y=\\\"freq\\\", data=kp_lens_df)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Stats of MagKP\\n\",\n    \"\\n\",\n    \"##### w/o preprocessing\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=2,699,094\\n\",\n    \"- #(KP)=41,605,964\\n\",\n    \"- #(unique KP)=6,880,853\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=1,520,307 (56.33%)\\n\",\n    \"- #(KP)=35,525,765 (85.39%)\\n\",\n    \"- #(unique KP)=5,784,959 (84.07%)\\n\",\n    \"\\n\",\n    \"##### w/ preprocessing (lowercase and stemming)\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=2,699,094\\n\",\n    \"- #(KP)=41,605,964\\n\",\n    \"- #(unique KP)=6,537,481 (diff between w/&w/o preprocessing: 343,372, 5.25% difference)\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=1,520,307\\n\",\n    \"- #(KP)=35,525,765 （85.39%）\\n\",\n    \"- #(unique KP)=5,493,997 (84.04%, diff between w/&w/o preprocessing: 290,962)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"magkp\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k', 'magkp']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/hdaqing/rum20/kp/data/kp/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_examples = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    dataset_examples[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, 'train.json')\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                ex_dict['abstract'] = ex_dict['question']\\n\",\n    \"                ex_dict['keywords'] = ex_dict['tags']            \\n\",\n    \"                del ex_dict['question']\\n\",\n    \"                del ex_dict['tags']\\n\",\n    \"\\n\",\n    \"            keywords = ex_dict['keywords']\\n\",\n    \"            ex_dict['fulltext'] = ''\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                ex_dict['keywords'] = keywords\\n\",\n    \"            keywords = [k.strip() for k in keywords]\\n\",\n    \"            ex_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"            dataset_examples[dataset_name].append(ex_dict)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check ratio of present/absent KPs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp_range = [0, 10]\\n\",\n      \"\\t num_doc = 1075018\\n\",\n      \"\\t num_total_kp = 5042509\\n\",\n      \"\\t num_unique_kp = 1177502\\n\",\n      \"\\t ratio_unique_kp = 23.35%\\n\",\n      \"\\t num_present_kp = 1624703\\n\",\n      \"\\t ratio_present_kp = 32.22%\\n\",\n      \"\\t num_absent_kp = 3417806\\n\",\n      \"\\t ratio_absent_kp = 67.78%\\n\",\n      \"kp_range = [10, 20]\\n\",\n      \"\\t num_doc = 808194\\n\",\n      \"\\t num_total_kp = 11297261\\n\",\n      \"\\t num_unique_kp = 2128224\\n\",\n      \"\\t ratio_unique_kp = 18.84%\\n\",\n      \"\\t num_present_kp = 2225699\\n\",\n      \"\\t ratio_present_kp = 19.70%\\n\",\n      \"\\t num_absent_kp = 9071562\\n\",\n      \"\\t ratio_absent_kp = 80.30%\\n\",\n      \"kp_range = [20, 30]\\n\",\n      \"\\t num_doc = 476153\\n\",\n      \"\\t num_total_kp = 11417185\\n\",\n      \"\\t num_unique_kp = 2294995\\n\",\n      \"\\t ratio_unique_kp = 20.10%\\n\",\n      \"\\t num_present_kp = 2459860\\n\",\n      \"\\t ratio_present_kp = 21.55%\\n\",\n      \"\\t num_absent_kp = 8957325\\n\",\n      \"\\t ratio_absent_kp = 78.45%\\n\",\n      \"kp_range = [30, 40]\\n\",\n      \"\\t num_doc = 215045\\n\",\n      \"\\t num_total_kp = 7232686\\n\",\n      \"\\t num_unique_kp = 1580889\\n\",\n      \"\\t ratio_unique_kp = 21.86%\\n\",\n      \"\\t num_present_kp = 1669954\\n\",\n      \"\\t ratio_present_kp = 23.09%\\n\",\n      \"\\t num_absent_kp = 5562732\\n\",\n      \"\\t ratio_absent_kp = 76.91%\\n\",\n      \"kp_range = [40, 50]\\n\",\n      \"\\t num_doc = 69542\\n\",\n      \"\\t num_total_kp = 3033018\\n\",\n      \"\\t num_unique_kp = 572174\\n\",\n      \"\\t ratio_unique_kp = 18.86%\\n\",\n      \"\\t num_present_kp = 573525\\n\",\n      \"\\t ratio_present_kp = 18.91%\\n\",\n      \"\\t num_absent_kp = 2459493\\n\",\n      \"\\t ratio_absent_kp = 81.09%\\n\",\n      \"kp_range = [50, 60]\\n\",\n      \"\\t num_doc = 26385\\n\",\n      \"\\t num_total_kp = 1419833\\n\",\n      \"\\t num_unique_kp = 235052\\n\",\n      \"\\t ratio_unique_kp = 16.55%\\n\",\n      \"\\t num_present_kp = 212819\\n\",\n      \"\\t ratio_present_kp = 14.99%\\n\",\n      \"\\t num_absent_kp = 1207014\\n\",\n      \"\\t ratio_absent_kp = 85.01%\\n\",\n      \"kp_range = [60, 70]\\n\",\n      \"\\t num_doc = 13011\\n\",\n      \"\\t num_total_kp = 832478\\n\",\n      \"\\t num_unique_kp = 146629\\n\",\n      \"\\t ratio_unique_kp = 17.61%\\n\",\n      \"\\t num_present_kp = 113556\\n\",\n      \"\\t ratio_present_kp = 13.64%\\n\",\n      \"\\t num_absent_kp = 718922\\n\",\n      \"\\t ratio_absent_kp = 86.36%\\n\",\n      \"kp_range = [70, 80]\\n\",\n      \"\\t num_doc = 7256\\n\",\n      \"\\t num_total_kp = 537191\\n\",\n      \"\\t num_unique_kp = 105971\\n\",\n      \"\\t ratio_unique_kp = 19.73%\\n\",\n      \"\\t num_present_kp = 69464\\n\",\n      \"\\t ratio_present_kp = 12.93%\\n\",\n      \"\\t num_absent_kp = 467727\\n\",\n      \"\\t ratio_absent_kp = 87.07%\\n\",\n      \"kp_range = [80, 90]\\n\",\n      \"\\t num_doc = 4106\\n\",\n      \"\\t num_total_kp = 344253\\n\",\n      \"\\t num_unique_kp = 77924\\n\",\n      \"\\t ratio_unique_kp = 22.64%\\n\",\n      \"\\t num_present_kp = 43896\\n\",\n      \"\\t ratio_present_kp = 12.75%\\n\",\n      \"\\t num_absent_kp = 300357\\n\",\n      \"\\t ratio_absent_kp = 87.25%\\n\",\n      \"kp_range = [90, 100]\\n\",\n      \"\\t num_doc = 2358\\n\",\n      \"\\t num_total_kp = 221677\\n\",\n      \"\\t num_unique_kp = 58556\\n\",\n      \"\\t ratio_unique_kp = 26.42%\\n\",\n      \"\\t num_present_kp = 29940\\n\",\n      \"\\t ratio_present_kp = 13.51%\\n\",\n      \"\\t num_absent_kp = 191737\\n\",\n      \"\\t ratio_absent_kp = 86.49%\\n\",\n      \"kp_range = [100, 110]\\n\",\n      \"\\t num_doc = 1250\\n\",\n      \"\\t num_total_kp = 129846\\n\",\n      \"\\t num_unique_kp = 39733\\n\",\n      \"\\t ratio_unique_kp = 30.60%\\n\",\n      \"\\t num_present_kp = 17488\\n\",\n      \"\\t ratio_present_kp = 13.47%\\n\",\n      \"\\t num_absent_kp = 112358\\n\",\n      \"\\t ratio_absent_kp = 86.53%\\n\",\n      \"kp_range = [110, 120]\\n\",\n      \"\\t num_doc = 492\\n\",\n      \"\\t num_total_kp = 55893\\n\",\n      \"\\t num_unique_kp = 22093\\n\",\n      \"\\t ratio_unique_kp = 39.53%\\n\",\n      \"\\t num_present_kp = 7473\\n\",\n      \"\\t ratio_present_kp = 13.37%\\n\",\n      \"\\t num_absent_kp = 48420\\n\",\n      \"\\t ratio_absent_kp = 86.63%\\n\",\n      \"kp_range = [120, 130]\\n\",\n      \"\\t num_doc = 146\\n\",\n      \"\\t num_total_kp = 18013\\n\",\n      \"\\t num_unique_kp = 10305\\n\",\n      \"\\t ratio_unique_kp = 57.21%\\n\",\n      \"\\t num_present_kp = 2661\\n\",\n      \"\\t ratio_present_kp = 14.77%\\n\",\n      \"\\t num_absent_kp = 15352\\n\",\n      \"\\t ratio_absent_kp = 85.23%\\n\",\n      \"kp_range = [130, 140]\\n\",\n      \"\\t num_doc = 48\\n\",\n      \"\\t num_total_kp = 6441\\n\",\n      \"\\t num_unique_kp = 4896\\n\",\n      \"\\t ratio_unique_kp = 76.01%\\n\",\n      \"\\t num_present_kp = 1044\\n\",\n      \"\\t ratio_present_kp = 16.21%\\n\",\n      \"\\t num_absent_kp = 5397\\n\",\n      \"\\t ratio_absent_kp = 83.79%\\n\",\n      \"kp_range = [140, 150]\\n\",\n      \"\\t num_doc = 15\\n\",\n      \"\\t num_total_kp = 2164\\n\",\n      \"\\t num_unique_kp = 1808\\n\",\n      \"\\t ratio_unique_kp = 83.55%\\n\",\n      \"\\t num_present_kp = 230\\n\",\n      \"\\t ratio_present_kp = 10.63%\\n\",\n      \"\\t num_absent_kp = 1934\\n\",\n      \"\\t ratio_absent_kp = 89.37%\\n\",\n      \"kp_range = [150, 160]\\n\",\n      \"\\t num_doc = 8\\n\",\n      \"\\t num_total_kp = 1221\\n\",\n      \"\\t num_unique_kp = 1135\\n\",\n      \"\\t ratio_unique_kp = 92.96%\\n\",\n      \"\\t num_present_kp = 116\\n\",\n      \"\\t ratio_present_kp = 9.50%\\n\",\n      \"\\t num_absent_kp = 1105\\n\",\n      \"\\t ratio_absent_kp = 90.50%\\n\",\n      \"kp_range = [160, 170]\\n\",\n      \"\\t num_doc = 3\\n\",\n      \"\\t num_total_kp = 492\\n\",\n      \"\\t num_unique_kp = 491\\n\",\n      \"\\t ratio_unique_kp = 99.80%\\n\",\n      \"\\t num_present_kp = 31\\n\",\n      \"\\t ratio_present_kp = 6.30%\\n\",\n      \"\\t num_absent_kp = 461\\n\",\n      \"\\t ratio_absent_kp = 93.70%\\n\",\n      \"kp_range = [170, 180]\\n\",\n      \"\\t num_doc = 4\\n\",\n      \"\\t num_total_kp = 701\\n\",\n      \"\\t num_unique_kp = 665\\n\",\n      \"\\t ratio_unique_kp = 94.86%\\n\",\n      \"\\t num_present_kp = 62\\n\",\n      \"\\t ratio_present_kp = 8.84%\\n\",\n      \"\\t num_absent_kp = 639\\n\",\n      \"\\t ratio_absent_kp = 91.16%\\n\",\n      \"kp_range = [180, 190]\\n\",\n      \"\\t num_doc = 3\\n\",\n      \"\\t num_total_kp = 554\\n\",\n      \"\\t num_unique_kp = 539\\n\",\n      \"\\t ratio_unique_kp = 97.29%\\n\",\n      \"\\t num_present_kp = 36\\n\",\n      \"\\t ratio_present_kp = 6.50%\\n\",\n      \"\\t num_absent_kp = 518\\n\",\n      \"\\t ratio_absent_kp = 93.50%\\n\",\n      \"kp_range = [190, 200]\\n\",\n      \"\\t num_doc = 8\\n\",\n      \"\\t num_total_kp = 1557\\n\",\n      \"\\t num_unique_kp = 1109\\n\",\n      \"\\t ratio_unique_kp = 71.23%\\n\",\n      \"\\t num_present_kp = 44\\n\",\n      \"\\t ratio_present_kp = 2.83%\\n\",\n      \"\\t num_absent_kp = 1513\\n\",\n      \"\\t ratio_absent_kp = 97.17%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def if_present_phrase(src_str_tokens, phrase_str_tokens):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    :param src_str_tokens: a list of strings (words) of source text\\n\",\n    \"    :param phrase_str_tokens: a list of strings (words) of a phrase\\n\",\n    \"    :return:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    match_flag = False\\n\",\n    \"    match_pos_idx = -1\\n\",\n    \"    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\\n\",\n    \"        match_flag = True\\n\",\n    \"        # iterate each word in target, if one word does not match, set match=False and break\\n\",\n    \"        for seq_idx, seq_w in enumerate(phrase_str_tokens):\\n\",\n    \"            src_w = src_str_tokens[src_start_idx + seq_idx]\\n\",\n    \"            if src_w != seq_w:\\n\",\n    \"                match_flag = False\\n\",\n    \"                break\\n\",\n    \"        if match_flag:\\n\",\n    \"            match_pos_idx = src_start_idx\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"    return match_flag\\n\",\n    \"\\n\",\n    \"stat_dicts = []\\n\",\n    \"\\n\",\n    \"for start in range(0, 200, 10):\\n\",\n    \"    unique_kp_set = set()\\n\",\n    \"    num_total_kp, num_unique_kp, num_present_kp, num_absent_kp = 0, 0, 0, 0\\n\",\n    \"    exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) >= start and len(ex['keywords']) < start + 10]\\n\",\n    \"    for ex_id, ex in enumerate(exs):\\n\",\n    \"        for p in ex['keywords']:\\n\",\n    \"            unique_kp_set.add(p)\\n\",\n    \"            num_total_kp += 1\\n\",\n    \"            src_tokens = (ex['title'] + ' ' + ex['abstract']).lower().split()\\n\",\n    \"            tgt_tokens = p.lower().split()\\n\",\n    \"            if if_present_phrase(src_tokens, tgt_tokens):\\n\",\n    \"                num_present_kp += 1\\n\",\n    \"            else:\\n\",\n    \"                num_absent_kp += 1\\n\",\n    \"#         if ex_id > 1000:\\n\",\n    \"#             break\\n\",\n    \"          \\n\",\n    \"    stat = {\\n\",\n    \"        'kp_range': '[%d, %d]' % (start, start + 10),\\n\",\n    \"        'num_doc': len(exs),\\n\",\n    \"        'num_total_kp': num_total_kp,\\n\",\n    \"        'num_unique_kp': len(unique_kp_set),\\n\",\n    \"        'ratio_unique_kp': len(unique_kp_set) / num_total_kp,\\n\",\n    \"        'num_present_kp': num_present_kp,\\n\",\n    \"        'ratio_present_kp': num_present_kp / num_total_kp,\\n\",\n    \"        'num_absent_kp': num_absent_kp,\\n\",\n    \"        'ratio_absent_kp': num_absent_kp / num_total_kp,\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    stat_dicts.append(stat)\\n\",\n    \"    for k, v in stat.items():\\n\",\n    \"        if k == 'kp_range':\\n\",\n    \"            print(k, '=', v)\\n\",\n    \"        elif k.startswith('ratio'):\\n\",\n    \"            print('\\\\t', k, '=', '%.2f%%' % (v * 100.0))\\n\",\n    \"        else:\\n\",\n    \"            print('\\\\t', k, '=', v)\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None]\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kpstat_df = pd.DataFrame(stat_dicts, columns=list(stat_dicts[0].keys()))\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"fig, axes=plt.subplots(2, 2, figsize=(12, 9))\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"num_doc\\\", data=kpstat_df, ax=axes[0, 0])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_unique_kp\\\", data=kpstat_df, ax=axes[0, 1])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_present_kp\\\", data=kpstat_df, ax=axes[1, 0])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_absent_kp\\\", data=kpstat_df, ax=axes[1, 1])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp_range = [0, 5]\\n\",\n      \"\\t num_doc = 242700\\n\",\n      \"\\t num_total_kp = 845631\\n\",\n      \"\\t num_unique_kp = 318082\\n\",\n      \"\\t ratio_unique_kp = 37.61%\\n\",\n      \"\\t num_present_kp = 404802\\n\",\n      \"\\t ratio_present_kp = 47.87%\\n\",\n      \"\\t num_absent_kp = 440829\\n\",\n      \"\\t ratio_absent_kp = 52.13%\\n\",\n      \"kp_range = [5, 10]\\n\",\n      \"\\t num_doc = 248247\\n\",\n      \"\\t num_total_kp = 1423963\\n\",\n      \"\\t num_unique_kp = 467033\\n\",\n      \"\\t ratio_unique_kp = 32.80%\\n\",\n      \"\\t num_present_kp = 618408\\n\",\n      \"\\t ratio_present_kp = 43.43%\\n\",\n      \"\\t num_absent_kp = 805555\\n\",\n      \"\\t ratio_absent_kp = 56.57%\\n\",\n      \"kp_range = [10, 15]\\n\",\n      \"\\t num_doc = 9475\\n\",\n      \"\\t num_total_kp = 106882\\n\",\n      \"\\t num_unique_kp = 48461\\n\",\n      \"\\t ratio_unique_kp = 45.34%\\n\",\n      \"\\t num_present_kp = 39789\\n\",\n      \"\\t ratio_present_kp = 37.23%\\n\",\n      \"\\t num_absent_kp = 67093\\n\",\n      \"\\t ratio_absent_kp = 62.77%\\n\",\n      \"kp_range = [15, 20]\\n\",\n      \"\\t num_doc = 4179\\n\",\n      \"\\t num_total_kp = 71033\\n\",\n      \"\\t num_unique_kp = 12502\\n\",\n      \"\\t ratio_unique_kp = 17.60%\\n\",\n      \"\\t num_present_kp = 33997\\n\",\n      \"\\t ratio_present_kp = 47.86%\\n\",\n      \"\\t num_absent_kp = 37036\\n\",\n      \"\\t ratio_absent_kp = 52.14%\\n\",\n      \"kp_range = [20, 25]\\n\",\n      \"\\t num_doc = 3813\\n\",\n      \"\\t num_total_kp = 83462\\n\",\n      \"\\t num_unique_kp = 8837\\n\",\n      \"\\t ratio_unique_kp = 10.59%\\n\",\n      \"\\t num_present_kp = 41734\\n\",\n      \"\\t ratio_present_kp = 50.00%\\n\",\n      \"\\t num_absent_kp = 41728\\n\",\n      \"\\t ratio_absent_kp = 50.00%\\n\",\n      \"kp_range = [25, 30]\\n\",\n      \"\\t num_doc = 2799\\n\",\n      \"\\t num_total_kp = 75139\\n\",\n      \"\\t num_unique_kp = 7639\\n\",\n      \"\\t ratio_unique_kp = 10.17%\\n\",\n      \"\\t num_present_kp = 37382\\n\",\n      \"\\t ratio_present_kp = 49.75%\\n\",\n      \"\\t num_absent_kp = 37757\\n\",\n      \"\\t ratio_absent_kp = 50.25%\\n\",\n      \"kp_range = [30, 35]\\n\",\n      \"\\t num_doc = 1685\\n\",\n      \"\\t num_total_kp = 53393\\n\",\n      \"\\t num_unique_kp = 5449\\n\",\n      \"\\t ratio_unique_kp = 10.21%\\n\",\n      \"\\t num_present_kp = 26650\\n\",\n      \"\\t ratio_present_kp = 49.91%\\n\",\n      \"\\t num_absent_kp = 26743\\n\",\n      \"\\t ratio_absent_kp = 50.09%\\n\",\n      \"kp_range = [35, 40]\\n\",\n      \"\\t num_doc = 764\\n\",\n      \"\\t num_total_kp = 28040\\n\",\n      \"\\t num_unique_kp = 3992\\n\",\n      \"\\t ratio_unique_kp = 14.24%\\n\",\n      \"\\t num_present_kp = 13900\\n\",\n      \"\\t ratio_present_kp = 49.57%\\n\",\n      \"\\t num_absent_kp = 14140\\n\",\n      \"\\t ratio_absent_kp = 50.43%\\n\",\n      \"kp_range = [40, 45]\\n\",\n      \"\\t num_doc = 313\\n\",\n      \"\\t num_total_kp = 13028\\n\",\n      \"\\t num_unique_kp = 2397\\n\",\n      \"\\t ratio_unique_kp = 18.40%\\n\",\n      \"\\t num_present_kp = 6473\\n\",\n      \"\\t ratio_present_kp = 49.69%\\n\",\n      \"\\t num_absent_kp = 6555\\n\",\n      \"\\t ratio_absent_kp = 50.31%\\n\",\n      \"kp_range = [45, 50]\\n\",\n      \"\\t num_doc = 94\\n\",\n      \"\\t num_total_kp = 4378\\n\",\n      \"\\t num_unique_kp = 1258\\n\",\n      \"\\t ratio_unique_kp = 28.73%\\n\",\n      \"\\t num_present_kp = 2168\\n\",\n      \"\\t ratio_present_kp = 49.52%\\n\",\n      \"\\t num_absent_kp = 2210\\n\",\n      \"\\t ratio_absent_kp = 50.48%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stat_dicts = []\\n\",\n    \"step_size = 5\\n\",\n    \"\\n\",\n    \"for start in range(0, 50, step_size):\\n\",\n    \"    unique_kp_set = set()\\n\",\n    \"    num_total_kp, num_unique_kp, num_present_kp, num_absent_kp = 0, 0, 0, 0\\n\",\n    \"    exs = [ex for ex in dataset_examples[\\\"kp20k\\\"] if len(ex['keywords']) >= start and len(ex['keywords']) < start + step_size]\\n\",\n    \"    for ex_id, ex in enumerate(exs):\\n\",\n    \"        for p in ex['keywords']:\\n\",\n    \"            unique_kp_set.add(p)\\n\",\n    \"            num_total_kp += 1\\n\",\n    \"            src_tokens = (ex['title'] + ' ' + ex['abstract']).lower().split()\\n\",\n    \"            tgt_tokens = p.lower().split()\\n\",\n    \"            if if_present_phrase(src_tokens, tgt_tokens):\\n\",\n    \"                num_present_kp += 1\\n\",\n    \"            else:\\n\",\n    \"                num_absent_kp += 1\\n\",\n    \"#         if ex_id > 1000:\\n\",\n    \"#             break\\n\",\n    \"          \\n\",\n    \"    stat = {\\n\",\n    \"        'kp_range': '[%d, %d]' % (start, start + step_size),\\n\",\n    \"        'num_doc': len(exs),\\n\",\n    \"        'num_total_kp': num_total_kp,\\n\",\n    \"        'num_unique_kp': len(unique_kp_set),\\n\",\n    \"        'ratio_unique_kp': len(unique_kp_set) / num_total_kp,\\n\",\n    \"        'num_present_kp': num_present_kp,\\n\",\n    \"        'ratio_present_kp': num_present_kp / num_total_kp,\\n\",\n    \"        'num_absent_kp': num_absent_kp,\\n\",\n    \"        'ratio_absent_kp': num_absent_kp / num_total_kp,\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    stat_dicts.append(stat)\\n\",\n    \"    for k, v in stat.items():\\n\",\n    \"        if k == 'kp_range':\\n\",\n    \"            print(k, '=', v)\\n\",\n    \"        elif k.startswith('ratio'):\\n\",\n    \"            print('\\\\t', k, '=', '%.2f%%' % (v * 100.0))\\n\",\n    \"        else:\\n\",\n    \"            print('\\\\t', k, '=', v)\\n\",\n    \"        \\n\",\n    \"kpstat_df = pd.DataFrame(stat_dicts, columns=list(stat_dicts[0].keys()))\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"fig, axes=plt.subplots(2, 2, figsize=(12, 9))\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"num_doc\\\", data=kpstat_df, ax=axes[0, 0])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_unique_kp\\\", data=kpstat_df, ax=axes[0, 1])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_present_kp\\\", data=kpstat_df, ax=axes[1, 0])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_absent_kp\\\", data=kpstat_df, ax=axes[1, 1])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Manually examine examples containing too many phrases (e.g. >20, **num_noisy_examples= 745916**)\\n\",\n    \" - All phrases are lowercased.\\n\",\n    \" - All hyphens/parentheses are removed. But words in parentheses are preserved.\\n\",\n    \" - Found phrases in French (imagerie medicale, ondelette), Spanish (analisis datos) etc.\\n\",\n    \" - It appears that MagKP contains a lot of data from IEEE which contain a lot of errors. (**num_contain_long_kps=514582**)\\n\",\n    \"     - 4 groups: IEEE Keywords, INSPEC: Controlled Indexing, INSPEC: Non-Controlled Indexing, and author keywords\\n\",\n    \"     - it often contains some wrong and very long phrases, concatenation of multiple phrases. This can be easily filtered by phrase length.\\n\",\n    \"         e.g. https://ieeexplore.ieee.org/abstract/document/4101114/keywords#keywords, \\\"normalized random number area efficient architecture large scale implementation biological neural networks plausible neural networks spiking neural networks reconfigurable hardware multiplier less hardware architecture single fpga synaptic multiplication and gate\\\"\\n\",\n    \"     - Oftentimes it doesn't completely match the record on IEEE website. Say \\\"membrane potential\\\" and \\\"integrate and fire\\\" appear in the abstract, but not in any of keyword fields.\\n\",\n    \" - Also some papers do not have keywords originally. Very likely they are automatically annotated? (**num_no_long_kps=231334**)\\n\",\n    \"     - say 'id': 'dbe8cad4-3c1f-4414-95a0-338c7cb3184d', https://www.sciencedirect.com/science/article/pii/S1077201404000452\\n\",\n    \"     - 'id': 'dde7b89e-5251-4991-9e3a-7c7191a20240', https://link.springer.com/chapter/10.1007/11925941_1\\n\",\n    \"         \\\"These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.\\\"\\n\",\n    \" - Some have original keywords, but still many are new.\\n\",\n    \"     - 'id': 'dc7b584d-66f5-4ae5-bc21-e38715fd7403', https://www.sciencedirect.com/science/article/pii/0045790694900175\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"745916\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) > 20]\\n\",\n    \"exs = sorted(exs, key=lambda x: len(ex['keywords']))\\n\",\n    \"print(len(exs))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'id': 'dbe06305-ce86-46da-b693-7ae062acc315',\\n\",\n       \" 'title': 'A novel adaptive routing scheme for the QoS-based multimedia services in mobile ad-hoc networks',\\n\",\n       \" 'abstract': 'A mobile ad-hoc network is composed of only mobile nodes, which are distributed dynamically, without any wired backbone or centralized entities. Since the existing works on ad-hoc routing protocol are mostly biased toward a military application, we need a new routing scheme for the support of multimedia services in mobile ad-hoc networks. Therefore, we propose a novel scheme that can support a variety of traffic attributes and can be applicable to high-speed and multimedia data services in mobile ad-hoc networks by using adaptive transmission power level. As a result of simulation, the proposed scheme has better performance than conventional method, which is performed with uniform transmission power level, in view of route query delay time',\\n\",\n       \" 'keywords': ['land mobile radio quality of service multimedia communication telecommunication network routing adaptive systems telecommunication traffic data communication transport protocols delays',\\n\",\n       \"  'routing intelligent networks ad hoc networks multimedia systems local area networks quality of service spine network topology mobile radio mobility management telecommunication traffic',\\n\",\n       \"  'data communication',\\n\",\n       \"  'transport protocols',\\n\",\n       \"  'telecommunication traffic',\\n\",\n       \"  'telecommunication network routing',\\n\",\n       \"  'land mobile radio',\\n\",\n       \"  'adaptive systems',\\n\",\n       \"  'multimedia data',\\n\",\n       \"  'multimedia communication',\\n\",\n       \"  'mobile ad hoc network',\\n\",\n       \"  'distribution dynamics',\\n\",\n       \"  'adaptive routing',\\n\",\n       \"  'mobile node',\\n\",\n       \"  'delay time',\\n\",\n       \"  'quality of service',\\n\",\n       \"  'ad hoc routing',\\n\",\n       \"  'route query delay time qos based multimedia services mobile ad hoc networks adaptive routing mobile nodes ad hoc routing protocol military application multimedia services traffic attributes high speed data services multimedia data services adaptive transmission power level simulation performance',\\n\",\n       \"  'multimedia services',\\n\",\n       \"  'high speed',\\n\",\n       \"  'delays'],\\n\",\n       \" 'fulltext': ''}\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"exs[50]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check if all the phrases are lowercase? yes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"num_pure_lower_kps= 41605964\\n\",\n      \"num_non_lower_kps= 0\\n\",\n      \"num_hyphen_kps= 0\\n\",\n      \"num_bracket_kps= 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_pure_lower_kps, num_non_lower_kps = 0, 0\\n\",\n    \"num_hyphen_kps, num_bracket_kps = 0, 0\\n\",\n    \"for ex in dataset_examples[\\\"magkp\\\"]:\\n\",\n    \"    for kp in ex['keywords']:\\n\",\n    \"        if kp.lower() == kp:\\n\",\n    \"            num_pure_lower_kps += 1\\n\",\n    \"        else:\\n\",\n    \"            num_non_lower_kps += 1\\n\",\n    \"        if '-' in kp:\\n\",\n    \"            num_hyphen_kps += 1\\n\",\n    \"        if '(' in kp or ')' in kp:\\n\",\n    \"            num_bracket_kps += 1\\n\",\n    \"            \\n\",\n    \"print('num_pure_lower_kps=', num_pure_lower_kps)\\n\",\n    \"print('num_non_lower_kps=', num_non_lower_kps)\\n\",\n    \"\\n\",\n    \"print('num_hyphen_kps=', num_hyphen_kps)\\n\",\n    \"print('num_bracket_kps=', num_bracket_kps)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check if all noisy MagKP examples are from IEEE? No, likely 68.99%\\n\",\n    \"\\n\",\n    \"Assume all IEEE data examples contain certain number of very long phrases (>=10 words, due to the mistaken concatenation of multiple phrases).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"num_noisy_examples= 745916\\n\",\n      \"num_contain_long_kps= 514582\\n\",\n      \"num_no_long_kps= 231334\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) > 20]\\n\",\n    \"exs = sorted(exs, key=lambda x: len(ex['keywords']))\\n\",\n    \"no_long_exs = []\\n\",\n    \"\\n\",\n    \"print('num_noisy_examples=', len(exs))\\n\",\n    \"\\n\",\n    \"num_contain_long_kps, num_no_long_kps = 0, 0\\n\",\n    \"\\n\",\n    \"for ex in exs:\\n\",\n    \"    found_long_kp = False\\n\",\n    \"    for kp in ex['keywords']:\\n\",\n    \"        if len(kp.split()) >= 10:\\n\",\n    \"            found_long_kp = True\\n\",\n    \"            break\\n\",\n    \"    if found_long_kp:\\n\",\n    \"        num_contain_long_kps += 1\\n\",\n    \"    else:\\n\",\n    \"        num_no_long_kps += 1\\n\",\n    \"        no_long_exs.append(ex)\\n\",\n    \"        \\n\",\n    \"print('num_contain_long_kps=', num_contain_long_kps)\\n\",\n    \"print('num_no_long_kps=', num_no_long_kps)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"no_long_exs[105500]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count #kp per document\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-21T21:49:26.929171Z\",\n     \"start_time\": \"2020-09-21T21:49:07.863773Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DescribeResult(nobs=2699094, minmax=(1, 438), mean=15.414788814320657, variance=168.752782332351, skewness=1.8635894274050995, kurtosis=7.294107031214651)\\n\",\n      \"Percentile@0 = 1.000000\\n\",\n      \"Percentile@1 = 1.000000\\n\",\n      \"Percentile@2 = 1.000000\\n\",\n      \"Percentile@3 = 1.000000\\n\",\n      \"Percentile@4 = 1.000000\\n\",\n      \"Percentile@5 = 1.000000\\n\",\n      \"Percentile@6 = 2.000000\\n\",\n      \"Percentile@7 = 2.000000\\n\",\n      \"Percentile@8 = 2.000000\\n\",\n      \"Percentile@9 = 2.000000\\n\",\n      \"Percentile@10 = 3.000000\\n\",\n      \"Percentile@11 = 3.000000\\n\",\n      \"Percentile@12 = 3.000000\\n\",\n      \"Percentile@13 = 3.000000\\n\",\n      \"Percentile@14 = 3.000000\\n\",\n      \"Percentile@15 = 4.000000\\n\",\n      \"Percentile@16 = 4.000000\\n\",\n      \"Percentile@17 = 4.000000\\n\",\n      \"Percentile@18 = 4.000000\\n\",\n      \"Percentile@19 = 4.000000\\n\",\n      \"Percentile@20 = 5.000000\\n\",\n      \"Percentile@21 = 5.000000\\n\",\n      \"Percentile@22 = 5.000000\\n\",\n      \"Percentile@23 = 5.000000\\n\",\n      \"Percentile@24 = 5.000000\\n\",\n      \"Percentile@25 = 6.000000\\n\",\n      \"Percentile@26 = 6.000000\\n\",\n      \"Percentile@27 = 6.000000\\n\",\n      \"Percentile@28 = 6.000000\\n\",\n      \"Percentile@29 = 7.000000\\n\",\n      \"Percentile@30 = 7.000000\\n\",\n      \"Percentile@31 = 7.000000\\n\",\n      \"Percentile@32 = 7.000000\\n\",\n      \"Percentile@33 = 8.000000\\n\",\n      \"Percentile@34 = 8.000000\\n\",\n      \"Percentile@35 = 8.000000\\n\",\n      \"Percentile@36 = 8.000000\\n\",\n      \"Percentile@37 = 9.000000\\n\",\n      \"Percentile@38 = 9.000000\\n\",\n      \"Percentile@39 = 9.000000\\n\",\n      \"Percentile@40 = 10.000000\\n\",\n      \"Percentile@41 = 10.000000\\n\",\n      \"Percentile@42 = 10.000000\\n\",\n      \"Percentile@43 = 10.000000\\n\",\n      \"Percentile@44 = 11.000000\\n\",\n      \"Percentile@45 = 11.000000\\n\",\n      \"Percentile@46 = 11.000000\\n\",\n      \"Percentile@47 = 11.000000\\n\",\n      \"Percentile@48 = 12.000000\\n\",\n      \"Percentile@49 = 12.000000\\n\",\n      \"Percentile@50 = 12.000000\\n\",\n      \"Percentile@51 = 12.000000\\n\",\n      \"Percentile@52 = 13.000000\\n\",\n      \"Percentile@53 = 13.000000\\n\",\n      \"Percentile@54 = 13.000000\\n\",\n      \"Percentile@55 = 14.000000\\n\",\n      \"Percentile@56 = 14.000000\\n\",\n      \"Percentile@57 = 14.000000\\n\",\n      \"Percentile@58 = 15.000000\\n\",\n      \"Percentile@59 = 15.000000\\n\",\n      \"Percentile@60 = 16.000000\\n\",\n      \"Percentile@61 = 16.000000\\n\",\n      \"Percentile@62 = 16.000000\\n\",\n      \"Percentile@63 = 17.000000\\n\",\n      \"Percentile@64 = 17.000000\\n\",\n      \"Percentile@65 = 18.000000\\n\",\n      \"Percentile@66 = 18.000000\\n\",\n      \"Percentile@67 = 18.000000\\n\",\n      \"Percentile@68 = 19.000000\\n\",\n      \"Percentile@69 = 19.000000\\n\",\n      \"Percentile@70 = 20.000000\\n\",\n      \"Percentile@71 = 20.000000\\n\",\n      \"Percentile@72 = 20.000000\\n\",\n      \"Percentile@73 = 21.000000\\n\",\n      \"Percentile@74 = 21.000000\\n\",\n      \"Percentile@75 = 22.000000\\n\",\n      \"Percentile@76 = 22.000000\\n\",\n      \"Percentile@77 = 23.000000\\n\",\n      \"Percentile@78 = 23.000000\\n\",\n      \"Percentile@79 = 24.000000\\n\",\n      \"Percentile@80 = 25.000000\\n\",\n      \"Percentile@81 = 25.000000\\n\",\n      \"Percentile@82 = 26.000000\\n\",\n      \"Percentile@83 = 26.000000\\n\",\n      \"Percentile@84 = 27.000000\\n\",\n      \"Percentile@85 = 28.000000\\n\",\n      \"Percentile@86 = 28.000000\\n\",\n      \"Percentile@87 = 29.000000\\n\",\n      \"Percentile@88 = 30.000000\\n\",\n      \"Percentile@89 = 31.000000\\n\",\n      \"Percentile@90 = 32.000000\\n\",\n      \"Percentile@91 = 33.000000\\n\",\n      \"Percentile@92 = 34.000000\\n\",\n      \"Percentile@93 = 35.000000\\n\",\n      \"Percentile@94 = 37.000000\\n\",\n      \"Percentile@95 = 39.000000\\n\",\n      \"Percentile@96 = 41.000000\\n\",\n      \"Percentile@97 = 44.000000\\n\",\n      \"Percentile@98 = 50.000000\\n\",\n      \"Percentile@99 = 61.000000\\n\",\n      \"Percentile@100 = 438.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tgt_nums = [len(ex['keywords']) for ex in dataset_examples[\\\"magkp\\\"]]\\n\",\n    \"data = tgt_nums\\n\",\n    \"print(scipy.stats.describe(data))\\n\",\n    \"\\n\",\n    \"for p in np.linspace(0, 100, 101):\\n\",\n    \"    percentile = np.percentile(data, p, interpolation='lower')\\n\",\n    \"    print('Percentile@%.0f = %.6f' % (p, percentile))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Histogram of #(kp per document) < 61\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T00:01:00.862762Z\",\n     \"start_time\": \"2020-09-22T00:01:00.344346Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\\n\",\n      \"  warnings.warn(msg, FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n < 61]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=60, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Histogram of #(kp per document) < 11\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:58:02.104309Z\",\n     \"start_time\": \"2020-09-22T03:58:01.792437Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 10)')\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 10]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=10, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 10)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n>=20 and n <= 100]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=80, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T05:18:43.851712Z\",\n     \"start_time\": \"2020-08-12T05:18:43.465149Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#     sns.distplot(np.asarray(tgt_nums, dtype=int), bins=15, color=\\\"r\\\", kde=False, rug=False);\\n\",\n    \"    \\n\",\n    \"    # Plot a simple histogram with binsize determined automatically\\n\",\n    \"#     sns.distplot(tgt_nums, kde=False, color=\\\"b\\\", ax=ax)\\n\",\n    \"\\n\",\n    \"#     # Plot a kernel density estimate and rug plot\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, rug=True, color=\\\"r\\\")\\n\",\n    \"\\n\",\n    \"#     # Plot a filled kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, color=\\\"g\\\", kde_kws={\\\"shade\\\": True})\\n\",\n    \"\\n\",\n    \"#     # Plot a histogram and kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=True, color=\\\"m\\\", ax=ax)\\n\",\n    \"    \\n\",\n    \"# sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"skyblue\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"teal\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"sns.distplot(tgt_nums[\\\"magkp\\\"] , color=\\\"teal\\\", label=\\\"MagKP\\\", bins=100, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"inspec\\\"] , color=\\\"red\\\", label=\\\"Inspec\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"krapivin\\\"] , color=\\\"olive\\\", label=\\\"Krapivin\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"nus\\\"] , color=\\\"gold\\\", label=\\\"NUS\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"semeval\\\"] , color=\\\"teal\\\", label=\\\"Semeval\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"\\n\",\n    \"ax.set(xlabel='Number of keyphrases in doc', ylabel='Number of documents')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count unique phrases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:52:33.765262Z\",\n     \"start_time\": \"2020-08-12T06:52:33.759476Z\"\n    }\n   },\n   \"source\": [\n    \"##### only count documents that #(kp)>10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:08:07.865368Z\",\n     \"start_time\": \"2020-08-12T05:32:53.686432Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=2699094\\n\",\n      \"#(KP)=29690034\\n\",\n      \"#(unique KP)=11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"do_preprocess = True\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"for ex_dict in dataset_tgt_dict['magkp']:\\n\",\n    \"    title = json_dict['title']\\n\",\n    \"    abstract = json_dict['abstract']\\n\",\n    \"    fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"    keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"    if isinstance(keywords, str):\\n\",\n    \"        keywords = keywords.split(';')\\n\",\n    \"        json_dict['keywords'] = keywords\\n\",\n    \"\\n\",\n    \"    if len(keywords) > 10:\\n\",\n    \"        num_data += 1\\n\",\n    \"        for keyword in keywords:\\n\",\n    \"            num_kp += 1\\n\",\n    \"            if do_preprocess:\\n\",\n    \"                tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                keyword = '_'.join(tokens)\\n\",\n    \"\\n\",\n    \"            unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#####  count all documents #(kp)>0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:42:02.179127Z\",\n     \"start_time\": \"2020-08-12T16:40:39.391069Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=2699094\\n\",\n      \"#(KP)=41605964\\n\",\n      \"#(unique KP)=6880853\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'magkp'\\n\",\n    \"do_preprocess = False\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"kp_len_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"        if len(keywords) > 0:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = ' '.join(tokens)\\n\",\n    \"#                 print(keyword)\\n\",\n    \"                \\n\",\n    \"                tokens = [t for t in keyword.split()]\\n\",\n    \"                kp_len_counter[len(tokens)] = kp_len_counter[len(tokens)] + 1\\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T05:11:24.875312Z\",\n     \"start_time\": \"2020-08-12T05:11:24.111497Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7ff5c4604590>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_kp_freqs = [v for k,v in unique_kp_counter.items() if v > 5000]\\n\",\n    \"sns.distplot(tmp_kp_freqs, color=\\\"teal\\\", \\n\",\n    \"             title=\\\"Frequency of unique phrases\\\", label=\\\"MagKP\\\",\\n\",\n    \"             bins=50, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  KP length distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:42:10.332438Z\",\n     \"start_time\": \"2020-08-12T16:42:08.442570Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#kp_len=1, freq=7813072, accum/total=18.78%\\n\",\n      \"#kp_len=2, freq=21476033, accum/total=70.40%\\n\",\n      \"#kp_len=3, freq=6337508, accum/total=85.63%\\n\",\n      \"#kp_len=4, freq=1705544, accum/total=89.73%\\n\",\n      \"#kp_len=5, freq=502647, accum/total=90.94%\\n\",\n      \"#kp_len=6, freq=281022, accum/total=91.61%\\n\",\n      \"#kp_len=7, freq=217690, accum/total=92.13%\\n\",\n      \"#kp_len=8, freq=230406, accum/total=92.69%\\n\",\n      \"#kp_len=9, freq=226450, accum/total=93.23%\\n\",\n      \"#kp_len=10, freq=226740, accum/total=93.78%\\n\",\n      \"#kp_len=11, freq=216440, accum/total=94.30%\\n\",\n      \"#kp_len=12, freq=195025, accum/total=94.77%\\n\",\n      \"#kp_len=13, freq=171045, accum/total=95.18%\\n\",\n      \"#kp_len=14, freq=154596, accum/total=95.55%\\n\",\n      \"#kp_len=15, freq=146705, accum/total=95.90%\\n\",\n      \"#kp_len=16, freq=150455, accum/total=96.26%\\n\",\n      \"#kp_len=17, freq=159578, accum/total=96.65%\\n\",\n      \"#kp_len=18, freq=164405, accum/total=97.04%\\n\",\n      \"#kp_len=19, freq=158950, accum/total=97.42%\\n\",\n      \"#kp_len=20, freq=142297, accum/total=97.77%\\n\",\n      \"#kp_len=21, freq=119340, accum/total=98.05%\\n\",\n      \"#kp_len=22, freq=95724, accum/total=98.28%\\n\",\n      \"#kp_len=23, freq=75454, accum/total=98.46%\\n\",\n      \"#kp_len=24, freq=60110, accum/total=98.61%\\n\",\n      \"#kp_len=25, freq=49592, accum/total=98.73%\\n\",\n      \"#kp_len=26, freq=43438, accum/total=98.83%\\n\",\n      \"#kp_len=27, freq=39221, accum/total=98.93%\\n\",\n      \"#kp_len=28, freq=35621, accum/total=99.01%\\n\",\n      \"#kp_len=29, freq=32786, accum/total=99.09%\\n\",\n      \"#kp_len=30, freq=30577, accum/total=99.16%\\n\",\n      \"#kp_len=31, freq=28432, accum/total=99.23%\\n\",\n      \"#kp_len=32, freq=25857, accum/total=99.30%\\n\",\n      \"#kp_len=33, freq=24294, accum/total=99.35%\\n\",\n      \"#kp_len=34, freq=22616, accum/total=99.41%\\n\",\n      \"#kp_len=35, freq=21041, accum/total=99.46%\\n\",\n      \"#kp_len=36, freq=19390, accum/total=99.51%\\n\",\n      \"#kp_len=37, freq=17688, accum/total=99.55%\\n\",\n      \"#kp_len=38, freq=16803, accum/total=99.59%\\n\",\n      \"#kp_len=39, freq=15122, accum/total=99.62%\\n\",\n      \"#kp_len=40, freq=13949, accum/total=99.66%\\n\",\n      \"#kp_len=41, freq=12812, accum/total=99.69%\\n\",\n      \"#kp_len=42, freq=11536, accum/total=99.72%\\n\",\n      \"#kp_len=43, freq=10867, accum/total=99.74%\\n\",\n      \"#kp_len=44, freq=10065, accum/total=99.77%\\n\",\n      \"#kp_len=45, freq=8981, accum/total=99.79%\\n\",\n      \"#kp_len=46, freq=8204, accum/total=99.81%\\n\",\n      \"#kp_len=47, freq=7637, accum/total=99.83%\\n\",\n      \"#kp_len=48, freq=6935, accum/total=99.84%\\n\",\n      \"#kp_len=49, freq=6330, accum/total=99.86%\\n\",\n      \"#kp_len=50, freq=5695, accum/total=99.87%\\n\",\n      \"#kp_len=51, freq=5179, accum/total=99.88%\\n\",\n      \"#kp_len=52, freq=4734, accum/total=99.90%\\n\",\n      \"#kp_len=53, freq=4257, accum/total=99.91%\\n\",\n      \"#kp_len=54, freq=3847, accum/total=99.92%\\n\",\n      \"#kp_len=55, freq=3488, accum/total=99.92%\\n\",\n      \"#kp_len=56, freq=3413, accum/total=99.93%\\n\",\n      \"#kp_len=57, freq=2797, accum/total=99.94%\\n\",\n      \"#kp_len=58, freq=2479, accum/total=99.94%\\n\",\n      \"#kp_len=59, freq=2368, accum/total=99.95%\\n\",\n      \"#kp_len=60, freq=2232, accum/total=99.96%\\n\",\n      \"#kp_len=61, freq=1869, accum/total=99.96%\\n\",\n      \"#kp_len=62, freq=1731, accum/total=99.96%\\n\",\n      \"#kp_len=63, freq=1603, accum/total=99.97%\\n\",\n      \"#kp_len=64, freq=1465, accum/total=99.97%\\n\",\n      \"#kp_len=65, freq=1192, accum/total=99.97%\\n\",\n      \"#kp_len=66, freq=1069, accum/total=99.98%\\n\",\n      \"#kp_len=67, freq=1061, accum/total=99.98%\\n\",\n      \"#kp_len=68, freq=905, accum/total=99.98%\\n\",\n      \"#kp_len=69, freq=778, accum/total=99.98%\\n\",\n      \"#kp_len=70, freq=742, accum/total=99.99%\\n\",\n      \"#kp_len=71, freq=621, accum/total=99.99%\\n\",\n      \"#kp_len=72, freq=587, accum/total=99.99%\\n\",\n      \"#kp_len=73, freq=522, accum/total=99.99%\\n\",\n      \"#kp_len=74, freq=435, accum/total=99.99%\\n\",\n      \"#kp_len=75, freq=423, accum/total=99.99%\\n\",\n      \"#kp_len=76, freq=349, accum/total=99.99%\\n\",\n      \"#kp_len=77, freq=339, accum/total=99.99%\\n\",\n      \"#kp_len=78, freq=314, accum/total=99.99%\\n\",\n      \"#kp_len=79, freq=276, accum/total=99.99%\\n\",\n      \"#kp_len=80, freq=242, accum/total=100.00%\\n\",\n      \"#kp_len=81, freq=199, accum/total=100.00%\\n\",\n      \"#kp_len=82, freq=217, accum/total=100.00%\\n\",\n      \"#kp_len=83, freq=168, accum/total=100.00%\\n\",\n      \"#kp_len=84, freq=148, accum/total=100.00%\\n\",\n      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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(16, 12))\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"kp_lens = sorted([(kp_len, freq) for kp_len, freq in kp_len_counter.items()], key=lambda k:k[0])\\n\",\n    \"\\n\",\n    \"accum_kp_count = 0\\n\",\n    \"total_kp_count = sum(freq for _, freq in kp_lens)\\n\",\n    \"for kp_len, freq in kp_lens:\\n\",\n    \"    accum_kp_count += freq\\n\",\n    \"    print('#kp_len=%d, freq=%d, accum/total=%.2f%%' % (kp_len, freq, accum_kp_count / total_kp_count * 100))\\n\",\n    \"    \\n\",\n    \"print(len(kp_lens))\\n\",\n    \"kp_lens_df = pd.DataFrame(kp_lens, columns=['#kp_len', 'freq'])\\n\",\n    \"ax = sns.barplot(x=\\\"#kp_len\\\", y=\\\"freq\\\", data=kp_lens_df)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/split_magkp.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T02:05:01.616539Z\",\n     \"start_time\": \"2020-09-22T02:04:24.631467Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import scipy\\n\",\n    \"\\n\",\n    \"from nltk.stem.porter import PorterStemmer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T02:28:17.985909Z\",\n     \"start_time\": \"2020-09-22T02:28:17.982061Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def normalize_title_str(title):\\n\",\n    \"    title = title.lower()\\n\",\n    \"    title = re.sub(r'\\\\W', ' ', title)\\n\",\n    \"    tokens = title.split()\\n\",\n    \"    return '_'.join(tokens)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load existing scientific datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:06:22.112940Z\",\n     \"start_time\": \"2020-09-22T03:06:14.589528Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"File not found, skip /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/inspec/inspec_train.json\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/inspec/inspec_valid.json\\n\",\n      \"Found 1500 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/inspec/inspec_test.json\\n\",\n      \"Found 500 data points\\n\",\n      \"File not found, skip /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/krapivin/krapivin_train.json\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/krapivin/krapivin_valid.json\\n\",\n      \"Found 1844 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/krapivin/krapivin_test.json\\n\",\n      \"Found 460 data points\\n\",\n      \"File not found, skip /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/nus/nus_train.json\\n\",\n      \"File not found, skip /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/nus/nus_valid.json\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/nus/nus_test.json\\n\",\n      \"Found 211 data points\\n\",\n      \"File not found, skip /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/semeval/semeval_train.json\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/semeval/semeval_valid.json\\n\",\n      \"Found 144 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/semeval/semeval_test.json\\n\",\n      \"Found 100 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_train.json\\n\",\n      \"Found 514154 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_valid.json\\n\",\n      \"Found 19992 data points\\n\",\n      \"Loading from /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/kp20k/kp20k_test.json\\n\",\n      \"Found 19987 data points\\n\",\n      \"loaded 558892 docs\\n\",\n      \"non-dup 557887 docs\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"datasets_to_avoid = ['inspec', 'krapivin', 'nus', 'semeval', 'kp20k']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"\\n\",\n    \"titles_to_avoid = set()\\n\",\n    \"num_doc = 0\\n\",\n    \"for dataset_name in datasets_to_avoid:\\n\",\n    \"    for split in ['train', 'valid', 'test']:\\n\",\n    \"        num_doc_split = 0\\n\",\n    \"        input_json_path = os.path.join(json_base_dir, dataset_name, '%s_%s.json' % (dataset_name, split))\\n\",\n    \"        if os.path.exists(input_json_path):\\n\",\n    \"            print('Loading from %s' % input_json_path)\\n\",\n    \"        else:\\n\",\n    \"            print('File not found, skip %s' % input_json_path)\\n\",\n    \"            continue\\n\",\n    \"\\n\",\n    \"        with open(input_json_path, 'r') as input_json:\\n\",\n    \"            for json_line in input_json:\\n\",\n    \"                json_dict = json.loads(json_line)\\n\",\n    \"                title = json_dict['title']\\n\",\n    \"                \\n\",\n    \"                keywords = json_dict['keywords']\\n\",\n    \"                if isinstance(keywords, str):\\n\",\n    \"                    keywords = keywords.split(';')\\n\",\n    \"                assert len(keywords) > 0\\n\",\n    \"                \\n\",\n    \"                title = normalize_title_str(title)\\n\",\n    \"                titles_to_avoid.add(title)\\n\",\n    \"                num_doc += 1\\n\",\n    \"                num_doc_split += 1\\n\",\n    \"        print('Found %d data points' % num_doc_split)\\n\",\n    \"\\n\",\n    \"print('loaded %d docs' % num_doc)\\n\",\n    \"print('non-dup %d docs' % len(titles_to_avoid))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load MagKP and only retain non-duplicate data points\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T04:06:35.340973Z\",\n     \"start_time\": \"2020-09-22T04:05:51.575612Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loaded 2699094 docs\\n\",\n      \"non-dup 2686643 docs\\n\",\n      \"non-dup 518908 docs that #(kp) in [3, 6]\\n\",\n      \"non-dup 1512921 docs that #(kp)>10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"titles_to_jsonstr = {}\\n\",\n    \"titles_to_jsonstr_lessnoisy = {} # \\n\",\n    \"titles_to_jsonstr_noisy = {} # \\n\",\n    \"num_doc = 0\\n\",\n    \"\\n\",\n    \"input_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_train.json')\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        title = normalize_title_str(title)\\n\",\n    \"\\n\",\n    \"        if title not in titles_to_avoid and title not in titles_to_jsonstr:\\n\",\n    \"            titles_to_jsonstr[title] = json_line\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"            assert len(keywords) > 0\\n\",\n    \"            if len(keywords) >= 3 and len(keywords) <= 6:\\n\",\n    \"                titles_to_jsonstr_lessnoisy[title] = json_line\\n\",\n    \"            elif len(keywords) > 10:\\n\",\n    \"                titles_to_jsonstr_noisy[title] = json_line\\n\",\n    \"                \\n\",\n    \"        num_doc += 1\\n\",\n    \"\\n\",\n    \"print('loaded %d docs' % num_doc)\\n\",\n    \"print('non-dup %d docs' % len(titles_to_jsonstr))\\n\",\n    \"print('non-dup %d docs that #(kp) in [3, 6]' % len(titles_to_jsonstr_lessnoisy))\\n\",\n    \"print('non-dup %d docs that #(kp)>10' % len(titles_to_jsonstr_noisy))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Dump jsons to disk\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T05:58:49.736943Z\",\n     \"start_time\": \"2020-09-22T05:58:47.743403Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ERROR! Session/line number was not unique in database. History logging moved to new session 190\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_LN_train.json')\\n\",\n    \"\\n\",\n    \"with open(output_json_path, 'w') as output_json:\\n\",\n    \"    for json_line in titles_to_jsonstr_lessnoisy.values():\\n\",\n    \"        output_json.write(json_line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T06:18:21.847211Z\",\n     \"start_time\": \"2020-09-22T06:18:18.817011Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"output_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_Nsmall_train.json')\\n\",\n    \"\\n\",\n    \"with open(output_json_path, 'w') as output_json:\\n\",\n    \"    for line_id, json_line in enumerate(titles_to_jsonstr_noisy.values()):\\n\",\n    \"        if line_id >= len(titles_to_jsonstr_lessnoisy):\\n\",\n    \"            break\\n\",\n    \"        output_json.write(json_line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T06:04:06.187119Z\",\n     \"start_time\": \"2020-09-22T06:03:58.178311Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"ERROR! Session/line number was not unique in database. History logging moved to new session 193\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_Nlarge_train.json')\\n\",\n    \"\\n\",\n    \"with open(output_json_path, 'w') as output_json:\\n\",\n    \"    for line_id, json_line in enumerate(titles_to_jsonstr_noisy.values()):\\n\",\n    \"        output_json.write(json_line)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T06:20:15.561921Z\",\n     \"start_time\": \"2020-09-22T06:20:07.480872Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MagKP-N-small: #(doc)=518908, #(kp)=12122092, #(avg_kp)=23.360773\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"input_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_Nsmall_train.json')\\n\",\n    \"num_doc_noisy = 0\\n\",\n    \"num_kp_noisy = 0\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        title = normalize_title_str(title)\\n\",\n    \"\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"\\n\",\n    \"        num_doc_noisy += 1\\n\",\n    \"        num_kp_noisy += len(keywords)\\n\",\n    \"\\n\",\n    \"print('MagKP-N-small: #(doc)=%d, #(kp)=%d, #(avg_kp)=%.6f' % (num_doc_noisy, num_kp_noisy, num_kp_noisy / num_doc_noisy))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T06:13:10.136246Z\",\n     \"start_time\": \"2020-09-22T06:12:44.964269Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MagKP-N-large: #(doc)=1512921, #(kp)=35312484, #(avg_kp)=23.340600\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"input_json_path = os.path.join(json_base_dir, 'magkp', 'magkp_Nlarge_train.json')\\n\",\n    \"num_doc_noisy = 0\\n\",\n    \"num_kp_noisy = 0\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        title = normalize_title_str(title)\\n\",\n    \"\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"\\n\",\n    \"        num_doc_noisy += 1\\n\",\n    \"        num_kp_noisy += len(keywords)\\n\",\n    \"\\n\",\n    \"print('MagKP-N-large: #(doc)=%d, #(kp)=%d, #(avg_kp)=%.6f' % (num_doc_noisy, num_kp_noisy, num_kp_noisy / num_doc_noisy))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/test.py",
    "content": "import json\nimport tqdm\nimport numpy as np\n\npred_path = \"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseq_pred/kp20k.pred\"\npred_path = \"/Users/memray/project/kp/OpenNMT-kpg/output/aaai20/catseqd_pred/kp20k.pred\"\n\nkeys = ['gold_num',\n        'unique_pred_num', 'dup_pred_num', 'pred_sents_local_count', 'topseq_pred_num',\n        'beam_num', 'beamstep_num']\nnum_doc = 0\nstat_dict = {k: [] for k in keys}\n\nfor l in tqdm.tqdm(open(pred_path, 'r')):\n    pred_dict = json.loads(l)\n    #     print(pred_dict.keys())\n    print(pred_dict['topseq_pred_sents'])  # top beam, a sequence of words\n    print(pred_dict['topseq_preds'])  # a sequence of indices\n\n    print(pred_dict['pred_sents'])  # unique phrases\n    print(pred_dict['ori_pred_sents'])  # beams, each is a list of words, seperated by <sep>\n    #     print(pred_dict['ori_preds'])\n\n    print(pred_dict['unique_pred_num'])\n    if num_doc > 10:\n        break\n\n    num_doc += 1\n    stat_dict['gold_num'].append(len(pred_dict['gold_sent']))\n    stat_dict['unique_pred_num'].append(pred_dict['unique_pred_num'])\n    stat_dict['dup_pred_num'].append(pred_dict['dup_pred_num'])\n    stat_dict['pred_sents_local_count'].append(len(pred_dict['pred_sents']))\n    stat_dict['beam_num'].append(pred_dict['beam_num'])\n    stat_dict['beamstep_num'].append(pred_dict['beamstep_num'])\n    stat_dict['topseq_pred_num'].append(len(pred_dict['topseq_pred_num']))\n\n    print('#(doc)=%d' % num_doc)\n    for k, v in stat_dict.items():\n        print('avg(%s) = %d/%d = %f' % (k, np.sum(v), num_doc, np.mean(v)))\n"
  },
  {
    "path": "notebook/transfer_analysis.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load packages\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:17:20.769558Z\",\n     \"start_time\": \"2020-11-26T05:17:19.079496Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import fnmatch\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"import tqdm\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"# !pip install -q adjustText\\n\",\n    \"# from adjustText import adjust_text # not quite useful\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def find_dirs(target_name, root_path):\\n\",\n    \"    match_files = []\\n\",\n    \"    for root, dirs, files in os.walk(root_path):\\n\",\n    \"        for dir_name in dirs:\\n\",\n    \"            if target_name == dir_name:\\n\",\n    \"                match_files.append(os.path.join(root, dir_name))\\n\",\n    \"    return match_files\\n\",\n    \"\\n\",\n    \"def find_files(pattern, root_path, recursive=False):\\n\",\n    \"    match_files = []\\n\",\n    \"    if recursive:\\n\",\n    \"        for root, dirs, files in os.walk(root_path):\\n\",\n    \"            for file_name in files:\\n\",\n    \"                if fnmatch.fnmatch(file_name, pattern):\\n\",\n    \"                    match_files.append(os.path.join(root_path, file_name))\\n\",\n    \"    else:\\n\",\n    \"        # list all subfiles\\n\",\n    \"        for file_name in os.listdir(root_path):\\n\",\n    \"            if fnmatch.fnmatch(file_name, pattern):\\n\",\n    \"                match_files.append(os.path.join(root_path, file_name))\\n\",\n    \"\\n\",\n    \"    return match_files\\n\",\n    \"\\n\",\n    \"def load_eval_results(eval_file_dict, previous_eval_dict_list=[]):\\n\",\n    \"    '''\\n\",\n    \"    Load eval results from json files and return a dict with averaged scores.\\n\",\n    \"    input eval_file_dict is a dict in which key is a dir path and value is a list of eval files.\\n\",\n    \"    '''\\n\",\n    \"    eval_dict_list = previous_eval_dict_list\\n\",\n    \"    loaded_evals_set = set(eval_dict['path'] for eval_dict in previous_eval_dict_list)\\n\",\n    \"    new_evals_set = set()\\n\",\n    \"    \\n\",\n    \"    exp_set =set()\\n\",\n    \"    for eval_dir_name, eval_file_list in tqdm.tqdm(eval_file_dict.items(), desc='Processing eval dir'):\\n\",\n    \"        for eval_file in tqdm.tqdm(eval_file_list, desc='Processing eval dir %s' % eval_dir_name, miniters=100):\\n\",\n    \"    #         print(eval_file)\\n\",\n    \"            new_evals_set.add(eval_file)\\n\",\n    \"            if eval_file in loaded_evals_set:\\n\",\n    \"                continue\\n\",\n    \"                \\n\",\n    \"            name_fields = eval_file.split('/')\\n\",\n    \"\\n\",\n    \"            eval_dict = {}\\n\",\n    \"            \\n\",\n    \"\\n\",\n    \"            eval_dict['path'] = eval_file\\n\",\n    \"            eval_dict['exp_group'] = name_fields[-5]\\n\",\n    \"\\n\",\n    \"            eval_dict['exp_name'] = name_fields[-1][: name_fields[-1].rfind('_step')]\\n\",\n    \"            exp_fields = name_fields[-1][: name_fields[-1].rfind('.')].split('-')\\n\",\n    \"            \\n\",\n    \"#             if eval_dict['exp_name'] in exp_set:\\n\",\n    \"#                 continue\\n\",\n    \"#             exp_set.add(eval_dict['exp_name'])\\n\",\n    \"\\n\",\n    \"#             for i, k in enumerate(name_fields):\\n\",\n    \"#                 print('%d: %s' % (i, k))\\n\",\n    \"\\n\",\n    \"#             for i, k in enumerate(exp_fields):\\n\",\n    \"#                 print('%d: %s' % (i, k))\\n\",\n    \"\\n\",\n    \"            if '-v3' in eval_file:\\n\",\n    \"                eval_dict['test_name'] = name_fields[-1][name_fields[-1].rfind('step'): name_fields[-1].rfind('.')]\\n\",\n    \"                eval_dict['tokenization'], eval_dict['train_mode'], _ = name_fields[-4].split('-')\\n\",\n    \"\\n\",\n    \"                eval_dict['model_base'] = exp_fields[4]\\n\",\n    \"                eval_dict['order'] = exp_fields[3]\\n\",\n    \"                eval_dict['train_dataset'] = exp_fields[2]\\n\",\n    \"                eval_dict['step'] = int(eval_dict['test_name'][eval_dict['test_name'].find('_')+1: eval_dict['test_name'].find('-')])\\n\",\n    \"\\n\",\n    \"                eval_dict['test_dataset'] = exp_fields[-2]\\n\",\n    \"                eval_dict['decoding_method'] = eval_dict['test_name'].split('-')[-1]\\n\",\n    \"                eval_dict['decoding_terminate'] = name_fields[-4].split('-')[-1]\\n\",\n    \"                eval_dict['beam_width'] = name_fields[-3].split('-')[-2][4:]\\n\",\n    \"                eval_dict['max_length'] = name_fields[-3].split('-')[-1][6:]\\n\",\n    \"            else:\\n\",\n    \"                eval_dict['test_name'] = name_fields[-1][name_fields[-1].rfind('step'): name_fields[-1].rfind('.')]\\n\",\n    \"                eval_dict['tokenization'], eval_dict['train_mode'], _ = name_fields[-4].split('-')\\n\",\n    \"\\n\",\n    \"                eval_dict['model_base'] = exp_fields[3]\\n\",\n    \"                eval_dict['order'] = exp_fields[2]\\n\",\n    \"                eval_dict['train_dataset'] = exp_fields[0]\\n\",\n    \"                eval_dict['step'] = int(eval_dict['test_name'][eval_dict['test_name'].find('_')+1: eval_dict['test_name'].find('-')])\\n\",\n    \"\\n\",\n    \"                eval_dict['test_dataset'] = exp_fields[-2]\\n\",\n    \"                eval_dict['decoding_method'] = eval_dict['test_name'].split('-')[-1]\\n\",\n    \"                eval_dict['decoding_terminate'] = name_fields[-4].split('-')[-1]\\n\",\n    \"                eval_dict['beam_width'] = name_fields[-3].split('-')[-2][4:]\\n\",\n    \"                eval_dict['max_length'] = name_fields[-3].split('-')[-1][6:]\\n\",\n    \"\\n\",\n    \"            eval_full_dict = json.load(open(eval_file, 'r'))\\n\",\n    \"            eval_avg_dict = {k: np.average(v) for k,v in eval_full_dict.items()}\\n\",\n    \"\\n\",\n    \"            \\n\",\n    \"#             for k,v in eval_dict.items():\\n\",\n    \"#                 print('\\\\t%s : %s' % (k,v))\\n\",\n    \"\\n\",\n    \"            for k, v in eval_avg_dict.items():\\n\",\n    \"                if k.endswith('_num'):\\n\",\n    \"                    eval_dict[k] = v\\n\",\n    \"            eval_dict.update(eval_avg_dict)\\n\",\n    \"\\n\",\n    \"            #             print(eval_dict)\\n\",\n    \"            \\n\",\n    \"            eval_dict_list.append(eval_dict)\\n\",\n    \"\\n\",\n    \"#             break\\n\",\n    \"#         continue\\n\",\n    \"    \\n\",\n    \"    # some results are actually removed from disk.\\n\",\n    \"    eval_dict_list = [eval_dict for eval_dict in eval_dict_list if eval_dict['path'] in new_evals_set]\\n\",\n    \"            \\n\",\n    \"    return eval_dict_list\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def peak_index(group, x_index, y_index):\\n\",\n    \"    max_x, max_y = 0, -1\\n\",\n    \"\\n\",\n    \"    for id_label, _ in group.iterrows():\\n\",\n    \"        if group.at[id_label , y_index] > max_y:\\n\",\n    \"            max_y = group.at[id_label , y_index]\\n\",\n    \"            max_x = group.at[id_label , x_index]\\n\",\n    \"            \\n\",\n    \"    return max_x, max_y\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def get_max_change(group, y_index, skip_first=5):\\n\",\n    \"    max_change = 0.0\\n\",\n    \"    max_change_rate = 0.0\\n\",\n    \"\\n\",\n    \"    for idx, (id_label, _) in enumerate(group.iterrows()):\\n\",\n    \"        if idx < skip_first:\\n\",\n    \"            continue\\n\",\n    \"        if idx == skip_first:\\n\",\n    \"            prev_id_label = id_label\\n\",\n    \"            continue\\n\",\n    \"        if abs(group.at[id_label, y_index] - group.at[prev_id_label, y_index]) > max_change:\\n\",\n    \"            max_change = abs(group.at[id_label, y_index] - group.at[prev_id_label, y_index])\\n\",\n    \"            max_change_rate = max_change / group.at[prev_id_label, y_index]\\n\",\n    \"            \\n\",\n    \"    return max_change, max_change_rate\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def plot_testing_curve(df, y_index, title='', plot_valid_peak=True):\\n\",\n    \"    fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"\\n\",\n    \"    if plot_valid_peak:\\n\",\n    \"        valid_peak_x, valid_peak_y = peak_index(df[df.test_dataset.str.endswith('kp20k_valid2k')], x_index='step', y_index=y_index)\\n\",\n    \"    #     print(valid_peak_x, valid_peak_y)\\n\",\n    \"        valid_box_props = dict(facecolor='w', alpha=0.5)\\n\",\n    \"    \\n\",\n    \"    peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"    \\n\",\n    \"    for key, grp in df.groupby(['test_dataset']):\\n\",\n    \"#         print(key)\\n\",\n    \"#         print(grp.shape)\\n\",\n    \"#         display(grp)\\n\",\n    \"        if key.endswith('kp20k_valid2k'):\\n\",\n    \"            ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', linestyle='dashed', markersize=8.0, linewidth=4)\\n\",\n    \"        else:\\n\",\n    \"            ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', markersize=8.0, linewidth=4)\\n\",\n    \"\\n\",\n    \"        peak_x, peak_y = peak_index(grp, x_index='step', y_index=y_index)\\n\",\n    \"        variance = grp[y_index].var()\\n\",\n    \"        max_change, max_change_rate = get_max_change(grp, y_index=y_index)\\n\",\n    \"\\n\",\n    \"        if not plot_valid_peak or peak_x == valid_peak_x:\\n\",\n    \"            ax.annotate('%s peak=%.3f (@step=%d)' % (key, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#             ax.annotate('peak=%.3f (@step=%d), var=%.3f, ckpt\\\\u2195=%.3f(%.1f%%)' % (peak_y, peak_x, variance, max_change, max_change_rate * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"        else:\\n\",\n    \"            valid_peak_y = grp[grp.step == valid_peak_x][y_index].item()\\n\",\n    \"            ax.annotate('%s peak=%.3f (@step=%d), valid\\\\u2193=%.3f(%.1f%%), ' % (key, peak_y, peak_x, valid_peak_y, (peak_y-valid_peak_y)/valid_peak_y * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#             ax.annotate('peak=%.3f (@step=%d), var=%.3f, ckpt\\\\u2195=%.3f(%.1f%%), valid\\\\u2193=%.3f(%.1f%%), ' % (peak_y, peak_x, variance, max_change, max_change_rate * 100, valid_peak_y, (peak_y-valid_peak_y)/valid_peak_y * 100), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"\\n\",\n    \"    if plot_valid_peak:\\n\",\n    \"        plt.axvline(x=valid_peak_x, color='k', linestyle='--')\\n\",\n    \"    plt.legend(loc='best')\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"def brief_eval_results(df, base_metric, metrics='all', ignore_valid=False):\\n\",\n    \"    '''\\n\",\n    \"    Given complete results of a set of training experiments (performance of all ckpts on each dataset),\\n\",\n    \"    return best-test/best-valid/last-ckpt score of each dataset\\n\",\n    \"    '''\\n\",\n    \"    assert base_metric is not None, \\\"base metric must be given!\\\"\\n\",\n    \"    if isinstance(metrics, str):\\n\",\n    \"        if metrics == 'present':\\n\",\n    \"            metrics = ['present_exact_precision@5', 'present_exact_recall@5', 'present_exact_f_score@5', 'present_exact_precision_hard@5', 'present_exact_f_score_hard@5', 'present_exact_precision@10', 'present_exact_recall@10', 'present_exact_f_score@10', 'present_exact_precision_hard@10', 'present_exact_f_score_hard@10', 'present_exact_precision@k', 'present_exact_recall@k', 'present_exact_f_score@k', 'present_exact_precision_hard@k', 'present_exact_f_score_hard@k', 'present_exact_precision@M', 'present_exact_recall@M', 'present_exact_f_score@M', 'present_exact_precision_hard@M', 'present_exact_f_score_hard@M', 'present_exact_advanced_auc', 'present_exact_advanced_ap', 'present_exact_advanced_mrr', 'present_exact_advanced_sadr', 'present_exact_advanced_ndcg', 'present_exact_advanced_alpha_ndcg@5', 'present_exact_advanced_alpha_ndcg@10']\\n\",\n    \"        elif metrics == 'absent':\\n\",\n    \"            metrics = ['absent_exact_precision@10', 'absent_exact_recall@10', 'absent_exact_f_score@10', 'absent_exact_precision_hard@10', 'absent_exact_f_score_hard@10', 'absent_exact_precision@50', 'absent_exact_recall@50', 'absent_exact_f_score@50', 'absent_exact_precision_hard@50', 'absent_exact_f_score_hard@50', 'absent_exact_precision@M', 'absent_exact_recall@M', 'absent_exact_f_score@M', 'absent_exact_precision_hard@M', 'absent_exact_f_score_hard@M', 'absent_exact_advanced_auc', 'absent_exact_advanced_ap', 'absent_exact_advanced_mrr', 'absent_exact_advanced_sadr', 'absent_exact_advanced_ndcg', 'absent_exact_advanced_alpha_ndcg@5', 'absent_exact_advanced_alpha_ndcg@10']\\n\",\n    \"        else:\\n\",\n    \"            metrics = ['present_exact_precision@5', 'present_exact_recall@5', 'present_exact_f_score@5', 'present_exact_precision_hard@5', 'present_exact_f_score_hard@5', 'present_exact_precision@10', 'present_exact_recall@10', 'present_exact_f_score@10', 'present_exact_precision_hard@10', 'present_exact_f_score_hard@10', 'present_exact_precision@k', 'present_exact_recall@k', 'present_exact_f_score@k', 'present_exact_precision_hard@k', 'present_exact_f_score_hard@k', 'present_exact_precision@M', 'present_exact_recall@M', 'present_exact_f_score@M', 'present_exact_precision_hard@M', 'present_exact_f_score_hard@M', 'present_exact_advanced_auc', 'present_exact_advanced_ap', 'present_exact_advanced_mrr', 'present_exact_advanced_sadr', 'present_exact_advanced_ndcg', 'present_exact_advanced_alpha_ndcg@5', 'present_exact_advanced_alpha_ndcg@10', 'absent_exact_precision@10', 'absent_exact_recall@10', 'absent_exact_f_score@10', 'absent_exact_precision_hard@10', 'absent_exact_f_score_hard@10', 'absent_exact_precision@50', 'absent_exact_recall@50', 'absent_exact_f_score@50', 'absent_exact_precision_hard@50', 'absent_exact_f_score_hard@50', 'absent_exact_precision@M', 'absent_exact_recall@M', 'absent_exact_f_score@M', 'absent_exact_precision_hard@M', 'absent_exact_f_score_hard@M', 'absent_exact_advanced_auc', 'absent_exact_advanced_ap', 'absent_exact_advanced_mrr', 'absent_exact_advanced_sadr', 'absent_exact_advanced_ndcg', 'absent_exact_advanced_alpha_ndcg@5', 'absent_exact_advanced_alpha_ndcg@10']\\n\",\n    \"    else:\\n\",\n    \"        assert isinstance(metrics, list)\\n\",\n    \"\\n\",\n    \"    str_rows = []\\n\",\n    \"    self_peak_df = pd.DataFrame()\\n\",\n    \"    valid_peak_df = pd.DataFrame()\\n\",\n    \"    last_ckpt_df = pd.DataFrame()\\n\",\n    \"    \\n\",\n    \"    # group rows by those values and return the ones with best valid scores\\n\",\n    \"    for exp_name, exp_grp in df.groupby(['exp_name', 'beam_width', 'decoding_method', 'decoding_terminate']):\\n\",\n    \"#         print(exp_name)\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='test_dataset', ascending=True)\\n\",\n    \"        \\n\",\n    \"        if not ignore_valid:\\n\",\n    \"            valid_peak_step, _ = peak_index(exp_grp[exp_grp.test_dataset.str.endswith('kp20k_valid2k')], x_index='step', y_index=base_metric)\\n\",\n    \"\\n\",\n    \"        for testset, testset_grp in exp_grp.groupby(['test_dataset']):            \\n\",\n    \"            self_peak_step, _ = peak_index(testset_grp, x_index='step', y_index=base_metric)\\n\",\n    \"            last_step = testset_grp['step'].max()\\n\",\n    \"            \\n\",\n    \"            try:\\n\",\n    \"                train_mode = testset_grp.loc[testset_grp.step == self_peak_step, 'train_mode'].values[0]\\n\",\n    \"            except Exception as e:\\n\",\n    \"                print('Error while locating best test score!')\\n\",\n    \"                print('self_peak_step=%s' % self_peak_step)\\n\",\n    \"                print(testset_grp.iloc[0]['path'])\\n\",\n    \"                print(testset_grp.shape)\\n\",\n    \"                display(testset_grp)\\n\",\n    \"                raise e\\n\",\n    \"\\n\",\n    \"            order = testset_grp[testset_grp.step==self_peak_step].order.values[0]\\n\",\n    \"            decoding_terminate = testset_grp[testset_grp.step==self_peak_step].decoding_terminate.values[0]\\n\",\n    \"            str_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step}\\n\",\n    \"#             self_peak_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step, 'valid_peak_step': valid_peak_step}\\n\",\n    \"#             valid_peak_row = {'exp_name': exp_name, 'train_mode': train_mode, 'test_dataset': testset, 'order': order, 'decoding_terminate': decoding_terminate, 'self_peak_step': self_peak_step, 'valid_peak_step': valid_peak_step}\\n\",\n    \"            if not ignore_valid:\\n\",\n    \"                str_row['valid_peak_step'] = valid_peak_step\\n\",\n    \"\\n\",\n    \"            for metric in metrics:\\n\",\n    \"                self_peak_value = testset_grp[testset_grp.step==self_peak_step][metric].values[0]\\n\",\n    \"\\n\",\n    \"                if not ignore_valid:\\n\",\n    \"                    try:\\n\",\n    \"                        valid_peak_value = testset_grp[testset_grp.step==valid_peak_step][metric].values[0]\\n\",\n    \"                    except Exception:\\n\",\n    \"                        print('Error while locating best valid score!')\\n\",\n    \"                        print('metric=', metric)\\n\",\n    \"                        print('valid_peak_step=', valid_peak_step)\\n\",\n    \"                        print('len(testset_grp)=', len(testset_grp))\\n\",\n    \"                        print(next(iter(testset_grp.path), 'no match'))\\n\",\n    \"                        display(testset_grp)\\n\",\n    \"                        display(testset_grp[testset_grp.step==valid_peak_step])\\n\",\n    \"                        display(testset_grp[testset_grp.step==valid_peak_step][metric])\\n\",\n    \"                        return\\n\",\n    \"                else: \\n\",\n    \"                    valid_peak_value = 0.0\\n\",\n    \"\\n\",\n    \"                str_row[metric] = '%.4f (%.4f)' % (self_peak_value, valid_peak_value)\\n\",\n    \"#                 print('%s@%s - %s = %.4f' % (testset, self_peak_step, metric, testset_grp[testset_grp.step==self_peak_step][metric].item()))\\n\",\n    \"#                 display(testset_grp[testset_grp.step==self_peak_step][metric])\\n\",\n    \"            self_peak_row = testset_grp[testset_grp.step==self_peak_step]\\n\",\n    \"            valid_peak_row = testset_grp[testset_grp.step==valid_peak_step] if not ignore_valid else self_peak_row\\n\",\n    \"            last_step_row = testset_grp[testset_grp.step==last_step]\\n\",\n    \"            \\n\",\n    \"            str_rows.append(str_row)\\n\",\n    \"            self_peak_df = self_peak_df.append(self_peak_row)\\n\",\n    \"            last_ckpt_df = last_ckpt_df.append(last_step_row)\\n\",\n    \"            if not ignore_valid:\\n\",\n    \"                valid_peak_df = valid_peak_df.append(valid_peak_row)\\n\",\n    \"    \\n\",\n    \"    str_summary_df = pd.DataFrame(str_rows).sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    self_peak_df = self_peak_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    last_ckpt_df = last_ckpt_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"    \\n\",\n    \"    if not ignore_valid:\\n\",\n    \"        valid_peak_df = valid_peak_df.sort_values(by=['test_dataset', 'exp_name'])\\n\",\n    \"  \\n\",\n    \"    return str_summary_df, self_peak_df, valid_peak_df, last_ckpt_df\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Scan all pred and eval files\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:19:21.268834Z\",\n     \"start_time\": \"2020-11-26T05:19:10.109389Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total pred = 43845\\n\",\n      \"Total eval = 70285\\n\",\n      \"[0]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40\\n\",\n      \"\\t#(pred)=2351, #(eval)=4661\\n\",\n      \"[1]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40\\n\",\n      \"\\t#(pred)=2338, #(eval)=4648\\n\",\n      \"[2]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40\\n\",\n      \"\\t#(pred)=2351, #(eval)=4661\\n\",\n      \"[3]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40\\n\",\n      \"\\t#(pred)=2322, #(eval)=4632\\n\",\n      \"[4]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40\\n\",\n      \"\\t#(pred)=852, #(eval)=1680\\n\",\n      \"[5]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40\\n\",\n      \"\\t#(pred)=846, #(eval)=1680\\n\",\n      \"[6]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40\\n\",\n      \"\\t#(pred)=841, #(eval)=1680\\n\",\n      \"[7]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40\\n\",\n      \"\\t#(pred)=846, #(eval)=1680\\n\",\n      \"[8]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40\\n\",\n      \"\\t#(pred)=1997, #(eval)=3994\\n\",\n      \"[9]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40\\n\",\n      \"\\t#(pred)=7691, #(eval)=10510\\n\",\n      \"[10]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40\\n\",\n      \"\\t#(pred)=3934, #(eval)=7822\\n\",\n      \"[11]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40\\n\",\n      \"\\t#(pred)=1970, #(eval)=3936\\n\",\n      \"[12]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-stackexchange-topmodels\\n\",\n      \"\\t#(pred)=1, #(eval)=1\\n\",\n      \"[13]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6\\n\",\n      \"\\t#(pred)=280, #(eval)=280\\n\",\n      \"[14]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6\\n\",\n      \"\\t#(pred)=280, #(eval)=280\\n\",\n      \"[15]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6\\n\",\n      \"\\t#(pred)=280, #(eval)=280\\n\",\n      \"[16]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6\\n\",\n      \"\\t#(pred)=1910, #(eval)=1896\\n\",\n      \"[17]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6\\n\",\n      \"\\t#(pred)=280, #(eval)=280\\n\",\n      \"[18]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6\\n\",\n      \"\\t#(pred)=1680, #(eval)=1680\\n\",\n      \"[19]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6\\n\",\n      \"\\t#(pred)=1680, #(eval)=1680\\n\",\n      \"[20]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6\\n\",\n      \"\\t#(pred)=1680, #(eval)=1680\\n\",\n      \"[21]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6\\n\",\n      \"\\t#(pred)=1680, #(eval)=1680\\n\",\n      \"[22]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6\\n\",\n      \"\\t#(pred)=1680, #(eval)=1680\\n\",\n      \"[23]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6\\n\",\n      \"\\t#(pred)=3209, #(eval)=6418\\n\",\n      \"[24]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-stackexchange\\n\",\n      \"\\t#(pred)=60, #(eval)=60\\n\",\n      \"[25]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6\\n\",\n      \"\\t#(pred)=140, #(eval)=140\\n\",\n      \"[26]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6\\n\",\n      \"\\t#(pred)=140, #(eval)=140\\n\",\n      \"[27]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6\\n\",\n      \"\\t#(pred)=246, #(eval)=246\\n\",\n      \"[28]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6\\n\",\n      \"\\t#(pred)=140, #(eval)=140\\n\",\n      \"[29]/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6\\n\",\n      \"\\t#(pred)=140, #(eval)=140\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/'\\n\",\n    \"eval_dirs = find_dirs('eval', output_base_dir)\\n\",\n    \"\\n\",\n    \"# only return all direct .eval subfiles, since we have previous evals that don't want to include, \\n\",\n    \"#     such as /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam1-maxlen40/eval/selfterminating\\n\",\n    \"eval_file_dict = {eval_dir[: eval_dir.rfind('/')]: find_files('*.json', eval_dir) for eval_dir in eval_dirs}\\n\",\n    \"pred_file_dict = {eval_dir[: eval_dir.rfind('/')]: find_files('*.pred', eval_dir[: eval_dir.rfind('/')]+'/pred', recursive=True) for eval_dir in eval_dirs}\\n\",\n    \"\\n\",\n    \"print('Total pred = %d' % (sum([len(v) for v in pred_file_dict.values()]))) # 32157\\n\",\n    \"print('Total eval = %d' % (sum([len(v) for v in eval_file_dict.values()]))) # 36929\\n\",\n    \"for dir_id, (dir_name, file_list) in enumerate(eval_file_dict.items()):\\n\",\n    \"    print('[%d]%s\\\\n\\\\t#(pred)=%d, #(eval)=%d' % (dir_id, dir_name, len(pred_file_dict[dir_name]) if dir_name in pred_file_dict else 0, len(file_list)))\\n\",\n    \"    \\n\",\n    \"# Total pred = 34611\\n\",\n    \"# Total eval = 41129\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:19:21.276281Z\",\n     \"start_time\": \"2020-11-26T05:19:21.270618Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"30\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40 - 4661\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40 - 4648\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40 - 4661\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40 - 4632\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40 - 3994\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40 - 10510\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40 - 7822\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40 - 3936\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6 - 280\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6 - 280\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6 - 280\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6 - 1896\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6 - 280\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6 - 1680\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6 - 6418\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6 - 140\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6 - 140\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6 - 246\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6 - 140\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6 - 140\\n\",\n      \"28\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# remove stackexchange and beam1\\n\",\n    \"kp20k_eval_file_dict = {}\\n\",\n    \"print(len(eval_dirs))\\n\",\n    \"for k, v in eval_file_dict.items():\\n\",\n    \"    if 'stackexchange' in k: # or '-beam1-' in k:\\n\",\n    \"        continue\\n\",\n    \"#     if 'kp20k-v3' not in k:\\n\",\n    \"#         continue\\n\",\n    \"    kp20k_eval_file_dict[k] = v\\n\",\n    \"    print('%s - %d' % (k, len(v)))\\n\",\n    \"\\n\",\n    \"print(len(kp20k_eval_file_dict))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load EVAL information from each eval json file\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:24:08.154226Z\",\n     \"start_time\": \"2020-11-26T05:19:21.277627Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Processing eval dir:   0%|          | 0/28 [00:00<?, ?it/s]\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40: 100%|██████████| 4661/4661 [00:00<00:00, 1183464.55it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40: 100%|██████████| 4648/4648 [00:00<00:00, 1171582.03it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40: 100%|██████████| 4661/4661 [00:00<00:00, 1170988.38it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40: 100%|██████████| 4632/4632 [00:00<00:00, 1177955.26it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40: 100%|██████████| 1680/1680 [00:00<00:00, 586518.29it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam10-maxlen40: 100%|██████████| 1680/1680 [00:00<00:00, 1073332.94it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam1-maxlen40: 100%|██████████| 1680/1680 [00:00<00:00, 1114941.57it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam25-maxlen40: 100%|██████████| 1680/1680 [00:00<00:00, 1089261.20it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir 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   \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6: 100%|██████████| 280/280 [00:00<00:00, 776209.60it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6: 100%|██████████| 1680/1680 [00:00<00:00, 728915.97it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6: 100%|██████████| 1680/1680 [00:00<00:00, 786256.50it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6: 100%|██████████| 1680/1680 [00:00<00:00, 738000.70it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6: 100%|██████████| 1680/1680 [00:00<00:00, 781027.57it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6: 100%|██████████| 1680/1680 [00:00<00:00, 766082.92it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6: 100%|██████████| 6418/6418 [00:00<00:00, 795668.10it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam16-maxlen6: 100%|██████████| 140/140 [00:00<00:00, 450308.71it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam8-maxlen6: 100%|██████████| 140/140 [00:00<00:00, 522887.41it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6: 100%|██████████| 246/246 [00:00<00:00, 654694.66it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam32-maxlen6: 100%|██████████| 140/140 [00:00<00:00, 543706.07it/s]\\n\",\n      \"\\n\",\n      \"Processing eval dir /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam64-maxlen6: 100%|██████████| 140/140 [00:00<00:00, 579666.89it/s]\\n\",\n      \"Processing eval dir: 100%|██████████| 28/28 [03:46<00:00,  8.08s/it]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"70224\\n\",\n      \"Stored 'all_eval_result_dicts' (list)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# load individual eval from *.eval.json files (it takes 57:26 to finish)\\n\",\n    \"\\n\",\n    \"# reload all_eval_result_dicts\\n\",\n    \"%store -r all_eval_result_dicts\\n\",\n    \"# print(len(all_eval_result_dicts)) # 56879\\n\",\n    \"# incrementally load new evals\\n\",\n    \"all_eval_result_dicts = load_eval_results(kp20k_eval_file_dict, previous_eval_dict_list=all_eval_result_dicts)\\n\",\n    \"print(len(all_eval_result_dicts)) # 56894\\n\",\n    \"\\n\",\n    \"# save it to disk\\n\",\n    \"%store all_eval_result_dicts\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Convert to Pandas DataFrames\\n\",\n    \" - previous steps are loading eval dicts from disk\\n\",\n    \" \\n\",\n    \" \\n\",\n    \"meng17-one2seq-kp20k-v3    5744\\n\",\n    \"meng17-one2one-kp20k-v3    1680\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:24:35.106007Z\",\n     \"start_time\": \"2020-11-26T05:24:08.156032Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(67058, 121)\\n\",\n      \"meng17-one2seq-kp20k-v3           26262\\n\",\n      \"meng17-one2seq-kp20k-v2           18602\\n\",\n      \"meng17-one2one-kp20k-v3            8400\\n\",\n      \"meng17-one2seq-kp20k-topmodels     6720\\n\",\n      \"meng17-one2one-kp20k               3252\\n\",\n      \"meng17-one2one-kp20k-v2            3016\\n\",\n      \"meng17-one2one-kp20k-topmodels      806\\n\",\n      \"Name: exp_group, dtype: int64\\n\",\n      \"semeval          9775\\n\",\n      \"nus              9611\\n\",\n      \"kp20k_valid2k    9553\\n\",\n      \"duc              9538\\n\",\n      \"inspec           9535\\n\",\n      \"krapivin         9531\\n\",\n      \"kp20k            9515\\n\",\n      \"Name: test_dataset, dtype: int64\\n\",\n      \"kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse = 40\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue = 40\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1 = 25\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1 = 50\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D128-E128-DO0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue = 40\\n\",\n      \"kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse = 20\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue = 10\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue = 20\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue = 20\\n\",\n      \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue = 20\\n\",\n      \"kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1 = 20\\n\",\n      \"kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 50\\n\",\n      \"kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 53\\n\",\n      \"kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 52\\n\",\n      \"kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 52\\n\",\n      \"kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 53\\n\",\n      \"kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 52\\n\",\n      \"kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 54\\n\",\n      \"kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-kp20k-alphabetical_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 55\\n\",\n      \"kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-kp20k-length_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 55\\n\",\n      \"kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-kp20k-no_sort_reverse-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse = 60\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 54\\n\",\n      \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse = 53\\n\",\n      \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 53\\n\",\n      \"kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 53\\n\",\n      \"kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 55\\n\",\n      \"kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 60\\n\",\n      \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse = 54\\n\",\n      \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 53\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.0-Copytrue = 20\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.1-Copytrue = 20\\n\",\n      \"magkp-meng17-one2one-BS128-OPTadagrad-LR0.05-L1-H-D150-E100-DO0.0-Copytrue = 20\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue = 6\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Covtrue-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copyfalse-Covfalse-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covfalse-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.5-Copytrue-Covtrue-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1 = 30\\n\",\n      \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue = 20\\n\",\n      \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue = 20\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue = 30\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue = 30\\n\",\n      \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue = 30\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue = 20\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue = 20\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue = 20\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue = 20\\n\",\n      \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue = 20\\n\",\n      \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue = 30\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%store -r all_eval_result_dicts\\n\",\n    \"all_eval_df = pd.DataFrame(all_eval_result_dicts, columns=all_eval_result_dicts[0].keys())\\n\",\n    \"\\n\",\n    \"# correct some wrong information in evals\\n\",\n    \"for index_label, row_series in all_eval_df.iterrows():\\n\",\n    \"    all_eval_df.at[index_label , 'exp_group'] = all_eval_df.at[index_label , 'path'].split('/')[9]\\n\",\n    \"    \\n\",\n    \"    # correct the wrong information in One2One evals\\n\",\n    \"    if all_eval_df.at[index_label , 'test_dataset'].startswith('IF1'):\\n\",\n    \"        _, test_name = all_eval_df.at[index_label , 'test_name'].split('-')\\n\",\n    \"        all_eval_df.at[index_label , 'test_dataset'] = test_name\\n\",\n    \"        all_eval_df.at[index_label , 'decoding_method'] = 'exhaustive'\\n\",\n    \"    if all_eval_df.at[index_label , 'model_base'] == 'BS128':\\n\",\n    \"        all_eval_df.at[index_label , 'model_base'] = 'rnn'\\n\",\n    \"    all_eval_df.at[index_label , 'exp_group'] = all_eval_df.at[index_label , 'path'].split('/')[9]\\n\",\n    \"\\n\",\n    \"all_eval_df = all_eval_df[all_eval_df.beam_num.notnull()]\\n\",\n    \"print(all_eval_df.shape)\\n\",\n    \"all_eval_df[:5]\\n\",\n    \"print(all_eval_df.exp_group.value_counts())\\n\",\n    \"print(all_eval_df.test_dataset.value_counts())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# correct the wrong information in evals\\n\",\n    \"for exp_name, exp_group in all_eval_df.groupby('exp_name'):\\n\",\n    \"    max_step = len(exp_group.step.unique())\\n\",\n    \"    print('%s = %d' % (exp_name, max_step))\\n\",\n    \"\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:24:35.138817Z\",\n     \"start_time\": \"2020-11-26T05:24:35.107630Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(15474, 121)\\n\",\n      \"kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         2100\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse          2100\\n\",\n      \"kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            2100\\n\",\n      \"kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            2100\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                        1400\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1                   911\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1                  351\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1                  313\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1                          312\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1                          301\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1                         301\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse                                               280\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue                                                      280\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1       217\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covfalse-Contextboth-IF1      214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copyfalse-Covfalse-Contextboth-IF1     214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1       214\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Covtrue-Contextboth-IF1       185\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.5-Copytrue-Covtrue-Contextboth-IF1       185\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1       184\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1                         156\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue                                                    140\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.0-Copytrue                                                      140\\n\",\n      \"magkp-meng17-one2one-BS128-OPTadagrad-LR0.05-L1-H-D150-E100-DO0.0-Copytrue                                            140\\n\",\n      \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                      140\\n\",\n      \"magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.1-Copytrue                                                      140\\n\",\n      \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue                                                      140\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue                                          70\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue                                          70\\n\",\n      \"kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D128-E128-DO0.1-Copytrue                                          70\\n\",\n      \"magkp-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue                                           6\\n\",\n      \"Name: exp_name, dtype: int64\\n\",\n      \"meng17-one2one-kp20k-v3           8400\\n\",\n      \"meng17-one2one-kp20k              3252\\n\",\n      \"meng17-one2one-kp20k-v2           3016\\n\",\n      \"meng17-one2one-kp20k-topmodels     806\\n\",\n      \"Name: exp_group, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"one2one_eval_df = all_eval_df.loc[all_eval_df['train_mode'] == 'one2one']\\n\",\n    \"print(one2one_eval_df.shape)\\n\",\n    \"print(one2one_eval_df.exp_name.value_counts())\\n\",\n    \"print(one2one_eval_df.exp_group.value_counts())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:24:35.202019Z\",\n     \"start_time\": \"2020-11-26T05:24:35.140230Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(51584, 121)\\n\",\n      \"meng17-one2seq-kp20k-v3           26262\\n\",\n      \"meng17-one2seq-kp20k-v2           18602\\n\",\n      \"meng17-one2seq-kp20k-topmodels     6720\\n\",\n      \"Name: exp_group, dtype: int64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"one2seq_eval_df = all_eval_df.loc[all_eval_df['train_mode'] == 'one2seq']\\n\",\n    \"\\n\",\n    \"print(one2seq_eval_df.shape)\\n\",\n    \"print(one2seq_eval_df.exp_group.value_counts())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-25T10:13:20.235892Z\",\n     \"start_time\": \"2020-10-25T10:13:20.050075Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"'path,'\\n\",\n      \"'exp_group,'\\n\",\n      \"'exp_name,'\\n\",\n      \"'test_name,'\\n\",\n      \"'tokenization,'\\n\",\n      \"'train_mode,'\\n\",\n      \"'model_base,'\\n\",\n      \"'order,'\\n\",\n      \"'train_dataset,'\\n\",\n      \"'step,'\\n\",\n      \"'test_dataset,'\\n\",\n      \"'decoding_method,'\\n\",\n      \"'decoding_terminate,'\\n\",\n      \"'beam_width,'\\n\",\n      \"'max_length,'\\n\",\n      \"'present_tgt_num,'\\n\",\n      \"'absent_tgt_num,'\\n\",\n      \"'present_pred_num,'\\n\",\n      \"'absent_pred_num,'\\n\",\n      \"'unique_pred_num,'\\n\",\n      \"'dup_pred_num,'\\n\",\n      \"'beam_num,'\\n\",\n      \"'beamstep_num,'\\n\",\n      \"'present_exact_correct@5,'\\n\",\n      \"'present_exact_precision@5,'\\n\",\n      \"'present_exact_recall@5,'\\n\",\n      \"'present_exact_f_score@5,'\\n\",\n      \"'present_exact_precision_hard@5,'\\n\",\n      \"'present_exact_f_score_hard@5,'\\n\",\n      \"'present_exact_correct@10,'\\n\",\n      \"'present_exact_precision@10,'\\n\",\n      \"'present_exact_recall@10,'\\n\",\n      \"'present_exact_f_score@10,'\\n\",\n      \"'present_exact_precision_hard@10,'\\n\",\n      \"'present_exact_f_score_hard@10,'\\n\",\n      \"'present_exact_correct@k,'\\n\",\n      \"'present_exact_precision@k,'\\n\",\n      \"'present_exact_recall@k,'\\n\",\n      \"'present_exact_f_score@k,'\\n\",\n      \"'present_exact_precision_hard@k,'\\n\",\n      \"'present_exact_f_score_hard@k,'\\n\",\n      \"'present_exact_correct@M,'\\n\",\n      \"'present_exact_precision@M,'\\n\",\n      \"'present_exact_recall@M,'\\n\",\n      \"'present_exact_f_score@M,'\\n\",\n      \"'present_exact_precision_hard@M,'\\n\",\n      \"'present_exact_f_score_hard@M,'\\n\",\n      \"'absent_exact_correct@10,'\\n\",\n      \"'absent_exact_precision@10,'\\n\",\n      \"'absent_exact_recall@10,'\\n\",\n      \"'absent_exact_f_score@10,'\\n\",\n      \"'absent_exact_precision_hard@10,'\\n\",\n      \"'absent_exact_f_score_hard@10,'\\n\",\n      \"'absent_exact_correct@50,'\\n\",\n      \"'absent_exact_precision@50,'\\n\",\n      \"'absent_exact_recall@50,'\\n\",\n      \"'absent_exact_f_score@50,'\\n\",\n      \"'absent_exact_precision_hard@50,'\\n\",\n      \"'absent_exact_f_score_hard@50,'\\n\",\n      \"'absent_exact_correct@M,'\\n\",\n      \"'absent_exact_precision@M,'\\n\",\n      \"'absent_exact_recall@M,'\\n\",\n      \"'absent_exact_f_score@M,'\\n\",\n      \"'absent_exact_precision_hard@M,'\\n\",\n      \"'absent_exact_f_score_hard@M,'\\n\",\n      \"'present_partial_correct@5,'\\n\",\n      \"'present_partial_precision@5,'\\n\",\n      \"'present_partial_recall@5,'\\n\",\n      \"'present_partial_f_score@5,'\\n\",\n      \"'present_partial_precision_hard@5,'\\n\",\n      \"'present_partial_f_score_hard@5,'\\n\",\n      \"'present_partial_correct@10,'\\n\",\n      \"'present_partial_precision@10,'\\n\",\n      \"'present_partial_recall@10,'\\n\",\n      \"'present_partial_f_score@10,'\\n\",\n      \"'present_partial_precision_hard@10,'\\n\",\n      \"'present_partial_f_score_hard@10,'\\n\",\n      \"'present_partial_correct@k,'\\n\",\n      \"'present_partial_precision@k,'\\n\",\n      \"'present_partial_recall@k,'\\n\",\n      \"'present_partial_f_score@k,'\\n\",\n      \"'present_partial_precision_hard@k,'\\n\",\n      \"'present_partial_f_score_hard@k,'\\n\",\n      \"'present_partial_correct@M,'\\n\",\n      \"'present_partial_precision@M,'\\n\",\n      \"'present_partial_recall@M,'\\n\",\n      \"'present_partial_f_score@M,'\\n\",\n      \"'present_partial_precision_hard@M,'\\n\",\n      \"'present_partial_f_score_hard@M,'\\n\",\n      \"'absent_partial_correct@10,'\\n\",\n      \"'absent_partial_precision@10,'\\n\",\n      \"'absent_partial_recall@10,'\\n\",\n      \"'absent_partial_f_score@10,'\\n\",\n      \"'absent_partial_precision_hard@10,'\\n\",\n      \"'absent_partial_f_score_hard@10,'\\n\",\n      \"'absent_partial_correct@50,'\\n\",\n      \"'absent_partial_precision@50,'\\n\",\n      \"'absent_partial_recall@50,'\\n\",\n      \"'absent_partial_f_score@50,'\\n\",\n      \"'absent_partial_precision_hard@50,'\\n\",\n      \"'absent_partial_f_score_hard@50,'\\n\",\n      \"'absent_partial_correct@M,'\\n\",\n      \"'absent_partial_precision@M,'\\n\",\n      \"'absent_partial_recall@M,'\\n\",\n      \"'absent_partial_f_score@M,'\\n\",\n      \"'absent_partial_precision_hard@M,'\\n\",\n      \"'absent_partial_f_score_hard@M,'\\n\",\n      \"'present_exact_advanced_auc,'\\n\",\n      \"'present_exact_advanced_ap,'\\n\",\n      \"'present_exact_advanced_mrr,'\\n\",\n      \"'present_exact_advanced_sadr,'\\n\",\n      \"'present_exact_advanced_ndcg,'\\n\",\n      \"'present_exact_advanced_alpha_ndcg@5,'\\n\",\n      \"'present_exact_advanced_alpha_ndcg@10,'\\n\",\n      \"'absent_exact_advanced_auc,'\\n\",\n      \"'absent_exact_advanced_ap,'\\n\",\n      \"'absent_exact_advanced_mrr,'\\n\",\n      \"'absent_exact_advanced_sadr,'\\n\",\n      \"'absent_exact_advanced_ndcg,'\\n\",\n      \"'absent_exact_advanced_alpha_ndcg@5,'\\n\",\n      \"'absent_exact_advanced_alpha_ndcg@10,'\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue                                      1680\\n\",\n       \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                           1680\\n\",\n       \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue                                      1680\\n\",\n       \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue                                      1680\\n\",\n       \"kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                 1120\\n\",\n       \"kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1       1120\\n\",\n       \"kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1           1120\\n\",\n       \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                    1120\\n\",\n       \"kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                1120\\n\",\n       \"magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                    1120\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse                                   1120\\n\",\n       \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue                                    1120\\n\",\n       \"magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue                                    1120\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1        1120\\n\",\n       \"kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1                 1120\\n\",\n       \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue                                       645\\n\",\n       \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue                                       582\\n\",\n       \"kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue                                       575\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                               560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue                                    560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue                                     560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                               560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse                                       560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue                                        560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse                                       560\\n\",\n       \"kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue                                     560\\n\",\n       \"kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue             394\\n\",\n       \"kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                    384\\n\",\n       \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                    382\\n\",\n       \"kpgen-meng17-kp20k-length-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                             382\\n\",\n       \"kpgen-meng17-kp20k-no_sort-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                            381\\n\",\n       \"kpgen-meng17-kp20k-alphabetical-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                       381\\n\",\n       \"kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue             380\\n\",\n       \"kpgen-meng17-kp20k-verbatim_prepend-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                   379\\n\",\n       \"kpgen-meng17-kp20k-random-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                             378\\n\",\n       \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 372\\n\",\n       \"kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue                 369\\n\",\n       \"kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse                  365\\n\",\n       \"kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue           362\\n\",\n       \"kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       351\\n\",\n       \"kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue       349\\n\",\n       \"kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse               348\\n\",\n       \"kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue     140\\n\",\n       \"kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue     140\\n\",\n       \"kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue            140\\n\",\n       \"kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         140\\n\",\n       \"kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue         140\\n\",\n       \"Name: exp_name, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"for col_name in all_eval_df.columns:\\n\",\n    \"    print(\\\"'%s,'\\\" % col_name)\\n\",\n    \"all_eval_df.loc[all_eval_df['exp_group'].str.startswith('meng17-one2seq-kp20k')].exp_name.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Dump all previous intermediate outputs into disk for later Colab use\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:25:09.297225Z\",\n     \"start_time\": \"2020-11-26T05:24:35.203264Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"with open('_eval_analysis.pkl', 'wb') as tmp_pkl:\\n\",\n    \"    pickle.dump([eval_dirs, eval_file_dict, pred_file_dict, kp20k_eval_file_dict, all_eval_result_dicts,\\n\",\n    \"                    one2one_eval_df, one2seq_eval_df, all_eval_df], tmp_pkl)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Start from here @Eric @Tong\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:26:12.403716Z\",\n     \"start_time\": \"2020-11-26T05:25:09.298711Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Stored 'eval_dirs' (list)\\n\",\n      \"Stored 'eval_file_dict' (dict)\\n\",\n      \"Stored 'pred_file_dict' (dict)\\n\",\n      \"Stored 'kp20k_eval_file_dict' (dict)\\n\",\n      \"Stored 'all_eval_result_dicts' (list)\\n\",\n      \"Stored 'one2one_eval_df' (DataFrame)\\n\",\n      \"Stored 'one2seq_eval_df' (DataFrame)\\n\",\n      \"Stored 'all_eval_df' (DataFrame)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# SAVE pred/eval paths and dataframes\\n\",\n    \"%store eval_dirs\\n\",\n    \"%store eval_file_dict\\n\",\n    \"%store pred_file_dict\\n\",\n    \"%store kp20k_eval_file_dict\\n\",\n    \"%store all_eval_result_dicts\\n\",\n    \"%store one2one_eval_df\\n\",\n    \"%store one2seq_eval_df\\n\",\n    \"%store all_eval_df\\n\",\n    \"\\n\",\n    \"# LOAD using store magic (doesn't work on Colab)\\n\",\n    \"%store -r eval_dirs\\n\",\n    \"%store -r eval_file_dict\\n\",\n    \"%store -r pred_file_dict\\n\",\n    \"%store -r kp20k_eval_file_dict\\n\",\n    \"%store -r all_eval_result_dicts\\n\",\n    \"%store -r one2one_eval_df\\n\",\n    \"%store -r one2seq_eval_df\\n\",\n    \"%store -r all_eval_df\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T05:26:17.547680Z\",\n     \"start_time\": \"2020-11-26T05:26:12.405349Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"30\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"28\\n\",\n      \"70224\\n\",\n      \"(15474, 121)\\n\",\n      \"(51584, 121)\\n\",\n      \"(67058, 121)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import pickle\\n\",\n    \"# load from pickle\\n\",\n    \"with open('_eval_analysis.pkl', 'rb') as tmp_pkl:\\n\",\n    \"    eval_dirs, eval_file_dict, pred_file_dict, kp20k_eval_file_dict, all_eval_result_dicts, one2one_eval_df, one2seq_eval_df, all_eval_df = pickle.load(tmp_pkl)\\n\",\n    \"#     data = []\\n\",\n    \"#     while True:\\n\",\n    \"#         packet = tmp_pkl.recv(4096)\\n\",\n    \"#         if not packet: break\\n\",\n    \"#         data.append(packet)\\n\",\n    \"#     eval_dirs, eval_file_dict, pred_file_dict, kp20k_eval_file_dict, all_eval_result_dicts, one2one_eval_df, one2seq_eval_df, all_eval_df = pickle.loads(b\\\"\\\".join(data))\\n\",\n    \"\\n\",\n    \"print(len(eval_dirs))\\n\",\n    \"print(len(eval_file_dict))\\n\",\n    \"print(len(pred_file_dict))\\n\",\n    \"print(len(kp20k_eval_file_dict))\\n\",\n    \"print(len(all_eval_result_dicts))\\n\",\n    \"print(one2one_eval_df.shape)\\n\",\n    \"print(one2seq_eval_df.shape)\\n\",\n    \"print(all_eval_df.shape)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## #1 Plot the testing curve\\n\",\n    \"Models to plot:\\n\",\n    \" - One2one: kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1\\n\",\n    \" - One2seq: \\n\",\n    \"     - kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     - kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1\\n\",\n    \"     \\n\",\n    \"Messages:\\n\",\n    \" - One2one converges much faster than One2seq\\n\",\n    \" - If we check the testing curve close\\n\",\n    \"     - The optimal time-step on transfered datasets is very different from train/valid dataset, which can lead to huge gap between valid-peak with real-peak (up to 16.6% for one2one, 7.2% for one2seq).\\n\",\n    \"     - One2seq is less rough than one2one. Which means for the top 10 results, the one2seq is more stable with respect to ranking, partly because it takes into account the correlation between phrases in a sequence.\\n\",\n    \"     - The more distance between the training and testing dataset, the larger the performance gap between real/valid peak. A few-shot learning might be useful for this domain adaptation.\\n\",\n    \"     \\n\",\n    \"TODO:\\n\",\n    \" - Run pred&eval of step=5000 for seq2seq\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:56: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: RuntimeWarning: divide by zero encountered in double_scalars\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:57: RuntimeWarning: divide by zero encountered in double_scalars\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"# exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse']\\n\",\n    \"\\n\",\n    \"exp_eval_df = exp_eval_df.sort_values(by='step', ascending=True)\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.beam_width == '200']\\n\",\n    \"# print(exp_eval_df.step.value_counts())\\n\",\n    \"# print(exp_eval_df.step.value_counts())\\n\",\n    \"\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[exp_eval_df.step % 5000 == 0] # keep % 10000\\n\",\n    \"exp_eval_df = exp_eval_df.loc[(exp_eval_df.step % 10000 == 6000) | (exp_eval_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# display(exp_eval_df)\\n\",\n    \"# plot 7 datasets\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_f_score_hard@10', title='All datasets, present_exact_f_score_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_precision_hard@10', title='All datasets, present_exact_precision_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_recall@10', title='All datasets, present_exact_recall@10')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@50', title='All datasets, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot kp20k and kp20k_valid2k\\n\",\n    \"kp20k_eval_df = exp_eval_df[exp_eval_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='present_exact_f_score_hard@10', title='KP20k and KP20k_valid2k, present_exact_f_score_hard@10')\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='absent_exact_recall@50', title='KP20k and KP20k_valid2k, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot recall@M\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_recall@M', title='All datasets, present_exact_recall@M')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@M', title='All datasets, absent_exact_recall@M')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='unique_pred_num', title='All datasets, unique_pred_num')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_sadr', title='All datasets, present_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_sadr', title='All datasets, absent_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_auc', title='All datasets, present_exact_advanced_auc')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_auc', title='All datasets, absent_exact_advanced_auc')\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot One2Seq (beam_width=50) hard metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:56: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: RuntimeWarning: divide by zero encountered in double_scalars\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:57: RuntimeWarning: divide by zero encountered in double_scalars\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse']\\n\",\n    \"\\n\",\n    \"# print(exp_eval_df.shape)\\n\",\n    \"# print(exp_eval_df.test_dataset.value_counts())\\n\",\n    \"# print(exp_eval_df.step.value_counts())\\n\",\n    \"\\n\",\n    \"exp_eval_df = exp_eval_df.sort_values(by='step', ascending=True)\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.beam_width == '50']\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# print(exp_eval_df.decoding_terminate.value_counts())\\n\",\n    \"# print(exp_eval_df.shape)\\n\",\n    \"# display(exp_eval_df)\\n\",\n    \"# print(exp_eval_df.path.unique())\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[exp_eval_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[(exp_eval_df.step % 10000 == 4000) | (exp_eval_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# plot 7 datasets\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_f_score_hard@10', title='All datasets, present_exact_f_score_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_precision_hard@10', title='All datasets, present_exact_precision_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_recall@10', title='All datasets, present_exact_recall@10')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@50', title='All datasets, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot kp20k and kp20k_valid2k\\n\",\n    \"kp20k_eval_df = exp_eval_df[exp_eval_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='present_exact_f_score_hard@10', title='KP20k and KP20k_valid2k, present_exact_f_score_hard@10')\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='absent_exact_recall@50', title='KP20k and KP20k_valid2k, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot recall@M\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_recall@M', title='All datasets, present_exact_recall@M')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@M', title='All datasets, absent_exact_recall@M')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='unique_pred_num', title='All datasets, unique_pred_num')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_sadr', title='All datasets, present_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_sadr', title='All datasets, absent_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_auc', title='All datasets, present_exact_advanced_auc')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_auc', title='All datasets, absent_exact_advanced_auc')\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot MagKP exps\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"One2One\\n\",\n    \"- V2\\n\",\n    \"magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue\\n\",\n    \"magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue\\n\",\n    \"magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue\\n\",\n    \"\\n\",\n    \"- V3\\n\",\n    \"\\n\",\n    \"One2Seq\\n\",\n    \"- V2\\n\",\n    \"magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue\\n\",\n    \"magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue\\n\",\n    \"\\n\",\n    \"- V3\\n\",\n    \"'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"\\n\",\n    \"- V3 + KP20k fine-tune\\n\",\n    \"'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plot curves\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 358,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-24T00:19:59.330576Z\",\n     \"start_time\": \"2020-11-24T00:19:58.545074Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(140, 121)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:187: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"nus\\n\",\n      \"0.4633748852793502\\n\",\n      \"0.47788649089015295\\n\",\n      \"kp20k\\n\",\n      \"0.35101416081356307\\n\",\n      \"0.3815645475895672\\n\",\n      \"semeval\\n\",\n      \"0.3658028499278499\\n\",\n      \"0.3562085137085137\\n\",\n      \"inspec\\n\",\n      \"0.429311717298083\\n\",\n      \"0.3910899485993763\\n\",\n      \"krapivin\\n\",\n      \"0.36650551796172\\n\",\n      \"0.3910339346288834\\n\",\n      \"kp20k_valid2k\\n\",\n      \"0.33763650831913017\\n\",\n      \"0.37125271223106043\\n\",\n      \"duc\\n\",\n      \"0.1995196632371957\\n\",\n      \"0.17192841429854416\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:60: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:61: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == \\n\",\n    \"# 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"# 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"                              \\n\",\n    \"# 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"\\n\",\n    \"# 'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                              \\n\",\n    \"# 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                              \\n\",\n    \"# 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"exp_eval_df = exp_eval_df.sort_values(by='step', ascending=True)\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.beam_width == '50']\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# print(exp_eval_df.decoding_terminate.value_counts())\\n\",\n    \"print(exp_eval_df.shape)\\n\",\n    \"# display(exp_eval_df)\\n\",\n    \"# print(exp_eval_df.path.unique())\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[exp_eval_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[(exp_eval_df.step % 10000 == 4000) | (exp_eval_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# plot 7 datasets\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_f_score@k', title='All datasets, present_exact_f_score@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_precision_hard@10', title='All datasets, present_exact_precision_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_recall@10', title='All datasets, present_exact_recall@10')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@50', title='All datasets, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot kp20k and kp20k_valid2k\\n\",\n    \"kp20k_eval_df = exp_eval_df[exp_eval_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"# plot_testing_curve(kp20k_eval_df, y_index='present_exact_f_score@10', title='KP20k and KP20k_valid2k, present_exact_f_score_hard@10')\\n\",\n    \"# plot_testing_curve(kp20k_eval_df, y_index='absent_exact_recall@50', title='KP20k and KP20k_valid2k, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot recall@M\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_recall@M', title='All datasets, present_exact_recall@M')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@M', title='All datasets, absent_exact_recall@M')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='unique_pred_num', title='All datasets, unique_pred_num')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_sadr', title='All datasets, present_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_sadr', title='All datasets, absent_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_auc', title='All datasets, present_exact_advanced_auc')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_auc', title='All datasets, absent_exact_advanced_auc')\\n\",\n    \"\\n\",\n    \"for test_dataset in exp_eval_df.test_dataset.unique():\\n\",\n    \"    test_df = exp_eval_df.loc[exp_eval_df.test_dataset == test_dataset]\\n\",\n    \"    print(test_dataset)\\n\",\n    \"    print(test_df.loc[exp_eval_df.step == 5000]['present_exact_f_score@k'].item())\\n\",\n    \"    print(test_df.loc[exp_eval_df.step == 100000]['present_exact_f_score@k'].item())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Compare valid scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-26T03:15:23.297590Z\",\n     \"start_time\": \"2020-10-26T03:15:14.460836Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(2130, 121)\\n\",\n      \"present valid_kp_df\\n\",\n      \"(70, 121)\\n\",\n      \"All data\\n\",\n      \"(2130, 121)\\n\",\n      \"absent valid_kp_df\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:93: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(70, 121)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:156: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"# pd.options.display.max_colwidth = 100\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_avg_only = False\\n\",\n    \"kp_exps = {\\n\",\n    \"    'Baseline': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'One2One MagKP+KP20k alternate': 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"    'One2One MagKP+KP20k mixed': 'kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"#     'MagKP_LN+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"#     'MagKP_Nsmall+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'MagKP_Nlage+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"\\n\",\n    \"    'MagKP+KP20k alternate': 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP+KP20k mixed': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'MagKP+KP20k mixed, no-copy': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"\\n\",\n    \"    'MagKP_LN+KP20k-FT': 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP_Nsmall+KP20k-FT': 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP_Nlarge+KP20k-FT': 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP+KP20k-FT': 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP20k+KP20k-FT': 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"} \\n\",\n    \"\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 5000 == 0) | (kp_df.step % 6000 == 0)]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = kp_exps.keys()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"if plot_avg_only:\\n\",\n    \"    for k,v in bar_values.items():\\n\",\n    \"        bar_values[k] = v[-1]\\n\",\n    \"    datasets = [datasets[-1]]\\n\",\n    \"        \\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"#     display(tmp_df)\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"    \\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = kp_exps.keys()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"if plot_avg_only:\\n\",\n    \"    for k,v in bar_values.items():\\n\",\n    \"        bar_values[k] = v[-1]\\n\",\n    \"    datasets = [datasets[-1]]\\n\",\n    \"        \\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"pd.options.display.float_format = '{:,.1f}'.format\\n\",\n    \"with pd.option_context('display.max_colwidth', -1, 'display.max_rows', None):\\n\",\n    \"    value_cols = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"    tmp_df = valid_kp_df[['exp_name', 'model_base', 'train_dataset', 'test_dataset'] + value_cols]\\n\",\n    \"    for col in value_cols:\\n\",\n    \"        tmp_df[col] = tmp_df[col].map(lambda v: v * 100.0)\\n\",\n    \"\\n\",\n    \"#     tmp_df.columns = [' '.join(c.split('_')) for c in tmp_df.columns]\\n\",\n    \"    df_list = []\\n\",\n    \"    for exp in kp_exps:\\n\",\n    \"        for i in ordered_datasets:\\n\",\n    \"            df_list.append(tmp_df[(tmp_df['exp_name']==exp) & (tmp_df['test_dataset']==i)])\\n\",\n    \"    ordered_df = pd.concat(df_list)\\n\",\n    \"    tmp_df = ordered_df[value_cols]\\n\",\n    \"\\n\",\n    \"#     display(ordered_df)\\n\",\n    \"#     print(tmp_df.to_latex(index=False))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Compare peak scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-10-26T03:16:23.533860Z\",\n     \"start_time\": \"2020-10-26T03:16:14.748402Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(2130, 121)\\n\",\n      \"present peak_kp_df\\n\",\n      \"(70, 121)\\n\",\n      \"All data\\n\",\n      \"(2130, 121)\\n\",\n      \"absent peak_kp_df\\n\",\n      \"(70, 121)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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KzRoUQQgghhPgn/OuDQoAEhIQQQsjvAiGEEEIIIe6xf/3ysb9ycXXEQXfvm11gMJOdlX/P6xVCCCGEEEIIIYT4N3rogkIOOi0dR++85/XuXvgy2WUo16pVIw4cOIxer7/nbbjZrFnvUbv2Y3Tr1p0dO7ZhMBjo1u3Nf/Sa/3YXL54nLi6O5577z4NuCgBJSYn88stPvPxyl9uWnT17OmfPnqF69RrMmDHnPrTuzh079jPbt2/j8uVY7OzsqV37Md58szeVKlVWyuzfv5ePP95ETEw0w4e/y6uvdlPOFRQUMHv2dM6fP4dGo2HIkJG0bNn6QXRFCCGEEEIIIUQZPHRBoUdR586vPegmlMhqtaJSqe7bko6LFy/w449H/lVBoV27tt82KHTt2lUOHfqGiIhvUavv/4pNi8WCRqO5ZZnVq1cQFXWJ3r374eTkTG5uLhcu/MHUqeMZNWocTzxRH4CaNWvx3nuzCQ/foDw3Li6WvLxcfvzxe/R6PZ9+uoMTJ44xdeoE5s5diJOTM56enpQr5w6AwWAgKSkBo9EEgIODA97ePuh0un9mAIQQQgghhBBClEiCQndh69bNHDv2M5mZGQQHD+Hpp58DYPr0KcTFxWIyGalUyY+JE6fh6upKXFwMs2ZNp6CgAKvVQvv2HenR4y1MJhOrVi3n119PYDKZqVGjBqNHTyw2C2nt2jDy8/MZOnQke/fu5uDBCFxcXImKuoSLizOhofPx9PQCYMuWjRw69F8sFgteXhUYP36ycq6s1q4NIyYmmvz8PJKTk6lSpQoTJ4bg7OzM2rVhJCTEk5+fR0JCPEuXriYj4xoffvgBmZkZmEwmunbtTocOnSgoKCA0NISYmCg0Gi3+/lWYOXMuAPv2fcWXX36OxWLB2dmZMWMm4O9ftdT+abVa1qxZSV5eLn369KBBgyBGjhxbYvtLG1cHBwdGjx5Gixatef31N4iOjmLMmOGsWLEWDw9Pxo0bSWZmJgaDgccfr8PYsZOws7MDYPPm9Rw8GIFKpcbR0ZHly9fwwQfzSUpKoE+fHlSuXJnQ0PnF2pKXl8vw4QMxGAro168n7dt3KHHGl9Vq5YMP5nPy5DHs7OzR6x1ZsWIdAD/8cIR161ZhNptRq1VMnjydgICa/PTTj4SFLcVqtVKunDsTJkzG17cyJ08e56OPPqB+/QacO3eW3r3706BBEEuWLOLSpYsYDAZq1arNq692w87OjkuXIklNTWHOnPdJSIgHCoM//v5VqFSpMkuWLGLChKkYjQYCAx9DpVKhVqvJzMzkwoXzyrbmX3+9n2nTZgIQEFCLGjUC+P3332jcuCkpKclotXY4Ozuj1WqpWLGyMrbp6ekkJsZTrVqNO7pPhRBCCCGEEEL8PRIUugtqtZqVK9cRFxfDwIH9qV8/CHd3D0aMGEO5cuUAWLVqOVu2bGTQoGF8+eU2mjdvSZ8+AwDIysoCCgM4Tk5OrF69CYDlyz9i8+b1BAcPueX1z507y8aNW/H29mHevFC2bfuU4OAh7N+/l/j4eMLCNqBWq9m+fRtLly4mJCT0jvv422+nWL/+Yzw8PJk9ezobNqxh6NCRAPz660nWrdtCuXLlMJvNjBw5mJCQUKpUqUpeXi79+79F3br1iImJJjs7m/Dwz4v0+/TpU3zzzUGWLVuNvb09R4/+wJw5M5QgSGn9GzBgID/+eKTE4MvNbjWu06bN5J13+lCrViALF85l9OgJVKjgjc1mIyQkFDe3cthsNkJDQ9izZyedO7/Gvn1f8f33h1mxYi1OTs5kZmagVqt5991xLFv2IWvXbi61LXq9EwsWfMiAAW+xYcPHpZaLjLzA8eO/8PHH21Cr1cpYxcXFMm9eKMuWrcbPzx+j0YjZbCI9/RqhodNYsmQV1apV56uvdhASMoVVqzYAEBUVyZgxExg1ahwAc+fOpEGDhkyYMJXLl+NYsmQR58+f4/nn27NgwSxmzpyL1Wpl48Y1nD//B1WrVsNsNtOt25sEBTXk119P8vjjdYq02dHREY1Gg5+fH7GxMaSmpuDmVnj/azQaKlashEqlwt+/CrGxMaSlpeLs7IxGo1FmLtlsNlQqlFlDQgghhBBCCCHuHwkK3YWXXnoZAH//qtSqFciZM7/TqtVTRER8xYEDEZjNJvLzC/Dz8wegQYMgli37EJPJRMOGjWjYsBEAP/xwmNzcXA4d+gYAk8lIQEDN216/Xr36eHv7AFCnTl2OHfsZgO+/P8wff5yjX7+eAFgsZpydne+qjy1atMbDw1Pp7+LFC5RzzZu3VIJfly/HERsbTUjIJOW8yWQiJiaagICaxMXFsHDhPIKCnqRFi1ZKvyMjL/LOO32AwsBAdnbWbftXVrcaV3d3DyZOnMbw4QN57bU3lDZZrVa2bg3np59+xGq1kJ2djYODw5/1HaFz51dxciocy+uBj3upYsXKWK0W5s6dScOGjWjRojAXz7FjP9OsWQvlXrK3t8fe3p6TJ09Qo0YtqlWrDsCLL3Zi4cJ55OXlAlC5sh9169ZT6v/++8OcO3eGTz4Jx2AwYLXaqFKlKnq9/s+ZPiqOHDlEfHw8a9eGk5SUSL9+b9KjR2/Kl/cu8vpcV1BQgLu7OxrNjY8Rs9kMgLOzM2aziStX0oiJicLe3h6jsehW4jdmGdnw8ip/z8ZSCCGEEEIIIUTZSFDob7LZAFScPn2KHTu+YMWKdbi7u3PgQAS7dn0JwNNPP0fduvX45ZefCA/fwJ49u5g2bSY2G4wePYEnn2x8R9e0t7dXHqvVGiwWy59tsdG7dz8laFWaPXt28fnnnwDQo8dbPP98+zL18TpHR/1N52y4uZUrdRbMli2fc/z4MX766QdWrVrGxo2fYLNBhw6dGDBg4B31r6xuN64XLvxBuXLlSEtLVY4dPBjBb7/9yvLlq9Hrndi0aR2XL8ddr/GOrn83nJ2d2bz5M06dOsGJE8dYsWIJ69aF3+LahTNsSnPza3S9/OzZ7+Pp6UlsbCyBgbWVMxqNFqPRSHR0FPXrB6HVavHz86dq1eoUFOSTnZ1N+fJFgzZmsxmTyfTn8y4B4OnppcxwAsjOzqZlyzb4+1fl6tUrqNVF8xrVqhWI1WolMzNDWUomhBBCCCGEEOL+uf9Zb/8f2LNnF1A4SyYy8jx16tQlOzsbJydn3NzcMBqNShmA+PjLeHh48uKLHenb923Onj0DQKtWbfj00y0YDAVAYf6ZmJjou25Xq1Zt2L59m/LF3Gg0cvHihWLlOnToxIYNH7Nhw8elBoR+/PF70tPTAdi3b7cyu+mv/P2r4ODgQETEHuVYbGwMubk5pKamoFZraNPmaYYPH01GRjrZ2Vm0bNmaiIg9pKamAIWJkP/449xt++fk5EROTs5ty91qXM+e/R9ffPE5GzZsJSMjnR07tgGQk5ONm1s59PrCaxw8GKHU17JlG3bs+EKZhZOZmfFne5zJzb19e8oiPT0dg8FAs2YtGDhwKM7OziQmJtCkSXN++ulHJUBlNBrJy8ulTp16REZeIDY2BijM0VSrViB6vVOJ9bds2Ybw8I2YTGbUajUZGRkkJiYA/LlcLYNq1arz+++nMZvNJCTEEx0dxZUraRw9+r0yowoKA4EFBfm4uLjg4+Or5AJq1eopdu/eDhS+N86dO0uzZi1Qq9Xk5eXi6OhYrF1qtZpy5dxJSkpUZhkJIYQQQgghhLg/HrqZQgUGM7sX3nomzN3WW1b29vYMGtSPjIwMxo6dhLu7B82ateDAgX306PEaFSpUoHbtx5TgzzffHOTAgQjs7LSoVCpGjBgNQM+efVi7NowBA3r9uSuVin793qZq1Wp31Yd27TqQmZnBsGHvAIVLol555XVq1qx1x3U1atSYOXNmkJiYgL9/FYYOHVViOa1Wy7x5i/joo4Vs3boZi8WKh4cHM2bM5dKlSFauXPpnWyz07NkHL6/yeHmV5513BjNhwrtYLFbMZhPPPNOW2rUfu2WbnnyyCVu3htO7d3eCghqWmmi6tHH19PRi+vQpTJ4cgru7B9OmhRIc3Ic6dZ6gXbuXOHLkMD17dqV8+fLUrx+EwWBQxjUtLZV33umLRqNBr9ezbNlqatQIwN+/Cm+91ZUqVareNtfRraSmpjBvXigWiwWLxUKzZi2oU+cJbDYr/fsHM378KGw2G/b29kybFkqNGgFMmhTC5MnjMJtNuLi4MnbsRKWugoL8P/P1qLBarXTr1oP169fQv39PVCoVer2ekSPH4ePjS5s2z7Bp03r69BmAr29F3nqrG9WqVefJJxsREbGH7t17kpaWBhQm3P78863k5uZy4sQxPv54M6NHj8fHx5ennnqGzZvX061bZ9RqNePGTUKrtSMuLqbEgNDNrFYbZrMJrfah+0gSQgghhBBCiIeWymaz/fNrY8ro6tUcrNaizUlOjsXHp8oDatGDp9WqMZut9/WaN+92Jh6s67uB+fj4YjAYiI+Po0qVquh0DiWcu4ynpyc5OTnk5+cpO4VZrVauXr2KRqMmOzuL/Px8VCo11apVR6vVEhMTzZYtGzAajXTs+Aqurm7YbFbi4y8TFxdLmzZPA4UzutRqNSqVmqpVqynL10wmM7Gx0bi4uKLRaPDyKk9eXi46nQOXL8fh4uJCRkYGFStWwsXFhdzcHDQaDTqdA1arlStX0sjOzqJ69YA/g3jifngQny33wqP+O+FBKF/ehbS07AfdDPGQkPtFlJXcK+JOyP0iykruleLUahWenqXnGpZ/ywvxL2W1WsnJyaZq1erKDCVnZxcyMzPx8rIvds7FxZmrV68qu4Fdp1ar8fDwICYmmooVKxIbG4NKpSI/Px87OztMJhPduvXk6tUrbNv2CfHxl//cVcyfN9/sTbVq1cnPLyAh4TJ2dvY4OzujUqnIy8vFyclZ2UksJyeHihUrAXDt2jUKCvKVct7e3ri4uABgsVhJSUnGbDajUqlwcHCkcmV/CQgJIYQQQgghxH0mQSFRTP/+wQ+6CbeVnn6NUaOGFjv+1FPP0Lfv2w+gRdC//1vFkmLXqVOXsWNv7My2e/cOvvjis2LPnTw5hJo1A4scMxoNgAqdTqcc0+kcyM/PK/GcyVS4/Orm3cCuS0tLLbJTmNVqITk5CY1Gg4+PL5mZ6Tz22ON4eAzEZrOh0WioUePGzB2drnAGn8FQQKVKlQHIyEgnOTkZsKHT6XB391ACP87OzkpQyGQykZqaQmpqCrVq1cbV1RVXV9cyjqoQQgghhBBCiH+KBIXEQ8nd3aPUHc8elLVrN9+2TMeOnenYsXOZ6rNabcVmz2g0aqxWS7Fz+fn5fwaFiu/ilZ+fT35+Pt7ePphMhdvCu7mVw9e3olLGZrOSkpKCzVa4Pb2vr2+R+u3t7ZVlYdd3h/P3r1pq26/njhJCCCGEEEII8e8l6zWE+JdSqwvzAd3MYrGiVmuKnLPZbKSkJOPi4qIs5bru+jlvb28AZcc3b28fpUx+fh5paak4ORUuT6tevRrJyUmYTEY0mhv73mdmZuLqWu4f6asQQgghhBBCiPtPgkJC/EvZ2+sA259LxQoZDAXY29sXOWe1WikoyCc9PYPc3FxiY6MBiIy8SG5uLgUF+SQkJHDhwnlyc3MBuHQpkry8PKVOmw0MBgMODjqysrJRq9UkJyeh+jObdF5eHmazWVkeJoQQQgghhBDi4SdBISH+pdRqNc7OLqSlpWG1WsnLyyMnJxs3N7ci5wAqVfJDpQI/Pz8qV/YHoGrVauj1egICauLs7IxOp6Nq1aoABATUwMXFCa1WTW5uHlarBScnJ0CF2WzGZDIX2R4+MzOzxJlIQgghhBBCCCEeXpJTSIh/MR8fH5KSkrh48QIajQZvb190OgdMJhM5OTno9XoiIy+i0WioWLESer2TMgPIZivMO2SxmMnMzECFipjoGAAunL9ABRcvHO10ZGdnAZCRkaFcV6fTYWdXmDvIarWSnZ2lJJgWQgghhBBCCPH/w0MXFHJ3s0drr7t9wTtkNhpIzzTetpzRaCQsbBlHjhxCq9Wi0+no2/cd2rR5+m9df9eu7XzxxafYbDZUKhU9evTihRdeVM6fPn2KlSuXkp6ejsViISioIUOHjrqnuzjt3bub2bOnM2rUOF59tStQGJYMy9EAACAASURBVFjo2rUzeXk57Nnz37uuOykpkQED3lLqiI+/zLvvDqVnzz506vQKr73WEXt7e+zs7LFaLfTu3Z+2bV/4R+s+cuQQ69evwWQyYrNBhw6d6N69p1Lvrl3b2bJlIzabjWbNWjBy5FjUajV79+7mxx+PEBo6/67HY+jQd+je/S1atmzN4cOH8PLy4vHH6xYrp9FoqVzZr9hxOzs7AgNrFzmm1aoxm63o9Xpq1378prL21K1bl4LknCLl129dR8/X3qJu3bokJ6eU2la1Wk2tWoGlnn9Y3GqcH4STJ49jNptp0qTZLctlZmYwfvy7FBQU8Pzz7ejRo9d9amHZmc1m9u37ioMHI8jISEenc6BZsxZ07/4Wer1eKbd06WK+++4bkpIS2bTpE6pXD1DOxcXFMmvWe2RmZuLm5saUKdPx8/N/EN0RQgghhBDikfHQBYW09jqiZr16z+utPvkL4PZBoYUL55Kfn8/mzZ+h0+mIiopk9OjhuLq60qBBw7u+fuXKfixZsgpXV1dSU1Po27cH9eo1wM+vMvHxl5k8eSwzZ84jKOhJrFYrS5cuYurUCXz44fK7vmZJatUKJCJijxIUOnXqBK6uruTl5dzmmWV36VIkY8eOYMiQETz33PPK8dDQeVSvHsCFC38wcGB/GjVqSrlyNxIbnzx5nH37vmLy5PfuSd0eHl7Mn78IL6/y5OTk0L9/Tx5/vA716weRmJjA+vWrWbduC25ubowZM5z9+/fSvv1L92wcrjty5BC1az92V8EKi8Vy10u6Nn66gW6d37ir594LZnPRJWr/tL8zzv+EU6dOkJ+ff9ug0PHjv+Di4sLKlevuU8tusFqtqFQqJbdUSYxGIxMmjKZmzVpMmTKdChW8MRgMHDwYwciRg5k37wPc3T0AaN36aV5//Q2GDHm7WD3vvz+HLl1e54UXXmT//r0sWDCbjz5a+Y/1TQghhBBCCPEQBoUepOTkJL755iBffPEVOl3hbKXq1QPo1asf69ev5sMPV7B3724OHozAxcWVqKhLuLg4Exo6H09PLwC2bNnIoUP/xWKx4OVVgfHjJ+Pp6UXDho2U61So4I2npxepqan4+VVm06Z1dOjwMkFBTwKFMzcGDx5B164vc/r0KerXD+K11zrSrl0Hjh37matXr9C9e09efbUbAHFxMXz44QdkZmZgMpno2rU7HTp0KrGPFStWIiMjg+joKKpVq87evbt58cWXWLdulVJm+vQpxMXFYjIZqVTJj4kTpykzlsLClvHNNwdxdXUjKOhJTpw4VmSr9jNn/seUKeMYN24SzZu3KrENtWrVRq/Xk5SUUCQodDt3WnedOjeCA87OzlSpUrjrVv36QRw69F9at34Kd3d3oHAr+b17dxcLCqWkJDNp0lh69OjFc8/9p8i548d/YfXqFRiNBiwWC7169Ss2++nnn4/y/feHOX78F3bv3km3bj14qUNH9u77ii+++ByLxYyzszPjxk2iSpWqfLV7JwcOHsDdvRzR0dFMnDiVDz9cyGOP1eHMmd9JS0vj2WfbMmjQMAC2bg3nv/89gNVqQavSMir4XWpWr8nisA8AGDphMBp7LSEhs1CrVaxbt5rY2GiMRiNPPFGfiROnAKUHBEoydOg71KwZyMWL50lLS+XZZ/9DcPAQ5dwTT9Tn7Nn/YW9vz4IFH3L06Pds2rQOg8GInZ0dw4a9S926TxAXF8OsWdMpKCjAarXQvn1HevR4C5PJxKpVy/n11xOYTGZq1KjB6NET0ev1zJr1Hvb29ly+HEdqagp16jzBlCnT+eWXn4qNc2kBvitXrrB48XxSUpIxGAy0bfsCvXr1Iz39Gm+/3ZvQ0HnUrv04+/Z9xa5d21myJIzY2BgWLpxLQUE+RqORTp1eoWvXHgDk5OTw0UcL+eOPs6hUaurXb8DLL7/Kzp1fYrVaOX78F5577nneeqtPsbacPHmcZcs+JC8vlz59ejBq1Fjq1w8qVi49/RrvvTeF9PSrADRq1IThw0cDsHnzeg4ejEClUuPo6Mjy5WtQq9Vs2rSBffu+AuCxx+owcuRY9Ho9a9eGkZAQT35+HgkJ8SxdupqMjGulfoaEhS3luef+U+QzRafT8dJLL+PvX4WPPvqAkJBQAOrXb1DimKenX+PChT9YtGgZAG3bvsCiRfNJT09X3oNCCCGEEEKIe0+CQnfg0qVIKlXyw9XVrcjxOnXqsmbNCuXnc+fOsnHjVry9fZg3L5Rt2z4lOHgI+/fvJT4+nrCwDajVarZv38bSpYuVL0zXnTx5nJycHGrXrq1ct3fv/kXKaLVaatUKJDLygvIlsaCggLCw9SQlJdKrVzfaty9cNvXee1MICQmlSpWq5OXl0r//W9StW48qVaqW2M927Tqwb99X9OkzgN9/P02fPgOKBIVGjBijBGtWrVrOli0bGTRoGN9/f5gff/yeDRu2otPpmDJlfJF68/LyGDVqCLNnL6BRoyaljvPJk8cxGo1KwuSy+Lt1x8bGcPbs74wbNwkoDPb4+Pgq5729fZTt3K+7ePECM2ZMYfToCSXOEqtVqzbLl69Bo9Fw7dpV+vd/iyZNmhdZ8te0aXNatWpD7dqPKUG807+dYv+uHbw/dhL2dnb8cvoUM6ZM5MOQUFCp+P33X9m8+RNlWZlKpSI1NZkVK9aQnZ1Dt24v89JLL+Pn50+7dh3o3r0nWq2a7/cf4oOVC1kxfyUjg99lx74dLJ27HI9qFUhOTmHp0sXUqfMEQ4eOxGq1smjRfHbv3slLL3Uu8+twXUxMFIsXL8doNDJwYF/q1q1Hy5atAYiKimThwiVotVoSEuLZsGEtH3ywBCcnZ6KiLjFmzHC+/HIPX365jebNW9KnzwAAsrIKcx9t2bIRJycnVq/eBMDy5R+xefN6JfAUFXWJxYuXo1ar6dv3TY4f/7nEcS5NaOg0+vQZQIMGDTGZTIwYMYjHHnucxo2bMWlSCO+9N4UpU6azevUKVqxYi1arxdfXl8WLl2Nvb09eXh7vvNObJk2aU7VqNT76aCGOjo5s2LAVtVpNRkYG5cqV4+WXu5Cfn8/QoSNLbUvDho0YMGDgbZcrHjiwDx8fH2Xm4PWx2rfvK77//jArVqzFycmZzMwM1Go1R4/+QETEHlauXIde70RoaAgbNqxh8ODhAPz660nWrdtCuXLlMJvNjBw5uMTPEG9vH86ePcOwYe+SlZXFggWzSUiIp2nT5vzxx1kWLVpGePhGsrKybrnUNSUlBS+vCsqsN41Gg5dXeVJTUyQoJIQQQgghxD9IgkJ3wGazlalcvXr18fb2AQoDRseO/QzA998f5o8/ztGvX2HemuuzQG4WHR1FaGgIISGz0Okc7ui6bdsWLpfy9a2Ii4sraWmpWK1WYmOjCQmZpJQzmUzExESXGhR69tn/0K9fT/z8/Gnd+uliy5MiIr7iwIEIzGYT+fkFSt6PU6eO8+yzbXF0dASgffsObNiwVnmeTudA/foN2L79cxo0aFhs6dCUKeOxt9fh5OTErFnzcHFx4eLF88yaNR2A/Pw8srKy6NOncAbGU089Q9++b9913ddduXKFCRPeZdSo8Xh5lS/TWEdGRjJ58ljmz19M1arVSiyTkZHOnDkziI+PQ6PRkpWVSVxcLHXrPnHLuo8cOUxUXCzDQiYCYAOyc28s3wusVRe1zZXEy5kAGA1mguq3RKNRKzOeEhLi8fPz5/z5c2zevL4wmbTFxuXE+FKve+zYT1y8eJ6dO78srNdowN//7nK6tG//ElqtFq1Wy3PPPc/Jk8eUoNB//tNOeX1+/vkoCQnxDBnyjvJci8XCtWtXadAgiGXLPsRkMtGwYSNlNt0PPxwmNzeXQ4e+AcBkMhIQUFN5fuvWTysz+QIDA0lIiKdx47K1Oz8/n1OnThRJup2Xl0tMTAyNGzejYcNG/Oc/LzBkyABmzVqgvM8LCgpYunQukZEXUKnUXLmSRmTkBapWrcaPPx5hzZpw1OrCzR7vZPZbWdWp8wSffvoxy5Z9SIMGDWnatDkAP/xwhM6dX8XJqfBzxs2t8NrHj/9C27YvKMc7derChx++r9TXvHlLpZ2XL8eV+hmSn5/H44/XASA8fD01a9Zi5sy5HDgQwcGDEUDhLngJCZdxda1zz/sthBBCCCGE+HskKHQHatQIICHhMllZmUVmC5058z9q1LjxpdTe3l55rFZrsFgsQGFwp3fvfrz00ssl1n/5chxjx45g7NhJRZZZBATU5MyZ34skszabzVy4cJ433riRGLnodQt3nQIVbm7l2LDh4zL3U6/XU6dOXVauXMKSJWFFzp0+fYodO75gxYp1uLu7c+BABLt2fan071a5RzQaNaGh85k8eRwhIROZPn1OkeDN9bw/N6tZM1Bp+61yCt1N3VC4bGXkyMHFln95e/uQnJyk/JySkkyFCt7KzxUqVCAvL49Tp06UGhRauHAuLVu2YfbsBahUKt54owtGo6HU8bnBRrunnqHPayXn+3FwcCx2rPhrb8FkMjF16niWLl1NnTqPE382htf6dSn9qjYbEydOKzJDysfHG7PZChQGMZOSkggLW0Zk5EW0Wi2zZs3D379qqXXGxcWSlpaqBGkK67EQExOFwWAgMzODpk2bM3XqDKAwP01UVCRXrqRRsWIlxoyZSExMNOHhG9izZxfTps3EZoPRoyfw5JMlR3p0upLff2VhsxXm0FmzZlOp+Y4uXjxPuXLlSEtLVY6FhS3Dw8OTdeu2oNVqGTVqCEbj7XOU3St169Zj/fotHDv2M/v37yU8fAMrVqylMKRYEht/fave/N51dLyRHNpms5X6GXLu3BlUqsJgV1TUJYYOHQXAU089zapVhUvBrl27ioeH5y3b7+3tzZUrqUqOLIvFwpUraUXec0IIIYQQQoh7T/2gG/Aw8fWtyDPPtOX99+diMBR+uY+KimTTpnXKjJVbadWqDdu3b1OWdhiNRi5evABAQkI87747jJEjx9C8ecsiz+vZsw9ffbWDU6dOAIXJX5cv/5DKlf1um9za378KDg4ORETsUY7FxsaQm3vrxNE9e/ahX7/gYoGU7OxsnJyccXNzw2g0smfPLuVcw4aN+Pbbr//MAWNl//69xeq1s7Nj1qz5mEwmQkImYjabb9mOO3GndWdmZjBy5BBefbUrHTsWXSL11FPPcuTId6Snp2O1Wtm9ewfPPnsjaOTq6srixcvZv38vW7eGl1h/dnY2vr6+qFQqjh37iYSEyyWWc3JyIifnxuvRqlUbDn5/mLSrhflhLFYLF6IvlWkMbnY9l9H1L9Y79m0vcl7vqCc3L1f5uXHjZnzxxWdKECUrK5PExATlfHJyMiqVivfem8XatZuZOnW6MlPmryIi9nLt2jXy8/M5fvwXgoJu5MzSaDR4epbHza0c9esH8fPPR4mKutG/6OgoatYMRK93onHjpnTr1oO+fd/m7Nkzyvh8+ukWDIYC4PpMnujbjsdfx7kker0T9esHER6+QTmWkpLM1atXAPj00y2YTGbWrt1CePgGLl48D0BOTjYVKnij1WqJiork9Olflee3aNGarVs3KTP+rs9CcnJyuu37sKwSExNwcnKmbdsXGDZsFOfP/4HVaqVlyzbs2PEFeX++zpmZhddu1KgpX399gLy8XGw2G199taPUZZe3+gypWrU6Z878DkD16jX48ccjQOGsSICzZ/9HXl5eqffJde7uHgQE1OLrr/cD8PXX+6lZM1CWjgkhhBBCCPEPe+hmCpmNhj93Crv39ZbF6NETCAtbSs+eXbGz02Jvr2PEiNFKEuhbadeuA5mZGQwbVrhUxmq18sorr1OzZi1WrFhCVlYGa9aEsWZN4eycQYOG0bJlS/z9qxAaOp+wsGVkZGRgsZhp0KAhoaHzbntNrVbLvHmL+OijhWzduhmLxYqHhwczZsy95fOqVatOtWrVix1v1qwFBw7so0eP16hQoQK1az9205f1p/j999/o06c7Xl7lqVPnCbKzs4vVURi8WcDkyWOVWT33yp3UHR6+kcuX49i580tlydTrr79Bhw6dqFSpMr179yc4uA8ATZo04/nn2xd5vrOzMx98sJRx40ZSUJBfLDA4aNBQFi6cR3j4RmrUCCgym+xmL7zwIrNmTefbb/9Lt2496NixE/1e787UD+ZhtVkxm820adKMWtVq3NFYODk5079/MG+/3QsfHx8a1y06s6bry914d+pIHJwcCQmZxYABwWzcuJZRo4YAKuzs7Bg7dgIVKvhitVrJycmmatXqaDQa9Ho9zs4uZGZmUqGCQ7Fr16xZi3HjRpCVlUWDBg2VpWNQOAvFxcWFgoJ8fH0rMm3aTObOnYnBYMBkMuLvX5V27TrwzTcHOXAgAjs7LSqVihEjChMn9+zZh7VrwxgwoNefS7JU9Ov3dqkztkob59ISTU+bNpOPPvqAXr0Kcw/p9U5MnDiNlJRktm37lFWrNuLu7s6ECVMICZnEmjWb6N27PzNnTuPAgX1UqlSJBg1uJIMeNuxdPvpoIW+91Q2NRkNQUENGjhxLmzbPMHnyWPr06VFqoumyOnXqBJ98Eo5Go8VmszJ27ETUajXt2nUgLS2Vd97pq7xuy5atpnnzlkRHXyI4uC8AtWs/Xixv2XW3+gwpV86ZGjVqEhGxh549+7JgwWz69etJkybNKF++Avv27WHChKlKXYsXL+C7777l2rWrjBw5BFdXN8LDPwNg7NhJhIaGsH79GlxcXJg6dfpdj4cQQgghhBCibFS2siasuQ+uXs3Bai3anOTkWHx8qjygFj14Wq1aWb7zMMjLy0Wvd8JqtTJ37ky8vMrzzjuDH3SzHiparZqcyJJnBjkH1FByCf1VRT+3Uu8VrVZNQXLJs1IcfJxJTk4p8dz15WMFBfnExsYSGFhbOXf16lXy8/OUhNfXDR36Du3adaBFi1Y4O7sQFRVJYOBjxZYWpqWlYjab8fWtqBy7vnyscOmWCicnJ8qXr3Bft65/VNyrz5aCggLGj3+Xhg2f5JVXXsfV1RWz2czJk8dRqQpnoN1Lj/rvhAehfHkX0tKKB/iFKIncL6Ks5F4Rd0LuF1FWcq8Up1ar8PR0LvW8fNMS99TMmSEkJydiMBgIDHyMN9/s9aCbJO4Bq9WmJEq+TqNRY7UWz9djtVoxGo24u3tgMpnu6DoajYYqVarh4OCAxWIhJSWZpKREJZm5+PdxcHBg4cKP2LVrO5MmjSE3NwdHRz1PPtmYbt3efNDNE0IIIYQQQtyCBIXEPTVnzvu3LyQeOmq1Cqu16KwSi8WKWq3583FhEurc3FxGjBiNh4fnLZOOQ2HOIpPJhI+Pr1I2KyuTzMwMDAYDrq5ueHt7Exl5UUlAnJubS0pKEiaTGUdHR3x9fbGzs7/ldUpy9Oj3hIUtL3Y8OHgwzZu3uuP6/q6bd9m72V/zXS1YMJszZ/5XpIxGo2Ht2s3/eBtvRavV0qXL63Tp8voDbYcQQgghhBDizkhQSAhxW/b2OsCG0Wj48zEYDAXKrmfXk1BXr16DS5cucvXqFTIy0pXnR0ZepFKlyuj1hbtaZWZmUtLCVa1Wi6dneXJzc/5MzHwjsGQ2m0lIuIyPT0WcnZ25ciWNhISE2+YSKknz5q0eSPCnNDfvsncrY8dOum0ZIYQQQgghhCgr2X1MCHFbarUaZ2cX0tLSsFqt5OXlkZOTjZubm5KE2surPFqtloCAmri4uOLi4kblyoXLvqpWLVwSBoXBnStX0pQAkdVqVXbm0mrtsLe3R6PRYLVaSUlJRq93QqPRkJOTjU7ngKurK2q1Gi+v8hgMBmUnQCGEEEKIO5WVlcnEiWNo27YVr776EgcORNz2OcOHD6RVq0ZFdrq9XT27d++gW7fO/Oc/rXn33WFcuZJ2z/sihBB3Q4JCQogy8fHxwWazcfHiBRITE/D29kWncyAvLw+brTDnkEqlQqu1w8HBEbPZhEZTuLxMq9UqOYni4y9jMhnJyipMmH3x4nmuXCnc8t1kMhIfH8fVq1fJyclBpVJRsWJhImqDwYBOp1Pao1arsbOzw1jGnQOFEEIIIf5q4cJ52NnZsWvXAaZNC2XhwjlERZW84QfAgQP7sFiK51S8VT2nTp0gLGwZc+YsZO/eb6hYsSLvvTf5H+uTEELcCQkKCfH/kMViJj7+MufP/0Fk5EUyMkresexmmZkZf/7X6sa6LovFotQTHR2Ni4srgYG1CQioqcwSur5MLDr6ErGxMcCNJNT29vbUrv24kjMoPz8fm81GYOBjVK8eAEBg4GOUL18eAFdXN2rUqImnpyeurq5UrFgJrdYOKJxRVDzZtaZYriMhhBBCiLLIz8/nu+++YcCAgej1eurXb0CrVm3Yv39vieVzcnJYt241gwYNv6N6fvjhCM8805bq1WtgZ2dHnz4D+PXXkyQkxP/jfRRCiNuRoJAQ/w9dz/ETEFCTihUrkZiYiNFS+k5gBkNBiccTExOL1JOSklSkbHJyEjabFZVKTc2agVSo4A0UTUJ9nc1mIyUlGW9v79smoS6JWq0uFgCyWi3FAkVCCCGEePTczTKwYcOCMZvNVKxYSTlWubI/+/Z9VWI9ixbNJz4+jhEjBgLQrt3TbNiwhsuXY1GrNfj7V1HK1qhRi+joKKDwbyDbTckUrz+Oior8e50WQoh74KFLNO1STofDXew2dDsFJiPZGbIMRTz8ruf4qVq1OhqNBr1ej6urC9kFOXg6uRcrb7EU5ghycXEhIyNDOW6z2cjKyiIgIACdzg6dzg5XV1eys7NxctJTUFBATk72n8mlIzGZjDg6OgJFk1Df3K6CgnwSEhKuXwEonoS6NDqdjszMGzOerFYrRqNJSXwthBBCiEfXzcu3Ll68wLhxIwgIqEn16jXIyspkzpyZHDv2E25u5QgOHgrYMJmK/8Pshx+OYLVaCQx8jNOnT/H++7MJCKiJ0Wjg7NnCHUCvB3XGjJlIu3YdOH36FM7Ozkod69atYt26VdSoUROA5s1bEhIykc6dX8XPz4/161ejUqkoKCj5n3JCCHE/PXRBIQc7e7p+Ouie1/tZtxVkc/ugkNFoJCxsGUeOHEKr1aLT6ejb9x3atHn6b11/167tfPHFp9hsNlQqFT169OKFF15Uzp8+fYqVK5eSnp6OxWIhKKghQ4eOwtXV9W9d92Z79+5m9uzpjBo1jldf7QoU/tLr2rUzeXk57Nnz37uuOykpkQED3lLqiI+/zLvvDqVnzz506vQKr73WEXt7e+zs7LFaLfTu3Z+2bV/4R+s+cuQQ69evwWQyYrNBhw6d6N69p1Lvrl3b2bJlIzabjWbNWjBy5FjUajV79+7mxx+PEBo6/67H46/mzp1J+/YvUb9+0N+uy2g08Mcf55g/f7ayVbmDgwPZeVkllk9JScbBwbHYjJvC9fIqzIY0zH++NdQYyMvNJTfLhMnqjJ2dHVevXgUgJiYGb28f7OzsyMnJpkqVqkXqU6vVBATUVH42mczExkZTtWo1JffQ9f+k2WyFj61WKyqVipycHHbv3kHTps3Jzs7Cyalw9zGdTlckz9D9tHZtGL169cPOzu6W5bZv38bnn29Fp9OxbNlq9Hqn+9TCsouLi+WTT8I5c+Z31Go1Pj4V6dq1O0FBTyplLl48zwcfzOPChfM0b96y2P2/YcMa9u7dDcCLL3akT58B97UPQggh/n8oKYDz/PPtbnluyJC3OX36FJs3f6Ys32rSpDmTJo0hMTEBq9XK44/XUQJGY8YMx2q1KLmBnn66GYsWLaNu3XpcuPAHfn43Zvzk5eWxfv1qkpMTcXNz4/JlCAvbQN++Pfjgg3nUqlUbR0dHcnNzAEhIiOfQof+i1+uVv1EaNWpCv37BTJkyjpycHLp164Fer1dmWAshxIP00AWFHrSFC+eSn5/P5s2fodPpiIqKZPTo4bi6utKgQcO7rrdyZT+WLFmFq6srqakp9O3bg3r1GuDnV5n4+MtMnjyWmTPnERT0JFarlaVLFzF16gQ+/HD5Pewd1KoVSETEHiUodOrUCVxdXcnLy7ln17h0KZKxY0cwZMgInnvueeV4aOg8qlcP4MKFPxg4sD+NGjWlXLlyyvmTJ4+zb99XTJ783j2p28PDi/nzF+HlVZ6cnBz69+/J44/XoX79IBITE1i/fjXr1m3Bzc2NMWOGs3//Xtq3f+mejcPNJkyYes/qslptxQI8arUGq6147h2D2UheQR7Ozs7FlmbZbDY0mr/Wo8JqLfzvWEFBAQaDAWdnV2rUCCAhIYGkpAS0Wq2ShNpkMhEdfYlq1QrX0F/PD3S9nVCYhPr6crIrV65w9eqN3TiysjLx9CyP2Wzik0/C6dLldVJSkklMTMTR0aHIdO/7bf3/sXefAU1dbQDH/0nYW1FAURQc4KpIrXW1Vmvdo1brqoM6cIFbXCiKDBcqOHChiLvuat22+hardVsXggIyZLjYI2S8H1JTKeCqtbWe3yeSc+/JuTc3IXnynOesX0OfPv1fGBTauXMbM2b4UKtWnbc0sj8oFAp0dJ7/Nn/69M+Eha1l0KChjBvnia6uLvHxcYSELCU6+jY9e/YFoEyZsri7jyc6+jYXLvxapI8rVy7x00/H2bhxOwBubq44O7v8pfdEQRAE4f30vIyfktrS0lLIzc0FoHJlO20/CQnxpKen4+RUm5s3rxMTc5eUlGTq13cGwNDQiEmTpjF9+iRA8yOjubk5ajU8fvyIIUOGc/XqZQwNDbl/P4moqEhthtCgQd8Ams9CoaGr8PKajVKpJCEhniVLFjJihAczZkzFxqaCdjzdu/fUfr6Oj7/Hhg2h2NtX+/tPqCAIwguIoNArSElJ5scfj7Fr1wFt5N/BoToDBgxi/fo1BAWFcPDgfo4dO4ypqRkxMXcxNTXB13c+lpblANi8eQMnT55AqVRSrpwVkydPx9KyHC4uDbWPY2VljaVlOdLS0qhcJ9d+KQAAIABJREFUuRLh4evo2LGr9ld7qVTKyJFj6NmzK1evXqZ+/Qb06NGZdu06cv78rzx69JA+ffrRvXsvAOLj4wgKWkRGRjqFhYX07NmHjh27lHiMFSvakp6eTmxsDPb2Dhw8uJ8OHTqxbt1q7TazZ3sRH3+PwkI5traVmTp1pjZjadWq5fz44zHMzMxp0OBDLl48r81WAbhx4zpeXp54ek6jSZPmJY6hZk0njIyMSE5OKhIUepFX7btOnbra+01MTKhSxf73DwsNOHnyBJ980oIyZTTTrTp3/pKDB/cXCwqlpqYwbdok+vYdwOeff1GkLTR0FfHxceTk5JCQEI+jYy369RvIsmVLSElJpkWLVowaNQYAd3c3+vTpT5MmzRg92p2PHGvRrW0H7iUlMHW+P0EzfSlvackvv0SwatUqCuVydHR0+dZ1FI41awOwcuVyjh07gqWlJRUqFA2WqFRKpJLitXfSsh9RsXJFsrI0Qb/IyFvs3LkNB4fqxMZqVszwmjaeqlXsuHL1GkuXr6ZmTUcS4uP4umd/rKysWbt2JTEx0cjlcmrUcGTYsFGYm5uzbt1qjh8/gp6ePhIJBAevwtTUlBs3rrNy5VJycnIAGDJkOE2bNtdmfHXp8hVnz54mPz+fKVNmUr58eSZNGkN2djajRrlhYGDAypXrSr0ODh06wO7dO1AqlZiYmDBx4hTs7KoSFraWqKjb+PsvID8/n6FDBzBy5GiaNGnOsmVLuHLlEoWFhVhYWDB16kztB7nTp39m3brVKBQKpFIJ06fPZt++3QCMGDEIiUTK0qWaY/uzmTOnkpSUyJw5M3F0rIW3t2+JY963bzfffbcFXV091GoVPj5zqVKlKnFxsQQFLeTx40eo1Wr69OlP+/adSExMYMECf9LTnyCTyXBzG0Xjxk0BaN68ISNHjuaXXyKoX78BQ4eOKPF9x9raiocPH7BhQyhBQSFFpu/Z2VXFz28BkyePo1GjJlStak+5cuUpV6489+7FFhv/iRPHaNu2I/r6BgC0bduREyeOiaCQIAiC8EqeFmwOD99erGCzq+uQYm0ff9yUzZvDcXMbRWBgQJF+7t6NRkdHly+/7MHNm9f55JMWHDlykCZNmpOXl4ujoxM7dmwFNJ+tf/31DAqFArVaRefOXdm1S/NDh46OLoWFhSxatBRPz3HMnRuIsbEJw4cPolIlO65cuYihoSEtWrTE3382JiYmGBubUFCQT8OGjQDNCqpJSQnY21cjNTWV+fP9+PrrPm80418QBOF1iaDQK7h79w62tpUxMzMvcn+dOnVZuzZEe/vWrZts2LAVa2sb5s3zZefO7QwbNoojRw6SmJjIqlVhSKVS9uzZybJlS4p9Ubx06QLZ2dk4OTlpH3fgwMFFttHR0aFmTUfu3InSTjnKz89n1ar1JCffZ8CAXrRvr5k2NWuWF97evlSpUpXc3BwGD+5P3bofFJve81S7dh05dOgArq5DuHbtKq6uQ4oEhcaMmagN1qxevYLNmzcwYoQHERH/45dfIggL00yV8fKaXKTf3Nxcxo0bhb//Au0/yZJcunQBuVxOpUp2pW7zZ3+173v34rh58xqentMATbDn2V93rK1tSEtLLbJPdHQUPj5eTJgwpdQvv7dvR7J27UYMDQ0ZNKgfK1cuY+HCYJRKJV9/3YUuXboV+VVLKpUya5Yvgwb2oXoVe4LC1jDadQjlLS25n5rCuvVrmDFtHkZGxsQnxOLrN4XVK7dz/sIv/PzzKdav34Kuri5jx45A/UxmUH5+PnqyohktKrWKAkUBCQkJqFQqntY/jIuLxdV1KB4e49i/fy9z5y1m5YrFv5+neIYMGUKTRu6opWWYPduLTz9txdSpM1CpVEyZMp7jx4/QsWNXtm7dxIEDR39ftj4HPT19srKyWLjQnwULgilXrhwPHz5k6NABhIdrPnhlZGRQt+4HDBs2iqNHD7FyZTAhIesYP34yQ4b0JyxsS6nPLWimWf744zGWL1+Dnp4eZ86cJiDAh5CQdQwYMIgJEzzYuXObdgrU0+Bhv36uuLuPBWD//r2EhAQze3YA8fH3mDfPl+XL11C5sh1yuRyFopAJEyazZ88OQkLWPbcWko9PAD16dNZmqpVmxYogwsO3Y21tg1wuR6VSoVAomDJlAm5uI2nVqvXv50dT82n2bC+6du1Gp05fEhsbg7v7UDZt2qkNYmqyCTWv2dLed+bM8Wffvt0MGDAIIyMjTpw4xsaN6zE3t8DBoRp169Zj0CA39u/fg4fH+Oee99TUlCJTzaytbbh69dJz9xEEQRD+2140DWzWrClEREQUaSut8POePTvZsiUctVrNzZvXtQWd799PQi4v0AaEBgzozaJFS8nM1NQgLFu2LKtWLQPg+vVrVKjwiF9++RmpVEZ6+hPKldOsfiqRSPjf/37i8uWLANSo4cS1a78BmhVdDQwM0NPTx9TUjI8/bkpy8n0AOnbsTEjIUnJyshkxYgz9+n2NWq3i7t07mJmZU6FCRUBTfmL2bC+SkhIxMjKmQ4fODBky/G89/4IgCC9LBIVewbOrBjzPBx/Ux9raBtAEjM6f10y1iIj4H5GRtxg0SFO3RqlUFClKBxAbG4Ovrzfe3n7aX91f9nFbt9ZMl6pQoSKmpmY8eJCGSqXi3r1YvL2nabcrLCwkLi621KBQq1ZfMGhQPypXtuOTTz7T1nt56vDhAxw9ehiFopC8vHxtUOPy5Qu0atVaW2y4ffuOhIWFavfT1zegfn1n9uzZgbOzS7FpLV5ek9HT08fY2Bg/v3mYmpoSHX0bP7/ZAOTl5ZKZmYmrq2Y6S4sWLfn226Gv3fdTDx8+ZMqU8YwbN1n74eBF7ty5w/Tpk5g/fwlVq9qXul2jRo21z3H16tWpVq2mtgCznV0VkpISiwSFQPMBZuLQkUwMmE23Nu1p/PuX7fO/XSUpKRGvmWO02ypVStLTH3P9+mVat26jDVB88UU7du/e8Xtx53wyM7OoZG5T5HGkEin2ZSujb2VMWtoDVCrNvPoKFSri7OwMSGjbtj3h4evIzsqhsFCJtbUNDRtoMqyMjIy4cuUy8fH3+O67zb8XuM7Gzs4OUzNT7Oyq4OMzg48/bkrTpp9gZGTM9etXSU6+z8SJfyzlKpFISEpKwNzcAkNDI5o1+wSAOnXqsWzZkpd6Pp46ffp/3LkTjZubK6B57WRlaWopSaVSZs6cg6trX6ytbVixYq12v7NnT7N79w7y8nK19QUAzp//lcaNm2qfIz09vWIFtN8EF5eP8Pf34ZNPPqVJk+bY2lYiJuYuSqVSGxACMDe3IDc3hzt3oujQQZPtZ2/vQPXqjty4cY3mzT8FKJLR9rz3ndu3b9G79zdkZKSzZk0IISFr0dc3wN3djRo1amJvX434+Htv/HgFQRCE/74XTQPT0ys+Day0ws/5+XlUrepAbOxdbR0fubyAmJi7yOUFuLmNYvXq5SQlJeDpOY4ePXqhVsOjRw/p1esbNm/egK1tZW7c+I38/HzUajVxcbHauogqlQobm4qYmZmRk5PNypVLmTHDB3d3N5RKFTY2FYrUDapQoSIRERcIDV0FgFoNO3Zs5ZtvBmg/m/bo0Vl7DKampmzYsO3vPuWCIAivRQSFXoGmbkoCmZkZRbKFbty4rl1dACjypVEqlWm/ZKrVagYOHESnTl1L7D8hIZ5Jk8YwadI07XxngOrVa3DjxrUixawVCgVRUbfp3fuPwshFH1eKUqkAJJibW7www+JZRkZG1KlTl5Url7J06aoibVevXmbv3l2EhKyjTJkyHD16mO+/3609vuctNS6TSfH1nc/06Z54e09l9uyAIsGbkrIpatRw1I79eTWFXqdvgCdPHjN27Mhi07+srW1ISUnW3k5NTSlSDNDKyorc3FwuX7743KDQsytjSaUy9PX//BwpS9qNO3GxmJuY8uDxo2fu1RS8HjpoYrHt/xw2tLDQZIxER0chk8moWLEievkyomKjmLPIBz2ZLg3queA+2AMdHZ3faxA924vmeaxYUfMLV0JyFk8y8jE2NkRXV/b7+CUgkTDCYwLlypRBKtNBz7wMOkbG6OrIWLVqPdeuXeXSpQsMHtyPwMClqNVQrVoNli9fU+wYkpPvo6f3RzbTH9fwy3taMLy0X9/u37+PVColKyuTgoJ8dHRMSElJZunSRaxZE07FirZcu3aV2bO9Sjmzfw9//wXcunWDixcvMHr0cCZOnIq1dcnFJ0sLEj/72jM0/CN76XnvO5rXrJSkpERq1nSkTJmyANpsu8ePH1G2rOULx1/y68XmOXsIgiAI/2UvMw3swIEDxaaBeXiMx99/VpF+bt++hZ6eHn369MfffxbNmn3C4cM/cPHieeTyAoyNjfnii7asXr0cS8tyxMTcZd26NYCaqlUdaNeuI5s3b8De3oELF84xZYoXc+fOoVatOowf78nQoQMxMjLC1NSEqKhIJBIJDx8+0NYaKijIx9rahsqVq6BQKPj11zN89NHHZGVlcujQAaysrDExMeHixfM8eJDKnj07AUhPf8LMmVP55psB9Ovn+vafBEEQhJdUvMiIUKoKFSrSsmVrFi6cS0GBZjmmmJg7hIev0/4q8DzNm3/Knj07yczUZC7I5XKio6MAzUoF48d7MHbsRJo0aVZkv379XDlwYK82pVWlUrFiRRCVKlV+Yc0OO7sqGBgYcPjwD9r77t2L0/7SUZp+/VwZNGhYsUCKZjlyE8zNzZHL5fzww/faNheXhvz003Hy8/NRqVQcOXKwWL+6urr4+c2nsLAQb++pKBSv9qX/eV6174yMdMaOHUX37j3p3PnLIm0tWrTi559P8eTJE1QqFfv376VVqz+CRmZmZixZsoIjRw6ydeumN3YMoAky7j12mFX+C8nIymT/iaMAfFivPmfP/kJ8wh81XaLvRALwQT0XTpw4Rl5eHkqlksOHf0BXVw9HRyeqV6+BhYUmiFnTviYbl24idMl63Ad7FHlcqVSGubkFycn3uXFDs+Tq8eNHcbCvSq2aNliVM0b2pwLWHzZpztETRzGqUBmjCpXILSwkLeU+OTk5pKen06DBhwwePAwHh2rExNylbt0PSEyM59KlC9o+bt268cJsOGNjY/Lz81/4nD79oPh0qp9SqSQy8hYAmZmZ+Ph4MWuWP61bt2X+fD8AcnJy0NHRxdLSEpVKxd69u7T9NWrUhLNnfyEhIR7QvGZzczW1kIyMjF/4OnoZCoWC+/eTqF27Lv37u9KoUWOio29jZ1cVmUzGjz8e126bkZGOsbEJ1avX5NChA4Dm9Xz3bhS1a9ctsf/nve/UqOHIlSuXsLWtxJ07UaSnp5OXl/f7B205oaErS60/9qyWLT/nyJEfKCjIp6AgnyNHfiiS4SQIgiC8XxIS7iGVyrTTvACqVatJbGyMts3e/o8f1dLSUrG0tKRWLU2dxMTEeG0/arWaOnXqadssLa24cyeaqCjNZ6CndQJBk/0Nah4+TAM0dTVHjdKshrlr13aMjY3IzNT84HbvXiyenpqp4zk5OcTGxtC/vyu2tpUBzZT2p/bv38uuXdtxcqrN9OmefPHFJ/Tp8xUPHz5gxgxNRntQ0ArCw7ezfv1m1q/fTLly5Zk0aRpffdXzDZ5ZQRCEN++dyxTKL5TzXa+QF2/4Gv2+jAkTprBq1TL69euJrq4Oenr6jBkzoUg9jdK0a9eRjIx0PDzcAE1wp1u3r6lRoyYhIUvJzExn7dpVrF2ryc4ZMcKDZs2aYWdXBV/f+axatZz09HSUSgXOzi74+s574WPq6Ogwb95igoMD2bp1I0qlirJly+LjM/e5+9nbO2Bv71Ds/saNm3L06CH69u2BlZUVTk61uHnzBgDNm7fg2rXfcHXtQ7ly5alTpx5ZWVnF+tAEbxYwffokbVbPm/IqfW/atIGEhHj27dutLRz89de96dixC7a2lRg4cDDDhrkCmmlgbdq0L7K/iYkJixYtw9NzLPn5eS8VGHyRrKwsvL2n4TlsFGXMzZk6Ygwes6ZRq3oNqlexZ9YsX5YtW4BcXoBCocDJsS41qjvR8MMmJCXf4dtv+1KuXHkaNPiQBw8evPgBS2Bv78DPP58kNHQVBgb6TP79A1NJBg4fw+a1K/Ac4YpEIkFXV5cBw0eTU8aUqVMnIpcXoFKpqFnTiRYtWqKvr8/cuYtYvjyIoKBAFIpCKla0Zd68xc8dk5mZOW3atGfgwN6YmpqVWmja2dkFN7eRTJkyHqVShUJRSMuWrXFyqkVAgA8dO3ahfn1n6tatx5gxI9i7dydfftmDli1b069fL6ytrWnQ4EOuXr0MaFYx8fScjrf3VJRKFTKZlOnTZ1OtWnV69/6G0aOHo69vUGqh6ZehUqnw85tFdnYWEokUa2trhg93R0dHh7lzA1m8eD5hYWuQSKT06dOPdu064u3ty4IF/nz33RZkMhleXj7aekJ/Vtr7Tq1aTnTp0o0ZM6YQHLySwYOHMW7cSMzMLHB2duGnn47Ts6dmFUTQZHKNHDmE/Px85PICunXrwODBbnTq9CUuLg359NOW9O+vSddv167DS70nCoIgCP9NeXl5xUokmJiYkJubU6wtMvImqakpVKxoi4GBpnTCunVrmDp1JsePa34Y8/AYr227cuUCOjq6uLmNZNWq5bRs2ZpevfoydOhAhg0bSVjYWoYOHcm2bZvQ09NlypSZuLu7IZFIyMnNZXVYOCaV7dE3t8T2s45krPRHpShk2DB3unTpRq9e36BQKEhNTcHNzZXy5a3w8BhP48ZN6dKlGwEBPpw//ysGBoaMHetJgwaaxWLMzYsujiKVSjE1NX1u7UFBEIR/A4n6ZQvWvAWPHmVrl6l+KiXlHjY2VUrZ479PR0eKQlF8KfF/q9zcHIyMjFGpVMydO4dy5crj5jbynx7WO0VHR0r2nbsltplUr8b9hIwS2ypUMnvu9L38lJKzWgxsTEhJ0WTWXLv2G2FhawkMDAbAxsaanMzEEvczNqtEXEZuiW1VzY3eqev2ffT0veXkyRNs376FESM8qFevPhKJhLS0VH7++SSdOn2pXWnx3+J9/5/wTyhf3pQHD4oH+AWhJOJ6EQCioiIZMWIwJ06c1t63desmLl++yJAhwxgxYjC//fYbqakZDBvmiqNjbdLSUhk3bhJff92FZs0+5eLFc9oaQ6dO/cpvv11l5MjBVKpkx/37iVhYlOHJk8d07dqdNm3aM3LkYPr1+5bdu7czbZo3586d5dy5X3n8+CFyuZx58+Yxe8kyqrTtjkX1OkRtW8WTqGvoGplgJFExc6YvH330cbFj6dGjM5Mne5XYJrw94r1FeFniWilOKpVgaWlSavs7lykk/LvNmeNNSsp9CgoKcHSsxTffDPinh/TekEgkpCXGldhmVanqWx2L8O747LPPqVLFnq1bN7J48XwkEik2NhXo0aPXvy4gJAiCILwbKleuglKpJCEhXrtYw507UdjbO2jb4uLiUKn0iIy8xd27d5BKpdppYLdu3WDOnHl4eo5FrVbTsWNrnv7ulZaWwueft8HTczpt27bg5s1rTJw4hYiIC8yaNZ2CggLs7auRkZFBbOxddu7UlDowNpYx1WsGxjaV0DU2oc7gCdrxrungUuqXyJ079/+NZ0oQBOGfJ4JCwhsVELDwnx6C8BfUq/eBNkvo32zy5HGkpqYWuc/a2vqF09D+LgsW+GvrMD0lk8kIDd2ovf3sSnrPKqmm1dtmb+/AtGne/+gYBEEQhP8OQ0NDWrRoydq1K5kyZQbR0beJiDhFSMg6bVtwcDBjxkwmIGAhPj4zmDdvEQYGhgwdOpDQ0I2Ym1uwd+8hFizwByT07v0N7u5u6OjoMHLkMOzsrGjVqhUnT57kzJmfsLKy4tSpH6levToNG9bDwcGWkJBgLl36hc8++4zg4GCMK9phZG37wvELgiC8T15q+lhsbCxTpkwhPT0dCwsL5s2bR9WqVUvcNiYmhm7dutG3b18mT578SoMR08eKe9emjwl/3etOH6tY2fy5mUIvM33sz8T0sf+ud/W95X3/n/BPEGnYwqsQ14vwVGZmhrb+jpmZOcOHe9CmTTtSUlLo168HLi4uXL58GQsLCyZMmEDnzp25ePEiffv25eihI1RxqApAeno606ZNIyIigoKCAubNm8fdu5rPSfn5+Rw5ckT7Q5GDgwNjBjhQ3lJTx+da5APCdlzj4ZNcTe2ftv0wKFu+2Fiflykk/DuI9xbhZYlrpbgXTR97qaDQgAED6N69O127dmXfvn3s2rWL8PDwYtsplUpcXV2xsrLCyspKBIXegHf1i5vw+kRQSHgb3tX3lvf9f8I/QXy4El6FuF6El1W+vCnLpn5bYpt7wHqiF0aU2FZjYnMCAwNLbJswYQIXj04qse3DNgsYevBSiW0iKPTvJ95bhJclrpXiXhQUeuGS9I8ePeLmzZt06tQJgE6dOnHz5k0eP35cbNvVq1fz2WeflZpFJAiCIAiCIAiCIAiCIPw7vDAolJycjLW1NTKZDNDUybCysiI5ObnIdpGRkURERODq6vq3DFQQBEEQBEEQBEEQBEF4c95IoenCwkJmzJhBQECANnj0OkpKaUpLk6Kj88LY1X/a+378wrtJXLf/fu/icySVSilf3vSfHsZ7R5xz4VWI6+X9opLLkerpldhWWKhEV/f1vxu8TeK6/fcTz5HwssS18mpeGBSqUKECqampKJVKZDIZSqWStLQ0KlSooN3mwYMHxMfH4+bmBkBmZiZqtZrs7GzmzJnz0oMpqaaQSqUqUveijKkeOgZvfplkRX4BT7LkL9yuR4/OFBbK2b37oDYA9sMP3xMQ4MO4cZPo3r3Xa4/Bz28WTk61tH2sWRPCmTOnCQxcypkzEQQHB2JjUxGFopAqVaoyebIXZmbmf6HvCAIDl5Xat4mJKTNmTCYm5i56evqUKVOGSZOmYWtbCYDHjx8xZ85MkpOT0dfXx9NzOnXq1NWep/nzF+PgUP21zsWlSxdYvjyI0NCNZGVl8f33u/nmm4Gv1dfrunTpAgqFgkaNGr/Vx/2nvqhnZ2dz9Oghvvrq6zfS35uoVxMauooBAwahq6v73O327NnJjh1b0dfXZ/nyNRgZGf/lx37T4uPvsW3bJm7cuIZUKsXGpiI9e/ahQYMPtdtER99m0aJ5REXdpkmTZvj6zi/SR1jYWg4e1CyN26FDZ1xdh7z2eN7VmkIqlUrME3/LxNx84VWI6+X9U768Kae7di+xrdm+XfhMOFBi28zATn/nsF6ZuG7/3cR7i/CyxLVS3ItqCr0wKGRpaUmtWrU4cOAAXbt25cCBA9SqVYuyZctqt6lYsSK//vqr9vbSpUvJzc195ULTL0PHQL/Ufzx/RbN9u+AlgkIAlpblOHfuDE2aNAfg0KEDODrWemNjUavVLFmygOjoKFasWIWBgeYLbsOGjfD1nY9KpWLmzCls2BCKh8f4Ivu6u7sxffosKlSo+MK+g4NXYWJiUmrfo0aNpX37TjRt+glSqZRdu7Yzf74fQUEhAKxcuYz69RuwePFyrl69go+PF9u27UEikbyxcwGQnZ3Fli3hrxUUUigU6Oi8XkLc5csXycvLe+tBoWcpVUpk0rfzC1tOTg579ux8Y0GhN2H9+jX06dP/hUGhnTu3MWOGD7Vq1XlLI/vDy1xjp0//TFjYWgYNGsq4cZ7o6uoSHx9HSMhSoqNv07NnXwDKlCmLu/t4oqNvc+HCr0X6uHLlEj/9dJyNG7cD4ObmirOzC87OLn/PgQmCIAiCIAiC8J/3Ut+WZ82axZQpU1ixYgVmZmbMmzcPgKFDhzJ69Gjq1av3tw7y36Z9+84cPHiAJk2ac/9+EgUF+Tg4VNO2X7hwjjVrQpDLC1AqlQwYMIjWrdsCEBsbg7//bPLz86hRw5HExAQGDhxMs2afAKBUqvDzm8WTJ09YvHgZxsbFV3GSSqW4uHzEmTMlr8pQmj/3ra9vUGybZ/uWSqU0b95C21anzgd8991W7e2ffjrOjh2arIX69Z3R09MjMvJmsS/mW7du4uzZ0/j5LdAGoZ6aPduL+Ph7FBbKsbWtzNSpMzEzMyuyzaJF88jOzsbVtS8GBgasXLmOhw8fsmTJfFJTUygoKKB167YMGDAI0GQpderUlYsXz1Oxoi1t23YgOHgRtWvX4caNa4CE2bP9qVrVnkePHjJr1nRycnKQy+U0bdqMkSPHcPfuHfbt241KpeLChXN8/nkb+vd35cyZCMLD11FQIEdXVxcPj/HUrftq1/+lSxcICgrE0dGJO3eikMlkTJs2C3t7By5dusDSpYuoXdWB2zF36fdld+o51mLl5g3EJNxDIZHgVPMDXAeORCaTsX3HBiIiTqCrq4eevg6zpnthYmzM7agowjdvIjdPszrYyFFj+LBaA5JTkxk20Y0ubbtw9uJZCgrymT5zFjY2FVm9ejk5OdmMHTsKfX195s1bVOoxHDl6ggMHT5Anl2NkbMJgj4lUrGzHrs1hxN2NYmngEvLz8xk6dAAjR46mSZPmLFu2hCtXLlFYWIiFhQVTp87ExkaTcXj69M+sW7cahUKBVCph+vTZ7Nu3G4ARIwYhkUhZunQVpqbFU0FnzpxKUlIic+bMxNGxFt7eviWOed++3Xz33RZ0dfVQq1X4+MylSpWqxMXFEhS0kMePH6FWq+nTpz/t23ciMTGBBQv8SU9/gkwmw81tFI0bNwWgefOGjBw5ml9+iaB+/QYMHTqCzZs3cPLkCZRKJeXKWTF58nQsLcvx8OEDNmwIJSgoBCMjI+147Oyq4ue3gMmTx9GoUROqVrWnXLnylCtXnnv3YouN/8SJY7Rt21H7um3btiMnThwTQSFBEARBEARBEF7bSwWFqlWrxo4dO4rdv2bNmhK39/Dw+Guj+pdzcWnInj07yMzM5NChA7Rr15HIyFva9po1nVixYi3tXWCCAAAgAElEQVQymYzHjx8xeHB/GjVqgpmZGXPmzKRXr760bduByMibuLm5Ful7w4a1VK/uSGBgcKnZB3K5nIiI/+Hk9GrZSX+17927v6N5808ByMhIR61WY2FhoW23trYhLS1VGxRSqTRZSenp6SxcGFxitseYMRO1faxevYLNmzcwYkTR62f8+MkMGdKfsLAt2vt8fWfi6joEZ2cXCgsLGTNmBLVq1eajjzRZPQ8fPmTp0lWAJggTG3uXadNm4uk5nQ0bQtmwIRRvb19MTEyZN28xRkZGKBQKxo935+zZX2jcuCldu35FXl4e7u5jAUhKSiQsLJRFi5ZibGxCTMxdJk4cze7dP7zcE/CMu3ejGTt2Ig0afMihQwfw9fUmNHTj72138Og7AI+BgwEIXBPCB7VqM2HoCIwc7PGc5MmPPx2iSeMW7Pt+O+vX7kFfXx/zsjpkPkwhOyeHFatX4T19OmXLlOXxk8dMmjaNdYvXA5CZlUFtxzoM6TeUY6eOsnx5MHPmzMXNbRQTJ45myZLlzx37b9ducOp/p1m5ci338xRcPn+GlYsC8FkcQrc+AwiYPoHvvttGZOQtmjRpps2o69fPVXsu9+/fS0hIMLNnBxAff49583xZvnwNlSvbIZfLUSgKmTBhMnv27CAkZF2RYMqf+fgE0KNHZ3x95z13uuKKFUGEh2/H2toGuVz++9RUBVOmTMDNbSStWrUGNNc2aAKWXbt2o1OnL4mNjcHdfSibNu2kTJkygGYa07JlqwE4cuQgiYmJrFoVhlQqZc+enSxbtgRvb1/27dvNgAGDMDIy4sSJY2zcuB5zcwscHKpRt249Bg1yY//+PcWy/v4sNTWlyFQza2sbrl4teWldQRAEQRAEQRCEl/FGCk2/byQSaNXqC06cOMqJE0cJCQktEhRKT39CQIAPiYnxyGQ6ZGZmEB9/D3t7e2Jj7/LFF+0AcHKqTbVqRb/ENmjQkCtXLnH9+m/FMgAuXDiHq6tmmkm9evXp3/9bABYs8OfGjesAJCUlMGnSGHR0NAGYxYuXUaZM2dfu+6ktW8KJi4slOHjlS5+ngAAf6tath7e3b6lTyg4fPsDRo4dRKArJy8uncmW7F/abl5fH5csXSU9P196Xm5tDXFycNijUrl3HIvvY2VWhZk0nAOrUqcfp0z8Dmi/2K1YEce3ab4CaR48eER0dpc0Iedavv54hKSmRUaPctPcplUoeP35E2bKWLxz3sypVqqz9gt+2bQfmz/cjJycbgMqV7ahdw1G77ZnLF4iMucPOg/uR6uuTnZ2LZdnyGBoaYVuxMkFL/Wjg3IiOndsgk8mIvB1JWloqs/3+yJiRSCQkpSRhbmqOoYEhTT/SHF/tmnUI2RDySmM/c/Ycd2NiGTx4AHKlCrVaTU62Zt6uVCrFffJMpo/6FisrG1asWKvd7+zZ0+zevYO8vFyUSqX2/vPnf6Vx46ba515PTw+9UgpW/hUuLh/h7+/DJ598SpMmzbG1rURMzF2USqU2IARgbm5Bbm4Od+5E0aFDFwDs7R2oXt2RGzeuaQOj7dv/UQshIuJ/REbeYtCgfgAolQptVtzt27fo3fsbMjLSWbMmhJCQtejrG+Du7kaNGjWxt69GfPy9N368giAIgiAIgiAILyKCQq+pfftODBumqelhbm5RpC0wcC7Nmn2Kv/8CJBIJvXt/hVxegFqt+XL+vJo7DRq48OWX3fHymoyv7zwaNmyobXta9+fPJk2apv37eTWF/tz3s4Gh0voG2LVrO8eOHSYoKAQDA83UlafHnJ6ers30SU1NwcrKWrufs3OD34M3T7SBqWddvXqZvXt3ERKyjjJlynD06GG+/353qefmKbVahUQiYe3a8FIznoyMDIvc1tP7ozi5VCrVBiW2b99MVlYmq1eH/T5lyg+5vKCUx1Xz8cdNmDHD57nju3v3DnPmzATAxeVDRo+e8MJjepahYdGxq9VqZo/zpKKVNSbVq3E/IUPbNtd/BZGR17h2/TKurt8wY8o01Go1VapUZe6cP4JCVpWqkp+STXJqMrq6fwRcnj0XL00N7dq2xt1jMnEZucWa01LuI5FIycrKpKAgHx0dE1JSklm6dBFr1oRTsaIt165dZfZsrz86fAv8/Rdw69YNLl68wOjRw5k4cSrW1tYlbqtWlzymZ1+7hoZ/ZC+p1WoGDhxEp05dS+xLIpGSlJRIzZqO2tdCw4aNAF46qGhtbUNKSrL2tub1ZvPC/QRBEARBEARBEErz7q1H/C9ha1uJoUNHMnBg8dV/srKyqFChAhKJhPPnz5KUlACAiYkJVavac+zYEQBu344kJuZusf0bNmzErFl+eHl5cvnyxTc67mf7vnLlxVNP9u3bzb59u1m0aHmxlc5atmzN3r07Abh69QoFBQVFCm537NiF3r37MWbMCB4+fFCs76ysLIyNTTA3N0cul/PDD9+XOAZjY2Py8/NRKBQAGBkZU79+AzZtCtNuk5qawqNHD194PCWNwdKyHPr6+jx4kEZExKkij/s0ewegUaPG/PrrmSLP2a1bN4r1Wa1adcLCthAWtqXUgFBiYgJXr14G4Nixwzg4VMfYuOSK8E1cGrJt/x6UKk3wJjMzg9TUZPLycsnITKdOHWd69/oWB4fqxCfEU8vRieTk+/x2/Zq2j5s3b5Qa6HjKyMiIgoKCFwaJmjT+iGPHfyItLRUAlVJJTHQkANlZmSyb68OcOQG0bt2W+fP9AE0Rax0dXSwtLVGpVOzdu0vbX6NGTTh79hcSEuIBzRTG3Nyc38dU9Dl4XQqFgvv3k6hduy79+7vSqFFjoqNvY2dXFZlMxo8/Htdum5GRjrGxCdWr1+TQIc2KKffuxXH3bhS1a9ctsf/mzT9lz56dZGZmao8hOjoKgBo1HLly5RK2tpW4cyeK9PR08vLyuHjxPHK5nNDQlXTs2OWFx9Cy5eccOfIDBQX5FBTkc+TID0UynARBEARBEARBEF7VO5cppMgv0KwU9jf0+6q6dv2qxPtHjHAnMHAemzZtoFq16lSrVkPb5uU1m4AAH7Zt24SjYy2qVatRrPgyaII33t5+TJ06qdQMntf1tG8vL8/n9p2bm8PChQHY2FRg3LhRAOjq6rJmzQYAhg93x8dnJocPd0NfX58ZM3yQSovGGdu0aY+enh5jxoxg4cLgIhlMjRs35ejRQ/Tt2wMrKyucnGpx82bxIIuZmTlt2rRn4MDemJqasXLlOmbOnENw8CIGDOgFaIIHU6fOxNKy3Cudi6+/7s2MGZP59tu+WFlZ8+GHH2nbPv20JdOnT8LVta+20PTMmXOYO3cOBQUFKBSF1KtX/7VWvKpRoybHjh0hKCgQmUyKl9fsUrcd2e9bVm/byLBpk5Dp64NayiBXd2Q6OixYOFNTH0etom7dOjT5uDF6enp4TZnK+vBw1q5fh0KhwK6KPb4TSy7A/JSpqSktWrRkzJgRGBublFpo+oMP6jLItR8TJ44jv7AQhUJB409a4lDDiVWLAvisbUecnRtQq1ZdxowZwd69O/nyyx60bNmafv16YW1tTYMGH2qDYpUr2+HpOR1v76kolSpkMinTp8+mWrXq9O79DaNHD0df36DUQtMvQ6XSFFnPzs5CIpFibW3N8OHu6OjoMHduIIsXzycsbA0SiZQ+ffrRrl1HvL19WbDAn+++24JMJsPLy0dbT+jP2rXrSEZGOh4ebtrH69bta2rUqEmXLt2YMWMKwcErGTx4GOPGjcTMzAJnZxd++uk4PXv25YMPnAFITr7PyJFDyM/PRy4voFu3Dgwe7EanTl/i4tKQTz9tSf/+vVCroV27DkVqDAmCIAiCIAiCILwqifpF6QNv0aNH2ahURYeTknIPG5sq/9CI3ry8vDwMDAyQSCTExsbg4TGMLVt2FVtx6ykdHWmx1ceEd9ulSxdYvjxIW1j6z3R0pGTfKZ5BBhSbPvasipXNSUuMK7Ht6fSxkhjYmJCSklpim42NNTmZiSW2GZtVKnH6GEBV8+Kr5r3PTp48wfbtWxgxwoN69eojkUhIS0vl559P0qnTl+jr67+4kzfsXX1v+a/9T3gXlC9vyoMHWf/0MIR3hLhe3i+ZmRksWhTA/06cwFSmQ/fy1jR+pqxCs3278JlwgLiky/wWdYT8giykUh0qWjmxc98qwvw0i4tk5uRz8koMKY+zkEmldOvRk54GLZBJZUSm3mXTxb3ceRiHVCKlSYtmlC9fvsRFKCZMmMDFo5NKHOuHbRYw9GDJWfJrOriI6/ZfTry3CC9LXCvFSaUSLC1LnpUC72Cm0Lvu2rWrLF8exNM6KpMnTy81ICQIwn/DZ599TpUq9mzdupHFi+cjkUixsalAjx69/pGAkCAIgiC8CYGB89DT02VJDSfi8/MJSrxHZQMDbPUNimxXvmxV2jRzx0DPhEJFAed+28GSJUt4Gj46eSUGQ31dBnX4iIJCBRHnz2NsqaJL3dZky3Np5/QpLpVGIpVK2ZJxnJMnT9KhQ4e3f8CCIAj/QSIo9JY1atSYRo0a/9PDEP5BLi4NS80S+jfx85tFevoTVMpC7X1WVuXx9fF6zl5/n2dX2XtKJpMVOZfR0bfx8ys+Fa9795507vzl3z7G57G3d2DaNO9/dAyCIAiC8KoyMzMICJjD+fNnMTe3YNgwd9q0aUdeXh6nTv3IgQMHuD92Ik8UhaiB2bF3MJTJqGdsSv1sTZaysWEZth+aCmgWYFCplcRtvMwHDja0cHYgM7cAc2MDthy/Qm6+HCubCkSpYgFoWLlekfH069eP/fv3v9VzIAiC8F8mgkKCIJRo+vRZz50+9rY9u8peaWrUcCQsbMtbGI0gCIIgvB8CA+ehq6vL998fJTo6Ck/PMVSvXgOFohCpVIa9vT33gRqGRrQtY0lcfj7DbCsRnnKfJUuWAJq6eS0/HsrJc6EolPnIZLro6OhSvZJm9c0q1hZci0mhe4t6GOjpsO/sXWJy7pU4nvPnz5da408QBEF4dWL1MUEQBEEQBEEQinmaDTRkyHCMjIyoX9+Z5s0/5ciRg+Tl5RVZLKWsrh4Wurrk/75aqgQJ9+79EdixKutAz3Z+dPt8JhXK1cTCwoKKlpoSCgWFCnR1ZOw4+RvhRy7RpEkT4tOTSc5MKzKe2EcJrFixgo8//vgtHL0gCML7QQSFBEEQBEEQBEEoJiHhHlKpDDu7Pwr8V6tWk9jYGAwNDcnJKbqIRXx+PtF5uYyMusXFrAwGDhxYrE8jQ3Ny8jJQqVRIJBLUajV3kh5hbqzPiC6NGdLxI7KyNEVi7z1O0u53PyMV78NLmDZtGhUqVPibjlgQBOH9I6aPCYIgCIIgCIJQzJ+zgQBMTEzIzc2hcuUqKJVK4uLitG35KhVty5ajdRlLTqU/xtbWFkgvsn9O3hOeZCZqVw/LlysoVKhIz87nSXYeFiZ/FKkuUMgBSMt6iNfBQHo36MyXX35JYGDg33PAgvACpdXY+rPjx48QGrqKx48foaurR+PGTRk3bhLGxprX0xdffFJk+4KCArp168G4cZ4A5Ofns2zZEn766RgKhYLq1WuyfPmav/8AhfeSCAoJgiAIgiAIglBMSdlAOTk5GBkZY2hoSIsWLQkODqaTSkV8fh5XsjOZVsWBMrq61DMxZfz48TRwGExs4kWsLB0wMrAgMvZndHUMaNasGZCDob4uZkb6WJoZ8cOZSOQKBbaVqyKTyLA0LsPDnCdM+2EhHWu3okPtz/6R8yAIT5VWY8vBoVqR7erVq09IyDosLCzIzc1lwQJ/1qwJYezYSQAcO/azdtu8vDy6dGlDy5attffNn++HUqlg06admJmZER0d9XYOUHgvvXNBIXMzQ/T03/yw5QUKMjLz3ni/giAIgiAIgvBvVlr2w9NsoISEeCpXtuP48SOsX7+awsJCOnX6AheXhmRnZzMm+hYmMh0KVWp842JQq9XIUQOgyN2Nro4BlyN/QF6Yh0qlwNDAnGPHjmHUvDZ2VhZ0aOzEz7/FUlCoQCKRUKFCBRLi4qla1pbvrx8nJesBWy99z9ZL3yPdLEMulzNo0KB/+KwJ/1UvWnEvPHw7RkZGPHiQilqtZvDgfhgbmxTJBrK2ttFmA6nVagoLC1EqlajVam020FM+PjPIy8tDLtdkxsXHxxER8T/27PlBm1nk5FTr7Z4E4b3yzgWF9PR18Jlw4I33OzOw00tt16NHZwoL5ezefRCZTAbADz98T0CAD+PGTaJ7916vPQY/v1k4OdXS9rFmTQhnzpwmMHApZ85EEBwciI1NRRSKQqpUqcrkyV6YmZn/hb4jCAxcVmrfJiamzJgxmZiYu+jp6VOmTBkmTZqGrW0lAB4/fsScOTNJTk5GX18fT8/p1KlTV3ue5s9fjIND9dc+H8+KjLzJ9u1b8Pb2fSP9Abi7u9GnT3+aNfvkxRu/A06d+gmZSkHNGjX+6aEAcOnSBRQKBY0aNX7udhkZ6UyePJ78/HzatGlH374D3tIIX55CoeDQoQMcO3aY9PQn6Osb0LhxU/r06a9NfwdYtmwJp079SHLyfcLDtxW5/uPj7+HnN4uMjAzMzc3x8ppN5cp2/8ThCIIgCEIRz8t+aNGiJWvXrmTKlBno6uoCEBq6CRubCixY4I+trR0DHetwJiOdmkZGlNXR5ZGikDX3E4mRF2BXoT7WltVwdurAg8exnDi7Ch2ZLuXLl9c+fhlTQ1rUt6esmRHZeXJuPpbTtd4XmOgb0/fDrvT9sKt22xoTm4vpY8Lf6kUr7j2tsVWvXn169+5HZOQtZs3yK5YNtHDhUjw9x5CTk4OBgQEymaxINhBAUlIi58+fxdDQEIlEAsCNG9exsbEhNHQVR44cxNKyHIMGufHZZ5+/3RMhvDdEoenXYGlZjnPnzmhvHzp0AEfHNxe9VavVLF48n8uXL7JixSrtspsNGzYiLGwL4eHbkUgkbNgQWmxfd3c3kpPvv1TfwcEv7rt9+05s3ryTDRu28sknLZg/30/b18qVy6hfvwHbtu1m/PjJ+Ph4oVar39h5eJaTU+03GhB6GxQKxVt9vFOnThJ1J/qtPubzXL58kXPnzr5wuwsXzmFqakpY2Ja3HhBSqVQvvGblcjmenuNITEzAy2s24eHbWbZsNdbWNowdO5InTx5rt/3kk89Ytmw1NjbFC2AuXBjAV199zbZtu/nqq69ZsMD/jR+PIAiCILyq560wBjBhwhTk8gI6d/6CoKBAJk6choNDNdLSUvnxx2Pcvn0bgPvyAvzvxTAi6iYB92KQAlZWVliVddA+VkziBfR0DXGp1QU9PT3t/UqliiPno1n5/a9899NvODs70+/Dbm/1PAgCvNqKe9bWNpQrV57c3BwApFIpiYkJ2vb69Z05cuQUe/Yc5KOPPsbc3IL69RsUebyAgDkUFBRoM4IAHjxIIybmLsbGJuzde5hx4zzx85tFXFzs33z0wvvqncsU+jdo374zBw8eoEmT5ty/n0RBQX6ReaQXLpxjzZoQ5PIClEolAwYMonXrtgDExsbg7z+b/Pw8atRwJDExgYEDB2uzVZRKFX5+s3jy5AmLFy/D2NgIhUJV5PGlUikuLh9x5kzEK437z33r6xsU2+bZvqVSKc2bt9C21anzAd99t1V7+6efjrNjx35A86anp6dHZORNatWqU6TPrVs3cfbsafz8FhQrVti8eUOGDh3Bzz+fIiMjg8mTp3Phwjl+/fUXFAoFc+bMo2pVey5dusDy5UGEhm7k0qULzJ/vx9q1GzExMcHPbxZly1oyYoQHOTnZLF26mLt3o5HL5TRo0BAPj3HIZDLtuVcqFVSt6qBN0XxV7u5u1KjhSHT0bR48SKNVqy8YNmyUtq1evfrcvHkdPT09FiwI4syZCMLD11FQIEdXVxcPj/HUrVuP+Pg4/Pxmk5+fj0qlpH37zvTt25/CwkJWbQnnt8ibKBQK7CvbMebboRgaGOLj442iEO7fT+TRozRq1qzNaPepXLl6noiIU+jq6HDsxHG6dupMq89aljj+R48fErQmiLSHqRQUFNC2QwfatetEeno6kyaNYfLk6VSvXpMffzzGyZM/snCeN/fiEwhaupL8/Hzk8kI6dWjDAFfNMefmZBO+Mpi7UZFIJBKc6tZnQK9e7Nu3G5VKxYUL5/j88zb07+9abCxPn9fc3BxcXfsybtykYv8sAZ48ecysWV48efII0AQxR4+eAMDGjes5duwwEokUQ0NDVqxYi1QqZdOmMO0H2lq16jB27CSMjIwIDV1FUlIieXm5JCUlsmzZGtLTHxMUtIiMjHQKCwvp2bMPHTt2AWDVqmV8/vkX2tsA+vr6dOrUFTu7KgQHL9IGLOvXdy7xnD958pioqEgWL14OQOvWbVm8eD5PnjzRBmYFQRAE4Z9Q2gpjV65cAsDMzJxlIcsw0NUEcS5cuEC7dp+RnZ2NoaEhw4YNgwWL6V7emu7lrbV9zI+PpXv37jyJk2jvsylXk/yCLGytaxF9/5D2fn09Hfq2/uN/qPuECUQvfLXPucK77W0UcC4sLGT27OlERt4iJSWZ4OCVuLg0LLL9814Pn3/+RbEaW9HRUVy7dpU2bT7FwMAAf/+FxcZcvrwVaWmpKJVKbTYQwI8/Hufx44fUr9+AlJRk7f36+vro6OgwcOBgdHR0aNDgQxo0aMi5c2epWtX+Fc6qILwcERR6DS4uDdmzZweZmZkcOnSAdu06Ehl5S9tes6YTK1asRSaT8fjxIwYP7k+jRk0wMzNjzpyZ9OrVl7ZtOxAZeRM3N9cifW/YsJbq1R0JDAxGR6fkp0culxMR8b9Xnlv6V/vevfs7mjf/FNBM+VGr1VhYWGjbra1tSEtL1QaFVCo1S5YsID09nYULg7Upx39mYmLK2rXh/PjjcaZOncDs2QEMH+7O5s0bCA9fx8yZc4ps7+LSkHbtOjJ3rg/Nmn1KQkI8kyd7AbB06WKcnV2YMmUGKpWK2bO9+OGH7+nSpRtz5szk66970759J65fv8bIkYNf6fw9Ky4uhiVLViCXyxk+/Fvq1v1AG9iLiblDYOBSdHR0SEpKJCwslEWLlmJsbEJMzF0mThzN7t0/sHv3Tpo0aYar6xAAMjMzAdi4cQPGRsYs95kLwJptm9j6/R4G9ewLQHx8LLNmBiKRSJjoOZSrv12kgXMjmjdvQaUK1nRq3+G5Y/cP8mdAzwHUr+NMYWEhE30nYGNTEWdnF0aPnsCiRfMZM2YCW7ZsJDR0AzKZAhtrKxbMnYOeni55eXmM9JjIJy3aIylrw4aQYAwMDZkXEoZUKiUzI53qdhXp2vUr8vLycHcfW+pYXFwaMmTIcH755Wd8feeXut3Ro4ewsbEhKGhFkXN16NABIiL+R0hIKMbGJmRkpCOVSjlz5jRHjhxk5cp1GBkZ4+vrTVjYWkaOHA3AlSuXWLduMxYWFigUCsaOHYm3ty9VqlQlNzeHwYP7U7fuB1hb23Dz5g08PMaTmZnJggX+JCUl8vHHTYiMvMnixcvZtGkDmZmZmJmZlTr+1NRUypWz0k45lclklCtXnrS0VBEUEgRBEP5Rz1th7CkDXT16bh+hvV3NsxGFmQU8upCEra0tf85Rf1Qo53ZuDsu+/JL1S64CUKgo4GrkQVo1dvvbjkV4d72tAs4ffODM11/3xctrEmvXhhAVdbtY3aBnXw/Hjx9h69aNZGSkc+PGNeRyOVFRkdSs6QTA99/vRiKRYGBggEKhYMIED7766usiQagbN67z4EEa+vr62n5zc3NZvXo5SqWSdu06Eha2VttWrdq/oxSE8P4QQaHXIJFAq1ZfcOLEUU6cOEpISGiRoFB6+hMCAnxITIxHJtMhMzOD+Ph72NvbExt7ly++0ES9nZxqU61a0Zo7DRo05MqVS1y//hvOzi5F2i5cOIerqyYwUK9effr3/xaABQv8uXHjOgBJSQlMmjQGHR1NAGbx4mWUKVP2tft+asuWcOLiYgkOXvnS5ykgwIe6devh7e1bJCr+Z59/3gYAR0cnQELTps1/v12LU6d+KnGfAQMGMXbsSJYvX8LatZu0Qa6IiP9x69YNtm3bDGiWc7SysiYnJ5vY2Lu0basJmNStW+8v1Ttq374TOjr/Z+++46qq/weOv85dXC4bBRFFme6BiCu1sjRXlqmlOdIy03KLW1Sc5MBBjsxcuUpTyz2b7m3DhQIiIMje3P3748pVBEdmlr/v5/l4+Hh47znncwYHuLzP+/N+K1AoFLz66mucPXvKGhRq1aqN9XhOnDhGQkI8Awfe/QBkNBpJT08jMLAeixcvRK/XExQUbH1Scfjwz+RkZPDrnSmKeoMB33ueVjRs2Myacu3rU4Xk5AdPF7xfQWEB5/84R2bW3fawhfpC4uNvEhgYRO3adWje/GXGjRvJ2LETKVfOg7zseAq1Wj6PXEp0dCySTCItLZ1r16IIaOjB2ZNHCV9kyc4BcHRyftDun1jNmrX55psNLF68kMDAIBo1agLAkSO/0rFjZ+vTIac7+y7KTip6/403OrFw4d0nN02aNLUGNG/ejOPGjRgmTx5vXa7X64mNjaGgIJ8aNSxBznXrVhEQUIVp0z5l//69HDiwFwBvbx8SEm7i6Fg8Q04QBEEQngdFHcZcnFQoVJY/WiXJgIuLE25uDg/cTulog0NAGUaMGMHI+ypSHM3KJMBWg5eXF2AJCv12dR8+Fetjrynzj52L8Hy6v4DzvVO2Pv54cLF1y5XzKPb6/ilb9/rpp0M4O7tas9CVSiXv3HnImp+fj1yuKBGEur/jXu3adXnrrbe5fPkiYWEz6NXrHSZPHs/KleuJirqCWq1m6dKV+Pr6cebMaYYN+5gWLVqyf/8eatSoRe3adZHJFPzww/5i5UZWrFhGYGAQBw/u45VXWhYLCgUGBlGunAfr1q2mZ88+XLz4B+fOnWHgwKF/70ILwgOIoNATatv2dfr370NgYAY7JNYAACAASURBVJD1D9EiERGf0rTpi8ycOQdJkujWrRM6nRazGSRJemiApF69IDp27Exo6BimT59FcPDdlMbg4IalZlOMGnX3j9lBgz5iwoQwypf3fOTY9waGHjQ2wJYt33DgwF4WLlyKWm2ZclZ0zpmZmdY/rpOTk3B3v5s2HBhYj3PnzpCZmWENTJWmKMAhk8lQqe5mE8lkMoxGY6nb5ObmkpychFKpIjs7Ew+Pol8QZmbOnGsthl0kLy/3odf9XhERs/j9d8sHmKlTZ1KpkvdD17fUpLk7tq2tptiyRo2aMHHi1BLbvfzyq9SqVYeTJ4+zbt1qdu3azqRJ0zCbzQzp8yH1atYudX8q5d05+A+7Rg86VkmSWDb3C2vgSu1hT1JSsnWdmJjrODk5kZaWan1vxcq1uLq6MGbUMORyOaPHTkKr1T72fv+uWrXqsGrVek6dOsG+fbtZt241S5euAB5UD8hc4ut97+v7v0ZOTs6sXr2hxCiXLv2JJFk+6EZHX2fQoOEAvPTSy3zxhWUqWHp6Gq6uD/+AW65cOVJTb2M0GpHL5RiNRlJTU4p9vwiCIAjCv6Gow1h84i1M6y1Ts08fjqesrYLoGZ0B8J2wpdRtzSYzcXFxUNG72PtHszJpV8at2HvJqVHkF2Ry9cZRAHT6PPbeTqZ+FU/qVy3+uU343/KoKYz3u3DhvLWAs42NDQEBVWnZslmJaWeWGR3tkCSp2LQzhUKJVqvl1VdfKxaEWrZsMXFxsWi1Wjp1as/kydOpW7ceCQk3MRqN9O7djZSU28hkMl5/vSVOTs6EhIzD19ePpKQkQkIGYTabqVu3Hl98sYSlSz8jJycbvV6PQqHk3Xd7Ws/hzJlTxMXFAtCtWycyMzOYNGkcPXq8R8+efQgPj2DWrOmsW7caD4/yhIZOoXJl73/sayD8bxOFpp9QhQoV6dfvE3r3/rDEspycHMqXL48kSZw6dZyEBEv02t7eHm9vHw4c2AfAlSuXiY6+XmL74OCGhIXNIDR0NOfOnXmqx33v2A/6QXuv77/fyvffb2XevMUlOp21aNGS7777FrD8cNZqtcUi4O3bv0G3bj0ZOvRjUlNTnup5hIdP4fXXOzJhQhhhYROsKc5Nm77IunVrrIGSzMxMEhMTsLOzx8fHz5rdcfHiH0RHXyt17JCQMaxevYHVqzc8MCC0d+9uDAYDBQUF/PjjIYKCgjEaDWi1WhIS4rl2LYqsrCwaNmzMiRPHin2dT58+QXT0NX799SfS0tIIDAyid+++XLz4JwDNm7/EN7t3EJOTw43CAqIyM4i6GWfd3mDUkZWTREZWAjpDAQajHgA7Ozvy8/Mfet00thrqVK/Dhi3rre8lJydZiyVv374Ng8FARMQitm7dzNWrluKReXl5uLmVvVOb6Qa//3HRun1QwxfYsXmjtWBz9p0sJDs7uxLzrp9U0dewZcvWDB48nCtXLmMymWja9EW++26L9eufdWffwcGNOHRoP/n5eZjNZnbu/I7g4Ialjl2pUmXUajV79+6yvnfjRix5ebl4e/vy55+/A+Dr68fRo5aU5MOHfwEs91F+fn6Jp1b3c3Fxxd+/CgcPWr73Dx7cR0BAVTF1TBAEQfjX2dra8tJLLYiMjKRQb+LP23kcu5nNq34lM38zLiShyyy0tJzPLCDpYDRNmjQpts61/HwyDHqCHYpPq3618QDavzSKds1H0K75CNzd3WlRz5fafiWbMwj/Wx5nCuO97i3g7OlZEXt7e7Zv38+kSdOJiAgnOvo6SUlJnDt3hj/++I2WLZuxaNECunbtwb59PxMebskeL2qIcvDgPg4f/pUjR34hMzMDD4/y+Pn54+paht9+O8+PPx7kxImj3LqViMlkQi6X4+VVmQEDBlGnTiAHD+5n6NABGAwGFAolv/76M/37D2Tbtt3Mn78YpVKJk5NTsWLSCxcuYfPm7WzevJ1Vq9ZTtqwbo0aNp1OndwDL585ly1Zx8OBh1q3bzEsvlV4rVBCehucuU0inNTx2+/i/Ou5f9eabnUp9/+OPBxERMYt169bg5+dfbF5oaOgUwsOn8vXX66hatTp+fgElfgiCJXgzefIMxo0b9dBaK0+iaOzQ0NEPHTs/P4+5c8Px8CjP8OGWosJKpZLly9cAMGDAIKZOncTevW9hY2PDxIlTrVOIirz2WltUKhVDh37M3LmRpWYw/VWbNm1Aq9XSs2dvJEmiRYuWzJo1gylTZjJ0aAhLlkTSp8+7SJKEUqliyJAQPD0rEBo6hZkzp/DNN+upWrU6NWvWeuJjqFq1GsOGfUJqagotWrSkadPmJCTEI0lQvrwnnp4ViI+Po3JlbyZNmsann1o6CxgMemrWrM3o0RM4fPgXli2zZJtIkoyhQy1P5zp1epvEhASmT59053pK9HirCwFelTCZTOj0+WhsnVAqbFHIVej0eZjNRtq2bc/kSeM5cuzoQwtNTxgxkcUrF/H+kN4A2Ds50L//IFJSUti583vmzFmAk5MzAwcOZeLEcSxa+Ck9ur/Dp7Pmc+jQT5QvX546te9OlXpvwGDWfB7JqI96IZPLqVGnHnXGjefFF1swYcIo+vTp/sBC04/r3LkzfP31OuRyBWaziVGjxiGTyWjTpj0pKbf56KP3kcvlaDQaFi9eTpMmTbl+PYr+/S3TIKtVq0Hv3qXXkFIoFMyaNZ/IyAg2blyL0WjC1dWVqVM/xdnZHj+/APbu3UXPnu8zZ85MPvigJw0bNsbNzZ09e3YxduxE61gLFszh559/JD09jWHDBuLo6MS6dZsAS0bf9OmTWbXqSxwcHJg4ccoTXw9BEARBeJpCQsYSETGTbvsu4ahSMKiRJ5Wd1dzO1dF/+zX2vG+Zql6YksetA9cxFuiR2ypxCCjDtGnTuNjn7kPSI9kZ1HdwxPZOHb0iNiq7Yq/lcjk2KgUqRfH1hP8990/ZAssDSY3Gcs9kZ2cRFjaWw4cPF8sGsrd3IC4uFrPZhEajISUlGbPZTN++PZHL5SgUSmxtNcWmiOXn5/Htt18DcOzYYS5cOEft2nVRKORIkoRer+f27WSSkm5x7NgRXF3L4OcXQGxsDEOGhBAREc6QISOYMyec33+/wOLFC0hLS8PBwTLVcuDAIUyZMoFvv92Bi4sre/bs5KWXWnD2bPEH/ffPNJHJZDg4OKDRaBCEZ00y/1M9xJ9AWlouJlPxw0lKuoGHR+UHbPH8KSgoQK1WI0kSMTHRDB7cnw0btjywSK1CISvRfUz4dw0a9BHvvtvLWkMILK3No6Ku4O3tay0il5iYgEKheOgUIaPRSHJyEkajES+vSgDExcViq9fjIC8Zs5V5ehITE4uL493gWkZ2Ag52Zank7c7t+NhS9+Ne0ZvCpNKzdu6fPnYvD49y5GXHl7rMzrEisVmlZyZ5O5Xsmve8KiwsZMyYEQQF1eett97G0dERg8HA2bOnkSRo0KDxv32IT+R5/dny/+13wvPAzc2BlJScf/swhOeEuF+eX25uDtbpYvfznbClWKHpe23qupQjb5a+XdPvtzA1ZGepyyZFvM6ice+XumxQ+KoHdh8LGNmMiIiIUpeFhIRwZv+oUpfVf20O/XaXniW/vF2QuG//RQUFBbRt24K1azdZPw9PmzaJsmXd+PjjwUyePB6VSs7w4eOswZ2lS1diMOjp3/99VCoV+/b9THJyErt2befy5Ut3soVu0bp1W2sZh759e3LlymXrft97733eeuttXF3L0KJFEypWrIReryM9PR212gZ7e3sOHTpE8+bN8fHxYe3atZw/f54JE0K5di2K8PC5uLqWZcyY4XTp0pXTp0+yaNEXvP56Sz79dB61atWx7uutt9oxceLUEt3OhKdP/B4qSSaTKFOmZCJKkecuU+h5Z4koL6SoFsqYMRMe2rVIeD7odFpAKtZVwMZGTUFB6UGT/Px84uPjMJlMSJKMihUtc+nNZjMFBQXYyBXEawsxAxqZHFelEgnLkxS5TIFeX4BSqUanL0RCQi4rvbOb8Pep1WoiIiLZvn0b48ePJC8vF1tbDfXrN6Br1x7/9uEJgiAIgiA814qmMH755eeMHTuRqKgrHD78M0uXrrQWod65cycajYbk5FvUr9+Qfft2UbVqDcxmM/XrW0oElCvnQdmybqSk/ExaWgoymWQtLQCQlZWFr68/kyZNp0+fbqjVtjg6OpGenobJZLLW+LGxUaPVajGX8aDfrjOkpqWRlplFnUYvYNTpMBVaprUVFhZSrVp1vL19+O67b/ngg/788stPKJUq0UFMeK6IoNAz1rBhYxo2fD4zCwSLRYu+KPGeyWQuMXVOLpdhMpVeBFqj0VClSjX0ej2ZmZnWbnEGgwGz2Uye0Uj5Ox1Abut1ZBr0uCiUSJIl/To3P81aYtnBroy1GHKR02fPsHbD3bpBCqUKk95Ev579aBxcfO7/sxAVdYUZM0pOl+rc+R06dOhofX1vJ70icrmcFSvW/uPH+DAKhYJOnd6mU6e3/9XjEARBEIS/w8HRFrWN+Pgv/PeEhIwlPHwqHTq0wtHRyVrA+ejRIxgMBuuD15iYaM6cOcXhwz/j6OiEyWRmzJgJ1nGioq5y7dpVa63Lom5jRqOR27eTAYk+fboB8MUXS/jiiyWEh4cDMHz4cN566y2+/PJLvvrqK2R5uSQe2Y/ZZELpYI/ZbMZQmIdapcJoNOLuXg65XE6tWrU5f/4sc+eGo1QqmTZtFra2tgDodDrrsRgMlvqjKpXqsRvgCMKzIH4rCMJTIJNJmEzFp+IYjSZksofPk1cqldjb23HrVgLe3r5327orFMjv/LJwlCvIMhhwUVi6ruUXZuJg745CrsRg1JOTl4qjXfH9BAfVJziovvX1w6aPPQsBAVVL7e51v3s76QmCIAiC8HSpbRR0CPm+1GU7It58xkcjCHc5OjoRHl5yWqCdnQZX1zJ4enqSkpJD//4DKV/ek/379zBnzkLatm1Bdna2tUZPfn4e3bu/R1BQMKNHD8PDw1LIPCMjHZPJhJ2dPevXf8u5c6eZMiUUuUxi7uyp2GmUFKT8xPkff2f9ukOWwI1CQWGapVmOPjcbpb0jHg1fpvsLgcyfPx8fHz9OnTrBN99spHHjF5g9ewFXrlxi7NgRzJ0bSUBAVbp370xS0i0ARowYBMDmzdufSp1VQXhaRFBIEJ4ClcoGMKPTae/8H7TaQlQq1cM3BMxmy1MEsGTFKJUPngpWWFiIQm6DQm4ZVyFXoZCr0BueXXt4QRAEQRAEQXgWHlaE+mHTzoqCLuPHj2Tdus0cOWLpIFunTl0MBj3ff7+VOnXqkJUeS/iYl9m86zL7f4mhbg13HOxVZOXo8KgRhHe7d0g5fxzPpq3wavkm2sw0tm9YiLOzC46OjkRFXaVx4xesHc2qV69JjRq1OHXqJAEBVfn22x3P9oIJwhMQLekF4SmQyWTY2zuQkpKCyWQiPz+f3NwcnJycSqyblZWFXq/HbDaj1+tITb1t7a4A4OzsQrbBgNFsxmQ2k200YCu3fKva2tpiMGoxGi1BJKNRh8GoRS4XNYUEQRAEQRCE/zYXBxVubg6l/nNxKPkw1curMkajkdjYWOt7165dxcfHF7BMO9PptHTo0IqwsAnWaWdZWVmYzWZu3ozDzk7O1at/AnDq1AkGDvyQKlX8effddwE4fCqe5o288PFyYmjYATKztXh6elKplaXEgXvQC8Qd2MrhMX04O28Cubm5dOhgyayrXr0Gv/12jqioKwBcvXqZCxfO4+8vagoJzw+RKSQIT4mHhwe3bt0iKuoqcrmccuXKY2OjRq/XExNzHR8fP5RKJTqdlpSU2xiNRuRyOXZ29ri5uVnHcXd3ozA9nQStFgnQyOU436k5ZGdnh63aiZz8NMwmE5JMhq2NI0qF+l86a0EQBEEQBEF4PAq1zUO71ZGjK/ZeUTZQZGQkw4aNLZYNBMWnne3fv4c6dQLv1PAxU6NGLZKTb9GgQQNsbGzw9vYmLy+Ppk2bsnXrVi5dukS9mh4kJOXw9faL5OXrkZAo52bHli1bGH0sBgDv9l0x6gq5feYokiTRqlUrevf+EIB69erzwQcfERo6hvT0dJydXejV631RQ1Z4rjx3QSEnRxWqezo8PS06rZasbN2jVxSEB5DLFVSs6FXifaVSSZUq1ayv3dzccXNzf+A4kiRRRqmkzAOmkalV9qhVD24pKAiCIAiCIAj/X4SEjCUiYmaJItRJSUn06vU2a9duxsPDg5iYaJYu/YycnGwcHBxp3LgpX375BStXWgJIJpOJo0ePsnPnTpRKJYGBgbxaLx2VUk7XDtXR6Y18MmEffbvWwcXFBbAEhWRyBQFd+hLQpS8Aoe2CirU879y5K507d33m10UQnpbnLiiksrFh0bj3n/q4g8JXAY8OCnXp0gG9XsfWrbuRyy3FfXft2k54+FSGDx/1t34gzJgRRrVq1a1jLF++lGPHjhAR8RnHjh0mMjICDw9PDAY9lSt7M2ZMKI6OJacnAZw9e5rFixeW6Np061Yib7/9Bh07dmbkyHHW9z78sBe7dh167GMdNOgj3n23F02bNsdoNPLpp9NIS0tj5sw5rF+/hm3bvqVsWTd0Oi21a9dl5Mhx5OfnMW3aJBIS4lGpVFSo4MWoUePv/NCFuLgbzJgRRlZWFk5OToSGTsHLqxIAzZoFs3//L2g0moce14oVy6z7LjJx4lSmTZsEQHZ2Fvn5eXh4WOYZd+jQkc6d33ns8/4nGI0Gbt26RV5eHnK5HA8Pj1K/MfOMRhKvRqHX6wEJpUKNna0zkiQjJuYaP/16iTp1ApGwFKiWySTKON69XrnaPNLyMzGajChkcsrYuWCnevj1fFKJiYkcO3aUN9/s9Mh1Z86cwsWLf+Lr68fUqeH/yPH8XadOnWDbtm+5efMGSqWKatWq06NHbypUqGhdZ9++3WzY8BWxsTEMGTKi2M+CwsJCZs6cwpUrl5DL5QwcOIymTZv/G6ciCIIgCILwXHF0dCJy4UIU9z0wdXNz4Pz589aH+/37D6R//4HF1nFxcbD+XyaT0axZM5o1awZASEgIZ/aPsi5XKeV8ObvdP3gmgvDf9NwFhf4LypQpy8mTx2jSxPIDZc+enVStWv2pjW82m1mwYA5RUVdZsmQZarWl3kxwcEOmT5+NyWRi0qSxrFmzgsGDR/zl8W1tNfz6609069az1MyWe3Xp0uGhBdL0ej1hYeORJIlZs+ZZiyS3adOeQYOGodPpGDy4P999t4XWrdtauwEALF68kM8//4xx4ywBm7lzw+nU6W1at27Hvn27mTNnJpGRn//l8yva972KOl/t3r2Do0d/Zfr02X953NLIZBIyWcmWkiaTCbPZUkT6UZKSkpAkCX//ALRaLfHxN/FQKFDd1+LeRibD18eb27fyMJtN5BVkUFCYhcbWhZjYa5w8fZSmjRthqyqZYaTX60nOSaW8ozsalS35unxuZafg7VqxxLpPw61biWzfvu2RQaH09DR++ukH9u790dp57VkqmsL3MMuXLyU6+joffNAPf/8qgCXoGhY2niFDQqhduy4AAQFVCAubybp1q0uMsXHjWjQaDd988x03b8YxcGA/vv562yODnIIgCIIgCAIolMoHJgZ8Mm0Fbm5PfyaJIPyvEEGhJ9C2bQd2795JkybNSExMQKstxNfXz7r89OmTLF++FJ1Oi9Fo5L33PqBly9YAxMREM3PmFAoLCwgIqEp8/E169+5rzRowGk3MmBFGRkYG8+cvws5Og8FQvNW5TCYjKKgBx44dfqLjV6mUdOvWi+XLlzJlyswnvApQWFjA6NHDcHNzZ8yY0FL/uFapVNStG0hc3A0cHZ2sASGAmjVrsW3bFsDSJvLq1cvMn78YgJYtWzN//mwyMjKsmURgCbYsWjSftLQ0JkwIe6zuXk/TihXLiI2NoaAgn6SkJLy9veneZxgaO3u2bFpNclIi2sICkpMSWbliFampqSxcOI+srEz0ej3vvPMu7du/QWFhIdOnTyYmJhqj0YCfXwAzZsxGo9Fw9uxJ9u7eiWQyYafRMLRPP7w8K3Do15/4+bOzKORq4m7GYKtWM2hQCEZHGV9/s4r8glxGjxtN7Ro1+ajvh8WOW683IJNkKCUFS1cv4cKf58krzCfAJ4DQsCmYTCamTAmlQYNGvP76m9y8eYOpUyexYsUa1Eoj40Onkp2dg1anpVrVKgwf+ol17O++XsuRHw8gSRI2alumzFvC3LmfkpiYQJ8+3alYsWKpQbj8/DyGDBmAVlvIBx/0pG3b9nTt2qPEeiaTiXnzZnP27CmUShUaja11HvmRI7+ycuUXGAwGZDKJCROm4O8fwPHjR1m2bBEmkwlnZxdGjRpPxYpenD17msjIedStG8ilSxfp3bsvgYH1+Oyz+Vy/HoVOp6NevWAGDx6OXC7n8OGfuX072dpRokj9+g2YMyeSsWNHsGjRFygUCnx9/QFKDW4dOnSA0NAwALy8KlGtWnWOHz/KK6+0/Gs3oCAIgiAIwv9TBr0RNzeHR694H5lCRtTc0v8uChjZ7O8eliD8vyeCQk8gKCiYbds2k52dzZ49O2nTpj2XL1+yLq9SpRpLlnyJXC4nPT2Nvn170bBhExwdHZk2bRJdu3andet2XL58kY8+6lNs7DVrvsTfvyoREZEoFKV/eXQ6HYcP/0K1ak+endSp09t0796ZqKgr2Nv/9R++APPmzaJJk2aMGzcJSSqZLQOQm5vLyZMnSkzRMplMbNu2hWbNXgQgOTmZsmXdrYEluVxO2bJu3L6dbA0K6XQ6Zs4Mw8PDk7CwGQ/c5969uzh9+iQAtWvXJSRkzBOd34P89ts5Vq3agKtrGT79dCrfbVlL9/c+BuDKxd+YPnsZDo5OaDQawsJCmTx5OpUre5Ofn0ffvr2oVasOsbEx5OTksGLFV9y4ccPaNvPChXMcP36M0DETqWhnx8kL55i7fCkLJ08H4OLFP5g8eQYuLi6sWbOSn346RK8eA+jW9X1Onv7ZmjKbkVOAna0KlcJyPW1tbVEplHz17RrsNHZEzFhASm46e3fsYM2albz11tsMHz6a0aOH4ePjx7Jli+nffyDu7uXIzbrJ+HEhODk6YjabmTVnAXv3HaRb9378fGAPZ44fZsq8pWjs7MjJzkImkzFy5FgiI+eXmL54L43GjjlzFvLhh72smVyluXbtKqdPn2TDhm+RyWRkZ2cDlumGs2ZNZ/Hi5Xh5VUKn02Ew6MnISGf69El89tkX+Pj4snPnd0yZEsry5WsAiI6+xsiRYxk+fDQAn346jcDAIMaOnWgNju3atZ033niLLVs2MXnyDAA+/3wRx48fpXLlyhgMBoYMCeHFF1/m+PEjNGv20kPvmeTkJMqVK2997e7uwe3bSQ/dRhAEQRAE4X+JQilnasjOUpdNinj9GR+NIPzvEEGhJyBJ8MorrTh0aD+HDu1n6dIVxYJCmZkZhIdPJT4+DrlcQXZ2FnFxN/Dx8SEm5jqtWrUBoFq1Gvj5+Rcbu169YM6fP8sff/xGYGBQsWWnT5+kT5/ugCXY0avXk9dWsrGxoU+fD1m2bDEhIWOLLRszZjjJyckApKamWPcpl8uL/ZHfqNELnD59ktjYGGtbyCJFgRmZTOKFF5rRvv0bxZbPnz8Hjcb2L9XzCQkZzKuvvkb37r0eul5p08eephdeaI6raxnAUpNo5qd3s63qBjXC4U6dp5s347hxI4bJk8dbl+v1emJjY/D3DyAuLpaFCyPw8qpEp06W63DkyC/ExEQzafoklJKEGcjJy707ft1A/LzrYDIZqValJn9e/MO6TK1WU9ZRA5KEVqcnK68QVwdb5DIZkgQONvYcPnmYgoICDvx6AKVMgcFooEo1SxFsZ2dnBg8ezqRJY2nf/k2CgxsClgDe5s3bOHnqLCaTiZzcXGzuFHs/e+IIrdp3RGNnmeLo8IAaV3+Hp2dFTCZL3aqgoGBeeMGSVXfq1AkaN37BWndKpVKhUqk4e/YMfn5VrPdku3ZvEBExi/z8PAAqVvSiVq061vEPH/6FS5f+5Ouv1wOW+j/u7uUASyDS2dmZY8cOEx19jS+//IpbtxL54IMemEwmfHz8iIm5/tTPWRAEQRAEQRAE4VkQQaEn1Lbt6/Tv34fAwCCcnJyLLYuI+JSmTV9k5sw5SJJEt26d0Om0mM2WzlIPynABqFcviI4dOxMaOobp02cRHHx3ulVRTaGnpV27DmzcuJbz588We3/WrPnW/3fp0uGBWRyvvvoajRo1Yfjwgcyfv7hYYOhhgZlFixYQHx/HrFnzrVNtypUrR2rq3TbtRqOR1NQU6x/nYMnQOnHiGG+91QVbW1uysjIZemcaU6VKlZ9KkeJdu7azefPXAHTv3ovXXmv70PXNZrO1sDNYAjP3LnNycn7g9Vu/fjPHjh3mhx8Osn37Ntas+RqzGVq1ak2H19pRrpSpcSqVJRgjk8lRKm3Q6Qusy+RyufXeUquUFOoMaPVGNDYycnNzSc1LRylTMPiT0dSsUYvE7Nt4OpbDqaIrSUmWIGB09HUcHR1JS0u1jvvDj7/w+5+XWDAvHI1Gw/qNm4iPT7xzjg+9PE+Fvb09a9du4ty5M5w5c4qlSz9j5cp1wIN2buYh32LY2t5fx8fMzJlzixWNLlJ0PaOjr9OkSTMUCgVeXpXw9rbc6+npadYA4cOUK+dBcvIta9bb7dtJxaZSCoIgCIIgCIIg/BuefWXX/ycqVKhIv36f0Lv3hyWW5eTkUL58eSRJ4tSp4yQk3AQsf9x6e/tw4MA+AK5cuUx0dMksg+DghoSFzSA0dDTnzp35x85BLpfTr9/HrFz5xROP0bp1OwYMGMSwYZ8QExP9yPWXLVvMlSuXCA+PKFYPyMXFFX//Khw8aLk2Bw/uIyCgarF6Qu+/348GDRoSEjKYvLxca8Bl9eoNT61rVfv2GCn9xQAAIABJREFUb1jHfFBA6OjRw2RkZACwa9cOatQKLHW9SpUqo1ar2bt3l/W9GzdiycvL5fbtZGQyOS1atKJr1x5kZGSQk5NN06bNOXToADkZ6QAYTUauPiQTxXwnKqPRaMjNzX3geoWFhdgq1TRr2JxN2zeBEdQKG9Kz06xft6tXr7B79w7mz19CdnaW9bhzc/NwcnS07CMvjx9++MU6bv3GTTmw6zsK8vMByMnOAsDOzo68vAcfz1+RkZGBVqulceMXGDBgEPb29iQmJtCwYROOHz/KzZtxgCWrJz8/j5o163Dt2lVu3IgFLIXgAwKqotHYlTp+06Yvsm7dGoxGIwCZmZkkJiYA3JmuloWvrx8nThzFYDCQkBBPbGw06enp7Nu3m6ZNX3zkObRo8Srff78VsGSQXbp0kcaNm/zdSyMIgiAIgiAIgvC3PHeZQjqt9k77+Kc/7l/1oM5KH388iIiIWaxbtwY/P3/8/AKsy0JDpxAePpWvv15H1arV8fMLwN7evsQYwcENmTx5BuPGjXri7KDr16N46627bRWDgxvywQcfFVunRYuWrF//lXVqzZNo06Y9ZrOZYcM+YcGCJQ9cLzr6OmvXrsLLqxIDBnwAQPnyntYivqNGjWf69MmsWvUlDg4OTJw4pcQYPXv2wcZGzbBhnxAR8RmO/8B0pUcJDm5AePhUEhMTqFy5Mj3eH17qegqFglmz5hMZGcHGjWsxGk24uroydeqnXL9+jc8/XwSAVqvlzTc74epaBo3Gjo4duzAnMgKz2YzBYODFho2p4uNHocmEyWQpOm4yGdDqC5BJlrhurVqB7Ny9iSEhw6lZoya9e/VBbzBhr7lbUyhFn0LnN99G9+16PhrZD6PZiFKu4KMBn+Dl5c28ebMYMmQEzs7ODB8+mjFjhvPCC01p1aoFR4+d4IN+Aylbpgy1a9dAq9UB8GLLNqSnpjBx6EfI5HJsNRomz12Mv38AlSpVplevd6hc2ftvZbjdvp3MrFnTMRqNGI1GGjd+gZo1ayOTyRg9egKTJ4/DaDQhl8uYMGEKfn7+hIZOZcqUCRiNRpydXZg0adoDxx86NIQlSyLp0+ddJElCqVQxZEgInp4VePPNzixevJBx4yZx4cJ5PvzwPSpXrsyLL77Mxo1fMWLEGBwcLDW5DhzYy5IlkeTkZPPrrz+zbt0a5s1bhI+PL927v8eMGWF07drxznGPf2CQShAEQRAE4XmVnZ1FePg0Tp06jpOTM/37D+K119qUWO9Edibfpd4m22BAIUnUtnOg7j0POA8cXUJq5g3rZ90jf3xGhyBLDc6k9ByOX4zjdkYekgRX84bQo+xruGqcS+xHEIRHk8zmZzEB5PGkpeViMhU/nKSkG3h4VP6XjujpKygoQK1WI0kSMTHRDB7cnw0btuDo6Fjq+gqFrET3MeHfs2LFMgoKCqxT4xQKGdduZpa6rr+X82N97YxGA7du3SIvLw+5XI6HhweK1FQMZjMJWi0VbGxQSBIZBj15SJZOW5IMpdIWjdoJSZJhNhsp1GdQWFgIgFwmw/6eQtPuFb1Jjk0ksyAHo8mITJLhbOuIs60jag976/Sx+3l4lCMvO77UZXaOFYnNyi91mbdTya55z6vFixeSmprChx8OoEKFiphMJq5fv0ZU1BXatevwbx/eE3tef7b8f/ud8Dxwc3MgJSXn3z4M4Tkh7pf/Njc3BzqEfF/qsh0RbxI9o3Opy3wnbOGdbz4uddmmrks58mbp2zX9fstDCwc/qMX4oPBVD+0mFRERUeqykJAQzuwfVeqy+q/Nod/us6UuW94uSNy3T8nkyeMxm82MHTuRqKirjB49lKVLV1o7Nbu5OXDkzc6k63UoJRkOCgWFJiNfJSUS8OYbmDIsGfgHji7Bp2IQ/pUaA8Xvl9ikDPQGI5XLOSNJErcVFYg9eZWpbUs+qBX3y/8e8XuoJJlMokyZkokoRZ67TKHn3e+/X2Dx4oUU1UMZM2bCAwNCwv8f9wd+3NzccXKyZDnJ5QoqVvQCLE9Xbt++jUFnycSxk8usczxdFEoMchkGgwGz2YROl4feUIizQ3kkSY6fnx/xsdfJyddiNJrIK9Qh19ggv1O3yUntiJNa3Gt/1cCBQzl27AiRkRHcvp2MQqHE3z+AHj16/9uHJgiCIAiC8J9RUFDAzz//wFdffYNGo6Fu3UCaNXuRfft28/HHg4ut66osXjtTQuLGjRt4OZZeluFe3h4uxV737NmTHjve/fsnIAj/o0RQ6Blr2LAxDRs2fmrj9e3by1oLpUjNmrUYNWr8A7YQ/o6+ffs/0XZJSUlIkoS/fwBarZb4+DjUahtsbNTF1rO11eDr60Nh7A1MmEnT68kwGCijVFrXsbN1xkZVMtJrMBjIyivEpC9k5qczMJnMmMxmFHIZCqWKZsHN6N21zxMd/9/1OPfpjh3fsWXLphLbTpgwmYCAqv/4MT5MkyZNadKk6b96DIIgCIIgCP9lN2/eQCaTU6nS3YxeP78qJZraFLman8fC+BsUmEyoJImlvXvzw7a7GfjnL+/m/KVdONi70/qE2wP3e+rUKSq5VHh6JyII/2NEUOg5d2+LeOG/yWQykZubg7e3L3K5HI1Gg4ODIzk5OdjZFe+EpVDYlNje8JgzPLOzc1DIZLiULcPCufMwYyYlMx9XR1s8K/lSmPR0Cj8/ice5Tzt06EiHDh2fwdEIgiAIgiAIT1tBQUGJWqn29vbW2qXZ2VmEhY3llyt/4iBX0NmtHIur1CBDr+fnzHQqVKgAZBKbcI7c/DRMJgMymQIJ6N+/P283r46T/d0Hqpm5Baw/eB65/BTTWo+wvv/r9VOsP/s9aXnpeP5QET8/P7y9vZ/BFRCE55MICgnCP0yn0wISNjZ3Az62tmpSMlPJSy9Z4NzDpixxhYWYMCMB7ve1ps8vzCK/MAu5TIlG7YhCYfnlqNUWopDfbSgoISGXSxiNz1/dGEEQBEEQBOH5YmtrW6L7bF5enrW5RkTELFQqJQsCqhFXWMjC+Bt4qdVUsFFT296BESNGUM+3L26u3rR9cThqlT16g5aTv21GodYSm5xBXfvy1rEPnrkGZqhVqxa1ylcBIDUvg4iflhP62mDqV6xFYgMjn3zyCd27d8fW1vbZXQxBeI6IlvSC8A8zmczIZMW/1WQyOWZT6RlAdnYaKqnVVLRR46RQoJDubluunAfODuVxcfTERmVHTp7lKYplPyYkmVR8P0j8h2rJC4IgCIIgCP9PeXlVxmg0cvNmnPW9a9eu4uPja603NHToUNQyOVU0dgTaO3A0yzJdzGg2Exdn2c7O1gX1PaUSJElmaaZyz0faC9cTScnMw8vdiYoVK1rfT8vLwE6lIdirNpIk8fLLL6NUKsnOzv6Hz14Qnl8iU0gQ/mEymWRtJV/EZDKWCODcTyFJ2MrkpOh0eN7JMtJobMmULEWobVR26PT56AwFqFUOyGSyEoEmM2Yk6eH7EQRBEARBEITH9aC287a2trz0Ugu+/PJzxo6dyMaNa9m/fw9qtZrvv9+KyWTCzc2NROBYViauSiXxhYVczstl9s1YPDw8ANDpC7gWd4Lfo/ZjMNzNqr9s1lHXvzzp2fkc/u0GQQGeyO77PO1f1hsv5/KcuHGeYK86HDx4ELlcjqur67O8RILwXBFBIUH4h6lUNoAZnU575/9QWFiIpHh0op6Zx68pZGOjJjvzbnE+M2aMRjNyuUgIFARBEARBEJ6OiIhZKJVKtm/fb2077+8fgK+vHyEhYwkPn0qHDq2wt3cgJGQsHTt2Zu/e3UyfPokZM2bwOpCo0/JjRjoFJhN/5ufhKJdTt25dwPLw9EbieYxGPSChtrEndOIYfj9kaUhy8Mw1TGYzF67fwmgyIUXd4oC0n2/fX4JcJuOVgBeY88MX6Ix6VOtV1mwhQRBK99wFhVycbFGonv5hG3QGMrIKHrlely4d0Ot1bN26G7lcDsCuXdsJD5/K8OGj6Ny56xMfw4wZYVSrVt06xvLlSzl27AgREZ9x7NhhIiMj8PDwxGDQU7myN2PGhOLo6FTqWGfPnmbIkAEMGDCInj37WN9bvHjhMylO3aVLB2bPno+vrz+DBn3Eu+/2omnT5o+17e7dOzh69FemT58NwJEjvzJ3bjgzZszGxcWVbt3ewsfHD7PZhFpty8iRYwkIqMrq1V9y8OB+5HI5crmc/v0H0qhREwCMRiMLFszlxImjSJJEz559rEWN77/uD3LrVqJ130V69nyPHbv2kHI7CYC4G9fxquSLJEmU93AjImIRMpkMe3sHUlJSKF/ek8LCQrKzc1CVVZfYh7FAj06nByzBoEyDHvWdqWcms5mcnFyKcmd1+nz0Bi0aW2cAHB0duHUrEa3egEqpsLSkl8tQyP5+UGjNVxvo/u7b/5lfqJs2baBVqza4uDz8qc8vv/zEsmWLUKlUTJkyk0qVvJ/NAf4FqakpbNy4jjNnTgHg6lqGN9/sxEsvtbCuk5Jym6lTJ3L16mUqVqxU4nt4+/ZtrF+/BrPZTOPGLzBs2KgSUxYFQRAEQRD+rke1nXd0dCI8PKLEdp6enshkMlJTUwHo7FYOO5mc49mZuKtUeKpsMKtUYAa1jT1N63Vnz+EFdHp1Elm5yWzYsIFXqzmTkpmH3mDkk46NkctknLgYR7mAevT3sHyuP59wkVUnNxP++mj8ylZC29qZ9957j7Zt21K2bNlneq0E4Xnx3AWFFCoFUXMPP/VxA0Y2e+x1y5Qpy8mTx2jSxLLNnj07qVq1+lM7FrPZzIIFc4iKusqSJctQqy3F2YKDGzJ9+mxMJhOTJo1lzZoVDB484oHjlClTlk2bNvLmm51xcHB4asf3tKxYsYzy5T1p167DA9c5cGAvn3++iIiIz/D19ePWrUTs7e1ZvXoDAJs2bSQ8fCorV66nevWadOvWE7VaTVTUVQYP/ojvv9+LjY2a/fv3kJBwk6+/3kZWVhYffNCD4OCGlC/v+ZeO+d59AygUMvxrvmB93fPtV5g8/TPUtrb4ezljMFimjXl4eHDr1i2ioq4il8vx9PQkzZSF2Wii8HYeanc7JLkMk95IdHQ0RoMe2Z3pYy4Ky7epGUi5nUxhgRajyYhKaYODXVnkMuWdY1HgZKcmJ1+LMU+LUiHDya5kN7Mn8dW6r3nn7bf+Q0GhjQQHN3xkUOj777fSt+8AXnml5TM6srsMBgMKxcN/xF68+Adz54bTvft7fPTRJ9jY2JCcnMSqVcu5cOEsQ4aEAGBrq6Fv3/7k5eWxcuUXxcZITExg1arlrFy5HicnJ0aOHMK+fbtp2/b1f+zcBEEQBEH43/RX285fuHCe0aOHkpdn6UDWpk0b+MryWTq2sIBUvY4hFSvxS2ZGse1SM+Ows3Xht6v7uB53AqNZT2WHAHILtGTna1m95wwAeoMRWUwKF+0vsLDTZKLT4qjpUYUAN28A6tSpg7u7OwkJCSIoJAgP8NwFhf4L2rbtwO7dO2nSpBmJiQlotYX4+t7NHjl9+iTLly9Fp9NiNBp5770PaNmyNQAxMdHMnDmFwsICAgKqEh9/k969+1qzaIxGEzNmhJGRkcH8+Yuws9NYAwtFZDIZQUENOHbs4cGxsmXdqFWrNuvXr2HAgEHFlmVkpBMWFkpGRhpgCTgNGRLC7t07OHBgL/b2Dly/HoWbmzvDho1iyZKF3Lx5k+rVazBp0jQkSWL//r1s3rwRg8GS2TJw4DCCgxv+vYt7j+++28LGjWuJjPycChUqlrpOgwaN+OKLxQDWrCAAf/8AzGYzWVlZuLur+eGHA3To0BGZTIaLiwvNm7/Ejz8epHv394qNd/bsaRYsmENY2Ax8ff2f2rnI5QoqVvSyvlYoZKSlZyHJZdiWtwTsUpKTCR06kjde78iZ40fR6nQM7dOPstWqk5Rym08mjuXtru9y5PBRXnqxFa+0aMuGDSv48+IFDAY91apX5f0ePSjjqGHvgf1s37kDpVKJyWRm1ux5eKjdiEuIY9GKz8jKzsJg0NOlw9u81eMdADp2bEuPHr05ceIoOTk59O7dl06dOrPws88BGDxsNDJJxry5M0q0Gy1y7uQxtm38CsmoR6FQMnjwCGrVqs3evbvYsmUTS5euQCaTMXz4QFq0eJWOHbuwceM6Dh3aj9FoQKWysWZ+Afzxx28sXryQ/Px8AAYOHMqlS3+SmppCaOgYVCobJk+ejo+Pb4ljiYyM4LffzhEXd4Nt2zbz2WfLSj3mX3/9ieXLlyKTyTEaDQwfPpqgoGBSUm6zYMEc4uNvAtCyZWt69Xqf9PQ05swJJzExHrPZzLvv9rIGYLp06cDrr7/JmTOn8PSswLhxk9izZydbt27GaDRib2/PyJFjqVTJm8LCQiIiZjF79sJiH1LKlfNg7NiJzJ49g5Mnj9OwYWPs7e0JDAzi7NnTJY7/p58O0bz5S7i4uADQoUNHdu/eIYJCgiAIgiA8dUVt512cVCjulEUoX74sp09rcXOzfKY16LRkZFlqYNatG8i+fT+TknKbESMGsW/fPrqZTMQVFnAmJ5tXXFxxVRbvtBsTf4bM7Ftk5SThUSYAZydPKvuW4eDps3R6sRbvtQ6yrnsuKhF339q859YWgAA3H749v4fotDh8y1Ti4sWLJCUlUbNmzWdxeQThuSSCQk8gKCiYbds2k52dzZ49O2nTpj2XL1+yLq9SpRpLlnyJXC4nPT2Nvn170bBhExwdHZk2bRJdu3andet2XL58kY8+6lNs7DVrvsTfvyoREZEPzDLQ6XQcPvwL1ao9OjupT58Pee+9bnTp0q3Y+/v378HDw4OFC5cAFKvIf+nSRb766mvc3csxevQwpkwJZdGiL1Cr1fTt25PTp0/SoEEjGjVqTKtWrZEkibi4WIYO/YRt23Y/7mV8qLNnz/D77xdYsWIdZcu6PXC9H388aA0g3Gvv3l1UqFARd/dyACQnJ+HhcbeFZblyHty+nVxsm/3797Bp00YiIj7Dzc291P3l5ubSp0936+vFiz//S+f1KLnZOfj7B/BBuze4cOlPZixewFfzFgGQnZuDt7cP7du8C8DmLWvR2Nkx+9OlAGz9fjXfbttKr+49WL32Kz6btwC3smXR6/WU8fDAkKZlesRUJoyYSOWKlckvyKd/SD/qNW2AWq0BQKPRMHduJJcu/cmcOeF06tSZoYMHsH3Hbj5bMPuhrTyTEhPYumE142bMo4anG1evRjFy5BC2bt1FmzbtOXfuDEuXfoa9vT2Ojk507NgFgDZt2vPuuz0BOHXqBHPmhPPFF6vJzs5i/PhRzJgxm9q162I0GsnLy6Nhw8bs2PEd06fPemjgbsiQEK5evfLIqYtffrmMkJCx1K1bD6PRSGGhZRrp1KkTadKkKTNmzAEg8069pgUL5uLr60d4+FxSU1Pp27cHVatWsx5LamqqNQB14cI5fvjhAIsXL0elUnHs2BHCw6eydOlKDh3aT9u27Slbtixnz55myZJIbGxsqF27Lo6OTvTr9zFz54bTsGHjh90yj3VvC4IgCIIgPA1FbecVKhuiZ3QG4MafqZCcZ33tO2ELoCu2nZubO4MGDScsbDzHcnNRy+Q4yOW87V6ONL2OXWkpvJyfj6MtZOUmcy3uOABxSb9Rwb06ixYtoFvHtiSmZRPofzfTXymXoVKpcLK1BKRql6/Ku/XfIPzgUjILsiizx4169eoV61AmCEJxIij0BCQJXnmlFYcO7efQof0sXbqiWFAoMzOD8PCpxMfHIZcryM7OIi7uBj4+PsTEXKdVqzYAVKtWAz+/4n/U1qsXzPnzZ/njj98IDAwqtuz06ZPWgETt2nXp1ev9Rx6ri4srHTp0ZPXqL4tNoalZszbffLOBxYsXEhgYVCzLpk6dutZgSkBAVTw8ylszQ/z9A0hIuEmDBo1ISIgnLGwCKSkpKBQK0tPTSEtLpUyZB6dm7tjxHVu2WIrEpaenoVAo2LRpIwD9+39inZJXuXJl0tPTOXBgnzVgUKQoMGM2m/H0rMCECWHFlp87d4bly5eyYMHiR16fIrt27cDGxoaFC5dgZ1d6FgyUPn0sJTuz1HXNZjOKxygmfS+FQkGbNu3Ij46hbvWa2KhUxN9KRGNri0qppGXL17gVbwngnTp1lIKCPI4d+9myP4xUqlABgDq1ahG5eBGNGjQguH59KvgEcDnxOjfibzB17hTr/nR6PbGxMVSrZnl60rz5S4AlsJmenoZWq+Vx/XbmBMmJCUwZORCVXIbZbKnllJ6ehqtrGUaMGE3fvr0wGAzFauJcuXKJtWtXkZ2dhUwms7Yx/eOP3/H29qF2bUvRQblcjqOj41+6no+jfv1gFi2aT4sWLWnc+AV8ff3Jz8/njz9+Y/78u/eQs7OldtPp0ycZNGgYAGXLlqVJk2acPXvaGhRq06a9dZsjR37h2rUoa/DXbDaTk5NtPe833uiEyWRi1qzpzJ0bSfnynowfP5KqVavj4uIq2qcKgiAIgvCfUtR2PjY21vpeTEYhlZ0eXbJArbbFbDbzedWa7E9PZWtKMiOvXQVAIck4fPgwGpvLtHtxBB5l/fnxxJd0fGUCMpncmhF9v0Y1KjEofG6x8iIdar5Kh5qvApYSIRERJWscCYJwlwgKPaG2bV+nf/8+BAYG4eTkXGxZRMSnNG36IjNnzkGSJLp164ROp8VsBkmSHtoivF69IDp27Exo6BimT59FcHCwdVlRTaG/qnv39+jRozPVqlWzvlerVh1WrVrPqVMn2LdvN+vWrWbp0hUAqFR3UzhlMpm1Y5bltRyj0QhAWNgEBg0azosvvozJZKJly2bodMWfCtyvQ4eO1gLPD6spVKZMWaZMCWfw4P4AxQJD9wdm7vXHH78xbdokwsMjihUVLlfOg6SkW1Svbgl+3J9d4e8fwIUL54iJiaFWrdqApbPC779fAGDq1Jko70ttfRRJktDeum59nZWdw4jJloBMZb8qfBAy8JFjmM1muHO7qG3Uxe4dM2Y++nAYtWtbgoeeXk7cjo8FYNyoMURdu8Zvf/zOhMmTGDd+Mi4KR5wcnVixYGWxfag97ElKsmSWFJ1jURH1oq/14zCbzdQNbsTA0RPxdio57TEtLY38/HwkSSIvLw87O3v0ej0TJ45h0aLlVK1ajdTUFDp2bHv33J+BIUNCuH79GmfOnGLixLF07drDOt3zQe7/Hr73tUZzN5vKbIb27d/gww8HlBjDbAaZTCIrKxNHRye8vCoB0KRJM2tATq0uWYz8fkX3dpHk5CRrUFcQBEEQBOFpKmo7HxkZyYeOJq5nFHDsZjbz2pacyr9//x7q1KlHuXLlSE5OYvnyJTRp0gRu3uIlZ1ca3dMwZ296KjQIxt5kqdXp7uqHxtaZP6/9QE3/Vzhz5gwJqVk0rV25xH4EQfh7RHuaJ1ShQkX69fuE3r0/LLEsJyeH8uXLI0kSp04dJyHBUpPE3t4eb28fDhzYB8CVK5eJjr5eYvvg4IaEhc0gNHQ0586d+dvHam9vT9euPVmz5m4wIDExATs7e1q2bM3gwcO5cuUyJpPpIaOUlJubay3UvHPn948MCP1V7u7liIz8nG3bNrNx47pHrn/p0p9MmjSOadNmUbVqtWLLWrRoyY4d32EymcjIyODXX3/mpZdesS6vWrUaM2fOZdq0idZrHhIyhtWrN7B69Yan0rXKydGBFfPnsmL+/7F33/E1X/8Dx1/33txsGQ2S2DKMEgmCGKVae5SWqpqpIEg0NIQQQWQgYoaUiF17FLVi1957kxBB7Ox1R35/3OZWmpugRev7O8/Hw+Phfs75nHs+n9yM+77v8z7TCAmZorOPUqlk164dAFy6fo1chYLyRRTDrufaiC2/rdNm82RkZHA/MRGVSkXS48dUcXSk69ffUNvZhZs3r1O+bHlN0e39u7Rj3Eu8R0ZG+mvnbmxsRHpGZrF9atWtz4XTJ7h/N0577Nq1KwAoFArGj/dnyJAf6ddvIOPHj0GpVGrrbuUHMTZuXKc918mpFnfvxnP58kVAE6DKz5wxMTEhPf31834TCQl3sbd3oFu372nVqi3Xrl3F2NiYmjVrsXbtn8HH/OVjrq712bJlEwDPnz/j2LEj1K7tqnPsxo0/Y+fObdrlXCqVSptVWKVKVc6dO4u5uQVpaak8eJCIUqnk+PEjqNVqFi1aQKtWbV87/2bNvuDQoYO8fPkStVrN1q2/8sUXLf/RPREEQRAEQSiKr+9osrOz6b7uGlN+T8S7QRkqWhjyJD2Xr1de5d79BEqVKsGTJw/w8vKgVaumeHn1p0oVByZNmgSAgVSKuZ5c+89AolkGZmigydiXSmU0q9ePh0+usXZXAOPGjaNlXUc+KWH8b166IPxP+ugyhZS5yrfaKextxn1bnTp9o/P44MHeRERMYcWKpdjbO2Bv76htCwiYSFhYEKtXr6Bq1erY2zvqLNrr6lqf8eND8Pcf+beyg/6qS5durF+/Wvv43LkzrF69AplMj7w8NSNH+r/1FtY//vgTY8aMoGTJUn9kTJm//qS3ZG1tw5w587UZQ59//kWRfSMippCbm0N4eKj22LhxQdjbO9C6dTuuXr1M9+5fA5paS38tXm1v78DUqTMZNWo4w4f7FVhS96GYmpUgMTEB7xVLyc7JYazXMOR6unf8+qZzD9asXYKf/yCkEilyfRldO3+NjbU1syLnkJGZgUQioaRVSXy/7oJelh5hY8OIjJnD6l9XoVarsbSwJCx82mvn9W2XzozwC8BAX7/IQtO2ZcvjNSqQ+TMmM1+pQKFQ4OTkTPXqNZg3bzYODlW1GThnz54iOjqKwYOH4uHhyYABfbC2tsHN7c+d3MzMzAkJmcqcOTPIzs5CIpHi5eVDvXoN6Nq1O6GhQRgaGhZZaPpNRUVFapd6mpqa4u8fCEBg4CSmT59C797dkEpltGzZml693Bk2bATh4aH07dudvLwlga1lAAAgAElEQVQ8Bg3yLlBo/lUuLnUYOHAIo0f/hEqlRqlU0Lx5C6pVq86XX7bE23sgzZt/iZ/fWMaPH4O+vj7OzrU5e/Y0HTp00i41ValUdO3aEYUil/T0dL7+uh0dOnTCw8OTsmXL0bevB56e7gDUr+/2RsEkQRAEQRCEv8PMzJx58+ZpawjlK22qz6Yen1KxfAW6rRkMZcDGqwY2f7Tf4FmRy8A6l7Km8bRpBPn+pj1mUcKG1k1+BCAwogOR/q8vnSEIwtuT5H2oNRpv4PnzdNTqgtNJSrqHjc3/TppgVlYWhoaaZUDx8XEMHerJypUbiqyVoqcnLbQMR/jv0NOTcvu+7ppCDuUtCiwfe5WBrT13XiQUOJa/+9ie2IOk39Z9nqmDPQ/vp+hse3X52F+VLleJ7CTdmTWvLh/7KxsbazJSE3W2mZiV426K7uwhXcvHhMIuXDjP7NkRDBgwmHr1GiCTyUhOTubAgT18/nkLbR2j9+Fj/dnyv/Y74WNQqlQJnj5N+7enIXwkxOvlv61UqRJ09N2ss21rRKdCb/Lz2Y3doHmTr8Pa76I40kn3eY03byjwJv9Vxb3J9w5bXKBGzKuKqxHj6+vLmdiROtvqtgpnwHbd26ZHt6sjXrc6lDAzwtCg6BwC8XoR/ovex++h1NQUwsImcerUcczNLfD09KZVqzaF+u3Zs4uYmPm8ePEcuVwfN7dGDB8+UluzNihoHGfOnCQrK5tPPrGiZ88+2tIqANnZ2URGzmT//t0olUocHKowd270P56/VCrByqrourkfXabQx+7SpQvMnTsL0AS/Ro0a+16K5wqC8N/n7OxCSEg4q1YtY8ECzU6AVlZWdOjQ6b0GhARBEARBEF7H0ECv2CCiIPx/ERExBblczpYtsdy6dRM/Px8cHBwLrRZwcnImKmoRFhYWZGZmEh4eSnR0FMOGaYKPvXq5M3r0OPT19bl37y5Dh3ri6FhVu6v41KkhqFRKVqxYj5mZGbdu3fwg1yeCQh9Y/fpur91i+m14ePQuVAy4Ro2ajBw55p09h/BhlLK2Zv7q5a/v+C+bMWse167dQCqTk6vSZJrIZDJCI2P+lfksXhzNwYP7Cx2fMSMSS8tPAHj58gXDh3sX6tOsWXN++GHAe59jcWxsbBg+3O9fnYMgCIIgCIIgCIVlZWVx8OA+li1bg7GxMc7OLjRp0pRdu7YzePDQAn2trW0KPJZKpSQm3tc+fjWIJJFo/j14kEi1atVJSLjL4cO/s2nTNm1mUX6w6H0TQaGP3KtbewvChzDcZwhQ/PKxD+mHHwa8NrBjaflJkTvWCYIgCIIgCIIg6HL//j2kUhkVKvxZvsDevgrnz+teXnjhwnn8/HzIyMjA0NCQ0NCC9VunTZvMjh1bycnJoUqVqjRs2BiAK1cuY2NjQ0zMfHbt2o6VVUn69RvI559/+f4u7g9i9zFBEARBEARBEARBEP7fSk1Nwd9/BC1aNKFLlw7Exu4ENJlCr26ys2fPLlatWs7x40fo0KElwcHjC+zmvHnzBgwNDTE0NEIu19fuxgxw+fIlEhMTkMv1MTU1JTdXod1d+enTJ8TF3cHExJRff93J8OF+hIRM4O7d+Pd+7SIoJAiCIAiCIAiCIAjC/1uv1g0KDAwmIiKMuLg7GBkZFQj6ODk58/XX3+Lm1pi1azeTnZ3FDz/01AaT7OwcWLduK3v2HMLXdzSLFi3g+vVr7Nmzi3HjRnHp0gVkMhkNGzZBoVDw00+aEhcHD+4DYM2aX+jd+zsSE+9Tu7YrJ08eR6FQEBDgR9euHWnSxJWzZ0+/02sXQSFBEARBEARBEARBEP5fyq8b1L//oEJ1g8qXr4hKpeL+fc3O0dbWNjx4cJ/Kle0AuH79GtnZ2dpg0vLli7R1hNRqFWq1mgcPEnFycmbx4pXs3XuEdeu2AJCXp2kD6Ny5KzKZjO3b9zFlynSio6NIT/9zF7VatVwYN24SVlZW7/z6RVBIEARBEARBEARBEIT/l4qqGxQfH4eRkRHNmjVn4cKfycrK4uLF8xw8uJ+NG9fSqlVTkpIeMXDgEIyNjXn8+BF169YnKGgcX3zRiKCgcZiYmNCwYWP09fU5ffoEmZmZqFQqXrx4zqNHDylTpgwAbdt2wMbGlhUrlqBSqVAqlVy7dpUGDRoil8vp1q0Hzs4uSKWyd379H12haXNzQ/T15e983NxcBSkp2e98XEEQBEEQBEEQBEEQ/pv+WjcIwNTUlMzMDAB8fUcTFhZEx44tMTMzx89vLK1atWHnzm0EB4+nTJmyAMTHx3HmzCmysjKxsrKiSpXqVK5sh76+Pjk5OaxYsZQJE8YCIJFI0NPTY/TocQDo6elRtWp1YmLms3Dhz+jr6+PvH0jFipXe+/V/dEEhfX05ERER73xcX19f4PVBoa5dO6JQ5LJx43ZkMk2Ubtu2LYSFBTF8+Ei6dPnub88hJGQC1apV144RHR3FsWNHiIiYw7Fjh5k9OwIbmzIolQoqVqzEqFEBmJmZ6xzr7NnT/PjjIAYN8qZXL3ftsblzZxETs5xHjx7Sv39vtm3b+7fn+z7ExMwnKysLb+9hAGzevJFfflnKjBlzefw4iZEjff5I4VNiZVWSUaMCsLUtQ0TEFM6cOYlcro+xsRE+PiOoVu1TALKzswkNnciNG9eQyWR4eQ2jcePPAPD2Hsj33/fWPi7K2bOntc+d78cff+LnnyNJz8hCqVSS9Og+5cpXBqBiZQfCJ4e9j1v0QaWlpbF27Qa6d+vyb09FKyZmPn369EMuLz44vGnTetatW4WBgQFz50ZjbGzygWb45hIS7rF69QquXLmEVCrFxqYM3bp9T+3adbV9bt26wfTpU7h58wYNGzYmOHhqgTGWLFnI9u1bAWjXriPu7v0/6DUIgiAIgiAIwoeUmppCWNgkTp06jrm5BZ6e3rRq1aZQvz17dhETM58XL54jl+vj5taI4cNHYmJiSm5uLhERkzl9+iTJycnk5uZw7NgR7W5gGRkZZGZm0rNnV548eYK1tTWBgcE0bfq5dnxb2zKYmZkRGTmDRYt+wdPTC1vbMsTG7iAycgEA4eGhbNq0nm+/7a7dDfnixfP4+v6Iu7sHzs61teMFBYWhUgVz+fIlzp07zRdftHiPd/FPH11Q6L/AyqokJ08eo2HDJgDs2PEbVatWf2fj5+XlMXNmOLdu3WTevPkYGmrezLq61ic4eCpqtZrAwNEsXRrD0KE/FTvPtWtX0alTF0qUKPFO5qZSqbTBsH8iJGQCbdt2oE4d1yL7rFy5jO3btzJ3bjSlSpXm8eMkKlWyIyZmOQBz5kxnzpwZhIaG4+bWCB8fX/T09Dhy5BCBgf6sXbsZgFWrlmNsbMyaNb9y/34CXl4DWL16E8bGxm8151efO9+iRcu4fT+Zp0+SGDd6EKHTot/yTry9d/U1eBNpaWmsWbvxPxUUWrw4mu+/7/3aoND69asZNy6I6tVrfKCZ/UmpVKKnV/yP1yNHDrFkyUL69RvA8OF+yOVyEhLuEhU1h1u3btCtWw8ALC0/wdv7J27dusHp0ycKjHH+/Fn279/D8uVrABg40B0Xlzq4uNR5PxcmCIIgCIIgCP+yV4tC37p1Ez8/HxwcHLGzsy/Qz8nJmaioRVhYWJCZmUl4eCjR0VEMGzYSlUpF6dLWREYuwMzMnHbtviAgYBQrVqzF1rYMV65c5NatG0ydOhM3t0YcO3aEceNGsX79ViwtPwHAyMiIrKxsbV0g0AST8j+MTk1N4fjxo/z222ZWr16Bp6c3tWo5ExQ0jq+/7sLevbH07NlXZ/BKKpWyadN6OnX6Rhu8Sk1NRaHI5erVy9r30bGxOwgPD9U+v1qtJicnh4ULl1Ot2pvFKERQ6G9o27Yj27f/RsOGTXj48AE5OdkFXoCnT58kOjqK3NwcVCoVffr0o0WL1oAmpSw0dCLZ2Vk4OlYlMfE+fft6aDNVVCo1ISETePnyJTNmRGJiYoxSqS7w/FKplDp16nHs2OFi51myZClq1nTil1+WMmiQd7F9J04MICHhHgpFLmXLlsffPxAzMzPOnj3N7NnTcXZ24dq1q/Tt60GVKlUJDh7P8+fPKVu2LHl50KCBG126fEdGRjpz5szgzp1b5ObmUru2K0OHDn/rIMaCBfM4ceIYkZHRWFhY6Ozj6lqfuXNnAxTI9KlZ04mnT5+gVquRSqXs3bubgIAJAJQvX4Fq1apz/PjRQpHXPXt2sXr1L4SGhlO6tPVbzfefunrxEsvnx1DJ3o6khIdIlAr8PL2oWLY8569eIWrFYmpWrc7th4l06tiDT6vXYvHSedy7F4dCkUvNGi6MCfAHYNXaNfx++PAfyywlhEwMojRw9eZVFiybr02D/KGHBw1dG/Lw4UP69u1B69ZtOXPmFDk5OXh7D+PTT2sybdpk0tMzGDjIBwNDA+bMnFrkNRzcvYPYrRtRq1R8Ym6Gr+9oKlSoxJIlC7l58wahoeFkZ2czYEAfhgz5kYYNmxAZOZPz58+iUCiwsLDA3z8QGxtbQBM0WbRoAUqlEqlUwtixE9m8eSMAgwf3QyKRMmfOfJ0Bz8BAfx48SGTSpECqVq3O+PHBOue8efNG1q5diVyuT16emqCgyVSsWIm7d+OZNWsaL148Jy8vj++/703bth1ITLxPeHgoyckvkclkDBzohZtbIwCaNHFlyJAfOXr0MM7OtRkwYDC//LKUAwf2olKpKFmyNKNGjcXKqiTPnj1l6dIYZs2KKhCcrFChEiEh4YwaNZz69RtSqVJlSpYsRcmSpbh3r/B2lHv37qZ16/YYGBgC0Lp1e/bu3S2CQoIgCIIgCML/pPyi0MuWrSlUFHrw4KEF+lpb2xR4LJVKtUWgjYyM8PDw1La1bNmSI0eOcPfuDRSKdE6cOEaJEiX46qs25OYqaNSoCUZGRmzcuI727TthbW2Nvr4BSqUCFxdNts/Lly84dOgA1at/ikqlYsyYkTx9+oTAwEmUKmXNyJE/YmJiyrffdqdGjVra9za6glfXr1/F2NikQPDK2tqGDh1asnhxNF9+2Qpb2zK0atWWVq3aaq9j+/atLFmykKpVq73xPRVBob+hTh1XNm1aR2pqKjt2/EabNu25fv2atr1KlWrMm7cQmUzGixfP8fDoTf36DTEzM2PSpEC++64HrVu34/r1qwwc6F5g7KVLF+LgUJWIiNlFZhrk5uZy+PDvbxT5c3fvT58+3enatXux/Xx8RmiDLwsWzOOXX5Zqv6ni4m4zYsRohg/3A2Ds2JHUrl0Xd/f+JCU9ok+f7jRo4AbAnDkzcHGpw+jR41Cr1UycGMC2bVv46quvXzvXfDt2bOWTT6z4+edFmJiY6uyjVqs5cGAfVapULdS2YcNaGjZsglSqqaP++HES1ta22vbSpW148iSpwDm//LKUkydPMHPmvELrSfPdvRuHu7sme0MulxMdvfSNr+lNJMTfpc+g/nRo1o6Ni2KY8nMk8yZNASD+fgI+PwzAv207Ht5PYW5UODU+dcZr8EjUajUzZ4ewdetmalWrwq9bNrMsZjEGBgZkZmVhoK9PWloa06MimDJuClaflOT5i2d4jvRk8awlYAxpaalUrVqdXr3c//ghu5jJkyMYMWI07u7fs+DnWcXO/dqlCxz/fR8Tps1Frq/PwyvnCAsLIipqEX369MPXdyjr16/WLoHKz7Lr1ctdu1Rw69ZfiYqazcSJYSQk3GPKlGDmzo2mfPkK5ObmolQq8PUdxaZN64iKWlRspldQUBhdu3YkOHgKdnYORfabN28Wy5atwdrahtzcXNRqNUqlktGjfRk4cIg2cJiSkgxogqedOn1Nhw6diY+Pw9t7ACtWrMfS0hLQvC7zU0V37dpOYmIi8+cv0Ub6IyNnMn58MJs3b6RPn34YGxuzd+9uli9fjLm5BXZ29tSs6US/fgPZunVTsZmAoHltv7rUzNrahgsXzhZ7jiAIgiAIgiB8rIoqCn3+vO6/gS9cOI+fnw8ZGRkYGhoSGjpNZz8fHx927tzJ6NGjMTQ0pEGDBty4cYP+/ftz+vRphg79Cblcn+zsbAYP7kdaWiolSphha1sGIyNjsrKyuHr1CpcvazKMtmz5laysTPr3H0SLFq2Jjd1BqVKluXfvLjEx81EoFAC0bPkZa9du5vTpEzRq9Jm2CHVCwj08Pb20wavc3FwUCgUGBgaYmJhw+fJFbGxskUgkBa4jPz7x1+PFEUGhv0EigS++aMnevbHs3RtLVFRMgaBQcvJLwsKCSExMQCbTIzU1hYSEe1SuXJn4+Du0bKlZ71it2qfY2xd8w1q7tivnz5/l8uWLhT7tP336pDYo4eTkTO/eP7x2rpaWn9CxY2eWLFlY7JrEnTt/IzZ2J0qlgqysbMqXr6BtK1euPDVr1tI+Pnv2DMOGjQTAxsaWunXradsOH/6da9eusHr1L4Cmnk9+1s3ixdEcPLgf0LyZvXjxPEZGmjf2Y8eOx9FRE+CpXr0Gt27d4PjxY3z5ZcsC88wPzOTl5eHg4MDQocMLtO/Zs4vdu3cyd+6bL+NatEgTdZ02bVaxS5J0LR97l6zL2FLdqSYALZo0Zcai+WRkZgJQ1saWTx3/DICdOn2U27evs2XrWgBycnOoWKksDWo7U7ZMWabPnkUdl9rUc62LsZERly5d4NHjR/gF+WnHkAAPkh5Q2q4MhoZG1KvXANAENRcvXvhWcz974gj34m4T4DMQALlUQmpqKqCJyAcGTsLdvQfW1jbMm/fn2MePH2HjxnVkZWmq8Guv79QJ3NwaaV+H+vr66Ovrv9Wc3kSdOvUIDQ3is8+a0rBhE8qWLUdc3B1UKlWB7xdzcwsyMzO4ffsm7dp9BUDlynY4OFTlypVLNGnSFNDsGpDv8OHfuX79Gv369QJApVJqA443blyje/eepKQkEx0dRVTUQgwMDPH2HoijYxUqV7YnIeHeO79eQRAEQRAEQfiYva4odHJyMv7+owrUG9q16yBPnz5hy5ZN2lUJf12yJZfr4ejoSPPmzVGpVBw+fJgXL15w6NAhAKZPn0Jo6DQaNWqCl5eP9rmjombzyy/LaN/+SywsLAkICKJVqzYcPXoEPz8f2rTRvD+Ij4/j2bNnSKVSzMzMcXNrzKBBXpibW/Dy5ctCRai7du3OZ599rn2eHj26kJT0SPt44sQAatasha1tGe2xpKRHXLhwDn//wLe6pyIo9De1bdsBT09N/Q5z84LLmyIiJtO4cVNCQ8ORSCR07/4Nubk55OVpvsDFRe1q165D585dCAgYRXDwFFxd/6y5k19T6G316NGHnj27UK2a7hSyCxfO8euvG4iKWoSlpSWxsTvZsmWjtj0/cPOqoq8hj9DQaZQtW65Qyw8/DOCHHwYAxdcUqlTJjkGDhuLrq1ny9mpgqLjAzMGD+1mwYB6zZkXxySdW2uPW1jY8fvxIm83x5ElSgeetUaMmp06dICnpkTYI4e8/gkePHgIwb94/rxN05949QmfOAaC2U018x056q/ONDA0LHsjLY5TfJGys//whUKa8OU8S7xIeNplr169z8fIlfvIbyfix48iTyLCvZMfs0MhCY79QpxYIhkmlsgIBmjeRl5fH563a062vpshxJfOCyx4fPnyIVColLS2VnJxs9PRMSUp6xJw504mOXkaZMmW5dOkCEycG5I/4Vs//d4WGhnPt2hXOnNEUZh8xwh9ra91LB/PydM/p1e+FV79X8vLy6Nu3Hx06dNI5lkQi5cGDRKpUqapdl+zqWh+AFy+eF3gNF8Xa2qbAL4fHj5MoXdqmmDMEQRAEQRAE4eNlZGRERkZ6gWOv1vEJCgoqst5QzZq1GDCg7x8f1pbA3b0/X331NePGjeLq1cva90S3b98mLi4OhUKBvr4+tWvX5ty584SGTmDq1Jls3ryR06dPkpKSjFqtpkQJM4KCwqhXrwHx8XF4ePQmIeEuEomEsLAghg0bobMIdT5LS0ttEer84FV+Ikm+9eu3olQq8fX9kbJly+LnN7bQvdm5cxu1arlod0N7U9K36i1olS1bjgEDhtC3b+GdftLS0rC11aRynTp1nAcPNOsWTU1NqVSpMrt37wLgxo3rxMXdKXS+q2t9JkwIISDAj3PnzvzjuZqamvLdd71YunSRzva0tDRMTEwxNzcnNzeXbdu2FDte7dp1tbsdPX6cxNmzp7RtjRs3ZcWKpdqgQnJyMg8fPnjrOTs4OBIREcmsWdPYu3f3a/sfOXKIyMgZTJ8eWSBaCtC8+Zfa9Zr37ydw7dpV3NwaatsbNGjEiBH+jBzpo/16hIVNY8mSlSxZsvKd7FplX7EiMTOmETNjGt793HX2efzwEdcvXwFg39HDVC5fAZMilkjVc23Epk0rtfc5NTWFhw8fkJmVRUpqCjVr1KDHd92pUL4CCfcTcHJyJvFRIucu/ZlWef3WtSIDHflMTEzIycl5bZCorltjft+7k+dPnwCaYtj52XOpqakEBQUwYUIoLVq0ZurUEEDzw1tPT46VlRVqtZpff92gHa9+/YYcP36U+/cTAM2Syfzov7GxSaFfBH+HUqnk4cMHfPppTXr3dqd+fTdu3bpBhQqVkMlk7Nu3R9s3JSUZExNTHByqsGPHbwDcu3eXO3du8umnNXWO36RJUzZtWq/NmMrNzeXWrZsAODpW5fz5s5QtW47bt2+SnJxMVlYWZ86cIjc3l5iYn2nf/qvXXkPz5l+ya9c2cnKyycnJZteubR9slwJBEARBEARB+NA0O1GrtO8TAG7fvknlynZkZWURGxtL//6DCtUbAli39hdycrI5evQos2bN5Oef5xAUNIb09FQaNGhAWloaAGXLlsXJyYlKlSrRs2dPSpUqhZGRIba2ZTh16qS2xk+tWrXp2bMv6elpvHjxHNDU9Q0OnkJkZDT6+vo0adKUCRPGAAWDV0UpVao0DRo00p6TT61WM2nSOORyPX76aZTOc3fu3FZg5cKb+ugyhXJzFX9sH//ux31bnTp9o/P44MHeRERMYcWKpdjbO2Bv76htCwiYSFhYEKtXr6Bq1erY2zvqrGHj6lqf8eND8Pcf+beyg/6qS5durF+/Wmebm1sjYmN30KNHV0qXLk21atW5evVKkWP5+PgSHDyevXt3U7FiJZycnLW1f3x8fJk3bzbu7t8jkUiQy/X58Ufft45WQn5gaA6+vkOBPG02hS5hYRPR05MTEPDnN8isWfMwN7egR48+hIRM4LvvOiOVSvHzG1Pom7Fu3XqMGTOe0aN/YtKkKW9VmOtdqWhXmWMHD7E2ZjkoFIwqpjh4vx+8WbZ8Pj+N7I8ECXK5nJF+o9CXqJgcHk5Obi55eWrsK9vRsIEbZmZmhI4JI2pJFJExc1AoFZSxLkPo2MnFzsnc3Jwvv/ic/gOHYlrCtMhC09WdXOjuPpDwCaNRq1RI1CqaN29BtWrVCQsLon37r3B2dqFmTSd8fAbz66/r6dy5K82bt6BXr++wtramdu26XLhwDtAUBPfzG8v48f6oVGpkMiljx07E3t6B7t178uOPgzAwMCyy0PSbUKs1Rd3T09OQSKRYW1szaJA3enp6TJ4cwYwZU1myJBqJRMr33/eiTZv2jB8fTHh4KGvXrkQmkxEQEKTNQPurNm3ak5KSzNChA7XP9/XX3+LoWOWPTyRGM3v2z3h4eDJ8+BDMzCxwcanD/v176NatB7VquQDw6NFDhgzpT3Z2Nrm5OXz9dTs8PAbSoUNn6tRxpWnT5vTu/R15edCmTbsCNYYEQRAEQRAE4X+JkZERzZo1Z+HCnxk9ehy3bt3g8OGDREUt+qPekFRbbyg2dgelS9ty585N7t6N58TJEziXqc7DqHOYAwZqOXv27NGO27x5c0CTVFGmTBmuXLnC8+fPUShySU1NRa1W4+ZWlyZNmrBjxw5MTY0Y4fsjy5cv1gapSpQoQYkSJbCwsESlUpGSkqwtbp0fvHodlUpVYEezvLw8Jk+exIsXL5g2bZbO2sMXL57n2bOnNG/+5VvfU0ne61IFPqDnz9NRqwtOJynpHjY2FYs44+OTlZWFoaEhEomE+Pg4hg71ZOXKDZiZmensr6cnLbT72L8tJycbmUwPPT09nj17xoABfZg1ax4VKlT6t6f2wenpSbl9P1lnm0N5C3IeFc4EAzCwtefOiz+j21cvXmLlwiUEz47A/pMKpN/WfZ6pgz0P76fobMtfPqZL6XKVyE7SnV1jaGNKUtJjnW02NtZkpCbqbDMxK8fdlEydbX9dPiYUduDAXtasWcngwUNxcnJGIpHw5MljDh06QIcOnTEwMHivz/9f/NnyJv7Xfid8DEqVKsHTp2n/9jSEj4R4vfy3lSpVgo6+m3W2bY3oRFxIF51tdmM30G3NYJ1ta7+L4kgn3ec13ryBIN/fdLYFRnQg0l93fUzvsMXcmqZ7l13HEU2IiIjQ2ebr68uZ2JE62+q2CmfAdt2FaKPb1RGvWx3E60W8Xv6rUlNTCAsL4tSpE5iZmTNo0FBatWrDvn17CAwczfr1v2FjY8P8+XPZtGk9GRnpmJtbkJaWyvIe0zEzNOVJ2jP6rR6FBAn6MjkqiZq8vDyaNm2Ko6MjSUlJbNu2TbtiQi6X0q19Ndp/6UB2jhL/KQfxH+JGvVZBNG7cmNGjxxUoG9GmzedkZGT8UVLCgwYNGjJypA9RUYsK7FwOmuBVrVq1sba25vHjJIKDx2NmZk5oaDgA4eGh3Lp1k5kz5xW52c6UKSHk5uYwblxQoTapVIKVle7NlOAjzBT62F26dIG5c2eRXzNl1KixRQaE/qvu379PcPB48vLyUKmU/PDDgP+XASFB+Cc+//xLKlaszKpVy5kxYyoSiRQbG1u6dv3uvQeEBEEQBEEQBOFjZWZmTlhY4WBfuXLlMDQ0xMZGU2PT09MLMzNzzp07Q8+efRg/3h8zQ01wpHSJknh/1ocDt1UjWwQAACAASURBVE8wuYMfGS1MGTJkCI6OmlU+NjY29OvXj127NKVfvHrZUd5W8759/bbrfFavHJ9YGjFixAiMjIyxti5Y13PnzgM8fvwYP79hrFy5nB07fsPX1x87O3uSkpLo3ftbli9fh42NDfHxcURFzdHuaJZfhBo0xaM3b96Ivr4+nTq11o4/cuQY7Vb0OTk57N+/+2+vMBJBoQ+sfn036td3e2fjeXj0LlTvpUaNmowcOaaIM/45BwdHbSEs4d34tJYTwbN1f4rxXxIQGMyTJ08BkMrk5KrUlCxtzciJU/6V+YSHh3LlyuUCx2QyWYFi5Ldu3SAkZGKhc7t06UbHjp3f+xyLU7myHWPGjP9X5yAIgiAIgiAI/wterTeUv4FQ/pItIyMj0tMLrpzIzM3GSK7Z0EepVGprgYJmydbBgwdRKBRMnTqVcf7ehI5qBsCVm8949jKLzbG3QCJDqVQSGOhPz5596NXLXTuGtbU1ixf/QocOLYmJWa4th2JjY8Pu3Ye0/Tw9vfD09NJ5TTY2thw+fLrY6zYwMGDnzgNvdpN0EEGhj9z73CJdEP4qOChA+//ilo99KG8S/HR0rCqCmIIgCIIgCILwEbEsoY+eoe7seWV2Di/TcgsdNzIyomXLljrrDdnaltHU6kl5TFlza/bfPs61x7epYGHLk7RnRM1cT9myf9bBPXz4MMnJybRv3x6pVMrjZxnaNn/vhixZd4nnL7P4eeF6vv++B97ew3Fza1RoTmq1muzsbJ4+fVJsjdx/kwgKCYIgCIIgCIIgCILwn6FnaFBs/Sl0BIUAxo8fz4gRfnTs2BIzM/MCS7bUajUxx9fg94Un5+5f5vjds+jJ9Dhw5wRftm9J9erVAbh06RLXrl1DKpWybNkyli5dCnlqDp9KpEm9cqz77TrPXmQxxrsh1rZlkMv1KF/emooVrTly5AiWlpZUrVqV5NR0ImfOoESJElSsWPm93at/SgSFBEEQBEEQhP8UTRHRSZw6dRxzcws8Pb1p1apNoX579uwiJmY+L148Ry7Xx82tEcOHj9Tuirphwxq2b/+NuLjbtGjRmrFjJxQ4Pzs7m8jImezfvxulUomDQxXmzo3+EJcoCIIgvAcWFhY66w3Z2Nhw9OhRfLp50nPFMMwMTPFtPoDPHdx4kv4c780T6NxZU1oiKysLExMTcnJyMDAwoH379nxZJ5USJvo8fZHJ3iP3kOtJGTxmF7LAA2QrVUzeexrrZEOenr/A3R3ryEl+jrmJMVWrViciYs5/umaoCAoJgiAIgiAI/ykREVOQy+Vs2RLLrVs38fPzwcHBsdCOLU5OzkRFLcLCwoLMzEzCw0OJjo5i2DDNjkIlS5aib18PTp48Rk5OTqHnmTo1BJVKyYoV6zEzM+PWrZsf5PoEQRCED8/CwoKAVkMLHS9tasW5c+e0O9XVr1+f+vXra9tf3amu1CfGrJzzlbbtrzvVlXJxo5SLpobwx7JTnQgKCYIgCIIgCP8ZWVlZHDy4j2XL1mBsbIyzswtNmjRl167tDB5c8I/5v+72IpVKSUy8r33crNkXAFy/fpWnT58U6JuQcJfDh39n06Zt2syiatWqv49LEgRBEN4hpUJFqVIlimjLLbJN0O2jCwpZWhigJ9d/5+MqFbm8TC78CdJfde3aEYUil40btyOTyQDYtm0LYWFBDB8+ki5dvvvbcwgJmUC1atW1Y0RHR3Hs2BEiIuZw7NhhZs+OwMamDEqlgooVKzFqVABmZub/YOzDREREFjm2qWkJxo0bRVzcHfT1DbC0tGTkyDGULVsOgBcvnjNpUiCPHj3CwMAAP7+x1KhRU3ufpk6dgZ2dQ7Hz2r59q/a5840cOYbw8FAAsrIyefbsKeXLVwSgYcPGRVZm/5g8fPiQfft38UXb1q/v/AGkp6cTG7uDb7759rV9Fy1ewe+HjvCJVSn8p0Z+gNm9vevXr7J27Spu3bqBXC6nYsXK9OjRG0fHqto+J08eZ/78ucTF3aZLl+/w9h6mbVOpVMycOY0TJ44ikUjo1cv9X9+pTBAE4f+L+/fvIZXKqFChovaYvX0Vzp8/q7P/hQvn8fPzISMjA0NDQ0JDp73R81y5chkbGxtiYuaza9d2rKxK0q/fQD7//Mt3ch2CIAjC+6EnlxHk+5vOtsCIDkT6/6CzzTts8fuc1kfrowsK6cn1talb71LdVuHA64NCAFZWJTl58hgNGzYBYMeO36ha9d19spSXl8fMmeHcunWTefPmY2hoAoCra32Cg6eiVqsJDBzN0qUxDB36U4Fzvb0HMnbsBGxty+gausDYs2fPx9TUtMixvbyG0bZtBxo1+gypVMqGDWuYOjWEWbOiAPj550icnWszY8ZcLlw4T1BQAKtXb0IikbzV9eY/96vyd4s6e/Y0c+fO+iC7rKlUKm2g73179Ogh+3bE/meCQhkZGWzatP6NgkLrNvzKqhUxlK1Q44PvPqZUKtHTK/7H1ubNG9m7NxYPj0HUrOmETCbj+vWrzJoVQZcu3WjevAUAZcqUZdSosRw4sI/c3IKF6mJjd/DgwX1Wr95ESkoK/fr1xNW1fpHfV4IgCMK7k5WVpf37JJ+pqSmZmZqdX3TVG9q16yBPnz5hy5ZN2NjYAgXrDalUKqysSpKRka7NCtq/fy9xcXe4ezeeli3b0KFDJ/z8hlGpkh3p6eksXBjFjRvXkcmkuLjUZdiwkZQsWfLD3gxBEARBeM8+uqDQf0Hbth3Zvv03GjZswsOHD8jJyS6wxv306ZNER0eRm5uDSqWiT59+tGihefMfHx9HaOhEsrOzcHSsSmLiffr29aBx488AUKnUhIRM4OXLl8yYEYmJiTFKpbrA80ulUurUqcexY4ffat5/HdvAwLBQn1fHlkqlNGnSTNtWo0Yt1q5dpX28f/8e1q3bCoCzswv6+vpcv36V6tVrFBhz1aoVHD9+hJCQ8EJ/5L1v27dvJTZ2ByYmJiQmJmJubs64cUGUKlWa7du3smdPLJaWFsTHx+PvPw5LSytmzpzK48dJ5OTk0KJFa/r06YdarWb69KmcPXsKuVwfY2MjoqIWAXD+7HE2b/wFRW4uenpyerkPwaHKp5w5c5oZ4SFUr+LIlRs3kUgkBP40jErlyzFt2mQePEzE33sY1ra2DBs7qshrWPPbZn4/eQyVSo11uXL06zsMS8tPmDtvKsbGJvzg7kVy8guGDO3BaN8R2FWuTMTMGTx4+BCFUoGtjQ1BwVPRR6q5J3u2seG39QDo6cmZPmsOCxbMJSMjnWHDvDAwMGDKlOk65+Lz02hyc3MZMSqARo2a0rGPp85+CxfOJzZ2J/r6BkgkMHv2fEqUKMHlyxeZO3cWmZmaYJKXlw/167tx7doVZs6cRnZ2FoaGRgwbNoLq1Wvw6NFD+vfvzTffdOP06ZO0bt2W9u07sWDBPM6fP4NCocTe3h5fX3+MjY25efM6Bw/uY/r0yALBo2rVPiUiYjbDhnlRp44r5uYWlCtXHoBDhw4Wmv++fbvp2LEzUqkUS0tLPvusGfv376FHjz6ve8kJgiAI/5CRkREZGekFjmVkZGBsrPmQrLh6Qw0aNGLChDEsWvRLgXpDc+fO4sCBvQXqDZmbmyOVSunQoRMKhYLatetSu7YrJ08ep3z5Cnz11Tc0aOCGTKbH9OlTCA2dyPTpcz74/RAEQRCE90kEhf6GOnVc2bRpHampqezY8Rtt2rTn+vVr2vYqVaoxb95CZDIZL148x8OjN/XrN8TMzIxJkwL57rsetG7djuvXrzJwoHuBsZcuXYiDQ1UiImYXmRGRm5vL4cO/v/W693869saNa2nSpCkAKSnJ5OXlYWFhoW23trbhyZPH2qCQWq3JSkpOTmbatNnI5XKdz3n69Enc3XsAYGtbhrCwN0v7flMXL15gyZJfqFChEosWLWDWrGnazKRLl86zZMkq7ZK4YcOG4O7eHxeXOigUCnx8BlO9+qeYm1tw+vRJVq5cj1QqJTU1FYDExPv8un4FfgFTMDY2IfF+POEh/sz6eTUA8fcTGTXUixGDPVm+bgPL129g3HAfRowYzbQZUwieXbgy/qv2HP6dB48fMWdCKFKplF0XzrJk2TyG+wTQ38OH0WOGcOLkYXbs/JVevfpiV1mz1eGAfh6YmZkBsGLVSpYvX4JHl36cu3SOFetXMCcsEitLKzKzMjExNWXgQC9GjPiRmTPnFjufWdMn82Wrr5gzcyolrR11Zgqlp6WycuVytm6NxcDAkMzMDPT1DUhNTWHMmJGEhEzFyckZlUpFRkYGCoWCsWP98PcPpF69Bpw+fZKxY/1Ys+ZXAFJSUqhUqTIeHpoA1JIlCzExMSE6ehkA8+bNZvnyxXh6erF27Sq8vIahp6fH2rWr2L59K6VLW2NpaUmHDp349tvviY3dybffdi/2Oh8/TtJ+0gx/vrYFQRCE9698+YqoVCru30+gfPkKANy+fZPKle1eW29IpVLx4EEiULjekEQiKVBvqHXrduzevRMzM3OeP39WoG/Dho0LPNYsMx74Pi5XEARBEP5VIij0N0gk8MUXLdm7N5a9e2OJioopEBRKTn5JWFgQiYkJyGR6pKamkJBwj8qVKxMff4eWLTVbqlar9in29gVr7tSu7cr582e5fPkiLi51CrS9GjxxcnKmd2/NWsnw8FCuXLkMwIMH9xk50gc9PU0AZsaMSCwtP/nbY+dbuXIZd+/GM3v2z298n8LCgqhZ04nx44OLXVKma/nYu1SrljMVKlQCoGPHzvTp82dAwMnJRRsQysrK4ty5MyQnJ2vbMzMzuHv3Lm3bdkCtVjF58iTq1HGlUSNNZtfx48d48vghwYGv1KNRq0hJfgFAhTJlqGKnCdR8WrUKR0+feau5Hz17mpvxdxgc4AdAnp4ecrkmw8vAwADfn8bjN8oTF5d6dO3ajSeJdwHYd/AABw/9jlKpJDs7h8p2dpr5njlG6+atsbK0AsDYyPidb49obGxChQoVCQoaR4MGjWjU6DOMjU24fPkSlSpVxsnJGQCZTIaZmRl37txGLpdTr14DQPN6kMvlJCTcw9jYGH19A774oqV2/CNHficjI4MDB/YBoFDk4uDgCGiW5dnbOxAXd4ddu7Yzb95CcnKy8fDoTevW7bCzs+fcubf7GgiCIAgflpGREc2aNWfhwp8ZPXoct27d4PDhg0RFLSpUbyg2dgelS9ty585NkpIeERU1G0NDI1q0aIK5uQWtW7djw4Y1ZGRkIJPJ8PHxRalUcuDAXmJi5qNSqVi9egWlS1tz4sQxzp07g5eXT6Elai4utalc2U47x61bf2XFiiW8ePEcJycXxowJpGTJUv/WLRMEQRCEv00Ehf6mtm074OnpjotLHczNLQq0RURMpnHjpoSGhiORSOje/Rtyc3PIy9N8SlVcgKR27Tp07tyFgIBRBAdPwdXVVdtWVPBk5Mgx2v8XV1Por2O/GhgqLjCzYcMadu/eyaxZURgaagIS+decnJyszRZ6/DiJ0qWttee5uNT+I8jyUhuYGjCgLwqFAmNjY+bNW1jkfXhTJ04cIypKk8rdqlWb1y7vycvLK3D/jY2NXmlTI5FIWLhwmc5MquXL13Lu3BnOnDlFVNQcFi1aAeRRy6Ueg4b6F+qf8uQF+vp/ZkdJpVJUKtVbXmEePTt3oe0fu6eYOtjz8H6KtjUx8S5Gxia8TH6BUqkE4MrVq+zYtYupIaGYm5tz8NDv7Pv9kPb63zepTMbChUs5d+4cZ8+exsOjFxERc4p87r9+TfLlHzIyMizQnpcHvr6jqVu3no5zNP3i4+OoW7cexsbGGBsbU7NmLUBTHN3Kyuq112BtbUNS0iNt1ttfM4cEQRCEf05XbaBWrTQfnPn6jiYsLIiOHVuir2+AgYEhgwf3Iy8PcnNziI+/Q+XK9sTHx7Fx4zoyMtI5evQwMpkMB4cqrFq1kVu3bjJs2GAUCgWgqR04atRPfPFFS7y8fIiKWsSLF88ZPtyLhw8fMGHCWAICJlKxYiXGjx+jXaJ24MBeQkMnMnbsBADOnTvD/PlzmT37Z8qXr8CsWdOYMGEskZEL/q1bKQiCIAh/m/TfnsDHqmzZcgwYMIS+ffsXaktLS8PW1haJRMKpU8d58ECTqmxqakqlSpXZvXsXADduXCcu7k6h811d6zNhQggBAX7vPKvh1bGL2sXjVZs3b2Tz5o1Mnz630E5nzZu34NdfNbVpLlw4T05OToGC2+3bf0X37r3w8RnMs2dPAYiOXsqSJSvfSUAIoEGDhixZspIlS1YWGRC6dOkC9+8nAJoaQ3Xq1NXZz9jYBGfn2qxYsUR77PHjJJ4/f8bLly/JycnBza0RgwZ5Y2pqysOHD6hfvyEXz58i8X689pw7t6+/dt4mJibaujrFaVjHla17dpH2R22F3Nxc4u/e/mNuj1i0eC6TJs7E1qYs8+fPAyAjMwMTY2NKlCiBQqFgz7592vEa1WvMrv27ePFHJlNmVia5ubkYGxuTk5PzN4JWhWVlZpKc/JLatevi4eGJnZ09cXF3cHKqxd278Vy+fBHQ/HGemppKxYqVyM3N5ezZ04CmuLhSqdTuOPdXTZo0Zc2aX8jJydZcQ2YGd+9q7n/p0tbExd2hcmU7zp07Q1ZWFi9fvuTy5YtkZmaycuUybaZecZo3b8HWrb+iVqt5+fIlhw4d1G5rLAiCILwbr9YGCgwMJiIiTPt3kZmZOWFhEezZc5jFi39h2bI17Np1kGnTZiGVStm8eSMAnp5e2NqWwcqqJJs3a/6+Ski4S1LSI5ydXWjUqAkSiYTw8Fn8/PMiypQpx5Ejv6Ovr4+FhQV2dva0atWWMmXKUqNGTZo1a65dota//yBevHjOggXzqFmzlvZ3zZEjh2jevAV2dvbI5XLc3ftz/vxZ7bI1QRAEQfiYfHSZQkpF7h87hb37cd9Wp07f6Dw+eLA3ERFTWLFiKfb2DtjbO2rbAgImEhYWxOrVK6hatTr29o46iy+7utZn/PgQ/P1HvvOlVfljBwT4FTt2ZmYG06aFYWNjy/Dhmm3g5XI50dFLARg0yJugoEB27vwaAwMDxo0LQiotGGds1aot+vr6+PgMZtq02f/K7k0uLnWJiZlPfHycttB0UQIDJzF79nT69PkO0ASK/P0Dyc7OZsqUYFQqFSqVCje3RtSo4YS+vh6Df/QnOmoaitwclEoljlVrYu9Qrdg5OTg4Ylu2LKMG/4htubJFFppu2aQZKWlp/BQ8HgCJXM6XX3SgXNmKRMwIolfPAZSxLcfA/sMYM84Lu/LlqFu7Dgd+P8gQn6FYWVnhYO/A3QRNYNKlpgs9u/TEN3A4UqkUuVxOxMw5lChRgmbNmuPjMxgTE9MiC02/icyMdPxCA8nJyUatVlOlSjWaNWuOgYEBISFTmTNnBtnZWUgkUry8fKhXrwEhIVMLFJoODp5SZA2qXr3ciYmZT//+ff54vUno128AlSpV5ptvuhEZOZPw8Jm0bNmGwYM9KF3amkaNPmPz5g24u/fXLhe8cOE8EyaMISMjg7y8PPbujWX06HE0aNCQ1q3bcfXqZbp3/xqgwHmCIAjCP/e62kCverU2UNmy5cnLy+PWrZvace7cuUWHDp14/vwpMpkeTZt+rh2nZMnSyGR6NGzYmAsXzpOS8hIjIyMePEgkISFBu5W9TCZjxAhN1m/+EjV9fX28vQfi7u5BZmaW9sO0vLy8Atmv+f+Pi7stflcIgiAIH52PLij0MjmHN906/n1Yv36rzuP5KcUA9eq5sXr1Jp39bG3LsmDBUiQSCfHxcQwd6knlyvaFxtCM04CdO/ehVKpxcalDu3YdXzu/olKXdY392297AIoc29jYhEOHThX5XFZWJZk1a57Otlfv0+eff8nnn3+ps1+7dh2Lva46dVz/8Xb0RkaGTJgQ8kbPbWVVkokTQ3WOo1kuVpiTcz2cnAsvZapb15UF06ZoH9euWUP7WE9Pj5ETA95o/l3bdqBr2w5AweVjUydHafsYGhqxevUGbU0hv59GFBijdLlKZCdpso3at+xA+5Yd/jzXypSkpMd4efm80Xz2xm4ptt2qVGkWLVpWaNc80NSrmj9/caHj1avX0Hnc1rYM27btLXBMT08PT08vPD29CvWvWdOJRo0a4+c3nMGDh9Kt2/eApiD4qVMnqFHDSdvX2dmFTZu267yGV98cCILw/hW3jOhVr24xLpfr4+bWiOHDR2q3GH/dONnZ2URGzmT//t0olUocHKowd270B7tO4U9/rQ1kbm6Is7MTp06dolSpEgX65uYq+P3349oAjlQqRSqVkpWVxb59u8nLy+Pbb78nLS0VU1NT7O2rcP78WWJjd2BlVRIDAwO2bt3Ezp3bqVixMk+fPsHe3lGbpTR37kwuXDjHJ59YoVQqycrKwtjYmB9/HMQ333xL585d2bJlE5mZGYCmCPX48f507tyF8uXLs3hxNBKJhOzs7A9+HwVBEAThn/rogkIfu0uXLjB37ixA86nSqFFjtbtECYLwz3Xt2p1q1T5l2TJNQVKZTI/y5Svw/fe9iq3nJbw9lUrJo0ePtJ+ylypVGnNz80JtUqkEkPxRt0uKiYkJ1tY2yGQyUlNTePr06R81P/KQSKRYW1tjYWEJaN7E370bp33O7Ow0du7cjbt7fx49esi3336FkdGftcF69uyLu3vhZb3Cf1txW4y/6tUtxjMzMwkPDy2wxfjrxpk6NQSVSsmKFesxMzPTZpsIH15WVlaBTGl9fTnHjh3j9u3bREQU3JnT19cXZ2cXdu06yNOnT1i3bhU3blynY8eWGBkZY2Jiip2dPUePHuHFi+eo1SoyMzO09YYyMzOYMiUEiUSCXC4nOHgqRkZGxMTMZ/HiP4OC7u7f88MPA/jss2akpaWSnPySxYujWbw4GoVCgVqt+bDD1bU+/fp5EhDgR3p6Ot991wNjY+MCdRUFQRAE4WMhgkIfWP36btSv7/ZvT+P/jddlIv1XBPzoi0qtqedjINNHnZNDdYcqDOv372x/GxIygadPnyKX66FWaQp0li5diuCgP7Objh49zKzI2YXO7f6DJ5Va6s4M+1Bq1qylLS4tvD9JSUlIJBIcHBzJyckhMTEBQ0NNQdhX2zIzM3j06CEVK1ZCT09OUtIj7t6NR6lUIpNJ0dPTw9CwBNbW1jx8+JDHj5MwMjIiJyeHR48eAZrsLUNDI3Jy0mnatDmgqd8GmqUbf80KiY+PIzh4vLbGR9Wq1Rk2bESB3YOED6uoLJ6/LiN6+vQxeXl5eHj0wsTEtEA2kLW1DampKfj7j+DkyWNIJBLKldNsWf7Xcc6cOUlGRgZLliwkKCiMhIS7HD78O5s2bdNmFlWrVr24KQvvkZGRERl/1MvLp1Aoilw6nK9UqdI0bfoFp0+fZM+ew9y8eZ3Bgz0AzYYaenp6pKWlYWxsgqenF9euXeHixQvMmxdNlSrVuHHjGqNH/8S0abPx8PDEw8MT0Cwp9vPzwcPDk6ysLCQSCatWbaR8ec3ra9KkgruLdenSjS5dugGQkHCPpUtjtJnfgiAIgvAxEUEhQfgPCJ7956ei9p9UIP124QLkH1L+ckMbG2syUnUXzmzUqAllatTR2Sb871Or1aSnp1Gpkh0ymQxjY2NMTUuQkpLyf+ydd3gUVduH751tyWaz6Z1AKKGHGqoI0ruoCCovoKKCiEpHRCkiiAihCQQEpAuIIEVFUFBeUUC69BYgvZdNsn1nvz82WQgJgddPQHTu6+IiM+fMmTNnzu7seeZ5fg/+/qoSZZ6eOvLz88nLy8PPzx+TyYgoOqhWLRKTyUhCQjz+/gHIZE4DkVwuJy8vDy8vb5dWR5Uq1UhLS0UQBJdWyIoVSwDYuvU7rl2LK+EV4u8fwLRpMwkODkEURbZu3cyUKRNYvXrjwxy2fzV38uKx2awlwoiiourz/PP9uXDhPFOmTC/lDTRx4nhOnjyB3W5DpVKRkHCDuLirJdpJSkrk55/3otFoSE11GhbPnj1DcHAwK1YsZffu7/Dz82fQoMF3DG+WuL+Eh1fCbreTkBDvMrxkZWXh4+Nz12PtdrvL4Ht7O23atGXv3j20adOWP/44yR9/nKRu3frUrFkbcIYr165dl/Xr1/D6628RFBREWloqy5YtpnHjpoDTYNWmTVuWL1/C+PETuXz5IgcO7Cc29nMAzGYzSUkJVK5clbS0ND75ZDp9+rwgeX5LSEhISDySSEYhCQkJiX8I5YVz3Ypen0dmZiY2m7VUONfd2jEaDWRkZGAyGXE4HGRmZhAYGIRSqUStdsNoNGCxmAEZarXadU5BEMjOziY7OwuA4OBQ5HK565xJSYk4HM7wMZ1Oh8ViKQo3A4cDrl27it0uIooily5dxGg0cujQQQAGDnwOmUyGl5c3O3ZsZcSIsXh6euLp6dQlcTgcCIJAYmLCfRt7CSd/xhuoeJ4UFha4vIG0Wi1nz/5Bz54dAaeW2DffbEen8yIjI506depy7do1bDYrWq0n27ZtwcvLC5vNSufObTCbzdSv35CMjAzMZqcOYUJCPHFxV4mPv4Gfnz+PP/4E06dPISKiCgUFBSxfHsvFixeQywUaNGjMiBFj8ff3f2hj+XfjXnWfvv32W+bOnXdPuk9yuYJp0yYzb95ijh27xLVr1/D19WX1aqf2YkhICI899hjbt31Nk6bNCAkJITk5mVWrltKyZUsCAjyxWdUlDDhdunTn55/38fXXW/jpp7288MIAtm37ikOHfmXixPFMnTqDU6dO0rRpM4YOHUR+vh5PTx3Nmz/G66/f1KobPXo8M2ZMpWfPjuh0Xowe/a4rDNFisfDBB++TlJSIRuNBt249efXV1x/MjZCQkJCQkPiLkYxCEhISEv8QygvnuhV3dw1VqlQGBOx2O2lpqWRmZrgy/NzajtFoIDExkdTUFORyOTqdDm9vHwTBj5SUZARBv57XbQAAIABJREFUIDU1BS8vL7Kzs7Db7RgMBsD5Nr/Y6KNSqZDLBURRxOFwYLc7wyVF0eESjQUZNpu1aL8dUXQaiWQyBzabDZnMWe/ChbNFelECS5euoVq16uj1eYwYMYwff9zt8igB6NLlCYxGI6IousJEJJzcj0W+wwFVq1Yr4Q20a9cO4uLisNlsZGZmULFipRLeQP37v8ibbw4u4Q20detm8vL0gHN+WK1WlixZSVzcFT7++EOeeKIDMTG9uHjxAmPHjmDfvh94//0pKBQK3nlnIrt27USr9cRkMrqMTgcP/opMJmPHjj0uz7IaNWrx+++HCA+vyJNPPkOzZs2RyxXMmTOTjz76gDlzPn1g9+PvTnl6TSXnkhcDBgziqad6YzAY+OijKbz88n/Izs5yGY7tdhGtVku1atU4efIE3bt3QKNxRxAEMjIyXB6Cer2en3/+Ga1Wy0fTJ5Orv5lopFvbKhzbM5bGnWbx9tujGTSoP506tcbhcDBw4CAGD37DVdfX15e5c2cBMubMmcWAAS/zwgv9y71enc6LGTNiyizz9PSUvA4lJCQkJP4xCHevIiEhISHxd6c4nMvfP6BUONftKJVKFIqS7wQsFkuZ7eTl5aFUKvD29iY0NIzc3BzUahUKhRxRFPHx8cFoNODursHHxwetVkuFCuEuL6Ji8vLykMlkhIWFFy380jGbTQiCzOUhFBoahlwup6AgH0GQu8qqV69J1aqR+Pr6oVaryc/PJzs7G63Wk5o1a6NQKPD19aNr1+7k5uaW0Cn5/vuf+f77nxk5chzVq9e8T6P/aHLrIn/SpGnExMwgLq506GqjRo2Ijf2c3bv38+WX27Hb7SxbFluqnU2btmOzWbl27SqpqSnUr9+Ali0fRxSdi/Rbhd6DgoLx9w/AYCgsM8V4UlIiPXv2YsOGrYBTK2b//n2Eh1dEpVKRk5ONh4cWuVyBKNrR6/OoX78RoiiyaNE8hg59C0FwGj2Dg0MwGo3ExV1BEIQS6c8zMtIBZzapdu064OGhxc3Njd69n+P06VP3c/gfKYo9vV599fVS6eOh5FyaM2cOsbELXHPpwoXzmEwmduzYw/Dho7FYLMyYMYvNm3fg6+tPWFgF+vR5nm3bthEaGkrlypXp378/YWFhZGdnk5qaysiRI6lZ1Y9KYTrGv9EcrUbJ3l9vkJiiB5wGnP/8ZyALFy7Dz8+P6OimJfrfu/dzbNq0jR9++C+bN2+/q0FIQkJCQkLi34RkFJKQkJD4B1BWyJZa7eYy9txOYaGBS5cucPnyRfLz8/H19S3VTrGByNNTh9VqLWFoUqnUgIP8/HxUKjVKpRKLxYJKpUKlUgEOV3pmURQxm014eGhRKORFRiBc7TgcTkOVRqMBwG4Xi9pxnsNiMaNUKtFqPTAajQC4uZUWqS32UCpyMnDh7u7OU0/1Ztq0yeTkZP+/xvmfwt0W+cVizh06tKJfv378/vsh17G3huJt376VvXv3cPDgAV588XmAEu34+wdw+fIlYmMX4HA4OHXqhKudy5cvcerUSXr16ozD4SAu7iqJiQklUowbjQbkcjk3btzg2rU4Tp48jt1udxmeli1bjE6nQ6PxwN3dHV9fP9LT0xk48Hl++ulHRFEkOrqpKxNhcHAI69atwmazodFoSUlJplmzFqXG59Sp45Io+S3cnj4eoGrV6ly7FldqLkVHR1O7dhSvvOL03ElNTWHw4DfQaDRERFRGEAROnToJUOQh6BSG9/LyIj4+nujoaNf3gUKhQKVSYTAYOPJHCm8PiqZerUBUKjmRlX355fdErHaR0FBfhg0bQocOj6NQKPD21hAQ4InOW/NQxktCQkJCQuJR4pELH9N5a1Ar5X95u2arHX2u4S9vV0JCQuJBUByGdSvOcC1nmNbtOkHBwcFUr14Tq9VKTk42WVlZJCYmFmn4iFy6dAGZTCgy2KgwmYzk5eVSWFiI3W4r0gaSkZWVRXh4OAaDgfz8fBwO0aUbZDIZ0evzKCwsBEClUt+iHSTDYCgkP98NhUKOzWbDbDZjtztTPnt5eSEIAu7uGtLS0ggNDcNgMOBwiNSsWZsaNWpis9k4fPggTZo0Iz9fz65d3xAYGFQizfXN8RExmUxkZKTj4+N7H+/Eo8GdFvknTx4HSnp+ZGQk8MorrzJr1nSMRmc4VmRkDTp0aIVcLkcQ5Hz33T5+++0XJk16l/z8fAoLDXzzzXa+/PILRFHEz88Pk8nE6tUraN++ExUrVuLKlYuAAzc3N+RyBTIZ9OvXGy8vbzw8tFy7Fsenn87BarWSmZmBp6cnlSpFYLfbOXjwV4YOfYXatety+vQftGvXAQAPDy3Z2VnYbKLLIBob+ynt2nVAq9UyY0YMM2dOY926VXh4eFCpUgSVKkWUGJsrVy6zcuVyPv647NChfyO3p48H0Gq1GAyFZc6lJk2aoVQq6d27L+PGjaBevQYAKASnwXfjxnWsX78ad3d3+vbty/nz57l+/ToAX3/9NVarFbncaUD29fXl+vXrCIKMkMCbfQgK0JCYmo9SLvDad8dd+3NMVmYfvox3lpJl3aRkCBISEhKPKrm5uXyaeIOzhQV4yhX0DgiiuZd3qXpxCUe4eP0A+sIMlAo33D45h5voQBCcHsrZegP7T8WRnlPI9qMd6V+9Jy0r33w+/HL1COuPbyd7fR4qlYqmTZsSERHxoC7zb8EjZxRSK+UlHv5/Fff6w+HZZ3titVrYuvU7l1bGt9/uYMaMqYwcOZbevZ/7032YPn0KNWvWcrWxbFksBw/+SkzMpxw8eIAFC2IIDg7FZrNSqVIE77zzPlqtJxMnvkNc3FVUKjU+Pj6MHTuBsLAKAGRnZ/Hhh5NISUlBrVYzbtx71KlT13Utn3wylypVqv3PfT1+/CiLFs1nxYq1AJw7d4YJE8YyduwEHnvscVq1iqZq1WqADEGQMWzYCKKjm7Jjx9ds2bLJtSjs128gnTt3c7W7atVyvvtuJ+BMJ//SS68CsGLFUoxGI2++OeKufSs+t0zmXCB36tSFlJQUVyjA9etxhIaGFXkhwIoVa1338lFm48b1NIqqg3cZX5YPg+++20nduvVKLBTK4vTpU3zyyXQUCgVvvTWKRo2iH1AP7x29Xs/mzRs4ePBXbDYbnp6edO7cje7dn3SFxFgsFsaPH83Fi+cA+PbbvSXaOHDgvyxePB+73U6NGrWYMGEybm5upc71ZxEEGaIolthnt4sIgnNul9YbSqBSpUqo1W6YTCZMJhPVqkWSlpaKXp9HxYoRWK0WkpKSXOFcxXpASqWKkJAQ4uNvAJCYmFhkHJBht8sICAgkIyMdh8NBcnKS67OYkZHm6o9crsBkMlFQkI/NZgNsXLvmDDfx8fFFrXYjLy8PDw8PMjMzuXz5IgAymcDMmXNwd3enZs3avPfeOBwOEbVajcFgYO7chQAunZyqVZ0ZzpYtiy0yKlT+y8b8Uaa8Rf7totDR0dG0afME/v6BPPvsc4wa9RZarZYdO/awa9c3zJs3i4SEeEJCQpHJZGRkpOPpqSMlJQmAZs1a8s477/HMM90RBDnffLOdFi0e4/TpP6hWrQYrVqxh27avmDdvNg6Hg06durJ162amTn2fF198hZUrlxEYGMyNG9dZsGAJR44cIjc3h1mz5jN8+FAUCjkDBrwMwMKFS4vmE1y8eN71XPL39+f777+lSpWqLF26EoANG9Zx4sSxEmOQmJjAmDFvM3z4aOrXb3i/b8MjQ1np4wsLC9FoPMqdS25ubnh4eDBlygQ+/3w9nl6e2MxWagVXY0zb19h94b8UnEjHkWvGYDCgVqsZMGAAKSkp7Nq1i/DwcMxmZ5nGrWSqerVKgclku+/XLiEhISHxcJg6dSoKmYx5kTWJN5mYn3iDcDc3wm7TyrTZrTSu3Qs/n4qYzYUcOrQFHflE16iAKDr49tAF6lYOplerOjTpNYQhrw6mks9kwryDySzMIebnZbzf6S2en/c6I0aM4IcffqBfv364u7s/pCt/8EjhY38CPz9/fv/9oGt7165vqFGj1l/WvsPhYO7cTzhx4hiLFy91pWeNjm7KqlVfsGbNJmQyGatXrwCga9cerF//FatXb+Dxx9vwySfTXW0tWbKQ+vUbsnHjVkaNeoepU993CTjeC999t5MVK5aWW+f48aNMmDCWyZOn8dhjj7v2x8Z+zurVGxg0aDCTJ7+LKIpUqBDOp59+xpo1m5g1az4LFsSQkpIMwMmTx/nppx9Zu3YTa9du4qeffnS9tf5fiY39nFWrvmDVqi/o128go0e/49ouTlVdvH2/DELFC5MHxcaNX5SpH/Ow+O67nSQkxN+13u7d39G1aw9WrvzigRuE7uUeJSUlMmLEG/j5+bNgQSyrVn3B1Kkfk5SUyKRJ77oMMYIg8MIL/Zk3b3GpNgwGA598Mp2ZM+eyadM2NBoNGzas/Uuv5dZQq2LMZhMqlapMvSGdzhkGJopiUdiVo8iDKASA3NwclEolMpkz/bJKpUKj0eBwiCiVSlJSkvH01CGXy/H19SU8PBxRdCCXC/j5+VGpUiUEQUAmEwgODkYmkyGXK6hevQaRkdVdXk0Gg4GQkDBq1qxN1aqRAK6HsMViJicnBwCFQomXlw8BAQH4+TkzQs2aNY8mTZoik8lwc3NnwoQpNGzonEP5+QVMmfIeXbo8wXPPPUViYgIxMZ+WCK/7N1PeIr+8UCGt1pP4+Oukpqag0WiIiqqHTCZj9+7vXKnB1Wo3NBoP2rXrVHRsVQICAnF312C329i4cR2TJ09AEATq1KlLRkYGixYtcJ0rOTkRq9WC3W5nx46vAYiIqIxSqSQpKZGKFSNQKJS8/HI/RFHknXcmurJCeXl54+fnj5+fPx4entjtdjw9PYmMrOFKW17MlSuXSoSIpaamMGLEG7z00it06dL9rx/0R5hb074XUzx+5c0ld3d3jEaTK318REQEIiIyZPh7+NC4Ql12X/wvlXxC0Wg0WCwW8vPz+emnn6hduzbp6emuUDLjbQYgi8WGm9sj925TQkJCQuIeMBqN7Nmzh6cDgnAT5FTXeNBA68lvebml6laPaEmgXxXkggKNuxc9e/YkJSsfgJx8A4VGCw2qhSDIZLRo0YLaQdXYd8W5ls8qzMFDpSE6PAqZTEbFihVRKpXo9foHer0PG+lp+ifo2rUn3333DS1atCI5OQmz2eT6QQpw9OjvLFsWi8Vixm63M3DgIDp06Aw44+Y/+ugDTCYjkZE1SExM4MUXX3EZU+x2kenTp5CTk8PcuQvx8NBgs5V8+y8IAo0aNeHgwQMIgkCrVm1cZXXq1OPLLze4tn/66Uc2b3Z63tSv3wCVSsWFC+eoVatOiTY3bFjHoUO/Mn36rDJDL+7EgQP/JSbmYz7+OIaaNWuXWadJk+bk5eWRl5dXYtEfGBiEn58/6enphISEsnfvD3Tu3N2VKalz5+7s3fsDDRqU9OK6evUKH3zwHiNHjqNhw8b33Ne/ilatonn55dc4cuQwen0eT/V5mabNWwPQv087nu8/mJPHD9OiWRNeerIzX3y9nf2/HcQuivj7+jL2jSGEhsDRg4fZvGY9glxAtIu8OPQ1ateLIjMzg5nzZ5OelYnZYqFdi1b06/UMAE891Z3HH+vAqT+OkZObRa+ez9Gt69N8tWUdmZkZfDx7NiqVktHDR1IxPLzM/p+7dI7P1izFYHCG9AwZNoyqVatz+vQfLF48n9mzF+Dh4cH8+TFUqFCBlwY8w959+9n69U5XZqghgwfxeBunN1pS/HVWxc4nLycbh8NBj2df4IybgosXzzNv3myWLYtl2LDhNGnSrFRfvvhiDXv3/oCbmxt79nzP0qWfl8qUBU5vorlzP0EUnVmoXnxxEB07dqGgoIAFC2K4cOEcMplA/foNGDXqHQwGA/PmzeL8+bMAdO7cjf79XwLgzTcHExVVn3PnzqBSqZg1az4HDx5gzZrPMZstKJVK3nprFHXrRuFwOPjoow94//0pJTzqfHx8GDJkGGvWfM7Ondvo1esZFAoFTZo0cxk5b+XQod+oWbMW4eEVi+5jb6ZNm8LLL792t+l2zwiCgFbrSUZGBiEhoS4vnEqVIkrpDTnFo5UYjcaieeBAo9G62lGr3VxGJGemMBteXl44HE69HpPJRGBgAIWFBgRBjtVqQS4XkMmcOkYGQ2FR6JqjKFW8jtTUFJRKJfn5+Wi1WhQKBTablapVq7kMRE4BbCVyufPRFBAQSEBAYInrTE294fq7vOxA7dp1cIUUSZTm1kV+8bwsXuTfPVRIcAk0h4c7DUfnz59l374fUKlUZGZmEBVVD73eaXS02WzYbDYsFjOiKNKkSTNatmjG4thYfvrpB3r06EJUVF3OnDmD2WymZ89uKJVKjh8/xttvj2Tbtq0cOXIInc4Lo9HIoUO/4uGhZejQt+jXb6Crj3v27KJevYYEBQWRlpbKsmWLefzxJ1zfPbemLb98+SIHDuwnNvZzADIy0nn77dd55pk+PPXUs/d9/B813N3d7zh+ISGhJebSjh07OH36FJUrV0alUmOzWWnQwOl1pdFo8PfwJdeYh8lq5mrmDQrMhbSt1oKIiAgcDgc7duygbt26BAUFcebMGXx8fIiIiMAuiqSkF7hCyNIyDURU8HqYwyIhISEhcZ8o/r0RrLr5Mi9c7c7FovVLeRw5cgRfnVNTrixXCAdwI9vpzVzNP4Jw7xAO3zhJFXsLrl+/7nrh+W9CMgr9CRo1iubrrzej1zs1LLp06c6FC+dd5dWr12Tx4uXI5XKys7N45ZUBNG3aAp1Ox4cfTuK55/rRuXM3Llw4x+DBL5Voe/Xq5VSrVoOYmAWlsgMVY7FYOHDgv9SsWdo7aevWL2nVymmgyMvLxeFw4O19M5woKCiY9PQ0l1FIFB3MmzeL3NxcZs9egFKpLNXmnUhMjGfq1IksWfJ5CaPY7ezb9wOBgUEuj6dijh8/SkFBATVrOjMCpaWlljDyBAUFc+pUSU+ho0d/Z8GCGD74YEa5IqBDhw5yhaxMnDi1KJTtr0MQBJYs+ZykpHheeeUlatSKwsvLeX0Oh4P3P5hLtXBvdm5YSVJKCrEzP0IQBLZ9v5vFq9Yw7ZNotqzbwMvDhlCzbh1Eux2T2enh8cEHk3ihczfq1ayN1WZl7EdTqVGlKo2j6gNgtpj5+KNFpKenMmLUy7R9ogvP9u7Pvp+/Y/yYMVQqJ1wrvyCfObExzJw4Ez9ff7KyM3n9ndeZO3cRUVH1eOKJ9ixcOJemTZuTnJzEhx9+hNmQSnR0Q9q1bY1MJiMhIZEx70zk8TbdsdttzJoynudfGkzz1u2c59DnERUewjff7OSFFwaU8B67nX79BnLtWlyJsMmyWL9+NX379qNLl+44HA4KCpxvpRcsiMHd3Z1VqzYgCAK5uc63B6tWLUcURdas2YTBUMiQIYOoWjWSFi0eAyAu7goxMZ+iUChISkpk1aoVzJnzKR4eWuLirjJmzNts3fotJ04co2bN2lSpUo0rVy4zZ85M7HY7TZs2Jz8/n2HDhjNmzNv0KjLa3Ym0tFSCgkJc28Wfw7+a4OBgUlJSuHz5EnK5nKCgENRqN/R6PQ6HiNVqLRKEdnrgiKIdo9FY5M1zs39eXl5kZWVy9eplANRqtUtXCByIop3U1FRXfavVgsPh9DSSOSwkJNxwiT0rFQJmQyo6nQ6z2UJyclKRt6KDoKBgl0GoZOibA1EUkclkJTJWSfx1lLfIt9msJTw/duzY4fIMSkx0akI1btzU1U716jW4ePE8cXFXGTZsOPPnx/D111vYv38fI0eOZP/+/XTu3AabzUZUVBRhYSEUGgzYbFYK8q0MGTIYmQxEuzP+PyysAmPGvMvw4UOZNGkCADKZjMLCQmbMmEqTJs3Zv38fK1YsZfHiBajVagRB4Nlnnyc29lPy8/V4eupo3vwxXn99mOs6Ro8ez4wZU+nZsyM6nRejR7/renbt3LmN5OQkVq5cxsqVy1zH/PDDLw/idjwS3Gn8UlNTcTgcLFw4jylTpnPgwAF++eVnlEole/Z8T0hIKO7uGoxGI/Pnf47eVECkfwT91g7H4XAQ6R9BJd8wfvzxRzw9nR6MR44ccX0nnDlzBo1GQ5P6IXz5zQUG9Y3CahO5FJfN80/Wcnk/izarS2VetNkQi76XJCQkJCQeLnp9HjNmfOgK7R8y5E06depSqkylUqPRaNDr9SiVSmcWUYcDuUzGr3k57M7OJM9uY9CFM3hGR9Mg8mkqhjrXRykZl9h3+GZ0iyADpVygcY0w1CoFC78+iEKQ8dm39TCbzYTonC8d5YJAu8iWzNr3GR9FOb39O3bs+D+tif8J3JNR6Nq1a4wfP57c3Fy8vb2ZOXNmKfGlLVu2sGrVKgRBQBRF+vTpw8CBA8tu8BFHJoN27Tqyd+8e9u7dQ2zsihJGodzcHGbMmEpiYjxyuQK9Po/4+BtUrlyZa9eu0rGj80PgDJcoaaxo2DCakyePc+bMH6U8ZI4e/Z2XXuoHQFRUfZeGQjFffLGG69evsWDBknu+lhkzplK3bhSTJ09zLb4OHjzA0qXOD4Ven4fNZuOXX/YD0Lt3X3r2fAqgyD1fy44dWxkxYmyptosNM76+vnz00ewSZdeuxTFt2mQmT55epmdIWRw5cojDh39j7txF+PsHlFs3NvZzVyaj+0GPHr0AqFQpgojKkVy5dI7GTZwGh8fbdHbV+/XIUS5evcpro8cBTk8wDw9nv2rXj2L98lU0a9WS+tGNCI+ohMlk4vjxY2Sn3VxwG0wm4pOTXEahVo85jS+BgcF4aD3Jys6gQljFe+r32QtnSElLYdzUca59MpmM1NRkqlWrTp8+zzN58gRWrlzmMpqYgZTkVKavmk1mVhYKuYLs7ByysjJJTkxFtNtdBiEAT91f/+a2UaNo1q1bRWpqCk2aNHfpYv322y8sX77OZVgoNoAePfo7w4ePQSaT4eGhpUOHThw9+rvLKNSxYxeX0fXw4YMkJSUybNhg1/nsdjvZ2VlcuHCehg2dn8NPPpnOsGEjqFevPrNnz8BicXoV3a7j8zCRyxVUqFDaQ0ylUiKTCa4HXEBAIEqlgoKCQvz9/blx40YJI3RxNjCr1UZYWAWys7NuMTQFkZmZhd1uK2pb5Ur9LYoihUZ7UV8Ep0HIYsdisaPT+RRlrJKhVCrw8fEhKysTrVaL2s2Nq1cvY7U6PdGKQ1SqVKlWlMlM4n5wp0X+9evXMZvNnDhxjIYNG3P16lU2bfoCm83KuXNnEUUH77zznqudSpUqc/HiBZRKK+vWrea99z6gU6cuBAR4EhMTw9mzZzEXGb1Pnz7N6dOn2bt3L1X9LrHqqzMkJuupXNEbpULg5Ll0IiMj+fXXI6SlpbF8+RqqV6/JxYvnGT9+FDNnziEyssYdr2nIkGF3LCvPs2zQoMEMGjS4zDIJJ3cav+DgYHbu3OOaSz4+Pkyc+CGdOnUhNTWV/v2fxWg00rNnRxQKBQq5gkuZ19CoNOSb8nmr9YsAXL16FavVikzm1EdTKBS0aNGCWrWcL8AG9a3HmxP3MOTETW/M9z75L807O9/2HvloFOacTADOLJ0BQFLjvajVkjeRhISExMPk1uQVly9fYty44VSrFkmVKlVLlK1YsZTt27eyePFycnKyGTPmbb7PzqS7XwAmu4hFFAlRqRhVIYKFdjMHTqynhy4UrcaXw39sBkCt0vL+xHf4YMpkIiv4IxcE2jasws7fLiCXy2nSpAmKVBtKwfm792TSOVb+vpkZPcbRZUY/3n33XXbv3k3Xrl3x9/d/mMP2QLkno9DkyZPp168fvXr1Yvv27UyaNIk1a9aUqNO5c2eeeeYZZDIZBQUF9OzZk6ZNm7q8QP5pdO3agyFDXqJBg0Z43SbsGxPzMY891pqPPpqFTCbj+eefwWIx43Bw1zffDRs24qmnevP+++8wbdpMoqNvhltFRzdl2rRPyjxuy5ZN/PDD98yfH+sSry3uV7ExD5weC4GBQa7jGjRoyIkTx8jNzXFl5GnRohUtWrQCnLowKSnJvPLKkFLndHfXMHfuIkaOfIN582aVMgzdyTCTkBDP2LHDGTt2AvXrN3DtDwoKJjU1xbXt7Guwazs8vCLXrsVx4cI5V8jc6tUr+Oknp6Dv22//NSLFr732oiv99uLFy+9a34GjxD11c7spSuZwOBjwbG+6d2hX6rgBg18h/tp1zp06zYIZs+j69JO0aP04MhksmvrxHT3FlMqbi+Tixfi94gCqRlRhwUcLb/Y3WEtqqtNrxWAoJCMjvSjM52Ys7bQZs3l98CBaPdYcURTp1rMPZnPZqc7vB3379uOxx1pz5Mhh5s37hCZNmjN48BvlHOFMeX4rt94jd/eb89LhcNCsWQsmTpx6h3acBqecnGzXfG3Vqg379v0A3EypXB5BQcGcOHHUtX375/D/g1yQIRPK/k5xiA7soqOE3lCxwLrJZCqV9r24rLCwALPZQsWKlXB3dy/xOTYaDYiiiLe3NwqFktzcXFQqBW5u7uh0OvR5WQQFeACQnFqAUiFgNNlQYUHupsHN33ndBgBlIQlZOVSvEOLSE5J4cNxpkR8REUH79h3Ztm0LNWvWpnXr1qxdu5Zly1YTEhJK165t0ev1eHl5c/HCKfbt+4Hu3bsTE1O6rbNnnSGcPXv2JD09nXPnztG3b18qVKhAQqCWl/tEUSHEk6wcI+/M+Jm6NQLw8vLi8uVL1K/f0BWWXKtWHWrXrsuRI7+XaxSSeDjcOpcCAjzJyHBqOQQHB/Pjjwdc9QICPLk8+0CZbYwcObJcI7vWQ8WqOT1K7a9QoQL8kU6zSZ+WWVbcFwkJCQmJB8/tySvq129Aq1at2b37O1566dUSZW+9NZLc3Gz27t3DSy+9ikwm42xBAd39Aqjh4YE5XaSBVoefSsULTz3D3JhPuZbSRPw4AAAgAElEQVR0lEohDTCanbqqbaJfom/fvnz26WwuxmfQvE5FfD2dv2Nf7d6Et2euoNfj3WhfvSUAcVnx1AmuTmRABIIgEBgYSGBgIElJSf8qo9BdVzNZWVmcO3eOHj2cD+IePXpw7tw5srOzS9TTarWuRZfJZHK97fmnEhZWgddee4MXX3y1VFl+fj4hISHIZDKOHDlEUlIC4ByjiIjK/PDDbgAuXrxAXNzVUsdHRzdlypTpvP/+uFKZUcpi+/atbN++lTlzFqG7zUujbdsObNv2FQCnTp3EbDaXEMXu3v1Jnn++P8OHDyUzM+PeB6AIrVbL3LmLOXv2NPPmzbpr/aSkREaNeosRI8a4vDZu9rU9u3d/i9lswmw2sXv3tyU0QYKDQ5k7dxFLlixi7949ALz44isuwei/SqR42bLVrFr1RbkGoW+/3QFAfHw8N65foWpk2ULjjzWNZvv3u8kvCneyWK1cuXYdgOTEJCpWjqDLUz15rG0b4i5dxl3jToMGDdm4c5urjfSsTLJzc+7abw8PDwoNhnLr1K1Zl8SURE6cvhmWd+7cWZeL/YIFc+nYsQtvvz2amJiZrlTiBQWFhAQ7F/O7vv/B5dERGl4RQS7n0H/3udrL1+fd7M9t4qN/lvj4G4SFVeCpp3rTp88LLq2gli0fZ8OGNa7+F4ePRUc345tvtuNwODAYCtm7dw/R0U3LbLtp0+YcPnywxGexuP3IyBousXMfH1/Onj2Dw+Hg11+dISVbtnx5x3ZvpXnzFpw/f87lAbNt25a/TO9GJsgouHK1zH/FxqJb9YaKhaX1+nxX2vdby7KzszEYCgkNDcXT0wOFQnD9M5mMJCQkoNN5olAoXAaiYkO0XC7HZLZhNtvI05ux2UVsdhGVUo67uzt2swmxKE24aDFjN5uQKyVPoL8jo0ePx2Ix07NnR0aPHu3yIsrLy3OFChmNRmbHzMZqtbJn127q16lH/Tr16Ne+N5dnH+DgwYMcOnQIm83Grl27SE5Oplu3bi5x/2vxuXy66igvj/6WcR/9hAwY+Wo0VrtIixbRnD59kszMxCIjQwKnT5+iXv26D3dg/kX4eKoICPAs85+3l/sdy2zWe39RISEhISHxz6a85BXllbm7u+Pv70+e3YpZFLlhNCICLYrWukeOHEGhUJGrTyUjJx5RdD57fjm2htatW5OtN5Ce61yHZOc710crdx2lcePGXM9OpElFZwRGZEBlzqVeJi7L+Rs9MzOT1NRU/Pz87vvY/J24q6dQSkoKQUFBrh9xcrmcwMBAUlJSSgkw7d27lzlz5hAfH8/o0aOpUeOvf5tnttrvOX38/9ru/8qddESGDn2TmJiZrFu3mqpVq5V4A/7++x8wY8ZUNm5cR40atahaNbJMYefo6KZMnjydd98de0fvIHB6dsyePYPg4BBGjnS6zSuVSpYtWw3A66+/ydSpk/j++6dRq9VMnDi1lGdDp05dUalUDB8+lNmzFxASEvo/jYNWq2XOnEWMHDmsTI+hW4mN/RS9Ppfly5eyfLkz7nPo0Ldo1qwFjRpF07p1WwYMeA6HA7p06VZKSDowMIj58xczatRbmEwmund/8n/q61+FSqVi6NBB5OXlMWjwKJee0O10fqINefp83n5/MuD03OjVtTN1WrZn08o1pCanIMgFPDw8eG3EmwB88MF0Zn/4Aa+OHwWAxt2dMa+9ga932ecopm/fF1iwaCFqtfqOQtOeWk8+mjCD2FWxLFzxKVablQoVKzJmzLvs3LkNi8XMM8/0QSaT0bLl43z88TTGjx3GsKGvMmnKdPz9/ahXry46nSfgDFcaO+VjPl80ly3rVyGTyejx7AtEhT/Dk08+w6JF89iwYS1vvFG20PS98tVXGzl+/BhKpQKlUsXIkc459tZbo1iwIIYBA55DLpfTsGEjRowYy0svvcrcuZ8wcKBTp6hz5240b96yzLbDwysyadKHfPzxh5jNZmw2K1FR9alVqw6NGzdh+fIlXL9+jXHj3iuhKXTo0G9Urly1hNDtq68OJCMjjfz8fJ5+uhvNmrVg/PiJaDQejBs3gXHjRiCKIpGRNRg+fMyfHo8/w+16Q6GhoajVblitVgoKCtBoNFy+fIliSb7k5GSSk5Jw4MBd6UaYVzBpeamIor1EljtnSJrT+GQ2Oz0ik9OcD2G5XMBb54a7uwIPDw9UOh+MWWk47HZkghyVzhu5278n3efDxsdThcKt7OxrNpOZnPyb3n/leX7cHio0pu1rPFGtOekFWbyxeSLDHnd+JhYvXowoiq7fDykpKfz222907doVgKOnU8nONSOTyahRxY8X+9RFrVKglAssy1Tg1+4pnn91CNb8PJQeOkJbd6ftE60lz48HhMJNza+9epdZ9tj2LUwd/U2ZZRM+7kxAgOf97JqEhISExCNCeckryiv79tsdOBwO/JUqhl8+j4dcgVYu53RhAacK89l74QwgQ6cN4HqS0xtfJhOw2kzk5JiwWKzYRefv0RtpeSgVciw2O9EN62JOzGf+/pV82G0UUSE1eKHxk8z4MZbx388qWk80dHqh/ov4S4Wm27dvT/v27UlOTmbYsGG0bt2aKlXuLAZ8O35+pY0j6enON9TFGApMlO8L8ee59Tx3Ytu2b8vcP3nyzdCTFi1a8tVX28usFx4ezuefr0Umk3HtWhxvvPEakZGRKBRCiTac7bTg+++LPTCiefLJXqXa0+k8OXjwzt5EQUGBLFpUtsbQrdfSoUNHOnToWKpOWecEaNq0KU2brndt+/h4sWrVOtf2oUNlp5L/+OPyvYmGDBnKkCFDy9xfTFBQIOvXb7pjG3c6dzF3uof/C88+25eBA18C4ErCzdSI6zbvK1W375M96PtkaZf3kRPfLbNtPz9/3ntzRJll27Z9S3LCzQX50sUbXX/36vU0LRo3vGvfa0bWYv70m+mfi8PHnnzyaZ588mnX/v/8ZyDBwUEU6hPp2KEtHTu0dZW98vIAPHShXM8zEFYxgokz55c6T5s2bWjTpk2p/bdz+7wvi3Hjyh4rHx+vMo/X6bR3bHfJktIeYC1btqRly7KMRgKTJn3ApEkT6N//RRYuXIJKpaKwsIAKFSpQr1591OqbQnS3fgZup23bdrRtezOM0G63k5SUSEFBQZFWTzDe3qW1L3JycsnOzsJsNiMIcry9vQgKCnJ5YprNZlItZiyiA0EGPgolHkWLcACTyUhycnJRSKQ7kZGRqFQ3+6xQqKlTp3TmQFNqaS+vMK/gEuGGtxMcHIynxlZmGYDSU4fSU1dm2b18/4LT60lacP557rTIb/bVlwQE3MFgZLWXGPOAAE+WL//MtV0cDhSo9eOrlxe79q9du7bMkLJi/vNUHf7zVJ07loc93pmwxzuX2i/d/783CqWShe++XGbZmzNWPuDeSPNF4t6R5orE/4I0X+6N0FB/DIbCEuMlk9nw8fG6Y5nNZuGzzxaxcuVKMsdNcJUlmEwsTY4n2WKhYcOG5KTLkAsKmtd/jhx9MkfPfE1ufipNmzYiJe4cV5OzsVhttKlfmTb1KwPO59DvU79lwPpRGCxGNCp3etZpT8867Ykc06rc3y1/lkdhrtzVKBQSEkJaWhp2ux25XI7dbi9KIR5yx2NCQ0OJiori559//p+MQllZBYhiyUwRznS2fx8h1/8vJ06cYNGi+RS/jR837j00Gu0dr1GhEP5R1/9PwWZzzst7Xcj+G/mnzNuwsIrMmbOQjRvX88UXa7Hb7eh0XnTo0InAwJA/fZ1JSU5x1KpVIzGbzSQmxqNUKksJr9tsNgICgnBzcysyJCUgkwn4+fnjcDi4cSMeD0FOkEqBWRRJs5hRCW4oZTJsNhvx8TcIDg5Fq9WSmZlBfHw8ERGVy/1ueRjz+l7HURRFyVPkNsrL6nErBw78SOy1K6RZLbgLAs103vQOCEIuk6FQyhk3dA1HTm8hM/cGgqCgYnA9GtfpxZS5vcjIyGfnzm2sW7eK7OwsoqIaMGHCJGrVuvdn/F+FdP8fDI/Cj9h7QZovJfmn3Nf7gTRXSiPNlzsjzZd7Q6v1x2azcfz4WcLDnYlxTp48TWhoxTLLfvxxH5cvX+bTT5dSo0YNMm9pK9dmRW+3836lKgzYuJHHmneiSoUmAPjoQunY0hk1MymmB483c0YWlZeD0lFu6V/H32GuCIKsTAecYu5qFPLz86NWrVp888039OrVi2+++YZatWqVCh27evUqVas6U7tmZ2dz+PBhOnXq9P/s/j+Ppk2b07Rp84fdDYn/BwcOHL17pYfMxs1fcvDwIde2QqlCtIrMnhKDz13C0O4HO3duY8uWL0vtf++9ySVEY195ZUAp4ew6deoyduyE2w99oDgX2sPKzWz0vyCKIgUF+UREVEEul6PRaNBqnamYAwPdsNttpKSkUFhYiFwuJyAgEI1GgyAI6HReGIq0o7KyMrFYzFiRobfb8BDkqAWBArsNH4WS7OwcZDIZaWkppKfL8PTUYTabsNutyOUqCgvzSU9Pd6WqDwoKQqcr25NH4uFSnuHn1swd69atZNq0SXzyyXQ8PT3p2LEzgwcPK9KAMtLN15//5uVw1WhgT3YmlwyFvFvJadg5cnoLNrsFtUqLwZjHlYTD2EUrNms3rl07z7Jli1mzZg2VKlVi+vTpTJ8+iXXr7uwdJ/Hoces88/X1pYdDoPltyTQAvv76a3b9shB9YQZKhRsRoQ1pULMbguD0UtQXmvj5ZByp2fnIBYGqYX60rlfZdfwvV4+w/vh2sgqz8ffwZWCTZ4ik1QO7TgkJCQmJB4O7uztt2rRl+fIljB8/kcuXL3LgwH5iYz/H3d2d9h06sG7dCqZNm8ZXX33FsWNH+Pjjj2nTpkWJds4XFrAkOYE3QsMJU7uxYsUKjKZ8l1HoWuIxvD1D0Hr4snDhQrLzjYT6eaJWKkjNzketVOCtdSMnJ4fPDm4gKqQGHqr7l6n6UeOewsemTJnC+PHjWbx4MTqdjpkzZwLw2muv8fbbbxMVFcWmTZv49ddfUSgUOBwO+vfvT6tW0gNeQuJh8Hyfvjzfp69rO7BCRJnhQA+Knj2fomfPp+5ab8WKtQ+gNw8fi8UMyFCrb4brqNVuGI1OY09qaioymYxq1W56Ebm5qVGr3TAYDK407Q6HMztaRbUKuwPSLRZEHAhFHpc5Odk4HDK8PYMRHSL6vAxAICUpm7BwfxISEvDSuuHlrsFstZEQn4Cfl4bg8Mq3d1niIXOndK4hIaElMncEBATRuHFTqlWLpG/fF3jnnVF4eq5jwICX6NevH8/OjsFboWR+ZC2+y8pgd04W+3KyaQ3oCzMpNGTTpukgQgNq8sux1cQlHCVPr2fO1HcI81ax+/OPAHA3Wjhy5Cjx8fEPd2Ak/lJunWcZGQm8OmAA4W5uhN3mwWg0Gmlcuxd+PhUxmwvZf/Rzzsf9TJ1q7QH4+WQc7molg7o1wWy1sf3AOU7HpQKQWZhDzM/LeL/TWzSuUJejCX/w8Y9L6J71nwd+vRISEhIS95/Ro8e7tAh1Oi9X8orU1FT+u38/8lB3GjdtjEN0gAwmTHqPdydOwGEVqe6uYXylKuzMysAkisQk3gBAmH0Bf+8I5HKnOeN68gmS0zcADpR7lYQHetO2ofOll77QxMGz8RjMVjYd6Eldr2qMa1c6s/a/mXsyClWtWpXNmzeX2r9s2TLX3xMm3L83+c6Fzz83k5mEhMS/C1F0lBJ8l8sFRNFerheRSmXCZDIRHOwM3/X3D0Cv16O32dEpFKgFgXy7DWXR16XdLqKQqwEZgkyOUumG2WLA4XBgtdoQZDLUCudjQK1UgMyMvZyU0A+b4ixz/zbKS+favn3HEpk7nn76WYxGIydPHicgIJBOnbpw/PhN3blMq5X2Pn4oBYF4s4kQlZpkiwmAisFRXLj+C0F+VTGa9OgLM5ArlMTHxzvHvsT4O/++dOkSlShpMJB4NLl9nkVHR9NA68lvebn0CQwuUbdfv35cOeYUmta4exER1oi0zCuucr3BTL2qISjkAgq5iopB3mTpizwcC3PwUGmIDo8CoEnF+qiVKsnAKCEhIfEP5dbkFbcSHBzMyZMn6buptJ4swJfPxbq0EMdVLPnC8vaEB22b3swIPimmRwltu+rhAVQPDwCcmkLFWogSN/lLhabvBwqFisJCPR4eOskwJCEh8Y9AEGSItxlf7HYRQZDf0YsoPz+PvLw8wsIqkJp6M7TMz8+XvIwM8kxOgWelTEAugwK7HXBgsRrI1ZtRKt2x2czIAJlMhkIhxwFk5BYik4FCLiDD+b/FYuFK5nVkspuGKx93Hb6a0mEkDwqHw0FhoR6F4t+Xwv5OKVtPnjxebuYOgFOnTlC58k3dnw4+fhzW55FltRJnNOIuCNT1cGpGRFRozKUbv7Jp1wTAQZBfVcwWAzVq1CAi2Ifvf79E3SrBeGvd+P18IgAmkwkko9A/grLmWbjanYtFc6k80rOu4uUZ5NquXzWES4mZhPnrMFtt3EjLoXktp15ENf8Iwr1DOHzjJNHh9fg9/iRKuZIaNWqwb1/pZA0SEhISEn9/PHXuuKnLNi2IVguC8t/3++1R4m9vFPLxCSAnJ4OCgty7V/4HIghCqcWjxN8HQRAo0JedDy85OQ9bXk6ZZQrHDQoKs8o+zuTAXFj2cfrkG+TpjWV3JjmXfH3ZbdqSZdj0prL7Irqh1+vLLBNFExZT2X3JK7CTb7SUWZZcqJLmbTmIogOjMY/ExKsoijx1cnNzkcvlpKebMJvzSU294aqv1+sxGAz4+voRH38ZcL51sdmsxMdfRSeXI3M4MNrtCDIQBTlGHKBWYzIZAQcYcxBkCkSHHYcocvFiCjarpYRnkFIhJ1dvwuIQMZn0BGj9KDbF2yyFpFsKH+p8UShU+PgE3LXeP43yDD/u7u4UFpYMDS0sLESj8eDbb3dw7txZrFYbHTq0wtfXl8dEO1cMhRzUO5+pdT28aaT1ZOvWrez6ZS5ODyCnF1Ba1lXCg6PQaDR4ebihUSvZsPcUAEG+WpRygeDgYNKOJPLKxndwU9w0ZPau35VJkkbMI0VZ88xdLmAS7Xc4wsnVhN/JykukWf2bIcth/jrOXk9j6c7DOBxQs2IAVUKdWpRyQaBdZEtm7fsMi92KUlAwvsPraDSStoOEhITEo4qbWkHP0WVn394Z04u46WVnP63y3pb72S2Je+RvbxSSyxX4+98509k/nYAAz7+FYrlE2QQEeDL+T34B3our5O00us1V8lZud5W8lfJcJSPHNLpj+sXRo0dzbM/YMsvqdJrFa98dL7NsWbfa0ry9C0uXLgVkLtG9sWOHExv7OSaTlZEjR7B3768AHDt2hHHjRhIZWZ25cxfRtWtb1qzZRGio823+Z5+NIe/oMa4YDbTQeXO8QM/0ypHIZTLey80kIyOLFvWfJ68gjfNXf0apdKN3xynsOzabzIw0nn68LoHeHvxw9DJXk7J59okoXntvDn369GH7K58hF+Ql+i3NlwdPeYaf8PBK2O12EhLiXZk7rly5hEKhYMmShVSvXhONRsOOHXtIT4+nf//+KIDx4ZUJdVPzeUoSmzPSiMrOxuGwo5C70apRf05d3IWb2pPEtLOcP3+en0/GEeij5bl29TFbbWz97xnsooPIyEhO4/Qa2vTip6Xmi8SjQ1nzzGgXcSvnniaknubk+W9p1/x13FROg5Ioimz/9Rx1KwfTp00UFpudvcev8NuZG7wFnEw6x8rfNzOjxziq+lfkSuYNPtz9KQ3Pt7uflychISEhISFxB6R82hISEhIPgdGjx2OxmOnZsyNTprznEt1Tqdwwm82cOOHUgVm1ajkWi5kLF87Ro0cHbDYb8+ffNMokJydzoiAfk10k2WJmdHgESkGg0G4nLS2NVg37c/rSbs5c/hGFXIVS4Qz1qVu3LmqlAl9PdwpNFjLyCvH10pCQftMr8+UN43jxizHM2/85eSbJaPOwuNXwU8yVK5eoXLlKiaweRqORP/44yf79+zhx4ihTp87g2LHfefVVpxdGTo7Ti6uBp47qHh5o5QpaeflwuiCfV199FZVSg8bdi5CA6lQMqUdOXhJuak9+++038gpNBPlokQsyRNGBzS7iq3PHy8vrYQ2LRDno9Xm8++4YOnRoRe/ePdiz5/sy6+3a9Q2DBvWnU6c2jBs3EqvVyvXrca7yK8ZCki0m3rx0jhGXL7AuNRl7kbbU+bj9/HJsLTa7lb2HYvnl2GqMJj25ubkUGC3UqxqMXC7grlZSq1Ig19Oc8y8uK546wdWJDIhAkAlUD6hMjcDK/Pbbb/d/YCQkJCQkJCRK8bf3FJKQkJD4J3In0b2IiAjat+/Itm1bqFmzNq+9NtTlRZSfr2fixPHExCwAnF5ECQkJhKvd+KBytRLteCoUVKhQAX1hOrWqPMEfl3bjrQtFrXQHoH379vz44w+u8I6IYB9SsvT4e3ng4+PD3KcmUsUvHL2pgNjf1jN73zI+7Dbqvo+LRGnKS+cKJbN6uLm5I5PJmDEjBoPBiM1mQ6VyhnWtXevM7nc0X8/Qi+dw4EAtCNTxcHp4NKzVg99Pf8XmPRMR7Tbc3XRYrCZq1KhB3crBHDwXz4HT11EqnJ4jTWpWKNHPlzeMQyaT0TCsNi836/OghkeiDO6Ura5Klaol6plMJoYPH03t2nXJzc1h4MDnmDz5PZYs+Zxjxy5xsiCf+h6evB4WjkG0MzvhOvtyslEePMgfF3dTt1oHalVpjUwQOHJmKwdPbcTXtx86jZrTcak0igzDarNz4UYG/l4eAEQGVOark7uIy4qnil9Frmbe4GzqZV6r8RaHDx9+GMMlISEhISHxr0YyCklISEg8IMoT4TOZbeQX6UXdKXXnb7/9SnZ2FqmpqQQHB7Nq1XJMJhNJDgdDL54DIFKjYVR4BAALFy7kxf6vk1eQilLhhlKhonGdXjgcIvPnz6dSkA/ZegMGs4XEjDyCfLRUDPLGw8ODyABnGz4aL4a2/A8D1o/CYLmDnpXEfae8dK4DBvRh7drNBAcH89ZbQ/jjj5OMHTscu92pBTNr1kfExCxg7dq1jGnZih9zspABgkyghsaD5wOdIdrVKjbDbrdy+vIebDYz4CAqsgOtWrVi3wZvLiZkkJlXiMlio2bFAKqG+gGgc9OWaUTc+H7XhzRa/27Ky1Y3dOhbJeo+/fSzrr8DAgJ57rn/sHXrZnr27IiPjw86uYI2Pr7o7Tbej7tCc50XyRYTixcvxi5aOR/3E+fjfgLAWxdKrj4FgG7Na/LLH9c4fikJmUxGhQAvHo+KACAqpAYvNH6SGT/GkmvMQ+fmSZ8G3WnVqpVkFJKQkJCQkHgISEYhCQkJiQfE3UT4igO07uRF1LBhIxQKBVarU7D500+XMmvWh1h++bVUymiAjIwMTJZ8Oj82HH+fiq79/8fenUdHVSTu/3/Sne6ks5OQDUmALASQTUUZFMEFBBFEYRhGlhmGxWV08KcZERQREAxhjB9ZBBdGRtFRBlxYBFlE0SAwKCD4RQiRLWACGAhJms7W6d8fjK2xEwIhgcB9v87xnPStunWrmhLjc25VFZUUKjs7W/f3ueHMUfSSfvgxV5t2nf1IaJeMeSR8ffDrOdEg2Crv/739Ex4eqO3bt7vrvf3WfJ08dWZ+ZGTs1kMPjXC/WSZJNpNZzW3+ejSmiSrjZwuWl7zUq8vf1SDoTFhU3R4xNotvpSFiYWFhpc9A3TrbaXXV2bPne915Z2899NDfFB4eqCldbtXm/FP6U1QjPR+XqBezDuie8EiNXrDAY3+73fu+kMt1ZsP48BB/9evSusrn9Ln6dvW5+vYajhAAANQmQiEAuExUtozo008/1ZPhnoHQ9/ZCvf7EE7r5uj9XCIQkydcaoMaNG1e5vOPbb7/VibwcNQqOUGHxab228V21iU6Sv5XTgeoDb6tPNad4nAmFKtuEOqu4SI18fCq998dju7V5xyLdcv1IdyAkyXOPGLNJLZtEVB8iuggRL4WznVb3W4EhPvL93zHB77//vjIydmv69GkKDQ2UJCX5+emLvBN6OGOXyiXdFHTmtLrfOpn/o3buXa2uHYbX/oAAAECdIhQCgHqgvKxE4eGe/7MlSWUlxe63P367jGjixIkKnfcv5ZaWaPy+TE2JS1CYxaplucdVUFykz/87z91OeGicbus4StKZpWUPj/xTpcs7srKyNH3l/ymvKF9+FpvaX9VKY257oG6/AFRwtqWG5+q3IeKZPWLy9VSTOI+6Gzdu1IZt76hLh2EeIWJoaOhZ94jZc2yf/K1+HiFiYGDl8xl162yn1f2Wr8WqPyx8SKe+P67DS3crbtg1enDN05Kk9wa8rBezDqprSAM91SROxa5y92l1nX/VRoH9J322+XVdd/U9igjznFsAAKB+IxQCgHrA5G09p7c/fru0LDw8UBvm/UthFqvmJrVyXx8T20w3LXnfY4nHz1q2bFnl8o7evXsraXdIDUeC2lDdUsNz9esQsUGDBhoa2UhX+fh6hIhz5sxRaVnVIeLZ9ojJyT+ut7Z8QIhYT1T2htjPp9VJZ04mS0l5Tlu2bFJoaKhK47x1Ymu24oa0ky3ylzeM3nnnHZ0oK9XKEz/p87wT6hgUohuDQrTkp2OSpIUrx8kll5zOUplMZm3a/p5y8w5J6i1JKi1zasPOA9p7JFfl5S41DPbXIxf3qwAAAOeAUAgAgCvUr0PE8PBAbeh7Jnj8bYi4oJI9Yn7tbHvEdE3oqK4JHWux17gQ1Z1W9+uTyT7//BM9//zzirm3hfwaB1Vox2w2K9hs1m0NwtQpKESzjhzSt4X5auJ75gTDPreM1ZqNLyuxSSc1b3Kj3l8zUbHR7dz3f7btB5W7XBrS/Rr5WL31U57n8jUAAHDpEQoBQD1X4iytcmkZ8GvMFUhVn1Z34MABffrpas2c+ar8/Py0du1aSf+FrFEAACAASURBVFLWkj068vFeSZJ/bLDi/tRegwYNktebb+vdo9ladeInlblcspnM7tPqMrM2qfB0rnZmrNa3u1eq3OXUZ5tfl/SoThY4tC/7pIbfeZ2s/9vMPqJBQKV9BQAAlxahEADUc1azRX9Y+FClZf8ZOPci9wb1GXMFUtUnGJaUFMlq9dG1114n6cwbYrf87S7ZD5xUsyHtPOrH+tr05P/2oJp1+KCirD4K8j7zq2Pb5j3UtnkPSdLajXMVERqntklnPuecKFCQn482f5+l3YeOy9/XqhtaxtTJWAEAwIUxXeoOAAAAoO5VdjKZ2dcsZ4nzrPd9mXdSB4oc6hna0KPM7jipY7k/KC6mg/taoaNEufmnZfU2a3ivDurarpnWfr1XP/zwQ+0MBAAA1BreFAIAALiMne20uqLiMhXkOyRVfjKZs9gps9VcZdtbC/L1/vEc/T2mmQK9PZ+x7/DXCg9tpgC/MPc1b7NJJi8vXd8iRiaTl64KD9ZV4cFKT0/XjYqvyRABAEAdIRQCAAC4jFV3Wl3B/36u7GSyopxC+UR4HlcvSTsLC/SvnCP6/xo3UWNf30rr7D/8ta6Ov63CtYbBfjUbCAAAuOhYPgYAAGAAvz6ZzOFw6JtvvtGp3ccV2j7Ko+7GjRv12o+H9fBVsYqzVR7yHD+xX6eL8hXbqOJ+RI0aBinQz0df7zms8nKXfszN15GfTqlz5851Mi4AAFBzvCkEAABgEL8+maxBgwZq3CdJvhEBKskr0p7Zm5X0SEdZQ3w1Z84cOcqdeinroPveRD8/PR7T1P153+GvFRPVRhbvim8RmU0m3dWphdZtzdQ3GUcU6Oej7tclKj4+XnuVfbGGCgAAzgGhEAAAgEH8+mSy8PBA92l11hBftRnf1V1vwYIF2tC3/1nb6th2QJVlYUF+GnBL21roMQAAqEssHwMAAAAAADAg3hQCAAC4QpWXlSg8PPBSdwMAANRThEIAAABXKJO3VfumVr4MLO7p9y9ybwAAQH3D8jEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUAgAAAAAAMCBCIQAAAAAAAAMiFAIAAAAAADAgQiEAAAAAAAADIhQCAAAAAAAwIEIhAAAAAAAAAyIUusLl55/SuHF/V7dundW/f2+tXv1JpfVWrlyu4cOH6I47uuree3tpzpwZKisrc5e///5CjRgxVLfe2klTp070uP/TT9do8ODfq3v3LhoyZIC++OLzOhoRAAAAAACoDd6XugOoW2lpqbJYLFq6dLX27s3QmDGPKiEhUXFx8RXqFRUV6dFHk9WqVWvl5Z3Uk08+rsDAtzV06DBJUsOG4frzn0fov//dqOLi4gr3Hj9+TM8994xSUtL0u9/dqI0bN+iZZ57U4sXL1KBB6MUaKgAAAAAAOA+8KXQFczgcWr9+nUaOfFB+fn5q1669OnfuolWrVnjUvffe36tdu2tksVgUHh6hO+7oqZ07v3WXd+16m7p0uUVBQcEe9x47dkwBAYHq1OkmeXl56cYbO8tms+nIkcN1Oj4AAAAAAFBzhEJXsKysgzKZzIqNbeK+Fh/fXPv376v23m+/3aZmzeLO6TktWrRU06bNlJ6+Xk6nU1988bksFqvi4xNr3HcAAAAAAFC3WD52BXM4HAoICKhwLSAgQKdP289638cfL9Xu3d/rySefOafnmM1m9ezZS5MmjVdJSYm8vb313HOpstlsNe47AAAAAACoW7wpdAWz2Wyy2wsrXLPb7fLz86/yni+++FyvvDJbL7wwUyEhIef0nC1bNmvOnFmaNetVffbZRs2e/ZpSU5/T3r17Lqj/AAAAAACg7hAKXcFiYprI6XQqK+uQ+1pmZkaVy8I2bfpK06dPUWrqi4qPTzjn5+zdm6F27a5RixatZDKZ1LLl1WrVqrW2bPnvBY8BAAAAAADUDUKhK5jNZlPXrrdq3rxX5HA4tGPHdqWnr1ePHr086n7zzRZNnvyMpkyZrlatWnuUl5WVqbi4WOXl5Sovd6q4uNh9ZH3Llq20Y8c295tBGRm79e2325WQwJ5CAAAAAADUV+wpdIVLTh6rlJTJ6tOnu4KCgpWcPE5xcfHKycnR0KEDtGDBIkVFRelf/5onu71QTzzxqPvetm2vUVraTEnSm2/+U/Pnv+4uW7Vqpf7yl1EaMeIBXXPNdRo+/H6NH/+kTpw4oZCQBho69C+64YbfXfTxAgAAAACAc0ModIULCgpWSkqax/WoqCitWfOl+/OsWa+etZ0RIx7QiBEPVFnev/9A9e8/sOYdBQAAAAAAFxWh0BUgP/+UUlKe05YtmxQcHKIHHnhEAwbcI6vVUqHehx9+qAULFujAgQPy8/NX9+49dP/9D8vb27vKdu64o6f7/mXLPtLbb/9LJ07kqk2b9nrqqQlq2DD8oo4VAAAAAADUDkKhK0BaWqosFouWLl2tvXszNGbMo7rhhvZaunRphXq7du1SXFyc3nvvPWVmZunJJx9XYODbGjp0WJXtJCQkKi4uXtu2faNXX31ZM2e+opiYWM2Y8YImTnxas2e/dglGDAAAAAAALhQbTV/mHA6H1q9fp5EjH5Sfn5/atWuvzp27aMmSJR51W7VqpejoaFmtVoWHR+iOO3pq585vz9rOqlUrJEkbNnypW2/tpri4eFksFg0bNlLbt2/VkSOHL+p4AQAAAABA7SAUusxlZR2UyWRWbGwT97X4+ObKzMys9t5vv93mPp6+qnb2798nSXK5XHK5XO6yn3/et6/65wAAAAAAgPqHUOgy53A4FBAQUOFaQECA7Hb7We/7+OOl2r37e91339CztnP69Jl2OnW6SZ99tkaZmXtVXFyk+fNfl5eXl4qKimpxNAAAAAAA4GIhFLrM2Ww22e2FFa7Z7Xb5+/tXec/atWv1yiuzNXHi80pNnaJu3TprwoSxys8/5dGOn5+/Vq5crjlzZsrhcGjEiCHq1et2RUZGyc/PTxERkcrPP6Vx4/6ubt06q3//3lq9+pNKn/vGG6+pc+cO2rJl84UPHAAAAAAAXBBCoctcTEwTOZ1OZWUdcl/LzMxQQkJCpfWzsrI0fvx4paa+qA8++I97Y+nx4yerrKxMX32VXqGdZs3iVFRUpEcfTdaqVeu1ePEyNWnSTAUF+XI6nWrWLL7CBtUTJkxRWlqK9u37ocJzjxw5rM8//1RhYQ3r5osAAAAAAADnhVDoMmez2dS1662aN+8VORwO7dixXenp69W3b1+PukeOHNG6des0a9YsNWsWX2Fj6euv76ioqGi9/PJLFdrp0aOX7r3392rRopWysg6qYcNwdep0k1asWKYBA+6TxWI56wbVP3vxxel66KG/yWKxXKyvBgAAAAAAnAWh0BUgOXmsSkqK1adPd02c+LSSk8cpMTFRhYWFeuONN1RYeGZ52datW1VSUqL7779fvXt3U1lZmWbMSHO3c9ddd6ugIL9CO3Fx8ZKkkpISTZo0Xt2736x///stxcTEauTIB6vdoFqS1q1bK4vFW506db5I3wgAAAAAAKiO96XuAC5cUFCwUlLSPK4HBARo+PDh7s99+vSRJCUnJ2vt2i/0zDNjlZY2010eFtZQsbFNNXv2ax5tBQYG6s0339PHHy/VvHmvKDX1JZnN5mo3qD59+rRee+1lvfji7FoZKwAAAAAAqB2EQpexBsE2eVtr9kdY1QbVfn5Vb1D9xRef65VXZuull+YoJCTknNr55z9fVY8evdSo0VU16icAAAAAAKgbhEKXMW+rt/a+kF5pWeLfz75U69cbVMfExEr6ZWPpymza9JWmT5+i6dNfUnx8wjm38803W3T8+FF9+OFiSVJe3klNmDBOgwf/SUOGDDuv8QIAAAAAgNrDnkIGVdUG1T169PKo+803WzR58jOaMmW6WrVqfV7tzJgxR2+9tVDz57+j+fPfUcOG4XriiafUr98fLso4AQAAAABA5XhTyMCSk8cqJWWy+vTprqCgYPfG0jk5ORo6dIAWLFikqKgo/etf82S3F+qJJx5139u27TXu/YiqakeSgoNDKjzTZDIpMDBQfn5+F2+gAAAAAADAA6GQgVW1QXVUVJTWrPnS/XnWrFdr1E5lFi9edn6dBAAAAAAAdeKcQqH9+/dr7NixysvLU0hIiFJTU9W0adMKdV5++WWtWLFCZrNZ3t7eeuyxx3TzzTfXRZ9xgcqdpQoPD6y0rKy0RCfzii9yjwAAAAAAwMV2TqHQs88+q0GDBqlv375asmSJJkyYoLfeeqtCnbZt22r48OGy2WzavXu3hgwZovT0dPn6+tZJx1FzJrNF36x+otKy6+74hyRCIQAAAAAArnTVbjSdm5urXbt2qXfv3pKk3r17a9euXTpx4kSFejfffLNsNpskKSkpSS6XS3l5eXXQZQAAAAAAAFyoakOh7OxsRUZGymw2S5LMZrMiIiKUnZ1d5T0fffSRYmNjFRUVVXs9BQAAAAAAQK2p9Y2m//vf/2rGjBl64403zvvesLCA2u5OvZOXl6enn35aGzZsUIMGDfT444+rT58+HvUyMjKUmpqq7777Tnl5edqzZ0+F8h9++EHJyQ9r59YdCrYF6i83DNCNza6VJJU6yzR69Ght2LBBhYWF6t27txo1anTOfaxqvyHgfDCPcD6YLzhXzBWcD+YLzhVzBeeD+YJzdTnMlWpDoejoaB09elROp1Nms1lOp1PHjh1TdHS0R91t27bpiSee0Jw5cxQXF3fencnNLVR5ueu877ucPPvsM3K5vLRkySrt3ZuhMWMeVUREjPsI958VFBSrc+dbdN999+nhhx/W8eMF7rKysjLdf/+DGjJkkJ5qPVLfZe/R5NUz1aTBs7oq5MzbWddee618fHy0Zs2a8+7jr5+Fs7sc/iW/VJhHnpgvVW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     \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"# pd.options.display.max_colwidth = 100\\n\",\n    \"\\n\",\n    \"plot_avg_only = False\\n\",\n    \"kp_exps = {\\n\",\n    \"    'Baseline': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"    'One2One MagKP+KP20k alternate': 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n    \"    'One2One MagKP+KP20k mixed': 'kpgen-meng17-magkp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'kpgen-meng17-magkp-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"\\n\",\n    \"#     'MagKP_LN+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_LN-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"#     'MagKP_Nsmall+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_Nsmall-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'MagKP_Nlage+KP20k mixed': 'kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',   \\n\",\n    \"\\n\",\n    \"    'MagKP+KP20k alternate': 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP+KP20k mixed': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#     'MagKP+KP20k mixed, no-copy': 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"    \\n\",\n    \"    'MagKP_LN+KP20k-FT': 'kpgen-meng17-MagKP_LN+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP_Nsmall+KP20k-FT': 'kpgen-meng17-MagKP_Nsmall+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP_Nlarge+KP20k-FT': 'kpgen-meng17-MagKP_Nlarge+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP+KP20k-FT': 'kpgen-meng17-magkp+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"    'MagKP20k+KP20k-FT': 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue'\\n\",\n    \"} \\n\",\n    \"\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 5000 == 0) | (kp_df.step % 6000 == 0)]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"print('present peak_kp_df')\\n\",\n    \"_, peak_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(peak_kp_df.shape)\\n\",\n    \"# display(peak_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"# metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = peak_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = kp_exps.keys()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in peak_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"if plot_avg_only:\\n\",\n    \"    for k,v in bar_values.items():\\n\",\n    \"        bar_values[k] = v[-1]\\n\",\n    \"    datasets = [datasets[-1]]\\n\",\n    \"        \\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent peak_kp_df')\\n\",\n    \"_, peak_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(peak_kp_df.shape)\\n\",\n    \"# display(peak_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = peak_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = kp_exps.keys()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in peak_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"if plot_avg_only:\\n\",\n    \"    for k,v in bar_values.items():\\n\",\n    \"        bar_values[k] = v[-1]\\n\",\n    \"    datasets = [datasets[-1]]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Summary (used in paper Table 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Present  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 321,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:16:35.080712Z\",\n     \"start_time\": \"2020-11-23T19:16:34.351927Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"# BaseRNN-one2one\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"# Transformer-one2one\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# Transformer-one2one, MagKP20k\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-magkp-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# BigRNN-one2one\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"# BaseRNN-one2seq\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# Transformer-one2seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# Transformer-one2seq, MagKP20k\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-magkp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"# BigRNN-one2seq\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"     kp20k_one2one_df.at[index_label, 'test_dataset'] = 'one2one - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2seq_df.iterrows():\\n\",\n    \"     kp20k_one2seq_df.at[index_label, 'test_dataset'] = 'one2seq - ' + kp20k_one2seq_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"# combine both and plot\\n\",\n    \"combined_kp20k_df = kp20k_one2one_df.append(kp20k_one2seq_df, ignore_index=True)\\n\",\n    \"combined_df = one2one_df.append(one2seq_df, ignore_index=True)\\n\",\n    \"\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False, title='Present: One2One vs. One2Seq')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 322,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:16:38.271556Z\",\n     \"start_time\": \"2020-11-23T19:16:37.947528Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False, title='Absent: One2One vs. One2Seq')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Valid-peak\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 325,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:20:01.983221Z\",\n     \"start_time\": \"2020-11-23T19:19:57.386911Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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D79k3Cwv6d1xkWFkanTp2xsbEjPT2d06dPMn78SBYtWoGLS7MC1XXFikVYWVkxb96SbNeBVqlUmJiYMG/eEgYM6M22bZvo3bufRpnTp0/SunUzVCoVVatW5+uv+2g86Ll7928AKlXSDMIzE+HdvfuPBKdCCCGEKDxnzvwOQECAHwEBfhr7+vYdSP/+rtSqVZt9+35iz55dPH2aTOnSZahVqzYTJ07LMgQ4PT0dlUqlse3Jkyfs2bOH6OgozM0t+OCDlvTv75rlST/AoUM/k56eTtu2MqS3sPj4eGJnZ8/ixSuVgL927Tr07NmVbds24e4+WikbExPD6tU+SvBYvXoNOnduy7Fjh5UvtmvXrsDa2oaVK72UxEbOzi707t2djRt9mTdvSbb1uHMniB9+2EaHDp9gYWH5xtWvoFq2bK38rFarqVOnHoaGhsyePZ3RoydgavrvfOmoqCd4efkrIxMaN3ahV68v8PX1Yu1aX2JiYggJecD8+Us0eqnbtPk3IdgPP2wjPPwRGzduU+6/UaPGJCTE4+/vQ5cumg90PvqoLYMGDSY9XUWjRo25fv0qhw//Qv/+rhgaGinZsytUqJTvEROQMfLB03MV7dt30kicVqtWbXr27Mq+fT/xxRdfAjBo0FAuXPgLD48peHjMYdmyRXzyyaca71358hUZOfLfrNzp6enUqVOPzz7ryJkzv9OixYcArFmzAlNTM9at20DJkhk90w0bNlKOq1atBnp6+nkOVc1Ny5atGDJkhMa2FSsWU7++I/PnL1W2OTo68cUXndm2bTMjRowhMTGBdevW8sEHHzJ37iKlXOPGLhrnGjZspPKzSqWiYcNGBAc/4KefdhYoOA0NfciRI4fYuvVHdHR0SE9Px8/Pm717d5OcnMSHH/4PMzMzoqOjmTLFg4EDB7N06QJ69eqrLOvVrNn71KhRCzs7e6KinrBz53YmTx7Lt9/O5OOP2wMZvbNAliHBxsYmAMTHx+W7zgUhwakQQgjxjspP9seOHTvTsWPnfJ3v1KnzWbbNnj2HmJikfB3fvftXdO/+Vb7KirwlJydz+/ZNevXqqxG42NnZU6dOPS5evKBRvly58hq9mubmFpibWxAe/giAlJSnXLp0QfmSm5aWppR1cnLm0KGD2dYjMjKSSZPGYG9vj7v7qEKun/lL1w8yAszM3rJMufXeJyYmsHGjHydOHOXx43CNawUHB2sEp1ZW1hoBk46ODh9++D+2bAlApVJhamqKnZ09Xl6riYqKokGDhhr3CXD27O/UqlUbW1s7jWs5Ozdh797d3Lt3lypVqirb/xvsVK5chfPnz+V4P/l17doVEhMTadOmnUY9ypSxokKFily6dFEJTnV1dfHwmEPfvl/h6toXW1s7hg8fk+Wcu3btYPfunYSGhpCcnKxsf/DgPpAxjSAw8DI9e/ZSAtNX4YMPPtR4HRz8gIcPQ+jVq6/GvZYoUZL33qvL5csZn8/AwCskJyfxySef5nr+mzdv4Oe3jhs3rhMTE630iud3ekSmP/44Rb16DbCzswcyem1/+GHb/w+Fr8DPP+9jx47vad26DQBNmrgQFfWE4OAHyrVGjRqf5d5dXfuybt0aJTjNrN9/16l+1SQ4FUIIId4lr3m9zni1Ku9C4pWIj49DrVZnmxXZ0tJSCeoyZfaIPE9PT49nzzLWV42LiyM9PZ0NG3zZsME322uqVCqNJYJiY2MYNWoIarWapUtXY2BgWMj103+p+mW6ePEvZR5mpuwetmSaO3cG58+fo39/N6pWrUapUqW4fv0aS5cu4NmzFI2yz/cU/7vNgtTUVGJiorGwsGTZsjX4+Xmzbt1qYmNjsbW158sveykZUaOjowkJCaZlyybZ1ue/awb/d2TC8+34MqKjowEYOXJItvv/20Zly5ajTp26nDt3hs6du2YJLjOHw3bv/hWNG4/A2NgYlUqNq2sf5X2Mj49DpVJhZaU5B7Ww/fdzGB0dBcD8+bOYP39WlvKZQ3oz3/vc6hce/oiRIwdTsWJlRo4ch7W1Dbq6Ovj4eHH//t0C1TM4+IHGfNe9e3fTq1df5bPSoEFDrl+/quwvUaIkZmbmxMbGANkHwhkPTFrj6bmKyMhISpcurXyG4uLiKF363/cms8c0u9/HwiDBqRBCCPEOed3rdV50qSVfNoqIsbEJWlpaREU9ybLvyZMn2Q6tzo2RkTHa2tp07dqNtm07ZFvm+cAvMTGB0aPdiYuLZc0a3ywJboq6fs+rUaMmvr4B+bpOSkoKp06dpG/fgRrJfjLnP/5XdvcXFRWFnp4eZmYZ8yLt7cvy7bczUavV3Llzm507t7NkyXxsbGxxcWmGiYkpdeqYM2LE2CzngoL3vr2ozDaZMsVDmXv4vP8mOdu37yfOnTtD9eo18fPz4YMPPsTGxlbZf/ToIRo2dNboUQ8NfahxDmNjE7S1tYmIiCjMW8nivz2Emb3frq7DaNTIOUt5XV09jXIRERFZkiRlOnv2DxISEpg5c57GUl0pKU8LXM+0tHSN9zksLJSqVaspr7W1talSpdpz5dOIi4vV6M3Pzr89pRmv/51b+rdGcHr37j8a+wub/L8QQgghhHgLlSpViurVa3L8+BH69RukJE559CiMq1ev8Nln3Qt8vrp163PnThDVqtXIMdCDjKGY48aNJCwslFWr1ikJi96U+v2XgYGhkk04L6mpqaSnp2cZ9vvzz/uyLf/4cThXrwYqQ3vT09M5fvwINWu+l6WOWlpaVK1aHXf3Uezb9xN37/6Ni0szGjd2YefO7Vhb22BubpHv+8pJZubWlJSUPEpqqlOnLgYGhoSEBOe53NODB/dZsWIxn37aDTe3ofTp8yUzZ37LqlXrlLZ++vQpBgZGGscdOKA53aBkyZLUrVufQ4d+pm/fAdlmQ4aM3uEXCfZyUr58BWxt7bh372969eqTY7k6depSqpQBe/b8mGWeaaanTzPq9fxn5sGD+wQGXs7y0CYvVlZW3Lr17wNGc3MLHj0K1SgTFhaqzCs+cuQXypSxyjJU/HlpaWkcP34Ua2sbpQe5du26mJmZcfjwQRo1aqyUPXTo5/9/WFKvQPXOLwlOhRBCCCHyoKeGC00KJ9Pri17/RQwY4Mb48SMZP34Un376OcnJSaxf742RkRE9e35d4PO5u49m2LCBjB49jI4dO2NpWZrY2Bhu3bqJSqVi8GB3AKZMGU9g4GVGjBhDcnKyRiZoe/uySibVoqrfyzAyMuK99+qwbdtmLC1LY2Zmxv79e4iIeJxteQsLS6ZPn0T//q6YmZmze/cOgoMfMHbsJCAjWdSKFYtp3foj7O3LoVKpOHBgLzo6Ojg6ZiT96d79K44dO8yQIQPo3v1LypevSHJyMvfv3+PKlYsaCXvyo1y58ujo6LB//x5MTEzQ19enfPkKGsOus2NoaMSQIcNZtmwhMTHRNGnSFENDIyIjI7h48QINGjSkTZu2pKamMmPGVGxsbBk2bCQlSpRg+vTZDB06kI0b1ysZdRs3bsrmzRsJCPCjZs33uHDhPMePH81y3aFDRzBs2CBcXfvRo8dXWFlZExoaQlDQbWX+ZMWKlbl8+RKnT/+GpaUlpqZm2NraFeh9eZ6WlhajR49n4sQxpKam0arV/zA1NSMqKoqrV69gbW1Njx5fY2BgiJvbUJYtW8SUKeNo06YdBgYGBAXdRl9fn88/74GTkzM6OjrMnj2dHj2+5smTSNavX4eVlQ3qAk59cHRsxObNG0lMTMDQ0IgWLVoREOBPlSrVKF++IgcP7uPGjWvY2dlz6NBBli1byMSJ3yo9w4cPH+TUqV9p0qQZ1tY2REU94ccff+DWrRt4eMxRrqOrq8uAAW4sWbKA0qXL4OTkzIUL59m/fw8jR45TEo4VNglOhRBCCCHykviMV/NV7NVq0qQpCxcux9/fh2nTJqGnp0uDBg0ZMmQ4pUuXKfD5qlevgY9PAP7+3ixfvpjExATMzMypVq06Xbp8ppQ7ezYjE/Ty5YuznGPy5Om0b9+pSOv3sjw85rB48XyWLl1IiRIlaNXqf4wYMZbx40dmKWtvX5avvurNunVrCAkJxsbGFg+POcoyHJaWllhb27Bt2xYiIsLR1y+Bg0MVFi5crix9Y2RkhKenHxs2+LB5cwAREY8xMjKmfPkKtGzZqsD1NzU1Y9So8WzeHIC7uyvp6en5Xue0S5fPsLa2ZsuW7zh8+BfS0tIoU6YM9eo1UIaXrlu3hrt3/8HXd6OyHEzt2nXp23cg/v4+ODk5U7duffr2HUBCQjzbt28hJeUZDRo4snTpKr74QjMJW82a7+HpuR5f33UsX76IZ89SsbGxUT5HAG5uw1iwYDbTpk0kJSUl3+uc5sbFpTlr1vgQEODH/PmzefYsBQsLS957r7bGOrWffdYdCwtLtmz5jhkzvkVXV5cKFTLWMQWoXNmBadNms369FxMnjsbOrixubsM4e/YPLl78q0B1ql27DhUqVMLHx5ORI8fRr99A7t+/i5tbRsbqatVq0KnTp+zdu4vbt28yZcoMjSVibG3tiY6OZu3alcTFxVKyZElq1HiPJUtWZen57dLlc0CLbds2s3Xrd1hb2zBq1Hi6du32Ym9oPmipMwcYv0EiIuKLugqvjJmZQb6zFoo3i7Rd8SbtV3xJ2xWuVCP91z/nNL5gQweLSmRkGKVL2+Zd8B2io6NNeroktSqOpO2Kt9za786dIAYP7sfXX/fhm2/6AxlZsZOTkyhXrjxRUU9IS0vTmN/6uuX197RMGeNst+d/ML4QQgghhBBCiCJVpUpVFi5czvbtWxg8uD9Hjx4G1FhZWRMR8ZhLly4yd+4Mli5dUNRVLTAZ1iuEEEIIIcQ7TqVSoVLl3NOqpaWlJDIqLp5fnzQ7Ojo6r30dz8LSoEFDvvtuOwEBfsoc4Ew2NrZ07Ni5WK4bLcGpEEIIIYQQ7zh/fx/8/X1y3G9jY8uOHXtz3P+mCQsLpVu3T3Itk995tm8qCwtLRo4cx4gRYwkPDychIR4zM7MXmq/9ppDgVAghxAs7fvwIR478ws2bN4iOjsba2poWLVrRu3dfJevj+fPnOHBgL1evXlEW93Z2bkL//q75WhIhJiaG5ctXcvr0bzx5EomlpSUuLs3o23eQkvEzMjKSHTu28eefZwkJeYCenh4ODlXp23cg9es7vtL3QAgh3gadO3elWbP3c9yvp6f/Gmvz8kqXLpPn2rWva33YV01LSwsbGxvApqir8tIkOBVCCPHCtm7dhLW1Da6uQylTxoqgoFv4+Xlz4cJ5vLz80NbWZvfunSQnJ/PNN/2xs7MnJCSY9evXcfbsGTZu3Jpl0fbnqdVq3N2Hcu/eXfr3d6NChYrcu3cXX18vbt26iZeXH1paWty6dYOjRw/ToUMn3nuvNqmpaeza9QPu7q7Mn7801y9cQgghMoK54tzj9l96enr5XrtWvDkkOBVCCPHCFixYpvReQsYcGGNjE+bM8eDixb9o2LARY8ZMzFKmXLnyDBs2iGPHDtOxY+fsTg1AcPADLl26yLhxk+ncuSsAjo5OaGtrsXjxfIKD71O+fEXq1q3P1q07NRY4d3ZuQq9eX7BlS4AEp0IIIUQxINl6hRBCvLDng85MNWu+B6AsSJ99mYyn2ZGREbmePy0tFQBDQ82F4Y2MMlLQq1QZq6EZGxtrBKaQsYB41arVlXoIIYQQ4s0mwakQQohCdelSxoLiFStWyrHMxYsXAKhQoWKu56pUyQEnJyc2bFjPzZvXSUpK4vr1q2zY4EuTJk1zvUZqaipXr17JtYwQQggh3hwyrFcIIUShiYh4jK/vOpycnHOc65OUlMjKlUuoWLES77/fMtfzaWlpsXatF2PHjmPAgN7K9qZNmzNr1vxcj/Xz8yYi4jHTp88u8H0IIYQQ4vWT4FQIIUShSEpKYuLEMejo6DB58vRsy6SlpeHhMYWIiAg8PddnGYqbHQ+P6Vy7FsjYsZOoWLES9+7dxc/Pm6lTJ7BgwTK0tbMOAjp06CCbNm2gT58B1KvX4KXvTQghhBCvngSnQgghXlpKSgoTJ44mNPQhq1d7Y2VlnaWMSqVizhwPzp8/x8KFy6lSpWqe5/3991McOLCf5cvX4uTkDED9+o7Y2y+5qQcAACAASURBVNszatQwTp8+maX39dSpk8yd60HHjp3p39+1UO5PCEPDZ6CVWnQVUOuRmFi8lvIQQoiCkuBUCCHES0lLS2Pq1PHcuHGNZcvW4uBQJdtyixbN49ixw8yatUAJNPPy9993gH8TKGWqWbM2APfu3eP95xLxnj9/jmnTJvLBBx8ybtzkF7gbIXKglcoffzQrssu7uJwGik9wumfPLk6ePM6dO0EkJMRja2tHu3Yd6datJ3p6ekVdvVfuwoXzDB/uxrJla2jUqHFRV0fDgQN7UalUuWZKL06Cgm5x8uQJunXrgYmJaVFX54VlZrnfsWMvAGFhoXTt2pHJk6fTvn2nQrnGgQN7mTt3RpbtVapUY8OGLRrbwsMfsWrVUv788yxqNTg5OTN8+Jj/X0/11ZHgVAghxAtTqVTMmDGVv/76k4ULl1O7dp1sy61atYx9+3YzZYoHH3zQMt/nt7S0BOD69WsaX/CuX78KQJky/67Jd/XqFSZNGkPDho2YNm1WtsN9hRCvx4YNvjg5OTNixCeYmppx5colfH29uH79GrNnLyjq6r3TDhzYS3p6+lsUnN7G39+Hjz9uX6yD04KKjIxgx47v+eOPUzx8GIJarcbW1o73329J167dKFPGKsdjZ82aT5ky/45wKlWqlMb+p0+fMmLEYPT09JgyZQZaWuDj48nw4a5s3LgtS/nCJMGpEKJIHT9+hCNHfuHmzRtER0djbW1Nixat6N27LwYGGcuHnD9/jgMH9nL16hUiIyMpXbo0zs5N6N/fFXNzi1zPn5SUyLx5s7hz5xYRERHo6upSrlwFPv+8Ox9/3F6j7M8/7+PUqV+5efMG4eGPaNeuI1OmeLyqW38rLF26gOPHj9C7dz9KlizF1auByj4rKyusrKzZtGkD33+/mQ4dPqFs2fIaZczNzbG3L6u8btGiMW3bdmDSpGn///pDfH09mT17On36DKBChYrcv38Pf38frKys+eCDDwG4f/8e48aNxNTUjJ49e3Hz5g2NeuYUNAshXo316zdpLCPl6OiEWq1m/fp1PHwYovF7L8SbQq1Wk5aW9sb37u/Zs4vlyxdTubIDHTt2oWzZcpQsWZKQkGCOHDnErl0/MG7cFFq3/ijb46tWrU7ZsuVyPX9o6EO2bNmplHNwqErPnl356aed9Ojx9Su5L5DgVAhRxLZu3YS1tQ2urkMpU8aKoKBb+Pl5c+HCeby8/NDW1mb37p0kJyfzzTf9sbOzJyQkmPXr13H27Bk2btyKgYFBjudPTU1FR0eHAQMGYWpqSWrqM44ePcysWdOIiYmme/evlLK//HKAmJgYGjVqzPHjR17H7Rd7Z878DkBAgB8BAX4a+/r2HUj//q5Kmf3797B//x6NMv99AJCeno5KpVJeGxoasXnzVpYvX8GWLQE8eRKJpWVpmjV7n379Biltf+1aIPHxccTHxzF8uFuWep46db5Q7leI4ujMmd/x9/chKOg2enq6ODo6MXiwO+XLV1TKDBs2iPT0dPr1G4Sn50ru37+HnZ09rq5Dad68hcb5goJu4+vryeXLl3j27BnVq1fHzc1dI/lYdusbZ2bwjoyMUILTBw/u4+m5isDAyyQlJWJmZk6tWrXx8JijJEyLiYnB19eTU6dOEhsbg62tHd27f0Xnzl01zn/+/DnWrl3BvXt3KV26DF9+2Ztr1wI1hkoWhvXr13H69EkePgxBV1cXB4eqDBo0NNuHYImJCcyZ48Fvv/2KSqWiWbP3GTlyLKamZkqZ7du38tNPOwkLC0NfXx97+7L07t2PFi0+VMr8+usxNm8O4O+/g9DT08PJqTHDho3SGGL5+eedqFu3Pk2bNsff34fw8EdUqFCJ4cPHUK9efSCjnS9dyljKq3lzJyBjHv/q1d75uve82kKlUjF8uBuPHoWxYcNWjIyMgIwpGgMHfsNnn33B0KEjADhy5Bf27NnFP//cISUlhbJly/HFF1/Srl1HjWumpaWxbdsmDh7cT2joQwwMDKhWrSYjRozh2rVAZZhqjx6fKsf88MMebG3t8ryfzPfM0dGJLVsCePgwhJkz59OixYc8ffoUPz9vjh8/QkTEY8qUsaJjx8706tVXY2ROdHQ069ev4/fffyM6OgpzcwsaNHBkwoRv0dfXJyQkGH9/b65cucyTJ0+wtCxN48ZNGDRoKCYmJvl635+3c+f3eHmtYdq0mbRs2VpjX4MGDenUqQvHjh1h7lwPSpTQz/L7mx+nT5/kvfdqawSwdnb21KlTj1OnTkpwKoR4ey1YsEzjS0yDBg0xNjZR5l40bNiIMWMmZilTrlx5hg0bxLFjh3MdmmRqaoaHxxzMzAyIiUkCwMWlOcHBD9i/f49GcLp06WrlH87Zs38U9q2+lfLzhS+/X3og+yDS1tZW6UnNSfv2nQptTo4Qb5MzZ35n/PiRODo6MXPmXJKSklm/3oshQwbg779FY+hfaGgIK1YsoVevPpiamrFt2yamTBnP5s07lC+pt27dZOjQAVStWp0JE6ZQsmRJdu/eyciRQ/D09KNGjZo51uXSpQtoa2tTrlx5Zdv48aMwNjZi7NiJmJqaERERwZkzp1Gr1UBGcDd4cD9SUlLo128QtrZ2nDt3hiVL5pOa+ozPP+8BwL17dxk3bgQ1atTEw2MuqanP8PPzJjk5udCH+EdEPOaLL77Eysqa5ORkDh06wLBhA/H1/S5LoreVK5fi5OSMh8ccQkIesG7dWiIjI1i1ah0Ahw79zJo1y5XM4ikpT7lz5w7x8bHKOXbv3sHixfNp374TffsOICkpCT8/b9zdB/3/A1pDpezlyxd58OA+Awa4UbJkSby91zJhwkh++GEvxsbGjBkzkVmzviU9XaXMyzc0NCQ/8tMW2traTJs2iz59vmTRojnMmDGPlJSnTJ8+mUqVKjNo0BDlfKGhD2nZsjVff90HLS0tLl++yPz5s0hJeUqXLp8r5Tw8JvPbb7/SrVtPnJycefbsGZcvX+DJk0hcXJrzzTf92bhxvcZQVUvL0vluzwsXzhMUdIu+fQdibm6Bra0daWlpjB49jHv37tKnT38qV67CtWuBbNy4nri4ONzdRwEQFxfH4MH9iIuL45tv+uHgUJXo6ChOnfqVtLRU9PX1iYyMoEwZa4YPH42xsQmhoQ8JCPAnKGgE69b557ueAA8e3GP16uUsWrQix9wNaWlptGr1P1JSnjJ//iy+/74hhoZGGmWGDBlATEw05ubmNG/eAlfXoRpDou/e/YfmzT/Icu6KFStz4sSrfXgvwakQokhl93S9Zs33gIwvADmX+fcJ/IswNTUlNfWZxjaZoyiEeNv4+HhiZ2fP4sUrlZ7I2rXr0LNnV7Zt24S7+2ilbExMDKtX+yjBY/XqNejcuS3Hjh2md+9+AKxduwJraxtWrvRShj46O7vQu3d3Nm70Zd68JdnW486dIH74YRsdOnyChYWlcr2QkAfMn79Eo3enTZu2ys8//LCN8PBHbNy4TalXo0aNSUiIx9/fhy5dPkdXV5eNG9djYGDA0qVrlPlwtWvXpXv3LpQuXYbCNHHit8rP6enpNG7swu3bX7Bv30+MHDlWo2zFipWfW1qrKSYmpsyc+S3nz5/DycmZq1ev4OBQhb59ByrHuLg0V35OSkrC03MV7dt30liiq1at2vTs2ZV9+37iiy++VLYnJibi778FExMTdHS0MTe3YMCA3vzxx2natGlLpUqVMTAwJD09vcDTHfLbFlZW1kyYMJUpU8bh7OzC1auBhIeHsX79Jo3hspmfKcjocW3QoCFPnkSya9dOJTj9668/OXHiGCNGjKVbtx5K+edzF2T2wuc1VDUn8fFxrF//nUZAe/Dgfq5cucTq1d7Ur+8IoASD/v4+fP31N5ibW7B9+xZCQx/i6xtAtWo1lOM/+ujfz3D9+o7KOSDjc2lvX46hQwdw+/ZNjePysmnTRj76qK1Sl7//vsOiRXO5desGtrZ2DB8+hrFjh/PDD3to27YD27dv4fDhX+jS5TMgI2jv23cgtWq9R4kSJbly5RKbNwcQGHgZH58ASpQoAUBcXCzGxll7dU1MTIiPj893fV+EBKdCiDfOpUt/AVCxYqUcy1y8mDEsqUKFivk6Z+Y8ksTEBE6cOMbZs39ofMEQBWCoT6rW67tcvFqVdyEhRBbJycncvn2TXr36aqwpnDk8L/PvaKZy5cpr9Gqam1tgbm5BePgjAFJSnnLp0gV69eqLlpYWaWlpSlknJ2cOHTqYbT0iIyOZNGkM9vb2So8TZDwktLOzx8trNVFRUcqomOedPfs7tWrVVnqzMjk7N2Hv3t3cu3eXKlWqcvVqIE2aNNNI1GJtbUOdOvUICwvN9X1KT09Xemoh40Flbg8r//zzLAEBfvz99x3i4v7t4bS1tc9StlWr/2m8/vDD/zF79nSuXr2Ck5MzNWu+x65dO1i2bCHNm7egTp16lCxZUil/7doVEhMTadOmncb9lyljRYUKFbl06aJGcFq7dh2NoaKVK2dkT89sw5eR37aAjHwBnTt3ZcmS+Tx79oxJk6ZRvnwFjfMFBz/A19eLy5cvEhX1RJnSoa//b1bqc+fOoKWlxSefdHnp+ufkvffqZOlpPXv2D2xsbKldu26We/Xx8eTatUCaN2/BuXNnqFGjVq4BZmpqKlu3fsfBg/t59OgRz56lKPsePLhfoOD09OmTzJmzCMhIWjRmjDvVqlVn4cLlREZGsGjRXKWslpYWjRs35dKlC0pw2rixC40buyhlHB2dcHCowqRJYzl06Gc6deqicfx/Pf978qpIcCqEeKNERDzG13cdTk7Oyvyk/0pKSmTlyiVUrFgpyxqXOdm6dQtz584BQFdXlxEjxmaZ1yLyJ1ULHM/cyLtgIbnoUkv+WQnxAuLj41Cr1dkOcbS0tMwSsGTXU6Knp8ezZxmjTOLi4khPT2fDBl82bPDN9poqlUojsIuNjWHUqCGo1WqWLl2tMQRVS0uLZcvW4Ofnzbp1q4mNjcXW1p4vv+zFp59m9JxFR0cTEhJMy5ZNsr1eZnD45Emk0iP7PAsLizyD0xEjBivzMOHf+fLZuXXrJuPGjcDZ2YWJE7/F0rI0OjrazJ8/WyPo+Pf6mnXS09PD2NhYGfXTtm0HUlJS2L//J3bt2oGuri5NmjTD3X0UtrZ2REdHAzBy5JAs54asbfbfbLWZgV52dSuo/LZFpnbtOvLTTz9ibm6h0ZMIGT3Co0YNpWTJkri5DcPevix6enrs2rVDIzdBXFwsJiYmlChRklclu9+P6OgoHj0Ky/FeY2NjlfrltWa3l9dqdu78nj59BlCnTj0MDAx4/PgxU6aMU3638iMuLpbY2FjlgcPp07+RkpLCzJnzlQcaOjo6zJz574N3a2sbbt3K/f918+YtKFWqFDduXFOCU2NjkyztCRAfH4+xsXG+6/wi5P+9EOKNkZSUxMSJY9DR0dEYvvS8tLQ0PDymEBERgafneo3egNy0bduOypWrExMTw6lTJ1m+fBHa2trK00QhhHjbGBuboKWlRVTUkyz7njx5UuBlN4yMjNHW1qZr1260bdsh2zLPB6aJiQmMHu1OXFwsa9b4Zru0hb19Wb79diZqtZo7d26zc+d2liyZj42NLS4uzTAxMaVOHXNGjBib5VhA6Y2ztCyd7X1GRUXleV/jx08mKSlJeZ3bMOATJ46io6PD3LmLNP7/xMfHKcl/NK+vWafU1FTi4+OVa2hpadGly2d06fIZcXFx/PnnGVavXs60aZPw8dmotNGUKR5UqlQ5y/lzSwhY2PLbFpDRqzdv3kwqV3YgJCQYL69VDB8+Rtl/7doVHj0KY80aXyVZE2T0Yj/P1NSMuLg4UlKevrIANbseQlNTU2xt7Zk1a162x9jY2Cn1i4jIfXrR0aOHaNu2A336DFC2JScnF7ieme9NiRIZDxwePQqlXLnyGj3t/+2FjYp6opF8KzfPvw+VKlXm7t1/spS5d+8fKlbM+jksTBKcCiHeCCkpKUycOJrQ0IesXu2NlZV1ljIqlYo5czw4f/4cCxcuz/Np5fMsLCyoUSPjD3iTJk1JSXnKmjUr6Nixc74DXCGEKE5KlSpF9eo1OX78CP36DUJHRweAR4/CuHr1Cp991r3A56tbtz537gRRrVqNXIe+Pn36lHHjRhIWFsqqVevynAuopaVF1arVcXcfxb59P3H37t+4uDSjcWMXdu7cjrW1Ta5Lh9WuXYczZ06TnJysDO0ND39EYODlPOecPp+1OC8pKU/R1tbR2PbXX38SHv4o2+ywx44d0Ujad/z4EVQqFbVr181S1sTEhNat23D9+lV++ulHAOrUqYuBgSEhIcGFNtpHX1+fmJiYAh+X37YAWLFiMREREfj7b+b330+xcuUSnJ1daNKkKZDx+QA0/v/GxcVx6tSvGudxdm7Cpk0b2Lt3t5L86r8y57GmpLx873Cmxo2bcuLEMUqVMsh1+pCzcxM2blxPUNBtqlatlm2Zp0+fZvme8d/M9flhamqGvn4JQkJCcHCogrm5BY8fh2uMVnh+lEB6ejpHjvzC11/3yfW8J0+eIDk5mVq1aivbmjf/gDVrVmgs+xQWFkpg4GXc3NwLXPeCkG9kQogil5aWxtSp47lx4xrLlq3FwaFKtuUWLZrHsWOHmTVrQY5Z6vKrRo2a/PzzPqKinmQbCAshxNtgwAA3xo8fyfjxo/j0089JTk5i/XpvjIyM6Nmz4MtBuLuPZtiwgYwePYyOHTtjaVma2NgYbt26iUqlYvDgjC+uU6aMJzDwMiNGjCE5OVljfWN7+7KYm5tz504QK1YspnXrj7C3L4dKpeLAgb3o6Ojg6NgIgO7dv+LYscMMGTKA7t2/pHz5iiQnJ3P//j2uXLnI/PlLAfjmm/4cP36E0aOH0rNnb1JTU/HzW5ftUN+X0bhxU7Zv38rcuTNo374TwcEP2LAh+15hyOhpmjt3Bq1btyE4+D7e3p7Ur++o/A9bsGAOBgYG1K5dB3NzC4KDH/DLLwdo1ChjOKmhoRFDhgxn2bKFxMRE06RJUwwNjYiMjODixQs0aNBQI4FUflSsWIldu3Zw9Ogh7O3LYmBgkK8APb9tceLEUfbu3c23387E3r4s3br14M8/zzB37gw2btyKubkFtWvXw9DQkKVLF9C/vyvJyckEBKzH1NSMhIQE5ZqOjk60bNmKVauW8fhxOI6OjUhLS+Py5Yu4uDTD0dFJ6cn78cfttGvXUVne52XWKm3Tph0HDuxlxIjB9OjxFVWqVCM1NZXQ0BBOnTrJvHlLKFmyJF988SWHDx9k1Kgh9O7dHweHKv8/QutXxo2bhIGBIY0bu/Dzz/uoXLkKZcuW49dfj3H16pUC10lbW5v69R05ceIoDg5VaNzYhWXLFrJ27Uq+/roPUVFPWLduDQAhIQ9YuXIpBgaGtGnTTjnHyJFDcHR0onJlB0qUKMGVK5fZtm0TVapU0xh63anTp+zcuZ1Jk8YwcOBgtLS08PHxwsrKJssSToVNglMhRJFSqVTMmDGVv/76k4ULl+eYPXDVqmXs27ebKVM8NLL0vaiLFy9QqpRBnk9/hRACALUeLi6ni/T6L6JJk6YsXLgcf38fpk2bhJ6eLg0aNGTIkOEvlMW2evUa+PgE4O/vzfLli0lMTMDMzJxq1aprTJM4ezZjfePlyxdnOcfkydNp374TlpaWWFvbsG3bFiIiwtHXL4GDQxUWLlyuLEljZGSEp6cfGzb4sHlzABERjzEyMqZ8+Qq0bNlKOWfFipVYtGgFa9euYPr0SZQubcVXX/27zmlhadzYhZEjx/L991s4ceIYlSs7MHXqDDZuXJ9t+REjxnDq1EmmT59EenrmOqfjlP1169Zj//49/PLLARITEyhdugxt2rTTmPPapctnWFtbs2XLdxw+/AtpaWmUKVOGevUa5Nhbl5uvvvqGBw/uM3/+bJKTk/K9zml+2iI8/BELFsyhTZt2fPxxe+XYSZOm06dPD+bM8WDRohWYm5szd+5iVq9extSpEyhdujTduvUkLi4Wf38fjet6eMxl8+aN/PzzfrZvz1g7tUaNWnTqlNEjXbVqNfr1G8SePbvYu3c3KpUq3+uc5kRXV5clS1axadMG9uzZRVhYKCVLlsLeviwuLs2UnlBjY2M8Pdfj7e3J5s0biI2NxcLCEkdHJ3R1M35nR40aD6jx9l4LgItLMzw85jBw4DcFrle3bj3w8JhMu3YdsbOzZ+rUGcyZM4Nt2zahr6+Pm5s7K1cuYcKE0bRu3YbJk6drBOmVKjnwyy8HePz4Mampz7CysqZz58/o02eARiKqUqVKsWKFF6tWLWHWrOmo1WqcnBoxfPiYVz6UXEv9OtIuFVBExKtNUVyUnl9rURQv0navxuLF89i9eye9e/ejadP3NfZZWVlhZWXNpk0b8PJaTYcOn9Cp06caZczNzZUhJwAtWjSmbdsOyrqYu3fv5Pr1q3zwQXMMDc2Ii4vl2LHDHD16GDe3YRrDXe7e/Yd79zLmWCxaNA8Hhyp07doNgPr1G2a7pM27KNVI//UnRIovvOFa7zppv5xFRoZRurRtUVfjjaKjo016evHNmJ25ZnZ+1mR+2xT3tnvX5dR+s2ZN48aNayxatAJ7+7KkpaURHPyAMmWsMDIy4u7df7Czs3ulSaTyI6+/p2XKZJ9YSXpOhRBF6syZjKfrAQF+BAT4aezLzJiYWWb//j1Z5mm0a9eRKVM8lNfp6elKOnoAB4cqnDr1K4sXLyI2NhZTUzMqVKjEwoXLadq0uca5jh07rPHE9uLFv5Qn7itXemFu7vTyNyyEEEII8YImTJjK/Pmz6NPnSz7/vDstW7amXLmMOd23bt3kxImjHD16iOXL12Jnl3V5ozed9Jy+ZtL7VnxJ2xVv0n6FR3reijdpv5xJz2lWxb33TXpO89d2z6/lmf25dLLNavumUqlUGg+q/0tLS0tJEPamyqv9fv31ONu2fce1a1eVe9XT08PJyZlevfpSt279HI99HaTnVAghhBBCiOc8P7JG5Cyn9TwzZc4TLi7mzZvJzz/vy3F/fufZvslatPiQFi0+JDExgcePH6OtrY2NjS0lSpQo6qq9FAlOhRBFw1Cf1Nf4EDZeXXyf/AshhBCvkq9vQK77Xya5UFHo128Qn332RY77X+f6sK+aoaERlSplXWO3uJLgVAhRJFK1eP1DC1/b1YQQQojio0aNWkVdhUJla2tX7AJqkSHn1ZOFEEIIIYQQQojXRIJTIYQQQggyFrlPS0st6moIIUSxlpaWirb2i4WZEpwKIYQQQgBGRqbExETy9GkSb+BiBkII8UZTq9U8fZpETEwkRkamL3QOmYIlhBBCCAHo65fE3NyKhIQYEhJii7o6bwRdXW3S0iShXHEkbVe8Fdf209PTx9zc6oWX6pHgVAghhBDi/+no6GBqalnU1XhjyBrRxZe0XfH2rrafDOsVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkJDgVQgghhBBCCFHkdPMqcPDgQfbv38/Vq1d58uQJtra2tGnTBldXV4yMjJRysbGxLFy4kCNHjpCSkkL9+vWZNGkS1atXf6U3IIQQQgghhBCi+Muz59TPzw9tbW1GjRqFr68vPXv2ZOvWrfTr1w+VSgWAWq1m8ODB/Pbbb3z77besXLmStLQ0evfuzaNHj175TQghhBBCCCGEKN7y7Dn18vLCwsJCee3s7IyZmRkTJkzg7NmzuLi4cPToUf766y82btxIkyZNAGjQoAGtW7fG19eXqVOnvro7EEIIIYQQQghR7OXZc/p8YJqpTp06AISHhwNw7NgxrKyslMAUwNjYmA8//JCjR48WVl2FEEIIIYQQQrylXigh0rlz5wBwcHAA4M6dO1SrVi1LuSpVqhAaGkpiYuJLVFEIIYQQQgghxNsuz2G9/xUeHs7KlStp2rSp0oMaGxuLvb19lrJmZmYAxMXFYWhomO9rmJkZFLRaxYaOjvZbfX9vM2m7whWZnv5ar6el9Xb/bXmdpO2KN2k/URDyv6/4krYr3t7V9itQcJqYmMjgwYPR0dFh3rx5yna1Wo2WllaW8mq1+oUqFROT9ELHFQdmZgZv9f29zaTtCpfaSP/1Xk/9dv9teZ2k7Yo3aT9REPK/r/iStive3vb2K1PGONvt+Q5OU1JSGDx4MCEhIXz33XfY2Ngo+0xNTYmNjc1yTOY2ExOTgtZXCCGEEEIIIcQ7JF9zTlNTU3F3dycwMBBvb+8sa5dWqVKFoKCgLMf9/fff2NnZFWhIrxBCCCGEEEKId0+ewalKpWLs2LH88ccfrF27lvr162cp07p1a8LDw5VESQAJCQkcP36cVq1aFW6NhRBCCCGEEEK8dfIc1jtjxgwOHjyIm5sbpUqV4tKlS8o+GxsbbGxsaNWqFQ0aNGDcuHGMHz8eExMTvL29UavVDBgw4JXegBBCCCGEEEKI4i/P4PS3334DwMvLCy8vL419w4YNw93dEpAL/wAAIABJREFUHW1tbby8vFiwYAEzZswgJSWF+vXrExAQgK2t7aupuRBCCCGEEEKIt0aewemxY8fydSIzMzONDL5CCCGEEEIIIUR+5SshkhBCCCGEEEII8SpJcCqEEEIIIYQQosjle51TIYQQQgjxZnn8OJzNmzdy8+YN7ty5TUpKCj/8sAdbWzuNcv/88ze+vl5cuxZIYmICNjZ2dOjwCd269UBXN/evg7GxMfj7+3L69G88eRKJpaUlLi7N6Nt3EObm5hpl4+Li8Pf34ddfjxEdHYWZmTlOTs5MmeJR2LcuhHgLSXAqhBBCCFFMhYQEc+zYEapXr0G9eg04d+5MljKRkREMH+5K6dJWDB8+BjMzM86fP8fatSuIjo5iyJDhOZ5frVYzceJogoMf0L+/GxUqVOTevbv4+npx69ZNvLz80NLSAjIC0yFDBqClBQMHDsbW1o7IyAgCAy+/svsXQrxdJDgVQgghhCim6td3ZO/eQwDs3bs72+D09OnfiImJYe3a9ZQvXwGAhg0bERoawi+/7M81OL1//z6BgVcYN24ynTt3BcDR0QltbS0WL55PcPB9ypevCMC6df/H3n3HVVk2fhz/HgRU5sEtaol7JYg4cJZZjixHrszMHJFPuUpTc5RZbh/LzNx7PT8tByo4kscet+YeYA5MUkxKgTBR4Pz+MMkTQ1A4t+Dn/U+v+zrX9AY7X+9xTdeff97U4sUr5ezsktxH06bNsmq5AHI5njkFAADIoezsHvxVLiHhjiTJ2dnZqtzFxVVJSZZ02965k3ZbScnt//zzTwUHb9TLL7exCqYAkBmEUwAAgFzsueeaymw2a+rUibp8+RfFxf2hHTtCtHnzJnXu/Hq6bcuVKycfH18tXDhPoaGndPPmTZ06dUILF85V3br1VLq0lyQpLOy04uPj5eFRQCNGfKgmTerrhRcaatiwD3T58i+2WCaAXIDbegEAAHKxAgUKaubMBRo69AN17NhakmQymdSjx9t6/fU3021rMpk0adKXGjNmlHr16pZcXq9eA40ZMz75OCrqmiTp66+/VN269TRhwhRdv35Ds2ZNV9++AVqy5D9ycnJO0T8A3I9wCgAAkItdv35dw4cPVv78+fTZZxPk5uauQ4cOatGieXJwcFDXrt3TbT9hwmc6efK4Bg0aptKlvRQefkHz58/WiBFDNGHCVNnZ2SXf3lu8uKdGjx6b/JKkEiVKKiCguzZvDlLbtu2ze6kAcjjCKQAAQC62fPliXblyRatXB8rNzU3S3ZcaJSYmau7cmWrVqo3MZnOqbXfs2KFt2zbriy9myM+vtqS7L2EqUaKEBg58T7t2/aCGDZ+Vu7u7JMnPr3ZyMJWkqlWrydnZWWfOhGXzKgHkBjxzCgAAkIudO3dWJUuWTA6m91SpUlUJCQmKiLiUZtuffjojSapcuYpVeeXK1SRJ4eHhkiQvrzKSZBVM72dnl3o5ANyPcAoAAJCLFSxYUBEREYqJibEqP3XqpCSpcOHCabYtVKiQVd2/256walukSFFVqlRFBw7slcXy9xuAT5w4pri4uBThFgBSQzgFAADIwUJCtikkZJvCwk5Lkvbu3a2QkG06fPhHSVLr1q8qPv6W3n//PX3//VYdPLhfc+Z8oxUrlqhRo+dUtGix5L4aN66jceM+TT5u2vQFFSpUWJ999rHWrFmtQ4cOas2a1frss49VpEhRNWr0XHLdd955T+HhFzRixIfas2eXgoI2aNSoYXr66dJ64YXmNvrTAJCT8cwpAABADjZy5FCr4ylT7r5F18fHV9Onz1a1as/o66/nauHCOZo2bbLi4uJUrJinunfvpdde62rVNjExUUlJScnHLi4umjVrgebPn63lyxfrt9+iVLBgIdWv31A9erwtJyen5Lp+frU1fvy/NW/eLA0fPlj58uWXv399vftuf+XNmy8b/wQA5BYmy/33Xjwmrl2LNXoK2cZsdtKNGzeNngYeAucua91xcZTv3tM2G++wfxXZx8bbbLzcjHOXs3H+kBn8vy/n4tzlbLn9/BUu7JpqOVdOAQAAcgpnR92x4buFYi1JD64EAFmEcAoAAJBD3DHJ9le+bTYagCcdL0QCAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABjO3ugJAACAJ8evv17VsmWLFBp6WmfPnlF8fLxWrVqv4sU9U9Q9ceK45s+frVOnjishIUGeniXUrVsPNW3aLN0xoqNvaMGCudq163/67bcoFSxYUP7+9fXWW2/Lw8PDqu769Wu0cuVSXblyWcWKFVenTl3Upk37LF0zACBjCKcAAMBmIiIuafv2bapYsZK8vWto//69qdbbvXunPvpokF54obk+/vgz2ds7KDz8gm7fvp1u/xaLRUOHvq9Ll35Wz57v6OmnSys8/ILmzp2psLBQzZw5XyaTSdLdYDpp0lh17dpdtWrV0cGD+zVlygRZLFLbtgRUALA1wikAALAZHx9fBQZukSQFBq5NNZzevBmnsWNHq23bDurf/4Pk8lq16jyw/0uXftbx48c0ePBHat26nSTJ19dPdnYmTZ48XpcuXdRTT5VWQkKCZs+eoWbNWiog4N3kelFR1zR37jd6+eU2srfnaxIA2BLPnAIAAJuxs3vwV4/t27fpxo3r6tz59Uz3n5BwR5Lk7OxsVe7i4ipJSkqySJJOnjyuGzeuq1mzFlb1mjd/SdHR0Tp27EimxwYAPBrCKQAAeKwcO3ZEbm7uOn/+rLp166TGjeuoXbuXNH/+bCUmJqbb1surrHx8fLVw4TyFhp7SzZs3derUCS1cOFd169ZT6dJekqQLF879Vb/cP9qX+evz89mwMgBAerhfBQAAPFaioqJ069YtjR49Qm++2UsVK1bSwYP7tWjRPP3xR6z69fsgzbYmk0mTJn2pMWNGqVevbsnl9eo10Jgx45OPY2JiJEmurq5W7V1d3SRJsbExWbkkAEAGEE4BAMBjxWJJ0u3b8Xr77T7q3LmrpLvPg8bEROu771apR48Aubi4pNl+woTPdPLkcQ0aNEylS3spPPyC5s+frREjhmjChKmys7OTxXL39t57L0cCABiP23oBAMBjxc3NXZJUq1Zdq/JateoqISEh+Zbc1OzevVPbtm3WyJGfqk2bV+Xj46s2bV7VyJGjtWfPLu3a9YPVGPeuoN5z74rpvSuoAADbIZwCAIDHyr3nPlNe1Lx3tTPtry/nzp2VJFWuXMWqvHLlapKk8PBwqzH+GXTvPWt673MAgO0QTgEAwGOlUaNnJUl79+6xKt+3b48cHfOqTJmyabYtWLCgJOnUqZNW5adOnZAkFS5cWJJUrVp1mc1mbd0abFVvy5Ygubm565lnvB9pDQCAzOOZUwAAYFMhIdskSWFhpyVJe/fultlsltnsoRo1aqpMmXJq2fJlzZs3UxZLkipUuPtCpA0b1unNN3vKyckpua/GjeuoefOXNGzYqL+On9Ps2TP02Wcfq3v3Xnr66dK6eDFcCxbMUZEiRdWo0XOSJHt7e/Xq9Y6mTJmgQoUKy8+vtg4dOqiNG9drwIDBcnBwsPGfCgCAcAoAAGxq5MihVsdTptx9i66Pj6+mT58tSRo8+CMVKlRY3377f/r9999UrJin3ntvoDp2fM2qbWJiopKSkpKPnZ1dNGvWAs2fP1vLly/Wb79FqWDBQqpfv6F69HjbKti2adNekkkrVy7TihVLVLRoMQ0c+KHateuQTSsHAKTHZLn3urrHyLVrsUZPIduYzU66ceOm0dPAQ+DcZa07Lo7y3XvaZuMd9q8i+9h4m42Xm3HucjbOX87G+UNG8b0lZ8vt569wYddUy7lyCgAAso2bKU4mFxuGG4uD4uIcbTceACDLEE4BAEC2MVnitWdPfZuN5++/SxLhFAByIt7WCwAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIZjKxkAQI7y669XtWzZIoWGntbZs2cUHx+vVavWq3hxz+Q6V65cVocOr6TaPigoRK6uqW/+LUmbNgVq7NjRaX6+bl2wChYs9FdfG7Rz5w6Fhp7W1auRatGilYYP/+ThFgYAwBOOcAoAyFEiIi5p+/Ztqlixkry9a2j//r1p1n3jjbdUv34jqzInJ6d0+/f3b6CZMxf8o9SiIUMGytOzRHIwlaTNmzfpxo0bqlWrjkJCtmV6LQAA4G+EUwBAjuLj46vAwC2SpMDAtemGU0/PEqpW7ZlM9e/h4SEPDw+rsqNHDys6Olo9egRYlf/739NlZ3f3CZl9+/ZkahwAAGCNZ04BADnKvTBoS0FBG+Tg4KCmTV80fC4AAORW/F8VAJBrzZo1XY0b11GzZo01ZMhAnTt3NtN9xMffUkjINtWr10Du7uZsmCUAAJC4rRcAkAs5ODiqdet2ql27rsxmD128GK4lSxbonXd6aM6cRSpd2ivDff3ww38VFxenFi1aZeOMAQAAV04BALlOoUKFNHjwR2rcuIm8vWvolVfaavr02TKZTFq8eH6m+goK2iiz2UN169bPptkCAACJcAoAeEIULVpM1at76/TpkxluExUVpR9/3K8XX2wue3tuNgIAIDsRTgEATwyLRTKZTBmuv2XLJiUmJnJLLwAANkA4BQA8ESIjI3X8+FFVqVItw22CgzeqbNnyKl++YjbODAAASLwQCQCQA4WEbJMkhYWdliTt3btbZrNZZrOHatSoqa++miqLJUlVq1aXh4eHfv45XEuWLJSdnUndur1l1VfjxnXUvPlLGjZslFV5WFiozp8/p/feG5DmPC5cOK/w8POSpPj4eEVGXkmem49PzRT7pQIAgLQRTgEAOc7IkUOtjqdMGS9J8vHx1fTps+XlVUZr136rTZs26M8/b8rd3SxfXz/16NFbTz1V2qptYmKikpKSUowRFLRBefLk0YsvtkhzHtu3b9WCBXOSjw8f/lGHD/8oSZo2baY8PPwedokAADxxCKcAgBxn586D6X7eqlVrtWrV+pH6GjBgkAYMGJRu2549A9SzZ0CGxgEAAOkjnAJ44vz661UtW7ZIoaGndfbsGcXHx2vVqvUqXtwzuc6VK5fVocMrqbYPCgqRq6trumMkJSVp2bJFWrfuO/3++28qVeppvfVWLz377PPJdQ4dOqh+/d5Js4+ZMxeoWrVnMrm63MfNFCeTS7ztBrQ4KC7O0XbjAQAASYRTAE+giIhL2r59mypWrCRv7xrav39vmnXfeOMt1a/fyKrMycnpgWPMmfONVq5cqt69/6VKlSpr27bNGjlyqCZOnCp//waSpIoVK2nmzAUp2o4fP0axsdGqXLlKJleWO5ks8dqzx3Z7jPr775JEOAUAwNYIpwCeOD4+vgoM3CJJCgxcm2449fQskemrl9ev/66VK5eqa9fu6tLlDUmSr6+ffvklQjNnTk8Op87OLin6joy8oosXL6hz567KkydPpsYFAADIydhKBsATx84ue//q27dvj+7cuZPiRTovvthC586d1eXLv6TZNjh4oywWi1q0eClb5wgAAPC4IZwCQDpmzZquxo3rqFmzxhoyZKDOnTv7wDYXLpyXo6OjSpYsZVXu5VVGkhQefiHNtsHBm1ShQiWVKVPu0SYOAACQw3BbLwCkwsHBUa1bt1Pt2nVlNnvo4sVwLVmyQO+800Nz5ixS6dJeabaNiYmRi4urTCaTVbmbm/tfn0en2u7EiWOKiPhZ/fun/4ZYAACA3IgrpwCQikKFCmnw4I/UuHETeXvX0CuvtNX06bNlMpm0ePH8B7S26B+59G6pxZJuq6CgDbK3t9cLLzR/+IkDAADkUIRTAMigokWLqXp1b50+fTLdeq6uboqNjU0RRmNjYyT9fQX1frdv39b27dvk799AZrM56yYNAACQQxBOASATLBaluF33n7y8yuj27dv65ZcIq/J7z5qmdkvwzp0/KDY2Ri1atMq6yQIAAOQgGXrmNDIyUnPmzNGJEycUGhqqW7du6fvvv1fJkiWT60REROj5559Ptf2BAwfk5uaWNTMGAINERkbq+PGjatTo2XTr1a1bTw4ODtqyJUg9erydXL55c5DKlCkrT88SKdoEB2+Qu7u76tVrkNXTBgAAyBEyFE4vXryooKAgVa1aVX5+ftq5c2eadQMCAtSkSROrMmdn50ebJQBksZCQbZKksLDTkqS9e3fLbDbLbPZQjRo19dVXU2WxJKlq1ery8PDQzz+Ha8mShbKzM6lbt7es+mrcuI6aN39Jw4aNkiR5eBRQx45dtHTpQjk5OalChUravn2rDh06oHHjpqSYy/Xrv2vfvj1q27a97O15Tx0AAHgyZehbUK1atbR7925J0qpVq9INp6VKlZKPj0/WzA4AssnIkUOtjqdMGS9J8vHx1fTps+XlVUZr136rTZs26M8/b8rd3SxfXz/16NFbTz1V2qptYmKikpKSrMrefvtfyp8/v1atWqnff/9NTz31tD79dJwaNGiUYi5btgQpMTFRzZtzSy8AAHhyZSicZveG9QCQ3dxMcTK5xCcfHz6yI53acerUuak6dW6a5uf3+7uv+8otDurevZe6d+/1wLl16vS6OnV6/YH1AAAAcrMsv39sypQp+vjjj5U/f37VqlVLAwcOVMWKFbN6GADIFJMlXnv21LfZeP7+uyQ52mw8AACAnC7Lwqmjo6M6deqkBg0aqECBAjp//rxmzpypzp07a/Xq1SpbtmyG+zKbnbJqWo+dPHnscvX6cjPOXdaKSkw0egrZys5kyrU/L5y7nI3zl7PZ+vyZTLn7e1luxveWnO1JPX9ZFk6LFCmiTz/9NPnYz89PDRs21EsvvaRvvvlGkydPznBfN27czKppPXbMZqdcvb7cjHOXtSwuufuqYpLFothc+vPCucvZOH85m63Pn8WSu7+X5WZ8b8l+v/56VcuWLVJo6GmdPXtG8fHxWrVqvYoX90yzzcSJn2v9+jV68cUWGjVqTJr1zGYnHTt2Wt99t0qHDx/U5cu/yMnJSZUqVVGvXn1UvnwFq/q3bt3SsmWLtHXrZv3661WZzWbVqFFTvXq9k+58jFK4sGuq5dn6MGnx4sVVs2ZNHT9+PDuHAQAAAACbioi4pO3bt8nV1VXe3jUeWP/48aPaujU4wzuZHDiwV4cPH1Tz5q00YcJUvf/+UN24cUMBAd0VGnraqu748WO0fPlivfJKG02e/KV69+6jo0cPq3//Prp5M+f8I0W271lgsVgeuGE9AAAAAOQkPj6+CgzcIkkKDFyr/fv3plk3ISFBEyd+rm7demjduu8y1P/zzzdTu3YdrbJUzZq11L79y1q1aoVGjrx712p8/C2FhGxTly7d1KVLt+S6Hh4FNGhQPx0/flR16vg/zBJtLluvnF6+fFmHDh2St7d3dg4DAAAAADaVmR1Nli9frKSkJHXu3DXDbcxmc4qLfC4uLipV6ilFRV1LLktMTFRiYqKcnJz/UffurbP/3O7ucZbhK6fBwcGSpBMnTkiSfvjhBxUoUEAFChRQ7dq1NX78eCUlJcnHx0cFChTQhQsXNHv2bNnZ2SkgICB7Zg8AAAAAj7FffonQokXzNGnSl3JwcHikvmJionXhwjm1bPlycpmTk7OaNWup1atXqmrVaqpUqYoiI69oxowvVa5cBfn51X7UJdhMhsNp//79rY5Hjx4tSapdu7aWLFmicuXKacWKFVqzZo3i4uLk4eGhunXr6t1331WZMmWydtYAAAAAkANMmjRWjRs3ka+v3yP3NXXqJFksFnXs2MWq/KOPPtYXX0xWv37vJJdVqVJNU6d+/ciB2JYyHE7DwsLS/bx9+/Zq3779I08IAAAAAHKDzZs3KTT0lJYtW/3IfS1ZskBbtwZr6NCRKlmylNVnc+Z8oy1bNunddweocuUquno1UgsWzNGgQf00ffps5c+f/5HHt4VsfyESAAAAADxpbt68qa++mqrXX39Tjo55FRsbK+nuM6AJCQmKjY1V/vz5ZW//4Ei2du1qzZr1tXr37qNWrVpbfXb+/DktXbpQQ4eOUKtWbZLLq1Spptdea6fAwLXq2PG1rF1cNiGcAgAAAEAWi46+oRs3rmvWrK81a9bXVp9t375V27dv1dixk9Wo0bPp9hMcvFFTpkxQ585d9eabPVN8fv78WUlSpUpVrcpLlXpKLi6uunjxwqMtxIYIpwAAAACQxQoUKKhp02amKP/kk+EqU6asunXroTJlyqXbx44dIRo37lO1atVG7703IM1xJOn06ZMqV658cvnPP1/UH3/EqnDhIo+wCtsinAIAAADAQwgJ2SZJCgs7LUnau3e3zGazzGYP1ahRM9WXIDk6OqpAgYIpPmvcuI6aN39Jw4aNkiQdOXJIo0cPV9my5dSyZSudOHH8vj4cVKFCJUmSt3cNlStXQdOnT1VsbIwqVbr7zOmiRfPk4uKi5s1bZcvaswPhFAAAAAAewsiRQ62Op0wZL0ny8fHV9OmzM9VXYmKi1Z6kP/54QLdv39aZM2Hq08f6dt5ixYpr9epASVKePHn05ZcztHjxAq1fv0Zz586S2WxWtWrV1atXgIoVK/YwSzME4RQAAAAAHsLOnQcz3eZeqHxQXz17Bqhnz4AM9enublbfvgPVt+/ATM/ncUI4BQAAAID0ODvqjsl2w8Vakh5cKRcinAIAAABAOu6YJN+9p2023mH/Kk9kULMzegIAAAAAABBOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMJy90RMAAABAzvDrr1e1bNkihYae1tmzZxQfH69Vq9areHHP5DoHD+7Xpk2BOnHimKKiolSoUCHVrl1XPXsGyMOjQKbG27o1WKNHj1DhwkW0Zs2mFJ/HxMRowYI52rFju65f/11ms4f8/Gpr+PBPHnWpAAxAOAUAAECGRERc0vbt21SxYiV5e9fQ/v17U9RZu/Zb/fnnn3rzzZ7y9CyhiIhLmjdvlvbt26tFi1bIyckpQ2PFxsbqq6+mqmDBgql+HhMTo3/9q5dMJql37z4qXtxTUVHXdPz40UdaIwDjEE4BAACQIT4+vgoM3CJJCgxcm2o4/eCDofLw8Eg+rlGjpkqVekrvvfe2tm/fqlatWmdorBkzpqlcufIqWLCQDh7cn+LzWbOm688/b2rx4pVydnZJLm/atFlmlwXgMcEzpwAAAMgQO7sHf3W8P5jeU7lyFUlSVNS1DI1z7NgRbdmySe+/PyTVz//8808FB2/Uyy+3sQqmAHI2rpwCAAAgWx0+fEiS9PTTpR9YNyEhQRMnjlWXLt1UsmSpVOuEhZ1WfHy8PDwKaMSID7V79y7lyWMnP7/a6tv3fXl6lsjK6QOwEa6cAgAAINvcvBmnadOmqHRpLzVs+OwD6y9dulB37txW167d06xz7wrs119/KTu7PJowYYoGDx6uM2fC1LdvgG7ejMui2QOwJa6cAgAAIFskJCTok0+G69q1a/rmm3myt0//q2dExCUtXrxAY8dOUt68edOsl5RkkSQVL+6p0aPHymQySZJKlCipgIDu2rw5SG3bts+6hQCwCcIpAAAAslxSUpI+//wTHTy4XxMnfqFy5co/sM0XX0xSzZp+qlr1GcXGxkqS7ty5I4vFotjYWDk6Oihv3nxyd3eXJPn51U4OppJUtWo1OTs768yZsOxZFIBsRTgFAABAlps0aZy2b9+qMWMmyM+vdobahIdfUGTkFbVo8VyKz1q0eE4dOrym/v0/kJdXGUmyCqb3s7NLvRzA441wCgAAgCz11VdTtWHDWg0f/okaNXo2w+0++WSsbt+OtypbunSRzpw5rU8/Ha8iRYpKkooUKapKlarowIG9slgsySH1xIljiouLS347MICchXAKAACADAsJ2Sbp7htzJWnv3t0ym80ymz1Uo0ZNLV26UP/5zzK99NIrKlnyKZ04cTy5rYeHh0qUKJl83LhxHTVv/pKGDRslSapW7ZkU4wUFbZCDg6N8ff2syt955z198EFfjRjxoVq1aqMbN65rzpxv9PTTpfXCC82zfN0Ash/hFAAAABk2cuRQq+MpU8ZLknx8fDV9+mzt3btbkrRx43pt3Ljeqm6LFq00fPgnyceJiYlKSkp6qHn4+dXW+PH/1rx5szR8+GDly5df/v719e67/ZU3b76H6hOAsQinAAAASJWbKU4mF+vbbA8f2ZFOizgtWDj1Ab3+vc3L333FSRYHxcU5pqh9f5j9J3//+vL3r/+A8QDkFIRTAAAApMpkideePbYJf/7+uySlDKcAnhx2Rk8AAAAAAADCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAACtNaZ3AAAgAElEQVTAcPZGTwB4VL/+elXLli1SaOhpnT17RvHx8Vq1ar2KF/e0qhcTE6MZM77U//73X8XHx6tq1erq1+99lS1bLlPjbd0arNGjR6hw4SJas2ZTis/Xr1+jlSuX6sqVyypWrLg6deqiNm3aP9IaAQAAgNyOK6fI8SIiLmn79m1ydXWVt3eNVOtYLBYNHfq+9u3bowEDBuuzzyYqMTFB/foF6Ndfr2Z4rNjYWH311VQVLFgw1c/Xr1+jSZPGqnHjJpoy5Ss991xTTZkyQWvWrH6otQEAAABPCq6cIsfz8fFVYOAWSVJg4Frt3783RZ2dO3fo2LEjmjZtpnx9/SRJ1apVV4cOr2j58sUaMGBwhsaaMWOaypUrr4IFC+ngwf1WnyUkJGj27Blq1qylAgLelST5+vopKuqa5s79Ri+/3Eb29vzKAQAAAKnhyilyPDu7B/8Y79z5gwoVKpwcTCXJxcVF9es31P/+tyND4xw6dEhbtmzS++8PSfXzkyeP68aN62rWrIVVefPmLyk6OlrHjh3J0DgAAADAk4hwiifChQvnVaZM2RTlXl5ldPVqpG7evJlu+4SEBI0e/bG6dOmmkiVLpTHGub/6tH6G1curTPIcAAAAAKSOcIonQkxMjFxdXVOUu7m5S5JiY2PSbb906ULdvn1bXbt2T3cMSSnGcXV1y9AYAAAAwJOMB+DwhLBIMqUstVge2DIi4pIWL16gadOmKW/evGmP8FdfJlPKcQAAAIBHYYsdKlauXKpDh35UWNgp/fbbb3rrrd7q2TPAqk5UVJRWr16pAwf2KSLiZzk4OKhs2fJ6663e8vHxfaQ1cuUUTwRXV7dUr1zeK7t3dTM1X3wxSTVr+ql6dW/FxsYqNjZWd+7ckcViUWxsrOLjb0n6+yrsvSuomRkDAAAASI8tdqgIDFyr69d/V8OGz6ZZJyzstL7/fqsaNmyszz6boI8++kSOjo7q2zdAu3b972GXJ4krp3hCeHmV0YED+1KUh4dfUNGixeTk5JRm2/DwC4qMvKJ69eqm+KxFi+fUocNr6t//g/ueLT2nQoUKJde596zpvc8BAACAzLLFDhVLlvyf7OzslJCQoLVrv021TvXqPlqx4lurXShq166rN97oqOXLF6t+/YYPu0TCKZ4MDRo01qZNgTp8+EfVqFFTkhQX94d27fqfXnihWbptP/lkrG7fjpeLSz798cfdq6RLly7SmTOn9emn41WkSFFJd3/xzWaztm4NVq1adZLbb9kSJDc3dz3zjHc2rQ4AAOREhw4d1Jw53ygsLFR58+ZVvXr19e67A1SgQOr7qd/v8uVfNGPGlzp4cL8SEhJUuXJVvftuf1WqVCW5zs8/X9R3363S4cMHdfnyL3JyclKlSlXUq1cflS9fITuXhmyQFTtUPCicZmSM1N7jYm9vr/LlKyo09NQD26eHcIpcISRkm6S7txlI0t69u2U2m2U2e6hGjZpq0KCRqlWrrjFjRulf/+onV1c3LVmyQBaLRV26dLPqq3HjOmre/CUNGzZKklSt2jOSJLPZSTdu3H2rb1DQBjk4OFr94tvb26tXr3c0ZcoEFSpUWH5+tXXo0EFt3LheAwYMloODQ7b/OQAAgJzh6NHDGjjwXdWp46/PP5+g6OhozZnzjfr376N585bK0dExzbbR0Tf0r3/1kpOTkwYP/kh58+bTf/6zTH37vqM5cxapdGkvSdKBA3t1+PBBNW/eShUrVlJsbKyWL1+sgIDumjFjnipVqmyr5cJG0tuhIjh4o27evJnuHYMP686dOzpx4pjKlSv/SP0QTpErjBw51Op4ypTxku7e/jB9+mzZ2dlp4sSpmj79C02ZMkG3b8erWrXqmjZtpooWLWbVNjExUUlJSQ81jzZt2ksyaeXKZVqxYomKFi2mgQM/VLt2HR6qPwAAkDvNnz9HxYoV19ixk5Nvj3z66dLq3ftNbdiwLt3vDmvWrNb1679r+vTZyVvc1axZSx07tta8ebM0Zszd70HPP99M7dp1tHpZY82atdS+/ctatWqFRo78NBtXCCPExMSoePHiKcrv36EiO8Lp/Pmzde3ar/r4488eqR/CKXIuZ0fd+evv2pAjx9Ksduev/+Z3KazBYz/XP29muPOP43t9/bM81vJ3YB0+/JM0x2vT5lW1afNqmp8DAACcOnVczZq1tHpur3LlqnJ3d9cPP4SkG05PnTqhkiVLWe29nj9/fnl7+2j37v8pISFBkmQ2m1O0dXFxUalSTykq6loWrgaPj4ffoeJhbdkSrKVLF6p7915pvqgpowinyLHumCTfvadtNt5h/yr8wgAAgCxhZ2cne/uUj/w4ODjqwoVzD902Pj5ely9HqFChKqm0lGJionXhwjm1bPnyw00cj7VH2aHiYezc+YPGjv1ErVq1TrHlzMNgKxkAAADAxp566mmdPHncqiwy8op++y0qxbZ0qbWNiPhZ0dE3ksuSkpJ0+vRJSSm3tbvf1KmTZLFY1LFjl0eYPR5XXl5lkneKuF9GdqjIrIMH92vUqKFq1Og5DR78UZb0maFwGhkZqTFjxqhTp07y9vZWxYoVFRERkaJedHS0hg8frjp16sjHx0fdu3dXWFhYlkwUAAAAyC06dHhNp0+f1OzZM3T9+u+6eDFcY8aMkp2dnUym9L+it279qiwWiz777GP98kuEoqKi9MUXk3TlymVJSrP9kiULtHVrsAYO/NDqlmDkHg0aNNa1a7/q8OEfk8vu7VDRoEGjLBvnxIljGjbsA9WsWUujRo3J0Ft+MyJDdylevHhRQUFBqlq1qvz8/LRz584UdSwWi/r06aOIiAiNHDlSbm5umj17trp166Z169apWLFiqfQMAAAAPHlefLGFLl4M14oVS7V48XyZTCY1afKC6tatp/PnU175ul+JEiU1atQY/fvfE9WpUxtJUoUKldSxYxetWLFEBQsWStFm7drVmjXra/Xu3UetWrXOljUh+2XnDhWSFBp6SleuXFZS0t1nVMPDLySP6e/fQPny5dPFi+EaPHiA3N3Neu21NxQaav2Y3b2dLh5GhsJprVq1tHv3bknSqlWrUg2n33//vX788UctWrRIdevWlSTVqFFDzz//vObOnasRI0Y89CQBAACA3KZ37z7q2rW7Ll/+RR4eHipQoKBef729qld/8N7ozz77vBo2fFaXLv0sBwcHlShRUpMnj1ORIkVTXBQKDt6oKVMmqHPnrnrzzZ7ZtRzYQHbvUPHtt/+noKANycchIduSw+mqVetVvLinTp48rtjYGMXGxqhfv3dSzHHnzoMPvb4MhdOMXKbdvn27ihQpkhxMpbsbtD733HP6/vvvCacAAADAP+TPn19ly5aTdPcq2MWL4Ro6dGSG2ubJkyd5T9OoqGv6/vut6tLlDas6O3aEaNy4T9WqVRu9996ArJ08so2bKU4ml/gU5YeP7EinVZwkydnFXp+PHSRpUKqfp+wrTrI4KC7OUcOHf5LurhSS1LLly9n2Qq0se/no2bNnVaFChRTl5cqV09q1axUXFydnZ+esGg4AAADIsc6cCdXevbtVoUIlSdKxY0e0YsUSdenSTc888/eV08jIK+rUqY26d++lt97qLUlKSEjQjBlfysenppydnXXhwjktWbJQXl5l1Llz1+S2R44c0ujRw1W2bDm1bNlKJ078/QImR0eH5LHx+DFZ4rVnT32bjefvv0uSo83GS0uWhdPo6GiVKFEiRfm9/ZViYmIyHE7N5qzfGPZxkSePXa5eny1FJSbadDyTKXf/bNqarc+frdmZTLn254Vzl7Nx/nK23Hz+cvu5+6cCBVx14MBerVixRLdv31aZMmU0atTHatu2nVW9uLh8SkxMlKNjnuQ/n4SEBEVGXtbkyVsUGxujokWL6dVXX1Xv3m8rf/78ku5+5zx58ohu376tM2fC1KeP9e28np6e2rJlm20Wmwvk5t896fH5/cuycGqxWGQyZc2Grzdu3MyKKT2WzGanXL0+W7K42PZfdyyW3P2zaWu2Pn+2lmSxKDaX/rxw7nI2zl/OlpvPX24/d3J21J37viq7l/bSvxcsTFHtWkKC1bFj0aIKOXIsxWejp32Vou0fkv74q04+i51ef72HXn+9R5pT4ntNxuXm3z3J9r9/hQu7plqeZeHU3d1d0dHRKcrvlbm5Ze2GrwAAAEBOccck+e49/eCKWeSwf5Ws+6IP2EjWbEiju8+W/vTTTynKz507J09PT543BQAAAACkKcvC6fPPP6+rV69q//79yWV//PGHQkJC1KRJk6waBgAAAACQC2X4an9wcLAk6cSJE5KkH374QQUKFFCBAgVUu3ZtNWnSRDVq1NDgwYP14Ycfys3NTbNnz5bFYlGvXr2yZ/YAAAAAgFwhw+G0f//+VsejR4+WJNWuXVtLliyRnZ2dZs6cqQkTJmj06NGKj4+Xj4+PFi9erOLFi2ftrAEAAAAAuUqGw2lYWNgD65jNZo0bN+6RJgQAAAAAePJk2TOnAAAAAAA8LMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADGdv9AQAAAAA2MaxY0e0YMEc/fTTGd2+fVslS5ZUu3Yd1apV6zTbhIae0rp1a3T06CFdvRopd3ezvL1rqHfvPvL0LJFcb9OmQI0dOzrNftatC1bBgoWydD3IXQinAAAAwBPg7NmfNGDAu6patZqGDBmufPnyKSTke40fP0Z37txR27btU223bdsWhYefU/v2neXlVUbXrl3TokVz1atXNy1YsExFixaTJPn7N9DMmQv+0dqiIUMGytOzBMEUD0Q4BQAAAJ4A33+/RUlJiZowYaqcnJwkSbVq1dXZsz8pOHhjmuH09dfflIeHh1VZ9ere6tDhFQUGrlWvXu9Ikjw8PFLUO3r0sKKjo9WjR0A2rAi5DeEUeAgPc0uMJM2a9bVCQ08pLCxUMTHR+uijj9Wy5csp6q1cuVSHDv2osLBT+u233/TWW73Vsyd/qQMAgId3584d2dvbK2/evFblLi6uio2NSbPdPwOnJBUrVlxms4euXfs13TGDgjbIwcFBTZu++HCTxhOFFyIBmXTvlpiEhAQNGTJcn38+QZUqVdH48WO0Zs3qdNuuXv0fxcfHq169BunWCwxcq+vXf1fDhs9m4cwBAMCTrGXLVpKkL76YrKioa4qNjdX69Wv044/71alTl0z1FR5+Qdev/66nn/ZKs058/C2FhGxTvXoN5O5ufqS548nAlVMgkx72lhhJ2rz5v7Kzs1NExCUFB29Ms96SJf8nOzs7JSQkaO3ab7N8DQAA4MlTpkw5ffXVLH300WCtWbNKkmRvb6/Bg4epadNmGe4nISFBkyePk9nske5dYz/88F/FxcWpRYtWjzx3PBkIp0AmPewtMZJkZ5exmxUyWg8AACCjLl36WcOHfygvrzIaNGiY8ubNq507d2jSpHFydMyrF19skaF+pk6dqOPHj2rSpC/l5uaWZr2goI0ymz1Ut279rFoCcjm+AQOZlJW3xAAAANjKrFlfy97eXhMnfqH69RvKz6+2BgwYrCZNXtCXX05WUlLSA/uYOXO61q9fo2HDRql27bpp1ouKitKPP+7Xiy82l70918OQMfykAJmUVbfEAAAA2NL582dVrlyFFGGxcuWq2ro1WNev/57udi+LFs3T0qULNWDAIDVv/lK6Y23ZskmJiYnc0otMIZwCmZRVt8QAAADYUoECBfXTT2d0584dOTg4JJefOnVCjo555ebmnmbbVatWas6cb/T22/9S+/adHzhWcPBGlS1bXuXLV8ySuePJQDgFMun+W2Lu/cujn19tRUdH68svJ6tp02Y8MwoAAB47r77aUSNHDtWQIQPVtm175c2bTzt37tC2bZvVqVMXOTg4KDLyijp1aqPu3Xvprbd6S5K2bdusadOmqE6devL1raUTJ44n9+ns7CwvrzJW44SFher8+XN6770BNl0fcj7CKZBJj3pLDAAAgBGee66pJk36UsuWLdKECZ8pPv62SpQoqfffH6LWrdtJkiwWixITE62eP923b48sFov27dutfft2W/Xp4+Or6dNnW5UFBW1Qnjx5uJsMmUY4BTLpUW6JAQAAsAU3U5xMLvEpypu+4KOmL/ik0uKWJKlceXcdPrLjr7I4SdLYcYM1dtzgdEaLkywOiotzlCQNGDBIAwYMeoTZ40lFOAUy6WFviZGkw4d/1I0b1/X7779JkkJDTyl//vyS7v5r5j2hoad05cplJSVZJN3d6DokZJskyd+/gfLly2er5QIAgBzIZInXnj2228LF33+XJEebjYfciXAKZNLD3hIjSfPmzdKRI4eSj7/7bpW+++7uG3937jyYXP7tt/+noKANycchIduSw+mqVetVvLhntq0PAAAAMALhFMig+2+PeZhbYiRpwcKp6Yzwd72x4wZr7NiPkm+PAQAAAHI7wimQQdweAwAAAGQf9rsAAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOHss7Kzffv2qVu3binKXV1ddfDgwawcCgAAAACQi2RpOL1nxIgReuaZZ5KP8+TJkx3DAAAAAAByiWwJp2XLlpWPj092dA0AAAAAyIV45hQAAAAAYLhsuXI6aNAgXb9+XW5ubmrQoIE++OADeXp6ZsdQAAAAAIBcIEvDqaurq3r06KFatWrJxcVFp06d0qxZs7R//36tXbtWBQsWzFA/ZrNTVk7rsZInj12uXp8tRSUmGj2FbGVnMuXqnxXOX87FucvZOH85W24+f5y7nI3zl7M9LucvS8NplSpVVKVKleTj2rVrq1atWurQoYMWL16sgQMHZqifGzduZuW0Hitms1OuXp8tWVwcjZ5CtkqyWBSbi39WOH85F+cuZ+P85Wy5+fxx7nI2zl/OZuvzV7iwa6rl2f7MadWqVVW6dGmdOHEiu4cCAAAAAORQNnkhksViscUwAAAAAIAcKtvD6fHjxxUeHi5vb+/sHgoAAAAAkENl6TOnH3zwgUqWLKmqVavK1dVVp0+f1qxZs1S0aFF17do1K4cCAAAAAOQiWRpOK1SooA0bNmjp0qW6deuWChUqpBdffFF9+/ZVgQIFsnIoAAAAAEAukqXhNCAgQAEBAVnZJQAAAADgCWCTFyIBAAAAAJAewikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHCEUwAAAACA4QinAAAAAADDEU4BAAAAAIYjnAIAAAAADEc4BQAAAAAYjnAKAAAAADAc4RQAAAAAYDjCKQAAAADAcIRTAAAAAIDhCKcAAAAAAMMRTgEAAAAAhiOcAgAAAAAMRzgFAAAAABiOcAoAAAAAMBzhFAAAAABgOMIpAAAAAMBwhFMAAAAAgOEIpwAAAAAAwxFOAQAAAACGI5wCAAAAAAxHOAUAAAAAGI5wCgAAAAAwHOEUAAAAAGA4wikAAAAAwHD2Rk/gcXH1aqS++urfOnBgnywWyc+vtvr1+0DFihVLt11o6CmtW7dGR48e0tWrkXJ3N8vbu4Z69+4jT88SyfV+/vmivvtulY4ePaSIiEtycnJSpUpV1KtXH5UvXyG7lwcAAAAAjzXCqaRbt26pf/8+cnBw0PDho2UySXPmfKN+/QK0aNFK5c+fP82227ZtUXj4ObVv31leXmV07do1LVo0V716ddOCBctUtOjdcHvgwF4dPnxQbdu21VNPlVVsbKyWL1+sgIDumjFjnipVqmyr5QIAAADAY4dwKmn9+jW6fPkXLV/+rUqWLCVJKlu2vF57rZ3WrftWnTt3TbPt66+/KQ8PD6uy6tW91aHDKwoMXKtevd6RJD3/fDO1a9dRHh7OunHjpiSpZs1aat/+Za1atUIjR36aTasDAAAAgMcfz5xK2rXrB1WtWi05mEqSp2cJPfOMt3bu/CHdtv8MppJUrFhxmc0eunbt1+Qys9ksk8lkVc/FxUWlSj2lqKhrj7gCAAAAAMjZCKeSLlw4Ly+vsinKS5cuo/Dw85nuLzz8gq5f/11PP+2VZp2rVyM1ZMhAnT59UkePHtZHHw1WZGRkhvqPj4/X119/qdatm6lJk/oKCHhLR44cSlEvKSlJS5YsUPv2L6tJk3p6883X9N//fp/p9QAAAABAdiOcSoqJiZarq1uKcjc3N8XGxmaqr4SEBE2ePE5ms4datWqdap17z7geOXJY9vb2GjBgkCIifla/fgH6888/HzjG+PFjFBi4Rj17vqOJE6eqYMFCev/9vvrppzCrenPmfKP582erXbuOmjx5mqpWraaRI4dqz56dmVoTAAAAAGQ3wulf/nnLrSRZLJZM9zN16kQdP35Uo0aNkZtbysAr3X3G9ZdfIhQX94cGDRqmNm3aa/z4f+vq1UitW/dtuv3/9NMZbd0arL5939crr7SVn19tffrpOBUtWlRz585Mrnf9+u9auXKpunbtri5d3pCvr58+/HC4fH39NHPm9EyvCwAAAACyE+FUkqur2/+3d99xURz9H8A/KKDSBP0FG4hYDmnSFI6qghEwCkSjYmJBrFRji2CJ/bEC6hmkaBQ1GmsARcH6KGrEFqPmiRqN5RCxgICicgfM7w+e2yfLHQJ6guL3/XrdHzs7uzs7uzu3szszi6KiQrnw58+fQ1tbu8briYtbi9TUXxAZ+T3s7YVVxktO3g3GGMaNC+Lerta0j+vp0yehqqoKD4++XJiqqir69PHEuXNnIZFIAABZWb9CKpWib19v3vJ9+3rj9u1byMl5UOP9IoQQQgghhJD3jSqnAIyNO+LOHfm+pXfv/o0OHTrWaB1JSRuwdesmTJo0FV5eX1QZLzU1Fffv30OnTp0xatQY3rya9HG9c+c22rRpi6ZNm8otK5VKkZ0t/m+8v6Gurs4b5Amo2NeKfbtTo/0ihBBCCCGEkLpAlVMALi5u+M9/ruHBg2wu7OHDHFy9+jucnd2qXX7Xrp+RmLgO48cH46uv/KuMd+LEccyZMwsqKioQCp3l5tekj2tRUVGV/WMB4PnzIi6elpa2XHNlHZ3m/50v/6aYEEIIIYQQQuqLUiunDx8+RHh4OOzs7GBra4vQ0FDk5OQocxPvxYABX6J16zaIjJyKzMx/49SpE4iImAp9/dbw9R3IxcvNfYiePR2wcWMiF3bkSAbWrImCg4MTbG174Nq1q9zvn29jL1++hPnzZ0EgEKBRo0Z4+vQJF+/mzesAatbHlTGmsH8sUHlZBkXR3qYfLSGEEEIIIYS8b6rKWtGrV68watQoqKurY9myZQCA1atXY+TIkUhNTYWGhoayNqVcmupQ1VJH1PoN+GHlcixcNBeMMdjaOyB0+ndQ09eF9L9Rpc1UUVZWhlJVFUi11AEAv17MAmMMWVlnkJV1hrdqK7vuWLXhRwDA+auXIJFI8OeffwIAMjIOICPjAICK76Lu3r2vRn1cdXSa49Ej+U/OFBVVvHGVvVXV1q54C1u5Mit7syp7g0oIIYQQQgghHwKlVU537twJsViM9PR0GBkZAQBMTEzg6emJHTt2YPTo0cralFJJVQDbsxUVRgweX/EDcADAAXERIC7iLxC3A2sArJEt88U3FT8FTuIf67bpDcT1xm+OZpgyejSkUinWrdvAi1+TPq7Gxh1x8uRxvH79mtfv9O7dv6Gmpsb1MTU27giJRIIHD7J5/U5lfU07dKj6G6yEEEIIIYQQUteU1qz32LFjsLKy4iqmAGBoaAhbW1scPXpUWZtpEN6lj6uLixtKS0tx/PgRLqy0tBTHjh1Gjx5CqKtXvNEVCp2gpqaGQ4cO8pbPyDiIjh07oW3bdkrcI0IIIYQQQgh5N0p7c3rr1i14eNZtua8AABxhSURBVHjIhXfu3Bnp6enK2kyDMGDAl9izZyciI6di3LggqKioIDExTmEf16FD/RAQMBajR48DAHTpYgIPj8+xenUUSktL0aZNWyQn78bDhzn4/vtF3LJ6ei0wZMjX2Lp1EzQ0NCAQdMWxY4dx6dJ5LFkSVef7TAghhBBCCCFvosKUNEKOhYUFAgICMG3aNF54TEwMEhMT8Z///EcZm2kwcnJysGTJEpw+fRqMMTg6OmLmzJkwMDDg4mRnZ8PDwwOhoaEICwvjwl+/fo2YmBjs378fRUVF6Nq1K6ZNmwYHBwfeNsrKyhAfH49du3bhyZMnMDY2RkhICLy8vOpsPwkhhBBCCCGkJpRaOR09ejSmTp3KC6fKKSGEEEIIIYSQ6iitz6mOjg4KC+W/nVlYWMh9g5MQQgghhBBCCFFEaZXTzp0746+//pILv337Njp37qyszRBCCCGEEEIIaYCUVjl1d3fH77//DrFYzIVlZ2fj0qVLcHd3V9ZmCCGEEEIIIYQ0QErrc/ry5Uv4+vqiadOmmDRpElRUVLB69WoUFxcjNTUVmpqaytgMIYQQQgghhJAGSGmVU6BmI9ASQgghhBBCCCGVKbVySgghhBBCCCGEvA2l9TklhBBCCCGEEELeFlVOCSGEEEIIIYTUO6qcvgcjRozAiBEj6jsZnxyRSAQTE5P6TgZ5B7JjWFpa+k7refHiBdauXQt/f384ODige/fu8Pf3x5EjRxTGv3DhAvz9/dGtWzc4OztjyZIleP36NS9OREQE3Nzc3ild7wvlW+1ULqOzsrJgYmKCrKysapc1MTGBSCTiptPT0xEWFobevXujW7du8PT0RFRUFF68eMFbbu/evTAxMcG9e/eUtyO1oKxzRBlqk9/KWI4QUrfofoy8C6qckgZj8ODB2LFjR30ng3wAcnJysH37dvTo0QPLly9HTEwMOnTogJCQEPz000+8uNevX0dgYCBatGiB+Ph4fPvtt9i7dy8iIiLqKfX151PNN3Nzc+zYsQPm5ua1XvbHH39Eo0aNMHnyZKxfvx7Dhg3D9u3bERgYiPLy8veQ2o/f2+b3uxwnQgghHwfV+k4AIcrSunVrtG7dur6TQT4ABgYGOHLkCJo1a8aFubq64uHDh0hMTMQ333zDhYtEIrRu3RqrV6+GmpoaAEBNTQ0zZszAuHHjPqkb4U8137S0tGBtbf1Wy8bFxaFFixbctL29PXR1dTFjxgxkZWXB0dFRWcmsc1KpFKqqqlBRUVHqet82v9/lOBFCyMdKIpFAXV29vpNRZ+jN6TtKS0uDl5cXLCws8MUXX+Dw4cO8+bKmXNnZ2bxwRU0eSktLkZCQgH79+sHS0hJCoRBjxozB7du33/t+NASV89TExAQxMTHYvHkz3N3dYWNjg+HDh+Ovv/7iLZeZmQl/f3/Y2dnBxsYGnp6eWLt2rdx6b9y4gREjRsDKygouLi5YvXq13JuR/Px8zJ07F66urrCwsICXl5fCt7lisRjTp0+Hs7MzLCws4OHhgUWLFik5RxqGkydPwsbGBgsWLIBYLIaJiQl++uknLFmyBI6OjrCyssKECRN415iGhgavgiVjYWGBx48fc9NSqRSZmZnw9vbmKlgAuOmjR4++MW179uyBubk5EhISlLCnytVQ8k0ikcDe3h5Lly6Vm3fgwAGYmJjgzz//BABcuXIF4eHhcHNz45rYRkdHyzU1rkxRc9GysjLExMTAxcUFVlZWGDFihFzZAYBXMZWxtLQEADx69OiN27127RqcnJwQGhqKkpKSN8Z9H6o6R5YvXw4XFxdYWlqiqKgI+fn5+P777+Hp6QkrKyv07NkTU6dOldu/mpaVlfN73rx5cHJykmtyLJFI0KNHDyxevFjhckBFE+1hw4bhzJkz+PLLL2FlZYX+/ftX2RT9Uyc7Rnfv3sX48eNhY2OD3r17Y+3atdwxqs19S1JSEry9vdGtWzf06NEDAwcOlLsP+pTduXMHISEhcHR0hKWlJXr16oXw8HDuXK/JPYPseFy6dAmTJk2CjY0NnJycEB8fD6DiOvbz84O1tTUGDRqEa9euyaXj0KFDGDJkCKysrNC9e3eEh4cjJyeHmz9u3DgMHDhQbrnHjx/DzMwMmzZt4tJbk7Kgobh37x6mT58Od3d3dOvWDR4eHpg7dy4KCwu5OImJibCwsMCzZ8/klu/Xrx+Cg4O56VevXmHFihVwd3eHhYUF3N3dsW7dOoXl46FDhzB79mwIhUI4OTnVOD0ySUlJcHd3h6WlJb766itcunQJ7u7ucq2bxGIxpk6dCqFQCAsLC/j6+tb7NUxvTt/BmTNnMHXqVPTq1QsRERHIz8/H4sWLUVpaCmNj41qvb8qUKThy5AhGjRoFR0dHSCQSnD9/Hk+ePEGnTp3ewx40fPv27YOxsTFmzZoFqVSK5cuXIzg4GAcPHoSqqirEYjGCgoLg6emJ4OBgqKmp4d69exCLxXLrCgkJwaBBgzBhwgScOnUKsbGxaNSoEcLCwgBU9NcbNmwYSkpKEBYWBgMDA2RmZmLevHmQSCRcHzexWIzBgwejWbNmCAsLg5GREXJzc3Hq1Kk6zZuPQXJyMmbPno2goCCEhIRwN0sJCQkwNTXFkiVLkJeXh5iYGIwZMwb79+/nVZYqu3DhAjp27MhN379/HyUlJejSpQsvXpMmTdC+fXvcunWrynXFx8dDJBJh4cKFCv/U61NDyjd1dXV4eXlh//79mD59Oho3bszNS01NhUAggKmpKQDg4cOH6Nq1K7788ktoamrir7/+QmxsLMRiMWJiYqrd1j+JRCLEx8dj9OjRcHZ2xrVr1xAUFFSjZc+dOwcAbyy3T506hbCwMAwYMABz587l7VddqOociYuLg6WlJRYuXIiysjI0adIEOTk5UFdXx5QpU9CiRQs8fvwYP/74I4YNG4aDBw+iSZMmvHVXV1ZW5ufnh+3bt+P06dPo2bMnF378+HEUFRXB19f3jfsiFouxePFijB8/Hnp6eti4cSPCw8Nx8OBBGBkZvWNONUyhoaEYOHAgAgICcOzYMYhEIrRp0waDBg2q8TpSU1OxbNkyBAcHo3v37igpKcGNGzdQUFDwHlP+cZk4cSK0tbUxb9486Onp4dGjRzhx4gTKy8trfM8gExERAV9fXwwdOhTp6emIjo5GUVERTp48iYkTJ0JDQwMrVqxASEgIDh8+zL1p2759O+bNm4eBAwciJCQExcXFEIlEGD58OFJTU6GlpQU/Pz9MmTIFt27dQufOnblt7t+/HwDQv39/AEBBQUGtyoKP3ePHj9G6dWvMnDkTzZs3h1gsRnx8PMaPH889RPDx8UF0dDQOHDjAa1107do13L59G5MmTQJQ8QJK9sIpKCgIJiYmuHz5MmJjY1FYWChXaVy4cCHc3NywfPlySCSSGqcHAHbt2oV//etf+Oqrr+Dl5YX79+9j2rRpKCoq4m3j4cOHGDJkCFq2bInIyEi0aNECBw4cQFhYGH744Qd4eHi8l3ytFiNvbejQoczb25uVlZVxYZcvX2YCgYANHz6cMcbYnj17mEAgYGKxmLfsmjVrmEAg4KbPnDnDBAIBS0pKqpvEN0CV81QgELDPP/+cSSQSLuzgwYNMIBCwixcv8qafP39e7Xrj4+N54bNmzWLW1tassLCQMcbY2rVrmYWFBbtz545cPHt7eyaVShljjE2fPp1ZW1uz3Nzcd9rfhkiW11KplCUkJDAzMzO2c+dObr5YLGYCgUDuurtw4QITCAS8uJX9/PPPTCAQsJSUFC7s4sWLTCAQsBMnTsjF9/f3ZyNHjuSmZ8yYwVxdXVlZWRlbsGABs7KyYsePH3/HPVaOhp5vsnSePHmSC8vLy2NmZmYsISFB4TLl5eVMKpWy5ORkZmJiwvLz87l5w4cP58poxhg7e/YsEwgE7OzZs4wxxgoKCpi1tTWbM2cOb53x8fFMIBCwNWvWVJnW3NxcJhQKWUBAAC9c9l9w9+5dlpKSwszNzdmqVatqngnvqKbniJ+fHysvL3/jukpLS1lOTg4TCATs0KFDctuorqysnN+MMda3b182efJk3nJBQUHM29ubm1a03PDhw5mZmRmv3H369Cnr2rUrW7duXQ1y5tMiO0a7d+/mhffv35+NHj2aMVbz+5b58+czPz+/95/oj1ReXh4TCATsyJEjCufX9J5BdjxEIhEXRyqVMqFQyMzMzNj9+/e58CNHjjCBQMCysrIYY4y9ePGC2drasoiICN42xGIxMzc3Zxs3bmSMMfbq1Stma2vLVq5cyYvn4+PDxo4dW+U+VlcWNDRSqZSdP3+eCQQC9scff3DhAQEBbMiQIby4ixYtYj169GAlJSWMMcZ++eUXJhAI2Llz53jxYmNjmbm5OXv69Clj7H/lXHBw8Fulp6ysjLm5uckdt4yMDCYQCNiMGTO4sMjISObg4MD7f5Ttj4+PT7Xbf1+oWe9bKisrw7Vr1+Dp6YlGjf6XjVZWVmjXrl2t13f69GmoqKhgyJAhykzmJ8/JyYn3RkggEACoeFoEAKamplBTU8PkyZORnp6OvLy8Ktfl7e3Nm/7iiy/w8uVL3Lx5E0BF82ArKysYGBigtLSU+7m4uKCgoIB7m3T69Gn06tULrVq1Uuq+NiRLliyBSCTC6tWrMXjwYLn5la87Ozs7tG7dGpcvX1a4vqysLCxatAi+vr7w8fHhwhljAKCwT51s3j+VlZVh8uTJ2L9/PzZu3IhevXrVdtfeq4aab3Z2dmjfvj1SUlK4sLS0NJSXl/PS9eLFC6xYsQJ9+vSBpaUlzM3N8d1334ExVqtRcm/evImXL18qvObfpLi4GEFBQWjcuDGWLFmiME5SUhIiIyMxc+ZM7ol6XaruHPHw8FB4XLdt2wYfHx/Y2NjAzMyMO4Z37tyRi1tdWanIgAEDcPToUW6U44KCAq65YnWMjIzQoUMHbrply5Zo2bIlr9ki4at8DXbp0qXW+WVpaYk///wTCxcuxJkzZ/Dq1SslpvDjp6enB0NDQ0RFRWHnzp24e/cub35N7xlk/jnquaqqKnfeGxoacuGyFi6ye5zLly/jxYsX8PHx4W2jdevWMDY2xoULFwAATZs2Rd++fbFv3z6uDL9x4wauX78udw3Wpiz42EkkEsTFxcHLywvdunWDubk593b0n/vr6+uLy5cvc8e4tLQUBw4cgJeXF/cGOzMzE+3atYONjQ3vWDg7O0Mqlcr9D3/++edvlZ7c3Fzk5ubCy8uLt6yHhwdUVfkNZjMzM9GzZ09oa2vLnYPXr1+XG3W+rlCz3rf07NkzSKVS/N///Z/cPEVh1SkoKEDz5s3RtGlTZSSP/Ffz5s1507JCQta/y8jICOvXr0diYiK+++47SCQSWFpaYvr06bC3t+ct27JlS4XTsr54+fn5uHfvXpUDwciaOhUUFNDATdXYv38/OnfuzPWzqEzRNdayZUuF/V6uXLmCoKAgCIVCru+ajK6uLgAo7K9RVFTEa94EVFR+Tpw4AaFQiG7dutV4f+pKQ843Hx8fbNiwAcXFxdDU1ERKSgqEQiHvIU9kZCTOnDmD8PBwmJqaolmzZrhy5QoWLFhQqz6dT548ASCfX28q20tKShAUFITs7Gxs2bKlyms8LS0NrVq1gqenZ43To0zVnSP6+vpyYVu2bMGiRYswevRouLi4QEdHB4wxDBkyRGG+VldWKuLr64u1a9ciIyMDgwYNQlpaGkpLSzFgwIBq96lyOQ9UlPWypnBEnqL/xtrml5+fH0pKSrB7925s27YNqqqq6NmzJyIiImBgYKDM5H6UVFRUsHHjRohEIkRFRaGgoAAGBgYYM2YMvv766xrfM8hUPmZqamrQ0dGRCwPAHUvZA/eAgACF2/jnOv38/LB3715kZWVBKBQiJSUFmpqavKadtS0LPnbR0dHYunUrgoODYWNjA01NTTx69EhunIC+ffti/vz5SE1NRXh4OE6fPo2nT5/yuiTk5+fjwYMHNT7en3322VulR/b/Vbkcbty4MfT09Hhh+fn5SE5ORnJyssI0PXv2DFpaWlVlz3tDldO3pKenBzU1NTx9+lRu3tOnT7m3p7L291KplBen8kmop6eHwsJCvH79miqodUwoFEIoFEIikeDixYtYs2YNJkyYgKNHj/IGO8nLy4OGhgZvGvjfzZyuri5atGiBWbNmKdyOrB+yrN8JqVpSUhICAwMxbtw4JCQkQFNTkzdf0XWXl5fH9T2UuXHjBsaOHQtTU1OIRCK5fpWGhoZQV1eXG+impKQEYrFY7slj8+bNsWLFCkycOBFTpkxBVFSU3JPI+tSQ801WeTl8+DCsrKxw9epVLFu2jLfto0ePIjQ0FKNGjeLC3/S2riqym4KnT5/y+tUqyj+gonwPCwvD1atXsXHjxjd+308kEmHOnDkYMWIEkpKSFN6AvE/VnSOK3pqmpaXB0dGR1ydKUb98merKSkUMDQ1ha2uL1NRUDBo0CPv27YO9vT3atGlT430jylHT+xYVFRX4+/vD398fhYWFOH36NJYuXYrJkydj165ddZbeD5mhoSGWL18OxhiuX7+OrVu3Yv78+WjXrl2N7xnehexB4tKlS+UeGgLgXf/29vZo27YtUlNTYW9vj7S0NHh6evLuSWtbFnzs0tLS4OvryxvU6OzZs3LxNDQ00KdPH+zbtw/h4eFITU2FoaEh7OzsuDi6urowMDDAqlWrFG6rcqvLqsri6tIj+0+p3BKwrKxMbtAmXV1d2NnZYdy4cQrTVF8t/KhZ71tq3LgxLCwskJGRwRtl6/fff8eDBw+46bZt2wIA7yautLRUbvAbZ2dnMMaoQK9H6urqcHR0xNixY/Hy5Uu5kQoPHjzIm05LS4OGhgbXVNjV1RV37txB27ZtYWlpKfeTPX1ydnbG8ePH3/gW4VPXuXNnbNmyBffu3cPYsWPlmpZUvu4uXryI3Nxc3mcm7t69i8DAQBgYGCA+Pl7hQx91dXW4uroiPT2dN1Joeno6JBIJ3N3d5ZZxcHBAYmIiTp48iSlTpsiNMFqfGnK+tW/fHjY2NkhNTUVKSgo0NDR4zZ4kEgnKysrkKr2//PJLrbYDVIz0raGhofCar6y8vBzTpk3Dr7/+itjY2Go/ddKqVSts2bIF5eXlGDlyZJ2XA9WdI4q8fv1aLl/37t1bZfzqysqq+Pr64ty5c8jKysJvv/1Woya9RPlqet/yT82bN0e/fv3g7e2tcFTrT52KigpMTU0RGRkJoCJva3rP8C5sbW2hqamJe/fuKdzGPwe6U1FRwYABA5CRkYETJ04gNzdX7hqsbVnwsavN/vr6+uL+/fvIzMzE0aNH5QZyc3V1RW5uLjQ0NBQeC0Ujv79NemSfVUxPT+eFHzlyRO5/19XVFTdu3ECXLl0Upqm+Pl/z4Tzy/wiFh4cjMDAQwcHB8Pf3R35+PkQiEe9JuKWlJdq3b4/ly5ejvLwc6urq2LZtm9wTSaFQCE9PTyxduhQPHz6EUChEaWkpzp8/j169esHBwaGud++TsH37dly4cAFubm5o06YNnj17hvj4eOjr68vdSO3cuRPl5eWwtLTEqVOnsGvXLoSFhXHNagICAnDgwAF8/fXXCAgIgLGxMV69eoW///4bFy5cwLp16wAAYWFh+Pe//w1/f39MnDgR7du3x6NHj5CZmYmVK1fWeR58qDp16oTNmzdj5MiRGDt2LNavX8/NKy4u5l130dHR6NChA/dHmpeXh8DAQEilUoSHh8v13TEzM+MK3bCwMAwdOhTffvstvvnmG2RnZ2PFihXw9PSEhYWFwrR1794d69evx7hx4zB58mRER0e/cbTbutSQ883X1xcLFizAzZs30adPH95Tf21tbVhbW2Pjxo3Q19eHnp4e9uzZ81atFHR0dDBq1CjExcVBU1MTLi4uuHr1Knbv3i0Xd/78+UhPT8fEiRPRrFkzXr+hqr69rK+vjy1btiAgIAAjRozA5s2b6/QJ9ZvOEUVcXV2RmJiIuLg4dOvWDWfPnkVGRkaV8asrK6vi7e2NRYsWYfr06VwfOFL3anrfMmfOHGhqasLa2hotW7bE3bt3kZKSAmdn53pK+Yfl+vXrWLx4Mfr16wcjIyOUlZXhl19+gaqqKoRCIYyMjGp0z/AutLS08N1332HBggXIz8+Hm5sbtLW18ejRI5w/fx729va8pvO+vr6Ij4/H3Llz0aZNG7nuTbUtCz52rq6uSE5OhkAggJGREQ4dOoTffvtNYVwnJyfo6+tj1qxZePXqFW88BKCiX/3evXsREBCAwMBAdO3aFRKJBGKxGMeOHcMPP/yg8FNutU1Po0aNEBoaitmzZ2PWrFnw8vKCWCxGYmIitLW1eW9kw8PDMXjwYHzzzTcYPnw42rVrh6KiIty8eRNisbjKsRPeN6qcvgMnJyesXLkSIpEIoaGhMDIywsyZM7F582YujqqqKmJjY7FgwQJERkaiefPmGDVqFKysrHjf0gQq2pInJiYiOTkZmzdvhpaWFiwtLRUOWkGUo2vXrjh58iSio6ORl5cHXV1d2NraYuXKlXJvjGJjY7Fw4ULExsZCW1sbQUFBvKYV2tra+Pnnn/HDDz8gMTERjx8/hra2NoyNjXk3WQYGBti1axdWrVqFqKgoFBcXo1WrVvU3ZPcHrGPHjti6dStGjhyJMWPGYP78+QCA8ePH4/79+4iIiMCrV6/g4OCAOXPmcBWdW7ducS0YJkyYILfeo0ePcn2iTE1NsWHDBqxcuRLjx4+HtrY2fH19MWXKlDemzc7ODhs2bMDYsWMxadIkrFq16oP5SHZDzbd+/fph8eLFePLkicLPi0RFRWHevHmYP38+mjZtCm9vb8yaNUvhvlQnLCwMjDHs3r0bP/30E6ysrBAXFyc3KFJmZiaAik+wxMXF8eaFhoZW+fmUzz77DJs3b0ZAQABGjhxZ5xXUqs4RRUJCQlBUVIRNmzahpKQE9vb2WL9+Pfr06aMwfnVlZVV0dHTQu3dvZGRkoH///vXS14nU/L7F1tYWe/fuRUpKCp4/fw59fX34+PggPDy8HlP/4fjss8/Qtm1bbNq0Cbm5uWjSpAkEAgHi4uK4B3g1uWd4V/7+/mjTpg3Wr1+P/fv3o7S0FK1atUL37t3lunR06tQJFhYWuHbtGiZMmCDXtLS2ZcHHbvbs2WCMcU1x3dzcEBUVpfC+vFGjRujfvz9+/PFH2NjYyH3GSk1NDRs2bEBCQgJ27NiB7OxsaGhowNDQEL169arRg9qapmfw4MEoLi5GUlISUlNT0aVLF6xYsQJBQUHQ1tbm4rVt2xZ79uyBSCRCdHQ0nj17Bl1dXXTp0qVeW66oMEVDKxJCOCKRCGvXrsUff/zxQfUv/BRlZ2fDw8MDixYtooc2tUD5RuoClZWEEPJhunLlCgYPHoxly5Z98F0m6N+DEEIIIYQQQhoAsViMbdu2wc7ODlpaWrh9+zbi4+NhYGBQbyPF1wZVTgkhhNSbsrIyhd9GlWnUqBHv26yEEEIIqVrTpk1x8+ZNJCcno6ioCDo6OnBycsLUqVOr7df6IaBmvYQQQuqNu7s7b4Tzyt7Ub5MQQgghDQtVTgkhhNSbGzducB+MV0RfX7/evrVGCCGEkLpFlVNCCCGEEEIIIfWOOvIQQgghhBBCCKl3VDklhBBCCCGEEFLvqHJKCCGEEEIIIaTeUeWUEEIIIYQQQki9+3+KvlT4KB7ZrAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Present F-score@10\\n\",\n    \"# change to use F@O as anchor metric for present KPG\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score@k')\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"ax.legend(loc='center left')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Present F-score@O\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"     \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"# Absent results\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"        kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Phrase number\\n\",\n    \"\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Self-peak\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 327,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T19:26:22.896462Z\",\n     \"start_time\": \"2020-11-23T19:26:17.997111Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Present F-score@10\\n\",\n    \"# change to use F@O as anchor metric for present KPG\\n\",\n    \"_, self_peak_summary_df, _, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score@k')\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_advanced_sadr', 'present_exact_advanced_auc']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = self_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = self_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in self_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"ax.legend(loc='center left')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"     \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Present F-score@O\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"_, self_peak_summary_df, _, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = self_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = self_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in self_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"     \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# absent   \\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"_, self_peak_summary_df, _, _ = brief_eval_results(combined_df, base_metric=metric_name)\\n\",\n    \"metric_names = [metric_name]\\n\",\n    \"\\n\",\n    \"datasets = self_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = self_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in self_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets) * 100.0\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.2f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"      \\n\",\n    \"# number of phrases   \\n\",\n    \"_, self_peak_summary_df, valid_peak_summary_df, _ = brief_eval_results(combined_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"metric_names = ['beam_num', 'unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = self_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = self_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in self_peak_summary_df.iterrows():\\n\",\n    \"    train_mode = row_series.train_mode\\n\",\n    \"    \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute Statistic Significance between One2One and One2Seq\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T19:52:55.378308Z\",\n     \"start_time\": \"2020-08-12T19:52:52.479791Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"duc\\n\",\n      \"T-test on testset duc:\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.5714285714285715), mean=0.1367563608170575, variance=0.014993705810133684, skewness=0.7445382035367543, kurtosis=0.05828938637050385)\\n\",\n      \"\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.625), mean=0.15902501063018462, variance=0.015268666695762056, skewness=0.5943168508580654, kurtosis=0.07485535073359628)\\n\",\n      \"Ttest_relResult(statistic=-3.3529164665694124, pvalue=0.0008997911753705149)\\n\",\n      \"==================================================\\n\",\n      \"inspec\\n\",\n      \"T-test on testset inspec:\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.7499999999999999), mean=0.3203698818737471, variance=0.027663875105232976, skewness=0.20025285080462765, kurtosis=-0.38264981429972256)\\n\",\n      \"\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.8571428571428571), mean=0.381651447837591, variance=0.02742971895343227, skewness=0.16078165532565022, kurtosis=-0.25092602622627114)\\n\",\n      \"Ttest_relResult(statistic=-9.957505049922169, pvalue=1.9965851328919563e-21)\\n\",\n      \"==================================================\\n\",\n      \"kp20k\\n\",\n      \"T-test on testset kp20k:\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 0.8235294117647058), mean=0.2779562283353565, variance=0.024724542635172393, skewness=-0.011476160393475335, kurtosis=-0.4925356706113866)\\n\",\n      \"\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 0.9090909090909091), mean=0.25874385381418014, variance=0.025235155315174736, skewness=0.1075969439625255, kurtosis=-0.5103597352351805)\\n\",\n      \"Ttest_relResult(statistic=23.871041265753774, pvalue=3.3438422401932963e-124)\\n\",\n      \"==================================================\\n\",\n      \"kp20k_valid2k\\n\",\n      \"T-test on testset kp20k_valid2k:\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.8235294117647058), mean=0.28204402840956855, variance=0.024032528709123393, skewness=0.009485627202265905, kurtosis=-0.4303931766119766)\\n\",\n      \"\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.7499999999999999), mean=0.26351008055322767, variance=0.024712964938893047, skewness=0.04553285269169188, kurtosis=-0.6299365151490472)\\n\",\n      \"Ttest_relResult(statistic=7.374135365851898, pvalue=2.4104891983390634e-13)\\n\",\n      \"==================================================\\n\",\n      \"krapivin\\n\",\n      \"T-test on testset krapivin:\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.6666666666666665), mean=0.2724484426094845, variance=0.02388752876808955, skewness=0.06484344833656193, kurtosis=-0.554595692398772)\\n\",\n      \"\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.625), mean=0.2651850409903457, variance=0.02383589872666965, skewness=0.005622911158916607, kurtosis=-0.6623277406593884)\\n\",\n      \"Ttest_relResult(statistic=1.3634528897745772, pvalue=0.17340817698329275)\\n\",\n      \"==================================================\\n\",\n      \"nus\\n\",\n      \"T-test on testset nus:\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.35977218193071714, variance=0.0234693053366113, skewness=-0.053179277515829834, kurtosis=-0.2697108654208371)\\n\",\n      \"\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.36317369604642674, variance=0.02507595517234991, skewness=-0.29978570877191907, kurtosis=-0.0818470831234066)\\n\",\n      \"Ttest_relResult(statistic=-0.4344582849165332, pvalue=0.664401757497143)\\n\",\n      \"==================================================\\n\",\n      \"semeval\\n\",\n      \"T-test on testset semeval:\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.7499999999999999), mean=0.3437584144527643, variance=0.0247539458207435, skewness=-0.03488189761346301, kurtosis=-0.27907456230871075)\\n\",\n      \"\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.6666666666666666), mean=0.34430079831589117, variance=0.02451922429063192, skewness=0.07260867329929079, kurtosis=-0.6146200833079902)\\n\",\n      \"Ttest_relResult(statistic=-0.04392018709093214, pvalue=0.9650564887184025)\\n\",\n      \"==================================================\\n\",\n      \"Stats of One2One, freq=50000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.7499999999999999), mean=0.2773400604822976, variance=0.028928923278886888, skewness=0.21295949644399262, kurtosis=-0.537781386903954)\\n\",\n      \"\\n\",\n      \"Stats of One2Seq, freq=50000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.8571428571428571), mean=0.2994618592437217, variance=0.03045566321782634, skewness=0.1876014822751765, kurtosis=-0.44333938518770255)\\n\",\n      \"T-test on five testsets:\\n\",\n      \"Ttest_relResult(statistic=-6.966701540739821, pvalue=4.747968339841069e-12)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>69.805195</td>\\n\",\n       \"      <td>331.642857</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.727273</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.100601</td>\\n\",\n       \"      <td>0.115919</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.115919</td>\\n\",\n       \"      <td>1.207792</td>\\n\",\n       \"      <td>0.120779</td>\\n\",\n       \"      <td>0.166011</td>\\n\",\n       \"      <td>0.136756</td>\\n\",\n       \"      <td>0.120779</td>\\n\",\n       \"      <td>0.136756</td>\\n\",\n       \"      <td>1.025974</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>3.691558</td>\\n\",\n       \"      <td>0.055446</td>\\n\",\n       \"      <td>0.484908</td>\\n\",\n       \"      <td>0.098701</td>\\n\",\n       \"      <td>0.055446</td>\\n\",\n       \"      <td>0.098701</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.009740</td>\\n\",\n       \"      <td>0.000031</td>\\n\",\n       \"      <td>0.005952</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.000031</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.834416</td>\\n\",\n       \"      <td>0.240233</td>\\n\",\n       \"      <td>0.163032</td>\\n\",\n       \"      <td>0.189772</td>\\n\",\n       \"      <td>0.240233</td>\\n\",\n       \"      <td>0.189772</td>\\n\",\n       \"      <td>1.506494</td>\\n\",\n       \"      <td>0.193492</td>\\n\",\n       \"      <td>0.259036</td>\\n\",\n       \"      <td>0.217046</td>\\n\",\n       \"      <td>0.193492</td>\\n\",\n       \"      <td>0.217046</td>\\n\",\n       \"      <td>1.295455</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>4.337662</td>\\n\",\n       \"      <td>0.071434</td>\\n\",\n       \"      <td>0.621703</td>\\n\",\n       \"      <td>0.127106</td>\\n\",\n       \"      <td>0.071434</td>\\n\",\n       \"      <td>0.127106</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001838</td>\\n\",\n       \"      <td>0.011788</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.001838</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000554</td>\\n\",\n       \"      <td>0.017822</td>\\n\",\n       \"      <td>0.001066</td>\\n\",\n       \"      <td>0.000554</td>\\n\",\n       \"      <td>0.001066</td>\\n\",\n       \"      <td>0.012987</td>\\n\",\n       \"      <td>0.000124</td>\\n\",\n       \"      <td>0.025324</td>\\n\",\n       \"      <td>0.000246</td>\\n\",\n       \"      <td>0.000124</td>\\n\",\n       \"      <td>0.000246</td>\\n\",\n       \"      <td>0.091300</td>\\n\",\n       \"      <td>0.207958</td>\\n\",\n       \"      <td>0.144624</td>\\n\",\n       \"      <td>0.224051</td>\\n\",\n       \"      <td>0.293580</td>\\n\",\n       \"      <td>0.317872</td>\\n\",\n       \"      <td>0.380552</td>\\n\",\n       \"      <td>0.000037</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.000937</td>\\n\",\n       \"      <td>0.001059</td>\\n\",\n       \"      <td>0.002780</td>\\n\",\n       \"      <td>0.002780</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>25.331169</td>\\n\",\n       \"      <td>31.766234</td>\\n\",\n       \"      <td>103.159091</td>\\n\",\n       \"      <td>3254.538961</td>\\n\",\n       \"      <td>422.370130</td>\\n\",\n       \"      <td>11909.139610</td>\\n\",\n       \"      <td>0.727273</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.100148</td>\\n\",\n       \"      <td>0.116142</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.116142</td>\\n\",\n       \"      <td>1.405844</td>\\n\",\n       \"      <td>0.140747</td>\\n\",\n       \"      <td>0.190990</td>\\n\",\n       \"      <td>0.159025</td>\\n\",\n       \"      <td>0.140584</td>\\n\",\n       \"      <td>0.158870</td>\\n\",\n       \"      <td>1.168831</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>2.275974</td>\\n\",\n       \"      <td>0.096578</td>\\n\",\n       \"      <td>0.304134</td>\\n\",\n       \"      <td>0.142257</td>\\n\",\n       \"      <td>0.096578</td>\\n\",\n       \"      <td>0.142257</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.824675</td>\\n\",\n       \"      <td>0.244157</td>\\n\",\n       \"      <td>0.165272</td>\\n\",\n       \"      <td>0.192976</td>\\n\",\n       \"      <td>0.244157</td>\\n\",\n       \"      <td>0.192976</td>\\n\",\n       \"      <td>1.652597</td>\\n\",\n       \"      <td>0.207966</td>\\n\",\n       \"      <td>0.277746</td>\\n\",\n       \"      <td>0.233240</td>\\n\",\n       \"      <td>0.207534</td>\\n\",\n       \"      <td>0.232979</td>\\n\",\n       \"      <td>1.360390</td>\\n\",\n       \"      <td>0.227053</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>0.226866</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>2.678571</td>\\n\",\n       \"      <td>0.133099</td>\\n\",\n       \"      <td>0.410399</td>\\n\",\n       \"      <td>0.194576</td>\\n\",\n       \"      <td>0.133099</td>\\n\",\n       \"      <td>0.194576</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001438</td>\\n\",\n       \"      <td>0.007767</td>\\n\",\n       \"      <td>0.002351</td>\\n\",\n       \"      <td>0.001229</td>\\n\",\n       \"      <td>0.002085</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000814</td>\\n\",\n       \"      <td>0.008529</td>\\n\",\n       \"      <td>0.001362</td>\\n\",\n       \"      <td>0.000268</td>\\n\",\n       \"      <td>0.000516</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000796</td>\\n\",\n       \"      <td>0.009451</td>\\n\",\n       \"      <td>0.001330</td>\\n\",\n       \"      <td>0.000796</td>\\n\",\n       \"      <td>0.001330</td>\\n\",\n       \"      <td>0.076831</td>\\n\",\n       \"      <td>0.270145</td>\\n\",\n       \"      <td>0.204509</td>\\n\",\n       \"      <td>0.205482</td>\\n\",\n       \"      <td>0.228552</td>\\n\",\n       \"      <td>0.286359</td>\\n\",\n       \"      <td>0.342041</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>44.402000</td>\\n\",\n       \"      <td>381.750000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.764000</td>\\n\",\n       \"      <td>0.352800</td>\\n\",\n       \"      <td>0.276021</td>\\n\",\n       \"      <td>0.289861</td>\\n\",\n       \"      <td>0.352800</td>\\n\",\n       \"      <td>0.289861</td>\\n\",\n       \"      <td>2.834000</td>\\n\",\n       \"      <td>0.283400</td>\\n\",\n       \"      <td>0.426882</td>\\n\",\n       \"      <td>0.320370</td>\\n\",\n       \"      <td>0.283400</td>\\n\",\n       \"      <td>0.320370</td>\\n\",\n       \"      <td>2.604000</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>6.284000</td>\\n\",\n       \"      <td>0.142254</td>\\n\",\n       \"      <td>0.834030</td>\\n\",\n       \"      <td>0.237851</td>\\n\",\n       \"      <td>0.142254</td>\\n\",\n       \"      <td>0.237851</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.048575</td>\\n\",\n       \"      <td>0.015503</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.015503</td>\\n\",\n       \"      <td>0.190000</td>\\n\",\n       \"      <td>0.003800</td>\\n\",\n       \"      <td>0.090361</td>\\n\",\n       \"      <td>0.007166</td>\\n\",\n       \"      <td>0.003800</td>\\n\",\n       \"      <td>0.007166</td>\\n\",\n       \"      <td>0.338000</td>\\n\",\n       \"      <td>0.000887</td>\\n\",\n       \"      <td>0.141355</td>\\n\",\n       \"      <td>0.001758</td>\\n\",\n       \"      <td>0.000887</td>\\n\",\n       \"      <td>0.001758</td>\\n\",\n       \"      <td>1.952000</td>\\n\",\n       \"      <td>0.466169</td>\\n\",\n       \"      <td>0.361676</td>\\n\",\n       \"      <td>0.380171</td>\\n\",\n       \"      <td>0.466169</td>\\n\",\n       \"      <td>0.380171</td>\\n\",\n       \"      <td>3.212000</td>\\n\",\n       \"      <td>0.360859</td>\\n\",\n       \"      <td>0.531851</td>\\n\",\n       \"      <td>0.403184</td>\\n\",\n       \"      <td>0.360859</td>\\n\",\n       \"      <td>0.403184</td>\\n\",\n       \"      <td>2.992000</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>6.512000</td>\\n\",\n       \"      <td>0.152374</td>\\n\",\n       \"      <td>0.891328</td>\\n\",\n       \"      <td>0.254628</td>\\n\",\n       \"      <td>0.152374</td>\\n\",\n       \"      <td>0.254628</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.030517</td>\\n\",\n       \"      <td>0.131412</td>\\n\",\n       \"      <td>0.046328</td>\\n\",\n       \"      <td>0.030517</td>\\n\",\n       \"      <td>0.046328</td>\\n\",\n       \"      <td>0.254000</td>\\n\",\n       \"      <td>0.010489</td>\\n\",\n       \"      <td>0.219348</td>\\n\",\n       \"      <td>0.019604</td>\\n\",\n       \"      <td>0.010489</td>\\n\",\n       \"      <td>0.019604</td>\\n\",\n       \"      <td>0.472000</td>\\n\",\n       \"      <td>0.002053</td>\\n\",\n       \"      <td>0.306846</td>\\n\",\n       \"      <td>0.004066</td>\\n\",\n       \"      <td>0.002053</td>\\n\",\n       \"      <td>0.004066</td>\\n\",\n       \"      <td>0.316208</td>\\n\",\n       \"      <td>0.407457</td>\\n\",\n       \"      <td>0.213142</td>\\n\",\n       \"      <td>0.491125</td>\\n\",\n       \"      <td>0.585661</td>\\n\",\n       \"      <td>0.543668</td>\\n\",\n       \"      <td>0.622040</td>\\n\",\n       \"      <td>0.014343</td>\\n\",\n       \"      <td>0.032080</td>\\n\",\n       \"      <td>0.030609</td>\\n\",\n       \"      <td>0.045255</td>\\n\",\n       \"      <td>0.050635</td>\\n\",\n       \"      <td>0.030401</td>\\n\",\n       \"      <td>0.030594</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>22.192000</td>\\n\",\n       \"      <td>47.114000</td>\\n\",\n       \"      <td>84.350000</td>\\n\",\n       \"      <td>3164.972000</td>\\n\",\n       \"      <td>427.300000</td>\\n\",\n       \"      <td>12297.024000</td>\\n\",\n       \"      <td>1.912000</td>\\n\",\n       \"      <td>0.382400</td>\\n\",\n       \"      <td>0.300901</td>\\n\",\n       \"      <td>0.314813</td>\\n\",\n       \"      <td>0.382400</td>\\n\",\n       \"      <td>0.314813</td>\\n\",\n       \"      <td>3.382000</td>\\n\",\n       \"      <td>0.339504</td>\\n\",\n       \"      <td>0.507734</td>\\n\",\n       \"      <td>0.381651</td>\\n\",\n       \"      <td>0.338200</td>\\n\",\n       \"      <td>0.380574</td>\\n\",\n       \"      <td>3.018000</td>\\n\",\n       \"      <td>0.372335</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>0.372107</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>4.902000</td>\\n\",\n       \"      <td>0.231086</td>\\n\",\n       \"      <td>0.685163</td>\\n\",\n       \"      <td>0.328642</td>\\n\",\n       \"      <td>0.231086</td>\\n\",\n       \"      <td>0.328642</td>\\n\",\n       \"      <td>0.060000</td>\\n\",\n       \"      <td>0.007667</td>\\n\",\n       \"      <td>0.034241</td>\\n\",\n       \"      <td>0.011426</td>\\n\",\n       \"      <td>0.006000</td>\\n\",\n       \"      <td>0.009651</td>\\n\",\n       \"      <td>0.076000</td>\\n\",\n       \"      <td>0.004841</td>\\n\",\n       \"      <td>0.040797</td>\\n\",\n       \"      <td>0.007132</td>\\n\",\n       \"      <td>0.001520</td>\\n\",\n       \"      <td>0.002879</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.004693</td>\\n\",\n       \"      <td>0.042297</td>\\n\",\n       \"      <td>0.006863</td>\\n\",\n       \"      <td>0.004693</td>\\n\",\n       \"      <td>0.006863</td>\\n\",\n       \"      <td>2.052000</td>\\n\",\n       \"      <td>0.481097</td>\\n\",\n       \"      <td>0.373897</td>\\n\",\n       \"      <td>0.392583</td>\\n\",\n       \"      <td>0.481097</td>\\n\",\n       \"      <td>0.392583</td>\\n\",\n       \"      <td>3.586000</td>\\n\",\n       \"      <td>0.393555</td>\\n\",\n       \"      <td>0.579552</td>\\n\",\n       \"      <td>0.439360</td>\\n\",\n       \"      <td>0.392156</td>\\n\",\n       \"      <td>0.438213</td>\\n\",\n       \"      <td>3.248000</td>\\n\",\n       \"      <td>0.451361</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>0.451054</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>5.104000</td>\\n\",\n       \"      <td>0.252699</td>\\n\",\n       \"      <td>0.742746</td>\\n\",\n       \"      <td>0.358221</td>\\n\",\n       \"      <td>0.252699</td>\\n\",\n       \"      <td>0.358221</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.030429</td>\\n\",\n       \"      <td>0.116493</td>\\n\",\n       \"      <td>0.044221</td>\\n\",\n       \"      <td>0.026631</td>\\n\",\n       \"      <td>0.040757</td>\\n\",\n       \"      <td>0.106000</td>\\n\",\n       \"      <td>0.016993</td>\\n\",\n       \"      <td>0.143181</td>\\n\",\n       \"      <td>0.025687</td>\\n\",\n       \"      <td>0.006701</td>\\n\",\n       \"      <td>0.012553</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.016077</td>\\n\",\n       \"      <td>0.145531</td>\\n\",\n       \"      <td>0.024023</td>\\n\",\n       \"      <td>0.016077</td>\\n\",\n       \"      <td>0.024023</td>\\n\",\n       \"      <td>0.316342</td>\\n\",\n       \"      <td>0.494065</td>\\n\",\n       \"      <td>0.259221</td>\\n\",\n       \"      <td>0.498763</td>\\n\",\n       \"      <td>0.541210</td>\\n\",\n       \"      <td>0.523011</td>\\n\",\n       \"      <td>0.605785</td>\\n\",\n       \"      <td>0.013818</td>\\n\",\n       \"      <td>0.026776</td>\\n\",\n       \"      <td>0.026776</td>\\n\",\n       \"      <td>0.025499</td>\\n\",\n       \"      <td>0.026069</td>\\n\",\n       \"      <td>0.041039</td>\\n\",\n       \"      <td>0.041345</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>49.054135</td>\\n\",\n       \"      <td>380.151148</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>2242.250213</td>\\n\",\n       \"      <td>1.393005</td>\\n\",\n       \"      <td>0.278601</td>\\n\",\n       \"      <td>0.486837</td>\\n\",\n       \"      <td>0.330571</td>\\n\",\n       \"      <td>0.278601</td>\\n\",\n       \"      <td>0.330571</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>0.192304</td>\\n\",\n       \"      <td>0.644470</td>\\n\",\n       \"      <td>0.277956</td>\\n\",\n       \"      <td>0.192300</td>\\n\",\n       \"      <td>0.277950</td>\\n\",\n       \"      <td>1.336519</td>\\n\",\n       \"      <td>0.353454</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>0.353453</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>3.033071</td>\\n\",\n       \"      <td>0.064979</td>\\n\",\n       \"      <td>0.899688</td>\\n\",\n       \"      <td>0.115484</td>\\n\",\n       \"      <td>0.064979</td>\\n\",\n       \"      <td>0.115484</td>\\n\",\n       \"      <td>0.148196</td>\\n\",\n       \"      <td>0.014820</td>\\n\",\n       \"      <td>0.069768</td>\\n\",\n       \"      <td>0.023406</td>\\n\",\n       \"      <td>0.014820</td>\\n\",\n       \"      <td>0.023406</td>\\n\",\n       \"      <td>0.290639</td>\\n\",\n       \"      <td>0.005813</td>\\n\",\n       \"      <td>0.133821</td>\\n\",\n       \"      <td>0.010994</td>\\n\",\n       \"      <td>0.005813</td>\\n\",\n       \"      <td>0.010994</td>\\n\",\n       \"      <td>0.439786</td>\\n\",\n       \"      <td>0.001159</td>\\n\",\n       \"      <td>0.200463</td>\\n\",\n       \"      <td>0.002301</td>\\n\",\n       \"      <td>0.001159</td>\\n\",\n       \"      <td>0.002301</td>\\n\",\n       \"      <td>1.420523</td>\\n\",\n       \"      <td>0.322414</td>\\n\",\n       \"      <td>0.560786</td>\\n\",\n       \"      <td>0.381636</td>\\n\",\n       \"      <td>0.322414</td>\\n\",\n       \"      <td>0.381636</td>\\n\",\n       \"      <td>1.961775</td>\\n\",\n       \"      <td>0.214185</td>\\n\",\n       \"      <td>0.711640</td>\\n\",\n       \"      <td>0.308473</td>\\n\",\n       \"      <td>0.214180</td>\\n\",\n       \"      <td>0.308466</td>\\n\",\n       \"      <td>1.373943</td>\\n\",\n       \"      <td>0.417629</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>0.417628</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>3.045580</td>\\n\",\n       \"      <td>0.066355</td>\\n\",\n       \"      <td>0.915690</td>\\n\",\n       \"      <td>0.117828</td>\\n\",\n       \"      <td>0.066355</td>\\n\",\n       \"      <td>0.117828</td>\\n\",\n       \"      <td>0.155751</td>\\n\",\n       \"      <td>0.032226</td>\\n\",\n       \"      <td>0.147540</td>\\n\",\n       \"      <td>0.050534</td>\\n\",\n       \"      <td>0.032226</td>\\n\",\n       \"      <td>0.050534</td>\\n\",\n       \"      <td>0.317306</td>\\n\",\n       \"      <td>0.010812</td>\\n\",\n       \"      <td>0.242365</td>\\n\",\n       \"      <td>0.020409</td>\\n\",\n       \"      <td>0.010812</td>\\n\",\n       \"      <td>0.020409</td>\\n\",\n       \"      <td>0.495172</td>\\n\",\n       \"      <td>0.001955</td>\\n\",\n       \"      <td>0.328999</td>\\n\",\n       \"      <td>0.003879</td>\\n\",\n       \"      <td>0.001955</td>\\n\",\n       \"      <td>0.003879</td>\\n\",\n       \"      <td>0.384625</td>\\n\",\n       \"      <td>0.455979</td>\\n\",\n       \"      <td>0.332556</td>\\n\",\n       \"      <td>0.552072</td>\\n\",\n       \"      <td>0.606808</td>\\n\",\n       \"      <td>0.518431</td>\\n\",\n       \"      <td>0.524368</td>\\n\",\n       \"      <td>0.029264</td>\\n\",\n       \"      <td>0.063326</td>\\n\",\n       \"      <td>0.061357</td>\\n\",\n       \"      <td>0.071243</td>\\n\",\n       \"      <td>0.080194</td>\\n\",\n       \"      <td>0.047877</td>\\n\",\n       \"      <td>0.048025</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>23.765698</td>\\n\",\n       \"      <td>45.438435</td>\\n\",\n       \"      <td>83.928604</td>\\n\",\n       \"      <td>2966.514084</td>\\n\",\n       \"      <td>412.683394</td>\\n\",\n       \"      <td>12031.902837</td>\\n\",\n       \"      <td>1.280182</td>\\n\",\n       \"      <td>0.256094</td>\\n\",\n       \"      <td>0.456092</td>\\n\",\n       \"      <td>0.307406</td>\\n\",\n       \"      <td>0.256036</td>\\n\",\n       \"      <td>0.307370</td>\\n\",\n       \"      <td>1.754991</td>\\n\",\n       \"      <td>0.176175</td>\\n\",\n       \"      <td>0.607375</td>\\n\",\n       \"      <td>0.258744</td>\\n\",\n       \"      <td>0.175499</td>\\n\",\n       \"      <td>0.258091</td>\\n\",\n       \"      <td>1.053335</td>\\n\",\n       \"      <td>0.306870</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>0.306367</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>2.187522</td>\\n\",\n       \"      <td>0.100247</td>\\n\",\n       \"      <td>0.730980</td>\\n\",\n       \"      <td>0.167464</td>\\n\",\n       \"      <td>0.100247</td>\\n\",\n       \"      <td>0.167464</td>\\n\",\n       \"      <td>0.046380</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.022021</td>\\n\",\n       \"      <td>0.007750</td>\\n\",\n       \"      <td>0.004638</td>\\n\",\n       \"      <td>0.007347</td>\\n\",\n       \"      <td>0.059639</td>\\n\",\n       \"      <td>0.002694</td>\\n\",\n       \"      <td>0.028441</td>\\n\",\n       \"      <td>0.004288</td>\\n\",\n       \"      <td>0.001193</td>\\n\",\n       \"      <td>0.002262</td>\\n\",\n       \"      <td>0.060940</td>\\n\",\n       \"      <td>0.002545</td>\\n\",\n       \"      <td>0.029014</td>\\n\",\n       \"      <td>0.004015</td>\\n\",\n       \"      <td>0.002545</td>\\n\",\n       \"      <td>0.004015</td>\\n\",\n       \"      <td>1.320158</td>\\n\",\n       \"      <td>0.307409</td>\\n\",\n       \"      <td>0.541522</td>\\n\",\n       \"      <td>0.366387</td>\\n\",\n       \"      <td>0.307346</td>\\n\",\n       \"      <td>0.366348</td>\\n\",\n       \"      <td>1.813279</td>\\n\",\n       \"      <td>0.201625</td>\\n\",\n       \"      <td>0.682945</td>\\n\",\n       \"      <td>0.293340</td>\\n\",\n       \"      <td>0.200889</td>\\n\",\n       \"      <td>0.292642</td>\\n\",\n       \"      <td>1.127233</td>\\n\",\n       \"      <td>0.388850</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>0.388123</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>2.252214</td>\\n\",\n       \"      <td>0.110255</td>\\n\",\n       \"      <td>0.787605</td>\\n\",\n       \"      <td>0.182605</td>\\n\",\n       \"      <td>0.110255</td>\\n\",\n       \"      <td>0.182605</td>\\n\",\n       \"      <td>0.051083</td>\\n\",\n       \"      <td>0.021917</td>\\n\",\n       \"      <td>0.090091</td>\\n\",\n       \"      <td>0.032731</td>\\n\",\n       \"      <td>0.019755</td>\\n\",\n       \"      <td>0.030940</td>\\n\",\n       \"      <td>0.070646</td>\\n\",\n       \"      <td>0.011505</td>\\n\",\n       \"      <td>0.111533</td>\\n\",\n       \"      <td>0.018058</td>\\n\",\n       \"      <td>0.004938</td>\\n\",\n       \"      <td>0.009324</td>\\n\",\n       \"      <td>0.072197</td>\\n\",\n       \"      <td>0.010883</td>\\n\",\n       \"      <td>0.113058</td>\\n\",\n       \"      <td>0.016922</td>\\n\",\n       \"      <td>0.010883</td>\\n\",\n       \"      <td>0.016922</td>\\n\",\n       \"      <td>0.311136</td>\\n\",\n       \"      <td>0.441994</td>\\n\",\n       \"      <td>0.334838</td>\\n\",\n       \"      <td>0.479550</td>\\n\",\n       \"      <td>0.508923</td>\\n\",\n       \"      <td>0.491106</td>\\n\",\n       \"      <td>0.496273</td>\\n\",\n       \"      <td>0.010310</td>\\n\",\n       \"      <td>0.024681</td>\\n\",\n       \"      <td>0.024466</td>\\n\",\n       \"      <td>0.018006</td>\\n\",\n       \"      <td>0.019405</td>\\n\",\n       \"      <td>0.025164</td>\\n\",\n       \"      <td>0.025249</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>48.918000</td>\\n\",\n       \"      <td>379.279500</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.403000</td>\\n\",\n       \"      <td>0.280600</td>\\n\",\n       \"      <td>0.497367</td>\\n\",\n       \"      <td>0.334004</td>\\n\",\n       \"      <td>0.280600</td>\\n\",\n       \"      <td>0.334004</td>\\n\",\n       \"      <td>1.948000</td>\\n\",\n       \"      <td>0.194800</td>\\n\",\n       \"      <td>0.661260</td>\\n\",\n       \"      <td>0.282044</td>\\n\",\n       \"      <td>0.194800</td>\\n\",\n       \"      <td>0.282044</td>\\n\",\n       \"      <td>1.345500</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>3.031500</td>\\n\",\n       \"      <td>0.064932</td>\\n\",\n       \"      <td>0.899449</td>\\n\",\n       \"      <td>0.115291</td>\\n\",\n       \"      <td>0.064932</td>\\n\",\n       \"      <td>0.115291</td>\\n\",\n       \"      <td>0.153500</td>\\n\",\n       \"      <td>0.015350</td>\\n\",\n       \"      <td>0.069898</td>\\n\",\n       \"      <td>0.024154</td>\\n\",\n       \"      <td>0.015350</td>\\n\",\n       \"      <td>0.024154</td>\\n\",\n       \"      <td>0.291000</td>\\n\",\n       \"      <td>0.005820</td>\\n\",\n       \"      <td>0.135362</td>\\n\",\n       \"      <td>0.011027</td>\\n\",\n       \"      <td>0.005820</td>\\n\",\n       \"      <td>0.011027</td>\\n\",\n       \"      <td>0.438500</td>\\n\",\n       \"      <td>0.001154</td>\\n\",\n       \"      <td>0.201439</td>\\n\",\n       \"      <td>0.002291</td>\\n\",\n       \"      <td>0.001154</td>\\n\",\n       \"      <td>0.002291</td>\\n\",\n       \"      <td>1.427500</td>\\n\",\n       \"      <td>0.322982</td>\\n\",\n       \"      <td>0.566254</td>\\n\",\n       \"      <td>0.382858</td>\\n\",\n       \"      <td>0.322982</td>\\n\",\n       \"      <td>0.382858</td>\\n\",\n       \"      <td>1.986000</td>\\n\",\n       \"      <td>0.216015</td>\\n\",\n       \"      <td>0.722818</td>\\n\",\n       \"      <td>0.311294</td>\\n\",\n       \"      <td>0.216015</td>\\n\",\n       \"      <td>0.311294</td>\\n\",\n       \"      <td>1.384000</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>3.045000</td>\\n\",\n       \"      <td>0.066299</td>\\n\",\n       \"      <td>0.916028</td>\\n\",\n       \"      <td>0.117614</td>\\n\",\n       \"      <td>0.066299</td>\\n\",\n       \"      <td>0.117614</td>\\n\",\n       \"      <td>0.159500</td>\\n\",\n       \"      <td>0.032977</td>\\n\",\n       \"      <td>0.149964</td>\\n\",\n       \"      <td>0.051809</td>\\n\",\n       \"      <td>0.032977</td>\\n\",\n       \"      <td>0.051809</td>\\n\",\n       \"      <td>0.315500</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.248631</td>\\n\",\n       \"      <td>0.020779</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.020779</td>\\n\",\n       \"      <td>0.491500</td>\\n\",\n       \"      <td>0.001985</td>\\n\",\n       \"      <td>0.336167</td>\\n\",\n       \"      <td>0.003939</td>\\n\",\n       \"      <td>0.001985</td>\\n\",\n       \"      <td>0.003939</td>\\n\",\n       \"      <td>0.391085</td>\\n\",\n       \"      <td>0.461993</td>\\n\",\n       \"      <td>0.338297</td>\\n\",\n       \"      <td>0.559063</td>\\n\",\n       \"      <td>0.612446</td>\\n\",\n       \"      <td>0.514048</td>\\n\",\n       \"      <td>0.519904</td>\\n\",\n       \"      <td>0.030876</td>\\n\",\n       \"      <td>0.066772</td>\\n\",\n       \"      <td>0.064009</td>\\n\",\n       \"      <td>0.073106</td>\\n\",\n       \"      <td>0.081964</td>\\n\",\n       \"      <td>0.050022</td>\\n\",\n       \"      <td>0.050202</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>23.730500</td>\\n\",\n       \"      <td>46.566500</td>\\n\",\n       \"      <td>85.292500</td>\\n\",\n       \"      <td>2992.986500</td>\\n\",\n       \"      <td>414.778000</td>\\n\",\n       \"      <td>12097.758000</td>\\n\",\n       \"      <td>1.293500</td>\\n\",\n       \"      <td>0.258700</td>\\n\",\n       \"      <td>0.465987</td>\\n\",\n       \"      <td>0.311486</td>\\n\",\n       \"      <td>0.258700</td>\\n\",\n       \"      <td>0.311486</td>\\n\",\n       \"      <td>1.788500</td>\\n\",\n       \"      <td>0.179152</td>\\n\",\n       \"      <td>0.622748</td>\\n\",\n       \"      <td>0.263510</td>\\n\",\n       \"      <td>0.178850</td>\\n\",\n       \"      <td>0.263122</td>\\n\",\n       \"      <td>1.058500</td>\\n\",\n       \"      <td>0.304263</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>0.303867</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>2.213500</td>\\n\",\n       \"      <td>0.100874</td>\\n\",\n       \"      <td>0.737942</td>\\n\",\n       \"      <td>0.169063</td>\\n\",\n       \"      <td>0.100874</td>\\n\",\n       \"      <td>0.169063</td>\\n\",\n       \"      <td>0.053000</td>\\n\",\n       \"      <td>0.005369</td>\\n\",\n       \"      <td>0.025780</td>\\n\",\n       \"      <td>0.008537</td>\\n\",\n       \"      <td>0.005300</td>\\n\",\n       \"      <td>0.008437</td>\\n\",\n       \"      <td>0.065000</td>\\n\",\n       \"      <td>0.002430</td>\\n\",\n       \"      <td>0.030288</td>\\n\",\n       \"      <td>0.004285</td>\\n\",\n       \"      <td>0.001300</td>\\n\",\n       \"      <td>0.002465</td>\\n\",\n       \"      <td>0.067000</td>\\n\",\n       \"      <td>0.002251</td>\\n\",\n       \"      <td>0.031330</td>\\n\",\n       \"      <td>0.003958</td>\\n\",\n       \"      <td>0.002251</td>\\n\",\n       \"      <td>0.003958</td>\\n\",\n       \"      <td>1.338000</td>\\n\",\n       \"      <td>0.308816</td>\\n\",\n       \"      <td>0.545680</td>\\n\",\n       \"      <td>0.368356</td>\\n\",\n       \"      <td>0.308816</td>\\n\",\n       \"      <td>0.368356</td>\\n\",\n       \"      <td>1.847000</td>\\n\",\n       \"      <td>0.203396</td>\\n\",\n       \"      <td>0.692416</td>\\n\",\n       \"      <td>0.296106</td>\\n\",\n       \"      <td>0.203041</td>\\n\",\n       \"      <td>0.295663</td>\\n\",\n       \"      <td>1.137500</td>\\n\",\n       \"      <td>0.380898</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>0.380290</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>2.280500</td>\\n\",\n       \"      <td>0.110588</td>\\n\",\n       \"      <td>0.792416</td>\\n\",\n       \"      <td>0.183784</td>\\n\",\n       \"      <td>0.110588</td>\\n\",\n       \"      <td>0.183784</td>\\n\",\n       \"      <td>0.060000</td>\\n\",\n       \"      <td>0.022542</td>\\n\",\n       \"      <td>0.094527</td>\\n\",\n       \"      <td>0.033962</td>\\n\",\n       \"      <td>0.020549</td>\\n\",\n       \"      <td>0.032297</td>\\n\",\n       \"      <td>0.076500</td>\\n\",\n       \"      <td>0.011409</td>\\n\",\n       \"      <td>0.116486</td>\\n\",\n       \"      <td>0.018144</td>\\n\",\n       \"      <td>0.005133</td>\\n\",\n       \"      <td>0.009699</td>\\n\",\n       \"      <td>0.078000</td>\\n\",\n       \"      <td>0.010686</td>\\n\",\n       \"      <td>0.117942</td>\\n\",\n       \"      <td>0.016827</td>\\n\",\n       \"      <td>0.010686</td>\\n\",\n       \"      <td>0.016827</td>\\n\",\n       \"      <td>0.310270</td>\\n\",\n       \"      <td>0.442734</td>\\n\",\n       \"      <td>0.334244</td>\\n\",\n       \"      <td>0.483828</td>\\n\",\n       \"      <td>0.512278</td>\\n\",\n       \"      <td>0.477469</td>\\n\",\n       \"      <td>0.482639</td>\\n\",\n       \"      <td>0.010785</td>\\n\",\n       \"      <td>0.025619</td>\\n\",\n       \"      <td>0.025395</td>\\n\",\n       \"      <td>0.019122</td>\\n\",\n       \"      <td>0.020814</td>\\n\",\n       \"      <td>0.024922</td>\\n\",\n       \"      <td>0.025033</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>46.528261</td>\\n\",\n       \"      <td>378.417391</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.332609</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.483465</td>\\n\",\n       \"      <td>0.317053</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.317053</td>\\n\",\n       \"      <td>1.893478</td>\\n\",\n       \"      <td>0.189348</td>\\n\",\n       \"      <td>0.640867</td>\\n\",\n       \"      <td>0.272448</td>\\n\",\n       \"      <td>0.189348</td>\\n\",\n       \"      <td>0.272448</td>\\n\",\n       \"      <td>1.204348</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>2.952174</td>\\n\",\n       \"      <td>0.064462</td>\\n\",\n       \"      <td>0.894865</td>\\n\",\n       \"      <td>0.116644</td>\\n\",\n       \"      <td>0.064462</td>\\n\",\n       \"      <td>0.116644</td>\\n\",\n       \"      <td>0.217391</td>\\n\",\n       \"      <td>0.021739</td>\\n\",\n       \"      <td>0.085324</td>\\n\",\n       \"      <td>0.032630</td>\\n\",\n       \"      <td>0.021739</td>\\n\",\n       \"      <td>0.032630</td>\\n\",\n       \"      <td>0.373913</td>\\n\",\n       \"      <td>0.007478</td>\\n\",\n       \"      <td>0.139386</td>\\n\",\n       \"      <td>0.013917</td>\\n\",\n       \"      <td>0.007478</td>\\n\",\n       \"      <td>0.013917</td>\\n\",\n       \"      <td>0.556522</td>\\n\",\n       \"      <td>0.001465</td>\\n\",\n       \"      <td>0.207167</td>\\n\",\n       \"      <td>0.002901</td>\\n\",\n       \"      <td>0.001465</td>\\n\",\n       \"      <td>0.002901</td>\\n\",\n       \"      <td>1.378261</td>\\n\",\n       \"      <td>0.315008</td>\\n\",\n       \"      <td>0.556890</td>\\n\",\n       \"      <td>0.369044</td>\\n\",\n       \"      <td>0.315008</td>\\n\",\n       \"      <td>0.369044</td>\\n\",\n       \"      <td>1.958696</td>\\n\",\n       \"      <td>0.213756</td>\\n\",\n       \"      <td>0.704711</td>\\n\",\n       \"      <td>0.304214</td>\\n\",\n       \"      <td>0.213756</td>\\n\",\n       \"      <td>0.304214</td>\\n\",\n       \"      <td>1.278261</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>2.971739</td>\\n\",\n       \"      <td>0.066235</td>\\n\",\n       \"      <td>0.913145</td>\\n\",\n       \"      <td>0.119715</td>\\n\",\n       \"      <td>0.066235</td>\\n\",\n       \"      <td>0.119715</td>\\n\",\n       \"      <td>0.230435</td>\\n\",\n       \"      <td>0.043473</td>\\n\",\n       \"      <td>0.167630</td>\\n\",\n       \"      <td>0.064693</td>\\n\",\n       \"      <td>0.043473</td>\\n\",\n       \"      <td>0.064693</td>\\n\",\n       \"      <td>0.426087</td>\\n\",\n       \"      <td>0.014361</td>\\n\",\n       \"      <td>0.264867</td>\\n\",\n       \"      <td>0.026690</td>\\n\",\n       \"      <td>0.014361</td>\\n\",\n       \"      <td>0.026690</td>\\n\",\n       \"      <td>0.669565</td>\\n\",\n       \"      <td>0.002587</td>\\n\",\n       \"      <td>0.360654</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.002587</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.379387</td>\\n\",\n       \"      <td>0.450949</td>\\n\",\n       \"      <td>0.335888</td>\\n\",\n       \"      <td>0.545130</td>\\n\",\n       \"      <td>0.600735</td>\\n\",\n       \"      <td>0.502666</td>\\n\",\n       \"      <td>0.515249</td>\\n\",\n       \"      <td>0.035782</td>\\n\",\n       \"      <td>0.086806</td>\\n\",\n       \"      <td>0.083077</td>\\n\",\n       \"      <td>0.081692</td>\\n\",\n       \"      <td>0.095008</td>\\n\",\n       \"      <td>0.059456</td>\\n\",\n       \"      <td>0.060308</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>22.808696</td>\\n\",\n       \"      <td>45.680435</td>\\n\",\n       \"      <td>86.208696</td>\\n\",\n       \"      <td>3251.073913</td>\\n\",\n       \"      <td>435.997826</td>\\n\",\n       \"      <td>12531.176087</td>\\n\",\n       \"      <td>1.286957</td>\\n\",\n       \"      <td>0.257391</td>\\n\",\n       \"      <td>0.466974</td>\\n\",\n       \"      <td>0.305464</td>\\n\",\n       \"      <td>0.257391</td>\\n\",\n       \"      <td>0.305464</td>\\n\",\n       \"      <td>1.847826</td>\\n\",\n       \"      <td>0.185256</td>\\n\",\n       \"      <td>0.625793</td>\\n\",\n       \"      <td>0.265185</td>\\n\",\n       \"      <td>0.184783</td>\\n\",\n       \"      <td>0.264538</td>\\n\",\n       \"      <td>1.200000</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>2.389130</td>\\n\",\n       \"      <td>0.110275</td>\\n\",\n       \"      <td>0.762951</td>\\n\",\n       \"      <td>0.182055</td>\\n\",\n       \"      <td>0.110275</td>\\n\",\n       \"      <td>0.182055</td>\\n\",\n       \"      <td>0.052174</td>\\n\",\n       \"      <td>0.005510</td>\\n\",\n       \"      <td>0.024268</td>\\n\",\n       \"      <td>0.008305</td>\\n\",\n       \"      <td>0.005217</td>\\n\",\n       \"      <td>0.008053</td>\\n\",\n       \"      <td>0.067391</td>\\n\",\n       \"      <td>0.002912</td>\\n\",\n       \"      <td>0.028127</td>\\n\",\n       \"      <td>0.004666</td>\\n\",\n       \"      <td>0.001348</td>\\n\",\n       \"      <td>0.002522</td>\\n\",\n       \"      <td>0.069565</td>\\n\",\n       \"      <td>0.002822</td>\\n\",\n       \"      <td>0.028325</td>\\n\",\n       \"      <td>0.004505</td>\\n\",\n       \"      <td>0.002822</td>\\n\",\n       \"      <td>0.004505</td>\\n\",\n       \"      <td>1.330435</td>\\n\",\n       \"      <td>0.307725</td>\\n\",\n       \"      <td>0.545264</td>\\n\",\n       \"      <td>0.360798</td>\\n\",\n       \"      <td>0.307725</td>\\n\",\n       \"      <td>0.360798</td>\\n\",\n       \"      <td>1.900000</td>\\n\",\n       \"      <td>0.211663</td>\\n\",\n       \"      <td>0.700680</td>\\n\",\n       \"      <td>0.300782</td>\\n\",\n       \"      <td>0.211189</td>\\n\",\n       \"      <td>0.300135</td>\\n\",\n       \"      <td>1.252174</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>2.439130</td>\\n\",\n       \"      <td>0.119472</td>\\n\",\n       \"      <td>0.813916</td>\\n\",\n       \"      <td>0.196661</td>\\n\",\n       \"      <td>0.119472</td>\\n\",\n       \"      <td>0.196661</td>\\n\",\n       \"      <td>0.060870</td>\\n\",\n       \"      <td>0.028910</td>\\n\",\n       \"      <td>0.102949</td>\\n\",\n       \"      <td>0.041320</td>\\n\",\n       \"      <td>0.026625</td>\\n\",\n       \"      <td>0.039525</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.015114</td>\\n\",\n       \"      <td>0.123522</td>\\n\",\n       \"      <td>0.023168</td>\\n\",\n       \"      <td>0.006671</td>\\n\",\n       \"      <td>0.012392</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.014311</td>\\n\",\n       \"      <td>0.125238</td>\\n\",\n       \"      <td>0.021737</td>\\n\",\n       \"      <td>0.014311</td>\\n\",\n       \"      <td>0.021737</td>\\n\",\n       \"      <td>0.335451</td>\\n\",\n       \"      <td>0.467640</td>\\n\",\n       \"      <td>0.357506</td>\\n\",\n       \"      <td>0.503576</td>\\n\",\n       \"      <td>0.537226</td>\\n\",\n       \"      <td>0.476757</td>\\n\",\n       \"      <td>0.488681</td>\\n\",\n       \"      <td>0.012829</td>\\n\",\n       \"      <td>0.031475</td>\\n\",\n       \"      <td>0.031475</td>\\n\",\n       \"      <td>0.020634</td>\\n\",\n       \"      <td>0.022043</td>\\n\",\n       \"      <td>0.024188</td>\\n\",\n       \"      <td>0.024279</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>47.971564</td>\\n\",\n       \"      <td>382.943128</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>2.184834</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.444856</td>\\n\",\n       \"      <td>0.402446</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.402446</td>\\n\",\n       \"      <td>2.952607</td>\\n\",\n       \"      <td>0.295261</td>\\n\",\n       \"      <td>0.565834</td>\\n\",\n       \"      <td>0.359772</td>\\n\",\n       \"      <td>0.295261</td>\\n\",\n       \"      <td>0.359772</td>\\n\",\n       \"      <td>2.597156</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>4.848341</td>\\n\",\n       \"      <td>0.106813</td>\\n\",\n       \"      <td>0.843396</td>\\n\",\n       \"      <td>0.182876</td>\\n\",\n       \"      <td>0.106813</td>\\n\",\n       \"      <td>0.182876</td>\\n\",\n       \"      <td>0.213270</td>\\n\",\n       \"      <td>0.021327</td>\\n\",\n       \"      <td>0.046272</td>\\n\",\n       \"      <td>0.023420</td>\\n\",\n       \"      <td>0.021327</td>\\n\",\n       \"      <td>0.023420</td>\\n\",\n       \"      <td>0.597156</td>\\n\",\n       \"      <td>0.011943</td>\\n\",\n       \"      <td>0.122822</td>\\n\",\n       \"      <td>0.020429</td>\\n\",\n       \"      <td>0.011943</td>\\n\",\n       \"      <td>0.020429</td>\\n\",\n       \"      <td>0.924171</td>\\n\",\n       \"      <td>0.002396</td>\\n\",\n       \"      <td>0.182038</td>\\n\",\n       \"      <td>0.004682</td>\\n\",\n       \"      <td>0.002396</td>\\n\",\n       \"      <td>0.004682</td>\\n\",\n       \"      <td>2.232227</td>\\n\",\n       \"      <td>0.505064</td>\\n\",\n       \"      <td>0.509617</td>\\n\",\n       \"      <td>0.463853</td>\\n\",\n       \"      <td>0.505064</td>\\n\",\n       \"      <td>0.463853</td>\\n\",\n       \"      <td>3.109005</td>\\n\",\n       \"      <td>0.344811</td>\\n\",\n       \"      <td>0.650430</td>\\n\",\n       \"      <td>0.418135</td>\\n\",\n       \"      <td>0.344811</td>\\n\",\n       \"      <td>0.418135</td>\\n\",\n       \"      <td>2.739336</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>5.033175</td>\\n\",\n       \"      <td>0.116308</td>\\n\",\n       \"      <td>0.897778</td>\\n\",\n       \"      <td>0.198631</td>\\n\",\n       \"      <td>0.116308</td>\\n\",\n       \"      <td>0.198631</td>\\n\",\n       \"      <td>0.322275</td>\\n\",\n       \"      <td>0.063100</td>\\n\",\n       \"      <td>0.120033</td>\\n\",\n       \"      <td>0.070450</td>\\n\",\n       \"      <td>0.063100</td>\\n\",\n       \"      <td>0.070450</td>\\n\",\n       \"      <td>0.919431</td>\\n\",\n       \"      <td>0.025341</td>\\n\",\n       \"      <td>0.234054</td>\\n\",\n       \"      <td>0.042969</td>\\n\",\n       \"      <td>0.025341</td>\\n\",\n       \"      <td>0.042969</td>\\n\",\n       \"      <td>1.464455</td>\\n\",\n       \"      <td>0.004690</td>\\n\",\n       \"      <td>0.320374</td>\\n\",\n       \"      <td>0.009153</td>\\n\",\n       \"      <td>0.004690</td>\\n\",\n       \"      <td>0.009153</td>\\n\",\n       \"      <td>0.429044</td>\\n\",\n       \"      <td>0.541626</td>\\n\",\n       \"      <td>0.321157</td>\\n\",\n       \"      <td>0.575096</td>\\n\",\n       \"      <td>0.664121</td>\\n\",\n       \"      <td>0.582729</td>\\n\",\n       \"      <td>0.624832</td>\\n\",\n       \"      <td>0.016737</td>\\n\",\n       \"      <td>0.070061</td>\\n\",\n       \"      <td>0.065042</td>\\n\",\n       \"      <td>0.060403</td>\\n\",\n       \"      <td>0.078266</td>\\n\",\n       \"      <td>0.049930</td>\\n\",\n       \"      <td>0.059064</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>22.649289</td>\\n\",\n       \"      <td>45.862559</td>\\n\",\n       \"      <td>84.919431</td>\\n\",\n       \"      <td>3274.933649</td>\\n\",\n       \"      <td>434.398104</td>\\n\",\n       \"      <td>12527.127962</td>\\n\",\n       \"      <td>2.033175</td>\\n\",\n       \"      <td>0.406635</td>\\n\",\n       \"      <td>0.405661</td>\\n\",\n       \"      <td>0.373284</td>\\n\",\n       \"      <td>0.406635</td>\\n\",\n       \"      <td>0.373284</td>\\n\",\n       \"      <td>2.976303</td>\\n\",\n       \"      <td>0.298697</td>\\n\",\n       \"      <td>0.565945</td>\\n\",\n       \"      <td>0.363174</td>\\n\",\n       \"      <td>0.297630</td>\\n\",\n       \"      <td>0.362394</td>\\n\",\n       \"      <td>2.431280</td>\\n\",\n       \"      <td>0.392704</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>0.392478</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>3.725118</td>\\n\",\n       \"      <td>0.174411</td>\\n\",\n       \"      <td>0.675854</td>\\n\",\n       \"      <td>0.261203</td>\\n\",\n       \"      <td>0.174411</td>\\n\",\n       \"      <td>0.261203</td>\\n\",\n       \"      <td>0.061611</td>\\n\",\n       \"      <td>0.006161</td>\\n\",\n       \"      <td>0.011570</td>\\n\",\n       \"      <td>0.007286</td>\\n\",\n       \"      <td>0.006161</td>\\n\",\n       \"      <td>0.007286</td>\\n\",\n       \"      <td>0.099526</td>\\n\",\n       \"      <td>0.003577</td>\\n\",\n       \"      <td>0.017176</td>\\n\",\n       \"      <td>0.005211</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"      <td>0.003370</td>\\n\",\n       \"      <td>0.104265</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.017607</td>\\n\",\n       \"      <td>0.005084</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.005084</td>\\n\",\n       \"      <td>2.118483</td>\\n\",\n       \"      <td>0.484790</td>\\n\",\n       \"      <td>0.482832</td>\\n\",\n       \"      <td>0.443366</td>\\n\",\n       \"      <td>0.484790</td>\\n\",\n       \"      <td>0.443366</td>\\n\",\n       \"      <td>3.123223</td>\\n\",\n       \"      <td>0.345138</td>\\n\",\n       \"      <td>0.644575</td>\\n\",\n       \"      <td>0.416962</td>\\n\",\n       \"      <td>0.343913</td>\\n\",\n       \"      <td>0.416103</td>\\n\",\n       \"      <td>2.582938</td>\\n\",\n       \"      <td>0.474214</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>0.473938</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>3.971564</td>\\n\",\n       \"      <td>0.198323</td>\\n\",\n       \"      <td>0.754379</td>\\n\",\n       \"      <td>0.295786</td>\\n\",\n       \"      <td>0.198323</td>\\n\",\n       \"      <td>0.295786</td>\\n\",\n       \"      <td>0.151659</td>\\n\",\n       \"      <td>0.045667</td>\\n\",\n       \"      <td>0.077653</td>\\n\",\n       \"      <td>0.050156</td>\\n\",\n       \"      <td>0.044387</td>\\n\",\n       \"      <td>0.049496</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.023144</td>\\n\",\n       \"      <td>0.104366</td>\\n\",\n       \"      <td>0.031757</td>\\n\",\n       \"      <td>0.012529</td>\\n\",\n       \"      <td>0.021001</td>\\n\",\n       \"      <td>0.327014</td>\\n\",\n       \"      <td>0.021469</td>\\n\",\n       \"      <td>0.105829</td>\\n\",\n       \"      <td>0.029209</td>\\n\",\n       \"      <td>0.021469</td>\\n\",\n       \"      <td>0.029209</td>\\n\",\n       \"      <td>0.360912</td>\\n\",\n       \"      <td>0.566511</td>\\n\",\n       \"      <td>0.350501</td>\\n\",\n       \"      <td>0.509725</td>\\n\",\n       \"      <td>0.568610</td>\\n\",\n       \"      <td>0.539049</td>\\n\",\n       \"      <td>0.581340</td>\\n\",\n       \"      <td>0.004899</td>\\n\",\n       \"      <td>0.025594</td>\\n\",\n       \"      <td>0.024212</td>\\n\",\n       \"      <td>0.010563</td>\\n\",\n       \"      <td>0.013570</td>\\n\",\n       \"      <td>0.029372</td>\\n\",\n       \"      <td>0.032255</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>48.530000</td>\\n\",\n       \"      <td>378.250000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.920000</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.304143</td>\\n\",\n       \"      <td>0.321593</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.321593</td>\\n\",\n       \"      <td>2.950000</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.473317</td>\\n\",\n       \"      <td>0.343758</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.343758</td>\\n\",\n       \"      <td>2.430000</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>5.450000</td>\\n\",\n       \"      <td>0.114519</td>\\n\",\n       \"      <td>0.836617</td>\\n\",\n       \"      <td>0.195946</td>\\n\",\n       \"      <td>0.114519</td>\\n\",\n       \"      <td>0.195946</td>\\n\",\n       \"      <td>0.180000</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.022961</td>\\n\",\n       \"      <td>0.019800</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.019800</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.009200</td>\\n\",\n       \"      <td>0.061082</td>\\n\",\n       \"      <td>0.015819</td>\\n\",\n       \"      <td>0.009200</td>\\n\",\n       \"      <td>0.015819</td>\\n\",\n       \"      <td>0.800000</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.101568</td>\\n\",\n       \"      <td>0.004158</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.004158</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>0.470994</td>\\n\",\n       \"      <td>0.378530</td>\\n\",\n       \"      <td>0.397349</td>\\n\",\n       \"      <td>0.470994</td>\\n\",\n       \"      <td>0.397349</td>\\n\",\n       \"      <td>3.150000</td>\\n\",\n       \"      <td>0.351866</td>\\n\",\n       \"      <td>0.563634</td>\\n\",\n       \"      <td>0.409536</td>\\n\",\n       \"      <td>0.351866</td>\\n\",\n       \"      <td>0.409536</td>\\n\",\n       \"      <td>2.620000</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>5.590000</td>\\n\",\n       \"      <td>0.122265</td>\\n\",\n       \"      <td>0.884154</td>\\n\",\n       \"      <td>0.208801</td>\\n\",\n       \"      <td>0.122265</td>\\n\",\n       \"      <td>0.208801</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.072059</td>\\n\",\n       \"      <td>0.089026</td>\\n\",\n       \"      <td>0.077427</td>\\n\",\n       \"      <td>0.072059</td>\\n\",\n       \"      <td>0.077427</td>\\n\",\n       \"      <td>0.870000</td>\\n\",\n       \"      <td>0.026946</td>\\n\",\n       \"      <td>0.167713</td>\\n\",\n       \"      <td>0.045661</td>\\n\",\n       \"      <td>0.026946</td>\\n\",\n       \"      <td>0.045661</td>\\n\",\n       \"      <td>1.570000</td>\\n\",\n       \"      <td>0.005546</td>\\n\",\n       \"      <td>0.255692</td>\\n\",\n       \"      <td>0.010820</td>\\n\",\n       \"      <td>0.005546</td>\\n\",\n       \"      <td>0.010820</td>\\n\",\n       \"      <td>0.344738</td>\\n\",\n       \"      <td>0.439681</td>\\n\",\n       \"      <td>0.237985</td>\\n\",\n       \"      <td>0.495311</td>\\n\",\n       \"      <td>0.605620</td>\\n\",\n       \"      <td>0.525368</td>\\n\",\n       \"      <td>0.573741</td>\\n\",\n       \"      <td>0.013270</td>\\n\",\n       \"      <td>0.084758</td>\\n\",\n       \"      <td>0.082866</td>\\n\",\n       \"      <td>0.038479</td>\\n\",\n       \"      <td>0.058214</td>\\n\",\n       \"      <td>0.052090</td>\\n\",\n       \"      <td>0.064509</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>22.800000</td>\\n\",\n       \"      <td>45.280000</td>\\n\",\n       \"      <td>83.750000</td>\\n\",\n       \"      <td>3258.980000</td>\\n\",\n       \"      <td>433.570000</td>\\n\",\n       \"      <td>12591.410000</td>\\n\",\n       \"      <td>1.930000</td>\\n\",\n       \"      <td>0.386000</td>\\n\",\n       \"      <td>0.310670</td>\\n\",\n       \"      <td>0.324662</td>\\n\",\n       \"      <td>0.386000</td>\\n\",\n       \"      <td>0.324662</td>\\n\",\n       \"      <td>2.960000</td>\\n\",\n       \"      <td>0.296333</td>\\n\",\n       \"      <td>0.473295</td>\\n\",\n       \"      <td>0.344301</td>\\n\",\n       \"      <td>0.296000</td>\\n\",\n       \"      <td>0.344015</td>\\n\",\n       \"      <td>2.510000</td>\\n\",\n       \"      <td>0.349171</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>0.348837</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>4.010000</td>\\n\",\n       \"      <td>0.184256</td>\\n\",\n       \"      <td>0.626297</td>\\n\",\n       \"      <td>0.272150</td>\\n\",\n       \"      <td>0.184256</td>\\n\",\n       \"      <td>0.272150</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.014567</td>\\n\",\n       \"      <td>0.011500</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.011500</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.003684</td>\\n\",\n       \"      <td>0.015817</td>\\n\",\n       \"      <td>0.005624</td>\\n\",\n       \"      <td>0.002200</td>\\n\",\n       \"      <td>0.003816</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.003416</td>\\n\",\n       \"      <td>0.015817</td>\\n\",\n       \"      <td>0.005179</td>\\n\",\n       \"      <td>0.003416</td>\\n\",\n       \"      <td>0.005179</td>\\n\",\n       \"      <td>2.030000</td>\\n\",\n       \"      <td>0.467807</td>\\n\",\n       \"      <td>0.382451</td>\\n\",\n       \"      <td>0.396574</td>\\n\",\n       \"      <td>0.467807</td>\\n\",\n       \"      <td>0.396574</td>\\n\",\n       \"      <td>3.190000</td>\\n\",\n       \"      <td>0.353353</td>\\n\",\n       \"      <td>0.557835</td>\\n\",\n       \"      <td>0.409015</td>\\n\",\n       \"      <td>0.353020</td>\\n\",\n       \"      <td>0.408730</td>\\n\",\n       \"      <td>2.710000</td>\\n\",\n       \"      <td>0.431871</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>0.431491</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>4.200000</td>\\n\",\n       \"      <td>0.209371</td>\\n\",\n       \"      <td>0.704316</td>\\n\",\n       \"      <td>0.308704</td>\\n\",\n       \"      <td>0.209371</td>\\n\",\n       \"      <td>0.308704</td>\\n\",\n       \"      <td>0.160000</td>\\n\",\n       \"      <td>0.061492</td>\\n\",\n       \"      <td>0.072762</td>\\n\",\n       \"      <td>0.062335</td>\\n\",\n       \"      <td>0.056679</td>\\n\",\n       \"      <td>0.061220</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.033354</td>\\n\",\n       \"      <td>0.091858</td>\\n\",\n       \"      <td>0.040554</td>\\n\",\n       \"      <td>0.014513</td>\\n\",\n       \"      <td>0.024630</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.031701</td>\\n\",\n       \"      <td>0.094163</td>\\n\",\n       \"      <td>0.038028</td>\\n\",\n       \"      <td>0.031701</td>\\n\",\n       \"      <td>0.038028</td>\\n\",\n       \"      <td>0.289219</td>\\n\",\n       \"      <td>0.501281</td>\\n\",\n       \"      <td>0.296191</td>\\n\",\n       \"      <td>0.454212</td>\\n\",\n       \"      <td>0.508012</td>\\n\",\n       \"      <td>0.519445</td>\\n\",\n       \"      <td>0.575881</td>\\n\",\n       \"      <td>0.006943</td>\\n\",\n       \"      <td>0.060787</td>\\n\",\n       \"      <td>0.060787</td>\\n\",\n       \"      <td>0.014852</td>\\n\",\n       \"      <td>0.019784</td>\\n\",\n       \"      <td>0.031381</td>\\n\",\n       \"      <td>0.035286</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                 path  \\\\\\n\",\n       \"8   /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"14  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"13  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"15  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"7   /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"18  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"9   /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"17  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"12  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"19  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"11  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"16  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"10  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"20  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"\\n\",\n       \"                         exp_group  \\\\\\n\",\n       \"8             meng17-one2one-kp20k   \\n\",\n       \"14  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"13            meng17-one2one-kp20k   \\n\",\n       \"15  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"7   meng17-one2one-kp20k-topmodels   \\n\",\n       \"18  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"9             meng17-one2one-kp20k   \\n\",\n       \"17  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"12            meng17-one2one-kp20k   \\n\",\n       \"19  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"11            meng17-one2one-kp20k   \\n\",\n       \"16  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"10            meng17-one2one-kp20k   \\n\",\n       \"20  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"\\n\",\n       \"                                             exp_name  \\\\\\n\",\n       \"8   kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"14  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"13  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"15  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"7   kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"18  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"9   kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"17  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"12  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"19  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"11  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"16  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"10  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"20  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"\\n\",\n       \"                               test_name tokenization train_mode model_base  \\\\\\n\",\n       \"8             step_100000-duc-exhaustive       meng17    one2one        rnn   \\n\",\n       \"14             step_50000-duc-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"13         step_100000-inspec-exhaustive       meng17    one2one        rnn   \\n\",\n       \"15          step_50000-inspec-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"7           step_100000-kp20k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"18           step_50000-kp20k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"9   step_100000-kp20k_valid2k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"17   step_50000-kp20k_valid2k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"12       step_100000-krapivin-exhaustive       meng17    one2one        rnn   \\n\",\n       \"19        step_50000-krapivin-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"11            step_100000-nus-exhaustive       meng17    one2one        rnn   \\n\",\n       \"16             step_50000-nus-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"10        step_100000-semeval-exhaustive       meng17    one2one        rnn   \\n\",\n       \"20         step_50000-semeval-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"\\n\",\n       \"              order train_dataset    step   test_dataset decoding_method  \\\\\\n\",\n       \"8           one2one         kp20k  100000            duc      exhaustive   \\n\",\n       \"14  verbatim_append         kp20k   50000            duc      exhaustive   \\n\",\n       \"13          one2one         kp20k  100000         inspec      exhaustive   \\n\",\n       \"15  verbatim_append         kp20k   50000         inspec      exhaustive   \\n\",\n       \"7           one2one         kp20k  100000          kp20k      exhaustive   \\n\",\n       \"18  verbatim_append         kp20k   50000          kp20k      exhaustive   \\n\",\n       \"9           one2one         kp20k  100000  kp20k_valid2k      exhaustive   \\n\",\n       \"17  verbatim_append         kp20k   50000  kp20k_valid2k      exhaustive   \\n\",\n       \"12          one2one         kp20k  100000       krapivin      exhaustive   \\n\",\n       \"19  verbatim_append         kp20k   50000       krapivin      exhaustive   \\n\",\n       \"11          one2one         kp20k  100000            nus      exhaustive   \\n\",\n       \"16  verbatim_append         kp20k   50000            nus      exhaustive   \\n\",\n       \"10          one2one         kp20k  100000        semeval      exhaustive   \\n\",\n       \"20  verbatim_append         kp20k   50000        semeval      exhaustive   \\n\",\n       \"\\n\",\n       \"   decoding_terminate beam_width max_length  present_tgt_num  absent_tgt_num  \\\\\\n\",\n       \"8            fullbeam        200          6         7.860390        0.204545   \\n\",\n       \"14           fullbeam         50         40         7.860390        0.204545   \\n\",\n       \"13           fullbeam        200          6         7.716000        2.110000   \\n\",\n       \"15           fullbeam         50         40         7.716000        2.110000   \\n\",\n       \"7            fullbeam        200          6         3.331916        1.930555   \\n\",\n       \"18           fullbeam         50         40         3.331916        1.930555   \\n\",\n       \"9            fullbeam        200          6         3.327500        1.937500   \\n\",\n       \"17           fullbeam         50         40         3.327500        1.937500   \\n\",\n       \"12           fullbeam        200          6         3.228261        2.513043   \\n\",\n       \"19           fullbeam         50         40         3.228261        2.513043   \\n\",\n       \"11           fullbeam        200          6         5.985782        5.677725   \\n\",\n       \"16           fullbeam         50         40         5.985782        5.677725   \\n\",\n       \"10           fullbeam        200          6         6.710000        8.360000   \\n\",\n       \"20           fullbeam         50         40         6.710000        8.360000   \\n\",\n       \"\\n\",\n       \"    present_pred_num  absent_pred_num  unique_pred_num  dup_pred_num  \\\\\\n\",\n       \"8          69.805195       331.642857         0.000000      0.000000   \\n\",\n       \"14         25.331169        31.766234       103.159091   3254.538961   \\n\",\n       \"13         44.402000       381.750000         0.000000      0.000000   \\n\",\n       \"15         22.192000        47.114000        84.350000   3164.972000   \\n\",\n       \"7          49.054135       380.151148       497.658378    497.658378   \\n\",\n       \"18         23.765698        45.438435        83.928604   2966.514084   \\n\",\n       \"9          48.918000       379.279500         0.000000      0.000000   \\n\",\n       \"17         23.730500        46.566500        85.292500   2992.986500   \\n\",\n       \"12         46.528261       378.417391         0.000000      0.000000   \\n\",\n       \"19         22.808696        45.680435        86.208696   3251.073913   \\n\",\n       \"11         47.971564       382.943128         0.000000      0.000000   \\n\",\n       \"16         22.649289        45.862559        84.919431   3274.933649   \\n\",\n       \"10         48.530000       378.250000         0.000000      0.000000   \\n\",\n       \"20         22.800000        45.280000        83.750000   3258.980000   \\n\",\n       \"\\n\",\n       \"      beam_num  beamstep_num  present_exact_correct@5  \\\\\\n\",\n       \"8     0.000000      0.000000                 0.727273   \\n\",\n       \"14  422.370130  11909.139610                 0.727273   \\n\",\n       \"13    0.000000      0.000000                 1.764000   \\n\",\n       \"15  427.300000  12297.024000                 1.912000   \\n\",\n       \"7   497.658378   2242.250213                 1.393005   \\n\",\n       \"18  412.683394  12031.902837                 1.280182   \\n\",\n       \"9     0.000000      0.000000                 1.403000   \\n\",\n       \"17  414.778000  12097.758000                 1.293500   \\n\",\n       \"12    0.000000      0.000000                 1.332609   \\n\",\n       \"19  435.997826  12531.176087                 1.286957   \\n\",\n       \"11    0.000000      0.000000                 2.184834   \\n\",\n       \"16  434.398104  12527.127962                 2.033175   \\n\",\n       \"10    0.000000      0.000000                 1.920000   \\n\",\n       \"20  433.570000  12591.410000                 1.930000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@5  present_exact_recall@5  \\\\\\n\",\n       \"8                    0.145455                0.100601   \\n\",\n       \"14                   0.145455                0.100148   \\n\",\n       \"13                   0.352800                0.276021   \\n\",\n       \"15                   0.382400                0.300901   \\n\",\n       \"7                    0.278601                0.486837   \\n\",\n       \"18                   0.256094                0.456092   \\n\",\n       \"9                    0.280600                0.497367   \\n\",\n       \"17                   0.258700                0.465987   \\n\",\n       \"12                   0.266522                0.483465   \\n\",\n       \"19                   0.257391                0.466974   \\n\",\n       \"11                   0.436967                0.444856   \\n\",\n       \"16                   0.406635                0.405661   \\n\",\n       \"10                   0.384000                0.304143   \\n\",\n       \"20                   0.386000                0.310670   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@5  present_exact_precision_hard@5  \\\\\\n\",\n       \"8                  0.115919                        0.145455   \\n\",\n       \"14                 0.116142                        0.145455   \\n\",\n       \"13                 0.289861                        0.352800   \\n\",\n       \"15                 0.314813                        0.382400   \\n\",\n       \"7                  0.330571                        0.278601   \\n\",\n       \"18                 0.307406                        0.256036   \\n\",\n       \"9                  0.334004                        0.280600   \\n\",\n       \"17                 0.311486                        0.258700   \\n\",\n       \"12                 0.317053                        0.266522   \\n\",\n       \"19                 0.305464                        0.257391   \\n\",\n       \"11                 0.402446                        0.436967   \\n\",\n       \"16                 0.373284                        0.406635   \\n\",\n       \"10                 0.321593                        0.384000   \\n\",\n       \"20                 0.324662                        0.386000   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@5  present_exact_correct@10  \\\\\\n\",\n       \"8                       0.115919                  1.207792   \\n\",\n       \"14                      0.116142                  1.405844   \\n\",\n       \"13                      0.289861                  2.834000   \\n\",\n       \"15                      0.314813                  3.382000   \\n\",\n       \"7                       0.330571                  1.923000   \\n\",\n       \"18                      0.307370                  1.754991   \\n\",\n       \"9                       0.334004                  1.948000   \\n\",\n       \"17                      0.311486                  1.788500   \\n\",\n       \"12                      0.317053                  1.893478   \\n\",\n       \"19                      0.305464                  1.847826   \\n\",\n       \"11                      0.402446                  2.952607   \\n\",\n       \"16                      0.373284                  2.976303   \\n\",\n       \"10                      0.321593                  2.950000   \\n\",\n       \"20                      0.324662                  2.960000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@10  present_exact_recall@10  \\\\\\n\",\n       \"8                     0.120779                 0.166011   \\n\",\n       \"14                    0.140747                 0.190990   \\n\",\n       \"13                    0.283400                 0.426882   \\n\",\n       \"15                    0.339504                 0.507734   \\n\",\n       \"7                     0.192304                 0.644470   \\n\",\n       \"18                    0.176175                 0.607375   \\n\",\n       \"9                     0.194800                 0.661260   \\n\",\n       \"17                    0.179152                 0.622748   \\n\",\n       \"12                    0.189348                 0.640867   \\n\",\n       \"19                    0.185256                 0.625793   \\n\",\n       \"11                    0.295261                 0.565834   \\n\",\n       \"16                    0.298697                 0.565945   \\n\",\n       \"10                    0.295000                 0.473317   \\n\",\n       \"20                    0.296333                 0.473295   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@10  present_exact_precision_hard@10  \\\\\\n\",\n       \"8                   0.136756                         0.120779   \\n\",\n       \"14                  0.159025                         0.140584   \\n\",\n       \"13                  0.320370                         0.283400   \\n\",\n       \"15                  0.381651                         0.338200   \\n\",\n       \"7                   0.277956                         0.192300   \\n\",\n       \"18                  0.258744                         0.175499   \\n\",\n       \"9                   0.282044                         0.194800   \\n\",\n       \"17                  0.263510                         0.178850   \\n\",\n       \"12                  0.272448                         0.189348   \\n\",\n       \"19                  0.265185                         0.184783   \\n\",\n       \"11                  0.359772                         0.295261   \\n\",\n       \"16                  0.363174                         0.297630   \\n\",\n       \"10                  0.343758                         0.295000   \\n\",\n       \"20                  0.344301                         0.296000   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@10  present_exact_correct@k  \\\\\\n\",\n       \"8                        0.136756                 1.025974   \\n\",\n       \"14                       0.158870                 1.168831   \\n\",\n       \"13                       0.320370                 2.604000   \\n\",\n       \"15                       0.380574                 3.018000   \\n\",\n       \"7                        0.277950                 1.336519   \\n\",\n       \"18                       0.258091                 1.053335   \\n\",\n       \"9                        0.282044                 1.345500   \\n\",\n       \"17                       0.263122                 1.058500   \\n\",\n       \"12                       0.272448                 1.204348   \\n\",\n       \"19                       0.264538                 1.200000   \\n\",\n       \"11                       0.359772                 2.597156   \\n\",\n       \"16                       0.362394                 2.431280   \\n\",\n       \"10                       0.343758                 2.430000   \\n\",\n       \"20                       0.344015                 2.510000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@k  present_exact_recall@k  \\\\\\n\",\n       \"8                    0.132831                0.132831   \\n\",\n       \"14                   0.149253                0.149253   \\n\",\n       \"13                   0.334620                0.334620   \\n\",\n       \"15                   0.372335                0.371915   \\n\",\n       \"7                    0.353454                0.353452   \\n\",\n       \"18                   0.306870                0.306082   \\n\",\n       \"9                    0.358846                0.358846   \\n\",\n       \"17                   0.304263                0.303589   \\n\",\n       \"12                   0.345168                0.345168   \\n\",\n       \"19                   0.327904                0.327904   \\n\",\n       \"11                   0.432855                0.432855   \\n\",\n       \"16                   0.392704                0.392309   \\n\",\n       \"10                   0.337615                0.337615   \\n\",\n       \"20                   0.349171                0.348546   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@k  present_exact_precision_hard@k  \\\\\\n\",\n       \"8                  0.132831                        0.132831   \\n\",\n       \"14                 0.149253                        0.149253   \\n\",\n       \"13                 0.334620                        0.334620   \\n\",\n       \"15                 0.372107                        0.371915   \\n\",\n       \"7                  0.353453                        0.353452   \\n\",\n       \"18                 0.306367                        0.306082   \\n\",\n       \"9                  0.358846                        0.358846   \\n\",\n       \"17                 0.303867                        0.303589   \\n\",\n       \"12                 0.345168                        0.345168   \\n\",\n       \"19                 0.327904                        0.327904   \\n\",\n       \"11                 0.432855                        0.432855   \\n\",\n       \"16                 0.392478                        0.392309   \\n\",\n       \"10                 0.337615                        0.337615   \\n\",\n       \"20                 0.348837                        0.348546   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@k  present_exact_correct@M  \\\\\\n\",\n       \"8                       0.132831                 3.691558   \\n\",\n       \"14                      0.149253                 2.275974   \\n\",\n       \"13                      0.334620                 6.284000   \\n\",\n       \"15                      0.371915                 4.902000   \\n\",\n       \"7                       0.353452                 3.033071   \\n\",\n       \"18                      0.306082                 2.187522   \\n\",\n       \"9                       0.358846                 3.031500   \\n\",\n       \"17                      0.303589                 2.213500   \\n\",\n       \"12                      0.345168                 2.952174   \\n\",\n       \"19                      0.327904                 2.389130   \\n\",\n       \"11                      0.432855                 4.848341   \\n\",\n       \"16                      0.392309                 3.725118   \\n\",\n       \"10                      0.337615                 5.450000   \\n\",\n       \"20                      0.348546                 4.010000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@M  present_exact_recall@M  \\\\\\n\",\n       \"8                    0.055446                0.484908   \\n\",\n       \"14                   0.096578                0.304134   \\n\",\n       \"13                   0.142254                0.834030   \\n\",\n       \"15                   0.231086                0.685163   \\n\",\n       \"7                    0.064979                0.899688   \\n\",\n       \"18                   0.100247                0.730980   \\n\",\n       \"9                    0.064932                0.899449   \\n\",\n       \"17                   0.100874                0.737942   \\n\",\n       \"12                   0.064462                0.894865   \\n\",\n       \"19                   0.110275                0.762951   \\n\",\n       \"11                   0.106813                0.843396   \\n\",\n       \"16                   0.174411                0.675854   \\n\",\n       \"10                   0.114519                0.836617   \\n\",\n       \"20                   0.184256                0.626297   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@M  present_exact_precision_hard@M  \\\\\\n\",\n       \"8                  0.098701                        0.055446   \\n\",\n       \"14                 0.142257                        0.096578   \\n\",\n       \"13                 0.237851                        0.142254   \\n\",\n       \"15                 0.328642                        0.231086   \\n\",\n       \"7                  0.115484                        0.064979   \\n\",\n       \"18                 0.167464                        0.100247   \\n\",\n       \"9                  0.115291                        0.064932   \\n\",\n       \"17                 0.169063                        0.100874   \\n\",\n       \"12                 0.116644                        0.064462   \\n\",\n       \"19                 0.182055                        0.110275   \\n\",\n       \"11                 0.182876                        0.106813   \\n\",\n       \"16                 0.261203                        0.174411   \\n\",\n       \"10                 0.195946                        0.114519   \\n\",\n       \"20                 0.272150                        0.184256   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@M  absent_exact_correct@10  \\\\\\n\",\n       \"8                       0.098701                 0.000000   \\n\",\n       \"14                      0.142257                 0.000000   \\n\",\n       \"13                      0.237851                 0.100000   \\n\",\n       \"15                      0.328642                 0.060000   \\n\",\n       \"7                       0.115484                 0.148196   \\n\",\n       \"18                      0.167464                 0.046380   \\n\",\n       \"9                       0.115291                 0.153500   \\n\",\n       \"17                      0.169063                 0.053000   \\n\",\n       \"12                      0.116644                 0.217391   \\n\",\n       \"19                      0.182055                 0.052174   \\n\",\n       \"11                      0.182876                 0.213270   \\n\",\n       \"16                      0.261203                 0.061611   \\n\",\n       \"10                      0.195946                 0.180000   \\n\",\n       \"20                      0.272150                 0.100000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@10  absent_exact_recall@10  \\\\\\n\",\n       \"8                    0.000000                0.000000   \\n\",\n       \"14                   0.000000                0.000000   \\n\",\n       \"13                   0.010000                0.048575   \\n\",\n       \"15                   0.007667                0.034241   \\n\",\n       \"7                    0.014820                0.069768   \\n\",\n       \"18                   0.005121                0.022021   \\n\",\n       \"9                    0.015350                0.069898   \\n\",\n       \"17                   0.005369                0.025780   \\n\",\n       \"12                   0.021739                0.085324   \\n\",\n       \"19                   0.005510                0.024268   \\n\",\n       \"11                   0.021327                0.046272   \\n\",\n       \"16                   0.006161                0.011570   \\n\",\n       \"10                   0.018000                0.022961   \\n\",\n       \"20                   0.010000                0.014567   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score@10  absent_exact_precision_hard@10  \\\\\\n\",\n       \"8                  0.000000                        0.000000   \\n\",\n       \"14                 0.000000                        0.000000   \\n\",\n       \"13                 0.015503                        0.010000   \\n\",\n       \"15                 0.011426                        0.006000   \\n\",\n       \"7                  0.023406                        0.014820   \\n\",\n       \"18                 0.007750                        0.004638   \\n\",\n       \"9                  0.024154                        0.015350   \\n\",\n       \"17                 0.008537                        0.005300   \\n\",\n       \"12                 0.032630                        0.021739   \\n\",\n       \"19                 0.008305                        0.005217   \\n\",\n       \"11                 0.023420                        0.021327   \\n\",\n       \"16                 0.007286                        0.006161   \\n\",\n       \"10                 0.019800                        0.018000   \\n\",\n       \"20                 0.011500                        0.010000   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score_hard@10  absent_exact_correct@50  \\\\\\n\",\n       \"8                       0.000000                 0.000000   \\n\",\n       \"14                      0.000000                 0.000000   \\n\",\n       \"13                      0.015503                 0.190000   \\n\",\n       \"15                      0.009651                 0.076000   \\n\",\n       \"7                       0.023406                 0.290639   \\n\",\n       \"18                      0.007347                 0.059639   \\n\",\n       \"9                       0.024154                 0.291000   \\n\",\n       \"17                      0.008437                 0.065000   \\n\",\n       \"12                      0.032630                 0.373913   \\n\",\n       \"19                      0.008053                 0.067391   \\n\",\n       \"11                      0.023420                 0.597156   \\n\",\n       \"16                      0.007286                 0.099526   \\n\",\n       \"10                      0.019800                 0.460000   \\n\",\n       \"20                      0.011500                 0.110000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@50  absent_exact_recall@50  \\\\\\n\",\n       \"8                    0.000000                0.000000   \\n\",\n       \"14                   0.000000                0.000000   \\n\",\n       \"13                   0.003800                0.090361   \\n\",\n       \"15                   0.004841                0.040797   \\n\",\n       \"7                    0.005813                0.133821   \\n\",\n       \"18                   0.002694                0.028441   \\n\",\n       \"9                    0.005820                0.135362   \\n\",\n       \"17                   0.002430                0.030288   \\n\",\n       \"12                   0.007478                0.139386   \\n\",\n       \"19                   0.002912                0.028127   \\n\",\n       \"11                   0.011943                0.122822   \\n\",\n       \"16                   0.003577                0.017176   \\n\",\n       \"10                   0.009200                0.061082   \\n\",\n       \"20                   0.003684                0.015817   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score@50  absent_exact_precision_hard@50  \\\\\\n\",\n       \"8                  0.000000                        0.000000   \\n\",\n       \"14                 0.000000                        0.000000   \\n\",\n       \"13                 0.007166                        0.003800   \\n\",\n       \"15                 0.007132                        0.001520   \\n\",\n       \"7                  0.010994                        0.005813   \\n\",\n       \"18                 0.004288                        0.001193   \\n\",\n       \"9                  0.011027                        0.005820   \\n\",\n       \"17                 0.004285                        0.001300   \\n\",\n       \"12                 0.013917                        0.007478   \\n\",\n       \"19                 0.004666                        0.001348   \\n\",\n       \"11                 0.020429                        0.011943   \\n\",\n       \"16                 0.005211                        0.001991   \\n\",\n       \"10                 0.015819                        0.009200   \\n\",\n       \"20                 0.005624                        0.002200   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score_hard@50  absent_exact_correct@M  \\\\\\n\",\n       \"8                       0.000000                0.009740   \\n\",\n       \"14                      0.000000                0.000000   \\n\",\n       \"13                      0.007166                0.338000   \\n\",\n       \"15                      0.002879                0.080000   \\n\",\n       \"7                       0.010994                0.439786   \\n\",\n       \"18                      0.002262                0.060940   \\n\",\n       \"9                       0.011027                0.438500   \\n\",\n       \"17                      0.002465                0.067000   \\n\",\n       \"12                      0.013917                0.556522   \\n\",\n       \"19                      0.002522                0.069565   \\n\",\n       \"11                      0.020429                0.924171   \\n\",\n       \"16                      0.003370                0.104265   \\n\",\n       \"10                      0.015819                0.800000   \\n\",\n       \"20                      0.003816                0.110000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@M  absent_exact_recall@M  absent_exact_f_score@M  \\\\\\n\",\n       \"8                   0.000031               0.005952                0.000062   \\n\",\n       \"14                  0.000000               0.000000                0.000000   \\n\",\n       \"13                  0.000887               0.141355                0.001758   \\n\",\n       \"15                  0.004693               0.042297                0.006863   \\n\",\n       \"7                   0.001159               0.200463                0.002301   \\n\",\n       \"18                  0.002545               0.029014                0.004015   \\n\",\n       \"9                   0.001154               0.201439                0.002291   \\n\",\n       \"17                  0.002251               0.031330                0.003958   \\n\",\n       \"12                  0.001465               0.207167                0.002901   \\n\",\n       \"19                  0.002822               0.028325                0.004505   \\n\",\n       \"11                  0.002396               0.182038                0.004682   \\n\",\n       \"16                  0.003500               0.017607                0.005084   \\n\",\n       \"10                  0.002127               0.101568                0.004158   \\n\",\n       \"20                  0.003416               0.015817                0.005179   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision_hard@M  absent_exact_f_score_hard@M  \\\\\\n\",\n       \"8                        0.000031                     0.000062   \\n\",\n       \"14                       0.000000                     0.000000   \\n\",\n       \"13                       0.000887                     0.001758   \\n\",\n       \"15                       0.004693                     0.006863   \\n\",\n       \"7                        0.001159                     0.002301   \\n\",\n       \"18                       0.002545                     0.004015   \\n\",\n       \"9                        0.001154                     0.002291   \\n\",\n       \"17                       0.002251                     0.003958   \\n\",\n       \"12                       0.001465                     0.002901   \\n\",\n       \"19                       0.002822                     0.004505   \\n\",\n       \"11                       0.002396                     0.004682   \\n\",\n       \"16                       0.003500                     0.005084   \\n\",\n       \"10                       0.002127                     0.004158   \\n\",\n       \"20                       0.003416                     0.005179   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@5  present_partial_precision@5  \\\\\\n\",\n       \"8                    0.834416                     0.240233   \\n\",\n       \"14                   0.824675                     0.244157   \\n\",\n       \"13                   1.952000                     0.466169   \\n\",\n       \"15                   2.052000                     0.481097   \\n\",\n       \"7                    1.420523                     0.322414   \\n\",\n       \"18                   1.320158                     0.307409   \\n\",\n       \"9                    1.427500                     0.322982   \\n\",\n       \"17                   1.338000                     0.308816   \\n\",\n       \"12                   1.378261                     0.315008   \\n\",\n       \"19                   1.330435                     0.307725   \\n\",\n       \"11                   2.232227                     0.505064   \\n\",\n       \"16                   2.118483                     0.484790   \\n\",\n       \"10                   2.000000                     0.470994   \\n\",\n       \"20                   2.030000                     0.467807   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@5  present_partial_f_score@5  \\\\\\n\",\n       \"8                   0.163032                   0.189772   \\n\",\n       \"14                  0.165272                   0.192976   \\n\",\n       \"13                  0.361676                   0.380171   \\n\",\n       \"15                  0.373897                   0.392583   \\n\",\n       \"7                   0.560786                   0.381636   \\n\",\n       \"18                  0.541522                   0.366387   \\n\",\n       \"9                   0.566254                   0.382858   \\n\",\n       \"17                  0.545680                   0.368356   \\n\",\n       \"12                  0.556890                   0.369044   \\n\",\n       \"19                  0.545264                   0.360798   \\n\",\n       \"11                  0.509617                   0.463853   \\n\",\n       \"16                  0.482832                   0.443366   \\n\",\n       \"10                  0.378530                   0.397349   \\n\",\n       \"20                  0.382451                   0.396574   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@5  present_partial_f_score_hard@5  \\\\\\n\",\n       \"8                           0.240233                        0.189772   \\n\",\n       \"14                          0.244157                        0.192976   \\n\",\n       \"13                          0.466169                        0.380171   \\n\",\n       \"15                          0.481097                        0.392583   \\n\",\n       \"7                           0.322414                        0.381636   \\n\",\n       \"18                          0.307346                        0.366348   \\n\",\n       \"9                           0.322982                        0.382858   \\n\",\n       \"17                          0.308816                        0.368356   \\n\",\n       \"12                          0.315008                        0.369044   \\n\",\n       \"19                          0.307725                        0.360798   \\n\",\n       \"11                          0.505064                        0.463853   \\n\",\n       \"16                          0.484790                        0.443366   \\n\",\n       \"10                          0.470994                        0.397349   \\n\",\n       \"20                          0.467807                        0.396574   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@10  present_partial_precision@10  \\\\\\n\",\n       \"8                     1.506494                      0.193492   \\n\",\n       \"14                    1.652597                      0.207966   \\n\",\n       \"13                    3.212000                      0.360859   \\n\",\n       \"15                    3.586000                      0.393555   \\n\",\n       \"7                     1.961775                      0.214185   \\n\",\n       \"18                    1.813279                      0.201625   \\n\",\n       \"9                     1.986000                      0.216015   \\n\",\n       \"17                    1.847000                      0.203396   \\n\",\n       \"12                    1.958696                      0.213756   \\n\",\n       \"19                    1.900000                      0.211663   \\n\",\n       \"11                    3.109005                      0.344811   \\n\",\n       \"16                    3.123223                      0.345138   \\n\",\n       \"10                    3.150000                      0.351866   \\n\",\n       \"20                    3.190000                      0.353353   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@10  present_partial_f_score@10  \\\\\\n\",\n       \"8                    0.259036                    0.217046   \\n\",\n       \"14                   0.277746                    0.233240   \\n\",\n       \"13                   0.531851                    0.403184   \\n\",\n       \"15                   0.579552                    0.439360   \\n\",\n       \"7                    0.711640                    0.308473   \\n\",\n       \"18                   0.682945                    0.293340   \\n\",\n       \"9                    0.722818                    0.311294   \\n\",\n       \"17                   0.692416                    0.296106   \\n\",\n       \"12                   0.704711                    0.304214   \\n\",\n       \"19                   0.700680                    0.300782   \\n\",\n       \"11                   0.650430                    0.418135   \\n\",\n       \"16                   0.644575                    0.416962   \\n\",\n       \"10                   0.563634                    0.409536   \\n\",\n       \"20                   0.557835                    0.409015   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@10  present_partial_f_score_hard@10  \\\\\\n\",\n       \"8                            0.193492                         0.217046   \\n\",\n       \"14                           0.207534                         0.232979   \\n\",\n       \"13                           0.360859                         0.403184   \\n\",\n       \"15                           0.392156                         0.438213   \\n\",\n       \"7                            0.214180                         0.308466   \\n\",\n       \"18                           0.200889                         0.292642   \\n\",\n       \"9                            0.216015                         0.311294   \\n\",\n       \"17                           0.203041                         0.295663   \\n\",\n       \"12                           0.213756                         0.304214   \\n\",\n       \"19                           0.211189                         0.300135   \\n\",\n       \"11                           0.344811                         0.418135   \\n\",\n       \"16                           0.343913                         0.416103   \\n\",\n       \"10                           0.351866                         0.409536   \\n\",\n       \"20                           0.353020                         0.408730   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@k  present_partial_precision@k  \\\\\\n\",\n       \"8                    1.295455                     0.217284   \\n\",\n       \"14                   1.360390                     0.227053   \\n\",\n       \"13                   2.992000                     0.433587   \\n\",\n       \"15                   3.248000                     0.451361   \\n\",\n       \"7                    1.373943                     0.417629   \\n\",\n       \"18                   1.127233                     0.388850   \\n\",\n       \"9                    1.384000                     0.420597   \\n\",\n       \"17                   1.137500                     0.380898   \\n\",\n       \"12                   1.278261                     0.410627   \\n\",\n       \"19                   1.252174                     0.399462   \\n\",\n       \"11                   2.739336                     0.506244   \\n\",\n       \"16                   2.582938                     0.474214   \\n\",\n       \"10                   2.620000                     0.425488   \\n\",\n       \"20                   2.710000                     0.431871   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@k  present_partial_f_score@k  \\\\\\n\",\n       \"8                   0.217284                   0.217284   \\n\",\n       \"14                  0.226769                   0.226866   \\n\",\n       \"13                  0.433587                   0.433587   \\n\",\n       \"15                  0.450800                   0.451054   \\n\",\n       \"7                   0.417627                   0.417628   \\n\",\n       \"18                  0.387705                   0.388123   \\n\",\n       \"9                   0.420597                   0.420597   \\n\",\n       \"17                  0.379873                   0.380290   \\n\",\n       \"12                  0.410627                   0.410627   \\n\",\n       \"19                  0.399462                   0.399462   \\n\",\n       \"11                  0.506244                   0.506244   \\n\",\n       \"16                  0.473732                   0.473938   \\n\",\n       \"10                  0.425488                   0.425488   \\n\",\n       \"20                  0.431160                   0.431491   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@k  present_partial_f_score_hard@k  \\\\\\n\",\n       \"8                           0.217284                        0.217284   \\n\",\n       \"14                          0.226769                        0.226769   \\n\",\n       \"13                          0.433587                        0.433587   \\n\",\n       \"15                          0.450800                        0.450800   \\n\",\n       \"7                           0.417627                        0.417627   \\n\",\n       \"18                          0.387705                        0.387705   \\n\",\n       \"9                           0.420597                        0.420597   \\n\",\n       \"17                          0.379873                        0.379873   \\n\",\n       \"12                          0.410627                        0.410627   \\n\",\n       \"19                          0.399462                        0.399462   \\n\",\n       \"11                          0.506244                        0.506244   \\n\",\n       \"16                          0.473732                        0.473732   \\n\",\n       \"10                          0.425488                        0.425488   \\n\",\n       \"20                          0.431160                        0.431160   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@M  present_partial_precision@M  \\\\\\n\",\n       \"8                    4.337662                     0.071434   \\n\",\n       \"14                   2.678571                     0.133099   \\n\",\n       \"13                   6.512000                     0.152374   \\n\",\n       \"15                   5.104000                     0.252699   \\n\",\n       \"7                    3.045580                     0.066355   \\n\",\n       \"18                   2.252214                     0.110255   \\n\",\n       \"9                    3.045000                     0.066299   \\n\",\n       \"17                   2.280500                     0.110588   \\n\",\n       \"12                   2.971739                     0.066235   \\n\",\n       \"19                   2.439130                     0.119472   \\n\",\n       \"11                   5.033175                     0.116308   \\n\",\n       \"16                   3.971564                     0.198323   \\n\",\n       \"10                   5.590000                     0.122265   \\n\",\n       \"20                   4.200000                     0.209371   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@M  present_partial_f_score@M  \\\\\\n\",\n       \"8                   0.621703                   0.127106   \\n\",\n       \"14                  0.410399                   0.194576   \\n\",\n       \"13                  0.891328                   0.254628   \\n\",\n       \"15                  0.742746                   0.358221   \\n\",\n       \"7                   0.915690                   0.117828   \\n\",\n       \"18                  0.787605                   0.182605   \\n\",\n       \"9                   0.916028                   0.117614   \\n\",\n       \"17                  0.792416                   0.183784   \\n\",\n       \"12                  0.913145                   0.119715   \\n\",\n       \"19                  0.813916                   0.196661   \\n\",\n       \"11                  0.897778                   0.198631   \\n\",\n       \"16                  0.754379                   0.295786   \\n\",\n       \"10                  0.884154                   0.208801   \\n\",\n       \"20                  0.704316                   0.308704   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@M  present_partial_f_score_hard@M  \\\\\\n\",\n       \"8                           0.071434                        0.127106   \\n\",\n       \"14                          0.133099                        0.194576   \\n\",\n       \"13                          0.152374                        0.254628   \\n\",\n       \"15                          0.252699                        0.358221   \\n\",\n       \"7                           0.066355                        0.117828   \\n\",\n       \"18                          0.110255                        0.182605   \\n\",\n       \"9                           0.066299                        0.117614   \\n\",\n       \"17                          0.110588                        0.183784   \\n\",\n       \"12                          0.066235                        0.119715   \\n\",\n       \"19                          0.119472                        0.196661   \\n\",\n       \"11                          0.116308                        0.198631   \\n\",\n       \"16                          0.198323                        0.295786   \\n\",\n       \"10                          0.122265                        0.208801   \\n\",\n       \"20                          0.209371                        0.308704   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@10  absent_partial_precision@10  \\\\\\n\",\n       \"8                    0.000000                     0.001838   \\n\",\n       \"14                   0.000000                     0.001438   \\n\",\n       \"13                   0.110000                     0.030517   \\n\",\n       \"15                   0.080000                     0.030429   \\n\",\n       \"7                    0.155751                     0.032226   \\n\",\n       \"18                   0.051083                     0.021917   \\n\",\n       \"9                    0.159500                     0.032977   \\n\",\n       \"17                   0.060000                     0.022542   \\n\",\n       \"12                   0.230435                     0.043473   \\n\",\n       \"19                   0.060870                     0.028910   \\n\",\n       \"11                   0.322275                     0.063100   \\n\",\n       \"16                   0.151659                     0.045667   \\n\",\n       \"10                   0.320000                     0.072059   \\n\",\n       \"20                   0.160000                     0.061492   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@10  absent_partial_f_score@10  \\\\\\n\",\n       \"8                   0.011788                   0.003088   \\n\",\n       \"14                  0.007767                   0.002351   \\n\",\n       \"13                  0.131412                   0.046328   \\n\",\n       \"15                  0.116493                   0.044221   \\n\",\n       \"7                   0.147540                   0.050534   \\n\",\n       \"18                  0.090091                   0.032731   \\n\",\n       \"9                   0.149964                   0.051809   \\n\",\n       \"17                  0.094527                   0.033962   \\n\",\n       \"12                  0.167630                   0.064693   \\n\",\n       \"19                  0.102949                   0.041320   \\n\",\n       \"11                  0.120033                   0.070450   \\n\",\n       \"16                  0.077653                   0.050156   \\n\",\n       \"10                  0.089026                   0.077427   \\n\",\n       \"20                  0.072762                   0.062335   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@10  absent_partial_f_score_hard@10  \\\\\\n\",\n       \"8                           0.001838                        0.003088   \\n\",\n       \"14                          0.001229                        0.002085   \\n\",\n       \"13                          0.030517                        0.046328   \\n\",\n       \"15                          0.026631                        0.040757   \\n\",\n       \"7                           0.032226                        0.050534   \\n\",\n       \"18                          0.019755                        0.030940   \\n\",\n       \"9                           0.032977                        0.051809   \\n\",\n       \"17                          0.020549                        0.032297   \\n\",\n       \"12                          0.043473                        0.064693   \\n\",\n       \"19                          0.026625                        0.039525   \\n\",\n       \"11                          0.063100                        0.070450   \\n\",\n       \"16                          0.044387                        0.049496   \\n\",\n       \"10                          0.072059                        0.077427   \\n\",\n       \"20                          0.056679                        0.061220   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@50  absent_partial_precision@50  \\\\\\n\",\n       \"8                    0.000000                     0.000554   \\n\",\n       \"14                   0.000000                     0.000814   \\n\",\n       \"13                   0.254000                     0.010489   \\n\",\n       \"15                   0.106000                     0.016993   \\n\",\n       \"7                    0.317306                     0.010812   \\n\",\n       \"18                   0.070646                     0.011505   \\n\",\n       \"9                    0.315500                     0.010988   \\n\",\n       \"17                   0.076500                     0.011409   \\n\",\n       \"12                   0.426087                     0.014361   \\n\",\n       \"19                   0.091304                     0.015114   \\n\",\n       \"11                   0.919431                     0.025341   \\n\",\n       \"16                   0.312796                     0.023144   \\n\",\n       \"10                   0.870000                     0.026946   \\n\",\n       \"20                   0.310000                     0.033354   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@50  absent_partial_f_score@50  \\\\\\n\",\n       \"8                   0.017822                   0.001066   \\n\",\n       \"14                  0.008529                   0.001362   \\n\",\n       \"13                  0.219348                   0.019604   \\n\",\n       \"15                  0.143181                   0.025687   \\n\",\n       \"7                   0.242365                   0.020409   \\n\",\n       \"18                  0.111533                   0.018058   \\n\",\n       \"9                   0.248631                   0.020779   \\n\",\n       \"17                  0.116486                   0.018144   \\n\",\n       \"12                  0.264867                   0.026690   \\n\",\n       \"19                  0.123522                   0.023168   \\n\",\n       \"11                  0.234054                   0.042969   \\n\",\n       \"16                  0.104366                   0.031757   \\n\",\n       \"10                  0.167713                   0.045661   \\n\",\n       \"20                  0.091858                   0.040554   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@50  absent_partial_f_score_hard@50  \\\\\\n\",\n       \"8                           0.000554                        0.001066   \\n\",\n       \"14                          0.000268                        0.000516   \\n\",\n       \"13                          0.010489                        0.019604   \\n\",\n       \"15                          0.006701                        0.012553   \\n\",\n       \"7                           0.010812                        0.020409   \\n\",\n       \"18                          0.004938                        0.009324   \\n\",\n       \"9                           0.010988                        0.020779   \\n\",\n       \"17                          0.005133                        0.009699   \\n\",\n       \"12                          0.014361                        0.026690   \\n\",\n       \"19                          0.006671                        0.012392   \\n\",\n       \"11                          0.025341                        0.042969   \\n\",\n       \"16                          0.012529                        0.021001   \\n\",\n       \"10                          0.026946                        0.045661   \\n\",\n       \"20                          0.014513                        0.024630   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@M  absent_partial_precision@M  \\\\\\n\",\n       \"8                   0.012987                    0.000124   \\n\",\n       \"14                  0.000000                    0.000796   \\n\",\n       \"13                  0.472000                    0.002053   \\n\",\n       \"15                  0.110000                    0.016077   \\n\",\n       \"7                   0.495172                    0.001955   \\n\",\n       \"18                  0.072197                    0.010883   \\n\",\n       \"9                   0.491500                    0.001985   \\n\",\n       \"17                  0.078000                    0.010686   \\n\",\n       \"12                  0.669565                    0.002587   \\n\",\n       \"19                  0.091304                    0.014311   \\n\",\n       \"11                  1.464455                    0.004690   \\n\",\n       \"16                  0.327014                    0.021469   \\n\",\n       \"10                  1.570000                    0.005546   \\n\",\n       \"20                  0.310000                    0.031701   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@M  absent_partial_f_score@M  \\\\\\n\",\n       \"8                  0.025324                  0.000246   \\n\",\n       \"14                 0.009451                  0.001330   \\n\",\n       \"13                 0.306846                  0.004066   \\n\",\n       \"15                 0.145531                  0.024023   \\n\",\n       \"7                  0.328999                  0.003879   \\n\",\n       \"18                 0.113058                  0.016922   \\n\",\n       \"9                  0.336167                  0.003939   \\n\",\n       \"17                 0.117942                  0.016827   \\n\",\n       \"12                 0.360654                  0.005121   \\n\",\n       \"19                 0.125238                  0.021737   \\n\",\n       \"11                 0.320374                  0.009153   \\n\",\n       \"16                 0.105829                  0.029209   \\n\",\n       \"10                 0.255692                  0.010820   \\n\",\n       \"20                 0.094163                  0.038028   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@M  absent_partial_f_score_hard@M  \\\\\\n\",\n       \"8                          0.000124                       0.000246   \\n\",\n       \"14                         0.000796                       0.001330   \\n\",\n       \"13                         0.002053                       0.004066   \\n\",\n       \"15                         0.016077                       0.024023   \\n\",\n       \"7                          0.001955                       0.003879   \\n\",\n       \"18                         0.010883                       0.016922   \\n\",\n       \"9                          0.001985                       0.003939   \\n\",\n       \"17                         0.010686                       0.016827   \\n\",\n       \"12                         0.002587                       0.005121   \\n\",\n       \"19                         0.014311                       0.021737   \\n\",\n       \"11                         0.004690                       0.009153   \\n\",\n       \"16                         0.021469                       0.029209   \\n\",\n       \"10                         0.005546                       0.010820   \\n\",\n       \"20                         0.031701                       0.038028   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_auc  present_exact_advanced_ap  \\\\\\n\",\n       \"8                     0.091300                   0.207958   \\n\",\n       \"14                    0.076831                   0.270145   \\n\",\n       \"13                    0.316208                   0.407457   \\n\",\n       \"15                    0.316342                   0.494065   \\n\",\n       \"7                     0.384625                   0.455979   \\n\",\n       \"18                    0.311136                   0.441994   \\n\",\n       \"9                     0.391085                   0.461993   \\n\",\n       \"17                    0.310270                   0.442734   \\n\",\n       \"12                    0.379387                   0.450949   \\n\",\n       \"19                    0.335451                   0.467640   \\n\",\n       \"11                    0.429044                   0.541626   \\n\",\n       \"16                    0.360912                   0.566511   \\n\",\n       \"10                    0.344738                   0.439681   \\n\",\n       \"20                    0.289219                   0.501281   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_mrr  present_exact_advanced_sadr  \\\\\\n\",\n       \"8                     0.144624                     0.224051   \\n\",\n       \"14                    0.204509                     0.205482   \\n\",\n       \"13                    0.213142                     0.491125   \\n\",\n       \"15                    0.259221                     0.498763   \\n\",\n       \"7                     0.332556                     0.552072   \\n\",\n       \"18                    0.334838                     0.479550   \\n\",\n       \"9                     0.338297                     0.559063   \\n\",\n       \"17                    0.334244                     0.483828   \\n\",\n       \"12                    0.335888                     0.545130   \\n\",\n       \"19                    0.357506                     0.503576   \\n\",\n       \"11                    0.321157                     0.575096   \\n\",\n       \"16                    0.350501                     0.509725   \\n\",\n       \"10                    0.237985                     0.495311   \\n\",\n       \"20                    0.296191                     0.454212   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_ndcg  present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"8                      0.293580                             0.317872   \\n\",\n       \"14                     0.228552                             0.286359   \\n\",\n       \"13                     0.585661                             0.543668   \\n\",\n       \"15                     0.541210                             0.523011   \\n\",\n       \"7                      0.606808                             0.518431   \\n\",\n       \"18                     0.508923                             0.491106   \\n\",\n       \"9                      0.612446                             0.514048   \\n\",\n       \"17                     0.512278                             0.477469   \\n\",\n       \"12                     0.600735                             0.502666   \\n\",\n       \"19                     0.537226                             0.476757   \\n\",\n       \"11                     0.664121                             0.582729   \\n\",\n       \"16                     0.568610                             0.539049   \\n\",\n       \"10                     0.605620                             0.525368   \\n\",\n       \"20                     0.508012                             0.519445   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"8                               0.380552                   0.000037   \\n\",\n       \"14                              0.342041                   0.000000   \\n\",\n       \"13                              0.622040                   0.014343   \\n\",\n       \"15                              0.605785                   0.013818   \\n\",\n       \"7                               0.524368                   0.029264   \\n\",\n       \"18                              0.496273                   0.010310   \\n\",\n       \"9                               0.519904                   0.030876   \\n\",\n       \"17                              0.482639                   0.010785   \\n\",\n       \"12                              0.515249                   0.035782   \\n\",\n       \"19                              0.488681                   0.012829   \\n\",\n       \"11                              0.624832                   0.016737   \\n\",\n       \"16                              0.581340                   0.004899   \\n\",\n       \"10                              0.573741                   0.013270   \\n\",\n       \"20                              0.575881                   0.006943   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"8                   0.000115                   0.000115   \\n\",\n       \"14                  0.000000                   0.000000   \\n\",\n       \"13                  0.032080                   0.030609   \\n\",\n       \"15                  0.026776                   0.026776   \\n\",\n       \"7                   0.063326                   0.061357   \\n\",\n       \"18                  0.024681                   0.024466   \\n\",\n       \"9                   0.066772                   0.064009   \\n\",\n       \"17                  0.025619                   0.025395   \\n\",\n       \"12                  0.086806                   0.083077   \\n\",\n       \"19                  0.031475                   0.031475   \\n\",\n       \"11                  0.070061                   0.065042   \\n\",\n       \"16                  0.025594                   0.024212   \\n\",\n       \"10                  0.084758                   0.082866   \\n\",\n       \"20                  0.060787                   0.060787   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"8                     0.000937                    0.001059   \\n\",\n       \"14                    0.000000                    0.000000   \\n\",\n       \"13                    0.045255                    0.050635   \\n\",\n       \"15                    0.025499                    0.026069   \\n\",\n       \"7                     0.071243                    0.080194   \\n\",\n       \"18                    0.018006                    0.019405   \\n\",\n       \"9                     0.073106                    0.081964   \\n\",\n       \"17                    0.019122                    0.020814   \\n\",\n       \"12                    0.081692                    0.095008   \\n\",\n       \"19                    0.020634                    0.022043   \\n\",\n       \"11                    0.060403                    0.078266   \\n\",\n       \"16                    0.010563                    0.013570   \\n\",\n       \"10                    0.038479                    0.058214   \\n\",\n       \"20                    0.014852                    0.019784   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_alpha_ndcg@5  absent_exact_advanced_alpha_ndcg@10  \\n\",\n       \"8                             0.002780                             0.002780  \\n\",\n       \"14                            0.001991                             0.001991  \\n\",\n       \"13                            0.030401                             0.030594  \\n\",\n       \"15                            0.041039                             0.041345  \\n\",\n       \"7                             0.047877                             0.048025  \\n\",\n       \"18                            0.025164                             0.025249  \\n\",\n       \"9                             0.050022                             0.050202  \\n\",\n       \"17                            0.024922                             0.025033  \\n\",\n       \"12                            0.059456                             0.060308  \\n\",\n       \"19                            0.024188                             0.024279  \\n\",\n       \"11                            0.049930                             0.059064  \\n\",\n       \"16                            0.029372                             0.032255  \\n\",\n       \"10                            0.052090                             0.064509  \\n\",\n       \"20                            0.031381                             0.035286  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"' \\\\n'\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import stats\\n\",\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"sample_freq = 50000\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"if sample_freq == 5000:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"else:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % sample_freq == 0)] # keep % 10k, 20k, 50k\\n\",\n    \"\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"     kp20k_one2one_df.at[index_label, 'test_dataset'] = 'one2one - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"    \\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"if sample_freq == 5000:\\n\",\n    \"    one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"else:\\n\",\n    \"    one2seq_df = one2seq_df.loc[(one2seq_df.step % sample_freq == 0)] # keep % 10k, 20k, 50k\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2seq_df.iterrows():\\n\",\n    \"     kp20k_one2seq_df.at[index_label, 'test_dataset'] = 'one2seq - ' + kp20k_one2seq_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"# combine both and plot\\n\",\n    \"combined_kp20k_df = kp20k_one2one_df.append(kp20k_one2seq_df, ignore_index=True)\\n\",\n    \"combined_df = one2one_df.append(one2seq_df, ignore_index=True)\\n\",\n    \"\\n\",\n    \"# obtain the test scores by best valid performance\\n\",\n    \"str_summary_df, self_peak_summary_df, valid_peak_summary_df = brief_eval_results(combined_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"one2one_outofdistribution, one2seq_outofdistribution = [], []\\n\",\n    \"for test_dataset, exp_group in valid_peak_summary_df.groupby('test_dataset'):\\n\",\n    \"    print(test_dataset)\\n\",\n    \"    one2one_row = exp_group.loc[exp_group['train_mode'] == 'one2one']\\n\",\n    \"    one2seq_row = exp_group.loc[exp_group['train_mode'] == 'one2seq']\\n\",\n    \"    \\n\",\n    \"    with open(one2one_row['path'].values[0], 'r') as one2one_eval_file:\\n\",\n    \"        one2one_eval = json.load(one2one_eval_file)\\n\",\n    \"    with open(one2seq_row['path'].values[0], 'r') as one2seq_eval_file:\\n\",\n    \"        one2seq_eval = json.load(one2seq_eval_file)\\n\",\n    \"        \\n\",\n    \"    # run T-test for individual testset\\n\",\n    \"    print('T-test on testset %s:' % test_dataset)\\n\",\n    \"    print(len(one2one_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2one_eval['present_exact_f_score@10']))\\n\",\n    \"    \\n\",\n    \"    print('')\\n\",\n    \"    print(len(one2seq_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2seq_eval['present_exact_f_score@10']))\\n\",\n    \"    ttest = stats.ttest_rel(one2one_eval['present_exact_f_score@10'], one2seq_eval['present_exact_f_score@10'])\\n\",\n    \"    print(ttest)\\n\",\n    \"    print('=' * 50)\\n\",\n    \"    \\n\",\n    \"    if not test_dataset.startswith('kp20k'):\\n\",\n    \"        one2one_outofdistribution.extend(one2one_eval['present_exact_f_score@10'])\\n\",\n    \"        one2seq_outofdistribution.extend(one2seq_eval['present_exact_f_score@10'])\\n\",\n    \"\\n\",\n    \"# run T-test on five testsets\\n\",\n    \"print('Stats of One2One, freq=%d' % sample_freq)\\n\",\n    \"print(len(one2one_outofdistribution))\\n\",\n    \"print(stats.describe(one2one_outofdistribution))\\n\",\n    \"print('')\\n\",\n    \"print('Stats of One2Seq, freq=%d' % sample_freq)\\n\",\n    \"print(len(one2seq_outofdistribution))\\n\",\n    \"print(stats.describe(one2seq_outofdistribution))\\n\",\n    \"print('T-test on five testsets:')\\n\",\n    \"ttest = stats.ttest_rel(one2one_outofdistribution, one2seq_outofdistribution)\\n\",\n    \"print(ttest)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"display(valid_peak_summary_df)\\n\",\n    \"\\n\",\n    \"# Present results\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"modes = valid_peak_summary_df.train_mode.unique()\\n\",\n    \"bar_values = {'%s - %s' % (mode, metric_name): [] for mode in modes for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.train_mode, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"''' \\n\",\n    \"'''\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute Gap Significance for One2One models (gap=Score_max_among_all_samples-Score_min_among_all_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T20:22:41.176534Z\",\n     \"start_time\": \"2020-08-12T20:22:38.739910Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"duc\\n\",\n      \"T-test on testset duc:\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.5714285714285715), mean=0.1240018880821837, variance=0.014013153757859649, skewness=0.8280140209754177, kurtosis=0.18949299840948264)\\n\",\n      \"\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.5714285714285715), mean=0.1367563608170575, variance=0.014993705810133684, skewness=0.7445382035367543, kurtosis=0.05828938637050385)\\n\",\n      \"Ttest_relResult(statistic=-3.001338004385848, pvalue=0.0029085132408209614)\\n\",\n      \"==================================================\\n\",\n      \"inspec\\n\",\n      \"T-test on testset inspec:\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.8421052631578948), mean=0.32429694315801766, variance=0.026105377185854553, skewness=0.25360223613675537, kurtosis=-0.25263831936896075)\\n\",\n      \"\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.7499999999999999), mean=0.3203698818737471, variance=0.027663875105232976, skewness=0.20025285080462765, kurtosis=-0.38264981429972256)\\n\",\n      \"Ttest_relResult(statistic=1.001874320808005, pvalue=0.3168898719133819)\\n\",\n      \"==================================================\\n\",\n      \"kp20k\\n\",\n      \"T-test on testset kp20k:\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 0.8235294117647058), mean=0.278800915528659, variance=0.024943723715449298, skewness=-0.014828251159089728, kurtosis=-0.5103355798795768)\\n\",\n      \"\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 0.8235294117647058), mean=0.2779562283353565, variance=0.024724542635172393, skewness=-0.011476160393475335, kurtosis=-0.4925356706113866)\\n\",\n      \"Ttest_relResult(statistic=1.6286708532393661, pvalue=0.1033984772027146)\\n\",\n      \"==================================================\\n\",\n      \"kp20k_valid2k\\n\",\n      \"T-test on testset kp20k_valid2k:\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.8235294117647058), mean=0.28287264555675606, variance=0.024674767183369837, skewness=0.061345127639821084, kurtosis=-0.42507137527518246)\\n\",\n      \"\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.8235294117647058), mean=0.28204402840956855, variance=0.024032528709123393, skewness=0.009485627202265905, kurtosis=-0.4303931766119766)\\n\",\n      \"Ttest_relResult(statistic=0.5029810454230335, pvalue=0.615033009746552)\\n\",\n      \"==================================================\\n\",\n      \"krapivin\\n\",\n      \"T-test on testset krapivin:\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.6666666666666666), mean=0.26333446401667293, variance=0.02408344686592948, skewness=0.15505469160421698, kurtosis=-0.483826343767495)\\n\",\n      \"\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.6666666666666665), mean=0.2724484426094845, variance=0.02388752876808955, skewness=0.06484344833656193, kurtosis=-0.554595692398772)\\n\",\n      \"Ttest_relResult(statistic=-2.765373141918726, pvalue=0.0059149347716784765)\\n\",\n      \"==================================================\\n\",\n      \"nus\\n\",\n      \"T-test on testset nus:\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.365868622238561, variance=0.02524619769468459, skewness=-0.19022781923212875, kurtosis=-0.2805066797967588)\\n\",\n      \"\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.35977218193071714, variance=0.0234693053366113, skewness=-0.053179277515829834, kurtosis=-0.2697108654208371)\\n\",\n      \"Ttest_relResult(statistic=1.3198277039413808, pvalue=0.1883294948612155)\\n\",\n      \"==================================================\\n\",\n      \"semeval\\n\",\n      \"T-test on testset semeval:\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.7499999999999999), mean=0.35177721248479754, variance=0.026757949803960776, skewness=-0.010721997310314084, kurtosis=-0.4163863028676844)\\n\",\n      \"\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.7499999999999999), mean=0.3437584144527643, variance=0.0247539458207435, skewness=-0.03488189761346301, kurtosis=-0.27907456230871075)\\n\",\n      \"Ttest_relResult(statistic=0.9641251052819224, pvalue=0.33733236623561513)\\n\",\n      \"==================================================\\n\",\n      \"Stats of One2One-freq10000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.8421052631578948), mean=0.27476308239189806, variance=0.029762688602177012, skewness=0.23919631765551128, kurtosis=-0.5363141459420739)\\n\",\n      \"\\n\",\n      \"Stats of One2One-freq50000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.7499999999999999), mean=0.2773400604822976, variance=0.028928923278886888, skewness=0.21295949644399262, kurtosis=-0.537781386903954)\\n\",\n      \"T-test on five testsets:\\n\",\n      \"Ttest_relResult(statistic=-1.3153045552722886, pvalue=0.18859865171018966)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>85.720779</td>\\n\",\n       \"      <td>299.870130</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.694805</td>\\n\",\n       \"      <td>0.138961</td>\\n\",\n       \"      <td>0.098413</td>\\n\",\n       \"      <td>0.111953</td>\\n\",\n       \"      <td>0.138961</td>\\n\",\n       \"      <td>0.111953</td>\\n\",\n       \"      <td>1.090909</td>\\n\",\n       \"      <td>0.109091</td>\\n\",\n       \"      <td>0.151873</td>\\n\",\n       \"      <td>0.124002</td>\\n\",\n       \"      <td>0.109091</td>\\n\",\n       \"      <td>0.124002</td>\\n\",\n       \"      <td>0.915584</td>\\n\",\n       \"      <td>0.119022</td>\\n\",\n       \"      <td>0.119022</td>\\n\",\n       \"      <td>0.119022</td>\\n\",\n       \"      <td>0.119022</td>\\n\",\n       \"      <td>0.119022</td>\\n\",\n       \"      <td>4.126623</td>\\n\",\n       \"      <td>0.049967</td>\\n\",\n       \"      <td>0.541126</td>\\n\",\n       \"      <td>0.090996</td>\\n\",\n       \"      <td>0.049967</td>\\n\",\n       \"      <td>0.090996</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.795455</td>\\n\",\n       \"      <td>0.236934</td>\\n\",\n       \"      <td>0.162384</td>\\n\",\n       \"      <td>0.188140</td>\\n\",\n       \"      <td>0.236934</td>\\n\",\n       \"      <td>0.188140</td>\\n\",\n       \"      <td>1.428571</td>\\n\",\n       \"      <td>0.185839</td>\\n\",\n       \"      <td>0.250347</td>\\n\",\n       \"      <td>0.208878</td>\\n\",\n       \"      <td>0.185839</td>\\n\",\n       \"      <td>0.208878</td>\\n\",\n       \"      <td>1.198052</td>\\n\",\n       \"      <td>0.206885</td>\\n\",\n       \"      <td>0.206885</td>\\n\",\n       \"      <td>0.206885</td>\\n\",\n       \"      <td>0.206885</td>\\n\",\n       \"      <td>0.206885</td>\\n\",\n       \"      <td>4.727273</td>\\n\",\n       \"      <td>0.062171</td>\\n\",\n       \"      <td>0.669677</td>\\n\",\n       \"      <td>0.113168</td>\\n\",\n       \"      <td>0.062171</td>\\n\",\n       \"      <td>0.113168</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001653</td>\\n\",\n       \"      <td>0.010749</td>\\n\",\n       \"      <td>0.002766</td>\\n\",\n       \"      <td>0.001653</td>\\n\",\n       \"      <td>0.002766</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000558</td>\\n\",\n       \"      <td>0.017396</td>\\n\",\n       \"      <td>0.001073</td>\\n\",\n       \"      <td>0.000558</td>\\n\",\n       \"      <td>0.001073</td>\\n\",\n       \"      <td>0.003247</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.021213</td>\\n\",\n       \"      <td>0.000229</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.000229</td>\\n\",\n       \"      <td>0.093452</td>\\n\",\n       \"      <td>0.188868</td>\\n\",\n       \"      <td>0.127280</td>\\n\",\n       \"      <td>0.222677</td>\\n\",\n       \"      <td>0.306971</td>\\n\",\n       \"      <td>0.345667</td>\\n\",\n       \"      <td>0.404280</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.005237</td>\\n\",\n       \"      <td>0.005237</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>78</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>69.805195</td>\\n\",\n       \"      <td>331.642857</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.727273</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.100601</td>\\n\",\n       \"      <td>0.115919</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.115919</td>\\n\",\n       \"      <td>1.207792</td>\\n\",\n       \"      <td>0.120779</td>\\n\",\n       \"      <td>0.166011</td>\\n\",\n       \"      <td>0.136756</td>\\n\",\n       \"      <td>0.120779</td>\\n\",\n       \"      <td>0.136756</td>\\n\",\n       \"      <td>1.025974</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>0.132831</td>\\n\",\n       \"      <td>3.691558</td>\\n\",\n       \"      <td>0.055446</td>\\n\",\n       \"      <td>0.484908</td>\\n\",\n       \"      <td>0.098701</td>\\n\",\n       \"      <td>0.055446</td>\\n\",\n       \"      <td>0.098701</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.009740</td>\\n\",\n       \"      <td>0.000031</td>\\n\",\n       \"      <td>0.005952</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.000031</td>\\n\",\n       \"      <td>0.000062</td>\\n\",\n       \"      <td>0.834416</td>\\n\",\n       \"      <td>0.240233</td>\\n\",\n       \"      <td>0.163032</td>\\n\",\n       \"      <td>0.189772</td>\\n\",\n       \"      <td>0.240233</td>\\n\",\n       \"      <td>0.189772</td>\\n\",\n       \"      <td>1.506494</td>\\n\",\n       \"      <td>0.193492</td>\\n\",\n       \"      <td>0.259036</td>\\n\",\n       \"      <td>0.217046</td>\\n\",\n       \"      <td>0.193492</td>\\n\",\n       \"      <td>0.217046</td>\\n\",\n       \"      <td>1.295455</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>0.217284</td>\\n\",\n       \"      <td>4.337662</td>\\n\",\n       \"      <td>0.071434</td>\\n\",\n       \"      <td>0.621703</td>\\n\",\n       \"      <td>0.127106</td>\\n\",\n       \"      <td>0.071434</td>\\n\",\n       \"      <td>0.127106</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001838</td>\\n\",\n       \"      <td>0.011788</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.001838</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000554</td>\\n\",\n       \"      <td>0.017822</td>\\n\",\n       \"      <td>0.001066</td>\\n\",\n       \"      <td>0.000554</td>\\n\",\n       \"      <td>0.001066</td>\\n\",\n       \"      <td>0.012987</td>\\n\",\n       \"      <td>0.000124</td>\\n\",\n       \"      <td>0.025324</td>\\n\",\n       \"      <td>0.000246</td>\\n\",\n       \"      <td>0.000124</td>\\n\",\n       \"      <td>0.000246</td>\\n\",\n       \"      <td>0.091300</td>\\n\",\n       \"      <td>0.207958</td>\\n\",\n       \"      <td>0.144624</td>\\n\",\n       \"      <td>0.224051</td>\\n\",\n       \"      <td>0.293580</td>\\n\",\n       \"      <td>0.317872</td>\\n\",\n       \"      <td>0.380552</td>\\n\",\n       \"      <td>0.000037</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.000115</td>\\n\",\n       \"      <td>0.000937</td>\\n\",\n       \"      <td>0.001059</td>\\n\",\n       \"      <td>0.002780</td>\\n\",\n       \"      <td>0.002780</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>52.216000</td>\\n\",\n       \"      <td>369.698000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.776000</td>\\n\",\n       \"      <td>0.355200</td>\\n\",\n       \"      <td>0.278714</td>\\n\",\n       \"      <td>0.292117</td>\\n\",\n       \"      <td>0.355200</td>\\n\",\n       \"      <td>0.292117</td>\\n\",\n       \"      <td>2.878000</td>\\n\",\n       \"      <td>0.287800</td>\\n\",\n       \"      <td>0.432416</td>\\n\",\n       \"      <td>0.324297</td>\\n\",\n       \"      <td>0.287800</td>\\n\",\n       \"      <td>0.324297</td>\\n\",\n       \"      <td>2.640000</td>\\n\",\n       \"      <td>0.337863</td>\\n\",\n       \"      <td>0.337863</td>\\n\",\n       \"      <td>0.337863</td>\\n\",\n       \"      <td>0.337863</td>\\n\",\n       \"      <td>0.337863</td>\\n\",\n       \"      <td>6.704000</td>\\n\",\n       \"      <td>0.128952</td>\\n\",\n       \"      <td>0.885955</td>\\n\",\n       \"      <td>0.220645</td>\\n\",\n       \"      <td>0.128952</td>\\n\",\n       \"      <td>0.220645</td>\\n\",\n       \"      <td>0.078000</td>\\n\",\n       \"      <td>0.007800</td>\\n\",\n       \"      <td>0.041071</td>\\n\",\n       \"      <td>0.012202</td>\\n\",\n       \"      <td>0.007800</td>\\n\",\n       \"      <td>0.012202</td>\\n\",\n       \"      <td>0.176000</td>\\n\",\n       \"      <td>0.003520</td>\\n\",\n       \"      <td>0.087947</td>\\n\",\n       \"      <td>0.006643</td>\\n\",\n       \"      <td>0.003520</td>\\n\",\n       \"      <td>0.006643</td>\\n\",\n       \"      <td>0.338000</td>\\n\",\n       \"      <td>0.000926</td>\\n\",\n       \"      <td>0.147841</td>\\n\",\n       \"      <td>0.001836</td>\\n\",\n       \"      <td>0.000926</td>\\n\",\n       \"      <td>0.001836</td>\\n\",\n       \"      <td>1.964000</td>\\n\",\n       \"      <td>0.467958</td>\\n\",\n       \"      <td>0.362419</td>\\n\",\n       \"      <td>0.381157</td>\\n\",\n       \"      <td>0.467958</td>\\n\",\n       \"      <td>0.381157</td>\\n\",\n       \"      <td>3.246000</td>\\n\",\n       \"      <td>0.364067</td>\\n\",\n       \"      <td>0.537777</td>\\n\",\n       \"      <td>0.406608</td>\\n\",\n       \"      <td>0.364067</td>\\n\",\n       \"      <td>0.406608</td>\\n\",\n       \"      <td>3.040000</td>\\n\",\n       \"      <td>0.438239</td>\\n\",\n       \"      <td>0.438239</td>\\n\",\n       \"      <td>0.438239</td>\\n\",\n       \"      <td>0.438239</td>\\n\",\n       \"      <td>0.438239</td>\\n\",\n       \"      <td>6.854000</td>\\n\",\n       \"      <td>0.135288</td>\\n\",\n       \"      <td>0.928281</td>\\n\",\n       \"      <td>0.231421</td>\\n\",\n       \"      <td>0.135288</td>\\n\",\n       \"      <td>0.231421</td>\\n\",\n       \"      <td>0.090000</td>\\n\",\n       \"      <td>0.028940</td>\\n\",\n       \"      <td>0.126133</td>\\n\",\n       \"      <td>0.043974</td>\\n\",\n       \"      <td>0.028940</td>\\n\",\n       \"      <td>0.043974</td>\\n\",\n       \"      <td>0.252000</td>\\n\",\n       \"      <td>0.010328</td>\\n\",\n       \"      <td>0.217613</td>\\n\",\n       \"      <td>0.019301</td>\\n\",\n       \"      <td>0.010328</td>\\n\",\n       \"      <td>0.019301</td>\\n\",\n       \"      <td>0.484000</td>\\n\",\n       \"      <td>0.002120</td>\\n\",\n       \"      <td>0.308720</td>\\n\",\n       \"      <td>0.004197</td>\\n\",\n       \"      <td>0.002120</td>\\n\",\n       \"      <td>0.004197</td>\\n\",\n       \"      <td>0.326509</td>\\n\",\n       \"      <td>0.399050</td>\\n\",\n       \"      <td>0.203907</td>\\n\",\n       \"      <td>0.504316</td>\\n\",\n       \"      <td>0.607636</td>\\n\",\n       \"      <td>0.545872</td>\\n\",\n       \"      <td>0.626369</td>\\n\",\n       \"      <td>0.016366</td>\\n\",\n       \"      <td>0.031944</td>\\n\",\n       \"      <td>0.030421</td>\\n\",\n       \"      <td>0.045386</td>\\n\",\n       \"      <td>0.051120</td>\\n\",\n       \"      <td>0.030722</td>\\n\",\n       \"      <td>0.031491</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>83</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>44.402000</td>\\n\",\n       \"      <td>381.750000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.764000</td>\\n\",\n       \"      <td>0.352800</td>\\n\",\n       \"      <td>0.276021</td>\\n\",\n       \"      <td>0.289861</td>\\n\",\n       \"      <td>0.352800</td>\\n\",\n       \"      <td>0.289861</td>\\n\",\n       \"      <td>2.834000</td>\\n\",\n       \"      <td>0.283400</td>\\n\",\n       \"      <td>0.426882</td>\\n\",\n       \"      <td>0.320370</td>\\n\",\n       \"      <td>0.283400</td>\\n\",\n       \"      <td>0.320370</td>\\n\",\n       \"      <td>2.604000</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>0.334620</td>\\n\",\n       \"      <td>6.284000</td>\\n\",\n       \"      <td>0.142254</td>\\n\",\n       \"      <td>0.834030</td>\\n\",\n       \"      <td>0.237851</td>\\n\",\n       \"      <td>0.142254</td>\\n\",\n       \"      <td>0.237851</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.048575</td>\\n\",\n       \"      <td>0.015503</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.015503</td>\\n\",\n       \"      <td>0.190000</td>\\n\",\n       \"      <td>0.003800</td>\\n\",\n       \"      <td>0.090361</td>\\n\",\n       \"      <td>0.007166</td>\\n\",\n       \"      <td>0.003800</td>\\n\",\n       \"      <td>0.007166</td>\\n\",\n       \"      <td>0.338000</td>\\n\",\n       \"      <td>0.000887</td>\\n\",\n       \"      <td>0.141355</td>\\n\",\n       \"      <td>0.001758</td>\\n\",\n       \"      <td>0.000887</td>\\n\",\n       \"      <td>0.001758</td>\\n\",\n       \"      <td>1.952000</td>\\n\",\n       \"      <td>0.466169</td>\\n\",\n       \"      <td>0.361676</td>\\n\",\n       \"      <td>0.380171</td>\\n\",\n       \"      <td>0.466169</td>\\n\",\n       \"      <td>0.380171</td>\\n\",\n       \"      <td>3.212000</td>\\n\",\n       \"      <td>0.360859</td>\\n\",\n       \"      <td>0.531851</td>\\n\",\n       \"      <td>0.403184</td>\\n\",\n       \"      <td>0.360859</td>\\n\",\n       \"      <td>0.403184</td>\\n\",\n       \"      <td>2.992000</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>0.433587</td>\\n\",\n       \"      <td>6.512000</td>\\n\",\n       \"      <td>0.152374</td>\\n\",\n       \"      <td>0.891328</td>\\n\",\n       \"      <td>0.254628</td>\\n\",\n       \"      <td>0.152374</td>\\n\",\n       \"      <td>0.254628</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.030517</td>\\n\",\n       \"      <td>0.131412</td>\\n\",\n       \"      <td>0.046328</td>\\n\",\n       \"      <td>0.030517</td>\\n\",\n       \"      <td>0.046328</td>\\n\",\n       \"      <td>0.254000</td>\\n\",\n       \"      <td>0.010489</td>\\n\",\n       \"      <td>0.219348</td>\\n\",\n       \"      <td>0.019604</td>\\n\",\n       \"      <td>0.010489</td>\\n\",\n       \"      <td>0.019604</td>\\n\",\n       \"      <td>0.472000</td>\\n\",\n       \"      <td>0.002053</td>\\n\",\n       \"      <td>0.306846</td>\\n\",\n       \"      <td>0.004066</td>\\n\",\n       \"      <td>0.002053</td>\\n\",\n       \"      <td>0.004066</td>\\n\",\n       \"      <td>0.316208</td>\\n\",\n       \"      <td>0.407457</td>\\n\",\n       \"      <td>0.213142</td>\\n\",\n       \"      <td>0.491125</td>\\n\",\n       \"      <td>0.585661</td>\\n\",\n       \"      <td>0.543668</td>\\n\",\n       \"      <td>0.622040</td>\\n\",\n       \"      <td>0.014343</td>\\n\",\n       \"      <td>0.032080</td>\\n\",\n       \"      <td>0.030609</td>\\n\",\n       \"      <td>0.045255</td>\\n\",\n       \"      <td>0.050635</td>\\n\",\n       \"      <td>0.030401</td>\\n\",\n       \"      <td>0.030594</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>57.251914</td>\\n\",\n       \"      <td>368.631060</td>\\n\",\n       \"      <td>498.548456</td>\\n\",\n       \"      <td>498.548456</td>\\n\",\n       \"      <td>498.548456</td>\\n\",\n       \"      <td>2225.124531</td>\\n\",\n       \"      <td>1.395107</td>\\n\",\n       \"      <td>0.279021</td>\\n\",\n       \"      <td>0.486950</td>\\n\",\n       \"      <td>0.330973</td>\\n\",\n       \"      <td>0.279021</td>\\n\",\n       \"      <td>0.330973</td>\\n\",\n       \"      <td>1.929204</td>\\n\",\n       \"      <td>0.192920</td>\\n\",\n       \"      <td>0.645607</td>\\n\",\n       \"      <td>0.278801</td>\\n\",\n       \"      <td>0.192920</td>\\n\",\n       \"      <td>0.278801</td>\\n\",\n       \"      <td>1.335468</td>\\n\",\n       \"      <td>0.356062</td>\\n\",\n       \"      <td>0.356062</td>\\n\",\n       \"      <td>0.356062</td>\\n\",\n       \"      <td>0.356062</td>\\n\",\n       \"      <td>0.356062</td>\\n\",\n       \"      <td>3.112423</td>\\n\",\n       \"      <td>0.056935</td>\\n\",\n       \"      <td>0.914882</td>\\n\",\n       \"      <td>0.102515</td>\\n\",\n       \"      <td>0.056935</td>\\n\",\n       \"      <td>0.102515</td>\\n\",\n       \"      <td>0.138340</td>\\n\",\n       \"      <td>0.013834</td>\\n\",\n       \"      <td>0.064341</td>\\n\",\n       \"      <td>0.021801</td>\\n\",\n       \"      <td>0.013834</td>\\n\",\n       \"      <td>0.021801</td>\\n\",\n       \"      <td>0.275079</td>\\n\",\n       \"      <td>0.005502</td>\\n\",\n       \"      <td>0.125970</td>\\n\",\n       \"      <td>0.010401</td>\\n\",\n       \"      <td>0.005502</td>\\n\",\n       \"      <td>0.010401</td>\\n\",\n       \"      <td>0.416421</td>\\n\",\n       \"      <td>0.001132</td>\\n\",\n       \"      <td>0.188574</td>\\n\",\n       \"      <td>0.002246</td>\\n\",\n       \"      <td>0.001132</td>\\n\",\n       \"      <td>0.002246</td>\\n\",\n       \"      <td>1.422625</td>\\n\",\n       \"      <td>0.322163</td>\\n\",\n       \"      <td>0.560310</td>\\n\",\n       \"      <td>0.381347</td>\\n\",\n       \"      <td>0.322163</td>\\n\",\n       \"      <td>0.381347</td>\\n\",\n       \"      <td>1.967429</td>\\n\",\n       \"      <td>0.214673</td>\\n\",\n       \"      <td>0.713126</td>\\n\",\n       \"      <td>0.309208</td>\\n\",\n       \"      <td>0.214673</td>\\n\",\n       \"      <td>0.309208</td>\\n\",\n       \"      <td>1.374293</td>\\n\",\n       \"      <td>0.419127</td>\\n\",\n       \"      <td>0.419127</td>\\n\",\n       \"      <td>0.419127</td>\\n\",\n       \"      <td>0.419127</td>\\n\",\n       \"      <td>0.419127</td>\\n\",\n       \"      <td>3.121279</td>\\n\",\n       \"      <td>0.057824</td>\\n\",\n       \"      <td>0.926865</td>\\n\",\n       \"      <td>0.104055</td>\\n\",\n       \"      <td>0.057824</td>\\n\",\n       \"      <td>0.104055</td>\\n\",\n       \"      <td>0.144694</td>\\n\",\n       \"      <td>0.030910</td>\\n\",\n       \"      <td>0.140663</td>\\n\",\n       \"      <td>0.048417</td>\\n\",\n       \"      <td>0.030910</td>\\n\",\n       \"      <td>0.048417</td>\\n\",\n       \"      <td>0.303247</td>\\n\",\n       \"      <td>0.010551</td>\\n\",\n       \"      <td>0.235907</td>\\n\",\n       \"      <td>0.019914</td>\\n\",\n       \"      <td>0.010551</td>\\n\",\n       \"      <td>0.019914</td>\\n\",\n       \"      <td>0.471156</td>\\n\",\n       \"      <td>0.001959</td>\\n\",\n       \"      <td>0.318803</td>\\n\",\n       \"      <td>0.003886</td>\\n\",\n       \"      <td>0.001959</td>\\n\",\n       \"      <td>0.003886</td>\\n\",\n       \"      <td>0.389728</td>\\n\",\n       \"      <td>0.454564</td>\\n\",\n       \"      <td>0.330770</td>\\n\",\n       \"      <td>0.556240</td>\\n\",\n       \"      <td>0.613373</td>\\n\",\n       \"      <td>0.520436</td>\\n\",\n       \"      <td>0.526383</td>\\n\",\n       \"      <td>0.027356</td>\\n\",\n       \"      <td>0.059837</td>\\n\",\n       \"      <td>0.058040</td>\\n\",\n       \"      <td>0.066860</td>\\n\",\n       \"      <td>0.075342</td>\\n\",\n       \"      <td>0.043846</td>\\n\",\n       \"      <td>0.044019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>77</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>49.054135</td>\\n\",\n       \"      <td>380.151148</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>497.658378</td>\\n\",\n       \"      <td>2242.250213</td>\\n\",\n       \"      <td>1.393005</td>\\n\",\n       \"      <td>0.278601</td>\\n\",\n       \"      <td>0.486837</td>\\n\",\n       \"      <td>0.330571</td>\\n\",\n       \"      <td>0.278601</td>\\n\",\n       \"      <td>0.330571</td>\\n\",\n       \"      <td>1.923000</td>\\n\",\n       \"      <td>0.192304</td>\\n\",\n       \"      <td>0.644470</td>\\n\",\n       \"      <td>0.277956</td>\\n\",\n       \"      <td>0.192300</td>\\n\",\n       \"      <td>0.277950</td>\\n\",\n       \"      <td>1.336519</td>\\n\",\n       \"      <td>0.353454</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>0.353453</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>0.353452</td>\\n\",\n       \"      <td>3.033071</td>\\n\",\n       \"      <td>0.064979</td>\\n\",\n       \"      <td>0.899688</td>\\n\",\n       \"      <td>0.115484</td>\\n\",\n       \"      <td>0.064979</td>\\n\",\n       \"      <td>0.115484</td>\\n\",\n       \"      <td>0.148196</td>\\n\",\n       \"      <td>0.014820</td>\\n\",\n       \"      <td>0.069768</td>\\n\",\n       \"      <td>0.023406</td>\\n\",\n       \"      <td>0.014820</td>\\n\",\n       \"      <td>0.023406</td>\\n\",\n       \"      <td>0.290639</td>\\n\",\n       \"      <td>0.005813</td>\\n\",\n       \"      <td>0.133821</td>\\n\",\n       \"      <td>0.010994</td>\\n\",\n       \"      <td>0.005813</td>\\n\",\n       \"      <td>0.010994</td>\\n\",\n       \"      <td>0.439786</td>\\n\",\n       \"      <td>0.001159</td>\\n\",\n       \"      <td>0.200463</td>\\n\",\n       \"      <td>0.002301</td>\\n\",\n       \"      <td>0.001159</td>\\n\",\n       \"      <td>0.002301</td>\\n\",\n       \"      <td>1.420523</td>\\n\",\n       \"      <td>0.322414</td>\\n\",\n       \"      <td>0.560786</td>\\n\",\n       \"      <td>0.381636</td>\\n\",\n       \"      <td>0.322414</td>\\n\",\n       \"      <td>0.381636</td>\\n\",\n       \"      <td>1.961775</td>\\n\",\n       \"      <td>0.214185</td>\\n\",\n       \"      <td>0.711640</td>\\n\",\n       \"      <td>0.308473</td>\\n\",\n       \"      <td>0.214180</td>\\n\",\n       \"      <td>0.308466</td>\\n\",\n       \"      <td>1.373943</td>\\n\",\n       \"      <td>0.417629</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>0.417628</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>0.417627</td>\\n\",\n       \"      <td>3.045580</td>\\n\",\n       \"      <td>0.066355</td>\\n\",\n       \"      <td>0.915690</td>\\n\",\n       \"      <td>0.117828</td>\\n\",\n       \"      <td>0.066355</td>\\n\",\n       \"      <td>0.117828</td>\\n\",\n       \"      <td>0.155751</td>\\n\",\n       \"      <td>0.032226</td>\\n\",\n       \"      <td>0.147540</td>\\n\",\n       \"      <td>0.050534</td>\\n\",\n       \"      <td>0.032226</td>\\n\",\n       \"      <td>0.050534</td>\\n\",\n       \"      <td>0.317306</td>\\n\",\n       \"      <td>0.010812</td>\\n\",\n       \"      <td>0.242365</td>\\n\",\n       \"      <td>0.020409</td>\\n\",\n       \"      <td>0.010812</td>\\n\",\n       \"      <td>0.020409</td>\\n\",\n       \"      <td>0.495172</td>\\n\",\n       \"      <td>0.001955</td>\\n\",\n       \"      <td>0.328999</td>\\n\",\n       \"      <td>0.003879</td>\\n\",\n       \"      <td>0.001955</td>\\n\",\n       \"      <td>0.003879</td>\\n\",\n       \"      <td>0.384625</td>\\n\",\n       \"      <td>0.455979</td>\\n\",\n       \"      <td>0.332556</td>\\n\",\n       \"      <td>0.552072</td>\\n\",\n       \"      <td>0.606808</td>\\n\",\n       \"      <td>0.518431</td>\\n\",\n       \"      <td>0.524368</td>\\n\",\n       \"      <td>0.029264</td>\\n\",\n       \"      <td>0.063326</td>\\n\",\n       \"      <td>0.061357</td>\\n\",\n       \"      <td>0.071243</td>\\n\",\n       \"      <td>0.080194</td>\\n\",\n       \"      <td>0.047877</td>\\n\",\n       \"      <td>0.048025</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>57.205500</td>\\n\",\n       \"      <td>367.945000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.408500</td>\\n\",\n       \"      <td>0.281700</td>\\n\",\n       \"      <td>0.498524</td>\\n\",\n       \"      <td>0.335665</td>\\n\",\n       \"      <td>0.281700</td>\\n\",\n       \"      <td>0.335665</td>\\n\",\n       \"      <td>1.953500</td>\\n\",\n       \"      <td>0.195350</td>\\n\",\n       \"      <td>0.662105</td>\\n\",\n       \"      <td>0.282873</td>\\n\",\n       \"      <td>0.195350</td>\\n\",\n       \"      <td>0.282873</td>\\n\",\n       \"      <td>1.337000</td>\\n\",\n       \"      <td>0.355796</td>\\n\",\n       \"      <td>0.355796</td>\\n\",\n       \"      <td>0.355796</td>\\n\",\n       \"      <td>0.355796</td>\\n\",\n       \"      <td>0.355796</td>\\n\",\n       \"      <td>3.101500</td>\\n\",\n       \"      <td>0.056658</td>\\n\",\n       \"      <td>0.912156</td>\\n\",\n       \"      <td>0.101936</td>\\n\",\n       \"      <td>0.056658</td>\\n\",\n       \"      <td>0.101936</td>\\n\",\n       \"      <td>0.149500</td>\\n\",\n       \"      <td>0.014950</td>\\n\",\n       \"      <td>0.067968</td>\\n\",\n       \"      <td>0.023511</td>\\n\",\n       \"      <td>0.014950</td>\\n\",\n       \"      <td>0.023511</td>\\n\",\n       \"      <td>0.275000</td>\\n\",\n       \"      <td>0.005500</td>\\n\",\n       \"      <td>0.126504</td>\\n\",\n       \"      <td>0.010417</td>\\n\",\n       \"      <td>0.005500</td>\\n\",\n       \"      <td>0.010417</td>\\n\",\n       \"      <td>0.406500</td>\\n\",\n       \"      <td>0.001104</td>\\n\",\n       \"      <td>0.185664</td>\\n\",\n       \"      <td>0.002191</td>\\n\",\n       \"      <td>0.001104</td>\\n\",\n       \"      <td>0.002191</td>\\n\",\n       \"      <td>1.434000</td>\\n\",\n       \"      <td>0.324073</td>\\n\",\n       \"      <td>0.569714</td>\\n\",\n       \"      <td>0.384889</td>\\n\",\n       \"      <td>0.324073</td>\\n\",\n       \"      <td>0.384889</td>\\n\",\n       \"      <td>1.998000</td>\\n\",\n       \"      <td>0.216978</td>\\n\",\n       \"      <td>0.726183</td>\\n\",\n       \"      <td>0.312869</td>\\n\",\n       \"      <td>0.216978</td>\\n\",\n       \"      <td>0.312869</td>\\n\",\n       \"      <td>1.377000</td>\\n\",\n       \"      <td>0.415721</td>\\n\",\n       \"      <td>0.415721</td>\\n\",\n       \"      <td>0.415721</td>\\n\",\n       \"      <td>0.415721</td>\\n\",\n       \"      <td>0.415721</td>\\n\",\n       \"      <td>3.112500</td>\\n\",\n       \"      <td>0.057608</td>\\n\",\n       \"      <td>0.924953</td>\\n\",\n       \"      <td>0.103574</td>\\n\",\n       \"      <td>0.057608</td>\\n\",\n       \"      <td>0.103574</td>\\n\",\n       \"      <td>0.154500</td>\\n\",\n       \"      <td>0.032151</td>\\n\",\n       \"      <td>0.145589</td>\\n\",\n       \"      <td>0.050471</td>\\n\",\n       \"      <td>0.032151</td>\\n\",\n       \"      <td>0.050471</td>\\n\",\n       \"      <td>0.300000</td>\\n\",\n       \"      <td>0.010612</td>\\n\",\n       \"      <td>0.239197</td>\\n\",\n       \"      <td>0.020067</td>\\n\",\n       \"      <td>0.010612</td>\\n\",\n       \"      <td>0.020067</td>\\n\",\n       \"      <td>0.456500</td>\\n\",\n       \"      <td>0.001972</td>\\n\",\n       \"      <td>0.322262</td>\\n\",\n       \"      <td>0.003912</td>\\n\",\n       \"      <td>0.001972</td>\\n\",\n       \"      <td>0.003912</td>\\n\",\n       \"      <td>0.391062</td>\\n\",\n       \"      <td>0.458255</td>\\n\",\n       \"      <td>0.333496</td>\\n\",\n       \"      <td>0.560159</td>\\n\",\n       \"      <td>0.615080</td>\\n\",\n       \"      <td>0.510986</td>\\n\",\n       \"      <td>0.516684</td>\\n\",\n       \"      <td>0.028624</td>\\n\",\n       \"      <td>0.060060</td>\\n\",\n       \"      <td>0.057473</td>\\n\",\n       \"      <td>0.068145</td>\\n\",\n       \"      <td>0.075958</td>\\n\",\n       \"      <td>0.047304</td>\\n\",\n       \"      <td>0.047434</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>79</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>48.918000</td>\\n\",\n       \"      <td>379.279500</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.403000</td>\\n\",\n       \"      <td>0.280600</td>\\n\",\n       \"      <td>0.497367</td>\\n\",\n       \"      <td>0.334004</td>\\n\",\n       \"      <td>0.280600</td>\\n\",\n       \"      <td>0.334004</td>\\n\",\n       \"      <td>1.948000</td>\\n\",\n       \"      <td>0.194800</td>\\n\",\n       \"      <td>0.661260</td>\\n\",\n       \"      <td>0.282044</td>\\n\",\n       \"      <td>0.194800</td>\\n\",\n       \"      <td>0.282044</td>\\n\",\n       \"      <td>1.345500</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>0.358846</td>\\n\",\n       \"      <td>3.031500</td>\\n\",\n       \"      <td>0.064932</td>\\n\",\n       \"      <td>0.899449</td>\\n\",\n       \"      <td>0.115291</td>\\n\",\n       \"      <td>0.064932</td>\\n\",\n       \"      <td>0.115291</td>\\n\",\n       \"      <td>0.153500</td>\\n\",\n       \"      <td>0.015350</td>\\n\",\n       \"      <td>0.069898</td>\\n\",\n       \"      <td>0.024154</td>\\n\",\n       \"      <td>0.015350</td>\\n\",\n       \"      <td>0.024154</td>\\n\",\n       \"      <td>0.291000</td>\\n\",\n       \"      <td>0.005820</td>\\n\",\n       \"      <td>0.135362</td>\\n\",\n       \"      <td>0.011027</td>\\n\",\n       \"      <td>0.005820</td>\\n\",\n       \"      <td>0.011027</td>\\n\",\n       \"      <td>0.438500</td>\\n\",\n       \"      <td>0.001154</td>\\n\",\n       \"      <td>0.201439</td>\\n\",\n       \"      <td>0.002291</td>\\n\",\n       \"      <td>0.001154</td>\\n\",\n       \"      <td>0.002291</td>\\n\",\n       \"      <td>1.427500</td>\\n\",\n       \"      <td>0.322982</td>\\n\",\n       \"      <td>0.566254</td>\\n\",\n       \"      <td>0.382858</td>\\n\",\n       \"      <td>0.322982</td>\\n\",\n       \"      <td>0.382858</td>\\n\",\n       \"      <td>1.986000</td>\\n\",\n       \"      <td>0.216015</td>\\n\",\n       \"      <td>0.722818</td>\\n\",\n       \"      <td>0.311294</td>\\n\",\n       \"      <td>0.216015</td>\\n\",\n       \"      <td>0.311294</td>\\n\",\n       \"      <td>1.384000</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>0.420597</td>\\n\",\n       \"      <td>3.045000</td>\\n\",\n       \"      <td>0.066299</td>\\n\",\n       \"      <td>0.916028</td>\\n\",\n       \"      <td>0.117614</td>\\n\",\n       \"      <td>0.066299</td>\\n\",\n       \"      <td>0.117614</td>\\n\",\n       \"      <td>0.159500</td>\\n\",\n       \"      <td>0.032977</td>\\n\",\n       \"      <td>0.149964</td>\\n\",\n       \"      <td>0.051809</td>\\n\",\n       \"      <td>0.032977</td>\\n\",\n       \"      <td>0.051809</td>\\n\",\n       \"      <td>0.315500</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.248631</td>\\n\",\n       \"      <td>0.020779</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.020779</td>\\n\",\n       \"      <td>0.491500</td>\\n\",\n       \"      <td>0.001985</td>\\n\",\n       \"      <td>0.336167</td>\\n\",\n       \"      <td>0.003939</td>\\n\",\n       \"      <td>0.001985</td>\\n\",\n       \"      <td>0.003939</td>\\n\",\n       \"      <td>0.391085</td>\\n\",\n       \"      <td>0.461993</td>\\n\",\n       \"      <td>0.338297</td>\\n\",\n       \"      <td>0.559063</td>\\n\",\n       \"      <td>0.612446</td>\\n\",\n       \"      <td>0.514048</td>\\n\",\n       \"      <td>0.519904</td>\\n\",\n       \"      <td>0.030876</td>\\n\",\n       \"      <td>0.066772</td>\\n\",\n       \"      <td>0.064009</td>\\n\",\n       \"      <td>0.073106</td>\\n\",\n       \"      <td>0.081964</td>\\n\",\n       \"      <td>0.050022</td>\\n\",\n       \"      <td>0.050202</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>54.089130</td>\\n\",\n       \"      <td>365.610870</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.339130</td>\\n\",\n       \"      <td>0.267826</td>\\n\",\n       \"      <td>0.484399</td>\\n\",\n       \"      <td>0.318251</td>\\n\",\n       \"      <td>0.267826</td>\\n\",\n       \"      <td>0.318251</td>\\n\",\n       \"      <td>1.826087</td>\\n\",\n       \"      <td>0.182609</td>\\n\",\n       \"      <td>0.620367</td>\\n\",\n       \"      <td>0.263334</td>\\n\",\n       \"      <td>0.182609</td>\\n\",\n       \"      <td>0.263334</td>\\n\",\n       \"      <td>1.186957</td>\\n\",\n       \"      <td>0.341882</td>\\n\",\n       \"      <td>0.341882</td>\\n\",\n       \"      <td>0.341882</td>\\n\",\n       \"      <td>0.341882</td>\\n\",\n       \"      <td>0.341882</td>\\n\",\n       \"      <td>3.043478</td>\\n\",\n       \"      <td>0.056973</td>\\n\",\n       \"      <td>0.912094</td>\\n\",\n       \"      <td>0.104321</td>\\n\",\n       \"      <td>0.056973</td>\\n\",\n       \"      <td>0.104321</td>\\n\",\n       \"      <td>0.180435</td>\\n\",\n       \"      <td>0.018043</td>\\n\",\n       \"      <td>0.066702</td>\\n\",\n       \"      <td>0.026499</td>\\n\",\n       \"      <td>0.018043</td>\\n\",\n       \"      <td>0.026499</td>\\n\",\n       \"      <td>0.345652</td>\\n\",\n       \"      <td>0.006913</td>\\n\",\n       \"      <td>0.132739</td>\\n\",\n       \"      <td>0.012888</td>\\n\",\n       \"      <td>0.006913</td>\\n\",\n       \"      <td>0.012888</td>\\n\",\n       \"      <td>0.534783</td>\\n\",\n       \"      <td>0.001455</td>\\n\",\n       \"      <td>0.202800</td>\\n\",\n       \"      <td>0.002880</td>\\n\",\n       \"      <td>0.001455</td>\\n\",\n       \"      <td>0.002880</td>\\n\",\n       \"      <td>1.367391</td>\\n\",\n       \"      <td>0.315251</td>\\n\",\n       \"      <td>0.559234</td>\\n\",\n       \"      <td>0.370365</td>\\n\",\n       \"      <td>0.315251</td>\\n\",\n       \"      <td>0.370365</td>\\n\",\n       \"      <td>1.906522</td>\\n\",\n       \"      <td>0.209482</td>\\n\",\n       \"      <td>0.696897</td>\\n\",\n       \"      <td>0.298997</td>\\n\",\n       \"      <td>0.209482</td>\\n\",\n       \"      <td>0.298997</td>\\n\",\n       \"      <td>1.260870</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>0.407583</td>\\n\",\n       \"      <td>3.060870</td>\\n\",\n       \"      <td>0.058221</td>\\n\",\n       \"      <td>0.926750</td>\\n\",\n       \"      <td>0.106515</td>\\n\",\n       \"      <td>0.058221</td>\\n\",\n       \"      <td>0.106515</td>\\n\",\n       \"      <td>0.197826</td>\\n\",\n       \"      <td>0.039706</td>\\n\",\n       \"      <td>0.150155</td>\\n\",\n       \"      <td>0.058599</td>\\n\",\n       \"      <td>0.039706</td>\\n\",\n       \"      <td>0.058599</td>\\n\",\n       \"      <td>0.404348</td>\\n\",\n       \"      <td>0.013809</td>\\n\",\n       \"      <td>0.258688</td>\\n\",\n       \"      <td>0.025688</td>\\n\",\n       \"      <td>0.013809</td>\\n\",\n       \"      <td>0.025688</td>\\n\",\n       \"      <td>0.650000</td>\\n\",\n       \"      <td>0.002608</td>\\n\",\n       \"      <td>0.354814</td>\\n\",\n       \"      <td>0.005160</td>\\n\",\n       \"      <td>0.002608</td>\\n\",\n       \"      <td>0.005160</td>\\n\",\n       \"      <td>0.370623</td>\\n\",\n       \"      <td>0.438493</td>\\n\",\n       \"      <td>0.325852</td>\\n\",\n       \"      <td>0.546935</td>\\n\",\n       \"      <td>0.600735</td>\\n\",\n       \"      <td>0.499567</td>\\n\",\n       \"      <td>0.511675</td>\\n\",\n       \"      <td>0.028881</td>\\n\",\n       \"      <td>0.074416</td>\\n\",\n       \"      <td>0.071483</td>\\n\",\n       \"      <td>0.074560</td>\\n\",\n       \"      <td>0.085085</td>\\n\",\n       \"      <td>0.051881</td>\\n\",\n       \"      <td>0.052376</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>82</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>46.528261</td>\\n\",\n       \"      <td>378.417391</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.332609</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.483465</td>\\n\",\n       \"      <td>0.317053</td>\\n\",\n       \"      <td>0.266522</td>\\n\",\n       \"      <td>0.317053</td>\\n\",\n       \"      <td>1.893478</td>\\n\",\n       \"      <td>0.189348</td>\\n\",\n       \"      <td>0.640867</td>\\n\",\n       \"      <td>0.272448</td>\\n\",\n       \"      <td>0.189348</td>\\n\",\n       \"      <td>0.272448</td>\\n\",\n       \"      <td>1.204348</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>0.345168</td>\\n\",\n       \"      <td>2.952174</td>\\n\",\n       \"      <td>0.064462</td>\\n\",\n       \"      <td>0.894865</td>\\n\",\n       \"      <td>0.116644</td>\\n\",\n       \"      <td>0.064462</td>\\n\",\n       \"      <td>0.116644</td>\\n\",\n       \"      <td>0.217391</td>\\n\",\n       \"      <td>0.021739</td>\\n\",\n       \"      <td>0.085324</td>\\n\",\n       \"      <td>0.032630</td>\\n\",\n       \"      <td>0.021739</td>\\n\",\n       \"      <td>0.032630</td>\\n\",\n       \"      <td>0.373913</td>\\n\",\n       \"      <td>0.007478</td>\\n\",\n       \"      <td>0.139386</td>\\n\",\n       \"      <td>0.013917</td>\\n\",\n       \"      <td>0.007478</td>\\n\",\n       \"      <td>0.013917</td>\\n\",\n       \"      <td>0.556522</td>\\n\",\n       \"      <td>0.001465</td>\\n\",\n       \"      <td>0.207167</td>\\n\",\n       \"      <td>0.002901</td>\\n\",\n       \"      <td>0.001465</td>\\n\",\n       \"      <td>0.002901</td>\\n\",\n       \"      <td>1.378261</td>\\n\",\n       \"      <td>0.315008</td>\\n\",\n       \"      <td>0.556890</td>\\n\",\n       \"      <td>0.369044</td>\\n\",\n       \"      <td>0.315008</td>\\n\",\n       \"      <td>0.369044</td>\\n\",\n       \"      <td>1.958696</td>\\n\",\n       \"      <td>0.213756</td>\\n\",\n       \"      <td>0.704711</td>\\n\",\n       \"      <td>0.304214</td>\\n\",\n       \"      <td>0.213756</td>\\n\",\n       \"      <td>0.304214</td>\\n\",\n       \"      <td>1.278261</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>0.410627</td>\\n\",\n       \"      <td>2.971739</td>\\n\",\n       \"      <td>0.066235</td>\\n\",\n       \"      <td>0.913145</td>\\n\",\n       \"      <td>0.119715</td>\\n\",\n       \"      <td>0.066235</td>\\n\",\n       \"      <td>0.119715</td>\\n\",\n       \"      <td>0.230435</td>\\n\",\n       \"      <td>0.043473</td>\\n\",\n       \"      <td>0.167630</td>\\n\",\n       \"      <td>0.064693</td>\\n\",\n       \"      <td>0.043473</td>\\n\",\n       \"      <td>0.064693</td>\\n\",\n       \"      <td>0.426087</td>\\n\",\n       \"      <td>0.014361</td>\\n\",\n       \"      <td>0.264867</td>\\n\",\n       \"      <td>0.026690</td>\\n\",\n       \"      <td>0.014361</td>\\n\",\n       \"      <td>0.026690</td>\\n\",\n       \"      <td>0.669565</td>\\n\",\n       \"      <td>0.002587</td>\\n\",\n       \"      <td>0.360654</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.002587</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.379387</td>\\n\",\n       \"      <td>0.450949</td>\\n\",\n       \"      <td>0.335888</td>\\n\",\n       \"      <td>0.545130</td>\\n\",\n       \"      <td>0.600735</td>\\n\",\n       \"      <td>0.502666</td>\\n\",\n       \"      <td>0.515249</td>\\n\",\n       \"      <td>0.035782</td>\\n\",\n       \"      <td>0.086806</td>\\n\",\n       \"      <td>0.083077</td>\\n\",\n       \"      <td>0.081692</td>\\n\",\n       \"      <td>0.095008</td>\\n\",\n       \"      <td>0.059456</td>\\n\",\n       \"      <td>0.060308</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>56.691943</td>\\n\",\n       \"      <td>368.350711</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>2.137441</td>\\n\",\n       \"      <td>0.427488</td>\\n\",\n       \"      <td>0.429895</td>\\n\",\n       \"      <td>0.392144</td>\\n\",\n       \"      <td>0.427488</td>\\n\",\n       \"      <td>0.392144</td>\\n\",\n       \"      <td>3.009479</td>\\n\",\n       \"      <td>0.300948</td>\\n\",\n       \"      <td>0.575393</td>\\n\",\n       \"      <td>0.365869</td>\\n\",\n       \"      <td>0.300948</td>\\n\",\n       \"      <td>0.365869</td>\\n\",\n       \"      <td>2.592417</td>\\n\",\n       \"      <td>0.435701</td>\\n\",\n       \"      <td>0.435701</td>\\n\",\n       \"      <td>0.435701</td>\\n\",\n       \"      <td>0.435701</td>\\n\",\n       \"      <td>0.435701</td>\\n\",\n       \"      <td>4.976303</td>\\n\",\n       \"      <td>0.092170</td>\\n\",\n       \"      <td>0.863524</td>\\n\",\n       \"      <td>0.161187</td>\\n\",\n       \"      <td>0.092170</td>\\n\",\n       \"      <td>0.161187</td>\\n\",\n       \"      <td>0.218009</td>\\n\",\n       \"      <td>0.021801</td>\\n\",\n       \"      <td>0.049569</td>\\n\",\n       \"      <td>0.025139</td>\\n\",\n       \"      <td>0.021801</td>\\n\",\n       \"      <td>0.025139</td>\\n\",\n       \"      <td>0.563981</td>\\n\",\n       \"      <td>0.011280</td>\\n\",\n       \"      <td>0.113848</td>\\n\",\n       \"      <td>0.019259</td>\\n\",\n       \"      <td>0.011280</td>\\n\",\n       \"      <td>0.019259</td>\\n\",\n       \"      <td>0.905213</td>\\n\",\n       \"      <td>0.002439</td>\\n\",\n       \"      <td>0.180395</td>\\n\",\n       \"      <td>0.004761</td>\\n\",\n       \"      <td>0.002439</td>\\n\",\n       \"      <td>0.004761</td>\\n\",\n       \"      <td>2.189573</td>\\n\",\n       \"      <td>0.494205</td>\\n\",\n       \"      <td>0.494643</td>\\n\",\n       \"      <td>0.452337</td>\\n\",\n       \"      <td>0.494205</td>\\n\",\n       \"      <td>0.452337</td>\\n\",\n       \"      <td>3.170616</td>\\n\",\n       \"      <td>0.347149</td>\\n\",\n       \"      <td>0.651976</td>\\n\",\n       \"      <td>0.420083</td>\\n\",\n       \"      <td>0.347149</td>\\n\",\n       \"      <td>0.420083</td>\\n\",\n       \"      <td>2.734597</td>\\n\",\n       \"      <td>0.506856</td>\\n\",\n       \"      <td>0.506856</td>\\n\",\n       \"      <td>0.506856</td>\\n\",\n       \"      <td>0.506856</td>\\n\",\n       \"      <td>0.506856</td>\\n\",\n       \"      <td>5.151659</td>\\n\",\n       \"      <td>0.099476</td>\\n\",\n       \"      <td>0.912741</td>\\n\",\n       \"      <td>0.173598</td>\\n\",\n       \"      <td>0.099476</td>\\n\",\n       \"      <td>0.173598</td>\\n\",\n       \"      <td>0.327014</td>\\n\",\n       \"      <td>0.064010</td>\\n\",\n       \"      <td>0.127012</td>\\n\",\n       \"      <td>0.073078</td>\\n\",\n       \"      <td>0.064010</td>\\n\",\n       \"      <td>0.073078</td>\\n\",\n       \"      <td>0.872038</td>\\n\",\n       \"      <td>0.024576</td>\\n\",\n       \"      <td>0.223158</td>\\n\",\n       \"      <td>0.041588</td>\\n\",\n       \"      <td>0.024576</td>\\n\",\n       \"      <td>0.041588</td>\\n\",\n       \"      <td>1.450237</td>\\n\",\n       \"      <td>0.004837</td>\\n\",\n       \"      <td>0.318129</td>\\n\",\n       \"      <td>0.009429</td>\\n\",\n       \"      <td>0.004837</td>\\n\",\n       \"      <td>0.009429</td>\\n\",\n       \"      <td>0.421844</td>\\n\",\n       \"      <td>0.523403</td>\\n\",\n       \"      <td>0.307670</td>\\n\",\n       \"      <td>0.575886</td>\\n\",\n       \"      <td>0.665095</td>\\n\",\n       \"      <td>0.572421</td>\\n\",\n       \"      <td>0.616615</td>\\n\",\n       \"      <td>0.018235</td>\\n\",\n       \"      <td>0.063796</td>\\n\",\n       \"      <td>0.058345</td>\\n\",\n       \"      <td>0.062098</td>\\n\",\n       \"      <td>0.077747</td>\\n\",\n       \"      <td>0.042823</td>\\n\",\n       \"      <td>0.052390</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>81</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>47.971564</td>\\n\",\n       \"      <td>382.943128</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>2.184834</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.444856</td>\\n\",\n       \"      <td>0.402446</td>\\n\",\n       \"      <td>0.436967</td>\\n\",\n       \"      <td>0.402446</td>\\n\",\n       \"      <td>2.952607</td>\\n\",\n       \"      <td>0.295261</td>\\n\",\n       \"      <td>0.565834</td>\\n\",\n       \"      <td>0.359772</td>\\n\",\n       \"      <td>0.295261</td>\\n\",\n       \"      <td>0.359772</td>\\n\",\n       \"      <td>2.597156</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>0.432855</td>\\n\",\n       \"      <td>4.848341</td>\\n\",\n       \"      <td>0.106813</td>\\n\",\n       \"      <td>0.843396</td>\\n\",\n       \"      <td>0.182876</td>\\n\",\n       \"      <td>0.106813</td>\\n\",\n       \"      <td>0.182876</td>\\n\",\n       \"      <td>0.213270</td>\\n\",\n       \"      <td>0.021327</td>\\n\",\n       \"      <td>0.046272</td>\\n\",\n       \"      <td>0.023420</td>\\n\",\n       \"      <td>0.021327</td>\\n\",\n       \"      <td>0.023420</td>\\n\",\n       \"      <td>0.597156</td>\\n\",\n       \"      <td>0.011943</td>\\n\",\n       \"      <td>0.122822</td>\\n\",\n       \"      <td>0.020429</td>\\n\",\n       \"      <td>0.011943</td>\\n\",\n       \"      <td>0.020429</td>\\n\",\n       \"      <td>0.924171</td>\\n\",\n       \"      <td>0.002396</td>\\n\",\n       \"      <td>0.182038</td>\\n\",\n       \"      <td>0.004682</td>\\n\",\n       \"      <td>0.002396</td>\\n\",\n       \"      <td>0.004682</td>\\n\",\n       \"      <td>2.232227</td>\\n\",\n       \"      <td>0.505064</td>\\n\",\n       \"      <td>0.509617</td>\\n\",\n       \"      <td>0.463853</td>\\n\",\n       \"      <td>0.505064</td>\\n\",\n       \"      <td>0.463853</td>\\n\",\n       \"      <td>3.109005</td>\\n\",\n       \"      <td>0.344811</td>\\n\",\n       \"      <td>0.650430</td>\\n\",\n       \"      <td>0.418135</td>\\n\",\n       \"      <td>0.344811</td>\\n\",\n       \"      <td>0.418135</td>\\n\",\n       \"      <td>2.739336</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>0.506244</td>\\n\",\n       \"      <td>5.033175</td>\\n\",\n       \"      <td>0.116308</td>\\n\",\n       \"      <td>0.897778</td>\\n\",\n       \"      <td>0.198631</td>\\n\",\n       \"      <td>0.116308</td>\\n\",\n       \"      <td>0.198631</td>\\n\",\n       \"      <td>0.322275</td>\\n\",\n       \"      <td>0.063100</td>\\n\",\n       \"      <td>0.120033</td>\\n\",\n       \"      <td>0.070450</td>\\n\",\n       \"      <td>0.063100</td>\\n\",\n       \"      <td>0.070450</td>\\n\",\n       \"      <td>0.919431</td>\\n\",\n       \"      <td>0.025341</td>\\n\",\n       \"      <td>0.234054</td>\\n\",\n       \"      <td>0.042969</td>\\n\",\n       \"      <td>0.025341</td>\\n\",\n       \"      <td>0.042969</td>\\n\",\n       \"      <td>1.464455</td>\\n\",\n       \"      <td>0.004690</td>\\n\",\n       \"      <td>0.320374</td>\\n\",\n       \"      <td>0.009153</td>\\n\",\n       \"      <td>0.004690</td>\\n\",\n       \"      <td>0.009153</td>\\n\",\n       \"      <td>0.429044</td>\\n\",\n       \"      <td>0.541626</td>\\n\",\n       \"      <td>0.321157</td>\\n\",\n       \"      <td>0.575096</td>\\n\",\n       \"      <td>0.664121</td>\\n\",\n       \"      <td>0.582729</td>\\n\",\n       \"      <td>0.624832</td>\\n\",\n       \"      <td>0.016737</td>\\n\",\n       \"      <td>0.070061</td>\\n\",\n       \"      <td>0.065042</td>\\n\",\n       \"      <td>0.060403</td>\\n\",\n       \"      <td>0.078266</td>\\n\",\n       \"      <td>0.049930</td>\\n\",\n       \"      <td>0.059064</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_40000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>56.380000</td>\\n\",\n       \"      <td>366.810000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.990000</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.318959</td>\\n\",\n       \"      <td>0.334811</td>\\n\",\n       \"      <td>0.398000</td>\\n\",\n       \"      <td>0.334811</td>\\n\",\n       \"      <td>3.020000</td>\\n\",\n       \"      <td>0.302000</td>\\n\",\n       \"      <td>0.485085</td>\\n\",\n       \"      <td>0.351777</td>\\n\",\n       \"      <td>0.302000</td>\\n\",\n       \"      <td>0.351777</td>\\n\",\n       \"      <td>2.540000</td>\\n\",\n       \"      <td>0.352486</td>\\n\",\n       \"      <td>0.352486</td>\\n\",\n       \"      <td>0.352486</td>\\n\",\n       \"      <td>0.352486</td>\\n\",\n       \"      <td>0.352486</td>\\n\",\n       \"      <td>5.720000</td>\\n\",\n       \"      <td>0.103600</td>\\n\",\n       \"      <td>0.874427</td>\\n\",\n       \"      <td>0.180833</td>\\n\",\n       \"      <td>0.103600</td>\\n\",\n       \"      <td>0.180833</td>\\n\",\n       \"      <td>0.180000</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.024885</td>\\n\",\n       \"      <td>0.020150</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.020150</td>\\n\",\n       \"      <td>0.440000</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.056324</td>\\n\",\n       \"      <td>0.015038</td>\\n\",\n       \"      <td>0.008800</td>\\n\",\n       \"      <td>0.015038</td>\\n\",\n       \"      <td>0.760000</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.098199</td>\\n\",\n       \"      <td>0.004153</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.004153</td>\\n\",\n       \"      <td>2.080000</td>\\n\",\n       \"      <td>0.478924</td>\\n\",\n       \"      <td>0.387256</td>\\n\",\n       \"      <td>0.404496</td>\\n\",\n       \"      <td>0.478924</td>\\n\",\n       \"      <td>0.404496</td>\\n\",\n       \"      <td>3.210000</td>\\n\",\n       \"      <td>0.357626</td>\\n\",\n       \"      <td>0.570505</td>\\n\",\n       \"      <td>0.415783</td>\\n\",\n       \"      <td>0.357626</td>\\n\",\n       \"      <td>0.415783</td>\\n\",\n       \"      <td>2.700000</td>\\n\",\n       \"      <td>0.438224</td>\\n\",\n       \"      <td>0.438224</td>\\n\",\n       \"      <td>0.438224</td>\\n\",\n       \"      <td>0.438224</td>\\n\",\n       \"      <td>0.438224</td>\\n\",\n       \"      <td>5.810000</td>\\n\",\n       \"      <td>0.109150</td>\\n\",\n       \"      <td>0.913483</td>\\n\",\n       \"      <td>0.190196</td>\\n\",\n       \"      <td>0.109150</td>\\n\",\n       \"      <td>0.190196</td>\\n\",\n       \"      <td>0.270000</td>\\n\",\n       \"      <td>0.069235</td>\\n\",\n       \"      <td>0.087304</td>\\n\",\n       \"      <td>0.074543</td>\\n\",\n       \"      <td>0.069235</td>\\n\",\n       \"      <td>0.074543</td>\\n\",\n       \"      <td>0.860000</td>\\n\",\n       \"      <td>0.026469</td>\\n\",\n       \"      <td>0.163776</td>\\n\",\n       \"      <td>0.044814</td>\\n\",\n       \"      <td>0.026469</td>\\n\",\n       \"      <td>0.044814</td>\\n\",\n       \"      <td>1.510000</td>\\n\",\n       \"      <td>0.005643</td>\\n\",\n       \"      <td>0.249968</td>\\n\",\n       \"      <td>0.010999</td>\\n\",\n       \"      <td>0.005643</td>\\n\",\n       \"      <td>0.010999</td>\\n\",\n       \"      <td>0.349028</td>\\n\",\n       \"      <td>0.430952</td>\\n\",\n       \"      <td>0.228896</td>\\n\",\n       \"      <td>0.514550</td>\\n\",\n       \"      <td>0.618622</td>\\n\",\n       \"      <td>0.521619</td>\\n\",\n       \"      <td>0.576123</td>\\n\",\n       \"      <td>0.014259</td>\\n\",\n       \"      <td>0.087534</td>\\n\",\n       \"      <td>0.085606</td>\\n\",\n       \"      <td>0.038275</td>\\n\",\n       \"      <td>0.058379</td>\\n\",\n       \"      <td>0.059390</td>\\n\",\n       \"      <td>0.067076</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>80</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2one-kp20k</td>\\n\",\n       \"      <td>kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...</td>\\n\",\n       \"      <td>step_100000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>48.530000</td>\\n\",\n       \"      <td>378.250000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>1.920000</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.304143</td>\\n\",\n       \"      <td>0.321593</td>\\n\",\n       \"      <td>0.384000</td>\\n\",\n       \"      <td>0.321593</td>\\n\",\n       \"      <td>2.950000</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.473317</td>\\n\",\n       \"      <td>0.343758</td>\\n\",\n       \"      <td>0.295000</td>\\n\",\n       \"      <td>0.343758</td>\\n\",\n       \"      <td>2.430000</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>0.337615</td>\\n\",\n       \"      <td>5.450000</td>\\n\",\n       \"      <td>0.114519</td>\\n\",\n       \"      <td>0.836617</td>\\n\",\n       \"      <td>0.195946</td>\\n\",\n       \"      <td>0.114519</td>\\n\",\n       \"      <td>0.195946</td>\\n\",\n       \"      <td>0.180000</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.022961</td>\\n\",\n       \"      <td>0.019800</td>\\n\",\n       \"      <td>0.018000</td>\\n\",\n       \"      <td>0.019800</td>\\n\",\n       \"      <td>0.460000</td>\\n\",\n       \"      <td>0.009200</td>\\n\",\n       \"      <td>0.061082</td>\\n\",\n       \"      <td>0.015819</td>\\n\",\n       \"      <td>0.009200</td>\\n\",\n       \"      <td>0.015819</td>\\n\",\n       \"      <td>0.800000</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.101568</td>\\n\",\n       \"      <td>0.004158</td>\\n\",\n       \"      <td>0.002127</td>\\n\",\n       \"      <td>0.004158</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>0.470994</td>\\n\",\n       \"      <td>0.378530</td>\\n\",\n       \"      <td>0.397349</td>\\n\",\n       \"      <td>0.470994</td>\\n\",\n       \"      <td>0.397349</td>\\n\",\n       \"      <td>3.150000</td>\\n\",\n       \"      <td>0.351866</td>\\n\",\n       \"      <td>0.563634</td>\\n\",\n       \"      <td>0.409536</td>\\n\",\n       \"      <td>0.351866</td>\\n\",\n       \"      <td>0.409536</td>\\n\",\n       \"      <td>2.620000</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>0.425488</td>\\n\",\n       \"      <td>5.590000</td>\\n\",\n       \"      <td>0.122265</td>\\n\",\n       \"      <td>0.884154</td>\\n\",\n       \"      <td>0.208801</td>\\n\",\n       \"      <td>0.122265</td>\\n\",\n       \"      <td>0.208801</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.072059</td>\\n\",\n       \"      <td>0.089026</td>\\n\",\n       \"      <td>0.077427</td>\\n\",\n       \"      <td>0.072059</td>\\n\",\n       \"      <td>0.077427</td>\\n\",\n       \"      <td>0.870000</td>\\n\",\n       \"      <td>0.026946</td>\\n\",\n       \"      <td>0.167713</td>\\n\",\n       \"      <td>0.045661</td>\\n\",\n       \"      <td>0.026946</td>\\n\",\n       \"      <td>0.045661</td>\\n\",\n       \"      <td>1.570000</td>\\n\",\n       \"      <td>0.005546</td>\\n\",\n       \"      <td>0.255692</td>\\n\",\n       \"      <td>0.010820</td>\\n\",\n       \"      <td>0.005546</td>\\n\",\n       \"      <td>0.010820</td>\\n\",\n       \"      <td>0.344738</td>\\n\",\n       \"      <td>0.439681</td>\\n\",\n       \"      <td>0.237985</td>\\n\",\n       \"      <td>0.495311</td>\\n\",\n       \"      <td>0.605620</td>\\n\",\n       \"      <td>0.525368</td>\\n\",\n       \"      <td>0.573741</td>\\n\",\n       \"      <td>0.013270</td>\\n\",\n       \"      <td>0.084758</td>\\n\",\n       \"      <td>0.082866</td>\\n\",\n       \"      <td>0.038479</td>\\n\",\n       \"      <td>0.058214</td>\\n\",\n       \"      <td>0.052090</td>\\n\",\n       \"      <td>0.064509</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                 path  \\\\\\n\",\n       \"26  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"78  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"22  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"83  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"27  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"77  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"24  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"79  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"25  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"82  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"23  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"81  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"21  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"80  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"\\n\",\n       \"                         exp_group  \\\\\\n\",\n       \"26            meng17-one2one-kp20k   \\n\",\n       \"78            meng17-one2one-kp20k   \\n\",\n       \"22            meng17-one2one-kp20k   \\n\",\n       \"83            meng17-one2one-kp20k   \\n\",\n       \"27  meng17-one2one-kp20k-topmodels   \\n\",\n       \"77  meng17-one2one-kp20k-topmodels   \\n\",\n       \"24            meng17-one2one-kp20k   \\n\",\n       \"79            meng17-one2one-kp20k   \\n\",\n       \"25            meng17-one2one-kp20k   \\n\",\n       \"82            meng17-one2one-kp20k   \\n\",\n       \"23            meng17-one2one-kp20k   \\n\",\n       \"81            meng17-one2one-kp20k   \\n\",\n       \"21            meng17-one2one-kp20k   \\n\",\n       \"80            meng17-one2one-kp20k   \\n\",\n       \"\\n\",\n       \"                                             exp_name  \\\\\\n\",\n       \"26  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"78  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"22  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"83  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"27  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"77  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"24  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"79  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"25  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"82  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"23  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"81  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"21  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"80  kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-D...   \\n\",\n       \"\\n\",\n       \"                               test_name tokenization train_mode model_base  \\\\\\n\",\n       \"26             step_40000-duc-exhaustive       meng17    one2one        rnn   \\n\",\n       \"78            step_100000-duc-exhaustive       meng17    one2one        rnn   \\n\",\n       \"22          step_40000-inspec-exhaustive       meng17    one2one        rnn   \\n\",\n       \"83         step_100000-inspec-exhaustive       meng17    one2one        rnn   \\n\",\n       \"27           step_40000-kp20k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"77          step_100000-kp20k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"24   step_40000-kp20k_valid2k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"79  step_100000-kp20k_valid2k-exhaustive       meng17    one2one        rnn   \\n\",\n       \"25        step_40000-krapivin-exhaustive       meng17    one2one        rnn   \\n\",\n       \"82       step_100000-krapivin-exhaustive       meng17    one2one        rnn   \\n\",\n       \"23             step_40000-nus-exhaustive       meng17    one2one        rnn   \\n\",\n       \"81            step_100000-nus-exhaustive       meng17    one2one        rnn   \\n\",\n       \"21         step_40000-semeval-exhaustive       meng17    one2one        rnn   \\n\",\n       \"80        step_100000-semeval-exhaustive       meng17    one2one        rnn   \\n\",\n       \"\\n\",\n       \"      order train_dataset    step   test_dataset decoding_method  \\\\\\n\",\n       \"26  one2one         kp20k   40000            duc      exhaustive   \\n\",\n       \"78  one2one         kp20k  100000            duc      exhaustive   \\n\",\n       \"22  one2one         kp20k   40000         inspec      exhaustive   \\n\",\n       \"83  one2one         kp20k  100000         inspec      exhaustive   \\n\",\n       \"27  one2one         kp20k   40000          kp20k      exhaustive   \\n\",\n       \"77  one2one         kp20k  100000          kp20k      exhaustive   \\n\",\n       \"24  one2one         kp20k   40000  kp20k_valid2k      exhaustive   \\n\",\n       \"79  one2one         kp20k  100000  kp20k_valid2k      exhaustive   \\n\",\n       \"25  one2one         kp20k   40000       krapivin      exhaustive   \\n\",\n       \"82  one2one         kp20k  100000       krapivin      exhaustive   \\n\",\n       \"23  one2one         kp20k   40000            nus      exhaustive   \\n\",\n       \"81  one2one         kp20k  100000            nus      exhaustive   \\n\",\n       \"21  one2one         kp20k   40000        semeval      exhaustive   \\n\",\n       \"80  one2one         kp20k  100000        semeval      exhaustive   \\n\",\n       \"\\n\",\n       \"   decoding_terminate beam_width max_length  present_tgt_num  absent_tgt_num  \\\\\\n\",\n       \"26           fullbeam        200          6         7.860390        0.204545   \\n\",\n       \"78           fullbeam        200          6         7.860390        0.204545   \\n\",\n       \"22           fullbeam        200          6         7.716000        2.110000   \\n\",\n       \"83           fullbeam        200          6         7.716000        2.110000   \\n\",\n       \"27           fullbeam        200          6         3.331916        1.930555   \\n\",\n       \"77           fullbeam        200          6         3.331916        1.930555   \\n\",\n       \"24           fullbeam        200          6         3.327500        1.937500   \\n\",\n       \"79           fullbeam        200          6         3.327500        1.937500   \\n\",\n       \"25           fullbeam        200          6         3.228261        2.513043   \\n\",\n       \"82           fullbeam        200          6         3.228261        2.513043   \\n\",\n       \"23           fullbeam        200          6         5.985782        5.677725   \\n\",\n       \"81           fullbeam        200          6         5.985782        5.677725   \\n\",\n       \"21           fullbeam        200          6         6.710000        8.360000   \\n\",\n       \"80           fullbeam        200          6         6.710000        8.360000   \\n\",\n       \"\\n\",\n       \"    present_pred_num  absent_pred_num  unique_pred_num  dup_pred_num  \\\\\\n\",\n       \"26         85.720779       299.870130         0.000000      0.000000   \\n\",\n       \"78         69.805195       331.642857         0.000000      0.000000   \\n\",\n       \"22         52.216000       369.698000         0.000000      0.000000   \\n\",\n       \"83         44.402000       381.750000         0.000000      0.000000   \\n\",\n       \"27         57.251914       368.631060       498.548456    498.548456   \\n\",\n       \"77         49.054135       380.151148       497.658378    497.658378   \\n\",\n       \"24         57.205500       367.945000         0.000000      0.000000   \\n\",\n       \"79         48.918000       379.279500         0.000000      0.000000   \\n\",\n       \"25         54.089130       365.610870         0.000000      0.000000   \\n\",\n       \"82         46.528261       378.417391         0.000000      0.000000   \\n\",\n       \"23         56.691943       368.350711         0.000000      0.000000   \\n\",\n       \"81         47.971564       382.943128         0.000000      0.000000   \\n\",\n       \"21         56.380000       366.810000         0.000000      0.000000   \\n\",\n       \"80         48.530000       378.250000         0.000000      0.000000   \\n\",\n       \"\\n\",\n       \"      beam_num  beamstep_num  present_exact_correct@5  \\\\\\n\",\n       \"26    0.000000      0.000000                 0.694805   \\n\",\n       \"78    0.000000      0.000000                 0.727273   \\n\",\n       \"22    0.000000      0.000000                 1.776000   \\n\",\n       \"83    0.000000      0.000000                 1.764000   \\n\",\n       \"27  498.548456   2225.124531                 1.395107   \\n\",\n       \"77  497.658378   2242.250213                 1.393005   \\n\",\n       \"24    0.000000      0.000000                 1.408500   \\n\",\n       \"79    0.000000      0.000000                 1.403000   \\n\",\n       \"25    0.000000      0.000000                 1.339130   \\n\",\n       \"82    0.000000      0.000000                 1.332609   \\n\",\n       \"23    0.000000      0.000000                 2.137441   \\n\",\n       \"81    0.000000      0.000000                 2.184834   \\n\",\n       \"21    0.000000      0.000000                 1.990000   \\n\",\n       \"80    0.000000      0.000000                 1.920000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@5  present_exact_recall@5  \\\\\\n\",\n       \"26                   0.138961                0.098413   \\n\",\n       \"78                   0.145455                0.100601   \\n\",\n       \"22                   0.355200                0.278714   \\n\",\n       \"83                   0.352800                0.276021   \\n\",\n       \"27                   0.279021                0.486950   \\n\",\n       \"77                   0.278601                0.486837   \\n\",\n       \"24                   0.281700                0.498524   \\n\",\n       \"79                   0.280600                0.497367   \\n\",\n       \"25                   0.267826                0.484399   \\n\",\n       \"82                   0.266522                0.483465   \\n\",\n       \"23                   0.427488                0.429895   \\n\",\n       \"81                   0.436967                0.444856   \\n\",\n       \"21                   0.398000                0.318959   \\n\",\n       \"80                   0.384000                0.304143   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@5  present_exact_precision_hard@5  \\\\\\n\",\n       \"26                 0.111953                        0.138961   \\n\",\n       \"78                 0.115919                        0.145455   \\n\",\n       \"22                 0.292117                        0.355200   \\n\",\n       \"83                 0.289861                        0.352800   \\n\",\n       \"27                 0.330973                        0.279021   \\n\",\n       \"77                 0.330571                        0.278601   \\n\",\n       \"24                 0.335665                        0.281700   \\n\",\n       \"79                 0.334004                        0.280600   \\n\",\n       \"25                 0.318251                        0.267826   \\n\",\n       \"82                 0.317053                        0.266522   \\n\",\n       \"23                 0.392144                        0.427488   \\n\",\n       \"81                 0.402446                        0.436967   \\n\",\n       \"21                 0.334811                        0.398000   \\n\",\n       \"80                 0.321593                        0.384000   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@5  present_exact_correct@10  \\\\\\n\",\n       \"26                      0.111953                  1.090909   \\n\",\n       \"78                      0.115919                  1.207792   \\n\",\n       \"22                      0.292117                  2.878000   \\n\",\n       \"83                      0.289861                  2.834000   \\n\",\n       \"27                      0.330973                  1.929204   \\n\",\n       \"77                      0.330571                  1.923000   \\n\",\n       \"24                      0.335665                  1.953500   \\n\",\n       \"79                      0.334004                  1.948000   \\n\",\n       \"25                      0.318251                  1.826087   \\n\",\n       \"82                      0.317053                  1.893478   \\n\",\n       \"23                      0.392144                  3.009479   \\n\",\n       \"81                      0.402446                  2.952607   \\n\",\n       \"21                      0.334811                  3.020000   \\n\",\n       \"80                      0.321593                  2.950000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@10  present_exact_recall@10  \\\\\\n\",\n       \"26                    0.109091                 0.151873   \\n\",\n       \"78                    0.120779                 0.166011   \\n\",\n       \"22                    0.287800                 0.432416   \\n\",\n       \"83                    0.283400                 0.426882   \\n\",\n       \"27                    0.192920                 0.645607   \\n\",\n       \"77                    0.192304                 0.644470   \\n\",\n       \"24                    0.195350                 0.662105   \\n\",\n       \"79                    0.194800                 0.661260   \\n\",\n       \"25                    0.182609                 0.620367   \\n\",\n       \"82                    0.189348                 0.640867   \\n\",\n       \"23                    0.300948                 0.575393   \\n\",\n       \"81                    0.295261                 0.565834   \\n\",\n       \"21                    0.302000                 0.485085   \\n\",\n       \"80                    0.295000                 0.473317   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@10  present_exact_precision_hard@10  \\\\\\n\",\n       \"26                  0.124002                         0.109091   \\n\",\n       \"78                  0.136756                         0.120779   \\n\",\n       \"22                  0.324297                         0.287800   \\n\",\n       \"83                  0.320370                         0.283400   \\n\",\n       \"27                  0.278801                         0.192920   \\n\",\n       \"77                  0.277956                         0.192300   \\n\",\n       \"24                  0.282873                         0.195350   \\n\",\n       \"79                  0.282044                         0.194800   \\n\",\n       \"25                  0.263334                         0.182609   \\n\",\n       \"82                  0.272448                         0.189348   \\n\",\n       \"23                  0.365869                         0.300948   \\n\",\n       \"81                  0.359772                         0.295261   \\n\",\n       \"21                  0.351777                         0.302000   \\n\",\n       \"80                  0.343758                         0.295000   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@10  present_exact_correct@k  \\\\\\n\",\n       \"26                       0.124002                 0.915584   \\n\",\n       \"78                       0.136756                 1.025974   \\n\",\n       \"22                       0.324297                 2.640000   \\n\",\n       \"83                       0.320370                 2.604000   \\n\",\n       \"27                       0.278801                 1.335468   \\n\",\n       \"77                       0.277950                 1.336519   \\n\",\n       \"24                       0.282873                 1.337000   \\n\",\n       \"79                       0.282044                 1.345500   \\n\",\n       \"25                       0.263334                 1.186957   \\n\",\n       \"82                       0.272448                 1.204348   \\n\",\n       \"23                       0.365869                 2.592417   \\n\",\n       \"81                       0.359772                 2.597156   \\n\",\n       \"21                       0.351777                 2.540000   \\n\",\n       \"80                       0.343758                 2.430000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@k  present_exact_recall@k  \\\\\\n\",\n       \"26                   0.119022                0.119022   \\n\",\n       \"78                   0.132831                0.132831   \\n\",\n       \"22                   0.337863                0.337863   \\n\",\n       \"83                   0.334620                0.334620   \\n\",\n       \"27                   0.356062                0.356062   \\n\",\n       \"77                   0.353454                0.353452   \\n\",\n       \"24                   0.355796                0.355796   \\n\",\n       \"79                   0.358846                0.358846   \\n\",\n       \"25                   0.341882                0.341882   \\n\",\n       \"82                   0.345168                0.345168   \\n\",\n       \"23                   0.435701                0.435701   \\n\",\n       \"81                   0.432855                0.432855   \\n\",\n       \"21                   0.352486                0.352486   \\n\",\n       \"80                   0.337615                0.337615   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@k  present_exact_precision_hard@k  \\\\\\n\",\n       \"26                 0.119022                        0.119022   \\n\",\n       \"78                 0.132831                        0.132831   \\n\",\n       \"22                 0.337863                        0.337863   \\n\",\n       \"83                 0.334620                        0.334620   \\n\",\n       \"27                 0.356062                        0.356062   \\n\",\n       \"77                 0.353453                        0.353452   \\n\",\n       \"24                 0.355796                        0.355796   \\n\",\n       \"79                 0.358846                        0.358846   \\n\",\n       \"25                 0.341882                        0.341882   \\n\",\n       \"82                 0.345168                        0.345168   \\n\",\n       \"23                 0.435701                        0.435701   \\n\",\n       \"81                 0.432855                        0.432855   \\n\",\n       \"21                 0.352486                        0.352486   \\n\",\n       \"80                 0.337615                        0.337615   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@k  present_exact_correct@M  \\\\\\n\",\n       \"26                      0.119022                 4.126623   \\n\",\n       \"78                      0.132831                 3.691558   \\n\",\n       \"22                      0.337863                 6.704000   \\n\",\n       \"83                      0.334620                 6.284000   \\n\",\n       \"27                      0.356062                 3.112423   \\n\",\n       \"77                      0.353452                 3.033071   \\n\",\n       \"24                      0.355796                 3.101500   \\n\",\n       \"79                      0.358846                 3.031500   \\n\",\n       \"25                      0.341882                 3.043478   \\n\",\n       \"82                      0.345168                 2.952174   \\n\",\n       \"23                      0.435701                 4.976303   \\n\",\n       \"81                      0.432855                 4.848341   \\n\",\n       \"21                      0.352486                 5.720000   \\n\",\n       \"80                      0.337615                 5.450000   \\n\",\n       \"\\n\",\n       \"    present_exact_precision@M  present_exact_recall@M  \\\\\\n\",\n       \"26                   0.049967                0.541126   \\n\",\n       \"78                   0.055446                0.484908   \\n\",\n       \"22                   0.128952                0.885955   \\n\",\n       \"83                   0.142254                0.834030   \\n\",\n       \"27                   0.056935                0.914882   \\n\",\n       \"77                   0.064979                0.899688   \\n\",\n       \"24                   0.056658                0.912156   \\n\",\n       \"79                   0.064932                0.899449   \\n\",\n       \"25                   0.056973                0.912094   \\n\",\n       \"82                   0.064462                0.894865   \\n\",\n       \"23                   0.092170                0.863524   \\n\",\n       \"81                   0.106813                0.843396   \\n\",\n       \"21                   0.103600                0.874427   \\n\",\n       \"80                   0.114519                0.836617   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score@M  present_exact_precision_hard@M  \\\\\\n\",\n       \"26                 0.090996                        0.049967   \\n\",\n       \"78                 0.098701                        0.055446   \\n\",\n       \"22                 0.220645                        0.128952   \\n\",\n       \"83                 0.237851                        0.142254   \\n\",\n       \"27                 0.102515                        0.056935   \\n\",\n       \"77                 0.115484                        0.064979   \\n\",\n       \"24                 0.101936                        0.056658   \\n\",\n       \"79                 0.115291                        0.064932   \\n\",\n       \"25                 0.104321                        0.056973   \\n\",\n       \"82                 0.116644                        0.064462   \\n\",\n       \"23                 0.161187                        0.092170   \\n\",\n       \"81                 0.182876                        0.106813   \\n\",\n       \"21                 0.180833                        0.103600   \\n\",\n       \"80                 0.195946                        0.114519   \\n\",\n       \"\\n\",\n       \"    present_exact_f_score_hard@M  absent_exact_correct@10  \\\\\\n\",\n       \"26                      0.090996                 0.000000   \\n\",\n       \"78                      0.098701                 0.000000   \\n\",\n       \"22                      0.220645                 0.078000   \\n\",\n       \"83                      0.237851                 0.100000   \\n\",\n       \"27                      0.102515                 0.138340   \\n\",\n       \"77                      0.115484                 0.148196   \\n\",\n       \"24                      0.101936                 0.149500   \\n\",\n       \"79                      0.115291                 0.153500   \\n\",\n       \"25                      0.104321                 0.180435   \\n\",\n       \"82                      0.116644                 0.217391   \\n\",\n       \"23                      0.161187                 0.218009   \\n\",\n       \"81                      0.182876                 0.213270   \\n\",\n       \"21                      0.180833                 0.180000   \\n\",\n       \"80                      0.195946                 0.180000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@10  absent_exact_recall@10  \\\\\\n\",\n       \"26                   0.000000                0.000000   \\n\",\n       \"78                   0.000000                0.000000   \\n\",\n       \"22                   0.007800                0.041071   \\n\",\n       \"83                   0.010000                0.048575   \\n\",\n       \"27                   0.013834                0.064341   \\n\",\n       \"77                   0.014820                0.069768   \\n\",\n       \"24                   0.014950                0.067968   \\n\",\n       \"79                   0.015350                0.069898   \\n\",\n       \"25                   0.018043                0.066702   \\n\",\n       \"82                   0.021739                0.085324   \\n\",\n       \"23                   0.021801                0.049569   \\n\",\n       \"81                   0.021327                0.046272   \\n\",\n       \"21                   0.018000                0.024885   \\n\",\n       \"80                   0.018000                0.022961   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score@10  absent_exact_precision_hard@10  \\\\\\n\",\n       \"26                 0.000000                        0.000000   \\n\",\n       \"78                 0.000000                        0.000000   \\n\",\n       \"22                 0.012202                        0.007800   \\n\",\n       \"83                 0.015503                        0.010000   \\n\",\n       \"27                 0.021801                        0.013834   \\n\",\n       \"77                 0.023406                        0.014820   \\n\",\n       \"24                 0.023511                        0.014950   \\n\",\n       \"79                 0.024154                        0.015350   \\n\",\n       \"25                 0.026499                        0.018043   \\n\",\n       \"82                 0.032630                        0.021739   \\n\",\n       \"23                 0.025139                        0.021801   \\n\",\n       \"81                 0.023420                        0.021327   \\n\",\n       \"21                 0.020150                        0.018000   \\n\",\n       \"80                 0.019800                        0.018000   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score_hard@10  absent_exact_correct@50  \\\\\\n\",\n       \"26                      0.000000                 0.000000   \\n\",\n       \"78                      0.000000                 0.000000   \\n\",\n       \"22                      0.012202                 0.176000   \\n\",\n       \"83                      0.015503                 0.190000   \\n\",\n       \"27                      0.021801                 0.275079   \\n\",\n       \"77                      0.023406                 0.290639   \\n\",\n       \"24                      0.023511                 0.275000   \\n\",\n       \"79                      0.024154                 0.291000   \\n\",\n       \"25                      0.026499                 0.345652   \\n\",\n       \"82                      0.032630                 0.373913   \\n\",\n       \"23                      0.025139                 0.563981   \\n\",\n       \"81                      0.023420                 0.597156   \\n\",\n       \"21                      0.020150                 0.440000   \\n\",\n       \"80                      0.019800                 0.460000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@50  absent_exact_recall@50  \\\\\\n\",\n       \"26                   0.000000                0.000000   \\n\",\n       \"78                   0.000000                0.000000   \\n\",\n       \"22                   0.003520                0.087947   \\n\",\n       \"83                   0.003800                0.090361   \\n\",\n       \"27                   0.005502                0.125970   \\n\",\n       \"77                   0.005813                0.133821   \\n\",\n       \"24                   0.005500                0.126504   \\n\",\n       \"79                   0.005820                0.135362   \\n\",\n       \"25                   0.006913                0.132739   \\n\",\n       \"82                   0.007478                0.139386   \\n\",\n       \"23                   0.011280                0.113848   \\n\",\n       \"81                   0.011943                0.122822   \\n\",\n       \"21                   0.008800                0.056324   \\n\",\n       \"80                   0.009200                0.061082   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score@50  absent_exact_precision_hard@50  \\\\\\n\",\n       \"26                 0.000000                        0.000000   \\n\",\n       \"78                 0.000000                        0.000000   \\n\",\n       \"22                 0.006643                        0.003520   \\n\",\n       \"83                 0.007166                        0.003800   \\n\",\n       \"27                 0.010401                        0.005502   \\n\",\n       \"77                 0.010994                        0.005813   \\n\",\n       \"24                 0.010417                        0.005500   \\n\",\n       \"79                 0.011027                        0.005820   \\n\",\n       \"25                 0.012888                        0.006913   \\n\",\n       \"82                 0.013917                        0.007478   \\n\",\n       \"23                 0.019259                        0.011280   \\n\",\n       \"81                 0.020429                        0.011943   \\n\",\n       \"21                 0.015038                        0.008800   \\n\",\n       \"80                 0.015819                        0.009200   \\n\",\n       \"\\n\",\n       \"    absent_exact_f_score_hard@50  absent_exact_correct@M  \\\\\\n\",\n       \"26                      0.000000                0.000000   \\n\",\n       \"78                      0.000000                0.009740   \\n\",\n       \"22                      0.006643                0.338000   \\n\",\n       \"83                      0.007166                0.338000   \\n\",\n       \"27                      0.010401                0.416421   \\n\",\n       \"77                      0.010994                0.439786   \\n\",\n       \"24                      0.010417                0.406500   \\n\",\n       \"79                      0.011027                0.438500   \\n\",\n       \"25                      0.012888                0.534783   \\n\",\n       \"82                      0.013917                0.556522   \\n\",\n       \"23                      0.019259                0.905213   \\n\",\n       \"81                      0.020429                0.924171   \\n\",\n       \"21                      0.015038                0.760000   \\n\",\n       \"80                      0.015819                0.800000   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision@M  absent_exact_recall@M  absent_exact_f_score@M  \\\\\\n\",\n       \"26                  0.000000               0.000000                0.000000   \\n\",\n       \"78                  0.000031               0.005952                0.000062   \\n\",\n       \"22                  0.000926               0.147841                0.001836   \\n\",\n       \"83                  0.000887               0.141355                0.001758   \\n\",\n       \"27                  0.001132               0.188574                0.002246   \\n\",\n       \"77                  0.001159               0.200463                0.002301   \\n\",\n       \"24                  0.001104               0.185664                0.002191   \\n\",\n       \"79                  0.001154               0.201439                0.002291   \\n\",\n       \"25                  0.001455               0.202800                0.002880   \\n\",\n       \"82                  0.001465               0.207167                0.002901   \\n\",\n       \"23                  0.002439               0.180395                0.004761   \\n\",\n       \"81                  0.002396               0.182038                0.004682   \\n\",\n       \"21                  0.002127               0.098199                0.004153   \\n\",\n       \"80                  0.002127               0.101568                0.004158   \\n\",\n       \"\\n\",\n       \"    absent_exact_precision_hard@M  absent_exact_f_score_hard@M  \\\\\\n\",\n       \"26                       0.000000                     0.000000   \\n\",\n       \"78                       0.000031                     0.000062   \\n\",\n       \"22                       0.000926                     0.001836   \\n\",\n       \"83                       0.000887                     0.001758   \\n\",\n       \"27                       0.001132                     0.002246   \\n\",\n       \"77                       0.001159                     0.002301   \\n\",\n       \"24                       0.001104                     0.002191   \\n\",\n       \"79                       0.001154                     0.002291   \\n\",\n       \"25                       0.001455                     0.002880   \\n\",\n       \"82                       0.001465                     0.002901   \\n\",\n       \"23                       0.002439                     0.004761   \\n\",\n       \"81                       0.002396                     0.004682   \\n\",\n       \"21                       0.002127                     0.004153   \\n\",\n       \"80                       0.002127                     0.004158   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@5  present_partial_precision@5  \\\\\\n\",\n       \"26                   0.795455                     0.236934   \\n\",\n       \"78                   0.834416                     0.240233   \\n\",\n       \"22                   1.964000                     0.467958   \\n\",\n       \"83                   1.952000                     0.466169   \\n\",\n       \"27                   1.422625                     0.322163   \\n\",\n       \"77                   1.420523                     0.322414   \\n\",\n       \"24                   1.434000                     0.324073   \\n\",\n       \"79                   1.427500                     0.322982   \\n\",\n       \"25                   1.367391                     0.315251   \\n\",\n       \"82                   1.378261                     0.315008   \\n\",\n       \"23                   2.189573                     0.494205   \\n\",\n       \"81                   2.232227                     0.505064   \\n\",\n       \"21                   2.080000                     0.478924   \\n\",\n       \"80                   2.000000                     0.470994   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@5  present_partial_f_score@5  \\\\\\n\",\n       \"26                  0.162384                   0.188140   \\n\",\n       \"78                  0.163032                   0.189772   \\n\",\n       \"22                  0.362419                   0.381157   \\n\",\n       \"83                  0.361676                   0.380171   \\n\",\n       \"27                  0.560310                   0.381347   \\n\",\n       \"77                  0.560786                   0.381636   \\n\",\n       \"24                  0.569714                   0.384889   \\n\",\n       \"79                  0.566254                   0.382858   \\n\",\n       \"25                  0.559234                   0.370365   \\n\",\n       \"82                  0.556890                   0.369044   \\n\",\n       \"23                  0.494643                   0.452337   \\n\",\n       \"81                  0.509617                   0.463853   \\n\",\n       \"21                  0.387256                   0.404496   \\n\",\n       \"80                  0.378530                   0.397349   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@5  present_partial_f_score_hard@5  \\\\\\n\",\n       \"26                          0.236934                        0.188140   \\n\",\n       \"78                          0.240233                        0.189772   \\n\",\n       \"22                          0.467958                        0.381157   \\n\",\n       \"83                          0.466169                        0.380171   \\n\",\n       \"27                          0.322163                        0.381347   \\n\",\n       \"77                          0.322414                        0.381636   \\n\",\n       \"24                          0.324073                        0.384889   \\n\",\n       \"79                          0.322982                        0.382858   \\n\",\n       \"25                          0.315251                        0.370365   \\n\",\n       \"82                          0.315008                        0.369044   \\n\",\n       \"23                          0.494205                        0.452337   \\n\",\n       \"81                          0.505064                        0.463853   \\n\",\n       \"21                          0.478924                        0.404496   \\n\",\n       \"80                          0.470994                        0.397349   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@10  present_partial_precision@10  \\\\\\n\",\n       \"26                    1.428571                      0.185839   \\n\",\n       \"78                    1.506494                      0.193492   \\n\",\n       \"22                    3.246000                      0.364067   \\n\",\n       \"83                    3.212000                      0.360859   \\n\",\n       \"27                    1.967429                      0.214673   \\n\",\n       \"77                    1.961775                      0.214185   \\n\",\n       \"24                    1.998000                      0.216978   \\n\",\n       \"79                    1.986000                      0.216015   \\n\",\n       \"25                    1.906522                      0.209482   \\n\",\n       \"82                    1.958696                      0.213756   \\n\",\n       \"23                    3.170616                      0.347149   \\n\",\n       \"81                    3.109005                      0.344811   \\n\",\n       \"21                    3.210000                      0.357626   \\n\",\n       \"80                    3.150000                      0.351866   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@10  present_partial_f_score@10  \\\\\\n\",\n       \"26                   0.250347                    0.208878   \\n\",\n       \"78                   0.259036                    0.217046   \\n\",\n       \"22                   0.537777                    0.406608   \\n\",\n       \"83                   0.531851                    0.403184   \\n\",\n       \"27                   0.713126                    0.309208   \\n\",\n       \"77                   0.711640                    0.308473   \\n\",\n       \"24                   0.726183                    0.312869   \\n\",\n       \"79                   0.722818                    0.311294   \\n\",\n       \"25                   0.696897                    0.298997   \\n\",\n       \"82                   0.704711                    0.304214   \\n\",\n       \"23                   0.651976                    0.420083   \\n\",\n       \"81                   0.650430                    0.418135   \\n\",\n       \"21                   0.570505                    0.415783   \\n\",\n       \"80                   0.563634                    0.409536   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@10  present_partial_f_score_hard@10  \\\\\\n\",\n       \"26                           0.185839                         0.208878   \\n\",\n       \"78                           0.193492                         0.217046   \\n\",\n       \"22                           0.364067                         0.406608   \\n\",\n       \"83                           0.360859                         0.403184   \\n\",\n       \"27                           0.214673                         0.309208   \\n\",\n       \"77                           0.214180                         0.308466   \\n\",\n       \"24                           0.216978                         0.312869   \\n\",\n       \"79                           0.216015                         0.311294   \\n\",\n       \"25                           0.209482                         0.298997   \\n\",\n       \"82                           0.213756                         0.304214   \\n\",\n       \"23                           0.347149                         0.420083   \\n\",\n       \"81                           0.344811                         0.418135   \\n\",\n       \"21                           0.357626                         0.415783   \\n\",\n       \"80                           0.351866                         0.409536   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@k  present_partial_precision@k  \\\\\\n\",\n       \"26                   1.198052                     0.206885   \\n\",\n       \"78                   1.295455                     0.217284   \\n\",\n       \"22                   3.040000                     0.438239   \\n\",\n       \"83                   2.992000                     0.433587   \\n\",\n       \"27                   1.374293                     0.419127   \\n\",\n       \"77                   1.373943                     0.417629   \\n\",\n       \"24                   1.377000                     0.415721   \\n\",\n       \"79                   1.384000                     0.420597   \\n\",\n       \"25                   1.260870                     0.407583   \\n\",\n       \"82                   1.278261                     0.410627   \\n\",\n       \"23                   2.734597                     0.506856   \\n\",\n       \"81                   2.739336                     0.506244   \\n\",\n       \"21                   2.700000                     0.438224   \\n\",\n       \"80                   2.620000                     0.425488   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@k  present_partial_f_score@k  \\\\\\n\",\n       \"26                  0.206885                   0.206885   \\n\",\n       \"78                  0.217284                   0.217284   \\n\",\n       \"22                  0.438239                   0.438239   \\n\",\n       \"83                  0.433587                   0.433587   \\n\",\n       \"27                  0.419127                   0.419127   \\n\",\n       \"77                  0.417627                   0.417628   \\n\",\n       \"24                  0.415721                   0.415721   \\n\",\n       \"79                  0.420597                   0.420597   \\n\",\n       \"25                  0.407583                   0.407583   \\n\",\n       \"82                  0.410627                   0.410627   \\n\",\n       \"23                  0.506856                   0.506856   \\n\",\n       \"81                  0.506244                   0.506244   \\n\",\n       \"21                  0.438224                   0.438224   \\n\",\n       \"80                  0.425488                   0.425488   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@k  present_partial_f_score_hard@k  \\\\\\n\",\n       \"26                          0.206885                        0.206885   \\n\",\n       \"78                          0.217284                        0.217284   \\n\",\n       \"22                          0.438239                        0.438239   \\n\",\n       \"83                          0.433587                        0.433587   \\n\",\n       \"27                          0.419127                        0.419127   \\n\",\n       \"77                          0.417627                        0.417627   \\n\",\n       \"24                          0.415721                        0.415721   \\n\",\n       \"79                          0.420597                        0.420597   \\n\",\n       \"25                          0.407583                        0.407583   \\n\",\n       \"82                          0.410627                        0.410627   \\n\",\n       \"23                          0.506856                        0.506856   \\n\",\n       \"81                          0.506244                        0.506244   \\n\",\n       \"21                          0.438224                        0.438224   \\n\",\n       \"80                          0.425488                        0.425488   \\n\",\n       \"\\n\",\n       \"    present_partial_correct@M  present_partial_precision@M  \\\\\\n\",\n       \"26                   4.727273                     0.062171   \\n\",\n       \"78                   4.337662                     0.071434   \\n\",\n       \"22                   6.854000                     0.135288   \\n\",\n       \"83                   6.512000                     0.152374   \\n\",\n       \"27                   3.121279                     0.057824   \\n\",\n       \"77                   3.045580                     0.066355   \\n\",\n       \"24                   3.112500                     0.057608   \\n\",\n       \"79                   3.045000                     0.066299   \\n\",\n       \"25                   3.060870                     0.058221   \\n\",\n       \"82                   2.971739                     0.066235   \\n\",\n       \"23                   5.151659                     0.099476   \\n\",\n       \"81                   5.033175                     0.116308   \\n\",\n       \"21                   5.810000                     0.109150   \\n\",\n       \"80                   5.590000                     0.122265   \\n\",\n       \"\\n\",\n       \"    present_partial_recall@M  present_partial_f_score@M  \\\\\\n\",\n       \"26                  0.669677                   0.113168   \\n\",\n       \"78                  0.621703                   0.127106   \\n\",\n       \"22                  0.928281                   0.231421   \\n\",\n       \"83                  0.891328                   0.254628   \\n\",\n       \"27                  0.926865                   0.104055   \\n\",\n       \"77                  0.915690                   0.117828   \\n\",\n       \"24                  0.924953                   0.103574   \\n\",\n       \"79                  0.916028                   0.117614   \\n\",\n       \"25                  0.926750                   0.106515   \\n\",\n       \"82                  0.913145                   0.119715   \\n\",\n       \"23                  0.912741                   0.173598   \\n\",\n       \"81                  0.897778                   0.198631   \\n\",\n       \"21                  0.913483                   0.190196   \\n\",\n       \"80                  0.884154                   0.208801   \\n\",\n       \"\\n\",\n       \"    present_partial_precision_hard@M  present_partial_f_score_hard@M  \\\\\\n\",\n       \"26                          0.062171                        0.113168   \\n\",\n       \"78                          0.071434                        0.127106   \\n\",\n       \"22                          0.135288                        0.231421   \\n\",\n       \"83                          0.152374                        0.254628   \\n\",\n       \"27                          0.057824                        0.104055   \\n\",\n       \"77                          0.066355                        0.117828   \\n\",\n       \"24                          0.057608                        0.103574   \\n\",\n       \"79                          0.066299                        0.117614   \\n\",\n       \"25                          0.058221                        0.106515   \\n\",\n       \"82                          0.066235                        0.119715   \\n\",\n       \"23                          0.099476                        0.173598   \\n\",\n       \"81                          0.116308                        0.198631   \\n\",\n       \"21                          0.109150                        0.190196   \\n\",\n       \"80                          0.122265                        0.208801   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@10  absent_partial_precision@10  \\\\\\n\",\n       \"26                   0.000000                     0.001653   \\n\",\n       \"78                   0.000000                     0.001838   \\n\",\n       \"22                   0.090000                     0.028940   \\n\",\n       \"83                   0.110000                     0.030517   \\n\",\n       \"27                   0.144694                     0.030910   \\n\",\n       \"77                   0.155751                     0.032226   \\n\",\n       \"24                   0.154500                     0.032151   \\n\",\n       \"79                   0.159500                     0.032977   \\n\",\n       \"25                   0.197826                     0.039706   \\n\",\n       \"82                   0.230435                     0.043473   \\n\",\n       \"23                   0.327014                     0.064010   \\n\",\n       \"81                   0.322275                     0.063100   \\n\",\n       \"21                   0.270000                     0.069235   \\n\",\n       \"80                   0.320000                     0.072059   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@10  absent_partial_f_score@10  \\\\\\n\",\n       \"26                  0.010749                   0.002766   \\n\",\n       \"78                  0.011788                   0.003088   \\n\",\n       \"22                  0.126133                   0.043974   \\n\",\n       \"83                  0.131412                   0.046328   \\n\",\n       \"27                  0.140663                   0.048417   \\n\",\n       \"77                  0.147540                   0.050534   \\n\",\n       \"24                  0.145589                   0.050471   \\n\",\n       \"79                  0.149964                   0.051809   \\n\",\n       \"25                  0.150155                   0.058599   \\n\",\n       \"82                  0.167630                   0.064693   \\n\",\n       \"23                  0.127012                   0.073078   \\n\",\n       \"81                  0.120033                   0.070450   \\n\",\n       \"21                  0.087304                   0.074543   \\n\",\n       \"80                  0.089026                   0.077427   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@10  absent_partial_f_score_hard@10  \\\\\\n\",\n       \"26                          0.001653                        0.002766   \\n\",\n       \"78                          0.001838                        0.003088   \\n\",\n       \"22                          0.028940                        0.043974   \\n\",\n       \"83                          0.030517                        0.046328   \\n\",\n       \"27                          0.030910                        0.048417   \\n\",\n       \"77                          0.032226                        0.050534   \\n\",\n       \"24                          0.032151                        0.050471   \\n\",\n       \"79                          0.032977                        0.051809   \\n\",\n       \"25                          0.039706                        0.058599   \\n\",\n       \"82                          0.043473                        0.064693   \\n\",\n       \"23                          0.064010                        0.073078   \\n\",\n       \"81                          0.063100                        0.070450   \\n\",\n       \"21                          0.069235                        0.074543   \\n\",\n       \"80                          0.072059                        0.077427   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@50  absent_partial_precision@50  \\\\\\n\",\n       \"26                   0.003247                     0.000558   \\n\",\n       \"78                   0.000000                     0.000554   \\n\",\n       \"22                   0.252000                     0.010328   \\n\",\n       \"83                   0.254000                     0.010489   \\n\",\n       \"27                   0.303247                     0.010551   \\n\",\n       \"77                   0.317306                     0.010812   \\n\",\n       \"24                   0.300000                     0.010612   \\n\",\n       \"79                   0.315500                     0.010988   \\n\",\n       \"25                   0.404348                     0.013809   \\n\",\n       \"82                   0.426087                     0.014361   \\n\",\n       \"23                   0.872038                     0.024576   \\n\",\n       \"81                   0.919431                     0.025341   \\n\",\n       \"21                   0.860000                     0.026469   \\n\",\n       \"80                   0.870000                     0.026946   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@50  absent_partial_f_score@50  \\\\\\n\",\n       \"26                  0.017396                   0.001073   \\n\",\n       \"78                  0.017822                   0.001066   \\n\",\n       \"22                  0.217613                   0.019301   \\n\",\n       \"83                  0.219348                   0.019604   \\n\",\n       \"27                  0.235907                   0.019914   \\n\",\n       \"77                  0.242365                   0.020409   \\n\",\n       \"24                  0.239197                   0.020067   \\n\",\n       \"79                  0.248631                   0.020779   \\n\",\n       \"25                  0.258688                   0.025688   \\n\",\n       \"82                  0.264867                   0.026690   \\n\",\n       \"23                  0.223158                   0.041588   \\n\",\n       \"81                  0.234054                   0.042969   \\n\",\n       \"21                  0.163776                   0.044814   \\n\",\n       \"80                  0.167713                   0.045661   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@50  absent_partial_f_score_hard@50  \\\\\\n\",\n       \"26                          0.000558                        0.001073   \\n\",\n       \"78                          0.000554                        0.001066   \\n\",\n       \"22                          0.010328                        0.019301   \\n\",\n       \"83                          0.010489                        0.019604   \\n\",\n       \"27                          0.010551                        0.019914   \\n\",\n       \"77                          0.010812                        0.020409   \\n\",\n       \"24                          0.010612                        0.020067   \\n\",\n       \"79                          0.010988                        0.020779   \\n\",\n       \"25                          0.013809                        0.025688   \\n\",\n       \"82                          0.014361                        0.026690   \\n\",\n       \"23                          0.024576                        0.041588   \\n\",\n       \"81                          0.025341                        0.042969   \\n\",\n       \"21                          0.026469                        0.044814   \\n\",\n       \"80                          0.026946                        0.045661   \\n\",\n       \"\\n\",\n       \"    absent_partial_correct@M  absent_partial_precision@M  \\\\\\n\",\n       \"26                  0.003247                    0.000115   \\n\",\n       \"78                  0.012987                    0.000124   \\n\",\n       \"22                  0.484000                    0.002120   \\n\",\n       \"83                  0.472000                    0.002053   \\n\",\n       \"27                  0.471156                    0.001959   \\n\",\n       \"77                  0.495172                    0.001955   \\n\",\n       \"24                  0.456500                    0.001972   \\n\",\n       \"79                  0.491500                    0.001985   \\n\",\n       \"25                  0.650000                    0.002608   \\n\",\n       \"82                  0.669565                    0.002587   \\n\",\n       \"23                  1.450237                    0.004837   \\n\",\n       \"81                  1.464455                    0.004690   \\n\",\n       \"21                  1.510000                    0.005643   \\n\",\n       \"80                  1.570000                    0.005546   \\n\",\n       \"\\n\",\n       \"    absent_partial_recall@M  absent_partial_f_score@M  \\\\\\n\",\n       \"26                 0.021213                  0.000229   \\n\",\n       \"78                 0.025324                  0.000246   \\n\",\n       \"22                 0.308720                  0.004197   \\n\",\n       \"83                 0.306846                  0.004066   \\n\",\n       \"27                 0.318803                  0.003886   \\n\",\n       \"77                 0.328999                  0.003879   \\n\",\n       \"24                 0.322262                  0.003912   \\n\",\n       \"79                 0.336167                  0.003939   \\n\",\n       \"25                 0.354814                  0.005160   \\n\",\n       \"82                 0.360654                  0.005121   \\n\",\n       \"23                 0.318129                  0.009429   \\n\",\n       \"81                 0.320374                  0.009153   \\n\",\n       \"21                 0.249968                  0.010999   \\n\",\n       \"80                 0.255692                  0.010820   \\n\",\n       \"\\n\",\n       \"    absent_partial_precision_hard@M  absent_partial_f_score_hard@M  \\\\\\n\",\n       \"26                         0.000115                       0.000229   \\n\",\n       \"78                         0.000124                       0.000246   \\n\",\n       \"22                         0.002120                       0.004197   \\n\",\n       \"83                         0.002053                       0.004066   \\n\",\n       \"27                         0.001959                       0.003886   \\n\",\n       \"77                         0.001955                       0.003879   \\n\",\n       \"24                         0.001972                       0.003912   \\n\",\n       \"79                         0.001985                       0.003939   \\n\",\n       \"25                         0.002608                       0.005160   \\n\",\n       \"82                         0.002587                       0.005121   \\n\",\n       \"23                         0.004837                       0.009429   \\n\",\n       \"81                         0.004690                       0.009153   \\n\",\n       \"21                         0.005643                       0.010999   \\n\",\n       \"80                         0.005546                       0.010820   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_auc  present_exact_advanced_ap  \\\\\\n\",\n       \"26                    0.093452                   0.188868   \\n\",\n       \"78                    0.091300                   0.207958   \\n\",\n       \"22                    0.326509                   0.399050   \\n\",\n       \"83                    0.316208                   0.407457   \\n\",\n       \"27                    0.389728                   0.454564   \\n\",\n       \"77                    0.384625                   0.455979   \\n\",\n       \"24                    0.391062                   0.458255   \\n\",\n       \"79                    0.391085                   0.461993   \\n\",\n       \"25                    0.370623                   0.438493   \\n\",\n       \"82                    0.379387                   0.450949   \\n\",\n       \"23                    0.421844                   0.523403   \\n\",\n       \"81                    0.429044                   0.541626   \\n\",\n       \"21                    0.349028                   0.430952   \\n\",\n       \"80                    0.344738                   0.439681   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_mrr  present_exact_advanced_sadr  \\\\\\n\",\n       \"26                    0.127280                     0.222677   \\n\",\n       \"78                    0.144624                     0.224051   \\n\",\n       \"22                    0.203907                     0.504316   \\n\",\n       \"83                    0.213142                     0.491125   \\n\",\n       \"27                    0.330770                     0.556240   \\n\",\n       \"77                    0.332556                     0.552072   \\n\",\n       \"24                    0.333496                     0.560159   \\n\",\n       \"79                    0.338297                     0.559063   \\n\",\n       \"25                    0.325852                     0.546935   \\n\",\n       \"82                    0.335888                     0.545130   \\n\",\n       \"23                    0.307670                     0.575886   \\n\",\n       \"81                    0.321157                     0.575096   \\n\",\n       \"21                    0.228896                     0.514550   \\n\",\n       \"80                    0.237985                     0.495311   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_ndcg  present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"26                     0.306971                             0.345667   \\n\",\n       \"78                     0.293580                             0.317872   \\n\",\n       \"22                     0.607636                             0.545872   \\n\",\n       \"83                     0.585661                             0.543668   \\n\",\n       \"27                     0.613373                             0.520436   \\n\",\n       \"77                     0.606808                             0.518431   \\n\",\n       \"24                     0.615080                             0.510986   \\n\",\n       \"79                     0.612446                             0.514048   \\n\",\n       \"25                     0.600735                             0.499567   \\n\",\n       \"82                     0.600735                             0.502666   \\n\",\n       \"23                     0.665095                             0.572421   \\n\",\n       \"81                     0.664121                             0.582729   \\n\",\n       \"21                     0.618622                             0.521619   \\n\",\n       \"80                     0.605620                             0.525368   \\n\",\n       \"\\n\",\n       \"    present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"26                              0.404280                   0.000000   \\n\",\n       \"78                              0.380552                   0.000037   \\n\",\n       \"22                              0.626369                   0.016366   \\n\",\n       \"83                              0.622040                   0.014343   \\n\",\n       \"27                              0.526383                   0.027356   \\n\",\n       \"77                              0.524368                   0.029264   \\n\",\n       \"24                              0.516684                   0.028624   \\n\",\n       \"79                              0.519904                   0.030876   \\n\",\n       \"25                              0.511675                   0.028881   \\n\",\n       \"82                              0.515249                   0.035782   \\n\",\n       \"23                              0.616615                   0.018235   \\n\",\n       \"81                              0.624832                   0.016737   \\n\",\n       \"21                              0.576123                   0.014259   \\n\",\n       \"80                              0.573741                   0.013270   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"26                  0.000000                   0.000000   \\n\",\n       \"78                  0.000115                   0.000115   \\n\",\n       \"22                  0.031944                   0.030421   \\n\",\n       \"83                  0.032080                   0.030609   \\n\",\n       \"27                  0.059837                   0.058040   \\n\",\n       \"77                  0.063326                   0.061357   \\n\",\n       \"24                  0.060060                   0.057473   \\n\",\n       \"79                  0.066772                   0.064009   \\n\",\n       \"25                  0.074416                   0.071483   \\n\",\n       \"82                  0.086806                   0.083077   \\n\",\n       \"23                  0.063796                   0.058345   \\n\",\n       \"81                  0.070061                   0.065042   \\n\",\n       \"21                  0.087534                   0.085606   \\n\",\n       \"80                  0.084758                   0.082866   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"26                    0.000000                    0.000000   \\n\",\n       \"78                    0.000937                    0.001059   \\n\",\n       \"22                    0.045386                    0.051120   \\n\",\n       \"83                    0.045255                    0.050635   \\n\",\n       \"27                    0.066860                    0.075342   \\n\",\n       \"77                    0.071243                    0.080194   \\n\",\n       \"24                    0.068145                    0.075958   \\n\",\n       \"79                    0.073106                    0.081964   \\n\",\n       \"25                    0.074560                    0.085085   \\n\",\n       \"82                    0.081692                    0.095008   \\n\",\n       \"23                    0.062098                    0.077747   \\n\",\n       \"81                    0.060403                    0.078266   \\n\",\n       \"21                    0.038275                    0.058379   \\n\",\n       \"80                    0.038479                    0.058214   \\n\",\n       \"\\n\",\n       \"    absent_exact_advanced_alpha_ndcg@5  absent_exact_advanced_alpha_ndcg@10  \\n\",\n       \"26                            0.005237                             0.005237  \\n\",\n       \"78                            0.002780                             0.002780  \\n\",\n       \"22                            0.030722                             0.031491  \\n\",\n       \"83                            0.030401                             0.030594  \\n\",\n       \"27                            0.043846                             0.044019  \\n\",\n       \"77                            0.047877                             0.048025  \\n\",\n       \"24                            0.047304                             0.047434  \\n\",\n       \"79                            0.050022                             0.050202  \\n\",\n       \"25                            0.051881                             0.052376  \\n\",\n       \"82                            0.059456                             0.060308  \\n\",\n       \"23                            0.042823                             0.052390  \\n\",\n       \"81                            0.049930                             0.059064  \\n\",\n       \"21                            0.059390                             0.067076  \\n\",\n       \"80                            0.052090                             0.064509  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'\\\\n'\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import stats\\n\",\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"sample_freq_1 = 10000\\n\",\n    \"sample_freq_2 = 50000\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue']\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"if sample_freq_1 == 5000:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep %5000\\n\",\n    \"else:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % sample_freq_1 == 0)] # keep %10k, 20k, 50k\\n\",\n    \"one2one_df_1 = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"for index_label, row_series in one2one_df_1.iterrows():\\n\",\n    \"     one2one_df_1.at[index_label, 'exp_name'] = one2one_df_1.at[index_label, 'exp_name'] + '-freq%d' % sample_freq_1\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"if sample_freq_2 == 5000:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep %5000\\n\",\n    \"else:\\n\",\n    \"    one2one_df = one2one_df.loc[(one2one_df.step % sample_freq_2 == 0)] # keep %10k, 20k, 50k\\n\",\n    \"\\n\",\n    \"one2one_df_2 = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"for index_label, row_series in one2one_df_2.iterrows():\\n\",\n    \"     one2one_df_2.at[index_label, 'exp_name'] = one2one_df_2.at[index_label, 'exp_name'] + '-freq%d' % sample_freq_2\\n\",\n    \"        \\n\",\n    \"# combine both and plot\\n\",\n    \"combined_df = one2one_df_1.append(one2one_df_2, ignore_index=True)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# obtain the test scores by best valid performance\\n\",\n    \"str_summary_df, self_peak_summary_df, valid_peak_summary_df = brief_eval_results(combined_df, base_metric='present_exact_f_score@10')\\n\",\n    \"# display(valid_peak_summary_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2one_freq1, one2one_freq2 = [], []\\n\",\n    \"for test_dataset, exp_group in valid_peak_summary_df.groupby('test_dataset'):\\n\",\n    \"    print(test_dataset)\\n\",\n    \"    freq1_row = exp_group.loc[exp_group['exp_name'].str.endswith('freq%d' % sample_freq_1)]\\n\",\n    \"    freq2_row = exp_group.loc[exp_group['exp_name'].str.endswith('freq%d' % sample_freq_2)]\\n\",\n    \"    \\n\",\n    \"    with open(freq1_row['path'].values[0], 'r') as one2one_freq1_eval_file:\\n\",\n    \"        one2one_freq1_eval = json.load(one2one_freq1_eval_file)\\n\",\n    \"    with open(freq2_row['path'].values[0], 'r') as one2one_freq2_eval_file:\\n\",\n    \"        one2one_freq2_eval = json.load(one2one_freq2_eval_file)\\n\",\n    \"        \\n\",\n    \"    # run T-test for individual testset\\n\",\n    \"    print('T-test on testset %s:' % test_dataset)\\n\",\n    \"    print(len(one2one_freq1_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2one_freq1_eval['present_exact_f_score@10']))\\n\",\n    \"    \\n\",\n    \"    print('')\\n\",\n    \"    print(len(one2one_freq2_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2one_freq2_eval['present_exact_f_score@10']))\\n\",\n    \"    ttest = stats.ttest_rel(one2one_freq1_eval['present_exact_f_score@10'], one2one_freq2_eval['present_exact_f_score@10'])\\n\",\n    \"    print(ttest)\\n\",\n    \"    print('=' * 50)\\n\",\n    \"    \\n\",\n    \"    if not test_dataset.startswith('kp20k'):\\n\",\n    \"        one2one_freq1.extend(one2one_freq1_eval['present_exact_f_score@10'])\\n\",\n    \"        one2one_freq2.extend(one2one_freq2_eval['present_exact_f_score@10'])\\n\",\n    \"\\n\",\n    \"# run T-test on five testsets\\n\",\n    \"print('Stats of One2One-freq%d' % sample_freq_1)\\n\",\n    \"print(len(one2one_freq1))\\n\",\n    \"print(stats.describe(one2one_freq1))\\n\",\n    \"print('')\\n\",\n    \"print('Stats of One2One-freq%d' % sample_freq_2)\\n\",\n    \"print(len(one2one_freq2))\\n\",\n    \"print(stats.describe(one2one_freq2))\\n\",\n    \"print('T-test on five testsets:')\\n\",\n    \"ttest = stats.ttest_rel(one2one_freq1, one2one_freq2)\\n\",\n    \"print(ttest)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"display(valid_peak_summary_df)\\n\",\n    \"\\n\",\n    \"# Present results\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"groups = valid_peak_summary_df.exp_name.unique()\\n\",\n    \"bar_values = {'%s - %s' % (group, metric_name): [] for group in groups for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"''' \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compute Gap Significance for One2Seq models (gap=Score_max_among_all_samples-Score_min_among_all_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T20:25:46.768466Z\",\n     \"start_time\": \"2020-08-12T20:25:44.437441Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"duc\\n\",\n      \"T-test on testset duc:\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.625), mean=0.15684752915477584, variance=0.014647897712379286, skewness=0.7126967947493295, kurtosis=0.4842528943604565)\\n\",\n      \"\\n\",\n      \"308\\n\",\n      \"DescribeResult(nobs=308, minmax=(0.0, 0.625), mean=0.15902501063018462, variance=0.015268666695762056, skewness=0.5943168508580654, kurtosis=0.07485535073359628)\\n\",\n      \"Ttest_relResult(statistic=-0.5316171278181215, pvalue=0.5953756515793138)\\n\",\n      \"==================================================\\n\",\n      \"inspec\\n\",\n      \"T-test on testset inspec:\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.8421052631578948), mean=0.38417682744394344, variance=0.027810654015699533, skewness=-0.06570811676582702, kurtosis=-0.19534418235489914)\\n\",\n      \"\\n\",\n      \"500\\n\",\n      \"DescribeResult(nobs=500, minmax=(0.0, 0.8571428571428571), mean=0.381651447837591, variance=0.02742971895343227, skewness=0.16078165532565022, kurtosis=-0.25092602622627114)\\n\",\n      \"Ttest_relResult(statistic=0.7251653967286315, pvalue=0.4686904855894354)\\n\",\n      \"==================================================\\n\",\n      \"kp20k\\n\",\n      \"T-test on testset kp20k:\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 1.0), mean=0.26158172581932937, variance=0.02547599668241945, skewness=0.09930748940024499, kurtosis=-0.5162865183974699)\\n\",\n      \"\\n\",\n      \"19987\\n\",\n      \"DescribeResult(nobs=19987, minmax=(0.0, 0.9090909090909091), mean=0.25874385381418014, variance=0.025235155315174736, skewness=0.1075969439625255, kurtosis=-0.5103597352351805)\\n\",\n      \"Ttest_relResult(statistic=6.490093923405796, pvalue=8.779666960628789e-11)\\n\",\n      \"==================================================\\n\",\n      \"kp20k_valid2k\\n\",\n      \"T-test on testset kp20k_valid2k:\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.7499999999999999), mean=0.26365626023444766, variance=0.025069350264951933, skewness=0.05673036392541371, kurtosis=-0.5959470341024642)\\n\",\n      \"\\n\",\n      \"2000\\n\",\n      \"DescribeResult(nobs=2000, minmax=(0.0, 0.7499999999999999), mean=0.26351008055322767, variance=0.024712964938893047, skewness=0.04553285269169188, kurtosis=-0.6299365151490472)\\n\",\n      \"Ttest_relResult(statistic=0.10858930895167422, pvalue=0.91353913909669)\\n\",\n      \"==================================================\\n\",\n      \"krapivin\\n\",\n      \"T-test on testset krapivin:\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.6666666666666666), mean=0.2708535687790444, variance=0.02459567628914805, skewness=-0.06679815694728365, kurtosis=-0.7105440780997516)\\n\",\n      \"\\n\",\n      \"460\\n\",\n      \"DescribeResult(nobs=460, minmax=(0.0, 0.625), mean=0.2651850409903457, variance=0.02383589872666965, skewness=0.005622911158916607, kurtosis=-0.6623277406593884)\\n\",\n      \"Ttest_relResult(statistic=1.7794999334617954, pvalue=0.07581941806985751)\\n\",\n      \"==================================================\\n\",\n      \"nus\\n\",\n      \"T-test on testset nus:\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.3665558427431042, variance=0.02372419387150121, skewness=-0.17664522679492564, kurtosis=-0.22549011596243274)\\n\",\n      \"\\n\",\n      \"211\\n\",\n      \"DescribeResult(nobs=211, minmax=(0.0, 0.7499999999999999), mean=0.36317369604642674, variance=0.02507595517234991, skewness=-0.29978570877191907, kurtosis=-0.0818470831234066)\\n\",\n      \"Ttest_relResult(statistic=0.6961972874297504, pvalue=0.48707510463902226)\\n\",\n      \"==================================================\\n\",\n      \"semeval\\n\",\n      \"T-test on testset semeval:\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.7499999999999999), mean=0.3493667955109905, variance=0.02334243999286617, skewness=-0.010293528917556428, kurtosis=-0.46521948833293925)\\n\",\n      \"\\n\",\n      \"100\\n\",\n      \"DescribeResult(nobs=100, minmax=(0.0, 0.6666666666666666), mean=0.34430079831589117, variance=0.02451922429063192, skewness=0.07260867329929079, kurtosis=-0.6146200833079902)\\n\",\n      \"Ttest_relResult(statistic=0.6471092536452687, pvalue=0.5190588737121207)\\n\",\n      \"==================================================\\n\",\n      \"Stats of one2seq-freq5000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.8421052631578948), mean=0.3022609605509165, variance=0.03065430497818151, skewness=0.12176181456569621, kurtosis=-0.5540339427393559)\\n\",\n      \"\\n\",\n      \"Stats of one2seq-freq50000\\n\",\n      \"1579\\n\",\n      \"DescribeResult(nobs=1579, minmax=(0.0, 0.8571428571428571), mean=0.2994618592437217, variance=0.03045566321782634, skewness=0.1876014822751765, kurtosis=-0.44333938518770255)\\n\",\n      \"T-test on five testsets:\\n\",\n      \"Ttest_relResult(statistic=1.522745547125509, pvalue=0.12802273896304983)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>99</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>26.983766</td>\\n\",\n       \"      <td>38.652597</td>\\n\",\n       \"      <td>113.103896</td>\\n\",\n       \"      <td>2877.782468</td>\\n\",\n       \"      <td>397.006494</td>\\n\",\n       \"      <td>11338.480519</td>\\n\",\n       \"      <td>0.720779</td>\\n\",\n       \"      <td>0.144156</td>\\n\",\n       \"      <td>0.100478</td>\\n\",\n       \"      <td>0.115804</td>\\n\",\n       \"      <td>0.144156</td>\\n\",\n       \"      <td>0.115804</td>\\n\",\n       \"      <td>1.389610</td>\\n\",\n       \"      <td>0.138961</td>\\n\",\n       \"      <td>0.189414</td>\\n\",\n       \"      <td>0.156848</td>\\n\",\n       \"      <td>0.138961</td>\\n\",\n       \"      <td>0.156848</td>\\n\",\n       \"      <td>1.146104</td>\\n\",\n       \"      <td>0.145175</td>\\n\",\n       \"      <td>0.145175</td>\\n\",\n       \"      <td>0.145175</td>\\n\",\n       \"      <td>0.145175</td>\\n\",\n       \"      <td>0.145175</td>\\n\",\n       \"      <td>2.425325</td>\\n\",\n       \"      <td>0.095782</td>\\n\",\n       \"      <td>0.323610</td>\\n\",\n       \"      <td>0.143397</td>\\n\",\n       \"      <td>0.095782</td>\\n\",\n       \"      <td>0.143397</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.827922</td>\\n\",\n       \"      <td>0.240267</td>\\n\",\n       \"      <td>0.163123</td>\\n\",\n       \"      <td>0.190207</td>\\n\",\n       \"      <td>0.240267</td>\\n\",\n       \"      <td>0.190207</td>\\n\",\n       \"      <td>1.642857</td>\\n\",\n       \"      <td>0.205093</td>\\n\",\n       \"      <td>0.274161</td>\\n\",\n       \"      <td>0.229839</td>\\n\",\n       \"      <td>0.205093</td>\\n\",\n       \"      <td>0.229839</td>\\n\",\n       \"      <td>1.360390</td>\\n\",\n       \"      <td>0.221199</td>\\n\",\n       \"      <td>0.221199</td>\\n\",\n       \"      <td>0.221199</td>\\n\",\n       \"      <td>0.221199</td>\\n\",\n       \"      <td>0.221199</td>\\n\",\n       \"      <td>2.818182</td>\\n\",\n       \"      <td>0.128951</td>\\n\",\n       \"      <td>0.426914</td>\\n\",\n       \"      <td>0.192010</td>\\n\",\n       \"      <td>0.128951</td>\\n\",\n       \"      <td>0.192010</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001933</td>\\n\",\n       \"      <td>0.011709</td>\\n\",\n       \"      <td>0.002999</td>\\n\",\n       \"      <td>0.001559</td>\\n\",\n       \"      <td>0.002704</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001015</td>\\n\",\n       \"      <td>0.012578</td>\\n\",\n       \"      <td>0.001492</td>\\n\",\n       \"      <td>0.000342</td>\\n\",\n       \"      <td>0.000663</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000976</td>\\n\",\n       \"      <td>0.012703</td>\\n\",\n       \"      <td>0.001418</td>\\n\",\n       \"      <td>0.000976</td>\\n\",\n       \"      <td>0.001418</td>\\n\",\n       \"      <td>0.076260</td>\\n\",\n       \"      <td>0.096550</td>\\n\",\n       \"      <td>0.200341</td>\\n\",\n       \"      <td>0.209200</td>\\n\",\n       \"      <td>0.233658</td>\\n\",\n       \"      <td>0.282014</td>\\n\",\n       \"      <td>0.335770</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>141</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-duc-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>duc</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.860390</td>\\n\",\n       \"      <td>0.204545</td>\\n\",\n       \"      <td>25.331169</td>\\n\",\n       \"      <td>31.766234</td>\\n\",\n       \"      <td>103.159091</td>\\n\",\n       \"      <td>3254.538961</td>\\n\",\n       \"      <td>422.370130</td>\\n\",\n       \"      <td>11909.139610</td>\\n\",\n       \"      <td>0.727273</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.100148</td>\\n\",\n       \"      <td>0.116142</td>\\n\",\n       \"      <td>0.145455</td>\\n\",\n       \"      <td>0.116142</td>\\n\",\n       \"      <td>1.405844</td>\\n\",\n       \"      <td>0.140747</td>\\n\",\n       \"      <td>0.190990</td>\\n\",\n       \"      <td>0.159025</td>\\n\",\n       \"      <td>0.140584</td>\\n\",\n       \"      <td>0.158870</td>\\n\",\n       \"      <td>1.168831</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>0.149253</td>\\n\",\n       \"      <td>2.275974</td>\\n\",\n       \"      <td>0.096578</td>\\n\",\n       \"      <td>0.304134</td>\\n\",\n       \"      <td>0.142257</td>\\n\",\n       \"      <td>0.096578</td>\\n\",\n       \"      <td>0.142257</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.824675</td>\\n\",\n       \"      <td>0.244157</td>\\n\",\n       \"      <td>0.165272</td>\\n\",\n       \"      <td>0.192976</td>\\n\",\n       \"      <td>0.244157</td>\\n\",\n       \"      <td>0.192976</td>\\n\",\n       \"      <td>1.652597</td>\\n\",\n       \"      <td>0.207966</td>\\n\",\n       \"      <td>0.277746</td>\\n\",\n       \"      <td>0.233240</td>\\n\",\n       \"      <td>0.207534</td>\\n\",\n       \"      <td>0.232979</td>\\n\",\n       \"      <td>1.360390</td>\\n\",\n       \"      <td>0.227053</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>0.226866</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>0.226769</td>\\n\",\n       \"      <td>2.678571</td>\\n\",\n       \"      <td>0.133099</td>\\n\",\n       \"      <td>0.410399</td>\\n\",\n       \"      <td>0.194576</td>\\n\",\n       \"      <td>0.133099</td>\\n\",\n       \"      <td>0.194576</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001438</td>\\n\",\n       \"      <td>0.007767</td>\\n\",\n       \"      <td>0.002351</td>\\n\",\n       \"      <td>0.001229</td>\\n\",\n       \"      <td>0.002085</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000814</td>\\n\",\n       \"      <td>0.008529</td>\\n\",\n       \"      <td>0.001362</td>\\n\",\n       \"      <td>0.000268</td>\\n\",\n       \"      <td>0.000516</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000796</td>\\n\",\n       \"      <td>0.009451</td>\\n\",\n       \"      <td>0.001330</td>\\n\",\n       \"      <td>0.000796</td>\\n\",\n       \"      <td>0.001330</td>\\n\",\n       \"      <td>0.076831</td>\\n\",\n       \"      <td>0.270145</td>\\n\",\n       \"      <td>0.204509</td>\\n\",\n       \"      <td>0.205482</td>\\n\",\n       \"      <td>0.228552</td>\\n\",\n       \"      <td>0.286359</td>\\n\",\n       \"      <td>0.342041</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.000000</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>103</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>23.304000</td>\\n\",\n       \"      <td>51.262000</td>\\n\",\n       \"      <td>91.370000</td>\\n\",\n       \"      <td>3072.194000</td>\\n\",\n       \"      <td>419.990000</td>\\n\",\n       \"      <td>12056.604000</td>\\n\",\n       \"      <td>1.956000</td>\\n\",\n       \"      <td>0.391200</td>\\n\",\n       \"      <td>0.308453</td>\\n\",\n       \"      <td>0.321771</td>\\n\",\n       \"      <td>0.391200</td>\\n\",\n       \"      <td>0.321771</td>\\n\",\n       \"      <td>3.410000</td>\\n\",\n       \"      <td>0.342456</td>\\n\",\n       \"      <td>0.508636</td>\\n\",\n       \"      <td>0.384177</td>\\n\",\n       \"      <td>0.341000</td>\\n\",\n       \"      <td>0.383022</td>\\n\",\n       \"      <td>3.082000</td>\\n\",\n       \"      <td>0.383891</td>\\n\",\n       \"      <td>0.383410</td>\\n\",\n       \"      <td>0.383630</td>\\n\",\n       \"      <td>0.383410</td>\\n\",\n       \"      <td>0.383410</td>\\n\",\n       \"      <td>5.032000</td>\\n\",\n       \"      <td>0.228810</td>\\n\",\n       \"      <td>0.702070</td>\\n\",\n       \"      <td>0.326277</td>\\n\",\n       \"      <td>0.228810</td>\\n\",\n       \"      <td>0.326277</td>\\n\",\n       \"      <td>0.068000</td>\\n\",\n       \"      <td>0.009752</td>\\n\",\n       \"      <td>0.034760</td>\\n\",\n       \"      <td>0.012508</td>\\n\",\n       \"      <td>0.006800</td>\\n\",\n       \"      <td>0.010607</td>\\n\",\n       \"      <td>0.074000</td>\\n\",\n       \"      <td>0.006018</td>\\n\",\n       \"      <td>0.037760</td>\\n\",\n       \"      <td>0.007198</td>\\n\",\n       \"      <td>0.001480</td>\\n\",\n       \"      <td>0.002795</td>\\n\",\n       \"      <td>0.076000</td>\\n\",\n       \"      <td>0.005858</td>\\n\",\n       \"      <td>0.038427</td>\\n\",\n       \"      <td>0.006914</td>\\n\",\n       \"      <td>0.005858</td>\\n\",\n       \"      <td>0.006914</td>\\n\",\n       \"      <td>2.094000</td>\\n\",\n       \"      <td>0.490360</td>\\n\",\n       \"      <td>0.381910</td>\\n\",\n       \"      <td>0.400368</td>\\n\",\n       \"      <td>0.490360</td>\\n\",\n       \"      <td>0.400368</td>\\n\",\n       \"      <td>3.634000</td>\\n\",\n       \"      <td>0.397998</td>\\n\",\n       \"      <td>0.582956</td>\\n\",\n       \"      <td>0.443556</td>\\n\",\n       \"      <td>0.396319</td>\\n\",\n       \"      <td>0.442218</td>\\n\",\n       \"      <td>3.300000</td>\\n\",\n       \"      <td>0.461203</td>\\n\",\n       \"      <td>0.460657</td>\\n\",\n       \"      <td>0.460908</td>\\n\",\n       \"      <td>0.460657</td>\\n\",\n       \"      <td>0.460657</td>\\n\",\n       \"      <td>5.212000</td>\\n\",\n       \"      <td>0.249388</td>\\n\",\n       \"      <td>0.757745</td>\\n\",\n       \"      <td>0.354508</td>\\n\",\n       \"      <td>0.249388</td>\\n\",\n       \"      <td>0.354508</td>\\n\",\n       \"      <td>0.084000</td>\\n\",\n       \"      <td>0.032900</td>\\n\",\n       \"      <td>0.118128</td>\\n\",\n       \"      <td>0.045370</td>\\n\",\n       \"      <td>0.027315</td>\\n\",\n       \"      <td>0.041615</td>\\n\",\n       \"      <td>0.114000</td>\\n\",\n       \"      <td>0.018267</td>\\n\",\n       \"      <td>0.146094</td>\\n\",\n       \"      <td>0.025424</td>\\n\",\n       \"      <td>0.006798</td>\\n\",\n       \"      <td>0.012736</td>\\n\",\n       \"      <td>0.120000</td>\\n\",\n       \"      <td>0.017234</td>\\n\",\n       \"      <td>0.148405</td>\\n\",\n       \"      <td>0.023559</td>\\n\",\n       \"      <td>0.017234</td>\\n\",\n       \"      <td>0.023559</td>\\n\",\n       \"      <td>0.331321</td>\\n\",\n       \"      <td>0.367722</td>\\n\",\n       \"      <td>0.263744</td>\\n\",\n       \"      <td>0.510444</td>\\n\",\n       \"      <td>0.557479</td>\\n\",\n       \"      <td>0.529657</td>\\n\",\n       \"      <td>0.612187</td>\\n\",\n       \"      <td>0.013150</td>\\n\",\n       \"      <td>0.017159</td>\\n\",\n       \"      <td>0.030473</td>\\n\",\n       \"      <td>0.024821</td>\\n\",\n       \"      <td>0.025569</td>\\n\",\n       \"      <td>0.039086</td>\\n\",\n       \"      <td>0.039453</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>143</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-inspec-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>inspec</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>7.716000</td>\\n\",\n       \"      <td>2.110000</td>\\n\",\n       \"      <td>22.192000</td>\\n\",\n       \"      <td>47.114000</td>\\n\",\n       \"      <td>84.350000</td>\\n\",\n       \"      <td>3164.972000</td>\\n\",\n       \"      <td>427.300000</td>\\n\",\n       \"      <td>12297.024000</td>\\n\",\n       \"      <td>1.912000</td>\\n\",\n       \"      <td>0.382400</td>\\n\",\n       \"      <td>0.300901</td>\\n\",\n       \"      <td>0.314813</td>\\n\",\n       \"      <td>0.382400</td>\\n\",\n       \"      <td>0.314813</td>\\n\",\n       \"      <td>3.382000</td>\\n\",\n       \"      <td>0.339504</td>\\n\",\n       \"      <td>0.507734</td>\\n\",\n       \"      <td>0.381651</td>\\n\",\n       \"      <td>0.338200</td>\\n\",\n       \"      <td>0.380574</td>\\n\",\n       \"      <td>3.018000</td>\\n\",\n       \"      <td>0.372335</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>0.372107</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>0.371915</td>\\n\",\n       \"      <td>4.902000</td>\\n\",\n       \"      <td>0.231086</td>\\n\",\n       \"      <td>0.685163</td>\\n\",\n       \"      <td>0.328642</td>\\n\",\n       \"      <td>0.231086</td>\\n\",\n       \"      <td>0.328642</td>\\n\",\n       \"      <td>0.060000</td>\\n\",\n       \"      <td>0.007667</td>\\n\",\n       \"      <td>0.034241</td>\\n\",\n       \"      <td>0.011426</td>\\n\",\n       \"      <td>0.006000</td>\\n\",\n       \"      <td>0.009651</td>\\n\",\n       \"      <td>0.076000</td>\\n\",\n       \"      <td>0.004841</td>\\n\",\n       \"      <td>0.040797</td>\\n\",\n       \"      <td>0.007132</td>\\n\",\n       \"      <td>0.001520</td>\\n\",\n       \"      <td>0.002879</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.004693</td>\\n\",\n       \"      <td>0.042297</td>\\n\",\n       \"      <td>0.006863</td>\\n\",\n       \"      <td>0.004693</td>\\n\",\n       \"      <td>0.006863</td>\\n\",\n       \"      <td>2.052000</td>\\n\",\n       \"      <td>0.481097</td>\\n\",\n       \"      <td>0.373897</td>\\n\",\n       \"      <td>0.392583</td>\\n\",\n       \"      <td>0.481097</td>\\n\",\n       \"      <td>0.392583</td>\\n\",\n       \"      <td>3.586000</td>\\n\",\n       \"      <td>0.393555</td>\\n\",\n       \"      <td>0.579552</td>\\n\",\n       \"      <td>0.439360</td>\\n\",\n       \"      <td>0.392156</td>\\n\",\n       \"      <td>0.438213</td>\\n\",\n       \"      <td>3.248000</td>\\n\",\n       \"      <td>0.451361</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>0.451054</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>0.450800</td>\\n\",\n       \"      <td>5.104000</td>\\n\",\n       \"      <td>0.252699</td>\\n\",\n       \"      <td>0.742746</td>\\n\",\n       \"      <td>0.358221</td>\\n\",\n       \"      <td>0.252699</td>\\n\",\n       \"      <td>0.358221</td>\\n\",\n       \"      <td>0.080000</td>\\n\",\n       \"      <td>0.030429</td>\\n\",\n       \"      <td>0.116493</td>\\n\",\n       \"      <td>0.044221</td>\\n\",\n       \"      <td>0.026631</td>\\n\",\n       \"      <td>0.040757</td>\\n\",\n       \"      <td>0.106000</td>\\n\",\n       \"      <td>0.016993</td>\\n\",\n       \"      <td>0.143181</td>\\n\",\n       \"      <td>0.025687</td>\\n\",\n       \"      <td>0.006701</td>\\n\",\n       \"      <td>0.012553</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.016077</td>\\n\",\n       \"      <td>0.145531</td>\\n\",\n       \"      <td>0.024023</td>\\n\",\n       \"      <td>0.016077</td>\\n\",\n       \"      <td>0.024023</td>\\n\",\n       \"      <td>0.316342</td>\\n\",\n       \"      <td>0.494065</td>\\n\",\n       \"      <td>0.259221</td>\\n\",\n       \"      <td>0.498763</td>\\n\",\n       \"      <td>0.541210</td>\\n\",\n       \"      <td>0.523011</td>\\n\",\n       \"      <td>0.605785</td>\\n\",\n       \"      <td>0.013818</td>\\n\",\n       \"      <td>0.026776</td>\\n\",\n       \"      <td>0.026776</td>\\n\",\n       \"      <td>0.025499</td>\\n\",\n       \"      <td>0.026069</td>\\n\",\n       \"      <td>0.041039</td>\\n\",\n       \"      <td>0.041345</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>102</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>24.520839</td>\\n\",\n       \"      <td>48.330865</td>\\n\",\n       \"      <td>93.038375</td>\\n\",\n       \"      <td>2895.182869</td>\\n\",\n       \"      <td>409.084905</td>\\n\",\n       \"      <td>11881.238255</td>\\n\",\n       \"      <td>1.299545</td>\\n\",\n       \"      <td>0.259988</td>\\n\",\n       \"      <td>0.462279</td>\\n\",\n       \"      <td>0.311866</td>\\n\",\n       \"      <td>0.259909</td>\\n\",\n       \"      <td>0.311814</td>\\n\",\n       \"      <td>1.775104</td>\\n\",\n       \"      <td>0.178172</td>\\n\",\n       \"      <td>0.613406</td>\\n\",\n       \"      <td>0.261582</td>\\n\",\n       \"      <td>0.177510</td>\\n\",\n       \"      <td>0.260950</td>\\n\",\n       \"      <td>1.067994</td>\\n\",\n       \"      <td>0.310903</td>\\n\",\n       \"      <td>0.310174</td>\\n\",\n       \"      <td>0.310444</td>\\n\",\n       \"      <td>0.310174</td>\\n\",\n       \"      <td>0.310174</td>\\n\",\n       \"      <td>2.195677</td>\\n\",\n       \"      <td>0.097724</td>\\n\",\n       \"      <td>0.734336</td>\\n\",\n       \"      <td>0.163878</td>\\n\",\n       \"      <td>0.097724</td>\\n\",\n       \"      <td>0.163878</td>\\n\",\n       \"      <td>0.056287</td>\\n\",\n       \"      <td>0.006179</td>\\n\",\n       \"      <td>0.027039</td>\\n\",\n       \"      <td>0.009384</td>\\n\",\n       \"      <td>0.005629</td>\\n\",\n       \"      <td>0.008936</td>\\n\",\n       \"      <td>0.067494</td>\\n\",\n       \"      <td>0.003026</td>\\n\",\n       \"      <td>0.032201</td>\\n\",\n       \"      <td>0.004803</td>\\n\",\n       \"      <td>0.001350</td>\\n\",\n       \"      <td>0.002560</td>\\n\",\n       \"      <td>0.068745</td>\\n\",\n       \"      <td>0.002825</td>\\n\",\n       \"      <td>0.032736</td>\\n\",\n       \"      <td>0.004433</td>\\n\",\n       \"      <td>0.002825</td>\\n\",\n       \"      <td>0.004433</td>\\n\",\n       \"      <td>1.338920</td>\\n\",\n       \"      <td>0.310563</td>\\n\",\n       \"      <td>0.546752</td>\\n\",\n       \"      <td>0.370121</td>\\n\",\n       \"      <td>0.310479</td>\\n\",\n       \"      <td>0.370067</td>\\n\",\n       \"      <td>1.833041</td>\\n\",\n       \"      <td>0.203504</td>\\n\",\n       \"      <td>0.688778</td>\\n\",\n       \"      <td>0.296070</td>\\n\",\n       \"      <td>0.202784</td>\\n\",\n       \"      <td>0.295392</td>\\n\",\n       \"      <td>1.142793</td>\\n\",\n       \"      <td>0.392430</td>\\n\",\n       \"      <td>0.391362</td>\\n\",\n       \"      <td>0.391759</td>\\n\",\n       \"      <td>0.391362</td>\\n\",\n       \"      <td>0.391362</td>\\n\",\n       \"      <td>2.264122</td>\\n\",\n       \"      <td>0.107731</td>\\n\",\n       \"      <td>0.792412</td>\\n\",\n       \"      <td>0.179099</td>\\n\",\n       \"      <td>0.107731</td>\\n\",\n       \"      <td>0.179099</td>\\n\",\n       \"      <td>0.061390</td>\\n\",\n       \"      <td>0.023418</td>\\n\",\n       \"      <td>0.095162</td>\\n\",\n       \"      <td>0.034751</td>\\n\",\n       \"      <td>0.020855</td>\\n\",\n       \"      <td>0.032666</td>\\n\",\n       \"      <td>0.079302</td>\\n\",\n       \"      <td>0.012081</td>\\n\",\n       \"      <td>0.114826</td>\\n\",\n       \"      <td>0.018672</td>\\n\",\n       \"      <td>0.005081</td>\\n\",\n       \"      <td>0.009596</td>\\n\",\n       \"      <td>0.080903</td>\\n\",\n       \"      <td>0.011330</td>\\n\",\n       \"      <td>0.116161</td>\\n\",\n       \"      <td>0.017298</td>\\n\",\n       \"      <td>0.011330</td>\\n\",\n       \"      <td>0.017298</td>\\n\",\n       \"      <td>0.316835</td>\\n\",\n       \"      <td>0.370134</td>\\n\",\n       \"      <td>0.338765</td>\\n\",\n       \"      <td>0.484131</td>\\n\",\n       \"      <td>0.513992</td>\\n\",\n       \"      <td>0.493537</td>\\n\",\n       \"      <td>0.498814</td>\\n\",\n       \"      <td>0.013183</td>\\n\",\n       \"      <td>0.015901</td>\\n\",\n       \"      <td>0.030902</td>\\n\",\n       \"      <td>0.021980</td>\\n\",\n       \"      <td>0.023667</td>\\n\",\n       \"      <td>0.029606</td>\\n\",\n       \"      <td>0.029667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>144</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>23.765698</td>\\n\",\n       \"      <td>45.438435</td>\\n\",\n       \"      <td>83.928604</td>\\n\",\n       \"      <td>2966.514084</td>\\n\",\n       \"      <td>412.683394</td>\\n\",\n       \"      <td>12031.902837</td>\\n\",\n       \"      <td>1.280182</td>\\n\",\n       \"      <td>0.256094</td>\\n\",\n       \"      <td>0.456092</td>\\n\",\n       \"      <td>0.307406</td>\\n\",\n       \"      <td>0.256036</td>\\n\",\n       \"      <td>0.307370</td>\\n\",\n       \"      <td>1.754991</td>\\n\",\n       \"      <td>0.176175</td>\\n\",\n       \"      <td>0.607375</td>\\n\",\n       \"      <td>0.258744</td>\\n\",\n       \"      <td>0.175499</td>\\n\",\n       \"      <td>0.258091</td>\\n\",\n       \"      <td>1.053335</td>\\n\",\n       \"      <td>0.306870</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>0.306367</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>0.306082</td>\\n\",\n       \"      <td>2.187522</td>\\n\",\n       \"      <td>0.100247</td>\\n\",\n       \"      <td>0.730980</td>\\n\",\n       \"      <td>0.167464</td>\\n\",\n       \"      <td>0.100247</td>\\n\",\n       \"      <td>0.167464</td>\\n\",\n       \"      <td>0.046380</td>\\n\",\n       \"      <td>0.005121</td>\\n\",\n       \"      <td>0.022021</td>\\n\",\n       \"      <td>0.007750</td>\\n\",\n       \"      <td>0.004638</td>\\n\",\n       \"      <td>0.007347</td>\\n\",\n       \"      <td>0.059639</td>\\n\",\n       \"      <td>0.002694</td>\\n\",\n       \"      <td>0.028441</td>\\n\",\n       \"      <td>0.004288</td>\\n\",\n       \"      <td>0.001193</td>\\n\",\n       \"      <td>0.002262</td>\\n\",\n       \"      <td>0.060940</td>\\n\",\n       \"      <td>0.002545</td>\\n\",\n       \"      <td>0.029014</td>\\n\",\n       \"      <td>0.004015</td>\\n\",\n       \"      <td>0.002545</td>\\n\",\n       \"      <td>0.004015</td>\\n\",\n       \"      <td>1.320158</td>\\n\",\n       \"      <td>0.307409</td>\\n\",\n       \"      <td>0.541522</td>\\n\",\n       \"      <td>0.366387</td>\\n\",\n       \"      <td>0.307346</td>\\n\",\n       \"      <td>0.366348</td>\\n\",\n       \"      <td>1.813279</td>\\n\",\n       \"      <td>0.201625</td>\\n\",\n       \"      <td>0.682945</td>\\n\",\n       \"      <td>0.293340</td>\\n\",\n       \"      <td>0.200889</td>\\n\",\n       \"      <td>0.292642</td>\\n\",\n       \"      <td>1.127233</td>\\n\",\n       \"      <td>0.388850</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>0.388123</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>0.387705</td>\\n\",\n       \"      <td>2.252214</td>\\n\",\n       \"      <td>0.110255</td>\\n\",\n       \"      <td>0.787605</td>\\n\",\n       \"      <td>0.182605</td>\\n\",\n       \"      <td>0.110255</td>\\n\",\n       \"      <td>0.182605</td>\\n\",\n       \"      <td>0.051083</td>\\n\",\n       \"      <td>0.021917</td>\\n\",\n       \"      <td>0.090091</td>\\n\",\n       \"      <td>0.032731</td>\\n\",\n       \"      <td>0.019755</td>\\n\",\n       \"      <td>0.030940</td>\\n\",\n       \"      <td>0.070646</td>\\n\",\n       \"      <td>0.011505</td>\\n\",\n       \"      <td>0.111533</td>\\n\",\n       \"      <td>0.018058</td>\\n\",\n       \"      <td>0.004938</td>\\n\",\n       \"      <td>0.009324</td>\\n\",\n       \"      <td>0.072197</td>\\n\",\n       \"      <td>0.010883</td>\\n\",\n       \"      <td>0.113058</td>\\n\",\n       \"      <td>0.016922</td>\\n\",\n       \"      <td>0.010883</td>\\n\",\n       \"      <td>0.016922</td>\\n\",\n       \"      <td>0.311136</td>\\n\",\n       \"      <td>0.441994</td>\\n\",\n       \"      <td>0.334838</td>\\n\",\n       \"      <td>0.479550</td>\\n\",\n       \"      <td>0.508923</td>\\n\",\n       \"      <td>0.491106</td>\\n\",\n       \"      <td>0.496273</td>\\n\",\n       \"      <td>0.010310</td>\\n\",\n       \"      <td>0.024681</td>\\n\",\n       \"      <td>0.024466</td>\\n\",\n       \"      <td>0.018006</td>\\n\",\n       \"      <td>0.019405</td>\\n\",\n       \"      <td>0.025164</td>\\n\",\n       \"      <td>0.025249</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>101</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>24.464500</td>\\n\",\n       \"      <td>48.384000</td>\\n\",\n       \"      <td>93.111500</td>\\n\",\n       \"      <td>2905.150000</td>\\n\",\n       \"      <td>409.844500</td>\\n\",\n       \"      <td>11906.859500</td>\\n\",\n       \"      <td>1.299500</td>\\n\",\n       \"      <td>0.259900</td>\\n\",\n       \"      <td>0.468845</td>\\n\",\n       \"      <td>0.312857</td>\\n\",\n       \"      <td>0.259900</td>\\n\",\n       \"      <td>0.312857</td>\\n\",\n       \"      <td>1.788500</td>\\n\",\n       \"      <td>0.179341</td>\\n\",\n       \"      <td>0.622615</td>\\n\",\n       \"      <td>0.263656</td>\\n\",\n       \"      <td>0.178850</td>\\n\",\n       \"      <td>0.263116</td>\\n\",\n       \"      <td>1.064000</td>\\n\",\n       \"      <td>0.304832</td>\\n\",\n       \"      <td>0.304210</td>\\n\",\n       \"      <td>0.304465</td>\\n\",\n       \"      <td>0.304210</td>\\n\",\n       \"      <td>0.304210</td>\\n\",\n       \"      <td>2.209500</td>\\n\",\n       \"      <td>0.098078</td>\\n\",\n       \"      <td>0.738750</td>\\n\",\n       \"      <td>0.164736</td>\\n\",\n       \"      <td>0.098078</td>\\n\",\n       \"      <td>0.164736</td>\\n\",\n       \"      <td>0.062000</td>\\n\",\n       \"      <td>0.006294</td>\\n\",\n       \"      <td>0.029596</td>\\n\",\n       \"      <td>0.009947</td>\\n\",\n       \"      <td>0.006200</td>\\n\",\n       \"      <td>0.009825</td>\\n\",\n       \"      <td>0.075500</td>\\n\",\n       \"      <td>0.002852</td>\\n\",\n       \"      <td>0.035230</td>\\n\",\n       \"      <td>0.005006</td>\\n\",\n       \"      <td>0.001510</td>\\n\",\n       \"      <td>0.002862</td>\\n\",\n       \"      <td>0.076000</td>\\n\",\n       \"      <td>0.002586</td>\\n\",\n       \"      <td>0.035330</td>\\n\",\n       \"      <td>0.004518</td>\\n\",\n       \"      <td>0.002586</td>\\n\",\n       \"      <td>0.004518</td>\\n\",\n       \"      <td>1.341000</td>\\n\",\n       \"      <td>0.309750</td>\\n\",\n       \"      <td>0.549483</td>\\n\",\n       \"      <td>0.369786</td>\\n\",\n       \"      <td>0.309750</td>\\n\",\n       \"      <td>0.369786</td>\\n\",\n       \"      <td>1.845500</td>\\n\",\n       \"      <td>0.203699</td>\\n\",\n       \"      <td>0.693016</td>\\n\",\n       \"      <td>0.296454</td>\\n\",\n       \"      <td>0.203206</td>\\n\",\n       \"      <td>0.295911</td>\\n\",\n       \"      <td>1.144000</td>\\n\",\n       \"      <td>0.382541</td>\\n\",\n       \"      <td>0.381571</td>\\n\",\n       \"      <td>0.381963</td>\\n\",\n       \"      <td>0.381571</td>\\n\",\n       \"      <td>0.381571</td>\\n\",\n       \"      <td>2.275500</td>\\n\",\n       \"      <td>0.107668</td>\\n\",\n       \"      <td>0.794117</td>\\n\",\n       \"      <td>0.179347</td>\\n\",\n       \"      <td>0.107668</td>\\n\",\n       \"      <td>0.179347</td>\\n\",\n       \"      <td>0.066000</td>\\n\",\n       \"      <td>0.023300</td>\\n\",\n       \"      <td>0.097700</td>\\n\",\n       \"      <td>0.035282</td>\\n\",\n       \"      <td>0.021333</td>\\n\",\n       \"      <td>0.033548</td>\\n\",\n       \"      <td>0.084000</td>\\n\",\n       \"      <td>0.011723</td>\\n\",\n       \"      <td>0.119394</td>\\n\",\n       \"      <td>0.018800</td>\\n\",\n       \"      <td>0.005246</td>\\n\",\n       \"      <td>0.009923</td>\\n\",\n       \"      <td>0.085000</td>\\n\",\n       \"      <td>0.010887</td>\\n\",\n       \"      <td>0.120758</td>\\n\",\n       \"      <td>0.017265</td>\\n\",\n       \"      <td>0.010887</td>\\n\",\n       \"      <td>0.017265</td>\\n\",\n       \"      <td>0.313800</td>\\n\",\n       \"      <td>0.369182</td>\\n\",\n       \"      <td>0.335396</td>\\n\",\n       \"      <td>0.484483</td>\\n\",\n       \"      <td>0.514268</td>\\n\",\n       \"      <td>0.480191</td>\\n\",\n       \"      <td>0.485185</td>\\n\",\n       \"      <td>0.012895</td>\\n\",\n       \"      <td>0.015542</td>\\n\",\n       \"      <td>0.030987</td>\\n\",\n       \"      <td>0.022108</td>\\n\",\n       \"      <td>0.024344</td>\\n\",\n       \"      <td>0.029908</td>\\n\",\n       \"      <td>0.029912</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>142</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-kp20k_valid2k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k_valid2k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.327500</td>\\n\",\n       \"      <td>1.937500</td>\\n\",\n       \"      <td>23.730500</td>\\n\",\n       \"      <td>46.566500</td>\\n\",\n       \"      <td>85.292500</td>\\n\",\n       \"      <td>2992.986500</td>\\n\",\n       \"      <td>414.778000</td>\\n\",\n       \"      <td>12097.758000</td>\\n\",\n       \"      <td>1.293500</td>\\n\",\n       \"      <td>0.258700</td>\\n\",\n       \"      <td>0.465987</td>\\n\",\n       \"      <td>0.311486</td>\\n\",\n       \"      <td>0.258700</td>\\n\",\n       \"      <td>0.311486</td>\\n\",\n       \"      <td>1.788500</td>\\n\",\n       \"      <td>0.179152</td>\\n\",\n       \"      <td>0.622748</td>\\n\",\n       \"      <td>0.263510</td>\\n\",\n       \"      <td>0.178850</td>\\n\",\n       \"      <td>0.263122</td>\\n\",\n       \"      <td>1.058500</td>\\n\",\n       \"      <td>0.304263</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>0.303867</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>0.303589</td>\\n\",\n       \"      <td>2.213500</td>\\n\",\n       \"      <td>0.100874</td>\\n\",\n       \"      <td>0.737942</td>\\n\",\n       \"      <td>0.169063</td>\\n\",\n       \"      <td>0.100874</td>\\n\",\n       \"      <td>0.169063</td>\\n\",\n       \"      <td>0.053000</td>\\n\",\n       \"      <td>0.005369</td>\\n\",\n       \"      <td>0.025780</td>\\n\",\n       \"      <td>0.008537</td>\\n\",\n       \"      <td>0.005300</td>\\n\",\n       \"      <td>0.008437</td>\\n\",\n       \"      <td>0.065000</td>\\n\",\n       \"      <td>0.002430</td>\\n\",\n       \"      <td>0.030288</td>\\n\",\n       \"      <td>0.004285</td>\\n\",\n       \"      <td>0.001300</td>\\n\",\n       \"      <td>0.002465</td>\\n\",\n       \"      <td>0.067000</td>\\n\",\n       \"      <td>0.002251</td>\\n\",\n       \"      <td>0.031330</td>\\n\",\n       \"      <td>0.003958</td>\\n\",\n       \"      <td>0.002251</td>\\n\",\n       \"      <td>0.003958</td>\\n\",\n       \"      <td>1.338000</td>\\n\",\n       \"      <td>0.308816</td>\\n\",\n       \"      <td>0.545680</td>\\n\",\n       \"      <td>0.368356</td>\\n\",\n       \"      <td>0.308816</td>\\n\",\n       \"      <td>0.368356</td>\\n\",\n       \"      <td>1.847000</td>\\n\",\n       \"      <td>0.203396</td>\\n\",\n       \"      <td>0.692416</td>\\n\",\n       \"      <td>0.296106</td>\\n\",\n       \"      <td>0.203041</td>\\n\",\n       \"      <td>0.295663</td>\\n\",\n       \"      <td>1.137500</td>\\n\",\n       \"      <td>0.380898</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>0.380290</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>0.379873</td>\\n\",\n       \"      <td>2.280500</td>\\n\",\n       \"      <td>0.110588</td>\\n\",\n       \"      <td>0.792416</td>\\n\",\n       \"      <td>0.183784</td>\\n\",\n       \"      <td>0.110588</td>\\n\",\n       \"      <td>0.183784</td>\\n\",\n       \"      <td>0.060000</td>\\n\",\n       \"      <td>0.022542</td>\\n\",\n       \"      <td>0.094527</td>\\n\",\n       \"      <td>0.033962</td>\\n\",\n       \"      <td>0.020549</td>\\n\",\n       \"      <td>0.032297</td>\\n\",\n       \"      <td>0.076500</td>\\n\",\n       \"      <td>0.011409</td>\\n\",\n       \"      <td>0.116486</td>\\n\",\n       \"      <td>0.018144</td>\\n\",\n       \"      <td>0.005133</td>\\n\",\n       \"      <td>0.009699</td>\\n\",\n       \"      <td>0.078000</td>\\n\",\n       \"      <td>0.010686</td>\\n\",\n       \"      <td>0.117942</td>\\n\",\n       \"      <td>0.016827</td>\\n\",\n       \"      <td>0.010686</td>\\n\",\n       \"      <td>0.016827</td>\\n\",\n       \"      <td>0.310270</td>\\n\",\n       \"      <td>0.442734</td>\\n\",\n       \"      <td>0.334244</td>\\n\",\n       \"      <td>0.483828</td>\\n\",\n       \"      <td>0.512278</td>\\n\",\n       \"      <td>0.477469</td>\\n\",\n       \"      <td>0.482639</td>\\n\",\n       \"      <td>0.010785</td>\\n\",\n       \"      <td>0.025619</td>\\n\",\n       \"      <td>0.025395</td>\\n\",\n       \"      <td>0.019122</td>\\n\",\n       \"      <td>0.020814</td>\\n\",\n       \"      <td>0.024922</td>\\n\",\n       \"      <td>0.025033</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>104</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>23.671739</td>\\n\",\n       \"      <td>46.989130</td>\\n\",\n       \"      <td>95.773913</td>\\n\",\n       \"      <td>3151.547826</td>\\n\",\n       \"      <td>426.680435</td>\\n\",\n       \"      <td>12225.017391</td>\\n\",\n       \"      <td>1.315217</td>\\n\",\n       \"      <td>0.263043</td>\\n\",\n       \"      <td>0.474356</td>\\n\",\n       \"      <td>0.311374</td>\\n\",\n       \"      <td>0.263043</td>\\n\",\n       \"      <td>0.311374</td>\\n\",\n       \"      <td>1.886957</td>\\n\",\n       \"      <td>0.189450</td>\\n\",\n       \"      <td>0.632191</td>\\n\",\n       \"      <td>0.270854</td>\\n\",\n       \"      <td>0.188696</td>\\n\",\n       \"      <td>0.270022</td>\\n\",\n       \"      <td>1.208696</td>\\n\",\n       \"      <td>0.331696</td>\\n\",\n       \"      <td>0.331696</td>\\n\",\n       \"      <td>0.331696</td>\\n\",\n       \"      <td>0.331696</td>\\n\",\n       \"      <td>0.331696</td>\\n\",\n       \"      <td>2.452174</td>\\n\",\n       \"      <td>0.110893</td>\\n\",\n       \"      <td>0.771984</td>\\n\",\n       \"      <td>0.183004</td>\\n\",\n       \"      <td>0.110893</td>\\n\",\n       \"      <td>0.183004</td>\\n\",\n       \"      <td>0.071739</td>\\n\",\n       \"      <td>0.008502</td>\\n\",\n       \"      <td>0.027916</td>\\n\",\n       \"      <td>0.012205</td>\\n\",\n       \"      <td>0.007174</td>\\n\",\n       \"      <td>0.010705</td>\\n\",\n       \"      <td>0.089130</td>\\n\",\n       \"      <td>0.004847</td>\\n\",\n       \"      <td>0.032262</td>\\n\",\n       \"      <td>0.007556</td>\\n\",\n       \"      <td>0.001783</td>\\n\",\n       \"      <td>0.003310</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.004624</td>\\n\",\n       \"      <td>0.032987</td>\\n\",\n       \"      <td>0.007182</td>\\n\",\n       \"      <td>0.004624</td>\\n\",\n       \"      <td>0.007182</td>\\n\",\n       \"      <td>1.345652</td>\\n\",\n       \"      <td>0.310922</td>\\n\",\n       \"      <td>0.549312</td>\\n\",\n       \"      <td>0.364027</td>\\n\",\n       \"      <td>0.310922</td>\\n\",\n       \"      <td>0.364027</td>\\n\",\n       \"      <td>1.943478</td>\\n\",\n       \"      <td>0.214840</td>\\n\",\n       \"      <td>0.706115</td>\\n\",\n       \"      <td>0.305127</td>\\n\",\n       \"      <td>0.213979</td>\\n\",\n       \"      <td>0.304165</td>\\n\",\n       \"      <td>1.258696</td>\\n\",\n       \"      <td>0.400129</td>\\n\",\n       \"      <td>0.400129</td>\\n\",\n       \"      <td>0.400129</td>\\n\",\n       \"      <td>0.400129</td>\\n\",\n       \"      <td>0.400129</td>\\n\",\n       \"      <td>2.491304</td>\\n\",\n       \"      <td>0.119425</td>\\n\",\n       \"      <td>0.821873</td>\\n\",\n       \"      <td>0.196689</td>\\n\",\n       \"      <td>0.119425</td>\\n\",\n       \"      <td>0.196689</td>\\n\",\n       \"      <td>0.082609</td>\\n\",\n       \"      <td>0.031563</td>\\n\",\n       \"      <td>0.107291</td>\\n\",\n       \"      <td>0.045096</td>\\n\",\n       \"      <td>0.028446</td>\\n\",\n       \"      <td>0.042124</td>\\n\",\n       \"      <td>0.126087</td>\\n\",\n       \"      <td>0.016154</td>\\n\",\n       \"      <td>0.127288</td>\\n\",\n       \"      <td>0.025198</td>\\n\",\n       \"      <td>0.007051</td>\\n\",\n       \"      <td>0.013095</td>\\n\",\n       \"      <td>0.128261</td>\\n\",\n       \"      <td>0.015045</td>\\n\",\n       \"      <td>0.130101</td>\\n\",\n       \"      <td>0.023258</td>\\n\",\n       \"      <td>0.015045</td>\\n\",\n       \"      <td>0.023258</td>\\n\",\n       \"      <td>0.341869</td>\\n\",\n       \"      <td>0.394736</td>\\n\",\n       \"      <td>0.353547</td>\\n\",\n       \"      <td>0.511024</td>\\n\",\n       \"      <td>0.544717</td>\\n\",\n       \"      <td>0.470495</td>\\n\",\n       \"      <td>0.482040</td>\\n\",\n       \"      <td>0.014667</td>\\n\",\n       \"      <td>0.017008</td>\\n\",\n       \"      <td>0.040569</td>\\n\",\n       \"      <td>0.022848</td>\\n\",\n       \"      <td>0.026502</td>\\n\",\n       \"      <td>0.027258</td>\\n\",\n       \"      <td>0.027258</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>145</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-krapivin-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>krapivin</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>3.228261</td>\\n\",\n       \"      <td>2.513043</td>\\n\",\n       \"      <td>22.808696</td>\\n\",\n       \"      <td>45.680435</td>\\n\",\n       \"      <td>86.208696</td>\\n\",\n       \"      <td>3251.073913</td>\\n\",\n       \"      <td>435.997826</td>\\n\",\n       \"      <td>12531.176087</td>\\n\",\n       \"      <td>1.286957</td>\\n\",\n       \"      <td>0.257391</td>\\n\",\n       \"      <td>0.466974</td>\\n\",\n       \"      <td>0.305464</td>\\n\",\n       \"      <td>0.257391</td>\\n\",\n       \"      <td>0.305464</td>\\n\",\n       \"      <td>1.847826</td>\\n\",\n       \"      <td>0.185256</td>\\n\",\n       \"      <td>0.625793</td>\\n\",\n       \"      <td>0.265185</td>\\n\",\n       \"      <td>0.184783</td>\\n\",\n       \"      <td>0.264538</td>\\n\",\n       \"      <td>1.200000</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>0.327904</td>\\n\",\n       \"      <td>2.389130</td>\\n\",\n       \"      <td>0.110275</td>\\n\",\n       \"      <td>0.762951</td>\\n\",\n       \"      <td>0.182055</td>\\n\",\n       \"      <td>0.110275</td>\\n\",\n       \"      <td>0.182055</td>\\n\",\n       \"      <td>0.052174</td>\\n\",\n       \"      <td>0.005510</td>\\n\",\n       \"      <td>0.024268</td>\\n\",\n       \"      <td>0.008305</td>\\n\",\n       \"      <td>0.005217</td>\\n\",\n       \"      <td>0.008053</td>\\n\",\n       \"      <td>0.067391</td>\\n\",\n       \"      <td>0.002912</td>\\n\",\n       \"      <td>0.028127</td>\\n\",\n       \"      <td>0.004666</td>\\n\",\n       \"      <td>0.001348</td>\\n\",\n       \"      <td>0.002522</td>\\n\",\n       \"      <td>0.069565</td>\\n\",\n       \"      <td>0.002822</td>\\n\",\n       \"      <td>0.028325</td>\\n\",\n       \"      <td>0.004505</td>\\n\",\n       \"      <td>0.002822</td>\\n\",\n       \"      <td>0.004505</td>\\n\",\n       \"      <td>1.330435</td>\\n\",\n       \"      <td>0.307725</td>\\n\",\n       \"      <td>0.545264</td>\\n\",\n       \"      <td>0.360798</td>\\n\",\n       \"      <td>0.307725</td>\\n\",\n       \"      <td>0.360798</td>\\n\",\n       \"      <td>1.900000</td>\\n\",\n       \"      <td>0.211663</td>\\n\",\n       \"      <td>0.700680</td>\\n\",\n       \"      <td>0.300782</td>\\n\",\n       \"      <td>0.211189</td>\\n\",\n       \"      <td>0.300135</td>\\n\",\n       \"      <td>1.252174</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>0.399462</td>\\n\",\n       \"      <td>2.439130</td>\\n\",\n       \"      <td>0.119472</td>\\n\",\n       \"      <td>0.813916</td>\\n\",\n       \"      <td>0.196661</td>\\n\",\n       \"      <td>0.119472</td>\\n\",\n       \"      <td>0.196661</td>\\n\",\n       \"      <td>0.060870</td>\\n\",\n       \"      <td>0.028910</td>\\n\",\n       \"      <td>0.102949</td>\\n\",\n       \"      <td>0.041320</td>\\n\",\n       \"      <td>0.026625</td>\\n\",\n       \"      <td>0.039525</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.015114</td>\\n\",\n       \"      <td>0.123522</td>\\n\",\n       \"      <td>0.023168</td>\\n\",\n       \"      <td>0.006671</td>\\n\",\n       \"      <td>0.012392</td>\\n\",\n       \"      <td>0.091304</td>\\n\",\n       \"      <td>0.014311</td>\\n\",\n       \"      <td>0.125238</td>\\n\",\n       \"      <td>0.021737</td>\\n\",\n       \"      <td>0.014311</td>\\n\",\n       \"      <td>0.021737</td>\\n\",\n       \"      <td>0.335451</td>\\n\",\n       \"      <td>0.467640</td>\\n\",\n       \"      <td>0.357506</td>\\n\",\n       \"      <td>0.503576</td>\\n\",\n       \"      <td>0.537226</td>\\n\",\n       \"      <td>0.476757</td>\\n\",\n       \"      <td>0.488681</td>\\n\",\n       \"      <td>0.012829</td>\\n\",\n       \"      <td>0.031475</td>\\n\",\n       \"      <td>0.031475</td>\\n\",\n       \"      <td>0.020634</td>\\n\",\n       \"      <td>0.022043</td>\\n\",\n       \"      <td>0.024188</td>\\n\",\n       \"      <td>0.024279</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>98</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>23.478673</td>\\n\",\n       \"      <td>49.075829</td>\\n\",\n       \"      <td>95.758294</td>\\n\",\n       \"      <td>3166.227488</td>\\n\",\n       \"      <td>428.734597</td>\\n\",\n       \"      <td>12338.137441</td>\\n\",\n       \"      <td>2.028436</td>\\n\",\n       \"      <td>0.405687</td>\\n\",\n       \"      <td>0.396093</td>\\n\",\n       \"      <td>0.368173</td>\\n\",\n       \"      <td>0.405687</td>\\n\",\n       \"      <td>0.368173</td>\\n\",\n       \"      <td>2.990521</td>\\n\",\n       \"      <td>0.301083</td>\\n\",\n       \"      <td>0.579405</td>\\n\",\n       \"      <td>0.366556</td>\\n\",\n       \"      <td>0.299052</td>\\n\",\n       \"      <td>0.364819</td>\\n\",\n       \"      <td>2.431280</td>\\n\",\n       \"      <td>0.392193</td>\\n\",\n       \"      <td>0.391719</td>\\n\",\n       \"      <td>0.391930</td>\\n\",\n       \"      <td>0.391719</td>\\n\",\n       \"      <td>0.391719</td>\\n\",\n       \"      <td>3.781991</td>\\n\",\n       \"      <td>0.174484</td>\\n\",\n       \"      <td>0.695568</td>\\n\",\n       \"      <td>0.261233</td>\\n\",\n       \"      <td>0.174484</td>\\n\",\n       \"      <td>0.261233</td>\\n\",\n       \"      <td>0.090047</td>\\n\",\n       \"      <td>0.009005</td>\\n\",\n       \"      <td>0.025682</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.009005</td>\\n\",\n       \"      <td>0.010988</td>\\n\",\n       \"      <td>0.127962</td>\\n\",\n       \"      <td>0.003799</td>\\n\",\n       \"      <td>0.029338</td>\\n\",\n       \"      <td>0.006150</td>\\n\",\n       \"      <td>0.002559</td>\\n\",\n       \"      <td>0.004341</td>\\n\",\n       \"      <td>0.132701</td>\\n\",\n       \"      <td>0.003232</td>\\n\",\n       \"      <td>0.030285</td>\\n\",\n       \"      <td>0.005285</td>\\n\",\n       \"      <td>0.003232</td>\\n\",\n       \"      <td>0.005285</td>\\n\",\n       \"      <td>2.090047</td>\\n\",\n       \"      <td>0.483705</td>\\n\",\n       \"      <td>0.475679</td>\\n\",\n       \"      <td>0.439502</td>\\n\",\n       \"      <td>0.483705</td>\\n\",\n       \"      <td>0.439502</td>\\n\",\n       \"      <td>3.109005</td>\\n\",\n       \"      <td>0.347584</td>\\n\",\n       \"      <td>0.651987</td>\\n\",\n       \"      <td>0.420085</td>\\n\",\n       \"      <td>0.345459</td>\\n\",\n       \"      <td>0.418307</td>\\n\",\n       \"      <td>2.568720</td>\\n\",\n       \"      <td>0.474196</td>\\n\",\n       \"      <td>0.473629</td>\\n\",\n       \"      <td>0.473881</td>\\n\",\n       \"      <td>0.473629</td>\\n\",\n       \"      <td>0.473629</td>\\n\",\n       \"      <td>4.047393</td>\\n\",\n       \"      <td>0.198370</td>\\n\",\n       \"      <td>0.770084</td>\\n\",\n       \"      <td>0.295725</td>\\n\",\n       \"      <td>0.198370</td>\\n\",\n       \"      <td>0.295725</td>\\n\",\n       \"      <td>0.180095</td>\\n\",\n       \"      <td>0.049461</td>\\n\",\n       \"      <td>0.086772</td>\\n\",\n       \"      <td>0.053353</td>\\n\",\n       \"      <td>0.047029</td>\\n\",\n       \"      <td>0.052502</td>\\n\",\n       \"      <td>0.355450</td>\\n\",\n       \"      <td>0.022692</td>\\n\",\n       \"      <td>0.109743</td>\\n\",\n       \"      <td>0.031019</td>\\n\",\n       \"      <td>0.012740</td>\\n\",\n       \"      <td>0.021400</td>\\n\",\n       \"      <td>0.369668</td>\\n\",\n       \"      <td>0.020372</td>\\n\",\n       \"      <td>0.112448</td>\\n\",\n       \"      <td>0.027467</td>\\n\",\n       \"      <td>0.020372</td>\\n\",\n       \"      <td>0.027467</td>\\n\",\n       \"      <td>0.367128</td>\\n\",\n       \"      <td>0.403375</td>\\n\",\n       \"      <td>0.350856</td>\\n\",\n       \"      <td>0.513696</td>\\n\",\n       \"      <td>0.581022</td>\\n\",\n       \"      <td>0.545461</td>\\n\",\n       \"      <td>0.586076</td>\\n\",\n       \"      <td>0.013532</td>\\n\",\n       \"      <td>0.016544</td>\\n\",\n       \"      <td>0.048320</td>\\n\",\n       \"      <td>0.023496</td>\\n\",\n       \"      <td>0.026923</td>\\n\",\n       \"      <td>0.040065</td>\\n\",\n       \"      <td>0.043178</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>140</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-nus-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>nus</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>5.985782</td>\\n\",\n       \"      <td>5.677725</td>\\n\",\n       \"      <td>22.649289</td>\\n\",\n       \"      <td>45.862559</td>\\n\",\n       \"      <td>84.919431</td>\\n\",\n       \"      <td>3274.933649</td>\\n\",\n       \"      <td>434.398104</td>\\n\",\n       \"      <td>12527.127962</td>\\n\",\n       \"      <td>2.033175</td>\\n\",\n       \"      <td>0.406635</td>\\n\",\n       \"      <td>0.405661</td>\\n\",\n       \"      <td>0.373284</td>\\n\",\n       \"      <td>0.406635</td>\\n\",\n       \"      <td>0.373284</td>\\n\",\n       \"      <td>2.976303</td>\\n\",\n       \"      <td>0.298697</td>\\n\",\n       \"      <td>0.565945</td>\\n\",\n       \"      <td>0.363174</td>\\n\",\n       \"      <td>0.297630</td>\\n\",\n       \"      <td>0.362394</td>\\n\",\n       \"      <td>2.431280</td>\\n\",\n       \"      <td>0.392704</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>0.392478</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>0.392309</td>\\n\",\n       \"      <td>3.725118</td>\\n\",\n       \"      <td>0.174411</td>\\n\",\n       \"      <td>0.675854</td>\\n\",\n       \"      <td>0.261203</td>\\n\",\n       \"      <td>0.174411</td>\\n\",\n       \"      <td>0.261203</td>\\n\",\n       \"      <td>0.061611</td>\\n\",\n       \"      <td>0.006161</td>\\n\",\n       \"      <td>0.011570</td>\\n\",\n       \"      <td>0.007286</td>\\n\",\n       \"      <td>0.006161</td>\\n\",\n       \"      <td>0.007286</td>\\n\",\n       \"      <td>0.099526</td>\\n\",\n       \"      <td>0.003577</td>\\n\",\n       \"      <td>0.017176</td>\\n\",\n       \"      <td>0.005211</td>\\n\",\n       \"      <td>0.001991</td>\\n\",\n       \"      <td>0.003370</td>\\n\",\n       \"      <td>0.104265</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.017607</td>\\n\",\n       \"      <td>0.005084</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.005084</td>\\n\",\n       \"      <td>2.118483</td>\\n\",\n       \"      <td>0.484790</td>\\n\",\n       \"      <td>0.482832</td>\\n\",\n       \"      <td>0.443366</td>\\n\",\n       \"      <td>0.484790</td>\\n\",\n       \"      <td>0.443366</td>\\n\",\n       \"      <td>3.123223</td>\\n\",\n       \"      <td>0.345138</td>\\n\",\n       \"      <td>0.644575</td>\\n\",\n       \"      <td>0.416962</td>\\n\",\n       \"      <td>0.343913</td>\\n\",\n       \"      <td>0.416103</td>\\n\",\n       \"      <td>2.582938</td>\\n\",\n       \"      <td>0.474214</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>0.473938</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>0.473732</td>\\n\",\n       \"      <td>3.971564</td>\\n\",\n       \"      <td>0.198323</td>\\n\",\n       \"      <td>0.754379</td>\\n\",\n       \"      <td>0.295786</td>\\n\",\n       \"      <td>0.198323</td>\\n\",\n       \"      <td>0.295786</td>\\n\",\n       \"      <td>0.151659</td>\\n\",\n       \"      <td>0.045667</td>\\n\",\n       \"      <td>0.077653</td>\\n\",\n       \"      <td>0.050156</td>\\n\",\n       \"      <td>0.044387</td>\\n\",\n       \"      <td>0.049496</td>\\n\",\n       \"      <td>0.312796</td>\\n\",\n       \"      <td>0.023144</td>\\n\",\n       \"      <td>0.104366</td>\\n\",\n       \"      <td>0.031757</td>\\n\",\n       \"      <td>0.012529</td>\\n\",\n       \"      <td>0.021001</td>\\n\",\n       \"      <td>0.327014</td>\\n\",\n       \"      <td>0.021469</td>\\n\",\n       \"      <td>0.105829</td>\\n\",\n       \"      <td>0.029209</td>\\n\",\n       \"      <td>0.021469</td>\\n\",\n       \"      <td>0.029209</td>\\n\",\n       \"      <td>0.360912</td>\\n\",\n       \"      <td>0.566511</td>\\n\",\n       \"      <td>0.350501</td>\\n\",\n       \"      <td>0.509725</td>\\n\",\n       \"      <td>0.568610</td>\\n\",\n       \"      <td>0.539049</td>\\n\",\n       \"      <td>0.581340</td>\\n\",\n       \"      <td>0.004899</td>\\n\",\n       \"      <td>0.025594</td>\\n\",\n       \"      <td>0.024212</td>\\n\",\n       \"      <td>0.010563</td>\\n\",\n       \"      <td>0.013570</td>\\n\",\n       \"      <td>0.029372</td>\\n\",\n       \"      <td>0.032255</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>100</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_75000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>75000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>23.780000</td>\\n\",\n       \"      <td>49.330000</td>\\n\",\n       \"      <td>97.010000</td>\\n\",\n       \"      <td>3171.300000</td>\\n\",\n       \"      <td>428.210000</td>\\n\",\n       \"      <td>12440.260000</td>\\n\",\n       \"      <td>1.940000</td>\\n\",\n       \"      <td>0.388000</td>\\n\",\n       \"      <td>0.314265</td>\\n\",\n       \"      <td>0.327538</td>\\n\",\n       \"      <td>0.388000</td>\\n\",\n       \"      <td>0.327538</td>\\n\",\n       \"      <td>3.010000</td>\\n\",\n       \"      <td>0.301000</td>\\n\",\n       \"      <td>0.479292</td>\\n\",\n       \"      <td>0.349367</td>\\n\",\n       \"      <td>0.301000</td>\\n\",\n       \"      <td>0.349367</td>\\n\",\n       \"      <td>2.600000</td>\\n\",\n       \"      <td>0.363218</td>\\n\",\n       \"      <td>0.363218</td>\\n\",\n       \"      <td>0.363218</td>\\n\",\n       \"      <td>0.363218</td>\\n\",\n       \"      <td>0.363218</td>\\n\",\n       \"      <td>4.040000</td>\\n\",\n       \"      <td>0.180375</td>\\n\",\n       \"      <td>0.630679</td>\\n\",\n       \"      <td>0.267522</td>\\n\",\n       \"      <td>0.180375</td>\\n\",\n       \"      <td>0.267522</td>\\n\",\n       \"      <td>0.120000</td>\\n\",\n       \"      <td>0.012429</td>\\n\",\n       \"      <td>0.016107</td>\\n\",\n       \"      <td>0.013912</td>\\n\",\n       \"      <td>0.012000</td>\\n\",\n       \"      <td>0.013523</td>\\n\",\n       \"      <td>0.130000</td>\\n\",\n       \"      <td>0.004500</td>\\n\",\n       \"      <td>0.017357</td>\\n\",\n       \"      <td>0.006791</td>\\n\",\n       \"      <td>0.002600</td>\\n\",\n       \"      <td>0.004492</td>\\n\",\n       \"      <td>0.130000</td>\\n\",\n       \"      <td>0.004261</td>\\n\",\n       \"      <td>0.017357</td>\\n\",\n       \"      <td>0.006418</td>\\n\",\n       \"      <td>0.004261</td>\\n\",\n       \"      <td>0.006418</td>\\n\",\n       \"      <td>2.060000</td>\\n\",\n       \"      <td>0.472352</td>\\n\",\n       \"      <td>0.386805</td>\\n\",\n       \"      <td>0.401143</td>\\n\",\n       \"      <td>0.472352</td>\\n\",\n       \"      <td>0.401143</td>\\n\",\n       \"      <td>3.190000</td>\\n\",\n       \"      <td>0.355147</td>\\n\",\n       \"      <td>0.560904</td>\\n\",\n       \"      <td>0.411272</td>\\n\",\n       \"      <td>0.355147</td>\\n\",\n       \"      <td>0.411272</td>\\n\",\n       \"      <td>2.770000</td>\\n\",\n       \"      <td>0.437941</td>\\n\",\n       \"      <td>0.437941</td>\\n\",\n       \"      <td>0.437941</td>\\n\",\n       \"      <td>0.437941</td>\\n\",\n       \"      <td>0.437941</td>\\n\",\n       \"      <td>4.220000</td>\\n\",\n       \"      <td>0.203886</td>\\n\",\n       \"      <td>0.706746</td>\\n\",\n       \"      <td>0.301802</td>\\n\",\n       \"      <td>0.203886</td>\\n\",\n       \"      <td>0.301802</td>\\n\",\n       \"      <td>0.200000</td>\\n\",\n       \"      <td>0.064504</td>\\n\",\n       \"      <td>0.078108</td>\\n\",\n       \"      <td>0.067849</td>\\n\",\n       \"      <td>0.062430</td>\\n\",\n       \"      <td>0.066884</td>\\n\",\n       \"      <td>0.320000</td>\\n\",\n       \"      <td>0.029377</td>\\n\",\n       \"      <td>0.100447</td>\\n\",\n       \"      <td>0.040891</td>\\n\",\n       \"      <td>0.016040</td>\\n\",\n       \"      <td>0.027182</td>\\n\",\n       \"      <td>0.330000</td>\\n\",\n       \"      <td>0.027416</td>\\n\",\n       \"      <td>0.101764</td>\\n\",\n       \"      <td>0.037968</td>\\n\",\n       \"      <td>0.027416</td>\\n\",\n       \"      <td>0.037968</td>\\n\",\n       \"      <td>0.292198</td>\\n\",\n       \"      <td>0.328403</td>\\n\",\n       \"      <td>0.312790</td>\\n\",\n       \"      <td>0.464882</td>\\n\",\n       \"      <td>0.515668</td>\\n\",\n       \"      <td>0.514999</td>\\n\",\n       \"      <td>0.568920</td>\\n\",\n       \"      <td>0.010042</td>\\n\",\n       \"      <td>0.011127</td>\\n\",\n       \"      <td>0.077889</td>\\n\",\n       \"      <td>0.016367</td>\\n\",\n       \"      <td>0.024115</td>\\n\",\n       \"      <td>0.036571</td>\\n\",\n       \"      <td>0.040301</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>146</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...</td>\\n\",\n       \"      <td>meng17-one2seq-kp20k-topmodels</td>\\n\",\n       \"      <td>kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...</td>\\n\",\n       \"      <td>step_50000-semeval-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2seq</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>verbatim_append</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>semeval</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>6.710000</td>\\n\",\n       \"      <td>8.360000</td>\\n\",\n       \"      <td>22.800000</td>\\n\",\n       \"      <td>45.280000</td>\\n\",\n       \"      <td>83.750000</td>\\n\",\n       \"      <td>3258.980000</td>\\n\",\n       \"      <td>433.570000</td>\\n\",\n       \"      <td>12591.410000</td>\\n\",\n       \"      <td>1.930000</td>\\n\",\n       \"      <td>0.386000</td>\\n\",\n       \"      <td>0.310670</td>\\n\",\n       \"      <td>0.324662</td>\\n\",\n       \"      <td>0.386000</td>\\n\",\n       \"      <td>0.324662</td>\\n\",\n       \"      <td>2.960000</td>\\n\",\n       \"      <td>0.296333</td>\\n\",\n       \"      <td>0.473295</td>\\n\",\n       \"      <td>0.344301</td>\\n\",\n       \"      <td>0.296000</td>\\n\",\n       \"      <td>0.344015</td>\\n\",\n       \"      <td>2.510000</td>\\n\",\n       \"      <td>0.349171</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>0.348837</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>0.348546</td>\\n\",\n       \"      <td>4.010000</td>\\n\",\n       \"      <td>0.184256</td>\\n\",\n       \"      <td>0.626297</td>\\n\",\n       \"      <td>0.272150</td>\\n\",\n       \"      <td>0.184256</td>\\n\",\n       \"      <td>0.272150</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.014567</td>\\n\",\n       \"      <td>0.011500</td>\\n\",\n       \"      <td>0.010000</td>\\n\",\n       \"      <td>0.011500</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.003684</td>\\n\",\n       \"      <td>0.015817</td>\\n\",\n       \"      <td>0.005624</td>\\n\",\n       \"      <td>0.002200</td>\\n\",\n       \"      <td>0.003816</td>\\n\",\n       \"      <td>0.110000</td>\\n\",\n       \"      <td>0.003416</td>\\n\",\n       \"      <td>0.015817</td>\\n\",\n       \"      <td>0.005179</td>\\n\",\n       \"      <td>0.003416</td>\\n\",\n       \"      <td>0.005179</td>\\n\",\n       \"      <td>2.030000</td>\\n\",\n       \"      <td>0.467807</td>\\n\",\n       \"      <td>0.382451</td>\\n\",\n       \"      <td>0.396574</td>\\n\",\n       \"      <td>0.467807</td>\\n\",\n       \"      <td>0.396574</td>\\n\",\n       \"      <td>3.190000</td>\\n\",\n       \"      <td>0.353353</td>\\n\",\n       \"      <td>0.557835</td>\\n\",\n       \"      <td>0.409015</td>\\n\",\n       \"      <td>0.353020</td>\\n\",\n       \"      <td>0.408730</td>\\n\",\n       \"      <td>2.710000</td>\\n\",\n       \"      <td>0.431871</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>0.431491</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>0.431160</td>\\n\",\n       \"      <td>4.200000</td>\\n\",\n       \"      <td>0.209371</td>\\n\",\n       \"      <td>0.704316</td>\\n\",\n       \"      <td>0.308704</td>\\n\",\n       \"      <td>0.209371</td>\\n\",\n       \"      <td>0.308704</td>\\n\",\n       \"      <td>0.160000</td>\\n\",\n       \"      <td>0.061492</td>\\n\",\n       \"      <td>0.072762</td>\\n\",\n       \"      <td>0.062335</td>\\n\",\n       \"      <td>0.056679</td>\\n\",\n       \"      <td>0.061220</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.033354</td>\\n\",\n       \"      <td>0.091858</td>\\n\",\n       \"      <td>0.040554</td>\\n\",\n       \"      <td>0.014513</td>\\n\",\n       \"      <td>0.024630</td>\\n\",\n       \"      <td>0.310000</td>\\n\",\n       \"      <td>0.031701</td>\\n\",\n       \"      <td>0.094163</td>\\n\",\n       \"      <td>0.038028</td>\\n\",\n       \"      <td>0.031701</td>\\n\",\n       \"      <td>0.038028</td>\\n\",\n       \"      <td>0.289219</td>\\n\",\n       \"      <td>0.501281</td>\\n\",\n       \"      <td>0.296191</td>\\n\",\n       \"      <td>0.454212</td>\\n\",\n       \"      <td>0.508012</td>\\n\",\n       \"      <td>0.519445</td>\\n\",\n       \"      <td>0.575881</td>\\n\",\n       \"      <td>0.006943</td>\\n\",\n       \"      <td>0.060787</td>\\n\",\n       \"      <td>0.060787</td>\\n\",\n       \"      <td>0.014852</td>\\n\",\n       \"      <td>0.019784</td>\\n\",\n       \"      <td>0.031381</td>\\n\",\n       \"      <td>0.035286</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                  path  \\\\\\n\",\n       \"99   /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"141  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"103  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"143  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"102  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"144  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"101  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"142  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"104  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"145  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"98   /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"140  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"100  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"146  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output...   \\n\",\n       \"\\n\",\n       \"                          exp_group  \\\\\\n\",\n       \"99   meng17-one2seq-kp20k-topmodels   \\n\",\n       \"141  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"103  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"143  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"102  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"144  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"101  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"142  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"104  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"145  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"98   meng17-one2seq-kp20k-topmodels   \\n\",\n       \"140  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"100  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"146  meng17-one2seq-kp20k-topmodels   \\n\",\n       \"\\n\",\n       \"                                              exp_name  \\\\\\n\",\n       \"99   kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"141  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"103  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"143  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"102  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"144  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"101  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"142  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"104  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"145  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"98   kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"140  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"100  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"146  kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-L...   \\n\",\n       \"\\n\",\n       \"                               test_name tokenization train_mode model_base  \\\\\\n\",\n       \"99             step_75000-duc-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"141            step_50000-duc-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"103         step_75000-inspec-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"143         step_50000-inspec-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"102          step_75000-kp20k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"144          step_50000-kp20k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"101  step_75000-kp20k_valid2k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"142  step_50000-kp20k_valid2k-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"104       step_75000-krapivin-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"145       step_50000-krapivin-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"98             step_75000-nus-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"140            step_50000-nus-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"100        step_75000-semeval-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"146        step_50000-semeval-exhaustive       meng17    one2seq        rnn   \\n\",\n       \"\\n\",\n       \"               order train_dataset   step   test_dataset decoding_method  \\\\\\n\",\n       \"99   verbatim_append         kp20k  75000            duc      exhaustive   \\n\",\n       \"141  verbatim_append         kp20k  50000            duc      exhaustive   \\n\",\n       \"103  verbatim_append         kp20k  75000         inspec      exhaustive   \\n\",\n       \"143  verbatim_append         kp20k  50000         inspec      exhaustive   \\n\",\n       \"102  verbatim_append         kp20k  75000          kp20k      exhaustive   \\n\",\n       \"144  verbatim_append         kp20k  50000          kp20k      exhaustive   \\n\",\n       \"101  verbatim_append         kp20k  75000  kp20k_valid2k      exhaustive   \\n\",\n       \"142  verbatim_append         kp20k  50000  kp20k_valid2k      exhaustive   \\n\",\n       \"104  verbatim_append         kp20k  75000       krapivin      exhaustive   \\n\",\n       \"145  verbatim_append         kp20k  50000       krapivin      exhaustive   \\n\",\n       \"98   verbatim_append         kp20k  75000            nus      exhaustive   \\n\",\n       \"140  verbatim_append         kp20k  50000            nus      exhaustive   \\n\",\n       \"100  verbatim_append         kp20k  75000        semeval      exhaustive   \\n\",\n       \"146  verbatim_append         kp20k  50000        semeval      exhaustive   \\n\",\n       \"\\n\",\n       \"    decoding_terminate beam_width max_length  present_tgt_num  absent_tgt_num  \\\\\\n\",\n       \"99            fullbeam         50         40         7.860390        0.204545   \\n\",\n       \"141           fullbeam         50         40         7.860390        0.204545   \\n\",\n       \"103           fullbeam         50         40         7.716000        2.110000   \\n\",\n       \"143           fullbeam         50         40         7.716000        2.110000   \\n\",\n       \"102           fullbeam         50         40         3.331916        1.930555   \\n\",\n       \"144           fullbeam         50         40         3.331916        1.930555   \\n\",\n       \"101           fullbeam         50         40         3.327500        1.937500   \\n\",\n       \"142           fullbeam         50         40         3.327500        1.937500   \\n\",\n       \"104           fullbeam         50         40         3.228261        2.513043   \\n\",\n       \"145           fullbeam         50         40         3.228261        2.513043   \\n\",\n       \"98            fullbeam         50         40         5.985782        5.677725   \\n\",\n       \"140           fullbeam         50         40         5.985782        5.677725   \\n\",\n       \"100           fullbeam         50         40         6.710000        8.360000   \\n\",\n       \"146           fullbeam         50         40         6.710000        8.360000   \\n\",\n       \"\\n\",\n       \"     present_pred_num  absent_pred_num  unique_pred_num  dup_pred_num  \\\\\\n\",\n       \"99          26.983766        38.652597       113.103896   2877.782468   \\n\",\n       \"141         25.331169        31.766234       103.159091   3254.538961   \\n\",\n       \"103         23.304000        51.262000        91.370000   3072.194000   \\n\",\n       \"143         22.192000        47.114000        84.350000   3164.972000   \\n\",\n       \"102         24.520839        48.330865        93.038375   2895.182869   \\n\",\n       \"144         23.765698        45.438435        83.928604   2966.514084   \\n\",\n       \"101         24.464500        48.384000        93.111500   2905.150000   \\n\",\n       \"142         23.730500        46.566500        85.292500   2992.986500   \\n\",\n       \"104         23.671739        46.989130        95.773913   3151.547826   \\n\",\n       \"145         22.808696        45.680435        86.208696   3251.073913   \\n\",\n       \"98          23.478673        49.075829        95.758294   3166.227488   \\n\",\n       \"140         22.649289        45.862559        84.919431   3274.933649   \\n\",\n       \"100         23.780000        49.330000        97.010000   3171.300000   \\n\",\n       \"146         22.800000        45.280000        83.750000   3258.980000   \\n\",\n       \"\\n\",\n       \"       beam_num  beamstep_num  present_exact_correct@5  \\\\\\n\",\n       \"99   397.006494  11338.480519                 0.720779   \\n\",\n       \"141  422.370130  11909.139610                 0.727273   \\n\",\n       \"103  419.990000  12056.604000                 1.956000   \\n\",\n       \"143  427.300000  12297.024000                 1.912000   \\n\",\n       \"102  409.084905  11881.238255                 1.299545   \\n\",\n       \"144  412.683394  12031.902837                 1.280182   \\n\",\n       \"101  409.844500  11906.859500                 1.299500   \\n\",\n       \"142  414.778000  12097.758000                 1.293500   \\n\",\n       \"104  426.680435  12225.017391                 1.315217   \\n\",\n       \"145  435.997826  12531.176087                 1.286957   \\n\",\n       \"98   428.734597  12338.137441                 2.028436   \\n\",\n       \"140  434.398104  12527.127962                 2.033175   \\n\",\n       \"100  428.210000  12440.260000                 1.940000   \\n\",\n       \"146  433.570000  12591.410000                 1.930000   \\n\",\n       \"\\n\",\n       \"     present_exact_precision@5  present_exact_recall@5  \\\\\\n\",\n       \"99                    0.144156                0.100478   \\n\",\n       \"141                   0.145455                0.100148   \\n\",\n       \"103                   0.391200                0.308453   \\n\",\n       \"143                   0.382400                0.300901   \\n\",\n       \"102                   0.259988                0.462279   \\n\",\n       \"144                   0.256094                0.456092   \\n\",\n       \"101                   0.259900                0.468845   \\n\",\n       \"142                   0.258700                0.465987   \\n\",\n       \"104                   0.263043                0.474356   \\n\",\n       \"145                   0.257391                0.466974   \\n\",\n       \"98                    0.405687                0.396093   \\n\",\n       \"140                   0.406635                0.405661   \\n\",\n       \"100                   0.388000                0.314265   \\n\",\n       \"146                   0.386000                0.310670   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score@5  present_exact_precision_hard@5  \\\\\\n\",\n       \"99                  0.115804                        0.144156   \\n\",\n       \"141                 0.116142                        0.145455   \\n\",\n       \"103                 0.321771                        0.391200   \\n\",\n       \"143                 0.314813                        0.382400   \\n\",\n       \"102                 0.311866                        0.259909   \\n\",\n       \"144                 0.307406                        0.256036   \\n\",\n       \"101                 0.312857                        0.259900   \\n\",\n       \"142                 0.311486                        0.258700   \\n\",\n       \"104                 0.311374                        0.263043   \\n\",\n       \"145                 0.305464                        0.257391   \\n\",\n       \"98                  0.368173                        0.405687   \\n\",\n       \"140                 0.373284                        0.406635   \\n\",\n       \"100                 0.327538                        0.388000   \\n\",\n       \"146                 0.324662                        0.386000   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score_hard@5  present_exact_correct@10  \\\\\\n\",\n       \"99                       0.115804                  1.389610   \\n\",\n       \"141                      0.116142                  1.405844   \\n\",\n       \"103                      0.321771                  3.410000   \\n\",\n       \"143                      0.314813                  3.382000   \\n\",\n       \"102                      0.311814                  1.775104   \\n\",\n       \"144                      0.307370                  1.754991   \\n\",\n       \"101                      0.312857                  1.788500   \\n\",\n       \"142                      0.311486                  1.788500   \\n\",\n       \"104                      0.311374                  1.886957   \\n\",\n       \"145                      0.305464                  1.847826   \\n\",\n       \"98                       0.368173                  2.990521   \\n\",\n       \"140                      0.373284                  2.976303   \\n\",\n       \"100                      0.327538                  3.010000   \\n\",\n       \"146                      0.324662                  2.960000   \\n\",\n       \"\\n\",\n       \"     present_exact_precision@10  present_exact_recall@10  \\\\\\n\",\n       \"99                     0.138961                 0.189414   \\n\",\n       \"141                    0.140747                 0.190990   \\n\",\n       \"103                    0.342456                 0.508636   \\n\",\n       \"143                    0.339504                 0.507734   \\n\",\n       \"102                    0.178172                 0.613406   \\n\",\n       \"144                    0.176175                 0.607375   \\n\",\n       \"101                    0.179341                 0.622615   \\n\",\n       \"142                    0.179152                 0.622748   \\n\",\n       \"104                    0.189450                 0.632191   \\n\",\n       \"145                    0.185256                 0.625793   \\n\",\n       \"98                     0.301083                 0.579405   \\n\",\n       \"140                    0.298697                 0.565945   \\n\",\n       \"100                    0.301000                 0.479292   \\n\",\n       \"146                    0.296333                 0.473295   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score@10  present_exact_precision_hard@10  \\\\\\n\",\n       \"99                   0.156848                         0.138961   \\n\",\n       \"141                  0.159025                         0.140584   \\n\",\n       \"103                  0.384177                         0.341000   \\n\",\n       \"143                  0.381651                         0.338200   \\n\",\n       \"102                  0.261582                         0.177510   \\n\",\n       \"144                  0.258744                         0.175499   \\n\",\n       \"101                  0.263656                         0.178850   \\n\",\n       \"142                  0.263510                         0.178850   \\n\",\n       \"104                  0.270854                         0.188696   \\n\",\n       \"145                  0.265185                         0.184783   \\n\",\n       \"98                   0.366556                         0.299052   \\n\",\n       \"140                  0.363174                         0.297630   \\n\",\n       \"100                  0.349367                         0.301000   \\n\",\n       \"146                  0.344301                         0.296000   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score_hard@10  present_exact_correct@k  \\\\\\n\",\n       \"99                        0.156848                 1.146104   \\n\",\n       \"141                       0.158870                 1.168831   \\n\",\n       \"103                       0.383022                 3.082000   \\n\",\n       \"143                       0.380574                 3.018000   \\n\",\n       \"102                       0.260950                 1.067994   \\n\",\n       \"144                       0.258091                 1.053335   \\n\",\n       \"101                       0.263116                 1.064000   \\n\",\n       \"142                       0.263122                 1.058500   \\n\",\n       \"104                       0.270022                 1.208696   \\n\",\n       \"145                       0.264538                 1.200000   \\n\",\n       \"98                        0.364819                 2.431280   \\n\",\n       \"140                       0.362394                 2.431280   \\n\",\n       \"100                       0.349367                 2.600000   \\n\",\n       \"146                       0.344015                 2.510000   \\n\",\n       \"\\n\",\n       \"     present_exact_precision@k  present_exact_recall@k  \\\\\\n\",\n       \"99                    0.145175                0.145175   \\n\",\n       \"141                   0.149253                0.149253   \\n\",\n       \"103                   0.383891                0.383410   \\n\",\n       \"143                   0.372335                0.371915   \\n\",\n       \"102                   0.310903                0.310174   \\n\",\n       \"144                   0.306870                0.306082   \\n\",\n       \"101                   0.304832                0.304210   \\n\",\n       \"142                   0.304263                0.303589   \\n\",\n       \"104                   0.331696                0.331696   \\n\",\n       \"145                   0.327904                0.327904   \\n\",\n       \"98                    0.392193                0.391719   \\n\",\n       \"140                   0.392704                0.392309   \\n\",\n       \"100                   0.363218                0.363218   \\n\",\n       \"146                   0.349171                0.348546   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score@k  present_exact_precision_hard@k  \\\\\\n\",\n       \"99                  0.145175                        0.145175   \\n\",\n       \"141                 0.149253                        0.149253   \\n\",\n       \"103                 0.383630                        0.383410   \\n\",\n       \"143                 0.372107                        0.371915   \\n\",\n       \"102                 0.310444                        0.310174   \\n\",\n       \"144                 0.306367                        0.306082   \\n\",\n       \"101                 0.304465                        0.304210   \\n\",\n       \"142                 0.303867                        0.303589   \\n\",\n       \"104                 0.331696                        0.331696   \\n\",\n       \"145                 0.327904                        0.327904   \\n\",\n       \"98                  0.391930                        0.391719   \\n\",\n       \"140                 0.392478                        0.392309   \\n\",\n       \"100                 0.363218                        0.363218   \\n\",\n       \"146                 0.348837                        0.348546   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score_hard@k  present_exact_correct@M  \\\\\\n\",\n       \"99                       0.145175                 2.425325   \\n\",\n       \"141                      0.149253                 2.275974   \\n\",\n       \"103                      0.383410                 5.032000   \\n\",\n       \"143                      0.371915                 4.902000   \\n\",\n       \"102                      0.310174                 2.195677   \\n\",\n       \"144                      0.306082                 2.187522   \\n\",\n       \"101                      0.304210                 2.209500   \\n\",\n       \"142                      0.303589                 2.213500   \\n\",\n       \"104                      0.331696                 2.452174   \\n\",\n       \"145                      0.327904                 2.389130   \\n\",\n       \"98                       0.391719                 3.781991   \\n\",\n       \"140                      0.392309                 3.725118   \\n\",\n       \"100                      0.363218                 4.040000   \\n\",\n       \"146                      0.348546                 4.010000   \\n\",\n       \"\\n\",\n       \"     present_exact_precision@M  present_exact_recall@M  \\\\\\n\",\n       \"99                    0.095782                0.323610   \\n\",\n       \"141                   0.096578                0.304134   \\n\",\n       \"103                   0.228810                0.702070   \\n\",\n       \"143                   0.231086                0.685163   \\n\",\n       \"102                   0.097724                0.734336   \\n\",\n       \"144                   0.100247                0.730980   \\n\",\n       \"101                   0.098078                0.738750   \\n\",\n       \"142                   0.100874                0.737942   \\n\",\n       \"104                   0.110893                0.771984   \\n\",\n       \"145                   0.110275                0.762951   \\n\",\n       \"98                    0.174484                0.695568   \\n\",\n       \"140                   0.174411                0.675854   \\n\",\n       \"100                   0.180375                0.630679   \\n\",\n       \"146                   0.184256                0.626297   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score@M  present_exact_precision_hard@M  \\\\\\n\",\n       \"99                  0.143397                        0.095782   \\n\",\n       \"141                 0.142257                        0.096578   \\n\",\n       \"103                 0.326277                        0.228810   \\n\",\n       \"143                 0.328642                        0.231086   \\n\",\n       \"102                 0.163878                        0.097724   \\n\",\n       \"144                 0.167464                        0.100247   \\n\",\n       \"101                 0.164736                        0.098078   \\n\",\n       \"142                 0.169063                        0.100874   \\n\",\n       \"104                 0.183004                        0.110893   \\n\",\n       \"145                 0.182055                        0.110275   \\n\",\n       \"98                  0.261233                        0.174484   \\n\",\n       \"140                 0.261203                        0.174411   \\n\",\n       \"100                 0.267522                        0.180375   \\n\",\n       \"146                 0.272150                        0.184256   \\n\",\n       \"\\n\",\n       \"     present_exact_f_score_hard@M  absent_exact_correct@10  \\\\\\n\",\n       \"99                       0.143397                 0.000000   \\n\",\n       \"141                      0.142257                 0.000000   \\n\",\n       \"103                      0.326277                 0.068000   \\n\",\n       \"143                      0.328642                 0.060000   \\n\",\n       \"102                      0.163878                 0.056287   \\n\",\n       \"144                      0.167464                 0.046380   \\n\",\n       \"101                      0.164736                 0.062000   \\n\",\n       \"142                      0.169063                 0.053000   \\n\",\n       \"104                      0.183004                 0.071739   \\n\",\n       \"145                      0.182055                 0.052174   \\n\",\n       \"98                       0.261233                 0.090047   \\n\",\n       \"140                      0.261203                 0.061611   \\n\",\n       \"100                      0.267522                 0.120000   \\n\",\n       \"146                      0.272150                 0.100000   \\n\",\n       \"\\n\",\n       \"     absent_exact_precision@10  absent_exact_recall@10  \\\\\\n\",\n       \"99                    0.000000                0.000000   \\n\",\n       \"141                   0.000000                0.000000   \\n\",\n       \"103                   0.009752                0.034760   \\n\",\n       \"143                   0.007667                0.034241   \\n\",\n       \"102                   0.006179                0.027039   \\n\",\n       \"144                   0.005121                0.022021   \\n\",\n       \"101                   0.006294                0.029596   \\n\",\n       \"142                   0.005369                0.025780   \\n\",\n       \"104                   0.008502                0.027916   \\n\",\n       \"145                   0.005510                0.024268   \\n\",\n       \"98                    0.009005                0.025682   \\n\",\n       \"140                   0.006161                0.011570   \\n\",\n       \"100                   0.012429                0.016107   \\n\",\n       \"146                   0.010000                0.014567   \\n\",\n       \"\\n\",\n       \"     absent_exact_f_score@10  absent_exact_precision_hard@10  \\\\\\n\",\n       \"99                  0.000000                        0.000000   \\n\",\n       \"141                 0.000000                        0.000000   \\n\",\n       \"103                 0.012508                        0.006800   \\n\",\n       \"143                 0.011426                        0.006000   \\n\",\n       \"102                 0.009384                        0.005629   \\n\",\n       \"144                 0.007750                        0.004638   \\n\",\n       \"101                 0.009947                        0.006200   \\n\",\n       \"142                 0.008537                        0.005300   \\n\",\n       \"104                 0.012205                        0.007174   \\n\",\n       \"145                 0.008305                        0.005217   \\n\",\n       \"98                  0.010988                        0.009005   \\n\",\n       \"140                 0.007286                        0.006161   \\n\",\n       \"100                 0.013912                        0.012000   \\n\",\n       \"146                 0.011500                        0.010000   \\n\",\n       \"\\n\",\n       \"     absent_exact_f_score_hard@10  absent_exact_correct@50  \\\\\\n\",\n       \"99                       0.000000                 0.000000   \\n\",\n       \"141                      0.000000                 0.000000   \\n\",\n       \"103                      0.010607                 0.074000   \\n\",\n       \"143                      0.009651                 0.076000   \\n\",\n       \"102                      0.008936                 0.067494   \\n\",\n       \"144                      0.007347                 0.059639   \\n\",\n       \"101                      0.009825                 0.075500   \\n\",\n       \"142                      0.008437                 0.065000   \\n\",\n       \"104                      0.010705                 0.089130   \\n\",\n       \"145                      0.008053                 0.067391   \\n\",\n       \"98                       0.010988                 0.127962   \\n\",\n       \"140                      0.007286                 0.099526   \\n\",\n       \"100                      0.013523                 0.130000   \\n\",\n       \"146                      0.011500                 0.110000   \\n\",\n       \"\\n\",\n       \"     absent_exact_precision@50  absent_exact_recall@50  \\\\\\n\",\n       \"99                    0.000000                0.000000   \\n\",\n       \"141                   0.000000                0.000000   \\n\",\n       \"103                   0.006018                0.037760   \\n\",\n       \"143                   0.004841                0.040797   \\n\",\n       \"102                   0.003026                0.032201   \\n\",\n       \"144                   0.002694                0.028441   \\n\",\n       \"101                   0.002852                0.035230   \\n\",\n       \"142                   0.002430                0.030288   \\n\",\n       \"104                   0.004847                0.032262   \\n\",\n       \"145                   0.002912                0.028127   \\n\",\n       \"98                    0.003799                0.029338   \\n\",\n       \"140                   0.003577                0.017176   \\n\",\n       \"100                   0.004500                0.017357   \\n\",\n       \"146                   0.003684                0.015817   \\n\",\n       \"\\n\",\n       \"     absent_exact_f_score@50  absent_exact_precision_hard@50  \\\\\\n\",\n       \"99                  0.000000                        0.000000   \\n\",\n       \"141                 0.000000                        0.000000   \\n\",\n       \"103                 0.007198                        0.001480   \\n\",\n       \"143                 0.007132                        0.001520   \\n\",\n       \"102                 0.004803                        0.001350   \\n\",\n       \"144                 0.004288                        0.001193   \\n\",\n       \"101                 0.005006                        0.001510   \\n\",\n       \"142                 0.004285                        0.001300   \\n\",\n       \"104                 0.007556                        0.001783   \\n\",\n       \"145                 0.004666                        0.001348   \\n\",\n       \"98                  0.006150                        0.002559   \\n\",\n       \"140                 0.005211                        0.001991   \\n\",\n       \"100                 0.006791                        0.002600   \\n\",\n       \"146                 0.005624                        0.002200   \\n\",\n       \"\\n\",\n       \"     absent_exact_f_score_hard@50  absent_exact_correct@M  \\\\\\n\",\n       \"99                       0.000000                0.000000   \\n\",\n       \"141                      0.000000                0.000000   \\n\",\n       \"103                      0.002795                0.076000   \\n\",\n       \"143                      0.002879                0.080000   \\n\",\n       \"102                      0.002560                0.068745   \\n\",\n       \"144                      0.002262                0.060940   \\n\",\n       \"101                      0.002862                0.076000   \\n\",\n       \"142                      0.002465                0.067000   \\n\",\n       \"104                      0.003310                0.091304   \\n\",\n       \"145                      0.002522                0.069565   \\n\",\n       \"98                       0.004341                0.132701   \\n\",\n       \"140                      0.003370                0.104265   \\n\",\n       \"100                      0.004492                0.130000   \\n\",\n       \"146                      0.003816                0.110000   \\n\",\n       \"\\n\",\n       \"     absent_exact_precision@M  absent_exact_recall@M  absent_exact_f_score@M  \\\\\\n\",\n       \"99                   0.000000               0.000000                0.000000   \\n\",\n       \"141                  0.000000               0.000000                0.000000   \\n\",\n       \"103                  0.005858               0.038427                0.006914   \\n\",\n       \"143                  0.004693               0.042297                0.006863   \\n\",\n       \"102                  0.002825               0.032736                0.004433   \\n\",\n       \"144                  0.002545               0.029014                0.004015   \\n\",\n       \"101                  0.002586               0.035330                0.004518   \\n\",\n       \"142                  0.002251               0.031330                0.003958   \\n\",\n       \"104                  0.004624               0.032987                0.007182   \\n\",\n       \"145                  0.002822               0.028325                0.004505   \\n\",\n       \"98                   0.003232               0.030285                0.005285   \\n\",\n       \"140                  0.003500               0.017607                0.005084   \\n\",\n       \"100                  0.004261               0.017357                0.006418   \\n\",\n       \"146                  0.003416               0.015817                0.005179   \\n\",\n       \"\\n\",\n       \"     absent_exact_precision_hard@M  absent_exact_f_score_hard@M  \\\\\\n\",\n       \"99                        0.000000                     0.000000   \\n\",\n       \"141                       0.000000                     0.000000   \\n\",\n       \"103                       0.005858                     0.006914   \\n\",\n       \"143                       0.004693                     0.006863   \\n\",\n       \"102                       0.002825                     0.004433   \\n\",\n       \"144                       0.002545                     0.004015   \\n\",\n       \"101                       0.002586                     0.004518   \\n\",\n       \"142                       0.002251                     0.003958   \\n\",\n       \"104                       0.004624                     0.007182   \\n\",\n       \"145                       0.002822                     0.004505   \\n\",\n       \"98                        0.003232                     0.005285   \\n\",\n       \"140                       0.003500                     0.005084   \\n\",\n       \"100                       0.004261                     0.006418   \\n\",\n       \"146                       0.003416                     0.005179   \\n\",\n       \"\\n\",\n       \"     present_partial_correct@5  present_partial_precision@5  \\\\\\n\",\n       \"99                    0.827922                     0.240267   \\n\",\n       \"141                   0.824675                     0.244157   \\n\",\n       \"103                   2.094000                     0.490360   \\n\",\n       \"143                   2.052000                     0.481097   \\n\",\n       \"102                   1.338920                     0.310563   \\n\",\n       \"144                   1.320158                     0.307409   \\n\",\n       \"101                   1.341000                     0.309750   \\n\",\n       \"142                   1.338000                     0.308816   \\n\",\n       \"104                   1.345652                     0.310922   \\n\",\n       \"145                   1.330435                     0.307725   \\n\",\n       \"98                    2.090047                     0.483705   \\n\",\n       \"140                   2.118483                     0.484790   \\n\",\n       \"100                   2.060000                     0.472352   \\n\",\n       \"146                   2.030000                     0.467807   \\n\",\n       \"\\n\",\n       \"     present_partial_recall@5  present_partial_f_score@5  \\\\\\n\",\n       \"99                   0.163123                   0.190207   \\n\",\n       \"141                  0.165272                   0.192976   \\n\",\n       \"103                  0.381910                   0.400368   \\n\",\n       \"143                  0.373897                   0.392583   \\n\",\n       \"102                  0.546752                   0.370121   \\n\",\n       \"144                  0.541522                   0.366387   \\n\",\n       \"101                  0.549483                   0.369786   \\n\",\n       \"142                  0.545680                   0.368356   \\n\",\n       \"104                  0.549312                   0.364027   \\n\",\n       \"145                  0.545264                   0.360798   \\n\",\n       \"98                   0.475679                   0.439502   \\n\",\n       \"140                  0.482832                   0.443366   \\n\",\n       \"100                  0.386805                   0.401143   \\n\",\n       \"146                  0.382451                   0.396574   \\n\",\n       \"\\n\",\n       \"     present_partial_precision_hard@5  present_partial_f_score_hard@5  \\\\\\n\",\n       \"99                           0.240267                        0.190207   \\n\",\n       \"141                          0.244157                        0.192976   \\n\",\n       \"103                          0.490360                        0.400368   \\n\",\n       \"143                          0.481097                        0.392583   \\n\",\n       \"102                          0.310479                        0.370067   \\n\",\n       \"144                          0.307346                        0.366348   \\n\",\n       \"101                          0.309750                        0.369786   \\n\",\n       \"142                          0.308816                        0.368356   \\n\",\n       \"104                          0.310922                        0.364027   \\n\",\n       \"145                          0.307725                        0.360798   \\n\",\n       \"98                           0.483705                        0.439502   \\n\",\n       \"140                          0.484790                        0.443366   \\n\",\n       \"100                          0.472352                        0.401143   \\n\",\n       \"146                          0.467807                        0.396574   \\n\",\n       \"\\n\",\n       \"     present_partial_correct@10  present_partial_precision@10  \\\\\\n\",\n       \"99                     1.642857                      0.205093   \\n\",\n       \"141                    1.652597                      0.207966   \\n\",\n       \"103                    3.634000                      0.397998   \\n\",\n       \"143                    3.586000                      0.393555   \\n\",\n       \"102                    1.833041                      0.203504   \\n\",\n       \"144                    1.813279                      0.201625   \\n\",\n       \"101                    1.845500                      0.203699   \\n\",\n       \"142                    1.847000                      0.203396   \\n\",\n       \"104                    1.943478                      0.214840   \\n\",\n       \"145                    1.900000                      0.211663   \\n\",\n       \"98                     3.109005                      0.347584   \\n\",\n       \"140                    3.123223                      0.345138   \\n\",\n       \"100                    3.190000                      0.355147   \\n\",\n       \"146                    3.190000                      0.353353   \\n\",\n       \"\\n\",\n       \"     present_partial_recall@10  present_partial_f_score@10  \\\\\\n\",\n       \"99                    0.274161                    0.229839   \\n\",\n       \"141                   0.277746                    0.233240   \\n\",\n       \"103                   0.582956                    0.443556   \\n\",\n       \"143                   0.579552                    0.439360   \\n\",\n       \"102                   0.688778                    0.296070   \\n\",\n       \"144                   0.682945                    0.293340   \\n\",\n       \"101                   0.693016                    0.296454   \\n\",\n       \"142                   0.692416                    0.296106   \\n\",\n       \"104                   0.706115                    0.305127   \\n\",\n       \"145                   0.700680                    0.300782   \\n\",\n       \"98                    0.651987                    0.420085   \\n\",\n       \"140                   0.644575                    0.416962   \\n\",\n       \"100                   0.560904                    0.411272   \\n\",\n       \"146                   0.557835                    0.409015   \\n\",\n       \"\\n\",\n       \"     present_partial_precision_hard@10  present_partial_f_score_hard@10  \\\\\\n\",\n       \"99                            0.205093                         0.229839   \\n\",\n       \"141                           0.207534                         0.232979   \\n\",\n       \"103                           0.396319                         0.442218   \\n\",\n       \"143                           0.392156                         0.438213   \\n\",\n       \"102                           0.202784                         0.295392   \\n\",\n       \"144                           0.200889                         0.292642   \\n\",\n       \"101                           0.203206                         0.295911   \\n\",\n       \"142                           0.203041                         0.295663   \\n\",\n       \"104                           0.213979                         0.304165   \\n\",\n       \"145                           0.211189                         0.300135   \\n\",\n       \"98                            0.345459                         0.418307   \\n\",\n       \"140                           0.343913                         0.416103   \\n\",\n       \"100                           0.355147                         0.411272   \\n\",\n       \"146                           0.353020                         0.408730   \\n\",\n       \"\\n\",\n       \"     present_partial_correct@k  present_partial_precision@k  \\\\\\n\",\n       \"99                    1.360390                     0.221199   \\n\",\n       \"141                   1.360390                     0.227053   \\n\",\n       \"103                   3.300000                     0.461203   \\n\",\n       \"143                   3.248000                     0.451361   \\n\",\n       \"102                   1.142793                     0.392430   \\n\",\n       \"144                   1.127233                     0.388850   \\n\",\n       \"101                   1.144000                     0.382541   \\n\",\n       \"142                   1.137500                     0.380898   \\n\",\n       \"104                   1.258696                     0.400129   \\n\",\n       \"145                   1.252174                     0.399462   \\n\",\n       \"98                    2.568720                     0.474196   \\n\",\n       \"140                   2.582938                     0.474214   \\n\",\n       \"100                   2.770000                     0.437941   \\n\",\n       \"146                   2.710000                     0.431871   \\n\",\n       \"\\n\",\n       \"     present_partial_recall@k  present_partial_f_score@k  \\\\\\n\",\n       \"99                   0.221199                   0.221199   \\n\",\n       \"141                  0.226769                   0.226866   \\n\",\n       \"103                  0.460657                   0.460908   \\n\",\n       \"143                  0.450800                   0.451054   \\n\",\n       \"102                  0.391362                   0.391759   \\n\",\n       \"144                  0.387705                   0.388123   \\n\",\n       \"101                  0.381571                   0.381963   \\n\",\n       \"142                  0.379873                   0.380290   \\n\",\n       \"104                  0.400129                   0.400129   \\n\",\n       \"145                  0.399462                   0.399462   \\n\",\n       \"98                   0.473629                   0.473881   \\n\",\n       \"140                  0.473732                   0.473938   \\n\",\n       \"100                  0.437941                   0.437941   \\n\",\n       \"146                  0.431160                   0.431491   \\n\",\n       \"\\n\",\n       \"     present_partial_precision_hard@k  present_partial_f_score_hard@k  \\\\\\n\",\n       \"99                           0.221199                        0.221199   \\n\",\n       \"141                          0.226769                        0.226769   \\n\",\n       \"103                          0.460657                        0.460657   \\n\",\n       \"143                          0.450800                        0.450800   \\n\",\n       \"102                          0.391362                        0.391362   \\n\",\n       \"144                          0.387705                        0.387705   \\n\",\n       \"101                          0.381571                        0.381571   \\n\",\n       \"142                          0.379873                        0.379873   \\n\",\n       \"104                          0.400129                        0.400129   \\n\",\n       \"145                          0.399462                        0.399462   \\n\",\n       \"98                           0.473629                        0.473629   \\n\",\n       \"140                          0.473732                        0.473732   \\n\",\n       \"100                          0.437941                        0.437941   \\n\",\n       \"146                          0.431160                        0.431160   \\n\",\n       \"\\n\",\n       \"     present_partial_correct@M  present_partial_precision@M  \\\\\\n\",\n       \"99                    2.818182                     0.128951   \\n\",\n       \"141                   2.678571                     0.133099   \\n\",\n       \"103                   5.212000                     0.249388   \\n\",\n       \"143                   5.104000                     0.252699   \\n\",\n       \"102                   2.264122                     0.107731   \\n\",\n       \"144                   2.252214                     0.110255   \\n\",\n       \"101                   2.275500                     0.107668   \\n\",\n       \"142                   2.280500                     0.110588   \\n\",\n       \"104                   2.491304                     0.119425   \\n\",\n       \"145                   2.439130                     0.119472   \\n\",\n       \"98                    4.047393                     0.198370   \\n\",\n       \"140                   3.971564                     0.198323   \\n\",\n       \"100                   4.220000                     0.203886   \\n\",\n       \"146                   4.200000                     0.209371   \\n\",\n       \"\\n\",\n       \"     present_partial_recall@M  present_partial_f_score@M  \\\\\\n\",\n       \"99                   0.426914                   0.192010   \\n\",\n       \"141                  0.410399                   0.194576   \\n\",\n       \"103                  0.757745                   0.354508   \\n\",\n       \"143                  0.742746                   0.358221   \\n\",\n       \"102                  0.792412                   0.179099   \\n\",\n       \"144                  0.787605                   0.182605   \\n\",\n       \"101                  0.794117                   0.179347   \\n\",\n       \"142                  0.792416                   0.183784   \\n\",\n       \"104                  0.821873                   0.196689   \\n\",\n       \"145                  0.813916                   0.196661   \\n\",\n       \"98                   0.770084                   0.295725   \\n\",\n       \"140                  0.754379                   0.295786   \\n\",\n       \"100                  0.706746                   0.301802   \\n\",\n       \"146                  0.704316                   0.308704   \\n\",\n       \"\\n\",\n       \"     present_partial_precision_hard@M  present_partial_f_score_hard@M  \\\\\\n\",\n       \"99                           0.128951                        0.192010   \\n\",\n       \"141                          0.133099                        0.194576   \\n\",\n       \"103                          0.249388                        0.354508   \\n\",\n       \"143                          0.252699                        0.358221   \\n\",\n       \"102                          0.107731                        0.179099   \\n\",\n       \"144                          0.110255                        0.182605   \\n\",\n       \"101                          0.107668                        0.179347   \\n\",\n       \"142                          0.110588                        0.183784   \\n\",\n       \"104                          0.119425                        0.196689   \\n\",\n       \"145                          0.119472                        0.196661   \\n\",\n       \"98                           0.198370                        0.295725   \\n\",\n       \"140                          0.198323                        0.295786   \\n\",\n       \"100                          0.203886                        0.301802   \\n\",\n       \"146                          0.209371                        0.308704   \\n\",\n       \"\\n\",\n       \"     absent_partial_correct@10  absent_partial_precision@10  \\\\\\n\",\n       \"99                    0.000000                     0.001933   \\n\",\n       \"141                   0.000000                     0.001438   \\n\",\n       \"103                   0.084000                     0.032900   \\n\",\n       \"143                   0.080000                     0.030429   \\n\",\n       \"102                   0.061390                     0.023418   \\n\",\n       \"144                   0.051083                     0.021917   \\n\",\n       \"101                   0.066000                     0.023300   \\n\",\n       \"142                   0.060000                     0.022542   \\n\",\n       \"104                   0.082609                     0.031563   \\n\",\n       \"145                   0.060870                     0.028910   \\n\",\n       \"98                    0.180095                     0.049461   \\n\",\n       \"140                   0.151659                     0.045667   \\n\",\n       \"100                   0.200000                     0.064504   \\n\",\n       \"146                   0.160000                     0.061492   \\n\",\n       \"\\n\",\n       \"     absent_partial_recall@10  absent_partial_f_score@10  \\\\\\n\",\n       \"99                   0.011709                   0.002999   \\n\",\n       \"141                  0.007767                   0.002351   \\n\",\n       \"103                  0.118128                   0.045370   \\n\",\n       \"143                  0.116493                   0.044221   \\n\",\n       \"102                  0.095162                   0.034751   \\n\",\n       \"144                  0.090091                   0.032731   \\n\",\n       \"101                  0.097700                   0.035282   \\n\",\n       \"142                  0.094527                   0.033962   \\n\",\n       \"104                  0.107291                   0.045096   \\n\",\n       \"145                  0.102949                   0.041320   \\n\",\n       \"98                   0.086772                   0.053353   \\n\",\n       \"140                  0.077653                   0.050156   \\n\",\n       \"100                  0.078108                   0.067849   \\n\",\n       \"146                  0.072762                   0.062335   \\n\",\n       \"\\n\",\n       \"     absent_partial_precision_hard@10  absent_partial_f_score_hard@10  \\\\\\n\",\n       \"99                           0.001559                        0.002704   \\n\",\n       \"141                          0.001229                        0.002085   \\n\",\n       \"103                          0.027315                        0.041615   \\n\",\n       \"143                          0.026631                        0.040757   \\n\",\n       \"102                          0.020855                        0.032666   \\n\",\n       \"144                          0.019755                        0.030940   \\n\",\n       \"101                          0.021333                        0.033548   \\n\",\n       \"142                          0.020549                        0.032297   \\n\",\n       \"104                          0.028446                        0.042124   \\n\",\n       \"145                          0.026625                        0.039525   \\n\",\n       \"98                           0.047029                        0.052502   \\n\",\n       \"140                          0.044387                        0.049496   \\n\",\n       \"100                          0.062430                        0.066884   \\n\",\n       \"146                          0.056679                        0.061220   \\n\",\n       \"\\n\",\n       \"     absent_partial_correct@50  absent_partial_precision@50  \\\\\\n\",\n       \"99                    0.000000                     0.001015   \\n\",\n       \"141                   0.000000                     0.000814   \\n\",\n       \"103                   0.114000                     0.018267   \\n\",\n       \"143                   0.106000                     0.016993   \\n\",\n       \"102                   0.079302                     0.012081   \\n\",\n       \"144                   0.070646                     0.011505   \\n\",\n       \"101                   0.084000                     0.011723   \\n\",\n       \"142                   0.076500                     0.011409   \\n\",\n       \"104                   0.126087                     0.016154   \\n\",\n       \"145                   0.091304                     0.015114   \\n\",\n       \"98                    0.355450                     0.022692   \\n\",\n       \"140                   0.312796                     0.023144   \\n\",\n       \"100                   0.320000                     0.029377   \\n\",\n       \"146                   0.310000                     0.033354   \\n\",\n       \"\\n\",\n       \"     absent_partial_recall@50  absent_partial_f_score@50  \\\\\\n\",\n       \"99                   0.012578                   0.001492   \\n\",\n       \"141                  0.008529                   0.001362   \\n\",\n       \"103                  0.146094                   0.025424   \\n\",\n       \"143                  0.143181                   0.025687   \\n\",\n       \"102                  0.114826                   0.018672   \\n\",\n       \"144                  0.111533                   0.018058   \\n\",\n       \"101                  0.119394                   0.018800   \\n\",\n       \"142                  0.116486                   0.018144   \\n\",\n       \"104                  0.127288                   0.025198   \\n\",\n       \"145                  0.123522                   0.023168   \\n\",\n       \"98                   0.109743                   0.031019   \\n\",\n       \"140                  0.104366                   0.031757   \\n\",\n       \"100                  0.100447                   0.040891   \\n\",\n       \"146                  0.091858                   0.040554   \\n\",\n       \"\\n\",\n       \"     absent_partial_precision_hard@50  absent_partial_f_score_hard@50  \\\\\\n\",\n       \"99                           0.000342                        0.000663   \\n\",\n       \"141                          0.000268                        0.000516   \\n\",\n       \"103                          0.006798                        0.012736   \\n\",\n       \"143                          0.006701                        0.012553   \\n\",\n       \"102                          0.005081                        0.009596   \\n\",\n       \"144                          0.004938                        0.009324   \\n\",\n       \"101                          0.005246                        0.009923   \\n\",\n       \"142                          0.005133                        0.009699   \\n\",\n       \"104                          0.007051                        0.013095   \\n\",\n       \"145                          0.006671                        0.012392   \\n\",\n       \"98                           0.012740                        0.021400   \\n\",\n       \"140                          0.012529                        0.021001   \\n\",\n       \"100                          0.016040                        0.027182   \\n\",\n       \"146                          0.014513                        0.024630   \\n\",\n       \"\\n\",\n       \"     absent_partial_correct@M  absent_partial_precision@M  \\\\\\n\",\n       \"99                   0.000000                    0.000976   \\n\",\n       \"141                  0.000000                    0.000796   \\n\",\n       \"103                  0.120000                    0.017234   \\n\",\n       \"143                  0.110000                    0.016077   \\n\",\n       \"102                  0.080903                    0.011330   \\n\",\n       \"144                  0.072197                    0.010883   \\n\",\n       \"101                  0.085000                    0.010887   \\n\",\n       \"142                  0.078000                    0.010686   \\n\",\n       \"104                  0.128261                    0.015045   \\n\",\n       \"145                  0.091304                    0.014311   \\n\",\n       \"98                   0.369668                    0.020372   \\n\",\n       \"140                  0.327014                    0.021469   \\n\",\n       \"100                  0.330000                    0.027416   \\n\",\n       \"146                  0.310000                    0.031701   \\n\",\n       \"\\n\",\n       \"     absent_partial_recall@M  absent_partial_f_score@M  \\\\\\n\",\n       \"99                  0.012703                  0.001418   \\n\",\n       \"141                 0.009451                  0.001330   \\n\",\n       \"103                 0.148405                  0.023559   \\n\",\n       \"143                 0.145531                  0.024023   \\n\",\n       \"102                 0.116161                  0.017298   \\n\",\n       \"144                 0.113058                  0.016922   \\n\",\n       \"101                 0.120758                  0.017265   \\n\",\n       \"142                 0.117942                  0.016827   \\n\",\n       \"104                 0.130101                  0.023258   \\n\",\n       \"145                 0.125238                  0.021737   \\n\",\n       \"98                  0.112448                  0.027467   \\n\",\n       \"140                 0.105829                  0.029209   \\n\",\n       \"100                 0.101764                  0.037968   \\n\",\n       \"146                 0.094163                  0.038028   \\n\",\n       \"\\n\",\n       \"     absent_partial_precision_hard@M  absent_partial_f_score_hard@M  \\\\\\n\",\n       \"99                          0.000976                       0.001418   \\n\",\n       \"141                         0.000796                       0.001330   \\n\",\n       \"103                         0.017234                       0.023559   \\n\",\n       \"143                         0.016077                       0.024023   \\n\",\n       \"102                         0.011330                       0.017298   \\n\",\n       \"144                         0.010883                       0.016922   \\n\",\n       \"101                         0.010887                       0.017265   \\n\",\n       \"142                         0.010686                       0.016827   \\n\",\n       \"104                         0.015045                       0.023258   \\n\",\n       \"145                         0.014311                       0.021737   \\n\",\n       \"98                          0.020372                       0.027467   \\n\",\n       \"140                         0.021469                       0.029209   \\n\",\n       \"100                         0.027416                       0.037968   \\n\",\n       \"146                         0.031701                       0.038028   \\n\",\n       \"\\n\",\n       \"     present_exact_advanced_auc  present_exact_advanced_ap  \\\\\\n\",\n       \"99                     0.076260                   0.096550   \\n\",\n       \"141                    0.076831                   0.270145   \\n\",\n       \"103                    0.331321                   0.367722   \\n\",\n       \"143                    0.316342                   0.494065   \\n\",\n       \"102                    0.316835                   0.370134   \\n\",\n       \"144                    0.311136                   0.441994   \\n\",\n       \"101                    0.313800                   0.369182   \\n\",\n       \"142                    0.310270                   0.442734   \\n\",\n       \"104                    0.341869                   0.394736   \\n\",\n       \"145                    0.335451                   0.467640   \\n\",\n       \"98                     0.367128                   0.403375   \\n\",\n       \"140                    0.360912                   0.566511   \\n\",\n       \"100                    0.292198                   0.328403   \\n\",\n       \"146                    0.289219                   0.501281   \\n\",\n       \"\\n\",\n       \"     present_exact_advanced_mrr  present_exact_advanced_sadr  \\\\\\n\",\n       \"99                     0.200341                     0.209200   \\n\",\n       \"141                    0.204509                     0.205482   \\n\",\n       \"103                    0.263744                     0.510444   \\n\",\n       \"143                    0.259221                     0.498763   \\n\",\n       \"102                    0.338765                     0.484131   \\n\",\n       \"144                    0.334838                     0.479550   \\n\",\n       \"101                    0.335396                     0.484483   \\n\",\n       \"142                    0.334244                     0.483828   \\n\",\n       \"104                    0.353547                     0.511024   \\n\",\n       \"145                    0.357506                     0.503576   \\n\",\n       \"98                     0.350856                     0.513696   \\n\",\n       \"140                    0.350501                     0.509725   \\n\",\n       \"100                    0.312790                     0.464882   \\n\",\n       \"146                    0.296191                     0.454212   \\n\",\n       \"\\n\",\n       \"     present_exact_advanced_ndcg  present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"99                      0.233658                             0.282014   \\n\",\n       \"141                     0.228552                             0.286359   \\n\",\n       \"103                     0.557479                             0.529657   \\n\",\n       \"143                     0.541210                             0.523011   \\n\",\n       \"102                     0.513992                             0.493537   \\n\",\n       \"144                     0.508923                             0.491106   \\n\",\n       \"101                     0.514268                             0.480191   \\n\",\n       \"142                     0.512278                             0.477469   \\n\",\n       \"104                     0.544717                             0.470495   \\n\",\n       \"145                     0.537226                             0.476757   \\n\",\n       \"98                      0.581022                             0.545461   \\n\",\n       \"140                     0.568610                             0.539049   \\n\",\n       \"100                     0.515668                             0.514999   \\n\",\n       \"146                     0.508012                             0.519445   \\n\",\n       \"\\n\",\n       \"     present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"99                               0.335770                   0.000000   \\n\",\n       \"141                              0.342041                   0.000000   \\n\",\n       \"103                              0.612187                   0.013150   \\n\",\n       \"143                              0.605785                   0.013818   \\n\",\n       \"102                              0.498814                   0.013183   \\n\",\n       \"144                              0.496273                   0.010310   \\n\",\n       \"101                              0.485185                   0.012895   \\n\",\n       \"142                              0.482639                   0.010785   \\n\",\n       \"104                              0.482040                   0.014667   \\n\",\n       \"145                              0.488681                   0.012829   \\n\",\n       \"98                               0.586076                   0.013532   \\n\",\n       \"140                              0.581340                   0.004899   \\n\",\n       \"100                              0.568920                   0.010042   \\n\",\n       \"146                              0.575881                   0.006943   \\n\",\n       \"\\n\",\n       \"     absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"99                   0.000000                   0.000000   \\n\",\n       \"141                  0.000000                   0.000000   \\n\",\n       \"103                  0.017159                   0.030473   \\n\",\n       \"143                  0.026776                   0.026776   \\n\",\n       \"102                  0.015901                   0.030902   \\n\",\n       \"144                  0.024681                   0.024466   \\n\",\n       \"101                  0.015542                   0.030987   \\n\",\n       \"142                  0.025619                   0.025395   \\n\",\n       \"104                  0.017008                   0.040569   \\n\",\n       \"145                  0.031475                   0.031475   \\n\",\n       \"98                   0.016544                   0.048320   \\n\",\n       \"140                  0.025594                   0.024212   \\n\",\n       \"100                  0.011127                   0.077889   \\n\",\n       \"146                  0.060787                   0.060787   \\n\",\n       \"\\n\",\n       \"     absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"99                     0.000000                    0.000000   \\n\",\n       \"141                    0.000000                    0.000000   \\n\",\n       \"103                    0.024821                    0.025569   \\n\",\n       \"143                    0.025499                    0.026069   \\n\",\n       \"102                    0.021980                    0.023667   \\n\",\n       \"144                    0.018006                    0.019405   \\n\",\n       \"101                    0.022108                    0.024344   \\n\",\n       \"142                    0.019122                    0.020814   \\n\",\n       \"104                    0.022848                    0.026502   \\n\",\n       \"145                    0.020634                    0.022043   \\n\",\n       \"98                     0.023496                    0.026923   \\n\",\n       \"140                    0.010563                    0.013570   \\n\",\n       \"100                    0.016367                    0.024115   \\n\",\n       \"146                    0.014852                    0.019784   \\n\",\n       \"\\n\",\n       \"     absent_exact_advanced_alpha_ndcg@5  absent_exact_advanced_alpha_ndcg@10  \\n\",\n       \"99                             0.000000                             0.000000  \\n\",\n       \"141                            0.001991                             0.001991  \\n\",\n       \"103                            0.039086                             0.039453  \\n\",\n       \"143                            0.041039                             0.041345  \\n\",\n       \"102                            0.029606                             0.029667  \\n\",\n       \"144                            0.025164                             0.025249  \\n\",\n       \"101                            0.029908                             0.029912  \\n\",\n       \"142                            0.024922                             0.025033  \\n\",\n       \"104                            0.027258                             0.027258  \\n\",\n       \"145                            0.024188                             0.024279  \\n\",\n       \"98                             0.040065                             0.043178  \\n\",\n       \"140                            0.029372                             0.032255  \\n\",\n       \"100                            0.036571                             0.040301  \\n\",\n       \"146                            0.031381                             0.035286  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'\\\\n'\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from scipy import stats\\n\",\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"sample_freq_1 = 5000\\n\",\n    \"sample_freq_2 = 20000\\n\",\n    \"\\n\",\n    \"# prepare for one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue']\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.loc[(one2seq_df.step % sample_freq_1 == 0)]\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"one2seq_df_1 = one2seq_df\\n\",\n    \"for index_label, row_series in one2seq_df_1.iterrows():\\n\",\n    \"     one2seq_df_1.at[index_label, 'exp_name'] = one2seq_df_1.at[index_label, 'exp_name'] + '-freq%d' % sample_freq_1 \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df = one2seq_df.loc[(one2seq_df.step % sample_freq_2 == 0)]\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"one2seq_df_2 = one2seq_df\\n\",\n    \"for index_label, row_series in one2seq_df_2.iterrows():\\n\",\n    \"     one2seq_df_2.at[index_label, 'exp_name'] = one2seq_df_2.at[index_label, 'exp_name'] + '-freq%d' % sample_freq_2\\n\",\n    \"        \\n\",\n    \"# combine both and plot\\n\",\n    \"combined_df = one2seq_df_1.append(one2seq_df_2, ignore_index=True)\\n\",\n    \"# display(combined_df)\\n\",\n    \"\\n\",\n    \"# obtain the test scores by best valid performance\\n\",\n    \"str_summary_df, self_peak_summary_df, valid_peak_summary_df = brief_eval_results(combined_df, base_metric='present_exact_f_score@10')\\n\",\n    \"# display(valid_peak_summary_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"one2seq_freq1, one2seq_freq2 = [], []\\n\",\n    \"for test_dataset, exp_group in valid_peak_summary_df.groupby('test_dataset'):\\n\",\n    \"    print(test_dataset)\\n\",\n    \"    freq1_row = exp_group.loc[exp_group['exp_name'].str.endswith('freq%d' % sample_freq_1)]\\n\",\n    \"    freq2_row = exp_group.loc[exp_group['exp_name'].str.endswith('freq%d' % sample_freq_2)]\\n\",\n    \"    \\n\",\n    \"    with open(freq1_row['path'].values[0], 'r') as one2seq_freq1_eval_file:\\n\",\n    \"        one2seq_freq1_eval = json.load(one2seq_freq1_eval_file)\\n\",\n    \"    with open(freq2_row['path'].values[0], 'r') as one2seq_freq2_eval_file:\\n\",\n    \"        one2seq_freq2_eval = json.load(one2seq_freq2_eval_file)\\n\",\n    \"        \\n\",\n    \"    # run T-test for individual testset\\n\",\n    \"    print('T-test on testset %s:' % test_dataset)\\n\",\n    \"    print(len(one2seq_freq1_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2seq_freq1_eval['present_exact_f_score@10']))\\n\",\n    \"    \\n\",\n    \"    print('')\\n\",\n    \"    print(len(one2seq_freq2_eval['present_exact_f_score@10']))\\n\",\n    \"    print(stats.describe(one2seq_freq2_eval['present_exact_f_score@10']))\\n\",\n    \"    ttest = stats.ttest_rel(one2seq_freq1_eval['present_exact_f_score@10'], one2seq_freq2_eval['present_exact_f_score@10'])\\n\",\n    \"    print(ttest)\\n\",\n    \"    print('=' * 50)\\n\",\n    \"    \\n\",\n    \"    if not test_dataset.startswith('kp20k'):\\n\",\n    \"        one2seq_freq1.extend(one2seq_freq1_eval['present_exact_f_score@10'])\\n\",\n    \"        one2seq_freq2.extend(one2seq_freq2_eval['present_exact_f_score@10'])\\n\",\n    \"\\n\",\n    \"# run T-test on five testsets\\n\",\n    \"print('Stats of one2seq-freq%d' % sample_freq_1)\\n\",\n    \"print(len(one2seq_freq1))\\n\",\n    \"print(stats.describe(one2seq_freq1))\\n\",\n    \"print('')\\n\",\n    \"print('Stats of one2seq-freq%d' % sample_freq_2)\\n\",\n    \"print(len(one2seq_freq2))\\n\",\n    \"print(stats.describe(one2seq_freq2))\\n\",\n    \"print('T-test on five testsets:')\\n\",\n    \"ttest = stats.ttest_rel(one2seq_freq1, one2seq_freq2)\\n\",\n    \"print(ttest)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"display(valid_peak_summary_df)\\n\",\n    \"\\n\",\n    \"# Present results\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"groups = valid_peak_summary_df.exp_name.unique()\\n\",\n    \"bar_values = {'%s - %s' % (group, metric_name): [] for group in groups for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"''' \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Copy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### RNN models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-19T20:55:26.216823Z\",\n     \"start_time\": \"2020-11-19T20:55:25.759265Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"one2one_exps = [\\n\",\n    \"                'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"                'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"                ]\\n\",\n    \"\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2one_exps)]\\n\",\n    \"# one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[((one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0))] # keep % 10000 and 5000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"# kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset == 'kp20k']\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"    expname = 'RNN KPG-One2One'\\n\",\n    \"    expname += '_copy' if kp20k_one2one_df.at[index_label, 'exp_name'].find('Copytrue') > 0 else ''\\n\",\n    \"    rename = expname + ' - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"    kp20k_one2one_df.at[index_label, 'exp_name'] = expname\\n\",\n    \"    kp20k_one2one_df.at[index_label, 'test_dataset'] = rename\\n\",\n    \"    \\n\",\n    \"# # prepare one2seq data\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"               ]\\n\",\n    \"\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"# one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue']\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[(one2seq_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"# kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset == 'kp20k']\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2seq_df.iterrows():    \\n\",\n    \"    expname = 'RNN KPG-One2Seq'\\n\",\n    \"    expname += '_copy' if kp20k_one2seq_df.at[index_label, 'exp_name'].find('Copytrue') > 0 else ''\\n\",\n    \"    rename = expname + ' - ' + kp20k_one2seq_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"    kp20k_one2seq_df.at[index_label, 'exp_name'] = expname\\n\",\n    \"    kp20k_one2seq_df.at[index_label, 'test_dataset'] = rename\\n\",\n    \"\\n\",\n    \"# combine both and plot\\n\",\n    \"combined_kp20k_df = kp20k_one2one_df.append(kp20k_one2seq_df, ignore_index=True)\\n\",\n    \"# combined_df = one2one_df.append(one2seq_df, ignore_index=True)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# display(combined_kp20k_df)\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-21T22:55:34.961768Z\",\n     \"start_time\": \"2020-11-21T22:55:31.206435Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(900, 121)\\n\",\n      \"['one2one' 'one2seq+copy+cov' 'one2one+copy' 'one2one+copy+cov'\\n\",\n      \" 'one2seq+copy' 'one2seq']\\n\",\n      \"present valid_kp_df\\n\",\n      \"(42, 121)\\n\",\n      \"All data\\n\",\n      \"(900, 121)\\n\",\n      \"absent valid_kp_df\\n\",\n      \"present_exact_precision@5\\n\",\n      \"10\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_80000-kp20k-exhaustive.json\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65019</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_80000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_80000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>80000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>64.303097</td>\\n\",\n       \"      <td>383.164857</td>\\n\",\n       \"      <td>499.718767</td>\\n\",\n       \"      <td>499.718767</td>\\n\",\n       \"      <td>499.718767</td>\\n\",\n       \"      <td>2198.695452</td>\\n\",\n       \"      <td>1.405664</td>\\n\",\n       \"      <td>0.281133</td>\\n\",\n       \"      <td>0.493077</td>\\n\",\n       \"      <td>0.334212</td>\\n\",\n       \"      <td>0.281133</td>\\n\",\n       \"      <td>0.334212</td>\\n\",\n       \"      <td>1.934357</td>\\n\",\n       \"      <td>0.193436</td>\\n\",\n       \"      <td>0.651547</td>\\n\",\n       \"      <td>0.280349</td>\\n\",\n       \"      <td>0.193436</td>\\n\",\n       \"      <td>0.280349</td>\\n\",\n       \"      <td>1.322209</td>\\n\",\n       \"      <td>0.358082</td>\\n\",\n       \"      <td>0.358082</td>\\n\",\n       \"      <td>0.358082</td>\\n\",\n       \"      <td>0.358082</td>\\n\",\n       \"      <td>0.358082</td>\\n\",\n       \"      <td>3.125632</td>\\n\",\n       \"      <td>0.051071</td>\\n\",\n       \"      <td>0.921608</td>\\n\",\n       \"      <td>0.092751</td>\\n\",\n       \"      <td>0.051071</td>\\n\",\n       \"      <td>0.092751</td>\\n\",\n       \"      <td>0.116526</td>\\n\",\n       \"      <td>0.011653</td>\\n\",\n       \"      <td>0.055430</td>\\n\",\n       \"      <td>0.018413</td>\\n\",\n       \"      <td>0.011653</td>\\n\",\n       \"      <td>0.018413</td>\\n\",\n       \"      <td>0.237705</td>\\n\",\n       \"      <td>0.004754</td>\\n\",\n       \"      <td>0.109618</td>\\n\",\n       \"      <td>0.008993</td>\\n\",\n       \"      <td>0.004754</td>\\n\",\n       \"      <td>0.008993</td>\\n\",\n       \"      <td>0.370341</td>\\n\",\n       \"      <td>0.000970</td>\\n\",\n       \"      <td>0.168707</td>\\n\",\n       \"      <td>0.001926</td>\\n\",\n       \"      <td>0.000970</td>\\n\",\n       \"      <td>0.001926</td>\\n\",\n       \"      <td>1.432731</td>\\n\",\n       \"      <td>0.323188</td>\\n\",\n       \"      <td>0.563621</td>\\n\",\n       \"      <td>0.382964</td>\\n\",\n       \"      <td>0.323188</td>\\n\",\n       \"      <td>0.382964</td>\\n\",\n       \"      <td>1.970831</td>\\n\",\n       \"      <td>0.214266</td>\\n\",\n       \"      <td>0.714967</td>\\n\",\n       \"      <td>0.309202</td>\\n\",\n       \"      <td>0.214266</td>\\n\",\n       \"      <td>0.309202</td>\\n\",\n       \"      <td>1.363436</td>\\n\",\n       \"      <td>0.419890</td>\\n\",\n       \"      <td>0.419890</td>\\n\",\n       \"      <td>0.419890</td>\\n\",\n       \"      <td>0.419890</td>\\n\",\n       \"      <td>0.419890</td>\\n\",\n       \"      <td>3.134988</td>\\n\",\n       \"      <td>0.051741</td>\\n\",\n       \"      <td>0.930557</td>\\n\",\n       \"      <td>0.093897</td>\\n\",\n       \"      <td>0.051741</td>\\n\",\n       \"      <td>0.093897</td>\\n\",\n       \"      <td>0.122079</td>\\n\",\n       \"      <td>0.029043</td>\\n\",\n       \"      <td>0.133421</td>\\n\",\n       \"      <td>0.045557</td>\\n\",\n       \"      <td>0.029043</td>\\n\",\n       \"      <td>0.045557</td>\\n\",\n       \"      <td>0.263371</td>\\n\",\n       \"      <td>0.009787</td>\\n\",\n       \"      <td>0.219326</td>\\n\",\n       \"      <td>0.018476</td>\\n\",\n       \"      <td>0.009787</td>\\n\",\n       \"      <td>0.018476</td>\\n\",\n       \"      <td>0.424376</td>\\n\",\n       \"      <td>0.001769</td>\\n\",\n       \"      <td>0.299567</td>\\n\",\n       \"      <td>0.003510</td>\\n\",\n       \"      <td>0.001769</td>\\n\",\n       \"      <td>0.003510</td>\\n\",\n       \"      <td>0.392332</td>\\n\",\n       \"      <td>0.443644</td>\\n\",\n       \"      <td>0.331991</td>\\n\",\n       \"      <td>0.560372</td>\\n\",\n       \"      <td>0.617860</td>\\n\",\n       \"      <td>0.525254</td>\\n\",\n       \"      <td>0.531011</td>\\n\",\n       \"      <td>0.023159</td>\\n\",\n       \"      <td>0.029962</td>\\n\",\n       \"      <td>0.050672</td>\\n\",\n       \"      <td>0.058712</td>\\n\",\n       \"      <td>0.065879</td>\\n\",\n       \"      <td>0.039788</td>\\n\",\n       \"      <td>0.039954</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65067</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_50000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_50000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>50000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>65.524191</td>\\n\",\n       \"      <td>380.746585</td>\\n\",\n       \"      <td>499.830040</td>\\n\",\n       \"      <td>499.830040</td>\\n\",\n       \"      <td>499.830040</td>\\n\",\n       \"      <td>2194.497073</td>\\n\",\n       \"      <td>1.377946</td>\\n\",\n       \"      <td>0.275589</td>\\n\",\n       \"      <td>0.484120</td>\\n\",\n       \"      <td>0.327580</td>\\n\",\n       \"      <td>0.275589</td>\\n\",\n       \"      <td>0.327580</td>\\n\",\n       \"      <td>1.899034</td>\\n\",\n       \"      <td>0.189903</td>\\n\",\n       \"      <td>0.640823</td>\\n\",\n       \"      <td>0.275427</td>\\n\",\n       \"      <td>0.189903</td>\\n\",\n       \"      <td>0.275427</td>\\n\",\n       \"      <td>1.295692</td>\\n\",\n       \"      <td>0.351092</td>\\n\",\n       \"      <td>0.351092</td>\\n\",\n       \"      <td>0.351092</td>\\n\",\n       \"      <td>0.351092</td>\\n\",\n       \"      <td>0.351092</td>\\n\",\n       \"      <td>3.120779</td>\\n\",\n       \"      <td>0.049884</td>\\n\",\n       \"      <td>0.921662</td>\\n\",\n       \"      <td>0.090830</td>\\n\",\n       \"      <td>0.049884</td>\\n\",\n       \"      <td>0.090830</td>\\n\",\n       \"      <td>0.103367</td>\\n\",\n       \"      <td>0.010337</td>\\n\",\n       \"      <td>0.049108</td>\\n\",\n       \"      <td>0.016343</td>\\n\",\n       \"      <td>0.010337</td>\\n\",\n       \"      <td>0.016343</td>\\n\",\n       \"      <td>0.214339</td>\\n\",\n       \"      <td>0.004287</td>\\n\",\n       \"      <td>0.098415</td>\\n\",\n       \"      <td>0.008107</td>\\n\",\n       \"      <td>0.004287</td>\\n\",\n       \"      <td>0.008107</td>\\n\",\n       \"      <td>0.340171</td>\\n\",\n       \"      <td>0.000896</td>\\n\",\n       \"      <td>0.155139</td>\\n\",\n       \"      <td>0.001779</td>\\n\",\n       \"      <td>0.000896</td>\\n\",\n       \"      <td>0.001779</td>\\n\",\n       \"      <td>1.405063</td>\\n\",\n       \"      <td>0.318976</td>\\n\",\n       \"      <td>0.556646</td>\\n\",\n       \"      <td>0.377912</td>\\n\",\n       \"      <td>0.318976</td>\\n\",\n       \"      <td>0.377912</td>\\n\",\n       \"      <td>1.940061</td>\\n\",\n       \"      <td>0.211564</td>\\n\",\n       \"      <td>0.707091</td>\\n\",\n       \"      <td>0.305457</td>\\n\",\n       \"      <td>0.211564</td>\\n\",\n       \"      <td>0.305457</td>\\n\",\n       \"      <td>1.339020</td>\\n\",\n       \"      <td>0.415492</td>\\n\",\n       \"      <td>0.415492</td>\\n\",\n       \"      <td>0.415492</td>\\n\",\n       \"      <td>0.415492</td>\\n\",\n       \"      <td>0.415492</td>\\n\",\n       \"      <td>3.130835</td>\\n\",\n       \"      <td>0.050549</td>\\n\",\n       \"      <td>0.930585</td>\\n\",\n       \"      <td>0.091965</td>\\n\",\n       \"      <td>0.050549</td>\\n\",\n       \"      <td>0.091965</td>\\n\",\n       \"      <td>0.108971</td>\\n\",\n       \"      <td>0.027748</td>\\n\",\n       \"      <td>0.127036</td>\\n\",\n       \"      <td>0.043503</td>\\n\",\n       \"      <td>0.027748</td>\\n\",\n       \"      <td>0.043503</td>\\n\",\n       \"      <td>0.239806</td>\\n\",\n       \"      <td>0.009338</td>\\n\",\n       \"      <td>0.209081</td>\\n\",\n       \"      <td>0.017627</td>\\n\",\n       \"      <td>0.009338</td>\\n\",\n       \"      <td>0.017627</td>\\n\",\n       \"      <td>0.396007</td>\\n\",\n       \"      <td>0.001712</td>\\n\",\n       \"      <td>0.288096</td>\\n\",\n       \"      <td>0.003396</td>\\n\",\n       \"      <td>0.001712</td>\\n\",\n       \"      <td>0.003396</td>\\n\",\n       \"      <td>0.384463</td>\\n\",\n       \"      <td>0.435554</td>\\n\",\n       \"      <td>0.327189</td>\\n\",\n       \"      <td>0.553477</td>\\n\",\n       \"      <td>0.611862</td>\\n\",\n       \"      <td>0.522618</td>\\n\",\n       \"      <td>0.528239</td>\\n\",\n       \"      <td>0.019484</td>\\n\",\n       \"      <td>0.025503</td>\\n\",\n       \"      <td>0.044057</td>\\n\",\n       \"      <td>0.051971</td>\\n\",\n       \"      <td>0.058513</td>\\n\",\n       \"      <td>0.035134</td>\\n\",\n       \"      <td>0.035267</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65140</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_90000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_90000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>90000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>65.082904</td>\\n\",\n       \"      <td>381.555711</td>\\n\",\n       \"      <td>499.639916</td>\\n\",\n       \"      <td>499.639916</td>\\n\",\n       \"      <td>499.639916</td>\\n\",\n       \"      <td>2206.406064</td>\\n\",\n       \"      <td>1.407965</td>\\n\",\n       \"      <td>0.281593</td>\\n\",\n       \"      <td>0.494686</td>\\n\",\n       \"      <td>0.335078</td>\\n\",\n       \"      <td>0.281593</td>\\n\",\n       \"      <td>0.335078</td>\\n\",\n       \"      <td>1.931205</td>\\n\",\n       \"      <td>0.193121</td>\\n\",\n       \"      <td>0.651469</td>\\n\",\n       \"      <td>0.280097</td>\\n\",\n       \"      <td>0.193121</td>\\n\",\n       \"      <td>0.280097</td>\\n\",\n       \"      <td>1.315455</td>\\n\",\n       \"      <td>0.357647</td>\\n\",\n       \"      <td>0.357647</td>\\n\",\n       \"      <td>0.357647</td>\\n\",\n       \"      <td>0.357647</td>\\n\",\n       \"      <td>0.357647</td>\\n\",\n       \"      <td>3.121679</td>\\n\",\n       \"      <td>0.050312</td>\\n\",\n       \"      <td>0.921625</td>\\n\",\n       \"      <td>0.091532</td>\\n\",\n       \"      <td>0.050312</td>\\n\",\n       \"      <td>0.091532</td>\\n\",\n       \"      <td>0.116226</td>\\n\",\n       \"      <td>0.011623</td>\\n\",\n       \"      <td>0.055351</td>\\n\",\n       \"      <td>0.018379</td>\\n\",\n       \"      <td>0.011623</td>\\n\",\n       \"      <td>0.018379</td>\\n\",\n       \"      <td>0.237204</td>\\n\",\n       \"      <td>0.004744</td>\\n\",\n       \"      <td>0.109946</td>\\n\",\n       \"      <td>0.008976</td>\\n\",\n       \"      <td>0.004744</td>\\n\",\n       \"      <td>0.008976</td>\\n\",\n       \"      <td>0.368840</td>\\n\",\n       \"      <td>0.000970</td>\\n\",\n       \"      <td>0.168012</td>\\n\",\n       \"      <td>0.001926</td>\\n\",\n       \"      <td>0.000970</td>\\n\",\n       \"      <td>0.001926</td>\\n\",\n       \"      <td>1.434783</td>\\n\",\n       \"      <td>0.323520</td>\\n\",\n       \"      <td>0.564660</td>\\n\",\n       \"      <td>0.383525</td>\\n\",\n       \"      <td>0.323520</td>\\n\",\n       \"      <td>0.383525</td>\\n\",\n       \"      <td>1.968530</td>\\n\",\n       \"      <td>0.214008</td>\\n\",\n       \"      <td>0.714734</td>\\n\",\n       \"      <td>0.308951</td>\\n\",\n       \"      <td>0.214008</td>\\n\",\n       \"      <td>0.308951</td>\\n\",\n       \"      <td>1.358333</td>\\n\",\n       \"      <td>0.420250</td>\\n\",\n       \"      <td>0.420250</td>\\n\",\n       \"      <td>0.420250</td>\\n\",\n       \"      <td>0.420250</td>\\n\",\n       \"      <td>0.420250</td>\\n\",\n       \"      <td>3.132036</td>\\n\",\n       \"      <td>0.050988</td>\\n\",\n       \"      <td>0.930496</td>\\n\",\n       \"      <td>0.092685</td>\\n\",\n       \"      <td>0.050988</td>\\n\",\n       \"      <td>0.092685</td>\\n\",\n       \"      <td>0.122330</td>\\n\",\n       \"      <td>0.029122</td>\\n\",\n       \"      <td>0.133798</td>\\n\",\n       \"      <td>0.045685</td>\\n\",\n       \"      <td>0.029122</td>\\n\",\n       \"      <td>0.045685</td>\\n\",\n       \"      <td>0.263821</td>\\n\",\n       \"      <td>0.009790</td>\\n\",\n       \"      <td>0.219845</td>\\n\",\n       \"      <td>0.018483</td>\\n\",\n       \"      <td>0.009790</td>\\n\",\n       \"      <td>0.018483</td>\\n\",\n       \"      <td>0.422925</td>\\n\",\n       \"      <td>0.001774</td>\\n\",\n       \"      <td>0.299158</td>\\n\",\n       \"      <td>0.003519</td>\\n\",\n       \"      <td>0.001774</td>\\n\",\n       \"      <td>0.003519</td>\\n\",\n       \"      <td>0.392803</td>\\n\",\n       \"      <td>0.444007</td>\\n\",\n       \"      <td>0.332339</td>\\n\",\n       \"      <td>0.560342</td>\\n\",\n       \"      <td>0.618109</td>\\n\",\n       \"      <td>0.524573</td>\\n\",\n       \"      <td>0.530251</td>\\n\",\n       \"      <td>0.023062</td>\\n\",\n       \"      <td>0.029863</td>\\n\",\n       \"      <td>0.050363</td>\\n\",\n       \"      <td>0.058387</td>\\n\",\n       \"      <td>0.065657</td>\\n\",\n       \"      <td>0.039336</td>\\n\",\n       \"      <td>0.039491</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64935</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_40000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_40000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>40000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>69.573923</td>\\n\",\n       \"      <td>379.558863</td>\\n\",\n       \"      <td>499.955071</td>\\n\",\n       \"      <td>499.955071</td>\\n\",\n       \"      <td>499.955071</td>\\n\",\n       \"      <td>2186.779607</td>\\n\",\n       \"      <td>1.309101</td>\\n\",\n       \"      <td>0.261820</td>\\n\",\n       \"      <td>0.453733</td>\\n\",\n       \"      <td>0.308959</td>\\n\",\n       \"      <td>0.261820</td>\\n\",\n       \"      <td>0.308959</td>\\n\",\n       \"      <td>1.834492</td>\\n\",\n       \"      <td>0.183449</td>\\n\",\n       \"      <td>0.612040</td>\\n\",\n       \"      <td>0.264245</td>\\n\",\n       \"      <td>0.183449</td>\\n\",\n       \"      <td>0.264245</td>\\n\",\n       \"      <td>1.245309</td>\\n\",\n       \"      <td>0.326462</td>\\n\",\n       \"      <td>0.326462</td>\\n\",\n       \"      <td>0.326462</td>\\n\",\n       \"      <td>0.326462</td>\\n\",\n       \"      <td>0.326462</td>\\n\",\n       \"      <td>3.144044</td>\\n\",\n       \"      <td>0.047042</td>\\n\",\n       \"      <td>0.922958</td>\\n\",\n       \"      <td>0.086077</td>\\n\",\n       \"      <td>0.047042</td>\\n\",\n       \"      <td>0.086077</td>\\n\",\n       \"      <td>0.094361</td>\\n\",\n       \"      <td>0.009436</td>\\n\",\n       \"      <td>0.044166</td>\\n\",\n       \"      <td>0.014876</td>\\n\",\n       \"      <td>0.009436</td>\\n\",\n       \"      <td>0.014876</td>\\n\",\n       \"      <td>0.200280</td>\\n\",\n       \"      <td>0.004006</td>\\n\",\n       \"      <td>0.091981</td>\\n\",\n       \"      <td>0.007576</td>\\n\",\n       \"      <td>0.004006</td>\\n\",\n       \"      <td>0.007576</td>\\n\",\n       \"      <td>0.317356</td>\\n\",\n       \"      <td>0.000839</td>\\n\",\n       \"      <td>0.144985</td>\\n\",\n       \"      <td>0.001665</td>\\n\",\n       \"      <td>0.000839</td>\\n\",\n       \"      <td>0.001665</td>\\n\",\n       \"      <td>1.337019</td>\\n\",\n       \"      <td>0.307492</td>\\n\",\n       \"      <td>0.532685</td>\\n\",\n       \"      <td>0.362890</td>\\n\",\n       \"      <td>0.307492</td>\\n\",\n       \"      <td>0.362890</td>\\n\",\n       \"      <td>1.878571</td>\\n\",\n       \"      <td>0.207230</td>\\n\",\n       \"      <td>0.688472</td>\\n\",\n       \"      <td>0.298089</td>\\n\",\n       \"      <td>0.207230</td>\\n\",\n       \"      <td>0.298089</td>\\n\",\n       \"      <td>1.282834</td>\\n\",\n       \"      <td>0.393683</td>\\n\",\n       \"      <td>0.393683</td>\\n\",\n       \"      <td>0.393683</td>\\n\",\n       \"      <td>0.393683</td>\\n\",\n       \"      <td>0.393683</td>\\n\",\n       \"      <td>3.151098</td>\\n\",\n       \"      <td>0.047622</td>\\n\",\n       \"      <td>0.932227</td>\\n\",\n       \"      <td>0.087095</td>\\n\",\n       \"      <td>0.047622</td>\\n\",\n       \"      <td>0.087095</td>\\n\",\n       \"      <td>0.099265</td>\\n\",\n       \"      <td>0.026350</td>\\n\",\n       \"      <td>0.119815</td>\\n\",\n       \"      <td>0.041264</td>\\n\",\n       \"      <td>0.026350</td>\\n\",\n       \"      <td>0.041264</td>\\n\",\n       \"      <td>0.224346</td>\\n\",\n       \"      <td>0.009000</td>\\n\",\n       \"      <td>0.201261</td>\\n\",\n       \"      <td>0.016989</td>\\n\",\n       \"      <td>0.009000</td>\\n\",\n       \"      <td>0.016989</td>\\n\",\n       \"      <td>0.370541</td>\\n\",\n       \"      <td>0.001660</td>\\n\",\n       \"      <td>0.278550</td>\\n\",\n       \"      <td>0.003294</td>\\n\",\n       \"      <td>0.001660</td>\\n\",\n       \"      <td>0.003294</td>\\n\",\n       \"      <td>0.356063</td>\\n\",\n       \"      <td>0.407376</td>\\n\",\n       \"      <td>0.305429</td>\\n\",\n       \"      <td>0.531947</td>\\n\",\n       \"      <td>0.589744</td>\\n\",\n       \"      <td>0.516701</td>\\n\",\n       \"      <td>0.522477</td>\\n\",\n       \"      <td>0.018024</td>\\n\",\n       \"      <td>0.023429</td>\\n\",\n       \"      <td>0.040661</td>\\n\",\n       \"      <td>0.048145</td>\\n\",\n       \"      <td>0.054120</td>\\n\",\n       \"      <td>0.031966</td>\\n\",\n       \"      <td>0.032130</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64958</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_70000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_70000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>70000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>65.139291</td>\\n\",\n       \"      <td>382.153850</td>\\n\",\n       \"      <td>499.780257</td>\\n\",\n       \"      <td>499.780257</td>\\n\",\n       \"      <td>499.780257</td>\\n\",\n       \"      <td>2197.898184</td>\\n\",\n       \"      <td>1.402462</td>\\n\",\n       \"      <td>0.280492</td>\\n\",\n       \"      <td>0.492116</td>\\n\",\n       \"      <td>0.333434</td>\\n\",\n       \"      <td>0.280492</td>\\n\",\n       \"      <td>0.333434</td>\\n\",\n       \"      <td>1.926902</td>\\n\",\n       \"      <td>0.192690</td>\\n\",\n       \"      <td>0.649185</td>\\n\",\n       \"      <td>0.279223</td>\\n\",\n       \"      <td>0.192690</td>\\n\",\n       \"      <td>0.279223</td>\\n\",\n       \"      <td>1.317256</td>\\n\",\n       \"      <td>0.356607</td>\\n\",\n       \"      <td>0.356607</td>\\n\",\n       \"      <td>0.356607</td>\\n\",\n       \"      <td>0.356607</td>\\n\",\n       \"      <td>0.356607</td>\\n\",\n       \"      <td>3.126582</td>\\n\",\n       \"      <td>0.050425</td>\\n\",\n       \"      <td>0.921777</td>\\n\",\n       \"      <td>0.091653</td>\\n\",\n       \"      <td>0.050425</td>\\n\",\n       \"      <td>0.091653</td>\\n\",\n       \"      <td>0.115425</td>\\n\",\n       \"      <td>0.011543</td>\\n\",\n       \"      <td>0.054974</td>\\n\",\n       \"      <td>0.018246</td>\\n\",\n       \"      <td>0.011543</td>\\n\",\n       \"      <td>0.018246</td>\\n\",\n       \"      <td>0.234903</td>\\n\",\n       \"      <td>0.004698</td>\\n\",\n       \"      <td>0.108659</td>\\n\",\n       \"      <td>0.008888</td>\\n\",\n       \"      <td>0.004698</td>\\n\",\n       \"      <td>0.008888</td>\\n\",\n       \"      <td>0.366488</td>\\n\",\n       \"      <td>0.000963</td>\\n\",\n       \"      <td>0.166613</td>\\n\",\n       \"      <td>0.001910</td>\\n\",\n       \"      <td>0.000963</td>\\n\",\n       \"      <td>0.001910</td>\\n\",\n       \"      <td>1.428529</td>\\n\",\n       \"      <td>0.322629</td>\\n\",\n       \"      <td>0.562686</td>\\n\",\n       \"      <td>0.382244</td>\\n\",\n       \"      <td>0.322629</td>\\n\",\n       \"      <td>0.382244</td>\\n\",\n       \"      <td>1.963076</td>\\n\",\n       \"      <td>0.213743</td>\\n\",\n       \"      <td>0.713398</td>\\n\",\n       \"      <td>0.308420</td>\\n\",\n       \"      <td>0.213743</td>\\n\",\n       \"      <td>0.308420</td>\\n\",\n       \"      <td>1.357833</td>\\n\",\n       \"      <td>0.419250</td>\\n\",\n       \"      <td>0.419250</td>\\n\",\n       \"      <td>0.419250</td>\\n\",\n       \"      <td>0.419250</td>\\n\",\n       \"      <td>0.419250</td>\\n\",\n       \"      <td>3.135888</td>\\n\",\n       \"      <td>0.051086</td>\\n\",\n       \"      <td>0.930760</td>\\n\",\n       \"      <td>0.092784</td>\\n\",\n       \"      <td>0.051086</td>\\n\",\n       \"      <td>0.092784</td>\\n\",\n       \"      <td>0.121079</td>\\n\",\n       \"      <td>0.028970</td>\\n\",\n       \"      <td>0.132906</td>\\n\",\n       \"      <td>0.045428</td>\\n\",\n       \"      <td>0.028970</td>\\n\",\n       \"      <td>0.045428</td>\\n\",\n       \"      <td>0.260369</td>\\n\",\n       \"      <td>0.009730</td>\\n\",\n       \"      <td>0.218290</td>\\n\",\n       \"      <td>0.018368</td>\\n\",\n       \"      <td>0.009730</td>\\n\",\n       \"      <td>0.018368</td>\\n\",\n       \"      <td>0.420573</td>\\n\",\n       \"      <td>0.001764</td>\\n\",\n       \"      <td>0.297739</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.001764</td>\\n\",\n       \"      <td>0.003500</td>\\n\",\n       \"      <td>0.391089</td>\\n\",\n       \"      <td>0.442332</td>\\n\",\n       \"      <td>0.331116</td>\\n\",\n       \"      <td>0.559074</td>\\n\",\n       \"      <td>0.616880</td>\\n\",\n       \"      <td>0.525029</td>\\n\",\n       \"      <td>0.530749</td>\\n\",\n       \"      <td>0.022862</td>\\n\",\n       \"      <td>0.029445</td>\\n\",\n       \"      <td>0.049769</td>\\n\",\n       \"      <td>0.057737</td>\\n\",\n       \"      <td>0.064942</td>\\n\",\n       \"      <td>0.039546</td>\\n\",\n       \"      <td>0.039706</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65088</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_60000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_60000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>60000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>63.940661</td>\\n\",\n       \"      <td>383.046130</td>\\n\",\n       \"      <td>499.819683</td>\\n\",\n       \"      <td>499.819683</td>\\n\",\n       \"      <td>499.819683</td>\\n\",\n       \"      <td>2198.704158</td>\\n\",\n       \"      <td>1.393256</td>\\n\",\n       \"      <td>0.278651</td>\\n\",\n       \"      <td>0.488622</td>\\n\",\n       \"      <td>0.331383</td>\\n\",\n       \"      <td>0.278651</td>\\n\",\n       \"      <td>0.331383</td>\\n\",\n       \"      <td>1.917646</td>\\n\",\n       \"      <td>0.191765</td>\\n\",\n       \"      <td>0.647134</td>\\n\",\n       \"      <td>0.278231</td>\\n\",\n       \"      <td>0.191765</td>\\n\",\n       \"      <td>0.278231</td>\\n\",\n       \"      <td>1.305949</td>\\n\",\n       \"      <td>0.354560</td>\\n\",\n       \"      <td>0.354560</td>\\n\",\n       \"      <td>0.354560</td>\\n\",\n       \"      <td>0.354560</td>\\n\",\n       \"      <td>0.354560</td>\\n\",\n       \"      <td>3.114024</td>\\n\",\n       \"      <td>0.051048</td>\\n\",\n       \"      <td>0.920748</td>\\n\",\n       \"      <td>0.092750</td>\\n\",\n       \"      <td>0.051048</td>\\n\",\n       \"      <td>0.092750</td>\\n\",\n       \"      <td>0.112173</td>\\n\",\n       \"      <td>0.011217</td>\\n\",\n       \"      <td>0.053110</td>\\n\",\n       \"      <td>0.017710</td>\\n\",\n       \"      <td>0.011217</td>\\n\",\n       \"      <td>0.017710</td>\\n\",\n       \"      <td>0.230800</td>\\n\",\n       \"      <td>0.004616</td>\\n\",\n       \"      <td>0.106247</td>\\n\",\n       \"      <td>0.008732</td>\\n\",\n       \"      <td>0.004616</td>\\n\",\n       \"      <td>0.008732</td>\\n\",\n       \"      <td>0.360534</td>\\n\",\n       \"      <td>0.000945</td>\\n\",\n       \"      <td>0.164131</td>\\n\",\n       \"      <td>0.001875</td>\\n\",\n       \"      <td>0.000945</td>\\n\",\n       \"      <td>0.001875</td>\\n\",\n       \"      <td>1.421424</td>\\n\",\n       \"      <td>0.321252</td>\\n\",\n       \"      <td>0.560312</td>\\n\",\n       \"      <td>0.380792</td>\\n\",\n       \"      <td>0.321252</td>\\n\",\n       \"      <td>0.380792</td>\\n\",\n       \"      <td>1.957823</td>\\n\",\n       \"      <td>0.213060</td>\\n\",\n       \"      <td>0.711754</td>\\n\",\n       \"      <td>0.307673</td>\\n\",\n       \"      <td>0.213060</td>\\n\",\n       \"      <td>0.307673</td>\\n\",\n       \"      <td>1.349777</td>\\n\",\n       \"      <td>0.417249</td>\\n\",\n       \"      <td>0.417249</td>\\n\",\n       \"      <td>0.417249</td>\\n\",\n       \"      <td>0.417249</td>\\n\",\n       \"      <td>0.417249</td>\\n\",\n       \"      <td>3.124731</td>\\n\",\n       \"      <td>0.051753</td>\\n\",\n       \"      <td>0.930046</td>\\n\",\n       \"      <td>0.093952</td>\\n\",\n       \"      <td>0.051753</td>\\n\",\n       \"      <td>0.093952</td>\\n\",\n       \"      <td>0.117727</td>\\n\",\n       \"      <td>0.028489</td>\\n\",\n       \"      <td>0.130584</td>\\n\",\n       \"      <td>0.044668</td>\\n\",\n       \"      <td>0.028489</td>\\n\",\n       \"      <td>0.044668</td>\\n\",\n       \"      <td>0.256267</td>\\n\",\n       \"      <td>0.009643</td>\\n\",\n       \"      <td>0.216027</td>\\n\",\n       \"      <td>0.018205</td>\\n\",\n       \"      <td>0.009643</td>\\n\",\n       \"      <td>0.018205</td>\\n\",\n       \"      <td>0.414069</td>\\n\",\n       \"      <td>0.001745</td>\\n\",\n       \"      <td>0.295350</td>\\n\",\n       \"      <td>0.003462</td>\\n\",\n       \"      <td>0.001745</td>\\n\",\n       \"      <td>0.003462</td>\\n\",\n       \"      <td>0.389232</td>\\n\",\n       \"      <td>0.440237</td>\\n\",\n       \"      <td>0.330284</td>\\n\",\n       \"      <td>0.556893</td>\\n\",\n       \"      <td>0.615213</td>\\n\",\n       \"      <td>0.522060</td>\\n\",\n       \"      <td>0.527682</td>\\n\",\n       \"      <td>0.021970</td>\\n\",\n       \"      <td>0.028466</td>\\n\",\n       \"      <td>0.048659</td>\\n\",\n       \"      <td>0.056378</td>\\n\",\n       \"      <td>0.063418</td>\\n\",\n       \"      <td>0.038249</td>\\n\",\n       \"      <td>0.038443</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64971</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_100000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_100000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>100000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>64.123780</td>\\n\",\n       \"      <td>383.209636</td>\\n\",\n       \"      <td>499.721219</td>\\n\",\n       \"      <td>499.721219</td>\\n\",\n       \"      <td>499.721219</td>\\n\",\n       \"      <td>2203.482013</td>\\n\",\n       \"      <td>1.411067</td>\\n\",\n       \"      <td>0.282213</td>\\n\",\n       \"      <td>0.495357</td>\\n\",\n       \"      <td>0.335613</td>\\n\",\n       \"      <td>0.282213</td>\\n\",\n       \"      <td>0.335613</td>\\n\",\n       \"      <td>1.931305</td>\\n\",\n       \"      <td>0.193131</td>\\n\",\n       \"      <td>0.650633</td>\\n\",\n       \"      <td>0.279970</td>\\n\",\n       \"      <td>0.193131</td>\\n\",\n       \"      <td>0.279970</td>\\n\",\n       \"      <td>1.321809</td>\\n\",\n       \"      <td>0.358676</td>\\n\",\n       \"      <td>0.358676</td>\\n\",\n       \"      <td>0.358676</td>\\n\",\n       \"      <td>0.358676</td>\\n\",\n       \"      <td>0.358676</td>\\n\",\n       \"      <td>3.119528</td>\\n\",\n       \"      <td>0.051086</td>\\n\",\n       \"      <td>0.921153</td>\\n\",\n       \"      <td>0.092789</td>\\n\",\n       \"      <td>0.051086</td>\\n\",\n       \"      <td>0.092789</td>\\n\",\n       \"      <td>0.117476</td>\\n\",\n       \"      <td>0.011748</td>\\n\",\n       \"      <td>0.055900</td>\\n\",\n       \"      <td>0.018568</td>\\n\",\n       \"      <td>0.011748</td>\\n\",\n       \"      <td>0.018568</td>\\n\",\n       \"      <td>0.239506</td>\\n\",\n       \"      <td>0.004790</td>\\n\",\n       \"      <td>0.110747</td>\\n\",\n       \"      <td>0.009062</td>\\n\",\n       \"      <td>0.004790</td>\\n\",\n       \"      <td>0.009062</td>\\n\",\n       \"      <td>0.371592</td>\\n\",\n       \"      <td>0.000974</td>\\n\",\n       \"      <td>0.169482</td>\\n\",\n       \"      <td>0.001933</td>\\n\",\n       \"      <td>0.000974</td>\\n\",\n       \"      <td>0.001933</td>\\n\",\n       \"      <td>1.437735</td>\\n\",\n       \"      <td>0.323827</td>\\n\",\n       \"      <td>0.564773</td>\\n\",\n       \"      <td>0.383745</td>\\n\",\n       \"      <td>0.323827</td>\\n\",\n       \"      <td>0.383745</td>\\n\",\n       \"      <td>1.968780</td>\\n\",\n       \"      <td>0.214100</td>\\n\",\n       \"      <td>0.714324</td>\\n\",\n       \"      <td>0.308983</td>\\n\",\n       \"      <td>0.214100</td>\\n\",\n       \"      <td>0.308983</td>\\n\",\n       \"      <td>1.363136</td>\\n\",\n       \"      <td>0.420483</td>\\n\",\n       \"      <td>0.420483</td>\\n\",\n       \"      <td>0.420483</td>\\n\",\n       \"      <td>0.420483</td>\\n\",\n       \"      <td>0.420483</td>\\n\",\n       \"      <td>3.129634</td>\\n\",\n       \"      <td>0.051778</td>\\n\",\n       \"      <td>0.930216</td>\\n\",\n       \"      <td>0.093968</td>\\n\",\n       \"      <td>0.051778</td>\\n\",\n       \"      <td>0.093968</td>\\n\",\n       \"      <td>0.123530</td>\\n\",\n       \"      <td>0.029197</td>\\n\",\n       \"      <td>0.134221</td>\\n\",\n       \"      <td>0.045801</td>\\n\",\n       \"      <td>0.029197</td>\\n\",\n       \"      <td>0.045801</td>\\n\",\n       \"      <td>0.266173</td>\\n\",\n       \"      <td>0.009825</td>\\n\",\n       \"      <td>0.220465</td>\\n\",\n       \"      <td>0.018549</td>\\n\",\n       \"      <td>0.009825</td>\\n\",\n       \"      <td>0.018549</td>\\n\",\n       \"      <td>0.425777</td>\\n\",\n       \"      <td>0.001770</td>\\n\",\n       \"      <td>0.299955</td>\\n\",\n       \"      <td>0.003513</td>\\n\",\n       \"      <td>0.001770</td>\\n\",\n       \"      <td>0.003513</td>\\n\",\n       \"      <td>0.392483</td>\\n\",\n       \"      <td>0.443859</td>\\n\",\n       \"      <td>0.332175</td>\\n\",\n       \"      <td>0.560682</td>\\n\",\n       \"      <td>0.617881</td>\\n\",\n       \"      <td>0.524209</td>\\n\",\n       \"      <td>0.529985</td>\\n\",\n       \"      <td>0.023845</td>\\n\",\n       \"      <td>0.030482</td>\\n\",\n       \"      <td>0.051203</td>\\n\",\n       \"      <td>0.059227</td>\\n\",\n       \"      <td>0.066489</td>\\n\",\n       \"      <td>0.040820</td>\\n\",\n       \"      <td>0.040992</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64911</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_20000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_20000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>20000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>62.695252</td>\\n\",\n       \"      <td>388.627058</td>\\n\",\n       \"      <td>499.948166</td>\\n\",\n       \"      <td>499.948166</td>\\n\",\n       \"      <td>499.948166</td>\\n\",\n       \"      <td>2181.367589</td>\\n\",\n       \"      <td>1.241907</td>\\n\",\n       \"      <td>0.248381</td>\\n\",\n       \"      <td>0.432954</td>\\n\",\n       \"      <td>0.293451</td>\\n\",\n       \"      <td>0.248381</td>\\n\",\n       \"      <td>0.293451</td>\\n\",\n       \"      <td>1.757893</td>\\n\",\n       \"      <td>0.175789</td>\\n\",\n       \"      <td>0.589513</td>\\n\",\n       \"      <td>0.253498</td>\\n\",\n       \"      <td>0.175789</td>\\n\",\n       \"      <td>0.253498</td>\\n\",\n       \"      <td>1.170261</td>\\n\",\n       \"      <td>0.306918</td>\\n\",\n       \"      <td>0.306918</td>\\n\",\n       \"      <td>0.306918</td>\\n\",\n       \"      <td>0.306918</td>\\n\",\n       \"      <td>0.306918</td>\\n\",\n       \"      <td>3.059989</td>\\n\",\n       \"      <td>0.050842</td>\\n\",\n       \"      <td>0.909836</td>\\n\",\n       \"      <td>0.092417</td>\\n\",\n       \"      <td>0.050842</td>\\n\",\n       \"      <td>0.092417</td>\\n\",\n       \"      <td>0.064792</td>\\n\",\n       \"      <td>0.006479</td>\\n\",\n       \"      <td>0.029893</td>\\n\",\n       \"      <td>0.010180</td>\\n\",\n       \"      <td>0.006479</td>\\n\",\n       \"      <td>0.010180</td>\\n\",\n       \"      <td>0.162406</td>\\n\",\n       \"      <td>0.003248</td>\\n\",\n       \"      <td>0.074781</td>\\n\",\n       \"      <td>0.006142</td>\\n\",\n       \"      <td>0.003248</td>\\n\",\n       \"      <td>0.006142</td>\\n\",\n       \"      <td>0.286786</td>\\n\",\n       \"      <td>0.000742</td>\\n\",\n       \"      <td>0.130517</td>\\n\",\n       \"      <td>0.001472</td>\\n\",\n       \"      <td>0.000742</td>\\n\",\n       \"      <td>0.001472</td>\\n\",\n       \"      <td>1.275829</td>\\n\",\n       \"      <td>0.297221</td>\\n\",\n       \"      <td>0.517540</td>\\n\",\n       \"      <td>0.351050</td>\\n\",\n       \"      <td>0.297221</td>\\n\",\n       \"      <td>0.351050</td>\\n\",\n       \"      <td>1.806274</td>\\n\",\n       \"      <td>0.200570</td>\\n\",\n       \"      <td>0.669366</td>\\n\",\n       \"      <td>0.288685</td>\\n\",\n       \"      <td>0.200570</td>\\n\",\n       \"      <td>0.288685</td>\\n\",\n       \"      <td>1.211588</td>\\n\",\n       \"      <td>0.379275</td>\\n\",\n       \"      <td>0.379275</td>\\n\",\n       \"      <td>0.379275</td>\\n\",\n       \"      <td>0.379275</td>\\n\",\n       \"      <td>0.379275</td>\\n\",\n       \"      <td>3.070796</td>\\n\",\n       \"      <td>0.051709</td>\\n\",\n       \"      <td>0.922164</td>\\n\",\n       \"      <td>0.093923</td>\\n\",\n       \"      <td>0.051709</td>\\n\",\n       \"      <td>0.093923</td>\\n\",\n       \"      <td>0.069345</td>\\n\",\n       \"      <td>0.024131</td>\\n\",\n       \"      <td>0.109031</td>\\n\",\n       \"      <td>0.037722</td>\\n\",\n       \"      <td>0.024131</td>\\n\",\n       \"      <td>0.037722</td>\\n\",\n       \"      <td>0.186671</td>\\n\",\n       \"      <td>0.008362</td>\\n\",\n       \"      <td>0.187219</td>\\n\",\n       \"      <td>0.015783</td>\\n\",\n       \"      <td>0.008362</td>\\n\",\n       \"      <td>0.015783</td>\\n\",\n       \"      <td>0.340121</td>\\n\",\n       \"      <td>0.001556</td>\\n\",\n       \"      <td>0.266650</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.001556</td>\\n\",\n       \"      <td>0.003088</td>\\n\",\n       \"      <td>0.333828</td>\\n\",\n       \"      <td>0.385183</td>\\n\",\n       \"      <td>0.295277</td>\\n\",\n       \"      <td>0.513160</td>\\n\",\n       \"      <td>0.569919</td>\\n\",\n       \"      <td>0.507513</td>\\n\",\n       \"      <td>0.513002</td>\\n\",\n       \"      <td>0.010899</td>\\n\",\n       \"      <td>0.015074</td>\\n\",\n       \"      <td>0.027533</td>\\n\",\n       \"      <td>0.037136</td>\\n\",\n       \"      <td>0.042423</td>\\n\",\n       \"      <td>0.025029</td>\\n\",\n       \"      <td>0.025205</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>64930</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_10000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_10000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>10000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>63.507980</td>\\n\",\n       \"      <td>393.350328</td>\\n\",\n       \"      <td>499.968429</td>\\n\",\n       \"      <td>499.968429</td>\\n\",\n       \"      <td>499.968429</td>\\n\",\n       \"      <td>2177.906089</td>\\n\",\n       \"      <td>1.135688</td>\\n\",\n       \"      <td>0.227138</td>\\n\",\n       \"      <td>0.391363</td>\\n\",\n       \"      <td>0.266176</td>\\n\",\n       \"      <td>0.227138</td>\\n\",\n       \"      <td>0.266176</td>\\n\",\n       \"      <td>1.646470</td>\\n\",\n       \"      <td>0.164647</td>\\n\",\n       \"      <td>0.546434</td>\\n\",\n       \"      <td>0.235964</td>\\n\",\n       \"      <td>0.164647</td>\\n\",\n       \"      <td>0.235964</td>\\n\",\n       \"      <td>1.095712</td>\\n\",\n       \"      <td>0.276643</td>\\n\",\n       \"      <td>0.276643</td>\\n\",\n       \"      <td>0.276643</td>\\n\",\n       \"      <td>0.276643</td>\\n\",\n       \"      <td>0.276643</td>\\n\",\n       \"      <td>3.062491</td>\\n\",\n       \"      <td>0.049631</td>\\n\",\n       \"      <td>0.903028</td>\\n\",\n       \"      <td>0.090357</td>\\n\",\n       \"      <td>0.049631</td>\\n\",\n       \"      <td>0.090357</td>\\n\",\n       \"      <td>0.037124</td>\\n\",\n       \"      <td>0.003712</td>\\n\",\n       \"      <td>0.016929</td>\\n\",\n       \"      <td>0.005810</td>\\n\",\n       \"      <td>0.003712</td>\\n\",\n       \"      <td>0.005810</td>\\n\",\n       \"      <td>0.121579</td>\\n\",\n       \"      <td>0.002432</td>\\n\",\n       \"      <td>0.055068</td>\\n\",\n       \"      <td>0.004593</td>\\n\",\n       \"      <td>0.002432</td>\\n\",\n       \"      <td>0.004593</td>\\n\",\n       \"      <td>0.237554</td>\\n\",\n       \"      <td>0.000608</td>\\n\",\n       \"      <td>0.107224</td>\\n\",\n       \"      <td>0.001207</td>\\n\",\n       \"      <td>0.000608</td>\\n\",\n       \"      <td>0.001207</td>\\n\",\n       \"      <td>1.166959</td>\\n\",\n       \"      <td>0.278624</td>\\n\",\n       \"      <td>0.482123</td>\\n\",\n       \"      <td>0.327508</td>\\n\",\n       \"      <td>0.278624</td>\\n\",\n       \"      <td>0.327508</td>\\n\",\n       \"      <td>1.698504</td>\\n\",\n       \"      <td>0.191969</td>\\n\",\n       \"      <td>0.636878</td>\\n\",\n       \"      <td>0.275274</td>\\n\",\n       \"      <td>0.191969</td>\\n\",\n       \"      <td>0.275274</td>\\n\",\n       \"      <td>1.131235</td>\\n\",\n       \"      <td>0.352803</td>\\n\",\n       \"      <td>0.352803</td>\\n\",\n       \"      <td>0.352803</td>\\n\",\n       \"      <td>0.352803</td>\\n\",\n       \"      <td>0.352803</td>\\n\",\n       \"      <td>3.074699</td>\\n\",\n       \"      <td>0.050697</td>\\n\",\n       \"      <td>0.920637</td>\\n\",\n       \"      <td>0.092262</td>\\n\",\n       \"      <td>0.050697</td>\\n\",\n       \"      <td>0.092262</td>\\n\",\n       \"      <td>0.041277</td>\\n\",\n       \"      <td>0.021674</td>\\n\",\n       \"      <td>0.097140</td>\\n\",\n       \"      <td>0.033821</td>\\n\",\n       \"      <td>0.021674</td>\\n\",\n       \"      <td>0.033821</td>\\n\",\n       \"      <td>0.145645</td>\\n\",\n       \"      <td>0.007735</td>\\n\",\n       \"      <td>0.171823</td>\\n\",\n       \"      <td>0.014592</td>\\n\",\n       \"      <td>0.007735</td>\\n\",\n       \"      <td>0.014592</td>\\n\",\n       \"      <td>0.293791</td>\\n\",\n       \"      <td>0.001438</td>\\n\",\n       \"      <td>0.248759</td>\\n\",\n       \"      <td>0.002854</td>\\n\",\n       \"      <td>0.001438</td>\\n\",\n       \"      <td>0.002854</td>\\n\",\n       \"      <td>0.301206</td>\\n\",\n       \"      <td>0.351010</td>\\n\",\n       \"      <td>0.271279</td>\\n\",\n       \"      <td>0.482890</td>\\n\",\n       \"      <td>0.540021</td>\\n\",\n       \"      <td>0.499486</td>\\n\",\n       \"      <td>0.505033</td>\\n\",\n       \"      <td>0.006223</td>\\n\",\n       \"      <td>0.008979</td>\\n\",\n       \"      <td>0.016726</td>\\n\",\n       \"      <td>0.026793</td>\\n\",\n       \"      <td>0.031014</td>\\n\",\n       \"      <td>0.020847</td>\\n\",\n       \"      <td>0.020964</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>65046</th>\\n\",\n       \"      <td>/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_30000-kp20k-exhaustive.json</td>\\n\",\n       \"      <td>meng17-one2one-kp20k-topmodels</td>\\n\",\n       \"      <td>one2one+copy</td>\\n\",\n       \"      <td>step_30000-kp20k-exhaustive</td>\\n\",\n       \"      <td>meng17</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>rnn</td>\\n\",\n       \"      <td>one2one</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>30000</td>\\n\",\n       \"      <td>kp20k</td>\\n\",\n       \"      <td>exhaustive</td>\\n\",\n       \"      <td>fullbeam</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>3.331916</td>\\n\",\n       \"      <td>1.930555</td>\\n\",\n       \"      <td>67.122330</td>\\n\",\n       \"      <td>387.862160</td>\\n\",\n       \"      <td>499.795317</td>\\n\",\n       \"      <td>499.795317</td>\\n\",\n       \"      <td>499.795317</td>\\n\",\n       \"      <td>2212.192275</td>\\n\",\n       \"      <td>1.297944</td>\\n\",\n       \"      <td>0.259589</td>\\n\",\n       \"      <td>0.453381</td>\\n\",\n       \"      <td>0.307410</td>\\n\",\n       \"      <td>0.259589</td>\\n\",\n       \"      <td>0.307410</td>\\n\",\n       \"      <td>1.822485</td>\\n\",\n       \"      <td>0.182248</td>\\n\",\n       \"      <td>0.613309</td>\\n\",\n       \"      <td>0.263487</td>\\n\",\n       \"      <td>0.182248</td>\\n\",\n       \"      <td>0.263487</td>\\n\",\n       \"      <td>1.218592</td>\\n\",\n       \"      <td>0.325737</td>\\n\",\n       \"      <td>0.325737</td>\\n\",\n       \"      <td>0.325737</td>\\n\",\n       \"      <td>0.325737</td>\\n\",\n       \"      <td>0.325737</td>\\n\",\n       \"      <td>3.113824</td>\\n\",\n       \"      <td>0.048512</td>\\n\",\n       \"      <td>0.920384</td>\\n\",\n       \"      <td>0.088561</td>\\n\",\n       \"      <td>0.048512</td>\\n\",\n       \"      <td>0.088561</td>\\n\",\n       \"      <td>0.079352</td>\\n\",\n       \"      <td>0.007935</td>\\n\",\n       \"      <td>0.037354</td>\\n\",\n       \"      <td>0.012516</td>\\n\",\n       \"      <td>0.007935</td>\\n\",\n       \"      <td>0.012516</td>\\n\",\n       \"      <td>0.177716</td>\\n\",\n       \"      <td>0.003554</td>\\n\",\n       \"      <td>0.081917</td>\\n\",\n       \"      <td>0.006722</td>\\n\",\n       \"      <td>0.003554</td>\\n\",\n       \"      <td>0.006722</td>\\n\",\n       \"      <td>0.294141</td>\\n\",\n       \"      <td>0.000761</td>\\n\",\n       \"      <td>0.133854</td>\\n\",\n       \"      <td>0.001511</td>\\n\",\n       \"      <td>0.000761</td>\\n\",\n       \"      <td>0.001511</td>\\n\",\n       \"      <td>1.329664</td>\\n\",\n       \"      <td>0.306932</td>\\n\",\n       \"      <td>0.534519</td>\\n\",\n       \"      <td>0.362968</td>\\n\",\n       \"      <td>0.306932</td>\\n\",\n       \"      <td>0.362968</td>\\n\",\n       \"      <td>1.866763</td>\\n\",\n       \"      <td>0.205814</td>\\n\",\n       \"      <td>0.687491</td>\\n\",\n       \"      <td>0.296630</td>\\n\",\n       \"      <td>0.205814</td>\\n\",\n       \"      <td>0.296630</td>\\n\",\n       \"      <td>1.263471</td>\\n\",\n       \"      <td>0.396105</td>\\n\",\n       \"      <td>0.396105</td>\\n\",\n       \"      <td>0.396105</td>\\n\",\n       \"      <td>0.396105</td>\\n\",\n       \"      <td>0.396105</td>\\n\",\n       \"      <td>3.124431</td>\\n\",\n       \"      <td>0.049183</td>\\n\",\n       \"      <td>0.929612</td>\\n\",\n       \"      <td>0.089714</td>\\n\",\n       \"      <td>0.049183</td>\\n\",\n       \"      <td>0.089714</td>\\n\",\n       \"      <td>0.085756</td>\\n\",\n       \"      <td>0.026003</td>\\n\",\n       \"      <td>0.118332</td>\\n\",\n       \"      <td>0.040701</td>\\n\",\n       \"      <td>0.026003</td>\\n\",\n       \"      <td>0.040701</td>\\n\",\n       \"      <td>0.204983</td>\\n\",\n       \"      <td>0.008743</td>\\n\",\n       \"      <td>0.195679</td>\\n\",\n       \"      <td>0.016502</td>\\n\",\n       \"      <td>0.008743</td>\\n\",\n       \"      <td>0.016502</td>\\n\",\n       \"      <td>0.347326</td>\\n\",\n       \"      <td>0.001572</td>\\n\",\n       \"      <td>0.269380</td>\\n\",\n       \"      <td>0.003120</td>\\n\",\n       \"      <td>0.001572</td>\\n\",\n       \"      <td>0.003120</td>\\n\",\n       \"      <td>0.356005</td>\\n\",\n       \"      <td>0.418582</td>\\n\",\n       \"      <td>0.307765</td>\\n\",\n       \"      <td>0.531174</td>\\n\",\n       \"      <td>0.589561</td>\\n\",\n       \"      <td>0.518493</td>\\n\",\n       \"      <td>0.524123</td>\\n\",\n       \"      <td>0.013252</td>\\n\",\n       \"      <td>0.033337</td>\\n\",\n       \"      <td>0.032549</td>\\n\",\n       \"      <td>0.041366</td>\\n\",\n       \"      <td>0.046638</td>\\n\",\n       \"      <td>0.028207</td>\\n\",\n       \"      <td>0.028360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                                                                                                                                                                                                                                                            path  \\\\\\n\",\n       \"65019  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_80000-kp20k-exhaustive.json    \\n\",\n       \"65067  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_50000-kp20k-exhaustive.json    \\n\",\n       \"65140  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_90000-kp20k-exhaustive.json    \\n\",\n       \"64935  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_40000-kp20k-exhaustive.json    \\n\",\n       \"64958  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_70000-kp20k-exhaustive.json    \\n\",\n       \"65088  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_60000-kp20k-exhaustive.json    \\n\",\n       \"64971  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_100000-kp20k-exhaustive.json   \\n\",\n       \"64911  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_20000-kp20k-exhaustive.json    \\n\",\n       \"64930  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_10000-kp20k-exhaustive.json    \\n\",\n       \"65046  /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_30000-kp20k-exhaustive.json    \\n\",\n       \"\\n\",\n       \"                            exp_group      exp_name  \\\\\\n\",\n       \"65019  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"65067  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"65140  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"64935  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"64958  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"65088  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"64971  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"64911  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"64930  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"65046  meng17-one2one-kp20k-topmodels  one2one+copy   \\n\",\n       \"\\n\",\n       \"                          test_name tokenization train_mode model_base  \\\\\\n\",\n       \"65019  step_80000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"65067  step_50000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"65140  step_90000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"64935  step_40000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"64958  step_70000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"65088  step_60000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"64971  step_100000-kp20k-exhaustive  meng17       one2one    rnn         \\n\",\n       \"64911  step_20000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"64930  step_10000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"65046  step_30000-kp20k-exhaustive   meng17       one2one    rnn         \\n\",\n       \"\\n\",\n       \"         order train_dataset    step test_dataset decoding_method  \\\\\\n\",\n       \"65019  one2one  kp20k         80000   kp20k        exhaustive       \\n\",\n       \"65067  one2one  kp20k         50000   kp20k        exhaustive       \\n\",\n       \"65140  one2one  kp20k         90000   kp20k        exhaustive       \\n\",\n       \"64935  one2one  kp20k         40000   kp20k        exhaustive       \\n\",\n       \"64958  one2one  kp20k         70000   kp20k        exhaustive       \\n\",\n       \"65088  one2one  kp20k         60000   kp20k        exhaustive       \\n\",\n       \"64971  one2one  kp20k         100000  kp20k        exhaustive       \\n\",\n       \"64911  one2one  kp20k         20000   kp20k        exhaustive       \\n\",\n       \"64930  one2one  kp20k         10000   kp20k        exhaustive       \\n\",\n       \"65046  one2one  kp20k         30000   kp20k        exhaustive       \\n\",\n       \"\\n\",\n       \"      decoding_terminate beam_width max_length  present_tgt_num  \\\\\\n\",\n       \"65019  fullbeam           200        6          3.331916          \\n\",\n       \"65067  fullbeam           200        6          3.331916          \\n\",\n       \"65140  fullbeam           200        6          3.331916          \\n\",\n       \"64935  fullbeam           200        6          3.331916          \\n\",\n       \"64958  fullbeam           200        6          3.331916          \\n\",\n       \"65088  fullbeam           200        6          3.331916          \\n\",\n       \"64971  fullbeam           200        6          3.331916          \\n\",\n       \"64911  fullbeam           200        6          3.331916          \\n\",\n       \"64930  fullbeam           200        6          3.331916          \\n\",\n       \"65046  fullbeam           200        6          3.331916          \\n\",\n       \"\\n\",\n       \"       absent_tgt_num  present_pred_num  absent_pred_num  unique_pred_num  \\\\\\n\",\n       \"65019  1.930555        64.303097         383.164857       499.718767        \\n\",\n       \"65067  1.930555        65.524191         380.746585       499.830040        \\n\",\n       \"65140  1.930555        65.082904         381.555711       499.639916        \\n\",\n       \"64935  1.930555        69.573923         379.558863       499.955071        \\n\",\n       \"64958  1.930555        65.139291         382.153850       499.780257        \\n\",\n       \"65088  1.930555        63.940661         383.046130       499.819683        \\n\",\n       \"64971  1.930555        64.123780         383.209636       499.721219        \\n\",\n       \"64911  1.930555        62.695252         388.627058       499.948166        \\n\",\n       \"64930  1.930555        63.507980         393.350328       499.968429        \\n\",\n       \"65046  1.930555        67.122330         387.862160       499.795317        \\n\",\n       \"\\n\",\n       \"       dup_pred_num    beam_num  beamstep_num  present_exact_correct@5  \\\\\\n\",\n       \"65019  499.718767    499.718767  2198.695452   1.405664                  \\n\",\n       \"65067  499.830040    499.830040  2194.497073   1.377946                  \\n\",\n       \"65140  499.639916    499.639916  2206.406064   1.407965                  \\n\",\n       \"64935  499.955071    499.955071  2186.779607   1.309101                  \\n\",\n       \"64958  499.780257    499.780257  2197.898184   1.402462                  \\n\",\n       \"65088  499.819683    499.819683  2198.704158   1.393256                  \\n\",\n       \"64971  499.721219    499.721219  2203.482013   1.411067                  \\n\",\n       \"64911  499.948166    499.948166  2181.367589   1.241907                  \\n\",\n       \"64930  499.968429    499.968429  2177.906089   1.135688                  \\n\",\n       \"65046  499.795317    499.795317  2212.192275   1.297944                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision@5  present_exact_recall@5  \\\\\\n\",\n       \"65019  0.281133                   0.493077                 \\n\",\n       \"65067  0.275589                   0.484120                 \\n\",\n       \"65140  0.281593                   0.494686                 \\n\",\n       \"64935  0.261820                   0.453733                 \\n\",\n       \"64958  0.280492                   0.492116                 \\n\",\n       \"65088  0.278651                   0.488622                 \\n\",\n       \"64971  0.282213                   0.495357                 \\n\",\n       \"64911  0.248381                   0.432954                 \\n\",\n       \"64930  0.227138                   0.391363                 \\n\",\n       \"65046  0.259589                   0.453381                 \\n\",\n       \"\\n\",\n       \"       present_exact_f_score@5  present_exact_precision_hard@5  \\\\\\n\",\n       \"65019  0.334212                 0.281133                         \\n\",\n       \"65067  0.327580                 0.275589                         \\n\",\n       \"65140  0.335078                 0.281593                         \\n\",\n       \"64935  0.308959                 0.261820                         \\n\",\n       \"64958  0.333434                 0.280492                         \\n\",\n       \"65088  0.331383                 0.278651                         \\n\",\n       \"64971  0.335613                 0.282213                         \\n\",\n       \"64911  0.293451                 0.248381                         \\n\",\n       \"64930  0.266176                 0.227138                         \\n\",\n       \"65046  0.307410                 0.259589                         \\n\",\n       \"\\n\",\n       \"       present_exact_f_score_hard@5  present_exact_correct@10  \\\\\\n\",\n       \"65019  0.334212                      1.934357                   \\n\",\n       \"65067  0.327580                      1.899034                   \\n\",\n       \"65140  0.335078                      1.931205                   \\n\",\n       \"64935  0.308959                      1.834492                   \\n\",\n       \"64958  0.333434                      1.926902                   \\n\",\n       \"65088  0.331383                      1.917646                   \\n\",\n       \"64971  0.335613                      1.931305                   \\n\",\n       \"64911  0.293451                      1.757893                   \\n\",\n       \"64930  0.266176                      1.646470                   \\n\",\n       \"65046  0.307410                      1.822485                   \\n\",\n       \"\\n\",\n       \"       present_exact_precision@10  present_exact_recall@10  \\\\\\n\",\n       \"65019  0.193436                    0.651547                  \\n\",\n       \"65067  0.189903                    0.640823                  \\n\",\n       \"65140  0.193121                    0.651469                  \\n\",\n       \"64935  0.183449                    0.612040                  \\n\",\n       \"64958  0.192690                    0.649185                  \\n\",\n       \"65088  0.191765                    0.647134                  \\n\",\n       \"64971  0.193131                    0.650633                  \\n\",\n       \"64911  0.175789                    0.589513                  \\n\",\n       \"64930  0.164647                    0.546434                  \\n\",\n       \"65046  0.182248                    0.613309                  \\n\",\n       \"\\n\",\n       \"       present_exact_f_score@10  present_exact_precision_hard@10  \\\\\\n\",\n       \"65019  0.280349                  0.193436                          \\n\",\n       \"65067  0.275427                  0.189903                          \\n\",\n       \"65140  0.280097                  0.193121                          \\n\",\n       \"64935  0.264245                  0.183449                          \\n\",\n       \"64958  0.279223                  0.192690                          \\n\",\n       \"65088  0.278231                  0.191765                          \\n\",\n       \"64971  0.279970                  0.193131                          \\n\",\n       \"64911  0.253498                  0.175789                          \\n\",\n       \"64930  0.235964                  0.164647                          \\n\",\n       \"65046  0.263487                  0.182248                          \\n\",\n       \"\\n\",\n       \"       present_exact_f_score_hard@10  present_exact_correct@k  \\\\\\n\",\n       \"65019  0.280349                       1.322209                  \\n\",\n       \"65067  0.275427                       1.295692                  \\n\",\n       \"65140  0.280097                       1.315455                  \\n\",\n       \"64935  0.264245                       1.245309                  \\n\",\n       \"64958  0.279223                       1.317256                  \\n\",\n       \"65088  0.278231                       1.305949                  \\n\",\n       \"64971  0.279970                       1.321809                  \\n\",\n       \"64911  0.253498                       1.170261                  \\n\",\n       \"64930  0.235964                       1.095712                  \\n\",\n       \"65046  0.263487                       1.218592                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision@k  present_exact_recall@k  \\\\\\n\",\n       \"65019  0.358082                   0.358082                 \\n\",\n       \"65067  0.351092                   0.351092                 \\n\",\n       \"65140  0.357647                   0.357647                 \\n\",\n       \"64935  0.326462                   0.326462                 \\n\",\n       \"64958  0.356607                   0.356607                 \\n\",\n       \"65088  0.354560                   0.354560                 \\n\",\n       \"64971  0.358676                   0.358676                 \\n\",\n       \"64911  0.306918                   0.306918                 \\n\",\n       \"64930  0.276643                   0.276643                 \\n\",\n       \"65046  0.325737                   0.325737                 \\n\",\n       \"\\n\",\n       \"       present_exact_f_score@k  present_exact_precision_hard@k  \\\\\\n\",\n       \"65019  0.358082                 0.358082                         \\n\",\n       \"65067  0.351092                 0.351092                         \\n\",\n       \"65140  0.357647                 0.357647                         \\n\",\n       \"64935  0.326462                 0.326462                         \\n\",\n       \"64958  0.356607                 0.356607                         \\n\",\n       \"65088  0.354560                 0.354560                         \\n\",\n       \"64971  0.358676                 0.358676                         \\n\",\n       \"64911  0.306918                 0.306918                         \\n\",\n       \"64930  0.276643                 0.276643                         \\n\",\n       \"65046  0.325737                 0.325737                         \\n\",\n       \"\\n\",\n       \"       present_exact_f_score_hard@k  present_exact_correct@M  \\\\\\n\",\n       \"65019  0.358082                      3.125632                  \\n\",\n       \"65067  0.351092                      3.120779                  \\n\",\n       \"65140  0.357647                      3.121679                  \\n\",\n       \"64935  0.326462                      3.144044                  \\n\",\n       \"64958  0.356607                      3.126582                  \\n\",\n       \"65088  0.354560                      3.114024                  \\n\",\n       \"64971  0.358676                      3.119528                  \\n\",\n       \"64911  0.306918                      3.059989                  \\n\",\n       \"64930  0.276643                      3.062491                  \\n\",\n       \"65046  0.325737                      3.113824                  \\n\",\n       \"\\n\",\n       \"       present_exact_precision@M  present_exact_recall@M  \\\\\\n\",\n       \"65019  0.051071                   0.921608                 \\n\",\n       \"65067  0.049884                   0.921662                 \\n\",\n       \"65140  0.050312                   0.921625                 \\n\",\n       \"64935  0.047042                   0.922958                 \\n\",\n       \"64958  0.050425                   0.921777                 \\n\",\n       \"65088  0.051048                   0.920748                 \\n\",\n       \"64971  0.051086                   0.921153                 \\n\",\n       \"64911  0.050842                   0.909836                 \\n\",\n       \"64930  0.049631                   0.903028                 \\n\",\n       \"65046  0.048512                   0.920384                 \\n\",\n       \"\\n\",\n       \"       present_exact_f_score@M  present_exact_precision_hard@M  \\\\\\n\",\n       \"65019  0.092751                 0.051071                         \\n\",\n       \"65067  0.090830                 0.049884                         \\n\",\n       \"65140  0.091532                 0.050312                         \\n\",\n       \"64935  0.086077                 0.047042                         \\n\",\n       \"64958  0.091653                 0.050425                         \\n\",\n       \"65088  0.092750                 0.051048                         \\n\",\n       \"64971  0.092789                 0.051086                         \\n\",\n       \"64911  0.092417                 0.050842                         \\n\",\n       \"64930  0.090357                 0.049631                         \\n\",\n       \"65046  0.088561                 0.048512                         \\n\",\n       \"\\n\",\n       \"       present_exact_f_score_hard@M  absent_exact_correct@10  \\\\\\n\",\n       \"65019  0.092751                      0.116526                  \\n\",\n       \"65067  0.090830                      0.103367                  \\n\",\n       \"65140  0.091532                      0.116226                  \\n\",\n       \"64935  0.086077                      0.094361                  \\n\",\n       \"64958  0.091653                      0.115425                  \\n\",\n       \"65088  0.092750                      0.112173                  \\n\",\n       \"64971  0.092789                      0.117476                  \\n\",\n       \"64911  0.092417                      0.064792                  \\n\",\n       \"64930  0.090357                      0.037124                  \\n\",\n       \"65046  0.088561                      0.079352                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision@10  absent_exact_recall@10  \\\\\\n\",\n       \"65019  0.011653                   0.055430                 \\n\",\n       \"65067  0.010337                   0.049108                 \\n\",\n       \"65140  0.011623                   0.055351                 \\n\",\n       \"64935  0.009436                   0.044166                 \\n\",\n       \"64958  0.011543                   0.054974                 \\n\",\n       \"65088  0.011217                   0.053110                 \\n\",\n       \"64971  0.011748                   0.055900                 \\n\",\n       \"64911  0.006479                   0.029893                 \\n\",\n       \"64930  0.003712                   0.016929                 \\n\",\n       \"65046  0.007935                   0.037354                 \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score@10  absent_exact_precision_hard@10  \\\\\\n\",\n       \"65019  0.018413                 0.011653                         \\n\",\n       \"65067  0.016343                 0.010337                         \\n\",\n       \"65140  0.018379                 0.011623                         \\n\",\n       \"64935  0.014876                 0.009436                         \\n\",\n       \"64958  0.018246                 0.011543                         \\n\",\n       \"65088  0.017710                 0.011217                         \\n\",\n       \"64971  0.018568                 0.011748                         \\n\",\n       \"64911  0.010180                 0.006479                         \\n\",\n       \"64930  0.005810                 0.003712                         \\n\",\n       \"65046  0.012516                 0.007935                         \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score_hard@10  absent_exact_correct@50  \\\\\\n\",\n       \"65019  0.018413                      0.237705                  \\n\",\n       \"65067  0.016343                      0.214339                  \\n\",\n       \"65140  0.018379                      0.237204                  \\n\",\n       \"64935  0.014876                      0.200280                  \\n\",\n       \"64958  0.018246                      0.234903                  \\n\",\n       \"65088  0.017710                      0.230800                  \\n\",\n       \"64971  0.018568                      0.239506                  \\n\",\n       \"64911  0.010180                      0.162406                  \\n\",\n       \"64930  0.005810                      0.121579                  \\n\",\n       \"65046  0.012516                      0.177716                  \\n\",\n       \"\\n\",\n       \"       absent_exact_precision@50  absent_exact_recall@50  \\\\\\n\",\n       \"65019  0.004754                   0.109618                 \\n\",\n       \"65067  0.004287                   0.098415                 \\n\",\n       \"65140  0.004744                   0.109946                 \\n\",\n       \"64935  0.004006                   0.091981                 \\n\",\n       \"64958  0.004698                   0.108659                 \\n\",\n       \"65088  0.004616                   0.106247                 \\n\",\n       \"64971  0.004790                   0.110747                 \\n\",\n       \"64911  0.003248                   0.074781                 \\n\",\n       \"64930  0.002432                   0.055068                 \\n\",\n       \"65046  0.003554                   0.081917                 \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score@50  absent_exact_precision_hard@50  \\\\\\n\",\n       \"65019  0.008993                 0.004754                         \\n\",\n       \"65067  0.008107                 0.004287                         \\n\",\n       \"65140  0.008976                 0.004744                         \\n\",\n       \"64935  0.007576                 0.004006                         \\n\",\n       \"64958  0.008888                 0.004698                         \\n\",\n       \"65088  0.008732                 0.004616                         \\n\",\n       \"64971  0.009062                 0.004790                         \\n\",\n       \"64911  0.006142                 0.003248                         \\n\",\n       \"64930  0.004593                 0.002432                         \\n\",\n       \"65046  0.006722                 0.003554                         \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score_hard@50  absent_exact_correct@M  \\\\\\n\",\n       \"65019  0.008993                      0.370341                 \\n\",\n       \"65067  0.008107                      0.340171                 \\n\",\n       \"65140  0.008976                      0.368840                 \\n\",\n       \"64935  0.007576                      0.317356                 \\n\",\n       \"64958  0.008888                      0.366488                 \\n\",\n       \"65088  0.008732                      0.360534                 \\n\",\n       \"64971  0.009062                      0.371592                 \\n\",\n       \"64911  0.006142                      0.286786                 \\n\",\n       \"64930  0.004593                      0.237554                 \\n\",\n       \"65046  0.006722                      0.294141                 \\n\",\n       \"\\n\",\n       \"       absent_exact_precision@M  absent_exact_recall@M  \\\\\\n\",\n       \"65019  0.000970                  0.168707                \\n\",\n       \"65067  0.000896                  0.155139                \\n\",\n       \"65140  0.000970                  0.168012                \\n\",\n       \"64935  0.000839                  0.144985                \\n\",\n       \"64958  0.000963                  0.166613                \\n\",\n       \"65088  0.000945                  0.164131                \\n\",\n       \"64971  0.000974                  0.169482                \\n\",\n       \"64911  0.000742                  0.130517                \\n\",\n       \"64930  0.000608                  0.107224                \\n\",\n       \"65046  0.000761                  0.133854                \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score@M  absent_exact_precision_hard@M  \\\\\\n\",\n       \"65019  0.001926                0.000970                        \\n\",\n       \"65067  0.001779                0.000896                        \\n\",\n       \"65140  0.001926                0.000970                        \\n\",\n       \"64935  0.001665                0.000839                        \\n\",\n       \"64958  0.001910                0.000963                        \\n\",\n       \"65088  0.001875                0.000945                        \\n\",\n       \"64971  0.001933                0.000974                        \\n\",\n       \"64911  0.001472                0.000742                        \\n\",\n       \"64930  0.001207                0.000608                        \\n\",\n       \"65046  0.001511                0.000761                        \\n\",\n       \"\\n\",\n       \"       absent_exact_f_score_hard@M  present_partial_correct@5  \\\\\\n\",\n       \"65019  0.001926                     1.432731                    \\n\",\n       \"65067  0.001779                     1.405063                    \\n\",\n       \"65140  0.001926                     1.434783                    \\n\",\n       \"64935  0.001665                     1.337019                    \\n\",\n       \"64958  0.001910                     1.428529                    \\n\",\n       \"65088  0.001875                     1.421424                    \\n\",\n       \"64971  0.001933                     1.437735                    \\n\",\n       \"64911  0.001472                     1.275829                    \\n\",\n       \"64930  0.001207                     1.166959                    \\n\",\n       \"65046  0.001511                     1.329664                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision@5  present_partial_recall@5  \\\\\\n\",\n       \"65019  0.323188                     0.563621                   \\n\",\n       \"65067  0.318976                     0.556646                   \\n\",\n       \"65140  0.323520                     0.564660                   \\n\",\n       \"64935  0.307492                     0.532685                   \\n\",\n       \"64958  0.322629                     0.562686                   \\n\",\n       \"65088  0.321252                     0.560312                   \\n\",\n       \"64971  0.323827                     0.564773                   \\n\",\n       \"64911  0.297221                     0.517540                   \\n\",\n       \"64930  0.278624                     0.482123                   \\n\",\n       \"65046  0.306932                     0.534519                   \\n\",\n       \"\\n\",\n       \"       present_partial_f_score@5  present_partial_precision_hard@5  \\\\\\n\",\n       \"65019  0.382964                   0.323188                           \\n\",\n       \"65067  0.377912                   0.318976                           \\n\",\n       \"65140  0.383525                   0.323520                           \\n\",\n       \"64935  0.362890                   0.307492                           \\n\",\n       \"64958  0.382244                   0.322629                           \\n\",\n       \"65088  0.380792                   0.321252                           \\n\",\n       \"64971  0.383745                   0.323827                           \\n\",\n       \"64911  0.351050                   0.297221                           \\n\",\n       \"64930  0.327508                   0.278624                           \\n\",\n       \"65046  0.362968                   0.306932                           \\n\",\n       \"\\n\",\n       \"       present_partial_f_score_hard@5  present_partial_correct@10  \\\\\\n\",\n       \"65019  0.382964                        1.970831                     \\n\",\n       \"65067  0.377912                        1.940061                     \\n\",\n       \"65140  0.383525                        1.968530                     \\n\",\n       \"64935  0.362890                        1.878571                     \\n\",\n       \"64958  0.382244                        1.963076                     \\n\",\n       \"65088  0.380792                        1.957823                     \\n\",\n       \"64971  0.383745                        1.968780                     \\n\",\n       \"64911  0.351050                        1.806274                     \\n\",\n       \"64930  0.327508                        1.698504                     \\n\",\n       \"65046  0.362968                        1.866763                     \\n\",\n       \"\\n\",\n       \"       present_partial_precision@10  present_partial_recall@10  \\\\\\n\",\n       \"65019  0.214266                      0.714967                    \\n\",\n       \"65067  0.211564                      0.707091                    \\n\",\n       \"65140  0.214008                      0.714734                    \\n\",\n       \"64935  0.207230                      0.688472                    \\n\",\n       \"64958  0.213743                      0.713398                    \\n\",\n       \"65088  0.213060                      0.711754                    \\n\",\n       \"64971  0.214100                      0.714324                    \\n\",\n       \"64911  0.200570                      0.669366                    \\n\",\n       \"64930  0.191969                      0.636878                    \\n\",\n       \"65046  0.205814                      0.687491                    \\n\",\n       \"\\n\",\n       \"       present_partial_f_score@10  present_partial_precision_hard@10  \\\\\\n\",\n       \"65019  0.309202                    0.214266                            \\n\",\n       \"65067  0.305457                    0.211564                            \\n\",\n       \"65140  0.308951                    0.214008                            \\n\",\n       \"64935  0.298089                    0.207230                            \\n\",\n       \"64958  0.308420                    0.213743                            \\n\",\n       \"65088  0.307673                    0.213060                            \\n\",\n       \"64971  0.308983                    0.214100                            \\n\",\n       \"64911  0.288685                    0.200570                            \\n\",\n       \"64930  0.275274                    0.191969                            \\n\",\n       \"65046  0.296630                    0.205814                            \\n\",\n       \"\\n\",\n       \"       present_partial_f_score_hard@10  present_partial_correct@k  \\\\\\n\",\n       \"65019  0.309202                         1.363436                    \\n\",\n       \"65067  0.305457                         1.339020                    \\n\",\n       \"65140  0.308951                         1.358333                    \\n\",\n       \"64935  0.298089                         1.282834                    \\n\",\n       \"64958  0.308420                         1.357833                    \\n\",\n       \"65088  0.307673                         1.349777                    \\n\",\n       \"64971  0.308983                         1.363136                    \\n\",\n       \"64911  0.288685                         1.211588                    \\n\",\n       \"64930  0.275274                         1.131235                    \\n\",\n       \"65046  0.296630                         1.263471                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision@k  present_partial_recall@k  \\\\\\n\",\n       \"65019  0.419890                     0.419890                   \\n\",\n       \"65067  0.415492                     0.415492                   \\n\",\n       \"65140  0.420250                     0.420250                   \\n\",\n       \"64935  0.393683                     0.393683                   \\n\",\n       \"64958  0.419250                     0.419250                   \\n\",\n       \"65088  0.417249                     0.417249                   \\n\",\n       \"64971  0.420483                     0.420483                   \\n\",\n       \"64911  0.379275                     0.379275                   \\n\",\n       \"64930  0.352803                     0.352803                   \\n\",\n       \"65046  0.396105                     0.396105                   \\n\",\n       \"\\n\",\n       \"       present_partial_f_score@k  present_partial_precision_hard@k  \\\\\\n\",\n       \"65019  0.419890                   0.419890                           \\n\",\n       \"65067  0.415492                   0.415492                           \\n\",\n       \"65140  0.420250                   0.420250                           \\n\",\n       \"64935  0.393683                   0.393683                           \\n\",\n       \"64958  0.419250                   0.419250                           \\n\",\n       \"65088  0.417249                   0.417249                           \\n\",\n       \"64971  0.420483                   0.420483                           \\n\",\n       \"64911  0.379275                   0.379275                           \\n\",\n       \"64930  0.352803                   0.352803                           \\n\",\n       \"65046  0.396105                   0.396105                           \\n\",\n       \"\\n\",\n       \"       present_partial_f_score_hard@k  present_partial_correct@M  \\\\\\n\",\n       \"65019  0.419890                        3.134988                    \\n\",\n       \"65067  0.415492                        3.130835                    \\n\",\n       \"65140  0.420250                        3.132036                    \\n\",\n       \"64935  0.393683                        3.151098                    \\n\",\n       \"64958  0.419250                        3.135888                    \\n\",\n       \"65088  0.417249                        3.124731                    \\n\",\n       \"64971  0.420483                        3.129634                    \\n\",\n       \"64911  0.379275                        3.070796                    \\n\",\n       \"64930  0.352803                        3.074699                    \\n\",\n       \"65046  0.396105                        3.124431                    \\n\",\n       \"\\n\",\n       \"       present_partial_precision@M  present_partial_recall@M  \\\\\\n\",\n       \"65019  0.051741                     0.930557                   \\n\",\n       \"65067  0.050549                     0.930585                   \\n\",\n       \"65140  0.050988                     0.930496                   \\n\",\n       \"64935  0.047622                     0.932227                   \\n\",\n       \"64958  0.051086                     0.930760                   \\n\",\n       \"65088  0.051753                     0.930046                   \\n\",\n       \"64971  0.051778                     0.930216                   \\n\",\n       \"64911  0.051709                     0.922164                   \\n\",\n       \"64930  0.050697                     0.920637                   \\n\",\n       \"65046  0.049183                     0.929612                   \\n\",\n       \"\\n\",\n       \"       present_partial_f_score@M  present_partial_precision_hard@M  \\\\\\n\",\n       \"65019  0.093897                   0.051741                           \\n\",\n       \"65067  0.091965                   0.050549                           \\n\",\n       \"65140  0.092685                   0.050988                           \\n\",\n       \"64935  0.087095                   0.047622                           \\n\",\n       \"64958  0.092784                   0.051086                           \\n\",\n       \"65088  0.093952                   0.051753                           \\n\",\n       \"64971  0.093968                   0.051778                           \\n\",\n       \"64911  0.093923                   0.051709                           \\n\",\n       \"64930  0.092262                   0.050697                           \\n\",\n       \"65046  0.089714                   0.049183                           \\n\",\n       \"\\n\",\n       \"       present_partial_f_score_hard@M  absent_partial_correct@10  \\\\\\n\",\n       \"65019  0.093897                        0.122079                    \\n\",\n       \"65067  0.091965                        0.108971                    \\n\",\n       \"65140  0.092685                        0.122330                    \\n\",\n       \"64935  0.087095                        0.099265                    \\n\",\n       \"64958  0.092784                        0.121079                    \\n\",\n       \"65088  0.093952                        0.117727                    \\n\",\n       \"64971  0.093968                        0.123530                    \\n\",\n       \"64911  0.093923                        0.069345                    \\n\",\n       \"64930  0.092262                        0.041277                    \\n\",\n       \"65046  0.089714                        0.085756                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision@10  absent_partial_recall@10  \\\\\\n\",\n       \"65019  0.029043                     0.133421                   \\n\",\n       \"65067  0.027748                     0.127036                   \\n\",\n       \"65140  0.029122                     0.133798                   \\n\",\n       \"64935  0.026350                     0.119815                   \\n\",\n       \"64958  0.028970                     0.132906                   \\n\",\n       \"65088  0.028489                     0.130584                   \\n\",\n       \"64971  0.029197                     0.134221                   \\n\",\n       \"64911  0.024131                     0.109031                   \\n\",\n       \"64930  0.021674                     0.097140                   \\n\",\n       \"65046  0.026003                     0.118332                   \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score@10  absent_partial_precision_hard@10  \\\\\\n\",\n       \"65019  0.045557                   0.029043                           \\n\",\n       \"65067  0.043503                   0.027748                           \\n\",\n       \"65140  0.045685                   0.029122                           \\n\",\n       \"64935  0.041264                   0.026350                           \\n\",\n       \"64958  0.045428                   0.028970                           \\n\",\n       \"65088  0.044668                   0.028489                           \\n\",\n       \"64971  0.045801                   0.029197                           \\n\",\n       \"64911  0.037722                   0.024131                           \\n\",\n       \"64930  0.033821                   0.021674                           \\n\",\n       \"65046  0.040701                   0.026003                           \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score_hard@10  absent_partial_correct@50  \\\\\\n\",\n       \"65019  0.045557                        0.263371                    \\n\",\n       \"65067  0.043503                        0.239806                    \\n\",\n       \"65140  0.045685                        0.263821                    \\n\",\n       \"64935  0.041264                        0.224346                    \\n\",\n       \"64958  0.045428                        0.260369                    \\n\",\n       \"65088  0.044668                        0.256267                    \\n\",\n       \"64971  0.045801                        0.266173                    \\n\",\n       \"64911  0.037722                        0.186671                    \\n\",\n       \"64930  0.033821                        0.145645                    \\n\",\n       \"65046  0.040701                        0.204983                    \\n\",\n       \"\\n\",\n       \"       absent_partial_precision@50  absent_partial_recall@50  \\\\\\n\",\n       \"65019  0.009787                     0.219326                   \\n\",\n       \"65067  0.009338                     0.209081                   \\n\",\n       \"65140  0.009790                     0.219845                   \\n\",\n       \"64935  0.009000                     0.201261                   \\n\",\n       \"64958  0.009730                     0.218290                   \\n\",\n       \"65088  0.009643                     0.216027                   \\n\",\n       \"64971  0.009825                     0.220465                   \\n\",\n       \"64911  0.008362                     0.187219                   \\n\",\n       \"64930  0.007735                     0.171823                   \\n\",\n       \"65046  0.008743                     0.195679                   \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score@50  absent_partial_precision_hard@50  \\\\\\n\",\n       \"65019  0.018476                   0.009787                           \\n\",\n       \"65067  0.017627                   0.009338                           \\n\",\n       \"65140  0.018483                   0.009790                           \\n\",\n       \"64935  0.016989                   0.009000                           \\n\",\n       \"64958  0.018368                   0.009730                           \\n\",\n       \"65088  0.018205                   0.009643                           \\n\",\n       \"64971  0.018549                   0.009825                           \\n\",\n       \"64911  0.015783                   0.008362                           \\n\",\n       \"64930  0.014592                   0.007735                           \\n\",\n       \"65046  0.016502                   0.008743                           \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score_hard@50  absent_partial_correct@M  \\\\\\n\",\n       \"65019  0.018476                        0.424376                   \\n\",\n       \"65067  0.017627                        0.396007                   \\n\",\n       \"65140  0.018483                        0.422925                   \\n\",\n       \"64935  0.016989                        0.370541                   \\n\",\n       \"64958  0.018368                        0.420573                   \\n\",\n       \"65088  0.018205                        0.414069                   \\n\",\n       \"64971  0.018549                        0.425777                   \\n\",\n       \"64911  0.015783                        0.340121                   \\n\",\n       \"64930  0.014592                        0.293791                   \\n\",\n       \"65046  0.016502                        0.347326                   \\n\",\n       \"\\n\",\n       \"       absent_partial_precision@M  absent_partial_recall@M  \\\\\\n\",\n       \"65019  0.001769                    0.299567                  \\n\",\n       \"65067  0.001712                    0.288096                  \\n\",\n       \"65140  0.001774                    0.299158                  \\n\",\n       \"64935  0.001660                    0.278550                  \\n\",\n       \"64958  0.001764                    0.297739                  \\n\",\n       \"65088  0.001745                    0.295350                  \\n\",\n       \"64971  0.001770                    0.299955                  \\n\",\n       \"64911  0.001556                    0.266650                  \\n\",\n       \"64930  0.001438                    0.248759                  \\n\",\n       \"65046  0.001572                    0.269380                  \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score@M  absent_partial_precision_hard@M  \\\\\\n\",\n       \"65019  0.003510                  0.001769                          \\n\",\n       \"65067  0.003396                  0.001712                          \\n\",\n       \"65140  0.003519                  0.001774                          \\n\",\n       \"64935  0.003294                  0.001660                          \\n\",\n       \"64958  0.003500                  0.001764                          \\n\",\n       \"65088  0.003462                  0.001745                          \\n\",\n       \"64971  0.003513                  0.001770                          \\n\",\n       \"64911  0.003088                  0.001556                          \\n\",\n       \"64930  0.002854                  0.001438                          \\n\",\n       \"65046  0.003120                  0.001572                          \\n\",\n       \"\\n\",\n       \"       absent_partial_f_score_hard@M  present_exact_advanced_auc  \\\\\\n\",\n       \"65019  0.003510                       0.392332                     \\n\",\n       \"65067  0.003396                       0.384463                     \\n\",\n       \"65140  0.003519                       0.392803                     \\n\",\n       \"64935  0.003294                       0.356063                     \\n\",\n       \"64958  0.003500                       0.391089                     \\n\",\n       \"65088  0.003462                       0.389232                     \\n\",\n       \"64971  0.003513                       0.392483                     \\n\",\n       \"64911  0.003088                       0.333828                     \\n\",\n       \"64930  0.002854                       0.301206                     \\n\",\n       \"65046  0.003120                       0.356005                     \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_ap  present_exact_advanced_mrr  \\\\\\n\",\n       \"65019  0.443644                   0.331991                     \\n\",\n       \"65067  0.435554                   0.327189                     \\n\",\n       \"65140  0.444007                   0.332339                     \\n\",\n       \"64935  0.407376                   0.305429                     \\n\",\n       \"64958  0.442332                   0.331116                     \\n\",\n       \"65088  0.440237                   0.330284                     \\n\",\n       \"64971  0.443859                   0.332175                     \\n\",\n       \"64911  0.385183                   0.295277                     \\n\",\n       \"64930  0.351010                   0.271279                     \\n\",\n       \"65046  0.418582                   0.307765                     \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_sadr  present_exact_advanced_ndcg  \\\\\\n\",\n       \"65019  0.560372                     0.617860                      \\n\",\n       \"65067  0.553477                     0.611862                      \\n\",\n       \"65140  0.560342                     0.618109                      \\n\",\n       \"64935  0.531947                     0.589744                      \\n\",\n       \"64958  0.559074                     0.616880                      \\n\",\n       \"65088  0.556893                     0.615213                      \\n\",\n       \"64971  0.560682                     0.617881                      \\n\",\n       \"64911  0.513160                     0.569919                      \\n\",\n       \"64930  0.482890                     0.540021                      \\n\",\n       \"65046  0.531174                     0.589561                      \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_alpha_ndcg@5  \\\\\\n\",\n       \"65019  0.525254                              \\n\",\n       \"65067  0.522618                              \\n\",\n       \"65140  0.524573                              \\n\",\n       \"64935  0.516701                              \\n\",\n       \"64958  0.525029                              \\n\",\n       \"65088  0.522060                              \\n\",\n       \"64971  0.524209                              \\n\",\n       \"64911  0.507513                              \\n\",\n       \"64930  0.499486                              \\n\",\n       \"65046  0.518493                              \\n\",\n       \"\\n\",\n       \"       present_exact_advanced_alpha_ndcg@10  absent_exact_advanced_auc  \\\\\\n\",\n       \"65019  0.531011                              0.023159                    \\n\",\n       \"65067  0.528239                              0.019484                    \\n\",\n       \"65140  0.530251                              0.023062                    \\n\",\n       \"64935  0.522477                              0.018024                    \\n\",\n       \"64958  0.530749                              0.022862                    \\n\",\n       \"65088  0.527682                              0.021970                    \\n\",\n       \"64971  0.529985                              0.023845                    \\n\",\n       \"64911  0.513002                              0.010899                    \\n\",\n       \"64930  0.505033                              0.006223                    \\n\",\n       \"65046  0.524123                              0.013252                    \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_ap  absent_exact_advanced_mrr  \\\\\\n\",\n       \"65019  0.029962                  0.050672                    \\n\",\n       \"65067  0.025503                  0.044057                    \\n\",\n       \"65140  0.029863                  0.050363                    \\n\",\n       \"64935  0.023429                  0.040661                    \\n\",\n       \"64958  0.029445                  0.049769                    \\n\",\n       \"65088  0.028466                  0.048659                    \\n\",\n       \"64971  0.030482                  0.051203                    \\n\",\n       \"64911  0.015074                  0.027533                    \\n\",\n       \"64930  0.008979                  0.016726                    \\n\",\n       \"65046  0.033337                  0.032549                    \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_sadr  absent_exact_advanced_ndcg  \\\\\\n\",\n       \"65019  0.058712                    0.065879                     \\n\",\n       \"65067  0.051971                    0.058513                     \\n\",\n       \"65140  0.058387                    0.065657                     \\n\",\n       \"64935  0.048145                    0.054120                     \\n\",\n       \"64958  0.057737                    0.064942                     \\n\",\n       \"65088  0.056378                    0.063418                     \\n\",\n       \"64971  0.059227                    0.066489                     \\n\",\n       \"64911  0.037136                    0.042423                     \\n\",\n       \"64930  0.026793                    0.031014                     \\n\",\n       \"65046  0.041366                    0.046638                     \\n\",\n       \"\\n\",\n       \"       absent_exact_advanced_alpha_ndcg@5  absent_exact_advanced_alpha_ndcg@10  \\n\",\n       \"65019  0.039788                            0.039954                             \\n\",\n       \"65067  0.035134                            0.035267                             \\n\",\n       \"65140  0.039336                            0.039491                             \\n\",\n       \"64935  0.031966                            0.032130                             \\n\",\n       \"64958  0.039546                            0.039706                             \\n\",\n       \"65088  0.038249                            0.038443                             \\n\",\n       \"64971  0.040820                            0.040992                             \\n\",\n       \"64911  0.025029                            0.025205                             \\n\",\n       \"64930  0.020847                            0.020964                             \\n\",\n       \"65046  0.028207                            0.028360                             \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>path</th>\\n\",\n       \"      <th>exp_group</th>\\n\",\n       \"      <th>exp_name</th>\\n\",\n       \"      <th>test_name</th>\\n\",\n       \"      <th>tokenization</th>\\n\",\n       \"      <th>train_mode</th>\\n\",\n       \"      <th>model_base</th>\\n\",\n       \"      <th>order</th>\\n\",\n       \"      <th>train_dataset</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"      <th>decoding_method</th>\\n\",\n       \"      <th>decoding_terminate</th>\\n\",\n       \"      <th>beam_width</th>\\n\",\n       \"      <th>max_length</th>\\n\",\n       \"      <th>present_tgt_num</th>\\n\",\n       \"      <th>absent_tgt_num</th>\\n\",\n       \"      <th>present_pred_num</th>\\n\",\n       \"      <th>absent_pred_num</th>\\n\",\n       \"      <th>unique_pred_num</th>\\n\",\n       \"      <th>dup_pred_num</th>\\n\",\n       \"      <th>beam_num</th>\\n\",\n       \"      <th>beamstep_num</th>\\n\",\n       \"      <th>present_exact_correct@5</th>\\n\",\n       \"      <th>present_exact_precision@5</th>\\n\",\n       \"      <th>present_exact_recall@5</th>\\n\",\n       \"      <th>present_exact_f_score@5</th>\\n\",\n       \"      <th>present_exact_precision_hard@5</th>\\n\",\n       \"      <th>present_exact_f_score_hard@5</th>\\n\",\n       \"      <th>present_exact_correct@10</th>\\n\",\n       \"      <th>present_exact_precision@10</th>\\n\",\n       \"      <th>present_exact_recall@10</th>\\n\",\n       \"      <th>present_exact_f_score@10</th>\\n\",\n       \"      <th>present_exact_precision_hard@10</th>\\n\",\n       \"      <th>present_exact_f_score_hard@10</th>\\n\",\n       \"      <th>present_exact_correct@k</th>\\n\",\n       \"      <th>present_exact_precision@k</th>\\n\",\n       \"      <th>present_exact_recall@k</th>\\n\",\n       \"      <th>present_exact_f_score@k</th>\\n\",\n       \"      <th>present_exact_precision_hard@k</th>\\n\",\n       \"      <th>present_exact_f_score_hard@k</th>\\n\",\n       \"      <th>present_exact_correct@M</th>\\n\",\n       \"      <th>present_exact_precision@M</th>\\n\",\n       \"      <th>present_exact_recall@M</th>\\n\",\n       \"      <th>present_exact_f_score@M</th>\\n\",\n       \"      <th>present_exact_precision_hard@M</th>\\n\",\n       \"      <th>present_exact_f_score_hard@M</th>\\n\",\n       \"      <th>absent_exact_correct@10</th>\\n\",\n       \"      <th>absent_exact_precision@10</th>\\n\",\n       \"      <th>absent_exact_recall@10</th>\\n\",\n       \"      <th>absent_exact_f_score@10</th>\\n\",\n       \"      <th>absent_exact_precision_hard@10</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@10</th>\\n\",\n       \"      <th>absent_exact_correct@50</th>\\n\",\n       \"      <th>absent_exact_precision@50</th>\\n\",\n       \"      <th>absent_exact_recall@50</th>\\n\",\n       \"      <th>absent_exact_f_score@50</th>\\n\",\n       \"      <th>absent_exact_precision_hard@50</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@50</th>\\n\",\n       \"      <th>absent_exact_correct@M</th>\\n\",\n       \"      <th>absent_exact_precision@M</th>\\n\",\n       \"      <th>absent_exact_recall@M</th>\\n\",\n       \"      <th>absent_exact_f_score@M</th>\\n\",\n       \"      <th>absent_exact_precision_hard@M</th>\\n\",\n       \"      <th>absent_exact_f_score_hard@M</th>\\n\",\n       \"      <th>present_partial_correct@5</th>\\n\",\n       \"      <th>present_partial_precision@5</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_f_score@5</th>\\n\",\n       \"      <th>present_partial_precision_hard@5</th>\\n\",\n       \"      <th>present_partial_f_score_hard@5</th>\\n\",\n       \"      <th>present_partial_correct@10</th>\\n\",\n       \"      <th>present_partial_precision@10</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_f_score@10</th>\\n\",\n       \"      <th>present_partial_precision_hard@10</th>\\n\",\n       \"      <th>present_partial_f_score_hard@10</th>\\n\",\n       \"      <th>present_partial_correct@k</th>\\n\",\n       \"      <th>present_partial_precision@k</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>present_partial_f_score@k</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_f_score_hard@k</th>\\n\",\n       \"      <th>present_partial_correct@M</th>\\n\",\n       \"      <th>present_partial_precision@M</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_f_score@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_f_score_hard@M</th>\\n\",\n       \"      <th>absent_partial_correct@10</th>\\n\",\n       \"      <th>absent_partial_precision@10</th>\\n\",\n       \"      <th>absent_partial_recall@10</th>\\n\",\n       \"      <th>absent_partial_f_score@10</th>\\n\",\n       \"      <th>absent_partial_precision_hard@10</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@10</th>\\n\",\n       \"      <th>absent_partial_correct@50</th>\\n\",\n       \"      <th>absent_partial_precision@50</th>\\n\",\n       \"      <th>absent_partial_recall@50</th>\\n\",\n       \"      <th>absent_partial_f_score@50</th>\\n\",\n       \"      <th>absent_partial_precision_hard@50</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@50</th>\\n\",\n       \"      <th>absent_partial_correct@M</th>\\n\",\n       \"      <th>absent_partial_precision@M</th>\\n\",\n       \"      <th>absent_partial_recall@M</th>\\n\",\n       \"      <th>absent_partial_f_score@M</th>\\n\",\n       \"      <th>absent_partial_precision_hard@M</th>\\n\",\n       \"      <th>absent_partial_f_score_hard@M</th>\\n\",\n       \"      <th>present_exact_advanced_auc</th>\\n\",\n       \"      <th>present_exact_advanced_ap</th>\\n\",\n       \"      <th>present_exact_advanced_mrr</th>\\n\",\n       \"      <th>present_exact_advanced_sadr</th>\\n\",\n       \"      <th>present_exact_advanced_ndcg</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>present_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"      <th>absent_exact_advanced_auc</th>\\n\",\n       \"      <th>absent_exact_advanced_ap</th>\\n\",\n       \"      <th>absent_exact_advanced_mrr</th>\\n\",\n       \"      <th>absent_exact_advanced_sadr</th>\\n\",\n       \"      <th>absent_exact_advanced_ndcg</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@5</th>\\n\",\n       \"      <th>absent_exact_advanced_alpha_ndcg@10</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"Empty DataFrame\\n\",\n       \"Columns: [path, exp_group, exp_name, test_name, tokenization, train_mode, model_base, order, train_dataset, step, test_dataset, decoding_method, decoding_terminate, beam_width, max_length, present_tgt_num, absent_tgt_num, present_pred_num, absent_pred_num, unique_pred_num, dup_pred_num, beam_num, beamstep_num, present_exact_correct@5, present_exact_precision@5, present_exact_recall@5, present_exact_f_score@5, present_exact_precision_hard@5, present_exact_f_score_hard@5, present_exact_correct@10, present_exact_precision@10, present_exact_recall@10, present_exact_f_score@10, present_exact_precision_hard@10, present_exact_f_score_hard@10, present_exact_correct@k, present_exact_precision@k, present_exact_recall@k, present_exact_f_score@k, present_exact_precision_hard@k, present_exact_f_score_hard@k, present_exact_correct@M, present_exact_precision@M, present_exact_recall@M, present_exact_f_score@M, present_exact_precision_hard@M, present_exact_f_score_hard@M, absent_exact_correct@10, absent_exact_precision@10, absent_exact_recall@10, absent_exact_f_score@10, absent_exact_precision_hard@10, absent_exact_f_score_hard@10, absent_exact_correct@50, absent_exact_precision@50, absent_exact_recall@50, absent_exact_f_score@50, absent_exact_precision_hard@50, absent_exact_f_score_hard@50, absent_exact_correct@M, absent_exact_precision@M, absent_exact_recall@M, absent_exact_f_score@M, absent_exact_precision_hard@M, absent_exact_f_score_hard@M, present_partial_correct@5, present_partial_precision@5, present_partial_recall@5, present_partial_f_score@5, present_partial_precision_hard@5, present_partial_f_score_hard@5, present_partial_correct@10, present_partial_precision@10, present_partial_recall@10, present_partial_f_score@10, present_partial_precision_hard@10, present_partial_f_score_hard@10, present_partial_correct@k, present_partial_precision@k, present_partial_recall@k, present_partial_f_score@k, present_partial_precision_hard@k, present_partial_f_score_hard@k, present_partial_correct@M, present_partial_precision@M, present_partial_recall@M, present_partial_f_score@M, present_partial_precision_hard@M, present_partial_f_score_hard@M, absent_partial_correct@10, absent_partial_precision@10, absent_partial_recall@10, absent_partial_f_score@10, absent_partial_precision_hard@10, absent_partial_f_score_hard@10, absent_partial_correct@50, absent_partial_precision@50, absent_partial_recall@50, absent_partial_f_score@50, absent_partial_precision_hard@50, ...]\\n\",\n       \"Index: []\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Series([], Name: present_exact_precision@5, dtype: float64)\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"ename\": \"TypeError\",\n     \"evalue\": \"cannot unpack non-iterable NoneType object\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m-------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mTypeError\\u001b[0m             Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-13-def958537464>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m     72\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkp_df\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     73\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'absent valid_kp_df'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 74\\u001b[0;31m \\u001b[0m_\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0m_\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalid_kp_df\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0m_\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mbrief_eval_results\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkp_df\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mbase_metric\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'absent_exact_recall@50'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     75\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     76\\u001b[0m \\u001b[0;31m# ensure the dataframe is sorted first\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mTypeError\\u001b[0m: cannot unpack non-iterable NoneType object\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = {     \\n\",\n    \"        'one2one': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"        'one2one+copy+cov': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"        'one2one+copy': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n    \"        'one2seq': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"        'one2seq+copy+cov': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"        'one2seq+copy': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse'\\n\",\n    \"    \\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps.values())]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print(kp_df.exp_name.unique())\\n\",\n    \"\\n\",\n    \"print('present valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Phrase number\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Transformer models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-21T22:56:03.711037Z\",\n     \"start_time\": \"2020-11-21T22:56:03.297168Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"test_dataset = 'kp20k'\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"one2one_exps = [\\n\",\n    \"                'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"                'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                ]\\n\",\n    \"\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2one_exps)]\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df = one2one_df.loc[one2one_df.beam_width == '200']\\n\",\n    \"# kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset == test_dataset]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"    expname = 'Transformer KPG-One2One'\\n\",\n    \"    expname += '_copy' if kp20k_one2one_df.at[index_label, 'exp_name'].find('Copytrue') > 0 else ''\\n\",\n    \"    rename = expname + ' - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"    kp20k_one2one_df.at[index_label, 'exp_name'] = expname\\n\",\n    \"    kp20k_one2one_df.at[index_label, 'test_dataset'] = rename\\n\",\n    \"    \\n\",\n    \"# # prepare one2seq data\\n\",\n    \"one2seq_exps = [\\n\",\n    \"                'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"                'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',]\\n\",\n    \"\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"# kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"kp20k_one2seq_df = one2seq_df[one2seq_df.test_dataset == test_dataset]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2seq_df.iterrows():    \\n\",\n    \"    expname = 'Transformer KPG-One2Seq'\\n\",\n    \"    expname += '_copy' if kp20k_one2seq_df.at[index_label, 'exp_name'].find('Copytrue') > 0 else ''\\n\",\n    \"    rename = expname + ' - ' + kp20k_one2seq_df.at[index_label, 'test_dataset']\\n\",\n    \"\\n\",\n    \"    kp20k_one2seq_df.at[index_label, 'exp_name'] = expname\\n\",\n    \"    kp20k_one2seq_df.at[index_label, 'test_dataset'] = rename\\n\",\n    \"\\n\",\n    \"# combine both and plot\\n\",\n    \"combined_kp20k_df = kp20k_one2one_df.append(kp20k_one2seq_df, ignore_index=True)\\n\",\n    \"# combined_df = one2one_df.append(one2seq_df, ignore_index=True)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# display(combined_kp20k_df)\\n\",\n    \"plot_testing_curve(combined_kp20k_df, y_index=y_index, plot_valid_peak=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Sum up (used in paper)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T06:57:34.248312Z\",\n     \"start_time\": \"2020-11-22T06:57:25.987702Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"All data\\n\",\n      \"(700, 121)\\n\",\n      \"absent valid_kp_df\\n\",\n      \"(28, 121)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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tFBw/u1+7dKRow4CG7tsXFxcrPz9evv/4qScrPz1dBQYEk6cKF89q1K0l5eXmyWq36xz/+rl27ktS+fQdJUlbWWU2dOkkjRozS8OEPV98EAQAAAABArUVNoSoWHR2juLhFGjKkn/z8/BUdPVfh4REym80aO3aU1q//RCEhITKbT2vUqKG26/r06aqQkAZKSflKkpM+/XSjli2LU1FRsUJCQjR1arS6d39AkvTZZ5/q1KmTWrs2QWvXJtj62Lnzb9U7WQAAAAAAUGsQClWRQH83mdzcFRTkq8TEP9idDwry1f79+6973VJHjhyxa1dUWKDAwEC9+659H9eMH/+0xo9/unIGDgAAAAAADIFQqIqY3NyVvnhkhfsJn7dRUn7FBwQAAAAAAHAdagoBAAAAAAAYEKEQAAAAAACAAREKAQAAAAAAGBChEAAAAAAAgAERCgEAAAAAABgQoRAAAAAAAIABEQoBAAAAAAAYEKEQAAAAAACAAREKAQAAAAAAGBChEAAAAAAAgAERCgEAAAAAABiQqaYHAACoWdnZlxQX97L27PlG/v4Biop6Vv37D7Rrl56epnfffUtHjvxLly5d0pEjR0qc//zCOe2+dEEn8/PV0ddfExreZTtXWFykVadO6GeLRecKf9Wcxk3VytunyueGysf3BQAA4M7BnUIAYHDx8Uvl6uqqLVuStWDBK4qPj1N6+k927Uwmk3r37quYmBdL7SfAZNKQuvXVzT+w1PPNPb30VMO75O/CekRtxvcFAADgzkEoBAAGZrFYlJLyhSZOnCQvLy9FRrZVt249lJS0za5taGhTDR48XGFhEaX21d7XX/f5+snHxcXunMnJWf3r1FMLL285O1X6NFBN+L4AqGrZ2Zc0d+4s9e3bTSNHDlZy8o5S26Wnp2nmzGfVsWNHjf/xkN35y9ZCvXPimCYd+admpx3RN5culjj/14vnFfPTUU0+clgTJkzQL79kVcl8AMDREQoBgIFlZh6Ts7OLQkOb2I5FRLRQRkZ6DY4KjorvC4CqVta7ERcvXlxqPx+aT8vk5KS3mrfSUw3v0vozp3Qy/4ok6UherjZmndFzd4XqnRatdNdddyk2dl6VzgsAHBWhEAAYmMVikY9PyVotPj4+ysvLraERwZHxfQFQlcpzN2Lz5s3tzuUXFen7nGz9LihYHs4uauHlrbY+vvp//3e30P7L2erg669G7h4yOTnrmWee0f79e3Xy5IkqnyMAOBpCIQAwME9PT+XmXi5xLDc3V15e3jU0Ijgyvi8AqlJl3Y1oLsiXs5MU4uZuO9bY3VOn8vMlScXFpV+Xnp5W9kEDQC1HKAQABta4cRNZrVZlZh63HUtLO6qwsPAaHBUcFd8XAFWpsu5GzC8qkqdzyXplni7OulJklSTd6+OrPTmX/n97dx7eVJn2cfzbpFu6pAW6sbTQBQqoqKMCsir7AKUCIi7wKooLLihWEVRQFEVU3AUHmXEBRhFkR9RRRhCXoqMMzjCUpRUK3SjQli5J2iTvH5HQ2CJbN+jvc11cV3POfU6eJ3lITu7zLGRaLNgcDt588028vLywWCxnVwERkXOQkkIiIo2YyWSid++rWbDgLcrKyti2bSubN29k4MDBVWKdTidWq5Xy8nIA198Oh3u/3emk3OHA4XTiwPW3vdLt2HKHwx1f8dt+54lu10qDpPYiIrWppnoj+hmOJ4COKbM78P8tUdQxMIjksAjePLCPh/ek0bJlSwICAoiIiDy7CoiInIO0zquISCOXkjKFWbOeIimpP2ZzCCkpU4mLiycnJ4exY0excOFSoqKiyMnJZtSoYe7jOnXqRDNvH15ISARgTX4eqw8dX73lu6JChjUL55pw10X2o+m7OFThShC8lLkXgOfj2tVVNaWG1Gd7+fLAAfz8QuqqqiKNRlFRIbNmPc0PP3xPSEgod955LwMGDKoSl56+mzfeeIWdO3dQUFDA+v+70GP/UWsFL397gJ+yiwnx8+aWSyO5Oi60ynkW/zuPRYmJvPzym1xxRRf39sq9EaOjY4Az640Y5euH3Qm5NiuRvw0hy7RaaOF3fDhZ3ybN6NukGQAtBgxg7ty5J1wtUUTkfKakkIhII2c2hzBr1pwq26OiovjHP752P27evAWbN//ofhweHsw3ySPdj68Jj3T/oK/OsWSAnFts9nLCw4Pdj8PDg1mwYH6VuPDwYLZu3VrpcSJpaWkeMWfbXlq1asXBg0dPq/wicnKVV/zatWsnkyffT0JCW+LiPJMkx1b8uvnmsdxzzz1VzvNmajY+Bi8+GNWePYctPLFhL3FN/Wkd6u+OyTpqZfPeQsLDw6scX7k34pQp09i1K43Nmzcyb97fqsQ6nU5sNpu7N+KxnoU+BgN+BgOXBZtZcTCPcc1bss9SxtbiIh5tHeeOzS230dLXj8MV5bw1fTqjRt2A2Ww+8xdRROQcpaSQiEgj9Psf+iIn4mv04bolE876PB+NnlcDpRGRmnZsxa/3319SZcWvCRPu84iNiWlDTEwbSksPVzmPpdzBN/uKmDcsAZOPkQsjA+kaHcyXewq49bIod9y81GzG/SmK+WkV1ZbnTHsj3rlzu0dvxDFRzXkn+wD37/ofQUZvxka2oKWfKzlV7nQyPyuTPJsNf4OR6/veyk033XbWr6WIyLlISSERkUaoJn7o60e+iMi570Qrfm3d+tNpnWd/kWvFr1bm40O0YpuY+CX3+CTRX/9aiLfBi86tgpmfdqTa85xu79Xf91o9JsjozX2tWlfZDhBgNPJU7PGl7LunpKgXoog0WkoKiYiIiIg0UjW14pelwkGgj+eKX4E+BsrKXRM+l5XbeffnXJ7p1wZwDf8KDQ1Qr1URkXqmpJCIiIiISCNVUyt++XsbKC33XPGrtNyB6bdE0aJ/59EnLpSoYF8AvLy8ePqrVwnOXHgWpVevVRGRs6WkkIiIiIjUilNd1QpgyZLFLF78PjablUt+mwPGx2AAIMtqYVFuNnstZQQbvRkVEcVlwccnBd5UcJhPDuVTWFFB24AAxkW1rJP6nQ9qasWvVmbXil8Hiqy0/G0IWcYRC61DXH9vzS4hv7ScdWmu+YgKbQ68PsolokdrInpWP8xLRERqn6G+CyAiIiIi56fKq1pNnz6TOXNmkZ6+p0pcaup3LFr0Hq+8MpcNGzZw0GZjZX4eAHank9f37+PioGBeb9uBm6Na8HZWJjk2KwBppSV8fDCX+1rF8Hq79oT5+PKXrMw6ree5rPKKX2VlZWzbtpXNmzcycODgKrFOpxOr1epe8ctmd2Czu1b98vcx0C3GzMKteVjKHfw3r4TvMovoG+9akn5W/zbMG5bAG0nxvJEUT0REBK2S2tOssxJ4IiL1SUkhEREREalxx1a1Gj/+riqrWv3e+vVrGTo0mbi4eEJCQkgKi+CbwgIAsm1WCioqGNCkGQYvLzoEBtHWFMC3v+3fWlzE5cEhtPTzx9vLQFKzcHaWlbJv3746re+5LCVlCjablaSk/jz55GMeK37179+TnJwcAHJysunbtztDhgwBIHnxdm5fuct9nnu7NMdmd3D90v8xe9N+7u3Swr0cvdnfm6YmH/c/o9GI0eSN0U8DF0RE6pM+hUVERESkxp3OqlYZGen07Nnb/Tjaz58iewXF9gpwVj23Ezhgtbj+rmY/wM6dO7n44i5nVYfG4kxW/Ep/puqKX8F+3ky/+tSGgm3YsOGsV8EUEZGzd0pJoYyMDKZMmUJBQQGhoaHMnj2bNm3aeMRs3ryZl156iZ07dzJ27FgeeeSR2iiviIiIiJwDTmdVq7KyUgIDj8eajK7JiS12B1F+fgR7G/n0cD79m4axo7SYtNJS2ge6JkLuFBTMW1mZXBXalEhfX1YfysMLsFgstVe5c1Sw2YS/euaIiEglp/St8MQTT3DjjTeSnJzMqlWrmD59Ou+//yQepFoAACAASURBVL5HTHR0NDNnzuSzzz7DZrPVSmFFRERE5NxwOqtamUwBlJQcTxZZHK5VrPyNBry9vLivZWsW52bxyaF82phMXGE24+3lBUDHwCCSwyJ488A+yhx2BjQNw99gICoqqhZrd27y9/MmKWXVWZ9nzZzkGiiNiIg0BCedU+jQoUNs376doUOHAjB06FC2b9/O4cOHPeJat25Nx44d8fbW3QcRERGRxq7yqlbHnGhVq9jYOHbv3ul+nGmxYDZ6E2R0XVdG+/szpXUcr7frQEp0Gw6W24jzD3DH923SjOfi2/Fq2w5cFmzG7nTStm3bWqydiIjI+eGkSaHs7GwiIyMx/taN12g0EhERQXZ2dq0XTkRERETOTaezqtWgQUNYt241GRnpFBYWsubQQbqHhLr3Z1oslDscWB0OPv1t6flj+8sdDvZbLTidTg6V23gv+wD9mzYjJCSkzuoqIiJyrmpQ3XqaNQs6eVAjFB4eXN9FcGtIZZGqGtL705DKIg2f2oucKrWVhq/ye/TsszN59NFHGTZsAKGhocyYMYMuXS4hKyuLIUOGsG7dOlq0aEFS0kDy87N44IEJWCwWLvHx4ZqwCPd5visqYFPBYexOaBsQQEp0G3wMrnub5U4n87MyybPZ8DcY6RESyvCwyCplEfkjaisNX0N6jxpSWaSqhvT+NKSynMhJk0LNmzcnNzcXu92O0WjEbreTl5dH8+bNa7wwhw4V43CcYAmJc0xNvvkHDx49q+MbUlmkejX1HtXE+9OQyiJVnQtfLGdC7aV2nI/tRW2ldtRUW7HZy/E1+rgfh4aGMnfu3CpxLVq04Oeff/bYNm7cOMaNGwfAN8meK1tdFxHFdRHVzxEUYDTyVGz1Q8XUXjydj58JNUVtpXY0pN8hDaksUr2G9DukIZXlbBkMXn/YAeekSaFmzZrRoUMH1q5dS3JyMmvXrqVDhw40bdq0RgsqIiIi1SsqKmTWrKf54YfvCQkJ5c4772XAgEHVxi5ZspjFi9/HarUwcOBAbjY58DW6elTkFtt4IzWLHQdL8TEY6NHazJ1XNMdocE3Y++muw3z0n3yOlFVwQUQAk7q1pOrsLyIn5mv0Oetlxj8aPa+GSiMiIiInc9I5hQCefPJJFi1axMCBA1m0aBEzZswA4Pbbb+eXX34B4Mcff6RXr1688847fPjhh/Tq1Yuvv/669kouIiLSSMyZMxsfHx9Wr/6c6dNnMmfOLNLT91SJS039jkWL3uOVV+aydOka9u/fz6Ktee79b6RmEervzeJR7XkjKZ5fcktYm+ZaOGJbTgnv/pzLE1fH8NHo9kQG+fLc15l1VkcREZHTVVRUyNSpD9GvXw9GjhzK559/esLYd999lwd27eCendv5W/Z+yh0O9758m42XM3/l3p3beWDXDhblZGF3Hh/BsqWokMfSdzFh53YGDx7Mpk1f1Wa1ROrUKc0pFB8fz9KlS6tsf/vtt91/X3755WzatKnmSiYiIiKUlZWxceMG3n9/CQEBAVx88SX06NGLzz77hAkT7vOIXb9+LUOHJhMXFw/A3XffzaS7xnPrZa5hN7nF5SQlNsPXaKCpycBlLYLZW2ABIHV/ET1bh9A61B+AGzuFM2ZZGvv27UNERKQhqnzTZNeunUyefD8JCW3d34PHpKZ+x/z583k4pg2h3j68sX8vK/PzGPXbsNSFuVmYjd68nNCeUoedFzN/ZcORw/Rv2owj5eW8nbWf+1rFcFFgEBUPTmTixIksW7aGJk00ekbOfafUU0hERETqR2bmXgwGIzExrd3b4uPbkZGRXiU2IyOdhITjc6skJiZyxFJBkaUCgOT2zdj0ayGWCgf5peX8mHWUy1oeHzNf6aYox/7cufP4MuEiIiINxbGbJuPH31XlpsnvrV+/lmuvvZaWfv4EGo0khUXwTWGBe39+eTlXmEPwMRgI8fbhosBgsmyumyaHK8oJMBroFBSMl5cXV111FSaTiQMH9tdZXUVqk5JCIiIiDVhZWRlBQZ6TAwYFBVFaWlJNbCmBgcdjg4NdCZ+yClcX+YsiA9hbYGHkB9sZuyyNts1MdIt2xVzeMpiv9xaSccSCtcLB3/+dhxdgsVhqqWYiIiJn7nRvmrRv3979ONrPnyJ7BcV2102Tfk2akVpUiNXh4Eh5Ob8UH+XCQNf3Y6y/iea+fvx8tAiH08kXX3yBj48v8fHVT3Avcq5pUEvSi4g0FnU1cfAxi/+dx6J/5/FsvzZc2uLEqw9Iw2MymSgpKfbYVlJSQkBAYDWxAZSUHE8WFRe7jjN5G3A4nTz+xV7+3K4Jc/4ch6XcwcvfHuBvP+Vy22VRXNo8iDEXRzDzq32UlNsZ3iEMk4+BqKgoqDp9kYiISL063ZsmlWNNRiMAFruDICMkBgSwqeAw9+zcjgPobg7lT0GupJDBy4tuIaHMz9pPudOBb0oKTz31HCaTqfYqJ1KH1FNIRKQe1MXEwcdkHbWyeW8hTU26D3Auio5ujd1uJzPz+Nw+u3fvJDa26rpgsbFx7N59fLjXjh07aOLvjdnfm6NWOwdLyxnW3jWnkNnfm/4JTfjhwPGlUpPaN+Ovw9vx4XUd6N7ajN3ppG1b3QkVEZGG53Rvmhy7UQJgcdgB8De6bpq8lLmXPwWbmdeuI6+1bU+Jw87Sg7kA/LekmKV5uUyOiWV+4gUsXLiQ2bOfZteutFqsnUjdUVJIRKSOne4Y+GMTB5vNZu6++26+2HN8DHxucTk9W4f8NnGwj8fEwcfMS81m3J+i8P5d7yE5N5hMJnr3vpoFC96irKyMbdu2snnzRgYOHFwldtCgIaxbt5qMjHSKioqYN28e/eJDAQjx9yYqyId1aYexO5wU2+x8secIsU1cE0vb7A5+PWLB6XSSV2zjte8OcE37ZoSEhNRpfUVERE7F6d40SUs7nsTJtFgwG70JMnpTYrdzuKKcvk2a4WMwEGT0pkdIE34pPuqObRcQQKzJhMHLi06dOtGx44X88MOW2q+kSB1QUkhEpI7V5cTBX/9aiLfBi86tgqucW84dKSlTsNmsJCX158knHyMlZSpxcfHk5OTQv39PcnJyAOjatRs33jiWiRPv4tprk2jZsiVjLolwn+fxq2L4Meso13/0P25bsROjwYs7L28OgM3uZPbXmQz/YDsPfJJOh/AAxl4SWS/1FREROZnTvWmybNkyDlgtlNjtrDl0kO4hrpsmwd7ehPn48M+Cw9idTkrtdr4pPEK0v+umSazJxK6yUvZZygDYvn07//73Vo/rM5FzmcYSiIjUsZqaONiMa+LgT3cdZuQH23E4oV98qHvi4LJyO+/+nMsz/drUWl2kbpjNIcyaNafK9qioKP7xj689tl1//Riuv34MAOHhwaQ/M9K9L76piecHVr2DChDka2TeMF3giojIuSMlZQqzZj1FUlJ/zOYQj5smY8eOYuHCpURFRdG1azfGjx/PCy+8gM3p5LJgM9eEHb9pcm/LGD7IzWb9oYMYvLxoHxDI9RGumyaJAYEkh0Uw90AmhfYKwu+7j7Fjx9G5c9f6qrZIjVJSSESkjtXVxMGL/p1Hn7hQooJ9a7dCUuOCzSb8/fQVLSIi8kdO56bJuHHjaLdybbXnifE38Ujr6m+aAPRt0oy+TZoB0H3Vxxw8ePSEsSLnGl1xiojUscpj4KOjY4CTTxzct29/wHPi4EJLhcfEwb5GA/0TmvD+VldSaGt2Cfml5az7beLpQmsFz27KZNSFYUypu+rKGfD38yYpZdVZn2fNnOQaKI2IiEj9stnLCQ/XUHiR2qCkkIhIHas8Bn7KlGns2pXG5s0bmTfvb1ViBw0awrPPzmDAgD/TrFnYCScOHnlBGGUVDo+Jg2f1b0OF0+k+1/3r9nDH5c25vKWWpBcREZFzh6/Rh+uWTDirc3w0el4NlUbk/KKJpkVE6kFdTBxs9vemqcnH/c/g5UWQrxGTj7Fe6iwiIiIiIg2LegqJiNSRyvPEhIcHs2DB/Cox4eHBbN261WPbffdN4L77jt8dO9WJg3/vvZGJZ1JsERERERE5TykpJCJSR2pinhjNESMiIiL1qaiokFmznuaHH74nJCSUO++8lwEDBlUb++677/LWRzuw2h10jzFzb9cW+Bpdg1WG/327R6zN7mBIu6bc3aUFucU2blm+E39vV6xh2aWYu0YSeVVs7VZOzmun23bf3LWDcqeDy4LNjI1sgY/B1R4npP2u7Tod9Altyk1RLci32ZicvhM/LwPGSy/F6XRy0003c8st42u9fmdKSSERERERERE5JXPmzMbHx4fVqz9n166dTJ58PwkJbYmLi/eIS039jvnz5zNrQBuamnx4+qu9LNqax62XRQGw4saO7lhLuYMblu6gZ5sQj3Msu74DRoMXcY99fNZzComcbtt9OKYNod4+vLF/Lyvz8xgV4Wq78xKPt12rw8EDu3Zwudmz7b7RrgO9Vi8/J1aq05xCIiIiIiIiclJlZWVs3LiB8ePvIiAggIsvvoQePXrx2WefVIldv34t1157La1D/Qn2M3JDpwi+2FNQ7Xm/3ldIqL+RCyMCarsK0kidSdtt6edPoNFIUlgE3xRW33Z/PFpIsLeRdqZzt+0qKSQiIiIiIiInlZm5F4PBSExMa/e2+Ph2ZGSkV4nNyEinffv27sdxTfw5YqmgyFJRJfbLPQX0jQvFy8vLY/vNH6cxZtkOpk6dSkWJrQZrIo3N2bTdaD9/iuwVFNurtt1vCgvoZq7adh/ek0avXr149tkZFBRUn1BqKJQUEhERERERkZMqKysjKCjIY1tQUBClpSXVxJZ6xAb6ulY/LatweMTlFdv4JbeEfvFN3NvMfkZeHRzPeyMTeX1IAiUlJexd5jmPi8jpOJu2azK62q7F7tl2D5XbSCstoXvI8bYb5G1kWut4XohPZPny5ZSWlvDUU4/XZFVqnJJCIiIiIiIiclImk4mSkmKPbSUlJQQEBFYTG0Bx8fHYUpvdtd3b8yfol+kFdIwIICrY9/ixPkbahZkwGrxoYvJm2rRpFO85jL2aXkYip+Js2q7F4Wq7/kbPtvttYQFtTQGE+x5vu/4GI7EmE0YvL8LCwpg0aTJbtnxf5bkbEiWFRERERERE5KSio1tjt9vJzNzn3rZ7905iY+OqxMbGxpGWluZ+nH7EQhN/b8z+nmsdfbmngH5xTX5/uIffD80ROV1n03YzLRbMRm+CjJ5t99vCAo9eQtU51nadzrMpfe1SUkhEREREREROymQy0bv31SxY8BZlZWVs27aVzZs3MnDg4CqxgwYNYdmyZewtsHDUaufDXw7SLz7UI2Z7Xin5ZeX0bGP22L7jYCn7C604nE6KLBXMnDmTwDahGP21eLacmTNpuwesFkrsdtYcOkj3EM+2u7u0lCMV5Vwe7Nl295SVkm11td0jR47wyisvcumll1UZutaQKCkkIiIiIiIipyQlZQo2m5WkpP48+eRjpKRMJS4unpycHPr370lOTg4AXbt2Y/z48Uz5PIObl6cREejDmEsiPM71xZ4jdI8xE+Bj9NieU2zj8S9/ZcQH27lrzW58fX1pPeqCOqujnJ9Ot+2+sC+Dh/ek0czHh2vCPNvuN0VHuCzY7J5v6JiD5TZe3v8rd+/cTlJSEj4+Pjz55DN1VsczoVSriIiIiIiIeAg2m/D3q/pzMTw8mAUL5le7fevWrR7bxo0bR++stSd8jolXtqx2+1WxoVwVe7xnRtxjz3PdkgmnWnRpxGz2csLDg6vdd7ptt93KE7fdm6Oqb7tdzaF0NbvabvdVH3Pw4NFTLXq9UVJIREREREREPPj7eZOUsuqszrFmTnINlUbk1PgafWokgfjR6Hk1UJpzg4aPiYiIiIiIiIg0QkoKiYiIiIiIiIg0QkoKiYiIiIiIiIg0QkoKiYiIiIiIiIg0QppoWkRERERERERqXFFRIbNmPc0PP3xPSEgod955LwMGDKo29t133+Wtj3ZgtTvoHmPm3q4t8DUe78fyVUYBf992kLwSG038vUnp3ooLIwMptzuY/fV+dh0qI+/9ROLHXUpQbJO6quI5T0khEREREREREalxc+bMxsfHh9WrP2fXrp1Mnnw/CQltiYuL94hLTf2O+fPnM2tAG5qafHj6q70s2prHrZdFAfBTVjHv/JTLlF7RJIaZOFxW4XH8BREBXNOhGc//VFxndTtfaPiYiIiIiIiIiNSosrIyNm7cwPjxdxEQEMDFF19Cjx69+OyzT6rErl+/lmuvvZbWof4E+xm5oVMEX+wpcO9f9O9cbuwUTofwAAxeXoQF+BAW4AOAj9HA8I5hXBgZiMGgFMfp0ismIiIiIiIiIjUqM3MvBoORmJjW7m3x8e3IyEivEpuRkU779u3dj+Oa+HPEUkGRpQK7w8muQxYKLXZuXbGTMct2MDc1C2uFo07qcb5TUkhEREREREREalRZWRlBQUEe24KCgigtLakmttQjNtDX6Npe4aDAUkGFw8nmfYW8ODCWN4cmsOewhQ9+OVi7FWgklBQSERERERERkRplMpkoKfGc46ekpISAgMBqYgMoLj4eW2qzu7Z7G9yTTSe1b0bTAB9C/L0Z3rEZP+w/WoulbzyUFBIRERERERGRGhUd3Rq73U5m5j73tt27dxIbG1clNjY2jrS0NPfj9CMWmvh7Y/b3JtjPSFiAN151UurGR0khEREREREREalRJpOJ3r2vZsGCtygrK2Pbtq1s3ryRgQMHV4kdNGgIy5YtY2+BhaNWOx/+cpB+8aHu/f0TmrB6x2EKyio4arWz8n+H6NIq2L3fZndgs7vmGHLaHTjK7Tidztqv5HlAS9KLiIiIiIiISI1LSZnCrFlPkZTUH7M5hJSUqcTFxZOTk8PYsaNYuHApUVFRdO3ajfHjxzPllRew2p30iDEz5pII93lu7BRBkcXO+JU78TUa6NnGzPWdwt37b1+5i7yScteD93MB6DDpSnybmOq0vuciJYVEakhRUSGzZj3NDz98T0hIKHfeeS8DBgyqNnbJksUsXvw+VquF3r37MHv2s+59kz9LZ8fBMowGVwfJZgHeLLimHQB7CyzM+WY/2UdtACQ0NXFX5+a0DvWv5dqJiIiIiIhUFWw24e9XfWohPDyYBQvmV7t969atHtvGjRtH76y11Z7H2+DFvV1bcG/XFtXuf29kIgBxj33MdUsmnE7xGz0lhURqyJw5s/Hx8WH16s/ZtWsnkyffT0JCW+Li4j3iUlO/Y9Gi93j11XmEhYXz6KMP8dprrzGiUszdXZozqG3TKs/RLMCHx3rHEBHog8MJa9MO89ymTOYNa1vLtRMREREREanK38+bpJRVZ32eNXOSa6A0cro0p5BIDSgrK2Pjxg2MH38XAQEBXHzxJfTo0YvPPvukSuz69WsZOjSZuLh4zGYzt9wynhUrVpzS8wT5GokM8sXLy9WLyOCFu9eQiIiIiIiIyOlQTyGRGpCZuReDwUhMTGv3tvj4dmzd+lOV2IyMdHr27O1+nJDQjvz8fIosYZj9Xf8l3/kpl3d+yqWV2Y+bL42gU1SQxzmu/WA7ZRUOnE4YW2msrYiIyJkMZ7bZrFwZ6c29XVu4l/79o+HMAD9nFzM3NYuDJeUkhgXwYPeWVF1PRkRERBoyJYVEakBZWRlBQZ6Jm6CgIEpLS6qJLSUwMMgjDqCswoEZuPVPUcSE+uFt8GLjr4U8uWEfbyTF0yLYz33Mshs6Yil38MWeI0QE+dZOpURE5Jx0JsOZ27eP5bakq1i0NY9bL4tyx5xoOHOhpYKZX+3jgStb0iU6mPd/zmPWpkxW13rtREREpCZp+JhIDTCZTJSUFHtsKykpISAgsJrYAEpKSirFuY4zebv+O7YPDyDAx4iv0UD/+CZ0jAjgh/3FVc7j72NgcGJTXty8n4KyipqsjoiInKPOdDhzSEgIN3SK4Is9Baf0PN/sK6J1qB8924TgazQw5uIIMo5Y2LNnT01XSURERGqRkkIiNSA6ujV2u53MzH3ubbt37yQ2tmpH+tjYOHbv3lkpbhdhYceHjv2e1x88r9MJVruD/NLyMy67iIicP040nDkjI71KbEZGOgkJxxcqiGvizxFLBUWW4zca3vkpl9FL/kfK+nS25Ry/QbGvwEpspWV+/X0MNA/2Zffu3TVdJREREalFSgqJ1ACTyUTv3lezYMFblJWVsW3bVjZv3sjAgYOrxA4aNIR161aTkZFOUVER7733V4YPHw5Asc3Ovw4cxWZ3YHc42ZBewC95JVzWwjXE7KesYnYfKsPucFJiszP/x2yCfI3EhPpVeR4REWl8zmY4c6Cv0bW9wgG4hjO/M6IdC69NZFC7Jjy5YR9ZR62/xdgJ9PG8jAz0MXr0hBUREZGGT3MKidSQlJQpzJr1FElJ/TGbQ0hJmUpcXDw5OTmMHTuKhQuXEhUVRdeu3bjxxrFMnHgXVquVq67qw8SJE9n/wndUOJy8tzWP/YVWDF7QKsSP6Ve1plWIK+lTYrMzb0sW+aUV+Bq9aBdmYma/Nu5JQUVEpHE7m+HMpTa7a3ul4czH9I9vwsaMQn7YX0xyBz9M3kZKyx0e5ysttxMYGAinNgJNREREGgAlhUTOQrDZhL+f679ReHgwCxbMrxITHh7M1q1bPbbdd98E7rtvQpXYUH9vXhsSX2X7MT3bhNCzTchJy3UmK89YrRZ69+7D7NnPVonJtVmZlrGby4PN3NEiGoB8m43J6Tvx8zqekPpzszCGhWk1NBGR+lJ5OHN0dAxw8uHMffv2ByD9iIUm/t6nNJw5JtSPLyvNP2Qpd5B91EZCQgL8WHP1ERERkdqlpJDIWfD38yYpZdVZn2fNnOQaKM1xZ7LyTFhYOI8++hCvvfYaV/7ufItysoj1N1GdN9p1wOj1RzMfiYhIXak8nHnKlGns2pXG5s0bmTfvb1ViBw0awrPPzmDAgD/j69uGD385SL/4UMA1nDntYCkXRQVi9HKthvlLXgl3XtEcgG4xZv76rxw27y2kc6tgFm/Lo00Tf+Lj45UUEhEROYdozInIeeZMV54xm83ccst4VqxY4RGTWlSAyWikQzVDD0REpOFJSZmCzWYlKak/Tz75mMdw5v79e5KTkwPgMZz56quvJiLQhzGXuHp7HhvOfP2SHYxe8j9W7zjkMZw51N+bx3vH8N7PuYz68H+k5ZcytVd0vdVZREREzox6ComcZ0608szWrT9Vic3ISKdnz97uxwkJ7cjPz6e4SRhBRm/K7HZWHszj4Zg2bCo4Uu3zPbwnDS+gY2AQ14VHEeytjxURkbpUeSgznPlw5vRnRrq3n2w4M8ClLYJ4+5p2Z1N0ERERqWf69SZynjmblWeOHWexOwgywor8PHqGNqGpj2+VY4O8jUxrHU+Mvz/FdjuLcrOYn72flOg2NVshERH5QzUxlLmmhzGLiIjIuUHDx0TOM2ez8syx4/yNBvZZytheUsyAps2qfR5/g5FYkwmjlxch3t6MiWzOf0uKKbPba7A2IiIiIiIiUluUFBI5z1ReeeaYk608czxuF2FhrqFjO0pLyC+38dDunTywawefHT7Ev44W8WTG7hM8s2uyaWeN1kZERERERERqi5JCIueZyivPlJWVsW3bVjZv3sjAgYOrxA4aNIR161aTkZFOUVER7733V4YPHw5A79CmzI5vx4zYeGbExnNVkyZ0Cgrmwd+Gh+0pKyXbasXhdFJsr+DvuVm0DwgkwGisy+qKiIiIiIjIGVJSSOQ8dCYrz1x7bRJRUc2ZOHEiAH4GAyHePu5/fl4GfLy8MP82kfTBchsv7/+Vu3duZ1r6bry9DNzZolW91VlEREREREROjyaaFjlP2OzlhIcHA2e+8swfuSY80uNxV3MoXc2hZ1FiERERERERqU9KComcJ3yNPly35OTJnZP5aPS8GiiNiIiIiIiINHQaPiYiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0ggpKSQiIiIiIiIi0gh513cBGrKiokJmzXqaH374npCQUO68814GDBhUbeySJYtZvPh9rFYLvXv3YfbsZ937jlorePnbA/yUXUyInze3XBrJ1XGh7v0/ZxczNzWLgyXlJIYF8GD3lkQG+dZKWYrtFbyTfYD/lhQTbPRmZHgkXUNcZalwOvhL1n5+LSvjUEU5k6Pb0D4w6IxfPxERERERERFpuNRT6A/MmTMbHx8fVq/+nOnTZzJnzizS0/dUiUtN/Y5Fi97jlVfmsnTpGrKyDvDaa6+597+Zmo2PwYsPRrXn4R6teCM1i70FFgAKLRXM/Gof/3dJJB9d34G2zUzM2pRZa2VZlJONt5cXr7Rtz+0tWrEwN4sDVot7f1tTALe3aEWIUflCERERERERkfOZkkInUFZWxsaNGxg//i4CAgK4+OJL6NGjF5999kmVXxrbDwAAFutJREFU2PXr1zJ0aDJxcfGYzWZuuWU8K1asAMBS7uCbfUWMvTQSk4+RCyMD6RodzJd7CgD4Zl8RrUP96NkmBF+jgTEXR5BxxEJmobXGy2J1OPjX0SKGh0fibzDSLiCQS4KC+bbQVRZvLwMDmobRLiAQg1eNv6QiIiIiIiIi0oAoKXQCmZl7MRiMxMS0dm+Lj29HRkZ6ldiMjHQSEtq6HycktCM/P58iSwX7i6wYvKCV2c+9P7aJib2/JX32FViJbWJy7/P3MdA82Nfdk6imylJsryDH5ipLlO/xskT7mciyWqucR0RERERERETOb0oKnUBZWRlBQZ7z6QQFBVFaWlJNbCmBlebeOXZcWYUDS4WDQB+jR3ygj4GycvtvMXYCfQy/22+krNxRo2Wx2B1YHQ5MBs+ymIwGLA57lfOIiIiIiIiIyPntlJJCGRkZjB49moEDBzJ69Gh+/fXXKjF2u50ZM2bQr18/+vfvz9KlS2u6rHXKZDJRUlLssa2kpISAgMBqYgMoKSmpFOc6zuRtwN/bQGm5Z9KltNyB6bdEkcnbSGmlBJBrvx1TpURRTZTF32jAz1A1AVRmd+D/u0SRiIiIiIiIiJz/Tikp9MQTT3DjjTfy2WefceONNzJ9+vQqMWvWrGHfvn18/vnnLFmyhNdff539+/fXeIHrSnR0a+x2O5mZ+9zbdu/eSWxsXJXY2Ng4du/eWSluF2FhYZj9vWll9sPuhANFx4doZRyx0DrENYQrJtSPjCPHh4pZyh1kH7XROtS/RssSZPQmytdVllzb8bJkWi208POrch4REREREREROb+dNCl06NAhtm/fztChQwEYOnQo27dv5/Dhwx5xn3zyCaNGjcJgMNC0aVP69evHp59+WjulrgMmk4neva9mwYK3KCsrY9u2rWzevJGBAwdXiR00aAjr1q0mIyOdoqIi3nvvrwwfPhxwzRHULcbMwq15WMod/DevhO8yi+gb71oGvluMmV8LLGzeW4jN7mDxtjzaNPEnOsSvxsviZzBwWbCZFQfzsDoc7CotYWtxEd1+W5IeoNzhoNzh6rlUgZNyhwOn01lzL6yIiIiIiIiINAgnXXc8OzubyMhIjEbXECOj0UhERATZ2dk0bdrUI65Fixbux82bNycnJ+e0CmNoYEtePfzwVJ55ZgZJSf0JCQnh4YenkpCQQE5ONjfdNIrFi5cSFdWcbt26c9NN/8fEiXdhtVq56qo+TJw4kZz5uwG4v38oc77YwQ1LdxDs78P9V7cnvnUkAGEh8MRgP97YuIsXNh+gfZSZx4dcjLf5+OTTBoPXWZdlW5qrLLc1CWX+zh08sHsHQT4+jGvXnriISPdzTU79jvzflqh/KXMvAK907kq4v8ldFvEUUWmi8LPhHRJ+1ucID2h68qBT4Bdx9mVRW6leTbSXmmgrUDPtpSbaCqi9VEefLdVTW6mePluqp/ZSlT5bqqe2Uj19tlRP7aUqfbZUryG0lZOVwct5km4g//nPf3jkkUdYt26de9vgwYN54YUXuOCCC9zbkpKSeOaZZ+jUqRMAb7/9Nrm5uTz++ONnU34REREREREREakFJx0+1rx5c3Jzc7HbXRMU2+128vLyaN68eZW4rKws9+Ps7GyioqJquLgiIiIiIiIiIlITTpoUatasGR06dGDt2rUArF27lg4dOngMHQMYNGgQS5cuxeFwcPjwYb744gsGDhxYO6UWEREREREREZGzctLhYwB79uxhypQpFBUVYTabmT17NnFxcdx+++1MnDiRiy66CLvdzlNPPcU333wDwO23387o0aNrvQIiIiIiIiIiInL6TikpJCIiIiIiIiIi55eTDh8TEREREREREZHzj5JCIiIiIiIiIiKNkJJCIiIiIiIiIiKNkJJCIiIiIiIiIiKNkJJCIiIiIiIiIiKNkJJCDUhiYiIlJSX1XQypB8nJyVgslvouhpxDzuTz4s0332TIkCEMGzaMESNG8PXXX7v32e12ZsyYQb9+/ejfvz9Lly5175syZQqLFi2qsbKLSMNQ29cdv/zyCykpKSeNe/XVV/nkk09qrRwiIr/3+uuvM3v27PouhkiDoKSQSAOwatUq/P3967sYcp7r1KkTy5YtY/Xq1Tz77LNMmjTJnYxcs2YN+/bt4/PPP2fJkiW8/vrr7N+/v55L3Licb4m+yhfcX3755QkvvlNTUxkxYgQAP/30E9dffz2DBw9m8ODBzJ49G4fDUSVO6k5FRcUZH3vRRRcxZ86ck8bdf//9DB48+IyfR0REpLKz+e5qjLzruwCN2eeff85LL71EaGgovXr1AuDAgQOMHTuW1NRUAPbv38/IkSPdj//5z3/y+uuvU1FRgcFg4LnnnqN9+/b1VgepGYmJifz0008EBgbSp08fkpOT+fbbbzl48CC33norY8aMweFw8NRTT/H999/j6+tLQEAAH374obuNjBgxgh9++AGr1coTTzzB5ZdfDsDGjRuZN28eNpsNHx8fpk6dyiWXXALAsmXLeP/99wHw8fHhL3/5C2FhYfX2OsjpczgcPPfcc+Tn5/Pcc88xffp0vL292b9/P9nZ2VxxxRVMnz4dX19fevbs6T4uMTERp9NJQUEBUVFRfPLJJ4waNQqDwUDTpk3p168fn376KePHj/d4vu+//55nnnmGOXPm0K5du7qurvxOp06duPXWWzGZTOzYsYMxY8awefNm/P39PRJ9BQUFXHPNNVx55ZW0atWqzsvZt29f+vbte9K4oKAgnnvuOdq0aYPNZuPmm29m9erVXHPNNXVQysarus+RwMBAfv31V44cOcLy5ctJSUkhIyOD8vJyYmJiePbZZwkJCSE1NZVnnnmGCy64gB07dmA0GnnuuedISEggNTWV2bNns3z5ch599FESExO5+eabAdi5cycTJkzgiy++YOrUqVx44YWMGTOG119/nYyMDI4ePUpmZiYxMTG8+uqrmEymen6V5HQlJiYyadIk/vGPf1BQUMDkyZMZOHBglWvbyo8PHTpESkoKhw4dAuDKK6/k0Ucfrc9qyGkoKyvjkUceYffu3Xh7exMbG8urr77KihUr+Pvf/47dbicoKIgnn3ySuLg4li9fztq1awkODiYtLY3IyEimTZvG888/z969e7nwwgt58cUX8fLyori4mFmzZpGWlobVaqVLly5MnTqVn3/+mZkzZ7Jy5Up3OUaMGMGUKVOIjY3lwQcfpKSkBKvVSu/evZk8eXI9vkLye9V9t0ycOJGxY8fSr18/ADZs2MA777zDwoULycvLY+bMmWRlZWG1WhkyZAh33XUXAH369GHkyJF8//33REdHM2nSpBO+/0ePHuXRRx9l165dREZGEhkZSbNmzXjkkUew2Wy8/PLL/PDDD5SXl9OuXTuefPJJAgMD6+11qm1KCtWTQ4cOMW3aND744APi4uJ4++23T3pMRkYGjz/+OIsXL3ZfMNtstjoordQ1i8XCkiVL2L9/P0lJSQwfPpy9e/fy3XffsX79egwGA4WFhe74goICEhMTeeSRR9iyZQsPPvggX3zxBTk5OcydO5e//vWvBAUFsWvXLm6//Xa++uorUlNT+ctf/sLf//53wsPDKSkpwdtbHwnnEqvVytSpU2nZsiVz5szBy8sLgH//+998+OGH+Pn5cccdd/DRRx8xZswYj2NXrlxJTEwMUVFRAGRnZ9OiRQv3/ubNm5OTk+NxzOrVq3nvvfdYsGABkZGRtVy7xquhJfp+/PHHs77gXr58OV999RWvvfYaAC+//DKffPIJkZGRXHTRRe64ys/v6+tLx44dycrKqnK+oqIi7r33Xvr06cMtt9xy6i+uVHGiz5Gff/6ZRYsWERAQAMBjjz1G06ZNAdf79/bbb/PQQw8BkJaWxuOPP07nzp1ZsWIFkydPZvny5R7PM2LECJ555hl3Umj58uUMHz7c/XyV/ec//2HZsmUEBwdz2223sWbNGq677rpaew2k9gQFBfHxxx/zr3/9iwceeICBAwf+YfyaNWto0aIF7777LoDHtY40fJs3b6aoqMg9HLSwsJAff/yR9evXs3jxYnx9fdm4cSOPPvooH374IeAaZrpmzRqioqK48847SUlJYdGiRZhMJoYPH853331Ht27dmDVrFldccQXPPPMMDoeDhx56iI8//pjrrruO0tJSduzYQfv27dm5cydFRUVcccUV2Gw23nrrLQIDAykvL+e2225j06ZN7pvxUv+q+24ZPnw4K1eudCeFVqxYwciRIwF45JFHuPvuu93v7y233MJFF11E9+7dATh48CALFy4EXN9vJ3r/33zzTcxmM59++ikFBQWMGDHC/fm0YMECgoODWbZsGQAvvPAC8+fPZ9KkSXX62tQl/QKsJ1u3bqVjx47ExcUBMHr0aF588cU/PObbb7+lV69etGnTBnBdMPv6+tZ2UaUeHOtG36pVK8xmMzk5OURHR2O323nsscfo0qULV199tTvex8eHYcOGAdC5c2f8/f1JT0/nX//6F/v27eOmm25yx1ZUVJCfn89XX31FcnIy4eHhAOd19vt8NX78eIYMGcJtt93msX3w4MHu9/Oaa67h888/90gKbdmyhVdffZW//e1vp/xcy5cvx8/Pj/fee4+goKCaqYBU0RATfZdffnmNXnBv2LCBDRs2sHLlSvz9/bnnnnuqjTt06BCfffYZ8+fP99h+4MAB7rvvPu644w4GDRp0wueRU3Oiz5FBgwa5E0LgGua8Zs0aysvLKS0tdV+LALRu3ZrOnTsDrjnypk2bRnFxscf5Lr/8ckpKStixYwcJCQmsXbuWJUuWVFumHj16YDabAVdvuH379tVEVaUeHLueueSSS8jLy8Nqtf5h/MUXX8w777zD7Nmz6dy5Mz169KiLYkoNad++Penp6cyYMYPOnTtz1VVXsWHDBnbs2MGoUaMAcDqdFBUVuY/505/+5P7e6tChAy1btiQ4ONh9vr1799KtWzc2bNjAtm3beOeddwDXDdRj31vJycmsWLGCqVOneiSc7XY7zz//PD///DNOp5P8/Hx27NihpFADUt13yz333MOsWbM4fPgwXl5ebNmyhdmzZ1NaWsqWLVs4fPiw+/iSkhL27NnjTgpV7ln8R+9/amoqjz/+OAChoaHuBBS4rlOKi4v57LPPALDZbOf9yBwlheqJ0+msdrvZbPbYV/nL80THyPnHz8/P/bfRaMRutxMcHMy6detITU3lu+++48UXX2TFihXVHu90Ot0/Jnv27Mnzzz9fJ+WWutWlSxe+/vprbrjhBo8fb5VVbgvguvv/8MMPM3fuXHdSGlwJg6ysLDp16gRUTSgkJiby448/snv3bvfwQ6l5DTXRV5MX3KmpqR71ufbaa5k7d65HTHFxMRMmTODWW2+lY8eO7u0HDx7k//7v/5g9e7Z7iKycnRN9jlT++8cff+SDDz7gww8/pGnTpqxZs4aPPvrotJ8rOTmZlStX0rlzZ+Lj42nZsmW1cb//DjxZIkEarmPvpdFoBFw3pry9vU94rXvppZeycuVKvv32W1atWsX8+fP54IMP6rbQcsaio6P55JNP+P7779m0aRMvv/wyffv2ZeTIkdx///3VHvP7/+/VXQOD63pm7ty5REdHVznH8OHDue6663jwwQc9Es7vvPMORUVFLF26FD8/P6ZNm6bPkwbkRN8tJpOJvn37sm7dOsA1BD0gIIDi4mK8vLxYtmwZPj4+1Z6z8nfXH73/v78+rszpdPLEE09w5ZVX1nCNGy5NNF1PLr30UrZv386vv/4K4J4ANDg4mPLycvbu3QvA2rVr3cf06NGDTZs2uY+x2WxV7sTJ+evw4cNYLBZ69erFQw89RHBwMJmZmQCUl5ezZs0awPUBa7VaiY2NpXv37nz99dfs2rXLfZ5t27YBcPXVV7Nq1Sry8/MBV6ZdwxHPLffeey/dunVj/PjxHp8Fn376KaWlpVRUVLB69Wq6dOkCuN77SZMm8dprr3HBBRd4nGvQoEEsXboUh8PB4cOH+eKLLzy6+V9wwQW88cYbPPzww2zZsqVuKtgIHfuBXlpaesKYEyX63nzzzWoTfcdkZ2e778aCK9GXn5/P7t27T1qu4cOHs27dOqxWK2vXrnXfiat8wbVmzRr69et30gvuk93gKCsr46677qJ79+7ceuutHvtCQkKIjY1l06ZNJy2znJoTfY5UVlRURFBQEKGhodhsNj7++GOP/Xv37uXHH38EXMN/2rVrV22icfjw4axdu5alS5dq0vBGLCws7ITXupmZmQQFBTFkyBCmTp3Kf//7X/dk89Lw5eTkYDQa6devH1OnTuXw4cP06dOHVatWuXuq2u12/vOf/5z2ufv06cP8+fPdSaLDhw+7r4NbtGhBfHw8M2fOJCEhwZ1wPnr0KOHh4fj5+ZGbm8uXX35ZQzWVmvBH3y0jRoxgxYoVrFixwv19ERQUxGWXXebRgzg7O5uDBw9We/4/ev+7dOniHhZfWFjosa9Pnz68++677sVYiouL2bNnT81VvAFSUqieNGvWjKeffpq77rqL66+/3n0HBVxjK8eNG8fYsWM9trdp04ann36aSZMmMWzYMEaPHs2BAwfqo/hSD7Kzsxk3bhzDhg1j2LBh9OrVy91jIzQ0lL179zJq1ChmzJjBSy+9hK+vL23atOGFF17gscceY9iwYfz5z3923z3p3Lkzd9xxh/ucN998s0d3Xjk3HBtCc8stt1BQUADAFVdcwT333MOQIUNo3ry5ey6OGTNmYLFYmD59OsnJySQnJ5OWlga47uC3atWKAQMGcN1113HPPfdUuRuXmJjIW2+9xeOPP+6xypXUnIaa6KvJC+4rr7yS9evXU1pait1u97gItFqt3HXXXVx88cXV3lX29fVl7ty57Nmzh5kzZ6oHbQ2p7nOksl69ehETE8Of//xnxo8f79F7C1xDPtauXcuIESNYuHDhCXuntmjRgoSEBLZs2cKAAQNqpS7S8Hl7e5/wWnfLli0MHz6c5ORkxo8fz4wZMzAY9HPlXJGWlsbo0aMZNmwYo0aN4o477uCKK67ggQceYMKECQwbNoyhQ4eeUXLm0UcfxWAwkJycTFJSEuPHjyc3N9e9f8SIEXz00UcMHz7cvW3s2LH89NNPXHPNNY2u58e54I++Wy6//HKKi4spLi726Bn84osvsmfPHpKSkkhKSmLSpEkn/P3yR+//Pffcw6FDhxgyZAgPP/wwf/rTn9w3M+644w7at2/PtddeS1JSEjfeeON5nxTycuqKSuSc9vtVPKRxmzJlinsVHzm3VF6F8P3332f16tUsWLCA5557jqCgIPbs2UNWVpbHRNMjR47kwIEDHvMBPf/88yQmJmK323nqqaf45ptvALj99tsZPXo04NlO9uzZw4QJE5g2bZrHxNW/t2rVKiZPnszzzz9PcnIy4Jrf5/7776eiooKoqCgCAwNp06YN9913H6+//jqlpaU88sgj1U40vX79eiIiIujSpQv//Oc/Wb58OYsXL2bmzJkeE04PGjSICRMmeKxkVVFRwcMPP0xgYCBPPfWUfjTWo8rvi4iIyLmgvLwch8OBn58fxcXF3HDDDUydOpVu3brVd9HqhZJCIuc4JYWkMiWFzj96T6UhU1JIRETONYcOHeL222/HbrdjtVoZOnQo9957b30Xq94oKSQiItKAKSkkIiIiIrVFSSERERFh48aNvPTSS1W2P/jgg/Tu3bseSiQiIiIitU1JIRERERERERGRRkgzM4qIiIiIiIiINEJKComIiIiIiIiINEJKComIiIiIiIiINEJKComIiIiIiIiINEJKComIiIiIiIiINEL/D2wL/8xNQkUrAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = {\\n\",\n    \"        'one2one': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"        'one2one+copy': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"        'one2seq': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"        'one2seq+copy': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# kp_exps = {\\n\",\n    \"#         'one2one': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"#         'one2one+copy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"#         'one2seq': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"#         'one2seq+copy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"# }\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps.values())]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"############## present F@10\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## present F@O\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k']\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"print('All data')\\n\",\n    \"print(kp_df.shape)\\n\",\n    \"print('absent valid_kp_df')\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"print(valid_kp_df.shape)\\n\",\n    \"# display(valid_kp_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50', 'absent_exact_advanced_sadr']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"# display(df.transpose())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# Phrase number\\n\",\n    \"_, _, valid_peak_summary_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@10')\\n\",\n    \"metric_names = ['unique_pred_num', 'present_pred_num', 'absent_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_peak_summary_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"bar_values = {'%s - %s' % (exp_name, metric_name): [] for exp_name in exp_names for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"# metric_name = 'present_exact_f_score_hard@10'\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_peak_summary_df.iterrows():\\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.exp_name, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(16,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Plot average scores (used in paper)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 351,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T20:30:52.362678Z\",\n     \"start_time\": \"2020-11-23T20:30:41.926548Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>w/o copy</th>\\n\",\n       \"      <th>w/ copy</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>RNN-O2O</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-O2O</th>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>RNN-O2S</th>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-O2S</th>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         w/o copy  w/ copy\\n\",\n       \"RNN-O2O 0.0       0.1     \\n\",\n       \"TF-O2O  0.2       0.1     \\n\",\n       \"RNN-O2S 0.0       0.0     \\n\",\n       \"TF-O2S  0.1       0.1     \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# 2. Compare different architecture variants\\n\",\n    \"import pandas as pd\\n\",\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"ordered_datasets = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'duc', 'average']\\n\",\n    \"\\n\",\n    \"kp_exps = {\\n\",\n    \"        'RNN-O2O': 'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n    \"        'RNN-O2O+copy': 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n    \"        'RNN-O2S': 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n    \"        'RNN-O2S+copy': 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n    \"        'TF-O2O': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"        'TF-O2O+copy': 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"        'TF-O2S': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copyfalse-Covfalse',\\n\",\n    \"        'TF-O2S+copy': 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in kp_exps.items()}\\n\",\n    \"\\n\",\n    \"# prepare data\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(kp_exps.values())]\\n\",\n    \"kp_df = kp_df.loc[(kp_df.step % 10000 == 6000) | (kp_df.step % 5000 == 0)] # keep % 10000\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"kp_df = kp_df.loc[(kp_df.beam_width == '50') | (kp_df.beam_width == '200')]\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"kp_df = kp_df.loc[kp_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp_df.iterrows():\\n\",\n    \"    expname = kp_df.at[index_label, 'exp_name']\\n\",\n    \"    kp_df.at[index_label, 'exp_name'] = long2short[expname]\\n\",\n    \"\\n\",\n    \"############## present F@10\\n\",\n    \"# change to use F@O as anchor metric for present KPG\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"f10_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# display(f10_df)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############## present F@O\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='present_exact_f_score@k')\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"fo_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"# display(fo_df)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"############## absent\\n\",\n    \"_, _, valid_kp_df, _ = brief_eval_results(kp_df, base_metric='absent_exact_recall@50')\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_kp_df.test_dataset.unique()\\n\",\n    \"exp_names = valid_kp_df.exp_name.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {exp_name: [] for exp_name in exp_names}\\n\",\n    \"for index_label, row_series in valid_kp_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.exp_name].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"datamodel_names = bar_values.keys()\\n\",\n    \"model_names = ['RNN-O2O', 'TF-O2O', 'RNN-O2S', 'TF-O2S']\\n\",\n    \"avg_bar_values = {'w/o copy': [0.0] * 4, 'w/ copy': [0.0] * 4}\\n\",\n    \"\\n\",\n    \"for shortmodel_name, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    if '+' in shortmodel_name:\\n\",\n    \"        copy_mode = 'w/ copy'\\n\",\n    \"        model_name = shortmodel_name.split('+')[0]\\n\",\n    \"    else:\\n\",\n    \"        copy_mode = 'w/o copy'\\n\",\n    \"        model_name = shortmodel_name\\n\",\n    \"    avg_bar_values[copy_mode][model_names.index(model_name)] = np.mean(_v)\\n\",\n    \"#     print(shortmodel_name, _v)\\n\",\n    \"#     print(copy_mode, model_name, np.mean(_v))\\n\",\n    \"# print(avg_bar_values)\\n\",\n    \"    \\n\",\n    \"r50_df = pd.DataFrame(avg_bar_values, index=model_names)\\n\",\n    \"display(r50_df)\\n\",\n    \"\\n\",\n    \"f10_df = f10_df * 100.0\\n\",\n    \"fo_df = fo_df * 100.0\\n\",\n    \"r50_df = r50_df * 100.0\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 352,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T20:30:52.724086Z\",\n     \"start_time\": \"2020-11-23T20:30:52.363839Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7fbf1c671150>\"\n      ]\n     },\n     \"execution_count\": 352,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x576 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"sns.set_palette(\\\"Set2\\\")\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\\n\",\n    \"# f10_df.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"# for p in axes[0].patches:\\n\",\n    \"#     axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.05))  \\n\",\n    \"# axes[0].set_ylabel(\\\"Present F@10\\\")\\n\",\n    \"\\n\",\n    \"fo_df.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.02, p.get_height() + 0.05))    \\n\",\n    \"axes[0].set_ylabel(\\\"Present F@O\\\") \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"r50_df.plot.bar(ax=axes[1], legend=True, rot=0)\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.04, p.get_height() + 0.05))    \\n\",\n    \"axes[1].set_ylabel(\\\"Absent R@50\\\") \\n\",\n    \"axes[1].legend(loc='upper left')\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Phrase Order on One2Seq Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq - Present Phrase Prediction\\n\",\n    \"\\n\",\n    \"Terminology:\\n\",\n    \" - present_pred_num means valid phrases (no <unk>, doesn't contain ,/.) that can be found in the source text.\\n\",\n    \" - verbatim_prepend means (absent_phrases+present_phrases)\\n\",\n    \" - verbatim_append means (present_phrases+absent_phrases)\\n\",\n    \"    \\n\",\n    \"**verbatim_prepend** outputs significantly more present_phrases and **verbatim_append/alphabetic** outputs significantly more absent_phrases, which means the more noisy hidden state lead to more diverse following predictions.\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-16T20:43:01.112594Z\",\n     \"start_time\": \"2020-11-16T20:42:51.562467Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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5cfIMd5ugXIymwb99fDBs2hBUr1lj3tW3bMR179Ohq1qxDzZp10rsbIiIiIs8cJa5eUPcXbz9+/BhXrlxK9eLtNWvWYf36QGudXLlyM2TIMN54o1SqYiclNjaWn36ax5o1q7h79y7//HOUgQP70LXrR+TLlx8Ae3t7smXLTmhoCABnzpzmxx9nsW/fX1y/fp1MmTKxceM6SpUqHS92aheGFxGxhTnOQsfhG55Y/NmDfJ5YbJEHWSwWLBYLBoMezBcREZH0p79IXlDTpk1h376/yJMnD87O2ZKte3/x9lu3IvD2LgxAkSJFmTBhCiNHfkOHDl3ImjUrYWE3Uh07MRaLhX79PmP69O+tyaR8+fLz998H6datPWfOnLbWtbe3Jyoqij/+2EWnTu9z6tQJvL0LYzIZ8fN7D4sljtu3b8eLv2LFUjZuXI+Dg0OqplSKiDwr5s+fS/PmDfHxqUKbNs3Ytm1LknUrVSrL0qX+NGvWkHr1qjNlyrfExcXFqzN58kRq165Ks2YN2LXrd+v+1at/pXVrP3x8qtCsWUNWrvw5Qfy5c2dRr151/Px8433hER0dzeTJE2nSpB6+vjUZO3Ykd+9GERkZSe/enxAaGoKPT2V8fCoTGhrCrFnT+PrrwdbjDx48wAcfdKR27Xdp0qQea9bc+4Jk584ddOjQipo136FJk3rMmjUtzeP4sB49uhIQsNK6/fBUykqVyrJy5TJatGhM7dpVGTduNBaLJdG6f/65m1atmlKr1juMHz86XuyHz/XhaXgRERH8739f07BhLRo1qmOdGp+ce+13ZMKEMdSq9Q6tWjXlr7/2xDu3adOm0L17R6pXr8Tly5eSbcdsNjN58kTq1atOs2YN2blzh81jOGPGVLp374iPTxV69fqIsLAw4N6Tdg0a1I5X38/Plz///MM6LoMG9eXrrwfj41OFtm3f4/z5c8yfP4f69X1o0qQee/bstqkfIiIi8uxQ4uoFtXjxStas2cTEid/j5uaWbN3Gjf1Yt24bCxYsoXnzVgBky5adcuXepEqVd+nUqRsDBw5l6tRJzJ8/J1WxE7N9+zZ2795JgwaN+eqr4cC9xNUPP8wmJiaGiRO/sdaNjo7GZDIxdOggSpUqw7BhozlwYD8dOnTho48+YdSo8bz00svx4g8a9DXr129j+vS5iS40LyLyrMuTJy/ffz+Tdeu20qFDF4YNG0xoaGiS9X/7bQuzZs1j1qyFbN++jdWrf7WWHT16mJdfzsfq1Rtp1aoto0YNsyZjXF1zMGbMRNav38aAAUOYNGk8x48fsx57/fo1bt4MY8WKQAYO/IoxY0Zw/vxZAKZOncSFC+eYO/cnFi9eQUhICHPmzMTJyYlvvvkWNzd3NmzYzoYN23Fzc4/X36CgIHr37knTps1ZtWojc+YspHDhIgA4OjoyaNDXrF27hTFjJrJy5c/89tvWxzSyKdu5cwczZsxj7txFbNmygT/+2JWgTlhYGAMH9qVLl+6sXr2JPHnycujQQZvbGDHiS4xGI/7+K5kzZyF//rk7XkItKUePHiF37jysXr3p/z+7+xAeftNavm7dGvr0Gcj69dvw8sqVbDsBASvYuXM7s2cvZNaseWzdusnm/m/YsJb+/b9k1ar1xMTEsGjRfJuP3blzO7Vq1SUwcDOFCxfl888/Ji4ujpUrA2nfvjNjx460OZaIiIg8G5S4ekE97sXbvb0LU7hwUVasWPbITzHt2/cXAHXrNsDZORsODg6EhASTJ09e3nijFHv37iEoKIjo6GjCw28SFRVJePhNPvywJz/8MAlnZ2cqVqzMxYsXrP+ZzWZiYmK5ePECJpPJ5oXhRUSeRdWq1cDNzR2DwUD16jXJm/dl/vnncJL1W7duR7Zs2fHy8qJ585Zs3LjOWubllYsGDRpjNBqpU6c+166Fcv36NQAqVqxEnjx5sbOzo1SpMpQv/xYHD+6PF7tr1w+xt7enVKkyVKhQic2bN2KxWAgIWEHPnp+TLVt2MmfOQtu2HZJ82cbDNmwIpGzZ8vj41MZkMpE9uwuFCxcFoHTpshQq5I3BYMDbuzA1atTiwIG9qR3CNGvduj3Ozs54eXlRqlRZTpz4N0GdXbt2kD9/AapWrYHJZKJ581bkyJHTpvjXr19j9+6dfPLJ5zg5OeHqmoPmzVvZNHYuLq40b94Kk8lE9eo1efnlfPGelKpb15eCBQthMpkID7+ZbDubN2+kefOWeHp6kS1bdt5/v4ONI3SvnZdfzoeDgyPVqvkkOkZJKVGiJG++WQGTyUTVqjUIC7tBmzbtMZlM1KhRkytXLtv8ohgRERF5Nuhf7/LY3L17N943t2kVExMN3PvW3GAwUKTIK5w4cZzo6GgcHByBe08A5MyZk7i4OO7cuUOWLFmIiLjFnj1/EB19lw4dWiUau0WLxlSsWIkxYyY+cj9FRDKqwMBVLF78E0FBlwGIjIzk5s2wJOt7eHhaf/byymVdOxCIl1BxdHS0xoN7U8nnzJnBhQvnsVjiiIqKomBBb2t9Z2dnnJycMJvj4sUOC7tBVFQUnTq1sda1WCwJpigm5erVq0l+SXLkyGF++GESZ86cIiYmhpiYGKpWrZ5izPXrA61P65QoUYpx476zqS8Py5kz/nhFRt5JUCc0NDTemNvZ2cXbTk5Q0BViY2Np2PC/KXVxcRabjnd398DOzs667ekZ/1o/GCOldkJDQ/Dw8LKW3X+7sC1y5vzvaeykxigpD96PDg4OZM/ugtFoBMDe/t5LXCIj7+Ds7GxzTBEREcnYlLiSVEnqW8x9+/7izJlTlCpVJt5+s9lMbGwsUVFR1n/wpKRAgYL/H/NP67flhw4d5OefF3P06L0nBoKDg9i0aR1Go5HbtyMwm818/vnHvPFGSQoV8iYs7AZbt27GbDbTrVsPFiyYi729PR9/3CveH8wiIs+boKArjBkzgokTp1K8+OsYjUbat2+FJZkXHgYHX6VgwUIAXL0alGBqXmKio6MZNOgLBg0aSuXK72Iymejf/3PrNEK495kRGRlpTShcvRpEwYKFyJ7dBQcHB+bPX4K7u0eC2A8mVxLj6enJ0aNHEi0bOnQgTZs255tvvsPBwYFvvx2XbNLuPlve+ufk5ERUVJR1+9q1aynGTYybmxvbt1+1blssFoKD/9tOrh0PDy8yZbJn1aqNqX56OCQkGIvFYh3fq1eDqFSpirX8wXFPqZ2cOd0IDg6ybl+9GpSgTmo9fN5ms9m6fqaIiIi8uDRV8Dm1du1q5s6dybRpPxAWFkZERARz585k7tyZrF27Ol7d2NhYIiJuMXfuTLZu3QzA77//Zq0fFHTFWvfXX1cAsHXrJlau/JklSxYxbNgQPvusB5kzZ6ZHj0/jxb5+/TqhoSHWhNN9Bw7ss8Y/duwfAH7+eQlz587k2rVruLrmYObMafz66wrKlXuTl1/Oz5Qp33LjxnUAAgJ+Ydu2LbRp056oqCiioqKoUqUqEyZMoUePXgwa9DVjxkwkJiaGo0cP4+joSJYsWahatQYlSpR8vIMtIpKBREZGYmdnh6urC3BvAfUzZ04le8yiRfMJDw/n6tUgli5dRLVqKb/BMDb23tNMLi6uGI1Gdu36PdGFsWfM+IGYmBgOHtzPzp3bqVq1BgaDAV/fxnz33Xjr7/WQkGDrelA5cuTk5s2bREREJNq2j08d/vprD5s2bSA2NpabN8M4ceI4AHfu3CFbtuw4ODhw9OhhNmxYm+K52Mrbuwjbtm0mKiqKixcvsHr1L2mKU6FCJc6cOc22bZuJjY1l6VJ/6/RLgMKFi3Dw4D6CgoKIiIhgwYI51jI3NzfKl3+TyZMncvt2BHFxcVy6dJH9+1OeDhkWdoOlS/2JjY1l8+aNnDt3lgoV3k60bkrtVKvmw7JliwkOvkp4eDgLFvyYprF40Esv5SM6OpqdO3cQGxvLjz/OIiYm5pHjioiIyLNNT1w9p1at+oUDB/bF2zdz5g8AlCxZmtq161n330tcRVjLAbZt22J9C1WJEiXx8spl/fnYsaMcOvT3/7+NyA4vLy8aNmxCy5ZtbZ4qsHfvn8yZMyPePn//Bdaff/zRn+HDhzBmzAjrPjc3N27fvk1kZCS3bt2kV68+NGnSnCVLFhEZeYc6derHi1e6dFk8Pb3Yv38vDg4ONvVLRORZV6BAQd57rzXdunXEYLCjdu16vP76G8keU6nSO3Tq9D63b0dQp0596tdvmGI7mTNn4ZNPejNkSH9iYqJ5++3K8Z7egXsJqGzZnGnUqDYODo707t2ffPnyA9C9+8fMnTuTrl07cPNmGO7u7jRq5Mebb1YgX7781KhRk+bNGxIXZ2bBgqXx4np5efHNN98yefJERo8eTtasWenSpfv/L9bdl8mTJzJ+/BhKlSpNtWo1kkyApdZ777Xm2LGj+PrWxNvbGx+f2vHezGcrFxcXhg0bxcSJ3zBy5FBq1aob7xqVK/cW1arVpH37FmTP7kLr1u3YseM3a/mgQV/zww+TaNOmOXfu3CZ37jy0bt0uxXaLFXuNixfPU69edVxdczJ8+GiyZ3dJsn5y7fj6NuLChXO0b9+KLFmy0KJFG/bu/TPVY/GgrFmz0qdPP0aPHobZHEerVm0TfSJPREREXix2FktykweeLSEhWozzYS4umQkLS37tiPffb05kZCTLlgWkGG/Llo0MHtyPAQO+pG5d3xTrpyZ2Yi5evEBoaAhubu7kzfsS33//LT/9NJ/x4ydTvvxbALRu7ce5c2eZN88/3toqAF27tufff4+xdWvir8ceP340y5cvZenSX8mVK3ea+vi8sOVeEblP9wuEhl7BzS2Xddtob8Ic9+Q+Uo0GO8zRsY89bqVKZfH3X0HevC899tgARqPBusaVJK1Hj67UqlUXX99GTyT+mjUBBASsZOrUWU8k/uOi+8U2D//+eRHpc0hSQ/eL2Er3SuLc3dN37Ug9cSUZWt68L8X7x9Tu3TvJkiVLvG+mX331Nc6dO0twcHCCxFVISDCurjmeWn9F5MX1JJJK8eI/0egiIiIiIhmTElfPGQd7E5YHvvGPvBONvclo3bYz2HH3Cf/jKjVCQ0O5fTsCT0+vFBdvX7bMn9OnT9GhQxecnJys+2vVqsvatav55Zefeeutitb9O3b8RkhIML6+jZ9Y/0VERDKKsWNHsn59YIL9NWvW4bXXXn9q/fDxqZzo/m+++Y433ij11PohIiIizwclrp4zljgL3w7fnGT5J4OqAfcWb7+/6HpYWBgxMTHMnTsTuPe68gfXwAoKumJd0P3MmdPAvcXb778BqXbtetY1sFIbe9q0yQQGruK7736gdOmy1v29e/ckd+485M9fEDs7O/bs2c327VupWLES7dp1indO5cq9SY0atdi4cR29e/ekYsXKBAVd4eefF5MzpxudOnWNV//AgX3W9b8eXBg+a9asALRv3znJ8RMRed7s2PFXendBgMmTpz9yjD59BtCnz4Aky22Z4v84bNiw/am0IyIiIi8GJa5eUKlZvP3y5UvxFm6HpBdvT23spBQvXoJNm9YTGLgKgHz5CvDZZ31p2LAJRqMxQf1Bg4bi7V2Y1at/5bvvxpE1qzPvvludrl0/TPBa95QWhlfiSkRERERERCRj0OLszxl7kzHFJ66iY7VSiiSkhQglNXS/aHFkW2mxbUkN3S+20e8ffQ5J6uh+EVvpXklcei/ObkjX1kVERERERERERJKgqYIvGJMxFju7xBdnt7MzEhWtXKaIiIiIiIiIZAxKXL1g4uJimTf680TL2vYdhx7CExEREREREZGMQlkKERGR54yfny9//vnHU2/3ypXLVKpUltjYxJ/sfZqCgoLw8amM2fz8ruuYXtdZRERE5GnSE1ciIiKPgbODGYPlySVs4uxM3Lqb8K2q6cnPz5e+fQdRrtyb6d2VBLy8vNiwYXt6d0NEREREHpESVyIiIo+BwRLLxcndnlj8vD2mARkrcSWPT2xsLCaT/iwTEREReZimCoqIiDzH4uLimD9/Ls2bN6Ru3eoMHtyP8PCbwH9T+wIDV9GkST3q1avOjz/Osh57924Uw4d/Se3aVWnd2o+FC3+kceO6AAwbNpirV4Po2/czfHwqs3Dhj9bjNmxYa403dwYlVhMAACAASURBVO7MNPd91qxpfP31YOv2w1MRe/ToyowZU+nevSM+PlXo1esjwsLCEq17+fIlevToio9PFT799EPGjx9tjb1v31/W87rvwWl4yY1hUu63/8svy2nYsDYNG9Zi0aIF8c5t0KAv+PrrwdSs+Q6BgatSbGft2tU0bVqfunXjX6eUxnDw4H4MGzYEH58qtGnTnGPHjlrLK1Uqy8WLF6zbI0Z8xfTp38cbl4ULf6R+fR8aNqzFb79tZdeuHbRo0YQ6daoxb95sm/ohIiIiklZKXImIiDzHli3zZ/v2rUyePJ2VKwNxdnZm3LjR8er8/fcBFi36mYkTpzJ37kzOnj0DwOzZMwgKusKSJb8wYcIU1q8PtB4zePAwPD29GD16PBs2bKd163aJxps9e4Y13pOwYcNa+vf/klWr1hMTE8OiRfMTrTd06CCKFn2F1as30r59ZwIDV9vchi1jmJR9+/7C338F48dPYeHCufHWpNq+fRvvvludtWu3ULNm7WTbOXPmNOPGjWLw4K9ZuTKQ8PCbhIQE29SH33//jRo1arJ27RYqVarC+PFjbD7369evER0dzcqVgXTq9AFjxgxn3bpAZs+ez5QpM5g7dyaXLl20OZ6IiIhIailxJSIi8hz75ZfldO36IR4entjb29OxYze2bt0UbwH1Dh264ODgSOHCRfD2LszJk/8CsHnzRt5/vwPZsmXDw8MTP78WNrUZP14Ra7wnoW5dX15+OR8ODo5Uq+bDiRMJ2woKCuLYsaN07twde3t7SpYszdtvV7a5DVvGMCkdO3bFycmJQoW8qVvXl40b11nLihcvQZUq72IwGHBwcEy2na1bN1GxYiVKliyNvb09nTt3x87Ozqb+v/56SSpUqITRaKRWrbqcPHnC5nM3Gk20bdsRk8lEjRo1CQsLo1mzlmTOnIWCBQuRP39BTp06aXM8ERERkdTSYgoiIiLPsaCgKwwY0AeD4b8kh9Fo5MaN69btHDlyWn92cHAkMjISgGvXQvDw8LSWPfhzch6M5+j4X7z4/Qri/febWbfTupB6zpxuD7V1J0Gd0NAQnJ2dcXJysu7z8spFcPBVm9pIbgzd3T2SPfbBMfP0zMWpU6cSLUupndDQEDw8vKz7nZycyJ49u039z5kz/vWIjr5r85pa2bNnx2i8t7aavb0DADly5LCWOzg4JDrmIiIiIo+LElciIiLPMQ8PT/r3H0KJEiUTlF25cjnZY3PmdCMkJJgCBQoCJEj02PrET2Jseeufk5MTUVFR1u1r166lqS03Nzdu3bpFZGSkNXl19WqQtf8Pt2M2mwkLu2HdTm4MUxIcfJV8+fJb23Rz+y/R9vD4JddOzpxunDv335TLqKgobt5Mfp0tWzg6OiYY45SScSIiIiJPk6YKioiIPMcaNWrK9OnfExR0BYAbN26wfftWm46tWrUG8+fPITw8nJCQYH7+eUm8clfXHFy+fOlxd9mqcOEiHDy4j6CgICIiIliwYE6a4nh55aJo0VeZNWsaMTExHDx4gN9//y9p9tJL+YiOvsvOnTuIjY3lxx9nERMTYy1/lDGcO3cmUVFRnD59ijVrAqhevWaSdZNr5913q7Nz5w4OHjxATEwMM2f+gMViSeVIJFS4cBE2bFiL2Wxm9+6dHDiw75FjioiIiDxOeuJKRETkOdasWUssFgu9en1EaGgorq6uVK9ek8qV303x2A4duvDNNyNp3rwBOXO64eNTmzVrAqzl77/fngkTxjJ16ne0bduJqlWrP9a+lyv3FtWq1aR9+xZkz+5C69bt2LHjtzTF+vLL4YwY8RV161bjtddep3btukRERACQNWtWPv+8H6NHD8NsjqNVq7bxnjp6lDEsWbI0773XCIvFQsuWbShf/q0k6ybXTsGChfjss74MHTqQqKgo3nuv1WN5MuqTT3ozfPhXLF++lCpV3qFy5XceOaaIiIjI42RneRxf12UQISG30rsL6c7eZOTb4ZuTLO81uCI/jv480bK2fcdxNybTk+qaZHAuLpkJC9M6JWIb3S8QGnoFN7dc1m1nBzMGS8qLdadVnJ2JW3eNTyy+LVasWMamTeuZPHm6zccYjQbM5rgn2Ku0mTVrGpcuXWTIkGFPJP6VK5dp1qwBW7futmktKbkno94vGc3Dv39eRPocktTQ/SK20r2SOHd353RtX39JiYiIPAb3kkrpm1h63EJDQ7l8+SLFi5fg4sXz+PsvoGnT5undLRERERF5gShxJSIiIomKjY1h7NiRXLlymaxZnalevSaNGzdL+cAXxPr1gYwdOzLBfk/PXIwdO/Gp9ePzz3vy99/7E+x///0OtG3b8an1Q0RERORJUOJKREREEuXllYv585ekXPEZ1KlTt0eOUbNmHWrWrJNk+Y4dfz1yG7YYN+67p9KOiIiISHrQWwVFRERERERERCRDUuJKREREREREREQyJCWuREREREREREQkQ1LiSkREREREREREMiQlrkREREREREREJENS4kpEREReGAcP7qdlyybp3Y0nqlKlsly8eCG9uyEiIiLyWJjSuwMiIiLPA1NmC2aL+YnFN9oZib1j98TiPy1r1gQQELCSqVNnpUv7b7xRikWLlqdL2yIiIiKSekpciYiIPAZmi5kPVw94YvG/rzeSZ/1jOzY2Nr278Mwxm80Yjcb07oaIiIhIunm2/wIWERGRBPz8fGnSpDnr1q0mKOgKb75ZkYEDv8LBwQGAX39dwcKFPxIeHk6JEm/Qp88A3Nzck4xnsViYNGk869evJSYmGi+vXHz55XAKFvQmIiKCCRPG8McfO3FwcMTXtxFt23bEYDBYn6569dViBAaupnTpMuza9TuxsbH4+FTGaDSydu3WVJ/fiBFf4e7uQdeuHwKwb99fDBs2hBUr1qR4/g/X/fffY4waNYwLFy5QocLb2NlBnjwv0bXrh4k+HVapUln8/VeQN+9LREdHM33692zevIGYmBiqVHmXnj0/w8HBMcm+32+/cWM/Fi9eiJNTZrp2/ZCaNetYz83BwYGgoCscOLCP//1vHG+8USrZdn76aR6LFy8E7OjSpbvNY+jo6ERQ0GUOHNhP/vwF+OqrEeTJk5crVy7TrFkDtm7djcl070/FHj26UqtWXXx9G/3/uKzg1VdfY82aAJydszNkyNdcuHCemTN/IDo6mo8++oQ6deqn7sKKiIiIJEJrXImIiDyHtmzZwLhxk1i69FdOnTpBYGAAAHv3/sm0aZP5+utR/PLL2v9PQiX/pNiePbs5cGA/ixYtZ+3arQwd+j+yZXMBYMKEMdy+HcGSJb8wefJ01q5dw5o1v1qPPXr0MLlz5yEgYD1Dhgyjd+/+vPba62zYsD1NSatHPf8HxcTE0L9/b2rVqktg4GaqVq3O1q2bbW5j6tRJXLhwjrlzf2Lx4hWEhIQwZ87MFI+7fv0aN2+GsWJFIAMHfsWYMSM4f/6stXzDhrW0bduR9et/o0SJksm2s3v3ThYtWsCECVPw91/BX3/tsbn/Gzeuo0OHLgQGbiZv3peYPn2KzccePXqEQoUKs3r1Jnx8avHllwP455+j+PuvYMiQYYwfP4Y7d+7YHE9EREQkKUpciYiIPIf8/Frg5uZOtmzZefvtypw48S8A69cHUq9eA4oWfQV7e3u6devB4cN/c+XK5SRjmUwm7ty5w7lzZ7FYLOTPXwA3NzfMZjObN2+gW7ceZM6chVy5ctOiRWvWrl1jPdbNzR0/vxaYTKZkn0R63JI6/wcdOXKI2NhYmjdvhclkomrVGrz66ms2xbdYLAQErKBnz8/Jli07mTNnoW3bDmzatN6m4zt37o69vT2lSpWhQoVKbN680VpWqdI7lChREoPBgL29fbLtbN68gbp1fSlY0BsnJyc6duxqU/sA77xTlWLFimMymahZs3aiY5SUXLlyU69eA4xGI9Wr1yQ4+CodOnTG3t6e8uXfIlOmTFy6pAXiRURE5NFpqqCIiMhzKEeOnNafHRwcCQ0NBSA0NJQiRV6xlmXOnJns2V0ICQkmV67cicYqU6YcTZs2Z/z40QQHB1G5clV69PiEu3fvEhMTg5dXLmtdL69chIaGWLc9PDxT1e+DB/fTu3dPADw9c7FgwZJUHX9fUuf/oNDQENzdPbCz+2/Re09PL5vih4XdICoqik6d2lj3WSwW4uLiUjzW2dkZJycn63ZyY5ZSO6GhoRQt+mq8WLZ6eIwiIyNtPtbVNccDxzokEs+BO3dsjyciIiKSFCWuREREXiBubm5cvXrFuh0ZGcnNm2G4u3ske1yzZi1o1qwFN25cZ/Dgfvz003w6duyKyWQiKOgKBQoUBODq1aB462U9mBRKbPthb7xRig0btidbx9HRibt3o6zb169fS7Z+UnLmdCMkJBiLxWLtV3BwEHny5E20nWvX/kt+Zc/ugoODA/PnL0lx7B5269YtIiMjrcmrq1eDKFiwkLX8wTFKqR03NzeCg69at69eDUpVXxLj6HivX3fvRmEyZQXSPsYiIiIij0pTBUVERF4gPj61WbMmgBMnjhMdHc20aVMoVqx4kk9bAfzzzxGOHDlMbGwsjo5O2Ns7YDAYMBqNVKvmw/Tp33Pnzm2Cgq6wePFCatWqm2QsV9cchIQEExMTk+ZzKFy4CLt2/U54+E2uXQtlyZJFaYpTvHgJjEYjS5f6Exsby7Ztmzl69Ii13Nu7MGfOnObEiePcvXuX2bOnW8sMBgO+vo357rvx3LhxHYCQkGD++GOXTW3PmjWNmJgYDh7cz86d26latUai9VJqp2rVGgQGruLMmdNERUUxZ86MNI3Fg1xdXXF392DdukDMZjOrVv3CpUsXHzmuiIiISFoocSUiIvICKVu2PJ07f8DAgV/QsGFtLl++yNChI5M95vbt24wZM5w6dari51ef7Nmz07Ll+wB8+mkfnJycaN68IR9+2Bkfn9rUq9cgyVhlypSjQIGCNGhQi3r1qqfpHGrVqou3dxH8/Brw2Wc9qF7dJ01xMmXKxMiRYwkMDKBOnWps2rSBd96pZi1/+eV8tG/fmU8//ZCWLZtQokTJeMd37/4xefO+RNeuHahZ8x0+/fRDzp8/l2K7OXLkxNnZmUaNajN06CB69+5Pvnz5k6yfXDsVKrxNs2Yt+eST7rz3XiNKly6bprF42BdfDGTRovnUq1edM2dO8/rrbzyWuCIiIiKpZWexWCzp3YnHJSTkVnp3Id3Zm4x8OzzpNyL1GlyRH0d/nmhZ277juBuT6Ul1TTI4F5fMhIXpDVBiG90vEBp6BTe3/9YTMmW2YLaYn1h7RjsjsXeSn2aXERmNBszmlNd9yihGjPgKd3cPunb98InE37fvL4YNG8KKFWtSrvwCetbul/Ty8O+fF5E+hyQ1dL+IrXSvJM7d3Tld29caVyIiIo/BvaTSk/tYjX1ikUVEREREMi4lrkRERCTe2/weltJi6RLfvHmzmT9/ToL9JUqUonXrtk+tH23aNI+3EP99ffoMoGbNOk+tHyIiIiKPQokrERERseltfi+CgQO/euQYbdt2pG3bjkmWP61pggsWLHkq7YiIiIg8SVqcXUREREREREREMiQlrkREREREREREJENS4kpERERERERERDIkJa5ERERERERERCRDUuJKREREREREREQyJCWuREREJFVmzZrG118PTu9uPLLPP+9JYOCq9O7GE/O8XCcRERF5sZnSuwMiIiLPg6xGM5hjn1wDRhMRZuOTi/8CGjfuu/TugoiIiIikQIkrERGRx8Ecy77O3Z5Y+NIzpwGpT1zFxsZiMunj/lmn6ygiIiIvKpv+Apo2bRpHjhzhyJEjXLx4kTx58rB58+Yk658+fZpvvvmGP//8k5iYGIoVK8bHH39MhQoVEtSNi4tj3rx5+Pv7c+nSJXLkyEGdOnXo2bMnmTNnTvuZPQOuX7/GrFnT2LXrd65fv0aOHDmpUqUqnTp1w9nZ2VqvUqWyycbp0qU77dp1Srm9mxf5rPcX7PljDzGxZrJndeS1/J6UKJQLg51dvLqbNm3gjz92cvz4Mc6ePY3ZbGbp0l/JlSt3kvHPnDnNjz/OYv/+vwgPD8fFxZVXXilGnz79yZEjZ4r9ExGRx8PPz5dGjZqyYcNazp8/x4YN21m0aAEBASu4ceMGnp6edOnyIe+8UxWANWsCCAhYyWuvvc6qVb/g7OzMZ5/1pUKFtwG4fPkSI0cO5fjxY7z2WnFefjlfvPZ27NjGDz9MITQ0GG/vIvTu3Z/8+QsA0LhxPZo0aca6dWu4dOki1avXpFu3jxgxYih//32AYsWKM2zYKLJly5bq87xy5TLNmjVg69bd1qROjx5dqVWrLr6+jVI8rwfrms1mpk6dRGBgAJkzZ6VFi9ZMmDDGGtvPz5e+fQdRrtybwL1peJcuXWTIkGEAHD58iMmTJ3D27Gk8PXPxySefU7p08p/fPXp0pXjxEuzdu4fz589RqlQZBgz4kmzZslvPrV+/QcyePYNcuXIzZcqMZNtJ6TolN4YDB37FjBlTuXs3iubNW1n/rhgx4ivc3T3o2vVDAPbt+4thw4awYsUa4N699qSur4iIiAjYmLgaP348Li4uFCtWjFu3biVb9/z587Rs2RKj0Ujnzp3JmjUrS5cupXPnzsyYMYOKFSvGqz9y5Ejmz5+Pj48PHTt25NSpU8yfP5+jR48yd+5cDIbncxmuGzeu07Vre0JDQ2jYsAkFChTizJlTrFy5jIMH9zF16mwcHR0BGDz460RjzJ49nUuXLvL221VSbO/qtVNs/mM6LtmdKVEoF072Ji4E32T732e5Hh5JtdKF4tVfsWIpR48ewdu7MHny5OX8+XPJxv/jj13079+bPHny4OfXAlfXHNy4cYMjR/7m9u3bSlyJiDxlGzeuZ8yYibi4uGAymciTJy/ffz+THDlysmXLRoYNG8xrr63Ezc0NgKNHD1OnTn1Wr97Ir7+uYNSoYaxcGYidnR1Dhw6iePHXGT9+MkePHqZPn0+pXPkdAM6fP8dXXw3kf//7hlKlyrJ48UL69u3FggVLyZQpEwBbt25mwoQpmM1mOnRozYkTx+nXbzD58xekd++eLFvmT8eOXZ/IOCR3Xg8KCFjBzp3bmT17IU5OTgwc+IXNbYSEBPPFF58yePBQ3nyzInv37mHQoL4sXLgMV1fXZI9du3Y148dPIleuPAwfPoSJE7+xJsMA9u/fx8KFyzAY7FJsJ7nrlJK//z7AokU/c/78ebp2bcc771SzJh9Tkp7XV0RERJ5/NiWuNm7cyEsvvQRA/fr1uXPnTpJ1x40bR3h4OMuXL+fVV18FoFGjRtSvX5+hQ4eydu1a6x+LJ06cYMGCBdSsWZNJkyZZY+TNm5fhw4ezevVqfH1903xyGdm8eXMICrrCl18Ox8entnV/8eIlGDp0EP7+C2jfvjMAtWrVTXB8cPBVrly5zCuvFMPbu3CK7e09shI77Jg9awabFowHoEShXGzed4ojZ6/yysvu8eoPGvQ1bm5umEwmxo8fnWzi6saN6wwdOohSpcowevR4TWUQEckA/Pzew9PTy7pdrVoN68/Vq9dk/vy5/PPPYSpXfhcAL69cNGjQGIA6deozbtworl+/RkxMLMeOHWXixO+xt7enZMnSvP12ZWuszZs3UKFCJcqVewuAli3fZ+lSfw4dOmh9EsjP7z3rFxhvvFESV9ccFCnyCgBVqrzL3r1/PrFxSOq8cuZ0i1dv8+aNNG/e0jpm77/fgf3799rUxrp1a6hQoSIVKlQCoFy5t3jllVfZvft36tSpn+yxtWrVpWBBbwA6d+5Ohw6tGDjwK2t5x45dcXJy+v92/JNsp1Spsslep5R06NAFBwdHChcugrd3YU6e/NfmxFV6Xl8RERF5/tn0ONP9pFVK7ty5w+bNmylfvrw1aQWQJUsW/Pz8OHv2LIcOHbLuX7VqFRaLhXbt2sWL07x5c5ycnPj1119tavdZtG/fXzg4OFCjRq14+6tXr4m9vQNr1gQke/yaNQHExcVRv37DFNu6G32HG+GX8chZkLx58sQrezWfBwD/nAuOt9/Ly8vmBNTKlT8THn6TDz/siclkIioqitjYJ7hAsYiIpMjT0zPedmDgKtq3b0Xt2u9Su/a7nDlzips3w6zlDz4Ze/+J38jISEJDQ3B2drYmT+BeMui+0NCQeNsGgwEPD09CQ0Os+1xdc1h/dnBwTLB9505koufw+ec98fGpjI9PZdavD7T53B+U1Hk9LDQ0BA+P/xJ9Xl5eCeokJSgoiC1bNlnHtnbtd/n77wOEhoameKyHx3/XycsrF7GxsfGuy4PJx+TaSek6peTBcXJwcEx0jJKS1usrIiIiYovH+mjM8ePHiY6OpmTJkgnK7u87dOgQJUqUAODw4cMYDAbr9n0ODg688sor8ZJcz5uYmGjs7R0STFUwGAw4ODhw+fIlwsLCcHFxSXCsxWJh9eoAHB0d8fGplaD8YXFx95JIJqN9gjKT8V7uMuh68lNAk7Nr1+9kyZKFiIhbtG/fipMn/8VgMFC8eAk+/rgXr776Wppji4hIWv33+RIUdIUxY0YwceJUihd/HaPRSPv2rbBYUo7i5ubGrVu3iIyMtCZFrl4Nsn5+ubm5c+rUSWt9i8VCcPBV3NzcE42XGim99c/R8V5/7t6NwmTKCtxbPzItcuZ0Izg4yLp99WpQvHJHRyfu3o2ybj/YjqenJ7Vq1aVv30Gpbjc4+Gq8Nk0mE9mzu8Tbb0s7QUFXkr1OaZXceYuIPGue9hrDERERzJjxPdu2bSE8/Ca5c+eladPmNGrUNMHv59SuMZya2JI2GfV+CQ8PZ+3a1ezatYNz584SFhaGp6cnJUuWoX37TvG+9ErMyZMn6NSpDWazmWHDRlG1ao1k62cEjzVxFRx876mdh7/lfXDf1atX49V3dXXF3j5hQsXT05P9+/cTHR2daPmzrkCBgpw/v4UTJ45TuHBR6/4TJ45z61Y4cO8PzsQSV3v3/smVK5eoW9eXLFmyptiWo4MzDvZZCL1xjqioqHhll0JvAnArMjrN53L+/DnMZjOff/4xVavWoH37Tly5coUff5zFxx93Y/r0HylYsFDKgURE5ImIjIzEzs4OV9d7nymrV//KmTOnbDrWyysXRYu+yqxZ0+jW7SOOHj3C779vp1Kle+srVqtWgwUL5vLXX3soWbI0S5YsIlMme15//Y0ndj73ubq64u7uwbp1gTRs2ITAwFVcunQxTbGqVfNh2bLFVKxYGUdHJxYs+DFeeeHCRdi4cT1vvfU2J0/+y9atm3jzzXvrdtasWYcuXdrxxx+7KFu2PLGxsRw5coi8eV+K90RVYtavD6R27XrkypWbmTN/4N13q2M0Jv72yOTaSek6pVXhwkXw919Au3adiImJYcmSRY8UT0QkvTztNYZjYmLo1etD/v33OH5+75EvXwF2795pnbLeqVP8NxGnZo3h1MaW1MvI98vRo4eZMmUiZcqUo0mTZri4uHD69Cl++WU5W7ZsYOrU2RQoUDDRduLi4hg9ejj29g5ERia9BFRG81gTV/cfK08s0eTg4BCvzv2fk0pK3a8fFRVlc+LKxSVjvYUwNDSUKVMm89tvv3HtWihubm5Ur16Djz7qQceOHdi+fRtffTWAvn37UbhwYVasWMHs2bOsxw8Y8DnVqlVn4MD436yuW7cKgBYtmic458g79xJQN8IvE7h9AhZLHJVLt+WVAlU4eDyQL/r1J6/DbRwdMnHg5GUOnb73jXJMrJl+/QfS54t+5M2b96HzuJeQ/OijzoSHh2Nvb0+ePHlo0KAhzZu/R2TkHcxmM/Xq1Wf06DHW48qUKUnHju1ZuHAO48aNf0yjKk+K0WjIcP8PScal+wXCwgwYjf/NuLeLe7LfbtrZ2WFMxQtLDIb/+uft7U3Llm3o1q0jBoOBOnXq8frrb2Aw2GE0GjAY7O7FNxoeinFv39dfj2TYsCHUrVuN4sVLUKdOPSIibmE0GihQoCBffjmciRPHEhISQuHCRfjmm4k4Ojok2hc7u//i3m/Dzo4Ebduqf//BjB37P6ZPn4Kvb6NUnZednZ3150aNmnDx4nnat29FlixZaNXqffbu/ROj8V7fu3X7kCFDBlCnTlVKlSpDzZp1CA+/idFoIHfu3IwZM57Jk7/lq68GYjQaKFasOH369E/2vOzs7Khdux4jRw7l3LmzlCpVmr59B1jbBOL9nFI7yV2npCTWzoPjUrduffbu3YOfXwNy5cpFvXoNWLRoQbyYj/v6pvVeeJGYTPodrM8hSQ2j0cDixfP//wnksdStW89a9uab5fjiiz788ssSunX7AID33vNLECMoKIgRI77itdeKU7Zsyl/O+Psv4p9/jtK//wBat24DQNu2rfn000+YP38OLVs2J3fu/5ZxGTt2DO7uHphMJkaMGM758+fIls0p0fs8tbHFdvd/t/zww7cZ9n55/fVXCQhYzcsvvxwvho9Pdbp06cy8eTOZMGFiou3Mnz+fs2dP07FjJ6ZMmUyWLA7PxO9SO4vFlokC/7m/OPvmzZsTlK1bt46ePXvy5Zdf0qpVq3hlJ0+epF69enTr1o3PPvsMAF9fX65du8bOnTsTxPrkk09Yu3Ythw4dsjlxFRKS9uluj9uNG9fp0qVdggztL78sp0CBgkydOpudO3fw7bdjuXbtv8fu3d09yZYtG6dOnaBBg8bcuHGd//1vnLU8PDycRo1qkytXbhYuXJagXXuTkYnDNrLu90ncvHWVWPNdKpduy0u5Xufg8XWcvPAbd+/+93RVVid77sbEEmeB7NmyY2cwMmvW/HhTPLp168CRI4d4771W5M9fkLt3ozh48ABbtmykbNnyHDlymMjIO0yYMMX6mvD7mjatT3R0NAEB6x/n8MoT4OKSmbCwZyfrLulL9wuEhl7Bze2/NYSyGs1gfoLr+xlNRJgTfxInBEg0IwAAIABJREFUIzMaDZjNcendjVS7cuUyzZo1YOvW3U/spSM9enSlVq26+Po2eiLxn0XP6v3ytD38++dFpM8hSQ0Xl8w0bNiQixfPs3HjjnjTruLi4qhRozJubm4sWfJLkjHmzp3JzJk/0Lt3fxo1appim927d+LEieOsXr3J+lAGwMGD+/nooy507/4xrVu3S/TY8eNHs3z50iSnCj5KbEne/d8t7dq1fGbulwfVrVsdFxcXfvrp5wRlV68G0aZNczp06IKLiwsjRw61eaqgu7tzinWepMf6lZaHx72Fvh+cDnjf/X0PTiP08PDgxo0bREcnnKZ29erVJKcRPgvuvzVw4MCv6NXrCxo1akqvXl8wcOBXnDjxL/7+C6hWrQbLl6+hd+9+ALRu3Y4VK1Zjb58Jo9FIz56fx0taAaxfv4bo6Gj+j73zDm+qeuP4J02bdNDdUlqgLTLaskFkbyjQsgUR2cviAGSKDAVkiAIyBREQBBQUFWSPskcR2avsUQp075Fm/v6ISRvSiYUf1fN5Hh7NOfeec3J6m9587/t+344d8zZlv3n/BMmpUVSt2NLYJpFYUNs/iNB9e3mzeTWsZZaUsraiV6uaqNRa3B3tWLJkEYmJCaxZ853JeH5++spAPXv2pnPnbvTs2ZuZM+fSvftbnD17BkdHRwBcXV15FldXN2Pqo0AgEPybSdNISUP+4v6VQNFKIBAIBIJXhcJ6DOdG0T2Gtdy6dYPKlf1MRAiAgIBqWFhYEB5+/bnex4scW5BNSbxe0tLSyMhINym4kpMFC77Ey6ssvXq9U+BYrxrF+gixSpUqyGQyLl68aNZnaKtevbqxrXr16pw4cYLLly9Tr162oVlWVhY3btwwaStp5Fc18IsvZrJ79w4GDRqGVCrl0KFQnJ1dePfd94mPj+PWrZtUq1aDpUu/NjOCO3fuLywtLQkK0ocrrlmzkrVrV+W6hqTUKLM2GxsbNFodCqWaBgHleRqvF5WiElLp11+v4O7YsZUdO7YazzEIkgZGjAjh4sXzxtdRUU8BGDCgN6tXr8ffv6qxLzY2BkdHJxYu/EoYBwoEAoHgP0dgYLNc2+fPz994vjjZv38P8+bNMWv38PBk48ZfXto6CotarSYuLpa0tDQ0GjVSqSX29va4ubmb+H/FxsYSHx+b6xju7h65PlB7loiIB2Rk5B21Y2trh7e3DwDp6ek8epS35wyAt7cvtra5p1woFAoePLgP6PDyKoeDg0OB6xMUTGHNk/O7Z/7gg4/o06d/gXM9ew/8LPXq1WfRouWFOv7Ze2bQpxatX7+Gc+f+IjY2FgcHB6pU8adPn/7Url23wPUJ8udlegynpqaQlZWFu3tpsz6ZTIaDg6NJ9d2i8CLHFmRTEq+XH35Yg1qtpkOHjmZ9Bw/uJyzsBCtWrHlhkeQvkmJdsZ2dHa1ateLAgQPcuHEDf399pE56ejq//vorvr6+JhUEg4ODWblyJT/88IOJSPXLL7+QmZlJ586di3N5L5XCKrRyuZxLly7QsGFjduzYxtKlC9FoNFy+fJErVy4RHNyZgIBq3L9/l61bt6DRaGjatLlJqWmAUaPG4ujoxOZNG3kU8ZSAii3RatVEPL1ktraYxDQAXBxsCLsWgZWlBY2r+dC6+wD2HzjMyZPHGD58BFeuXOLUqeOUKeNFTEwMmZkZJCUloVRmYWtri1arQyKBTp26sWXLJvz8/E1yqU+cOEZsbAxOTs5s2/abMA4UCAQCQYnB09OLEyfO/uNxDhw4nmffsmXf5dlXnLRrF0S7dkEvZa5/ilqt5uHD+6jVapycnJHJ5CiVWSQlJZKRkYGPjy8Wz3i9lS7tgVRqektrMMwtCFdXNxwdNWbtqakppKWlUqpUtvAhl8vx9DT3jNHptERFRSGVSo0VHc2P0REV9RQLCwlabZFcOgT5UBTzZAOGe+ac+PkFFGq+gQOH5Jrae/DgAU6dOp6rAbOTkxMjR441a3/WfyguLpZhw/qhVmvo2vVNypcvT1xcHNu3b2XUqPeYO/drGjduWqh1CnKnV68+HD9+lE8/ncRHH42lQoVK3L9/lyVLFmBpaYlarTYrZGVgx45tAPlmveTEMI6VlVWu/TKZLM+5/p9jC7IpadfL4cOhbN68kfr1G9GxYxeTvtTUVJYsWUDnzt2oXr1mHiO82hRKuNq2bRtPnjwBICEhAZVKxfLl+qcJXl5edOuW/QE+btw4Tp8+zZAhQxg0aBB2dnZs2bKF6OhoVq5caSLk+Pn50bdvXzZu3MiIESNo0aIFd+/eZcOGDdSvX79EC1eFUWjffXcAr7/+BhqNhnPnznLq1Al0Oh3e3j7GKhLXrl1hzJiPsba25uHDB5w9eyZX1bZZs5Zcv36V23duAfA4+jqvlc8WAx9HhxN+7zCO627xKFof0njk4n1Uag0dG/njXdqJ4KAOJKcoOHnyGJaWlly7dgWpVGosgz158gQiIx8Zx6xatTpjx07E3z+AxMQEQkP38fnnn9K4cTOiop7y228/Y2dnR1JSIqNHj6dnz94AdOnSnSlTJrBhw1o6duxCmTL/bY8GgUAgEAgEemJjY1GpVHh5lcXBwdHYbmNjw5Mnj0lIiDfx4QQoVcr+ua0l8noSHh8fi0QiwdExOyrK0tLSaI+Qk5SUZECHo6NjnpHkiYkJKJVZuLi4ikiIYsRgzTFt2iwCAzsY26tXr8mMGVPZvHkjgwYNMzmnWbOWuXoGFYY33miYa/sPP6xBJpPRvr25QGxtbUP79sEFjr1nz06SkpL44ov5NGvW0tjetm17evfuzo4dW4Vw9Q+pVasO06fPYfHieUyYMBoAqVRKp05dSUxM5Nixw9jZ2Zmdl5KSwvHjR/Dx8aVWrdqFmssgmKpUqlz7lUploQX2lzm2IJuSdL2EhZ3g888/xc8vgJkzvzD7W/TNN4vQanW8997IQq3nVaRQwtVvv/3GmTNnTNoWL14MQP369U2EKx8fHzZt2sT8+fP57rvvUKlUVK1aldWrV9O4cWOzsSdPnkzZsmX5+eefOXLkCM7OzvTr149Ro0aZPVErSRRGofXyKktY2EkAFIpMfH1fY+TIMSxfvgS5XM477/Rn3brV7Nmzk+DgToSHXwPgyhXzKKr09HQWLZpH2zbtCD1oboReytYZCwtLfv55C/EJeuHM08WextV9cLbPfjpouPELCztBcrJe4Lp58waAiWhlY2OLTqcjJiYKPz9/pk6dQaVKldm1aztLliygVCl7WrZsw8OHD3jw4B6dO3c325+jRw9z8OB+YRwoEAgEAoEAgIyMdCQSC+ztTdPo7O0dkEiekpycbCZcAWg0GiwsLIrFgiAjIwOlUom9vYNZJFduGDxOno3iMaBSqYiLi8XV1R1LS+FVV5wU1prjWdLT05DLrYslXebSpQtERDykTZt2JmJrTrRaLZmZGdja2uV5jaanpwOYXd8uLq5YWFhgbZ17NJ+gaLRu3ZYWLVpx794dMjIy8Pb2+duyZQBSqZRy5cqbnVMYj+Fnsbd3QC6XExsbY9anVCpJSUnGze350j9f5NgCU0rC9XL69CmmTPmYChVe4+uvl5k9kLl58wa7dm1n6NDhpKQk//2wRf9ABfTp1pGRjyhd2uOV9hcv1Kf1hg0bijRoxYoVWbFiRaGOlUqlDBkyhCFDhhRpjledwii0I0eORa1WM2xYfywsLPj++43IZDIWL56PTCYnOLgz69at5sKFc3Tv3pN9+44SFNTamGaYM5922LD+qNVqDh0+iJuzD1UrtUKRlV1l0dG+DK0bhDDm08YMeac7l+9F0aiat4loBRiN8g0eEosWLadevfomx4wYEcKVK5e4ffsmkydPQCaT0bBhE4YP/5B+/QYZj9NqtQQGNqNKFX9hHCgQCAQCgaBA9DYEErMv94Y2lUqJWq02ERwePLiHVqsFJNjY2ODq6kapUgV7iuSF4cGdk5NzgccqlUoyMtKxsbE1u9cxEB0dhZWVDBcXF+MXBkHxUFhrjpz3zAMHvkNGRjpSqZSAgGoMHDiURo2aPPcadu7UVxXLqzpobGwMgYHNyMrKwtramvr1GzF8+If4+PiaHFe/fkM2blzHggVf8uGHH1GuXHni4mJZu3Y1NjY29O7d77nXKDBFKpWaZMQYPIZr134916iWnTu3m3gMFwYLCwuqVPHn9u2bKJVKE0EgPPwaWq0Wf//Cpai+zLEF5rzK18uff4YxefIEvL19WbRoea7eidHRUeh0Olav/pbVq78161+4cB6Qu+/eq0TJc+UqQRRGoc3I0D9dsbfPDnM3pBkaVFBDamFuRnD29va0aNGKo0cPExjYgTIeHmz55TeOnFlN+TL6/NXMrFRS0+Owtdb/0baz0c+TplDi4mBqIGpQeS9duoinZ1lef/0Ns/fl5VWWGjVq4e3ty5w50/HyKsvp06c4d+4My5evoWLFSsZ1C+NAgUAgEAgEhcXaWk5KShYKhcLkC4FCoUCr1XtRqdUqLC0tkUotcHJyxsbGBgsLKUplFomJCURGRuDp6ZVnBFR+aDQaUlJSsLKS5WmynpNskSv3uVJSkklLS8XHx1cUpHkBFMU82d7eni5dulOjRi3s7e2JiHjIL79s4uOPRzNp0mcEBxfdoiQ9PY3Dh0MLvGeuVKkyFhYWXL9+jd9++8Xsnhmgbt16jB07kTVrvmXkyGwP2HLlvFm5ch2+vhWKvD5BwWi1WhYtmo9Wq2XgQPNAihs3rnPnzi1atGhl5jFsQK1W8/hxJHK5NWXKlDG2t23bnitXLrF9++9GyxSAX375CalUSuvWgc+97hc5tiBvXqXr5cyZ00yaNJ7y5b1ZvHh5nhGfVatWY+bMuWbtFy6c4/fft9C7dz+qVauOl1e5Qu3B/wshXL1gclNob9+6Sb26dfBwkIBDKTzLeBAVHYNMm4zcppQxzXDatMmAPjc+LOxkrkZwvXr1oUwZL44ePcyBA3tN5n4UdRmAs9e2wjXo0FQf+VXaWf8UMio+Fe/Spjda169fRSaToVRm0bFj51xvsiZPngbo83d1Oh2enl5MmDCZkSOHs3Tp18ZqKsI4UCAQCAQCQVFwcXElJSWVx48j8fDwQC63Jisri5iYKCQSCTqdzmhubl7u2x5HRycePLhHdHQ0pUrZm1QhLAz6exttvn5VBnQ6HcnJyVhYmKc2gl4Ei4mJ/ltcK1gEExSdopgn9+rVx+z8jh27MGDA2yxZ8jUtW7YplFiZkwMH9qFQKAq8ZzbQqlVbmjZtbnbPbMDJyRk/v6rUq1cfb29vIiIi2LRpAxMmjGbZspV4eJRB8PxkZGQQEjKQZs1a4uVVlrS0NEJD93HzZjghIR9Qt655RXtDRF2nTrlH1IH+wX/fvj2pXbuuSdGNLl26s3v3DpYuXcjTp0/x9a1AWNhJjh07zMCBQ80M+i9ePG+sQnnjRjgAv/32izGCNGfaa1HHFhSdV/l6uXHjOp98Mg7Q0bFjZ06fPmU2j8Fbz83NnVat2pr1Z2ZmAlCtWvVc+181hHD1EjEotBqtlu7OcUQu0z9NaeGuZXOUjtVj+zJy+W/GNMM5c6YDcPz4EU6dOk6nTl1JSIjn+PGjxgsNTFVUS6kFu3+9ysOnl4wVBQNea4Gbkw+lbPU3eGXdHLCztuL6g2hqV/ZC9rffwq1bt7lw4Rz29g5oNBpjNYL4+DhcXd3M3s+vv24GoFq1GtSqVYdatepw4cI5srIUyOXWwjhQIBAI/gXs3r2DHTu2sWLFmmIZb/3673ny5DGffPJpsYxXkijuvSwK8+bNwd29dK5+P68SdnZ2eHmVJSYmKoe3pgQnJyfUajVpaam5eqDm3FsnJ2fi4mJRKDILVYY8J/oIKkmhorXS09NQq1U4OTnnuqaYmGh0OnKNPBcUD89rnmzA0dGJrl178P3333H16mXq18/dfD0vdu36A6lUalbBq6A1P3vPDLB9+1YWLJjL2rU/8tpr2ZFYDRo0YsiQvqxc+Q2ffTazSOsTmGJlZUXFipUIDd1HfHwccrk1AQFVWbBgKQ0aNDI7PitLQWjoPkqX9si1vzDzLVq0nFWrlhMauo+UlGTKli3HmDETePPNXmbHnzv3F2vXrjJp27x5o/H/c35+F3VsQdF5la+Xe/fuolRmAbBkyde5jleYohAlCSFcvSDyU2hHvR9CrfRsVbRnNTdORqSw+lwUSXPn4f2aP5cvX0KhUBAQUI333x9JhQqv4ezswsCB7wCwfv0aGjbU/0LkVFFlllKunpKi1iiNwpWbkw/eXrWM80ktLGhWswJ7z9zit6NXqeZbGqVaw4+jPqJUKXuSk5No3Lip8UZrwIC3qVmzNlWq+OPu7k5SUhJ//XWGc+fOULFiJXr10q/J09OLCxfOkZqailxuLYwDBQLBfwq5zBLdCyxzL7GQkKVUv7DxXwTnz59l5szP2Lp1t7FtwIB/l6dlSWHChMn/7yUUGgcHB+zt7cnKykKr1SKTybC0tOTBg/uApEDzWEOkt1qtKdK8CoUChSKTUqVK5RktnhNDmmBuIpdCkUlychJubu5oNBo0GkOao/53WKNRo1QqsbS0LNHFiF4Fnsc8OSeGCoMGk/3CcvfuHcLDr5vcMxeWZ++ZATZsWIePj6+JaAVQsWIlfHx8jZE4gufHysqKGTO+KPTxcrk1e/ceKfA4T08vTpw4m2ufvb09Y8dOZOzYiQWOM3TocIYOHV7gcc8ztqDovMrXS3Bw5+dKby7uMV4mQrh6QeSn0LZrVpfIZdnClZ1Myrz2FVh/MYbDx46TuH0n7u4e9O8/mEGDhhnNPuPj43jw4B5AgTc5Kelx+fZXLueGpdSCv25EcvLqQ6QWEpo0bY7c2o49e3aahDf27NmbM2dO8/vvW0hJSUYut8bb24fhwz+kZ8/e2NjoDd4fPYpAKpUaw+WFcaBAIPgvodPqWDzr0Asb/6OprV/Y2Hlh+JItELxMDObrOaOy1WoVCoUCW1vbAu+BDIVmiloxLluIKtiUXR/9pa9MZ7gPyolKpf/diYuLzdXPMzo6CgAfnwq5ni8oGkU1T87Jo0cRALi45O5Hkxc7dmwD8k8Jym/OnPfMAHFxMZQtm7vHjF78FJ/HAoHgv4sQrl4Q+Su0WWYtjtaWjGzoRbkRK0lWmlelyWkEt2TJt9StW8+Yt/9s5Zz0zERuPzyF3MqWbm0/w1Jq9fcYGh48eEBqRhb2tnIqeLpQwTP7j3TvMdMJ7tQFFxdXGjduamwfPPhdBg9+F4C0tDRsbGzMPCNOnTrBlSuXaNiwsUlVHWEcKBAIBC+XDRvWcfPmdWbN+srYtmjRfEDH6NETSEtLY+nSrzl9+iQSiQXBwZ0ZOnQ4Uqn073SrrQQEVGPPnl10797z72gFHQsXfsXevbtwdXVj7NiJxoqzu3Zt56ef1hMTE4OTkzN9+w6gW7ceZGZmMn78R6hUSgIDmwGwadPv/PHH7zx+HMlnn83k6dMnvPVWFyZN+ow1a1aSkZHBe+99iJ9fAHPnziQ6Oop27YIKfPL4+HEkX345izt3biORQP36jRg7diL29vYA9OzZma5d32Tfvt3ExcXRvHlLxo37BLlcbowK6969Jz///CM2NraEhHxAu3ZBgF4E+e675Rw6dACVSkXz5i0ZNWoscrm18dxevfrw448/IJVaEBLyoTFtKDk5iTlzZnDhwnl8fHyoX7/oqQM5adq0Hps3bzVGkMyePR1399KEhHxQ4FpyHgvw00/r+fnnHwEJ7777PnPnzjSOPWJECO3bBxurpD2b4vjw4QMWLvyKmzdv4OTkxLBh79OmTf5/z2fPno5MJuPx40iuXbuKn58/U6fOoEwZT+N7GzPmY7Zs2YRGo2HLlu0m85QqVYouXbob0yWSkhKZM+dzLl403VuVSkVSUiJSqdREENLpdCiVStq0acL48Z+wefOPJCUlERjYgbFjP0an07F+/fdER0czb94iAOP1eeTIaSwtLRkxIoSaNWtz/vxZ7ty5RZUq/nz88VRmzJjKyZPH8fb2YebMuXh6emFtbZ2r0W1GRjpJSYm4uLhibW3zSpceL6nkZp6c1z1zdHQU27b9hqOjIzVq1DS252WebECpVHLgwB6ze+acFPWe2de3Anfv3uHq1StUr17D2H716mUePYr4R5UP/4s8GwWdmaE02qNAyYxiFrwYxLVSMhDC1StIYY3gMjMzeeutLjRv3hIfH1/s7R14HBnBrqNbUGuUNKnTzyhaAWQokun59seUdXPgzebVzeY9cvQYycnJ9OkzIM+nlOfPn2Xp0oU0adIML6+ySKVSwsOvsX//HpycnBg1apzJ8cI4UCD495GQEM/ixV9x9OhREhLicXFxpXnzVgwdOtwoFABs2rSRkyeP8ejRQ1JSUnBwcMDb25eePXvTokWrQs+XlpbGqlXLOXr0MCkpyXh5laNHj15069bDxAxXrVazcOFXhIdfJzr6KRkZGbi5uRMQUI1+/QZSpYq/2dhRUVGsX7+Gc+f+IjY2FgcHB6pU8adPn/7Url0yU5kDA9uzbt0q0tPTsLMrhUaj4fDhA8yePR+A2bOn4ezswubN21AoMvn449GULu1Bt249ALh+/Rpt2rRjx479aDRqDh48wPXr12jZsg27dh3k6NFDTJkygS1btuPg4IizswtffbUIL6+yXLx4nvHjRxEQUA0/P3/mz1/MzJmfsWXLduLiYklKSiI+Po7U1BSio6OM6VPXr19l06bfOX78KLNnT6N69ZqMGKH3q5k2bTINGjSmSZNmeb5nnU5H//6DqFWrLsnJSUyZMoHFi+fz9tt9jGla+/fvYcGCpdjY2DBx4hh++GENISEfkJGRQXx8HBERD5k7dyH37t3hq69m4+8fgLe3LytWLOXJk0jWrfsJpVLJzJmfsXDhPN588y0eP35EfHwcCQlxbN26m7Nn/2Tq1Ik0a9YSBwcHvv76S2QyOX/8sZenTx8zduxIY1rSiyAhIZ709DS2bdvDX3+dNllLTk6fPsWmTRtZvHg5np5l+fLLWYWeIzMzkzFjPmTo0OHMn7+Eu3fvMHbsh1So8BqvvVYx33P379/LvHmLqFq1OsuXL2HGjKkmfl/Hjx9h1aq1PH78lEePIvjoo/d5553+jBw5hjt3brNo0XyaNGnOa69V5Ouvv0SpVLJy5VoSExOYPn0KpUt7cP/+PbRaLV5eZU0is1QqFffv3wX0wsGqVetJT09n2LB+NGnSjKpVq/2dlmiVryn7wYP7+frrZSQmJjJjxhQ++WQM48Z9wpQp0/nii89Zu3YVkydPw8rKKtd0Q51OS1JSItbWNrmWLRcUjX9yzxwR8ZCdO7eRmZnJtGmzjSl7kLd5soHjx48U+z3z0KHDmTx5AmPGfEi3bj0oV648kZGP2LbtVywtrRg8OKQYd+7fT0FR0P+PKGbBq4m4VkoGQrh6AUhllmjy9TnJv0pNYY3g5HI5LVu25vr1qxw7doTMzAycnJwp41aFqhVb4ebsXaR1b9++A4BOnbrmeYy3tw9+fv6cOnWcxMQE1Go17u6l6dq1BwMGDDbL8RfGgQLBv4vExARCQgYRFxdL165vUqFCRe7fv8u2bb9y6dJ5Vqz43piWER5+DU9PLxo1aoKjoxMpKSkcPhzKlCkTGDbsvUKZRKtUKsaM+YBbt27Ss+fb+PhU4PTpUyxYMJeEhHgTLwiVSsWNG+HUrFkLL69gbG1tiY6OZteu7YSEDGLBgqUm5crj4mIZNqwfarWGrl3fpHz58sTFxbF9+1ZGjXqPuXO/zvNJ+qtMmTKeVKniz7FjRwgK6sT5838hl1tTvXoNEhLiOX36FHv3HjamOPXq1Yft27cahStXVzdjhKzhC5mTkzO9evVBIpHQpk07Nm/eyKlTJ+jQoaPJHtWp8zr16zfk0qUL+PllC4UPH95HrVbj5OSMnV0pkpISSUpKJDk5GcCYFh8QUBW53Jo2bdpRubI/Go0GP78Azp//Cz8/f9zc3HN9z+XKlTdGIdna2tGqVVt27NiGtbUN6elpAPTo0ctYkWvAgCEsXDiPkJAPSEtLAfRp8TKZFX5+/jRo0JhDh0IZOHAoO3Zs5YcfNuPg4MiDB/cJCurEd98t5733RvD06ROkUiktW7YhLi6WRo2aYmNjS0TEQwICqnLkyCHWr/8ZGxsbXnutEkFBnV6oT41UasmgQcOwtLQ0WUvO6A2AQ4cOEBzc2eilM2RICKGh+wo1x6lTxylTxtMYyeXn50+LFq05cuRggcJV48ZNjIJwSMgHdOjQkujoKOPPpX//wX9/VqRx9OghnJ2dqVPndWQyOU2aNOfSpQscOXIQHx9fjh49zIIFSwEdpUrZ07BhY27fvomdnR0uLq75pt/17TsIe3t77O3tqVOnHrdv36JsWf2DNCur/COggoM74+zsQmpqCq+//gaxsbG88UYDQF8xbvXqbwu1j4Li4Z/dMztRr159+vQZQNWq5g908yO7cljx3TM3bdqChQu/4aefNrBr13bS09Owt7enfv2GDBo0zCQVUiAQCP5rCOHqBaDR6hgy60Ce/b99lv8XocIawclkMrOqTDJLaZ6KcSlbF87+GcYPX47LtX/Z0sVkqfI3I/X1rcCsWV8WuLacCONAgeDfw/r1a4mKespXX82jcePsqKnq1WsyY8ZUNm/eaBSkPv/c/HOsV693GDq0Pz/+uJ7+/QcXWKp+x45thIdfZ/To8UYxpUuX7kyZMoENG9bSsWMXY6qRjY0Na9ZsMBuja9ce9OjRkU2bNpgIV3v27CQpKYkvvphPs2Ytje1t27and+/u7NixtUQKVwCBgR0IDd1PUFAnDhzYR2BgBwCiop6iVqvp2rWD8VitVkfp0h7G1x4eHmbjubuXNolC8fDwNPr2hIWdZO3aVTx6FIFOp0WhUJiYC2s0GlQqFV5eZXFwcMTa2hpraxs8Pb2MxTucnfX5vvSeAAAgAElEQVRp62XLlsPGxgZvbx+cnPSG105OzqjVGhIS4nF1dcs1GiYxMYFFi+Zx6dJFMjLS0Wp1ODg44OnpxZ07twAoXTo73Ue/fr0XpKurO/b2Dvj7BxAV9RSlUomHR5m/I8QSUSgUDB3aDwCdDkCHVqvFyckZe3uHv/+rL2zi4uKCtbU1mZkZJCUlotFontnbvEvZz5s3h/379wB6Aed5TOwdHR1Noj8Ma3mWuLg4/PyyPSYNv0OFISrqKdevX6VDh5bGNo1GU6jqRTl/Bra2tjg4OBAXF2fcl9KlPZBILChbthw6nY779+8xalS2OG2Yx7C31arVMApUVatW59GjiDw9gmQyGf7+VQFwdXU1thv2qHx5H9zc3Hn8ODLf9+Di4oqtrS3+/lVxcTlijBoEvTiS237nxNHRqVAVCwWF45/cM+dHfubJAAsXflPgGM9zz/z662+Y/J0SCAQCgR4hXAkEAoGg0Jw/fxa5XE5QUDDJyZnG9jZt2vHFFzPZvXtHvpFUlpaWuLu7c+/eHdRqdYHC1YEDe7G2tqZz5+4m7b169eHo0cMcPLifvn0H5juGs7MzMpmc1NRUk/b09HQAsygeFxdXLCwssLYuuYbJrVq1ZdmyRcTERHPs2GG+/XYtoBcOrKxk7NwZmo9xtbkwFBsbg06nM4pG0dFRNG3aHKVSydSpHzN16gyaNWuJpaUlkyaNQ6dXeJBIJH+fZ2FiQgxgb++ARFJwNTWJRIKFhQVardZkDTn59ttlgIQfftiEo6MTx44dYeHCr0yOiYmJMv5/dHQUbm5ugN7UOS0tlczMzBzHRlOpUmUcHZ2Qy+Vs2PBLvlXDSpVyICMjg6ysbA9LJydnpFIpMTHR+Pj4GufNiwkTJhdY+c/a2hqFQmF8HR8fX+RqZgBubm7ExEQbXz+7LhsbG7N5DJQu7UHt2nVZtGh5kefN+TPIyMggJSXF+HMATH62+c2j0WiKtLeFJb/3LRAIBAKB4P+HqL/7iqGwskJio87zn6Wt/stAQkI88+bN4c03O9KyZUPefLMjixbNN/liptPpuB95jhPnN/DHoTls3v0JHbt0Y+epcKISUvNaghlxcXGsXPkNY8eOpFOntjRtWo/Zs6fnebxOp2P//r28994QOnZsQ2Bgc/r168XatauMKRsGLlw4x4IFXzJgwNsEBjanU6e2vP/+EA4c2Gv84iMQCF4dVColMpncTDywsLBALpfz5Mljs5LiKSnJJCYm8uDBfdauXcWff4ZRt249E1Pa3NBqtdy6dYPKlf3Mjg0IqIaFhQXh4dfNztNoNEYvpfDwa8yYMZXMzAwzY9v69RsCsGDBl1y4cI7Y2BjCw68xffoUbGxs6N27X6H35VXDkGI1Z84MPD298PWtAOgFi/r1G7Bs2SLS09PQarU8fhzJhQvn8h0vKSmRLVs2o1arOXQolIcPH9CoURPUahUqlcoo0oSFneTMmdPG81xcXI2i0LPXjEQiMbY9W71Qq9WgVqvJysoiK0uBSqXE1tYuz2pyGRkZ2NraUqqUPbGxMWzatN7smN9/30JMTDQpKcls2LCWNm3amfSvWbMSlUrFrVs3OX36JK1atcXCwoLOnbuzZMnXJCYmAHoR788/w0zOVatVgD5Vz4BUKqVFi9Z8//1KFAoF9+/fY+/eXfnuc0FUrlyFAwf2otFoOH361HOnHbZq1ZY9e3Zy//49FAoFa9euMumvVKkKR48eQqFQEBn5iF27/jD2NWnSjEePIti7dxdqtRq1Wk14+DUePLhf4LxhYae4dOkiKpWK1atXULVqdcqUKWOUSiXof+8lOebZt898nhext6Df30uXzhMVFUVaWhobN679x2MKig+5zBKZpdT4z2CgbPgnl4nn8QKBQPBvRXzCv2KodBo+2DUlz/7lHeeQmJiSp8fM5csXqFNxEJZSGVqtmlMXf8LZwQsfrzqUsnWhTiMnNvywji1HrhBYrzL+3rn7heQkIuIBGzaspXRpD/z9q3L69Kl8j//uu+Vs2LCW119/g8GD38XS0pILF86xZs1KwsJOsnLlWuOXlRUrlhIbG0Pz5i3p0aMSCkUmBw8eYMaMqZw/f5aJE6cWaf8EAsGLpUKF14iIOMyNG+GUKeNjbL99+yapqXqvoOjoKGOaF8A777xp9DIyfOEcN+6TAudKTU0hKysr14gSmUyGg4NjrmXmHz68z4AB2VVMS5UqRf/+g+nXb5DJcXXr1mPs2ImsWfMtI0dmpyOVK+fNypXrjGJPSSUwsD2zZk3jgw9GmbRPnfo53367lH79epGRkY6XV9kCo9aqVq1GZGQEHTu2wdnZlVmzvjSmO3300Xg++2wSKpWSJk2a0bRpc+N5Pj6+NGnSnIkTRwMSfvxxi7FPoVCg1erTrDQaFZBtjPz06RNKlbL/+7gsZDJ5vsU8Bg9+l1mzptGhQ0vKli1P+/bB/PLLT8/sRwfGjh1BXFwsTZu2YODAocY+FxdX7O3tCQkZiJWVFaNHTzBG8rz//kjWrVtNSMhgkpOTcHd3p1u3nkb/HJ1OR1JSElZWMmxtbU3mHDPmY774YgZdurTHx8eH4ODOnD+fd/pRQXz00XhmzZrO779voXnzFjRr1uK5xmnUqAlvvfUOH330PhKJvqqgIU0R4O23+3LjxnU6d25HpUqVCAzswNmzZwC9h9jChctYunQhy5YtRKvVUalSZUaOHFPgvIGB7Vm79juuXbtClSr+fPbZTNBB9FP9w7S4mHSspNkP1qZO+pKffv6OpUvN5ynuvQV4442GtG7djkGDeuPo6ETfvgM5ceLYPxpTUHwIA2WBQCD47yLR/YvCWmJjCx9F9EKxlBbocRW5LPfKII6jV/H+zvyFq0VzF7NlyyamTZtl9C0BfUrNjBlTqenXgRqVA9FqNcQmPsDDNdssdcynjVkxYyQ/hl5EIpEwNLieUUQaMHFBrh5XGRnpZGUpcXZ2JikpiU6d2hIU1IkpU6abHatWq+nQoSXe3r6sXr3e5On4559/yv79e1i79kejweSFC+eoWbO2SbqQVqtl1Kj3uHjxPOvXbzbxShG8OJycbElKyt+bQyC4dOkCI0cOp3z58owYMYYKFSpx//5dlixZwNOnT1Cr1XzzzWpq1aptPOfixfMolUpiY2M4fDgUCwsLPvpofJ5eNAaio6Po0aMT7dsH8+mnn5v1v/lmRxwcHFm3zlSgyMzM5OrVy6jVKiIjI9m/fzf+/tX44INRZobNhw6FsnPnH9SrVx9vb28iIiLYtGkD1tY2LFu2Ml9Pori4p7i5ZXsDPVtOubgpqeWYFYpMHjx4gJWVFR4eHsjl1mRlZRETE4VKpUKn0+Ht7Wsi+mRkpKPT6VCp1KSmpiCR6NMcZbL8jbOfRa1Wc+fOLSZNGs/kydOMJto5OX/+LDNnfsbWrbuJinpKUlIir71WqVBzabVaIiIeolAo8Pb2MROuShJNm9Zj8+atRpP74mb27Om4u5cmJOQDk3YJ2cJVbnh42vOvuVEtRp79/PkvkJ+PK+iFK6Vak2e/4L+FuF4EhUVcK4XD3d2+4INeICLiqgRi8Jhp27a9SXubNu2YO3cm9x79RY3KgVhYSE1EKwO21jLKujlw90kCGVkq7Kzzvzm3tbXD1tauUGvTaPSpHa6urmYpHQYfi5y+MXXqvG42hoWFBS1btubixfPcu3dXCFcvgYSEeBYv/oqjR4+SkBCPi4srzZu3YujQ4djbZ39IXb9+lf3793DzZjh37twmMzOTyZOnERzcudBznTp1gj/++I27d++QmJiAlZUMLy8vOnToSNeuPUxSws6fP8uoUe/lO97y5aupWTNbJElLS2PVquUcPXqYlJRkvLzK0aNHL7p165FviXNB4ahVqw7Tp89hyZL5TJgwGtBHUXXq1JXExESOHTuMnZ3p54WhihhAx45dmDZtMh98MJQNG7bkWw7eUJ1QpVLl2q9UKo3H5MTGxsZEoOjYsQtDhvRjypQJfP31MmP79u1bWbBgLmvX/mjyOdOgQSOGDOnLypXf6CNCCklJFJVeBnZ2dnh5lSUmJorIyEd/t0pwcnJCrVaTlpZq9vci598cJycnHj+OJCLiARUqVCzQF+1loU+zfIRCocDT06tEi1YCgUAgEAgErzJCuCqBFOQxk5ISj0KZhrWsVJ5jpGUqsbCQILcq3ktALremVq06/PlnGBs3rqNlyzZIpVIuXDjH1q2/0r59EOXLexc4TkyMvtKUi4trAUcK/imJiQl5pp5eunSeFSu+N4oDYWEn+f33LXh7+1KpUmWuXLlc5Pnu3r2DhYVe6HB1dSMrS8GlSxdZsuRrTp06wcKF3xivbV/fCrlG2qhUSr76ag6Ojk4mJaxVKhVjxnzArVs36dnzbXx8KnD69CkWLJhLQkI8Q4cONxtLUHRat25L164dOX/+MhkZGXh7++Ds7MK77w5AKpUWGLERFNSJgwf3c+zYITp16pbncfb2DsjlcmPluZwolUpSUpJxc6uby5mm2Nra0qJFK3788QceP440Rnpt2LAOHx9fM3G8YsVK+Pj4Prd/kMAcBwcH7O3tycrKQqvVIpPJsLS0/NsXSVJgdJOjoxPLly/mzz/DzP72tWsXVKCpeXFj8AZLT0+nTBkvHB0dX+r8ryr9+vUiOvqpWfvL/PlcunSB8eNH5dp34MDxl7YOgUAgEAgExYcQrkogBo+Z27dvGlPuQO8xk5Ki95jJyEzKU7h6EJVIdGIaft7uWEqL359/2rRZzJo1nW+/XfZ3pSe9Ce+AAUMYNiz/6BmAuLhYtm//HS+vsiaRNIIXw/r1a4mKespXX82jceNWxvbq1WsyY8ZUNm/eaKwS1717T/r0GYCNjQ2HD4c+l3DVv/8gs7aePXuzYMGXbN26hfDwa0YxysXFNdcS6wcO7EWr1dKhQ0eTymg7dmwjPPw6o0ePp2dPvcdRly7dmTJlAhs2rKVjxy5FKvsuyBupVGry+RMfH8etWzepXfv1XKOgcmKovGb4vMoLCwsLqlTx5/btmyiVShNxIzz8GlqtFn//gEKtN3vOZKNwFRcXk2e6okajQaMREVTFiUQiMbk21GoVCoUCW1vbPE3XDeh0Wvr3H8zYsRNxdXXL99jcWL58NZ6eXrn21a1bj61bdxd6rGzRKo0yZTxN/NxKMidO/DN/KICNG3/Js69du6B/PH5hqFWrjhCoBAKBQCD4lyGqCpZAevXqg4WFBZ9+OomwsBNERUURFnaSzz6bZPwSr9Yocz03IuIRB87exs5aRrMavi9kffrUr7J06NCRadNmMX36bFq0aM0PP6xh/frv8z1XoVAwadJ4MjMzmTJlej7l2gXFhSH1NCjIVCBq06YdMpmc3bt3GNtcXFzNPIKKizJl9F5COStj5sXOnfoKV507dzVpP3BgL9bW1nTu3N2kvVevPqjVag4e3F9MqxXkRKvVsmjRfLRaLQMHDgH0PlMZGeaeaRqNht9/1xt0V6tWw9iuVqt5+PABUVGmJe3btm2PQqFg+/bfTdp/+eUnpFIprVsHGtsSExPRarVmc8bHx3H4cCg2NrZUqJCdPu3rW4GIiIdcvXrF5PirVy/z6FEE/v5VC7sFgiKi0+mIjo4GdEYhSqvVotGYe0jodDoSExMBsLGxNWnPysrKM5X0RWAuWjm/tLkFAoFAIBAI/qsIVaAEYvCYWbx4npnHTHJSEkeOHsLK0jziIS0jnvdHfAVAlyYB2MjNjdj/KQqFgvfeG4Kfnx8zZnxhbG/btj3Tpk1izZqVtGrVBm9vX7Nzs7KymDRpHDdvhjNlynRq1apT7OsTmFNQ6umTJ49JSkoq9qiCjIx0lEoV6elpXLlyiR9/XI+jo6NJ6l9uPHnymPPnz1KzZm2T60ir1XLr1g2qVPE38ckCCAiohoWFBeHh14v1PfwXycjIICRkIIGBgbi4lCYtLY3Q0H3cvBlOSMgH1K1bD4DIyAhGjAihZcs2eHv74ODgSGxsDKGh+4iIeEhQUCeT3/HY2Bj69u1J7dp1WbbsO2N7ly7d2b17B0uXLuTp06f4+lYgLOwkx44dZuDAoSaV5g4c2MMvv2yiefOWeHqWxcrKkkePItizZxepqSlMnDjVJOJn6NDhTJ48gTFjPqRbtx6UK1eeyMhHbNv2K5aWVgwenHsRDUHR0Go13Lt3D3t7e6ysrNBotKSmJqNQKHBzK230RFMqlUREPMDe3gGZTIZUKkWtVpOSkoxSqcTR0cnER0qlUnH//l1sbW3N/qYYqk0a6s9kZSmMbba2tiYeWhkZ6UaRVaFQAJCUlGiMAnNzy66++/TpE9LT07C1tUMisTBWyzQgl8sLjDgUCAQCgUAgEBQNIVyVUFq3bkuLFq24d++OicdMSMhAJBIL7O1MUynSMhIIDVuB1EpNt6ZVcXMsnNl6UTl8OJTIyAjee+9Ds75Wrdpy8OABLl++aPYlQy9ajefs2TNMnDg11/QwwYvBkHp640Y4Zcr4GNtv375Jaqo+lSs6OqrYhas5c2Zw5Eh2BY+qVaszduxEEzP43Ni1azs6nY7OnU29kVJTU8jKysLdvbTZOTKZDAcHR+MXV8HzY2VlRcWKldi9exexsbHI5dYEBFRlwYKlNGjQyHicu7sH7doFc/nyRY4dO0JGRjqlSpWicmU/Bg0aZlIRtaD5Fi1azqpVywkN3WdM9RszZgJvvtnL5NiaNesQHn6dkyePk5AQj0qlwsXFlXr16vPWW72pUaOWyfFNm7Zg4cJv+OmnDezatZ309DTs7e2pX78hgwYNM0mFFDw/Eonkb//FZNRqNRKJBTY2NpQr502pUtkp7ZaWljg4OJKZmUlqaiparRap1AK53BpXV/d8jfyf5dnfdYVCYRSlXF3dTYSr9PQM4uNNj09IiDf+f07hSqHIBAxiV7rZvK6u7kK4EggEAoFAIChmhHBVgsnNY+bmzZt4uFbEUprtBWMQrVRqBd+uWM6Z7ate2JoMXxY0GvN0HUMKiPqZcqJKpZLJkyfw11+n+fjjKXTq1NXsXMGLo1evPhw/fpRx48YyYsQYKlSoxP37d1myZAGWlpao1WrjF77iZPDgELp27UFSUiLnz5/j7t3bpKQk53uORqNhz56d2NnZ0apVW5M+wxqtrHKPJJTJZC/kffzXsLKyYsaML3BysiUpyTwV0ICTkxPjxk0s9Lienl55euzY29szduxExo7Nfzx//wCmTZtV6DkBXn/9DV5//Y0inSMoGhKJRZ5eYjmxtLQskgedTCbLM52zKGme7u7uuLu7F3wgULFi5UKPKxAIBAKBQCAoHoTH1b+EbI8ZDdUqZX+hN4hWSlUGrRuEEBDgn+cYeXnMFAVf3woA7N2706xvzx59W0BANWObUqlk0qTxnDkTxvjxk8yiaAQvHkPqaXp6OhMmjKZnz0588slY6tatR+PGzQCMqTzFScWKlXjjjQYEBnZg4sQpBAd3Zvz4UVy+fDHPc86cCSMmJpq2bdubRTUYXufld6NUKkUkhEAgEAgEAoFAIBCUMETEVQnE4DHTrFlLvLzKmnjMvP/eCFIi9eKRSq3g4OkVpGcm4OfblJS0WHbv2cuNiOyUCO/Sjtha66OzYmJic/WYAVi3bjWQXZnr7t3bxrbatetSu7a+JH3jxs0ICKhGWNhJPvzwXVq0aA3oOHr0MJcuXaBVq7b4+WWLZ59/PpU//zxFvXr1sba2Zt8+08pOFStWplIl8YT7RdO6dVu6du3I+fOXTVJP3313AFKplHLlyr/wNXTo0JGvv/6SP/74Lc9qktmm7OYCp729A3K5nNjYGLM+pVJJSkoybm51i3fR/yHkMkt0Wp3xdWaGEpml1PhaYiEhSymq8P0b2b17Bzt2bGPFijWFOl4iAbIvFbRaLTkd9H5Y/z1Pnjzmk08+LdZ1llTWrFnJ48eRfPbZzJc+97hxo2jbth1BQZ1e+twvg//n3goEAoFAICg+hHBVAjF4zISG7iM+Ps7EY6ZZk6YsnqX3DcpSZpCWkQDAzQcnADj1TDBL92bVjMJVfqxe/a3J61u3bnLr1k0ABg9+1yhcSaVSFi9ezoYN6zh69BArVixBIpFQrlx53n9/JG+/3ddknBs3wgE4e/YMZ8+eMZt38OB3hXD1ksgt9fTWrZvUrv36S4lUUiqVaLVaUlJScu1PTEzg5MnjVKxYOdc0IAsLC6pU8ef27ZsolUpksuzrOjz8GlqtFn//gBe2/n87Oq3O+NmSGx9Nbf0SV/NqYi3TotOZV8UrLiQSKQplCQiU1kH0U3110KvXLrJoyWxWr9xi7B44YEhOXUvwf2TBgiX/7yUIBAKBQCAQFIgQrkogBo+Zgihl60LfTgtM2sZ82pgfvhyX6/FeXp55eszk1Z4btrZ2DB/+IcOHmxu0P8uvv+4o9LiCl0d26qmWgQOHPNcYarWax48jkcutKVOmjLE9Pj4OV1c3s+N//XUzANWq1ch1vD17dqFWq+ncOW8PtLZt23PlyiW2b/+dnj17G9t/+eUnpFIprVsHPtd7EQgKg06nYX0en6/FwYCJC3jZGf5q9b8vik6n06HT6YxVAwUlA7VajaWluG0VCAQCgeC/iLgDKGHYqSVAVp79Kmzz7BMIcsOQehoYGIiLS2mT1NOQkA+oW7ee8dioqKfs3bsLgPv37wFw8uQxYmKiAX26n8FcOTY2JtfU0wED3qZmzdpUqeKPu7s7SUlJ/PXXGc6dO0PFipXo1eudXNe5e/d2ZDI57drlXXGyS5fu7N69g6VLF/L06VN8fSsQFnaSY8cOM3DgULy8yv6zzRIISgAbNqzj5s3rzJr1lbFt0aL5gI7RoyeQlpbG0qVfc/r0SSQSC4KDOzN06HCkUunfaYFbCQioxp49u+jeveffqcI6Fi78ir17d+Hq6sbYsROpV68+oK/0+dNP64mJicHJyZl+fQfQqEE7FIpMZs2eiEqtok+/IACWLdnAzt0/Efl3+tbTp094660uTJr0GWvWrCQjI4P33vsQP78A5s6dSXR0FO3aBRVozG9Yd5Uq/rmuccSIEGrUqMXFi+e4efMm69dvxsnJucB9yGu8gvdwG9Wq1WDnzj+MxQUaNWoCwJMnj5kzZwY3b96gWrXqeHv75Pm+CsKwf0eOnDaKOiNGhNC+fTCdO3crcC05j9VoNKxYsZQ9e3Zga1uK3r37snDhV8axe/bszMSJU3njjQaAeRre1atXWLZsIQ8e3MPDw5OPPhpn8vcjN0aMCKF69ZqcDjvNkyePqFatFiM+mIi9vQMxMVG89+E7fPLJVL7/fhWenl58882qfOd5nr017OGUKdNZtWoFWVkKevXqw8CBQwGYPXs67u6lCQn5AIDz588yc+ZnbN2qtzbo2bMzb775Fvv27ebx40jatGnH8OEfMnv2DC5fvkjVqtWZOXNukapiCgQCgUAgMEUIVyUNtZrzw4bn2V1j3YaXuBjBvwFD6unu3buIjY01ST1t0KCRybFPnjw2Sxs9evQwR48eBqBmzdoFVgXr2bM3Z86c5vfft5CSkoxcbo23tw/Dh39Iz569sbGxMTvnypVLPHhwn8DADvne/FtZWbFo0XJWrVpOaOg+UlKSKVu2HGPGTODNN3sVdksEghJNYGB71q1bRXp6GnZ2pdBoNBw+fIDZs+cDMHv2NJydXdi8eRsKRSYffzya0qU96NatBwDXr1+jTZt27NixH41GzcGDB7h+/RotW7Zh166DHD16iClTJrBly3YcHBxxdnbhq68W4eVVlosXzzN+/ChKu/tS8bUqTJ3ypVmqYG5cv36VTZt+59KlC3zyyVgaNGjEokXLUavVDB7cl1at2lKnzusFjJH3GgH27dvN/PlLjGLGp59OLHAf8hqv4D28SlBQJ3btCmX79q3MnTuTbdv2IJFImDFjKtWr1+Drr5dx/fpVJkwYTbNmLZ7/B14A+a0lJzt2bOXUqeN8//2P2NjYMGXKx4WeIzY2ho8/Hs2nn86gQYPGnDt3hqlTJ/Ljj7/i7Oyc77l79+5iyqQv8ShdhiXLvmDN2qWMHjXF2H/xwnl+/PFXLCwkBc7zT/b28uWLbNr0GxEREYSEDKRFi9bGgjMFceTIIRYu/AaNRsPgwX25ffsmn3zyKb6+rzF+/Ch+/XUzQ4aEFGosgUAgEAgE5gjhSiD4j2NIPXVysiUpKSPfY+vWrVfotFFPT69cjx08+F0GD363SGusUaNWoec1RBQUFKEhEPxbKVPGkypV/Dl27AhBQZ04f/4v5HJrqlevQUJCPKdPn2Lv3sPI5dbY2NjQq1cftm/fahRdXF3djKm2higeJydnevXqg0QioU2bdmzevJFTp07QoUNHGjduapy7Tp3XqV+/IeHhl6n4WpVCr3nQoGHI5XLq12+ItbUNbdu2x9nZBYBatWpz+/bNAoWr/NYIEBzcmddeqwhQqH3Ia7z69RsWeG6ZMp506dIdgKCgTixYMJeEhHhUKjU3blxn0aLlyGQyateuS5MmzQq9T89DXmt5NmX70KFQevV6Bw8PfWp3//6DuXDhXKHm2LdvN40aNaZRI/218MYbDfH3D+D06ZMFGr93aB+Mj7deIHrn7SGMm/AuIz/8xNg/ZEiI8YHGvn2b85ynTp16/2hvBw9+F7ncmsqVq1CpUmXu3LlVaOGqZ8+3cXFxBfTXq7OzC1Wq6AvRNG/eknPn/ir0OgQCgUAgEJgjhCuBQCAQCP5lBAZ2IDR0P0FBnThwYB+BgR0AfbqvWq2ma9cOxmO1Wh2lS3sYX3t4eJiN5+5e2iRCx8PDk7g4fYXasLCTrF27ikePItDptCgUCjxKexdpvQaRCkAulz/z2pqMjPxF9YLWCJi8x8LsQ17jFeZcg4gBGItbZGZmkpSUhL29vUlkaZkynsZ062cZN24Uly9fAGDChMm0axdU4D48S15reZa4uFhKl872I8zpTd5G/jsAACAASURBVFgQUVFRHD58kJMnjxvb1Go1derknyoIPLPnHqg1alJTk41tBiGtoHni4mKLtLfPknOf5HLrXPcoL569Xs2v38KPJRAIBAKBwBwhXAkE/1HkMkt02uzaXpkZSmSWUuNriYWELOW/z5hZIPgv0KpVW5YtW0RMTDTHjh3m22/XAlC6dBmsrGTs3Bmaj9G1xKwlNjYGnU5nFHKio6No2rQ5SqWSqVM/ZurUGTRr1hJLS0smTRoHOl0eI7048lqjgZwiVGH2Ia/xCreHuePm5kZqaiqZmZlGgSU6Ososbc9AQVX/rK31Y2RlKbC0LAXoo8meB1dXN2Jiooyvo6OjTPqtrW3IylIYX+ecx8PDg/btg5k4cWqR580pLMXFxWAptcTe3pH4+FizY/ObJyrqaZH2trDk974FAoFAIBC8HERJHYHgP4pOq2PxrEPGf4s+P2jyOqeoJRAIShbOzs7UqfM6c+bMwNPTy5jy5ObmRv36DVi2bBHp6WlotVoeP44sMCUsKSmRLVs2o1arOXQolIcPH9CoURPUahUqlQonJ2ekUilhYSc5c+a08TxHJxfSUlNIT097oe83vzXmRmH2Ia/xnncPQR8B5OcXwJo1K1GpVFy6dNEkeqioODs74+5emn379qDRaNi58w8eP458rrFatw7k119/JiYmmpSUFDZu/MGkv3LlKoSG7ket1qc7Hjly0NjXrl0QJ08e588/w9BoNGRlZXH+/NlCRTvt27+HR48ekJWlYNPPa2nUsDlSqTTXY/Obp7j3Nuf7Dgs7SUpKMvHxcfzyy6Z/PKZAIBAIBIKiIYQrgUAgEAj+hQQGtufs2TPGNEEDU6d+jlqtol+/XgQFtWLq1I+Jj4/Ld6yqVasRGRlBx45t+O675cya9SWOjk7Y2trx0Ufj+eyzSQQFtSI0dK9JlFO5st40bdqa90f0pd/ATiQk5D/PPyGvNeZFQfuQ33jPs4cGpk2bxfXrVwkObs3atd/RoUPelVILw8cfT2HTpg107NiG+/fvUaNGrecap3PnbtSv35BBg/owdGg/mjdvZdI/bNh7PH4cSVBQK9asWWlyXXl4lOGLLxawYcNaOnUKpEePjmzatAFtIR6AtG8fzNJvvmTIuz1QqZQMHTIqz2MLmqe499awvkqVqtCzZxfGjh1BmzaB/3hMgUAgEAgERUOi0+n+NWEVsbGp/+8l6LGUMmTWgTy7f/usKZHLcq8u4zh6Fe/vnJJrH8APbWZyfmj+VQUXzTqUZ/+YTxvzw5fjcu0bMHEBWSqrPM8V/LuQWUpZnM+18tHU1ijVmpe4IsGrjLhezImLe4qbW3YVTWuZFp3uxe2BRCJFoXz1nzdJgOinef899vC0p7hvPHbv3sGOHdtYsWLNKzleSeTp0ye89VYXjhw5XeSUyMIyYkQI7dsH06BemzyPeRHXy7+BZz9//guIv0OCoiCuF0FhEddK4XB3t/+/zi88rgQCgUAgKAb0otKrLywJBAKBQCAQCAQlCSFcCQQCgUAgeOWZN28O+/fvMWtv1y6IatVq/B9WJCgMgYHNcm2fPz9/4/niZP/+PcybN8es3cPDk40bf3lp6xAIBAKBQPB8COFKIBAIBALBK8+ECZOZMGFynv3BwZ2Lba7g4M7FOl5JxNPTixMnzv7jcQ4cyNsgfdmy7wpMLS0O2rULol27oBc6h0AgEAgE/2PvvsOiON4Ajn+5O7pSlGov2I0aa+yigqCiqGg0FuwtltijqNEYY4mF2CUaNdi7UQRB7L3H2HunKnDSufL7g7By0g1Gfzqf5/F53N3ZmbndBe7em3lHeH/EnAZBEARBEARBEARBEAThoyQCV4IgCIIgCIIgCIIgCMJHSQSuBEEQBEEQBEEQBEEQhI+SCFwJgiAIgiAIgiAIgiAIHyURuBIEQRAEQRAEQRAEQRA+SiJwJQiCIAifkf379zJkSL98L/u2a9ev0H9Q53c6NzN//PE7s2fP+Fd1hIS8oFGj2qhUqnzq1edrzJgR+Pvv+9DdeG9Wr17Jjz9O+dDdEARBEAQBUHzoDgiCIAjCp6CgkT562vdXv1YPXiemvL8GPiKXLl1gxoyp7Nq1X9rXq1ffD9gj4W3z5y/60F0QBEEQBOEzIQJXgiAIgpAP9LTwxOfCe6u/xMDa761uIWtarRatVotM9v4HqatUKhQK8dYsv4nrKgiCIAj/38RUQUEQBEH4xPj6rqVLl/Y4OTWhR4/OHD16OMuyjRrVZtu2zXTu3J42bVqwdOmvaDQanTJLlnjj4uJI587tOH36pLTfz+9Punf3wMmpCZ07t2f37h0Z6t++cz2efdszaGhXjh4PkvYnJyezZIk3HTu2wc3NmV9++ZmkpEQSEhIYO3YkkZERODk1xsmpMZGRERmmbv311xUGD+6Li0szOnZsw/79ewE4deoEffp8g7NzUzp2bMPq1SvzfP2GDRvIypVLGTKkLy1aNOLFi+fExsYya9aPtG/fCnd3V3x8lqFWq0lOTsbFpRkPHtyTzo+KiqJ584ZERb0C4OTJ4/Tu/Q0uLs0YPLgv9+7dlcp6eLixfv1aPD274uTUGJVKxfr1a3F3d8XJqQndunXkwoVzAGg0Gunetm7dgilTvkepjMnz64PMp00OGzaQvXt3A2+miWZ179OXVavVLFniTZs2LejcuT07dmzVqdvDw43z589K5759L2/fucFEr2H08GzLqLH9uHb9So79HzZsICtWLGHAgF60atWU778fLV2LtNe2b99uOnZsw8iRQwC4du1v6Znx9OzGpUtvAs0vXjxn2LCBODk14bvvhhITE53ra+jvv4+OHdvQpk0L1q1bLR2fOXMaPj7LpO1Lly7QoUNradvDw42NG//A07MrLVs2YtasH3n16iVjxozAyakJI0cORalU5tgPQRAEQfjUicCVIAiCIHxiihYtxrJlqzhw4Ah9+gxgxowpREZGZln+2LHDrF79B6tXb+D48aP4+f0pHbtx4xolSpTEz+8g33zTi9mzZ6DVps6JtLQsxNy53gQGHmXSpKksXryA27dvSedGR7/itTKGVSu3MeLb71mxcgGPnzwCYPnyxTx9+pi1azeyZcsuIiIiWLNmFcbGxsyb9ytWVtYEBR0nKOg4VlbWOv0NDQ1l7NgRdOrUhX37DrJmzQbKlSsPgJGREZMn/0hAwGHmzvVm9+4dHDt2JM/X8MCB/Ywb50Vg4FHs7OyZOfMH5HI5mzfvZs2aDZw/f4a9e3djYGBAkyaOBAUdkM49dCiIGjVqYmlZiNu3bzFr1o+MGzcJP79g2rfvyPffjyY5OVkqf/BgIHPnehMQcJgXL56xc+c2Vq36g6CgYyxYsAR7+yIAbN++mePHj7BkiQ+7d/tTsGBB5s+fk+fXllvZ3fv09u7dxalTx/n99w2sXv0HR44E57qNiIhwZs6aiEenHvyx5k88ew5h7rwfchU4CgjwY+LEqezeHYBcLsfbe57O8cuXL7Fhw3YWLFhMREQ448d/h6dnX/bvP8SwYSOZPHkCUVFRAEyfPpkKFSri53eQ3r374+/vl+vXcPXqFTZt2oG393LWrl3Fo0cPc33ukSOHWLhwKZs27eTkyeOMHTuCQYOG4ud3EK1Ww/btm3NdlyAIgiB8qkTgShAEQRA+Mc2bt8TKyhqZTEaLFs4UK1aCmzevZVm+e3dPzMzMsbOzo0uXbhw8+CYIY2dnT7t2HZDL5bi6tuXly0hevXoJQIMGjShatBh6enp8+WUt6tb9ir/+uqxTd7eufdHXN6BKlRrUqlmPQ4cOotVq2bt3FyNGjMHMzBwTE1N69epDcHBgrl5fUJA/tWvXxcnJBYVCgbm5BeXKVQCgZs3alC3rgEwmw8GhHC1btuLKlYt5vYS0bu1GmTJlUSgUKJUxnDlzipEjx2BsbIylZSG6dPlG6q+TkwsHD77p+8GDATg5tQJSgzrt23ekSpWq0jXU19fn+vW/pfIeHl9ja2uHoaERMpmc5ORkHj58gEqlwt6+CEWLFgNgz56dDBw4FBsbWwwMDOjbdxBHjgS/t2Tz2d379A4dOkiXLt2wtbXDzMycnj375LqNAwf2U+vLetSq+RUymYwa1WvjULY8ly6fyfHcVq1aU6aMA8bGxvTvP4RDh4JQq9XS8b59B2JsbIyhoREHDuynfv0G1K/fCJlMRp06X1GxYiXOnDlJaGgot27doH//IRgYGFCjRk0aNmyc69fQp88ADA2NKFeuPA4O5bh3706uz/Xw+JpChQpjbW1D9eo1qFy5KuXLV/wnINqMu3dv57ouQRAEQfhUiQn/giAIgvCJ8fffx5YtGwkNfQFAQkJCtiNYbGxspf/b2dkTGRkhbRcqVFj6v5GRkVQfwOnTJ1mz5jeePn2CVqshMTGRMmUcpPIFTAtiZGQsbVtb2REZGUF0dBSJiYn069dDOqbVajNMUcxKWFiYFMx52/Xr11ixYjEPH94nJSWFlJQUHB1b5Kre9NJfk9DQEFQqFe3bu0j7NBqtVKZWrTokJydy/fo1ChcuzN27d2ja1FE6199/Hzt2bJHOTUlJ0bnGtrZv2ipWrDgjRozh9999ePjwAfXqfcXw4aOxsrImNDSESZPGIZPpSeXlcjlRUa+wtrbR6f+YMSO4ejU1iDhu3CScnV3zfA2yu/fpRUZGYGNjJ23b2dllKJOV0NBQTp05wvmLp6R9arWaqlW+zPHct59blUql85zb2r7pR2hoKIcPB3Py5HFpn0ql4ssvaxMZGUHBggUxNjbWqS88PCxXryH9dTI0NMr0GmXF0rKQzrlvb8fH574uQRAEQfhUicCVIAiCIHxCQkNDmDt3Jt7ey6la9Qvkcjm9e39DJjO8JOHhYZQpUxaAsLDQDFPzMpOcnMzkyeOZPHk6jRs3Q6FQMHHiGNI3FBv3msTEBCl4FREZRuUqFTE3t8DQ0BBf360ZAi4Aenp6GfalZ2try40b1zM9Nn26F506dWHevEUYGhry66/zczXtLLs+2NjYoa9vwL59BzNN8i2TyXB0dOLgwQMUKlSIBg0aY2Ji+s+5tvTq1RdPz37Ztaaz5ezsgrOzC3Fxscyd+zPLly9iypQZ2NjYMnHiVKpVq5Fj/3Na9S/tniQlJaJQFADIdDRVbhQubEV4eKi0HRYWqnPcyMiYpKREaTt9Oza2tjRt4szQwWPz3G76wFJYWKg0+i6zgJOtrS2tWrVmwoTJGY6Fhobw+vVrEhISpOBVWFhojs9hTrJ73YIgCIIg5J6YKigIgiAIn5CEhAT09PSwtLQAUhOoP3x4P9tzNm3yRalUEhYWyrZtm2je3CnHdlSq1NFMFhaWyOVyTp8+yblzGad3bd66lpSUFG7cvMrFS2do7tgSmUyGm1sHFi1aICUwj4gI5+zZ00DqCJaYmBhiY2MzbdvJyZULF84RHBwkjbJJm1IVHx+PmZk5hoaG3LhxjaCggBxfS06srKyoW7ceS5Z4ExcXi0aj4fnzZ1y+/GYKopOTC4cOBRIY6C9NEwRo164De/bs5Pr1a2i1WhISEjh16gTx8XGZtvXkySMuXjxPcnIyBgaGGBoaIpPJAXB374SPzzJCQ0OA1CTwx48feafXZGlpibW1DQcO+KNWq9m3bw/Pnz97p7qaN3di+/YthIeHoVQqWb9+nc7xcuXKc/BgICqVilu3bujkwGrl7MqFC6e4fOWclOz+2vUrRL6MeLuZDAID/Xn48AGJiYmsWrWCZs1aIJfLMy3r7OzKyZPHOXv2NGq1mqSkJC5dukB4eBh2dvZUqFCJ1atXkpKSwl9/XdEZmfWuypUrz+nTJ1EqY3j5MpKtWzf96zoFQRAE4XMkRlwJgiAIwiekdOkyfP11dwYN6otMpoeLSxu++KJ6tuc0atSUfv16EhcXi6trW9q2bZ9jOyYmpowcOZapUyeSkpJMw4aNadSoiU4ZC4tCFDAtSP9BHhgaGDFowChKliyFFhgyZDhr165i4MA+xMREY21tjbu7B/Xq1adkyVK0bOlMly7t0WjUrF+/TadeOzs75s37lSVLvJkz5ycKFCjAgAFDKFeuAmPGTGDJEm8WLJjLl1/WpHnzllkGwPJi8uQfWbFiMT16dCE+Po4iRYrSvbundLxKlaoYGRkTGRnJV181lPZXrFiZ8eO9WLhwLs+ePcHQ0JAvvqhBjRqZT4VLTk5hxYrFPHr0CIVCwRdfVGP8eC8AOnfuhlarZdSob4mMjMTS0pIWLZxp3LjZO72m8eO9WLBgDj4+S2nTpn2Oz0lW3Nzcefr0Mb17f4OpqSldu/bg4sXz0vH+/QczbZoXrq6O1KhREycnF2m1PFtbO76f8BN/+K5kofdP/+Qmq8igAaNybLdVq9b8/PM0Hj9+TI0aNRk7dmKWZW1t7Zg1az7Lly9i2jQv5HIZlSpVYcyY1HN++OEnZs6cRuvWzalS5QtcXFr/6+emVavWXLhwDg+Pdtjb29O6tRubN2/4V3UKgiAIwudIT5vZ8jD/pyIiXn/oLqRSyOn7U1CWh3dMbcSzJQMzPWb+3W8M2eeV5bnrWszgUr9BWR7/Yq0v3j8dyvL4qCkNWDdnTKbHek2YT1KKfpbnCp8WA4WcX7N5VkZObk6ySp3lceHzIp6XjCIjQ7Cyspe2Cxrpo/ce/6Jq9eB1Ykq+19uoUW02b95FsWLF86U+PSAsJOu/x7b2Bflk3ngImQoJeUHnzu04cuRMplMr03vX52XYsIG0atUaNzf3f9fZ/1Nv//75HIi/Q0JeiOdFyC3xrOSOtXXBD9q+GHElCIIgCPngfQSVBEEQBEEQBOFzJwJXgiAIgiB8dpycGme6f968RVSvnvOKdsL7900PV/RkGROkz5uXfeL5/BQY6M8vv/ycYb+trT3r12/9z/ohCIIgCJ8zEbgSBEEQhM/YiRMXPnQXPoigoH+ffFvInL19kXx5rjau989yquCSJT7/uv7ccHZ2xdnZ9T9pSxAEQRCEzIlVBQVBEARBEARBEARBEISPkghcCYIgCIIgCIIgCIIgCB8lEbgSBEEQBEEQBEEQBEEQPkoicCUIgiAIgiAIgiAIgiB8lETgShAEQRAEQRAEQRAEQfgoicCVIAiCIHxiPDzcOH/+7H/ebkjICxo2qo1arf7P2/7U/fHH78yePeNDd+O9uXb9Cv0Hdf7Q3RAEQRAE4SOk+NAdEARBEIRPgZGRDK32/QVs9PTkJCZq3lv978LDw40JEyZTp069D92VT16vXn0/dBcEQRAEQRA+CBG4EgRBEIR8oNWq+e23395b/QMGDAD03lv9HxOVSoVC8d+8RVGr1cjl8v+krc/Ff3n/BEEQBEH49ImpgoIgCILwCdNoNPj6rqVLl/a0bt2CKVO+R6mMAVKn9jVqVBt//3107NiGNm1asG7dauncpKREfvrpB1xcHOne3YMNG9bRoUNrAGbMmEJYWCgTJozGyakxGzask847djyIgYO/xrNve7bvWJ9jH1evXsnkyeP58ccpODs3xd9/X7b9Hj16ODt2bNGpw9OzG0ePHgLg8eNHfPfdUFxdm9OtW0eCg4OkcjNnTmPevFmMHTuCli0bcenSBU6fPkGPHp1xcmqCu7srGzf6SuVPnjxO797f4OLSjMGD+3Lv3t283gLJ21M4V69eyY8/TgFyvhfpywIEBPjRqVNbWrdOLZe+7pkzp+Hjs0wqe+nSBem+AURGRuDlNY62bVvSuXM7tm3bnGPf0+7R1KkTcXJqQt++3bl7947Oa1u/fi2enl1xcmqMSqXKtp2kpERmzpyGi4sjI77rzb17t3J9DTdu9MXTsyutWjVl6tSJJCUlAbB//16GDOmnU75Ro9o8e/ZUui7z5s1mzJgRODk1ZsiQvrx8Gcmvv87HxcWRb77pxJ07ueuHIAiCIAj/HRG4EgRBEIRP2Pbtmzl+/AhLlviwe7c/BQsWZP78OTplrl69wqZNO/D2Xs7atat49OghAL///huhoSFs3bqHhQuXEhjoL50zZcoMbG3tmDNnAUFBx+ne3VM6dvPWNRb/+gfTps5n6/Y/ePbscY79PH78KM2atSAg4DDOzi7Z9tvJqRUHDx6Qzn348AFhYSHUr9+IhIQERo36FicnF/buDWTatJ9ZsGA2Dx7cl8oHBQXQq1dfAgOPUa1aDWbPnsG4cZMICjrGH39soVatOgDcvn2LWbN+ZNy4Sfj5BdO+fUe+/340ycnJ73Anciere5Hew4cPmD9/NlOm/Mju3f4olTFERITnqn6NRsP48aNwcCjPrl3+eHsvY+vWTZw9ezrHc48fP4qjYwv8/Q/RsqULkyaNRaVSSccPHgxk7lxvAgIOI5PJsm3n999/4/nzZ2zdupupk+dy+OiBrJrN4PDhIObPX8y2bX9y//5d/P335uncAQOGsG/fQfT1DRg8uC/ly1fAz+8gzZq1YPHihbmuSxAEQRCE/4YIXAmCIAjCJ2zPnp0MHDgUGxtbDAwM6Nt3EEeOBOsEHPr0GYChoRHlypXHwaEc9+6ljqQ5dOggPXv2wczMDBsbWzw8uuaqzS6dPTE0NKR0KQdKlSzLo8f3czynatVqNGnSDJlMhqGhUbb9btrUkbt37xAaGgJAYKA/TZo4YmBgwKlTx7Gzs6dNm3YoFAoqVKhI06bNOXIkWGqrUaOmVKtW45+2DJHLFTx8+IC4uFjMzMyoUKEiAHv37qJ9+45UqVIVuVyOq2tb9PX1uX7971xf/7zK6l6kd+RIMA0aNKJGjZoYGBjQv/8Q9PRyN4305s0bREdH0afPAPT19SlatBjt2rkTHByY47kVKlTC0bElCoWCrl27k5ycpHMtPDy+xtbWDkNDoxzbOXToIL169cXMzBwrKxvauHbM5RUCD4+uWFlZY2ZmTsOGjXVGfuWkSRNHKlashKGhIU2aNMPAwABX17bI5XJatHDm7t3bua5LEARBEIT/hkhAIAiCIAifsNDQECZNGodM9iawIZfLiYp6JW0XKlRY+r+hoREJCQkAvHwZgY2NrXQs/f+zY2lRKF19hiQkJuR4ztt1Z9dva2sb6tdvxMGDB+jRozfBwYGMH+8lnXfjxjVcXJpJ56nValq1ejNV7u22Zs6cy7p1q1mxYgkODuUYPHgYVatWIzQ0BH//fTrTElNSUoiMjMjQ/z/++B1f3zUAODu7Mm7cpBxfc2ayuhfpRUZGYGNjJ20bGxtjbm6eq/pDQ0N4+TLyreujoXr1Gjmem/66yWQyrK1tda6Fre2b4zm18/azZW2du2cLMl6jyMjIXJ9raZn+2TR6a9sw0+stCIIgCMKHJQJXgiAIgvAJs7GxZeLEqVSrljEwERLyIttzCxe2IiIinNKlywAQHh6mczy3o3xy4+26sus3QMuWrViz5jdq1KhJUlISNWvWls6rUaMm3t7LMj0vs7YqVarC7NkLUKlU7NixhalTJ7Jzpx82Nrb06tUXT89+WdT0Rq9efXNc+c/IyJikpERp+9WrlznWm5nCha14/PjNFMLExERiYmJy1Y6trS329kXYvHlXnttNf/81Gg0REWFYWVmnK/HmuubUTuHCVoSHh1G2TFkAIiNzN9UxO2+/7pcvcx/QEgRBEATh4yWmCgqCIAjCJ8zdvRM+PsukaXVRUVEcP34kV+c6OrbE13cNSqWSiIhwduzYqnPc0rIQL148z+8uAzn3u379hoSGhrBq1QpatHBCJkt9S9OwYWOePn1CQIAfKpUKlUrFzZvXM80VBakjqAID/YmNjUWhUGBqairV1a5dB/bs2cn169fQarUkJCRw6tQJ4uPj3uk1lStXnoMHA1GpVNy6dUNn+mJeNGvWglOnTvDXX1dISUlh1aoVaLVanXZOnz6JUhnDy5eRbN26STpWqVIVTExMWb9+LUlJiajVah48uMfNm9dzbPf27ZscPXoIlUrF1q0b0dc3oEqVLzItm1M7jo4tWb9+LUqlksiXEez3z3sg7W0ODuV4+PABd+/eJikpid9/9/nXdQqCIAiC8OGJwJUgCIIgfMI6d+5Go0ZN/klY3oRBg3pz40bOQQpIzbdkY2NDly7t+O67oTRr1hx9fX3peM+evVm3bjUuLs10VuL7L/ptYGBA06aOXLhwDicnF2m/iYkpCxcuITg4EHd3F9q1a8Xy5YtJSck6oXpAwH48PNxwdm7K7t07mTJlBgAVK1Zm/HgvFi6ci6urI127urN/f+4Tgb+tf//BPH/+DFdXR1avXqnT77woU6Yso0dPYPp0L9q3d6FgwYJYW9tIx1u1ao2DQ3k8PNoxevQwWrRwko7J5XLmzl3I3bt36Ny5PW3atGT27J+IjY3Nsd3GjZsSHByEq2tzDhzYz8yZv6BQZD54P6d2+vYdiK2tHZ27tOPHGeNo2sQp03ryokSJkvTu3Z/vvhtKt24dsxytJwiCIAjC/xc9bfqv6PJJXFwcvr6++Pn58ezZMwwMDChdujRdunShQ4cOOkP0Hzx4wLx58zh//jwpKSlUrlyZ4cOHU79+/Ty3GxHxOj9fxrtTyOn7U1CWh3dMbcSzJQMzPWb+3W8M2eeV5bnrWszgUr9BWR7/Yq0v3j8dyvL4qCkNWDdnTKbHek2YT1KKfqbHhE+PgULOr9k8KyMnNydZpf4PeyR8zMTzklFkZAhWVvbStpGRDK32/V0DPT05iYma91Z/buzatZ3g4ECWLMl6JIseEBaS9d9jW/uC5PsbDwEPDzcmTJhMnTr13kv9q1ev5PnzZ0ydOiNf6xXPy7t5+/fP50D8HRLyQjwvQm6JZyV3rK0LftD28z3HlUajYcCAAVy+fBl3d3d69OhBQkICfn5+TJw4kfv37zNu3DgAnjx5Qrdu3ZDL5fTv358CBQqwbds2+vfvz2+//UaDBg3yu3uCIAiC8F6kBpXyL+dTRv990CoyMpIXL55RtWo1nj17wubN6+nUqct/3g9BEARBgNQgNXm5nQAAIABJREFU+po1v2V5XC6Xc/ToWQA27Mv8y/rUYzBgwJBc5TAEePjwAevWreby5QsolUosLCypWLEy48ZN1Fkw4m1TpnzP4cMHKV26DL6+WzMcj42N5bfflnH06GGUyhiKFClGp05dcHfvlK95JAXh/12+B67++usvLl68iKenJ5MmvVlR55tvvsHV1ZUtW7ZIgav58+ejVCrZuXMnlSpVAsDd3Z22bdsyffp0AgICxA+sIAiCIHwgKlUKv/zyMyEhLyhQoCAtWjjToUPnd6prxswJ3Lx5FT2Z7t/1nj375JjUXPhvjBkzgqtXL2fY37Nnn/+sDxERYYwc1VvaTv+8+Ppuw87OLpOzBEH4XDRt2pxixYpn2H///l02bvSlYcMm0r4GNb7JtI6rdw4QG/9Sp2x2zp49zcSJYylatCgeHl2xtCxEVFQU169fJS4uLsvA1cmTxzl69BCGhoaZHk9JSWHUqKHcuXMbD4+vKVmyNGfOnGL+/Nm8evWSftnMshGEz02+B67SchfY2Njo7DcwMMDS0pLk5NQcE/Hx8Rw6dIi6detKQSsAU1NTPDw8WLRoEX///TfVqlXL7y4KgiAIgpALdnb2mX5D/C6meM0BxNSv92X79nfPvZVm/vxF+dCTf8fa2paN6/2lbfG8CIKQnoNDORwcymXYP3fuTADatm0v7StdrFaGcvEJ0cTFv6JSpcqZ1vO2qKhXTJ8+mS+/rMWcOQuyzOuXoZ34eObPn03Hjp05ceJYpmX27t3NzZs3+O67sXh4dAVSFwXx8hqHr+8a2rRph53d5zUlWBCyku/J2atVq4aZmRmrVq3C39+fFy9e8ODBA+bPn8/169cZPnw4ALdv3yY5OZkaNTImzkzb9/fff+d39wRBEARBEARBEIRPRGJiIsHBgVhb21CvXvZ5ku8/PY8WLe3bdchV3bt370CpjGHo0BEoFAoSExNRqVQ5nufjswy1Ws2AAUOyLBMUFICRkRFubrp96dLlG1QqFcHBgbnqoyB8DvJ9xJW5uTnLly/Hy8uL7777TtpvamrK4sWLadmyJQDh4eEA2NraZqgjbV9YWFh+d08QBEEQBEEQBEH4RBw6FERcXBweHl2Ry+VZltNqtTx4dg653ABn59yt6nr69ElMTU2JjX1N797fcO/eHWQyGVWrVmP48FFUqlQlwzk3blxj586t/PDDTExNC2Rar0aj4c6dW5QvXzHDVMJKlaogk8m4efNGrvoofDh5ybmW5tSpE2zZspHbt2+SkpKMtbUtdevWY/ToCbluNzc515RKJQEBfpw+fYLHjx8RHR2Nra0tNWrUonfvftja6k69P3XqBHv27OD+/XtERb1CX9+AIkWK4OLShvbtOwGfWHJ2ABMTE8qXL0/z5s2pWbMm0dHRbNy4kTFjxrBs2TIaNmxIQkICkDqF8G1pP7xpZXLLwsLk33c+Hyjjs15yOyc5ZfR6nxm/ZHp6H801FN6/hByeUz2ZeB6EN8TzklF0tAy5PN8HLv/f02hySCKvB3KZuG5CKvG8vBuFQvbZ/c4Vf4eErAQE7EVPT49u3b6WnoHMnpewl3eJjX9FmWJ1KFiwIMYmGT+Hvu3ZsydoNBrGjh2Bs3Mrvv12KM+fP8fHZyUjRgxm06bNOlMOVSoV8+bNokGDBnTqlDptUSbTQy7X/ZmNjo4mKSmJIkXsM3luTbCwsCAq6qV4pv8D/+Z3S9u2rSlfvmyG/Xfu3GHNmt9p1sxR59xly5aybNlSGjZsxLBhwzAyMiY0NIQ7d27n+l6fPHmCESOGU7x4cXr06EnhwoV59eoVf/11BblcLdVz7dpFli71pl69r+jevTsWFpbcu3eXbdu2cuTIQdav30DZsg5SvS9ePMbQ0AAPDw+srKxJSkrk0qWLLFq0gHPnTrN27ZoPmn883wNXt2/fpmvXrkycOJFu3bpJ+9u2bUvbtm2ZMmUKQUFBGBsbA0g5r9JLSkoCkMrkVnR0/L/oeT5SZB3pz0lOeRzeZ54HjVb78VxD4b0zyOE51WrE8yC8IZ6XjFQqDWr1f7/S38cux7c0WsR1EyTieXk3KpXms/udK/4OCZl58uQRly5dolatuhQoUEh6BjJ7Xu49SR35UrZE3Vw/L3FxcajVapydXRk3bjIAdepAiRJlGTFiMIsWLeHHH2dJ5X191/D48SNmzJgj1a/RaFGrdX9mw8Je/fM/Wab9UCj0iYuLF8/0f+Df/G6xtS2OrW3GxQJOn0591lq1aiude/78WZYtW0r//oPp3bt/hnNyc6+jol4xbtw4atTImHPNw6O7Tj2FC9uzceMOihYtJpVp0cKVmjXrMWrUtyxc6M1PP83VOT+tjjRt2nTExMSMXbu2ffD84/n+FdbatWtJSkrCxUV3+KWxsTHNmjXj+fPnPH/+XErentl0wLR9mU0jFARBEARBEARBEIR9+/YA4ObWPttyScnxPA29hlkBG2wKlcl1/QYGqTOBXF3b6uyvWbM2trZ2XL58Udr37NlT1qxZRa9efXWCBZkxMjICUlcWzExycrJURvj/klXONV/fNVhaFqJHj95AagL/HEcdvyUvOdfs7Ytk+hzWqVMPMzNzHjy4n6s201bzjYmJyVNf81u+B67ScldldhPSLqpKpaJ8+fIYGBhw5cqVDOXS9lWtWjW/uycIgiAIgvDJ+OWXn1m7dtWH7sZ747d/L0OG9PvQ3RAE4SOkUqkICNiPmZk5TZo4Zlv20fOLaDQqyhavm6c20gZbFC5cOMOxwoWteP1aKW0vWbIQMzMzmjRx5Nmzp9I/tVpNSoqKZ8+eEhkZCUDBgmYYGhoSERGeod7k5GSUyhisrKzz1Ffh45CWc611azcp51pCQgJ//XWZypWrsG/fHtzdXXF2boKTU2N++GEir169zFXdb+dca9myEc2bN2Do0P7cvHk9V3XExsYSHx8n5cJ6W3x8HNHR0Tx//oyAAD82bPgDc3NzqlevnrsL8J7k+1TBsmXLcuLECXbu3MmAAQOk/UqlkuDgYMzNzSlRogQKhQJHR0eCgoK4desWFStWBFKHY27fvp1SpUp90KFogiAIgpAXBUxBD/V7q1+LnNi491b9f8Zv/1727t3N8uWrP3RXPgnjxk360F0QBEH4IE6ePM6rVy/p3LlbpnmT07v/9BwyPTllitXOUxuVKlXh8eNHhIeHU6aMg86xiIhwLC0LSduhoaFERkbQs2eXTOvq2rUDDRo0Yu5cb2QyGeXLV+Tu3dskJyfr9P/mzetoNBoqVqyUp74KH4d9+/agp6dHmzbtpH1pAczr169x/vxZunf3xMGhPFevXmbbts3cv3+PVat8cxxl9+TJY9RqNWPGDMfRsSW9e/cjJCSEdetWM3z4IHx81lGmTMacW+mtW7calUqFi0ubTI///PN0jhw5JG1XrlyV0aMnYGZmloerkP/yPXDl6enJnj17mD9/Pnfu3KFmzZrExMSwdetWIiIimDp1qjQXc8yYMZw5c4a+ffvSu3dvTE1N2bZtG2FhYaxcufKDJv8SBEEQhLzQQ831EzPfW/1VGnkB755D8WOgVv+7wJ5arc52xaj8otVq0Wq1yERS8HylUql08nEIgiD8G35+qdME27bNfprgy+inRClfUNzuC4wMM18ZTaVS8fz5MwwNjaSpUQCtWrUmIMCPPXt28NVXDaT9J04cIyIiHDe3DtK+b78dSWzs6wx1z58/BwMDA4YPH0XhwlbS/pYtW/H333/x55878fDoKu3funUjcrmc5s2dcrgCQk5ys+rf6ZMXALh6+wB/3w3MUGbDPhg6dCTffNMzx/YCAvy4evUKxsbGeHp2w9DQgOLFS1C7dupIv+joKCZMmIybmzsATZo0IyQkhGPHDtO2bUv09GTY2trRooUTXbp0y7AqZXx8HBqNBkNDQ4KDA7l58wadOnXh559/YcSIwaxdu0on59rbDh8+yObN66lbt75OYC29Pn0G0r59J6Kjo7h06SL3799Fqfyw0wThPQSuihYtyrZt21i6dCmnT59m//79GBoaUqlSJb7//nucnZ2lsiVLlmTTpk3MmzcPHx8fUlJSqFy5MqtWraJBgwbZtCIIgiAIQlY8PNzo2LELBw74ERoaQr16DfDymiat2vvnn7vYsGEdSqWSatWqM27cpGynJGi1WhYvXkBgYAApKcnY2dnzww8/UaaMA7GxsSxcOJezZ09haGhEOzd3Wjl1RiaTcehwAEHB+yjnUIkjRw5QtWoNLl0+i0qlwsmpMXK5nICAI1m2O3Nmap9DQ0O4cuUSs2bNp3r1L/HxWcahQ0GkpKTQpEkzRowYjaGhEd27ezB06EgaNmwMpH4Qad++FQsWLKVChYpcu/Y3S5Ys5NGjB9ja2jNy5Bhq1kz99n3YsIF88UV1rly5yO3bt/njj81cvXqFNWtWER0dhYWFBQMGDMHZ2RVI/UZ10yZfXr58SeXKVRg/3gs7O/t3ul+NGtVm8+ZdFCtWXHrd1tY2DBw4lEuXLjBjxlS6dPmGDRvWIZfLGDjwW+kNZ/qyABs3/sGWLRsAPQYMGMLs2TOkuocNG0irVq2lN8z73xr99vjxIxYunMvt27ewsLCgf/8htGiR/QenmTOnYWBgwPPnz7h+/RoVKlRk8uTp0rVo1Kg2o0aNZ9u2TajVarZt+zNDO108etOwQeo0n9evY1i8dC7Xb1yhaJESNGzUMNfXcOzY79m8eQPR0dE4ObkwevR49PT0WL16Jc+fP2Pq1BkAhIS8oHPndhw5cgaFQsGwYQOpVq0Gly5d4P79u3z5ZW28vH7A23seJ08ep0SJksyYMRt7+yJ5uKvCx+BdlqpPs3PnNhYsmAPAvn0HsbCwyFWbt2/fYs0aH65e/YvExASKFi2Gm5s7nTp9nWngXaVSsWvXdvz99/LkyWPkcjlFixajXbuOuLt3ksoNGzaQK1cuZdlu7dp18fZelqs+fgoiIyM4e/Y0lSpV0VkZLTP3n6YlZa+XZZmIiHC6d/egRo2aLFniI+2vU6ceLVu24uDBA4wdO4IGDRoTGhrCjh1bKFzYin79BuqUzczSpb9ibGyMo2NLnf3t2nVg//69LF68kJCQEEqVKs3p0yc5duwwnp79KFKkaI7XQche06bNpb+t6d2/f5eNG31p2LBJhmO1KrfH0MBU2m7VvjJly1XIVXvLly8G4IsvqtO0aXMSExMIDg5izZrUKf0ymYxWrVpL5X18lnHs2GEAihcvSfv2Hbh8+SKrV6/k9OmTrFz5ZiW/9PnQvvqqIfXq1efMmVPMnz+bPn0GZMi59rbTp0/w449TqFChEjNmzMpykFDZsg7Sz5STkwu7d+9g7NgR2Nmtp1atWrm6Du/De/naq0SJEsyZMydXZcuWLcvy5cvfRzcEQRAE4bN1+HAQ8+cvxsDAgCFD+uHvvxd3dw8uXjzPypVLWLBgKaVLl2HpUm9++GESS5dm/eHu3LkzXLlymU2bdlKgQAEeP35EgQKp31ovXDiXuLhYtm7dQ0xMDKNHDUNfUYCWLVKHoN+9e5NGDZuzZvUuVCoVf984naepgkFBAfzyy6/MnetNSkoKy5cv5sWLZ6xduxGFQsG0aZNZs2YVgwcPkz5cpAWuzp07g7m5BRUqVCQiIpzx479jypTp1KvXgIsXzzF58gQ2bNiOpaUlAAcO7GfevEWUKFGSxMREvL3nsWrVOkqUKEVkZKT0jeOxY0fw9V3DnDkLKVasOOvXr2XaNC9WrPj9ne9Xdl69eklcXCy7d/tz/vwZJk+eQOPGzTIM2z9z5hSbNq3n11+XYW9flDlzfsp1GwkJCYwa9S39+g1i3rxF3L9/j9Gjv6V06TI5TjsIDAzgl1+8qVy5KsuWLWL69Mk69/f48SP4+KzF0NAwQzsP7t9j5HdDKV68FCWKl8Zn1a8YGOiz2mc7YeGhzJw1IdcBo1OnTvDbb38QFxdH//49aNiwsc4IiewEBweyYMESzM0tGDy4D4MG9WXMmAl4eU1j1qwfWbPmNyZN+iFXdQkfj3f50AqpQZGVK5dgbGxCQkLuV3W7cuUSo0cPw9S0AB4eX2NhYcn582dZtGgBDx8+ZMIEL53yKSkpTJgwmsuXL+Dk5EL79p1Qq9U8e/aEsLBQnbKenn2loHN6wcFBnDp1PMvX8qnav38varU602uSnkqdwqPnlzExMqeIde6CD2+bPHk6Dg7l8PP7k0WL5lOgQEGaNWvBwIFD/1UeKn19fby9l/Hbb8s4ePAASmUMRYsWY9SocXTsmPl0QyFvHBzK4eBQLsP+uXNTR8hnNlqvmF1VCpi8mQLq6tqcZFXOI8ZVKhXJycmYmZkxe/YCafpn587dGDKkH9ev/42Jiam0X6VSsW3bJhwcynPv3h0sLCxwd/fA3d0DuVxBYKA/9+7dodw/QbO9e3dLecT79RtImTIOtGvXAS+vcfj6rqF06bI8fJh5wvUzZ07h5TWe0qXLsGDBkgwjubLj4tKGBQvmsHnz5k8vcCUIgiAIwofl4dFVekPdsGFj7t69A0BgoD9t2rSjQoXU3JKDBg3D1dWRkJAXWQYIFAoF8fHxPH78iMqVq1CqVGkgdereoUNB/P77BkxMTDExMaVr1+7s379fClwVsrSijWtHgHea5teoUVOqVasBgIGBAXv37mLdus2YmZkD0KtXH6ZPn8zgwcNwcnKhT5/uJCYmYmRkRFBQAE5OqascHziwn/r1G1C/fiMA6tT5iooVK3HmzElptajWrd2kII1cLkcm0+PBg/vY2tphZWWFlVXqFI89e3bSs2dv6Tr06tUXX981hIaGvPOoq+zI5Qp69+6PQqGgfv1GGBub8OTJY6pW/UKn3KFDQf+8htRvSvv2HcjBgwdy1capU8exs7OXRnJVqFCRpk2bc+RIcI6BqwYNGlKjRk0ABg4ciotLM8LCQrG1TZ1u07NnH+l+BQcHZmjnq3pNOH3mGEWLlODM2WMsnP87RkbGlCxRGlfXttmOMkmve/feFCxYkIIFC/Lll7W5e/dOrgNXrVu7Sasv1avXgEePHkqjJxwdW7Jq1Ypc1SN8XN7lQyukTu0qUqQoZcqU5cAB/1y35+09Dz09PVas+F16njp27MzcuTP5889duLi0oXr1GlL5tWtXcfHiORYuXCqN/sxKnTpfZbp/3brVGBgY0KqVa677+Sno1asvvXr1zbGcQq5PF5ecp/Hb2xfhxIkLmdehUNCjR29pNbi82r59b5bHChYsyOjRExg9esI71S3kXVar/qWXkpKIXK6PTJb79y0nTx7n9WtlhpxrMpkMJ6dWXL/+N7GxsdJ7FLVaRVJSEgUKpAaR0udLS3u/YWRkLO0LCgpAJpOh0Wh0cq516fINR48e5sWLZzp1pDl79jSTJo2jRIlSeHsvy3OuquTkZDQazQdfVVAErgRBEAThE5R+tRhDQyNpJaPIyEjKl68oHTMxMcHc3IKIiPAsA1e1atWhU6cuLFgwh/DwUBo3dmTYsJEkJSWRkpKiE6yxs7Pn1atIabvwv1wVycbGVvp/dHQUiYmJ9OvXQ9qn1WqlbyCLFStOqVKlOHnyGA0bNuHEiWOsWbMBSE2ae/hwMCdPHpfOValUfPnlmw+L6dsyNjZm+vRZbNrky+zZM/jii+oMGzaKkiVLERYWwq+/zmfJEu90/UidavJ24OqXX34mMDD1g2/Pnn1y9UHrbebm5jq5oYyMjDIdBRIZGUmFCm+S+eYliBYaGsKNG9dwcWkm7VOr1TpTGrJiY/MmH4yJiQlmZmZERkZKgav01zWzdlQpKpo2cUapjEGtVmNV+M0zY2f7pu6cpF/1K6trlBXdnxdDChUqpLOdl7qEj1tOH1qPHj3MyZPHWLHid3bt2p7repVKJffu3aFu3foZlqBv3dqNP//cxf79f0qBq4SEBLZt20yjRk2oWbM2Wq2WhIR4TExMM6s+U3/9dZknTx7TooWzFBz+VChMtKi1WY9ykevJUcWLfMifu3eZDpy26l/VqtVo2jT1C4pOztOl437H5qFSJaGnJ6OwRXHqnDKgdt3U3xWRkZHs2LGF27dvcefOTaKjo3F1bYuX17RMc66pVCo2bvyDzZvX/7NHy6BBvfnxx9mULFmK6tW/lL6cKV++IiEhL7h8+SI7d26jRo2adO/ugUajYfr0n7lz5xYlS5bi4cMHOjnXKlWqgp6eHnFxcTRv/iYtE6SOPJ84cSzFi5fg11+XZft74uXLSJ0cbGm2b98M8OmtKigIgiAIwsfLysqKsLAQaTshIYGYmGisrW2yPa9z56507tyVqKhXTJnyPRs3+tK370AUCgWhoSGULl0GgLCwUAoVevPGRw/dDxZ5XXglfXlzcwsMDQ3x9d2aZX/TpgtqNBpKlSotTROytbWlVavWTJgwOVdtAdSrV5969eqTlJSIj89y5sz5iWXLVmFjY0uvXn2lfFfZGTduUo4r/xkZGZGYmChtv3z5Msf7kRkrKyvCw8Ok7benGhkbG2doJ42NjS01atR8pxw54eFv2omPj0epVErfFoPudX27HT0gLCQ1mXFa8v3IlxEUK1oi09fwLrJ73cLnJ+1Dq4dH1wyjQOPiUnP2tWvXkcqVq+YpcJWSkgyQ6apghoap+65fvybt++uvy8THx1GhQiW8vefh5/cnCQnxWFhY4ObWgX79BuW4mMG+fakflHOaLvf/SK1VM9Qv69+dy9r8jPgoK7zLdOC0Vf9Sk6i/mQ5soG+MQ4mvsLYshYG+Mcq4CG49OMaoMSOYOHEqrVu78eTJI3x912BjY0vFipU5c+YUkHnONa1Wy/ffj+bMmVPI5XLMzMzR04P79+/h6dmVHj166wSSlixZyJIlC9HT06NDBw927twmLRKTmJhAUlISpUuXpWzZchlyrmm1WhQKhU7OtVu3bvD992MALW3auEl9TS/9l1O9en1NtWo1KF++ItbW1kRHR3P+/DkuXjxH2bIOeHp6vsMdyj/ip10QBEEQPiNOTi5MmzYJJycXSpYszcqVS6lcuWq2eYRSl+bWUqFCRYyMjDEwMEQmk0mrHvn4LGPKlOkolUo2b9lAm9ads6yrkGUhIiLCSUlJQV9fP099l8lkuLl1YNGiBYwePR7Lf+p68OC+NHKiRYtW+PgsQ6lUStMEAZydXRkwwJOzZ09Tu3ZdVCoV16//TbFixXVGBKV59eolN25co1atuhgaGmJiYiK9gWzfvhOrVi3HwaE8ZcqUJTY2lnPnztC8ecsM9eRGuXLlCQoKoHTpMpw/f5YrVy690zLojo4tmTXrR1xc2mBvXyTDt9AODuU5evQQbm7uREZG4Oe3R5pW0LBhY1asWEJAgB8tW7YC4O7d2xgbm0hTItNERETw8mUEADEx0Vy6dIG9e3dRunRZtm/fQunSZaTRVgAvXjwjMTEBtVqNnZ09jx49YPfu7bRt644ecPfeLYyNjClWrCT16jZmy9a1DBs6nvDwUPwD/LC0LMStWzeyfN3pR6hotVrCwkKJjX2NXC7n9u2bmJqacvnyBR4/fkThwlasX79G53yNRkNU1Cvu3r2NRqMhOjqKpKQktFqtWOE6H+RlRMSmTes5efIYT58+RqlUYmZmRokSpfDw6IpTi9z9fOWUxLxAgQI6S9UHBwdx9uwpbt++xYMH99BqtTpJ0TPz8OED1q1bzeXLF1AqlVhYWFKhQiXMzMy5fv1vkpISpWAVwOXLqVPQ0geWnzx5DMDWrZvQ19dn6NARmJmZExTkj6/vGiIiwpk8eTpZiYuL5fDhg9jbF6VWrTo5XxhB+ATldTrwkyePuHr1ChYWFlhb2+hMB65YJmOQq2zxuhy+uIhFixbQrFkLKlasxN69QVhaWhIdHU3btqm/lzLLuXb8+FHOnDmFhYUlr18rmTVrHiVLlubXX+cRFBTAunWrsbGxxcGhPKVLl6FBg0bo6elx5Mghdu7cBqR+2RMaGiIlZtfX12fixKkZcq4ZGRlha2uvk3PtwYP7JCcnAbBo0YJMr1/6wJWHR1fOnTvDzp3bUCpjMDQ0okSJkgwa9C0eHl2lKY0fighcCYIgCMJnpHbtuvTvPxgvr/G8fv2aL76oxvTpP2d7TlxcHIsXL+DFi+cYGBhQt259unVLXRb6u+/G4e39C126tMfAwBA3N3daOGY9EqlWrTqULl2Gdu1aIZPp4ecXnKf+DxkynLVrVzFwYJ9/RopZ4+7uIQWurKysqFq1GleuXNJZEtrW1o5Zs+azfPkipk3zQi6XUalSFcaMmZhpOxqNls2bNzBjxlT09PRwcCjPmDHfA9C0qSMJCfFMmzaJ0NBQChQoQO3add85cDVy5Fh++mkaO3duo0mTpjRu3PSd6qlfvyGdO3dj5Mgh6OmlriqYNk0R4Ouvu3Pr1g3c3JxxcHDAycmFCxfOAanBn4ULl7B4ceo3vhqNFgeHcgwfPipDOwULFpTydxgbm+Do2JIDB/y5desGxYuXZOTIsTrlFQp9LC0LIZcrUKvVjB8/GV/fNfj4LAP0KFG8DH08U1dFHNBvJEuWzaHvgE4ULVKC1q3duHDhLPb2GVfXev1aSWzsa2mhAEgNXCUmJiCXKzAxMcHW1o5Chay4ePEC/fv3wsLCkp49e3PixDEgdWnxpKREkpNT/umjHLlcTmJiAqGhodjb53/Oss9NXkZE3Lx5HXv7ItSv3xBzcwuUSiWHDx/Ey2scTx4PAbJfPQ5yTmIeGxtLrVp1pRXbdu3axo0b1ylSpCharRYAU9Osp+udPXuaiRPHUrRoUTw8umJpWYioqCiuX7+Kq2sbtmzZyKRJ4+nffxDm5hZcuHCO1atXIpfLSUp6M/IvPj4OSH2O//hjCyVLlgKgRQsnhg8fRECAH927e0qjWd8WFHSAxMRE2rRxEwFWQUgnu+nAaaMUY2JimDvXO8dRlYYGpnTs4MFvq1Zw7dpV6tb9KtPpvJmdDquoAAAgAElEQVTlXDt//uw/bUUzefJ0qlf/EoAffviJmJhozp07g0wmo2TJkvzww5vFVL74ojrHjx9Bo9HQvn1HVq5cKn3Rl5KSkmnOtbZtnShY8M3fQkidoty6tVu2ry+9Pn0G0KfPgFyX/6+JwJUgCIIg5AMtcqo08sq54L+oP7feTgTbr98gne20VWtyq3btuqxbtznTY2ZmZkydOkPaTj/1q7mjC80dXXTK6+vr88svv+aqXS+vaRn2GRoaMmjQtwwa9G2W5/36a+arFVepUlVnmfP03t5vZWWVZVlIXWXHxaVNlsfzomLFyqxfvzXTYzVr1mbXrv06+9Lf37evUc+evenZs7e0PXv2m3tjYWHBwoVLdcqnfzZKlCiVq3tjZGQkTYcyMDDA2toGL69phIaGEB0dpROkyCzZcf36DbG3L0JKSjLly1ck/J/nBVKng3pNTBdwtC/IgAFDMu3Hy5cR6OnpYW5uptNOqVJl+Okn3dWtp0z5kfv372Fqakrx4iVo164DAE+fPmH8+MmULl1GCsaNGfO99FrMzc2pU6ceW7bszvG6CJnLy4iI9MHmNF26dKNfv574rl9He8fpyPRk2baXXRLztMTGbm5v2pw8+UfMzc0ZMMATGxtbnVFRb4uKesX06ZP58stazJmzIMNUPo1Gg6GhEZs3b2DAgNRpNcbGJgwfPgofn2Wo1W9yNqWNyKpcuaoUtErj4tKGy5cvcuXKpSwDV35+e5DL5dLIsU9FTiP0kOlRfZojkD8j9LLLWZQZrVZLUNABdu7cytOnj0lOTsHW1o4WLZzo0qVbhtXaYmNj+e23ZRw9ehilMoYiRYrRqVMX3N07iYDje5LVdGCVSoW//z709PRo29Y919OB077AiI6OznUfkpKSOHnyKJAaEHo7X2Taz39IyAu+/XakzrH58+dgZWVNWFgoT58+AVKTtBsaGhIREZ6hreTkZJTKGKysaua6f/+PROBKEARBEPJBbByQh+CSIHxKNBoNSmUMCoV+jsts6+npoVAoSEpKlEa45FV8fDzJyckULGiGXJ7z21mFQoFMpodG8yZwoFarSUpKxNS0gM4KUJCaED86OoqYmGhMTEzeqY9C1nKzqlcahUKBtbV16jQ+jRrk2QeuMpOWxNzAwAAjI2OaNHGUjtnZ2bFlywaePHnEV181JDw8jJCQF6jVamlUVEjIc+LiYgkM9EepjGHo0BEoFAoSExNRKBRSAEsmkzFw4FB69uzD/fv3AC0ODuXRarX88svPVKnyZiVQG5vUPHbpFxVIk5Yg+fVrZaav5/79e9y8eYMGDRq9Uz68j1naCD09AzVrr2yT9ieExhJx8gnmFd7kz8uPEXpZ5SzKio/PMnx911CrVh369BmAQqHg8uWLrF69ktOnT7Jy5RopIJWSksKoUUO5c+c2Hh5fU7Jkac6cOcX8+bN59eplhi+VhPyRlsPq7aDuyZPHiYqKwsjIKEOwKDvPn6dO67W1McNQP0Xan/Z/uUz371hycjKTJo0jPDw1yPT2FLvExERu3HiT706t1kj/Dw4O5PTpE/TrN4hVq1ZIi8/IZDLKl6/I3bu3SU5O1vmblZrOQfNOKQb+n4jAlSAIgiAI/PXXZcaOHZHpsaCg45nuzw89enTRSRafZty4SblKfi68f9ndozSvXyvRaDRYWhYiMjJSyoGVOT1Ai4mJaep0v/goVOpkNBo1Mj0ZcrkBxkYFUcgNpTPefj41Gi2gRU9Pxu+/+xIfn/Wqf0ZGxujr66PRaDA1LYBWq0WpVBIVlZqkPS4ujgcP7mNmZiZNF9T7Z1RPXFxclvm1ChQoQLFiJbJ5nUJWskuQDvyzwqSGmJhoDh8+yNmzp6lVqw5yed7y4qVJmx6UnJxM+/adMgQqQ0ND0Wg0nDqV+rtuxIjBOscHDPDE2NiY0qXLYmpqSmzsa3r3/oZ79+4gk8moWrUaw4ePolKlKkDqggBVq74JUh0+fBCtViutAgZIZdM+3KaXNqois6XtAfbuTR0B2Lbtp5eUPW2Enp6xij3aN397Yh/fAqBQzTfTd/NjhF5WOYsyo1Kp2LZtE+XLV2ThwqVS3kN3dw/kcgWBgf7cu3eHcuUqAKn36ebNG3z33Vg8PLoC0K5dB7y8xuHru4Y2bdrlafVXIWdpOazSTwdOs2mTLwC9e/fPEEzSaNQkpyRgoG+ssz8uIYp927djZKDgWtAGbh1KHX2u1mgIj4oFQMubwFVycjITJ47l3LnTDB8+mvXr17Jq1UqMjIypXbsuMTHRrF69kpiYN6O3AgL20aKFE69fv2bRovm4ublz7dpVAJ3X0LJlK/7++y/+/HOn9DwBbN26Uco5+ikTgStBEARBEKhe/cv3GqDKSlZT5IT8kdlUvbzK7h6lBRcfP34EpE7102g0GQIDYWEh0jfHoKVgQTNsbGy5f/8u/2PvPAOjKLs2fG3JpveEkoQkkEbvICJVQheQIiAogoAgSFd5raivDZUiIlKlWZEapNfQQu8tIUAIgYRsetvN1u/HsJNsdjcJvOoHuNcvmJl9ZrJlnnnuc859ZDIHHB1ckErlGIx6iosLyCtIx83FBxA8O0p/P/V6PYmJ15DL5dSqFUZRUSGennqz8+l0WnHxr1ar0Gg0+Pr64evrR0aGkszMDFxcXMWFp6OjIxkZSgoKCggJCRUzbUylXV5e3jg7m2deVdTxzY5tSmdEVFgaBnTo0In/vP0On7z7I3fSL5OZcxuVOg9HhSvengHUD7ctNphMzB0dHSkuLhZLE0sbrOfm5uLh4YFUKiMnJ5upU6fj7e3N+vV/cObMKRo0aIRSmS5mSUyePI5WrZ7h+ef7oVKpWblyGRMmjGHx4pXUqhVmMbbQ8cuBDh2eFa8rICCQBg0acfHieeLjrxIVVRsQvnMxMRuQyWRWyx41Gg27dm3Dx8eX1q3bPPB7/zhi0OjJuXAPBw9H3CMsM9RK86AZei4urlY9i6yh1+soLi7G19dXvHeYMHVSdXIqET527dqOk5MTvXr1NTt24MAhxMbuY8+enQwd+v/bqe1Jo6TTprkpe2rqXS5ePI+rq6uZN5QJnV7DltivCapaHw+3Kjg6uJBXmE5i8jEMRi2dm4UhLyWyF6o0rI29aDHOJ5+8z7FjR2jevCVeXl688MKLbNq0TiyNBmjcuClDh77CypXLqFq1GnFxhxk/fjRarZaiIhXXrydy6dIFOnaMNhM2e/fuy9atm/nuuzmkpqYSGlqTuLjDHDiwj1deGWkh1D1p2GdcO3bs2LFjx44dOw9NcXExKlURLi6uomBl8sAy4eAgJzMzk8LCApycnDAaDRgMBkKCQ8nPNRedHBWu5OanUajKxUg1ypKXl4fRaMDT0xOJRGK1NFHoFJiNXq/Dz8+f/Px89Ho9BoOBrKwsnJycqFEjmMzMTDIy0sVsrMLCAjIylGRnZwESsZTR2dkZT09Pi/PYeXDKZkRYM2+/dSuJ27eT2bdvN97ePmg0xeTk5HDk7C94ewQQEtAENxcfVMV5XLsVx47D39F6WxU6de5mcT6TiblEIhFb1dsyWI+JWU9OTrbow3bkyCEArl69QkREJGlpQuZh48ZNOXhwP/n5ecyfv5ioqNpMnDiWb775gsLCQpKSbuDt7U1ISE0SEuJRq1XUrl3boqx1ypS3GD9+NJMnj2PAgEF4enqyZ88urly5xIgRo6lWzfL7f/DgfnJzcxkyZNi/RjzNuZSOoViPX6saSKSWvlB/dYaeLRwdnWjUqAnHjsXx008r6NChEzKZjDNnTrFhw1q6du1OjRpCFqbBYCAh4SqRkbVxdHQ0G6dOnXpIpVKuXLHdLdVOCZXtTKrVatm0aQNyuQOLFy/giy8+wdPTi4iISHJzcwHo3r0XKSm3xdeaghTFxflU84skM/c2yann0ek1QsmnEVzdXDiXmIrRaCQiyM/Cm8xoMLBq1Y/s2LFVDOKcPHlcbH5Smtat2yKRwNq1QuZWTk4OPj4+3Lhxnfz8PGQyGXl5wu+7Z8/eHDok+GRlZWVy714aX389lxUrlrJ79w7y8nIJDAxiypS36Ndv4MO/wY8J/467nR07duzYsWPHjp2/BVPJg5eXl81jnJycUamKkMsdCAmpyd27d7h9+xa1aoaRj3mZn1Qiw0HuiEarQq/TISuzOBfOJ8HT0/b51Go1Op0Wd3cPfH398PLyJinpJhpNCkajAZlMjkQiwdfXF6NRELOMRiEjLCsri6pVq6JUKu8LbIJ4VdprxM7DUzYjoiLz9nfe+ZAdO7Yy7o0xtG/+KkHV6pkdFx7cii37v+LbebPp2KmLxeezZYuQ3WU0GunV6/lyDdaVyntiG/rS/PFHDH5+frRv3wq9XkerVq3FjmEgNFGoWrUaN25cR6VSIZVKycnJwWhM4pln2vDKKyMJDg61GDcysjY//PAjS5Ys4I8/fkWj0RASEsq7786w2Q3M9P6VNrV/0sk6dRck5mWCpXnxxX6iMCGTyWjf/lnemf4uq74/+5dfy4wZn/Lppx+xcOF8Fi6cDwi+fcOGvcqoUSUlpvn5eRQXF1v1IFMoFHh4eJKRUV5JtR0Tle1MeuDAPgoLC/Dy8iI6uisBAQFkZGSwadN6MQN37drfRNGoNDuPfI9cpmBQ9y/YfuhbitS51KhWHy/36jzTKYBfVv7IjhPXuK3MpVPTcDxcnRjVswVLt5zgxMlTbN22nbZt29O//yBycrLZsGEtWq2GH3740azBwurVK7h8+SIKhSNarY5Ro17j8uXL7Nu3GxAyLm/fTuaXX1bxyy+rxNfNmfM1AEuXrmLq1OlMnTr9r3uDHxPswpUdO48BlY00AFy+fJGdO7cRH3+FxMRrqFSqch+AbJGWlsrSH5dw6tQJlEolHh4eREbWZsiQl2ncuKRrxenTJy28IMqyYMFSGjZsbHVfYuI1Ro58Cb1ez3//+yUdOz5cO3k7dv4/MBgM9kWsnX81RqOR3NxcZDIZbm7uNo8r7YEldAL0Ij8/j/yCfKw1NRBM1CVIy/gfqdVq1GoVbm5uYntwa5SIad6AME+6u7uTlZWJk5MzhYWFZGZm4O7ugaenF3K5A+np93BxcSEwMAgQ5kGFwhGNpph799JITb0LgIODAm9vb/Fv+acpKbl8/NDpdGzfvhUPD08zg/SylDVvl0ql7NmzE3VQgcWxzo7uVPEN43baBbKzs0RjczA3Mf/qq7kALF++pFyD9dK8995HZt3lXFycyc/PJzS0pkUZrq+vH0qlkL3344+/ERYWXu7YJsLDI5g5c47N/WUp2xn0SUedUUhhci5utbxx9Ha2esxnn32NRqNBqUxn377daDTFFBYW/i3X4+CgICAgEH//njz11NNIJBL279/LypXLUCgUvPLKSOG61er7x1u/TykUCvEYO+VTmc6kchcj23f+CcC8hfMJiygx5u8zsBcv9h+E0QDTp79ndt82lQO3ajRI9LdqUuc5fL2CKVRlIZcpGPpiT7S3TrD+4CUuJ6XTOKw6vp4l5aVKpZLevfvy9tslnaW7du3BsGGDmDv3G779doG4/eWXh7N27W8cPLifESNG8+KLwwD49NOP2L79T8aMGW8m0p05c4r16/9g8OCXqFevPgEBQf/DO/l4Yxeu7Nh5DKhspAEgLu4w69f/QXBwKOHhEVy4cP6Bz1ekzmXY8CHodHr69OlHjRo1yMjIICZmAxMnjuXLL2eLvgqhoTX54INPLMbQajV89dXneHp6UbdufavnMRgMzJz5KQqFIyqVbWNdO48X/7TQWp54WnqxYosPPvgP+/btpmbNWqxebe7lU97Yw4ePYODAF3F397a3tLbzRKFUKis0Vzd1L8rKykSv1yGXy0lMTMBgMFC9eoBFNpSpjbhpuym7SfCQEsQpvUGLSp2HVqe+v1/C3bspVK1aXVz4m0q1CgoKbJimS4iIiCQvLw+5XE5eXi6pqXfR6XRw30BXKpXi4uKCUplu1lrc19cPPz9/JBIJeXlCNzcHBwc0mmIzsUir1ZCefo+srCyLxZTJ+D0nJxuNplj0Nipt/G4iOTmpXFN5FxdXgoNDLMYvKMjBxaX8zo2PKocPHyQrK5MXXnjRwgetNGXN24uLiwHQaK2/X0XqXBwcHPD1djLr+rV1y3oA+j7/HE4KA2qNlLi4wzYN1n18yvdP8vb2IT8/n8zMTIt9SmU6UqkUZ2fnCs3b7VSerFPCb96nWYDNY0oHVHv27M2MGe8yesyrdGw2CUfFX9cVVK1WM3bsq0RFRfHxxyXG8NHRXZkx4x2WLVtEx46dCA4OFcultVqt1bE0Go1FSbWdylNW3E5LTyMuLg6XQA9mJfwICebHN2vRnNi9+2nUqImZuG0qBw6sWhcnhXBfreobRkFRFn/u/4oqPmFATyQSCeGBvtzNyOP41RT8PF3Q6syDCCtWLKVx46Y0btyUwMAgGjVqwvHjR/n00xnUrVsfiUTC8eNHOXhwP61btxFFToCaNWsCQhZm6U6rKpUKgHr16v/rg/t24cqOnceAykQaTPTtO4AhQ4bh7OzMvn27H0q4upFykpycHL744hvatu0gbo+O7srgwX3ZvHmDKFz5+PjStWsPizF27dqOwWCgW7eeNiONa9f+TlLSDYYMGcayZYse+DrtPJr800Krid69+9KoUROzbRW1CT98+CCxsXst/CcqO7bRaCQzM+3hLvgJRSKRoFRab+EOIFMUir5BgIV4YA2T+K1SFZGbm4tKpUKtFsSNgIBAMavmYVCr1dy4kQhAUFANPDwsfYxyc3PIysq6L2AYcXBwwNPT875Br3lGkF6vR6lMv++5osfBQYGvr6/N7mCPIsXFahwcLMVYtVotZillZAiLycTEBDQaDQ4OCuRy+f1yQNBqVaLYo9VquXXrGi4uruTlCQv+pKSbFBUV4urqSF62Hq1WRV5hOlKJDINRj0QixUnhhkqVi0SiR6FwxGg0kpJyQxzXwUGB0WhAp9Ph7e1NdnY2bm5u3LhxlXv37uLi4srdu7cs/g53dw9kMhlGoxE3NzckEgm5ublcuXIHf/+q+Pj4cPPmdfR6PY6OgaSn38Hb21s0cDYaITMzg+JiNQqFIIKZSE+/R0aGEhcXVzw8PEQRLCXlhtiVzoTRqLX6Pufm5lJQkE+1atXF97k0Dg4KnJ0fT+FqyxbbZW4qlQqj0YiLi4uZebterxfL9/y8SoQ8g0FPflEGGdnJZOYk06N7N36fW9LpUq83sGnbSVwcHUg6sglj+/aAlOTkW+j1eqZNm0DHjtEMHz6S1NRUVq5cxqVLF8q9/uDgEJKTb7Fr13azAMuhQwdQKtNxcHCwOXZp83Y7lcOoN5B9Lg2ZsxzPOv6Vfl337s+xZ89ObqddIDz4qb/sevbt201KSjJjx4632NexYzR79uzi/PmzBAeH4u7ugaOjo9X5TaPRkJeXi59fU4t9dipHWXF76+YtYDDi08x6OanynvD7LC8zuCIKVIKAnngnk8Q75uJ1TMwGAEaMGC0KqY6OgjB5+vRJYmP3AhASUpOpU6fTuXNXCgoKKCws4MKFc/z88yo8PT1tBvvt2IUrO3YeW8pGGkxUFC2sDFqtkLrs52f+kODjI3RRKd0xxRa2unqYuHcvjSVLfmDEiNfK9UWx8/jxTwutJurXb2hVRLVFUVERs2Z9Sb9+L3Do0IG/dOx/Mwq5jDVL4m3un/R+HTS6EjPunJwCVCrLiLRJ6GzXriPt2gktnpctW8TKlcvuLwrcuHDh/EOVQpswGAyMGTOCpKSbqFRF98uVa5sds2jR96xevZxmzVrQtm175HI5Z86cYs+eXdStW59Fi5aLGXdarZZx40aSkBDPgAGDCAmpydGjRzhwYB8jRoxm5MgxD3WdjwpfffUZMTEb+Oqrufj5VScjQ8nbb79FZGQUS5euZt++3XzwwTviZ3LtWjxvvPGa6P3Tq1df/P0vsnv3DpKTb9G9+3NEP9uNmT9uYvP+r3Bz8SUr9zYSiZTubafi7SFn0vvPit+XPXt2MmPGu/Tp058pU95CLpcze/ZM1q//g+joruzevYNevZ5n+/Yt6HQ6XnzxJTw9vbh79y47d24TM3tdXFx5+unWfPzxF8TFHeKXX1bTosVTHDy4n6tXr+Dp6YVKVcSXX84mOzufd999x+J7lp9/imnTpvDyyyMYM0ZYxOp0OgYPfoHg4FCWLl1lVkb8yScfsHPnNpYv/5mIiCgA/EqC/mYMGdKftLRUNm7cZlVIfdSobJZtRoaSo0eP4O3twzfffG6RZZuSkswbb7xG8+ZPcf78WUJCQtm+fYv4fenZoxde0lqo1HnEJx0iPesmyqwb98/hwJRJE4lZ/Kl43uupWag1OppGBpCcnsOro0Zz7VqiWJ7Vpk173nvvI86ePU1S0k3atevA1q2bAVi3bg1ubm5kZWWyadN60c6gSpWqAJw4cYw335xI69ZtSUtLZd263/H19SM7OwutVkuXLt3NSgxN5u0rVizlk09KMnXslE9efCa6Ag1+rYKQyitfll9Rht7DYvKk0usty3VNHUh19+9XUqmUyMjaXLsWj0ajMcswvHLlEgaDQcxctfPglBa3AV55dTjHqiZYPTYvIYObly7TtWsPi0Dle+99xMcz/su3n+412+7m4sPQ52aJ/y9Qabh48x4ero681LkJsvv393PXUzlw7iYTJ05l4MAh4vFqtVrsQjpw4IsMHvyS2fjvv/82+/eXnLNu3fpMnTodd3dzYa1Hj14P/YzzpGEXruzYeUwpG2n4Kwnwj+Ly9b3MmjWT8eMnERRUg4wMJcuXL8XZ2dni5luWu3fvcPr0SRo2bGzVjBRg1qyZBAQEMnDgi+zcue0vvX47jx5/p9BaGpMpbkUZVACLFy9Ar9czevTrFQpXDzq2ncrz/yV0QsVZnzqdjj/++JXIyNrMmfO9KEQ8//wAZDI5O3duIzExQRQiNm/eyJUrl5k8+U0GDBgMCNl67733FqtXL6dnz95mra0fJ6z9hrdu3YzBYKB3735WX+PvX5Xo6G78+edGQMKWLZtwd3cnIiKK4cNH0fl+B7hrt+LQaIsoVAnvr6d7NTzdLLMlTQGRQYOGWGTyHj58EH//KjRp0pzNmzfi6Oh03xxXi4+PL2Fh4Vy8KHxfiooKxZKLatUCcHBQsHbt7+TkZGM0GgkMDOS99z4iODiU06dLfIxK3wOqVxfKlkwlkAB6vY7i4uL7mXjmi2y/+ypVRYGfc+fOkJx8i06dujwWohVUPsvW9H3JycnG09PLIsvW378qXbr0EE2Kk5NvsW7d7+L3pWf3nsz7bB95BelcStyDwqHEY6a6XwTe3uaZl5eT7gHg5uTI5iNXiIyMYNy4SXz33Wy0Wi3nzp0hI0PJqVMnLIS33377Sfy3s7OLhZ3BkCHDOHhwP/PmzcLNzZ0OHTrx2mvjeOmlgahURXTv/pzZ8Sbz9jNnTlX6fbUDmacFXzlrZYIqlQpDkcws4xGoMENPLlPg6vxwWbqhoUI51/btf9KpU2ezfdu2Cf5KpctBo6O7cuHCOWJi1otzAsCaNb8gk8l49lnzMexUjrKdScujOLOI5HWX8a/izxtvTHmo86nVarYevYpOZyD66QhRtAKIquHPxeQcli5dhJOTM82btyQ3N4dlyxaJXovWvMxGjHiNPn36k5OTzenTp7h+/Rp5ebkPdX3/FuzClR07jyllIw1/JVX9wnn7zXdYtGQBEyaUZAgEBQWzaNEKceK2xZYtMWL3Hmvs2bOTuLhD/PDDsn9NK+dHiQfxoALhAWHhwvkcjTuG3qDHxzOQhpFdqeZnKTbYwiS0VqlSlX79epCXl4eXlze1a9flrbfesRCwdDodGzasZdu2zSQn30ImkxEYGETv3v14/vn+Vs8xc+ZnfP75x+LfEBJSkzlz5pt5GZi4fPki69evYcaMz3B1rbjk5ttvvxHHDgoKpl+/F3jhhcF2b6u/iX9C6KxM1ueDChG7dm3HycmJXr36mh07cOAQYmP3sWfPToYOfeUv+xv+SawFS4YNe5Vhw161+RovLy9atmzFpk3reOGFF5k0aZrV45LTLgAS9Hot7q5+5OTd5bet0/HzCaXz5aqERwpZcOWZUqtURQwc+KIY4Z4x41Patesg7t+3b7coXEFJxkTNmrWYPfs7oCSjq3v3XhZBl7L3gObNWwLg41NSAuro6ESjRk04diyOn35aQYcOnZDJZJw5c4oNG9bStWt3atQItvk3QOlsZevz56NIZcXnYcNe5bnn+uDs7GJVfPby8mLSpGnExu7Fw8OTjRu3mWWpmO63Pl5BdGszmYOnVmI0GtDqVCgcLH2Mnm9TD73BwMrtp3F3VrBk0UJkDp6sW/c7t24lUVCQz7Jli5k+/T0xG/K114aTkHCV/fuPsmbNryxZsoAhQ162ELb79h3AuHETLc5ZpUoVbt1KwtfX8l7l6+tHQsLVCt9POwJKpZL8xCxcAj1wrmo5T99Ovs0bo1+nQ4dOBAeH4OHhiVKZbpGhZ6JInSt6FnVuPc5srBUrlgIlmVrXr18Tt5k8iwBat25LnTr1iIs7zPjxo2nf/lnASGzsPs6dO0PHjtFERZVk7fbu3ZetWzfz3XdzSE1NJTS0JnFxhzlwYB+vvDKyQtHFTgnujnqkRh0Au7YK3nWD+vbEUyF8ZvkSy4y84mwV11ecAYmEWfPmWIjblUGv1zLtremkZxcQ3TyCQD8Ps/1OCjnfz5/Hhx/9V7zngfC9GTr0FVauXGb1OTMsLJywMMFAvnPnbmzcuI4335zI/PmLbTa0+rdjXzHasfMY8iCRhofFy9ubqKi6NG/ekuDgYJKTk/n119W89dZk5s9fRNWq1ay+Tq/Xs23bn7i6ulo1EczPz2fevFn06vU89es3/Fuu3U75PIgH1Z07KYwdOxK5TEadsI4o5E4kJh9l77HFdGw5mur+kcxXbxgAACAASURBVJU656+/rgaEUqoBAwbj7e1DdnY2ly6dp7Cw0EyQ0Ov1vP32FM6cOUnnzt3o06c/er2elJRk7t2z9JNKSIhHIpHi5eUlLiZPnz7JjRuJzJ49k88++9rseJ1Ox8yZn9GiRSuLiGlZ5HI5bdq0o1WrZ/Dz8ycjQ8mWLZuYN28WiYkJvPvujEr9/XYejL8zo9REZbI+H0SIMBgMJCRcJTKytkVWXp069ZBKpVy5Ys1M/NHH3VHP9i0bkEgkDH6+m7hQADBI5OQX2/6MyvM0AkhNvUtOXipgxGDU4+8dSuPaPSksyuJi4m7Gjhsl+gLpdDru3EnB0dGJatXM5yCJREKXLt0ZN24kPj6+og+jLSqbMVFcrLa4B2zevJGNG9cC8Mwzbc3GmDHjUz799CMWLpzPwoXzxWsbNuxVRo0qvwNvYWEB+/btpnr1QJo1a1HusY86Dys+V8a8vVhTxMFTq9Dq1LRtPoy9R217ZN7JyKNQreGpOjVwcXGhWCt8vrduJREWFsHevTuZNm26GERTKtPx9vZ5aDsD09jp6enUqhVuts80tp3KUZFnUZUqVejSpQfnz5/lwIH9FBUV4ubmJnrRbdm6Gdhs8br0rOtm/09OTmLp0oVm2xIS4klIEEreS3sWyWQyvv12AatXryA2di8//DAPiURCUFANXn99AoMGDbXolj137gKWLFnA7t07yMvLJTAwiOef78+pU8f5/fefcXBQ0KxZC8aNm2j1mb6goIAlSxYQG7uPvLxcAgKC6N9/IM8/3/9fFTyTGnWkzB+D3mBk49p43BUyoq78TkqCkF3nOdk8IKvJVnF9+RkMGj21hjchLCIco+rBzqnXa4k9uZy0jASebRpG7WDrPmvh4eEsX/4LKSm3ychQ4ufnT1BQDRYs+BaAkJDQCs/VrVtPZs+eyaZN6+zClQ3swpUdO48hFflH/a8k3jrKb9s2sHz5T2YPXk899TSvvjqURYu+58MP/2v1tcePx5Gefo8+ffpZ7Zby/fdzMRiMjB074W+5djsV8yClWQsXzqegIJ9VK35h25o7ANQMas6fsV9z4uJ6enWYXuGD04UL57h58wYeHp789NMfFWbZHToUy6lTx5kz53uaNm1e7rHZ2VmsWvUjLVu2YubM2eLYBoOBN9+cdD8KepZGjUoeAn79dTUpKcl8/vnXtoYVadiwscUDRO/efXnzzUls3bqZnj37mI39JPKgGXq3biURe+JH0rNuPFSG3pkzp1i0SMis+e23n9m0aR01agTTr99AoqO7Wv2+xcUdYuXKH0lMTKjUIuDDD//D8eNHkcvlDB8+xCxCXpbKChH5+XkUFxdbbQigUCjw8PAU/VEeN24lXef0ufM0ruYKGz4mpdS+oDcWYeoKWJaMDCXHjsVRp049MbJcmqKiQl4f/xqmbn8+noFU84tEr9fi5OhORHBrLl3fI/oCKZXpDB06gMaNmzJ//mJACIaA4EOXmJhAbm4uQ4YMK/c+4+npVemMiWXLFuPnJ3gXyWQysrOzKCgoEPdrtTqzsR0cFAQEBOLv35OnnnoaiUTC/v17WblyGQqFwqyLVFl27dqBWq2mZ89ej+2C1JQVsW/nVgoLCxk6aAA+zjpAeJ/+V6GzoCiL3XE/oNEW0anVGFxdbAtBeoOBW2nZAFTzLfGN6dq1B9u3b0GtVlNYWEhy8i1q1QoTDdZ79er70HYGprE3bVpHq1atxe2lx7ZTOcrzLALw8vZi2rTpFtsTE69x/fo15FIpOzaVBAuy8+5y5cZ+alRrIG4zBee8vX0YOPBFzp07y9Gjh83GW758iTgHmua7MWPGM2bMeJKTk/jhh+84c+Y0y5cv4fDhA+Tk5Jh1y3Z3d2fq1OlMnSpca2zsXt5/fzrh4ULpalZWJr/+uprY2H3I5TICA2uIwpROp2PKlHEWvomzZn1JVlYmSUk3bXZFhidT9Dqakk+2WkefOr4oZNZ9zzQ590UrtY5awxvjUv3BDdn1eh2xJ1eQqkzg/Xf/Q+7V2ApfExRUwywwfPToEVxdXWnQoFGFr9VoNBgMBrGjrR1L7MKVHTuPGTqdju3bt+Lh4Um7dh3/lnNcur6H0NBQi2hhWFg4ISGhnD172uZryytziI+/ypYtMYwcOYa8vFyxljs7OwsQ2qqnpNymSpWq5bbJtvPXYy06rlKpOHz4AE2aNCMyMoptCMKVg9yR8BpPcT5hO5k5t/HzLr/0ZcGCeQAMHfoKcrkctVqNXC63ubA8ceIYbdq0o2nT5hiNRlSqIjGCWpaNG9eRl5fLuHETLcZ++eXhHD8ex9Gjh0VxKSXlNsuXL+WVV14lMDDood4rqVRqdewnlQfN0Bs9ejgatf6hM/Tmzv2GzMwM/P2rMHz4KNRqFXv27OLjj9/n9OmTTJ/+vtnx8fFXWL/+D3ERUFhYwJo1v/L66yNZtmy1WZMJrVbLxIljSEiIp1atcAYMGMTRo0fYsWOrzeuprBBh8rBwcHCwOo5CobDqc/E4sGGTkInUNeLByiy2bt2MXq+3WfaWm5vL3bt3xP9nZN8iI9u8C6C3t3e5vkBJSTcB6NSpszj/2BI9TLRs2Ypq1arbzJgoTYcOz3LwYCzr1v1Ofn4+zs7OREREER3dhZUrl5ndA9RqNWPHvkpUVBQff1xiwB0d3ZUZM95h2bJFdOzYyab345Ytm5DJZH+LBcA/hSkr4rftN5AAT2fHkTK/5PP7X4TOwsJC9hz9gUJVFlGhbcgrUJKZcxuA/MJMtm7bTpFag4uT8PxQqNJwNlHoyujmVPJM0aLFU6KZP8CmTetwdHQSDdYjI6OYPXujaGeQnCx8J/fv30NqqjCeybwdYPjwUVbHtmbePnLkaw/ztj6R5OXlsmrVcg4e3I9SmY6Liws1a4YxatRYsy6+ORfTUcYlo04rAIkE52puVGkXAj2tj2sKzinkMhJOCaXcWbkpnIsXBMiUe5f4M/ZrqoSncubsmfslo6uJiIji6aev0a5dRxYu/A65XM64cRNZt24NV65cArCakS6VSgkJCSUp6Sbnz58DBKNtU9lyaXQ6HXPmfE2VKlX5/vulODg4MG7cSHQ6HUajgTp1GuLl5S0KU97ePjZ9E1euXAZg03dTq9WWK3o9rs1Cdl4TxOiu4dbnI02Oius/nkGn1hH2SmNcAjysHgeIWbzurmV90nTEnlxOqjKelg3683yf3qyshHBVmrVrf+PGjeuMGDEaZ+cSS4HMzAyrFhZr1/4GQL16DSz22RGwC1d27DxmVCaN/n+lSJ2LQW/dFFav16PX66zuy87O4vDhg4SFRVC7dl2L/ffupWE0Glm6dKFFWjbAnDlCBszSpausvt7O34e10qzr16+h0WisTqImsSozN7lc4Uqn03H5svDAFxVVm+HDh5CYmIBUKqV+/YZMmDDFrCwHhKhTVFQd5s79hi1bYlCpivDy8qJXr76MHDnGTPCKizuMq6srBQX5FmMPGfIyYG6ePH/+HDw8PGjXriMpKbfF7Xq9Hq1WR0rKbZycnEX/IltYM2Z+UnmYDL2uz0zGx1PIdiqboVcRoaE1uX79GuPHTyI6uisAL7zwIhMnjmXz5o288MJgM1F9x45t4iLAZNLbqlVrRo58WfSvMbF580YSEuJxcXFh/vzFeHh40Lt3X0aOfIn4+KsWn+eDCBGmDFOt1rJDIgjfa2tZqI86Op2OmK3bcVfIaB1sewFgjYo8sKpXD+D40TNER3clryCdnu3exMvDvCzo3M0VXI2/Ih5/6FCJWbpOpyM5OQmAli2fNjM/Lg+5XC5mTFTESy8N56WXhltsT0tLZeXKZWbfmX37dpOSkszYsZbjduwYzZ49uzh//qxV4er69USuXLlM69ZtrGbtPU6k5BZzKb2IxtVcqeZe+eeUygidBUVCoCs+6ZDZPmX2TT786GP6tq0nClelkZXJzHj//Y/R6bTs37+XjRvX4e7uQYcOnRg69BWmTBlnZmdgEkf37Nklvr60eXtp4co0dnh4BFu2xFiYt5ft1vxvJS0tlQkTxqBWFdG393OEBtcgv6CAhMTrFGbfxVNRl3yJlPSDt0jddR3n6m5UfbYWEiD7fBo3fz7PjqjtdOn4XIXnupd5nT1HF2E06pHLHWkc1YO0jGvMmfsNUqmUJk2aiQ02TPNdVlYGS5cuJCcnm/j4K0ilUgwGg9X5LiQklKtXL9Ojx3Ns27YVo9FoVbQCIaM4I0PJqFFjcXFxYf36P0Rh6uDBWK5evcyWLXuYMeMdVq9eTlhYhFXfxN69+xEbu48GDRqiVFrP5H0Sm4VkFmk5eTefKD9nanpbzqf6Yh3Xl59Bk6PG76kgijOKKM4QMt92SLZh1Mho0eIpsWTZlMXbtEkz6gSWdAQ8fOZnUpVXqeYXgVymYOu27VxNLnmf/Txd8PMsCahOmjyVatWDCA2thUQi4fjxoxw8uJ/WrdtYZNkOGzaIhg0bExlZG39/f3Jycjhx4jinTh0nLCycgQNf/EvfsycJu3Blx84jRGWiT6Y0+jNnThId3QaJREpERCQvvzycp58u39OjLKYOK4VF2ew/sQyj0UDbpsPwdKvKreRbXLx4AY2mmIkTLX052rQpKeFasGApDRs2Zts2oQV5+/bP8tVXn3Hq1AmUSiUeHh5ERtamR49e/Pe/X1qMdebMKdav/4PBg1+iXr36BAQ8XCaMnYfHmtm/qazJ2iLKxUkQNlXq8lOaDx8+iF6vQyaT8Z//TKVjx2iGDx9Jaqqw6JswYYzoX1OaNWt+vR+JnIiHhye7dm1j9erlKJXpvP/+x+Jxycm30Ov1TJs2wWLsDz74D2BunpyWlkZGhpKXXx5o9XoHD+5L69Zt+OqrueX+XbdvJ1uM/W+ivAy9pk2bi6IVWGbolYdOp+PMmVMWGaVSqZQOHZ7l7NnT3Lhx3Uy4KijIZ/DgoWadpSIiomjSpJmFf01MjGDoOnDgELOszzp16hEff5Vjx47QosVTYtbngwgR7u4eODo6olSmWxyr0WjIy8vFz69phe/to8bhwwfJzMqyWZahdnBAIhOCGRLF/RbxDnokzsI2mUSGrqj8khRfr2DyCtIpUudaCFfp6fds+gIdPnxQNFO2len2d2HtHmC6Z5qM30uj1+sB0On0VsfbvHkjAM899/iYsttiR+L9jIgHzNCrSOgMCAgwa08PoNYUsG7nDGoFNWfNuu9YObOkAYCHqxMNa1Xj/I00i89ELpfTtGkL9u/fy9dff0vLlq0A+PLL/1rYGbRr14FDh2L573+/tOrdWRa5XG5T8LQj8MknH6DX61n3y0qKf3sPks8C8KwPkJBMSsIf6F+eSdreGzhVcSXiteZI7t9//FoFkfDDCeZ8PZtnWnaosMHKqUsbxX/XrtmWqJptiKrZhtt5mzlwYL/VZ5x69eoDsGLFMnr37sumTeuRyWQW811gYA1u3rzB5Mlvcvz4MUJCQmnfviM//iiUMpcNhly9evn++EJAsHRDj8zMTE6dOkFy8i2xoUdiYgJ169a3yKo6fFjohOzp6W1TuHoSm4Xsup6NwWg720pfpEWTLWQ2ZxxLgRIXAz5ZJzw7zpu3sEKvvaxc4VklLeMaaRnXOHLWfH/L2kFmwlWDBvXZuWuP6JMYElKTqVOn06dPPwufzgEDBnP8+FHWr/+DvLxcHB2dCA4OYcyY8QwYMNgsO8uOOXbhyo6dRwRT9EmlKqJnzz7UqBFMYWEB168niguhjAwlcXFC7b1UKmXkyLFIJLBz5zbefnsKH3zwCQ0bNmb79i0A3Lx5AxAmuPR0oS10t249xQiLqcOKXOaITOqATi8sABpGduXg6ZVMmTKebt160LNnb7KyMjl58gRGo4Hhw0fh5+fHV199jqenF3XrChP81q0xKBQK1q79FYPBSJ8+/ahRowYZGRnExGzgww//w5dfzrYwzVWpBLfEevXqV+qh0M5fiy2z//JKn6RSYZtOryl3bJPQqtfr6dSpC++995G4LyqqNhMnjhX9a0qTn5/HqlW/i4aWnTp15o03XmP79i107dqTFi0EE3aVqgi9Xk+XLt3Nxq5VK4xp04SFR2nz5PHjJ1FQkG9xnbNmzUShUDBhwhSzFO7c3Bw8Pc2NeTUajfhQWtaY+d9CeRl6Deo3RJNhfnzpDL3yKC+jND1duA9ae+C0lhVYt259cRFQq1YYBoNBzJxYsWKp2DGqNIcOHeDQoQNi1ueDCBFSqZTIyNpcuxaPRqMxu/4rVy5hMBioXbtOuX//o4jpN2xroaA16hm3Rchqy7kkfEarz69ji+wIAAt6fo7pcdOWuXrNwGbcTDnJtVtHCKhS4i+Vcu8S6eX4Apmu7a/A1rU9yD3A1HG3ssbvpcfbtWtbpUzlH3V0Oh17rufYzND7K4TOB8HVWfgdFqgt5yrTs5UpC8puZ/DPcPbsac6fP8vkyW/i7+dHksGIzmDESW4ujF84dx6j3ohXw6qiaAUgkUnxaliVtN03OHgwlm7dbNQMIhj5Z+fdxUHujFanJqxGS3Ffvbr1OHBgPykplvOSn58gZun1OsLCwjEajbi7u1vMd2q1CicnJ9zc3MVu2SZvKxA8Pvv2HSD+v3RAsGxDD5OAlpGhpHHjpkilUvR6vYWwdvnyRTZuXIeLiys5OdlW/+4ntVnI4AZVGNzAdkaqwtuZRp88a3Xfgp6fY1SZSx+mLF6FXMa3n+4Vtz/fydySYMoHrc1E8bKMGvkqLw+rXOnliBGjGTFidKWOtWOOXbiyY+cRwRR9WrHiN5tlSmvX/o7RaMTPz5/Fi1eKWQQDBgzm1VeHMmfO13zwwccWZXixsfuIjd0HCGbTZVODdXoNDSO7cD5B8HsIqlaf7+b9wKrVK9mzZxeFhQW4u7vTuvUzDB8+ioiIKHbt2o7BYKBbt57I5XIuXDhHUtJNwsMjSEy8xhdffEPbth3Ec0RHd2Xw4L5s3rzhsX8wf9KwZfZfXumTwSBsk8tsP7yb/EokEilGo4Hu3c1T+ps2bY6fnz9HjhxixYqlotAKwkJi377dZkLr00+34ezZ08ya9QW//bYBQDRAvXs3heXLl4hdv3bsELwsHB0dRWEVBP8Ra3z//bc4OztbCKfTpk3Ez8+PqKg6ZmOnpCQzYMAgs7H/TZSfoefPnTLCVWUz9GwZM2dkKImJWU9AQKDVbjvWIuYmgevatXhq1QojPz8PnU5H/foNGTRoiNmxpqxPX18/Jk9+U8z6fFAhIjq6KxcunCMmZr1Z2dqaNb8gk8l49tnyu1g+aph+ww3q1aWmt3UT3NS7qdzbLwiCamUhAHnxGWhzBeE7rWkqVb0EjzRr5uoA1f0jCQlowq27Z9h3bAmBVetSqMom/uYh/PwsfYHOnj3N4cMHiYs7jJOTM2q1yqrnUFpaKtu3b+HMmVNkZ5cs8I4fP8qkSa8THh5J27btady4qc1re5B7QOvWbalTp16ljd9NHDy4v1Km8o8DsQcPl2uc/CBC519BFW/hO5GWaRmwuHz5Iq6urgQHhwB2O4N/ClMAtmrVarwx9W0OHr6EwQiB7gqGNKrCs7UEoVhz/9lDqrD0RJM6CNsuXbpYrnBlMAiCqFanoppfBG4uJYEPo1FoCmHyLSuNqQS5UaMm7NolPBu7uZUYfJvmu+zsLCIja/PDD/PE8tLSzzKlLQnAPCBYtqGHSQxVq9UoFArc3NzJy8s1Cx6W7op88+Z1m76Jj2OzEJlCjt5gLOeIv9dMvlhTxKXE3dxOu0iROhcHuSNe7tVoGNkNKGm0kJqZz6n4FNJzClFrdLg6O5Cs/4IhQyrvnRoff5Xlyxdz/vw51GoVgYFB9Or1PP37D7LI0HrjjddseguXvRdlZGSwbt3vxMdfJSHhCjk5OXTv/pxZYPdx5vGeHe3YeUIoHX3y8/NDp9Oh0+ks/FBMC6T+/QeZPdzK5XI6d+7GokXfk5eXZ+YBUh4SiQS5zJEGkZ1xVJibX7do3pJGjZvZfG1ZsaNBg0YcOnSShQvnk5h4zcLHwcfHF6lUipOTZQpsjx696NGjV6Wu2c5fS3lm/6bP0FrpU5FaiEQ7O9n2vDH5lfj4+JKVlYmvr2WmjKurKxkZSotFwr17aSxdutBMaPX2FjI+TJkuILTDvnUrCZVKbWGebDQauHcvrTJvg01sGTOPHPkanTt3+5/GflypKEPPWiZCZTL0bBkzq9Vq3nnnTVQqFW+//T4//bQCwGxxEBOzAQ8PDzOh01RC9tNPK+jatYd4fYGBQRYCpSnr08HBwWzfgwoRvXv3ZevWzXz33RxSU1MJDa1JXNxhDhzYxyuvjLTa5fBRxvQb7tenF6RssXpM6t1U0vbeNNuWe1lJ7mVhUXR30F1RuCqNqTT+0KFY7t5NxUHuiJuLD9n5qaRlXMPBwZng6g0ZN3EoX3/9OQkJ8eTm5uLn54ebm5vYql6tFj47a55Dd+/esSpAZGZmkJmZwalTJ5BIEFvdg7CYXbXqR3bs2Mrdu3eQSmXcuZPCuXNnKSoqNLsHNGnSnEWLvjdbJHTu3I3mzVtWyvjdRGVN5R8H1sfcN/K3kaH3INjKgnsQAv08cHVy4HLSPYqKipA5CCL6tWsJnDlzih49eonPU3Xr1rPbGfwDmMzuZ878jNDgIKY9E4RWb2TDlQy+PpSCzmCkS7g3tWoJgYOCG9n4tzK/hxTcFITo9PTy53gnR3dkUgf0Bi01A827FKekCP1Ry3Zx0+l0YgfZKlWqEROzHoVCYSYgmeYTnU5HdnaWWXlp6TkwP9987NIBwbJZ7RqNxuwYR0eFeKyJ0l2Rx4wZgZOTk9Us8sexWYjeYOTVT3fZ3L/uw78v6J2aepdtB+eg0xcTVqMlHq7+aHRqcvJSxeddgFtp2Ww+cgVPNycahlXDWeFAVn4RO3bsZO/e/axa9VuFHoVnz55m6tQ3cHV1Y8CAQXh5eXPixDHmzZvNzZs3zXw5TXh5eTFhwlSL7WWfKZKTk1i9ejlVqlSldu26HD165CHfkUcTu3Blx84jQOno09tvT+HYsSPo9XqCg2swduQIenUXDIodjEL6sbebHE9FsVlbaUdHYaKrKPpUmhMX1uPm4kPtmu24ecd216ay3L17h9OnT9KwYWMLk9mWLVvx008rmDVrJuPHTyIoqAYZGUqWL1+Ks7Mzgwe/VOnz2Pn7Ka80q1atcBQKBZcuXbB4XUa2kFrv62m5IDVh8iv59NMZbN++hfT0dItOlUVFRfj7V2HDBqGr2927dxg4sA9169Zn8eIVZseaBKvSHih16tTj1q0kXn99glnrcYC+fXvY9MYpy9q1m61ut/uUWFJRhp7p4bs0lcnQs2bMXFxczDvvTCM+/grvvfcRXl5efPihpRCxZs0vgHlGqelhXyqVml2fLfN0sHzIl8lkfPvtAlavXlEpIcLBwYG5cxewZMkCdu/eQV5eLoGBQUyZ8hb9+ln3VXuUMf2GPRXFpMy3Llw1bd7UZmmGsL8ZRkFbEssy0tJSGTnyZVSqInr3ep6Ec0XiIqG6fxShgYKn4930q0x/R8iA69dvIF5eXty8eZ2YmA24u3tUuEho2rQ5hw6d5NixON55500CAwPp0qU73t4+ZGdnc+nSefr06S9e28GDJ3jrrUksXryAtm3b07//IHJystmwYS1arYYVK36lZs1a4vinT5+0WCQ8iPG7iTlzvq/0sY8yGRlKDh89ZtM4Gf6aDD2AC9eEBa5eL/yes/NSWfrjcs5evU2AnweBfoJAJZNKaduwJtuPJzB6zFiee64fhYWFrFnzC15e3mad1fz8/K3aFdjtDP5aioqEZ1kXFxd+/OE77i16A4DWwR68uiGeFWfuER3mRVhEOG5h3uRdzeDujkR8mgr39qwzqeRfywRArS4u91xGowGJRMjUuXnnFJ7uVXBUuJKmvMa5+98hnc58Tvj119WkpQlZWHfuCOKWs7N517nSgeXU1LuMGjVWLC8tnWWlUqnMyktLBwQjIwVDeNOcVLZ0VavVIZFIxO2luyL7+1cRfROtZU49qc1C/i4+/Oh9jEY9Pdu9WW5A9mxiKhKJhAHtG+DsWPK80HPgcD77/Av27dvNwIFDbL4ehM7JEomEhQt/FDO0+vV7ga+++oyYmA1069bTolu1k5MzXbv2qPDvqF27Dps378Lb25ucnByee+7Jul/ZhSs7dh4BSkefatSowXvvfYRGo2Htmp94d8YnKHcso0u4N273DQdj1yyjvXK3WVvpM2eELKuKok8mdu3awZ30K3R55g2kUuutqW2xZUsMRqPRauefpk2bM3XqdJYtW8iECSUPhEFBwSxatEIsvbHzaGCrNAuEh8rWrdty4MA+Eq7Fi9u1umISbx/D3dUPXy/Bu0gu06HXq0lJScHJyTw63uu57mzfvoVNm9aZiUuHDh1AWca/JiAgkAYNGnHx4nni46+K2Sx6vZ6YmA3IZDJatGglHt+1a49Kj23nf6dyGXpKwLzcuTIZemWNmQXR6k1OnjzO9Onviw9tpTNKV69ezqJF3zNnzvcWZaAajbCgmTFD6H5Ynnl6dHRXvvzyv4SFWXZPdHFxfSAhwt3dnalTpzN1asUdFB8V/ukSjdKl8QHVqpp5i5Tm6s1YpFIpy5ctwsurxGcqIjyUzz7/goMHdtGvv/UsJhPZ2Vl8/PH7NGnSjJkzZ5dbinfwYCxHjx6hd+++vP12SdS7a9ceDBs2iLlzv+HbbxeI25/0RcKDYhKfy8u2etgMvbKcj99u9v/svDssXCSIWy1rB4nCFUBEkB9ymZSkPBnffz8XBwcFzZu34PXXJz72HRwfR0yeS9HRXc2CBe6OMp4K8mDPjRxScovxBkIG1idl01WUR5JRHhYCZgovJwKfiyJl01VcXV2tnULkzr3L6PQafDxroMy6wfZD3wIglzkyedJkvpk9UwxuQIk41LBhY06dOsG1a1fx8PC0EHnKCFfKyQAAIABJREFUVhXYKi/NyFAyeHBfsaTLVNZ16dIFmjVrYTYnlS5dNTX0cHf3EH0TS3dFjo3dh8FgoHr16ly6dMGiK/KT2izk7+Ds2dOcO3eG5vWex9nJA4NBj8Gotxpo0+h0yGRSHBXm84j/fYsXa1UlpcnLyyMxMYGWLZ+2KCvs0aMXMTEb2Lo1xkK4AsG3TKUqwsXFVRRjy+Li4oqLS/m/iccZu3Blx84jQOno07x5i8SJ/Lno1nTr0VOMPtX0dqJJdVfibuez7FQaL99MIl+rYOvWzWI6aEXRJ4D8/Hxmz/2G8OCn8PcOfaBr1ev1bNv2J66urjYjj15e3kRF1aV585YEBweTnJzMr7+u5q23JjN//iKqVn24lH87fy22SrNKM3bsG5w6dYKJE8cRXLUVDnInEpOPolLn0qHFSHHyNBh0LPxkEit3nCbQz4N+7Up8X4ZNn0V0dFd2797Bm29OpHXrtqSlpbJu3e/4+lr610yZ8hbjx49m8uRxDBgwCE9PT/bs2cWVK5cYMWK0mSjWosVTDzS2nf+NymToXbh4nqhq5ibkpTP05DIdEonO5jkkEhl5BTreffctTpw4yttvv2ezhKr0IqCscFXWv+ZJNU//K/gnSzQsS+O16PQaq4sErbYYR4WCjQs/QVrqQT0pTSgTcnJytHhNWTZuXEdeXi7jxk1ELpejVquRy+VWBazTpwVRtEeP3mbbAwODaNSoCcePHyUtLU28Bz3pi4QHZdiwV5kwaigp822bFD9Mhp41ynYXhPINlGtW92HG3FkUax+8+6TdzuCvpUoVQSy0Zh/g4yz8Lgs0gnG/3NmB0MEN0BZoKM4oQuoow7mqG/mJQsaVqYGLLRJvC23lWjUahLuLDzn5aRgx4u0RQK9e7flm9kz0ej3XriUQERHJ/PlzcHd3JynpJl5e3uTkZNO9+3OcPHkcjUbLsWNxeHh4ivOdVqulRo1gRo9+XTznvn172LtXuJ8GBQUzZsw4sby0SZNm+Pr6sXnzRgYOHCLOSZcvXzIrXT137gwGg4GGDRtz6FAsMTHrrXZF/vXXkhLp0l2R7fNd5TFVvbg4e7P/+DLuKq9iNBpwd/WjQUQXagaV2KYEV/EiLauA3Sev0SQiEGdHOZl5RWyeN4/Q0JpER3cp91xarXk5aGlKV86URalMp3PnthQXF+Pk5ETLlk8zZsz4Cr//Txp24cqOnUcAW9EnTw8Ps+hTsJcT77QL5tu4O6y7lMHa+2Uq1asHMHXqdGbO/LTC6BPA99/PxWgw0Lh25UoKS3P8eBzp6ffo06ef1RtvTMwGZs36kuXLfzYrC3vqqad59dWhLFr0PR9++N8HPq+dvx5rpVllCQqqwQ8/LGPxovnExe3FYNDj4xlIx5ajqe4fWelzvf/+x4SHR7BlSwzz5s3Czc2dDh068dpr4ywil4LR6Y8sWbKAP/74FY1GQ0hIKO++O8Pq4uFBxrbzv1HZDL0qbdri7REAWGboGQw6Vs2cht5gILdQjYNMhrtLiQAxeMoXvPPOOxw/Hsebb75T7vez7CLAxUUo57DmXwNPnnn644it0nhri4Tq/lFcuHbLYpFw6EIS3u7OdOlccYZTXNxhXF1dKSjIZ/jwISQmJiCVSqlfvyETJkwxM9evzKLi8uWLD+239CTw/22gbOfxp06demzcuE7sFFuajCKhtM3LybwSwMFNgYNbifiSlyAIV61aPWPzPEplOqnKeHy9gvH2EMoM/bxDxP1H4g4BwjP41KlvMHDgEOLjr5KZad5dxNSIA2DatAl4eHiwdeteWrduy/79e0hLS+WZZ9qhUCgoKipi/vy5uLt7kJ+fR506denYMRqdTsetW0k4OjoxadKbzJjxDuPHj6JGjRAuXDjHpEmvm5WumuakceMmolSm8913c2jTpj316jUgIeEqV65con37Z4mO7mKzK7J9vqscpqqXY+f/wN3Vj6cbDcZg0HHl5gGOnP0Fg1GPyZy9eVQQqmItl2+lE3+75HvyTOvWfDjj8woDGT4+vnh5eXHp0gWKi9XivAKlK2fumb3GVIkQHh6BVCrl8uVLrFu3hlOnjrNgwTKbgecnEbtwZcfOI8CDRJ/cHWW83yGYbJUOTfQEDA6ehIdHcuyYkHFVkfpuavf82uixpF4rQqMVsr3UxQUAqIrzyS/MEHxqrJQQlvjbWF9Mrl69gpCQUAsvo7CwcEJCQm12xrDzz1O2NMsWoaE1+ebruTbLeUx4uDoxoV9rq/vkcvkD+UWFh0cwc+acSh37oGPbeTgqm6F3+vQJ9h5dRO1a7Wxm6AEUqjT8vOusRYbeBx9+xLFjR2jevCVOTk7s2LHV7BxhYRGEhwslfXK53GwR0KtXX5v+NfDkmac/jpQtjZ/xwSdsXX/ObJFgallfL7wTUQ3d2LBhg9kiIaSqF91aRuLq6kqxbcsy8Xx6vZ5p0ybQsWM0w4ePJDU1lZUrlzFhwhgWL15JrVphAKJ/1enTJ8TvGAhGx5cvC1HwypbjP6n8fxoo23kyaNu2A99+O4udO7cxcXSJ72lWkZa42/kEuisI8LCdTVl0J4+s06k0adbErKSqrJn/n1s2YzQaCK9h2U24WFPIgh+W4uXlxRdfzOLnn1fy888rKC4uJjg4hFatnmbt2jUEBAQyZsx4Zs2aiUwmIzMzQ6waGDv2DeLiDlNcrOaDD6bz1FOt2bx5AxkZSqpXr05+fh4NGjQELP3aHB0dWblyGYcOCeXQarWKZ5+N5ujRI2ZzUnBwiOibGBu7z8I3USKR2OyKbJ/vKoep6sVB7kj0068jkwrrrqBqDYjZ9zlnr27FYJgICE2tXJ0dqVHFi1oBPjg5yEnNyuf4iRPMmPEuX35Zfjm6RCJh4MAhLF68gHfffZtRo8bg6enFyZPHWbZsETKZjOJic9P8d9+dYfb/jh2jadOmHRMmjOG772Yzd+4C/i3YhSs7dh4BHib65O0sJ6hJY3I1wuRuimLfvXuHQYOeR6lMx8XFhZo1wxg1aiyNGglmtyNHCg8Jixb/YPVaTl7aAJeg37B6hEeYt+3Ozs7i8OGDVKsWwPLlS7h+PZHs7CwcHBQEBATQrVtPlMp7BAVZ+lPExR3izp0UiouL6d79WZo1a8G4cROtTpwFBQVmk3RAQBD9+w/k+ef726zrtlM+chcjeqPe5n6ZRIauyP7e2rFNZTP0lixazrTJH3Ip8eEy9K5cvQLAyZPHOXnyuMX+ESNGm4kKzz4bLS4CKvKvedLM0x9HypbGuzo7EX/SyWyRUCuoORKJFIlEgr+/v8Ui4dz1VLYfT2C4TgeUX/qlUhWh1+vp0qW7WUvwqKjaTJw4lhUrlvLJJ18A0KVLD1au/JGlSxfh5ORM8+Ytyc3NYdmyReTm5gA8cp247Nh53PDw8GD8+El8/fXnDH31NTr6qNDpjWxJyEJnMPL6U9XFY1P33ECTWYRLoAdSJzmq1HyyTqfi4K7gg08+Mhu3rDg0YvhI8lJqcufeFXbHLaCaXyTOju4UqrJJTD6GAQ1ffjmLBg0a8eWXs83GWrXqRwwGA0OGDKNjx2hRHNq0qcRbLSioBosWLeeNN0Zz+PBBjh49QrVq1albtz4XLpzjlVdG2pxXnnmmLc880xYQ7DtMz7w7d26zmJMe1jfRPt9VDlPVS0hAE1G0AnBUuBBYtR43U05y65Zgd7D71DVSM/MZEt0YB7mwLgsL9KVrv6F8OfNrtm37s9xnJBCa/qjVan777WdGj34FEMz/J0yYwuLFC8w6Z9uiUaMmNGrUhDNnTllkbj3J2IUrO3YeAUpHn155ZaRY7qLMyKhU9Onq1cts3rwRhULBqVPH6dmzDzVqBJOfn8eFC+e5di1BFK4mT36L27eTkUklnDueIo5x485JtDo1NQObEVS1HkGBluLTtm1b0Ol0hIWFIZXKeO65Pvj6+lFcrObcubPMmzcbZ2cXbt1K4uLFC9Sv3wCA2Ni9vP/+dIxGI7VqhdG5czfWrPmV118fybJlq83KubRaLVOmjCMhIZ4BAwYRElKTo0ePMGvWl2RlZVpkUNipHHqjnnFb3rW5f0HPz7FPCXbKo7IZejVr1qJ9i4qPs5WhF7NxwwP70JReBFTE42ie/iRhqzS+9CIhr0CJp3tV4s79hvZsGr1aRpotEjxdndh/9gZ/btlK9x79yz2fQuGISlVE9/9j776jo6r2No5/J5PeQ0IPSSCUEHpVaYLSpAsIFqRcqqKoiPIiKrbrFa+IqIAgCMi10puASJMqJfQW6UVCEiG9zSR5/xhnYJgkhKYBns9aLi/77HPOztxxyHlm//Z+pINde9269SlZshS7dl3eUdfX15dPPpnEe++9yYcf/tvWXrt2XZ56qg+zZk3Hy8v7pl8DkXtd585d8ff354fvZvL17pM4YSCiuAevNg2mWonL5VYepb1JOXaR5GMXyTHl4OLnRtD9wZRsGkqFoFJguryuqyeWUl8juXiTiQnL79LengE4OTlz5ORGsrLScHP1omRQJf778euUzuN3XSj833cVK1ZizpwltuApNvYCLi4uDuFQQeu13ezfSfntinwrrn0vsFa9eLj5OByztiUlJ5GclsmRM/HUrFDK9veR1cMPP8wHY//L7t07rxlcOTk5MWjQszz9dD+OHTsK5FKxYmVyc3P573/fp1q1GoUad+nSZdi1ayfJyckKrkTk73Plt0+DB/elfftOmExmFi+a4/Dt09e7LnAuOYsqQR4EL1rC7oPHWLZsMQaDAR8fX6ZP/x9Bf+1ucf78H0ycOIGkpES6d+8JYPu3q7PRVvqVlp7AkVOWGVvBJasRUqYWvr6+ZJntU/+fflqMq6sbo0e/ja+v/e5g3bs/zrhxY1mwYA4Gg4GXXhpKly7dKF26DJMnfwqAi4srr732FhERVbn//kb07/8006dPZeTIy7s3LVmykEOHDvLiiyNsNfmdOj3K6NGvMHv2DNq372Tb7l5Ebg/N0JPbpaDSeOtDQpYpjdT0S5w8F0WPx7rjkv2HXb+KZQNZt/s4UVG7rhlclShRglOnTuZ5v8DAIKKjD9u1hYdXZMaMbzl79gzx8XEEBRUnOLgckyZZdiO71xbDFbldHnzwITq1alzgYv7+kSXwj8xn50ezmagB9ud+FVEdMkxEDRhMjZmzAfDzKcVD9zlu1BIaGubwe25+FA7dvaxVL2kZCQ7HrDsiFwsIICXdEozm5jqu8Zf91/vIXMj3E4CHh4ftC36AtWt/ITc3126H7IKcOXMao9GIj0/+uzXfbRRciRQR1m+fvvnma6ZN+wKDwYnaNavxcm1Pu2+fwou5s+t8ClF/pJC192NKlChFs2bN+eWXlTz9dN+/dmkyYzbnv2vX1Y6d2Q4UtNgq7Nu3h5MnT9CqVVuH0MrKumDt4MHPsWvXTpYtW0xKSjI5OTmEhZVnzJj3qFSpCgCVKlWhTp16rFnzMy+/PNJWE75q1Qrc3d3p2PFRu2v36PEk69evZfXqn3nqqT6F/tlE5Ppphp7cLgWVxlsfEtzdfGz/Oycnx6Gf9cGhMCUVVatW49Spk8TGxjqsvRgXF0tAQLE8zwsOLmdX9r5162a8vLyoUaPWNe8pt15SUiLf/G8mi9b8RFpGIi7Obvj7lKJm5baUCKxg1/fE+YvsPnqeuIQUsnNy8fZwpVwJf3oX4j5RUTsYNmxInsesO7ZdHlMSK1YsY8uWjZw6dZKEhARKlixJ7dr16Nu3v8MOyvHx8cyb9wNHjhwmOvoQCQkJPPJIB7sS1rtNUV7Mv6Adbg0GIxlZTn/ziOSf0LRpcz6dMI4T56KoXqkVLs6WWcHpGUmcjdmPj1cQ5cqVI8DHHYMBjp+/yAPVQnFzvfw70NJlywCoWjXS1nb1mmsFSUxMYMqUSfj7+9OlS3dbe0pKCh4eHhiN9jO8Nm/eyL59e7j//ka2Wcz3Av3WKVKEPPjgQzz44OVtov1cMx2+iWoc6kfjUD8Agp+bQmKWG5MnfwY47tIUHBzCG2+8Q5s27fK9Z25uLsfPbsNodKVbyzG4uOQ93bRGjVoO05zT0lLJyjKRmprCvn17+Oabr/Hz86Nz56706mUJl2bPnsGUKRN54YURttDKKjKyOjt3buf06VNUqBBOTk4O0dGHqVw5wuGDuGrVajg5OXHo0MGCXkIRESnCri6Nd/W1zLK68iHBxysIVxdPDAYn1q3/la73V7B7SDh0yhJ6RUZe3s49v4eENm3asWLFMhYtmmf3TfbGjb8SFxfr8CVJXubO/Z7jx4/Rr99APDw8bvo1kOsTE3Oe558fTHp6GiGl6+DrVZwscwYJSedtAafVb4fOsO3QGUJK+HNf1RCcjU4kp2cSn5h2Xffs1OlR2xILVlevmXfw4H4mTvyEevUa0LXrY/j7+3P8+DEWLZrP2rWrmDz5K9uC/wCnT59k9uwZlChRkoiISLZu3Xydr8Sdpygv5m/d4TYvvUeOAxRc3Qt8fX0ZNuwl/vPBe6zc9Cnh5RqSk2Mm+tQWcnKyaVC9KwDuri7UrliGXb//wfdr9hAZVhJ3V2fO/5lM9NktlC0bbFcmePWaa1Zbtmzk229n06DBfRQrFsiFCzEsWbKQ5OQkPvjgY/z9/W19o6J28Nln42ncuCllypTFaDRy6NABfv55Of7+/gwb5vj+nTlzGgCZmZYy2mPHfre11a5dl9q16976F/FvouBK5B90q76JunqXptGj3yIrK4sffviGd999E7PZTPv2nfI898Kfv5OSdpEKwQ3yDa3y8/77b7Nu3eWd5iIjqzN8+Eh8fC7XicfHxwGOv/Bd2RYfH0eFCuEkJyeRmZmZZ19XV1d8ff1s1xMRkTvP1aXxnTp24cDRww4PCW6unkSUb8qh4+v5fk2y3UPCkTNx+Hm507nT5b/X8ntIaNDgPlq2bMMvv6xkxIhhNGrUlJiY88yb9wOBgUH0729fQjRihGXTkLCwChgMBrZt28qGDeto1KgJffr0d/h57uaHhKLinXfeIDs7m2//9yPffLEv336/bdvGtkNnuK9qORpWzXvtosKqXr1mgV/6gaXU7Ntv51G2bLBd+wMPNOGll4YyffoXvPfeh7b2iIiqLFmyioCAABISEujQoeXVlxSRf8CjXbqxcdUZDh5by54jKzBgICgglMZ1nqJEsfK2fo2rh+Lv7cHBkxfYeeQs2Tm5eHm40q3ro/Tp+0yh1kAsVaoMLi6uzJ37A0lJifj5+VO/foO/dpEMs+sbEhJKlSoRbN68gUuXLmI2mylevASdO3ejd+9+eT4vTZv2hd2fo6OPEB19BLBsbnMn/52k4ErkH3Srvom6epcm64K3zZq1oEePzkydOpFHHumAk5Pjt0dHT/8GQHhIw+sdPv36DaJz524kJFwiKmonx479TlKS/bef1h2YrlyE18rV1dWuT0F9rf21o5OIyJ3tytL4KVMnYTbl5vmQUKdqR7r2fIBpX3xu95BQo0Ip7qtaDm9vLzJN177f66+/TcWKlVi2bDGffjoOb28fmjd/mEGDnrXbHAQsgcXq1T+zfPlSAEJDyzN8+Eg6d+7qUK4Bd/dDQlGwe3cUe/fu5sUXRxAUVJycnGxycrNxNro69J0x82s83FyoX8USJGWZs3ExOt3wbsTp6ek4OTnlW4pTunSZPNsbNLgPX18/jh8/Ztfu6emFp6dXnueIyD8rpHRNQkrXLLCPwWCgevmSVC9f0q6996uvOGwqk9+C/OXLV+Djjz8r1JjCwsrz3ntjC9XXKr9NAO4GCq5E7gL57dLk6+tLkybNWLFiGadPnyIsrLzdeZlZaZyJ2Y+vdwlKFLNfI6IwwsMrEh5uWTOkVau2LFw4jxEjhvH551OpWbM2AO7ulllcJpPj00VWVpZdn4L6Wvtb+4iIyJ3LWhp/5UYhVzMYDDzapTNJR3695vUK2rXL2dmZXr360qtX32tep2/fAfTtO+Ca/azu5oeEomDLFsvGMSVLlmL4yy+wafNGcnNz8PEKokal1pQPrgeA2ZzJrt27CSnuy8GTF9h2+CypGVk4G50oXzqAZjXLF3QbBxMmfMT7778NQHBwCF27PsZjjz1eqBAsJSWFtLRUKlQIv86fVkRuFy+zAcjM85h1B0op2hRcidwFCtqlKTDQssNgcnKSw7GT53aSk2MmvNz1z7bKS9u27fn447EsWjTPFlxZv82Oi4t1CM7i4mLt+vj4+OLm5mZrv1JWVhZJSYkEBenbaxGRO0VBDwugBwYp2JVLIYSUC+GBWo+Tk2Pm0Ilf2bz7W3Jyswkv15DktD/Jzs4m5lIyp2MTqFe5LEF+XvzxZxJ7jp4nPjGNvhkZGIx5z+i2cnZ2pkmTZtx/f2OCgooTHx/HsmWL+PTTcRw9Gs1rr4255phnzZqO2Wymbdv2t+Q1EJFbII9dKK2sO1BK0abgSuQuUNAuTdYQKK+dk46d2YaTwUiF4Pq3ZBxZWVnk5OSQlHQ5JIuIsOywceDAPho0uM+u/8GD+/Hy8iIkJBQAJycnKleO4Pffj5CVlWUrJQQ4dOgAOTk5RERURURE7hAFPCyAHhikYFcuhTB50pdMGrsBgOBSNVi89n12H/6JCsH1MZktywikZ5p5qE441f4q5QkvG4irs5Fth8+ydNlPdOzUs8D71axZ2/bFm1WnTo8yYsQL/PTTEtq370ytWrXzOduypf333/+Phg0fyHdtURERuX7aLkHkLtC0aXM8Pb34+efltl/ywLL18oYN6wgODrHb1hvg4KEDXEr6g7IlI3F387n6koBll6ZTp04SExNDUlIin3/+CT17dqFFiwfo0KElzz8/mD17dtn6z537PQDVqtUAYP78Obz00lAAFi2abze233+PZteunbRo0RJn58sZ+qVLF8nIyOChhxrRpEl92z9Dhw4EYNOmDXZjzM3N5eefVzBkyL9o3/5hWrVqRq9ePZgx40tSU1MKfN2OHv2dBx+8jyZN6rN27S8F9hUREZG/V35LIbi5elK2ZDUyMpNJSonD6GQ5ZgAiQuzXLYsItcxK3xkVdUNjcHJy4umn+wKwdeumfPtt2bKRd955gypVqvLuu/+54bW1RETEkWZcidwFrt6lqX37TphMZhYunIvJZGL48Fcdzlm8eCEA4SH3ORyzsu7SFBlZjUuXLpGenkb79p2ZP/9HihULJCUlmZUrf2Lfvj1s376NnTu3ER5ekR49niA+Po4pUz7Hw8OT9PQ04uJiGTp0AB07Pkpqaio//vgt/v4B9O9v/038Cy+MYNy4sVy4cJ6GDR+gePHi/P57NEeOHALgoYda2fWfOnUSs2fPoF69BvTrNxBnZ2d27drJ9OlT2LJlE1OmzMjzl8ecnBzGjn0PV1c30tOvb5tskbtRUlIiX389gw0b1hEXF0u2cy7uJbwo9VAFvMP88zxn/vw5fPyxZeHQpUt/sdvGOT87dkbx2fy8t4EPKxVA7yv+bDabGT/+Qw4dOsiFC+dJS0sjKKg4VatWo1evPlSuHGF3/q5dO1mz5hf27Ini/PnzuLm5Uq5cCF279qBlyzZ6kBS5wxS0FILHX1+6ZZnS8PayLIvg5uqM0Wj/vbyXu2X2dnJS8g2Pw7oQe0JCQp7Ht27dzOjRr/618PLnhdpdTERECk/Blchd4spdmqZN+wKDwYnq1WswZsx7DtPeMzMz+HnVCjzd/ShTvMo1r33q1Em8vLyZOfN7goKCcHd3Z9u2rZw9e4Zlyxbj5uZOSEgogwcPpXv3x/Hw8ODdd8dQpkxZKlQIZ+XK5bz55rvMnfsDEyd+gouLK/XrN+CZZ4Y5bOX6wAONmTHjG778chLr168lKmo7ZcsGExBQjNTUFNq0ecTW12w2M2fOd1SuHMH48RNtuyZ26dIdo9GZn39eztGj0VSq5Pgzzp37AydPHufJJ3szffqUG3nJi7y/K4i4WnxiKj+s2UtObi6P3Fc5zz5btmxk1qyvOHo0GhcXV+rVa8Czz1q2oS/I0aO/079/L7Kzs3n33Q9o0ULbid8KMTHnef75wbZwOiQ8mFnbfiD9Qgqm5LzXJ7o6nL5e1cJKUibI167N28N+pzCTycThw4eoWbMWZcq0w9PTkwsXLrBs2WIGDerLuHGfUa9eA1v/yZM/Iy4ulmbNmtOtW0UyMtJZvXoVb7/9OlFROxg58vXrHqeI/HMKWgohLcOyi7G7mw8ebj6UKlWKmJgYTOZsXJwv7wCZkm75DAsoFnDD4zhz5jQAxYo5Lrvw229beO21VwgJCeOTTybh6+vr0EdERG6OgiuRu4h1l6ZrcXNzZ80vG/LdycmqdOkyfP75VJ57bhADBz5DUFAQZrOZJ554mn79BuZ73vr1a9m06Ve++OIrFiyYC0DDhg/QuvUj+Z5zJR8fH4YPH8nw4SMB2LNnF0OHDuThh1vj6+tn65edbSYzM5PAwEBbaGUVFGT59tXd3cPh+hcuxPDll5Pp12/QDQUzRcXVwZSnpyfly4czYMAQSpYslWcQkbA/lmNfWcolqo1sgrPX5aCgoCAiJSWZ7fvnc+b8PjJNafh4BlI5rAmVQh+wm8WSm5vLT1uP2P684rdolt/3AHPmLLZ9Y71+/Rpef30kFStW4tlnXyAm5jxz5/7Ar7+uxWh0xs/Pj8qVI3jyyafttpLXLLnb55133iA7O9sWThs8zPzkvKXAc8aNG2sXTl+v0oE+DiU9V/Pw8GD6dMc1kDp37ka3bu357rvZdsHVM888T82atTEaLz+0PvbYEwwbNoQlSxby2GOPU6FCxeseq4j8M5o2bc6ECeP4+eflDOw/yNaenpHE2Zj9+HgF4fPXbKt2j7Tlqxkz2X/iAnUqlbH13Xc8BoDGjR6wtZnNZs6dO4ubmzulSpWytScmJuDnZ/97QVZWFl99NdVyjcZN7Y5t27aVUaNGUK5cCBMmTLL7HUVERG4dBVcid7AMFxcMRnO+x40GI+aKchkYAAAgAElEQVS0myuNuXIr6ldffYnffttMdnY2wcEh9Os3gDZt2tn1T01NYfz4D+nUqSuRkdVtwdXNWLp0EQAdO3axa3dzc6dWrTr89tsW/ve/mTRv/jBGo5Fdu3ayYMFc2rR5hHLlQhyuZ33g7tHjCX7++fofuIuCq2fIlCsXQmpqCseOHSUuLpYpUyY6BBGL0tYRs/YETq5GcrKyHa6ZXxBhMpl4btgz/H7qEFXCmuDnXZI/4g6zff88MjKTqVmlja3vnmPnSUrNgL/CLA93V9IysmzHLaVf/6VEiZJMnDiNtLRU+vZ9AqPRiMmURWRkNRo0uI/FixcwbNgQPvjgYxo1agLcG7Pk/gm7d0exd+9uXnxxhC2czk7PKPCc9WvWOYTTN8JkzsZgMOBsvL4lNwMCAnB1dSM52b70p06deg59nZycaN78IXbvjuL48WMKrkTuIFcuhfCvAb3xd69GTo6Z6FNbyMnJpkH1rra+vXv1YsHcH9i07yQJKekE+Xlx/s8kjpyJJ7i4H61atsScY+lrXQqhdu26fP75VNs1Xn55GEFBQVSpUtW2q+DKlcs5e/Y03bv3JDKyuq3v4cMH+b//exnIpX37jmzd6lgCffXvSDNnTgMgM9MyC+zYsd9tbbVr17X7skZERC5TcCVyBzPlZvPsstH5Hp/U/n1u9j/zK7eiLleuHKNHv0VWVhY//PAN7777Jmaz2W7nnMmTPyM3N4chQ567qftapaamsHbtL5QuXdZuZoXVmDHv8d57b/HFF5/zxRefA2AwGOjd+18MGDDEof/q1T+zZctGJk+ebrco/J3m6hkyV8oviDi77AiuAR54lPTi0p4LducUFEQsWbKQgwcPUL9aF6qUt3zbXDH0fn7dMZMDR1dToZzl/5fktEy2HjxNvSpl8fV0Z82uYwT5enL6iuBq166dxMfHMWDAEDw9PZk37wcSEhL4z38+Yu7cHzh8+CATJkymZcs2PP74oyxZsoBGjZrcNbPkiqL8wmnXQA9KNS9PQK1Sdv2zM8x8/Nm4mw6nf91zgl92HgXA39udGhVKUSu8dJ59s7OzSU5OJjvbTGzsBb777n+kp6fxwAONC3Uva5lRsWKO6+SISNFmXQrhu29ns+fwCgwYCAoIpXGdpyhRrLytn7e3F92aVWfrwdOcOH+Rgydj8fZwpX6VsjSIKIfRaLQFV/lp3vwhNmxYz7x5P5CcnIyHhweVKlWhf/9BtGrV1q7v8ePHyMqyBFCffvpxnte7OriaNu0Luz9HRx8hOtoyS7lfv4EKrkRE8nHnPrWJyN/iyq2oP/10im1Xn2bNWtCjR2emTp3II490wMnJiX379rBo0XzefPNdvL1vzcKkq1atJCMjg/btO+a5sLKLiytlypSlePH23HefpWxt3bo1zJo1HVdXV/r06W/rm5yczKefjqNjxy5Ur17zlozvn5BXMGU2m3F3dwfyDyIASrYoT9aldLvrXSuIWLVqBe7u7lQMud+uPaJ8M87E7OPUH7uBDqzbfRxfL3fuqxrCkTNxeY798OGDwOWdJ1NTUwEICipOZGR1du7czunTpyhVqjROTk62Us+7YZZcUZBXeWluruWYNZxu0aIV0b8f5PSZM5yed5CzS4/gUdqH4veXwy+yOOdXHYOc6wunLyX9wfIN48nNzcGn7CDKlw4grGQAXh6upKZnceDkBTbsPclvB88wZWkzPDw8qVOnLoMGDSU0NIxTp07Qu/fjtut5e3vTsWMXjh8/RufObUhKSsLfP4CIiEheeWWUXUAVHx/H4sXzKVOmrMN6fyJyZ3jwwYdo9XCray5x4OHmQos64bSoE15gv9Kly7Bx4w6H9l69+tKrV99Cjaldu460a9exUH2t8rqniIhcm4IrESlQfltR+/r60qRJM1asWMbp06coWzaYsWP/Tf36DR2+lbwZy5Ytwmg02s3qssrIyGDIkH9RpUoV3n77P7b2li3bMGbMKKZPn0KLFg8TEhIGwMSJn5CTk8uQIc/fsvH9E65Vvnn1LLkRI/6PiRMnkJ6dwYW1J/AsZ79wbEFBRE5ODtHRh4moUhWj0cXuWKB/CAYMXEw8w8+rfuFkzCW6P1gDJ6f8y1Pj4y2BlnVR/oYN7+d//5vJuHFjqVGjFgBRUdvZtu03PDw8ePzxXnfNLLl/Wn7lpd9+a1lDyhpOv/vum1SrUZ3M8k7EbjoNuZCTlc3J7/dRrF5pLkad56333il0OJ2bm8Nve+dgdHLBnJ1JWGgoHR6oesXxXI798ScAWeZsnnziMdw9fFmwYC6DB/dl8uSvKF26LOPHT8RsNnH27Fnmz/+RZcsWU65cCN27P05AQDEuXbrEgQN7SU1NtQVXGRkZjBo1gvT0dMaOHa/3j0gR52U2AHlvCGHC8+8djIiIFBn6DU5EClTQVtSBgZYSteTkJObP/5HTp0/y3HMvcvbsGVuftDTLjJrz58+RmppC2bLBhb73sWNHOXToII0aNXHYfRBg7dpfOHv2NEOGDHU41qJFS1avXsXevbsJCQnjyJHDLFu2mP79B5OUlEhSkmU3okuXLgJw8eKfnD17hhIlSuLq6upwvaLkWuWbYWGW0glrEDFhwke4ublRrncNoidvJ/18iu1aqacT+XPHuXyDiOTkJDIzM/N8/Y1GZ1xdvUhJu8S48Z9QLawkpQN9Chx7RoZl7SRrCFq3bn2GDx/J9Olf2GZjffLJRwQHhzBlykwCA4N49dUX7vhZckVBfuWlO3fuYPPmDbZw+p13/oPBw8yzy14jKyGDS7tjKNclglNzD3Ix6jze5QNo1bY1uekF3OwKR05sJDE5hsjwFuyNXulw/Pj5i5yOTSSsVAAnYy7h6urKv/41iDZt2tG7d08++eQjJkyYRIMG9wGW/2a/+moqrq6ulChRkqef7pfnfTMzMxk16mWOHDnE6NFvUatWnet/0UTk72U2EzVgcJ6Hasx03KhBRETuDQquRKRABW1FHRdnaQsIKEZMTAw5OTmMGDEsz+sMHNgHDw8PVq3aUOh7L1myEIAOHbrkedw6eyc723HRCmtpnNls+feFCzHk5uYybdoXDmtMAIwf/18Apk37moiIyEKP8Z9wrfLNc+fOApaZZ4cPH2TRovmMee9tFmSvwzciiEu7LTss5WTncGbRYbwr5B9E2IImVxfHg4DRyZmUtD/x9najUfXQa47dWs5oMplsbf7+AVSpEom7uxu//rqODh26sGnTr7zyyotERla7K2bJ/dMKKi8tKJx29raEuDlZOZCTC7kQdF8wZ8+cITfD8ivE1eF0+dDL74PU9EvsObKCGpVb4+bqlefYzsUlARAZVoKTMZdISLCEymXLBlOrVh22bdtKTEyMbeevhQvnkZycRPv2nVi2bDHHjx8jJCTUbjaVJbQawY4d2xg58nWHdWZERERE5M6h4EpECnTlVtR9+vTH09MyVT8+Pp4NG9YRHBxCcHA52rXrSM2atRzOnz9/Drt27WTUqDfx8blcopbfVtRWWVlZrFq1nGLFAm27yl3NOrNoxYqlPPxwK7tjy5cvBSzBG0BkZDXeffcDh2vs2rWT+fPn8PjjvahWrTplyhR+Rtg/pTDlmwD+/v6XyzfbtmbBsnW2IALgYtR5MuNTKdO2Yr5BhHVdMVOWCTwcx2IyZ2Iyp9PjsadIPbmDjCxLIJWWafm36a8A0RpUBQUVByyhZ1hYeRYvXsC4cR8wY8Y3rFq1ElhHjx5P8Nhjj9Ov35OcP3+OAQOG3PGz5P5pBZWX1qljWQz46nDanGYi66Ilzby07wKZ8ZbA9OR3++j53WMO97CG0+vXXt5Za/u++Xh7FiOifDNOnNuZ59iycyzBc/pf75lixQJsx9zcLEHnwYP7bZ8TW7ZswsvLy7Y+Wu/ePXFycqJ69Zo8//xLhIdX4rXXXmH79q28+upoOnTofD0vlYiIiIgUMQquRKRAV25FPXhwX9q374TJZGbhwrmYTCaGD38VgEqVKlOpUmWH8zdv3ghA48bN7HaDy28raqsNG9aRmJjIk0/2znddmkaNmlK1ajW2bNnE0KEDefDBh4Bc1q9fy549u2jRoiVVqkQAlsCkRYuWDtdIT7c8mFerVj3P40VRYco3ATZu/PVy+eaZM2T+mUZmXJrteGZsKuTCidl76Dk77yDC+tqvXrMKV5dN+PuUombltpQIrIDZbMJktrx+X0z9Ms+xnv8zGYDTp08SEhJqm8124MA+GjS4j9mzZ1KiREkWLJhnCxuff34wISEheHv7kJSUeM1Zcp6eXjRseD/PPjuMMmXK2vWJj49n3rwfOHLkMNHRh0hISOCRRzowevRbeY73blVQeemSJQtxdXV1CKcPTdhCTroZgD+3n8M7PAD/6iUxujszsO6T5GY5AfmH08fP7eRc7EGa1u+Lk5PR1p6advk9CFDM13K/nUfOAdCsqSWozsjIYP/+vQDExsbY/Sxms5l161ZjNBoZPfot/vzzT2bNms5zzw2iSpWq7Nu3hxEjRtGxY96zNUVERETkzqHgSkSuyboV9TfffM20aV9gMDhRvXoNxox577bt0rV06SKAAmdLGI1GJkyYxOzZM1m/fg2TJ3+KwWAgOLgczzzzPD17PnVbxvZPK0z5poeHB4cOHSiwfDNhfywGZydCukXmGUT4+fljMpkICAjg0qVLRJR/kD8TT3Mp6Q9KBFYg7tIJAFydPWjc5D7iTh0i02Tm9IVEMk2WwCPIz5P4xDRKliwNQJ069QgMDGLJkoX06PEk8fGxGAxOrFu3moyMdGrUqEXz5g+xevUqkpIScXZ2ZsyY9+zGvXTpIn77bQv+/gE0adKMwMDiLF48n2ee6c/06bNts7rAEpjNnj2DEiVKEhERydatm7kXXau8NDc3h7i4WFs4bSYLJycDOU4Git8fTEZcGhjAu7w/bsU8adHyIXLTLb9C5BVOZ5nS2XnA8t/wkeMbCClVwzaWGTNm4pKTRgl/b7zcLbsKGgyQlJZJ2eJ++AcEcOjQAaZPn0JiYgLAXyGVMy4uzqSmppD711aIr7zyGq1bPwJAlSoRDBs2hL17d1O/fkPc3d1ZufInu9chPLwSFStWui2vsYiIiIjcHgquRKRQHnzwob9mNF2f0aPfynN2S35bUVuNHz+xUNf39PRi8OChDB7suEB7YdzIdtb/tMKUbz7xRC/++9/3KVGiJHXr1icbM2t+XUN2hhm34p5kXkilXJcIjB4u+FUtTouWD2FKhnPnzuLnZwkfXFxcmDXre9avX8P48R+Sk2PibMx+srLSqVK+CdEnNwEGHmn2Em/8pyOzxr4MQFpGFl+vjMKUnYPHX2tjWRd+d3Z25oUXRjBmzCiGDh2Av38AcXGxuLq6UqxYIO+88x+KFy9B1arVePbZAZjNZkJDw6hQoSJgKTEdO/bfADz33Iu0bdsegObNW9C//9NMnz6VkSNH216riIiqLFmyioCAABISEujQ4c6YVXerFaa89IUXRrBq1QpLOO3khHtpb0o1L49XqOX9cGrOfo5Oi6LK8/dd835RBxfDX+HS1apXr87u7ZvYc+w8WaZsnJ2dKO7nRXqmmXNxiXR5tBsAtWvXpV27jixduogzZ84wdepEu7XRhg172S7Yrlu3Pk5OTuTk5LBjxzZ27NjmcO9+/QYquBIRERG5wyi4ErmHJCUl8vXXM9iwYR1xcbEYcLEr/QKIv3Sak+d2MnDQLKJ//5309HTefON1OnZob3ctg8FIxl8zdK62efNGFi2ax7FjR7l06SIuLq6UKVOGtm3b07lzN9tDNEBU1A6GDRtS4LgnTZpmm9m1a9dO1qz5hT17ojh//jxubq6UKxdC1649aNmyjW1NprtZYco3Gza83zZLbt261RicnPD4K4j4c+cfZF5IxbdKEM5el9eGspZventbdgbs1q0HQUFBtGvXkWXLFrEv+hcA0jOT+HXHTM5e2E/1Si3x9rQvWbyUko6LixFTdg4JKZbF3efN+9EWXvXtOwA3NzdmzZpOQsIlwDIjqHnzh9m8eSNnz55h4cK5GI3OZGebOX78mC242rVrJykplvLDK99HlSpVoU6deqxZ8zMvvzzSVuLo6emFp2fei4LfSwpTXhoRUZXHHnscwLar4JUCapcmYV8siQfj7NqvDqcPHz7EsTPbqVm5DWFlLTv5JafGk5Fp2c2yRo1qFM+NxcfDDaPR/jMkISWdJl0H4udfmuDgckyaNAGAMWPeo2HD+wF46qnunDp1kvr1Gzj8LBERkURHH2bduq2Fe2FEREREpMhTcCVyj4iJOc/zzw8mPT2N9u07Uz4sjJ8W7CIh6TxpGYm2fn/EHiL65CbKlw/D38OZ9HTY/NP3XDqwxu56vUeOA/IOro4dO4qTk5EOHToTGBhEZmYGe/bs5tNPP2bz5o2MHz/RFjCFhZXnjTfecbiGyZTFhx++j5+fP5GR1W3tkyd/RlxcLM2aNadbt4pkZKSzevUq3n77daKidjBy5Os3/VrdCQpTvnnlLLkrg4g/d/5R4LU9PT1JSUkmLKy83ULeLs6WhbJT0y9iMDhRv9qjVA5r7HD+2dhE0jIsM2OS0zMB+P77/9mO9+07gMaNm9K4cVMAdu7czrffzubXX9eyfPkSfHx8aNjwfjw9vVi+fCnFil0OWw4fPghYZuQ1aGA/8ycysjo7d27n9OlTVKgQXshX8t5wdXmpj1s2TrmWcs6EP88DEFLCGz9Xy/9fyQbH/7ZzTZZF1LPTTQ7H4HIwvmrVciCXvdEr2Bu9wqHffz/6GABnoxPm7Bxa1qtI1VBLsObv7UHdOnXINFlmhW3duhkvLy9q1Li88UPJkqU4deokr776EomJiXbhdWzsBQICitndz2w28+23X7Ny5U/88cc5PDw8qVOnLoMGDSU0NMyur4JxERERkaJHwZXIPeKdd94gOzubmTO/JygoCFdnIyf2+Dr0qxTWiMjw5rzydgveGNaH878lX/e9nn66r0Nb9+6PM27cWBYsmMOhQwdsYVSxYoF5blW/atUKcnJyaNu2vd3i7M888zw1a9bGaLy82PNjjz3BsGFDWLJkIY899rhtds7d7uryzcthRKZD3yuDiJCukYR0jXToYy3fHDVqBLGxF+wW8s7JNvPZp1NITI7h/lo9CS/XMN9xlSxmmbFVJaQ4retXovfIcbYgIi/16jWgXj372TPx8XH06vUYZcqUtVtHLT7eMtunePESDtextsXHxym4usrV5aV+rkbOfj6Yi2kmVv/yO2V9XHFa9C5HTTnkkkupV76yOz83J5f4bWcB8Az2s7VbdwdNSkrinXdeJz09jZYPt2bP9jNkZ5tITb+Ev28ZShQrz4U/jxF9ahMGgwEvdxe83F25cCkl3zHPnfs9x48fo1+/gXh4XN7S8sIFy0LtBoOB55570S68BujY8dHL487N5f/+bzhbt26madMH6datJwkJl1iwYC6DB/dl8uSvKF++gq2/gnERERGRokfBlcg9YPfuKPbu3c2LL44gKCgIs9lMjjkrz74ebj63bRzW7eyTk68dhlkXZ+/Y0X5x9jp16jn0dXJyonnzh9i9O8qurOxe45Rr5uzng/M85vdi3rv+5SWvhbxdnY0c3u7M4rXvs/vwT1QIro8hj1k5CSnprNrxO17urjStEXZDP0dGRgajRo0gPT2dsWPH2wWXGRmW0sMr12mycnV1tesjl11dXtqtU3v+3BfHsuiLmHNyeeY+y+L555IzeXXlCR5K+zexaacwerpgSsokYd8FMuPTCKhdCu8wx91Bvby88PLyZubM7ylTqiQT3lvjMAZTdiacgrfHvEnc3lUcPRfP8t+iAVi86SC+Xu4U8/Fg7rz5bN6ynQ0b1tGoURP69Olvd50RI0axcOE8Vq/++a8+TalVqw6HDh0gNzeXli1b2/pu2LCerVs306nTo7z66uW1z9q0aUfv3j355JOPmDBhkq1dwbiIiIhI0aPgSuQesGXLJsBSYnNl6ZePVxA1KrWmfLBjGHQrpKWlkpVlIjU1hX379vDNN1/j5+dnV/qXlz/+OEdU1A5q1qxNSEhYoe5lLYG6sqzsbmN0dSY7J+8Fry1uTRlTfgt5u7l6UrZkNU6c3UFSShx+PiXtzktMzWDBhgMAdGpcFQ+3/GdZ5SczM5NRo17myJFDjB79FrVq1bE77u5uKVe8cpFuq6ysLLs+Yu/K8tKJU74EcxYRxT14tWkw1UpY1gEL8nThoQr+HD54mNizp8jOzMbo7oxHKW9KPhiGf82SeV47NTWVgQOf+SsYN2HOzsLZ6JpnX2vAeKVSxXz4/Vw8h0/Fsi36c0JDyzN8+Eg6d+5qFyKBJbyuUaMWlSpVZtmyxXz66Ti8vX2oUqUqhw8f5NKli7a+UVGWDSDatetkd42yZYOpVasO27ZtJSYmxhaqKxgXERERKXoUXIncA06fPgWQZ+nX5t3fkpObXWDp1416//23Wbfu8syLyMjqDB8+Eh+fgmd1LVu2mNzcXDp27FKo+8THx7F48XyHsrK7TXZOLv96b1W+x+e92eSW3KeghbytM/KyTGl27X/8cZ4FGw5gMufwaNNIgvyuf0F0S2g1gh07tjFy5Ot5lpAGBRUHLDN9wsLK2x2Li4u16yOOrOWlfq6Zec7O83N3Zuh9ZfB78UueWTo6jyvYK126DE891Ydvvpl1zWA8vFxDwss1pOXDjZi1Y5nddRpWLUfDquUArllaCpbdKXv16kuvXn1tbZMmfcrhwwftwmuTKf8w083N0nbw4H5bcJWfeyEYFxERESmq8l5ZWUTuKleXfrVu/QidOnah1QNDcXXxYPfhn8jNzbnl9+3XbxDjx09kzJj3bOvOJCUlFnhOdnY2y5cvxcvLixYtWl7zHleWlY0e/ZZdWZncmKpVqwGXH9avZF3I3/2KktKUtIsMeXYoWSYzXZpEUtzf+7rvmZWVxWuvvcL27Vt59dXRdOjQOc9+ERGWtbkOHNjncOzgwf14eXkREhJ63fe/2xhdncHZmP8/t2h2HtgH48nJiYx54x3ur9kDJydnNu/+lmNntt2ye+Unv/Daun5VVNR2u/4ZGRkcPLgfgNjYmBu6toiIiIj8PfSEJ3IPuNHSr5sVHl6R8HBLWU2rVm1ZuHAeI0YM4/PPp+b7ALht2xZiYy/QuXPXa5Z8XausTG7M1Qt5e3p6ApCekcTZmP34eAXh4xUEWEKrX7ZMxmDMonPjSEoE5B9aWRfydnNzt5vhkpWVxahRI9i2bQsjRowqcKZdnTr1CAwMYsmShfTo8aRtbL//Hs2uXTtp166jwkv+vtl54BiMe3m4c2SHO8GlalxzTbRboaA10Vq3bsesWV8xbdoU3N09qF+/IYmJCUyfPoXExATb+TdybRERERH5e+g3MJF7wI2Uft0Obdu25+OPx7Jo0bx8g6vLi7IXXCZYmLIyuTFXL+Tdvn0ncnOyWbHpf+TkZNOgelcATOYMVm+dTGr6RXr2eIyLx3dzKcU+BAgpcXkHOutC3rVr1+Xzz6fa2t9553V++20z9es3xN3dnZUrf7K7Rnh4JSpWrARYSsReeGEEY8aMYujQAXTs+Cipqan8+OO3+PsH0L+/Y/nbzJnTAMt7BuDYsd9tbbVr16V27bo3+5Ld0/6pYByuHV77+vryySeTeO+9N/nww3/b2mvXrstTT/Vh1qzpeHnlHbYqGBcREREpGhRcidwDqlatxsKF8wpd+nW7ZGVlkZOTQ1JSUp7HL126yKZNGwgPr2QrCcvvOoUpK5Mbd+VC3tOmfYGTkxO+XsE0rvMUJYpZ1pbKzEojJc2yEPYPP87J8zqPNq12zXsdPnwIgB07trFjh2NZWb9+A23BFcBDD7XEzc2NWbOmM3HiJ7i4uFK/fgOeeWYYxYuXcDh/2rQv7P4cHX2E6OgjtmsruLo5/1QwXtjwOjy8IjNmfMvZs2eIj48jKKg4wcHlmDRpAgChoWE3fG0RERERuf0UXIncA66n9Ot65Ff69eef8QQGOl5v7tzvAahWrUae11u+fBlms5mOHfMPoq6nrExujnUhbwBXZyMT3ltjd9zbsxhPdRgHwEtvNGLW2JcLvF7p0mXYuHGHQ/vcuUuue2yNGzelceOmheqb1z3l1vkngvEbCa+Dg8sRHFzO9uetWzfj5eVFjRq1bvraIiIiInL7KLgSuQcUtvQLLGsWnTi3k2lfHeHoOctsmhPnL5KSbimzqhJSHF9Py9pT+ZV+9e7dk5o1a1O5cgTFixcnISGB7du3sXPnNsLDK9KjxxN5jvOnnxbj6upG69b5z264nrIyKTwvswHIzPe4Cc+/bzByR7k6GHf1tYRUtysYvxXh9dy533P8+DH69RuIh4fHLb22iIiIiNxaCq5E7hGFKf0CSE27yN4jK9h75PK5x/64yLE/LCFW6UBfW3CVn+7dH2fbtq3Mnz+HpKRE3NzcCQkJZfDgoXTv/rjdg6LVvn17OHnyBK1atcXX1zffa19vWZkUktlM1ADH9aGsasyc/TcORu4kVwfjnTp24cDRw0Sf2pJnMD7tqxnsPnyGP5PSAcdg3OpWrIkGMGLEMMqUKUtYWAUMBgPbtm1lw4Z1NGrUhD59+tudq2BcREREpOhRcCVyD7lW6RdAyaCKPNVh3E2VfvXrN5B+/QZe19hq1KhVqJKuGykrE5Hb68pgfMrUSZhNuQQFhOYZjH8xZarduVcH49dyveF19eo1Wb36Z5YvXwpAaGh5hg8fSefOXTEajTd1bRERERG5/W5bcJWQkMCUKVP45ZdfiImJwcvLi0qVKvHCCy9Qv359W7/jx4/z0UcfsX37dkwmE5GRkTz//PM88MADt2toIveMgsq/VPolIreSNRjPLxQHSzC+47ct1wzF4datida37wD69h1QqL4KxkVERESKntsSXJ07d46nn36atLQ0unfvTlhYGCkpKRw5coQLFzUWpl4AACAASURBVC7Y+p0+fZonnngCo9HIgAED8Pb2Zs6cOQwYMIAvv/ySRo0a3Y7hidw7Cij/UumXiNwoheIiIiIi8ne5LcHVK6+8QnZ2NosXL7Ztk52XcePGkZSUxPz586latSoAXbp0oUOHDrz99tusWLECg8FwO4YoIiIiN0qhuIiIiIj8TZxu9QW3b9/Ozp07GTBgACVKlMBkMpGenu7QLy0tjTVr1tCwYUNbaAXg5eVF9+7dOXnyJPv27bvVwxMRERERERERkTvELQ+u1q9fD0Dp0qUZMmQItWrVonbt2rRp04ZFixbZ+h05coSsrCxq167tcA1rm4IrEREREREREZF71y0vFTxx4gQAb7zxBqGhoXzwwQdkZWUxc+ZMXn31VcxmM926dSM2NhaAkiVLOlzD2nblelgiIiIiIiIiInJvueXBVWpqKmAp+fv6669xdXUFoFWrVrRs2ZLx48fz6KOP2soHrcev5ObmBpBniWFB/P2LxoKwSWlZN3zutVb0up0rfjkZDEXmNbxX3Mx7BfR+udfos0UKS58tcj302SKFpc8WuR76bJHC0meLXMstD67c3d0BaN++vV0o5efnx0MPPcTChQs5ceIEHh4eAGRlOb5JMzMtOxVZ+xRWQkLajQ771nI23vCpuTd5/Gbk5OYWndfwXnET7xXQ++Weo88WKSx9tsj10GeLFJY+W+R66LNFCkufLUVe8eI+/+j9b/kaV9Yyv+LFizscs7YlJibadhvMqxzQ2pZXGaGIiIiIiIiIiNwbbnlwVbNmTQBiYmIcjlnbAgMDqVy5Mq6uruzevduhn7WtevXqt3p4IiIiIiIiIiJyh7jlwVXLli3x8vJi8eLFtvWuAGJjY1m9ejVhYWGEhobi5eVFixYt2LZtG4cPH7b1S01NZe7cuYSFhdlCMBERERERERERuffc8jWu/Pz8GDlyJG+++SY9e/akW7dumEwmvvvuO0wmE2+88Yat78svv8zWrVv517/+Rd++ffHy8mLOnDlcuHCBKVOmYDDczmXURERERERERESkKLvlwRVAz549CQgIYNq0aUyYMAGDwUCdOnX46KOPqFevnq1faGgo3333HR999BFTp07FZDIRGRnJtGnTaNSo0e0YmoiIiIiIiIiI3CFuS3AF0Lp1a1q3bn3NfuHh4UyePPl2DUNERERERERERO5Qty24kjtbkyb182z38PBg1aoN+Z43f/4cPv54LABLl/6Cv7//Ne/13HOD2L07Kt/j9es35JNPJtm1mc1mFiyYy/LlSzh9+hRGo5GyZYPp1KkrXbp0s+t75MhhZsyYyt69e8jISKds2WA6duxCt249MRpvbutVEREREREREbl9FFxJvmrVqkOnTo/atTk75/+WiY+PY8qUz/Hw8CQ9Pa3Q9+nT51907NjFoX316lVs3ryBxo2b2bWbTCZGjhzOrl07aNWqLZ07dyM7O5uzZ09z4YL9bpa7d0cxfPhzeHl50717T/z9A9i+/Tc+/fRjTpw4wciRows9ThERERERERH5eym4knyVKVOWNm3aFbr/uHFjKVOmLBUqhLNy5fJCn9egwf15ts+aNR1XV1fatHnErn3mzGns3LmN8eMnUrdu3jPDrD755CMMBgNffPEVZcsGA9C162N8+OG/Wbx4AW3btqdWrdqFHquIiIiIiIiI/H2c/ukBSNFmMplIS7v27Kn169eyadOvvPLKazg53Xz53Z49uzh9+hRNmzbH19fP1p6ens6cOd/TpEkz6tatT25uLmlpqXleIykpiaNHo6lVq64ttLJq164jAD/9tPimxyoiIiIiIiIit4dmXEm+1q1bzc8/Lyc7Oxt//wAefrgVAwc+i7e3t12/1NQUxo//kE6duhIZWZ0FC+be9L2XLl0E4FBCuGfPLtLSUqlSpSqffPIRy5YtJj09DX9/fzp2fJT+/QfbyhlNpiwA3N3dHa7v5mZpO3Bg/02PVURERERERERuDwVXkqeqVavRokVLgoPLkZqawtatm5g370d2745i8uSv8PT0tPWdPPkzcnNzGDLkuVty79TUFNau/YXSpctSr14Du2OnT58C4Mcfv8PFxYVnnx2Gr68fq1YtZ/bsGcTFxfL6628DUKxYIP7+/hw4sI/MzAxbWAWwa9cOAGJjL9ySMYuIiIiIiIjIrafgSvL05Zez7P78yCMdCA+vxNSpk5gz5zv69OkPwL59e1i0aD5vvvmuw0ysG7Vq1UoyMjJo374jBoPB7pi1LDA5OYmvv/6B0NAwAB5+uBXPPz+YFSuW8dRTfShfvgIGg4EePZ5k6tRJvPbaqwwYMBg/P3927NjG9OlTMBqNZGZm3JIxi4iIiIiIiMitpzWupNCefLI3Li4ubNmyCbCsfzV27L+pX78hrVq1vWX3WbZsEUajkfbtOzkcs86aioysbgutrNq2bQ9YdhK06tWrL717/4tdu3YycGAfevTozGefjWfo0Bfx8fHF09Prlo1bRERERERERG4tzbiSQnN2diYoqDgJCQkAzJ//I6dPn+S5517k7Nkztn7WWVHnz58jNTXFYWH0ghw7dpRDhw7SqFETihcv4XC8RAlLW2BgoMOxwMAgwDIby8rJyYlBg57l6af7cezYUSCXihUrk5uby3//+z7VqtUo9NhERERERERE5O+l4EoKLTMzk9jYC7awJyYmhpycHEaMGJZn/4ED++Dh4cGqVRsKfY8lSxYC0KFDlzyPV61aDYDY2FiHY3FxlraAgGIOxzw8PKhe/XJItXbtL+Tm5nL//Y0KPTYRERERERER+XspuBIHiYkJ+Pn5O7RPm/YF2dnZNG7cFIB27TpSs2Yth37z589h166djBr1Jj4+vrZ2s9nMuXNncXNzp1SpUg7nZWVlsWrVcooVC6RRoyZ5jq1MmbLUqFGL/fv3cuTIYapUiQAgOzubxYsXYDQaadDg/mv+fFOmTMLf358uXboX2FdERERERERE/jkKrsTBrFnTOXBgP3Xr1qdkyZKkpaWzdesmoqJ2EBlZne7dewJQqVJlKlWq7HD+5s0bAWjcuBn+/pcDsLi4WJ56qju1a9fl88+nOpy3YcM6EhMTefLJ3jg75//WfOmlVxg6dCAvvvgs3bv3xM/Pj9WrV3Ho0AH69RtoF4pt2bKRb7+dTYMG91GsWCAXLsSwZMlCkpOT+OCDj+3GJyIiIiIiIiJFi4IrcVCnTj1OnjzB8uVLSUpKxMnJieDgEAYNepaePZ/Czc3tttx36dJFAHTo0LnAfpUrRzB58ld8+aVlh8OsrCxCQ8N47bUxtGvX0a5vqVJlcHFxZe7cH0hKSsTPz5/69RvQp09/QkLCbsvPISIiIiIiIiK3hoIrcdC0aXOaNm1+w+ePHv0Wo0e/5dBeunQZNm7cke9548dPLPQ9KlasxNix46/Zr3z5Cnz88WeFvq6IiIiIiIiIFB0KrsTG3dkDNydDvsdzDZCcYfobRyQiIiIiIiIi9zIFV3JZDpyemv+MqJBB9f/GwYiIiIiIiIjIvc7pnx6AiIiIiIiIiIhIXhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkaTgSkREREREREREiiQFVyIiIiIiIiIiUiQpuBIRERERERERkSJJwZWIiIiIiIiIiBRJCq5ERERERERERKRIUnAlIiIiIiIiIiJFkoIrEREREREREREpkhRciYiIiIiIiIhIkeT8Tw9ARERERERERORucfr0SWbMmEZ09GHi4+Mwm82ULFmKBx5ozBNP9CYoKMiu//79e5k9ewbR0UdITEwkKCiIevUa0KtXX8qWDS70fU+cOM6sWdPZtWsHSUlJ+PsHEBERySuvjKJYsUBbP7PZzLfffs3KlT/xxx/n8PDwpE6dugwaNJTQ0DC7az733CB2747K956NGjVixowZhR7jjfh/9u48IKrqbeD4lxlWUUR2gRQVRdRcgczdXMPADZdEFMO0LKlMs8wyW7XcstREFpd+ae6KirlmmlsKmilSLoEkAioIwrCNvH/wMjoN2yCg1fP5530598w519v9nbnz3OecI4ErIYQQQgghhBBCiCqSkpLCrVs36datJ7a2diiVSq5cucT27VvYt28PK1d+R716VgAcP36Ut956HUdHZ4YMGY6lpSVXr15m+/Yt/PjjAVavXoetrV25fZ44cYx33pmKk5MTfn4jqVfPirS0NM6f/5WsrCxN4KqwsJC3357C8eNH6dq1O0OHjiA9PY0tWzYycWIgy5aF06hRY027Y8e+wKhRI3X6i4qK4uDBg/Ts2bOKrlrpJHAlhBBCCCGEEEIIUUU8PLzw8PDSKW/Tpj3vv/82u3ZF4u8/FoD1679DoVDwzTfhWFpaauo2atSEuXM/5uDBfQwfPqrM/tLSbjN79kzatevA3LkLMDQsPdRz+PAhjh8/iq/vYN56611Neb9+3owZM4JFi+bx5ZdLNeWenh2xta2j086yZcswNjbG19e3zHOrCjWyxpVKpeKZZ57Bzc2NDz/8UOf4lStXmDRpEp6enrRt25ZRo0Zx7Nixmjg1IYQQQgghhBBCiGrn4OAAQGZmpqYsKysLY2MT6tTRDg4VTyc0NTUrt92tWzeRkXGHSZOCMTQ0JCcnh4KCghLrRkefAsDbWzvg5OTkTJs27Th9+iQ3btwos79Tp05x9epV+vTpoxVsqy41ErhavHgxaWlpJR5LSEjg+eef58yZM4wfP55p06aRnZ3N+PHjOXr0aE2cnhBCCCGEEEIIIUSVys3NJT09nZSUZE6ePM4XX3wKwNNPd9bU8fLqSHZ2Fp988gF//PE7qakpnDhxjK+/XoSLSyN69+5bbj/Hjv2Mubk5d+9mEhg4it69u/DMM52YNGk8sbHnterm5+cBYGpqqtOOiUlR2YULv5XZ38aNGwEYNmxYuedWFap9quD58+dZtWoV06ZNY86cOTrH58+fT0ZGBps3b8bd3R2AQYMG8dxzzzF79mx2796NgYFBdZ+mEEIIIYQQQgghRJXZsWMrCxd+ofm7fn1H3n//I9q0aacpCwgYR1paGrt2bWfPnihN+dNPd+aDDz6hVi3zcvtJSIhHrVbz5puT6dmzN4GBQSQlJbFqVRiTJ08kJGQVjRs3AdCsXxUd/Quurk01beTk5GgCVikppWdc3b17l927d+Ps7EzHjh0reCUeTrUGrtRqNe+99x5du3alT58+OoGr7OxsDhw4gJeXlyZoBWBubo6fnx+LFy/m3LlztG7dujpPUwghhBBCCCGEEKJKde3agwYNXFCpVPzxRxxHjhwiPV17NppCocDW1hYPDy+6detBnTp1OXfuLJs2fc+sWTOYM6fsNasAVKps1Go1ffs+y7vvfqApd3NrTnDwS6xcGcqHH34GQN++3qxaFU5o6HJMTc3w8PDizp10wsKWc+dOOlAUxCrNjh07UKlUDB06tMaSjKo1cLVy5UquXLnC4sWLSzweFxdHXl4ebdu21TlWXCaBKyGEEEIIIYQQQvzT2NnZY2dnD0C3bj3o3v0ZXnxxDLm5uQQEjAPg009nc+7cr6xZ871m+l737j1xdnZm3rw5REXtwMdnUJn9GBuboFJl8+yzz2mVt2/vgb29AzExpzVlFhYWLFq0lI8/fp/PP/9EU962bXv8/ceyalUY5ua1S+1r48aNKJVKhg4dqt/FeAjVFri6du0aX331FZMmTcLZ2ZnExESdOikpKQDY29vrHCsuS05OrnCflpa1Knm2VSsjO6/Sny0vXlm98cyyW1cYGDw21/jf4mHuFXi090vx/XD16lW++WYpFy7EkpqaQkFBAfXr16dr126MG/cCtra2Op89dOgQa9as4sKFC+Tl5WFvb0+nTp15992Z5fYbGDiWU6d+KfV4x45PExoapvk7Pz+fiIhwIiMjSUy8Rq1atfD09CI4+DUaN25cYhuXL19i+fLlnDx5gjt37mBlZUXLlq14//1ZmkUSH4V/6tgiY0fN+zeMLaLmyNgiKkrGFqEPGVtERf2XxhYPjza4u7uzdesmJk9+haSk6+zZE8WoUaNwcLDSqjtokC/z5s3h/PmzBASUvaugg4MDV69ewcXFWed87O3tuHDhglZ5hw6t2bJlKwkJ8aSkpGJnZ0uDBg2ZP38eAC1auJX474qLi+PcuXP06NGjxDhOdam2wNUHH3yAs7Mz48aNK7WOSqUCwNjYWOeYiYmJVp2KSE/P1vMsq4mhstIfLXzI4w+n7NbvFRaS8bhc43+Lh7hX4NHeL/cKC0lPz+by5XiSkpLp0qU7trZ2KJVKrly5xIYN69m5cycrV35HvXr3B+Hw8BDCw0Pw8nqaceMmYGpqSnLyDS5f/qNC/xv29x/Ls8/66JTv37+Xo0cP89RTnTXtFBYWMm3aaxw/fpSuXbszePAw0tPT2LJlI6NGjWTZsnDNHO9iJ04c4513puLk5MTQoSOoV8+KtLQ0zp//laSkmxgaPsIHmX/o2FJ8r4ga9C8YW0QNkrFFVJSMLUIfMraIivqPjS1ZWSru3En//99SCQBkZ+fptHP7dtHOgypVbrl9uLm5c/XqFS5fjsfW1knrWFLSDerVsyqxDQsLWywsihIN0tOzOXToEObm5jRu3Fyrvq1t0Y6HGzZsAGpuUfZi1RK42rZtGz///DPffvstRkZGpdYzMyva1jEvTzfCmpubq1VHCPF48vDwwsPDS6e8TZv2vP/+2+zaFYm//1gAfvnlBOHhIYwf/xKBgeMr1Z+nZ8kLAK5aFYaxsTH9+j2rKTt8+BDHjx/F13cwb731rqa8Xz9vxowZwaJF8/jyy6Wa8rS028yePZN27Towd275c8mFENUrIeFPIiJC+f33i9y8mUpBQQH29g48/XRnnn9+TIkZkEePHuH7778jLi6W/Pw8bG3t8fJ6iilTppfbX0zMaQ4c2MfZs9EkJSVhYmLME080YMiQ4fTu3U9nHYf9+/dy4sRR4uIu8uefV1Cr1WzYsJ369R112n711QmcORNdat8eHl4sWrS01ONCCCGE+Oe4desm1ta6zynR0ae4evUy7dp1AKBBg4YolUoOH/6RiRNfoU6dOpq6u3ZFAuDu3kJTVlBQwF9/JWJiYoqDg4OmvF8/b3bv3sm2bZvo2LGTpvzIkZ9ITU3Bx2dwuee8ceM6rly5zLhxL5YYh8nLyyMyMhIbGxt69OhR/kWoQlX+qywvL485c+bQvXt3bG1tiY+PB+5P+cvMzCQ+Pp569ephZ2endexBxWU1mX4mhKg6xQNpZmampmzNmgjq1bNi9OhAoGiDBlNTUxQKxUP1dfZsDAkJ8fTq1RcLi7qa8ujoUwB4e/tq1XdycqZNm3acPHmcGzduaM5169ZNZGTcYdKkYAwNDcnJycHQ0FACWEI8IikpKdy6dZNu3XpqZXRu376Fffv2lJnRGRQ0USujsyKWLfuK1NQUunXrwdChruTkqNi/fy+zZ88kOvoU06drT2fesmUDFy6cx9W1KU5OziQkxJfa9tixL5S4PkVxtmjnzt0qeFVEWWo62AlFuyutWLGUQ4cOkpFxB0dHZ4YOHc6gQbqL1pYVwAwNXU3z5i20yuLiLhIREcKvv54lJ0eFk5MzPj6DGDp0BErlw2UoCCH08ziPLwUFBSxc+DmxsRdITk4iOzsbGxtb3N1bMnr0WJo1a17ptkXlzJs3h1u3btKhgyf29g7k5eURFxfL/v17qFWrFq+++joAFhZ1GTbsedat+5YXXvDHx2cQFhYWnDt3lj17dmvG/WKpqSn4+/vRtm17vv46RFPu6fkUvXv3Y9++H5g6NZhOnbpy40YSmzZ9j7W1DUFBE7TOb+rUYBwdnXBxaYyBgQEnTx7n8OEf6dSpC2PHBpX4b9q3bx/p6emMHz++xn8fVXlvOTk53L59mx9//JEff/xR5/j27dvZvn07b731FiNHjsTY2JgzZ87o1Csua9WqVVWfohCiGuTm5qJSqcjLy+XPP6+ybFnRpgxPP90ZKJr2e/ZsDB07dmLHjm2sXBnKzZupmJiY0KVLN157bSpWVtaV6nvHjm0AOj8K8/OLsjmLFzl8kIlJUdmFC79pAlfHjv2Mubk5d+9mEhg4ikuXfkehUNCqVWsmT34Dd/eWlTo/cZ8+D31hYcuJiFhRYjuTJr3GqFEB5fb3sFku7733NgcP7qNRo8asWbNe65i+2TlCfzWd0fnyy5Np3bqtVkBg2LDnCQ5+icjIrQwbNpLGjV01x2bO/BAbGxsMDQ1ZsGBumYErfbJFReXVdLAzPz+fN96YxO+/x+HnN4KGDRtx/PhR5s+fw+3btwgKmqjzGUtLSyZPnqJT7uioPbXjzJlopkx5FXPz2vj5jcDSsh6//HKCxYsXcPXqVaZPf1enDSFE9Xmcx5f8/HwuXoyldes2ODp6U6tWLZKTk9m5czsTJgQyf/5XdOjgWam2ReX07t2P3bt38MMPu/5/F0EDHBwcGDhwCM8/P0YrW+qVV16jQYOGREZuZc2aCPLy8rC1tWPQID9eeGFCmQulP2jmzNm4ujZl587tLF48n9q169CjRy8mTJiEjY32usOtWrVm//49REXtAKBhw0ZMmTKdgQOHlPpiZOPGjQD4+flV4oo8nCoPXJmZmfHll1/qlN++fZvZs2fTtWtX/Pz8cHNzw9zcnJ49e7J3714uXrxI8+ZFkeCsrCw2btyIi4uL7CgoxD/Ejh1bWbjwC83f9es78v77H9GmTTsAEhOvoVarOX/+N3755QT+/mNxdW3Gr7/GsGHDOi5fvkRo6JoSg0xlycq6y8GD+6hf30nrCxnQrF8VHf0Lrq5NNeU5OTlcuPAbACkpNzTlCQnxqNVq3nxzMj179iYwMIikpCRWrQpj8uSJhISsonHjJvpdGKFF34c+gODgKdSta6lV5ubmXqH+HibL5eefD3Po0AHNmot/p292jqg61ZXRWZy2/yCFQkGPHs9w5kw0V65c1gpcPfjQWRmlZYuKyqvpYGdk5FZiYy/w+utT8fMbCYCv72DefXcaa9ZEMGCALw4O9bU+Y2pqRr9+3uW2vWjRPAwMDPjmm3CcnJwBGDJkGJ9//gnbt2+hf/8BtGmjuzO3EKJ6PM7ji5mZGWFha3TaGDhwKEOHDmDt2jVaz8mVGbuEfnr16kOvXn0qVNfAwABf38H4+pY/na9+fUeOHDlV4jFDQ0NGjw7UPAuVJTBwvN73Znh4uF71q1KVB66MjIzo37+/TnnxroINGjTQOv7mm29y/PhxXnjhBQIDAzE3N2fDhg0kJyezfPlyeWMtxD9E1649aNDABZVKxR9/xHHkyKH/f7tQJDu7aHG/9PQ0pk+fqQkmdO/ek1q1zImIWEFU1A4GD9Yvgr937w/k5OQwYICPznjRt683q1aFExq6HFNTMzw8vLhzJ52wsOXcuZMOFAWxiqlU2ajVavr2fZZ33/1AU+7m1pzg4JdYuTKUDz/8TK/zE9r0eegr1rVrjxLXDKqIyma5ZGdnM3/+HIYMGcaRIz+VWEff7BxReY8yoxPu74L8MG2UpLRsUVH1qivYuXfvbkxNTXXWDhk+fBSHDh1k//49OmMawL1791CpsqlVy7zEZ92MjAwuXfodL6+nNUGrYt7ePmzfvoVdu7ZL4Ooh1XQWcGUydQsKCtiyZSNRUZEkJMSjVCpxcnLG13cIgwbd34o+OvoUwcEvldhvp05d+PzzReWen6icx218eVC9evUwNjbROreqalsUMTU0w0RResyi0AAyc/Jr8Iz+nR75wi0NGzZk7dq1zJs3j5CQEPLz82nRogWhoaF06tSp/AaEEI8FOzt77OyK1qTr1q0H3bs/w4svjiE3N5eAgHGarBWFQqHzpvnZZ58jImIFMTGn9Q5c7dy5DaVSyYABvjrHLCwsWLRoKR9//D6ff/6Jprxt2/b4+49l1aowrdRbY2MTVKpsnn32Oa122rf3wN7egZiY03qdm6i4kh76HpSVdRcTE9MqmU9fkSyXkJClqNVqXnzx5VIDV/pm54jKe1QZnQA3b6ayfftmHB2daN266oIEZWWLiodXE8HOe/fu8fvvF2nWrLlOZqa7e0sUCgWxsRd0PpeamkKfPl3Jzc3F1NQUL6+nmTjxFRo2dNHUqchU9/Pnf6v4BRElquksYH0zdfPz85k+fQoxMafo06c/AwcORa1Wk5iYQHLyjRL78PUdrBkbi9na2lXo/ETFPM7ji1qtJjMzE7W6gJSUZNau/RaVKltzbg/TtijFPUgIKTkDCqDBBI8aPJl/rxoLXDk7OxMXF1fisSZNmrBs2bKaOhUhRA1wdW1K06ZubNmyj9e1jwAAIABJREFUkYCAcZrNGOrUqYOxsbFW3eIdNzIzM/Tq4/LlS8TGXqBTpy6lPpQ1aeJKRMR3JCZe4+bNVGxsbHF2foKlS4umND/4Q8HOzo74+D+xttZ9mLC2tuH33y/qdX6idOU99D1o7Njnyc7OQqlU4u7ekrFjg0qsV1HlZblcuPAbmzevZ9asTyq8psCDqis757/sUWV05uTk8M47U1GpVMydu7BKFyItK1tUPLyaCHZmZmaQm5tb4vePsbExFhZ1uXkzVavc0dGJJ59sg6trUxQKBRcunGfTpvWcPn2SpUvDaNKkKNhtZWWNpaUl58+fIzc3RxOsAoiJKfqBlJKiu7mR0E9NZwHrm6m7cmUop0+fZOHCJbRvX7Efv61ata7QVFRReY/r+AIQH3+VMWNGav6uXbs2AQHjtKaOVbZtIR6lR55xJYT498rNzSUj4w5Q9BBub+9ASkoyOTk5Wl/WqalFP/T//lazPJGRWwF47rnyp9k4Oz+Bs/MTmr+PHz+Kubk5Tz7ZRlPm7t6S+Pg/SUlJ0cmUSU1N0fv8ROnKe+iDoiCnr+9gnnyyDXXq1CEhIZ7169fy1luv88477+Pt7aN3v+VluRQUFDB37id4enas8LoED6qu7Jz/ukeR0Zmbm8s777xJXFws7777gU4Gw8MqK1tUPLyaCHYWTzU3MjIq8bixsbHWdHSAGTNmaf3ds2dvunTpxuTJE/nqqwWazSIMDAwYPnwUISFLmTHjLcaPn0jdupacOnWSsLDlKJVKcnO12xZVp7qygPXJ1FWpVGzYsI4uXbrRvr0HhYWFmuml5VGpVCgUilLXaBQP53EdXwDq13di4cIlFBTkk5iYyJ49u7h79y75+fma+7WybQvxKEngSgjxUG7duqnJmHpQdPQprl69rPWQ1q+fN6tXh7Nt2yZGjPDXlG/ZUrRDRceO97NoCgoK+OuvRExMTEtcADkvL4+9e6OwsrKmU6cuep3zxo3ruHLlMuPGvYiZmZnW+e3evZNt2zbRseP9qcpHjvxEamqKzjoAovLKe+iDonUW/m7AAF/GjBnB4sUL6NGjF7Vq1dKr3/KyXNauXUNiYgKffvpFCZ8uW3Vm5wht1Z3RWRS0msqpUyeZPn1mlWcvVCRbVDycmgh2Fr+Ayc8vee2SvLy8Ck1PbdOmHW3atCMm5rRWdtXo0YHk5OSwbt3/ePHFoqwfM7NaTJ78hmY6s6gajzILGErO1D17Nobs7Czc3NxZtGgeO3duR6XKxtLSEh+fwQQFTSzxe+bLL+fx6aezAXB2bsCQIcMYNmykZHZWocd5fDEzM8PT8ynN3wMG+PLCC6N5991pLFjw9UO1LcSjJE/VQoiHMm/eHG7dukmHDp7Y2zuQl5dHXFws+/fvoVatWrz66uuauv7+Yzh06ABLlnzJtWsJuLo25ddfz7JnTxQdOnhqZbikpqbg7+9H27bt+frrEJ1+Dx/+kTt37jBq1JgyAwRTpwbj6OiEi0tjDAwMOHnyOIcP/0inTl0YOzZIq66n51P07t2Pfft+YOrUYDp16sqNG0ls2vQ91tY2BAVNqIIrJqD8h77S1K1rycCBQwkPD+G3337Fy6vkxddLU1aWS2LiNSIiQhk79gWdxZDLU93ZOUJXdWV05uXlMWPGNH755ThvvfUuzz03sMrPXZ9sUVE1qiPYWaeOBSYmJpp77EF5eXlkZNzBxqZ9hc6vfn1HYmJOk5mZqQlcKRQKJkyYREDAOC5fvgQU4urajMLCQr744lNatnyyQm2L8j2qLGAoPVM3ISEegPXr12JkZMSkScFYWNRl794o1qyJIDU1hZkzZ2vqGxoa0qVLNzp27IyNjS03b6ayc+c2Fi+ez6VLv+tk+4mq8ziPL7Vq1aJ7957873+r+OuvRJycnKt07BKipkjgSgjxUHr37sfu3Tv44Ydd/58xY4CDgwMDBw7h+efHaGVLmZvXZsmSUEJDl3H48CF27NiGra09AQHjCAwcr7XmQ3mK1ykq70dlq1at2b9/D1FROwBo2LARU6ZMZ+DAISX2N3PmbFxdm7Jz53YWL55P7dp16NGjFxMmTMLGxrbC5yf08/eHvrIUry2Snp6uVx/lZbl8/fVCLCws6NatJ4mJ1zTlarWa/PwCEhOvYWpqprXLFFR/ds5/WU1ndObl5fHOO1M5efIYU6e+Uy27/T1Mtqh4OFUd7FQoFDRr1pw//ogjLy9P6wdqbOx57t27R/PmFVu0+9q1BJRKJXXqWOgcMzMzo1Wr+0Gqgwf3UVhYqJUZLB7Oo8oCLitTNzs7CygKcKxe/b1mTc5evfowefJEdu/eib//WBo1agxA69Ztdaao+/oOZurU19i1K5IBAwbKLpTV6HEeX3JzcwHIyLiDk5NzlbYtRE2RwJUQ4qH06tVHr7WALC0tmTr1HaZOfafMevXrO3LkSOk7dCxcuKRC/QUGjicwcHyFz8/Q0JDRowO1FrEUNePBh76yXLuWAICVVdWuiXbjxg1u3kwlIGB4icdHjhyss6V4TWTn/JfVdEbnhx/O5MSJo3h4eGFqasoPP+zSOp8mTZri6tpU8/eZM9GcORMNwMWLsQBs2rSe2rWLFvUvaeypaLaoqJyaDnb27t2Pc+fOsn37Zvz87i+IvH79dyiVSp555v59d/fuXczMzHRemhw9eoRz587SsWOnctckunMnneXLl2JpacmgQfptNiBK9yiygMvL1C3OvGvRopXWRjIA/fsPICbmNGfORGsCVyVRKBQEBARy8uQxjh//WQJXD+lxHl/S0tKoW7cuCoVC55wPHtyHmVktGjVqUqm2hXgcyBOTEKJSTA3NMFGUvl5CoQFk5pQ8d178d1X0oa+goICcnBxNAKBYcvINtm7dRN26dXnyydaa8qpYE+2VV17j7l3dhXjnz5+LsbExkye/oXXuNZGd819X0xmdxcGnU6dOcurUSZ3j48a9qBW4On36FyIiVmjVWbfuW83/X1LgqqLZoqJyajrY6es7mF27Ivnqq4UkJSXh4tKIY8d+5qefDjJ2bBCOjk6autHRp/jqq4V07twVR0cnlEolsbHn2bMnCktLS4KD39T6txw7doTvvluDp+dTWFlZk5x8g8jIrWRmZjBnzgIsLS2r8Ur+t1V3FnBFMnWLp5uVttMxVGztvspmKQtdj/P4sndvFOvXr6Vbtx7Ur++EkZEh164lEBW1k8zMDKZPn6mV+aVP20I8DiRwJYSonHuQEFJ6RlSDCRXbtln8t1T0oU+lUjFsmC/duvWgYUMX6tSxICEhnh07tqJSqZg16xOt7eGrYk20BxczfdCSJV9iZmZGz569tcr1zc4R+qvpjM6NGyP1Or+goIkEBU3U6zMVzRYVlVPTwU4jIyMWLVrKihVL2bfvB81UnDfemMaQIdrZmw0aNMTNrTlHjx4mLe02BQUF2NraMXDgUMaMGaczhdnBwREjI2M2bvyejIw71K1riYeHJ2PHBtGggUtVXC5RhurKAq5opq67e0vg/sLtD9Jn7b7KZikLXY/z+NK6dTtiYy/w88+HuX37Fvn5+VhZWePh4cWwYSO1dtHWt20hHgcSuBJCCFFjKvrQZ2JiQo8ez3Dhwm/89NOPmp2UPDy8GDVqDC1atNKr3+rIctE3O0dUjGRziofxKKav16lThylTpjNlyvQy23BxacTHH8+t8Lk1atSYBQu+qnB9ob+azgLWJ1PX0dGJJ59sw2+//Upc3EXc3JoDResubt++BaVSiafn/amJd+6kU7eudhZeXl4e4eFFL3M6d+5a0csiSvE4jy/Nm7sza9bHFT43fdoW4nEggSshhBA1pqIPfcbGxrz99nsVbreq1kQrSWlZOPpm54gKkmxOUUES5BQPq6azgPXN1H3jjWm88sqLvP76JPz8RlC3bl32799LbOx5xo17USso9uabwdjY2ODm5q7ZVfCHH6JITEzAz2+E3i98/stkbBHi8SOBKyGEENVKHgCFENVCgpziIdV0FrC+mbrNmjVn2bJwVqxYyoYNa8nLy6NhQxdmzJiFt7eP1md79HiGw4cPsWnT92RmZmJmZkbTpm4EBU2gT5/+lbxC/1Eytgjx2JHAlRBCiOolD4BCCCEeQzWdBVyZTF1X16bMnbuw3HqyI7IQ4t9MAldCCCGEEEKIfzXJ/hVCiH8uCVwJIYQQQggh/t0k+1cIIf6xFI/6BIQQQgghhBBCCCGEKIkEroQQQgghhBBCCCHEY0kCV0IIIYQQQgghhBDisSSBKyGEEEIIIYQQQgjxWJLAlRBCCCGEEEIIIYR4LEngSgghhBBCCCGEEEI8liRwJYQQQgghhBBCCCEeSxK4EkIIIYQQQgghhBCPJcNHfQLi3yUhIZ49e6I4efI4168nkpubh5OTMz179mL48FGYmZlp1T9wYB/r13/HpUu/Y2CgoGnTZgQEBPL0010q1F909CmCg18q8VinTl34/PNFOuUFBQVs2bKRqKhIEhLiUSqVODk54+s7hEGDhj5U20IIIYQQQgghhKg6ErgSVWrnzu1s3ryBLl260bdvfwwNDYmOPs2KFcs4cGAfISERmJiYAvDttyv55puvadbMjaCglzAwgD17onjrrTd4770P6dv32Qr36+s7mDZt2mmV2dra6dTLz89n+vQpxMScok+f/gwcOBS1Wk1iYgLJyTceqm0hhBBCCCGEEEJULQlciSrVs2cvAgLGUbt2bU3ZoEF+ODs/werV4ezYsY2hQ0dw+/YtwsKW07hxE0JCVmFoWHQr+vmN5IUX/Fm48As6d+6KuXnt0rrS0qpVa/r18y633sqVoZw+fZKFC5fQvr1HlbYthBBCCCGEEEKIqiVrXIkq1bx5C62gVbFevfoCcOXKZQDOnfuV/Px8+vR5VhO0AjA0NKRPn/5kZmZw+PAhvfpWqVTk5uaWeXzDhnV06dKN9u09KCwsJDs7q0raFkIIIYQQQgghRNWTwJWoESkpyQBYWVkDkJ+fB4CpqalO3eKphOfP/1bh9r/8ch59+nSlV6/OjBw5hPXr11JYWKhV5+zZGLKzs3Bzc2fRonn07dudvn2789xzvVm+fAkFBQWVblsIIYQQQgghhBBVT6YKimqnVqtZuTIUpVJJnz79AGjUqAkA0dG/MGzYSK36MTGnAEhJKXnNqQcZGhrSpUs3OnbsjI2NLTdvprJz5zYWL57PpUu/M2PGLE3dhIR4ANavX4uRkRGTJgVjYVGXvXujWLMmgtTUFGbOnF2ptoUQQgghhBBCCFH1JHAlqt3ixfM5f/4cEye+QoMGLgA0aeKKp+dTHD58iKVLv8Tb2xeAXbsiOX78KAA5OeVPzWvdui2tW7fVKvP1HczUqa+xa1ckAwYMpE2bouPF0wIzMzNYvfp7GjYsOpdevfowefJEdu/eib//WBo1aqx320IIIYQQQgghhKh6MlVQVKsVK5axadN6fH0HExAwTuvY7Nmf0b17T9au/ZbRo4cxevQwDh7cx5Qp0wEwNzevVJ8KhYKAgEAAjh//WVNePAWxRYtWmqBVsf79BwBw5kx0pdoWQgghhBBCCCFE1ZOMK1FtwsKWs2pVGN7ePkybNkPnuIWFBZ988gW3b9/i2rUEzMzMcHVtxokTRRlXfw8u6aN+fUcA0tPTNWV2dnYAWFtb69S3trYBirKxKtO2EEIIIYQQQgghqp4ErkS1CA8PISJiBf37D+Dtt9/DwMCg1LpWVtaaRdsBjh0rymTq2LFzpfu/di3h/9u20pS5u7cEICUlRad+ampRWb16VjrHKtK2EEIIIYQQQgghqp5MFRRVLiJiBeHhIfTr582MGbNQKCp+m128eIEdO7bRtm17rfWjCgoKiI//kxs3tBdsv3NHN+spLy+P8PAQADp37qopd3R04skn2xAbe564uIuacrVazfbtW1AqlXh6dqxU20IIIYQQQgghhKh6knElqtSmTesJC1uOvb0DHh5e7N27W+u4lZWVJji0YsUyEhMTcHdvSe3atYmLi2Pnzu3Y2Njy3nsfan0uNTUFf38/2rZtz9dfh2jK33wzGBsbG9zc3DU7//3wQxSJiQn4+Y2gRYtWWu288cY0XnnlRV5/fRJ+fiOoW7cu+/fvJTb2POPGvYiDg0Ol2xZCCCGEEEIIIUTVksCVqFIXL14AIDn5Bp988oHO8bZt22sCV82auXHq1ElOnjxBbm4OdnYO+PmNICBgHHXq1KlQfz16PMPhw4fYtOl7MjMzMTMzo2lTN4KCJtCnT3+d+s2aNWfZsnBWrFjKhg1rycvLo2FDF2bMmIW3t89DtS2EEEIIIYQQQoiqJYErobeEhHj27Ini5MnjXL+eSG5uHk5OzvTr14v335vKnE/f0/nMT4eP8O2333P+Qiy9enXG1tYeL6+nWL48okJ91q/vyJEjpwC4dOkPgoJGo1ar+eijOTptFBQU8N13q/H39+P69b8wM6tFu3btmTDhFRo2dMHVtSlz5y4ssZ/y2hZCCCGEEEIIIUTNkcCV0NvOndvZvHkDXbp0o2/f/hgaGhIdfZolS5YRueV/fPhmV4yNlZr6m3bFsSkqjk6dOhIUNBFTU1OSk29w+fIfevd979495s79GGNjE1SqbJ3jhYWFvP32FI4fP0rXrt0ZOnQE6elpbNmykYkTA1m2LJxGjRpXqm0hhBBCCCGEEELULAlcCb317NmLgIBx1K5dW1M2aJAfTRo7syI0goPHEujXvREA5y6msikqDr8Bbrz38ZdkZilLa7ZCNm78nj//vMKoUWMIC1uuc/zw4UMcP34UX9/BvPXWu5ryfv28GTNmBIsWzePLL5dWqm0hhBBCCCGEEELULNlVUOitefMWWkGrYv369gYgMSlDU7Ztzx9Y1DFmYJ+mAGRnZ3Pv3r1K9ZucfIMVK5YxbtwE7O0dSqwTHV00ndDb21er3MnJmTZt2nH69EmdnQkr2rYQQgghhBBCCCFqlgSuRJVJTk4BoG4dEwBycgu4ePkWrg3r8eOxBHr3fY6+fbvRp09XZs16h9u3b+nV/vz5c3F0dGL48OdLrZOfnweAqampzjETk6KyCxd+q1TbQgghhBBCCCGEqFkyVVBUCbVazfKQMJQKAzp5OAOQnJrFvXuFXPozjXMXUwkKeoEnGrjx668xbNiwjsuXLxEauqbEINPf7d+/h2PHjrBsWRiGhqXftsXrV0VH/4Kra1NNeU5OjiZglZKinXFV0baFEEIIIYQQQghRs+RXuqgSixfP59dzvzHCxx1H+6JphKrcAgAy7ubx4vNtePmlF8nMUtK9e09q1TInImIFUVE7GDzYr8y2MzMzWbx4Pj4+g2jVqnWZdfv29WbVqnBCQ5djamqGh4cXd+6kExa2nDt30oGiIFZl2hZCCCGEEEIIIUTNkqmC4qGtWLGMTZvWM3ToIAb2vZ/lZGxUtBC7gQF08XTW+syzzz4HQEzM6XLbX7JkEffuFfLSS5PLrWthYcGiRUtxcnLi888/Yfjwgbz44lhycnLw9x8LgLn5/fW59GlbCCGEEEIIIYQQNUsyrsRDCQtbzqpVYXh7+/Deu29z4edPNcesLYumAJrXMsbISHs3QWtrGwAyMzMoS1zcRXbu3E5Q0EQyMu6QkXEHgLS02wDcvn2LxMRr2NnZY2xsDECTJq5ERHxHYuI1bt5MxcbGFmfnJ1i69EsAGjZ0qXTbQgghhBBCCCGEqDkSuBKVFh4eQkTECvr3H8Dbb7+HgUGh1vG6FqbY1DPjVrqK3LwCrWOpqUULuderZ1VmH8nJNygsLCQ09BtCQ7/ROb5w4RcAhIaupnnzFlrHnJ2fwNn5Cc3fx48fxdzcnCefbPPQbQshhBBCCCGEEKL6SeBKVEpExArCw0Po18+bGTNmoVAoALVOvS5ezmz94Q/2H4mn/TP3y7ds2QhAx46dNWUFBQX89VciJiamODg4ANCiRUs++miOTrsxMafZvHkDI0eOpmXLVjg6OuvUedDGjeu4cuUy48a9iJmZWZW2LYQQQgghhBBCiOohgSuht02b1hMWthx7ewc8PLzYu3c3AKYm9/jr92vUrWPCk83tAHiulysnzyTxv63nyVXOpaFLM3799Sx79kTRoYMnvXr10bSbmpqCv78fbdu25+uvQwCwsbGlZ8/eOuegUqkAaNmylc7xqVODcXR0wsWlMQYGBpw8eZzDh3+kU6cujB0bpKlXmbaFEEIIIYQQQghRcyRwJfR28eIFoGiq3SeffKBz3N3VWhO4qmVmxPuvd2bDzov8+ONPpKVvx9bWnoCAcQQGjkepVOp8/mG1atWa/fv3EBW1A4CGDRsxZcp0Bg4cUi39CSGEEEIIIYQQonpI4EpUmFpxDxOTQj78cBYffjhL57iJ0T1+O/yJTrlFbROCRrShZZd3ycwqPXBUv74jR46cqtC5eHv74O3tU+KxwMDxBAaOr1A7+rYthBBCCCGEEEKImiOBK1Fh6ntqVqxYUerxVycFlXpMCCGEEEIIIYQQQl8SuBJCCCGEEEIIIYR4TCQkxLNnTxQnTx7n+vVEcnPzcHJypmfPXgwfPkqz4VhhYSF79kRx9OhhLl6M5ebNVOrWtaRp02aMGRNEy5atKtTfq69O4MyZ6FKPd+rUiYiICK2ygoICvvvuO7Zs2cLVq1dRKpU0aNCAESNGMHLkSJ02Ll26xLJlyzhx4gTp6elYWVnx5JNPMnv2bGxsbMo8PwlcCSGE+Meo6S/x6OhTBAe/VOKxTp268PnnizR/Z2RksHv3To4dO0J8/J+kp6djb29P27YdCAwMwt7eodJti8qp6ful2NWrV1i1KoyYmFNkZGRgaVmP5s1bMG3aO1hZWQNl//cvtnRpKK1bt9X8fePGDVavDuP06V9ITU3FwsKCZs2aM2pUAG3bttfz6gghhBDicbVz53Y2b95Aly7d6Nu3P4aGhkRHn2bFimUcOLCPkJAITExMycvL46OP3qdp02b06tUXR0dHbt68ybZtm3nppXHMnDmbfv28y+1v7NgX8PEZpFO+f/9ejh49TM+ePbXK8/LyePnllzlx4gQ+Pj6MHDmSgoIC4uPjuX79uk47hw8f5pVXXqFBgwYEBARgbW3N7du3iYmJ4e7duxK4EkII8e9R01/ixXx9B9OmTTutMltbO62/L1z4jSVLFtGhgydDhgzD0tKSK1cus23bZg4e3MuyZeE0atS4Um2LynkU98uJE8d4552pODk54ec3knr1rEhLS+P8+V/JysrSBK5cXBrx3nsf6nw+Pz+Pzz//lLp1LWnR4n7A7ObNVMaPH01BgZqBA4fwxBNPcPPmTbZv30Jw8EvMmbOATp26VM2FE0IIIcQj1bNnLwICxlG7dm1N2aBBfjg7P8Hq1eHs2LGNoUNHoFQq+eqr5bRr10Hr876+gwkIGM7XXy+iT5/+KBSKMvvz9OxYYvmqVWEYGxvj6+urVb506VKOHTtGeHg4HTuW/Nlit27dYurUqXh5ebFs2TKMjIzKrF8SCVwJIYT4x6jpL/FirVq1Ljdw0bChC999twknJ2et8qef7sIbb7xCWNg3fPzx55VqW1ROTd8vaWm3mT17Ju3adWDu3AUYGpb+mGVlZV3if/e9e3dz7949+vcfoPX5qKgdpKen89ln8+jatYemvHfvfowcOZjIyC0SuBJCCCH+JZo3b1Fiea9efVm9OpwrVy4DYGhoqPP8AkXPGW3btufQoYOkpd3G2rrsjKaSnD0bQ0JCPL169cXS0lJTnp2dzerVq+nVqxcdO3aksLCQrKwsreetB61du5b09HSmTZuGkZERKpUKQ0NDvQJYErgSQgjxj/Eov8RVKhUKhQITE5MSj9ev71hiuafnU1hY1NWcW2XaFpVT0/fL1q2byMi4w6RJwRgaGpKTk4OhoWGZAay/27FjGwA+PgO1yrOysgCwsbHVOUeFQoGpqVmF+xAlq+jUUijKsNyzJ4q4uFguXfoDlUrFjBmz9NqVuLz1RDw8vFi0aKlWWUFBAVu2bCQqKpKEhHiUSiVOTs74+g5h0KChmnpHjx5h27ZNXL58ibS02xgZGePo6Ej//gMYOHCojDVCCPEPlZKSDKDJ4C67bgpGRkbUrl2nUn3dfybRnkJ46tQpsrKyaNmyJR9//DGbNm0iOzubevXqMXz4cIKDg7WefX766Sdq165NZmYmAwcO5OLFiygUCtq1a8fbb79N69atyz0XCVwJIYT4x6vuL/Evv5zHp5/OBsDZuQFDhgxj2LCRGBgYlPvZu3fvkp2dRePGTaq8bVE51XW/HDv2M+bm5ty9m0lg4CguXfodhUJBq1atmTz5DdzdW5b5+evX/yI6+hStW7elQQMXrWNeXh359tuVzJ8/l1deeQ1n5ye4eTOViIhQzMzMGDlydLnnJ8pW0amlUPTfevPmDTRo4IKra1POnftV7/7KW0+kc+duWuX5+flMnz6FmJhT9OnTn4EDh6JWq0lMTCA5+YZW3cuXL6FQKHnuuYFYW9uQm5vD2bNnWLx4AUePHmHhwiUyxgghxD+MWq1m5cpQlEolffr0K7PusWNHiI09T79+3pV6WZGVdZeDB/dRv74THTp4ah27evUqAKtWrcLIyIhp06ZhaWlJZGQky5cvJzk5mblz52rVV6vVjB8/nv79+zNp0iT++usvli1bxpgxY9iwYQNNmzYt83wkcCWEeKRq+g23Pm+hCwoKWLjwc2JjL5CcnER2djY2Nra4u7dk9OixNGvWvMQ+KrIws6g61fklbmhoSJcu3ejYsTM2NrbcvJnKzp3bWLx4Ppcu/c6MGbPKbWPVqjAKCgro339Albct9Fed90tCQjxqtZo335xMz569CQwMIikpiVWrwpg8eSIhIatKDWBCUeCksLCwxGBG+/YeTJkynbCwb5g8eaKm3Nm5AcuXr8TFpVG55yfKVtGppQCDB/sxatQYzMzMOHhwX6UCV+WtJ9Kv37ORVabPAAAgAElEQVRa5StXhnL69EkWLlxC+/YeZbYdEBCoU+bnN5L58+eyZcsGYmPPa62hJoQQomZV5jfQwYP7uHXrFgC//XZO5yVXsWvXEvjoo1nY2trx6qtvAPpn4u7d+wM5OTkMGOCDgYEBr732Grt376Zp06Z4exctdXDnzh0iIyNp0qQJ169f5/jx45iYmLB161Z++ukn2rRpQ1BQEFlZWajVanx8fJgzZ46mj5YtWzJmzBiWLFnCokVlb0okgSshxCNV02+49XkLnZ+fz8WLsbRu3QZHR29q1apFcnIyO3duZ8KEQObP/0rnDURFF2YWJavpL3EoyohasWIphw4dJCPjDo6OzgwdOpxBg4bSunVbza5u0dGnmDfvM83ndu2KZNeuSK0+/r4L3MGD+1i37lusra0JCVnC/PlztAKZc+Ys0Pq8r+9gpk59jV27IhkwYCBt2rRFVK3Fi+dz/vw5Jk58pdR7BUq/X8qiUmWjVqvp2/dZ3n33A025m1tzgoNfYuXKUD788LMSP6tWq4mK2oG5uTk9e/YusY6lZT3c3Frg4eFFgwYNSEhIYO3aNUyb9jpff71cZ+dKoZ+KTi2FimXrVcaD64lYWNTVlKtUKjZsWEeXLt1o396DwsJCVKpsatUy16t9B4eieyQzM7NKz1sIIYR+9P0NtHHj9wBYWVlx+/btUtu9fv0vXnvtZQwMYN68xdSrVw/QPxN3585tKJVKBgzw5eefD7Nnzx5MTYvOp/j/tmnThiZNmpCcnMyQIUNQq9U89dRT/PTTT7Ru3ZqLFy8yZswYjIyMUKvVDBkyROtcn3rqKRwdHTl58mS510sCV0KIalVeIOLvb7gTEv7kxIljGBkZc/nyHwQEjGD69Jl06OBZ4TfcZW0Z//e30A+uMXLq1Em6dr0fiAoNXU1Y2Bqt+nfv3iU5+Qa7dkXy+uuTaNiwkSbIkZ6eVuGFmUXJavpLPD8/nzfemMTvv8fh5zeChg0bcfz4UebPn8Pt27cICrqf2fLgLnDx8VdZvTqCzp270rVr9xJ3gTt27AgffDATgNq169C//4ByA5kKhYKAgEBOnjzG8eM/S+CqHPoEOgHmzfuMrVs3YWhoxKpVYRw79jNBQRN1AtCl3S8AcXEXiYgI4ddfz5KTo8LJyRkfn0EMHToCY2MTVKpsnn32OU39Y8eOsGpVOFAUyJw58x6TJgXj6Oik1efJk8dISUlm4MAhmJqacunSHwQFjUatVvPRR3PIzMxk/vw5RET8j8aNXTWfe+qpp3nhBX+WL1/C++9/VGXXVtynz9TSh1XaeiJnz8aQnZ2Fm5s7ixbNY+fO7ahU2VhaWuLjM5igoIklfudkZ2eRl5dPVtZdzp07y//+t5q6detKtlUV0Hf8SUj4k2XLviImJpqCgnyaNWte4vhTlrKeb9q2ba+pFx19iuDgl8ps68EXLTExpzlwYB9nz0aTlJSEiYkxTzzRgCFDhtO7dz+ZVipENdAny1elUgHg7e1Dx46deP/9d0psMynpOsHBL6FSqfjyy6U0aXL/eUGfTNzLly8RG3uBTp26YG5em/nz5+Dv78+BAweA+y9BbG2L1t3cunUraWlpLFmyBGNjY3766SfatWvHjBkz6Nu3L0qlEgAbG921Qm1tbblw4UK510t+VQkhqpU+gYi//krkpZeCUCqVDBw4hI0b13H37l2mTHmVefMW4+n5VLn9VWbLeEtLSzp08GT//r2MGhVAkyZFc6z//sOyOMgRF3cRQ0ND7OzsadCgoSbIoVAoHnph5v+6mv4Sj4zcSmzsBV5/fSp+fiOBoqynd9+dxpo1EQwY4IuDQ31Aexe4GzeSWL06AisrG0xMTHV2gTt+/CgzZkyjsPAe7dt7Mn/+4grfB8WLvKenp1f4uv1X6TO+LFr0BVu3bsLY2JjAwPHUrl2HyMgtOuNLWffLmTPRTJnyKubmtfHzG4GlZT1++eUEixcv4OrVq9jZ2REf/yfW1kUBjkOHDjBz5nRcXZvi4FCflJRkzp6N4eWXgwgLW6O10PqDAYt79+4xd+7HmkAYwJo1K2nY0EUraAXQpIkrDRu6lLnIt6g8faaWPqyy1hNJSIgHYP36tRgZGTFpUjAWFnXZuzeKNWsiSE1NYebM2TptfvrpbH788YDm7xYtWjFlynTq1KncQr3ivso+3/j7j8HcvHaJ409Z9Hm+efBFy4Py8/NKfNGybNlXpKam0K1bD4YOdSUnR8X+/XuZPXsm0dGnmD59ZhVdtX+38oKZqakpWsdzcnJQKpXk5+djYGCAm5u7VjCzosHO8paouHr1CqGh3/DLLydQqbIxMDCgsBDq169Phw5eBAYGYWZWi927d3Ls2BHi4/8kPT0dW1s7jIyMuHMnnaysu1oZ6QYGBmVmrEuws3wVzfINDw9h3bpv6d9/AG+//R6HDh0o8XM3biQRHPwSd+/eZdGipaUuafJ3JWXiRkZuBeC55wYRErIUtVrN66+/rglcPfnkk//fZ9H6infv3gXAzs6OuLg4AKytrbGxsUGhUGBlZUV2djY3btygWbNmfzvvG1hZWZV7nvJrSghRrfQJRHzzzdfcvZtJWNgabt68ycaN6/D1HczevbtZsGAu3323qdwvwopuGV/8Fjo3N5fCwkJ++eUkdevWJSDgBZ0HerVaTWZmJpGRW4iNvUDTpm788Ucc3t4+BAaO1wQ5GjVq8lALM4ua/xLfu3c3pqam+PgM1iofPnwUhw4dZP/+Pfj7j9Vp99q1BKAo0+vvu8CdOHGMGTOmUaeOBbdv3yI4eIpegcwH2xZlq+j4Eh4eosnOW7YsDDc3dwD69x9AQMBwzfiSnHyjzPtl0aJ5GBgY8M034Tg5OQMwZMgwPv/8E7Zv38JTTz1NfPyfpKSk0KCBCwsXfoGdnT1LloTi7++HtbUNn3++kKCgAMLCQpg+/V0A0tJu8/PPh2nSpCnNm7dg/fq1/PnnFUaNGkNY2HIAbt5M0fT5d2q1GrW6oGov7r9QZTJk3nwzmKSk6xgaGjJnzsd6Zcjok8XyYIZMUtJfWtm/D8rMzGD16u9p2NAFgMaNm3D+/Dl2797Jvn0/UK+eldYP1nHjJjBwYFFGcHT0aS5f/oOMjDuVuHri7yr7fNO0qRugO/5U1fMNaL9oedDevbt1XrQAvPzyZFq3bqvJigAYNux5goNfIjJyK8OGjdQJmgtd5QUzPT292LZtC126dMPLqyPr1n1LXl4++fl52NjYkpV1/2Wto6NThYKd5S1R8ccfv/POO1OpV88KlSobF5dG2NnZc+vWLZo2bcaePbs4eHAvL78czJIli+jQwZMhQ4ZRu3YdwsOX89dftzEyMiIgYByXLv2heVk7ZswLFc5YF/p5MMs3ImIF4eEh9OvnzYwZs1AoFCV+5saNJCZPnkhmZgYLFy6heXP3UtvPyLhDfHw89+6pSUq6rpOJm5eXx969UVhZWVOvXj02b17PrFmfaI11TzzxBO3btycmJobz58/TpUsXQkJC+OCDD1CpVCiVSqysrHjzzTepVasWQUFBzJ49m3Xr1tGt2/2NRw4cOEBycjLDhw8v97pI4EoIUa0qGohQqVT8/PNPtGvXgcaNXZk3bw5KpRJv7+cwNTUlNPSbCi0mW9Et40t6Cz1p0mStQblYfPxVxowZqfn7+vW/CAgYx+jRgcD9IEd8/J8oFAaVXphZlK46vsTv3bvH779fpFmz5iiVSuLj/8TExBQHBwfc3VuiUCj49dez+Ptrt5uXl0d4eAhQtHbR6tXhml3gTp48zjvvTOWJJxqgVCrJzc0pNZDp6OhE3bqWpbbduXPXqrh0/2oVGV+K7xeFQkHbtu01QSuAWrVq4eMziNDQbzhy5BCLFy8o9X7JyMjg0qXf8fJ6Gnt7B637xdvbh+3bt2jqbtu2CaVS+f8ZEi8RHX2K1NQUfHwG07SpG+3adeDAgT28+eZ0DA0NiYraSUFBAT4+A0lOvsGKFcsYN24Clpb37w8Xl0ZcvnyJ3347R6tWT2rKf/vtV65dS+DppztX1WX919I3QyYw0J+8vFzc3Vvw7LM+emfI6JPF4uLSCEdHJ5KSkggOnoKFhQVwP0PGxMQElUpFixatNEGr4h+sxXWfeaYPjRo10ZqK3KSJqyZrsE+f/mzduompU4P5+usQrfX4hP4q83xTHLQC7fGnKp9vyvL3Fy3F2rXroFNXoVDQo8cznDkTzZUrlyVwVQHlBTO9vDqyZcsuateuzXvvvU1eXh5hYWs4eHA/q1eHM2LEKDZu/J4FC+bi6tqs3GBneUtUpKXd1hx//fWpKBQKnRcgffs+yxtvvMJPPx3ku+82aY5v3ryB27dvM2TIMDZv3sCVK5f49NMvNC9rlUplhTPWRcU9mOVbWHiPsLAV2Ns74OHhxd69uwG4cOE8AFevFo0x2dlZTJ78EklJ1/HzG0FCQrwmS7eYp+dTmunus2fP5MSJY5pjf8/EPXz4R+7cucPzz49m3rw5eHp2pFevPjrn+t577+Hv78+4ceMICAigf//+7N27F7VaDcCkSZNwcXFh/fr1NGnShNOnT7Njxw5efPFFevTowfXr1/n222+xtbXl1VdfLffaSOBKCPFI/H3NkMuX/yAvL4+WLZ/UWTy5Zcuih7nY2AvlPthVdMv44rfQixZ9QUJCPLGx53n11YmYmpri5fU0Eye+ovlxUL++EwsWfM1bb72Ora0dlpaW3L17l/z8fAwNDTVBjry8PKCwUgszi9JV15e4m5s7ubm52NrakZqagr+/H23btufrr0MwNjbGwqIu0dG/8PbbU3Bzc9fs/PfDD1EkJibg5zeCuLiLml3gLl68wNtvvwkUMmCAD8uXL+XePTWvvz4Jd/eW+PmNwN6+viaQWb++I05OzqW2LWvQVF7x+JKcfINt2zZjZWXN7du3qFWrFj/8sEtTz8rKSjO+fPbZR2Rk3Cn1fnF1LZpCbGpqqnO/FAc7kpOT6d27H/v2/cCffxZtFX3lymXWrInA2tqGoKAJQNFD4unTv5CQEE/jxk3YtWs7xsYm9O3rzccfz8LR0Ynhw59nz54oTf9BQROZMWMab7zxCoMGDcXZ+QkSE6+xdetGDA2NGDduQjVdzX8PfTJkpk9/g7y8XLp06c5nnxVl2umbIaNPFktaWhrXr/9Fp05dGDbs/ouS4gyZdu08OHr0sGYa6oM/SIcMGcb06W/g4tKoxDVMHtS//wAWLJjLtm2bJHBVTcp6vvm76ni+Kc31638RHX1K86KlYv+WFK1/iyhbecHM7OwsateurRPMVCiUrF4dTmLiNU0wMynpernBzhMnjv1fe3ceV1P+/wH8VVq1SCJ72W5R2fcy1khpCmUZ2beGwVhmMBg75ZvJyL4lshfKPmMbUoSxZh3GEBWR9kXd8/uj3z263dtm6+L1fDzm8ZjO+rn3Hp9zzvvz/nw+hQ5RsX9/sLi+Ro2ayMjIQHZ2tlyAq0WLVjA0LIeYmGdyQS1ZRvrYsT/i+PE/xECsrLH24MGQd8pYp8LlfQeSPYfExcVi4cI5CtueO3cWY8f+iMTERMTEPAUAMbtc8bhrxH/H7u79ceFCBMzMzNGwYROFTFxZgFsqFRAd/RiLFv1P6TEbNGiAHTt2YNmyZQgICEB6ejp0dHTwzTffwNnZGY8ePcLGjRsxatQoBAYGwtvbGxYWFggODsbixYthYGCAbt26YeLEiTA1NS3yu2Hgiog+OWVjhsTHvwAA3Lt3B+fPh+Pbb3ti4MChAAATk0py2xSmuFPGy1qhraxs8M03HZGQ8AqHDoXCzq49zpw5jcuXI7Fq1UbUqVMXurq6sLCwRHZ2NurXt8K0abMwbJgHZsz4Cb/9tkIMciQlJUIqFeQGZpaVydS0Mq5cufz+X95X6GPdxOfOXQQA0NTUVLpeS0sLZcqUQUJCAoKDdyE5ORm6urqoV88Cw4ePQqdO9nB3/1acBe7kyT+RlZX5/2WWny3w+vWruH79KsLCLomBTHV19QKPbW/v8E7fFcnXL1paWgCAV69yZ50MCzuDsLAz4raNGzfF5MnTAEB8aCvsoc/IyAhRUTfE31nmypVLAHJfWDdv3o66deshMDAAAHDp0gV06NAZo0aNETMlKlZ8W6elpqbg0aN/YW/vgIsXzyMiIgyrV29UaDm3s2sPX9+V2L59Kw4dCkVqagoMDAzQsmVrDBkyQu7lhpQrbobMunWr8OjRvzA2roBFi/4nBqhKmiFTkiyWvOOJ5CV7gejTpx/Cw8+KgYS8L6S3bt0EALlZCAuSlZUFqVSKpKSkIrelkivs+Ub27z6vj/F8U5BDh0LFhpbiiI9/gdDQvahatRqDnO+pqGBm3vWyYGZ2dnaRwc6IiHOFDlFR1Pr69a2QkpKCtLRUuR4BeTPS37x5I7e+fn0rqKmp4fnzOFhZ2UBbW1uufLLG3Nu3ix5smwB9PUANuRlKK1auQXDwbvTu7YoxnoMAAIsXzUFKqvw+p04dx6xZ08SeH1WqVEVY2KVin7N167Zy2+fPxPX1XYno6CcYNKgfBg8eVuAwBQBgaWmJNWvWYPfu3WJXwLxjWNnZ2aFXr15YunQpfHx8MGrUKIwa9W4NbQxcEdEnp2w6+oyMDAC5g1o7Ojrjp59+EbeXvXzKtilKSaaM/+WX2eKxDx8+gDJl1OHruwLjxo2Gn99vWLZsldy5NTU1UbZsWbRv3xHbtgXg6dNoVKtW/f+DHBqQSrPEFvG8KlQwwb17d0r4TdH69asVbuJei2bJbSOgDA4cLPlNPDExd/DzN2/eKN0+KysL1avXwJo1m5TuHxERJjcLnKOjMxwdncX19vbfID09Db6+K+W6FckCma9evcKBA38U74ugYtHXA7y8fBAVdQPjf/gew4cPAQAcOHgYM2fNxZzZs9DFXr6LzNOn0QByWyAnTJhc6PH79PkO69atwvLlvli/PgDlyhkhNHQfNm5cK3YN1dDQgIfHEDx58hiHDoVi/fotCg99eeu0li1bIyzsEpKTk+Hh4QZnZ1dYWzdUev5mzVqUaAYyKp78XZG3bMn9N+/k9K1CV+SSZMjIZGdn4+nTaLFraf4slrzjieSdPCRvhkzz5q1gY9MIN29ex927d8QX0qSkRCxb5gMAWLrUC3/8cQTjxk1EpUqmqFBBcfamoKCd//85FF+I6f0V9nyjrJHkYz7f5JWTk4MjRw6KDS1FycjIwPTpU5Ceng5vb19OMvMeigpm5l+fkyMV9y0q2Pn48X/IyckpcIgKdfUykEoLXr9uXQCOHDmI7OxsODg4iedITk4SM9IDAjbKrdfS0oKBgSGSkhKVlk/WmFucYCzlBq2iwhYi6PAd7D1yD+1b10Cv9rnLAMDKbgaAMoUf5D0py8RdscIXhoaG+OabjoiOfgIASEvTQ3Z2Nt68eYP//vsPurq6qFQp9xpYu3YtateurTDwuoWFBWrXro2LFy++dzlZCxHRJyULROTNqAKA8PCzAHK70EybNkuuC0ZuF7zcLjpFCQ3d905TxudthW7UqAkaNWqCK1cuIzMzA9raOuK537x5AwDIzMzNuEhKSkS1atWRlZUFPT09vH6dhefPnyuMBfHixXOUL8/Btkti48a1CAjYCBeXHnI38fxyb+olZ2BgCG1tbbx48VxhXVZWFpKSEmFi0lTJnrkKmrZeJv8Mc3kxkPlxrFy5Ejt37UEnWzO0tngqXjPxj54BAN7ky5QCSla/eHgMQUZGBnbu3IaRI3O7QOjqlsW4cRPFWXdk8tcZRZ1z5cplkEoFeHqOK9ZnpQ9DWVfkcuWMkJj4+v+778p3LZW9ND569C82b94AIHdGLwA4d+6MGARzcHASx3fJ27V0zpyFClkssvFEvvtukFyAIH+GzMSJP2Hs2JH48ccxyMjIgCBIMX68JwRBQMeOndGggY34QqqhoYkmTZpCIrFExYoV8fr1a1y8GInLlyNRp05d9OnT/yN/s1+GrVv9cffuHdy9ewcxMU9RuXIVBAUdULqt7PmmfHljBARsxJYt/qhXT4JGjZoAKF5dUNiA/mlpafjtN2+555sTJ/6ElZUNwsL+gpubMwRBwJ49oeLstDKRkRF4/jwOpqaV4eHhjoSEV9DU1ELVqlXh4OAEF5feYuZMZmYmpk+fjLt3b2PGjDli+endFBXMzL9e1pgiW59f3mBnenoacnJyChyiInf2wIKHsFiyZCGiom6gZcs2cHL6VlwvK9/Ll/E4deq4wnpZuQrLWC9uMJaA4MN3sffIPbRrWR0j+zf+5DMyKsvEjY2NRXz8CwwcqHzQ9K5du6JDhw5YuzZ3Apm4uDjUrFlT6bbZ2dlyz0fvioErIvpkZIGI/BlV/v7rcfLkcQC5GQX5W7jj43MDC/kHJFVGNmV8zZrmcoMny6aMv3xZecQ/fyt0lSpVceXKZSQmJqJSJR25IIfsRq6rWxa1atURgxzW1o3w+nUCQkKC0bp1W/HYYWFnxIGZqXg2bVoHf//1cHBwwpxfZ+B2+IcfG0xdXR0SiSXu37+LrKws8WEQAG7fjoJUKi1wVpb8s8ApU7++lTjDHAOZH9/GjWvh7++P9q1rYHhf+Ywlo3K5L4XPXyi2AJekflFXV8eoUWMwcOBQPHjwDwABdetKIAgC/ve/RXJZLLLjvXjxXKELjyxYKtvm7t07OHQoFMOHj0ZSUqLYbTEh4RWA3K6O0dFPUKmSqdx1Su9PWVdkWTbmkSMHceTIQXHbxo2bYvr0XwHk/ib79u2RO9Zff53CX3+dAgA0bNhYYWBiqVSqNItFFgTv0eNtNqCyDBmJxBKrV2/C+vWrcO5cbmOPgYEBxo2bJGZ7yl5Ia9WqitevX2Pv3j1ISkqEtrYOatY0w+jRY+Hm1k9hBkVSbu3alTA0LAeJxAIpKckFbid7vgEAExMTdO3qCDU14I8/jmDrVn8AUNpIkr/+KWxA/7Jly8LMzFzufrJv3x7cuhUFbW3tQl8OZddYlSpV0bx5S1SoYILMzAxcu3YVy5f/hvDwMPj6rkRWVhamT5+CS5ciMXXqTKUzE1LxFdRYKwtUnj59QmF4DFkwEyg62KmlpY309LQCh6iIi4sFAKXrjYzK4+bN67C0bID58xfLBUtk5bt+/SosLOorrJeVS1n5ZGUsTmMQAWvWbkDwkbuwa1Edowc0gbr6+wet8mf5yrx8GV/sTNyxYyco1HmGhrqYO3cutLW1MW3aNFSs+Pa5qW7durh79y6uXr2Kxo3fdi2+cuUKHj16hPbt27/352Lgiog+ibyBiLwZVcHBu7Fx49r/n5o3HmfPnpZ7yTM2NkZUVO74HRUrViqyhVs2ZXz+wZOB3BeBV69eYvr0yUW2Qj958hhqamr4/vvhaN++I6pUqYYKFUxw69ZNDBzYB8nJyZg6dSZ0dHRw7doVSKVStG1ri0qVKuH48WOYMmU82rZth9jYGAQH75IbmJkKpzhroPDexyzoJt6lSzfcuHENoaF7xVlxAGD37u0oU6YMOnVSnEUFgNwscAXp1s0RR48eYiDzE5DVL849HNHXvoxCa2XNqobQ1FDHtes3FPaV1S8FBSCV0dXVlZvZ79Sp4xAEQe53lh0vKuqGwgx0t27dhJ6eHmrWNAOQO2abIAjYsGENNmxYo3A+X9/cgVE3bNhSonJS4ZS9VM6YMQenT5/AzJlTMXnyNPTs6Sa3j+zeU7OmWbHHFKlSpSpOnDiHadMm4ebN6wpZLL6+KxX2kWXIyLoiy9StWw/e3r5iV+R587yUdkV+/fo1tm7dXfwvg5TatWu/2NV34MA+SE9PV9hGVv+oqanB3LwW1q/fIgYl3dz6YciQ/nj06F9cv35VYd/89U9hA/pfvfq3QhbLzJnzYGJigsGD+yE2NlZp4CpvQ4vseUjGza0fli71xr59e3D9+lVs2eKPixfP4+efZ8gFUqnkCmqsBd4GKpUNjyELZgJFBzuLyuyWBa7yrz9/PlwM0P/22wro6cnPqC2b8EZbW0dhfVZWFpKTk6Curv7OGeuUKzh4N1avWQ+T8rqwtqiI8MvRcuvLGWjD6v97j8fGxuDo0UMASpblm/ff/KBBfdGwYeNiZeIqmzm3YkUDLFmyBGXLloWDg/xYrOPGjcMPP/yAoUOHol+/fjA3N8ejR4+wc+dOaGpqFmvWwKIwcEVEH51iIOJtRtWdO7mDN8oq3keP/sX8+b+K6xs2bIy4uFhUr14T+vr6Ci91+Vu4ZVPG3717V2472ZTx1avXyNcKrY2aNc3lWqHDw8Nw48Y1WFs3RJUqVXHu3Fm8evUSmZmZkEqlMDWtAm9vX9jYNAIgH+To188DdevWw6FDoVi+fCn09Q0UBmamgskCmXlnDdTRluLpvdz+9eUMtGFjmdtV58WrNIRFRuPc7U24czd3BreS3sS//bYnDh8+AD8/X8TExMDcvBYiIs7hzJlTGDx4OKpWraa0nHlngStIixatxBnmGMj8ePLWL/PmzlKanaejrYEm1qa4dOlv3L9/D/Xq5Y7BkJaWhgMH9qN69Zpo0MBK3L6gQKcyiYmvsXbtKhgZGcHV9W2Qo0mTZqhQwQQHDuxHnz7foWzZsgCA+/fv4cqVy3B0dBZfbhs0sML8+V4Kx75y5TL27t2Dfv08YGVljapVCx4glUomb1fkubOnQk3t7Qt/zeq5L3mJiUVnyBRHbterkmWxsCuyaihsUGLgbf3TuHFTXL36N7p2dZTr7qmhoYFu3Ryxdu1KXLt2pcj6p0mTZsjOzpbLGM87oH9MzDPcvHlDDJxXrlxZfL6pVMkUsbExCmUsqqFFVsf5+f2Gu3fvYMqU6cUewJ2UK6ixVkaWMWlsXEFhvSyYqaGhgaiowhtbisrs1tbWQWZmhtz6Cxci8MsvP0FdvQyMjIxgaGgot9+FCxGYMeNn6OjoQBCkCplTt29HQRAEmJpWfqeMdXpL9g4Un5CONYFXFPPH9U0AACAASURBVNbXr1sB/Ubk/v+zZ0+LfAfKn+Wbn5tbP0RGnv8ombidO3fGpk2bsHHjRuzduxfJyckwNDSEnZ0dxowZg/r13/96+OCBq3///RehoaE4d+4cHj9+jMzMTNSsWRMODg4YPHiw+OAm8/DhQ/j4+ODixYt48+YNGjRogHHjxqFNmzYfumhEVAqUByIAIHfwSZdvu4qDbT9+/AQDBg6DhoYG3N2/g56eHg4c2If4+BdYsmQZmjZtLrZwx8Q8g7v7twqBCNmU8QsXzkb//gNRvXoNrFz5uzhl/OzZC8Wb6Zkzp+Hn5wtr64bQ1tbB4cMHcPt2FP744wiMjIzwyy+zxYwIIDcl2tNzGP755x5Onz6JR4/+VRrk8PAYIg4STiUju4kXNGtg/boV3gauXqZhz6E7AN6+oJX0Jq6pqYlly1Zh/fpVOH78mDhm2cSJP6FXL+X9+m/cuCbOApf/gS+/mTPnMpD5EeWvXw4fOSYGOQH5QGe/b+vjzsNUTJr0A/r0Uaxf8r44FBTojIgIw/btW9GiRSsYG1dAXFwsDhzYj+TkJHh5/QYjIyNxWw0NDUyYMAWzZ0/H2LEj4OzcE6mpqdi9ezuMjMpj+PC3s4KZmFRUOmCyLLvDysq6WAMqU/HkzdBz76SGW+cWya3PzsyGpoY6rl+/rrBvSTP0srKy8MsvP5Uoi4VdkT8Peesfc/PauHr1bzx69FAcF83Y2BgtWrSGtnbui7+GhsY71z+yAf0BYOLEsXB17Y3q1WsgOvqJ+HxTv76V0sBV/oaWtLRUZGW9QWpqCm7cuIZt23IzxO7cuY3mzVtCR0dHbmw3AKhTpx7q1q33Yb/AL1RhjbWy9Vu3+sPUtDKeP4/Dgwf/KA1m1q1bD2fOnML9+/dQq1ZtPH0aDalUkAt2pqWl4ujRQ9i3bw+qVKkqBjtlmd2tW9vi/PlzYuZ3ZOR5TJ8+BcbGxoiNjUHbtu3kyiZbX6NGTdjbO2DNGr8CM9IdHZ2xadO6Emesf41y1KXQ1lbsPTBv3mx4L56Fm2eVj+GaV953oKIUNEHR0KEjMXToyGIdoyAnT54scF2bNm0+agzngweugoODsW3bNnTq1AnOzrmtiRcuXMCyZctw5MgR7N69W4zcPn78GP3790eZMmUwYsQI6OvrY8+ePRgxYgTWr1+Ptm3bFnE2IlJ1xQlEGE6wFf+eNa4ZDoVlY9u2zXjz5g0kEkv4+CxXmrKqTEmmjK9Z0wwWFpYIDz+LhIRXyM7ORsWKleDi0huDBg1VmC3lXYIcVLS8N/R582Zj3rzZcuu1NaVKb+oN6plgu9+3sLKbgeTUwmdcKWyWQQMDA0yaNBWTJk0tVnltbBoV++FBNsMcA5kfR0kCnZUr6iPAfx2W+q565/qlcuWq0NTUQlDQLiQlJaJcOSM0b94CgwcPFwfdzatTpy7Q1tZGQMBGrFy5DJqaWmjevAW+/3680tmY6OP7lBl6svGCIiMjSpTFwq7In4e89c/+/UEAgGPHDotBn8aNm6JFi9a4ciX3fmFt3RB6enolrn/i41+IA/pPmTIdu3ZtV/p8c+DAfoV9lTW0LFo0F6dPv335bNDAGnFxsXj5Mh6XLkXi0qVIheMMHTqSgatiUNZYm9etWzcQHLwHpqaV0bt3H2zatA5jx45Eq1Ztoa2tjevXr4jBzGrVquPy5YuYNOkHdO/eA9u3b4Guri6ysrLEYGfezO5z586ievUa+OabjmJm97RpM7FixTIcP34Mnp7DcPv2LQiCFPHxL6Cvb4A6deqI1+uzZ0+xdetmAAKcnJyhr2+AypWr4vfflyIiIhwVK1ZEUlKS2Fg7cOBQnDt3tsQZ61+jHGkO1q9fr3TdD2OGf+LSfL4+eOCqW7duGD16NAwMDMRl/fv3h5mZGdasWYOgoCB4eHgAAJYuXYqkpCTs3btXTB9zdXVFjx49MHfuXBw9evSTj6pPRB+GLBhRkkAEAFSrbIDfl71fIKK4U8abm9fCggXeRW6XV0mDHFS0wm7oAG/qJK+wQGdhdQsA1K5dC15evxV5joLql1q1auO33/xKVF5b23awtW1X9IZKODo6i4Nu0/v71Bl68+bNxIUL4SXOYmFXZNWjrweoqwPqaoCBXm63Uq9Fs8SMcQFlMHLUWFy8eAHffTcQjo65M7CtWrUc58+HAwDU1cuUuP7JyMiQG9C/UaMmaNmydbHLrayhZejQUXBx6Y3XrxPw99+X8eDBfcyYMadExyXlimpMkWVCxsXFYtWq5eLyU6f+BADo6enJBTNXr96INWv8sH9/MIDcCULyBztnzpyLSpVMsX37FkRHP8HhwwfkMrtlmd+7d+9ATk42gNyJIlJSkrFsmY/Sz7F8ufx1euFC7jVsbl5LbKxVU1NjYy59Uh88cGVjY6N0uaOjI9asWYN79+4ByG21OnnyJFq2bCnX51FPTw9ubm5Yvnw5bty4gYYNGyo9HhGpNrYuENHHwLqF3tWnztC7c+c2AJQoi4VdkVWTGnKQmfYCWZnZiApTDI5b2c3A3LmL4e09Hzt2BGL79q0AcoNQkyZNhbf3Aujp6ZXonLljo03G3bu3FQb0fx916tRFnTq53Uvt7R2wf38wpkwZjxUr1qFhw8ZF7E1A4V2/5s2bDW0tNQhS5TM8ArmBzpTUos9jbl50Y4uGhgbGjBmPMWPGF7j+Y2V+szGXPqVPNjh7bGzurAYmJrlTMN69mzuYW97pEmVkyxi4IiIiIqIPYcaMOZgxY474t4FejtIghMz7ZugFBR0ocRnZFfnzZWhoiIUL/4dXr17iyZPH0NXVRd26EjFbxczMvNjHepcB/d+Vg4MTfvvNGyEhwQxcFVNxMsULq1us7GYAKLxnARHJ+ySBq5ycHKxatQoaGhro0aMHgLeDDJqamipsL1sWFxf3KYpHRERERF+YgrIiZDgaBX0MxsYVYGz8drbHiIhzAIDWrW0L2kXOuwzo/z6ysrIglUqRlJT0Uc9DRPQ+PkngatGiRbh69SomTZqE2rVrA3g7U07e6TNltLW15bYpLiOjskVv9AkkpWW9875FPUN93Gesj3t0dXU1lfmNVMX7XCvAl3u98FpRjnWLcrxeFLFuUY7XinJfat3yvuPn8XpR9CXXLepqBf/eQk5K4fsWcK3cvHkTBw+GoHnzFmjf/u0g+m/evMGTJ0+gq6uDKlWqisuzsrIwbdqPiIyMwK+/zoa7e/HHCtLW1gQAGBrqKi1LfPwLpV1It2/3BwA0a9b0g1/vX2rd8r5Ytyj6kuuW98Fr5a2PHrhatmwZAgMD0bdvX4we/XbqZ11dXQC5FXR+mZmZctsU1+vXae9R0g9I491TPwtuFyze+vfzcY8ulQpITlaR30hVvMe1Any51wuvlQKwblGK14sSrFuU4rVSANYtSvF6UeILrlukgoCkfO8SR48eQmxsDLS1BCSlZCI7W8C+o7nj9ZoY66Jdyxq5+0oF/PbbUkRHP0b9+lbQ19fH3bt3cehQKExMKmL69Nly7ykxMc/g7v6twoD+M2f+jLCwMDRv3hJSqTp27QqSK0/+Af2vXv0bV6/+DQC4du06AMDfPwD6+voAgCFDRojburh8i4YNG0MisUTFihXx+vVrXLwYicuXI1GnTl18+63bh3+XYt2iFOsWJb7guuV9qNK1UrGiQdEbfUQfNXDl5+eH1atXo1evXpg7d67cukqVcge/VNYdULZMWTdCIiIiIiKij+3gwRAxMCSz59AdALmD+csCVwAgkVjg0qVIREZeQGZmBipVqgw3t74YOHCo3GzrhSnpgP6XL1+Ev798VuHOnYHi/+cNXLm59UNk5Hns3bsHSUmJ0NbWQc2aZhg9eizc3PqVOGGAiOhT+miBqxUrVmDFihVwdXXFwoUL5aYNBgCJRAItLS1cvXpVYV/ZMmtr649VPCIiIiIiIgDKx0Rbv34tAEBbU4qbZwsebBsA2rfvhPbtOxXrXB9qQP/hw0dj+PDRRW+I3KDX0KEjS3R8IiJV8VECVytWrICfnx9cXFywePFiqKurK2yjp6eHjh074s8//8SdO3dgaWkJAEhNTUVQUBDMzc05oyAREREREX10hY2JVtR4aERE9HF98MDVtm3b4Ofnh6pVq6Jt27Y4cEC+5cDExAS2trmzakyePBnnz5/HsGHDMGTIEOjp6WHPnj2Ii4vD2rVrFbK0iIiIiIiIiIjo6/HBA1c3btwAADx79gxTp05VWN+yZUsxcGVmZoYdO3bAx8cH69atw5s3b9CgQQNs2LABbdu2VdiXiIiIiIiIiIi+Hh88cOXl5QUvL69ib1+nTh2sXr36QxeDiIiIiIiIiIg+c4qDTxEREREREREREakABq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSQxcERERERERERGRSmLgioiIiIiIiIiIVBIDV0REREREREREpJIYuCIiIiIiIiIiIpXEwBUREREREREREakkBq6IiIiIiIiIiEglMXBFREREREREREQqiYErIiIiIiIiIiJSSaUeuJJKpdi8eTMcHBxgY2OD9u3bw8vLC2lpaaVdNCIiIiIiIiIiKkUapV2ARYsWYevWrbC3t8ewYcPw4MEDbN26Fbdu3cLmzZuhrl7qsTUiIiIiIiIiIioFpRq4un//PgIDA9G1a1f4+fmJy6tXr44FCxbg0KFDcHZ2LsUSEhERERERERFRaSnVdKaDBw9CEAQMHjxYbnmfPn2gq6uL0NDQUioZERERERERERGVtlINXN28eRPq6upo2LCh3HJtbW1YWlrixo0bpVQyIiIiIiIiIiIqbaUauHr+/DnKly8PLS0thXWmpqZISEhAVlZWKZSMiIiIiIiIiIhKm5ogCEJpnbxLly7Izs7G6dOnFdb9/PPPCAkJwcWLF2FoaPjpC0dERERERERERKWqVDOudHV1C8yoyszMBADo6Oh8yiIREREREREREZGKKNXAVaVKlQrsDhgXF1dgN0IiIiIiIiIiIvrylWrgytraGlKpFNevX5dbnpmZiTt37sDa2rqUSkZERERERERERKWtVANXjo6OUFNTQ0BAgNzy3bt3Iz09Hc7OzqVUMiIiIiIiIiIiKm2lOjg7AMyfPx+BgYGwt7dH+/bt8eDBA2zduhVNmzZFQEAA1NVLNbZGRERERERERESlpNQDVzk5OQgICMCuXbvw9OlTlC9fHo6Ojhg/fjz09PRKs2hERERERERERFSKSj1wRUREREREREREpAz74RERERERERERkUpi4Ooz1qlTJwwcOLC0i0Ef0YULF2BhYYG9e/eWdlHoM7F3715YWFjgwoULpV2UYvHz84OFhQWio6NLuygfFX+XDy86OhoWFhbw8/OTW25hYYFp06YV6xjv+zkLKsPnRtWvz4EDB6JTp07vvH9Jrgkioo9J1etbIlWlUdoFICIi1RYVFYWDBw/i/Pnz4gt+zZo10atXL/Tp0weampoK+1y7dg2+vr64du0a1NTU0KRJE0yZMgX169f/1MX/YvF3+TgiIyNx5MgRXLp0CdHR0dDW1oa5uTk8PDzg5OQENTW10i4iERHRFykxMRF2dnbIysrCkiVL4OLiUtpFIhXBwBWRCmvRogWuX78ODQ3+U6XSs2HDBoSHh6NLly5wd3eHVCrFqVOnMG/ePJw8eRIbNmyQe5m/evUqBg4cCFNTU0yYMAEAEBgYiO+++w47d+6EhYVFaX2ULwp/F+WuX7/+XjMS+/j4IDY2Fvb29vDw8EB6ejoOHz6MyZMn4/z581iwYMEHLC0Vx8aNG99r//e9JoiI6NM4cOAA3rx5g+rVqyMoKIiBKxLxbZhIhamrq0NbW7u0i0FfOQ8PD3h5ecldix4eHpgyZQoOHDiA06dPo2PHjuK6BQsWQFNTE9u2bYOpqSkAoHv37ujevTu8vb2xadOmT/4ZvkT8XZR73zpzypQpaNasGcqUKSMuGzRoEAYNGoQ9e/Zg0KBBkEgk71vML0ZKSgr09fU/6jm0tLTea3/eR4mIPg9BQUFo1aoVOnfujEWLFuHx48eoWbNmqZRFEASkpaVBT0+vVM5P8tj89BmIiYnBhAkT0KxZMzRt2hSenp54/PixwnaFjbVR0DgeL168wIIFC9C5c2dYW1ujTZs2GDp0KM6dO/fRPg8VX/4xrvL+HRwcDCcnJ1hbW6Njx45Yv369wv5///03RowYAVtbW9jY2KBdu3YYOXIkrl69Km4juzbu37+PBQsWwNbWFg0bNoS7uzsiIiKUlis8PBzDhg1D8+bNYWNjA2dnZ+zYsUPptrdu3cL48ePRtm1bWFtbo3379pg0aZLSa5g+ntWrV8PCwgLz58+HVCoVx3wJDw9Hnz590KhRI9ja2mLBggVIS0uT27dZs2ZKX/wcHR0BAPfv3xeX/ffff7hx4wYcHBzE4AgAmJqawsHBAeHh4Xjx4kWhZc3JycGvv/4KS0tLpdf1l+RL/11ycnLQrl079OzZU+l6WabX8ePHAeQGQXx9feHu7o5WrVrB2toa9vb28PHxQXp6erHOqWw8I6lUirVr16JTp05inRUaGqp0/5YtW8oFrYDcRoRu3boBkP9eC3L27Fk0adIE3333HRITE4tVblVU0PUZERGB/v37o0mTJvj+++8BAHFxcfDy8oKLiwtatGgBGxsbODo6Yt26dcjJyZE7rmyMl/DwcPj5+aFjx46wtraGs7MzDh06pFCO/GNc/fjjj7C2tsarV68Utn348CEsLCywcOFCcZmya0K27MqVK/Dw8EDjxo3RqlUrzJgxA6mpqe/1vVHJyK6HiIgIbNy4EV26dIG1tTW6deuGffv2iduV9Dk3JiYG06dPF6+vNm3aoF+/fnLHpI8nMzMTfn5+6NatGxo1aoTmzZvD2dkZ3t7ectsV95lSNrbvnTt3MGTIEDRp0gRt2rSBt7c3srOzkZmZCW9vb7Rr1w42NjYYMGAAHjx4oHCcrKwsrFmzBk5OTrCxsUHz5s3h6emJW7duids8ePAAFhYWWLx4sdLPNmnSJLk66MGDB5gzZw6cnJzQpEkTNGrUCL169cLu3bvf5yv86kRFReH27dvo2bMnnJ2doaGhgeDgYHF9dnY2bG1t4ebmpnT/bdu2wcLCAqdOnRKXZWZmYtWqVXB0dISNjQ1atGgBT09P3LlzR27f8PBwWFhYYP/+/di6dSu6d+8OGxsbBAQEAMjNWp86dSq6du2KRo0aiff4EydOKC1LREQE+vTpg4YNG8LOzg6LFi3CnTt3YGFhgVWrVsltK5VKERgYiJ49e4rHHjx4MCIjI9/pe/xSMeNKxSUlJWHAgAGIjY1Fv379UKdOHVy8eBGDBg1CRkbGex07Ojoa/fv3x8uXL+Hi4gJra2ukp6fj2rVrCA8Ph62t7Qf6FPSh7dy5E/Hx8XBzc4OhoSFCQ0Ph4+ODypUrw9nZGUDuw/uwYcNgYmKCQYMGoUKFCoiPj8fff/+NO3fuoHHjxnLHnDp1KtTV1TFy5EikpKRg165dGDFiBNavX4+2bduK2+3atQuzZ89G48aN4enpCV1dXYSHh2POnDl4/Pgxpk6dKm576tQpjBs3DmXLloWbmxvMzMzw4sULhIWF4d69e6XWgvI1kUqlmDdvHnbs2IHJkydj1KhR4rqoqCgcO3YM7u7ucHFxwYULF7B161bcv38f/v7+RXatiY2NBQBUqFBBXHbjxg0AQJMmTRS2b9y4MYKDgxEVFYUOHTooPWZGRgYmTZqEM2fOwNvb+4tNEf9afpcyZcrA2dkZGzduxL179xQylfbv34/y5cujffv2AHKDH0FBQejatSt69OgBDQ0NREZGYsOGDbh9+/Y7dxlbvHgxtmzZghYtWmDIkCF4+fIl5s2bhxo1ahT7GMq+V2X27duHmTNnomPHjli6dOlnme1T2PV58+ZNHDt2DH369JELSN69exd//PEH7O3tUbNmTbx58wZnz57F0qVLER0djXnz5imcx8fHB2lpaejfvz+A3ADGpEmTkJmZiV69ehVYvp49e+LIkSM4fPgwPDw85NaFhISI2xTl9u3b8PT0RK9evdCjRw9ERkYiKCgI6urqmD9/fpH704fl6+uLjIwM9O3bF1paWtixYwemTZuGmjVrolmzZiU6VnZ2NoYOHYq4uDh89913MDc3R0pKCu7evYtLly4V6/qg9zN37lwEBwfD1dUVjRs3hlQqxaNHj+QGJS/JMyWQWw8PHToUjo6O6NatG86dO4dNmzZBXV0d//zzDzIyMjBq1CgkJCRg06ZNGDNmDI4cOSLeN9+8eYPhw4fjypUrcHFxwYABA5CSkoLdu3ejf//+CAwMhI2NDerUqQMbGxscPHgQP//8s1xjRkpKCk6cOIF27drB2NgYQO7YiJcuXUKHDh1QvXp1pKen4+jRo5g1axYSEhIwevToT/CNf/6CgoJQtmxZdO3aFWXLlkWHDh2wf/9+TJgwAerq6tDQ0ECPHj2wefNmPHjwAHXq1JHbf//+/ahQoQLatWsHIDdIOWzYMFy7dg2urq4YOHAgkpKSsHv3bvTr1w/bt29HgwYN5I7h7++PpKQk9O7dGxUrVkTVqlUBAMeOHcOjR4/g6OiIqlWrIiEhAfv27cOYMWPg6+srNhoCuYkGI0eOhJGREUaNGgV9fX1x7ExlpkyZgiNHjqB79+5wc3NDRkYGQkNDMWTIEKxatarAZ7OvjkAqbenSpYJEIhGCgoLkli9YsECQSCSCh4eHuOzJkyeCRCIRli9frnCc5cuXCxKJRHjy5Im4bMSIEYJEIhHOnDmjsH1OTs4H/BT0rs6fPy9IJBIhODhY7m9bW1shMTFR3C4tLU1o1aqV0KdPH3FZQECAIJFIhGvXrhV6Dtm14ebmJmRmZorLY2JihMaNGwsODg7isri4OMHa2lqYNGmSwnHmz58vWFpaCv/9959cmVq3bi3ExsYqbM9r7OMIDg4WJBKJcP78eSE9PV0YM2aMYGVlJezbt09uO4lEIkgkEuHPP/+UWz5//nxBIpEIBw8eLPQ8KSkpQqdOnYRmzZoJCQkJ4vKNGzcKEolEOH36tMI+p0+fFiQSibBz505xWd66KSEhQejbt6/QuHFj4ezZs+/y8VXW1/y73Lt3T5BIJIK3t7fc8v/++0+QSCTC/PnzxWWZmZlCVlaWwjF8fX0V6rOC7nkSiUSYOnWq+PeDBw8ECwsLYdCgQUJ2dra4/ObNm4KFhYXCvVGZ2NhYoVmzZkLnzp3lype/DGvXrhUkEokwe/bsz6qOK+n1ee7cOYVjpKenC1KpVGH5lClTBEtLSyEuLk7hfB06dBCSkpLE5UlJSUKHDh2EFi1aCOnp6eJyDw8PoWPHjuLf2dnZgq2trdC7d2+5c0mlUqFDhw5Cjx49FMqd95qQLbOwsBCuXLkit3zkyJFCgwYNhJSUFIXPQh+H7HpwcXGRew6JjY0VrKyshIkTJwqCULLn3Nu3bwsSiURYt27dp/kQpKBFixbCiBEjClxfkmdKQRCEjh07ChKJRDh8+LDctj179hQsLCwET09PuTpI9hyc9z3H399f6btPcnKy0L59e7n3qsDAQKX3zd27dwsSiUQ4duyYuCw1NVXhM+Tk5AgeHh5C06ZN5e4beetbeisjI0No0aKFXF39559/KvwGsn/bS5culdv/wYMHgkQiERYtWiQuW79+vWBhYaFwz0pMTBTatWsnDB48WFx27tw5QSKRCC1bthRevnypUD5lv3Fqaqpgb2+vcM9xdXUVbGxshOjoaHFZVlaW4O7uLkgkEmHlypXi8sOHDwsSiUTYs2eP3DGysrIEFxcXwd7eXuG8Xyt2FVRxx48fh4mJCVxdXeWWjxw58r2O+/r1a5w9exbt2rUTo9J5cRBT1da7d28YGhqKf+vq6qJx48Z49OiRuMzAwAAAcOLECWRmZhZ5zCFDhsiNIyLL3nr48KGYan3s2DFkZWXBzc0Nr169kvuvU6dOkEqlYvfCsLAwJCQkYOjQoXJdk2R4cguVXQAAE+ZJREFUjX1ciYmJGDp0KMLDw7F69WqFOgQAatWqhS5dusgtk2VW/PnnnwUeOycnBz/99BOio6MxZ84cGBkZietk3bmUjUkjW6asy9ezZ8/Qv39/PHnyBIGBgbCzsyvGp/z8fI2/S7169WBlZYUDBw5AKpWKy/fv3w8Act+BlpaWOBtidnY2EhMT8erVKzHr89q1ayU+/4kTJyAIAoYOHSrXam5lZVWszOL09HT88MMPSE9Ph5eXl9LZGmUZSkuXLsWECRMwZ86cz7KOK871aWlpKZeFK6OjoyNOBpCVlYXXr1/j1atXsLOzg1Qqxc2bNxX26d+/v3ivAnLvW/369UNiYmKhU8XLMvlu3Lgh1xXowoULePbsWbGzaRo3bqyQfdy6dWtkZ2fj6dOnxToGfTjfffedXB1lamqKWrVqyT3bFJfsurpw4QJevnz5oYpIJaCvr49//vkH9+7dU7q+JM+UMqampujevbvcsqZNm0IQBAwcOFBuQpLmzZsDyO0qLxMaGoratWvDyspK7nxZWVlo27YtLl++LPZocXJygqampnivkgkJCYGRkZFcFkzZsmXF/8/MzERCQgJev34NW1tbpKSk4OHDhyX45r5Of/zxBxITE+XuOx06dECFChXkugtaWlrC0tISoaGhEARBXK4s2zY0NBT16tWDpaWl3O+dnZ2NNm3a4OLFi8jKypIrR69evcRMurzy/sbp6elISEhARkYGWrZsifv374vDOcTFxeHWrVuwt7dHtWrVxH00NTUxcOBAheOGhobC0NAQnTp1kitjcnIyOnTogP/++w9Pnjwp9vf4JWNXQRX35MkT2NjYKIy3UalSJbnARUk9fvwYgiAopEfS56F69eoKy4yMjPD69WvxbycnJ4SGhmLNmjXYvHkzGjVqBDs7Ozg5OclVpDL5023zLnvy5Anq1KkjviAMGTKkwLLFx8cDgPigyWusdEybNg1paWkIDAwUH97yU/aby+qWgm6SUqkUv/zyC06cOIGJEyeiR48ecut1dXUBQOFBIO8y2TZ5eXp6IicnB6GhoTAzMyv8w33GvtbfxdXVFQsXLkR4eLgY/JI9UFpbW8ttu23bNuzcuRP//POPXKALwDuNFyX7zmrXrq2wrk6dOggLCytw38zMTIwdOxY3b96El5dXgb9ZQEAAUlNTMXHiRHh6epa4jKqiONenubm50uXZ2dlYt24dQkJC8N9//8m9UAC5Qx/kV9BvAkBhTM78XF1dsWnTJoSEhGDSpEkAcl9cZEGt4lDWVVQW8M17P6VPo6Df412CiNWqVYOnpyfWrVsHOzs71K9fH61bt4aDgwMaNmz4IYpLRfjll1/w888/w9nZGTVq1ECrVq3QsWNHdOrUCerq6iV6ppRR9vxbrlw5petk70l5/y0/ePAAGRkZaNOmTYHnTEhIQJUqVcTg1IkTJ5CcnAwDAwNER0fj0qVLCkHW1NRUrFixAkeOHEFMTIzCMZXVfyQvKCgIxsbGqFy5slywsW3btjh69ChevXolBpRcXV3h5eWF8+fPo02bNhAEAQcOHICFhQUsLS3FfR8+fIg3b94U+nu/fv0alSpVEv8u6B734sULLFu2DCdPnlQ6vmJycjLKli0rPnPUqlVLYRtlyx4+fIikpKRCyxgfH1+ioQ2+VAxcfQbyth7klf+hsKDtgNwHSmX7FrYPqa78gUxltLS04O/vj+vXr+Ps2bO4dOkSli9fjhUrVmDp0qWwt7cv8hj5rzHZ397e3nKVfF6yipXXWOlydHTE3r17sWrVKqxatQo6OjoK2xS3bsm7fMaMGdi/fz9++OEHpS/osuvi+fPnCuvi4uIAQGkGXo8ePbBr1y6sWrUKixcv/iyzVYrja/1devTogSVLlmD//v2ws7PDpUuX8OTJE0yZMkVuO39/f3h5ecHOzg6DBg1CpUqVoKmpibi4OEybNq3A76A4lH2vhR0vMzMTY8aMQXh4OBYsWFDouF62tra4ePEidu3aBScnp8/2AbM416eyACcAeHl5YevWrXB0dISnpyeMjY2hqamJqKgo+Pj4KAQhgfe7P1hYWKB+/foIDQ3FxIkTkZGRgWPHjsHW1hYVK1Ys1jEKu5e+z7VG76ao+qUkz7kAMHHiRLi5ueH06dO4dOkSgoKCsHHjRowYMQI//fTTe5eXCtelSxecPHkSf/31Fy5evIjw8HAEBQWhefPm8Pf3L9EzpUxh/2YLun7y/lsWBAESiQTTp08v8Dh5s21cXV3x559/4ujRo3B3d0dISAgEQVDIRp08eTJOnz6NPn36oEWLFihXrhw0NDTw119/YfPmzUrrP3rryZMnuHDhAgRBECdCyU825hMAODs7w8fHByEhIWjTpg0uXLiAp0+fKkzCIQgC6tevj59//rnAc+fNTgeg9L4nlUoxbNgwPHr0CIMGDYKVlRUMDAxQpkwZ7NmzB4cPH37ne4YgCKhYsSKWLFlS4DZ169Z9p2N/aRi4UnE1atTAo0ePkJOTI1dZP3/+HMnJyXLbyloclLVI52+5NDMzg5qamtwMGvRlatiwodi6GBMTA1dXVyxbtkwhcPXgwQO5VgoAYmqz7MFB1gpRvnx5pV1F8pK1pN+6dYsD/ZcCZ2dntGnTBj///DNGjx6NNWvWKLxw/vPPPwr7yeqW/A+LsuDI3r178f3332PcuHFKz2tjYwMAuHLlCtzd3eXWXb16FWpqarCyslLYb9SoUTAzM8OSJUuQnZ2NJUuWFCtA+7n5Wn8XY2NjfPPNNzh+/DhSU1Oxf/9+qKur49tvv5XbLiQkBNWqVcP69evlXkLOnDlT4nPKyL6zBw8eKHx/BXXfyMrKwtixY3Hu3DnMnz+/wBmMZCQSCcaPH48hQ4bAw8MDAQEBBbbaqrLiXJ8FCQkJQYsWLeDr6yu3PG/LeX4PHjxA586dFZYByjMr8nN1dcXixYtx/vx5vHjxAqmpqRx0+wtWkudcmRo1amDgwIEYOHAgMjMzMXz4cGzYsAHDhg0rcqIFen9GRkZwcXGBi4sLBEGAj48PNmzYgBMnTpTomfJDMTMzQ0JCAlq3bl2shpj27dvD2NgY+/fvh7u7u9jVMG/WXlJSEk6fPg0XFxeFSSjCw8M/+Gf4Eu3duxeCIGDBggVy3cdlli1bhuDgYDFwZWJiAjs7Oxw7dgyzZ89GSEgINDQ0FLJtzczM8OrVK7Rp0+a9Gkpu3bqFe/fuYfz48Rg7dqzcup07d8r9Lbt3/fvvvwrHUbbMzMwM4eHhaNKkSbHvt1+rL7NJ+wvSuXNnxMfHK/SvVjYVub6+PipWrIjz58/LRX2fPHkiTjUuY2RkhG+++QZnzpxRWqmypfHzpyyNtXLlyjA2Nlb60Ld582a5bkSxsbE4cOAAatWqJXbd6N69O7S0tODn56d0Vsvk5GTxGLa2tihfvjz8/f2VZnnwGvv4nJycsHTpUly+fBkjR45UmOL933//VagbZHVL3jGWBEHAzJkzERwcDE9PT/z4448FntPMzAzW1tY4evSomMkD5Gb1HD16FK1bty4wG2L48OH45ZdfcPDgQUyePFlpC/qX4Gv9XXr27In09HSEhobi6NGjaNu2rUKWl7q6OtTU1OTqh+zsbKX3vOLq1KkT1NTU4O/vj5ycHHF5VFSU0vtfVlYWxowZg7CwMMydO1ch0FeQevXqYcuWLcjJyYGHh4fSadg/B0VdnwVRV1dXqNfT0tKwefPmAvfZsWOHXCNccnIydu7cCUNDQ7Rs2bLIc8qmSw8JCUFISAgMDAwUAmH05SjJc25ycjLevHkjt0xbW1tsVHuXbsdUfDk5OQrd49TU1MThIxITE0v0TPmhuLq64sWLF/D391e6Pn/XRE1NTTg5OeHy5cs4cOAAHj16pBAclwXA8td/z58/x549ez5g6b9MUqkU+/btg0Qigbu7OxwcHBT+69GjB+7du4fr16+L+7m6uiItLQ2hoaE4duwY7OzsYGJiIndsV1dXxMXFISAgQOm58//eBZE12OX/je/cuYOTJ0/KLatcuTLq16+PP//8U66b85s3b7B161aFY7u6uiI7O1uh0aekZfwaMONKxY0YMQIHDx7ErFmzEBUVhbp16yIyMhJXr15F+fLlFbYfMGAAli1bhhEjRqBLly54/vw5du7ciXr16onTocvMmjULt27dwsiRI+Hq6gorKytkZmbi2rVrqFatGtOoP3OrV6/GuXPnxKl5BUHAqVOn8PDhQ4wYMUJh+5ycHAwYMABOTk5ITU3Fzp07kZmZiZkzZ4rbVK5cGXPmzMHMmTPh6OiIb7/9FtWqVcOrV69w7949HD9+HIcOHUL16tWhq6uLhQsXYsKECXB2doabm5vY8hEWFoYhQ4YoDEBNH56DgwM0NDTw448/ii3N+vr6AHKzRH766Se4u7vDzMwMFy5cwLFjx9CyZUu5aX2XLFmCoKAgWFpaonbt2uIAmDI1a9ZEkyZNxL9nzJiBQYMGYcCAAeJU9YGBgRAEQSGNO7/BgwdDQ0MD8+fPR05ODn777Telg2F/7r7G36V9+/YwMjKCj48PUlJSlGbGODg4YOnSpRg5ciTs7e2RkpKCgwcPQkPj3R9X6tSpgwEDBiAwMBCDBw9G165d8fLlS2zbtg2WlpYKmcdTpkzB2bNn0bZtW+jo6Ch8r/nH0Mh/Ltl5Bg0ahM2bN6NevXrvXPbSUtj1WZBu3bph165d+PHHH9G2bVvEx8cjODhYoRtGXuXLl4e7uzt69+4NQRCwd+9ePHv2DAsWLChWy7Ns2vNjx44hMzMTbm5u0NbWLvHnpc9HcZ9zL1y4gFmzZqFr166oVasW9PT0cPPmTQQFBaFRo0ZKx1ejDyc1NRV2dnbo1KkTGjRoAGNjY0RHR2PHjh0oV64cOnbsCFNT02I/U34ogwYNQnh4OJYsWYLz58+jdevW0NfXx7Nnz3D+/HloaWkpBBd69uyJrVu3ipNu5M8U1tfXh62tLUJDQ6GjowMbGxs8ffoUu3btQvXq1TleXhHCwsIQExNTaGZz165d4efnh6CgIDHbrXPnzihXrhyWLFmC1NRUpZOJDBkyBOHh4Vi8eDEiIiLQsmVL6OnpISYmBhEREdDT0yswiJlX3bp1Ubt2baxbtw6pqamoVasWHj58iF27dkEikSAqKkpu+6lTp2LEiBHo27cv+vXrBwMDAxw+fFhsPMub/eXk5IQzZ84gICAAN2/eRPv27VG+fHnExsbi77//RkxMDI4dO1as7/JLx8CViitXrhy2bdsGLy8v7N+/H4IgoFWrVtiyZYvSwQxHjhyJ5ORkhIaGIjIyEnXr1sXChQsRFRWlELiqUaMGgoODsXLlSpw5cwYhISEwNDSEpaUl+vbt+4k+IX0sXbp0wYsXL3D06FHEx8dDR0cHZmZmWLBggdKbg7e3N3bu3In169cjKSkJFhYW8PLyUujm17t3b5ibm2PTpk3YtWsXkpOTYWRkhFq1amHChAlyWRudO3fG9u3bsWbNGgQFBSE1NRUmJiZo1qwZLCwsPvp3QLm6dOmCFStWYNy4cRg2bBg2bNgAIHdWtenTp8PX1xc7d+6Evr4+PDw8MHHiRLkUetlsYHfu3FE6TkDPnj3lAiRNmzbF1q1bsWzZMvz+++/ist9//73AF/68BgwYAE1NTfz6668YP348fv/9d6Wz4X3uvrbfRUtLCz169EBgYCD09fWVBq6HDx8OQRAQFBSEhQsXomLFiujevTt69+4tF7QrqRkzZsDExAS7d+/GkiVLYG5ujl9//RX//fefQuBK9r2Gh4crzcj64YcfCv2+zM3N5YJX/v7+xfp+VU1B12dBpk+fDj09PRw9ehQnTpxAlSpV0LdvX9jY2BQ4+PKUKVNw6dIlbNu2DfHx8TA3N4ePj0+xB1cHcq/zU6dOAUCh45DRl6G4z7kWFhawt7dHZGSkOKNplSpVMHr0aAwbNqwUP8HXQUdHB4MHD0ZERAQiIiKQmpqKSpUqoVOnThg9erSYbVuSZ8oPQVNTE2vXrsX27dsREhICPz8/ALnjQNrY2ChtULGysoJEIsG9e/fQtm1bVK5cWWGb//3vf1i6dClOnjyJffv2wdzcHBMnToSGhkah42lR7qDsAAode1cikcDc3ByHDx/GL7/8Ah0dHWhpaaF79+5ilq6ybFstLS1s2LABgYGBCA0Nlfu9GzVqVOyu5Zqamli3bh2WLFmCffv2IT09HRKJBD4+Prh+/bpC4KpNmzZYv349fH19sXbtWhgaGsLJyQkODg7o37+/wjha3t7eaN26NXbv3o21a9ciOzsbJiYmsLa2Rr9+/YpVxq+BmsD+OkRfNT8/P6xYsQInTpz4oK1apNosLCzQs2dPeHl5lXZRKA/+LvQ12Lt3L6ZPn44tW7agVatWpV0cIiKij+7w4cOYOHEifv/9dzg4OJR2cT47HOOKiIiIiIiIiOg9SaVShfHZsrKysHnzZmhqahZrDEdSxK6CREREVGKvXr2SG+xcmbJly0JPT+8TlYiIiIiodKWnp6Nr165wdnaGubk5Xr9+jUOHDuHevXvw9PSEsbFxaRfxs8TAFREREZWYm5ub3Iw5yvzwww8YN27cJyoRERERUenS0tLCN998g+PHj+PFixcQBAG1a9fGnDlz0L9//9Iu3meLY1wRERFRiV2+fBmZmZmFblOjRg3UqFHjE5WIiIiIiL5EDFwREREREREREZFK4uDsRERERERERESkkhi4IiIiIiIiIiIilcTAFRERERERERERqSQGroiIiIiIiIiISCUxcEVERERERERERCrp/wCazTX+1gLLpAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pd.set_option('display.max_rows', None)\\n\",\n    \"pd.set_option('display.max_columns', None)\\n\",\n    \"pd.set_option('display.width', None)\\n\",\n    \"pd.set_option('display.max_colwidth', -1)\\n\",\n    \"\\n\",\n    \"one2seq_exps = ['kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_f_score@10')\\n\",\n    \"ax.legend(loc='lower left')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"'''\\n\",\n    \"# SADR\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_advanced_sadr')\\n\",\n    \"metric_names = ['present_exact_advanced_sadr']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_sadr')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"# AUC\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_advanced_auc')\\n\",\n    \"metric_names = ['present_exact_advanced_auc']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_exact_advanced_auc')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"'''    \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"metric_names = ['beam_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='beam_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='unique_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['present_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='present_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_pred_num']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0, title='absent_pred_num')\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Heat map\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-16T20:39:00.545540Z\",\n     \"start_time\": \"2020-11-16T20:38:49.758233Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - present_exact_f_score@10',\\n\",\n      \"       'length - present_exact_f_score@10',\\n\",\n      \"       'no_sort - present_exact_f_score@10',\\n\",\n      \"       'random - present_exact_f_score@10',\\n\",\n      \"       'verbatim_append - present_exact_f_score@10',\\n\",\n      \"       'verbatim_prepend - present_exact_f_score@10',\\n\",\n      \"       'alphabetical_reverse - present_exact_f_score@10',\\n\",\n      \"       'length_reverse - present_exact_f_score@10',\\n\",\n      \"       'no_sort_reverse - present_exact_f_score@10'],\\n\",\n      \"      dtype='object')\\n\",\n      \"(7, 9)\\n\",\n      \"Index(['alphabetical - present_exact_f_score@10',\\n\",\n      \"       'length - present_exact_f_score@10',\\n\",\n      \"       'no_sort - present_exact_f_score@10',\\n\",\n      \"       'random - present_exact_f_score@10',\\n\",\n      \"       'verbatim_append - present_exact_f_score@10',\\n\",\n      \"       'verbatim_prepend - present_exact_f_score@10',\\n\",\n      \"       'alphabetical_reverse - present_exact_f_score@10',\\n\",\n      \"       'length_reverse - present_exact_f_score@10',\\n\",\n      \"       'no_sort_reverse - present_exact_f_score@10'],\\n\",\n      \"      dtype='object')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7fe9555a0e10>\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x1152 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = ['kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"import copy\\n\",\n    \"# prepare one2seq data\\n\",\n    \"############ beam_width=1 ############\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '1']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'selfterminating'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1 = copy.copy(df)\\n\",\n    \"df_f1 = df_f1.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"                   'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"                   'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"                   'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"                   'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"                   'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"                   'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"                   'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"                   'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"                   'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1 = df_f1.div(df_f1.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1.columns)\\n\",\n    \"normalized_df_f1.columns = [column_name_map[item] for item in normalized_df_f1.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"############ beam_width=10 ############\\n\",\n    \"'''\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '10']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_10 = copy.copy(df)\\n\",\n    \"df_f1_10 = df_f1_10.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_10.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"                   'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"                   'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"                   'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"                   'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"                   'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"                   'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"                   'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"                   'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"                   'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_10 = df_f1_10.div(df_f1_10.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_10.columns)\\n\",\n    \"normalized_df_f1_10.columns = [column_name_map[item] for item in normalized_df_f1_10.columns]\\n\",\n    \"\\n\",\n    \"############### beam_width=25 ##################\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '25']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_25 = copy.copy(df)\\n\",\n    \"df_f1_25 = df_f1_25.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_25.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"                   'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"                   'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"                   'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"                   'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"                   'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"                   'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"                   'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"                   'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"                   'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_25 = df_f1_25.div(df_f1_25.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_25.columns)\\n\",\n    \"normalized_df_f1_25.columns = [column_name_map[item] for item in normalized_df_f1_25.columns]\\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"############ beam_width=50 ############\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"    \\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"\\n\",\n    \"df_f1_50 = copy.copy(df)\\n\",\n    \"df_f1_50 = df_f1_50.drop(['kp20k_valid2k'], axis=0)\\n\",\n    \"print(df_f1_50.shape)\\n\",\n    \"column_name_map = {\\n\",\n    \"                   'random - present_exact_f_score@10': \\\"random\\\",\\n\",\n    \"                   'alphabetical - present_exact_f_score@10': \\\"alpha\\\",\\n\",\n    \"                   'alphabetical_reverse - present_exact_f_score@10': \\\"alpha-reverse\\\",\\n\",\n    \"                   'length - present_exact_f_score@10': \\\"length\\\",\\n\",\n    \"                   'length_reverse - present_exact_f_score@10': \\\"length-reverse\\\",\\n\",\n    \"                   'no_sort - present_exact_f_score@10': \\\"no-sort\\\",\\n\",\n    \"                   'no_sort_reverse - present_exact_f_score@10': \\\"no-sort-reverse\\\",\\n\",\n    \"                   'verbatim_append - present_exact_f_score@10': \\\"pres-abs\\\",\\n\",\n    \"                   'verbatim_prepend - present_exact_f_score@10': \\\"abs-pres\\\"}\\n\",\n    \"\\n\",\n    \"normalized_df_f1_50 = df_f1_50.div(df_f1_50.sum(axis=1), axis=0)\\n\",\n    \"print(normalized_df_f1_50.columns)\\n\",\n    \"normalized_df_f1_50.columns = [column_name_map[item] for item in normalized_df_f1_50.columns]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 18,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n    \"'''\\n\",\n    \"f, axes = plt.subplots(2, 2, figsize=(20, 9))\\n\",\n    \"sns.heatmap(normalized_df_f1, annot=df_f1, linewidths=.5, ax=axes[0][0], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"axes[0][0].set_ylabel(\\\"F1@10\\\")\\n\",\n    \"axes[0][0].set_xlabel(\\\"(a) Greedy Decoding\\\")\\n\",\n    \"\\n\",\n    \"sns.heatmap(normalized_df_f1_10, annot=df_f1_10, linewidths=.5, ax=axes[0][1], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"axes[0][1].set_ylabel(\\\" \\\") \\n\",\n    \"axes[0][1].set_xlabel(\\\"(b) Beam Width = 10\\\") \\n\",\n    \"\\n\",\n    \"sns.heatmap(normalized_df_f1_25, annot=df_f1_25, linewidths=.5, ax=axes[1][0], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"axes[1][0].set_ylabel(\\\"F1@10\\\")\\n\",\n    \"axes[1][0].set_xlabel(\\\"(c) Beam Width = 25\\\") \\n\",\n    \"\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.5, ax=axes[1], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"axes[1][1].set_ylabel(\\\" \\\") \\n\",\n    \"axes[1][1].set_xlabel(\\\"(d) Beam Width = 50\\\") \\n\",\n    \"f.tight_layout()\\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, figsize=(12, 16), sharex=True)\\n\",\n    \"sns.heatmap(normalized_df_f1, annot=df_f1, linewidths=.5, ax=axes[0], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.5, ax=axes[1], cmap=cmap, fmt='.3f', cbar=False)\\n\",\n    \"\\n\",\n    \"# sns.heatmap(normalized_df_f1_50, annot=df_f1_50, linewidths=.2, ax=axes, cmap=cmap, fmt='.3f', cbar=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq - Absent Phrase Prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-16T20:40:48.269032Z\",\n     \"start_time\": \"2020-11-16T20:40:42.605607Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\\"\\\\n# SADR\\\\n_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_advanced_sadr')\\\\nmetric_names = ['absent_exact_advanced_sadr']\\\\ndatasets = valid_one2seq_df.test_dataset.unique()\\\\norders = valid_one2seq_df.order.unique()\\\\nbar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\\\nfor index_label, row_series in valid_one2seq_df.iterrows():   \\\\n    for metric_name in metric_names:\\\\n        bar_value = row_series[metric_name]\\\\n        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\\\n\\\\nkp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\\\nfor k, v in bar_values.items():\\\\n    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\\\n    bar_values[k].append(np.mean(v))\\\\ndatasets = np.append(datasets, 'Average')\\\\ndf = pd.DataFrame(bar_values, index=datasets)\\\\nax = df.plot.bar(figsize=(20,9), rot=0)\\\\nfor p in ax.patches:\\\\n    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\\\n    \\\\n    \\\\n    \\\\n    \\\\n    \\\\n    \\\\n# AUC\\\\n_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_advanced_auc')\\\\nmetric_names = ['absent_exact_advanced_auc']\\\\ndatasets = valid_one2seq_df.test_dataset.unique()\\\\norders = valid_one2seq_df.order.unique()\\\\nbar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\\\nfor index_label, row_series in valid_one2seq_df.iterrows():   \\\\n    for metric_name in metric_names:\\\\n        bar_value = row_series[metric_name]\\\\n        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\\\n\\\\nkp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\\\nfor k, v in bar_values.items():\\\\n    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\\\n    bar_values[k].append(np.mean(v))\\\\ndatasets = np.append(datasets, 'Average')\\\\ndf = pd.DataFrame(bar_values, index=datasets)\\\\nax = df.plot.bar(figsize=(20,9), rot=0)\\\\nfor p in ax.patches:\\\\n    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\\\n\\\"\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = ['kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-length_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-no_sort_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kpgen-meng17-kp20k-alphabetical_reverse-rnn-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n    \"                'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1', \\n\",\n    \"                'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"'''\\n\",\n    \"# SADR\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_advanced_sadr')\\n\",\n    \"metric_names = ['absent_exact_advanced_sadr']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"# AUC\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_advanced_auc')\\n\",\n    \"metric_names = ['absent_exact_advanced_auc']\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"orders = valid_one2seq_df.order.unique()\\n\",\n    \"bar_values = {'%s - %s' % (order, metric_name): [] for order in orders for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s' % (row_series.order, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))  \\n\",\n    \"'''\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Decoding Strategy on One2Seq Learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-%s-%s' % (one2seq_df.at[index_label , 'decoding_terminate'], one2seq_df.at[index_label , 'decoding_method'], one2seq_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# print(peak_one2seq_df.iloc[0].path)\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"decoding_terminates = valid_one2seq_df.decoding_terminate.unique()\\n\",\n    \"decoding_methods = valid_one2seq_df.decoding_method.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s - %s' % (decoding_terminate, decoding_method, metric_name): [] for decoding_method in decoding_methods for decoding_terminate in decoding_terminates for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s - %s' % (row_series.decoding_terminate, row_series.decoding_method, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############ Absent\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-%s-%s' % (one2seq_df.at[index_label , 'decoding_terminate'], one2seq_df.at[index_label , 'decoding_method'], one2seq_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# print(peak_one2seq_df.iloc[0].path)\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"decoding_terminates = valid_one2seq_df.decoding_terminate.unique()\\n\",\n    \"decoding_methods = valid_one2seq_df.decoding_method.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s - %s' % (decoding_terminate, decoding_method, metric_name): [] for decoding_method in decoding_methods for decoding_terminate in decoding_terminates for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s - %s' % (row_series.decoding_terminate, row_series.decoding_method, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"############ Present + Absent\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['exp_name', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# display(one2seq_df)\\n\",\n    \"\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-%s-%s' % (one2seq_df.at[index_label , 'decoding_terminate'], one2seq_df.at[index_label , 'decoding_method'], one2seq_df.at[index_label , 'exp_name'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"\\n\",\n    \"# print(peak_one2seq_df.iloc[0].path)\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"# metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10', 'absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"decoding_terminates = valid_one2seq_df.decoding_terminate.unique()\\n\",\n    \"decoding_methods = valid_one2seq_df.decoding_method.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s - %s - %s' % (decoding_terminate, decoding_method, metric_name): [] for decoding_method in decoding_methods for decoding_terminate in decoding_terminates for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['%s - %s - %s' % (row_series.decoding_terminate, row_series.decoding_method, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Beam Width\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-07T20:14:37.584541Z\",\n     \"start_time\": \"2020-11-07T20:14:37.547767Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_index='present_exact_f_score@10'\\n\",\n    \"\\n\",\n    \"# prepare for one2one data\\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"kp20k_one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in kp20k_one2one_df.iterrows():\\n\",\n    \"     kp20k_one2one_df.at[index_label, 'test_dataset'] = 'one2one - ' + kp20k_one2one_df.at[index_label, 'test_dataset']\\n\",\n    \"    \\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.beam_width == '50']\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2One - Present Phrase Prediction\\n\",\n    \"\\n\",\n    \"    - For present prediction, beam_width>8 is already enough. Actual unique #kp increases significantly using larger beam size.\\n\",\n    \"    - For absent, the corresponding increase is very clear. The gain from beam=64 to beam=200 is small.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[  8  16  32  64 200]\\n\",\n      \"[  8  16  32  64 200]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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TvuczMTIYOHUrjxo0PKF+9ejUPPPAAs2fP5owzzuDLL788RAtl57rrrjvufRyNoqIiKlSIZjRyxD2EJEmSJElS2SkqKqJ///4kJSXRr18/du7cCUBOTg6dOnWiVatW9OjRg88+27sd7/jx40lLSyM5OZm+ffvG6mdlZTFkyBA6d+5Mo0aNePfddxkwYADx8fFkZWXF+mvXrh116tQ5aBzjx4/n+uuv54wzzgCgdu3aRzWPiRMnMnTo0Nhxr169mDlzJgDVq1dnxIgRJCcn065dO7744gsARo4cyahRo2LzTU5Opn379tx66620aNHiiO1OmzaN9u3bk5qayiWXXHLQiqf9xcXFcdttt9GmTRvatGnDhx9+GLtvv/zlL+ncuTO33XYbO3bsYMCAAaSlpdGyZUteeWXvFslff/01P/3pT0lKSuKyyy7j66+/Puz9ONScs7KyyM7OPqAewMyZM+nUqROXXnopTZo04fbbb2fy5Mm0adOGxMREPvroo8N/AcfIQEiSJEmSpBNo5cqVDBo0iMWLF3Paaafx+OOPU1hYyA033EB2djY5OTkMGDCAESNGANCnTx/mzZvHokWLiI+PZ8KECbG2Nm/ezIwZM3jkkUfIzMxk+PDhLF26lCVLlpCbm3vYcaxatYpVq1bRoUMH2rVrxxtvvFFmc9yxYwft2rVj0aJFdOzYkfHjxx9U5+c//zljxoxhzpw5pWpzw4YN3Hfffbz99tssWLCA1q1b8/DDDx/2mtNOO425c+cydOhQhg0bFitftWoVb7/9Nn/4wx+4//776dKlC/PmzeOdd97h1ltvZceOHYwbN46qVauyePFiRowYQU5OzjHP+dsWLVrE6NGjWbJkCU8//TSrVq1i7ty5DBw4kEcffbRU9+W7iua6KEmSJEmSTpL69evToUMHAK688krGjBnDBRdcQF5eHt26dQNg9+7dsVU9eXl53HHHHWzZsoXt27fTo0ePWFuZmZkEQUBiYiJnnXUWiYmJACQkJJCfn09KSsohx1FUVMTq1auZOXMm69at4/zzzycvL4/TTz/9mOdYqVIlevXqBUCrVq146623Dji/detWtmzZQqdOnQC46qqreP311w/b5r/+9S+WLVsWu3fffPMN7du3P+w1l19+eezv4cOHx8ovueQSypcvD+xddfTqq6/GVi4VFBSwdu1aZs2axY033ghAUlISSUlJxzTnkqSlpcW+5x//+Md0794dgMTERN55550jXn8sDIQkSZIkSTqBit/ifcBxGIYkJCSUuFomKyuLl19+meTkZCZOnBh7fAqgcuXKAJQrVy72ed9xUVHRYcdRr1492rVrR8WKFWnYsCFNmzZl9erVpKWlxeqMHTs2ttLlH//4B3Xr1o2dq1ChAnv27IkdFxQUxD5XrFgxNs/y5csfNJYwDA+6D0dqNwxDunXrxrPPPnvYee1v/z72/1ytWrUDxvLCCy/QtGnTw15/JIea8/7zCcOQb775JnbNt7+z/b/PI31/x8pHxiRJkiRJOoHWrl0bC36effZZ0tPTadq0KevXr4+VFxYWsnTpUgC2bdtGnTp1KCwsZPLkyWU2jp/85CexVSgbNmxg1apVNGrU6IA6119/Pbm5ueTm5h4QBsHePXpyc3PZs2cPn3zyCXPnzi1136effjo1atTgvffeAzhgXodqt127dsyePTu2F9DOnTtZtWrVYft5/vnnY38fajVRjx49ePTRR9n70nRYuHAhAB07doyNKy8vj8WLF5d6fvuLi4uLPW72yiuvUFhY+J3aKWuuEJIkSZIkRdcRXhN/PMTHxzNp0iQGDx5M48aNGTJkCJUqVSI7O5sbb7yRrVu3UlRUxLBhw0hISODee++lbdu2NGjQgMTERLZt23ZU/f3qV79iypQp7Ny5k3r16jFw4EBGjhxJjx49mDZtGs2bN6d8+fL8/ve/p2bNmqVut0OHDjRs2JDExERatGhBamrqUY3rL3/5CwMGDKBq1aoHPAZ3qHbPPPNMJk6cyOWXX86uXbsAuO+++2jSpMkh+9i1axdt27Zlz549h1xZdOeddzJs2DCSkpIIw5C4uDimTp3KkCFD+PnPf05SUhIpKSm0adPmqOa3z7XXXstFF11EmzZt6Nq16wGrk06mYF8CdjK1bt06nD9//skehiTpOxp73YzvdN31T3Qp45FIkiQd3vLly4mPjz/Zw9C35Ofn06tXL/Ly8sqszbi4OObPn0+tWrXKrM3vs5L+bQdBkBOGYeuS6vvImCRJkiRJUsT4yJgkSZIkSTqp4uLivvPqoIsvvpg1a9YcUPbQQw+Rn59fBiM7WNu2bWOPrO3z9NNPx97w9kNhICRJkiRJkn6wXnrppRPa3wcffHBC+ztefGRMkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWLcQ0iSJEmSFFmJk8p2I+Al/ZeUaXvS8eIKIUmSJEmSTpD8/HxatGhxQvu84IILSE5OJiEhgeuuu47du3cDcOutt9KsWTOSkpK4+OKL2bJly3Efy1133cXbb7993Ps5HuLi4tiwYcPJHkaZMRCSJEmSJOk/2F//+lcWLVpEXl4e69ev529/+xsA3bp1Iy8vj8WLF9OkSRMeeOCB4z6W3/72t/z3f//3ce+ntIqKik72EE4aAyFJkiRJkk6goqIi+vfvT1JSEv369WPnzp0A5OTk0KlTJ1q1akWPHj347LPPABg/fjxpaWkkJyfTt2/fWP2srCyGDBlC586dadSoEe+++y4DBgwgPj6erKysWH+nnXZarN9vvvmGIAgA6N69OxUq7N1Jpl27dqxbt+6o5jFy5EhGjRoVO27RogX5+fnk5+cTHx/PtddeS0JCAt27d+frr7+OjTk7OxuAN954g2bNmpGens6NN95Ir169DtsuwDPPPEObNm1ISUlh8ODBsdVOJalevTo333wzqampdO3alfXr1wOQkZHBb37zGzp16sTo0aNZv349ffv2JS0tjbS0NGbPng3Axo0b6d69Oy1btmTw4MGEYXjIvg4354yMDObPnw/Ahg0biIuLA2DixIn85Cc/ITMzk4YNG/LYY4/x8MMP07JlS9q1a8emTZtK/V18FwZCkiRJkiSdQCtXrmTQoEEsXryY0047jccff5zCwkJuuOEGsrOzycnJYcCAAYwYMQKAPn36MG/ePBYtWkR8fDwTJkyItbV582ZmzJjBI488QmZmJsOHD2fp0qUsWbKE3NzcWL0ePXpQu3ZtTj31VPr163fQmP785z9z4YUXltkcV69ezfXXX8/SpUs5/fTTeeGFFw44X1BQwLXXXstrr73GP//5Tz7//PMjtrl8+XKef/55Zs+eTW5uLuXLl2fy5MmHrL9jxw5SU1NZsGABnTp14p577omd27JlC++++y4333wzN910E8OHD2fevHm88MILDBw4EIB77rmH9PR0Fi5cSO/evVm7du0xzbkkeXl5TJkyhblz5zJixAiqVq3KwoULad++PU899dQRrz8WbiotSZIkSdIJVL9+fTp06ADAlVdeyZgxY7jgggvIy8ujW7duAOzevZs6deoAe0ODO+64gy1btrB9+3Z69OgRayszM5MgCEhMTOSss84iMXHvJtkJCQnk5+eTkpICwJtvvklBQQE/+9nPmDFjRqwfgPvvv58KFSrws5/9rMzm2LBhw1jfrVq1iq3w2WfFihU0bNiQxo0bx+7Dk08+edg2p0+fTk5ODmlpaQB8/fXX1K5d+5D1y5Urx2WXXRZrv0+fPrFz+8oB3n77bZYtWxY7/uqrr9i2bRuzZs3ixRdfBKBnz56cccYZxzTnknTu3JlTTz2VU089lRo1apCZmQlAYmIiixcvPuL1x8JASJIkSZKkE2jfI1v7H4dhSEJCAnPmzDmoflZWFi+//DLJyclMnDiRmTNnxs5VrlwZ2Bt+7Pu87/jb++NUqVKF3r1788orr8QCoUmTJjF16lSmT59+0LgARowYwd///neAA1YcAVSoUIE9e/bEjgsKCg4aF0D58uVjj08d7j4cqd0wDOnfv/933uto//6qVasW+7xnzx7mzJnDKaecUuoxluRQc95/Pvvfo29fs/93WNL3V9YMhCRJkiRJkXUyXhO/du1a5syZQ/v27Xn22WdJT0+nadOmrF+/PlZeWFjIqlWrSEhIYNu2bdSpU4fCwkImT57M2WefXeq+tm/fHru+qKiIf/zjH5x//vnA3j18HnroId59912qVq1a4vX3338/999/f4nn4uLimDp1KgALFixgzZo1pR5Xs2bNWLNmDR999BE//vGPefbZZ4/YbteuXbnooosYPnw4tWvXZtOmTWzbto0GDRqU2MeePXvIzs7mpz/9KVOmTCE9Pb3Eet27d+exxx7j1ltvBfYGXykpKXTs2JHJkydzxx138Prrr7N58+ZSz29/cXFx5OTk0KZNm9j+Sd8H7iEkSZIkSdIJFB8fz6RJk0hKSmLTpk0MGTKESpUqkZ2dzW233UZycjIpKSm8//77ANx77720bduWbt260axZs6Pqa8eOHfTu3ZukpCSSk5OpXbs21113HQBDhw5l27ZtdOvWjZSUlFh5afXt25dNmzaRkpLCuHHjaNKkSamvrVKlCk8++SQ9e/YkPT39gFDnUO02b96c++67j+7du5OUlES3bt1iG2+XpFq1aixdupRWrVoxY8YM7rrrrhLrjRkzhvnz55OUlETz5s154oknALj77ruZNWsWqampTJs2jXPOOafU89vfLbfcwrhx4zjvvPO+V6+tDw63S/aJ0rp163DfjtuSpB+esdfN+E7XXf9ElzIeiSRJ0uEtX76c+Pj4kz0MfcvMmTMZNWpUbGVQWahevTrbt28vs/a+70r6tx0EQU4Yhq1Lqu8KIUmSJEmSpIhxDyFJkiRJknRSZWRkkJGR8Z2ubdu2Lbt27Tqg7Omnnz4uq4M2btxI165dDyqfPn06NWvWLPP+jicDIUmSJEmS9IP1wQcfnLC+atasedDb1n6ofGRMkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWLcQ0iSJEmSFFnLm5XtK+jjVywv0/ak48UVQpIkSZIknSD5+fm0aNHihPb5zTffMGjQIJo0aUKzZs144YUXDjifnZ1NEATMnz//uI/lrrvu4u233z7u/RwPcXFxbNiw4WQPo8y4QkiSJEmSpP9g999/P7Vr12bVqlXs2bOHTZs2xc5t27aNMWPG0LZt2xMylt/+9rcnpJ/SKioqokKFaEYjrhCSJEmSJOkEKioqon///iQlJdGvXz927twJQE5ODp06daJVq1b06NGDzz77DIDx48eTlpZGcnIyffv2jdXPyspiyJAhdO7cmUaNGvHuu+8yYMAA4uPjycrKivX35z//mV//+tcAlCtXjlq1asXO3XnnnfzqV7+iSpUqRz2PkSNHMmrUqNhxixYtyM/PJz8/n/j4eK699loSEhLo3r07X3/9dWzM2dnZALzxxhs0a9aM9PR0brzxRnr16nXYdgGeeeYZ2rRpQ0pKCoMHD2b37t2HHF/16tW5+eabSU1NpWvXrqxfvx6AjIwMfvOb39CpUydGjx7N+vXr6du3L2lpaaSlpTF79mwANm7cSPfu3WnZsiWDBw8mDMND9nW4OWdkZMRWX23YsIG4uDgAJk6cyE9+8hMyMzNp2LAhjz32GA8//DAtW7akXbt2BwR3x4OBkCRJkiRJJ9DKlSsZNGgQixcv5rTTTuPxxx+nsLCQG264gezsbHJychgwYAAjRowAoE+fPsybN49FixYRHx/PhAkTYm1t3ryZGTNm8Mgjj5CZmcnw4cNZunQpS5YsITc3ly1btgB7g5/U1FQuueQSvvjiCwAWLlzIJ598EgtiytLq1au5/vrrWbp0KaeffvpBj6kVFBRw7bXX8tprr/HPf/6Tzz///IhtLl++nOeff57Zs2eTm5tL+fLlmTx58iHr79ixg9TUVBYsWECnTp245557Yue2bNnCu+++y80338xNN93E8OHDmTdvHi+88AIDBw4E4J577iE9PZ2FCxfSu3dv1q5de0xzLkleXh5Tpkxh7ty5jBgxgqpVq7Jw4ULat2/PU089dcTrj0U010VJkiRJknSS1OjcCwAAACAASURBVK9fnw4dOgBw5ZVXMmbMGC644ALy8vLo1q0bALt376ZOnTrA3tDgjjvuYMuWLWzfvp0ePXrE2srMzCQIAhITEznrrLNITEwEICEhgfz8fOrVq8e6devo0KEDDz/8MA8//DC33HILkyZNYvjw4UycOPG4zLFhw4akpKQA0KpVq9gKn31WrFhBw4YNady4cew+PPnkk4dtc/r06eTk5JCWlgbA119/Te3atQ9Zv1y5clx22WWx9vv06RM7t68c4O2332bZsmWx46+++opt27Yxa9YsXnzxRQB69uzJGWeccUxzLknnzp059dRTOfXUU6lRowaZmZkAJCYmsnjx4iNefywMhCRJkiRJOoGCIDjoOAxDEhISmDNnzkH1s7KyePnll0lOTmbixInMnDkzdq5y5crA3vBj3+d9x0VFRdSsWZOqVaty8cUXA3DJJZcwYcIEtm3bRl5eHhkZGQB8/vnn9O7dm1dffZXWrVvH2hkxYgR///vfAcjNzT1gXBUqVGDPnj2x44KCgoPGBVC+fPnY41OHuw9HajcMQ/r3788DDzxQ4nVHsn9/1apVi33es2cPc+bM4ZRTTin1GEtyqDnvP5/979G3r9n/O9z3/R1PPjImSZIkSYqs+BXLy/RPaaxduzYW/Dz77LOkp6fTtGlT1q9fHysvLCxk6dKlwN6Nn+vUqUNhYeFhH5EqSRAEZGZmxkKk6dOn07x5c2rUqMGGDRtie/60a9fuoDAI9m5InZube1AYBHvfurVgwQIAFixYwJo1a0o9rmbNmrFmzRo++uij2H04Urtdu3YlOzubL7/8EoBNmzbx8ccfH7KPPXv2xPYrmjJlCunp6SXW6969O4899ljseN9cO3bsGLvfr7/+Ops3by71/PYXFxdHTk4OQGw83wcGQpIkSZIknUDx8fFMmjSJpKQkNm3axJAhQ6hUqRLZ2dncdtttJCcnk5KSwvvvvw/AvffeS9u2benWrRvNmjU76v4eeughRo4cSVJSEk8//TR/+MMfymQeffv2ZdOmTaSkpDBu3DiaNGlS6murVKnCk08+Sc+ePUlPT6dBgwZHbLd58+bcd999dO/enaSkJLp16xbbeLsk1apVY+nSpbRq1YoZM2Zw1113lVhvzJgxzJ8/n6SkJJo3b84TTzwBwN13382sWbNITU1l2rRpnHPOOaWe3/5uueUWxo0bx3nnnfe9em19cLhdsk+U1q1bh/t23JYk/fCMvW7Gd7ru+ie6lPFIJEmSDm/58uXEx8ef7GHoW2bOnMmoUaOYOnVqmbVZvXp1tm/fXmbtfd+V9G87CIKcMAxbl1TfFUKSJEmSJEkR46bSkiRJkiTppMrIyIhtcH202rZty65duw4oe/rpp4/L6qCNGzfStWvXg8qnT59OzZo1y7y/48lASJIkSZIk/WB98MEHJ6yvmjVrlrjB9g+Rj4xJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYx7CEmSJEmSImvsdTPKtL3rn+hSpu1Jx4srhCRJkiRJOkHy8/Np0aLFCetv586d9OzZk2bNmpGQkMDtt98eO7dr1y4uu+wyzj33XNq2bUt+fn7s3AMPPMC5555L06ZNefPNN4/7OF999VUefPDB497P8ZCVlUV2dvbJHsZRMxCSJEmSJOk/2C233MKKFStYuHAhs2fP5vXXXwdgwoQJnHHGGXz44YcMHz6c2267DYBly5bx3HPPsXTpUt544w1+8YtfsHv37uM6xt69ex8QVp1sRUVFJ3sIx52BkCRJkiRJJ1BRURH9+/cnKSmJfv36sXPnTgBycnLo1KkTrVq1okePHnz22WcAjB8/nrS0NJKTk+nbt2+sflZWFkOGDKFz5840atSId999lwEDBhAfH09WVhYAVatWpXPnzgBUqlSJ1NRU1q1bB8Arr7xC//79AejXrx/Tp08nDENeeeUVfvrTn1K5cmUaNmzIueeey9y5c0s9v5kzZ9KrV6/Y8dChQ5k4cSIAcXFx3H333aSmppKYmMiKFSsAmDhxIkOHDgVgzZo1tG/fnrS0NO68806qV69+xHYPde9KkpGRwbBhwzjvvPNo0aJFbG4jR45k0KBBdO/enauvvprdu3dz6623kpaWRlJSEn/84x8BCMOQoUOH0rx5c3r27MmXX3552PtxqDmPHDmSUaNGxeq1aNGC/Px88vPzadasGQMHDqRFixb87Gc/4+2336ZDhw40btz4qL6LwzEQkiRJkiTpBFq5ciWDBg1i8eLFnHbaaTz++OMUFhZyww03kJ2dTU5ODgMGDGDEiBEA9OnTh3nz5rFo0SLi4+OZMGFCrK3NmzczY8YMHnnkETIzMxk+fDhLly5lyZIl5ObmHtDvli1beO211+jatSsAn376KfXr1wegQoUK1KhRg40bNx5QDlCvXj0+/fTTMpt/rVq1WLBgAUOGDDkgENnnpptuYsiQIcybN4//+q//OmJ7h7t3h7Jjxw7ef/99Hn/8cQYMGBArz8nJ4ZVXXmHKlClMmDCBGjVqMG/ePObNm8f48eNZs2YNL730EitXrmTJkiWMHz+e999//5jn/G0ffvghN910E4sXL2bFihVMmTKF9957j1GjRvG73/3uiNeXhptKS5IkSZJ0AtWvX58OHToAcOWVVzJmzBguuOAC8vLy6NatGwC7d++mTp06AOTl5XHHHXewZcsWtm/fTo8ePWJtZWZmEgQBiYmJnHXWWSQmJgKQkJBAfn4+KSkpwN5VSZdffjk33ngjjRo1AvaudPm2IAgOWV5W+vTpA0CrVq148cUXDzo/e/ZsXnjhBQCuuuqq2KNsh7Jy5cpD3rtDufzyywHo2LEjX331FVu2bAH2Prp2yimnADBt2jQWL14c2x9o69atrF69mlmzZnH55ZdTvnx56tatS5cuR95I/Ehz/raGDRse8F127do19j3vv9fTsTAQkiRJkiTpBPp2uLIvhElISGDOnDkH1c/KyuLll18mOTmZiRMnMnPmzNi5ypUrA1CuXLnY533H+++DM2jQIBo3bsywYcNiZfXq1eOTTz6hXr16FBUVsXXrVn70ox/FyvdZt24ddevWPWBML730Evfccw8Af/rTn2jdunXsXIUKFdizZ0/suKCg4IBr942zfPnyh9yrp6QA6lDtHu7eHUpJ3wFAtWrVYmVhGPLoo48eEMAB/OMf/zjqgKykOR/uPn37u9z/ey6r/Y0MhCRJkiRJkXUyXhO/du1a5syZQ/v27Xn22WdJT0+nadOmrF+/PlZeWFjIqlWrSEhIYNu2bdSpU4fCwkImT57M2WeffVT93XHHHWzdupU//elPB5T37t2bSZMm0b59e7Kzs+nSpQtBENC7d2+uuOIKfvnLX/Lvf/+b1atX06ZNmwOuvfjii7n44otL7K9BgwYsW7aMXbt2UVBQwPTp00lPTy/1eDt06MBzzz3HlVdeyeTJk4/Y7uHu3aE8//zzdO7cmffee48aNWpQo0aNg+r06NGDcePG0aVLFypWrMiqVas4++yz6dixI3/84x+5+uqr+fLLL3nnnXe44oorSj2/feLi4pg6dSoACxYsYM2aNUfdxrEwEJIkSZIk6QSKj49n0qRJDB48mMaNGzNkyBAqVapEdnY2N954I1u3bqWoqIhhw4aRkJDAvffeS9u2bWnQoAGJiYls27at1H2tW7eO+++/n2bNmpGamgrs3Yx54MCBXHPNNVx11VWce+65/OhHP+K5554D9j6idOmll9K8eXMqVKjA2LFjKV++fKn7rF+/PpdeeilJSUk0btyYli1bHtX9GT16NFdccQWjR4+mb9++R2z3cPfuUM444wzOO+88vvrqK/785z+XWGfgwIHk5+eTmppKGIaceeaZvPzyy1x88cXMmDGDxMREmjRpQqdOnY5qfvv07duXp556ipSUFNLS0mjSpMl3aue7Ckp6NvBEa926dTh//vyTPQxJ0nc09roZ3+m6k/EbOUmSFG3Lly8nPj7+ZA9DR6F69eps3769zNrLyMhg1KhRBzzm9p+gpH/bQRDkhGFY4kR9y5gkSZIkSVLE+MiYJEmSJEn63vquq4Ouv/56Zs+efUDZTTfddMCm3GXp4osvPmgfoIceeuigTam/LwyEJEmSJEmREoZhmb5GXd9PY8eOPaH9vfTSSye0v/19l+2AfGRMkiRJkhQZVapUYePGjd/pf6Cl76MwDNm4cSNVqlQ5qutcISRJkiRJiox69eqxbt061q9ff7KHIpWZKlWqUK9evaO6xkBIkiRJkhQZFStWpGHDhid7GNJJ5yNjkiRJkiRJEWMgJEmSJEmSFDEGQpIkSZIkSRFjICRJkiRJkhQxBkKSJEmSJEkRYyAkSZIkSZIUMQZCkiRJkiRJEWMgJEmSJEmSFDEGQpIkSZIkSRFjICRJkiRJkhQxBkKSJEmSJEkRYyAkSZIkSZIUMQZCkiRJkiRJEWMgJEmSJEmSFDEGQpIkSZIkSRFjICRJkiRJkhQxBkKSJEmSJEkRYyAkSZIkSZIUMQZCkiRJkiRJEWMgJEmSJEmSFDEGQpIkSZIkSRFjICRJkiRJkhQxpQ6EgiAoHwTBwiAIphYfNwyC4IMgCFYHQfB8EASVissrFx9/WHw+7vgMXZIkSZIkSd/F0awQuglYvt/xQ8AjYRg2BjYD1xSXXwNsDsPwXOCR4nqSJEmSJEn6nihVIBQEQT2gJ/Cn4uMA6AJkF1eZBPyk+PNFxccUn+9aXF+SJEmSJEnfA6VdIfT/gF8Be4qPawJbwjAsKj5eB5xd/Pls4BOA4vNbi+tLkiRJkiTpe+CIgVAQBL2AL8MwzNm/uISqYSnO7d/uoCAI5gdBMH/9+vWlGqwkSZIkSZKOXWlWCHUAegdBkA88x95Hxf4fcHoQBBWK69QD/l38eR1QH6D4fA1g07cbDcPwyTAMW4dh2PrMM888pklIkiRJkiSp9I4YCIVh+OswDOuFYRgH/BSYEYbhz4B3gH7F1foDrxR/frX4mOLzM8IwPGiFkCRJkiRJkk6Oo3nL2LfdBvwyCIIP2btH0ITi8glAzeLyXwK3H9sQJUmSJEmSVJYqHLnK/y8Mw5nAzOLP/we0KaFOAXBJGYxNkiRJkiRJx8GxrBCSJEmSJEnSD5CBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRYyBkCRJkiRJUsQYCEmSJEmSJEWMgZAkSZIkSVLEGAhJkiRJkiRFjIGQJEmSJElSxBgISZIkSZIkRcwRA6EgCKoEQTA3CIJFQRAsDYLgnuLyhkEQfBAEweogCJ4PgqBScXnl4uMPi8/HHd8pSJIkSZIk6WiUZoXQLqBLGIbJQApwQRAE7YCHgEfCMGwMbAauKa5/DbA5DMNzgUeK60mSJEmSJOl74oiBULjX9uLDisV/QqALkF1cPgn4SfHni4qPKT7fNQiCoMxGLEmSJEmSpGNSqj2EgiAoHwRBLvAl8BbwEbAlDMOi4irrgLOLP58NfAJQfH4rULOENgcFQTA/CIL569evP7ZZSJIkSZIkqdRKFQiFYbg7DMMUoB7QBogvqVrx3yWtBgoPKgjDJ8MwbB2GYeszzzyztOOVJEmSJEnSMTqqt4yFYbgFmAm0A04PgqBC8al6wL+LP68D6gMUn68BbCqLwUqSJEmSJOnYleYtY2cGQXB68edTgP8GlgPvAP2Kq/UHXin+/GrxMcXnZ4RheNAKIUmSJEmSJJ0cFY5chTrApCAIyrM3QPprGIZTgyBYBjwXBMF9wEJgQnH9CcDTQRB8yN6VQT89DuOWJEmSJEnSd3TEQCgMw8VAyxLK/4+9+wl9u7wAuKRMRidJkiRJkqQyd1R7CEmSJEmSJOmHz0BIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJCliDIQkSZIkSZIixkBIkiRJkiQpYgyEJEmSJEmSIsZASJIkSZIkKWIMhCRJkiRJkiLGQEiSJEmSJClijhgIBUFQPwiCd4IgWB4EwdIgCG4qLv9REARvBUGwuvjvM4rLgyAIxgRB8GEQBIuDIEg93pOQJEmSJElS6ZVmhVARcHMYhvFAO+D6IAiaA7cD08MwbAxMLz6G/4+9Ow+rqtr/OP7ezAiOKMiggkBqAuJQ6e+aZWY5lGVZZnVT07rZdLNMmyyt7qVug9W1Uetmk5aaQ2aDSoMNalhoppYoKDgAoszD4cD+/XHgBHJAUDDxfF7P4yOsvdc+Z9lqr72+a9gwHIis+HMr8Gqjf2sRERERERERETlhxw0ImaZ50DTNnyt+zgN2AMHAFcCCitMWAFdW/HwF8I5pswFoYxhGYKN/cxEREREREREROSEN2kPIMIxQoDewEQgwTfMg2IJGgH/FacFAapVsaRVpIiIiIiIiIiJyGqh3QMgwDF9gKXCPaZq5dZ3qIM10cL1bDcNIMAwjITMzs75fQ0RERERERERETlK9AkKGYbhjCwa9b5rmxxXJ6ZVLwSr+zqhITwM6VckeAhw49pqmab5hmmY/0zT7dejQ4US/v4iIiIiIiIiINFB93jJmAG8CO0zTfL7KoZXA+IqfxwMrqqTfVPG2sf5ATuXSMhERERERERER+eu51eOcvwF/B341DCOxIu0h4CngI8MwJgH7gGsqjq0GRgBJQCEwsVG/sYiIiIiIiIiInJTjBoRM0/wOx/sCAQxxcL4J3HGS30tERERERERERJpIg94yJiIiIiIiIiIizZ8CQiIiIiIiIiIiTkYBIRERERERERERJ6OAkIiIiIiIiIiIk1FASERERERERETEySgg1IzdfPPN+Pv7ExUVZU9LTEykf//+xMbG0q9fPzZt2gSAaZrcfffdREREEBMTw88//+zwmhdeeCHdunUjNjaW2NhYMjIyqh1fsmQJhmGQkJAAwJo1a+jbty/R0dH07duX+Pj4JiqtiIiIiIiIiDQWBYSasQkTJvD5559XS5s+fTqPPfYYiYmJPP7440yfPh2Azz77jF27drFr1y7eeOMNpkyZUut133//fRITE0lMTMTf39+enpeXx0svvcR5551nT2vfvj2ffPIJv/76KwsWLODvf/97I5dSRERERERERBqbAkLN2KBBg2jXrl21NMMwyM3NBSAnJ4egoCAAVqxYwU033YRhGPTv35/s7GwOHjzYoM+bOXMm06dPx8vLy57Wu3dv+2f07NmT4uJiSkpKTqZYIiIiIiIiItLEFBA6w7zwwgvcf//9dOrUiWnTphEXFwfA/v376dSpk/28kJAQ9u/f7/AaEydOJDY2lieeeALTNAH45ZdfSE1N5bLLLqv1s5cuXUrv3r3x9PRsxBKJiIiIiIjUj6NtNcaOHWvfEiM0NJTY2Fj7sa1btzJgwAB69uxJdHQ0xcXFNa45a9YsgoOD7ddYvXp1teP79u3D19eXZ5991p72+eef061bNyIiInjqqaeaoKQiJ08BoTPMq6++ypw5c0hNTWXOnDlMmjQJwB7YqcowjBpp77//Pr/++ivr169n/fr1vPvuu5SXlzN16lSee+65Wj/3t99+Y8aMGbz++uuNVxgRERERcXoN6eCnpKTg7e1tP3bbbbc5vGZtHfzS0lLGjx9PdHQ0PXr0sA+uAsyZM4eePXsSFRXFuHHjHAYO5K/naFuNDz/80L4lxtVXX81VV10FgNVq5cYbb+S1117jt99+4+uvv8bd3d3hdadOnWq/xogRI2ocGz58uP33srIy7rjjDj777DO2b9/OwoUL2b59eyOXVOTkKSB0hlmwYIH9BnfNNdfYN5UOCQkhNTXVfl5aWpp9qVdVwcHBALRs2ZLrr7+eTZs2kZeXx7Zt27jwwgsJDQ1lw4YNjBo1yr6xdFpaGqNHj+add94hPDy8qYsoIiIizVxDR/DB8Qh8Veeff749f1BQEFdeeSVgWzYfExNjf+HGd999B8DevXvp27cvsbGx9OzZk9dee62JSisnqyEdfIDw8HD7sbr+uzrq4C9evJiSkhJ+/fVXNm/ezOuvv05KSgr79+/npZdeIiEhgW3btlFWVsaiRYuapsByUhxtq1HJNE0++ugjxo0bB8CXX35JTEwMvXr1AsDPzw9XV9cGfd7y5cvp2rUrPXv2tKdt2rSJiIgIunbtioeHB9dddx0rVqw4wRJJU2qK9mjdunX06dOH2NhYBg4cSFJSEgDPP/88Z599NjExMQwZMoS9e/cC8NVXX9k/LzY2Fi8vL5YvX95EJa5OAaEzTFBQEN988w0A8fHxREZGAjBq1CjeeecdTNNkw4YNtG7dmsDAwGp5rVYrhw8fBmyjI6tWrSIqKorWrVtz+PBhUlJSSElJoX///qxcuZJ+/fqRnZ3NyJEjiYuL429/+9upLaw0SFPc7JKTkznvvPOIjIxk7NixWCwWAN5++206dOhgv/b8+fPteaZPn07Pnj3p0aMHd999t8PZayIicmZraAcfao7AH2v9+vX2/AMGDLDnHzJkCFu2bCExMZG33nqLyZMnAxAYGMgPP/xAYmIiGzdu5KmnnuLAgQONXFJpDA3p4J8swzAoKCjAarVSVFSEh4cHrVq1ArCnWa1WCgsLHQ6uyult/fr1BAQE2PtIf/zxB4ZhcOmll9KnTx/+85//1Jp37ty5xMTEcPPNN3P06FEACgoKePrpp3nssceqnduQ7Trkr9UU7dGUKVPsL2q6/vrrefLJJwHb/rsJCQls3bqVMWPG2F8ANXjwYPvnxcfH06JFCy655JJGLqljCgg1Y+PGjWPAgAH8/vvvhISE8OabbzJv3jzuu+8+evXqxUMPPcQbb7wBwIgRI+jatSsRERHccsstvPLKK/brVAYBSkpKuPTSS+2jaMHBwdxyyy11foe5c+eSlJTEE088Ueur6uX00BQ3uxkzZjB16lR27dpF27ZtefPNN+3Hxo4da7925cP3Dz/8wPfff8/WrVvZtm0bP/30kz2AKSIizqOhHXxHI/C1ycvLIz4+3j5DyNfX175MvqCgwP6zh4eHfd/DkpISysvLT6pM8tc4toMPtgGr3r17c8EFF7B+/fpa8zrq4I8ZMwYfHx8CAwPp6BjuLQAAIABJREFU3Lkz06ZNo127dgQHBzNt2jQ6d+5MYGAgrVu3PmUdNmk8CxcurHZvsVqtfPfdd7z//vt89913LFu2jHXr1tXIN2XKFHbv3k1iYiKBgYHcd999ADz22GNMnToVX1/faufXd7sO+es1RXtU24ueBg8eTIsWLQDo378/aWlpNfIuWbKE4cOH289ram6n5FOk0e3o3oNHgUdbt4HWbWyJz9hmcbxXeVKJBW64kR0Vv94JvLx7d41rJSYmAuDj48PmzZuP+9lff/21/edHHnmERx555ITKIKfWoEGDSElJcXis8mYXHx9vT6u82fn4+NSaJz4+ng8++ACA8ePHM2vWLKZMmVLrdzAMg+LiYiwWC6ZpUlpaSkBAwIkXSprMzTffzKpVq/D392fbtm2ALcj3+++/A5CdnU2bNm1ITEwkKyuLFz+5l70Zv9O/26VcO/Buh9dc9uPrbNv3I64ubrRvFcSNF9pGRbKyshgzZgw//fQTEyZMYO7cuTXyjho1ij179ti/i4icuY7t4FeOwK9Zs6bWGatVLVu2jCFDhthndVSmPfjgg2RkZPDpp5/a01NTUxk5ciRJSUk888wzmvHRDB3bwQ8MDGTfvn34+fmxefNmrrzySn777bdq9QFsHfyZM2diGAYzZ87kvvvu46233mLTpk24urpy4MABjh49yvnnn8/FF19M27ZtWbFiBcnJybRp04ZrrrmG9957jxtvvPFUF1lOkNVq5eOPP67W3wkJCeGCCy6gffv2gG0Q/eeff2bIkCHV8lZ9Xr3lllvsL9rZuHEjS5YsYfr06WRnZ+Pi4oKXlxd9+/at13Ydcno70fZo/vz5jBgxAm9vb1q1asWGDRtqnPPmm286HHhftGgR9957b+MV4jg0Q0hEar3ZHTv9taqsrCzatGmDm5strnzsVNilS5cSExPDmDFj7A3igAEDGDx4MIGBgQQGBnLppZfSo0ePJiyZnKiGzCjz8vLisn4TGT3A8cadlbqH9OWha97koWvm4986hC9/+cCe/4knnqi1Yf34449rjLzJ6achy1KzsrIYPHgwvr6+3HnnnbVec/HixfTs2RMXFxf7vnXHy79w4UKio6OJiYlh2LBh9qXQ0nwc28GvbQS+vvkBRo8ezc6dO1m+fDkzZ860p3fq1ImtW7eSlJTEggULSE9Pb5xCyClR2cEfO3asPc3T0xM/Pz8A+vbtS3h4OH/88UeNvAEBAbi6uuLi4sItt9xi33fzgw8+YNiwYbi7u+Pv78/f/vY3EhISWLt2LWFhYXTo0AF3d3euuuoqfvjhh1NTUGkUa9eupXv37oSEhNjTLr30UrZu3UphYSFWq5VvvvmGs88+u0begwcP2n9etmyZva1bv369fVuNe+65h4ceeog777yTc845h127dpGcnIzFYmHRokWMGjWq6QspjepE26M5c+awevVq0tLSmDhxYo0Az3vvvUdCQgL3339/tfSDBw/y66+/cumllzZeIY5DASEROaGbXV1TYS+//HJSUlLYunUrF198MePHjwcgKSmJHTt2kJaWxv79+4mPj+fbb79t5NJIY2jI9FkfHx/CA6Nxd3X8Vo5KPTr1w9XFtlFjWMDZZBcctucfOHAgXl5eNfLk5+fz/PPPayZiM9DQIGJdQcBKUVFRfPzxxwwaNKhaem35rVYr//znP/nqq6/YunUrMTExDmecyenLUQd/48aNTJ8+ndDQUF544QX+/e9/1/rfNSsri02bNjFy5EiHxwcNGsTu3btrBAqDgoLo2bNnncuL5PTjqIOfmZlJWVkZAHv27GHXrl107dq1Rt7aOvidO3cmPj4e0zQpKChgw4YNdO/enc6dO7NhwwYKCwsxTZN169ZpUOs05WhbDbDNvDg2WNy2bVvuvfdezjnnHGJjY+nTp4/9/jF58mT7YMT06dPtgw1fffUVc+bMqfM7uLm5MXfuXPvg57XXXluvJa9y+jjR9igzM5MtW7Zw3nnnAbbBsarB47Vr1/Kvf/2LlStX2pctV/roo48YPXp0rW+6awpaMibi5BxNn61t+mvVkfj27duTnZ2N1WrFzc2t2lTYypE5sE2rnTFjBmB74Orfv7890DR8+HA2bNhQo7MnpzdH+zU01I87P6NP+IXHPa9yGv+pWkctJ64hy1Irg4CVb92oTW2drdrym6Zp78T5+fmRm5tLREREwwsjfxlHHfyqQZpZs2bVObNs8eLFXHbZZdUCzElJSYSHh2MYBj///DMWiwU/Pz/S0tLw8/PD29ubo0eP8v3335/SafpSf+PGjePrr7/m8OHDhISEMHv2bCZNmuSwg//tt9/y6KOP4ubmhqurK6+99pp9gGPy5Mncdttt9OvXj+nTp5OYmIhhGISGhvL6668DcMcddzBx4kSioqIwTZOJEycSExMD2PYX6tOnD25ubvTu3Ztbb7311P5DSL0sXLjQYfrbb7/tMP3GG290uPSv6ktR3n333eN+7qxZs6r9PmLEiBqvp5fm40Tbo7Zt25KTk8Mff/zBWWedxZo1a+zPM7/88gv/+Mc/+Pzzz/H396/xmQsXLiQuLq6JSuSYAkIiTu5Eb3aGYTB48GCWLFnCddddx4IFC7jiiisA26hb5VvsVq5cab8Jdu7cmXnz5vHggw9imibffPMN99xzT1MXURqZo+UYDfH5z+/j4uLKOZEX13leYmIiSUlJzJkzp9ZAgzQPjRFErA93d3deffVVoqOj8fHxITIykpdffrlJP1NOTEM6+HUZMWIE8+fPtw9ILFq0iAceeKDaOUuXLuWdd97B3d0db29vPvzwQwzDYMeOHdx3330YhoFpmkybNo3o6OhGLac0joZ08K+++mquvvpqh+fXp4Pv6+vL4sWLHR6bPXs2s2fPPs63lVNpR/cTm6XVY+eO458kTqEp2qN58+Zx9dVX4+LiQtu2bXnrrbcAuP/++8nPz+eaa64BbH2jlStXApCSkkJqaioXXHBB4xeyDgoIiTiJprjZPf3001x33XU88sgj9O7dm0mTJgHw0ksvsXLlStzc3GjXrp39gW3MmDHEx8cTHR2NYRgMGzaMyy+/vCmKK03E0Yyyhtjw+xds2/sjd1/27HHftvHjjz+yefNmQkNDsVqtZGRkcOGFF1bb2F6ah5MNItZXaWkpr776Kr/88gtdu3blrrvuIi4uTksOTwPHdtocvRhjxzPPMgNgw0Z2vPAiULPTduwI/OrVq6v97uj+MGPGDPtM1aqGDh3K1q1bG1IMOQXUwReRplDbveV47RG3Od4js672aPTo0YwePbpGnrVr19b6/UJDQ6vtx3qqKCAkcoarvPnV9+G7UuWDVV03u65du9o3YawqLi7O4XRHV1dX+5RsaZ4czSirr+37NrE2cRH/HDUHD/ea+wUda8qUKfa31qWkpHDZZZcpGNQMnWwQsSEq35oZHh4OwLXXXstTTz3V5J8rIiIi0hwpICQiIjU0dEbZo+9fT3FpIdayUramfM8dI58msG0o73/zLAPPvpwuHbrx0ff/xVpWytxPba+bD/XvwR1cZPs5NJTc3FwsFgvLly/nyy+/dPiWD2l+TiaI2FDBwcFs376dzMxMOnToUG3dvoiIiIhUp4CQiIjU0NANGR+/4QOH6TdcMM3+86xxtW/IeLw9gkJDQ9m2bVud58hfq6FBxNqCgFU3fV22bBl33XUXmZmZjBw5ktjYWL744os68z/22GMMGjQId3d3unTpUmudFREREXF2hqNXR59q/fr1Mytf6Sf1o/XVUl+qK9IQJ1pf4i88sY1773jtohPKJ3893VukIVRfpL5UV6QhVF+kvpy5rhiGsdk0zX6Ojrmc6i8jIiIiIiIiIiJ/LQWEREREREREREScjAJCIiIiIiIiIiJORgEhEREREREREREno4CQiIiIiIiIiIiTUUBIRERERERERMTJKCAkIiIiIiIiIuJkFBASEREREREREXEyCgiJiIiIiIiIiDgZBYRERERERERERJyMAkIiIiIiIiIiIk5GASERERERERERESejgJCIiIiIiIiIiJNRQEhERERERERExMkoICQiIiIiIiIi4mQUEBIRERERERERcTIKCImIiIiIiIiIOBkFhEREREREREREnIwCQiIiIiIiIiIiTkYBIRERERERERERJ6OAkIiIiIiIiIiIk1FASERERERERETEySggJCIiIiIiIiLiZBQQEhERERERERFxMgoIiYiIiIiIiIg4GQWEREREREREREScjAJCIiIiIiIiIiJORgEhEREREREREREno4CQiIiIiIiIiIiTUUBIRERERERERMTJKCAkIiIiIiIiIuJkFBASEREREREREXEyCgiJiIiIiIiIiDgZBYRERERERERERJyMAkIiIiIiIiIiIk5GASERERERERERESejgJCIiIiIiIiIiJNRQEhERERERERExMkoICQiIiIiIiIi4mQUEBIRERERERERcTIKCImIiIiIiIiIOBkFhEREREREREREnIwCQiIiIiIiIiIiTkYBIRERERERERERJ6OAkIiIiIiIiIiIk1FASERERERERETEySggJCIiIiIiIiLiZBQQEhERERERERFxMgoIiYiIiIiIiIg4GQWEREREREREREScjAJCIiIiIiIiIiJORgEhEREREREREREno4CQiIiIiIiIiIiTUUBIRERERERERMTJKCAkIiIiIiIiIuJkFBASEREREREREXEyCgiJiIiIiIiIiDgZBYRERERERERERJyMAkIiIiIiIiIiIk5GASERERERERERESejgJCIiIiIiIiIiJNRQEhERERERERExMkcNyBkGMZbhmFkGIaxrUpaO8Mw1hiGsavi77YV6YZhGC8ZhpFkGMZWwzD6NOWXFxERERERERGRhqvPDKG3gWHHpD0ArDNNMxJYV/E7wHAgsuLPrcCrjfM1RURERERERESksRw3IGSa5rfAkWOSrwAWVPy8ALiySvo7ps0GoI1hGIGN9WVFREREREREROTknegeQgGmaR4EqPjbvyI9GEitcl5aRVoNhmHcahhGgmEYCZmZmSf4NUREREREREREpKEae1Npw0Ga6ehE0zTfME2zn2ma/Tp06NDIX6N5efHFF4mKiqJnz5688MILABw5coShQ4cSGRnJ0KFDOXr0qMO8z2ZkcHnyHi5L3sO/0tMxTds/962pqYxOSeby5D3MOnSIsor0xMRE+vfvT2xsLP369WPTpk2nppAiIiIiIiIicto40YBQeuVSsIq/MyrS04BOVc4LAQ6c+Nc7823bto158+axadMmtmzZwqpVq9i1axdPPfUUQ4YMYdeuXQwZMoSnnnqqRt5figr5paiI5aFhrAgNY1txET8VFQLwfFAQy0LDWBkaxtEyK1/k5QEwffp0HnvsMRITE3n88ceZPn36KS2viIiIiIiIiPz1TjQgtBIYX/HzeGBFlfSbKt421h/IqVxaJo7t2LGD/v3706JFC9zc3LjgggtYtmwZK1asYPx42z/x+PHjWb58eY28BgYlZjmlponFNLGa4OfqBoCvqysAVqDUNO1TtwzDIDc3F4CcnByCgoKavIwiIiIiIiINNWfOHHr27ElUVBTjxo2juLiYdevW0adPH2JjY7lx3172Wiw18v1QUMCYlGSuSE5mTEoyGwoK7Mc+zc0lOjqamJgYhg0bxuHDh4H6r9AQOZPU57XzC4EfgW6GYaQZhjEJeAoYahjGLmBoxe8Aq4E9QBIwD7i9Sb71GSQqKopvv/2WrKwsCgsLWb16NampqaSnpxMYaNuPOzAwkIyMjBp5Y729ObeFDxfsTuKC3Un8zceHcE9P+/FbUlM5P2kXPi6uXNKyJQAvvPAC999/P506dWLatGnExcWdmoJKo3LUOJ5//vnExsYSGxtLUFAQV155ZY18O4qLGbc3hcuT93BlcjKfVQQHAX4sKODqlGRGpyRz4769JCUlAVBSUsLYsWOJiIjgvPPOIyUl5VQVU0RETnMn2h4BRP2+k9EV7c4daWn29Krt0cCBA9UenUGO17kfOHCgw849wBtZWVy6Zzcj9uzhu4J8e/r6gnxG7NlDREREtRn1EyZMICwszF4XExMTm7x80rj279/PSy+9REJCAtu2baOsrIxFixYxZcoU3n//fRITExnZshWvZx2ukbeNqyuvhISwIiyMuMAgHjhkm6NgNU3iMtL56quv2Lp1KzExMcydOxegXis05PRWV5s0OiWZC5KSuHN/msO8B0pLmZy6j8sqtmPZX2q7F8XHx9OnTx+ioqIYP348VqsVgBUrVhATE2PfiuW77747ZeVsTPV5y9g40zQDTdN0N00zxDTNN03TzDJNc4hpmpEVfx+pONc0TfMO0zTDTdOMNk0zoemL0Lz16NGDGTNmMHToUIYNG0avXr1wc3OrV969Fgt7LCXEh0fwVXgEGwsLSCgstB+f16kT34RHYDHL2ViR/uqrrzJnzhxSU1OZM2cOkyZNapJySdOprXFcv349iYmJJCYmMmDAAK666qoaeb1dXIgLDOKTsK680SmEuIx0csvKAHg8/RD/CbQtNRzZshVPPvkkAG+++SZt27YlKSmJqVOnMmPGjFNaXhEROT2dTHsE4GkYLAsNY1loGC+HhNjTq7ZH119/vdqjM0R9OvfXX3+9w859UkkJn+Xl8kloGG+EhPBEejplpkmZafJkejqvh4Swfft2Fi5cyPbt2+35nnnmGXtdjI2NPZXFlUZitVopKirCarVSWFhIUFBQtRUPeeXldHBzr5HvbC8v/CvSIzw8KCkvx1Jejoltg9uCggJM0yQ3N9e+YqI+KzTk9HW8NmlZaBix3l4M9W3pMP+DBw9wczs/VoV15cMuobRzdaPcNBk/fjyLFi1i27ZtdOnShQULbC9bHzJkCFu2bCExMZG33nqLyZMnn8riNprG3lRaTsCkSZP4+eef+fbbb2nXrh2RkZEEBARw8KAtkn3w4EH8/f1r5Fubn0cvL298XFzwcXHhfB9fthQVVTvH08WFwb4tic+37SG0YMEC+4PZNddco02lmylHjWOlvLw84uPjHY7Ihnp4EOrhAYC/mzt+bm4cqQgIGRjkl5fbrlFe7rBxHDNmDOvWrbNvXi7Ng6PRkvqOnD6XmcGo5D2MSt5TbUbZ/QcOMGKPLf3hgwcpK7ONlmxN+Z5/L55M3JJbeXrpFHYf/PWUlFEaT1PUl0pPph/C19fX/vu+ffsYPHgwvXv3JiYmhtWrVzdZuaRpnGh7VJeq7VHV5e1qj5q/43Xuc3JyHHbu4/PzGd6yFR4uLoR4eNDZ3YNfi4v5tbiYzu4edPLwwMPDg+uuu44VK1bUyC/NU3BwMNOmTaNz584EBgbSunVrLrnkEubPn8+IESMICQlhZW4Ot7RrV+d1vszPo4eXFx4uLrgbBo8GBBAdHU1QUBDbt2+3D5DXZ4WGnN7qapMKysvYWFjIkCrPIZWSSkooA/7PxwcAHxcXvF1cyC4rw9PTk7POOguAoUOHsnTpUgB8fX0xDNvGLAUFBfafmxsFhE4DlTebffv28fHHHzNu3DhGjRpljz4uWLCAK664oka+IDd3fioqxGqalJomPxUV0tXTg4LycjIrprJZTZNvC/IJ87AtJQsKCuKbb74BbNPfIiMjT0URpRHV1jhWWrZsGUOGDKFVq1Z1XmdrURGlpklnd9uD1+MdO3JbWiqDdyexMjeHBx54ALBF2zt1su0V7+bmRuvWrcnKymqi0kljq220BI4/cvpNfj7bi4v5ODSMRV1CeevIEfIrAoiXtWrFp2G2De1LzHJ+2GnryHcL7sODY+bx4Jg3uPHCaXzw7XOnrrBy0pqqvgBsKy4ir6KTX+nJJ5/k2muv5ZdffmHRokXcfrtWmjcnJ9seWUyTa1JSuG5vCmsrXn4B1dujd999V+3RGaI+nft3333XYec+w1pKxyoz6APc3Ui3lpJuLaWj+5/pISEh7N+/3/77ww8/TExMDFOnTqWkpKRpCyiN7ujRo6xYsYLk5GQOHDhAQUEB7733HnPmzGH16tWkpaUxunVrns6sPXCzq6SE5zMzmRXQEbDtrbooO5tffvmFAwcOEBMToy00zhDHa5PW5uXTv4WPfa/dqlIsFlq6uHD3/jSuSknmmYwMykyTtq6ulJaWkpBgW/i0ZMkSUlNT7fmWLVtG9+7dGTlyJG+99VbTF7IJKCB0Grj66qs5++yzufzyy3n55Zdp27YtDzzwAGvWrCEyMpI1a9bYH4YSEhLs09EuadmSTu4eXFmxzr67pyeDfVtSVF7OHfvTuDLZlt7O1ZWxbdoAMG/ePO677z569erFQw89xBtvvPGXlVtOTG2NY6WFCxcybty4Oq+RabXywMGD/KtjIC4V0ex3jh7htZBOfBUewejWrbn33nsBHI6+NtcIuLOqa7SkLkmWEs5p0QI3w6CFiwvdvDxZX7Ep4wUVoyKGYRDt5c3RgkwAPN297fWjpLQYUF1pbpqivpSZJs9mZDKtQ/XZrnrRQfN2su3RuvBwFoeG8kxgEE9lpLOvYu+Yqu3RxIkT1R6dIerTuZ84caLDzr2jeWBGbekVdSIuLo6dO3fy008/ceTIEZ5++ulGLY80vbVr1xIWFkaHDh1wd3fnqquu4vvvv2fLli2cd955AAxv2YpfjlkhUelQaSl3708jrmMgnStmyO8sLgYgPDwcwzC49tpr+eGHHwDqtUJDTl/Ha5M+zctlRCvHy8XKMNlcVMT9Hfz5qEsoaaUWlufkYBgGixYtYurUqZx77rm0bNmy2vYuo0ePZufOnSxfvpyZM2c2eRmbggJCf7HoBdFkT87GdYYr5feWc0/aPUQviObCVReScVMGXo94kXFTBhd8cgHRC6KZ+NtENp6/EQBXw2B2x46sCuvKqrCuzPAPAKC9mxsfdQlleVgYn4R15ZGAjrhVNI4DBw5k8+bNbNmyhY0bN9K3b9+/rOxyYhw1jpUNWVZWFps2bWLkyJG15s8vK+O2tFTu7tCeXt7eAByxWvm9pMT++/CWrezXDAkJsUfCrVYrOTk5tDvO1Fw5fdQ1WnK8kdPunl6szy+gqLyco1YrmwoLOWQtrXZOqWmyMjeHszudY0/bkvwdT3w4gdc+f5gbLpjWtAWURtVU9eWD7KMM9vWlwzF75M2aNYv33nuPkJAQRowYwX//+9+mL6Q0mpNtjyr39+jk4cG5LVqwo6S4Rns0duxYtUdniPp07seOHeuwcx/g5s6hitnvAOmlVvzd3Ono5s6h0j/T09LS7IHlwMBADMPA09OTiRMnapuEZqhz585s2LCBwsJCTNNk3bp1nH322eTk5PDHH38Atk3owyuCPVXllpUxZX8aUzt0oE+LFvb0AHc3dpeUkJlpG8has2YNPXr0AKjXCg05fR2vTfq1qIgLfGouFwPo6OZOD09POnl44GYYDPFtyfYSW/BwwIABrF+/nk2bNjFo0CCHK2wGDRrE7t277W+sa04UEBJpZhw1jpUN2eLFi7nsssvw8vJymNdimtx1YD9XtGrNsJZ/TuFv5epKXnk5KRWjsz8WFDhsHJcsWcJFF12kEdlmpLbRkvqMnP7Nx4fzfX24ft9eph08QC8vb3twudIT6Yfo16IFEYEx9rReYQOZOfZtbr3kcT5NeLupiyiNqCnqS4a1lC/y8rihbdsaeRYuXMiECRNIS0tj9erV/P3vf6f8mGVlcvo6mfYop6wMS8V/66NWKz8XFRHu4VmjPaqts6b2qPmpT+d+zZo1Djv3g319+SwvF0t5OWkWC3tLLUR7eRHl5cXeUgtpFgsWi4VFixYxatQoAPtMD9M0Wb58OVFRUaeusNIozjvvPMaMGUOfPn2Ijo6mvLycW2+9lXnz5nH11VfTq1cvVubm2Gefxufn8d/DtkDPB9lH2Wex8GpWlv1thllWWyDx9vbtGTRoEDExMSQmJvLQQw8B1LpCQ5qH47VJF/r64uniOPwR5eVFbnk5RyoCzxsKCwmv2HKlcnuXkpISnn76aW677TYAkpKS7DNXf/75ZywWC35+fk1axqZQv9dZichpo2rj6ObmRu/evbn11lsBWLRoUY3Ga1txER9mZ/NEx0A+z81lc2Eh2WVlLMvNAeDfHQPp4eXF4wEd+ef+/bgY0MrFhUXPPAPYNj3/+9//TkREBO3atbPvJyLNQ9XREsA+WnLjjTcC2EdOn332WYf5b/Nrz21+7QHbRtKd3f98UH/58GGOlJXxUkBHvnaQNyIohsNfHyC/KAdf79aNWi5pGg2pL9c4yO+ovuwoLmGvxcKwPbsBKCwrIyIigqSkJN58800+//xzwDYCV1xczOHDhzVNv5k4mfZoj6WEWYfScTGg3IRb2vkR4Wl7+K7aHnV89137vgxqj5q32upLSEgIV199NS4uLrRt25aHqnTufysu5q72HYj09OTSli25PCUZVwwe8Q/AtSIY+LB/ALekpeLaowc333wzPXv2BOCGG24gMzMT0zSJjY3ltdde+8vKLscXvSDa8YGu4P6gbTZhIon0W9TPlj4NyilnQVwX+6kX+bbkooo3SFVtj451XZu2zN6xo0a6n58f69atO4lSyF/peG3S9cfsZ1e1TXI1DO7v4M/NqamYQE8vT8ZUbLnyzDPPsGrVKsrLy5kyZQoXXXQRAEuXLuWdd97B3d0db29vPvzww2Y5SGGcDm9n6Nevn1m5UZOzqfXmdxwfxVmPf5IDPXbWvPlJ86C6Iidi48aN3Hzzzfz00094e3szYcIE+vXrx5gxYwgMDMQ0TaZOnYqXlxdPPfUUO7r3sOctM03yystp4+rK78XF3H/wAB+HhuFmGCzJzubjnBze6tQJLxcX4i98GYDMnP20b2V7a0xq5h+89vkjPHlj7Q3kHa9ddEr+HaR+GlJfxi+v/iafuupLVeekpZKfnw/A8OHDGTuwJDe4AAAgAElEQVR2LBMmTGDHjh0MGTKE/fv3N8sHKmehtkjqS3VFGkL1RepLdaXhDMPYbJpmP0fHNENIROQMVttoyfDhwx2OnFYdLbGaJjfu2wuAr4sLTwcG2Tv3s9MPEeTuzriK4xGb32F435tITP6WjX+swdXFDXdXD26+eKY696e5Yx+sMiMzaRveFsPVwKuzFwnnJ/DgkAex5lnBBK/OXgSND2I89a8vtXnuuee45ZZbmDNnDoZh8Pbbb6u+iIiIiJwiCgiJiJyBqnXyHU23Hm87ZGCwhS0MWDoAgI+8vInqaNvM1dPFhVVhXR1e/9du3av9Ht/3JgCGxo5jaGzdb7mT01vA6AACRgdUSwubEebgTCtR9awvVVXODgI4++yz+f7770/q+4qIiIjIidGm0iIiIiIiIiIiTkYBIRERERERERERJ6OAkIiIiIiIiIiIk1FASERERERERETEySggJCIiIiIiIiLiZBQQEhERERERERGpp99//53Y2Fj7n1atWvHCCy+wePFievbsiYuLCwkJCbXmv/nmm/H39ycqKsrh8WeffRbDMDh8+DAAX3/9Na1bt7Z/3uOPP94o5dBr50VERERERERE6qlbt24kJiYCUFZWRnBwMKNHj6awsJCPP/6Yf/zjH3XmnzBhAnfeeSc33XRTjWOpqamsWbOGzp07V0s///zzWbVqVeMVAs0QEhERERERERE5IevWrSM8PJwuXbrQo0cPunXrdtw8gwYNol27dg6PTZ06lf/85z8YhtHYX7UGBYRERERERERERE7AokWLGDduXKNca+XKlQQHB9OrV68ax3788Ud69erF8OHD+e233xrl87RkTERERERERESkgSwWCytXriQuLu6kr1VYWMi//vUvvvzyyxrH+vTpw969e/H19WX16tVceeWV7Nq166Q/UzOEREREREREREQa6LPPPqNPnz4EBASc9LV2795NcnIyvXr1IjQ0lLS0NPr06cOhQ4do1aoVvr6+AIwYMYLS0lL7htMnQzOEREREREREREQaaOHChY22XCw6OpqMjAz776GhoSQkJNC+fXsOHTpEQEAAhmGwadMmysvL8fPzO+nPVEBIREREREREROQYL98WX+sxS2kxnyxfTT/v63l5o+28Lcnfsfj7/1JYmsvIkSOJjY3liy++4MCBA0yePJnVq1cDMG7cOL7++msOHz5MSEgIs2fPZtKkSbV+1pIlS3j11Vdxc3PD29ubRYsWNcqm0woIiYiIiIiIiIg0gIe7F/+ZsLxaWq+wgfQKG8gdr11ULT0oKMgeDALbzKLjSUlJsf985513cuedd57cF3ZAewiJiIiIiIiIiDgZBYRERERERERERJyMAkIiIiIiIiIiIk5GASERERERERERESejgJCIiIiIiIiIiJNRQEhERERERERExMkoICQiIiIiIiIi4mQUEBIRERERERERcTIKCImIiIiIiIiIOBkFhEREREREREREnIwCQiIiIiIiIiIiTkYBIRERERERERERJ6OAkIiIiIiIiIiIk1FASERERERERETEySggJCIiIiIiIiLiZNz+6i9wpikrK6Nfv34EBwezatUqJkyYwDfffEPr1q0BePvtt4mNja2RL+XZFAp3F+Jzlg9dpnaxp6fNS6Pg9wJcvV0BCJ4cjHcXb9bl5fHfw4cxDHDD4AF/f/q2aHFqCikiIiIiIiIizZoCQo3sxRdfpEePHuTm5trTnnnmGcaMGVNnvvYj2lNeUs7Rr4/WONZxbEdan9O6Wlp/Hx8u8vXFMAx+Ly7m3oMH+DSsa+MUQkRERESkDscOgk6aNImEhARM0+Sss86ibGgZrl6u1fJk/5DN4c8O238vTismfFY43l28SV+SztEfjtL3aBmbz+pWLd9nubm8nHUYA+ju6cUzQUGnoogiImc8BYQaUVpaGp9++ikPP/wwzz//fIPy+p7tS/6O/Hqf7+Py52q/ItPEaNCnyenieA9Tb7/9Nr6+vg7zWrIsJD2UhP+V/rQf3p6SgyWkvpL65/FMC/6j/YE27CguZnb6IUpMEzcMZgYEEOPtfYpKKY3p2Dpzww03kJCQgLu7O+eeey6vv/467u7uNfJtm7gNrxAvANz93Olyj20mYtbaLLK+zMKSYaH7f7vbz99UWMCd+/cTXHGtob4tub19+1NQQmlM9akvjtRWX1JfS6UopQjD1eDhPC9mdeyIu2GQU1bGI4cOkmopxdPF4MmOgUR6ep6yckrjqE+bdKz8bfkcWnwIs8zEcDXoOLYjvmfb2q1yazkH3z1Iwc4CRh6Ff7ZvzyUtW/FURjobCwsBKC4v50hZGRsjzzqVRZVGcOwg6Jw5c2jVqhUA9957L9+t/Y4Ol3WolqfN/7Whzf+1AaA4tZi9L+3Fu4vteaRlbEvaXdyOfVP/qJYnxWJh3pEs3u/chdaurmRZrU1dNGkCJxJAtGRa2PXQLjw72toT73BvgicEA1CUUkTa/DQuTS9nkI8vD/n7YxgG2WVl3HdgP/tLSwl2d+f5oGBau7rW+D5y+qutzuw6YqsTwZODa9SZSsf2kwAu3p2Ej4srLhUrbBaHhgLweV4uLx8+zB6LhQ+7dCHKy7n6SNpDqBHdc889/Oc//8HFpfo/68MPP0xMTAxTp06lpKSkwddNX5rOrkd2cfCDg5SXltvT1+blMTJ5D7elpfJkx8CT/v5y6lU+TFWaM2cOW7ZsYevWrXTu3Jm5c+fWmvfQB4fwjf4zWOQZ6EnEExFEPBFB+OxwXDxcaNXX9mD2XGYGt/u1Z1loGHe2b89zmRlNVyhpUsfWmRtuuIGdO3fy66+/UlRUxPz58x3mc/FwsdePys49QIvIFoTeH4q7X80gUl9vb5aFhrEsNEzBoGaqsetLmwFtiIyLJOLJCErMcpZmZwPwRlYW3T29WB4WRlzHQP6dkd60BZMmcSJtkmtLV7rc04XIJyMJuSWEtDfS7McyP8nErZUbZz19Fp+EhnGOt21p+wP+AfZ7yw1t23Kxb8umL5w0qspB0MmTJ9vTKoNBpmlSVFTE8UYrszdm0+a8NvbfW0S0wL1NzbZoSXY217dpa+/U+7lpPLs5Ot795cjaIw7zefh72NujymAQwIEFBwieEMznYV3ZW2phfUEBAPOzsujfwofPu4bTv4UP849kNW3BpMnUVmcin4zE3c+91joDNftJld7u1IlloWH2YBBApIcnLwUH089JB8sVEGokq1atwt/fn759+1ZLj4uLY+fOnfz0008cOXKEp59+ukHXDbgmgMi4SMIfC6esoIzDq/+cZntxy5Z8GtaVucEhvHQ4s1HKIadOfR6mDMPx01Tu5lw8OnjgGex4BD5/ez4e/h54tPcAwMCgoNwWTMwvL8ffreYDl5z+HNWZESNGYBgGhmFw7rnnkpaWVscVavLu4o1HB4/G/qpyGmiK+tKyV0t7/mgvbw5VjNTvtpTQv2Ifu66enhwoLeWwRvGblRNtk7y7eOPe1tameAZ7Ypaa9sGro+uP2meIuBgGbR105Ffn5jGy4nOk+ahtEHTixIl07NiRnTt34nexX53XyNmYQ+v+res8ByCl1EKKxcINe/dy3d4U1hfUf0a9nB4aI4BYVWl2KWVFZbSIaIFhGFzRqjXr8m31Ij4/nysr9m69snVr1uWpvjRHx6szpsWstc4cr590rHBPT8I8nHdWswJCjeT7779n5cqVhIaGct111xEfH8+NN95IYGAghmHg6enJxIkT2bRpU4Ou697GHcMwcHF3oc3ANhTtKapxTr8WLUgtLeWoHr6blfo8TN1111018pWXlJO5OpMOV3aocazSsQ9ZD/j780xmBhftTuKZzAzu6VB7Xjl91VZnAEpLS3n33XcZNmyYw7zlpeUkzUpi9+O7yd2c6/CcYyUWFTE6JZlb01LZdQKzG+Wv1ZT1xbSarMzNYaCPDwDdPL1Ym58HwNaiIg6UlpKuNqlZOdE2qarchFy8unjh4u5CWUEZAOkfp5P0WBL37N9fI0i4v7SUtFIL5+mlGM1KbYOgAP/73/84cOAAPXr0IGdTTq3XKNxdiIuni31pal3KTJO9pRbe7tyZZwODePTQIXLLyk6qDHJqnUwA0ZJpIenRJPbE7aHgd9ssIOtRK+7t/hzcDHBzI8NaCkBWmZUOFcHnDm5uHClTW9Qc1VVndv5zJyUHSxzWmbr6SYZhMDktlTEpyXxUMcNZFBBqNHFxcaSlpZGSksKiRYu46KKLeO+99zh48CBgi2QuX76cqKioBl23NLvUnj/v5zx7pHOvxYJpmgBsLy6m1DRpo/WxzUZ9H6Y+/PDDGsfTl6XT/tL2ta6ZLbeWk/dLXrWNyBdlZ/OAvz/x4RHM6ODPzEMHG68wckrUVWcAbr/9dgYNGsT555/v8Hi357oRMSuCTrd14uAHBynJqDvAc7anF2vDI2xLOtq05a79DZtJIn+tpq4vB945QL8WLehX0ZG/pV07csrKGZ2SzPvZR+nh5YVapObjZNqkSsX7izn00SGCJtg2+zXLTaxHrLSIaEHE7Ahivb155pjlyp/l5nJJy5a41jIbVk5PtQ2CVnJ1dWXs2LHkJtQ++JCzMYfW5x1/dhBAgJs7F/n64m4YhHh4EOrhwV6L5aTLIafGyQQQ3dq40e35bkQ8HkHguEBSX0+lrKjM3geqSneRM8fx6kz3F7rjGeTpsM7U1U96v3NnloaG8XpIJxZmHyWhYi87Z6eAUBO74YYbiI6OJjo6msOHD/PII48AkJCQUG0K3J5/7yH1lVTyt+ezc+pO8n61jbSmvZ7Grkd2kfRIEtZ8Kx1G2aKda/LyGJWSzOiUZJ5IP8RzgUG1Li+S0099H6aWLl1aI2/RniIOfXiI3+/7nawvs8hclUnW2j/XR+dvzcerixdurf+cmr8iN4ehFXs0DGvZkl+Li5uwdNIU6qozs2fPJjMzs87N7CuXdHj4e+DT3YfivXXXAV9XV/vm9Rf4+mI1Tc1CbEaasr5kLM/AmmdlRgd/e5qvqyv/DgxkWWgYT3UM5IjVSoiDzc3l9HQybRJA6ZFS9r20j5BbQ/D0tw1cufq6YngY9r3sLm3Zku3HtD2r83K1XKwZcjQI+u6775KUlATYBjE/+eQTPAIdL0c2y01yfsqptn9QXYa09GVTRcftqNXKXouFTh5a6txcnEwA0cXdBTdf2/Osd6htibvlkAX3du6UHim1n5dutdKhYjsEP1c3MiueVzKtVtq5as+p5uZ4dcZwMWh9bmuHdaauflLllhl+bm4M8fVla3HNlTfOSP+HNJZZf45yXAhc2M+WFj+o6kmp8OwnAPQD5odANJ0B6PqQ41fGh80Ic5g+2c+PyX51r82W01dcXBxxcXEAfP311zz77LP2h6mIiAj7w1T37t1r5K1aV9KXpePq5VptymTOhhza9K/+kOXv5sZPRYWc28KHDYWFdFFHrdlxVGfee+895s+fzxdffMG6descLg0CKCsow/CwLT215lkpTCqk/Yi6N4nOtFpp7+qKYRhsLSqiHDQLsRlpqvpy5Jsj5G3LI2x6GC7P/fmSg9yyMrxcXPAwDJbk5NCvRQt8VV+ajYa0SXvZWy1vWUEZe+fsJWBMAD6RPvZ0wzBoFduKgp0F+J7ty4bCAsKr7NGQbCkht6yMWCd7m8uZyjRNxo8fT25uLqZp0qtXL/yvsAWNc3/JpSi5iICrAgAo+L0A97buePhXD+oc+vAQ2RuyKTNNBu9O4urWrbmzfQcGtvDhh4ICLkvegysG0zr4qz1qRup7f3EUQLTmWm3BZRcDS4YFS7oF9w7uuPm64ertSmFSIabpzorcHG5o0xaAwb6+LM/J4RY/P5bn5HBRLW/rldNXfepMbmKuwzpTWz+pvKScgvIyfFxcKSwv54eCQqa0V18aFBASOfVmHTNFOsUKf1gwZ7Vm/P8KyS0xMU3o1dGFV0d6w6x5rPy9lPRCX/vDVG3KS8rJ/y3fPmW/0uyAjsRlpFMGeBgGs/VWujPGbbfdRpcuXRgwYAAAV111FY8++ihFyUUc+eoIwTcHU3KghP0L9mMYBqZp0n5Ee7yCbfs2ZK3JInN1JtYcK0kzk5hp+vBEx0C+zMtjUfZR3AwDT8PguSDNQjwTOKovdKHe9eXAggO4+7mz54k9jM4wGerbktvbt2ePxcIDBw/gahiEe3jwhO4xp79j26JKx2mTvqBLtQ5+1rosStJLyFyZSeZK2wsuQu8Pxa2VGwHXBpD2RhoHPzjIylxX/lWlXnyam8uIVq10X2kOaqsrVBkEfbwt3w+teiSVaG/boGer3q1o1fvPmWC+PXzxfbRmJ73j2I50HNuRj+Kqz0Y1DIMZ/gHMOPESyKlUR32p6/7if3skUD2AWPB7ARnLMjBcDXCBoPFB9hlDQTcFkTY/jWEZ5Zzv48Ogij3tbvHzY+qB/SzNySbQ3Z05QcG1fh35i9VVVyo5qDNJ7u54dfIiaLytv3Ns0NkRa46VG/fts/1smoxs1YrzfWz3obV5efwrI50jZWVMSUuju6cX8zp1OvnyNROGozWYp1q/fv3MhISEv/prnJz6VGgHosM6n1C+YxvL+uqxc8cJ5ZNGpLoiDdFM6kv8hS+fUL47XrvohPKJA82krujecppQfZH6Ul2RhlB9kfpqJnXlTHjGNQxjs2ma/Rwd0x5CIiIiIiIiIiJORgEhEREREREREREno4CQiIiIiIiIiIiTUUBIRERERERERMTJKCAkIiIiIiIiIuJkFBASEREREREREXEyCgiJiIiIiIiIiDgZBYRERERERERERJyMAkIiIiIiIiIiIk5GASERERERERERESfj9ld/gdNRamoqN910E4cOHcLFxYVbb72Vf/7zn9x///188skneHh4EB4ezv/+9z/atGlTI392scnklUVsyyjHMOCtUV4M6GT7p/7vRgtzf7Lg5gIjI93gVlue4tRi9r+9n/KicnCB8EfDcfFQvE5EREREREREGp8iDg64ubnx3HPPsWPHDjZs2MDLL7/M9u3bGTp0KNu2bWPr1q2cddZZxMXFOcz/z8+LGRbhxs47fdlymw89OrgC8FWylRW/l7L1Nh9+u92Xaf/nAYBZZpL6eipB44OI/HckYQ+EYbgZp6y8cnJSU1MZPHgwPXr0oGfPnrz44osALF68mJ49e+Li4kJCQkKd1ygrN+n9ej6XfVBoT5u7yULES3kYs3M5XFhuT8/+IZtdj+xi1yO72P3kbor2FTVNwUREROSMVq9nmANltebPLjYZ81Eh3efm0+PlfH5Mtdry/1bKrod2sW3iNoqSqz+nFKcWs/uJ3ex6yPYsU24pd3RpOQ2dbH0Bx8+86/ZYSXosiaSZSez51x5K0ksAKC8tZ98r+/hj+h/sfnw3lkxL0xVOGt1x68vs3BOqL2lvppE0M4ldj+xi39x9lBVXv0bOTzlsm1Dz3iOOKSDkQGBgIH369AGgZcuW9OjRg/3793PJJZfg5mab6dO/f3/S0tJq5M0tMfl2r5VJvd0B8HA1aONlC+68mmDhgYGeeFYEe/x9bP/8+dvy8erkhXdnbwDcfN0wXBQQai5qCyBGRUXx8ccfM2jQoONe48WNFnq0r/6/4986ubL2Jh+6tK5eFzw6eND1wa5EPhmJ/yh/Drx9oFHLI02rtsbxyJEjDB06lMjISIYOHcrRo0cd5p++ppier9gevO/+rBjTNKsdH7WwkKhX8u2/52zKqfWhXE5/J1tfZqwpJuqVfKJeyefDbaX29OSj5Zw3P5/I/+az75V9lFv/7JBV1pldD+0i9bXUpi2gNKqTHaC4eUUR/s/kVbuHgK1z3/OVfFxm51a7j2T/kE3SzCT7n20Tt1G0V/eZ5uRkn2FqGwSN8neh812daXFWi2rnaxC0eWuqZ94pnxYT8o8Q/r+9O4+vojz7P/65ck4WEsImiMEgi1EE1KIiqLXuS62KC6ggWlSs+1Ktv6q19Wl/WsWlVVv9Pe6V1gUtauFBBbeqVZ+KuNQFRBCi7KLEQAjZTq7fHzPBQ3ISckIOySHf9+vli3Nm7pkzA5f33Pd13zNTdEMRXffryurpqwEoeaOESG6EXW/dle2O2o6Vf1+ZkvOS1NhsvPSLbHYfieKl4PQCim4oYpcbdyFzu0zWvLxm47rYhhjfvvQtnQZ2avXz2VYpIbQZxcXFfPDBB4wcOXKT5Q8//DDHHHNMg/KLSmrplWucPa2Cve4r49zpG1hfFXTYPv+2ln99WcPIB8s4+JH1vLssyGZWrgyy4MW3F7Pwvxay+vnVKT4raU2NJRAHDx7MoEGDNrv90rW1PLeghnP3ztpk+V4FEfp3a/i/aO4uuUTyggo0d+dcqtdUNygj7VdjF8dJkyZx+OGHs2DBAg4//HAmTZrUYNu3l9Tw1pIYH12QxycX5vHu8hivf/n9qMgz86rpvGkYkV2YnbBRLulhS+Lluc+reX9ljA8vyOOdc/O47e1K1lYG16OrX67giv2yWXBpZyK5EUreCBJKlSsrWT1jNQOvG8guN+1CwekFW/V8ZctsaWftrGGZzDyjYV2x+/YZPHNqpwaN924HdKPohiKKbiii8LxCMntm0qmfGuHpZEvaME0Ngg7uFSG7ILvBNhoETW+pavOaETw2g+DPaPdgAH7dB+vofmB3ALru25X1c9c3GAiT9itV8RLpFFyL3B2vcoirQr5+5mt6/qQnlql6pbmUEGpCWVkZo0eP5s4776RLly4bl//+978nGo0yfvz4BtvU1ML7K2q5cHgmH5zfmbxMY9KblRvXlVTAvyfmcduROZw6tTyo1GqhfEE5hecXMvBXA1n73lrK5pY12Le0f40lEJvy85kV3HpEDi1pD5W8UUL+nvnJbyhtprGL47Rp05gwYQIAEyZM4B//+EeDbQ2oqHGqYlAZg+qY0zsvCJyyKueP/1vFrw/atAGe0ycnYaNc0sOWxMvc1bUc3C9KNMPIyzJ+0DvCzIU1uDuvLo4xZkjQ4O5+YHfWvb8OgJLXS+hxeI+NSedoFz1qMJ1saeP7oH5RenRqeDEa3CvCoJ5Nj+R+9853dBvZ8LmKkj6SbcM0NQjaGA2Cbjtas8374PE5fPnHL/nsis/47u3v6HVsLwCqS6rJ7BEkHC1iZHTKIFbW9C1G0j61dh9p6YNL+ezyz6hcUcl2R2wHwIYvN1C9ppouw7o03EAapYRQI6qrqxk9ejTjx4/n5JNP3rh88uTJzJgxg8ceewyzhtFZ2MUo7GKMLAwa0WOGRHl/Ze3GdScPjmJmjNgxQoZBbF2MaPcoeYPyiOZHycjOIH/PfDYUa8p1umksgdiUGZ9Xs32esU+fzU+ZbPB788ooeaOE3qf2TnpbaR/iL46rVq2ioCCYjVFQUMDXX3/doPz+faMc2j9KwR/WUfCHdRy9c3Tj9PzfvFrJL/bPIlcjItusZOPlBztEeGFhDeXVzjfltfyzuIYlpbV8u8HplgPRsIUV7R6luiSYaVi5spKqlVUsunERX/zfL1j30bqtd4LSqlrS+N4Spe+U0nW/rlvlt6T1taQN09QgaKM0CLpNaO027x3/rqLflf3Y7Y7d6HZgN1Y+Ed4alii/qGZO2klFH6nw3EJ2u3M3svtkUzq7FK91Vjy+gh3G7tCah94hKCGUgLszceJEBg8ezJVXXrlx+cyZM7nllluYPn06ubmJb7/YoXMGfbtmMP+bIHv9yuIahoT3PZ64WyavLg4etvf5tzGqYhDJj5C/Rz4VSyuorazFY876+evJ6ZOT4rOU1tRYAnFz3voqxvT5NfS/cx1jp27g1cU1nPHM5pOBFUsqWPbwMna6fCeinTWCn45acnFcuKaWed/UsvTKfJZdmc+rxTHe+LKGD1fGWFhSy0mDM1N81NJWWhIvR+0c5SdFUQ54aD3jnt7A/n0jRDMg4Wz7ugZ2LVSuqmTANQPoe2Fflv1lGbH1Go1NNy2Jly1R/kU5GdkZ5BSq7ZKOWtqGaWoQtDEaBE1/rd3mXb2+lv+sipG7c9C36jqyK+ULgwcIZ/bI3PhoBI85tRtqN85glfSQyj6SZRhdR3Rl7Zy11FbUUrmsksWTFjP/F/PZ8MUGvrzrSz0/sxnUk0zgrbfe4m9/+xt77LEHw4YNA+Cmm27isssuo7KykiOPPBIIHix97733snz5cs59rJznxwcV2Z+PyWH8MxuoisHA7hn85YTgPulz9srknGnBAz6zIjD5xE5cbkYkL0LPo3vyxe++AIP8PfPJH6bbgNJFYwnE5rj5iBxuPiJoQL9WXMPtb1fx6MlNP3+h6tsqvvrzV/Q9ry/ZO+hWoHSU6OLYu3dvVqxYQUFBAStWrGD77bdvsN2z86rZb8cInbOC3vsxRVH+vTRGfpbx3vIY/e9cR00tfL3eybx5EQOvHbhVz0tSo/nx0vBhm9cdlM114W2Epz9dzi7bZdAz1/iuAmpqnWiGUVNSQ2a3IJkY7R4ld+dcLGpk9coie4dsKldVkjtQz6BKFy1tfG+J0ndK6TpSs4PS0Za0YeIHQQf1jGwyCNqY/D3y+eaFb6itrMWixvr56+l5VM8tOQXZilLR5q2pdUorILKykuwdsin7pGzjre75w/IpebOE3KJcSt8tJW9wXsI7NKR9SkW8uDuVqyrJ7p2Nu7P2w7VkFWQRyY0w+O7BG7dfdPMiCsYW0GmAnmu3OUoI1dP/mucA6Hf1DNbGLb/oDYcxdxEBvguXzYwrXzz++8bysB0izDmvc4N9Z0Ws0c5+twO60e0A3XufjhpLIFZWVnLppZeyevVqjj32WIYNG8asWbNYvq6Wc6dXbEwgNuZP71Ry61tVrCxz9vzv9VTsvYwdz9mR1dNWU1NWw/K/hm8Xi0DRb4tSfZrSShq7OI4aNYrJkydzzTXXMHnyZE444YQG2+7UNYMH3q/i2tos3OH1L2v4+cgsjh+UyYX7Bg/cK8/oD+cAABI2SURBVP6uluMeL8eUDNomJBcv922ybazW+a7C2S43g49WxfhoVS1H7RzctnzogAhT59YwdvdMSt4sIX+vYBCiy95dKH2nlO4/6k7NuhoqV1WStX29J5VLu7Ulje8W/2atU/puqRLQaapZbZg51QzbIYNZZ+Q1aMM0Ngj67LxqPvvTZ8TWxSi+o5hOO3Wi/1X9NQia5rY0XhKJZhgPHJ/DuLu/wszIyM2gcGIhAN0P6s7S+5fy+S8/J5IXoe+FfbfKeUrr2Gy8rIxx7OPlScWLA8seWBa8at4hp28OfSb02UpntG2y9vCk9uHDh3tTr0HdmuoSPMkqzjm9RdvtMWCnFm331M01Ldpu8GfzWrSdNKRYkWTUxUvF0k9Z9djVZPbqH7xWA+h+0E/J6jOIb6ZNombtaqJdetHzhGuJdMpn6prTuHdOFQ+O6kSs1rnouQre+CqGAT8uivLHoze9RWNjQui2IQCsfW8tyx9dTmxdjIzcjI2N8sa0NF5ePeSeFm138b2HtWi7bVl83ZJMvCzpfj5zlsc2xktFjbP3fesB6JJt3HtcDsN2CKbaLyqpZezUctZscFbvnE/h+YVkZGbg7qycspKyj8sgA3od14tu+yUerFDd0j40J148Vs2al+4jtqGUjOzOZG0/gA0TvmjQ+B73dDmvFcf4pjx4YP3vDslm4t5ZPDuvmktfqGB1uRPLi2xSj5TNK2PV31ex8/U7N3mcipe2p3aLJEPxIs21rcfKttDGNbP33H14onWaISQishXlFA6l39UzEq7rPfamBsuG94nw4KhgxDWSYdx3fNNTX/t3y+CTizqzR/i9yz5d6LKP3raQrrYkXnKixtyLG85WhWAkf/bPgnXxDSszo2BcAYzb0iOXttBUvOTuekC9JafTJz9jk5HYJ0YnHpU9aXDmxmeU1W+Idx7cmc7XJ44zERERad/0UGkRERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCSERERERERERkQ5GCaEOaObMmQwaNIiioiImTZrUYH1lZSWnnXYaRUVFjBw5kuLi4o3rbr75ZoqKihg0aBCzZs1q9j4lvSlmRCRVVL9IMhQvkgzFiyRLMSMdjRJCHUwsFuPiiy/mhRdeYO7cuTzxxBPMnTt3kzIPPfQQ3bt3Z+HChVxxxRVcffXVAMydO5cpU6bw6aefMnPmTC666CJisViz9inpSzEjIqmi+kWSoXiRZCheJFmKGemIlBDqYGbPnk1RUREDBw4kKyuLsWPHMm3atE3KTJs2jQkTJgAwZswYXnnlFdydadOmMXbsWLKzsxkwYABFRUXMnj27WfuU9KWYEZFUUf0iyVC8SDIUL5IsxYx0REoIdTDLli2jb9++G78XFhaybNmyRstEo1G6du3Kt99+2+i2zdmnpC/FjIikiuoXSYbiRZKheJFkKWakI0pJQsjMfmxm881soZldk4rfkJZx9wbLzKxZZZJdLtsGxYyIpIrqF0mG4kWSoXiRZClmpCNq9YSQmUWAe4BjgCHAODMb0tq/Iy1TWFjIkiVLNn5funQpffr0abRMTU0NpaWl9OjRo9Ftm7NPSV+KGRFJFdUvkgzFiyRD8SLJUsxIR5SKGUIjgIXuvsjdq4ApwAkp+B1pgX333ZcFCxawePFiqqqqmDJlCqNGjdqkzKhRo5g8eTIAU6dO5bDDDsPMGDVqFFOmTKGyspLFixezYMECRowY0ax9SvpSzIhIqqh+kWQoXiQZihdJlmJGOqJoCva5I7Ak7vtSYGQKfkdaIBqNcvfdd3P00UcTi8U455xzGDp0KNdffz3Dhw9n1KhRTJw4kTPPPJOioiJ69OjBlClTABg6dCinnnoqQ4YMIRqNcs899xCJRAAS7lO2DYoZEUkV1S+SDMWLJEPxIslSzEhHZInua9yiHZqdAhzt7ueG388ERrj7pfXKnQecF34dBMxv1QPZNvQEvmnrg5C0oFiRZChepLkUK5IMxYs0l2JFkqF4keZSrCTWz917JVqRihlCS4G+cd8LgeX1C7n7/cD9Kfj9bYaZzXH34W19HNL+KVYkGYoXaS7FiiRD8SLNpViRZChepLkUK8lLxTOE3gV2MbMBZpYFjAWmp+B3RERERERERESkBVp9hpC715jZJcAsIAI87O6ftvbviIiIiIiIiIhIy6TiljHc/Xng+VTsu4PRLXXSXIoVSYbiRZpLsSLJULxIcylWJBmKF2kuxUqSWv2h0iIiIiIiIiIi0r6l4hlCIiIiIiIiIiLSjikh1E6Y2W/N7Kq2Pg5pO2b2dlsfg6QXM+tvZp80s2xfM/unmc0zs0/N7PK4dT3M7CUzWxD+2T1crnpJZBuXTD2S5H4fNLMhmymj656IbDVmVmxmPdv6OKQhMzvJzNzMdmvrY+lolBASaSfc/YC2PgbZptUAv3D3wcB+wMVxnbVrgFfcfRfglfC7tJF0TvSZ2SNmNib8nDAhYGZnmdnd4ecrzWyumX1kZq+YWb9w+SFmNiMVxyjJMbMWPW/S3c9197mbKaPrnoiIAIwD3iR4Q/kWMbPIlh9Ox6GEUBsys+vMbL6ZvQwMCpe9ZmbDw889zaw4/Bwxs9vN7OOw4Xxp2x25pIKZlYV/HhLGwVQz+8zMHjMzC9dNius83R4ue8TM7jWzf5nZ52Z2XLg8Yma3mdm7Yfnz437rl2Es/cfMJrXF+UrrMrOBZvaBmf0fM5tmZjPD+uW/ANx9hbu/H35eB8wDdgw3PwGYHH6eDJyYYP8/M7MXzKxT6s9GktBuE33NSQgAHwDD3X1PYCpwa+qPTBpTrx75u5n9D/CimXUOE3bvh9eOE8Ly/cPr1OTwOjPVzHLDda+Z2XAzu9DMbo37jbPM7M/h581e9yQ9hbExz8weCJPVL5pZpybauUPNbLaZfRjG0i5tegLSYmaWZ2bPhW3MT8zsNDPbx8xeN7P3zGyWmRWEZV8zszvM7I0wXvY1s2csGMi4MW6fZ8TFx31hG7epuuUf4W99ambnbf2/BUmGmXUGfghMJEwImdmTZvaTuDKPmNnoxvo34XXkn2b2OPBxuCxhHJjZxLDP9FpYR9UNUvUys6fDfb9rZj/cen8LbUcJoTZiZvsQBPxewMnAvpvZ5DxgALBX2HB+LLVHKG1sL+DnwBBgIPBDM+sBnAQMDWPgxrjy/YGDgWOBe80sh6BSLXX3fQni62dmNsDMjiHo8I909x+gDljaM7NBwNPA2cBqYAQwHhgGnFLX+I4r358gxt4JF/V29xUQJI6A7euVvwQ4HjjR3Tek7ESkgbZM9JnZYDObHfe9v5l9FH6+PmwsfWJm9yfqvNfr+J0dNr5eJ2j0ER7zP929PPz6b6AwwX72Df8OBm72L0xaLEE9sj8wwd0PAyqAk9x9b+BQ4A9x/+aDgPvD69Ja4KJ6u55K0M6pcxrwZIJDaHDda43zkja1C3CPuw8FvgNGN1H2AuAudx8GDAeWboXjk9T4MbDc3X/g7rsDM4E/A2PcfR/gYeD3ceWr3P0g4F5gGnAxsDtwlpltZ2aDCeqNH4bxESNo4zRVt5wT/tZw4DIz2y5F5yqt40Rgprt/Dqwxs72BKQT/pphZFnA4wVvME/Zvwv2MAK5z97qBsQZxYGZ9gN8QDKIdCcTfonYXcEe479HAgyk743ZECaG28yPgWXcvd/e1wPTNlD8CuNfdawDcfU2qD1Da1Gx3X+rutcCHBAmftQSN8gfN7GSgPK78U+5e6+4LgEUEldtRwE/N7EOCjv92BI2zI4C/1HXCFEtprxdBA+oMd/8wXPaSu38bJm+eAQ6sKxyOwjwN/DysezbnTOAYYLS7V7buoUtT2jrR5+7zgKy4RMxpwFPh57vdfd+wsd8JOK6J8ygAfkfQwT+SoMOfyETghXrbHkDQSTjB3Rc19huyxRqrR+quDwbcFCYEXyZIOvYO1y1x97fCz48SV98AuPtqYJGZ7Rd2ygYBb9FQouuepLfFcfH0Hk3/m/4v8Cszuxrop8GHtPYxcISZ3WJmPwL6EiR4XgrbpL9m0+T/9LjtPg0HOioJ2rN9CRIB+wDvhtsfDgzcTN1ymZn9h2CgoS9B+1far3EECSDCP8cRtAcOM7NsgnboG2G90Fj/BoLryOK4/SaKgxHA6+6+xt2rgb/HlT8CuDvc93Sgi5nlt/7pti8tui9cWo0nWFbD94m6nLjl1kh52TbFd7xjQNTda8xsBMGFcCxwCXBYWKZ+bDhBzFzq7rPiV5jZjxOUl/RVCiwh6Gx/Gi5LFA+YWSZBguExd38mbv0qMytw9xVh5/3ruHWfECQgCoH4i6ykVl0HfbS7f2pmwwgTfQBmVpfomxN+b0mibylBMqi6iXJPAacCkwgSQqeFyw81s18CuUAPgtj7n0b2MRJ4LWy8Y2ZPArvGFzCzMwhG8A6OWzwYuB84yt2XN+OcpOUS1SPr49aPJ4jJfdy92oLbfOraKAnrm3qeJIijzwgGwxKVaXDdS+YEpF2q/2/aiUbaue7+uJm9QzDTeZaZnevur261I5VW4+6fh3dC/AS4GXiJINGzfyOb1MVJLZvGTC1BPWDAZHe/NsG2DeoWMzuEoGO/v7uXm9lrbNqnknYkTOYdBuxuZg5ECK4jvwReA44maHs8UbcJifs3hxB33WoiDpq6HTkjLN+hEtKaIdR23gBOsuB+6nyCUVqAYoIsOMCYuPIvAhdY+HDH8PYh6UDCDl9Xd3+eYFr9sLjVp5hZhpntTDDVfj4wC7gwTAJgZruaWR5BLJ1j3z/nQbGU3qoIptr+1MxOD5cdacEDhTuF694Kb+94CJjn7n+st4/pwITw8wSCRESdD4DzgenhNFvZOuI76HValOgLyyRK9PUnwS1a9TwJnGpmuwLu7gvCW1L/H8H0/z2AB9h8Y7vRJLSZHQFcB4yqNwttBcGsyL02s2/ZconqkXhdga/DZNChQL+4dTuZWV1Hr+6hoPU9E+5/HIlvF5OOo5gE7dxwJuIid/8TwTVpz61/aNIawrZCubs/CtxOMCjQq66eMLNMMxuaxC5fAcaY2fbh9j0sfAEBieuWrkBJmATYjeDWIGm/xgB/dfd+7t7f3fsSDEAeSDBb6GyCO2vqEkCN9W/qaywOZgMHm1n3sF8dfyvriwQD7oT7ju9rbbOUEGoj4TMfniSYFv008K9w1e0EQf42EP9axAeBr4CPwqlviRpssm3LB2aEU/ZfB66IWzc/XPYCcIG7VxDEzFzgfQveWHQfwUyjmQSNrTnhlEi9VjzNuft6glt2riC4AL4J/I2wfnH3OQSJhTMJpt9+GP5X97C+SQRJpAUEt/RMqrf/Nwni5DnT61q3lnaR6HP3LwhG9n/D943tuuTPN2GiekyibeO8AxwS3rufCZxSt8LM9iKom0a5+9f1tvuOYLbATeFIn6RQgnok3mPAcDObQzBb6LO4dfOACeG1qQfw3wn2XUJwPern7rPrr5cOpbF27mnAJ2G7ZDfgr21xcNIq9gBmh/+W1wHXE1wnbgn7MB8CzX7DYPhygl8TPOD+I4IZRwXhukR1y0wgGpa9geB2IWm/xgHP1lv2NEFf90XgIOBld68K1yXs3yTYb8I4cPdlwE0EbZOXw32VhttcRnCt+8jM5hI822ybZ4ln7YpIujCzR4AZ7j61rY9F2p6ZnUXw1qZLNldW2qfwWUAz3H13M+tG0Ph9lCCplwcUAY+7++/M7ECCAYWPCabXA/zK3Z8Pp2E/BexEMKBwiruvMbPfAmXufruZHU2YEHT3bxo5nquA24AB7l4cLruR4NbVYoKZTF+6+2/j66NwevZV7j7HzM4GriWY9fMhEHH3Syx4y+Ye4XKAr9x9VJgAusrdjzOznQiS3ee4e93zkaQdiI/VNj4UERGRZjGzzu5eFs4QehZ42N3rJ6U6DCWERNKcEkISTwmhbZP+XaU9UkJIRETSjZndTvB8oRyCWUiXN/Jsuw5BCSEREZF2TgkhEREREWltSgiJiIh0cGZ2D5s+wBrgLnf/S1scj4iIiIiknhJCIiIiIiIiIiIdjN4yJiIiIiIiIiLSwSghJCIiIiIiIiLSwSghJCIiIiIiIiLSwSghJCIiIiIiIiLSwSghJCIiIiIiIiLSwfx/Gf0avcMrCvUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"# one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2one_df.at[index_label , 'exp_name'], one2one_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='present_exact_f_score_hard@10')\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10']\\n\",\n    \"metric_names = ['present_exact_f_score_hard@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"print(beam_widths)\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"# unique number of phrases\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"print(beam_widths)\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2One - Absent Phrase Prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"# one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2one_df.at[index_label , 'exp_name'], one2one_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = sorted([int(b) for b in valid_one2one_df.beam_width.unique()])\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"# one2one_df = one2one_df[one2one_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2one_df.at[index_label , 'exp_name'], one2one_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, peak_one2one_df, valid_one2one_df = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# display(peak_one2one_df)\\n\",\n    \"# print(peak_one2one_df.shape)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score_hard@5', 'present_exact_f_score_hard@10', 'present_exact_f_score_hard@k', 'present_exact_f_score_hard@M']\\n\",\n    \"metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = sorted([int(b) for b in valid_one2one_df.beam_width.unique()])\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2Seq\\n\",\n    \"    - For exhaustive, it's clear that the larger beam witdh the better performance. But for self-terminating, the other way around, essentially due to the preference of shorter sequences.\\n\",\n    \"    - The absent performance of exhaustive is poor and of self-terminating is negligible.\\n\",\n    \"    - Alert: we don't have statistics like unique_pred_num for self-terminating due to carelessness.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2Seq - Present Prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-07T20:14:04.213785Z\",\n     \"start_time\": \"2020-11-07T20:13:59.857532Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"######### Exhaustive\\n\",\n    \"\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2seq_df.at[index_label , 'exp_name'], one2seq_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = valid_one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"# Plot unique_pred_num\\n\",\n    \"metric_names = ['unique_pred_num']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = valid_one2seq_df.beam_width.unique()\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \" \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"######### Self-terminating\\n\",\n    \"\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'selfterminating'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2seq_df.at[index_label , 'exp_name'], one2seq_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10']\\n\",\n    \"# metric_names = ['present_exact_f_score@10']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = valid_one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2Seq - Absent Prediction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-07T20:15:40.374318Z\",\n     \"start_time\": \"2020-11-07T20:15:36.709796Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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o29TCPenkYFxdXatWqjY9Pf2rXrvPQBbi9vV8lPT0Nf//8aXRGo5HXX+9HnTp1mTnzXUaO9KdmzVpMnDiVt956g2bNmtOmjRcREUcYNKgvjo6ONGzYiEuX8hNcnp7P4+PjS0DAaBQKJRYWGoKClmFra0fXrq8wdOgAKlWq/NAFuIsyfvxEQkI+wNd3IAqFAo3GgnHjJlKtWnVmzJhLcHAQPj79USpVdOnSjSFDfIu9BkXp3bs/q1YtZ/Hi5XTp4s2oUX44Ojrh5dWBffs+wdd3uHl01Lp1q7lyJQqlUoVGo2H69Nnm0Urduv2LS5cuMGDA6wAFjnucFKayGBdWzpKTMzEan/lulCkHh0okJWWUdzPEM6CsY2XmzHcBBVOnTic6+gqTJo1nzZoNhe7mdvLkd8yfP5sVK9ZgZ2fP++9PplEjd0aN8ic1NYWzZyPx8upAhQoVOHPmNO+9N4mZM+fRocMLnD9/lvffn8zy5SG4uTXgp58uExAwhlmz/k2rVm3KrC+iMHltESUlsSJKQ+JFlJTEyrPv5s1fcHauXXzBMqBWK83r4AjxZ8orVnbv3sGJE98xapQ/rq7567/GxcUSGXmKXr36mKfCPQl//NtUKhXY2ekeWl5GJgkhytTEiVNZsGAOPXp0oXJlayZOfJd69Vy4efMmPj792Lx5F87OzrRp48WgQT6MGzeS3NxcXnjhJfz83v61FgWffvoJS5YswGg04ezszLhxE+nQ4QUg/xuIYcNG8P77U0hJSaFKFRt8fN6URJIQQgghhBDimdG37wAaNmz867pbv6BSqalZsxYDBw55oomkv0JGJv1Nybc2oqQkVkRpSLyIkpJYEaUh8SJKSmLl2Scjk/4Z/Px8Ci3O7e7ehEmTppVLe6ZMeYdbt24V2Obk5ERQ0DJAYgVkZJIQQgghhBBCCCHKUWjo5vJuQgEPkkai7EgySQhRJmwqWaC2rFB8wT+hz8klNaP4Ox8IIYQQQgghhCg/kkwSQpQJtWUFjvfs80h1tNv3CUgySQghhBBCCCGeasryboAQQgghhBBCCCGEeHZIMkkIIYQQQgghhBBClJhMcxNCCCGEEEIIUS5srC1QWzzauptF0eflknFPX2y59u1bEBHxDVqttszbUJzTp0+ybt1qYmKu0qfP/zF2bIB5n8FgYPnyJZw69R0KhYIhQ3zp0aNXmZ6/b98eLFq0jHr1XMu03j9KTEzg9OmT9OzZ+7Gep6QyMjLYv38PgwcPLe+mFLgGY8eOYOBAH9q162Def/nyJXbu3E509BU0Gg21a9dl0CAf3NwamMuEhq5j797d2Ns7ANC0qQcTJ04BHm8cSTJJCCGEEEIIIUS5UFtUIObfj7buZlHqvfcJlCCZVJ6qVavOlCnv8dVXX5CXV3Dd0IiII8THx7Jjx17S09MZNmwwLVq0omrVauXU2r8uMTGB/fv3PjXJpMzMDLZtCyt1MslgMKBSqR5Tqwrbt28PR49G4Oc3kiZNmqJSqbh8+RIrViylT5/+vPhiZ3NZb+9XCyQjH3iccSTJJCGEEEIIIYQQ/1jbt28mMvIU6elpvP32GF544WUALl68wNq1K7l37x4Aw4ePxMurPXq9nsmTA0hPTyc3N5fGjd2ZNGkaGo2Gw4cP8Pnn4eh0lbh2LRoHB0cCAiYRErKC2NhYGjVqzIwZc1EoFNSoUROAY8e+LtSmL774nB49eqFUKrGxsaFDh058+eV/GTTojVL1LSIinF27tqPX3wdgzJgAWrRoVWD/jz9+z507SfTvP5A+ff4Po9FIcPAizp2LRKOxQKutyJo1GwA4ceJbwsI2kJubh0ajwd9/Ak2aNOXcuTN88EEwjRu7c/Hij4CC2bPnU6dOXYKDF5GYGI+v7yBq1KjBvHmLHtrerVs38dVXRzEYDNjbOzJlynvY2dmzYMEcdDod/v4TSElJZsQIXxYsWIKbWwNmz36fGzd+4f79PKpXr8m7786gcuXKABw8uI9du3YAoNFoWLRoGcHBQWRmZuLrOwhLS0vWrt1QZFse9MnDoxlRUZcYOtSPZs08WblyGdeuRZOXl4enZwv8/d9BpVKRlHSb5csXExcXC0Dnzt3w8Xmz2GtQlJ9+uszXX39BcPAq1Orf0jYNGzZm6dIPCAgYQ/PmLbC2rvKn9ZRVHBVFkklCCCGEEEIIIf6xlEola9du4MaNnxk50g8PD0/Uag1Llsxn8eIPsLe3586dO7z11huEhX2MTqdj5sx5WFtXwWQyMW/eTA4d2kevXn0BiIq6RFjYDhwdnZg8OYDZs99n1aoPsbS0xM9vCGfOnKZly9Z/2qZbt27i7FzV/NjJyZnbt2+Vum+tW7ehS5duKBQKbtz4mfHjR7N372Hz/pSUZFavXk9KSjJvvjkYD4/mGI0Gzpw5zbZtu1Eqldy9exeA+Pg4Nm4MJTh4JVZWOmJirhEYOI49ew4BcP36NaZNm8Hkye+xaVMomzaFMnPmPCZMmMzq1SsIDd38p2397LPDxMXFsW7dRpRKJXv37mbVquXmOkaM8OWbb75iz56dDBr0hnmq1/jxgVSpkp9U+fDDELZu3cSoUf6cO3eGzZv/Q0jIR9jZ2ZOVlYVKpWLChCkMH+7Dxo3bin3+YmKuEhg4lXfemQzAwoVzadasOVOnTsdoNDJ79vscOrSf1157nTlzptO2bTv+/e/FAKSlpZXoGhRl587tjBkTgFqtZufO7Rw+fABHRydsbGzo3r0n/foNJCIinH79BgBw9GgEkZEnsbW1w8/vbZo0eQ4ouzgqiiSThBBCCCGEEEL8Y3Xv3hOAWrXqUL9+Ay5e/BGVSkViYgKBgePM5RQKBfHxsbi5NWD79i2cPPkdRqOBjIwMLC0tzeWee84DR0cnANzcGuDsXBWdTgeAq6sb8fGxxSaTykp8fByzZr1HUlISarWalJRkkpPvYGdnX6DvtrZ2eHm15/z5s7zySneMRgMLF86lefMWeHnlr+Fz6tQJ4uPjGDNmhLl+g8FASkoyALVq1aZ+/YYAuLs35fjxY6Vq67fffsPly1EMGzbk17r15uetQgVL5sxZyPDhPrRq1YbevfuZjwsPP0hERDh6/X2ys3OoWbMWACdOHMfb+1VzX//Kulg1atQ0J2YetDEq6iI7dmwFICcnB0dHJ7Kysrhw4QeWLVttLvsgwVXcNShKYmICLi6uxMRc47PPDhMS8hG5uTn4+fnQrdu/qFfPhfPnzwLQq1cfhg71Q61WExl5kqlTJ7J1665iRy09KkkmCSGEEEIIIYQQgMkEoMBkAhcXN1avXl+oTHj4IX744X+EhKxHq7UiLGwDsbE3zPstLCzMvyuVSix+t8C4UqnCYDAU2w4nJ2du3kykUSN3oPAIkwc2bQrlyy+PAjBu3ASaN29RYP+sWe8xduw7dOz4Akajkc6d2xdan+m3vptQKECn07F5807Onz/L2bORrFmzkg0btmAymWjdui3Tp88pdOzPP1//Qz+VJernH88/dOgwc4Kr8Dli0GqtSElJRq/Xo1ar+f7783z66SesWbMBGxsbIiLC2b9/j7m+R1Wx4h8TUCbmz19C9eo1CmzNysp6aB2luQYPKBQKAK5fj+H551ui1WrRarXmxFZKSjJ2dnYABZJSLVu2wdHRiZiYa3h6Pl/iOPorlGVSixBCCCGEEEII8Qw6dGg/ALGxN7h69Qru7k1o0uQ54uJucO7cGXO5qKiLmEwmMjMzsLauglZrRWZmJp9/Hl7mbXrxxc4cOPApRqOR1NRUjh37mk6dXipUbuhQPzZu3MbGjdsKJZIAMjMzzYstHzy4r1AS48iRgwCkpqZy8uR3eHq2IDU1ldzcXNq08WLkyLHodDoSEuJp1aoNp06dICbmmvn4qKiLxfbFykrHvXuZxZZr374je/fuNk+ry8vLIzr6JwASEuJZsWIpq1Z9SPXqNVm/fg2Qf2c2Kysd1tbW5OXlma8lQLt2HQgPP2QeOZWVlUVeXh5WVlbk5OSg15d+gfZ27TqyZcsmc6IsLS2NhIR4c6Jn587fps49mOZW3DUoyoOEUN269Th//izZ2dmkpqZy4cIPZGVlsW1bGF26eAOQlHTbfFx09BVu3kykVq3aQMnj6K+QkUlCCCGEEEIIIcqFPi83/85rj6HekrKwsGDUqGGkpaUxadI0bGxsAVi4MJjVq1ewYsVS9Pr7VKtWnaCgZXh7d+fYsW8YMqQ/Dg4OeHh4kptb8vM98P33/2PWrGncu3cPk8nE0aMRTJ06ndat29Kt27+4dOkCAwa8DoCv7/BCo2FKYty4CUybFoi9vQPNmjXH2tq6wH4nJ2dGjx5OcvIdfHx8cXFx5cqVywQFzcNgMGAwGGjTxgt396YolUpmzJjLwoVzyc3NRa+/T9OmHuZRLw/j4uJKrVq18fHpT+3adR66ALe396ukp6fh758/jc5oNPL66/2oU6cuM2e+y8iR/tSsWYuJE6fy1ltv0KxZc9q08SIi4giDBvXF0dGRhg0bcelSfoLL0/N5fHx8CQgYjUKhxMJCQ1DQMmxt7eja9RWGDh1ApUqVH7oAd1HGj59ISMgH+PoORKFQoNFYMG7cRKpVq86MGXMJDg7Cx6c/SqWKLl26MWSIb7HXoCi9e/dn1arlLF68nC5dvBk1yg9HRye8vDqwb98nBeJh3brVXLkShVKpQqPRMH36bPNopbKKo6IoTGUx9qucJSdnYjQ+890oUw4OlUhKyijvZohnQFnFioNDJY73fLTburbb94nE7VNOXltESUmsiNKQeBElJbHy7Lt58xecnWs/kXOp1Ur0euMTOZd4tj2NsbJ79w5OnPiOUaP8cXV1AyAuLpbIyFP06tXHPBWurPzxb1OpVGBnp3toeRmZJIQQQgghhBBCCPEU6dt3AA0bNv51Ta5fUKnU1KxZi4EDh5R5IumvkGSSEEIIIYQQQgghngg/P59Ci3O7uzdh0qRp5dKeKVPe4fbtW/x+zpaTkxNBQcvKpT2/16TJcwXuJvc0kWSSEEIIIYQQQgghnojQ0M3l3YQCgoKWPZXT3J52cjc3IYQQQgghhBBCCFFikkwSQgghhBBCCCGEECUmySQhhBBCCCGEEEIIUWKSTBJCCCGEEEIIIYQQJSYLcAshhBBCCCGEKBeVqlTAUmNR5vXm3M8jO/N+mdcrhMgnySQhhBBCPDPu3k1nwYK5REaexNq6Cm+/PZauXb2LLPvxx1vZujWM3NwcOnV6icDAd7GwyP/AMmfOdM6ePU12dg62tnYMHvwGPXr0AiAxMYF+/V6jYsWK5roGDx6Kr+/wx99BIYT4h7HUWND/41FlXu/O/1tDNsUnk9q3b0FExDdotdoyb0NxNm78iP/+NwKVSoVKpeLtt8fQunVbAEJD17F3727s7R0AaNrUg4kTp5Tp+fv27cGiRcuoV8+1TOv9o8TEBE6fPknPnr0f63lKKiMjg/379zB48NDybkqBazB27AgGDvShXbsO5v2XL19i587tREdfQaPRULt2XQYN8sHNrYG5zJ/FisFgYPnyJZw69R0KhYIhQ3zN73celSSThBBCCPHMWLo0CI1Gw/79EURH/8TkyeNxdXWjXj2XAuVOnTrBli2bWLFiDfb2DkybFkho6DpGjfIHYMgQX6ZOnY6FhQW//PIz/v5v4+bWgIYNG5nrOHLkS9RqeaskhBDi8WjUyJ0BA4ZgaWlJdPRP+PuPYN++cCpUsATA2/tVxo4NKOdWPrrExAT279/71CSTMjMz2LYtrNTJJIPBgEqlekytKmzfvj0cPRqBn99ImjRpikql4vLlS6xYsZQ+ffrz4oudzWUfFisREUeIj49lx469pKenM2zYYFq0aEXVqtUeuX3yDkkIIYQQz4Ts7Gy+/voLwsI+RqvV4uHRjPbtO/LZZ4fNSaIHjhw5SPfuPc1JJl/f4cyZM91c7vfJJ4Ui/yc+Pq5AMkkIIcQ/w/btm4mMPEV6ehpvvz2GF154GYCLFy+wdu1K7t27B8Dw4SPx8mqPXq9n8uQA0tPTyc3NpXFjdyZNmoZGo+Hw4QN8/nk4Ol0lrl2LxsHBkYCASYSErCA2NpZGjRozY8ZcFAqFeRQSgKurGyaTifT0dBwdLcusbxER4ezatZ0z/mUAACAASURBVB29Pn+U1pgxAbRo0arA/h9//J47d5Lo338gffr8H0ajkeDgRZw7F4lGY4FWW5E1azYAcOLEt4SFbSA3Nw+NRoO//wSaNGnKuXNn+OCDYBo3dufixR8BBbNnz6dOnboEBy8iMTEeX99B1KhRg3nzFj20vVu3buKrr45iMBiwt3dkypT3sLOzZ8GCOeh0Ovz9J5CSksyIEb4sWLAEN7cGzJ79Pjdu/ML9+3lUr16Td9+dQeXKlQE4eHAfu3btAECj0bBo0TKCg4PIzMzE13cQlpaWrF27oci2POiTh0czoqIuMXSoH82aebJy5TKuXYsmLy8PT88W+Pu/g0qlIinpNsuXLyYuLhaAzp274ePzZrHXoCg//XSZr7/+guDgVQW+2GrYsDFLl35AQMAYmjdvgbV1lT+t54svPqdHj14olUpsbGzo0KETX375XwYNeuNPjysJSSYJIYQQ4pkQG/sLSqWKWrVqm7e5uNTnf/87V6js9esxdOjQyfzY1bU+KSnJpKenmd94LVmykCNHDpCbm0v9+g1o27ZdgTr69u2BQqGgZcvWjB49nipV/vwNmxBCiGeTUqlk7doN3LjxMyNH+uHh4YlarWHJkvksXvwB9vb23Llzh7feeoOwsI/R6XTMnDkPa+sqmEwm5s2byaFD++jVqy8AUVGXCAvbgaOjE5MnBzB79vusWvUhlpaW+PkN4cyZ07Rs2bpAG8LDD1G9eg0cHZ3M244ejSAy8iS2tnb4+b1NkybPlbpvrVu3oUuXbigUCm7c+Jnx40ezd+9h8/6UlGRWr15PSkoyb745GA+P5hiNBs6cOc22bbtRKpXcvXsXyP/SZePGUIKDV2JlpSMm5hqBgePYs+cQANevX2PatBlMnvwemzaFsmlTKDNnzmPChMmsXr2C0NDNf9rWzz47TFxcHOvWbUSpVLJ3725WrVpurmPECF+++eYr9uzZyaBBb5ineo0fH2j+H/3hhyFs3bqJUaP8OXfuDJs3/4eQkI+ws7MnKysLlUrFhAlTGD7ch40btxX7/MXEXCUwcCrvvDMZgIUL59KsWXOmTp2O0Whk9uz3OXRoP6+99jpz5kynbdt2/PvfiwFIS0sr0TUoys6d2xkzJgC1Ws3Onds5fPgAjo5O2NjY0L17T/r1G0hERDj9+g0AHh4rt27dxNm5qrleJydnbt++VWy/S0KSSUIIIYR4JmRnZ6PT6Qps0+l0ZGXdK6JsFlZWugLlALKysszJpPw3h5O4cOFHzp8/Y15Pydq6Ch99FIara33u3k0nODiIOXPeJzh41ePqmhBCiHLUvXtPAGrVqkP9+g24ePFHVCoViYkJBAaOM5dTKBTEx8fi5taA7du3cPLkdxiNBjIyMrC0/G000XPPeZiTQm5uDXB2rmr+P+Tq6kZ8fGyBZNL582dZv34Ny5evNm/r1asPQ4f6oVariYw8ydSpE9m6dVexI1H+KD4+jlmz3iMpKQm1Wk1KSjLJyXews7Mv0HdbWzu8vNpz/vxZXnmlO0ajgYUL59K8eQu8vPLX8Dl16gTx8XGMGTPCXL/BYCAlJfnX56829es3BMDdvSnHjx8rVVu//fYbLl+OYtiwIb/WrTc/bxUqWDJnzkKGD/ehVas29O7dz3xcePhBIiLC0evvk52dQ82atQA4ceI43t6vmvv6V9bFqlGjZoEk3rfffkNU1EV27NgKQE5ODo6OTmRlZXHhwg8sW/bbNXyQ4CruGhQlMTEBFxdXYmKu8dlnhwkJ+Yjc3Bz8/Hzo1u1f1KvnwvnzZ4Gyi5XSkmSSEEIIIZ4JFStW5N69zALb7t27h1ZrVURZrXlaQn65/OP++EZSpVLh4dGMiIjD7N27m379BqDVamnYsDGQ/+b6nXcm07OnN/fuZRZIUAkhhPj7MZkAFJhM4OLixurV6wuVCQ8/xA8//I+QkPVotVaEhW0gNvaGef+DLycgf9SThUWF3z1WYTAYzI8vXPiBuXNnsGDBUmrVqmPe/vtEQ8uWbXB0dCIm5hqens8XaMumTaF8+eVRAMaNm0Dz5i0K7J816z3Gjn2Hjh1fwGg00rlze/Ly8h7SdxMKRf4XMJs37+T8+bOcPRvJmjUr2bBhCyaTidat2zJ9+pxCx/788/U/9FNZoJ8lYTKZGDp0mDnBVfgcMWi1VqSkJKPX61Gr1Xz//Xk+/fQT1qzZgI2NDRER4ezfv8dc36OqWPGPCSgT8+cvoXr1GgW2ZmVlPbSO0lyDBxQKBZA/0vr551ui1WrRarXmxFZKSjJ2dnbAn8eKk5MzN28m0qiRO1B4pNKjUJZJLUIIIYQQj1nNmrUxGAwF3rBfvfoTdevWK1S2bt16XL360+/KRWNra/fQb+kMBgPx8XFF7nvwhq4M3pMKIYR4Ch06tB+A2NgbXL16BXf3JjRp8hxxcTc4d+6MuVxU1EVMJhOZmRlYW1dBq7UiMzOTzz8P/0vnjYq6yIwZ7zJ3bhANGjQssC8p6bb59+joK9y8mVhgmvcDQ4f6sXHjNjZu3FYokQSQmZlpXmz54MF9hZIYR44cBCA1NZWTJ7/D07MFqamp5Obm0qaNFyNHjkWn05GQEE+rVm04deoEMTHXCvShOFZWukJfBhWlffuO7N272zytLi8vj+jo/P/lCQnxrFixlFWrPqR69ZqsX78GyL8zm5WVDmtra/Ly8szXEqBduw6Ehx8yj5zKysoiLy8PKysrcnJy0Ov1xbbpj9q168iWLZvMibK0tDQSEuLNiZ6dO3+bOvdgmltx16AoDxJCdevW4/z5s2RnZ5OamsqFCz+QlZXFtm1hdOmSfzfbP4uVF1/szIEDn2I0GklNTeXYsa/p1OmlUve7KDIySQghhBDPhIoVK9Kp04t89NFapk6dTnT0Fb799mvzoqC/5+39KvPnz6Zr11ews7Nn06ZQXnmlOwCpqSmcPRuJl1cHKlSowJkzp/nvfz9j5sx5QP6Cq5Uq6ahRoxYZGXdZvnwJnp7PF5piJ4QQ4tHl3M9j5/+teSz1lpSFhQWjRg0jLS2NSZOmYWNjC8DChcGsXr2CFSuWotffp1q16gQFLcPbuzvHjn3DkCH9cXBwwMPDk9zc3FK3cenSIPLyclm8eL552/Tpc3BxcWXdutVcuRKFUqlCo9EwffrsP50W9TDjxk1g2rRA7O0daNasOdbW1gX2Ozk5M3r0cJKT7+Dj44uLiytXrlwmKGgeBoMBg8FAmzZeuLs3RalUMmPGXBYunEtubi56/X2aNvUwj3p5GBcXV2rVqo2PT39q167z0AW4vb1fJT09DX///Gl0RqOR11/vR506dZk5811GjvSnZs1aTJw4lbfeeoNmzZrTpo0XERFHGDSoL46OjjRs2IhLl/ITXJ6ez+Pj40tAwGgUCiUWFhqCgpZha2tH166vMHToACpVqvzQBbiLMn78REJCPsDXdyAKhQKNxoJx4yZSrVp1ZsyYS3BwED4+/VEqVXTp0o0hQ3yLvQZF6d27P6tWLWfx4uV06eLNqFF+ODo64eXVgX37PsHXd7h5dNSfxUq3bv/i0qULDBjwOkCB4x6VwlQWY7/KWXJyJkbjM9+NMuXgUImkpIzyboZ4BpRVrDg4VOJ4zz6PVEe7fZ9I3D7l5LVFlNTjipW7d9NZsGAOkZGnqFzZmpEj/ena1ZubN2/i49OPzZt34ezsDMCOHVvYujWM3NxcXnjhJQID38XCwoLU1FSmT5/C1as/YTSacHZ2pm/fAbz2Wv4brc8/D+fDD0NITU3BysqKFi1aM3r0uL/0Jl6UjLy2iJKSWHn23bz5C87OhUfYPA5qtRK93vhEziWebU9jrOzevYMTJ75j1Ch/XF3dAIiLiyUy8hS9evUxj5wuK3/821QqFdjZPfyLNEkm/U3JP1pRUpJMEqUhry2ipCRWRGlIvIiSklh59kkySTyNntZYuXDhB3bu3E5s7C+oVGpq1qzFwIFDzAudl6XSJpNKNM3t+vXrTJ06lbS0NKpUqUJQUBB16tQpUMZgMDBv3jyOHTuGQqFgxIgR9OuXv8L6J598wsaN+bf3MxqN9OvXjzfeeAOAlStXsm3bNhwdHQFo3rw5M2fOLFWnhRBCCCGEEEII8fTz8/MptDi3u3sTJk2aVi7tmTLlHW7fvlVgbUQnJyeCgpaVS3t+r0mT5wrcTe5pUqJk0syZMxk0aBA9e/Zk3759zJgxg7CwsAJlDhw4wI0bN4iIiCAtLY1evXrRtm1batSoQbdu3ejduzcKhYLMzEx69OhBq1ataNgwP5vWq1cvpkyZUva9E0IIIcQzzaaSBWrLCsUX/BP6nFxSM0q+doYQQgghHp/Q0M3l3YQCgoKWPbUjk55mxSaTkpOTuXTpEv/5z38A6N69O3PnziUlJQVbW1tzucOHD9OvXz+USiW2trZ07tyZ8PBwhg8fXmDBypycHO7fv1/m8/uEEEII8fejtqxQJlNokWSSEEIIIUSZURZXIDExEScnJ1QqFQAqlQpHR0cSExMLlatWrZr5cdWqVbl586b58dGjR3n11Vd58cUXGT58OA0aNDDvO3ToED169GDYsGGcP3/+kTslhBBCCCGEEEIIIR6PEk1zKwsvv/wyL7/8MgkJCYwZM4aOHTtSr149BgwYwMiRI9FoNBw/fpzRo0dz+PBhbGxsSlz3ny0K9U/m4FCpvJsgnhFPU6w8TW0RRZNrJErqaYqVp6ktomhyjURJSaw8227fVqJWFzumocw8yXOJZ9s/PVaUSmWpXl+LTSZVrVqVW7duYTAYUKlUGAwGbt++TdWqVQuVS0hI4Lnn8heH+uNIpQeqVatG06ZN+eqrr6hXrx4ODg7mfe3ataNq1apER0fTqlWrEndC7uZWmNzpQpRUWd7NrSxI3D7d5LVFlJS8tojSkNcWUVISK88+o9H4xNamkXVwRElJrOT/bf7+9fWR7+ZmZ2dHo0aNOHjwID179uTgwYM0atSowHpJAN7e3uzatYuuXbuSlpbGf//7X7Zu3QrAtWvXcHFxASAlJYVTp07RtWtXAG7duoWTkxMAUVFRxMfHU7du3VJ2WwghhBBCCCHEs6YsbrRQFH1OLhnZ+mLLtW/fgoiIb9BqtWXehuKEhq5j797d2NvnD7Bo2tSDiRPzb0xlMBhYvnwJp059h0KhYMgQX3r06FWm5+/btweLFi2jXj3XMq33jxITEzh9+iQ9e/Z+rOcpqYyMDPbv38PgwUPLuykFrsHYsSMYONCHdu06mPdfvnyJnTu3Ex19BY1GQ+3adRk0yAc3t9+WDSqvOCrRNLdZs2YxdepUQkJCqFy5MkFBQQC89dZbjBs3jqZNm9KzZ0++//57c5JozJgx1KxZE4CPP/6Y48ePo1arMZlMDBkyhPbt2wMQHBzMxYsXUSqVaDQaFi1aVGC0khBCCCGEEEKIv6eyuNFCUdrt+wRKkEwqb97erzJ2bECh7RERR4iPj2XHjr2kp6czbNhgWrRoRdWqhWf/PO0SExPYv3/vU5NMyszMYNu2sFInkx7M1npS9u3bw9GjEfj5jaRJk6aoVCouX77EihVL6dOnPy++2NlctjziqETJJBcXF3bt2lVo+/r1682/q1QqZs+eXeTx06ZNe2jdDxJTQgghhBBCCCHEk7Z9+2YiI0+Rnp7G22+P4YUXXgbg4sULrF27knv37gEwfPhIvLzao9frmTw5gPT0dHJzc2nc2J1Jk6ah0Wg4fPgAn38ejk5XiWvXonFwcCQgYBIhISuIjY2lUaPGzJgxt9i7m3/xxef06NELpVKJjY0NHTp04ssv/8ugQW+Uqm8REeHs2rUdvf4+AGPGBNCiRasC+3/88Xvu3Emif/+B9OnzfxiNRoKDF3HuXCQajQVabUXWrNkAwIkT3xIWtoHc3Dw0Gg3+/hNo0qQp586d4YMPgmnc2J2LF38EFMyePZ86deoSHLyIxMR4fH0HUaNGDebNW/TQ9m7duomvvjqKwWDA3t6RKVPew87OngUL5qDT6fD3n0BKSjIjRviyYMES3NwaMHv2+9y48Qv37+dRvXpN3n13BpUrVwbg4MF97Nq1A+DXwSvLCA4OIjMzE1/fQVhaWrJ27YYi2/KgTx4ezYiKusTQoX40a+bJypXLuHYtmry8PDw9W+Dv/w4qlYqkpNssX76YuLhYADp37oaPz5vFXoOi/PTTZb7++guCg1ehVv+WtmnYsDFLl35AQMAYmjdvgbV1lT+tp6ziqChPbAFuIYQQQgghhBDiaaNUKlm7dgM3bvzMyJF+eHh4olZrWLJkPosXf4C9vT137tzhrbfeICzsY3Q6HTNnzsPaugomk4l582Zy6NA+evXqC0BU1CXCwnbg6OjE5MkBzJ79PqtWfYilpSV+fkM4c+Y0LVu2BuDo0QgiI09ia2uHn9/bNGmSvwbxrVs3cXb+bZ1iJydnbt++Veq+tW7dhi5duqFQKLhx42fGjx/N3r2HzftTUpJZvXo9KSnJvPnmYDw8mmM0Gjhz5jTbtu1GqVRy9+5dAOLj49i4MZTg4JVYWemIiblGYOA49uw5BMD169eYNm0Gkye/x6ZNoWzaFMrMmfOYMGEyq1evIDR085+29bPPDhMXF8e6dRtRKpXs3bubVauWm+sYMcKXb775ij17djJo0BvmqV7jxwdSpUp+UuXDD0PYunUTo0b5c+7cGTZv/g8hIR9hZ2dPVlYWKpWKCROmMHy4Dxs3biv2+YuJuUpg4FTeeWcyAAsXzqVZs+ZMnTodo9HI7Nnvc+jQfl577XXmzJlO27bt+Pe/FwOQlpZWomtQlJ07tzNmTABqtZqdO7dz+PABHB2dsLGxoXv3nvTrN5CIiHD69RsAPP44Kookk4QQQgghhBBC/GN1794TgFq16lC/fgMuXvwRlUpFYmICgYHjzOUUCgXx8bG4uTVg+/YtnDz5HUajgYyMDCwtLc3lnnvOA0fH/HWB3dwa4OxcFZ0ufyFjV1c34uNjadmyNb169WHoUD/UajWRkSeZOnUiW7fuKna0SWnEx8cxa9Z7JCUloVarSUlJJjn5DnZ29gX6bmtrh5dXe86fP8srr3THaDSwcOFcmjdvgZdX/ho+p06dID4+jjFjRpjrNxgMpKQk//r81aZ+/YYAuLs35fjxY6Vq67fffsPly1EMGzbk17r15uetQgVL5sxZyPDhPrRq1YbevfuZjwsPP0hERDh6/X2ys3OoWbMWACdOHMfb+1VzX//Kulg1atQ0J2YetDEq6iI7duSvD52Tk4OjoxNZWVlcuPADy5atNpd9kOAq7hoUJTExARcXV2JirvHZZ4cJCfmI3Nwc/Px86NbtX9Sr58L582cBnkgcFUWSSUIIIYQQQgghBGAyASgwmcDFxY3Vq9cXKhMefogffvgfISHr0WqtCAvbQGzsDfN+CwsL8+9KpRILiwq/e5x/h3SgQDKhZcs2ODo6ERNzDU/P53FycubmzUQaNXIHCo8weWDTplC+/PIoAOPGTaB58xYF9s+a9R5jx75Dx44vYDQa6dy5PXl5eQ/puwmFAnQ6HZs37+T8+bOcPRvJmjUr2bBhCyaTidat2zJ9+pxCx/788/U/9FNp7mdJmUwmhg4dZk5wFT5HDFqtFSkpyej1etRqNd9/f55PP/2ENWs2YGNjQ0REOPv37zHX96gqVvxjAsrE/PlLqF69RoGtWVlZD62jNNfggQfTIK9fj+H551ui1WrRarXmxFZKSjJ2dnZA2cTRX6Esk1qEEEIIIYQQQohn0KFD+wGIjb3B1atXcHdvQpMmzxEXd4Nz586Yy0VFXcRkMpGZmYG1dRW0WisyMzP5/PPwv3TepKTb5t+jo69w82YitWrVBuDFFztz4MCnGI1GUlNTOXbsazp1eqlQHUOH+rFx4zY2btxWKJEEkJmZaV5s+eDBfYWSGEeOHAQgNTWVkye/w9OzBampqeTm5tKmjRcjR45Fp9ORkBBPq1ZtOHXqBDEx1wo8J8WxstJx715mseXat+/I3r27zdPq8vLyiI7+CYCEhHhWrFjKqlUfUr16TdavXwPk35nNykqHtbU1eXl55msJ0K5dB8LDD5lHTmVlZZGXl4eVlRU5OTno9aVfoL1du45s2bLJnChLS0sjISHenOjZufO3qXMPprkVdw2K8iAhVLduPc6fP0t2djapqalcuPADWVlZbNsWRpcu3kDZxNFfISOThBBCCCGEEEKUC31Obv6d1x5DvSVlYWHBqFHDSEtLY9KkadjY2AKwcGEwq1evYMWKpej196lWrTpBQcvw9u7OsWPfMGRIfxwcHPDw8CQ3t+Tne2DdutVcuRKFUqlCo9Ewffps8yiTbt3+xaVLFxgw4HUAfH2HFxoNUxLjxk1g2rRA7O0daNasOdbW1gX2Ozk5M3r0cJKT7+Dj44uLiytXrlwmKGgeBoMBg8FAmzZeuLs3RalUMmPGXBYunEtubi56/X2aNvUwj3p5GBcXV2rVqo2PT39q167z0AW4vb1fJT09DX///Gl0RqOR11/vR506dZk5811GjvSnZs1aTJw4lbfeeoNmzZrTpo0XERFHGDSoL46OjjRs2IhLl/ITXJ6ez+Pj40tAwGgUCiUWFhqCgpZha2tH166vMHToACpVqvzQBbiLMn78REJCPsDXdyAKhQKNxoJx4yZSrVp1ZsyYS3BwED4+/VEqVXTp0o0hQ3yLvQZF6d27P6tWLWfx4uV06eLNqFF+ODo64eXVgX37PikQD08ijoqiMJXF2K9ylpycidH4zHejTDk4VCIpKaO8myGeAWUVKw4OlR75tq7t9n0icfuUk9cWUVLy2iJKQ15bRElJrDz7bt78BWfn2k/kXGq1Er3e+ETOJZ5tT2Os7N69gxMnvmPUKH9cXd0AiIuLJTLyFL169Sn2joCl9ce/TaVSgZ2d7qHlZWSSEEIIIYQQQgghxFOkb98BNGzY+Nc1uX5BpVJTs2YtBg4cUuaJpL9CkklCCCGEEEIIIYR4Ivz8fAotzu3u3oRJk6aVS3umTHmH27dv8fs5W05OTgQFLSuX9vxekybPFbib3NNEkklCCCGEEEIIIYR4IkJDN5d3EwoIClr2VE5ze9rJ3dyEEEIIIYQQQjwxf4Nle4X4W/krf5OSTBJCCCGEEEII8UQolSoMhtLfkl0I8fjcv5+HSlW6iWuSTBJCCCGEEEII8URUrKgjIyMNk0mmFAlR3kwmE3l5uaSlJaHTVSnVsbJmkhBCCCGEEEKIJ0KnsyY1NYlbt+KAxzvdTalUYjRK0koU758cKyqVmkqVbKhY0apUx0kySQghhBBCCCHEE6FQKLC1dXwi53JwqERSUsYTOZd4tkmslJ5McxNCCCGEEEIIIYQQJSbJJCGEEEIIIYQQQghRYpJMEkIIIYQQQgghhBAlJmsmCSGEEEKIv6W7d9NZsGAukZEnsbauwttvj6VrV+8iy3788Va2bg0jNzeHTp1eIjDwXSwsLACYM2c6Z8+eJjs7B1tbOwYPfoMePXoBcOHCj3z00RquXLmMSqWkWbPnCQiYhL29/RPrpxBCCPGkycgkIYQQ5ebu3XTefTeQzp3b06dPdyIiwh9a9uOPt/Laa93o1q0T8+fPJi8vz7xvzpzp9OzZja5dOzFgQG8OHPjUvO/ChR8JCBjNK6+8RPfunXn//SncuXPnsfZLCPF0WLo0CI1Gw/79EcyYMY+lSxcQE3OtULlTp06wZcsmli8PYdeuAyQkxBMaus68f8gQX3btOkBExNcEBQWzfv0aLl+OAiAj4y6vvdab3bv3s3v3QbRaLfPnz35ifRRCCCHKgySThBBClBv5oCeEeFyys7P5+usvGD58JFqtFg+PZrRv35HPPjtcqOyRIwfp3r0n9eq5ULlyZXx9h3PkyEHz/nr1XMyjlBSK/J/4+DgA2rZtx0svdcbKSoelpSV9+vwfP/74/ZPppBBCCFFOJJkkhBCiXMgHPSHE4xQb+wtKpYpatWqbt7m41Of69ZhCZa9fj8HV1c382NW1PikpyaSnp5m3LVmykJdfbsegQX2xs7Onbdt2RZ73++/PUbduvTLsiRBCCPH0kWSSEEKIciEf9IQQj1N2djY6na7ANp1OR1bWvSLKZmFlpStQDiArK8u8LTBwKhER37B69Ud07PiiOYH9e1evRvOf/3zEmDHjy6obQgghxFNJkklCCCHKhXzQE0I8ThUrVuTevcwC2+7du4dWa1VEWS337t37Xbn847RabYFyKpUKD49mJCXdZu/e3QX2xcXFEhg4jvHjJ+Lh4VlW3RBCCCGeSpJMEkL8P3v3Hh9leed9/DvHZIbJJEAOEyCBJBw9AD2KIkaBkChkoSrCUui2SgW0prXxAHWLoLZZ3Gaf6laxigdO+5RStspRwLWywFqkW5GnokBMgEgy4RCSkMlkkszM8wd1YJwIAySQwOf9evVV7uv+Xfd9/V65HTI/ruu6gcuCL3oA2lNaWm/5/X6Vlx8KtZWU7Gt1ZmJGRqZKSvadEbdf3bp1V3x8QqvX9vv9oaW0kuR2V+onP3lA3//+fcrLG9uGWQAA0DFRTAIAXBZ80QPQnmw2m7Kzb9OiRS/J6/Vq9+5d2rZti3Jz74iIzcsbq3XrVqusrFR1dXVavPhV3X77OEnSiRPVeuedjWpoaJDf79eOHe/rnXc26hvf+KYk6ejRIyoomKk775yoCRPuvqQ5AgBwuVBMAgBcFnzRA9DeCgtnq6nJp/z8HM2b94QKC+coMzNLbrdbOTkj5Ha7JUnDht2kKVOmqaBgpu6+O18uV6ruu2/G369i0JtvrtKdd96h228fqRde+LUKCgo1YsStkqQ1a95URcVhvf76K8rJGRH6HwAAVzJDMBgMXu5BXKzjx+sVCHT6NNpUUlKcjh49ebmHgU6grZ6VpKQ4bR9/10VdY/hbq3huO7i2/mypq6tVUdFT2rlzh5zOeM2c+ZDGjMmT2+3WpTt8VAAAIABJREFUtGkTtXTpSrlcLknS7363TMuXL5HP59Ott47UI4/MkdVq1YkTJ/Tznz+ukpJ9CgSCcrlcuvvuyfqHf/iOJOm1117Wa6+9LJvNFnbvzZu3tlkeiMRnC84Hv7cgWjwrOB88L4gWz0oko9Gg7t0dX3meYtIViv8YEC2+8OF88NmCaPHZgvPBZwuixbOC88HzgmjxrEQ6VzHJfAnHAgAAALSLrnFWmWNjLvo6LY0+nTjZ1AYjAgDgykUxCQBwSfGFD0B7MMfGXPQsNunUTDbx2QIAwFlRTAIAXFJ84QMAAAA6N97mBgAAAAAAgKhRTAIAAAAAAEDUKCYBAAAAAAAgahSTAAAAAAAAEDWKSQAAAAAAAIgaxSQAAAAAAABEjWISAAAAAAAAokYxCQAAAAAAAFGjmAQAAAAAAICoUUwCAAAAAABA1CgmAQAAAAAAIGoUkwAAAAAAABA1ikkAAAAAAACIGsUkAAAAAAAARI1iEgAAAAAAAKJGMQkAAAAAAABRo5gEAAAAAACAqFFMAgAAAAAAQNQoJgEAAAAAACBqFJMAAAAAAAAQNYpJAAAAAAAAiFpUxaSysjJNmjRJubm5mjRpkg4cOBAR4/f7NX/+fI0ePVo5OTlauXJl6NyqVauUn5+v8ePHKz8/X0uWLImqHwAAAAAAADoWczRBTz75pKZMmaLx48frrbfe0ty5c8MKQpK0Zs0aHTp0SJs2bVJNTY0mTJigG2+8Ub169VJubq7uvPNOGQwG1dfXKz8/X9/+9rc1cODAs/YDAAAAAABAx3LOmUnHjx/Xnj17NG7cOEnSuHHjtGfPHlVXV4fFrV+/XhMnTpTRaFS3bt00evRovf3225Ikh8Mhg8EgSWpsbFRzc3Po+Gz9AAAAAOBSqKur1Zw5j2j06Jt1113jtGnTV38nWbFiuf7hH3KVm5utX/5yvpqamiRJTU1NKip6SnfdNU45ObfoBz+Yovff3x7Wd82aNzVp0gTl5IzQT3/6kI4dO9queQFAezhnMamyslIpKSkymUySJJPJpOTkZFVWVkbE9ejRI3Scmpoqt9sdOv6v//ovjR07VrfddpumT5+uAQMGRNUPAAAAANpbcfECWSwWrV69SXPnPqPi4iKVln4WEbdjx/tatmyxfv3rF7Vy5RpVVBzWq6/+VtKpLTySk1P0m9+8rI0b39P06bM0d+4cVVZWSJI+/PB/9dvfvqCiomKtX/+uevTooXnznrikeQJAW4hqmVtbGDVqlEaNGqWKigo9+OCDuuWWW5SZmdkm1+7e3dEm17nSJCXFXe4hoJPoSM9KRxoLWteRfkYdaSyI1JF+Ph1pLGhdR/oZdaSxIFJ7/HwaGhr03//9J61Zs0a9e6eod+8UjRo1Slu3vqMbbhgaFvunP23UPfdMDLX/5CcFeuSRRzR37s8kxWn27EdCsRMm3KFXX12oysoDGjx4gD78cIfuuOP2UN+f/vTHuuWWW+T1nlB6enqb5wX+e0b0eFbOzzmLSampqaqqqpLf75fJZJLf79eRI0eUmpoaEVdRUaHBgwdLipxx9IUePXro+uuv13vvvafMzMyo+53N8eP1CgSC59XnSpeUFKejR09e7mGgE2irZ6WtPnx5bju2tnhe2vIvap6XjovPFpwPPlsQrfb6HXffvk9lMBjlcCSGrt+rV4Z27fprxP0++WSvvvWtm0LtiYm9dOzYMZWUlCs+PiEstrr6uA4cOKDu3Xvo6NGT8nh88vmaQn2PH6+XJP3lLx/JZuva5nld7fhOhGjxrEQyGg1nnbhzzmVu3bt316BBg7R27VpJ0tq1azVo0CB169YtLC4vL08rV65UIBBQdXW13nnnHeXm5kqSPvvs9PTQ6upq7dixQ/379z9nPwAAAABob16vVw5H+Jcmh8OhhgZPK7EN6tLFERYnnZrddKaWlhbNn/9z5eWNVe/efSRJN944XH/602aVlOyXz9eo119/RQaDQY2NjW2cEQC0r6iWuc2bN0+zZ8/Wiy++KKfTqQULFkiSfvjDH6qgoEDXX3+9xo8fr48++khjxoyRJD344INKS0uTJK1YsULbt2+X2WxWMBjU1KlTdfPNN0vSWfsBAAAAQHuz2WzyeOrD2jwej+z2Lq3E2uXxeM6IO9XPbreH2gKBgJ5++ueyWMz66U8fD7V/85vf1r33ztA///Njqq+v16RJU2S325WcnNLWKQFAu4qqmJSVlaWVK1dGtL/yyiuhP5tMJs2fP7/V/j/72c++8tpn6wcAAAAA7S0trbf8fr/Kyw8pLe3U3kUlJfuUkRG5x2tGRqZKSvZp1Kicv8ftV7du3UNL3ILBoP7lX55WdXW1fvWr52Q2h3/luuuue3TXXfdIkg4dOqjFi19VRkZWe6YHAG3unMvcAAAAAOBKZrPZlJ19mxYtekler1e7d+/Stm1blJt7R0RsXt5YrVu3WmVlpaqrq9Pixa/q9tvHhc7/6ldFOnCgTAsW/B/FxMSG9fX5fCotLVEwGJTb7dazz/5CEyf+o5xOZ7vnCABt6ZK9zQ0AAAAAOqrCwtkqKnpK+fk5cjrjVVg4R5mZWXK73Zo2baKWLl0pl8ulYcNu0pQp01RQMFM+n0+33jpS9903Q5Lkdlfqrbf+U1arVePHn94H9tFHf6YxY25XU1OT5s//Zx0+/Lns9i664458TZ8+83KlDAAXjGISAAAAgKue0xmvoqLiiHaXy6XNm7eGtU2ePFWTJ09tJTZV27b95SvvERcXp8WLf3fxgwWAy4xiEgAAAICrStc4q8yxMRd1jZZGn06cbGqjEQFA50IxCQAAAMBVxRwbo+3j77qoawx/a5VEMQnAVYoNuAEAAAAAABA1ikkAAAAAAACIGsUkAAAAAAAARI1iEgAAAAAAAKLGBtwAQurqalVU9LR27vyz4uMTNGPGjzRmTF6rsStWLNfy5Uvk8zUqO3ukFiz4pSSpORDQ0qoKfeLxqD7gV4rFqjuTUjTYERfq+0Fdrd46dkTVLc3qZrborqQUfT3OeUlyBAAAAABcHGYmAQgpLl4gi8Wi1as3ae7cZ1RcXKTS0s8i4nbseF/Lli3Wr3/9olauXKOKisN6/vnnJUkBSd3MFj2enqEX+g3ShKRkLawo17GmU287OdHcrFcqPtekZJde7DdI9yS79HJFuepaWi5lqgAAAACAC0QxCYAkyev1asuWdzV9+kzZ7XYNGTJUN998izZuXB8Ru2HDWo0bN16ZmVlyOp36/ven649//KMkKcZo1ISkFCVarTIaDBrqcCrJYtGBRq8kqbqlWXaTUYMdcTIYDBriiJPVaNSRZl6tCwAAAACdAcUkAJKk8vKDMhpNSk/vHWrLyuqvsrLSiNiyslL17dsvdNy3b38dO3ZM9f7I2UW1LS1yNzWpZ0ysJCkj1qZUa4w+PFmnQDCov56sk8VgUNrfzwMAAAAAOjb2TAIg6dTMJIfDEdbmcDjU0OBpJbZBXbo4wuIkqdEfkMN0Oq4lGNTLFeUaHp+g1JgYSZLRYNBN8Ql6ueJzNQcDMhsMmtUzXTFGatsAAAAA0BlQTAIgSbLZbPJ46sPaPB6P7PYurcTa5fF4zog71S/WdLogFAgGtajic5kNBn03pUeo/WNPvVYeqdJj6RnqHRurg41ePf/5IT2c1lvD2zopAAAAAECbYyoAAElSWlpv+f1+lZcfCrWVlOxTRkZmRGxGRqZKSvadEbdfiYmJcphO1aeDwaBedx9Wnb9FD/ZMl9lgCMWWNzaqv92uDJtNRoNBGTa7Mm027fFEzoACAAAAAHQ8FJMASDo1Myk7+zYtWvSSvF6vdu/epW3btig3946I2Ly8sVq3brXKykpVV1enxYtf1Xe+853Q+aVVFar0+VTQK13WLy1fy7DZtN/boEN/35D7YKNX+xoa1Cs2pn0TBAAAAAC0CZa5AQgpLJytoqKnlJ+fI6czXoWFc5SZmSW3261p0yZq6dKVcrlcGjbsJk2ZMk0FBTPl8/l0660jVVBQoJ1b39ex5ia9V3NCZoNBD+/fG7r291w9dGN8ggbYu2h8YrJePFyuWn+L4kwmje2epOu6xF3GzAEAAAAA0aKYBCDE6YxXUVFxRLvL5dLmzVvD2iZPnqrJk6eGjq1WqyQp0WLVawOvO+t9RnXtrlFdu7fBiAF0FnV1tSoqelo7d/5Z8fEJmjHjRxozJq/V2BUrlmv58iXy+RqVm5ur3EBAFqNRzYGAllZV6BOPR/UBv1IsVt2ZlKLBjlPF6Pdra7TEXRG6TlBBNQWDWvW3vyklpXer9wIAAMD5o5gEAADaXXHxAlksFq1evUn79+/TY4/9WH379lNmZlZY3I4d72vZssV67rmFSkxM0rx5s/XmsSOamOxSQFI3s0WPp2eom8Wi3Z6TWlhRrqf79FWi1aob4xN0Y3xC6Frbak5ozfEjuvbaa3XsWL0AAADQNigmAVe5Jn+zkpJYYgag/Xi9Xm3Z8q6WLFkhu92uIUOG6uabb9HGjes1a9ZDYbEbNqzVuHHjQ0WmBx54QAX3TdfEZJdijEZNSEoJxQ51OJVksehAo1eJf58deab/qavRTfFdZTjjJQAAAAC4eBSTgKuc1WTRPStmXfR1fj9pYRuMBsCVqLz8oIxGk9LTTy81y8rqr127/hoRW1ZWqhEjskPHAwYMUJ2/RfX+ltAbI79Q29Iid1OTesbERlznWHOT9jZ49ANXzzbMBAAAABJvcwMAAO3M6/XK4XCEtTkcDjU0eFqJbVCXLqdj4+JOzZxs9AfC4lqCQb1cUa7h8QlKjYl8G+T/1Naov82upFZmLAEAAODiUEwCAADtymazyeMJ37PI4/HIbu/SSqxdHs/pIlN9/al+sabTv7IEgkEtqvhcZoNB303p0eo9/6f21BI3AAAAtD2KSQAAoF2lpfWW3+9XefmhUFtJyT5lZGRGxGZkZKqkZF/o+NNPP5XTZA4tcQsGg3rdfVh1/hY92DNd5lb2Q9rf4FFNS7O+Gedsh2wAAABAMQkAALQrm82m7OzbtGjRS/J6vdq9e5e2bdui3Nw7ImLz8sZq3brVKisrVV1dnRYuXKjhZ7yhbWlVhSp9PhX0SpfV2PqvMdtra/SNuHjZTKZ2ywkAAOBqRjEJAAC0u8LC2Wpq8ik/P0fz5j2hwsI5yszMktvtVk7OCLndbknSsGE3acqUaSoomKm7785Xz549NSExWdKpTbXfqzmhQ75GPbx/r2bt3aNZe/fo/dqa0H2aAwHtPFkbVoACAABA2+JtbgAAoN05nfEqKiqOaHe5XNq8eWtY2+TJUzV58lRJUlJSnLaPv0uSlGix6rWB1531PhajUS/0v6aNRg0AAIDWMDMJAAAAAAAAUWNmEgAAaHNN/mYlJcVd7mEAAACgHVBMAgAAbc5qsuieFbMu+jq/n7SwDUYDAABw+dTV1aqo6Gnt3PlnxccnaMaMH2nMmLxWY1esWK7ly5fI52tUdvZIPfLIHFmtVjU1Nam4+F/0l798oLq6OvXq1Uv33/+gbrxxeKhvY2OjfvObX+tPf9qslpYW9e3bXy+88Eq75EQxCQAAAAAAoJ0UFy+QxWLR6tWbtH//Pj322I/Vt28/ZWZmhcXt2PG+li1brOeeW6jExCT97GeP6NVXf6tZsx6S3+9XcnKKfvObl5WS4tL772/X3LlztGTJ75Sa2kOS9Oyzv5Df36Jly/4gp9Op/fv3tVtO7JkEAAAAAADQDrxer7ZseVfTp8+U3W7XkCFDdfPNt2jjxvURsRs2rNW4ceOVmZklp9Op739/ujZsWCtJstlsuu++GUpN7SGj0ajhw0eoR48e2rv3E0nSoUMHtG3bf+uxx55Q165dZTKZNHDgoHbLi5lJAIALcqHTdXNzc5UbCMhiNKo5ENDSqgp94vGoPuBXisWqO5NSNNhxeq8dXyCg3x9xa+fJWvmDQaXFxGp278xLlSYAAABwwcrLD8poNCk9vXeoLSurv3bt+mtEbFlZqUaMyA4d9+3bX9XVx1VbW6P4+ISw2Orq4yovP6SMjFOzmz7++G9yuVx69dXfauPG9erePVH33nu/br11VLvkRTEJAHBBLnS67rx5s/XmsSOamOxSQFI3s0WPp2eom8Wi3Z6TWlhRrqf79FWi1SpJWuw+rEBQeiajnxwmkw75Gi9DtgAAAMD583q9cjgcYW0Oh0MNDZ5WYhvUpYsjLE6SGhoawopJLS0tmj//58rLG6vevftIko4ePaLS0s+UnT1Sb775tv72t9167LGfqE+fTPXpk9HmebHMDQBw3i5muu4DDzyg7bU1kqQYo1ETklKUaLXKaDBoqMOpJItFBxq9kqRKn0+76k/qn1w95DSbZTQY1CfWdklzBQAAAC6UzWaTx1Mf1ubxeGS3d2kl1i6Px3NG3Kl+drs91BYIBPT00z+XxWLWT3/6eKg9JiZGZrNZ//RP98lisehrX/uGvva1b+qDD/7c1ilJopgEALgAXzVdt6ysNCK2rKxUffv2Cx0PGDBAdf4W1ftbImJrW1rkbmpSz5hYSVJpY4O6Wyx689gRFez/RD8v26+/1NW2Q0YAAABA20tL6y2/36/y8kOhtpKSfcrIiNy2ISMjUyUl+86I269u3bqHZiUFg0H9y788rerqav3iF8/KbD692Cwrq1/E9doTxSQAwHm7mOm6cXGn9kNq9AfC4lqCQb1cUa7h8QlKjYmRJJ1obtZhn082o1H/1neApqb00KuVh1XBUjcAAAB0AjabTdnZt2nRopfk9Xq1e/cubdu2Rbm5d0TE5uWN1bp1q1VWVqq6ujotXvyqbr99XOj8r35VpAMHyrRgwf9RzN//8fULQ4d+XSkpLi1b9oZaWlq0e/cuffjh/+qGG25sl7woJgEAztvFTNetrz/VL9Z0+q+gQDCoRRWfy2ww6LspPULtVqNRJhmUn5gss8GoAfYuGmjvoo+/dG8AAACgoyosnK2mJp/y83M0b94TKiyco8zMLLndbuXkjJDb7ZYkDRt2k6ZMmaaCgpm6++58uVypuu++GZIkt7tSb731nyop2afx43OVkzNCOTkjtGnTBkmS2WxWUVGx3n9/u/LybtWzz/5C//zP80N7KrU1NuAGrgAX+lat7OyRWrDgl5KkQEtAh9fu1cnPquX3tsjazabU0Vly9u8e6hto8qtiY4lqPj6ioD8gm8uhvvd945LkiI7lzOm6aWnpks49XXfUqBxJ0qeffiqnySyH6dRfQcFgUK+7D6vO36Kf9Oots8EQ6tvrS//iAgAAAHQ2Tme8ioqKI9pdLpc2b94a1jZ58lRNnjy1ldhUbdv2l7PeJzMzS7/97esXN9goMTMJuAKc+VatuXOfUXFxkUpLP4uI++KtWr/+9YtauXKNKioO6/nnn5ckBQNBWZwx6nvv13Xdz25R6qhMHfz939R0whvqX776U/m9zRr40A26bs4t6nH7pV2Xi47jYqbrLly4UMPPeBvF0qoKVfp8KuiVLqsx/K+l/vYu6m6xaN3xo/IHg9rf4NGnXo+u6xLX7jkCAAAAaB0zk4BO7ou3ai1ZsiLirVqzZj0UFnvmW7Uk6fvfn65nnpmrngWDZbKa5Bp5elaJc0CirF1j1VBxUtauNjUe86hu7zFdUzhcpthTHx32Hs5Llyg6nMLC2Soqekr5+TlyOuPDputOmzZRS5eulMvlCpuu6/P5lJeXq9wTJyVJx5qb9F7NCZkNBj28f2/o2t9z9dCN8QkyGwx6qFe63qg8rPXHj6q7xarpqb1CeyoBAAAAHUXXOKvMsRf3e2pLo08nTja10YjaD8UkoJP7qrdq7dr114jYsrJSjRiRHTru27e/jh07ppSGZpntlrDY5vom+Y57FZt8ag+chs/rZI2PlftPZTrxkVsWh1Upt2Uo4drkdsoMHd2FTtdNSorT9vF3SZISLVa9NvC6s96nZ0ysnuiT1U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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"######### Exhaustive\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2seq_df.at[index_label , 'exp_name'], one2seq_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = valid_one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"######### Self-terminating\\n\",\n    \"\\n\",\n    \"one2seq_exps = ['kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"\\n\",\n    \"# prepare one2seq data\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'].isin(one2seq_exps)]\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.step % 5000 == 0] # keep % 10000 and 5000\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by='step', ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'selfterminating'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = '%s-beam%s' % (one2seq_df.at[index_label , 'exp_name'], one2seq_df.at[index_label , 'beam_width'])\\n\",\n    \"\\n\",\n    \"_, peak_one2seq_df, valid_one2seq_df = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@10')\\n\",\n    \"\\n\",\n    \"# ensure the dataframe is sorted first\\n\",\n    \"# print(peak_one2seq_df.shape)\\n\",\n    \"# display(peak_one2seq_df)\\n\",\n    \"\\n\",\n    \"# metric_names = ['present_exact_f_score@5', 'present_exact_f_score@10', 'present_exact_f_score@k', 'present_exact_f_score@M']\\n\",\n    \"metric_names = ['absent_exact_recall@10', 'absent_exact_recall@50']\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = valid_one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'beam%s - %s' % (beam_width, metric_name): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values['beam%s - %s' % (row_series.beam_width, metric_name)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    bar_values[k].append(np.mean(_v))\\n\",\n    \"datasets = np.append(datasets, 'Average')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame(bar_values, index=datasets)\\n\",\n    \"ax = df.plot.bar(figsize=(20,9), rot=0)\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2One vs. One2Seq Present\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### RNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:04.426190Z\",\n     \"start_time\": \"2020-11-26T07:22:57.296897Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.319557</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>0.326624</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0.326885</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>64</td>\\n\",\n       \"      <td>0.327016</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>0.327054</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0     8  0.319557   RNN\\n\",\n       \"1    16  0.326624   RNN\\n\",\n       \"2    32  0.326885   RNN\\n\",\n       \"3    64  0.327016   RNN\\n\",\n       \"4   200  0.327054   RNN\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'F@O': [0.26723133263019827, 0.31357115175153655, 0.31867138526915223, 0.32248722461089524], 'model': ['RNN', 'RNN', 'RNN', 'RNN']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.267231</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.313571</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0.318671</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0.322487</td>\\n\",\n       \"      <td>RNN</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0     1  0.267231   RNN\\n\",\n       \"1    10  0.313571   RNN\\n\",\n       \"2    25  0.318671   RNN\\n\",\n       \"3    50  0.322487   RNN\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 288x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"rnn_one2one_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"# '''\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"rnn_one2seq_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=rnn_one2one_present_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=rnn_one2seq_present_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:14.189804Z\",\n     \"start_time\": \"2020-11-26T07:23:08.188905Z\"\n    },\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.304694</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>0.312574</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0.312238</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>64</td>\\n\",\n       \"      <td>0.312586</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>0.312619</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0     8  0.304694    TF\\n\",\n       \"1    16  0.312574    TF\\n\",\n       \"2    32  0.312238    TF\\n\",\n       \"3    64  0.312586    TF\\n\",\n       \"4   200  0.312619    TF\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:51: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1295, 121)\\n\",\n      \"{'Beam': [1, 10, 25, 50], 'F@O': [0.2746217920232182, 0.3059079509812458, 0.328056885778184, 0.32964581915149765], 'model': ['TF', 'TF', 'TF', 'TF']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Beam</th>\\n\",\n       \"      <th>F@O</th>\\n\",\n       \"      <th>model</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.274622</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>10</td>\\n\",\n       \"      <td>0.305908</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0.328057</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0.329646</td>\\n\",\n       \"      <td>TF</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Beam       F@O model\\n\",\n       \"0     1  0.274622    TF\\n\",\n       \"1    10  0.305908    TF\\n\",\n       \"2    25  0.328057    TF\\n\",\n       \"3    50  0.329646    TF\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"metric_names = ['present_exact_f_score@k']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"tf_one2one_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"# one2seq_df = one2seq_df.loc[(one2seq_df.step >= 100000) | (one2seq_df.beam_width == 50) | (one2seq_df.beam_width == 1)] # a workaround, some kp20k results are missing\\n\",\n    \"\\n\",\n    \"print(one2seq_df.shape)\\n\",\n    \"# a workaround, some kp20k results are missing\\n\",\n    \"# beam_step_TBR = []\\n\",\n    \"# for beam_width in one2seq_df['beam_width'].unique():\\n\",\n    \"# #     print('beam_width=', beam_width)\\n\",\n    \"#     beam_df = one2seq_df.loc[one2seq_df.beam_width == beam_width]\\n\",\n    \"#     for step in one2seq_df['step'].unique():\\n\",\n    \"#         step_df = one2seq_df.loc[one2seq_df.step == step]\\n\",\n    \"#         print('beam=%d, step=%d, #(dataset)=%d, step_df.shape=%s'\\n\",\n    \"#               % (beam_width, step, len(step_df.test_dataset.unique()), step_df.shape))\\n\",\n    \"        \\n\",\n    \"#         if len(step_df.test_dataset.unique()) < 7:\\n\",\n    \"#             print('remove! ', (beam_width, step, step_df.shape[0]))\\n\",\n    \"#             beam_step_TBR.append((beam_width, step, step_df.shape[0]))\\n\",\n    \"\\n\",\n    \"# # some rows cannot be detected by df_TBR, don't know why!\\n\",\n    \"# for beam_width, step, num_dp in beam_step_TBR:\\n\",\n    \"#     df_TBR = one2seq_df.loc[(one2seq_df['beam_width'] == beam_width) & (one2seq_df['step'] == step)]\\n\",\n    \"#     print('Removing: ', (beam_width, step, step_df.shape[0]), ' \\\\t Found:', df_TBR.shape[0])\\n\",\n    \"#     one2seq_df.drop(df_TBR.index, inplace = True) \\n\",\n    \"#     print(one2seq_df.shape)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='present_exact_f_score@k') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'present_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'F@O': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['F@O'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"display(df)\\n\",\n    \"tf_one2seq_present_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=tf_one2one_present_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"F@O\\\", data=tf_one2seq_present_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### One2One vs. One2Seq Absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### RNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:19.860753Z\",\n     \"start_time\": \"2020-11-26T07:23:14.191146Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:53: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'R@50': [0.002497695838612458, 0.01161554165530662, 0.019129676534856244, 0.024819682898928285], 'model': ['RNN', 'RNN', 'RNN', 'RNN']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"rnn_one2one_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('RNN')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"rnn_one2seq_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=rnn_one2one_absent_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=rnn_one2seq_absent_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:25.846007Z\",\n     \"start_time\": \"2020-11-26T07:23:19.862179Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'Beam': [1, 10, 25, 50], 'R@50': [0.01326445780812537, 0.06125211421026306, 0.08131404633692148, 0.10118755119067635], 'model': ['TF', 'TF', 'TF', 'TF']}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0, 0.5, '')\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot One2One \\n\",\n    \"one2one_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-one2one-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2one_df = one2one_df.loc[(one2one_df.step % 10000 == 6000) | (one2one_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"one2one_df['beam_width'] = one2one_df['beam_width'].astype(int)\\n\",\n    \"one2one_df = one2one_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2one_df.iterrows():\\n\",\n    \"    one2one_df.at[index_label , 'exp_name'] = 'One2One-beam%s' % one2one_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2one_df, _ = brief_eval_results(one2one_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"metric_names = ['absent_exact_recall@50']\\n\",\n    \"\\n\",\n    \"datasets = valid_one2one_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2one_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {beam_width: [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2one_df.iterrows():   \\n\",\n    \"    for metric_name in metric_names:\\n\",\n    \"        bar_value = row_series[metric_name]\\n\",\n    \"        bar_values[row_series.beam_width].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(5,6))\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"'''\\n\",\n    \"import copy\\n\",\n    \"tf_one2one_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot One2Seq\\n\",\n    \"one2seq_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kpgen-meng17-kp20k-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue']\\n\",\n    \"one2seq_df['beam_width'] = one2seq_df['beam_width'].astype(int)\\n\",\n    \"one2seq_df = one2seq_df.sort_values(by=['beam_width', 'step'], ascending=True)\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"one2seq_df = one2seq_df.loc[one2seq_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"\\n\",\n    \"for index_label, row_series in one2seq_df.iterrows():\\n\",\n    \"    one2seq_df.at[index_label , 'exp_name'] = 'One2Seq-beam%s' % one2seq_df.at[index_label , 'beam_width']\\n\",\n    \"\\n\",\n    \"_, _, valid_one2seq_df, _ = brief_eval_results(one2seq_df, base_metric='absent_exact_recall@50') \\n\",\n    \"\\n\",\n    \"# display(valid_one2seq_df)\\n\",\n    \"metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"datasets = valid_one2seq_df.test_dataset.unique()\\n\",\n    \"beam_widths = one2seq_df.beam_width.unique()\\n\",\n    \"\\n\",\n    \"bar_values = {'%s' % (beam_width): [] for beam_width in beam_widths for metric_name in metric_names}\\n\",\n    \"\\n\",\n    \"for index_label, row_series in valid_one2seq_df.iterrows():   \\n\",\n    \"    bar_value = row_series[metric_name]\\n\",\n    \"    bar_values['%s' % (row_series.beam_width)].append(float(bar_value))\\n\",\n    \"\\n\",\n    \"# print(bar_values)\\n\",\n    \"avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"for k, v in bar_values.items():\\n\",\n    \"    _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"    avg_bar_values['Beam'].append(int(k))\\n\",\n    \"    avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"    avg_bar_values['model'].append('TF')\\n\",\n    \"\\n\",\n    \"print(avg_bar_values)\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(avg_bar_values)\\n\",\n    \"# display(df)\\n\",\n    \"tf_one2seq_absent_df = copy.copy(df)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"plt.figure(figsize=(4,6))\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"ax = sns.barplot(x=\\\"Beam\\\", y=\\\"F@10\\\", data=df)\\n\",\n    \"ax.set_ylim(0, 0.3)\\n\",\n    \"ax.set_yticklabels([])\\n\",\n    \"ax.set_ylabel('')\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.3f' % (p.get_height()), (p.get_x() + 0.15, p.get_height() * 1.005))   \\n\",\n    \"# '''\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 14,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 3), sharey=True)\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=tf_one2one_absent_df, ax=axes[0])\\n\",\n    \"# df1.plot.bar(ax=axes[0], legend=False, rot=0)\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))   \\n\",\n    \"axes[0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"\\n\",\n    \"# df2.plot.bar(ax=axes[1], rot=0)\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"sns.barplot(x=\\\"Beam\\\", y=\\\"R@50\\\", data=tf_one2seq_absent_df, ax=axes[1])\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.3f' % (p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))    \\n\",\n    \"axes[1].set_xlabel(\\\"One2Seq\\\") \\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Combine all (used in paper)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-26T07:23:26.640766Z\",\n     \"start_time\": \"2020-11-26T07:23:25.847352Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x432 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 18,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 16,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 16,\\n\",\n    \"                             \\\"legend.fontsize\\\": 16})\\n\",\n    \"\\n\",\n    \"sns.set_palette(\\\"tab10\\\", n_colors=10)\\n\",\n    \"\\n\",\n    \"f, axes = plt.subplots(2, 2, figsize=(10, 6), sharex=False, sharey=False)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"model_names = ['RNN', 'TF']\\n\",\n    \"one2one_beams = [8, 16, 32, 64, 200]\\n\",\n    \"one2seq_beams = [1, 10, 25, 50]\\n\",\n    \"\\n\",\n    \"# one2one_present\\n\",\n    \"concat_df = pd.concat([rnn_one2one_present_df, tf_one2one_present_df], ignore_index=True, sort=False)\\n\",\n    \"# display(one2one_present_df)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2one_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2one_beams.index(row_series.Beam)] = float(row_series['F@O'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2one_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"# display(one2one_present_df)\\n\",\n    \"g = concat_df.plot.bar(ax=axes[0][0], legend=True, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[0][0].patches:\\n\",\n    \"    axes[0][0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.005), rotation=0)   \\n\",\n    \"axes[0][0].set_ylabel(\\\"Present (F@O)\\\")\\n\",\n    \"axes[0][0].legend(loc='lower left')\\n\",\n    \"g.set_ylim(25, 35)\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# one2seq_present\\n\",\n    \"concat_df = pd.concat([rnn_one2seq_present_df, tf_one2seq_present_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2seq_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2seq_beams.index(row_series.Beam)] = float(row_series['F@O'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2seq_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[0][1], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[0][1].patches:\\n\",\n    \"    axes[0][1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.01), rotation=0)   \\n\",\n    \"g.set_ylim(0, 35)\\n\",\n    \"g.set(yticklabels=[])\\n\",\n    \"# g.set(yticks=[])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"g.set(ylabel=None)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"sns.set_palette(\\\"tab10_r\\\", n_colors=10)\\n\",\n    \"# one2one_absent\\n\",\n    \"concat_df = pd.concat([rnn_one2one_absent_df, tf_one2one_absent_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2one_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2one_beams.index(row_series.Beam)] = float(row_series['R@50'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2one_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[1][0], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[1][0].patches:\\n\",\n    \"    axes[1][0].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.005), rotation=0)   \\n\",\n    \"axes[1][0].set_ylabel(\\\"Absent (R@50)\\\")\\n\",\n    \"axes[1][0].set_xlabel(\\\"One2One\\\")\\n\",\n    \"axes[1][0].legend(loc='upper left')\\n\",\n    \"g.set_ylim(0, 15)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# one2seq_absent\\n\",\n    \"concat_df = pd.concat([rnn_one2seq_absent_df, tf_one2seq_absent_df], ignore_index=True, sort=False)\\n\",\n    \"avg_bar_values = {model_name: [0.0]*len(one2seq_beams) for model_name in model_names}\\n\",\n    \"for index_label, row_series in concat_df.iterrows():   \\n\",\n    \"    avg_bar_values[row_series.model][one2seq_beams.index(row_series.Beam)] = float(row_series['R@50'])\\n\",\n    \"concat_df = pd.DataFrame(avg_bar_values, index=one2seq_beams)\\n\",\n    \"concat_df = concat_df * 100.0\\n\",\n    \"g = concat_df.plot.bar(ax=axes[1][1], legend=False, rot=0)\\n\",\n    \"\\n\",\n    \"for p in axes[1][1].patches:\\n\",\n    \"    axes[1][1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.001, p.get_height() + 0.01), rotation=0)   \\n\",\n    \"# axes[1][1].set_ylabel(\\\"One2Seq Absent (R@50)\\\")\\n\",\n    \"axes[1][1].set_xlabel(\\\"One2Seq\\\")\\n\",\n    \"g.set(yticklabels=[])\\n\",\n    \"# g.set(yticks=[])\\n\",\n    \"# g.set(ylabel=None)\\n\",\n    \"g.set_ylim(0, 15)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"from matplotlib.ticker import MaxNLocator\\n\",\n    \"axes[1][0].yaxis.set_major_locator(MaxNLocator(integer=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Effect of Architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(['magkp-meng17-random-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-random-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-alphabetical-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-alphabetical-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-no_sort-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-length-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-random-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-verbatim_prepend-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-no_sort-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusefalse-Covfalse-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-length-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.3-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-no_sort-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-verbatim_prepend-transformer-BS4096-LR0.5-Layer4-Heads8-Dim512-Emb512-Dropout0.2-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'magkp-meng17-verbatim_append-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-alphabetical-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-length-transformer-BS4096-LR0.5-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1',\\n\",\n       \"       'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L4-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.002-Layer1-Dim512-Emb128-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L2-H4-Dim128-Emb128-Dropout0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D128-E128-DO0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n       \"       'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L2-H4-D128-E128-DO0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-one2one-BS128-LR0.002-L1-H-D150-E100-DO0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L4-H8-D512-E512-DO0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-one2one-BS128-OPTadagrad-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covfalse-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.25-Copytrue-Covtrue-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copyfalse-Covfalse-Contextboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.0-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.5-Copytrue-Covtrue-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.5-Copytrue-Covtrue-Contextboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-rnn-BS128-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1',\\n\",\n       \"       'kp20k-meng17-one2one-BS128-LR0.05-L1-H8-D512-E128-DO0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse',\\n\",\n       \"       'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse',\\n\",\n       \"       'magkp20k-meng17-verbatim_append-transformer-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \"       'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \"       'magkp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-OPT-LR0.05-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covtrue',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse',\\n\",\n       \"       'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Covfalse',\\n\",\n       \"       'magkp20k-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue',\\n\",\n       \"       'magkp20k-meng17-one2one-BS128-LR0.05-L1-H-D150-E100-DO0.0-Copytrue',\\n\",\n       \"       'magkp20k-meng17-one2one-BS128-LR0.05-L1-D512-E128-DO0.1-Copytrue',\\n\",\n       \"       'magkp-meng17-one2one-transformer-BS4096-LR0.05-L6-H8-D512-E512-DO0.1-Copytrue'],\\n\",\n       \"      dtype=object)\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"all_eval_df.exp_name.unique()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### One2One\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - [7]['kp20k_valid2k' 'nus' 'semeval' 'kp20k' 'inspec' 'duc' 'krapivin']\\n\",\n      \"(280, 121)\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - [7]['semeval' 'kp20k_valid2k' 'krapivin' 'inspec' 'duc' 'nus' 'kp20k']\\n\",\n      \"(911, 121)\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10': [0.00032467532467532473, 0.04039430353823858, 0.06842784980073414, 0.06873645721741518, 0.04685716433495115, 0.10031317913118709, 0.07364274609978945, 0.05695662506385585], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10': [0.1240018880821837, 0.32429694315801766, 0.278800915528659, 0.28287264555675606, 0.26333446401667293, 0.365868622238561, 0.35177721248479754, 0.28442181300937824]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - present_exact_f_score_hard@10 = 8\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50 = 7\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 7\\n\",\n      \"['duc' 'inspec' 'kp20k' 'kp20k_valid2k' 'krapivin' 'nus' 'semeval'\\n\",\n      \" 'Average']\\n\",\n      \"{'kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50': [0.0, 0.023323088023088023, 0.058231797832538304, 0.05360714285714285, 0.03986628709454796, 0.055115389442821835, 0.03658567821067821, 0.03810419763725959], 'kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50': [0.0, 0.0929945165945166, 0.13248352632452295, 0.13661190476190474, 0.13927457807892588, 0.11269831924825102, 0.06120905483405483, 0.09646741426316799]}\\n\",\n      \"kp20k-meng17-one2one-BS128-LR0.05-L1-D150-E100-DO0.0-Copyfalse-Covfalse - absent_exact_recall@50 = 8\\n\",\n      \"kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1 - absent_exact_recall@50 = 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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"
  },
  {
    "path": "notebook/transfer_dataset_stats.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T03:16:09.514882Z\",\n     \"start_time\": \"2020-11-22T03:15:31.760701Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('..'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"import kp_evaluate\\n\",\n    \"import onmt.keyphrase.utils as utils\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import scipy\\n\",\n    \"\\n\",\n    \"from nltk.stem.porter import *\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"\\n\",\n    \"def stem_word_list(word_list):\\n\",\n    \"    return [stemmer.stem(w.strip()) for w in word_list]\\n\",\n    \"\\n\",\n    \"def if_present_duplicate_phrases(src_seq, tgt_seqs, stemming=True, lowercase=True):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Check if each given target sequence verbatim appears in the source sequence\\n\",\n    \"    :param src_seq:\\n\",\n    \"    :param tgt_seqs:\\n\",\n    \"    :param stemming:\\n\",\n    \"    :param lowercase:\\n\",\n    \"    :param check_duplicate:\\n\",\n    \"    :return:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    if lowercase:\\n\",\n    \"        src_seq = [w.lower() for w in src_seq]\\n\",\n    \"    if stemming:\\n\",\n    \"        src_seq = stem_word_list(src_seq)\\n\",\n    \"\\n\",\n    \"    present_indices = []\\n\",\n    \"    present_flags = []\\n\",\n    \"    duplicate_flags = []\\n\",\n    \"    phrase_set = set()  # some phrases are duplicate after stemming, like \\\"model\\\" and \\\"models\\\" would be same after stemming, thus we ignore the following ones\\n\",\n    \"\\n\",\n    \"    for tgt_seq in tgt_seqs:\\n\",\n    \"        if lowercase:\\n\",\n    \"            tgt_seq = [w.lower() for w in tgt_seq]\\n\",\n    \"        if stemming:\\n\",\n    \"            tgt_seq = stem_word_list(tgt_seq)\\n\",\n    \"\\n\",\n    \"        # check if the phrase appears in source text\\n\",\n    \"        # iterate each word in source\\n\",\n    \"        match_flag, match_pos_idx = if_present_phrase(src_seq, tgt_seq)\\n\",\n    \"\\n\",\n    \"        # if it reaches the end of source and no match, means it doesn't appear in the source\\n\",\n    \"        present_flags.append(match_flag)\\n\",\n    \"        present_indices.append(match_pos_idx)\\n\",\n    \"\\n\",\n    \"        # check if it is duplicate\\n\",\n    \"        if '_'.join(tgt_seq) in phrase_set:\\n\",\n    \"            duplicate_flags.append(True)\\n\",\n    \"        else:\\n\",\n    \"            duplicate_flags.append(False)\\n\",\n    \"        phrase_set.add('_'.join(tgt_seq))\\n\",\n    \"\\n\",\n    \"    assert len(present_flags) == len(present_indices)\\n\",\n    \"\\n\",\n    \"    return np.asarray(present_flags), \\\\\\n\",\n    \"           np.asarray(present_indices), \\\\\\n\",\n    \"           np.asarray(duplicate_flags)\\n\",\n    \"\\n\",\n    \"def if_present_phrase(src_str_tokens, phrase_str_tokens):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    :param src_str_tokens: a list of strings (words) of source text\\n\",\n    \"    :param phrase_str_tokens: a list of strings (words) of a phrase\\n\",\n    \"    :return:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    match_flag = False\\n\",\n    \"    match_pos_idx = -1\\n\",\n    \"    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\\n\",\n    \"        match_flag = True\\n\",\n    \"        # iterate each word in target, if one word does not match, set match=False and break\\n\",\n    \"        for seq_idx, seq_w in enumerate(phrase_str_tokens):\\n\",\n    \"            src_w = src_str_tokens[src_start_idx + seq_idx]\\n\",\n    \"            if src_w != seq_w:\\n\",\n    \"                match_flag = False\\n\",\n    \"                break\\n\",\n    \"        if match_flag:\\n\",\n    \"            match_pos_idx = src_start_idx\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"    return match_flag, match_pos_idx\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Stats of Four Transfer Datasets\\n\",\n    \"\\n\",\n    \"#### KP20k-train\\n\",\n    \"    num_doc= 514154\\n\",\n    \"    avg_src_len= 161.00254203993356\\n\",\n    \"    num_tgt= 2710067\\n\",\n    \"    num_unique_tgt= 680136\\n\",\n    \"    avg_tgt_len= 2.0392241225032444\\n\",\n    \"    num_present_doc= 489040\\n\",\n    \"    num_present_tgt= 1715640 , #avg=3.34\\n\",\n    \"    num_absent_doc= 420673\\n\",\n    \"    num_absent_tgt= 994427 , #avg=1.93\\n\",\n    \"    \\n\",\n    \"#### OpenKP-train\\n\",\n    \"    num_doc= 134894\\n\",\n    \"    avg_src_len= 1104.5965647100686\\n\",\n    \"    num_tgt= 294186\\n\",\n    \"    num_unique_tgt= 206806\\n\",\n    \"    avg_tgt_len= 1.9732210234341538\\n\",\n    \"    num_present_doc= 134894\\n\",\n    \"    num_present_tgt= 288417 , #avg=2.14\\n\",\n    \"    num_absent_doc= 5095\\n\",\n    \"    num_absent_tgt= 5769 , #avg=0.04\\n\",\n    \"    \\n\",\n    \" \\n\",\n    \"#### KPTimes-train   \\n\",\n    \"    num_doc= 259923\\n\",\n    \"    avg_src_len= 803.653481992744\\n\",\n    \"    num_tgt= 1308180\\n\",\n    \"    num_unique_tgt= 104797\\n\",\n    \"    avg_tgt_len= 2.1985628888990814\\n\",\n    \"    num_present_doc= 232110\\n\",\n    \"    num_present_tgt= 628506 , #avg=2.42\\n\",\n    \"    num_absent_doc= 242719\\n\",\n    \"    num_absent_tgt= 679674 , #avg=2.61\\n\",\n    \"\\n\",\n    \"#### Stackex\\n\",\n    \"    num_doc= 298965\\n\",\n    \"    avg_src_len= 207.50310571471576\\n\",\n    \"    num_tgt= 803868\\n\",\n    \"    num_unique_tgt= 8102\\n\",\n    \"    avg_tgt_len= 1.3293140167291146\\n\",\n    \"    num_present_doc= 252114\\n\",\n    \"    num_present_tgt= 464119 , #avg=1.55\\n\",\n    \"    num_absent_doc= 204656\\n\",\n    \"    num_absent_tgt= 339749 , #avg=1.14\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T05:39:07.881950Z\",\n     \"start_time\": \"2020-11-23T05:38:37.978143Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"0\\n\",\n      \"10000\\n\",\n      \"20000\\n\",\n      \"30000\\n\",\n      \"40000\\n\",\n      \"50000\\n\",\n      \"60000\\n\",\n      \"70000\\n\",\n      \"80000\\n\",\n      \"90000\\n\",\n      \"100000\\n\",\n      \"110000\\n\",\n      \"120000\\n\",\n      \"130000\\n\",\n      \"140000\\n\",\n      \"150000\\n\",\n      \"160000\\n\",\n      \"170000\\n\",\n      \"180000\\n\",\n      \"190000\\n\",\n      \"200000\\n\",\n      \"210000\\n\",\n      \"220000\\n\",\n      \"230000\\n\",\n      \"240000\\n\",\n      \"250000\\n\",\n      \"260000\\n\",\n      \"270000\\n\",\n      \"280000\\n\",\n      \"290000\\n\",\n      \"300000\\n\",\n      \"310000\\n\",\n      \"320000\\n\",\n      \"330000\\n\",\n      \"340000\\n\",\n      \"350000\\n\",\n      \"360000\\n\",\n      \"370000\\n\",\n      \"380000\\n\",\n      \"390000\\n\",\n      \"400000\\n\",\n      \"410000\\n\",\n      \"420000\\n\",\n      \"430000\\n\",\n      \"440000\\n\",\n      \"450000\\n\",\n      \"460000\\n\",\n      \"470000\\n\",\n      \"480000\\n\",\n      \"490000\\n\",\n      \"500000\\n\",\n      \"510000\\n\",\n      \"num_doc= 514154\\n\",\n      \"avg_src_len= 161.00254203993356\\n\",\n      \"num_tgt= 2710067\\n\",\n      \"num_unique_tgt= 680136\\n\",\n      \"avg_tgt_len= 2.0392241225032444\\n\",\n      \"num_present_doc= 489040\\n\",\n      \"num_present_tgt= 1715640 , #avg=3.34\\n\",\n      \"num_absent_doc= 420673\\n\",\n      \"num_absent_tgt= 994427 , #avg=1.93\\n\",\n      \"openkp\\n\",\n      \"0\\n\",\n      \"10000\\n\",\n      \"20000\\n\",\n      \"30000\\n\",\n      \"40000\\n\",\n      \"50000\\n\",\n      \"60000\\n\",\n      \"70000\\n\",\n      \"80000\\n\",\n      \"90000\\n\",\n      \"100000\\n\",\n      \"110000\\n\",\n      \"120000\\n\",\n      \"130000\\n\",\n      \"num_doc= 134894\\n\",\n      \"avg_src_len= 1104.5965647100686\\n\",\n      \"num_tgt= 294186\\n\",\n      \"num_unique_tgt= 206806\\n\",\n      \"avg_tgt_len= 1.9732210234341538\\n\",\n      \"num_present_doc= 134894\\n\",\n      \"num_present_tgt= 288417 , #avg=2.14\\n\",\n      \"num_absent_doc= 5095\\n\",\n      \"num_absent_tgt= 5769 , #avg=0.04\\n\",\n      \"kptimes\\n\",\n      \"0\\n\",\n      \"10000\\n\",\n      \"20000\\n\",\n      \"30000\\n\",\n      \"40000\\n\",\n      \"50000\\n\",\n      \"60000\\n\",\n      \"70000\\n\",\n      \"80000\\n\",\n      \"90000\\n\",\n      \"100000\\n\",\n      \"110000\\n\",\n      \"120000\\n\",\n      \"130000\\n\",\n      \"140000\\n\",\n      \"150000\\n\",\n      \"160000\\n\",\n      \"170000\\n\",\n      \"180000\\n\",\n      \"190000\\n\",\n      \"200000\\n\",\n      \"210000\\n\",\n      \"220000\\n\",\n      \"230000\\n\",\n      \"240000\\n\",\n      \"250000\\n\",\n      \"num_doc= 259923\\n\",\n      \"avg_src_len= 803.653481992744\\n\",\n      \"num_tgt= 1308180\\n\",\n      \"num_unique_tgt= 104797\\n\",\n      \"avg_tgt_len= 2.1985628888990814\\n\",\n      \"num_present_doc= 232110\\n\",\n      \"num_present_tgt= 628506 , #avg=2.42\\n\",\n      \"num_absent_doc= 242719\\n\",\n      \"num_absent_tgt= 679674 , #avg=2.61\\n\",\n      \"stackex\\n\",\n      \"0\\n\",\n      \"10000\\n\",\n      \"20000\\n\",\n      \"30000\\n\",\n      \"40000\\n\",\n      \"50000\\n\",\n      \"60000\\n\",\n      \"70000\\n\",\n      \"80000\\n\",\n      \"90000\\n\",\n      \"100000\\n\",\n      \"110000\\n\",\n      \"120000\\n\",\n      \"130000\\n\",\n      \"140000\\n\",\n      \"150000\\n\",\n      \"160000\\n\",\n      \"170000\\n\",\n      \"180000\\n\",\n      \"190000\\n\",\n      \"200000\\n\",\n      \"210000\\n\",\n      \"220000\\n\",\n      \"230000\\n\",\n      \"240000\\n\",\n      \"250000\\n\",\n      \"260000\\n\",\n      \"270000\\n\",\n      \"280000\\n\",\n      \"290000\\n\",\n      \"num_doc= 298965\\n\",\n      \"avg_src_len= 207.50310571471576\\n\",\n      \"num_tgt= 803868\\n\",\n      \"num_unique_tgt= 8102\\n\",\n      \"avg_tgt_len= 1.3293140167291146\\n\",\n      \"num_present_doc= 252114\\n\",\n      \"num_present_tgt= 464119 , #avg=1.55\\n\",\n      \"num_absent_doc= 204656\\n\",\n      \"num_absent_tgt= 339749 , #avg=1.14\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"dataset_names = ['kp20k', 'openkp', 'kptimes', 'stackex']\\n\",\n    \"# dataset_names = ['kp20k', 'openkp']\\n\",\n    \"\\n\",\n    \"KP_DATASET_FIELDS = {'kp20k': ('title', 'abstract', 'keywords', None),\\n\",\n    \"                     'stackex': ('title', 'question', 'tags', 'categories'),\\n\",\n    \"                     'openkp': ('url', 'text', 'KeyPhrases', None),\\n\",\n    \"                     'kptimes': ('title', 'abstract', 'keyword', 'categories')}\\n\",\n    \"dataset_split = 'train'\\n\",\n    \"json_base_dir = '/zfs1/hdaqing/rum20/kp/data/kp/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_src_lens, dataset_tgt_lens, dataset_tgt_nums  = {}, {}, {}    \\n\",\n    \"dataset_unique_kp_count, dataset_unique_preskp_count, dataset_unique_abskp_count = {}, {}, {}\\n\",\n    \"\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    src_len, tgt_len, tgt_num = [], [], []\\n\",\n    \"    num_present_doc, num_present_tgt = 0, 0\\n\",\n    \"    num_absent_doc, num_absent_tgt = 0, 0\\n\",\n    \"    \\n\",\n    \"    unique_kp_count, unique_preskp_count, unique_abskp_count = defaultdict(int), defaultdict(int), defaultdict(int)\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s.json' % dataset_split)\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for ex_id, json_line in enumerate(input_json):\\n\",\n    \"            if ex_id % 10000 == 0: print(ex_id)\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"            \\n\",\n    \"            title_field, text_field, keyword_field, _ = KP_DATASET_FIELDS[dataset_name]\\n\",\n    \"\\n\",\n    \"            src_str = ex_dict[title_field] + ' . ' + ex_dict[text_field]\\n\",\n    \"            if isinstance(ex_dict[keyword_field], str):\\n\",\n    \"                tgt_kps = ex_dict[keyword_field].split(';')\\n\",\n    \"            else:\\n\",\n    \"                tgt_kps = ex_dict[keyword_field]\\n\",\n    \"\\n\",\n    \"            src_seq = [t for t in re.split(r'\\\\W', src_str) if len(t) > 0]\\n\",\n    \"            tgt_seqs = [[t for t in re.split(r'\\\\W', p) if len(t) > 0] for p in tgt_kps]\\n\",\n    \"#             [kp_set.add(' '.join(p)) for p in tgt_seqs]\\n\",\n    \"            \\n\",\n    \"            present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs, stemming=True, lowercase=True)\\n\",\n    \"            \\n\",\n    \"            present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\\n\",\n    \"            absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\\n\",\n    \"            \\n\",\n    \"            for p in tgt_seqs:\\n\",\n    \"                unique_kp_count[' '.join(p)] += 1\\n\",\n    \"            for p in present_tgts:\\n\",\n    \"                unique_preskp_count[' '.join(p)] += 1\\n\",\n    \"            for p in absent_tgts:\\n\",\n    \"                unique_abskp_count[' '.join(p)] += 1\\n\",\n    \"            \\n\",\n    \"            num_present_tgt += len(present_tgts)\\n\",\n    \"            num_absent_tgt += len(absent_tgts)\\n\",\n    \"            if len(present_tgts) > 0: num_present_doc += 1\\n\",\n    \"            if len(absent_tgts) > 0: num_absent_doc += 1\\n\",\n    \"                                 \\n\",\n    \"            src_len.append(len(src_seq))\\n\",\n    \"            tgt_num.append(len(tgt_seqs))\\n\",\n    \"            tgt_len.extend([len(tgt_seq) for tgt_seq in tgt_seqs])\\n\",\n    \"    \\n\",\n    \"    print('num_doc=', len(src_len))\\n\",\n    \"    print('avg_src_len=', np.mean(src_len))\\n\",\n    \"    print('num_tgt=', sum(tgt_num))\\n\",\n    \"    print('num_unique_tgt=', len(unique_kp_count))\\n\",\n    \"    print('avg_tgt_len=', np.mean(tgt_len))\\n\",\n    \"    print('num_present_doc=', num_present_doc)\\n\",\n    \"    print('num_present_tgt=', num_present_tgt, ', #avg=%.2f' % (num_present_tgt / len(tgt_num)))\\n\",\n    \"    print('num_absent_doc=', num_absent_doc)\\n\",\n    \"    print('num_absent_tgt=', num_absent_tgt, ', #avg=%.2f' % (num_absent_tgt / len(tgt_num)))\\n\",\n    \"\\n\",\n    \"    dataset_src_lens[dataset_name] = src_len\\n\",\n    \"    dataset_tgt_lens[dataset_name] = tgt_len\\n\",\n    \"    dataset_tgt_nums[dataset_name] = tgt_num\\n\",\n    \"    dataset_unique_kp_count[dataset_name] = unique_kp_count\\n\",\n    \"    dataset_unique_preskp_count[dataset_name] = unique_preskp_count\\n\",\n    \"    dataset_unique_abskp_count[dataset_name] = unique_abskp_count\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Stored 'dataset_src_lens' (dict)\\n\",\n      \"Stored 'dataset_tgt_lens' (dict)\\n\",\n      \"Stored 'dataset_tgt_nums' (dict)\\n\",\n      \"Stored 'dataset_unique_kp_count' (dict)\\n\",\n      \"Stored 'dataset_unique_preskp_count' (dict)\\n\",\n      \"Stored 'dataset_unique_abskp_count' (dict)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# SAVE pred/eval paths and dataframes\\n\",\n    \"%store dataset_src_lens\\n\",\n    \"%store dataset_tgt_lens\\n\",\n    \"%store dataset_tgt_nums\\n\",\n    \"%store dataset_unique_kp_count\\n\",\n    \"%store dataset_unique_preskp_count\\n\",\n    \"%store dataset_unique_abskp_count\\n\",\n    \"\\n\",\n    \"# LOAD using store magic (doesn't work on Colab)\\n\",\n    \"%store -r dataset_src_lens\\n\",\n    \"%store -r dataset_tgt_lens\\n\",\n    \"%store -r dataset_tgt_nums\\n\",\n    \"%store -r dataset_unique_kp_count\\n\",\n    \"%store -r dataset_unique_preskp_count\\n\",\n    \"%store -r dataset_unique_abskp_count\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Common absent phrases in each dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"**************************************************\\n\",\n      \"kp20k 393767\\n\",\n      \"('algorithms', 2134)\\n\",\n      \"('data mining', 2020)\\n\",\n      \"('design', 1841)\\n\",\n      \"('machine learning', 1727)\\n\",\n      \"('performance', 1387)\\n\",\n      \"('experimentation', 1338)\\n\",\n      \"('theory', 1308)\\n\",\n      \"('performance evaluation', 1245)\\n\",\n      \"('information retrieval', 1157)\\n\",\n      \"('cryptography', 1106)\\n\",\n      \"('reliability', 1095)\\n\",\n      \"('pattern recognition', 1092)\\n\",\n      \"('finite element method', 1085)\\n\",\n      \"('computational complexity', 1064)\\n\",\n      \"('image processing', 1038)\\n\",\n      \"('security', 986)\\n\",\n      \"('verification', 973)\\n\",\n      \"('classification', 924)\\n\",\n      \"('optimization', 915)\\n\",\n      \"('cybernetics', 896)\\n\",\n      \"**************************************************\\n\",\n      \"openkp 5154\\n\",\n      \"('Baby Names', 41)\\n\",\n      \"('Definition', 23)\\n\",\n      \"('Dictionary', 21)\\n\",\n      \"('United States', 20)\\n\",\n      \"('mean', 19)\\n\",\n      \"('Think Baby Names', 19)\\n\",\n      \"('definitions', 16)\\n\",\n      \"('Holiday Inn', 16)\\n\",\n      \"('FCRA', 15)\\n\",\n      \"('Recipes', 13)\\n\",\n      \"('definition', 13)\\n\",\n      \"('DIN', 10)\\n\",\n      \"('CHLORHEXADINE SOLUTION', 10)\\n\",\n      \"('Lee High School', 10)\\n\",\n      \"('Fairfax County Public Schools', 10)\\n\",\n      \"('Lyme Disease Association', 10)\\n\",\n      \"('Definitions', 9)\\n\",\n      \"('Synonyms', 9)\\n\",\n      \"('New York', 8)\\n\",\n      \"('Our Baby Namer', 8)\\n\",\n      \"**************************************************\\n\",\n      \"kptimes 49712\\n\",\n      \"('Computers and the Internet', 7148)\\n\",\n      \"('NYC', 5878)\\n\",\n      \"('Politics and Government', 5653)\\n\",\n      \"('Basketball', 4655)\\n\",\n      \"('Baseball', 4593)\\n\",\n      \"('US Politics', 4337)\\n\",\n      \"('Economic Conditions and Trends', 4173)\\n\",\n      \"('Barack Obama', 3981)\\n\",\n      \"('Football', 3825)\\n\",\n      \"('2016 Presidential Election', 3710)\\n\",\n      \"('Obama Barack', 3687)\\n\",\n      \"('United States Politics and Government', 3606)\\n\",\n      \"('Decisions and Verdicts', 3139)\\n\",\n      \"('Accidents and Safety', 3111)\\n\",\n      \"('College Athletics', 3065)\\n\",\n      \"('United States Economy', 3034)\\n\",\n      \"('Great Britain', 3020)\\n\",\n      \"('Terrorism', 2986)\\n\",\n      \"('Murders and Attempted Murders', 2945)\\n\",\n      \"('Appointments and Executive Changes', 2799)\\n\",\n      \"**************************************************\\n\",\n      \"stackex 6472\\n\",\n      \"('c', 9664)\\n\",\n      \"('linux', 8901)\\n\",\n      \"('shell script', 5429)\\n\",\n      \"('bash', 4404)\\n\",\n      \"('java', 4355)\\n\",\n      \"('javascript', 4121)\\n\",\n      \"('shell', 3942)\\n\",\n      \"('text processing', 3816)\\n\",\n      \"('python', 3598)\\n\",\n      \"('object oriented', 3457)\\n\",\n      \"('performance', 3417)\\n\",\n      \"('beginner', 3351)\\n\",\n      \"('seo', 3112)\\n\",\n      \"('command line', 2669)\\n\",\n      \"('design patterns', 2569)\\n\",\n      \"('machine learning', 2241)\\n\",\n      \"('complexity theory', 2165)\\n\",\n      \"('cc complexity theory', 2058)\\n\",\n      \"('algorithms', 2054)\\n\",\n      \"('reference request', 1998)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for dataset, kp_set in dataset_unique_abskp_count.items():\\n\",\n    \"    print('*' * 50)\\n\",\n    \"    print(dataset, len(kp_set))\\n\",\n    \"    kp_list = sorted(kp_set.items(), key=lambda k:k[1], reverse=True)\\n\",\n    \"    \\n\",\n    \"    for p in kp_list[:20]:\\n\",\n    \"        print(p)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Cherry-pick transfered labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\\n\",\n      \"  return torch._C._cuda_getDeviceCount() > 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import random\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"module_path = os.path.abspath(os.path.join('../'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"import onmt.keyphrase.eval as eval\\n\",\n    \"import importlib\\n\",\n    \"importlib.reload(eval)\\n\",\n    \"import json\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"openkp\\n\",\n      \"kptimes\\n\",\n      \"stackex\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k_train100k', 'openkp_train100k', 'kptimes_train100k', 'stackex_train100k']\\n\",\n    \"\\n\",\n    \"KP_DATASET_FIELDS = {'kp20k': ('title', 'abstract', 'keywords', None),\\n\",\n    \"                     'stackex': ('title', 'question', 'tags', 'categories'),\\n\",\n    \"                     'openkp': ('url', 'text', 'KeyPhrases', None),\\n\",\n    \"                     'kptimes': ('title', 'abstract', 'keyword', 'categories')}\\n\",\n    \"\\n\",\n    \"examples_dict = {}\\n\",\n    \"\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    input_path = '/zfs1/hdaqing/rum20/kp/data/kp/json/%s/train.json' % dataset_name\\n\",\n    \"    tl_path = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_%s_train.pred' % dataset_name\\n\",\n    \"    dataset_name = dataset_name[: dataset_name.index('_')]\\n\",\n    \"    print(dataset_name)\\n\",\n    \"    examples_dict[dataset_name] = []\\n\",\n    \"\\n\",\n    \"    with open(input_path, 'r') as input_json, open(tl_path, 'r') as tl_json:\\n\",\n    \"        for ex_id, (json_line, tl_line) in enumerate(zip(input_json, tl_json)):\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"            tl_dict = json.loads(tl_line)\\n\",\n    \"\\n\",\n    \"            title_field, text_field, keyword_field, _ = KP_DATASET_FIELDS[dataset_name]\\n\",\n    \"\\n\",\n    \"            if isinstance(ex_dict[keyword_field], str):\\n\",\n    \"                tgt_kps = ex_dict[keyword_field].split(';')\\n\",\n    \"            else:\\n\",\n    \"                tgt_kps = ex_dict[keyword_field]\\n\",\n    \"\\n\",\n    \"            _ex_dict = {}\\n\",\n    \"            _ex_dict['title'] = ex_dict[title_field]\\n\",\n    \"            _ex_dict['text'] = ex_dict[text_field]\\n\",\n    \"            _ex_dict['keyphrase'] = tgt_kps\\n\",\n    \"            tl_kps = [' '.join(p) for p in tl_dict['pred_sents'] if len(p) > 0]\\n\",\n    \"            _ex_dict['transfered_keyphrase'] = tl_kps\\n\",\n    \"    #         print(tgt_kps)\\n\",\n    \"    #         print(tl_kps)\\n\",\n    \"\\n\",\n    \"            examples_dict[dataset_name].append(_ex_dict)\\n\",\n    \"            if ex_id > 10000: break\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### kp20k\\n\",\n    \"\\n\",\n    \"2123\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7906\\n\",\n      \"==================================================\\n\",\n      \"[Source]: enriched human-centered multimedia computing through inspirations from disabilities and deficit-centered computing solutions . The paradigm of human-centered multimedia computing (HCMC) has emerged recently as a result of the increasing emphasis on integrating the concept of human-centeredness in various aspects of multimedia computing. While many theories have been proposed to advance this paradigm, it is our belief that a complete understanding of the issues surrounding HCMC requires capturing a complementary (yet enriching) perspective through inspirations drawn from studying human disabilities and deficits. In this paper, we present the need for understanding human deficiencies in sensory, neural, and cognitive sensing/actuations which could reveal innate components of human interaction that benefits researchers, designers and developers of new multimedia solutions. We illustrate how technologies that were started with assistive and rehabilitative goals have broader impacts to the general population. More importantly, this opens up new research issues that would otherwise not have been seen when the focus is only on the 'able' population. The study and understanding of the disabilities and deficits leads to a better understanding of human requirements in any human machine interaction which is important in advancing the vision and core principles of HCMC \\n\",\n      \"[GROUND-TRUTH] #(all)=4, #(present)=1, #(absent)=3\\n\",\n      \"\\t\\tassistive technology\\n\",\n      \"\\t\\trehabilitative technology\\n\",\n      \"\\t\\t[human centered multimedia computing]\\n\",\n      \"\\t\\thuman centered computing\\n\",\n      \"[PREDICTION] #(all)=9, #(valid)=8, #(present)=5, #(valid&present)=5, #(valid&absent)=3\\n\",\n      \"\\t\\tScore N/A\\t[human centered multimedia computing] \\t[correct!]\\n\",\n      \"\\t\\tScore N/A\\t[human centeredness] \\t\\n\",\n      \"\\t\\tScore N/A\\t[multimedia] \\t\\n\",\n      \"\\t\\tScore N/A\\tdisability centered computing \\t\\n\",\n      \"\\t\\tScore N/A\\t[machine interaction] \\t\\n\",\n      \"\\t\\tScore N/A\\t[disability] \\t\\n\",\n      \"\\t\\tScore N/A\\tAssistive technology \\t[correct!]\\n\",\n      \"\\t\\t\\tScore N/A\\tDisability centered computing \\t\\n\",\n      \"\\t\\tScore N/A\\tMethods \\t\\n\",\n      \"\\n\",\n      \" ======================================================= \\n\",\n      \"[GROUND-TRUTH] #(all)=4, #(present)=1, #(absent)=3\\n\",\n      \"\\n\",\n      \"[PREDICTION] #(all)=9, #(valid)=8, #(present)=5, #(valid&present)=5, #(valid&absent)=3\\n\",\n      \"\\n\",\n      \" --- batch all_exact F1 @k: \\t0.2500\\n\",\n      \" --- batch all_exact F1 @10: \\t0.3333\\n\",\n      \" --- batch present_exact F1 @k: \\t1.0000\\n\",\n      \" --- batch present_exact F1 @10: \\t0.3333\\n\",\n      \" --- batch absent_exact F1 @50: \\t0.3333\\n\",\n      \" --- batch absent_exact F1 @M: \\t0.3333\\n\",\n      \" =======================================================\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\\n\",\n      \"  return array(a, dtype, copy=False, order=order)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\\"\\\\nfor ex_idx, ex_dict in enumerate(examples_dict['kp20k']):\\\\n    if ex_idx < 2000:\\\\n        continue\\\\n    if ex_idx > 3000:\\\\n        break\\\\n        \\\\n    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\\\n    printout, eval_dict = eval.eval_and_print(src_text, \\\\n                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\\\n                                              pred_scores=None, return_eval=True)\\\\n    \\\\n    if eval_dict['absent_exact']['f_score@50'] > 0.0:\\\\n        print(ex_idx)\\\\n        print(printout)\\\\n\\\"\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"rnd_idx = random.randint(0, len(examples_dict['kp20k'])-1)\\n\",\n    \"print(rnd_idx)\\n\",\n    \"ex_dict = examples_dict['kp20k'][2123]\\n\",\n    \"src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                          tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                          pred_scores=None, return_eval=True)\\n\",\n    \"print(printout)\\n\",\n    \"\\n\",\n    \"'''\\n\",\n    \"for ex_idx, ex_dict in enumerate(examples_dict['kp20k']):\\n\",\n    \"    if ex_idx < 2000:\\n\",\n    \"        continue\\n\",\n    \"    if ex_idx > 3000:\\n\",\n    \"        break\\n\",\n    \"        \\n\",\n    \"    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"    printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                              pred_scores=None, return_eval=True)\\n\",\n    \"    \\n\",\n    \"    if eval_dict['absent_exact']['f_score@50'] > 0.0:\\n\",\n    \"        print(ex_idx)\\n\",\n    \"        print(printout)\\n\",\n    \"'''\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### openkp\\n\",\n    \"127 celebrity, inferred personal life\\n\",\n    \"\\n\",\n    \"1723 coined a place in PA: Battlinville Pennsylvania \\n\",\n    \"\\n\",\n    \"1803 infered Food Safety Council\\n\",\n    \"\\n\",\n    \"2494 inferred election and Minnesota State Capitol\\n\",\n    \"\\n\",\n    \"2541 inferred Traditional IRA, Personal financial problems, Financial savers \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"==================================================\\n\",\n      \"[Source]: http://budgeting.thenest.com/pay-penalty-early-withdrawal-thrift-savings-plan-college-tuition-taxes-24837.html . Calculate Tax Savings Do I Pay a Penalty on Early Withdrawal From a Thrift Savings Plan for College Tuition on My Taxes ? by Naomi Smith You have other options besides taking an early TSP withdrawal for college expenses . For most federal and military employees , the Thrift Savings Plan offers comparable benefits to private - sector individual retirement accounts and 401 ( k ) plans . However , the options for penalty - free early withdrawals are not as generous as with other retirement plans . The TSP allows you to withdraw your money early , but if it ' s going for college tuition you ' ll get stuck with a 10 percent penalty as well as any taxes owed on the distribution . You may find other options more advantageous . Early Withdrawal An early withdrawal is defined as any distribution from your TSP before you reach retirement age : 59 ½ for federal employees , or 55 for uniformed service . Such withdrawals are subject to applicable taxes and a 10 percent penalty , unless the amount is for an annuity , death benefit , divorce settlement , qualified medical expenses or if you become totally and permanently disabled . You can not replace such withdrawals so you suffer a permanent reduction in the amount of savings . You ca n ' t make up the difference by contributing more later , TSP Loans Rather than take a withdrawal and penalty , consider whether a TSP loan is a better way to go . You wo n ' t earn any returns on the borrowed amount , but you can repay it , so you wo n ' t adversely affect your total retirement savings . You may only borrow up to the amount of contributions plus their earnings ; you can not borrow against matching funds . You can borrow for any purpose , including education , for up to five years , and up to 15 years to purchase a home . Rollover to an IRA If you are eligible for a traditional or Roth IRA , consider rolling over your TSP into one of these accounts . Both types of IRA allow early withdrawals for qualified education expenses without assessing the 10 percent additional penalty . You ' ll still owe any tax due on the distributions . If you have a pre - tax TSP and choose to roll over an amount into a Roth IRA , you will be subject to tax on the amount rolled over , otherwise it ' s a tax - free transaction . Other Considerations Any early distribution from your TSP requires a 20 percent withholding for federal taxes . You may get this amount back when you file your tax return , but it does mean you wo n ' t be able to put the full benefit of the distribution toward your educational expenses until much later . The 10 percent penalty is assessed when you file your tax return . Depending on your age and field of study , you may find the negative aspects of an early TSP withdrawal are far outweighed by the potential for increased earning power with a References Thrift Savings Plan : Important Tax Information About Payments from Your TSP Account Thrift Savings Plan : TSP Loans - Loan Basics IRS Publication 590 : Individual Retirement Arrangements About the Author Naomi Smith has been writing full - time since 2009 , following a career in finance . Her fiction has been published by Loose Id and Dreamspinner Press , among others . She holds a Master of Science in financial economics from the London School of Economics and a Bachelor of Arts in political economy from the University of California , Berkeley . Photo Credits \\n\",\n      \"[GROUND-TRUTH] #(all)=3, #(present)=3, #(absent)=0\\n\",\n      \"\\t\\t[Thrift Savings Plan]\\n\",\n      \"\\t\\t[Early Withdrawal]\\n\",\n      \"\\t\\t[TSP Loans]\\n\",\n      \"[PREDICTION] #(all)=9, #(valid)=9, #(present)=6, #(valid&present)=6, #(valid&absent)=3\\n\",\n      \"\\t\\tScore N/A\\tTraditional IRA \\t\\n\",\n      \"\\t\\tScore N/A\\t[Roth IRA] \\t\\n\",\n      \"\\t\\tScore N/A\\t[TSP Loans] \\t[correct!]\\n\",\n      \"\\t\\tScore N/A\\t[Early Withdrawal] \\t[correct!]\\n\",\n      \"\\t\\tScore N/A\\t[401 k] \\t\\n\",\n      \"\\t\\tScore N/A\\t[Rollover to an IRA] \\t\\n\",\n      \"\\t\\tScore N/A\\tPersonal financial problems \\t\\n\",\n      \"\\t\\tScore N/A\\tFinancial savers \\t\\n\",\n      \"\\t\\tScore N/A\\t[Thrift] \\t\\n\",\n      \"\\n\",\n      \" ======================================================= \\n\",\n      \"[GROUND-TRUTH] #(all)=3, #(present)=3, #(absent)=0\\n\",\n      \"\\n\",\n      \"[PREDICTION] #(all)=9, #(valid)=9, #(present)=6, #(valid&present)=6, #(valid&absent)=3\\n\",\n      \"\\n\",\n      \" --- batch all_exact F1 @k: \\t0.3333\\n\",\n      \" --- batch all_exact F1 @10: \\t0.3333\\n\",\n      \" --- batch present_exact F1 @k: \\t0.6667\\n\",\n      \" --- batch present_exact F1 @10: \\t0.4444\\n\",\n      \" --- batch absent_exact F1 @50: \\t0.0000\\n\",\n      \" --- batch absent_exact F1 @M: \\t0.0000\\n\",\n      \" =======================================================\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\\"\\\\ncount = 0\\\\nfor ex_idx, ex_dict in enumerate(examples_dict['openkp']):\\\\n    if ex_idx < 2000:\\\\n        continue\\\\n    if ex_idx > 3000:\\\\n        break\\\\n        \\\\n    if len(ex_dict['keyphrase']) < 3: continue\\\\n\\\\n    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\\\n    printout, eval_dict = eval.eval_and_print(src_text, \\\\n                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\\\n                                              pred_scores=None, return_eval=True)\\\\n    \\\\n    if eval_dict['present_exact']['f_score@k'] > 0.4:\\\\n        print(ex_idx)\\\\n        print(printout)\\\\n        count += 1\\\\n        if count > 100:\\\\n            break\\\\n\\\"\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ex_dict = examples_dict['openkp'][2541]\\n\",\n    \"src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                          tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                          pred_scores=None, return_eval=True)\\n\",\n    \"print(printout)\\n\",\n    \"'''\\n\",\n    \"count = 0\\n\",\n    \"for ex_idx, ex_dict in enumerate(examples_dict['openkp']):\\n\",\n    \"    if ex_idx < 2000:\\n\",\n    \"        continue\\n\",\n    \"    if ex_idx > 3000:\\n\",\n    \"        break\\n\",\n    \"        \\n\",\n    \"    if len(ex_dict['keyphrase']) < 3: continue\\n\",\n    \"\\n\",\n    \"    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"    printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                              pred_scores=None, return_eval=True)\\n\",\n    \"    \\n\",\n    \"    if eval_dict['present_exact']['f_score@k'] > 0.4:\\n\",\n    \"        print(ex_idx)\\n\",\n    \"        print(printout)\\n\",\n    \"        count += 1\\n\",\n    \"        if count > 100:\\n\",\n    \"            break\\n\",\n    \"'''\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### KPTimes\\n\",\n    \"- 959 inferred topics about Illegal immigration\\n\",\n    \"- 1134 education\\n\",\n    \"- 1199 apple tech\\n\",\n    \"- 2518 Charter Challenges Comcast-Time Warner Cable Deal inferred Mergers and acquisitions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"==================================================\\n\",\n      \"[Source]: Apple Profit Soars 73% as Sales Rise . SAN FRANCISCO, July 25 — Apple on Wednesday reported a 73 percent jump in quarterly profit on strong sales of Macs and iPods, beating Wall Street forecasts. It also alleviated some concerns about early sales of the iPhone . Investors were spooked on Tuesday when AT&T, which provides service for the phone, said it had activated just 146,000 iPhones in the day and a half from its release to the end of the quarter, far fewer than some analysts had expected. That sent Apple’s stock down 6 percent. But Apple executives said on Wednesday that the company had actually sold 270,000 iPhones in that period, a number that seemed to calm investors’ fears. The executives said Apple expects to sell 1 million iPhones this quarter, and reiterated its goal of selling 10 million phones by the end of 2008. The company plans to release the phone in Europe in the fourth quarter. “Our view is the starting gun has been fired and we’re off to a great start,” said Timothy D. Cook, Apple’s chief operating officer, in a conference call with analysts. “Our primary focus is not on initial sales but on creating a third business for Apple.” By comparison, Apple executives said, it took nearly two years for Apple to sell 1 million iPods. It is not entirely clear why so many early iPhone customers did not activate their phones right away, but Apple executives acknowledged that many customers experienced activation problems during the first week. Peter Oppenheimer, Apple’s chief financial officer, apologized for the problems and said they had been fixed. Shares of Apple climbed more than 9 percent, or $12.92, in after-hours trading. They closed at $137.26, up $2.37, in regular trading. The gains more than made up for the losses on Tuesday. Investor excitement over the iPhone’s potential has helped drive up Apple shares more than 50 percent since the product was announced in January. The company’s profit was $818 million, or 92 cents a share, compared with $472 million, or 54 cents a share, in the quarter a year earlier. Analysts had forecast a profit of $645.6 million, or 72 cents a share, according to a survey by Thomson Financial. The company shipped 1.76 million Macs during the quarter, representing 33 percent growth over the year-ago quarter, and 9.82 million iPods, for growth of 21 percent. “We’re thrilled to report the highest June quarter revenue and profit in Apple’s history, along with the highest quarterly Mac sales ever,” said Steven P. Jobs , Apple’s chief executive. Apple’s revenue for the quarter increased to $5.41 billion from $4.37 billion last year, beating Apple’s own projection of $5.1 billion. Apple has decided to book sales from the iPhone handsets, which cost $500 or $600 depending on the model, as subscription revenue over 24 months. The company recognized $5 million in revenue from the iPhone and related products during the fiscal third quarter, which ended June 30. The company’s gross margins rose substantially during the third quarter, to 36.9 percent from 30.3 percent in last year’s quarter. Mr. Oppenheimer said Apple’s margins benefited from favorable pricing for components like memory chips. “The upside was clearly the health of the Mac business,” said A. C. Sacconaghi, an analyst with Sanford C. Bernstein & Company. “Apple’s in a really attractive position, where the Mac’s component prices are falling but they’re able to charge the same prices.” Apple issued a conservative forecast for the fourth quarter, predicting revenue of about $5.7 billion and earnings per share of about 65 cents, as well as a drop in profit margins. Mr. Oppenheimer noted that the fourth quarter includes the school buying season, in which Apple typically offers costly back-to-school promotions and sells more lower-margin entry-level Macs. He also said he expected to see component prices rise somewhat during the quarter. Analysts have become accustomed to restrained forecasts from Apple. “Guidance for Apple has become a throwaway,” Mr. Sacconaghi said. “They guide conservatively and routinely beat it.” \\n\",\n      \"[GROUND-TRUTH] #(all)=5, #(present)=2, #(absent)=3\\n\",\n      \"\\t\\tApple Inc\\n\",\n      \"\\t\\tCompany Reports\\n\",\n      \"\\t\\t[iPhone]\\n\",\n      \"\\t\\t[Sales]\\n\",\n      \"\\t\\tJobs Steven P\\n\",\n      \"[PREDICTION] #(all)=10, #(valid)=10, #(present)=6, #(valid&present)=6, #(valid&absent)=4\\n\",\n      \"\\t\\tScore N/A\\t[AT T] \\t\\n\",\n      \"\\t\\tScore N/A\\t[Thomson Financial] \\t\\n\",\n      \"\\t\\tScore N/A\\t[Macs] \\t\\n\",\n      \"\\t\\tScore N/A\\t[chief financial officer] \\t\\n\",\n      \"\\t\\tScore N/A\\t[Timothy D Cook] \\t\\n\",\n      \"\\t\\tScore N/A\\t[iPhone] \\t[correct!]\\n\",\n      \"\\t\\tScore N/A\\tApple Inc \\t[correct!]\\n\",\n      \"\\t\\tScore N/A\\tCorporate affairs \\t\\n\",\n      \"\\t\\tScore N/A\\tFirst quarter \\t\\n\",\n      \"\\t\\tScore N/A\\tComputer companies of the United States \\t\\n\",\n      \"\\n\",\n      \" ======================================================= \\n\",\n      \"[GROUND-TRUTH] #(all)=5, #(present)=2, #(absent)=3\\n\",\n      \"\\n\",\n      \"[PREDICTION] #(all)=10, #(valid)=10, #(present)=6, #(valid&present)=6, #(valid&absent)=4\\n\",\n      \"\\n\",\n      \" --- batch all_exact F1 @k: \\t0.0000\\n\",\n      \" --- batch all_exact F1 @10: \\t0.2667\\n\",\n      \" --- batch present_exact F1 @k: \\t0.0000\\n\",\n      \" --- batch present_exact F1 @10: \\t0.2500\\n\",\n      \" --- batch absent_exact F1 @50: \\t0.2857\\n\",\n      \" --- batch absent_exact F1 @M: \\t0.2857\\n\",\n      \" =======================================================\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\\"\\\\ncount = 0\\\\nfor ex_idx, ex_dict in enumerate(examples_dict['kptimes']):\\\\n    if ex_idx < 1000:\\\\n        continue\\\\n    if ex_idx > 2000:\\\\n        break\\\\n        \\\\n    if len(ex_dict['keyphrase']) < 3: continue\\\\n\\\\n    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\\\n    printout, eval_dict = eval.eval_and_print(src_text, \\\\n                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\\\n                                              pred_scores=None, return_eval=True)\\\\n    \\\\n    if eval_dict['absent_exact']['f_score@50'] > 0.0:\\\\n        print(ex_idx)\\\\n        print(printout)\\\\n        count += 1\\\\n        if count > 100:\\\\n            break\\\\n\\\"\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ex_dict = examples_dict['kptimes'][1199]\\n\",\n    \"src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                          tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                          pred_scores=None, return_eval=True)\\n\",\n    \"print(printout)\\n\",\n    \"'''\\n\",\n    \"count = 0\\n\",\n    \"for ex_idx, ex_dict in enumerate(examples_dict['kptimes']):\\n\",\n    \"    if ex_idx < 1000:\\n\",\n    \"        continue\\n\",\n    \"    if ex_idx > 2000:\\n\",\n    \"        break\\n\",\n    \"        \\n\",\n    \"    if len(ex_dict['keyphrase']) < 3: continue\\n\",\n    \"\\n\",\n    \"    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"    printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                              pred_scores=None, return_eval=True)\\n\",\n    \"    \\n\",\n    \"    if eval_dict['absent_exact']['f_score@50'] > 0.0:\\n\",\n    \"        print(ex_idx)\\n\",\n    \"        print(printout)\\n\",\n    \"        count += 1\\n\",\n    \"        if count > 100:\\n\",\n    \"            break\\n\",\n    \"'''\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### StackEx\\n\",\n    \"- 216 infer awk\\n\",\n    \"- 2177 Brain computer interface\\n\",\n    \"- 3432 Logic based programming \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"'''\\n\",\n    \"rnd_idx = random.randint(0, len(examples)-1)\\n\",\n    \"print(rnd_idx)\\n\",\n    \"ex_dict = examples_dict['kp20k'][2123]\\n\",\n    \"src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                          tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                          pred_scores=None, return_eval=True)\\n\",\n    \"print(printout)\\n\",\n    \"'''\\n\",\n    \"count = 0\\n\",\n    \"for ex_idx, ex_dict in enumerate(examples_dict['stackex']):\\n\",\n    \"    if ex_idx < 3000:\\n\",\n    \"        continue\\n\",\n    \"    if ex_idx > 4000:\\n\",\n    \"        break\\n\",\n    \"        \\n\",\n    \"    if len(ex_dict['keyphrase']) < 2: continue\\n\",\n    \"\\n\",\n    \"    src_text = ex_dict['title'] + ' . ' + ex_dict['text']\\n\",\n    \"    printout, eval_dict = eval.eval_and_print(src_text, \\n\",\n    \"                                              tgt_kps=ex_dict['keyphrase'], pred_kps=ex_dict['transfered_keyphrase'], \\n\",\n    \"                                              pred_scores=None, return_eval=True)\\n\",\n    \"    \\n\",\n    \"    if eval_dict['absent_exact']['f_score@50'] > 0.0:\\n\",\n    \"        print(ex_idx)\\n\",\n    \"        print(printout)\\n\",\n    \"        count += 1\\n\",\n    \"        if count > 100:\\n\",\n    \"            break\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## KP Overlap between datasets\\n\",\n    \"\\n\",\n    \"#### not lowercased\\n\",\n    \"    kptimes 104797\\n\",\n    \"    kptimes (=104797) ∩ stackex (=8102) = 36\\n\",\n    \"    kptimes (=104797) ∩ kp20k (=680136) = 184\\n\",\n    \"    kptimes (=104797) ∩ openkp (=206806) = 8057\\n\",\n    \"    stackex 8102\\n\",\n    \"    stackex (=8102) ∩ kptimes (=104797) = 36\\n\",\n    \"    stackex (=8102) ∩ kp20k (=680136) = 4509\\n\",\n    \"    stackex (=8102) ∩ openkp (=206806) = 1485\\n\",\n    \"    kp20k 680136\\n\",\n    \"    kp20k (=680136) ∩ kptimes (=104797) = 184\\n\",\n    \"    kp20k (=680136) ∩ stackex (=8102) = 4509\\n\",\n    \"    kp20k (=680136) ∩ openkp (=206806) = 11678\\n\",\n    \"    openkp 206806\\n\",\n    \"    openkp (=206806) ∩ kptimes (=104797) = 8057\\n\",\n    \"    openkp (=206806) ∩ stackex (=8102) = 1485\\n\",\n    \"    openkp (=206806) ∩ kp20k (=680136) = 11678\\n\",\n    \"    \\n\",\n    \" \\n\",\n    \"#### lowercased\\n\",\n    \"    kptimes 104797\\n\",\n    \"    kptimes (=104797) ∩ stackex (=8102) = 556\\n\",\n    \"    kptimes (=104797) ∩ kp20k (=680136) = 3679\\n\",\n    \"    kptimes (=104797) ∩ openkp (=206806) = 8899\\n\",\n    \"    stackex 8102\\n\",\n    \"    stackex (=8102) ∩ kptimes (=104797) = 556\\n\",\n    \"    stackex (=8102) ∩ kp20k (=680136) = 4509\\n\",\n    \"    stackex (=8102) ∩ openkp (=206806) = 2678\\n\",\n    \"    kp20k 680136\\n\",\n    \"    kp20k (=680136) ∩ kptimes (=104797) = 3679\\n\",\n    \"    kp20k (=680136) ∩ stackex (=8102) = 4509\\n\",\n    \"    kp20k (=680136) ∩ openkp (=206806) = 21832\\n\",\n    \"    openkp 206806\\n\",\n    \"    openkp (=206806) ∩ kptimes (=104797) = 8899\\n\",\n    \"    openkp (=206806) ∩ stackex (=8102) = 2678\\n\",\n    \"    openkp (=206806) ∩ kp20k (=680136) = 21832\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kptimes 104797\\n\",\n      \"kptimes (=104797) ∩ stackex (=8102) = 556\\n\",\n      \"acer\\n\",\n      \"actor\\n\",\n      \"addiction\\n\",\n      \"adhd\\n\",\n      \"adobe\\n\",\n      \"adult education\\n\",\n      \"advertising\\n\",\n      \"aggression\\n\",\n      \"aging\\n\",\n      \"airbnb\\n\",\n      \"alcohol\\n\",\n      \"altruism\\n\",\n      \"amazon\\n\",\n      \"ancestry com\\n\",\n      \"android\\n\",\n      \"animation\\n\",\n      \"ansys\\n\",\n      \"anthropology\\n\",\n      \"antibiotics\\n\",\n      \"anxiety\\n\",\n      \"==================================================\\n\",\n      \"kptimes (=104797) ∩ kp20k (=680136) = 3679\\n\",\n      \"1960s\\n\",\n      \"1996 presidential election\\n\",\n      \"20 questions\\n\",\n      \"2008 presidential election\\n\",\n      \"3 d\\n\",\n      \"311\\n\",\n      \"3m\\n\",\n      \"5g\\n\",\n      \"911\\n\",\n      \"aaa\\n\",\n      \"abacus\\n\",\n      \"abandonment\\n\",\n      \"abba\\n\",\n      \"abc\\n\",\n      \"abe\\n\",\n      \"abortion\\n\",\n      \"absenteeism\\n\",\n      \"ac dc\\n\",\n      \"accelerometer\\n\",\n      \"accountants\\n\",\n      \"==================================================\\n\",\n      \"kptimes (=104797) ∩ openkp (=206806) = 8899\\n\",\n      \"13th amendment\\n\",\n      \"19 kids and counting\\n\",\n      \"1900 galveston hurricane\\n\",\n      \"1930s\\n\",\n      \"2010 fifa world cup\\n\",\n      \"2012 summer olympics\\n\",\n      \"2013\\n\",\n      \"2014\\n\",\n      \"3m\\n\",\n      \"401k\\n\",\n      \"41\\n\",\n      \"49ers\\n\",\n      \"4chan\\n\",\n      \"5g\\n\",\n      \"a midsummer night s dream\\n\",\n      \"a p\\n\",\n      \"a raisin in the sun\\n\",\n      \"a series of unfortunate events\\n\",\n      \"a24\\n\",\n      \"aaa\\n\",\n      \"==================================================\\n\",\n      \"stackex 8102\\n\",\n      \"stackex (=8102) ∩ kptimes (=104797) = 556\\n\",\n      \"acer\\n\",\n      \"actor\\n\",\n      \"addiction\\n\",\n      \"adhd\\n\",\n      \"adobe\\n\",\n      \"adult education\\n\",\n      \"advertising\\n\",\n      \"aggression\\n\",\n      \"aging\\n\",\n      \"airbnb\\n\",\n      \"alcohol\\n\",\n      \"altruism\\n\",\n      \"amazon\\n\",\n      \"ancestry com\\n\",\n      \"android\\n\",\n      \"animation\\n\",\n      \"ansys\\n\",\n      \"anthropology\\n\",\n      \"antibiotics\\n\",\n      \"anxiety\\n\",\n      \"==================================================\\n\",\n      \"stackex (=8102) ∩ kp20k (=680136) = 4509\\n\",\n      \"2 sat\\n\",\n      \"2d\\n\",\n      \"2d graphics\\n\",\n      \"3 sat\\n\",\n      \"32 bit\\n\",\n      \"3d\\n\",\n      \"3d objects\\n\",\n      \"3d scanner\\n\",\n      \"3d structure\\n\",\n      \"3g\\n\",\n      \"4d\\n\",\n      \"4g\\n\",\n      \"4k\\n\",\n      \"64 bit\\n\",\n      \"802 1x\\n\",\n      \"a b testing\\n\",\n      \"a star\\n\",\n      \"aac\\n\",\n      \"abap\\n\",\n      \"abbreviations\\n\",\n      \"==================================================\\n\",\n      \"stackex (=8102) ∩ openkp (=206806) = 2678\\n\",\n      \"3d printer\\n\",\n      \"3d scanner\\n\",\n      \"64 bit\\n\",\n      \"abap\\n\",\n      \"abbreviations\\n\",\n      \"abi\\n\",\n      \"abnormal psychology\\n\",\n      \"access\\n\",\n      \"access control\\n\",\n      \"access control lists\\n\",\n      \"access point\\n\",\n      \"accessibility\\n\",\n      \"account management\\n\",\n      \"accounts\\n\",\n      \"accuracy\\n\",\n      \"acer\\n\",\n      \"acm\\n\",\n      \"active directory\\n\",\n      \"active learning\\n\",\n      \"activity\\n\",\n      \"==================================================\\n\",\n      \"kp20k 680136\\n\",\n      \"kp20k (=680136) ∩ kptimes (=104797) = 3679\\n\",\n      \"1960s\\n\",\n      \"1996 presidential election\\n\",\n      \"20 questions\\n\",\n      \"2008 presidential election\\n\",\n      \"3 d\\n\",\n      \"311\\n\",\n      \"3m\\n\",\n      \"5g\\n\",\n      \"911\\n\",\n      \"aaa\\n\",\n      \"abacus\\n\",\n      \"abandonment\\n\",\n      \"abba\\n\",\n      \"abc\\n\",\n      \"abe\\n\",\n      \"abortion\\n\",\n      \"absenteeism\\n\",\n      \"ac dc\\n\",\n      \"accelerometer\\n\",\n      \"accountants\\n\",\n      \"==================================================\\n\",\n      \"kp20k (=680136) ∩ stackex (=8102) = 4509\\n\",\n      \"2 sat\\n\",\n      \"2d\\n\",\n      \"2d graphics\\n\",\n      \"3 sat\\n\",\n      \"32 bit\\n\",\n      \"3d\\n\",\n      \"3d objects\\n\",\n      \"3d scanner\\n\",\n      \"3d structure\\n\",\n      \"3g\\n\",\n      \"4d\\n\",\n      \"4g\\n\",\n      \"4k\\n\",\n      \"64 bit\\n\",\n      \"802 1x\\n\",\n      \"a b testing\\n\",\n      \"a star\\n\",\n      \"aac\\n\",\n      \"abap\\n\",\n      \"abbreviations\\n\",\n      \"==================================================\\n\",\n      \"kp20k (=680136) ∩ openkp (=206806) = 21832\\n\",\n      \"2\\n\",\n      \"20th century\\n\",\n      \"21st century skills\\n\",\n      \"25\\n\",\n      \"2d animation\\n\",\n      \"2d array\\n\",\n      \"3 dimensional objects\\n\",\n      \"3 methylindole\\n\",\n      \"304 stainless steel\\n\",\n      \"3d digital imaging\\n\",\n      \"3d graphics\\n\",\n      \"3d laser scanning\\n\",\n      \"3d map\\n\",\n      \"3d maps\\n\",\n      \"3d model\\n\",\n      \"3d modeling\\n\",\n      \"3d models\\n\",\n      \"3d perception\\n\",\n      \"3d printing\\n\",\n      \"3d puzzles\\n\",\n      \"==================================================\\n\",\n      \"openkp 206806\\n\",\n      \"openkp (=206806) ∩ kptimes (=104797) = 8899\\n\",\n      \"13th amendment\\n\",\n      \"19 kids and counting\\n\",\n      \"1900 galveston hurricane\\n\",\n      \"1930s\\n\",\n      \"2010 fifa world cup\\n\",\n      \"2012 summer olympics\\n\",\n      \"2013\\n\",\n      \"2014\\n\",\n      \"3m\\n\",\n      \"401k\\n\",\n      \"41\\n\",\n      \"49ers\\n\",\n      \"4chan\\n\",\n      \"5g\\n\",\n      \"a midsummer night s dream\\n\",\n      \"a p\\n\",\n      \"a raisin in the sun\\n\",\n      \"a series of unfortunate events\\n\",\n      \"a24\\n\",\n      \"aaa\\n\",\n      \"==================================================\\n\",\n      \"openkp (=206806) ∩ stackex (=8102) = 2678\\n\",\n      \"3d printer\\n\",\n      \"3d scanner\\n\",\n      \"64 bit\\n\",\n      \"abap\\n\",\n      \"abbreviations\\n\",\n      \"abi\\n\",\n      \"abnormal psychology\\n\",\n      \"access\\n\",\n      \"access control\\n\",\n      \"access control lists\\n\",\n      \"access point\\n\",\n      \"accessibility\\n\",\n      \"account management\\n\",\n      \"accounts\\n\",\n      \"accuracy\\n\",\n      \"acer\\n\",\n      \"acm\\n\",\n      \"active directory\\n\",\n      \"active learning\\n\",\n      \"activity\\n\",\n      \"==================================================\\n\",\n      \"openkp (=206806) ∩ kp20k (=680136) = 21832\\n\",\n      \"2\\n\",\n      \"20th century\\n\",\n      \"21st century skills\\n\",\n      \"25\\n\",\n      \"2d animation\\n\",\n      \"2d array\\n\",\n      \"3 dimensional objects\\n\",\n      \"3 methylindole\\n\",\n      \"304 stainless steel\\n\",\n      \"3d digital imaging\\n\",\n      \"3d graphics\\n\",\n      \"3d laser scanning\\n\",\n      \"3d map\\n\",\n      \"3d maps\\n\",\n      \"3d model\\n\",\n      \"3d modeling\\n\",\n      \"3d models\\n\",\n      \"3d perception\\n\",\n      \"3d printing\\n\",\n      \"3d puzzles\\n\",\n      \"==================================================\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for dataset, kp_set in dataset_kp_sets.items():\\n\",\n    \"    print(dataset, len(kp_set))\\n\",\n    \"    for sec_dataset, sec_kp_set in dataset_kp_sets.items():\\n\",\n    \"        if dataset == sec_dataset: continue\\n\",\n    \"        if len(kp_set) < len(sec_kp_set):\\n\",\n    \"            smaller = kp_set\\n\",\n    \"            larger = sec_kp_set\\n\",\n    \"        else:\\n\",\n    \"            smaller = sec_kp_set\\n\",\n    \"            larger = kp_set\\n\",\n    \"        smaller = set(p.lower().strip() for p in smaller)\\n\",\n    \"        larger = set(p.lower().strip() for p in larger)\\n\",\n    \"        \\n\",\n    \"#         print('smaller')\\n\",\n    \"#         for p in sorted(smaller)[:20]:\\n\",\n    \"#             print(p)\\n\",\n    \"#             print('-')\\n\",\n    \"#         print('larger')\\n\",\n    \"#         for p in sorted(larger)[:20]:\\n\",\n    \"#             print(p)\\n\",\n    \"#             print('-')\\n\",\n    \"        num_intersec = 0\\n\",\n    \"        intersec_kps = []\\n\",\n    \"        for p in smaller:\\n\",\n    \"            if p in larger: \\n\",\n    \"                num_intersec += 1\\n\",\n    \"                intersec_kps.append(p)\\n\",\n    \"        \\n\",\n    \"        print('%s (=%d) ∩ %s (=%d) = %d' % (dataset, len(kp_set), sec_dataset, len(sec_kp_set), num_intersec))\\n\",\n    \"\\n\",\n    \"        for p in sorted(intersec_kps)[:20]:\\n\",\n    \"            print(p)\\n\",\n    \"        print('=' * 50)\\n\",\n    \"            \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize histogram of two datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-10T04:06:47.180742Z\",\n     \"start_time\": \"2020-11-10T04:06:46.899160Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7fa94f6a28d0>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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DgAXbv3g2FQoHbt29j/Pjx6N+/vxjrjRs38Pnnn0Or1WLcuHHw9fXF66+/Xuf3hKOkEhFVoWfPnuJzU927d8ft27cNbvf888+ja9euAIAePXrgzp07AIBevXph3759+PTTT3Hq1Ck4OjpW2ZednR3efPNN8XVubi5mzZqFgIAATJ8+HQ8ePEBqaqq4fuTIkbCxsUHz5s0xbNgw/Prrr3XOF2BRIKJGrGvXrkhLSxOHtK7o6Ydinzxga4hSqRT/Xy6Xo6ysDADg7++PPXv24LnnnsPWrVsxf/78KmNp2rQpZDKZ+DoiIgK9e/fG4cOH8fXXX6Nt27bQarUG9xUEody+dcGiQESNlqenJ9RqNcLCwlBQUADg8axoO3bsQFFRUZ3bT0tLQ5s2bTB69GhMnz4dly5dAgDY29uLD/RWJT8/H+3atYNMJsOvv/6KtLS0cutjYmJQVlaGoqIiHDt2DH369KlzvACvKRBRI/fRRx9h48aNePvtt2Frayvekvr888/Xue1vv/0Whw8fhq2tLWQyGUJDQwEAgwcPRkxMDAIDA8ULzRX97W9/w9KlS7F161Z07NgRnTt3Lrf+xRdfxKRJk5CZmYmhQ4ea5HoCAMiE6uadq+eejN/BsY8aht1xKRYZ+8gcY/UD1vXZANKNfeTt7V1umbmeU9CXlUFuods6Ta3i2EcTJkzA5MmTjSoEht7zqn47AZ4+IiILM0dBAIDiKs6/U/Wso4wSETUiX375pdna5pECERGJWBSIiEjEokBERCIWBSIiEvFCMxE1aqWlpYiKikJsbCxsbGxgY2ODDh06YNasWfjTn/70TG0eP34cLi4u6Natm4mjNT8WBSKyKJ1eB4UZbku1a2LcfAqLFi3Co0ePEB0dDUdHRwiCgGPHjuHGjRt1Kgo+Pj4sCkREtaWQK/BNUqzJ2x3rE1DjNk8Pnf1ksDqZTCYOTFdSUoK1a9fi7NmzKC0tRadOnRAREYHmzZsjJCQESqUSt27dwr1799CjRw9oNBr89NNPSEhIwC+//ILo6GhMmjQJI0eOxJYtW3Do0CEAwEsvvYQlS5agefPmKCwsxIoVK8QhMN566y1MmTLF5O+HsXhNgYgarZqGzt62bRscHBywd+9exMTEwMXFBVu2bBHXX79+HVu3bkVsbCwuX76MX375BQMGDIBarcaUKVMQExODkSNH4vvvv8ehQ4fw9ddf4/Dhw9DpdIiMjAQAREZGQq/XiwPfxcTE4Pvvv5ckf0N4pEBE9L9+++03/O1vf8OjR48wYMAA/Oc//0FBQQHi4uIAPD5y6NLl/4ZNGTx4sDhMRNeuXXH79m288sorldo9deoUhg0bJk7kM3bsWKxatUpcFxoaCplMBnt7ewwfPhynTp3CwIEDzZ2uQSwKjYxOp4dCwQNEIuD/hs5++PAhHB0d8ac//QkxMTHYtWsXkpKSIAgCwsPD8fLLLxvc39ihtasb2trQOlMNg/0sWBQaGYVCjt1xKZL3a65B6YjqwtPTE4MGDcKSJUuwcuVKODg4AIA4bLZarcYXX3wBX19fNGnSBAUFBcjMzISXl1e17VYcGrtfv3745JNPMGHCBDRv3hx79+5Fv379xHV79+5Fz549UVhYiKNHj2LBggVmyrhmLApE1KitXr0akZGReOedd2BjYwNHR0e4uLhgypQp6NSpEzZs2IB33nkHMpkMMpkMM2bMqLEovPXWW1i0aBGOHTsmXmi+evUqxo0bBwDw8fHB1KlTAQDTpk3D8uXLMWLECHHfV1991bxJV4NDZ9dz5sjFUkcKHDq7fjNFPjXdXmpoGGdz3ZJapiuDjcI6/u6tOHR2bdR26GzreMeIqF6o6fbSjjp35Bbnmbxf56ZOlZZpH2lh05w/cbXFK45ERCRiUSAiIpHkRWHDhg3o3Lkzrl27BgBITU1FUFAQ/P39ERQUhFu3bkkdEhFJRkADvozZ4DzLey1pUbh8+TL+85//lLvQGB4ejuDgYMTFxSE4OBhhYWFShkREEtLLBOjKDN/LT6ZXWloKm1rOUy1ZUSgpKcGyZcsQHh4uPpiRk5OD5ORkBAQ8HqMkICAAycnJyM3NlSosIpJQnpCPnD9yIOh5tGBuer0emZmZVQ7hURXJLs2vW7cOb731Fjw8PMRlGRkZcHV1hULx+HY0hUIBFxcXZGRkwNnZ2ei2k5KSKi1LTEyse9D1hClzUalUSE9PN1l7xusi9it9/13M9n2wpu8ZUPd8VCoVMqr5fO9BBqFYh4cPHtSpn4ocmzqgpKTEpG1aA71eD71ejzt37hi9jyRF4fz587h06RLmzZtnlvb5nELtSP2cwNP9WuI5BQBm+T5Y0/cMMF0+bjV8vg/wCA/wqM79PG2sd+9Ky6zp8zF1Lk+eUzBEktNHZ8+exc2bNzFo0CCo1Wrcu3cP77//Pm7fvo3MzExxvBCdToesrCy4ublJERYREVUgSVGYMmWKOMZ4QkIC2rZti+3bt2PYsGHw9vZGbOzjh11iY2Ph7e1dq1NHRERkOhZ/3C8iIgIhISGIjIyEo6MjNBqNpUMiImq0LFIUEhISxP/38vJCdHS0JcIgIqIK+EQzERGJWBSIiEjEokBERCIWBSIiErEoEBGRiEWBiIhELApERCRiUSAiIhGLAhERiVgUiKyQTl/7iWysZURRqhuLj31ERKankCvwTVJsrfbJSE+vcdjrmoz1CajT/mR5PFIgIiIRiwIREYmMLgrffvutweXHjh0zWTBERGRZRheFxYsXG1weFhZmsmCIiMiyarzQ/GTCZ0EQKk3+fOfOHSiVSvNERkREkquxKLzxxhuQyWQQBAFvvPFGuXWtW7fGzJkzzRYcERFJq8aikJKSAgAYP348du3aZfaAiIjIcoy+psCCQERk/Yx+eO3OnTv49NNPceXKFRQVFZVbd/LkSVPHRUREFmB0UZg3bx48PDywcOFCNG3a1JwxERGRhRhdFK5fv449e/ZALufzbkRE1sroX/hevXohOTnZnLEQEZGFGX2k0K5dO7z//vsYMmQIWrduXW7d7NmzTR4YERFJz+iiUFxcDLVajbKyMty7d8+cMRERkYUYXRRWr15tzjiIiKgeqNUtqVXx8PCocf9p06bh7t27kMvlaNasGf7+97/D29sbqampCAkJQV5eHpycnKDRaODp6WlsWEREZEJGF4Wnh7t4QiaTAQCuXLlS4/4ajQYODg4AgOPHjyM0NBQHDhxAeHg4goODERgYiJiYGISFhWHnzp21zYOIiEzA6KLwZLiLJ/744w9s2LABfn5+Ru3/pCAAQEFBAWQyGXJycpCcnIzPP/8cABAQEIDly5cjNzcXzs7OxoZGREQm8szTcbZp0waLFy+Gv78/RowYYdQ+ixcvxs8//wxBELBt2zZkZGTA1dUVCoUCAKBQKODi4oKMjAwWBSIiC6jTHM03b95EcXGx0duvXLkSAHDw4EF8/PHHJruVNSkpqdKyxMREk7RdH5gyF5VKhfT0dJO1Z7wuYr/S99/FbN+H+vo9U6lUyHiG9/lZ9inHxwRtPGO/hj6L+vr5PAupcjG6KAQHB4vXEIDHt6j+9ttvmD59eq07HTlyJMLCwtC2bVtkZmZCp9NBoVBAp9MhKysLbm5utWrPx8cHdnZ24uvExESoVKpax1UfmSMX9zpOzl6XftPT0y3Svzm+D/X9e+ZWy/c5Iz291vuYol9TqfhZ1PfPpzZMnYtWqzX4xzRQi6IwZsyYcq+bNm2KLl26GHWnUGFhIR4+fCj+2CckJKBFixZo1aoVvL29ERsbi8DAQMTGxsLb25unjoiILMToojBq1Khn7qS4uBizZ89GcXEx5HI5WrRogaioKMhkMkRERCAkJASRkZFwdHSERqN55n6IiKhujC4KpaWl2LRpE2JiYpCVlQUXFxcEBgbir3/9a41TcrZu3RrffPONwXVeXl6Ijo6uXdRERGQWRheFNWvW4OLFi1i6dKl4bjgyMhIFBQUIDQ01Z4xERCQRo4vCsWPHEBMTg5YtWwIAOnbsiK5duyIwMJBFgYjIShg9dPbTTzIbs5yISCo6va7SMqnuPDLUd0Nm9JHC0KFDMXXqVEyfPh3u7u74/fffsWnTJgwdOtSc8RER1UghV+CbpNhyy0x1i21NxvoEmL0PKRldFObPn49NmzZh2bJlyMrKgqurK4YPH46pU6eaMz4iIpJQjaePEhMTsWbNGiiVSsyePRvfffcdLly4gPj4eJSUlHA2NiIiK1JjUdi8eTN69eplcF2fPn0QFRVl8qCIiMgyaiwKV65cwYABAwyu69evX5WPShMRUcNTY1EoKChAaWmpwXVlZWUoLCw0eVBERGQZNRaFjh074qeffjK47qeffkLHjh1NHhQREVlGjUVh4sSJCA8PR3x8PPR6PQBAr9cjPj4eERERmDRpktmDJCIiadR4S+qIESOQnZ2NhQsXorS0FE5OTsjLy4NSqcSsWbMQEGBd9+gSETVmRj2nMGnSJIwZMwbnz59HXl4enJyc4OvrC3t7e3PHR0REEjL64TV7e/sq70IiIiLrYPTYR0REZP1YFIiISMSiQEREIhYFIiISsSgQEZGIRYGIiEQsCkREJGJRICIiEYsCERGJWBSIiEjEokBWT6fTm6VdlUplkX6JzMnosY+IGiqFQo7tW/aZvN38/Hw4ODhUuf79KW+bvE8ic+ORAhERiSQ5Urh//z4WLFiA27dvQ6lUokOHDli2bBmcnZ2RmpqKkJAQcUhujUYDT09PKcIiC3B3d7d0CERUDUmKgkwmw5///Gf06dMHAKDRaPDJJ59g1apVCA8PR3BwMAIDAxETE4OwsDDs3LlTirBIYsVpl2s85WIeXSTuj6jhkuT0kZOTk1gQAKBHjx5IT09HTk4OkpOTxdnbAgICkJycjNzcXCnCIiKiCiS/pqDX67Fnzx6o1WpkZGTA1dUVCoUCAKBQKODi4oKMjAypwyIiIljg7qPly5ejWbNmGD9+PJKTk03SZlJSUqVliYmJJmm7PjBlLiqVCunp6SZrz3hdkJ+fDwDif6Vkrj5ratdS30OVSoWMZ/icn2WfcnxM0IYJ+5UkFh9pPmepvkuSFgWNRoO0tDRERUVBLpfDzc0NmZmZ0Ol0UCgU0Ol0yMrKgpubW63a9fHxgZ2dnfg6MTGxxnvIGwpz5GKpi70ODg4WuqYAs/RpTC6W/B661fJzzkhPr/U+pujXVCr2a6p8jGHuz9nUvwNardbgH9OAhKeP1q5di6SkJGzcuBFKpRIA0KpVK3h7eyM2NhYAEBsbC29vbzg7O0sVFhERPUWSI4Xr168jKioKnp6eGDduHACgffv22LhxIyIiIhASEoLIyEg4OjpCo9FIEZJF6XR6KBTG1WNrOeIhooZBkqLwwgsv4OrVqwbXeXl5ITo6Woow6g2FQo7dcSlGbZuenm7S0z3B/rw9k4iqxieaiYhIxKJAREQiFgUiIhKxKBARkYhFgYiIRCwKREQkYlEgIiIRiwIREYlYFIiISMSiQEREIhYFIiISsSgQEZGIRYGIiEQsCkREdaDT68zeh6Eh9M3Vr+TTcRIRWROFXIFvkmLN2oehWeTG+gSYpS8eKRARkYhFgYiIRCwKREQkYlEgIiIRiwIREYlYFIiISMSiQEREIhYFIiISsSgQEZGIRYGIiEQsCkREJGJRICIikSRFQaPRQK1Wo3Pnzrh27Zq4PDU1FUFBQfD390dQUBBu3bolRThERFQFSYrCoEGD8NVXX6Fdu3blloeHhyM4OBhxcXEIDg5GWFiYFOEQEVEVJCkKfn5+cHNzK7csJycHycnJCAh4PPxrQEAAkpOTkZubK0VIRERkgMWuKWRkZMDV1RUKhQIAoFAo4OLigoyMDEuFRETU6FnFJDtJSUmVliUmJlogEuOoVCqkp6cbvX1ttq1ZFxO3Z3y/+fn5ACD+V0rm6rOmdi31PVSpVMh4hs/5WfYpx8cEbZiwX0likSjnSn34mOf7ZbGi4ObmhszMTOh0OigUCuh0OmRlZVU6zWQMHx8f2NnZia8TExMNTl9Xn7hXmEWpKunp6UZva+q+Tc3BwQH5+flwcHCwSN+mZkwulvweVpypqyaGZveSol9TqdivqfJ5lr5NrWoreigAAA4gSURBVKpcnvX7pdVqDf4xDVjw9FGrVq3g7e2N2NjH09jFxsbC29sbzs7OlgqJiKjRk+RIYcWKFYiPj0d2djYmTZoEJycnHDlyBBEREQgJCUFkZCQcHR2h0WikCIfI6gkSTCZP1kmSorBkyRIsWbKk0nIvLy9ER0dLEQI9pTjtsgV67WKBPi0v94f/Z5F+nV8Nski/1PDxiWYiIhKxKBARkYhFgYiIRCwKREQkYlEgIiIRiwIREYlYFIiISMSiQEREIhYFIiISsSgQEZGIRYGIiEQsCkREJGJRICIiEYsCERGJWBSIiEjEokBERCKLzdFMZO10Or3FJrvR6fQY6xNQu5186t4vZ3xr+FgUiMxEoZBj+5Z9Fun7/Slv46e9y2u1T35+PhwcHOrUb/93/l6n/cnyePqIiIhELApERCRq1EVBp9NbOoQaubu7WzoEImpEGvU1BYVCjt1xKZL3G+zfBcVpl43a1hTnecvrYsK2iMjaNOojBSIiKq9RHykQWSudTm+RO4EawilZqh6LApEZ/VGYY5F+FQo5Plq7pVb7aLVa2NnZ1anfkA+n1Gl/sjyePiIiIlG9KAqpqakICgqCv78/goKCcOvWLUuHRETUKNWLohAeHo7g4GDExcUhODgYYWFhlg6JiKhRsnhRyMnJQXJyMgICHo/TEhAQgOTkZOTm5lo4MiKixsfiF5ozMjLg6uoKhUIBAFAoFHBxcUFGRgacnZ2r3VcQBABASUlJpXVardao/m3k0t8todVqoVQa99bbNbE1eltT921KT/o1dT616dvUaspFq9WiiZ3S5P0a41n6lssApbJu8Wq1WtgIijq1Yap+7eRKSWKRImdDuRj7G2eIod/MJ2TCk19WC0lKSsLChQtx5MgRcdmwYcOwZs0avPjii9Xum5+fj2vXrpk7RCIiq+Tj41PpjjOLHym4ubkhMzMTOp0OCoUCOp0OWVlZcHNzq3Hf5s2bo1OnTrC1tYVMJpMgWiKihu/JsYChI0OLF4VWrVrB29sbsbGxCAwMRGxsLLy9vWs8dQQAcrncxENAEBE1bhY/fQQAN27cQEhICB4+fAhHR0doNBp07NjR0mERETU69aIoEBFR/WDxW1KJiKj+YFEgIiIRiwIREYlYFIiISMSiQEREIos/p1AbGo0GcXFx+P3333H48GF06tQJd+/exfTp08Vt8vPzUVBQgDNnzgB4PAJrSEgI8vLy4OTkBI1GA09PTwtlUJ6hfADgxIkTWLduHQRBgF6vx8yZMzFkyBAA9TefqnI5efIk1q1bh7KyMrRo0QKrV6+Gh4cHgPqbCwDcv38fCxYswO3bt6FUKtGhQwcsW7YMzs7O1cZdH3OqLpeqPjegfuYCVJ2PTCarMk+gfuZT3Wczbdo03L17F3K5HM2aNcPf//53eHt7mz8XoQE5e/askJ6eLrz++uvC1atXDW6zYsUKYenSpeLrCRMmCAcPHhQEQRAOHjwoTJgwQZJYjWEoH71eL/j5+Ymvr1y5IvTo0UPQ6XSCINTffAzlkpeXJ/Tu3Vu4efOmIAiP4508ebK4T33NRRAE4f79+8Kvv/4qvv7oo4+ERYsWCYJQfdz1Mafqcqnu31R9zEUQqs6nujwFoX7mU13MDx8+FJd/9913wsiRI8XX5sylQRWFJ6oqClqtVujTp4+QlJQkCIIgZGdnCyqVSigrKxMEQRDKysoElUol5OTkSBpvTSoWhd69ewvnzp0TBEEQzpw5IwwZMkQQhIaRz9O5XLhwQRg2bJi47v79+0KnTp2EnJycBpHL044dOyb893//d7VxN5ScnuTytIr/phpKLoJgOJ+KyxtKPlXlcuDAAWHUqFGCIJg/lwZ1+qgmCQkJcHV1FQfSq8sIrJYik8nw6aefYtq0aWjWrBkKCwuxefNmAA0vn+effx7Z2dm4ePEiunXrhsOHDwN4nIcgCA0mF71ejz179kCtVlf7GTSEnJ7OpToN5btWVT4VlzeEfAzlsnjxYvz8888QBAHbtm0DYP5crOpC8759+/D2229bOow6KSsrw+bNmxEZGYkTJ05g06ZN+PDDD1FYWGjp0GrNwcEBa9euxerVqzF69Gjk5OTA0dERNjYN62+R5cuXo1mzZhg/frylQ6kza8oFqDqfhpinoZhXrlyJkydP4sMPP8THH38sSRwN619nNTIzM3H27Nlyb1xdRmC1lCtXriArKwsqlQoAoFKp0LRpU9y4cQPt2rVrcPn069cP/fr1AwBkZ2dj+/bt8PDwQHFxcYPIRaPRIC0tDVFRUZDL5dV+pwRBqNc5VcylOg3h305V+RhaXt/zqemzGTlyJMLCwnD//n2z52I1RwoHDhzAwIED0bJlS3HZ0yOwAqjVCKyW0rZtW9y7dw83b94E8HiwwOzsbDz33HMNMp8//vgDwOND43/+858YN24cmjVr1iByWbt2LZKSkrBx40ZxiOHq4q7PORnKpTr1OReg6nyqWl6f8zEUc2FhITIyMsRtEhIS0KJFCzg5OZk9lwY1IN6KFSsQHx+P7OxstGzZEk5OTuLkPP7+/li8eDFeffXVcvvU5xFYq8rn0KFD2Lp1qzhHxKxZszB48GAA9TefqnJZvHgx/v3vf6O0tBSvvPIKQkNDxUk96msuAHD9+nUEBATA09MTTZo0AQC0b98eGzdurDbu+phTdblU92+qPuYCVJ3PnDlzqswTqJ/5VJXL0qVLMW3aNBQXF0Mul6NFixZYuHCheL3UnLk0qKJARETmZTWnj4iIqO5YFIiISMSiQEREIhYFIiISsSgQEZGIRYEs6ubNmxg5ciR8fX3Ru3dvrF27FgBw7tw5+Pv7Wzi68k6fPl3plmeprF+/HvPmzbNI39S4sCiQRW3btg29e/fG+fPny4354ufnh7i4OAtGZjmWLD5ELApkUenp6XjhhRcsHYaorKzM0iEQWRSLAlnMe++9h9OnT2PZsmXw9fVFaWmpuK7iX8tqtRqbN2/GsGHD0KtXLyxatAharbbctlFRUejTpw/UajUOHTok7ltSUgKNRoPXXnsN/fr1Q1hYGB49elRu3y1btuCVV17BokWLjI4/MzMTM2fORN++faFWq7Fz505x3fr16zF79mwsWLAAvr6+GD58OC5duiSuv3z5snjabNasWZgzZw7Wrl2LoqIi/OUvf0FWVhZ8fX3h6+uLzMxMAEBpaWmV7VVFrVZj27ZtGDFiBHr06IHQ0FBkZ2fjz3/+M3x9fTFx4kQ8ePAAAHD37l107twZ+/btw8CBA9GrVy/s2bMHFy9exIgRI+Dn54dly5YZ/f5Qw8SiQBazc+dO+Pn5ISwsDOfPn4etrW212x8+fBjbt2/Hd999h9TUVERGRorrsrOzcf/+ffz444/46KOPEBYWJo4ftWbNGqSmpuLgwYOIj49HVlaWOPTBk30fPHiAEydOYPny5UbFrtfrMXXqVHTu3Bk//PADduzYgR07duDHH38Ut0lISMDw4cNx7tw5qNVqse2SkhLMmDEDo0aNwpkzZxAQEIDjx48DAJo1a4atW7fCxcUF58+fx/nz5+Hq6lptezWJj4/H559/jri4OJw4cQJ/+ctfMHfuXJw+fRp6vR5ffvllue0vXLiA+Ph4rF27FqtWrUJUVBS++OILHDlyBN9++604qyFZJxYFajD+67/+C25ubnBycsLUqVPFMXqemD17NpRKJXr37o2BAwfi22+/hSAIiI6ORmhoKJycnGBvb48PPvig3L5yuRyzZs2CUqkUx5+pyaVLl5Cbm4sZM2ZAqVTCw8MDY8eOxdGjR8VtVCoVBg4cCIVCgcDAQKSkpAB4/KNbVlaG9957D7a2thgyZAheeumlGvusqr2ajB8/Hq1bt4arqyv8/PzQrVs3dO3aFUqlEm+88QaSk5PLbT99+nTY2dmhf//+aNasGQICAtCqVStx/4rbk3WxmqGzyfo9PTSwu7s7srKyxNeOjo5o1qxZpfW5ubkoLi7G6NGjxXXC/859/UTLli3FQfqM9fvvvyMrKwt+fn7iMp1OV+5169atxf9v0qQJtFotysrKkJWVBVdXV3HAw4q5VaWq9mqan+Lp/ezs7Cq1U1RUVG77Vq1aldu+4uuK25N1YVGgBuPpoYTT09Ph4uIivn748CGKiorEwpCRkYEXXngBLVu2RJMmTXDkyBHxNExFT/84G8vNzQ3t27dHfHx8rfdt06YNMjMzIQiC2HdGRgY8PDyeOR4iU+HpI2owdu/ejXv37iEvL0+86Py09evXo6SkBOfOncPJkycxdOhQyOVyjBkzBqtWrUJOTg6AxxeInz73/yy6desGe3t7bNmyBY8ePYJOp8O1a9dw8eLFGvft0aMHFAoFdu3ahbKyMhw/frzcReNWrVohLy8P+fn5dYqR6FmwKFCDERAQgMmTJ2Pw4MHw8PDA1KlTxXWtW7eGo6MjBgwYgHnz5iEiIgJeXl4AgPnz56NDhw4YO3YsevbsiYkTJyI1NbVOsSgUCmzatAkpKSkYNGgQ+vbtiyVLlqCgoKDGfZVKJdavX4+9e/eiV69eOHToEF577TVxghUvLy8MHz4cgwcPhp+fn3j3EZEUOJ8CNQhqtRorVqwQp/Z82unTpzF//nz88MMPFojMNMaMGYNx48Y1+DnGqeHjkQKRBZw5cwZ//PEHysrKcODAAVy9ehUDBgywdFhEvNBM9LSoqChs3ry50nKVSoVt27aZrJ/U1FTMmTMHRUVF8PDwwGeffVbuwrmx0tPTMXz4cIPrjhw5And397qGSo0MTx8REZGIp4+IiEjEokBERCIWBSIiErEoEBGRiEWBiIhELApERCT6/3YZ3T8qt1RnAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.__version__\\n\",\n    \"penguins = sns.load_dataset(\\\"penguins\\\")\\n\",\n    \"sns.histplot(data=penguins, x=\\\"flipper_length_mm\\\", hue=\\\"species\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-10T04:09:19.376341Z\",\n     \"start_time\": \"2020-11-10T04:09:18.196431Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k and MagKP')\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"Greens_r\\\", 8)[6], label=\\\"MagKP\\\", bins=np.arange(31) - 0.5, kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"w\\\", linewidth=0.2))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n >= 3 and n <= 6]\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-LN\\\", bins=np.arange(31)-0.5, \\n\",\n    \"             color=\\\"c\\\",\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"w\\\", linewidth=0.1))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10 and n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"Blues_r\\\", 8)[4], label=\\\"MagKP-N\\\", bins=np.arange(31)-0.5, kde=False, rug=False, hist_kws=dict(alpha=0.5, edgecolor=\\\"w\\\", linewidth=0.2))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=sns.color_palette(\\\"hls\\\", 8)[0], label=\\\"KP20k\\\", bins=np.arange(31) - 0.5, kde=False, rug=False, hist_kws=dict(alpha=0.6, edgecolor=\\\"k\\\", linewidth=1.5))\\n\",\n    \"\\n\",\n    \"plt.xlim([-1, 30])\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k and MagKP')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 203,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T21:06:33.627156Z\",\n     \"start_time\": \"2020-11-23T21:06:32.172778Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2391870\\n\",\n      \"521542\\n\",\n      \"1520307\\n\",\n      \"521542\\n\",\n      \"511653\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 0, '#(phrase) per paper')\"\n      ]\n     },\n     \"execution_count\": 203,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 30]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP\\\",\\n\",\n    \"             bins=np.arange(31) - 0.5, color=\\\"w\\\",\\n\",\n    \"             hist_kws=dict(alpha=1.0, edgecolor=\\\"k\\\", linewidth=5.0),\\n\",\n    \"             kde=False, kde_kws={\\\"color\\\": \\\"k\\\", \\\"lw\\\": 3, \\\"label\\\": \\\"KDE\\\"})\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n >= 3 and n <= 6]\\n\",\n    \"magkpln_tgt_nums = tmp_tgt_nums\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-LN\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Blues_r\\\", 8)[3],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.8, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-Nlarge\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Greys_r\\\", 8)[5],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=0.8, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n > 10]\\n\",\n    \"tmp_tgt_nums = tmp_tgt_nums[: len(magkpln_tgt_nums)]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"MagKP-Nsmall\\\", \\n\",\n    \"             bins=np.arange(31)-0.5, color=sns.color_palette(\\\"Greys_r\\\", 8)[2],\\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"k\\\", linewidth=0.0))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"print(len(tmp_tgt_nums))\\n\",\n    \"sns.distplot(tmp_tgt_nums, label=\\\"KP20k\\\", \\n\",\n    \"             bins=np.arange(31) - 0.5, color=sns.color_palette(\\\"hls\\\", 8)[0], \\n\",\n    \"             kde=False, rug=False, hist_kws=dict(alpha=1.0, edgecolor=\\\"red\\\", linewidth=2.5))\\n\",\n    \"\\n\",\n    \"plt.xlim([-1, 30])\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"ax.set_ylabel('#(papers)')\\n\",\n    \"ax.set_xlabel('#(phrase) per paper')\\n\",\n    \"# ax.set_title('Histogram of #(kp per document) of KP20k and MagKP')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check #(unique_kp) in each dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T05:38:33.869419Z\",\n     \"start_time\": \"2020-11-23T05:38:31.184Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"magkp\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k', 'magkp']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_tgt_dict = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    dataset_tgt_dict[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                json_dict['abstract'] = json_dict['question']\\n\",\n    \"                json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"                del json_dict['question']\\n\",\n    \"                del json_dict['tags']\\n\",\n    \"\\n\",\n    \"            title = json_dict['title']\\n\",\n    \"            abstract = json_dict['abstract']\\n\",\n    \"            fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"            keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                json_dict['keywords'] = keywords\\n\",\n    \"            keywords = [k.lower().strip() for k in keywords]\\n\",\n    \"            \\n\",\n    \"            dataset_tgt_dict[dataset_name].append(keywords)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T00:22:10.562675Z\",\n     \"start_time\": \"2020-11-23T00:22:09.463568Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# prepare Magkp subsets\\n\",\n    \"dataset_tgt_dict['magkp_ln'] = [kps for kps in dataset_tgt_dict[\\\"magkp\\\"] if len(kps) >= 3 and len(kps) <= 6]\\n\",\n    \"dataset_tgt_dict['magkp_nlarge'] = [kps for kps in dataset_tgt_dict[\\\"magkp\\\"] if len(kps) > 10]\\n\",\n    \"dataset_tgt_dict['magkp_nsmall'] = dataset_tgt_dict['magkp_nlarge'][: len(dataset_tgt_dict['magkp_ln'])]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-23T21:27:32.323475Z\",\n     \"start_time\": \"2020-11-23T21:26:12.282108Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"**************************************************\\n\",\n      \"kp20k\\n\",\n      \"num_doc= 514154\\n\",\n      \"num_unique_kp= 699791\\n\",\n      \"num_kp= 2710067\\n\",\n      \"len_kp= 1.9230266262789812\\n\",\n      \"max_kp_in_doc= 110\\n\",\n      \"len_kp_list= 91\\n\",\n      \"**************************************************\\n\",\n      \"magkp\\n\",\n      \"num_doc= 2699094\\n\",\n      \"num_unique_kp= 6880853\\n\",\n      \"num_kp= 41605964\\n\",\n      \"len_kp= 3.4161944427005704\\n\",\n      \"max_kp_in_doc= 438\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_ln\\n\",\n      \"num_doc= 521542\\n\",\n      \"num_unique_kp= 579244\\n\",\n      \"num_kp= 2331072\\n\",\n      \"len_kp= 2.726639932185707\\n\",\n      \"max_kp_in_doc= 6\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_nlarge\\n\",\n      \"num_doc= 1520307\\n\",\n      \"num_unique_kp= 5784959\\n\",\n      \"num_kp= 35525765\\n\",\n      \"len_kp= 3.376301903702848\\n\",\n      \"max_kp_in_doc= 438\\n\",\n      \"len_kp_list= 100\\n\",\n      \"**************************************************\\n\",\n      \"magkp_nsmall\\n\",\n      \"num_doc= 521542\\n\",\n      \"num_unique_kp= 2236091\\n\",\n      \"num_kp= 12193980\\n\",\n      \"len_kp= 3.3652286620119107\\n\",\n      \"max_kp_in_doc= 262\\n\",\n      \"len_kp_list= 100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"for dataset, kps_list in dataset_tgt_dict.items():\\n\",\n    \"    kp_set = set()\\n\",\n    \"    num_kp = 0\\n\",\n    \"    max_kp_in_doc = 0\\n\",\n    \"    max_len_kp = 0\\n\",\n    \"    len_kp_list = []\\n\",\n    \"    for kps in kps_list:\\n\",\n    \"        for kp in kps:\\n\",\n    \"            kp_set.add(kp)\\n\",\n    \"            num_kp += 1\\n\",\n    \"            num_word = len(kp.split())\\n\",\n    \"            len_kp_list.append(num_word)\\n\",\n    \"            if num_word > max_len_kp:\\n\",\n    \"                max_len_kp = num_word\\n\",\n    \"        if len(kps) > max_kp_in_doc:\\n\",\n    \"            max_kp_in_doc = len(kps)\\n\",\n    \"    num_unique_kp = len(kp_set)\\n\",\n    \"    print('*' * 50)\\n\",\n    \"    print(dataset)\\n\",\n    \"    print('num_doc=', len(kps_list))\\n\",\n    \"    print('num_unique_kp=', num_unique_kp)\\n\",\n    \"    print('num_kp=', num_kp)\\n\",\n    \"    print('len_kp=', np.mean(len_kp_list))\\n\",\n    \"    print('max_kp_in_doc=', max_kp_in_doc)\\n\",\n    \"    print('len_kp_list=', max_len_kp)\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### print num_paper binned by num_kp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-11-22T03:20:08.168149Z\",\n     \"start_time\": \"2020-11-22T03:20:08.113779Z\"\n    },\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"1884\\n\",\n      \"15433\\n\",\n      \"88651\\n\",\n      \"136732\\n\",\n      \"135117\\n\",\n      \"68930\\n\",\n      \"25108\\n\",\n      \"12786\\n\",\n      \"6306\\n\",\n      \"3871\\n\",\n      \"2054\\n\",\n      \"1517\\n\",\n      \"1088\\n\",\n      \"945\\n\",\n      \"859\\n\",\n      \"817\\n\",\n      \"844\\n\",\n      \"793\\n\",\n      \"866\\n\",\n      \"856\\n\",\n      \"807\\n\",\n      \"731\\n\",\n      \"743\\n\",\n      \"676\\n\",\n      \"657\\n\",\n      \"604\\n\",\n      \"546\\n\",\n      \"500\\n\",\n      \"492\\n\",\n      \"440\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 30]\\n\",\n    \"for bin_count in np.bincount(tmp_tgt_nums):\\n\",\n    \"    print(bin_count)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Stats of KP20k\\n\",\n    \"\\n\",\n    \"##### w/o preprocessing\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(data examples)=514,154 \\n\",\n    \"- #(KP)=2,710,067\\n\",\n    \"- #(unique KP)=710,218\\n\",\n    \" \\n\",\n    \" \\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=19,336 (3.76%)\\n\",\n    \"- #(KP)=401,763 (14.82%)\\n\",\n    \"- #(unique KP)=52,176 (7.35%)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##### w/ preprocessing\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=514,154\\n\",\n    \"- #(KP)=2,710,067\\n\",\n    \"- #(unique KP)=625,058 (diff between w/&w/o preprocessing: 85,160)\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=19,336\\n\",\n    \"- #(KP)=401,763 (14.82%)\\n\",\n    \"- #(unique KP)=48,125 (7.70%, diff between w/&w/o preprocessing: 4,051)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count #kp per document\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DescribeResult(nobs=514154, minmax=(1, 110), mean=5.270924664594655, variance=14.141117540879774, skewness=5.39192287405869, kurtosis=40.41415445884668)\\n\",\n      \"Percentile@0 = 1.000000\\n\",\n      \"Percentile@1 = 2.000000\\n\",\n      \"Percentile@2 = 2.000000\\n\",\n      \"Percentile@3 = 2.000000\\n\",\n      \"Percentile@4 = 3.000000\\n\",\n      \"Percentile@5 = 3.000000\\n\",\n      \"Percentile@6 = 3.000000\\n\",\n      \"Percentile@7 = 3.000000\\n\",\n      \"Percentile@8 = 3.000000\\n\",\n      \"Percentile@9 = 3.000000\\n\",\n      \"Percentile@10 = 3.000000\\n\",\n      \"Percentile@11 = 3.000000\\n\",\n      \"Percentile@12 = 3.000000\\n\",\n      \"Percentile@13 = 3.000000\\n\",\n      \"Percentile@14 = 3.000000\\n\",\n      \"Percentile@15 = 3.000000\\n\",\n      \"Percentile@16 = 3.000000\\n\",\n      \"Percentile@17 = 3.000000\\n\",\n      \"Percentile@18 = 3.000000\\n\",\n      \"Percentile@19 = 3.000000\\n\",\n      \"Percentile@20 = 3.000000\\n\",\n      \"Percentile@21 = 4.000000\\n\",\n      \"Percentile@22 = 4.000000\\n\",\n      \"Percentile@23 = 4.000000\\n\",\n      \"Percentile@24 = 4.000000\\n\",\n      \"Percentile@25 = 4.000000\\n\",\n      \"Percentile@26 = 4.000000\\n\",\n      \"Percentile@27 = 4.000000\\n\",\n      \"Percentile@28 = 4.000000\\n\",\n      \"Percentile@29 = 4.000000\\n\",\n      \"Percentile@30 = 4.000000\\n\",\n      \"Percentile@31 = 4.000000\\n\",\n      \"Percentile@32 = 4.000000\\n\",\n      \"Percentile@33 = 4.000000\\n\",\n      \"Percentile@34 = 4.000000\\n\",\n      \"Percentile@35 = 4.000000\\n\",\n      \"Percentile@36 = 4.000000\\n\",\n      \"Percentile@37 = 4.000000\\n\",\n      \"Percentile@38 = 4.000000\\n\",\n      \"Percentile@39 = 4.000000\\n\",\n      \"Percentile@40 = 4.000000\\n\",\n      \"Percentile@41 = 4.000000\\n\",\n      \"Percentile@42 = 4.000000\\n\",\n      \"Percentile@43 = 4.000000\\n\",\n      \"Percentile@44 = 4.000000\\n\",\n      \"Percentile@45 = 4.000000\\n\",\n      \"Percentile@46 = 4.000000\\n\",\n      \"Percentile@47 = 4.000000\\n\",\n      \"Percentile@48 = 5.000000\\n\",\n      \"Percentile@49 = 5.000000\\n\",\n      \"Percentile@50 = 5.000000\\n\",\n      \"Percentile@51 = 5.000000\\n\",\n      \"Percentile@52 = 5.000000\\n\",\n      \"Percentile@53 = 5.000000\\n\",\n      \"Percentile@54 = 5.000000\\n\",\n      \"Percentile@55 = 5.000000\\n\",\n      \"Percentile@56 = 5.000000\\n\",\n      \"Percentile@57 = 5.000000\\n\",\n      \"Percentile@58 = 5.000000\\n\",\n      \"Percentile@59 = 5.000000\\n\",\n      \"Percentile@60 = 5.000000\\n\",\n      \"Percentile@61 = 5.000000\\n\",\n      \"Percentile@62 = 5.000000\\n\",\n      \"Percentile@63 = 5.000000\\n\",\n      \"Percentile@64 = 5.000000\\n\",\n      \"Percentile@65 = 5.000000\\n\",\n      \"Percentile@66 = 5.000000\\n\",\n      \"Percentile@67 = 5.000000\\n\",\n      \"Percentile@68 = 5.000000\\n\",\n      \"Percentile@69 = 5.000000\\n\",\n      \"Percentile@70 = 5.000000\\n\",\n      \"Percentile@71 = 5.000000\\n\",\n      \"Percentile@72 = 5.000000\\n\",\n      \"Percentile@73 = 5.000000\\n\",\n      \"Percentile@74 = 6.000000\\n\",\n      \"Percentile@75 = 6.000000\\n\",\n      \"Percentile@76 = 6.000000\\n\",\n      \"Percentile@77 = 6.000000\\n\",\n      \"Percentile@78 = 6.000000\\n\",\n      \"Percentile@79 = 6.000000\\n\",\n      \"Percentile@80 = 6.000000\\n\",\n      \"Percentile@81 = 6.000000\\n\",\n      \"Percentile@82 = 6.000000\\n\",\n      \"Percentile@83 = 6.000000\\n\",\n      \"Percentile@84 = 6.000000\\n\",\n      \"Percentile@85 = 6.000000\\n\",\n      \"Percentile@86 = 6.000000\\n\",\n      \"Percentile@87 = 7.000000\\n\",\n      \"Percentile@88 = 7.000000\\n\",\n      \"Percentile@89 = 7.000000\\n\",\n      \"Percentile@90 = 7.000000\\n\",\n      \"Percentile@91 = 7.000000\\n\",\n      \"Percentile@92 = 8.000000\\n\",\n      \"Percentile@93 = 8.000000\\n\",\n      \"Percentile@94 = 8.000000\\n\",\n      \"Percentile@95 = 9.000000\\n\",\n      \"Percentile@96 = 10.000000\\n\",\n      \"Percentile@97 = 13.000000\\n\",\n      \"Percentile@98 = 19.000000\\n\",\n      \"Percentile@99 = 25.000000\\n\",\n      \"Percentile@100 = 110.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"data = tgt_nums[\\\"kp20k\\\"]\\n\",\n    \"print(scipy.stats.describe(data))\\n\",\n    \"\\n\",\n    \"for p in np.linspace(0, 100, 101):\\n\",\n    \"    percentile = np.percentile(data, p, interpolation='lower')\\n\",\n    \"    print('Percentile@%.0f = %.6f' % (p, percentile))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T04:00:35.137583Z\",\n     \"start_time\": \"2020-09-22T04:00:35.105311Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"429430/514154\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n >=3 and n <= 6]\\n\",\n    \"print('%d/%d' % (len(tmp_tgt_nums), len(tgt_nums[\\\"kp20k\\\"])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:57:34.284991Z\",\n     \"start_time\": \"2020-09-22T03:57:34.098594Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k (truncated at 10)')\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"] if n <= 10]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=10, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k (truncated at 10)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:17:46.526807Z\",\n     \"start_time\": \"2020-09-22T03:17:46.241163Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of KP20k')\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"kp20k\\\"]]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=100, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of KP20k')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count unique phrases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##### only count documents that #(kp)>10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:08:27.482077Z\",\n     \"start_time\": \"2020-08-12T06:08:13.670328Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=19336\\n\",\n      \"#(KP)=401763\\n\",\n      \"#(unique KP)=48125\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'kp20k'\\n\",\n    \"do_preprocess = True\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"        \\n\",\n    \"        if len(keywords) > 10:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = '_'.join(tokens)\\n\",\n    \"                    \\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#####  count all documents #(kp)>0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:37:34.579653Z\",\n     \"start_time\": \"2020-08-12T16:37:26.863175Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=514154\\n\",\n      \"#(KP)=2710067\\n\",\n      \"#(unique KP)=710218\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'kp20k'\\n\",\n    \"do_preprocess = False\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"kp_len_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"        \\n\",\n    \"        if len(keywords) > 0:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = ' '.join(tokens)\\n\",\n    \"                    \\n\",\n    \"                tokens = [t for t in keyword.split()]\\n\",\n    \"                kp_len_counter[len(tokens)] = kp_len_counter[len(tokens)] + 1\\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"start_time\": \"2020-09-22T00:02:32.633Z\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_kp_freqs = [v for k,v in unique_kp_counter.items() if v > 1000]\\n\",\n    \"sns.distplot(tmp_kp_freqs, color=\\\"teal\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  KP length distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:40:25.845738Z\",\n     \"start_time\": \"2020-08-12T16:40:25.254505Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#kp_len=0, freq=63, accum/total=0.00%\\n\",\n      \"#kp_len=1, freq=907946, accum/total=33.51%\\n\",\n      \"#kp_len=2, freq=1267094, accum/total=80.26%\\n\",\n      \"#kp_len=3, freq=418621, accum/total=95.71%\\n\",\n      \"#kp_len=4, freq=87761, accum/total=98.95%\\n\",\n      \"#kp_len=5, freq=19873, accum/total=99.68%\\n\",\n      \"#kp_len=6, freq=5200, accum/total=99.87%\\n\",\n      \"#kp_len=7, freq=1625, accum/total=99.93%\\n\",\n      \"#kp_len=8, freq=670, accum/total=99.96%\\n\",\n      \"#kp_len=9, freq=268, accum/total=99.97%\\n\",\n      \"#kp_len=10, freq=147, accum/total=99.97%\\n\",\n      \"#kp_len=11, freq=219, accum/total=99.98%\\n\",\n      \"#kp_len=12, freq=234, accum/total=99.99%\\n\",\n      \"#kp_len=13, freq=98, accum/total=99.99%\\n\",\n      \"#kp_len=14, freq=38, accum/total=99.99%\\n\",\n      \"#kp_len=15, freq=41, accum/total=99.99%\\n\",\n      \"#kp_len=16, freq=30, accum/total=99.99%\\n\",\n      \"#kp_len=17, freq=19, accum/total=100.00%\\n\",\n      \"#kp_len=18, freq=9, accum/total=100.00%\\n\",\n      \"#kp_len=19, freq=21, accum/total=100.00%\\n\",\n      \"#kp_len=20, freq=14, accum/total=100.00%\\n\",\n      \"#kp_len=21, freq=12, accum/total=100.00%\\n\",\n      \"#kp_len=22, freq=5, accum/total=100.00%\\n\",\n      \"#kp_len=23, freq=17, accum/total=100.00%\\n\",\n      \"#kp_len=24, freq=11, accum/total=100.00%\\n\",\n      \"#kp_len=25, freq=6, accum/total=100.00%\\n\",\n      \"#kp_len=26, freq=4, accum/total=100.00%\\n\",\n      \"#kp_len=27, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=28, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=29, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=31, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=32, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=33, freq=3, accum/total=100.00%\\n\",\n      \"#kp_len=34, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=35, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=36, freq=2, accum/total=100.00%\\n\",\n      \"#kp_len=40, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=50, freq=1, accum/total=100.00%\\n\",\n      \"#kp_len=91, freq=1, accum/total=100.00%\\n\",\n      \"39\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(16, 12))\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"kp_lens = sorted([(kp_len, freq) for kp_len, freq in kp_len_counter.items()], key=lambda k:k[0])\\n\",\n    \"\\n\",\n    \"accum_kp_count = 0\\n\",\n    \"total_kp_count = sum(freq for _, freq in kp_lens)\\n\",\n    \"for kp_len, freq in kp_lens:\\n\",\n    \"    accum_kp_count += freq\\n\",\n    \"    print('#kp_len=%d, freq=%d, accum/total=%.2f%%' % (kp_len, freq, accum_kp_count / total_kp_count * 100))\\n\",\n    \"    \\n\",\n    \"print(len(kp_lens))\\n\",\n    \"kp_lens_df = pd.DataFrame(kp_lens, columns=['#kp_len', 'freq'])\\n\",\n    \"ax = sns.barplot(x=\\\"#kp_len\\\", y=\\\"freq\\\", data=kp_lens_df)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Stats of MagKP\\n\",\n    \"\\n\",\n    \"##### w/o preprocessing\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=2,699,094\\n\",\n    \"- #(KP)=41,605,964\\n\",\n    \"- #(unique KP)=6,880,853\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=1,520,307 (56.33%)\\n\",\n    \"- #(KP)=35,525,765 (85.39%)\\n\",\n    \"- #(unique KP)=5,784,959 (84.07%)\\n\",\n    \"\\n\",\n    \"##### w/ preprocessing (lowercase and stemming)\\n\",\n    \"\\n\",\n    \"All documents\\n\",\n    \"- #(DP)=2,699,094\\n\",\n    \"- #(KP)=41,605,964\\n\",\n    \"- #(unique KP)=6,537,481 (diff between w/&w/o preprocessing: 343,372, 5.25% difference)\\n\",\n    \"\\n\",\n    \"For documents whose \\\\#(kp)>10\\n\",\n    \"- #(DP)=1,520,307\\n\",\n    \"- #(KP)=35,525,765 （85.39%）\\n\",\n    \"- #(unique KP)=5,493,997 (84.04%, diff between w/&w/o preprocessing: 290,962)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### load data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"magkp\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k', 'magkp']\\n\",\n    \"\\n\",\n    \"# json_base_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/' # path to the json folder\\n\",\n    \"json_base_dir = '/zfs1/hdaqing/rum20/kp/data/kp/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_examples = {}\\n\",\n    \"    \\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    dataset_examples[dataset_name] = []\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, 'train.json')\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for json_line in input_json:\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"            if dataset_name == 'stackexchange':\\n\",\n    \"                ex_dict['abstract'] = ex_dict['question']\\n\",\n    \"                ex_dict['keywords'] = ex_dict['tags']            \\n\",\n    \"                del ex_dict['question']\\n\",\n    \"                del ex_dict['tags']\\n\",\n    \"\\n\",\n    \"            keywords = ex_dict['keywords']\\n\",\n    \"            ex_dict['fulltext'] = ''\\n\",\n    \"\\n\",\n    \"            if isinstance(keywords, str):\\n\",\n    \"                keywords = keywords.split(';')\\n\",\n    \"                ex_dict['keywords'] = keywords\\n\",\n    \"            keywords = [k.strip() for k in keywords]\\n\",\n    \"            ex_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"            dataset_examples[dataset_name].append(ex_dict)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check ratio of present/absent KPs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp_range = [0, 10]\\n\",\n      \"\\t num_doc = 1075018\\n\",\n      \"\\t num_total_kp = 5042509\\n\",\n      \"\\t num_unique_kp = 1177502\\n\",\n      \"\\t ratio_unique_kp = 23.35%\\n\",\n      \"\\t num_present_kp = 1624703\\n\",\n      \"\\t ratio_present_kp = 32.22%\\n\",\n      \"\\t num_absent_kp = 3417806\\n\",\n      \"\\t ratio_absent_kp = 67.78%\\n\",\n      \"kp_range = [10, 20]\\n\",\n      \"\\t num_doc = 808194\\n\",\n      \"\\t num_total_kp = 11297261\\n\",\n      \"\\t num_unique_kp = 2128224\\n\",\n      \"\\t ratio_unique_kp = 18.84%\\n\",\n      \"\\t num_present_kp = 2225699\\n\",\n      \"\\t ratio_present_kp = 19.70%\\n\",\n      \"\\t num_absent_kp = 9071562\\n\",\n      \"\\t ratio_absent_kp = 80.30%\\n\",\n      \"kp_range = [20, 30]\\n\",\n      \"\\t num_doc = 476153\\n\",\n      \"\\t num_total_kp = 11417185\\n\",\n      \"\\t num_unique_kp = 2294995\\n\",\n      \"\\t ratio_unique_kp = 20.10%\\n\",\n      \"\\t num_present_kp = 2459860\\n\",\n      \"\\t ratio_present_kp = 21.55%\\n\",\n      \"\\t num_absent_kp = 8957325\\n\",\n      \"\\t ratio_absent_kp = 78.45%\\n\",\n      \"kp_range = [30, 40]\\n\",\n      \"\\t num_doc = 215045\\n\",\n      \"\\t num_total_kp = 7232686\\n\",\n      \"\\t num_unique_kp = 1580889\\n\",\n      \"\\t ratio_unique_kp = 21.86%\\n\",\n      \"\\t num_present_kp = 1669954\\n\",\n      \"\\t ratio_present_kp = 23.09%\\n\",\n      \"\\t num_absent_kp = 5562732\\n\",\n      \"\\t ratio_absent_kp = 76.91%\\n\",\n      \"kp_range = [40, 50]\\n\",\n      \"\\t num_doc = 69542\\n\",\n      \"\\t num_total_kp = 3033018\\n\",\n      \"\\t num_unique_kp = 572174\\n\",\n      \"\\t ratio_unique_kp = 18.86%\\n\",\n      \"\\t num_present_kp = 573525\\n\",\n      \"\\t ratio_present_kp = 18.91%\\n\",\n      \"\\t num_absent_kp = 2459493\\n\",\n      \"\\t ratio_absent_kp = 81.09%\\n\",\n      \"kp_range = [50, 60]\\n\",\n      \"\\t num_doc = 26385\\n\",\n      \"\\t num_total_kp = 1419833\\n\",\n      \"\\t num_unique_kp = 235052\\n\",\n      \"\\t ratio_unique_kp = 16.55%\\n\",\n      \"\\t num_present_kp = 212819\\n\",\n      \"\\t ratio_present_kp = 14.99%\\n\",\n      \"\\t num_absent_kp = 1207014\\n\",\n      \"\\t ratio_absent_kp = 85.01%\\n\",\n      \"kp_range = [60, 70]\\n\",\n      \"\\t num_doc = 13011\\n\",\n      \"\\t num_total_kp = 832478\\n\",\n      \"\\t num_unique_kp = 146629\\n\",\n      \"\\t ratio_unique_kp = 17.61%\\n\",\n      \"\\t num_present_kp = 113556\\n\",\n      \"\\t ratio_present_kp = 13.64%\\n\",\n      \"\\t num_absent_kp = 718922\\n\",\n      \"\\t ratio_absent_kp = 86.36%\\n\",\n      \"kp_range = [70, 80]\\n\",\n      \"\\t num_doc = 7256\\n\",\n      \"\\t num_total_kp = 537191\\n\",\n      \"\\t num_unique_kp = 105971\\n\",\n      \"\\t ratio_unique_kp = 19.73%\\n\",\n      \"\\t num_present_kp = 69464\\n\",\n      \"\\t ratio_present_kp = 12.93%\\n\",\n      \"\\t num_absent_kp = 467727\\n\",\n      \"\\t ratio_absent_kp = 87.07%\\n\",\n      \"kp_range = [80, 90]\\n\",\n      \"\\t num_doc = 4106\\n\",\n      \"\\t num_total_kp = 344253\\n\",\n      \"\\t num_unique_kp = 77924\\n\",\n      \"\\t ratio_unique_kp = 22.64%\\n\",\n      \"\\t num_present_kp = 43896\\n\",\n      \"\\t ratio_present_kp = 12.75%\\n\",\n      \"\\t num_absent_kp = 300357\\n\",\n      \"\\t ratio_absent_kp = 87.25%\\n\",\n      \"kp_range = [90, 100]\\n\",\n      \"\\t num_doc = 2358\\n\",\n      \"\\t num_total_kp = 221677\\n\",\n      \"\\t num_unique_kp = 58556\\n\",\n      \"\\t ratio_unique_kp = 26.42%\\n\",\n      \"\\t num_present_kp = 29940\\n\",\n      \"\\t ratio_present_kp = 13.51%\\n\",\n      \"\\t num_absent_kp = 191737\\n\",\n      \"\\t ratio_absent_kp = 86.49%\\n\",\n      \"kp_range = [100, 110]\\n\",\n      \"\\t num_doc = 1250\\n\",\n      \"\\t num_total_kp = 129846\\n\",\n      \"\\t num_unique_kp = 39733\\n\",\n      \"\\t ratio_unique_kp = 30.60%\\n\",\n      \"\\t num_present_kp = 17488\\n\",\n      \"\\t ratio_present_kp = 13.47%\\n\",\n      \"\\t num_absent_kp = 112358\\n\",\n      \"\\t ratio_absent_kp = 86.53%\\n\",\n      \"kp_range = [110, 120]\\n\",\n      \"\\t num_doc = 492\\n\",\n      \"\\t num_total_kp = 55893\\n\",\n      \"\\t num_unique_kp = 22093\\n\",\n      \"\\t ratio_unique_kp = 39.53%\\n\",\n      \"\\t num_present_kp = 7473\\n\",\n      \"\\t ratio_present_kp = 13.37%\\n\",\n      \"\\t num_absent_kp = 48420\\n\",\n      \"\\t ratio_absent_kp = 86.63%\\n\",\n      \"kp_range = [120, 130]\\n\",\n      \"\\t num_doc = 146\\n\",\n      \"\\t num_total_kp = 18013\\n\",\n      \"\\t num_unique_kp = 10305\\n\",\n      \"\\t ratio_unique_kp = 57.21%\\n\",\n      \"\\t num_present_kp = 2661\\n\",\n      \"\\t ratio_present_kp = 14.77%\\n\",\n      \"\\t num_absent_kp = 15352\\n\",\n      \"\\t ratio_absent_kp = 85.23%\\n\",\n      \"kp_range = [130, 140]\\n\",\n      \"\\t num_doc = 48\\n\",\n      \"\\t num_total_kp = 6441\\n\",\n      \"\\t num_unique_kp = 4896\\n\",\n      \"\\t ratio_unique_kp = 76.01%\\n\",\n      \"\\t num_present_kp = 1044\\n\",\n      \"\\t ratio_present_kp = 16.21%\\n\",\n      \"\\t num_absent_kp = 5397\\n\",\n      \"\\t ratio_absent_kp = 83.79%\\n\",\n      \"kp_range = [140, 150]\\n\",\n      \"\\t num_doc = 15\\n\",\n      \"\\t num_total_kp = 2164\\n\",\n      \"\\t num_unique_kp = 1808\\n\",\n      \"\\t ratio_unique_kp = 83.55%\\n\",\n      \"\\t num_present_kp = 230\\n\",\n      \"\\t ratio_present_kp = 10.63%\\n\",\n      \"\\t num_absent_kp = 1934\\n\",\n      \"\\t ratio_absent_kp = 89.37%\\n\",\n      \"kp_range = [150, 160]\\n\",\n      \"\\t num_doc = 8\\n\",\n      \"\\t num_total_kp = 1221\\n\",\n      \"\\t num_unique_kp = 1135\\n\",\n      \"\\t ratio_unique_kp = 92.96%\\n\",\n      \"\\t num_present_kp = 116\\n\",\n      \"\\t ratio_present_kp = 9.50%\\n\",\n      \"\\t num_absent_kp = 1105\\n\",\n      \"\\t ratio_absent_kp = 90.50%\\n\",\n      \"kp_range = [160, 170]\\n\",\n      \"\\t num_doc = 3\\n\",\n      \"\\t num_total_kp = 492\\n\",\n      \"\\t num_unique_kp = 491\\n\",\n      \"\\t ratio_unique_kp = 99.80%\\n\",\n      \"\\t num_present_kp = 31\\n\",\n      \"\\t ratio_present_kp = 6.30%\\n\",\n      \"\\t num_absent_kp = 461\\n\",\n      \"\\t ratio_absent_kp = 93.70%\\n\",\n      \"kp_range = [170, 180]\\n\",\n      \"\\t num_doc = 4\\n\",\n      \"\\t num_total_kp = 701\\n\",\n      \"\\t num_unique_kp = 665\\n\",\n      \"\\t ratio_unique_kp = 94.86%\\n\",\n      \"\\t num_present_kp = 62\\n\",\n      \"\\t ratio_present_kp = 8.84%\\n\",\n      \"\\t num_absent_kp = 639\\n\",\n      \"\\t ratio_absent_kp = 91.16%\\n\",\n      \"kp_range = [180, 190]\\n\",\n      \"\\t num_doc = 3\\n\",\n      \"\\t num_total_kp = 554\\n\",\n      \"\\t num_unique_kp = 539\\n\",\n      \"\\t ratio_unique_kp = 97.29%\\n\",\n      \"\\t num_present_kp = 36\\n\",\n      \"\\t ratio_present_kp = 6.50%\\n\",\n      \"\\t num_absent_kp = 518\\n\",\n      \"\\t ratio_absent_kp = 93.50%\\n\",\n      \"kp_range = [190, 200]\\n\",\n      \"\\t num_doc = 8\\n\",\n      \"\\t num_total_kp = 1557\\n\",\n      \"\\t num_unique_kp = 1109\\n\",\n      \"\\t ratio_unique_kp = 71.23%\\n\",\n      \"\\t num_present_kp = 44\\n\",\n      \"\\t ratio_present_kp = 2.83%\\n\",\n      \"\\t num_absent_kp = 1513\\n\",\n      \"\\t ratio_absent_kp = 97.17%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def if_present_phrase(src_str_tokens, phrase_str_tokens):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    :param src_str_tokens: a list of strings (words) of source text\\n\",\n    \"    :param phrase_str_tokens: a list of strings (words) of a phrase\\n\",\n    \"    :return:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    match_flag = False\\n\",\n    \"    match_pos_idx = -1\\n\",\n    \"    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\\n\",\n    \"        match_flag = True\\n\",\n    \"        # iterate each word in target, if one word does not match, set match=False and break\\n\",\n    \"        for seq_idx, seq_w in enumerate(phrase_str_tokens):\\n\",\n    \"            src_w = src_str_tokens[src_start_idx + seq_idx]\\n\",\n    \"            if src_w != seq_w:\\n\",\n    \"                match_flag = False\\n\",\n    \"                break\\n\",\n    \"        if match_flag:\\n\",\n    \"            match_pos_idx = src_start_idx\\n\",\n    \"            break\\n\",\n    \"\\n\",\n    \"    return match_flag\\n\",\n    \"\\n\",\n    \"stat_dicts = []\\n\",\n    \"\\n\",\n    \"for start in range(0, 200, 10):\\n\",\n    \"    unique_kp_set = set()\\n\",\n    \"    num_total_kp, num_unique_kp, num_present_kp, num_absent_kp = 0, 0, 0, 0\\n\",\n    \"    exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) >= start and len(ex['keywords']) < start + 10]\\n\",\n    \"    for ex_id, ex in enumerate(exs):\\n\",\n    \"        for p in ex['keywords']:\\n\",\n    \"            unique_kp_set.add(p)\\n\",\n    \"            num_total_kp += 1\\n\",\n    \"            src_tokens = (ex['title'] + ' ' + ex['abstract']).lower().split()\\n\",\n    \"            tgt_tokens = p.lower().split()\\n\",\n    \"            if if_present_phrase(src_tokens, tgt_tokens):\\n\",\n    \"                num_present_kp += 1\\n\",\n    \"            else:\\n\",\n    \"                num_absent_kp += 1\\n\",\n    \"#         if ex_id > 1000:\\n\",\n    \"#             break\\n\",\n    \"          \\n\",\n    \"    stat = {\\n\",\n    \"        'kp_range': '[%d, %d]' % (start, start + 10),\\n\",\n    \"        'num_doc': len(exs),\\n\",\n    \"        'num_total_kp': num_total_kp,\\n\",\n    \"        'num_unique_kp': len(unique_kp_set),\\n\",\n    \"        'ratio_unique_kp': len(unique_kp_set) / num_total_kp,\\n\",\n    \"        'num_present_kp': num_present_kp,\\n\",\n    \"        'ratio_present_kp': num_present_kp / num_total_kp,\\n\",\n    \"        'num_absent_kp': num_absent_kp,\\n\",\n    \"        'ratio_absent_kp': num_absent_kp / num_total_kp,\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    stat_dicts.append(stat)\\n\",\n    \"    for k, v in stat.items():\\n\",\n    \"        if k == 'kp_range':\\n\",\n    \"            print(k, '=', v)\\n\",\n    \"        elif k.startswith('ratio'):\\n\",\n    \"            print('\\\\t', k, '=', '%.2f%%' % (v * 100.0))\\n\",\n    \"        else:\\n\",\n    \"            print('\\\\t', k, '=', v)\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None]\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kpstat_df = pd.DataFrame(stat_dicts, columns=list(stat_dicts[0].keys()))\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"fig, axes=plt.subplots(2, 2, figsize=(12, 9))\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"num_doc\\\", data=kpstat_df, ax=axes[0, 0])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_unique_kp\\\", data=kpstat_df, ax=axes[0, 1])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_present_kp\\\", data=kpstat_df, ax=axes[1, 0])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_absent_kp\\\", data=kpstat_df, ax=axes[1, 1])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp_range = [0, 5]\\n\",\n      \"\\t num_doc = 242700\\n\",\n      \"\\t num_total_kp = 845631\\n\",\n      \"\\t num_unique_kp = 318082\\n\",\n      \"\\t ratio_unique_kp = 37.61%\\n\",\n      \"\\t num_present_kp = 404802\\n\",\n      \"\\t ratio_present_kp = 47.87%\\n\",\n      \"\\t num_absent_kp = 440829\\n\",\n      \"\\t ratio_absent_kp = 52.13%\\n\",\n      \"kp_range = [5, 10]\\n\",\n      \"\\t num_doc = 248247\\n\",\n      \"\\t num_total_kp = 1423963\\n\",\n      \"\\t num_unique_kp = 467033\\n\",\n      \"\\t ratio_unique_kp = 32.80%\\n\",\n      \"\\t num_present_kp = 618408\\n\",\n      \"\\t ratio_present_kp = 43.43%\\n\",\n      \"\\t num_absent_kp = 805555\\n\",\n      \"\\t ratio_absent_kp = 56.57%\\n\",\n      \"kp_range = [10, 15]\\n\",\n      \"\\t num_doc = 9475\\n\",\n      \"\\t num_total_kp = 106882\\n\",\n      \"\\t num_unique_kp = 48461\\n\",\n      \"\\t ratio_unique_kp = 45.34%\\n\",\n      \"\\t num_present_kp = 39789\\n\",\n      \"\\t ratio_present_kp = 37.23%\\n\",\n      \"\\t num_absent_kp = 67093\\n\",\n      \"\\t ratio_absent_kp = 62.77%\\n\",\n      \"kp_range = [15, 20]\\n\",\n      \"\\t num_doc = 4179\\n\",\n      \"\\t num_total_kp = 71033\\n\",\n      \"\\t num_unique_kp = 12502\\n\",\n      \"\\t ratio_unique_kp = 17.60%\\n\",\n      \"\\t num_present_kp = 33997\\n\",\n      \"\\t ratio_present_kp = 47.86%\\n\",\n      \"\\t num_absent_kp = 37036\\n\",\n      \"\\t ratio_absent_kp = 52.14%\\n\",\n      \"kp_range = [20, 25]\\n\",\n      \"\\t num_doc = 3813\\n\",\n      \"\\t num_total_kp = 83462\\n\",\n      \"\\t num_unique_kp = 8837\\n\",\n      \"\\t ratio_unique_kp = 10.59%\\n\",\n      \"\\t num_present_kp = 41734\\n\",\n      \"\\t ratio_present_kp = 50.00%\\n\",\n      \"\\t num_absent_kp = 41728\\n\",\n      \"\\t ratio_absent_kp = 50.00%\\n\",\n      \"kp_range = [25, 30]\\n\",\n      \"\\t num_doc = 2799\\n\",\n      \"\\t num_total_kp = 75139\\n\",\n      \"\\t num_unique_kp = 7639\\n\",\n      \"\\t ratio_unique_kp = 10.17%\\n\",\n      \"\\t num_present_kp = 37382\\n\",\n      \"\\t ratio_present_kp = 49.75%\\n\",\n      \"\\t num_absent_kp = 37757\\n\",\n      \"\\t ratio_absent_kp = 50.25%\\n\",\n      \"kp_range = [30, 35]\\n\",\n      \"\\t num_doc = 1685\\n\",\n      \"\\t num_total_kp = 53393\\n\",\n      \"\\t num_unique_kp = 5449\\n\",\n      \"\\t ratio_unique_kp = 10.21%\\n\",\n      \"\\t num_present_kp = 26650\\n\",\n      \"\\t ratio_present_kp = 49.91%\\n\",\n      \"\\t num_absent_kp = 26743\\n\",\n      \"\\t ratio_absent_kp = 50.09%\\n\",\n      \"kp_range = [35, 40]\\n\",\n      \"\\t num_doc = 764\\n\",\n      \"\\t num_total_kp = 28040\\n\",\n      \"\\t num_unique_kp = 3992\\n\",\n      \"\\t ratio_unique_kp = 14.24%\\n\",\n      \"\\t num_present_kp = 13900\\n\",\n      \"\\t ratio_present_kp = 49.57%\\n\",\n      \"\\t num_absent_kp = 14140\\n\",\n      \"\\t ratio_absent_kp = 50.43%\\n\",\n      \"kp_range = [40, 45]\\n\",\n      \"\\t num_doc = 313\\n\",\n      \"\\t num_total_kp = 13028\\n\",\n      \"\\t num_unique_kp = 2397\\n\",\n      \"\\t ratio_unique_kp = 18.40%\\n\",\n      \"\\t num_present_kp = 6473\\n\",\n      \"\\t ratio_present_kp = 49.69%\\n\",\n      \"\\t num_absent_kp = 6555\\n\",\n      \"\\t ratio_absent_kp = 50.31%\\n\",\n      \"kp_range = [45, 50]\\n\",\n      \"\\t num_doc = 94\\n\",\n      \"\\t num_total_kp = 4378\\n\",\n      \"\\t num_unique_kp = 1258\\n\",\n      \"\\t ratio_unique_kp = 28.73%\\n\",\n      \"\\t num_present_kp = 2168\\n\",\n      \"\\t ratio_present_kp = 49.52%\\n\",\n      \"\\t num_absent_kp = 2210\\n\",\n      \"\\t ratio_absent_kp = 50.48%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None,\\n\",\n       \" None]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x648 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"stat_dicts = []\\n\",\n    \"step_size = 5\\n\",\n    \"\\n\",\n    \"for start in range(0, 50, step_size):\\n\",\n    \"    unique_kp_set = set()\\n\",\n    \"    num_total_kp, num_unique_kp, num_present_kp, num_absent_kp = 0, 0, 0, 0\\n\",\n    \"    exs = [ex for ex in dataset_examples[\\\"kp20k\\\"] if len(ex['keywords']) >= start and len(ex['keywords']) < start + step_size]\\n\",\n    \"    for ex_id, ex in enumerate(exs):\\n\",\n    \"        for p in ex['keywords']:\\n\",\n    \"            unique_kp_set.add(p)\\n\",\n    \"            num_total_kp += 1\\n\",\n    \"            src_tokens = (ex['title'] + ' ' + ex['abstract']).lower().split()\\n\",\n    \"            tgt_tokens = p.lower().split()\\n\",\n    \"            if if_present_phrase(src_tokens, tgt_tokens):\\n\",\n    \"                num_present_kp += 1\\n\",\n    \"            else:\\n\",\n    \"                num_absent_kp += 1\\n\",\n    \"#         if ex_id > 1000:\\n\",\n    \"#             break\\n\",\n    \"          \\n\",\n    \"    stat = {\\n\",\n    \"        'kp_range': '[%d, %d]' % (start, start + step_size),\\n\",\n    \"        'num_doc': len(exs),\\n\",\n    \"        'num_total_kp': num_total_kp,\\n\",\n    \"        'num_unique_kp': len(unique_kp_set),\\n\",\n    \"        'ratio_unique_kp': len(unique_kp_set) / num_total_kp,\\n\",\n    \"        'num_present_kp': num_present_kp,\\n\",\n    \"        'ratio_present_kp': num_present_kp / num_total_kp,\\n\",\n    \"        'num_absent_kp': num_absent_kp,\\n\",\n    \"        'ratio_absent_kp': num_absent_kp / num_total_kp,\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    stat_dicts.append(stat)\\n\",\n    \"    for k, v in stat.items():\\n\",\n    \"        if k == 'kp_range':\\n\",\n    \"            print(k, '=', v)\\n\",\n    \"        elif k.startswith('ratio'):\\n\",\n    \"            print('\\\\t', k, '=', '%.2f%%' % (v * 100.0))\\n\",\n    \"        else:\\n\",\n    \"            print('\\\\t', k, '=', v)\\n\",\n    \"        \\n\",\n    \"kpstat_df = pd.DataFrame(stat_dicts, columns=list(stat_dicts[0].keys()))\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"fig, axes=plt.subplots(2, 2, figsize=(12, 9))\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"num_doc\\\", data=kpstat_df, ax=axes[0, 0])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_unique_kp\\\", data=kpstat_df, ax=axes[0, 1])\\n\",\n    \"g.set(xticklabels=[])\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_present_kp\\\", data=kpstat_df, ax=axes[1, 0])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\",\n    \"\\n\",\n    \"g = sns.barplot(x=\\\"kp_range\\\", y=\\\"ratio_absent_kp\\\", data=kpstat_df, ax=axes[1, 1])\\n\",\n    \"plt.setp(g.get_xticklabels(), rotation=90)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Manually examine examples containing too many phrases (e.g. >20, **num_noisy_examples= 745916**)\\n\",\n    \" - All phrases are lowercased.\\n\",\n    \" - All hyphens/parentheses are removed. But words in parentheses are preserved.\\n\",\n    \" - Found phrases in French (imagerie medicale, ondelette), Spanish (analisis datos) etc.\\n\",\n    \" - It appears that MagKP contains a lot of data from IEEE which contain a lot of errors. (**num_contain_long_kps=514582**)\\n\",\n    \"     - 4 groups: IEEE Keywords, INSPEC: Controlled Indexing, INSPEC: Non-Controlled Indexing, and author keywords\\n\",\n    \"     - it often contains some wrong and very long phrases, concatenation of multiple phrases. This can be easily filtered by phrase length.\\n\",\n    \"         e.g. https://ieeexplore.ieee.org/abstract/document/4101114/keywords#keywords, \\\"normalized random number area efficient architecture large scale implementation biological neural networks plausible neural networks spiking neural networks reconfigurable hardware multiplier less hardware architecture single fpga synaptic multiplication and gate\\\"\\n\",\n    \"     - Oftentimes it doesn't completely match the record on IEEE website. Say \\\"membrane potential\\\" and \\\"integrate and fire\\\" appear in the abstract, but not in any of keyword fields.\\n\",\n    \" - Also some papers do not have keywords originally. Very likely they are automatically annotated? (**num_no_long_kps=231334**)\\n\",\n    \"     - say 'id': 'dbe8cad4-3c1f-4414-95a0-338c7cb3184d', https://www.sciencedirect.com/science/article/pii/S1077201404000452\\n\",\n    \"     - 'id': 'dde7b89e-5251-4991-9e3a-7c7191a20240', https://link.springer.com/chapter/10.1007/11925941_1\\n\",\n    \"         \\\"These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.\\\"\\n\",\n    \" - Some have original keywords, but still many are new.\\n\",\n    \"     - 'id': 'dc7b584d-66f5-4ae5-bc21-e38715fd7403', https://www.sciencedirect.com/science/article/pii/0045790694900175\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"745916\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) > 20]\\n\",\n    \"exs = sorted(exs, key=lambda x: len(ex['keywords']))\\n\",\n    \"print(len(exs))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'id': 'dbe06305-ce86-46da-b693-7ae062acc315',\\n\",\n       \" 'title': 'A novel adaptive routing scheme for the QoS-based multimedia services in mobile ad-hoc networks',\\n\",\n       \" 'abstract': 'A mobile ad-hoc network is composed of only mobile nodes, which are distributed dynamically, without any wired backbone or centralized entities. Since the existing works on ad-hoc routing protocol are mostly biased toward a military application, we need a new routing scheme for the support of multimedia services in mobile ad-hoc networks. Therefore, we propose a novel scheme that can support a variety of traffic attributes and can be applicable to high-speed and multimedia data services in mobile ad-hoc networks by using adaptive transmission power level. As a result of simulation, the proposed scheme has better performance than conventional method, which is performed with uniform transmission power level, in view of route query delay time',\\n\",\n       \" 'keywords': ['land mobile radio quality of service multimedia communication telecommunication network routing adaptive systems telecommunication traffic data communication transport protocols delays',\\n\",\n       \"  'routing intelligent networks ad hoc networks multimedia systems local area networks quality of service spine network topology mobile radio mobility management telecommunication traffic',\\n\",\n       \"  'data communication',\\n\",\n       \"  'transport protocols',\\n\",\n       \"  'telecommunication traffic',\\n\",\n       \"  'telecommunication network routing',\\n\",\n       \"  'land mobile radio',\\n\",\n       \"  'adaptive systems',\\n\",\n       \"  'multimedia data',\\n\",\n       \"  'multimedia communication',\\n\",\n       \"  'mobile ad hoc network',\\n\",\n       \"  'distribution dynamics',\\n\",\n       \"  'adaptive routing',\\n\",\n       \"  'mobile node',\\n\",\n       \"  'delay time',\\n\",\n       \"  'quality of service',\\n\",\n       \"  'ad hoc routing',\\n\",\n       \"  'route query delay time qos based multimedia services mobile ad hoc networks adaptive routing mobile nodes ad hoc routing protocol military application multimedia services traffic attributes high speed data services multimedia data services adaptive transmission power level simulation performance',\\n\",\n       \"  'multimedia services',\\n\",\n       \"  'high speed',\\n\",\n       \"  'delays'],\\n\",\n       \" 'fulltext': ''}\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"exs[50]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check if all the phrases are lowercase? yes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"num_pure_lower_kps= 41605964\\n\",\n      \"num_non_lower_kps= 0\\n\",\n      \"num_hyphen_kps= 0\\n\",\n      \"num_bracket_kps= 0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_pure_lower_kps, num_non_lower_kps = 0, 0\\n\",\n    \"num_hyphen_kps, num_bracket_kps = 0, 0\\n\",\n    \"for ex in dataset_examples[\\\"magkp\\\"]:\\n\",\n    \"    for kp in ex['keywords']:\\n\",\n    \"        if kp.lower() == kp:\\n\",\n    \"            num_pure_lower_kps += 1\\n\",\n    \"        else:\\n\",\n    \"            num_non_lower_kps += 1\\n\",\n    \"        if '-' in kp:\\n\",\n    \"            num_hyphen_kps += 1\\n\",\n    \"        if '(' in kp or ')' in kp:\\n\",\n    \"            num_bracket_kps += 1\\n\",\n    \"            \\n\",\n    \"print('num_pure_lower_kps=', num_pure_lower_kps)\\n\",\n    \"print('num_non_lower_kps=', num_non_lower_kps)\\n\",\n    \"\\n\",\n    \"print('num_hyphen_kps=', num_hyphen_kps)\\n\",\n    \"print('num_bracket_kps=', num_bracket_kps)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Check if all noisy MagKP examples are from IEEE? No, likely 68.99%\\n\",\n    \"\\n\",\n    \"Assume all IEEE data examples contain certain number of very long phrases (>=10 words, due to the mistaken concatenation of multiple phrases).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"num_noisy_examples= 745916\\n\",\n      \"num_contain_long_kps= 514582\\n\",\n      \"num_no_long_kps= 231334\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"exs = [ex for ex in dataset_examples[\\\"magkp\\\"] if len(ex['keywords']) > 20]\\n\",\n    \"exs = sorted(exs, key=lambda x: len(ex['keywords']))\\n\",\n    \"no_long_exs = []\\n\",\n    \"\\n\",\n    \"print('num_noisy_examples=', len(exs))\\n\",\n    \"\\n\",\n    \"num_contain_long_kps, num_no_long_kps = 0, 0\\n\",\n    \"\\n\",\n    \"for ex in exs:\\n\",\n    \"    found_long_kp = False\\n\",\n    \"    for kp in ex['keywords']:\\n\",\n    \"        if len(kp.split()) >= 10:\\n\",\n    \"            found_long_kp = True\\n\",\n    \"            break\\n\",\n    \"    if found_long_kp:\\n\",\n    \"        num_contain_long_kps += 1\\n\",\n    \"    else:\\n\",\n    \"        num_no_long_kps += 1\\n\",\n    \"        no_long_exs.append(ex)\\n\",\n    \"        \\n\",\n    \"print('num_contain_long_kps=', num_contain_long_kps)\\n\",\n    \"print('num_no_long_kps=', num_no_long_kps)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"no_long_exs[105500]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count #kp per document\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-21T21:49:26.929171Z\",\n     \"start_time\": \"2020-09-21T21:49:07.863773Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DescribeResult(nobs=2699094, minmax=(1, 438), mean=15.414788814320657, variance=168.752782332351, skewness=1.8635894274050995, kurtosis=7.294107031214651)\\n\",\n      \"Percentile@0 = 1.000000\\n\",\n      \"Percentile@1 = 1.000000\\n\",\n      \"Percentile@2 = 1.000000\\n\",\n      \"Percentile@3 = 1.000000\\n\",\n      \"Percentile@4 = 1.000000\\n\",\n      \"Percentile@5 = 1.000000\\n\",\n      \"Percentile@6 = 2.000000\\n\",\n      \"Percentile@7 = 2.000000\\n\",\n      \"Percentile@8 = 2.000000\\n\",\n      \"Percentile@9 = 2.000000\\n\",\n      \"Percentile@10 = 3.000000\\n\",\n      \"Percentile@11 = 3.000000\\n\",\n      \"Percentile@12 = 3.000000\\n\",\n      \"Percentile@13 = 3.000000\\n\",\n      \"Percentile@14 = 3.000000\\n\",\n      \"Percentile@15 = 4.000000\\n\",\n      \"Percentile@16 = 4.000000\\n\",\n      \"Percentile@17 = 4.000000\\n\",\n      \"Percentile@18 = 4.000000\\n\",\n      \"Percentile@19 = 4.000000\\n\",\n      \"Percentile@20 = 5.000000\\n\",\n      \"Percentile@21 = 5.000000\\n\",\n      \"Percentile@22 = 5.000000\\n\",\n      \"Percentile@23 = 5.000000\\n\",\n      \"Percentile@24 = 5.000000\\n\",\n      \"Percentile@25 = 6.000000\\n\",\n      \"Percentile@26 = 6.000000\\n\",\n      \"Percentile@27 = 6.000000\\n\",\n      \"Percentile@28 = 6.000000\\n\",\n      \"Percentile@29 = 7.000000\\n\",\n      \"Percentile@30 = 7.000000\\n\",\n      \"Percentile@31 = 7.000000\\n\",\n      \"Percentile@32 = 7.000000\\n\",\n      \"Percentile@33 = 8.000000\\n\",\n      \"Percentile@34 = 8.000000\\n\",\n      \"Percentile@35 = 8.000000\\n\",\n      \"Percentile@36 = 8.000000\\n\",\n      \"Percentile@37 = 9.000000\\n\",\n      \"Percentile@38 = 9.000000\\n\",\n      \"Percentile@39 = 9.000000\\n\",\n      \"Percentile@40 = 10.000000\\n\",\n      \"Percentile@41 = 10.000000\\n\",\n      \"Percentile@42 = 10.000000\\n\",\n      \"Percentile@43 = 10.000000\\n\",\n      \"Percentile@44 = 11.000000\\n\",\n      \"Percentile@45 = 11.000000\\n\",\n      \"Percentile@46 = 11.000000\\n\",\n      \"Percentile@47 = 11.000000\\n\",\n      \"Percentile@48 = 12.000000\\n\",\n      \"Percentile@49 = 12.000000\\n\",\n      \"Percentile@50 = 12.000000\\n\",\n      \"Percentile@51 = 12.000000\\n\",\n      \"Percentile@52 = 13.000000\\n\",\n      \"Percentile@53 = 13.000000\\n\",\n      \"Percentile@54 = 13.000000\\n\",\n      \"Percentile@55 = 14.000000\\n\",\n      \"Percentile@56 = 14.000000\\n\",\n      \"Percentile@57 = 14.000000\\n\",\n      \"Percentile@58 = 15.000000\\n\",\n      \"Percentile@59 = 15.000000\\n\",\n      \"Percentile@60 = 16.000000\\n\",\n      \"Percentile@61 = 16.000000\\n\",\n      \"Percentile@62 = 16.000000\\n\",\n      \"Percentile@63 = 17.000000\\n\",\n      \"Percentile@64 = 17.000000\\n\",\n      \"Percentile@65 = 18.000000\\n\",\n      \"Percentile@66 = 18.000000\\n\",\n      \"Percentile@67 = 18.000000\\n\",\n      \"Percentile@68 = 19.000000\\n\",\n      \"Percentile@69 = 19.000000\\n\",\n      \"Percentile@70 = 20.000000\\n\",\n      \"Percentile@71 = 20.000000\\n\",\n      \"Percentile@72 = 20.000000\\n\",\n      \"Percentile@73 = 21.000000\\n\",\n      \"Percentile@74 = 21.000000\\n\",\n      \"Percentile@75 = 22.000000\\n\",\n      \"Percentile@76 = 22.000000\\n\",\n      \"Percentile@77 = 23.000000\\n\",\n      \"Percentile@78 = 23.000000\\n\",\n      \"Percentile@79 = 24.000000\\n\",\n      \"Percentile@80 = 25.000000\\n\",\n      \"Percentile@81 = 25.000000\\n\",\n      \"Percentile@82 = 26.000000\\n\",\n      \"Percentile@83 = 26.000000\\n\",\n      \"Percentile@84 = 27.000000\\n\",\n      \"Percentile@85 = 28.000000\\n\",\n      \"Percentile@86 = 28.000000\\n\",\n      \"Percentile@87 = 29.000000\\n\",\n      \"Percentile@88 = 30.000000\\n\",\n      \"Percentile@89 = 31.000000\\n\",\n      \"Percentile@90 = 32.000000\\n\",\n      \"Percentile@91 = 33.000000\\n\",\n      \"Percentile@92 = 34.000000\\n\",\n      \"Percentile@93 = 35.000000\\n\",\n      \"Percentile@94 = 37.000000\\n\",\n      \"Percentile@95 = 39.000000\\n\",\n      \"Percentile@96 = 41.000000\\n\",\n      \"Percentile@97 = 44.000000\\n\",\n      \"Percentile@98 = 50.000000\\n\",\n      \"Percentile@99 = 61.000000\\n\",\n      \"Percentile@100 = 438.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tgt_nums = [len(ex['keywords']) for ex in dataset_examples[\\\"magkp\\\"]]\\n\",\n    \"data = tgt_nums\\n\",\n    \"print(scipy.stats.describe(data))\\n\",\n    \"\\n\",\n    \"for p in np.linspace(0, 100, 101):\\n\",\n    \"    percentile = np.percentile(data, p, interpolation='lower')\\n\",\n    \"    print('Percentile@%.0f = %.6f' % (p, percentile))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Histogram of #(kp per document) < 61\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T00:01:00.862762Z\",\n     \"start_time\": \"2020-09-22T00:01:00.344346Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\\n\",\n      \"  warnings.warn(msg, FutureWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n < 61]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=60, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Histogram of #(kp per document) < 11\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-09-22T03:58:02.104309Z\",\n     \"start_time\": \"2020-09-22T03:58:01.792437Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 10)')\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n <= 10]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=10, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 10)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_tgt_nums = [n for n in tgt_nums[\\\"magkp\\\"] if n>=20 and n <= 100]\\n\",\n    \"sns.distplot(tmp_tgt_nums, color=\\\"teal\\\", label=\\\"MagKP\\\", bins=80, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"\\n\",\n    \"ax.set_title('Histogram of #(kp per document) of MagKP (truncated at 60)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T05:18:43.851712Z\",\n     \"start_time\": \"2020-08-12T05:18:43.465149Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#     sns.distplot(np.asarray(tgt_nums, dtype=int), bins=15, color=\\\"r\\\", kde=False, rug=False);\\n\",\n    \"    \\n\",\n    \"    # Plot a simple histogram with binsize determined automatically\\n\",\n    \"#     sns.distplot(tgt_nums, kde=False, color=\\\"b\\\", ax=ax)\\n\",\n    \"\\n\",\n    \"#     # Plot a kernel density estimate and rug plot\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, rug=True, color=\\\"r\\\")\\n\",\n    \"\\n\",\n    \"#     # Plot a filled kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=False, color=\\\"g\\\", kde_kws={\\\"shade\\\": True})\\n\",\n    \"\\n\",\n    \"#     # Plot a histogram and kernel density estimate\\n\",\n    \"#     sns.distplot(tgt_nums, hist=True, color=\\\"m\\\", ax=ax)\\n\",\n    \"    \\n\",\n    \"# sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"skyblue\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"kp20k\\\"] , color=\\\"teal\\\", label=\\\"KP20k\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"sns.distplot(tgt_nums[\\\"magkp\\\"] , color=\\\"teal\\\", label=\\\"MagKP\\\", bins=100, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"inspec\\\"] , color=\\\"red\\\", label=\\\"Inspec\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"krapivin\\\"] , color=\\\"olive\\\", label=\\\"Krapivin\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"nus\\\"] , color=\\\"gold\\\", label=\\\"NUS\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"# sns.distplot(tgt_nums[\\\"semeval\\\"] , color=\\\"teal\\\", label=\\\"Semeval\\\", bins=15, kde=False, rug=False, hist_kws=dict(alpha=0.7))\\n\",\n    \"\\n\",\n    \"ax.set(xlabel='Number of keyphrases in doc', ylabel='Number of documents')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Count unique phrases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:52:33.765262Z\",\n     \"start_time\": \"2020-08-12T06:52:33.759476Z\"\n    }\n   },\n   \"source\": [\n    \"##### only count documents that #(kp)>10\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T06:08:07.865368Z\",\n     \"start_time\": \"2020-08-12T05:32:53.686432Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=2699094\\n\",\n      \"#(KP)=29690034\\n\",\n      \"#(unique KP)=11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"do_preprocess = True\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"for ex_dict in dataset_tgt_dict['magkp']:\\n\",\n    \"    title = json_dict['title']\\n\",\n    \"    abstract = json_dict['abstract']\\n\",\n    \"    fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"    keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"    if isinstance(keywords, str):\\n\",\n    \"        keywords = keywords.split(';')\\n\",\n    \"        json_dict['keywords'] = keywords\\n\",\n    \"\\n\",\n    \"    if len(keywords) > 10:\\n\",\n    \"        num_data += 1\\n\",\n    \"        for keyword in keywords:\\n\",\n    \"            num_kp += 1\\n\",\n    \"            if do_preprocess:\\n\",\n    \"                tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                keyword = '_'.join(tokens)\\n\",\n    \"\\n\",\n    \"            unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#####  count all documents #(kp)>0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:42:02.179127Z\",\n     \"start_time\": \"2020-08-12T16:40:39.391069Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#(DP)=2699094\\n\",\n      \"#(KP)=41605964\\n\",\n      \"#(unique KP)=6880853\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'magkp'\\n\",\n    \"do_preprocess = False\\n\",\n    \"\\n\",\n    \"stemmer = PorterStemmer()\\n\",\n    \"\\n\",\n    \"json_base_dir = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json' # path on CRC\\n\",\n    \"input_json_path = os.path.join(json_base_dir, dataset_name, '%s_train.json' % dataset_name)\\n\",\n    \"\\n\",\n    \"unique_kp_counter = defaultdict(lambda: 0)\\n\",\n    \"kp_len_counter = defaultdict(lambda: 0)\\n\",\n    \"num_data = 0\\n\",\n    \"num_kp = 0\\n\",\n    \"\\n\",\n    \"with open(input_json_path, 'r') as input_json:\\n\",\n    \"    for json_line in input_json:\\n\",\n    \"        json_dict = json.loads(json_line)\\n\",\n    \"\\n\",\n    \"        if dataset_name == 'stackexchange':\\n\",\n    \"            json_dict['abstract'] = json_dict['question']\\n\",\n    \"            json_dict['keywords'] = json_dict['tags']            \\n\",\n    \"            del json_dict['question']\\n\",\n    \"            del json_dict['tags']\\n\",\n    \"\\n\",\n    \"        title = json_dict['title']\\n\",\n    \"        abstract = json_dict['abstract']\\n\",\n    \"        fulltext = json_dict['fulltext'] if 'fulltext' in json_dict else ''\\n\",\n    \"        keywords = json_dict['keywords']\\n\",\n    \"\\n\",\n    \"        if isinstance(keywords, str):\\n\",\n    \"            keywords = keywords.split(';')\\n\",\n    \"            json_dict['keywords'] = keywords\\n\",\n    \"            \\n\",\n    \"        if len(keywords) > 0:\\n\",\n    \"            num_data += 1\\n\",\n    \"            for keyword in keywords:\\n\",\n    \"                num_kp += 1\\n\",\n    \"                if do_preprocess:\\n\",\n    \"                    tokens = [stemmer.stem(t) for t in keyword.lower().split()]\\n\",\n    \"                    keyword = ' '.join(tokens)\\n\",\n    \"#                 print(keyword)\\n\",\n    \"                \\n\",\n    \"                tokens = [t for t in keyword.split()]\\n\",\n    \"                kp_len_counter[len(tokens)] = kp_len_counter[len(tokens)] + 1\\n\",\n    \"                unique_kp_counter[keyword] = unique_kp_counter[keyword] + 1\\n\",\n    \"\\n\",\n    \"print('#(DP)=%d' % num_data)\\n\",\n    \"print('#(KP)=%d' % num_kp)\\n\",\n    \"print('#(unique KP)=%d' % len(unique_kp_counter))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T05:11:24.875312Z\",\n     \"start_time\": \"2020-08-12T05:11:24.111497Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.axes._subplots.AxesSubplot at 0x7ff5c4604590>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAfUAAAFlCAYAAADyLnFSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAU+UlEQVR4nO3dfayk1X0f8O+vrE3axDVg1pRccBY32yj+J/ZmRYnchNakBGhqiGMk7CisHCpESypbduXgWmpTqX/YrRqnViOvSbCCIxNDEyxWEYmDALdqJWMvNuYlmLJebc2yG3b9hlO5iQs5/WPO2sPl3r13d2dm9x4+H2k0z3OeMzO/c5+Z53uflzu3WmsBADa+v3GyCwAAZkOoA8AghDoADEKoA8AghDoADEKoA8AgNp3sApLk7LPPblu2bDnZZQDAQjz44INfa61tnvXznhKhvmXLluzevftklwEAC1FV/3sez+vwOwAMQqgDwCCEOgAMQqgDwCCEOgAMQqgDwCCEOgAMQqgDwCCEOgAMQqgDwCDWFepVta+qHqmqh6pqd287q6ruqaon+/2Zvb2q6sNVtaeqHq6qbfMcAAAwcSx76v+otfb61tr2Pn9Tkntba1uT3Nvnk+TyJFv77fokH5lVsQDA6k7k8PuVSW7t07cmuWqq/eNt4rNJzqiqc0/gdQCAdVjvf2lrSf60qlqSj7bWbk5yTmvtYJK01g5W1at736UkT009dn9vOzijmtf0lre/PfuefnrV5VuWlnLnbbctqhwAWIj1hvobW2sHenDfU1VfPkrfWqGtvahT1fWZHJ7Pa17zmnWWsT77nn46SzfcsPrynTtn+noAcCpY1+H31tqBfn8oyaeSXJjkmSOH1fv9od59f5Lzpx5+XpIDKzznza217a217Zs3z/z/xAPAS86aoV5VP1hVrzgyneTSJI8m2ZVkR++2I8ldfXpXkmv7VfAXJXn2yGF6AGB+1nP4/Zwkn6qqI/1va639SVV9PskdVXVdkq8mubr3vzvJFUn2JPlOknfMvGoA4EXWDPXW2t4kP7FC+9eTXLJCe0ty40yqAwDWzTfKAcAghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8AghDoADEKoA8Ag1h3qVXVaVX2xqv6oz19QVQ9U1ZNVdXtVvby3n97n9/TlW+ZTOgAw7Vj21N+Z5PGp+Q8m+VBrbWuSbya5rrdfl+SbrbUfTfKh3g8AmLN1hXpVnZfknyT5nT5fSd6U5A96l1uTXNWnr+zz6csv6f0BgDla7576byZ5b5K/7vOvSvKt1tpzfX5/kqU+vZTkqSTpy5/t/QGAOVoz1Kvq55Mcaq09ON28Qte2jmXTz3t9Ve2uqt2HDx9eV7EAwOrWs6f+xiRvrqp9ST6ZyWH330xyRlVt6n3OS3KgT+9Pcn6S9OWvTPKN5U/aWru5tba9tbZ98+bNJzQIAGAdod5ae19r7bzW2pYk1yS5r7X2S0nuT/LW3m1Hkrv69K4+n778vtbai/bUAYDZOpG/U/+1JO+uqj2ZnDO/pbffkuRVvf3dSW46sRIBgPXYtHaX72utfSbJZ/r03iQXrtDnL5NcPYPaAIBj4BvlAGAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQQh0ABiHUAWAQa4Z6Vf1AVX2uqr5UVY9V1b/r7RdU1QNV9WRV3V5VL+/tp/f5PX35lvkOAQBI1ren/ldJ3tRa+4kkr09yWVVdlOSDST7UWtua5JtJruv9r0vyzdbajyb5UO8HAMzZmqHeJv5Pn31Zv7Ukb0ryB7391iRX9ekr+3z68kuqqmZWMQCwonWdU6+q06rqoSSHktyT5CtJvtVae6532Z9kqU8vJXkqSfryZ5O8apZFAwAvtq5Qb60931p7fZLzklyY5MdX6tbvV9orb8sbqur6qtpdVbsPHz683noBgFUc09XvrbVvJflMkouSnFFVm/qi85Ic6NP7k5yfJH35K5N8Y4Xnurm1tr21tn3z5s3HVz0A8D3rufp9c1Wd0af/ZpKfTfJ4kvuTvLV325Hkrj69q8+nL7+vtfaiPXUAYLY2rd0l5ya5tapOy+SXgDtaa39UVX+W5JNV9e+TfDHJLb3/LUl+r6r2ZLKHfs0c6gYAllkz1FtrDyd5wwrtezM5v768/S+TXD2T6gCAdfONcgAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwCKEOAIMQ6gAwiDVDvarOr6r7q+rxqnqsqt7Z28+qqnuq6sl+f2Zvr6r6cFXtqaqHq2rbvAcBAKxvT/25JO9prf14kouS3FhVr0tyU5J7W2tbk9zb55Pk8iRb++36JB+ZedUAwIusGeqttYOttS/06b9I8niSpSRXJrm1d7s1yVV9+sokH28Tn01yRlWdO/PKAYAXOKZz6lW1JckbkjyQ5JzW2sFkEvxJXt27LSV5auph+3sbADBH6w71qvqhJH+Y5F2ttW8fresKbW2F57u+qnZX1e7Dhw+vtwwAYBXrCvWqelkmgf6J1tqdvfmZI4fV+/2h3r4/yflTDz8vyYHlz9lau7m1tr21tn3z5s3HWz8A0K3n6vdKckuSx1trvzG1aFeSHX16R5K7ptqv7VfBX5Tk2SOH6QGA+dm0jj5vTPLLSR6pqod6279O8oEkd1TVdUm+muTqvuzuJFck2ZPkO0neMdOKAYAVrRnqrbX/kZXPkyfJJSv0b0luPMG6AIBj5BvlAGAQQh0ABiHUAWAQQh0ABiHUAWAQ6/mTtuHs3bs32y6++Kh9tiwt5c7bbltQRQBw4l6Sof7d55/P0g03HLXPvp07F1QNAMyGw+8AMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDEOoAMAihDgCDWDPUq+pjVXWoqh6dajurqu6pqif7/Zm9varqw1W1p6oerqpt8yweAPi+9eyp/26Sy5a13ZTk3tba1iT39vkkuTzJ1n67PslHZlMmALCWNUO9tfbfk3xjWfOVSW7t07cmuWqq/eNt4rNJzqiqc2dVLACwuuM9p35Oa+1gkvT7V/f2pSRPTfXb39sAgDmb9YVytUJbW7Fj1fVVtbuqdh8+fHjGZQDAS8/xhvozRw6r9/tDvX1/kvOn+p2X5MBKT9Bau7m1tr21tn3z5s3HWQYAcMTxhvquJDv69I4kd021X9uvgr8oybNHDtMDAPO1aa0OVfX7Sf5hkrOran+Sf5vkA0nuqKrrknw1ydW9+91JrkiyJ8l3krxjDjUDACtYM9Rba29bZdElK/RtSW480aIAgGPnG+UAYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBr/p36S9XevXuz7eKLV12+ZWkpd9522wIrAoCjE+qr+O7zz2fphhtWXb5v584FVgMAa3P4HQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGIdQBYBBCHQAGselkF7BR7d27N9suvnjV5VuWlnLnbbctsCIAXuqE+nH67vPPZ+mGG1Zdvm/nzgVWAwBCfW7syQOwaEJ9TuzJA7BoLpQDgEEIdQAYhFAHgEEIdQAYhFAHgEEIdQAYhFAHgEEIdQAYhFAHgEEIdQAYhK+JPUl8NzwAsybUTxLfDQ/ArDn8DgCDEOoAMAiH309RzrkDcKyE+inKOXcAjpXD7wAwCKEOAIMQ6gAwCOfU4SXsLW9/e/Y9/fSqy12QCRuLUN+gXB3PLOx7+mkXZMJA5hLqVXVZkv+c5LQkv9Na+8A8XuelbK2r4+9773uFPjAMR5XWZ+ahXlWnJfmtJP84yf4kn6+qXa21P5v1a7G6Ew39JPnzAwfyd374h497uQ+ZDRHMiqNK6zOPPfULk+xpre1Nkqr6ZJIrkwj1U8haoZ8kX37Pe/KTR+mz1nIfMhsiYLHmEepLSZ6amt+f5O/P4XU4xa113n+EIwFr7Ynv3bcvSwusZ9bWWofJia8HRzM4FYzyPqzW2myfsOrqJD/XWvtnff6Xk1zYWvuXy/pdn+T6PvtjSZ5Yx9OfneRrMyz3VGFcG4txbSzGtbG8VMb1I621zbN+kXnsqe9Pcv7U/HlJDizv1Fq7OcnNx/LEVbW7tbb9xMo79RjXxmJcG4txbSzGdWLm8eUzn0+ytaouqKqXJ7kmya45vA4AMGXme+qtteeq6leTfDqTP2n7WGvtsVm/DgDwQnP5O/XW2t1J7p7DUx/T4foNxLg2FuPaWIxrYzGuEzDzC+UAgJPDP3QBgEFsiFCvqsuq6omq2lNVN53selZSVedX1f1V9XhVPVZV7+ztv15VT1fVQ/12xdRj3tfH9ERV/dxU+4rj7RcfPlBVT1bV7f1CxEWMbV9VPdLr393bzqqqe3ot91TVmb29qurDvfaHq2rb1PPs6P2frKodU+0/2Z9/T39sLWBMPza1Th6qqm9X1bs24vqqqo9V1aGqenSqbe7rZ7XXmPO4/mNVfbnX/qmqOqO3b6mq/zu13nZOPeaY6j/az2iO45r7+66qTu/ze/ryLQsY1+1TY9pXVQ/19o20vlbbtp+an7HW2il9y+Riu68keW2Slyf5UpLXney6Vqjz3CTb+vQrkvyvJK9L8utJ/tUK/V/Xx3J6kgv6GE872niT3JHkmj69M8k/X9DY9iU5e1nbf0hyU5++KckH+/QVSf44SSW5KMkDvf2sJHv7/Zl9+sy+7HNJfqo/5o+TXH4S3mN/nuRHNuL6SvIzSbYleXSR62e115jzuC5NsqlPf3BqXFum+y17nmOqf7Wf0ZzHNff3XZJ/kWRnn74mye3zHtey5f8pyb/ZgOtrtW37KfkZm/kGZta3PtBPT82/L8n7TnZd66j7rky+/361D+sLxpHJXwv81Grj7Sv7a/n+Bu0F/eY8ln15cag/keTcPn1ukif69EeTvG15vyRvS/LRqfaP9rZzk3x5qv0F/RY0vkuT/M8+vSHXV5ZtJBexflZ7jXmOa9myX0jyiaP1O576V/sZzXl9zf19d+SxfXpT71eLWF+9rqeSbN2I62tZjUe27afkZ2wjHH5f6WtnT+lv3uyHtd6Q5IHe9Kv9MMzHpg6frDau1dpfleRbrbXnlrUvQkvyp1X1YE2+CTBJzmmtHUySfv/q3n6s41rq08vbF+maJL8/Nb/R11eymPWz2mssyq9ksldzxAVV9cWq+m9V9dO97XjqP1nbnHm/7773mL782d5/EX46yTOttSen2jbc+lq2bT8lP2MbIdRXOr96yl6yX1U/lOQPk7yrtfbtJB9J8neTvD7JwUwOQSWrj+tY2xfhja21bUkuT3JjVf3MUfpupHGln298c5L/2ptGWF9HM8Q4qur9SZ5L8onedDDJa1prb0jy7iS3VdXfzvHVfzLGvIj33clcl2/LC39x3nDra4Vt+7HWs5DP2EYI9XV97eypoKpelslK/0Rr7c4kaa0901p7vrX210l+O5P/YpesPq7V2r+W5Iyq2rSsfe5aawf6/aEkn8pkDM9U1blJ0u8P9e7HOq79fXp5+6JcnuQLrbVnkjHWV7eI9bPaa8xVv8Do55P8UuvHJVtrf9Va+3qffjCT881/7zjrX/g2Z0Hvu+89pi9/ZZJvzH40L9Rf6y1Jbj/SttHW10rb9uOoZyGfsY0Q6hvia2f71Yq3JHm8tfYbU+3nTnX7hSRHrgzdleSamlyRekGSrZlcLLHiePvG6/4kb+2P35HJuZ25qqofrKpXHJnO5Pzzo73+HSvUsivJtf0K0IuSPNsPG306yaVVdWY/tHhpJuf6Dib5i6q6qP8Mr13EuKa8YA9io6+vKYtYP6u9xtxU1WVJfi3Jm1tr35lq31xVp/Xp12ayfvYeZ/2r/YzmOa5FvO+mx/vWJPcd+aVozn42k3PG3zvEvJHW12rb9uOoZzGfsXldTDDjCxOuyOSKw68kef/JrmeVGv9BJodMHk7yUL9dkeT3kjzS23dl6gKOJO/vY3oiU1d8rzbeTK50/VySPZkcLj59AeN6bSZX1n4pyWNH6snkXNy9SZ7s92f19kryW732R5Jsn3quX+m170nyjqn27ZlsxL6S5L9kxhfvHGVsfyvJ15O8cqptw62vTH4pOZjk/2XyW/91i1g/q73GnMe1J5Pzkkc+Y0eu5v7F/v78UpIvJPmnx1v/0X5GcxzX3N93SX6gz+/py18773H19t9NcsOyvhtpfa22bT8lP2O+UQ4ABrERDr8DAOsg1AFgEEIdAAYh1AFgEEIdAAYh1AFgEEIdAAYh1AFgEP8fmGTL7sanNM4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(8, 6))\\n\",\n    \"\\n\",\n    \"tmp_kp_freqs = [v for k,v in unique_kp_counter.items() if v > 5000]\\n\",\n    \"sns.distplot(tmp_kp_freqs, color=\\\"teal\\\", \\n\",\n    \"             title=\\\"Frequency of unique phrases\\\", label=\\\"MagKP\\\",\\n\",\n    \"             bins=50, kde=False, rug=False, hist_kws=dict(alpha=0.7, edgecolor=\\\"k\\\", linewidth=1))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"####  KP length distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"ExecuteTime\": {\n     \"end_time\": \"2020-08-12T16:42:10.332438Z\",\n     \"start_time\": \"2020-08-12T16:42:08.442570Z\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"#kp_len=1, freq=7813072, accum/total=18.78%\\n\",\n      \"#kp_len=2, freq=21476033, accum/total=70.40%\\n\",\n      \"#kp_len=3, freq=6337508, accum/total=85.63%\\n\",\n      \"#kp_len=4, freq=1705544, accum/total=89.73%\\n\",\n      \"#kp_len=5, freq=502647, accum/total=90.94%\\n\",\n      \"#kp_len=6, freq=281022, accum/total=91.61%\\n\",\n      \"#kp_len=7, freq=217690, accum/total=92.13%\\n\",\n      \"#kp_len=8, freq=230406, accum/total=92.69%\\n\",\n      \"#kp_len=9, freq=226450, accum/total=93.23%\\n\",\n      \"#kp_len=10, freq=226740, accum/total=93.78%\\n\",\n      \"#kp_len=11, freq=216440, accum/total=94.30%\\n\",\n      \"#kp_len=12, freq=195025, accum/total=94.77%\\n\",\n      \"#kp_len=13, freq=171045, accum/total=95.18%\\n\",\n      \"#kp_len=14, freq=154596, accum/total=95.55%\\n\",\n      \"#kp_len=15, freq=146705, accum/total=95.90%\\n\",\n      \"#kp_len=16, freq=150455, accum/total=96.26%\\n\",\n      \"#kp_len=17, freq=159578, accum/total=96.65%\\n\",\n      \"#kp_len=18, freq=164405, accum/total=97.04%\\n\",\n      \"#kp_len=19, freq=158950, accum/total=97.42%\\n\",\n      \"#kp_len=20, freq=142297, accum/total=97.77%\\n\",\n      \"#kp_len=21, freq=119340, accum/total=98.05%\\n\",\n      \"#kp_len=22, freq=95724, accum/total=98.28%\\n\",\n      \"#kp_len=23, freq=75454, accum/total=98.46%\\n\",\n      \"#kp_len=24, freq=60110, accum/total=98.61%\\n\",\n      \"#kp_len=25, freq=49592, accum/total=98.73%\\n\",\n      \"#kp_len=26, freq=43438, accum/total=98.83%\\n\",\n      \"#kp_len=27, freq=39221, accum/total=98.93%\\n\",\n      \"#kp_len=28, freq=35621, accum/total=99.01%\\n\",\n      \"#kp_len=29, freq=32786, accum/total=99.09%\\n\",\n      \"#kp_len=30, freq=30577, accum/total=99.16%\\n\",\n      \"#kp_len=31, freq=28432, accum/total=99.23%\\n\",\n      \"#kp_len=32, freq=25857, accum/total=99.30%\\n\",\n      \"#kp_len=33, freq=24294, accum/total=99.35%\\n\",\n      \"#kp_len=34, freq=22616, accum/total=99.41%\\n\",\n      \"#kp_len=35, freq=21041, accum/total=99.46%\\n\",\n      \"#kp_len=36, freq=19390, accum/total=99.51%\\n\",\n      \"#kp_len=37, freq=17688, accum/total=99.55%\\n\",\n      \"#kp_len=38, freq=16803, accum/total=99.59%\\n\",\n      \"#kp_len=39, freq=15122, accum/total=99.62%\\n\",\n      \"#kp_len=40, freq=13949, accum/total=99.66%\\n\",\n      \"#kp_len=41, freq=12812, accum/total=99.69%\\n\",\n      \"#kp_len=42, freq=11536, accum/total=99.72%\\n\",\n      \"#kp_len=43, freq=10867, accum/total=99.74%\\n\",\n      \"#kp_len=44, freq=10065, accum/total=99.77%\\n\",\n      \"#kp_len=45, freq=8981, accum/total=99.79%\\n\",\n      \"#kp_len=46, freq=8204, accum/total=99.81%\\n\",\n      \"#kp_len=47, freq=7637, accum/total=99.83%\\n\",\n      \"#kp_len=48, freq=6935, accum/total=99.84%\\n\",\n      \"#kp_len=49, freq=6330, accum/total=99.86%\\n\",\n      \"#kp_len=50, freq=5695, accum/total=99.87%\\n\",\n      \"#kp_len=51, freq=5179, accum/total=99.88%\\n\",\n      \"#kp_len=52, freq=4734, accum/total=99.90%\\n\",\n      \"#kp_len=53, freq=4257, accum/total=99.91%\\n\",\n      \"#kp_len=54, freq=3847, accum/total=99.92%\\n\",\n      \"#kp_len=55, freq=3488, accum/total=99.92%\\n\",\n      \"#kp_len=56, freq=3413, accum/total=99.93%\\n\",\n      \"#kp_len=57, freq=2797, accum/total=99.94%\\n\",\n      \"#kp_len=58, freq=2479, accum/total=99.94%\\n\",\n      \"#kp_len=59, freq=2368, accum/total=99.95%\\n\",\n      \"#kp_len=60, freq=2232, accum/total=99.96%\\n\",\n      \"#kp_len=61, freq=1869, accum/total=99.96%\\n\",\n      \"#kp_len=62, freq=1731, accum/total=99.96%\\n\",\n      \"#kp_len=63, freq=1603, accum/total=99.97%\\n\",\n      \"#kp_len=64, freq=1465, accum/total=99.97%\\n\",\n      \"#kp_len=65, freq=1192, accum/total=99.97%\\n\",\n      \"#kp_len=66, freq=1069, accum/total=99.98%\\n\",\n      \"#kp_len=67, freq=1061, accum/total=99.98%\\n\",\n      \"#kp_len=68, freq=905, accum/total=99.98%\\n\",\n      \"#kp_len=69, freq=778, accum/total=99.98%\\n\",\n      \"#kp_len=70, freq=742, accum/total=99.99%\\n\",\n      \"#kp_len=71, freq=621, accum/total=99.99%\\n\",\n      \"#kp_len=72, freq=587, accum/total=99.99%\\n\",\n      \"#kp_len=73, freq=522, accum/total=99.99%\\n\",\n      \"#kp_len=74, freq=435, accum/total=99.99%\\n\",\n      \"#kp_len=75, freq=423, accum/total=99.99%\\n\",\n      \"#kp_len=76, freq=349, accum/total=99.99%\\n\",\n      \"#kp_len=77, freq=339, accum/total=99.99%\\n\",\n      \"#kp_len=78, freq=314, accum/total=99.99%\\n\",\n      \"#kp_len=79, freq=276, accum/total=99.99%\\n\",\n      \"#kp_len=80, freq=242, accum/total=100.00%\\n\",\n      \"#kp_len=81, freq=199, accum/total=100.00%\\n\",\n      \"#kp_len=82, freq=217, accum/total=100.00%\\n\",\n      \"#kp_len=83, freq=168, accum/total=100.00%\\n\",\n      \"#kp_len=84, freq=148, accum/total=100.00%\\n\",\n      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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x864 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(figsize=(16, 12))\\n\",\n    \"sns.set(style=\\\"whitegrid\\\")\\n\",\n    \"kp_lens = sorted([(kp_len, freq) for kp_len, freq in kp_len_counter.items()], key=lambda k:k[0])\\n\",\n    \"\\n\",\n    \"accum_kp_count = 0\\n\",\n    \"total_kp_count = sum(freq for _, freq in kp_lens)\\n\",\n    \"for kp_len, freq in kp_lens:\\n\",\n    \"    accum_kp_count += freq\\n\",\n    \"    print('#kp_len=%d, freq=%d, accum/total=%.2f%%' % (kp_len, freq, accum_kp_count / total_kp_count * 100))\\n\",\n    \"    \\n\",\n    \"print(len(kp_lens))\\n\",\n    \"kp_lens_df = pd.DataFrame(kp_lens, columns=['#kp_len', 'freq'])\\n\",\n    \"ax = sns.barplot(x=\\\"#kp_len\\\", y=\\\"freq\\\", data=kp_lens_df)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"toc-autonumbering\": true,\n  \"toc-showcode\": false,\n  \"toc-showmarkdowntxt\": false,\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/transfer_figures.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"def peak_index(group, x_index, y_index):\\n\",\n    \"    max_x, max_y = 0, -1\\n\",\n    \"\\n\",\n    \"    for id_label, _ in group.iterrows():\\n\",\n    \"        try:\\n\",\n    \"            if group.at[id_label , y_index] is not None:\\n\",\n    \"                if isinstance(group.at[id_label , y_index], pd.core.series.Series):\\n\",\n    \"                    if len(group.at[id_label , y_index]) > 1:\\n\",\n    \"                        new_v = group.at[id_label , y_index].iloc[0].item()\\n\",\n    \"                    if len(group.at[id_label , y_index]) == 0:\\n\",\n    \"                        continue\\n\",\n    \"                else:\\n\",\n    \"                    new_v = group.at[id_label , y_index]\\n\",\n    \"#                 print(new_v)\\n\",\n    \"                if new_v > max_y:\\n\",\n    \"                    max_y = new_v\\n\",\n    \"                    max_x = group.at[id_label , x_index]\\n\",\n    \"        except Exception as e:\\n\",\n    \"            print(group.at[id_label , y_index])\\n\",\n    \"            print(type(group.at[id_label , y_index]))\\n\",\n    \"            print(isinstance(group.at[id_label , y_index], pd.core.series.Series))\\n\",\n    \"            print(len(group.at[id_label , y_index]))\\n\",\n    \"            raise e\\n\",\n    \"            \\n\",\n    \"    return max_x, max_y\\n\",\n    \"\\n\",\n    \"def plot_testing_curve(df, y_index, title=''):\\n\",\n    \"    peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"    \\n\",\n    \"    fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"    for key, grp in df.groupby(['exp_name']): \\n\",\n    \"        ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', markersize=8.0, linewidth=4)\\n\",\n    \"        peak_x, peak_y = peak_index(grp, x_index='step', y_index=y_index)\\n\",\n    \"        variance = grp[y_index].var()\\n\",\n    \"        ax.annotate('%s peak=%.3f (@step=%d)' % (key, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#         display(grp)\\n\",\n    \"#         break\\n\",\n    \"\\n\",\n    \"    plt.legend(loc='best')\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def best_testscores_by_dev(df, dev_dataset, test_dataset, anchor_metric_name, ignore_duplicate=True):\\n\",\n    \"    dev_df = df.loc[df['test_dataset'] == dev_dataset]\\n\",\n    \"    test_df = df.loc[df['test_dataset'] == test_dataset]\\n\",\n    \"    dev_df = dev_df.sort_values(by=anchor_metric_name, ascending=False)\\n\",\n    \"    test_df = test_df.sort_values(by=anchor_metric_name, ascending=False)\\n\",\n    \"\\n\",\n    \"    if len(dev_df) == 0: \\n\",\n    \"        print('No valid score found! Return None')\\n\",\n    \"        return None\\n\",\n    \"\\n\",\n    \"    if len(test_df) == 0: \\n\",\n    \"        print('No test score found! Return None')\\n\",\n    \"        return None\\n\",\n    \"\\n\",\n    \"    dev_row = dev_df.iloc[0].to_frame().transpose()\\n\",\n    \"    \\n\",\n    \"    test_row = None\\n\",\n    \"    for idx, dev_row in dev_df.iterrows():\\n\",\n    \"        best_step = dev_row.step\\n\",\n    \"        test_row = test_df.loc[test_df.step == best_step]\\n\",\n    \"        if len(test_row) == 1:\\n\",\n    \"            dev_row = dev_row.to_frame().transpose()\\n\",\n    \"            test_row = test_row.iloc[0].to_frame().transpose()\\n\",\n    \"            break\\n\",\n    \"        elif len(test_row) > 1:\\n\",\n    \"            print('Found multiple rows (%d rows): exp=%s' % (\\n\",\n    \"                len(test_row), exp_name))\\n\",\n    \"            display(test_row)\\n\",\n    \"            if ignore_duplicate:\\n\",\n    \"                dev_row = dev_row.to_frame().transpose()\\n\",\n    \"                test_row = test_row.iloc[0].to_frame().transpose()\\n\",\n    \"                break\\n\",\n    \"            else:\\n\",\n    \"                raise ValueError()\\n\",\n    \"#     display(dev_row[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"#     display(test_row[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    return test_row\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load all .eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"10\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"40\\n\",\n      \"50\\n\",\n      \"60\\n\",\n      \"70\\n\",\n      \"80\\n\",\n      \"90\\n\",\n      \"100\\n\",\n      \"['stackex_test' 'openkp_valid2k_test' 'duc_test' 'kptimes_test'\\n\",\n      \" 'openkp_test' 'kptimes_valid2k_test' 'stackex_valid2k_test'\\n\",\n      \" 'jptimes_test' 'kp20k_valid2k_test' 'kp20k_test']\\n\",\n      \"['bartFT_presabs_openkp_100k' 'bartFT_presabs_openkp_100k_rerun'\\n\",\n      \" 'bartFT_presabs_kp20k_100k_rerun' 'transformer_presabs_stackex'\\n\",\n      \" 'bartFT_presabs_kptimes_100k_rerun' 'transformer_presabs_openkp'\\n\",\n      \" 'transformer_presabs_kptimes' 'bartFT_presabs_kp20k_100k'\\n\",\n      \" 'bartFT_presabs_stackex_100k_rerun' 'transformer_presabs_kp20k']\\n\",\n      \"['stackex_test' 'openkp_valid2k_test' 'duc_test' 'kptimes_test'\\n\",\n      \" 'openkp_test' 'kptimes_valid2k_test' 'stackex_valid2k_test'\\n\",\n      \" 'jptimes_test' 'kp20k_valid2k_test' 'kp20k_test']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# fulldata\\n\",\n    \"report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/cross_domain_exps/kp_o2s_fulldata_devbest/report'\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"    df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    df = df.loc[df.pred_name == pred_name]\\n\",\n    \"    df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"    print(len(all_eval_df))\\n\",\n    \"print(all_eval_df.test_dataset.unique())\\n\",\n    \"print(all_eval_df.exp_name.unique())\\n\",\n    \"print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot Heatmap for Transfer Across Domains\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer_presabs_kp20k 10\\n\",\n      \"transformer_presabs_openkp 10\\n\",\n      \"transformer_presabs_kptimes 10\\n\",\n      \"transformer_presabs_stackex 10\\n\",\n      \"{'TF-KP20k': {'KP20k': 0.2950610537355346, 'OpenKP': 0.0331670194536841, 'KPTimes': 0.0238392735042735, 'StackEx': 0.1173166666666666}, 'TF-OpenKP': {'KP20k': 0.0301053071419493, 'OpenKP': 0.183441689345832, 'KPTimes': 0.0518436904761904, 'StackEx': 0.0521833333333333}, 'TF-KPTimes': {'KP20k': 0.0092436171050924, 'OpenKP': 0.0286765447031549, 'KPTimes': 0.5025780158730159, 'StackEx': 0.0138125}, 'TF-StackEx': {'KP20k': 0.0405206069849953, 'OpenKP': 0.0104576151597621, 'KPTimes': 0.0042925793650793, 'StackEx': 0.5023770833333333}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# 'BART-KP20k': 'bartFT_presabs_kp20k_100k_rerun',\\n\",\n    \"# 'BART-OpenKP': 'bartFT_presabs_openkp_100k_rerun',\\n\",\n    \"# 'BART-KPTimes': 'bartFT_presabs_kptimes_100k_rerun',\\n\",\n    \"# 'BART-StackEx': 'bartFT_presabs_stackex_100k_rerun',\\n\",\n    \"\\n\",\n    \"'TF-KP20k': 'transformer_presabs_kp20k',\\n\",\n    \"'TF-OpenKP': 'transformer_presabs_openkp',\\n\",\n    \"'TF-KPTimes': 'transformer_presabs_kptimes',\\n\",\n    \"'TF-StackEx': 'transformer_presabs_stackex'\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"testname_map = {\\n\",\n    \"    'kp20k_test': 'KP20k', \\n\",\n    \"    'openkp_test': 'OpenKP', \\n\",\n    \"    'kptimes_test': 'KPTimes', \\n\",\n    \"    'stackex_test': 'StackEx', \\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"train_test_scores = {}\\n\",\n    \"\\n\",\n    \"for short_name, exp_name in short2long.items():\\n\",\n    \"    exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"    exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    print(exp_name, len(exp_grp))\\n\",\n    \"    train_test_scores[long2short[exp_name]] = {}\\n\",\n    \"    \\n\",\n    \"#     display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    \\n\",\n    \"    for dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        train_test_scores[long2short[exp_name]][testname_map[test_dataset]] = best_test_row[anchor_metric_name].values[0]\\n\",\n    \"        \\n\",\n    \"print(train_test_scores)\\n\",\n    \"        \\n\",\n    \"# avg_bar_values = {'Beam': [], 'R@50': [], 'model': []}\\n\",\n    \"# kp20k_valid2k_idx = datasets.tolist().index('kp20k_valid2k')\\n\",\n    \"# for k, v in bar_values.items():\\n\",\n    \"#     _v = [e for i,e in enumerate(v) if i != kp20k_valid2k_idx]\\n\",\n    \"#     avg_bar_values['Beam'].append(int(k))\\n\",\n    \"#     avg_bar_values['R@50'].append(np.mean(_v))\\n\",\n    \"#     avg_bar_values['model'].append('TF')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>KP20k</th>\\n\",\n       \"      <th>OpenKP</th>\\n\",\n       \"      <th>KPTimes</th>\\n\",\n       \"      <th>StackEx</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-KP20k</th>\\n\",\n       \"      <td>29.506105</td>\\n\",\n       \"      <td>3.316702</td>\\n\",\n       \"      <td>2.383927</td>\\n\",\n       \"      <td>11.731667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-OpenKP</th>\\n\",\n       \"      <td>3.010531</td>\\n\",\n       \"      <td>18.344169</td>\\n\",\n       \"      <td>5.184369</td>\\n\",\n       \"      <td>5.218333</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-KPTimes</th>\\n\",\n       \"      <td>0.924362</td>\\n\",\n       \"      <td>2.867654</td>\\n\",\n       \"      <td>50.257802</td>\\n\",\n       \"      <td>1.381250</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>TF-StackEx</th>\\n\",\n       \"      <td>4.052061</td>\\n\",\n       \"      <td>1.045762</td>\\n\",\n       \"      <td>0.429258</td>\\n\",\n       \"      <td>50.237708</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                KP20k     OpenKP    KPTimes    StackEx\\n\",\n       \"TF-KP20k    29.506105   3.316702   2.383927  11.731667\\n\",\n       \"TF-OpenKP    3.010531  18.344169   5.184369   5.218333\\n\",\n       \"TF-KPTimes   0.924362   2.867654  50.257802   1.381250\\n\",\n       \"TF-StackEx   4.052061   1.045762   0.429258  50.237708\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train_test_matrix = pd.DataFrame(data=train_test_scores)\\n\",\n    \"train_test_matrix = train_test_matrix.transpose() * 100.0\\n\",\n    \"display(train_test_matrix)\\n\",\n    \"\\n\",\n    \"# sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"#                              \\\"axes.titlesize\\\": 24,\\n\",\n    \"#                              \\\"axes.labelsize\\\": 14,\\n\",\n    \"#                              \\\"xtick.labelsize\\\": 14,\\n\",\n    \"#                              \\\"ytick.labelsize\\\": 14,\\n\",\n    \"#                              \\\"legend.fontsize\\\": 14})\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n    \"sns.set(font_scale=1.6)\\n\",\n    \"sns.heatmap(train_test_matrix, \\n\",\n    \"            cmap='Blues',\\n\",\n    \"#             cmap='Reds',\\n\",\n    \"#             cmap='coolwarm', \\n\",\n    \"            annot=True, fmt=\\\".1f\\\", \\n\",\n    \"            annot_kws={'size':16},\\n\",\n    \"            cbar=False,\\n\",\n    \"            square=True,\\n\",\n    \"#             xticklabels=False,\\n\",\n    \"#             yticklabels=False\\n\",\n    \"           )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"bartFT_presabs_kp20k_100k_rerun 10\\n\",\n      \"bartFT_presabs_openkp_100k_rerun 10\\n\",\n      \"bartFT_presabs_kptimes_100k_rerun 10\\n\",\n      \"bartFT_presabs_stackex_100k_rerun 10\\n\",\n      \"{'BART-KP20k': {'KP20k': 0.3247736233549484, 'OpenKP': 0.1901093639754056, 'KPTimes': 0.1131522723475355, 'StackEx': 0.23204375}, 'BART-OpenKP': {'KP20k': 0.194497983279298, 'OpenKP': 0.4270512045156738, 'KPTimes': 0.1766549603174603, 'StackEx': 0.1869166666666666}, 'BART-KPTimes': {'KP20k': 0.0251171736853645, 'OpenKP': 0.1116167725027719, 'KPTimes': 0.6450049603174604, 'StackEx': 0.1177729166666666}, 'BART-StackEx': {'KP20k': 0.0609741117857706, 'OpenKP': 0.0411601653059167, 'KPTimes': 0.0713909126984126, 'StackEx': 0.5696531250000001}}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>KP20k</th>\\n\",\n       \"      <th>OpenKP</th>\\n\",\n       \"      <th>KPTimes</th>\\n\",\n       \"      <th>StackEx</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>BART-KP20k</th>\\n\",\n       \"      <td>32.477362</td>\\n\",\n       \"      <td>19.010936</td>\\n\",\n       \"      <td>11.315227</td>\\n\",\n       \"      <td>23.204375</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>BART-OpenKP</th>\\n\",\n       \"      <td>19.449798</td>\\n\",\n       \"      <td>42.705120</td>\\n\",\n       \"      <td>17.665496</td>\\n\",\n       \"      <td>18.691667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>BART-KPTimes</th>\\n\",\n       \"      <td>2.511717</td>\\n\",\n       \"      <td>11.161677</td>\\n\",\n       \"      <td>64.500496</td>\\n\",\n       \"      <td>11.777292</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>BART-StackEx</th>\\n\",\n       \"      <td>6.097411</td>\\n\",\n       \"      <td>4.116017</td>\\n\",\n       \"      <td>7.139091</td>\\n\",\n       \"      <td>56.965313</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                  KP20k     OpenKP    KPTimes    StackEx\\n\",\n       \"BART-KP20k    32.477362  19.010936  11.315227  23.204375\\n\",\n       \"BART-OpenKP   19.449798  42.705120  17.665496  18.691667\\n\",\n       \"BART-KPTimes   2.511717  11.161677  64.500496  11.777292\\n\",\n       \"BART-StackEx   6.097411   4.116017   7.139091  56.965313\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'BART-KP20k': 'bartFT_presabs_kp20k_100k_rerun',\\n\",\n    \"'BART-OpenKP': 'bartFT_presabs_openkp_100k_rerun',\\n\",\n    \"'BART-KPTimes': 'bartFT_presabs_kptimes_100k_rerun',\\n\",\n    \"'BART-StackEx': 'bartFT_presabs_stackex_100k_rerun',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"testname_map = {\\n\",\n    \"    'kp20k_test': 'KP20k', \\n\",\n    \"    'openkp_test': 'OpenKP', \\n\",\n    \"    'kptimes_test': 'KPTimes', \\n\",\n    \"    'stackex_test': 'StackEx', \\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"train_test_scores = {}\\n\",\n    \"\\n\",\n    \"for short_name, exp_name in short2long.items():\\n\",\n    \"    exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"    exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    print(exp_name, len(exp_grp))\\n\",\n    \"    train_test_scores[long2short[exp_name]] = {}\\n\",\n    \"    \\n\",\n    \"#     display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    \\n\",\n    \"    for dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        train_test_scores[long2short[exp_name]][testname_map[test_dataset]] = best_test_row[anchor_metric_name].values[0]\\n\",\n    \"        \\n\",\n    \"print(train_test_scores)\\n\",\n    \"\\n\",\n    \"train_test_matrix = pd.DataFrame(data=train_test_scores)\\n\",\n    \"train_test_matrix = train_test_matrix.transpose() * 100.0\\n\",\n    \"display(train_test_matrix)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=(6, 6))\\n\",\n    \"sns.set(font_scale=1.6)\\n\",\n    \"sns.heatmap(train_test_matrix, \\n\",\n    \"#             cmap='Blues',\\n\",\n    \"            cmap='Reds',\\n\",\n    \"#             cmap='coolwarm', \\n\",\n    \"            annot=True, fmt=\\\".1f\\\", \\n\",\n    \"            annot_kws={'size':16},\\n\",\n    \"            cbar=False,\\n\",\n    \"            square=True,\\n\",\n    \"#             xticklabels=False,\\n\",\n    \"#             yticklabels=False\\n\",\n    \"           )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### TF fewshot results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# TF-fewshot\\n\",\n    \"report_dirs = ['/zfs1/pbrusilovsky/rum20/kp/transfer_exps/tf_FT_devbest/report',\\n\",\n    \"               '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT_devbest/report',\\n\",\n    \"               '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/tf_PTFT_devbest/report',\\n\",\n    \"               '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_devbest/report',\\n\",\n    \"              ]\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"\\n\",\n    \"for report_dir in report_dirs:\\n\",\n    \"    for fname in os.listdir(report_dir):\\n\",\n    \"        if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"        df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"        df = df.loc[df.pred_name == pred_name]\\n\",\n    \"        df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"#     print(len(all_eval_df))\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\\n\",\n    \"# print(all_eval_df.exp_name.unique())\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-fewshot100-lr3e4-step2k 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-kp20k-fewshot1k-lr3e4-step4k 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-kp20k-fewshot10k-lr3e4-step8k 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5 7 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-openkp-fewshot100-lr3e4-step2k 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-openkp-fewshot1k-lr3e4-step4k 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-openkp-fewshot10k-lr3e4-step8k 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5 3 openkp_valid2k_test openkp_test\\n\",\n      \"transformer-kptimes-fewshot100-lr3e4-step2k 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-kptimes-fewshot1k-lr3e4-step4k 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-kptimes-fewshot10k-lr3e4-step8k 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5 4 kptimes_valid2k_test kptimes_test\\n\",\n      \"transformer-stackex-fewshot100-lr3e4-step2k 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-stackex-fewshot1k-lr3e4-step4k 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-stackex-fewshot10k-lr3e4-step8k 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5 3 stackex_valid2k_test stackex_test\\n\",\n      \"transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5 3 stackex_valid2k_test stackex_test\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'FT-KP20k-100': 'transformer-kp20k-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KP20k-1k': 'transformer-kp20k-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KP20k-10k': 'transformer-kp20k-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-OpenKP-100': 'transformer-openkp-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-OpenKP-1k': 'transformer-openkp-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-OpenKP-10k': 'transformer-openkp-fewshot10k-lr3e4-step8k', \\n\",\n    \"'FT-KPTimes-100': 'transformer-kptimes-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KPTimes-1k': 'transformer-kptimes-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KPTimes-10k': 'transformer-kptimes-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-StackEx-100': 'transformer-stackex-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-StackEx-1k': 'transformer-stackex-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-StackEx-10k': 'transformer-stackex-fewshot10k-lr3e4-step8k',\\n\",\n    \"\\n\",\n    \"'DA+FT-KP20k-100': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-1k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-10k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-100': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-1k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-10k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-100': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-1k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-10k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-100': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-1k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-10k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+FT-KP20k-100': 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-1k': 'transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-10k': 'transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-100': 'transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-1k': 'transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-10k': 'transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-100': 'transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-1k': 'transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-10k': 'transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-100': 'transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-1k': 'transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-10k': 'transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+DA+FT-KP20k-100': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-1k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-10k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-100': 'transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-1k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-10k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-100': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-1k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-10k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-100': 'transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-1k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-10k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"# for short_name in short2long.keys():\\n\",\n    \"#     model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"#     test_dataset = test_dataset.lower()\\n\",\n    \"#     bar_dicts.append({\\n\",\n    \"#         'model_setting': model_setting,\\n\",\n    \"#         'test_dataset': dataset_name, \\n\",\n    \"#         'train_setting': train_setting,\\n\",\n    \"#         'score': random.uniform(20, 50)\\n\",\n    \"#     })\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        print(exp_name, len(exp_grp), dev_dataset, test_dataset)\\n\",\n    \"#         display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    \\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0 if best_test_row is not None and len(best_test_row) > 0 else 0.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# print(train_test_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"        palette=[_set3[8], _set3[2], _set3[7], _set3[9]] #_set3[7:]\\n\",\n    \"    )\\n\",\n    \"#     g.set_ylim(22, 38)\\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() - 0.05, p.get_height() + 0.1), rotation=0)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"F@O\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Present\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'FT-KP20k-100': 'transformer-kp20k-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KP20k-1k': 'transformer-kp20k-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KP20k-10k': 'transformer-kp20k-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-OpenKP-100': 'transformer-openkp-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-OpenKP-1k': 'transformer-openkp-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-OpenKP-10k': 'transformer-openkp-fewshot10k-lr3e4-step8k', \\n\",\n    \"'FT-KPTimes-100': 'transformer-kptimes-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KPTimes-1k': 'transformer-kptimes-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KPTimes-10k': 'transformer-kptimes-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-StackEx-100': 'transformer-stackex-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-StackEx-1k': 'transformer-stackex-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-StackEx-10k': 'transformer-stackex-fewshot10k-lr3e4-step8k',\\n\",\n    \"\\n\",\n    \"'DA+FT-KP20k-100': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-1k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-10k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-100': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-1k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-10k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-100': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-1k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-10k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-100': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-1k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-10k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+FT-KP20k-100': 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-1k': 'transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-10k': 'transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-100': 'transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-1k': 'transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-10k': 'transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-100': 'transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-1k': 'transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-10k': 'transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-100': 'transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-1k': 'transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-10k': 'transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+DA+FT-KP20k-100': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-1k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-10k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-100': 'transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-1k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-10k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-100': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-1k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-10k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-100': 'transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-1k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-10k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'present_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"        palette=_set3[7:] + [_set3[2], _set3[3]]\\n\",\n    \"    )\\n\",\n    \"#     g.set_ylim(22, 38)\\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.05, p.get_height() + 0.1), rotation=0)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"Present F@O\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'FT-KP20k-100': 'transformer-kp20k-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KP20k-1k': 'transformer-kp20k-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KP20k-10k': 'transformer-kp20k-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-OpenKP-100': 'transformer-openkp-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-OpenKP-1k': 'transformer-openkp-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-OpenKP-10k': 'transformer-openkp-fewshot10k-lr3e4-step8k', \\n\",\n    \"'FT-KPTimes-100': 'transformer-kptimes-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-KPTimes-1k': 'transformer-kptimes-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-KPTimes-10k': 'transformer-kptimes-fewshot10k-lr3e4-step8k',\\n\",\n    \"'FT-StackEx-100': 'transformer-stackex-fewshot100-lr3e4-step2k',\\n\",\n    \"'FT-StackEx-1k': 'transformer-stackex-fewshot1k-lr3e4-step4k',\\n\",\n    \"'FT-StackEx-10k': 'transformer-stackex-fewshot10k-lr3e4-step8k',\\n\",\n    \"\\n\",\n    \"'DA+FT-KP20k-100': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-1k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KP20k-10k': 'transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-100': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-1k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-OpenKP-10k': 'transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-100': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-1k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-KPTimes-10k': 'transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-100': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-1k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'DA+FT-StackEx-10k': 'transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+FT-KP20k-100': 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-1k': 'transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-KP20k-10k': 'transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-100': 'transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-1k': 'transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-OpenKP-10k': 'transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-100': 'transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-1k': 'transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5',\\n\",\n    \"'PT+FT-KPTimes-10k': 'transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-100': 'transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-1k': 'transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+FT-StackEx-10k': 'transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'PT+DA+FT-KP20k-100': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-1k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KP20k-10k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-100': 'transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-1k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-OpenKP-10k': 'transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-100': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-1k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-KPTimes-10k': 'transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-100': 'transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-1k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5',\\n\",\n    \"'PT+DA+FT-StackEx-10k': 'transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"        palette=[_set3[4], _set3[3], _set3[6], _set3[5]]\\n\",\n    \"    )\\n\",\n    \"#     g.set_ylim(22, 38)\\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() - 0.02, p.get_height() + 0.05), rotation=0, fontsize=10)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"Absent Recall@50\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot different DA strategies (TL/NP/RS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 86,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# fulldata\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2s_fulldata_devbest/report/'\\n\",\n    \"\\n\",\n    \"# TF-fewshot\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/report/'\\n\",\n    \"report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT_devbest/report'\\n\",\n    \"\\n\",\n    \"# BART-fewshot\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v1-DA1e6_FT1e5/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/report/'\\n\",\n    \"\\n\",\n    \"# BART-mag\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/report/'\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"    df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    df = df.loc[df.pred_name == pred_name]\\n\",\n    \"    df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"#     print(len(all_eval_df))\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\\n\",\n    \"# print(all_eval_df.exp_name.unique())\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 87,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-DA_TLtf_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_RS_40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_RS_40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_RS_40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k 3 kp20k_valid2k_test kp20k_test\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'TL-KP20k-100': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL-KP20k-1k': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL-KP20k-10k': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"# 'TL beam1-KP20k-100': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot100',\\n\",\n    \"# 'TL beam1-KP20k-1k': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"# 'TL beam1-KP20k-10k': 'transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"\\n\",\n    \"# 'NP-KP20k-100': 'transformer-DA_NP_40k-FT_kp20k_fewshot100',\\n\",\n    \"# 'NP-KP20k-1k': 'transformer-DA_NP_40k-FT_kp20k_fewshot1k',\\n\",\n    \"# 'NP-KP20k-10k': 'transformer-DA_NP_40k-FT_kp20k_fewshot10k',\\n\",\n    \"\\n\",\n    \"'RS-KP20k-100': 'transformer-DA_RS_40k-FT_kp20k_fewshot100',\\n\",\n    \"'RS-KP20k-1k': 'transformer-DA_RS_40k-FT_kp20k_fewshot1k',\\n\",\n    \"'RS-KP20k-10k': 'transformer-DA_RS_40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"# 'TL beam3-KP20k-100': 'transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100',\\n\",\n    \"# 'TL beam3-KP20k-1k': 'transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k',\\n\",\n    \"# 'TL beam3-KP20k-10k': 'transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"# 'TL beam5-KP20k-100': 'transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100',\\n\",\n    \"# 'TL beam5-KP20k-1k': 'transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k',\\n\",\n    \"# 'TL beam5-KP20k-10k': 'transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"# 'TL beam10-KP20k-100': 'transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100',\\n\",\n    \"# 'TL beam10-KP20k-1k': 'transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k',\\n\",\n    \"# 'TL beam10-KP20k-10k': 'transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:NP=2:8-KP20k-100': 'transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:NP=2:8-KP20k-1k': 'transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:NP=2:8-KP20k-10k': 'transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:NP=5:5-KP20k-100': 'transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:NP=5:5-KP20k-1k': 'transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:NP=5:5-KP20k-10k': 'transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:NP=8:2-KP20k-100': 'transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:NP=8:2-KP20k-1k': 'transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:NP=8:2-KP20k-10k': 'transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:RS=2:8-KP20k-100': 'transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:RS=2:8-KP20k-1k': 'transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:RS=2:8-KP20k-10k': 'transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:RS=5:5-KP20k-100': 'transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:RS=5:5-KP20k-1k': 'transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:RS=5:5-KP20k-10k': 'transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"    \\n\",\n    \"'TL:RS=8:2-KP20k-100': 'transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100',\\n\",\n    \"'TL:RS=8:2-KP20k-1k': 'transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k',\\n\",\n    \"'TL:RS=8:2-KP20k-10k': 'transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"# for short_name in short2long.keys():\\n\",\n    \"#     model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"#     test_dataset = test_dataset.lower()\\n\",\n    \"#     bar_dicts.append({\\n\",\n    \"#         'model_setting': model_setting,\\n\",\n    \"#         'test_dataset': dataset_name, \\n\",\n    \"#         'train_setting': train_setting,\\n\",\n    \"#         'score': random.uniform(20, 50)\\n\",\n    \"#     })\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        print(exp_name, len(exp_grp), dev_dataset, test_dataset)\\n\",\n    \"#         display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    \\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# print(train_test_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 95,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_set2 = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Paired\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 1, figsize=(8, 4), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == 'kp20k']\\n\",\n    \"\\n\",\n    \"g = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"    ax=axes, alpha=0.9,\\n\",\n    \"    palette=[_set2[5], _set2[1]] + _set2[0:6:2] + _set2[6:12:2]\\n\",\n    \"#     palette=_set2[1:]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in axes.patches:\\n\",\n    \"    axes.annotate('%.1f' % (p.get_height()), (p.get_x() + 0.01, p.get_height() + 0.1), rotation=0)\\n\",\n    \"axes.set_title(\\\"\\\")\\n\",\n    \"axes.set_xlabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"axes.set_ylabel(\\\"F@O\\\")\\n\",\n    \"axes.legend(loc='upper left')\\n\",\n    \"\\n\",\n    \"alphas = [1.0] * 3 + [0.2] * 9\\n\",\n    \"for bar, alpha in zip(g.containers[0], alphas):\\n\",\n    \"    bar.set_alpha(alpha)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### TL Zero-shot\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 92,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# TF-DA-zeroshot\\n\",\n    \"report_dirs = ['/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/report',\\n\",\n    \"              '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/report'\\n\",\n    \"             ]\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for report_dir in report_dirs:\\n\",\n    \"    for fname in os.listdir(report_dir):\\n\",\n    \"        if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"        df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"        df = df.loc[df.pred_name == pred_name]\\n\",\n    \"        df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 99,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"bart-DA_kp20k-TL_RS-lr1e5-step10k 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"bart-DA_openkp-TL_RS-lr1e5-step10k 4 openkp_valid2k_test openkp_test\\n\",\n      \"bart-DA_kptimes-TL_RS-lr1e5-step10k 5 kptimes_valid2k_test kptimes_test\\n\",\n      \"bart-DA_stackex-TL_RS-lr1e5-step10k 4 stackex_valid2k_test stackex_test\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# 'TF-KP20k-zeroshot': 'transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k',\\n\",\n    \"# 'TF-OpenKP-zeroshot': 'transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k',\\n\",\n    \"# 'TF-KPTimes-zeroshot': 'transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k', \\n\",\n    \"# 'TF-StackEx-zeroshot': 'transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k',\\n\",\n    \"# 'NP-KP20k-zeroshot': 'transformer_DA-NP_kp20k_step100k',\\n\",\n    \"# 'RS-KP20k-zeroshot': 'transformer_DA-RS_kp20k_step100k',\\n\",\n    \"# 'TL-KP20k-zeroshot': 'transformer_DA-TL_kp20k_step100k',\\n\",\n    \"    \\n\",\n    \"'BART-KP20k-zeroshot': 'bart-DA_kp20k-TL_RS-lr1e5-step10k',\\n\",\n    \"'BART-OpenKP-zeroshot': 'bart-DA_openkp-TL_RS-lr1e5-step10k',\\n\",\n    \"'BART-KPTimes-zeroshot': 'bart-DA_kptimes-TL_RS-lr1e5-step10k', \\n\",\n    \"'BART-StackEx-zeroshot': 'bart-DA_stackex-TL_RS-lr1e5-step10k',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['zeroshot']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"# for short_name in short2long.keys():\\n\",\n    \"#     model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"#     test_dataset = test_dataset.lower()\\n\",\n    \"#     bar_dicts.append({\\n\",\n    \"#         'model_setting': model_setting,\\n\",\n    \"#         'test_dataset': dataset_name, \\n\",\n    \"#         'train_setting': train_setting,\\n\",\n    \"#         'score': random.uniform(20, 50)\\n\",\n    \"#     })\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        print(exp_name, len(exp_grp), dev_dataset, test_dataset)\\n\",\n    \"#         display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"    \\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# print(train_test_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 100,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"No handles with labels found to put in legend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7ff7afcb1e50>\"\n      ]\n     },\n     \"execution_count\": 100,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 360x252 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"_set2 = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Paired\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 1, figsize=(5, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"subbar_df = bar_df\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", font_scale=1.0, rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 18,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 18,\\n\",\n    \"                             \\\"legend.fontsize\\\": 18})\\n\",\n    \"\\n\",\n    \"sns.set(font_scale=1.2,\\n\",\n    \"        rc={\\\"font.size\\\": 14,\\n\",\n    \"            \\\"axes.titlesize\\\": 24,\\n\",\n    \"            \\\"axes.labelsize\\\": 20,\\n\",\n    \"            \\\"xtick.labelsize\\\": 20,\\n\",\n    \"            \\\"ytick.labelsize\\\": 20,\\n\",\n    \"            \\\"legend.fontsize\\\": 20})\\n\",\n    \"g = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"test_dataset\\\", y=\\\"score\\\",\\n\",\n    \"    ax=axes, alpha=1.0,\\n\",\n    \"    palette=[_set2[10], _set2[7], _set2[8], _set2[9]]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in axes.patches:\\n\",\n    \"    axes.annotate('%.1f' % (p.get_height()), (p.get_x() + 0.25, p.get_height() + 0.1), rotation=0)\\n\",\n    \"# axes.set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"# axes.set_xticklabels([])\\n\",\n    \"axes.set_xlabel(\\\"Zeroshot\\\", fontsize=14)\\n\",\n    \"axes.set_ylabel(\\\"F@O\\\",fontsize=14)\\n\",\n    \"# _, ylabels = plt.yticks()\\n\",\n    \"# g.set_yticklabels(ylabels, size=15)\\n\",\n    \"axes.legend(loc='upper left')\\n\",\n    \"g.tick_params(labelsize=12)\\n\",\n    \"\\n\",\n    \"axes.legend([],[], frameon=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### BART fewshot\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# BART-fewshot\\n\",\n    \"report_dirs = [\\n\",\n    \"#     '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT/report/',\\n\",\n    \"#     '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_DAFT/report/',\\n\",\n    \"#     '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT/report/'\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT_devbest/report/',\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_DAFT_devbest/report/',\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTFT_devbest/report/',\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT_devbest/report/'\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for report_dir in report_dirs:\\n\",\n    \"    for fname in os.listdir(report_dir):\\n\",\n    \"        if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"        df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"        df = df.loc[df.pred_name == pred_name]\\n\",\n    \"        df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"#     print(len(all_eval_df))\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\\n\",\n    \"# print(all_eval_df.exp_name.unique())\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"FT KP20k 100\\n\",\n      \"FT KP20k 1k\\n\",\n      \"FT KP20k 10k\\n\",\n      \"PT+DA+FT KP20k 100\\n\",\n      \"PT+DA+FT KP20k 1k\\n\",\n      \"PT+DA+FT KP20k 10k\\n\",\n      \"FT OpenKP 100\\n\",\n      \"FT OpenKP 1k\\n\",\n      \"FT OpenKP 10k\\n\",\n      \"PT+DA+FT OpenKP 100\\n\",\n      \"PT+DA+FT OpenKP 1k\\n\",\n      \"PT+DA+FT OpenKP 10k\\n\",\n      \"FT KPTimes 100\\n\",\n      \"FT KPTimes 1k\\n\",\n      \"FT KPTimes 10k\\n\",\n      \"PT+DA+FT KPTimes 100\\n\",\n      \"PT+DA+FT KPTimes 1k\\n\",\n      \"PT+DA+FT KPTimes 10k\\n\",\n      \"FT StackEx 100\\n\",\n      \"FT StackEx 1k\\n\",\n      \"FT StackEx 10k\\n\",\n      \"PT+DA+FT StackEx 100\\n\",\n      \"PT+DA+FT StackEx 1k\\n\",\n      \"PT+DA+FT StackEx 10k\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# 'FT-KP20k-100': 'bart-FT_kp20k_fewshot100-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'FT-KP20k-1k': 'bart-FT_kp20k_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'FT-KP20k-10k': 'bart-FT_kp20k_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-KP20k-100': 'bart-FT_kp20k_fewshot100-bs16_step4k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"'FT-KP20k-1k': 'bart-FT_kp20k_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"'FT-KP20k-10k': 'bart-FT_kp20k_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"'FT-OpenKP-100': 'bart-FT_openkp_fewshot100-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-OpenKP-1k': 'bart-FT_openkp_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-OpenKP-10k': 'bart-FT_openkp_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01', \\n\",\n    \"'FT-KPTimes-100': 'bart-FT_kptimes_fewshot100-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'FT-KPTimes-1k': 'bart-FT_kptimes_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-KPTimes-1k': 'bart-FT_kptimes_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01-rerun',\\n\",\n    \"'FT-KPTimes-10k': 'bart-FT_kptimes_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-StackEx-100': 'bart-FT_stackex_fewshot100-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-StackEx-1k': 'bart-FT_stackex_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"'FT-StackEx-10k': 'bart-FT_stackex_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"# 'DA+FT-KP20k-100': 'bart-kp20k-DA_step5k_bs256-FT100_bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-KP20k-1k': 'bart-kp20k-DA_step5k_bs256-FT1k_bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-KP20k-10k': 'bart-kp20k-DA_step5k_bs256-FT10k_bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-OpenKP-100': 'bart-openkp-DA_step5k_bs256-FT100_bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-OpenKP-1k': 'bart-openkp-DA_step5k_bs256-FT1k_bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-OpenKP-10k': 'bart-openkp-DA_step5k_bs256-FT10k_bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-KPTimes-100': 'bart-kptimes-DA_step5k_bs256-FT100_bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-KPTimes-1k': 'bart-kptimes-DA_step5k_bs256-FT1k_bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-KPTimes-10k': 'bart-kptimes-DA_step5k_bs256-FT10k_bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-StackEx-100': 'bart-stackex-DA_step5k_bs256-FT100_bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-StackEx-1k': 'bart-stackex-DA_step5k_bs256-FT1k_bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'DA+FT-StackEx-10k': 'bart-stackex-DA_step5k_bs256-FT10k_bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"# 'PT+FT-KP20k-100': 'bart-kp20k-PT_step40k-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-KP20k-1k': 'bart-kp20k-PT_step40k-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-KP20k-10k': 'bart-kp20k-PT_step40k-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-OpenKP-100': 'bart-openkp-PT_step40k-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-OpenKP-1k': 'bart-openkp-PT_step40k-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-OpenKP-10k': 'bart-openkp-PT_step40k-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-KPTimes-100': 'bart-kptimes-PT_step40k-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-KPTimes-1k': 'bart-kptimes-PT_step40k-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-KPTimes-10k': 'bart-kptimes-PT_step40k-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-StackEx-100': 'bart-stackex-PT_step40k-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-StackEx-1k': 'bart-stackex-PT_step40k-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'PT+FT-StackEx-10k': 'bart-stackex-PT_step40k-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"'PT+DA+FT-KP20k-100': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-KP20k-1k': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-KP20k-10k': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-OpenKP-100': 'bart-PT_step40k-DA_openkp_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-OpenKP-1k': 'bart-PT_step40k-DA_openkp_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-OpenKP-10k': 'bart-PT_step40k-DA_openkp_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-KPTimes-100': 'bart-PT_step40k-DA_kptimes_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-KPTimes-1k': 'bart-PT_step40k-DA_kptimes_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-KPTimes-10k': 'bart-PT_step40k-DA_kptimes_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-StackEx-100': 'bart-PT_step40k-DA_stackex_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-StackEx-1k': 'bart-PT_step40k-DA_stackex_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'PT+DA+FT-StackEx-10k': 'bart-PT_step40k-DA_stackex_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')\\n\",\n    \"                 ]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"# anchor_metric_name = 'all_exact_f_score@10'\\n\",\n    \"# anchor_metric_name = 'all_exact_advanced_sadr'\\n\",\n    \"# anchor_metric_name = 'all_exact_advanced_ndcg'\\n\",\n    \"# anchor_metric_name = 'all_exact_advanced_alpha_ndcg@10'\\n\",\n    \"# anchor_metric_name = 'all_exact_advanced_auc'\\n\",\n    \"# anchor_metric_name = 'all_exact_advanced_mrr'\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"avg_score_map = {}\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"#         print(exp_name, len(exp_grp), dev_dataset, test_dataset)\\n\",\n    \"        print(model_setting, dataset_name, train_setting)\\n\",\n    \"#         display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        avg_key = f\\\"{model_setting}-{train_setting}\\\"\\n\",\n    \"        avg_score = avg_score_map.get(avg_key, 0.0)\\n\",\n    \"        avg_score_map[avg_key] = avg_score + best_test_row[anchor_metric_name].values[0]\\n\",\n    \"\\n\",\n    \"avg_bar_dicts = []\\n\",\n    \"for avg_key, avg_score in avg_score_map.items():\\n\",\n    \"    model_setting, train_setting = avg_key.split('-')\\n\",\n    \"    avg_bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': avg_score / 4 * 100.0\\n\",\n    \"        })\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(20.0, 55.0)]\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 6,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"#         palette=[_set3[7], _set3[8], _set3[9], _set2[2]]\\n\",\n    \"        palette=[_set3[7], _set2[2]]\\n\",\n    \"    )\\n\",\n    \"    \\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"#         axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() - 0.01, p.get_height()+np.random.normal(0,0.8) + 0.05), rotation=0, fontsize=10)\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.05, p.get_height() + 0.05), rotation=0, fontsize=12)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"F@O\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\\n\",\n    \"        \\n\",\n    \"axes[0].set(ylim=(20, 55))\\n\",\n    \"# axes[0].set(ylim=(25, 70))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(20.0, 45.0)]\"\n      ]\n     },\n     \"execution_count\": 105,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=avg_bar_dicts)\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, ax = plt.subplots(1, 1, figsize=(6, 4), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"g = sns.barplot(\\n\",\n    \"    data=bar_df,\\n\",\n    \"    x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"    ax=ax, alpha=1.0,\\n\",\n    \"    palette=[_set3[7], _set3[8], _set3[9], _set2[2]]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in ax.patches:\\n\",\n    \"    ax.annotate('%.1f' % (p.get_height()), (p.get_x() - 0.01, p.get_height() + 0.05), rotation=0, fontsize=11)\\n\",\n    \"ax.set_title(\\\"Average across 4 datasets\\\")\\n\",\n    \"ax.set_xlabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"ax.set_ylabel(\\\"F@O\\\")\\n\",\n    \"ax.legend(loc='upper left')\\n\",\n    \"\\n\",\n    \"ax.set(ylim=(20, 45))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### present\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'BART-KP20k-fewshot100': 'bart_presabs_kp20k_fewshot100',\\n\",\n    \"'BART-KP20k-fewshot1k': 'bart_presabs_kp20k_fewshot1k',\\n\",\n    \"'BART-KP20k-fewshot10k': 'bart_presabs_kp20k_fewshot10k_step10k_rerun',\\n\",\n    \"'BART-OpenKP-fewshot100': 'bart_presabs_openkp_fewshot100',\\n\",\n    \"'BART-OpenKP-fewshot1k': 'bart_presabs_openkp_fewshot1k',\\n\",\n    \"'BART-OpenKP-fewshot10k': 'bart_presabs_openkp_fewshot10k_step10k_rerun', \\n\",\n    \"'BART-KPTimes-fewshot100': 'bart_presabs_kptimes_fewshot100',\\n\",\n    \"'BART-KPTimes-fewshot1k': 'bart_presabs_kptimes_fewshot1k',\\n\",\n    \"'BART-KPTimes-fewshot10k': 'bart_presabs_kptimes_fewshot10k_step10k_rerun',\\n\",\n    \"'BART-StackEx-fewshot100': 'bart_presabs_stackex_fewshot100',\\n\",\n    \"'BART-StackEx-fewshot1k': 'bart_presabs_stackex_fewshot1k',\\n\",\n    \"'BART-StackEx-fewshot10k': 'bart_presabs_stackex_fewshot10k_step10k_rerun',\\n\",\n    \"\\n\",\n    \"'BART+DA-KP20k-fewshot100': 'bart-kp20k-fewshot100-DA1e5_step3k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KP20k-fewshot1k': 'bart-kp20k-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KP20k-fewshot10k': 'bart-kp20k-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-OpenKP-fewshot100': 'bart-openkp-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-OpenKP-fewshot1k': 'bart-openkp-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-OpenKP-fewshot10k': 'bart-openkp-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-KPTimes-fewshot100': 'bart-kptimes-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KPTimes-fewshot1k': 'bart-kptimes-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KPTimes-fewshot10k': 'bart-kptimes-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-StackEx-fewshot100': 'bart-stackex-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-StackEx-fewshot1k': 'bart-stackex-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-StackEx-fewshot10k': 'bart-stackex-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'present_exact_f_score@5'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"        palette=[_set3[2], _set3[3]] #_set3[7:]\\n\",\n    \"    )\\n\",\n    \"#     g.set_ylim(22, 38)\\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.05, p.get_height() + 0.1), rotation=0)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"Present F@5\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x252 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'BART-KP20k-fewshot100': 'bart_presabs_kp20k_fewshot100',\\n\",\n    \"'BART-KP20k-fewshot1k': 'bart_presabs_kp20k_fewshot1k',\\n\",\n    \"'BART-KP20k-fewshot10k': 'bart_presabs_kp20k_fewshot10k_step10k_rerun',\\n\",\n    \"'BART-OpenKP-fewshot100': 'bart_presabs_openkp_fewshot100',\\n\",\n    \"'BART-OpenKP-fewshot1k': 'bart_presabs_openkp_fewshot1k',\\n\",\n    \"'BART-OpenKP-fewshot10k': 'bart_presabs_openkp_fewshot10k_step10k_rerun', \\n\",\n    \"'BART-KPTimes-fewshot100': 'bart_presabs_kptimes_fewshot100',\\n\",\n    \"'BART-KPTimes-fewshot1k': 'bart_presabs_kptimes_fewshot1k',\\n\",\n    \"'BART-KPTimes-fewshot10k': 'bart_presabs_kptimes_fewshot10k_step10k_rerun',\\n\",\n    \"'BART-StackEx-fewshot100': 'bart_presabs_stackex_fewshot100',\\n\",\n    \"'BART-StackEx-fewshot1k': 'bart_presabs_stackex_fewshot1k',\\n\",\n    \"'BART-StackEx-fewshot10k': 'bart_presabs_stackex_fewshot10k_step10k_rerun',\\n\",\n    \"\\n\",\n    \"'BART+DA-KP20k-fewshot100': 'bart-kp20k-fewshot100-DA1e5_step3k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KP20k-fewshot1k': 'bart-kp20k-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KP20k-fewshot10k': 'bart-kp20k-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-OpenKP-fewshot100': 'bart-openkp-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-OpenKP-fewshot1k': 'bart-openkp-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-OpenKP-fewshot10k': 'bart-openkp-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-KPTimes-fewshot100': 'bart-kptimes-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KPTimes-fewshot1k': 'bart-kptimes-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KPTimes-fewshot10k': 'bart-kptimes-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"'BART+DA-StackEx-fewshot100': 'bart-stackex-fewshot100-DA1e5_step1k-FT5e6_step1k',\\n\",\n    \"'BART+DA-StackEx-fewshot1k': 'bart-stackex-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-StackEx-fewshot10k': 'bart-stackex-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_pastel = sns.color_palette(\\\"pastel\\\").as_hex()\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"_set3 = sns.color_palette(\\\"Set3\\\").as_hex()\\n\",\n    \"# sns.set_palette(_set2[5:])\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 4, figsize=(16, 3.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"for fig_id, (data_name, dev_dataset, test_dataset) in enumerate(dev_test_pairs):\\n\",\n    \"    subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == data_name]\\n\",\n    \"\\n\",\n    \"    g = sns.barplot(\\n\",\n    \"        data=subbar_df,\\n\",\n    \"        x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"        ax=axes[fig_id], alpha=1.0,\\n\",\n    \"        palette=[_set3[6], _set3[5]] #_set3[7:]\\n\",\n    \"    )\\n\",\n    \"#     g.set_ylim(22, 38)\\n\",\n    \"    for p in axes[fig_id].patches:\\n\",\n    \"        axes[fig_id].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.05, p.get_height() + 0.1), rotation=0)\\n\",\n    \"    axes[fig_id].set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"    axes[fig_id].set_xlabel(\\\"\\\")\\n\",\n    \"    \\n\",\n    \"    if fig_id == 0:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"Absent Recall@50\\\")\\n\",\n    \"        axes[fig_id].legend(loc='upper left')\\n\",\n    \"    else:\\n\",\n    \"        axes[fig_id].set_ylabel(\\\"\\\")\\n\",\n    \"        axes[fig_id].legend([],[], frameon=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### DA-MagTL\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# BART-mag\\n\",\n    \"report_dirs = [\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT_devbest/report/', # TF MAG\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAbs256_FTbs16_devbest/report/', # BART MAG\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_devbest/report', # TF\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT_devbest/report/', # BART FT\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_DAFT_devbest/report/', # BART DAFT\\n\",\n    \"    '/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT_devbest/report/', # BART PTDAFT\\n\",\n    \"    '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/cross_domain_exps/kp_o2s_fulldata_devbest', # exps using all data\\n\",\n    \"    '/zfs1/pbrusilovsky/rum20/kp/transfer_exps_v1/empirical_mag/report/', # previous SOTA\\n\",\n    \"]\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for report_dir in report_dirs:\\n\",\n    \"    for fname in os.listdir(report_dir):\\n\",\n    \"        if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"        df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"        df = df.loc[df.pred_name == pred_name]\\n\",\n    \"        df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t100\\n\",\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t1k\\n\",\n      \"transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t10k\\n\",\n      \"transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t100\\n\",\n      \"transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t1k\\n\",\n      \"transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t10k\\n\",\n      \"transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t100\\n\",\n      \"transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t1k\\n\",\n      \"transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"TF\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t10k\\n\",\n      \"bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t100\\n\",\n      \"bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t1k\\n\",\n      \"bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01 3 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tKP20k-100k\\tKP20k\\t10k\\n\",\n      \"bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k100-bs16_step2k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t100\\n\",\n      \"bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k1k-bs16_step4k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t1k\\n\",\n      \"bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k10k-bs16_step8k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-1m\\tKP20k\\t10k\\n\",\n      \"bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k100-bs16_step2k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t100\\n\",\n      \"bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k1k-bs16_step4k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t1k\\n\",\n      \"bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k10k-bs16_step8k_clip01_labelsmooth01 4 kp20k_valid2k_test kp20k_test\\n\",\n      \"BART\\tPT+DA+FT\\tMagCS-12m\\tKP20k\\t10k\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# Transformer\\n\",\n    \"'TF,PT+DA+FT,KP20k-100k,KP20k,100': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5',\\n\",\n    \"'TF,PT+DA+FT,KP20k-100k,KP20k,1k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5',\\n\",\n    \"'TF,PT+DA+FT,KP20k-100k,KP20k,10k': 'transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'TF,PT+DA+FT,MagCS-1m,KP20k,100': 'transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5',\\n\",\n    \"'TF,PT+DA+FT,MagCS-1m,KP20k,1k': 'transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5',\\n\",\n    \"'TF,PT+DA+FT,MagCS-1m,KP20k,10k': 'transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5',\\n\",\n    \"\\n\",\n    \"'TF,PT+DA+FT,MagCS-12m,KP20k,100': 'transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5',\\n\",\n    \"'TF,PT+DA+FT,MagCS-12m,KP20k,1k': 'transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5',\\n\",\n    \"'TF,PT+DA+FT,MagCS-12m,KP20k,10k': 'transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5',\\n\",\n    \"\\n\",\n    \"# BART\\n\",\n    \"# FT\\n\",\n    \"# 'BART,FT,KP20k,100': 'bart-FT_kp20k_fewshot100-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'BART,FT,KP20k,1k': 'bart-FT_kp20k_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'BART,FT,KP20k,10k': 'bart-FT_kp20k_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01',\\n\",\n    \"# 'BART,FT,KP20k,100': 'bart-FT_kp20k_fewshot100-bs16_step4k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"# 'BART,FT,KP20k,1k': 'bart-FT_kp20k_fewshot1k-bs16_step4k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"# 'BART,FT,KP20k,10k': 'bart-FT_kp20k_fewshot10k-bs16_step8k_clipnorm01_labelsmooth01-rerun-seed177',\\n\",\n    \"\\n\",\n    \"# DAFT, DA bs256\\n\",\n    \"# 'BART,DA+FT,KP20k-100k,KP20k,100': 'bart-kp20k-DA_step5k_bs256-FT100_bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,KP20k-100k,KP20k,1k': 'bart-kp20k-DA_step5k_bs256-FT1k_bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,KP20k-100k,KP20k,10k': 'bart-kp20k-DA_step5k_bs256-FT10k_bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"    \\n\",\n    \"# 'BART,DA+FT,MagCS-1m,KP20k,100': 'bart-kp20k-DA_magcs1m_step5k_tlrs55-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,MagCS-1m,KP20k,1k': 'bart-kp20k-DA_magcs1m_step5k_tlrs55-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,MagCS-1m,KP20k,10k': 'bart-kp20k-DA_magcs1m_step5k_tlrs55-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"# 'BART,DA+FT,MagCS-12m,KP20k,100': 'bart-kp20k-DA_magcs12m_step20k_tlrs55-FT100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,MagCS-12m,KP20k,1k': 'bart-kp20k-DA_magcs12m_step20k_tlrs55-FT1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"# 'BART,DA+FT,MagCS-12m,KP20k,10k': 'bart-kp20k-DA_magcs12m_step20k_tlrs55-FT10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"# PTDAFT DA bs256\\n\",\n    \"'BART,PT+DA+FT,KP20k-100k,KP20k,100': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,KP20k-100k,KP20k,1k': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,KP20k-100k,KP20k,10k': 'bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"'BART,PT+DA+FT,MagCS-1m,KP20k,100': 'bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,MagCS-1m,KP20k,1k': 'bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,MagCS-1m,KP20k,10k': 'bart-PT_step40k-DA_magcs1m_step5k-FT1e5_kp20k10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"'BART,PT+DA+FT,MagCS-12m,KP20k,100': 'bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k100-bs16_step2k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,MagCS-12m,KP20k,1k': 'bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k1k-bs16_step4k_clip01_labelsmooth01',\\n\",\n    \"'BART,PT+DA+FT,MagCS-12m,KP20k,10k': 'bart-PT_step40k-DA_magcs12m_step20k-FT1e5_kp20k10k-bs16_step8k_clip01_labelsmooth01',\\n\",\n    \"\\n\",\n    \"# 'TF-KP20k-full': 'transformer_presabs_kp20k',\\n\",\n    \"# 'PrevSOTA-KP20k-full': 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"# dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_valid2k_test'), \\n\",\n    \"#                   ('openkp', 'openkp_valid2k_test', 'openkp_valid2k_test'),\\n\",\n    \"#                   ('kptimes', 'kptimes_valid2k_test', 'kptimes_valid2k_test'),\\n\",\n    \"#                   ('stackex', 'stackex_valid2k_test', 'stackex_valid2k_test')]\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_name, model_setting, da_dataset, ft_dataset, ft_setting = short_name.split(',')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"#         display(exp_grp[['exp_name', 'test_dataset', 'step', anchor_metric_name]])\\n\",\n    \"#         exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"\\n\",\n    \"        if not short_name.startswith('TF') and not short_name.startswith('BART'):\\n\",\n    \"            dev_dataset = dev_dataset[:-5]\\n\",\n    \"            test_dataset = test_dataset[:-5]\\n\",\n    \"        print(exp_name, len(exp_grp), dev_dataset, test_dataset)\\n\",\n    \"        print('\\\\t'.join([model_name, model_setting, da_dataset, ft_dataset, ft_setting]))\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_name': model_name+' '+model_setting,\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'DA_dataset': da_dataset, \\n\",\n    \"            'FT_setting': ft_setting,\\n\",\n    \"            'FT_dataset': ft_dataset, \\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# print(train_test_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_paired = sns.color_palette(\\\"Paired\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"subbar_df = bar_df.loc[(bar_df['FT_dataset'].str.lower() == 'kp20k') & (bar_df['model_name'] == 'TF PT+DA+FT')]\\n\",\n    \"g0 = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"FT_setting\\\", y=\\\"score\\\", hue=\\\"DA_dataset\\\",\\n\",\n    \"    ax=axes[0], alpha=0.8,\\n\",\n    \"    palette=_paired[4:6] + _paired[8:10] + _paired[6:8]\\n\",\n    \")\\n\",\n    \"# subbar_df = bar_df.loc[(bar_df['FT_dataset'].str.lower() == 'kp20k') & (bar_df['model_name'] == 'BART DA+FT')]\\n\",\n    \"# g1 = sns.barplot(\\n\",\n    \"#     data=subbar_df,\\n\",\n    \"#     x=\\\"FT_setting\\\", y=\\\"score\\\", hue=\\\"DA_dataset\\\",\\n\",\n    \"#     ax=axes[1], alpha=0.8,\\n\",\n    \"#     palette=[_paired[0], _paired[6], _paired[8]]\\n\",\n    \"# )\\n\",\n    \"subbar_df = bar_df.loc[(bar_df['FT_dataset'].str.lower() == 'kp20k') & (bar_df['model_name'] == 'BART PT+DA+FT')]\\n\",\n    \"g2 = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"FT_setting\\\", y=\\\"score\\\", hue=\\\"DA_dataset\\\",\\n\",\n    \"    ax=axes[1], alpha=0.8,\\n\",\n    \"    palette=[_paired[0], _paired[6], _paired[8]]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in axes[0].patches:\\n\",\n    \"    axes[0].annotate('%.2f' % (p.get_height()), (p.get_x() - 0.0, p.get_height() + 0.1), rotation=0, fontsize=10)\\n\",\n    \"# for p in axes[1].patches:\\n\",\n    \"#     axes[1].annotate('%.1f' % (p.get_height()), (p.get_x() + 0.025, p.get_height() + 0.1), rotation=0, fontsize=10)\\n\",\n    \"for p in axes[1].patches:\\n\",\n    \"    axes[1].annotate('%.2f' % (p.get_height()), (p.get_x() - 0.0, p.get_height() + 0.1), rotation=0, fontsize=10)\\n\",\n    \"\\n\",\n    \"axes[0].set_title(\\\"\\\")\\n\",\n    \"axes[0].set_xlabel(\\\"Transformer (PT+DA+FT)\\\")\\n\",\n    \"# axes[1].set_xlabel(\\\"BART (DA+FT)\\\")\\n\",\n    \"axes[1].set_xlabel(\\\"BART (PT+DA+FT)\\\")\\n\",\n    \"\\n\",\n    \"axes[0].set(ylim=(10, 30))\\n\",\n    \"axes[0].set_ylabel(\\\"F@O\\\")\\n\",\n    \"axes[1].set_ylabel(\\\"\\\")\\n\",\n    \"axes[0].legend(loc='upper left')\\n\",\n    \"\\n\",\n    \"# axes[0].grid(False)\\n\",\n    \"# axes[1].grid(False)\\n\",\n    \"\\n\",\n    \"# g1.legend_.remove()\\n\",\n    \"g2.legend_.remove()\\n\",\n    \"\\n\",\n    \"sns.despine(left=True, bottom=True, right=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### present\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['#cab2d6', '#6a3d9a', '#fb9a99', '#e31a1c', '#ffff99', '#b15928']\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7f9d50e91150>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# 'TF+DA-KP20k-fewshot100': 'transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-fewshot1k': 'transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-fewshot10k': 'transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-full': 'transformer_presabs_kp20k',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot100': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot1k': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot10k': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-full': 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k',\\n\",\n    \"\\n\",\n    \"'TF+MagDA-KP20k-fewshot100': 'transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-fewshot1k': 'transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-fewshot10k': 'transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-full': 'transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5',\\n\",\n    \"\\n\",\n    \"'BART+DA-KP20k-fewshot100': 'bart-kp20k-fewshot100-DA1e5_step3k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KP20k-fewshot1k': 'bart-kp20k-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KP20k-fewshot10k': 'bart-kp20k-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"# 'BART+DA-KP20k-fewshot100': 'bart_presabs_kp20k_fewshot100',\\n\",\n    \"# 'BART+DA-KP20k-fewshot1k': 'bart_presabs_kp20k_fewshot1k',\\n\",\n    \"# 'BART+DA-KP20k-fewshot10k': 'bart_presabs_kp20k_fewshot10k_step10k_rerun',\\n\",\n    \"'BART+DA-KP20k-full': 'bartFT_presabs_kp20k_100k_rerun',\\n\",\n    \"    \\n\",\n    \"'BART+MagDA-KP20k-fewshot100': 'bart-MagTL_step100k-FT_kp20k_fewshot100_lr5e6_step1k',\\n\",\n    \"'BART+MagDA-KP20k-fewshot1k': 'bart-MagTL_step100k-FT_kp20k_fewshot1k_lr5e6_step2k',\\n\",\n    \"'BART+MagDA-KP20k-fewshot10k': 'bart-MagTL_step100k-FT_kp20k_fewshot10k_lr5e6_step4k',\\n\",\n    \"'BART+MagDA-KP20k-full': 'bart-MagTL_step100k-kp20k_full_lr1e5_step50k',\\n\",\n    \"    \\n\",\n    \"'TF-KP20k-full': 'transformer_presabs_kp20k',\\n\",\n    \"'PrevSOTA-KP20k-full': 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'present_exact_f_score@5'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"        \\n\",\n    \"        if not short_name.startswith('TF') and not short_name.startswith('BART'):\\n\",\n    \"            dev_dataset = dev_dataset[:-5]\\n\",\n    \"            test_dataset = test_dataset[:-5]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_paired = sns.color_palette(\\\"Paired\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 1, figsize=(8, 4), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == 'kp20k']\\n\",\n    \"\\n\",\n    \"print(_paired[8:10] + _paired[4:6] + _paired[10:12])\\n\",\n    \"g = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"    ax=axes, alpha=0.9,\\n\",\n    \"    palette=_paired\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in axes.patches:\\n\",\n    \"    axes.annotate('%.1f' % (p.get_height()), (p.get_x() - 0.02, p.get_height() + 0.1), rotation=0)\\n\",\n    \"axes.set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"axes.set_xlabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"# axes.set(ylim=(10, 35))\\n\",\n    \"axes.set_ylabel(\\\"Present F@5\\\")\\n\",\n    \"axes.legend(loc='upper left')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### absent\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['#cab2d6', '#6a3d9a', '#fb9a99', '#e31a1c', '#ffff99', '#b15928']\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7f9d50336410>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"# 'TF+DA-KP20k-fewshot100': 'transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-fewshot1k': 'transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-fewshot10k': 'transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"# 'TF+DA-KP20k-full': 'transformer_presabs_kp20k',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot100': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot1k': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-fewshot10k': 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'TF+PT+DA-KP20k-full': 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k',\\n\",\n    \"\\n\",\n    \"'TF+MagDA-KP20k-fewshot100': 'transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-fewshot1k': 'transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-fewshot10k': 'transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5',\\n\",\n    \"'TF+MagDA-KP20k-full': 'transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5',\\n\",\n    \"    \\n\",\n    \"'BART+DA-KP20k-fewshot100': 'bart-kp20k-fewshot100-DA1e5_step3k-FT5e6_step1k',\\n\",\n    \"'BART+DA-KP20k-fewshot1k': 'bart-kp20k-fewshot1k-DA1e5_step1k-FT5e6_step2k',\\n\",\n    \"'BART+DA-KP20k-fewshot10k': 'bart-kp20k-fewshot10k-DA1e5_step1k-FT5e6_step4k',\\n\",\n    \"# 'BART+DA-KP20k-fewshot100': 'bart_presabs_kp20k_fewshot100',\\n\",\n    \"# 'BART+DA-KP20k-fewshot1k': 'bart_presabs_kp20k_fewshot1k',\\n\",\n    \"# 'BART+DA-KP20k-fewshot10k': 'bart_presabs_kp20k_fewshot10k_step10k_rerun',\\n\",\n    \"'BART+DA-KP20k-full': 'bartFT_presabs_kp20k_100k_rerun',\\n\",\n    \"    \\n\",\n    \"'BART+MagDA-KP20k-fewshot100': 'bart-MagTL_step100k-FT_kp20k_fewshot100_lr5e6_step1k',\\n\",\n    \"'BART+MagDA-KP20k-fewshot1k': 'bart-MagTL_step100k-FT_kp20k_fewshot1k_lr5e6_step2k',\\n\",\n    \"'BART+MagDA-KP20k-fewshot10k': 'bart-MagTL_step100k-FT_kp20k_fewshot10k_lr5e6_step4k',\\n\",\n    \"'BART+MagDA-KP20k-full': 'bart-MagTL_step100k-kp20k_full_lr1e5_step50k',\\n\",\n    \"    \\n\",\n    \"'TF-KP20k-full': 'transformer_presabs_kp20k',\\n\",\n    \"'PrevSOTA-KP20k-full': 'kpgen-meng17-magkp20k+kp20kFT-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue',\\n\",\n    \"}\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"report_metric_name = 'absent_exact_recall@50'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"for data_name, dev_dataset, test_dataset in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        model_setting, dataset_name, train_setting = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"        \\n\",\n    \"        if not short_name.startswith('TF') and not short_name.startswith('BART'):\\n\",\n    \"            dev_dataset = dev_dataset[:-5]\\n\",\n    \"            test_dataset = test_dataset[:-5]\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, test_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'model_setting': model_setting,\\n\",\n    \"            'test_dataset': dataset_name, \\n\",\n    \"            'train_setting': train_setting,\\n\",\n    \"            'score': best_test_row[report_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \\n\",\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 14,\\n\",\n    \"                             \\\"axes.titlesize\\\": 24,\\n\",\n    \"                             \\\"axes.labelsize\\\": 14,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 14,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 14,\\n\",\n    \"                             \\\"legend.fontsize\\\": 12})\\n\",\n    \"\\n\",\n    \"_husl = sns.color_palette(\\\"husl\\\").as_hex()\\n\",\n    \"_paired = sns.color_palette(\\\"Paired\\\").as_hex()\\n\",\n    \"_set2 = sns.color_palette(\\\"Set2\\\").as_hex()\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"whitegrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 1, figsize=(8, 4), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"subbar_df = bar_df.loc[bar_df['test_dataset'].str.lower() == 'kp20k']\\n\",\n    \"\\n\",\n    \"print(_paired[8:10] + _paired[4:6] + _paired[10:12])\\n\",\n    \"g = sns.barplot(\\n\",\n    \"    data=subbar_df,\\n\",\n    \"    x=\\\"train_setting\\\", y=\\\"score\\\", hue=\\\"model_setting\\\",\\n\",\n    \"    ax=axes, alpha=0.9,\\n\",\n    \"    palette=_paired[6:]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for p in axes.patches:\\n\",\n    \"    axes.annotate('%.1f' % (p.get_height()), (p.get_x() - 0.01, p.get_height() + 0.1), rotation=0)\\n\",\n    \"axes.set_title(subbar_df['test_dataset'].iloc[0])\\n\",\n    \"axes.set_xlabel(\\\"\\\")\\n\",\n    \"\\n\",\n    \"# axes.set(ylim=(10, 35))\\n\",\n    \"axes.set_ylabel(\\\"Absent Recall@50\\\")\\n\",\n    \"axes.legend(loc='upper left')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Self-learning results (10 iters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"400\\n\",\n      \"['kp20k_valid2k_test']\\n\",\n      \"['transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5']\\n\",\n      \"['kp20k_valid2k_test']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# fulldata\\n\",\n    \"report_dir = '/Users/memray/Downloads/report'\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"    df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    df = df.loc[df.pred_name == pred_name]\\n\",\n    \"    df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"    print(len(all_eval_df))\\n\",\n    \"print(all_eval_df.test_dataset.unique())\\n\",\n    \"print(all_eval_df.exp_name.unique())\\n\",\n    \"print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"short2long = {\\n\",\n    \"'KP20k 100k-KP20k-1': 'transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-2': 'transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-3': 'transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-4': 'transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-5': 'transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-6': 'transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-7': 'transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-8': 'transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-9': 'transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'KP20k 100k-KP20k-10': 'transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"\\n\",\n    \"'MagCS 1m-KP20k-1': 'transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-2': 'transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-3': 'transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-4': 'transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-5': 'transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-6': 'transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-7': 'transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-8': 'transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-9': 'transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5',\\n\",\n    \"'MagCS 1m-KP20k-10': 'transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5'\\n\",\n    \"\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"# long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"\\n\",\n    \"dev_test_pairs = [('kp20k', 'kp20k_valid2k_test', 'kp20k_test'), \\n\",\n    \"                  ('openkp', 'openkp_valid2k_test', 'openkp_test'),\\n\",\n    \"                  ('kptimes', 'kptimes_valid2k_test', 'kptimes_test'),\\n\",\n    \"                  ('stackex', 'stackex_valid2k_test', 'stackex_test')]\\n\",\n    \"setting_names = ['fewshot100', 'fewshot1k', 'fewshot10k']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"bar_dicts = []\\n\",\n    \"\\n\",\n    \"for data_name, dev_dataset, _ in dev_test_pairs:\\n\",\n    \"    for short_name, exp_name in short2long.items():\\n\",\n    \"        if not data_name in exp_name: continue\\n\",\n    \"\\n\",\n    \"        train_dataname, test_dataname, round_iter = short_name.split('-')\\n\",\n    \"        exp_grp = all_eval_df.loc[all_eval_df['exp_name'] == exp_name]\\n\",\n    \"        exp_grp = exp_grp.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"        best_test_row = best_testscores_by_dev(exp_grp, dev_dataset, dev_dataset, anchor_metric_name)\\n\",\n    \"        bar_dicts.append({\\n\",\n    \"            'train_dataname': train_dataname,\\n\",\n    \"            'test_dataname': test_dataname, \\n\",\n    \"            'round_iter': str(int(round_iter)),\\n\",\n    \"            'score': best_test_row[anchor_metric_name].values[0] * 100.0\\n\",\n    \"        })\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 97,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/p5/3r_xtg1x7kb4hc_r955731vc0000gn/T/ipykernel_84159/2541090818.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\\n\",\n      \"  g.set_xticklabels(['{:.0f}'.format(i) for i in range(1, 11)], fontsize=14)\\n\",\n      \"/var/folders/p5/3r_xtg1x7kb4hc_r955731vc0000gn/T/ipykernel_84159/2541090818.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\\n\",\n      \"  g.set_yticklabels(['{:.1f}'.format(i) for i in g.get_yticks()], fontsize=14)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 504x324 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set up the matplotlib figure\\n\",\n    \"bar_df = pd.DataFrame(data=bar_dicts)\\n\",\n    \"# display(bar_df)\\n\",\n    \"\\n\",\n    \"sns.set_theme(style=\\\"darkgrid\\\")\\n\",\n    \"f, axes = plt.subplots(1, 1, figsize=(7,4.5), sharex=True, sharey=True)\\n\",\n    \"f.tight_layout(pad=0.0)\\n\",\n    \"\\n\",\n    \"sns.set_context(\\\"paper\\\", rc={\\\"font.size\\\": 16,\\n\",\n    \"                             \\\"axes.titlesize\\\": 16,\\n\",\n    \"                             \\\"axes.labelsize\\\": 16,\\n\",\n    \"                             \\\"xtick.labelsize\\\": 18,\\n\",\n    \"                             \\\"ytick.labelsize\\\": 16,\\n\",\n    \"                             \\\"legend.fontsize\\\": 14})\\n\",\n    \"\\n\",\n    \"g = sns.lineplot(data=bar_df, x=\\\"round_iter\\\", y=\\\"score\\\", hue=\\\"train_dataname\\\",\\n\",\n    \"                 style=\\\"train_dataname\\\", markers=[\\\"o\\\", \\\"o\\\"],\\n\",\n    \"                 dashes=False, markersize=9)\\n\",\n    \"\\n\",\n    \"axes.set_title(\\\"\\\")\\n\",\n    \"axes.set_xlabel(\\\"Iter\\\", fontsize=16)\\n\",\n    \"axes.set_ylabel(\\\"F@O\\\", fontsize=16)\\n\",\n    \"axes.legend(loc='upper left', fontsize=14)\\n\",\n    \"\\n\",\n    \"g.set_xticklabels(['{:.0f}'.format(i) for i in range(1, 11)], fontsize=14)\\n\",\n    \"g.set_yticklabels(['{:.1f}'.format(i) for i in g.get_yticks()], fontsize=14)\\n\",\n    \"\\n\",\n    \"axes.set(xlim=(-0.2, 9.2))\\n\",\n    \"\\n\",\n    \"# label points on the plot\\n\",\n    \"for x, y in zip(bar_df['round_iter'], bar_df['score']):\\n\",\n    \"    plt.text(x=float(x)-1.9, # x-coordinate position of data label\\n\",\n    \"    y = y+0.02, # y-coordinate position of data label, adjusted to be 150 below the data point\\n\",\n    \"    s = '{:.2f}'.format(y), # data label, formatted to ignore decimals\\n\",\n    \"    color='black', fontsize=12) # set colour of line\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Others\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# fulldata\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2s_fulldata_devbest/report/'\\n\",\n    \"\\n\",\n    \"# TF-fewshot\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/report/'\\n\",\n    \"report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_devbest/report/'\\n\",\n    \"\\n\",\n    \"# BART-fewshot\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v1-DA1e6_FT1e5/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/report/'\\n\",\n    \"\\n\",\n    \"# BART-mag\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/report/'\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"    df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    df = df.loc[df.pred_name == pred_name]\\n\",\n    \"    df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\",\n    \"\\n\",\n    \"#     print(len(all_eval_df))\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\\n\",\n    \"print(all_eval_df.exp_name.unique())\\n\",\n    \"# print(all_eval_df.test_dataset.unique())\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"349.091px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/transfer_learning_curve.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"sns.set()\\n\",\n    \"\\n\",\n    \"def peak_index(group, x_index, y_index):\\n\",\n    \"    max_x, max_y = 0, -1\\n\",\n    \"\\n\",\n    \"    for id_label, _ in group.iterrows():\\n\",\n    \"        try:\\n\",\n    \"            if group.at[id_label , y_index] is not None:\\n\",\n    \"                if isinstance(group.at[id_label , y_index], pd.core.series.Series):\\n\",\n    \"                    if len(group.at[id_label , y_index]) > 1:\\n\",\n    \"                        new_v = group.at[id_label , y_index].iloc[0].item()\\n\",\n    \"                    if len(group.at[id_label , y_index]) == 0:\\n\",\n    \"                        continue\\n\",\n    \"                else:\\n\",\n    \"                    new_v = group.at[id_label , y_index]\\n\",\n    \"#                 print(new_v)\\n\",\n    \"                if new_v > max_y:\\n\",\n    \"                    max_y = new_v\\n\",\n    \"                    max_x = group.at[id_label , x_index]\\n\",\n    \"        except Exception as e:\\n\",\n    \"            print(group.at[id_label , y_index])\\n\",\n    \"            print(type(group.at[id_label , y_index]))\\n\",\n    \"            print(isinstance(group.at[id_label , y_index], pd.core.series.Series))\\n\",\n    \"            print(len(group.at[id_label , y_index]))\\n\",\n    \"            raise e\\n\",\n    \"            \\n\",\n    \"    return max_x, max_y\\n\",\n    \"\\n\",\n    \"def plot_testing_curve(df, y_index, title=''):\\n\",\n    \"    peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"    \\n\",\n    \"    fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"    for key, grp in df.groupby(['exp_name']): \\n\",\n    \"        ax = grp.plot(ax=ax, title=title, kind='line', x='step', y=y_index, label=key, style='-o', markersize=8.0, linewidth=4)\\n\",\n    \"        peak_x, peak_y = peak_index(grp, x_index='step', y_index=y_index)\\n\",\n    \"        variance = grp[y_index].var()\\n\",\n    \"        ax.annotate('%s peak=%.3f (@step=%d)' % (key, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"#         display(grp)\\n\",\n    \"#         break\\n\",\n    \"\\n\",\n    \"    plt.legend(loc='best')\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load all .eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# fulldata\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2s_fulldata/report/'\\n\",\n    \"\\n\",\n    \"# TF-fewshot\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/report/'\\n\",\n    \"# report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/report/'\\n\",\n    \"report_dir = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/report'\\n\",\n    \"\\n\",\n    \"# BART-fewshot\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v1-DA1e6_FT1e5/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/report/'\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/report/'\\n\",\n    \"\\n\",\n    \"# BART-mag\\n\",\n    \"# report_dir = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/report/'\\n\",\n    \"\\n\",\n    \"pred_name = 'beamsearch-width_50-maxlen_40'\\n\",\n    \"\\n\",\n    \"all_eval_df = None\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.split_nopunc.csv'): continue\\n\",\n    \"    df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    df = df.loc[df.pred_name == pred_name]\\n\",\n    \"    df = df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    all_eval_df = df if all_eval_df is None else pd.concat([all_eval_df, df], sort=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1060\\n\",\n      \"['kptimes_valid2k_test' 'stackex_valid2k_test' 'openkp_valid2k_test'\\n\",\n      \" 'kp20k_valid2k_test']\\n\",\n      \"['transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step2k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-stackex-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-openkp-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step10k_20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step25k_100k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step50k_100k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step10k_100k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step75k_100k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step15k_20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step5k_20k-FT_fewshot1k_step2k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step20k_lr1e5_warmup2k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step50k_lr1e4_warmup5k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr5e5_warmup10k'\\n\",\n      \" 'transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr1e5_warmup5k']\\n\",\n      \"['kptimes_valid2k_test' 'stackex_valid2k_test' 'openkp_valid2k_test'\\n\",\n      \" 'kp20k_valid2k_test']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(len(all_eval_df))\\n\",\n    \"print(all_eval_df.test_dataset.unique())\\n\",\n    \"print(all_eval_df.exp_name.unique())\\n\",\n    \"print(all_eval_df.test_dataset.unique())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1060\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step10k_100k-FT_fewshot1k_step2k_lr1e5 19\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step10k_20k-FT_fewshot1k_step2k_lr1e5 18\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step15k_20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr1e5_warmup5k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr5e5_warmup10k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step20k_lr1e5_warmup2k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step50k_lr1e4_warmup5k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step25k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step50k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step5k_20k-FT_fewshot1k_step2k_lr1e5 19\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step75k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5 20\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k 20\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step2k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5 30\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5 34\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5 40\\n\",\n      \"transformer-openkp-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-openkp-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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WrLLXxPRJR2Z69AxsR1Skla61kMhlWrlyJI0eOICUlBUJh0fOu1NRU5R/qBg0aKPc3MDBQ3qx16tQJY8aMwWeffYbY2Fi88sor+Oijj9RupoGiRLFhw4bK5YYNG0IqlSI5ObnMcgGAtbW1yuuWLou+vj6ys7ORkpKC3NxcDB06VLlNoVCoDOpjaWmpvBH67rvv8P333wMABgwYoKwR++eff2BkZISRI0dW+JkZGxvD3NwciYmJcHBwAPB09OY2bdpg8uTJyn2NjIzUmhRnZWXB2NhYubx79260b99epR/an3/+iUWLFgEA2rVrp2ye1rdvX3zzzTdq5Xsez/L9xcbGIjExEb6+vsp1MplMZbnkZyQUCmFnZ4fExETlOrlcjnnz5kEikeDTTz9Vri/rM8rOzoaxsTGMjIwAFH1mxd9f6c8vMzMTv/76K1auXKlyc/nuu+/iypUrAIAlS5Zg4MCB5X4WCQkJMDc3B1DUxLhLly7KpNjf3x979+7FhAkTyj0eKGpauXHjRnz77bdwc3PDBx98AB8fnzL3jYuLw7Rp05S/N6DoMyv5eyiOLaDo91KyGTQAHD9+HCtWrMCPP/6oLGtV4i06OhqRkZH47bffoKenp1zfv39/xMXFAQB++OEH5Xe7d+9eZUsDbffll19izZo16NevH5ycnDB9+vRyHxDFxcVh6dKlyuavQNF1IyEhQXmtKv0dlIzn8lQWsxXFe7EDBw7A2dkZr776alXetprS11IbGxvY2NgAKOoaMXfuXEyePFn526/ot92oUSPlejc3N0ybNg1btmxRuc6VZmBggNatW+PSpUu4dOmS8kHB1atXcenSJWWz76r8/Sn5HRQr/XegvL9RVVHZdas8tfE9ElHZmMwS1XPFTWeBoj+2J06cwI8//ggnJydkZmaiffv2UCgUVTrX+PHjMX78eCQnJ2PWrFnYvHkzZs2apbafra2tSo1HXFwcxGIxrK2tlf0bS5brWVhaWsLAwAAHDx6EnZ1dmfuUPPeUKVOU/UBLGj58ODIyMjBp0iRs3rxZeVMKQKUPZnZ2NtLT05W1mAUFBZg2bRrs7OzUmoo2b95cpQ/Wo0ePUFhYCBcXF+W6JUuW4IcffsDSpUsxf/58AMDAgQMrTL6qS3nfX+nvwsHBAU5OTjh69Gi55yr5GcnlciQkJCg/I4VCgU8++QRPnjzBDz/8AIlEoty3efPmKn1gc3JyEB0djWbNmsHc3Bw2NjaIjIxEly5dAACRkZHKZrUAYGZmhq+//hqzZs3CunXrlE0Rq9o/LTQ0FAkJCWjXrh3y8vJw+PBhyOVy5esVFBQgIyMDkZGRKk1HS/Py8sLGjRtRWFiIXbt2YdasWfjnn3/KjGt7e3ssXbpUWdaSigfGio+PV9Zex8XFqdSanz59GgsWLMCmTZtUmjq6uLhAJpMhKipKGWOlPy9XV1eMGTMGEydOxPbt2+Hq6goAGhkszNDQELm5ucrlpKSkcn/DVeHi4oIVK1ZALpfj6NGjmDlzJoKDg8v8DhwcHDBlypQKf2fx8fHKAatKfwflqSxmK4r3YtOnT8eZM2fwwQcfYOXKlRCJRFX7AP5T2bVUIBAor/FV+W2Xd2xFOnTogAsXLiAiIgKenp7o0KEDzp49i9DQULRv3x7Ai//9qYyhoaFy7ASgKHlOSUlR2aei61ZFauN7JKKysZkxUT3SoEEDPHr0qNzt2dnZ0NPTg6WlJXJzc7FixYoqnzs0NBTXr19HYWEhDA0Noaenp1LTVJK/vz+2b9+OR48eITs7GytXrkS/fv2ea7Tj0oRCIYYPH46lS5cqa7YSEhJU+nxV1cKFC9GkSRNMmTJF5Sbon3/+weXLl1FQUIDVq1ejTZs2cHBwQGFhIWbOnAl9fX0EBgaqvf8BAwbg1KlTuHz5MnJycrB69Wr06dNHpfbT2NgYmzdvxuXLl6ut1rUqKvr+rK2tVaYb8fLygrGxMTZt2oS8vDzIZDLcvn0boaGhyn3CwsJw9OhRSKVSbN++HXp6emjTpg0AYNGiRbh37x6+++47tWbeffr0wZ07d/DXX38hPz8f69evh5ubmzKRGzx4MDZu3Ij09HTcu3cPv/32m9pIrH5+fvjmm28wY8YMlTJVJCsrC6dOncKcOXMwcOBAuLm54fjx4xCJRDh48CD27duHffv24dChQ/D19a1wIKiCggL8+eefyMzMhEQigbGxscpnmZaWptJ0ctSoUVi1apXyAU9KSopaP9INGzYgNzcXd+7cwZ49e/Daa68BKJoOZO7cuVi7di28vLxUjjEyMkKfPn2wZs0a5OTk4MqVKzhx4gQGDRqksl9xH/S33npLOaCbJrRs2RJBQUGQyWQ4ffo0Ll269ELn279/v7KGz8zMDEDR9cHKygpCoVDlWjhy5Ehs2rRJOeBQZmYmDh8+rHK+LVu2ID09HfHx8crBxYrl5+crp20qKChAfn6+cltFMVtZvANFA7ytXr0aubm5mDdvXplTRz2LCxcuIDY2FgqFAvHx8fjmm2/Qq1cvAJX/tv/55x9ln/F79+5hw4YNymMr0r59e+zbtw9NmzaFnp4eOnTogN9++w1OTk7KlgQv8venKpo0aYL8/Hz8/fffKCwsxMaNG9Wm2qrouiWVSpGfnw+5XA6ZTIb8/HxlVwBNfI9EVITJLFE9MmnSJGzcuBG+vr7466+/1LYPHjwYjo6O6NatG/r3719hH7PSsrOzsWDBAnTo0AE9evSAhYUF3nnnnTL3HTZsGAYOHIixY8eiV69e0NPTU2lq+qLmzp2Lxo0b44033kDbtm0xYcIE5YjCz0IgEODzzz+Hvb09pk6dqrxB9ff3x/r16+Hn54ewsDB8/fXXAKCc4uXcuXPKkVJ9fHyUg500b94cS5YswYcffojOnTsjOztb2Xy4JDMzM2zduhWnT5/GqlWrnv+DeAYVfX+vv/467t69C19fX0ydOhUikQjfffcdIiMj0atXL3Ts2BELFixQaWbXq1cvHDp0CO3bt8f+/fuxdu1aSCQSxMbG4pdffkFERAS6du2q/Iz+/PNPAICVlRXWrl2LlStXon379ggNDVW5qZ05cyYaNWqEHj16YNy4cXjnnXfKnLe1S5cuWLp0KaZMmaIcKbksxaN7v/TSS/juu+/w1ltvKUe9LZ7D1dHRUdk008bGBmPGjMGBAwcqHPhm//796NmzJ9q2bYvdu3crY6Rp06bo378/evfuDV9fXyQkJGD8+PHo2bMn3n77bfj4+OCNN95QS8I7dOiAPn36YMKECXj77bfRtWtXAEVJbmZmpnKE8dLzuy5atAh5eXno3LkzPvjgAyxevLjM6XCGDBmCadOm4c0331SbJ7W2fPLJJzh16hR8fX1x4MAB5SjHz+vMmTPo378/fHx88OWXX2LlypUwMDCAoaEhpkyZglGjRsHX1xfXrl1Dnz598O6772LOnDlo27Yt/P391aZ86tWrF4YOHYrBgwfj5Zdfxuuvv67cVjxfMVA0ZVbJBwsVxWxl8V5MT08P69atQ3JyMubPn/9CiVBERARGjhwJb29vjBw5Em5ubsrpqCr7bV+4cAEDBw6Et7c3Jk2ahD59+lTYxLiYj48P8vPzlbWwzZo1g76+vkpz5hf5+1MVpqamWLRoERYsWIDu3bvD0NBQrQl2edctANi4cSO8vLywadMm/Pnnn8rWF4BmvkciKiJQVFf7DSKieiAgIAB2dnacQ7ACxfNq1mbNcl0VExODXr16ISwsrFpaLtDzcXNzw9GjR3WmvzA9O163iHQTa2aJiIiIiIhI5/AxLxFRPXH58mVMnDixzG3F835S1ZUc8bekykZLri9KjsJdkqOjY7UNMFVylOqSJk+eXObAbnVFeSNjlxx5ujbx2kJEmlKlZsYPHjxAQEAA0tLSYGFhgcDAQJXRNwFg/fr1OHToEIRCISQSCWbPno1u3boBKOpncOjQIYhEIigUCkyePFk5cEJubi4+/vhjhIWFQSQS4aOPPqp0bkUiIiIiIiKq36qUzI4fPx7Dhg3DoEGDsH//fvzxxx/YsWOHyj5nzpyBr68vDA0NERkZibFjx+Ls2bMwMDBAZmamco6whIQE9OvXD6dOnYK5uTnWrVuHx48f44svvkBUVBTGjBmDo0ePqszNRURERERERFRSpX1mk5OTER4eDn9/fwBFo3iGh4erzc3VrVs3GBoaAigaKEGhUCAtLQ0AVCauz8nJgUAgUI7gdvjwYYwYMQJA0ZxwrVu3VhtBkIiIiIiIiKikSvvMxsfHw87OTjm5s0gkgq2tLeLj45Vzg5W2b98+ODs7qwx5/vPPP2P79u14/Pgxli5dCktLSwBFE483bNhQuZ+Dg4PKpNVEREREREREpVX7AFAXL17E6tWrsXXrVpX1o0aNwqhRo3Dr1i18+OGH6NSpkzKhrctkMhnnESMiojpFLBZDIBBouhhERFTPVZrMOjg4ICEhATKZDCKRCDKZDImJiXBwcFDbNyQkBHPnzsWGDRvg6upa5vnc3Nxga2uLixcv4tVXX4WjoyNiY2OVtbzx8fHw8/N7pjeRnJwFuVy7pstNTU3BhQtnkJeXC6GQMyDVZyKREDIZH2iQ9mOsUlUoFAoIBAK0auWF5s1b1vrr29iYIikps9Zfl+hZMVZJV2h7rAqFAlhbm5S5rdJk1traGu7u7ggKCsKgQYMQFBQEd3d3tSbGoaGhmD17NtasWQMPDw+VbXfv3kWzZs0AAI8ePUJERIRyuW/fvvjll1/g6emJqKgo3LhxA99+++1zvVFtkZOTg3Pn/kaXLl3g4uLCp9f1nEQiQmGhTNPFIKoUY5WqKjMzA4cPH4a+vj6cnZtoujhERFRPVWk043v37iEgIAAZGRkwMzNDYGAgXF1dMXHiRMycOROenp4YNmwYYmNjYWdnpzxu+fLlcHNzw/vvv4+7d+9CLBZDJBLh3XffVU7Nk5OTg4CAAEREREAoFGLu3Lno3bv3M70JbauZjYmJRlxcNF555RVNF4W0ABME0hWMVXoWt2/fxsOH0WjfvnOtvq621yAQFWOskq7Q9litqGa2SsmsttO2ZDYq6j7S0p7g5Zdf1nRRSAswQSBdwVilZxEVFYWIiAh06vRSrb6utt90ERVjrNYsmUyK1NQkSKUFmi6KzhMKhVoxxo9YrAdLSxuIRKqNh1+omTG9uNjYWMTEPIKfX8dqO2d4eDgWLlyAPn1eweTJU6rtvJWZMOFNbN68BWJxxaEzbdpUZGZmQCKR4Msvl8He3h537tzBZ58tgUKhwKefLoSbm1uF54uNjcWoUSPh6uoKsViEmTPfx4oVK1BQkI+YmFi4urqiTZs2mD17ToVluXjxIhwcHNCoUaMqv8/Q0OsIDAyEUChE69at8dFHAQCArVu34NSpU3B0dMAXXyyFRCJRW5eYmIg1a1YjMHB5medev34dTpw4AUtLC0gkEsyY8b6yaX58fDz69n0Fx46dgK2tbZXKumfPHxg6dFiV3xsAbNiwHufOnQUAzJgxEx07dkJ2djbmzZuL9PR0DB/+BgYNGoT169ehbdu26NSp7JoXf//X0KCBDQCgQ4cOuHjxIjIzM5CRkYGGDZ3Qq1dvjBs3TuWY06f/wZo1qzFq1BgMG6Ze7nHjxuKnn3bik0/mY/LkyXB2blzhe3ne39eMGdNw+fJlrFixUvn+goIO4Oeff4a5uTmWL/8aJiYmZa4rLmN5Ll68iAsXzmPmzPdV1hd/92ZmZujRowfefHNCmcfv27cXmzf/gAYNbODp6YkPPvjwmd5bbTlz5jSWLw+EhYWl8vN4njh6XrGxsRX+1kpLTExEQMBHyM/Px/Tp01XKExMTg/nzAyAQCGBnZ49ly75CXl4eZs6cDqlUCmNjE3z99TcwNjbGTz/9hEOHgv5rTfQRvL29VV7n668D8dZbbyt/G8/yfqrzb0XJa6hEIsEPP2wGULXrWFnrMjIysGXLZsyb91G1lI+I6EWkpibBwMAIxsb27NL3gsRiIaRSzSazCoUC2dkZSE1NQoMG6mMzlYcjE9WC2NhYBAcHq61/kScgZ8+ewaxZsytNZF/0KYtCocDzVN5//PF87NixE++8MxE//bQdALB27RosX/41vv12BdatW1Ol83Tq1Anbtm3HsGHDcf78eWzbth1ff/2tcn1liSwAXLp0ETExj56p/I6Ojti69Uf89NNOpKQk4/bt20hOTsalSxfx00870aKFG06ePFHmuqqYO3cutmz5EYsXf4Yvv/wcubm5AIDjx49j8OAhOHnyZJXLunfv3md6bwAwcOAg7Nr1MzZu/B4bN24AAPz++2/o168ftm/fgT17fkdhYeVPOi0trbBt23Zs27YdU6dOw7Zt2/HRRx9jwICB2LZtu1oiCwAnT57E8uXflJnIPo/yfl+VWbhwMcaOfVq+wsJC/PrrL9i+fQcGDBiIX3/9pcx1z6v4tzh37lxs27a93ES22IQJb2Pbtu3VnshW55NXL682+OMP1fh7njiqLZs3/4Dp02fghx9+wPfff6+yzdTUFOvXb8T27T+hYcOGOHPmNMRiMb76ajm2b/8JPXv2xL59Re91//692LXrZ6xYsQpbt25ROU9WVhaSk1OUiWxhYSG+//47TJgwHhMmjMfGjRtQUFD2Z/K8sVyR4mtlcSJb1etYWeusra2RkpKCrKysai0jEdHzkEoLYGxsxkS2jhAIBDA2NnvmmnbWzNYQhUKBtKx8ZOYUYvtP/0NEWCiuXA3Bl198gU8/XQALCwt069YNycnJOHfuLPLz87Fw4SK4u7fChAlvwsOjFS5fvoI33hiBYcOGYf78jxEXFwuBQIhFixbj999/g4mJKXJycmBqaoq1a4uSwxkzZqJTp86YMOFNeHp6IjExEc7OzoiJiUFSUiJsbe3g7OyM06f/Qbdu3fHee1ORkpKChQs/RXZ2NlxdXfHppwuxfv06xMfHISEhEYGBy9UG/Dp4MAg3btxAQMDHGDx4IFxdXRETE4NPP10ET09PODk5ASiavkEoLJqjOCMjQzkKdmZmZrnnK0vLli1x8WLVbvJWrVqJK1euQCwWY+nSpdi/fx9OnDiBTp06YsaM9/HppwuQnJwMS0tLfPVVIA4eDMKJE8eRn18AfX09rFixUqVGRSyWQCQSIizsJtq37wAA6NixEw4eDIKhoaHautatPQEU3cR+8sl8jBo1Ej4+7cosq729PTp37oKbN2+iffv2uHQpGF9+uQwLFnyCkSNHqu2fnp6G999/HwKBAM2bN0enTp1w585tTJjwJiZNmgwTE2OsWLECUqkUw4YNw5AhQzFhwpto0aI5bt68iSFDhmL48DeU34+enp7yj0Bo6HXMn78AIpEIbm5uuH//gfJ179y5gzVrVuGrr5bD2Ni4St9DWa5evYJTp07i1q1IzJ07DytXrsRPP+1EbGwsNmxYjy+/XFrpOY4fP47Nm3+AkZERJkyYgKCgIISEXMX169ewZcuP2LhxA4KDgyEUCvH5518AAD76aB4sLCyQkpKM5cu/gZOTE2xsVGvNoqMfonnzFhCLxejUqRMWLVqIl15SX1dMoVBg2bKl8Pb2UY4BUJahQ4egefPmaN68OQBg5coV2LJlMz78cC5atnQv8/cHADt37sCBA/vx3ntT0bFjJ7XzfvjhB1i0aDH++uvIf7Xd6zB16ntYu3Ydli8PxK1bkZDL5QgMXA4HB0eMHj0SLVu6w8DAAOnp6dDX18ft27fRoUMHZGZm4vr1axgzZgwGDRqi0mJiwoQ3sW3bdnzyyXwIhUJERz9E+/YdMH36DJibm6uV61nj6OLFi/jhh00QiYQoKCjEypUrYW5uofY92traYsqUSZBKpbC0tMK3365Qnrf4tzZ8+Bto3759ud/FnTt38PHH8//7g2mMrKwsmJgUNVsq+V6KrltC6OvrK+NELBZDKpUCABwdG6KgoACZmRmwsLBQeY0LFy6gTZs2yuXFixehf39/TJ48BXK5HEFBQVi48FN89VVgtcVyRS5duojx48eid+8+GD/+zSpfx8pa9+qrfeHt7Y3z58+jT58+Fb4uEVFtYCJbtzzP98lktgYUSmVIycxHenYBFArg1f6DYe/QEOPfmYLExMdISUnG5s1bIBKJkJubi4kTJyE6+iHWr1+HwMCvAQD+/gPw/vuzMXHiOxg4cCASEh5j27YdyikRBg0arGy2N27cWGzaVPTUffLkScqmc7169Ya3tzfWr18Hd3d3LFv2FSZOfBc9e/bClCnv4Y03huO996Ziy5Yf8O67E+Ht7Y0VK77FtWvXAACNG7vgiy/Uk4tDhw7h5s0bmD//EwBFTff+97+fkZmZhSVLFmPDho0AiubY3bTpOyxcuBgAoFA8rREqWdtb+nxluXLlMlxcXKr0+V+7FoLt23dAKBRCoVCofFa7du1Ejx498Npr/bF7924cO3YUAGBlZY0lSz7Dli2bcezYcWVycuvWLaSkpKBp02aIjIxU3oCbmpogIyMDmZmZausAQCqVYsGC+Xj99eHo0KFDhf0QbW1t8eTJE6SkpMDc3AKmpqYwNjZWDrhWUkREBNq3b49p06YrY6F58xbYtq2o9nvSpIlYt249jI2NMXHiO/D39wcA9O37Gj766GOMHz8OgwcPhkSiB6CoufHw4W8AADIyMpU39iYmpsoHDnfv3sOuXbvw1VeBaolsamoKJkx4EwCwaNFiNGlS8aimbdu2Q9eu3arUfLg8x48fw7ffrkDDhg2hUChgYGAIJycnzJz5Pm7duoXExARs27Yd9+7dw+bNm/Duu5OQnp6Obdu2Izw8DFu2bMaiRYvVzqv6/k2QmZlZ5rpiX321DN7e3hUmsgCQkPAYO3fugpGREdLT0zBt2nQ8fBiFTz9dgB07dpb5++vZsxcGDhyEtLQ0TJr0Ln755TeIRCKV83p5eSE09Dpu3rwJiUQPhYWFEAqFEIlEmDVrNgwNDXH+/L/49ddf8f77s5CamopJkybD3t4en3wyH507d8HChYvg7/8avv76G8ydOw/vvPMWBg0aUu578fPzw+eff4GpU99DQkKCyoB/ZX+OVYsjhUKB777bhMOHD+O3335Dt27d1b7HhQsXY/36jTAwMMCaNasRHByMxo0bq/zW2rdvjxs3QtVGxPfwaIW5cz+CXC5T/qEs+j4zlGUtlpiYiPPnz6u0esnJycZvv/2KjRuLanM7dOiAAQP6QyaTKdcVi45+iKZNi0brP3HiOF57rT8sLCzw1lsT4OHhATs7OzRt2hSRkRHVFsvvvz8T6enpKuVYsWIlbGxsEBR0CHp6epgxYzr8/DqWec2q6joAcHJyQnh4eLkxQkREVJuYzNaAK7eSYGUkR+nWuQoFIJXL0aRpc+WN6YEDf+LgwSAIBEKVpxHNmjWHRCKBQCCERCLBwIGD8dFH8+Do6IgZM2aqnFcggPKGTCR62nLcw6OVyvmAosSpefOiGy0jIyPIZDLcv38fq1atACBATk4OPD2LahZbtSrqx/nOO29BJpPjm2++AQBs2fIDdux42l/Q2bkxjIyMYWRkjKyspzf7X3+9HAMGDIKzs/N/5Xz6/gSCp+Useb5///0XmzZ9j5YtW2LcuPE4f/483nprAmxtbbFw4aKKP/j/vPXWO5g//2NYWFio9Vm8f/8+wsPD8Ouvv6KgIB/9+vWHqakJ3N3dARTVAN+8eRNAUS3o0qVfKGuATExMkZCQAADIysqGmZlZmeuAouS7S5eu6NChw3+fRSDCwsLx7rvvqpU3MTERrq5NcfJkUY3l5MmTkJKSjH/++RsDBgxU2bddO19cvnwZH300F126dMPAgarbb92KxPTp0wAAaWmpSElJBQC4u7tDJBLB0dERyckpsLe3x/Hjx5GWlob+/YsSXlNTE2RlZUFfXx9ZWVkwNTUFAGzduhmBgcthYmKClJQUzJkzGwCwbdt2ZTPjF/UsTdknTZqM77//DjKZDJMmTVLZ9uDBfVy6dEmZYBfXqrVo0RxisRgtW7rj0aPVZZ63+P0DQHZ20fsvax0APHwYBX19vXJbEpTk4tIERkZGAABzcwsARQ+KipX1+yvug2llZYXGjV2QnJys1ofax8cH//zzD/Ly8tCiRQscOXIYLVsWzfm5desWBAdfgFQqVc75bWVlDXt7e+XxxdeBBg1sSlxvin6jqk9Gn343xb+T5s2bIzY2psxktipxVFrJ39/58/+W+T3m5uZg8eLFSExMQHJyMho3bozGjRur/dY8Pb3KjcmS153s7GyYmqo+LCooKMD8+R9jyZIlyn78RX38F2DmzPdhZmaGrKwsBAUF4dChw0hOTsGSJYvUEtpiERERmD59Bj75ZD5WrVqNmzdv4Nq1a/Dy8sLDhw+rLZZXry6/24aeXtGDq5deehl3796p8nWsvGsbEZGuyiuQ4khwNE5ejUVWbiFMDCXo2bYh+vo5w0CPKZGu4jdXA67fS8bLnqbKZbFYDJm8qGZOoQBkJbqs7d69G7///gcePYrGokVPk7WSN5MymQyvvfYaBg0ahMWLF+HmzRsqryeXK5Q33LISJy9541by3rTkuRUKBVxcmsDff4ByECKpVIrbt29DKCzab8uWH1Ve78svlyEg4COsXLkKBgYGiI5+iJycHGRlZcHYuOhG9Y8//vivBnmQ8jgzM3M8fvwYQqFQ5Ya25Pk6d+6Mzp2LapZjY2PRqVOnKg/uUszPzw8vvfQSNm36Hv/88w/EYrGyn6CLSxN07NgRffoUTZtUWFiIgweDcOtWJICimthGjRpBKpUiIOAjfPjhXGWT49atW2P37p/x9tvv4MKF8/Dy8ipzXVEZOsLBwQG7du3EhAlvYu7cpwOmXL9+XfnvhIQEnD//L959dyJ27NiOHTt2wsDAADk52fj000/Vklm5XI7p02cAAIYNG4KBAweqfLfu7u5YsWIVjIyMUFhYCIlEonxfnp6eiIuLg7W1FW7duoXdu/+HDRu+Ux7bpo03goMv4NVX++LWrUi4uhbVsn788Xxs2rQJ9vb2cHZuXC3Ja7H8/HwAwJ07t6t8jKOjIz777HOEhIRg+/bt8PcfAJms6Pfl4uKCzp07Y/78BQCKvt/ExETcuXMHMpkMt25FljsQWOPGLrh7t2i/8+fPo02bNmWuK963X7/++OabrzF37rwKyysUPv0dFjdrTU1NLVFm9d9f8X55eXmIjn4IS0tLtfO2bOn+X+2wD3x82iIwcBnmzPkAaWlpuHTpEnbs2Il///0XBw8GqZUDKP1wSbVZj6mpCZKSkmBgYIAnT54o19+6dQtNmzbD3bt3MWrU6DLfb1XjqKRbt2799/+i76es7/Hvv0+hcePGWL78a6xevUr5AKTkb23MmLEV1sy2aNEC165dQ4sWLVSaGBdbvHgRRo0apaxZBYB169bC27utclAmoVAAAwMDSCR6MDU1VfZ3L+bs3BhxcbHKcheXUyAQKL+DM2fOYPToMbC3t6+WWC6vZlZfX19ZuxoSchVjxoxBw4ZOVbqOlXdti4mJQZMmrmV+90RE2iqvQIovdlxBUlouCv8b6CgrtxCHg6Nx+VYSFoxv98IJbdeuvjh69LTyAXZVbNu2GcePH4VIJIRIJMbkydPg51fUtSgvLw9Lly7BrVsREIlEmDZtFrp06QYAmD59EkaNGqdcrglffrkYLVu6Y9iwEdi373fk5+djxIgxavsdOnQA//57Bl98sRxnzvyNH3/cjMLCotap/fsPxKhRYwEAW7Z8j9zcXEyfPqtay8lktgbkFUhVlhu7NMX2LRsQ+PknmDBxmkqNraenJ958czzatSu7TyVQVIMwY8Y0yGRymJgYo3nzFjhz5oxy+3vvTcXEie8AgDLReRYTJ07C4sWLkJWVBaFQgCVLPq9w/5YtW+Ktt97G/PkBWL78G9jbO+DTTxfg0aNoLFjwKQDgiy8+g6enJyZMeBO+vr6YPn0Gpk2bjg8//AAAsGDBgnLPV9lIyZWZMWM68vPzABTd0NnZ2WH16pUIDQ3FW2+9jUWLFmL37p+hUACzZs0CAKSlpWPixHehr6+PFStW4ujRv3Dz5k3lTfGsWbPh7e0NX19fjBs3Fg4ODhg3bhwkEj21dYmJSQCKvovPP/8MBw8exCuv9FUp49dff60czXj+/AWQSqXIycmGgYEBAMDIyBhpaanIy8tTrgOAGzduYPXqVZBKC5X9KD09PTFz5nS8+eYETJs2HdOnT4NCoYC5uTlWrSqquTl69C8EBi7D4MFDIJHo4dtvv0FycjImTZoIU1MTrF27HsOGvY558+Zi165dGD58uLIpsqmpKZYt+woBAfMQGLj8mUdorUj37t0xbtxYeHl5VvmYDRvWIzT0OnJycvDhh/PQvHlzrF69Eh98MAfffrsC1tYNMGHCmxAIBHjttdfQuXMXWFlZY+bMGUhNTcFXXxU9HFm69EucPv0P/v77FN54IwbDh7+BYcOGY/z4cTAzM8Py5V9DIpGorSs2bNgw/PDDJmzZshnvvKNe416Wb7/9Gnfu3IVCIcesWUWDl5X1+9u/fx/OnTsLuVyOd955V/lQoiSJRAKJRAIfn7bw9PTEgwcP4OXVBsbGxjAyMsLbb7+FFi1aVPlzLen114dj+vSpaNu2HSwtn/aXv3TpEnbv/hm+vu1hb2+PmzdvYtWqFbh79w7effdtrF+/8bniSCwWY/LkScjPz8eqVathYWGh9j1269YdmzZ9j7CwMJiamqBx46cJcfFv7dChQ3jttdfKfeDy9ttFrTby8/Mwdep0AEUjRzdv3gKFhYU4fvwY4uLi8NNPP2Hs2HHw8vLCli2b4e3tgxMnjqNv334YOXIkOnfujDFjRkEmk2PKlPdUXsPPzw9ffPE5Ro0aDQ8PD5w7dxajRo3GzJkz4OnpievXr2HkyFFwcXHBihXfVkssl1cze/r0P1i3bi0kEj20a9cOXl5FD2Oqch0rax0AhISEKK/zRETaIOJhKnYevYX45JxnPrZQKkfck2xMXXG63H0crI0w9hU3uDdWf7D8otzdPTBy5FgYGBjgzp3bmDFjEvbvPwJ9fQP8/PNPMDY2xi+/7MOjR9GYNm0idu/e+0zJcnUZPPj1Ku1nZdUAy5cXjT2TlZWFd94Zi1atPNCmjU+NlY3zzNaAj9ccwEutTdGxc9lPS0RCARrZlj1Xki6qbJoSbbdv315IpTK8/nrVfqjPStNzd1Z1OqW66lmnb6nPKorVqk6V9KzKm8ZIlxVPzWNt3QAffTQPQ4cOhZ9fR8hkMmXC/Ty/R03HcnJyMjZv/kE5VVlU1ANERERynlmicjBWa9bjxw9hb98YH39/HgmpuZUf8ALsLA2xbLL6YIwlFdfMGhgYYN26lUhOTsYnnyzG118vhVgsxoMH95GWlgYfn7aYM+cjtQfVCoUCffu+jJ9++hW2tnYYO/YNLFiwGC1bFnUbnDdvFvr29UfPnr1VamaPH/8Lu3fvwtKlX8PWVr37z19/HcLff5/EsmVF3QWLBgn1x8aNW5Cbm4tvvy2aiq6gIB8DBw7BG28UtbwqWTNbsla1sLAQK1cux9Wrl2FuboHmzd2QlpaCL75Q/9s0b95s9OrVB6+++prKOe7du4vPPluAWbPmqg2SWvy9lsR5ZmuZTwt7ZGU9Vg7QU5JAAJgaqdey0PM5fPgwfvllt3K5KvPO6pIFC+YjJiZWuTxt2vQKR2vVNj/99BNOnDiuXC5r3tmKPHjwAEuWLFYuGxjo47vvNlVnEV9YZmYmZsyYrrJu7dp1yr6i1UEXPgd6qmS3gkWLFmPjxg1Yt24dxGIRRo8eo7MPlqytrZWJLFA0Qr2enkEFRxAR1S8FBQVYunQxHBwaYvHiL5V5QHj4TWzcuBV6enqYO/d9/PnnHgwbNkLl2CNHDqJhQydlQpqQ8Bh2dk/nW7W1tUdi4mOVY3bt2o6LF4OxatWGMsekAICXXuqJNWu+RVpaGiwsLHDhwr9o3NgFjo4NkZOTjVWrNsDIyAAZGVmYNOlNdOjQCS4u5Q/ouX//H4iPj8POnb9BKpVi2rSJytlKSnr4MArh4Tcwb958lfWXLgVj7doVWLJkWbV0W2HNbA3IysnDtl/2waN5QzRydlX2kxIAEIsFaGBuwKHE6xGxWASpVHM1s0RVxVilqlAoFEhNTcXVqyHo0uVllabotYG1XaQrGKs1q7gG70WaGVdFVZsZd+3qCzc3d/Tq9QpGj3764P7LLxfDyakR3nyzqEvg0aOH8fffJ7F06dOuSyEhV/D55wuxatV6ODu7AAD69OmOX3/drxw345tvvoKTkxNGjhyL6dMnITc3F3Z29liyZGmZ3ZFK+uqrz9G0aXMMHz4Sn3wyF127voR+/fyRkpKMdetW4d69OwAESEiIx4cffozevV8tt2Z2/vy5ePnlnnjllX4AgF9//R9CQ6+p1Mw+efIEM2ZMwsSJU9GzZ28ARX1mT58+BblcjpUr15fbbY01s1rAxMgAb74xCLsPnMTV8NP4bxwliIQCtHa1wv1SA7FQ3aavL0Z+vrTyHYk0jLFKVWVgYIDOnV+q9USWiKg098aW+HJixwr32XfmPg4HRysHfypJIhain58zBnd78VpCH592CA4+j6FDh6uMeVKRmzdD8fnnC7Fs2bfKRBYA7OzskZAQr0xmExMfo21bX+V2D4/WuHQpGI8fx6NRI+cKX6NfvwFYvfobvPJKX1y7dhWfflo0Ps7336+HlZU1Fi5cAkCI2bOnoaCg4NnedCmpqSmYNWsqxowZr0xkizVq5IwHD+4jMjIcXbtWTxcVJrM1xNTYEOOH9cWMVWcgLTHC8IghHWFnVfsdt0lz+GSWdAVjlYiI6qK+fs64fCtJZTRjoCiRtbEwRF+/ipPBqnr77UnYs+dXzJkzHV9/vUo5y8epUyfwxhujIZFIcOTIIXTp0hUAEBERhoULP8bnnwfCza2lyrl69OiF/fv3oGXLVnj0KBoREeFYvPhL5XY/v8546aWemDv3fSxd+g1cXZuWW642bbyRk5ON775bj27dXlYm2llZmWjatGjKt9u3b+P69Wvo06dvuecBiqaJPHLkEHr27AOZTIpjx47Azq5o6r/09DTMmjUNw4a9AX//wWrH2ts7YsaMOfjgg5nIz89Hr16vVP6hVoJVhDVIXyJCi0bmKutuPkjRUGmIiIiIiOofAz0xFoxvh35+zjA1kkCAojFs+vk5V8u0PCWNHTsBPXr0xqxZU5GRUTRtmrt7K8yZMw1jxw6HnZ0dBg4cCgD49ttAFBTk4+uvl2LChNGYMGE07t27CwAYPXo8MjMzMWLEYMybNwvz5s2HkZGxymu1a9ce8+cvQkDAHNy+HVlhufr27Y8DB/aiX78BynVvvvkODhzYizFj3sDWrZvg7V35qMMDBw6FnZ09xo4djpkz30PLlh7KbTt3bsejR9HYv3+P8v0cPPinyvF2dvZYvXoDduz4EYcOHaj09SrDPrM17HDwQ/x26p5y2btZA8x83UuDJaLaxtou0hWMVdIFjFPSFYzVmlVW30ptVLLvqbYSi4WQltEEWxOetc8sa2ZrmIeLan+iyOhUlWbHRERERERE9OzYZ7aGOdmawMxYDxnZRZ2p8wpkuB+XgRaNLDRbMCIiIiIiqlGffLK4Vl7nwIF9+OOPX8t4/UVo3tytVsqgCUxma5hQIICHiyXOhyUo14U9SGEyS0RERERE1WLAgMEYMGCwpotR69jMuBa0KtXUOCyKg0AREREREb2IOjD0D5XwPN8nk9la4NFENZl9EJ+BrNxCDZWGiIiIiEi3icV6yM7OYEJbRygUCmRnZ0As1num49jMuBZYmOjDycYYMUnZAACFAoh8mArflrYaLhkRERERke6xtLRBamoSsrLSNF0UnScUCiGXa36AWrFYD5aWNs92TA2VhUrxaGKlTGaBovlmmcwSERERET07kUiMBg0cNF2MOkGXp5FiM+NaUrqpcdiDFDaLICIiIiIiek5MZmtJCycLiEVPP+7kjDwkpOZqsERERERERES6i8lsLdGTiODWyFxlXdgDjmpMRERERET0PJjM1iKPJtYqy0xmiYiIiIiIng+T2VrUysVSZTkiOhVSmeZHDiMiIiIiItI1VRrN+MGDBwgICEBaWhosLCwQGBgIFxcXlX3Wr1+PQ4cOQSgUQiKRYPbs2ejWrRsAYMmSJTh//jz09PRgZGSETz75BJ6engCAcePGIS4uDiYmJgCA8ePHY9iwYdX4FrWHk60JzIz1kJFdAADIL5DhflwGWjSy0GzBiIiIiIiIdEyVktlFixZh9OjRGDRoEPbv34+FCxdix44dKvt4eXnh7bffhqGhISIjIzF27FicPXsWBgYG6N69O+bPnw+JRIJTp05h9uzZOH78uPLYBQsWoEePHtX7zrSQUCCAh4slzoclKNfdfJDCZJaIiIiIiOgZVdrMODk5GeHh4fD39wcA+Pv7Izw8HCkpqv09u3XrBkNDQwCAm5sbFAoF0tLSAAA9evSARCIBAHh7e+Px48daMTGvJpQ1RQ8RERERERE9m0qT2fj4eNjZ2UEkEgEARCIRbG1tER8fX+4x+/btg7OzM+zt7dW27dq1Cy+//DKEwqcvvXz5cgwYMAAffvghEhIS1I6pS1q5qCazUfEZyMot1FBpiIiIiIiIdFOVmhk/i4sXL2L16tXYunWr2raDBw/iwIED2LVrl3Ld8uXL4eDgAJlMhu+//x6zZs3Czz///EyvaW1t8sLlri02NqZwcTBDVHwGAEABIDY1F12drSo+kHSajY2ppotAVCWMVdIFjFPSFYxV0hW6GquVJrMODg5ISEiATCaDSCSCTCZDYmIiHBwc1PYNCQnB3LlzsWHDBri6uqpsO3bsGFauXIlt27ahQYMGKucHimp8x48fj3Xr1kEul6vU3FYmOTkLcrmiyvtrWstGFspkFgDOX4+Fm6OZBktENcnGxhRJSZmaLgZRpRirpAsYp6QrGKukK7Q9VoVCQbmVl5VmjNbW1nB3d0dQUBAAICgoCO7u7rCyUq1JDA0NxezZs7FmzRp4eHiobDt16hSWLVuGLVu2wMnJSbleKpXiyZMnyuWDBw+iRYsWz5TI6iL1frOpUCh0JxknIiIiIiLSNIGiClnUvXv3EBAQgIyMDJiZmSEwMBCurq6YOHEiZs6cCU9PTwwbNgyxsbGws7NTHrd8+XK4ubmhY8eOkEgkKgnwtm3boK+vj7Fjx6KwsKjPqK2tLT755BO1Wt3K6FrNbEGhDDNWn0Gh9OkgWEsndYS9lZEGS0U1RdufdhEVY6ySLmCckq5grJKu0PZYrahmtkrJrLbTtWQWAL7dHYKwqFTl8pg+LdCrnVMFR5Cu0vYLBFExxirpAsYp6QrGKukKbY/VF2pmTDXDo4m1yjKn6CEiIiIiIqo6JrMaUrrfbER0KqSy+jn3LhERERER0bNiMqshTjbGMDPWUy7nF8hwLzZdgyUiIiIiIiLSHUxmNUQgEMDDpdSoxlFsakxERERERFQVTGY1qLXaFD1MZomIiIiIiKqCyawGtXKxVFmOis9EVm6hhkpDRERERESkO5jMapC5iT4a2T4dZloBIOJhavkHEBEREREREQAmsxpXelTjsAfJGioJERERERGR7mAyq2Fqg0A9SIFCodBQaYiIiIiIiHQDk1kNa+5kDon46deQnJGPxyk5GiwRERERERGR9mMyq2F6EhFaNLJQWcdRjYmIiIiIiCrGZFYLlNXUmIiIiIiIiMrHZFYLlJ5vNjI6DVKZXEOlISIiIiIi0n5MZrVAQxtjmBvrKZfzC2W4F5uuwRIRERERERFpNyazWkAgEKhP0RPFpsZERERERETlYTKrJdTnm2UyS0REREREVB4ms1qiValBoKLiM5GVW6ih0hAREREREWk3JrNawtxYD41sTZTLCgDhbGpMRERERERUJiazWoRNjYmIiIiIiKqGyawWKWsQKIVCoaHSEBERERERaS8ms1qkhZM5JOKnX0lKRj4ep+RosERERERERETaicmsFpGIRXBrZKGy7iabGhMREREREalhMqtlSjc1DmcyS0REREREpIbJrJYpncxGRqdBKpNrqDRERERERETaicmslmnYwBjmJnrK5fxCGe7FpmuwRERERERERNqHyayWEQgEaO2iWjvLfrNERERERESqqpTMPnjwACNGjMCrr76KESNGICoqSm2f9evXo3///hgwYACGDh2KM2fOKLctWbIEffv2xcCBAzFy5EjcuHFDue3Jkyd4++238eqrr2LgwIG4fv36i78rHdeK880SERERERFVqErJ7KJFizB69Gj89ddfGD16NBYuXKi2j5eXF37//XccOHAAS5cuxezZs5GXlwcA6N69Ow4cOIA///wTkydPxuzZs5XHffvtt/D19cVff/2FhQsXYu7cufV+btVWpWpmHz7ORGZOgYZKQ0REREREpH0qTWaTk5MRHh4Of39/AIC/vz/Cw8ORkqJaW9itWzcYGhoCANzc3KBQKJCWlgYA6NGjByQSCQDA29sbjx8/hlxeNKjRkSNHMHLkSACAr68v9PT0VGpu6yNzYz0425oolxUAIh6maq5AREREREREWqbSZDY+Ph52dnYQiUQAAJFIBFtbW8THx5d7zL59++Ds7Ax7e3u1bbt27cLLL78MoVCI1NRUKBQKWFk9rYl0cHDA48ePn+e91CmlRzVmv1kiIiIiIqKnxNV9wosXL2L16tXYunWr2raDBw/iwIED2LVrV7W+prW1SeU76Zgu3k44HBytXI58mIoGDUwgEAg0WCp6XjY2ppouAlGVMFZJFzBOSVcwVklX6GqsVprMOjg4ICEhATKZDCKRCDKZDImJiXBwcFDbNyQkBHPnzsWGDRvg6uqqsu3YsWNYuXIltm3bhgYNGgAALC0tAQApKSnK2tn4+Pgya3QrkpycBbm8bvWztTGVQE8sRIG0qDn2k/Q83LiVAAdrYw2XjJ6VjY0pkpIyNV0MokoxVkkXME5JVzBWSVdoe6wKhYJyKy8rbWZsbW0Nd3d3BAUFAQCCgoLg7u6u0jQYAEJDQzF79mysWbMGHh4eKttOnTqFZcuWYcuWLXByclLZ1rdvX+zevRsAcPnyZeTl5aF169ZVf3d1lEQsQgtnC5V1bGpMRERERERURKCowtDB9+7dQ0BAADIyMmBmZobAwEC4urpi4sSJmDlzJjw9PTFs2DDExsbCzs5Oedzy5cvh5uaGjh07QiKRqCTA27Ztg6WlJZKSkjB37lzExcVBX18fS5YsQdu2bZ/pTdTFmlkAOHoxGrtP3lUuezW1xqzhbTRYInoe2v60i6gYY5V0AeOUdAVjlXSFtsdqRTWzVUpmtV1dTWZjk7Lw6ZaLymU9iRBr3+8OibhKMyqRltD2CwRRMcYq6QLGKekKxirpCm2P1RdqZkya49jAGBYmesrlgkI57sWma7BERERERERE2oHJrBYTCATwcFHtmxwWxX6zRERERERETGa1HOebJSIiIiIiUsdkVsu1KlUzG/04E5k5BRoqDRERERERkXZgMqvlzIz14Gz3tMOzAkDEw1TNFYiIiIiIiEgLMJnVAWxqTEREREREpIrJrA5oXXoQqAcpqAMzKhERERERET03JrM6oJmTBfRKzC2bmpmP+OQcDZaIiIiIiIhIs5jM6gCJWAg3Z0uVdWFsakxERERERPUYk1kd4eFSKpnlfLNERERERFSPMZnVEaUHgYqMTkWhVK6h0hAREREREWkWk1kd4djAGBYmesrlgkI57sama7BEREREREREmsNkVkcIBAK12ln2myUiIiIiovqKyawOUUtm2W+WiIiIiIjqKSazOqRVqflmox9nIiOnQEOlISIiIiIi0hwmszrEzEgPje1MlcsKABFRqZorEBERERERkYYwmdUx7DdLRERERETEZFbnlNVvVqFQaKg0REREREREmsFkVsc0a2gOPcnTry01Mx9xyTkaLBEREREREVHtYzKrYyRiIdwaWaqsY1NjIiIiIiKqb5jM6iD2myUiIiIiovqOyawOKp3M3nqUikKpXEOlISIiIiIiqn1MZnWQo7URLE31lcsFhXLcjU3XYImIiIiIiIhqF5NZHSQQCODhwqbGRERERERUfzGZ1VHsN0tERERERPUZk1kd1crFEoISyw8TMpGRU6Cx8hAREREREdWmKiWzDx48wIgRI/Dqq69ixIgRiIqKUttn/fr16N+/PwYMGIChQ4fizJkzym379+/HgAED0KpVK+zcuVPluICAAHTv3h2DBg3CoEGDsHHjxhd7R/WEqZEenO1MVdaFR7F2loiIiIiI6gdxVXZatGgRRo8ejUGDBmH//v1YuHAhduzYobKPl5cX3n77bRgaGiIyMhJjx47F2bNnYWBgAHd3d6xcuRKbNm0q8/yTJk3C2LFjX/zd1DMeTazwMCFTuRz2IAUdW9lrsERERERERES1o9Ka2eTkZISHh8Pf3x8A4O/vj/DwcKSkqNYCduvWDYaGhgAANzc3KBQKpKWlAQBatGiBZs2aQShkq+bqVFa/WYVCoaHSEBERERER1Z5Ks8v4+HjY2dlBJBIBAEQiEWxtbREfH1/uMfv27YOzszPs7atWS/jjjz9iwIABmDp1Ku7du1fFolOzhubQkzz9CtOyChD3JFuDJSIiIiIiIqodVWpm/CwuXryI1atXY+vWrVXaf/bs2bCxsYFQKMS+ffvw7rvv4vjx48rkuSqsrU2et7g6z6uZDS5HJCiXHz7JgXcrBw2WiMpiY2Na+U5EWoCxSrqAcUq6grFKukJXY7XSZNbBwQEJCQmQyWQQiUSQyWRITEyEg4N6whQSEoK5c+diw4YNcHV1rVIB7OzslP8ePHgwli1bhsePH6Nhw4ZVfhPJyVmQy+tn89rmjmYqyWzwjXh0drfVYImoNBsbUyQlZVa+I5GGMVZJFzBOSVcwVklXaHusCoWCcisvK21mbG1tDXd3dwQFBQEAgoKC4O7uDisr1f6aoaGhmD17NtasWQMPD48qFy4h4WkidubMGQiFQpUElypWut/srehUFErlGioNERERERFR7RAoqjBi0L179xAQEICMjAyYmZkhMDAQrq6umDhxImbOnAlPT08MGzYMsbGxKono8uXL4ebmhqCgICxfvhwZGRmQSCQwNDTE1q1b0axZM0yYMAHJyckQCAQwMTHBvHnz4O3t/Uxvoj7XzCoUCny44V+kZuYr180d6Q13F6sKjqLapO1Pu4iKMVZJFzBOSVcwVklXaHusVlQzW6VkVtvV52QWALYeisDZ0KcDcvXr6IzhLzfTYImoJG2/QBAVY6ySLmCckq5grJKu0PZYfaFmxqT9PFzUp+ghIiIiIiKqy5jM1gGtXCwhKLEcnZCFjOwCjZWHiIiIiIiopjGZrQNMjfTgbK86nHZ4FGtniYiIiIio7mIyW0e0LjWqcRiTWSIiIiIiqsOYzNYRZfWbrQNjexEREREREZWJyWwd0bShOfQlIuVyWlYB4p5ka7BERERERERENYfJbB0hEQvh5myhso6jGhMRERERUV3FZLYO8SjVb/Ym+80SEREREVEdxWS2Dik9CNTt6DQUSmUaKg0REREREVHNYTJbh9hbGcHSVF+5XCCV405MugZLREREREREVDOYzNYhAoFArakx+80SEREREVFdxGS2jlGbb5bJLBERERER1UFMZusY98aWEJRYjk7MQkZ2gcbKQ0REREREVBOYzNYxpkZ6aGxvqrIunKMaExERERFRHcNktg5iv1kiIiIiIqrrmMzWQaX7zd6MSoFCodBQaYiIiIiIiKofk9k6qGlDc+hLRMrl9KwCxD7J1mCJiIiIiIiIqheT2TpILBKipbOFyjo2NSYiIiIiorqEyWwd1Yr9ZomIiIiIqA5jMltHle43e+tRGgqlMg2VhoiIiIiIqHoxma2j7K2MYGWmr1wulMpxJyZdgyUiIiIiIiKqPkxm6yiBQAAPFzY1JiIiIiKiuonJbB3G+WaJiIiIiKiuYjJbh7VysYKgxHJ0YhbSsws0Vh4iIiIiIqLqwmS2DjMxlMDFwVRlXXgUa2eJiIiIiEj3MZmt49jUmIiIiIiI6qIqJbMPHjzAiBEj8Oqrr2LEiBGIiopS22f9+vXo378/BgwYgKFDh+LMmTPKbfv378eAAQPQqlUr7Ny5U+W43NxczJo1C3369EHfvn1x6tSpF3tHpKKsQaAUCoWGSkNERERERFQ9qpTMLlq0CKNHj8Zff/2F0aNHY+HChWr7eHl54ffff8eBAwewdOlSzJ49G3l5eQAAd3d3rFy5Ev7+/mrHbdmyBSYmJjh27Bi+++47LFiwANnZ2S/4tqhY04bm0JeIlMvp2QWITeLnS0REREREuq3SZDY5ORnh4eHKRNTf3x/h4eFISVFtrtqtWzcYGhoCANzc3KBQKJCWlgYAaNGiBZo1awahUP3lDh8+jBEjRgAAXFxc0Lp1a5w+ffqF3hQ9JRYJ0dLZQmXdTTY1JiIiIiIiHVdpMhsfHw87OzuIREW1eyKRCLa2toiPjy/3mH379sHZ2Rn29vaVFiAuLg4NGzZULjs4OODx48dVKTtVUel+sxwEioiIiIiIdJ24uk948eJFrF69Glu3bq3uU5fL2tqk1l5LF3Vr1wj/O35HuXz7URrMLYygV6L5MdUsGxvTynci0gKMVdIFjFPSFYxV0hW6GquVJrMODg5ISEiATCaDSCSCTCZDYmIiHBwc1PYNCQnB3LlzsWHDBri6ulapAI6OjoiNjYWVVVHtYXx8PPz8/J7pTSQnZ0Eu56BG5dGDAtZm+kjOyAcAFEjl+PdajNrgUFQzbGxMkZSUqeliEFWKsUq6gHFKuoKxSrpC22NVKBSUW3lZaTNja2truLu7IygoCAAQFBQEd3d3ZfJZLDQ0FLNnz8aaNWvg4eFR5cL17dsXv/zyCwAgKioKN27cQLdu3ap8PFVOIBBwih4iIiIiIqpTqjSa8eLFi7Fz5068+uqr2LlzJ5YsWQIAmDhxIm7cuAEAWLJkCfLy8rBw4UIMGjQIgwYNwq1btwAUJcDdu3fHkSNHsHr1anTv3h13794FALzzzjvIyMhAnz59MHnyZHz22WcwMWGz4erm0cRaZZnJLBERERER6TKBog5MOspmxpXLyi3E+6vPoOSntHJ6F5ib6GusTPWFtjfdICrGWCVdwDglXcFYJV2h7bH6Qs2MqW4wMZTAxcFMZV14VKqGSkNERERERPRimMzWIx5NLFWWOd8sERERERHpKiaz9Ujp0YvDo1JQB1qZExERERFRPcRkth5p2tAc+npP55ZNzy5AbFK2BktERERERET0fJjM1iNikRDuzmxqTEREREREuo/JbD2jNt9sFJNZIiIiIiLSPUxm65nSyeztR2koKJRpqDRERERERETPh8lsPWNnaQhrMwPlcqFUjjsx6RosERERERER0bNjMlvPCAQC9abG7DdLREREREQ6hslsPdS6VDLLQaCIiIiIiEjXMJmth1o2toSgxHJMUhbSs/I1Vh4iIiIiIqJnxWS2HjIxlMDFwUxlXXhUqoZKQ0RERERE9OyYzNZTpfvNsqkxERERERHpEiaz9VTpfrNhUSlQKBQaKg0REREREdGzYTJbT7k6mkFfT6RczsguQExStgZLREREREREVHVMZuspsUgId2dLlXWcooeIiIiIiHQFk9l6TH2+2WQNlYSIiIiIiOjZMJmtx0r3m731KB0FhTINlYaIiIiIiKjqmMzWY7aWhrA201cuS2VyzF57FvvO3EdegVSDJSMiIiIiIqoYk9l6LL9Qhtx81ZrY3AIZDgdH44sdV5jQEhERERGR1mIyW48dCY5GfhnNigulciSl5eJIcLQGSkVERERERFQ5JrP12MmrsZDJy55btlAqx8mrsbVcIiIiIiIioqoRa7oApDlZuYWVbv/xUAR6tXOCs51pLZWKqGx5BVIcCY7GyauxyMothImhBD3bNkRfP2cY6PFSRkRERFTf8A6wHjMxlFSa0J4JjceZ0Hg0dzJHr3ZOaNvCBmIRK/SpduUVSPHFjitISstFoVQOoOhhy+HgaFy+lYQF49sxoSUiIiKqZ5iV1GM92zaERFy1ELgTk47v9odh3sZ/8ee5B0jPLqjh0hE9dSQ4WiWRLcb+3URERET1F5PZeqyvnzNsLAzVElqhUACRUFDmMWlZBdh35gE+XH8Omw6E4V5cOhSKsvvdElWXk1dj1RLZYoVSOU6FsH83ERERUX1TpXZ5Dx48QEBAANLS0mBhYYHAwEC4uLio7LN+/XocOnQIQqEQEokEs2fPRrdu3QAAubm5+PjjjxEWFgaRSISPPvoIPXr0AAAEBATg33//haWlJQCgb9++eO+996rxLVJ5DPTEWDC+HY4ER+NUSCyycgphYiRBD5+G6NPeCbei03HiSgwiHqaqHSuTK3AhLAEXwhLgYm+KXu2c0MHdFhKxSAPvhOq6Svt351S8nYiIiIjqniols4sWLcLo0aMxaNAg7N+/HwsXLsSOHTtU9vHy8sLbb78NQ0NDREZGYuzYsTh79iwMDAywZcsWmJiY4NixY4iKisKYMWNw9OhRGBsbAwAmTZqEsWPHVv+7o0oZ6IkxuJsrBndzVdvWtoUN2rawQeyTbJy8EoN/bz4ucyqfqMeZ2HIwAr+euovubRzRw6chrMwMaqP4VA8kpuVWuk9Vm8sTERERUd1R6R1gcnIywsPD4e/vDwDw9/dHeHg4UlJSVPbr1q0bDA0NAQBubm5QKBRIS0sDABw+fBgjRowAALi4uKB169Y4ffp0db4PqkENGxhj3Ktu+HZaZ4zq1Ry2loZl7peZU4iD5x9i3sbzWL/3Bm5Fp7IJMr2QrNxCrPr1eqX7FUjlOHThYS2UiIiIiIi0RaU1s/Hx8bCzs4NIVNR8VCQSwdbWFvHx8bCysirzmH379sHZ2Rn29vYAgLi4ODRs2FC53cHBAY8fP1Yu//jjj/jll1/QqFEjfPDBB2jatOkLvSmqGUYGEvRp3wi9fJ0Q9iAFJ67EIPRestp+coUCV24l4cqtJDjZGKNnOyd0amUPfT02QaaqK5TKsX7PDTxOyanS/r//fQ+GeiL0aOtUwyUjIiIiIm1Q7XNZXLx4EatXr8bWrVurtP/s2bNhY2MDoVCIffv24d1338Xx48eVyXNVWFubPG9x6TnZ2Zqhp58L4p5k4dC5KBy/+BDZeVK1/WKSsrHjyC388c999OngjNc6N4FDA2MNlFizbGw4T++zUCgUWPnzVdx6lKayvrG9KVIz85GZUwAjAwnyC6SQyp7W/u88dhu2DUzwcrtGtVziuoOxSrqAcUq6grFKukJXY7XSZNbBwQEJCQmQyWQQiUSQyWRITEyEg4OD2r4hISGYO3cuNmzYAFfXp30wHR0dERsbq6zJjY+Ph5+fHwDAzs5Oud/gwYOxbNkyPH78WKUmtzLJyVmQy9mcVRMkAAZ1boxXfRviQlgCTlyJQeyTbLX9snMLse+fe9j/zz14NrVG73ZOaNXECkJB2aMm1yU2NqZISsrUdDF0yv6zD3DqSozKuuZO5vhwpI9K/9hb0alY8et15UjHCgWw8ucQFOQXwqe5Ta2WuS5grJIuYJySrmCskq7Q9lgVCgXlVl5W2mfW2toa7u7uCAoKAgAEBQXB3d1drYlxaGgoZs+ejTVr1sDDw0NlW9++ffHLL78AAKKionDjxg3lSMcJCQnK/c6cOQOhUKiS4JJuMNAT42WfhvjsnQ6YO8oH7VrYoKw8VQEg9F4yVvx6HZ/8EIxjlx8hp4waXaq//r0Zj/1nH6iss7M0xIxhXmoDPbk5W2LakNYqU0nJFQps3BeG8CjVfv1EREREVLcIFFUYoefevXsICAhARkYGzMzMEBgYCFdXV0ycOBEzZ86Ep6cnhg0bhtjYWJVEdPny5XBzc0NOTg4CAgIQEREBoVCIuXPnonfv3gCACRMmIDk5GQKBACYmJpg3bx68vb2f6U2wZlY7Jafn4e9rsfjnWlyFU6vo64nQubU9erV1gmMdbIKs7U+7tEnkw1R8+8s1yEr8nk0MJfhkfDvYWRqVe9zFiAR8vz8MJa8C+hIRPhzpjaYNzWuwxHULY5V0AeOUdAVjlXSFtsdqRTWzVUpmtR2TWe1WKJXhYkQijl+JwcPHFf9Q3Btbonc7J7Rp1gBCYd1ogqztFwhtEZ+cjS93XEFO/tOaerFIiLmjvNHcyaLS409fj8O2w5Eq64z0xfhoTFs0smW/+qpgrJIuYJySrmCskq7Q9litKJmt9gGgiEqTiEXo4umAzq3tcS8uAyevxOBSZKJK7VuxiIepiHiYCmszA/Rs2xDd2jjCxFCigVJTbcrILsDKX6+rJLIA8K6/e5USWQDo3sYRuflS/HLyrnJdTr4U3/5yDR+PaQs7q/JrdomIiIhI97BmljQiPSsf/1yLw6lrsUjPKih3P4lYCL9Wdujdzgm2loY4EhyNk1djkZVbCBNDCXq2bYi+fs4w0NPe5zLa/rRL0woKZfj65xDci8tQWT/sJVf07+TyzOfbd+Y+/jwXpbLO2kwfH49tByszgxcoad3HWCVdwDglXcFYJV2h7bHKZsaktaQyOa7eTsLxKzG4G5Ne4b4SsRByuUKlRlciFsLGwhALxrfT2oRW2y8QmiRXKPDdvpu4fCtJZX03LwdM6NcSgucY7VqhUODn43dwvNRoyHZWRvh4TFuYGeu9UJnrMsYq6QLGKekKxirpCm2P1RcazZioJolFQnRwt8P8se2waEJ7dPVyUBuxtlihVK7WNLlQKkdSWi6OBEfXRnGpmv3x9z21RLaViyXGver2XIksAAgEAozs3RxdPO1V1iek5GDFL9eQk1f+YGREREREpDuYzJLWaGxvirdfc8e307pg+MtNYV3FJqGFUjlOXo2t4dJRdfv7WiwOl3oI0bCBMaYO9oRY9GKXJqFAgAn9WqKdm+pcs9GJWVj1WyjyC2QvdH4iIiIi0jwms6R1TAwl6NexMQKndML0oZ5wb2xZ6TFZuYXYdew2ohO0t4kEPXXjfjJ2/nVbZZ25sR7eH+4FI4PqaS4uEgoxaYAHWjdRnRP7bmw61u0JRaFUXi2vQ0RERESawWSWtJZQKEDbFjaYO8qnSgnOiSsxWPzjJSz+8SJOXImpcG5b0pxHiVnYuO8m5CW66+tJhJj5uhcamBtW62tJxEJMG+qJ5k6qc82GRaXi+z/DIJMzoSUiIiLSVUxmSSf0budUbl/a0qITsrDr2G3MWXcW3+2/iZsPkjlAmJZIzczHqt+uI69EM18BgMkDPNDEwaxGXlNfIsL7r7eBs53qwAFXbydh26FIlaSaiIiIiHQHk1nSCX39nGFjYaiW0AoFQHnjBEllClyMSMSKX67jo+/+xb4z95GUllsLpaWy5BVIsfr360jNzFdZP6JXc/i0sCnnqOphZCDGnBHecLBWnWv23M3H+Pn4HdSBQd2JiIiI6h1OzUM6I69AiiPB0TgVEousnEKYGEnQw6cherVzQmR0Gs6ExiHsfgoqi4SWzhbo5uWItm420JeIarzc2j7ceW2QyxVY+0cort9LVlnfq60TRvdp/twjFz+rlIw8LNt5FckZeSrr/Tu7YGh311opgzZjrJIuYJySrmCskq7Q9ljlPLNUb6Rk5OHczcc4GxqHpLS8Cvc11BfBz90OXb0c0cTBtMYSKm2/QNQ0hUKB/x27gxNXVed9bdPUGjOGeUEorJ1EtlhCag6+2nkV6dkFKuvf6NEMff2ca7Us2qa+xyrpBsYp6QrGKukKbY9VJrNU78gVCtx5lIYzofG4HJmIgkpGrm3YwBhdvRzQycMeZsZ61VoWbb9A1LSjlx5h94k7Kuuc7UwQMKYtDPSqZ+TiZxWTmIXA/11Fdp5UZf2bfd3wkndDjZRJU4pbPJy8Govs3EIYG0rQs21D9PVz1tj3Q1SR+n5NJd3BWCVdoe2xymSW6rXcfCkuRiTgbGg87sVlVLivSChAm2YN0NXLAZ6uVhAJX7xbubZfIGrS1dtJWL/nhkrTbyszfXwyzheWpvoaKxcA3I/LwNe7Q1TmnBUAmDTQA36t7DRXsFqUVyDFFzuuICktV2WqIolYCBsLQywY344JLWmd+nxNJd3CWCVdoe2xymSW6D+xT7JxLjQe/96MR0ZOxVP3mBvrobOnPbp6OsDB2vi5X1PbLxA15UF8BgJ3XVWpFTfQE2H+2HZwsi37glTbIh+mYsWv1yGVPS2jSCjA9KGeaNOsgQZLVjv2nbmPQxeiVd5/MYlYiH5+zhjcjX2JSbvU12sq6R7GKukKbY9VJrNEpUhlcty4l4wzofEIvZdc6fQszZzM0c3TAb4tbWGo/2w1Vdp+gagJT9Jz8cWOK8go0S9VKBBg1nAvtHa11mDJ1F27+wTr99yArMQ1RCIWYvbwNmjZ2FKDJatZaVn5+Oi78yo1sqWZGkmwema3WiwVUeXq4zWVdBNjlXSFtscqk1miCqRn5ePfsMc4GxqP+OScCvfVl4jQvqUtuno5oLmTeZUGjdL2C0R1y8mTYtnOK4h9kq2yfnxfN7yspf1RL4Q/xg9/hqs0h9bXE2HeKJ8am/9WU1Iy8nA4OBr/XIsrs0a2JAGALQE9a6dgRFVU366ppLsYq6QrtD1WK0pm2RmK6j1zE33082uMvh2ccS8uA2dD4xAckajSl7JYfqEMZ2/E4+yNeNhZGqKrlwM6t3bQeP9PbSGVybFh3w21RLafn7PWJrIA0LGVPfIKZNhx5JZyXX6BDCt+uYaPxrSFk412NIt+EU/Sc3H4QjTOhMZBKqvawz8D/ZqfuoqIiIjoebFmlqgM+QUyXL6ViDOh8bj9KK3CfQUCwNPVGt28HNCmWQOIRaqDRmn7067qolAo8OPhSJwNjVdZ79vSFlMGeUBYS3PJvojDwQ/x26l7KuvMjfXw8di2sLU00lCpXkxiWi4OnY/CuRuPVZpSV9W4V1qgR1unGigZ0fOpL9dU0n2MVdIV2h6rbGZM9AISUnJw9kY8zt2IR1pWQYX7mhhK0Lm1Pdq72+LGveR6Nd1J0L9R2HP6vsq6pg3NMHekD/QkulPDt+f0PQT9+1BlXQNzAwSMaQsrMwMNlerZPU7JwcF/o3A+LKHcPuGWpnqQy4uahhdW0OS4dzsnjOzVvNbnBCYqi7bfdBEVY6ySrtD2WGUyS1QN5HIFbj5IwdnQOITceVJpLZcAUOmDWZenO7kQ/hib/gxXWWdjYYBPxvvCzKh65+2taQqFAv87dgcnrsaorHewNsJHY9pq/fuJe5KNoPNRCA5PQHlX9wbmBujfqTG6eDpAKpPjSHA0ToXEIiu3EHpiIfIL1RNbr6bWmDzQ45kHQCOqbtp+00VUjLFKukLbY5XJLFE1y8wpwIWwBJwJjUNMUnblB/ynLk53cvtRGr7ZHaLSD9PYQIz549q90JRGmiRXKLD1YAT+vflYZX1jO1PMHeUDIwPtS+hiErNw4N8oXI5MRHlXQ1sLQ/Tv3BidPOzVmsMDT/+YhdxJwqY/w5FfqNpvvKGNMd5/3QsNzA1r4B0QVY2233QRFWOskq7Q9lhlMktUQxQKBR4mZOJMaDyCwxKQky+t9BhDfRHWzuquE31IK5OQmoMvd1xBVu7TOXtFQgE+HOkNN2fdntZGJpdj474wXL2dpLK+uZM55ozwhr6WNJ1++DgTQf9G4UqpcpZkb2WEAZ1d0KGVLURC9SS2WMk/ZtEJmVj9eyhSM/NV9jEz1sOMYZ5o6mhePW+A6Blp+00XUTHGKukKbY9VJrNEtaCgUIar/9VoVcbW0hC92jmhq6eDzjbbzMotxJc7LiMhNVdl/UT/VujU2l5DpapehVI51vx+HWFRqSrrW7taYeYwrzJrN2vLg/gMHDgXhWt3n5S7T8MGxvDv7IL2LW2r1N+19B+ztKx8rPk9FFGPVf/AiUVCvOvvjg7uds//Boiek7bfdBEVY6ySrtD2WGUyS1SLZq4+o1JTWREDPRG6ejqgVzsn2Fnpzmi5hVIZvtl9DXdi0lXWD+raBIO6NtFQqWpGfoEM3/5yDXdjVd+rr5sNJg/yqLCmsybcjU3HgXNRuHE/udx9nGxMMLCLC9q62TxTC4Cy/pjlF8qwOSgcV26p1/wO7tYEAzq7VGm+ZaLqou03XUTFGKukK7Q9VpnMEtWifWfu43BwNAql5Y8OW5oAgGdTa/T2dYKHi5VWJwdyhQI/HAhHcHiCyvrOre3xTn93rS7788rJK0Tg/0LwKDFLZX1XTwdMeK1lrTQZvxWdigP/RiG8VC1xSY3tTTGwswvaNG/wXGUq74+ZXKHA3tP3cfD8Q7VtHT3s8Fa/lpCItaPZNdV92n7TRVSMsUq6QttjtaJkVjfbNxJpsb5+zrh8KwlJabkqCa1YJIBELEJ+gRSln70oAITeS0bovWQ4WBuht28jdPawh76e9iUI+87cV0tkWzpbYEK/lnUykQUAIwMJPhjhjWW7riIhJUe5/uyNeBjqizGyV7Maee8KhQKRD1Px57ko3KpgvmNXRzMM7OICT1frGimHUCDAsJeawt7KCNsOR6qM5H0hLAFP0vMwfain1o/0TERERHVLlWpmHzx4gICAAKSlpcHCwgKBgYFwcXFR2Wf9+vU4dOgQhEIhJBIJZs+ejW7dugEAcnNz8fHHHyMsLAwikQgfffQRevToUem2qmLNLGmbvAKpynQnJoYS9PApmmc2v0CGv6/F4VRILDKyy5+31khfjO5tHNGzbUM0sNCO0WPPhMbhx0ORKuvsrYzwyfh2MDaQaKhUtSclIw/Ldl5BcobqoEgDu7hU6wjVCoUCYVEp+PNcFO6WaspdUjMncwzq0gStXCyrJYmtypPZW9GpWLfnBrLzVAc7a2BugPeHt0HDBro5gjXpDm2vQSAqxlglXaHtsfrCzYzHjx+PYcOGYdCgQdi/fz/++OMP7NixQ2WfM2fOwNfXF4aGhoiMjMTYsWNx9uxZGBgYYN26dXj8+DG++OILREVFYcyYMTh69CiMjY0r3FZVTGZJm5V3gSiUynE5MhHHLj9SG2CnJIEA8Glug97tnODmbKGx2s/wqBSs/PW6Sq2cqZEEn4z3ha2WJNu1ISElB8t2XVV7EDGyZzO80sH5hc6tUCgQei8Zf56LwoP4jHL3a+lsgQFdmqBlNcdDVf+YJaTmYPVvoXhcopYaKBqp+73BrdG6iXW1lYmoNG2/6SIqxlglXaHtsfpCyWxycjJeffVVBAcHQyQSQSaTwc/PD0ePHoWVlVWZxygUCvj6+uLgwYOwt7dH//798dVXX8HT0xMAMHnyZAwePBj9+vWrcFtVMZklbVbZBUKhUOBebAaOX3mEy5FJkFfwk3SyMUFvXyd0bGUHvVqcGib2STaW/nQFuSWmHpKIhZg3ygdNG9a/KVoeJWYhcNdVtamYJvRrie5tHJ/5fHKFAtfuPMGBc1F4mFB+rHi4WGJAlyZo0cjimV+jKp7lj1l2XiE27L2JiIeqfXiFAgHG9GmOHm2daqKIRFp/01XcMufk1actc3q2LWqZY6DH3l31ibbHKlExbY/VF+ozGx8fDzs7O4hERTfOIpEItra2iI+PLzeZ3bdvH5ydnWFvXzQ9R1xcHBo2bKjc7uDggMePH1e6jag+EAgEaOZkjmZO5kjpkYdTIbH451pcmSMixyRlYdvhSPz+9z285O2IHj4NYWVmUKPlS8/Kx6pfr6skskDRFDz1MZEFgEa2Jpj9Rht8s/sa8gtlyvXbj0TCUF+M9i1tq3QeuUKBq7eS8Oe5KMQkZZW7n1dTawzo7KJVn7exgQSz32iDnUdv4/T1OOV6uUKBn47eRnxKDkb2bF6lKYGI6oq8Aim+2HEFSam5KJQVjZmQlVuIw8HRuHwrCQvGt2NCS0RUjar9inrx4kWsXr0aW7dure5Tl6u8TJ1IW9jYmFZ5P7emNnhrkCdOX43Bn2fuI6qM5qZZuYU4eP4hDgdHo4uXIwZ0dUXLauo3WVJegRTLdl1Fckaeyvq3/FuhX7em1fpausbGxhSfGulj8eYLkP5306pQAD8cCINtAxP4VjAHq0yuwNlrsfjl+G08qqAm1s/DHiP6tEDzRpbVXv7yVDVWi304zhfNG9/D1gNhKNmo4PjlGKRlF2Lu2HYwqgf9qal2PWuc1padhyOQkJoDmUy1hU2hVI4nabk4feMxxvR111DpSBO0NVaJStPVWK00mXVwcEBCQgJkMpmymXFiYiIcHBzU9g0JCcHcuXOxYcMGuLo+HQzF0dERsbGxyprc+Ph4+Pn5VbqtqtjMmLTZ8zbd8Ha1Qpsmlrj9KA3HLscg5E4SSrdAlssVOHMtFmeuxcLF3hS9fZ3QvqUdJOIXn/tULldg/d4buFNqFN2XvR3R1cNOq5uj1BZHSwO8N8gD6/feVDYPl8oUWLrtIua80QZuzqpJqEwuR3B4AoL+fajW37Skdi1s4N/ZBY3ti/6w1NZn/byx2qWVHYwkQmz6M1ylpvpyRAI+WPUPZr7uhQbm9adfNdUsbWwOp1AoEHLnCX47cVtttPpiBVI5Dp57gFfasQn+89DF5tvaGKtEZdH2WH3hAaDGjRuH119/XTkA1O+//46ffvpJZZ/Q0FDMnDkTq1evRps2bVS2rV27FgkJCcpBnkaPHo2jR4/CxMSkwm1VxWSWtFl1XSCepOXi5NVYnL4ep9ZXsyQzYz28/F8TZHMT/ed+vd0n7uDopUcq61q7WuH9170gEr54slyXnA97jM0HwlHyKiQSCqAvESEnXwoTQwmaOZoh5kk2nqTnlXkOAYD27rbw7+QCJ1vNtDZ50ViNTsjE6t9DkZqpOtqzmbEeZgzzRFNH7WkmTbpLm266FAoFbtxPwd4z9/GwgoH8Stoa0LOGS1X3KJtvl5ryTiIWwsbCUGubb2tTrFLt0MWHLoD2x+oLJ7P37t1DQEAAMjIyYGZmhsDAQLi6umLixImYOXMmPD09MWzYMMTGxsLO7mnTuuXLl8PNzQ05OTkICAhAREQEhEIh5s6di969ewNAhduqisksabPqvkDkF8jwb9hjHL/8CPHJ5dfuiYQCdHC3RW/fRmjiYPZMr3HiSgx2Hbutss7Jxhgfj20HQ33tvRhr0smrMdh59HblO5YiEAAdW9mhfycXOGp4WpvqiNW0rHys+T1UbYRuiViId/q7o0MFza+JqkJbbroiolKw58x93Istf+TxskwZ5MHfwTPad+Y+DgdHqySyxcQiAV5p74zXX9a+ri/aEqtU86QyOTKzC7B89zUkp+dCWqKrgVgkgJWpAaYOaQ0jfTFEIiFEIgHEQkHRv4UCiISCWp+tomTinZ1bCGMtTrxfOJnVdkxmSZvV1B8zhUKB8KhUHLv8CKH3kivct2lDM/TxbYS2LWwgFlVcq3r97hOs+SNUpUmzhYkeFoz3rfHBpnTdwfNR+OOf+1XaVygQoFNrO/h3coGdlVENl6xqqitW8wtl2BwUjiu3ktS2De7WBAM6u2hsiinSfZpOEG4/SsO+M/cRGZ32XMcLAIzs1Rx92jeq1nLVZTNXnylzUMSSzI31YGNhCBsLg//+//Q/cxM9CDVwzdF0rNYl1VHjKZcrkF8oK/qvQPb034Uy5BfIkV8oRX6hHPkFMhT8tz6vUIYC5b5ytWMLCmXIK5CpTFv4vERCAcT/JbdiUYlEVyQsWhYKIBI+/ffTfYuSY5HwvyS55DnK2F8kEkKuUODwhYfIzClUKbu2tnZgMkukQbXxxywhJQcnrsTg7I145BXIyt3P0lQfPXwa4iVvR5ga6altf/g4E1/tuqrS71FfIkLAmLbK/ptUscnf/F1m7UFJ3ds44rVOjbVuft7qjFW5QoG9p+/j4PmHats6edhhQj/3aunbTfWPphKE+3EZ2HfmPm4+SCl3H+9mDRD3JBupWfmVXgf6+hXVJmoiydIlmTkFeH/N2Rc6h1gkfJrkmqsmvA0sDGrspp3JbPVIzyrAV7uuIDkjT6XGUygUwEhfjHYtbCArJ1EtTjbzC+XKwRqpYhKxEP38nDG4m2vlO9cSJrNEGlSbf8xy86U4eyMeJ67EIDE1t9z9xCIhOnrYoXsbR9y8n6x80ikAVPp9CgTAzGFeaNOsQY2Xva54+6uTFW4XANiipX3maiJWz92Ix7bDkWpPrZs5mWP6UE+YlfFQhagitZ0gRCdkYt+ZB7h290m5+7Rpao3B3VzR2N5UWYN0KiQWWTmFMDGSoHlDc1y7+0RtcKiOHnZ4+zX3SlvM1FfRCZlY+8cNtRH1q5uZkaREcluU7Nr+t2xhov/cU4wxma1YoVSO9Kx8pGUVIC0rH6lZ+UjLykdaZtFy8X+5+eU/pKeaYWokweqZ3TRdDCUms0QapIk/ZnKFAjfuJeP4lRiEVVCLAEAtgS1pTJ8W6MWRN59JZc3htO0PREk1Fau3olOxbs8NZOepDlzWwNwA7w9vg4Ya7itMuqW2rqmxT7Kx/8x9XC6juXwxDxdLDO7mWqU5oG8+SMb6vTeRX6r1jIeLJaYO8eR4BKVcjEjA1oMRKKikhrumiUUCWJeszTU3VGnOXPp706V+iDVFJpcjI7vwaUKamY/UrBIJ6n/JamVNx3VJRfdSyn0EgJWpPqQyBWRyBaQyufL/2paNaduDdyazRBqk6SezsU+ycfJKDM7djEdBYdVvClwdzbBgvG8NlqxuqmigEm1sulNSTcZqQmoOVv8WqjYlkaG+CFMHe8KjiVWNvC7VPTV9TU1IycH+cw8QHJZQ7s1pi0YWGNKtidr0W5V5+DgTK3+9howc1Zv4xnammDXc64VGoK8r5HIF/jh9D4cvRKttK50wFPfvCxjjg5x8GZLSckv8l4ektFw8SctVe5BWnUwMJcrk1tJUH8HhCcjMLVSZa1hb+yE+az9UuUKBrNxCpGU+rU1NK65ZzXxas5qRXaB1yVkxfYkI+hIh9CQiGOiJoC8RQU9S9H/9/5aL/i1UbjP4b5tyP+W+QuWyRCzE/rMPnvvvv1yhgEz2NMGVlUh0i/6vgEwuL/q/TA6pvGh/mVxedNx//1ceU2Ld03M+XXcmNE6lyXZp2vbgnckskQZpOpktlp1XiDPX43Hyaky508OUZGoower3tedCpit0dQoJoOZjNTuvEBv23kTEw1SV9UKBAGP6NEePtmwFQJWrqTh9kpaLP89F4d+bj5XzRpfW1NEMg7u7olVjy+cexCwxLRcrfrmm1hWkgbkBPhjhrTUDwmlCTl4hvv8zHDfuqw9q2L2NA8yM9PDP9Thl8+0ePlWr8czOK8ST/5Lb0glvckZetQzeUxX6EiFMDPUgFj0dtEcsEipHtS05cI/q9hL/Fj0dEEgsLLUsEioH/Clr36fnEEIqk2PVb9fxJC0PhSX6koqEApgaSfCStyOycqVqtam19Vk9C32JCKN6N4fef8mlgUQEvZKJaXEyKhbW6OCDuvT3X9cevDOZJdIgbUlmi8nlCoTceYL1e29UuJ+2NTHRJWX1mavqTZcm1UasSmVy7Dx6G6evx6lt6+3rhJE9mz93/zSqH6o7TlMy8hB0/iHOXI8r90bd2c4EQ7q5wqupdbXcDGfkFGD1b9fxIF71fZgYSjBreBu4Oj7bdGp1QeyTbKz7IxQJpZJ8sUiAsa+4oXsbxxp5XZlcjtSM/KLkNr10wptXp5rCahuBoGgecgsTfVia6MPCpOjfFqb6Rf830YOFqT5OXI7BkYu6kXjpyt9/XUq8ASazRBqlbclsMV3u20k1o7ZiVaFQ4OilR/j15F21ZpxeTa0xeaAH+w9SuaorTtOz8nHwwkP8HRJX7iinDW2MMbirK9q2aFDtNTp5BVJs3BemVgupJxFi6mBPeDW1rtbX02Yht5PwQ1C42mj85iZ6mDbEE82q0Ce5puTkSfEkXbXpcvF/T9Jrr1ZX15gYSv5LTP9LUE30YVkqWTUzlkAkrHzwM11LvHSFSuL9XzNzbUy8ASazRBqlrcmsrjUxoZpX27EacicJm/4MV5kKCgCcbIwx83UvNDDXrqmLSDu8aJxm5hTgcHA0Tl6JKXdwIXsrIwzq2gTt3W1rdOocqUyO7Ucice7GY5X1QoEAE/q1RFcvhxp7bW0gVygQdC4K+84+UNvm6miGaUM8YWmqvf2I5XIFUjPzlcntrmO3NT5gVU0z1Bcpk9OSyaqlydPaVHMT/Wqfek1Xajx1lbbeqxZjMkukQdp6geCTTipNE7EanZCJ1b+HIjUzX2W9mbEeZgzzRFNHzdXIkHZ63jjNySvEkYuPcOzyI7URhYs1MDfAoK5N0NHDrko1RtVBoVBg75n7CPpXfU7mId1d4d+pcY3289OU3HwpNgeFI+SO+pRHXT0dMO7VFpCIRRoo2fOr6CGxWCREj7YN0aedE6QlB+mRFc1/Kv1v0B+pTHVb8YA+JQcAUjlWea4S20oeU7y+5OBBMjnSsgoqfC8SsRBDurnCwlRPmaiam+jxvqCO0tZ71WJMZok0SJsvEHzSSSVpKlbTsvKx5vdQRD1WfW2JWIh3+rujg7tdrZeJtNezxmluvhTHLz/CkYuPkJtf9qi2Vmb6GNDZBV08HTQ25+vJqzHYdfS2WtP7Hm0bYkzvFnWqL3lCag7W/nEDcU+yVdYLBQKM6t0cPds21MkEXpceErN1FpWkzfeqAJNZIo3S9gsEUTFNxmp+oQybg8JxpYw5PQd3a4IBnV108uaWql9V4zS/QIaTV2NwODi63PEBzI314N/ZBd3bOFZ7s8jnceVWIr7/M1ytD2+7FjaYOKAV9CS6VVNZlpv3k/Hd/jDklHqwYGIowdTBrdGy8bNNd6RtdKUfoi4l3lTztP1elckskQZp+wWCqJimY1WuUGDv6fs4eF69uWUnDztM6OeuFQkHaVZlcVooleHvkDgcvPAQGdllN6U0MZTgtY6N0aNtQ+hrWYJ4+1Ea1vweqpbsNXcyx8zXvWBsINFQyV6MQqHAkYvR+P3ve2pzkDrbmWD6UM86109e09fUyrB1FhXT9lhlMkukQdp+gSAqpi2xeu5GPLYdjlQbJdTSVB8FhTJk50lhYihBz7a86aqPyotTqUyOM6HxCPo3Sq0PdjFjAzH6+jmjVzsnrY6b2KQsrPj1utr7cGxgjDlvtIGVmYGGSvZ88gtl2HY4EsHhCWrb/FrZYUK/llr3UKE6aMs1lagy2h6rTGaJNEjbLxBExbQpVm9Fp2LdnhvIziu7jyPA5nD1Vek4lcnl+PfGYxz4NwpP0vPKPMZAT4RX2jfCK+2dYWSgG7GSkpGHlb9eR2ypfqWWpvqY/UYbONmUfWOnbZ6k52LdnhuITshSWS8QAK+/3BR9OzjX2S4E2nRNJaqItscqk1kiDdL2CwRRMW2L1YTUHKz+LRSPU3LK3YcDldQPxc0hT16NRXZuIYz/64dobW6AwxceIiE1t8zj9CRC9G7XCH39nGFiqHvNc7PzCrH291DcjklXWW+oL8bMYZ5wc9bu/qWRD1OxYd9NtT7LRvpiTBnkgdaudXsuXW27phKVR9tjlckskQZp+wWCqJg2xmp2XiFmrTmr1uS4JFMjCVbP7FaLpaLaVN5ANRWRiIXo4dMQr3VsDDNjvRouYc0qlMqw6YD64GhikRCTBrSCb0tbDZWsfAqFAievxuLn43cgL3Wb6djAGDOGecLO0khDpas92nhNJSqLtsdqRcmsbrS1ISKiesnYQFJhIgsAmTmFkCsUENbRpor13ZHg6ConsiKhAC95O6J/JxdYmurXQulqnkQswnuDWuN/x2/j5NVY5XqpTI6N+25idJ8W6NXOSYMlVFUoleOno7dwNjRebZtP8wZ4178VDPV5+0lE1YNXEyIi0momhpJyp1Yp9tXOqxjf101n+hFS1Uhlchy99KjSRFYoEKCrlz38O7vUuRFxgaJaiTF9WsDSVB9//HNfuV4BYNex20jLysfQ7q4a73uampmP9Xtv4H5chtq2wV2bwL+LCx86EVG1YjJLRERarWfbhjgcHF1hQnM3Nh1LfryEVzs4Y0AXlzo5Mmp9IZPLERmdhovhCbh6Owl5BbJKj1k6yQ+2dbzZqkAgQP9OLjA31se2w5EqzXcPnn+ItMx8vNmvJcQizUxfdS82Hev23kB6lup0SPp6IkzybwWfFjYaKRcR1W1MZomISKv19XPG5VtJlTY1lckVOHThIS5GJGDcq27wrOODy9QlcoUCd2PScTEiAZcjE5GRU3FNfEmmRpI6n8iW1NXLAWbGetiw7wYKCp/+Hs7dfIz0nAJMHdy61kf3Pn09DjuP3oJUptolwM7SENOHeaFhA+NaLQ8R1R8cAIqohml7p3qiYtocq8Wj2Z4KiUVWTiFMjCTwa2WHhJRc3LifXOYxHdxtMbJXc1iY1I2+k3WNQqFA1ONMBIcn4FJkYrlzw1akPo9m/SA+A6t+u47MUom/i70pZg1vUysDX0llcuw+cUelL2+x1q5WmDLQA0YGujeKdHXR5msqUUnaHqsczZhIg7T9AkFUTBdjVaFQ4OrtJ/jf8dtlJkOG+iK8/lJTvOTTkH31tIBCoUBsUjaCIxJwMSIBSWllzwtbkrGBGAoABYUylZo/zjNcNH3Vil+uqX2OthaGmDOiTY3WWGdkF2DDvpu4/ShNbVu/js4Y1r0phML6/ZvTxWsq1U/aHqtMZok0SNsvEETFdDlWc/Ol2HvmPk5ciUFZf9VcHc0w/lU3ONuZ1n7hCI9TcnAxIgEXIxIR9yS70v0N9UVo28IGfu52aNnYElKZ/GnNfG4hTP6bZ7avn3O9TWSLpWcXYNWv1/EwQfW3a2okwazhbdDEwazaX/Ph40ys3ROKlAzVB0h6YiHe7u+ODu521f6aukiXr6lUv2h7rDKZJdIgbb9AEBWrC7Ea9TgD2w/fUruxB4pGvH2lfSMM6toE+nocIKqmPUnPxaWIRARHJCA6IavS/fUkQng3awA/dzu0drWCRFz2d1QX4rS65eZLsWHfTYQ9SFFZry8RYdqQ1mhdjf3HL4Q9xo+HI9X6r1ubGWDGME8+MCqBsUq6QttjlckskQZp+wWCqFhdiVWZXI6TV2Kx58x95JcxEq61mQHGvNIC3s0aaKB0dVtaVj4uRSbiYkQC7sWqT89SmlgkhFdTa3Rwt0Wbpg2q9JChrsRpdZPK5PjxUATOhyWorBcJBZjQryW6eDq80PnlcgV+//sejlyMVtvW0tkCUwa3hplRzffT1SWMVdIV2h6rFSWz9bttDhER1TkioRB92jdCOzcb/O/4HVy9naSyPTkjD2t+D0U7NxuM7l00dyc9v8ycAly5lYSLEQm4FZ2Gyh4ti4QCtHKxQgd3W/g0t4GRAW9FqoNYJMQ7/q1gYaKPw8FPE06ZXIEtByOQnl2Afn7OzzUXbVZuIb7/M0yt5hcAevs64Y0ezTQ2JRAR1W/8C0JERHWSlZkBpg/1RMidJOw6dlutf9+VW0kIe5CCod1d0bOtU70frOZZ5ORJEXInCcERCQh/kKoy52lZBABaNrZEB3dbtHOzhYlh/R3htiYJBQIM79EMFqb62H38jsqDhd//vofUzHyM6tX8mWI9JikL6/64gcS0XJX1YpEQ4191Q1evF6vxJSJ6EVVKZh88eICAgACkpaXBwsICgYGBcHFxUdnn7NmzWLFiBW7fvo1x48bho48+Um5LSkrCwoULERMTA6lUiilTpmDQoEEAgLVr1+J///sfbG1tAQBt27bFokWLquntERFRfefT3AbujS2x78wDHL8co5J45RXI8L/jd/Dvzcd4s29LNLZnf7/y5BfIcP3eEwSHJ+DG/WS1OUXL0qyhOTq428K3pS2nSKpFfXwbwdxYD5uDwlW+pxNXYpCelY+JA1qV2ye5pCu3krA5KBz5harN9S1M9DB9qBdcHat/cCkiomdRpT6z48ePx7BhwzBo0CDs378ff/zxB3bs2KGyz8OHD5GTk4MjR46goKBAJZn94IMP4OrqimnTpiElJQVDhw7Fzz//DAcHB6xduxY5OTkq+z8r9pklbabt/RCIitWHWI1OyMT2I7fwIF69P6dAAPRu1wiDuzWBoT4bLgFAoVSGG/dTcDEiAdfuPkFBobzSYxrbm6KDuy3at7RFA3PDai9TfYjT6hL5MBVr94QiN181GXVrZIEZwzzLnQNWrlDgz7MP8Oe5KLVtTRuaYdoQTz6cqALGKukKbY/VF+ozm5ycjPDwcPz4448AAH9/f3z++edISUmBlZWVcr/GjRsDAI4fP46CggKVc0RGRuLNN98EAFhZWaFly5Y4fPgw3n777ed7R0RERM/B2c4Un4xrh1Mhsfjjn3vIKzFAlEIBHLv8CJdvJWJsnxbwaWGjwZLWjrwCKY4ER+Pk1adT3rzs7YjG9qa4ducJrt5JUkuEyuLYwBh+7rbo4G4HO6uam9uUnk3LxpYIGNMOK3+9hrSsp/dmtx6lYdmuq5jzhrdan/HcfCl+OBCOa3efqJ2vextHjOnTAhIx+8cSkXaoNJmNj4+HnZ0dRKKi5igikQi2traIj49XSWYr4uHhgUOHDsHT0xMxMTEICQmBk5OTcvvBgwdx9uxZ2NjYYMaMGfDx8XmmN1Fepk6kLWxs2HSRdEN9idWRfc3Qp5MLfth/E+eux6lsS83Mx9o9N+DnYY/JQ7xgY1n9tYvaIDdfisWrT+NxcjYK/ptmJSu3EEHnH1bpeAdrY3TzaYju3g3RuAbmMq1IfYnT6mBjY4pv3zfHoh/OIybx6RRJsUnZWLbrKpZM7Ahne7P/1mVh2a6rKvsBRYN2TRriiX6dXJ5rAKn6jLFKukJXY7VW2lEFBARg6dKlGDRoEBwdHdGpUydlcjxy5EhMmTIFEokE586dw9SpU3Ho0CFYWlpW+fxsZkzaTNubbhAVq4+x+k6/lmjfogF++us2kjPyVLYFhz3GtdtJGNKtCXr5OkEkrDu1UXK5Aj8dvYXYpCzInuHvp6WpPvzc7dChlS0a25kqE5vajJv6GKcvSgBg3igfrPk9FHdj05Xrn6TlYtaKvyEWC8utgTc1kmDq4NZwc7bEkyeVzxdMTzFWSVdoe6y+UDNjBwcHJCQkQCaTQSQSQSaTITExEQ4OVR+9zsrKCt98841yeeLEiWjWrBkAwMbmaTOuLl26wMHBAXfu3EGHDh2qfH4iIqLn5dW0Ab541xJ/nnuAvy4+UhkgKr9Qht0n7+LfsKIBoprUcg1kdcnNl+JBfAbuxqTjTmw67selV6n5MACYGUnQvmVRAtu0oTmErJnTSSaGEnw40hvf/xmGkDtPmxAXyhQolJUdC43tTDFjmCeszAxqq5hERM+k0mTW2toa7u7uCAoKwqBBgxAUFAR3d/cqNzEGgNTUVJiamkIsFuP8+fO4ffs21qxZAwBISEiAnZ0dACAiIgKxsbFo0qTJc74dIiKiZ6evJ8LwHs3Q0cMeO45E4l6c6gBR0QlZ+GL7ZfRs64ShL7lq/QBRyel5uBub/l/ymoZHiVmofLhHdR+O9Iabs0WdqpWuz/QkIkwd0hq7jt7G39fiKtzXqYExPh7bFnqSykc9JiLSlCr9NV68eDECAgKwYcMGmJmZITAwEEBRDevMmTPh6emJy5cvY86cOcjKyoJCocDBgwfx5Zdfolu3bggNDcWXX34JoVAIS0tLfPfddzA0LOqDtGLFCoSFhUEoFEIikWD58uUqtbVERES1pZGtCT4e1w6nr8Xht7/vITdfqtymAHDiagyu3E7E6N4t0M7NRiv6D8rkcsQkZuNOTBruxqbjTkw6UjPzKz+wEqZGErRyqfqDa9INIqEQ4151w783Hyv7SpclPbuAiSwRab0qTc2j7dhnlrSZtvdDICrGWFWVnpWP3SfvIjg8ocztXk2tMbZPCzSwqN0BonLypLgfV5S03o1Nx/24DLV5QKtCIhZCKpOXWWMrEQvRz88Zg7u5VkOJqxfjtHq8/dXJCrcLAGwJ6Fk7hamjGKukK7Q9Vl+ozywREVF9ZG6ij8kDPdCltT1+OnoLSWmqA0SF3kvGguhgDOraBH18G0Esqv6muAqFAk/S85R9Xe/GpCE2KRvP8/jW3soIzZzM0byhOZo5mcPCRA9f/nQVSWm5KCxRQycRC2FjYYi+fs7V90ZI65gYSpCVW1j+dqOy56AlItImTGaJiIgq0Nr1/+3de1CU5QLH8d+yFxEvLGDAygreytE6pW4jdbLxSI6QCTmdmWyayCYzZxoy//APZCxmsiYpaiqlzKZ/dBo72ZBxdDSapJo6oni6DWcc0iURBSXAS4vKbZ/zR7qTx6Ngsuy+9v38xz7vwvM6v+H1x/Ps+yZp9eJM/fNfh7Rzz+GL7v7b1R3Uliq/dtce16KcSZqQFn9NP6unN6jDxwM6eOTkb+X16CmdCnT1/cb/4bDHaJxnxPny6taEtJEaEee65LhVj/q0c89hVX13VIEz3Roe59TsaWnKyUxXrIv/IlzPsqanaceewxf9IeMCpyNGs6elRWBWAHB12GYMhFm0b90ALiCrfTv6S0AbP63TgSOnLhmzSfrbtDT9fdZ4xcX2b1UrcLZb/vOl9cCRUzrUfPqKn2O8nJFxTk30ujUxLV43euOVnjJCTsf1edMmcjowznX16IWN/77syvyqR338QeMakVVYRbRn9UrbjCmzQJhF+y8I4AKy2j9BY/T1j83aUnVQHed6LhkfGefUjd541TWeUuBst4YPdSprepqyZ4zR6Y7u8591PamDR0+rqbXjD80hbdQwTfTGa+L5LcPJ7qFRcTOqwUBOB865rh5W5sOIrMIqoj2rlFkggqL9FwRwAVm9Oqc7uvSPXQe0+z///wZRv2ez/bZy+0cuVS5HjMaPHhkqrxPS4jWsnyu/1yNyCqsgq7CKaM8qN4ACAGCAjRzm0pLcm/XXv3i06dM6tZw4e9ljjVG/b9rkHu7SRK87dKOmMcnDw3JzKQAArI4yCwDANbh5bKJWL56h7bsbVPHNoat6r02SN3l4aNX1xrR4JcXH/mm2DAMAcC0oswAAXCOnw64Fd4/vV5mdMjYh9FnXCaPjNXQIl2IAAP4IrqAAAAyQPp/dOdSpFQ9NG8QZAQBw/eJDOAAADJCs6WmXfSSO0xGjrOk8uxMAgIFCmQUAYIDkZKbrBvfQSwrthWd35mSmR2hmAABcfyizAAAMkFiXQ6se9enezHSNiHPKJmlEnFP3ZqZr1aM+nt0JAMAA4qoKAMAAinU5tODu8Vpw9/hITwUAgOsaK7MAAAAAAMuhzAIAAAAALIcyCwAAAACwHMosAAAAAMByKLMAAAAAAMu5Lu5mHBNji/QUgCsio7AKsgorIKewCrIKq4jmrF5pbjZjjBnEuQAAAAAAcM3YZgwAAAAAsBzKLAAAAADAciizAAAAAADLocwCAAAAACyHMgsAAAAAsBzKLAAAAADAciizAAAAAADLocwCAAAAACyHMgsAAAAAsBzKLHAFJ06c0JIlS5Sdna3c3FwVFBSovb1dkvT9998rLy9P2dnZevzxx9XW1hZ6XzjGgP5at26dJk2apJ9++kkSWUX06ezsVHFxsebOnavc3Fw9++yzkqSff/5ZCxcuVHZ2thYuXKhDhw6F3hOOMaAvVVVVWrBgge6//37l5eWpsrJSEllFZJWUlCgrK+uia700+LmMiswaAJd14sQJU11dHfp6zZo1ZuXKlaa3t9fMmTPH1NTUGGOMKSsrM4WFhcYYE5YxoL9qa2vN4sWLzezZs01dXR1ZRVRavXq1efHFF00wGDTGGPPLL78YY4zJz883W7duNcYYs3XrVpOfnx96TzjGgCsJBoPm9ttvN3V1dcYYY/bv32+mTp1qent7ySoiqqamxjQ1NYWu9RcMdi6jIbOUWeAq7Ny50yxatMj88MMP5r777gu93tbWZqZOnWqMMWEZA/qjs7PTPPjgg6axsTF0gSOriDaBQMD4fD4TCAQuer21tdX4fD7T09NjjDGmp6fH+Hw+09bWFpYxoC/BYNDMmDHD7Nu3zxhjzN69e83cuXPJKqLG78vsYOcyWjLrGPy1YMCagsGgNm/erKysLDU3N2v06NGhscTERAWDQZ08eTIsY263e1DOEdb2xhtvKC8vT16vN/QaWUW0aWxslNvt1rp167Rnzx4NGzZMzzzzjGJjY5WSkiK73S5JstvtSk5OVnNzs4wxAz6WmJgYmX8AWIbNZtPrr7+up556SnFxcero6NCGDRvU3NxMVhF1BjuX0ZJZPjML9NPq1asVFxenRx55JNJTAS7x3Xffqba2Vg8//HCkpwJcUW9vrxobGzVlyhSVl5drxYoVevrpp3XmzJlITw24SE9Pj9555x299dZbqqqq0ttvv63ly5eTVSCKsDIL9ENJSYkaGhq0fv16xcTEyOPxqKmpKTTe3t6umJgYud3usIwBfampqZHf79c999wjSTp27JgWL16s/Px8soqo4vF45HA4NH/+fEnSbbfdpoSEBMXGxur48ePq7e2V3W5Xb2+vWlpa5PF4ZIwZ8DGgL/v371dLS4t8Pp8kyefzaejQoRoyZAhZRdTxeDyDmstoySwrs0AfXnvtNdXW1qqsrEwul0uSdMstt+jcuXPat2+fJOmDDz5QTk5O2MaAvjz55JP6+uuvtWvXLu3atUupqal677339MQTT5BVRJXExERlZmbqm2++kfTb3TDb2to0duxYTZ48Wdu2bZMkbdu2TZMnT1ZiYqKSkpIGfAzoS2pqqo4dO6b6+npJkt/vV1tbmzIyMsgqok44smeFzNqMMWZQfyJgIQcOHND8+fM1duxYxcbGSpK8Xq/Kysr07bffqri4WJ2dnUpLS9Mrr7yiUaNGSVJYxoCrkZWVpfXr1+umm24iq4g6jY2NKioq0smTJ+VwOLR8+XLNmjVLfr9fhYWFOn36tEaOHKmSkhKNHz9eksIyBvSloqJC7777rmw2myRp2bJlmjNnDllFRL3wwguqrKxUa2urEhIS5Ha7tX379kHPZTRkljILAAAAALActhkDAAAAACyHMgsAAAAAsBzKLAAAAADAciizAAAAAADLocwCAAAAACyHMgsAAAAAsBzKLAAAUWjt2rVasWJFpKcBAEDUoswCAAAAACzHZowxkZ4EAAB/Zhs2bNCmTZsUCASUnJyslStXqqCgQMYYuVwujRkzRhUVFfr111/10ksv6auvvpLNZtMDDzygZcuWyW63q7y8XB9++KGmTJmiTz75RDfccIOKi4t15513Rvr0AAAIC0ekJwAAwJ9ZfX293n//fX300UdKSUnRkSNHFAwGtXTpUjU0NKi0tDR0bGFhoZKSklRZWamzZ89q6dKl8ng8euihhyRJP/74o3JyclRdXa3PPvtMBQUF+vzzz+V2uyN0dgAAhA/bjAEAiCC73a6uri75/X51d3fL6/UqPT39kuNaW1v15ZdfqqioSHFxcUpKStJjjz2m7du3h45JTEzUokWL5HQ6NW/ePI0bN05ffPHFIJ4NAACDh5VZAAAiKCMjQ0VFRVq7dq0OHjyomTNnqrCw8JLjmpqa1NPTo5kzZ4ZeCwaD8ng8oa9TUlJks9lCX48ePVotLS3hPQEAACKEMgsAQITl5uYqNzdXgUBAzz33nEpLS5WRkXHRMampqXK5XKqurpbD8f8v38ePH5cxJlRom5ublZWVFfb5AwAQCWwzBgAggurr67V79251dXXJ5XJpyJAhiomJUVJSko4ePapgMChJSk5O1l133aU1a9YoEAgoGAzq8OHD2rt3b+h7tbe3a+PGjeru7taOHTvk9/s1a9asSJ0aAABhxcosAAAR1NXVpVdffVV+v19Op1PTpk3T888/L5fLpYqKCmVmZsrr9erjjz/Wyy+/rNLSUs2bN08dHR0aM2aMlixZEvpet956qxoaGnTHHXdo1KhRevPNN5WQkBDBswMAIHx4NA8AANeB8vJybdmyRZs3b470VAAAGBRsMwYAAAAAWA5lFgAAAABgOWwzBgAAAABYDiuzAAAAAADLocwCAAAAACyHMgsAAAAAsBzKLAAAAADAciizAAAAAADLocwCAAAAACznv6FXBb3h+WioAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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06YeTIkVi8eDGio6Px0ksvYfbs2bC0tCxwzPj4eNStW1e3XLduXajVaiQmJhYaFwA4ODjoHffZWExMTJCRkYGkpCRkZWVh6NChunWCIOhN1mhnZwcTExMAuXMi/N///R8AYMCAAbpfwE+cOAFzc3O8/vrrxT5mFhYWsLGxQXx8PFxdXQE8vRpL69atMXHiRN225ubmBYaOKBQKvS7gv/32G/z8/ODv769r+/vvv7FgwQIAQLt27XRd7AMCAvDFF18UiK88ypK/6OhoxMfH632x0Wg0esv5HyOxWAxnZ2e9Cfu0Wi0++ugjyGQyzJ8/X9de2GOUkZEBCwsL3XwFCoVCl79nH7/09HT8/vvvWLlypd4X5nfeeQfnz58HACxatKjY+RHi4uJgY2MDIHcoSZcuXXTFkMDAQPz1118YM2ZMkbcHgG+++QbfffcdvvzyS3h7e+P999/Xm3Azv5iYGEydOlWva7tYLNZ7PeQ9t4Dc10v+YQl5+wgNDcWSJUv02lu3bq37e8iQIdi9ezdOnDiB0aNHAwAiIyNx48YNbNu2DXK5vNj7VJKS8lNcbvPs2rULnp6eehOQhoWFYfz48QAANzc33RfK1q1b49dff32umIsSExODHTt2YMuWLbo2lUqlew77+fkhNDQUDx48gJ+fH6ysrHDu3DlcunQJHTp0AFDya6qoc2l8fDzc3Nz04nFzc0NcXFy5709Z8/zhhx/i66+/xquvvgobGxuMHTu2yKu2lPRYAfrPXzc3twKTdz548ADjx4/H3LlzCxRMijsXl0b+831JSnr/sLCw0A2lqlOnDubPn4+uXbtCoVAUeq4kIqLKwSIGUS2X/1fbXbt24ciRI/jxxx/h7u6O9PR0+Pn5lXr88Ztvvok333wTiYmJmDlzJjZs2KAbS52fk5OT3lUJYmJiIJVK4eDgoJu/oCzdhfOzs7ODqakp9uzZA2dn50K3yb/vSZMm6Y1vzzNs2DDdmPUNGzboTfiXf46FjIwMpKam6n5hVSqVmDp1KpydnQsMCWjcuLHeWPGHDx9CpVKhXr16urZFixZh/fr1WLZsGebOnQsAGDhwYLFfuitKUfl7Nheurq5wd3fHwYMHi9xX/scor9t93mMkCAI+/vhjPH78GOvXr4dMJtNt27hxY705LjIzMxEZGYlGjRrBxsYGjo6OuHHjBrp06QIg99fbvKEoAGBtbY0VK1Zg5syZ+Pbbb9GuXTsAKPXcCpcvX0ZcXBzatWuH7Oxs7Nu3D1qtVnc8pVKJtLQ03LhxQ2/YxLNatWqF7777DiqVCj///DNmzpyJEydOFPq8dnFxwbJly3Sx5pc34WlsbKyut0pMTEyBXjI7d+5E27Zt4eHhUez9E4lEeq/nBg0aYOTIkRg/fjw2bdqEBg0aFHv74pSUn+Jym2fatGk4deoU3n//faxcuRISiQTt27fHxYsXyx1Xebi6umLSpEmYPHlyoes7dOiAo0ePIjo6GpMmTYK1tTV27dqFixcv6uaNAEp/TszPyckJMTExem2xsbHo1q1bue9PWfPs6OioK4iFhYVh7Nix8PPz0w2Ny6+kxyov/jzPPn+jo6MxduxYTJkyBYMHDy5w2+LOxaVRlveS0rx/FLbv0r5HEhFRxeBwEqJapE6dOnj48GGR6zMyMiCXy2FnZ4esrCx89dVXpd735cuX8d9//0GlUsHMzAxyubzQSdOA3F+zN23ahIcPHyIjIwMrV65Ev379ynX1kmeJxWIMGzYMy5Yt0/2SHRcXpzdfQ2kFBwejfv36mDRpkt7EbidOnEBYWBiUSiW+/vprtG7dGq6urlCpVJgxYwZMTEywfPnyAvd/wIABOHbsGMLCwpCZmYmvv/4aL774ot4veBYWFtiwYQPCwsIqrJdFaRSXPwcHB71LabZq1QoWFhb4/vvvkZ2dDY1Gg1u3buHy5cu6ba5du4aDBw9CrVZj06ZNkMvlul4BCxYswN27d7Fu3boC3btffPFF3L59GwcOHEBOTg7WrFkDb29v3Rf4wYMH47vvvkNqairu3r2Lbdu2FZjI0t/fH1988QWmT5+uF1NxFAoFjh07hlmzZmHgwIHw9vbG4cOHIZFIsGfPHuzYsQM7duzA3r170b59+2In+FQqlfj777+Rnp4OmUwGCwsLvccyJSVFb4jWG2+8gVWrVukKe0lJSTh8+LDePteuXYusrCzcvn0b27dvR//+/fXW79ixo8DjkJaWhlOnTiEnJwdqtRp///03wsLCCnwZzptjZuzYsXpDTYqSk5Ojm+9AqVTqJmkEis9PSbkFciep/frrr5GVlYWPPvqo0MvdVoVhw4bht99+w3///QdBEJCZmYnjx4/repL4+fkhJCQE2dnZcHFxQfv27XHq1CmkpKSgWbNmAMp2TsyvR48eiIiIwK5du6BWq7F3717cuXMHL7zwAoDCz+MqlQo5OTkQBAFqtRo5OTnQaDR625Qlz/v27dMVIm1sbCASiXSxP3v8kh4rAPjll1/w6NEjpKSkYN26dbrnb1xcHN566y2MHDkSb7zxRpHxFHUuLq/8z9v8j11J7x///fcf7t27B61Wi+TkZCxZsgQdOnQo9zwyRERUPixiENUiEyZMwHfffYf27dsXeqnFwYMHw83NDd26dcPLL7+MNm3alHrfGRkZmDdvHjp06ICePXvC1tYW48aNK3TbV155BQMHDsSoUaPQu3dvyOVyvSEFz+vDDz+El5cXXnvtNbRt2xZjxozRXSGkLEQiET755BO4uLhgypQpug+9gYGBWLNmDfz9/XHt2jWsWLECAHSX6jxz5gz8/Px0V6DImySycePGWLRoET744AN07twZGRkZumEi+VlbW+OHH37AyZMnsWrVqvI/EGVQXP5effVV3LlzB+3bt8eUKVMgkUiwbt063LhxA71790bHjh0xb948vS8tvXv3xt69e+Hn54edO3di9erVkMlkiI6OxtatW3H9+nV07dpV9xj9/fffAAB7e3usXr0aK1euhJ+fHy5fvqxXTJsxYwY8PDzQs2dPjB49GuPGjUP37t0L3J8uXbpg2bJlmDRpku7KJ4XJu1pPjx49sG7dOowdOxaffvopgNw5JoYOHQo3Nzc4Ojrq/h85cqTuC2ZRdu7ciV69eqFt27b47bffdM+Rhg0b4uWXX0afPn3Qvn17xMXF4c0330SvXr3w9ttvw9fXF6+99lqB4kuHDh3w4osvYsyYMXj77bfRtWtX3bqLFy8iLi6uwKVV1Wo1Vq1ahY4dO6Jjx47YsmUL1qxZg/r16xeId8iQIZg6dSreeustvYJVYVq1aqUbGtOvXz+0atVKt664/JSU2zxyuRzffvstEhMTMXfuXIMUMlq2bIlPPvkEixcvhp+fH1566SVs375dt75+/fqwsLDQDX2wtLSEu7s72rZtC4lEAqBs58T87OzssG7dOvz444/w9/fHhg0bsG7dOt2QpjfffBMHDhyAn5+frrfE/Pnz0apVK+zevRvr1q1Dq1atsHPnzgL7Lm2er1y5gmHDhsHX1xeTJ0/Gxx9/rOvlM23aNAQFBaF9+/bYu3dviY8VkHvOfPvtt9GnTx94enrqem1s27YNDx8+xLfffqs7FxQ27Kqoc3F5BQQEoFWrVoiLi8O4cePQqlUrXRGxuPePhw8f4p133tFdbUYul5ep2E9ERBVDJLAPHBFRqQUFBcHZ2blcE8zVFqtXr8aDBw+qtCdJTRUVFYXevXvj2rVrFdJTiaiq9erVC0uWLEHnzp0NHQoREdUQ7IlBREREREREREaBP+sQEdUS+a/y8KyqnjixJnj55ZcLTMAIlHz1k+ouODgYu3btKtCe/+o9xij/FWrymzhxYqGT+9Z0xpznmvraIyKi0uFwEiIiIiIiIiIyChxOQkRERERERERGgUUMIiIiIiIiIjIKLGIQERERERERkVEwmok9k5MzoNVy+o7qysHBEomJCkOHQaXAXBkH5sl4MFfGozbnShAEqNVqQ4dRarU5V8aEeTIe1T1XYrEIdnYWhg6DqhGJRAKxuPA+F0ZTxNBqBRYxqjnmx3gwV8aBeTIezJXxqG250mq1OH8+BFFRD4r8MFgdSSRiaDRaQ4dBJWCejIcx5EoiMZ5zFFU+rVYLd3d39O7dG1KpftnCaIoYRERERFQ2//0XBo1GiVGjRkMmkxk6nFKTySRQqTSGDoNKwDwZj+qeK5EIkEolhg6DqhGNRoMTJ07g6NGjeOmll/TWsdxFREREVEPFxcWiU6fORlXAICIikkgk6NatG6KioiAI+r0ojbonhkajRnJyAtRqpaFDqfXi48XQap+/i5pUKoednSMkEqN+ahIREVULarUacrnc0GEQERGVmUwmg0ZTsAeRUX9TTE5OgKmpOSwsXCASiQwdTq0mlYqhVj9fEUMQBGRkpCE5OQF16rhWUGREREQEANHR0YiKegh//44Vts/w8HAEB8/Diy++hIkTJ1XYfksyZsxb2LBhY4Fx0s+aOnUK0tPTIJPJsHTpp3BxccHt27exePEiCIKA+fOD4e3tXez+oqOj8cYbr6NBgwaQSiWYMeNdfPXVV1AqcxAVFY0GDRqgdevWeO+9WcXGEhoaCldXV3h4eJT6fl6+/B+WL18OsViMFi1aYPbsIADADz9sxLFjx+Dm5oolS5ZBJpMVaIuPj8c333yN5cs/L3Tfa9Z8iyNHjsDOzhYymQzTp7+L5s2bAwBiY2MREPASDh06Aicnp1LFun37nxg69JVS3zcA+OyzT3Hjxg0AwK1bN/HPP/9ix46/sGHDetSp44iWLVvi/fc/wI4df0Gt1uDVV18t9vj/93/r0KaNr+4+x8fHIyhoNnJycjBt2jR06tS50LaPP56LiRMnwtPTq9B9d+zYAU2b+gAAOnTogNDQUCQmPoYgCKhTxxHDh7+Ofv366d3mjz/+wO+/b8XUqdPQo0ePMj0u+ZXmvgPAjRvXodUKaNasGeLj4zF16mTcvXsXoaFhkEqlCAkJwenTZzBjxrulPnb+fZZWVFQU5s4NgkgkgrOzCz799DNIJBKMHj0KP/20pcjbhYaGIiTkX7z33nt67X/88QfWrl2Ltm3b4osvvijy9qtXr8ahQ4dgY2ODXr16YezYsaWOuSRvvPEGfv3111Jtq1arMWfOHERFRaFnz56YMGGC3vqFCxdi//79eP/99zFs2DAAueeYxYsXIzMzEwMHDtS1x8fHo0+fPti1axe8vPSfm5999hnGjRsHR0fHMt2XqKgoPHz4EJ06dSrT7YoSFxeHSZMm4c6dO7h48aLeOfR///sfDhw4oHvsli1bhqtXr6JZs2aYN29eoW03btzAqVOnMH78+GKPa9RFDLVayQJGDSISiWBhYQ2FIsXQoRAREdU40dHRCAkJKVDE0Gq15Z708/TpU5g58z107dqt2O2e5xgAdF2Jy/qZb86cuXB3d8c///yDn37ahA8/nI3Vq7/B55+vgFgsxpIli7F69ZoS99OpUycsX/459u3bh7Nnz+J//9uE6OjoYosEzzp3LhRt27YtUxHDzc0NP/zwI0xMTDB79oe4desWHBwccO5cKH76aQs2btyAo0ePoH17vwJtLVq0LHH/H374ITp16oxHjx5h1qyZ2LjxR5iZmeHw4cMYPHgIjh49itdff71Usf71119lLmIEBc0BAFy/Ho7//e9/uvYxY94u8Uv7s3r27IX27dtjzZqn+dywYT2mTZuOpk29MWXKFHTq1LnQtpI0btwE//vfJt3ylClTSywuHDiwHz/9tAUmJiZluh/ldePGDajVGjRr1gw2NjbYsOEHvPvujArbZ2lZWVlhzZrvYGVlha+/XoVTp07ihRd6luv4Wq0WvXr1gp+fH1avXl3i9kFBQejcueR8VqajR4+iQYMGWLFiBSZOnIiEhAS9QsOUKVPQqlUrvd4FK1euxKeffgp7e3u9fW3atAmtW7cucAyFQoHExETdflUqFTZs2IAzZ84AADp27IgJEyYU2gsvOjoa//77b4UVMWxtbfG///0P06ZN02tXKpW4fv26bvnatWvIzMzEL7/8ggULFuDy5cuQSCQF2lq1aoXVq1dDEIRiz/dGXcQAyv5mRtUb80lExiBbqcb+kEgcvRCNjCwVLMxk6NW2LgL8PWEqN/q3VoPJ/7gqslSw5OP63LSCgNSMHDxO12DTT7/g+rXLOH/hIpYuWYL58+fB1tYW3bp1Q2JiIs6cOY2cnBwEBy+Aj08zjBnzFpo3b4awsPN47bXheOWVVzB37hzExERDJBJjwYKF+OOPbbC0tEJmZiasrKywevU3AIDp02egU6fOGDPmLbRs2RLx8fHw9PREVFQUEhLi4eTkDE9PT5w8eQLdunXH5MlTkJSUhODg+cjIyECjRg3x8cfzsWbNt4iNjUFcXDyWL/+8wIf8PXt248qVKwgKmoPBgweiQYMGiIqKwvz5C9CyZUu4u7sDAKRSKcTi3EkD09LS4Oqa2+MzPT29yP0VpmnTpggNDSnVY79q1UqcP38eUqkUy5Ytw86dO3DkyBF06tQR06e/i/nz5yExMRF2dnb47LPl2LNnN44cOYycHCVMTOT46quVqFPn6ZcfqVQGiUSMa9euws+vAwCgY8dO2LNnN8zMzAq05RUxVCoVPv54LoYNew1+fn6Fxuri4oLOnbvg6tWr8PPzw7lzIVi69FPMm/dxoUWM1NQUvPvuuxCLRWjUqDE6deqE27dvYcyYtzBhwkRYWlrgq6++glqtxiuvvIIhQ4ZizJi30KRJY1y9ehVDhgzFsGGv6fZ35MgR9Onzom55y5bN2LVrJyZPnoKOHZ9+2YqLi8PChQuwaNHiAj1E7OzskJmZqdd2+/ZtzJkz98kPZRZQKBSFtuUJDQ3Bn3/+oevdUl4HDx7A1atXMHHieCxe/AlCQ8/h7793AACCguYiISEBUVEPMXToK+jcuSOOH8+dvNDMzAzJycnYtetvmJiY4sMPPwKQWyw8cuQQAGDt2nVQKBSYPfsjZGQo4O3dFHPnfoxt27YhNTUF586FYPnyFUUWTxQKBYKCZmPmzJlo1Kix3rpff/1F79j59/nZZ5/jk08WIyIiAiYmJvjss+W4efMm1q//HhKJGEqlCitXroSNja1uf7mvu6fFS0EQ8Omny9CmjS/69+9f5OM3cOBANGnSBE2aNMGECRMK5PXBgwdYtGgRlEolOnfujClTpgAAvvjiC1hbW2P27Nnw8fHRu01gYKDu/LBw4UK0atUKx44dw4YNG6DRaDBlyhR0794dS5YswY0bN6DVavHFF1/Azc1Nt4/169dDLBZj3LhxRcZ+6dIlBAQEAAD8/f1x+fJl9O7dW7f+2eetSqVCTEwMgoODkZWVhXnz5qF+/fpISkqCQqFA3bp1Cxzj7NmzaNOmjW55/vz5GDBgACZPngytVou///4bH3/8MVasWIFDhw7h//7v/2Bubo5x48bh77//xoULF3Dp0iVs2rQJ3377LUJCQiASibBs2TIAwAcffABbW1skJSXhyy+/LLbwamJiUuhzbdu2bRg8eDC++eYb3eOSV2Dq3LkzLl26BIlEUqCtVatW8PLyQnh4uK5nWGH4iYCIiKgMspVqLNl8HgkpWVA9GUanyFJhX0gkwm4mYN6b7arVF+7qXBgQBAGCkPtFOyNbjc9/uYCElGyoNdX/cTUG2Uo14pOzkJapglwuQd+XB8PFtS7eHDcJ8fGPkJSUiA0bNkIikSArKwvjx09AZOQDrFnzLZYvXwEACAwcgHfffQ/jx4/DwIEDERf3CP/732bdr2SDBg1G27Zt0alTZ4wePQrff78BADBx4gTdL9y9e/dBmzZtsGbNt/Dx8cGnn36G8ePfQa9evTFp0mS89towTJ48BRs3rsc774xHmzZtsGrVV7h06RIAwMurHpYsWVbg/u3duxdXr17B3LkfA8jtev3LL78iPV2BRYsWYu3a7wDkznD//ffrEBy8EAAgCE+Hv+afLO7Z/RXm/Pkw1KtXr1SP/6VLF7Fp02aIxWIIgqD3WP388xb07NkT/fu/jN9++w2HDh0EANjbO2DRosXYuHEDDh06rPuid/PmTSQlJaFhw0a4ceMGLCwsAABWVpZIS0tDenp6gTYgt2v7vHlz8eqrw4osYORxcnLC48ePkZSUBBsbW1hZWcHCwgJpaWmwtrbW2/b69evw8/PDzJnvQqlUQyQS6fVWmDBhPL79dg0sLCwwfvw4BAYGAgACAvpj9uw5ePPN0Rg8eDBkstxfik+fPo1x494BAPTq1RsDBw5CSkoKJkx4B1u3bgMAJCTEF1nAKIpWq9H9QGZpaYn09LRC2wAgNPQcQkNDsHRpwQJGXoEGAFav/hZWVlbFHvell/ril19+wYYNG5Geno7jx49h06afkJaWivnz5+GTT5Zg797daNy4Cfz8OuDSpf9w6dIlTJ48BfPnz8MPP/wPpqamEAQBt27dhLOzM+bMmYsFC4Jx8+ZN/PPPGQQE9MPAgQMRHDwfly//h2HDhpU47KS4AgaQ24sg/7Hz7/P48WNwdXVFcPACnDp1Er//vhWtW7eBIAhYt+577Nu3D9u2bcM77+QOA4iPj8fZs2f1hpl99tmnaNOmTbEFDCC3WPXbb7/B3Ny80PUrV67E0qVL4erqilmzZuHRo0cYPXo0pk+fjoiICMydOxe//PKL3m3i4+OxdetWKBQKBAcH47vvvsMPP/yATZs2QavVYvz48ejevTvef/99mJmZ4Z9//sHWrVt1w1s2bMg9t40bNw5KpbJAIUMsFmPTpk3PvBatChRKn5WcnIybN2/i0KFDSExMxIoVK7B27Vps2rQJo0aNwsaNGwvcJiIiAo0b5+bv8OHDCAwMhK2tLUaPHo0WLVrA2dkZjRo1wvXr13Hw4EGsWrUK7u7uEAQBpqamcHd3x3vvvYcbN24gPj4eP/30E+7evYvvv/8eEyZMQGpqKrZs2YJr165h/fr1WLx4MaZNm4bU1FS9OL7++usChWUgtzATGhqKkSNH6ooY6enpumKIlZUVbt++DalUWqANADw8PHDv3j0WMfJU5w9yRERUfQmCgPRMFR6nZmN/yAM8SsqEVqs/U7ZKrUVsYgbmbwyFs50ZRCIRRCJAhNx/xU+WgXx/i0QQi3J7oT1ZzPf3k9vntT27zTP7xpNtxU8OIhKJoNVqcepyLDKy1bp4FVkq7P7nAY5eiIZ/M6cn2wnQCrldd7VaQKMVoBWE3PYnfxdo0wrQCAK0WuT+rRUgPLtd3nK+/ej2/2S5JCq1FrGPM7D4x3No4mkHK3MZrMzlT/6VwcpMrmuTSWvHRddUag3SM1VP/lciLVOpW07LVELx5N9HiRlobqPFsw+zIAAqjRae9RpBka2GVKLFzh07sH/fXojFYr1ekY0aNYZMJoNIJIZMJsPAgYMxe/ZHcHNzw/Tp+l3VRaLcL4UAIJE8zUXz5s309gfkfmFu3LgRAMDc3BwajQb37t3DqlVfARAhKysTzZu3AAA0a5b7QXbcuLHQaLS6cfEbN67H5s1Px9h7enrB3NwC5uYWUCiefnFYseJzDBgwCJ6enk/ifHr/RKKncebf3z///IPvv/8/NG3aFKNHv4mzZ89i7NgxcHJyQnDwghJzBABjx47D3LlzYGtrW2Aegnv37iE8/Bp+//13KJU56NfvZVhZWep+PW7atCmuXr0KILfXw7JlS/Dll18BACwtrRAXFwcAUCgyYG1tXWgbkFt06dKlKzp06PDksViOa9fC8c477xSINz4+Hg0aNMTRo0dx8+YNTJw4AUlJiThx4jgGDBiot227du0RFhaG999/H507d8XAgfrrb968gWnTpgIAUlKSkZSUDADw8fGBRCKBm5sbEhOT4OLiggcPIuDk5AQzMzMA0MVub28PL696SExMBAD8/vtWzJjxbqkLGIB+fjMyMmBlZV1oGwCsW7cWGzf+AJlMhlu3bmHZsqWoU6cOvvjiywLDScoiKuohbt68gbFjx+jabGxskZKSikuXLuLtt99GSEgIHj16BBcXF0ydOg2ffLIYMpkM06ZNBwDdF1YnJyekp6fj4cOH6N69OwCgefPmiIyMLFUshw4dxCuvvFpoAQNAocfOc+/ePezbtw9nzpyBRqPRDXPI/5w9e/YfALlDCebOnYNFixbp5kh48CACJibyIns55Ve/fv0iCxgAcP/+fXz0UW4vlbS0NMTFxeniKarI6OnpCQsLC1hYWCA9PR3Jycm4e/eubu6MxMRECIKADRs24OzZs1Cr1WjYsCGA3OLPnj17sHXrVgCAXC7HTz/9VOhxLC0tkZGRobtd3nmnKFZWVmjUqBHs7e1hb2+PlJQUpKWl4dGjR7q8F+fatWt49913ERQUhG+//RaXL1/W9WiIiIjApEmT8N1330Gj0WDSJP15i+7du4eQkBCMHj0aAHTDU5o0aQKpVAofHx/dc+vbb78tMZY8O3fuxIABAwrcz7xeTwqFAtbW1pBIJAXaSqvWfHOvil/OunZtj4MHTxb7onvW//63AYcPH4REIoZEIsXEiVPh75/bbS47OxvLli3CzZvXIZFIMHXqTHTpkjvmdNq0CXjjjdG65cqwdOlCNG3qg1deGY4dO/5ATk4Ohg8fWWC7vXt34ezZ0/jkk+U4deo4fvxxA1QqJQQBePnlgXjjjVEAgI0b/w9ZWVmYNm1mpcVMRFQegiAgLVOFx6lZSEzNxuMn/+f+ndumLMXkxYIAJD65XXWmFQQoslQ4cj7a0KGUigDgUXIWHiVnFbudqVzytMhhlr/Ykfuvu2smtCq1bp2JXFKueCr6RxGVWot0XSFCqStG5F/OX6zIVhacqb00pFIpNNqntxUgQnJ67hXefvn1V3yz7ic8io3Ct199irjkTKg1WqRlKmFqkvsaUarU6N+/PwYNGoSFCxfg6tUrevvXagXdB1KN5unrJf+XxvyjRvMXEwRBQL169REYOADNmzeHTCZBVlYObt26BbE4d7uNG3/UO97SpZ8iKGg2Vq5cBVNTU0RGPkBmZiYUCgUsLHKLKX/++eeTHiODdLeztrbBo0ePIBaLdUWXZ/fXuXNnXTfn6Oho3ZwYZeHv748ePXrg++//DydOnIBUKtVdya1evfro2LEjXnzxJQC5v1zu2bMbN2/mTnJ58+ZNeHh4QK1WIyhoNj744EPd0JIWLVrgt99+xdtvj8O//55Fq1atCm3LjaEjXF1d8fPPWzBy5Ch8+OFsXXz//fef7u+4uDicPfsP3nlnPDZv3oTNm7fA1NQUmZkZT7qq6xcptFotpk2bDplMgoEDB2LgwIF6ufXx8cFXX62Cubk5VCqVrmfDzZs30bJlS8TExMDBIfcX3CNHjqB37z662yoUClhaWiI7OxuRkQ9gZ2cHAJg4cRKOHj2CBg0aFjpPQGGaNGmCS5cuoUmTJrr9FtYGAEuXLsMnnyzGihVfokmT8hctnlW3rjtatGiJlStXAcjNNZA7hCc0NATjx0/Ali1bYGtrAyC3GLB06TLs2bMbO3fugIODAwD914qHhweuXQtHo0aNce3aNbzyyqt4+DASSmXxV2wcPHgIHj16hCNHDus95nmePbazs7Nun/Xq1cPAgQMxZsxY3f24ePEibt68CSC3cJX3q/rChQvwxhtvoGHDRrp9e3nVQ79+L+OLL1bohskUpaTh5fXr18fcuXPh5OQEjSa3Z01eLpOSkgq9mkVkZCQyMzORnp4OS0tL2NnZoUmTJti4Mbc3mkqlQkpKCkJDQ/HLL7/gzJkz2LVrF4DcwsTkyZMxZ84crFixAmq1usieGL6+vjh7Nvc1GBISgpdffrnY+2JmZgZzc3NkZWUhLS0NlpaWuH//PiIiIjBu3DjcunULjx490pszpl69eoiOzn3/VqlUenMGSSS572unTp3CyJEj4erqiqVLl+LChQv48ccfMXDgQN15qH79+ujatSvmz5+v21dcXBxu374NjUaDGzdu6IowZemJcf/+fdy4cQO//vor7ty5g59++glt27bF1q1b0b9/f/zzzz8YOnQoJBJJgTYAePjwYYmPW40oYlx/kIwtB28iNjGz5I2foVJrEfM4A1O+OlnkNq4O5hj1kjd8vOyeJ8xC+fg0x+uvj4KpqSlu376F6dMnYOfO/TAxMcWvv/4ECwsLbN26Aw8fRmLq1PH47be/ylQkqSiDB5duciV7+zr4/PPcMZwKhQLjxo1Cs2bN0bq1byVHSERUNK0gIC1DWaAwoStWpGXrCtxkvLKVGmQrNUhIKV0RSS4Vw8pcBkuzfD078hc+zPR7fZiZSJGj0pT4o4hELC5QlMgtQqgKLVaUtyhRVl71GmLTxrVY/snHGDN+qt66Jt7NEfTeRDRv5QutAGTlaKDWCEhRKCHJ0iJHpcWtB/H4ZP4HEAQtLCws4eDiiRxV7mOeo9Jg4qTJGD8+94P9s7/ilsb48ROwcOECKBQKSCRiLFy4uNjtmzZtirFj38bcuUH4/PMv4OLiivnz5+Hhw0jMm5f7oXzJksVo2bIlxox5C+3bt8e0adMxdeo0fPDB+wCgmyG/sP2VdOWTkkyfPg05ObnPxa++WglnZ2d8/fVKXL58GWPHvo0FC4Lx22+/QhCAmTNnAgBSUlIxfvw7MDExwVdfrXwyt8JVfPnllwCAmTPfQ5s2bdC+fXuMHj0Krq6uGD16NGQyeYG2+PgEALm5+OSTxdi7d2+BbvwrVqzQXZ1k7tx5UKvVyMzMgKmpKQDA3NwCKSnJyM7O1rUBwJUrV/D116ug0ah1c1a0bNkSM2ZMw1tvjcHUqdMwbdpUCIIAGxsbrFr1NYDcuSKWL/8UgwcP0Q0lOXHiOL755umvvJs3b8KZM6eh1Woxbtw7ugKIVCrDZ599jvfem4nZs4N0v5LnOX78ODZu3ICHDyMxc+a7WLXqa7z9dm5vmJycbEyZkjvxYGFtAODq6oqgoDlPClkrYW5uUd7U67G3t0f37t3x1lujIRZL4O/vj0mTJqNNmzZISkp8MjeHOVq1yi3MLF68CNHRUVAqlViyZCmuXLlSYJ+vvjoMs2d/iD//3IYmTbzRunVr2NnZYd68ubhz5zY+/HA2Jk+eiFu3bmLixPF4992ZAHK/5C5cuAgffvgBrK1tCgwxevbYMplct885cz7Gp58uxdtv5xYxRo8eDQsLS0ilUkycOAE5OTlYteprXLp0CYcPH0JMTAx++uknjBo1Gn365BZMXnnlFaxf/z02btygGz5UkmPHjuH777/Hw4cPMX36dKxevRrvvfce5s6dC6VSCZlMhm+++Qaff/45bt26BUEQ8P777xfYj4uLC+bOnYvIyEgsWLAAYrEYY8eOxZgxYwAAjRo1wvz582Fubo4333wT3t7eerfv3r07UlNTsWTJEgQHBxfZE6Nnz544cOAA3njjDfTo0QNOTk64fv06rl69imHDhuG7777D7t27IQgC4uLiMG3aNEyePBnjxo2DRqPBvHnz0LJlS12vj6CgIEyePFnvGB07dsSiRYswcuRItGjRQlewmDp1Klq2bIlLly5hxIgRqF+/PlasWIH//vsPmZmZmD17Npo0aYKvvvoKM2fOxKpVq1CnTh1dT4zAwEB06dIFDg4OmDp1KpKSknQ934rqiaFSqTB+/HjcuHED48aNw6xZs/Dhhx/q1r/xxhu6/cvlcowYMQI+Pj66QmthbREREQXmNHmWSBBK0ZezGkhMVBTouvvo0QO4uHhhzv+dRVwJv848L2c7M3w6sfhZXPN6YpiamuLbb1ciMTERH3+8ECtWLINUKsX9+/eQkpICX9+2mDVrdoHxdoIgICDgBfz00+9wcnLGqFGvYd68hWjaNLcb5kcfzURAQCB69eqj1xPj8OED+O23n7Fs2Qo4OTkXiOvAgb04fvwoPv0090mYO8lSIL77biOysrLw5ZefITs7C0qlEgMHDsFrr40AoN8TI38vCpVKhZUrP8eFC2GwsbFF48beSE1NxiefLC9w7I8+eg+9e7+Ivn376+3j7t07WLx4HmbO/BC+vu30bpOXV6ocjo5WSEgofnweGR7zVDZaQUCqQqkrUOQvTuQVLtQaFimqK4lYBI22+n8ckYhFkEpEyFEV/VySSkRQa6rPffFzeoihr7wKExPTkjeuACIRIJWIIZWInvyr/7dEXPQvrIIgIDUjt7ij0QqQiEWwMpfBxkJeqom/S7qEY3VX2ktpVicymQQqVemKcKW9LC5VjrLkqrRCQ0Px779ny3TZ1qKIRIBUWr7ecaVRlsukGoO8S6zWqVMHH3zwAV599VV07NgRGo0Gu3fvRmBgYLlea1FRUVi1alWxl7OtTDdu3MDJkyd1l6bNG+Lzzjvv6L0P8CxSwZRKJZYtWwhX17pYuHCp7sEOD7+K7777AXK5HB9++C7+/ns7XnlluN5t9+/fg7p13XWFiLi4R3B2dtWtd3JyQXz8I73b/PzzJoSGhmDVqrV6XSLz69GjF7755kukpKTA1tYW//77D7y86sHNrS4yMzOwatVayOVyZGZmYsKEt9ChQyfUq1e/yPu4c+efiI2NwZYt26BWqzF16ni9mXvzPHgQgfDwK/joo7l67efOhWD16q+waNGnqF+/QTGPJhHVJsV10ZdLJUhR5DxTmHharEhKy670L44mcgnq2JhCpdbicUoWCvvOLRGL4OfjhM4tXAAB0ApPJq/E00kshULbnvyLpxNdIve/p38Lgu6Y2ifb40lb3u309gdg79kHxQ6DMZGJMaRbA4jFIkjEIojEIkhEIojFT/4X5bbn/Z3bjgLb5N1enL9dnDvfh0Qszvd37tweErH+7QFgx6l72BcSWWiPGIlEhDYNHdDUyz63B0NW7vwPivy9GrJUBeZ+qGiaJ3N/FKcqChhikahAzxFrvaEzuX+HXo9DSlQsMhXpBYoYIgCmJhLIJGKoNQLUGi3UGm2hz+uyEITcXq4qNQAU/MJUWJFDJsl9LiSm5UCteTqHh0abW9TIzFbD1cG8Wl/BbN++fdi69TfdcuvWrfHee7MMGFHFmjdvLqKing4/mzp1WokThVam6vZ4r1z5ld7wnOHDX0e/fv0MFk9pnDt3DmvWPP113d29bqET6FaG9PR0TJ+uf0nOb7/9FnZ2tlVy/JogKChI9/eiRYuwZs0afP3115BKpRg1apTRFgubNm2Kpk2b6pbT09MhlxcsZBvnvXvGmwFNyz2cpDTyhpOUxvvvT0fv3i9hxIjReu29er2oGwbSr18gjh8/qlfEuHjxPNav/w6rVpV8rfI8P/zwPZydXfDFF18XexkoU1NTdOv2Ag4d2o9hw17Hvn270K9f7izR2dnZ+Pbbz3Dnzi2IRGI8fpyAO3duFVvEuHDhPPr1y63uSaVS9O3bD1eu/Ke3zePHjxEUNAuzZgXpXR7s3Ll/ERLyD1auXKPXTkS1W968RfHJmbovgYosFXb9E4E9Zx/ofYGvLKZyCerYmKGOjSkcbExRR/e/GRxsTGFhKoVIJCp0jiUAkEnFcLQ1w5t9vavNZNFarVBkYUAmFaNvB0+81KH4SceqSoC/J8JuJhT5uI4LbFbs46oVBGRmq/WHa2Q9nfxSkalCtlqLpJSsJ+3KatNjQiwSwdJcButnh7MUKE7k/mtuKtUVf4rj6WyJL/53D6dOHoNfB/8nhQzRk0KCCHIbUyif7EcMQI7cx1GjyS3WqLVaaNTCk+KNFmqNUOmFosKIACQlimEql+SbJFek97dYlDuB56NHsVUfIABf3zbw9W2j11bWWDp27Fiu21WFwoYHPXoUC6lUArW6dL/uf/bZZ3j8OKFC4qmIx7sivfHGG3jjjTf02qpbHp/NlYeHOz777DO9bcoas6enBzw9Pcp1X589tkKhQHZ25fWs/+qrrxAbW71yUpHefPNNvPnmm7rl8t5XiUSC999/v1o8Vjk5OQgNDdW7nGye6vEp6zn5eNlh6fiOxW5T3C88MqkY/fw9Mbjb8/cK8PVth5CQsxg6dJje2MHiXL16GZ98EoxPP/0Snp71dO3Ozi6Ii4vVTWgUH/8Ibdu2161v3rwFzp0LwaNHsfDwKP5DaL9+A/D111/gpZcCcOnSBcyf/wkA4P/+bw3s7R3www8/QyqV4r33ppY4KVBJkpOTMHPmFIwc+SZ69dKfNMjDwxP379/DjRvh6Nq1x3Mdh4iMm0qtRWRcOu7GpOHkpWjEFFKIFgRAU0HfmsxNpLrihMOT4kQdG1M4WJuijq0pzE2kpfql11Quxbw322F/SCSOXXzaa6Snb/W72lVJhYEA/+pRwAAKeVwzVbA0L/3jKhaJYGkmg6WZDK4OhW+Tf5iWIAjIVmryFT3yFz6eactUIT1LCWUxw0gKxPKkp4SuCGEmh5VFbhHi2WJFaYsSZWUql+KDMS9ix+Ew7D8WBpGggUQihqOtKZzszHBPXParuWg0WijVWuSoNFCqNFCqtMhR5/6rVGkMPixILM7t/ZPX20eS/3+JOLd3kSR/e75tJU97CYlQdD40Wi3ik7N0lwOW5ntMJeV4TI2ZiYkUOTlqQ4dR5YzlOZA/To1Gq/f6r05xArm9tOTV6P2TDE8mk6Fly5aFzo9Ra54pVfVB7u23J2D79t8xa9Y0rFixSjc79rFjR/DaayMgk8mwf/9edOnSFQBw/fo1BAfPwSefLIe3d1O9ffXs2Rs7d25H06bN8PBhJK5fD8fChUt16/39O6NHj1748MN3sWzZF2jQQH+Co/xat26DzMwMrFu3Bt26vaArsCgU6WjYsDGkUinu3buD//67hBdfDCj2PrZr1x779+9Fr14vQqNR49Ch/XBxyR32kpqagpkzp+KVV15DYODgArd1cXHD9Omz8P77M5CTk4PevV8q+UElIqMnCAIep2bjbkwq7kWn4W5MGh7Gp1foL+EWptKCxYl8vSrMTYvusVZWpnIpBndrgMHdGlTr+UuetzBQ1fI/rpVNJBLBzEQKMxMpnEo5b3eOSoM/T9zF8YvRhT53pRIR+rTzwKs9G1ZKUaI8TOVSvN6/I4Dif+ypCIIgIDNHjccpBYd8PU7NQkJqNnKqaBLT55E31MZULoW5iRSmJpLc54pcCrlUjEt3HyMrRwat1kp3G2miCPZWppjxaivYWZk86TVSPZ4Dlak6n/8qy9PeeHKo1E+fA7Kk3O8UFXHFw8Lk9kh82lPq2UtYa575NytHjQ17wpGcLoNaU3VxlpdYLIKDQ+FD44meVX2euZWsKj/IjRo1BiYmppg5cwq+/HI1AMDHpxlmzZqK5ORk+Pq2w8CBuZeQ+fLL5VAqc7BixdMxaPPnL0bDho0wYsSbWLp0IYYPHwyxWIyPPppbYKbkdu38MHfuAgQFzcKSJcvRpIl+ISS/gICXsWHDOqxZs0HX9tZb4/DJJ8HYs2cnPDw80aZNyVcRGThwKO7cuYNRo4bBxsYWTZs2R0pKEgBgy5ZNePgwEjt3bsfOndsBAMOGvY6XX356aS5nZxd8/fVazJo1HTk5Oejff0ChxyEi45WVo0ZEbG6x4l5MGu7FpCItU/Vc+7Q0k+UWJKzzD/d4OvzDzKTWvKWVSVUWBmo6E5kEQ7s3QHhEcpE/igzsWq/aFDCqmkgkgoWpDBYuMni5WBVYLwgCMrLVBSfgTc3G5buPK33IWGkJyL06S1aOBsnpOaW6jVojID4lC/M2hADI7Y1jbiqFuakUFqa5xRBzUxksTKUwM5XCwlSWu94k39957SZS3WVln1dFXw64NhEEAUq1FpnZamRkq3T/nvovBo+SMgtccECl1iI2MQNLNoXBrY7F06KCULDQoC6q+CAULEboChUV1CtRpdYiISUL+0Mi+b5ARqtGXJ2kust/lY+aSioVQ11Blyc0lrwaq9r4q4kxMqY8aQUBsY8zcC8mr2iRiuiEDFTkm4ulmQzfvNutAvdYcYwpV7VdReUq74uhMfRuMRbFTuwqFqGRuw3qu1ojK0f95H8NspS5f2fnqJGZo0F2jrpCzzuGZCqX5BY/8gofhRU7Ci2CSCF7coWHkubvqYhf4ivy/FdZBReVWoOMbHXu/1mqAkWJvH8zstUF2qrLvDmVwcpchq9nVJ/3VfbEoLLgOy0REZVJeqZSr2BxPzYNWTnl6yLuYG2KhnWtkZGtws3IlEI/MMqkYvRqW/d5wyaqMOzdUvFKGvb77qutSvwiKwgCclSaJ70onhQ7lGr95aIKIEo1MnPUyM7RIKeCL0NZHtlKDbKVGiSmla4nSH4yqRjmJlJotFpkZBUs7KjUWjxKzMSqbf+heT17SCRiSJ/MGSJ5MjeIVCx+MkeI+Mly4etzBCA1NUtvvTTf7UrbK6mwgosiS4V9IZEIu5mA2SN8odUKBQoNimeKEfkLEnlthRXGCFA8Z+9IIkNiEaMKfPzxwio5zq5dO/Dnn78XcvwFaNy4dFdXISLKT63R4mG84knRInc+i/iU8s0ebiKToL6rFRq42aChmzUauFnDxtIEQMm/GFanCSiJqOJVxIS5IpEIpnIpTOVS2FmZlDsWjVaLbKXmacEjX0Fk076bxRY5RMg9bxV3aePKplJrkaoufpJ2rSDg1sNU3HqYWqmxPDuRqlQi1i1L84oiYjHSMnKQolAWWnCJeZyBd785XalxGov8l7TOP3ltYf/GPs4odoiWpXnFzRNFVNVYxKhBBgwYjAEDBhs6DCIyUoIgIDk9B3dj0nA3OhX3YtIQ8Sgdak35Poy7OpijoZsNGtS1RkM3G7jVMS9yNnRjm4CSiCpedZkwVyIWw8JUDItCJgN+lJhZqqvdqdRaZOaokanrKfDk75x8f+fvVZCj1m2XVYOu9qEVBGjVAoz1N3+pRKQb0pM3bCcpNRsxiYUXCCRiEVo3coB/M5fcAk4hBYYiiw+i4osSeZcTLq2SrszY05c9HMl4lepT4f379xEUFISUlBTY2tpi+fLlqFevnt42p0+fxldffYVbt25h9OjRmD17tt76vXv34rvvvoMgCBCJRPjxxx9Rp06d574DefujmsFIpmghMipFjTPu6euOR0lP57K4G5OKVEX5LrFsYSpFw7o2aPCkh0UDV+syXw2EXfSJqLor7dXuZFIxbKRy2FjIy3wMrVZAlvJJQSP/UImc/PM1PFMgyVcwMfRlbqsbiVikN7/Is0UJ/XVSWJjJdOvkUnGB7xkl9Rx8J7BZtSi8G9MltonKqlQTe7755pt45ZVXMGjQIOzcuRN//vknNm/erLfNgwcPkJmZif3790OpVOoVMa5cuYLZs2dj06ZNcHR0RHp6OuRyOUxMSt/Vr7CJPR8/joWpqTksLKxZyDCwipjYUxAEZGSkITs7E3XquFZQZPQsTkJoHCpyAsIlm8MQn5yt16NCBJR7AjyxSAQPJ8snPSxye1k42ZnV2vMwX1PGg7kyHtU5V9V5YldBEKBUaZGRrcLufyJw+kpsoXMNicUiNKprg8buNtBocy/bqdZqn1y+UwuNVoBaI0Cj0T5Z/6Qt7+8nl/mECMhRanTbqJ+0a7TaCp8U09LsSfHBTKY3uWnu1V9ksDDLbXu2KGEiq/jL3Vbn50B+enGWY4hWVeLEnlQWJRYxEhMT0bdvX4SEhEAikUCj0cDf3x8HDx6Evb19ge1Xr16NzMxMvSLG+++/j06dOuHVV18td6CFFTE0GjWSkxOgLmHcH1U+sVgMrfb5x39KpXLY2TlCIqleJ9aapDp/MKSn8udJrdHqfoXLfNLVWPer25O2zCdjtnV/P/k3LUP53L/K2VmZoMGTYkUDN2t4uVjBRCapiLtZI/A1ZTyYK+PBXD0/Q1+dRBCeXDL0SWFDrSuI5C+Y5LYfuxCNf8MfFT65s0SMAH9PDOnOXnrPo7q/pljEoLIo8cwVGxsLZ2dnSCS5H1glEgmcnJwQGxtbaBGjMHfv3oW7uztGjhyJzMxMvPjii5g8eXKZqqJFPaldXOxKvQ8iyuXoaGXoEIxeVo4a24/dxt5/IpCeoYSVhRz9O9fD0J6NYWby9NSad535jCxV7v/Zqqd/Z6mgyLvcW95yvvWZ2SoostRQVuFM+XKpGI08bOHtZQ9vLzt4e9qhjq1ZlR3fWPE1ZTyYK+PBXD2/VbNewPZjt7HvnwikZSphbS5Hv0Leq55HReSppbczIr8+iUeJGXqTosqlYrg4WGB0YPMKi7c242uKaooqORtoNBrcvHkTP/74I5RKJd555x24ublh8ODBpd5HYT0xqPqo7tVdeoq5en55v27FJ2fqfjVKy1Bi6+Fb+Ov4HbjYWyBbpUHWk54S1f06852au6Bh3dy5LNwdLSGVPJ18U1Cp+XwpAV9TxoO5Mh7MVcV5qZ07XmrnrtemSMuCogL2XZF5ChrpW+QQjYqKtzar7q8p9sSgsiixiOHq6oq4uDhoNBrdcJL4+Hi4upZ+zgI3NzcEBARALpdDLpejd+/euHz5cpmKGEREhiQIAmIeZ+D6g2QcvRCNR0mZhWwD5Ki0eBBXfT8kPMvKXIbxA5oZOgwiIqrlOLkzEZVWiUUMBwcH+Pj4YPfu3Rg0aBB2794NHx+fUg8lAYDAwECcOHECgwYNglqtxr///ou+ffs+V+BERJVJEATEp2Th+oNk3Hjyf1qmYS4SJxaJYG4qhZmJBOYmuROZmZtIYfbkX72/89Y9+fvYhWgcPh/FS6wRERERUY1QquEkCxcuRFBQENauXQtra2ssX74cADB+/HjMmDEDLVu2RFhYGGbNmgWFQgFBELBnzx4sXboU3bp1w8svv4yrV6+if//+EIvF6Nq163NN8klEVBkSU7NxIzIZ1x/k/p+cnlMh+5WIRbriwtMChKzYAoS7qw2yM3Oee2b1AV3q4b+7ibzEGhERERHVCKW6xGp1wDkxqrfqPs6OnmKunkpV5OB6ZDJuPEjBjQfJiE/JqpD9mptIMXd0O11RQlbIdeZLUpF5MpZLwRkrvqaMB3NlPJgr48A8GY/qnivOiUFlwU+vRFRrKLJUuJmvp0VsYsF5LYpjIpfA28MWao0Wtx6mFH4pOKkYfdq7w62ORUWF/dw4zpiIiIiIagoWMYioxsrKUePmwxTdnBYP4xUoS38umVSMRnVt4ONlBx8vO3i5WEEqEeuuTsIhGkREREREVYtFDCKqMXJUGtyJSs2djDMyGRGx6dCWYcScRCxCQzdrNH1StGjgZgOZVFxgO1O5FPPebMchGkREREREVYyftInIaKnUWtyLSdVdQeRuTBo0ZZg7RyQC6rlY63paNKprAxO5pFS35RANIiIiIqKqxyIGEVUbeRNQHr0QDUWWCpZmMvRq+7R3g0arRURsuu4KIneiUqEs5NKhxfF0skRTLzs09bJDE3dbmJvyNEhEREREZCz46Z2IqoXC5plQZKmw998HOH4pGh5OVrgbnYpspaZM+3WrY4Gmnrbw8bKDt6cdLM1klRE+ERERERFVARYxiKha2B8SWWCiTABQawSkZahw7X5SqfbjZGv2pKeFLXw87WBjaVIZ4RIRERERkQGwiEFE1cLRC9EFChilYWdlopvToqmnHRxsTCshOiIiIiIiqg5YxCAig8rKUede4SNLVartrc1lujktfLzs4GRrBpFIVMlREhERERFRdcAiBhEZRGa2CofPR+HQuYfIyFaXuL2JTIJ5b7WHm4M5ixZERERERLUUixhEVKUUWSocOvcQh89HISun5OIFAMikYvTt4IG6dSwqOToiIiIiIqrOWMQgoiqRlqHEgXO5l0/NKeIKIyIAIpEIWkHQtcmkYjjamiHA37OKIiUiIiIiouqKRQwiqlQpihzsD4nE8YvRUBYxcaeZiRQvtndHt1ZuOHU5JneOjEwVLM1l6OlbFwH+njCV83RFRERERFTb8VsBEVWKpLRs7P33AU7+Fwu1pvDihYWpFC918ETvtu4wN809HQ3u1gCDuzWoylCJiIiIiMhIsIhBRBUqISULe84+wJkrsdBohUK3sTaXoa+/J3r61mUPCyIiIiIiKjV+eyCiChGXlIndZyNw9mqc3pwW+dlaytHP3wvd27jBRCap4giJiIiIiMjYsYhBRM8l+nEG9vwTgZDrcSiidgF7axO83NELXVu5QiZl8YKIiIiIiMqHRQwiKpfIuHTs/icC528moIjaBRxtTfFyp3ro3MIFUom4SuMjIiIiIqKah0UMIiqT+7Fp2HUmApfuPC5yG2d7cwR28kLH5s6QiFm8ICIiIiKiisEiBhGVyp2oVPz9z31cvZdU5DZ161ggsHM9+DV1glgsqsLoiIiIiIioNmARg4iKdTMyGX+ficD1B8lFbuPpZIkBXerBt4kjxCIWL4iIiIiIqHKwiEFEBQiCgPCIZOw6cx+3olKL3K6+qzUGdKmH1g0dIGLxgoiIiIiIKhmLGESkIwgCLt9NxK5/InAvJq3I7Rq722BAl3poXs+exQsiIiIiIqoyLGIQEbSCgIu3HmP3PxF4EJde5HZNPW0xsEt9eHvasnhBRERERERVjkUMolpMqxUQdjMeu/6JQHRCRpHbtahvjwFd6qGxu23VBUdERERERPQMFjGIaoFspRr7QyJx9EI0MrJUsDCTobG7NWISMxGXlFXk7do0qoPAzvXQwM26CqMlIiIiIiIqXKmKGPfv30dQUBBSUlJga2uL5cuXo169enrbnD59Gl999RVu3bqF0aNHY/bs2bp1q1evxi+//AInJycAQNu2bbFgwYKKuxdEVKRspRpLNp9HQkoWVGotAECRpcLF24lF3qadtyMGdK4HT2erqgqTiIiIiIioRKUqYixYsAAjRozAoEGDsHPnTgQHB2Pz5s1623h4eGDp0qXYv38/lEplgX0MHjxYr7BBRFVjf0ikXgGjKCIR0MHHGYGdvFDX0bKKoiMiIiIiIio9cUkbJCYmIjw8HIGBgQCAwMBAhIeHIykpSW87Ly8v+Pj4QCrlCBWi6uTohegSCxhdWrhg6fiOmDiwOQsYRERERERUbZVYcYiNjYWzszMkEgkAQCKRwMnJCbGxsbC3ty/1gfbs2YPTp0/D0dER06dPh6+vb5kCdXDgF6vqztGRQw+qm5jHCiiyVMVuIwIQNNa/agKiMuFryngwV8aDuTIezJVxYJ6MB3NFNUWVdJt4/fXXMWnSJMhkMpw5cwZTpkzB3r17YWdnV+p9JCYqoNUKlRglPQ9HRyskJBR9aU6qepfuPMb6XeElbmdpLmPuqiG+powHc2U8mCvjwVwZB+bJeFT3XInFIv5oTaVW4nASV1dXxMXFQaPRAAA0Gg3i4+Ph6upa6oM4OjpCJpMBALp06QJXV1fcvn27nCETUXG0WgF/nbyHb/64jKwcdbHbyqRi9PStW0WRERERERERPZ8SixgODg7w8fHB7t27AQC7d++Gj49PmYaSxMXF6f6+fv06oqOjUb9+/XKES0TFUWSpsOqP/7Drn4gC60Qi/WWZVAxHWzME+HtWTXBERERERETPqVTDSRYuXIigoCCsXbsW1tbWWL58OQBg/PjxmDFjBlq2bImwsDDMmjULCoUCgiBgz549WLp0Kbp164avvvoK165dg1gshkwmw+effw5HR8dKvWNEtc2DR+lY89cVPE7N1msXi0QY0q0+lGotjl+KhiJLBUszGXr61kWAvydM5ZyMl4iIiIiIjINIEASjmGiCc2JUb9V9nF1Nd+pyDLYcvFXgKiTW5jJMHtwC3p5P559hrowD82Q8mCvjwVwZD+bKODBPxqO654pzYlBZ8CdYIiOmUmvxy+FbOHEppsC6hnWtMWVwS9hZmRggMiIiIiIioorHIgaRkUpKy8aav67gfmzBqnrvdu4Y3qsRpJISp70hIiIiIiIyGixiEBmh8IgkrNt5DYoslV67XCrGWwFN0amFi4EiIyIiIiIiqjwsYhAZEUEQsC8kEn+euItnZ7NxsjXD1KEt4eHE8YRERERERFQzsYhBZCSyctTYuOc6LtxKKLCudUMHjB/QDOamMgNERkREREREVDVYxCAyAtGPM/Dt9iuIS8rUaxcBGNytPl7uXA9ikcgwwREREREREVURFjGIqrnQ63H4ce8N5Kg0eu0WplJMGNgcLRs4GCgyIiIiIiKiqsUiBlE1pdFqse3YXRw897DAOk9nS0wd0hKOtmYGiIyIiIiIiMgwWMQgqoZSM5RYt+Mqbj5MKbCua0tXjHqpCeQySdUHRkREREREZEAsYhBVM3eiUrF2xxWkKJR67VKJCCNebIIerd0g4vwXRERERERUC7GIQVRNCIKAoxei8duR29Bo9a+fam9tgimDW6KBm7WBoiMiIiIiIjI8FjGIqoEclQab99/A2WtxBdb5eNlh4qDmsDaXGyAyIiIiIiKi6oNFDCIDi0vOxJrtVxGVoCiwrn9HLwzpXh8SsdgAkREREREREVUvLGIQGdClO4+xflc4snLUeu2mcgnGvdwM7bwdDRQZERERERFR9cMiBpEBaLUCdp6+j13/RBRY51bHAlOHtICrg0XVB0ZERERERFSNsYhBVMUUWSp8v+sart5LKrDOr6kTxvZvClM5X5pERERERETP4jcloir04FE61vx1BY9Ts/XaxSIRXuvZEC/6efDyqUREREREREVgEYOoipy+HIufDt6ESq3Va7c2l2Hy4Bbw9rQzUGRERERERETGgUUMokqmUmvx6+FbOH4ppsC6RnVtMHlwC9hZmRggMiIiIiIiIuPCIgZRJUpKy8aav67ifmxagXW927ljeK9GkEp4+VQiIiIiIqLSYBGDqJKERyRh3c5rUGSp9NrlUjHeCmiKTi1cDBQZERERERGRcWIRg6iCCYKAfSGR+PPEXQiC/jonWzNMHdoSHk6WhgmOiIiIiIjIiLGIQVSBsnLU2LjnOi7cSiiwrnVDB4wf0AzmpjIDREZERERERGT8WMQgqiDRjzPw7fYriEvK1GsXARjcrT5e7lwPYl4+lYiIiIiIqNxYxCAqp2ylGvtDInH0QnSBeS/yWJhKMWFgc7Rs4FDF0REREREREdU8LGIQlUO2Uo0lm88jITkLKo220G08nS0xdUhLONqaVXF0RERERERENVOpru14//59DB8+HH379sXw4cMRERFRYJvTp09j6NChaNGiBZYvX17ofu7du4fWrVsXuZ7IWOwPiURCStEFDA9HS8wd1Y4FDCIiIiIiogpUqiLGggULMGLECBw4cAAjRoxAcHBwgW08PDywdOlSjBs3rtB9aDQaLFiwAH369Hm+iImqgaMXoqFSF17AAIAURQ7kMkkVRkRERERERFTzlVjESExMRHh4OAIDAwEAgYGBCA8PR1JSkt52Xl5e8PHxgVRa+AiV77//Hi+88ALq1av3/FETGVhRc2CUdj0RERERERGVXYlzYsTGxsLZ2RkSSe6vyhKJBE5OToiNjYW9vX2pDnLjxg2cPn0amzdvxtq1a8sVqIODZbluR1XH0dHK0CFUGQszGTKKKVRYW8ir9eNRnWOjp5gn48FcGQ/myngwV8aBeTIezBXVFJU+sadKpcL8+fPx6aef6goh5ZGYqIBWK1RgZFSRHB2tkJCQbugwqoythbzIIoZMKkaPNm7V9vGobbkyVsyT8WCujAdzZTyYK+PAPBmP6p4rsVjEH62p1EosYri6uiIuLg4ajQYSiQQajQbx8fFwdXUt1QESEhIQGRmJCRMmAADS0tIgCAIUCgU++eST54ueyAAep2Yh5nFGoetkUjEcbc0Q4O9ZxVERERERERHVfCUWMRwcHODj44Pdu3dj0KBB2L17N3x8fEo9lMTNzQ0hISG65dWrVyMzMxOzZ88uf9REBnTw3EPk7xMkFgGCAFiay9DTty4C/D1hKufVi4mIiIiIiCpaqb5pLVy4EEFBQVi7di2sra11l0gdP348ZsyYgZYtWyIsLAyzZs2CQqGAIAjYs2cPli5dim7dulXqHSCqShnZKpz6L1av7a2ApujW2s1AEREREREREdUepSpiNGzYENu2bSvQvn79et3f7du3x8mTJ0vc1/Tp08sQHlH1cuxCNHJUGt2yjaUcHZu7GDAiIiIiIiKi2qPES6wSUS6VWovD56P02vq0c4dMypcRERERERFRVeC3L6JSOnvtEdIylLplE7kEPX3rGjAiIiIiIiKi2oVFDKJS0AoCDoRG6rX1aO0Gc1OZgSIiIiIiIiKqfVjEICqFy3cSEZuYqVsWi0R4sb2HASMiIiIiIiKqfVjEICqF/SEP9JY7NHOCg42pgaIhIiIiIiKqnVjEICrB3ZhU3IpK1WsL6OBpoGiIiIiIiIhqLxYxiEqwP0R/Lozm9ezg6WxloGiIiIiIiIhqLxYxiIoRl5yJCzcT9NoC/L0MFA0REREREVHtxiIGUTEOhj6EkG/Z08kSzerZGSweIiIiIiKi2oxFDKIipGUqcfpKrF5bX39PiEQiA0VERERERERUu7GIQVSEo+ejoFJrdcv21ibwa+pkwIiIiIiIiIhqNxYxiAqRo9Lg6IVovbYX23tAKuFLhoiIiIiIyFD4jYyoEGeuxEKRpdItm5lI0b21mwEjIiIiIiIiIhYxiJ6h1Qo4GPpQr+0FXzeYmUgNFBEREREREREBLGIQFXDhVgLiU7J0yxKxCH3aeRgwIiIiIiIiIgJYxCDSIwgC9odG6rV1au4COysTA0VEREREREREeVjEIMrndlQq7sWk6bX19fc0UDRERERERESUH4sYRPnsD9HvhdGqoQPq1rEwUDRERERERESUH4sYRE/EPM7ApTuP9dr6sRcGERERERFRtcEiBtETB56ZC6O+qxWaeNgaJhgiIiIiIiIqgEUMIgApihycvfZIry3A3wsikchAEREREREREdGzWMQgAnDkfBTUGkG37GhrinZNHA0YERERERERET2LRQyq9bJy1Dh2IVqv7SU/T4jF7IVBRERERERUnbCIQbXeqcuxyMxR65YtzWTo2srVgBERERERERFRYVjEoFpNrdHi0Dn9CT17+taFiUxioIiIiIiIiIioKKUqYty/fx/Dhw9H3759MXz4cERERBTY5vTp0xg6dChatGiB5cuX6637888/MWDAAAwaNAgDBgzA5s2bKyR4oucVdiMeiWk5umWZVIze7dwNGBEREREREREVRVqajRYsWIARI0Zg0KBB2LlzJ4KDgwsUIjw8PLB06VLs378fSqVSb13fvn0xdOhQiEQiKBQKDBgwAB06dEDTpk0r7p4QlZEgCNgfot8Lo0sLF1hbyA0UERERERERERWnxJ4YiYmJCA8PR2BgIAAgMDAQ4eHhSEpK0tvOy8sLPj4+kEoL1kUsLS11l6rMzs6GSqXipSvJ4K4/SEZkvEK3LALQt4On4QIiIiIiIiKiYpVYxIiNjYWzszMkktw5AiQSCZycnBAbG1umAx05cgQvv/wyevbsiXfeeQfe3t7li5iogjzbC8O3iSOc7c0NFA0RERERERGVpFTDSSpC79690bt3b8TExGDq1Kno3r07GjRoUOrbOzhYVmJ0VBEcHa0MHUKp3Y9JxdX7+r2J3ujb1Kjuw/OoLffT2DFPxoO5Mh7MlfFgrowD82Q8mCuqKUosYri6uiIuLg4ajQYSiQQajQbx8fFwdS3fJSjd3NzQsmVLHD9+vExFjMREBbRaoVzHpMrn6GiFhIR0Q4dRar/uv6G33MjdBg4WMqO6D+VlbLmqrZgn48FcGQ/myngwV8aBeTIe1T1XYrGIP1pTqZU4nMTBwQE+Pj7YvXs3AGD37t3w8fGBvb19qQ9y9+5d3d9JSUkICQlBkyZNyhEu0fNLSstG6PU4vbZ+nAuDiIiIiIio2ivVcJKFCxciKCgIa9euhbW1te4SquPHj8eMGTPQsmVLhIWFYdasWVAoFBAEAXv27MHSpUvRrVs3bN26FWfOnIFUKoUgCBg1ahS6du1aqXeMqCiHwh5Ck69Xj4u9OVo3rmPAiIiIiIiIiKg0RIIgGMUYDQ4nqd6qexe1PJnZanyw9gyylRpd21sB3ujRpq4Bo6paxpKr2o55Mh7MlfFgrowHc2UcmCfjUd1zxeEkVBYlDichqklOXIrWK2BYW8jRuYWLASMiIiIiIiKi0mIRg2oNtUaLQ2EP9dp6t3OHTCoxUERERERERERUFixiUK3x77U4pCiUumUTmQQ9fWvPMBIiIiIiIiJjxyIG1QqCIOBAaKReW7dWrrA0kxkoIiIiIiIiIiorFjGoVrhyLxHRjzN0yyIR8KKfhwEjIiIiIiIiorJiEYNqhf0h+r0w/Jo6wdHWzEDREBERERERUXmwiEE13v3YNNyITNFrC/D3NEwwREREREREVG4sYlCN9+xcGE09bVHPxdpA0RAREREREVF5sYhBNVpCShbO3YjXawvw9zJQNERERERERPQ8WMSgGu3guYcQhKfLdR0t0LKBveECIiIiIiIionJjEYNqLEWWCqcux+i1BXTwhEgkMlBERERERERE9DxYxKAa69iFKChVWt2ynZUJ/Js5GzAiIiIiIiIieh4sYlCNpFJrcOR8lF5bn/bukEr4lCciIiIiIjJW/EZHNdKZq4+QlqnSLZvKJejRuq4BIyIiIiIiIqLnxSIG1ThaQcCB0Id6bS+0qQtzU6mBIiIiIiIiIqKKwCIG1TiXbj9GXFKmblkiFqFPe3cDRkREREREREQVgUUMqnH2h0TqLfs3c4a9tamBoiEiIiIiIqKKwiIG1Sh3olJxJzpVr61vB08DRUNEREREREQViUUMqlH2hTzQW25R3x4eTpYGioaIiIiIiIgqEosYVGM8SsrEpduP9doC/NkLg4iIiIiIqKZgEYNqjIOhkRDyLXs6W8LHy85g8RAREREREVHFYhGDaoS0DCVOX3mk1xbg7wmRSGSgiIiIiIiIiKiisYhBNcKR81FQa7S6ZQdrU/g1dTJgRERERERERFTRWMQgo5ej1ODohSi9tpf8PCAR8+lNRERERERUk/BbHhm901dikZGt1i1bmErRrbWrASMiIiIiIiKiysAiBhk1jVaLA6GRem0v+NaFqVxqoIiIiIiIiIiospSqiHH//n0MHz4cffv2xfDhwxEREVFgm9OnT2Po0KFo0aIFli9frrduzZo1ePnllzFgwAAMHToUp06dqpDgic7fTMDj1GzdslQiQp927gaMiIiIiIiIiCpLqX6uXrBgAUaMGIFBgwZh586dCA4OxubNm/W28fDwwNKlS7F//34olUq9da1atcLbb78NMzMz3LhxA6NGjcLp06dhampacfeEah1BELA/RL8XRucWLrCxNDFQRERERERERFSZSuyJkZiYiPDwcAQGBgIAAgMDER4ejqSkJL3tvLy84OPjA6m0YF2kW7duMDMzAwB4e3tDEASkpKRUQPhUm92MTEHEo3S9tr4dPA0UDREREREREVW2EntixMbGwtnZGRKJBAAgkUjg5OSE2NhY2Nvbl/mAO3bsgKenJ1xcXMp0OwcHyzIfi6qWo6NVlR5v7c5resv+zV3QqmnZnle1VVXnisqHeTIezJXxYK6MB3NlHJgn48FcUU1RpbMfhoaG4uuvv8YPP/xQ5tsmJiqg1QqVEBVVBEdHKyQkpJe8YQWJTlAg7HqcXlvPNm5VGoOxqupcUfkwT8aDuTIezJXxYK6MA/NkPKp7rsRiEX+0plIrcTiJq6sr4uLioNFoAAAajQbx8fFwdS3bJSwvXryIDz/8EGvWrEGDBg3KFy3RE/ufuSJJAzdrNHa3MVA0REREREREVBVKLGI4ODjAx8cHu3fvBgDs3r0bPj4+ZRpKcvnyZbz33nv45ptv0Lx58/JHSwQgOT0H/17T74UR0METIpHIQBERERERERFRVSjVJVYXLlyILVu2oG/fvtiyZQsWLVoEABg/fjyuXLkCAAgLC0P37t3x448/4rfffkP37t11l1JdtGgRsrOzERwcjEGDBmHQoEG4efNmJd0lqukOhz2EJt/QIic7M7Rt4mjAiIiIiIiIiKgqlGpOjIYNG2Lbtm0F2tevX6/7u3379jh58mSht//zzz/LGR6RvqwcNY5fitZr6+vnAbGYvTCIiIiIiIhqulL1xCCqLk7+F4OsHI1u2dJMhi4tyzY/CxERERERERknFjHIaKg1WhwKe6jX1rudO+QyiYEiIiIiIiIioqrEIgYZjXPX45GUlqNblkvF6NW2rgEjIiIiIiIioqrEIgYZBUEQsC9E/7KqXVq5wspcbqCIiIiIiIiIqKqxiEFG4VpEEqISFLplkSh3Qk8iIiIiIiKqPVjEIKOw/5leGO2aOMLJztxA0RAREREREZEhsIhB1d6DR+kIj0jWawvw9zJQNERERERERGQoLGJQtXcgVL8XRhMPWzRwszZQNERERERERGQoLGJQtfY4NQuh1+P12gI6eBooGiIiIiIiIjIkFjGoWjt0LgpaQdAtuzqYo1UjBwNGRERERERERIbCIgZVWxnZKpz8L0avrW8HT4hFIgNFRERERERERIbEIgZVW8cvRiNHpdEt21jI0am5iwEjIiIiIiIiIkNiEYOqJZVai8NhUXptfdq7QyblU5aIiIiIiKi2kho6AKL8spVq7A+JxIHQh3q9MORSMV7wrWvAyIiIiIiIiMjQWMSgaiNbqcaSzeeRkJIFlVqrt04mFUMi5lwYREREREREtRn75lO1sT8kstACBgDkqDTYHxJpgKiIiIiIiIioumARg6qNoxeiCy1gAIBaI+DYxegqjoiIiIiIiIiqExYxqNpQZKmKX59Z/HoiIiIiIiKq2VjEoGrDwrT4KVoszWVVFAkRERERERFVRyxiULVRx9asyHUyqRg9eXUSIiIiIiKiWo1FDKoW7sem4cGj9ELXyaRiONqaIcDfs4qjIiIiIiIiouqEl1glg9NqBfx04KZem0gEQMgdQtLTty4C/D1hKufTlYiIiIiIqDbjt0IyuBP/xSDimV4Y7w1rjRYNHAwUEREREREREVVHHE5CBpWWqcT2E3f12tp5O7KAQURERERERAWwiEEG9cfxu8jIVuuWTWQSvNG7sQEjIiIiIiIiouqKRQwymDtRqTh9OVavbWCXerC3NjVQRERERERERFSdlaqIcf/+fQwfPhx9+/bF8OHDERERUWCb06dPY+jQoWjRogWWL19e6nVUO2m0Wmw5qD+Zp6uDOV708zBQRERERERERFTdlaqIsWDBAowYMQIHDhzAiBEjEBwcXGAbDw8PLF26FOPGjSvTOqqdjl2IRmS8Qq9t1EvekErYOYiIiIiIiIgKV+I3xsTERISHhyMwMBAAEBgYiPDwcCQlJelt5+XlBR8fH0ilBS94Utw6qn1SFTn469Q9vTb/Zs7w8bIzUERERERERERkDEqsKsTGxsLZ2RkSiQQAIJFI4OTkhNjYWNjb21d6gHkcHCyr7FhUPo6OVqXabvOhW8jK0eiWzUykmDKsDefCqEKlzRUZFvNkPJgr48FcGQ/myjgwT8aDuaKawmi6RiQmKqDVCoYOg4rg6GiFhIT0Ere7GZmM4+ej9NoGda0PTY4KCQmqygqP8iltrsiwmCfjwVwZD+bKeDBXxoF5Mh7VPVdisYg/WlOplTicxNXVFXFxcdBocn8512g0iI+Ph6ura6UHRzWLWqPFloO39NrcHS3Qu11dA0VERERERERExqTEIoaDgwN8fHywe/duAMDu3bvh4+NTpUNJqGY4HBaF6McZem2jXvKGRMzJPImIiIiIiKhkpfr2uHDhQmzZsgV9+/bFli1bsGjRIgDA+PHjceXKFQBAWFgYunfvjh9//BG//fYbunfvjlOnTpW4jmqH5PQc7DxzX6+tSwsXNPGwNUxAREREREREZHREgiAYxUQTnBOjeitpnN26nVcRej1et2xmIsWnEzrC2kJeFeFRPtV9TCTlYp6MB3NlPJgr48FcGQfmyXhU91xxTgwqC/bjp0oXHpGkV8AAgKHdG7CAQURERERERGXCIgZVKpW64GSeXs5W6OnLyTyJiIiIiIiobFjEoEp18FwkHiVl6pZFAEb1bQKxWGS4oIiIiIiIiMgosYhBlSYxNRu7zkTotXVr7YaGbjaGCYiIiIiIiIiMGosYVGl+PXIbSrVWt2xhKsWrLzQ0YERERERERERkzFjEoEpx+W4iLtxK0Gt79YWGsDSTGSgiIiIiIiIiMnYsYlCFU6k1+OWQ/mSeDdys0a21m4EiIiIiIiIiopqARQyqcPv+jUR8SpZuWSQCRr/kDbGIk3kSERERERFR+bGIQRUqPiULe/59oNfW07cuvFysDBQRERERERER1RQsYlCFEQQBvxy6BVW+yTytzGUY0r2BAaMiIiIiIiKimoJFDKowl24/xuW7iXptr/VsBAtTTuZJREREREREz49FDKoQ2Uo1fjl8W6+tsbsNOrdwMVBEREREREREVNOwiEEVYtuR20hMy9Yti0UijHrJGyJO5klEREREREQVhEUMem6PkjKx/dgdvbbe7dzh4WRpoIiIiIiIiIioJmIRg56LIAj4+eBNqDVPJ/O0sZRjcLf6BoyKiIiIiIiIaiIWMei5nL+ZgGsRyXptw3s1gpmJ1EARERERERERUU3FIgaVW7ZSjV+P6E/m2dTTFv4+zgaKiIiIiIiIiGoyFjGo3P4+E4Hk9BzdskQswkhO5klERERERESVhEUMKpfoBAUOnXuo1/aSnwfq1rEwUERERERERERU07GIQWUmCAJ+PnQLGq2ga6tjY4oBXeoZLigiIiIiIiKq8VjEoDILCY/DjcgUvbZ3BreEqZyTeRIREREREVHlYRGDyiQzW42tR+/otTWvb4/OLV0NFBERERERERHVFixiUJnsPH0fqRlK3bJUIsKoF5twMk8iIiIiIiKqdCxiUKk9jFfgyPkovbYAfy8425sbKCIiIiIiIiKqTVjEoFLRCgJ+OngTWkF/Ms+XO3kZMCoiIiIiIiKqTUpVxLh//z6GDx+Ovn37Yvjw4YiIiCiwzenTpzF06FC0aNECy5cv11un0WiwaNEi9OnTBy+++CK2bdtWIcFT1fnnyiPciUrVa3ujT2OYyCQGioiIiIiIiIhqm1IVMRYsWIARI0bgwIEDGDFiBIKDgwts4+HhgaVLl2LcuHEF1u3atQuRkZE4ePAgtm7ditWrVyMqKqrAdlQ9ZWSrsO24/mSerRs6wLexo4EiIiIiIiIiotqoxCJGYmIiwsPDERgYCAAIDAxEeHg4kpKS9Lbz8vKCj48PpNKCl9ncu3cvhg0bBrFYDHt7e/Tp0wf79++voLtAlW37yXtIz1TplmVSMUa82MSAEREREREREVFtVGIRIzY2Fs7OzpBIcocNSCQSODk5ITY2ttQHiY2NhZubm27Z1dUVjx49Kke4VNUiHqXh+IVovbaXO3nB0dbMQBERERERERFRbVWw20Q15eBgaegQah2tVsBnv1yAkK/NtY4FRr/cHPJC5sJwdLSquuDouTBXxoF5Mh7MlfFgrowHc2UcmCfjwVxRTVFiEcPV1RVxcXHQaDSQSCTQaDSIj4+Hq6trqQ/i6uqKmJgYtGrVCkDBnhmlkZiogFYrlLwhVZgTl6JxKzJFr+31Xo2QmpJZYFtHRyskJKRXUWT0PJgr48A8GQ/myngwV8aDuTIOzJPxqO65EotF/NGaSq3E4SQODg7w8fHB7t27AQC7d++Gj48P7O3tS32QgIAAbNu2DVqtFklJSTh8+DD69u1b/qip0qVnKvHH8bt6be2aOKJlAwcDRURERERERES1XamuTrJw4UJs2bIFffv2xZYtW7Bo0SIAwPjx43HlyhUAQFhYGLp3744ff/wRv/32G7p3745Tp04BAAYNGgR3d3e89NJLeO211zB16lR4eHhU0l2iivDnibvIyFbrluUyMd7o09iAEREREREREVFtJxIEwSjGaHA4SdW5G52KpT+d12t79YWG6N/Rq8jbVPcuavQUc2UcmCfjwVwZD+bKeDBXxoF5Mh7VPVccTkJlYTQTe1ZH2Uo19odE4uiFaCiyVLA0k6FX27oI8PeEqdw4H1qtVsBPB2/qtbk6mOMlP/acISIiIiIiIsMyzm/a1UC2Uo0lm88jISULKrUWAKDIUmFfSCTCbiZg3pvtjLKQcexiNCLjFHpto15sAqmkVCOPiIiIiIiIiCoNv5mW0/6QSL0CRh6VWouElCzsD4k0UGTll5qhxPaT9/TaOvg4wade6SdxJSIiIiIiIqosLGKU09EL0QUKGHlUai0OnHuIjGxVFUf1fLYdu4OsnKeTeZrIJRjei5N5EhERERERUfVgfOMdqglFVvEFihylBjO/OY2WDRzg38wZbRrVgYlcUkXRld2thyn45+ojvbbBXevDzsrEQBERERERERER6WMRo5wszWQlFjI0WgGX7jzGpTuPYSKTwLdxHXRo5owW9e2r1RwTGq0WW56ZzLOuowV6t3M3UEREREREREREBbGIUU692tbFvpDIIoeUPCtHpcG/4XH4NzwOFqZStG/qBH8fZzTxtIVYJKrkaIt3JCwKUQkZem2jX/KuVoUWIiIiIiIiIhYxyinA3xNhNxMKTO4plYhgZiKFqYkUCclZhd42I1uNE5dicOJSDGwt5ejg4wz/Zs6o52IFURUXNJLTc7Dj9H29ts4tXNDEw7ZK4yAiIiIiIiIqCYsY5WQql2Lem+2wPyQSxy5GQ5GpgqW5DD196yLA3xMmMgkexKUjJDwOodfjkZyeU+h+UhRKHDz3EAfPPYSTnRn8nxQ03OpYVMn9+P3YHWQrNbplMxMphvVsVCXHJiIiIiIiIioLFjGeg6lcisHdGmBwtwaFrq/nYo16LtYY1rMRbj9MQUh4HM7diEdGtrrQ7eOTs7Drnwjs+icCnk6W8G/uDH8fZ9hbm1ZK/NcjkhASHqfXNrR7A9hYyCvleERERERERETPg0WMKiAWieDtaQdvTzuMeLEJrt1PQsj1OFy89Rg5Kk2ht4mMVyAyXoFtx+6isbsNOjZzRrumTrA2r5gCg1qjxZZDt/TaPJ0t0dO3boXsn4iIiIiIiKiisYhRxaQSMVo3qoPWjeogR6XBf3ceIyQ8DlfuJUKtEQq9ze2oVNyOSsXPh26jWX07+Ps4o20TR5iZlD99B889RGxipl7b6Je8IRYbdpJRIiIiIiIioqKwiGFAJjIJOvg4o4OPMzKyVTh/MwEh4XG4EZkMoZB6hlYQcPVeEq7eS8LmAzfRuqED/Ju5oFVDe8ikklIfNyktG3+f0Z/Ms1srVzSsa/O8d4mIiIiIiIio0rCIUU1YmMrQvbUburd2Q6oiB6E34hESHod7MWmFbq9SaxF2MwFhNxNgZiJB2yaO8G/mDB8vO0jExV8a9dcjt6FUPb2iioWpFK++0LBC7w8RERERERFRRWMRoxqysTTBi+098GJ7D8SnZCE0PA4h4XGIfpxR6PZZORqcufIIZ648grW5DH5Nc69w0rCudYFLtl69l4jzNxP02l55oSGsKmiuDSIiIiIiIqLKwiJGNedka4bAzvUQ2LkeouIVCLmeW9B4nJpd6PZpmSocuRCFIxeiUMfGFB18nOHbuA6u3EvE0QvRUGSp9Lav72qN7q3dquKuEBERERERET0XFjGMiLuTJdydLDG0ewPcjUnLvWTr9TikZaoK3f5xajb2/vsAe/99UOQ+X+vZEGIRJ/MkIiIiIiKi6o9FDCMkEonQqK4NGtW1weu9G+HGgxSEhMfh/K0EZOWoS70fsQi4/iAZ3p52lRgtERERERERUcVgEcPIScRiNK9vj+b17TG6bxNcvpuEkOtx+O/OY6jU2mJvqxWAYxejMbhbgyqKloiIiIiIiKj8WMSoQWRSCdp5O6KdtyOyctS4eDsBG3ZfL/Y2iiKGohARERERERFVN8Vfi5OMlpmJFJ1buMLSTFbsdpbmxa8nIiIiIiIiqi5YxKjherWtC5m08DTLpGL09K1bxRERERERERERlQ+LGDVcgL8nHG3NChQyZFIxHG3NEODvaaDIiIiIiIiIiMqGRYwazlQuxbw326GfvyeszGUQAbAyl6GfvyfmvdkOpnJOi0JERERERETGgd9gawFTuRSDuzXgVUiIiIiIiIjIqLEnBhEREREREREZhVIVMe7fv4/hw4ejb9++GD58OCIiIgpso9FosGjRIvTp0wcvvvgitm3bpluXkJCAyZMnY8CAAejXrx927txZYXeAiIiIiIiIiGqHUhUxFixYgBEjRuDAgQMYMWIEgoODC2yza9cuREZG4uDBg9i6dStWr16NqKgoAMBnn32GFi1aYNeuXfj555+xcuVKxMbGVuw9ISIiIiIiIqIarcQiRmJiIsLDwxEYGAgACAwMRHh4OJKSkvS227t3L4YNGwaxWAx7e3v06dMH+/fvBwDcuHED3bp1AwDY29ujadOm2LdvX0XfFyIiIiIiIiKqwUqc2DM2NhbOzs6QSCQAAIlEAicnJ8TGxsLe3l5vOzc3N92yq6srHj16BABo3rw59u7di5YtWyIqKgoXL16Eu7t7mQJ1cLAs0/ZU9RwdrQwdApUSc2UcmCfjwVwZD+bKeDBXxoF5Mh7MFdUUVXJ1kqCgICxbtgyDBg2Cm5sbOnXqpCuKlFZycga0WqGSIqTn5eBgicREhaHDoFJgrowD82Q8mCvjwVwZD+bKODBPxqO650osFsHOzsLQYZCRKLGI4erqiri4OGg0GkgkEmg0GsTHx8PV1bXAdjExMWjVqhUA/Z4Z9vb2+OKLL3Tbjh8/Ho0aNSpToHxSV3/sLWM8mCvjwDwZD+bKeDBXxoO5Mg7Mk/FgrqimKHFODAcHB/j4+GD37t0AgN27d8PHx0dvKAkABAQEYNu2bdBqtUhKSsLhw4fRt29fAEBycjLUajUA4OzZs7h165Zujg0iIiIiIiIiotIQCYJQ4hiNu3fvIigoCGlpabC2tsby5cvRoEEDjB8/HjNmzEDLli2h0WiwePFinDlzBkBub4vhw4cDAE6cOIGlS5dCLBbDzs4OwcHB8PHxqdx7RkREREREREQ1SqmKGEREREREREREhlbicBIiIiIiIiIiouqARQwiIiIiIiIiMgosYhARERERERGRUWARg4iIiIiIiIiMAosYRERERERERGQUWMQgIiIiIiIiIqMgNXQAVL0lJyfjo48+QmRkJORyOby8vLB48WLY29vD29sbTZo0gVicWwv7/PPP4e3tDQA4evQoPv/8c2g0GjRv3hyffvopzMzMDHlXaoVevXpBLpfDxMQEAPDBBx+gW7duuHTpEoKDg5GTk4O6detixYoVcHBwAIBi11HliIqKwtSpU3XL6enpUCgUCA0NLTKHAHNVFZYvX44DBw4gOjoau3btQpMmTQAA9+/fR1BQEFJSUmBra4vly5ejXr16z7WOnk9huSruPQsA37cMpKjXVXnPdzwXVp7CclXcexZQ/jxS+RV3rivva4e5IqMiEBUjOTlZ+Pfff3XLn332mTBnzhxBEAShSZMmgkKhKHAbhUIhdO7cWbh//74gCIIwd+5cYfXq1VUSb23Xs2dP4ebNm3ptGo1G6NOnj3Du3DlBEARhzZo1QlBQUInrqOosWbJEWLRokSAIhedQEJirqnLu3DkhJiamQB5Gjx4t7NixQxAEQdixY4cwevTo515Hz6ewXBX3niUIfN8ylKJeV+U53/FcWLmKylV++d+zBIHvW4ZQ1LmuvK8d5oqMDYeTULFsbW3h7++vW27Tpg1iYmKKvc3JkyfRokUL3a+Nr7/+Ovbt21eZYVIxrl69ChMTE7Rv3x5Abj72799f4jqqGkqlErt27cIrr7xS7HbMVdVo3749XF1d9doSExMRHh6OwMBAAEBgYCDCw8ORlJRU7nX0/ArLVXneswC+b1W2wnJVHL5vGU5JuSrtexbAXFWmos515X3tMFdkbDichEpNq9Xi119/Ra9evXRto0ePhkajQffu3TF9+nTI5XLExsbCzc1Nt42bmxtiY2MNEXKt9MEHH0AQBLRr1w6zZs0qkA97e3totVqkpKQUu87W1tYA0dc+R48ehbOzM5o3b65rezaH1tbWzJUBxcbGwtnZGRKJBAAgkUjg5OSE2NhYCIJQrnV5wxuo8hT2ngXwfau6Kev5judCwyrsPQvg+5Yh5T/Xlfe1w1yRsWFPDCq1Tz75BObm5hg1ahQA4Pjx49i+fTt+/vln3LlzB2vWrDFwhPTzzz/j77//xp9//glBELB48WJDh0Ql+PPPP/V+0WIOiSrGs+9ZAN+3qhue74zPs+9ZAPNoaIWd64hqOhYxqFSWL1+OBw8eYNWqVboJ0fK6G1paWmLYsGG4cOGCrj1/992YmJgydSOl8st7nOVyOUaMGIELFy4UyEdSUhLEYjFsbW2LXUeVLy4uDufOncOAAQN0bYXlMK+duTIMV1dXxMXFQaPRAAA0Gg3i4+Ph6upa7nVUuQp7zwL4vlXdlOd8x3Oh4RT2ngXwfcuQnj3Xlfe1w1yRsWERg0r01Vdf4erVq1izZg3kcjkAIDU1FdnZ2QAAtVqNAwcOwMfHBwDQrVs3XLlyBREREQCA3377Df369TNI7LVJZmYm0tPTAQCCIGDv3r3w8fFBixYtkJ2djbCwMAC5+QgICACAYtdR5fvrr7/Qo0cP2NnZASg6hwBzZUgODg7w8fHB7t27AQC7d++Gj48P7O3ty72OKk9h71kA37eqm/Ke73guNJxn37MAvm8ZUmHnuvK+dpgrMjYiQRAEQwdB1dft27cRGBiIevXqwdTUFADg7u6Od955B8HBwRCJRFCr1fD19cXcuXNhYWEBADh8+DBWrFgBrVYLHx8ffPbZZzA3NzfkXanxHj58iOnTp0Oj0UCr1aJhw4aYN28enJyccOHCBSxYsEDvsll16tQBgGLXUeXq27cvPv74Y3Tv3h1A8TkEmKuqsGTJEhw8eBCPHz+GnZ0dbG1tsWfPHty9exdBQUFIS0uDtbU1li9fjgYNGgBAudfR8yksV6tWrSr0PWvNmjW4ePEi37cMpLBcrVu3rtznO54LK09R50Cg4HsWwPctQynq8/maNWvK/dphrsiYsIhBREREREREREaBw0mIiIiIiIiIyCiwiEFERERERERERoFFDCIiIiIiIiIyCixiEBEREREREZFRYBGDiIiIiIiIiIwCixhEREREREREZBRYxCAiIjKQ1atX44MPPjB0GERERERGg0UMIiIiIiIiIjIKIkEQBEMHQUREVNN9//33+Omnn6BQKODk5IQ5c+Zg2rRpEAQBcrkcHh4e+Pvvv5Geno5PP/0UJ0+ehEgkwtChQzFjxgxIJBJs374dv//+O5o1a4adO3fC0dERCxYsQKdOnQx994iIiIiqhNTQARAREdV09+7dw88//4w//vgDzs7OiIqKglarxcSJE/HgwQN88cUXum2DgoLg4OCAgwcPIisrCxMnToSrqytef/11AMDly5cREBCAf//9F4cOHcK0adNw5MgR2NraGujeEREREVUdDichIiKqZBKJBEqlEnfv3oVKpYK7uzs8PT0LbPf48WOcOHECc+fOhbm5ORwcHDBmzBjs2bNHt429vT3eeustyGQy9O/fH/Xr18fx48er8N4QERERGQ57YhAREVUyLy8vzJ07F6tXr8adO3fQtWtXBAUFFdguJiYGarUaXbt21bVptVq4urrqlp2dnSESiXTLbm5uiI+Pr9w7QERERFRNsIhBRERUBQYMGIABAwZAoVAgODgYX3zxBby8vPS2cXFxgVwux7///guptPC36Li4OAiCoCtkxMbGolevXpUePxEREVF1wOEkRERElezevXs4e/YslEol5HI5TExMIBaL4eDggOjoaGi1WgCAk5MTunTpgs8++wwKhQJarRaRkZEIDQ3V7SspKQmbN2+GSqXCvn37cPfuXfTo0cNQd42IiIioSrEnBhERUSVTKpX48ssvcffuXchkMvj6+mLx4sWQy+X4+++/4e/vD3d3d/z111/4/PPP8cUXX6B///7IyMiAh4cHxo8fr9tXq1at8ODBA3Ts2BF16tTBN998Azs7OwPeOyIiIqKqw0usEhERGYnt27dj27Zt+PXXXw0dChEREZFBcDgJERERERERERkFFjGIiIiIiIiIyChwOAkRERERERERGQX2xCAiIiIiIiIio8AiBhEREREREREZBRYxiIiIiIiIiMgosIhBREREREREREaBRQwiIiIiIiIiMgosYhARERERERGRUfh/4jhpMb7G6VEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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gjAPD09DS4/pVRXr+tSL9+nK+vL6ZPn46VK1fi1q1b6Ny5M6ZNm1biaLPyriUAuLm56UdgApV/vd9++y0mTJhQ5dGARWsBCgLfQt26dcOgQYOwf/9+BAcHIz4+Htu2bcOPP/6o30etViM5OVk/Um7UqFH6wPm1117DV199VSx4JyIiIqoJDLyIqM4o+iFxx44dOHjwIL7//nt4eXkhOzsb7du3hyAIFTrX6NGjMXr0aKSmpmLKlClYt24dpkyZUmw/V1dXgzvvxcfHQyqVwtnZWb/eUtG6KsPR0RGWlpbYtWtXqVO1ip573Lhx+nWpiho+fDiysrLw5ptvYt26dfqwBYDBmlA5OTnIzMzUfzhVqVSYMGEC3Nzcik3TbNKkicFaPffv34darYafn5++be7cufjmm2+wcOFCTJ8+HQAwePBgfUBTk0p7/x5/Lzw8PODl5YV9+/aVeq6i10in0yEpKUl/jQRBwH/+8x88ePAA33zzDWQymX7fJk2aGKzJpVQqERMTg8aNG6NevXpwcXHBtWvX0KlTJwDAtWvX9NMhAcDe3h5LlizBlClTsGrVKgQHBwOAPiSsLe7u7pDL5fjnn39KvFmDr68vLC0t8eOPPyIkJAS2traoX78+fv31VwQHB+vDZj8/Pyxbtgw6nQ779u3D5MmTERERUe7zu7q6Giyin5CQoL/+lf3e8vPzg1arxd27d/V9teh1r0i/LsmgQYMwaNAgKBQKzJo1C0uXLsWSJUuK1VfetQQK1pQTBEF/bEJCAsLCwir8Gr/77ju88cYbqF+/Pvr27Vvh4wqVd01FIpH+56iHhwfGjRuHt99+u8R93d3dDc5X1Z+FRERERFXBKY1EZJbq169f5oLKOTk5kMvlcHR0RG5uLpYtW1bhc1+8eBEXLlyAWq2GlZUV5HK5/kP74wYOHIj169fj/v37yMnJwfLly9GvX78q3cXxcWKxGMOHD8fChQv1I8aSkpIM1peqqFmzZqFhw4YYN26cwRo6R48eRWRkJFQqFVasWIE2bdrAw8MDarUakydPhoWFBRYvXlzs9Q8aNAiHDx9GZGQklEolVqxYgd69exuMorKxscG6desQGRlZbaO3KqKs98/Z2Vl/F0UAaN26NWxsbPD1118jLy8PWq0WN27cwMWLF/X7XLlyBfv27YNGo8H69eshl8vRpk0bAMDs2bMRHR2NtWvXFptS2rt3b9y8eRN79+5Ffn4+Vq9ejYCAAP2Ux6FDh+Krr75CZmYmoqOjsWXLFjzzzDMG5wgNDcXSpUsxadIkg5pqk6urKzp16oRPP/0UCoUCOp0OMTEx+mmMQMEorx9//FE/ffHxx0DB2lCFoy3t7e0BoNTvq6IGDBiAr776CmlpaUhLS8Pq1asxaNAgAAXvZ0ZGhsFUZUEQkJ+fr19nLT8/HyqVCkDBCK7evXvjyy+/hFKpxJkzZ3Dw4EEMGTIEQMX69eNu376NkydPQqVSQS6Xw8LCwqC/xcXF6UdlVuRapqWlYcOGDVCr1fjzzz8RHR2Nbt26ASgYfZifnw+NRgOdTmfwOgs1btwY69atw7x583Dw4MFyr2959uzZg5ycHOh0Ohw/fhx//PGHPoAbPnw4fvnlF1y4cAGCIECpVOLIkSP6UXLDhg3Dxo0bkZqaiszMTPzwww/o3r37v66JiIiIqCIYeBGRWXrzzTfx1VdfISQkxGAR5UJDhw6Fp6cnunTpggEDBhRbL6gsOTk5mDFjBp566in06NEDDg4OGDNmTIn7Pvvssxg8eDBefvll9OzZE3K53GBa27/1wQcfwNfXF88//zzatWuHV199VX+nxMoQiUSYP38+3N3dMX78eOTn5wMoCOxWr16N0NBQXLlyBUuWLAEAnDt3DocPH8aJEyfQvn17/V3wIiMjARSMhJk7dy7ef/99dOzYETk5OfqpikXZ29vju+++w7Fjx/DFF19U/UJUQlnv33PPPYdbt27p17mSSCRYu3Ytrl27hp49e+Lpp5/GjBkzDKa19ezZE7t370b79u2xfft2rFy5EjKZDHFxcdi8eTOuXr2Kzp07669R4fpSTk5OWLlyJZYvX4727dvj4sWLBsHr5MmT4e3tjR49emDUqFEYM2YMunbtWuz1dOrUCQsXLsS4ceP0d4CsbZ999hnUajX69++P9u3bY/LkyUhJSdFvb9++PXJycgwCr6KPAeCvv/7CgAEDEBQUhAULFmD58uWlrjtX1Pjx49GyZUv96MAWLVro1yjz9/fHgAED0KtXL4SEhCApKQlxcXFo3bo1BgwYAKAg1AwPD9efb/bs2cjLy0PHjh3x3nvvYc6cOfrRYxXt10WpVCp8/vnnCA0NRefOnZGWloapU6cCgP55Q0ND9WFmedeydevWuHfvHp5++ml88cUX+PLLL+Ho6AigIDRs3bo15syZg8jISLRu3brEnzfNmjXD2rVrMXPmTBw9erTca1yWDRs2oGvXrggJCcFnn32GTz75RD9NuVWrVpg/fz7mzZuH9u3bo0+fPti6dav+2PHjx6NVq1bo27cv+vfvj+bNm5c6GoyIiIiouomEis7vISKiOqVwnSGup1O6lStX4t69e7U6Qo2eXFu3bsWWLVuwadMmY5dCREREZPY4wouIiIiIiIiIiOoULlpPRERmJzIyEmPHji1x27lz52q5GvPD61c58fHx+imSj9u1a5fBXSRNTdE7oxbl6emJXbt2GaEiIiIiotrBKY1ERERERERERFSncEojERERERERERHVKQy8iIiIiIiIiIioTmHgRUREREREREREdYrZLFqfnp4DnY7LjZFpcna2RWqqwthlEJWLfZXMBfsqmQP20yebVquFTqczdhkVYup9VSwWwdHRxthlEJEZEolEkEpLjrbMJvDS6QQGXmTS2D/JXLCvkrlgXyVzwH765MnMzMDJk8eQm6uEWGweE2YkEjG0WtMO5yQS87iWRGRadDod5HI5evToAS8vL4NtZhN4ERERERERGVN+fh6OHz+E0NCn4e/vD5FIZOySKkQmk0Ct1hq7jFKJRIBUKjF2GURkhgRBQEJCAg4ePIj+/fvD2dlZv40xOhERERERUQWkp6fB0dEJjRs3Npuwi4ioLhOJRPD09ETDhg2RkJBgsM2sR3hptRqkp6dAo1EZuxSqJlKpHI6OLpBIzLprEhEREVEdpNFoIJfLjV0GERE9Ri6XQ61WG7SZdaqQnp4CS0tr2Ni48zcsdYAgCMjJyUJ6egrq1/cwdjlERERERGWKi4tDbOx9hIY+XW3njIqKwqxZM9C7dx+89da4ajtveV599RWsW/dtqYs/F5owYTyys7Mgk8mwYMEiuLu74+bNm5g3by4EQcDMmbMQEBBQ5vni4uLw4osvoFGjRpBKJXjnnXewfPly5OfnIzY2Fv7+/mjbti3ee++9MmuJiIiAp6cnvL29K/w6L1y4gEWLFkEkEqFVq1aYPn06AGDdunU4ePAgPD098emnn0ImkxVrS0pKwhdffIGlS5eWeO6VK1di//79qFevHry8vJCSkoL8/HxERUWhefPmsLCwwLp16wyOUSqVeP311+Hq6oovv/yywq+jJC+++CI2bdpU7n7/+9//8NxzzwEAvvrqK/z000949tln8e677wIAbty4gdmzZ0MQBMyZMwfNmjUrsa08Bw4cQEhICBwcHCr8GqKiovDRRx8hJycHhw4dAgBs3boVWq0Ww4cPL/W4adOm4e2334avr69Be9++feHq6goAmD17Nho3blzi8XFxcZg3bx6USiUGDx5c5nNVRkREBP7++2/9tS1PWdc5KSkJ48aNw61bt3Du3DmD760ffvgBe/fu1b//CxcuxOXLl9G8eXPMmDHD4Dlyc3OxcOFCzJ8/v9KvpyrvaVmuXr2KuXPnQiwWY+rUqQgJCcHWrVvx3//+F66urmjVqhU+/PBDaDQafPzxx4iNjUWPHj3w5ptv4sCBA1CpVOjfv3+p56/QlMY7d+5gxIgR6Nu3L0aMGIG7d+8W2+f48eMYNmwYWrZsicWLF5d4ntu3b6NNmzalbq8sjUYFGxt7hl11hEgkgo2NPUfsEREREZFZiIuLQ0RERLH2f3P3xuPH/8KUKe+WG3b92ztECoIAQaj8TRc+/ng6Nmz4EWPGjMXGjesBACtXfonPPluCzz9fhlWrKhbadOjQAT/8sB7PPjscf/99Ehs3bsSyZcvQsWNHbNy4sdywCwBOnTqF+/fvV6p+T09PrF+/Hps2bUJqaiquX7+O1NRUREREYNOmTQgICMCBAwdKbKuIadOmYePGjVi0aBHWrVuHjRs3omnTpti4cWOxsAsArl27hpCQkH8ddlXGb7/9pv96+PDhxQK8FStWYNmyZVixYgVWrFhRalt5Dhw4gMzMzErV5uPjg82bN8PNza1Sx5VEp9PByckJGzduxMaNG0sNuwBg+fLlWLRoETZu3FhtYVdVlHWdHRwc8MMPP6Bt27YG7SqVClevXtU/vnLlCpRKJX7++Weo1WpcvHjRYP+dO3eiR48e+sf379/H1KlT8fLLL2Ps2LE4duxYqfVV5T0ty4oVK/DFF1/g22+/xVdffaVvf+ONN7Bx40Z8+OGHAIBDhw6hUaNG2LRpE86cOYOUlBSEhYVh9+7dZZ6/QiO8Zs+ejZEjR2LIkCHYvn07Zs2ahQ0bNhjs4+3tjQULFmDPnj1QqYoHFlqtFrNnz0avXr0q8pQVxrCrbuH7SURERESmTBAEZCjyka1UY/3Gn3H1ykWcOXsOCz75BDNnzoCDgwO6dOmC1NRUnDhxHPn5+Zg1azYCA5vj1VdfQYsWzREZeQbPPz8Czz77LKZP/xjx8XEQicSYPXsO/ve/LbC1tYNSqYSdnR1WriwIQiZNmowOHTri1VdfQatWrZCcnAwfHx/ExsYiJSUZrq5u8PHxwbFjR9GlS1e8/fZ4pKWlYdasmVAqc9CwYSPMnDkLq1evQkJCPJKSkrF48WdwcnIyeH27du3EpUuXMG3axxg6dDAaNWqE2NhYzJw5G61atdLfBU0qlUIsLlhoPisrCx4eBTM0srOzSz1fSZo1a4bTp4uHhiVZtmwZTp8+DalUisWLF+P333/H/v370bFjR0yZMgXTp0/HgwcP4OTkhCVLlmDHjh3Yv38/VCoV5HI5VqxYARcXF/35ZDIZJBIJLl++jKeeegoA0LFjR+zYsQPW1tbF2lq1agUAUKvVmDZtGkaMGKHfp6qWLFmCxMRESCQSvPLKK5g+fTpycnLg7++POXPm4I033sC6deuwfPlyKJVK/Oc//8GYMWPw+eefY9KkSQCAgIAAzJgxA1qtFjNmzMClS5fw3nvvoWvXrti+fTt++ukniMVizJkzB3Fxcbhx4wZGjRqFcePGoVOnToiOjjaoqaT3s6z3uNBHH32EuLg4iEQiLF68GH/99Reio6PRt29fDBs2rNhrW7lyJe7cuYO0tDR4enpi4cKFsLW1LfVaJSUlYebMmZg/f36pgdjWrVtx7NgxKJVKvPvuu8jMzMRLL70Ef39//Oc//4GFhQUOHz6MdevWQavVYvz48ejQoQPi4+Mxa9Ys5ObmYsaMGWjYsKHBOR/vRzKZDHPmzMGdO3dgaWmJJUuWIC8vD++//z40Gg0CAgIwZ84c/TkUCgXef/99vPfee2jSpEmpr7Gs62xhYQELC4tix2zZsgVDhw7Vh6bnz59Hx44dART03fPnz6N169b6/Y8cOaIfhHT37l2sWrUKH374Idzd3ZGdnY158+ZBpVKhV69e1fKeliUrKwvu7u4ACkae5eXlAQDWr1+Pbdu2YeLEiejQoQPOnz+P8PBwAEBoaCguXryInj17QiqVIj09HY6OjiWev9zAKzU1FVFRUfj+++8BAAMHDsT8+fORlpZm8MOxcOhg4bCyx3399dfo3r07lEollEpleU9LRERERERkUlQaLVKz8pCZo4IgAH0HDIW7RwOMHjMOScmJSE1LxTffrINEKkFebh7Gjn0TMTH3sHr1KixevAQAMHDgILzzzrsYO3YMBg8ejKSkRPzwwwYIggCRSIQhQ4aiXbt26NChI0aNehlff10wKuitt95Ehw4FH2J79uyFtm3bYvXqVQgMDMSiRZ9i7Ng3EBbWE+PGvY3nnx+OcePexuqv1mLwsy+haWAr/LBuFY6fPAUA8PX1wyefFP8gunv3bly+fAnTp/8HAJCcnIyff96E7GwF5s6dgzVrCkZgaLVafP31WsyaNQcAIAiPRpsVHTX2+PlKcuZMJHx9/Sp0/c+ePasPbwRBwDPPPIPg4GD9qLCwsDAMHDgQP//8M/bu3QsAcHZ2xieffIKvv/4a+/fvx4ABAwAUjKxKS0tD48aNcfXqVX3QYmdnh6ysLGRlZRVrAwrWcZs2bRqef/75EsOuTz/9FPXq1UOvXr3wyiuvlPuapkyZop/y9umnn+Ktt95CUFAQlixZgnPnzsHPzw+3b99GXFwcpFIpkpKS4OHhgatXr+Kpp57CpEmT9Nc8MzMT7777LjQaDebNm4dOnTph48aN2LRpE5KSkjB//nz897//1Y84K03R0YOF5y6prSi1Wo3ExET8+OOP+r7cpUsX/TTDkl4bADRp0gRvv/02Zs+ejfPnzxcbvVSoImFXIXt7e3zxxRcAgJ9//hkODg5Yu3YtNm/ejJdffhnfffcd1q9fD51Oh7Fjx6JZs2a4fv069u/fj9TUVCxZsgRr1qwxOOfj/cjKygqenp6YO3cujh49il9++QWvvfYavv/+e0ilUrz//vv6mXE5OTkGYdfBgwfxww8/GJy/W7dueOONN8q9ziVd91OnTuGll17SB17Z2dn6ab52dna4efOmwTEZGRn6vr1p0yYsXLgQ33//PSIiIuDu7o53330XS5YsQbdu3arlPRWLxViyZIlBDS1atMC0adPg5OSEGzduoH79+rh58yaysrLQq1cvDB06FBkZGXj99dfx22+/ITs7GzY2NvrXVBgGenl54c6dO1UPvBISEuDm5gaJpCC9l0gkcHV1RUJCQrHfBpTm2rVrOH78ODZs2FCs41SUs3PxpDc5WQyptOI3msxTabD75D0cjIxFdq4adlYy9AzxQv8OvrCUm/VyZnWKWCyGi4udscuoNHOsmZ5M7KtkLthXyRywn5o/rU6AQqlCpiIfmTkqZClUyMzJR6ZChSxFPjIU+cjKKdienZGI7s0t8fjnUEEANFodvH38EfsgFwDw586tOHJwL8RiMUQi4H6yAvlqLawcPJGSpYZaIyAlS4VuYf3wzrvvwc3dHa+98TZy8jTIUKiQkpELjVaHPK0UIhEgAEjLzodGq0MD38bIyFEhX61FgLcfspRqODrVh6e3H3Ly1LCwtEJMUhaio6Nx5coVACLk5SnRNKA5snPV6B7cDjJZwYginU6HZcuWQSwW4bvv1uHnn3+GTFbw2c/X1xf16tmjXj175OQo9O2fffYpnnnmGfj7F4yCEYvF+m0SiQQymaTY+U6cOIG1a9eiWbNmeOWVV/DPPyfx+uuvws3NzWAkTFneeOMNfPTRR3BwcCi2JlLha928eTPy8/MxYMAA2NnZITAwEAAQGBiIS5cuASj4wD9//nx9KGJnZ4fExEQABSNx7O3tS2wDgMjISHTu3BmhoaEACgKuK1euYOzYsQAKpjQWjq6prOjoaHz++ecQiUTIyclB69at0a5dO5w+fRoymQwWFhY4ceIEgoKCEBISglOnTuG9995Dly5dMHToUDg6OsLZ2RlAwciZwlE2MpkMXl5epY7MelzRWTdisbjUtqJkMhmeeeYZvP/++2jQoAHeeeedcl8bADRv3hxAwftz7969UgOvX375Be+++26Fpjq2aNFC/3XhWlO9e/fGDz/8gPT0dERHR+O1114DUDDAx9bWFo0bN4aTkxOcnJyQkZFR7JyP9yOZTIZdu3bh+PHj0Gg0aNu2LTIyMjBnzhxkZ2cjLi4OycnJAIA9e/bg+eef14/s6tmzJ3r27Fli7eVd58dt374dgwYNMmizs7ODQqEAYNh3S2JlZQWVSoVbt27hu+++w9SpU6HT6WBpaVlt7+mQIUNKDVjfe+89zJ8/HzY2NggICICjoyNkMhkAwMnJCX5+fnjw4AFsbW2Rk5Ojf00+Pj7lXhugFhatV6vVmDlzJhYtWqQPzaoiNVUBnc7wXxadTgeNpmJz1/NUGnyy4QxSMnKhfnhMdq4au07ew6mryZgxOvhfh16dO4dg375jsLa2rvAxP/ywDgcO7INEIoZEIsVbb01AaGiHgprz8rBw4Vxcv34VEokEEyZMQadOXQAAEye+iRdfHKV/XBMWLJiDZs0C8eyzI7Bt2/+Qn5+PESNeKrbf7t078Pfff+GTTz7DX38dwfffr4NaXfBbrwEDBuPFF18GAHz77X+Rm5uLiROnlPm8Op0OKSkV+2FsKlxc7MyuZnoysa+SuWBfJXPAflp98lQa7ImIwaGzcVDkqmFrJUNYuwYID/Wp9P/RtTodFEo1spVqZCtVyM5VIytHVfA492Gb8tHfOXnqYgFWaewkhp89pFIptDqt/rGoyAfUXX/8hi/XbkRCfCxWLlsErU6AIBQEbNDooBOA3Dw1Onbrja49+2HlsoW4ePES1Bod8lVa5ORpoNHqkPwgHQCgUmuRlaOCRisgS6mGJF9Abr4WCqUGaVl5yFdrkaFQwTojD2qNriAY8/JFj17haNy04MO6VqvB3TvRyFaqoVZrsW7dd/p6dToBn3yyEO+99z6WL/8ClpaWuHfvHjIzs6FQKGBtbQO1Wovffvvt4f/zB0GtLnjtdnb2uH8/DmKxWL/f4+d76qmn8dRTBYv7x8XF4emnO2Dx4s8KrlsFVzV5+umn0b17d6xduxZHjhwpuP7aghoaNmyIDh06oG/fvgAKPofu2LED169fB1AwCMPHxwcajQYffPABPvroI/30xlatWuHnn3/G2LFj8ffff6NNmzYlthXW4OHhgY0bN2LUqFGYNm2avr4LFy5U7IWUomHDhhg8eDBatmwJoGA02YMHD/DWW2/hueeeg42NDdavX4/ly5dDp9PpA4ghQ4Zg6NChxZaHcXJyQnx8PNRqNZKSkmBnVxDQl7eMTL169ZCYmPhwjWWbUtuK0mq1GDBgAIYOHYqZM2fi0qVLxd6fx1/bjRs3cO3aNXTr1g3Xrl3DkCFDSq3p7bffxoEDB/Q3NChLYVBUOPNMLpfj7Nmz8Pb2hqOjI5o2bYpvv/0WEokEarUaMpkM1tbWyM3NNRjZV9Tj/cjS0hJDhw7F66+/DqCgv23cuBG9evXCsGHD8N577+lHaD377LNISEjAgQMH0KtXrzJHeJV3nR93584dXLt2DZs2bcKtW7ewceNGtGvXDps3b0b//v3x999/Y9iwYQbH1KtXDwqFAra2tvprVHjNxGIxMjIyoFKpqu09vXjxYqkjvBo2bIjvvvsOaWlpWLRoEWQymb62vLw83Lt3D05OTggKCsLJkyfRunVrRERE6EdqxsbGGkw/fVy5/3p4eHggKSkJWq0WEokEWq0WycnJ+nml5UlJSUFMTAzefPNNAAVJsyAIUCgUVborQEmu3kvHj/uuIyG18lMl1Rod4h/kYPyy0hdm83C2xst9AhDoW/IwuX8jMLAFXnjhZVhaWuLmzRuYNOlNbN++BxYWlti0aSNsbGywefM23L8fgwkTxuKXX36vVKBWXYYOfa5C+zk51cdnny1H/fouUCgUGDPmZTRv3gJt2gTVcIVEREREZI5K+sW0IleNPyNiEHk9BR+NDCoIeXJUyM4tDKseC65yC9oUShVy8jS1Vruvnz/Wf7sGi+f/B6+OnWCwrWlAC0x79y20aF36/4Nzc5WYP/N96HQ6WFvbwLdhY0SeOqnf/uKoMZjx0WQAwEuvvFnp+p4f+SpWLl8EZY4CIpEYk9+bDkEAcvO1Je7frFkzvPba65g+fRo++2wp3N09MHPmDNy/H4MZM2YCAD75ZB5atWqFV199BSEhIZg4cRImTJiI998vWGS+6B3hHj9feXeALM/48eP1a/ysWLECbm5uWLZsGS5cuIA33ngDM2bMwM8//wxBEPSL3hdOi5LL5fjyyy+xZ88eXLp0Sf8BfOrUqfoRUy+++CI8PT3xyiuvQC6XF2srHLHzzjvvYM6cOdi1a5f+g3d1GDduHGbOnIns7GyIxWJ88skn8PLyQlZWFoKDg2FtbY3FixejUaNGOH36NJYtWwaNRlPqiDKJRIKXXnoJL730EsRiMWbNmgUAaN26NcaPH4/XX38dd+7cwaZNm5CRkYGsrCzMnj0bkyZNwpQpUwAUrOcNoMS2onJycvD2229Dq9XC1tYWTZs2RefOnTF37lyEh4eX+NqAglFCr7zyCjw9PREUFISEhAR8/PHHuHnzJl599VX9fnK5HEuWLME777yD6dOnw9/fv9zrmZWVhbFjx8La2hr29vZYsmQJxGIxXnvtNbz66qsAgMaNG2P27Nl4++23MWbMGP06aI97vB/JZDJ88sknGD16NADglVdewdNPP42PPvqoxBsczJ8/H1OnToW9vX2ZI7xKus5ff/01hgwZAicnJ4wdOxbXrl3DmDFjMHXqVHzwwQf6Y1988UWMGjVKf71GjhyJwMBAg/W7gIJw7dSpUwgLC0N+fj6kUil8fX3x+uuvw8vLC+vWrcN7771Xbe8pgFJHeG3ZsgV//PEHLC0t9f3zhx9+wF9//aWfciqTydCjRw/s3bsXL774Irp16wZXV1fodDqoVKpSpzMCgEiowMTQUaNG4bnnntMvWv+///2v1IJXrlwJpVKJjz76qErbS1PSCK/ExHtwd/fFx/89iaT03Eqdr7LcHK2w6K0OZe5TOMLL0tISq1YtR2pqKv7znzlYsmQhpFIp7ty5jYyMDAQFtcPUqR/ph+oVEgQB4eHdsXHjr3B1dcPLLz+PGTPmoFmzgiGBH344BeHhAxEW1stghNeBA3vxyy8/YeHCJXB1LT7Ec+/e3Thy5BAWLSq4+4ZGo8Gzzw7EV199i9zcXHz++afIy8uFSqXC4MHP4PnnRwIwHOFVdHSWWq3G8uWf4ezZSNSr54AmTQKQkZGGTz75rNhzf/jhu+jZszf69u1vcI7o6FuYN28Gpkz5AEFBwQbHFL6v5oS/4SVzwb5K5oJ9lcwB+2nV6HQCFLlqZClVyMpR4dDZWJy/lVrs//qmyE6SjV6trdGlW1ip+4hEAISCKYimzM+9/Om4o0a9jI0bf6zxWkQiQCqt+myg0mzduhVardaod92j0q1cuVK/Bpspq2v9SKlUYtGiRZg/fz5u3bqFVatW4T//+Q9cXFyQlJSEK1euICys9J9xZanN97Rw/fj+/fsDKLhrq0wm04dsQAWnNM6ZMwfTpk3DmjVrYG9vr1/Rf+zYsZg8eTJatWqFyMhITJ06FQqFAoIgYNeuXViwYAG6dKm5KXemSKVSYeHCOfDwaIA5cxboh4tGRV3GV199B7lcjg8+eAd//LEVzz47wuDYPXt2oUEDL31olZSUCDe3RyPpXF3dkZycaHDMTz+tx6lTEfjiizWl3tGiW7cwfPnl58jIyICDgwP++edv+Pr6wdOzAZTKHHzxxRrI5XIolUq8+eYreOqpDvDzK31Y4PbtvyEhIR4//rgFGo0GEyaMLXHE3717dxEVdQkffjjdoP306QisXLkMc+cuQsOGjcq4mkRERERkDlRqLbIejrjKzFEhO0f1MNB6FGxlKQvas3MrPoWwtlhZSGFvLYOdtRx21rKHf+SwsyraJsexyGvIybmvX8C5KJEIqGcjh4Pto7uoCQ+TL6HgAQQI+tcuPGwUioRjhWMR9G2CUORYw/MVPf7RMQUby7vGErHp3xl99+7d2LRpk/5x27Zt9aO2zNXt27cNRkdZWFhg3bp1Rqyo6tavX28wkqmii/RXh7rYN2qTtbW1frZd48aNMXbsWMyePRtpaWlwdXXFhx9+aOQKK6ZXr14GjzMzM/WL9ReqUODl7++PLVu2FGv/5ptv9F+HhITg2LHSpwUWKrx1anUaHd6sylMaK6JwSmNFvPfeJPTs2QcjR44yaA8L662fitiv30AcOXLIIPA6d+4MvvnmK3zxxeoK1/Xdd1/Dzc0dS5euKDZarChLS0t06dId+/fvwfDhL+DPP3egX7+BAArWCVu16lPcunUDIpEYDx6k4NatG2UGXmfPnkG/fgMhlUohlUrRt28/XLx43mCfBw8eYNq0qZg6dRrq139069/Tp/9BRMTfWL58tUE7EREREVWvf7MuliAIUOZrCoKqHBWylGr919lKVUGoVdimVCFPVfIUOWMQAbCxehhaFQmsbMsIs6SSit0Ia1jP1vhu021ERhyHX6PGkIgLRiWJRIBUIoJcZ4k8hfHDJJVSDUUpoZdIBNhayZCoK3904pIlS5CYmFADFRb3+AivoKAgg5EaQMEN1SqjQ4cOVTquplhZWeGzzwxnxZhKbZXVp08f9OnTx6Ctsq/lueeeq9Jx1dE3KsPU+lF1c3JywsyZMw3aqvpaq/qe/hs6nQ73799Heno6OnfubLCtTtyaMNDXEQvGPl3mPtv+uo0/I2L06wIUJZOK0S/UB0O7/PvRRkFBwYiIOIlhw4bD0tKyQsdcvnwR8+fPwqJFn8PHx0/f7ubmjqSkBP2c1OTkRLRrF6Lf3qJFS5w+HYHExAR4e5d9l4J+/QZhxYql6NMnHOfPn8XMmQWJ7n//uxpOTs747rufIJVK8e67E/QL11VVenoapkwZj5deGo2wMMPU1dvbB3fu3Ma1a1Ho3Lnbv3oeIiIiIipZaeti7f7nHk5eScSIHo2Rq9IahFb6UVgP27QmNMWw6OirwuDKvsjIKzurR0GWjZUUkgrc3awqbKws8NoLQ7Bj31+I2n8Cgk4HiUQMFwdLuDpa4XYNPW9laXU6XI/JgEqtRdG3USwC5DIJAnwcauwaVYVIBMj/5Q3EiOjJZW9vj4EDB8LKysqg/Yn5qRIe6oPI6ykG/+gDBWGXi4MVwkMrdlvL8rz++pvYuvVXTJ06EUuWfAEbm4JphocPH8Tzz4+ETCbDnj270alTQfJ49eoVzJr1MebPX4yAgGYG5+rRoye2b9+KZs2a4/79GFy9GoU5cxbot4eGdkS3bmH44IN3sHDhUjRqVPrCfW3atIVSmYO1a1ejS5fu+jBOociGv38TSKVS3L59CxcunEfv3uFlvsbg4BDs2bMbYWG9odVqsH//Hri5uQMAMjMzMGXKBDz77PMYOHBosWPd3T0xadJUvPfeZOTn56Nnzz7F9iEiIiKisul0AnLyCkbxKHLVUCgffZ2dq8aVO2lISM0pNsJHoxWQkpGHVb9fNk7hD9lYSmFnLYe9jRzZOSokpStRUr4mlRT8YvqZrqazDIattSVeHNrb2GWUq+PDEX6Hzz0a4dcjqGp3vqxpYrEIzs4lL89CRFRVpvWTrgZZyqWYMTr40Q99pRq21jXzQ//ll1+FhYUlpkwZj88/XwkACAxsjqlTJyA9PR1BQcEYPLjg1qCff74YKlU+lixZqD9+5sx58PdvjJEjR2PBgjkYMWIoxGIxPvxwOqytDW9NGhzcHtOnz8a0aVPxySeL0bSpYWhWVHj4AKxbtxarVz+aJ/7KK2Mwf/4s7Nq1Hd7ePmjbtvy7KQ4ePAy3bt3Cyy8PR716DmjWrAXS01MBAD/+uB7378dg+/at2L59KwBg+PAXMGDAYP3xbm7uWLFiDaZOnYT8/Hz07z+o3OckIiIiMgVFpwnm5KphU4lpgqUpGl5lK9XIeRhalRZmKZQqKPM0JrUoukQs0o+6sreRw85ajno2ctjZPGp7tM1wCmFJo9GAR7+Y7vd09fxi+kljKZdiaJdGGNqlEW+wQERPpArdpdEUlHWXRlNX9G6HVD5zeV+L4n8iyFywr5K5YF998vyb9aZqS3nBzIzRwZBLJVDkGYZURYOr7FwVcnI1yM5VQZGrMcnwqpClXFIwbbCE0Krga5k+3LK2lEIsqvraVXlFRyPV4C+mn1Sm/jOVI7yIqCbwXw8iIiIiMqrS1pv6MyIGkddTMGN0MCzlUgiCAK1OgE5X8Lfh17pS2kvZRytAJ5T09cN9hIftRc5zPSYdiak5xabeqTU6xD/IwTsr/oJGK5hkePU4iViEkGausLOWFYzEMgi0CtbCspBJyj9RNSk6GomIiKg6MPCqBf/5z5xaeZ4dO7bht99+LeH5Z6NJk4rdZZKIiIioNily1fh5/3UkpimLjeYvDJImLD8GEUTQmfjEBLW2duuztpDC1loGW6uCP3ZWMv3dCW/GZuDKnfQSF56vzhs2ERERmSoGXnXIoEFDMWjQUGOXQURERFSi3HwNYpKycSchG3cTs3AnIQspGXnlHicIgHmMm6o6G0tpQVhVGFpZyQzCLFsrOWytpAV3KLSSlXsnwvKmX1bXDZuIiIhMldkHXoIgQPQv1gsg02ImS8oRERFROVRqLWKSFbiTkIW7DwOuxFRlHY+tCthYSosEVYbBlZ21HDaWBaOwCtvKC6+qojZv2ERERGSKzPpfOqlUjpycLNjY2DP0qgMEQUBOThakUrmxSyEiIqJK0Gh1iE1R4G5CdkHAlZiNuJScGpmCKBaJIJGIIBaLICn69cM/YrG4yNeP2h89FhfbVvzrh+eQiAqe7+HXUffScT0mo9jUSwCQSkToGeyF57r7V3t4VVVcF4uIiJ5kZh14OTq6ID09BQpFhrFLoWoilcrh6Ohi7DKIiIioFFqdDgkPlLiT+Gjk1v1kBTRVXL9KLBLB1koKRa662GLwACCTiNGnvTeGdm0IsUhk1F9y9gz2KnOa4JDODU0m7CIiInrSmXXgJZFIUb++h7HLICIiIqqTdIKA5PRc/bTEO4lZiEnKhkqtK//gEogAuDtbw8/dHn4edmjoYQ9vV1sIglBmkDSgo69JBEnFpgnmqmFrxWmCREREpkgkmMmiSampihKHjxOZAhcXO6SkZBu7DKJysa+SuWBfrX2CICA1Mw93ErNxN6FgQfl7SdnIzddW+ZwuDpZo6GEPP3d7NPSwg4+bHawsSg6F8lQas1tviv2UzIWp91WxWARnZ1tjl0FEdYxp/u+BiIiIiP61whDp0NlHo5HC2hWESLn52od3SiyYlng3IRuKXHWVn8vRzgJ+7gWjtvw87ODnbg9bK1mFj+d6U0RERFSdGHgRERER1UF5Kk3BNMH0XKi1BdMEFblq7Pj7Lnb+fbfE9bIqys5a9nDklh38POzR0N0O9WwtqqlyIiIion+PgRcRERGRmVFrtMjMUSErR40spQpZOUX+PHwcm5JT4ogtQQAqk3VZW0j1I7YaPvzbyd6Cd8gmIiIik8bAi4iIiMjIBEFAnkprGF4p1QZBVqZSheyHgda/WVerLBYyCXzd7QymJro6WDHcIiIiIrPDwIuIiIioEspaF6vo4uqCICAnT2Mw8irz4dfZyoLRWUUfqzRVu/Phv+Hvaa+/Y6Kfhz08nKwhFjPcIiIiIvPHwIuIiIiogkpbF2vn3/ewPzIWfh52yMlVPwyx1NCa8B2mba1k+M/oEGOXQURERFQjGHgRERERlUGt0SE2RYG7idk4ei4O8Q9yiu2jEwTk5mtw9W56rdQkFolgZyODvbUc9jZy2FvLUc9Grm+rZyPHmRspOHEpARpt8dBNJhUjrF2DWqmViIiIyBgYeBERERE9pNHqEJeSg7uJWbibmI27CdmITVHUykgtqUSMejYyfYBlZ1MQXOlDLRs57K0LtttYySAuZ12txl71cDM2EykZuVAXmS4pk4rh4mCF8FCfmn5JREREREbDwIuIiIieSFqdDvEPlLib8DDcSszG/WQFNNrqW0vLUi4pElY9Cq3q2chh9/BxvYfbLeWSal0c3lIuxYzRwdgTEYPD5+KgUKphay1Dj6Di640RERER1TX8nw4RERHVeTqdgITUHH2wdTcxC/eTFNW+ULyVXIL3XgjSj8SSyyTVev7KspRLMbRLIwzt0siodRARERHVNgZeREREVKfoBAFJaUr9lMS7iVmISVIgX62t8jmd7S3g526PnHw1bt7PLHGKo0wqRu/23mjkaf9vyiciIiKiasDAi4iIiMyWIAhIzsjVB1t3E7JxLykbeaqqh1uOdhbwc7cr+ONhD193O9hbywEUuUsj18UiIiIiMmkMvIiIiMgk5Kk02BMRg0Nn45CTq4aNlQxh7R6tNyUIAh5k5j0cuVWw7ta9xGwo8zVVfk57GzkaFgm2/Nzt4GBrUer+XBeLiIiIyDyIBEGo+dsOVYPUVAV0tXCHJKKqcHGxQ0pKtrHLICoX+yqZqtJGTknEIlhZSOHlaov7SdnIyat6uGVrJUPDh8FWYcjlYCuv1oXi6cnCn6lkLky9r4rFIjg72xq7DCKqY/hrSCIiIqo1Op2AnDw1FLmGf/6JSkJiqhK6x34Pp9UJUOSqce1eeqWex8ZSqp+SWDA90R5O9hYMt4iIiIieEAy8iIiIqEo0Wh1y8jRQKFXFAiz9H6UairyHf+eqoczToLrHa1tZSA3W3PJzt0P9epYMt4iIiIieYAy8iIiI6rCi62IpctWwfWxdrEJqjRaKXM3DkEoFRZEgKztXjZxc9cPtj8Kt3PyqLwxfVRZyCfzc7ODnUTBqy8/DDi4OVhAz3CIiIiKiIhh4ERERVVJFQ6TqohMEaDQ6aLQCNFpdwR9dYdujdq1WB7VWePi3Dnn5Wmw7fhvZSjW0D9fBVOSqsePvu9h7KgauDtZQ5hcEWfnq2g+vKsNSLsHMV0Lg5mTNcIuIiIiIysXAi4iIqBL0i6un50KtLVhcXZGrxq6T93DkXBy6tfUEIDIIokr7+vGASqsVoNHpHgu3hGLrWv1bggDkq3W4n6Ko1vNWlJWFFLZWUthayWFrJYOtlQxJaUrcTcou8QY1MqkYfdp7w8PZxgjVEhEREZE5YuBFRERUAYIgIC4lB78cuomE1Bw8nkFpdQKylGrs+PuecQo0AhEAGysZbKxksHsYXNlYSWFnJS/421oOG0tZQbhlXRBu2VhKIZWIi52rtLs0yqRiuDhYITzUpxZfGRERERGZOwZeREREpchSqhB1Jw1X7qTh8t00ZCpUxi6pxohFokfBlGVhQPVoFFZhkGVrJYOtdUG4ZW0hhVhcPdMLLeVSzBgdjD0RMTh87tFU0R5BNTdVlIiIiIjqLv7vkYiI6CGNVofouExcvpOGy7fTcC8p29gl6UklYsikIkjEYkglIkgl4iJ/RI/9XfD1+VsPoNGWPh3S2kKKWa+GwNZKDisLidHvamgpl2Jol0YY2qURXFzskJJiOtefiIiIiMwLAy8iInpiCYKA5PRcXH44iutqTDryVf9u8Xa5VIz+T/tCIhFBJhFDUiyIKj2gMvhaKoZULIZEIoJELKpSGLXtr9v4MyLGYIpgIZlUjF4hXnB1tP5Xr5eIiIiIyBQx8CIioieKMk+Dq/fSceVOKi7fScODzLwKH2shk6CerRypmXn6ux4WJZOKER7qg8GdG1ZnyVUWHuqDyOspXBeLiIiIiJ44FQq87ty5g2nTpiEjIwMODg5YvHgx/Pz8DPY5fvw4li1bhhs3bmDUqFH46KOP9Nt+++03/PDDDxCLxdDpdBg+fDhGjx5drS+EiIioJDqdgDuJWQXrcN1Jw+24rErd9dDXzQ4tGzmhhZ8TGnvVg0arM5vF1Yuti6VUw9aa62IRERERUd0nEoTy/9c/evRoPPvssxgyZAi2b9+O3377DRs2bDDY5969e1AqldizZw9UKpVB4KVQKGBjYwORSASFQoFBgwbhq6++QrNmzSpcaGqqosRblROZAq41Q+biSemraVl5Betw3UnD1btpyMnTVPjYejZytGjohJYNndDczwn2NvJi++SpNAyRatiT0lfJvLGfkrkw9b4qFovg7Gxr7DKIqI4p93/lqampiIqKwvfffw8AGDhwIObPn4+0tDQ4OTnp9/P19QUAHDhwACqV4V2sbG0f/fDKy8uDWq02+sK4RERUd+Srtbgek/FwFFcqElKVFT5WKhGjqXe9hyGXM7xcbMr9N6ro4upERERERGR6yg28EhIS4ObmBolEAgCQSCRwdXVFQkKCQeBVnoMHD2LZsmWIiYnBe++9h4CAgKpXTURETzRBEHA/WYErdwvupngzNqPMuxE+zsPZGi0bOqNlIyc09XaAhUxSg9USEREREVFtq7V5Fz179kTPnj0RHx+PCRMmoGvXrmjUqOK/GecQVzJ1Li52xi6BqEJMta/m5muw9fBN7P77LrJzVLCzkaN/Rz8M69EEVhZSZGTn4/yNZJy7kYJz15ORnp1f4XPbWsnQtqkLggJcEdTUFS6OVjX4Sqi6mGpfJSqK/ZTMBfsqET1pyg28PDw8kJSUBK1WC4lEAq1Wi+TkZHh4eFTpCT09PdGqVSscOXKkUoEX1/AiU2bq6yIQFTLVvpqn0hRbCD4rR4Uth25i5/E7cLCVIzYlp8LnE4tEaNTAHi0fTlP0c7eDWPxwmqJGY5LXgAyZal8lKor9lMyFqfdVruFFRDWh3MDL2dkZgYGB2LlzJ4YMGYKdO3ciMDCwUtMZo6Oj4e/vDwBIS0tDREQE+vTpU/WqiYioTtkTEVPsrocAoNUKUOSqochVl3uO+vUs0bKRM1r4OSHQ1xHWllw8noiIiIjoSVWhTwNz5szBtGnTsGbNGtjb22Px4sUAgLFjx2Ly5Mlo1aoVIiMjMXXqVCgUCgiCgF27dmHBggXo0qULNm/ejBMnTkAqlUIQBLz88svo3Llzjb4wIiIyD5mKfOw9FVMs7CqPhVyCQB9H/R0VXR2teEMUIiIiIiICAIgEQTCLeYKc0kimzNSHiRMVMpW+mqHIx5nrKYi8lowb9zNQ0Z/uvu52D6cpOsG/QT1IJeIarZOMx1T6KlFZ2E/JXJh6X+WURiKqCZzvQUREtSI9Ox9nricj8loybsZmVjjkAgBLuQSfjusAe2t5jdVHRERERER1BwMvIiKqMenZ+Yi8lozI68m4VcmQq5BMKkaf9t4Mu4iIiIiIqMIYeBERUbVKy8pD5MPpirfiMit0jJeLDbJyVFDma6DRPorFZFIxXBysEB7qU1PlEhERERFRHcTAi4iI/rXUzDxEPpyuGB2fVaFjfN3t0L6ZK4IDXODmaI08lQZ7ImJw+FwcFEo1bK1l6BHUAOGhPrCU858rIiIiIiKqOH6CICKiKnmQmYvIaymIvJ6M2xUMufwKQ65mrnB1sDLYZimXYmiXRhjapVFNlEtERERERE8QBl5ERFRhKRm5+pFcdxIqdrenhh72aN/MFSEBLqj/WMhFRERERERUExh4ERFRmZIzcnHmWjJOX0vG3cSKhVyNPO0REuCKkGYuqF+PIRcREREREdUuBl5ERFRMcroSp68lI/JaCu4lVSzk8m9gj/YBrggOcIVzPcsarpCIiIiIiKh0DLyIiAgAkJSmROT1gpFcMUmKCh3TuEE9hDycruhkz5CLiIiIiIhMAwMvIqInWGJa4UiuZNxPLj/kEgFo7FUYcrnC0c6i5oskIiIiIiKqJAZeRER1XJ5Kgz0RMTh0Ng45uWpYWUrh7WqLbKUK8Q+U5R4vAtDE2wEhAS4IZshFRERERERmgIEXEVEdlqfS4JMNZ5CcroRGKwAAlHkaXI/JKPM4EYCm3g4IaeaK4AAXONgy5CIiIiIiIvPBwIuIqI7SaHX4ftdVJKTmQBDK318kAgIKQ66mLqjHkIuIiIiIiMwUAy8iojomNTMPRy/E46+L8chUqMrdP9DXESHNXNGuqQvq2chroUIiIiIiIqKaxcCLiKgO0OkEXIxOxZHzcbh0O7VCI7qAgqmLH7wYVKO1ERERERER1TYGXkREZiw9Ox9/XYjHsYvxSMvKr/TxttayGqiKiIiIiIjIuBh4ERGZGZ0g4MqdNBw5F4cLt1KhK2M4l0QsgiAI0JWwi0wqRo+gBjVYKRERERERkXEw8CIiMhOZOSocvxiPo+fj8SAzr8x9fdxs0b1tA7RpXB+fbz6PlIxcqDU6/XaZVAwXByuEh/rUdNlERERERES1joEXEZEJ0wkCrt1Lx5Hz8Th3IwXakoZqPSSXivFUczd0b9sADT3sIBKJAAAzRgdjT0QMDp+LgyJXDVsrGXoENUB4qA8s5fxngIiIiIiI6h5+0iEiMkFZShVOXErA0fPxSE7PLXPfBi426N62ATq0cIO1ZfE1uSzlUgzt0ghDuzSCi4sdUlKya6psIiIiIiIik8DAi4jIRAiCgBv3M3DkfDzOXE+GRlv6aC6pRIz2zVzRI6gB/BvY60dzEREREREREQMvIiKjU+Sq8fflRBw9H4eEVGWZ+7o7WaN7UAN0bOkOWyveYZGIiIiIiKgkDLyIiIxAEAREx2XhyPk4nL6WbLCg/OMkYhFCmrmie1tPNPV24GguIiIiIiKicjDwIiKqRco8DU5eKRjNFZuSU+a+ro5W6NbWE51aecDeWl5LFRIREREREZk/Bl5ERDVMEATcTczGkXNxiLiaBJW67NFcQU3qo1tQAwT6OkLM0VxERERERESVxsCLiKiG5OZrEHE1CUfOxSEmSVHmvvXrWaJrG090ae2BerYWtVQhERERERFR3cTAi4iomsUkZePI+XicvJKIfJW21P1EIqBt4/roHtQALfycIBZzNBcREREREVF1YOBFRFQFeSoN9kTE4NDZOChy1bC1kqGxpz3SFSrcS8ou81hHOwt0a+OJLm084WjH0VxERERERETVjYEXEVEl5ak0+GTDGaRk5OrvrqjIVeN8dGqpx4gAtPJ3Rve2DdDK3wkSsbiWqiUiIiIiInryMPAiIqqkPRExSEnPhVpb+uLzherZytGltSe6tvFA/XpWtVAdERERERERMfAiIqqk/ZH3yw27WjR0Qve2nmjTuD6kEo7mIiIiIiIiqk0MvIiIKkit0eH3v24jN7/0heiBgumL741oWys1ERERERERUXEMvIiIKuB+sgLf7LiC2JSccve1tZbVQkVERERERERUGgZeRERl0OkE7D0dg9+P3YZGK5S7v0wqRo+gBrVQGREREREREZWGgRcRUSkeZORi3c4o3IjNLLZNLBZBBECrexSCyaRiuDhYITzUpxarJCIiIiIioscx8CIieowgCDhxKRE/H7iBPFXx9bo6tHDDc938cfRCPA6fi4NCqYattQw9ghogPNQHlnL+aCUiIiIiIjImfiojIioiS6nC+j+v4dzNB8W22VhKMTq8Gdo3cwUADO3SCEO7NKrtEomIiIiIiKgc4orsdOfOHYwYMQJ9+/bFiBEjcPfu3WL7HD9+HMOGDUPLli2xePFig22rV6/GgAEDMGjQIAwbNgx//fVXtRRPRFSdzt96gFnrIkoMu1o2dMK8MaH6sIuIiIiIiIhMV4VGeM2ePRsjR47EkCFDsH37dsyaNQsbNmww2Mfb2xsLFizAnj17oFKpDLa1bt0ar7/+OqysrHDt2jW8/PLLOH78OCwtLavvlRARVVFuvgabD93EsQsJxbbJpWKMCGuM7kENIBKJjFAdERERERERVVa5I7xSU1MRFRWFgQMHAgAGDhyIqKgopKWlGezn6+uLwMBASKXFM7QuXbrAysoKABAQEABBEJCRkVEN5RMR/Ts3YzMw5/tTJYZdDT3sMef1p9CjnRfDLiIiIiIiIjNS7givhIQEuLm5QSKRAAAkEglcXV2RkJAAJyenSj/htm3b4OPjA3d390od5+xsW+nnIqpNLi52xi6BKkGt0WHTvmv47dBNFLnRIoCCOzC+0DsAz/dsAomkQjO/zQr7KpkL9lUyB+ynZC7YV4noSVOri9afOnUKK1aswHfffVfpY1NTFdA9/qmUyES4uNghJSXb2GVQBcWlKPDNjijEJCuKbXN3ssbYQc3R0MMeaWk5RqiuZrGvkrlgXyVzwH5K5sLU+6pYLOIAByKqduUGXh4eHkhKSoJWq4VEIoFWq0VycjI8PDwq9UTnzp3DBx98gDVr1qBRI97VjIhqn04QcOD0ffzv6G1otLpi23u288JzPfxhIZMYoToiIiIiIiKqLuUGXs7OzggMDMTOnTsxZMgQ7Ny5E4GBgZWaznjx4kW8++67+PLLL9GiRYt/VTARUVWkZubh211RuBaTUWybg60crw8IRMuGzrVfGBEREREREVU7kSAI5c4TjI6OxrRp05CVlQV7e3ssXrwYjRo1wtixYzF58mS0atUKkZGRmDp1KhQKBQRBgJ2dHRYsWIAuXbrg2WefRVxcHNzc3PTn/OyzzxAQEFDhQjmlkUyZqQ8Tf5IJgoB/riThx/3XkZuvLbb9qUBXvNwnALZWMiNUV/vYV8lcsK+SOWA/JXNh6n2VUxqJqCZUKPAyBQy8yJSZ+n8inlSKXDU27LmGyOspxbZZW0jxct+meLp55W6gYe7YV8lcsK+SOWA/JXNh6n2VgRcR1YRaXbSeiKi2XIxOxfe7ryIzR1VsW3M/R7zePxBO9pZGqIyIiIiIiIhqGgMvIqpT8lVabD58C0fOxRXbJpOKMby7P8KCvSAWiYxQHREREREREdUGBl5EVGdEx2di3Y4oJKXnFtvm626HsQObw7O+jREqIyIiIiIiotrEwIuIzJ5Gq8OOE3ex6+Q96B5bllAkAgZ28MOgTn6QSsRGqpCIiIiIiIhqEwMvIjJrCak5+HpHFO4lFl+I1dXRCmMHNod/g3pGqIyIiIiIiIiMhYEXEZklnSDg0JlYbDkSDbVGV2x796AGGNGjMSzkEiNUR0RERERERMbEwIuIzE56dj6+2xWFK3fTi22rZyPHa/2bobV/fSNURkRERERERKaAgRcRmZV/ohLx494bUOZrim0LDnDB6L4BsLOWG6EyIiIiIiIiMhUMvIjILChy1fhx33WcuppcbJuVhQQv9W6KDi3cIRKJjFAdERERERERmRIGXkRk8q7cScO3u6KQoVAV29bMxwFjBjSHcz1LI1RGREREREREpoiBFxGZjDyVBnsiYnDobBwUuWrYWsng4mCJOwnF78AolYjxXLdG6NXeG2KO6iIiIiIiIqIiGHgRkUnIU2nwyYYzSMnI1d91UZGrhiJXXWxfb1dbjB3UHF4utrVdJhEREREREZkBBl5EZBL2RMQYhF0lEYmA/k/7YkjnhpBKxLVYHREREREREZkTBl5EZBIOnIktN+ya9lI7NPFyqL2iiIiIiIiIyCwx8CIioxEEAddjMrA/8j6UeZpydgbDLiIiIiIiIqoQBl5EVOvUGi3+iUrCgchY3E9WVOgYW2tZDVdFREREREREdQUDLyKqNZmKfBw+F4fD5+KQrSy+GH1pZFIxegQ1qMHKiIiIiIiIqC5h4EVENe5uYhb2n47FqatJ0OqEUveTSUTQCTDYRyYVw8XBCuGhPrVRKhEREREREdUBDLyIqEZodTqcu/EA+yPv42ZsZqn7iURAuyYu6BXiBR83W+w9dR+Hz8VBoVTD1lqGHkENEB7qA0s5f1wRERERERFRxfATJBFVK2WeGscuJODgmVikZuWVup+VhQRdWnuiZ7AXXBys9O1DuzTC0C6NaqNUIiIiIiIiqqMYeBFRtUhIzcGBM7H4+1Ii8tXaUvdzc7RCrxBvdGzpDisL/ggiIiIiIiKi6sdPm0RUZYIg4MrdNByIjMXF6NQy923u54jeId5o5e8MsUhUSxUSERERERHRk4iBFxFVWr5ai5NXEnEgMhbxD3JK3U8mFaNDC3f0CvGCl4ttLVZIRERERERETzIGXkRUYWlZeTh0Ng5Hz8chJ09T6n4OtnL0DPZC1zaesLOW12KFRERERERERAy8iKgCouMysT/yPiKvpUAnCKXu19DDHr3beyEkwBVSibgWKyQiIiIiIiJ6hIEXEZVIo9Uh8noyDkTG4nZ8Vqn7iUUihDRzQe8Qb/g3qFeLFRIRERERERGVjIEXERlQ5Kpx9HwcDp2NQ3p2fqn72VhK0a1tA4S1awAne8tarJCIiIiIiIiobAy8iAgAEJeiwP7IWJy8kgi1Rlfqfh7O1ujd3hsdWrjDQiapxQqJiIiIiIiIKoaBF9ETTCcIuBSdiv2R9xF1N73MfVv7O6N3iDea+zlCJBLVUoVERERERERElcfAi+gJkKfSYE9EDA6djYMiVw1bKyn8POyRlKZESkZeqcfJZWJ0buWBnsFe8HC2qcWKiYiIiIiIiKqOgRdRHZen0uCTDWeQkpGrn6qoyNXg8u20Uo9xtrdEz2AvdGnjARtLWW2VSkRERERERFQtGHgR1XF7ImKQkp4Ltbb0dbkKNfGqh94h3ghqWh8SsbgWqiMiIiIiIiKqfgy8iOq4Q2fjyg27OrRwR+/2XvBzt6+lqoiIiIiIiIhqDgMvojpMEAQoctVl7iMCMHZQ89opiIiIiIiIiKgWcM4SUR22JyKm3H1srblGFxEREREREdUtDLyI6qgTlxKw5Uh0mfvIpGL0CGpQSxURERERERER1Y4KBV537tzBiBEj0LdvX4wYMQJ3794tts/x48cxbNgwtGzZEosXL67wNiKqfpdup+KHP6+VuY9MKoaLgxXCQ31qqSoiIiIiIiKi2lGhwGv27NkYOXIk9u7di5EjR2LWrFnF9vH29saCBQswZsyYSm0joup1Oz4Lq3+/BK1O0LdJxEDHFu6ws5ZBBMDOWoZ+oT6YMToYlnIu5UdERERERER1S7mfdFNTUxEVFYXvv/8eADBw4EDMnz8faWlpcHJy0u/n6+sLADhw4ABUKpXBOcraRkTVJzFNiS+2XIBK/eiujCIAbw1uiZBmrsYrjIiIiIiIiKgWlTvCKyEhAW5ubpBIJAAAiUQCV1dXJCQk1HhxRFRxmYp8LNt8vthdGUf2bsqwi4iIiIiIiJ4oZjOXydnZ1tglEJXJxcXOaM+tzFPjkw1n8CAzz6D9+V5N8UJ4oJGqIlNlzL5KVBnsq2QO2E/JXLCvEtGTptzAy8PDA0lJSdBqtZBIJNBqtUhOToaHh0dt1KeXmqqArsiaRESmxMXFDikp2UZ5brVGhy+2XMDt+EyD9s6tPdA3uIHR6iLTZMy+SlQZ7KtkDthPyVyYel8Vi0Uc4EBE1a7cKY3Ozs4IDAzEzp07AQA7d+5EYGCgwfpdRGQcOkHAup1RuHov3aC9tb8zXgkPgEgkMlJlRERERERERMZTobs0zpkzBz/++CP69u2LH3/8EXPnzgUAjB07FpcuXQIAREZGomvXrvj+++/xyy+/oGvXrvjrr7/K3UZEVSMIAn45cBOnryUbtPt72uPtoS0hEVfo25uIiIiIiIiozhEJgmAW8wQ5pZFMmTGGie/+5x7+dyTaoM3dyRofv9wOdtbyWq2FzIepT2kgKsS+SuaA/ZTMhan3VU5pJKKawCEgRGboxKWEYmFXPVs5po5ow7CLiIiIiIiInngMvIjMzMXoVHy/+5pBm5WFFFOfb4v69ayMVBURERERERGR6WDgRWRGbsdnYc22S9AVmYkslYgw+dlW8HblMHAiIiIiIiIigIEXkdlITFPiiy0XoFLr9G0iAG8OaoEAH0fjFUZERERERERkYhh4EZmBDEU+lm0+D0Wu2qD9pT5NEdLM1UhVEREREREREZkmBl5EJk6Zp8HyXy/gQWaeQfvAjr4Ia+dlpKqIiIiIiIiITBcDLyITptbosGrrRdxPVhi0d27tgWe6NDJSVURERERERESmjYEXkYnSCQLW7YzCtZgMg/Y2/s54JTwAIpHIOIURERERERERmTgGXkQmSBAEbDpwE6evJRu0+3vaY9zQlpCI+a1LREREREREVBp+aiYyQX9GxODgmViDNncna7wzvA0sZBIjVUVERERERERkHhh4EZmYE5cS8L8j0QZtDrZyTB3RBrZWMiNVRURERERERGQ+GHgRmZCL0an4fvc1gzYrCymmPt8W9etZGakqIiIiIiIiIvPCwIvIRETHZ2LNtkvQCYK+TSoRY/KzreDlamvEyoiIiIiIiIjMCwMvIhOQmKbEii0XoVLr9G0iAG8Oao4AH0fjFUZERERERERkhhh4ERlZhiIfyzafhyJXbdD+Up+mCGnmaqSqiIiIiIiIiMwXAy8iI1LmabD81wt4kJln0D6wox/C2nkZqSoiIiIiIiIi88bAi8hI1BodVm29iPvJCoP2zq098EyXhkaqioiIiIiIiMj8MfAiMgKdIOCbnVG4FpNh0N7G3xmvhAdAJBIZpzAiIiIiIiKiOoCBF1EtEwQBmw7cROS1ZIN2f097jBvaEhIxvy2JiIiIiIiI/g1+siaqZbv/uYeDZ2IN2jycrfHO8DawkEmMVBURERERERFR3cHAi6gWHb+YgN+O3jZoc7CV493n28DWSmakqoiIiIiIiIjqFgZeRLXkYvQD/PDnNYM2Kwsppj7fFvXrWRmpKiIiIiIiIqK6h4EXUS2Ijs/Emm2XoRMEfZtUIsbkZ1vBy9XWiJURERERERER1T0MvIhqWEJqDlZsuQiVWqdvEwF4c1BzBPg4Gq8wIiIiIiIiojqKgRdRDUrPzseyzRegyFUbtL/cpylCmrkaqSoiIiIiIiKiuo2BF1ENUeZpsPzXC0jNyjNoH9jRDz3aeRmpKiIiIiIiIqK6j4EXUQ1Qa7RYtfUiYlMUBu1dWnvgmS4NjVQVERERERER0ZOBgRdRNdPpBHyzIwrXYjIM2tv4O2N0eABEIpFxCiMiIiIiIiJ6QjDwIqpGgiBg04GbiLyeYtDu38Ae44a2hETMbzkiIiIiIiKimsZP30TVaPc/93DwbKxBm4ezNd55rg0sZBIjVUVERERERET0ZGHgRVRN/roYj9+O3jZoc7CVY+rzbWFrJTNSVURERERERERPHqmxCzBneSoN9kTE4NDZOChy1bC1kiGsXQOEh/rAUs5L+yQ5HZWI9X9eN2izspBi6vNt4VzP0khVERERERERET2ZmMpUUZ5Kg082nEFyei40Wh0AQJGrxp8RMYi8noIZo4MZetVxhYHn/shY5OZrDLZJJWJMfrYVvFxtjVQdERERERER0ZOLUxqraE9EDFKKhF2F1BodUjJysScixkiVUW0oDDx3/3OvWNgFAK/1D0CAj6MRKiMiIiIiIiIiBl5VdOhsHNSPhV2F1BodDp+Lq+WKqDbtiYh5OLpPKLZNIhYhKS3XCFUREREREREREcDAq8oUueqytyvL3k7m7dDZuGKj+wppdQIDTyIiIiIiIiIjYuBVReXddc/Wmnflq8sYeBIRERERERGZrgoFXnfu3MGIESPQt29fjBgxAnfv3i22z/HjxzFs2DC0bNkSixcvNtim1Woxd+5c9OrVC71798aWLVuqpXhjCmvXADJpyZdPJAK6t21QyxVRbTl5JbHcfRh4EhERERERERlPhQKv2bNnY+TIkdi7dy9GjhyJWbNmFdvH29sbCxYswJgxY4pt27FjB2JiYrBv3z5s3rwZK1euRGxs7L+v3ojCQ33g4mBVYuglCICro6URqqKadi8xGz/8ea3MfWRSMXoEMfAkIiIiIiIiMpZyA6/U1FRERUVh4MCBAICBAwciKioKaWlpBvv5+voiMDAQUqm02Dl2796N4cOHQywWw8nJCb169cKePXuq6SUYh6Vcihmjg9Ev1Ad2JUxv3HrsDvJVWiNURjUlS6nCqq0XodaUvHYXUBB2uThYITzUpxYrIyIiIiIiIqKiiqdTj0lISICbmxskEgkAQCKRwNXVFQkJCXBycqrQkyQkJMDT01P/2MPDA4mJ5U8LK8rZ2bZS+9eWscMcMXZYG9xPysakpYeh1RXctS89Ox+HLyZgVL9AI1dI1UGj1eGL/11Eala+QXtoC3dcu5uGLKUK9tZy9Ovoh2E9msDKotxvLSKjcXGxM3YJRBXCvkrmgP2UzAX7KhE9aczmU3lqqgK6h2GSKbIUA71DvLHnVIy+bevhm2jn7wRXR2sjVkbV4ecDN3Dx1gODtp7BXnipd1MABf+BSEnJBgAosnKhqPUKiSqmaF8lMmXsq2QO2E/JXJh6XxWLRSY7wIGIzFe5Uxo9PDyQlJQErbZgep5Wq0VycjI8PDwq/CQeHh6Ij4/XP05ISIC7u3sVyjVtgzr5oZ6NXP9YoxXwy8FbRqyIqsPflxNwINJwzbkAbweMCGtspIqIiIiIiIiIqCzlBl7Ozs4IDAzEzp07AQA7d+5EYGBghaczAkB4eDi2bNkCnU6HtLQ0HDhwAH379q161SbKykKK4T38DdrO33qAS7dTjVQR/Vt3ErLww5/XDdqc7C3w9tCWkEoqdM8HIiIiIiIiIqplFfrEPmfOHPz444/o27cvfvzxR8ydOxcAMHbsWFy6dAkAEBkZia5du+L777/HL7/8gq5du+Kvv/4CAAwZMgReXl7o06cPnn/+eUyYMAHe3t419JKMq0MLdzRuUM+g7ecDN6HRlr7QOZmmrBwVVm29ZPDeyaRiTBzWCvZFRvIRERERERERkWkRCYJgugtjFWHqa3gVdS8xG/N+OI2i1Q7v7o9+T/sarSaqHI1Wh6W/nMeN+xkG7W8MDETHlsWn85r6ughEhdhXyVywr5I5YD8lc2HqfZVreBFRTeCcrBrg626Hbm09Ddr++Psu0rPzSzmCTM3mQ7eKhV29Q7xLDLuIiIiIiIiIyLQw8Kohw7r5w8by0U0w81VabDnCBezNwfGLCTh4xnCR+mY+Dng+zL+UI4iIiIiIiIjIlDDwqiG2VjI807WRQds/V5KKjRoi03InIQsb9houUu9sb4FxQ1tCIua3CxEREREREZE54Cf4GtS9bQN4uxrORf95/w2zWYvsSZNZwiL1cqkYE4e1hr01F6knIiIiIiIiMhcMvGqQWCzCS72bGrTFJCtw9HyckSqi0mi0Oqz5/VKxddZe7dcMvu52RqqKiIiIiIiIiKqCgVcNa+rtgNDmbgZtW4/dhiJXbaSKqCSbDt7EzdhMg7Y+7b3xdAt3I1VERERERERERFXFwKsWPN+jMSxkEv3jnDwNth67bcSKqKi/LsTj8FnDUXeBvo4Y3oOL1BMRERERERGZIwZetcDRzgIDO/oatB09F4d7idlGqogKRcdnYuM+w0Xq69ezxNtcpJ6IiIiIiIjIbPETfS3p094Hbo5W+scCgJ8O3IAgcAF7Y8lU5GP11kvQaB+9BwWL1LeCrZXMiJURERERERER0b/BwKuWyKRivNiriUHbrdhM/HMlyUgVPdk0Wh1Wb7uMDIXKoP21/oHwceMi9URERERERETmjIFXLWrtXx9t/J0N2n49cgu5+RojVfTk+vnATdx6bJH68FCfYjcYICIiIiIiIiLzw8Crlr3QqwmkEpH+caZChR1/3zVeQU+go+fjcOSc4SL1Lfwc8Vw3LlJPREREREREVBcw8Kplbo7W6PuUj0Hb/tP3kZCaY6SKniy34jLx474bBm0uDpZ4a0hLiMWiUo4iIiIiIiIiInPCwMsIBnbwg6Odhf6xVidg04GbXMC+hqVn52P175eg1RVZpF4mxqRhrblIPREREREREVEdwsDLCCzkEowIa2zQdvlOGs7ffGCkiuo+tUaHNb9fQuZji9S/3j8QXq62RqqKiIiIiIiIiGoCAy8jad/MFc18HAzaNh28CZVaa5yC6rif9t9AdHyWQVv/p33xVCAXqSciIiIiIiKqaxh4GYlIJMLIXk0hFj1aN+pBZh72nIoxYlV105FzcTh2Id6grWUjJwzr2shIFRERERERERFRTWLgZURerrbo0a6BQdvuk/fwIDPXSBXVPTdjM/DTfsNF6l0drPDW4BZcpJ6IiIiIiIiojmLgZWRDuzQ0WDBdpdHh10O3jFhR3VGwSP1lg0XqLWQSTHy2FWwsuUg9ERERERERUV3FwMvIbCxleK67v0Fb5PUURN1NM1JFdYNao8Pq3y8hK8dwkfoxAwLh5cJF6omIiIiIiIjqMgZeJqBzaw/4udsZtP184CY0Wp2RKjJvgiBg477ruP3YIvUDOvgipJmrkaoiIiIiIiIiotrCwMsEiEUivNSnqUFb/IMcHDobZ6SKzNvhc3E4fjHBoK21vzOe6cJF6omIiIiIiIieBAy8TIS/Zz10auVu0Lb9+G1kPjYlj8p2434GNh24adDm5miFNwc15yL1RERERERERE8IBl4m5LnujWFlIdE/zs3X4rcj0UasyLykZeVhze+XDBepl0sw8dnWsOYi9URERERERERPDAZeJqSejRxDOjU0aDt+KQHR8ZlGqsh8qDXagkXqlWqD9jcGNEeD+jZGqoqIiIiIiIiIjIGBl4kJC/aC52MBzc/7b0AnCKUcQYIgYMPe67iTkG3QPqijH4IDXIxUFREREREREREZCwMvEyOViDGyVxODtjsJ2Tjx2CLs9Mihs3E4cSnRoK2NvzOGdGlYyhFEREREREREVJcx8DJBzf2cio1M+t/RaCjz1KUc8eS6HpNebJF6dydrjB3UAmIRF6knIiIiIiIiehIx8DJRI8IaQy599PZkK9XYdvyOESsyPamZeViz7bLBdE9LuQSTnm0Fa0upESsjIiIiIiIiImNi4GWi6tezQv+nfQ3aDp2JQ2yKwkgVmRaVWotVv19C9mOL1I8d1BwezlyknoiIiIiIiOhJxsDLhIWH+qB+PUv9Y50g4Of9NyA84QvYC4KA9Xuu416i4SL1gzv5IagJF6knIiIiIiIietIx8DJhcpkEL/Q0XMD+WkwGIq+nGKki03AgMhYnrxguUt+2cX0M7sxF6omIiIiIiIiIgZfJC2pSHy38HA3aNh+6iXyV1kgVGdfVe+nYfOiWQZuHszXGDmrOReqJiIiIiIiICAADL5MnEokwsndTSMSPwpy0rHzs+ueeEasyjgeZufjqsUXqrSwkmDisFawsuEg9ERERERERERVg4GUGPJxt0CvEy6BtT0QMkjNyjVRR7ctXa7Fq6yUoch8tUi8CMHZQCy5ST0REREREREQGGHiZicGdGqKejVz/WKPV4ZcDN41YUe0pWKT+GmKSDO9QOaRLQ7RtXN9IVRERERERERGRqWLgZSasLKR4rru/Qdv5Ww9w6XaqkSqqPftP38c/V5IM2oKa1MfAjn7GKYiIiIiIiIiITFqFAq87d+5gxIgR6Nu3L0aMGIG7d+8W20er1WLu3Lno1asXevfujS1btui3paSk4O2338agQYPQr18/bN++vdpewJOkQ0t3+DewN2j7+cBNaLQ6I1VU86LupmHzYcNF6j3r2+CNgVyknoiIiIiIiIhKVqHAa/bs2Rg5ciT27t2LkSNHYtasWcX22bFjB2JiYrBv3z5s3rwZK1euRGxsLADg008/RcuWLbFjxw789NNPWL58ORISEqr3lTwBxCIRXu4dgKIxT1KaEvtP3zdaTTXpQUYu1m6/giJr1MPKQopJXKSeiIiIiIiIiMpQbuCVmpqKqKgoDBw4EAAwcOBAREVFIS0tzWC/3bt3Y/jw4RCLxXByckKvXr2wZ88eAMC1a9fQpUsXAICTkxOaNWuGP//8s7pfyxPB190OXdt6GrT98fddpGfnG6mi6pWn0mDbX7cxecVf+HDtyWKL1L81uDncnKyNVyARERERERERmbxyh8kkJCTAzc0NEokEACCRSODq6oqEhAQ4OTkZ7Ofp+SiI8fDwQGJiIgCgRYsW2L17N1q1aoXY2FicO3cOXl6Gdx0sj7OzbaX2r8vGPtMaZ66n6MOgfJUWO07ew3svBRu5sn8nN1+DOSuOITE1BypN8WmaI3oHoOfTDY1QWcW4uNgZuwSiCmFfJXPBvkrmgP2UzAX7KhE9aWplXti0adOwcOFCDBkyBJ6enujQoYM+QKuo1FQFdDqh/B2fEEM6N8RP+2/oHx85G4unA13R1NvBeEX9S78fu424FAW0JbzPIhGQk5OPlJRsI1RWPhcXO5Otjago9lUyF+yrZA7YT8lcmHpfFYtFHOBARNWu3CmNHh4eSEpKglarBVCwOH1ycjI8PDyK7RcfH69/nJCQAHd3dwAF0xiXLl2KP/74A2vXrkVOTg4aN25cna/jidM9yBNeLob/KPy8/4ZZhoI5eWrsiYjBzpN3Swy7AEAQgCPn42q5MiIiIiIiIiIyR+UGXs7OzggMDMTOnTsBADt37kRgYKDBdEYACA8Px5YtW6DT6ZCWloYDBw6gb9++AID09HRoNBoAwMmTJ3Hjxg39mmBUNRKxGC/1bmLQFpOswFEzCoVikxX44c9reG/VCfx6+JbB4vQlUSjVZe9ARERERERERIQKTmmcM2cOpk2bhjVr1sDe3h6LFy8GAIwdOxaTJ09Gq1atMGTIEFy4cAF9+vQBAEyYMAHe3t4AgIsXL2LBggUQi8VwdHTE2rVrYWVlVUMv6ckR4OOI0OZuiIhK0rdtPXYb7QPdYGslM2JlpdPqdDh34wEOnonF9fsZlTrW1to0XxMRERERERERmRaRIJQ3rsY0cA2vkqVn52P61/8gX63Vt3UPaoDRfQOMWFVx2UoVjl2Ix+FzcUjLqvwdJWVSMfqF+mBol0Y1UN2/Z+rrIhAVYl8lc8G+SuaA/ZTMhan3Va7hRUQ1oVYWraea42hngYEdffHb0dv6tqPn4tCtjSd83Y1/J5a7iVk4GBmLiKvJ0GiL33mxkKOdBbq28UBEVDJSs/KgLnKXRplUDBcHK4SH+tRGyURERERERERk5hh41QF92vvg+MUEJKXnAgAEAD8duIGPX2oHkUhU6/VotDpEXk/GwTOxiI7LKnPfAG8H9Az2QlDT+pCIxej7lA/2RMTg8Lk4KJRq2FrL0COoAcJDfWApZ3clIiIiIiIiovIxQagDZFIxXuzVBF9suahvuxWbiX+uJKFDS/daqyNTkY8j5+Nx5HwcMhWqUveTS8V4uoU7egZ7wdvVcOiypVyKoV0amezURSIiIiIiIiIyfQy86ojW/vXRxt8ZF6JT9W2/HrmFtk3qw8qi5t5mQRBwOz4LB8/E4vS1ZGjLWGetfj1LhLXzQufWHia7qD4RERERERERmT8GXnXIC72a4MrdNGi0BaFTpkKFHX/fxfM9Glf7c6k1Wpy6WjBt8W5i2QtgtvBzRM9gb7T2d4ZYXPtTLImIiIiIiIjoycLAqw5xc7RG36d8sOvkPX3b/tP30aW1BzycbarlOdKy8nD4XByOXYhHtlJd6n4Wcgk6tSyYtlhdz01EREREREREVBEMvOqYgR388PflRKRn5wMAtDoBmw7cxLvPt6nyAvaCIODG/QwcPBOLszceQCeUPm3RzdEKYcFe6NzKo0anUhIRERERERERlYaJRB1jIZdgRFhjrN1+Rd92+U4azt98gKCmLpU6V75ai4ioJBw8E4v7yYpS9xMBaOXvjF7BXmje0AliI9wZkoiIiIiIiIioEAOvOqh9M1ccOReHazEZ+rZNB2+iRUMnyGWSco9/kJGLQ+fi8NeFeOTkaUrdz8pCii6tPdCjXQO4OVpXR+lERERERERERP8aA686SCQSYWSvppjz/Wn99MMHmXnYeyoGgzo1LPEYQRBw9V46Dp6JxfmbD1D6pEXAs74NegZ7oUMLN1jK2YWIiIiIiIiIyLQwraijvFxt0aNdAxw8E6tv23XyHjq29IBzPUt9W55Kg78vJ+LgmVgkpCpLPZ9IBLRtXB+9gr3QzNexyuuBERERERERERHVNAZeddjQLg0REZUERW7B3RRVGh0++Opv2FrJ8FSgK3SCgIioJOTma0s9h42lFF3beqJHUAPUr2dVW6UTEREREREREVUZA686zMZShiGdG+Kn/TcM2hW5ahw6G1fmsT6utugZ7IXQ5m4VWveLiIiIiIiIiMhUMPCq47JyVBABZa7JVUgsEiE4wAU9g73QxKsepy0SERERERERkVli4FXHHT4XV27YZW8tQ9e2DdAjqAEc7SxqpS4iIiIiIiIioprCwKuOK1y/qyxLxneCTCquhWqIiIiIiIiIiGoeU446ztZKVuZ2O2sZwy4iIiIiIiIiqlOYdNRxYe0alBpoyaRi9AhqUMsVERERERERERHVLAZedVx4qA9cHKyKhV4yqRguDlYID/UxUmVERERERERERDWDgVcdZymXYsboYPQL9YGdtQwiFExj7Bfqgxmjg2Ep5zJuRERERERERFS3MO14AljKpRjapRGGdmlk7FKIiIiIiIiIiGocR3gREREREREREVGdwsCLiIiIiIiIiIjqFAZeRERERERERERUpzDwIiIiIiIiIiKiOoWBFxERERERERER1Slmc5dGsVhk7BKIysQ+SuaCfZXMBfsqmQP2UzIXptxXTbk2IjJfIkEQBGMXQUREREREREREVF04pZGIiIiIiIiIiOoUBl5ERERERERERFSnMPAiIiIiIiIiIqI6hYEXERERERERERHVKQy8iIiIiIiIiIioTmHgRUREREREREREdQoDLyIiIiIiIiIiqlMYeBERERERERERUZ3CwIuIiIiIiIiIiOoUBl5EZUhPT8fYsWPRt29fDBo0CBMnTkRaWhoA4Pz58xg8eDD69u2L119/HampqfrjytpGVNNWrVqFgIAA3LhxAwD7Kpme/Px8zJ49G3369MGgQYMwc+ZMAMCdO3cwYsQI9O3bFyNGjMDdu3f1x5S1jaimHD58GEOHDsWQIUMwePBg7Nu3DwD7KhnX4sWLERYWZvBvPVD1fsk+S0R1lkBEpUpPTxf++ecf/eNPP/1U+PjjjwWtViv06tVLOH36tCAIgrB69Wph2rRpgiAIZW4jqmmXL18WxowZI/To0UO4fv06+yqZpPnz5wsLFiwQdDqdIAiCkJKSIgiCIIwaNUrYtm2bIAiCsG3bNmHUqFH6Y8raRlQTdDqdEBISIly/fl0QBEG4evWq0LZtW0Gr1bKvklGdPn1aiI+P1/9bX6iq/ZJ9lojqKo7wIiqDg4MDQkND9Y/btm2L+Ph4XL58GRYWFggJCQEAvPDCC9izZw8AlLmNqCapVCrMmzcPc+bM0bexr5KpycnJwbZt2/DOO+9AJBIBAOrXr4/U1FRERUVh4MCBAICBAwciKioKaWlpZW4jqklisRjZ2dkAgOzsbLi6uiI9PZ19lYwqJCQEHh4eBm1V/RnKPktEdZnU2AUQmQudTodNmzYhLCwMCQkJ8PT01G9zcnKCTqdDRkZGmdscHByMUDk9KVasWIHBgwfDy8tL38a+Sqbm/v37cHBwwKpVqxAREQEbGxu88847sLS0hJubGyQSCQBAIpHA1dUVCQkJEASh1G1OTk7GfDlUh4lEInzxxRcYP348rK2tkZOTg6+//hoJCQnsq2Ryqtov2WeJqC7jCC+iCpo/fz6sra3x8ssvG7sUomLOnTuHy5cvY+TIkcYuhahMWq0W9+/fR/PmzbF161a8//77mDRpEpRKpbFLIzKg0Wjw3//+F2vWrMHhw4fx1VdfYcqUKeyrREREZoIjvIgqYPHixbh37x7Wrl0LsVgMDw8PxMfH67enpaVBLBbDwcGhzG1ENeX06dOIjo5Gz549AQCJiYkYM2YMRo0axb5KJsXDwwNSqVQ/faZNmzZwdHSEpaUlkpKSoNVqIZFIoNVqkZycDA8PDwiCUOo2oppy9epVJCcnIzg4GAAQHBwMKysrWFhYsK+SyfHw8KhSv2SfJaK6jCO8iMqxbNkyXL58GatXr4ZcLgcAtGzZEnl5eYiMjAQA/PLLLwgPDy93G1FNefPNN3H8+HEcOnQIhw4dgru7O7799lu88cYb7KtkUpycnBAaGooTJ04AKLg7WGpqKvz8/BAYGIidO3cCAHbu3InAwEA4OTnB2dm51G1ENcXd3R2JiYm4ffs2ACA6Ohqpqanw9fVlXyWTU1bfq+o2IiJzJxIEQTB2EUSm6ubNmxg4cCD8/PxgaWkJAPDy8sLq1atx9uxZzJ49G/n5+WjQoAGWLFmC+vXrA0CZ24hqQ1hYGNauXYumTZuyr5LJuX//PqZPn46MjAxIpVJMmTIF3bp1Q3R0NKZNm4asrCzY29tj8eLFaNSoEQCUuY2opvzxxx/45ptv9DdYmDx5Mnr16sW+Skb1ySefYN++fXjw4AEcHR3h4OCAXbt2Vblfss8SUV3FwIuIiIiIiIiIiOoUTmkkIiIiIiIiIqI6hYEXERERERERERHVKQy8iIiIiIiIiIioTmHgRUREREREREREdQoDLyIiIiIiIiIiqlMYeBERERERERERUZ3CwIuIiMhIVq5ciffff9/YZRARERER1TkMvIiIiIiIiIiIqE4RCYIgGLsIIiKiuu7rr7/Gxo0boVAo4Orqio8//hgTJ06EIAiQy+Xw9vbGH3/8gezsbCxatAjHjh2DSCTCsGHDMHnyZEgkEmzduhW//vormjdvju3bt8PFxQWzZ89Ghw4djP3yiIiIiIhMitTYBRAREdV1t2/fxk8//YT//e9/cHNzQ2xsLHQ6Hd566y3cu3cPS5cu1e87bdo0ODs7Y9++fcjNzcVbb70FDw8PvPDCCwCAixcvIjw8HP/88w/279+PiRMn4uDBg3BwcDDSqyMiIiIiMj2c0khERFTDJBIJVCoVoqOjoVar4eXlBR8fn2L7PXjwAEePHsX06dNhbW0NZ2dnvPrqq9i1a5d+HycnJ7zyyiuQyWTo378/GjZsiCNHjtTiqyEiIiIiMn0c4UVERFTDfH19MX36dKxcuRK3bt1C586dMW3atGL7xcfHQ6PRoHPnzvo2nU4HDw8P/WM3NzeIRCL9Y09PTyQnJ9fsCyAiIiIiMjMMvIiIiGrBoEGDMGjQICgUCsyaNQtLly6Fr6+vwT7u7u6Qy+X4559/IJWW/E90UlISBEHQh14JCQkICwur8fqJiIiIiMwJpzQSERHVsNu3b+PkyZNQqVSQy+WwsLCAWCyGs7Mz4uLioNPpAACurq7o1KkTPv30UygUCuh0OsTExODUqVP6c6WlpWHDhg1Qq9X4888/ER0djW7duhnrpRERERERmSSO8CIiIqphKpUKn3/+OaKjoyGTyRAUFIR58+ZBLpfjjz/+QGhoKLy8vPD777/js88+w9KlS9G/f3/k5OTA29sbY8eO1Z+rdevWuHfvHp5++mnUr18fX375JRwdHY346oiIiIiITI9IEATB2EUQERFR+bZu3YotW7Zg06ZNxi6FiIiIiMikcUojERERERERERHVKQy8iIiIiIiIiIioTuGURiIiIiIiIiIiqlM4wouIiIiIiIiIiOoUBl5ERERERERERFSnMPAiIiIiIiIiIqI6hYEXERERERERERHVKQy8iIiIiIiIiIioTmHgRUREREREREREdcr/ARJ+fCtbefAVAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# short2long = {\\n\",\n    \"# 'BART-KP20k': 'bartFT_presabs_kp20k_100k_rerun',\\n\",\n    \"# 'BART-OpenKP': 'bartFT_presabs_openkp_100k_rerun',\\n\",\n    \"# 'BART-KPTimes': 'bartFT_presabs_kptimes_100k_rerun',\\n\",\n    \"# 'BART-StackEx': 'bartFT_presabs_stackex_100k_rerun',\\n\",\n    \"\\n\",\n    \"# 'TF-KP20k': 'transformer_presabs_kp20k',\\n\",\n    \"# 'TF-OpenKP': 'transformer_presabs_openkp',\\n\",\n    \"# 'TF-KPTimes': 'transformer_presabs_kptimes',\\n\",\n    \"# 'TF-StackEx': 'transformer_presabs_stackex'\\n\",\n    \"# }\\n\",\n    \"# long2short = {long: short for short, long in short2long.items()}\\n\",\n    \"\\n\",\n    \"long2short = {n: n for n in sorted(all_eval_df.exp_name.unique())}\\n\",\n    \"datasets = ['kp20k_valid2k_test', 'kptimes_valid2k_test', 'openkp_valid2k_test', 'stackex_valid2k_test']\\n\",\n    \"\\n\",\n    \"anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"\\n\",\n    \"kp_df = all_eval_df.loc[all_eval_df['exp_name'].isin(long2short)]\\n\",\n    \"# kp_df = kp_df.loc[(kp_df.step % 10000 == 0)]\\n\",\n    \"kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"print(len(kp_df))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for exp_name, exp_grp in kp_df.groupby(['exp_name']):\\n\",\n    \"    print(exp_name, len(exp_grp))\\n\",\n    \"    fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"    exp_grp = exp_grp.loc[exp_grp['test_dataset'].isin(datasets)]\\n\",\n    \"\\n\",\n    \"    for test_dataset, data_grp in exp_grp.groupby(['test_dataset']): \\n\",\n    \"        data_grp = data_grp.drop_duplicates()\\n\",\n    \"#         print(test_dataset, len(data_grp))\\n\",\n    \"        peak_x, peak_y = peak_index(data_grp, x_index='step', y_index=anchor_metric_name)\\n\",\n    \"        \\n\",\n    \"        ax.annotate('%s peak=%.3f (@step=%d)' % (exp_name, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"        ax = data_grp.plot(ax=ax, kind='line', x='step', y=anchor_metric_name, title=exp_name, label=test_dataset[:-5], style='-o', markersize=8.0, linewidth=4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot each exp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"536\\n\",\n      \"536\\n\",\n      \"transformer-kp20k-PT_step200k-DA_step10k_100k-FT_fewshot1k_step2k_lr1e5 19\\n\",\n      \"kp20k_valid2k_test 19\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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eIiYnB8ePHsX79Rtja2kIQBHz00UfQaLTo0aOH0WOzQYOG2T6rAQMGYOjQoQZ5kMmkWLdug7h89OhRvPPOOwD0NwYzbhg6Ojri/v37kMlk2dIAoEKFCggOfsiglSxOnkFreHg4vL29xaGuZTIZvLy8EB4enu0Oe4bdu3fD19cXZcuWBQA8ffoU5cuXF9f7+PiI/VaIiEqy+GQVwp8n4Wl0Mk5dewrb1Oz9NAUBUGt1qOhXFdHxaZBKpdi/5w/8deQQZFIpJBIJVGotAAFVq1aDUqmERCKFQqHABx90xcSJX6NcuXL44ovRBvuVSCBeaMlkL2tX6tR5eTGScbHq5eWF6tWrAQDs7e2h1Wrx8OFDLFu2BIAEycnJqFdPX1NYu3YdAMCQIYOg1eqwePFiAMC6dT9h8+at4r59ff1gb+8Ae3sHJCa+vMhbtGghOnfuAl9f3/R8vpyrViJ5mc/M+/vf//6HH3/8AbVq1UK/fv1x5swZDBo0EF5eXggImG5KUWDQoCGYMmUyXF1dMXr0GIN1Dx8+RFDQTezYsQMqVRree+99ODk5wt/fH4C+RvfGjRsA9LWa8+bNEWt9HB2dxH6SiYlJcHZ2NpoG6IPsli1b4bXXXkv/LAJx82YQhgwZgoRkNZJSNdCmzykYFRWJCr6VsHvfYdy+fRvDhn2GmJho/PPP3+jc+QOD/Ddp0hQXLlzAxIkT0LJla3zwgeH6O3duY9SokQCAuLhYxMTEAgD8/f0hk8lQrlw5REfHiL/bmWUuk6SkJDg56d/L99+vxrp1600KWPWfkyOSkl7W0kqlMqNp+s8sEYcOHcTPP/8CANi7dy/++ON3tGrVCvXrN0Dnzh9kK0NTPXz4EIcOHcLp06eh1WrRoEEDNGrUCN9+uxiCIOD99zvh+PFj8PDwyPE7lvFdsbGxBQDEx78Qr3PKl6+AmJgYk/Kyfv1aBAYuzBZEAcjz+53T9zPzMRsa+ggNGjTA48ePsX79eqxatVp8/l9/HUH37j3yDFgz79OY2NhYPHz4EJ9+qq9NjomJhiAIYm3g22+3w+7du3PcZ61atXDmzP8QHPwQ58+fx8CBAwDoa+pTUpIxY8YMREZGIDo6Gn5+fvDz88v2PapXr754gyarnI7fDCqVClOmTMbMmTMhl8vh5OQotiZITEyEk5OTQVpSUmKuo7/HxsaiSpUqWLVqBX788Sfs3r0LycnJqFatOsLCwjBy5CjMnj0LCoUCo0Z9YfBcY8emsc9KoVDm+H4B/Q2BS5cuii1UnJycMr2nJDg5OUEmk2VLI7JkhT5P67lz5/Ddd99h/fr1hbpfD4/sJ3SyXJ6ePPlZO5ah6QRBQEx8Kp5EJCI0IgGPIxLEv/FJKoNt/d1ePpbL5dBmqlmCRIrkNC0ALXbu/BXLv9+C8KdPsGLJfDyNTkaqSofHUUlQKNKgUmvxNCoBzVu1RZu3OmBR4Gycv3QZGp0AjU5AmkoLnSAgNTUZEokEgiBAoZBBKpVAqVRALpdBJpNCoZCJ6QqFXHwsl0tRpUoVfPDBB6hbty4AfS3hgwf3oVTqt8vcTUQqlSAwMBBTpkzC8uXLYWtri8ePQ6FWpyEhIQFOTk5QKGT47bffIJNJ0aNHN/G5rq6uiI6OgkQigZOTo5iHzPt7883WePNNfYueJ0+eoGXLN8Rg+eXnKU1/H8bnE2zV6g20a/cWvv/+e5w+fRI2NkpIJPq5GKtWrYKWLd9A+/btAehrlvbt24d79+5CoZDh3r27qFTJDxKJgMmTJ2HSpEnw8dEHeI0aNcCOHdsxbNhnOHfuPzRq1NBomlwuRYsWLVC+fDls3/4z+vXrhylTpkAQBES/SMXJMxeA9Irm588jcfniOXzUewB27/wFK9esQ7mybkhJTsY333yDbt0+NHhvOp0GX345FgDQpUsXdO/+ocFnUbt2bSxfvhz29vZQq9VQKBSQSiV48OAe6tevj2fPwuHt7QmFQpbtc/T3r4UbN66hZs2aSE5OgpubC6RSCRYsWIA5c2ZjyZIlOd7E1t8s0R97Li5OSEtLg0qVigcPHqB69WpG0xQKGZycHDF8+HBMmzYVixYtQvfuH6J7d/171jcfzV7O+uNWlmP5Zy7rDz/sisGDB4tlrVAoEB39HN7e3mjWrClGjhyJ3r17QyoFunTpjB49umHatGm4fTso23dF/95cEBGhv8kfFvYE3t6ekMvl4mtmzUPGZzN16lSsXfsTKlQoDz8/wxpZrVab7bWVSoW4j5y+n/fv38Pbb7+Fe/fuokuXLkhLS8G0ad9gwYIFcHHRn9flcim6d++OyMgI/PPPCbRr185o2WWcJ7J+rpnL1curDGrWrIF16/StRPSfp1xshnv+/FX4+fkaPF8ul4rfrfv39d+tatWqolWrVpg2bZpYLsePH0eVKpWxdOkSLF26VDw3Zf0eXbt2DYsWLTLIf506dTBp0iSjx29mU6fOQL9+fVGrVk0AQLVqVfHgwX1IpfqbZY0aNTRIO3fuLBo1amjwfqpUqYyIiGfw968FjUYNhUIGiUR/bOhHixdw5coldOnSGUqlEs2aBWLfvn3Yt28PypYtC602Ocdj89KlS9k+K0HQYsgQwybnUqkUmzbpA9lr166hTp06sLXVt5xo0qQxfv31V3Tu3Annzv2Hbt26QSaTZUtTKGR4+jQM77//vsFx6u7uYNXXBNacd3opz6DVx8cHERER0Gq1kMlk0Gq1iIyMFPsfZXb58mVMmDABq1evRpUqVcT0cuXKISwsTPxRCw8PR/PmzfOV0ejoROh07BhuDTw9nRAVVfCBHkqCVJUGh8+G4vilMCSmqOFop8BbjcujQ3Nf2CoL/V5Rocic56QUNRysIM/FTR+cpuFpdBKePk//F52Ep8+TkZKW/ykB/CpVxaZ1qxE4+xsMHDrSYF2NmnUw6cthqFO/Ubbn6XSATgBiXiRg9rTx0Ol0sLd3gEsZXyQm/4PouFSERiSgW8+B6NN3ACQABgwehscRCUhTaRERnQSlUoGUVDWSklVITFJBo9FBpdZArdZCpxOgVmsxZMhQzJgxHYmJCdDqgC/GTUH0i1SEP09CVGwyXByUYi2pTiegWrUaGDBgECZMmICFCxfD27ssJk2ajMePQzF16jSo1VrMnDkT9erVwyef9EXTpk0xatQXGD58JMaMGQsAmDp1qpiHrPuTy/XHoUajE/OYWU7pGT7/fDjS0lIBAEuWLIWHhye++24pLl++gkGDBmP69ABs27YNggCMHTsWWq0OMTGxGDhwEGxsbLBkyVIcOHAQ169fR2DgQgDA2LFfomHDhmjcuAl69eoNHx8ffPJJXygUSoO0Xr0/QfizCKg1WnwyYCgWLpiHzT/vRJu270Kj1UEQ9DXua3/4Ds4urlDIFRj+xXhoNRokJycjRSPFgycvIJNKEB4RhYePo2Bvbw+ZVAK5TIqrVy5izaoV0Gg1aN78dajVWtStWxfDhw/HgAEDMWLESAwb9nl6DZgLli37DjqdgIMHD2Hu3Lno2vVDSCQy/PXXMaxbtxaPH4di5MhRWLbsOwwYMAhTpkxGWloqRowYJZaPl5c3Jk6chPHjJ2Dp0qWwtzds3vrDD9/j4MEDEAQB4eHPMHz4CAwd+hkGDRoEpdIG8+bNg1qtNZomCECLFi0RExOLWbNm45tvpuZZzjqdAI1Gm2P5CwKgVmvRps3/Yf78uejXrz8AoF+/fmjb9i14eJRBtWrV4e3tg5iYGNSr1wBxcfH44ouR0Gp1cHR0QOXKVQ1eJyMfI0aMxLhxX0Gn06JXr94ApGjVqhVmzJiBd99tjzp16mLZsiW4d+8eBgwYgFWr1kCr1X9v586dj0mTvkZg4EKDPtPx8dlfu0WLN8R9vvx+JkIqlWDmzNnQanW4d+8++vfvDx8fH9StWx8//fQjHj9+Ita6zZkzV/wMAwJmYMKE8bC3d8zWXF2r1UGj0UIQJAaft7Fy7devP/r3HwCJRIKqVati6tRp6NevP2xtbaBU2mDu3HkG5aLR6CCVyjB48BCkpaVh2bLv4OrqCjc3d3zySV9IJBJ07NgRrVu3wZo1a3Dt2nU4OTmiQoWKYt5HjNDXWO7Zsw8dO3bE+vUbs5W5Wq01evzu3r0L1avXgFqtxpEjR/DkSRg2bNiIvn37oV27dujWrQd69+4DV1cXLFiwEIBUTHN2dsbChYsM3k/Llq3x22870LJla3h4lMGtW7fxwQddMGjQYPj7++PevbsYMuRT2Nk5YMqUyQgLewKVSoU5c+ZCoVBi6tQpuHPnDiZP/ibbseng4Jjts5JIZDm+XwA4fPhPvPVWO3G5Ro1akMsV6N27N2rVqgV/f31rmaxparUWwcHBqFathvhc/XkwCVKpdV7X8ZrUekilklwrKSWCCUOE9evXDz169BAHYtq5cye2bNlisM21a9cwevRofPfdd2JzhgwrVqxAREQE5syZg5CQEPTp0wdHjhwx2hwmJwxarUdpP0GkqjSYs/kiouJSoNa87KunkElRxtUWE3o3go1CBp0gQBD0F1qCIEAn6IMinU6ADoCgE6DLlC5uCwE6Xfq2Oe3DYH9Ztk0f9TVzukqtw8H/HiExRS02TQQAuUwCd2dbfNWzITycbSGVSoy845JHpxPw/EUKnj5PNghQw6OTkZbDBbGp/N0i0aX9G/AqWz7vjc1AAv0Phyz9n1QqQYpKA0EnVgLqt5MAcpkUPh72kEqMHxf9+vXFli1bja6zBrt37xL7muVOgEYrQKPVGfzVanX6xzr9d6+4SCXptWRSCWQyCWRSKeQySba0gQMHYO3adeLNACpcCoUsxwC6qKxatVLsp23Jzp07h//+O1PgJt7FJT9lOG3aVEydqq8lHj9+HIYPH4natWsjNTUVR44cydZk31TF+Vndvn0bp0+fwpAhn4ppe/fuQe3aDQ1uqFiT0n5Nak3yClpN+qWaMWMGJk2ahNWrV8PZ2RmBgYEAgKFDh2L06NGoV68eZs6cidTUVAQEvBxRbeHChahZsyaGDBmCSZMm4Z133oFUKsWsWbPyFbASWZPDZ0MRFZsCtdZweHu1Vofw6GSMW3naTDnLP41WQGRsCiZ+fwYSAA52CjjZK+Bkp4CTvRJO9go42ivgZKd/7GSvhGPGNvZKKOSWPRW0RqtDVFxKplrTZDx9noRnMckGNxxehUIuhY+7PXzKOCAuMQ1psVFISsr+AyqRAA62CtjbyqHV6WsStFoBWp3+RoY207+iJAAmvY4gAGqNDqERiZBKXga6UqkEUon+sUarQ3yyCjJJenrGNunLRenQoUP49dft4nKDBg1MmgbHGEHQN7nWGgSlLx9rtQKK+5bqskWz8Cw8XFz+ZMBQ1GvQGIC+Fl6n0UGdy/MlANLUWjyLToKNjVJ/k0KWHtym197KpBKD/sY5EQQBgYsW4+rVqxAE/bHco8fH6Na1s0nPLwqff/4ZUlNfzpk5ffoMo9PgWJItW7bg2LGj4vLbb7dDv379iuW1z58/j1WrVorLpg5wRS/Nnj1HfDx9+kysWrUCd+/eg52dLT77bJgZc2a6WrVqoVatWuKyVqtFYmKS2HebyJxMqmm1BKxptR6l+a6WIAgYufRfpKqK9+66pbJVyvSBrRjUpge7dunBrr3SIM1WKcvXRa6pzbDVGh0iYrLXmj6LSS60INBGIUO5Mvbw8XBAuTIOKOfhgHJl7FHGxU4M0FJVGizYdBLlFI/R/LVmcHR2gQSS9FpLCcq42Jr0/nU6fU25VtBB0AnQpteca9Nr3DP+anQChMKJvYuERArIJPr3nxHwSpAR4AJSiQQSqSR9m5dpphAEAYkpGiSlaqDTCZBKJXCwlcPRTp7tMxYE/Q0CjU6AVqeDVgtodfrAVKtDsfz2SIBcA1+JBMVaW5tBf6Mh46/UoAZeLpUAEgliE1Kh0RrWJuf3mC6J5HIZNBr+Fliz0lyGGo0GN2/ehE4nweuvt7La73Fpvia1NoXSPNgSMGi1HqX1BPH0eRJ+OXoXN0NizZ0VqyWXScQAVl+L+/Jx1lpcpVyKZTuvZWuGLZdJ4GSnxGu1vRAZm4Kn0cmIjE0utAt+Oxs5ypdxyBagujnbmBRQpao02PfPDTwKuQsZtJDLpPB0tYWXmx1k0sKrmbaxkSMtTaMPXjPVDqozagk1+ppCtfg4vUmrhZ9nJRJALpVCJkuvDZRJIM/c/FWmr8l9Gp0EtUZnJJCSwtVRCbVGB5VGB5VaC4226N+zXCaBUi6DUiF9+VfxclkiAe6ExkGl1iJzEUglgFIhQ01fV0glEqi1OqjV+nJUabRQa3T696LWpT82fL45SSSAh7MtypdxgEwmgT40Lz0yvoOlmVanQ2RsCqLiUqHR6orsfFdUSnMZSqVSuLm5o3bt+gZz+1qb0npNao0YtFKxK20niORUDfaeDsaxi09MvuC3s5GJNWzS9CZ4+koL/QV31nQxLVONk8G2WZ6TkWawXmq4r8zP/+/mM6iL4cLdmjjZK9JrSx3g42GvD07LOBgMPmTJCvo9VKm1SEhWIz5ZhYRkFdbsvvnK/XhLA2cHJTycbeHhYosy6X8zP7azybs3TkbLgROXX7YcaNsof4OhCYKApFQNYhPS0v+lIjYhDXGJaYhJSENcenqSkamXipKNQgZ3Zxu4O9nA3dlW/098bAN3J1vYKI2P+mutSttvYVY5ju8gl8LT1Q5T+zex+EH+SnsZlgQsQ+tRKH1aiSg7nSDg1LVw/P7PAyQk59Z77CWFXIr3mvuia+sqeW9cjNycbHDobKjRfpwZee70RiUkpqiRkKxGYrIKCemPEzI9TkxW6dNS1EhMVkNnBffEXB2VYm2pTxkHlPPQ9z91tleaO2tmoVTI4OEig4eLvg9T+9cq5npsdHjNF+80q4ikFDUSU9RISk3/m6JBYooaialqJKXo/yWmavSPU9VISbOeQFgi0X9HMoJSD2dblHF5+djD2RbKHKZZyQ9bpRxdW1dB19ZVCnyhJZFI4GingKOdAhW9cv7xT1NrEZeoD2IzB7OxCWmITdT/fZGoKrTvcJpai/DoZIRHJ+e4jYOt3Ggw6+6sX3ZzsoFcZr01PiWBWqNFUqq+2X1KqgZJqWokp2qQnPbyccbfRxEJiIlPM7IPHSJjk3Hov1B82MayfguJyHIxaCUqgPthL/DzX3cR8sz4RaVSIdVPi5Cp9jLj7nKH5r7FlU2TdWjuiwt3onK8I96huW96s0obuDramLRPnSAgOVWDhGSVGOwmpAe1+uX0ADdZjcQU/WNVIQ1+ZIyHs216bWnmZr32sLdVFNlrlgR5HRvvva6vBXS0U8A7H/vVaHX6i18jwe7LxxnrNOJyURwjMqnEICD1cEkPStMfl8RgyUYhg7ebPbzd7HPcRqcT8CJJJQaz+traVIMANyI2pdDypA+GEvE4MtHoegn0Ndovg9mXAW1GoOvioDRpkC9rn5asoHkWBAGpKq0YaCanqtM/d3V6EKrRB55p6cFolsC0sAao02gF7D8Tgsi4FNSo6IoaFV1RzsPeKlqxEJF5sHkwFbqS3BQjLjENO/9+gP/deGZ0vYezLXq9XQ21K7nhz3OP9c38ktVwtM9/M7/iVhhNE19VmkprUHMrBrYpKiSmB7gJ6QFuZB4Xy0q5FP3a1xSb91rq515UCvN7aHBsmPl4Vqm1RoPdzMHtmRvPcm2qr1RIMfC9WijjbAcPF9MDneJkLefRXf8+xKGzodBoswczEglgbyMX+w8XB5lUAldHm2zBbObaW7lMgrlbLhV5s9XC/g4aa2orl0ng4mCDD9tUhlqjSw8y9cGovvbzZWCanKpBSprGYvutO9opUDM9gK1R0RUVvRzN/r20lu8h5YxlaD3Yp5WKXUk8QWi0Ovx14TH2ng5BmpGRgRVyKd5/3Q8dmvsWSjNBc7OGMtx98iEO/ReabWohwHKbYRcnayjDorL75MM8m7tb+rFhLeVnSr9FG4UMiSlqxMTra2oz/sbGpyE6Xr8cl5hWbMGUVIIcB6uSSIDyHg4o7+X4cu5rXdZ5rjPPd61fNjZPtkwmhUqlTZ9bO/N2Rvaly/z87PNwW2qgWZTsbGSoXsFVDGT9yjoVe4sHa/keUs5YhtaDfVqJXtG1B9H45dg9RMQY74vVtJYXPm5bFWVc7Io5Z6WbKU2aqXTisVF8bJVyTO3fJM+aeP30Vkr4lXUyup+MpshZg9nMQW58oqpQ5sPNLf4TBODJ8yQ8eZ5UCK9U8sikEtjbymFvq4C9jRwOtnLY28rFOabFxzZyXL4XhfO3Iws8OndKmhbXHkTj2oNoAPoWElXLuYhBbJVyziXiJjERmYY1rVToSspdrYjYZGw/eg9X038wsyrv6YA+7WrA38+tmHNW9KylDC2p2aqlsZYyLCrWfmyU9vIzRqPVITYhDTHxqYjJ9PdlkJta7KMiWyOlXGoYaNrog9CMANTgsc3L7RxsFVAqpCb3O821Ft7FFn3a1UDws3jcffwC957E5Xt+c5lUgsrlnFGzor42tmp5F5NG6c4Pfg+tH8vQerB5MBU7az9BpKo02P+/RzhyPtToHWJ7Gzk+bFMF/9eonFXMM1cQ1l6GxDK0diy/gklTafW1sxlBbXz2INdYFw9rJ5dJ8HqdsunBZuZa0IyA82W6Ql58v1um3jzS6nR4HJmIu6FxuPM4Dncfx+X7BoREAvh5O6FGehBbvaIrHO1ebaA9fg+tX2kvQ2sadI5BKxU7az1BCIKA/25G4Le/7yMuUZVtvQTAmw3L4cM2VeBUwqdDsdYypJdYhtaN5Vc0BEHAb38/wNELj43elJRJJahf1R1Na3lnnx9bgizzXRufJztj2d3dAS/ikg3myc5tTu6X82tnX7/3dDD+PPfYqvtp54dOEBD+PAl3H+uD2DuP4/DCyO9yXip4OogDO9Wo6Gry6PcZ+D20fqW5DK1trmT2aSUyQcizePz81z3cD3thdH31Ci7o065Gjv2xiIjI8kkkEnzQshKuPYjO8UJuaOc6hTd6sE3h9Lns+LofLt19Xmr6aUslEpT3dER5T0e0bVwBgiAgMi4Fd0PjxED2+YvUPPfzJCoJT6KScPxSGADA281OXxPrqw9iS8pYFNZUm5aBeTaNIAhQqXVIVWuRqtIgTaVFqvhPg1SVNj1Nk76NVtwmNCIB0S9Ss40FoNboEBWXgsNnQ63qZhdrWqnQWdNdrfhkFf745yFOXn1qdIAPNycbfNS2Kpr7e5eq+eOsqQzJOJahdWP5Fa3i6PNc2GVo7f20C1tMfCruPn4ZxIZHGx8sMTcezjYGNbFl3e2RptaKgUlSihoOFhRMaXU6aDQC1FodNFodNBodElPV+H73TcQkpBq0HpDJJHB1sMEn71SHImPAKgEQ9P9BgH7gsYwVQnpa5m3SF9O3E5A5YtCn6xOEjOeIz3+5H4PnpKep1TrsPxOChGS1wcjYMqkEzg5K9HizCmyVckilEvGfTJL+V5rD3yzrpVJ9awVvb2fERCdClt6ioaBMrbXU6nQGgWWaWovUNH1waRhUarJtk6bWIiUjLVNAWlTRj5O9At+Nbl1Ee88/Ng+mYmcNF1sarQ4nLodh98lgpKRl7zcjl0nQ/jVfvN/Cz+w/UuZgDWVIuWMZWjeWn/VjGRav+CSVGMTefRyHx5GJ+b7Yd7KTQ60VoNboDIIpuUwKD2cbDO9aF1KpJD1gNAwexcfpz9ekL+sfC/rH6dtmbGeYZrg//Toh02MdrOOK3XJJJDAMdiXZg19pDsFxbHwaYhPTciwDpVwKATDahN9SSQCsm/SWubMhYvNgoiyCQmLwy9F7CMthSoOG1cqg19vV4OVmX8w5IyIiooJwdlCiaS0vNK3lBQBITlXj3pMX4sBOIeEJ0OUR9SWkGB/8SaPVISI2BTM2nC/0fFPxEQToa6MLOA1TblRWFKxmcLR/tYHKihuDVio1nsel4NcT93HxTpTR9WXd7dG7XXXUq+JRzDkjIiKiwmRvq0CDamXQoFoZAPrmnQ+exov9Yh88jYdGa32BBpU+SrkUNkoZbJUy2CjksLWRwVaRvqyUwVYph236elulHDbp6y7djcSFO1FGB51TyKVo26i8Gd5NwTFopRIvTa3Fof8e4dDZUKPNNmyVMnzQsjLaNa0AuaxkTmFDRERUmtkq5ahTyR11KrkD0DfjDA6PF2ti7z95gTS1ZU+HJJEACpkUcpkUcpkEcrkUsQk5N1kF9M1ha1R0NdiHJP2B/m96OvSjVWckZe7/KaZne87L/WTuLpp5O0nG3jM95+ytCKOBVAa5TIK6lT2gEwRodQJ06f+0QvrfzGmZ/xrZXkhfpy2mLoYSQAwwbZVy2ChlsFPK9IGkzcuA0jZTsClub2QbG6WswNMr1q3ijtDIpBIzgBuDViqxBEHAxTtR+PX4PUTHpxndpmW9sujxZlW45HMYfCIiIrJeCrlUHIAJ0A9yNOa7U0g2Ms5FBokE8PFwgFwmeRk8yqXpj/VBpFwmzbQu03YyKRRyKWSyrGkSg/UZAakifd/i/uQSo8HL7pMPc7wpb6nTIXm42BZbnjP6lguCflConIJcrU4HnYAcg+K/r4ThbFCE0eBXLpPgrSYV8GGrKlAqpBYzcKetUo6p/ZuUmAHcrCu3RCZ6EpmIn4/exe3QOKPrK/s4oc87NVC1nEvxZoyIiIgsjkwqRbumFawuAOzQ3BcX7kRZVW2aOfIsyTTvcUFU8HJAyLOEHPPctVVl2CgLZ4qrwmSrlKNr6yoWd9wWBINWKlESU9TYczIYxy8/Mdpcxtlege7/VxUt6/lAaiF3woiIiMj8rDEAtMbaNOaZCoJT3lChM8cw/zqdgH+vPsUf/z5EYoo623qZVIK3m1TABy0rw96WJ5a8cKoG68cytG4sP+vHMrROBvPhpqjhaMfAxJrxe2g9OOUNlXh3H8fh57/uIjQy0ej6OpXd0fvt6ihXxqGYc0ZERETWJHNzSgY8RJaDQStZrdiENPx24j7+C4owur6Miy16v10dDauXsZhO8URERERElD8MWsniZTTVOX7pZVOdCl4OeBgWb3QyZ6VCivdbVEKH1ypCIbe8TvFERERERGQ6Bq1k0VJVGszZfNFgUITEFDVuP4ozun3z2t746P+qwt3ZthhzSURERERERYVBK1m0w2dDs43iZ0xFL0f0aVcdNX3diilnRERERERUHBi0kkU7fiksz4C1X/uaeLNBuQLPvUVERERERJaLQStZNGPT12QmAdC2UfniyQwRERERERU7qbkzQJSTlDQN8qo8dbRXFE9miIiIiIjILBi0kkXSaHVYtes6dELO2yjkUtayEhERERGVcAxayeLoBAHrDtxCUEhsjtso5FJ4utqhQ3PfYswZEREREREVN/ZpJYsiCAK2H7uHs0ERBumujkpotAKSUtRwtFegbaPy6NDcF7ZKHsJERERERCUZr/jJohw6G4qjF54YpHm72WFyvyZwtleaKVdERERERGQubB5MFuP09XDs/PuBQZqLgxLjejZkwEpEREREVEoxaCWLcPX+c2w4eNsgzc5Ghi8/bgBPVzsz5YqIiIiIiMyNQSuZ3YOwF1iz+wZ0wsuhguUyCb7oVh++3k5mzBkREREREZkbg1Yyq6fPk7Dst6tQaXRimgTAZ53roJafm/kyRkREREREFsGkoDU4OBg9e/ZE+/bt0bNnT4SEhGTb5tSpU+jWrRvq1q2LwMBAg3VRUVEYPnw4OnfujPfeew979uwplMyTdYtNSMOSHVeQlKoxSO/7bg00reVlplwREREREZElMSlonT59Ovr06YM///wTffr0QUBAQLZtKlasiLlz52LIkCHZ1i1YsAB169bFvn37sG3bNixduhTh4eGvnnuyWkmpaizZcQUx8WkG6Z3fqIS2jSuYKVdERERERGRp8gxao6OjERQUhE6dOgEAOnXqhKCgIMTExBhs5+fnB39/f8jl2WfRuX37Nlq3bg0AcHd3R61atXDo0KHCyD9ZIZVai+U7ryEsKskgvU2DcujaurKZckVERERERJYoz6A1PDwc3t7ekMlkAACZTAYvL6981ZTWqVMHBw8ehCAIePz4MS5fvoynT58WPNdktbQ6HX7YexP3nrwwSG9UvQz6ta8BiURippwREREREZElyl4tWgQmTZqEefPmoUuXLihXrhxatGghBsGm8vBwLKLcUVHw9Mw+6q8gCFi18you33tukF67sju+GfI6bBT5OyaoaBkrQ7IuLEPrxvKzfixD68cytH4sw5Ihz6DVx8cHERER0Gq1kMlk0Gq1iIyMhI+Pj8kv4u7ujsWLF4vLQ4cORbVq1fKV0ejoROh0Qt4bktl5ejohKiohW/rukw/x53+PDNLKl3HA8C51EB+XXFzZIxPkVIZkPViG1o3lZ/1YhtaPZWj9WIbWQyqV5FpJmWfzYA8PD/j7+2P//v0AgP3798Pf3x/u7u4mZyI2NhYajX6E2DNnzuDu3btiH1kqHU5ceoK9p0MM0tydbfDlxw3gYKswT6aIiIiIiMjimdQ8eMaMGZg0aRJWr14NZ2dncUqboUOHYvTo0ahXrx4uXLiAcePGITExEYIg4MCBA5g7dy5at26Na9euYe7cuZBKpXBzc8P3338POzu7In1jZDku3I7E1iN3DdIcbOUY93FDuDvbmilXRERERERkDSSCIFhFm1s2D7YemZti3H4UiyU7rkCjfVl2SrkUE3o3QtXyLubKIuWBzWmsH8vQurH8rB/L0PqxDK0fy9B6vHLzYKKCCo1IwIo/rhkErFKJBMO71mXASkREREREJmHQSkXieVwKlu64ipQ0rUH6wPdqoUG1MmbKFRERERERWRsGrVToXiSm4dsdV/EiSWWQ3v3NKmhV3/RRp4mIiIiIiIplnlYqPVJVGszfdgkRMYZT2LRrWgEdX/czU66IiIiIiMhasaaVCo1Gq8PqXTdw73GcQfpr/l7o9XZ1SCQS82SMiIiIiIisFoNWKhQ6QcCGg7dwIzjGIN3fzw1D3q8NKQNWIiIiIiIqAAatVCh2nniAMzcjDNL8vJ0wqls9KOQ8zIiIiIiIqGAYTdArO3w2FIfPhRqkebnaYezHDWBnw27TRERERERUcAxa6ZWcufEMO07cN0hzdbTBuJ4N4OKgNFOuiIiIiIiopGA1GBXYjYfRWH/wlkGajVKG6UNfh4uNzEy5IiIiIiKikoQ1rVQgweHxWLXrBrQ6QUyTSSX4ols9VKvgar6MERERERFRicKglfLtWUwylu64ijS1VkyTABjauTZqV3I3X8aIiIiIiKjEYdBK+RKXmIYlv15BYoraIL1Xu+p4zd/bTLkiIiIiIqKSikErmSw5VYOlO67i+YtUg/T3W/jhnaYVzZQrIiIiIiIqyRi0kknUGi1W/nENjyMTDdJb1fNBtzZVzJQrIiIiIiIq6Ri0Up50OgE/7gvC7dA4g/QGVT0w4L2akEgk5skYERERERGVeAxaKVeCIGDb0bu4eCfKIL1qeWd83rUuZFIeQkREREREVHQYcVCu9v0vBCcuhRmk+XjYY0yPBrBRcC5WIiIiIiIqWgxaKUf/XAnD7pPBBmluTjYY93FDONopzJQrIiIiIiIqTRi0klGX7kZh8593DNLsbeQY93EDeLjYmilXRERERERU2jBopWzuPo7DD3tvQhBepinkUozuUR/lPR3NlzEiIiIiIip1GLSSgSdRiVi+8xrUGp2YJpEAn3epgxoVXc2XMSIiIiIiKpUYtJIo+kUqlu64iuQ0jUH6gA610Ki6p5lyRUREREREpRmDVgIAJKaosWTHFcQmpBmkf9i6Mto0KGemXBERERERUWnHoJWQptLiu9+uIjw62SD9rcbl0emNSubJFBERERERERi0lnoarQ5r9tzAg6fxBulNa3qiT7sakEgkZsoZERERERERg9ZSTRAEbDp8G9ceRBuk1/J1xdDOtSGVMmAlIiIiIiLzkps7A1S8UlUaHD4biuOXwpCYos62vqKXI0Z1qw+FXGaG3BERERERERli0FqKpKo0mLP5IqLiUgymtMng4WyDLz9uAHtbHhZERERERGQZ2Dy4FDl8NjTHgBUAGlQrA1dHm2LOFRERERERUc4YtJYixy+F5RiwAsD525HFmBsiIiIiIqK8MWgtRYz1YTVYn5z7eiIiIiIiouLGoLUUcbRT5L7ePvf1RERERERExY1BaynStlH5HNcp5NJc1xMREREREZkDg9ZSpE5lN6PpCrkUnq526NDct5hzRERERERElDvObVKK3HvyIluak70CbRuVR4fmvrBV8nAgIiIiIiLLYlKUEhwcjEmTJiEuLg6urq4IDAxEpUqVDLY5deoUlixZgrt376Jfv36YOHGiuC46OhqTJ09GeHg4NBoNmjdvjqlTp0IuZ5BUnIJCYg2W+71bA20bVzBTboiIiIiIiPJmUvPg6dOno0+fPvjzzz/Rp08fBAQEZNumYsWKmDt3LoYMGZJt3ffff4+qVati37592Lt3L27evIkjR468eu7JZGlqLe49iTNIq13Z3TyZISIiIiIiMlGeQWt0dDSCgoLQqVMnAECnTp0QFBSEmJgYg+38/Pzg7+9vtPZUIpEgKSkJOp0OKpUKarUa3t7ehfQWyBR3H8dBoxXE5TIutvBytTNjjoiIiIiIiPKWZ9AaHh4Ob29vyGQyAIBMJoOXlxfCw8NNfpERI0YgODgYrVq1Ev81adKk4LmmfLsZbHiToU5ld0gkEjPlhoiIiIiIyDTF0qn08OHDqFmzJjZt2oSkpCQMHToUhw8fRocOHUzeh4eHYxHmsOS7m2UQphb1y8PT06nIXq8o903Fg2Vo/ViG1o3lZ/1YhtaPZWj9WIYlQ55Bq4+PDyIiIqDVaiGTyaDVahEZGQkfHx+TX2Tr1q2YN28epFIpnJyc8NZbb+Hs2bP5ClqjoxOh0wl5b0jZxCWmISQ8XlyWACjnZouoqIQieT1PT6ci2zcVD5ah9WMZWjeWn/VjGVo/lqH1YxlaD6lUkmslZZ7Ngz08PODv74/9+/cDAPbv3w9/f3+4u5s+iE+FChXw77//AgBUKhXOnDmD6tWrm/x8ejVBIYZNgyv5OMPRTmGm3BAREREREZnOpNGDZ8yYga1bt6J9+/bYunUrZs6cCQAYOnQorl+/DgC4cOEC2rRpgw0bNmD79u1o06YNTp48CQCYMmUKLl68iM6dO6Nr166oVKkSPv744yJ6S5TVzWDDqW7qVHYzU06IiIiIiIjyRyIIglW0uWXz4IIRBAHjVp3Gi0SVmDaxTyPU9C26wJVNMawfy9D6sQytG8vP+rEMrR/L0PqxDK3HKzcPJusW9jzJIGC1UchQtbyLGXNERERERERkOgatJVzWqW5q+rpCLmOxExERERGRdWD0UsLdzDIIU51Kpg+gRUREREREZG4MWkswtUaHu6FxBmm1KzNoJSIiIiIi68GgtQS7H/YCKo1OXHZ1VKKch70Zc0RERERERJQ/DFpLsKz9WetUdodEIjFTboiIiIiIiPKPQWsJxv6sRERERERk7Ri0llAJySqEPjOcl6o2g1YiIiIiIrIyDFpLqFuPYiFkWq7o5QhnB6XZ8kNERERERFQQDFpLKGP9WYmIiIiIiKwNg9YSSBAEBLE/KxERERERlQAMWkugiNgURMenictymRTVK7iYMUdEREREREQFw6C1BMraNLhmRRcoFTIz5YaIiIiIiKjgGLSWQFmD1trsz0pERERERFaKQWsJo9HqcDs01iCN/VmJiIiIiMhaMWgtYYLD45Gq0orLTvYKVPByNGOOiIiIiIiICo5BawmTbaqbSu6QSiRmyg0REREREdGrYdBawtzMMtVNbTYNJiIiIiIiK8agtQRJTtUg+GmCQVodDsJERERERERWjEFrCXI7NBY6QRCXfTzs4eZkY8YcERERERERvRoGrSVItv6srGUlIiIiIiIrx6C1BMnan5VT3RARERERkbVj0FpCPI9LQWRsirgsk0pQ09fVfBkiIiIiIiIqBAxaS4istaxVy7vAVik3U26IiIiIiIgKB4PWEoL9WYmIiIiIqCRi0FoC6HQCbj2KNUhjf1YiIiIiIioJGLSWAI8iEpCUqhGXHWzlqFTWyYw5IiIiIiIiKhwMWkuArE2D/f3cIJVKzJQbIiIiIiKiwsOgtQTIGrTWZn9WIiIiIiIqIRi0WrlUlQb3w14YpLE/KxERERERlRQMWq3c3cdx0OoEcdnL1Q6ernZmzBEREREREVHhYdBq5W5wqhsiIiIiIirBGLRauaAQw6luarNpMBERERERlSAMWq1YbEIanj5PEpclEsDfz9V8GSIiIiIiIipkDFqtWFCIYdPgKj7OsLdVmCk3REREREREhY9BqxXLOtUN+7MSEREREVFJY1LQGhwcjJ49e6J9+/bo2bMnQkJCsm1z6tQpdOvWDXXr1kVgYKDBuq+//hpdunQR/9WqVQvHjh0rlDdQWukEIVtNK/uzEhERERFRSSM3ZaPp06ejT58+6NKlC/bs2YOAgABs3rzZYJuKFSti7ty5OHz4MFQqlcG6hQsXio9v376NAQMGoHXr1oWQ/dLrSWQi4pPV4rKtUoYq5ZzNmCMiIiIiIqLCl2dNa3R0NIKCgtCpUycAQKdOnRAUFISYGMNaPj8/P/j7+0Muzz0O3rlzJzp37gylUvkK2aasowbX8nWDXMbW3kREREREVLLkWdMaHh4Ob29vyGQyAIBMJoOXlxfCw8Ph7p6/5qgqlQr79u3Dxo0b851RDw/HfD+nJLsb9sJguXk9H3h6OpkpN9lZUl6oYFiG1o9laN1YftaPZWj9WIbWj2VYMpjUPLiwHD16FOXKlYO/v3++nxsdnQidTiiCXFkftUaLmw+jDdJ8y9gjKirBTDky5OnpZDF5oYJhGVo/lqF1Y/lZP5ah9WMZWj+WofWQSiW5VlLm2Z7Ux8cHERER0Gq1AACtVovIyEj4+PjkOzO///47unfvnu/nkaG7T15ArdGJy+7ONijrbm/GHBERERERERWNPINWDw8P+Pv7Y//+/QCA/fv3w9/fP99Ng589e4aLFy+ic+fOBcspiYKyTnVTyR0SicRMuSEiIiIiIio6Jo3cM2PGDGzduhXt27fH1q1bMXPmTADA0KFDcf36dQDAhQsX0KZNG2zYsAHbt29HmzZtcPLkSXEfu3btQtu2beHi4lIEb6N04fysRERERERUWpjUp7Vq1ar47bffsqX/9NNP4uOmTZvi33//zXEfw4cPL0D2KKv4JBVCIxPFZQkAfz8382WIiIiIiIioCHGOFCsT9MiwltXX2wlO9pw+iIiIiIiISiYGrVYmKNhwflY2DSYiIiIiopKMQasVEQQBN0OyDsLEpsFERERERFRyMWi1IuHRyYhNSBOXlXIpqlVwNV+GiIiIiIiIihiDViuStZa1RkVXKOQsQiIiIiIiKrkY8VgRTnVDRERERESlDYNWK6HR6nAnNM4grU4lBq1ERERERFSyMWi1Eg/CXiBNrRWXXRyUKO/pYMYcERERERERFT0GrVbiZojhVDe1K7lDIpGYKTdERERERETFg0Grlcjen5VT3RARERERUcnHoNUKJKWqEfIs3iCtNvuzEhERERFRKcCg1QrcComFILxcLu/pAFdHG/NliIiIiIiIqJgwaLUCQVnmZ+WowUREREREVFowaLUCNzg/KxERERERlVIMWi1cZGwynr9IFZflMglqVHQ1X4aIiIiIiIiKEYNWC5d1qptq5V1go5CZKTdERERERETFi0GrhQti02AiIiIiIirFGLRaMK1Oh6BHhjWtDFqJiIiIiKg0YdBqwULCE5CSphGXHe0U8PV2MmOOiIiIiIiIiheDVgt2M8tUN/5+bpBKJGbKDRERERERUfFj0GrB2J+ViIiIiIhKOwatFiolTYMHT+MN0upUYtBKRERERESlC4NWC3UnNA5anSAue7vbw8PF1ow5IiIiIiIiKn4MWi1U1v6sdVnLSkREREREpRCDVgt1M0t/1tqV3cyUEyIiIiIiIvNh0GqBYuJT8SwmWVyWSiSo5cuglYiIiIiISh8GrRYoay1rlfLOsLORmyk3RERERERE5sOg1QKxPysREREREZEeg1YLoxMEBIXEGqTV5vysRERERERUSjFotTCPIxKRmKIWl+1s5Kjs42TGHBEREREREZkPg1YLk7VpcC1fV8ikLCYiIiIiIiqdGA1ZmKyDMNVl02AiIiIiIirFGLRakDS1FveexBmksT8rERERERGVZgxaLci9x3HQaAVxuYyLLbxc7cyYIyIiIiIiIvNi0GpBsvZnrV3JHRKJxEy5ISIiIiIiMj8GrRbkZrDhVDfsz0pERERERKWdSUFrcHAwevbsifbt26Nnz54ICQnJts2pU6fQrVs31K1bF4GBgdnWHzx4EJ07d0anTp3QuXNnPH/+/JUzX5K8SEzDk6hEcVkCoJafm/kyREREREREZAHkpmw0ffp09OnTB126dMGePXsQEBCAzZs3G2xTsWJFzJ07F4cPH4ZKpTJYd/36daxcuRKbNm2Cp6cnEhISoFQqC+9dlABBIYa1rJV8nOBopzBTboiIiIiIiCxDnjWt0dHRCAoKQqdOnQAAnTp1QlBQEGJiDPtf+vn5wd/fH3J59jh448aNGDx4MDw9PQEATk5OsLGxKYz8lxhZ+7PWYdNgIiIiIiKivGtaw8PD4e3tDZlMBgCQyWTw8vJCeHg43N1NC6wePHiAChUq4JNPPkFycjLeeecdDB8+PF+DDHl4OJq8rbURBAG3Qw1rWt9oWAGenk5mytGrs+a8kx7L0PqxDK0by8/6sQytH8vQ+rEMSwaTmge/Kq1Wizt37mDDhg1QqVT49NNPUa5cOXTt2tXkfURHJ0KnE/Le0Ao9iUpETHyauGyjkKGMgwJRUQlmzFXBeXo6WW3eSY9laP1YhtaN5Wf9WIbWj2Vo/ViG1kMqleRaSZln82AfHx9ERERAq9UC0AegkZGR8PHxMTkT5cqVQ4cOHaBUKuHo6Ii3334b165dM/n5JV1QsGHT4Jq+rpDLOLAzERERERFRnpGRh4cH/P39sX//fgDA/v374e/vb3LTYEDfD/bUqVMQBAFqtRr//fcfatWqVfBclzA3swzCVKcS+7MSEREREREBJk55M2PGDGzduhXt27fH1q1bMXPmTADA0KFDcf36dQDAhQsX0KZNG2zYsAHbt29HmzZtcPLkSQDA+++/Dw8PD3Ts2BFdu3ZFtWrV0KNHjyJ6S9ZFrdHhzmPDoLU2B2EiIiIiIiICAEgEQbCKjqIltU/rrUexWPTLZXHZ1VGJb0e2zNcgVZaG/QesH8vQ+rEMrRvLz/qxDK0fy9D6sQytxyv3aaWiFZR1qptK7lYdsBIRERERERUmBq1mdjOY87MSERERERHlhEGrGSWmqPHomWGThdochImIiIiIiEjEoNWMgkJikLmXbkUvRzg7KM2WHyIiIiIiIkvDoNWMjPVnJSIiIiIiopcYtJqJIAi4GZxlflb2ZyUiIiIiIjLAoNVMImJTEB2fKi7LZVJUr+BixhwRERERERFZHgatZpJ11OAaFV2gVMjMlBsiIiIiIiLLxKDVTLL1Z2XTYCIiIiIiomwYtJqBRqvD7dAs/Vk5CBMREREREVE2DFrNIDg8HilpWnHZyV6BCl6OZswRERERERGRZWLQagZZ+7PWruQOqURiptwQERERERFZLgatZhAUwqbBREREREREpmDQWsySUzV4+DTeII2DMBERERERERnHoLWY3Q6NhU4QxGUfD3u4OdmYMUdERERERESWi0FrMbuZdaobNg0mIiIiIiLKEYPWYhYUzPlZiYiIiIiITMWgtRg9j0tBRGyKuCyTSlDT19V8GSIiIiIiIrJwDFqLUdamwVXLu8BWKTdTboiIiIiIiCwfg9ZidDPrVDdsGkxERERERJQrBq3FRKcTcIuDMBEREREREeULg9Zi8igiAUmpGnHZwVaOSmWdzJgjIiIiIiIiy8egtZjczDJqcC0/N0ilEjPlhoiIiIiIyDowaC0mQVmbBrM/KxERERERUZ4YtBaDNJUW9568MEhjf1YiIiIiIqK8MWgtBncex0KrE8RlL1c7eLramTFHRERERERE1oFBazG4GWw41U1tNg0mIiIiIiIyCYPWYpCtPyubBhMREREREZmEQWsRi01IQ9jzJHFZIgH8/VzNlyEiIiIiIiIrwqC1iGWtZa3i4wx7W4WZckNERERERGRdGLQWsZtZgtbabBpMRERERERkMgatRUgQBASFGA7CxPlZiYiIiIiITMegtQg9iUpCfJJKXLZVylClnLMZc0RERERERGRdGLQWoZvBhk2Da/m6QS7jR05ERERERGQqRlBFKGt/VjYNJiIiIiIiyh8GrUVErdHi7uM4g7TaldzMkxkiIiIiIiIrZVLQGhwcjJ49e6J9+/bo2bMnQkJCsm1z6tQpdOvWDXXr1kVgYKDBuhUrVqBFixbo0qULunTpgpkzZxZK5i3ZvScvoNboxGV3ZxuUdbc3Y46IiIiIiIisj9yUjaZPn44+ffqgS5cu2LNnDwICArB582aDbSpWrIi5c+fi8OHDUKlU2fbRtWtXTJw4sXBybQWy9metXckdEonETLkhIiIiIiKyTnnWtEZHRyMoKAidOnUCAHTq1AlBQUGIiTEMyvz8/ODv7w+53KQ4uMTL2p+1LvuzEhERERER5VueEWZ4eDi8vb0hk8kAADKZDF5eXggPD4e7u+mB2IEDB3Dq1Cl4enriiy++QKNGjfKVUQ8Px3xtb04vEtMQGpEoLkskQKvGFeHiaGPGXBUvT08nc2eBXhHL0PqxDK0by8/6sQytH8vQ+rEMS4ZiqRbt1asXPv/8cygUCpw+fRojRozAwYMH4eZm+sBE0dGJ0OmEIsxl4fkv6JnBsq+XE1QpKkSlZG82XRJ5ejohKirB3NmgV8AytH4sQ+vG8rN+LEPrxzK0fixD6yGVSnKtpMyzebCPjw8iIiKg1WoBAFqtFpGRkfDx8TE5E56enlAoFACAli1bwsfHB/fu3TP5+dYmKDjWYLl2ZY4aTEREREREVBB5Bq0eHh7w9/fH/v37AQD79++Hv79/vpoGR0REiI9v3bqFsLAwVK5cuQDZtXyCIGTvz1qJ/VmJiIiIiIgKwqTmwTNmzMCkSZOwevVqODs7i1PaDB06FKNHj0a9evVw4cIFjBs3DomJiRAEAQcOHMDcuXPRunVrLFmyBDdv3oRUKoVCocDChQvh6elZpG/MXJ7FJCM2IU1cVsqlqFbB1XwZIiIiIiIismImBa1Vq1bFb7/9li39p59+Eh83bdoU//77r9HnZ523tSS7kWWqmxoVXaGQmzQdLhEREREREWXBaKqQBRmZn5WIiIiIiIgKhkFrIdJodbj9OM4gjfOzEhERERERFRyD1kL08Gk80lRacdnFQYnyng5mzBEREREREZF1Y9BaiLL2Z61dyQ0SicRMuSEiIiIiIrJ+DFoLUVCWqW7qsGkwERERERHRKzFp9GDKW1KqGsHh8QZpHISJiAqLVqtBbGwUNBqVSdtHRkqh0+mKOFdUVFh+1o9laP1YhtaPZWh55HIl3Nw8IZPlLwxl0FpIbj+KhSC8XC7v6QBXRxvzZYiISpTY2CjY2trDwaGsSd0O5HIpNBr+UFsrlp/1YxlaP5ah9WMZWhZBEJCUFI/Y2CiUKeOTr+eyeXAhuZmlP2sd1rISUSHSaFRwcHBmP3kiIiKyShKJBA4Ozia3GsuMQWshucn+rERUxBiwEhERkTUr6LUMg9ZCEBmXgqi4VHFZLpOgRkVX82WIiIiIiIiohGCf1kIQlKVpcLXyLrBRyMyUGyIivVSVBofPhuL4pTAkpqjhaKfAW43Lo0NzX9gqefonIiIi68CrlkKQrT8rmwYTkZmlqjSYs/kiouJSoE4fhCIxRY1DZ0Nx4U4UpvZv8kqB67p1P6B//8FQKBSFleVcqdVqTJ78FaKiItGkSTOMHv1VsbxufvTo0RkLFy5FlSrVTH7O3r278Pvvv0IQBEgkEvTp0x/t23cEAGi1Wixbthhnz/4PEokEffsOROfOXQEAc+fOQK1a/ujevWeu+w8Pf4pevT5E5cpVIQg62NraYfz4Sfjf/07hxIljAICwsMdwdXWHg4MDACAwcAm8vcua/B7+/fdvlClTBrVr1zX5OcacPPk3NmxYC7VaBUEA3n//A/Tu3Vdcv3HjWhw8uA8A0LFjZwwc+Gme69at+wEpKSkYNWpsnq/fqlVTVK2qL7u0NBVq1qyFgQM/ReXKVcRttFotunfvhFq1/LFgwZICvc+EhATs3fsHPvlkQIGen9nPP2/Bvn278OTJYyxYsAQtW7YW18XERGP27ACEh4fDxsYGX3/9DerU0ZdRfo/VlSuX4Z9/jiM8/Ck2b95u8LzQ0EeYO3cGXrx4ARcXF0ydOhMVK/rmua5Vq6Y4cuRf2Nvb5/ra69b9gF27dqJMGU8xrVOnD7B//14AQETEM9ja2sLFxRUAMGHCFPF9ZvXjj6vx99/H4OrqhtWr15r03vPL1O9mVjt2/Ix33ukANzf9NeS9e3ewZEkg7t69gxYtWmLOnIXitvk5rvPzmgW1ceNaHD16BDKZFDKZHMOGjUTz5i0AAKNGfYbevfsZHJu5yevYnDlzKi5duoDo6OcmHT+A4Xkww3ffrRaPGVMdPLgP//vfSYOyyMuLF3GYPTsAYWFPoFAoUKGCLyZMmAI3NzcAwI0b17Fo0TykpaXBx8cHAQGz4ebmjvDwp/j00344cOBYvvKYX5m/h+PHj8aXX36N8uUrZNsuczl++20gLl48B4VCCXt7O4wZMx61atUGULDfQVMxaH1FOp2AW49iDdIYtBJRUbr1KBZbj9xBeHRyvp+r1ujw9HkSRiz5N8dtfDzs0ffdmvD3c8txmw0bfkLv3v2MBq0ajQZyeeH+vNy9ewfPnj3D1q078vW8oshLYe63QoWKWLHiBzg7uyAyMgKDBvVB/foNUbFiBRw5cghhYY+xffsuvHjxAoMHf4KmTV+Dj0+5fL2Go6MjNm78GQCwY8cvmD9/Ftav34YBA4YAyP9FZVYnT/6NWrX8XzlodXcvg4ULl6JMGU8kJiZiyJC+qF27Dho0aIQrVy7hxImj2LLlVwDAZ58NRMOGjdGwYeNc1+XXmjXrYW9vD51Oh717/8Dw4UOwfv1WlCtXHgBw9uwZlCnjiWvXriImJhru7h75fo3ExAT8/PPmQglaGzVqjDffbIv582dlW/f99yvRoEEjLF26ClevXsHs2dPwyy9/FKg/WevW/4ePPuqFkSOHZlu3ePF8dOv2Edq374g//zyIRYvmYfny7/Nclx8dOryfLUDr0aMXgPwFidu3b8Pvv+8XAwZLsmPHL2ja9DUxgHRzc8eoUeNw794dXLhwtlhes6D8/eugV6++sLW1xb17d/HFF59hz57DsLGxLaScvjzndurUBaNHj0Pnzu/m6/mZz4PFKeNmZOPGTQEAq1Z9h++/X4HJkwOg0+kwe/Y0TJkyAw0aNMTGjWuxZs0KTJkyvdjzCQCLFy83abvXX38DY8Z8BblcjtOnTyIgYDJ27NhTxLlj0PrKgp/FIzlNIy472ing6+1kxhwRUUm3+fBtRMSmFNn+w6OTsfnwbcwf1sLo+m+/DQQADB8+GBKJFCtW/IDly7+FTCZDaOgjJCcnY+PGnzFz5lSEhj6CWq1C+fIVMXlyAJydnXHp0gUsX74EtWvXwc2b1wFIMHPmPFSqVBmhoSGYO3cmUlNTodNp8d57ndGqVWvMmjUVz59HYeDAPujXbyBatGiFZcsW4datmwD0F7UZQcCoUZ+hevWauHnzOpydnfHWW+/gr78Ow9HRCQ8e3IOnpxfGjp2AVauW4cmTJ/D3r42AgNmQSCRISkrEihVL8eDBPahUKjRq1BRffPElZDJZtv3m9gP/yy9b8d9/pzF37iL8+us2hIQE48WLODx/HoXKlatg8uTpcHR0FC9kAMDLyxseHmUQGRmJihUr4Pjxv9C5c1dIpVK4ubmhdes3ceLEUfTp09/gtS5duoDvvluM6dPn5Hl3u1mz5vjxx1V5HgPGXL9+FUuXLoROJ0Cj0WDAgMFwdnbBqVP/4sKFc9i3bw969uyD997rhEOH9uOPP36DVquFo6Mjxo+fBF/fSjh4cB+OHDkEGxsbhIU9gbu7B6ZNmwVPTy+D2jFHR0f4+VXGs2fhaNCgEY4d+wvt278vXgS3b/8+jh37Cw0bNs51XWYPHtzHrFlTMXbsBDRq1CTX9yqVStG1aw9cvnwRu3btxMiRYwAABw7sQdeu3XDjxnUcPnwgW1lkptPpsHjxAly6dF6skVizZj2WLAlEYmIiBg7sA1tbW3z//Xo8f/4cy5YtRETEM6SlpaFdu/bo338wAH3Nxdtvv4vz588iKSkRH3/cWwzQ/P3r5Pj6J04cxW+/6WufGzRoCIVCgdu3g7I9J/Ox6ujoaHRfDRo0NJoeGxuDu3dvY+lS/THVrl17LF26ELGxsQCEHNdlDhp1Oh1WrlyK6OhofPPNDCiVyhzf06sYMeJTqFRpGDt2OF57rQVGjhyT43E6bNggjB07HvXq1cPixQtw5colbN26AxqNBl26tMfOnftx//7dbN+Hd97pAAB4+PABRo/+HJGREahTpx6mTp0JiUSCmJhoLFo0H0+fPoEgCOjdux/ee68TNm1ah+fPozB16kQolTaYPn0OKleugjJlPPHoUXCu78uU43rPnj+wY8fPUCiUEAQdZs1agL//PpbtNStUqIgff1yNK1cuQqVSo1q1avjqq8mwt7fH3LkzIJfLERz8EHFxcWjUqDHGjZsIhUIh1qoCQLVq1SEIAl68eAEvL8Og9ejRP7F9+zbMm7cIXl7eeZaZsXNukybNctz+zJlT2Lx5PdLSVFAoFPjii3Fo2LBBnq9TEBERzzBlygT06dMfb7/9Dlq1aopBg4bi5Ml/kJaWimHDRuL//u9tODu7GJzn69Spi127fgcA3LlzC0qlUvx+de3aAx991Dlb0KpSqTBnznR4enph1KixRm88jRkzAj16fIzWrf8PAHD69Els374VK1b8gF9+2Ypjx45Aq9VAqbTB+PGTUL16zWz7yFxLGhz8EPPmzURKSgqqVq0KlerlaL+Zb3DWrVsPUVGR0Ol0kEoNh0oy5dySHwxaX0GqSoOdfz8wSHO2V0Cl1rK/GBGVWF99NRG7dv0m1kpluHfvLlau/BF2dnYAgDFjxsPV1RWAvlnetm2bMHz4FwCA4OAHmDIlAF9//Q02bVqHTZvWYfr0Ofjjj51o1aoN+vUbBACIj4+Hs7MzJk6cilWrvsO6dVsAAKtXL4dOp8Pmzb8iOTkJw4YNRpUq1dCiRUsAwNOnT7B69VrI5XIcPLgPt24FYfPm7fDy8sbXX4/FzJlTsWLFD7Czs8OQIX1x4cI5NGvWHCtWLEXDho0xadI06HQ6zJw5FQcO7MUHH3yYbb/G6HQCli1bhBcvXmDx4uViTfS1a5exYcPPcHf3wLx5M7Fx49psNUeXLl1AYmIiatWqBUB/UVS27Mt57Ly9yyIyMsLgOUeOHMKOHb9g8eLl8PT0yrPsTpw4avRixRTbtm1C79798M47HSAIAhITE+Hk5IRWrdoY1HRdvXoZx4//hVWrfoJSqcSZM6cxf/4srFmzPv2zuIqNG7fB17cS1q//Ed99tzhbc7tHj0IQFHQdX389RfwsMl+Qe3uXxdWrl/Jcl+H8+bNYsWIJZs6cb9DcNy+1a9fF+fP6Wq64uDhcvHgB33wzA76+lbBw4dxcg9Z79+7i8uUL2Lr1N0ilUsTHxwMAxo2biE8/7WdQ6zNnTgAGDvwUDRs2hlqtxpgxw+HvXxvNmr0OQB8crl+/FTEx0Rg06BM0aNAY1apVz/G1X7yIgyAI4vcv43OJjIwQg9acjtX8iIiIQJkyXpDJ9ON4yGQylCnjicjICAiCkOO6jKBVpVJh3rwZ8PEpjxkz5uZYC3z48AFcuHAOAFCvXgN89dXEfOd19eq1aNWqqXjeyu04bdKkGS5cOI969erh+vUrsLGxwfPnz/Hs2VP4+VWGnZ2d0e9DhocPH2DZstWQSqUYNOgTXLhwFs2avY5lyxajSpWqmD9/MZ4/f44hQ/qiZs1aGDBgCPbt2405cwLz1azS1ON69ervsG3b7yhTpgxUKhV0Op3R19y4cS0cHBzw00+b05+3HFu2bMCwYSMBAEFBN7BmzXoolUpMmDAGe/f+ka2G+/DhAyhfvkK2oHTbtk04d+4sli1bna8AJq9zboawsCfYuHEdlixZAQcHRzx8+ADjx4/G3r2HAABJSUkYMqQfBEFAu3bvonfvfgUexfbevbuYPXsavvpqEho0aCSmS6VSbNz4M0JDQ/D550PQoEEjg1psnU6HXbt+R6tWbQBkP8e7urpCp9MhPv6FmBYf/wJTpkzAm2++hY8+6pVjnjp27IRDhw6IQevBg3vRsWNnAPqbuhldLc6fP4tFi+bjxx835voeZ88OwEcf9cJ773XCjRvXMWLEEKPb/f77DrRo0cogYC2Mc4sxjKwKKKO/WPjzJIP0iNgUzNl88ZX7ixER5aR/h1oFbh5siozmwfn1f//3thiwAsDhw/tx5MhhaDRqpKSkin3ZAMDX1w81auiDszp16uH06ZMAgIYNG2H16uVITU1F48ZNDe5QZ3bhwjmMGTM+fc43R7Rr9y4uXDgnBq3vvNPB4CKnfv0G4kVU9eo1UbasD5yc9K1iqlWrjrCwx2jWrDlOnfoXt27dxPbt2wAAqampBhdfWfeb1fz5s1CvXn2x5jbDG2+0FpuSdurUBcuWLTJ4XnDwQ8yZMx3Tp881uUndgQP7YGNjg+++Ww0Hh5wvAjNq9QRBQLly5fHNNzNM2n9WjRs3xaZN6xEW9gTNmr2eY7/B06f/xf379/DZZwMB6CeTT0iIF9fXr98Avr6VAACdO3dF//6GF2LPnz/HpEnjMG7cJIN+jAV1/vx/OHv2f1i6dFW+9ycIgvj4zz8PoGXL1rC3d0D9+g2h0Whx48Y11K1b3+hzy5cvD41GgwULZqNx46Z44w3jza9TUlJw+fJFxMXFiWnJyUkICQkRg9ZOnboAANzdPfDGG61w+fLFXINWU+R0rBanr776Am+//S769OmX63bGmge/qtyO06ZNX8OmTevRsWNHODu7oGHDxrh48RzCw5+KNX25fR9at/4/2NjYAABq1qyZvo3+vJXxPsqUKYMWLVri0qULBer/l5/junHjZpg7dzpatmyNFi1aGe2zmPGZJCUl4e+/jwMA1GqVwXH21lvviDcq33uvE/7++7hB0Hr58kX89NMaLFtm2Jpj/fof4e1dFosXf5fvACavc26Gs2fPICzsCUaO/ExM02q1iI6OhodHGezadRBubu6IjY3BxInj4OTkLI4RkB/379/HN99MwMKFy1CpUmWDdRnfU1/fSqhRQ19D3KrVm+L6pUsXwd7eDt27f2zSa6lUKowY8SkGDx6Gt95ql+u2b775FlasWIIXL+IAAFeuXMLUqfpuA3fu3MKWLRsQH/8CUqkUjx+H5rqvpKREBAc/EMdXqFu3ntFj9OjRP/HXX4exatVPBulFdW5hVFVAh8+GIio2BUKWdK1OQFRcCg6fDUXX1qbfzSUiMpW/nxvmDn091232ng7GgTOPxEGYMlPIpXivuW+hn6Ps7V8GrFevXsbu3b9jzZr1cHNzw5Ejh7F37x/ieqXSRnwslUqh1WoB6APfunXr49y5/7B160YcOLAXAQGz850XOzvDwTkyNzmUSqVZXl8mvj4gYN68xTle1GXs9+zZM1izZgUA4N13O4g1bg0bNkoPQGJN7if2+HEoJkwYgwkTphg0w/T2Lotnz8LFmrGsd+WrVauOq1cvIyQkRLxg/vbbQFy/fhUAMGvWPCgUykLry/Xxx33QsmUbnD9/FsuWLUSzZq/js89GZNsuYxClTz/9PN+vERsbg7FjR+CTT/obXKRlfBYZIiKewcurbJ7rAKBiRV8EBz/E7dtB4gXkpk3rxIGoRo8el+PNkVu3glClin7wloMH9yE2NhY9euhrLxITE3HgwN4cg1ZHRyds2bIDly9fxIUL57BmzQqsX78123aCoINEIsHatZsLrf91xgAzcXFxYm2r/nN5eQOmIMdqVt7e3nj+PBJarRYymf579Px5VPrrCLms02vUqAnOnj2Dbt0+gq2tLV68iMOYMfpjytfXD7NmzS9QvkyR23Fat2593L17G6dPn0KTJs3QqFETHDiwF0+fhonb5/Z9sLHJfL7JfH4pPMaO65zMm6fvSnHx4gWMHv05xo+fLN7gy0wQgK++mpRrE9yc3LhxDbNnB2D+/G/Fm1IZ6tTRt1h49izc4OalKbKey3MiCAKaN2+BadMM+3fL5VJoNDoolS/7Cr/7bgdcv341W9Ca03k9My8vLyQnJ+Py5YvZgtbcrFy5DE+ehCIwcKlYK5n13BUXFwepVApnZxckJSVBLlegdu26OH36H7z5Zlux1YIxtra2aNXqTfz112EAQKtWb8LOzg5qtRrTpk3EypU/oWbNWnj+PApdu75ncr5z8s8/J/Djj6vx3XdrsvXtL4xzizGcp7WAjl8Kg1qb/WIQ0A90cuJyWDHniIjopY4t/ODpageF3PA0r5BL4elqhw7N83fhkJW9vQOSkhJzXJ+QkAAHB0e4uLhApVLhwIG9Ju33yZPHcHf3QMeOnTFo0FAEBd00ul3Tpq/hwIE9EAQByclJOHbsCJo1a16g95JZy5ZtsHXrJvEiMy4uDk+fZj+fN2/eAhs3/oyNG382uLB5//0P0KtXX4wZMxzPn0eJ6f/736n0fn764KdxY/1FYVjYE4wb9wXGjh2f7SKybdt22LdvN3Q6HWJjY3Hy5D/4v/97W1xfs2YtzJ27CLNmTcXlyxcB6JtuZ+Qr64XjqwoNfYTy5Suga9fu+Oij3mJ/YgcHB4OmkS1btsbhwwfEpsxarRa3b98S11+/flW803/gwF40aaIPGF+8iMPYsSPRvfvH6NSpa5bP4m38+ecBpKWlIi0tFX/+eUAManNbBwBly5bDkiUr8f33q3Ds2BEAwIABQ8TPyVjAqh+IaRfOnj2Drl174Natm0hISMCePYexc+c+7Ny5D1u2/IoTJ44iNTU12/MBIDY2FqmpqWjevAU+/3wUHB0d8fRpGBwcHJCamgqNRj8ehr29Axo0aIStWzeKz42IeIbo6Ofi8qFD+8V9njlzOscg2/Aza4fdu3cCAK5evYK0tDTUrOkvrs/pWM0PNzd3VKtWA0eP/glAX/NSvXpNuLm55bouw+DBn6FZs9cwbtwoJCUlwsXFVSyXogxYgdyPU6VSiRo1amHz5g1o2rQ56tSph2vXruLBg/uoU6cegJy/D7lp2vQ17Nu3GwAQHf08vSz154Ks36O8GDuujdFoNHj6NAy1a9dFv34D8dprr+PevTtGX7NVqzb49ddtSEvTH9P6Gv+XfWpPnDiGlJQUaDQaHD58UPzu3rp1EwEBkzF7diBq1qyVLQ/Nm7+B8eMnY8KEMXj48EG29YXhtddex9mzZwz2n1EmsbEx4vctNTUVp079i2rVahjJp/HzembOzs5Ytmw1/vzzILZvN7wJlfE79/hxKO7duyMeKz/8sAp37tzC/PnfGtxArVnTH2lpabh69QoAYPfunWjb9uW5SyqVYPLkANjbO2L69Mnie8jJe+91xsGD+3Hw4H507PgBAEClSoNWqxVvFv3xx2+57gMAHBwcUaVKNTEADgq6gYcP74vrT58+iZUrl2LJkpVGBwYsjHOLMaxpLaDEFHXu65NzX09EVJRslXJM7d8Eh8+G4sTlMCQmq+For0DbRoUzT2uvXp9g9OjPYWNjixUrfsi2/vXX38CRI4fQu3c3uLi4omHDRjkGoJkdP/4Xjhw5DIVCDolEgjFjjE9tM3Dgp1i6dCH699c3TWvfviNef/2NV3pPADBmzFdYvXo5Bg7sDYlEAoVCidGjvxJHjzXFu+++B6VSiTFjhouDNTVo0AgzZkxBVFQkKlWqglGjvgQArFmzAvHxcVi79gesXav/HIcP/wItW7ZE+/YdERR0A716fSi+56z5qFatOhYuXIaJE7/El19+bTAgSmHbuXM7Ll26CIVCDoVCiS+/nABA/9nPnTsTJ04cEwdi+uyzEZg0aRy0Wh00GjXatm2HWrX0AVO9eg3SB8F6LA7EBABbt27C48eh2LPnD+zZo6+V/+ijXnj//Q/QuHFTtGnTFn376su7Q4eOYj/W3NZl8PYui+++W41x475AWlqa2Ncrq+HD9YMf6ae8qYk1a9ahXLny+PnnzWjXrr1BUzdPTy/UqFELJ04cxXvvdcq2r4iIZ5g3bza0Wi20Wi1ef/0N1KlTD1KpFO+++x4GDOgFJydnfP/9egQEzMby5UvE49ne3gGTJwfAw6MMAH3N6eDBfZGUlIh+/QaKU/P8/PNm/PbbdsTFxWLevBlQKm2wdesOODg44vPPR2HWrAAcPvwhbGxsMG3arGwDpWQ9VnMamXrZskX4558TiImJxtixI+Hs7CKO5D1hwhTMmTMdGzashZOTE6ZNmyk+L7d1Gfr2HQgbG1uMHTsC3367As7OLkbzUNgaNmyc63HatGmz9IGrakMmk6FChQooV66c2Lw1p+9DbsaOHY9Fi+ZhwIBeEAQBn38+SqzJ79GjF+bNmwVbW1tMnz4Htra2GDHiU6SmpkKlSsOHH3bEkCGfGdzQMeW41ul0mDt3BhITEyCRSOHt7Y3PPx9l9DX79h2Idet+wKef9k8/ViQYPHioWKPo718b48aNRGxsLBo1aoIPPugGQN/CQ6VKw6JF88TXnTZtlnicAkCTJs0wZcp0TJo0DnPmBIrdQ/JrypQJYjDap093VKlSFUuWrETFir4ICJiNBQtmIy0tDRqNGvXqNUC9evVw7doVrF37fXqttwZvvNHK5Ca6xjg6OmLJkpX4+uuxSE1NFafY0mq1GDSoD1JTU9OntXHHw4cPsGXLBlSs6IvPP9efX3x8ymH+/MWQSqWYNm0WFi2aB5VKhbJlfbK1LJJIJPjqq4lYuXIZJk/+CnPmLBSbnmfVoEFDJCcniY8BfQA6ZMgwDB3aH87OLmjb9m2jz81q6tSZmDdvJrZu3YgqVaqJU9oAwPz5MyGXKzB16su+5VmnEDL13JIfEiFzhw0LFh2dCJ3OcrI6+ruTuQauTvYKfDe6YNMHWDtPTydERSWYOxv0CliGlufZs0coW9bP5O0zmkSR+RVkTsWSWn4FmefQWhVWGRblvIeUu5L6PSyIgs4/a27FVYamzjtMesauaaRSCTw8ch6fgc2DC+itxuWzNbvLoJBL0baR6XfliYiIiIiIyDjWtBZQxujBUXEpBgOdZPQXK82jB7OWzvqxDC0Pa1pLF3OW35Ah/bINHFOnTl1MmDDFLPmxdPv27cbvv+/Ilh4QMBNVqrzaCL/FacOGn/DPPyeypS9durJQB1MpahMnfomICMOpoby9vREYuDTf+7Km8+i9e3cwd2725tfdu39coFFyi1JO35lvvple4Cm5cmJNZWiKwjy+zakgNa0MWl9BqkpTZP3FrBkDHuvHMrQ8DFpLF5af9WMZWj+WofVjGVqmggStpTeyKgS2Sjm6tq7CqW2IqFgIgmC2+RSJiIiIXlVB60vZp5WIyArI5UokJcUX+GRPREREZE6CICApKR5yuTLvjbNgTSsRkRVwc/NEbGwUEhPjTNpeKpVCp2OTKGvF8rN+LEPrxzK0fixDyyOXK+Hm5pn/5xVBXoiIqJDJZHKUKeNj8vbsl2zdWH7Wj2Vo/ViG1o9lWHKweTARERERERFZLAatREREREREZLGspnmwVMoRM60Jy8v6sQytH8vQurH8rB/L0PqxDK0fy9A65FVOVjNPKxEREREREZU+bB5MREREREREFotBKxEREREREVksBq1ERERERERksRi0EhERERERkcVi0EpEREREREQWi0ErERERERERWSwGrURERERERGSxGLQSERERERGRxWLQSkRERERERBZLbu4MkHWJjY3F119/jdDQUCiVSvj5+WHWrFlwd3dHzZo1UaNGDUil+nshCxcuRM2aNQEAx48fx8KFC6HValGnTh3Mnz8fdnZ25nwrpdZbb70FpVIJGxsbAMD48ePRunVrXLlyBQEBAUhLS0P58uWxaNEieHh4AECu66h4PXnyBCNHjhSXExISkJiYiHPnzuVYtgDL0NwCAwPx559/IiwsDPv27UONGjUAAMHBwZg0aRLi4uLg6uqKwMBAVKpU6ZXWUdEwVoa5/SYC4O+iBcnpO1jQ8ybPqcXPWBnm9psIFLx8yQIJRPkQGxsr/Pfff+LyggULhMmTJwuCIAg1atQQEhMTsz0nMTFReOONN4Tg4GBBEARhypQpwooVK4olv5Rd27ZthTt37hikabVaoV27dsL58+cFQRCEVatWCZMmTcpzHZnfnDlzhJkzZwqCYLxsBYFlaAnOnz8vPH36NFsZ9evXT9i9e7cgCIKwe/duoV+/fq+8joqGsTLM7TdREPi7aEly+g4W5LzJc6p55FSGmWX+TRQE/i6WJGweTPni6uqK5s2bi8sNGzbE06dPc33Ov//+i7p164q1AL169cKhQ4eKMpuUTzdu3ICNjQ2aNm0KQF9Ghw8fznMdmZdKpcK+ffvQvXv3XLdjGZpf06ZN4ePjY5AWHR2NoKAgdOrUCQDQqVMnBAUFISYmpsDrqOgYK8OC/CYC/F00B2Pllxv+LlqevMrQ1N9EgGVojdg8mApMp9Phl19+wVtvvSWm9evXD1qtFm3atMEXX3wBpVKJ8PBwlCtXTtymXLlyCA8PN0eWKd348eMhCAKaNGmCcePGZSsjd3d36HQ6xMXF5brO1dXVDLmnDMePH4e3tzfq1KkjpmUtW2dnZ5ahhQoPD4e3tzdkMhkAQCaTwcvLC+Hh4RAEoUDrMpqlUvEz9psI8HfRGuT3vMlzqmUy9psI8HexpGBNKxXY7NmzYW9vj759+wIA/v77b/zxxx/Ytm0b7t+/j1WrVpk5h2TMtm3bsHfvXvz+++8QBAGzZs0yd5aogH7//XeDO8osWyLzyfqbCPB30RrwvFlyZP1NBFi+JQmDViqQwMBAPHr0CMuWLRMHmMhosuHo6IiPPvoIly5dEtMzN5d6+vRpvproUOHK+OyVSiX69OmDS5cuZSujmJgYSKVSuLq65rqOzCciIgLnz59H586dxTRjZZuRzjK0PD4+PoiIiIBWqwUAaLVaREZGwsfHp8DryDyM/SYC/F20BgU5b/KcanmM/SYC/F0sSRi0Ur4tWbIEN27cwKpVq6BUKgEAL168QGpqKgBAo9Hgzz//hL+/PwCgdevWuH79OkJCQgAA27dvx3vvvWeWvJd2ycnJSEhIAAAIgoCDBw/C398fdevWRWpqKi5cuABAX0YdOnQAgFzXkfns2rULb775Jtzc3ADkXLYAy9BSeXh4wN/fH/v37wcA7N+/H/7+/nB3dy/wOip+xn4TAf4uWoOCnjd5TrU8WX8TAf4uljQSQRAEc2eCrMe9e/fQqVMnVKpUCba2tgCAChUq4NNPP0VAQAAkEgk0Gg0aNWqEKVOmwMHBAQBw9OhRLFq0CDqdDv7+/liwYAHs7e3N+VZKpcePH+OLL76AVquFTqdD1apVMXXqVHh5eeHSpUuYPn26wdDvZcqUAYBc15F5tG/fHt988w3atGkDIPeyBViG5jZnzhwcOXIEz58/h5ubG1xdXXHgwAE8ePAAkyZNQnx8PJydnREYGIgqVaoAQIHXUdEwVobLli0z+pu4atUqXL58mb+LFsRY+X3//fcFPm/ynFr8cjqPAtl/EwH+LpY0DFqJiIiIiIjIYrF5MBEREREREVksBq1ERERERERksRi0EhERERERkcVi0EpEREREREQWi0ErERERERERWSwGrURERERERGSxGLQSERERERGRxWLQSkREZCYrVqzA+PHjzZ0NIiIii8aglYiIiIiIiCyWRBAEwdyZICIiKul+/PFHbNmyBYmJifDy8sLkyZMxatQoCIIApVKJihUrYu/evUhISMD8+fPx77//QiKRoFu3bhg9ejRkMhn++OMP7NixA7Vr18aePXvg6emJ6dOno0WLFuZ+e0REREVGbu4MEBERlXQPHz7Etm3bsHPnTnh7e+PJkyfQ6XQYNmwYHj16hMWLF4vbTpo0CR4eHjhy5AhSUlIwbNgw+Pj4oFevXgCAa9euoUOHDvjvv//w119/YdSoUTh27BhcXV3N9O6IiIiKFpsHExERFTGZTAaVSoUHDx5ArVajQoUK8PX1zbbd8+fP8c8//2DKlCmwt7eHh4cHBg4ciAMHDojbuLu7Y8CAAVAoFOjYsSMqV66Mv//+uxjfDRERUfFiTSsREVER8/Pzw5QpU7BixQrcv38frVq1wqRJk7Jt9/TpU2g0GrRq1UpM0+l08PHxEZe9vb0hkUjE5XLlyiEyMrJo3wAREZEZMWglIiIqBp07d0bnzp2RmJiIgIAALF68GH5+fgbblC1bFkqlEv/99x/kcuM/0RERERAEQQxcw8PD8dZbbxV5/omIiMyFzYOJiIiK2MOHD3HmzBmoVCoolUrY2NhAKpXCw8MDYWFh0Ol0AAAvLy+0bNkSCxYsQGJiInQ6HUJDQ3Hu3DlxXzExMdi8eTPUajUOHTqEBw8e4M033zTXWyMiIipyrGklIiIqYiqVCt9++y0ePHgAhUKBRo0aYdasWVAqldi7dy+aN2+OChUqYNeuXVi4cCEWL16Mjh07IikpCRUrVsTQoUPFfdWvXx+PHj3C66+/jjJlymD58uVwc3Mz47sjIiIqWpzyhoiIyEr88ccf+O233/DLL7+YOytERETFhs2DiYiIiIiIyGIxaCUiIiIiIiKLxebBREREREREZLFY00pEREREREQWi0ErERERERERWSwGrURERERERGSxGLQSERERERGRxWLQSkRERERERBbr/wEZfI1NkdbkMAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step10k_20k-FT_fewshot1k_step2k_lr1e5 18\\n\",\n      \"kp20k_valid2k_test 18\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step15k_20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr1e5_warmup5k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step100k_lr5e5_warmup10k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step20k_lr1e5_warmup2k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step50k_lr1e4_warmup5k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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rp5Xz1+USiV+XMvL1///0X69b9gDp16mDgwE9w7tw5DB7sD09PT8ycOSvX2GUYPHgopk2bCmdn52yJyqNHjxAUdAu7du2CQpGGrl3fhYODPXx9fQFoa3Rv3rwJQFtrs2DBPCxbpq35sLd3QFhYGAAgMTEJjo6OBucB2iT79dfbomXLlunfRQBu3QrCsGHDspU3PDwc1apVx/Hj2hrIkSNHIDo6Cv/8cxLdu7+nt26zZs0RGBiIyZMn4fXX2+G99/SX3717B2PGfAYAiI2NQXR0DADA19cXEokEPj4+iIqKRrly5XD06FHExsbi3Xe1ia2Dgz0SExNhZWWFxMREODg4AAA2blyPgIDFsLe3R3R0NCZM+AIAsHnzFl3z4IKIiYnBo0ePMGzYUABAdHQUAEAmk+P8+f/Qr18/nDhxAjExMfD3H4wRI1zwww/fQ61WY8SIEQBe7ddeXl5ISEhARESErgawXr16uHXrFtzc3PIsS0a8WrVqZfAGw4gRI7O9d4bg4Ee4ePGiLnnPqE2tVasmpFIp6tTxxbNn2mdvBUHAjBnTMW7ceN2+kpiYiL/+Ooiffvo5z3K6urrlmLACMHgsN27cOP21rqhcuQqioqLg6empe42zs7Mu0cs4fxjaj/7556TBc1XW88HKlYab/js4OCAxMTH9Myfp9q/Mtm3biurVq6NpU23LhMzniaSkJDg4aL+zGTNmYtKkiejTp49u/83qypUreOONN3Dw4AH07z8A9erVxdSpUzFq1Gjs378Pn3/+BdasWY3JkyfBz6872rVrr3utoX1TEASD39WoUaMxatRog2UAgGPHjuKtt94CoD1PJyUl6paJxRKD84go/5i0EpFZSFWoMG/rJUTEpkCp0tbYJaYo8df5pwi8G4HpnzSDTCo2kGSmTxudhKqhKeLOL6RSKdTpd88FABpBhGfhSZCIRdi+4yds3rYT4S9fYOGCObrXZL4oVKvV6NatG3r06IHZs2fh5k39Z8E0GkF3MahWazJtQ5zpb2T6+9WEIAioUqUq/Py6o149bZKqUqlw7949iMXa9TZs2KT3fvPnL8SUKZPxzTcrYG1tjadPnyA5ORmJiYm6GoPdu3dDJBKhR48eutc5Ojrh5cuXEIvFuiQ76/batGmjq6EICQlB69atERCwOK+vWE+rVq3QoUMHrFv3A/755x9IpVLds71VqlTFa6+9hrfffgeAtknogQP7cffuHQDamtWKFStCpVJhypTJmDhxkq6pcP369bFz588YMmQo/vvvHBo2bGhwnrYMr8Hb2xs7dmyHv/8gTJr0qinntWvXdH+HhYXh3Ll/MWzYcGzdugVbt26HtbU1kpOTMGPGjGxJq0ajwZgxYwEAvXu/j/fee08vtr6+vli+fAVsbW2hVCp1Qy7dvXsXDRo0wIsXL+Dm5oq7d+9i586fsGbNq468GjVqjPPn/0Pnzl1w9+4dVKumrTWdOnUa1q1bh3LlyqFSpcoFTlKzcnFxQa1aNfHDDz9CIpFAqVRCJBKhXr16+Pnnn7Bx42YcOXIESqUCVlZW8PHxwddfz8WVK1ewZcsWdOnSNdu+XLFiRQQF3UKHDh1w69YtVKxYSXv8qXMfzzMjXtu2bUPfvv2yLc/63n5+3XXbrFKlCtq0aYNp06YD0O5T4eHhuH//PtRqNe7evYOKFSsCAL77bhUaN26KVq1e023b3t4eI0eOxPTp/8PChQEQi3PuTiS3ZdqyZD+WM5rUpqam4unTJ3BxcdF7TVxcLF6+fAknJyfd+cPQfrRz50789ttuPHv2FLNmvbqBlPV8kFNNa6NGjfHrr7+gS5eu+O+/c+jZs6feOmfPnsXVq1ewdOmrztVq1aqFq1evolatWrrPoVAoIJfLYWVlpXceAYBKlSohNPQFAO3+oFQq02tRX50PT5w4gbp160IqleKrryZDqVRgwIABaNeuve5YymnfNPRd5VXT+u+//+r6HrC1tUVqahqSk5Pw8OFDVK9e3eA8AHj+/FmeLReI6BUmrURkFg6df6qXsGZQqjR4EZmEMd+cgsZMO1qsXKU6tmxYg4C5/4P/8M9089UaATVq1cWnwwejXsMmSFNo8Cw8EUqVGjEJabC1ESBAQFJSEsaO/QxqtQb29naoWbMW/v33rG47o0aNxvDh2hqBjIQmP4YPH4HZs2chMTERYrEIc+bMzXX9OnXqYPDgIZg2bQoWL16KcuW8MWPGdDx79hTTp88AAMyb9zUaNGgAf/9BaN68OcaMGYvPPhuDiRO/BABMnz49x+1JpYX76Rk7dgzS0lIBaC+Wvby8sHLlN7h+/ToGDx6CWbNmYufOnyEIwOeffw4AiI2Nw/Dhw2BlZYXly7/BkSOHcfPmTSxbtgwA8PnnX6Bx48Zo3rw5Bg4cAG9vbwwcOBAymTzbvPBwbUcvY8aMxdy5X+PAgQN4550uemVcsmQJXFycIZPJMG3adKhUKiQnJ8Ha2hoAYGtrh9jYGKSmpurmAcCNGzewcuUKqFRK3bOJDRo0wLhxYzBokD8++2wMxoz5DIIgwMnJCStWaGv4jhw5jICAhejZ833IZHIsW7YUUVFRGDFiOBwc7LFq1Wr07v0BvvpqEnbs2IEPP/xQ14TYwcEBCxcuwpQpXyEgYLHe876FIRaL8ckngzB06BCIRCJUr14d06fPQJMmTXD48GE4ODjAy8sLLi7OALRNma9fv4bk5GRMnGi4t99Ond7EkSNHMGjQQLi7e2DIkKFITU3FypXf4MsvJ2DZsuX48ssvcPnyZTx58gRDhgxB7dp1dPGaP38uDh48qGseniHre9esWVNvm25u7vD3HwSRSIRu3bqhTZvX4erqhnHjxiImJhqLFi1GeHg4NmxYj8aNm+DYsaPo0qWr7rnltm3bIS4uDgsXLsD//jc92+cy5ObNm1ixYjkePLiPYcOGYPXqtQaP5T17/sTZs2eg0WgwdOiwbGNHu7i4YM2a1bhz5w5GjRoFAAb3owYNGmDQoE/QrJn+89lZj9+calpdXV0hl1vhk08GoE6dOmjQoCEiIyOwe/dujBz5KRYunA87O3sMHuyPqlWrYNasORgyRNtqIi0tFaNHjwEALFy4AMHBwVCrVfD3129O7eTkDKVSCbVanZ4k78LAgQMxdepUuLi4QC6X4/79exgzZiwOHz6Mn3/egdTUVPj5dQcAlCvnjc8/H49x48Yb3DcNfVe51bQGBwfDx8dH7xgeMWIEhg8fBrncCgsWLMhx3vnz59Ghwxt57gdEpCUSLKS/7aioRGjM9YqV4OHhgIiIhLxXJJMzx1gpVRqM//Y0UhW515aYk5aeT9H7g48gk1sVeltiESCXSSCXiWElk0AulcDGWgqVqnA9AxeVgQMHYNu27XmvaKb+/PMPqFRqfPDBB8WyfZlMkuszrcXN338Q1q/fUOibAWVBUcUqJCQE3367Mt+tBEzBUo/fnGJ1+PAhSCRSvPXWW/jhh+/h5OSE3r0/gEwmw7lz/6JixUq6Z2vzqyS/q8mTv8LChYt0tesnT56Ai4sHKleuViLvX1TM8ZqCDDP3WInFIri52ee4nL9wRGQyYTHJ+OfqC5y5HlqiCatMKoaNXAIbKymsraSwtZLCxkqabZ61lSTTMilsrLTLj116jojgF0hOSoRTlqRVBO2JVyMIMPaWoEYAUhXq9O9AOzaoLpGVinUJrUwqhgii3DdWyvz111/45ZeduulGjRoZNQyOpZg+fRqePw/RTX/22ZhsPduas23btuHYsVdjZr755lsGh8ExJ8HBwZgzZ7Zu2traCt9/v67E3v/TT0cgNTVNN21MB1f0SufOr1o1jBgxEr/8shPDhg2BWq1Bu3btdL19m7vMNzwEQUB8fDzKlatowhIRmTfWtFKRMPe7N/SKqWOlUmtw7UEkTl4Jwa3HMfl6rUgEveRR71+2JFSSvm7GP4lumVRSuCGqUxUqLN18BJUd49GiZSvI5da68kklIrg7WQMiQK0SoFBpoFRpoFRr/y/MGTdj+zKpBDKpCHKpBFKJSO+5Pyp5UqkEKpXltBIoyxgry1FWYqVWq/Hw4UNERUWjXbs3La7FhKmvKch45h4r1rQSkVmIikvFqWsvcOr6C8QlKvL1WqlEhHdaVETvDtXNIkGzlksx0f8d/Hn0Iv46fhEiQQ2JRAwPZ2t4utjgUQ6dqQiCgDSlGslpKqSkqpCcpkJyqgrqQtyQE4kAm0y1xbZWUthYSyHO3NGTRoPwmBRExKZCpdZAmqmskjw6fqG8WVlJkZamyntFMjnGynKUlViJRCI4ODiibdtOFpewEpUkHh1EVGw0GgE3g6Nw8soLXHsYmWsto41cArFYhDSlGir1qxVlUjE8nG3g16aKWSSsGazlUvTt1hpA60JtRxAERMSm4ElYIp68TMCTsAQ8eZmAxBRlgbcpEgE+7nao7OUAHzc7nLwagrhEOZTqV0NQyKK13+v0T5oV2XBCZZW5372mVxgry8FYEVFmRl2pBAcHY8qUKYiNjYWzszMCAgKyDQK/evVqHDx4EGKxdszBL774Au3atQMApKSkYOrUqbh16xYkEgkmT56Mjh07FvmHISLzEJeYhlPXQ3Hqagii4tNyXbe6jyPeaFIeLep4QiMIOHT+KU5cCUFishL2tjJ0bFK047SaG5FIBE8XW3i62KJFHe3YioIgADIprgS91Etk45KMq6EWBCAkIgkhEUk5rqNUafAyKhnf7r6OFnW8YGetraW1tZbBVvd34ZtSExERERWWUVeBs2bNQr9+/dCjRw/s2bMHM2fOxNatW/XWadiwIYYMGQIbGxvcuXMHAwYMwJkzZ2BtbY0NGzbA3t4ef//9Nx4/foz+/fvjyJEjsLOzK5YPRUQlTyMIuPMkBievhODK/chcm7xaySVoXa8c3mjsg0pe+gPQ92xXDT3bWVbviUVNJBLBw8UWTWt5oGmtV8OPxCam6SWxT8ISEJ3HTYHcaGMWiztPYnNcRy4Tw85apmt2bJeezNqmz9P+LYWtlTbZzZz8WltJ9Jop5yVVocKh809x/HIIElOUsLeRoVPT0n3TgoiIiPKW51VAVFQUgoKCsGmTdvB5Pz8/zJ07F9HR0XB1ddWtl1GrCgC1a9eGIAiIjY1FuXLl8Ndff2HRokUAtIN0169fH6dOnULXrl2L+vMQUQlLTFHizPVQ/HM1BGExKbmuW9HTHh2blEerul6wsWISkl/O9lZwrmGFRjXcdfPikxV4qktiE/HkZTwiYlOL7D0VSg0UyjTEJOQ/ORYh/Xnb9MRWL/nNUrMrlYjw64mHiE1M0zUPT0xR4q/zTxF4N4LNmImIiMqwPK8AQkND4eXlBYlEAgCQSCTw9PREaGioXtKa2Z9//olKlSqhXLlyAIAXL16gfPnyuuXe3t54+fJlvgqaW29SZB48PBzyXonMQmFjJQgCbj+Oxl/nHuPstRdQ5jKeqFwqRrsm5dG1dRXUquRiVs+lmjtj4uQBoHplN715ickKPHoRh4fP47Dt4G0o1aYZ71UAtJ1NpamAuIJtQ6nSICw6GQfOP8PI9xuY7f7D85/lYKwsB2NlGRgny2HJsSry29YXLlzAypUrsXHjxiLdLoe8MW/sMMFyFCZWyakqnLv1EievhuT6vCQAeLvZ4o3G5dGmQTnYWcsAAJGRiQV637KosMeUt5M1vJ2sERmdhL/OPzV4Y0EsFqFqOQeU97BDcqoKSboejZVITv/bHAZFU2sEHDgbjNNXnqNGBWfUKO+EmhWdUNnLwSyeueX5z3IwVpaDsbIMjJPlMPdYFXrIG29vb4SFhUGtVkMikUCtViM8PBze3t7Z1r1y5QomTZqENWvWoFq1V8+k+fj4ICQkRFczGxoailatWhXk8xCRCQSHxuPklRCcvx0GhTLnWjuJWIRmtT3QsUl51KrobLa1YmVJl1aVEHg3AhGxKXqJa0avzF/2bZxjs1tBEJCqUKcntEqkpA/RYyi5TU5Nn057tTxNUbRjLMYnK3H5XgQu34vQfYaq3o6oWcEJNSs4oXp5J90NEiIiIio98kxa3dzc4Ovri/3796NHjx7Yv38/fH19szUNvn79Or744gt8++23qFevnt6yLl264JdffkGDBg3w+PFj3LhxA8uWLSvaT0JERSpNocb522E4cSUET17mfmfOw9kabzQuj9cbeMPRTl5CJSRjWMulmP5JswL1yiwSiWCTPv6rm5N1vt9bpdZoE11dUqtKT2qVunFqk9KT3Ut3I/I9Xq1SpcG9Z7G49yxWN6+8ux1qVnBCjQpOqFHBGR5O1rx5QkREZOFEgpB346+HDx9iypQpiI+Ph6OjIwICAlCtWjUMHz4c48aNQ4MGDdC7d2+EhITAy8tL97rFixejdu3aSE5OxpQpU3D79m2IxWJMmjQJb731Vr4KyubB5s3cmxzQK3nF6nl4Ik5eDcG5Wy+RkpZzTZlYJELjmu54o4kP6lZxzVcvsZS3snZM/Xn6UY7NmEUi7f6W36QWAJzs5ahZXpvA1qzghIqe9kXepLisxcqSMVaWg7EqGsXdKzvjZDnMPVZ5NQ82Kmk1B0xazZu5Hwj0iqFYKVVqBN6JwImrIXjwPPcec1wcrNChkQ/aNfKBi4NVcRa1TCtrx1SqQoV5Wy/l2Ix56oAmiIhNxf3ncXjwPA73n8ciNtG4cWszk8vEqObtqEtiq/s4wda6cBduZS1WloyxshyMVf4JgpDewkWNlDQV4pLS8OP+24hNSNO76SeTiOHhYlMkvbIzTpbD3GPFpJVKhLkfCPRK5li9jE7GP1dDcOZ6KJJSVTm+RgSgfjU3vNHEBw2ru0EiNn3nN6VdWTymMmoEjGnGLAgCouJScT/kVRIbEpGE/P5KiACU97DXPRdbo4IT3Bzz16S4LMbKUjFW5i1zrWBSihJ2ZjpWc3HUXmY8TpGiUCM1TaX9Oz35TFFkmlao0pcbWJamyleLFHcnazSq4Q4fN1t4u9nB290OjrYynv9KKXOPFZNWKhHmfiCUdVkvBKzkEtjZyBAVl/t4no62MrRr5IP2jXzg4WxTQqUlgMdUQSSnKvHwRTzuP4/Fg+dxePQiHopchmPKiYuDlbaH4gpOqFnBGRU87XK9UcNYWY6iilVxN7ksi/JqbWEuYzXnVE6pRARXB2sMeKcW1BohPbnMnFim/53xL3NyqlDnOnRcSbKzlqJcehLr42YHbzdbeLvbwd3RGmJx9mSW5z/LYe6xYtJKJcLcD4SyLDlViblbAhEZnwq12rhjqE4lZ7zRpDya1vIwiyFFyiIeU4WnUmvwLDwR95/F6mpk45Ly36TYSi5BdR/H9ETWGdV8HCESwSJqhEhfURxXqQoV5m0JRHhsClSZzqkZScvoXvXhYCOHlUwMuUwCiVhk0s7ATJ1gZzRZTVWokaZUI02hRqpSDUX6/2np8wPvhOP201iD13oiEVDezQ4VvOyh0QgQBEAjpP+vESAIAjQC0v/Peb5Gk2WdjOWajHna6dy2oVJpUBYvR2VSMbxcbOHjnl4r62YLHzc71KvlibjYZFMXj4xg7tcVTFqpRJj7gVCaCYKApFQVImJTdP8i41K1/8dq/zfmyLGzluL1Bt7o0NgH3m52xV5uyh2PqaInCAIiYlO0z8WmJ7EhkbmPN5wTmUTbMVTmn6WifE6Miocxx5VSpUFMYhpi4lMRk5CG6IQ0xMSnITpBOx0alYS0XIb+ykosEkGensDKpWJYySTaaalEO08mhlyasVyiW9cqY16mda0ybUe77NU2DdWC5af2Ms/kMiPBTF+eOeHMKRFNU6qRqlCbxXjPZYVErO313VouQXR8arEn2GIR4O5ko6uRzUhmvd1sYcshyMyKuV9XMGmlEmHuB4KlUyjVrxLR9P+1/1IRGZeC1EKMhykWizC4ax20qOMJuUxShKWmwuAxVTISU5R4GKJNYu8/i0Xwy4RCN9OTSkRwtJPDWi6FlUwCa3nmf9qLSatMf2deZiWTwNoqfVl6wlLYWjpT17SZEwcnGzwIjkJ0fKo2Gc30LyMpTUhWmrqYBSKViPSSXrlUgqQUBWKTFAaTRhEAW2spZFJxeuKpgcYyLglLJZEIsJFL04cZk8DaSgrb9OQzY+gxm0x/W8ulsE1fL/NymfTVOSO3XtmlEhEa13BHRU97hEYl40VUEl5GJRfokYqcONnJdcmsrqmxmx2c7eUGz2s8VxU9S3lOHGDSSiWEF9iFo9EIiElIQ2ScNhHVJqfpf8elIK4AvaQaSwRgw5ROxbZ9KhgeU6ahUmvw5GUC7qd37vQgJM6kSYxIBG2SK8ua5GZOfrMmvBJYy7TLxWJg48E7iI5P1WvKam7PCWYozEVrSpoqWwKqm45PQ0xCaq4dzhEVBbFYhLqVXV4ln/L05DNL0pl1eVHcoMoqv88JawQB0fGpCI1KRmhkEl5EJSM0KgmhUclITCm686CNlQTlXO20HUCl1866Olrjx323EBGbatbPNFsSS3lOPAOTVioRZfECOz8XVxlNeDMS0ciMmtL0WtOouNQCjUFZFBxsZVg5rp1J3ptyVhaPKXMkCALCY1JwL71zpwchcQiNKj3Pb9nIJXCyt4JMqm2iKpOKIZNKdNPS9Hl6yyRiyGViyCRiyGRiyCSSLNPamj79aXGevY7ndoHl7mSNIe/6IjlVlZ6EZklKE9KQkmYeCalIBDjayqFQsfYyg1QigpVMe5PFSibRtUCQp/9vJZPgeUQinrxMNPh9ScQiNKzmiuZ1vLTjNqc/JywWIf1/Uab5SJ/OtDzTfN26IhFEYu06evOyrK/bLkQQi4H9/z7B3xefQanOXiMpk4rRtVUl9GxXrSS+VqPkp1f23CQkK3Q1sqGRGclsEqLi04qx9FpisQh1KjmjaS0P7XlFmumf5NV56tUySaZlomIZ8aC4a4UFQYBaI0Cp0uj+KVTaDruUag2UyvT/M81XqTRQZFo/6HE0gkPjDTYRN8d9lUkrlYiydoGdW++BTnZyvNW8ImIT03TPlEbEpRbrBZU8/a6Zu5M13J1t4OFsAw8na3g42+C/oJf4O/C5weZB5njSIq2ydkxZkrErTrHGrgDEIpFeEqu9wHyVIEcnpCIyLtXkzz+KRICzvRVcHF79c3Ww1v7vaIWLd8Lxz5UXRictKrUGCqX2wlKhVEOh1CBNpf1foVRDocryf/rfaenrKjKvq1QjTbee/rKi+NokYpGuBj+n5FIv+ZRLYJ1pOtu66f8b06GfpdQKWUo5S4q9ow1u3QvXJrPpCe2LqCSEx6SY7GZ8VmKRyHCiayABlknFkOY0P/1vANj372PEJyn0PqNELIKDjQxvNq8IQD/hVKafB5TqjORSrb9ct45al4wW97nQ3Cot8kpay85RRVSEDp1/mu0HCwBUagFR8Wn45fiDIn0/kQhwdbCGh3N6UpqekGYkqLmNq+bnXAVXH0Tl+APbpVWlIi0rUWn3ZrMKOT4nJpOI8Waz8ujUtAJSFdpOaFKV2qEvMjq4SVWoXi1L/zsty3TGP5WBxMhSaQRB20EP1ECKacogEYvgbC+HS3oSqk1IreDq+GrayV6ea81MRU97BD2OMfqcKpVoL4Jti/GSK6MTpbQsifDRwOc4d+ulweRBKhGhXSMfvPta5Xwll8XFWi7F9E+avaoVTK+9KkitYHHKVs5C1F6WBjZWUlQu54DK5Rz05qvUGkTEpmibGkcl4UVG7Wx0MtIK0Q9HQWgEQdcxWHFSawTEJimw+5+Hxfo+RSXRwp7fZ00rFYmyVis05ptTSC7imlN7G5k2KXWySU9IreHhZAMPZ2u4OloX6mJCr3mQmV4IkL6ydkxZkpKsadEmItqkNiU9qc2c4Gb8naJLfFWvkuM0FR6ExJWZ4TmkEhGc7bVJqIujtfZ/Byu4OFjD1VH7d7XKboiOSiz0exVVk8viZsm1gjwHWob8xkkQhPReuNObGqc/P3vvWWyRtBYg41laTSuTVioSZeXHJTFFiT9PP8LxyyH5fm3Gc1naprvpSWl6k14PZxvYWJXMhUNZiZWlY5zMm6XcCMqr99A3GpfHG03KZ2qiptZ7JkqvuZpSv4mbMn2+ItOzVcr0Jm+KLM3eFKrCD3siEgG1KzrrJaGZm+7a28ogzqMjm7J4XFlKgp1VWYyVJSqqOP15+hEO/vfUYOsSsUiEKuUcUKmcg+4ck3F+Uqkyn38MzFdqLDYZlohFes2Y5ZmbLGd6bvfV4xbaJszy9GX3nsXi3rNYgy0tzPHxMDYPJioCGo2Af669wO//PDTqWTapRAS/NlX0klNHO3meF1REZBms5VL0bFcNPdtVM+uL6y6tKiHwbkSONW29OlQrkcQla6ciWZ/nUqg0OHU1BIF3IyzmAstSZN5XicxVXueqiR83LtC5Su/ck1eSa2CZSp19/r83Q/V6Y89KJhXjrWYV9BJM/YQz87OyOSyT5t15XV7yamlhaY+HMWklysO9Z7HY8fc9PAs3rklZxsXVe69XLeaSERHlzlyevxOJRJBKRJBKxLCxMrxOVW8HPItIKjUXWERkvOI6V+mde4qorM728pz7NTCjG2yW8py4sdg8mIqEOdc0FFR0fCp+PfkQ54PCDC6XiLW1pplrBficEBUVxslyMFZFp7ibsjJWloOxsgxlMU6W+qy4uceKzYOJ8kmpUuPwhWfYf+4xFMrsd9HkMjG6t6mC9o18cOzSc4t7ToiIyFyxKSsRmTtzacFS1vBbJUonCAKu3o/EzuP3ERGbanCd1+p64YM3qsPV0RoAeHFFREREVMbwBlvJY9JKBOBFZBJ+PnYft4KjDS6v5GmPfm/XQq2KziVbMCIiIiKiMo5JK5Vpyakq7D0bjGOXnhvssdLeRoZe7auhfSMfiMXs+ZeIiIiIqKQxaaUySSMIOHsjFLtPPkR8sjLbcrFIhI5Ny6NH26qwt5GZoIRERERERAQwaaUy6OGLOPz09z0EhxruQa1OJWf0e6sWKnjm3IMZERERERGVDCatVGbEJabht38e4uyNlwaXuzlaoU+nmmhW2wMiEZsCExERERGZAyatVOqp1BocDXyOvWeDkapQZ1ueMRB019cqw0omMUEJiYiIiIgoJ0xaqVS78SgKPx29j7DoZIPLm9X2QJ+ONeDubFPCJSMiIiIiImMwaaVSKSwmGb8ce4CrDyINLi/vboeP36qJulVcS7hkRERERESUH0xaqVRJVahw4NwTHL7wFCp19iFsbK2k6NGuKjo2KQ+pRGyCEhIRERERUX4waaVSQRAEnA8Kw64TDxCbqMi2XASgfWMfvN++Ghxt5SVfQCIiIiIiKhAmrWTxnrxMwI6j9/DgeZzB5TXKO6H/27VQuZxDCZeMiIiIiIgKi0krWaz4ZAX+OPUIp66+QPaGwICTvRwfdayB1+p6cQgbIiIiIiILxaSVLI5ao8GJyyH483QwktNU2ZZLJSK806IS3m1dGTZW3MWJiIiIiCyZUVf0wcHBmDJlCmJjY+Hs7IyAgABUqVJFb50zZ85g+fLluHfvHgYOHIjJkyfrlkVERGDmzJl4/vw5VCoVPv30U/To0aNIPwiVDbcfR+OnY/cREpFkcHmj6m7o+2ZNeLnalnDJiIiIiIioOBiVtM6aNQv9+vVDjx49sGfPHsycORNbt27VW6dixYqYP38+Dh06BIVCvyOcRYsWoX79+li7di2io6PRq1cvtGzZEt7e3kX3SahUi4xLwa7jDxB4N8Lgci8XG3z8Vk00rO5ewiUjIiIiIqLilGfSGhUVhaCgIGzatAkA4Ofnh7lz5yI6Ohqurq/GuKxcuTIA4OjRo9mS1jt37mDQoEEAAFdXV9SpUwd//fUXhgwZUmQfhEqHVIUKh84/xfHLIUhMUcLeRgYfdzs8ehFncAgbK7kE771eBW83r8ghbIiIiIiISqE8k9bQ0FB4eXlBIpEAACQSCTw9PREaGqqXtOamXr16OHjwIBo0aIDnz5/jypUrqFChQr4K6uZmn6/1qeR5eBSud96UNBVmrzyFl1FJUKg0AIDEFCXuPYs1uH6n5hUx6N26cHW0LtT7lkWFjRWVDMbJcjBWloOxshyMlWVgnCyHJceqRHqpmTJlChYsWIAePXrAx8cHrVu31iXBxoqKSoRGY6iPWDIHHh4OiIhIKNQ2/jz9CKFRSVCmJ6w5qVLOAf3froXq5Z2gTlMiIkJZqPcta4oiVlT8GCfLwVhZDsbKcjBWloFxshzmHiuxWJRrJWWeSau3tzfCwsKgVqshkUigVqsRHh6er+dRXV1dsXTpUt308OHDUaNGDaNfT2XD8cshuSasIgD+Xevg9YbeEHMIGyIiIiKiMiHPhwDd3Nzg6+uL/fv3AwD2798PX19fo5sGA0BMTAxUKu3QJOfOncO9e/fg5+dXwCJTaZWYkneNabtGPkxYiYiIiIjKEKOaB8+ePRtTpkzBmjVr4OjoiICAAADaGtNx48ahQYMGCAwMxIQJE5CYmAhBEHDgwAHMnz8f7dq1w/Xr1zF//nyIxWK4uLjg+++/h42NTbF+MLI8dtZSJKVmH3c1g72trARLQ0RERERE5sCopLV69er49ddfs83/8ccfdX83b94cp06dMvj6Dh06oEOHDgUsIpUVbo7WSEpNNLhMJhWjY5PyJVwiIiIiIiIyNY4RQmbh4Ys4PA3POWH1cLZBl1aVSrhURERERERkaiXSezBRbjQaAduP3NObJxYBgqBtEtyxSXl0aVUJ1nLurkREREREZQ2zADK5f669wJOX+l1wT+jTGHWrGN/ZFxERERERlU5sHkwmFZ+swO//PNSb16KOJxNWIiIiIiICwKSVTGz3yYd6PQZbySTo04lj+BIRERERkRaTVjKZhyFxOH09VG/ee22rwNXR2kQlIiIiIiIic8OklUzCUOdL3m62eLt5RROViIiIiIiIzBGTVjKJf66G4EmYfudLA96uBamEuyQREREREb3CDIFKXHyyArv/eaQ3r6WvJ3zZ+RIREREREWXBpJVK3G8nHyI5LWvnSzVNWCIiIiIiIjJXTFqpRD0MicOZLJ0v9WhbFS4OViYqERERERERmTMmrVRiNBoB247c1Zvn7WaLt5pXMFGJiIiIiIjI3DFppRJz8moInoYl6s1j50tERERERJQbZgtUIuKTFPidnS8REREREVE+MWmlEpGt8yU5O18iIiIiIqK8MWmlYvcgJA5nbmTpfOl1dr5ERERERER5Y9JKxUqjEbD9sH7nSz7udux8iYiIiIiIjMKklYrViSsheBqu3/lSf3a+RERERERERmLmQMUmPkmB30/pd77Uqq4XfCu7mKhERERERERkaZi0UrH57eRDpGTpfOmjjjVMWCIiIiIiIrI0TFqpWDx4nr3zpZ5t2fkSERERERHlD5NWKnJqjQbbj+h3vlTe3Q5vNmPnS0RERERElD9MWqnInbzygp0vERERERFRkWAWQUUqzkDnS6/V9UIddr5EREREREQFwKSVitRvJx/odb5kLZfgQ3a+REREREREBcSklYrM/eexOHvjpd48dr5ERERERESFwaSVioRarcH2I/f05pV3t0Mndr5ERERERESFwKSVisTBfx/jWZbOlwa8w86XiIiIiIiocIzKKIKDg9GnTx907twZffr0wePHj7Otc+bMGfTq1Qv169dHQECA3rKoqCiMGDEC3bt3R9euXTF79myoVKps2yDLFJekwPZDt/XmvVbPC7UrsfMlIiIiIiIqHKOS1lmzZqFfv344fPgw+vXrh5kzZ2Zbp2LFipg/fz6GDh2abdn333+P6tWrY9++fdi7dy9u3bqFI0eOFL70ZBZ+O/EAyan6nS99xM6XiIiIiIioCOSZtEZFRSEoKAh+fn4AAD8/PwQFBSE6OlpvvcqVK8PX1xdSqTTbNkQiEZKSkqDRaKBQKKBUKuHl5VVEH4FM6d6zWJy9maXzpXbV4GzPzpeIiIiIiKjw8kxaQ0ND4eXlBYlEAgCQSCTw9PREaGio0W8yevRoBAcHo23btrp/zZo1K3ipySyoNQY6X/Kww5vNypuoREREREREVNpkrxYtBocOHULt2rWxZcsWJCUlYfjw4Th06BC6dOli9Dbc3OyLsYRUEHtPP8TzCP3Ol8Z82BjlvJxMVCIyloeHg6mLQEZgnCwHY2U5GCvLwVhZBsbJclhyrPJMWr29vREWFga1Wg2JRAK1Wo3w8HB4e3sb/Sbbt2/HggULIBaL4eDggE6dOuH8+fP5SlqjohKh0QhGr0/FKy4xDdv/0u98qXU9L3g5WiEiIsFEpSJjeHg4MEYWgHGyHIyV5WCsLAdjZRkYJ8th7rESi0W5VlLm2TzYzc0Nvr6+2L9/PwBg//798PX1haurq9GFqFChAk6dOgUAUCgUOHfuHGrWrGn068n8/HryIVLS1LppW2spO18iIiIiIqIiZ1TvwbNnz8b27dvRuXNnbN++HXPmzAEADB8+HDdu3AAABAYGon379ti0aRN27tyJ9u3b4/Tp0wCAadOm4dKlS+jevTt69uyJKlWq4KOPPiqmj0TF7d6zWPybpfOlfp3rwImdLxERERERURETCYJgEW1u2TzYPKg1GszZdBHPI5J08yp42OG7SZ0QHZ2UyyvJXJh78xDSYpwsB2NlORgry8FYWQbGyXKYe6wK3TyYKLPjl0L0ElYAGPBObUgk3JWIiIiIiKjoMdMgo8UlpuHPM4/05rWuVw61KjqbpkBERERERFTqMWklo+06od/5ko2VBB91rG7CEhERERERUWnHpJWMcvdpDM7d0u98qWfbaux8iYiIiIiIihWTVsqTSq3B9r/v6c2r4GGHTs3Km6hERERERERUVjBppTwdvxyCEEOdL4m5+xARERERUfFi1kG5ik1Mw54snS+1qc/Ol4iIiIiIqGQwaaVc/XriQbbOlz7sWMOEJSIiIiIiorKESSvlSNv5UpjevJ7tqsHJTm6iEhERERERUVnDpJUMMtz5kj06NWXnS0REREREVHKYtJJBxy89N9D5Ui12vkRERERERCWKGQhlE5uYhj/PBOvNe52dLxERERERkQkwaaVsdp14gFRF5s6XpPiAnS8REREREZEJMGklPXefxuC/LJ0vvd+uKjtfIiIiIiIik2DSSjoqtQbbj+h3vlTR0x4d2fkSERERERGZCJNW0jl26TlCItn5EhERERERmQ9mIwQAiElIw56snS81KIeaFZxNUyAiIiIiIiIwaaV0vxrofOnDN9j5EhERERERmRaTVsKdJzH4L0i/86Ve7avBkZ0vERERERGRiTFpLeNUag22/63f+VIlT3u80cTHRCUiIiIiIiJ6hUlrGXc08DleZOt8qTY7XyIiIiIiIrPAzKQMi0lIw56z+p0vtW3gjRoVnExUIiIiIiIiIn1MWsuwXSceIC1T50u2VlJ88EZ1E5aIiIiIiIhIH5PWMur2kxicz9L50vvsfImIiIiIiMwMk9YySKXWYEfWzpe87NGxSXkTlYiIiIiIiMgwJq1lUE6dL4nFIhOViIiIiIiIyDAmrWWMwc6XGnqjRnl2vkREREREROZHauoCUPFLVahw6PxTHL8cgsQUpd4ydr5ERERERETmjElrKZeqUGHe1kuIiE2BUqXJtrz761XgaMvOl4iIiIiIyDwZ1Tw4ODgYffr0QefOndGnTx88fvw42zpnzpxBr169UL9+fQQEBOgt++qrr9CjRw/dvzp16uDYsWNF8gEod4fOP80xYRUBSE5VlXyhiIiIiIiIjGRUTeusWbPQr18/9OjRA3v27MHMmTOxdetWvXUqVqyI+fPn49ChQ1AoFHrLFi9erPv7zp07GDRoENq1a1cExae8HL8cYjBhBQABwMmrIXi/fbWSLRQREREREZGR8qxpjYqKQlBQEPz8/AAAfn5+CAoKQnR0tN56lStXhq+vL6TS3PPg3377Dd27d4dcziapJSHrM6zZlifnvpyIiIiIiMiU8qxpDQ0NhZeXFyQSCQBAIpHA09MToaGhcHV1zdebKRQK7Nu3D5s3b853Qd3c7PP9GgIc7eSIT1LkutzDw6FI3quotkPFj7GyDIyT5WCsLAdjZTkYK8vAOFkOS45ViXbEdPToUfj4+MDX1zffr42KSoRGIxRDqUq3Nxr7YP+5Jwa/O5lUjA6NfRARkVDo9/HwcCiS7VDxY6wsA+NkORgry8FYWQ7GyjIwTpbD3GMlFotyraTMs3mwt7c3wsLCoFarAQBqtRrh4eHw9vbOd2F2796N3r175/t1VHBdWlWCWJR9vkwqhoezDbq0qlTyhSIiIiIiIjJSnkmrm5sbfH19sX//fgDA/v374evrm++mwS9fvsSlS5fQvXv3gpWUCiQmIQ0qtX4tq52NDF1bVcL0T5rBWs5Rj4iIiIiIyHwZNeTN7NmzsX37dnTu3Bnbt2/HnDlzAADDhw/HjRs3AACBgYFo3749Nm3ahJ07d6J9+/Y4ffq0bht//PEHOnbsCCcnp2L4GJSTC7fD9aZ9K7tg1fh26NmuGhNWIiIiIiIye0ZlLdWrV8evv/6abf6PP/6o+7t58+Y4depUjtsYNWpUAYpHhSEIAi7cDtOb16qul4lKQ0RERERElH9G1bSSZQqJSEJoVLJuWiIWoWktDxOWiIiIiIiIKH+YtJZiF+7o17LWreIKexuZiUpDRERERESUf0xaSylt02D951lb+nqaqDREREREREQFw6S1lHoalojwmBTdtFQiQpOabBpMRERERESWhUlrKZW1A6b6Vd1ga83egomIiIiIyLIwaS2F2DSYiIiIiIhKCyatpdCjF/GIik/VTcukYjSq4W7CEhERERERERUMk9ZSKGsta8PqbrCxYtNgIiIiIiKyPExaSxmNICDwbtamwV4mKg0REREREVHhMGktZR48j0NMQppu2komQcPqbiYsERERERERUcExaS1lsvYa3KiGG6xkEhOVhoiIiIiIqHCYtJYiGo2AwLsRevPYNJiIiIiIiCwZk9ZS5O7TGMQnKXTTNlYSNKjmasISERERERERFQ6T1lLkwh39Dpga1/CATMqmwUREREREZLmYtJYSKrUGl7I0DW5V19NEpSEiIiIiIioaTFpLiTtPYpCYotRN21lLUbcKmwYTEREREZFlY9JaSly4rd80uGktD0glDC8REREREVk2ZjWlgEqtweV77DWYiIiIiIhKHyatpcDN4Ggkp6l00w62MtSp7Gy6AhERERERERURJq2lwMXbYXrTzWp7QiJmaImIiIiIyPIxs7FwCqUal+9H6s1rWYe9BhMRERERUenApNXC3XgUhTSFWjftZCdHrYrOpisQERERERFREWLSauGy9hrcvI4nxGKRiUpDRERERERUtJi0WrA0hRrXHmZpGuzLpsFERERERFR6MGm1YNceRkKh1OimXRysUL28kwlLREREREREVLSYtFqwrE2DW9TxhFjEpsFERERERFR6MGm1UClpKlx/GKU3r6Wvl4lKQ0REREREVDyYtFqoq/cjoVK/ahrs7mSNqt4OJiwRERERERFR0WPSaqEu3A7Tm27h6wkRmwYTEREREVEpY1TSGhwcjD59+qBz587o06cPHj9+nG2dM2fOoFevXqhfvz4CAgKyLT948CC6d+8OPz8/dO/eHZGRkdnWIeMkpSpxMzhab17LOmwaTEREREREpY/UmJVmzZqFfv36oUePHtizZw9mzpyJrVu36q1TsWJFzJ8/H4cOHYJCodBbduPGDXz33XfYsmULPDw8kJCQALlcXnSfooy5fC8Cao2gm/ZytUUlL3sTloiIiIiIiKh45FnTGhUVhaCgIPj5+QEA/Pz8EBQUhOho/Zq+ypUrw9fXF1Jp9jx48+bNGDJkCDw8PAAADg4OsLKyKoryl0kXs/Qa3LIOmwYTEREREVHplGdNa2hoKLy8vCCRSAAAEokEnp6eCA0Nhaurq1Fv8vDhQ1SoUAH9+/dHcnIy3n77bYwaNSpfiZabG2sSASAuMQ1BT2L05nV+vSo8PEzfCZM5lIGMw1hZBsbJcjBWloOxshyMlWVgnCyHJcfKqObBhaVWq3H37l1s2rQJCoUCw4YNg4+PD3r27Gn0NqKiEqHJ1CS2rDp5NUTveyjvbgdbiQgREQkmLJX2IDB1Gcg4jJVlYJwsB2NlORgry8FYWQbGyXKYe6zEYlGulZR5Ng/29vZGWFgY1Go1AG0CGh4eDm9vb6ML4ePjgy5dukAul8Pe3h5vvvkmrl+/bvTr6ZULQdl7DSYiIiIiIiqt8kxa3dzc4Ovri/379wMA9u/fD19fX6ObBgPa52DPnDkDQRCgVCrx33//oU6dOgUvdRkVl5iGu89i9ea19GWvwUREREREVHoZNeTN7NmzsX37dnTu3Bnbt2/HnDlzAADDhw/HjRs3AACBgYFo3749Nm3ahJ07d6J9+/Y4ffo0AODdd9+Fm5sbunXrhp49e6JGjRr44IMPiukjlV6BdyMgZGohXcnTHuVcbU1XICIiIiIiomImEgTBIh4U5TOtwMLtl3D/eZxuuneHani3dRXTFSgTc28nT68wVpaBcbIcjJXlYKwsB2NlGRgny2HusSr0M61kHqLjU/USVgBowabBRERERERUyjFptRCBd/THZq3q7QBPZxsTlYaIiIiIiKhkMGm1EBeyJK0t6rCWlYiIiIiISj8mrRYgMjYFj17E681ryaFuiIiIiIioDGDSagEuZqllrVHeCa6O1iYqDRERERERUclh0moBLtzO0jSYtaxERERERFRGMGk1c2ExyXgS9qp7ahGA5rWZtBIRERERUdnApNXMZa1lrVXRGS4OViYqDRERERERUcli0mrmLt4O05tuWZe9BhMRERERUdnBpNWMvYhMwvOIJN20WCRCs9oeJiwRERERERFRyWLSasYuZKll9a3sDEdbuYlKQ0REREREVPKYtJopQRAM9BrMpsFERERERFS2MGk1U8/CE/EyOlk3LRGL0LQWmwYTEREREVHZwqTVTF28o1/LWq+qK+xtZCYqDRERERERkWkwaTVD2qbB+s+ztqjDsVmJiIiIiKjsYdJqhh6/TEBEbKpuWioRoUlNNg0mIiIiIqKyh0mrGbqYpQOmBtXcYGstNVFpiIiIiIiITIdJq5kRBAEX72RpGuzLpsFERERERFQ2MWk1Mw9fxCMqPk03LZeK0biGuwlLREREREREZDpMWs1M1g6YGlZ3g7WcTYOJiIiIiKhsYtJqRjSCgMAsQ9209PUyUWmIiIiIiIhMj0mrGbn/LBaxiQrdtJVMggbV3UxYIiIiIiIiItNi0mpGLmSpZW1c0x1WMomJSkNERERERGR6TFrNhFqjwaVsTYPZazAREREREZVtTFrNxN2nsYhPVuqmbaykqF+VTYOJiIiIiKhsY9JqJrL2Gty0pjtkUoaHiIiIiIjKNmZFZkCl1uDS3Qi9eS3YazARERERERGTVnMQ9DgGSakq3bSdtRR1q7iYsERERERERETmwaikNTg4GH369EHnzp3Rp08fPH78ONs6Z86cQa9evVC/fn0EBAToLVu1ahVat26NHj16oEePHpgzZ06RFL60uJilaXCz2h6QSng/gYiIiIiISGrMSrNmzUK/fv3Qo0cP7NmzBzNnzsTWrVv11qlYsSLmz5+PQ4cOQaFQZNtGz549MXny5KIpdSmiVGlw+X6k3jw2DSYiIiIiItLKszovKioKQUFB8PPzAwD4+fkhKCgI0dHReutVrlwZvr6+kEqNyoMp3c3gKKSkvWoa7GArQ51KzqYrEBERERERkRnJM2kNDQ2Fl5cXJBIJAEAikcDT0xOhoaH5eqMDBw6ge/fuGDJkCK5cuVKw0pZCF2/rj83avLYnJGI2DSYiIiIiIgKMbB5cWH379sWnn34KmUyGs2fPYvTo0Th48CBcXIzvbMjNzb4YS2gaaUo1rj3Ubxr8dusq8PBwMFGJCsdSy10WMVaWgXGyHIyV5WCsLAdjZRkYJ8thybHKM2n19vZGWFgY1Go1JBIJ1Go1wsPD4e3tbfSbeHh46P5+/fXX4e3tjfv376Nly5ZGbyMqKhEajWD0+pYg8E44UtLUumknezk87eWIiEgwYakKxsPDwSLLXRYxVpaBcbIcjJXlYKwsB2NlGRgny2HusRKLRblWUubZDtXNzQ2+vr7Yv38/AGD//v3w9fWFq6ur0YUIC3vVO+7t27cREhKCqlWrGv360urCHf2mwS1qe0IsFpmoNERERERERObHqObBs2fPxpQpU7BmzRo4OjrqhrQZPnw4xo0bhwYNGiAwMBATJkxAYmIiBEHAgQMHMH/+fLRr1w7Lly/HrVu3IBaLIZPJsHjxYr3a17IoVaHC9Qf6TYNbstdgIiIiIiIiPUYlrdWrV8evv/6abf6PP/6o+7t58+Y4deqUwddnHbeVgGsPoqBQaXTTro5WqFbe0YQlIiIiIiIiMj/sptZELtwO05tuUccTYhGbBhMREREREWXGpNUEUtJUuPFIf5xbNg0mIiIiIiLKjkmrCVy5HwGV+lXTYA9na1QpZ7ldUBMRERERERUXJq0mcOG2fq/BLX29IGLTYCIiIiIiomyYtJawxBQlbgXrNw1uUcfTRKUhIiIiIiIyb0xaS9jlexFQawTddDlXW1T0zHkgXSIiIiIiorKMSWsJu5il1+CWvp5sGkxERERERJQDJq0lKD5ZgdtPYvXmtWCvwURERERERDli0lqCLt2NgEZ41TS4vIcdyrvbmbBERERERERE5o1JawnK1jSYHTARERERERHliklrCYlNTMPdp7F681qyaTAREREREVGumLSWkMA74RAyTVfysoeXq63JykNERERERGQJmLSWkAt3wvWmWctKRERERESUNyatJSA6PhUPnsfpzWvB51mJiIiIiIjyxKS1BFzMUsta1dsRHs42JioNERERERGR5WDSWgIu3M7aNJi1rERERERERMZg0lrMImJTEBwarzePTYOJiIiIiIiMw6S1mF3IMjZrjQpOcHW0NlFpiIiIiIiILAuT1mJ2MUvT4FbsNZiIiIiIiMhoTFqL0cvoZDwNT9RNi0RA89oeJiwRERERERGRZWHSWoyyNg2uXdEZTvZWJioNERERERGR5WHSWoyyNg1uyabBRERERERE+cKktZiERCQiJDJJNy0WidCMTYOJiIiIiIjyhUlrMck6NqtvFRc42MpNVBoiIiIiIiLLxKS1GAiCgAt3sjQN5tisRERERERE+caktRg8C09EWHSybloiFqEpmwYTERERERHlG5PWYpC1aXC9qq6ws5aZqDRERERERESWi0lrERMEIdtQNy192TSYiIiIiIioIJi0FrHHLxMQGZeqm5ZKxGhSk02DiYiIiIiICsKopDU4OBh9+vRB586d0adPHzx+/DjbOmfOnEGvXr1Qv359BAQEGNzOo0eP0KhRoxyXlwZZa1kbVHOFjZXURKUhIiIiIiKybEYlrbNmzUK/fv1w+PBh9OvXDzNnzsy2TsWKFTF//nwMHTrU4DbUajVmzZqFt956q3AlNmMaQcDFrL0G+3qZqDRERERERESWL88qwKioKAQFBWHTpk0AAD8/P8ydOxfR0dFwdXXVrVe5cmUAwNGjR6FQKLJtZ926dXjjjTeQnJyM5OTkbMtLg0ch8YiOT9NNy6ViNKrhZsISEREVHbVahZiYCKhU2c/xZVl4uBgajcbUxSAjMFaWg7GyDIyT5TCXWEmlcri4eEAiyV9L1DzXDg0NhZeXFyQSCQBAIpHA09MToaGheklrbu7cuYMzZ85g69atWLNmTb4KmMHNzb5ArytJv58J1ptuUa8cKpZ3MVFpSp6Hh4Opi0BGYqwsg7nF6dGjR7Czs4O9vQ9EIpGpi0NEREQWRBAEJCTEITk5BtWqVcvXa4v9YUulUokZM2Zg4cKFusS3IKKiEqHRCEVYsqKl0Qg4fSVEb17jaq6IiEgwUYlKloeHQ5n5rJaOsbIM5hinpKRkeHm5Q60WAJjv+bikSaViqFSmv3tNeWOsLAdjZRkYJ8thLrGysXFAWFhMtmscsViUayVlnkmrt7c3wsLCoFarIZFIoFarER4eDm9vb6MKFhERgadPn2LEiBEAgPj4eAiCgMTERMydO9eobViCe89iEZf0qsmclVyCBtXYNJiIShfWsBIREVFBFfQ6Is+k1c3NDb6+vti/fz969OiB/fv3w9fX1+imwT4+Pjh//rxuetWqVUhOTsbkyZMLVGBzdSFLB0xNarpDLit4zTIREREREREZ2Tx49uzZmDJlCtasWQNHR0fdkDXDhw/HuHHj0KBBAwQGBmLChAlITEyEIAg4cOAA5s+fj3bt2hXrBzAHao0Gl+5m6TW4DnsNJiJKVahw6PxTHL8cgsQUJextZOjUtDy6tKoEazmHAyMiIqK8iQRBsIgHk8z5mdZbwdFY9stV3bSNlRQrxraFTGrUiEKlgjk+f0eGMVaWwRzj9PLlE5QrV9no9VMVKszbegkRsSlQZnqORiYVw8PZBtM/aVaoxHXDhh/wySdDIJPJCryN/FAqlZg69UtERISjWbMWGDfuSwDm85wQAHzwQXcsXvwNqlWrYfRr9u79A7t3/wJBECASidCv3yfo3LkbAO1wdStWLMX58/9CJBJhwAB/dO/eEwAwf/5s1Knji969++S6/dDQF+jb931UrVodgqCBtbUNJk6cgn//PYMTJ44BAEJCnsHZ2RV2dnYAgICA5fDyKmf0Zzh16iTc3d1Rt279XNfLK1anT5/Epk3roVQqIAjAu+++h48/HqBbvnnzehw8uA8A0K1bd/j7D8tz2YYNPyAlJQVjxnye5+do27Y5qlfXxi4tTYHatevA338YqlZ91WGJWq1G795+qFPHF4sWLc9zm4YkJCRg797f0b//oAK9PoNGo8GMGZPx6NFDyOVWcHFxwaRJ01C+fAUAwNOnTzB//mzExcXByckJ06fPQcWKlXSf9ciRU7C1tTW47YxYbdjwA/744ze4u3volvn5vYf9+/cCAMLCXsLa2hpOTs4AgEmTpqFePcP7wbp1a3Dy5DE4O7tgzZr1hfrsOTH2uMhq166f8PbbXeDiom3JeP/+XSxfHoB79+6idevXMW/eYt26+dmn8vOeBSGVirF+/TocPXoEEokYEokUI0d+hlatWgMAxowZgY8/HojXXzeuEiuvc9icOdNx+XIgoqIic91/Mst8DsqwcuUa3T5jrIMH9+Hff0/rxSIvcXGxmDt3JkJCnkMmk6FChUqYNGkaXFy0HbXevHkDS5YsQFpaGry9vTFz5ly4uLgiNPQFhg0biAMHjuWrjLkxdP7LfBxOnDgOX3zxle74zSxzHJctC8ClSxcgk8lha2uD8eMnok6dugCM/w0ydD1R6GdaKW8XbofpTTet5V6mElYiKntuP4nB9iN3ERqV/yHMlCoNXkQmYfTyUzmu4+1miwHv1IZv5Zx7YN+06Ud8/PFAg0mrSqWCVFq0P3H37t3Fy5cvsX37rny9rjjKUpTbrVChIlat+gGOjk4IDw/D4MH90LBhY3h7++DIkb8QEvIMO3f+gbi4OAwZ0h/Nm7eEt7dPvt7D3t4emzf/BADYtetnLFz4NTZu3IFBg7Rju+f3wjar06dPok4d3zyT1ry4urpj8eJv4O7ugcTERAwdOgB169ZDo0ZNcPXqZZw4cRTbtv0CABgxwh+NGzdF48ZNc12WX2vXboStrS00Gg327v0do0YNxcaN2+HjUx4AcP78Obi7e+D69WuIjo6Cq2v++89ITEzATz9tLXTSCgBdu/qhTZt2EIvF2L37FyxePB8rV64FACxduhC9en2Izp274fDhg1iyZAG+/fb7fL9Hly7vZkvQPvigL4D8JYk7d+7A7t37dQmDOdm162c0b95Sl0C6uLhizJgJuH//LgIDz+fx6qJ5z4Ly9a2Hvn0HwNraGvfv38PYsSOwZ88hWFlZF1FJX53v/Px6YNy4Ceje/Z18vT7zOagkZdwIbNq0OQBg9eqV+P77VZg6dSY0Gg3mzp2BadNmo1Gjxti8eT3Wrl2FadNmlXg5AWDp0m+NWu+119pg/PgvIZVKcfbsacycORW7du0p5tIxaS00lVqDy/ci9Oa19GXTYCIq3bYeuoOwmJRi235oVDK2HrqDhSNbG1y+bJn2MZVRo4ZAJBJj1aof8O23yyCRSPD06RMkJydj8+afMGfOdDx9+gRKpQLly1fE1Kkz4ejoiMuXA/Htt8tRt2493Lp1A4AIc+YsQJUqVfH06WPMnz8Hqamp0GjU6Nq1O9q2bYevv56OyMgI+Pv3w8CB/mjdui1WrFiCO3eCIAgCunR5V5cEjBkzAjVr1satWzfg6OiITp3ext9/H4K9vQMePrwPDw9PfP75JKxevQLPnz+Hr29dzJw5FyKRCElJiVi16hs8fHgfCoUCTZo0x9ixX0AikWTbbm4XGT//vB3//XcW8+cvwS+/7MDjx8GIi4tFZGQEqlathqlTZ8He3l53MQUAnp5ecHNzT+9w0QfHj/+N7t17QiwWw8XFBe3adcCJE0fRr98neu91+XIgVq5cilmz5uV5h71Fi1ZYt261MbtBNjduXMM33yyGRiNApVJh0KAhcHR0wpkzpxAYeAH79u1Bnz790LWrH/76az9+//1XqNVq2NvbY+LEKahWrRoOHtyHI0f+gpWVFUJCnsPV1Q0zZnwNDw9PvRo6e3t7VK5cFS9fhqJRoyY4duxvdO78ru5CvHPnd3Hs2N9o3Lhprssye/jwAb7+ejo+/3wSmjRplutnFYvF6NnzA1y5cgl//PEbPvtsPADgwIE96NmzF27evIFDhw5ki0VmGo0Gy5cvxuXLF3W1ImvXbsTy5QFITEyEv38/WFtb4/vvNyIyMhIrVixGWNhLpKWl4a23OuOTT4YA0NaevPnmO7h48TySkhLx0Ucfo3fvPhCLxWjbtoPu/erVa4hdu34GAMTEROPevTv45httrN96qzO++WYxYmJi9JJGjUaD7777BlFRUfjf/2ZDLpfnvhMU0OjRw6BQpOHzz0ehZcvW+Oyz8Qb3kUqVqmDkyMH4/POJ8PWth6VLF+Hq1cvYvn0XVCoVevTojN9+248HD+5l2xfffrsLAODRo4cYN+5ThIeHoV69Bpg+fQ5EIhGio6OwZMlCvHjxHIIg4OOPB6JrVz9s2bIBkZERmD59MuRyK8yaNQ9Vq1aDu7sHnjwJzvVzGbNP7dnzO3bt+gkymRyCoMHXXy/CyZPHsr1nhQoVsW7dGly9egkKhRI1atTAl19Oha2tLebPnw2pVIrg4EeIjY1FkyZNMWHCZEilVrpaVQCoUaMmBEFAXFwcPD31k9ajRw9j584dWLBgCTw9875WNnS+a9asRY7rnzt3Blu3bkRamgIymQxjx05A/foN8nyfgggLe4lp0yahX79P8Oabb6Nt2+YYPHg4Tp/+B2lpqRg58jO88cabcHR00jvH1qtXH3/8sRsAcPfubcjlcjRq1BgA0LPnB/jww+7ZklaFQoF582bBw8MTY8Z8brAjo/HjR+ODDz5Cu3ZvAADOnj2NnTu3Y9WqH/Dzz9tx7NgRaDRqyGRyTJw4BTVr1s62jcy1pMHBj7BgwRykpKSgevXqUChedTab+eZi/foNEBERDo1GA7FYv8Iu82+QvX3hhy5l0loIqQoVtvx1B0mpKt08mVSMqt7mNbYiEVFp8+WXk/HHH7/qaqUy3L9/D999tw42NjYAgPHjJ8LZ2RmAtmngjh1bMGrUWABAcPBDTJs2E1999T9s2bIBW7ZswKxZ8/D777+hbdv2GDhwMABtr/eOjo6YPHk6Vq9eiQ0btgEA1qz5FhqNBjt27EJ8fAJGjhyCatVqoHXr1wEAL148x5o16yGVSnHw4D7cvh2ErVt3wtPTC1999TnmzJmOVat+gI2NDYYOHYDAwAto0aIVVq36Bo0bN8WUKTOg0WgwZ850HDiwF++993627Rqi0QhYsWIJ4uLisHTpt7qa6OvXr2DTpp/g6uqGBQvmYPPm9dlqry5fDkRiYiLq1KkDQHthVq7cq9ECvLzKITxcv3XRkSN/Ydeun7F06bfw8PDMM3YnThw1eMFkjB07tuDjjwfi7be76EYicHBwQNu27fVq265du4Ljx//G6tU/Qi6X49y5s1i48Gv8+OPm9O/iGjZv3oFKlapg48Z1WLlyabYmf0+ePEZQ0A189dU03XeROSnw8iqHa9cu57ksw8WL57Fq1XLMmbNQr7lvXurWrY+LF7U1bbGxsbh0KRD/+99sVKpUBYsXz881aX3w4B6uXAnE9u2/QiwWIz4+HgAwYcJkDBs2UK/mad68mfD3H4bGjZtCqVRi/PhR8PWtixYtXgOgTUI3btyO6OgoDB7cH40aNUWNGjX13u/333ehbdv26d9JGNzdPXXDHUokEri7eyA8PEyXtCoUCixYMBve3uUxe/b8HHsVPXToAAIDLwAAGjRohC+/zH9nnmvWrEfbts1154yc9pG1azeiWbMWCAy8CF/ferhx4yqsrKwQGRmJly9foHLlqrCxsTG4L2Z49OghVqxYA7FYjMGD+yMw8DxatHgNK1YsRbVq1bFw4VJERkZi6NABqF27DgYNGop9+/7EvHkB+Wrab+w+tWbNSuzYsRvu7u5QKBTQaDQG33Pz5vWws7PDjz9uTX/dt9i2bRNGjvwMABAUdBNr126EXC7HpEnjsXfv7+jT52O99zp06ADKl6+QLSndsWMLLlw4jxUr1uQrgcnrfJchJOQ5Nm/egOXLV8HOzh6PHj3ExInj8PvvBwAASUlJGDp0IARBwFtvvYOPPx5Y4F5s79+/h7lzZ+DLL6egUaMmuvlisRibN/+Ep08f49NPh6JRoyZ6tdgajQZ//LE70zGif351dnaGRqNBfHycbl58fBymTZuEDh064cMP++ZYpm7d/PDXXwd0SevBg3vRrVt3ANqWCh9/PABSqRjnzp3DkiULsW7d5lw/49y5M/Hhh33Rtasfbt68gdGjhxpcb/fuXWjduq1ewprTb1BhMWktoIxntUKjkvTmq9UaLNpxpdDPahERmbNPutQpcPNgY2Q0D86vN954U5ewAsChQ/tx5MghqFRKpKSk6p6nA4BKlSqjVi1tclavXgOcPXsaANC4cROsWfMtUlNT0bRpc7275JkFBl7A+PETIRKJYGdnj7feegeBgRd0Sevbb3fRu9Bq2LCR7kKuZs3aKFfOGw4O2pucNWrUREjIM7Ro0QpnzpzC7du3sHPnDgBAamqq3gVg1u1mtXDh12jQoKGu5jZDmzbtdE1J/fx6YMWKJXqvCw5+hHnzZmHWrPlGN+s7cGAfrKyssHLlGtjZ5XwhmlGrJwgCfHzK43//m23U9rNq2rQ5tmzZiJCQ52jR4rUcn108e/YUHjy4jxEj/AFkDGgfr1vesGEjVKpUBQDQvXtPfPKJ/sVgZGQkpkyZgAkTpug9S1lQFy/+h/Pn/8U336zO9/Yydz1y+PABvP56O9ja2qFhw8ZQqdS4efM66tdvaPC1Pj4VoFKpsGjRXDRt2hxt2hhufp2SkoIrVy4hNjZWNy85OQmPHz/WJa1+fj0AAK6ubmjTpi2uXLmkl7Tu2LEFjx8H56v575dfjsWbb76Dfv0G5rqeoebBhZXbPtK8eUts2bIR77zTBY6OTmjcuCkuXbqA0NAXupq+3PbFdu3egJWVFQCgdu3a6etozxkZn8Pd3R2tW7+Oy5cD85WoZsjPPtW0aQvMnz8Lr7/eDq1btzX4zGLGd5KUlISTJ48DAJRKhV6MO3V6W3eTsGtXP5w8eVwvab1y5RJ+/HEtVqzQb0mxceM6eHmVw9KlK/OdwOR1vstw/vw5hIQ8x2efjdDNU6vViI6OgpubO/744yBcXFwRExONyZMnwMHBUfd8fn48ePAA//vfJCxevAJVqlTVW5ZxjFSqVAW1amlriDO3RPjmmyWwtbVB794fGfVeCoUCo0cPw5AhI9Gp01u5rtuhQyesWrUccXGxAICrVy9j+vSvAWhrdLdt24SEhHiIRCI8e/Y0120lJSUiOPihrm+D+vUbGNxHjx49jL//PoTVq3/Um5/Tb1BhMasqoEPnnyIiJgVZu7HSCEBEbAoOnX+Knu2Mv5NKRGRJfCu7YP7w13Jd58/Tj/DX+ad6nTBlkEnF6NqqUpGfJ21tXyWs165dwZ9/7sbatRvh4uKCI0cOYe/e33XL5XIr3d9isRhqtRqANvGtX78hLlz4D9u3b8aBA3sxc2b+xxW3sdHvICRzs0exWJzl/SW69wcELFiwNMcLy4ztnj9/DmvXrgIAvPNOF12NW+PGTdITkBijn1V79uwpJk0aj0mTpumaqgHaGsOXL0Ph61sPQPaagRo1auLatSt4/Pix7qJ92bIA3LhxDQDw9dcLIJPJi+x5so8+6ofXX2+PixfPY8WKxWjR4jWMGDE623oZnSgNG/Zpvt8jJiYan38+Gv37f6J3oZjxXWQIC3sJT89yeS4DgIoVKyE4+BHu3AnSXcRu2bJB1xHVuHETcrw5cvt2EKpV03Ygc/DgPsTExOCDD7Q1KImJiThwYG+OSau9vT22bduFK1cuITDwAtauXYWNG7dnW08QNBCJRFi/fmuBnpP+7bed+Pvvw/j227Wwttbe8PDy8kJkZDjUajUkEu3+HRkZoXcDpkmTZjh//hx69foQ1tbWiIuLxfjx2nhWrlwZc+YszHdZjJXbPlK/fkPcu3cH586dQbNmLdCkSTMcOLAXL16E6NbPbV+0ssp8rGc+touOoX0qJwsWLMHt27dw6VIgxo37FBMnTtXdXMtMEIAvv5ySaxPcnNy8eR1z587EwoXLdDeEMtSrp20t8PJlqN6NQ2NkPY/mRBAEtGrVGjNmfG1wuVz+6lnhd97pghs3rmVLWnM6p2bm6emJ5ORkXLlyKVvSmpvvvluB58+fIiDgG12tZNbzRmxsLMRiMRwdnZCUlASpVIa6devj7Nl/0KFDR12rBUOsra3Rtm0H/P33IQBA27YdYGNjA6VSiRkzJuO7735EvXp18fJlGHr27Gp0uXPyzz8nsG7dGqxcuTbbc/UF+Q0yBnsLKqDjl0OgVBvugVCp0uDElZASLhERkXnp0qoSPJxtsnVMl9F7cJdW+bt4ycrW1g5JSYk5Lk9ISICdnT2cnJygUChw4MBeo7b7/PkzuLq6oVu37hg8eDiCgm4ZXK9585Y4cGAPBEFAcnISjh07ghYtWhXos2T2+uvtsX37Ft2FbmxsLF68yP6b0qpVa2ze/BM2b/5J7+Lq3XffQ9++AzB+/ChERr7qc+Hff88gJiYGgDb5adpUe2EaEvIcEyaMxeefT8x2Idux41vYt+9PaDQaxMTE4PTpf/DGG2/qlteuXQfz5y/B119Px5UrlwBom25nlCvrxWthPX36BOXLV0DPnr3x4Ycf4/ZtbWzs7Oz0mme+/no7HDp0QNeUWa1W486d27rlN25c09U2HDiwF82aaRPGuLhYfP75Z+jd+yP4+fXM8l28icOHDyAtLRVpaak4fPiALqnNbRkAlCvng+XLv8P336/GsWNHAACDBg3VfU+GElZtR0x/4Pz5c+jZ8wPcvn0LCQkJ2LPnEH77bR9++20ftm37BSdOHEVqaqrB7ysmJgapqalo1ao1Pv10DOzt7fHiRQjs7OyQmpoKlUr7eJOtrR0aNWqC7ds3614bFvYSUVGRuum//tqv2+a5c2d1Zf7zz93Yu/cPfPPNajg6OunWd3FxRY0atXD06GEA2lqZmjVr6z3POmTICLRo0RITJoxBUlIinJycdd/J/PkBBj9TUcltH5HL5ahVqw62b9+C5s1boV69Brh+/RoePnyAevW0z0jmtC/mpnnzlti3708AQFRUZPr3qD0Os+7DeTG0TxmiUqnw4kUI6tatj4ED/dGy5Wu4f/+uwfds27Y9fvllB9LStPuTtrb91TO1J04cQ0pKClQqFQ4dOqg7bm7fvoWZM6di7twA1K5dJ1sZWrVqg4kTp2LSpPF49Oih0Z8xP1q2fA3nz5/T235GTGJionX7empqKs6cOYUaNWoZKKfhc2pmjo6OWLFiDQ4fPoidO/VvAGX8xjx79hT379/V7Ss//LAad+/exsKFy/RuXtau7Yu0tDRcu3YVAPDnn7+hY8dX5w2xWISpU2fC1tYes2ZN1X2GnHTt2h0HD+7HwYP70a3bewAAhSINarVad7Po999/zXUbAGBnZ49q1WroEuCgoJt49OiBbvnZs6fx3XffYPny7wx2ypfTb1Bhsaa1gBJTlLkvT859ORFRaWctl2L6J81w6PxTnLgSgsRkJextZejYpGjGae3btz/GjfsUVlbWWLXqh2zLX3utDY4c+Qsff9wLTk7OaNy4SY4JaGbHj/+NI0cOQSaTQiQSYfz4Lw2u5+8/DN98sxj9+38EQRDQuXM3vPZam0J9JgAYP/5LrFnzLfz9P4ZIJIJMJse4cV/qeo81xjvvdIVcLsf48aN0nTU1atQEs2dPQ0REOKpUqYYxY74AAKxduwrx8bFYv/4HrF+v/R5HjRqLVq1ao3PnbggKuom+fd/Xfeas5ahRoyYWL16ByZO/wBdffKXXKUtR++23nbh8+RJkMilkMjm++GISAKBz526YP38OTpw4puuIacSI0ZgyZQLUag1UKiU6dnwL9etra4wbNGiU3gnWM11HTACwffsWPHv2FHv2/I49e7S18h9+2BfvvvsemjZtjvbtO2LAAO1zs126dNM9x5rbsgxeXuWwcuUaTJgwFmlpabrnzbIaNUrb+ZF2yJvaWLt2A3x8yuOnn7birbc66zW38/DwRK1adXDixFF07eqXbVvh4WEICJgHtVoNtVqN115rg3r1GkAsFuOdd7pi0KC+cHBwxPffb8TMmXPx7bfL8ckn2s9ga2uHqVNnws3NHQDg5OSMIUMGICkpEQMH+qN69RpITk7CsmWLUK6cN774Qvvco0wmw48/bgGgHX5m3rxZ2LRpPRwcHDBjxpxsZRwwwB9WVtb4/PPRWLZslV7iW5waN25qcB+pU8cXANC8eQvcuXMLvr51IZFIUKFCBfj4+Oiat+a0L+bm888nYsmSBRg0qC8EQcCnn47R1aJ/8EFfLFjwNaytrTFr1jxYW1tj9OhhSE1NhUKRhvff74ahQ0fo3UwxZp/SaDSYP382EhMTIBKJ4eXlhU8/HWPwPQcM8MeGDT9g2LBP0msDRRgyZLiuRtHXty4mTPgMMTExaNKkGd57rxcAbesKhSINS5Ys0L3vjBlf64ZvAoBmzVpg2rRZmDJlAubNC9A9mpFf06ZN0iWj/fr1RrVq1bF8+XeoWLESZs6ci0WL5iItLQ0qlRINGjSCr289XL9+FevXf59e661CmzZtjW6ia4i9vT2WL/8OX331OVJTU3XDW6nVagwe3A+pqanpw9q44tGjh9i2bRMqVqyETz/VHtve3j5YuHApxGIxZsz4GkuWLIBCoUC5ct7ZWvWIRCJ8+eVkfPfdCkyd+iXmzVusa3qeVaNGjZGcnKT7G9AmoEOHjsTw4Z/AyclJ76ZjbqZPn4MFC+Zg+/bNqFathm5IGwBYuHAOpFIZpk9/9Wx51iGEsv4G5bfHeUM4TmsBjVt5OtfE1cFWhpXjCtZ1vyUyxzElyTDGyjKYY5zyO05rWWFO47TmpKjGdbR0UqkYe/fuyfdYi2VdQcb/LSxLOK7KkpyGFmKctPIad9gcmFOsCjJOK5sHF1CnpuVzHItVJhWjYxPj74gTERERERGRYaxpLaCM3oMjYlP0OhnJeFarrPUebI61QmQYY2UZzDFOrGk1zJzuXpcWQ4cOzNZ5Tb169TFp0rRCbbe0xmrfvj+xe/eubPP/979ZBR5eyNQKGqvJk79AWJj+sExeXl4ICPimqIpmlu7fv4v587M3v+7d+6MC9ZJrrILEqTTuryWlMPu3OZ3/ClLTyqS1EFIVqmJ7VsvSmOMFNhnGWFkGc4wTk1bDzOlCgHLHWFkOxsoyME6Ww5xiVZCktWxlVkXMWi5Fz3bVOLQNEZUZgiAU6bhrREREVHYUtL6Uz7QSEZFRpFI5kpLiC/yDQ0RERGWXIAhISoqHVCrPe+UsWNNKRERGcXHxQExMBBITY01dFLMiFouh0ZhHkyvKHWNlORgry8A4WQ5ziZVUKoeLi0f+X1cMZSEiolJIIpHC3d3b1MUwO+b4/DEZxlhZDsbKMjBOlsPSY8XmwURERERERGS2mLQSERERERGR2bKY5sFiMXurNHeMkeVgrCwD42Q5GCvLwVhZDsbKMjBOlsOcY5VX2SxmnFYiIiIiIiIqe9g8mIiIiIiIiMwWk1YiIiIiIiIyW0xaiYiIiIiIyGwxaSUiIiIiIiKzxaSViIiIiIiIzBaTViIiIiIiIjJbTFqJiIiIiIjIbDFpJSIiIiIiIrPFpJWIiIiIiIjMltTUBSDzFhMTg6+++gpPnz6FXC5H5cqV8fXXX8PV1RW1a9dGrVq1IBZr730sXrwYtWvXBgAcP34cixcvhlqtRr169bBw4ULY2NiY8qOUCZ06dYJcLoeVlRUAYOLEiWjXrh2uXr2KmTNnIi0tDeXLl8eSJUvg5uYGALkuo+Lx/PlzfPbZZ7rphIQEJCYm4sKFCznGEGCsSkJAQAAOHz6MkJAQ7Nu3D7Vq1QIABAcHY8qUKYiNjYWzszMCAgJQpUqVQi2jwjEUq9x+swDwd8tEcjquCnq+47mw+BiKVW6/WUDB40gFl9u5rqDHjtnHSiDKRUxMjPDff//pphctWiRMnTpVEARBqFWrlpCYmJjtNYmJiUKbNm2E4OBgQRAEYdq0acKqVatKpLxlXceOHYW7d+/qzVOr1cJbb70lXLx4URAEQVi9erUwZcqUPJdRyZk3b54wZ84cQRAMx1AQGKuScvHiReHFixfZ4jBw4EDhzz//FARBEP78809h4MCBhV5GhWMoVrn9ZgkCf7dMJafjqiDnO54Li1dOscos82+WIPB3yxRyOtcV9NixhFixeTDlytnZGa1atdJNN27cGC9evMj1NadOnUL9+vV1tQl9+/bFX3/9VZzFpFzcvHkTVlZWaN68OQBtPA4dOpTnMioZCoUC+/btQ+/evXNdj7EqGc2bN4e3t7fevKioKAQFBcHPzw8A4Ofnh6CgIERHRxd4GRWeoVgV5DcL4O9WcTMUq9zwd8t08oqVsb9ZAGNVnHI61xX02LGEWLF5MBlNo9Hg559/RqdOnXTzBg4cCLVajfbt22Ps2LGQy+UIDQ2Fj4+Pbh0fHx+Ehoaaoshl0sSJEyEIApo1a4YJEyZki4erqys0Gg1iY2NzXebs7GyC0pc9x48fh5eXF+rVq6eblzWGjo6OjJUJhYaGwsvLCxKJBAAgkUjg6emJ0NBQCIJQoGUZzVWp+Bj6zQL4u2Vu8nu+47nQtAz9ZgH83TKlzOe6gh47lhAr1rSS0ebOnQtbW1sMGDAAAHDy5En8/vvv2LFjBx48eIDVq1ebuIS0Y8cO7N27F7t374YgCPj6669NXSTKw+7du/XuWDOGREUj628WwN8tc8PzneXJ+psFMI6mZuhcVxoxaSWjBAQE4MmTJ1ixYoWuA4uM5iP29vb48MMPcfnyZd38zM2xXrx4ka9mQVRwGd+zXC5Hv379cPny5WzxiI6OhlgshrOzc67LqPiFhYXh4sWL6N69u26eoRhmzGesTMPb2xthYWFQq9UAALVajfDwcHh7exd4GRUvQ79ZAH+3zE1Bznc8F5qOod8sgL9bppT1XFfQY8cSYsWklfK0fPly3Lx5E6tXr4ZcLgcAxMXFITU1FQCgUqlw+PBh+Pr6AgDatWuHGzdu4PHjxwCAnTt3omvXriYpe1mSnJyMhIQEAIAgCDh48CB8fX1Rv359pKamIjAwEIA2Hl26dAGAXJdR8fvjjz/QoUMHuLi4AMg5hgBjZUpubm7w9fXF/v37AQD79++Hr68vXF1dC7yMio+h3yyAv1vmpqDnO54LTSfrbxbA3y1TMnSuK+ixYwmxEgmCIJi6EGS+7t+/Dz8/P1SpUgXW1tYAgAoVKmDYsGGYOXMmRCIRVCoVmjRpgmnTpsHOzg4AcPToUSxZsgQajQa+vr5YtGgRbG1tTflRSr1nz55h7NixUKvV0Gg0qF69OqZPnw5PT09cvnwZs2bN0uvG3N3dHQByXUbFq3Pnzvjf//6H9u3bA8g9hgBjVRLmzZuHI0eOIDIyEi4uLnB2dsaBAwfw8OFDTJkyBfHx8XB0dERAQACqVasGAAVeRoVjKFYrVqww+Ju1evVqXLlyhb9bJmIoVt9//32Bz3c8FxafnM6BQPbfLIC/W6aS0/X56tWrC3zsmHusmLQSERERERGR2WLzYCIiIiIiIjJbTFqJiIiIiIjIbDFpJSIiIiIiIrPFpJWIiIiIiIjMFpNWIiIiIiIiMltMWomIiIiIiMhsMWklIiIiIiIis8WklYiIyERWrVqFiRMnmroYREREZo1JKxEREREREZktkSAIgqkLQUREVNqtW7cO27ZtQ2JiIjw9PTF16lSMGTMGgiBALpejYsWK2Lt3LxISErBw4UKcOnUKIpEIvXr1wrhx4yCRSPD7779j165dqFu3Lvbs2QMPDw/MmjULrVu3NvXHIyIiKjZSUxeAiIiotHv06BF27NiB3377DV5eXnj+/Dk0Gg1GjhyJJ0+eYOnSpbp1p0yZAjc3Nxw5cgQpKSkYOXIkvL290bdvXwDA9evX0aVLF/z333/4+++/MWbMGBw7dgzOzs4m+nRERETFi82DiYiIiplEIoFCocDDhw+hVCpRoUIFVKpUKdt6kZGR+OeffzBt2jTY2trCzc0N/v7+OHDggG4dV1dXDBo0CDKZDN26dUPVqlVx8uTJEvw0REREJYs1rURERMWscuXKmDZtGlatWoUHDx6gbdu2mDJlSrb1Xrx4AZVKhbZt2+rmaTQaeHt766a9vLwgEol00z4+PggPDy/eD0BERGRCTFqJiIhKQPfu3dG9e3ckJiZi5syZWLp0KSpXrqy3Trly5SCXy/Hff/9BKjX8Ex0WFgZBEHSJa2hoKDp16lTs5SciIjIVNg8mIiIqZo8ePcK5c+egUCggl8thZWUFsVgMNzc3hISEQKPRAAA8PT3x+uuvY9GiRUhMTIRGo8HTp09x4cIF3baio6OxdetWKJVK/PXXX3j48CE6dOhgqo9GRERU7FjTSkREVMwUCgWWLVuGhw8fQiaToUmTJvj6668hl8uxd+9etGrVChUqVMAff/yBxYsXY+nSpejWrRuSkpJQsWJFDB8+XLethg0b4smTJ3jttdfg7u6Ob7/9Fi4uLib8dERERMWLQ94QERFZiN9//x2//vorfv75Z1MXhYiIqMSweTARERERERGZLSatREREREREZLbYPJiIiIiIiIjMFmtaiYiIiIiIyGwxaSUiIiIiIiKzxaSViIiIiIiIzBaTViIiIiIiIjJbTFqJiIiIiIjIbP0fnMFlzd+An1MAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step25k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step50k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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sd8MYbhvMjI69h7NgxAIDk5CQkJibp35dEIoG3tzcSEhLh6anrvlqzZi39ug4O9lAoFLCysoJCoYCDgwMA4Ouvv0Jo6BL9PmaMguq6NPXftm1bo7uo5nXnzm2cOXMGwcFDAOha2urX98OaNWugUCgwZMgQnD17FlqtFjKZrMD9Jvf3RqFIw/379xEQoPte+fv748GDB0bF8v3332H8+A8LTFiBsu2z/v7+uHz5MoD8+yyQf18sip+ff6EJK1Dw/uXh4QEAaN68BVasWJFvnfr160MqlcLfPwD376/Cgwf3ERl5DUOHBuuX0Wg0WLbsM9y4cR0KhQKdO+vuCb537y6srOT6FngvL2/9BZq8Ctt/C3Pr1i3cu3cP+/f/joiICKxatRLz5y9AlSpOAHQX/bZv31bo+levXsXYseMwffo0rFy5CpcvX8KFCxfQpEkT3Lt3D6NGjcaGDV9Ao9Fg1KhRBusWtG/qPqt6Bp8VAKxaZVxXet1n4ACFQgFAdzxycHCARCIxquxZwKSViIgIgFqjRWxihi45TUhHTEIGHiak41FiBpQqw3tHnao+/VsqlUKjzf2cQhFS03WD+Oz6bhdWf7EdMQ8fYO3yRXjwWAGlWoOE1CxYybXQaLVQZGSjy2td0bPnG5g7dzYuX75k8FparaA/AdFonsaROzHI/Zi93MmDIAioVas2goJ66u8BVKvVuH79OsRi3XKbNm02eL0FCxZhypTJWLFiJaytrREVdQ8ZGRlQKBSws9MlNj/++OOTFuFe+vUcHavg0aNHEIvFBglQ7u21a9cO7drpWqyio6NLlbS0bt0aL730Er78cgP++ecfSKVS/b29tWrVRps2bfDqq68B0HWb3L9/HyIjrwHQDUZSo0YNqNVqTJkyGZ98Mknf7bJRo0bYtetbDBs2HCdPnkCTJk0KLNPF0AZeXl7YuXMH3ntvoEG30fDwcP3fsbGxOHHiP4wYMRLbtm3Ftm07YG1tjYyMdMycOTNf0qrVajF27DgAQN++b+KNN94wqNuAgAAsX74Stra2UKlUkMlkAHStb40bN8bDhw/h6uoChUIBe3t7JCUlQaPR7ZuBgU1x6tRJdO3aDZGR1+DrWxuA7v7kL7/8Ep6envDxqWlUHRRU16Wp/7KoVasW2rVrh2nTZgDQ1bVEIoFYLEZaWhpatXoBa9euwQsvtAaQf78BDL83gqBriYqIiEDLli1x9epVDBjwnsH+VZjRo9/HkSOH4etbx6B7aY6y7LORkZGF7rO6bRvui0Up7nmcBe1fOfvSnTt3Ckx+bty4AY1Gg8hI3XerWrXqaNSosb5FVqVS4dq1a8jMzMTWrduxe/cP+gtBNWvWwuuv98Bnny3FpEmfFtnSWtj+WzgBDg4OEIvFcHZ2hkKhgEqlhCAAcrkc58+fR40aNQzW8PGpiYcPo/Vx57TIi0QiiMW64+2xY8cwYMB78PT0xNy583D+/Hls3boVQUE99d+1gvbNuLi4fJ8VgBK1tAYGNsUPP3yHbt1ex8mTJ9C7d29IJFKjygDg/v376Nr19WI+N/PFpJWIiJ4rmdlqfWL6MCEdMfEZiEnMwOOkTGhLMUhFzVp1sHXT5widNx3BI8cYzKvv1xBTPhqNhk2aQQCg1gjQaoH0LDWyVIBKLeDuwwTMm/kJtFotbG3tYOdSHemZKqSkK5GsyEbw8JEYPnwYRCIRxowdW+L4Ro4chdmzQ6BQKCAWizBnzrwil/f398fQocMwbdoULFnyGTw9vTBz5gzcvx+FGTNmAgDmz5+Lxo0bIzh4CFq2bImxY8dhzJix+OSTjwEAM2bMKHR7Umnhpx6CICA1XYmMbDXuPkqDRCyCg60MVezk+hPucePG6p8tuHz5Cnh4eGDVqhW4ePEihg4dhpCQWdi161sIAjBhwgQAQHJyCkaOHPHkntYV+OOPg7h8+TKWLVsGAJgw4SM0bdoULVu2xKBBA+Hl5YVBgwZBJpPnK4uL040sOnbsOMybNxe//fZbvnvxli5dCmdnJ8hkMkybNgNqtRoZGemwtrYGANja2iE5OQlZWVn6MgC4dOkSVq1aCbVahTZtdPekNW7cGOPHj8WQIcEYM2Ysxo4dA0EQUKVKFaxcqWut+eOPgwgNXYTevd+ETCbHwoULcOPGTQiCFhMm6Aaz6tv3LXz66STs3LkT/fr10z+yycHBAYsWLcaUKZ8iNHSJQUIE6C5QfPfdt0hJSUFqaipmzJhZYF2XR/2XhL9/AFxdqyI4eAhEIhG6d++Ofv3ehr+/P9LS0iCXyyGRSNGsWTMA+feb27dv5dvmsGHDMG3aNKhUKrz88svw8PBAq1Yv6Pev3r3fxMyZ03Hz5g0MCQ7GuInTEJ+SBUGrwdQZ8zB75qeYPHkq6tSpY7DdsuyzVlZWWL684H02pwWvqH2xMIXVa979a/LkT5GamgqRCJg5M39vCFdXV4wfPw5JSYlYvFh3f3zHjh0xZMggiMUStG7dGoMHD0ZUVBRGjx4FT09Pgxbpvn37YuPGL7Fp01cYPnxEoS2tBe2/x48fg0ajxUsvvYSFCxfg6NF/8Pfff+Httx+gX7+3YWdnh8GDB0Kj0WDKlKlISUnFBx+Mhq2tLRwdHbF4seEFs9atW2P+/Hl4990BaNiwIf799zjefXcAxo8fh8aNGyM8/ALeeedd1KpVC8uXL8PFi+HIyMjAJ598inr16mHVqhX4+OOJWLZseb59s127F+HiYvhZAYW3tKpUKnzwwWhcvx6J0aNH4sMPJ6BJk0DI5VYYPHgg/P390bix7iKasWV3796Fv7+/UfuHORIJFjKMVEKCAlqtRYT6XHJzc8Djx0XfX0DmgXVlGVhPZSMIAlLSlYiJT8fDhAw8etJqGpOQjmSFcaOmFiWw6iP06t4ZTi5Vi1+4nIlFgFQihkQiglQihlQsgkQihvTJtEQsytfampKuRFqGChqtUGAiWJRBgwZi+/YdxS5XVoIgICYhAyqNFrnPTEQiQCYRw8vV1qh48/r555+gVmvw1ltvQSaTQKXSFL+SBQkOHoKvvtpUbsmguTDXuqqo/TS33PusOYuOjsbatauxaFGoqUMpN0uXhmLo0GFwda2KyZM/RZ8+fdC6dRtoNBr9RYHSfNeio6OxevWqUneHL6tr167hxIl/MXTocH3Zd9/tQrt2ncym+7BYLIKra+G3KTxbRzgiInquaLRaxCdnGbacJui6+GZml99D0+2spfBytYOnqy0SU7OgTIlDenpavqRVJAJs5BJYySVQawRoNFqoNQLUWi3K+HQaPa0AKNVaQA0ABZ/US8QiSCQiSMQiZKs0ELTQ34Or0QpIUSihyFTB2d4KIhHy3Z8LABB05RqtFopMVa5iQT8/z+IFTgiFFOZ9zSylBiq11qD86F9/4rdf90AEQCIRoXmzppjw0USIy5gUmIsZM6bhwYOn3WTHjBlb7GA6JVWSixbbt2/H4cOH9NOdO3fBoEGD8m6y0lTG55OXAAGCVncvu1Yr6P4rAMKTv9Oz1Pn2U+DJPe1qLXbt/hn79+7Rdz1u3DgQY8fpRoQWiZ50NX3y39zTQNn36Tt37mDOnNn6aWtrK6xfv6FMF62eN7m7+oeEzMb69Z9j7dq1kEol+u7iQNkvBlaWnDjtXKqj0+tv436cAg62MtjKRcjMzDTo6WHu2NJK5YKtQpaDdWUZnsd6ylKqceBUFI6ci4YiUwV7GxleaV4N3Vr7QCwS4VFihkF33piEdMQmZkCtKb/fBmcHK3i72sLL1Q5eVe3g5WILr6p2cLSV6U9EspRqhG75G9WtH6F1m9awtbUDoDsBlUpEqFrFusCTFkEQoNEAakELrUaARvvkn0YLjSBAoxFgGb/IpicCIBKLIBbr/paIRRCLRRDhyX/FgASiJ8uIIIauTCaTQKsp+9UDQRCgyFQjPUsNrVaAWCyCnbUU9jZSk52wCjnJlQBAEKCF7n7olHQltBrBIMkSARBLRKhiK4Poyef25H8Gf0OkuxAjQvH3Q5bXeyj956p7/1ptzvOHdYmn7nN5Mp0zD7pu+jnzdcs+Xc8Unn7eOUmtrlycK7kVPVnoaVlO/RguIxKJ9ClwkiI737GluGNVSUilEqjV5dMibo7fq4JoBQEJKVlQF/C5SiQiuDhYI1+4BV/rM05hVxWLKREEIFmRDY3WME6NRoWrVy7B080VL7RqU5qIKkRxLa1MWqlcPI8n2JaKdWUZnqd60goCklKz8dmu80hIzTJIQkUi3UmbphyP/xKxCO7ONrrE1NX2yT87eLrYwsbKuA5IWUo19v4VjvtRtyCGBlKJGG5O1nB3toFELC5+AwXSJa5KtRZKta7VUanS6KZVWqjUur8t41fbfEnEoict0bou1RKxSN/dWiI27GYt0Xe/1i0HiKDRahEZlQylSoPcu6VYBMhlEvj5OBW4DwhPWu40+hY83cBL2tytegbzc5fpWvo0ecr126ikfSLn+2jQQpir5bCweXkTL/3FBdHTbUIAHiUW3O1WIhbDyUEOQatr+c+54KPNufBTjj0ZnidiMSAVi/V1g5y6Qp4EONeFC319iQAxRJBKxdBqtQZ1my+BLmD9vK+hFQTcfZQGpUqTr/7lUglqetpDBJGuJVx/QeLphQbdxYjc5Tnz8l+UEJ5c4IBQwLZybQO5LgTl3palH4I1WiA52wotmgbizY51il+hkrB7MBERVQpBEJCt0kCRqUJ6phqKLBXSM1VPplVQZKp1f+cqV2SqkJGlLvQkQBAATSmzNCuZBJ6utk9bTp8kqe7ONpBKSptY6ljLpXi7awsALSr1AoNWEJCWoUJiahYSU7ORmJaFpCf/zZlOTlOWakApKppYJIKttRSCICAjq2qB+6xIBNxUWMHeRoZslQbZKo3uwoNKW64XXp5dRTwnNq7yoiBTsSp0TtijSgzjOZF24aFZJa3FYdJKRPSMK6rbrbW84J8BtUZrkFimZ6lzJZ+6xFOfhGaq9AlqeXbVNZajrSxPd15beLvawdnByqy6lJUHsUiEKnZyVLGTo7ZXwctotFqkKJRITM3G8u8vIEtZeNc9qUSEwLpVDVo9cl4HuVtHAH3XRYMWEjxtocm7bM59p/m29eQ1nm5L9/e1e0m4EZ1SYK8qEQB7WxmkEjHSs1T5HkFUGbSCYHBvb0EEAUhKy0ZSWnYlRUXlTSwSwcZKAhsrqe6f/Onf1lZS3I9Lw52YtAL3U4lYhHo1qqBuNadcvSR0Fy6yVTk9J3QXMbJzzc/O07pIVBkUGUUfz8wNk1YiomdYRrYKC7adxePkTH1CqchUYd+Je/jrXDSa1q+KbGWu1tEnCWh2EYmOqbg5Wefq0msH7ycDI9nbyEwdmlmRiMVwcbSGi6M1XmtVA7+fioJKnT/Jk0nFeL21D3p38DVBlPl1faEG5j/ZV3PHK5OK4eZkgxmDW+gvsqjUWmRkqaDIUiP9SWt9epbu4kr6k4sqGVk5rf1qZDyZl5GlqrSutJVNLhVDLpPASqYbCMxKJkZUrKLIFl6JWIT6NZyg0mihVmuhzhk4TKOFSqOFRiPo55l7S7EIgLWVFLZWElhbSWEjf5J05iSgcimsc/1tkJg+SU6traSQS8VFXuzKUqqL3E/H921S6MXAwgiCALVG0Ce0OYns06RX+7TVXp1/frbq6XpPy7SIjlcwGTYBEQAHO/nTi3055U/2K/2Fu9z/r7+YlzPv6YqiJzOebifP9p5uRr9NkQiIfqwo8nhnb2tZv51MWomILIRGq9W1eGY87XKblqv1M/ffOf/SCrmSqtUKSMtU4Vh4TCW/i9Kxt5Eh9P12pg7D4nRr7YOwyMeFnmB3a+1jwugMWculmDG4BQ6cisJf56OhyFDB3laGTs3y9wqQScWoYm+FKvaFdycsiKurPe5HJxsktRm5Et30PEmwIlONjGxd4ptdxsevSMQiyGUSyGViXWKp//ck2ZQ/LZPnLs+7rDxvmQQymbjAEZV/Pna73C5a5CRWusRWl9zqElstVGptnnm5kt8nCa9KnX+eWqOFWq3bzn+XY4rsqWElE+PdLvVha/Uk+ZTnSjitdJ9DZfSsKMl+aiyRSASZVASZVAy7chzMtaj6l0rEeKV5NbzWqgbUGl0dqTW6+4P1I59rdffZa/TzDf+r0QiwtpEhJTXLoEyt1V3w0G8rZ53c83K29WQwuuj49CITbLEIqOXlmOsRX0/uSc953Jc45x71XI8DK2i+wePBnv6dd1mJ5Ok97Xnn7z1+FwdOm//FwOK+/52aVTNBVKXHpJWIqBRK0+U2N5VaW2TiqRaA+KSMpwlohgoZ5fgIl4oilYhhbyOFvY0M9jYy2FnLYPfkb920bl5Omd2Tsn3/3S3yx/WV5pb142ouKuIEuyJZy6Xo3cG3wk74xGLdfam21iV/32qN7qLR3uN3cOziwwITLIlYhDYNPNClZY2nyemTJLOs91GXRnletMidWFUEJ3t5kceAri/4oGOgd4W8dklV9H5aXoqr/94dapf5GFBe9/SX5wWWivZ6Gx+cvW7+FwMt6aKlMYwaPfjOnTuYMmUKkpOT4eTkhNDQUNSqVctgmePHj2P58uW4fv06Bg0ahMmTnz7nKCEhAVOnTkVMTAzUajVat26NGTNmlOjhvBw92Lw9TyOdWjrWVdkV1j1MKtHdb/hG+9pQqrRIy1DqByTKSTzNufttbiIRYGedk1hKYW+dK8nMlYTaW0sNEtDiutYVprgud7m7hpobfqcsR3nUlaXtqzkX2Mz9ooWlfa6WoqLrv7yOf5ZW/5b0vdLH+eQCuznGCZTTI28GDx6Mvn37olevXvjll1/w448/Ytu2bQbL3Lt3DxkZGThw4ACUSqVB0rpgwQJIpVJMnjwZKpUKAwYMwNChQ9G9e3ej3wiTVvPGkzbLwboyjlqjRWq6EinpSqQolEhJz9b/ffVeEmITMyx+2HtAN8Lu4K5+uRJRXRJqYyUtsLthRbKUk4C8+J2yHOV5gm2J+6q5s6QTbNIpz+Mfv1cVy9x/q8r8yJuEhARERERg8+bNAICgoCDMmzcPiYmJcHFx0S9Xs2ZNAMChQ4egVCoNtiESiZCeng6tVgulUgmVSgUPD49SvSEiotISBAHpWWqkpCuRqtAlockK5ZPkNDtXgqosdpRQU7F70rLpkLfFs6B/tjIcPvsAf5y5X0SXuxpo28jTBO8kP0vpckfEfbVi5P5czf0Em8ofv1dUlGKT1piYGHh4eEAikQAAJBIJ3N3dERMTY5C0FuV///sfxo0bh/bt2yMzMxPvvfceWrRoUbbIieiZU9r7RFVqjUHCqfs7G6lPktKUdCVSnySlpngkS0HEIpG+VbOgZNPLzQGCWgN7W8N7PyXikt1P1qNtTZy/Ef/M3NNCREREz59KaWs/cOAA/Pz8sHXrVqSnp2PkyJE4cOAAunXrZvQ2imouJvPg5uZg6hDISOZYV5nZasxedRSPEtKhfJJcKTJV+O1kFE5GxKJf5/pIz1Q9eQZiFpLTspGYmoWktGykm1mrqFQixqsv+MDBTg5HOzkcbHX/zfnbwU4OO2tppT1DdOXEl7Hnrxv4/b+7SM1QwtFWjtfb1UKfTvVgY8UuV+XBHL9TVDDWleVgXVkG1pPlsOS6KvZsxcvLC7GxsdBoNJBIJNBoNIiLi4OXVyFPNS/Ajh07sHDhQojFYjg4OOCVV17BqVOnSpS08p5W88ZuPJbDXOvq52O38TA+HWqNYTdWtUaLuKRMrNsdXqnx5DxnrUrOP3s5qthZoYqdHNfvJyH8VkKBrbbFjnIoaJGpyEKmoujXL+96eq1FdbzWorpBmSI1E8WEQUYw1+8U5ce6shysK8vAerIc5l5XZb6n1dXVFQEBAdi3bx969eqFffv2ISAgwOiuwQBQvXp1HD16FE2aNIFSqcSJEyfw6quvGr0+ET27BEHA9fvJ+O3kvUrpumsll+RKRK2e/p172l4OB1tZoV1xOwR6FTnKIbvcEhEREZUfo0YPvnXrFqZMmYLU1FQ4OjoiNDQUvr6+GDlyJMaPH4/GjRsjLCwMEydOhEKhgCAIcHBwwIIFC9ChQwdERUUhJCQE8fHx0Gg0aN26NaZPn85H3jxDzP3qDT1lLnWVkq7Ef5dicDT8IWKTMsu0LZEIcLSTw8nOClXsdd1wq9jJ4fQkCXXUt5TKy20EQkt5jABVPNaV5WBdWQ7WlWVgPVkOc6+rcnnkjTlg0mrezP2LQE+Zsq60WgGX7yTgaHgMwm/GQ1OC77RUIsLLzarpE9HcLaP2NjKIxZX7eJaKxu+U5WBdWQ7WleVgXVkG1pPlMPe6KnP3YCKisopPzsTxSzE4djEGSWnZJV6/2PtEiYiIiOiZxaSViCqESq3FhZvxOBr+EBF3ElFUm6pELEJgXVfce6RAaoaS94kSERERkR6TViIqVw/j03E0/CH+u/wIimIeRePlaouOgd5o28gTjrbyCr9PlIiIiIgsD88CiajMspUanLkWh6MXH+Lmg5Qil5XLxHjB3wMdA71Rp5qjwbNKreVS9O7gy27ARERERKTHpJWISkUQBNyLTcPRCw9xMiIWWUpNkcvX8nRAx0BvtG7gARsrHnqIiIiIyDg8cySiEknPUuHklVgcC3+IqDhFkcvaWknRtqEnOgR6wcfDoZIiJCIiIqJnCZNWIiqWIAi4fj8ZR8MfIizyscFASQXx93FCh0BvtKjvBrlMUklREhEREdGziEkrERUqJV2J/y7F4Gj4Q8QmZRa5rKOdHC829kTHJt7wcLGtpAiJiIiI6FnHpJWIDGi1Ai7fScDR8BiE34yHRlv4w2pEIqCxrys6BnqjSR1XSCXiSoyUiIiIiJ4HTFqJCAAQn5yJYxdjcPxSDJLSsotctmoVa3Ro4oUXG3vBxdG6kiIkIiIioucRk1ai55hKrcWFm/E4Gv4QEXcSUXibKiARi9C8vhs6BnojoJYzxLkeVUNEREREVFGYtBI9B7KUahw4FYUj56KRnqmCjbUUni62iE3MQHqWush1vVxt8VKgN9o08oSjrbySIiYiIiIi0mHSSvSMy1KqMX/bWTxOyoRKoxv1NyNLjdsPUwtdRy4T4wV/D3QM9Eadao4QsVWViIiIiEyESSvRM27P0dt4lJABrVBU51+d2l4O6BDojdYBHrCx4uGBiIiIiEyPZ6VEzyBBEHDjQQr+DLuPs5GPi12+c4vq6NDECz4eDpUQHRERERGR8Zi0Ej1DVGotTl+NxZ9h9xEVqzBqHRGA916tX7GBERERERGVEpNWomdAiiIbf52Pxt/no5GaoSrRuva2sgqKioiIiIio7Ji0Elmwu49S8eeZBzh9NRYabfH3rOYlk4rRqVm1CoiMiIiIiKh8MGklsjAarRbnrsfjz7D7uPkgpdDlRAAC61bFS4Fe+OGf23icnAmVWqufL5OK4eZkg26tfSohaiIiIiKi0mHSSmQhFJkqHA1/iCPnHiAxNbvQ5azlEnRo4o3OLarB3dkWAOBX0xkHTkXhr/PRUGSqYG8jQ6dm1dCttQ+s5TwMEBEREZH54tkqkZmLjk/H4bD7+O/yIyhztZTm5e5sgy4tquPFxl75HldjLZeidwdf9O7gCzc3Bzx+nFbRYRMRERERlQsmrURmSCsIuHQrAYfC7uPK3aQil21QyxldWtZAkzquEItElRQhEREREVHlYNJKZEYys9X491IMDp99gNikzEKXk0vFaNvIE11aVEc1N/tKjJCIiIiIqHIxaSUyA3HJmTgc9gDHLz1EZram0OWcHazQuUV1dAz0hr0NH1VDRERERM8+Jq1EJiIIAq5FJeNQ2H1cuBGPoh5YU7daFXRpWR3N67tBKhFXWoxERERERKbGpJWokilVGpyMiMWhsAd48FhR6HISsQgvBLijS8saqO3lWIkREhERERGZDyatRJUkKS0bR849wD8XHkKRqSp0OQdb3eNoXm5WDU72VpUYIRERERGR+WHSSlTBbj1MwaGwBwi7FgeNtvBOwD7u9ujSsgZaN3CHTCqpxAiJiIiIiMwXk1aiCqDWaBEWGYdDYQ9w+2FqocuJREDzem7o0rI66tdwgoiPrCEiIiIiMsCklaiUspRqHDgVhSPnoqHIVMHeRoYXG3tCLpPgWPhDJCuUha5rYyXFS4HeeKV5NVR1sqnEqImIiIiILAuTVqJSyFKqMX/bWTxOzoRKrQUAKDJVOHj6fpHrebrYokvL6mjXyBPWcn79iIiIiIiKw7NmolI4cCrKIGEtTiNfF7zasgYa1naBmF2AiYiIiIiMxqSVqBSOnIsuNmGVy8R4sbEXurSoDi9Xu0qKjIiIiIjo2cKklagUinpkTY7lY16ErbWsEqIhIiIiInp2iU0dAJGlSUlXorgevg62MiasRERERETlgEkrUQlkZqux8vtwCIU/bhUyqRidmlWrvKCIiIiIiJ5hTFqJjKTWaPH5T5dwLzat0GVkUjHcnGzQrbVPJUZGRERERPTs4j2tREbQCgK+/u0qrtxNMih3c7JGZrYG6Zkq2NvK0KlZNXRr7cPH2RARERERlROeWRMZYfdft3DySqxBWS1PB3w6oBkTVCIiIiKiCsTuwUTF+ON0FA6cjjIoc3e2wYR+gUxYiYiIiIgqGJNWoiKciojFriM3DcocbWWY2L8pHO3kJoqKiIiIiOj5waSVqBARdxPx1b4IgzIruQQfvd0U7k42JoqKiIiIiOj5wqSVqAD3HqVh7Z5L0GifPttGIhZh7JuNUdPTwYSRERERERE9X5i0EuXxODkTK34IR5ZSY1A+rEcAGtZ2MVFURERERETPJyatRLmkZiix/LsLSE1XGpS/3aku2jb0NFFURERERETPLyatRE9kKzVY9cNFxCZlGpS/1qoGurX2MVFURERERETPNyatRADUGi3W/3IZd2JSDcpbN/DA26/UNVFURERERETEpJWee4IgYOuBa7h4K8GgvEEtZwzvEQCxSGSiyIiIiIiIiEkrPff2HL2Nfy89MijzcbfHmDcbQyrhV4SIiIiIyJR4Rk7PtcNnH2D/iXsGZVWrWOOjtwNhYyU1UVRERERERJTDqKT1zp076N+/P7p27Yr+/fvj7t27+ZY5fvw4+vTpg0aNGiE0NDTf/N9++w09e/ZEUFAQevbsifj4+DIHT1QWYdfi8M2f1w3K7G1kmNi/KarYW5koKiIiIiIiys2opqSQkBAMGDAAvXr1wi+//IJZs2Zh27ZtBsvUqFEDCxYswIEDB6BUGj4u5NKlS1i7di22bt0KNzc3pKWlQS6Xl9+7ICqhyKgkfPnrFQi5yuQyMSb0C4Sni63J4iIiIiIiIkPFtrQmJCQgIiICQUFBAICgoCBEREQgMTHRYLmaNWsiICAAUmn+PHjLli0YNmwY3NzcAAAODg6wsmJLFpnGgzgFVv94CWrN05RVLBLhf70bw9fb0YSRERERERFRXsW2tMbExMDDwwMSiQQAIJFI4O7ujpiYGLi4uBj1Irdu3UL16tXx3nvvISMjA6+++io++OADiEowKqurq73Ry5JpuLk5mDqEYsUlZWDl7ovIzFYblI/v3xSdWz0/z2K1hLoi1pMlYV1ZDtaV5WBdWQbWk+Ww5LqqlJFmNBoNIiMjsXnzZiiVSowYMQLe3t7o3bu30dtISFBAqxWKX5BMws3NAY8fp5k6jCIpMlVYtOMsElOzDMr7vuSLJrWczT7+8mIJdUWsJ0vCurIcrCvLwbqyDKwny2HudSUWi4pspCy2e7CXlxdiY2Oh0WgA6BLQuLg4eHl5GR2Et7c3unXrBrlcDnt7e3Tu3BkXL140en2islKqNFi9+yJiEjIMyjs3r47ubWqaKCoiIiIiIipOsUmrq6srAgICsG/fPgDAvn37EBAQYHTXYEB3H+zx48chCAJUKhVOnjwJf3//0kdNVAIarRZf/HIFN6NTDMpb+rnh3S71StRNnYiIiIiIKpdRj7yZPXs2duzYga5du2LHjh2YM2cOAGDkyJG4dOkSACAsLAwdO3bE5s2bsWvXLnTs2BHHjh0DAPTo0QOurq7o3r07evfujbp16+Ktt96qoLdE9JQgCNjxx3VcuGn4iCW/Gk4Y2bMBxGImrERERERE5kwkCIJF3CjKe1rNm7n2k//l+B38cvyOQVl1NztMea85bK1lJorKtMy1rsgQ68lysK4sB+vKcrCuLAPryXKYe12V+Z5WIkv194XofAmrq6MVPnq76XObsBIRERERWRomrfRMOn/9MbYfjDQos7OW4qO3m8LZgc8IJiIiIiKyFExa6Zlz80EKvth7Bbk7vsukYnz4ViC8q9qZLjAiIiIiIioxJq30THkYn45Vu8OhUmv1ZSIR8H6vhqhbvYoJIyMiIiIiotJg0krPjKS0bCz//gLSs9QG5YO7+qFZPTcTRUVERERERGXBpJWeCRlZKiz//gISU7MNynu3r42XmlYzUVRERERERFRWTFrJ4qnUGqz+8RKiH6cblL/c1Bs9X6xlmqCIiIiIiKhcMGkli6bVCtj4awSu3082KG9WryoGvuYHkUhkmsCIiIiIiKhcMGkliyUIAr45dB1hkY8NyutWq4LRbzSEWMyElYiIiIjI0jFpJYv128l7OHIu2qDMy9UW499qArlMYqKoiIiIiIioPDFpJYt0/GIMfvzntkGZs4MVJr7dFPY2MhNFRURERERE5Y1JK1mci7fiseX3awZlNlZSfPR2IFyrWJsoKiIiIiIiqghMWsmi3H6Yis9/vgytIOjLpBIxxvdtjOpu9iaMjIiIiIiIKgKTVrIYjxIzsPKHcChVWn2ZCMDoNxrAz8fZdIEREREREVGFYdJKFiFFkY3l312AIlNlUP7ea/XRws/dRFEREREREVFFY9JKZi8zW40V34cjPiXLoDyoXU280ry6iaIiIiIiIqLKwKSVzJpKrcXaPZcQFacwKG/fxAtvdvA1UVRERERERFRZmLSS2dIKAjbtj8DVe0kG5U3quGJINz+IRCITRUZERERERJVFauoAiHLLUqpx4FQUjpyLznf/KgD4ejvig16NIBHzegsRERER0fOASSuZjSylGvO3ncXj5Eyo1Np8892cbfDhW01gJZeYIDoiIiIiIjIFNleR2ThwKqrQhBUAAn1d4WArr+SoiIiIiIjIlJi0ktk4ci660IQVAE5dja3EaIiIiIiIyBwwaSWzUdA9rAbzM4qeT0REREREzx4mrWQ27G1kRc+3LXo+ERERERE9e5i0ktno1KxaofNkUnGR84mIiIiI6NnEpJXMRmBd1wLLZVIx3Jxs0K21TyVHREREREREpsZH3pDZuHwnMV+Zg60MnZpVQ7fWPrCWc3clIiIiInreMAsgs3E28rHBdPDr/ugY6G2iaIiIiIiIyBywezCZhbikDNyPU+inRSKgad2qJoyIiIiIiIjMAZNWMgvnrscbTNev7gRHO7mJoiEiIiIiInPBpJXMwtnrcQbTzf3cTBQJERERERGZEyatZHJJadm4FZ1qUNaiPpNWIiIiIiJi0kpm4PwNwwGYans5wMXR2kTREBERERGROWHSSiaXd9Tg5mxlJSIiIiKiJ5i0kkkpMlWIjEo2KGvh526aYIiIiIiIyOwwaSWTunAjHlpB0E9Xc7ODp4utCSMiIiIiIiJzwqSVTOpspOGowRyAiYiIiIiIcmPSSiaTma3GlbuJBmW8n5WIiIiIiHJj0komc+l2AtSap12D3ZysUcPd3oQRERERERGRuWHSSiaTd9TgFvXdIRKJTBQNERERERGZIyatZBIqtQYXbyUYlDX3Y9dgIiIiIiIyxKSVTOLKnSRkqzT66Sr2cvh6O5owIiIiIiIiMkdMWskkzl43HDW4eX03iNk1mIiIiIiI8mDSSpVOrdHiwo14g7KWHDWYiIiIiIgKwKSVKt31+8lIz1Lrp+2spajv42S6gIiIiIiIyGwxaaVKl3fU4Gb13CARc1ckIiIiIqL8mClQpdIKAs5dN0xaOWowEREREREVhkkrVarb0alISVfqp63kEjSs5WzCiIiIiIiIyJwxaaVKlXfU4MA6rpBJJSaKhoiIiIiIzJ1RSeudO3fQv39/dO3aFf3798fdu3fzLXP8+HH06dMHjRo1QmhoaIHbuX37NgIDAwudT882QRDy3c/anKMGExERERFREYxKWkNCQjBgwAAcPHgQAwYMwKxZs/ItU6NGDSxYsADDhw8vcBsajQYhISHo0qVL2SImi3U/ToH4lCz9tFQiRmNfVxNGRERERERE5q7YpDUhIQEREREICgoCAAQFBSEiIgKJiYkGy9WsWRMBAQGQSqUFbufLL7/Eyy+/jFq1apU9arJIeVtZG9V2gY1VwfsLERERERERABSbMcTExMDDwwMSie6+Q4lEAnd3d8TExMDFxcWoF7l27RqOHz+Obdu24fPPPy9VoK6u9qVajyqPm5tDkfPDbycYTL/cskax61DF4OduGVhPloN1ZTlYV5aDdWUZWE+Ww5LrqsKbuVQqFWbOnIlFixbpE9/SSEhQQKsVyjEyKk9ubg54/Dit0PkxCemIevR0vlgkgq+HfZHrUMUorq7IPLCeLAfrynKwriwH68oysJ4sh7nXlVgsKrKRstik1cvLC7GxsdBoNJBIJNBoNIiLi4OXl5dRATx+/BhRUVEYNWoUACA1NRWCIEChUGDevHlGvg2ydHmfzepf0wn2NjITRUNERERERJai2KTV1dUVAQEB2LdvH3r16oV9+/YhICDA6K7B3t7eOHXqlH56zZo1yMjIwOTJk0sfNVmcvPeztuCowUREREREZASjRg+ePXs2duzYga5du2LHjh2YM2cOAGDkyJG4dOkSACAsLAwdO3bE5s2bsWvXLnTs2BHHjh2ruMjJYiSkZOFurq7BIgDNmLQSEREREZERjLqntU6dOvjhhx/ylW/cuFH/d8uWLXH06NFitzVu3LgShEfPgrxdg+tUqwIneysTRUNERERERJbEqJZWorI4mydpbc5WViIiIiIiMhKTVqpQKelK3LifbFDWwo9JKxERERERGYdJK1WoCzceI/eDinw87OHmZGOyeIiIiIiIyLIwaaUKlbdrMEcNJiIiIiKikmDSShUmI0uFq3eTDMqa+7mbKBoiIiIiIrJETFqpwoTfTIBG+7RzsKeLLbxdbU0YERERERERWRomrVRh8nUN9nODSCQyUTRERERERGSJmLRShchWanD5doJBGR91Q0REREREJcWklSrE5TsJUKq1+mkXRyvU8nQwYURERERERGSJmLRShcjbNbh5fXYNJiIiIiKikmPSSuVOrdEi/Ga8QRkfdUNERERERKXBpJXK3dV7ScjM1uinHW1lqFfdyXQBERERERGRxWLSSuXubKRh1+Bm9d0gFrNrMBERERERlRyTVipXWq2A8zfyPOqGXYOJiIiIiKiUmLRSubrxIBlpGSr9tI2VFP41nU0YERERERERWTImrVSu8nYNblrXFVIJdzMiIiIiIiodZhNUbgRBKOBRN+4mioaIiIiIiJ4FTFqp3Nx9lIaktGz9tFwqRiNfFxNGRERERERElo5JK5WbvF2DG/u6wkomMVE0RERERET0LGDSSuVCEAScjYwzKGvhx1GDiYiIiIiobJi0UrmIik1DbFKmfloiFqFJnaomjIiIiIiIiJ4FTFqpXJy4FGMw3aCWC2ytpSaKhoiIiIiInhVMWqlc/HfxocE0uwYTEREREVF5YNJKZRaXlIE7D1P10yIR0LQuuwYTEREREVHZMWmlMjt3Pd5gun51JzjayU0UDRERERERPUuYtFKZnb1uOGpwc3YNJiIiIiKicsKklcokKS0bt6JTDcpa1GfSSkRERERE5YNJK5XJ+RuPDaZreznAxdHaRNEQEREREdGzhkkrlcnZSMOktYWfu4kiISIiIiKiZxGTVio1RaYKkVHJBmXN2TWYiIiIiIjKEZNWKrULN+KhFQT9dDU3O3i62JowIiIiIiIietYwaaVSOxtpOGowB2AiIiIiIqLyxqSVSiUzW40rdxMNytg1mIiIiIiIyhuTViqVS7cToNY87Rrs6WqLGu72JoyIiIiIiIieRUxaqVTyjhrctrE3RCKRiaIhIiIiIqJnFZNWKjGVWoOLtxIMyto19jJRNERERERE9Cxj0kolduVOErJVGv20k70c9X2cTRgRERERERE9q5i0UomdvW44anDz+m4Qi9k1mIiIiIiIyh+TVioRtUaLCzfiDcr4qBsiIiIiIqooTFqpRK7fT0Z6llo/bWctRX0fJ9MFREREREREzzQmrVQieUcNblbPDRIxdyMiIiIiIqoYzDbIaFpBwLnrhklrcz92DSYiIiIioorDpJWMdjs6FSnpSv20lVyChrU4ajAREREREVUcJq1ktLyjBgfWcYVMKjFRNERERERE9Dxg0kpGEQQh3/2szTlqMBERERERVTAmrWSU+3EKxKdk6aelEjGa1HE1YURERERERPQ8YNJKRsnbytqotgus5VITRUNERERERM8LJq1klLyjBrfgqMFERERERFQJmLRSsWIS0hEdn66fFotECKxb1YQRERERERHR84JJKxUrbyurf00n2NvITBQNERERERE9T4xKWu/cuYP+/fuja9eu6N+/P+7evZtvmePHj6NPnz5o1KgRQkNDDeatW7cOPXr0QM+ePdGnTx8cO3asXIKnypH3ftYWHDWYiIiIiIgqiVEj6YSEhGDAgAHo1asXfvnlF8yaNQvbtm0zWKZGjRpYsGABDhw4AKVSaTCvSZMmGDZsGGxsbHDt2jUMHDgQx48fh7W1dfm9E6oQCSlZuPsoTT8tAtCMSSsREREREVWSYltaExISEBERgaCgIABAUFAQIiIikJiYaLBczZo1ERAQAKk0fx7coUMH2NjYAAD8/PwgCAKSk5PLIXyqaHm7BtepVgVO9lYmioaIiIiIiJ43xba0xsTEwMPDAxKJBAAgkUjg7u6OmJgYuLi4lPgFf/75Z/j4+MDT07NE67m62pf4tajsLt4xvDjxUovqcHNzKHDZwsrJ/LCuLAPryXKwriwH68pysK4sA+vJclhyXVXqgzZPnz6NVatW4euvvy7xugkJCmi1QgVERYVJSVci4naCQVl9b0c8fpyWb1k3N4cCy8n8sK4sA+vJcrCuLAfrynKwriwD68lymHtdicWiIhspi+0e7OXlhdjYWGg0GgCARqNBXFwcvLy8ShTI+fPnMWnSJKxbtw6+vr4lWpdM48KNx8h9mcDHwx5uTjYmi4eIiIiIiJ4/xSatrq6uCAgIwL59+wAA+/btQ0BAQIm6Bl+8eBEfffQRVq9ejYYNG5Y+WqpUZ69z1GAiIiIiIjItox55M3v2bOzYsQNdu3bFjh07MGfOHADAyJEjcenSJQBAWFgYOnbsiM2bN2PXrl3o2LGj/tE2c+bMQVZWFmbNmoVevXqhV69eiIyMrKC3ROUhI0uFq3eTDMqa+7mbKBoiIiIiInpeGXVPa506dfDDDz/kK9+4caP+75YtW+Lo0aMFrv/jjz+WMjwylfCbCdDkuofY08UW3q62JoyIiIiIiIieR0a1tNLzJ1/XYD83iEQiE0VDRERERETPKyatlE+2UoPLeUYNbs77WYmIiIiIyASYtFI+l+8kQKnW6qddHa1Qy9Nyn+tERERERESWi0kr5ZO3a3Dz+u7sGkxERERERCbBpJUMqDVahN+MNyhr4ceuwUREREREZBpMWsnA1XtJyMzW6KcdbWWoW62KCSMiIiIiIqLnGZNWMnA20rBrcLP6bhCL2TWYiIiIiIhMg0kr6Wm1As7fyPOoG44aTEREREREJsSklfRuPEhGWoZKP21jJYV/TWcTRkRERERERM87Jq2kl7drcNO6rpBKuIsQEREREZHpMCMhAIAgCAU+6oaIiIiIiMiUmLQSAODuozQkpWXrp+UyMRr5upgwIiIiIiIiIiat9ETersGNfV1hJZOYKBoiIiIiIiIdJq2k6xocGWdQxlGDiYiIiIjIHDBpJTyMT0dsUqZ+WiIWoUmdqiaMiIiIiIiISIdJK+UbgKlBLRfYWktNFA0REREREdFTTFop3/2sLfzYNZiIiIiIiMwDk9bnXFxSBu7HKfTTIhHQtC67BhMRERERkXlg0vqcO3c93mC6fnUnONrJTRQNERERERGRISatz7mz1w1HDW7OrsFERERERGRGmLQ+x5LSsnErOtWgjI+6ISIiIiIic8Kk9Tl2/obhAEy1vRzh4mhtomiIiIiIiIjyY9L6HOOowUREREREZO6YtD6nFJkqREYlG5Q1Z9dgIiIiIiIyM0xan1MXbsRDKwj66WpudvB0sTVhRERERERERPkxaX1OnY00HDWYAzAREREREZE5YtL6HMrMVuPK3USDMnYNJiIiIiIic8Sk9Tl06XYC1JqnXYPdnKxRw93ehBEREREREREVjEnrcyj/qMHuEIlEJoqGiIiIiIiocExanzMqtQYXbyUYlPF+ViIiIiIiMldMWp8zV+4kIVul0U872ctR29vRhBEREREREREVjknrc+bsdcNRg5vXd4OYXYOJiIiIiMhMMWl9jqg1Wly4EW9Qxq7BRERERERkzpi0Pkeu309GepZaP21nLUV9HyfTBURERERERFQMqakDoIqXpVTjwKko/H4yyqC8ia8rJGJetyAiIiIiIvPFpPUZl6VUY/62s3icnAmVRmswL/JBMrKUaljLuRsQEREREZF5YjPbM+7AqSjEJWVApdbmm5eWrsKBU1EFrEVERERERGQemLQ+ox4nZ+LAqSjs++8e1BqhwGVUGi3+Oh9dyZEREREREREZj/1CnyExCekIi3yMs5FxiIpVGLWOIkNVwVERERERERGVHpNWCyYIAh48TsfZyDicjXyM6Pj0Em/D3lZWAZERUXkTBAEKRQoyMxXQajWmDodyiYsTQ6vNfwsGmR/WleVgXVkG1pPlMJe6kkrlcHZ2g0RSsjSUSauFEQQBdx+lIexJohqXlFnqbcmkYnRqVq0coyOiipKU9BgikQguLh6QSKQQiUSmDomekErFUBcwbgCZH9aV5WBdWQbWk+Uwh7oSBAHp6alISnqMqlW9SrQuk1YLoBUE3IpOwdnIxzgb+RgJqVnFriMCUK+GEwLruOJo+EMkpmUbDMYkk4rh5mSDbq19KjByIiovSmUWPDyqQyTiUARERERkeUQiEezsHKFQJJd4XSatZkqj1eJ6VDLCrj/GueuPkaJQFruOWCRCQE0ntPBzR7P6bqhiJwcAdGpeDQdOReGv89FQZKhgbytDp2bV0K21Dx93Q2QxBCasREREZNFK21OMGYsZUWu0uHovCWHX4nD+RjwUmcUPkiSViNCglgta+rmjab2qsLfJf4+qtVyK3h180buDb0WETUREREREVGGYtJqYUqXBlTuJCIt8jAs345GZrS52HblUjMa+rmjh54bAulVhY8VqJKL8spRqHDgVhSPnoqHIVMHeRoZXmrOXBREREVkWnrWYQJZSjUu3ExF2LQ4XbyUgW1X8SKBWcgkC67iipZ87Gvu6wkouqYRIichSZSnVmL/tLB4nZ+rvZ1dkqvD7qSiERT7GjMEtypy4btq0AYMHD4NMVjmjkKtUKkyd+jEeP45DixatMH78x5XyuiXx1ls9sWTJCvj61jV6nb17f8KPP34HQRAgEokwYMBgdO3aHQCg0WiwcuVnOHXqP4hEIgwcGIyePXsDABYsmA1//wD07du/yO3HxDzEO++8idq160AQtLC2tsEnn0zBf/8dx19/HQYAREffh5OTC+zs7AAAoaHL4eHhafR7OHr0b1StWhUNGjQyep2CHDv2NzZv/goqlRKCAPTo8QbefXegfv6WLV/ht99+BQB0794TwcEjip23adMGZGZmYuzYCcW+fvv2LVGnjq7usrOV8PPzR3DwCNSu/bSnkkajQd++QfD3D8DixctL9T7T0tKwd+8evPfekFKtn9vYsaMQGxurr7t+/d5Bjx5vAACiou5hwYLZSElJQZUqVTBjxhzUqKEby6J9+5b444+jsLW1Nep15syZgXPnwpCQEJ9vvcuXL2Hp0oXIzs6Gl5cXZs2aB2dnlyLnxcQ8xIgRg7B//+FiX3vBgtkICzuNKlWcAAC2trZo3bptqfbfhQvnICLiCnx962Du3EVGvfeSGjt2FN59dxBefLFDidbLe0w9ffokNmxYh9u3b6Jv3/4G+7Cx3/+SvmZpLVsWirNnT0Mmk8PW1gYffvgJ/P0bACj5cbGofVOpVGLKlI8RGRkBAEbtPwBw7lwYJk36EDVq1AQAyGQybNy41ah1cyvJ8SRHVNQ9LF26EAkJ8ZBIJAgIaIiPP54MKytrAMDx40fx+eeroNFo4OcXgGnTQmBtbY1z58Kwbt0qbNq0vcRxGivv9zA4eAA2bPhaH1tuOfVYq5YvZs6cjNu3b0Eut4KzszMmTZqGatWqAyj5scVYTForSUaWGuE34xEWGYfLdxINBkUqjK2VFM3qVUULP3c0rO0MmZSJKhEBV+8lYccfkYhJyCjxuiq1Fg/j0/G/5UcLXcbL1RYDX/NDQE3nIre1efNGvPvuoAJPdtRqNaTS8v2JuX49Eo8ePcKOHd+XaL2KiKU8t1u9eg2sWbMBjo5VEBcXi6FDB6BJk6bw8vLGH3/8jujo+9i16yekpKRg2LD30LLlC/Dy8i7Ra9jb22PLlm8AAN9//y0WLZqLr7/eiSFDhgMo/Ul2jmPH/oa/f0CZk1YXl6pYsmQFqlZ1g0KhwPDhA9GgQUMEBjbDhQvn8Ndfh7B9+3cAgFGjgtG0aXM0bdq8yHkltX7917C1tYVWq8XevXvwwQfD8fXXO+DtrRtt/9SpE6ha1Q0XL4YjMTEBLi6uJX4NhSIN33yzrVySVgCYMOGTAuvus88WoU+ffujatTsOHvwNS5cuxOrVX5TqNYKCemH8+Ino2fM1g3KtVot582Zi2rTZCAxsii1bvsL69WswbVpIkfNKauDAIfkStJLuv4mJCfj77yM4cOAviMXmN0ZA3mOqt3c1TJkyA3/9dRhKZfFjm5THa5ZWmzbt8OGHH0MqleLff49h1qyp+P77X8opSh21Wg2xWIx33x0IJycnTJjwvxKtX6uWb4UmgIWRyWQYN+4j1K/vD61Wi9mzp+Pbb3cgOHgEMjIysGTJAqxbtxE1avhg8eJ5+Pbb7Rg6dGSlxwlA/ztRnNdfD0K7dh0gFovx44/fYcmSBVi1an2FxsaktQIpMlU4f/0xwiIfI+JuIjRaodh1HGxlaFbPDS393eDv4wypxPwOqkRkWtsOXENsGR53VZyYhAxsO3ANi0a3LXSZZctCAQAffDAMIpEYa9ZswOrVyyCRSBAVdQ8ZGRnYsuUbzJkzA1FR96BSKVGtWg1MnToLjo6OOHcuDKtXL0eDBg1x5colACLMmbMQtWrVRlTUXSxYMAdZWVnQajV4/fWeaN++A+bOnYH4+McIDh6AQYOC0bZte6xcuRRXr14BAHTr1kOfBIwdOwr16vnhypVLcHR0xCuvvIo//zwAe3sH3Lp1A25u7pgwYRLWrVuJBw8eICCgAWbNmgeRSIT0dAXWrFmBW7duQKlUolmzlhg37iNIJJJ82/3ss9WFfkbffrsDJ0/+iwULluK773bi7t07SElJRnz8Y9Su7YupU0Ngb2+P5s1b6tdxd/eAq2tVxMXFwcvLG0eO/ImePXtDLBbD2dkZHTq8hL/+OoQBAwYbvNa5c2FYteozhITML7Y1o1Wr1vjyy3VFLlOYS5fCsWLFEmi1AtRqNYYMGQZHxyo4fvwowsJO49dff0H//gPw+utB+P33fdiz5wdoNBrY29vjk0+mwMenFn777Vf88cfvsLKyQnT0A7i4uGLmzLlwc3NHw4ZPk157e3vUrFkbjx7FIDCwGQ4f/hNdu/bQtwB07doDhw//iaZNmxc5L7ebN28gJGQ6JkyYhGbNWhT5XsViMXr3fgvnz5/FTz/txpgxHwIA9u//Bb1798Hly5dw4MD+fHWRm1arxfLlS3Du3Bl9C9T69V9j+fJQKBQKBAcPgLW1Nb744mvEx8dj5coliI19hOzsbHTp0hWDBw8DoGvh6Nz5NZw5cwrp6Qq8/fa7xbawJSUl4vr1a1ixQlfXXbp0xYoVS5CUlARn56cXpLRaLdauXYGEhARMnz4bcrm8wO21aNGqwPLIyKuQy+UIDGwKAOjd+y3069cT06aFFDkvN6VSifnzQ+Dm5o6xYydUyCO9MjLSMX78+8jOzsKwYQPx+us90L//e9ixYwv++ecINBoNqlZ1x+TJ0+HqWhW9e7+OzZt3wtnZBZ98Mh4ikQhLl65CUlIihg0biJ9++g3Hjv2NjRvXQyyWQKNR46OPPtV/ny9cOIcdO7YgPj4er7zSBR98MA4A8ODBfSxduhDJyUmQSCQYNWoM2rRpV+AxtXr1GgB0PRmKYsz3/+uvv8ShQwchl1tBJAJWr96AL7/8PN9risWiYo9/ly+HIzU1Fa+88irGjNG9r9wXDBo1aozHj+Og1WrzXRzIfVy0t7cvtt5y9v1z587A17cupk6dhVatWiMm5mGByxd23KkIt27dxNy5MzBhwiR4enphxIhB6NYtCGFhpyAIAj7+eAoCA5vBy8tbf6FRLBYjIKAh7t27AwA4efI/+PsH6HtA9O7dF/Pnz86XtKalpWH69El48cUO6N//vQLjeeedNzFvXijq1asPAPjxx+8QGXkN06aFYO3alQgPPwelUgUnJydMnToLnp75HzmTu5U0PPw8li1bDABo2rQ5BEHQv4f27V/Sr9OwYRN8//23+bZl7LHFWExay6Cg+8XaNfKEi4MVwm8lIDIqGVqh+ETVyV6OFvXd0cLPDfVrOEEs5vMXici8ffzxZPz00w/6VqkcN25cx9q1X8LGxgYA8OGHn8DJyQkA8OWXn2Pnzq36k7c7d25h2rRZ+PTT6di6dRO2bt2EkJD52LNnN9q374hBg4YCAFJTU+Ho6IjJk2cYdJX6/PPV0Gq12LbtO2RkpGP06GHw9a2Ltm1fBAA8fPgAn3/+FaRSKX777VdcvRqBbdt2wd3dA59+OgFz5szAmjUbYGNjg+HDByIs7DRatWqNNWtWoGnT5pgyZSa0Wi3mzJmB/fv34o033sy33YJotQJWrlyKlJQUfPbZan0LxsWL57F58zdwcXHFwoVzsGXLV/m6mJ07FwaFQgF/f38AQGzsI4MTCw8PT8TFxRqs88cfv+P777/FZ5+thpube7F199dfh1Cvnl+xyxVk586tePfdQXj11W4QBAEKhQIODg5o376jQVfF8PDzOHLkT6xbtxFyuRwnTvyLRYvmYv36r598FuHYsmUnfHxq4euvv8SqVZ9h/vwlBq91795dRERcwqefTtN/FrkTTQ8PT4SHnyt2Xo4zZ05h7doVmD17oUF33+I0aNAIZ86cAgAkJyfj7NkwTJ8+Gz4+tbBkyYIik9abN6/j/Pkw7NjxA8RiMVJTUwEAEydOxogRgwxaNebPn4Xg4BFo2rQ5VCoVPvzwAwQENECrVm0A6JLQr7/egcTEBAwd+h4CA5ujbt16AIDPP1+FDRvWom7d+vjgg3Fwc3NHbGwsqlZ1h0Si66UlkUhQtaob4uJi9UmrUqnEwoWz4eVVDbNnLyhVsph3H3VycoJWq0VqakqR83KkpqZg2rRJeOmlV9Cv3zuFvs6OHVvx66+6lrtOnTrrW1mNZWtrh6VLVxl87gcP/obo6Ghs2LAFYrEYP/20G2vXrkRIyHw0b94SZ8+ewcsvd0ZMzEOIRCKo1WqEhZ1G8+a6fe2rrzbg00+no1GjJtBoNMjKenoxMTb2Edat24iMjAz0798LQUG9UKOGD+bMmYFevd5EUFBv3LlzG2PHjsSOHbsLPaYWx5jvf2pqCr7//hv88ssBWFlZIyMjHXK5VYGvuXjxvCKPf3fv3sb69V9DqVTi/feHIjAwEG3atDd4vR9//B5t27Y3SFgLOy4aIz09HRs3bit2ueKOO/fvR2HYsPcgkUjRp08/vP56kNEx5HXmzCmsWbMcc+YsQu3avoiJeYiUlBTUrVsP48Z9hHPnwjB79nR8993PBsladnYW9u/fi/ffHwNAt594eBR9jH/0KAbTpk3CoEHB6NSpS6ExdevWAwcO7EO9ehMBAPv3/4rx43V/DxwYjAkTJkKt1uLXX3/G+vWrMWdO4d3jlUolQkKmYdaseWjevCUOH/4Te/b8UOCye/Z8j/btO+Zbv6zHlryMSlrv3LmDKVOmIDk5GU5OTggNDUWtWrUMljl+/DiWL1+O69evY9CgQZg8ebJ+nkajwfz583Hs2DGIRCKMGjUK/fr1K3PwplTY/WJ/nLlv1PqujtZo4eeGlv7u8PV2hLgCrioS0bNpcDf/UncPNkZO9+DSePnlzvqEFQAOHNiHP/44ALVahczMLP3VZADw8amJ+vV1yVnDho3x77/HAABNmzbD55+vRlZWFpo3b2nQEplbWNhpfPjhJ0+e+2aPLl1eQ1jYaX3S+uqr3QwSyyZNAuHu7gEAqFfPD56eXnBwcAAA1K1bD9HR99GqVWscP34UV69ewa5dOwEAWVlZ+vUK2m5eixbNRePGTfQttznateug70oaFNQLK1cuNVjvzp3bmD8/BCEhCwq8n6gg+/f/CisrK6xa9Tns7Apvtchp1RMEAd7e1TB9+myjtp9X8+YtsXXr14iOfoBWrdoYtIzm9u+/R3Hz5g2MGhUMQPdA+bS0VP38Jk0C9a0fPXv2xuDBhslKfHw8pkyZiIkTp6BqVbdSxZrbmTMncerUf1i9ej2cnUvWnVfIdfH54MH9ePHFDrC1tUOTJk2hVmtw+fJFNGrUpMB1vb2rQ61WY/Fi3Ylfu3YFd1/NzMzE+fNnkZycrC/LyEjH3bt39UlrUFAvAICLiyvatWuP8+fPom7depg5cy48PDyh0WiwfftmzJo1FevXbzLqvX388Th07vwaBgwYZNTy5U2pVOJ//xuBYcNG45VXCj8ZBwruHlxWx48fxbVrVzFsmO6+aY1GrW/9a9GiFcLCTj/pAdAYgiDgypXLT5LWVk+WaYnVq5fj5ZdfQZs27QxaOTt16gyxWKzvMRAd/QCurq64efM6unfX3XNcu7Yv6tbV9dzIe9JvDGO//3Z29qhWrQbmzQvBCy+0Qbt2un24sM+kqOPf668HQSqVQiqVonPn1xAWdsYgaT106CD+/PMA1q3baLDdwo6LxujWrYdRyxV13PHz88eePfthb2+Phw+jMWHC/1C1qhtatWpdoliAp8eTFSvWGRyfZDKZfjyC5s1bwsrKClFR9/QXl9RqNUJCpqFFi5YGLZVFSUiIx7hx72PGjDn6HguF6dYtCKNHD8EHH4zHvXt3oVCkITCwGQDg5Ml/8dNPPyAjIwMaTfFj6URF3YO1tbX+97dz51exdOmCfMvt3LkVd+/eyXfbQUUcW4xKWkNCQjBgwAD06tULv/zyC2bNmoVt2wyveNSoUQMLFizAgQMH8vW7//XXXxEVFYU//vgDycnJ6N27N9q2bYvq1auX2xupbAdORRkkrMbwcLFFSz83tPBzQ00Phwrp/kJEz76Ams5YMLJNkcv8fOw2fj8VVeAxSiYV4/XWPhXyGCxb26cJa3j4efz8849Yv/5rODs7448/DmDv3j36+XK5lf5vsVis/yF9+eXOaNSoCU6fPokdO7Zg//69mDVrXoljsbExbK3IfbVbLBbneX1Jrh9yAQsXfqYfVKKw7Z46dQLr16+BSKRLZHNa3Jo2bfYkAUnSD0ZTnPv3ozBp0oeYNGmawYmJh4cnHj2KQUBAQwD5W7Xq1q2H8PDzuHv3rj6BXLYsFJcuhQMA5s5dCJlMbnBPa1m8/fYAvPhiR5w5cworVy5Bq1ZtMGpU/vvKcgZRGjHi/RK/RlJSIiZM+B/ee2+wQSKT81nkiI19BHd3z2LnAUCNGj64c+c2rl6N0CeOW7du0g/kM378xEIvjly9GgFf3zoAgN9++xVJSUl4662eAHQXA/bv31to0mpvb4/t27/H+fNnERZ2GuvXr8HXX+/It5wgaCESifDVV9tKfJ90zoBDEokEb7/9LjZv3gitVgsPDw/Ex8dBo9FAItHt3/Hxjw0SkGbNWuDUqRPo06cfrK2Nu1BS0Ovn/uyTk5MhFovh6FilyHnp6emQSmVo0KAR/v33H7z0UidIJBLcunUT8+bNgkiki68iB1wTBAFDhgzTXxDIrUWLVtiy5Su4ubmjRYtWEAQBZ8+extmzZzB06CgAwPjxH+PWrZs4e/YMZs6cgv7939O3SBZ2fCtPBX3/CyKRSLBhw2ZcuhSOc+fCMHz4QCxbtkafTBkq+vhXlH/++Qtffvk5Vq1an+9e79IcF3Pk/l0pSlHHndxJvbd3NXTo8DIuXQrPl7Tu378XP/ywCwAwYMAgvPba6/m2lXM8uXYtwujkU6PRYO7cmXBwcMSECZP05R4enjh/Pkw/rTt2Pf2OOjg4wN3dEydP/lts0urp6Ylaterg5Mn/cP78WXTv3hMikQiPHsVgzZrl2Lx5B9zdvXDpUjjmzJlhVNyGDPOW3bt34c8/D2L16vX5jh/lcWzJq9gbJhMSEhAREYGgIF0TelBQECIiIpCYmGiwXM2aNREQEFDgwfa3335Dv379IBaL4eLigi5duuDAgQPl8gZM5ci5aKMS1mpudnjjxVqYO/wFLBzZGn1fqoNano5MWImoQnVr7QM3JxvIpIaHeZlUDDcnG3Rr7VPImsaztbVDerqi0PlpaWmws7NHlSpVoFQqsX//XqO2++DBfbi4uKJ7954YOnQkIiKuFLhcy5YvYP/+XyAIAjIy0nH48B+lumqe14svdsSOHVv1J5nJycl4+DA633KtW7fFli3fYPv2XQZdRHv0eAPvvDMQH374AeLjH+vL//vvOJKSkgDokp+c1pro6AeYOHEcJkz4RN9KnKNTpy749defodVqkZSUhGPH/sHLL3fWz/fz88eCBUsxd+4MnD9/FoCu6/aWLd9gy5Zvyv1erqioe6hWrTp69+6Lfv3e1d9PbGdnB4Xi6b7w4osdcODAfn03N41Gg2vXrurnX7oUjvv3owDoThJbtNAljCkpyZgwYQz69n0bQUG983wWnXHw4H5kZ2chOzsLBw/u1ye1Rc0DAE9Pbyxfvhbr16/B4cN/ANAN4pPzORWUsOoGYvoJp06dQO/eb+Hq1StIS0vDL78cwO7dv2L37l+xfft3+OuvQ8jKyirw80pKSkJWVhZat26L998fq2/lsbOzQ1ZWFtRq3WPubG3tEBjYDDt2bNGvGxv7CAkJ8frp33/fp9/miRP/onnzllCr1UhMTNAv8+efB+HrW+fJPdAuqFu3Pg4dOghA1wJWr56fwf2sw4aNQqtWL2DixLFFfpeL4ucXgOzsbISHXwAA/Pzzbn0XxqLmAYBYLMLUqbNga2uPkJCpUKvVqFOnrv57VdEjhLdv3xE//bRb321bqVTixo3rAABPTy+IxWIcOLAfLVq8gJYtX8Dvv++DVCqFp6fuQkFU1F3UqVMXb7/9Ll577XVcvRpR5OvZ2tqhbt36+rq8e/cObt26joYNG+vnl6QeCvr+FyQjIx3Jyclo1qwFhg8fDV/fOrh9+1aBr1nc8e/gwd+hVquRmZmJI0cOoWVL3XHs33+PYe3aFVi+fG2BA8UVdlwsT0Udd+Lj4/W9JlJTU3DmzEn9vZ9548w5LhSUsAJPjydffLFOfzwBdCPc//mnLr8JDz+P7Oxs1KxZC1qtFgsXzoZYLMaUKTMNcoA2bdri6tUI/fHw559/NDh2yeVWWLx4Ge7evY2VKz8z6PlRkO7dg7Bv3884dOggunXT5W45F4hcXFyh1Wrx888/Fv1BQtcTSvfdPQ9Ad1uJQpGmn//zzz9i796fsGLFOjg6Vsm3fnkcW/Iq9nJeTEwMPDw8DO6JcHd3R0xMDFxcjLtSEhMTA2/vpzuwl5cXHj16VKJAXV2Lv1m7MqVnqopd5ospnVHNzbzirkhubg6mDoGMxLqyDLnrKS5ODKnU+IHZ7KVyzB7WCr+duIfDZx9AkaGCva0MnVtUR/e2NcvlOa0DBuhOQKysrPD55xshEokgFov0cbZv/yL+/PN3DBjQB1WqOKFp0+aIiLgCqVQMiUQMkQj6ZXNP//33IRw8+PuTe55EmDhxUoHrjBgxCp99FoohQ3RdS19/PQjt2+u6qYlEIkgkT2MRi0UQiQync8eaO/aJEydh7dpVGDp0AEQiEWQyGSZM+AQ+PjXybTdH7mmJRIzu3XvA2toaH374AVasWAuxWISmTZthzpxpePz4MWrVqo0JEyZCKhVjw4a1SE1NwaZNG7Bp0wYAwJgx49GmTTv06BGEq1ev4N13+wAAhg8fCR+fGgYx+/v7YdmyVfjkkwn45JPJaNOmnUFsEokYQP6YcyvsfeW1Z893OHs2DDKZDDKZDB9/PBlSqRjduwdh3rwQ/P33Ybz77kB07x6E998fgylTPoZWq4FKpULnzq+iUaOGEItFaNKkKT7/fBXu34+Cq6srQkLmQyoV45tvtuLBgyjs3btH3yrfv/+7CArqhRdeeAEvv/wKBg3q/6S+e6BVK90Jc1Hzcuq6WjVvrF37BT78cAxUKiWCgt4o8D1+8IFu8COlUvfIm40bN8PHpwZCQ7fjtde6QSZ7OpK/l5cn/Pz88c8/h9GjR89820pIiMOiRfOg0Wig0WjQtu2LCAwMhFgsRteu3TFkyDtwdHTExo1bMHfuAqxcuUy/P9va2mL69BBIpbr7FJ2dnTF8+ECkpyswZMhQ+PnVR2ZmJj79dAJUKt05iZubG+bPX6yvxylTpmPu3BBs2fIVHBwcERIy16COpVIxgoOHwcbGBh99NAYrVqxFlSr5T0ABYPLkj/UXkN57ry98fetg1arPAYgxe/Z8hIYuePJYG2/Mnj3/yesUPi9nv5TJJJg8eSpWr16BadM+waJFS2FlZaWPD0C+Y0texu6/eb8LQUE9kZaWgnHjdC2ngiCgT59+CAjQ3bbQqtULCA+/AE9PXR1YWVmhadNm+vU3bFiH+/ejIJFIYG/vgOnTZ0EqFeeLJ/f03LkLEBq6AN9//w0kEilCQubBzU3XKpn3mHrr1k3MnDkV6enpAAQcPvwHpk+fhTZt2hn9/QeArKwMTJs2CdnZWRAEAX5+/ujcuTOkUnG+1yzu+Fe7di3873/DkZqags6dX9V3a160aA5kMhlmznx6e+DatV/oH1NU0HExd26Ql1QqNvh9yF23Q4cORFxcHNLS0vDmm93Rpk07TJ8+Cy1btiz0uHPs2BHs2bMbUqkUGo0G3bsHoVOnV4rcXwqS+3iybt3T40nz5i1RpYoTbt26of8Oz5u3EDY2Vvj332M4ePB31KlTFyNG6LrLNmkSiEmTpsLR0QFTp87A5MkfQavVoH59fwwaNMTgN8/GxgqLFi3F7NkzsHTpQkyZMr3Q0a87d+6M5cuXICCgAapX132+fn710bnzq3j33bfg5OSEdu1eRHj4eYPvYd7jgq2tNebNW4ilS3UDMTVr1hyenp6QSMTIzs7EsmWL4enphYkTdffmymRyfP31NoNtFHVsEYvFJT4XFQnFpOyXL1/G5MmTsX//fn1Z9+7dsXTpUjRs2DDf8mvWrEFGRobBPa09e/bEggUL0KSJrvvMxo0bERsbixkzjG+aTkhQQGvE6LuVZfyqY1AUkbg62MqwanzpHh1gidzcHPD4cVrxC5LJsa4sQ956evToHjw9a5owIiqMVCqGupieN6V5tt+z6rfffsV//x3LN/BSZTCmrsxRaZ7/a+ksta6eZQU9Woj1pFOS5w6bijnVVUHnNGKxqMhGymIv23t5eSE2NlbfTUCj0TwZij//MMlFbePhw6dDU8fExOi7VliqV5pXy9ftLodMKkanZtUqOSIiIiIiIqJnT7H9w1xdXREQEIB9+/ahV69e2LdvHwICAozuGgwA3bp1ww8//IDXXnsNycnJOHToEHbu3FmmwE2tW2sfhEU+zjcYU3neL0ZERM+G4cNHmzqEEhk+fFC+gWMaNmyESZOmlXnb3bv3RPfu+bvSWrJff/0ZP/74fb7y6dNDSv14odx27/61zNswxtKlC3HlymWDMolEon/MlKWoyP3XnJ04cRwbNnyer3z06P+hbdv2BaxRMmvXflnmbeTYvHkj/vnnr3zlK1asLfFATebAy8u70lpZn9f9u9juwQBw69YtTJkyRf+svNDQUPj6+mLkyJEYP348GjdujLCwMEycOBEKhQKCIMDBwQELFixAhw4dnoyYNRf//vsvAGDkyJHo379kw5abW/dg4OlzWv86H62/X6xTs2ro1tqnXO4XsyTscmo5WFeWgd2DLYc5dbmiorGuLAfryjKwniyHOdVVaboHG5W0mgNzTFrpKSZCloN1ZRnyJ61R8PCoDpHI+MGYqHKY04kAFY11ZTlYV5aB9WQ5zKWuBEFAbGxU+d/TSkREpieXWyM5OR5qtarYIe+JiIiIzI0gCEhPT4VUKi9+4Tyerz6sREQWytnZDQpFChITY6HVlv9D6qn0xGIxtFrTX72m4rGuLAfryjKwniyHudSVVCqHs7NbydergFiIiKiciUQiODg4wcHBydShUB7scm85WFeWg3VlGVhPlsPS64rdg4mIiIiIiMhsMWklIiIiIiIis2Ux3YPFYpGpQ6BisI4sB+vKMrCeLAfrynKwriwH68oysJ4shznXVXGxWcwjb4iIiIiIiOj5w+7BREREREREZLaYtBIREREREZHZYtJKREREREREZotJKxEREREREZktJq1ERERERERktpi0EhERERERkdli0kpERERERERmi0krERERERERmS0mrURERERERGS2pKYOgMxbUlISPv30U0RFRUEul6NmzZqYO3cuXFxc4Ofnh/r160Ms1l37WLJkCfz8/AAAR44cwZIlS6DRaNCwYUMsWrQINjY2pnwrz4VXXnkFcrkcVlZWAIBPPvkEHTp0wIULFzBr1ixkZ2ejWrVqWLp0KVxdXQGgyHlUMR48eIAxY8bop9PS0qBQKHD69OlC6xBgXVWG0NBQHDx4ENHR0fj1119Rv359AMCdO3cwZcoUJCcnw8nJCaGhoahVq1aZ5lHZFFRXRf1mAeDvlokU9r0q7fGOx8KKU1BdFfWbBZS+Hqn0ijrWlfa7Y/Z1JRAVISkpSTh58qR+evHixcLUqVMFQRCE+vXrCwqFIt86CoVCaNeunXDnzh1BEARh2rRpwpo1ayol3uddp06dhMjISIMyjUYjdOnSRThz5owgCIKwbt06YcqUKcXOo8ozf/58Yc6cOYIgFFyHgsC6qixnzpwRHj58mK8eBg0aJPz888+CIAjCzz//LAwaNKjM86hsCqqron6zBIG/W6ZS2PeqNMc7HgsrVmF1lVvu3yxB4O+WKRR2rCvtd8cS6ordg6lITk5OaN26tX66adOmePjwYZHrHD16FI0aNdK3Jrzzzjv4/fffKzJMKsLly5dhZWWFli1bAtDVx4EDB4qdR5VDqVTi119/Rd++fYtcjnVVOVq2bAkvLy+DsoSEBERERCAoKAgAEBQUhIiICCQmJpZ6HpVdQXVVmt8sgL9bFa2guioKf7dMp7i6MvY3C2BdVaTCjnWl/e5YQl2xezAZTavV4ttvv8Urr7yiLxs0aBA0Gg06duyIcePGQS6XIyYmBt7e3vplvL29ERMTY4qQn0uffPIJBEFAixYtMHHixHz14eLiAq1Wi+Tk5CLnOTk5mSD658+RI0fg4eGBhg0b6svy1qGjoyPryoRiYmLg4eEBiUQCAJBIJHB3d0dMTAwEQSjVvJzuqlRxCvrNAvi7ZW5KerzjsdC0CvrNAvi7ZUq5j3Wl/e5YQl2xpZWMNm/ePNja2mLgwIEAgL///ht79uzBzp07cfPmTaxbt87EEdLOnTuxd+9e/PjjjxAEAXPnzjV1SFSMH3/80eCKNeuQqHzk/c0C+Ltlbni8szx5f7MA1qOpFXSsexYxaSWjhIaG4t69e1i5cqV+AIuc7iP29vbo168fzp07py/P3R3r4cOHJeoWRKWX8znL5XIMGDAA586dy1cfiYmJEIvFcHJyKnIeVbzY2FicOXMGPXv21JcVVIc55awr0/Dy8kJsbCw0Gg0AQKPRIC4uDl5eXqWeRxWroN8sgL9b5qY0xzseC02noN8sgL9bppT3WFfa744l1BWTVirW8uXLcfnyZaxbtw5yuRwAkJKSgqysLACAWq3GwYMHERAQAADo0KEDLl26hLt37wIAdu3ahddff90ksT9PMjIykJaWBgAQBAG//fYbAgIC0KhRI2RlZSEsLAyArj66desGAEXOo4r3008/4aWXXoKzszOAwusQYF2ZkqurKwICArBv3z4AwL59+xAQEAAXF5dSz6OKU9BvFsDfLXNT2uMdj4Wmk/c3C+DvlikVdKwr7XfHEupKJAiCYOogyHzduHEDQUFBqFWrFqytrQEA1atXx4gRIzBr1iyIRCKo1Wo0a9YM06ZNg52dHQDg0KFDWLp0KbRaLQICArB48WLY2tqa8q088+7fv49x48ZBo9FAq9WiTp06mDFjBtzd3XHu3DmEhIQYDGNetWpVAChyHlWsrl27Yvr06ejYsSOAousQYF1Vhvnz5+OPP/5AfHw8nJ2d4eTkhP379+PWrVuYMmUKUlNT4ejoiNDQUPj6+gJAqedR2RRUVytXrizwN2vdunU4f/48f7dMpKC6+uKLL0p9vOOxsOIUdgwE8v9mAfzdMpXCzs/XrVtX6u+OudcVk1YiIiIiIiIyW+weTERERERERGaLSSsRERERERGZLSatREREREREZLaYtBIREREREZHZYtJKREREREREZotJKxEREREREZktJq1ERERERERktpi0EhERmciaNWvwySefmDoMIiIis8aklYiIiIiIiMyWSBAEwdRBEBERPeu+/PJLbN++HQqFAu7u7pg6dSrGjh0LQRAgl8tRo0YN7N27F2lpaVi0aBGOHj0KkUiEPn36YPz48ZBIJNizZw++//57NGjQAL/88gvc3NwQEhKCtm3bmvrtERERVRipqQMgIiJ61t2+fRs7d+7E7t274eHhgQcPHkCr1WL06NG4d+8ePvvsM/2yU6ZMgaurK/744w9kZmZi9OjR8PLywjvvvAMAuHjxIrp164aTJ0/izz//xNixY3H48GE4OTmZ6N0RERFVLHYPJiIiqmASiQRKpRK3bt2CSqVC9erV4ePjk2+5+Ph4/PPPP5g2bRpsbW3h6uqK4OBg7N+/X7+Mi4sLhgwZAplMhu7du6N27dr4+++/K/HdEBERVS62tBIREVWwmjVrYtq0aVizZg1u3ryJ9u3bY8qUKfmWe/jwIdRqNdq3b68v02q18PLy0k97eHhAJBLpp729vREXF1exb4CIiMiEmLQSERFVgp49e6Jnz55QKBSYNWsWPvvsM9SsWdNgGU9PT8jlcpw8eRJSacE/0bGxsRAEQZ+4xsTE4JVXXqnw+ImIiEyF3YOJiIgq2O3bt3HixAkolUrI5XJYWVlBLBbD1dUV0dHR0Gq1AAB3d3e8+OKLWLx4MRQKBbRaLaKionD69Gn9thITE7Ft2zaoVCr8/vvvuHXrFl566SVTvTUiIqIKx5ZWIiKiCqZUKrFs2TLcunULMpkMzZo1w9y5cyGXy7F37160bt0a1atXx08//YQlS5bgs88+Q/fu3ZGeno4aNWpg5MiR+m01adIE9+7dQ5s2bVC1alWsXr0azs7OJnx3REREFYuPvCEiIrIQe/bswQ8//IBvv/3W1KEQERFVGnYPJiIiIiIiIrPFpJWIiIiIiIjMFrsHExERERERkdliSysRERERERGZLSatREREREREZLaYtBIREREREZHZYtJKREREREREZotJKxEREREREZmt/wN8XPtxoo1nogAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step5k_20k-FT_fewshot1k_step2k_lr1e5 19\\n\",\n      \"kp20k_valid2k_test 19\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-DA_step75k_100k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"336    100\\n\",\n       \"337    200\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step75k_100k-FT_fewshot1k_step2k_lr1e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_100-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step75k_100k-FT_fewshot1k_step2k_lr1e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_200-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"376     50\\n\",\n       \"377    100\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_50-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_100-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"398     150\\n\",\n       \"399     200\\n\",\n       \"400     250\\n\",\n       \"401     300\\n\",\n       \"402     350\\n\",\n       \"403     400\\n\",\n       \"404     450\\n\",\n       \"405     500\\n\",\n       \"406     550\\n\",\n       \"407     600\\n\",\n       \"408     650\\n\",\n       \"409     700\\n\",\n       \"410     750\\n\",\n       \"411     800\\n\",\n       \"412     850\\n\",\n       \"413     900\\n\",\n       \"414     950\\n\",\n       \"415    1000\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     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\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_300-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_350-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_400-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_450-data_kp20k_valid2k_test.pred\\n\",\n      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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"416    50\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_50-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"436    50\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_50-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"457    100\\n\",\n       \"458    150\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_100-data_kp20k_valid2k_test.pred\\n\",\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_150-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"496    100\\n\",\n       \"Name: step, dtype: int64\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_100-data_kp20k_valid2k_test.pred\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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lx5t3fP//8g+3bt8HHxwdjxozFuXPn8Oab/nBzc8OCBQuRpMhCVHwaUtNVeBKjgJ21DLVsDHvg3nxzPObNmwsHBwdMmzbd4Ll68OABQkNv4rvvvoNSmYUBA16CnZ0tfH19Aeh6dG/cuAFA1+O0cuVyfPyxrnfL1tYO0dHRAACFIg329vaFtgG6Irtjp67wqN8cj56m4qttG/Hg3h1MmhgAQDc8OOfgQlkVlq+2bdsiJOQiZDIZLCwscO7cP2jTpg3at++AkJAQzJ49C8891wNDhgyBo6MTnJ2dAQCpqSm4fT8CTi7uEIklcK/tCYVCgeQ0JZQqbYk9VvlfC3nl3Pf777+Pbt26Y8iQIQbr79y5jSlTJgMAkpISkZCQCADw9fWFRCKBp6cn4uMTULt2bZw8eRJJSUl46aVB+tt/9dWXCAxco3/tFyfntVyYhw8f4OLFi/D31xWUrq66XvacIcG9e7+AW7duFSha875mwsIeF/raiogIx9q1a5CZmYmHDx8iISEeAPDdd99i2rTp+sJq+PDXDAryHOnp6UhLy73GY86ohZIIgoDLly8V6P3+888/sGFD8UPzBUGAo6MjTp8+jS++2IGNGzcgKysLnp6eSEpKqpD318CBA4s8ILJp06dYt249/Pz8MGPGdERERMDOzhYKhQIWFhZQKBSws7MrtI2IKhaLViIiM6NSa6HIUCE1XYnUDBUU6bq/FRkqpGaokJqugiJ7XWpyAro00OoLVqlUCo1WA0EAVBotlGotktOyIBWL8c033+Db735AZGQ4Fi9apL+/vIWCRqPBwIEDMXToUCxevAg3blw3iE2rFaBQKLK31ebZhzjP38jzd+6CIAioX78BBg0aDD8/3Q97tVqN//77D2KxbrsdOwxPNVmxYhXmzJmNTz7ZAEtLS4SFPUZ6ejoUCoW+V+jHH3/M7hEeqr+dvX0tPH36FGKx2KDQyLu/bt26oVs3XTEXERGBrl27IjBwDQRBQFR8OlQaJTTZs9prtAKSFbocONlbIjtctG7bHp27PoevdnyBk6dOAyIxMrNUyFSqUdfLG+06dMQLL+h6oNQqFX799Shuht5CllKDmzdvobZHHaSlZ+HDDz/EtBnvw66WE7JUGjT18cXX33yNUWP8cfbvv+Hr16LQtiyVGq3adEAtJ1f88uO3GPx/r+GtSdMhEgESsQiXLv+LLJUW6ZlqAAIE6A5qaLRapGYoAQEQgDz/C1BkqJCp1CAxNQu1Pb3Qt99ANPVpDgiASq1GQkIc5s6agYGDhsLKyho7d+3GgiUr8TROgVdHjgcATHzrDXR87gWo1FpExqVBAJCp1MDKthZiop9CrVYjPi4GNja2+tdukiILjnaWKEr+14Lh61KLKVOmQiaTYMiQIRgyZIjB69DX1xfr12+AtbU1VCoVZDIZAF3PZ8uWLREZGQlnZyfcuXMH+/d/jS1bDCcdmzt3HrZv347atWvD27tekTEC0L+WC1O/fn1069YN8+bNB6Ab1p2eng4LCwtIJBJcuXIFTZoUnH32zp076Nnzedy5cweDBw9BUlIyunTpghdf7Kvfz9q1gXjrrQno1KkTxowZrX9eJ016G6dPn0LDho3QunXrIntahwwZgszMLKSnp+H+/fto1KhRsY8zx40b1+Hr21x/cAwA4uJiIZXK4ODgUGB7b+96iIyMQK1aPlAqlfp2kUgEiUSCrKxMPHr0CI6OjujcuTOef/55bN++DX/++SekUqn+3Pn69RsUeA6OHDmMO3du658zLy8vREVFFtnTCgioVauW/uoXaWlpaN26DYKDz6Nfv/64c+c2GjZsUGgboBtJkHPggYieDYtWIqJyyFSqcSw4DKcvR0CRoYKtlQy929VB/87esJSX/qNVKwhIz1RDkaf4TM1QQSsSITpOoWvLLkRzCtNMZemH5VmJDSc/qle/EXbv2ILAZR/BP2AyNFogMVX3w7BBY1+MHj0afq3aIkulQVR8GlRqLRJTs2BhodUNM05OwcwZ06DVamFra4MmTZrqJ90DgHfeeRcBAbrCZMqUqaWOM0dAwEQsXrwICoUCYrEIS5YsK3Z7Hx8fvPnmW5g3bw7WrFmH2rU9sGDBfDx5Eob58xcAAJYvX4qWLVvC338cOnTogClTpmLy5Cn44IP3AQDz58/X769ps2YYO9Yfs+fMxtLlqyESSaDRapGQkolMpQYRcWlQa3IPAuQlAFBrBMQkZujbFsyeiqysLADA3IWrILVywNZtW3D+4mW8/NpobFq/Cvu+/gYQBIwb/y6S05SIjo3HhIDxkMktMHfBSvzwyyFcv3EDa9fqepfHTXgXvs1boolPK4wZPQqubrXRq/8ryBJkBdpi42OhFYDR/pOwZeMa/Pn7CTzfqy8EQRdrWqYaSalZiEnKMHgsarUW8clZhT7nigwVspQaJKcpMWz4WGz6ZBXS0xQQicSY9v48uNf2REpKCho3awlLKyvExkTDxd0LV69dwZ4dW6DWaNC6bUdkZfeeKrMvxSQIgEQiwaChr2L2jIkQicV4Z+osAEATHz/Men8GXnltFJ5GPsGhAz8gNTUFiUlJWLhgYaGvBak09314/fp1bNy4ARqNGl266Cb+admyJaZNm4Jx4/wxefIUTJkyGYKgK1I2bNDNTHzixHEEBq7CsGH/B5lMjo8/Xof4+HhMnBgAOztbbNq0GQBgZ2eHVatWY86cD7PPVS1dofL++zNx+fJlPH78GG+99RZ6934Bzs4u8PcfB5FIhIEDB6Jly5ZYsGA+rK2tUadOXUyePKXAfh48eIDx49+Eh4cH2rRpAx8fHyxatBD7938DQQBmzJiBnj2fx8qVy9GoUSN9UQ7ozpNevXoNZs6cgdmz5xTZ0woAEydOREDABMjlFli5UjfEefXqVZg160PExMRgwYKPcO/eXUyY8BaWLFmGOnXq4OTJk+jTx3Bo8OnTp9G7d+/C7gI9ez6P4OBg+Pr6QCqVIi4uDs8//z9MmPAWGjZshG+++QZTpkyFSCTC1KlTkJWVCQBYv/4TuLu7Y+PGT3Dt2jW8+eZbBZ4DAEhKSkZAwARYWFhg/fpPIJfLi+xpfeutCZg7dw4kEjEaNmyEpk2bwtPTEx9+OAv79u3D8OHDIZPJ8corrxZoyylYeT4rUcUQCeWZitEIeJ1W02bq136iXMzVs8tUqrF8zyXEJmVAlefapzKpGK61LDH55ZZQqgx7Q1PTVdmFqVL/t64IVUNbiR/DVuIM9GiiQv8Bgyt0vyIRIJWIIZWIIBWLIZGIcpclYkjEIqP8WBszZjT27i04RFSj1UKjEaDRZv/TaKHWCtBqBag1Wn27sb8RTx4/DI1Gg34Dh5a8MenJJCLIZBLIpWLIpWLIpBLIpAUvkFCWa3/6+4/Dl1/uMCh+TdHmzZ/pz+OsDgRBwNy5c/Dxx+sQHR2LhQsX4MMPP4S3dz2kpKTgn3/+Rv/+A8q1719++RlqtQavvvpqBUddUFDQXrRq1QqtWrUGAFy4EAytVgIfn+Yl3NK88DeF+TD1XJV0nVbT/iQmIjJBx4LDEJuYAZXGsBdTpdYiMj4dH31Rupllq0wlFGKCoHu8ussOFl4ESMSi3CI2p6gV6wpbSXZha7hPAclpuqJeoxUgEYsKPU807/Z5i1BNdgEal5xZoEitCmd+/w1HD/2kX/Zp3gL+EyZXyX1XlvAnj/HZJ7nXhrWwsMCSVRuMF1ARVBoBKo0a6dnLG9YuRfTTKIhEumGlEye+g06dOlXJgZS9e/caXHu0tBNckY5IJMLq1YEAACcnJ3z44Wxs3rwJERGRsLOzw/TpM4wbYCmNHs2cE1Uk9rRShTD1ozeUi7kqP7VGixsPErDll+tQa4z3eSQWiWBrJYWdtRy2VjLYWsv0f9tZy2CX02Ylx9krD6BKuInBw141OKcMAEQALC10vVNqja7gU2uqrsjT99aKRZBIRMjI0kCrFQxqbBF0R19tLKW5BWp2kWqqXwkiAJZy3XNdYSHmTKJVxj0qVdpibyECYGUhASDSn+Op+1+E7P/y/C/Srzdoy1lG7jnK+n0V2ibKt1/dHzmjEir7V4lYDMilkuweWTHkMl2vrJjDOE1KWXrFzcFvv52Aq6sn6tdvaOxQKhR/U5gPU88Ve1qJiJ6BVhDwX1gSzodG49KdGKRlqiv8PizlEthZy2BrJdcXna7ONpCKoCtErbKLUmsZbK1ksLaUlvoHtlvv5tgadBfn/v4DzZr5QiLRfezrCkYR5LUskZVTVACQAZAKAjTa3CG1Wq0AtVbQLWsFaDRVP4w2toruJ2eCIrFYpPtfpCuoxaLctvQsNdIz1YU+B6LsnFlAVnBlRcRXxu2V2UPRjRFrWQmCgJTkTKjzvb5yXqvOtSyh1QAqjQZqjQCVWqs/0FIRJBIRZBIRpBIJZFIRZBIxJFKRvlinyicIAhQZaqRlqqHVCvoDVrZWUrM9N1Sr1eLJkydITExCq1bPdo1kopqMRSsRUT6CIOBxdCrO34zGxdsxSEwtfEKaoogA1HG1ye75lGf3ehr2htrmWS7s3LuKOiJqZSHDpFFD8MuJv3D05D8QtFpIJGK4OljCzdEKD8QF77tkgr5oUKo0UKq1UKo1UKp0/6tUWqjUxffwVbWcYcoyaZ5/ktz/pdltuiHLxf841mi1uBOWBKVKY9DbKxYBcpkEzbwdICnX81rxzClWQBdvTGIGYpMyodGU7rWq1QrIUKqRkaVGplKj+z9LU2D4fnmIRICFXAJrCyks5VJYySWwspBCIhHp41RrtJDmidOUnk/A8Dk1vVh1Byi0gu5g2L2IZGSpNAUOWljIJGhS1wESiSi79x4o+yGcilOW51QkEsHGxgY9e74ACwsLI0VMFa2iJmOk0uPwYKoQpj7kgHIxV0WLik9DcGg0gm/FIDohveQbFEImFWNAZ28M6/FsQ8DMPU9qjRZJqVmIT8lEQkrO/5mIT8lCQkom4lIykVWGWZALIxaJYG8jQy1bCzjYyFHL1gK1bORwsM3+21aOWjZy1LKxKPTAwLPI+cHy+5XcHyy92prmDxaDWNNVsLU23VgrUkqaEuGxCoTHpiEi+//I+LRnft0BueVS3l8lErEItlYyDOxSD3KZ7lrHIpHudZpzbm3ucm7xJcqzXpx/GfnXZw/jzrtfFH0/SrUGW36+gfiUTIMeaalEBEc7C4zr7wOJWJTn9ACt/u+cc8TVat2kZfrTBzRaqDS6URi5pxToDlRpSthOrc3dn+YZe8l158zrDjRJJLpREJLsSeHy/i3NHiGRcx69wd8Fts17e912+W+v1Qo4cu4RUrLPvc//nL49tAXsrGT6icFkUjGkEuMdHKjs4qoiv6vMpRBMz1Rh5d5LiE3KNDg4JpOK4epghflj25tMvHmf07QMFWxM9DkFSh4ezKKVKoS5/8CuSZgrQ4mpWdmFajQePy35ebG1kqFtExfcepyI5DRlwdmDK+gLq7rnSRAEZGSpEZ9d0G47cANZqqJ7xuQyMUa/2ExfiDrYWsDWWmYS5yFW91xVJ87Otrh1LwbhsWkGBW10QkalzuJNNZtYJIJMJjaY3VouFWe36c6pzmm3kGWvl2Wfc513ffb2+c/Hzn9bqUR3YKTYme4r4btKEHRn3WuzZ2IXhNyedEEQoM3TlndZKwjIVGqw+afrhR5gcbC1wKg+TSCWiPUHQXIOkOSdDV6TfQBFnff/7IMlGo3hhH26/wvfPufUmJzb5d++pI8KuVQMO2uZfhZzuX6EjyTP37m5zV0WQyaTQCYR6/IvMXyNFHW7or4HqyL/FYlFK1UJ/mgzH8yV7jqTIXdicCE0GnfCkkocxmohk6BdUxd0bu6O5vWdIJWIK733qqbl6Ze/HuDX4DCDL9YcFdV7XVlqWq7MWVG5Uqk1iIpP1xey4bEKRMSmlfnUACJTIAIgk4n1s7wXJWfSvtxi0rCg1Ar5ik+tAAGFFJ/a3IKVqp5UIipQEMukYqSmq5CkyCq0yDbF71VOxEREBCBLqcGVe7G4EBqD6w/iS5whVyIWoWVDZ3Txc0frxi6wkBnOvGspl2JYj4Ym9YFvzvp39kbIndgijwj37+xtxOioupNJJfB2t4O3u51Be1qmChF5emXDYxW4F55spCirp5xhuspiRlrkkErEperpqukEoFTPZ2qGCqkZqsoPiCqVbki/GhllOMamUmvx+5UIs/oNw6KViKottUaLGw8TcCE0Gpfvxpb4JS4C4FPPEZ2bu6N9M1fYWJrGrKo1gaVcivlj29fIcy/JdNlYytDUywFNvRz0bdM2/gVFMT/0ZRIxuvi563qhUNgwySKWgTzrCunNQsGhlcg7zBIFh13GJmUUW+CJxSI0rlMr93rK2eeJ6v7lu8ZynjZp9jmeOZOX5ayTSLInNsv+W5p9nqhUmnsbaZ795czMDZRttEXOxE364aF5rtVc8O/soZ9aw6GfBsNLS7i9Os/M6f/ciCr2XFyRCHCys0BW9oR0SrWGRTaZJEW6eR2w4K8AIqpWtIKAu0+SEByqm/m3NJeoaeBhh86+7ujo6w5HO87uaCzsvSZz0LtdHbMZym5Ow+7LMtpCLBJBLBVV+ARrpeFgKy/TcyoIuuJXqdJCpc6ZbT377zyFrW429sK3Uaq1UGXP1J6zva4tz23zbF9V19ouTM61mcXigpOM6SchyzdhmFgEJKQWPow1h0Qsgk89R4ODHwaTZeU56KL7P9+67O2lBbbL83/efRaz/cG/Hxb5GpBKxOjdvg76tKsLlUarz3FOXlVqw5zn5k7Xps5eVqq12X9rDLbJ2Y86T1t52Vqb14F5Fq1EZPYEQUBYtEI/oVJpzkOr7WSNLs3d0bm5O9ydrKsgSiKqDsxpKLs5xVpgtIWJzspd1udUJBLpe5er6me3VitAqdbgwNmHOHUpvNCeYalEhO6tPPBCe688BWXBWa4LzGidb1ZsNzc7xMcr8syIXb7J8arTAZZh3RtU2etVEHQjBpR5C2CV7pJfp0LCcT40utCDGDKpGL3a1qmSGCsKJ2KiCsGJSMxHdcpVdEI6gkOjcT40Gk9LcYkaRzsLdPbVFare7rYmfbH66pSn6o65Mh8VlStzuoyQOcWalym/r8zlOa3q2YOfhbnNdGsOrwFze045ezBVCVP+ciFD5p6rxNQsXLylK1QfleISNTaWUnT0cUPn5u5o4uVgEpdIKQ1zz1NNwlyZD+bKfDBXFcOcZro3h0LQ3JjTNcVZtFKV4JeL+TDlXBV1YfGerT1x42ECzt98WqpL1MhlYrRr4orOzd3h18DJqBd2Ly9TzhMZYq7MB3NlPpgr88A8mQ9TzxUveUNEZqGwYSyKDBUO/fMIB/9+VOLtcy5R07m5O9o0doGFXFLibYiIiIjI9LFoJSKj02oF/PDHfUQnpBeYMKC4sSAiAM28HbIvUeMGWyvzmgmPiIiIiErGopWIqpQiQ4XwGAWexCoQHqNAeGwaIuIUpboQeo56tXWXqOnk6wYne8tKjJaIiIiIjI1FKxFVCrVGi6j49OzCNLdITVIoy73Pod0boHNzd9TmJWqIiIiIagwWrUT0TARBQJJCiSfZxWlOkRoVX3Co77Ows5JhaPcGFbY/IiIiIjIPLFqJqNSylBqEx+UO680pUNMy1c+8b5lUDLVGW+g5rDKpGL3amddFsImIiIioYrBoJaICtIKA2MQM3bDenAI1VoHYxIwSLzdTEolYBE8XG9R1tUFdN1t4udqijqstLOVirNh7uciLYPfv7P2M90xERERE5ohFK1ENkPf6p2kZKthkX/+0f2dvqDWCfmKkiFgFnsSUfWKkojjaWcDLzRZ1XW31RWptJ+sir5s6f2x7XliciIiIiAzwVyBRNae//mliBlQaw+ufHj73GNoKOO/UQiZBXVcb1HG1zS5SdQWqjWXZLkFjKZdiWI+GGNaj4TPHRERERETVA4tWomrupzMP8DQ+HVqh4PVPheIugloIEQA3RyuDYb1ebjZwcbCCWCSqwKiJiIiIiHRYtBJVQ2qNFlfvxePM1UhcfxBfrn3YWEpzh/a66XpQPZ1tYCGXVHC0RERERERFY9FKVI1EJ6bjr6tROHs9Cilppb8eat3sHtO8PagOtnKI2HtKREREREbGopXIzKnUWlz6LwZn/o3E7bCkMt/e1kqGpeM7VXxgREREREQVgEUrkZmKjEvDmauR+OfGUygyVMVuKwIKvVSNTCpGb17/lIiIiIhMGItWIjOSpdIg5HYM/rwaiXvhycVuaymXoItfbXRt7o7dx+/w+qdEREREZJZYtBKZgbDoVPx5NRLnb0YjI0td7LaN6tijZ2tPdPJx10+aZHD90wwVbK14/VMiIiIiMg/8tUpkojKy1Ai+FY0z/0bi0dPUYre1sZSia4va6NnaE3VdbQusz3v9U1dXO8TGFr8/IiIiIiJTwaKVyIQIgoAHUSk4828kLtyKQZZKU+z2Pt4O6NnaE+2buUIm5aVoiIiIiKj6YdFKZALSMlU4d+MpzlyNRHhsWrHb2lvL8FxLD/Rs7Ql3J+sqipCIiIiIyDhYtBIZiSAI+O9JEs5cjUTInViDSZLyEwHwa+CEnq090aaJC6QScdUFSkRERERkRCxaiapYSroS/1zX9ao+TUgvdltHOwt0b+mBHq084OJgVUUREhERERGZDhatRFVAKwi49SgRf16NxJX/YqHRFnbVVB2xSIRWjZzRs7UnWjZygkTMXlUiIiIiqrlYtBJVosTULJy9HoW/rkYiLjmz2G1dalmiR2tPdG/pAUc7iyqKkIiIiIjItLFoJapgGq0W1x8k4My/kbh2Px5aoeheVYlYhLZNXPB8mzrwre8IsUhUhZESEREREZk+Fq1E5ZSpVONYcBhOX46AIkMFG0spPF1sEJuUgSSFstjbujtZo2drDzzXwgP2NvIqipiIiIiIyPyUqmh9+PAh5syZg6SkJDg4OCAwMBD169c32Obs2bNYv349/vvvP4wZMwazZ882WH/06FF8/vnnEAQBIpEIO3fuhIuLS4U9EKKqlKlUY/meS4hNzIBKo5v1Ny1TjbvhyUXeRioRo4OPK55v7YmmXg4QsVeViIiIiKhEpSpaFy1ahJEjR2Lo0KE4cOAAFi5ciD179hhs4+XlhRUrVuDYsWNQKg17ma5fv47PPvsMu3fvhqurK1JTUyGXs3eJzNfBvx/haUI6tMVMqJSjjqsNerb2RFe/2rC1klVBdERERERE1UeJRWt8fDxCQ0Oxc+dOAMCgQYOwbNkyJCQkwMnJSb9dvXr1AAAnT54sULTu2rULb731FlxdXQEAdnZ2FfYAiKpSdGI6Tl4Mx6nL4SVu272VB55v7YmGnvbsVSUiIiIiKqcSi9aoqCi4u7tDIpEAACQSCdzc3BAVFWVQtBbn/v37qFu3LkaNGoX09HS8+OKLeOedd/hDnsyCIAj470kSTlx8gn/vxqHkvlVABOCtgb6VHRoRERERUbVXJRMxaTQa3LlzBzt37oRSqcSECRPg6emJYcOGlXofzs62lRcgVQhX1+rVg65Sa3H2agR++fM+HkQUfa5qYext5Cb9fJhybJSLeTIfzJX5YK7MB3NlHpgn82HOuSqxaPXw8EB0dDQ0Gg0kEgk0Gg1iYmLg4eFR6jvx9PRE//79IZfLIZfL8cILL+DatWtlKlrj4xWlOn+QjMPV1Q6xsanGDqNCKDJU+ONKBE5dDkdyCbMAF0YmFeP5Np4m+3xUp1xVZ8yT+WCuzAdzZT6YK/PAPJkPU8+VWCwqtpNSXNIOnJ2d4evri8OHDwMADh8+DF9f31IPDQZ058GePXsWgiBApVLh/Pnz8PHxKfXtiapCVHwa9hy/gw82/42fzjwosmD1dreFf38feDpbQyY1fAvJpGK4Olihf2fvqgiZiIiIiKjaK9Xw4MWLF2POnDnYsmUL7O3tERgYCAAICAjAtGnT0LJlS4SEhOC9996DQqGAIAg4cuQIVqxYgR49euCll17CjRs3MHDgQIjFYnTv3h2vvvpqpT4wotIQBAG3HifixMUnuHY/vsjtRADaNHFB345e+svVdGruhmPBYfj9SgQU6SrYWsvQq20d9O/sDUs5L4FMRERERFQRRIIgmMWYWw4PNm2mPuQgP5Vai+DQaJy4+AThsYoit7OQSdC9pQf6dKwLd0frKoyw8phbrmoq5sl8MFfmg7kyH8yVeWCezIep56qk4cHsDqIaJSVdiT8uR+D0lQikpBV9vqqjnQX6tK+Lnm08YWPJa6sSERERERkLi1aqESJiFfgt5An+uRENtUZb5HYNPOzQt6M32jdzhVRS4infRERERERUyVi0UrUlCAJuPEzAiYtPcPNhQpHbiURAu6au6NvRC43r1OL1g4mIiIiITAiLVqp2lCoNzt18it9CwhEZl1bkdpZyCXq29sQL7evC1cGqCiMkIiIiIqLSYtFK1UayIgunL0foZvPNUBW5nbO9JV7sUBc9WnvCyoJvASIiIiIiU8Zf7GT2wqJT8dvFJwi+FQ21pugZphvXqYW+Hb3QtqkLJGKer0pEREREZA5YtJJZ0goCrt2Px28Xn+DW48QitxOLROjg44oXO3qhkWetKoyQiIiIiIgqAotWMitZSg3+uRGFEyHhiE5IL3I7Kwspnm/jiRfa1YVzLcsqjJCIiIiIiCoSi1YyC4mpWTh9ORx/XIlAWqa6yO3cHKzQp0NdPNfSg+erEhERERFVA/xVTyYlU6nGseAwnL6sm0zJ2kIKRzsLRMWnQVv06apo6uWAvh290KaxC8RiXrKGiIiIiKi6YNFKJiNTqcbyPZcQm5QBlVoLAEjPUiM9q/CeVYlYhI6+bujb0Qv1a9tXZahERERERFRFWLSSyTgWHGZQsBbFxlKK/7Wtg97t6sLRzqKKoiMiIiIiImNg0Uom4/TliGILVpEIGP1iU3Rr4QELuaQKIyMiIiIiImNh0UomQ5GhKn4DAejVrm7VBENERERERCZBbOwAiADddVclJUygZGstq6JoiIiIiIjIVLBoJZNw4sITaIqZHlgmFaNX2zpVGBEREREREZkCFq1kdI+epuDHP+8XuV4mFcPVwQr9O3tXYVRERERERGQKeE4rGVWmUo1tB24a9LLKJCLIZRKkZ6phay1Dr7Z10L+zNyzlfLkSEREREdU0rALIqL4+eRfRiRkGbROH+KF9MzcjRURERERERKaEw4PJaC7cisbZa1EGbc+38WTBSkREREREeixaySjikjOw+9gdgzYPZ2u83ruJkSIiIiIiIiJTxKKVqpxGq8UXh0KRkaXWt0klIkwa4gcLucSIkRERERERkalh0UpV7sg/j3E3PNmg7dX/NYa3u52RIiIiIiIiIlPFopWq1N3wJBz4+6FBW4uGTujToa6RIiIiIiIiIlPGopWqTHqmCtsPhkLIvboN7K1lGP9Sc4hFIuMFRkREREREJotFK1UJQRCw5/gdxKdkGrSPH9QctWzkRoqKiIiIiIhMHYtWqhJ/X3+KC7diDNpe7OCFlg2djRQRERERERGZAxatVOmiE9Kx77f/DNq83Gzx6v8aGSkiIiIiIiIyFyxaqVKpNVpsO3gTWSqNvk0uFWPSED/IpHz5ERERERFR8Vg1UKX6+cwDPHqaatD2ep8m8HSxMVJERERERERkTli0UqW5+SgBvwaHGbS1b+qK51t7GikiIiIiIiIyNyxaqVKkpivx5eFQgzZHOwuMG+ADES9vQ0REREREpcSilSqcIAjYefQ2khVKfZsIQMCg5rC1khkvMCIiIiIiMjssWqnC/X4lAv/eizNoe6lbPfjUczRSREREREREZK5YtFKFCo9V4NvT9wzaGnraY8hzDYwUERERERERmTMWrVRhlCoNth28CZVaq2+zlEswcYgfpBK+1IiIiIiIqOxYSVCF+f73+4iITTNoG9OvGdwcrIwUERERERERmTsWrVQhLtx8ilOXww3auvq5o6tfbSNFRERERERE1QGLVnpmialZ2LD/ikGbq4MlRvdtZqSIiIiIiIioumDRSs9EKwjYcSQUqem5l7cRi0SYOMQPVhZSI0ZGRERERETVAYtWeibHL4Qh9FGiQduwHg3QyLOWkSIiIiIiIqLqhEUrldvDqBT89OcDgzYfbwcM7FLPSBEREREREVF1w6KVyiVTqcb2gzeh0Qr6NhtLKSYMag6xWGTEyIiIiIiIqDph0Url8vVvdxGdmGHQ5j/AF072lkaKiIiIiIiIqqNSFa0PHz7EiBEj0K9fP4wYMQKPHj0qsM3Zs2fx8ssvo0WLFggMDCx0Pw8ePEDr1q2LXE/m4cKtaJy9HmXQ1r9rfbRv5mqkiIiIiIiIqLoqVdG6aNEijBw5EsePH8fIkSOxcOHCAtt4eXlhxYoVGD9+fKH70Gg0WLRoEfr06fNsEZNRxSVlYPexOwZtHs7WGD/Ez0gRERERERFRdVZi0RofH4/Q0FAMGjQIADBo0CCEhoYiISHBYLt69erB19cXUmnhlznZvn07/ve//6F+/frPHjUZhUarxfbDocjIUuvbpBIRJg3xg6Wcl7chIiIiIqKKV2LRGhUVBXd3d0gkEgCARCKBm5sboqKiSrhlrtu3b+Ps2bPw9/cvd6BkfIf/eYx74ckGbcP/1xje7nZGioiIiIiIiKq7Su8eU6lUWLBgAVatWqUvfMvD2dm2AqOisrr5IB6H/n5o0Nbexw1vDPCFSKSbLdjVlcWruWCuzAPzZD6YK/PBXJkP5so8ME/mw5xzVWLR6uHhgejoaGg0GkgkEmg0GsTExMDDw6NUdxAbG4uwsDBMnDgRAJCSkgJBEKBQKLBs2bJSBxofr4A2z+VVqOqkZ6qwdu9F5H367W3kGPNiU8TFKQDo3gSxsalGipDKgrkyD8yT+WCuzAdzZT6YK/PAPJkPU8+VWCwqtpOyxKLV2dkZvr6+OHz4MIYOHYrDhw/D19cXTk5OpQrA09MTwcHB+uVNmzYhPT0ds2fPLtXtybgEQcDuY3cQn5Jl0D7+JV/Y28iNFBUREREREdUUpZo9ePHixQgKCkK/fv0QFBSEJUuWAAACAgJw/fp1AEBISAh69uyJnTt3Yv/+/ejZsyf++uuvyoucqsTZ61G4eDvGoK1vRy+0bOhspIiIiIiIiKgmEQmCYBZjbjk8uOo9TUjHkp0XkaXS6Nu83Wzx0dgOkEkNj3eY+pADysVcmQfmyXwwV+aDuTIfzJV5YJ7Mh6nnqqThwaXqaaWaR63RYtvBmwYFq1wqxqShfgUKViIiIiIiosrC6oMK9dOZB3j81PBozBt9msDD2cZIERERERERUU3EopUKuPkoAceCwwza2jdzRc/WnkaKiIiIiIiIaioWrWQgJV2JLw+FGrQ52llgXH8f/fVYiYiIiIiIqgqLVtITBAE7j9xCcppS3yYCMHFwc9hayYwXGBERERER1VgsWknv9OUIXL0fb9D2Urf6aObtaKSIiIiIiIiopmPRSgCA8BgFvj19z6Ctkac9hjxX3zgBERERERERgUUrAVCqNNh28CbUGq2+zVIuwcQhfpBK+BIhIiIiIiLjYUVC+O73e4iISzNoG9uvGVwdrIwUERERERERkQ6L1hruyt1YnL4cYdDW1a82uvjVNlJEREREREREuVi01mCJqVnYefS2QZurgyVG921qpIiIiIiIiIgMsWitobSCgC8Ph0KRodK3ScQiTBrSAlYWUiNGRkRERERElItFaw11PDgMtx4nGrQN69EADT3tjRQRERERERFRQSxaa6CHUSn46cwDgzYfbwcM6FzPSBEREREREREVjkVrDZORpca2gzeh0Qr6NhtLKQIG+0EsFhkxMiIiIiIiooJ48mINkKlU41hwGE5fjjA4hzXHmwN94WhnYYTIiIiIiIiIiseitZrLVKqxfM8lxCZlQKXWFljfvZUH2jV1NUJkREREREREJePw4GruWHBYkQUrANhby6s4IiIiIiIiotJj0VrNnb4cUWTBCgB/XYuswmiIiIiIiIjKhkVrNVfYOawG69OLX09ERERERGRMLFqrOVsrWfHrrYtfT0REREREZEwsWqu53u3qoKgr2cikYvRqW6dqAyIiIiIiIioDFq3VXJ8OdSEU0i6TiuHqYIX+nb2rPCYiIiIiIqLS4iVvqrkHkSkQ8lWttlYy9G5XB/07e8NSzpcAERERERGZLlYs1dyFWzEGy8+38cS4/j5GioaIiIiIiKhsODy4GlOptbhyN9agrZOPm5GiISIiIiIiKjsWrdXYjYfxyMjS6JftrWVo6u1gvICIiIiIiIjKiEVrNXYx39Dg9j5ukIiZciIiIiIiMh+sYKoppUqDK/fiDNo4NJiIiIiIiMwNi9Zq6tr9eGQpc4cGO9jK0cTLwXgBERERERERlQOL1mrq4m3DocEdfNwgFomMFA0REREREVH5sGithrKUGly9n39osLuRoiEiIiIiIio/Fq3V0NX7cVCqtPplJ3sLNKxjb8SIiIiIiIiIyodFazV0Id+swR05NJiIiIiIiMwUi9ZqJiNLjWv34w3aOvlyaDAREREREZknFq3VzL/34qDW5A4Ndqllifq17YwYERERERERUfmxaK1mLuYfGuzrBhGHBhMRERERkZli0VqNpGeqcP1BvqHBnDWYiIiIiIjMGIvWauTyf3HQaAX9srujFbzdbY0YERERERER0bNh0VqNXLydf2iwO4cGExERERGRWWPRWk0oMlQIfZRg0NbJx81I0RAREREREVUMFq3VxOX/Yg2GBns4W6OOq40RIyIiIiIiInp2LFqriQu3og2WO3FoMBERERERVQMsWquBlHQlbj1ONGjr5MuhwUREREREZP5YtFYDl+7EQsgdGYy6rrbwcObQYCIiIiIiMn+lKlofPnyIESNGoF+/fhgxYgQePXpUYJuzZ8/i5ZdfRosWLRAYGGiwbvPmzXjppZcwePBgvPzyy/jrr78qJHjSuZhvaHBH9rISEREREVE1IS3NRosWLcLIkSMxdOhQHDhwAAsXLsSePXsMtvHy8sKKFStw7NgxKJVKg3WtWrXCW2+9BSsrK9y+fRujR4/G2bNnYWlpWXGPpIZKUmThTliSQRuHBhMRERERUXVRYk9rfHw8QkNDMWjQIADAoEGDEBoaioQEw8ur1KtXD76+vpBKC9bBPXr0gJWVFQCgWbNmEAQBSUlJFRA+hdyOQZ6Rwajnbgd3R2ujxUNERERERFSRSuxpjYqKgru7OyQSCQBAIpHAzc0NUVFRcHJyKvMd/vLLL/D29kbt2rXLdDtnZ9sy31dN8O/9eIPlXh284OpqZ5RYjHW/VHbMlXlgnswHc2U+mCvzwVyZB+bJfJhzrko1PLiiXLhwARs3bsRXX31V5tvGxyug1Qolb1iDJKRkIvShYY+3r1ctxMamVnksrq52RrlfKjvmyjwwT+aDuTIfzJX5YK7MA/NkPkw9V2KxqNhOyhKHB3t4eCA6OhoajQYAoNFoEBMTAw8PjzIFcuXKFcyaNQubN29Gw4YNy3RbKlzI7RiD5QYe9nB1sDJSNERERERERBWvxKLV2dkZvr6+OHz4MADg8OHD8PX1LdPQ4GvXrmHmzJn49NNP4efnV/5oycCFfEUrJ2AiIiIiIqLqplSXvFm8eDGCgoLQr18/BAUFYcmSJQCAgIAAXL9+HQAQEhKCnj17YufOndi/fz969uypv7TNkiVLkJmZiYULF2Lo0KEYOnQo7ty5U0kPqWaIS8rAg8gUg7aOPixaiYiIiIioeinVOa2NGjXC999/X6D9iy++0P/doUMHnDlzptDb//jjj+UMj4py8Y5hL2vjurXgZM9LCBERERERUfVSqp5WMj0XbhkWrexlJSIiIiKi6ohFqxmKTkzH46e5s3+JAHRoxqKViIiIiIiqHxatZuhivl7Wpl4OcLSzMFI0RERERERElYdFqxm6yFmDiYiIiIiohmDRamai4tPwJEahXxaJgHYcGkxERERERNUUi1Yzk39osI+3I2rZyI0UDRERERERUeVi0WpmLnBoMBERERER1SAsWs1IeKwCkXFp+mWJWIT2HBpMRERERETVGItWM5J/aLBvfUfYWsmMFA0REREREVHlY9FqJgRBKDA0uKMPe1mJiIiIiKh6Y9FqJp7EKBCdkK5flohFaNfU1YgRERERERERVT4WrWbiQr6hwS0aOMHGkkODiYiIiIioemPRagYEQcCFW9EGbZ183Y0UDRERERERUdVh0WoGHj1NRVxypn5ZKhGjTRMXI0ZERERERERUNVi0moH8swa3bOgEKwupkaIhIiIiIiKqOixaTZwgCLh4m0ODiYiIiIioZmLRauLuR6YgPiVLvyyXitG6sbMRIyIiIiIiIqo6LFpNXP6hwa0au8BSzqHBRERERERUM7BoNWFaQUDIHcOitZOPm5GiISIiIiIiqnosWk3YvfBkJKbmDg22kEnQshGHBhMRERERUc3BotWE5b82a5smLrCQSYwUDRERERERUdVj0WqitFoBIXdiDdo4NJiIiIiIiGoaFq0m6s6TJKSkKfXLVhYStGjoZMSIiIiIiIiIqh6LVhN1Mf/Q4MaukEk5NJiIiIiIiGoWFq0mSKPVFhwa7MuhwUREREREVPOwaDVBtx4nQpGh0i/bWErh14BDg4mIiIiIqOZh0WqCLtwyvDZr26aukEqYKiIiIiIiqnlYCZkYtUaLK/9xaDARERERERHAotXkhD5KQFqmWr9sayWDj7ejESMiIiIiIiIyHhatJib/0OD2zTg0mIiIiIiIai5WQyZEpdbgyt18Q4N9ODSYiIiIiIhqLhatJuTGwwRkZGn0y/bWMjTj0GAiIiIiIqrBWLSakIv5hwb7uEEsFhkpGiIiIiIiIuNj0WoilCoNrtyLM2jj0GAiIiIiIqrpWLSaiGv345GlzB0a7GArRxMvB+MFREREREREZAJYtJqIC7cNhwZ38HGDWMShwUREREREVLOxaDUBWUoNruUfGuzrbqRoiIiIiIiITAeLVhNw9X4clGqtftnJ3gINPe2NGBEREREREZFpYNFqAi7kmzW4I4cGExERERERAWDRanQZWWpcux9v0MahwURERERERDosWo3s37txUGtyhwa71LJE/dp2RoyIiIiIiIjIdLBoNbKL+WYN7ujrBhGHBhMREREREQFg0WpU6ZkqXH+Qb2iwD4cGExERERER5WDRakSX/4uDRivol90dreDtbmvEiIiIiIiIiExLqYrWhw8fYsSIEejXrx9GjBiBR48eFdjm7NmzePnll9GiRQsEBgYarNNoNFiyZAn69OmDF198Ed9//32FBG/uLtyONlju6OvOocFERERERER5lKpoXbRoEUaOHInjx49j5MiRWLhwYYFtvLy8sGLFCowfP77AukOHDiEsLAwnTpzAt99+i02bNiE8PPzZozdjigwVbj1KNGjr5OtmpGiIiIiIiIhMU4lFa3x8PEJDQzFo0CAAwKBBgxAaGoqEhASD7erVqwdfX19IpdIC+zh69CiGDx8OsVgMJycn9OnTB8eOHaugh2CeLv8XazA02MPZGnVcbIwYERERERERkekpWGHmExUVBXd3d0gkEgCARCKBm5sboqKi4OTkVKo7iYqKgqenp37Zw8MDT58+LVOgzs7V61zPf+8ZTsDUq70X3NzsjRRNxXB15aV6zAVzZR6YJ/PBXJkP5sp8MFfmgXkyH+acqxKLVlMRH6+ANk/PpDlLSVPi6r1Ygza/eg6IjU01UkTPztXVzqzjr0mYK/PAPJkP5sp8MFfmg7kyD8yT+TD1XInFomI7KUscHuzh4YHo6GhoNBoAukmVYmJi4OHhUeogPDw8EBkZqV+OiopC7dq1S3376ubSnRgIeervuq628HDm0GAiIiIiIqL8SixanZ2d4evri8OHDwMADh8+DF9f31IPDQaA/v374/vvv4dWq0VCQgJOnjyJfv36lT9qM3fxdozBMidgIiIiIiIiKlypZg9evHgxgoKC0K9fPwQFBWHJkiUAgICAAFy/fh0AEBISgp49e2Lnzp3Yv38/evbsib/++gsAMHToUNStWxd9+/bFa6+9hsmTJ8PLy6uSHpJpS1Jk4U5YkkFbRxatREREREREhSrVOa2NGjUq9NqqX3zxhf7vDh064MyZM4XeXiKR6Avdmi7kdgzynplbz90O7o7WRouHiIiIiIjIlJWqp5UqzgUODSYiIiIiIio1Fq1VKCElE/fCkw3aOvqwaCUiIiIiIioKi9YqFJKvl7WBhz1cHKyMFA0REREREZHpY9FahTg0mIiIiIiIqGxYtFaRuKQMPIhMMWjj0GAiIiIiIqLisWitIvmvzdq4bi042VsaKRoiIiIiIiLzwKK1ihQYGsxeViIiIiIiohKxaK0C0YnpePw0Vb8sAtC+GYtWIiIiIiKikrBorQIXbxn2sjb1coCjnYWRoiEiIiIiIjIfLFqrwIVbnDWYiIiIiIioPFi0VrKo+DSExyr0yyIRhwYTERERERGVFovWSpZ/aLCPtyPsbeRGioaIiIiIiMi8sGitZAVmDebQYCIiIiIiolJj0VqJwmMViIxL0y9LxCIODSYiIiIiIioDFq2VKP8ETL71HWFrJTNSNEREREREROaHRWslEQQBF29FG7R18nE3UjRERERERETmiUVrJXkSo0B0YoZ+WSIWoW1TFyNGREREREREZH5YtFaS/EODWzRwgo0lhwYTERERERGVBYvWSiAIAi7kHxrsy6HBREREREREZcWitRI8epqKuORM/bJUIkabJhwaTEREREREVFYsWivBxXxDg1s1coaVhdRI0RAREREREZkvFq0VTBAEXLxtODS4ow+vzUpERERERFQeLFor2P3IFMSnZOmX5VIxWjd2NmJERERERERE5otFawXLPwFTq8YusJRzaDAREREREVF5sGitQFpBQMhtw/NZO3FoMBERERERUbmxC7AC3QtPRpJCqV+2kEnQshGHBhMRVWcqlQpxcVFQq5Ulb0xGFRMjhlarNXYYVArMlXlgnsyHqeRKKpXD0dEVEknZylAWrRUo/9DgNk1cYCGTGCkaIiKqCk+ePIGlpTVsbGpDJBIZOxwqhlQqhlpt/B9tVDLmyjwwT+bDFHIlCALS0lKQmBgLFxePMt2Ww4MriFYrIOROrEEbhwYTEVV/GRmZsLGxZ8FKRERUDJFIBBsb+3KNTGLRWkHuhCUiJS03AVYWErRoyKHBREQ1AQtWIiKikpX3+5JFawW5kG8CprZNXCGT8uklIiIiIiJ6FjyntQJotFpcyjc0uCOHBhMRUSEylWocCw7D6csRUGSoYGslQ+92ddC/szcvkUZERFQIfjtWgFuPE6HIUOmXbSyl8GvgZMSIiIjIFGUq1Vi+5xJikzKgyp4QQ5Ghwq/BYQi5E4v5Y9s/c+G6Y8c2jB37FmQyWUWEXCKVSoW5c99HbGwM2rfviGnT3q+S+y2LV18djDVrPkHTpk1LfZuDB3/Gjz9+C0EQIBKJMHLkWPTrNxAAoNFosGHDOgQH/wORSITRo/0xePAwAMCKFYvh4+OLV14ZUez+o6Ii8frr/4cGDRpBELSwtLTCBx/MwT//nMXvv58CAEREPIGDgxNsbGwAAIGB6+HuXrvUj+HMmT/g4uKC5s1blPo2hfnrrz+wc+eXUKmUEATgpZeG4I03RuvX79r1JY4ePQQAGDhwMPz9J5S4bseObcjIyMCUKTNKvP/u3TugUaPGEIl0I9j69u2P338/BZVKBbVahSdPwtCgQSMAQNOmzTBv3qJC9/PkSRgWLpwDAHjjjTHo23dAmZ6H0oiKisSECWNw5MipMt3u7t07CAsLwwsvvKhv++yzDfjzz9OIiorEnj370bBhY/267t074MSJM7C2ti53rIXdZ3mEhT3G2rUrER8fB4lEAl9fP7z//mxYWFji8uUQbN68ETt27C3Vvo4ePYR//vkLy5evKXT9hQvnsW3bZjx4cA+vvDKiVK8fQPe+DAm5gFq1HAAAvXq9gHHjxpfqtnnlfJbkzUVJKvuz5FnkfR+ePfsnrl79F5MnTy+wXd48Xr9+FZs3b4RCkQoA6Nq1O959dxpEIlGJ+XsWLForwIVb+YYGN3WFVMKhwURENcmtx4kIOnEHUfHpZb6tSq1FZFwa3l1/pshtPJytMbpvM/jWcyx2Xzt3foE33hhTaNGqVqshlVbsV/9//93B06dPERT0XZluVxmxVOR+69b1wqZN22BvXwsxMdF4882RaNWqDTw8PHHixK+IiHiC/ft/RnJyMt56axQ6dOgEDw/PMt2Hra0tdu36GgDw3XffYNWqpfjqq336H9NTpkzEG2+MwXPP9SjXY/jrrz/g4+P7zEWrk5ML1qz5BC4urlAoFBg/fjSaN/dD69Zt8e+/l/H77yexd++3AICJE/3Rpk07tGnTrth1ZfX5518ZFGgjR44FkFsk5jyPxfnzz9No0aI13n9/dpnvv7Ldvfsf/vnnL4MCskeP/2H48NcxeXJAld1nechkMkydOhNNm/pAq9Vi8eKP8M03QQYHLyqCRqOBp2cdzJkzH7//fgpKZdkm8xk9elylFoBFqYrPkorQvfvz6N79+RK3s7GxwUcfLYaXlzeUSiWmT38Hx48fRf/+L1VqfCxan5Fao8Xl/LMG+3JoMBFRTbPn2G1EJ2ZU2v6j4tOx59htrJrUtchtPv44EADwzjtvQSQSY9Ombfj0048hkUgQFvYY6enp2LXrayxZMh9hYY+hUilRp44X5s5dCHt7e1y+HIJPP12P5s39cPPmdQAiLFmyEvXrN0BY2COsWLEEmZmZ0Go1GDBgMLp374GlS+cjLi4W/v4jMWaMP7p27Y4NG9bi1q2bAID+/V/CqFHjAOiKsCZNmuHmzeuwt7dH794v4rffjsHW1g7379+Fq6sbZsyYhc2bNyA8PBy+vs2xcOEyiEQipKUpsGnTJ7h//y6USiXatu2AqVNnQiKRFNjvunWfFvkcffNNEM6f/xsrVqzFt9/uw6NHD5GcnIS4uFg0aNAQc+cugq2tLdq166C/jZubO5ydXRATEwMPD0+cPv0bBg8eBrFYDEdHR/To8Tx+//2kvpDKcflyCDZuXIdFi5aX2DPTsWNnbN++udhtinL9+lV88skaaLUC1Go1xo17C/b2tXD27BmEhFzAoUMHMGLESAwYMAi//noYP/30PTQaDWxtbfHBB3Pg7V0fR48ewokTv8LCwgIREeFwcnLGggVL4erqBj+/3KLX1tYW9eo1wNOnUWjdui1OnfoN/fq9BAsLSwBAv34v4dSp39CmTbti1+V1//49LF06HzNmzELbtu3L9RyU5MSJX/Hdd99Aq9Xi+vWrWLFiDSwsLLFhwxpERz9FVlYW+vTph7Fj30Jw8Dn88MN+rF27EYmJCRgypB+WLFmF3r37YN++3VAoFAgIeAfr16/B5csXIZPJYW1thc8//0p/f9u2bcb5838jMzMTc+YsROvWbQAAv/56GN98sxcikQiennXx4YfzIBaL8eWXW5GengZ//5Fo06YtZsyYpb9NcbRaLT777BPEx8fjo48WQy6XF9gmMTEBixfPR2JiPACgQ4dOGDdufKH3efPmDWzduglpaWkAgAkT3ka3bt31Bwf69x+EkJBgCIKA99+fg/bt28PDw1NfZInFYvj6+uHx44cF4khNTcVHH83Cc8/1wIgRo0p8bDnvn2bNfPHff3cQEPCO/gDOmTN/FNi+uM+IymBKnyVXr/6LDRvWYOfO3IM348ePwZQpM+DtXQ+LF3+E9PQ0ZGUp0a3bc3j33YK9qfl7Sbdv34JTp07Azs7e4H2Z9/7lcjmaNm2Gp0+jCuwvOvop5s2bhZEjxz7zgRGAReszyVSqsfvX20jPUuvbZFIxGnjYGTEqIiKqqd5/fzZ+/vn7Ar1Sd+/+h88+2w4rKysAwPTpH8DBwQGA7ofJvn278c47UwEADx/ex7x5C/Hhhx9h9+4d2L17BxYtWo6ffvoB3bv3xJgxbwIAUlJSYG9vj9mz5xsM/9uy5VNotVrs2fMt0tPTMGnSW2jYsDG6dn0OABAZGY4tW76EVCrF0aOHcOtWKPbs2Q83N3d8+OEMLFkyH5s2bYOVlRXGjx+NkJAL6NixMzZt+gRt2rTDnDkLoNVqsWTJfBw5chBDhvxfgf0WRqsV8PHHa5CUlIR16z7V90Rfu3YFO3d+DScnZ6xcuQS7dn1ZYMjh5cshUCgU8PHxAaD7MVa7du41Bt3dayMmxvBa7TlF0rp1n8LVteSD2b//fhJNmjQrcbvC7Nu3G2+8MQYvvtgfgiBAoVDAzs4O3bv3NBheePXqFZw+/Rs2b/4Ccrkc5879jVWrluqLrWvXrmLXrn3w9q6Pr77ajo0b1xUY5vf48SOEhl7Hhx/O0z8XeX/QurvXxtWrl0tcl+PixWBs2rQeS5asQoMGDYt8jDkHYgBgwYKlaNSo9MMzAaBv3wF48iTMYEjyjBnvwt9/Atq0aQeVSoXp09+Br29ztG7dFkuWzIdarUZIyAX4+bXEpUsX0Lt3H1y6dBEjR47FvXv/4cqVEAQFfQ+xWIyUlBT9fSUnJ6NFi1aYNGkyTpz4FVu3forPP/8KDx7cw9atn2HHjiC4uLjgiy8+xyefrMXSpaswYcLbZR5WqVQqsXLlYnh41MHixSuKnJX1xIlfUadOHWzcuAVA7ns3/32mpqZi3bqVWLv2U7i4uCAuLg4BAWOxZ8+3+sfVuHETTJ06E5cvh2Dx4o/w448HIRbnvueysjJx5MhBvP32ZIMYnj6Nwrx5szBmjD969epT6sf48OEDzJo1Dy1atCpx25I+I/bv/xoHDvyEOnXqYtKkKahfv0Gp48hLqxWwYcNaJCcnm8xnSevWbZCRkYF79+6iceMmuH//HlJTU9CmTTsolUoEBn4Ce3tbZGYq8d57U3D+/D/o0qVbkY/x7Nkz+PvvM9i582tYWFhg7twPCt0uMTEBf/xxGmvXbjBov3v3PyxbtgDvvz8HrVu3LfJ+yoJFaznlnJcUFZ9m0K7RaLEq6EqFnJdERETmY2x/n3IPDy6NnOHB5fG//72gL1gB4Nixwzhx4hjUahUyMjLh5eWtX+ftXQ9Nm+p+UPn5tcTff/8FAGjTpi22bPkUmZmZaNeug0HvQV4hIRcwffoH2dfjs0WfPn0REnJBX7S++GJ/g8KyVavWcHNzBwA0adIMtWt7wM5Od/C3ceMmiIh4go4dO+Ps2TO4desm9u/fBwDIzMzU366w/ea3atVStGrVWt9zm6Nbtx5wctJdom7QoKHYsGGtwe0ePnyA5csXYdGiFfoew5IcOXIIFhYW2LhxC2xsbIvcTqFQwN9/JARBgKdnHXz00eJS7T+/du06YPfurxAREY6OHbsY9Izm9fffZ3Dv3l1MnOgPABAEAampucVWq1at4e1dHwAwePAwjB37usHt4+LiMGfOe3jvvTlwcXEtV6x5Xbx4HsHB/+CTTzaXuL/8B2KeVUZGBq5cuYSkpCR9W3p6Gh49eoSOHbugYcNGuHnzBkJCLsDffwK2bNkIpVKJW7dC0bJl6+zzadVYvXoZ2rXrgG7dcodwW1lZ63sE/fxa4rPPNgDQFSxduz4HFxcXAMDQoS/D339kuR/D++9PxQsv9MXIkWOK3c7PryW+/fZrbN68EW3atEPnzoWP1rhx4yqioiLxwQfT9G0ikQgREU9Qq5YDZDKZ/lzMdu06wMLCAo8fP0KDBroDCGq1GosWzUP79h0MhpnGx8dh6tS3MX/+klL1HudVt65XqQpWAMV+Rkyc+C6cnV0gFovx66+H8f77U/HddwfK1Qu7atVStGzZyqQ+SwDdqJZffz2EqVPfw9GjhzBgwCCIRCJotVps2bIRN25cgyAIiI+Px927/xVbtF65EoLevV/Uv+cGDRqK3bt3GGyTnp6G2bPfw+uvj9Z/ZwDAvXv38NFHs7BmzYZyHxgoDKuqcjoWHIbYxAwIgmG7VgBikzJwLDgMw3oUfcSQiIiqF996jlgR0KXYbX756wF+DQ7TT8KUl0wqxoDO3pXy3WFtnVuwXr16Bb/88iM+//wrODo64sSJYzh48Cf9erncQv+3WCyGRqMBoCt8W7RohQsXziMoaBeOHDmIhQuXlTkWKyvDwiPvcEaxWJzv/iX6+wcErFy5DnXq1C12v8HB5/D555sA6CbsyRlm16ZNW1y5chlJSYlwdCzdZIlPnoRh1qzpmDVrnsGPbXf32nj6NAq+vn4ACvaWNG7cBFevXsGjR4/0BeTHHwfi+vWrAIClS1dCJpMbnNP6LF57bSSee64nLl4MxoYNa9CxYxdMnPhuge1yJlGaMOHtMt9HYmICZsx4F6NGjUXv3rk9ZTnPRY7o6Kdwc6td4joA8PLyxsOHD3D7dqi+yNm9ewd+//0URCJg6tT3ijw48qwEQQuRSIQvv9xT6MGOdu064NKlC7h58wY++GAuHB2dcerUCTRp0hQWFhawsLDA3r3f4cqVSwgJuYDPP9+Er74KAgDI5bnnk+veQ+oC+68Ibdu2R3DwObz88nBYWhZdBLVo0Qo7d+7DxYvBOH78KIKCduHzz3cU2E4QgEaNmmDz5i8KrIuKiiw2Fo1Gg6VLF8DOzh4zZswyWGdnZwc3t9o4f/7vMhet+T8vilf0Z0TeHsoBAwZh06ZPEBsbY/C+BYC5cz/QP9YtW76AtbVNgX3pPksuGe2zpCj9+w/CpEn+mDhxMk6ePI5t23YCAL79dh9SU1OwY8ceSCQyBAaugFKZVaq4i5KZmYkPP5yJTp26GEzKBgBubm5IT0/HlSuXKrRo5WxB5XT6cgRUmoI/OgDdhBq/X4mo4oiIiMjU9e/sDVcHqwLX8ZZJxXB1sEL/zt5F3LL0rK1tkJamKHJ9amoqbGxsUatWLSiVShw5crBU+w0PfwInJ2cMHDgYb74ZgNDQm4Vu16FDJxw5cgCCICA9PQ2nTp1Ax46dy/VY8nruuZ4ICtqtL2KTkpIQGVnwu7Zz567Ytetr7Nr1tcF5YS+9NAQjR47G9OnvIC4udy6Kf/45i8TERAC6c7ratesIAIiICMd7703FjBkf6HuJc/Tq1QeHDv0CrVaLxMRE/PXXn/jf/17Qr2/WzAcrVqzF0qXzceXKJQC6ods5ceX0ZlaUsLDHqFOnLoYNewXDh7+hP5/YxsYGCkXua+G553rg2LEj+uGHGo0Gt2/f0q+/fv0qnjwJAwAcOXIQ7dvrCsbk5CTMmDEZr7zyGgYNGpbvuXgBx48fQVZWJrKyMnH8+BF9UVvcOgCoXdsT69d/hq1bN+PUqRMAgHHjxmPXrq+xd+/+SitYAd37pHXrtggK2qVvi45+ivj4OABA+/adcPToIbi5uUMmk6FDh47YsWMb2rfXvT4SExORmZmJzp274u23p8DW1rbQ12Ne7dp1wLlzf+vv49ChX9CxYycABXNVGm+9NREdO3bCe+9NKfY9HxkZkT3qoR+mTp2JO3duQ6vVFrjPFi1aITw8DJcvh+jbbt26CSG7h0alUuG3344B0B38ysrKQv36DaDVarFy5WKIxWLMmbOgwDBludwCq1d/jEePHmDDhnX6/VW04j4jYmNzJ00NDj4HsVhcaO/+qlXr9O/TwgpWQPdZ8vrrxvssKUrt2rVRv35DbNiwDvXrN9QXv6mpqXB2doGFhQViY2Nw9uyfxe4HANq164jTp08iIyMDGo0GR4/mfk9kZWVh9uyZaN68RaEHwOzt7bFhwxYcP34U+/cHlXhfpcWe1nLKe4mbQtenF7+eiIhqHku5FPPHtsex4DD8fiUCinQVbK1l6NW24q7T+vrrozBt2tuwsLDEpk3bCqzv0qUbTpz4FW+88TJq1XJAmzZtiyxA8zp9+jecOHEMMpkUIpEI06cXfmkbf/8J+OSTNRg7VnceZb9+A4sdhlZa06e/jy1bPoW//xsQiUSQyeSYNu19eHrWKfU++vcfCKlUhunT39FP1tS6dVssXjwPsbExqF+/IaZMmQkA+PzzTUhJScKXX27Dl1/qnsd33pmKzp27ol+/gQgNvYHXX/8//WPOH0fjxk2wZs0GzJ49EzNnfljkkMyK8MMP+3H58iXIZFLIZHLMnKnr6erXbyBWrFiC338/pZ+IaeLEdzFnznvQaLRQq1Xo1asPfHx8AQAtW7bOngTriX4iJgAICtqNJ0/CcODATzhwQNcrP3z463jppSFo164DevbshdGjR+if45zzWItbl8PdvTY2btyC996biqysLAwcOLjSnqf8Fi5chk8/Xa9/rVpb22Du3IVwdnaBn18LJCcnoUMHXeHRvn1HbNu2WV+0xsREIzBwOTQaDTQaDbp06QY/v5aIjn5a5P01bNgYb789BTNnTs6eiKkOZs2al73/TvjmmyCMG/cG2rZthxkzZmHDhrX488/fkZAQjxkzJsPevlaBWbpHj/aHhYUlZsx4Fx9/vAn29rUK3O+VK5fw7bf7IBZLIAhazJo1F2KxuND7XL16PTZv3oiNGz+GWq2Cp2cdBAZ+AgCoVasW7t79D19/vQeCIGDx4hWQyWQ4c+YMjh//FQ0bNsL48bqhyi1bGs7SLJPJsGxZIJYtW4A1a1Zg1izdBFRldfXqv1i8eB7S0tIgCAJOnTqBOXMWoHPnrsV+RixfvhiJifEQicSwsbHB6tXrn2mW8b59B0Aul5vcZ8nAgYOwbNlC/XsX0L1XFyyYjZEjh8PFxU3/Gi7Oc8/1wI0b1+Dv/4Z+IqbYWF2BfvjwAVy5cgnJycm4cOE8gIKXELK1tcX69Z/hww9nIDMzs0JmkhYJlXW4o4LFxyug1ZpOqNM2/lVs4WpnLcPGaeWbot4cubraITY21dhhUCkwV+aBeTIfMTFP4ObmZewwqBSkUjHUeYZml+VaodVdZV5fsTzy54qMq6jrzzJPOubwWWJKuXr69DFq165n0CYWi+DsXPR5uxweXE6929UpMLwrh0wqRq+2pT/yS0RERERERIVjT2s55cweHJuUYTChRs55STVt9mD2CpkP5so8ME/mgz2t5sOUehrKY/z4MXkmptLx82uhH2JanZQ3V+fOncW2bVsKtE+a9C66du1eEaGZrLVrV+LmzRsGbRKJRH85qspQ3jzVpNdyRXqW17cpff6Vp6eVReszyFSqK/W8JHPCH9jmg7kyD8yT+WDRaj5M6UcbFY+5Mg/Mk/kwpVyVp2itWZVVBbOUSzGsR0Ne2oaIqIYTBKHAjJlERERkqLz9paU6p/Xhw4cYMWIE+vXrhxEjRuDRo0cFttFoNFiyZAn69OmDF198Ed9//71+XXx8PCZOnIjBgwdjwIABWLx4MdTqyrlmFRERUVWysrJEWlpKpV3GgYiIqDoQBAFpaSmQSuUlb5xPqXpaFy1ahJEjR2Lo0KE4cOAAFi5ciD179hhsc+jQIYSFheHEiRNISkrCsGHD0LVrV9StWxdbt25Fo0aNsH37dqhUKowcORInTpzAwIEDyxwwERGRKfHy8sK9ew+hUCQZOxQqgVgshlZrGsPjqHjMlXlgnsyHqeRKKpXD0bHgNXJLvF1JG8THxyM0NBQ7d+4EAAwaNAjLli1DQkICnJyc9NsdPXoUw4cPh1gshpOTE/r06YNjx45hwoQJEIlESEtLg1arhVKphEqlgru7e5mDJSIiMjUymQwuLh7GDoNKgeeKmw/myjwwT+bD3HNV4vDgqKgouLu7QyKRANDNQubm5oaoqKgC23l6euqXPTw88PSp7gLL7777Lh4+fIju3bvr/7Vvb3hxaSIiIiIiIqL8qmQipmPHjqFZs2bYvXs30tLSEBAQgGPHjqF///6l3kdxs0mRaXB1tTN2CFRKzJV5YJ7MB3NlPpgr88FcmQfmyXyYc65KLFo9PDwQHR0NjUYDiUQCjUaDmJgYeHh4FNguMjISrVq1AmDY8xoUFISVK1dCLBbDzs4OvXv3RnBwcJmK1sTENJO75A3lcna2RXy8wthhUCkwV+aBeTIfzJX5YK7MB3NlHpgn82HquRKLRXB0tClyfYlFq7OzM3x9fXH48GEMHToUhw8fhq+vr8H5rADQv39/fP/99+jbty+SkpJw8uRJ7Nu3DwBQt25dnDlzBq1atYJSqcS5c+fw4osvlumBFPcgyDSwN9x8MFfmgXkyH8yV+WCuzAdzZR6YJ/NhzrkSCaWYo//+/fuYM2cOUlJSYG9vj8DAQDRs2BABAQGYNm0aWrZsCY1Gg6VLl+Lvv/8GAAQEBGDEiBEAgLCwMCxatAhxcXHQaDTo3LkzPvroI0ilvEwsERERERERFa1URSsRERERERGRMZQ4ezARERERERGRsbBoJSIiIiIiIpPFopWIiIiIiIhMFotWIiIiIiIiMlksWomIiIiIiMhksWglIiIiIiIik8WilYiIiIiIiEyW1NgBkGlLTEzEhx9+iLCwMMjlctSrVw9Lly6Fk5MTmjVrhqZNm0Is1h37WLNmDZo1awYAOH36NNasWQONRgM/Pz+sWrUKVlZWxnwoNULv3r0hl8thYWEBAPjggw/Qo0cP/Pvvv1i4cCGysrJQp04drF27Fs7OzgBQ7DqqHOHh4Zg8ebJ+OTU1FQqFAhcuXCgyhwBzVRUCAwNx/PhxRERE4NChQ2jatCkA4OHDh5gzZw6SkpLg4OCAwMBA1K9f/5nW0bMpLFfFfWcB4PeWkRT1virv5x0/CytPYbkq7jsLKH8eqfyK+6wr73vH5HMlEBUjMTFROH/+vH559erVwty5cwVBEISmTZsKCoWiwG0UCoXQrVs34eHDh4IgCMK8efOETZs2VUm8NV2vXr2EO3fuGLRpNBqhT58+wsWLFwVBEITNmzcLc+bMKXEdVZ3ly5cLS5YsEQSh8BwKAnNVVS5evChERkYWyMOYMWOEX375RRAEQfjll1+EMWPGPPM6ejaF5aq47yxB4PeWsRT1virP5x0/CytXUbnKK+93liDwe8sYivqsK+97xxxyxeHBVCwHBwd07txZv9ymTRtERkYWe5szZ86gRYsW+t6E119/Hb/++mtlhknFuHHjBiwsLNChQwcAunwcO3asxHVUNZRKJQ4dOoRXXnml2O2Yq6rRoUMHeHh4GLTFx8cjNDQUgwYNAgAMGjQIoaGhSEhIKPc6enaF5ao831kAv7cqW2G5Kg6/t4ynpFyV9jsLYK4qU1GfdeV975hDrjg8mEpNq9Xim2++Qe/evfVtY8aMgUajQc+ePTF16lTI5XJERUXB09NTv42npyeioqKMEXKN9MEHH0AQBLRv3x7vvfdegXw4OTlBq9UiKSmp2HUODg5GiL7mOX36NNzd3eHn56dvy59De3t75sqIoqKi4O7uDolEAgCQSCRwc3NDVFQUBEEo17qc4apUeQr7zgL4vWVqyvp5x89C4yrsOwvg95Yx5f2sK+97xxxyxZ5WKrVly5bB2toao0ePBgD88ccf+Omnn7Bv3z7cu3cPmzdvNnKEtG/fPhw8eBA//vgjBEHA0qVLjR0SleDHH380OGLNHBJVjPzfWQC/t0wNP+/MT/7vLIB5NLbCPuuqIxatVCqBgYF4/PgxNmzYoJ/AImf4iK2tLYYPH47Lly/r2/MOx4qMjCzTsCAqv5znWS6XY+TIkbh8+XKBfCQkJEAsFsPBwaHYdVT5oqOjcfHiRQwePFjfVlgOc9qZK+Pw8PBAdHQ0NBoNAECj0SAmJgYeHh7lXkeVq7DvLIDfW6amPJ93/Cw0nsK+swB+bxlT/s+68r53zCFXLFqpROvXr8eNGzewefNmyOVyAEBycjIyMzMBAGq1GsePH4evry8AoEePHrh+/ToePXoEANi/fz8GDBhglNhrkvT0dKSmpgIABEHA0aNH4evrixYtWiAzMxMhISEAdPno378/ABS7jirfzz//jOeffx6Ojo4Ais4hwFwZk7OzM3x9fXH48GEAwOHDh+Hr6wsnJ6dyr6PKU9h3FsDvLVNT3s87fhYaT/7vLIDfW8ZU2Gdded875pArkSAIgrGDINN19+5dDBo0CPXr14elpSUAoG7dupgwYQIWLlwIkUgEtVqNtm3bYt68ebCxsQEAnDx5EmvXroVWq4Wvry9Wr14Na2trYz6Uau/JkyeYOnUqNBoNtFotGjVqhPnz58PNzQ2XL1/GokWLDKYxd3FxAYBi11Hl6tevHz766CP07NkTQPE5BJirqrB8+XKcOHECcXFxcHR0hIODA44cOYL79+9jzpw5SElJgb29PQIDA9GwYUMAKPc6ejaF5WrDhg2Ffmdt3rwZV65c4feWkRSWq61bt5b7846fhZWnqM9AoOB3FsDvLWMp6vf55s2by/3eMfVcsWglIiIiIiIik8XhwURERERERGSyWLQSERERERGRyWLRSkRERERERCaLRSsRERERERGZLBatREREREREZLJYtBIREREREZHJYtFKRERkJJs2bcIHH3xg7DCIiIhMGotWIiIiIiIiMlkiQRAEYwdBRERU3W3fvh179+6FQqGAm5sb5s6diylTpkAQBMjlcnh5eeHgwYNITU3FqlWrcObMGYhEIrz88suYNm0aJBIJfvrpJ3z33Xdo3rw5Dhw4AFdXVyxatAhdu3Y19sMjIiKqNFJjB0BERFTdPXjwAPv27cMPP/wAd3d3hIeHQ6vVYtKkSXj8+DHWrVun33bOnDlwdnbGiRMnkJGRgUmTJsHDwwOvv/46AODatWvo378/zp8/j99++w1TpkzBqVOn4ODgYKRHR0REVLk4PJiIiKiSSSQSKJVK3L9/HyqVCnXr1oW3t3eB7eLi4vDnn39i3rx5sLa2hrOzM/z9/XHkyBH9Nk5OThg3bhxkMhkGDhyIBg0a4I8//qjCR0NERFS12NNKRERUyerVq4d58+Zh06ZNuHfvHrp37445c+YU2C4yMhJqtRrdu3fXt2m1Wnh4eOiX3d3dIRKJ9Muenp6IiYmp3AdARERkRCxaiYiIqsDgwYMxePBgKBQKLFy4EOvWrUO9evUMtqlduzbkcjnOnz8PqbTwr+jo6GgIgqAvXKOiotC7d+9Kj5+IiMhYODyYiIiokj148ADnzp2DUqmEXC6HhYUFxGIxnJ2dERERAa1WCwBwc3PDc889h9WrV0OhUECr1SIsLAwXLlzQ7yshIQF79uyBSqXCr7/+ivv37+P555831kMjIiKqdOxpJSIiqmRKpRIff/wx7t+/D5lMhrZt22Lp0qWQy+U4ePAgOnfujLp16+Lnn3/GmjVrsG7dOgwcOBBpaWnw8vJCQECAfl+tWrXC48eP0aVLF7i4uODTTz+Fo6OjER8dERFR5eIlb4iIiMzETz/9hO+//x7ffPONsUMhIiKqMhweTERERERERCaLRSsRERERERGZLA4PJiIiIiIiIpPFnlYiIiIiIiIyWSxaiYiIiIiIyGSxaCUiIiIiIiKTxaKViIiIiIiITBaLViIiIiIiIjJZLFqJiIiIiIjIZP0/btmgXGE2VjQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k 20\\n\",\n      \"kp20k_valid2k_test 20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dataset_groups = [\\n\",\n    \"    ['kp20k', 'kp20k_test', 'kp20k_valid2k_test'],\\n\",\n    \"#     ['kp20k', 'duc_test'],\\n\",\n    \"#     ['openkp', 'openkp_test', 'openkp_valid2k_test'],\\n\",\n    \"#     ['openkp', 'duc_test']\\n\",\n    \"#     ['kptimes', 'kptimes_test', 'kptimes_valid2k_test', 'jptimes_test'],\\n\",\n    \"#     ['kptimes', 'duc_test']\\n\",\n    \"#     ['stackex', 'stackex_test', 'stackex_valid2k_test'],\\n\",\n    \"#     ['stackex', 'duc_test']\\n\",\n    \"#     ['duc_test']\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"for dataset_group in dataset_groups:\\n\",\n    \"    data_grps = kp_df.loc[kp_df.test_dataset.isin(dataset_group)]\\n\",\n    \"    data_grps = data_grps.sort_values(by='step', ascending=True)\\n\",\n    \"    print(len(data_grps))\\n\",\n    \"\\n\",\n    \"    anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"    peak_box_props = dict(boxstyle=\\\"round\\\", fc=\\\"w\\\", ec=\\\"0.5\\\", alpha=0.8)\\n\",\n    \"    \\n\",\n    \"    print(len(data_grps))\\n\",\n    \"    for exp_name, exp_grp in data_grps.groupby(['exp_name']):\\n\",\n    \"        if dataset_group[0] not in exp_name and dataset_group[0] != 'duc_test':\\n\",\n    \"            continue\\n\",\n    \"        \\n\",\n    \"        fig, ax = plt.subplots(figsize=(16,5))\\n\",\n    \"    \\n\",\n    \"        print(exp_name, len(exp_grp))\\n\",\n    \"        for test_dataset, data_grp in exp_grp.groupby(['test_dataset']): \\n\",\n    \"            print(test_dataset, len(data_grp))\\n\",\n    \"            peak_x, peak_y = peak_index(data_grp, x_index='step', y_index=anchor_metric_name)\\n\",\n    \"            ax.annotate('%s peak=%.3f (@step=%d)' % (exp_name, peak_y, peak_x), xy=(peak_x, peak_y), textcoords='data', size=8, bbox=peak_box_props)\\n\",\n    \"            ax = data_grp.plot(ax=ax, kind='line', x='step', y=anchor_metric_name, label=exp_name+' - '+test_dataset[:-5], style='-o', markersize=8.0, linewidth=4)\\n\",\n    \"\\n\",\n    \"            threshold = 0.1 # kp20k=0.1, openkp=0.2, kptimes=0.25, stackex=0.3, duc=0.06\\n\",\n    \"            if len(data_grp[data_grp[anchor_metric_name] < threshold]) > 0:\\n\",\n    \"                display(data_grp[data_grp[anchor_metric_name] < threshold]['step'])\\n\",\n    \"                for pred_path in data_grp[data_grp[anchor_metric_name] < threshold]['pred_file_path']:\\n\",\n    \"                    eval_path = pred_path[:-4] + 'spacyeval'\\n\",\n    \"                    report_path = pred_path[:-4] + 'report'\\n\",\n    \"                    \\n\",\n    \"                    if os.path.exists(pred_path):  \\n\",\n    \"                        print(pred_path)\\n\",\n    \"#                         os.remove(pred_path)\\n\",\n    \"#                     if os.path.exists(eval_path):  \\n\",\n    \"#                         print(eval_path)\\n\",\n    \"#                         os.remove(eval_path)\\n\",\n    \"#                     if os.path.exists(report_path):  \\n\",\n    \"#                         print(report_path)\\n\",\n    \"#                         os.remove(report_path)\\n\",\n    \"                    \\n\",\n    \"    #         display(grp)\\n\",\n    \"    \\n\",\n    \"        plt.legend(loc='best')\\n\",\n    \"        plt.show()\\n\",\n    \"    \\n\",\n    \"    break\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Delete problematic pred/eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"460\\n\",\n      \"bart_presabs_kp20k_fewshot100 22\\n\",\n      \"kp20k_test 10\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>#ab_doc</th>\\n\",\n       \"      <th>#ab_pred</th>\\n\",\n       \"      <th>#ab_tgt</th>\\n\",\n       \"      <th>#beam</th>\\n\",\n       \"      <th>#beamstep</th>\\n\",\n       \"      <th>#doc</th>\\n\",\n       \"      <th>#dup_pred</th>\\n\",\n       \"      <th>#pre_doc</th>\\n\",\n       \"      <th>#pre_pred</th>\\n\",\n       \"      <th>#pre_tgt</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_recall@1</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_recall@3</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>16637</td>\\n\",\n       \"      <td>36.230150</td>\\n\",\n       \"      <td>1.987292</td>\\n\",\n       \"      <td>55.312053</td>\\n\",\n       \"      <td>2157.181768</td>\\n\",\n       \"      <td>19987</td>\\n\",\n       \"      <td>122.938710</td>\\n\",\n       \"      <td>18989</td>\\n\",\n       \"      <td>1.233152</td>\\n\",\n       \"      <td>3.275179</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.053823</td>\\n\",\n       \"      <td>0.028991</td>\\n\",\n       \"      <td>0.018529</td>\\n\",\n       \"      <td>0.036002</td>\\n\",\n       \"      <td>0.032030</td>\\n\",\n       \"      <td>0.035037</td>\\n\",\n       \"      <td>0.036026</td>\\n\",\n       \"      <td>0.028991</td>\\n\",\n       \"      <td>200</td>\\n\",\n       \"      <td>kp20k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>16637</td>\\n\",\n       \"      <td>107.204283</td>\\n\",\n       \"      <td>1.987292</td>\\n\",\n       \"      <td>257.056136</td>\\n\",\n       \"      <td>8689.528243</td>\\n\",\n       \"      <td>19987</td>\\n\",\n       \"      <td>2083.173863</td>\\n\",\n       \"      <td>18989</td>\\n\",\n       \"      <td>7.714164</td>\\n\",\n       \"      <td>3.275179</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.101193</td>\\n\",\n       \"      <td>0.118781</td>\\n\",\n       \"      <td>0.053389</td>\\n\",\n       \"      <td>0.224639</td>\\n\",\n       \"      <td>0.128430</td>\\n\",\n       \"      <td>0.175775</td>\\n\",\n       \"      <td>0.230663</td>\\n\",\n       \"      <td>0.118781</td>\\n\",\n       \"      <td>900</td>\\n\",\n       \"      <td>kp20k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>16637</td>\\n\",\n       \"      <td>107.275979</td>\\n\",\n       \"      <td>1.987292</td>\\n\",\n       \"      <td>251.395207</td>\\n\",\n       \"      <td>8645.629759</td>\\n\",\n       \"      <td>19987</td>\\n\",\n       \"      <td>2137.247561</td>\\n\",\n       \"      <td>18989</td>\\n\",\n       \"      <td>7.733277</td>\\n\",\n       \"      <td>3.275179</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.101692</td>\\n\",\n       \"      <td>0.118378</td>\\n\",\n       \"      <td>0.052101</td>\\n\",\n       \"      <td>0.224919</td>\\n\",\n       \"      <td>0.127971</td>\\n\",\n       \"      <td>0.176442</td>\\n\",\n       \"      <td>0.229821</td>\\n\",\n       \"      <td>0.118378</td>\\n\",\n       \"      <td>1100</td>\\n\",\n       \"      <td>kp20k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>16637</td>\\n\",\n       \"      <td>116.085556</td>\\n\",\n       \"      <td>1.987292</td>\\n\",\n       \"      <td>266.844649</td>\\n\",\n       \"      <td>9022.422274</td>\\n\",\n       \"      <td>19987</td>\\n\",\n       \"      <td>2128.525392</td>\\n\",\n       \"      <td>18989</td>\\n\",\n       \"      <td>7.515135</td>\\n\",\n       \"      <td>3.275179</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.102611</td>\\n\",\n       \"      <td>0.117059</td>\\n\",\n       \"      <td>0.051837</td>\\n\",\n       \"      <td>0.219297</td>\\n\",\n       \"      <td>0.125725</td>\\n\",\n       \"      <td>0.173609</td>\\n\",\n       \"      <td>0.223735</td>\\n\",\n       \"      <td>0.117059</td>\\n\",\n       \"      <td>1600</td>\\n\",\n       \"      <td>kp20k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>16637</td>\\n\",\n       \"      <td>58.192925</td>\\n\",\n       \"      <td>1.987292</td>\\n\",\n       \"      <td>107.973683</td>\\n\",\n       \"      <td>3925.345324</td>\\n\",\n       \"      <td>19987</td>\\n\",\n       \"      <td>619.078501</td>\\n\",\n       \"      <td>18989</td>\\n\",\n       \"      <td>1.090609</td>\\n\",\n       \"      <td>3.275179</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.064991</td>\\n\",\n       \"      <td>0.031534</td>\\n\",\n       \"      <td>0.022436</td>\\n\",\n       \"      <td>0.035021</td>\\n\",\n       \"      <td>0.033784</td>\\n\",\n       \"      <td>0.034912</td>\\n\",\n       \"      <td>0.035021</td>\\n\",\n       \"      <td>0.031534</td>\\n\",\n       \"      <td>1800</td>\\n\",\n       \"      <td>kp20k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 217 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   #ab_doc    #ab_pred   #ab_tgt       #beam    #beamstep   #doc    #dup_pred  \\\\\\n\",\n       \"0    16637   36.230150  1.987292   55.312053  2157.181768  19987   122.938710   \\n\",\n       \"4    16637  107.204283  1.987292  257.056136  8689.528243  19987  2083.173863   \\n\",\n       \"5    16637  107.275979  1.987292  251.395207  8645.629759  19987  2137.247561   \\n\",\n       \"6    16637  116.085556  1.987292  266.844649  9022.422274  19987  2128.525392   \\n\",\n       \"9    16637   58.192925  1.987292  107.973683  3925.345324  19987   619.078501   \\n\",\n       \"\\n\",\n       \"   #pre_doc  #pre_pred  #pre_tgt  ...  present_partial_precision_hard@M  \\\\\\n\",\n       \"0     18989   1.233152  3.275179  ...                          0.053823   \\n\",\n       \"4     18989   7.714164  3.275179  ...                          0.101193   \\n\",\n       \"5     18989   7.733277  3.275179  ...                          0.101692   \\n\",\n       \"6     18989   7.515135  3.275179  ...                          0.102611   \\n\",\n       \"9     18989   1.090609  3.275179  ...                          0.064991   \\n\",\n       \"\\n\",\n       \"   present_partial_precision_hard@k  present_partial_recall@1  \\\\\\n\",\n       \"0                          0.028991                  0.018529   \\n\",\n       \"4                          0.118781                  0.053389   \\n\",\n       \"5                          0.118378                  0.052101   \\n\",\n       \"6                          0.117059                  0.051837   \\n\",\n       \"9                          0.031534                  0.022436   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@10  present_partial_recall@3  \\\\\\n\",\n       \"0                   0.036002                  0.032030   \\n\",\n       \"4                   0.224639                  0.128430   \\n\",\n       \"5                   0.224919                  0.127971   \\n\",\n       \"6                   0.219297                  0.125725   \\n\",\n       \"9                   0.035021                  0.033784   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@5  present_partial_recall@M  \\\\\\n\",\n       \"0                  0.035037                  0.036026   \\n\",\n       \"4                  0.175775                  0.230663   \\n\",\n       \"5                  0.176442                  0.229821   \\n\",\n       \"6                  0.173609                  0.223735   \\n\",\n       \"9                  0.034912                  0.035021   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@k  step  test_dataset  \\n\",\n       \"0                  0.028991   200    kp20k_test  \\n\",\n       \"4                  0.118781   900    kp20k_test  \\n\",\n       \"5                  0.118378  1100    kp20k_test  \\n\",\n       \"6                  0.117059  1600    kp20k_test  \\n\",\n       \"9                  0.031534  1800    kp20k_test  \\n\",\n       \"\\n\",\n       \"[5 rows x 217 columns]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k_valid2k_test 12\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>#ab_doc</th>\\n\",\n       \"      <th>#ab_pred</th>\\n\",\n       \"      <th>#ab_tgt</th>\\n\",\n       \"      <th>#beam</th>\\n\",\n       \"      <th>#beamstep</th>\\n\",\n       \"      <th>#doc</th>\\n\",\n       \"      <th>#dup_pred</th>\\n\",\n       \"      <th>#pre_doc</th>\\n\",\n       \"      <th>#pre_pred</th>\\n\",\n       \"      <th>#pre_tgt</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>present_partial_precision_hard@M</th>\\n\",\n       \"      <th>present_partial_precision_hard@k</th>\\n\",\n       \"      <th>present_partial_recall@1</th>\\n\",\n       \"      <th>present_partial_recall@10</th>\\n\",\n       \"      <th>present_partial_recall@3</th>\\n\",\n       \"      <th>present_partial_recall@5</th>\\n\",\n       \"      <th>present_partial_recall@M</th>\\n\",\n       \"      <th>present_partial_recall@k</th>\\n\",\n       \"      <th>step</th>\\n\",\n       \"      <th>test_dataset</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>1671</td>\\n\",\n       \"      <td>107.5235</td>\\n\",\n       \"      <td>2.0025</td>\\n\",\n       \"      <td>256.6530</td>\\n\",\n       \"      <td>8679.4840</td>\\n\",\n       \"      <td>2000</td>\\n\",\n       \"      <td>2089.9670</td>\\n\",\n       \"      <td>1895</td>\\n\",\n       \"      <td>7.7045</td>\\n\",\n       \"      <td>3.2625</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.102567</td>\\n\",\n       \"      <td>0.120510</td>\\n\",\n       \"      <td>0.053238</td>\\n\",\n       \"      <td>0.227533</td>\\n\",\n       \"      <td>0.126723</td>\\n\",\n       \"      <td>0.176313</td>\\n\",\n       \"      <td>0.233133</td>\\n\",\n       \"      <td>0.120510</td>\\n\",\n       \"      <td>900</td>\\n\",\n       \"      <td>kp20k_valid2k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1671</td>\\n\",\n       \"      <td>109.2775</td>\\n\",\n       \"      <td>2.0025</td>\\n\",\n       \"      <td>252.5100</td>\\n\",\n       \"      <td>8682.7465</td>\\n\",\n       \"      <td>2000</td>\\n\",\n       \"      <td>2160.0810</td>\\n\",\n       \"      <td>1895</td>\\n\",\n       \"      <td>7.6785</td>\\n\",\n       \"      <td>3.2625</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.101695</td>\\n\",\n       \"      <td>0.120038</td>\\n\",\n       \"      <td>0.051983</td>\\n\",\n       \"      <td>0.227052</td>\\n\",\n       \"      <td>0.129137</td>\\n\",\n       \"      <td>0.178667</td>\\n\",\n       \"      <td>0.231937</td>\\n\",\n       \"      <td>0.120038</td>\\n\",\n       \"      <td>1100</td>\\n\",\n       \"      <td>kp20k_valid2k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>1671</td>\\n\",\n       \"      <td>115.3735</td>\\n\",\n       \"      <td>2.0025</td>\\n\",\n       \"      <td>266.1735</td>\\n\",\n       \"      <td>8992.7490</td>\\n\",\n       \"      <td>2000</td>\\n\",\n       \"      <td>2132.1625</td>\\n\",\n       \"      <td>1895</td>\\n\",\n       \"      <td>7.5090</td>\\n\",\n       \"      <td>3.2625</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0.100634</td>\\n\",\n       \"      <td>0.118080</td>\\n\",\n       \"      <td>0.053482</td>\\n\",\n       \"      <td>0.218367</td>\\n\",\n       \"      <td>0.127855</td>\\n\",\n       \"      <td>0.172713</td>\\n\",\n       \"      <td>0.223205</td>\\n\",\n       \"      <td>0.118080</td>\\n\",\n       \"      <td>1600</td>\\n\",\n       \"      <td>kp20k_valid2k_test</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>3 rows × 217 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   #ab_doc  #ab_pred  #ab_tgt     #beam  #beamstep  #doc  #dup_pred  #pre_doc  \\\\\\n\",\n       \"4     1671  107.5235   2.0025  256.6530  8679.4840  2000  2089.9670      1895   \\n\",\n       \"5     1671  109.2775   2.0025  252.5100  8682.7465  2000  2160.0810      1895   \\n\",\n       \"7     1671  115.3735   2.0025  266.1735  8992.7490  2000  2132.1625      1895   \\n\",\n       \"\\n\",\n       \"   #pre_pred  #pre_tgt  ...  present_partial_precision_hard@M  \\\\\\n\",\n       \"4     7.7045    3.2625  ...                          0.102567   \\n\",\n       \"5     7.6785    3.2625  ...                          0.101695   \\n\",\n       \"7     7.5090    3.2625  ...                          0.100634   \\n\",\n       \"\\n\",\n       \"   present_partial_precision_hard@k  present_partial_recall@1  \\\\\\n\",\n       \"4                          0.120510                  0.053238   \\n\",\n       \"5                          0.120038                  0.051983   \\n\",\n       \"7                          0.118080                  0.053482   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@10  present_partial_recall@3  \\\\\\n\",\n       \"4                   0.227533                  0.126723   \\n\",\n       \"5                   0.227052                  0.129137   \\n\",\n       \"7                   0.218367                  0.127855   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@5  present_partial_recall@M  \\\\\\n\",\n       \"4                  0.176313                  0.233133   \\n\",\n       \"5                  0.178667                  0.231937   \\n\",\n       \"7                  0.172713                  0.223205   \\n\",\n       \"\\n\",\n       \"   present_partial_recall@k  step        test_dataset  \\n\",\n       \"4                  0.120510   900  kp20k_valid2k_test  \\n\",\n       \"5                  0.120038  1100  kp20k_valid2k_test  \\n\",\n       \"7                  0.118080  1600  kp20k_valid2k_test  \\n\",\n       \"\\n\",\n       \"[3 rows x 217 columns]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dataset_groups = [\\n\",\n    \"    ['kp20k', 'kp20k_test', 'kp20k_valid2k_test'],\\n\",\n    \"#     ['openkp', 'openkp_test', 'openkp_valid2k_test', 'duc_test'],\\n\",\n    \"#     ['kptimes', 'kptimes_test', 'kptimes_valid2k_test', 'jptimes_test', 'duc_test'],\\n\",\n    \"#     ['stackex', 'stackex_test', 'stackex_valid2k_test'],\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"for dataset_group in dataset_groups:\\n\",\n    \"    data_grps = kp_df.loc[kp_df.test_dataset.isin(dataset_group)]\\n\",\n    \"    data_grps = data_grps.sort_values(by='step', ascending=True)\\n\",\n    \"    print(len(data_grps))\\n\",\n    \"\\n\",\n    \"    anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"    \\n\",\n    \"    for exp_name, exp_grp in data_grps.groupby(['exp_name']):\\n\",\n    \"        \\n\",\n    \"        print(exp_name, len(exp_grp))\\n\",\n    \"        for test_dataset, data_grp in exp_grp.groupby(['test_dataset']): \\n\",\n    \"            print(test_dataset, len(data_grp))\\n\",\n    \"            \\n\",\n    \"            if len(data_grp[data_grp[anchor_metric_name] < 0.1]):\\n\",\n    \"                display(data_grp[data_grp[anchor_metric_name] < 0.1])\\n\",\n    \"\\n\",\n    \"        break\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Others\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"kp_df = None\\n\",\n    \"\\n\",\n    \"train_test_mappings = {\\n\",\n    \"    'kp20k': ['kp20k', 'kp20k_valid2k', 'inspec', 'krapivin', 'semeval', 'nus', 'duc'],\\n\",\n    \"#     'openkp': ['openkp', 'openkp_valid2k', 'duc'],\\n\",\n    \"#     'kptimes': ['kptimes', 'kptimes_valid2k', 'jptimes', 'duc'],\\n\",\n    \"#     'stackex': ['stackex', 'stackex_valid2k', 'duc'],\\n\",\n    \"}\\n\",\n    \"test_train_mappings = {}\\n\",\n    \"for train, tests in train_test_mappings.items():\\n\",\n    \"    for test in tests:\\n\",\n    \"        test_train_mappings[test] = train\\n\",\n    \"\\n\",\n    \"for fname in os.listdir(report_dir):\\n\",\n    \"    if not fname.endswith('.spacy.csv'): continue\\n\",\n    \"    kp_df = pd.read_csv(os.path.join(report_dir, fname))\\n\",\n    \"    kp_df = kp_df.loc[kp_df.pred_name == pred_name]\\n\",\n    \"    kp_df = kp_df.sort_values(by='step', ascending=True)\\n\",\n    \"\\n\",\n    \"    test_dataset_name = fname[: fname.find('_test')]\\n\",\n    \"    corr_train_name = test_train_mappings[test_dataset_name]\\n\",\n    \"    exp_names = kp_df.exp_name.unique()\\n\",\n    \"    \\n\",\n    \"#     print(fname)\\n\",\n    \"    print(test_dataset_name)\\n\",\n    \"    print(corr_train_name)\\n\",\n    \"#     print(len(kp_df))\\n\",\n    \"#     print(kp_df.columns)\\n\",\n    \"#     [print(c) for c in kp_df.columns]\\n\",\n    \"#     print(kp_df.exp_name.unique())\\n\",\n    \"\\n\",\n    \"    anchor_metric_name = 'all_exact_f_score@k'\\n\",\n    \"\\n\",\n    \"    plot_testing_curve(kp_df, y_index=anchor_metric_name, title='fewshot curve, '+ anchor_metric_name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot One2Seq (beam_width=50) hard metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:56: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: RuntimeWarning: divide by zero encountered in double_scalars\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:57: RuntimeWarning: divide by zero encountered in double_scalars\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1']\\n\",\n    \"# exp_eval_df = all_eval_df.loc[all_eval_df['exp_name'] == 'kp20k-meng17-verbatim_append-rnn-BS64-OPTadagrad-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse']\\n\",\n    \"\\n\",\n    \"# print(exp_eval_df.shape)\\n\",\n    \"# print(exp_eval_df.test_dataset.value_counts())\\n\",\n    \"# print(exp_eval_df.step.value_counts())\\n\",\n    \"\\n\",\n    \"exp_eval_df = exp_eval_df.sort_values(by='step', ascending=True)\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.beam_width == '50']\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_terminate == 'fullbeam'] # topbeamends/fullbeam\\n\",\n    \"exp_eval_df = exp_eval_df.loc[exp_eval_df.decoding_method == 'exhaustive'] # exhaustive/selfterminating\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# print(exp_eval_df.decoding_terminate.value_counts())\\n\",\n    \"# print(exp_eval_df.shape)\\n\",\n    \"# display(exp_eval_df)\\n\",\n    \"# print(exp_eval_df.path.unique())\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[exp_eval_df.step % 10000 == 0] # keep % 10000\\n\",\n    \"# exp_eval_df = exp_eval_df.loc[(exp_eval_df.step % 10000 == 4000) | (exp_eval_df.step % 5000 == 0)] # keep % 10000 and 5000\\n\",\n    \"\\n\",\n    \"# plot 7 datasets\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_f_score_hard@10', title='All datasets, present_exact_f_score_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_precision_hard@10', title='All datasets, present_exact_precision_hard@10')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_recall@10', title='All datasets, present_exact_recall@10')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@50', title='All datasets, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot kp20k and kp20k_valid2k\\n\",\n    \"kp20k_eval_df = exp_eval_df[exp_eval_df.test_dataset.str.startswith('kp20k')]\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='present_exact_f_score_hard@10', title='KP20k and KP20k_valid2k, present_exact_f_score_hard@10')\\n\",\n    \"plot_testing_curve(kp20k_eval_df, y_index='absent_exact_recall@50', title='KP20k and KP20k_valid2k, absent_exact_recall@50')\\n\",\n    \"\\n\",\n    \"# plot recall@M\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='present_exact_recall@M', title='All datasets, present_exact_recall@M')\\n\",\n    \"plot_testing_curve(exp_eval_df, y_index='absent_exact_recall@M', title='All datasets, absent_exact_recall@M')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='unique_pred_num', title='All datasets, unique_pred_num')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_sadr', title='All datasets, present_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_sadr', title='All datasets, absent_exact_advanced_sadr')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='present_exact_advanced_auc', title='All datasets, present_exact_advanced_auc')\\n\",\n    \"# plot_testing_curve(exp_eval_df, y_index='absent_exact_advanced_auc', title='All datasets, absent_exact_advanced_auc')\\n\",\n    \"\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"349.091px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  },\n  \"varInspector\": {\n   \"cols\": {\n    \"lenName\": 16,\n    \"lenType\": 16,\n    \"lenVar\": 40\n   },\n   \"kernels_config\": {\n    \"python\": {\n     \"delete_cmd_postfix\": \"\",\n     \"delete_cmd_prefix\": \"del \",\n     \"library\": \"var_list.py\",\n     \"varRefreshCmd\": \"print(var_dic_list())\"\n    },\n    \"r\": {\n     \"delete_cmd_postfix\": \") \",\n     \"delete_cmd_prefix\": \"rm(\",\n     \"library\": \"var_list.r\",\n     \"varRefreshCmd\": \"cat(var_dic_list()) \"\n    }\n   },\n   \"types_to_exclude\": [\n    \"module\",\n    \"function\",\n    \"builtin_function_or_method\",\n    \"instance\",\n    \"_Feature\"\n   ],\n   \"window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/transfer_pseudo_labels_and_np.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\\n\",\n      \"  warnings.warn(msg, category=FutureWarning)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import json\\n\",\n    \"exec('from __future__ import unicode_literals')\\n\",\n    \"\\n\",\n    \"import copy\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import re\\n\",\n    \"import sys\\n\",\n    \"import random\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import spacy\\n\",\n    \"spacy_nlp = spacy.load('en_core_web_sm')\\n\",\n    \"\\n\",\n    \"module_path = os.path.abspath(os.path.join('../'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"module_path = os.path.abspath(os.path.join('../onmt'))\\n\",\n    \"if module_path not in sys.path:\\n\",\n    \"    sys.path.append(module_path)\\n\",\n    \"\\n\",\n    \"from onmt.translate.translator import build_translator\\n\",\n    \"from onmt.constants import ModelTask\\n\",\n    \"import onmt.keyphrase.eval as eval\\n\",\n    \"from onmt.keyphrase.pke.utils import compute_document_frequency\\n\",\n    \"from onmt.keyphrase.utils import validate_phrases, if_present_duplicate_phrases\\n\",\n    \"from onmt.utils.parse import ArgumentParser\\n\",\n    \"import onmt.keyphrase.pke as pke\\n\",\n    \"\\n\",\n    \"from kp_gen_eval_transfer import _get_parser\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<module 'onmt.keyphrase.eval' from '/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/onmt/keyphrase/eval.py'>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import importlib\\n\",\n    \"importlib.reload(eval)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Wiki-transferred KPs\\n\",\n    \"\\n\",\n    \"Also see **onmt.keyphrase.kp_inference.py**.\\n\",\n    \"\\n\",\n    \"This part of notebook is meant to manually find a good prefix `prompt` to generate good pseudo labels. The actually code to generate pseudo labels is\\n\",\n    \"```\\n\",\n    \"### for kp20k/openkp/kptimes/stackex\\n\",\n    \"python kp_gen_magkp_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled -data_dir /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\\n\",\n    \"\\n\",\n    \"### for Mag\\n\",\n    \"python kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled -data_dir /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Load functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def eval_and_print(src_text, tgt_kps, pred_kps, pred_scores, unk_token='<unk>'):\\n\",\n    \"    src_seq = [t.text.lower() for t in spacy_nlp(src_text, disable=[\\\"textcat\\\"])]\\n\",\n    \"    tgt_seqs = [[t.text.lower() for t in spacy_nlp(p, disable=[\\\"textcat\\\"])] for p in tgt_kps]\\n\",\n    \"    pred_seqs = [[t.text.lower() for t in spacy_nlp(p, disable=[\\\"textcat\\\"])] for p in pred_kps]\\n\",\n    \"\\n\",\n    \"    topk_range = ['k', 10]\\n\",\n    \"    absent_topk_range = ['M']\\n\",\n    \"    metric_names = ['f_score'] # 'precision', 'recall', 'f_score'\\n\",\n    \"\\n\",\n    \"    # 1st filtering, ignore phrases having <unk> and puncs\\n\",\n    \"    valid_pred_flags = validate_phrases(pred_seqs, unk_token)\\n\",\n    \"    # 2nd filtering: filter out phrases that don't appear in text, and keep unique ones after stemming\\n\",\n    \"    present_pred_flags, _, duplicate_flags = if_present_duplicate_phrases(src_seq, pred_seqs)\\n\",\n    \"    # treat duplicates as invalid\\n\",\n    \"    valid_pred_flags = valid_pred_flags * ~duplicate_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"    valid_and_present_flags = valid_pred_flags * present_pred_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"    valid_and_absent_flags = valid_pred_flags * ~present_pred_flags if len(valid_pred_flags) > 0 else []\\n\",\n    \"\\n\",\n    \"    # compute match scores (exact, partial and mixed), for exact it's a list otherwise matrix\\n\",\n    \"    match_scores_exact = eval.compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs,\\n\",\n    \"                                              do_lower=True, do_stem=True, type='exact')\\n\",\n    \"    # split tgts by present/absent\\n\",\n    \"    present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs)\\n\",\n    \"    present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\\n\",\n    \"    absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\\n\",\n    \"\\n\",\n    \"    # filter out results of invalid preds\\n\",\n    \"    valid_preds = [seq for seq, valid in zip(pred_seqs, valid_pred_flags) if valid]\\n\",\n    \"    valid_present_pred_flags = present_pred_flags[valid_pred_flags]\\n\",\n    \"\\n\",\n    \"    valid_match_scores_exact = match_scores_exact[valid_pred_flags]\\n\",\n    \"\\n\",\n    \"    # split preds by present/absent and exact/partial/mixed\\n\",\n    \"    valid_present_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if present]\\n\",\n    \"    valid_absent_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if ~present]\\n\",\n    \"    present_exact_match_scores = valid_match_scores_exact[valid_present_pred_flags]\\n\",\n    \"    absent_exact_match_scores = valid_match_scores_exact[~valid_present_pred_flags]\\n\",\n    \"\\n\",\n    \"    all_exact_results = eval.run_classic_metrics(valid_match_scores_exact, valid_preds, tgt_seqs, metric_names, topk_range)\\n\",\n    \"    present_exact_results = eval.run_classic_metrics(present_exact_match_scores, valid_present_preds, present_tgts, metric_names, topk_range)\\n\",\n    \"    absent_exact_results = eval.run_classic_metrics(absent_exact_match_scores, valid_absent_preds, absent_tgts, metric_names, absent_topk_range)\\n\",\n    \"\\n\",\n    \"    eval_results_names = ['all_exact', 'present_exact', 'absent_exact']\\n\",\n    \"    eval_results_list = [all_exact_results, present_exact_results, absent_exact_results]\\n\",\n    \"\\n\",\n    \"    print_out = print_predeval_result(src_text,\\n\",\n    \"                                      tgt_seqs, present_tgt_flags,\\n\",\n    \"                                      pred_seqs, pred_scores, present_pred_flags, valid_pred_flags,\\n\",\n    \"                                      valid_and_present_flags, valid_and_absent_flags, match_scores_exact,\\n\",\n    \"                                      eval_results_names, eval_results_list)\\n\",\n    \"\\n\",\n    \"    print('[#present_tgts=%d] ' % len(present_tgts), str(present_tgts))\\n\",\n    \"    print('[#absent_tgts=%d]' % len(absent_tgts), str(absent_tgts))\\n\",\n    \"    \\n\",\n    \"    print('[#valid_present_preds=%d]' % len(valid_present_preds), str(valid_present_preds))\\n\",\n    \"    print('[#valid_absent_preds=%d]' % len(valid_absent_preds), str(valid_absent_preds))\\n\",\n    \"    \\n\",\n    \"    print('match_scores_exact', str(match_scores_exact))\\n\",\n    \"    \\n\",\n    \"    print('valid_match_scores_exact', str(valid_match_scores_exact))\\n\",\n    \"    print('all_exact_results', str(all_exact_results))\\n\",\n    \"    \\n\",\n    \"    print('present_exact_match_scores', str(present_exact_match_scores))\\n\",\n    \"    print('present_exact_results', str(present_exact_results))\\n\",\n    \"    \\n\",\n    \"    print('absent_exact_match_scores', str(absent_exact_match_scores))\\n\",\n    \"    print('absent_exact_results', str(absent_exact_results))\\n\",\n    \"    print(print_out)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def print_predeval_result(src_text,\\n\",\n    \"                          tgt_seqs, present_tgt_flags,\\n\",\n    \"                          pred_seqs, pred_scores, present_pred_flags, valid_pred_flags,\\n\",\n    \"                          valid_and_present_flags, valid_and_absent_flags, match_scores_exact,\\n\",\n    \"                          results_names, results_list):\\n\",\n    \"    print_out = '=' * 50\\n\",\n    \"    print_out += '\\\\n[Source]: %s \\\\n' % (src_text)\\n\",\n    \"\\n\",\n    \"    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\\\n' % \\\\\\n\",\n    \"                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\\n\",\n    \"    print_out += '\\\\n'.join(['\\\\t\\\\t[%s]' % ' '.join(phrase) if is_present else '\\\\t\\\\t%s' % ' '.join(phrase) for phrase, is_present in zip(tgt_seqs, present_tgt_flags)])\\n\",\n    \"\\n\",\n    \"    print_out += '\\\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\\\\n\",\n    \"                 '#(valid&present)=%d, #(valid&absent)=%d\\\\n' % (len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags), sum(valid_and_present_flags), sum(valid_and_absent_flags))\\n\",\n    \"    print_out += ''\\n\",\n    \"    preds_out = ''\\n\",\n    \"    for p_id, (word, match, is_valid, is_present) in enumerate(zip(pred_seqs, match_scores_exact, valid_pred_flags, present_pred_flags)):\\n\",\n    \"        score = pred_scores[p_id] if pred_scores else \\\"Score N/A\\\"\\n\",\n    \"\\n\",\n    \"        preds_out += '%s\\\\n' % (' '.join(word))\\n\",\n    \"        if is_present:\\n\",\n    \"            print_phrase = '[%s]' % ' '.join(word)\\n\",\n    \"        else:\\n\",\n    \"            print_phrase = ' '.join(word)\\n\",\n    \"\\n\",\n    \"        if match == 1.0:\\n\",\n    \"            correct_str = '[correct!]'\\n\",\n    \"        else:\\n\",\n    \"            correct_str = ''\\n\",\n    \"\\n\",\n    \"        pred_str = '\\\\t\\\\t[%d] %s\\\\t%s \\\\t%s\\\\n' % (p_id + 1, '[%.4f]' % (-score) if pred_scores else \\\"Score N/A\\\",\\n\",\n    \"                                                print_phrase, correct_str)\\n\",\n    \"        if not is_valid:\\n\",\n    \"            pred_str = '\\\\t%s' % pred_str\\n\",\n    \"\\n\",\n    \"        print_out += pred_str\\n\",\n    \"\\n\",\n    \"    print_out += \\\"\\\\n ======================================================= \\\\n\\\"\\n\",\n    \"\\n\",\n    \"    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\\\n' % \\\\\\n\",\n    \"                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\\n\",\n    \"    print_out += '\\\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\\\\n\",\n    \"                 '#(valid&present)=%d, #(valid&absent)=%d\\\\n' % (len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags), sum(valid_and_present_flags), sum(valid_and_absent_flags))\\n\",\n    \"\\n\",\n    \"    for name, results in zip(results_names, results_list):\\n\",\n    \"        # print @5@10@O@M for present_exact, print @50@M for absent_exact\\n\",\n    \"        if name in ['all_exact', 'present_exact', 'absent_exact']:\\n\",\n    \"            if name.startswith('all') or name.startswith('present'):\\n\",\n    \"                topk_list = ['10', 'k']\\n\",\n    \"            else:\\n\",\n    \"                topk_list = ['M']\\n\",\n    \"\\n\",\n    \"            for topk in topk_list:\\n\",\n    \"                print_out += \\\"\\\\n --- batch {} F1 @{}: \\\\t\\\".format(name, topk) \\\\\\n\",\n    \"                             + \\\"{:.4f}\\\".format(results['f_score@{}'.format(topk)])\\n\",\n    \"        else:\\n\",\n    \"            # ignore partial for now\\n\",\n    \"            continue\\n\",\n    \"\\n\",\n    \"    print_out += \\\"\\\\n =======================================================\\\"\\n\",\n    \"\\n\",\n    \"    return print_out\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Load translator and model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"0\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/torchtext/data/field.py:150: UserWarning: Field class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\\n\",\n      \"  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/torchtext/data/field.py:36: UserWarning: TextMultiField class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\\n\",\n      \"  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\\n\",\n      \"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/torchtext/data/field.py:36: UserWarning: RawField class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.\\n\",\n      \"  warnings.warn('{} class will be retired soon and moved to torchtext.legacy. Please see the most recent release notes for further information.'.format(self.__class__.__name__), UserWarning)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading pretrained vocabulary from /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp\\n\",\n      \"Vocab size=50265, base vocab size=50265\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# specify GPU device\\n\",\n    \"print(torch.cuda.is_available())\\n\",\n    \"torch.cuda.set_device(0)\\n\",\n    \"print(torch.cuda.current_device())\\n\",\n    \"\\n\",\n    \"# Supervised Deep Keyphrase Model, using OpenNMT 2.x pipeline\\n\",\n    \"parser = _get_parser()\\n\",\n    \"config_path = '/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/config/transfer_kp/infer/keyphrase-one2seq-controlled.yml'\\n\",\n    \"opt = parser.parse_args('-config %s' % (config_path))\\n\",\n    \"\\n\",\n    \"ckpt_path = '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bart_kppretrain_wiki_1e5/ckpts/checkpoint_step_100000.pt'\\n\",\n    \"opt.__setattr__('models', [ckpt_path])\\n\",\n    \"opt.__setattr__('fairseq_model', True)\\n\",\n    \"opt.__setattr__('encoder_type', 'bart')\\n\",\n    \"opt.__setattr__('decoder_type', 'bart')\\n\",\n    \"opt.__setattr__('pretrained_tokenizer', True)\\n\",\n    \"opt.__setattr__('copy_attn', False)\\n\",\n    \"\\n\",\n    \"opt.__setattr__('valid_batch_size', 1)\\n\",\n    \"opt.__setattr__('batch_size_multiple', 1)\\n\",\n    \"opt.__setattr__('bucket_size', 128)\\n\",\n    \"opt.__setattr__('pool_factor', 256)\\n\",\n    \"\\n\",\n    \"opt.__setattr__('beam_size', 1)\\n\",\n    \"opt.__setattr__('gpu', 0)\\n\",\n    \"\\n\",\n    \"if isinstance(opt.data, str): setattr(opt, 'data', json.loads(opt.data.replace('\\\\'', '\\\"')))\\n\",\n    \"setattr(opt, 'data_task', ModelTask.SEQ2SEQ)\\n\",\n    \"ArgumentParser._get_all_transform(opt)\\n\",\n    \"\\n\",\n    \"translator = build_translator(opt, report_score=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# check if model is on GPU\\n\",\n    \"print(translator._gpu)\\n\",\n    \"print(translator._use_cuda)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loaded #(docs)=10000\\n\",\n      \"4399\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_name = 'jptimes'\\n\",\n    \"dataset_path = '/zfs1/hdaqing/rum20/kp/data/kp/json/%s/test.json' % dataset_name\\n\",\n    \"\\n\",\n    \"with open(dataset_path, 'r') as f:\\n\",\n    \"    ex_dicts = [json.loads(l) for l in f.readlines()]\\n\",\n    \"    for ex in ex_dicts:        \\n\",\n    \"        if dataset_name.startswith('openkp'):\\n\",\n    \"            ex['title'] = ''\\n\",\n    \"            ex['abstract'] = ex['text']\\n\",\n    \"            ex['keywords'] = ex['KeyPhrases']\\n\",\n    \"            ex['dataset_type'] = 'webpage'\\n\",\n    \"        elif dataset_name.startswith('stackex'):\\n\",\n    \"            ex['abstract'] = ex['question']\\n\",\n    \"            ex['keywords'] = ex['tags'].split(';')\\n\",\n    \"            ex['dataset_type'] = 'qa'\\n\",\n    \"        elif dataset_name.startswith('kp20k') or dataset_name.startswith('duc'):\\n\",\n    \"            ex['keywords'] = ex['keywords'].split(';') if isinstance(ex['keywords'], str) else ex['keywords']\\n\",\n    \"            ex['dataset_type'] = 'scipaper'\\n\",\n    \"        elif dataset_name.startswith('kptimes') or dataset_name.startswith('jptimes'):\\n\",\n    \"            ex['keywords'] = ex['keyword'].split(';') if isinstance(ex['keyword'], str) else ex['keyword']\\n\",\n    \"            ex['dataset_type'] = 'news'\\n\",\n    \"        else:\\n\",\n    \"            print('????')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print('Loaded #(docs)=%d' % (len(ex_dicts)))\\n\",\n    \"doc_id = random.randint(0, len(ex_dicts))\\n\",\n    \"doc_id = 4399\\n\",\n    \"ex_dict = ex_dicts[doc_id]\\n\",\n    \"\\n\",\n    \"print(doc_id)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Loading pretrained vocabulary from /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp\\n\",\n      \"Vocab size=50265, base vocab size=50265\\n\",\n      \"Loading pretrained vocabulary from /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Translating in batches: 0it [00:00, ?it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Vocab size=50265, base vocab size=50265\\n\",\n      \"<present>10<header>5<category>5<seealso>2<infill>0<s>Search warrants tied to former Trump lawyer Michael Cohen released  . WASHINGTON - Months before the FBI raided Michael Cohen’s office and hotel room, investigators were examining the flow of foreign money into his bank accounts and looking into whether the funds might be connected to a plan to lift sanctions on Russia, according to court filings unsealed Wednesday. The five search warrant applications, made in the early weeks and months of special counsel Robert Mueller’s Russia investigation in 2017, were made public in response to requests from The Associated Press and other media organizations. Ultimately, Cohen was not charged by Mueller or by prosecutors in New York with anything related to Russian collusion or illegal influence peddling, but the documents shed further light on how he capitalized financially on his closeness to the president immediately following the 2016 election. Cohen, who at the time was the president’s personal lawyer and a close aide and confidant, quickly cut deals to act as a highly paid consultant to several foreign and domestic companies with business interests linked to federal government decisions. Investigators said in the warrant applications that a corporate entity Cohen had created, Essential Consultants LLC, had received multiple deposits from foreign businesses and entities, including from companies they said had “significant ties to foreign governments or are entities controlled by foreign governments.” Essential Consultants received funds from U.S. and foreign corporations who appear to have approached Cohen “in connection with political objectives in the Trump administration,” investigators wrote. Investigators were especially curious about deposits of more than $416,000 from an account linked to an investment management firm, Columbus Nova, LLC. The warrants link that firm, and the holding company that controls it, to Viktor Vekselberg, a Russian oligarch with ties to Russian President Vladimir Putin. In an application to search his Trump Organization email account, prosecutors said Cohen exchanged over 230 phone calls and 950 text messages with the CEO of Columbus Nova between Nov. 8, 2016, and July 14, 2017. There were no text messages or phone calls before Election Day in 2016, prosecutors said. Columbus Nova has said in a statement that it is solely owned and controlled by Americans. It has described as false any allegation that Vekselberg used Columbus Nova as a conduit for payments to Cohen. The warrant applications also make clear that investigators at the time were examining whether any of the fund transfers were connected to Cohen’s involvement in a plan, described months earlier in a New York Times story, to try to get the U.S. to lift sanctions on Russia. Cohen has acknowledged offering his insights into Trump’s administration to multiple corporate clients, but said he broke no laws in doing so. Representatives for Cohen, who began serving a three-year prison sentence this month, declined comment Wednesday. The newly unsealed material reveals nothing about Trump’s own role in the crimes that put Cohen behind bars. Cohen is now serving a three-year prison sentence for tax evasion, lying to Congress about a Trump real estate project in Moscow, and campaign finance violations related to hush-money payments he orchestrated to two women who claimed to have had affairs with Trump, the porn actress Stormy Daniels and erotic model Karen McDougal. The warrant applications, which covered requests to search Cohen’s email accounts, including one associated with the Trump Organization, were blacked out in certain sections to protect the secrecy of an ongoing federal investigation into Cohen’s campaign finance crimes. Cohen has said he arranged payments to McDougal and Daniels at Trump’s behest, which the president has denied along with the affairs.\\n\",\n      \"michael cohen<sep>donald trump<sep>viktor vekselberg<sep>russia probe<sep>robert mueller<sep>u.s .\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Translating in batches: 1it [00:01,  1.53s/it]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Total translation time (s): 1.530445\\n\",\n      \"Average translation time (s): 1.530445\\n\",\n      \"Tokens per second: 1.306810\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_pres, num_header, num_cat, num_seealso, num_infill = 10, 5, 5, 2, 0\\n\",\n    \"\\n\",\n    \"control_prefix = '<present>%d<header>%d<category>%d<seealso>%d<infill>%d<s>' \\\\\\n\",\n    \"    % (num_pres, num_header, num_cat, num_seealso, num_infill)\\n\",\n    \"\\n\",\n    \"new_ex_dict = copy.copy(ex_dict)\\n\",\n    \"new_ex_dict['src_control_prefix'] = control_prefix\\n\",\n    \"# new_ex_dict['title'] = ex_dict['title']    \\n\",\n    \"\\n\",\n    \"scores, preds = translator.translate(\\n\",\n    \"    src=[new_ex_dict],\\n\",\n    \"    batch_size=opt.batch_size,\\n\",\n    \"    attn_debug=opt.attn_debug,\\n\",\n    \"    opt=opt\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[autoreload of onmt.translate.translator failed: Traceback (most recent call last):\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/IPython/extensions/autoreload.py\\\", line 245, in check\\n\",\n      \"    superreload(m, reload, self.old_objects)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/IPython/extensions/autoreload.py\\\", line 394, in superreload\\n\",\n      \"    module = reload(module)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/imp.py\\\", line 314, in reload\\n\",\n      \"    return importlib.reload(module)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/importlib/__init__.py\\\", line 169, in reload\\n\",\n      \"    _bootstrap._exec(spec, module)\\n\",\n      \"  File \\\"<frozen importlib._bootstrap>\\\", line 630, in _exec\\n\",\n      \"  File \\\"<frozen importlib._bootstrap_external>\\\", line 728, in exec_module\\n\",\n      \"  File \\\"<frozen importlib._bootstrap>\\\", line 219, in _call_with_frames_removed\\n\",\n      \"  File \\\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/onmt/translate/translator.py\\\", line 24, in <module>\\n\",\n      \"    from onmt.keyphrase.eval import eval_and_print\\n\",\n      \"ImportError: cannot import name 'eval_and_print' from 'onmt.keyphrase.eval' (/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/onmt/keyphrase/eval.py)\\n\",\n      \"]\\n\",\n      \"[autoreload of onmt.transforms.keyphrase failed: Traceback (most recent call last):\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/IPython/extensions/autoreload.py\\\", line 245, in check\\n\",\n      \"    superreload(m, reload, self.old_objects)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/site-packages/IPython/extensions/autoreload.py\\\", line 394, in superreload\\n\",\n      \"    module = reload(module)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/imp.py\\\", line 314, in reload\\n\",\n      \"    return importlib.reload(module)\\n\",\n      \"  File \\\"/ihome/hdaqing/rum20/anaconda3/envs/kp/lib/python3.7/importlib/__init__.py\\\", line 169, in reload\\n\",\n      \"    _bootstrap._exec(spec, module)\\n\",\n      \"  File \\\"<frozen importlib._bootstrap>\\\", line 630, in _exec\\n\",\n      \"  File \\\"<frozen importlib._bootstrap_external>\\\", line 728, in exec_module\\n\",\n      \"  File \\\"<frozen importlib._bootstrap>\\\", line 219, in _call_with_frames_removed\\n\",\n      \"  File \\\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/onmt/transforms/keyphrase.py\\\", line 13, in <module>\\n\",\n      \"    class KeyphraseTransform(Transform):\\n\",\n      \"  File \\\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/onmt/transforms/__init__.py\\\", line 33, in register_transfrom_cls\\n\",\n      \"    'Cannot register duplicate transform ({})'.format(name))\\n\",\n      \"ValueError: Cannot register duplicate transform (keyphrase)\\n\",\n      \"]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[#present_tgts=3]  [['robert', 'mueller'], ['michael', 'cohen'], ['viktor', 'vekselberg']]\\n\",\n      \"[#absent_tgts=3] [['u.s', '.'], ['donald', 'trump'], ['russia', 'probe']]\\n\",\n      \"[#valid_present_preds=8] [['viktor', 'vekselberg'], ['robert', 'mueller'], ['russian', 'president'], ['russian', 'collusion'], ['vladimir', 'putin'], ['michael', 'cohen'], ['fbi', 'raids'], ['were', 'made', 'public', 'in']]\\n\",\n      \"[#valid_absent_preds=8] [['donald', 'trump'], ['search', 'warrant', 'documents'], ['american', 'people', 'of', 'ukrainian', '-', 'jewish', 'descent'], ['american', 'people', 'of', 'russian', '-', 'jewish', 'descent'], ['list', 'of', 'topics', 'characterized', 'as', 'pseudoscience'], ['list', 'of', 'people', 'from', 'new', 'york', 'city'], ['cohen', 'family'], ['in', 'a', 'court', 'filing', 'on']]\\n\",\n      \"match_scores_exact [1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \"valid_match_scores_exact [1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \"all_exact_results {'f_score@k': 0.5, 'f_score@10': 0.5}\\n\",\n      \"present_exact_match_scores [1. 1. 0. 0. 0. 1. 0. 0.]\\n\",\n      \"present_exact_results {'f_score@k': 0.6666666666666666, 'f_score@10': 0.5454545454545454}\\n\",\n      \"absent_exact_match_scores [1. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n      \"absent_exact_results {'f_score@M': 0.18181818181818182}\\n\",\n      \"==================================================\\n\",\n      \"[Source]: Search warrants tied to former Trump lawyer Michael Cohen released  . WASHINGTON - Months before the FBI raided Michael Cohen’s office and hotel room, investigators were examining the flow of foreign money into his bank accounts and looking into whether the funds might be connected to a plan to lift sanctions on Russia, according to court filings unsealed Wednesday. The five search warrant applications, made in the early weeks and months of special counsel Robert Mueller’s Russia investigation in 2017, were made public in response to requests from The Associated Press and other media organizations. Ultimately, Cohen was not charged by Mueller or by prosecutors in New York with anything related to Russian collusion or illegal influence peddling, but the documents shed further light on how he capitalized financially on his closeness to the president immediately following the 2016 election. Cohen, who at the time was the president’s personal lawyer and a close aide and confidant, quickly cut deals to act as a highly paid consultant to several foreign and domestic companies with business interests linked to federal government decisions. Investigators said in the warrant applications that a corporate entity Cohen had created, Essential Consultants LLC, had received multiple deposits from foreign businesses and entities, including from companies they said had “significant ties to foreign governments or are entities controlled by foreign governments.” Essential Consultants received funds from U.S. and foreign corporations who appear to have approached Cohen “in connection with political objectives in the Trump administration,” investigators wrote. Investigators were especially curious about deposits of more than $416,000 from an account linked to an investment management firm, Columbus Nova, LLC. The warrants link that firm, and the holding company that controls it, to Viktor Vekselberg, a Russian oligarch with ties to Russian President Vladimir Putin. In an application to search his Trump Organization email account, prosecutors said Cohen exchanged over 230 phone calls and 950 text messages with the CEO of Columbus Nova between Nov. 8, 2016, and July 14, 2017. There were no text messages or phone calls before Election Day in 2016, prosecutors said. Columbus Nova has said in a statement that it is solely owned and controlled by Americans. It has described as false any allegation that Vekselberg used Columbus Nova as a conduit for payments to Cohen. The warrant applications also make clear that investigators at the time were examining whether any of the fund transfers were connected to Cohen’s involvement in a plan, described months earlier in a New York Times story, to try to get the U.S. to lift sanctions on Russia. Cohen has acknowledged offering his insights into Trump’s administration to multiple corporate clients, but said he broke no laws in doing so. Representatives for Cohen, who began serving a three-year prison sentence this month, declined comment Wednesday. The newly unsealed material reveals nothing about Trump’s own role in the crimes that put Cohen behind bars. Cohen is now serving a three-year prison sentence for tax evasion, lying to Congress about a Trump real estate project in Moscow, and campaign finance violations related to hush-money payments he orchestrated to two women who claimed to have had affairs with Trump, the porn actress Stormy Daniels and erotic model Karen McDougal. The warrant applications, which covered requests to search Cohen’s email accounts, including one associated with the Trump Organization, were blacked out in certain sections to protect the secrecy of an ongoing federal investigation into Cohen’s campaign finance crimes. Cohen has said he arranged payments to McDougal and Daniels at Trump’s behest, which the president has denied along with the affairs. \\n\",\n      \"[GROUND-TRUTH] #(all)=6, #(present)=3, #(absent)=3\\n\",\n      \"\\t\\tu.s .\\n\",\n      \"\\t\\t[robert mueller]\\n\",\n      \"\\t\\tdonald trump\\n\",\n      \"\\t\\trussia probe\\n\",\n      \"\\t\\t[michael cohen]\\n\",\n      \"\\t\\t[viktor vekselberg]\\n\",\n      \"[PREDICTION] #(all)=17, #(valid)=16, #(present)=8, #(valid&present)=8, #(valid&absent)=8\\n\",\n      \"\\t\\t[1] [1.8993]\\t[viktor vekselberg] \\t[correct!]\\n\",\n      \"\\t\\t[2] [1.8993]\\t[robert mueller] \\t[correct!]\\n\",\n      \"\\t\\t[3] [1.8993]\\t[russian president] \\t\\n\",\n      \"\\t\\t[4] [1.8993]\\t[russian collusion] \\t\\n\",\n      \"\\t\\t[5] [1.8993]\\t[vladimir putin] \\t\\n\",\n      \"\\t\\t[6] [1.8993]\\tdonald trump \\t[correct!]\\n\",\n      \"\\t\\t[7] [1.8993]\\t[michael cohen] \\t[correct!]\\n\",\n      \"\\t\\t[8] [1.8993]\\t[fbi raids] \\t\\n\",\n      \"\\t\\t[9] [1.8993]\\tsearch warrant documents \\t\\n\",\n      \"\\t\\t[10] [1.8993]\\tamerican people of ukrainian - jewish descent \\t\\n\",\n      \"\\t\\t[11] [1.8993]\\tamerican people of russian - jewish descent \\t\\n\",\n      \"\\t\\t[12] [1.8993]\\tlist of topics characterized as pseudoscience \\t\\n\",\n      \"\\t\\t[13] [1.8993]\\tlist of people from new york city \\t\\n\",\n      \"\\t\\t[14] [1.8993]\\tcohen family \\t\\n\",\n      \"\\t\\t\\t[15] [1.8993]\\tto the trump organization . the warrants \\t\\n\",\n      \"\\t\\t[16] [1.8993]\\t[were made public in] \\t\\n\",\n      \"\\t\\t[17] [1.8993]\\tin a court filing on \\t\\n\",\n      \"\\n\",\n      \" ======================================================= \\n\",\n      \"[GROUND-TRUTH] #(all)=6, #(present)=3, #(absent)=3\\n\",\n      \"\\n\",\n      \"[PREDICTION] #(all)=17, #(valid)=16, #(present)=8, #(valid&present)=8, #(valid&absent)=8\\n\",\n      \"\\n\",\n      \" --- batch all_exact F1 @10: \\t0.5000\\n\",\n      \" --- batch all_exact F1 @k: \\t0.5000\\n\",\n      \" --- batch present_exact F1 @10: \\t0.5455\\n\",\n      \" --- batch present_exact F1 @k: \\t0.6667\\n\",\n      \" --- batch absent_exact F1 @M: \\t0.1818\\n\",\n      \" =======================================================\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"src_text = new_ex_dict['title'] + ' . ' + new_ex_dict['abstract']\\n\",\n    \"\\n\",\n    \"# print results\\n\",\n    \"eval_and_print(src_text, tgt_kps=ex_dict['keywords'], pred_kps=preds[0], pred_scores=scores[0])\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Noun phrases\\n\",\n    \"\\n\",\n    \"Use min_len=2, max_len=6, ignore single word.\\n\",\n    \"See **onmt.keyphrase.extract_np.py** for more examples.\\n\",\n    \"\\n\",\n    \"Some issues:\\n\",\n    \"1. Most common, participles are mistakenly tagged as VERB.\\n\",\n    \"  - image segmentation through evolved cellular automata ['NOUN', 'NOUN', 'ADP', 'VERB', 'ADJ', 'NOUN']\\n\",\n    \"  - HQCRFF-based modulator ['PROPN', '-', 'VERB', 'NOUN']\\n\",\n    \"  - collection of organized data ['NOUN', 'ADP', 'VERB', 'NOUN']\\n\",\n    \"  - irregularly-sampled data ['ADV', '-', 'VERB', 'NOUN']\\n\",\n    \"2. NP containing numbers, rare.\\n\",\n    \"  - sidewall angle of 90 ['ADJ', 'NOUN', 'ADP', 'NUM']\\n\",\n    \"3. Single-word phrases are ignored, and seemingly no simple way to resolve. Some abbreviations and acronyms are ignored.\\n\",\n    \"4. Starting with ADP.\\n\",\n    \"  - in-band zeros ['ADP', '-', 'NOUN', 'NOUN']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def noun_chunks_by_pos_regex(text, min_len, max_len):\\n\",\n    \"    '''\\n\",\n    \"    https://files.ifi.uzh.ch/cl/hess/classes/ecl1/termerCIE.html\\n\",\n    \"        (Adjective | Noun)* (Noun Preposition)? (Adjective | Noun)* Noun\\n\",\n    \"    https://www.aclweb.org/anthology/D09-1027.pdf\\n\",\n    \"        (JJ)*(NN|NNS|NNP)+\\n\",\n    \"    :param doc:\\n\",\n    \"    :param min_len:\\n\",\n    \"    :param max_len:\\n\",\n    \"    :return:\\n\",\n    \"    '''\\n\",\n    \"    doc = spacy_nlp(text, disable=[\\\"textcat\\\"])\\n\",\n    \"\\n\",\n    \"    np_regex = r'((^ADJ|^NOUN|^PROPN)(ADP|-|ADJ|NOUN|PROPN)*?)?(NOUN|PROPN)+'\\n\",\n    \"    cands = []\\n\",\n    \"    # a two-layer loop to get all n-grams\\n\",\n    \"    for i in range(0, len(doc) - 1):\\n\",\n    \"        for k in range(min_len, max_len + 1):\\n\",\n    \"            if i + k > len(doc): break\\n\",\n    \"            span = doc[i: i + k]\\n\",\n    \"            pos = ['-' if t.text=='-' else t.pos_ for t in span]\\n\",\n    \"            pos_str = ''.join(pos)\\n\",\n    \"\\n\",\n    \"            cands.append((span, pos_str, pos))\\n\",\n    \"\\n\",\n    \"#     for np_id, (np, pos_str, pos) in enumerate(cands):\\n\",\n    \"#         print('[%d]' % np_id, np, str(pos), '[match]' if re.fullmatch(np_regex, pos_str) else '')\\n\",\n    \"        \\n\",\n    \"    cands = [span.text for span, pos_str, pos in cands if re.fullmatch(np_regex, pos_str)]\\n\",\n    \"\\n\",\n    \"    return cands\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"****************************************************************************************************\\n\",\n      \"kp20k_valid2k\\n\",\n      \"****************************************************************************************************\\n\",\n      \"0\\n\",\n      \"1000\\n\",\n      \"****************************************************************************************************\\n\",\n      \"openkp_valid2k\\n\",\n      \"****************************************************************************************************\\n\",\n      \"0\\n\",\n      \"1000\\n\",\n      \"****************************************************************************************************\\n\",\n      \"kptimes_valid2k\\n\",\n      \"****************************************************************************************************\\n\",\n      \"0\\n\",\n      \"1000\\n\",\n      \"****************************************************************************************************\\n\",\n      \"stackex_valid2k\\n\",\n      \"****************************************************************************************************\\n\",\n      \"0\\n\",\n      \"1000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dataset_names = ['kp20k_train100k', 'kptimes_train100k', 'openkp_train100k', 'stackex_train100k']\\n\",\n    \"dataset_names = ['kp20k', 'inspec', 'krapivin', 'nus', 'semeval', 'openkp', 'kptimes', 'jptimes', 'stackex', 'duc']\\n\",\n    \"dataset_names = ['kp20k_valid2k', 'openkp_valid2k', 'kptimes_valid2k', 'stackex_valid2k']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    print('*' * 100)\\n\",\n    \"    print(dataset_name)\\n\",\n    \"    print('*' * 100)\\n\",\n    \"    input_path = '/zfs1/hdaqing/rum20/kp/data/kp/json/%s/test.json' % dataset_name\\n\",\n    \"    output_path = '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_nounphrase/checkpoint_step_9500-data_%s_test.pred' % dataset_name\\n\",\n    \"\\n\",\n    \"    with open(input_path, 'r') as input_jsonl, open(output_path, 'w') as output_jsonl:\\n\",\n    \"        for l_id, l in enumerate(input_jsonl):\\n\",\n    \"            if l_id % 1000 == 0: print('%d' % l_id)\\n\",\n    \"            ex = json.loads(l)\\n\",\n    \"\\n\",\n    \"            if dataset_name.startswith('openkp'):\\n\",\n    \"                src_text = ex['text']\\n\",\n    \"            elif dataset_name.startswith('stackex'):\\n\",\n    \"                src_text = ex['title'] + ' . ' + ex['question']\\n\",\n    \"            else:\\n\",\n    \"                src_text = ex['title'] + ' . ' + ex['abstract']\\n\",\n    \"\\n\",\n    \"            nps = noun_chunks_by_pos_regex(src_text, min_len=2, max_len=6)\\n\",\n    \"\\n\",\n    \"#             print(src_text)\\n\",\n    \"#             for np_id, np in enumerate(nps):\\n\",\n    \"#                 print('[%d]' % np_id, np)\\n\",\n    \"\\n\",\n    \"            # remove duplicates and write to file\\n\",\n    \"            nps = list(set(nps))\\n\",\n    \"            output_ex = {'pred_sents': nps}\\n\",\n    \"            output_jsonl.write(json.dumps(output_ex) + '\\\\n')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/visualize_domain_gap.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import re\\n\",\n    \"import json\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from collections import defaultdict\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 70,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"num_doc= 20000\\n\",\n      \"avg_src_len= 160.3073\\n\",\n      \"num_tgt= 106150\\n\",\n      \"num_unique_tgt= 56086\\n\",\n      \"avg_tgt_len= 2.0350635892604805\\n\",\n      \"openkp\\n\",\n      \"num_doc= 20000\\n\",\n      \"avg_src_len= 1118.87665\\n\",\n      \"num_tgt= 46490\\n\",\n      \"num_unique_tgt= 37221\\n\",\n      \"avg_tgt_len= 1.9704022370402237\\n\",\n      \"kptimes\\n\",\n      \"num_doc= 20000\\n\",\n      \"avg_src_len= 800.7895\\n\",\n      \"num_tgt= 101033\\n\",\n      \"num_unique_tgt= 21135\\n\",\n      \"avg_tgt_len= 2.192719210554967\\n\",\n      \"stackex\\n\",\n      \"num_doc= 20000\\n\",\n      \"avg_src_len= 208.24075\\n\",\n      \"num_tgt= 53827\\n\",\n      \"num_unique_tgt= 4847\\n\",\n      \"avg_tgt_len= 1.3337915915804337\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from collections import defaultdict\\n\",\n    \"\\n\",\n    \"num_doc = 20000\\n\",\n    \"\\n\",\n    \"dataset_names = ['kp20k', 'openkp', 'kptimes', 'stackex']\\n\",\n    \"# dataset_names = ['kp20k', 'openkp']\\n\",\n    \"\\n\",\n    \"KP_DATASET_FIELDS = {'kp20k': ('title', 'abstract', 'keywords', None),\\n\",\n    \"                     'stackex': ('title', 'question', 'tags', 'categories'),\\n\",\n    \"                     'openkp': ('url', 'text', 'KeyPhrases', None),\\n\",\n    \"                     'kptimes': ('title', 'abstract', 'keyword', 'categories')}\\n\",\n    \"dataset_split = 'train'\\n\",\n    \"json_base_dir = '/zfs1/hdaqing/rum20/kp/data/kp/json' # path on CRC\\n\",\n    \"\\n\",\n    \"dataset_src_lens, dataset_tgt_lens, dataset_tgt_nums  = {}, {}, {}    \\n\",\n    \"dataset_unique_kp_count, dataset_unique_preskp_count, dataset_unique_abskp_count = {}, {}, {}\\n\",\n    \"\\n\",\n    \"dataset_examples_dict = {}\\n\",\n    \"\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    src_len, tgt_len, tgt_num = [], [], []\\n\",\n    \"    data_examples = []\\n\",\n    \"    num_present_doc, num_present_tgt = 0, 0\\n\",\n    \"    num_absent_doc, num_absent_tgt = 0, 0\\n\",\n    \"    \\n\",\n    \"    unique_kp_count, unique_preskp_count, unique_abskp_count = defaultdict(int), defaultdict(int), defaultdict(int)\\n\",\n    \"    print(dataset_name)\\n\",\n    \"\\n\",\n    \"    input_json_path = os.path.join(json_base_dir, dataset_name, '%s.json' % dataset_split)\\n\",\n    \"    \\n\",\n    \"    with open(input_json_path, 'r') as input_json:\\n\",\n    \"        for ex_id, json_line in enumerate(input_json):\\n\",\n    \"            if ex_id >= num_doc: break\\n\",\n    \"            ex_dict = json.loads(json_line)\\n\",\n    \"            \\n\",\n    \"            title_field, text_field, keyword_field, _ = KP_DATASET_FIELDS[dataset_name]\\n\",\n    \"\\n\",\n    \"            src_str = ex_dict[title_field] + ' . ' + ex_dict[text_field]\\n\",\n    \"            if isinstance(ex_dict[keyword_field], str):\\n\",\n    \"                tgt_kps = ex_dict[keyword_field].split(';')\\n\",\n    \"            else:\\n\",\n    \"                tgt_kps = ex_dict[keyword_field]\\n\",\n    \"            data_examples.append({\\n\",\n    \"                'id': ex_id,\\n\",\n    \"                'src': src_str,\\n\",\n    \"                'tgt': tgt_kps\\n\",\n    \"            })\\n\",\n    \"\\n\",\n    \"            src_seq = [t for t in re.split(r'\\\\W', src_str) if len(t) > 0]\\n\",\n    \"            tgt_seqs = [[t for t in re.split(r'\\\\W', p) if len(t) > 0] for p in tgt_kps]\\n\",\n    \"#             [kp_set.add(' '.join(p)) for p in tgt_seqs]\\n\",\n    \"            \\n\",\n    \"            for p in tgt_seqs:\\n\",\n    \"                p = ' '.join([w.lower() for w in p])\\n\",\n    \"                unique_kp_count[p] += 1\\n\",\n    \"                                             \\n\",\n    \"            src_len.append(len(src_seq))\\n\",\n    \"            tgt_num.append(len(tgt_seqs))\\n\",\n    \"            tgt_len.extend([len(tgt_seq) for tgt_seq in tgt_seqs])\\n\",\n    \"    \\n\",\n    \"    print('num_doc=', len(src_len))\\n\",\n    \"    print('avg_src_len=', np.mean(src_len))\\n\",\n    \"    print('num_tgt=', sum(tgt_num))\\n\",\n    \"    print('num_unique_tgt=', len(unique_kp_count))\\n\",\n    \"    print('avg_tgt_len=', np.mean(tgt_len))\\n\",\n    \"    \\n\",\n    \"    dataset_examples_dict[dataset_name] = data_examples\\n\",\n    \"    dataset_src_lens[dataset_name] = src_len\\n\",\n    \"    dataset_tgt_lens[dataset_name] = tgt_len\\n\",\n    \"    dataset_tgt_nums[dataset_name] = tgt_num\\n\",\n    \"    dataset_unique_kp_count[dataset_name] = unique_kp_count\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'id': 19999,\\n\",\n       \" 'src': 'On the relations between ELECTRE TRI-B and ELECTRE TRI-C and on a new variant of ELECTRE TRI-B . We study the relations between ELECTRE TRI B and ELECTRE TRI C. We propose and motivate a new variant of ELECTRE TRI B. We analyze the limits and merits of this variant',\\n\",\n       \" 'tgt': ['decision with multiple attributes',\\n\",\n       \"  'sorting models',\\n\",\n       \"  'electre tri-b',\\n\",\n       \"  'electre tri-c']}\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset_examples_dict['kp20k'][-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dataset_unique_kp_count_sorted = {}\\n\",\n    \"for dataset_name in dataset_names:\\n\",\n    \"    unique_kp_count = dataset_unique_kp_count[dataset_name]\\n\",\n    \"    unique_kp_count = sorted(list(unique_kp_count.items()), key=lambda x:x[1], reverse=True)\\n\",\n    \"    dataset_unique_kp_count_sorted[dataset_name] = unique_kp_count\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 72,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k\\n\",\n      \"('paper', 367)\\n\",\n      \"('performance', 334)\\n\",\n      \"('design', 271)\\n\",\n      \"('systems', 239)\\n\",\n      \"('simulation', 239)\\n\",\n      \"('algorithms', 223)\\n\",\n      \"('algorithm', 216)\\n\",\n      \"('optimization', 180)\\n\",\n      \"('data mining', 167)\\n\",\n      \"('use', 165)\\n\",\n      \"openkp\\n\",\n      \"('dictionary', 328)\\n\",\n      \"('definition', 309)\\n\",\n      \"('recipe', 132)\\n\",\n      \"('error', 86)\\n\",\n      \"('united states', 78)\\n\",\n      \"('recipes', 67)\\n\",\n      \"('difference', 66)\\n\",\n      \"('meaning', 45)\\n\",\n      \"('florida', 41)\\n\",\n      \"('history', 34)\\n\",\n      \"kptimes\\n\",\n      \"('baseball', 899)\\n\",\n      \"('football', 723)\\n\",\n      \"('basketball', 714)\\n\",\n      \"('computers and the internet', 611)\\n\",\n      \"('china', 540)\\n\",\n      \"('nyc', 495)\\n\",\n      \"('terrorism', 474)\\n\",\n      \"('politics and government', 462)\\n\",\n      \"('soccer', 420)\\n\",\n      \"('new york city', 377)\\n\",\n      \"stackex\\n\",\n      \"('c', 1390)\\n\",\n      \"('linux', 1040)\\n\",\n      \"('bash', 768)\\n\",\n      \"('java', 757)\\n\",\n      \"('python', 635)\\n\",\n      \"('javascript', 510)\\n\",\n      \"('shell script', 483)\\n\",\n      \"('debian', 457)\\n\",\n      \"('seo', 448)\\n\",\n      \"('algorithms', 443)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for dataset_name in dataset_names:\\n\",\n    \"    print(dataset_name)\\n\",\n    \"    for r in dataset_unique_kp_count_sorted[dataset_name][:10]:\\n\",\n    \"        print(r)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Set up Transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"Tesla V100-PCIE-32GB\\n\",\n      \"cuda:3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"print(torch.cuda.is_available())\\n\",\n    \"print(torch.cuda.get_device_name())\\n\",\n    \"device = torch.device(\\\"cuda:3\\\")\\n\",\n    \"print(device)\\n\",\n    \"print(torch.cuda.device_count())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from transformers import AutoTokenizer, AutoModel\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"roberta-large\\\")\\n\",\n    \"model = AutoModel.from_pretrained(\\\"roberta-large\\\").to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"cuda:3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# dict_keys(['input_ids', 'attention_mask'])\\n\",\n    \"inputs = tokenizer(\\\"Hello world!\\\", return_tensors=\\\"pt\\\").to(device)\\n\",\n    \"print(inputs['input_ids'].device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = model(**inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"cuda:3\\n\",\n      \"torch.Size([1, 5, 1024])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# last_hidden_state=[B,L,H], pooler_output=[B,H]\\n\",\n    \"print(outputs['last_hidden_state'].device)\\n\",\n    \"print(outputs['last_hidden_state'].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'input_ids': tensor([[    0, 31414,   232,   328,     2]], device='cuda:3'), 'attention_mask': tensor([[1, 1, 1, 1, 1]], device='cuda:3')}\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"inputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"torch.Size([1, 1024])\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"outputs['pooler_output'].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"outputs['pooler_output'].is_cuda\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate Phrase Embedding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Phrase CLS\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"kp20k 1000\\n\",\n      \"openkp 1000\\n\",\n      \"kptimes 1000\\n\",\n      \"stackex 1000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"phrases = []\\n\",\n    \"labels = []\\n\",\n    \"\\n\",\n    \"for d_id, d_name in enumerate(['kp20k', 'openkp', 'kptimes', 'stackex']):\\n\",\n    \"#     _phrases = [p for doc in dataset_examples_dict[d_name] for p in doc['tgt']] # take all phrases\\n\",\n    \"    _phrases = [t[0] for t in dataset_unique_kp_count_sorted[d_name][:1000]] # take high-frequency phrases\\n\",\n    \"    _labels = [d_name] * len(_phrases)\\n\",\n    \"    phrases.extend(_phrases)\\n\",\n    \"    labels.extend(_labels)\\n\",\n    \"    print(d_name, len(_phrases))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 74,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4000\\n\",\n      \"4000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(len(phrases))\\n\",\n    \"print(len(labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 75,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['gvim', 'crash', 'framebuffer', 'counting complexity', 'computational physics', 'game development', 'parallel', 'mv', 'route', 'resolution']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(phrases[-10:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 76,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = tokenizer(phrases, return_tensors=\\\"pt\\\", padding=True).to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 77,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dict_keys(['input_ids', 'attention_mask'])\\n\",\n      \"torch.Size([4000, 14])\\n\",\n      \"torch.Size([4000, 14])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(inputs.keys())\\n\",\n    \"print(inputs['input_ids'].shape)\\n\",\n    \"print(inputs['attention_mask'].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 78,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 1, 1, ..., 0, 0, 0],\\n\",\n       \"       [1, 1, 1, ..., 0, 0, 0],\\n\",\n       \"       [1, 1, 1, ..., 0, 0, 0],\\n\",\n       \"       ...,\\n\",\n       \"       [1, 1, 1, ..., 0, 0, 0],\\n\",\n       \"       [1, 1, 1, ..., 0, 0, 0],\\n\",\n       \"       [1, 1, 1, ..., 0, 0, 0]])\"\n      ]\n     },\n     \"execution_count\": 78,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"inputs['attention_mask'].cpu().numpy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 79,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4000\\n\",\n      \"4000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"batch_size = 16\\n\",\n    \"phrase_batches = [phrases[i: i+batch_size] for i in range(0, len(phrases), batch_size)]\\n\",\n    \"\\n\",\n    \"cls_outputs, last_hidden_states = [], []\\n\",\n    \"for b in phrase_batches:\\n\",\n    \"    inputs = tokenizer(b, return_tensors='pt', padding=True).to(device)\\n\",\n    \"    outputs = model(**inputs)\\n\",\n    \"    # last_hidden_state=[B,L,H], pooler_output=\\n\",\n    \"    cls_output = outputs['pooler_output'].detach().cpu().numpy().tolist() # [B,H]\\n\",\n    \"    last_hidden_state = outputs['last_hidden_state'].detach().cpu().numpy().tolist() # [B,L,H]\\n\",\n    \"    \\n\",\n    \"    cls_outputs.extend(cls_output)\\n\",\n    \"    last_hidden_states.extend(last_hidden_state)\\n\",\n    \"\\n\",\n    \"print(len(cls_outputs))\\n\",\n    \"print(len(last_hidden_states))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 80,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[t-SNE] Computing 91 nearest neighbors...\\n\",\n      \"[t-SNE] Indexed 4000 samples in 0.003s...\\n\",\n      \"[t-SNE] Computed neighbors for 4000 samples in 0.562s...\\n\",\n      \"[t-SNE] Computed conditional probabilities for sample 1000 / 4000\\n\",\n      \"[t-SNE] Computed conditional probabilities for sample 2000 / 4000\\n\",\n      \"[t-SNE] Computed conditional probabilities for sample 3000 / 4000\\n\",\n      \"[t-SNE] Computed conditional probabilities for sample 4000 / 4000\\n\",\n      \"[t-SNE] Mean sigma: 0.114854\\n\",\n      \"[t-SNE] KL divergence after 250 iterations with early exaggeration: 82.827118\\n\",\n      \"[t-SNE] KL divergence after 1000 iterations: 1.929949\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.manifold import TSNE\\n\",\n    \"import seaborn as sns\\n\",\n    \"import pandas as pd  \\n\",\n    \"\\n\",\n    \"tsne = TSNE(n_components=2, verbose=1, random_state=123)\\n\",\n    \"z = tsne.fit_transform(cls_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 81,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(array(['kp20k', 'kptimes', 'openkp', 'stackex'], dtype=object),\\n\",\n       \" array([1000, 1000, 1000, 1000]))\"\n      ]\n     },\n     \"execution_count\": 81,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.DataFrame()\\n\",\n    \"df[\\\"y\\\"] = labels\\n\",\n    \"df[\\\"comp-1\\\"] = z[:,0]\\n\",\n    \"df[\\\"comp-2\\\"] = z[:,1]\\n\",\n    \"\\n\",\n    \"np.unique(df[\\\"y\\\"], return_counts=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 115,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[Text(0.5, 1.0, 'Visualizing phrases from four domains with T-SNE projection')]\"\n      ]\n     },\n     \"execution_count\": 115,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=[16, 9])\\n\",\n    \"sns.scatterplot(x=\\\"comp-1\\\", y=\\\"comp-2\\\", hue=df.y.tolist(),\\n\",\n    \"                palette=sns.color_palette(\\\"hls\\\", 4), s=40,\\n\",\n    \"                data=df).set(title=\\\"Visualizing phrases from four domains with T-SNE projection\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 114,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(figsize=[16, 9])\\n\",\n    \"sns.scatterplot(x=\\\"comp-1\\\", y=\\\"comp-2\\\", hue=df.y.tolist(),\\n\",\n    \"                palette=sns.color_palette(\\\"hls\\\", 4), s=40,\\n\",\n    \"                data=df).set(title=\\\"Visualizing phrases from four domains with T-SNE projection\\\")\\n\",\n    \"\\n\",\n    \"for i, (p, dname) in enumerate(zip(phrases, labels)):\\n\",\n    \"    if i % 83 == 0:\\n\",\n    \"        ax.annotate('[%s] %s' % (dname, p), (z[i][0], z[i][1]), fontsize=6)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebook/wikibart_inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2022-03-06 00:55:47.260454: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import json\\n\",\n    \"import logging\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"from dataclasses import dataclass, field\\n\",\n    \"from typing import Optional\\n\",\n    \"\\n\",\n    \"import datasets\\n\",\n    \"import nltk  # Here to have a nice missing dependency error message early on\\n\",\n    \"from nltk.stem import *\\n\",\n    \"import numpy as np\\n\",\n    \"from datasets import load_dataset, load_metric\\n\",\n    \"\\n\",\n    \"from filelock import FileLock\\n\",\n    \"from transformers import (\\n\",\n    \"    AutoConfig,\\n\",\n    \"    AutoModelForSeq2SeqLM,\\n\",\n    \"    AutoTokenizer,\\n\",\n    \"    DataCollatorForSeq2Seq,\\n\",\n    \"    HfArgumentParser,\\n\",\n    \"    Seq2SeqTrainer,\\n\",\n    \"    Seq2SeqTrainingArguments,\\n\",\n    \"    set_seed, TrainingArguments, TrainerState, TrainerControl,\\n\",\n    \"    TrainerCallback\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"logger = logging.getLogger(__name__)\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    nltk.data.find(\\\"tokenizers/punkt\\\")\\n\",\n    \"except (LookupError, OSError):\\n\",\n    \"    if is_offline_mode():\\n\",\n    \"        raise LookupError(\\n\",\n    \"            \\\"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files\\\"\\n\",\n    \"        )\\n\",\n    \"    with FileLock(\\\".lock\\\") as lock:\\n\",\n    \"        nltk.download(\\\"punkt\\\", quiet=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"@dataclass\\n\",\n    \"class ModelArguments:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    model_name_or_path: str = field(\\n\",\n    \"        metadata={\\\"help\\\": \\\"Path to pretrained model or model identifier from huggingface.co/models\\\"}\\n\",\n    \"    )\\n\",\n    \"    config_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"Pretrained config name or path if not the same as model_name\\\"}\\n\",\n    \"    )\\n\",\n    \"    tokenizer_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"Pretrained tokenizer name or path if not the same as model_name\\\"}\\n\",\n    \"    )\\n\",\n    \"    cache_dir: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"Where to store the pretrained models downloaded from huggingface.co\\\"},\\n\",\n    \"    )\\n\",\n    \"    use_fast_tokenizer: bool = field(\\n\",\n    \"        default=True,\\n\",\n    \"        metadata={\\\"help\\\": \\\"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\\\"},\\n\",\n    \"    )\\n\",\n    \"    model_revision: str = field(\\n\",\n    \"        default=\\\"main\\\",\\n\",\n    \"        metadata={\\\"help\\\": \\\"The specific model version to use (can be a branch name, tag name or commit id).\\\"},\\n\",\n    \"    )\\n\",\n    \"    use_auth_token: bool = field(\\n\",\n    \"        default=False,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Will use the token generated when running `transformers-cli login` (necessary to use this script \\\"\\n\",\n    \"            \\\"with private models).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    resize_position_embeddings: Optional[bool] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to automatically resize the position embeddings if `max_source_length` exceeds \\\"\\n\",\n    \"            \\\"the model's position embeddings.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"@dataclass\\n\",\n    \"class DataTrainingArguments:\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Arguments pertaining to what data we are going to input our model for training and eval.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    lang: str = field(default=None, metadata={\\\"help\\\": \\\"Language id for summarization.\\\"})\\n\",\n    \"\\n\",\n    \"    dataset_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The name of the dataset to use (via the datasets library).\\\"}\\n\",\n    \"    )\\n\",\n    \"    dataset_config_name: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The configuration name of the dataset to use (via the datasets library).\\\"}\\n\",\n    \"    )\\n\",\n    \"    text_column: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The name of the column in the datasets containing the full texts (for summarization).\\\"},\\n\",\n    \"    )\\n\",\n    \"    keyphrase_column: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The name of the column in the datasets containing the summaries (for summarization).\\\"},\\n\",\n    \"    )\\n\",\n    \"    train_file: Optional[str] = field(\\n\",\n    \"        default=None, metadata={\\\"help\\\": \\\"The input training data file (a jsonlines or csv file).\\\"}\\n\",\n    \"    )\\n\",\n    \"    validation_file: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"An optional input evaluation data file to evaluate the metrics (rouge) on \\\"\\n\",\n    \"            \\\"(a jsonlines or csv file).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    test_file: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"An optional input test data file to evaluate the metrics (rouge) on \\\" \\\"(a jsonlines or csv file).\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    overwrite_cache: bool = field(\\n\",\n    \"        default=False, metadata={\\\"help\\\": \\\"Overwrite the cached training and evaluation sets\\\"}\\n\",\n    \"    )\\n\",\n    \"    preprocessing_num_workers: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\\"help\\\": \\\"The number of processes to use for the preprocessing.\\\"},\\n\",\n    \"    )\\n\",\n    \"    max_source_length: Optional[int] = field(\\n\",\n    \"        default=1024,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total input sequence length after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_target_length: Optional[int] = field(\\n\",\n    \"        default=128,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total sequence length for target text after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    val_max_target_length: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The maximum total sequence length for validation target text after tokenization. Sequences longer \\\"\\n\",\n    \"            \\\"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`.\\\"\\n\",\n    \"            \\\"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used \\\"\\n\",\n    \"            \\\"during ``evaluate`` and ``predict``.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    pad_to_max_length: bool = field(\\n\",\n    \"        default=False,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to pad all samples to model maximum sentence length. \\\"\\n\",\n    \"            \\\"If False, will pad the samples dynamically when batching to the maximum length in the batch. More \\\"\\n\",\n    \"            \\\"efficient on GPU but very bad for TPU.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_train_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of training examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_eval_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of evaluation examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    max_predict_samples: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"For debugging purposes or quicker training, truncate the number of prediction examples to this \\\"\\n\",\n    \"            \\\"value if set.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    num_beams: Optional[int] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, \\\"\\n\",\n    \"            \\\"which is used during ``evaluate`` and ``predict``.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    ignore_pad_token_for_loss: bool = field(\\n\",\n    \"        default=True,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"Whether to ignore the tokens corresponding to padded labels in the loss computation or not.\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"    source_prefix: Optional[str] = field(\\n\",\n    \"        default=\\\"\\\", metadata={\\\"help\\\": \\\"A prefix to add before every source text (useful for T5 models).\\\"}\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    forced_bos_token: Optional[str] = field(\\n\",\n    \"        default=None,\\n\",\n    \"        metadata={\\n\",\n    \"            \\\"help\\\": \\\"The token to force as the first generated token after the decoder_start_token_id.\\\"\\n\",\n    \"            \\\"Useful for multilingual models like mBART where the first generated token\\\"\\n\",\n    \"            \\\"needs to be the target language token (Usually it is the target language token)\\\"\\n\",\n    \"        },\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    def __post_init__(self):\\n\",\n    \"        if self.dataset_name is None and self.train_file is None and self.validation_file is None:\\n\",\n    \"            raise ValueError(\\\"Need either a dataset name or a training/validation file.\\\")\\n\",\n    \"        else:\\n\",\n    \"            if self.train_file is not None:\\n\",\n    \"                extension = self.train_file.split(\\\".\\\")[-1]\\n\",\n    \"                assert extension in [\\\"csv\\\", \\\"json\\\"], \\\"`train_file` should be a csv or a json file.\\\"\\n\",\n    \"            if self.validation_file is not None:\\n\",\n    \"                extension = self.validation_file.split(\\\".\\\")[-1]\\n\",\n    \"                assert extension in [\\\"csv\\\", \\\"json\\\"], \\\"`validation_file` should be a csv or a json file.\\\"\\n\",\n    \"        if self.val_max_target_length is None:\\n\",\n    \"            self.val_max_target_length = self.max_target_length\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model_name_or_path = 'memray/bart_wikikp'\\n\",\n    \"cache_dir = './hf_cache'\\n\",\n    \"dataset_name='midas/duc2001'\\n\",\n    \"num_beams=1\\n\",\n    \"max_length=128\\n\",\n    \"max_target_length=128\\n\",\n    \"padding='max_length'\\n\",\n    \"prefix='<present>10<header>5<category>5<seealso>2<infill>0<s>'\\n\",\n    \"# Get the column names for input/target.\\n\",\n    \"text_column = 'document'\\n\",\n    \"keyphrase_column = 'extractive_keyphrases'\\n\",\n    \"\\n\",\n    \"training_args = Seq2SeqTrainingArguments(per_device_eval_batch_size=8, output_dir=cache_dir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PyTorch: setting up devices\\n\",\n      \"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['wikibart_inference.ipynb', '--config_name', 'memray/bart_wikikp', '--model_name_or_path', 'memray/bart_wikikp', '--tokenizer_name', 'memray/bart_wikikp', '--dataset_name', 'midas/duc2001', '--do_predict', '--output_dir', 'kp_output/duc2001/', '--overwrite_output_dir', '--per_device_eval_batch_size', '32', '--predict_with_generate', '--text_column', 'document', '--keyphrase_column', 'extractive_keyphrases', '--source_prefix', '<present>10<header>5<category>5<seealso>2<infill>0<s>', '--num_beams', '1', '--generation_max_length', '60']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sys.argv = ['wikibart_inference.ipynb'] +\\\\\\n\",\n    \"('--config_name memray/bart_wikikp --model_name_or_path memray/bart_wikikp --tokenizer_name memray/bart_wikikp --dataset_name midas/duc2001 --do_predict --output_dir kp_output/duc2001/ --overwrite_output_dir --per_device_eval_batch_size 32 --predict_with_generate --text_column document --keyphrase_column extractive_keyphrases --source_prefix <present>10<header>5<category>5<seealso>2<infill>0<s> --num_beams 1 --generation_max_length 60'.split())\\n\",\n    \"print(sys.argv)\\n\",\n    \"\\n\",\n    \"parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\\n\",\n    \"model_args, data_args, training_args = parser.parse_args_into_dataclasses()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"config = AutoConfig.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    use_fast=model_args.use_fast_tokenizer,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"model = AutoModelForSeq2SeqLM.from_pretrained(\\n\",\n    \"    model_args.model_name_or_path,\\n\",\n    \"    from_tf=bool(\\\".ckpt\\\" in model_args.model_name_or_path),\\n\",\n    \"    config=config,\\n\",\n    \"    cache_dir=model_args.cache_dir,\\n\",\n    \"    revision=model_args.model_revision,\\n\",\n    \"    use_auth_token=True if model_args.use_auth_token else None,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.resize_token_embeddings(len(tokenizer))\\n\",\n    \"\\n\",\n    \"if (\\n\",\n    \"        hasattr(model.config, \\\"max_position_embeddings\\\")\\n\",\n    \"        and model.config.max_position_embeddings < data_args.max_source_length\\n\",\n    \"    ):\\n\",\n    \"        if model_args.resize_position_embeddings is None:\\n\",\n    \"            logger.warning(\\n\",\n    \"                f\\\"Increasing the model's number of position embedding vectors from {model.config.max_position_embeddings} \\\"\\n\",\n    \"                f\\\"to {data_args.max_source_length}.\\\"\\n\",\n    \"            )\\n\",\n    \"            model.resize_position_embeddings(data_args.max_source_length)\\n\",\n    \"        elif model_args.resize_position_embeddings:\\n\",\n    \"            model.resize_position_embeddings(data_args.max_source_length)\\n\",\n    \"        else:\\n\",\n    \"            raise ValueError(\\n\",\n    \"                f\\\"`--max_source_length` is set to {data_args.max_source_length}, but the model only has {model.config.max_position_embeddings}\\\"\\n\",\n    \"                f\\\" position encodings. Consider either reducing `--max_source_length` to {model.config.max_position_embeddings} or to automatically \\\"\\n\",\n    \"                \\\"resize the model's position encodings by passing `--resize_position_embeddings`.\\\"\\n\",\n    \"            )\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess_function(examples):\\n\",\n    \"    # remove pairs where at least one record is None\\n\",\n    \"    inputs, targets = [], []\\n\",\n    \"    for i in range(len(examples[text_column])):\\n\",\n    \"        if examples[text_column][i] is not None and examples[keyphrase_column][i] is not None:\\n\",\n    \"            inputs.append(examples[text_column][i])\\n\",\n    \"            targets.append(examples[keyphrase_column][i])\\n\",\n    \"\\n\",\n    \"    inputs = examples[text_column]\\n\",\n    \"    targets = ['<sep>'.join(kps) for kps in examples[keyphrase_column]]\\n\",\n    \"    inputs = [prefix + ' '.join(inp) for inp in inputs]\\n\",\n    \"    model_inputs = tokenizer(inputs, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # Setup the tokenizer for targets\\n\",\n    \"    with tokenizer.as_target_tokenizer():\\n\",\n    \"        labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\\n\",\n    \"    # padding in the loss.\\n\",\n    \"    labels[\\\"input_ids\\\"] = [\\n\",\n    \"        [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\\\"input_ids\\\"]\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"    model_inputs[\\\"labels\\\"] = labels[\\\"input_ids\\\"]\\n\",\n    \"    return model_inputs\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Metric\\n\",\n    \"def postprocess_text(preds, labels, sep_token):\\n\",\n    \"    stemmer = PorterStemmer()\\n\",\n    \"    preds = [pred.lower().replace('</s>', '').replace('<pad>', '').split(sep_token) for pred in preds]\\n\",\n    \"    labels = [label.lower().replace('<s>', '').replace('<pad>', '').split(sep_token) for label in labels]\\n\",\n    \"    preds = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in pred] for pred in preds]\\n\",\n    \"    labels = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in label] for label in labels]\\n\",\n    \"    preds = [[p.strip() for p in pred if len(p.strip()) > 0] for pred in preds]\\n\",\n    \"    labels = [[p.strip() for p in label if len(p.strip()) > 0] for label in labels]\\n\",\n    \"\\n\",\n    \"    return preds, labels\\n\",\n    \"\\n\",\n    \"def compute_metrics(eval_preds):\\n\",\n    \"    preds = eval_preds.predictions\\n\",\n    \"    labels = eval_preds.label_ids\\n\",\n    \"    if isinstance(preds, tuple):\\n\",\n    \"        preds = preds[0]\\n\",\n    \"    print(preds.shape)\\n\",\n    \"    if len(preds.shape) == 3:\\n\",\n    \"        preds = preds.argmax(axis=-1)\\n\",\n    \"    \\n\",\n    \"    raw_decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=False)\\n\",\n    \"    # Replace -100 in the labels as we can't decode them.\\n\",\n    \"    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\\n\",\n    \"    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)\\n\",\n    \"\\n\",\n    \"    # Some simple post-processing\\n\",\n    \"    decoded_preds, decoded_labels = postprocess_text(raw_decoded_preds, decoded_labels, tokenizer.sep_token)\\n\",\n    \"\\n\",\n    \"    precs, recalls, f_scores = [], [], []\\n\",\n    \"    num_match, num_pred, num_gold = [], [], []\\n\",\n    \"    for raw_pred, pred, label in zip(raw_decoded_preds, decoded_preds, decoded_labels):\\n\",\n    \"        pred_set = set(pred)\\n\",\n    \"        label_set = set(label)\\n\",\n    \"        match_set = label_set.intersection(pred_set)\\n\",\n    \"        p = float(len(match_set)) / float(len(pred_set)) if len(pred_set) > 0 else 0.0\\n\",\n    \"        r = float(len(match_set)) / float(len(label_set)) if len(label_set) > 0 else 0.0\\n\",\n    \"        f1 = float(2 * (p * r)) / (p + r) if (p + r) > 0 else 0.0\\n\",\n    \"        precs.append(p)\\n\",\n    \"        recalls.append(r)\\n\",\n    \"        f_scores.append(f1)\\n\",\n    \"        num_match.append(len(match_set))\\n\",\n    \"        num_pred.append(len(pred_set))\\n\",\n    \"        num_gold.append(len(label_set))\\n\",\n    \"        \\n\",\n    \"#         print(f'raw_PRED: {raw_pred}')\\n\",\n    \"        print(f'PRED: num={len(pred_set)} - {pred_set}')\\n\",\n    \"        print(f'GT: num={len(label_set)} - {label_set}')\\n\",\n    \"        print(f'p={p}, r={r}, f1={f1}')\\n\",\n    \"        print('-' * 20)\\n\",\n    \"\\n\",\n    \"    result = {\\n\",\n    \"        'precision@M': np.mean(precs) * 100.0,\\n\",\n    \"        'recall@M': np.mean(recalls) * 100.0,\\n\",\n    \"        'fscore@M': np.mean(f_scores) * 100.0,\\n\",\n    \"        'num_match': np.mean(num_match),\\n\",\n    \"        'num_pred': np.mean(num_pred),\\n\",\n    \"        'num_gold': np.mean(num_gold),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    result = {k: round(v, 4) for k, v in result.items()}\\n\",\n    \"    return result\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Reusing dataset duc2001 (./hf_cache/midas___duc2001/raw/0.0.1/7888b46165d8a58f49f00e28410b46b1f22fabfd72a9e89f3e80a4e2d27e4a9b)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"46779f64551348a5ab215c46ee563137\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"08f9f508c1a74ef499b54ea3ee67c023\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Running tokenizer on prediction dataset:   0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set seed before initializing model.\\n\",\n    \"set_seed(training_args.seed)\\n\",\n    \"raw_datasets = load_dataset(\\n\",\n    \"    dataset_name, \\\"raw\\\", cache_dir=cache_dir\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"column_names = raw_datasets[\\\"test\\\"].column_names\\n\",\n    \"max_target_length = data_args.val_max_target_length\\n\",\n    \"padding = \\\"max_length\\\" if data_args.pad_to_max_length else False\\n\",\n    \"prefix = data_args.source_prefix if data_args.source_prefix is not None else \\\"\\\"\\n\",\n    \"predict_dataset = raw_datasets[\\\"test\\\"]\\n\",\n    \"\\n\",\n    \"with training_args.main_process_first(desc=\\\"prediction dataset map pre-processing\\\"):\\n\",\n    \"    predict_dataset = predict_dataset.map(\\n\",\n    \"        preprocess_function,\\n\",\n    \"        batched=True,\\n\",\n    \"        num_proc=data_args.preprocessing_num_workers,\\n\",\n    \"        remove_columns=column_names,\\n\",\n    \"        load_from_cache_file=not data_args.overwrite_cache,\\n\",\n    \"        desc=\\\"Running tokenizer on prediction dataset\\\",\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"# Data collator\\n\",\n    \"label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id\\n\",\n    \"data_collator = DataCollatorForSeq2Seq(\\n\",\n    \"    tokenizer,\\n\",\n    \"    model=model,\\n\",\n    \"    label_pad_token_id=label_pad_token_id,\\n\",\n    \"    pad_to_multiple_of=8 if training_args.fp16 else None,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"# print(torch.cuda.is_available())\\n\",\n    \"# print(torch.cuda.get_device_name())\\n\",\n    \"# # device = torch.device(\\\"cuda:2\\\")\\n\",\n    \"# torch.cuda.set_device(1)\\n\",\n    \"# device = torch.device()\\n\",\n    \"# print(device)\\n\",\n    \"print(torch.cuda.device_count())\\n\",\n    \"\\n\",\n    \"model = model.to('cuda:0')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"***** Running Prediction *****\\n\",\n      \"  Num examples = 308\\n\",\n      \"  Batch size = 32\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"    <div>\\n\",\n       \"      \\n\",\n       \"      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\\n\",\n       \"      [10/10 00:26]\\n\",\n       \"    </div>\\n\",\n       \"    \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(308, 128)\\n\",\n      \"PRED: num=26 - {'panamerican world airway', 'aardvark', 'and the follow develop today:', 'list of aircraft', 'articl contain video clip', 'and', 'lockerbi', 'scotland, scotland', 'scotland', 'aviation-rel death', 'histori of aviat', 'aerojet aircraft', 'airbu a380', 'aeroship aircraft', 'pan american world airway flight 103', 'pan american', 'histori', 'scotland,', 'larnockerbi flight 103 flight 103 (wednesday night)', 'aerial aircraft', 'aircraft accid', 'panamer', 'lancasterbi', 'aerospac accid', 'airlin', 'aero'}\\n\",\n      \"GT: num=3 - {'pan american world airway flight 103', 'lockerbi', 'crash'}\\n\",\n      \"p=0.07692307692307693, r=0.6666666666666666, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'cultur of the southern unit state', 'bunki', 'buddi mcintyr', 'texa', 'tunnel alley', 'american geographi', 'histori of the unit state (1825–1921)', 'historyof the unit state (1921–present)', 'tropic storm', 'and a', 'american weather', 'and', 'histori', 't tornado alley', 'geographi of the western unit state and the caribbean', 'tornado alley', 'and the univers of texa at austin,', 'nation weather servic'}\\n\",\n      \"GT: num=8 - {'tornado season', 'tornado watch', 'texa', 'tornado warn', 'tornado', 'spring thunderstorm', 'disast research', 'properti damag'}\\n\",\n      \"p=0.05555555555555555, r=0.125, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'forest and', 'the aircraft were', 'the u.s. air forc', 'the two aircraft', 'mountain and hill rang of germani and austria', '20th centuri', 'the air forc', 'blackforest', 'black forest', 'the', 'f-16', 'baden-soellingen', 'unit state', 'hahn air base', 'forest and woodland of germani (state)', 'black', 'ramstein air base and spangdahlen air base.', 'histori', 'marxzell-burbach', 'mainz', 'region of germani'}\\n\",\n      \"GT: num=5 - {'pilot', 'bodenheim', 'crash', 'train mission', 'aircraft'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the forest servic', 'fire manag plan', 'wild yellowston nation park', 'forestri', 'articl contain video clip', 'the nation park servic', 'fire in the unit kingdom', 'western', 'fire-fight equip', 'the', 'western fire season', 'unit state', 'fire season', 'charl philpot', 'forest fire polici', 'forestri manag', 'forest servic', 'forest', 'forest fire', 'in the unit state', 'sustain forest manag', 'and the possibl of multipl fires. the report said that', 'nation park servic'}\\n\",\n      \"GT: num=7 - {'nation forest', 'natur fire', 'forest fire polici', 'panel', 'western fire season', 'fire manag plan', 'recommend halt'}\\n\",\n      \"p=0.13043478260869565, r=0.42857142857142855, f1=0.2\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'arson', 'arsen and old lace', 'is a', 'mark twain nation forest', 'mark twain', 'and the', 'the', 'natur disast', 'is the', 'rolla', 'ozark hillbilli', 'missouri', 'the forest servic is', 'is', 'terror tactic', 'forestri crime', 'forest fire', 'mark t. twain nation forest', 'in the unit state', 'ron mcdonald', 'dale smallwood', 'the forest is a lot more'}\\n\",\n      \"GT: num=7 - {'pass truck', 'arson problem', 'missouri', 'crimin investig', 'arsonist', 'investig', 'forest fire'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'reagan administr', 'labor day weekend radio address', 'american societi', '20th centuri', 'peopl with disabl', 'the', 'american worker', 'labor depart', 'thoma j.-n.y.', 'histori', 'vietnam war', \\\"in fact thing have gotten worse. '' the presid said that\\\", 'american citizen', 'vetto pen', 'peopl of the american revolut', 'american peopl', 'thoma j. downey', 'the presid', 'peopl from west virginia'}\\n\",\n      \"GT: num=7 - {'welfar program', 'unemploy', 'presid reagan', 'welfar legisl', 'welfar reform', 'american', 'work requir'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'fountain hill', 'qujet', 'marquett gener hospit', 'succasunna', 'boe b-52 stratolaunch', 'and debruzzi were list in stabl condit at', 'aircraft first flown in 1940', 'quadruple-aircraft ballist missil system', 'stephenson', 'oper histori', 'unit state', 'unit kingdom', 'aerial bomb of citi', 'quadjet', 'the base hospital.', 'mulberri', 'k.i. sawyer air forc base'}\\n\",\n      \"GT: num=4 - {'crew member', 'train flight', 'crash', 'pilot'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'anti–trust case', 'central sell organ', 'anti-trust', 'unit kingdom', 'of the', 'offic of fair trade', 'de beer', 'unit state', 'the offic of fair', 'anti –trust law in the unit kingdom', 'zair', 'world trade organ member economi', 'of de beers.', 'diamond cartel', 'unit nation gener assembl observ', 'the complaint wa file by consolid gold field plc, a british mine concern', 'of', 'mineralco sa', 'soviet union', 'anti -trust law'}\\n\",\n      \"GT: num=7 - {'fair trade', 'central sell organ', 'south africa', 'world diamond trade', 'investig', 'de beer diamond organ', 'defens move'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'crisi in the unit state', 'senat', '1988 in agricultur', 'corn', 'ag agricultur depart', 'a $ 3.9 billion', 'unit nation econom commiss for africa', 'crop product', 'agroecolog', 'patrick j. leahi', 'and the', 'and', 'unit state', 'the unit states. the report came hour after presid reagan sign a $', 'senat agricultur committe', 'and $3.9', 'agricultur depart', '1988 event', 'ewen m. wilson', 'agronomi'}\\n\",\n      \"GT: num=9 - {'annual agricultur depart survey', 'presid reagan', 'food product', 'sharp reduct', 'deaden drought', 'higher retail food price', 'food price increas', 'disast relief bill', 'fall corn harvest'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'canadian sprinter of iranian descent', 'johnson', 'controversi', '1946 birth', 'southeast asia', 'toronto star', 'she and johnson', 'that she and johnson took steroids. she also said she had been', 'stanozolol', 'canadian peopl of iranian-jewish descent', 'she', 'canadian olymp sprinter', 'canadian male sprinter', 'that', 'mazda optimist track club', 'ben johnson', 'steroid', 'canadian olympian', 'johnson and', 'angella issajenko', 'world championship'}\\n\",\n      \"GT: num=6 - {'canadian olymp sprinter', 'angella issajenko', 'olymp gold medal', 'illeg drug', 'steroid stanozolol', 'ben johnson'}\\n\",\n      \"p=0.14285714285714285, r=0.5, f1=0.22222222222222224\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'and that the', 'that the engin had been shut down', 'b 737-200', 'feder aviat administr', 'belfast', 'unit kingdom', 'the airplan', 'bird of prey', 'aircraft first flown in 1981', 'collis', 'the', 'daili star', 'civil aviat author', 'b boe 737', 'east midland airport', 'accid and incid', '1980 unit state airlin', 'cfm56', 'the aircraft', 'the plane', 'boe 737'}\\n\",\n      \"GT: num=7 - {'wrong engin', 'injur pilot', 'crash', 'undamag right engin', 'engin monitor system', 'boe 737', 'left engin'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'by countri', 'asia-pacif', 'observ', 'of the', 'kuala lumpur', 'astro- eclips', 'list of eclips and partial eclips of the sun', \\\"the event. in the citi of davao, the city' mayor\\\", 'the', 'kurukshetra', 'asia', 'solar eclips', 'bolivia', 'bengal', \\\"the city'\\\", 'cultur aspect of death', 'boulder, colo.', 'astronom event', 'baguio citi', 'celesti cartographi', 'of', 'the town of'}\\n\",\n      \"GT: num=8 - {'sun', 'wit', 'moon', 'total eclips', 'tourist', 'wide area', 'partial eclips', 'solar eclips'}\\n\",\n      \"p=0.045454545454545456, r=0.125, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the pollut', 'exxon ship co.', 'the oil', 'of the oil', 'north slope', 'blight reef', 'bligh reef', 'blanchard island', 'blachford island', 'princ william sound', 'oil slick', 'blind area', 'and the', 'the', 'fujian province, republ of china', 'exxon baton roug', 'the oil pollut', 'denni kelso', 'richard golob', 'blow-up island', 'oil spill', 'effect'}\\n\",\n      \"GT: num=6 - {'major environment catastroph', 'oil spill', 'alaska', 'cleanup effort', 'crude oil', 'cleanup equip'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cantor fitzgerald', 'north florida junior colleg', 'tampa', 'madison, florida, unit state', '1885 establish in florida', 'north florida', 'tornado', 'the', 'tallahassee, florida', 'tornado,', 'madison counti memori hospit', 'and other util worker were on their way to the town', 'to help with the', 'histori', 'the cleanup,', 'the tornado,', 'tallahasse', 'citi in madison county, florida (u.s. state)', '1901-present', 'cultur of the central florida', 'coral springs, florida,'}\\n\",\n      \"GT: num=5 - {'thunderstorm', 'tornado', 'death', 'madison', 'destruct'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=15 - {'armenian earthquak', 'in 1988, the strongest earthquak in the unit state wa a magnitud of 7.5 on the richter scale.', 'eartholog scienc', 'richter scale', '1988', 'gulf of alaska', 'artifici extinct', 'articl contain video clip', 'soviet central asia', 'china', 'histori', 'earth quak', 'earthquak', 'modern era', 'u.s. geolog survey'}\\n\",\n      \"GT: num=7 - {'armenian earthquak', 'deadli earthquak', 'earthquak death', 'properti damag', 'offshor earthquak', 'earthquak death toll', 'chines earthquak'}\\n\",\n      \"p=0.06666666666666667, r=0.14285714285714285, f1=0.09090909090909091\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'american diabet associ', 'in the develop of', 'is a major factor in the', 'phenylalanin ammonia lyas', 'e-numb addit', 'and', 'diabet', 'amino acid', 'diabetics,', 'oxford univers', 'new zealand', 'garcia', 'amlin', 'of diabetes,', 'garth cooper', 'pancreat hormon', 'ammylin', \\\"st. luke' - roosevelt hospit center\\\", 'amphenyl compound', 'medic use', 'intern diabet feder', 'diabetes,'}\\n\",\n      \"GT: num=10 - {'insulin secret', 'pancreat hormon', 'amylin', 'blood sugar level', 'obes', 'diabet', 'american diabet associ', 'new treatment', 'diseas process', 'hormon research'}\\n\",\n      \"p=0.13636363636363635, r=0.3, f1=0.18749999999999997\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'kika de la garza', 'forest fire suppress', '20th centuri', 'articl contain video clip', 'and the u-s.,', 'the', 'u-s. depart of agricultur', '1990 farm bill', 'american wildfir', 'u.s. forest servic', 'histori', 'the u.s., the u.s. forest', 'forestry-rel death', 'the unit states.', 'forest fire', '1871 establish in the unit state', '1988', 'u-.s.', 'hous interior committe'}\\n\",\n      \"GT: num=7 - {'fire fight polici', 'summer fire', 'joint hear', 'hous agricultur committe', 'yellowston nation park', 'stubborn wildfir', 'forest fire practic'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'stanislau nation forest in wyom', 'ami vanderbilt', 'the largest fire', 'current situat', 'control monday after the blaze consum more than 1,100 acres.', 'the', 'americanwildfir', 'y yosemit nation park in california', 'shoshon nation forest', 'western unit state', 'yoseph', 'yosemit nation park', 'cultur of the pacif northwest', 'michigan', 'in the', 'american cultur', 'american wildfir', 'histori of the rocki mountain', 'the unit states.', '1871 establish in new york (state)', '1851 establish in montana', 'wyom', 'the fire wa', 'north america'}\\n\",\n      \"GT: num=7 - {'wildfir', 'wyom', 'firefight', 'blaze', 'illeg firework', 'steadi rain', 'forest fire'}\\n\",\n      \"p=0.041666666666666664, r=0.14285714285714285, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'latin american and caribbean american', 'u-s. district court', 'u.s. constitut', 'dan stein', 'censu', 'census', 'sampl (statistical)', 'upscal', '1990 censu', 'the', 'sampl (statistics)', 'unit state', 'that the question should be ad to the', 'histori', 'the u.', 'tom ridg', 'sampl (statistic)', 'jan meyer'}\\n\",\n      \"GT: num=5 - {'hous apportion', 'american immigr reform', 'censu bureau', 'nation head count', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'aircraft first flown in 1975', 'aerospac aircraft', 'airlin of the unit state', 'twin piston-engin aircraft', 'the thunderbolt ii is the safest plane in the air forc', 'militari equip introduc in the 1970', 'raf alconburi', 'rafa bentwat', 'a-10 thunderbolt ii', 'oper histori', \\\"tohono o'odham indian reserv\\\", 'tuscon', 'unit state', 'unit kingdom', 'traci', 'aerial bomb of citi', 'in the world.'}\\n\",\n      \"GT: num=5 - {'pilot', 'second crash', 'routin train flight', 'britain', 'suspens'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'poverti reduct', 'roberto de abreau sodr', 'west berlin', 'organ base in new york', 'obe y. asamoah', 'brazil', 'meet', 'unitedn', 'gro harlem brundtland', 'third world', 'unit nation', 'the world are not do enough to help the third world.', 'unit state', 'unit nation gener assembl observ', 'susana ruiz cerutti', 'gener assembl'}\\n\",\n      \"GT: num=8 - {'42nd gener assembl', 'third world countri', 'foreign loan', 'brazil', 'intern econom relat', 'debt restructur', 'industri nation', 'foreign debt'}\\n\",\n      \"p=0.0625, r=0.125, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'a-10 thunderbolt ii', 'the citi of remscheid.', 'train flight were order in the citi of', 'athlet sport', 'unit kingdom', 'aerial warfar', 'dieter wellershoff', 'militari personnel kill in action', 'czechoslovakia', 'and the', 'the', 'terror', 'unit state', 'ramstein', 'aircraft hijack', 'u.s. air forc', 'histori', 'terror tactic', 'welt am sonntag', 'airlin'}\\n\",\n      \"GT: num=7 - {'militari aircraft', 'pilot', 'fatal crash', 'deadli accid', 'remscheid', 'fieri crash', 'west german'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'biographi', '1946 birth', 'olymp career', 'toronto', 'the boston globe', 'american sprinter from new york', 'american olymp medalist', 'american peopl of english descent', 'canadian sprinter', 'return to canada', 'of peopl who had been wait for him to arriv at the airport', 'saskatchewan', 'saskatoon', 'ben johnson', 'stonewal', 'american male sprinter of english-languag descent', 'sydney', 'to hi', 'to', 'to the'}\\n\",\n      \"GT: num=8 - {'disappoint', 'homecom', 'canadian', 'drug test', 'sprinter', 'olymp gold medal', 'ben johnson', 'illeg steroid stanzolol'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'fighter squadron 143', 'north carolina', 'the airplan', 'virginia beach', 'f-16a', 'the', 'unit state', 'aircraft accid in the unit state', 'the jet over a hangar at gillespi field,', 'the plane wa', 'unit airlin flight 11', 'f-14', 'unit nation air forc', 'airlin that reenter the atlant ocean', 'virginia', 'hill air forc base', 'accid', 'the aircraft', 'aerospac accid in california', 'american airlin flight 175'}\\n\",\n      \"GT: num=6 - {'injuri', 'pilot', 'atlant ocean', 'navi aircraft', 'navi aviat', 'atlant accid'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'canadian provinc', 'cultur', 'john furedi', 'member state of the unit nation', 'the countri wa on a roller coaster ride to the bottom, and then', 'g20 nation', 'canadian', 'sport', 'countri in north america', 'the', 'the olymp', 'univers of toronto of toronto and the globe and mail', 'john faughti', 'canada', 'pat reid', 'globe and mail of toronto', 'wayn gretzki', 'olymp medal'}\\n\",\n      \"GT: num=7 - {'strip', 'stanzolol', 'canadian', 'sprinter', 'olymp gold medal', 'ben johnson', 'drug scandal'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'polic', 'a', 'counti of california', 'polic brutality.', 'thoma j. clark', 'popul coastal place in california', \\\"the city' polic chief said the incid wa\\\", '1871 establish in california territori', 'long beach, california', 'polic brutal', 'curt livesay', 'erni kell', 'nation associ for the advanc of color peopl', 'a polic', 'burbank, california,', 'long beach citi manag', 'histori', 'recent histori', 'counti seat in california (u.s. state)', 'a result of', 'today', 'long island'}\\n\",\n      \"GT: num=8 - {'racism complaint', 'polic racism', 'lo angel area', 'white policeman', 'seriou injuri', 'long beach polic', 'polic brutal', 'black policeman'}\\n\",\n      \"p=0.045454545454545456, r=0.125, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the blaze wa', 'wildfir', 'bridger-teton nation forest and two other major fire in wyom', 'summer firework in the philippin', 'zion nation park', 'summer event in the netherland', 'hudson valley', 'the fire wa about', 'hiawatha nation forest', 'event', 'the weekend, but on tuesday, the fire wa', 'the', 'unit state', 'wa about', 'yosemit nation park and wyom', 'wa', 'u.s. forest servic', 'fourth of juli', 'summer in the czech republ', 'public holiday in the unit state', 'fourthofjuli'}\\n\",\n      \"GT: num=6 - {'forest fire', 'brush fire', 'firefight', 'utah', 'zion nation park', 'fire line'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'prepar', 'wildfir', 'custer nation forest', 'the largest fire', 'zion nation park', 'event', 'day of the year', 'the', 'upper peninsula', 'hiawatha nation park and wilder', 'in the', 'u.s. forest servic', 'yosemit nation park<infill>august 1', 'day', 'daysof the year (august)', 'the wilder area.', 'august (period)', 'august', \\\"the fire' size. the fire wa\\\"}\\n\",\n      \"GT: num=6 - {'nation forest', 'wildfir', 'fire', 'firefight', 'blaze', 'illeg firework'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'forestfir', 'westernst', 'in the western states:', 'list of environment issu', 'cultur geographi', 'forestri', 'western', 'climat chang', 'climat', 'climat scienc', 'citi', 'western unit state', 'climatechang', 'forest', 'climat of the unit state (census)', 'western state :', 'climatolog', 'climat engin', 'forest fire', 'fire develop', 'forest and grass fire', 'western state', 'climat evolut', 'forest fire develop', 'climat impact', 'climat resili'}\\n\",\n      \"GT: num=2 - {'forest fire develop', 'western state'}\\n\",\n      \"p=0.07692307692307693, r=1.0, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'u.s. air base at misawa', 'modern histori', 'athlet aircraft', 'aerial hijack', 'the', 'f-16', 'unit state', 'iwat prefectur', 'aircraft hijack', 'the unit', 'jane', 'sakhalin', 'the u.s.', 'aerospac accid and incid', 'terror tactic', 'ishat', 'militari equip introduc in the 1970', 'airlin', 'yuzhno sakhalinsk', 'the u.s.-japan militari base at misawasawa is about'}\\n\",\n      \"GT: num=5 - {'pilot', 'militari jet', 'crash', 'misawa', 'northern japan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'tunnel', 'ed ferguson', 'nation sever storm forecast center', '20th centuri', \\\"mother 's day\\\", 'tornado', 'and the', \\\"mother' day\\\", 't tornado season', 'the', 'american weather', 'index of american-rel articl', 'histori', 'state of the unit kingdom', '1871 establish in the unit state', 'the tornado', 'the air to form thunderclouds. the warm weather', 'state and territori establish in 1871', 'nation climat data center', 'the nation.', 'north america', 'nation weather servic'}\\n\",\n      \"GT: num=7 - {'tornado season', 'tornado death', 'tornado outbreak', 'storm', 'tornado', 'public awar', 'fatal'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'carl lewi', 'post-cold war', 'peopl of the cold war', 'canada', 'do', 'canadalcan peopl', 'ken read', 'wayn gretzki', 'did not do', 'did', 'to do drugs. he did not do drugs. he', 'olymp medalist', 'north american indigen peopl', 'histori', 'rexdal', 'canadian peopl', 'brian mulroney', 'canadian', 'do drugs.', 'jean charest'}\\n\",\n      \"GT: num=8 - {'american carl lewi', 'canadian', 'disappoint nation', 'sprinter', 'olymp gold medal', 'anabol steroid', 'ben johnson', 'drug scandal'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'civil war', 'histori and scienc of south america', 'the rebel are', 'are not', 'and the fact that the govern', 'anda', 'histori of peru', 'peru', 'coloni peru', 'ayacucho provinc', 'spanish colon of the america', 'the', 'and', 'histori (polit and scientific)', 'ayacuchso', 'the shine path', 'the guerrilla are', 'historyof peru', 'histori', 'independ', 'andean report', 'is not', 'shine path', 'are', 'spanish-speak countri and territori', 'the andes,'}\\n\",\n      \"GT: num=8 - {'huaycan', 'rebel movement', 'shantytown', 'shine path guerrilla', 'central highway', 'polit forc', 'public support', 'polit violenc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'a report', 'scienc magazin', 'a', 'climat chang commun and research program', 'grant w. branstat', 'a studi', 'temperatur pattern', 'a book', 'that the studi wa a', 'nation ocean and atmospher administr', 'climat chang', 'climat', 'scientif evid', 'climat scienc', 'boulder', 'climatolog', 'climat engin', 'camp spring', 'nation center for atmospher research', 'a paper', 'drought in the midwest'}\\n\",\n      \"GT: num=8 - {'new comput studi', 'ocean temperatur abnorm', 'temperatur abnorm', 'drought', 'pacif ocean temperatur', 'atmospher research', 'weather pattern', 'midwest'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'memberst of the european union', 'index of peru-rel articl', 'ayacucho', 'the govern', 'peruvian-speak countri and territori', 'peru', 'the', '1960', 'that ha kill', 'the govern say the death squad is a death squad', 'countri in south america', 'andean', 'member state of the african union', 'osman morot', 'govern', 'pucallpa', 'histori', 'colombia', 'maoist', 'sh shine path', 'member state of the unit nation', 'govern and', 'war in the andean region', 'shine path', 'death squad'}\\n\",\n      \"GT: num=8 - {'district attorney carlo escobar', 'shine path guerrilla', 'osman morot', 'polic post', 'rodrigo franco command', 'properti damag', 'death squad', 'rebel raid'}\\n\",\n      \"p=0.08, r=0.25, f1=0.12121212121212122\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'ocean', 'atlant current', 'antarct', 'landform of the atlant ocean', 'a', 'nation hurrican center', 'that wa', 'bob', 'havana', 'bob case', 'landscap of the pacif ocean', 'that', 'wa', 'atlant ocean', 'bob sheet', 'histori', 'dominican republ', 'wa a', 'the next day. it wa then hit by a storm', '1988', 'atlant hurrican'}\\n\",\n      \"GT: num=8 - {'first tropic depress', 'hurrican', 'typic atlant hurrican season', 'atlant storm season', 'coastal counti', 'hurrican coastal flood model', 'forecast', 'nation hurrican center'}\\n\",\n      \"p=0.047619047619047616, r=0.125, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a', 'hurrican gilbert', 'nation hurrican center', 'weather in the unit state', 'the media, he is not a reporter. he is', 'hurrican elena', 'hurri gilbert', 'hurricana', 'weather organ', 'a director', 'special event', 'director', 'labor day hurrican of 1935', 'weather extrem', 'weather servic', 'hurrican camil', 'director of the nation hurrican', 'weather station in theunit state', 'nation weather servic'}\\n\",\n      \"GT: num=8 - {'barometr pressur', 'intens hurrican', 'categori 5 hurrican', 'catastroph damag', 'western hemispher', 'hurrican gilbert', 'destruct gilbert', 'nation hurrican center'}\\n\",\n      \"p=0.10526315789473684, r=0.25, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'cook city, montana', '1895 establish in the unit state', 'co cook city, mont.', '1885 establish in montana', 'citi in the yellowston nation park and preserv', 'the', 'north platt', 'popul place establish in 1885', 'codi', 'the fire is gone and the town is', 'a ghost town. the', 'last year', 'u.s. forest servic', 'histori', 'north platm', 'the fire', 'bill', 'yellowston nation park', 'the town'}\\n\",\n      \"GT: num=6 - {'unpreced fire season', 'firefight effort', 'yellowston nation park', 'yellowston ecosystem', 'new wildfir season', 'forest fire'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'air accid', 'tuesday crash', 'the plane,', 'nation transport safeti board', 'feder aviat administr', 'the wreckage,', 'citi in new mexican', 'al albuquerqu', 'event', 'popul place establish in 1876', 'coronado airport', 'and the', 'albuquerque, new mexico', 'the', '1876 establish in new mexicali', 'the crash site. the plane wa', 'air accid in new mexico in 1979', 'plane,', 'offic of the medic investig', 'the airplane,', 'albany, new mexicano', 'cessna p210', 'single-engin airplan'}\\n\",\n      \"GT: num=7 - {'albuquerqu', 'crash site', 'wit', 'crumpl airplan', 'victim', 'investig', 'rescu crew'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'mid-atlant state', 'event', 'august-rel list', 'drought', 'month', 'august event', 'and the', 'west virginia', \\\"the agency' long-rang forecast through next monday,\\\", 'and', 'unit state', 'mississippi valley', 'august', 'august of thi year', 'ohio valley', 'great lake', 'august and the unit state', 'nation weather servic'}\\n\",\n      \"GT: num=9 - {'dri weather', 'western great lake region', 'drought region', 'extrem drought', 'water shortag', 'northern great plain', 'agricultur depart', 'littl relief', 'nation weather servic'}\\n\",\n      \"p=0.05555555555555555, r=0.1111111111111111, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'bolivar', 'hurolog advisori', 'caribbean', 'hurrican', 'to the surface.', 'san andr', 'hurolog', 'carmen de bolivar, colombia', 'track', 'huron shower', 'northwest caribbean', 'hurrican joan', 'bogota', 'nation hurrican center', 'bolivia', 'hurican'}\\n\",\n      \"GT: num=8 - {'hurrican forc', 'hurrican joan', 'tropic storm', 'open caribbean', 'panama', 'hurrican watch', 'colombia', 'atlant hurrican season'}\\n\",\n      \"p=0.0625, r=0.125, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'unit nation food and agricultur organ', 'agricorn', 'agricultur stabil and conserv servic', 'agenc', 'salli michael', 'respons', 'usda background', 'peter c. myer', 'richard e. lyng', 'american drought', 'unit state', 'ag agricultur depart', '1962 establish in the unit state', '1961 establish in north america', 'natur disast', 'drought in the carolina', 'to help feed livestock in drought counties. _ june 9.'}\\n\",\n      \"GT: num=9 - {'usda drought task forc', 'meat purchas', 'emerg relief measur', 'congression drought relief task forc', 'drought aid', 'agricultur depart', 'action list', 'conserv reserv program', 'drought panel'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'barometr pressur', 'historyof the unit state', 'hurrican gilbert', 'hurrican debbi', 'histori (1801–present)', 'hurricaneolog', '1901 to present', 'the', 'the wind is', 'history(s)', 'of heat and moisture, and', 'is', 'wind is', 'histori', 'hurrican camil', 'wind', '191900 hurrican', '1935 labor day hurrican', 'histori of the unit state', 'histori and technolog of the u.s.', 'huronicad'}\\n\",\n      \"GT: num=8 - {'barometr pressur', 'intens hurrican', 'storm surg', 'western hemispher', 'hurrican gilbert', 'hurrican fatal', 'nation hurrican center', 'tropic storm forc wind'}\\n\",\n      \"p=0.09523809523809523, r=0.25, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'albert g. bustament', 'william f. goodl', 'background', 'thoma j. ridg', 'mervyn m. dymal', 'censu', 'census', 'sampl (statistical)', '1990 censu', 'don edward', \\\"and rep. john m. o'donnell, r-calif., said they are\\\", 'not sure whether the bill are constitutional.', 'sampl (statistics)', 'unit state', 'bill to count illeg alien', 'popul', 'citizenship studi'}\\n\",\n      \"GT: num=5 - {'lawmak', 'censu bureau', 'hous seat', 'repres', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'memberst of the european union', 'member state of the mercosur', 'peru', 'wa captured. he', 'the capital. he wa', 'minoist', 'countri in south america', 'peruvian communist parti', 'moonshin', 'southern cone countri', 'osman morot', 'wa', 'histori', 'perú', 'maoist', 'mao tse-tung', 'wa arrest', 'member state of the unit nation', 'the leadership of the shine path. morot wa arrest in', 'shine path', 'peruvian revolut'}\\n\",\n      \"GT: num=7 - {'top militari leader', 'counterinsurg polic', 'osman morot', 'radic leader', 'maoist rebel group', 'peru', 'shine path movement'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'rail transport in the british isl', 'london-pari', 'eurotunnel', 'railway in the netherland', 'railway tunnel in franc', 'railport in the channel tunnel', '1992', 'rail transport in franc and germani', 'the', 'rail tunnel in the unit kingdom', 'london station', 'will', 'construct', 'to be carri on the train. the', 'channel tunnel', 'the tunnel will', 'histori', 'will be', 'dover', 'waterloo station', 'london'}\\n\",\n      \"GT: num=8 - {'british fear', 'english channel', 'tunnel speed', 'tunnel builder', 'tunnel traffic', 'french coast', 'freight', 'channel train'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the hous of representatives. on the other hand, the', 'census', 'by countri', 'latin american and caribbean american histori', 'two seat', 'citizenship studi', 'hous of repres', 'censu bureau', 'unit state', 'hous of congress', 'state', 'popul', 'one seat', 'survey methodolog', 'two', 'popul refer bureau', 'popul expert', 'would lose', 'one', 'hous', 'one hous seat', '1990 censu', \\\"william o'har\\\"}\\n\",\n      \"GT: num=5 - {'censu bureau', '1990 censu', 'hous seat', 'nation head count', 'illeg alien'}\\n\",\n      \"p=0.08695652173913043, r=0.4, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'the govern', 's slovenian', 'thegovernment.', 'mladina', 'to take action against', 's slovenia', 'countri in europ', 'bosnia and herzegovina', 'yugoslavia', 'milan kucan', 'state and territori establish in 1991', 'the', 'slovenian', 'militari action against dissid', 'svetozar visnjic', 'militari', 'slojian communist', 'southeastern european countri', 'state of europ', 'state with multipl capit', 'balkan countri', 'that the govern wa not plan', 'polit', 'the state', 'srijani', 'the countri'}\\n\",\n      \"GT: num=8 - {'polit dissid', 'yugoslavia', 'dissid tendenc', 'slovenia polic', 'ministri statement', 'communist govern', 'slovenian communist', 'feder militari command'}\\n\",\n      \"p=0.038461538461538464, r=0.125, f1=0.058823529411764705\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'govern agenc establish in 1935', 'hurrican camil', 'nationalrican center', 'a major', 'hurricana', 'weather organ', 'to be in the middl of a hurricane, and', \\\"it' a\\\", 'a', 'bob sheet', 'hurrican gilbert', '1959 establish in the unit state', 'special program', 'hurrican elena', 'weather servic', 'nation hurrican center', 'nation weather servic'}\\n\",\n      \"GT: num=7 - {'hurrican headquart', 'destruct hurrican gilbert', 'catastroph damag', 'western caribbean', 'bob sheet', 'nation hurrican center', 'atlant hurrican season'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'colorado state univers', 'landform of the atlant ocean', 'articl contain video clip', 'oceanographi', 'the', 'climat', 'than the', 'atlant', 'more intens than', 'hurrican season', 'the last', 'west', 'atlant ocean', 'nation hurrican confer', 'el nino', 'atlant hurrican', 'west africa', 'forecast', 'oceanograph model', 'that the storm system will be'}\\n\",\n      \"GT: num=8 - {'storm', 'annual hurrican forecast', 'atlant hurrican', 'atlant ocean', 'hurrican expert', 'turbul summer', 'william gray', 'hurrican season'}\\n\",\n      \"p=0.15, r=0.375, f1=0.21428571428571425\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'organ of the palestinian peopl', 'critic', 'tuni', 'richard murphi', 'organ design as terrorist by iran', 'wa not approv by the unit states.', 'khalil wazir', 'organis of the arab leagu', 'charl redman', 'unit state', 'assassin of khalil wazir', 'arafat', 'tunisi', 'palestin liber organ', 'organ crime', 'organis design as terrorist by china'}\\n\",\n      \"GT: num=12 - {'palestinian guerrilla', 'accus', 'american target', 'khalil wazir', 'isra offici', 'isra squad', 'plo leader yasser arafat', 'terrorist attack', 'unit state', 'polit assassin', 'plo offici', 'possibl plo attack'}\\n\",\n      \"p=0.125, r=0.16666666666666666, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'and canadian presid john baird said that johnson had been', 'summer olymp sport', 'controversi', 'carl lewi', 'biathlon at the olymp', 'that johnson', 'sarasparilla', \\\"canada' chief of mission\\\", 'canada', 'that he', \\\"canada' nation team\\\", 'that hi', 'canada in the summer olymp', 'olymp dope', 'that', 'summer paralymp sport', 'slovenia', 'summer olymp', 'sierra leon', 'opinion', 'drug', 'bi olymp', 'that the', 'omnisport', 'biathlet', 'and that'}\\n\",\n      \"GT: num=7 - {'canadian ben johnson', 'american carl lewi', 'olymp', 'sprinter', 'illeg anabol steroid', 'gold medal', 'drug use'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'[[nation velvet]]', 'actress elizabeth taylor', '[[nationalelvet (film)|nat velvet]].', 'she ha been treat for pneumonia at st. john', 'hospit in lo', 'american film actress', 'american actress of english-languag descent', 'elizabeth taylor', 'and the', '[[ butterfield 8 (film)]]', 'the', 'and', '1961 birth', 'american peopl of english descent', 'nation velvet', 'list of highest-gross film', 'health problem', '[[butterfield 8]]', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=5 - {'miss taylor', 'sinu infect', 'actress elizabeth taylor', 'health problem', 'pneumonia'}\\n\",\n      \"p=0.14285714285714285, r=0.6, f1=0.23076923076923073\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'boycott exxon', 'environment issu', 'the cleanup will be', 'nation press club', 'alcohol beverag control act of 1988', 'environment disast', 'samuel skinner', 'alaska', 'consum backlash', 'alaskan oil spill', 'a major environment disaster.', 'environment backlash', 'ralph nader', 'aftermath', 'environment impact of the energi industri', 'coast guard', 'al alaska'}\\n\",\n      \"GT: num=10 - {'boycott exxon', 'signific environment disast', 'oil industri', 'ga price', 'fish industri', 'cleanup strategi', 'alaskan oil spill', 'tanker exxon valdez', 'exxon valdez accid', 'oil compani'}\\n\",\n      \"p=0.11764705882352941, r=0.2, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of the', 'hurrican dean', 'of antiguan and', 'stuart', 'cultur of antigua & barbuda (1936–1953)', 'island, which is about the size of', 'hurrican', 'caribbean', 'hur franc', 'antigua and barbuda', 'hur hurrican', 'of', 'list of hurrican', 'antigen and', 'antarctica', 'antiguan', 'citi in antigua', 'st. thoma', 'hurrah'}\\n\",\n      \"GT: num=10 - {'second hurrican', 'atlant season', 'hurrican advisori', 'eastern caribbean', 'emerg suppli', 'hurrican watch', 'forecast', 'hurrican dean', 'hurrican warn', 'puerto rico'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'elizabethtaylor', 'to her', 'univers of southern california', '[[nation velvet (1945 film)|nat velvet]]', 'american film actress', 'american actress of english-languag descent', 'elizabeth taylor', '1941 birth', 'american peopl of english descent', 'nation velvet', 'list of highest-gross film', 'health problem', 'in a video messag to', '[[butterfield 8]]', \\\"[[who' afraid of virginia woolf]]\\\", 'to', 'american women in journal', 'pneumonia', 'hospit', 'to the'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'sinu infect', 'health problem', 'lung biopsi', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.15, r=0.5, f1=0.23076923076923075\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'mountain and foothil of the appalachian mountain', 'mine town in maine<infill>c of the central main', 'main suprem court', 'cumberland counti superior court', 'the right to keep and bear arm', 'perkin wrote that the court had no basi to dismiss the gun possess charge. he also', 'the', '19th centuri', 'cape and island', 'histori', 'municip and state constitut amend', 'cumberland', 'cedar falls, main', 'that the', 'stephen l. perkin', 'marijuana', 'jame e. tierney', 'index of maine-rel articl', 'citi in main'}\\n\",\n      \"GT: num=10 - {'firearm', 'gun', 'felon', 'constitut amend', 'restrict', 'gun possess charg', 'main constitut', 'right', 'crimin threaten', 'arm'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'depart of interior', 'exxon ship co.', 'the oil', 'steve cowper', 'unit fishermen of alaska', 'gregori cousin', 'river of alaska (u.s. state)', 'princ william sound', 'in the histori of the', '1980', 'princewilliam sound', 'alaska', 'princewil sound', 'the', \\\"the nation' largest oil spill\\\", 'oil spill in', 'expo valdez', 'histori', 'of', 'geographi of the pacif northwest', 'the histori', 'oil spill'}\\n\",\n      \"GT: num=7 - {'captain', 'environment damag', 'joseph hazelwood', 'full investig', 'feder regul', 'proper pilot', 'oil spill'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'tumor', 'be test for', 'for tb.', '1920 establish in theunit state', 'prevent', 'american civil liberti organ', 'tuberculosi', 'aids-viru', 'canadian civil liberti associ', 'organ establish in 1920', 'prison', 'that all inmat', 'treatment', 'center for diseas control', 'in prison be', 'drug treatment', 'american civil liberti union', 'the cdc said, note that the cdc ha recommend', 'tuberculosi in the unit state', 'be', 'tculosi in u.s. prison'}\\n\",\n      \"GT: num=4 - {'tuberculosi case', 'tuberculosi rate', 'airborn transmiss', 'cdc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'citi in the unit state', 'the genet suscept and you can do', 'cultur tourism in texa', 'american diabet associ', 'citi in greater san antonio', 'mexican-american', 'do more', 'univers of texa health scienc center', 'do', 'latin american and latino american', 'san antonio', 'health', 'diabet', 'yale univers', 'popul place establish in 1836', 'type i diabet', 'demograph', 'list of peopl from san antonio and the surround area', 'type ii diabet'}\\n\",\n      \"GT: num=10 - {'obes diabet', 'diabet patient', 'hispan diabet', 'minor', 'insulin', 'diabet studi', 'diabet test', 'american diabet associ', 'type ii diabet', 'hispan'}\\n\",\n      \"p=0.10526315789473684, r=0.2, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'controversi', 'foreign polici of slovenia and the european union', 's slovenian conflict', 's krupska', 'josima dukaj', 'srukom,', 'foreign relat of slovenia', 's drukom', 'slukom,', 'foreign affair of slovenia<infill>foreign relationsof yugoslavia', 'southeast european countri', 'slovenia', 'aleksandar prlja', 'foreignrel of croatia', 'serbia', 'josip broz tito', 'janez drnovsek', 's slovenia, a slovene, said that the', 'ljubljana', 'srijani'}\\n\",\n      \"GT: num=7 - {'slovenian presid', 'slovenian serbian conflict', 'yugoslav feder', 'region autonomi', 'econom contact', 'econom boycott', 'serbian action'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'unit kingdom', 'europ', 'cultur depict of cattl', 'govern ha', 'the', 'cattledog', 'martin raff', 'not', 'cow in the unit state', 'ha not been', 'cattl diseas', 'crisi in cattl farm', 'ha', 'the u.s. depart of agricultur ha', 'the govern ha', 'nation farmer union', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'ha been', 'not been', 'been', 'univers college, london', 'c cattl diseas', 'scraie'}\\n\",\n      \"GT: num=8 - {'sheep diseas', 'govern', 'scrapi', 'british cattl', 'mad cow diseas', 'bse', 'immun system', 'export'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'nation air forc', \\\"the pattern of the '40s, '50 and '60 when we had a tremend number of landfal\\\", 'prepar', 'histori', 'atlant hurrican', 'nation ocean and atmospher administr', 'atlant ocean', 'climat chang', '1930', 'of landfal hurricanes.', 'hurrican histori', 'max mayfield', 'north palm beach, fla.', 'climat scienc', 'climat resili', 'nation hurrican center', 'nation weather servic', 'atlant hurrican season'}\\n\",\n      \"GT: num=8 - {'hurrican hunter', 'weather satellit', 'coastal popul', 'hurrican activ', 'hurrican reconnaiss flight', 'air forc', 'forecast', 'atlant hurrican season'}\\n\",\n      \"p=0.05555555555555555, r=0.125, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'smithfield meat market', 'to the', 'diamond', 'african nation congress', 'south african', 'central sell organ', 'chemic element', 'south african have been pressur the organ to increas price', 'south africa', 'product', 'diamond industri', 'to', 'to $ 4.09 billion,', 'product by countri', 'articl contain video clip', 'peter miller', 'argyl diamond'}\\n\",\n      \"GT: num=7 - {'central sell organ', 'de beer diamond empir', 'rough diamond sale', 'diamond busi', 'world diamond industri', 'south african interest', 'diamond produc'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'of a', 'lui carlo galan', 'the countri is in a state of', 'carlo pizarro', 'virgilio barco', 'of the', 'of polit', 'antonio navarro', 'april 19 movement', 'spanish colon of the america', 'april 27 elect', 'coloni era', 'countri in south america', 'bernardo jaramillo', 'counti seat in colombia', 'republ', 'histori', 'colombia', 'of', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=10 - {'assassin', 'drug traffick', 'suicid mission', 'presidenti elect', 'gunman', 'drug baron', 'terrorist campaign', 'polit chao', 'candid carlo pizarro', 'colombia'}\\n\",\n      \"p=0.05, r=0.1, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'exxon valdez oil spill', 'exxonvaldez oil spill', 'cleanup', 'exxon exxon valdel oil spill, it cleanup', 'exxon valdez', 'coordin drill', '1962', 'coast', 'oil spill of exxon valdaz', 'major event', 'exxon valdess', 'environment event', 'exxon', 'cooper develop', 'cooper', 'occurr', 'exxon valdezoil spill', 'transport in alaska', 'chronolog', '1970', 'coastal geographi', 'ch chronolog', 'coal mine', 'chromolog', 'chronolog of the exxon valdz oil spill (1962)', 'exxonaldez'}\\n\",\n      \"GT: num=4 - {'exxon valdez oil spill', 'develop', 'chronolog', 'cleanup'}\\n\",\n      \"p=0.11538461538461539, r=0.75, f1=0.19999999999999998\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'tunnel', 'ill.', 'tornado fact', 'tropic cyclon', 'tornado', 'north', 'natur disast', 'natur hazard', 'to the northeast,', 'insur inform institut', 'indiana', 'missouri', 'the nation weather servic ha a tornado scale of', 'histori', 'list of tornado', 'southwest, northeast, northeast', 't tornado fact', 'illinoi', 'northwest,', 'nation weather servic'}\\n\",\n      \"GT: num=3 - {'tornado', 'thunderstorm', 'tornado fact'}\\n\",\n      \"p=0.1, r=0.6666666666666666, f1=0.1739130434782609\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'a predict of', 'cultur of the southern unit state', 'hurrican hugo', 'suitland', 'hurrican', 'massachussett institut of technolog', 'colin mcadi', 'weather forecast', 'nation ocean and atmospher administr', 'cultur histori of the unit state (1865–1953)', 'the weather is a', 'a', 'gil clark', 'american weather', 'a forecast', 'climatolog', 'histori', '20th centuri'}\\n\",\n      \"GT: num=10 - {'hurrican forecast', 'hurrican hugo', 'real forecast problem', 'forecast abil', 'landfal', 'supercomput predict', 'south carolina coast', 'satellit data', 'destruct path', 'satellit pictur'}\\n\",\n      \"p=0.05555555555555555, r=0.1, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'state and territori establish in 1822', \\\"the solomons. a 6 read is a `` minor '' earthquak ; a 7 read is\\\", 'golden', 'former dutch coloni', 'island countri', 'recent histori', 'a quak', 'solomon', 'pacif tsunami warn center', 'a', 'san francisco bay', 'pacific-wid tsunami', 'state of the pacif', 'san cristob', 'histori', 'earthquak', 'solomon island', 'u.s. geolog survey'}\\n\",\n      \"GT: num=7 - {'earthquak monitor', 'richter scale', 'major earthquak', 'widespread heavi damag', 'largest earthquak', 'epicent', 'solomon island'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'corazon aquino', 'assassin', 'rafael ileto', 'to protect the peopl and to protect the', \\\"new people' armi\\\", 'brig. gen. alexand aguirr', 'attack by firearm', 'assault on govern offici', 'the govern and', 'the', 'asia', 'philippin capit', 'terror in the unit state', 'kill by firearm in the philippin', 'govern', 'govern and', 'the philippin', 'nation secur advis', 'philippin'}\\n\",\n      \"GT: num=9 - {'gunmen', 'assasin attempt', 'makati', 'street violenc', 'communist rebel', 'polit assassin', 'rebel assassin', 'philippin', 'polic chief'}\\n\",\n      \"p=0.05263157894736842, r=0.1111111111111111, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'and her family. she wa also in the intens care unit', 'elizabethtaylor', 'michael wild', \\\"[[who' afraid of virginia woolf?]]\\\", '1946 birth', \\\"[[who' afraid?]]\\\", 'american film actress', 'elizabeth taylor', '[[ butterfield 8 (film)|butterfield 8]] and', 'and the', 'and', 'american peopl of english descent', 'hollywood actress', 'list of highest-gross film', 'health problem', '[[butterfield 8]]', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=6 - {'viral pneumonia', 'sinu infect', 'bacteri pneumonia', 'intraven therapi', 'yeast infect', 'elizabeth taylor'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'scientif opinion', 'vaccin', 'rttb', 'to continu to suppress the tuberculosi bacteria, which would have', 'dana-farb cancer institut', 'be', 'world health organ essenti medicin', 'stanford univers', 'nation institut of allergi and infecti diseas', 'be abl to', 'rtb drug', 'epidem', 'drug for aid', 'societi and cultur', 'william haseltin', 'rttem', 'sten vermund', 'to', 'american associ for the advanc of scienc', 'aid', 'been abl to'}\\n\",\n      \"GT: num=6 - {'aid epidem', 'remiss', 'tuberculosi bacteria', 'aid vaccin', 'aid infect', 'drug'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american', 'american societi', '18th-centuri introduct', 'health', 'unit state', 'that', 'tuberculosi', 'american cultur', 'the cdc said that', 'tculosi', 'acquir immun defici syndrom', 'peopl of african descent', 'center for diseas control', 'in 1988, the cdc said. in 1988, there were', 'that the', 'ethnic group in the unit state', 'u.s. tuberculosi', 'american peopl', 'aid'}\\n\",\n      \"GT: num=5 - {'complet statist', 'tuberculosi case', 'steadi declin', 'aid case', 'diseas control'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'western unit state', 'idlewildfir command post', 'wildfir', 'citi in the pacif northwest', \\\"'' it' a veri dri\\\", 'boe', 'western cultur', 'idealist', '1880 establish in the unit states<infill>western unit state', 'yellowston nation park', 'american weather', 'climat', 'the weather is not consist', 'and dri', 'idaho', 'nation weather servic', 'cultur of the unit kingdom'}\\n\",\n      \"GT: num=6 - {'fire danger', 'dri weather', 'western wildfir', 'firefight', 'blaze', 'contigu unit state'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'univers of medicin and dentistri', 'atlanta', 'american societi', 'newark, n.j', 'tuber tuberculosi', 'tubulatru', 'tubbularculosi', 'health', 'tuberculosi', 'american peopl', 'tusbularculosis.', 'aid viru', 'american cultur', 'the center for diseas control and prevention. the center for', 'tubularculosi', 'north american societi', 'tculosi', 'american lung associ', 'tubi', 'cultur', 'tub', 'treatment'}\\n\",\n      \"GT: num=6 - {'new health threat', 'lung diseas', 'tuberculosi', 'aid viru', 'tb case', 'diseas control'}\\n\",\n      \"p=0.09090909090909091, r=0.3333333333333333, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'henri waxman', 'he', 'carl lewi', '20th centuri', \\\"he'\\\", '19th-centuri american male writer and editor', \\\"that he' here to compet with me. '' he said he\\\", 'benjamin gilman', 'mel levin', 'benjamingilman', 'american male of english descent', \\\"i'm here to tell the peopl of thi countri\\\", 'histori', \\\"he' here to\\\", 'ben johnson', 'steroid', \\\"american men' basketbal player\\\", 'american non-fict writer', 'american male writer', 'to', 'american peopl'}\\n\",\n      \"GT: num=6 - {'control substanc', 'canadian', 'seoul', 'gold medal', 'anabol steroid', 'ben johnson'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'cultur view of the moon', 'san', 'san francisco', 'at 11:07 a.m. mst in san francisco ; and', 'a partial eclips', 'list of eclips and partial eclips by countri', 'in san', 'mazatlan', 'partial solar eclips', 'solar event', 'welder', 'observ', 'pasadena', 'unit state', 'edmonton', 'north america', 'solar eclips'}\\n\",\n      \"GT: num=7 - {'solar telescop', 'stun view', 'partial solar', 'eye injuri', 'eye damag', 'north america', 'solar eclips'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'of a', 'tracer fire', 'august and septemb', 'of an', 'of.', 'gila river basin', 'event', 'day of the year', 'big sur', 'fort collin', 'western unit state', 'day', 'of', 'gila nation forest', 'precipit', 'mescalero fish hatcheri', 'august (period)', 'august', 'of the black tiger fire in the roosevelt nation forest,', 'august 1'}\\n\",\n      \"GT: num=8 - {'arson fire', 'new mexico fire', 'fire', 'western state', 'forest', 'firefight', 'fire season', 'blaze'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'aterian-era record', 'a total solar eclips', 'a', 'astrobiolog', \\\"the tablet wa found in 1948 in the ruin of ugarit, an ancient citi near syria'\\\", 'ancient histori', 'british museum', 'griffith observatori', 'egyptian-styl calendar', 'syria', 'ugarit', 'asteroid', 'athropolog object', 'astronom object discov in 1948', 'histori', 'a solar', 'aten asteroid', 'astrophys', 'total solar eclips', 'natur', 'the tablet is'}\\n\",\n      \"GT: num=6 - {'syria', 'ancient observ', 'dutch scientist', 'clay tablet', 'reliabl record', 'solar eclips'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'1815 establish in the unit kingdom', ':', 'histori and cultur of the america', 'tornado and sever thunderstorm that have kill at least 27 peopl', 'north american', 'articl contain video clip', 'geolog of theamerican state', 'tornado', 'state-by-st', 'southern unit state (1815–1921)', 'unit state', 'north america', 'geographi of the american state', 'american weather', 'sever thunderstorm', 'histori', 'american geographi', 'sever', 'north american weather', 'geograph midpoint of the earth', 'tornad', 'histori of the unit state'}\\n\",\n      \"GT: num=2 - {'tornado', 'sever thunderstorm'}\\n\",\n      \"p=0.09090909090909091, r=1.0, f1=0.16666666666666669\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'cleanup', 'paul a. yost jr.', 'sacramento', 'environment disast', 'plan', 'will be treat and', 'alyeska pipelin servic co.', 'exxon', 'alcohol beverag control act', 'and', 'environment issu in alaska', 'alaskan oil spill of march 2010', 'and the oil-lac wastewat', 'exxxon', 'alakan oil industri', 'paul a., yost', 'alaska oil spill', 'bill lamoreaux', 'gulf of alaska', 'and oil-taint'}\\n\",\n      \"GT: num=9 - {'exxon tanker', 'crude oil price', 'valdez spill', 'pollut area', 'oil coastlin', 'exxon offici', 'alaskan coastlin', 'oil spill', 'cleanup plan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'census', 'by countri', 'to be', 'unit kingdom', 'latin american and caribbean american histori', 'suprem court', 'citizenship studi', 'robert mosbach', 'censu bureau', 'the', 'sampl (statistics)', 'unit state', 'popul', 'richard shelbi', 'crimin law of the unit state', 'sun belt', 'the hous of repres to be redistrict', '1990 censu', 'to', 'to the'}\\n\",\n      \"GT: num=7 - {'illeg alien', '1990 censu', 'censu bureau', 'hous seat', 'nation head count', 'congression reapportion', 'illeg resid'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'citi in the unit state', 'list of peopl from minneapoli', 'sayl belton', 'minneapoli', '1950', '20th centuri', 'hubert h. humphrey', 'capit in the american south', 'that are', 'citi in minnesota', 'are the', 'are not the onli one who are', 'popul place establish in 1836', 'univers of minnesota', 'john h. laux-', 'histori', 'john laux', 'u.s. senat', 'are', 'racial tension'}\\n\",\n      \"GT: num=6 - {'polic racism', 'polic misconduct', 'civil right', 'drug raid', 'brutal', 'racial harmoni'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {\\\"[[who' af afraid of virginia woolf?\\\", 'elizabethtaylor', 'to the hospital.', \\\"[[who' afraid of virginia woolf?]]\\\", \\\"who '' afraid\\\", 'american film actress', 'elizabeth taylor', 'list of anim right activist', '1913 birth', 'american peopl of english descent', 'health problem', \\\"she wa move to st. john' hospit and health center on april 23. she wa then move to\\\", 'butterfield 8', 'viral pneumonia', '[[butterfield 8]]', '19th-centuri american actress', 'to', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'viral pneumonia', 'bacteri pneumonia', 'yeast infect', 'recoveri', 'elizabeth taylor'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'survey methodolog', 'censu', 'to be taken up in the', 'census', '1990 censu', 'don edward', 'mervyn dymal', 'the issu of the censu', 'sampl (statistics)', 'samuel a. teller', 'unit state', 'histori', 'the senate.', 'tom ridg', 'popul', 'don'}\\n\",\n      \"GT: num=7 - {'censu number', '1990 censu', 'censu bureau', 'hous seat', 'nation head count', 'hous reapportion', 'illeg alien'}\\n\",\n      \"p=0.0625, r=0.14285714285714285, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'alp', 'the oil', 'exxon valdez', '2010', 'of the oil that wa', 'al alaska', 'geolog of the arctic', 'princ william sound', 'the', 'princewilliam sound', 'bald eagl', 'al arctic', 'alaska depart of environment conserv', 'histori', 'geographi of the pacif northwest', 'alaskan arctic', 'exxon corp.', 'oil spill', 'alcohol beverag control act of 2004'}\\n\",\n      \"GT: num=7 - {'tanker exxon valdez', 'wildlif popul', 'civil lawsuit', 'crude oil', 'oil spill', 'environment conserv', 'crimin indict'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'st. john hospit and health center', 'maria burton-carson', '20th-centuri american actress', 'american actress of english-jewish descent', 'american film actress', 'elizabeth taylor', \\\"in the hospital' intens care\\\", 'intens care', 'in', 'american peopl of english descent', 'nation velvet', 'st- john hospit', 'list of highest-gross film', 'elizabeth taylor (1928–present)', 'health problem', 'tracheotomi', 'intens', 'she wa taken off the ventil and wa in good spirits. she wa', 'american women in journal', 'pneumonia', 'hospit'}\\n\",\n      \"GT: num=3 - {'miss taylor', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.09523809523809523, r=0.6666666666666666, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'geographi of the american west', 'casper', 'wildfir', '1881 establish in north america', 'okefenok swamp', 'cheyenn', 'okefenskoot', 'the', 'climat', 'american west', 'region of the unit kingdom', 'west', 'the largest fire in the west', 'fire', 'ashley nation forest', 'fire.', 'were report contain saturday. the', '1871 establish in the unit state', 'the fire', 'western unit state and canada', 'diamond peak fire'}\\n\",\n      \"GT: num=6 - {'feder firefight effort', 'forest fire', 'fire season', 'firefight', 'fire crew', 'fire line'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clarenc thoma', 'yale law school', 'alan simpson', 'sup court of the unit kingdom', 'that he', 'supremaci in the unit state', 'supran court nomine', 'upscal', 'unit state', 'suprem court nomin', 'he wa a lawyer at the law firm of', 'unit nation suprem court nomine', 'that', 'unit state district court nomine', \\\"unit kingdom' highest court\\\", 'john melcher', 'u.s. circuit court of appeal', 'howard m. metzenbaum', 'that thoma'}\\n\",\n      \"GT: num=6 - {'clarenc thoma', 'senat', 'feder appeal judg', 'columbia', 'nomin', 'black offici'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'geographi of the southern unit state', 'hurrican hugo', 'louisiana', 'the u.s. coast', 'hurrican', 'florida key', 'histori (unit states)', 'the', 'american weather', 'had hit the u.s. coast, but it would have been much more deadli if it had hit almost anywher else, say bob sheets, director of the nation hurrican center', 'histori and scienc of the caribbean', 'historyof the unit kingdom', 'histori of the unit state (1876–1953)', 'histori', 'missisippi', '20th centuri', 'franci marion nation forest'}\\n\",\n      \"GT: num=8 - {'hurrican hugo', 'deadli storm', 'forecast', 'south carolina', 'damag', 'william gray', 'popul densiti', 'atlant hurrican season'}\\n\",\n      \"p=0.058823529411764705, r=0.125, f1=0.07999999999999999\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'to the gulf of alaska. the exxon', 'golob oil pollut bulletin', 'steve cowper', 'of the', 'blind spot', 'blight reef', 'bligh reef', 'blach reef', 'princ william sound', 'ed wieliczkiewicz', 'fujian province, republ of china', 'in alaska', 'of oil', 'depart of environment conserv', 'blanchard reef', 'ed wielech', 'of', 'seabe', 'oil spill', 'effect'}\\n\",\n      \"GT: num=6 - {'oil slick', 'environment disast', 'crude oil', 'oil spill', 'environment conserv', 'bligh reef'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'the studi also found that the drug isoniazidine, which is a', 'drug abus', 'new england journal of medicin', 'rttb', 'tumor', 'rtt', 'aid viuru', 'prevent', 'isoniazid', 'a drug that', 'tuberculosi', 'aid viru', 'is use', 'is', 'acquir immun defici syndrom', 'rtb', 'rttem', 'wikipedia medicin articl readi to translat', 'aid', 'treatment'}\\n\",\n      \"GT: num=6 - {'tuberculosi infect', 'drug abus', 'aid viru', 'drug addict', 'tuberculosi bacteria', 'medicin'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american civil war', 'war of independ', 'disastr relief', '1988 drought', '19th-centuri conflict', 'drought', '1914 in the american civil war (1865–1896)', 'and the', 'unit state', 'war involv the unit state', 'the disast relief', 'aftermath', 'disast relief', 'associ press', '1988', 'disast', 'post-war', 'war in the unit nation', 'intern war of the unit kingdom'}\\n\",\n      \"GT: num=4 - {'disast relief measur', 'drought', 'america', 'associ press'}\\n\",\n      \"p=0.10526315789473684, r=0.5, f1=0.17391304347826086\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'citi in the unit state', 'american diabet associ', 'citi in greater san antonio', 'mexican-american', 'san angelo, texa', 'univers of texa health scienc center', 'popul place establish in 1835', 'san antonio', 'health', 'diabet', 'yale univers', 'demograph', 'cultur tourism in texa and the unit kingdom', '1835 establish in texa', 'that she is go to have diabetes. she is go', 'to have', 'san andrea fault', 'to', 'mexican'}\\n\",\n      \"GT: num=7 - {'healthi diet', 'diabet patient', 'hispan diabet', 'minor', 'diabet studi', 'diabet test', 'american diabet associ'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'geographi', 'index of california-rel articl', 'california', 'probabl of a major earthquak in northern california is about 30 percent. the probabl of a', 'san francisco bay', 'the', 'state and territori establish in 1850', 'california in the unit state', 'u.s. geolog survey', 'a major earthquak', 'geolog', 'in the', 'the san', 'state of the unit kingdom', 'earthquak', 'u-s. earthquak center', 'california and the unit nation', 'california institu of technolog', 'whittier quak', 'san'}\\n\",\n      \"GT: num=8 - {'richter scale', 'larg earthquak', 'high probabl', 'strong quak', 'major earthquak', 'widespread heavi damag', 'earthquak center', 'northern california'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'american farm organ', 'organ base in bismarck, north dakota', 'north dakota.', 'drought of1988', 'drain of 1988 hit hardest in the upper midwest', 'the', 'american food industri', 'unit state', 'american feed associ', 'drought of 1988', \\\"cash in onth drought, ''\\\", 'organis base in chicago', 'the third stori in a four-part series, `` cash in', 'american farm bureau feder', 'histori', 'american public health associ', 'north dakota', 'cash in on the drought', 'north america'}\\n\",\n      \"GT: num=6 - {'north dakota', 'drought', 'upper midwest', 'farmer', 'disast relief aid', 'disast aid program'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'the center, and the center is', 'joe blow', 'histori of the pacif northwest', 'lagrand', 'biolog museum in idaho', 'reed jarvi', 'lightn', 'bibliographi of the bois interag fire center', 'the', 'nation fire hall of fame', 'bureau of land manag', '1940 and 1950', 'arnold hartigan', 'firefight', 'histori', 'lynn findley', 'fire hall', \\\"the country, ''\\\", 'the most danger place in the country,'}\\n\",\n      \"GT: num=6 - {'bois interag fire center', 'wildfir battl', 'worst blaze', 'firefight', 'wildfir command post', 'fire line'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'southern u.s. state', 'a', 'index of alabama-rel articl', 'alabama', 'deep south', 'a tornado', 'tornado', 'jefferson counti', 'been a', 'sever weather', '1836 establish in alabama', 'state of the unit state (1836–1921)', 'histori', 'alorton, ill.', 'and build were demolished. the tornado', 'greenwood, s.c.', 'wa report to have', 'southern unit state', 'state and territori establish in 1836', 'nation weather servic', 'nation guardsmen'}\\n\",\n      \"GT: num=9 - {'tornado watch', 'rescu', 'disast', 'tornado', 'huntsvil', 'victim', 'properti damag', 'sever thunderstorm', 'destruct'}\\n\",\n      \"p=0.047619047619047616, r=0.1111111111111111, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cultur tourism in chicago', 'richard j. daley', '1901 establish in illinoi', 'illinoi popul place on lake michigan', \\\"chicago. the city' mayor\\\", 'chicago', 'northeastern illinoi univers', 'harold washington', '20th centuri', 'the', 'citi', 'ha been a vocal critic of', 'cultur of chicago', 'richard m. daley', \\\"city'\\\", 'machin polit', \\\"the city'\\\", '1991 mayor elect', 'histori', 'citi in illinoi (u.s. state)', 'racial tension'}\\n\",\n      \"GT: num=7 - {'race relat', 'racism', 'new mayor', 'black', 'polic brutal', 'racial issu', 'chicago'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'environment issu', 'citizen scienc', 'exxon', 'princ william sound', 'exxon ship co.', 'darrel buttic', 'exxon valdez', 'blind carbon copi', 'black', 'unit state', 'the environment. the valdez is expect to be', 'to be repaired.', 'oil spill', 'artifici intellig', 'neil goldschmidt', 'articl contain video clip', 'blacksmith'}\\n\",\n      \"GT: num=6 - {'crimin charg', 'exxon crew', 'joseph hazelwood', 'tanker exxon valdez', 'annual her industri', 'crude oil'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'paul feeley', 'histori of peru', 'the countryside, the shine path ha set down', 'aguayaco', 'coloni peru', 'spanish colon of the america', 'histori and scienc of peru (1901–present)', 'the shine path', 'upper huallaga river valley', 'aucayacu', 'the revolut', 'historyof peru', 'histori', 'of law and order.', 'maoist', 'a system of', 'raul aranda', 'upris against the militari', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=7 - {'maoist shine path guerrilla', 'corrupt local offici', 'revolutionari justic', 'rebel', 'puritan honesti', 'peru', 'coca product'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'he also promot villag banking.', 'foundat of thenon-profit', 'he', 'bootstamp', 'sustain', 'villag', 'bootstrap', 'english word', 'john hatch, founder of the non-profit foundat for intern commun assist', 'poverti reduct', 'sustain develop goal', 'econom ideolog', 'other use', 'foundat', 'foundat for internationalcommun assist', 'bootstrap econom', 'applic', 'commun', 'foundationfor intern commun assist', 'john hatch', 'third-world', 'practic econom', 'third world', 'villag bank'}\\n\",\n      \"GT: num=5 - {'third world', 'privat enterpris', 'john hatch', 'villag bank', 'world poverti'}\\n\",\n      \"p=0.125, r=0.6, f1=0.20689655172413793\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'latin american and caribbean american', 'georg comstock', 'black peopl', 'black (human racial classification)', 'new england journal of medicin', 'is a', 'black women', 'georg curlin', 'health', 'is the', 'the disease, which is', 'peopl from african-american commun', 'tuberculosi', 'black and latino american', 'is', 'john hopkin univers', 'arkansa depart of health', 'peopl of african descent', 'been blame on the', 'cultur', 'nation institut of allergi and infecti diseas'}\\n\",\n      \"GT: num=6 - {'infecti diseas', 'tuberculosi', 'black american', 'racial differ', 'white', 'tb bacteria'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'drug traffick', 'm-19', 'lui carlo galan', 'carlo pizarro', 'outlin of colombia', 'to continu the fight against', 'medellin drug cartel', 'state and territori establish in 1810', 'spanish colon of the america', 'countri in south america', 'cesar', 'republ', 'histori', 'against the drug traffickers.', 'colombia', 'independ', 'cesar gaviria', 'spanish-speak countri and territori', 'the govern ha vow to'}\\n\",\n      \"GT: num=8 - {'assassin', 'drug traffick', 'medellin drug cartel', 'elect', 'terrorist act', 'cocain cartel', 'presidenti candid carlo pizarro', 'colombia'}\\n\",\n      \"p=0.15789473684210525, r=0.375, f1=0.22222222222222218\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'aircraft manufactur of south korea and the unit state', 'to testifi on behalf of the victims. the prosecut is', 'to the', 'aerospac industri in south korea', 'aerial bomb of the soviet union', 'no tae-u', 'choe kyu-ha', 'republ of korea (1949–present)', 'azerbaijan', 'aero- ballist missil of the unit kingdom', 'to', 'chong sung-hwa', 'aterom', 'histori', 'cho kyu', 'chon tu-hwan', 'chong kyong-sik', 'changwon and the khmer roug'}\\n\",\n      \"GT: num=5 - {'assassin', 'crimin evid', 'militari prosecut', 'assassin kim', 'concret evid'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'hurrican andrew', 'unit kingdom', 'hurci', 'wallac stickney', 'new york', 'unit state', 'a major contribut to the', 'hurvey', 'louisiana', \\\"the president' re-elect campaign.\\\", 'hurrican', 'harri poll', 'new hampshir', 'aftermath', 'storm', 'in the end', 'georg bush', 'homestead air forc base', 'huronicad'}\\n\",\n      \"GT: num=6 - {'louisiana', 'presid georg bush', 'disast', 'emerg relief', 'hurrican andrew', 'florida'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the compani ha a branch in', 'the area.', 'hurrican andrew', 'nelson robertson', 'tropic cyclon', 'n nelson robertson', 'the', 'storm involv the unit kingdom', 'unit state', 'hurican andrew (song)', 'gener accid', 'hurrican', 'in the', 'orlando', 'aftermath', 'hur hurrican', 'orlando.', 'lord airli', 'the loss adjust are', 'lord', 'are', 'insur'}\\n\",\n      \"GT: num=4 - {'insur', 'loss', 'hurrican andrew', 'insur claim'}\\n\",\n      \"p=0.09090909090909091, r=0.5, f1=0.15384615384615385\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the ground and bloodi him with nightsticks. the rev.', 'citi in missouri', 'cultur tourism in missouri<infill>c of the missouri territori', 'andi brez', 'roman cathol', \\\"the polic department'\\\", \\\"officer'\\\", 'joseph okoy', 'the', \\\"the officer'\\\", 'polic brutal', '1872 establish in missouri territori (u.s. state)', 'steven bishop', \\\"polic department'\\\", 'histori', \\\"officers'\\\", 'terri d. barn', 'recent histori', 'kansa city, missouri', \\\"officials'\\\", 'popul place establish in 1872', 'excess forc'}\\n\",\n      \"GT: num=6 - {'polic forc', 'accident shoot death', 'racism', 'black citizen', 'brutal', 'excess forc'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'content', \\\"na'ib al - ma'ayitah\\\", 'lebanon', 'na', 'abu-nid group', 'al jazeera', 'and the fra', 'the frc and the frc', \\\"na-'ib al-'ma `\\\", 'and', 'al-watan', 'alitalia', 'investig', 'and hi', 'arabic-languag media', \\\"al-'watan\\\", \\\"na'-ib al- ma'ayitah assassin\\\", \\\"na'ib al-ma ` ayitah in beirut\\\", \\\"naʼib al‐ma'aytah\\\", 'na-ib al', 'arabist'}\\n\",\n      \"GT: num=7 - {'intern terror', 'investig report', 'imyo', 'jordanian author', 'ayitah assassin', 'jordanian fundamentalist extremist group', 'lebanes secur author'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'siemen', 'to the', 'economi of slovenia', 'economist', 'to find new market in the former yugoslavia, and are look for', 'world trade', 'servic', 'sector', 'renault', 'slovenia', 'world trade organ member economi', 'trade', 'to export to', 'european union member economi of slovenia and croatia', 'sloven', 'economi of europ by countri', 'comecon', 'iskra', 'to', 'econom of europ'}\\n\",\n      \"GT: num=10 - {'independ', 'sloven enterpris', 'sloven export', 'foreign invest', 'capit inflow', 'slovenia', 'trade link', 'sloven offici', 'european commun countri', 'former yugoslavia'}\\n\",\n      \"p=0.05, r=0.1, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'hurrican hugo', 'the year', 'hurrican andrew', 'oakland fire disast', 'in the us in', 'the', 'unit state', 'insur loss', 'cost', 'hurvey', 'dade counti', 'gulf hurrican', 'hurrican', 'hurkel', 'lo angel riot', 'hur hurrican', 'georg bush', 'american insur servic group', 'year', 'hurrah', 'the us'}\\n\",\n      \"GT: num=6 - {'insur claim', 'insur industri', 'florida loss', 'hurrican andrew', 'properti claim', 'insur loss'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'are not', 'in a popul area, but are not consid', 'geographi', 'tonopah, nev.', 'iben brown', 'new caledonia', 'colorado', 'golden', '1871 establish in colorado territori', 'cottonwood, colorado,', 'colorado springs, colorado', 'earth quak', 'not', 'in popul areas,', 'earthquak', 'new marid', 'new madrid fault', 'citi in el paso county, colorado (u.s. state)', 'are', 'cactusville, colorado,'}\\n\",\n      \"GT: num=8 - {'richter scale', 'moder earthquak', 'major earthquak', 'monitor equip', 'widespread heavi damag', 'earthquak emerg kit', 'countless earthquak predict', 'earthquak center'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'hurrican hugo', 'the caribbean, the', 'hurrican andrew', 'holiday inn', 'the', 'cajun countri', 'unit state', 'the state of florida, had', 'in', 'state', 'index of unit states-rel articl', '1848 establish in new orlean', 'louisiana', 'cuba', 'state and territori establish in 1847', 'hurrican', 'the bahamas,', 'the unit', '1847 establish in the unit state', 'histori', 'state of the unit kingdom', 'lloyd', 'in the caribbean,'}\\n\",\n      \"GT: num=7 - {'louisiana', 'uninsur loss', 'tropic storm', 'hurrican andrew', 'landfal', 'new orlean', 'sever damag'}\\n\",\n      \"p=0.08695652173913043, r=0.2857142857142857, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'barber conabl', 'the world bank', 'a state of', 'world develop report', 'john charl flickner', 'world bank', 'intern organ base in the unit state', 'robert mcnamara', 'intern monetari fund', \\\"the bank' new presid\\\", 'in', 'is still in the', 'in a state of', 'j p morgan', 'unit nation gener assembl observ', 'histori', 'john williamson', 'unit state intern monetari fund ( imf)', 'develop', 'in the 1980s, but the bank', 'intern bank', 'presidenti failur', 'develop in the third world'}\\n\",\n      \"GT: num=7 - {'poverti allevi', 'lewi preston', 'presid', 'world bank', 'third world', 'washington', 'loan condit'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'iowa', 'gerald w. whittak', 'great plain', 'minnesota', 'averag', 'agroecolog', 'agenc establish in 1876', 'agricultur', 'the', 'unit state', 'agrica', 'agri-food and drug administr', 'agribusi', 'drought of 1988', 'the averag', 'average.', 'wisconsin', 'histori', 'in 1988. in 1987, the', 'agronomi', 'illinoi'}\\n\",\n      \"GT: num=9 - {'feder disast relief', 'agricultur depart analysi', 'emerg drought aid', 'favor financi posit', 'commod market price', 'solvenc posit', 'sever drought region', '1988 drought', 'feder payment'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'hosni mubarak', 'the country.', 'the govern', 'cairo', 'dougla hurd', 'modern histori', 'citi in egypt and the arab world', 'the', 'egypt', 'anwar sadat', 'hassan abu-basha', 'rifaat el-mahgoub', 'mediev citi', '21st centuri', 'abu nidal', 'histori', 'abdul-halim moussa', 'the state', 'popularli own enterpris in egypt<infill>outlin of egypt', 'the perpetr were in the area. the', 'cultur tourism in egypt', 'popul place in cairo'}\\n\",\n      \"GT: num=8 - {'assassin', 'iraqi agent', 'egyptian politician', 'terrorist activ', 'funer', 'egyptian moslem fundamentalist', 'death toll', 'islam extremist'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'economi of europ', 'sri lanka', 'sindh', 'econom of the european union', 'poverti', 'bishop of oxford', 'economi of the unit kingdom', 'tigr', 'the world. the world ha chang sinc the industri revolution, and', 'the', 'sudanes govern', 'unit', 'economist of asia', 'unit states.', 'the unit', 'misus of statist', 'oecd member economi of the world', 'world trade organ member economi', 'critic', 'bishop', 'third world', 'econom of the british empir', 'civil conflict'}\\n\",\n      \"GT: num=6 - {'real factor', 'third world poverti', 'jame skinner', 'african debt', 'debt burden', 'govern polici'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'osman morot barrionuevo', 'abimael guzman', 'the govern', 'histori of peru', 'and the govern', 'the', 'and', 'la republica', 'osman morot', 'histori and cultur of peru (1918–1953)', 'polit repress', 'historyof peru', 'histori', 'spanish empir', 'sinc the end of the war', 'elena iparraguirr revoredo', 'rosa angelica sala de la cruz', 'member state of the unit nation', 'shine path', 'polit develop', 'spanish-speak countri and territori', 'the presid'}\\n\",\n      \"GT: num=8 - {'imprison shine path leader abimael guzman', 'peac propos', 'polit defeat', 'shine path peac strategi document', 'peac talk', 'peac agreement', 'govern repres', 'genuin shine path statement'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'to take precaut to prevent accidents. he said the', 'u.s. central command', 'saudi-american relat', 'were fli', 'f-15', 'pentagon', 'fli', 'saudi arabian civil war', 'saudi citi', 'were', 'u.s. air forc', 'outlin of saudi arabian arabia', 'saudi arabia', 'histori', 'vietnam war', 'citi in the kingdom of saudi arabia', 'aircraft were', 'saudi town and citi', 'fighter-bomb', 'modern era'}\\n\",\n      \"GT: num=3 - {'regular train flight', 'oper desert shield', 'fatal crash'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'slogan', 'serbian-speak countri and territori', 'and the sloven govern', 'serb-speak nation and territori of europ', 'former countri in europ', 'milan kucan', 'croatia', 'the', 'verica rudar', 'foreign polici', 'belgrad', 'the sloven government.', 'serbia', 'serbo-croatian', 'foreign relat', 'sloven govern', 's sloven', 'belgium–serbia relat', 'balkan war', 'srijani', 'cultur', 'to the'}\\n\",\n      \"GT: num=10 - {'sloven politician', 'possibl polit contact', 'sloven independ', 'offici sloven deleg', 'feder republ', 'sloven attitud', 'normal', 'offici belgrad', 'yugoslav diplomat', 'sloven govern'}\\n\",\n      \"p=0.045454545454545456, r=0.1, f1=0.06250000000000001\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'royal exchang', 'georg lloyd-robert', 'hur hurrican', 'hurrican', 'the gulf', 'list of hurrican', 'warburg secur', 'hurrican andrew', 'hur franc', 'rainbow hurrican', 'the', 'georg lloyd', 'unit state', 'the us gulf coast. the', 'hurrah', 'sulfur acid hurrican', 'royal insur'}\\n\",\n      \"GT: num=7 - {'hurrican andrew', 'southern florida', 'new orlean', 'florida', 'damag claim', 'insur industri loss', 'sever properti damag'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'econom effici', 'post-cold war', 'the world bank', 'the world', 'polici', 'intern organ base in london', 'poverti reduct', 'develop in europ', 'poverti allevi', 'world bank', 'the', \\\"of the bank' new polici directives,\\\", 'intern organis base in the unit kingdom', 'develop bank', 'polici to fight poverti', 'possibl', 'unit nation gener assembl observ', 'histori', 'lewi preston', 'pessim', \\\"the bank'\\\"}\\n\",\n      \"GT: num=8 - {'poverti allevi', 'poverti assess', 'loan volum', 'bank polici', 'world bank', 'third world', 'poverti relief', 'poverti reduct object'}\\n\",\n      \"p=0.09523809523809523, r=0.25, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'aircraft crash in japan in april', 'athlet accid and fatal in japan by type', 'the dfaa ha', 'okinawa', '2010', 'aerospac accid and death in japan (2010)', 'f-15 fighter', 'the', 'u yokota', '44th fighter squadron', 'u.s. forc in japan', 'aerial accid and fire', 'airlin that reenter the atmospher', 'defens facil administr agenc', 'kadena air base', 'militari accid and incid', 'aerosafeti', 'the defens', 'the dod', 'the u.'}\\n\",\n      \"GT: num=4 - {'investig', 'routin train', 'crash', 'okinawa'}\\n\",\n      \"p=0.05, r=0.25, f1=0.08333333333333334\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'finnair', 'the moon will be between the earth and the', 'astrobiolog', 'astrolog', 'total eclips in finland', 'the sun,', 'the', 'astrolog sub-disciplin', 'the eclipse,', 'soviet border', 'solar eclips', 'astronom event in 2019', 'joensuu', 'origin of the solar eclips (geometry)', 'special event', 'falkland meteorolog servic', 'astrophys', 'total', 'total solar eclips', 'finland', 'a total'}\\n\",\n      \"GT: num=7 - {'watcher', 'total solar eclips', 'observ', 'finland', 'special eyeglass', 'solar eclips', 'total phase'}\\n\",\n      \"p=0.14285714285714285, r=0.42857142857142855, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'anglo american corpor', 'russia', 'angolan american corpor compani', 'komdragmet', 'the diamond industry.', 'the russian', 'diamantair', 'oversea compani', 'of the', 'konrad', 'dollar', 'the', 'compani base in johannesburg', 'the diamond of the russian feder and the', 'world trade organ member economi', 'african compani establish in 1847', 'diamond industri', 'yukutia', 'diamond', 'rosalmazzoloto', 'the industry.', 'angola'}\\n\",\n      \"GT: num=12 - {'rough diamond', 'exclus sale agreement', 'russian diamond', 'de beer', 'beleagu diamond industri', 'unoffici export', 'south africa', 'russian feder', 'state diamond centr', 'russian diamond industri', 'yakut govern', 'harri oppenheim'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'the unit state', 'hurrican hugo', 'hurricana', 'climat of the atlant ocean', 'hurrican', 'and the nation hurrican center, which is base on', 'atlant hurrican', 'nation ocean and atmospher administr', 'atlant ocean', '1990 atlant hurrican season and forecast', 'climat chang', 'the', 'atlant cyclon', 'hurrican gilbert', 'histori', 'nation hurrican center', 'atlant hurrican season'}\\n\",\n      \"GT: num=9 - {'destruct storm', 'caribbean', 'predict', 'coastal resid', 'hurrican activ', 'hugo', 'hurrican gilbert', 'hurrican emerg', 'atlant hurrican season'}\\n\",\n      \"p=0.11764705882352941, r=0.2222222222222222, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'assassin', 'hamburg dpa', 'of the', 'carlo salina de gortari', 'havana', 'the', 'unit state', 'assassin of lui donaldo colosio murrieta', 'colosio.', 'assalt peopl', 'the assassin of', 'polit murder in mexico and central america', 'baja california', 'ass murder in mexico', 'polit crime', 'baja', 'hondura', 'intern', 'of', 'reaction', 'habana', 'dpa in spanish', 'mexican', 'of colosio.', 'list of assassin by firearm'}\\n\",\n      \"GT: num=9 - {'assassin', 'pri', 'mexican govern', 'reaction', 'presidenti candid lui donaldo colosio', 'antidemocrat forc', 'colombian govern', 'mexican presid carlo salina', 'sympathi'}\\n\",\n      \"p=0.08, r=0.2222222222222222, f1=0.11764705882352941\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'kelli air forc base', 'strateg bomber', 'airlin of the unit state', 'c-5a transport', 'the', 'unit state', 'iraq', 'hahn air base and england air base in louisiana', 'richard swope', 'militari equip introduc in 1991 and 1992', 'oper histori', 'mcchord air base', 'air', 'c- 5a stratolaunch', 'the air force. the four were hospit and report in satisfactori condition,', 'the air', 'aircraft first flown in 1991', 'militari aircraft of the cold war', 'richard w. chase'}\\n\",\n      \"GT: num=7 - {'ramstein air base', 'major accid', 'reservist', 'massiv aircraft', 'crash', 'victim', 'west germani'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'latin american raini season', 'astronom sub-disciplin', 'mexican astronom societi', 'the state', 'mexico and the unit state', 'mexico', 'mexico citi', 'total solar eclips', 'astrophys', 'articl contain video clip', 'astrobiolog', 'astrolog', 'special event', 'the day of the eclipse. the state ha alreadi book', 'the eclipse,', 'the', 'solar eclips', 'branch of biolog'}\\n\",\n      \"GT: num=7 - {'slar eclips', 'mexico', 'total solar eclips', 'moon', 'tourist', 'eclips path', 'partial eclips'}\\n\",\n      \"p=0.1111111111111111, r=0.2857142857142857, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'rttb: forum research', 'latvia', 'most of the', 'the most', 'the', 'ogr rayon', 'tuberculosi', 'wikipedia tuberculosi articl', 'in the', 'societi and cultur', 'tculosi', 'most', 'rttem', 'anda mikelson', 'the most common type of tuberculosi is', 'inta pavlovska', 'wikipedia medicin articl readi to translat', 'cso', 'epidemiolog', 'state tuberculosi and lung diseas center', 'riga', 'treatment'}\\n\",\n      \"GT: num=5 - {'tuberculosi hospit', 'mortal', 'tuberculosi case', 'tuberculosi morbid', 'mandatori treatment'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cuango', 'southern african countri', 'angolan diamond industri', 'de beer', 'the', 'to keep it tight grip on the market. it is', 'countri in africa', 'economi', 'the diamond', 'diamond industri', 'republ', 'index of angola-rel articl', 'endiama', 'north american industri', 'diamond', 'member state of the unit nation', 'thediamonds.', 'that the', 'antwerp', 'angola', 'northwest territori'}\\n\",\n      \"GT: num=10 - {'first diamond mine', 'diamond hunter', 'rough diamond product', 'diamond cartel', 'world diamond busi', 'greedi rush', 'canada', 'diamond market', 'diamond price', 'diamond sale'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'koper', 'kanavank road tunnel', 'b balkan countri', 'b balkan', 'region of europ', 'most of the', 'the most', 'balkan', 'the', 'and it own, ha', 'the world.', 'bibliographi of bosnia and herzegovina', 'slovenia', 'bosnia and herzeg', 'bakal', 'and the balkans. slovenia, with it own', 'indep - endenc', 'kapel', 'most', 'polit', 'koper - burg', 'balograd', 'ljubljana', 'bavarian languag'}\\n\",\n      \"GT: num=9 - {'independ', 'sloven enterpris', 'slovenian economi', 'former feder', 'sloven border', 'normal econom tie', 'privatis polici', 'ration market reform', 'former yugoslavia'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'hurrican andrew', 'huron storm', 'rebuild in', 'joe her', 'andrew card', 'hurolog', 'the', 'unit state', 'hurrican irma', 'state', 'andrew', 'state,', \\\"the state' oil industri wa unaffect by the storm. the state\\\", 'georg bush last night', 'hurci andrew', 'andrew andrew', 'louisiana', 'hurrican', 'andrew day', 'wa expect to', 'aftermath', 'the state,', 'georg bush', 'huricayn', 'the us'}\\n\",\n      \"GT: num=6 - {'widespread destruct', 'louisiana', 'feder emerg', 'hurrican andrew', 'florida', 'massiv rebuild effort'}\\n\",\n      \"p=0.08, r=0.3333333333333333, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'econom statu', 'in the world.', 'economi of slovenia', 'foreign exchang', 'nation bank of yugoslavia', 'world bank', 'yugoslavia', 'european union member economi of slovenia and croatia', 'marko kranjec', 'intern monetari fund', 'economi of europ by countri', 'world trade organ member economi', 'economist of slovenia, or central bank, is the largest', 'ljuba', 'ljubljanska bank', 'foreign trade, direct invest and aid', 'econom of europ'}\\n\",\n      \"GT: num=10 - {'independ', 'feder govern', 'negoti', 'nation bank of yugoslavia', 'foreign exchang deposit', 'yugoslavia', 'sloven bank', 'unalloc feder debt', 'foreign creditor', 'sloven citizen'}\\n\",\n      \"p=0.11764705882352941, r=0.2, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the movement', 'lima america channel 4 televis network', 'abimael guzman', 'the govern', 'luzon', 'the govern ha not', 'peruvian civil war', 'state and territori establish in 1825', 'outlin of the peruvian arm forc', 'lrb', 'the agreement. the', 'the', 'state', 'luna', 'the shine path', 'lima', 'peac agreement', 'republ', 'histori', 'chiapa', 'state of the and', 'shine path', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=7 - {'shine path member', 'guerrilla armi', 'gener amnesti', 'econom support', 'peac agreement', 'peruvian govern', 'popular war'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'controversi', 'trade agreement of canada', 'north american free trade agreement', 'free trade agreement', 'treati of mexico', 'california gubernatori hope', 'california', 'anti-nafta', 'the', 'california and texa', 'unit state', 'california governor', 'california hous of repres', 'anti nafta', 'san francisco', 'the opposit ha been stalk by demonstrators, who', 'nafta', 'free-trad agreement', 'tort reform', 'the us'}\\n\",\n      \"GT: num=11 - {'california', 'nafta oppon', 'massiv campaign', 'side agreement', 'north american free trade agreement', 'nafta foe', 'free trade pact', 'lead propon', 'fair trade campaign', 'environment degrad', 'american public'}\\n\",\n      \"p=0.1, r=0.18181818181818182, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'airlin that crash', 'uh-1 huey', 'a total of', 'in the unit state', 'u.s. air forc', 'of', 'militari equip crash', 'the f-111 is a twin-engin aircraft that can carri up', 'aerospac accid and incid', \\\"iraq' aug. 2 takeov of kuwait\\\", 'f-4', 'aircraft crash', 'unit state', 'saudi arabia', 'athlet aircraft', 'iraq', 'aerial warfar', 'articl contain video clip'}\\n\",\n      \"GT: num=4 - {'crew member', 'crash', 'saudi arabia', 'oper desert shield'}\\n\",\n      \"p=0.05555555555555555, r=0.25, f1=0.0909090909090909\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'and texas, and', 'hurrican andrew', 'of the', 'allstat', 'hurican', '1901–1910', 'sear roebuck', 'unit state', 'allstat insur', 'hurricana', 'hurrican', 'and the nation associ of mutual', 'of texas,', 'of louisiana,', 'hur hurrican', 'of', 'british petroleum', 'rican', 'rican of the atlant ocean', 'hurrah', 'histori of the unit state'}\\n\",\n      \"GT: num=6 - {'emerg servic', 'disast loss', 'insur claim', 'hurrican andrew', 'new orlean', 'properti damag'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'new front', 'peruvian-speak countri', 'index of peru-rel articl', 'alberto fujimori', 'for more than a decade.', 'peru', 'peruvian civil war', 'been in the countri', 'member countri of the mercosur', 'mario varga llosa', 'countri in south america', 'southern cone countri', 'histori', 'maoist', 'sh shine path', 'the shine path ha', 'member state of the unit nation', 'shine path', 'alan garcia', 'the capital. the presid said the'}\\n\",\n      \"GT: num=11 - {'coastal urban area', 'presid alan garcia', 'indigen peopl', 'rebel attack', 'presidenti runoff', 'shine path rebel', 'power car bomb', 'lima', 'shine path leader abimael guzman', 'defens revolutionari movement', 'polit violenc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'british rail', 'britain', 'chinnel', 'british', 'modern histori', 'railway in franc &amp; germani', 'the chunnel, a project', 'rail transport in franc', 'the', 'rail tunnel in the unit kingdom', 'andr benard', 'franc &amp, unit kingdom rail transport', 'british public', 'albert mathieu', 'channel tunnel', 'the english', 'histori', 'that will', 'railway tunnel in franc and england', 'the french', 'the british', 'chunnel', 'the project', 'british island'}\\n\",\n      \"GT: num=8 - {'rail tunnel', 'easi conduit', 'english channel', 'budget', 'contractor disput', 'chunnel train', 'chunnel project', 'invest money'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'henri waxman', 'in the house, and the senat', 'anti-[nuclear] prolifer', 'a', 'anti-nuclear terror', 'north american free trade agreement', 'a trade', 'background', 'richard w. bush', 'ross perot', 'unit state', 'anti–nuclear terror in the unit state', 'been a', 'richard bush', 'unit nation gener assembl observ', 'trade', 'bill clinton', 'anti-(nuclear) prolifer', 'ha been', 'polit', 'been', 'nafta', 'trade agreement.', 'richard gephardt'}\\n\",\n      \"GT: num=13 - {'environment issu', 'congression democrat', 'mr ross perot', 'governor bill clinton', 'public disaffect', 'us elect campaign', 'republican establish', 'food safeti provis', 'north american', 'free trade agreement', 'nafta pact', 'hispan american', 'presid bush'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'develop organ base on contin', 'develop in africa', 'worldbank', 'clinton administr', 'polici', 'intern organ base in london', 'poverti reduct', 'clinton', 'world bank', 'and', 'world bank presid', 'the philippines,', 'lloyd bentsen', 'intern organis base in pari', 'unit nation gener assembl observ', 'histori', 'the unit states, japan and south korea, and', 'lewi preston', 'l lloyd bentson'}\\n\",\n      \"GT: num=8 - {'poverti reduct', 'lewi preston', 'develop countri', 'world bank', 'third world', 'adjust loan', 'poverti relief', 'bank lend'}\\n\",\n      \"p=0.15789473684210525, r=0.375, f1=0.22222222222222218\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'france–unit kingdom border', 'railway in the netherland', 'britain', 'railway tunnel in franc', 'the first time we have air pass between the two', 'rail transport in franc and germani', 'the', 'rail tunnel in the unit kingdom', 'the two', 'ice age', 'calai', 'two inch', 'construct', 'sangatt', 'channel tunnel', 'two', 'two inch of', 'daili express', 'two-inch probe', 'dover', 'chunnel', 'linkup'}\\n\",\n      \"GT: num=10 - {'histor linkup', 'continent europ', 'english channel', 'britain', 'chunnel', 'traffic congest', 'channel tunnel project', 'franc', 'cost overrun', 'transmanch link'}\\n\",\n      \"p=0.09090909090909091, r=0.2, f1=0.12500000000000003\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'jj pickl', 'trade agreement of canada', 'north american free trade agreement', 'free trade agreement', 'anti-nafta sentiment', 'unit nation gener assembl', 'background', 'ross perot', \\\"ford'\\\", 'anti–fre trade agreement in the unit state', 'the', 'unit state', 'have been work to build support for the pact. they have', 'north america free trade pact', 'carlo salina', 'in the', 'the unit', 'carter', 'opposit', 'ford', 'been', 'anti free trade agreement', 'guerrilla tactic', 'the us'}\\n\",\n      \"GT: num=7 - {'widespread public hostil', 'hous hear', 'environment protect', 'environment activist', 'impoverish immigr', 'north american free trade agreement', 'presid clinton'}\\n\",\n      \"p=0.041666666666666664, r=0.14285714285714285, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=26 - {'the american', 'unit food and commerci worker intern union', 'american and', 'american', 'free trade agreement', 'north american develop bank', 'background', 'latin american and latino american', 'the', 'unit state', 'would be', 'southwest voter registr project', 'anti-nuclear movement', 'the unit', 'northamerican trade agreement', 'world trade organ member economi', 'and the us', 'abel guerra', 'trade', 'richard lopez', 'negoti', 'nafta', 'trade agreement', 'univers of california in lo angel', 'north america', 'the us'}\\n\",\n      \"GT: num=8 - {'trilater north american develop bank', 'neg impact', 'key hispan group', 'worker right', 'north american trade agreement', 'nafta', 'stringent side agreement', 'hispan commun'}\\n\",\n      \"p=0.038461538461538464, r=0.125, f1=0.058823529411764705\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'the govern', 'unit kingdom', 'of the', 'western gener hospit', 'peter warhurst', 'europ', 'the', 'bse', 'transmiss spongial encephalopathi', 'scotland', 'rare diseas', 'the public', 'cjd', 'cattl diseas', 'infecti diseas', 'bovin spongiform encephalopathi', 'depart of health', 'creutzfeld-jacob diseas', 'wikipedia medicin articl readi to translat', 'prion', 'rare infecti diseas', 'cjd is not known to have caus the diseas in humans. the'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'causal link', 'bse case', 'cjd case', 'public anxieti', 'infecti protein', 'scientif evid'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'a', 'srijan', 'constitut', 's slovenia', 'milan kucan', 'sourc', 'sourc of law', 'constitut of yugoslavia', 'slobodan milosev', 'slovenia', 'yugoslav republ', 's slovenian presid', 'sourc for constitut document', 'histori', 'spartan', 'modern constitut', \\\"constitution, '' tanjug said.\\\", 'the new constitut will be', 'constitu assembl', 'constitut document'}\\n\",\n      \"GT: num=9 - {'slovenian presid', 'feder goal', 'yugoslav feder', 'milan kucan', 'serbia', 'new yugoslav confeder', 'full sovereignti', 'separatist tendenc', 'feder control'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {\\\"the state' own\\\", 'ljetnica', 'lubljan', 'countri in europ', 'outlin of slovenia', 'the', 'lojz peterl', 'the slovenian', 'northern republ', 'southeastern european countri', 'declar of sovereignti', 'slovenia', 'the slovenia', 'ljubljana nightli televis news', 'polit', 'lljubbana', 'ljze peterl', 'lithuania', 'would take preced over feder law and would take over', 'state and territori establish in 1918', 'srijani', 's slovenian-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'feder author', 'independ legal system', 'yugoslav republ', 'slovenian declar', 'slovenian control', 'full sovereignti', 'troubl yugoslav feder', 'loos confeder'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'sale are made through the central sell organis (cso ).', 'diamond industri associ', 'miner polici committe', 'the world', 'articl contain video clip', 'de beer', 'the', \\\"the country'\\\", 'central sell organis', 'gemston', 'debeer', 'diamond industri', 'intern cartel', 'intern', 'botswana', 'nichola oppenheim', 'diamond', 'product', 'sculptur'}\\n\",\n      \"GT: num=11 - {'de beer', 'individu diamond produc', 'south african group', 'rough diamond output', 'rough diamond sale', 'contract negoti', 'diamond trade', 'botswana diamond', 'central sell organis', 'exclus sale contract', 'botswana politician'}\\n\",\n      \"p=0.10526315789473684, r=0.18181818181818182, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=28 - {'enriqu bernal', 'chile', 'chicano', 'to be', 'is tri to', 'carlo tapia', 'conflict', 'endirecto', 'spanish-speak countri and territori', 'the shine path,', 'the', 'to becom a', 'drug war (1910–present)', 'histori of mexico', 'tobecom a', 'is', 'en directo', 'histori', 'spanish languag', 'is attempt to', 'the govern is', 'chiapa', 'enriqu', 'sh shine path', 'spanish word and phrase', 'cox said that the shine path is', 'to', 'state and territori establish in 1824'}\\n\",\n      \"GT: num=7 - {'peac negoti', 'peac propos', 'shine path faction', 'nation reconcili', 'second parti congress', 'govern favor', 'imprison shine path member'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'frederick ostbi', 'geographi of the unit state', 'historyof the unitedst', 'histori of the midwest', 'tornado', 'kentucki', 'west coast', 'to a', '1990', 'missouri', 'geolog of the american midwest', 'american weather', 'to predict the strength of storm and', 'histori', 'kansa', 'american geographi', 'to', 'north america', 'to the'}\\n\",\n      \"GT: num=7 - {'storm', 'tornado', 'death', 'flood', 'studi', 'tornado trend', 'damag'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'counti seat in california', 'the public and the police. the', 'citi of lo angel', 'daryl gate', 'of the', '20th centuri', 'citi in lo angel (u.s. state)', 'georg georg', 'georg w. bush', 'cultur tourism in california and the pacif northwest', 'the', 'the polic', 'nation guard', 'lo angel', 'histori', 'warren christoph', 'the department.', 'cultur of lo angel (upper lo angeles)', 'carotid choke hold', 'citi colleg of lo angel (l.a.', 'georg bush', 'the lapd'}\\n\",\n      \"GT: num=6 - {'violenc', 'brutal complaint', 'polic brutal', 'daryl gate', 'brutal polic forc', 'investig'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'creutzfieldt- jakob', 'john macgregor', 'unit kingdom', 'kent', 'creep cow', 'unit nation food and agricultur organ', 'the', 'singl market', 'unit state', 'biopesticid', 'transmiss spongial encephalopathi', 'to protect', 'bioviru', 'creutzfeldt-jakob diseas', 'the unit', 'rare diseas', 'histori', 'a new food polici', 'bovin spongiform encephalopathi', 'the british', 'the uk', 'creep cow mad', 'anim virolog', 'rare infecti diseas', 'the us'}\\n\",\n      \"GT: num=6 - {'diseas sheep', 'diseas scrapi', 'british beef import', 'british agricultur', 'infect feed', 'matern transmiss'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'davidmaclean', 'the govern said that the diseas wa not', 'greater kudu', 'to be', 'cattledom', 'be', 'ron davies, a labour agricultur spokesman', 'ron davi', 'arabian oryx', 'cattl diseas', 'spongiform', 'crisi in cattl farm', 'spongiform encephalopathi', 'cultur of the unit state', 'infecti diseas', 'in the unit kingdom', 'david maclean', 'c cattl diseas', 'to', 'cow', 'insect-born diseas', 'in sheep', 'to the'}\\n\",\n      \"GT: num=5 - {'antelop popul', 'scrapi', 'mad cow diseas', 'sheep encephalopathi', 'spongiform encephalopathi'}\\n\",\n      \"p=0.043478260869565216, r=0.2, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'hurrican hugo', 'landform of the atlant ocean', 'landscap of theatlant ocean (geography)', 'hurrican gilbert', 'articl contain video clip', 'atlantic,', 'and are given a name, if they reach a sustain wind of', 'the', 'atlant', 'coral gabl', 'hurrican diana', 'hurricana', 'hurrican', 'atlant ocean', 'histori', 'atlant sea', 'atlant hurrican', 'of the atlantic,', 'modern era', '1990 atlant hurrican season'}\\n\",\n      \"GT: num=5 - {'intens hurrican', 'devast storm', 'forc hurrican', 'forecast', 'atlant hurrican season'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'construct', 'the tunnel is now complete. the tunnel machin on the', 'francoi mitterrand', 'channel tunnel', 'the tunnel', 'rail transport in england', 'french presid', 'the french side.', 'daili express', 'margaret thatcher', 'chunnel', 'france–unit kingdom border cross', 'france-unit kingdom sport rivalri', 'the', 'railway tunnel in franc', 'histori', 'ice age', 'french side.'}\\n\",\n      \"GT: num=13 - {'first land link', 'histor linkup', 'channel tunnel', 'grow unif', 'english channel', 'chunnel', 'britain', 'continent ill', 'tunnel construct', 'tunnel train', 'franc', 'cost overrun', 'transmanch link'}\\n\",\n      \"p=0.1111111111111111, r=0.15384615384615385, f1=0.12903225806451615\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'transmiss', 'the zoo', 'transmiss spongic diseas', '[[mad cow disease]]', 'biolog and pharmacolog', \\\"[[ mad cow diseas ''\\\", 'anim diseas', \\\"[[zoo antelop catch mad cow disease]] ''\\\", 'the', 'biopesticid', 'the diseas may have been pass on to other animals.', 'zoo diseas', \\\"[[zoo antelop catch mad cow diseas ''\\\", 'rare diseas', 'drug for neglect diseas', 'london zoo', 'bovin spongiform encephalopathi', 'biolog caus', 'mad cow diseas', 'the kudu', 'rare anim diseas'}\\n\",\n      \"GT: num=6 - {'endang speci', 'mad cow diseas', 'kudu herd', 'similar transmiss', 'affect anim', 'london zoo'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'ayacucho', 'histori and mytholog of peru (1825–18 pacifism)', 'histori of peru', 'coloni peru', 'the meet wa held in', 'the', 'history, literatur and cultur of peru.', 'the shine path', 'razuhuillca', 'rafirmars en la base de unidad partidaria y construir la conquesta del power', 'nation counterterror director', 'polit repress', 'historyof peru', 'histori', 'peruvian civil war (1925–1937)', 'oscar ramirez durand', 'shine path', 'ayacucha', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'shine path central committe', 'black group', 'abimael guzman reinoso', 'shine path meet', 'peac accord', 'revolutionari violenc', 'shine path congress', 'arrest leader'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'famili plan', 'a presid', 'a', 'donna shalala', 'new jersey', '1946 birth', 'presidenti transit', 'be', 'new york', 'welfar', 'and the presid said he would let state experi with such programmes, even when he disagre with them', 'unit state', 'clinton dynasti', 'social safeti net', 'arkansa', 'famili support act', '1956 birth', 'michigan', 'clinton famili', 'welfar reform', 'clinton school of public servic', 'apresid', 'he said he', 'would'}\\n\",\n      \"GT: num=4 - {'bill clinton', 'famili support act', 'welfar reform', 'social safeti net'}\\n\",\n      \"p=0.125, r=0.75, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'biographi', 'person', 'a', 'carl lewi', 'olymp career', 'st.', 'toronto star', 'st. kitt', 'stanozolol', 'american sprinter from new jersey', 'canadian male weightlift', 'american peopl of maltes descent', 'canadian sprinter', 'a person physician and', 'american bodybuild', 'ben johnson', 'benjohnson', 'american male sprinter of world war ii', 'person physician', 'he said he had been', 's korean game'}\\n\",\n      \"GT: num=9 - {'ban steroid', 'stanozolol use', 'seoul olymp', 'jami astaphan', 'disgrac olymp sprinter', 'olymp gold medal', 'johnson scandal', 'person physician', 'ben johnson'}\\n\",\n      \"p=0.09523809523809523, r=0.2222222222222222, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=27 - {'yellow rose trail', 'the 1988 blaze', 'spring bring rebirth', 'spring', 'the aftermath', 'where', 'the park is', 'a place where', 'yellowston park', 'the', 'bald eagl', 'spring ha come to yellowston', 'cultur of the yellowston park', 'in the', 'citi in the yellowston nation park', 'where the', 'is', 'yellowston', 'histori', 'rhode island', 'much unscath', 'is a sign that the forest is', 'henri shovic', 'yelloweston park', 'museum in yellowston county, montana', 'the great fire', 'is still'}\\n\",\n      \"GT: num=8 - {'yellowston park fire', 'fire ecolog', 'natur fire', 'lodgepol pine', 'firefight effort', 'firefight crew', 'destruct blaze', 'lightn fire'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'1885 establish in california', 'list of american marathon record', 'marathon cours in california (u.s. state)', \\\"the men' field is led by\\\", 'race cours', 'san diego half marathon', 'san josé', 'trib 10k', 'the', 'san jose', 'race in san diego', 'alphonc swai', 'in the', '1880 establish in the unit state', 'san francisco marathon', 'race day', 'sammi rotich', 'american marathon', 'who place second in the'}\\n\",\n      \"GT: num=6 - {'runner', 'alphonc swai', 'race', 'challeng cours', 'homef half marathon', 'comeback bid'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'tokai region', 'richter scale', 'tokaido', 'of protest and', 'tokai', 'and', 'geograph region of japan and the pacif ocean', 'philippin sea plate', 'geolog', 'geolog region', 'the tokai region ha been the subject of a seri of', 'eurasian plate', 'of', 'geographi of japan', 'geograph of asia (disambiguation)', 'geograph region of asia', 'list of extrem point', 'geographi of japan (1912–present)', 'suruga trough'}\\n\",\n      \"GT: num=8 - {'richter scale', 'japan', 'tokai earthquak', 'seismic activ', 'earthquak warn', 'immin earthquak', 'gener earthquak predict research', 'coastal tokai region'}\\n\",\n      \"p=0.05263157894736842, r=0.125, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'boston marathon', 'aebb bido', 'abeb mekonnen', 'the world record on', 'abe mekonné', 'the course.', 'abeyb biko', 'list of boston marathon medal', \\\"the women' race in 2:08:39. in the women '' race on monday,\\\", 'gulf coast', 'marathon', '1872 establish in the unit state', 'olymp sport', 'abe bikila', 'histori', 'juma ikangaa', 'boston', 'ethiopia', 'recent histori', 'joan benoit samuelson', 'recent winner'}\\n\",\n      \"GT: num=8 - {'ethiopia', 'ingrid kristiansen', 'abeb bikila', 'race', 'african runner', 'domin group', 'marathon runner', 'consecut olymp gold medal'}\\n\",\n      \"p=0.047619047619047616, r=0.125, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'wildfir', \\\"smoker' paradox\\\", 'in the unit', 'health effect', 'mark linan', 'john hopkin univers school of hygien and public health', 'smog', 'carbon monoxide, which can caus', 'unit', 'unit states.', 'hydrocarbon (gas)', 'the unit', 'yosemit fire', 'u.s. forest servic', 'california depart of health servic', 'carbon monoxid', 'in the unit state', 'carboxyl acid', 'to be the deadliest', 'toxicolog', 'johnsburg'}\\n\",\n      \"GT: num=12 - {'carbon monoxid', 'hazard chemic', 'lung function', 'health servic', 'respiratori diseas', 'poison stew', 'unseen hazard', 'wildfir', 'fatal heart attack', 'wildfir smoke', 'california wildland firefight', 'respiratori protect'}\\n\",\n      \"p=0.09523809523809523, r=0.16666666666666666, f1=0.12121212121212123\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'western unit state', 'outlin of alaska and the arctic', '1901-present', 'juneau', 'princ william sound', 'valdez', 'exxon valdez', 'alumni of the univers of alaska', 'alaska', 'alcohol beverag control act', 'to move the ship to a new port', 'alaskan oil spill', 'in the unit states.', 'histori', 'oil spill', 'expo valdez spill', 'coast guard', 'al alaska'}\\n\",\n      \"GT: num=10 - {'devast oil spill', 'spill damag', 'complet cleanup', 'spill cost', 'alaska', 'exxon valdez spill', 'tanker exxon valdez', 'crude oil', 'fresh oil sheen', 'spill conting plan'}\\n\",\n      \"p=0.05555555555555555, r=0.1, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of the', 'african union member economi', 'de beer', 'global market', 'the', 'the fact that', 'oversea diamond industri', 'central sell organis', 'venetia', 'zimbabw', 'diamond industri', 'the lack of', 'botswana', 'of', 'diamond', 'south africa', 'to do their job. thi is a result of the', 'venetu', 'angola'}\\n\",\n      \"GT: num=9 - {'venetia', 'global diamond market', 'rough gem diamond output', 'south african diamond mine', 'de beer', 'russian export', 'turbul', 'diamond cartel', 'central sell organis'}\\n\",\n      \"p=0.15789473684210525, r=0.3333333333333333, f1=0.21428571428571427\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'athanasiu univers', 'seoul olymp', 'ace (racing)', 'ollan cassel', 'carl lewi', 'lewi', 'articl contain video clip', 'intern amateur athlet feder', 'aerob exercis', 'unit state', 'that johnson should be strip of hi world record. lewi ha the second-fastest legal time in history, 9.92, in finish second to', 'the athlet congress', 'ha', 'histori', 'aesthet', 'ben johnson', 'athlet', 'ha been', 'aerial sport', 'world championship'}\\n\",\n      \"GT: num=6 - {'world championship', 'canadian inquiri', 'ben johnson', 'carl lewi', 'world record', 'steroid use'}\\n\",\n      \"p=0.15, r=0.5, f1=0.23076923076923075\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'assault weapon', 'stockton', 'weapon and ammunit introduc in 1947', 'militari equip introduc in 1945', 'the nra ha becom a more effect advoc for gun-control', 'and the', 'rifl', 'use in crimin justic', 'unit state', 'gun-control measur', 'and', 'assault', 'stockton schoolyard', 'ak-47', 'nation rifl assn.', 'weapon of mass destruct', 'denni deconcini'}\\n\",\n      \"GT: num=7 - {'deconcini', 'firearm', 'gun lobbi', 'nra', 'constitut right', 'gun ban', 'gun control'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'bioterror', 'the govern', 'unit kingdom', 'the health', 'western gener hospit', 'richard lacey', 'keith meldrum', 'creutzfield-jacob diseas', 'biolog and human', 'the', 'bse', 'leed univers', 'have die of cjd in the uk sinc 1990.', 'biopesticid', 'transmiss spongial encephalopathi', 'medic', 'the public', 'is', 'infect', 'transvers myeliti', 'biovasculopathi', 'hematolog', 'bovin spongiform encephalopathi', 'the diseas', 'creutzfeld-jacob diseas'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'human brain diseas', 'mad cow diseas', 'public concern', 'matern transmiss', 'bse', 'epidem'}\\n\",\n      \"p=0.08, r=0.2857142857142857, f1=0.125\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'counti in africa by gdp', 'debswana', 'in the world.', 'de beer', 'cape provinc', 'member state of the unit nation', 'countri in africa', 'member countri of the african union', 'member state of the commonwealth', 'which wa the lowest', 'economi', 'outlin of botswana', 'diamond industri', 'jwaneng', 'central sell organis', 'kalahari desert', 'botswana'}\\n\",\n      \"GT: num=10 - {'jwaneng mine', 'de beer', 'south african group', 'diamond mine', 'russian export', 'underground mine', 'diamond output', 'world diamond product', 'central sell organis', 'botswana'}\\n\",\n      \"p=0.17647058823529413, r=0.3, f1=0.22222222222222224\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'cat', 'health concern', 'in the unit kingdom, and', 'british cuisin', 'european commun', 'british beef', 'the', 'health', 'european', 'the diseas is', 'is', 'cattl', \\\"mad cow '' diseas\\\", 'british brand', 'british product', 'british food and drink', 'not known whether the', 'bovin spongiform encephalopathi', 'is not known', 'scrapi', 'british cattl', 'mad cow diseas'}\\n\",\n      \"GT: num=6 - {'bovin spongiform encephalopathi', 'british beef import', 'mad cow diseas', 'health fear', 'trade friction', 'germani import ban'}\\n\",\n      \"p=0.09090909090909091, r=0.3333333333333333, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'make', 'controversi', 'oper rescu', 'nonpartisan organ in theunit state', 'william armstrong', 'the new law, the aclu ha', 'nazi germani', 'lo angel polic offic', 'freedom rider', '1920 establish in the unit state', 'organ establish in 1920', 'american civil right organ', 'lo angel', 'that would', 'polic state', 'american civil liberti union', 'institut for justic', 'would make', 'a new law that would', 'would', 'pain-compli law'}\\n\",\n      \"GT: num=4 - {'nonviol protest', 'polic brutal charg', 'polic abus', 'lo angel polic offic'}\\n\",\n      \"p=0.047619047619047616, r=0.25, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'oper', 'alyska pipelin servic co.', 'of the oil', 'steve cowper', 'transport', 'aleska pipelin safeti plan', 'of oil', 'transport in alaska', 'oil', 'oil-spil cleanup.', 'autopilot', 'to be readi for a spill of thi magnitude. the', 'aleyeska pipelin (company)', 'of', 'alisha', 'pipelin in alaska and the pacif ocean', 'ayleska pipelin', 'alyeska marin termin', 'bp america', 'aileska pipelin system', 'oil spill', 'exxon valdez oil spill disast', 'trans-alaska oil pipelin'}\\n\",\n      \"GT: num=7 - {'cleanup respons plan', 'cleanup measur', 'improv safeti', 'oil spill', 'exxon valdez oil spill disast', 'minim environment effect', 'oil compani'}\\n\",\n      \"p=0.08695652173913043, r=0.2857142857142857, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'north america free trade act', 'for an', 'controversi', 'north american free trade agreement', 'free trade agreement', 'ross perot', 'trade agreement of the unit kingdom', 'unit state', \\\"veterans'day\\\", 'to vote for', 'for a', 'vice presid', 'outsid the unit state', 'david bonior', 'unit nation gener assembl observ', 'outsid washington', 'treati of canada', 'for the', 'vote for nafta. the administr is also tri to get member to', 'for', 'nafta', 'arthur andersen & compani', 'al gore'}\\n\",\n      \"GT: num=8 - {'mexican congress', 'hous vote', 'trade pact', 'public opinion', 'congression district', 'debat victori', 'trade campaign', 'north american free trade agreement'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'sting', 'long beach', 'popul place establish in 1846', 'california', 'law enforc', 'california state capit', 'law and govern', 'polic misconduct', 'polic brutal', 'that he wa', 'long-term disabl', 'that', 'wa', 'that you', 'long beach polic offic', '1846 establish in california<infill>list of peopl from long beach', 'citi in california', 'and you were so angri that you were', 'he wa', 'counti seat in california (u.s. state)', 'longer-term'}\\n\",\n      \"GT: num=6 - {'racism', 'polic offic', 'don jackson', 'polic brutal', 'dickey', 'arrest'}\\n\",\n      \"p=0.047619047619047616, r=0.16666666666666666, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'alaskan oil spill', '1959 establish in new york (state)', 'oil compani establish in 1959', 'tuberculosi treatment in new', 'lawrenc g. rawl', \\\"''valdez'' cleanup\\\", 'princ william sound', 'exxon', 'the cleanup cost and the chang in account', '1980 in the unit state', 'drexel burnham lambert inc.', 'compani base in new jersey', '1990', 'to $ 1.38 billion,', 'to.', 'histori', 'exxon valdez accid', 'to', 'exxon corp.'}\\n\",\n      \"GT: num=11 - {'cleanup charg', 'lower net incom', 'expens environment disast', 'valdez spill', 'tanker exxon valdez', 'total cleanup cost', 'financi effect', 'massiv alaskan oil spill', 'valdez cleanup', 'exxon valdez accid', 'revenu'}\\n\",\n      \"p=0.05263157894736842, r=0.09090909090909091, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clean water act', 'exxon valdez', 'environment disast', 'exxon salvag crew', 'princ william sound', 'outcom', 'alcohol beverag control act', 'unit state', 'georg miller', 'the oil spill.', 'of the hous of repres on wednesday', 'the cleanup of the', 'hiroshima', 'environment issu', 'alaska oil spill', 'gulf of alaska', 'alaskan oil spill of march 24', 'respons', 'long island'}\\n\",\n      \"GT: num=8 - {'crimin charg', 'cleanup', 'joseph hazelwood', 'alaska', 'stricken tanker exxon valdez', 'massiv oil spill', 'wildlif death', 'exxon salvag crew'}\\n\",\n      \"p=0.05263157894736842, r=0.125, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'a major', 'american peopl of irish descent', 'american male non-fict writer', 'a', 'polit career', '19th-centuri american politician', 'richard m daley', 'chicago', 'american polit parti founder', 'richard daley.', 'richard j daley.', 'washington', 'north america free trade agreement', 'bill daley (politician)', 'a key', 'a leader in', 'been a', 'list of american polit parti chairmen', 'bill clinton', 'richard m', 'american nonprofit chief execut', 'nafta', 'georg bush', \\\"mr daley ha been a key support of the president' agenda and ha\\\"}\\n\",\n      \"GT: num=8 - {'grassroot opposit', 'side agreement', 'presid bill clinton', 'congression support', 'controversi battl', 'north america free trade agreement', 'william daley', 'free trade pact'}\\n\",\n      \"p=0.041666666666666664, r=0.125, f1=0.0625\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'the white hous ha said that the plan will', 'post-cold war', 'wisconsin and georgia', 'schoolfar', 'winni mae', 'sustain develop goal', 'welfar', 'spite (social policy)', 'the', 'and', 'unit state', 'georgia', 'the unit state', 'wisconsin', 'florida', 'welfar reform', 'histori', 'vermont', 'bill clinton', 'develop assist program', 'be a pilot', 'the state', 'intergovernment organ establish in 1945', 'the us', 'tommi thompson'}\\n\",\n      \"GT: num=5 - {'food stamp', 'wisconsin', 'presid bill clinton', 'welfar reform', 'georgia'}\\n\",\n      \"p=0.12, r=0.6, f1=0.19999999999999998\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'with the', \\\"st. john' hospit and medic center\\\", 'and the star ha a cover photograph of liz taylor', 'american women in film', '20th-centuri american actress', 'american film actress', 'malcolm forb', 'career', 'american peopl of maltes descent', 'marin del rey hospit', 'list of highest-gross film', 'with', 'nation enquir', 'liz taylor', 'tropic diseas', 'stereo', 'marina del rey', 'with a', 'aid'}\\n\",\n      \"GT: num=5 - {'pneumonia', 'rumor', 'celebr', 'addict', 'liz taylor'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'longbeach, california, unit state', 'beachcomb', 'counti seat in california, california (state)', 'the runner', 'popul coastal place in california', 'viktor gural', '1854 establish in california (u.s. state)', 'the race.', 'rex wilson', 'long beach, california', 'the', 'marathon', 'long-dist run', 'the heat did not help. the', 'ric sayr', 'beje', 'diann rodger', 'sport', 'bejing, china', 'long island'}\\n\",\n      \"GT: num=7 - {'patienc', 'winner', 'cours record', 'rex wilson', 'race', 'wen yanmin', 'long beach marathon'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the survivor and the bodi of the', 'unit airlin', 'the corps of', 'crash', 'unit flight 232', 'feder aviat administr', 'of the', 'aircraft first flown in 1981', 'unit airlin flight 22', 'flight', 'cabl news network', 'unit state air forc aircraft', 'the', 'airlin base in denver', 'tail engin explod', 'american airlin flight 232 (unit airlines)', 'aerospac accid and incid', 'of', 'terri e. branstad', 'the bodi of', 'unit air line flight 21'}\\n\",\n      \"GT: num=9 - {'emerg land', 'explos', 'crash land', 'crash victim', 'air crash', 'complet hydraul failur', 'tail engin', 'flight crew', 'violent crash'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'elizabethtaylor', \\\"sutton' law in medicin\\\", '20th-centuri american actress', 'santa monica hospit', 'american film actress', 'elizabeth taylor', 'liza todd-tivey', 'american peopl of english descent', 'that', 'list of highest-gross film', 'lung', 'health problem', 'list', 'that she', 'that the', '19th- centuri american actress and produc', 'santa monica', 'willi -lrb- the actor', 'pneumonia', 'of the biopsy, he said he wa not sure'}\\n\",\n      \"GT: num=7 - {'ventil', 'surgeri', 'actress', 'health problem', 'seriou condit', 'pneumonia', 'elizabeth taylor'}\\n\",\n      \"p=0.15, r=0.42857142857142855, f1=0.2222222222222222\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'counti seat in new york (state)', 'fbi', 'long beach', 'a', 'modern era', 'long island, new york', 'lo angel counti district attorney', 'lo angel counti districtattorney', 'today show', 'video', 'counti in new mexico', '1871 establish in new jersey', 'video of the incid', 'long beach citi council', 'histori', 'vacat citi in the unit state', 'jeff hill', 'borough of new york citi', 'that the video wa', 'long island', 'a video'}\\n\",\n      \"GT: num=7 - {'polic forc', 'civil right violat', 'lo angel', 'polic brutal', 'jackson', 'investig', 'dickey'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'western cultur', 'reynold', 'cultur geographi', \\\"reggi democrat '\\\", 'welfar', 'western', 'and the', 'the', 'of the famili in afdc need afdc are abl to take low-paid job', 'western philosophi', 'unit state', 'franklin roosevelt', 'the unit', 'american cultur', 'american cultur tradit', 'bill clinton', 'american folklor', 'georg bush', 'reagan democrat', 'reagan', 'polit aspect', 'the us'}\\n\",\n      \"GT: num=6 - {'deficit reduct', 'welfar benefit', 'presid bill clinton', 'welfar reform', 'healthcar reform', 'feder assist'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'is a member of the hous arm servic committee. he says:', '17th-centuri introduct', 'state of the unit nation', 'north american free trade agreement', 'ross perot', 'the', 'unit state', 'virginia and the unit state', 'unit', 'norton si whiski', 'the unit', 'norman sisiski', 'american cultur', 'virginia', 'state and territori establish in 1788', 'n ralph dombrow', 'american polit', 'that the', 'north american fta', 'outlin of the american peopl', 'florida state univers', 'nation polit', 'north america'}\\n\",\n      \"GT: num=8 - {'demonstr', 'judici system', 'foreign polici implic', 'north american free trade agreement', 'presid bill clinton', 'passion opposit', 'virginia', 'opposit organis'}\\n\",\n      \"p=0.08695652173913043, r=0.25, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'unit aircraft corpor', 'desoto nation forest', 'air accid', 'hattiesburg', 'caus', 'nation forest.', 'and wa report miss at about 9:30 p.m.', 'in the desoto', 'aircraft accid in mississippi', 'mickey leland', 'american airlin flight 11', 'gulfport', 'dixi youth world seri', 'unit state air forc', 'unit airlin flight 21', 'unit air line flight 11 (d-l)', 'investig', 'marlin fitzwat'}\\n\",\n      \"GT: num=6 - {'mississippi', 'aviat accid', 'light plane crash', 'airplan crash', 'freshman congressman larkin smith', 'wreckag'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'caría', 'countri in north america', 'carib basin', 'list of caribbean countri', 'north american free trade agreement', 'alexand watson, assist secretari of state', 'caricom', 'the', 'and', 'theu', 'carlo salinas, prime minist of beliz', 'the unit state', 'watson ha been brief on the propos', 'carlo salina', 'caribbean', 'economi', 'and the us', 'trade', 'bill clinton', 'man manuel esquivl', 'member state of the unit nation', 'alexand watson', 'manuel esquivel', 'the us'}\\n\",\n      \"GT: num=9 - {'competit mexico', 'central american', 'econom disloc', 'presid bill clinton', 'north american free trade agreement', 'pariti propos', 'possibl divers', 'nafta market', 'caribbean basin countri'}\\n\",\n      \"p=0.041666666666666664, r=0.1111111111111111, f1=0.06060606060606061\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'economi of the european union', 'econom of europ (disambiguation)', 'world health organ', 'organis for econom co-oper and develop', 'welfar', 'the cost of the welfar state. the', 'the', 'economist of europe, a group of', 'cost', 'olymp game', 'oecd', 'world trade organ member economi', 'key issu', 'welfar state', 'otto van der walt', 'economi of europ by countri', 'the welfar state', 'oecd countri', 'econom of europ'}\\n\",\n      \"GT: num=5 - {'nation welfar system', 'welfar cost', 'healthcar reform', 'presid clinton', 'budget deficit'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'the us,', 'blair hous', 'domest polici', 'the treaty, and he ha been abl to', 'north american free trade agreement', 'presid', 'asian pacif nation', 'the', '2016 elector', 'american rhode scholar', 'the unit', 'seattl', '2016 unit state presidenti elector', 'uruguay round', 'trade', 'bill clinton', 'clinton school of public servic', 'uganda round', 'nafta', 'american polit career', 'to get the treati through', 'american peopl', 'gatt'}\\n\",\n      \"GT: num=8 - {'trade pact', 'investor', 'intern protection', 'open trade environ', 'econom benefit', 'north american free trade agreement', 'presid clinton', 'global inflat'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'build and structur destroy in 1991 and 1992', 'lo angel counti fire depart', 'precaut', 'the beetl are abl to burrow into the bark and', 'california', 'southern california', 'california wildfir', 'drought', 'and the', 'the', 'depart of forestri', 'build and structur demolish in 1991', 'build and structur disestablish in 1992', 'bark beetl', 'is', 'u.s. forest servic', 'list of wildfir in california', 'fungi', 'the fire season is'}\\n\",\n      \"GT: num=11 - {'wildfir season', 'southern california neighborhood', 'fire offici', 'wildfir danger', 'fuel sourc', 'fire prevent regul', 'rainfal season', 'vulner fire zone', 'vulner foothil', 'prolong drought', 'high fire hazard'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'the govern', 'the militia', 'gun ownership', 'social philosophi', 'gun violenc', 'parad magazin', 'don kate', 'the', 'that the militia ha changed, the', 'michigan law review', 'the unit state', 'gun right', 'michigan', 'histori', 'gun control', 'gun cultur', 'gun safeti', 'the state', 'warren burger', 'modern time', 'the countri', 'cultur', \\\"warren burger'\\\", 'second amend'}\\n\",\n      \"GT: num=8 - {'handgun purchas', 'right', 'law enforc', 'gun ownership', 'kate', 'constitut', 'gun control', 'second amend'}\\n\",\n      \"p=0.125, r=0.375, f1=0.1875\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'1853 establish in california<infill>oxnard', 'polic', 'ventur counti public defend', 'the three', 'popul place establish in 1853', 'oxnard, california', 'of the five', 'the four', 'ventura counti', 'the', 'law and govern', 'canton capit of california', 'polic brutal', 'were arrest on june 15. the', 'ventura counti district attorney', 'william kadi', 'citi in california', 'edward brodi', 'of', 'beyond a reason doubt', 'vento counti', 'the five'}\\n\",\n      \"GT: num=6 - {'polic offic', 'forcibl brutal', 'polic brutal', 'victim', 'excess forc', 'arrest report'}\\n\",\n      \"p=0.045454545454545456, r=0.16666666666666666, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'prepar', 'wa also expect to', 'to be', 'and food. the coast guard', 'august and septemb', 'nation hurrican center', 'event', 'day of the year', 'morgan citi', \\\"port o'connor\\\", 'freeport', 'day', 'nation hurrican servic', 'hurrican chantal', 'chevron corp.', 'august (period)', 'august', 'to', 'august 1', 'be'}\\n\",\n      \"GT: num=9 - {'hurrican stapl', 'hurrican chantal', 'oil drill work vessel', 'coastal resid', 'coast guard', 'rescu diver', 'crew member', 'hurrican warn', 'hurrican season'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'venetia', 'diamond', 'de beer', 'carr kitcatt & aitken', 'the largest diamond mine in the world,', 'robin baxter-brown', 'diamond mine', 'global market', 'diamond industri associ', 'zim', 'and', 'zimbabw ministri of mine', 'diamond industri', 'toronto', 'zealand', 'and the second, cost', 'african union member economi'}\\n\",\n      \"GT: num=11 - {'diamond deposit', 'river ranch', 'diamond explor experi', 'diamond mine', 'south african group', 'zimbabw govern', 'diamond busi', 'exclus explor right', 'diamond trade', 'joint ventur', 'central sell organis'}\\n\",\n      \"p=0.058823529411764705, r=0.09090909090909091, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a hous in franc', 'franc and the british', 'properti market', 'french.', 'railway in franc & germani', 'hesdin', 'montreuil', 'the', 'and', 'calai', 'channel tunnel', 'mitterrand', 'franc & the unit kingdom', 'histori', 'the french', 'railway tunnel in franceand the unit state', 'hovercraft', 'hover craft', 'hepdin', 'modern time', 'rail transport in franc and the unit kingdom', 'french', 'and the bank are not happi with the fact that the tunnel is'}\\n\",\n      \"GT: num=10 - {'channel tunnel', 'start signal', 'britain', 'uk market', 'prospect buyer', 'price competit', 'recoveri', 'franc', 'properti market', 'french properti'}\\n\",\n      \"p=0.08695652173913043, r=0.2, f1=0.12121212121212122\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'person life and career', 'carllewi', 'the olympics.', 'american sportspeopl', 'the', 'carl lewi', 'stanozolol', 'toronto', 'drug use', 'american male film actor', 'american film produc', 'american race track and field coach', 'the use of the ban steroid furazabol in', 'furazabol', 'intern amateur athlet feder', 's korean olymp', 'american peopl of maltes descent'}\\n\",\n      \"GT: num=7 - {'seoul olymp', 'anabol steroid stanozolol', 'steroid furazabol', 'drug test', 'carl lewi', 'drug use', 'ben johnson'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'wikipedia medicin articl readyto translat', 'hiv/aid', 'of the', 'elizabeth taylor', 'pulmonari diseas', 'candida albican', 'bacteri pneumonia', 'the', 'biolog', 'candida', 'y yeast', 'bacteria', 'infecti diseas', 'bacteriolyt pneumonia', 'of', 'rttem', 'the infect is not relat to the viral pneumonia. the', 'santa monica', 'wikipedia medicin articl readi to translat', 'wikipedia: medicin articl contain video clip', 'pneumonia'}\\n\",\n      \"GT: num=7 - {'miss taylor', 'viral pneumonia', 'actress elizabeth taylor', 'bacteri pneumonia', 'intraven therapi', 'yeast infect', 'hospit'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'california', 'cultur tourism in california', '1901-present', 'lake elsinor', 'by late friday afternoon, author said.', 'casa del sol', 'cultur of the southern california', 'citi in riversid county, california', 'counti seat in california (u.s. state)', 'cleveland nation forest', 'california in popular cultur', 'histori', 'antelop valley', 'ortega highway', 'firefight were abl to extinguish the blaze', 'riversid counti', 'san mateo wilder'}\\n\",\n      \"GT: num=7 - {'brush fire', 'cleveland nation forest', 'firefight', 'blaze', 'damag', 'investig', 'fire crew'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'to the white hous', 'clinton administr', 'alfr r. emerson', 'north american develop bank', 'the', 'presid of bill clinton', '1946 establish in the unit state', 'the white house.', 'the white hous ha been', 'afl-cio', 'clinton famili', 'foreign relat', 'northamerican free trade agreement', 'trade', 'effort to impeach bill clinton (illustr in thi video)', 'bill clinton', 'nafta', 'presid of the unit nation', 'the administration.', 'to', 'esteban torr', 'north america'}\\n\",\n      \"GT: num=7 - {'hous vote', 'undecid congressman', 'presid bill clinton', 'clinton administr', 'north american free trade agreement', 'north american develop bank', 'job loss'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'of cattl in', 'unit kingdom', 'europ', 'cultur depict of cattl', 'in', 'cattledog', 'martin raff', 'in the', 'cow in the unit state', 'cattl diseas', 'crisi in cattl farm', 'nation farmer union', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'univers college, london', 'c cattl diseas', 'scraie', 'the unit state ha ban the use of'}\\n\",\n      \"GT: num=9 - {'bovin spongiform encephalopathi', 'sheep diseas', 'cattl feed', 'british cattl', 'mad cow diseas', 'import', 'ban', 'bse', 'export'}\\n\",\n      \"p=0.05263157894736842, r=0.1111111111111111, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'european singl market', 'brussel', 'the uk.', 'to take action against', 'intern trade', 'europ', 'british cuisin', 'bivin spongeiform enceopathi', 'british beef', 'bse', 'horst seehof', 'european commiss', 'british and european livestock', 'creutzfeld-jakob diseas', 'bovin diseas', 'the uk is also hope to persuad the european commiss', 'cattl', 'creutzfield-jakob diseas and creutzfeldt-jakov diseas', 'british food and drink', 'bovin spongiform encephalopathi', 'anim virolog'}\\n\",\n      \"GT: num=7 - {'bovin spongiform encephalopathi', 'germani', 'british export', 'mad cow diseas', 'british beef export', 'restrict', 'unilater ban'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'organiz allianc', 'amzigh cultur associ', 'of all', 'ronald l. dellum', 'ronald v. dellum', 'of the', 'organ of the amazigh peopl', 'u.s. agenc for intern develop', 'u-2', 'organ base in addi ababa', 'the leland plane', 'u-.s. militari', 'histori', \\\"the plane' disappear wa widespread. the\\\", 'of', 'ethiopia', 'amazigh cultur associ', 'organis base in ethiopia', 'u.-2'}\\n\",\n      \"GT: num=7 - {'crash site', 'texa congressman mickey leland', 'rescu oper', 'american helicopt', 'fugnido refuge camp', 'wreckag', 'heavi weather'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'canadian steroid abus', 'controversi', 'carl lewi', 'huey said that franci had told her', 'stanozolol', 'that he wa', 'that franci', 'charli franci', 'canadian olymp medalist', 'canadian coach charli franci', 'angella taylor issajenko', 'olymp dope', 'canadian male sprinter', 'that', 'canadian sprinter', 'ben johnson', 'canadian femal sprinter (age 18–24)', 'nbc-tv', 'biotechnolog', 'biogenesi'}\\n\",\n      \"GT: num=8 - {'urin sampl', 'canadian coach charli franci', 'ban steroid', 'seoul olymp', 'lynda huey', 'canadian inquiri', 'drug use', 'sprinter ben johnson'}\\n\",\n      \"p=0.05, r=0.125, f1=0.07142857142857144\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'prepar', 'france–unit kingdom border cross', 'railway tunnel in franc', 'of the', 'wallonia', 'organis for econom co-oper and develop', 'nord-pa de calai', 'of belgium.', 'belgium', 'e40 european motorway', 'construct', 'channel tunnel', 'france-uk border', 'economi', 'and the tunnel will be', 'of', 'brussel region', 'rail transport in franc and the unit kingdom', 'bruge', 'the centr of'}\\n\",\n      \"GT: num=11 - {'develop', 'channel tunnel', 'chunnel', 'traffic load', 'western flander', 'freight carrier', 'belgium', 'rapid increas', 'european metropolitan area', 'offici open', 'holidaymak'}\\n\",\n      \"p=0.1, r=0.18181818181818182, f1=0.12903225806451613\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'govern of colombia (1918–present)', 'mexico', 'a', 'polit of colombia', 'polit in colombia', 'polit by countri', 'polit parti', 'the assassin wa', 'jose franciscoruiz massi', \\\"mexico' congress\\\", 'politicsof colombia', 'coloni in colombia (1824–1948)', 'institut revolutionari parti', 'the two allegedli hire the gunman, and other accomplices, accord to testimoni', 'mexico and the unit state', 'colombia', 'polit', 'list of presid of colombia and the america', 'jose lui donaldo colosio', 'carlo fuent', 'jose francisco ruiz massieu'}\\n\",\n      \"GT: num=7 - {'assassin', 'mexico', 'crimin justic system', 'gulf drug cartel', 'violent resist', 'jose francisco ruiz massieu', 'alleg allianc'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'cincinnati red', 'are not', 'black belt', 'crimin justic', 'oakland', 'who are', 'black-american cultur', 'to stop peopl who are', 'unit state', 'and we have to use them', 'lo angel intern airport', 'civil liberti', 'black belt in the unit state', 'joe morgan', 'civil right organ', 'drug courier', 'the polic are not', 'american civil liberti union', 'major leagu basebal', 'are'}\\n\",\n      \"GT: num=7 - {'racism', 'polic offic', 'public affair', 'lo angel', 'joe morgan', 'clayton searl', 'drug'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a major', 'a', 'lo diego', 'state with multipl time zone', 'california', 'to make the state inelig for feder aid. the state', 'lo francisco', 'state and territori establish in 1850', 'california in the unit state', 'been a', 'censu', 'govern', 'lo angel', 'ha', 'peter chacon', 'state of the unit kingdom', 'california and the unit nation', \\\"major impact on the state'\\\", 'lo angel counti', 'ha been', 'lo angel citi atty.', 'polit', 'been'}\\n\",\n      \"GT: num=7 - {'california', 'feder aid', 'congression seat', 'censu count', 'censu bureau', '1990 censu', 'illeg alien'}\\n\",\n      \"p=0.043478260869565216, r=0.14285714285714285, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'cat', 'gerstman', 'cat in the unit state', 'europ', 'gottingen univers', 'the', 'creutzfeldt jakob diseas', 'gerstmann straussler syndrom', 'germani', 'europ and the unit kingdom', 'cattl diseas', 'crisi in cattl farm', 'cattl', 'infecti diseas', 'bovin spongiform encephalopathi', 'cultur of the czech republ', 'c cattl diseas', 'the countri', 'the german govern said that there had been no known case in germany. in the unit', 'cow', 'creutfeldt'}\\n\",\n      \"GT: num=6 - {'bovin spongiform encephalopathi', 'british beef import', 'possibl connect', 'mad cow diseas', 'cattl diseas', 'bse'}\\n\",\n      \"p=0.09523809523809523, r=0.3333333333333333, f1=0.14814814814814814\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'evergreen currant', 'california and the pacif ocean', 'chaparr countri', 'geographi', 'california', '1850 establish in california', 'pacif coast iri', 'southern california', 'the', 'outlin of california', 'than the', 'state and territori establish in 1850', 'state', 'geograph region', 'theciti', 'the citi', 'are more', 'jade', 'of the chaparral. the chaparr are', 'evergreen', 'ever green', 'state of the unit state', 'monkeyflow', 'manzanita'}\\n\",\n      \"GT: num=8 - {'firescap demonstr garden', 'maximum fire protect', 'four plant zone', 'wildfir', 'southern californian', 'firescap garden', 'ice plant', 'firescap'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'list of american women in music', 'american femal screenwrit', '20th centuri', 'american film actress', 'elizabeth taylor', 'in the treatment of', 'lo angel counti district attorney', 'the report said the doctor had been', '20', '1980', 'american peopl of english descent', 'american women in the art', 'betti ford clinic', 'peopl from san gabriel valley', 'lo angel', 'histori', 'of elizabeth taylor.', 'rancho mirag', 'american actress', 'ranvo mirag'}\\n\",\n      \"GT: num=6 - {'physician', 'actress elizabeth taylor', 'medic practic', 'rehabilit clinic', 'painkil', 'investig'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'state of the unit state (18alsh)', 'exxon ship co.', 'u.-s.', 'long beach', '20th centuri', 'be spill into', 'princ william sound', 'alaska', 'state and territori establish in 1959', 'alcohol beverag control', 'wa about. the oil wa', 'u-s. coast guard', 'western unit state', 'histori', 'the oil wa be', 'u.s. fish and wildlif servic', 'alaskan arctic', 'former russian coloni', 'oil spill', 'be', 'into the water.'}\\n\",\n      \"GT: num=5 - {'wildlif hurt', 'exxon valdez', 'alaskan oil spill', 'crude oil', 'cleanup equip'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'tunnel', 'take', 'list oftornado and tornado by countri', 'univers of chicago', 'to the point where they', 't tornado alley', 'taker', 'denver', 't take', 'tornado', 'geophys scienc', 'unit state', 'natur hazard', 'took', 't tornado', 'histori', 'weather hazard', 'tetsuya theodor fujita', 'taken', 'univers', 'tune in', 'have been'}\\n\",\n      \"GT: num=7 - {'violent storm', 'small twister', 'thunderstorm', 'natur tornado', 'tornado', 'fujita', 'scientist'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a major', 'a', 'martin luther king jr', 'a signific', 'gun legisl', 'gun violenc', 'jame bradi', 'ronald reagan', 'unit state', 'the abil to kill a target with a singl bullet. thi is', 'social conservat', 'gun right', 'black talon', 'gun control in the unit state', 'social theori', 'gun control', 'robert kennedi', 'jame brady, the former white hous press secretari', 'current debat'}\\n\",\n      \"GT: num=7 - {'gun control law', 'bradi bill', 'gun purchas', 'complet ban', 'us congress', 'gun ownership', 'restrict'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'exxon ship co.', 'long beach', 'exxon valdez', 'steve cowper', 'the accident.', 'gregori cousin', 'exxonmobil accid', 'pilot', 'princ william sound', 'shipwreck in alaska', 'transport accid in alaska (1980s)', 'the', 'cousin wa not charged. the coast guard is', '1980 unit state oil spill', 'accid', 'ship', 'caus', 'killer whale', 'still investig the accid and', 'list of exxon vald'}\\n\",\n      \"GT: num=11 - {'unqualifi mate', 'separ accid', 'oil contamin', 'coast guard regul', 'pilot', 'feder disast', 'exxon valdez spill', 'cleanup effort', 'investig', 'oil spill', 'environment conserv'}\\n\",\n      \"p=0.05, r=0.09090909090909091, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'antiwar movement', 'jim kolb', 'the us,', 'bill daley', 'n fta', 'jim jontz', 'jim daley, draft from hi chicago district', 'theus,', 'anti–nuclear weapon', 'the', 'unit state', 'tafta/tafta treati', 'anti-(nuclear) weapon in', 'unit nation', 'lori wallach', 'the unit states, is the', 'anti-nuclear weapon in the unit state', 'histori', 'treati of canada', 'jim kelleh', 'nafta'}\\n\",\n      \"GT: num=9 - {'global trade structur', 'side agreement', 'north american free trade agreement', 'presid bill clinton', 'jim jontz', 'polit battl', 'fair trade campaign', 'intens retail polit war', 'democrat coalit'}\\n\",\n      \"p=0.047619047619047616, r=0.1111111111111111, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'american on welfar', 'american societi', 'of the', 'hous of repres', 'aid to famili with depend children', '1980', 'the', 'demograph of the american peopl', 'unit state', 'ha increas in real terms, the', 'democraci', 'been a', 'welfar reform', 'histori', 'thoma downey', 'latino-american cultur', 'ha', 'ha been', 'been', 'demograph histori of the unit state', 'anthoni beilenson', 'trojan hors', 'the system ha', 'famili welfar reform act of 1987'}\\n\",\n      \"GT: num=9 - {'welfar program', 'welfar benefit', 'welfar depend', 'invest', 'welfar system', 'welfar reform', 'welfar recipi', 'american taxpay', 'hous democrat'}\\n\",\n      \"p=0.041666666666666664, r=0.1111111111111111, f1=0.06060606060606061\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'bradi bill', 'gun ownership', 'gari kleck', 'social philosophi', 'gun violenc', 'nra ha', 'unit state', 'not enact gun- control law', 'gun right', 'not', 'jame d. wright', 'nation rifl associ', 'gun-control debat', 'social theori', 'gun control', 'frank t. iorio mamaroneck', 'persian gulf war', 'laws.', 'gun cultur', 'to enact gun-control law in the unit states. the', 'gun-control', 'issu'}\\n\",\n      \"GT: num=8 - {'firearm', 'bradi bill', 'crimin', 'violent crime', 'ban', 'protect', 'gun control', 'prison survey'}\\n\",\n      \"p=0.09090909090909091, r=0.25, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'teotihuacan', 'jean-michel jarr', \\\"the sun' rays. ; the eclips will be the\\\", 'the world', 'kailua', 'mexico', 'articl contain video clip', 'asteroid', 'the', 'baja california', 'astrobiolog', 'occurr', 'astronom object', 'alan dyer', 'new age', 'solar eclips', 'athropogen radioact', 'the first time'}\\n\",\n      \"GT: num=7 - {'visitor', 'mexico', 'sun', 'tourism', 'eclips fan', 'total eclips', 'hawaii'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'howard metzenbaum', 'peopl with bipolar disord', 'equal employ opportun commiss', 'clarenc thoma', 'background', \\\"of thomas' record on abort and other issues. ;\\\", 'arlen specter', 'initi reaction', 'unit state suprem court justic', 'sup court of the unit state', 'juliu l. chamber', 'democrat', 'unit nation gener assembl observ', 'orrin hatch', 'cb thi morn', 'suprem court nomin', 'peopl from west virginia'}\\n\",\n      \"GT: num=7 - {'clarenc thoma', 'senat judiciari committe', 'conserv major', 'confirm hear', 'black justic', 'circuit court', 'columbia'}\\n\",\n      \"p=0.058823529411764705, r=0.14285714285714285, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'coke war', 'peru', 'use in differ countri', 'cultur of the southern unit state', 'in the upper huallagas, the shine', 'the', 'path. the', 'upperhuallaga valley', 'upper huallga', 'the shine path.', 'coffe cultur', 'carcinolog', 'cocain', 'coca-cola and the shine path', 'u.s.', 'state of emerg', 'upper huallaga', 'shine path', 'cannabi'}\\n\",\n      \"GT: num=7 - {'coca farmer', 'upper huallaga valley', 'drug traffick', 'coca grower', 'guerrilla', 'shine path', 'emerg control'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'histori and scienc of south america', 'alberto fujimori', 'histori of peru', 'the government, and', 'the army.', 'peru', 'fernando belaund', 'the', 'the militari', 'the shine path', 'santiago garcia', 'senderlogo', 'gener elect after 12 year of a militari dictatorship', 'polit histori', 'historyof peru', 'histori', 'gener elect', 'historyof south america (1901–present)', 'histori (1925–present), the shine path is', 'spanish-speak countri and territori'}\\n\",\n      \"GT: num=8 - {'next presid', 'arm forc', 'presid garcia', 'alberto fujimori', 'shine path', 'democrat legal', 'peru', 'emerg law'}\\n\",\n      \"p=0.1, r=0.25, f1=0.14285714285714288\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'index of california-rel articl', 'morgan hill', 'california', '1850 establish in california', 'south bay', 'the', 'climat', 'and san francisco,', 'fire season', 'state and territori establish in 1850', 'east bay', 'the citi of', 'u.s. forest servic', 'state of the unit kingdom', 'stand kindl', 'santa clara counti', 'california and the unit state', 'of san', 'forest and forest', 'and the east bay, and the south bay,'}\\n\",\n      \"GT: num=9 - {'nation forest', 'california depart', 'fire prevent unit', 'fire protect', 'heavi fuel', 'firefight effort', 'fire season', 'fire threat', 'wildfir precaut'}\\n\",\n      \"p=0.05, r=0.1111111111111111, f1=0.06896551724137932\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'organ of petroleum export countri', 'world trade organ member compani', 'to increas sale of diamond jewelry, and to promot', 'diamond', 'debeer', 'n.w. ayer & son', 'j. walter thompson co.', 'diamond mine', 'd', 'the', 'de beer consolid mine ltd.', 'diamond industri', 'histori', 'd diamond', 'modern era', 'chemic compani establish in 1891', 'tin cartel', 'harri oppenheim'}\\n\",\n      \"GT: num=8 - {'south african concern', 'promot campaign', 'diamond trade', 'diamond jewelri', 'world diamond market', 'world diamond product', 'de beer campaign', 'diamond produc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'joseph mcnamara', 'counti seat in california', 'polic', 'a', 'daryl gate', \\\"and the city' mayor, a democrat, ha\\\", 'citi in lo angel', 'american civil liberti union of southern california', 'stanford univers', 'law and govern', 'popul place establish in 1932', 'a model for', 'been a', 'lo angel', '1898 establish in california<infill>list of peopl from lo angel counti', 'hoover institut', 'christoph commiss', 'polic reform', 'counti in california (u.s. state)'}\\n\",\n      \"GT: num=5 - {'inadequ disciplin', 'racism', 'polic reform', 'polic brutal', 'investig'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'intern organ base in franc', 'sub-saharan africa', 'intern financ institut', 'debt servic', 'south america', 'to pay off their debts.', 'south korea', 'develop in europ', 'event', 'world bank', 'latin america', 'have', 'east asia', 'last year', 'unit nation gener assembl observ', 'middl east', 'have been abl to maintain their', 'and have', 'unit state intern monetari fund', 'been abl to'}\\n\",\n      \"GT: num=6 - {'creditor', 'third world countri', 'new loan', 'global debt burden', 'third world debtor', 'cash drain'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'santa clara counti', 'lrb-408-rrb- 287-3785', 'counti seat in california', 'countri in california (\\\"eastern unit states\\\")', 'and exercise. the diabet societi of santa clara counti also offer a', 'a', 'diabet societi', 'health', 'counticut', 'diabet', 'educ', \\\"alexian brother' hospit\\\", 'counti', 'counti in california (u.s. state)', 'program', 'a program', 'polyunsatur oil', '1854 establish in california territori'}\\n\",\n      \"GT: num=7 - {'hispan diabet', 'health profession', 'diabet societi', 'good nutrit', 'educ director', 'hispan lifestyl', 'hispan diet'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'marin sport', 'marathon in san diego', 'new york citi marathon', 'glendal', 'women', 'of glendora.', 'list of sport event in sandiego', 'result', 'san diego intern marathon', 'san gabriel valley', 'mindi ireland', 'marathon event in california', '1885 establish in california (u.s. state)', 'glendora', 'lake forest', 'tijuana', 'race result'}\\n\",\n      \"GT: num=6 - {'runner', 'marathon cours', 'san diego intern marathon', 'mexico citi', 'ernesto beatriz martinez', 'mari rollin'}\\n\",\n      \"p=0.058823529411764705, r=0.16666666666666666, f1=0.08695652173913045\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'pete wilson', 'census', 'a', 'thad cochran', 'the censu is', 'is a', 'daniel patrick moynihan', 'senate-hous confer committe', 'background', 'unit state senat', 'moynihan -lrb- r-n.y.. -rrb- argu that the', 'sampl (statistics)', 'unit state', 'popul', 'a censu', 'censu', 'samuel a.uelson', 'bob dole', 'robert a. mosbach'}\\n\",\n      \"GT: num=10 - {'feder aid', '1990 popul count', 'appropri bill', 'congression seat', 'censu bureau', 'hous seat', 'illeg immigr', 'voic vote', 'prohibit', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'1910 in the thirteen coloni', 'nation governor associ', 'historyof the unit state (1872–1919)', '20th centuri', 'l rb', 'histori of the unit state (1876–1922)', 'lrb', '19th centuri in the unit state', 'work and', 'bill clinton of arkansa', 'american cultur', 'welfar reform', 'histori', 'bill clinton', 'and the govern would be abl to', 'american public life', 'rrb', 'to', 'to work and to'}\\n\",\n      \"GT: num=4 - {'reform program', 'welfar reform', 'welfar recipi', 'welfar system'}\\n\",\n      \"p=0.05263157894736842, r=0.25, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'bush administr', 'in the city.', 'citi in california (u.s. state)', \\\"california. the city' mayor, john m. o'malley, said that the\\\", 'voic vote', 'california', 'be', '1850 establish in california', 'senate-hous confer committe', 'the citi would', 'popul', 'miguel a. pulido', 'demograph', 'immigr', 'california and the unit state', '1990 censu', 'californiaian societi', 'demograph statist', 'robert a. mosbach'}\\n\",\n      \"GT: num=11 - {'feder aid', 'illeg alien', '1990 popul count', 'senat', 'appropri bill', 'congression seat', 'censu bureau', 'hous seat', 'illeg immigr', 'voic vote', 'larg immigr popul'}\\n\",\n      \"p=0.05263157894736842, r=0.09090909090909091, f1=0.06666666666666667\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'census', 'of the', 'hous of repres', 'the censu bureau to count illeg immigr in the', 'the', 'sampl (statistics)', 'unit state', 'sampl (statistic)', 'the popul of', 'richard shelbi', 'censu', 'popul refer bureau', 'histori', 'feder for american immigr reform', 'of', 'sampl (statistical)', '1990 censu', 'immigr and natur servic', 'hous debat'}\\n\",\n      \"GT: num=7 - {'congression campaign', 'congression seat', 'censu bureau', '1990 censu', 'congression reapportion', 'illeg immigr', 'illeg alien'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'crime', 'current practic', 'joseph macnamara', 'criminolog', 'code of silenc', 'to report', 'briberi', 'california', 'the', 'unit state', 'the polic', 'code', 'human right', 'the polic are not requir to', 'crude languag', 'intern affair depart', 'santa clara counti', 'rape', 'san jose polic chief', 'to the'}\\n\",\n      \"GT: num=6 - {'injuri', 'brutal complaint', 'polic abus', 'polic misconduct', 'polic brutal', 'santa clara'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'cancer', 'univers of wisconsin', 'switzerland', 'rttem', 'the diseas is a', '20th centuri', \\\"alzheimer' diseas\\\", 'outlin of neurosci', 'a diseas that is', 'a', '2030', 'univers', 'd. carleton gajdusek', 'wikipedia medicin articl readi to translat', 'histori', 'brain diseas', 'a mysteri that', 'spongiform encephalopathi'}\\n\",\n      \"GT: num=8 - {'sheep diseas', 'cjd victim', 'alter protein', 'cjd case', 'british cattl', 'mad cow diseas', 'infect', 'infect sheep'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bay area', 'ernest kraul', 'california depart of forestri and fire protect', 'geographi', 'is a fire chief in the santa cruz counti fire depart', 'santa cruz counti', 'california', 'and the', '1891 establish in california', 'the', 'lo alto hill', 'popul place establish in 1891', 'bayarea', 'hill and canyon', 'fire', 'citi in alameda county, california', 'california depart', 'the fire', 'list of peopl from the bay area', 'the fire department.'}\\n\",\n      \"GT: num=9 - {'fire prevent', 'deadli firestorm', 'fire protect', 'fire hazard', 'fire offici', 'saratoga foothil', 'brush fire', 'oakland firefight', 'damag wildfir'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bermuda', 'to run a marathon, but to take a', 'a', 'fred lebow', 'articl contain video clip', 'tour, a', 'london marathon', 'a marathon vacation,', 'marathon', 'distanc run', 'outdoor recreat', 'a vacation, a', 'histori', 'bermudian', 'new york marathon', 'tour', 'tourism', \\\"newyork roadrunner' club\\\", 'modern era', 'road run'}\\n\",\n      \"GT: num=4 - {'restrict entri race', 'marathon tour', 'marathon vacat', 'run vacat'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'counti seat in california', 'counts.', 'count', 'countries.', 'santa ana', \\\"county'\\\", 'feder immigr reform act', \\\"the county' population. the counti\\\", 'popul', 'demograph', 'house-sen confer', 'georg frank', 'orang county, california', 'u.s. senat', 'citi in orang county,california', 'counti', 'hous', 'countri in california (\\\"orang county\\\")', 'immigr', 'orang counti', 'counti in california (u.s. state)'}\\n\",\n      \"GT: num=7 - {'orang counti resid', 'feder revenu', 'censu bureau', '1990 censu', 'repres', 'complet count', 'illeg alien'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'american indian', 'santa clara counti', 'demograph', 'counti seat in california', 'st. louis health center', 'the clinic', 'the', 'health', 'diabet', 'countri in california (\\\"san jose mercuri news\\\")', 'st- louis drive', 'counti in california (u.s. state)', 'list of peopl from santa clara county, california', 'the clinic is free and open to the public. ; the', 'santa cruz counti', 'morgan hill', 'morganhil', '1854 establish in california territori'}\\n\",\n      \"GT: num=5 - {'exercis', 'diabet manag program', 'diabet societi', 'contract diabet', 'hispan'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'central sell organ', 'shearson lehman secur', 'market', 'diamond fabric', 'diamond jewelri', 'far east', 'west germani', 'articl contain video clip', \\\"japan, the world' second biggest jewelri market, ha\\\", 'demand', 'the', 'been boost by the', 'de beer consolid mine ltd.', 'diamond industri', 'the rise in', 'diamond', 'diamond mine', 'world trade organ', 'antwerp'}\\n\",\n      \"GT: num=8 - {'south african concern', 'diamond dealer', 'diamond price increas', 'world diamond market', 'world diamond product', 'diamond jewelri market', 'diamond price', 'diamond sale'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'build and structur destroy in fire', 'of the u. forest servic in', 'pendleton', 'the region', 'california', 'the area is', 'list of wildfir', 'the', 'build and structur demolish in 1991', 'umatilla nation forest', 'upper cascad', 'precipit and fire season', 'persian gulf', 'fire corridor', 'american wildfir', 'citi in california', 'u.s. forest servic', 'histori', 'builder and structur lost in fire'}\\n\",\n      \"GT: num=10 - {'california', 'aggress approach', 'wildfir', 'prevent measur', 'brush fire', 'natur fire corridor', 'firefight', 'fire threat', 'fire season', 'sever fire spread'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'popul in miami county, florida', 'cleo', 'galveston', 'geographi', 'nation hurrican center', 'citi in miami (city)', 'the', 'climat', 'slam', 'hurrican season', 'big quak', 'lash', 'the miami', 'popul place establish in 1796', 'the citi', 'counti seat in florida', 'miami', 'list of peopl from miami', 'that the season is go to be a quiet one, the'}\\n\",\n      \"GT: num=6 - {'miami', 'tropic storm', 'hurrican specialist', 'tropic depress', 'hurrican season', 'nation hurrican center'}\\n\",\n      \"p=0.15789473684210525, r=0.5, f1=0.23999999999999996\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'colorado state univers', 'hurrican histori', 'of the', 'nation hurrican center', 'articl contain video clip', 'a few', 'the', 'huron', 'hurrican', 'atlant ocean', 'jamaica', 'histori', 'the caribbean, there have been', 'saffir-simpson scale', 'storm', 'huron outbreak', 'yucatan', 'some of the', 'south carolina', 'hurrah'}\\n\",\n      \"GT: num=6 - {'intens hurrican', 'feroci hurrican', 'hurrican pattern', 'catastroph storm', 'atlant region', 'atmospher condit'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'gambia', 'ocean', 'histori', 'wa a', 'atlant hurrican', 'wa', 'atlant ocean', 'land surfac effect of the sun', 'gloria', '1990', 'landform of the atlant ocean', 'a', 'scienc', 'atlant oceanographi', 'mauritania', 'hurrican season', 'the sahel region in the 1990s. he said the sahel', 'mali'}\\n\",\n      \"GT: num=9 - {'rainfal level', 'atlant hurrican', 'coastal resid', 'hurrican pattern', 'global hurrican activ', 'tropic storm season', 'west african sahel region', 'weather research', 'nation hurrican center'}\\n\",\n      \"p=0.05555555555555555, r=0.1111111111111111, f1=0.07407407407407407\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'second fastest', 'gerardo alcala', 'el-mostafa nechchadi', 'race cours', 'old style/chicago marathon', 'list of sport event in chicago<infill>th second-fastest time in the histori of the chicago marathon. he finish in 2:28:15, a pace of', 'paul davies-hal', 'the', 'chicago marathon and half-marathon', 'sunday, august 27', '1885 establish in illinoi', 'race in chicago', 'olymp sport', 'issaquah, wash.', 'race result', 'carlo montero', 'the second fastest time in', 'don janicki', 'chicago marathon'}\\n\",\n      \"GT: num=5 - {'race condit', 'steadi runner', 'patient', 'windi weather', 'distanc runner'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'vote system', 'electorally, the system is not', 'larri pressler', 'elect', 'be abl to vote', 'elect system', 'term limit', 'michael kinsley', 'ronald reagan', 'unit state', 'new republ', 'to vote', 'david pryor', 'elector system', 'histori', 'gerrymand', 'vote', 'polit system', 'electron vote', 'to be abl to', 'to'}\\n\",\n      \"GT: num=10 - {'unfair campaign financ law', 'congression career', 'legisl arrog', 'opinion poll', 'voluntari servic limit', 'limit congression term', 'unfair advantag', 'term limit', 'constitut amend', 'hous incumb'}\\n\",\n      \"p=0.047619047619047616, r=0.1, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'battleship', 'world war ii', 'the wisconsin is about 1,000 feet long, and the', 'wisconsin', 'marin corp marathon', 'the', 'washington', 'maritim warfar', 'ship type', 'unit state', 'is about', 'middl eastern', 'middl east', 'histori', 'shipbuild in the middl east', 'shipwreck in the unit state', 'marin', 'shipyard in washington'}\\n\",\n      \"GT: num=5 - {'battleship wisconsin', 'marathon run', 'marin corp marathon', 'washington', 'train'}\\n\",\n      \"p=0.1111111111111111, r=0.4, f1=0.1739130434782609\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {'construct', 'champlain', 'francoi mitterrand', 'channel tunnel', 'french presid', 'chalet', 'france–unit kingdom border cross', 'rail transport in franc and england', 'france-unit kingdom sport rivalri', 'the same', 'same.', 'railway tunnel in franc', 'histori', 'the channel tunnel', 'ice age', 'folkeston', 'same', 'and shook hands. the two countri have been'}\\n\",\n      \"GT: num=7 - {'rail tunnel', 'channel tunnel', 'continent europ', 'english channel', 'symbol mileston', 'brilliant sign', 'engin project'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'air accid', \\\"mcdonnel dougla corp.'\\\", 'the airplan', 'airlin', 'northwest airlin flight 255', 'the', 'aircraft which explod in flame', 'airbu md-82', 'the accident, the ntsb said that the', 'unit state air forc one', 'aviat accid', 'the airlin', 'accid', 'unit aviat administr', 'caus', \\\"the aircraft'\\\", 'unit technolog corp.', 'the plane', 'aerospac accid', 'airport safeti institut'}\\n\",\n      \"GT: num=8 - {'emerg land', 'aviat safeti issu', 'safeti investig', 'crash victim', 'northwest airlin jet', 'detroit', 'heavi load', 'feder crash inspector'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'newyork citi marathon (running)', 'list of new york citi marathon record', 'race. it wa the first time he had won the race.', 'race cours', 'wanda panfil', \\\"gerri o'hara\\\", 'ken martin', 'ken wakiihuri', 'result', '1894 establish in new york (state)', 'new york citi marathon', 'sunday, april 9', 'wa', 'kenyan', 'marathon record', 'race result', 'wa the', 'he wa', 'organ establish in 1894', 'miki gorman'}\\n\",\n      \"GT: num=6 - {'winner', 'wanda panfil', 'new york citi marathon', 'dougla wakiihuri', 'third consecut marathon victori', 'foreign'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'european rail link ltd.', 'construct', 'channel tunnel', 'confeder of british industri', 'railway in the unit kingdom', 'railport in the british isl', 'british rail', 'france–unit kingdom border cross', 'hous of common', 'rail transport in franc and germani', 'railway tunnel in franc', 'rail tunnel in the netherland', 'cecil parkinson', 'leed univers', 'histori', 'and the govern will have to pay for it.', '1992'}\\n\",\n      \"GT: num=6 - {'channel tunnel', 'refus', 'singl market', 'fast rail servic', 'total benefit', 'ad cost'}\\n\",\n      \"p=0.058823529411764705, r=0.16666666666666666, f1=0.08695652173913045\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'american review of respiratori diseas', 'boston', 'bioterror', 'caus', 'boston depart of public health', 'air pollut', 'boston public health depart', 'airbourn', 'iarc group 3 carcinogen', 'evolutionari biolog', 'air ventil', 'the same time as the other end.', '[[ sick build syndrome]]', 'air diseas', 'boston medic school', 'airborn diseas'}\\n\",\n      \"GT: num=5 - {'tuberculosi studi', 'poor air ventil', 'ventil system', 'infecti air', 'airborn infect'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'prepar', 'railway in the unit kingdom', 'is a', 'tunnel in franc', 'farthinglo villag', 'franc', 'the', 'rail tunnel in the netherland', 'rolls-royc', 'rail transport in franc & germani', 'the tunnel is', 'construct', 'channel tunnel', 'work on the tunnel', 'is', 'the largest is the one for', 'railway tunnel in franc and england', 'franc & germani (france)', 'canterburi', 'dover', 'margaret thatcher'}\\n\",\n      \"GT: num=7 - {'full benefit', 'british rail', 'tunnel treati', 'british industri', 'bore machin', 'british termin', 'domin trade link'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'franc', 'the', 'avion accid', 'terror', \\\"n'djamena\\\", 'aviat accid in africa', 'lebanon crisi', 'airlin accid', 'aviat accid', 'brazzavil', 'air accid in franc', 'niger', 'aircraft accid', 'the french', 'francoi mitterrand', 'caus', 'french government.', 'the attack on', 'the caller also claim respons for the', 'french'}\\n\",\n      \"GT: num=6 - {'rescu mission', 'terrorist bomb', 'lebanon crisi', 'crash site', 'terrorist attack', 'niger'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'california', 'saratoga', 'bay area', 'cultur', 'list of peopl from the bay area', 'popul place establish in 1876', 'are the best place to watch the eclipse. ;', 'citi in alameda counti', 'scienc', 'de anza colleg', 'cupertino', 'baja', 'orion telescop', 'bayarea', 'solar eclips', 'hawaii'}\\n\",\n      \"GT: num=6 - {'mexico', 'eclips viewer', 'californian', 'partial eclips', 'solar eclips', 'hawaii'}\\n\",\n      \"p=0.125, r=0.3333333333333333, f1=0.18181818181818182\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'hamilton spectat', 'seoul olymp', 'johnson', 'johnson \\\"a', '1946 birth', 'canadian sprinter of english-languag descent', 'johnson\\\"', 'olymp sprinter', 'return to competit', 'castelfranco veneto', 'career', 'american peopl of english descent', 'charli franci', 'ben johnson', 'canadian male sprinter (age 18–24)', 'hamilton spectat indoor game', 'anabol steroid', 'american sprinter who have been disqualifi', 'that johnson wa \\\"a littl bit more confident\\\" in', 'suspens'}\\n\",\n      \"GT: num=8 - {'news confer', 'seoul olymp', 'drug test', 'expect', 'hamilton spectat indoor game', 'anabol steroid', 'ben johnson', 'suspens'}\\n\",\n      \"p=0.25, r=0.625, f1=0.35714285714285715\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'person life', 'american rhode scholar', 'tom bradley', 'bill gate', 'the incident. ;', 'american civil liberti union', 'controversi', 'american nonprofit chief execut', 'apocalyps now', 'lo angel time', '1946 birth', 'american technolog chief execut and chief execut of compani', 'racial discrimin', 'ride of the valkyri', 'list of wealthiest histor figur', 'the lapd ha been order to investig the', 'rodney g. king'}\\n\",\n      \"GT: num=6 - {'excess forc', 'racism', 'minor suspect', 'polic brutal', 'investig', 'white polic offic'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'mountain peak', 'astrobiolog', 'articl contain video clip', 'the sun,', 'california institut of technolog', 'and the', 'new moon', 'the', 'solar eclips in the unit state', 'astronom object', 'athropogen element', 'occurr', 'chabot colleg', 'solar eclips', 'on the mountain,', 'asteroid', 'the sun', 'aten asteroid', 'the moon', 'solar and lunar eclips by countri', 'alan dyer'}\\n\",\n      \"GT: num=7 - {'solar scientist', 'shadow', 'mexico citi', 'sun', 'total eclips', 'hawaii', 'solar eclips'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'affirm action', 'controversi', 'virginia thoma', 'a group that', 'polit career', '1946 birth', 'of the nation council of churches, which is', 'american lawyer', 'suprem court nomin', 'american peopl of english descent', 'american legal scholar', 'that', 'u.s. chamber of congress', 'lifespr', 'howard univers', 'american women lawyer', 'that her', 'critic', 'that she', 'nation council of church', 'dean kelley'}\\n\",\n      \"GT: num=9 - {'critic', 'clarenc thoma', 'affirm action', 'virginia thoma', 'senat confirm hear', 'conserv viewpoint', 'suprem court', 'support spous', 'nomin'}\\n\",\n      \"p=0.14285714285714285, r=0.3333333333333333, f1=0.2\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'assassin', '1947 birth', 'the govern', 'sri lanka', 'india', 'indian politician kill in action', 'elect', 't.n. seshan', 'indian minist of financ', 'the', 'jawaharl nehru', 'the election. ;', 'indian polit leader', 'abdul rashid gandhi', 'indian male writer', 'indira gandhi', 'death', 'the blast, the govern announc that', 'boe 737'}\\n\",\n      \"GT: num=10 - {'assassin scene', 'indian polit', 'nation parliamentari elect', 'polit dynasti', 'india', 'gandhi', 'new delhi', 'congress parti', 'time bomb', 'blast'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'daron council', 'to the podium. ;', 'bradley johnson', 'american male weightlift', 'american marathon', 'drug test', 'return to the sport', 'alachua counti', '1988 summer olymp', 'the start of the race, johnson wa the first athlet to', 'alachua county, fla.', 'performance-enhanc steroid', 'sportspeopl from jacksonville, florida', 'american sprinter', 'career', 'american peopl of english descent', 'alchua counti'}\\n\",\n      \"GT: num=7 - {'slow start', 'johnson', 'record crowd', 'drug test', 'first race', 'daron council', 'first indoor loss'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'san francisco amateur astronom', 'a', 'is a', 'san mateo astronom societi', 'astrobiolog', 'astrolog', 'branch of biolog', 'california', 'astronom sub-disciplin', 'outlin of astrobiolog', 'tuxpan', 'san jose', 'solar eclips', 'to make reservations. ; the', 'special event', 'campbel', 'baja', 'astrophys', 'the eclips is'}\\n\",\n      \"GT: num=4 - {'special mylar viewer', 'hawaii', 'solar eclips', 'mexico citi'}\\n\",\n      \"p=0.05263157894736842, r=0.25, f1=0.08695652173913043\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'suprem court of the unit state', 'unit state constitut law', 'sup court of the netherland', 'anti-defam leagu', 'thoma', 'a strong', 'william brennan', 'to affirm action. ;', 'are like to be more conserv than those of justic clarenc thomas, who ha', 'roman cathol', 'right to privaci', 'strong opposit to', 'sup court of canada', 'supremaci in the unit kingdom', 'david souter', \\\"unit kingdom' highest court\\\", 'histori', 'unit nation gener assembl observ', 'justic of the suprem court', 'right'}\\n\",\n      \"GT: num=5 - {'clarenc thoma', 'racial prefer', 'affirm action', 'suprem court', 'nomin'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'bank in the unit state', 'banker are', 'financi servic compani establish in 1837', 'chicago', 'new york', 'bankruptci', 'list of bank', 'bank reserv', 'keef bruyett & wood inc.', 'san francisco', 'merril lynch & co.', 'bank of boston', 'histori', 'recent histori', 'the move is like to increas the cost of loan to develop countries. the', 'bank base in boston', 'bond of the unit kingdom', 'merril lynch', 'to', 'to the'}\\n\",\n      \"GT: num=4 - {'region bank', 'boston corp', 'third world loan', 'develop countri'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'sport medicin', 'a coach', 'champion athlet', 'a', \\\"st. martin'\\\", 'furazabol', 'and hi athlet to use them, he is', 'stanozolol', 'st.martin', 'sport scienc', 'olymp sport', 'chadwick, ontario', 'dr. frankenstein', 'histori', 'dianabol', 'champ', '1988', 'a trainer', 'the franci system', 'championship'}\\n\",\n      \"GT: num=10 - {'charli franci', 'steroid combin', 'canadian nation sprint coach', 'gold medal', 'toronto', 'world record', 'illeg steroid use', 'ben johnson', 'steroid', 'feder inquiri'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'i wa rais to be independ and to be', 'american peopl of irish descent', 'clarenc thoma', 'pinpoint', 'to be', 'savannah', 'peopl from savannah, georgia', 'earli childhood', 'welfar', 'washington post', 'american lawyer', 'and to', 'unit state suprem court justic', 'unit nation suprem court nomine', 'heritag foundat', 'earli year', 'earli life', 'booker t. washington', 'to'}\\n\",\n      \"GT: num=3 - {'grandpar', 'clarenc thoma', 'black power'}\\n\",\n      \"p=0.05263157894736842, r=0.3333333333333333, f1=0.09090909090909091\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'water', 'sustain', 'american', 'poverti', 'state of the union', 'domest polici council', 'welfar', 'sustain capit', 'the', 'unit state', 'primari', 'state', 'social respons capit', 'state ofth union', \\\"have been reluct to go along with the president' welfar reform proposals.\\\", 'current issu', 'the public', 'social', 'primari respons', 'welfar reform', 'in the unit state', 'the american public', 'the presid'}\\n\",\n      \"GT: num=6 - {'welfar program', 'feder govern', 'welfar depend', 'presid reagan', 'american', 'welfar reform'}\\n\",\n      \"p=0.08695652173913043, r=0.3333333333333333, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'shipwreck in the gulf of mexico', 'pensaola', 'world war ii', 'the crash occur on the flight deck of the aircraft carrier, the navi said. the aircraft carrier is the onli aircraft carrier', 'a aircraft carrier', 'pentagon', 't-2 buckey', 'pensacola', 'aircraft carrier', 'unit state', 'ship type', 'histori', 'shipbuild in the unit state', 'aerospac accid', 'airlin', 'articl contain video clip', 'meridian'}\\n\",\n      \"GT: num=6 - {'major damag', 'crash', 'flight deck', 'victim', 'aircraft carrier lexington', 'jet trainer'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'the highest concentr of tb wa in those age 25-', 'new jersey', 'health and safeti', 'health depart', 'counti seat in new jersey', 'alcohol', 'hiv', 'unit state depart of health and human resourc', 'health', 'hiv infect', 'alcohol abus', '18th-centuri establish in new york citi (state)', 'tuberculosi', 'the greatest concentr of tuberculosi wa in the 25-to-44 age group, account for 57 %', 'tuberculosi prevent and control', 'treatment of tuberculosi', 'drug', 'alcohol abus and neglect', 'new york citi', '1810 establish in the unit state', 'new yorker'}\\n\",\n      \"GT: num=5 - {'control', 'activ tuberculosi diseas', 'health depart', 'tuberculosi prevent', 'tuberculosi test'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'of the year. the', 'event', 'drought', 'day of the year', 'august (period)', 'august and septemb', 'special trade repres', 'unit state', 'august', 'dow jone industri averag', 'the year.', 'marin fitzwat (white house)', 'marlin fitzwat', 'august 1', 'richard lyng', 'day'}\\n\",\n      \"GT: num=9 - {'reagan administr', 'export enhanc program', 'worsen drought', 'drought damag', 'farm export', 'food price', 'farm economi', 'export enhanc fund', 'disast relief'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'the debt problem.', 'american rhode scholar', 'barber conabl', 'tom foley', '1947 birth', 'and the world bank ha been slow to take action on', 'world bank', 'american non-fict writer', 'american nonprofit chief execut', 'john lafalc', 'polit career', 'bill bradley', 'third world debt', 'barb conabl (politician)', 'american peopl of english descent', 'jame baker', 'debt problem'}\\n\",\n      \"GT: num=7 - {'barber conabl', 'critic', 'third world debtor', 'third world debt', 'debt burden', 'world bank presid', 'debt crisi'}\\n\",\n      \"p=0.11764705882352941, r=0.2857142857142857, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'is an', 'is a', 'tuber tuberculosi', 'world health organ', 'world public health organ essenti medicin', 'hiv', 'world health disast', 'global', 'hiroshi nakajima', 'tuberculosi', 'aid epidem', 'worldculosi', 'the diseas is', 'societi and cultur', 'is', 'acquir immun defici syndrom', 'rttem', 'wikipedia medicin articl readi to translat', 'in the develop world, where they are', 'treatment'}\\n\",\n      \"GT: num=7 - {'parallel epidem', 'aid epidem', 'deadliest infecti diseas', 'global tuberculosi', 'tuberculosi case', 'human immunodefici viru', 'world health organ'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'bay area', 'missoula, missouri', 'the midwest,', 'mississippi river', 'geographi', '1836 establish in missouri', 'the big one is', 'arnold', 'the', 'missourinet', 'geolog', 'missourian-speak countri and territori', 'in the', 'cultur tourism in missouri and arkansa', 'missouri', 'paragould', 'earthquak', 'region,', 'citi in the central unit state', 'the region,', 'new madrid'}\\n\",\n      \"GT: num=7 - {'predict method', 'earthquak surviv class', 'feder emerg help', 'earthquak expert', 'new madrid fault', 'earthquak prepared', 'quak insur'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'american peopl of irish descent', 'clarenc thoma', 'the stori of a man who ha been', 'who is', 'peopl from savannah, georgia', 'polit and philosoph transform', 'a man', 'malcolm x', 'who', 'senat judiciari committe', 'american lawyer', 'robert frost', 'earli life and educ', 'unit state suprem court justic', 'earli polit and philosoph influenc', 'unit nation suprem court nomine', 'nina simon', \\\"robert frost' poetic recollect\\\", 'william barclay allen', 'who ha'}\\n\",\n      \"GT: num=7 - {'clarenc thoma', 'senat judiciari committe', 'thoma sowel', 'polit chang', 'affirm action program', 'suprem court nomin', 'racial minor'}\\n\",\n      \"p=0.1, r=0.2857142857142857, f1=0.14814814814814817\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a', 'you can look at the eclipse. ; q can i look at', 'astrobiolog', 'astrolog', 'cupertino', 'articl contain video clip', 'california', 'mylar', 'astrolog sub-disciplin', 'san jose', 'orion telescop center', 'solar eclips', 'special event', 'baja', 'hawaii', 'astronom event', 'aborigin stone', 'a. ; a', 'astrophys'}\\n\",\n      \"GT: num=7 - {'shadow', 'sun', 'eyeshad', 'eye damag', 'eclips', 'san jose', 'sunglass'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'ethnic group', 'counti seat in california', 'health fair', 'citi in lo angel', 'american indian', 'list of peopl from lo angel counti', 'health', 'diabet', 'latino', 'cultur tourism in lo angel', 'olvera street plaza', 'demograph', 'lo angel', 'upjohn co.', 'american diabet assn.', '1833 establish in california (u.s. state)', 'and the american diabet assn. held a', 'popul place establish in 1833', 'silent killer', 'a diabet'}\\n\",\n      \"GT: num=7 - {'american indian', 'latino diabet', 'major diabet risk factor', 'blood pressur', 'uncontrol diabet', 'diabet complic', 'educ effort'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'boston marathon', 'the race in', 'race cours', 'cours', 'the', 'marathon', 'in', 'the runner-up in', 'olymp sport', 'abe mekonnen', 'boston', 'john treaci', '1872 establish in massachusett', 'gelindo bordin', 'list of boston marathon entrant', 'the boston marathon in 2017 ; and', 'john kelley', 'racecours', 'ed eyeston'}\\n\",\n      \"GT: num=7 - {'60th boston marathon', 'boston marathon', 'respect entrant', 'checkpoint record', 'race run', 'alarm rate', 'stori histori'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'dwighter d.hower', 'colorado', 'polit cultur', 'california', 'polit parti', 'and the', 'term limit', 'the', 'and', 'and are not paid for their services,', 'dwight d. eisenhow', '17th-centuri establish', 'american cultur', 'polit histori', 'histori', 'american polit histori', 'american polit', 'franklin d. roosevelt', 'the state legislatur', 'the legislatur', 'modern era', '22nd amend'}\\n\",\n      \"GT: num=7 - {'state lawmak', 'american polit', 'legisl reform', 'polit institut', 'term limit', 'state legislatur', 'oppos polit coalit'}\\n\",\n      \"p=0.09090909090909091, r=0.2857142857142857, f1=0.13793103448275862\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=25 - {'represent democraci', 'congress', 'constut', 'state legisl', 'constitut', 'thesam way. the', 'who', 'constitution', 'who are', 'term limit', 'the', 'elector allianc', 'repres', 'constit', 'sourc of law', 'the term limit', 'the term of', 'key issu', 'repres govern', 'represent govern', 'constitu state', 'that the', 'the same way.', 'the legisl who', 'apostol constitut'}\\n\",\n      \"GT: num=7 - {'repres democraci', 'interest group', 'term limit', 'elector allianc', 'state legisl', 'constitut restrict', 'constitut chang'}\\n\",\n      \"p=0.12, r=0.42857142857142855, f1=0.1875\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'second', 'constitut law', 'of the', 'second 10 amend', 'u-rrb', 'the', 'unit state', 'the second amend is a', 'right of the', 'amend right', 'u.s. vs. cruikshank', 'histori', 'a constitut', 'u.s. suprem court', 'william neali studio citi', 'nation', 'apostol constitut', 'feder', 'second amend'}\\n\",\n      \"GT: num=6 - {'gun control law', 'feder govern', 'arm', 'constitut law', 'second amend', 'secur'}\\n\",\n      \"p=0.10526315789473684, r=0.3333333333333333, f1=0.16\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'myer anderson', 'clarenc thoma', 'american peopl of irish descent', 'leola william', 'peopl from savannah, georgia', 'father', 'father and', 'the time he wa a young boy, and hi', 'hi', 'baby-boom', 'georgia', 'unit state feder judg', 'unit state suprem court', 'cathol school uniform', 'unit nation suprem court justic', 'earli year', 'earli life', 'american prosecutor', 'hi father wa', 'thurgood marshal'}\\n\",\n      \"GT: num=6 - {'clarenc thoma', 'senat confirm', 'thoma sowel', 'affirm action program', 'black conserv', 'suprem court nomine'}\\n\",\n      \"p=0.05, r=0.16666666666666666, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'be san francisco', 'chile', 'geographi', 'the quak', '1824 establish in south america', 'nazca plate', 'the', 'the ship', 'state and territori establish in 1818', 'geolog', '1818 establish in chile', 'ring of fire', 'the earthquak', 'earthquak', 'index of chile-rel articl', 'member state of the unit nation', 'san francisco earthquak in 1891, the captain of', 'univers of chile', 'santiago'}\\n\",\n      \"GT: num=10 - {'earth movement', 'chilean earthquak', 'chilean build', 'seismic countri', 'chilean seismolog', 'chile', 'consider damag', 'major earthquak', 'san francisco earthquak', 'massiv plate'}\\n\",\n      \"p=0.05263157894736842, r=0.1, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'mahanama beach', 'astrobiolog', 'astrolog', 'branch of biolog', 'mauna kea', 'mauna loa', 'and the', 'mountain view', 'the', 'the eclipse. ; the eclips wa visibl', 'astrolog sub-disciplin', 'solar eclips', 'astronom event in california', 'the sun.', 'special event', 'baja', 'mauna kea and mauna lani', 'the moon', 'astrophys', 'pacif', 'sun.'}\\n\",\n      \"GT: num=6 - {'mexico', 'sun', 'eclipsefest', 'eclips', 'moon obscur', 'hawaii'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'clarenc thoma', \\\"i 'd tell her to pray, '' she replied. ; the crowd roar with\\\", 'unit airlin', 'the crowd', 'atlanta', 'supremaci in the unit state', 'unit state suprem court nomine', 'background', 'the', 'unit nation clarenc thoma suprem court nomin (2005)', 'unit state suprem court justic', 'suprem court nomin', 'sweet field of eden baptist church', 'anita hill', 'abbey famble, deacon', 'background of the nomin', '23rd psalm', 'abraham fambl', 'pinpoint georgia'}\\n\",\n      \"GT: num=6 - {'birthplac', 'clarenc thoma', 'hometown crowd', 'georgia', 'senat vote', 'william'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'nov. 6 ballot', 'index of california-rel articl', 'propos on the nov. 6, 1996 ballot', 'richard l. mountjoy', 'california', 'robert presley', 'would be more', 'term limit', 'state and territori establish in 1850', 'state', 'to the public.', 'monrovia', 'riversid counti sheriff', 'govern', 'more', 'more respons to the', 'proposit 140', 'california and the unit nation', 'state of the unit state', 'and the california state senat -lrb-', 'legisl branch', 'i think that the legislatur would'}\\n\",\n      \"GT: num=9 - {'california', 'neg impact', 'special interest', 'polit process', 'term limit', 'polit chang', 'statewid officehold', 'state legisl', 'profession occup'}\\n\",\n      \"p=0.09090909090909091, r=0.2222222222222222, f1=0.1290322580645161\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'polic', 'controversi', 'use', 'martin luther king jr.', 'unit kingdom', 'wa arrested, he said that the', 'use of forc', 'west hartford', 'nunchaku', 'gandhi', 'unit state', 'lo angel polic beat of motorist rodney king', 'the polic', 'polic use', 'human right', 'human reproduct', 'rttem', 'abort', 'lo angel polic depart', 'wikipedia medicin articl readi to translat'}\\n\",\n      \"GT: num=6 - {'demonstr', 'seriou injuri', 'polic abus', 'polic brutal', 'excess forc', 'civil right demonstr'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'firestorm', 'the park.', 'the forest', 'donald despain', 'the', 'mountain and hill rang of yellowston counti', 'reconstruct', 'firewe', 'wildflow', 'citi in yellowston county, wyom', 'the number of tree that will be', 'histori', 'grant villag', 'yellowston nation park and preserv', 'wild geranium', 'yellowston nation park', 'popul place establish in 1872', 'yelloweston park', 'the great fire'}\\n\",\n      \"GT: num=6 - {'fire damag', 'scorch hillsid', 'firestorm', 'tini seed', 'worst fire', 'rebirth'}\\n\",\n      \"p=0.05263157894736842, r=0.16666666666666666, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'european union', 'caus of world war i', 'causal in intern relat', 'ant markov', 'cease-fir', 'european commun', 'caus of world war', 'milan kucan', 'outcom', 'the', 'the slovenian', 'yugoslav presid', 'slovenia', 'government. ;', 'caus by the bosnian war', 'balkan war', 'stipe mesic', '1910 in europ', \\\"the army' militari and polic forces. ; the slovenian govern\\\"}\\n\",\n      \"GT: num=12 - {'feder armi oper', 'feder troop', 'slovenian defens forc', 'breakaway republ', 'slovenia', 'croatia', 'yugoslav republ', 'serbia', 'slovenian independ', 'yugoslav armi', 'independ declar', 'slovenian fear'}\\n\",\n      \"p=0.05263157894736842, r=0.08333333333333333, f1=0.06451612903225808\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'polit corrupt', 'milton friedman', 'public and', 'constitut', 'edward h. crane', 'polit ideolog', 'constitution', \\\"citizens' republ\\\", 'term limit', 'the', 'public', 'unit state', 'polit institut', 'cato institut', 'and the public', 'the public', 'histori', 'polit terminolog', 'ralph nader', 'croni capit', 'term limit in the unit state'}\\n\",\n      \"GT: num=7 - {'corrupt influenc', 'true citizen congress', 'term limit', 'impervi congress', 'congression term limit', 'congression deleg', 'legisl influenc'}\\n\",\n      \"p=0.047619047619047616, r=0.14285714285714285, f1=0.07142857142857142\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'a key part of', 'polit corrupt', 'a major', 'shirley chisholm', 'a', 'is a', 'polit reform', 'of the', 'harri truman', 'madelein kunin', 'constitution', 'democrat parti', 'term limit', 'unit state', 'term limit propos', 'richardlamm', 'and the democrat party. the democrat parti', 'histori', 'polit terminolog', 'john f. kennedi', 'of', 'term-limit propos', 'jerri brown'}\\n\",\n      \"GT: num=8 - {'american polit', 'polit system', 'liber democrat', 'california initi', 'term limit', 'term limit measur', 'democrat officehold', 'career incumb'}\\n\",\n      \"p=0.043478260869565216, r=0.125, f1=0.06451612903225806\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'a fight with', 'a', 'a battl', 'reuter news servic', 'background', 'janez jansa and the militia', 'battl countri', 'bogataj vejko', 'fight in slovenia', 'battl involv croatia', 'jansa', 'slovenia', 'jajce, who wa in the middl of', 'u.s. civil war', 'balkan war', 'battl of jajc', '1962 in europ', 'ljubljana', 'bibliographi of the bosnian war'}\\n\",\n      \"GT: num=8 - {'croatian independ', 'civil war', 'yugoslavia', 'breakaway republ', 'feder soldier', 'strong central control', 'independ declar', 'independ grab'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'key featur', 'a', 'sourc (law)', 'constitut', 'the court found that the', 'larri smith', 'california suprem court', 'sources(law) that are not base on constitut law', 'dillon v. fiorina', 'term-limit initi', 'term limit', 'unit state', 'sourc', 'for a', 'bullock v. carter', 'sourc of law', 'first amend', 'a candid', 'right to vote for', 'constitut document', 'clement v. fash'}\\n\",\n      \"GT: num=7 - {'congression regul', 'term limit', 'constitut', 'congression officehold', 'suprem court', 'hous incumb', 'fair elect system'}\\n\",\n      \"p=0.09523809523809523, r=0.2857142857142857, f1=0.14285714285714285\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'the gate commiss', 'of polic', 'polic agenc establish in 1933', 'citi in lo angel', 'new york polic depart', 'polic chief around the countri', 'polic depart establish in 1932', 'list of lapd district', 'john a. arguel', 'knapp commiss', 'of the offic who were found to have', 'hubert william', 'polic academi', 'lo angel polic depart ( lapd)', 'a cultur of', 'lo angel', 'histori', 'of', 'lapd', 'recent histori'}\\n\",\n      \"GT: num=7 - {'racial bia', 'brutal complaint', 'lapd offic', 'rodney king incid', 'polic brutal', 'lo angel', 'excess forc'}\\n\",\n      \"p=0.05, r=0.14285714285714285, f1=0.07407407407407408\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=24 - {'of a', 'the issu of gun ownership and use. the second amend wa a', 'a', 'select servic', 'polit ideolog', 'nation firearm associ', 'nation guardsman', 'jame madison', '19th centuri', 'a handgun.', 'nation guard', 'a gun', 'unit state feder', 'nation rifl associ', 'a firearm.', 'elbridg gerri', 'elbrida gerri', 'histori', 'second congress', 'of the public', 'of', 'reaction', 'nation', 'second amend'}\\n\",\n      \"GT: num=6 - {'nation guard', 'gun ownership', 'american citizen', 'gun control', 'undeni right', 'second amend'}\\n\",\n      \"p=0.08333333333333333, r=0.3333333333333333, f1=0.13333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'boston marathon', 'and he is the oldest person to', 'mafia man', 'the race.', 'list of boston marathon medal', 'bill rodger', '1891 establish in massachusett (u.s. state)', 'john adelbert kelley', 'lesli pawson', 'to finish the', 'marathon cours in massachusett', 'to run the', 'organ establish in 1901', 'histori', 'boston', '1901-present', 'organ of the boston marathon', 'hopkinton, mass.', 'john kelley', 'to', 'peter foley'}\\n\",\n      \"GT: num=5 - {'runner', '60th boston marathon', 'oldest competitor', 'john kelley', 'train'}\\n\",\n      \"p=0.047619047619047616, r=0.2, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'amend to the second amend', 'roger pilon', 'i will give up my right to speak when they have cut out my tongu ; i will give', 'the', 'ohio militia', 'unit state', 'when they have', 'nation guard', 'unit state constitut', 'cato institut', 'have', 'histori', 'nation firearm act of 1934', 'ohio', 'nation', 'thoma jefferson', 'have the', 'feder', 'second amend'}\\n\",\n      \"GT: num=5 - {'nation guard', 'right', 'bear arm', 'gun control', 'second amend'}\\n\",\n      \"p=0.10526315789473684, r=0.4, f1=0.16666666666666666\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=22 - {'to keep and bear arms, but', 'to be', 'key featur', 'constitut', 'amend claus', 'to the', 'first congress of the unit state', 'firstcongress', 'jame madison', \\\"to render militari servic in person ''. the second amend wa\\\", 'sourc', 'sourc for law', 'bill of right', 'sourc of law', 'jame madison of virginia', 'to a', 'unit state constitut', 'first amend', 'to an', 'apostol constitut', 'to', 'second amend'}\\n\",\n      \"GT: num=7 - {'privat right', 'congress', 'arm', 'constitut amend', 'gun owner', 'gun control', 'second amend'}\\n\",\n      \"p=0.045454545454545456, r=0.14285714285714285, f1=0.06896551724137931\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=16 - {'polit corrupt', 'constitut term limit', 'robert livingston', 'daniel webster', 'term limit', 'and the peopl are the best judg who ought to repres them.', 'polit theori', 'unit state', 'henri clay', 'everett dirksen', 'feder', 'histori', 'term-limit propos', 'constitut', 't.k. wetherel', 'claud pepper'}\\n\",\n      \"GT: num=4 - {'first congress', 'constitut convent', 'term limit', 'natur right'}\\n\",\n      \"p=0.0625, r=0.25, f1=0.1\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=23 - {'constitut term limit', 'rcb--', 'the govern', 'the congress', 'polit law', 'constitut', 'federalist', 'the legislatur is not in session, the', 'presid', 'term limit', 'the', 'unit state', 'the hous', 'alexand hamilton', 'lrc-', 'federalist 72', 'citizen congress', 'the senat', 'histori', 'polit terminolog', 'lrb-', 'term limit in the unit state', 'rrb-'}\\n\",\n      \"GT: num=9 - {'citizen congress', 'polit process', 'legisl term limit', 'constitut amend', 'elector control process', 'civil right amend', 'execut term limit', 'officehold', 'polit repres'}\\n\",\n      \"p=0.043478260869565216, r=0.1111111111111111, f1=0.0625\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=17 - {'tuberculosi treatment in colorado spring', 'airlin accid', 'the faa report that the aircraft wa', 'unit boe 747', 'recent histori', 'unit airlin flight 11', 'colorado rocki', 'unit dc-10', 'citi in el paso county, colorado<infill>1871 establish in colorado territori', 'counti seat in colorado', 'feder aviat administr', 'colorado springs, colorado', 'a major area of investigation.', 'histori', 'sioux citi', 'persian gulf war', 'nation weather servic'}\\n\",\n      \"GT: num=5 - {'third crash', 'colorado spring', 'travel industri', 'unit flight', 'first high wind warn'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'jess helm', 'clarenc thoma', 'american peopl of irish descent', 'polit career', 'race', 'and he', 'lrb', 'and the', 'and', 'unit state suprem court justic', 'suprem court nomin', 'afdc', 'jessehel', 'peopl from east harlem', 'cathol school', 'peopl of african descent', 'the right way. he did it becaus he wa love', 'american prosecutor', 'rrb'}\\n\",\n      \"GT: num=7 - {'triumph', 'clarenc thoma', 'affirm action', 'conserv jurist', 'judg thoma', 'conserv black', 'thoma hear'}\\n\",\n      \"p=0.05263157894736842, r=0.14285714285714285, f1=0.07692307692307693\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=19 - {'borrow', 'that they', 'free- market', 'financi servic industri', 'bank', 'and', 'green revolut', 'microloan', 'bond-bas lend', 'that', 'monterrey', 'bank organ establish in 1947', 'financi market', 'free-market econom', 'accion intern', 'and the repay rate wa', 'bond', 'trickl up', 'and that'}\\n\",\n      \"GT: num=5 - {'tini third world busi', 'repay perform', 'poverti lend', 'microloan', 'favor credit rate'}\\n\",\n      \"p=0.05263157894736842, r=0.2, f1=0.08333333333333333\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'lo angel superior court', 'controversi', 'a', 'to file for', '1941 birth', 'g.p. group inc.', 'list of american woman model', 'the enquir', 'settlement of lawsuit', '1990', 'american woman', 'star tabloid', 'women in the art', 'nation enquir', \\\"st. john' hospit\\\", '1940 birth', 'initi public offer', 'lantana', 'the enquir also announc that it is plan to', 'american women in journal', 'a lawsuit against the'}\\n\",\n      \"GT: num=6 - {'miss taylor', 'actress', 'enquir', 'lawsuit', 'medic condit', 'pneumonia'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'el salvadoran', \\\"the polic chief' statement wa a\\\", 'rodney king beat', 'polit career', '1946 birth', 'american technolog chief execut', 'polic chief', 'that he', 'public servic', 'rambo', 'list of wealthiest histor figur', 'commun polic', 'swat', 'american peopl of english descent', 'american rhode scholar', 'that', 'bill gate', 'a statement that', 'that the', 'polic corp program'}\\n\",\n      \"GT: num=5 - {'lapd brutal', 'rodney king incid', 'polic brutal', 'lo angel', 'excess forc'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'robert bork', 'that i', 'american peopl of irish descent', 'court of appeal for the district of columbia circuit', 'clarenc thoma', 'of the kind of judici philosophi that he ha', 'unit kingdom', 'appel legal career', 'american judg of the unit state', 'that he', 'equal employ opportun commiss', 'american lawyer', 'ronald reagan', 'equal', 'american legal scholar', 'that', 'legal career', 'david souter', 'unit nation gener assembl observ', 'unit state suprem court nomin'}\\n\",\n      \"GT: num=6 - {'judici conservat', 'clarenc thoma', 'columbia circuit', 'minor', 'suprem court nomine', 'judg'}\\n\",\n      \"p=0.05, r=0.16666666666666666, f1=0.07692307692307691\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=18 - {\\\"to do so. ''\\\", 'croat', 'and we are not go to be abl to', 'croato-macedonian war', 'countri in europ', 'european commun', 'bosnia and herzegovina', 'balkan', 'southeastern european countri', 'outlin of bosnia and herzeg', 'slovenia', 'southeast european countri and territori', 'croatia', 'modern histori', 'croatian', 'histori', 'mikhail gorbachev', 'state and territori establish in 1992'}\\n\",\n      \"GT: num=7 - {'sloven risk', 'european commun', 'nation ident', 'yugoslavia', 'slovenia bond', 'independ declar', 'industri output'}\\n\",\n      \"p=0.05555555555555555, r=0.14285714285714285, f1=0.08\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=21 - {'elector histori of the unit state', 'world war ii', 'alan baron', 'of the unit', '1990 elect', 'elect', 'hart-bailey report', 'focu group', 'the voter', 'the', 'histori of the republican parti', 'the elector', 'elector signific document', '1990', 'the public', 'elector', 'electron histori of polit', 'histori', 'unit state in world war ii (1947–1953)', '[[hart- bailey report]]', 'elect histori of unit state govern'}\\n\",\n      \"GT: num=7 - {'washington politician', 'congression elect', 'limit committe assign', 'elector system', 'term limit', 'averag victori margin', 'hous incumb'}\\n\",\n      \"p=0.0, r=0.0, f1=0.0\\n\",\n      \"--------------------\\n\",\n      \"PRED: num=20 - {'american civil war', 'war of independ', 'to keep and', 'john sanford', \\\"taney' rule\\\", '19th-centuri conflict', 'conflict in 1865', 'the right to keep and bear arm', 'keep and', 'missouri compromis', 'outcom', 'reconstruct', 'intern war of the unit state', 'john taney', 'dred scott', 'the civil war, when the', 'to', 'racial tension', 'second amend', 'war involv the unit kingdom'}\\n\",\n      \"GT: num=6 - {'black peopl', 'racial paranoia', 'infam rule', 'gun control', 'second amend', 'dred scott'}\\n\",\n      \"p=0.1, r=0.3333333333333333, f1=0.15384615384615383\\n\",\n      \"--------------------\\n\",\n      \"***** predict metrics *****\\n\",\n      \"  predict_fscore@M           =      7.751\\n\",\n      \"  predict_loss               =     4.6972\\n\",\n      \"  predict_num_gold           =     7.1266\\n\",\n      \"  predict_num_match          =     1.0617\\n\",\n      \"  predict_num_pred           =     20.289\\n\",\n      \"  predict_precision@M        =     5.2456\\n\",\n      \"  predict_recall@M           =    16.3283\\n\",\n      \"  predict_runtime            = 0:00:29.82\\n\",\n      \"  predict_samples            =        308\\n\",\n      \"  predict_samples_per_second =     10.325\\n\",\n      \"  predict_steps_per_second   =      0.335\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Initialize our Trainer\\n\",\n    \"trainer = Seq2SeqTrainer(\\n\",\n    \"    model=model,\\n\",\n    \"    args=training_args,\\n\",\n    \"    train_dataset=train_dataset if training_args.do_train else None,\\n\",\n    \"    eval_dataset=eval_dataset if training_args.do_eval else None,\\n\",\n    \"    tokenizer=tokenizer,\\n\",\n    \"    data_collator=data_collator,\\n\",\n    \"    compute_metrics=compute_metrics if training_args.predict_with_generate else None,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Evaluation\\n\",\n    \"results = {}\\n\",\n    \"predict_results = trainer.predict(\\n\",\n    \"    predict_dataset, metric_key_prefix=\\\"predict\\\", max_length=max_length, num_beams=num_beams,\\n\",\n    \")\\n\",\n    \"metrics = predict_results.metrics\\n\",\n    \"max_predict_samples = len(predict_dataset)\\n\",\n    \"metrics[\\\"predict_samples\\\"] = min(max_predict_samples, len(predict_dataset))\\n\",\n    \"\\n\",\n    \"trainer.log_metrics(\\\"predict\\\", metrics)\\n\",\n    \"trainer.save_metrics(\\\"predict\\\", metrics)\\n\",\n    \"\\n\",\n    \"if trainer.is_world_process_zero():\\n\",\n    \"    if training_args.predict_with_generate:\\n\",\n    \"        predictions = tokenizer.batch_decode(predict_results.predictions)\\n\",\n    \"        predictions = [pred.lower().replace('</s>', '').replace('<pad>', '').strip().split(tokenizer.sep_token) for pred in predictions]\\n\",\n    \"        output_prediction_file = os.path.join(training_args.output_dir, \\\"generated_predictions.txt\\\")\\n\",\n    \"        with open(output_prediction_file, \\\"w\\\") as writer:\\n\",\n    \"            writer.write(\\\"\\\\n\\\".join([json.dumps(pred) for pred in predictions]))\\n\",\n    \"\\n\",\n    \"kwargs = {\\\"finetuned_from\\\": model_name_or_path, \\\"tasks\\\": \\\"keyphrasification\\\"}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'4.17.0'\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import transformers\\n\",\n    \"transformers.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "notebook/wikibart_inference_buggy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2022-03-06 00:00:30.734654: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import json\\n\",\n    \"import logging\\n\",\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"from dataclasses import dataclass, field\\n\",\n    \"from typing import Optional\\n\",\n    \"\\n\",\n    \"import datasets\\n\",\n    \"import nltk  # Here to have a nice missing dependency error message early on\\n\",\n    \"from nltk.stem import *\\n\",\n    \"import numpy as np\\n\",\n    \"from datasets import load_dataset, load_metric\\n\",\n    \"\\n\",\n    \"from filelock import FileLock\\n\",\n    \"from transformers import (\\n\",\n    \"    AutoConfig,\\n\",\n    \"    AutoModelForSeq2SeqLM,\\n\",\n    \"    AutoTokenizer,\\n\",\n    \"    DataCollatorForSeq2Seq,\\n\",\n    \"    HfArgumentParser,\\n\",\n    \"    Seq2SeqTrainer,\\n\",\n    \"    Seq2SeqTrainingArguments,\\n\",\n    \"    set_seed, TrainingArguments, TrainerState, TrainerControl,\\n\",\n    \"    TrainerCallback\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"logger = logging.getLogger(__name__)\\n\",\n    \"\\n\",\n    \"try:\\n\",\n    \"    nltk.data.find(\\\"tokenizers/punkt\\\")\\n\",\n    \"except (LookupError, OSError):\\n\",\n    \"    if is_offline_mode():\\n\",\n    \"        raise LookupError(\\n\",\n    \"            \\\"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files\\\"\\n\",\n    \"        )\\n\",\n    \"    with FileLock(\\\".lock\\\") as lock:\\n\",\n    \"        nltk.download(\\\"punkt\\\", quiet=True)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"PyTorch: setting up devices\\n\",\n      \"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model_name_or_path = 'memray/bart_wikikp'\\n\",\n    \"cache_dir = './hf_cache'\\n\",\n    \"dataset_name='midas/duc2001'\\n\",\n    \"num_beams=1\\n\",\n    \"max_length=128\\n\",\n    \"max_target_length=128\\n\",\n    \"padding='max_length'\\n\",\n    \"prefix='<present>10<header>5<category>5<seealso>2<infill>0<s>'\\n\",\n    \"# Get the column names for input/target.\\n\",\n    \"text_column = 'document'\\n\",\n    \"keyphrase_column = 'extractive_keyphrases'\\n\",\n    \"\\n\",\n    \"training_args = Seq2SeqTrainingArguments(per_device_eval_batch_size=8, output_dir=cache_dir)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loading configuration file https://huggingface.co/memray/bart_wikikp/resolve/main/config.json from cache at ./hf_cache/565baaa81871d621f544378240cf6cf001c3b0ae16becf577d6e92b9ae423bb8.8c38c569db3fa2298a8f1d96cb54b8e8a9e18344189994a575808bc3e0e8400a\\n\",\n      \"Model config BartConfig {\\n\",\n      \"  \\\"_name_or_path\\\": \\\"memray/bart_wikikp\\\",\\n\",\n      \"  \\\"activation_dropout\\\": 0.1,\\n\",\n      \"  \\\"activation_function\\\": \\\"gelu\\\",\\n\",\n      \"  \\\"add_bias_logits\\\": false,\\n\",\n      \"  \\\"add_final_layer_norm\\\": false,\\n\",\n      \"  \\\"architectures\\\": [\\n\",\n      \"    \\\"BartModel\\\"\\n\",\n      \"  ],\\n\",\n      \"  \\\"attention_dropout\\\": 0.1,\\n\",\n      \"  \\\"bos_token_id\\\": 0,\\n\",\n      \"  \\\"classif_dropout\\\": 0.1,\\n\",\n      \"  \\\"classifier_dropout\\\": 0.0,\\n\",\n      \"  \\\"d_model\\\": 1024,\\n\",\n      \"  \\\"decoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"decoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"decoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"decoder_layers\\\": 12,\\n\",\n      \"  \\\"decoder_start_token_id\\\": 2,\\n\",\n      \"  \\\"dropout\\\": 0.1,\\n\",\n      \"  \\\"early_stopping\\\": true,\\n\",\n      \"  \\\"encoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"encoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"encoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"encoder_layers\\\": 12,\\n\",\n      \"  \\\"eos_token_id\\\": 2,\\n\",\n      \"  \\\"forced_eos_token_id\\\": 2,\\n\",\n      \"  \\\"gradient_checkpointing\\\": false,\\n\",\n      \"  \\\"id2label\\\": {\\n\",\n      \"    \\\"0\\\": \\\"LABEL_0\\\",\\n\",\n      \"    \\\"1\\\": \\\"LABEL_1\\\",\\n\",\n      \"    \\\"2\\\": \\\"LABEL_2\\\"\\n\",\n      \"  },\\n\",\n      \"  \\\"init_std\\\": 0.02,\\n\",\n      \"  \\\"is_encoder_decoder\\\": true,\\n\",\n      \"  \\\"label2id\\\": {\\n\",\n      \"    \\\"LABEL_0\\\": 0,\\n\",\n      \"    \\\"LABEL_1\\\": 1,\\n\",\n      \"    \\\"LABEL_2\\\": 2\\n\",\n      \"  },\\n\",\n      \"  \\\"max_position_embeddings\\\": 1024,\\n\",\n      \"  \\\"model_type\\\": \\\"bart\\\",\\n\",\n      \"  \\\"no_repeat_ngram_size\\\": 3,\\n\",\n      \"  \\\"normalize_before\\\": false,\\n\",\n      \"  \\\"num_beams\\\": 4,\\n\",\n      \"  \\\"num_hidden_layers\\\": 12,\\n\",\n      \"  \\\"pad_token_id\\\": 1,\\n\",\n      \"  \\\"scale_embedding\\\": false,\\n\",\n      \"  \\\"task_specific_params\\\": {\\n\",\n      \"    \\\"summarization\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 128,\\n\",\n      \"      \\\"min_length\\\": 12,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_cnn\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 2.0,\\n\",\n      \"      \\\"max_length\\\": 142,\\n\",\n      \"      \\\"min_length\\\": 56,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_xsum\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 62,\\n\",\n      \"      \\\"min_length\\\": 11,\\n\",\n      \"      \\\"num_beams\\\": 6\\n\",\n      \"    }\\n\",\n      \"  },\\n\",\n      \"  \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n      \"  \\\"transformers_version\\\": \\\"4.17.0\\\",\\n\",\n      \"  \\\"use_cache\\\": true,\\n\",\n      \"  \\\"vocab_size\\\": 50264\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"loading configuration file https://huggingface.co/memray/bart_wikikp/resolve/main/config.json from cache at ./hf_cache/565baaa81871d621f544378240cf6cf001c3b0ae16becf577d6e92b9ae423bb8.8c38c569db3fa2298a8f1d96cb54b8e8a9e18344189994a575808bc3e0e8400a\\n\",\n      \"Model config BartConfig {\\n\",\n      \"  \\\"_name_or_path\\\": \\\"memray/bart_wikikp\\\",\\n\",\n      \"  \\\"activation_dropout\\\": 0.1,\\n\",\n      \"  \\\"activation_function\\\": \\\"gelu\\\",\\n\",\n      \"  \\\"add_bias_logits\\\": false,\\n\",\n      \"  \\\"add_final_layer_norm\\\": false,\\n\",\n      \"  \\\"architectures\\\": [\\n\",\n      \"    \\\"BartModel\\\"\\n\",\n      \"  ],\\n\",\n      \"  \\\"attention_dropout\\\": 0.1,\\n\",\n      \"  \\\"bos_token_id\\\": 0,\\n\",\n      \"  \\\"classif_dropout\\\": 0.1,\\n\",\n      \"  \\\"classifier_dropout\\\": 0.0,\\n\",\n      \"  \\\"d_model\\\": 1024,\\n\",\n      \"  \\\"decoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"decoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"decoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"decoder_layers\\\": 12,\\n\",\n      \"  \\\"decoder_start_token_id\\\": 2,\\n\",\n      \"  \\\"dropout\\\": 0.1,\\n\",\n      \"  \\\"early_stopping\\\": true,\\n\",\n      \"  \\\"encoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"encoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"encoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"encoder_layers\\\": 12,\\n\",\n      \"  \\\"eos_token_id\\\": 2,\\n\",\n      \"  \\\"forced_eos_token_id\\\": 2,\\n\",\n      \"  \\\"gradient_checkpointing\\\": false,\\n\",\n      \"  \\\"id2label\\\": {\\n\",\n      \"    \\\"0\\\": \\\"LABEL_0\\\",\\n\",\n      \"    \\\"1\\\": \\\"LABEL_1\\\",\\n\",\n      \"    \\\"2\\\": \\\"LABEL_2\\\"\\n\",\n      \"  },\\n\",\n      \"  \\\"init_std\\\": 0.02,\\n\",\n      \"  \\\"is_encoder_decoder\\\": true,\\n\",\n      \"  \\\"label2id\\\": {\\n\",\n      \"    \\\"LABEL_0\\\": 0,\\n\",\n      \"    \\\"LABEL_1\\\": 1,\\n\",\n      \"    \\\"LABEL_2\\\": 2\\n\",\n      \"  },\\n\",\n      \"  \\\"max_position_embeddings\\\": 1024,\\n\",\n      \"  \\\"model_type\\\": \\\"bart\\\",\\n\",\n      \"  \\\"no_repeat_ngram_size\\\": 3,\\n\",\n      \"  \\\"normalize_before\\\": false,\\n\",\n      \"  \\\"num_beams\\\": 4,\\n\",\n      \"  \\\"num_hidden_layers\\\": 12,\\n\",\n      \"  \\\"pad_token_id\\\": 1,\\n\",\n      \"  \\\"scale_embedding\\\": false,\\n\",\n      \"  \\\"task_specific_params\\\": {\\n\",\n      \"    \\\"summarization\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 128,\\n\",\n      \"      \\\"min_length\\\": 12,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_cnn\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 2.0,\\n\",\n      \"      \\\"max_length\\\": 142,\\n\",\n      \"      \\\"min_length\\\": 56,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_xsum\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 62,\\n\",\n      \"      \\\"min_length\\\": 11,\\n\",\n      \"      \\\"num_beams\\\": 6\\n\",\n      \"    }\\n\",\n      \"  },\\n\",\n      \"  \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n      \"  \\\"transformers_version\\\": \\\"4.17.0\\\",\\n\",\n      \"  \\\"use_cache\\\": true,\\n\",\n      \"  \\\"vocab_size\\\": 50264\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/vocab.json from cache at ./hf_cache/0a70d76b53defaf659033ffb90b9de64d6b2326bb5a38cc38a5e7c3eace62607.d50f8ad29498687fb81b6dfb05e7e382c68948f6f0090a8f480525e1687f27f8\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/merges.txt from cache at ./hf_cache/612304d2164891eaeed48d181aa97f880c0acfcce9e898a0cc1fa2657f1fd3b5.e0217a5ee14d7072fe493a900022cbfdfcad655e80c2a4ad241faad766d87674\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/tokenizer.json from cache at None\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/added_tokens.json from cache at None\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/special_tokens_map.json from cache at ./hf_cache/4b7a3619321a39f6780e0c775802e3523e52c1efd3a46e6d1baac9e1e8e234e6.898eb95aac9bb57440c2f57caa963ae18b9b10ba4731cc81020283286b0391fc\\n\",\n      \"loading file https://huggingface.co/memray/bart_wikikp/resolve/main/tokenizer_config.json from cache at ./hf_cache/8f332c077d2e24a74f7e702e82e9c160c5348dd463955ad9adb11efdbe62123b.ed0e060365fd6ef5ba621290d459d33cd16e3f7c6fb657560a0160584e78c6cf\\n\",\n      \"loading configuration file https://huggingface.co/memray/bart_wikikp/resolve/main/config.json from cache at /ihome/hdaqing/rum20/.cache/huggingface/transformers/565baaa81871d621f544378240cf6cf001c3b0ae16becf577d6e92b9ae423bb8.8c38c569db3fa2298a8f1d96cb54b8e8a9e18344189994a575808bc3e0e8400a\\n\",\n      \"Model config BartConfig {\\n\",\n      \"  \\\"_name_or_path\\\": \\\"memray/bart_wikikp\\\",\\n\",\n      \"  \\\"activation_dropout\\\": 0.1,\\n\",\n      \"  \\\"activation_function\\\": \\\"gelu\\\",\\n\",\n      \"  \\\"add_bias_logits\\\": false,\\n\",\n      \"  \\\"add_final_layer_norm\\\": false,\\n\",\n      \"  \\\"architectures\\\": [\\n\",\n      \"    \\\"BartModel\\\"\\n\",\n      \"  ],\\n\",\n      \"  \\\"attention_dropout\\\": 0.1,\\n\",\n      \"  \\\"bos_token_id\\\": 0,\\n\",\n      \"  \\\"classif_dropout\\\": 0.1,\\n\",\n      \"  \\\"classifier_dropout\\\": 0.0,\\n\",\n      \"  \\\"d_model\\\": 1024,\\n\",\n      \"  \\\"decoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"decoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"decoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"decoder_layers\\\": 12,\\n\",\n      \"  \\\"decoder_start_token_id\\\": 2,\\n\",\n      \"  \\\"dropout\\\": 0.1,\\n\",\n      \"  \\\"early_stopping\\\": true,\\n\",\n      \"  \\\"encoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"encoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"encoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"encoder_layers\\\": 12,\\n\",\n      \"  \\\"eos_token_id\\\": 2,\\n\",\n      \"  \\\"forced_eos_token_id\\\": 2,\\n\",\n      \"  \\\"gradient_checkpointing\\\": false,\\n\",\n      \"  \\\"id2label\\\": {\\n\",\n      \"    \\\"0\\\": \\\"LABEL_0\\\",\\n\",\n      \"    \\\"1\\\": \\\"LABEL_1\\\",\\n\",\n      \"    \\\"2\\\": \\\"LABEL_2\\\"\\n\",\n      \"  },\\n\",\n      \"  \\\"init_std\\\": 0.02,\\n\",\n      \"  \\\"is_encoder_decoder\\\": true,\\n\",\n      \"  \\\"label2id\\\": {\\n\",\n      \"    \\\"LABEL_0\\\": 0,\\n\",\n      \"    \\\"LABEL_1\\\": 1,\\n\",\n      \"    \\\"LABEL_2\\\": 2\\n\",\n      \"  },\\n\",\n      \"  \\\"max_position_embeddings\\\": 1024,\\n\",\n      \"  \\\"model_type\\\": \\\"bart\\\",\\n\",\n      \"  \\\"no_repeat_ngram_size\\\": 3,\\n\",\n      \"  \\\"normalize_before\\\": false,\\n\",\n      \"  \\\"num_beams\\\": 4,\\n\",\n      \"  \\\"num_hidden_layers\\\": 12,\\n\",\n      \"  \\\"pad_token_id\\\": 1,\\n\",\n      \"  \\\"scale_embedding\\\": false,\\n\",\n      \"  \\\"task_specific_params\\\": {\\n\",\n      \"    \\\"summarization\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 128,\\n\",\n      \"      \\\"min_length\\\": 12,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_cnn\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 2.0,\\n\",\n      \"      \\\"max_length\\\": 142,\\n\",\n      \"      \\\"min_length\\\": 56,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_xsum\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 62,\\n\",\n      \"      \\\"min_length\\\": 11,\\n\",\n      \"      \\\"num_beams\\\": 6\\n\",\n      \"    }\\n\",\n      \"  },\\n\",\n      \"  \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n      \"  \\\"transformers_version\\\": \\\"4.17.0\\\",\\n\",\n      \"  \\\"use_cache\\\": true,\\n\",\n      \"  \\\"vocab_size\\\": 50264\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"loading configuration file https://huggingface.co/memray/bart_wikikp/resolve/main/config.json from cache at ./hf_cache/565baaa81871d621f544378240cf6cf001c3b0ae16becf577d6e92b9ae423bb8.8c38c569db3fa2298a8f1d96cb54b8e8a9e18344189994a575808bc3e0e8400a\\n\",\n      \"Model config BartConfig {\\n\",\n      \"  \\\"_name_or_path\\\": \\\"memray/bart_wikikp\\\",\\n\",\n      \"  \\\"activation_dropout\\\": 0.1,\\n\",\n      \"  \\\"activation_function\\\": \\\"gelu\\\",\\n\",\n      \"  \\\"add_bias_logits\\\": false,\\n\",\n      \"  \\\"add_final_layer_norm\\\": false,\\n\",\n      \"  \\\"architectures\\\": [\\n\",\n      \"    \\\"BartModel\\\"\\n\",\n      \"  ],\\n\",\n      \"  \\\"attention_dropout\\\": 0.1,\\n\",\n      \"  \\\"bos_token_id\\\": 0,\\n\",\n      \"  \\\"classif_dropout\\\": 0.1,\\n\",\n      \"  \\\"classifier_dropout\\\": 0.0,\\n\",\n      \"  \\\"d_model\\\": 1024,\\n\",\n      \"  \\\"decoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"decoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"decoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"decoder_layers\\\": 12,\\n\",\n      \"  \\\"decoder_start_token_id\\\": 2,\\n\",\n      \"  \\\"dropout\\\": 0.1,\\n\",\n      \"  \\\"early_stopping\\\": true,\\n\",\n      \"  \\\"encoder_attention_heads\\\": 16,\\n\",\n      \"  \\\"encoder_ffn_dim\\\": 4096,\\n\",\n      \"  \\\"encoder_layerdrop\\\": 0.0,\\n\",\n      \"  \\\"encoder_layers\\\": 12,\\n\",\n      \"  \\\"eos_token_id\\\": 2,\\n\",\n      \"  \\\"forced_eos_token_id\\\": 2,\\n\",\n      \"  \\\"gradient_checkpointing\\\": false,\\n\",\n      \"  \\\"id2label\\\": {\\n\",\n      \"    \\\"0\\\": \\\"LABEL_0\\\",\\n\",\n      \"    \\\"1\\\": \\\"LABEL_1\\\",\\n\",\n      \"    \\\"2\\\": \\\"LABEL_2\\\"\\n\",\n      \"  },\\n\",\n      \"  \\\"init_std\\\": 0.02,\\n\",\n      \"  \\\"is_encoder_decoder\\\": true,\\n\",\n      \"  \\\"label2id\\\": {\\n\",\n      \"    \\\"LABEL_0\\\": 0,\\n\",\n      \"    \\\"LABEL_1\\\": 1,\\n\",\n      \"    \\\"LABEL_2\\\": 2\\n\",\n      \"  },\\n\",\n      \"  \\\"max_position_embeddings\\\": 1024,\\n\",\n      \"  \\\"model_type\\\": \\\"bart\\\",\\n\",\n      \"  \\\"no_repeat_ngram_size\\\": 3,\\n\",\n      \"  \\\"normalize_before\\\": false,\\n\",\n      \"  \\\"num_beams\\\": 4,\\n\",\n      \"  \\\"num_hidden_layers\\\": 12,\\n\",\n      \"  \\\"pad_token_id\\\": 1,\\n\",\n      \"  \\\"scale_embedding\\\": false,\\n\",\n      \"  \\\"task_specific_params\\\": {\\n\",\n      \"    \\\"summarization\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 128,\\n\",\n      \"      \\\"min_length\\\": 12,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_cnn\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 2.0,\\n\",\n      \"      \\\"max_length\\\": 142,\\n\",\n      \"      \\\"min_length\\\": 56,\\n\",\n      \"      \\\"num_beams\\\": 4\\n\",\n      \"    },\\n\",\n      \"    \\\"summarization_xsum\\\": {\\n\",\n      \"      \\\"length_penalty\\\": 1.0,\\n\",\n      \"      \\\"max_length\\\": 62,\\n\",\n      \"      \\\"min_length\\\": 11,\\n\",\n      \"      \\\"num_beams\\\": 6\\n\",\n      \"    }\\n\",\n      \"  },\\n\",\n      \"  \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n      \"  \\\"transformers_version\\\": \\\"4.17.0\\\",\\n\",\n      \"  \\\"use_cache\\\": true,\\n\",\n      \"  \\\"vocab_size\\\": 50264\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"loading weights file https://huggingface.co/memray/bart_wikikp/resolve/main/pytorch_model.bin from cache at ./hf_cache/9eb0837198222b91a9055db9a451859461cfb0e30ecdad5e6db7c02dc2af3f52.25862b70ea881164d94370c29f913286bf8ad2c4454315a24ef0287ed4a4e446\\n\",\n      \"All model checkpoint weights were used when initializing BartForConditionalGeneration.\\n\",\n      \"\\n\",\n      \"All the weights of BartForConditionalGeneration were initialized from the model checkpoint at memray/bart_wikikp.\\n\",\n      \"If your task is similar to the task the model of the checkpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Embedding(50265, 1024)\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"config = AutoConfig.from_pretrained(\\n\",\n    \"    model_name_or_path, cache_dir=cache_dir\\n\",\n    \")\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\n\",\n    \"    model_name_or_path, cache_dir=cache_dir, use_fast=True, revision='main'\\n\",\n    \")\\n\",\n    \"model = AutoModelForSeq2SeqLM.from_pretrained(\\n\",\n    \"    model_name_or_path,\\n\",\n    \"    config=config,\\n\",\n    \"    cache_dir=cache_dir\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.resize_token_embeddings(len(tokenizer))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Reusing dataset duc2001 (./hf_cache/midas___duc2001/raw/0.0.1/7888b46165d8a58f49f00e28410b46b1f22fabfd72a9e89f3e80a4e2d27e4a9b)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"8db0bf8ed7dc4adaae1f433cd7ae3c48\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Set seed before initializing model.\\n\",\n    \"set_seed(666)\\n\",\n    \"raw_datasets = load_dataset(\\n\",\n    \"        dataset_name, \\\"raw\\\", cache_dir=cache_dir\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess_function(examples):\\n\",\n    \"    # remove pairs where at least one record is None\\n\",\n    \"    inputs, targets = [], []\\n\",\n    \"    for i in range(len(examples[text_column])):\\n\",\n    \"        if examples[text_column][i] is not None and examples[keyphrase_column][i] is not None:\\n\",\n    \"            inputs.append(examples[text_column][i])\\n\",\n    \"            targets.append(examples[keyphrase_column][i])\\n\",\n    \"\\n\",\n    \"    inputs = examples[text_column]\\n\",\n    \"    targets = ['<sep>'.join(kps) for kps in examples[keyphrase_column]]\\n\",\n    \"    inputs = [prefix + ' '.join(inp) for inp in inputs]\\n\",\n    \"    model_inputs = tokenizer(inputs, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # Setup the tokenizer for targets\\n\",\n    \"    with tokenizer.as_target_tokenizer():\\n\",\n    \"        labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)\\n\",\n    \"\\n\",\n    \"    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\\n\",\n    \"    # padding in the loss.\\n\",\n    \"    labels[\\\"input_ids\\\"] = [\\n\",\n    \"        [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\\\"input_ids\\\"]\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"    model_inputs[\\\"labels\\\"] = labels[\\\"input_ids\\\"]\\n\",\n    \"    return model_inputs\\n\",\n    \"\\n\",\n    \"predict_dataset = raw_datasets[\\\"test\\\"]\\n\",\n    \"\\n\",\n    \"with training_args.main_process_first(desc=\\\"prediction dataset map pre-processing\\\"):\\n\",\n    \"    predict_dataset = predict_dataset.map(\\n\",\n    \"        preprocess_function,\\n\",\n    \"        batched=True,\\n\",\n    \"        num_proc=4,\\n\",\n    \"        load_from_cache_file=False,\\n\",\n    \"        desc=\\\"Running tokenizer on prediction dataset\\\",\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"# Data collator\\n\",\n    \"label_pad_token_id = -100\\n\",\n    \"data_collator = DataCollatorForSeq2Seq(\\n\",\n    \"    tokenizer,\\n\",\n    \"    model=model,\\n\",\n    \"    label_pad_token_id=label_pad_token_id\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Metric\\n\",\n    \"def postprocess_text(preds, labels, sep_token):\\n\",\n    \"    stemmer = PorterStemmer()\\n\",\n    \"    preds = [pred.lower().replace('</s>', '').replace('<pad>', '').split(sep_token) for pred in preds]\\n\",\n    \"    labels = [label.lower().replace('<s>', '').replace('<pad>', '').split(sep_token) for label in labels]\\n\",\n    \"    preds = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in pred] for pred in preds]\\n\",\n    \"    labels = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in label] for label in labels]\\n\",\n    \"    preds = [[p.strip() for p in pred if len(p.strip()) > 0] for pred in preds]\\n\",\n    \"    labels = [[p.strip() for p in label if len(p.strip()) > 0] for label in labels]\\n\",\n    \"\\n\",\n    \"    return preds, labels\\n\",\n    \"\\n\",\n    \"def compute_metrics(eval_preds):\\n\",\n    \"    preds = eval_preds.predictions\\n\",\n    \"    labels = eval_preds.label_ids\\n\",\n    \"    if isinstance(preds, tuple):\\n\",\n    \"        preds = preds[0]\\n\",\n    \"    if len(preds.shape) == 3:\\n\",\n    \"        preds = preds.argmax(axis=-1)\\n\",\n    \"    \\n\",\n    \"    raw_decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=False)\\n\",\n    \"    # Replace -100 in the labels as we can't decode them.\\n\",\n    \"    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\\n\",\n    \"    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)\\n\",\n    \"\\n\",\n    \"    # Some simple post-processing\\n\",\n    \"    decoded_preds, decoded_labels = postprocess_text(raw_decoded_preds, decoded_labels, tokenizer.sep_token)\\n\",\n    \"\\n\",\n    \"    precs, recalls, f_scores = [], [], []\\n\",\n    \"    num_match, num_pred, num_gold = [], [], []\\n\",\n    \"    for raw_pred, pred, label in zip(raw_decoded_preds, decoded_preds, decoded_labels):\\n\",\n    \"        pred_set = set(pred)\\n\",\n    \"        label_set = set(label)\\n\",\n    \"        match_set = label_set.intersection(pred_set)\\n\",\n    \"        p = float(len(match_set)) / float(len(pred_set)) if len(pred_set) > 0 else 0.0\\n\",\n    \"        r = float(len(match_set)) / float(len(label_set)) if len(label_set) > 0 else 0.0\\n\",\n    \"        f1 = float(2 * (p * r)) / (p + r) if (p + r) > 0 else 0.0\\n\",\n    \"        precs.append(p)\\n\",\n    \"        recalls.append(r)\\n\",\n    \"        f_scores.append(f1)\\n\",\n    \"        num_match.append(len(match_set))\\n\",\n    \"        num_pred.append(len(pred_set))\\n\",\n    \"        num_gold.append(len(label_set))\\n\",\n    \"        \\n\",\n    \"        print(f'raw_PRED: {raw_pred}')\\n\",\n    \"        print(f'PRED: num={len(pred_set)} - {pred_set}')\\n\",\n    \"        print(f'GT: num={len(label_set)} - {label_set}')\\n\",\n    \"        print(f'p={p}, r={r}, f1={f1}')\\n\",\n    \"        print('-' * 20)\\n\",\n    \"\\n\",\n    \"    result = {\\n\",\n    \"        'precision@M': np.mean(precs) * 100.0,\\n\",\n    \"        'recall@M': np.mean(recalls) * 100.0,\\n\",\n    \"        'fscore@M': np.mean(f_scores) * 100.0,\\n\",\n    \"        'num_match': np.mean(num_match),\\n\",\n    \"        'num_pred': np.mean(num_pred),\\n\",\n    \"        'num_gold': np.mean(num_gold),\\n\",\n    \"    }\\n\",\n    \"\\n\",\n    \"    result = {k: round(v, 4) for k, v in result.items()}\\n\",\n    \"    return result\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"Tesla V100-PCIE-32GB\\n\",\n      \"cuda:0\\n\",\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"print(torch.cuda.is_available())\\n\",\n    \"print(torch.cuda.get_device_name())\\n\",\n    \"device = torch.device(\\\"cuda:1\\\")\\n\",\n    \"print(device)\\n\",\n    \"print(torch.cuda.device_count())\\n\",\n    \"\\n\",\n    \"model = model.to(device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The following columns in the test set  don't have a corresponding argument in `BartForConditionalGeneration.forward` and have been ignored: other_metadata, id, abstractive_keyphrases, doc_bio_tags, document, extractive_keyphrases. If other_metadata, id, abstractive_keyphrases, doc_bio_tags, document, extractive_keyphrases are not expected by `BartForConditionalGeneration.forward`,  you can safely ignore this message.\\n\",\n      \"***** Running Prediction *****\\n\",\n      \"  Num examples = 308\\n\",\n      \"  Batch size = 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"    <div>\\n\",\n       \"      \\n\",\n       \"      <progress value='10' max='39' style='width:300px; height:20px; vertical-align: middle;'></progress>\\n\",\n       \"      [10/39 00:01 < 00:06, 4.73 it/s]\\n\",\n       \"    </div>\\n\",\n       \"    \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"ename\": \"RuntimeError\",\n     \"evalue\": \"CUDA out of memory. Tried to allocate 2.11 GiB (GPU 0; 31.75 GiB total capacity; 24.83 GiB already allocated; 915.50 MiB free; 29.66 GiB reserved in total by PyTorch)\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m                              Traceback (most recent call last)\",\n      \"\\u001b[0;32m/scratch/slurm-266562/ipykernel_245208/1162611636.py\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m     11\\u001b[0m \\u001b[0mresults\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0;34m}\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     12\\u001b[0m predict_results = trainer.predict(\\n\\u001b[0;32m---> 13\\u001b[0;31m     \\u001b[0mpredict_dataset\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmetric_key_prefix\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"predict\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmax_length\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmax_length\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnum_beams\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mnum_beams\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     14\\u001b[0m )\\n\\u001b[1;32m     15\\u001b[0m \\u001b[0mmetrics\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpredict_results\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmetrics\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer_seq2seq.py\\u001b[0m in \\u001b[0;36mpredict\\u001b[0;34m(self, test_dataset, ignore_keys, metric_key_prefix, max_length, num_beams)\\u001b[0m\\n\\u001b[1;32m    117\\u001b[0m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_max_length\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mmax_length\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mmax_length\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m \\u001b[0;32melse\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgeneration_max_length\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    118\\u001b[0m         \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_num_beams\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnum_beams\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mnum_beams\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m \\u001b[0;32melse\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgeneration_num_beams\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 119\\u001b[0;31m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0msuper\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpredict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtest_dataset\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mignore_keys\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mignore_keys\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmetric_key_prefix\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmetric_key_prefix\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    120\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    121\\u001b[0m     def prediction_step(\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer.py\\u001b[0m in \\u001b[0;36mpredict\\u001b[0;34m(self, test_dataset, ignore_keys, metric_key_prefix)\\u001b[0m\\n\\u001b[1;32m   2330\\u001b[0m         \\u001b[0meval_loop\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mprediction_loop\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0muse_legacy_prediction_loop\\u001b[0m \\u001b[0;32melse\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mevaluation_loop\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   2331\\u001b[0m         output = eval_loop(\\n\\u001b[0;32m-> 2332\\u001b[0;31m             \\u001b[0mtest_dataloader\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdescription\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"Prediction\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mignore_keys\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mignore_keys\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmetric_key_prefix\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmetric_key_prefix\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   2333\\u001b[0m         )\\n\\u001b[1;32m   2334\\u001b[0m         \\u001b[0mtotal_batch_size\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0meval_batch_size\\u001b[0m \\u001b[0;34m*\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mworld_size\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer.py\\u001b[0m in \\u001b[0;36mevaluation_loop\\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\\u001b[0m\\n\\u001b[1;32m   2447\\u001b[0m                 \\u001b[0;32mif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpreprocess_logits_for_metrics\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   2448\\u001b[0m                     \\u001b[0mlogits\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpreprocess_logits_for_metrics\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mlogits\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mlabels\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 2449\\u001b[0;31m                 \\u001b[0mpreds_host\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlogits\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mpreds_host\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m \\u001b[0;32melse\\u001b[0m \\u001b[0mnested_concat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mpreds_host\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mlogits\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;36m100\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   2450\\u001b[0m             \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcontrol\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcallback_handler\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mon_prediction_step\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstate\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcontrol\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   2451\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer_pt_utils.py\\u001b[0m in \\u001b[0;36mnested_concat\\u001b[0;34m(tensors, new_tensors, padding_index)\\u001b[0m\\n\\u001b[1;32m    104\\u001b[0m     ), f\\\"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}.\\\"\\n\\u001b[1;32m    105\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mlist\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtuple\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 106\\u001b[0;31m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtype\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnested_concat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mzip\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    107\\u001b[0m     \\u001b[0;32melif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mTensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    108\\u001b[0m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtorch_pad_and_concatenate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer_pt_utils.py\\u001b[0m in \\u001b[0;36m<genexpr>\\u001b[0;34m(.0)\\u001b[0m\\n\\u001b[1;32m    104\\u001b[0m     ), f\\\"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}.\\\"\\n\\u001b[1;32m    105\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mlist\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtuple\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 106\\u001b[0;31m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtype\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnested_concat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mzip\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    107\\u001b[0m     \\u001b[0;32melif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mTensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    108\\u001b[0m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtorch_pad_and_concatenate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer_pt_utils.py\\u001b[0m in \\u001b[0;36mnested_concat\\u001b[0;34m(tensors, new_tensors, padding_index)\\u001b[0m\\n\\u001b[1;32m    106\\u001b[0m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtype\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnested_concat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mn\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mzip\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    107\\u001b[0m     \\u001b[0;32melif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mTensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 108\\u001b[0;31m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtorch_pad_and_concatenate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m    109\\u001b[0m     \\u001b[0;32melif\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mndarray\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    110\\u001b[0m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mnumpy_pad_and_concatenate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnew_tensors\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpadding_index\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m~/anaconda3/envs/kp/lib/python3.7/site-packages/transformers/trainer_pt_utils.py\\u001b[0m in \\u001b[0;36mtorch_pad_and_concatenate\\u001b[0;34m(tensor1, tensor2, padding_index)\\u001b[0m\\n\\u001b[1;32m     68\\u001b[0m     \\u001b[0;34m\\\"\\\"\\\"Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary.\\\"\\\"\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     69\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensor1\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m1\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0mtensor1\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0mtensor2\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 70\\u001b[0;31m         \\u001b[0;32mreturn\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensor1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtensor2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdim\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     71\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     72\\u001b[0m     \\u001b[0;31m# Let's figure out the new shape\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m: CUDA out of memory. Tried to allocate 2.11 GiB (GPU 0; 31.75 GiB total capacity; 24.83 GiB already allocated; 915.50 MiB free; 29.66 GiB reserved in total by PyTorch)\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Initialize our Trainer\\n\",\n    \"trainer = Seq2SeqTrainer(\\n\",\n    \"    model=model,\\n\",\n    \"    args=training_args,\\n\",\n    \"    tokenizer=tokenizer,\\n\",\n    \"    data_collator=data_collator,\\n\",\n    \"    compute_metrics=compute_metrics\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"# Evaluation\\n\",\n    \"results = {}\\n\",\n    \"predict_results = trainer.predict(\\n\",\n    \"    predict_dataset, metric_key_prefix=\\\"predict\\\", max_length=max_length, num_beams=num_beams,\\n\",\n    \")\\n\",\n    \"metrics = predict_results.metrics\\n\",\n    \"max_predict_samples = len(predict_dataset)\\n\",\n    \"metrics[\\\"predict_samples\\\"] = min(max_predict_samples, len(predict_dataset))\\n\",\n    \"\\n\",\n    \"trainer.log_metrics(\\\"predict\\\", metrics)\\n\",\n    \"trainer.save_metrics(\\\"predict\\\", metrics)\\n\",\n    \"\\n\",\n    \"if trainer.is_world_process_zero():\\n\",\n    \"    if training_args.predict_with_generate:\\n\",\n    \"        predictions = tokenizer.batch_decode(predict_results.predictions)\\n\",\n    \"        predictions = [pred.lower().replace('</s>', '').replace('<pad>', '').strip().split(tokenizer.sep_token) for pred in predictions]\\n\",\n    \"        output_prediction_file = os.path.join(training_args.output_dir, \\\"generated_predictions.txt\\\")\\n\",\n    \"        with open(output_prediction_file, \\\"w\\\") as writer:\\n\",\n    \"            writer.write(\\\"\\\\n\\\".join([json.dumps(pred) for pred in predictions]))\\n\",\n    \"\\n\",\n    \"kwargs = {\\\"finetuned_from\\\": model_name_or_path, \\\"tasks\\\": \\\"keyphrasification\\\"}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'4.17.0'\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import transformers\\n\",\n    \"transformers.__version__\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "onmt/__init__.py",
    "content": "\"\"\" Main entry point of the ONMT library \"\"\"\nfrom __future__ import division, print_function\n\nimport onmt.inputters\nimport onmt.encoders\nimport onmt.decoders\nimport onmt.models\nimport onmt.utils\nimport onmt.modules\nfrom onmt.trainer import Trainer\nimport sys\nimport onmt.utils.optimizers\nonmt.utils.optimizers.Optim = onmt.utils.optimizers.Optimizer\nsys.modules[\"onmt.Optim\"] = onmt.utils.optimizers\n\n# For Flake\n__all__ = [onmt.inputters, onmt.encoders, onmt.decoders, onmt.models,\n           onmt.utils, onmt.modules, \"Trainer\"]\n\n__version__ = \"2.0.0rc2\"\n"
  },
  {
    "path": "onmt/bin/__init__.py",
    "content": ""
  },
  {
    "path": "onmt/bin/average_models.py",
    "content": "#!/usr/bin/env python\nimport argparse\nimport torch\n\n\ndef average_models(model_files, fp32=False):\n    vocab = None\n    opt = None\n    avg_model = None\n    avg_generator = None\n\n    for i, model_file in enumerate(model_files):\n        m = torch.load(model_file, map_location='cpu')\n        model_weights = m['model']\n        generator_weights = m['generator']\n\n        if fp32:\n            for k, v in model_weights.items():\n                model_weights[k] = v.float()\n            for k, v in generator_weights.items():\n                generator_weights[k] = v.float()\n\n        if i == 0:\n            vocab, opt = m['vocab'], m['opt']\n            avg_model = model_weights\n            avg_generator = generator_weights\n        else:\n            for (k, v) in avg_model.items():\n                avg_model[k].mul_(i).add_(model_weights[k]).div_(i + 1)\n\n            for (k, v) in avg_generator.items():\n                avg_generator[k].mul_(i).add_(generator_weights[k]).div_(i + 1)\n\n    final = {\"vocab\": vocab, \"opt\": opt, \"optim\": None,\n             \"generator\": avg_generator, \"model\": avg_model}\n    return final\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"\")\n    parser.add_argument(\"-models\", \"-m\", nargs=\"+\", required=True,\n                        help=\"List of models\")\n    parser.add_argument(\"-output\", \"-o\", required=True,\n                        help=\"Output file\")\n    parser.add_argument(\"-fp32\", \"-f\", action=\"store_true\",\n                        help=\"Cast params to float32\")\n    opt = parser.parse_args()\n\n    final = average_models(opt.models, opt.fp32)\n    torch.save(final, opt.output)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/bin/build_vocab.py",
    "content": "#!/usr/bin/env python\n\"\"\"Get vocabulary coutings from transformed corpora samples.\"\"\"\nfrom onmt.utils.logging import init_logger\nfrom onmt.utils.misc import set_random_seed, check_path\nfrom onmt.utils.parse import ArgumentParser\nfrom onmt.opts import dynamic_prepare_opts\nfrom onmt.inputters.corpus import build_vocab\nfrom onmt.transforms import make_transforms, get_transforms_cls\n\n\ndef build_vocab_main(opts):\n    \"\"\"Apply transforms to samples of specified data and build vocab from it.\n\n    Transforms that need vocab will be disabled in this.\n    Built vocab is saved in plain text format as following and can be pass as\n    `-src_vocab` (and `-tgt_vocab`) when training:\n    ```\n    <tok_0>\\t<count_0>\n    <tok_1>\\t<count_1>\n    ```\n    \"\"\"\n\n    ArgumentParser.validate_prepare_opts(opts, build_vocab_only=True)\n    assert opts.n_sample == -1 or opts.n_sample > 1, \\\n        f\"Illegal argument n_sample={opts.n_sample}.\"\n\n    logger = init_logger()\n    set_random_seed(opts.seed, False)\n    transforms_cls = get_transforms_cls(opts._all_transform)\n    fields = None\n\n    transforms = make_transforms(opts, transforms_cls, fields)\n\n    logger.info(f\"Counter vocab from {opts.n_sample} samples.\")\n    src_counter, tgt_counter = build_vocab(\n        opts, transforms, n_sample=opts.n_sample)\n\n    logger.info(f\"Counters src:{len(src_counter)}\")\n    logger.info(f\"Counters tgt:{len(tgt_counter)}\")\n\n    def save_counter(counter, save_path):\n        check_path(save_path, exist_ok=opts.overwrite, log=logger.warning)\n        with open(save_path, \"w\",encoding=\"utf8\") as fo:\n            for tok, count in counter.most_common():\n                fo.write(tok + \"\\t\" + str(count) + \"\\n\")\n\n    if opts.share_vocab:\n        src_counter += tgt_counter\n        tgt_counter = src_counter\n        logger.info(f\"Counters after share:{len(src_counter)}\")\n        save_counter(src_counter, opts.src_vocab)\n    else:\n        save_counter(src_counter, opts.src_vocab)\n        save_counter(tgt_counter, opts.tgt_vocab)\n\n\ndef _get_parser():\n    parser = ArgumentParser(description='build_vocab.py')\n    dynamic_prepare_opts(parser, build_vocab_only=True)\n    return parser\n\n\ndef main():\n    parser = _get_parser()\n    opts, unknown = parser.parse_known_args()\n    build_vocab_main(opts)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "onmt/bin/release_model.py",
    "content": "#!/usr/bin/env python\nimport argparse\nimport torch\n\n\ndef get_ctranslate2_model_spec(opt):\n    \"\"\"Creates a CTranslate2 model specification from the model options.\"\"\"\n    with_relative_position = getattr(opt, \"max_relative_positions\", 0) > 0\n    is_ct2_compatible = (\n        opt.encoder_type == \"transformer\"\n        and opt.decoder_type == \"transformer\"\n        and getattr(opt, \"self_attn_type\", \"scaled-dot\") == \"scaled-dot\"\n        and ((opt.position_encoding and not with_relative_position)\n             or (with_relative_position and not opt.position_encoding)))\n    if not is_ct2_compatible:\n        return None\n    import ctranslate2\n    num_heads = getattr(opt, \"heads\", 8)\n    return ctranslate2.specs.TransformerSpec(\n        (opt.enc_layers, opt.dec_layers),\n        num_heads,\n        with_relative_position=with_relative_position)\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description=\"Release an OpenNMT-py model for inference\")\n    parser.add_argument(\"--model\", \"-m\",\n                        help=\"The model path\", required=True)\n    parser.add_argument(\"--output\", \"-o\",\n                        help=\"The output path\", required=True)\n    parser.add_argument(\"--format\",\n                        choices=[\"pytorch\", \"ctranslate2\"],\n                        default=\"pytorch\",\n                        help=\"The format of the released model\")\n    parser.add_argument(\"--quantization\", \"-q\",\n                        choices=[\"int8\", \"int16\", \"float16\"],\n                        default=None,\n                        help=\"Quantization type for CT2 model.\")\n    opt = parser.parse_args()\n\n    model = torch.load(opt.model)\n    if opt.format == \"pytorch\":\n        model[\"optim\"] = None\n        torch.save(model, opt.output)\n    elif opt.format == \"ctranslate2\":\n        model_spec = get_ctranslate2_model_spec(model[\"opt\"])\n        if model_spec is None:\n            raise ValueError(\"This model is not supported by CTranslate2. Go \"\n                             \"to https://github.com/OpenNMT/CTranslate2 for \"\n                             \"more information on supported models.\")\n        import ctranslate2\n        converter = ctranslate2.converters.OpenNMTPyConverter(opt.model)\n        converter.convert(opt.output, model_spec, force=True,\n                          quantization=opt.quantization)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/bin/server.py",
    "content": "#!/usr/bin/env python\nimport configargparse\n\nfrom flask import Flask, jsonify, request\nfrom waitress import serve\nfrom onmt.translate import TranslationServer, ServerModelError\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nSTATUS_OK = \"ok\"\nSTATUS_ERROR = \"error\"\n\n\ndef start(config_file,\n          url_root=\"./translator\",\n          host=\"0.0.0.0\",\n          port=5000,\n          debug=False):\n    def prefix_route(route_function, prefix='', mask='{0}{1}'):\n        def newroute(route, *args, **kwargs):\n            return route_function(mask.format(prefix, route), *args, **kwargs)\n        return newroute\n\n    if debug:\n        logger = logging.getLogger(\"main\")\n        log_format = logging.Formatter(\n            \"[%(asctime)s %(levelname)s] %(message)s\")\n        file_handler = RotatingFileHandler(\n            \"debug_requests.log\",\n            maxBytes=1000000, backupCount=10)\n        file_handler.setFormatter(log_format)\n        logger.addHandler(file_handler)\n\n    app = Flask(__name__)\n    app.route = prefix_route(app.route, url_root)\n    translation_server = TranslationServer()\n    translation_server.start(config_file)\n\n    @app.route('/models', methods=['GET'])\n    def get_models():\n        out = translation_server.list_models()\n        return jsonify(out)\n\n    @app.route('/health', methods=['GET'])\n    def health():\n        out = {}\n        out['status'] = STATUS_OK\n        return jsonify(out)\n\n    @app.route('/clone_model/<int:model_id>', methods=['POST'])\n    def clone_model(model_id):\n        out = {}\n        data = request.get_json(force=True)\n        timeout = -1\n        if 'timeout' in data:\n            timeout = data['timeout']\n            del data['timeout']\n\n        opt = data.get('opt', None)\n        try:\n            model_id, load_time = translation_server.clone_model(\n                model_id, opt, timeout)\n        except ServerModelError as e:\n            out['status'] = STATUS_ERROR\n            out['error'] = str(e)\n        else:\n            out['status'] = STATUS_OK\n            out['model_id'] = model_id\n            out['load_time'] = load_time\n\n        return jsonify(out)\n\n    @app.route('/unload_model/<int:model_id>', methods=['GET'])\n    def unload_model(model_id):\n        out = {\"model_id\": model_id}\n\n        try:\n            translation_server.unload_model(model_id)\n            out['status'] = STATUS_OK\n        except Exception as e:\n            out['status'] = STATUS_ERROR\n            out['error'] = str(e)\n\n        return jsonify(out)\n\n    @app.route('/translate', methods=['POST'])\n    def translate():\n        inputs = request.get_json(force=True)\n        if debug:\n            logger.info(inputs)\n        out = {}\n        try:\n            trans, scores, n_best, _, aligns = translation_server.run(inputs)\n            assert len(trans) == len(inputs) * n_best\n            assert len(scores) == len(inputs) * n_best\n            assert len(aligns) == len(inputs) * n_best\n\n            out = [[] for _ in range(n_best)]\n            for i in range(len(trans)):\n                response = {\"src\": inputs[i // n_best]['src'], \"tgt\": trans[i],\n                            \"n_best\": n_best, \"pred_score\": scores[i]}\n                if len(aligns[i]) > 0 and aligns[i][0] is not None:\n                    response[\"align\"] = aligns[i]\n                out[i % n_best].append(response)\n        except ServerModelError as e:\n            model_id = inputs[0].get(\"id\")\n            if debug:\n                logger.warning(\"Unload model #{} \"\n                               \"because of an error\".format(model_id))\n            translation_server.models[model_id].unload()\n            out['error'] = str(e)\n            out['status'] = STATUS_ERROR\n        if debug:\n            logger.info(out)\n        return jsonify(out)\n\n    @app.route('/to_cpu/<int:model_id>', methods=['GET'])\n    def to_cpu(model_id):\n        out = {'model_id': model_id}\n        translation_server.models[model_id].to_cpu()\n\n        out['status'] = STATUS_OK\n        return jsonify(out)\n\n    @app.route('/to_gpu/<int:model_id>', methods=['GET'])\n    def to_gpu(model_id):\n        out = {'model_id': model_id}\n        translation_server.models[model_id].to_gpu()\n\n        out['status'] = STATUS_OK\n        return jsonify(out)\n\n    serve(app, host=host, port=port)\n\n\ndef _get_parser():\n    parser = configargparse.ArgumentParser(\n        config_file_parser_class=configargparse.YAMLConfigFileParser,\n        description=\"OpenNMT-py REST Server\")\n    parser.add_argument(\"--ip\", type=str, default=\"0.0.0.0\")\n    parser.add_argument(\"--port\", type=int, default=\"5000\")\n    parser.add_argument(\"--url_root\", type=str, default=\"/translator\")\n    parser.add_argument(\"--debug\", \"-d\", action=\"store_true\")\n    parser.add_argument(\"--config\", \"-c\", type=str,\n                        default=\"./available_models/conf.json\")\n    return parser\n\n\ndef main():\n    parser = _get_parser()\n    args = parser.parse_args()\n    start(args.config, url_root=args.url_root, host=args.ip, port=args.port,\n          debug=args.debug)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/bin/train.py",
    "content": "#!/usr/bin/env python\n\"\"\"Train models with dynamic data.\"\"\"\nimport copy\nimport os\nimport sys\nimport torch\nimport numpy as np\nfrom functools import partial\n\nfrom shutil import copy2\nfrom onmt.keyphrase import utils\nfrom onmt.utils.distributed import ErrorHandler, consumer, batch_producer\nfrom onmt.utils.misc import set_random_seed\nfrom onmt.modules.embeddings import prepare_pretrained_embeddings\nfrom onmt.utils.logging import init_logger, logger\n\nfrom onmt.models.model_saver import load_checkpoint\nfrom onmt.train_single import main as single_main, _build_train_iter\n\nfrom onmt.utils.parse import ArgumentParser\nfrom onmt.opts import train_opts\nfrom onmt.inputters.corpus import save_transformed_sample\nfrom onmt.inputters.fields import build_dynamic_fields, save_fields, \\\n    load_fields\nfrom onmt.transforms import make_transforms, save_transforms, \\\n    get_specials, get_transforms_cls\n\n# Set sharing strategy manually instead of default based on the OS.\ntorch.multiprocessing.set_sharing_strategy('file_system')\n\n\ndef prepare_fields_transforms(opt):\n    \"\"\"Prepare or dump fields & transforms before training.\"\"\"\n    transforms_cls = get_transforms_cls(opt._all_transform)\n    specials = get_specials(opt, transforms_cls)\n\n    fields = build_dynamic_fields(\n        opt, src_specials=specials['src'], tgt_specials=specials['tgt'])\n\n    # maybe prepare pretrained embeddings, if any\n    prepare_pretrained_embeddings(opt, fields)\n\n    if opt.dump_fields:\n        save_fields(fields, opt.save_data, overwrite=opt.overwrite)\n    if opt.dump_transforms or opt.n_sample != 0:\n        transforms = make_transforms(opt, transforms_cls, fields)\n    if opt.dump_transforms:\n        save_transforms(transforms, opt.save_data, overwrite=opt.overwrite)\n    if opt.n_sample != 0:\n        logger.warning(\n            \"`-n_sample` != 0: Training will not be started. \"\n            f\"Stop after saving {opt.n_sample} samples/corpus.\")\n        save_transformed_sample(opt, transforms, n_sample=opt.n_sample)\n        logger.info(\n            \"Sample saved, please check it before restart training.\")\n        sys.exit()\n    return fields, transforms_cls\n\n\ndef _init_train(opt):\n    \"\"\"Common initilization stuff for all training process.\"\"\"\n    ArgumentParser.validate_prepare_opts(opt)\n\n    if opt.train_from:\n        # Load checkpoint if we resume from a previous training.\n        checkpoint = load_checkpoint(ckpt_path=opt.train_from)\n        fields = load_fields(opt.save_data, checkpoint)\n        transforms_cls = get_transforms_cls(opt._all_transform)\n        if (hasattr(checkpoint[\"opt\"], '_all_transform') and\n                len(opt._all_transform.symmetric_difference(\n                    checkpoint[\"opt\"]._all_transform)) != 0):\n            _msg = \"configured transforms is different from checkpoint:\"\n            new_transf = opt._all_transform.difference(\n                checkpoint[\"opt\"]._all_transform)\n            old_transf = set(checkpoint[\"opt\"]._all_transform).difference(\n                opt._all_transform)\n            if len(new_transf) != 0:\n                _msg += f\" +{new_transf}\"\n            if len(old_transf) != 0:\n                _msg += f\" -{old_transf}.\"\n            logger.warning(_msg)\n    else:\n        checkpoint = None\n        fields, transforms_cls = prepare_fields_transforms(opt)\n\n    # Report src and tgt vocab sizes\n    for side in ['src', 'tgt']:\n        f = fields[side]\n        try:\n            f_iter = iter(f)\n        except TypeError:\n            f_iter = [(side, f)]\n        for sn, sf in f_iter:\n            if sf.use_vocab:\n                logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))\n    return checkpoint, fields, transforms_cls\n\n\ndef train(opt):\n    init_logger(opt.log_file)\n    ArgumentParser.validate_train_opts(opt)\n    ArgumentParser.update_model_opts(opt)\n    ArgumentParser.validate_model_opts(opt)\n\n    set_random_seed(opt.seed, False)\n    checkpoint, fields, transforms_cls = _init_train(opt)\n\n    new_data_dict = {}\n    # @memray: copy config file to exp folder\n    if hasattr(opt, 'config') and hasattr(opt, 'exp_dir'):\n        if not os.path.exists(opt.exp_dir):\n            os.mkdir(opt.exp_dir)\n        filename = opt.config[opt.config.rfind(os.sep) + 1:] if os.sep in opt.config else opt.config\n        copy2(opt.config, opt.exp_dir + os.sep + filename)\n    # @memray: handle the case if opt.data is a folder\n    for corpus_id, corpus_dict in opt.data.items():\n        if os.path.isdir(corpus_dict[\"path_src\"]):\n            src_file_paths = []\n            for root, dirs, files in os.walk(corpus_dict[\"path_src\"]):\n                for file in files:\n                    if file.endswith('.json'):\n                        src_file_paths.append(os.path.join(root, file))\n            src_file_paths = sorted(src_file_paths)\n            if len(src_file_paths) == 0:\n                raise Exception(\"Error: no JSON files found in for %s in: %s\" % (corpus_id, corpus_dict[\"path_src\"]))\n            for src_file_path in src_file_paths:\n                new_corpus_dict = copy.copy(corpus_dict)\n                new_corpus_dict[\"path_src\"] = src_file_path\n                new_corpus_dict[\"path_tgt\"] = src_file_path\n                src_file = src_file_path[len(corpus_dict[\"path_src\"]):]\n                if \"label_data\" in corpus_dict:\n                    new_corpus_dict[\"label_data\"] = [label_folder if label_folder.startswith('__') else os.path.join(label_folder, src_file) for label_folder in corpus_dict[\"label_data\"]]\n                else:\n                    new_corpus_dict[\"label_data\"] = None\n                new_data_dict[corpus_id + '-' + src_file.replace(os.path.sep, '-')] = new_corpus_dict\n        else:\n            new_data_dict[corpus_id] = corpus_dict\n\n    with utils.numpy_seed(opt.seed):\n        kv_pairs = list(new_data_dict.items())\n        np.random.shuffle(kv_pairs)\n        new_data_dict = {k:v for k,v in kv_pairs}\n    opt.data = new_data_dict\n\n    train_process = partial(\n        single_main,\n        fields=fields,\n        transforms_cls=transforms_cls,\n        checkpoint=checkpoint)\n\n    nb_gpu = len(opt.gpu_ranks)\n\n    if opt.world_size > 1:\n\n        queues = []\n        mp = torch.multiprocessing.get_context('spawn')\n        semaphore = mp.Semaphore(opt.world_size * opt.queue_size)\n        # Create a thread to listen for errors in the child processes.\n        error_queue = mp.SimpleQueue()\n        error_handler = ErrorHandler(error_queue)\n        # Train with multiprocessing.\n        procs = []\n        for device_id in range(nb_gpu):\n            q = mp.Queue(opt.queue_size)\n            queues += [q]\n            procs.append(mp.Process(target=consumer, args=(\n                train_process, opt, device_id, error_queue, q, semaphore),\n                daemon=True))\n            procs[device_id].start()\n            logger.info(\" Starting process pid: %d  \" % procs[device_id].pid)\n            error_handler.add_child(procs[device_id].pid)\n        producers = []\n        # This does not work if we merge with the first loop, not sure why\n        for device_id in range(nb_gpu):\n            # Get the iterator to generate from\n            train_iter = _build_train_iter(\n                opt, fields, transforms_cls, stride=nb_gpu, offset=device_id)\n            producer = mp.Process(target=batch_producer,\n                                  args=(train_iter, queues[device_id],\n                                        semaphore, opt,),\n                                  daemon=True)\n            producers.append(producer)\n            producers[device_id].start()\n            logger.info(\" Starting producer process pid: {}  \".format(\n                producers[device_id].pid))\n            error_handler.add_child(producers[device_id].pid)\n\n        for p in procs:\n            p.join()\n        # Once training is done, we can terminate the producers\n        for p in producers:\n            p.terminate()\n\n    elif nb_gpu == 1:  # case 1 GPU only\n        train_process(opt, device_id=0)\n    else:   # case only CPU\n        train_process(opt, device_id=-1)\n\n\ndef _get_parser():\n    parser = ArgumentParser(description='train.py')\n    train_opts(parser)\n    return parser\n\n\ndef main():\n    parser = _get_parser()\n\n    opt, unknown = parser.parse_known_args()\n    train(opt)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/bin/translate.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nimport codecs\nfrom itertools import repeat\n\nfrom onmt.utils.logging import init_logger\nfrom onmt.utils.misc import split_corpus\nfrom onmt.translate.translator import build_translator\n\nimport onmt.opts as opts\nfrom onmt.utils.parse import ArgumentParser\n\n\ndef translate(opt):\n    ArgumentParser.validate_translate_opts(opt)\n    logger = init_logger(opt.log_file)\n\n    translator = build_translator(opt, logger=logger, report_score=True)\n    translator.out_file = codecs.open(opt.output, 'w+', 'utf-8')\n    src_shards = split_corpus(opt.src, opt.shard_size)\n    tgt_shards = split_corpus(opt.tgt, opt.shard_size) \\\n        if opt.tgt is not None else repeat(None)\n    shard_pairs = zip(src_shards, tgt_shards)\n\n    for i, (src_shard, tgt_shard) in enumerate(shard_pairs):\n        logger.info(\"Translating shard %d.\" % i)\n        translator.translate(\n            src=src_shard,\n            tgt=tgt_shard,\n            batch_size=opt.batch_size,\n            batch_type=opt.batch_type,\n            attn_debug=opt.attn_debug,\n            align_debug=opt.align_debug,\n            opt=opt\n            )\n\n\ndef _get_parser():\n    parser = ArgumentParser(description='translate.py')\n\n    opts.config_opts(parser)\n    opts.translate_opts(parser)\n    return parser\n\n\ndef main():\n    parser = _get_parser()\n\n    opt = parser.parse_args()\n    translate(opt)\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/constants.py",
    "content": "\"\"\"Define constant values used across the project.\"\"\"\n\n\nclass DefaultTokens(object):\n    PAD = '<blank>'\n    BOS = '<s>'\n    EOS = '</s>'\n    UNK = '<unk>'\n    MASK = '<mask>'\n    SEP = '<sep>'\n    VOCAB_PAD = 'averyunlikelytoken'\n    SENT_FULL_STOPS = [\".\", \"?\", \"!\"]\n    PHRASE_TABLE_SEPARATOR = '|||'\n    ALIGNMENT_SEPARATOR = ' ||| '\n\n\nclass CorpusName(object):\n    VALID = 'valid'\n    TRAIN = 'train'\n    SAMPLE = 'sample'\n\n\nclass SubwordMarker(object):\n    SPACER = '▁'\n    JOINER = '￭'\n\n\nclass ModelTask(object):\n    LANGUAGE_MODEL = 'lm'\n    SEQ2SEQ = 'seq2seq'\n"
  },
  {
    "path": "onmt/decoders/__init__.py",
    "content": "\"\"\"Module defining decoders.\"\"\"\nfrom onmt.decoders.decoder import DecoderBase, InputFeedRNNDecoder, \\\n    StdRNNDecoder\nfrom onmt.decoders.transformer import TransformerDecoder, TransformerLMDecoder\nfrom onmt.decoders.cnn_decoder import CNNDecoder\nfrom onmt.decoders.bart_decoder import BARTDecoder\n\n\nstr2dec = {\"rnn\": StdRNNDecoder, \"ifrnn\": InputFeedRNNDecoder,\n           \"cnn\": CNNDecoder, \"transformer\": TransformerDecoder,\n           \"transformer_lm\": TransformerLMDecoder,\n           \"bart\": BARTDecoder}\n\n__all__ = [\"DecoderBase\", \"TransformerDecoder\", \"StdRNNDecoder\", \"CNNDecoder\",\n           \"InputFeedRNNDecoder\", \"str2dec\", \"TransformerLMDecoder\", \"BARTDecoder\"]\n"
  },
  {
    "path": "onmt/decoders/bart_decoder.py",
    "content": "\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\nimport logging\nimport os\n\nfrom torch import Tensor\n\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom fairseq.models.bart import BARTModel\nfrom fairseq.models.fairseq_encoder import EncoderOut\nfrom onmt.decoders.decoder import DecoderBase\n\n\nclass BARTDecoder(DecoderBase):\n    \"\"\"The Transformer decoder from \"Attention is All You Need\".\n    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`\n\n    .. mermaid::\n\n       graph BT\n          A[input]\n          B[multi-head self-attn]\n          BB[multi-head src-attn]\n          C[feed forward]\n          O[output]\n          A --> B\n          B --> BB\n          BB --> C\n          C --> O\n\n\n    Args:\n       num_layers (int): number of encoder layers.\n       d_model (int): size of the model\n       heads (int): number of heads\n       d_ff (int): size of the inner FF layer\n       copy_attn (bool): if using a separate copy attention\n       self_attn_type (str): type of self-attention scaled-dot, average\n       dropout (float): dropout parameters\n       embeddings (onmt.modules.Embeddings):\n          embeddings to use, should have positional encodings\n    \"\"\"\n\n    def __init__(self, opt, embeddings, bart_model=None, prev_checkpoint=None):\n        super(BARTDecoder, self).__init__()\n        self.opt = opt\n\n        if bart_model is None:\n            bart_dir = os.path.join(opt.cache_dir, 'bart.large')\n            bart_path = os.path.join(bart_dir, 'model.pt')\n            assert os.path.exists(bart_path), 'BART checkpoint is not found! %s ' % bart_path\n            logging.getLogger().info(\"Loading BART decoder from %s\" % bart_path)\n\n            bart_model = BARTModel.from_pretrained(bart_dir, checkpoint_file='model.pt')\n        else:\n            bart_model = bart_model\n\n        if prev_checkpoint:\n            bart_model.model.load_state_dict(prev_checkpoint['model'], strict=True)\n\n        self.model = bart_model.model.decoder\n        # override the original forward function\n        self.model.forward = forward_bart_decoder\n        self.model.extract_features = extract_features\n\n        self.embed_positions = self.model.embed_positions\n\n        # if embeddings is None:\n        #     raise NotImplementedError\n        #     # self.embed_tokens = self.model.embed_tokens\n        # else:\n        #     self.model.embed_tokens = embeddings\n        #     self.embed_tokens = embeddings\n        #     logging.getLogger().info('Replace BART embedding with token_embed.shape=%s'\n        #                              % (str(self.model.embed_tokens.weight.shape)))\n\n        self._std_attn_idx = -1\n        self._copy_attn_idx = -1\n\n    @classmethod\n    def from_opt(cls, opt, embeddings, **kwargs):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt, embeddings, **kwargs\n        )\n\n    def init_state(self, src, memory_bank, enc_hidden):\n        \"\"\"Initialize decoder state.\"\"\"\n        pass\n\n    def map_state(self, fn):\n        pass\n\n    def forward(self, tgt,\n                encoder_output=None,\n                incremental_state=None,\n                **kwargs):\n        \"\"\"\n        :param tgt: (tgt_len, batch_size, 1)\n        :param memory_bank: (src_len, batch_size, dim)\n        :param step:\n        :param memory_lengths: (batch_size)\n        :param encoder_output: encoder_out.shape=(src_len, batch_size, dim)\n        :param incremental_state: attention of previous timestep, each shape=(batch_size, num_head, 1, attn_dim)\n        :param kwargs:\n        :return:\n        \"\"\"\n        # make them batch-first, (tgt_len, batch_size, 1) -> (batch_size, tgt_len)\n        prev_output_tokens = tgt.squeeze(2).permute(1, 0)\n        # Inputs:\n        #     prev_output_tokens (LongTensor): previous decoder outputs of shape `(batch, tgt_len)`, for teacher forcing\n        #     encoder_out (optional): output from the encoder, used for encoder-side attention\n        #     incremental_state (dict): dictionary used for storing state during :ref:`Incremental decoding`\n        #     features_only (bool, optional): only return features without applying output layer (default: False).\n        # Returns a tuple:\n        #     - **output** decoder's output: if features_only shape=`(batch, tgt_len, hid_dim)` else shape=`(batch, tgt_len, vocab)`\n        #     - a dictionary with any model-specific outputs\n        #        - **attn**: if average_attn, attn=`(batch, tgt_len, src_len)`, else `(num_head, batch, tgt_len, src_len)`\n        #        - **inner_states**: output of each layer\n        alignment_layer = kwargs.pop('alignment_layer', None) # None means the last layer\n        alignment_heads = None  # None means return all heads and process it here\n        alignment_targets = kwargs.pop('alignment_targets', [])\n\n        output, extra = forward_bart_decoder(\n            self.model,\n            prev_output_tokens,\n            incremental_state=incremental_state,\n            encoder_out=encoder_output,\n            features_only=True,\n            average_attn=False,\n            alignment_layer = alignment_layer,\n            alignment_heads = alignment_heads,\n            return_all_hiddens=False,\n        )\n\n        # (batch, tgt_len, hid_dim) -> (tgt_len, batch, hid_dim)\n        dec_outs = output.transpose(0, 1).contiguous()\n        attn_heads = extra['attn'][0]\n\n        # @memray as of 20201216, fairseq disables returning attentions\n        if attn_heads is not None:\n            attns = {\"std\": attn_heads[self._std_attn_idx].transpose(0, 1).contiguous()}\n            attns[\"copy\"] = attn_heads[self._copy_attn_idx].transpose(0, 1).contiguous()\n            if kwargs.pop('return_all_attention', False):\n                # (num_head, batch_size, tgt_len, src_len) -> (tgt_len, batch_size, num_head, src_len)\n                attns[\"all\"] = attn_heads.permute(2, 1, 0, 3).contiguous()\n\n            for head_id, target in enumerate(alignment_targets):\n                attns[\"alignment_%s\" % target] = attn_heads[head_id].transpose(0, 1).contiguous()\n        else:\n            attns = None\n\n        return dec_outs, attns\n\n\ndef forward_bart_decoder(\n    self,\n    prev_output_tokens,\n    encoder_out: Optional[EncoderOut] = None,\n    incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,\n    features_only: bool = False,\n    full_context_alignment: bool = False,\n    alignment_layer: Optional[int] = None,\n    alignment_heads: Optional[int] = None,\n    average_attn: bool = False,\n    src_lengths: Optional[Any] = None,\n    return_all_hiddens: bool = False,\n):\n    \"\"\"\n    Args:\n        prev_output_tokens (LongTensor): previous decoder outputs of shape\n            `(batch, tgt_len)`, for teacher forcing\n        encoder_out (optional): output from the encoder, used for\n            encoder-side attention\n        incremental_state (dict): dictionary used for storing state during\n            :ref:`Incremental decoding`\n        features_only (bool, optional): only return features without\n            applying output layer (default: False).\n        full_context_alignment (bool, optional): don't apply\n            auto-regressive mask to self-attention (default: False).\n\n    Returns:\n        tuple:\n            - the decoder's output of shape `(batch, tgt_len, vocab)`\n            - a dictionary with any model-specific outputs\n    \"\"\"\n    x, extra = extract_features(\n        self,\n        prev_output_tokens,\n        encoder_out=encoder_out,\n        incremental_state=incremental_state,\n        full_context_alignment=full_context_alignment,\n        alignment_layer=alignment_layer,\n        alignment_heads=alignment_heads,\n        average_attn=average_attn,\n    )\n    if not features_only:\n        x = self.output_layer(x)\n    return x, extra\n\n\ndef extract_features(\n    self,\n    prev_output_tokens,\n    encoder_out: Optional[EncoderOut] = None,\n    incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,\n    full_context_alignment: bool = False,\n    alignment_layer: Optional[int] = None,\n    alignment_heads: Optional[int] = None,\n    average_attn=False,\n):\n    \"\"\"\n    Similar to *forward* but only return features.\n\n    Includes several features from \"Jointly Learning to Align and\n    Translate with Transformer Models\" (Garg et al., EMNLP 2019).\n\n    Args:\n        full_context_alignment (bool, optional): don't apply\n            auto-regressive mask to self-attention (default: False).\n        alignment_layer (int, optional): return mean alignment over\n            heads at this layer (default: last layer).\n        alignment_heads (int, optional): only average alignment over\n            this many heads (default: all heads).\n\n    Returns:\n        tuple:\n            - the decoder's features of shape `(batch, tgt_len, embed_dim)`\n            - a dictionary with any model-specific outputs\n    \"\"\"\n    if alignment_layer is None:\n        alignment_layer = self.num_layers - 1\n\n    # embed positions\n    positions = (\n        self.embed_positions(\n            prev_output_tokens, incremental_state=incremental_state\n        )\n        if self.embed_positions is not None\n        else None\n    )\n\n    # print(prev_output_tokens.shape)\n    if incremental_state is not None:\n        prev_output_tokens = prev_output_tokens[:, -1:]\n        if positions is not None:\n            positions = positions[:, -1:]\n\n    # embed tokens and positions\n    x = self.embed_scale * self.embed_tokens(prev_output_tokens)\n\n    if self.quant_noise is not None:\n        x = self.quant_noise(x)\n\n    if self.project_in_dim is not None:\n        x = self.project_in_dim(x)\n\n    if positions is not None:\n        x += positions\n\n    if self.layernorm_embedding is not None:\n        x = self.layernorm_embedding(x)\n\n    x = self.dropout_module(x)\n\n    # B x T x C -> T x B x C\n    x = x.transpose(0, 1)\n\n    self_attn_padding_mask: Optional[Tensor] = None\n    if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():\n        self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)\n\n    # decoder layers\n    attn: Optional[Tensor] = None\n    inner_states: List[Optional[Tensor]] = [x]\n\n    for idx, layer in enumerate(self.layers):\n        # print('layer=', idx)\n        if incremental_state is None and not full_context_alignment:\n            self_attn_mask = self.buffered_future_mask(x)\n        else:\n            self_attn_mask = None\n\n        x, layer_attn, _ = layer(\n            x,\n            encoder_out[\"encoder_out\"][0]\n            if (encoder_out is not None and len(encoder_out[\"encoder_out\"]) > 0)\n            else None,\n            encoder_out[\"encoder_padding_mask\"][0]\n            if (\n                encoder_out is not None\n                and len(encoder_out[\"encoder_padding_mask\"]) > 0\n            )\n            else None,\n            incremental_state,\n            self_attn_mask=self_attn_mask,\n            self_attn_padding_mask=self_attn_padding_mask,\n            need_attn=bool((idx == alignment_layer)),\n            need_head_weights=bool((idx == alignment_layer)),\n        )\n        inner_states.append(x)\n        if layer_attn is not None and idx == alignment_layer:\n            attn = layer_attn.float().to(x)\n\n    if attn is not None:\n        if alignment_heads is not None:\n            attn = attn[:alignment_heads]\n\n        # average probabilities over heads, [H x B x tgt_len x src_len] -> [B x tgt_len x src_len]\n        if average_attn:\n            attn = attn.mean(dim=0)\n\n    if self.layer_norm is not None:\n        x = self.layer_norm(x)\n\n    # T x B x C -> B x T x C\n    x = x.transpose(0, 1)\n\n    if self.project_out_dim is not None:\n        x = self.project_out_dim(x)\n\n    return x, {\"attn\": [attn], \"inner_states\": inner_states}"
  },
  {
    "path": "onmt/decoders/cnn_decoder.py",
    "content": "\"\"\"Implementation of the CNN Decoder part of\n\"Convolutional Sequence to Sequence Learning\"\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nfrom onmt.modules import ConvMultiStepAttention, GlobalAttention\nfrom onmt.utils.cnn_factory import shape_transform, GatedConv\nfrom onmt.decoders.decoder import DecoderBase\n\nSCALE_WEIGHT = 0.5 ** 0.5\n\n\nclass CNNDecoder(DecoderBase):\n    \"\"\"Decoder based on \"Convolutional Sequence to Sequence Learning\"\n    :cite:`DBLP:journals/corr/GehringAGYD17`.\n\n    Consists of residual convolutional layers, with ConvMultiStepAttention.\n    \"\"\"\n\n    def __init__(self, num_layers, hidden_size, attn_type,\n                 copy_attn, cnn_kernel_width, dropout, embeddings,\n                 copy_attn_type):\n        super(CNNDecoder, self).__init__()\n\n        self.cnn_kernel_width = cnn_kernel_width\n        self.embeddings = embeddings\n\n        # Decoder State\n        self.state = {}\n\n        input_size = self.embeddings.embedding_size\n        self.linear = nn.Linear(input_size, hidden_size)\n        self.conv_layers = nn.ModuleList(\n            [GatedConv(hidden_size, cnn_kernel_width, dropout, True)\n             for i in range(num_layers)]\n        )\n        self.attn_layers = nn.ModuleList(\n            [ConvMultiStepAttention(hidden_size) for i in range(num_layers)]\n        )\n\n        # CNNDecoder has its own attention mechanism.\n        # Set up a separate copy attention layer if needed.\n        assert not copy_attn, \"Copy mechanism not yet tested in conv2conv\"\n        if copy_attn:\n            self.copy_attn = GlobalAttention(\n                hidden_size, attn_type=copy_attn_type)\n        else:\n            self.copy_attn = None\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.dec_layers,\n            opt.dec_rnn_size,\n            opt.global_attention,\n            opt.copy_attn,\n            opt.cnn_kernel_width,\n            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n            embeddings,\n            opt.copy_attn_type)\n\n    def init_state(self, _, memory_bank, enc_hidden):\n        \"\"\"Init decoder state.\"\"\"\n        self.state[\"src\"] = (memory_bank + enc_hidden) * SCALE_WEIGHT\n        self.state[\"previous_input\"] = None\n\n    def map_state(self, fn):\n        self.state[\"src\"] = fn(self.state[\"src\"], 1)\n        if self.state[\"previous_input\"] is not None:\n            self.state[\"previous_input\"] = fn(self.state[\"previous_input\"], 1)\n\n    def detach_state(self):\n        self.state[\"previous_input\"] = self.state[\"previous_input\"].detach()\n\n    def forward(self, tgt, memory_bank, step=None, **kwargs):\n        \"\"\" See :obj:`onmt.modules.RNNDecoderBase.forward()`\"\"\"\n\n        if self.state[\"previous_input\"] is not None:\n            tgt = torch.cat([self.state[\"previous_input\"], tgt], 0)\n\n        dec_outs = []\n        attns = {\"std\": []}\n        if self.copy_attn is not None:\n            attns[\"copy\"] = []\n\n        emb = self.embeddings(tgt)\n        assert emb.dim() == 3  # len x batch x embedding_dim\n\n        tgt_emb = emb.transpose(0, 1).contiguous()\n        # The output of CNNEncoder.\n        src_memory_bank_t = memory_bank.transpose(0, 1).contiguous()\n        # The combination of output of CNNEncoder and source embeddings.\n        src_memory_bank_c = self.state[\"src\"].transpose(0, 1).contiguous()\n\n        emb_reshape = tgt_emb.contiguous().view(\n            tgt_emb.size(0) * tgt_emb.size(1), -1)\n        linear_out = self.linear(emb_reshape)\n        x = linear_out.view(tgt_emb.size(0), tgt_emb.size(1), -1)\n        x = shape_transform(x)\n\n        pad = torch.zeros(x.size(0), x.size(1), self.cnn_kernel_width - 1, 1)\n\n        pad = pad.type_as(x)\n        base_target_emb = x\n\n        for conv, attention in zip(self.conv_layers, self.attn_layers):\n            new_target_input = torch.cat([pad, x], 2)\n            out = conv(new_target_input)\n            c, attn = attention(base_target_emb, out,\n                                src_memory_bank_t, src_memory_bank_c)\n            x = (x + (c + out) * SCALE_WEIGHT) * SCALE_WEIGHT\n        output = x.squeeze(3).transpose(1, 2)\n\n        # Process the result and update the attentions.\n        dec_outs = output.transpose(0, 1).contiguous()\n        if self.state[\"previous_input\"] is not None:\n            dec_outs = dec_outs[self.state[\"previous_input\"].size(0):]\n            attn = attn[:, self.state[\"previous_input\"].size(0):].squeeze()\n            attn = torch.stack([attn])\n        attns[\"std\"] = attn\n        if self.copy_attn is not None:\n            attns[\"copy\"] = attn\n\n        # Update the state.\n        self.state[\"previous_input\"] = tgt\n        # TODO change the way attns is returned dict => list or tuple (onnx)\n        return dec_outs, attns\n\n    def update_dropout(self, dropout):\n        for layer in self.conv_layers:\n            layer.dropout.p = dropout\n"
  },
  {
    "path": "onmt/decoders/decoder.py",
    "content": "import torch\nimport torch.nn as nn\n\nfrom onmt.models.stacked_rnn import StackedLSTM, StackedGRU\nfrom onmt.modules import context_gate_factory, GlobalAttention\nfrom onmt.utils.rnn_factory import rnn_factory\n\nfrom onmt.utils.misc import aeq\n\n\nclass DecoderBase(nn.Module):\n    \"\"\"Abstract class for decoders.\n\n    Args:\n        attentional (bool): The decoder returns non-empty attention.\n    \"\"\"\n\n    def __init__(self, attentional=True):\n        super(DecoderBase, self).__init__()\n        self.attentional = attentional\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\n\n        Subclasses should override this method.\n        \"\"\"\n\n        raise NotImplementedError\n\n\nclass RNNDecoderBase(DecoderBase):\n    \"\"\"Base recurrent attention-based decoder class.\n\n    Specifies the interface used by different decoder types\n    and required by :class:`~onmt.models.NMTModel`.\n\n\n    .. mermaid::\n\n       graph BT\n          A[Input]\n          subgraph RNN\n             C[Pos 1]\n             D[Pos 2]\n             E[Pos N]\n          end\n          G[Decoder State]\n          H[Decoder State]\n          I[Outputs]\n          F[memory_bank]\n          A--emb-->C\n          A--emb-->D\n          A--emb-->E\n          H-->C\n          C-- attn --- F\n          D-- attn --- F\n          E-- attn --- F\n          C-->I\n          D-->I\n          E-->I\n          E-->G\n          F---I\n\n    Args:\n       rnn_type (str):\n          style of recurrent unit to use, one of [RNN, LSTM, GRU, SRU]\n       bidirectional_encoder (bool) : use with a bidirectional encoder\n       num_layers (int) : number of stacked layers\n       hidden_size (int) : hidden size of each layer\n       attn_type (str) : see :class:`~onmt.modules.GlobalAttention`\n       attn_func (str) : see :class:`~onmt.modules.GlobalAttention`\n       coverage_attn (str): see :class:`~onmt.modules.GlobalAttention`\n       context_gate (str): see :class:`~onmt.modules.ContextGate`\n       copy_attn (bool): setup a separate copy attention mechanism\n       dropout (float) : dropout value for :class:`torch.nn.Dropout`\n       embeddings (onmt.modules.Embeddings): embedding module to use\n       reuse_copy_attn (bool): reuse the attention for copying\n       copy_attn_type (str): The copy attention style. See\n        :class:`~onmt.modules.GlobalAttention`.\n       target_encoder (str):\n       detach_target_encoder (bool):\n    \"\"\"\n\n    def __init__(self, rnn_type, bidirectional_encoder, num_layers,\n                 hidden_size, attn_type=\"general\", attn_func=\"softmax\",\n                 coverage_attn=False, context_gate=None,\n                 copy_attn=False, dropout=0.0, embeddings=None,\n                 reuse_copy_attn=False, copy_attn_type=\"general\",\n                 target_encoder_type=None, detach_target_encoder=False,\n                 ):\n        super(RNNDecoderBase, self).__init__(\n            attentional=attn_type != \"none\" and attn_type is not None)\n\n        self.bidirectional_encoder = bidirectional_encoder\n        self.num_layers = num_layers\n        self.hidden_size = hidden_size\n        self.embeddings = embeddings\n        self.dropout = nn.Dropout(dropout)\n\n        # Decoder state\n        self.state = {}\n\n        # @memray: hack to change size for target encoding\n        self.input_size = self._input_size\n        if target_encoder_type == 'none':\n            target_encoder_type = None\n        if target_encoder_type is not None:\n            self.input_size += self.hidden_size\n\n        # Build the RNN.\n        self.rnn = self._build_rnn(rnn_type,\n                                   input_size=self.input_size,\n                                   hidden_size=hidden_size,\n                                   num_layers=1,\n                                   dropout=dropout)\n\n        # Set up the context gate.\n        self.context_gate = None\n        if context_gate is not None:\n            self.context_gate = context_gate_factory(\n                context_gate, self.input_size,\n                hidden_size, hidden_size, hidden_size\n            )\n\n        # Set up the standard attention.\n        self._coverage = coverage_attn\n        if not self.attentional:\n            if self._coverage:\n                raise ValueError(\"Cannot use coverage term with no attention.\")\n            self.attn = None\n        else:\n            self.attn = GlobalAttention(\n                hidden_size, coverage=coverage_attn,\n                attn_type=attn_type, attn_func=attn_func\n            )\n\n        if copy_attn and not reuse_copy_attn:\n            if copy_attn_type == \"none\" or copy_attn_type is None:\n                raise ValueError(\n                    \"Cannot use copy_attn with copy_attn_type none\")\n            self.copy_attn = GlobalAttention(\n                hidden_size, attn_type=copy_attn_type, attn_func=attn_func\n            )\n        else:\n            self.copy_attn = None\n\n        self._reuse_copy_attn = reuse_copy_attn and copy_attn\n        if self._reuse_copy_attn and not self.attentional:\n            raise ValueError(\"Cannot reuse copy attention with no attention.\")\n\n        # @memray\n        # Build the Target Encoder. Feed its output to the decoder as auxiliary input\n        self.target_encoder_type = target_encoder_type\n        self.target_encoder = None\n        if target_encoder_type == \"rnn\":\n            self.target_encoder = self._build_rnn(\"GRU\",\n                                       input_size=self.embeddings.embedding_size,\n                                       hidden_size=hidden_size,\n                                       num_layers=1,\n                                       dropout=dropout)\n        elif target_encoder_type != None and target_encoder_type != \"none\":\n            raise NotImplementedError(\"target_encoder_type other than RNN is not implemented.\")\n        self.detach_target_encoder = detach_target_encoder\n        self.bilinear_layer = nn.Bilinear(in1_features=hidden_size, in2_features=hidden_size, out_features=1)\n\n    @classmethod\n    def from_opt(cls, opt, embeddings, **kwargs):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.rnn_type,\n            opt.brnn,\n            opt.dec_layers,\n            opt.dec_rnn_size,\n            opt.global_attention,\n            opt.global_attention_function,\n            opt.coverage_attn,\n            opt.context_gate,\n            opt.copy_attn,\n            opt.dropout[0] if type(opt.dropout) is list\n            else opt.dropout,\n            embeddings,\n            opt.reuse_copy_attn,\n            opt.copy_attn_type,\n            opt.target_encoder_type,\n            opt.detach_target_encoder,\n        )\n\n    def init_state(self, src, memory_bank, encoder_final):\n        \"\"\"Initialize decoder state with last state of the encoder.\"\"\"\n        def _fix_enc_hidden(hidden):\n            # The encoder hidden is  (layers*directions) x batch x dim.\n            # We need to convert it to layers x batch x (directions*dim).\n            if self.bidirectional_encoder:\n                hidden = torch.cat([hidden[0:hidden.size(0):2],\n                                    hidden[1:hidden.size(0):2]], 2)\n            return hidden\n\n        if isinstance(encoder_final, tuple):  # LSTM\n            self.state[\"hidden\"] = tuple(_fix_enc_hidden(enc_hid)\n                                         for enc_hid in encoder_final)\n        else:  # GRU\n            self.state[\"hidden\"] = (_fix_enc_hidden(encoder_final), )\n\n        # Init the input feed.\n        batch_size = self.state[\"hidden\"][0].size(1)\n        h_size = (batch_size, self.hidden_size)\n        self.state[\"input_feed\"] = \\\n            self.state[\"hidden\"][0].data.new(*h_size).zero_().unsqueeze(0)\n        self.state[\"coverage\"] = None\n\n        # @memray\n        if self.target_encoder is not None:\n            self.state[\"src_hidden\"] = self.state[\"hidden\"][0]\n            if self.target_encoder_type == \"rnn\":\n                self.state[\"tgt_enc_hidden\"] = (self.state[\"src_hidden\"], )\n\n    def map_state(self, fn):\n        self.state[\"hidden\"] = tuple(fn(h, 1) for h in self.state[\"hidden\"])\n        self.state[\"input_feed\"] = fn(self.state[\"input_feed\"], 1)\n        if self._coverage and self.state[\"coverage\"] is not None:\n            self.state[\"coverage\"] = fn(self.state[\"coverage\"], 1)\n        if self.target_encoder is not None:\n            self.state[\"src_hidden\"] = fn(self.state[\"src_hidden\"], 1)\n            if self.target_encoder_type == \"rnn\":\n                self.state[\"tgt_enc_hidden\"] = tuple(fn(h, 1) for h in self.state[\"tgt_enc_hidden\"])\n\n    def detach_state(self):\n        self.state[\"hidden\"] = tuple(h.detach() for h in self.state[\"hidden\"])\n        self.state[\"input_feed\"] = self.state[\"input_feed\"].detach()\n\n    def forward(self, tgt, memory_bank, memory_lengths=None, step=None,\n                **kwargs):\n        \"\"\"\n        Args:\n            tgt (LongTensor): sequences of padded tokens\n                 ``(tgt_len, batch, nfeats)``.\n            memory_bank (FloatTensor): vectors from the encoder\n                 ``(src_len, batch, hidden)``.\n            memory_lengths (LongTensor): the padded source lengths\n                ``(batch,)``.\n\n        Returns:\n            (FloatTensor, dict[str, FloatTensor]):\n\n            * dec_outs: output from the decoder (after attn)\n              ``(tgt_len, batch, hidden)``.\n            * attns: distribution over src at each tgt\n              ``(tgt_len, batch, src_len)``.\n        \"\"\"\n\n        dec_state, dec_outs, attns = self._run_forward_pass(\n            tgt, memory_bank, memory_lengths=memory_lengths)\n\n        # Update the state with the result.\n        if not isinstance(dec_state, tuple):\n            dec_state = (dec_state,)\n        self.state[\"hidden\"] = dec_state\n        self.state[\"input_feed\"] = dec_outs[-1].unsqueeze(0)\n        self.state[\"coverage\"] = None\n        if \"coverage\" in attns:\n            self.state[\"coverage\"] = attns[\"coverage\"][-1].unsqueeze(0)\n\n        # Concatenates sequence of tensors along a new dimension.\n        # NOTE: v0.3 to 0.4: dec_outs / attns[*] may not be list\n        #       (in particular in case of SRU) it was not raising error in 0.3\n        #       since stack(Variable) was allowed.\n        #       In 0.4, SRU returns a tensor that shouldn't be stacked\n        if type(dec_outs) == list:\n            dec_outs = torch.stack(dec_outs)\n\n            for k in attns:\n                if type(attns[k]) == list and len(attns[k]) > 0:\n                    attns[k] = torch.stack(attns[k])\n        return dec_outs, attns\n\n    def update_dropout(self, dropout):\n        self.dropout.p = dropout\n        self.embeddings.update_dropout(dropout)\n\n\nclass StdRNNDecoder(RNNDecoderBase):\n    \"\"\"Standard fully batched RNN decoder with attention.\n\n    Faster implementation, uses CuDNN for implementation.\n    See :class:`~onmt.decoders.decoder.RNNDecoderBase` for options.\n\n\n    Based around the approach from\n    \"Neural Machine Translation By Jointly Learning To Align and Translate\"\n    :cite:`Bahdanau2015`\n\n\n    Implemented without input_feeding and currently with no `coverage_attn`\n    or `copy_attn` support.\n    \"\"\"\n\n    def _run_forward_pass(self, tgt, memory_bank, memory_lengths=None):\n        \"\"\"\n        Private helper for running the specific RNN forward pass.\n        Must be overriden by all subclasses.\n\n        Args:\n            tgt (LongTensor): a sequence of input tokens tensors\n                ``(len, batch, nfeats)``.\n            memory_bank (FloatTensor): output(tensor sequence) from the\n                encoder RNN of size ``(src_len, batch, hidden_size)``.\n            memory_lengths (LongTensor): the source memory_bank lengths.\n\n        Returns:\n            (Tensor, List[FloatTensor], Dict[str, List[FloatTensor]):\n\n            * dec_state: final hidden state from the decoder.\n            * dec_outs: an array of output of every time\n              step from the decoder.\n            * attns: a dictionary of different\n              type of attention Tensor array of every time\n              step from the decoder.\n        \"\"\"\n\n        assert self.copy_attn is None  # TODO, no support yet.\n        assert not self._coverage  # TODO, no support yet.\n\n        attns = {}\n        emb = self.embeddings(tgt)\n\n        if isinstance(self.rnn, nn.GRU):\n            rnn_output, dec_state = self.rnn(emb, self.state[\"hidden\"][0])\n        else:\n            rnn_output, dec_state = self.rnn(emb, self.state[\"hidden\"])\n\n        # Check\n        tgt_len, tgt_batch, _ = tgt.size()\n        output_len, output_batch, _ = rnn_output.size()\n        aeq(tgt_len, output_len)\n        aeq(tgt_batch, output_batch)\n\n        # Calculate the attention.\n        if not self.attentional:\n            dec_outs = rnn_output\n        else:\n            dec_outs, p_attn = self.attn(\n                rnn_output.transpose(0, 1).contiguous(),\n                memory_bank.transpose(0, 1),\n                memory_lengths=memory_lengths\n            )\n            attns[\"std\"] = p_attn\n\n        # Calculate the context gate.\n        if self.context_gate is not None:\n            dec_outs = self.context_gate(\n                emb.view(-1, emb.size(2)),\n                rnn_output.view(-1, rnn_output.size(2)),\n                dec_outs.view(-1, dec_outs.size(2))\n            )\n            dec_outs = dec_outs.view(tgt_len, tgt_batch, self.hidden_size)\n\n        dec_outs = self.dropout(dec_outs)\n        return dec_state, dec_outs, attns\n\n    def _build_rnn(self, rnn_type, **kwargs):\n        rnn, _ = rnn_factory(rnn_type, **kwargs)\n        return rnn\n\n    @property\n    def _input_size(self):\n        return self.embeddings.embedding_size\n\n\nclass InputFeedRNNDecoder(RNNDecoderBase):\n    \"\"\"Input feeding based decoder.\n\n    See :class:`~onmt.decoders.decoder.RNNDecoderBase` for options.\n\n    Based around the input feeding approach from\n    \"Effective Approaches to Attention-based Neural Machine Translation\"\n    :cite:`Luong2015`\n\n\n    .. mermaid::\n\n       graph BT\n          A[Input n-1]\n          AB[Input n]\n          subgraph RNN\n            E[Pos n-1]\n            F[Pos n]\n            E --> F\n          end\n          G[Encoder]\n          H[memory_bank n-1]\n          A --> E\n          AB --> F\n          E --> H\n          G --> H\n    \"\"\"\n\n    def _run_forward_pass(self, tgt, memory_bank, memory_lengths=None):\n        \"\"\"\n        See StdRNNDecoder._run_forward_pass() for description\n        of arguments and return values.\n        \"\"\"\n        # Additional args check.\n        input_feed = self.state[\"input_feed\"].squeeze(0)\n        input_feed_batch, _ = input_feed.size()\n        _, tgt_batch, _ = tgt.size()\n        aeq(tgt_batch, input_feed_batch)\n        # END Additional args check.\n\n        dec_outs = []\n        attns = {}\n        if self.attn is not None:\n            attns[\"std\"] = []\n        if self.copy_attn is not None or self._reuse_copy_attn:\n            attns[\"copy\"] = []\n        if self._coverage:\n            attns[\"coverage\"] = []\n        # @memray, for Orthogonal regularization and Semantic Coverage, may consume lots of memory\n        attns[\"dec_states\"] = []\n        attns[\"enc_states\"] = self.state['src_hidden'].squeeze(0) if 'src_hidden' in self.state else []\n\n        emb = self.embeddings(tgt)\n        assert emb.dim() == 3  # len x batch x embedding_dim\n\n        dec_state = self.state[\"hidden\"]\n        if self.target_encoder is not None:\n            tgt_enc_state = self.state[\"tgt_enc_hidden\"]\n        coverage = self.state[\"coverage\"].squeeze(0) \\\n            if self.state[\"coverage\"] is not None else None\n\n        # Input feed concatenates hidden state with\n        # input at every time step.\n        for emb_t in emb.split(1):\n            if self.target_encoder is None:\n                decoder_input = torch.cat([emb_t.squeeze(0), input_feed], 1)\n                rnn_output, dec_state = self.rnn(decoder_input, dec_state)\n            else:\n                tgt_enc_input = emb_t.squeeze(0)\n                tgt_enc_output, tgt_enc_state = self.target_encoder(tgt_enc_input, tgt_enc_state)\n                tgtenc2dec = tgt_enc_output\n                if self.detach_target_encoder:\n                    tgtenc2dec = tgt_enc_output.detach()\n\n                decoder_input = torch.cat([emb_t.squeeze(0), input_feed, tgtenc2dec], 1)\n                rnn_output, dec_state = self.rnn(decoder_input, dec_state)\n\n            if self.attentional:\n                decoder_output, p_attn = self.attn(\n                    rnn_output,\n                    memory_bank.transpose(0, 1),\n                    memory_lengths=memory_lengths)\n                attns[\"std\"].append(p_attn)\n            else:\n                decoder_output = rnn_output\n            if self.context_gate is not None:\n                # TODO: context gate should be employed\n                # instead of second RNN transform.\n                decoder_output = self.context_gate(\n                    decoder_input, rnn_output, decoder_output\n                )\n            decoder_output = self.dropout(decoder_output)\n            input_feed = decoder_output\n\n            dec_outs += [decoder_output]\n\n            # Update the coverage attention.\n            if self._coverage:\n                coverage = p_attn if coverage is None else p_attn + coverage\n                attns[\"coverage\"] += [coverage]\n\n            if self.copy_attn is not None:\n                _, copy_attn = self.copy_attn(\n                    decoder_output, memory_bank.transpose(0, 1))\n                attns[\"copy\"] += [copy_attn]\n            elif self._reuse_copy_attn:\n                attns[\"copy\"] = attns[\"std\"]\n\n            # @memray: return rnn hidden states for computing orth_reg and sem_cov\n            attns[\"dec_states\"] += [rnn_output]\n            if self.target_encoder is not None:\n                if \"tgtenc_states\" not in attns:\n                    attns[\"tgtenc_states\"] = []\n                attns[\"tgtenc_states\"] += [tgt_enc_output]\n\n        attns[\"dec_states\"] = torch.stack(attns[\"dec_states\"]).transpose(0, 1) # [B, T, H]\n        if self.target_encoder is not None:\n            attns[\"tgtenc_states\"] = torch.stack(attns[\"tgtenc_states\"]).transpose(0, 1) # [B, T, H]\n        return dec_state, dec_outs, attns\n\n    def _build_rnn(self, rnn_type, input_size,\n                   hidden_size, num_layers, dropout):\n        assert rnn_type != \"SRU\", \"SRU doesn't support input feed! \" \\\n            \"Please set -input_feed 0!\"\n        stacked_cell = StackedLSTM if rnn_type == \"LSTM\" else StackedGRU\n        return stacked_cell(num_layers, input_size, hidden_size, dropout)\n\n    @property\n    def _input_size(self):\n        \"\"\"Using input feed by concatenating input with attention vectors.\"\"\"\n        return self.embeddings.embedding_size + self.hidden_size\n\n    def update_dropout(self, dropout):\n        self.dropout.p = dropout\n        self.rnn.dropout.p = dropout\n        self.embeddings.update_dropout(dropout)\n"
  },
  {
    "path": "onmt/decoders/ensemble.py",
    "content": "\"\"\"Ensemble decoding.\n\nDecodes using multiple models simultaneously,\ncombining their prediction distributions by averaging.\nAll models in the ensemble must share a target vocabulary.\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.decoders.decoder import DecoderBase\nfrom onmt.models import NMTModel\nimport onmt.model_builder\n\n\nclass EnsembleDecoderOutput(object):\n    \"\"\"Wrapper around multiple decoder final hidden states.\"\"\"\n    def __init__(self, model_dec_outs):\n        self.model_dec_outs = tuple(model_dec_outs)\n\n    def squeeze(self, dim=None):\n        \"\"\"Delegate squeeze to avoid modifying\n        :func:`onmt.translate.translator.Translator.translate_batch()`\n        \"\"\"\n        return EnsembleDecoderOutput([\n            x.squeeze(dim) for x in self.model_dec_outs])\n\n    def __getitem__(self, index):\n        return self.model_dec_outs[index]\n\n\nclass EnsembleEncoder(EncoderBase):\n    \"\"\"Dummy Encoder that delegates to individual real Encoders.\"\"\"\n    def __init__(self, model_encoders):\n        super(EnsembleEncoder, self).__init__()\n        self.model_encoders = nn.ModuleList(model_encoders)\n\n    def forward(self, src, lengths=None):\n        enc_hidden, memory_bank, _ = zip(*[\n            model_encoder(src, lengths)\n            for model_encoder in self.model_encoders])\n        return enc_hidden, memory_bank, lengths\n\n\nclass EnsembleDecoder(DecoderBase):\n    \"\"\"Dummy Decoder that delegates to individual real Decoders.\"\"\"\n    def __init__(self, model_decoders):\n        model_decoders = nn.ModuleList(model_decoders)\n        attentional = any([dec.attentional for dec in model_decoders])\n        super(EnsembleDecoder, self).__init__(attentional)\n        self.model_decoders = model_decoders\n\n    def forward(self, tgt, memory_bank, memory_lengths=None, step=None,\n                **kwargs):\n        \"\"\"See :func:`onmt.decoders.decoder.DecoderBase.forward()`.\"\"\"\n        # Memory_lengths is a single tensor shared between all models.\n        # This assumption will not hold if Translator is modified\n        # to calculate memory_lengths as something other than the length\n        # of the input.\n        dec_outs, attns = zip(*[\n            model_decoder(\n                tgt, memory_bank[i],\n                memory_lengths=memory_lengths, step=step, **kwargs)\n            for i, model_decoder in enumerate(self.model_decoders)])\n        mean_attns = self.combine_attns(attns)\n        return EnsembleDecoderOutput(dec_outs), mean_attns\n\n    def combine_attns(self, attns):\n        result = {}\n        for key in attns[0].keys():\n            result[key] = torch.stack(\n                [attn[key] for attn in attns if attn[key] is not None]).mean(0)\n        return result\n\n    def init_state(self, src, memory_bank, enc_hidden):\n        \"\"\" See :obj:`RNNDecoderBase.init_state()` \"\"\"\n        for i, model_decoder in enumerate(self.model_decoders):\n            model_decoder.init_state(src, memory_bank[i], enc_hidden[i])\n\n    def map_state(self, fn):\n        for model_decoder in self.model_decoders:\n            model_decoder.map_state(fn)\n\n\nclass EnsembleGenerator(nn.Module):\n    \"\"\"\n    Dummy Generator that delegates to individual real Generators,\n    and then averages the resulting target distributions.\n    \"\"\"\n    def __init__(self, model_generators, raw_probs=False):\n        super(EnsembleGenerator, self).__init__()\n        self.model_generators = nn.ModuleList(model_generators)\n        self._raw_probs = raw_probs\n\n    def forward(self, hidden, attn=None, src_map=None):\n        \"\"\"\n        Compute a distribution over the target dictionary\n        by averaging distributions from models in the ensemble.\n        All models in the ensemble must share a target vocabulary.\n        \"\"\"\n        distributions = torch.stack(\n                [mg(h) if attn is None else mg(h, attn, src_map)\n                 for h, mg in zip(hidden, self.model_generators)]\n            )\n        if self._raw_probs:\n            return torch.log(torch.exp(distributions).mean(0))\n        else:\n            return distributions.mean(0)\n\n\nclass EnsembleModel(NMTModel):\n    \"\"\"Dummy NMTModel wrapping individual real NMTModels.\"\"\"\n    def __init__(self, models, raw_probs=False):\n        encoder = EnsembleEncoder(model.encoder for model in models)\n        decoder = EnsembleDecoder(model.decoder for model in models)\n        super(EnsembleModel, self).__init__(encoder, decoder)\n        self.generator = EnsembleGenerator(\n            [model.generator for model in models], raw_probs)\n        self.models = nn.ModuleList(models)\n\n\ndef load_test_model(opt):\n    \"\"\"Read in multiple models for ensemble.\"\"\"\n    shared_fields = None\n    shared_model_opt = None\n    models = []\n    for model_path in opt.models:\n        fields, model, model_opt = \\\n            onmt.model_builder.load_test_model(opt, model_path=model_path)\n        if shared_fields is None:\n            shared_fields = fields\n        else:\n            for key, field in fields.items():\n                try:\n                    f_iter = iter(field)\n                except TypeError:\n                    f_iter = [(key, field)]\n                for sn, sf in f_iter:\n                    if sf is not None and 'vocab' in sf.__dict__:\n                        sh_field = shared_fields[key]\n                        try:\n                            sh_f_iter = iter(sh_field)\n                        except TypeError:\n                            sh_f_iter = [(key, sh_field)]\n                        sh_f_dict = dict(sh_f_iter)\n                        assert sf.vocab.stoi == sh_f_dict[sn].vocab.stoi, \\\n                            \"Ensemble models must use the same \" \\\n                            \"preprocessed data\"\n        # @memray, warning: not tested\n        if opt.data_type == \"keyphrase\":\n            shared_fields[\"tgt\"].type = opt.tgt_type\n\n        models.append(model)\n        if shared_model_opt is None:\n            shared_model_opt = model_opt\n    ensemble_model = EnsembleModel(models, opt.avg_raw_probs)\n    return shared_fields, ensemble_model, shared_model_opt\n"
  },
  {
    "path": "onmt/decoders/transformer.py",
    "content": "\"\"\"\nImplementation of \"Attention is All You Need\" and of\nsubsequent transformer based architectures\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.decoders.decoder import DecoderBase\nfrom onmt.modules import MultiHeadedAttention, AverageAttention\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\nfrom onmt.utils.misc import sequence_mask\n\n\nclass TransformerDecoderLayerBase(nn.Module):\n    def __init__(self):\n        super(TransformerDecoderLayerBase, self).__init__()\n\n    def forward(self, *args, **kwargs):\n        \"\"\"Extend `_forward` for (possibly) multiple decoder pass:\n        Always a default (future masked) decoder forward pass,\n        Possibly a second future aware decoder pass for joint learn\n        full context alignement, :cite:`garg2019jointly`.\n\n        Args:\n            * All arguments of _forward.\n            with_align (bool): whether return alignment attention.\n\n        Returns:\n            (FloatTensor, FloatTensor, FloatTensor or None):\n\n            * output ``(batch_size, T, model_dim)``\n            * top_attn ``(batch_size, T, src_len)``\n            * attn_align ``(batch_size, T, src_len)`` or None\n        \"\"\"\n        with_align = kwargs.pop(\"with_align\", False)\n        output, attns = self._forward(*args, **kwargs)\n        top_attn = attns[:, 0, :, :].contiguous()\n        attn_align = None\n        if with_align:\n            if self.full_context_alignment:\n                # return _, (B, Q_len, K_len)\n                _, attns = self._forward(*args, **kwargs, future=True)\n\n            if self.alignment_heads > 0:\n                attns = attns[:, : self.alignment_heads, :, :].contiguous()\n            # layer average attention across heads, get ``(B, Q, K)``\n            # Case 1: no full_context, no align heads -> layer avg baseline\n            # Case 2: no full_context, 1 align heads -> guided align\n            # Case 3: full_context, 1 align heads -> full cte guided align\n            attn_align = attns.mean(dim=1)\n        return output, top_attn, attn_align\n\n    def _forward(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def update_dropout(self, dropout, attention_dropout):\n        raise NotImplementedError\n\n\nclass TransformerDecoderLayer(TransformerDecoderLayerBase):\n    \"\"\"Transformer Decoder layer block in Pre-Norm style.\n    Pre-Norm style is an improvement w.r.t. Original paper's Post-Norm style,\n    providing better converge speed and performance. This is also the actual\n    implementation in tensor2tensor and also avalable in fairseq.\n    See https://tunz.kr/post/4 and :cite:`DeeperTransformer`.\n\n    .. mermaid::\n\n        graph LR\n        %% \"*SubLayer\" can be self-attn, src-attn or feed forward block\n            A(input) --> B[Norm]\n            B --> C[\"*SubLayer\"]\n            C --> D[Drop]\n            D --> E((+))\n            A --> E\n            E --> F(out)\n\n\n    Args:\n        d_model (int): the dimension of keys/values/queries in\n            :class:`MultiHeadedAttention`, also the input size of\n            the first-layer of the :class:`PositionwiseFeedForward`.\n        heads (int): the number of heads for MultiHeadedAttention.\n        d_ff (int): the second-layer of the :class:`PositionwiseFeedForward`.\n        dropout (float): dropout in residual, self-attn(dot) and feed-forward\n        attention_dropout (float): dropout in context_attn (and self-attn(avg))\n        self_attn_type (string): type of self-attention scaled-dot, average\n        max_relative_positions (int):\n            Max distance between inputs in relative positions representations\n        aan_useffn (bool): Turn on the FFN layer in the AAN decoder\n        full_context_alignment (bool):\n            whether enable an extra full context decoder forward for alignment\n        alignment_heads (int):\n            N. of cross attention heads to use for alignment guiding\n    \"\"\"\n\n    def __init__(\n        self,\n        d_model,\n        heads,\n        d_ff,\n        dropout,\n        attention_dropout,\n        self_attn_type=\"scaled-dot\",\n        max_relative_positions=0,\n        aan_useffn=False,\n        full_context_alignment=False,\n        alignment_heads=0,\n    ):\n        super(TransformerDecoderLayer, self).__init__()\n\n        if self_attn_type == \"scaled-dot\":\n            self.self_attn = MultiHeadedAttention(\n                heads,\n                d_model,\n                dropout=attention_dropout,\n                max_relative_positions=max_relative_positions,\n            )\n        elif self_attn_type == \"average\":\n            self.self_attn = AverageAttention(\n                d_model, dropout=attention_dropout, aan_useffn=aan_useffn\n            )\n\n        self.context_attn = MultiHeadedAttention(\n            heads, d_model, dropout=attention_dropout\n        )\n        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)\n        self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)\n        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)\n        self.drop = nn.Dropout(dropout)\n        self.full_context_alignment = full_context_alignment\n        self.alignment_heads = alignment_heads\n\n    def update_dropout(self, dropout, attention_dropout):\n        self.self_attn.update_dropout(attention_dropout)\n        self.context_attn.update_dropout(attention_dropout)\n        self.feed_forward.update_dropout(dropout)\n        self.drop.p = dropout\n\n    def _forward(\n        self,\n        inputs,\n        memory_bank,\n        src_pad_mask,\n        tgt_pad_mask,\n        layer_cache=None,\n        step=None,\n        future=False,\n    ):\n        \"\"\"A naive forward pass for transformer decoder.\n\n        # T: could be 1 in the case of stepwise decoding or tgt_len\n\n        Args:\n            inputs (FloatTensor): ``(batch_size, T, model_dim)``\n            memory_bank (FloatTensor): ``(batch_size, src_len, model_dim)``\n            src_pad_mask (bool): ``(batch_size, 1, src_len)``\n            tgt_pad_mask (bool): ``(batch_size, 1, T)``\n            layer_cache (dict or None): cached layer info when stepwise decode\n            step (int or None): stepwise decoding counter\n            future (bool): If set True, do not apply future_mask.\n\n        Returns:\n            (FloatTensor, FloatTensor):\n\n            * output ``(batch_size, T, model_dim)``\n            * attns ``(batch_size, head, T, src_len)``\n\n        \"\"\"\n        dec_mask = None\n\n        if step is None:\n            tgt_len = tgt_pad_mask.size(-1)\n            if not future:  # apply future_mask, result mask in (B, T, T)\n                future_mask = torch.ones(\n                    [tgt_len, tgt_len],\n                    device=tgt_pad_mask.device,\n                    dtype=torch.uint8,\n                )\n                future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)\n                # BoolTensor was introduced in pytorch 1.2\n                try:\n                    future_mask = future_mask.bool()\n                except AttributeError:\n                    pass\n                dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)\n            else:  # only mask padding, result mask in (B, 1, T)\n                dec_mask = tgt_pad_mask\n\n        input_norm = self.layer_norm_1(inputs)\n\n        if isinstance(self.self_attn, MultiHeadedAttention):\n            query, _ = self.self_attn(\n                input_norm,\n                input_norm,\n                input_norm,\n                mask=dec_mask,\n                layer_cache=layer_cache,\n                attn_type=\"self\",\n            )\n        elif isinstance(self.self_attn, AverageAttention):\n            query, _ = self.self_attn(\n                input_norm, mask=dec_mask, layer_cache=layer_cache, step=step\n            )\n\n        query = self.drop(query) + inputs\n\n        query_norm = self.layer_norm_2(query)\n        mid, attns = self.context_attn(\n            memory_bank,\n            memory_bank,\n            query_norm,\n            mask=src_pad_mask,\n            layer_cache=layer_cache,\n            attn_type=\"context\",\n        )\n        output = self.feed_forward(self.drop(mid) + query)\n\n        return output, attns\n\n\nclass TransformerDecoderBase(DecoderBase):\n    def __init__(self, d_model, copy_attn, embeddings, alignment_layer):\n        super(TransformerDecoderBase, self).__init__()\n\n        self.embeddings = embeddings\n\n        # Decoder State\n        self.state = {}\n\n        # previously, there was a GlobalAttention module here for copy\n        # attention. But it was never actually used -- the \"copy\" attention\n        # just reuses the context attention.\n        self._copy = copy_attn\n        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n\n        self.alignment_layer = alignment_layer\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.dec_layers,\n            opt.dec_rnn_size,\n            opt.heads,\n            opt.transformer_ff,\n            opt.copy_attn,\n            opt.self_attn_type,\n            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n            opt.attention_dropout[0]\n            if type(opt.attention_dropout) is list\n            else opt.attention_dropout,\n            embeddings,\n            opt.max_relative_positions,\n            opt.aan_useffn,\n            opt.full_context_alignment,\n            opt.alignment_layer,\n            alignment_heads=opt.alignment_heads,\n            target_encoder_layers=opt.target_encoder_layers,\n            detach_target_encoder=opt.detach_target_encoder,\n        )\n\n    def init_state(self, src, memory_bank, enc_hidden):\n        \"\"\"Initialize decoder state.\"\"\"\n        self.state[\"src\"] = src\n        self.state[\"cache\"] = None\n\n    def map_state(self, fn):\n        def _recursive_map(struct, batch_dim=0):\n            for k, v in struct.items():\n                if v is not None:\n                    if isinstance(v, dict):\n                        _recursive_map(v)\n                    else:\n                        struct[k] = fn(v, batch_dim)\n\n        if self.state[\"src\"] is not None:\n            self.state[\"src\"] = fn(self.state[\"src\"], 1)\n        if self.state[\"cache\"] is not None:\n            _recursive_map(self.state[\"cache\"])\n\n    def detach_state(self):\n        raise NotImplementedError\n\n    def forward(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def update_dropout(self, dropout, attention_dropout):\n        self.embeddings.update_dropout(dropout)\n        for layer in self.transformer_layers:\n            layer.update_dropout(dropout, attention_dropout)\n\n\nclass TransformerDecoder(TransformerDecoderBase):\n    \"\"\"The Transformer decoder from \"Attention is All You Need\".\n    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`\n\n    .. mermaid::\n\n       graph BT\n          A[input]\n          B[multi-head self-attn]\n          BB[multi-head src-attn]\n          C[feed forward]\n          O[output]\n          A --> B\n          B --> BB\n          BB --> C\n          C --> O\n\n\n    Args:\n        num_layers (int): number of decoder layers.\n        d_model (int): size of the model\n        heads (int): number of heads\n        d_ff (int): size of the inner FF layer\n        copy_attn (bool): if using a separate copy attention\n        self_attn_type (str): type of self-attention scaled-dot, average\n        dropout (float): dropout in residual, self-attn(dot) and feed-forward\n        attention_dropout (float): dropout in context_attn (and self-attn(avg))\n        embeddings (onmt.modules.Embeddings):\n            embeddings to use, should have positional encodings\n        max_relative_positions (int):\n            Max distance between inputs in relative positions representations\n        aan_useffn (bool): Turn on the FFN layer in the AAN decoder\n        full_context_alignment (bool):\n            whether enable an extra full context decoder forward for alignment\n        alignment_layer (int): N° Layer to supervise with for alignment guiding\n        alignment_heads (int):\n            N. of cross attention heads to use for alignment guiding\n    \"\"\"\n\n    def __init__(\n        self,\n        num_layers,\n        d_model,\n        heads,\n        d_ff,\n        copy_attn,\n        self_attn_type,\n        dropout,\n        attention_dropout,\n        embeddings,\n        max_relative_positions,\n        aan_useffn,\n        full_context_alignment,\n        alignment_layer,\n        alignment_heads,\n        target_encoder_layers=0,\n        detach_target_encoder=False,\n    ):\n        super(TransformerDecoder, self).__init__(\n            d_model, copy_attn, embeddings, alignment_layer\n        )\n\n        self.transformer_layers = nn.ModuleList(\n            [\n                TransformerDecoderLayer(\n                    d_model,\n                    heads,\n                    d_ff,\n                    dropout,\n                    attention_dropout,\n                    self_attn_type=self_attn_type,\n                    max_relative_positions=max_relative_positions,\n                    aan_useffn=aan_useffn,\n                    full_context_alignment=full_context_alignment,\n                    alignment_heads=alignment_heads,\n                )\n                for i in range(num_layers)\n            ]\n        )\n\n        self.detach_target_encoder = detach_target_encoder\n        if target_encoder_layers > 0:\n            self.target_encoder_layers = nn.ModuleList(\n                [\n                    TransformerDecoderLayer(\n                        d_model,\n                        heads,\n                        d_ff,\n                        dropout,\n                        attention_dropout,\n                        self_attn_type=self_attn_type,\n                        max_relative_positions=max_relative_positions,\n                        aan_useffn=aan_useffn,\n                        full_context_alignment=full_context_alignment,\n                        alignment_heads=alignment_heads,\n                    )\n                    for i in range(target_encoder_layers)\n                ]\n            )\n            self.input_proj_layer = nn.Linear(d_model * 2, d_model)\n            self.bilinear_layer = nn.Bilinear(in1_features=d_model, in2_features=d_model, out_features=1)\n        else:\n            self.target_encoder_layers = None\n            self.input_proj_layer = None\n            self.bilinear_layer = None\n\n    def detach_state(self):\n        self.state[\"src\"] = self.state[\"src\"].detach()\n\n    def forward(self, tgt, memory_bank=None, step=None, **kwargs):\n        \"\"\"Decode, possibly stepwise.\"\"\"\n        if memory_bank is None:\n            memory_bank = self.embeddings(tgt)\n        if step == 0:\n            self._init_cache(memory_bank)\n\n        tgt_words = tgt[:, :, 0].transpose(0, 1)\n\n        emb = self.embeddings(tgt, step=step)\n        assert emb.dim() == 3  # len x batch x embedding_dim\n\n        output = emb.transpose(0, 1).contiguous()\n        src_memory_bank = memory_bank.transpose(0, 1).contiguous()\n\n        pad_idx = self.embeddings.word_padding_idx\n        src_lens = kwargs[\"memory_lengths\"]\n        src_max_len = self.state[\"src\"].shape[0]\n        src_pad_mask = ~sequence_mask(src_lens, src_max_len).unsqueeze(1)\n        tgt_pad_mask = tgt_words.data.eq(pad_idx).unsqueeze(1)  # [B, 1, T_tgt]\n\n        with_align = kwargs.pop(\"with_align\", False)\n        attn_aligns = []\n\n        if self.target_encoder_layers is not None:\n            for i, layer in enumerate(self.target_encoder_layers):\n                layer_cache = (\n                    self.state[\"cache\"][\"te_layer_{}\".format(i)]\n                    if step is not None\n                    else None\n                )\n                te_output = output\n                te_output, _, _ = layer(\n                    te_output,\n                    src_memory_bank,\n                    src_pad_mask,\n                    tgt_pad_mask,\n                    layer_cache=layer_cache,\n                    step=step,\n                    with_align=False,\n                )\n\n            te_output = self.layer_norm(te_output)\n            if self.detach_target_encoder:\n                te_output.detach()\n\n            output = torch.cat([output, te_output], 2) # [B, T, 2*H]\n            output = self.input_proj_layer(output) # [B, T, H]\n\n            for i, layer in enumerate(self.transformer_layers):\n                layer_cache = (\n                    self.state[\"cache\"][\"layer_{}\".format(i)]\n                    if step is not None\n                    else None\n                )\n                output, attn, attn_align = layer(\n                    output,\n                    src_memory_bank,\n                    src_pad_mask,\n                    tgt_pad_mask,\n                    layer_cache=layer_cache,\n                    step=step,\n                    with_align=with_align,\n                )\n                if attn_align is not None:\n                    attn_aligns.append(attn_align)\n        else:\n            for i, layer in enumerate(self.transformer_layers):\n                layer_cache = (\n                    self.state[\"cache\"][\"layer_{}\".format(i)]\n                    if step is not None\n                    else None\n                )\n                output, attn, attn_align = layer(\n                    output,\n                    src_memory_bank,\n                    src_pad_mask,\n                    tgt_pad_mask,\n                    layer_cache=layer_cache,\n                    step=step,\n                    with_align=with_align,\n                )\n                if attn_align is not None:\n                    attn_aligns.append(attn_align)\n\n        output = self.layer_norm(output) # (B, T, H)\n        dec_outs = output.transpose(0, 1).contiguous() # (T, B, H)\n        attn = attn.transpose(0, 1).contiguous() # (T, B, S)\n\n        attns = {\"std\": attn} # (T, B, S)\n        if self._copy:\n            attns[\"copy\"] = attn # (T, B, S)\n        if with_align:\n            attns[\"align\"] = attn_aligns[self.alignment_layer]  # `(B, Q, K)`\n            # attns[\"align\"] = torch.stack(attn_aligns, 0).mean(0)  # All avg\n\n        # @memray: return rnn hidden states for computing orth_reg and sem_cov\n        attns[\"enc_states\"] = src_memory_bank[:, 0, :] # [B, H], take the 1st token as the embedding of source text\n        attns[\"dec_states\"] = output # [B, T, H]\n        if self.target_encoder_layers is not None:\n            if \"tgtenc_states\" not in attns:\n                attns[\"tgtenc_states\"] = []\n            attns[\"tgtenc_states\"] = te_output # [B, T, H]\n\n        # TODO change the way attns is returned dict => list or tuple (onnx)\n        return dec_outs, attns\n\n    def _init_cache(self, memory_bank):\n        self.state[\"cache\"] = {}\n        batch_size = memory_bank.size(1)\n        depth = memory_bank.size(-1)\n\n        for i, layer in enumerate(self.transformer_layers):\n            layer_cache = {\"memory_keys\": None, \"memory_values\": None}\n            if isinstance(layer.self_attn, AverageAttention):\n                layer_cache[\"prev_g\"] = torch.zeros(\n                    (batch_size, 1, depth), device=memory_bank.device\n                )\n            else:\n                layer_cache[\"self_keys\"] = None\n                layer_cache[\"self_values\"] = None\n            self.state[\"cache\"][\"layer_{}\".format(i)] = layer_cache\n\n        if self.target_encoder_layers is not None:\n            for i, layer in enumerate(self.target_encoder_layers):\n                layer_cache = {\"memory_keys\": None, \"memory_values\": None}\n                if isinstance(layer.self_attn, AverageAttention):\n                    layer_cache[\"prev_g\"] = torch.zeros(\n                        (batch_size, 1, depth), device=memory_bank.device\n                    )\n                else:\n                    layer_cache[\"self_keys\"] = None\n                    layer_cache[\"self_values\"] = None\n                self.state[\"cache\"][\"te_layer_{}\".format(i)] = layer_cache\n\nclass TransformerLMDecoderLayer(TransformerDecoderLayerBase):\n    \"\"\"Transformer Decoder only layer block in GPT style.\n\n    .. mermaid::\n\n        graph LR\n        %% \"*SubLayer\" can be self-attn, src-attn or feed forward block\n            A(input) --> B[Norm]\n            B --> C[\"*SubLayer\"]\n            C --> D[Drop]\n            D --> E((+))\n            A --> E\n            E --> F(out)\n\n\n    Args:\n        d_model (int): the dimension of keys/values/queries in\n            :class:`MultiHeadedAttention`, also the input size of\n            the first-layer of the :class:`PositionwiseFeedForward`.\n        heads (int): the number of heads for MultiHeadedAttention.\n        d_ff (int): the second-layer of the :class:`PositionwiseFeedForward`.\n        dropout (float): dropout in residual, self-attn(dot) and feed-forward\n        attention_dropout (float): dropout in context_attn (and self-attn(avg))\n        self_attn_type (string): type of self-attention scaled-dot, average\n        max_relative_positions (int):\n            Max distance between inputs in relative positions representations\n        aan_useffn (bool): Turn on the FFN layer in the AAN decoder\n        full_context_alignment (bool):\n            whether enable an extra full context decoder forward for alignment\n        alignment_heads (int):\n            N. of cross attention heads to use for alignment guiding\n    \"\"\"\n\n    def __init__(\n        self,\n        d_model,\n        heads,\n        d_ff,\n        dropout,\n        attention_dropout,\n        self_attn_type=\"scaled-dot\",\n        max_relative_positions=0,\n        aan_useffn=False,\n        full_context_alignment=False,\n        alignment_heads=0,\n    ):\n        super(TransformerLMDecoderLayer, self).__init__()\n\n        if self_attn_type == \"scaled-dot\":\n            self.self_attn = MultiHeadedAttention(\n                heads,\n                d_model,\n                dropout=attention_dropout,\n                max_relative_positions=max_relative_positions,\n            )\n        elif self_attn_type == \"average\":\n            self.self_attn = AverageAttention(\n                d_model, dropout=attention_dropout, aan_useffn=aan_useffn\n            )\n\n        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)\n        self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)\n        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)\n        self.drop = nn.Dropout(dropout)\n        self.full_context_alignment = full_context_alignment\n        self.alignment_heads = alignment_heads\n\n    def _forward(\n        self, inputs, tgt_pad_mask, layer_cache=None, step=None, future=False\n    ):\n        \"\"\"A naive forward pass for transformer decoder.\n\n        # T: could be 1 in the case of stepwise decoding or tgt_len\n\n        Args:\n            inputs (FloatTensor): ``(batch_size, T, model_dim)``\n            tgt_pad_mask (bool): ``(batch_size, 1, T)``\n            layer_cache (dict or None): cached layer info when stepwise decode\n            step (int or None): stepwise decoding counter\n            future (bool): If set True, do not apply future_mask.\n\n        Returns:\n            (FloatTensor, FloatTensor):\n\n            * output ``(batch_size, T, model_dim)``\n            * attns ``(batch_size, head, T, T)``\n\n        \"\"\"\n        dec_mask = None\n\n        if step is None:\n            tgt_len = tgt_pad_mask.size(-1)\n            if not future:  # apply future_mask, result mask in (B, T, T)\n                future_mask = torch.ones(\n                    [tgt_len, tgt_len],\n                    device=tgt_pad_mask.device,\n                    dtype=torch.uint8,\n                )\n                future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)\n                # BoolTensor was introduced in pytorch 1.2\n                try:\n                    future_mask = future_mask.bool()\n                except AttributeError:\n                    pass\n                dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)\n            else:  # only mask padding, result mask in (B, 1, T)\n                dec_mask = tgt_pad_mask\n\n        inputs_norm = self.layer_norm_1(inputs)\n        if isinstance(self.self_attn, MultiHeadedAttention):\n            query, attns = self.self_attn(\n                inputs_norm,\n                inputs_norm,\n                inputs_norm,\n                mask=dec_mask,\n                layer_cache=layer_cache,\n                attn_type=\"self\",\n            )\n        elif isinstance(self.self_attn, AverageAttention):\n            query, attns = self.self_attn(\n                inputs_norm, mask=dec_mask, layer_cache=layer_cache, step=step\n            )\n\n        output = self.drop(query) + inputs\n\n        output_feedforward = self.feed_forward(self.layer_norm_2(output))\n\n        output_norm = self.drop(output_feedforward) + output\n\n        return output_norm, attns\n\n    def update_dropout(self, dropout, attention_dropout):\n        self.self_attn.update_dropout(attention_dropout)\n        self.feed_forward.update_dropout(dropout)\n        self.drop.p = dropout\n\n\nclass TransformerLMDecoder(TransformerDecoderBase):\n    \"\"\"The Transformer decoder from GPT-2\n\n    .. mermaid::\n\n       graph BT\n          A[input]\n          B[multi-head self-attn]\n          C[feed forward]\n          O[output]\n          A --> B\n          B --> C\n          C --> O\n\n\n    Args:\n        num_layers (int): number of decoder layers.\n        d_model (int): size of the model\n        heads (int): number of heads\n        d_ff (int): size of the inner FF layer\n        copy_attn (bool): if using a separate copy attention\n        self_attn_type (str): type of self-attention scaled-dot, average\n        dropout (float): dropout in residual, self-attn(dot) and feed-forward\n        attention_dropout (float): dropout in context_attn (and self-attn(avg))\n        embeddings (onmt.modules.Embeddings):\n            embeddings to use, should have positional encodings\n        max_relative_positions (int):\n            Max distance between inputs in relative positions representations\n        aan_useffn (bool): Turn on the FFN layer in the AAN decoder\n    \"\"\"\n\n    def __init__(\n        self,\n        num_layers,\n        d_model,\n        heads,\n        d_ff,\n        copy_attn,\n        self_attn_type,\n        dropout,\n        attention_dropout,\n        embeddings,\n        max_relative_positions,\n        aan_useffn,\n        full_context_alignment=None,\n        alignment_layer=None,\n        alignment_heads=None,\n    ):\n        super(TransformerLMDecoder, self).__init__(\n            d_model, copy_attn, embeddings, None\n        )\n        self.transformer_layers = nn.ModuleList(\n            [\n                TransformerLMDecoderLayer(\n                    d_model,\n                    heads,\n                    d_ff,\n                    dropout,\n                    attention_dropout,\n                    self_attn_type=self_attn_type,\n                    max_relative_positions=max_relative_positions,\n                    aan_useffn=aan_useffn,\n                    full_context_alignment=None,\n                    alignment_heads=None,\n                )\n                for i in range(num_layers)\n            ]\n        )\n\n    def init_state(self, src=None, memory_bank=None, enc_hidden=None):\n        super(TransformerLMDecoder, self).init_state(None, None, None)\n\n    def detach_state(self):\n        pass\n\n    def forward(self, tgt, memory_bank=None, step=None, **kwargs):\n        \"\"\"Decode, possibly stepwise.\"\"\"\n        if step == 0:\n            self._init_cache()\n\n        tgt_words = tgt[:, :, 0].transpose(0, 1)\n\n        emb = self.embeddings(tgt, step=step)\n        assert emb.dim() == 3  # len x batch x embedding_dim\n\n        output = emb.transpose(0, 1).contiguous()\n\n        pad_idx = self.embeddings.word_padding_idx\n        tgt_pad_mask = tgt_words.data.eq(pad_idx).unsqueeze(1)  # [B, 1, T_tgt]\n\n        with_align = kwargs.pop(\"with_align\", False)\n        assert not with_align, \"TransformerLMDecoder does not support align\"\n\n        for i, layer in enumerate(self.transformer_layers):\n            layer_cache = (\n                self.state[\"cache\"][\"layer_{}\".format(i)]\n                if step is not None\n                else None\n            )\n            output, attn, _ = layer(\n                output,\n                tgt_pad_mask,\n                layer_cache=layer_cache,\n                step=step,\n                with_align=with_align,\n            )\n\n        output = self.layer_norm(output)\n        dec_outs = output.transpose(0, 1).contiguous()\n        attn = attn.transpose(0, 1).contiguous()\n\n        attns = {\"std\": attn}\n        if self._copy:\n            attns[\"copy\"] = attn\n\n        # TODO change the way attns is returned dict => list or tuple (onnx)\n        return dec_outs, attns\n\n    def _init_cache(self, memory_bank=None):\n        self.state[\"cache\"] = {}\n\n        for i, layer in enumerate(self.transformer_layers):\n            layer_cache = {\"self_keys\": None, \"self_values\": None}\n            if isinstance(layer.self_attn, AverageAttention):\n                raise NotImplementedError\n            self.state[\"cache\"][\"layer_{}\".format(i)] = layer_cache\n"
  },
  {
    "path": "onmt/encoders/__init__.py",
    "content": "\"\"\"Module defining encoders.\"\"\"\nfrom onmt.encoders.bart_encoder import BARTEncoder\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.encoders.pretrained_encoder import PretrainedEncoder\nfrom onmt.encoders.transformer import TransformerEncoder\nfrom onmt.encoders.ggnn_encoder import GGNNEncoder\nfrom onmt.encoders.rnn_encoder import RNNEncoder\nfrom onmt.encoders.cnn_encoder import CNNEncoder\nfrom onmt.encoders.mean_encoder import MeanEncoder\n\n\nstr2enc = {\"ggnn\": GGNNEncoder, \"rnn\": RNNEncoder, \"brnn\": RNNEncoder,\n           \"cnn\": CNNEncoder, \"transformer\": TransformerEncoder,\n           \"mean\": MeanEncoder,\n           \"pretrained\": PretrainedEncoder, \"bart\": BARTEncoder}\n\n__all__ = [\"EncoderBase\", \"TransformerEncoder\", \"RNNEncoder\", \"CNNEncoder\",\n           \"MeanEncoder\", \"str2enc\", \"BARTEncoder\"]\n"
  },
  {
    "path": "onmt/encoders/bart_encoder.py",
    "content": "\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\nimport os\nimport random\n\nfrom typing import Any, Dict, List, Optional, Tuple\nimport fairseq\nimport torch.nn as nn\nimport torch\n\nfrom fairseq.models.bart import BARTModel\n\nfrom onmt.encoders.encoder import EncoderBase\nimport logging\n\nclass BARTEncoder(EncoderBase):\n    \"\"\"\n    large: 24-layer, 1024-hidden, 16-heads, 355M parameters\n    \"\"\"\n    def __init__(self, model_name, embeddings, cache_dir, max_src_length, vocab_size, opt,\n                 bart_model=None, prev_checkpoint=None):\n        super(BARTEncoder, self).__init__()\n        self.model_name = model_name\n        self.opt = opt\n\n        if bart_model is None:\n            bart_dir = os.path.join(opt.cache_dir, 'bart.large')\n            bart_path = os.path.join(bart_dir, 'model.pt')\n            assert os.path.exists(bart_path), 'BART checkpoint is not found! %s ' % bart_path\n            logging.getLogger().info('Loading BART encoder from %s' % bart_path)\n\n            bart_model = BARTModel.from_pretrained(bart_dir, checkpoint_file='model.pt')\n        else:\n            bart_model = bart_model\n\n        if prev_checkpoint:\n            bart_model.model.load_state_dict(prev_checkpoint['model'], strict=True)\n\n        self.model = bart_model.model.encoder\n        self.embed_tokens = self.model.embed_tokens\n        self.embed_positions = self.model.embed_positions\n        self.embed_fields = self.model.embed_tokens\n\n        # override the forward_embedding() function to support src label embedding\n        self.model.forward_embedding = forward_embedding\n        self.model.forward = forward_bart_encoder\n\n        # BART default max length of position embedding is 1024 (max_source_positions and max_target_positions)\n        pos_emb_len = self.embed_positions.num_embeddings\n        if max_src_length > pos_emb_len:\n            emb_len = max_src_length + 8\n            # new pos_embedding must be longer than src_length by at least 2 (1 for heading CLS, 1 for an offset)\n            # Does fairseq start position at 2? b/c it's padding_idx is 1\n            new_pos_embedding = fairseq.modules.LearnedPositionalEmbedding(emb_len, self.embed_positions.embedding_dim, padding_idx=self.embed_positions.padding_idx)\n            nn.init.normal_(new_pos_embedding.weight, mean=0, std=self.embed_positions.embedding_dim ** -0.5)\n            nn.init.constant_(new_pos_embedding.weight[self.embed_positions.padding_idx], 0)\n            new_pos_embedding.weight.data[:pos_emb_len] = self.model.embed_positions.weight.data\n            self.model.embed_positions = new_pos_embedding\n            self.embed_positions = new_pos_embedding\n            self.model.max_source_positions = max_src_length\n            logging.getLogger().info('Adjusted position size to %d, position_embed.shape=%s'\n                                     % (self.embed_positions.num_embeddings, str(self.embed_positions.weight.shape)))\n\n        # Expand token embeddings if necessary\n        token_emb_len = self.embed_tokens.num_embeddings\n        if vocab_size > token_emb_len:\n            new_token_embedding = nn.Embedding(vocab_size, self.embed_tokens.embedding_dim, padding_idx=self.embed_tokens.padding_idx)\n            nn.init.normal_(new_token_embedding.weight, mean=0, std=self.embed_tokens.embedding_dim ** -0.5)\n            nn.init.constant_(new_token_embedding.weight[self.embed_tokens.padding_idx], 0)\n            new_token_embedding.weight.data[:token_emb_len] = self.model.embed_tokens.weight.data\n            self.model.embed_tokens = new_token_embedding\n            self.embed_tokens = new_token_embedding\n            # set embed_fields to be word_embeddings, to call token embeddings easily\n            self.embed_fields = new_token_embedding\n\n            logging.getLogger().info('Adjusted vocab size to %d, token_embed.shape=%s'\n                                     % (self.embed_tokens.num_embeddings, str(self.embed_tokens.weight.shape)))\n\n\n    @classmethod\n    def from_opt(cls, opt, embeddings, **kwargs):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            model_name='bart',\n            embeddings=embeddings,\n            cache_dir=opt.cache_dir,\n            max_src_length=opt.src_seq_length_trunc,\n            # vocab_size should be additionally added (after reloading fields news_dataset.reload_news_fields())\n            vocab_size=opt.vocab_size,\n            opt=opt,\n            **kwargs\n        )\n\n\n    def forward(self, src, src_lengths):\n        \"\"\"\n        :returns\n            last_hidden_state:\n                Sequence of hidden-states at the output of the last layer of the model.\n            pooler_output: Last layer hidden-state of the first token of the sequence (classification token)\n                further processed by a Linear layer and a Tanh activation function.\n                The Linear layer weights are trained from the next sentence prediction (classification) objective during Bert pretraining.\n                This output is usually not a good summary of the semantic content of the input,\n                you’re often better with averaging or pooling the sequence of hidden-states for the whole input sequence.\n        \"\"\"\n        # input to BART must be batch_first, src should be (batch_size, sequence_length)\n        src = src.permute(1, 0, 2)\n        # don't know how to add token_type_ids because embedding is processed inside\n        src_tokens = src[:, :, 0]\n        if src.shape[2] > 1:\n            src_labels = src[:, :, 1]\n        else:\n            src_labels = None\n\n        #     'encoder_out', state of the last layer # T x B x C\n        #     'encoder_padding_mask',  # B x T\n        #     'encoder_embedding', token embeddings (w/o positional embeddings) # B x T x C\n        #     'encoder_states', states of each layer if return_all_hiddens=True # List[T x B x C]\n        encoder_output = self.model(self.model, src_tokens, src_lengths,\n                                    return_all_hiddens=False, src_labels=src_labels)\n        # return last_hidden_state and memory_bank in shape of [src_len, batch_size, hid_dim] and length as is\n        last_hidden_state = encoder_output['encoder_out'][0]\n\n        return last_hidden_state, last_hidden_state, src_lengths, encoder_output\n\n\ndef forward_embedding(self, src_tokens, src_labels, token_embedding: Optional[torch.Tensor] = None):\n    '''\n    See fairseq.models.transformer.py L376, forward_embedding()\n    Embed tokens and positions, both shape=[batch_size, src_len] and weights in embed_tokens\n    :param self: BART model object\n    :param src_tokens: text tokens\n    :param src_labels: feature labels\n    :return:\n    '''\n    if token_embedding is None:\n        token_embedding = self.embed_tokens(src_tokens)\n    x = embed = self.embed_scale * token_embedding\n\n    if self.embed_positions is not None:\n        x = embed + self.embed_positions(src_tokens)\n\n    if src_labels is not None:\n        x += self.embed_tokens(src_labels)\n\n    if self.layernorm_embedding:\n        x = self.layernorm_embedding(x)\n\n    x = self.dropout_module(x)\n\n    if self.quant_noise is not None:\n        x = self.quant_noise(x)\n\n    return x, embed\n\n\ndef forward_bart_encoder(self,\n                         src_tokens,\n                         src_lengths,\n                         return_all_hiddens: bool = False,\n                         src_labels=None,\n                         token_embeddings: Optional[torch.Tensor] = None,\n                         **unused):\n    \"\"\"\n    Args:\n        src_tokens (LongTensor): tokens in the source language of shape\n            `(batch, src_len)`\n        src_lengths (torch.LongTensor): lengths of each source sentence of\n            shape `(batch)`\n        return_all_hiddens (bool, optional): also return all of the\n            intermediate hidden states (default: False).\n\n    Returns:\n        namedtuple:\n            - **encoder_out** (Tensor): the last encoder layer's output of\n              shape `(src_len, batch, embed_dim)`\n            - **encoder_padding_mask** (ByteTensor): the positions of\n              padding elements of shape `(batch, src_len)`\n            - **encoder_embedding** (Tensor): the (scaled) embedding lookup\n              of shape `(batch, src_len, embed_dim)`\n            - **encoder_states** (List[Tensor]): all intermediate\n              hidden states of shape `(src_len, batch, embed_dim)`.\n              Only populated if *return_all_hiddens* is True.\n    \"\"\"\n    x, encoder_embedding = self.forward_embedding(self, src_tokens, src_labels, token_embeddings)\n\n    # B x T x C -> T x B x C\n    x = x.transpose(0, 1)\n\n    # compute padding mask\n    encoder_padding_mask = src_tokens.eq(self.padding_idx)\n\n    encoder_states = []\n\n    # encoder layers\n    for layer in self.layers:\n        x = layer(x, encoder_padding_mask)\n        if return_all_hiddens:\n            assert encoder_states is not None\n            encoder_states.append(x)\n\n    if self.layer_norm is not None:\n        x = self.layer_norm(x)\n\n    return {\n        \"encoder_out\": [x],  # T x B x C\n        \"encoder_padding_mask\": [encoder_padding_mask],  # B x T\n        \"encoder_embedding\": [encoder_embedding],  # B x T x C\n        \"encoder_states\": encoder_states,  # List[T x B x C]\n        \"src_tokens\": [],\n        \"src_lengths\": [],\n    }\n    # return EncoderOut(\n    #     encoder_out=x,  # T x B x C\n    #     encoder_padding_mask=encoder_padding_mask,  # B x T\n    #     encoder_embedding=None,  # B x T x C\n    #     encoder_states=None,  # List[T x B x C]\n    #     # encoder_embedding=encoder_embedding,  # B x T x C\n    #     # encoder_states=encoder_states,  # List[T x B x C]\n    #     src_tokens=None,\n    #     src_lengths=None,\n    # )\n\n"
  },
  {
    "path": "onmt/encoders/cnn_encoder.py",
    "content": "\"\"\"\nImplementation of \"Convolutional Sequence to Sequence Learning\"\n\"\"\"\nimport torch.nn as nn\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.utils.cnn_factory import shape_transform, StackedCNN\n\nSCALE_WEIGHT = 0.5 ** 0.5\n\n\nclass CNNEncoder(EncoderBase):\n    \"\"\"Encoder based on \"Convolutional Sequence to Sequence Learning\"\n    :cite:`DBLP:journals/corr/GehringAGYD17`.\n    \"\"\"\n\n    def __init__(self, num_layers, hidden_size,\n                 cnn_kernel_width, dropout, embeddings):\n        super(CNNEncoder, self).__init__()\n\n        self.embeddings = embeddings\n        input_size = embeddings.embedding_size\n        self.linear = nn.Linear(input_size, hidden_size)\n        self.cnn = StackedCNN(num_layers, hidden_size,\n                              cnn_kernel_width, dropout)\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.enc_layers,\n            opt.enc_rnn_size,\n            opt.cnn_kernel_width,\n            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n            embeddings)\n\n    def forward(self, input, lengths=None, hidden=None):\n        \"\"\"See :class:`onmt.modules.EncoderBase.forward()`\"\"\"\n        self._check_args(input, lengths, hidden)\n\n        emb = self.embeddings(input)\n        # s_len, batch, emb_dim = emb.size()\n\n        emb = emb.transpose(0, 1).contiguous()\n        emb_reshape = emb.view(emb.size(0) * emb.size(1), -1)\n        emb_remap = self.linear(emb_reshape)\n        emb_remap = emb_remap.view(emb.size(0), emb.size(1), -1)\n        emb_remap = shape_transform(emb_remap)\n        out = self.cnn(emb_remap)\n\n        return emb_remap.squeeze(3).transpose(0, 1).contiguous(), \\\n            out.squeeze(3).transpose(0, 1).contiguous(), lengths\n\n    def update_dropout(self, dropout):\n        self.cnn.dropout.p = dropout\n"
  },
  {
    "path": "onmt/encoders/encoder.py",
    "content": "\"\"\"Base class for encoders and generic multi encoders.\"\"\"\n\nimport torch.nn as nn\n\nfrom onmt.utils.misc import aeq\n\n\nclass EncoderBase(nn.Module):\n    \"\"\"\n    Base encoder class. Specifies the interface used by different encoder types\n    and required by :class:`onmt.Models.NMTModel`.\n\n    .. mermaid::\n\n       graph BT\n          A[Input]\n          subgraph RNN\n            C[Pos 1]\n            D[Pos 2]\n            E[Pos N]\n          end\n          F[Memory_Bank]\n          G[Final]\n          A-->C\n          A-->D\n          A-->E\n          C-->F\n          D-->F\n          E-->F\n          E-->G\n    \"\"\"\n\n    @classmethod\n    def from_opt(cls, opt, embeddings=None):\n        raise NotImplementedError\n\n    def _check_args(self, src, lengths=None, hidden=None):\n        n_batch = src.size(1)\n        if lengths is not None:\n            n_batch_, = lengths.size()\n            aeq(n_batch, n_batch_)\n\n    def forward(self, src, lengths=None):\n        \"\"\"\n        Args:\n            src (LongTensor):\n               padded sequences of sparse indices ``(src_len, batch, nfeat)``\n            lengths (LongTensor): length of each sequence ``(batch,)``\n\n\n        Returns:\n            (FloatTensor, FloatTensor, FloatTensor):\n\n            * final encoder state, used to initialize decoder\n            * memory bank for attention, ``(src_len, batch, hidden)``\n            * lengths\n        \"\"\"\n\n        raise NotImplementedError\n"
  },
  {
    "path": "onmt/encoders/ggnn_encoder.py",
    "content": "\"\"\"Define GGNN-based encoders.\"\"\"\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom onmt.encoders.encoder import EncoderBase\n\n\nclass GGNNAttrProxy(object):\n    \"\"\"\n    Translates index lookups into attribute lookups.\n    To implement some trick which able to use list of nn.Module in a nn.Module\n    see https://discuss.pytorch.org/t/list-of-nn-module-in-a-nn-module/219/2\n    \"\"\"\n    def __init__(self, module, prefix):\n        self.module = module\n        self.prefix = prefix\n\n    def __getitem__(self, i):\n        return getattr(self.module, self.prefix + str(i))\n\n\nclass GGNNPropogator(nn.Module):\n    \"\"\"\n    Gated Propogator for GGNN\n    Using LSTM gating mechanism\n    \"\"\"\n    def __init__(self, state_dim, n_node, n_edge_types):\n        super(GGNNPropogator, self).__init__()\n\n        self.n_node = n_node\n        self.n_edge_types = n_edge_types\n\n        self.reset_gate = nn.Sequential(\n            nn.Linear(state_dim*3, state_dim),\n            nn.Sigmoid()\n        )\n        self.update_gate = nn.Sequential(\n            nn.Linear(state_dim*3, state_dim),\n            nn.Sigmoid()\n        )\n        self.tansform = nn.Sequential(\n            nn.Linear(state_dim*3, state_dim),\n            nn.LeakyReLU()\n        )\n\n    def forward(self, state_in, state_out, state_cur, edges, nodes):\n        edges_in = edges[:, :, :nodes*self.n_edge_types]\n        edges_out = edges[:, :, nodes*self.n_edge_types:]\n\n        a_in = torch.bmm(edges_in, state_in)\n        a_out = torch.bmm(edges_out, state_out)\n        a = torch.cat((a_in, a_out, state_cur), 2)\n\n        r = self.reset_gate(a)\n        z = self.update_gate(a)\n        joined_input = torch.cat((a_in, a_out, r * state_cur), 2)\n        h_hat = self.tansform(joined_input)\n\n        output = (1 - z) * state_cur + z * h_hat\n\n        return output\n\n\nclass GGNNEncoder(EncoderBase):\n    \"\"\" A gated graph neural network configured as an encoder.\n       Based on github.com/JamesChuanggg/ggnn.pytorch.git,\n       which is based on the paper \"Gated Graph Sequence Neural Networks\"\n       by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel.\n\n    Args:\n       rnn_type (str):\n          style of recurrent unit to use, one of [LSTM]\n       state_dim (int) : Number of state dimensions in nodes\n       n_edge_types (int) : Number of edge types\n       bidir_edges (bool): True if reverse edges should be autocreated\n       n_node (int) : Max nodes in graph\n       bridge_extra_node (bool): True indicates only 1st extra node\n          (after token listing) should be used for decoder init.\n       n_steps (int): Steps to advance graph encoder for stabilization\n       src_vocab (int): Path to source vocabulary.(The ggnn uses src_vocab\n            during training because the graph is built using edge information\n            which requires parsing the input sequence.)\n    \"\"\"\n\n    def __init__(self, rnn_type, state_dim, bidir_edges,\n                 n_edge_types, n_node, bridge_extra_node, n_steps, src_vocab):\n        super(GGNNEncoder, self).__init__()\n\n        self.state_dim = state_dim\n        self.n_edge_types = n_edge_types\n        self.n_node = n_node\n        self.n_steps = n_steps\n        self.bidir_edges = bidir_edges\n        self.bridge_extra_node = bridge_extra_node\n\n        for i in range(self.n_edge_types):\n            # incoming and outgoing edge embedding\n            in_fc = nn.Linear(self.state_dim, self.state_dim)\n            out_fc = nn.Linear(self.state_dim, self.state_dim)\n            self.add_module(\"in_{}\".format(i), in_fc)\n            self.add_module(\"out_{}\".format(i), out_fc)\n\n        self.in_fcs = GGNNAttrProxy(self, \"in_\")\n        self.out_fcs = GGNNAttrProxy(self, \"out_\")\n\n        # Find vocab data for tree builting\n        f = open(src_vocab, \"r\")\n        idx = 0\n        self.COMMA = -1\n        self.DELIMITER = -1\n        self.idx2num = []\n        for ln in f:\n            ln = ln.strip('\\n')\n            if ln == \",\":\n                self.COMMA = idx\n            if ln == \"<EOT>\":\n                self.DELIMITER = idx\n            if ln.isdigit():\n                self.idx2num.append(int(ln))\n            else:\n                self.idx2num.append(-1)\n            idx += 1\n\n        # Propogation Model\n        self.propogator = GGNNPropogator(self.state_dim, self.n_node,\n                                         self.n_edge_types)\n\n        self._initialization()\n\n        # Initialize the bridge layer\n        self._initialize_bridge(rnn_type, self.state_dim, 1)\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.rnn_type,\n            opt.state_dim,\n            opt.bidir_edges,\n            opt.n_edge_types,\n            opt.n_node,\n            opt.bridge_extra_node,\n            opt.n_steps,\n            opt.src_vocab)\n\n    def _initialization(self):\n        for m in self.modules():\n            if isinstance(m, nn.Linear):\n                m.weight.data.normal_(0.0, 0.02)\n                m.bias.data.fill_(0)\n\n    def forward(self, src, lengths=None):\n        \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n        self._check_args(src, lengths)\n        nodes = self.n_node\n        batch_size = src.size()[1]\n        first_extra = np.zeros(batch_size, dtype=np.int32)\n        prop_state = np.zeros((batch_size, nodes, self.state_dim),\n                              dtype=np.int32)\n        edges = np.zeros((batch_size, nodes, nodes*self.n_edge_types*2),\n                         dtype=np.int32)\n        npsrc = src[:, :, 0].cpu().data.numpy().astype(np.int32)\n\n        # Initialize graph using formatted input sequence\n        for i in range(batch_size):\n            tokens_done = False\n            # Number of flagged nodes defines node count for this sample\n            # (Nodes can have no flags on them, but must be in 'flags' list).\n            flags = 0\n            flags_done = False\n            edge = 0\n            source_node = -1\n            for j in range(len(npsrc)):\n                token = npsrc[j][i]\n                if not tokens_done:\n                    if token == self.DELIMITER:\n                        tokens_done = True\n                        first_extra[i] = j\n                    else:\n                        prop_state[i][j][token] = 1\n                elif token == self.DELIMITER:\n                    flags += 1\n                    flags_done = True\n                    assert flags <= nodes\n                elif not flags_done:\n                    # The total number of integers in the vocab should allow\n                    # for all features and edges to be defined.\n                    if token == self.COMMA:\n                        flags = 0\n                    else:\n                        num = self.idx2num[token]\n                        if num >= 0:\n                            prop_state[i][flags][num+self.DELIMITER] = 1\n                        flags += 1\n                elif token == self.COMMA:\n                    edge += 1\n                    assert source_node == -1, 'Error in graph edge input'\n                    assert (edge <= 2*self.n_edge_types and\n                            (not self.bidir_edges or edge < self.n_edge_types))\n                else:\n                    num = self.idx2num[token]\n                    if source_node < 0:\n                        source_node = num\n                    else:\n                        edges[i][source_node][num+nodes*edge] = 1\n                        if self.bidir_edges:\n                            edges[i][num][nodes*(edge+self.n_edge_types)\n                                          + source_node] = 1\n                        source_node = -1\n\n        prop_state = torch.from_numpy(prop_state).float().to(src.device)\n        edges = torch.from_numpy(edges).float().to(src.device)\n\n        for i_step in range(self.n_steps):\n            in_states = []\n            out_states = []\n            for i in range(self.n_edge_types):\n                in_states.append(self.in_fcs[i](prop_state))\n                out_states.append(self.out_fcs[i](prop_state))\n            in_states = torch.stack(in_states).transpose(0, 1).contiguous()\n            in_states = in_states.view(-1, nodes*self.n_edge_types,\n                                       self.state_dim)\n            out_states = torch.stack(out_states).transpose(0, 1).contiguous()\n            out_states = out_states.view(-1, nodes*self.n_edge_types,\n                                         self.state_dim)\n\n            prop_state = self.propogator(in_states, out_states, prop_state,\n                                         edges, nodes)\n\n        prop_state = prop_state.transpose(0, 1)\n        if self.bridge_extra_node:\n            # Use first extra node as only source for decoder init\n            join_state = prop_state[first_extra, torch.arange(batch_size)]\n        else:\n            # Average all nodes to get bridge input\n            join_state = prop_state.mean(0)\n        join_state = torch.stack((join_state, join_state,\n                                  join_state, join_state))\n        join_state = (join_state, join_state)\n\n        encoder_final = self._bridge(join_state)\n\n        return encoder_final, prop_state, lengths\n\n    def _initialize_bridge(self, rnn_type,\n                           hidden_size,\n                           num_layers):\n\n        # LSTM has hidden and cell state, other only one\n        number_of_states = 2 if rnn_type == \"LSTM\" else 1\n        # Total number of states\n        self.total_hidden_dim = hidden_size * num_layers\n\n        # Build a linear layer for each\n        self.bridge = nn.ModuleList([nn.Linear(self.total_hidden_dim,\n                                               self.total_hidden_dim,\n                                               bias=True)\n                                     for _ in range(number_of_states)])\n\n    def _bridge(self, hidden):\n        \"\"\"Forward hidden state through bridge.\"\"\"\n        def bottle_hidden(linear, states):\n            \"\"\"\n            Transform from 3D to 2D, apply linear and return initial size\n            \"\"\"\n            size = states.size()\n            result = linear(states.view(-1, self.total_hidden_dim))\n            return F.leaky_relu(result).view(size)\n\n        if isinstance(hidden, tuple):  # LSTM\n            outs = tuple([bottle_hidden(layer, hidden[ix])\n                          for ix, layer in enumerate(self.bridge)])\n        else:\n            outs = bottle_hidden(self.bridge[0], hidden)\n        return outs\n"
  },
  {
    "path": "onmt/encoders/mean_encoder.py",
    "content": "\"\"\"Define a minimal encoder.\"\"\"\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.utils.misc import sequence_mask\nimport torch\n\n\nclass MeanEncoder(EncoderBase):\n    \"\"\"A trivial non-recurrent encoder. Simply applies mean pooling.\n\n    Args:\n       num_layers (int): number of replicated layers\n       embeddings (onmt.modules.Embeddings): embedding module to use\n    \"\"\"\n\n    def __init__(self, num_layers, embeddings):\n        super(MeanEncoder, self).__init__()\n        self.num_layers = num_layers\n        self.embeddings = embeddings\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.enc_layers,\n            embeddings)\n\n    def forward(self, src, lengths=None):\n        \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n        self._check_args(src, lengths)\n\n        emb = self.embeddings(src)\n        _, batch, emb_dim = emb.size()\n\n        if lengths is not None:\n            # we avoid padding while mean pooling\n            mask = sequence_mask(lengths).float()\n            mask = mask / lengths.unsqueeze(1).float()\n            mean = torch.bmm(mask.unsqueeze(1), emb.transpose(0, 1)).squeeze(1)\n        else:\n            mean = emb.mean(0)\n\n        mean = mean.expand(self.num_layers, batch, emb_dim)\n        memory_bank = emb\n        encoder_final = (mean, mean)\n        return encoder_final, memory_bank, lengths\n"
  },
  {
    "path": "onmt/encoders/pretrained_encoder.py",
    "content": "\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\n\nimport torch.nn as nn\nimport torch\nfrom transformers import AutoModel\n\nfrom onmt.encoders.encoder import EncoderBase\nimport logging\n\nclass ExtClassifier(nn.Module):\n    def __init__(self, hidden_size):\n        super(ExtClassifier, self).__init__()\n        self.linear1 = nn.Linear(hidden_size, 1)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x, mask=None):\n        h = self.linear1(x).squeeze(-1)\n        if mask:\n            sent_scores = self.sigmoid(h) * mask.float()\n        else:\n            sent_scores = self.sigmoid(h)\n\n        return sent_scores\n\n\nclass PretrainedEncoder(EncoderBase):\n    \"\"\"\n    base: 12-layer, 768-hidden, 12-heads, 125M parameters\n    large: 24-layer, 1024-hidden, 16-heads, 355M parameters\n    \"\"\"\n    def __init__(self, model_name, cache_dir, src_length, vocab_size, opt):\n        super(PretrainedEncoder, self).__init__()\n        self.model_name = model_name\n        self.opt = opt\n        self.model = AutoModel.from_pretrained(model_name, cache_dir=cache_dir)\n\n        # if src is longer than default 512, add some randomly initialized positional embeddings\n        pos_emb_len = self.model.embeddings.position_embeddings.weight.data.size(0)\n        if(src_length > pos_emb_len):\n            emb_len = src_length + 8\n            # new pos_embedding must be longer than src_length by at least 2 (1 for heading CLS, 1 for an offset)\n            new_pos_embeddings = nn.Embedding(emb_len, self.model.config.hidden_size)\n            new_pos_embeddings.weight.data[:pos_emb_len] = self.model.embeddings.position_embeddings.weight.data\n            new_pos_embeddings.weight.data[pos_emb_len:] = self.model.embeddings.position_embeddings.weight.data[-1][None,:].repeat(emb_len-pos_emb_len, 1)\n            self.model.embeddings.position_embeddings = new_pos_embeddings\n\n        # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.\n        self.model.resize_token_embeddings(vocab_size)\n        self.vocab_size = self.model.config.vocab_size\n        self.finetune = opt.finetune_encoder\n        logging.getLogger().info('Adjusted vocab size to %d' % self.model.config.vocab_size)\n        # set token_type_embeddings to be word_embeddings, to call token embeddings easily\n        self.model.embeddings.token_type_embeddings = self.model.embeddings.word_embeddings\n\n        if opt.ext_loss:\n            assert len(opt.ext_loss_types) > 0\n            assert len(opt.ext_loss_types) <= self.model.config.num_attention_heads\n            self.ext_loss = opt.ext_loss\n            self.lambda_ext_loss = opt.lambda_ext_loss\n            self.ext_loss_types = opt.ext_loss_types\n            self.ext_loss_decay_steps = opt.ext_loss_decay_steps\n\n            logging.getLogger().info('Added %d extractive classifiers: %s' % (len(opt.ext_loss_types), str(opt.ext_loss_types)))\n            dim_per_head = int(self.model.config.hidden_size / self.model.config.num_attention_heads)\n            self.dim_per_head = dim_per_head\n            self.ext_classifiers = {}\n            for ext_type in opt.ext_loss_types:\n                ext_type_name = 'ext_'+ext_type\n                self.ext_classifiers[ext_type_name] = ExtClassifier(dim_per_head)\n                setattr(self, ext_type_name, self.ext_classifiers[ext_type_name])\n\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            model_name=opt.pretrained_encoder,\n            cache_dir=opt.cache_dir,\n            src_length=opt.src_seq_length_trunc,\n            # vocab_size should be additionally added (after reloading fields news_dataset.reload_news_fields())\n            vocab_size=opt.vocab_size,\n            opt=opt\n        )\n\n    def forward(self, src, mask):\n        \"\"\"\n        :returns\n            last_hidden_state:\n                Sequence of hidden-states at the output of the last layer of the model.\n            pooler_output: Last layer hidden-state of the first token of the sequence (classification token)\n                further processed by a Linear layer and a Tanh activation function.\n                The Linear layer weights are trained from the next sentence prediction (classification) objective during Bert pretraining.\n                This output is usually not a good summary of the semantic content of the input,\n                you’re often better with averaging or pooling the sequence of hidden-states for the whole input sequence.\n        \"\"\"\n        # input to BERT must be batch_first, src and token_type_ids should be (batch_size, sequence_length)\n        input_ids = src[:, :, 0].permute(1, 0)\n        if src.shape[2] > 1:\n            token_type_ids = src[:, :, 1].permute(1, 0)\n        else:\n            token_type_ids = None\n\n        mask = mask.permute(1, 0)\n        if(self.finetune):\n            # last_hidden_scindertate: [batch_size, src_len, hid_dim], pooler_output: [batch_size, hid_dim]\n            # last_hidden_state, pooler_output = self.model(input_ids)\n            # last_hidden_state, pooler_output = self.model(input_ids, attention_mask=mask)\n            last_hidden_state, pooler_output = self.model(input_ids, attention_mask=mask, token_type_ids=token_type_ids)\n        else:\n            self.eval()\n            with torch.no_grad():\n                last_hidden_state, pooler_output = self.model(input_ids, attention_mask=mask, token_type_ids=token_type_ids)\n\n        ext_logits = None\n        if hasattr(self, 'ext_loss') and self.ext_loss:\n            ext_logits = {}\n            for cls_id, (cls_name, cls) in enumerate(self.ext_classifiers.items()):\n                cls_input = last_hidden_state[:, :, self.dim_per_head * cls_id: self.dim_per_head * (cls_id + 1)]\n                # input.shape=[batch_size, src_len, head_hid_dim], output.shape=[batch_size, src_len]\n                logit = cls(cls_input)\n                # make its shape to [length, batch_size]\n                ext_logits[cls_name] = logit.permute((1, 0))\n\n        # return last_hidden_state and memory_bank in shape of [src_len, batch_size, hid_dim] and length as is\n        last_hidden_state = last_hidden_state.permute(1, 0, 2)\n\n        return last_hidden_state, last_hidden_state, ext_logits\n"
  },
  {
    "path": "onmt/encoders/rnn_encoder.py",
    "content": "\"\"\"Define RNN-based encoders.\"\"\"\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.nn.utils.rnn import pack_padded_sequence as pack\nfrom torch.nn.utils.rnn import pad_packed_sequence as unpack\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.utils.rnn_factory import rnn_factory\n\n\nclass RNNEncoder(EncoderBase):\n    \"\"\" A generic recurrent neural network encoder.\n\n    Args:\n       rnn_type (str):\n          style of recurrent unit to use, one of [RNN, LSTM, GRU, SRU]\n       bidirectional (bool) : use a bidirectional RNN\n       num_layers (int) : number of stacked layers\n       hidden_size (int) : hidden size of each layer\n       dropout (float) : dropout value for :class:`torch.nn.Dropout`\n       embeddings (onmt.modules.Embeddings): embedding module to use\n    \"\"\"\n\n    def __init__(self, rnn_type, bidirectional, num_layers,\n                 hidden_size, dropout=0.0, embeddings=None,\n                 use_bridge=False):\n        super(RNNEncoder, self).__init__()\n        assert embeddings is not None\n\n        num_directions = 2 if bidirectional else 1\n        assert hidden_size % num_directions == 0\n        hidden_size = hidden_size // num_directions\n        self.embeddings = embeddings\n\n        self.rnn, self.no_pack_padded_seq = \\\n            rnn_factory(rnn_type,\n                        input_size=embeddings.embedding_size,\n                        hidden_size=hidden_size,\n                        num_layers=num_layers,\n                        dropout=dropout,\n                        bidirectional=bidirectional)\n\n        # Initialize the bridge layer\n        self.use_bridge = use_bridge\n        if self.use_bridge:\n            self._initialize_bridge(rnn_type,\n                                    hidden_size,\n                                    num_layers)\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.rnn_type,\n            opt.brnn,\n            opt.enc_layers,\n            opt.enc_rnn_size,\n            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n            embeddings,\n            opt.bridge)\n\n    def forward(self, src, lengths=None):\n        \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n        self._check_args(src, lengths)\n\n        emb = self.embeddings(src)\n        # s_len, batch, emb_dim = emb.size()\n\n        packed_emb = emb\n        if lengths is not None and not self.no_pack_padded_seq:\n            # Lengths data is wrapped inside a Tensor.\n            lengths_list = lengths.view(-1).tolist()\n            packed_emb = pack(emb, lengths_list)\n\n        memory_bank, encoder_final = self.rnn(packed_emb)\n\n        if lengths is not None and not self.no_pack_padded_seq:\n            memory_bank = unpack(memory_bank)[0]\n\n        if self.use_bridge:\n            encoder_final = self._bridge(encoder_final)\n        return encoder_final, memory_bank, lengths, None\n\n    def _initialize_bridge(self, rnn_type,\n                           hidden_size,\n                           num_layers):\n\n        # LSTM has hidden and cell state, other only one\n        number_of_states = 2 if rnn_type == \"LSTM\" else 1\n        # Total number of states\n        self.total_hidden_dim = hidden_size * num_layers\n\n        # Build a linear layer for each\n        self.bridge = nn.ModuleList([nn.Linear(self.total_hidden_dim,\n                                               self.total_hidden_dim,\n                                               bias=True)\n                                     for _ in range(number_of_states)])\n\n    def _bridge(self, hidden):\n        \"\"\"Forward hidden state through bridge.\"\"\"\n        def bottle_hidden(linear, states):\n            \"\"\"\n            Transform from 3D to 2D, apply linear and return initial size\n            \"\"\"\n            size = states.size()\n            result = linear(states.view(-1, self.total_hidden_dim))\n            return F.relu(result).view(size)\n\n        if isinstance(hidden, tuple):  # LSTM\n            outs = tuple([bottle_hidden(layer, hidden[ix])\n                          for ix, layer in enumerate(self.bridge)])\n        else:\n            outs = bottle_hidden(self.bridge[0], hidden)\n        return outs\n\n    def update_dropout(self, dropout):\n        self.rnn.dropout = dropout\n"
  },
  {
    "path": "onmt/encoders/transformer.py",
    "content": "\"\"\"\nImplementation of \"Attention is All You Need\"\n\"\"\"\n\nimport torch.nn as nn\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.modules import MultiHeadedAttention\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\nfrom onmt.utils.misc import sequence_mask\n\n\nclass TransformerEncoderLayer(nn.Module):\n    \"\"\"\n    A single layer of the transformer encoder.\n\n    Args:\n        d_model (int): the dimension of keys/values/queries in\n                   MultiHeadedAttention, also the input size of\n                   the first-layer of the PositionwiseFeedForward.\n        heads (int): the number of head for MultiHeadedAttention.\n        d_ff (int): the second-layer of the PositionwiseFeedForward.\n        dropout (float): dropout probability(0-1.0).\n    \"\"\"\n\n    def __init__(self, d_model, heads, d_ff, dropout, attention_dropout,\n                 max_relative_positions=0):\n        super(TransformerEncoderLayer, self).__init__()\n\n        self.self_attn = MultiHeadedAttention(\n            heads, d_model, dropout=attention_dropout,\n            max_relative_positions=max_relative_positions)\n        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)\n        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, inputs, mask):\n        \"\"\"\n        Args:\n            inputs (FloatTensor): ``(batch_size, src_len, model_dim)``\n            mask (LongTensor): ``(batch_size, 1, src_len)``\n\n        Returns:\n            (FloatTensor):\n\n            * outputs ``(batch_size, src_len, model_dim)``\n        \"\"\"\n        input_norm = self.layer_norm(inputs)\n        context, _ = self.self_attn(input_norm, input_norm, input_norm,\n                                    mask=mask, attn_type=\"self\")\n        out = self.dropout(context) + inputs\n        return self.feed_forward(out)\n\n    def update_dropout(self, dropout, attention_dropout):\n        self.self_attn.update_dropout(attention_dropout)\n        self.feed_forward.update_dropout(dropout)\n        self.dropout.p = dropout\n\n\nclass TransformerEncoder(EncoderBase):\n    \"\"\"The Transformer encoder from \"Attention is All You Need\"\n    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`\n\n    .. mermaid::\n\n       graph BT\n          A[input]\n          B[multi-head self-attn]\n          C[feed forward]\n          O[output]\n          A --> B\n          B --> C\n          C --> O\n\n    Args:\n        num_layers (int): number of encoder layers\n        d_model (int): size of the model\n        heads (int): number of heads\n        d_ff (int): size of the inner FF layer\n        dropout (float): dropout parameters\n        embeddings (onmt.modules.Embeddings):\n          embeddings to use, should have positional encodings\n\n    Returns:\n        (torch.FloatTensor, torch.FloatTensor):\n\n        * embeddings ``(src_len, batch_size, model_dim)``\n        * memory_bank ``(src_len, batch_size, model_dim)``\n    \"\"\"\n\n    def __init__(self, num_layers, d_model, heads, d_ff, dropout,\n                 attention_dropout, embeddings, max_relative_positions):\n        super(TransformerEncoder, self).__init__()\n\n        self.embeddings = embeddings\n        self.transformer = nn.ModuleList(\n            [TransformerEncoderLayer(\n                d_model, heads, d_ff, dropout, attention_dropout,\n                max_relative_positions=max_relative_positions)\n             for i in range(num_layers)])\n        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n\n    @classmethod\n    def from_opt(cls, opt, embeddings):\n        \"\"\"Alternate constructor.\"\"\"\n        return cls(\n            opt.enc_layers,\n            opt.enc_rnn_size,\n            opt.heads,\n            opt.transformer_ff,\n            opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n            opt.attention_dropout[0] if type(opt.attention_dropout)\n            is list else opt.attention_dropout,\n            embeddings,\n            opt.max_relative_positions)\n\n    def forward(self, src, lengths=None):\n        \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n        self._check_args(src, lengths)\n\n        emb = self.embeddings(src)\n\n        out = emb.transpose(0, 1).contiguous()\n        mask = ~sequence_mask(lengths).unsqueeze(1)\n        # Run the forward pass of every layer of the tranformer.\n        for layer in self.transformer:\n            out = layer(out, mask)\n        out = self.layer_norm(out)\n\n        return emb, out.transpose(0, 1).contiguous(), lengths, None\n\n    def update_dropout(self, dropout, attention_dropout):\n        self.embeddings.update_dropout(dropout)\n        for layer in self.transformer:\n            layer.update_dropout(dropout, attention_dropout)\n"
  },
  {
    "path": "onmt/inputters/__init__.py",
    "content": "\"\"\"Module defining inputters.\n\nInputters implement the logic of transforming raw data to vectorized inputs,\ne.g., from a line of text to a sequence of embeddings.\n\"\"\"\nfrom onmt.inputters.inputter import get_fields, build_vocab, filter_example\nfrom onmt.inputters.iterator import max_tok_len, OrderedIterator\nfrom onmt.inputters.dataset_base import Dataset\nfrom onmt.inputters.text_dataset import text_sort_key, TextDataReader\nfrom onmt.inputters.keyphrase_dataset import kp_sort_key, KeyphraseDataReader, KeyphraseDataset\nfrom onmt.inputters.datareader_base import DataReaderBase\n\n\nstr2reader = {\n    \"text\": TextDataReader, \"keyphrase\": KeyphraseDataReader}\nstr2sortkey = {\n    'text': text_sort_key, \"keyphrase\": text_sort_key\n}\nstr2dataset = {\n    'text': Dataset, \"keyphrase\": KeyphraseDataset\n}\n\n__all__ = ['Dataset', 'get_fields', 'DataReaderBase', 'filter_example',\n           'build_vocab', 'OrderedIterator', 'max_tok_len',\n           'text_sort_key', 'TextDataReader',\n           'kp_sort_key', 'KeyphraseDataReader',\n           'Dataset', 'KeyphraseDataset']\n"
  },
  {
    "path": "onmt/inputters/corpus.py",
    "content": "\"\"\"Module that contain shard utils for dynamic data.\"\"\"\nimport itertools\nimport json\nimport os\nfrom onmt.utils.logging import logger\nfrom onmt.constants import CorpusName\nfrom onmt.transforms import TransformPipe\nfrom onmt.inputters.dataset_base import _dynamic_dict\nfrom torchtext.data import Dataset as TorchtextDataset, \\\n    Example as TorchtextExample\n\nfrom collections import Counter\nfrom contextlib import contextmanager\n\nimport multiprocessing as mp\n\n\n@contextmanager\ndef exfile_open(filename, *args, **kwargs):\n    \"\"\"Extended file opener enables open(filename=None).\n\n    This context manager enables open(filename=None) as well as regular file.\n    filename None will produce endlessly None for each iterate,\n    while filename with valid path will produce lines as usual.\n\n    Args:\n        filename (str|None): a valid file path or None;\n        *args: args relate to open file using codecs;\n        **kwargs: kwargs relate to open file using codecs.\n\n    Yields:\n        `None` repeatly if filename==None,\n        else yield from file specified in `filename`.\n    \"\"\"\n    if filename is None:\n        from itertools import repeat\n        _file = repeat(None)\n    else:\n        import codecs\n        _file = codecs.open(filename, *args, **kwargs)\n    yield _file\n    if filename is not None and _file:\n        _file.close()\n\n\nclass DatasetAdapter(object):\n    \"\"\"Adapte a buckets of tuples into examples of a torchtext Dataset.\"\"\"\n\n    valid_field_name = (\n        'src', 'tgt', 'indices', 'src_map', 'src_ex_vocab', 'alignment',\n        'align')\n\n    def __init__(self, fields, is_train):\n        self.fields_dict = self._valid_fields(fields)\n        self.is_train = is_train\n\n    @classmethod\n    def _valid_fields(cls, fields):\n        \"\"\"Return valid fields in dict format.\"\"\"\n        return {\n            f_k: f_v for f_k, f_v in fields.items()\n            if f_k in cls.valid_field_name\n        }\n\n    @staticmethod\n    def _process(item, is_train):\n        \"\"\"Return valid transformed example from `item`.\"\"\"\n        example, transform, cid = item\n        # this is a hack: appears quicker to apply it here\n        # than in the ParallelCorpusIterator\n        maybe_example = transform.apply(\n            example, is_train=is_train, corpus_name=cid)\n        if maybe_example is None:\n            return None\n        maybe_example['src'] = ' '.join(maybe_example['src'])\n        if is_train:\n            maybe_example['tgt'] = ' '.join(maybe_example['tgt'])\n        else:\n            maybe_example['tgt'] = ''\n        if 'align' in maybe_example:\n            maybe_example['align'] = ' '.join(maybe_example['align'])\n\n        return maybe_example\n\n    def _maybe_add_dynamic_dict(self, example, fields):\n        \"\"\"maybe update `example` with dynamic_dict related fields.\"\"\"\n        if 'src_map' in fields and 'alignment' in fields:\n            example = _dynamic_dict(\n                example,\n                fields['src'].base_field,\n                fields['tgt'].base_field)\n        return example\n\n    def _to_examples(self, bucket, is_train=False):\n        examples = []\n        for item in bucket:\n            maybe_example = self._process(item, is_train=is_train)\n            if maybe_example is not None:\n                example = self._maybe_add_dynamic_dict(\n                    maybe_example, self.fields_dict)\n                ex_fields = {k: [(k, v)] for k, v in self.fields_dict.items()\n                             if k in example}\n                ex = TorchtextExample.fromdict(example, ex_fields)\n                examples.append(ex)\n        return examples\n\n    def __call__(self, bucket):\n        examples = self._to_examples(bucket, is_train=self.is_train)\n        dataset = TorchtextDataset(examples, self.fields_dict)\n        return dataset\n\n\nclass ParallelCorpus(object):\n    \"\"\"A parallel corpus file pair that can be loaded to iterate.\"\"\"\n\n    def __init__(self, name, src, tgt, align=None, extra_label_paths=None, dataset_type=None, examples=None):\n        \"\"\"Initialize src & tgt side file path.\"\"\"\n        self.id = name\n        self.src = src\n        self.tgt = tgt\n        self.align = align\n        self.dataset_type = dataset_type\n        self.examples = examples\n        self.extra_label_paths = extra_label_paths if extra_label_paths is not None else []\n\n    def load(self, offset=0, stride=1):\n        \"\"\"\n        Load file and iterate by lines.\n        `offset` and `stride` allow to iterate only on every\n        `stride` example, starting from `offset`.\n        \"\"\"\n        # extra_label_files = [exfile_open(extra_label_path, mode='rb') for extra_label_path in self.extra_label_paths]\n        logger.info(f\"Loading {repr(self)}...\")\n        # if the path starts with __, e.g. ___random_span, it will be processed later\n        extra_label_files = [[json.loads(l) for l in open(extra_label_path, mode='rb').readlines()]\n                             if not extra_label_path.startswith('__') else itertools.repeat(extra_label_path)\n                             for extra_label_path in self.extra_label_paths]\n        with exfile_open(self.src, mode='rb') as fs,\\\n                exfile_open(self.tgt, mode='rb') as ft,\\\n                exfile_open(self.align, mode='rb') as fa:\n            files = [fs, ft, fa] + extra_label_files\n            for i, lines in enumerate(zip(*files)):\n                sline, tline, align = lines[0], lines[1], lines[2]\n\n                if (i % stride) == offset:\n                    if self.dataset_type == 'keyphrase':\n                        sline = sline.decode('utf-8')\n                        example = json.loads(sline)\n                        # dps with empty src/tgt will be skipped\n                        example['src'] = example['src'] if 'src' in example else ''\n                        example['tgt'] = example['tgt'] if 'tgt' in example else ''\n\n                        # load extra labels\n                        if len(lines) > 2:\n                            label_exs = lines[3:]\n                            for labelset_id, label_ex in enumerate(label_exs):\n                                if isinstance(label_ex, str):\n                                    example.update({'target%d' % labelset_id: label_ex})\n                                else:\n                                    # concatenate if it's a list\n                                    if len(label_ex['pred_sents']) > 0 and not isinstance(label_ex['pred_sents'][0], str):\n                                        label_ex['pred_sents'] = [' '.join(p) for p in label_ex['pred_sents']]\n                                    example.update({'target%d' % labelset_id: label_ex['pred_sents']})\n                    else:\n                        sline = sline.decode('utf-8')\n                        tline = tline.decode('utf-8')\n                        example = {\n                            'src': sline,\n                            'tgt': tline\n                        }\n                    if align is not None:\n                        example['align'] = align.decode('utf-8')\n                    yield example\n\n    def __repr__(self):\n        cls_name = type(self).__name__\n        return '{}({}, {}, align={})'.format(\n            cls_name, self.src, self.tgt, self.align)\n\n\ndef get_corpora(opts, is_train=False):\n    corpora_dict = {}\n    if is_train:\n        for corpus_id, corpus_dict in opts.data.items():\n            if 'type' in corpus_dict and corpus_dict['type'] == 'keyphrase':\n                if corpus_id != CorpusName.VALID:\n                    corpora_dict[corpus_id] = ParallelCorpus(\n                        corpus_id,\n                        corpus_dict[\"path_src\"],\n                        corpus_dict[\"path_tgt\"],\n                        corpus_dict[\"path_align\"],\n                        dataset_type = corpus_dict[\"type\"],\n                        extra_label_paths = corpus_dict[\"label_data\"] if \"label_data\" in corpus_dict else None\n                    )\n            else:\n                if corpus_id != CorpusName.VALID:\n                    corpora_dict[corpus_id] = ParallelCorpus(\n                        corpus_id,\n                        corpus_dict[\"path_src\"],\n                        corpus_dict[\"path_tgt\"],\n                        corpus_dict[\"path_align\"],\n                        dataset_type = corpus_dict[\"type\"],\n                        extra_label_paths = corpus_dict[\"label_data\"] if \"label_data\" in corpus_dict else None\n                    )\n    else:\n        if CorpusName.VALID in opts.data.keys():\n            corpora_dict[CorpusName.VALID] = ParallelCorpus(\n                CorpusName.VALID,\n                opts.data[CorpusName.VALID][\"path_src\"],\n                opts.data[CorpusName.VALID][\"path_tgt\"],\n                opts.data[CorpusName.VALID][\"path_align\"],\n                dataset_type = opts.data[CorpusName.VALID][\"type\"]\n            )\n        else:\n            return None\n    return corpora_dict\n\n\nclass ParallelCorpusIterator(object):\n    \"\"\"An iterator dedicate for ParallelCorpus.\n\n    Args:\n        corpus (ParallelCorpus): corpus to iterate;\n        transform (Transform): transforms to be applied to corpus;\n        infinitely (bool): True to iterate endlessly;\n        skip_empty_level (str): security level when encouter empty line;\n        stride (int): iterate corpus with this line stride;\n        offset (int): iterate corpus with this line offset.\n    \"\"\"\n    def __init__(self, corpus, transform, infinitely=False,\n                 skip_empty_level='warning', stride=1, offset=0):\n        self.cid = corpus.id\n        self.corpus = corpus\n        self.transform = transform\n        self.infinitely = False # @memray, set to always False\n        if skip_empty_level not in ['silent', 'warning', 'error']:\n            raise ValueError(\n                f\"Invalid argument skip_empty_level={skip_empty_level}\")\n        self.skip_empty_level = skip_empty_level\n        self.stride = stride\n        self.offset = offset\n\n    def _tokenize(self, stream):\n        for example in stream:\n            src = example['src'].strip('\\n').split()\n            tgt = example['tgt'].strip('\\n').split()\n            example['src'], example['tgt'] = src, tgt\n            if 'align' in example:\n                example['align'] = example['align'].strip('\\n').split()\n            yield example\n\n    def _transform(self, stream):\n        for example in stream:\n            # NOTE: moved to DatasetAdapter._process method in iterator.py\n            # item = self.transform.apply(\n            # example, is_train=self.infinitely, corpus_name=self.cid)\n            item = (example, self.transform, self.cid)\n            if item is not None:\n                yield item\n        report_msg = self.transform.stats()\n        if report_msg != '':\n            logger.info(\"Transform statistics for {}:\\n{}\".format(\n                self.cid, report_msg))\n\n    def _add_index(self, stream):\n        for i, item in enumerate(stream):\n            example = item[0]\n            line_number = i * self.stride + self.offset\n            example['indices'] = line_number\n\n            # @memray skip keyphrase non-empty check\n            if not hasattr(self.corpus, 'dataset_type') or \\\n                    (hasattr(self.corpus, 'dataset_type') and self.corpus.dataset_type != 'keyphrase'):\n                if (len(example['src']) == 0 or len(example['tgt']) == 0 or\n                        ('align' in example and example['align'] == 0)):\n                    # empty example: skip\n                    empty_msg = f\"Empty line exists in {self.cid}#{line_number}.\"\n                    if self.skip_empty_level == 'error':\n                        raise IOError(empty_msg)\n                    elif self.skip_empty_level == 'warning':\n                        logger.warning(empty_msg)\n                    continue\n            yield item\n\n    def _iter_corpus(self):\n        if self.corpus.examples is None:\n            corpus_stream = self.corpus.load(\n                stride=self.stride, offset=self.offset)\n        else:\n            # added by @memray, if examples are given, directly transform and return them\n            corpus_stream = self.corpus.examples\n\n        # @memray, skip tokenization for keyphrase (json format)\n        if self.corpus.dataset_type != 'keyphrase':\n            tokenized_corpus = self._tokenize(corpus_stream)\n        else:\n            tokenized_corpus = corpus_stream\n        transformed_corpus = self._transform(tokenized_corpus)\n        indexed_corpus = self._add_index(transformed_corpus)\n        yield from indexed_corpus\n\n    def __iter__(self):\n        if self.infinitely:\n            while True:\n                _iter = self._iter_corpus()\n                yield from _iter\n        else:\n            yield from self._iter_corpus()\n\n\ndef build_corpora_iters(corpora, transforms, corpora_info, is_train=False,\n                        skip_empty_level='warning', stride=1, offset=0):\n    \"\"\"Return `ParallelCorpusIterator` for all corpora defined in opts.\"\"\"\n    corpora_iters = dict()\n    for c_id, corpus in corpora.items():\n        c_transform_names = corpora_info[c_id].get('transforms', [])\n        corpus_transform = [transforms[name] for name in c_transform_names]\n        transform_pipe = TransformPipe.build_from(corpus_transform)\n        logger.info(f\"{c_id}'s transforms: {str(transform_pipe)}\")\n        corpus_iter = ParallelCorpusIterator(\n            corpus, transform_pipe, infinitely=is_train,\n            skip_empty_level=skip_empty_level, stride=stride, offset=offset)\n        corpora_iters[c_id] = corpus_iter\n    return corpora_iters\n\n\ndef write_files_from_queues(sample_path, queues):\n    \"\"\"\n    Standalone process that reads data from\n    queues in order and write to sample files.\n    \"\"\"\n    os.makedirs(sample_path, exist_ok=True)\n    for c_name in queues.keys():\n        dest_base = dest_base = os.path.join(\n            sample_path, \"{}.{}\".format(c_name, CorpusName.SAMPLE))\n        with open(dest_base + \".src\", 'w', encoding=\"utf-8\") as f_src,\\\n                open(dest_base + \".tgt\", 'w', encoding=\"utf-8\") as f_tgt:\n            while True:\n                _next = False\n                for i, q in enumerate(queues[c_name]):\n                    item = q.get()\n                    if item == \"break\":\n                        _next = True\n                        break\n                    j, src_line, tgt_line = item\n                    f_src.write(src_line + '\\n')\n                    f_tgt.write(tgt_line + '\\n')\n                if _next:\n                    break\n\n\ndef build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):\n    \"\"\"Build vocab on (strided) subpart of the data.\"\"\"\n    sub_counter_src = Counter()\n    sub_counter_tgt = Counter()\n    datasets_iterables = build_corpora_iters(\n        corpora, transforms, opts.data, is_train=False,\n        skip_empty_level=opts.skip_empty_level,\n        stride=stride, offset=offset)\n    for c_name, c_iter in datasets_iterables.items():\n        for i, item in enumerate(c_iter):\n            maybe_example = DatasetAdapter._process(item, is_train=True)\n            if maybe_example is None:\n                continue\n            src_line, tgt_line = maybe_example['src'], maybe_example['tgt']\n            sub_counter_src.update(src_line.split(' '))\n            sub_counter_tgt.update(tgt_line.split(' '))\n            if opts.dump_samples:\n                build_sub_vocab.queues[c_name][offset].put(\n                    (i, src_line, tgt_line))\n            if n_sample > 0 and ((i+1) * stride + offset) >= n_sample:\n                if opts.dump_samples:\n                    build_sub_vocab.queues[c_name][offset].put(\"break\")\n                break\n        if opts.dump_samples:\n            build_sub_vocab.queues[c_name][offset].put(\"break\")\n    return sub_counter_src, sub_counter_tgt\n\n\ndef init_pool(queues):\n    \"\"\"Add the queues as attribute of the pooled function.\"\"\"\n    build_sub_vocab.queues = queues\n\n\ndef build_vocab(opts, transforms, n_sample=3):\n    \"\"\"Build vocabulary from data.\"\"\"\n\n    if n_sample == -1:\n        logger.info(f\"n_sample={n_sample}: Build vocab on full datasets.\")\n    elif n_sample > 0:\n        logger.info(f\"Build vocab on {n_sample} transformed examples/corpus.\")\n    else:\n        raise ValueError(f\"n_sample should > 0 or == -1, get {n_sample}.\")\n\n    if opts.dump_samples:\n        logger.info(\"The samples on which the vocab is built will be \"\n                    \"dumped to disk. It may slow down the process.\")\n    corpora = get_corpora(opts, is_train=True)\n    counter_src = Counter()\n    counter_tgt = Counter()\n    from functools import partial\n    queues = {c_name: [mp.Queue(opts.vocab_sample_queue_size)\n                       for i in range(opts.num_threads)]\n              for c_name in corpora.keys()}\n    sample_path = os.path.join(\n        os.path.dirname(opts.save_data), CorpusName.SAMPLE)\n    if opts.dump_samples:\n        write_process = mp.Process(\n            target=write_files_from_queues,\n            args=(sample_path, queues),\n            daemon=True)\n        write_process.start()\n    with mp.Pool(opts.num_threads, init_pool, [queues]) as p:\n        func = partial(\n            build_sub_vocab, corpora, transforms,\n            opts, n_sample, opts.num_threads)\n        for sub_counter_src, sub_counter_tgt in p.imap(\n                func, range(0, opts.num_threads)):\n            counter_src.update(sub_counter_src)\n            counter_tgt.update(sub_counter_tgt)\n    if opts.dump_samples:\n        write_process.join()\n    return counter_src, counter_tgt\n\n\ndef save_transformed_sample(opts, transforms, n_sample=3):\n    \"\"\"Save transformed data sample as specified in opts.\"\"\"\n\n    if n_sample == -1:\n        logger.info(f\"n_sample={n_sample}: Save full transformed corpus.\")\n    elif n_sample == 0:\n        logger.info(f\"n_sample={n_sample}: no sample will be saved.\")\n        return\n    elif n_sample > 0:\n        logger.info(f\"Save {n_sample} transformed example/corpus.\")\n    else:\n        raise ValueError(f\"n_sample should >= -1, get {n_sample}.\")\n\n    corpora = get_corpora(opts, is_train=True)\n    datasets_iterables = build_corpora_iters(\n        corpora, transforms, opts.data, is_train=False,\n        skip_empty_level=opts.skip_empty_level)\n    sample_path = os.path.join(\n        os.path.dirname(opts.save_data), CorpusName.SAMPLE)\n    os.makedirs(sample_path, exist_ok=True)\n    for c_name, c_iter in datasets_iterables.items():\n        dest_base = os.path.join(\n            sample_path, \"{}.{}\".format(c_name, CorpusName.SAMPLE))\n        with open(dest_base + \".src\", 'w', encoding=\"utf-8\") as f_src,\\\n                open(dest_base + \".tgt\", 'w', encoding=\"utf-8\") as f_tgt:\n            for i, item in enumerate(c_iter):\n                maybe_example = DatasetAdapter._process(item, is_train=True)\n                if maybe_example is None:\n                    continue\n                src_line, tgt_line = maybe_example['src'], maybe_example['tgt']\n                f_src.write(src_line + '\\n')\n                f_tgt.write(tgt_line + '\\n')\n                if n_sample > 0 and i >= n_sample:\n                    break\n"
  },
  {
    "path": "onmt/inputters/datareader_base.py",
    "content": "# coding: utf-8\n\n\n# several data readers need optional dependencies. There's no\n# appropriate builtin exception\nclass MissingDependencyException(Exception):\n    pass\n\n\nclass DataReaderBase(object):\n    \"\"\"Read data from file system and yield as dicts.\n\n    Raises:\n        onmt.inputters.datareader_base.MissingDependencyException: A number\n            of DataReaders need specific additional packages.\n            If any are missing, this will be raised.\n    \"\"\"\n\n    @classmethod\n    def from_opt(cls, opt):\n        \"\"\"Alternative constructor.\n\n        Args:\n            opt (argparse.Namespace): The parsed arguments.\n        \"\"\"\n\n        return cls()\n\n    @classmethod\n    def _read_file(cls, path):\n        \"\"\"Line-by-line read a file as bytes.\"\"\"\n        with open(path, \"rb\") as f:\n            for line in f:\n                yield line\n\n    @staticmethod\n    def _raise_missing_dep(*missing_deps):\n        \"\"\"Raise missing dep exception with standard error message.\"\"\"\n        raise MissingDependencyException(\n            \"Could not create reader. Be sure to install \"\n            \"the following dependencies: \" + \", \".join(missing_deps))\n\n    def read(self, data, side):\n        \"\"\"Read data from file system and yield as dicts.\"\"\"\n        raise NotImplementedError()\n"
  },
  {
    "path": "onmt/inputters/dataset_base.py",
    "content": "# coding: utf-8\nimport logging\nfrom itertools import chain, starmap\nfrom collections import Counter\n\nimport torch\nfrom torchtext.data import Dataset as TorchtextDataset\nfrom torchtext.data import Example\nfrom torchtext.vocab import Vocab\nfrom transformers import RobertaTokenizer, RobertaTokenizerFast\n\n\ndef _join_dicts(*args):\n    \"\"\"\n    Args:\n        dictionaries with disjoint keys.\n\n    Returns:\n        a single dictionary that has the union of these keys.\n    \"\"\"\n\n    return dict(chain(*[d.items() for d in args]))\n\n\ndef _dynamic_dict(example, src_field, tgt_field):\n    \"\"\"Create copy-vocab and numericalize with it.\n\n    In-place adds ``\"src_map\"`` to ``example``. That is the copy-vocab\n    numericalization of the tokenized ``example[\"src\"]``. If ``example``\n    has a ``\"tgt\"`` key, adds ``\"alignment\"`` to example. That is the\n    copy-vocab numericalization of the tokenized ``example[\"tgt\"]``. The\n    alignment has an initial and final UNK token to match the BOS and EOS\n    tokens.\n\n    Args:\n        example (dict): An example dictionary with a ``\"src\"`` key and\n            maybe a ``\"tgt\"`` key. (This argument changes in place!)\n        src_field (torchtext.data.Field): Field object.\n        tgt_field (torchtext.data.Field): Field object.\n\n    Returns:\n        ``example``, changed as described.\n    \"\"\"\n    if hasattr(src_field, 'pretrained_tokenizer'):\n        src = example['src'].split() # it should have been tokenized in a previous transform\n        # @memray (deprecated) process subwords, or it causes mismatches for copy.\n        #   - Simple lower and trim doesn't work since the new temp vocab cannot be merged with original vocab (out of index error)\n        #   - Similarly, strip('Ġġ') also cause problems:\n        #       - e.g. expect [2-degenerate] but model outputs ['Ġ2', '-', 'deg', 'Ġener', 'ATE'], i.e. [2-deg enerATE]\n        #       - this might be because some copied tokens are wrongly merged, say the prob of a copied token is merged into 'Ġener' rather than 'ener'\n        #       - thus 'Ġġ' in the src_ex_vocab here should not be trimmed\n        # if isinstance(src_field.pretrained_tokenizer, RobertaTokenizer) \\\n        #         or isinstance(src_field.pretrained_tokenizer, RobertaTokenizerFast):\n        #     src = [t.lower().strip('Ġġ') for t in src]\n        # else:\n        #     logging.warning('Using a pretrained tokenizer other than Roberta, '\n        #                     'the leading special token is not properly trimmed.')\n    else:\n        src = src_field.tokenize(example[\"src\"])\n    # make a small vocab containing just the tokens in the source sequence\n    unk = src_field.unk_token\n    pad = src_field.pad_token\n\n    # add init_token and eos_token according to src construction\n    # bos/eos will be added later in OpenNMT pipeline\n    if src_field.init_token:\n        src = [src_field.init_token] + src\n    if src_field.eos_token:\n        src.append(src_field.eos_token)\n    example[\"src_length\"] = len(src)\n\n    src_ex_vocab = Vocab(Counter(src), specials=[unk, pad])\n    unk_idx = src_ex_vocab.stoi[unk]\n    # Map source tokens to indices in the dynamic dict.\n    src_map = torch.LongTensor([src_ex_vocab.stoi[w] for w in src])\n    example[\"src_map\"] = src_map\n    example[\"src_ex_vocab\"] = src_ex_vocab\n\n    if \"tgt\" in example:\n        if hasattr(tgt_field, 'pretrained_tokenizer'):\n            # @memray we need to normalize the target vocab to resolve the unmatchable issue in copy loss\n            #   see copy_generator.py, collapse_copy_scores(), L29\n            #   deprecated, see above comments\n            # norm_stoi = {}\n            # for s, i in tgt_field.vocab.stoi.items():\n            #     norm_stoi[s.lower().strip('Ġġ')] = i\n            #     norm_stoi[s] = i\n            # setattr(tgt_field.vocab, 'norm_stoi', norm_stoi)\n            tgt = example['tgt'].split()\n            # if isinstance(src_field.pretrained_tokenizer, RobertaTokenizer) \\\n            #     or isinstance(src_field.pretrained_tokenizer, RobertaTokenizerFast):\n            #     tgt = [t.lower().strip('Ġġ') for t in tgt]\n        else:\n            tgt = tgt_field.tokenize(example[\"tgt\"])\n        mask = torch.LongTensor(\n            [unk_idx] + [src_ex_vocab.stoi[w] for w in tgt] + [unk_idx])\n        example[\"alignment\"] = mask\n        example[\"tgt_length\"] = len(mask)\n\n    return example\n\n\nclass Dataset(TorchtextDataset):\n    \"\"\"Contain data and process it.\n\n    A dataset is an object that accepts sequences of raw data (sentence pairs\n    in the case of machine translation) and fields which describe how this\n    raw data should be processed to produce tensors. When a dataset is\n    instantiated, it applies the fields' preprocessing pipeline (but not\n    the bit that numericalizes it or turns it into batch tensors) to the raw\n    data, producing a list of :class:`torchtext.data.Example` objects.\n    torchtext's iterators then know how to use these examples to make batches.\n\n    Args:\n        fields (dict[str, Field]): a dict with the structure\n            returned by :func:`onmt.inputters.get_fields()`. Usually\n            that means the dataset side, ``\"src\"`` or ``\"tgt\"``. Keys match\n            the keys of items yielded by the ``readers``, while values\n            are lists of (name, Field) pairs. An attribute with this\n            name will be created for each :class:`torchtext.data.Example`\n            object and its value will be the result of applying the Field\n            to the data that matches the key. The advantage of having\n            sequences of fields for each piece of raw input is that it allows\n            the dataset to store multiple \"views\" of each input, which allows\n            for easy implementation of token-level features, mixed word-\n            and character-level models, and so on. (See also\n            :class:`onmt.inputters.TextMultiField`.)\n        readers (Iterable[onmt.inputters.DataReaderBase]): Reader objects\n            for disk-to-dict. The yielded dicts are then processed\n            according to ``fields``.\n        data (Iterable[Tuple[str, Any]]): (name, ``data_arg``) pairs\n            where ``data_arg`` is passed to the ``read()`` method of the\n            reader in ``readers`` at that position. (See the reader object for\n            details on the ``Any`` type.)\n        sort_key (Callable[[torchtext.data.Example], Any]): A function\n            for determining the value on which data is sorted (i.e. length).\n        filter_pred (Callable[[torchtext.data.Example], bool]): A function\n            that accepts Example objects and returns a boolean value\n            indicating whether to include that example in the dataset.\n\n    Attributes:\n        src_vocabs (List[torchtext.data.Vocab]): Used with dynamic dict/copy\n            attention. There is a very short vocab for each src example.\n            It contains just the source words, e.g. so that the generator can\n            predict to copy them.\n    \"\"\"\n\n    def __init__(self, fields, readers, data, sort_key, filter_pred=None):\n        self.sort_key = sort_key\n        can_copy = 'src_map' in fields and 'alignment' in fields\n\n        read_iters = [r.read(dat[1], dat[0]) for r, dat in zip(readers, data)]\n\n        # self.src_vocabs is used in collapse_copy_scores and Translator.py\n        self.src_vocabs = []\n        examples = []\n        for ex_dict in starmap(_join_dicts, zip(*read_iters)):\n            if can_copy:\n                src_field = fields['src']\n                tgt_field = fields['tgt']\n                # this assumes src_field and tgt_field are both text\n                ex_dict = _dynamic_dict(\n                    ex_dict, src_field.base_field, tgt_field.base_field)\n                self.src_vocabs.append(ex_dict[\"src_ex_vocab\"])\n            ex_fields = {k: [(k, v)] for k, v in fields.items() if\n                         k in ex_dict}\n            ex = Example.fromdict(ex_dict, ex_fields)\n            examples.append(ex)\n\n        # fields needs to have only keys that examples have as attrs\n        fields = []\n        for _, nf_list in ex_fields.items():\n            assert len(nf_list) == 1\n            fields.append(nf_list[0])\n\n        super(Dataset, self).__init__(examples, fields, filter_pred)\n\n    def __getattr__(self, attr):\n        # avoid infinite recursion when fields isn't defined\n        if 'fields' not in vars(self):\n            raise AttributeError\n        if attr in self.fields:\n            return (getattr(x, attr) for x in self.examples)\n        else:\n            raise AttributeError\n\n    def save(self, path, remove_fields=True):\n        if remove_fields:\n            self.fields = []\n        torch.save(self, path)\n\n    @staticmethod\n    def config(fields):\n        readers, data = [], []\n        for name, field in fields:\n            if field[\"data\"] is not None:\n                readers.append(field[\"reader\"])\n                data.append((name, field[\"data\"]))\n        return readers, data\n"
  },
  {
    "path": "onmt/inputters/dynamic_iterator.py",
    "content": "\"\"\"Module that contain iterator used for dynamic data.\"\"\"\nfrom itertools import cycle\n\nfrom torchtext.data import batch as torchtext_batch\n\nfrom onmt.constants import CorpusName\nfrom onmt.inputters import str2sortkey, max_tok_len, OrderedIterator\nfrom onmt.inputters.corpus import get_corpora, build_corpora_iters, \\\n    DatasetAdapter, ParallelCorpus\nfrom onmt.transforms import make_transforms\n\n\nclass MixingStrategy(object):\n    \"\"\"Mixing strategy that should be used in Data Iterator.\"\"\"\n\n    def __init__(self, iterables, weights):\n        \"\"\"Initilize neccessary attr.\"\"\"\n        self._valid_iterable(iterables, weights)\n        self.iterables = iterables\n        self.weights = weights\n\n    def _valid_iterable(self, iterables, weights):\n        iter_keys = iterables.keys()\n        weight_keys = weights.keys()\n        if iter_keys != weight_keys:\n            raise ValueError(\n                f\"keys in {iterables} & {iterables} should be equal.\")\n\n    def __iter__(self):\n        raise NotImplementedError\n\n\nclass SequentialMixer(MixingStrategy):\n    \"\"\"Generate data sequentially from `iterables` which is exhaustible.\"\"\"\n\n    def _iter_datasets(self):\n        for ds_name, ds_weight in self.weights.items():\n            for _ in range(ds_weight):\n                yield ds_name\n\n    def __iter__(self):\n        for ds_name in self._iter_datasets():\n            iterable = self.iterables[ds_name]\n            yield from iterable\n\n\nclass WeightedMixer(MixingStrategy):\n    \"\"\"A mixing strategy that mix data weightedly and iterate infinitely.\"\"\"\n\n    def __init__(self, iterables, weights):\n        super().__init__(iterables, weights)\n        self._iterators = {\n            ds_name: iter(generator)\n            for ds_name, generator in self.iterables.items()\n        }\n\n    def _reset_iter(self, ds_name):\n        self._iterators[ds_name] = iter(self.iterables[ds_name])\n\n    def _iter_datasets(self):\n        for ds_name, ds_weight in self.weights.items():\n            for _ in range(ds_weight):\n                yield ds_name\n\n    def __iter__(self):\n        for ds_name in cycle(self._iter_datasets()):\n            iterator = self._iterators[ds_name]\n            # @memray directly yield to support lazy load of many shards\n            try:\n                item = next(iterator)\n                yield item\n            except StopIteration:\n                self._reset_iter(ds_name)\n                iterator = self._iterators[ds_name]\n                item = next(iterator)\n                yield item\n            finally:\n                yield item\n\n\nclass SimpleInfiniteMixer(MixingStrategy):\n    def __init__(self, iterables, weights):\n        super().__init__(iterables, weights)\n        self._iterators = {\n            ds_name: iter(generator)\n            for ds_name, generator in self.iterables.items()\n        }\n\n    def _reset_iter(self, ds_name):\n        self._iterators[ds_name] = iter(self.iterables[ds_name])\n\n    def __iter__(self):\n        for ds_name in cycle(self.iterables.keys()):\n            try:\n                iterable = self.iterables[ds_name]\n                yield from iterable\n            except StopIteration:\n                self._reset_iter(ds_name)\n\n\nclass DynamicDatasetIter(object):\n    \"\"\"Yield batch from (multiple) plain text corpus.\n\n    Args:\n        corpora (dict[str, ParallelCorpus]): collections of corpora to iterate;\n        corpora_info (dict[str, dict]): corpora infos correspond to corpora;\n        transforms (dict[str, Transform]): transforms may be used by corpora;\n        fields (dict[str, Field]): fields dict for convert corpora into Tensor;\n        is_train (bool): True when generate data for training;\n        batch_type (str): batching type to count on, choices=[tokens, sents];\n        batch_size (int): numbers of examples in a batch;\n        batch_size_multiple (int): make batch size multiply of this;\n        data_type (str): input data type, currently only text;\n        bucket_size (int): accum this number of examples in a dynamic dataset;\n        pool_factor (int): accum this number of batch before sorting;\n        skip_empty_level (str): security level when encouter empty line;\n        stride (int): iterate data files with this stride;\n        offset (int): iterate data files with this offset.\n\n    Attributes:\n        batch_size_fn (function): functions to calculate batch_size;\n        sort_key (function): functions define how to sort examples;\n        dataset_adapter (DatasetAdapter): organize raw corpus to tensor adapt;\n        mixer (MixingStrategy): the strategy to iterate corpora.\n    \"\"\"\n\n    def __init__(self, corpora, corpora_info, transforms, fields, is_train,\n                 batch_type, batch_size, batch_size_multiple=1, data_type=\"text\",\n                 bucket_size=2048, pool_factor=8192,\n                 skip_empty_level='warning', stride=1, offset=0):\n        self.corpora = corpora\n        self.transforms = transforms\n        self.fields = fields\n        self.corpora_info = corpora_info\n        self.is_train = is_train\n        self.init_iterators = False\n        self.batch_size = batch_size\n        self.batch_size_fn = max_tok_len if batch_type == \"tokens\" else None\n        self.batch_size_multiple = batch_size_multiple\n        self.device = 'cpu'\n        self.sort_key = str2sortkey[data_type]\n        self.bucket_size = bucket_size\n        self.pool_factor = pool_factor\n        if stride <= 0:\n            raise ValueError(f\"Invalid argument for stride={stride}.\")\n        self.stride = stride\n        self.offset = offset\n        if skip_empty_level not in ['silent', 'warning', 'error']:\n            raise ValueError(\n                f\"Invalid argument skip_empty_level={skip_empty_level}\")\n        self.skip_empty_level = skip_empty_level\n\n    @classmethod\n    def from_opts(cls, corpora, transforms, fields, opts, is_train,\n                  stride=1, offset=0):\n        \"\"\"Initilize `DynamicDatasetIter` with options parsed from `opts`.\"\"\"\n        batch_size = opts.batch_size if is_train else opts.valid_batch_size\n        if hasattr(opts, 'batch_size_multiple') and opts.batch_size_multiple is not None:\n            batch_size_multiple = opts.batch_size_multiple\n        else:\n            batch_size_multiple = 8 if opts.model_dtype == \"fp16\" else 1\n        return cls(\n            corpora, opts.data, transforms, fields, is_train, opts.batch_type,\n            batch_size, batch_size_multiple, data_type=opts.data_type,\n            bucket_size=opts.bucket_size if hasattr(opts, 'bucket_size') else 2048,\n            pool_factor=opts.pool_factor if hasattr(opts, 'pool_factor') else 8192,\n            skip_empty_level=opts.skip_empty_level if hasattr(opts, 'skip_empty_level') else 'warning',\n            stride=stride, offset=offset\n        )\n\n    def _init_datasets(self):\n        datasets_iterables = build_corpora_iters(\n            self.corpora, self.transforms,\n            self.corpora_info, self.is_train,\n            skip_empty_level=self.skip_empty_level,\n            stride=self.stride, offset=self.offset)\n        self.dataset_adapter = DatasetAdapter(self.fields, self.is_train)\n        datasets_weights = {\n            ds_name: int(self.corpora_info[ds_name]['weight'])\n            for ds_name in datasets_iterables.keys()\n        }\n        if self.is_train:\n            # self.mixer = WeightedMixer(datasets_iterables, datasets_weights)\n            self.mixer = SimpleInfiniteMixer(datasets_iterables, datasets_weights)\n        else:\n            self.mixer = SequentialMixer(datasets_iterables, datasets_weights)\n        self.init_iterators = True\n\n    def _bucketing(self):\n        buckets = torchtext_batch(\n            self.mixer,\n            batch_size=self.bucket_size,\n            batch_size_fn=None)\n        yield from buckets\n\n    def __iter__(self):\n        if self.init_iterators is False:\n            self._init_datasets()\n        for bucket in self._bucketing():\n            dataset = self.dataset_adapter(bucket)\n            train_iter = OrderedIterator(\n                dataset,\n                self.batch_size,\n                pool_factor=self.pool_factor,\n                batch_size_fn=self.batch_size_fn,\n                batch_size_multiple=self.batch_size_multiple,\n                device=self.device,\n                train=self.is_train,\n                sort=False,\n                sort_within_batch=True,\n                sort_key=self.sort_key,\n                repeat=False,\n            )\n            for batch in train_iter:\n                yield batch\n\n\ndef build_dynamic_dataset_iter(fields, transforms_cls, opts, is_train=True,\n                               stride=1, offset=0):\n    \"\"\"Build `DynamicDatasetIter` from fields & opts.\"\"\"\n    transforms = make_transforms(opts, transforms_cls, fields)\n    corpora = get_corpora(opts, is_train)\n    if corpora is None:\n        assert not is_train, \"only valid corpus is ignorable.\"\n        return None\n    return DynamicDatasetIter.from_opts(\n        corpora, transforms, fields, opts, is_train,\n        stride=stride, offset=offset)\n\n\ndef build_dynamic_dataset_iter_given_examples(examples, fields, transforms_cls, opts, is_train = False):\n    \"\"\"Build `DynamicDatasetIter` from fields & opts.\"\"\"\n    transforms = make_transforms(opts, transforms_cls, fields)\n    corpora = {}\n\n    corpora[CorpusName.VALID] = ParallelCorpus(\n        CorpusName.VALID,\n        opts.data[CorpusName.VALID][\"path_src\"],\n        opts.data[CorpusName.VALID][\"path_tgt\"],\n        opts.data[CorpusName.VALID][\"path_align\"],\n        dataset_type=opts.data[CorpusName.VALID][\"type\"],\n        examples=examples\n    )\n\n    return DynamicDatasetIter.from_opts(\n        corpora, transforms, fields, opts, is_train)\n"
  },
  {
    "path": "onmt/inputters/fields.py",
    "content": "\"\"\"Module for build dynamic fields.\"\"\"\nfrom collections import Counter, defaultdict\nimport torch\nfrom onmt.utils.logging import logger\nfrom onmt.utils.misc import check_path\nfrom onmt.inputters.inputter import get_fields, _load_vocab, \\\n    _build_fields_vocab, load_roberta_kp_tokenizer, reload_keyphrase_fields\n\n\ndef _get_dynamic_fields(opts):\n    # NOTE: not support nfeats > 0 yet\n    src_nfeats = 0\n    tgt_nfeats = 0\n    with_align = hasattr(opts, 'lambda_align') and opts.lambda_align > 0.0\n    fields = get_fields(opts.data_type, src_nfeats, tgt_nfeats,\n                        dynamic_dict=opts.copy_attn,\n                        src_truncate=opts.src_seq_length_trunc,\n                        tgt_truncate=opts.tgt_seq_length_trunc,\n                        with_align=with_align,\n                        data_task=opts.data_task)\n\n    return fields\n\n\ndef build_dynamic_fields(opts, src_specials=None, tgt_specials=None):\n    \"\"\"Build fields for dynamic, including load & build vocab.\"\"\"\n    fields = _get_dynamic_fields(opts)\n\n    counters = defaultdict(Counter)\n    logger.info(\"Loading vocab from text file...\")\n\n    _src_vocab, _src_vocab_size = _load_vocab(\n        opts.src_vocab, 'src', counters,\n        min_freq=opts.src_words_min_frequency)\n\n    if opts.tgt_vocab:\n        _tgt_vocab, _tgt_vocab_size = _load_vocab(\n            opts.tgt_vocab, 'tgt', counters,\n            min_freq=opts.tgt_words_min_frequency)\n    elif opts.share_vocab:\n        logger.info(\"Sharing src vocab to tgt...\")\n        counters['tgt'] = counters['src']\n    else:\n        raise ValueError(\"-tgt_vocab should be specified if not share_vocab.\")\n\n    logger.info(\"Building fields with vocab in counters...\")\n    fields = _build_fields_vocab(\n        fields, counters, 'text', opts.share_vocab,\n        opts.vocab_size_multiple,\n        opts.src_vocab_size, opts.src_words_min_frequency,\n        opts.tgt_vocab_size, opts.tgt_words_min_frequency,\n        src_specials=src_specials, tgt_specials=tgt_specials)\n\n    # load a Fairseq-trained model, such as BART\n    if opts.data_type == 'keyphrase':\n        tokenizer = None\n        if hasattr(opts, 'pretrained_tokenizer') and opts.pretrained_tokenizer:\n            tokenizer = load_roberta_kp_tokenizer(opts.src_vocab, opts.bpe_dropout)\n            setattr(opts, 'vocab_size', len(tokenizer))\n        fields = reload_keyphrase_fields(fields, opts, tokenizer=tokenizer)\n\n    return fields\n\n\ndef get_vocabs(fields):\n    \"\"\"Get a dict contain src & tgt vocab extracted from fields.\"\"\"\n    src_vocab = fields['src'].base_field.vocab\n    tgt_vocab = fields['tgt'].base_field.vocab\n    vocabs = {'src': src_vocab, 'tgt': tgt_vocab}\n    return vocabs\n\n\ndef save_fields(fields, save_data, overwrite=True):\n    \"\"\"Dump `fields` object.\"\"\"\n    fields_path = \"{}.vocab.pt\".format(save_data)\n    check_path(fields_path, exist_ok=overwrite, log=logger.warning)\n    logger.info(f\"Saving fields to {fields_path}...\")\n    torch.save(fields, fields_path)\n\n\ndef load_fields(save_data, checkpoint=None):\n    \"\"\"Load dumped fields object from `save_data` or `checkpoint` if any.\"\"\"\n    if checkpoint is not None:\n        logger.info(\"Loading fields from checkpoint...\")\n        fields = checkpoint['vocab']\n    else:\n        fields_path = \"{}.vocab.pt\".format(save_data)\n        logger.info(f\"Loading fields from {fields_path}...\")\n        fields = torch.load(fields_path)\n    return fields\n"
  },
  {
    "path": "onmt/inputters/inputter.py",
    "content": "# -*- coding: utf-8 -*-\nimport copy\nimport numpy as np\nimport glob\nimport os\nimport codecs\nimport math\n\nfrom collections import Counter, defaultdict, OrderedDict\nfrom functools import partial\nfrom itertools import chain, cycle\n\nimport torch\nimport torchtext\nfrom torchtext.data import Field, RawField, LabelField\nfrom torchtext.data.utils import RandomShuffler\nfrom torchtext.vocab import Vocab\n\nfrom onmt.constants import DefaultTokens, ModelTask\nfrom onmt.inputters.iterator import batch_iter\nfrom onmt.inputters.text_dataset import text_fields, TextMultiField\nfrom onmt.inputters.news_dataset import update_field_vocab\nfrom onmt.inputters import keyphrase_dataset\nfrom onmt.inputters.keyphrase_dataset import keyphrase_fields, KeyphraseDataset, KeyphraseField\nfrom onmt.utils.logging import logger\n# backwards compatibility\nfrom onmt.inputters.text_dataset import _feature_tokenize  # noqa: F401\n\nimport gc\n\n\n# monkey-patch to make torchtext Vocab's pickleable\ndef _getstate(self):\n    return dict(self.__dict__, stoi=dict(self.stoi))\n\n\ndef _setstate(self, state):\n    self.__dict__.update(state)\n    self.stoi = defaultdict(lambda: 0, self.stoi)\n\n\nVocab.__getstate__ = _getstate\nVocab.__setstate__ = _setstate\n\n\ndef make_src(data, vocab):\n    src_size = max([t.size(0) for t in data])\n    src_vocab_size = max([t.max() for t in data]) + 1\n    alignment = torch.zeros(src_size, len(data), src_vocab_size)\n    for i, sent in enumerate(data):\n        for j, t in enumerate(sent):\n            alignment[j, i, t] = 1\n    return alignment\n\n\ndef preprocessing_tokenize(data, tokenizer, max_length=None):\n    data = tokenizer.bos_token + data + tokenizer.eos_token\n    token_indices = tokenizer.encode(data,\n                                     add_special_tokens=False,\n                                     truncation=max_length is not None,\n                                     max_length=max_length)\n    return torch.tensor(token_indices, dtype=torch.int64)\n\n\ndef postprocessing_length_cap(data, vocab, max_length):\n    return [min(max_length, l) for l in data]\n\n\ndef make_tgt(data, vocab, pad_idx=0):\n    \"\"\"\n    pad zero to the data, size=[max_len, batch_size]\n    \"\"\"\n    data = [torch.tensor(t, dtype=torch.int64) for t in data]\n    tgt_size = max([t.size(0) for t in data])\n    alignment = torch.zeros(tgt_size, len(data)).long()\n    # add fill_value for cases that pad_index is not zero\n    alignment = alignment.new_full(alignment.shape, pad_idx)\n    for i, sent in enumerate(data):\n        alignment[:sent.size(0), i] = sent\n    return alignment\n\n\nclass AlignField(LabelField):\n    \"\"\"\n    Parse ['<src>-<tgt>', ...] into ['<src>','<tgt>', ...]\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        kwargs['use_vocab'] = False\n        kwargs['preprocessing'] = parse_align_idx\n        super(AlignField, self).__init__(**kwargs)\n\n    def process(self, batch, device=None):\n        \"\"\" Turn a batch of align-idx to a sparse align idx Tensor\"\"\"\n        sparse_idx = []\n        for i, example in enumerate(batch):\n            for src, tgt in example:\n                # +1 for tgt side to keep coherent after \"bos\" padding,\n                # register ['N°_in_batch', 'tgt_id+1', 'src_id']\n                sparse_idx.append([i, tgt + 1, src])\n\n        align_idx = torch.tensor(sparse_idx, dtype=self.dtype, device=device)\n\n        return align_idx\n\n\ndef parse_align_idx(align_pharaoh):\n    \"\"\"\n    Parse Pharaoh alignment into [[<src>, <tgt>], ...]\n    \"\"\"\n    align_list = align_pharaoh.strip().split(' ')\n    flatten_align_idx = []\n    for align in align_list:\n        try:\n            src_idx, tgt_idx = align.split('-')\n        except ValueError:\n            logger.warning(\"{} in `{}`\".format(align, align_pharaoh))\n            logger.warning(\"Bad alignement line exists. Please check file!\")\n            raise\n        flatten_align_idx.append([int(src_idx), int(tgt_idx)])\n    return flatten_align_idx\n\n\ndef make_align(data, vocab):\n    \"\"\"\n    pad zero to the data, size=[max_tgt_len, batch_size, max_src_len]\n    each input shape is [tgt_len, src_len]\n    \"\"\"\n    max_src_size = max([t.size(1) for t in data])\n    max_tgt_size = max([t.size(0) for t in data])\n\n    alignment = torch.zeros(max_tgt_size, len(data), max_src_size)\n    for i, sent in enumerate(data):\n        j,t = sent.shape\n        alignment[:j, i, :t] = sent\n    return alignment\n\n\ndef get_task_spec_tokens(data_task, pad, bos, eos):\n    \"\"\"\n    Retrieve pad/bos/eos tokens for each data tasks\n    \"\"\"\n    if data_task == ModelTask.SEQ2SEQ:\n        return {\n            \"src\": {\"pad\": pad, \"bos\": None, \"eos\": None},\n            \"tgt\": {\"pad\": pad, \"bos\": bos, \"eos\": eos},\n        }\n    elif data_task == ModelTask.LANGUAGE_MODEL:\n        return {\n            \"src\": {\"pad\": pad, \"bos\": bos, \"eos\": None},\n            \"tgt\": {\"pad\": pad, \"bos\": None, \"eos\": eos},\n        }\n    else:\n        raise ValueError(f\"No task specific tokens defined for {data_task}\")\n\n\ndef get_fields(\n    src_data_type,\n    n_src_feats,\n    n_tgt_feats,\n    pad=DefaultTokens.PAD,\n    bos=DefaultTokens.BOS,\n    eos=DefaultTokens.EOS,\n    dynamic_dict=False,\n    with_align=False,\n    src_truncate=None,\n    tgt_truncate=None,\n    data_task=ModelTask.SEQ2SEQ\n):\n    \"\"\"\n    Args:\n        src_data_type: type of the source input. Options are [text].\n        n_src_feats (int): the number of source features (not counting tokens)\n            to create a :class:`torchtext.data.Field` for. (If\n            ``src_data_type==\"text\"``, these fields are stored together\n            as a ``TextMultiField``).\n        n_tgt_feats (int): See above.\n        pad (str): Special pad symbol. Used on src and tgt side.\n        bos (str): Special beginning of sequence symbol. Only relevant\n            for tgt.\n        eos (str): Special end of sequence symbol. Only relevant\n            for tgt.\n        dynamic_dict (bool): Whether or not to include source map and\n            alignment fields.\n        with_align (bool): Whether or not to include word align.\n        src_truncate: Cut off src sequences beyond this (passed to\n            ``src_data_type``'s data reader - see there for more details).\n        tgt_truncate: Cut off tgt sequences beyond this (passed to\n            :class:`TextDataReader` - see there for more details).\n\n    Returns:\n        A dict mapping names to fields. These names need to match\n        the dataset example attributes.\n    \"\"\"\n\n    assert src_data_type in ['text', 'keyphrase'], \\\n        \"Data type not implemented\"\n    assert not dynamic_dict or src_data_type == 'text' or src_data_type == 'keyphrase', \\\n        'it is not possible to use dynamic_dict with non-text input'\n    fields = {}\n\n    fields_getters = {\"text\": text_fields, \"keyphrase\": text_fields}\n    task_spec_tokens = get_task_spec_tokens(data_task, pad, bos, eos)\n\n    # @memray\n    if src_data_type == 'keyphrase':\n        task_spec_tokens = {\n            \"src\": {\"pad\": pad, \"bos\": bos, \"eos\": eos},\n            \"tgt\": {\"pad\": pad, \"bos\": bos, \"eos\": eos},\n        }\n\n    src_field_kwargs = {\n        \"n_feats\": n_src_feats,\n        \"include_lengths\": True,\n        \"pad\": task_spec_tokens[\"src\"][\"pad\"],\n        \"bos\": task_spec_tokens[\"src\"][\"bos\"],\n        \"eos\": task_spec_tokens[\"src\"][\"eos\"],\n        \"truncate\": src_truncate,\n        \"base_name\": \"src\",\n    }\n    fields[\"src\"] = fields_getters[src_data_type](**src_field_kwargs)\n\n    tgt_field_kwargs = {\n        \"n_feats\": n_tgt_feats,\n        \"include_lengths\": False,\n        \"pad\": task_spec_tokens[\"tgt\"][\"pad\"],\n        \"bos\": task_spec_tokens[\"tgt\"][\"bos\"],\n        \"eos\": task_spec_tokens[\"tgt\"][\"eos\"],\n        \"truncate\": tgt_truncate,\n        \"base_name\": \"tgt\",\n    }\n    fields[\"tgt\"] = fields_getters[src_data_type](**tgt_field_kwargs)\n\n    indices = Field(use_vocab=False, dtype=torch.long, sequential=False)\n    fields[\"indices\"] = indices\n\n    if dynamic_dict:\n        src_map = Field(\n            use_vocab=False, dtype=torch.float,\n            postprocessing=make_src, sequential=False)\n        fields[\"src_map\"] = src_map\n\n        src_ex_vocab = RawField()\n        fields[\"src_ex_vocab\"] = src_ex_vocab\n\n        align = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=make_tgt, sequential=False)\n        fields[\"alignment\"] = align\n\n    if with_align:\n        word_align = AlignField()\n        fields[\"align\"] = word_align\n\n    # added by @memray, load some other meta information of each data example for keyphrase dataset\n    if src_data_type == 'keyphrase':\n        id = Field(use_vocab=False, dtype=torch.long, sequential=False)\n        fields[\"id\"] = id\n\n        # for Orthogonal Regularization and Semantic Coverage\n        sep_indices = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=make_tgt, sequential=False)\n        fields[\"sep_indices\"] = sep_indices\n\n    return fields\n\n\ndef load_roberta_kp_tokenizer(vocab_path, bpe_dropout):\n    try:\n        # from transformers import AutoTokenizer\n        from transformers import RobertaTokenizer, RobertaTokenizerFast, AddedToken\n    except ImportError:\n        raise ImportError(\n            'Please install huggingface/tokenizers with: '\n            'pip install transformers'\n        )\n    vocab_dir = os.path.dirname(vocab_path)\n    bpe_vocab = os.path.join(vocab_dir, 'vocab.json')\n    bpe_merges = os.path.join(vocab_dir, 'merges.txt')\n\n    assert os.path.exists(bpe_vocab) and os.path.exists(bpe_merges),\\\n        \"Both vocab and merges are needed to load Huggingface tokenizer\"\n\n    print('Loading pretrained vocabulary from %s' % (vocab_dir))\n    # tokenizer = RobertaTokenizer(vocab_file=bpe_vocab, merges_file=bpe_merges)\n    sep_token = '<sep>'\n    kp_special_tokens = ['<present>', '<absent>',\n                         '<category>', '<infill>', '<seealso>', '<header>',\n                         '<|endoftext|>', '<sep>', '<mask>',\n                         '<mixed>', '<number>', '<phrase>']\n\n    roberta_kp_tokenizer = RobertaTokenizer(vocab_file=bpe_vocab,\n                                 merges_file=bpe_merges,\n                                 sep=sep_token,  # doesn't work\n                                 additional_special_tokens=kp_special_tokens)\n\n    sep_token_id = roberta_kp_tokenizer.convert_tokens_to_ids(sep_token)\n    added_sep_token = AddedToken(sep_token, lstrip=False, rstrip=False)\n\n    roberta_kp_tokenizer.sep_token = sep_token\n    roberta_kp_tokenizer._sep_token = added_sep_token\n    roberta_kp_tokenizer.init_kwargs['sep_token'] = sep_token\n    roberta_kp_tokenizer.all_special_ids.append(sep_token_id)\n    roberta_kp_tokenizer.all_special_tokens.append(sep_token)\n    roberta_kp_tokenizer.all_special_tokens_extended.append(added_sep_token)\n    roberta_kp_tokenizer.special_tokens_map['sep_token'] = sep_token\n    roberta_kp_tokenizer.special_tokens_map_extended['sep_token'] = added_sep_token\n\n    roberta_kp_tokenizer.unique_no_split_tokens = roberta_kp_tokenizer.all_special_tokens\n    roberta_kp_tokenizer = RobertaTokenizerFast.from_pretrained(\"roberta-base\",\n                                                     __slow_tokenizer=roberta_kp_tokenizer,\n                                                     tokenizer_file=None,\n                                                     vocab_file=bpe_vocab,\n                                                     merges_file=bpe_merges)\n\n    print('Vocab size=%d, base vocab size=%d' % (len(roberta_kp_tokenizer), roberta_kp_tokenizer.vocab_size))\n    if isinstance(roberta_kp_tokenizer, RobertaTokenizerFast) and float(bpe_dropout) > 0.0:\n        roberta_kp_tokenizer._tokenizer.model.dropout = float(bpe_dropout)\n\n    return roberta_kp_tokenizer\n\n\ndef reload_keyphrase_fields(fields, opt, tokenizer=None):\n    # update the vocabs in src/tgt field\n    if tokenizer is not None:\n        pad_idx = tokenizer.pad_token_id\n        partial_make_tgt_pad = partial(make_tgt, pad_idx=pad_idx)\n\n        new_field = update_field_vocab(fields['src'].base_field, tokenizer)\n        if hasattr(new_field, 'postprocessing'):\n            new_field.postprocessing = partial_make_tgt_pad\n        fields['src'].fields[0] = (fields['src'].fields[0][0], new_field)\n\n        new_field = update_field_vocab(fields['tgt'].base_field, tokenizer)\n        if hasattr(new_field, 'postprocessing'):\n            new_field.postprocessing = partial_make_tgt_pad\n        fields['tgt'].fields[0] = (fields['src'].fields[0][0], new_field)\n\n    return fields\n\ndef deprecated_reload_keyphrase_fields(fields, opt, tokenizer=None):\n    \"\"\"\n    In preprocessing phrase, the fields doesn't contain feature column information, thus len(fields['src'].fields)==1\n    We add additional features on-the-fly, thus we need to add corresponding fields here\n    Additionally, if we load a pretrained model, we need to override all the vocabs in fields\n    :param fields: for using pretraiend encoder the fields can be None\n    :param opt:\n    :return:\n    \"\"\"\n    src_nfeats = 0\n    tgt_nfeats = 0\n    src_seq_length_trunc = opt.src_seq_length_trunc if hasattr(opt, 'src_seq_length_trunc') else None\n    tgt_seq_length_trunc = opt.tgt_seq_length_trunc if hasattr(opt, 'tgt_seq_length_trunc') else None\n    dynamic_dict = True\n    new_fields = get_fields(\n        opt.data_type,\n        src_nfeats,\n        tgt_nfeats,\n        dynamic_dict=dynamic_dict,\n        src_truncate=src_seq_length_trunc,\n        tgt_truncate=tgt_seq_length_trunc,\n    )\n\n    # masks are required by huggingface Transformers\n    # prepare tensors on our own and ignore previous src/tgt fields\n    if opt.data_format == 'jsonl' and opt.pretrained_tokenizer:\n        pad_idx = tokenizer.pad_token_id if tokenizer is not None else 0\n        partial_src_preprocessing = partial(preprocessing_tokenize, tokenizer=tokenizer, max_length=src_seq_length_trunc)\n        partial_tgt_preprocessing = partial(preprocessing_tokenize, tokenizer=tokenizer, max_length=tgt_seq_length_trunc)\n        partial_src_length_cap = partial(postprocessing_length_cap, max_length=src_seq_length_trunc)\n        partial_tgt_length_cap = partial(postprocessing_length_cap, max_length=tgt_seq_length_trunc)\n        partial_make_tgt = partial(make_tgt, pad_idx=pad_idx)\n\n        src = Field(\n            use_vocab=False, dtype=torch.long,\n            preprocessing=partial_src_preprocessing,\n            postprocessing=partial_make_tgt, sequential=False)\n        new_fields[\"src\"] = src\n\n        src_lengths = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=partial_src_length_cap, sequential=False)\n        new_fields[\"src_length\"] = src_lengths\n\n        tgt = Field(\n            use_vocab=False, dtype=torch.long,\n            preprocessing=partial_tgt_preprocessing,\n            postprocessing=make_tgt, sequential=False)\n        new_fields[\"tgt\"] = tgt\n\n        tgt_length = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=partial_tgt_length_cap, sequential=False)\n        new_fields[\"tgt_length\"] = tgt_length\n\n        # this is the field tokens on the source side\n        field = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=partial_make_tgt, sequential=False)\n        new_fields[\"src_field\"] = field\n\n        src_mask = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=partial_make_tgt, sequential=False)\n        new_fields[\"src_mask\"] = src_mask\n\n        tgt_mask = Field(\n            use_vocab=False, dtype=torch.long,\n            postprocessing=partial_make_tgt, sequential=False)\n        new_fields[\"tgt_mask\"] = tgt_mask\n\n    # update the vocabs in src/tgt field\n    if tokenizer is not None:\n        # update the vocab depending on what fields are used (OpenNMT uses a MultiField, pretraiend model uses normal Field)\n        new_field = update_field_vocab(new_fields['src'], tokenizer)\n        # no bos/eos for src, otherwise it causes length mismatch later in training\n        setattr(new_field, 'bos', None)\n        setattr(new_field, 'bos_token', None)\n        setattr(new_field, 'eos', None)\n        setattr(new_field, 'eos_token', None)\n        if hasattr(new_field, 'postprocessing'):\n            partial_make_tgt_pad = partial(make_tgt, pad_idx=tokenizer.pad_token_id)\n            setattr(new_field, 'postprocessing', partial_make_tgt_pad)\n        if hasattr(opt, 'field_label') and opt.field_label:\n            setattr(new_field, 'word_feat_share', True)\n            setattr(new_field, 'feat_num', 1)\n        new_fields['src'] = new_field\n\n        new_field = update_field_vocab(new_fields['tgt'], tokenizer)\n        if hasattr(new_field, 'postprocessing'):\n            partial_make_tgt_pad = partial(make_tgt, pad_idx=tokenizer.pad_token_id)\n            setattr(new_field, 'postprocessing', partial_make_tgt_pad)\n        new_fields['tgt'] = new_field\n    return new_fields\n\n\nclass IterOnDevice(object):\n    \"\"\"Sent items from `iterable` on `device_id` and yield.\"\"\"\n\n    def __init__(self, iterable, device_id):\n        self.iterable = iterable\n        self.device_id = device_id\n\n    @staticmethod\n    def batch_to_device(batch, device_id):\n        \"\"\"Move `batch` to `device_id`, cpu if `device_id` < 0.\"\"\"\n        curr_device = batch.indices.device\n        device = torch.device(device_id) if device_id >= 0 \\\n            else torch.device('cpu')\n        if curr_device != device:\n            if isinstance(batch.src, tuple):\n                batch.src = tuple([_.to(device) for _ in batch.src])\n            else:\n                batch.src = batch.src.to(device)\n            batch.tgt = batch.tgt.to(device)\n            batch.indices = batch.indices.to(device)\n            batch.alignment = batch.alignment.to(device) \\\n                if hasattr(batch, 'alignment') else None\n            batch.src_map = batch.src_map.to(device) \\\n                if hasattr(batch, 'src_map') else None\n            batch.align = batch.align.to(device) \\\n                if hasattr(batch, 'align') else None\n\n    def __iter__(self):\n        for batch in self.iterable:\n            self.batch_to_device(batch, self.device_id)\n            yield batch\n\n\ndef filter_example(ex, use_src_len=True, use_tgt_len=True,\n                   min_src_len=1, max_src_len=float('inf'),\n                   min_tgt_len=1, max_tgt_len=float('inf')):\n    \"\"\"Return whether an example is an acceptable length.\n\n    If used with a dataset as ``filter_pred``, use :func:`partial()`\n    for all keyword arguments.\n\n    Args:\n        ex (torchtext.data.Example): An object with a ``src`` and ``tgt``\n            property.\n        use_src_len (bool): Filter based on the length of ``ex.src``.\n        use_tgt_len (bool): Similar to above.\n        min_src_len (int): A non-negative minimally acceptable length\n            (examples of exactly this length will be included).\n        min_tgt_len (int): Similar to above.\n        max_src_len (int or float): A non-negative (possibly infinite)\n            maximally acceptable length (examples of exactly this length\n            will be included).\n        max_tgt_len (int or float): Similar to above.\n    \"\"\"\n\n    src_len = len(ex.src[0])\n    tgt_len = len(ex.tgt[0])\n    return (not use_src_len or min_src_len <= src_len <= max_src_len) and \\\n        (not use_tgt_len or min_tgt_len <= tgt_len <= max_tgt_len)\n\n\ndef _pad_vocab_to_multiple(vocab, multiple):\n    vocab_size = len(vocab)\n    if vocab_size % multiple == 0:\n        return\n    target_size = int(math.ceil(vocab_size / multiple)) * multiple\n    padding_tokens = [\"{}{}\".format(DefaultTokens.VOCAB_PAD, i)\n                      for i in range(target_size - vocab_size)]\n    vocab.extend(Vocab(Counter(), specials=padding_tokens))\n    return vocab\n\n\ndef _build_field_vocab(field, counter, size_multiple=1, **kwargs):\n    # this is basically copy-pasted from torchtext.\n    all_special = [\n        field.unk_token, field.pad_token, field.init_token, field.eos_token\n    ]\n    # @memray\n    if \"extra_special_tokens\" in kwargs:\n        all_special.extend(kwargs[\"extra_special_tokens\"])\n        del kwargs[\"extra_special_tokens\"]\n    all_special.extend(list(kwargs.pop('specials', [])))\n    specials = list(OrderedDict.fromkeys(\n        tok for tok in all_special if tok is not None))\n    field.vocab = field.vocab_cls(counter, specials=specials, **kwargs)\n    if size_multiple > 1:\n        _pad_vocab_to_multiple(field.vocab, size_multiple)\n\n\ndef _load_vocab(vocab_path, name, counters, min_freq=0):\n    \"\"\"Inplace update `counters`[`name`] with vocab in `vocab_path`.\n\n    Each line of `vocab_path` have a token, possible with a count.\n    If not with count, each token will be assigned one so that the order\n    of counters[name] will be same with `vocab_path`, and the minimum count\n    number to be `min_freq` which defaults 0.\n    \"\"\"\n    # counters changes in place\n    vocab, has_count = _read_vocab_file(vocab_path, name)\n    vocab_size = len(vocab)\n    logger.info('Loaded %s vocab has %d tokens.' % (name, vocab_size))\n    if not has_count:\n        for i, token in enumerate(vocab):\n            # keep the order of tokens specified in the vocab file by\n            # adding them to the counter with decreasing counting values\n            counters[name][token] = vocab_size - i + min_freq\n    else:\n        for token, count in vocab:\n            counters[name][token] = int(count)\n    return vocab, vocab_size\n\n\ndef _build_fv_from_multifield(multifield, counters, build_fv_kwargs,\n                              size_multiple=1):\n    for name, field in multifield:\n        _build_field_vocab(\n            field,\n            counters[name],\n            size_multiple=size_multiple,\n            **build_fv_kwargs[name])\n        logger.info(\" * %s vocab size: %d.\" % (name, len(field.vocab)))\n\n\ndef _build_fields_vocab(fields, counters, data_type, share_vocab,\n                        vocab_size_multiple,\n                        src_vocab_size, src_words_min_frequency,\n                        tgt_vocab_size, tgt_words_min_frequency,\n                        src_specials=None, tgt_specials=None):\n    src_specials = list(src_specials) if src_specials is not None else []\n    tgt_specials = list(tgt_specials) if tgt_specials is not None else []\n    build_fv_kwargs = defaultdict(dict)\n    build_fv_kwargs[\"src\"] = dict(\n        max_size=src_vocab_size, min_freq=src_words_min_frequency,\n        specials=src_specials)\n    build_fv_kwargs[\"tgt\"] = dict(\n        max_size=tgt_vocab_size, min_freq=tgt_words_min_frequency,\n        specials=tgt_specials)\n    tgt_multifield = fields[\"tgt\"]\n    _build_fv_from_multifield(\n        tgt_multifield,\n        counters,\n        build_fv_kwargs,\n        size_multiple=vocab_size_multiple if not share_vocab else 1)\n\n    if data_type == 'text' or data_type == 'keyphrase':\n        src_multifield = fields[\"src\"]\n        _build_fv_from_multifield(\n            src_multifield,\n            counters,\n            build_fv_kwargs,\n            size_multiple=vocab_size_multiple if not share_vocab else 1)\n\n        if share_vocab:\n            # `tgt_vocab_size` is ignored when sharing vocabularies\n            logger.info(\" * merging src and tgt vocab...\")\n            src_field = src_multifield.base_field\n            tgt_field = tgt_multifield.base_field\n            _all_specials = [item for item in src_specials + tgt_specials]\n            _merge_field_vocabs(\n                src_field, tgt_field, vocab_size=src_vocab_size,\n                min_freq=src_words_min_frequency,\n                vocab_size_multiple=vocab_size_multiple,\n                specials=_all_specials)\n            logger.info(\" * merged vocab size: %d.\" % len(src_field.vocab))\n\n    return fields\n\n\ndef build_vocab(train_dataset_files, fields, data_type, share_vocab,\n                src_vocab_path, src_vocab_size, src_words_min_frequency,\n                tgt_vocab_path, tgt_vocab_size, tgt_words_min_frequency,\n                vocab_size_multiple=1):\n    \"\"\"Build the fields for all data sides.\n\n    Args:\n        train_dataset_files: a list of train dataset pt file.\n        fields (dict[str, Field]): fields to build vocab for.\n        data_type (str): A supported data type string.\n        share_vocab (bool): share source and target vocabulary?\n        src_vocab_path (str): Path to src vocabulary file.\n        src_vocab_size (int): size of the source vocabulary.\n        src_words_min_frequency (int): the minimum frequency needed to\n            include a source word in the vocabulary.\n        tgt_vocab_path (str): Path to tgt vocabulary file.\n        tgt_vocab_size (int): size of the target vocabulary.\n        tgt_words_min_frequency (int): the minimum frequency needed to\n            include a target word in the vocabulary.\n        vocab_size_multiple (int): ensure that the vocabulary size is a\n            multiple of this value.\n\n    Returns:\n        Dict of Fields\n    \"\"\"\n\n    counters = defaultdict(Counter)\n\n    if src_vocab_path:\n        try:\n            logger.info(\"Using existing vocabulary...\")\n            vocab = torch.load(src_vocab_path)\n            # return vocab to dump with standard name\n            return vocab\n        except torch.serialization.pickle.UnpicklingError:\n            logger.info(\"Building vocab from text file...\")\n            # empty train_dataset_files so that vocab is only loaded from\n            # given paths in src_vocab_path, tgt_vocab_path\n            train_dataset_files = []\n\n    # Load vocabulary\n    if src_vocab_path:\n        src_vocab, src_vocab_size = _load_vocab(\n            src_vocab_path, \"src\", counters,\n            src_words_min_frequency)\n    else:\n        src_vocab = None\n\n    if tgt_vocab_path:\n        tgt_vocab, tgt_vocab_size = _load_vocab(\n            tgt_vocab_path, \"tgt\", counters,\n            tgt_words_min_frequency)\n    else:\n        tgt_vocab = None\n\n    for i, path in enumerate(train_dataset_files):\n        dataset = torch.load(path)\n        logger.info(\" * reloading %s.\" % path)\n        for ex in dataset.examples:\n            for name, field in fields.items():\n                try:\n                    f_iter = iter(field)\n                except TypeError:\n                    f_iter = [(name, field)]\n                    all_data = [getattr(ex, name, None)]\n                else:\n                    all_data = getattr(ex, name)\n                    # added by @memray, to support keyphrase task where tgt is a list of tokens\n                    if name=='tgt' and isinstance(all_data[0], list) and len(all_data[0]) > 0 and isinstance(all_data[0][0], list):\n                        all_data = [[p for d in all_data for p in d]]\n                for (sub_n, sub_f), fd in zip(\n                        f_iter, all_data):\n                    has_vocab = (sub_n == 'src' and src_vocab) or \\\n                                (sub_n == 'tgt' and tgt_vocab)\n                    if sub_f.sequential and not has_vocab:\n                        val = fd\n                        # added by @memray, to support keyphrase task where tgt is a list of tokens\n                        if isinstance(val, list) and len(val) > 0 and isinstance(val[0], list):\n                            for v_ in val:\n                                counters[sub_n].update(v_)\n                        else:\n                            counters[sub_n].update(val)\n\n        # Drop the none-using from memory but keep the last\n        if i < len(train_dataset_files) - 1:\n            dataset.examples = None\n            gc.collect()\n            del dataset.examples\n            gc.collect()\n            del dataset\n            gc.collect()\n\n    fields = _build_fields_vocab(\n        fields, counters, data_type,\n        share_vocab, vocab_size_multiple,\n        src_vocab_size, src_words_min_frequency,\n        tgt_vocab_size, tgt_words_min_frequency)\n\n    return fields  # is the return necessary?\n\n\ndef _merge_field_vocabs(src_field, tgt_field, vocab_size, min_freq,\n                        vocab_size_multiple, specials):\n    # in the long run, shouldn't it be possible to do this by calling\n    # build_vocab with both the src and tgt data?\n    init_specials = [tgt_field.unk_token, tgt_field.pad_token,\n                tgt_field.init_token, tgt_field.eos_token]\n    # @memray, add extra specials\n    all_specials = list(OrderedDict.fromkeys(\n        tok for tok in init_specials + specials\n        if tok is not None))\n    merged = sum(\n        [src_field.vocab.freqs, tgt_field.vocab.freqs], Counter()\n    )\n    merged_vocab = Vocab(\n        merged, specials=all_specials,\n        max_size=vocab_size, min_freq=min_freq\n    )\n    if vocab_size_multiple > 1:\n        _pad_vocab_to_multiple(merged_vocab, vocab_size_multiple)\n    src_field.vocab = merged_vocab\n    tgt_field.vocab = merged_vocab\n    assert len(src_field.vocab) == len(tgt_field.vocab)\n\n\ndef _read_vocab_file(vocab_path, tag):\n    \"\"\"Loads a vocabulary from the given path.\n\n    Args:\n        vocab_path (str): Path to utf-8 text file containing vocabulary.\n            Each token should be on a line, may followed with a count number\n            seperate by space if `with_count`. No extra whitespace is allowed.\n        tag (str): Used for logging which vocab is being read.\n    \"\"\"\n\n    logger.info(\"Loading {} vocabulary from {}\".format(tag, vocab_path))\n\n    if not os.path.exists(vocab_path):\n        raise RuntimeError(\n            \"{} vocabulary not found at {}\".format(tag, vocab_path))\n    else:\n        with codecs.open(vocab_path, 'r', 'utf-8') as f:\n            lines = [line.strip() for line in f if line.strip()]\n            first_line = lines[0].split(None, 1)\n            has_count = (len(first_line) == 2 and first_line[-1].isdigit())\n            if has_count:\n                vocab = [line.split(None, 1) for line in lines]\n            else:\n                vocab = [line.strip().split()[0] for line in lines]\n            return vocab, has_count\n\nif __name__ == '__main__':\n    tokenizer = load_roberta_kp_tokenizer('/zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json', bpe_dropout=0.0)\n    tokenizer.save_pretrained(\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/hf_ckpts/tokenizer/\")\n"
  },
  {
    "path": "onmt/inputters/iterator.py",
    "content": "\"\"\"Contains all methods relate to iteration.\"\"\"\nimport torchtext.data\n\nfrom onmt.utils.logging import logger\n\n\ndef batch_iter(data, batch_size, batch_size_fn=None, batch_size_multiple=1):\n    \"\"\"Yield elements from data in chunks of batch_size, where each chunk size\n    is a multiple of batch_size_multiple.\n\n    This is an extended version of torchtext.data.batch.\n    \"\"\"\n    if batch_size_fn is None:\n        def batch_size_fn(new, count, sofar):\n            return count\n    minibatch, size_so_far = [], 0\n    for ex in data:\n        minibatch.append(ex)\n        size_so_far = batch_size_fn(ex, len(minibatch), size_so_far)\n        if size_so_far >= batch_size:\n            overflowed = 0\n            if size_so_far > batch_size:\n                overflowed += 1\n            if batch_size_multiple > 1:\n                overflowed += (\n                    (len(minibatch) - overflowed) % batch_size_multiple)\n            if overflowed == 0:\n                yield minibatch\n                minibatch, size_so_far = [], 0\n            else:\n                if overflowed == len(minibatch):\n                    logger.warning(\n                        \"The batch will be filled until we reach %d,\"\n                        \"its size may exceed %d tokens\"\n                        % (batch_size_multiple, batch_size)\n                        )\n                else:\n                    yield minibatch[:-overflowed]\n                    minibatch = minibatch[-overflowed:]\n                    size_so_far = 0\n                    for i, ex in enumerate(minibatch):\n                        size_so_far = batch_size_fn(ex, i + 1, size_so_far)\n    if minibatch:\n        yield minibatch\n\n\ndef _pool(data, batch_size, batch_size_fn, batch_size_multiple,\n          sort_key, random_shuffler, pool_factor):\n    for p in torchtext.data.batch(\n            data, batch_size * pool_factor,\n            batch_size_fn=batch_size_fn):\n        p_batch = list(batch_iter(\n            sorted(p, key=sort_key),\n            batch_size,\n            batch_size_fn=batch_size_fn,\n            batch_size_multiple=batch_size_multiple))\n        for b in random_shuffler(p_batch):\n            yield b\n\n\nclass OrderedIterator(torchtext.data.Iterator):\n\n    def __init__(self,\n                 dataset,\n                 batch_size,\n                 pool_factor=1,\n                 batch_size_multiple=1,\n                 yield_raw_example=False,\n                 **kwargs):\n        super(OrderedIterator, self).__init__(dataset, batch_size, **kwargs)\n        self.batch_size_multiple = batch_size_multiple\n        self.yield_raw_example = yield_raw_example\n        self.dataset = dataset\n        self.pool_factor = pool_factor\n\n    def create_batches(self):\n        if self.train:\n            if self.yield_raw_example:\n                self.batches = batch_iter(\n                    self.data(),\n                    1,\n                    batch_size_fn=None,\n                    batch_size_multiple=1)\n            else:\n                self.batches = _pool(\n                    self.data(),\n                    self.batch_size,\n                    self.batch_size_fn,\n                    self.batch_size_multiple,\n                    self.sort_key,\n                    self.random_shuffler,\n                    self.pool_factor)\n        else:\n            self.batches = []\n            for b in batch_iter(\n                    self.data(),\n                    self.batch_size,\n                    batch_size_fn=self.batch_size_fn,\n                    batch_size_multiple=self.batch_size_multiple):\n                self.batches.append(sorted(b, key=self.sort_key))\n\n    def __iter__(self):\n        \"\"\"\n        Extended version of the definition in torchtext.data.Iterator.\n        Added yield_raw_example behaviour to yield a torchtext.data.Example\n        instead of a torchtext.data.Batch object.\n        \"\"\"\n        while True:\n            self.init_epoch()\n            for idx, minibatch in enumerate(self.batches):\n                # fast-forward if loaded from state\n                if self._iterations_this_epoch > idx:\n                    continue\n                self.iterations += 1\n                self._iterations_this_epoch += 1\n                if self.sort_within_batch:\n                    # NOTE: `rnn.pack_padded_sequence` requires that a\n                    # minibatch be sorted by decreasing order, which\n                    #  requires reversing relative to typical sort keys\n                    if self.sort:\n                        minibatch.reverse()\n                    else:\n                        minibatch.sort(key=self.sort_key, reverse=True)\n                if self.yield_raw_example:\n                    yield minibatch[0]\n                else:\n                    yield torchtext.data.Batch(\n                        minibatch,\n                        self.dataset,\n                        self.device)\n            if not self.repeat:\n                return\n\n\ndef max_tok_len(new, count, sofar):\n    \"\"\"\n    In token batching scheme, the number of sequences is limited\n    such that the total number of src/tgt tokens (including padding)\n    in a batch <= batch_size\n    \"\"\"\n    # Maintains the longest src and tgt length in the current batch\n    global max_src_in_batch, max_tgt_in_batch  # this is a hack\n    # Reset current longest length at a new batch (count=1)\n    if count == 1:\n        max_src_in_batch = 0\n        max_tgt_in_batch = 0\n    # Src: [<bos> w1 ... wN <eos>]\n    max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2)\n    # Tgt: [w1 ... wM <eos>]\n    max_tgt_in_batch = max(max_tgt_in_batch, len(new.tgt[0]) + 1)\n    src_elements = count * max_src_in_batch\n    tgt_elements = count * max_tgt_in_batch\n    return max(src_elements, tgt_elements)\n"
  },
  {
    "path": "onmt/inputters/keyphrase_dataset.py",
    "content": "# -*- coding: utf-8 -*-\nimport json\nimport re\nfrom functools import partial\n\nimport six\nimport torch\nimport numpy as np\nfrom torchtext.data import Field, RawField\nfrom tqdm import tqdm\n\nfrom onmt.keyphrase.utils import if_present_duplicate_phrases\nfrom onmt.inputters.datareader_base import DataReaderBase\n\nfrom itertools import chain, starmap\nfrom collections import Counter\n\nimport torch\nfrom torchtext.data import Dataset as TorchtextDataset\nfrom torchtext.data import Example\n\nfrom onmt.inputters.dataset_base import _join_dicts, _dynamic_dict\n\nnp.random.seed(2333)\n\nKP_DATASET_FIELDS = {'scipaper': ('title', 'abstract', 'keywords', None),\n                     'qa': ('title', 'question', 'tags', 'categories'),\n                     'webpage': ('url', 'text', 'KeyPhrases', None),\n                     'news': ('title', 'abstract', 'keyword', 'categories')}\n\nKP_CONCAT_TYPES = ['one2one', 'random',\n                   'pres_abs', 'abs_pres',\n                   'nosort', 'nosort_reverse',\n                   'alphab', 'alphab_reverse',\n                   'length', 'length_reverse']\n# previous version (used in empirical study)\n# KP_CONCAT_TYPES = ['random',\n#             'no_sort', 'no_sort_reverse',\n#             'alphabetical', 'alphabetical_reverse',\n#             'length', 'length_reverse',\n#             'verbatim_append', 'verbatim_prepend']\n\ndef infer_dataset_type(filepath):\n    dataset_type = None\n    if 'stack' in filepath:\n        dataset_type = 'qa'\n    elif 'openkp' in filepath:\n        dataset_type = 'webpage'\n    elif 'kptimes' in filepath or 'jptimes' in filepath:\n        dataset_type = 'news'\n    elif 'kp20k' in filepath or 'magkp' in filepath:\n        dataset_type = 'scipaper'\n    elif 'duc' in filepath or 'inspec' in filepath or 'krapivin' in filepath \\\n            or 'nus' in filepath or 'semeval' in filepath:\n        # duc is an outlier\n        dataset_type = 'scipaper'\n\n    assert dataset_type is not None, 'Fail to detect the data type of the given input file.' \\\n                                     'Accecpted values:' + KP_DATASET_FIELDS.keys()\n\n    print('Automatically detect the input data type as [%s], path: %s' % (dataset_type.upper(), filepath))\n\n    return dataset_type\n\n\ndef parse_src_fn(ex_dict, title_field, text_field):\n    concat_str = ex_dict[title_field] + ' . ' + ex_dict[text_field]\n    return concat_str\n\n\ndef kpdict_parse_fn(example, tokenizer, tgt_concat_type, dataset_type='scipaper', max_target_phrases=-1, lowercase=False):\n    assert dataset_type in KP_DATASET_FIELDS\n    title_field, text_field, keyword_field, category_field = KP_DATASET_FIELDS[dataset_type]\n\n    src_str = parse_src_fn(example, title_field, text_field)\n    if isinstance(example[keyword_field], str):\n        tgt_kps = example[keyword_field].split(';')\n    else:\n        tgt_kps = example[keyword_field]\n    if tgt_concat_type == 'one2one':\n        # sample one tgt from multiple tgts and use it as the only tgt\n        rand_idx = np.random.randint(len(tgt_kps))\n        tgt_str = tgt_kps[rand_idx]\n    elif tgt_concat_type in KP_CONCAT_TYPES:\n        # generate one2seq training data points\n        order = obtain_sorted_indices(src_str.lower().split(),\n                                      [kp.lower().split() for kp in tgt_kps],\n                                      sort_by=tgt_concat_type)\n        if max_target_phrases > 0 and len(order) > max_target_phrases:\n            order = order[: max_target_phrases]\n        tgt = [tgt_kps[idx] for idx in order]\n        tgt_str = tokenizer.sep_token.join(tgt)\n    else:\n        raise NotImplementedError('Unsupported target concatenation type ' + tgt_concat_type)\n\n    if lowercase:\n        return src_str.lower(), tgt_str.lower()\n    return src_str, tgt_str\n\n\nclass KeyphraseDataset(TorchtextDataset):\n    \"\"\"Contain data and process it.\n\n    A dataset is an object that accepts sequences of raw data (sentence pairs\n    in the case of machine translation) and fields which describe how this\n    raw data should be processed to produce tensors. When a dataset is\n    instantiated, it applies the fields' preprocessing pipeline (but not\n    the bit that numericalizes it or turns it into batch tensors) to the raw\n    data, producing a list of :class:`torchtext.data.Example` objects.\n    torchtext's iterators then know how to use these examples to make batches.\n\n    Args:\n        fields (dict[str, List[Tuple[str, Field]]]): a dict with the structure\n            returned by :func:`onmt.inputters.get_fields()`. Usually\n            that means the dataset side, ``\"src\"`` or ``\"tgt\"``. Keys match\n            the keys of items yielded by the ``readers``, while values\n            are lists of (name, Field) pairs. An attribute with this\n            name will be created for each :class:`torchtext.data.Example`\n            object and its value will be the result of applying the Field\n            to the data that matches the key. The advantage of having\n            sequences of fields for each piece of raw input is that it allows\n            the dataset to store multiple \"views\" of each input, which allows\n            for easy implementation of token-level features, mixed word-\n            and character-level models, and so on. (See also\n            :class:`onmt.inputters.TextMultiField`.)\n        readers (Iterable[onmt.inputters.DataReaderBase]): Reader objects\n            for disk-to-dict. The yielded dicts are then processed\n            according to ``fields``.\n        data (Iterable[Tuple[str, Any]]): (name, ``data_arg``) pairs\n            where ``data_arg`` is passed to the ``read()`` method of the\n            reader in ``readers`` at that position. (See the reader object for\n            details on the ``Any`` type.)\n        dirs (Iterable[str or NoneType]): A list of directories where\n            data is contained. See the reader object for more details.\n        sort_key (Callable[[torchtext.data.Example], Any]): A function\n            for determining the value on which data is sorted (i.e. length).\n        filter_pred (Callable[[torchtext.data.Example], bool]): A function\n            that accepts Example objects and returns a boolean value\n            indicating whether to include that example in the dataset.\n\n    Attributes:\n        src_vocabs (List[torchtext.data.Vocab]): Used with dynamic dict/copy\n            attention. There is a very short vocab for each src example.\n            It contains just the source words, e.g. so that the generator can\n            predict to copy them.\n    \"\"\"\n\n    def __init__(self, fields, readers, data, dirs, sort_key,\n                 filter_pred=None, tgt_concat_type=None, data_format=None, max_target_phrases=-1):\n        # if not using pre-trained tokenizer, this is set at line 594 in inputter.py and line 303 in translator.py\n        self.tgt_type = tgt_concat_type\n        # concatenate multiple tgt sequences with <sep> or keep them separate as a list of seqs (2D tensor)\n        # self.concat_tgt = False\n        self.data_format = data_format\n        self.sort_key = sort_key\n        self.max_target_phrases = max_target_phrases\n\n        # will be specified before training, one of [one2one, original, random, verbatim]\n\n        # build src_map/alignment no matter field is available\n        can_copy = True\n\n        self.dataset_type = infer_dataset_type(dirs[0])\n        dirs = [None, None]\n        read_iters = [r.read(dat[1], dat[0], dir_) for r, dat, dir_\n                      in zip(readers, data, dirs)]\n\n        # self.src_vocabs is temp vocab/alignment, used in collapse_copy_scores and Translator.py\n        self.src_vocabs = []\n        examples = []\n        for ex_dict in tqdm(starmap(_join_dicts, zip(*read_iters)), desc='Loading and parsing data'):\n            if can_copy:\n                src_field = fields['src']\n                tgt_field = fields['tgt']\n\n                if hasattr(src_field, 'pretrained_tokenizer'):\n                    tokenizer = src_field.pretrained_tokenizer\n\n                if data_format == 'jsonl':\n                    title_field, text_field, keyword_field, _ = KP_DATASET_FIELDS[self.dataset_type]\n                    src_str, tgt_str = kpdict_parse_fn(ex_dict['src'], tokenizer, tgt_concat_type,\n                                                       dataset_type=self.dataset_type, max_target_phrases=-1,\n                                                       lowercase=False)\n                    ex_dict['src'] = src_str\n                    ex_dict['tgt'] = tgt_str\n\n                    # src_str = parse_src_fn(ex_dict['src'], title_field, text_field)\n                    # if isinstance(ex_dict['src'][keyword_field], str):\n                    #     tgt_kps = ex_dict['src'][keyword_field].split(';')\n                    # else:\n                    #     tgt_kps = ex_dict['src'][keyword_field]\n                    # ex_dict['src'] = src_str\n                    # ex_dict['tgt'] = ';'.join(tgt_kps)\n\n                # this assumes src_field and tgt_field are both text\n                src_base_field = src_field.base_field if hasattr(src_field, 'base_field') else src_field\n                tgt_base_field = tgt_field.base_field if hasattr(tgt_field, 'base_field') else tgt_field\n                src_ex_vocab, ex_dict = _dynamic_dict(\n                    ex_dict, src_base_field, tgt_base_field)\n                self.src_vocabs.append(src_ex_vocab)\n            ex_fields = {k: [(k, v)] for k, v in fields.items() if\n                         k in ex_dict}\n            ex = Example.fromdict(ex_dict, ex_fields)\n            examples.append(ex)\n\n        # fields needs to have only keys that examples have as attrs\n        fields = []\n        for _, nf_list in ex_fields.items():\n            assert len(nf_list) == 1\n            fields.append(nf_list[0])\n\n        super(KeyphraseDataset, self).__init__(examples, fields, filter_pred)\n\n    def __getattr__(self, attr):\n        # avoid infinite recursion when fields isn't defined\n        if 'fields' not in vars(self):\n            raise AttributeError\n        if attr in self.fields:\n            return (getattr(x, attr) for x in self.examples)\n        else:\n            raise AttributeError\n\n    def save(self, path, remove_fields=True):\n        if remove_fields:\n            self.fields = []\n        torch.save(self, path)\n\n    def load_config(self, opt):\n        self.tgt_type = opt.tgt_type\n\n\nclass KeyphraseDataReader(DataReaderBase):\n    def read(self, sequences, side, _dir=None):\n        \"\"\"Read keyphrase data from disk. Current supported data format is JSON only.\n\n        Args:\n            sequences (str or Iterable[str]):\n                path to text file or iterable of the actual text data.\n            side (str): Prefix used in return dict. Usually\n                ``\"src\"`` or ``\"tgt\"``.\n            _dir (NoneType): Leave as ``None``. This parameter exists to\n                conform with the :func:`DataReaderBase.read()` signature.\n\n        Yields:\n            dictionaries whose keys are the names of fields and whose\n            values are more or less the result of tokenizing with those\n            fields.\n            src: title+abstract\n            tgt: a string of a keyword, or a string of concatenated keywords (delimited by <sep>)\n        \"\"\"\n        assert _dir is None or _dir == \"\", \\\n            \"Cannot use _dir with KeyphraseDataReader.\"\n        if isinstance(sequences, str):\n            sequences = DataReaderBase._read_file(sequences)\n        for i, line in enumerate(sequences):\n            try:\n                # default input is a json line\n                line = line.decode(\"utf-8\")\n                json_dict = json.loads(line)\n                try:\n                    # Note tgt could be a list of strings\n                    seq = json_dict[side]\n                    # torchtext field only takes numeric features\n                    id = json_dict['id']\n                except Exception:\n                    # temporary workaround for raw json data\n                    seq = json_dict\n                    id = i\n            except Exception:\n                # temporary workaround for plain text input\n                seq = line\n                id = i\n\n            try:\n                if id.rfind('_') != -1:\n                    id = id[id.rfind('_') + 1:]\n                id = int(id)\n            except Exception:\n                # if not convertible, use indices as id\n                id = i\n\n            yield {side: seq, \"indices\": i, 'id': id}\n\n\ndef obtain_sorted_indices(src_seq, tgt_seqs, sort_by):\n    \"\"\"\n    :param src_seq: used for verbatim and alphabetical\n    :param tgt_seqs:\n    :param sort_by:\n    :param absent_pos: must be one of [prepend, append and ignore], ignore means simply drop absent kps\n    :return:\n    \"\"\"\n    num_tgt = len(tgt_seqs)\n\n    if sort_by == 'random':\n        sorted_id = np.random.permutation(num_tgt)\n    elif sort_by.startswith('nosort'):\n        sorted_id = list(range(len(tgt_seqs)))\n    elif sort_by.startswith('alphab'):\n        sorted_tgts = sorted(enumerate(tgt_seqs), key=lambda x: '_'.join(x[1]))\n        sorted_id = [t[0] for t in sorted_tgts]\n    elif sort_by.startswith('length'):\n        sorted_tgts = sorted(enumerate(tgt_seqs), key=lambda x: len(x[1]))\n        sorted_id = [t[0] for t in sorted_tgts]\n    elif sort_by == 'pres_abs' or sort_by == 'abs_pres':\n        # obtain present flags as well their positions, lowercase should be done beforehand\n        present_tgt_flags, present_indices, _ = if_present_duplicate_phrases(src_seq, tgt_seqs)\n        # separate present/absent phrases\n        present_tgt_idx = np.arange(num_tgt)[present_tgt_flags]\n        absent_tgt_idx  = [t_id for t_id, present in zip(range(num_tgt), present_tgt_flags) if ~present]\n        absent_tgt_idx  = np.random.permutation(absent_tgt_idx)\n        # sort present phrases by their positions\n        present_indices = present_indices[present_tgt_flags]\n        present_tgt_idx = sorted(zip(present_tgt_idx, present_indices), key=lambda x: x[1])\n        present_tgt_idx = [t[0] for t in present_tgt_idx]\n\n        if sort_by == 'pres_abs':\n            sorted_id = np.concatenate((present_tgt_idx, absent_tgt_idx), axis=None)\n        elif sort_by == 'abs_pres':\n            sorted_id = np.concatenate((absent_tgt_idx, present_tgt_idx), axis=None)\n        else:\n            raise NotImplementedError('Unsupported sort_by value: ' + sort_by)\n            sorted_id = present_tgt_idx\n    else:\n        raise NotImplementedError('Unsupported sort_by value: ' + sort_by)\n\n    if sort_by.endswith('reverse'):\n        sorted_id = sorted_id[::-1]\n\n    return np.asarray(sorted_id, dtype=int)\n\n\ndef process_multiple_tgts(big_batch, tgt_type):\n    \"\"\"\n    This function is only used using original OpenNMT pipeline (data stored in .pt file).\n    If data is loaded from raw JSON with pre-trained tokenizer, it is processed on-the-fly.\n    :param big_batch: a list of examples\n            src: [1, src_len]\n            tgt: [num_kp, 1, kp_len]\n    :param tgt_type:\n            specify format of target and concatenate kps in tgt accordingly\n            if one2one: randomly pick up one phrase, tgt will be [1, kp_len]\n            if one2seq: tgt will be [1, concat_kps_len]\n    :return:\n    \"\"\"\n    new_batch = []\n    for ex in big_batch:\n        # a workaround: truncate to maximum 8 phrases (some noisy data points have many phrases)\n        if hasattr(ex, \"tgt\") and len(ex.tgt) > 8:\n            random_choise = np.random.choice(len(ex.tgt), 8)\n            if hasattr(ex, \"tgt\"):\n                ex.tgt = [ex.tgt[idx] for idx in random_choise]\n            if hasattr(ex, \"alignment\"):\n                ex.alignment = [ex.alignment[idx] for idx in random_choise]\n        # tgt = ex.tgt if hasattr(ex, \"tgt\") else None\n        # alignment = ex.alignment if hasattr(ex, \"alignment\") else None\n\n        # sep_indices, indicating the position of <SEP> and <EOS> after concatenating, only used in one2seq training\n        sep_indices = None\n        if tgt_type == 'one2one':\n            # sample one tgt from multiple tgts and use it as the only tgt\n            rand_idx = np.random.randint(len(ex.tgt))\n            tgt = ex.tgt[rand_idx]\n            alignment = ex.alignment[rand_idx] if hasattr(ex, \"alignment\") else None\n\n            ex.tgt = tgt\n            ex.alignment = alignment\n        elif tgt_type in KP_CONCAT_TYPES:\n            # generate one2seq training data points\n            order = obtain_sorted_indices(ex.src, ex.tgt, sort_by=tgt_type)\n            tgt = [ex.tgt[idx] for idx in order]\n            tgt = [t[0]+[SEP_token] for t in tgt[:-1]] + tgt[-1]\n            tgt = [np.concatenate(tgt, axis=None).tolist()]\n\n            # position of <SEP> and <EOS>\n            sep_indices = [[wid for wid, w in enumerate(t) if w==SEP_token] + [len(t)] for t in tgt]\n            sep_indices = torch.torch.from_numpy(np.concatenate(sep_indices, axis=None))\n\n            # print(\"len_tgt=%d\" % len(tgt[0]))\n            # print(\"sep_indices=%s\" % str(sep_indices.tolist()))\n\n            if hasattr(ex, \"alignment\"):\n                alignment = [ex.alignment[idx] for idx in order]\n                # remove the heading and trailing 0 for <s> and </s> in each subsequence\n                alignment = [a.numpy().tolist()[1:-1] for a in alignment]\n                # add pads 0 for <sep> between subsequences, <s> and </s> for whole final sequence\n                alignment = [[0]] + [t+[0] for t in alignment[:-1]] + [alignment[-1]] + [[0]]\n                # concatenate alignments to one Tensor, length should be len(tgt)+2\n                alignment = torch.torch.from_numpy(np.concatenate(alignment, axis=None))\n            else:\n                alignment = None\n\n            ex.tgt = tgt\n            ex.alignment = alignment\n            '''\n            elif tgt_type == 'no_sort':\n                # return tgts in original order\n                tgt = [t[0]+[SEP_token] for t in ex.tgt[:-1]] + ex.tgt[-1]\n                tgt = [np.concatenate(tgt, axis=None).tolist()]\n                if hasattr(ex, \"alignment\"):\n                    # remove the heading and trailing 0 for <s> and </s>\n                    alignment = [a.numpy().tolist()[1:-1] for a in ex.alignment]\n                    # add 0s for <sep>, <s> and </s>\n                    alignment = [0] + [t+[0] for t in alignment[:-1]] + [alignment[-1]] + [0]\n                    # concatenate alignments to one Tensor\n                    alignment = torch.torch.from_numpy(np.concatenate(alignment, axis=None))\n                else:\n                    alignment = None\n            '''\n        # no processing for 'multiple' (test phrase)\n        elif tgt_type == 'multiple':\n            pass\n        else:\n            raise NotImplementedError\n\n        setattr(ex, 'sep_indices', sep_indices)\n\n        if hasattr(ex, \"alignment\"):\n            if isinstance(alignment, list):\n                # for test phase (tgt_type='multiple'), with unprocessed multiple targets\n                assert len(tgt) == len(alignment)\n                assert all([len(t[0])+2==a.size()[0] for t,a in zip(tgt, alignment)])\n            else:\n                # for other training cases, with one target sequence\n                assert len(tgt[0]) + 2 == alignment.size()[0]\n\n        new_batch.append(ex)\n\n    return new_batch\n\n\ndef kp_sort_key(ex):\n    \"\"\"Sort using the number of tokens in the sequence.\"\"\"\n    if hasattr(ex, \"tgt\"):\n        return len(ex.src[0]), len(ex.tgt[0])\n    return len(ex.src[0])\n\n# deprecated\ndef max_tok_len(new, count, sofar):\n    \"\"\"\n    Specialized for keyphrase generation task\n    Note that the form of tgt has to be determined beforehand, i.e. shuffle/order/pad should have been done\n    In token batching scheme, the number of sequences is limited\n    such that the total number of src/tgt tokens (including padding)\n    in a batch <= batch_size\n    \"\"\"\n    # Maintains the longest src and tgt length in the current batch\n    global max_src_in_batch, max_tgt_in_batch  # this is a hack\n    # Reset current longest length at a new batch (count=1)\n    if count == 1:\n        max_src_in_batch = 0\n        max_tgt_in_batch = 0\n    # Src: [<bos> w1 ... wN <eos>]\n    max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2)\n    # Tgt: [w1 ... wM <eos>]\n    max_tgt_in_batch = max(max_tgt_in_batch, len(new.tgt[0]) + 1)\n    src_elements = count * max_src_in_batch\n    tgt_elements = count * max_tgt_in_batch\n    return max(src_elements, tgt_elements)\n\n\ndef copyseq_tokenize(text):\n    '''\n    moved to onmt.keyphrase.utils.meng17_tokenize()\n    '''\n    pass\n\n# mix this with partial\ndef _feature_tokenize(\n        string, layer=0, tok_delim=None, feat_delim=None, truncate=None, lower=False):\n    \"\"\"Split apart word features (like POS/NER tags) from the tokens.\n\n    Args:\n        string (str): A string with ``tok_delim`` joining tokens and\n            features joined by ``feat_delim``. For example,\n            ``\"hello|NOUN|'' Earth|NOUN|PLANET\"``.\n        layer (int): Which feature to extract. (Not used if there are no\n            features, indicated by ``feat_delim is None``). In the\n            example above, layer 2 is ``'' PLANET``.\n        truncate (int or NoneType): Restrict sequences to this length of\n            tokens.\n\n    Returns:\n        List[str] of tokens.\n    \"\"\"\n    if lower:\n        string = string.lower()\n\n    # @memray 20190308 to make tokenized results same between src/tgt, changed here back to a simple splitter (whitespace)\n    # move complicated tokenization into pre-preprocess in kp_data_converter.py\n    tokens = string.split(tok_delim)\n    # tokens = copyseq_tokenize(string)\n    if truncate is not None:\n        tokens = tokens[:truncate]\n    if feat_delim is not None:\n        tokens = [t.split(feat_delim)[layer] for t in tokens]\n    return tokens\n\n\nclass KeyphraseField(RawField):\n    \"\"\"Container for subfields.\n\n    Text data might use POS/NER/etc labels in addition to tokens.\n    This class associates the \"base\" :class:`Field` with any subfields.\n    It also handles padding the data and stacking it.\n\n    Args:\n        base_name (str): Name for the base field.\n        base_field (Field): The token field.\n\n    Attributes:\n        fields (Iterable[Tuple[str, Field]]): A list of name-field pairs.\n            The order is defined as the base field first, then\n            ``feats_fields`` in alphabetical order.\n    \"\"\"\n\n    def __init__(self, base_name, base_field):\n        super(KeyphraseField, self).__init__()\n        self.fields = [(base_name, base_field)]\n        self.type = None\n\n    @property\n    def base_field(self):\n        return self.fields[0][1]\n\n    def pad_seqs(self, batch, max_seq_num, max_seq_len, pad_token):\n        \"\"\"\n        batch is a list of seqs (each seq is a list of strings)\n        pad empty seqs to each example in batch, to make them equal number and length\n        :param batch:\n        :param max_seq_num:\n        :param max_seq_len:\n        :param pad_token:\n        :return:\n        \"\"\"\n        padded = []\n        for ex in batch:\n            padded.append([s[0] for s in ex] + [[pad_token] * max_seq_len] * (max_seq_num-len(ex)))\n        return padded\n\n    def process(self, batch, device=None):\n        \"\"\"Convert outputs of preprocess into Tensors.\n\n        Args:\n            batch (List[List[List[str]]]): A list of length batch size.\n                Each element is a list of the preprocess results for each\n                field (which are lists of str \"words\" or lists of \"phrases\" (lists of str \"words\").\n            device (torch.device or str): The device on which the tensor(s)\n                are built.\n\n        Returns:\n            torch.LongTensor or Tuple[torch.LongTensor, torch.LongTensor]:\n                A tensor of shape ``(seq_len, batch_size, len(self.fields))``\n                where the field features are ordered like ``self.fields``.\n                If the base field returns lengths, these are also returned\n                and have shape ``(batch_size,)``.\n        \"\"\"\n        # batch (list(list(list))): batch_size x len(self.fields) x seq_len\n        if self.type and self.type == 'multiple':\n            # print(self.type)\n            # data: a list of phrases (list of words), [batch_size,seq_num,seq_len]\n            batch_size = len(batch)\n            max_seq_num = max([len(tgts) for tgts in batch])\n            max_seq_len = max([len(p[0]) for e in batch for p in e])\n            # make all examples have equal number of tgts, [batch_size, max_seq_num, max_seq_len]\n            padded_data = self.pad_seqs(batch, max_seq_num, max_seq_len, self.base_field.pad_token)\n\n            # flatten it to [batch_size*max_seq_num, max_seq_len]\n            batch_by_feat = [seq for e in padded_data for seq in e]\n            # base_data: [max_seq_len, batch_size*max_seq_num]\n            base_data = self.base_field.process(batch_by_feat, device=device)\n            # include_lengths is typically False (KeyphraseField is a target field)\n            if self.base_field.include_lengths:\n                # base_data: [max_seq_len, batch_size], lengths: batch_size\n                base_data, lengths = base_data\n\n            # feature is actually not supported\n            feats = []\n            levels = [base_data] + feats\n            # data: [seq_len, batch_size*max_seq_num, len(self.fields)=1]\n            data = torch.stack(levels, 2)\n            # reshape it back to [seq_len, batch_size, max_seq_num, len(self.fields)=1]\n            data = torch.reshape(data, shape=(-1, batch_size, max_seq_num, 1))\n\n            if self.base_field.include_lengths:\n                return data, lengths\n            else:\n                return data\n\n        else:\n            # [batch_size, seq_num=1, seq_len] -> [1, batch_size, seq_len]\n            batch_by_feat = list(zip(*batch))\n            base_data = self.base_field.process(batch_by_feat[0], device=device)\n            if self.base_field.include_lengths:\n                # base_data: [max_seq_len, batch_size], lengths: batch_size\n                base_data, lengths = base_data\n\n            feats = []\n            levels = [base_data] + feats\n            # data: seq_len x batch_size x len(self.fields) (usually only words, so num_feat=1)\n            data = torch.stack(levels, 2)\n            if self.base_field.include_lengths:\n                return data, lengths\n            else:\n                return data\n\n    def preprocess(self, x):\n        \"\"\"Preprocess data.\n\n        Args:\n            x (str): A sentence string (words joined by whitespace).\n\n        Returns:\n            List[List[str]]: A list of length ``len(self.fields)`` containing\n                lists of tokens/feature tags for the sentence. The output\n                is ordered like ``self.fields``.\n        \"\"\"\n        # if x is a list of strings (multiple keyphrases)\n        if isinstance(x, list):\n            return [[f.preprocess(x_) for _, f in self.fields] for x_ in x]\n        else:\n            return [f.preprocess(x) for _, f in self.fields]\n\n    def __getitem__(self, item):\n        return self.fields[item]\n\n\ndef keyphrase_fields(**kwargs):\n    \"\"\"Create keyphrase fields.\n\n    Args:\n        base_name (str): Name associated with the field.\n        n_feats (int): Number of word level feats (not counting the tokens)\n        include_lengths (bool): Optionally return the sequence lengths.\n        pad (str, optional): Defaults to ``\"<blank>\"``.\n        bos (str or NoneType, optional): Defaults to ``\"<s>\"``.\n        eos (str or NoneType, optional): Defaults to ``\"</s>\"``.\n        truncate (bool or NoneType, optional): Defaults to ``None``.\n\n    Returns:\n        List[Tuple[str, KeyphraseField]]\n    \"\"\"\n\n    n_feats = kwargs[\"n_feats\"]\n    include_lengths = kwargs[\"include_lengths\"]\n    base_name = kwargs[\"base_name\"]\n    pad = kwargs.get(\"pad\", \"<blank>\")\n    bos = kwargs.get(\"bos\", \"<s>\")\n    eos = kwargs.get(\"eos\", \"</s>\")\n    # manually added in create_vocab()\n    # sep = kwargs.get(\"sep\", \"<sep>\")\n    truncate = kwargs.get(\"truncate\", None)\n    lower = kwargs.get(\"lower\", None)\n    fields_ = []\n    feat_delim = u\"￨\" if n_feats > 0 else None\n    for i in range(n_feats + 1):\n        name = base_name + \"_feat_\" + str(i - 1) if i > 0 else base_name\n        tokenize = partial(\n            _feature_tokenize,\n            layer=i,\n            truncate=truncate,\n            feat_delim=feat_delim,\n            lower = lower)\n        use_len = i == 0 and include_lengths\n        feat = Field(\n            init_token=bos, eos_token=eos,\n            pad_token=pad, tokenize=tokenize,\n            include_lengths=use_len, lower=lower)\n        fields_.append((name, feat))\n    assert fields_[0][0] == base_name  # sanity check\n    field = KeyphraseField(fields_[0][0], fields_[0][1])\n    return field\n\n"
  },
  {
    "path": "onmt/inputters/news_dataset.py",
    "content": "# -*- coding: utf-8 -*-\nimport copy\nimport json\nimport logging\nimport random\nimport time\nfrom collections import Counter\nfrom functools import partial\nfrom itertools import starmap\nfrom multiprocessing import pool, Pool\n\nimport six\nimport torch\nimport torchtext\nfrom transformers import AutoTokenizer, RobertaTokenizerFast, RobertaTokenizer, AddedToken\nfrom torchtext.data import Field, RawField\n\nfrom torchtext.data import Dataset as TorchtextDataset\nfrom torchtext.data import Example\nfrom tqdm import tqdm\n\nfrom onmt.inputters.datareader_base import DataReaderBase\nfrom onmt.inputters.dataset_base import _join_dicts, _dynamic_dict\n\n\nclass Token():\n    def __init__(self, token, field):\n        self.token = token\n        self.field = field\n\ndef process_news_example(ex_dict, tgt_fields, tokenizer=None, tgt_weights=None,\n                         add_meta_label=True, add_field_label=True,\n                         has_special_vocab=False,\n                         max_src_len=None, max_tgt_len=None,\n                         return_type='str'):\n    dataset = ex_dict['source']\n\n    if add_meta_label or add_field_label:\n        assert has_special_vocab, 'if add_meta_label or add_field_label, special_vocab must be given.'\n    # if tokenizer_fn is given, tokenize text on-the-fly\n    if tokenizer:\n        text = ex_dict['text'].replace('\\n', '[SEP_PAR]')\n        ex_dict['text_tokens'] = ['[SEP_PAR]'] + tokenizer.tokenize(text)\n        text_tokens = [Token(t, field='[PART_MAINBODY]') for t in ex_dict['text_tokens']]\n\n        title = ex_dict['title'].replace('\\n', ' ')\n        ex_dict['title_tokens'] = tokenizer.tokenize(title)\n        title_tokens = [Token(t, field='[PART_TITLE]') for t in ex_dict['title_tokens']]\n\n        summary = ex_dict['text'].replace('\\n', '[SEP_SUM]')\n        ex_dict['summary_tokens'] = tokenizer.tokenize(summary)\n        summary_tokens = [Token(t, field='[PART_SUMMARY]') for t in ex_dict['summary_tokens']]\n\n        if dataset == 'cnndm' or dataset == 'xsum':\n            desc = ex_dict['metadata']['description'].replace('\\n', '[SEP_SUM]')\n            ex_dict['desc_tokens'] = tokenizer.tokenize(desc)\n            desc_tokens = [Token(t, field='[PART_DESCRIPTION]') for t in ex_dict['desc_tokens']]\n        else:\n            desc_tokens = None\n\n    else:\n        ex_dict['text_tokens'] = ['[SEP_PAR]']+[w if w != '\\n' else '[SEP_PAR]' for w in ex_dict['text_tokens']]\n        text_tokens = [Token(t, field='[PART_MAINBODY]') for t in ex_dict['text_tokens']]\n\n        title_tokens = [w for w in ex_dict['title_tokens'] if w != '\\n']\n        title_tokens = [Token(t, field='[PART_TITLE]') for t in title_tokens]\n\n        summary_tokens = [w if w != '\\n' else '[SEP_SUM]' for w in ex_dict['summary_tokens']]\n        summary_tokens = [Token(t, field='[PART_SUMMARY]') for t in summary_tokens]\n\n        if 'desc_tokens' in ex_dict:\n            desc_tokens = [w if w != '\\n' else '[SEP_SUM]' for w in ex_dict['desc_tokens']]\n            desc_tokens = [Token(t, field='[PART_DESCRIPTION]') for t in desc_tokens]\n        else:\n            desc_tokens = None\n\n    # randomly select a target and use the rest as source\n    copied_tgt_fields = copy.copy(tgt_fields)\n    if 'description' in copied_tgt_fields and not desc_tokens:\n        copied_tgt_fields.remove('description')\n    if 'title' in copied_tgt_fields and not title_tokens:\n        copied_tgt_fields.remove('title')\n    if 'summary' in copied_tgt_fields and not summary_tokens:\n        copied_tgt_fields.remove('summary')\n    if len(copied_tgt_fields) == 0:\n        pass\n\n    if len(copied_tgt_fields) > 0:\n        tgt_field = random.choices(copied_tgt_fields, weights=tgt_weights, k=1)[0]\n    else:\n        # for cases during testing, but no valid target field\n        tgt_field = tgt_fields[0]\n\n    infill_placeholder_token = Token('[PART_INFILL_PLACE]', field='[PART_INFILL_PLACE]')\n\n    if tgt_field == 'summary':\n        # remove title to avoid info-leaking during multi-dataset training\n        #   changed from : src_tokens = title_tokens + sep_token + text_tokens\n        src_tokens = title_tokens + text_tokens\n        tgt_tokens = summary_tokens\n\n    elif tgt_field == 'title':\n        src_tokens = text_tokens\n        tgt_tokens = title_tokens\n\n    elif tgt_field == 'description' and desc_tokens:\n        src_tokens = title_tokens + text_tokens\n        tgt_tokens = desc_tokens\n\n    elif tgt_field == 'randomsent':\n        raise NotImplementedError\n    else:\n        raise NotImplementedError\n\n    # meta_label: prepend metadata labels to src tokens\n    if add_meta_label:\n        meta_tokens = get_meta_tokens(ex_dict, dataset, tgt_field)\n        src_tokens = meta_tokens + src_tokens\n\n    # RoBERTa model requires special tokens in order to work.\n    if tokenizer:\n        cls_token = Token(token=tokenizer.cls_token, field='[PART_METALABEL]')\n        src_tokens = [cls_token] + src_tokens\n\n    if max_src_len and len(src_tokens) > max_src_len:\n        src_tokens = src_tokens[: max_src_len - 1]\n        trunc_token = Token(token='[MAINBODY_TRUNCATED_END]', field='[PART_MAINBODY]')\n        src_tokens.append(trunc_token)\n\n    if max_tgt_len and len(tgt_tokens) > max_tgt_len:\n        tgt_tokens = tgt_tokens[: max_tgt_len - 1]\n\n    if return_type == 'str':\n        if add_field_label:\n            src = ' '.join([t.token + u'￨' + t.field for t in src_tokens]) + '\\n'\n        else:\n            src = ' '.join([t.token for t in src_tokens]) + '\\n'\n        tgt = ' '.join([t.token for t in tgt_tokens]) + '\\n'\n        # (OpenNMT will prepend a <s> to tgt, but tgt_mask is not used at all)\n        src_mask = torch.LongTensor([1] * len(src_tokens))\n        tgt_mask = torch.LongTensor([1] * (len(tgt_tokens) + 1))\n    else:\n        # TODO, directly use tokenizer to encode\n        src = torch.LongTensor([1] * len(src_tokens))\n        tgt = torch.LongTensor([1] * len(src_tokens))\n        src_mask = torch.LongTensor([1] * len(src_tokens))\n        tgt_mask = torch.LongTensor([1] * len(tgt_tokens))\n\n    new_ex_dict = {'src': src, 'tgt': tgt,\n                   'indices': ex_dict['indices'] if 'indices' in ex_dict else None,\n                    # for pretrained model\n                   'src_mask': src_mask,\n                   'tgt_mask': tgt_mask,\n                   'src_tokens': [t.token for t in src_tokens],\n                   'tgt_tokens': [t.token for t in tgt_tokens],\n                   }\n\n    return new_ex_dict\n\n\ndef process_tokenized_news_example(ex_dict, tokenizer_name, tokenizer,\n                                   tgt_fields, tgt_weights=None,\n                                   add_meta_label=True, add_field_label=False,\n                                   has_special_vocab=False,\n                                   max_src_len=None, max_tgt_len=None,\n                                   return_type='str'):\n    dataset = ex_dict['source']\n\n    if add_meta_label or add_field_label:\n        assert has_special_vocab, 'if add_meta_label or add_field_label, special_vocab must be given.'\n\n    if tokenizer_name not in ex_dict:\n        raise NotImplementedError('Tokenized data is not found in the json.')\n    tokenized_data = ex_dict[tokenizer_name]['token']\n    encoded_data = ex_dict[tokenizer_name]['code']\n\n    title_sents_tokens = tokenized_data['title_sents']\n    title_tokens = title_sents_tokens[0]\n    summary_tokens = tokenized_data['summary']\n    desc_tokens = tokenized_data['description']\n\n    title_sents_codes = encoded_data['title_sents']\n    title_codes = title_sents_codes[0]\n    summary_codes = encoded_data['summary']\n    desc_codes = encoded_data['description']\n\n    if len(title_sents_tokens) == 0 or len(title_sents_codes) == 0:\n        return None\n\n    masks = ex_dict[tokenizer_name]['oracle_mask']\n    oracle_sent_mask = ex_dict[tokenizer_name]['oracle_sent_mask']\n\n    # our model has special_vocab, released BART doesn't\n    if has_special_vocab:\n        # replace the [PAD] to [SEP_PAR] (a preprocessing mistake)\n        title_sents_tokens = [s if s!=['[PAD]'] else ['[SEP_PAR]'] for s in title_sents_tokens]\n        pad_token_id = tokenizer.convert_tokens_to_ids(['[PAD]'])\n        sep_token_id = tokenizer.convert_tokens_to_ids(['[SEP_PAR]'])\n        title_sents_codes = [s if s!=pad_token_id else sep_token_id for s in title_sents_codes]\n\n        # append a [SEP_PAR] to the beginning of title\n        title_sents_tokens = [['[SEP_PAR]']] + title_sents_tokens\n        title_sents_codes = [sep_token_id] + title_sents_codes\n        masks = {k: [[0]]+v for k,v in masks.items()}\n        oracle_sent_mask = [0] + oracle_sent_mask\n\n        # add a sentence head token `[HEAD_SENT]` to all sentences\n        title_sents_tokens = [s if len(s) == 1 else ['[HEAD_SENT]']+s for s in title_sents_tokens]\n        senthead_token_id = tokenizer.convert_tokens_to_ids('[HEAD_SENT]')\n        title_sents_codes = [s if len(s) == 1 else [senthead_token_id]+s for s in title_sents_codes]\n        masks = {k: [s if len(s) == 1 else [0]+s for s in v] for k,v in masks.items()}\n    else:\n        # replace the [PAD] to [SEP_PAR] (a previous mistake). tokens and codes may not match\n        dot_token_id = tokenizer.convert_tokens_to_ids(['.'])\n        if len(title_sents_tokens) > 1 and title_sents_tokens[1] == ['[PAD]']:\n            title_sents_tokens[1] = ['.']\n            title_sents_codes[1] = dot_token_id\n        new_title_sents_tokens = []\n        new_title_sents_codes = []\n\n        for sent_token, sent_code in zip(title_sents_tokens, title_sents_codes):\n            new_sent_token, new_sent_code = [], []\n            for t, c in zip(sent_token, sent_code):\n                if t!='[PAD]' and c < tokenizer.vocab_size:\n                    new_sent_token.append(t)\n                    new_sent_code.append(c)\n            assert len(new_sent_token) == len(new_sent_code), 'sentence lengths of token and code mismatch'\n            if len(new_sent_token) > 0 and len(new_sent_code) > 0:\n                new_title_sents_tokens.append(new_sent_token)\n                new_title_sents_codes.append(new_sent_code)\n        title_sents_tokens = new_title_sents_tokens\n        title_sents_codes = new_title_sents_codes\n\n    # randomly select a target and use the rest as source\n    copied_tgt_dict = {k:v for k,v in zip(tgt_fields, tgt_weights)}\n    if 'description' in copied_tgt_dict and not desc_tokens:\n        del copied_tgt_dict['description']\n    if 'title' in copied_tgt_dict and not title_tokens:\n        del copied_tgt_dict['title']\n    if 'summary' in copied_tgt_dict and not summary_tokens:\n        del copied_tgt_dict['summary']\n    if 'randomsent' in copied_tgt_dict and len(title_sents_tokens) < 4:\n        del copied_tgt_dict['randomsent']\n    if len(copied_tgt_dict) == 0:\n        return None\n    copied_tgt_fields = list(copied_tgt_dict.keys())\n    copied_tgt_weights = list(copied_tgt_dict.values())\n\n    if len(copied_tgt_fields) > 0:\n        tgt_field = random.choices(copied_tgt_fields, weights=copied_tgt_weights, k=1)[0]\n    else:\n        # for cases during testing, but no valid target field\n        tgt_field = tgt_fields[0]\n\n    # field token: sentence position tokens\n    field_tokens = []\n    sent_count = 0\n    for s in title_sents_tokens:\n        if len(s) == 1 and s[0] == '[SEP_PAR]':\n            field_tokens.append(['[SEP_PAR]'])\n        else:\n            sent_count = sent_count if sent_count <= 127 else 127\n            field_tokens.append(['[SENT_POS_%d]' % sent_count] * len(s))\n            sent_count += 1\n\n    # determine src/tgt\n    if tgt_field == 'summary':\n        src_tokens = title_sents_tokens\n        src_codes = title_sents_codes\n        tgt_tokens = summary_tokens\n        tgt_codes = summary_codes\n    elif tgt_field == 'description' and desc_tokens:\n        src_tokens = title_sents_tokens\n        src_codes = title_sents_codes\n        tgt_tokens = desc_tokens\n        tgt_codes = desc_codes\n    elif tgt_field == 'title':\n        tgt_tokens = title_sents_tokens[1]\n        tgt_codes = title_sents_codes[1]\n        src_tokens = title_sents_tokens\n        src_codes = title_sents_codes\n        src_tokens[1] = ['[HEAD_SENT]', tokenizer.mask_token]\n        src_codes[1] = [senthead_token_id, tokenizer.mask_token_id]\n        field_tokens[1] = [field_tokens[1][0]] * 2\n        masks = {k: [[0, 0] if si==1 else s for si, s in enumerate(v)] for k, v in masks.items()}\n    elif tgt_field == 'randomsent':\n        accum_len = 0\n        for sentid, sent in enumerate(title_sents_tokens):\n            if accum_len > max_src_len:\n                break\n            accum_len += len(sent)\n        max_sentid = sentid\n        sentid = random.randint(2, max_sentid)\n        iter_count = 0\n        while len(title_sents_tokens[sentid]) < 5 and iter_count < 5:\n            iter_count += 1\n            sentid = random.randint(2, max_sentid)\n        tgt_tokens = title_sents_tokens[sentid]\n        tgt_codes = title_sents_codes[sentid]\n        src_tokens = title_sents_tokens\n        src_codes = title_sents_codes\n        src_tokens[sentid] = ['[HEAD_SENT]', tokenizer.mask_token]\n        src_codes[sentid] = [senthead_token_id, tokenizer.mask_token_id]\n        field_tokens[sentid] = [field_tokens[sentid][0]] * 2\n        masks = {k: [[0, 0] if si==sentid else s for si, s in enumerate(v)] for k, v in masks.items()}\n    else:\n        raise NotImplementedError\n\n    # meta_label: prepend metadata labels to src tokens\n    if add_meta_label:\n        # RoBERTa model requires special tokens in order to work.\n        meta_tokens = get_meta_tokens(ex_dict, dataset, tgt_field,\n                                      cls_token=tokenizer.cls_token, token_only=False)\n        meta_texts = [t.token for t in meta_tokens]\n        meta_fields = [t.field for t in meta_tokens]\n        meta_codes = tokenizer.convert_tokens_to_ids(meta_texts)\n        assert len(meta_tokens) == len(meta_codes) == len(meta_codes)\n\n        src_tokens = [meta_texts] + src_tokens\n        src_codes = [meta_codes] + src_codes\n        field_tokens = [meta_fields] + field_tokens\n\n        masks = {k: [[0] * len(meta_tokens)]+v for k,v in masks.items()}\n        oracle_sent_mask = [0] + oracle_sent_mask\n    else:\n        src_tokens = [[tokenizer.cls_token]] + src_tokens\n        src_codes = [[tokenizer.cls_token_id]] + src_codes\n        field_tokens = [[tokenizer.cls_token]] + field_tokens\n        masks = {k: [[0]]+v for k,v in masks.items()}\n        oracle_sent_mask = [0] + oracle_sent_mask\n\n    # add sent-level token pointing to the [SEP_PAR] prior to the oracle sentence\n    oracle_sent_head_mask = [[0] * len(s) for s in src_tokens]\n    if has_special_vocab:\n        for sent_id, sent_mask in enumerate(oracle_sent_mask):\n            if sent_mask == 1:\n                # 1st token must be a `[HEAD_SENT]`\n                if src_tokens[sent_id][0] != '[HEAD_SENT]':\n                    print(src_tokens[sent_id])\n                    pass\n                # assert len(oracle_sent_head_mask[sent_id-1]) == 1\n                oracle_sent_head_mask[sent_id][0] = 1\n    masks['sentence_head'] = oracle_sent_head_mask\n\n    # flatten all data before returning\n    src_tokens = [t for s in src_tokens for t in s]\n    src_codes = [t for s in src_codes for t in s]\n    field_tokens = [t for s in field_tokens for t in s]\n    field_codes = tokenizer.encode(field_tokens)\n    masks = {k: [t for m in v for t in m] for k,v in masks.items()}\n\n    len_src = len(src_tokens)\n    if has_special_vocab:\n        assert len_src == len(src_codes) == len(field_codes) == len(masks['word'])\n    else:\n        assert len_src == len(src_codes)\n    assert len(tgt_tokens) == len(tgt_codes)\n\n    # truncate scr/tgt sequences\n    if max_src_len and len(src_tokens) > max_src_len:\n        src_codes = src_codes[: max_src_len]\n        src_tokens = src_tokens[: max_src_len]\n        field_tokens = field_tokens[: max_src_len]\n        field_codes = field_codes[: max_src_len]\n        masks = {k: v[: max_src_len] for k,v in masks.items()}\n    if max_tgt_len and len(tgt_tokens) > max_tgt_len:\n        tgt_tokens = tgt_tokens[: max_tgt_len]\n        tgt_codes = tgt_codes[: max_tgt_len]\n\n    # add bos and eos to tgt_tokens/tgt_codes\n    tgt_tokens = [tokenizer.bos_token] + tgt_tokens + [tokenizer.eos_token]\n    tgt_codes = [tokenizer.bos_token_id] + tgt_codes + [tokenizer.eos_token_id]\n\n    # tensorize them\n    src_codes = torch.LongTensor(src_codes)\n    field_codes = torch.LongTensor(field_codes)\n    tgt_codes = torch.LongTensor(tgt_codes)\n    masks = {'ext_' + k: torch.LongTensor(v) for k,v in masks.items()}\n\n    src = src_codes\n    tgt = tgt_codes\n\n    src_mask = torch.LongTensor([1] * len(src_tokens))\n    tgt_mask = torch.LongTensor([1] * len(tgt_tokens))\n\n    new_ex_dict = {\n        'src': src, 'tgt': tgt, 'src_field': field_codes,\n        'src_length': len(src_tokens), 'tgt_length': len(tgt_tokens),\n        'src_mask': src_mask, 'tgt_mask': tgt_mask,\n        'src_tokens': src_tokens, 'tgt_tokens': tgt_tokens,\n        'indices': ex_dict['indices'] if 'indices' in ex_dict else None,\n    }\n    new_ex_dict.update(masks)\n\n    return new_ex_dict\n\n\nDATASET_TOKEN_MAP = {'cnndm': '[DATASET_CNNDM]',\n                     'nyt': '[DATASET_NYT]',\n                     'newsroom': '[DATASET_NEWSROOM]',\n                     'xsum': '[DATASET_XSUM]',\n                     'gigaword5': '[DATASET_GIGAWORD5]',\n                     'newscrawl': '[DATASET_NEWSCRAWL]'\n                     }\nDENSITY_BIN_MAP = {'extractive': '[BIN_DENSITY_EXT]',\n                   'abstractive': '[BIN_DENSITY_ABS]',\n                   'mixed': '[BIN_DENSITY_MIX]',\n                   'unknown': '[BIN_DENSITY_UNK]'\n                   }\n\ndef get_meta_tokens(doc, dataset_name, tgt_field, cls_token=None, token_only=False):\n    # dataset label\n    if dataset_name == 'cnn' or dataset_name == 'dailymail':\n        dataset_name = 'cnndm'\n    if dataset_name == 'newyorktimes':\n        dataset_name = 'nyt'\n    dataset_token = Token(DATASET_TOKEN_MAP[dataset_name], field='[PART_METALABEL]')\n\n    # target type label\n    target_token = tgt_field\n    target_token = Token('[%s]' % target_token.upper(), field='[PART_METALABEL]')\n\n    # density bin label, currently only newsroom has density_bin labels\n    if dataset_name == 'newsroom':\n        density_bin = doc['metadata']['density_bin']\n        density_bin_token = Token(DENSITY_BIN_MAP[density_bin], field='[PART_METALABEL]')\n    else:\n        density_bin_token = Token(DENSITY_BIN_MAP['unknown'], field='[PART_METALABEL]')\n\n    meta_tokens = [dataset_token, target_token, density_bin_token]\n\n    # required by models like RoBERTa\n    if cls_token:\n        cls_token = Token(cls_token, field='[PART_METALABEL]')\n        meta_tokens = [cls_token] + meta_tokens\n\n    if token_only:\n        meta_tokens = [t.token for t in meta_tokens]\n\n    return meta_tokens\n\n\ndef process_news_examples_parallel(news_examples, tgt_fields, tgt_weights,\n                                   meta_label, field_label, has_special_vocab,\n                                   max_src_len=None, max_tgt_len=None,\n                                   tokenizer=None, multi_process=False):\n    # news_examples = list(news_examples)\n    # news_examples = list(news_examples)[:1000]\n\n    if multi_process:\n        \"\"\"pretrained_tokenizer seems not multi-processing safe\n        error out \"RuntimeError: received 0 items of ancdata\" after a few examples (484~490)\n        speed is slow as well ~20 it/s, single-processing is ~60 it/s\n        \"\"\"\n        processed_list = []\n        partial_fn = partial(process_news_example, tgt_fields=tgt_fields,\n                             tokenizer=tokenizer, tgt_weights=tgt_weights,\n                             add_meta_label=meta_label, add_field_label=field_label,\n                             has_special_vocab=has_special_vocab,\n                             max_src_len=max_src_len, max_tgt_len=max_tgt_len,\n                             )\n        with Pool(processes=4) as pool:\n            for processed_ex in tqdm(pool.imap(partial_fn, news_examples),\n                                          desc='Preparing src and tgt w/ multi-processing (tokenizing and field tokens)'):\n                processed_list.append(processed_ex)\n        \"\"\"\n        print('Preparing src and tgt w/ multiple processing (tokenizing and field tokens)')\n        start_time = time.clock()\n        pool = Pool(1)\n        processed_list = pool.map(partial(process_news_example, tgt_fields=tgt_fields,\n                                          tokenizer=tokenizer, tgt_weights=tgt_weights,\n                                          add_meta_label=meta_label, add_field_label=field_label),\n                                  news_examples)\n        pool.close()\n        end_time = time.clock()\n        print(\"Process finished, elapsed time=%.4f, speed=%.2f it/s\" % (end_time-start_time,\n                                                                        len(processed_list)/(end_time-start_time)))\n        \"\"\"\n    else:\n        processed_list = [process_news_example(ex, tgt_fields=tgt_fields,\n                                               tokenizer=tokenizer, tgt_weights=tgt_weights,\n                                               add_meta_label=meta_label, add_field_label=field_label,\n                                               max_src_len=max_src_len, max_tgt_len=max_tgt_len,)\n                          for ex in tqdm(news_examples,\n                                         desc='Preparing src and tgt w/ single processing (tokenizing and field tokens)')]\n\n    new_processed_list = []\n    for didx, d in enumerate(processed_list):\n        # filter out None items\n        if not d:\n            print('Error when loading data point %d, skip for now' % didx)\n            continue\n        d['indices'] = didx\n        new_processed_list.append(d)\n\n    return new_processed_list\n\n\ndef load_tokenized_news_examples(news_examples, tokenizer_name, tokenizer,\n                                 tgt_fields, tgt_weights,\n                                 meta_label, field_label,\n                                 has_special_vocab,\n                                 max_src_len=None, max_tgt_len=None, ):\n    processed_list = []\n    for ex_id, ex_dict in tqdm(enumerate(news_examples), desc='Loading tensorized src and tgt'):\n        # if ex_id >= 500:\n        #     break\n        try:\n            ex_data_dict = process_tokenized_news_example(ex_dict, tokenizer_name, tokenizer,\n                                                          tgt_fields=tgt_fields, tgt_weights=tgt_weights,\n                                                          add_meta_label=meta_label, add_field_label=field_label,\n                                                          has_special_vocab=has_special_vocab,\n                                                          max_src_len=max_src_len, max_tgt_len=max_tgt_len)\n            if ex_data_dict is None:\n                # logging.warning(\"No valid %s is found in data %d, \"\n                #                 \"or source text is faulty, title=`%s`, len(text)=%d\"\n                #                 % (str(tgt_fields), ex_id, ex_dict['title'], len(ex_dict['text'])))\n                continue\n            ex_data_dict['indices'] = ex_id\n            processed_list.append(ex_data_dict)\n        except Exception as e:\n            logging.error(\"Error while processing %d data: %s\" % (ex_id, ex_dict))\n            logging.getLogger().exception('Exception message: ' + str(e))\n            continue\n    return processed_list\n\n\ndef build_dynamic_dict_and_masks_parallel(read_iters, fields, boseos_added, alignment_loss, alignment_targets, multi_process=False):\n    src_vocabs = []\n    stemmed_src_vocabs = []\n    ex_dicts = []\n    if multi_process:\n        partial_fn = partial(_dynamic_dict,\n                             src_field=fields['src'].base_field,\n                             tgt_field=fields['tgt'].base_field,\n                             boseos_added=boseos_added)\n        with Pool(processes=4) as pool:\n            for src_ex_vocab, example in tqdm(pool.imap(partial_fn, starmap(_join_dicts, zip(*read_iters))),\n                                          desc='Preparing src and tgt w/ multi-processing (tokenizing and field tokens)'):\n                src_vocabs.append(src_ex_vocab)\n                ex_dicts.append(example)\n        \"\"\"\n        print('Processing news examples w/ multiple processing (building dynamic_dict)')\n        start_time = time.clock()\n        pool = Pool()\n        processed_list = pool.map(partial(_dynamic_dict,\n                                          src_field=fields['src'].base_field, tgt_field=fields['tgt'].base_field),\n                                  starmap(_join_dicts, zip(*read_iters)))\n        end_time = time.clock()\n        src_vocabs = [i[0] for i in processed_list]\n        ex_dicts = [i[1] for i in processed_list]\n        print(\"Process finished, elapsed time=%.4f, speed=%.2f it/s\" % (end_time-start_time,\n                                                                        len(processed_list)/(end_time-start_time)))\n        \"\"\"\n    else:\n        for ex_dict in tqdm(starmap(_join_dicts, zip(*read_iters)), desc='Processing news examples w/ single processing (building dynamic_dict)'):\n            if hasattr(fields['src'], 'base_field'):\n                src_field = fields['src'].base_field\n                tgt_field = fields['tgt'].base_field\n            else:\n                src_field = fields['src']\n                tgt_field = fields['tgt']\n            # this assumes src_field and tgt_field are both text\n            ex_dict, src_ex_vocab, stemmed_src_ex_vocab = _dynamic_dict(\n                ex_dict, src_field, tgt_field,\n                boseos_added=boseos_added,\n                alignment_loss=alignment_loss,\n                alignment_targets=alignment_targets\n            )\n            src_vocabs.append(src_ex_vocab)\n            stemmed_src_vocabs.append(stemmed_src_ex_vocab)\n            ex_dicts.append(ex_dict)\n\n    return ex_dicts, src_vocabs, stemmed_src_vocabs\n\n\nclass NewsDataset(TorchtextDataset):\n    \"\"\"Contain data and process it.\n\n    A dataset is an object that accepts sequences of raw data (sentence pairs\n    in the case of machine translation) and fields which describe how this\n    raw data should be processed to produce tensors. When a dataset is\n    instantiated, it applies the fields' preprocessing pipeline (but not\n    the bit that numericalizes it or turns it into batch tensors) to the raw\n    data, producing a list of :class:`torchtext.data.Example` objects.\n    torchtext's iterators then know how to use these examples to make batches.\n\n    Args:\n        fields (dict[str, List[Tuple[str, Field]]]): a dict with the structure\n            returned by :func:`onmt.inputters.get_fields()`. Usually\n            that means the dataset side, ``\"src\"`` or ``\"tgt\"``. Keys match\n            the keys of items yielded by the ``readers``, while values\n            are lists of (name, Field) pairs. An attribute with this\n            name will be created for each :class:`torchtext.data.Example`\n            object and its value will be the result of applying the Field\n            to the data that matches the key. The advantage of having\n            sequences of fields for each piece of raw input is that it allows\n            the dataset to store multiple \"views\" of each input, which allows\n            for easy implementation of token-level features, mixed word-\n            and character-level models, and so on. (See also\n            :class:`onmt.inputters.TextMultiField`.)\n        readers (Iterable[onmt.inputters.DataReaderBase]): Reader objects\n            for disk-to-dict. The yielded dicts are then processed\n            according to ``fields``.\n        data (Iterable[Tuple[str, Any]]): (name, ``data_arg``) pairs\n            where ``data_arg`` is passed to the ``read()`` method of the\n            reader in ``readers`` at that position. (See the reader object for\n            details on the ``Any`` type.)\n        dirs (Iterable[str or NoneType]): A list of directories where\n            data is contained. See the reader object for more details.\n        sort_key (Callable[[torchtext.data.Example], Any]): A function\n            for determining the value on which data is sorted (i.e. length).\n        filter_pred (Callable[[torchtext.data.Example], bool]): A function\n            that accepts Example objects and returns a boolean value\n            indicating whether to include that example in the dataset.\n\n    Attributes:\n        src_vocabs (List[torchtext.data.Vocab]): Used with dynamic dict/copy\n            attention. There is a very short vocab for each src example.\n            It contains just the source words, e.g. so that the generator can\n            predict to copy them.\n    \"\"\"\n    def __init__(self, fields, readers, data, dirs, sort_key,\n                 tokenizer=None, filter_pred=None, opt=None):\n        self.sort_key = sort_key\n        self.tokenizer = tokenizer\n        self.opt = opt\n\n        # build src_map/alignment no matter field is available\n        can_copy = True\n        boseos_added = False\n        if hasattr(opt, 'special_vocab_path'):\n            if opt.special_vocab_path is None or opt.special_vocab_path == 'none' or opt.special_vocab_path == 'None':\n                has_special_vocab = False\n            else:\n                has_special_vocab = True\n        else:\n            has_special_vocab = False\n\n        logging.getLogger(\"transformers.tokenization_utils\").setLevel(logging.ERROR)\n\n        # data is directly given if it's called from translate.py\n        if opt.data_format == 'srctgt' or data:\n            read_iters = [r.read(dat[1], dat[0], dir_) for r, dat, dir_\n                          in zip(readers, data, dirs)]\n        elif opt.data_format == 'jsonl':\n            # only for cases that directly load data from json files.\n            read_iters = [r.read_jsonl(dir_) for r, dir_\n                          in zip(readers, dirs)]\n            read_iters = read_iters[0]\n            tokenizer = self.tokenizer if self.tokenizer else None\n            read_iters = process_news_examples_parallel(read_iters, opt.tgt_fields, opt.tgt_weights,\n                                                        opt.meta_label, opt.field_label,\n                                                        has_special_vocab=has_special_vocab,\n                                                        max_src_len=opt.src_seq_length_trunc,\n                                                        max_tgt_len=opt.tgt_seq_length_trunc,\n                                                        tokenizer=tokenizer,\n                                                        multi_process=False)\n            read_iters = [[d for d in read_iters if d is not None]]\n        elif opt.data_format == 'jsonl_tensor':\n            # text has been tokenized and tensorized in advance (but not completely)\n            boseos_added = True\n            read_iters = [r.read_jsonl(dir_) for r, dir_\n                          in zip(readers, dirs)]\n            read_iters = read_iters[0]\n            tokenizer = self.tokenizer if self.tokenizer else None\n            read_iters = load_tokenized_news_examples(read_iters, opt.pretrained_tokenizer,\n                                                      tokenizer,\n                                                      opt.tgt_fields, opt.tgt_weights,\n                                                      opt.meta_label, opt.field_label,\n                                                      has_special_vocab=has_special_vocab,\n                                                      max_src_len=opt.src_seq_length_trunc,\n                                                      max_tgt_len=opt.tgt_seq_length_trunc)\n            read_iters = [[d for d in read_iters if d is not None]]\n        else:\n            raise NotImplementedError\n\n        # build dynamic_dict for copynet and masks for pretrained models\n        # self.src_vocabs is used in collapse_copy_scores and Translator.py\n        alignment_loss = opt.alignment_loss if hasattr(opt, 'alignment_loss') else None\n        alignment_targets = opt.alignment_targets if hasattr(opt, 'alignment_targets') else None\n        ex_dicts, self.src_vocabs, self.stemmed_src_vocabs = build_dynamic_dict_and_masks_parallel(read_iters, fields,\n                                                                          boseos_added=boseos_added,\n                                                                          alignment_loss=alignment_loss,\n                                                                          alignment_targets=alignment_targets\n                                                                          )\n\n        examples = []\n        for ex_dict in tqdm(ex_dicts, desc='Processing data examples'):\n            ex_fields = {k: [(k, v)] for k, v in fields.items() if\n                         k in ex_dict}\n            ex = Example.fromdict(ex_dict, ex_fields)\n            examples.append(ex)\n\n        # fields needs to have only keys that examples have as attrs\n        fields = []\n        for _, nf_list in ex_fields.items():\n            assert len(nf_list) == 1\n            fields.append(nf_list[0])\n        logging.getLogger().info(\"Loaded %d data examples from %s\" % (len(examples), str(dirs)))\n\n        super(NewsDataset, self).__init__(examples, fields, filter_pred)\n\n    def reload_fields(self):\n        pass\n\n    def __getattr__(self, attr):\n        # avoid infinite recursion when fields isn't defined\n        if 'fields' not in vars(self):\n            raise AttributeError\n        if attr in self.fields:\n            return (getattr(x, attr) for x in self.examples)\n        else:\n            raise AttributeError\n\n    def save(self, path, remove_fields=True):\n        if remove_fields:\n            self.fields = []\n        torch.save(self, path)\n\n    def load_config(self, opt):\n        self.opt = opt.opt\n\ndef load_dataset_from_jsonl(fields, paths, tokenizer, opt):\n    dataset = NewsDataset(\n        fields,\n        readers=[NewsDataReader()],\n        data=None,\n        dirs=paths,\n        sort_key=news_sort_key,\n        tokenizer=tokenizer,\n        opt=opt,\n    )\n\n    return dataset\n\nclass NewsDataReader(DataReaderBase):\n    def read(self, sequences, side, _dir=None):\n        \"\"\"Read text data from disk.\n        Read from both src and tgt files.\n        Args:\n            sequences (str or Iterable[str]):\n                path to text file or iterable of the actual text data.\n            side (str): Prefix used in return dict. Usually\n                ``\"src\"`` or ``\"tgt\"``.\n            _dir (NoneType): Leave as ``None``. This parameter exists to\n                conform with the :func:`DataReaderBase.read()` signature.\n\n        Yields:\n            dictionaries whose keys are the names of fields and whose\n            values are more or less the result of tokenizing with those\n            fields.\n        \"\"\"\n        assert _dir is None or _dir == \"\", \\\n            \"Cannot use _dir with TextDataReader.\"\n        if isinstance(sequences, str):\n            sequences = DataReaderBase._read_file(sequences)\n        for i, seq in enumerate(sequences):\n            if isinstance(seq, six.binary_type):\n                seq = seq.decode(\"utf-8\")\n            yield {side: seq, \"indices\": i}\n\n    def read_jsonl(self, sequences, _dir=None):\n        \"\"\"Read keyphrase data from disk. Current supported data format is JSON only.\n\n        Args:\n            sequences (str or Iterable[str]):\n                path to text file or iterable of the actual text data.\n            _dir (NoneType): Leave as ``None``. This parameter exists to\n                conform with the :func:`DataReaderBase.read()` signature.\n\n        Yields:\n            dictionaries whose keys are the names of fields and whose\n            values are more or less the result of tokenizing with those\n            fields.\n        \"\"\"\n        assert _dir is None or _dir == \"\", \\\n            \"Cannot use _dir with KeyphraseDataReader.\"\n        if isinstance(sequences, str):\n            sequences = DataReaderBase._read_file(sequences)\n        # we need to make indices be the real index of the list, so replace it with a counter\n        count = 0\n        for i, line in enumerate(sequences):\n            try:\n                # default input is a jsonl line\n                line = line.decode(\"utf-8\")\n                data = json.loads(line)\n            except Exception:\n                # data must be a dict\n                if not data or len(line.strip()) == 0 or not isinstance(data, dict):\n                    continue\n\n            # insert `indices`\n            count += 1\n            data['indices'] = count\n            yield data\n\n\ndef news_sort_key(ex):\n    \"\"\"Sort using the number of tokens in the sequence.\"\"\"\n    src_len = ex.src.shape[0] if isinstance(ex.src, torch.Tensor) else len(ex.src[0]) + 2\n    tgt_len = ex.tgt.shape[0] if isinstance(ex.tgt, torch.Tensor) else len(ex.tgt[0]) + 1\n\n    if hasattr(ex, \"tgt\"):\n        return src_len, tgt_len\n\n    return src_len\n\n\n# mix this with partial\ndef _feature_tokenize(\n        string, layer=0, tok_delim=None, feat_delim=None, truncate=None):\n    \"\"\"Split apart word features (like POS/NER tags) from the tokens.\n\n    Args:\n        string (str): A string with ``tok_delim`` joining tokens and\n            features joined by ``feat_delim``. For example,\n            ``\"hello|NOUN|'' Earth|NOUN|PLANET\"``.\n        layer (int): Which feature to extract. (Not used if there are no\n            features, indicated by ``feat_delim is None``). In the\n            example above, layer 2 is ``'' PLANET``.\n        truncate (int or NoneType): Restrict sequences to this length of\n            tokens.\n\n    Returns:\n        List[str] of tokens.\n    \"\"\"\n\n    tokens = string.split(tok_delim)\n    if truncate is not None:\n        tokens = tokens[:truncate]\n    if feat_delim is not None:\n        # some wierd bug appears in XSum (on￨[PART_MAINBODY] 0800 555 111￨[PART_MAINBODY])\n        tokens = [t.split(feat_delim) for t in tokens]\n        tokens = [t for t in tokens if len(t)>layer]\n        tokens = [t[layer] for t in tokens]\n    return tokens\n\n\nclass NewsMultiField(RawField):\n    \"\"\"Container for subfields.\n\n    Text data might use POS/NER/etc labels in addition to tokens.\n    This class associates the \"base\" :class:`Field` with any subfields.\n    It also handles padding the data and stacking it.\n\n    Args:\n        base_name (str): Name for the base field.\n        base_field (Field): The token field.\n        feats_fields (Iterable[Tuple[str, Field]]): A list of name-field\n            pairs.\n\n    Attributes:\n        fields (Iterable[Tuple[str, Field]]): A list of name-field pairs.\n            The order is defined as the base field first, then\n            ``feats_fields`` in alphabetical order.\n    \"\"\"\n\n    def __init__(self, base_name, base_field, feats_fields):\n        super(NewsMultiField, self).__init__()\n        self.fields = [(base_name, base_field)]\n        for name, ff in sorted(feats_fields, key=lambda kv: kv[0]):\n            self.fields.append((name, ff))\n\n        # added by @memray for post-feature control\n        self.meta_label = False\n        self.field_label = False\n        self.num_meta_label = None\n\n    @property\n    def base_field(self):\n        return self.fields[0][1]\n\n    def process(self, batch, device=None):\n        \"\"\"Convert outputs of preprocess into Tensors.\n\n        Args:\n            batch (List[List[List[str]]]): A list of length batch size.\n                Each element is a list of the preprocess results for each\n                field (which are lists of str \"words\" or feature tags.\n            device (torch.device or str): The device on which the tensor(s)\n                are built.\n\n        Returns:\n            torch.LongTensor or Tuple[LongTensor, LongTensor]:\n                A tensor of shape ``(seq_len, batch_size, len(self.fields))``\n                where the field features are ordered like ``self.fields``.\n                If the base field returns lengths, these are also returned\n                and have shape ``(batch_size,)``.\n        \"\"\"\n\n        # batch (list(list(list))): batch_size x len(self.fields) x seq_len\n        # hope to truncate batch here!!!\n        batch_by_feat = list(zip(*batch))\n        base_data = self.base_field.process(batch_by_feat[0], device=device)\n        if self.base_field.include_lengths:\n            # lengths: batch_size\n            base_data, lengths = base_data\n\n        feats = [ff.process(batch_by_feat[i], device=device)\n                 for i, (_, ff) in enumerate(self.fields[1:], 1)]\n        levels = [base_data] + feats\n        # data: seq_len x batch_size x len(self.fields)\n        data = torch.stack(levels, 2)\n        if self.base_field.include_lengths:\n            return data, lengths\n        else:\n            return data\n\n    def preprocess(self, x):\n        \"\"\"Preprocess data.\n        Args:\n            x (str): A sentence string (words joined by whitespace).\n\n        Returns:\n            List[List[str]]: A list of length ``len(self.fields)`` containing\n                lists of tokens/feature tags for the sentence. The output\n                is ordered like ``self.fields``.\n        \"\"\"\n\n        return [f.preprocess(x) for _, f in self.fields]\n\n    def __getitem__(self, item):\n        return self.fields[item]\n\n\ndef news_fields(**kwargs):\n    \"\"\"Create text fields.\n\n    Args:\n        base_name (str): Name associated with the field.\n        n_feats (int): Number of word level feats (not counting the tokens)\n        include_lengths (bool): Optionally return the sequence lengths.\n        pad (str, optional): Defaults to ``\"<blank>\"``.\n        bos (str or NoneType, optional): Defaults to ``\"<s>\"``.\n        eos (str or NoneType, optional): Defaults to ``\"</s>\"``.\n        truncate (bool or NoneType, optional): Defaults to ``None``.\n\n    Returns:\n        NewsMultiField\n    \"\"\"\n\n    n_feats = kwargs[\"n_feats\"]\n    include_lengths = kwargs[\"include_lengths\"]\n    base_name = kwargs[\"base_name\"]\n    # change from <pad> to [PAD] and <unk> to [UNK] to be compatible with BERT\n    pad = \"[PAD]\"\n    unk = \"[UNK]\"\n    bos = kwargs.get(\"bos\", \"<s>\")\n    eos = kwargs.get(\"eos\", \"</s>\")\n    truncate = kwargs.get(\"truncate\", None)\n    fields_ = []\n    feat_delim = u\"￨\" if n_feats > 0 else None\n    for i in range(n_feats + 1):\n        name = base_name + \"_feat_\" + str(i - 1) if i > 0 else base_name\n        tokenize = partial(\n            _feature_tokenize,\n            layer=i,\n            truncate=truncate,\n            feat_delim=feat_delim)\n        use_len = i == 0 and include_lengths\n        feat = Field(\n            init_token=bos, eos_token=eos,\n            pad_token=pad, unk_token=unk,\n            tokenize=tokenize,\n            include_lengths=use_len)\n        fields_.append((name, feat))\n    assert fields_[0][0] == base_name  # sanity check\n    field = NewsMultiField(fields_[0][0], fields_[0][1], fields_[1:])\n    return field\n\n\ndef update_field_vocab(field, tokenizer):\n    new_field = copy.copy(field)\n    setattr(new_field, 'lower', False)\n    setattr(new_field, 'pretrained_tokenizer', tokenizer)\n    # setattr(new_field, 'tokenize', partial(tokenizer.tokenize, add_special_tokens=False)) # will be tokenized in transform\n    setattr(new_field, 'bos_token', tokenizer.bos_token)\n    setattr(new_field, 'init_token', tokenizer.bos_token)\n    setattr(new_field, 'eos_token', tokenizer.eos_token)\n    setattr(new_field, 'pad_token', tokenizer.pad_token)\n    setattr(new_field, 'unk_token', tokenizer.unk_token)\n    if hasattr(tokenizer, 'sep_token'):\n        setattr(new_field, 'sep_token', tokenizer.sep_token)\n    if not hasattr(new_field, 'vocab_cls'):\n        setattr(new_field, 'vocab_cls', torchtext.vocab.Vocab(counter=Counter()))\n    setattr(new_field.vocab_cls, 'UNK', tokenizer.unk_token)\n\n    # Update vocab given the external pretrained vocab\n    if not hasattr(new_field, 'vocab'):\n        setattr(new_field, 'vocab', torchtext.vocab.Vocab(counter=Counter()))\n    stoi = tokenizer.get_vocab()\n    itos = [s for s,_ in sorted(tokenizer.get_vocab().items(), key=lambda x:x[1])]\n    setattr(new_field.vocab, 'UNK', tokenizer.unk_token)\n    setattr(new_field.vocab, 'unk_index', tokenizer.unk_token_id)\n\n    # will be used for copy loss (copy_generator.py, collapse_copy_scores(), L29), for cases pad_index is not 0\n    setattr(new_field.vocab, 'PAD', tokenizer.pad_token)\n    setattr(new_field.vocab, 'pad_index', tokenizer.pad_token_id)\n    setattr(new_field.vocab, 'stoi', stoi)\n    setattr(new_field.vocab, 'itos', itos)\n    setattr(new_field.vocab, 'freqs', Counter(itos))\n\n    return new_field\n\n\ndef load_pretrained_tokenizer(tokenizer_name, cache_dir, special_vocab_path=None,\n                              bpe_vocab=None, bpe_merges=None, bpe_dropout=0.0):\n    assert tokenizer_name or bpe_vocab, \"Either tokenizer_name or bpe_vocab must be set to load HF tokenizer.\"\n    print('Loading pretrained vocabulary, dumped to %s' % cache_dir)\n    if tokenizer_name:\n        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, cache_dir=cache_dir)\n        print('Vocab size=%d, base vocab size=%d' % (len(tokenizer), tokenizer.vocab_size))\n        if special_vocab_path is not None and special_vocab_path != 'none' and special_vocab_path != 'None':\n            special_tokens = [w.strip() for w in open(special_vocab_path, 'r').readlines()]\n            num_added_toks = tokenizer.add_tokens(special_tokens)\n            print('Added', num_added_toks, 'special tokens')\n            print('Vocab size=%d, base vocab size=%d' % (len(tokenizer), tokenizer.vocab_size))\n        else:\n            print('Special token vocab is not provided.')\n    else:\n        # initialize a slow tokenizer and convert it to fast, so that special tokens can be properly segmented\n        sep_token = '<sep>'\n        kp_special_tokens = ['<present>', '<absent>', '<category>']\n        tokenizer = RobertaTokenizer(vocab_file=bpe_vocab,\n                                     merges_file=bpe_merges,\n                                     sep=sep_token,  # doesn't matter\n                                     additional_special_tokens=kp_special_tokens)\n        sep_token_id = tokenizer.convert_tokens_to_ids(sep_token)\n        added_sep_token = AddedToken(sep_token, lstrip=False, rstrip=False)\n        tokenizer.sep_token = sep_token\n        tokenizer._sep_token = added_sep_token\n        tokenizer.init_kwargs['sep_token'] = sep_token\n        tokenizer.all_special_ids.append(sep_token_id)\n        tokenizer.all_special_tokens.append(sep_token)\n        tokenizer.all_special_tokens_extended.append(added_sep_token)\n        tokenizer.special_tokens_map['sep_token'] = sep_token\n        tokenizer.special_tokens_map_extended['sep_token'] = added_sep_token\n\n        tokenizer.unique_no_split_tokens = tokenizer.all_special_tokens\n\n        tokenizer = RobertaTokenizerFast.from_pretrained(\"roberta-base\",\n                                                         __slow_tokenizer=tokenizer, tokenizer_file=None,\n                                                         vocab_file=bpe_vocab,\n                                                         merges_file=bpe_merges)\n        print('Vocab size=%d, base vocab size=%d' % (len(tokenizer), tokenizer.vocab_size))\n\n    if isinstance(tokenizer, RobertaTokenizerFast) and float(bpe_dropout) > 0.0:\n        workaround_files = tokenizer._tokenizer.model.save(cache_dir, 'workaround')\n        tokenizer._tokenizer.model = type(tokenizer._tokenizer.model)(*workaround_files, dropout=float(bpe_dropout))\n    print(tokenizer.tokenize('<s> what is wrong with <mask> <sep> I do not know either <present>, <absent>, <category> </s>'))\n    print(tokenizer.encode('<s> what is wrong with <mask> <sep> I do not know either <present>, <absent>, <category> </s>'))\n\n    return tokenizer\n"
  },
  {
    "path": "onmt/inputters/text_dataset.py",
    "content": "# -*- coding: utf-8 -*-\nfrom functools import partial\n\nimport six\nimport torch\nfrom torchtext.data import Field, RawField\n\nfrom onmt.constants import DefaultTokens\nfrom onmt.inputters.datareader_base import DataReaderBase\n\n\nclass TextDataReader(DataReaderBase):\n    def read(self, sequences, side):\n        \"\"\"Read text data from disk.\n\n        Args:\n            sequences (str or Iterable[str]):\n                path to text file or iterable of the actual text data.\n            side (str): Prefix used in return dict. Usually\n                ``\"src\"`` or ``\"tgt\"``.\n\n        Yields:\n            dictionaries whose keys are the names of fields and whose\n            values are more or less the result of tokenizing with those\n            fields.\n        \"\"\"\n        if isinstance(sequences, str):\n            sequences = DataReaderBase._read_file(sequences)\n        for i, seq in enumerate(sequences):\n            if isinstance(seq, six.binary_type):\n                seq = seq.decode(\"utf-8\")\n            yield {side: seq, \"indices\": i}\n\n\ndef text_sort_key(ex):\n    \"\"\"Sort using the number of tokens in the sequence.\"\"\"\n    if hasattr(ex, \"tgt\"):\n        return len(ex.src[0]), len(ex.tgt[0])\n    return len(ex.src[0])\n\n\n# mix this with partial\ndef _feature_tokenize(\n        string, layer=0, tok_delim=None, feat_delim=None, truncate=None):\n    \"\"\"Split apart word features (like POS/NER tags) from the tokens.\n\n    Args:\n        string (str): A string with ``tok_delim`` joining tokens and\n            features joined by ``feat_delim``. For example,\n            ``\"hello|NOUN|'' Earth|NOUN|PLANET\"``.\n        layer (int): Which feature to extract. (Not used if there are no\n            features, indicated by ``feat_delim is None``). In the\n            example above, layer 2 is ``'' PLANET``.\n        truncate (int or NoneType): Restrict sequences to this length of\n            tokens.\n\n    Returns:\n        List[str] of tokens.\n    \"\"\"\n\n    tokens = string.split(tok_delim)\n    if truncate is not None:\n        tokens = tokens[:truncate]\n    if feat_delim is not None:\n        tokens = [t.split(feat_delim)[layer] for t in tokens]\n    return tokens\n\n\nclass TextMultiField(RawField):\n    \"\"\"Container for subfields.\n\n    Text data might use POS/NER/etc labels in addition to tokens.\n    This class associates the \"base\" :class:`Field` with any subfields.\n    It also handles padding the data and stacking it.\n\n    Args:\n        base_name (str): Name for the base field.\n        base_field (Field): The token field.\n        feats_fields (Iterable[Tuple[str, Field]]): A list of name-field\n            pairs.\n\n    Attributes:\n        fields (Iterable[Tuple[str, Field]]): A list of name-field pairs.\n            The order is defined as the base field first, then\n            ``feats_fields`` in alphabetical order.\n    \"\"\"\n\n    def __init__(self, base_name, base_field, feats_fields):\n        super(TextMultiField, self).__init__()\n        self.fields = [(base_name, base_field)]\n        for name, ff in sorted(feats_fields, key=lambda kv: kv[0]):\n            self.fields.append((name, ff))\n\n    @property\n    def base_field(self):\n        return self.fields[0][1]\n\n    def process(self, batch, device=None):\n        \"\"\"Convert outputs of preprocess into Tensors.\n\n        Args:\n            batch (List[List[List[str]]]): A list of length batch size.\n                Each element is a list of the preprocess results for each\n                field (which are lists of str \"words\" or feature tags.\n            device (torch.device or str): The device on which the tensor(s)\n                are built.\n\n        Returns:\n            torch.LongTensor or Tuple[LongTensor, LongTensor]:\n                A tensor of shape ``(seq_len, batch_size, len(self.fields))``\n                where the field features are ordered like ``self.fields``.\n                If the base field returns lengths, these are also returned\n                and have shape ``(batch_size,)``.\n        \"\"\"\n\n        # batch (list(list(list))): batch_size x len(self.fields) x seq_len\n        batch_by_feat = list(zip(*batch))\n        base_data = self.base_field.process(batch_by_feat[0], device=device)\n        if self.base_field.include_lengths:\n            # lengths: batch_size\n            base_data, lengths = base_data\n\n        feats = [ff.process(batch_by_feat[i], device=device)\n                 for i, (_, ff) in enumerate(self.fields[1:], 1)]\n        levels = [base_data] + feats\n        # data: seq_len x batch_size x len(self.fields)\n        # @memray, shape is actually [batch_size x seq_len x len(self.fields)]\n        data = torch.stack(levels, 2)\n        if self.base_field.include_lengths:\n            return data, lengths\n        else:\n            return data\n\n    def preprocess(self, x):\n        \"\"\"Preprocess data.\n\n        Args:\n            x (str): A sentence string (words joined by whitespace).\n\n        Returns:\n            List[List[str]]: A list of length ``len(self.fields)`` containing\n                lists of tokens/feature tags for the sentence. The output\n                is ordered like ``self.fields``.\n        \"\"\"\n\n        return [f.preprocess(x) for _, f in self.fields]\n\n    def __getitem__(self, item):\n        return self.fields[item]\n\n\ndef text_fields(**kwargs):\n    \"\"\"Create text fields.\n\n    Args:\n        base_name (str): Name associated with the field.\n        n_feats (int): Number of word level feats (not counting the tokens)\n        include_lengths (bool): Optionally return the sequence lengths.\n        pad (str, optional): Defaults to ``\"<blank>\"``.\n        bos (str or NoneType, optional): Defaults to ``\"<s>\"``.\n        eos (str or NoneType, optional): Defaults to ``\"</s>\"``.\n        truncate (bool or NoneType, optional): Defaults to ``None``.\n\n    Returns:\n        TextMultiField\n    \"\"\"\n\n    n_feats = kwargs[\"n_feats\"]\n    include_lengths = kwargs[\"include_lengths\"]\n    base_name = kwargs[\"base_name\"]\n    pad = kwargs.get(\"pad\", DefaultTokens.PAD)\n    bos = kwargs.get(\"bos\", DefaultTokens.BOS)\n    eos = kwargs.get(\"eos\", DefaultTokens.EOS)\n    truncate = kwargs.get(\"truncate\", None)\n    fields_ = []\n    feat_delim = u\"￨\" if n_feats > 0 else None\n    for i in range(n_feats + 1):\n        name = base_name + \"_feat_\" + str(i - 1) if i > 0 else base_name\n        tokenize = partial(\n            _feature_tokenize,\n            layer=i,\n            truncate=truncate,\n            feat_delim=feat_delim)\n        use_len = i == 0 and include_lengths\n        feat = Field(\n            init_token=bos, eos_token=eos,\n            pad_token=pad, tokenize=tokenize,\n            include_lengths=use_len)\n        fields_.append((name, feat))\n    assert fields_[0][0] == base_name  # sanity check\n    field = TextMultiField(fields_[0][0], fields_[0][1], fields_[1:])\n    return field\n"
  },
  {
    "path": "onmt/keyphrase/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\n\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    pass"
  },
  {
    "path": "onmt/keyphrase/baseline/evaluate.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport json\nimport os\n\nfrom kp_evaluate import init_opt, keyphrase_eval, kp_results_to_str, gather_eval_results\nfrom onmt.utils.logging import init_logger\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    opt = init_opt()\n    score_dicts = {}\n\n    for model_name in os.listdir(opt.pred_dir):\n        pred_dir = os.path.join(opt.pred_dir, model_name, \"pred\")\n        if not os.path.exists(pred_dir):\n            continue\n\n        for dataset in opt.testsets:\n            logger = init_logger(os.path.join(opt.output_dir, model_name, \"kp_evaluate.log\"))\n            logger.info(\"Evaluating model %s on %s\" % (model_name, dataset))\n\n            src_path = os.path.join(opt.data, dataset, \"%s_test.src\" % dataset)\n            tgt_path = os.path.join(opt.data, dataset, \"%s_test.tgt\" % dataset)\n            pred_path = os.path.join(opt.pred_dir, model_name, \"pred\", \"%s\" % dataset)\n\n            if not os.path.exists(os.path.join(opt.output_dir, 'eval')):\n                os.makedirs(os.path.join(opt.output_dir, 'eval'))\n\n            score_path = os.path.join(opt.output_dir, 'eval', model_name + '-%s.json' % dataset)\n\n            if not os.path.exists(score_path):\n                score_dict = keyphrase_eval(src_path=src_path,\n                                            tgt_path=tgt_path,\n                                            pred_path=pred_path,\n                                            unk_token = '<unk>',\n                                            verbose = opt.verbose,\n                                            logger = logger,\n                                            eval_topbeam=opt.topbeam,\n                                            model_name=model_name\n                                            )\n                if score_dict is not None:\n                    logger.info(kp_results_to_str(score_dict))\n                    with open(score_path, 'w') as output_json:\n                        output_json.write(json.dumps(score_dict))\n                    score_dicts[dataset] = score_dict\n            else:\n                logger.error(\"Skip evaluating as previous eval result exists\")\n\n        gather_eval_results(eval_root_dir=os.path.join(opt.output_dir),\n                            report_csv_dir=os.path.join(opt.output_dir, 'summary_%s.csv' % ('%s')))\n\n        logger.info(\"Done!\")"
  },
  {
    "path": "onmt/keyphrase/baseline/export_dataset.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport codecs\nimport json\nimport os\n\nfrom nltk.tag import StanfordPOSTagger\nfrom nltk.internals import find_jars_within_path\nimport re\nimport six\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\nclass Document(object):\n    def __init__(self):\n        self.name       = ''\n        self.title      = ''\n        self.abstract       = ''\n        self.fulltext       = ''\n        self.keyword    = []\n\n    def __str__(self):\n        return '%s\\n\\t%s\\n\\t%s\\n\\t%s' % (self.name, self.title, self.abstract, str(self.keyword))\n\n    def to_dict(self):\n        d = {}\n        d['name'] = self.name\n        d['title'] = re.sub('[\\r\\n]', ' ', self.title).strip()\n        d['abstract'] = re.sub('[\\r\\n]', ' ', self.abstract).strip()\n        d['fulltext'] = self.fulltext\n        d['keyword'] = ';'.join(self.keyword)\n        return d\n\n\nclass Dataset(object):\n    def __init__(self, **kwargs):\n        self.__dict__.update(kwargs)\n        self.name    = self.__class__.__name__.lower()\n        self.datadir = os.path.join(basedir, self.name.lower())\n        self.textdir = self.datadir + '/all_texts/'\n        self.keyphrasedir = self.datadir + '/gold_standard_keyphrases/'\n        self.train_test_splitted = False\n        self.title_abstract_body_separated = False\n\n        self.doc_list = []\n\n\n    def load_dataset(self):\n        self.load_text(self.textdir)\n        self.load_keyphrase(self.keyphrasedir)\n\n\n    def load_dataset_as_dicts(self):\n        return self._convert_docs_to_dicts(self.doc_list)\n\n\n    def _convert_docs_to_dicts(self, docs):\n        '''\n        :return: a list of dict\n        '''\n        dict_list = []\n        for d in docs:\n            dict_list.append(d.to_dict())\n\n        return dict_list\n\n\n    def load_train_test_dataset(self):\n        train_data = []\n        test_data = []\n        if self.train_test_splitted:\n            '''\n            if the split of train/test is given, return as it is\n            '''\n            for doc in self.doc_list:\n                if doc.name.startswith('train'):\n                    train_data.append(doc)\n                elif doc.name.startswith('test'):\n                    test_data.append(doc)\n                else:\n                    raise Exception('File must start with either train or test if train_test_splitted is on for class %s' % self.__class__)\n        else:\n            '''\n            if split is not given, take the first 20% for test, rest 80% for training\n            '''\n            # ensure files are sorted in an alphabetical order\n            doc_list = sorted(self.doc_list, key=lambda d:d.name)\n            test_data = doc_list[: int(len(doc_list) * 0.2)]\n            train_data = doc_list[int(len(doc_list) * 0.2): ]\n\n        train_data_dicts = self._convert_docs_to_dicts(train_data)\n        test_data_dicts = self._convert_docs_to_dicts(test_data)\n\n        return train_data_dicts, test_data_dicts\n\n\n    def dump_train_test_to_json(self):\n        train_data_dicts, test_data_dicts = self.load_train_test_dataset()\n        train_json_path = os.path.join(self.datadir, self.name.lower() + '_train.json')\n        with open(train_json_path, 'w') as train_json:\n            for d in train_data_dicts:\n                train_json.write(json.dumps(d) + '\\n')\n\n        test_json_path = os.path.join(self.datadir, self.name.lower() + '_test.json')\n        with open(test_json_path, 'w') as test_json:\n            for d in test_data_dicts:\n                test_json.write(json.dumps(d) + '\\n')\n\n\n    def load_text(self, textdir):\n        # ensure files are loaded in an alphabetical order\n        file_names = os.listdir(textdir)\n        file_names = sorted(file_names)\n\n        for fid, filename in enumerate(file_names):\n            # with codecs.open(textdir+filename, \"r\", encoding='utf-8', errors='ignore') as textfile:\n            with open(textdir+filename) as textfile:\n                try:\n                    lines = textfile.readlines()\n                    lines = [line.strip() for line in lines]\n\n                    if self.title_abstract_body_separated:\n                        '''\n                        title/abstract/fulltext are separated by --T/--A/--B\n                        '''\n                        T_index = None\n                        for line_id, line in enumerate(lines):\n                            if line.strip() == '--T':\n                                T_index = line_id\n                                break\n\n                        A_index = None\n                        for line_id, line in enumerate(lines):\n                            if line.strip() == '--A':\n                                A_index = line_id\n                                break\n\n                        B_index = None\n                        for line_id, line in enumerate(lines):\n                            if line.strip() == '--B':\n                                B_index = line_id\n                                break\n\n                        # lines between T and A are title\n                        title = ' '.join(lines[T_index + 1: A_index])\n                        # lines between A and B are abstract\n                        abstract = ' '.join(lines[A_index + 1: B_index])\n                        # lines after B are fulltext\n                        fulltext = '\\n'.join(lines[B_index + 1:])\n\n                        if T_index is None or A_index is None or B_index is None:\n                            print('Wrong format detected : %s' % (filename))\n                            print('Name: ' + textdir + filename)\n                            print('Title: ' + title.strip())\n                            if not T_index:\n                                print('line 0 should be --T: ' + ''.join(lines[0]).strip())\n                            if not A_index:\n                                print('line 2 should be --A: ' + ''.join(lines[2]).strip())\n                            if not B_index:\n                                print('line 4 should be --B: ' + ''.join(lines[4]).strip())\n                            print()\n                        else:\n                            pass\n                            # print('No Problem: %s' % filename)\n\n                    else:\n                        '''\n                        otherwise, 1st line is title, and rest lines are abstract\n                        '''\n\n                        # 1st line is title\n                        title = lines[0]\n                        # rest lines are abstract\n                        abstract = (' '.join([''.join(line).strip() for line in lines[1:]]))\n                        # no fulltext is given, ignore it\n                        fulltext = ''\n\n                    doc = Document()\n                    doc.name = filename[:filename.find('.txt')]\n                    doc.title = title\n                    doc.abstract = abstract\n                    doc.fulltext = fulltext\n                    self.doc_list.append(doc)\n\n                except UnicodeDecodeError as e:\n                    print('UnicodeDecodeError detected! %s' % (textdir+filename))\n                    print(e)\n\n\n    def load_keyphrase(self, keyphrasedir):\n        for did,doc in enumerate(self.doc_list):\n            phrase_set = set()\n\n            if os.path.exists(self.keyphrasedir + doc.name + '.keyphrases'):\n                with open(keyphrasedir+doc.name+'.keyphrases') as keyphrasefile:\n                    phrase_set.update([phrase.strip() for phrase in keyphrasefile.readlines()])\n\n            if os.path.exists(self.keyphrasedir + doc.name + '.keywords'):\n                with open(keyphrasedir + doc.name + '.keywords') as keyphrasefile:\n                    phrase_set.update([phrase.strip() for phrase in keyphrasefile.readlines()])\n\n            doc.keyword = list(phrase_set)\n\n\nclass INSPEC(Dataset):\n    def __init__(self, **kwargs):\n        super(INSPEC, self).__init__(**kwargs)\n        self.train_test_splitted = True\n\n\nclass NUS(Dataset):\n    def __init__(self, **kwargs):\n        super(NUS, self).__init__(**kwargs)\n        self.title_abstract_body_separated = True\n\n\nclass SemEval(Dataset):\n    def __init__(self, **kwargs):\n        super(SemEval, self).__init__(**kwargs)\n        self.train_test_splitted = True\n        self.title_abstract_body_separated = True\n\n\nclass KRAPIVIN(Dataset):\n    def __init__(self, **kwargs):\n        super(KRAPIVIN, self).__init__(**kwargs)\n        self.title_abstract_body_separated = True\n\n\nclass DUC(Dataset):\n    def __init__(self, **kwargs):\n        super(DUC, self).__init__(**kwargs)\n\n\n# aliases\ninspec = INSPEC\nnus = NUS\nsemeval = SemEval\nkrapivin = KRAPIVIN\nduc = DUC\n\n\ndef get_from_module(identifier, module_params, module_name, instantiate=False, kwargs=None):\n    if isinstance(identifier, six.string_types):\n        res = module_params.get(identifier)\n        if not res:\n            raise Exception('Invalid ' + str(module_name) + ': ' + str(identifier))\n        if instantiate and not kwargs:\n            return res()\n        elif instantiate and kwargs:\n            return res(**kwargs)\n        else:\n            return res\n    return identifier\n\n\ndef initialize_test_data_loader(identifier, kwargs=None):\n    '''\n    load testing data dynamically\n    :return:\n    '''\n    test_data = get_from_module(identifier.lower(), globals(), 'data_loader', instantiate=True,\n                           kwargs=kwargs)\n    return test_data\n\n\nPAD_WORD = '<pad>'\nUNK_WORD = '<unk>'\nBOS_WORD = '<s>'\nEOS_WORD = '</s>'\nDIGIT = '<digit>'\nSEP_WORD = '<sep>'\n\n\ndef copyseq_tokenize(text):\n    '''\n    The tokenizer used in Meng et al. ACL 2017\n    parse the feed-in text, filtering and tokenization\n    keep [_<>,\\(\\)\\.\\'%], replace digits to <digit>, split by [^a-zA-Z0-9_<>,\\(\\)\\.\\'%]\n    :param text:\n    :return: a list of tokens\n    '''\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', text)\n    # pad spaces to the left and right of special punctuations\n    text = re.sub(r'[_<>,\\(\\)\\.\\'%]', ' \\g<0> ', text)\n    # tokenize by non-letters (new-added + # & *, but don't pad spaces, to make them as one whole word)\n    tokens = filter(lambda w: len(w) > 0, re.split(r'[^a-zA-Z0-9_<>,#&\\+\\*\\(\\)\\.\\'%]', text))\n\n    # replace the digit terms with <digit>\n    tokens = [w if not re.match('^\\d+$', w) else DIGIT for w in tokens]\n\n    return tokens\n\n\ndef load_pos_tagger(stanford_base_dir):\n    # path = os.path.dirname(__file__)\n    # path = os.path.join(file_dir[: file_dir.rfind('pykp') + 4], 'stanford-postagger')\n    # print(path)\n    # jar = '/Users/memray/Project/stanford/stanford-postagger/stanford-postagger.jar'\n    jar = stanford_base_dir + '/stanford-postagger.jar'\n    model = stanford_base_dir + '/models/english-bidirectional-distsim.tagger'\n    pos_tagger = StanfordPOSTagger(model_filename=model, path_to_jar=jar)\n\n    stanford_base_dir = jar.rpartition('/')[0]\n    stanford_jars = find_jars_within_path(stanford_base_dir)\n    pos_tagger._stanford_jar = ':'.join(stanford_jars)\n\n    return pos_tagger\n\n\nextra_dataset_names = ['inspec', 'nus', 'semeval', 'krapivin', 'duc']\ndef export_extra_dataset_to_json():\n    for dataset_name in extra_dataset_names:\n        print('-' * 50)\n        print('Loading %s' % dataset_name)\n\n        dataset_loader = initialize_test_data_loader(dataset_name)\n        dataset_loader.load_dataset()\n        dataset_dict = dataset_loader.load_dataset_as_dicts()\n        train_data_dicts, test_data_dicts = dataset_loader.load_train_test_dataset()\n        dataset_loader.dump_train_test_to_json()\n\n        print('#(doc) = %d' % (len(dataset_dict)))\n        print('#(keyphrase) = %.3f' % (sum([len(d.keyword) for d in dataset_loader.doc_list]) / len(dataset_dict)))\n        print('#(train) = %d, #(test)=%d' % (len(train_data_dicts), len(test_data_dicts)))\n\n        print('\\nlen(title) = %.3f' % (sum([len(d.title.split()) for d in dataset_loader.doc_list]) / len(dataset_dict)))\n        print('len(abstract) = %.3f' % (sum([len(d.abstract.split()) for d in dataset_loader.doc_list]) / len(dataset_dict)))\n        print('len(fulltext) = %.3f' % (sum([len(d.fulltext.split()) for d in dataset_loader.doc_list]) / len(dataset_dict)))\n\n        # print(dataset_loader.doc_list[10])\n        # print(dataset_loader.doc_list[20])\n\n\n\ndef add_testset_postag(test_dataset_names, dataset_base_dir, stanford_base_dir):\n    pos_tagger = load_pos_tagger(stanford_base_dir)\n\n    for dataset_name in test_dataset_names:\n        abstract_key = 'abstract'\n        if dataset_name =='stackexchange':\n            abstract_key = 'question'\n\n        print('-' * 50)\n        print('Loading %s' % dataset_name)\n        json_path = os.path.join(dataset_base_dir, dataset_name, dataset_name+'_test.json')\n\n        dataset_dict_list = []\n        # load from json file\n        with open(json_path, 'r') as json_file:\n            for line in json_file:\n                dataset_dict_list.append(json.loads(line))\n\n        json_path = os.path.join(dataset_base_dir, dataset_name, dataset_name + '_test_postag.json')\n        if os.path.exists(json_path):\n            with open(json_path, 'r') as json_file:\n                lines = [l for l in json_file if len(l) > 0]\n                if len(lines) != len(dataset_dict_list):\n                    print(\"Number of previous results doesn't match original dataset\")\n                else:\n                    print('%s seems already processed, skip!' % dataset_name)\n                    continue\n        else:\n            print('Processing and dumping to %s' % dataset_name)\n        # dump to another json\n        with open(json_path, 'w') as json_file:\n            # postag title/abstract and insert into data example\n            for e_id, example_dict in enumerate(dataset_dict_list):\n                print('=' * 50)\n                print(e_id)\n                print(example_dict['title'])\n                print('len(title)=%d' % len(example_dict['title']))\n                print('len(abstract)=%d' % len(example_dict[abstract_key]))\n\n                if len(example_dict[abstract_key]) > 1000:\n                    print('truncate to 1000 words')\n                    example_dict[abstract_key] = example_dict[abstract_key][:1000]\n\n                if e_id % 10 == 0:\n                    print('Processing %d/%d' % (e_id, len(dataset_dict_list)))\n\n                title_postag_tokens = pos_tagger.tag(copyseq_tokenize(example_dict['title']))\n                print('#(title token)=%d : %s' % (len(title_postag_tokens), str(title_postag_tokens)))\n                abstract_postag_tokens = pos_tagger.tag(copyseq_tokenize(example_dict[abstract_key]))\n                print('#(abstract token)=%d : %s' % (len(abstract_postag_tokens), str(abstract_postag_tokens)))\n                example_dict['title_postag'] = ' '.join([str(t[0])+'_'+str(t[1]) for t in title_postag_tokens])\n                example_dict['abstract_postag'] = ' '.join([str(t[0])+'_'+str(t[1]) for t in abstract_postag_tokens])\n\n                # for example_dict in postag_dataset_dict_list:\n                json_file.write(json.dumps(example_dict) + '\\n')\n\n\ndef export_to_UTD_format(dataset_name, dataset_base_dir, dump_path):\n    # export test data only\n    plain_text_dir = os.path.join(dump_path, 'plain_text')\n    postag_text_dir = os.path.join(dump_path, 'text')\n    keyphrase_dir = os.path.join(dump_path, 'keyphrase')\n    list_file_path = os.path.join(dump_path, '%s_list.txt' % dataset_name)\n    dirs = [plain_text_dir, postag_text_dir, keyphrase_dir]\n    for dir in dirs:\n        if not os.path.exists(dir):\n            os.makedirs(dir)\n\n    title_key = 'title'\n    abstract_key = 'abstract'\n    keyword_key = 'abstract'\n    if dataset_name == 'stackexchange':\n        abstract_key = 'question'\n        keyword_key = 'tags'\n\n    text_filename_list = []\n    data_json_path = os.path.join(dataset_base_dir, dataset_name, dataset_name + '_test_postag.json')\n\n    with open(data_json_path, 'r') as data_json_file:\n        for doc_id, line in enumerate(data_json_file):\n            example_dict = json.loads(line)\n            text_filename_list.append('%s.txt' % doc_id)\n            with open(os.path.join(plain_text_dir, '%s.txt' % doc_id), 'w') as plain_text:\n                plain_text.write(example_dict[title_key] + '.\\n' + example_dict[abstract_key])\n            with open(os.path.join(postag_text_dir, '%s.txt' % doc_id), 'w') as postag_text:\n                postag_text.write(example_dict['title_postag'] + ' ._. ' + example_dict['abstract_postag'])\n            with open(os.path.join(keyphrase_dir, '%s.txt' % doc_id), 'w') as keyphrase_file:\n                for target in example_dict[keyword_key].split(';'):\n                    keyphrase_file.write(target+'\\n')\n\n    with open(list_file_path, 'w') as list_file:\n        for text_filename in text_filename_list:\n            list_file.write(text_filename+'\\n')\n\n    print('Export to UTD format done!')\n\n\ndef export_to_maui_format(dataset_name, dataset_base_dir, dump_path, mode=None):\n    assert mode in ['kea', 'maui']\n    title_key = 'title'\n    abstract_key = 'abstract'\n    keyword_key = 'abstract'\n    if dataset_name == 'stackexchange':\n        abstract_key = 'question'\n        keyword_key = 'tags'\n\n    # export both training and testing\n    for data_type in ['train', 'test']:\n        dump_dir_path = os.path.join(dump_path, data_type)\n        if not os.path.exists(dump_dir_path):\n            os.makedirs(dump_dir_path)\n\n        # only train KEA and Maui with 50k data, tried 100k and they OOM all the time\n        train_limit = 50000\n        # only 'kp20k' and 'stackexchange' have training data\n        if data_type=='train':\n            if dataset_name != 'kp20k' and dataset_name != 'stackexchange':\n                continue\n        data_json_path = os.path.join(dataset_base_dir, dataset_name, dataset_name + '_%s.json' % data_type)\n\n        with open(data_json_path, 'r') as data_json_file:\n            for doc_id, line in enumerate(data_json_file):\n                if data_type == 'train' and doc_id >= train_limit:\n                    break\n                example_dict = json.loads(line)\n                with open(os.path.join(dump_dir_path, '%s.txt' % doc_id), 'w') as text_file:\n                    text_file.write(example_dict[title_key] + '.\\n' + example_dict[abstract_key])\n                with open(os.path.join(dump_dir_path, '%s.key' % doc_id), 'w') as key_file:\n                    for target in example_dict[keyword_key].split(';'):\n                        if mode == 'maui':\n                            key_file.write(target + '\\t1\\n')\n                        elif mode == 'kea':\n                            key_file.write(target + '\\n')\n                        else:\n                            raise Exception('Wrong mode')\n\n    print('Export to %s format done!' % mode)\n\n\nif __name__ == '__main__':\n    dataset_names = ['stackexchange', 'inspec', 'nus', 'semeval', 'krapivin', 'duc', 'kp20k']\n    dataset_json_dir = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/'\n    # export_extra_dataset_to_json()\n    add_testset_postag(dataset_names,\n                       dataset_base_dir=dataset_json_dir,\n                       stanford_base_dir='/Users/memray/project/stanford/stanford-postagger/')\n\n    for dataset_name in dataset_names:\n        print('-=*=-' * 10)\n        print(\"Exporting %s\" % dataset_name)\n        # dump_path = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/baseline/utd/%s/' % dataset_name\n        # export_to_UTD_format(dataset_name, dataset_json_dir, dump_path)\n\n        dump_path = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/baseline/maui/%s/' % dataset_name\n        export_to_maui_format(dataset_name, dataset_json_dir, dump_path, mode='maui')\n\n        dump_path = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/baseline/kea/%s/' % dataset_name\n        export_to_maui_format(dataset_name, dataset_json_dir, dump_path, mode='kea')"
  },
  {
    "path": "onmt/keyphrase/best_scores_in_report.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nLoad averaged results from csv, containing scores of all ckpts.\nFor each exp group, return the best ckpt (ranked by valid performance).\n\"\"\"\nimport shutil\n\nimport configargparse\nimport os\n\nimport pandas as pd\n\n\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\ndev_test_pairs = [\n    ('kp20k_valid2k', 'kp20k'),\n    # ('kp20k_valid2k', 'duc'),\n    ('openkp_valid2k', 'openkp'),\n    ('openkp_valid2k', 'jptimes'),\n    ('openkp_valid2k', 'duc'),\n    ('kptimes_valid2k', 'kptimes'),\n    ('kptimes_valid2k', 'jptimes'),\n    ('kptimes_valid2k', 'duc'),\n    ('stackex_valid2k', 'stackex'),\n    # ('kp20k_valid2k', 'inspec'),\n    # ('kp20k_valid2k', 'krapivin'),\n    # ('kp20k_valid2k', 'nus'),\n    # ('kp20k_valid2k', 'semeval'),\n]\n\ndef main():\n    parser = configargparse.ArgumentParser()\n    parser.add_argument('-report_dir', type=str, required=True, help='Directory to all report csv files.')\n    parser.add_argument('-pred_name', type=str, required=False, help='Filter by pred_name, since there exists results by multiple decoding settings from the same ckpt.')\n    parser.add_argument('-export_dir', type=str, required=False, default=None, help='If set, the best pred/eval files will be copied to this place.')\n    parser.add_argument('-report_selfbest', action='store_true', help='')\n    parser.add_argument('-report_lastckpt', action='store_true', help='')\n    opt = parser.parse_args()\n\n    kp_df = None\n    for f in os.listdir(opt.report_dir):\n        if not f.endswith('.csv'): continue\n        print(f)\n        df = pd.read_csv(os.path.join(opt.report_dir, f))\n        kp_df = df if kp_df is None else pd.concat([kp_df, df], sort=True)\n\n    # rearrange cols since paths take a lot of space\n    cols = df.columns.tolist()\n    path_cols = [c for c in cols if c.endswith('_path')]\n    not_path_cols = [c for c in cols if not c.endswith('_path')]\n    kp_df = kp_df[not_path_cols + path_cols]\n\n    if opt.pred_name is None:\n        if len(kp_df.pred_name.unique()) > 1:\n            print('Found multiple decoding settings, please set opt.pred_name to avoid mixed results')\n            print(kp_df.pred_name.unique().tolist())\n            raise Exception()\n    else:\n        kp_df = kp_df.loc[kp_df.pred_name == opt.pred_name]\n\n    # kp_df = kp_df.loc[kp_df.exp_name.str.contains(\"PT_step200k\")]\n\n    print(len(kp_df))\n    # print(kp_df.columns)\n    for exp_name in kp_df.exp_name.unique():\n        print(exp_name, len(kp_df.loc[kp_df.exp_name == exp_name]))\n    exp_names = kp_df.exp_name.unique()\n\n    anchor_metric_name = 'all_exact_f_score@k'\n    # anchor_metric_name = 'present_exact_f_score@k'\n\n    for dev_test_pair in dev_test_pairs:\n        # for transfer results\n        dev_name = dev_test_pair[0] + '_test'\n        test_name = dev_test_pair[1] + '_test'\n        # for empirical results\n        # dev_name = dev_test_pair[0]\n        # test_name = dev_test_pair[1]\n\n        devbest_dev_rows, devbest_test_rows = None, None\n        selfbest_dev_rows, selfbest_test_rows = None, None\n        for exp_name in exp_names:\n            exp_df = kp_df.loc[kp_df.exp_name == exp_name]\n            dev_df = exp_df.loc[exp_df.test_dataset == dev_name]\n            test_df = exp_df.loc[exp_df.test_dataset == test_name]\n            if opt.report_lastckpt:\n                dev_df = dev_df.sort_values(by='step', ascending=False)\n                test_df = test_df.sort_values(by='step', ascending=False)\n            else:\n                dev_df = dev_df.sort_values(by=anchor_metric_name, ascending=False)\n                test_df = test_df.sort_values(by=anchor_metric_name, ascending=False)\n\n            if len(dev_df) == 0: continue\n            dev_row = dev_df.iloc[0].to_frame().transpose()\n            selfbest_dev_row = dev_row\n            selfbest_dev_rows = dev_row if selfbest_dev_rows is None else pd.concat([selfbest_dev_rows, dev_row])\n\n            if len(test_df) > 0:\n                test_row = test_df.iloc[0].to_frame().transpose()\n                selfbest_test_rows = test_row if selfbest_test_rows is None else pd.concat([selfbest_test_rows, test_row])\n\n            test_row = None\n            for idx, dev_row in dev_df.iterrows():\n                best_step = dev_row.step\n                test_row = test_df.loc[test_df.step == best_step]\n                if len(test_row) == 1:\n                    dev_row = dev_row.to_frame().transpose()\n                    test_row = test_row.iloc[0].to_frame().transpose()\n                    break\n                elif len(test_row) > 1:\n                    print('Found multiple rows (%d rows): exp=%s, data=%s' % (\n                        len(test_row), exp_name, str(dev_test_pair)))\n                    raise ValueError()\n                # elif len(test_row) == 0:\n                #     print('Corresponding test row not found: exp=%s, data=%s' % (exp_name, str(dev_test_pair)))\n                # else:\n                #     print('what?')\n\n            if test_row is not None and len(test_row) > 0:\n                devbest_dev_rows = dev_row if devbest_dev_rows is None else pd.concat([devbest_dev_rows, dev_row])\n                devbest_test_rows = test_row if devbest_test_rows is None else pd.concat([devbest_test_rows, test_row])\n\n                # move the best dev pred/eval to the specified place\n                if opt.export_dir is not None:\n                    with pd.option_context('display.max_colwidth', 9999999):\n                        pred_file_path = test_row.pred_file_path.to_string(index=False)\n                        eval_file_path = test_row.eval_file_path.to_string(index=False)\n                        target_dir = os.path.join(opt.export_dir, 'dev_best', exp_name, '')\n                        exp_name = test_row.exp_name.to_string(index=False)\n                        pred_file_name = pred_file_path[pred_file_path.rfind('/') + 1: ]\n                        eval_file_name = eval_file_path[pred_file_path.rfind('/') + 1:]\n                        target_pred_file = '-'.join([exp_name, pred_file_name])\n                        target_eval_file = '-'.join([exp_name, eval_file_name])\n\n                    if not os.path.exists(target_dir): os.makedirs(target_dir)\n                    shutil.copyfile(pred_file_path, target_dir + target_pred_file)\n                    shutil.copyfile(eval_file_path, target_dir + target_eval_file)\n            else:\n                devbest_dev_rows = selfbest_dev_row if devbest_dev_rows is None else pd.concat([devbest_dev_rows, selfbest_dev_row])\n                print('Cannot find valid dev-best rows: exp=%s, data=%s, dev_df.size=%d, test_df.size=%d'\n                      % (exp_name, str(dev_test_pair), len(dev_df), len(test_df)))\n            # print(exp_name)\n            # print('len(devbest_dev_rows)=', len(devbest_dev_rows))\n            # print('len(selfbest_dev_rows)=', len(selfbest_dev_rows))\n\n        print('Dev best - ' + dev_name)\n        print(devbest_dev_rows.sort_values(by=anchor_metric_name, ascending=False).to_csv()) if devbest_dev_rows is not None else print('Empty')\n        print('=' * 50)\n        print('Dev best - ' + test_name)\n        print(devbest_test_rows.sort_values(by=anchor_metric_name, ascending=False).to_csv()) if devbest_test_rows is not None else print('Empty')\n        print('=' * 30)\n\n        if opt.report_selfbest:\n            print('Self best - ' + dev_name)\n            print(selfbest_dev_rows.sort_values(by=anchor_metric_name, ascending=False).to_csv()) if selfbest_dev_rows is not None else print('Empty')\n            print('=' * 50)\n            print('Self best - ' + test_name)\n            print(selfbest_test_rows.sort_values(by=anchor_metric_name, ascending=False).to_csv()) if selfbest_test_rows is not None else print('Empty')\n\n        print('*' * 50)\n\n\nif __name__ == '__main__':\n    main()"
  },
  {
    "path": "onmt/keyphrase/bleu.py",
    "content": "# -*- coding: utf-8 -*-\n# Natural Language Toolkit: BLEU Score\n#\n# Copyright (C) 2001-2015 NLTK Project\n# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim\n# Contributors: Dmitrijs Milajevs\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\"\"\"BLEU score implementation.\"\"\"\n\nfrom __future__ import division\n\nimport math\n\nfrom nltk.tokenize import word_tokenize\nfrom collections import Counter\nfrom nltk.util import ngrams\n\n\ndef bleu(candidate, references, weights):\n    \"\"\"Calculate BLEU score (Bilingual Evaluation Understudy)\n\n    :param candidate: a candidate sentence\n    :type candidate: list(str)\n    :param references: reference sentences\n    :type references: list(list(str))\n    :param weights: weights for unigrams, bigrams, trigrams and so on\n    :type weights: list(float)\n\n    >>> weights = [0.25, 0.25, 0.25, 0.25]\n    >>> candidate1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n    ...               'ensures', 'that', 'the', 'military', 'always',\n    ...               'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n    >>> candidate2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n    ...               'forever', 'hearing', 'the', 'activity', 'guidebook',\n    ...               'that', 'party', 'direct']\n\n    >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n    ...               'ensures', 'that', 'the', 'military', 'will', 'forever',\n    ...               'heed', 'Party', 'commands']\n\n    >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n    ...               'guarantees', 'the', 'military', 'forces', 'always',\n    ...               'being', 'under', 'the', 'command', 'of', 'the',\n    ...               'Party']\n\n    >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n    ...               'army', 'always', 'to', 'heed', 'the', 'directions',\n    ...               'of', 'the', 'party']\n\n    >>> bleu(candidate1, [reference1, reference2, reference3], weights)\n    0.504...\n\n    >>> bleu(candidate2, [reference1, reference2, reference3], weights)\n    0\n\n    Papineni, Kishore, et al. \"BLEU: A method for automatic evaluation of\n    machine translation.\" Proceedings of the 40th annual meeting on association for\n    computational linguistics. Association for Computational Linguistics, 2002.\n    http://www.aclweb.org/anthology/P02-1040.pdf\n\n    \"\"\"\n    p_ns = [\n        _modified_precision(candidate, references, i)\n        for i, _ in enumerate(weights, start=1)\n    ]\n\n    try:\n        s = math.fsum(w * math.log(p_n) for w, p_n in zip(weights, p_ns))\n    except ValueError:\n        # some p_ns is 0\n        return 0\n\n    # bp = _brevity_penalty(candidate, references)\n    # return bp * math.exp(s)\n\n    return math.exp(s)\n\n\ndef _modified_precision(candidate, references, n):\n    \"\"\"Calculate modified ngram precision.\n\n    The normal precision method may lead to some wrong translations with\n    high-precision, e.g., the translation, in which a word of reference\n    repeats several times, has very high precision. So in the modified\n    n-gram precision, a reference word will be considered exhausted after\n    a matching candidate word is identified.\n\n    Paper examples:\n\n    >>> _modified_precision(\n    ...    'the the the the the the the'.split(),\n    ...    ['the cat is on the mat'.split(), 'there is a cat on the mat'.split()],\n    ...    n=1,\n    ... )\n    0.28...\n\n    >>> _modified_precision(\n    ...    'the the the the the the the'.split(),\n    ...    ['the cat is on the mat'.split(), 'there is a cat on the mat'.split()],\n    ...    n=2,\n    ... )\n    0.0\n\n    >>> _modified_precision(\n    ...    'of the'.split(),\n    ...    [\n    ...        'It is a guide to action that ensures that the military will forever heed Party commands.'.split(),\n    ...        'It is the guiding principle which guarantees the military forces always being under the command of the Party.'.split(),\n    ...        'It is the practical guide for the army always to heed the directions of the party'.split(),\n    ...    ],\n    ...    n=1,\n    ... )\n    1.0\n\n    >>> _modified_precision(\n    ...    'of the'.split(),\n    ...    [\n    ...        'It is a guide to action that ensures that the military will forever heed Party commands.'.split(),\n    ...        'It is the guiding principle which guarantees the military forces always being under the command of the Party.'.split(),\n    ...        'It is the practical guide for the army always to heed the directions of the party'.split(),\n    ...    ],\n    ...    n=2,\n    ... )\n    1.0\n\n    More examples:\n\n    >>> weights = [0.25, 0.25, 0.25, 0.25]\n    >>> candidate1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n    ...               'ensures', 'that', 'the', 'military', 'always',\n    ...               'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n    >>> candidate2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n    ...               'forever', 'hearing', 'the', 'activity', 'guidebook',\n    ...               'that', 'party', 'direct']\n\n    >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n    ...               'ensures', 'that', 'the', 'military', 'will', 'forever',\n    ...               'heed', 'Party', 'commands']\n\n    >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n    ...               'guarantees', 'the', 'military', 'forces', 'always',\n    ...               'being', 'under', 'the', 'command', 'of', 'the',\n    ...               'Party']\n\n    >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n    ...               'army', 'always', 'to', 'heed', 'the', 'directions',\n    ...               'of', 'the', 'party']\n\n    Unigrams:\n\n    >>> _modified_precision(\n    ...    candidate1,\n    ...    [reference1, reference2, reference3],\n    ...    n=1,\n    ... )\n    0.94...\n\n    >>> _modified_precision(\n    ...    candidate2,\n    ...    [reference1, reference2, reference3],\n    ...    n=1,\n    ... )\n    0.57...\n\n    Bigrams:\n\n    >>> _modified_precision(\n    ...    candidate1,\n    ...    [reference1, reference2, reference3],\n    ...    n=2,\n    ... )\n    0.58...\n\n    >>> _modified_precision(\n    ...    candidate2,\n    ...    [reference1, reference2, reference3],\n    ...    n=2,\n    ... )\n    0.07...\n\n    \"\"\"\n    counts = Counter(ngrams(candidate, n))\n\n    if not counts:\n        return 0\n\n    max_counts = {}\n    for reference in references:\n        reference_counts = Counter(ngrams(reference, n))\n        for ngram in counts:\n            max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram])\n\n    clipped_counts = dict((ngram, min(count, max_counts[ngram])) for ngram, count in counts.items())\n\n    return sum(clipped_counts.values()) / sum(counts.values())\n\n\ndef _brevity_penalty(candidate, references):\n    \"\"\"Calculate brevity penalty.\n\n    As the modified n-gram precision still has the problem from the short\n    length sentence, brevity penalty is used to modify the overall BLEU\n    score according to length.\n\n    An example from the paper. There are three references with length 12, 15\n    and 17. And a terse candidate of the length 12. The brevity penalty is 1.\n\n    >>> references = [['a'] * 12, ['a'] * 15, ['a'] * 17]\n    >>> candidate = ['a'] * 12\n    >>> _brevity_penalty(candidate, references)\n    1.0\n\n    In case a candidate translation is shorter than the references, penalty is\n    applied.\n\n    >>> references = [['a'] * 28, ['a'] * 28]\n    >>> candidate = ['a'] * 12\n    >>> _brevity_penalty(candidate, references)\n    0.2635...\n\n    The length of the closest reference is used to compute the penalty. If the\n    length of a candidate is 12, and the reference lengths are 13 and 2, the\n    penalty is applied because the candidate length (12) is less then the\n    closest reference length (13).\n\n    >>> references = [['a'] * 13, ['a'] * 2]\n    >>> candidate = ['a'] * 12\n    >>> _brevity_penalty(candidate, references)\n    0.92...\n\n    The brevity penalty doesn't depend on reference order. More importantly,\n    when two reference sentences are at the same distance, the shortest\n    reference sentence length is used.\n\n    >>> references = [['a'] * 13, ['a'] * 11]\n    >>> candidate = ['a'] * 12\n    >>> _brevity_penalty(candidate, references) == _brevity_penalty(candidate, reversed(references)) == 1\n    True\n\n    A test example from mteval-v13a.pl (starting from the line 705):\n\n    >>> references = [['a'] * 11, ['a'] * 8]\n    >>> candidate = ['a'] * 7\n    >>> _brevity_penalty(candidate, references)\n    0.86...\n\n    >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]\n    >>> candidate = ['a'] * 7\n    >>> _brevity_penalty(candidate, references)\n    1.0\n\n    \"\"\"\n    c = len(candidate)\n    ref_lens = (len(reference) for reference in references)\n    r = min(ref_lens, key=lambda ref_len: (abs(ref_len - c), ref_len))\n\n    if c > r:\n        return 1\n    else:\n        return math.exp(1 - r / c)"
  },
  {
    "path": "onmt/keyphrase/check_pred_file.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nSome pred files use up too much space, e.g. /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_95000/kp20k.pred is 8.3GB, beam=10 size=2.0GB.\n\nSo this\n\"\"\"\nimport json\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    pred_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kpgen-meng17-kp20k+MagKP_Nlarge-verbatim_append-transformer-L6H8-BS4096-LR0.05-L6-H8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue_step_120000/kp20k_valid2k.pred'\n\n    # ensure the pred is complete\n    with open(pred_path, 'r') as pred_file:\n        for lid, line in enumerate(pred_file):\n            try:\n                pred_dict = json.loads(line)\n            except:\n                print(\"Error occurs while loading line %d\" % (lid))\n                print(line)\n                continue\n            # for k,v in pred_dict.items():\n            #     print('%s' % k)\n"
  },
  {
    "path": "onmt/keyphrase/concat.py",
    "content": "fw = open(\"/home/yingyi/Documents/output/kp20k/roberta-base/tokenized/kp20k_train_all.json\", 'w')\nfrom tqdm import tqdm\nfor i in tqdm(range(1,8)):\n    f =open(\"/home/yingyi/Documents/output/kp20k/roberta-base/tokenized/train_\"+str(i)+\".json\",\"r\").readlines()\n    for line in f:\n        line = line.strip()\n        fw.write(line+\"\\n\")\n\nfw.close()\n"
  },
  {
    "path": "onmt/keyphrase/eval.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport math\nfrom collections import Counter\n\nimport scipy\nimport numpy as np\nfrom sklearn import metrics\n\nfrom onmt.keyphrase.utils import stem_word_list\n\nfrom nltk.translate.bleu_score import sentence_bleu as bleu\n\nfrom nltk.stem.porter import *\nstemmer = PorterStemmer()\n\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\nfrom onmt.keyphrase.utils import validate_phrases, if_present_duplicate_phrases\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\n\ndef compute_match_scores(tgt_seqs, pred_seqs, do_lower=True, do_stem=True, type='exact'):\n    '''\n    If type='exact', returns a list of booleans indicating if a pred has a matching tgt\n    If type='partial', returns a 2D matrix, each value v_ij is a float in range of [0,1]\n        indicating the (jaccard) similarity between pred_i and tgt_j\n    :param tgt_seqs:\n    :param pred_seqs:\n    :param do_stem:\n    :param topn:\n    :param type: 'exact' or 'partial'\n    :return:\n    '''\n    # do processing to baseline predictions\n    if type == \"exact\":\n        match_score = np.zeros(shape=(len(pred_seqs)), dtype='float32')\n    else:\n        match_score = np.zeros(shape=(len(pred_seqs), len(tgt_seqs)), dtype='float32')\n\n    target_number = len(tgt_seqs)\n    predicted_number = len(pred_seqs)\n\n    metric_dict = {'target_number': target_number, 'prediction_number': predicted_number, 'correct_number': match_score}\n\n    # convert target index into string\n    if do_lower:\n        tgt_seqs = [[w.lower() for w in seq] for seq in tgt_seqs]\n        pred_seqs = [[w.lower() for w in seq] for seq in pred_seqs]\n    if do_stem:\n        tgt_seqs = [stem_word_list(seq) for seq in tgt_seqs]\n        pred_seqs = [stem_word_list(seq) for seq in pred_seqs]\n\n    for pred_id, pred_seq in enumerate(pred_seqs):\n        if type == 'exact':\n            match_score[pred_id] = 0\n            for true_id, true_seq in enumerate(tgt_seqs):\n                match = True\n                if len(pred_seq) != len(true_seq):\n                    continue\n                for pred_w, true_w in zip(pred_seq, true_seq):\n                    # if one two words are not same, match fails\n                    if pred_w != true_w:\n                        match = False\n                        break\n                # if every word in pred_seq matches one true_seq exactly, match succeeds\n                if match:\n                    match_score[pred_id] = 1\n                    break\n        elif type == 'ngram':\n            # use jaccard coefficient as the similarity of partial match (1+2 grams)\n            pred_seq_set = set(pred_seq)\n            pred_seq_set.update(set([pred_seq[i]+'_'+pred_seq[i+1] for i in range(len(pred_seq)-1)]))\n            for true_id, true_seq in enumerate(tgt_seqs):\n                true_seq_set = set(true_seq)\n                true_seq_set.update(set([true_seq[i]+'_'+true_seq[i+1] for i in range(len(true_seq)-1)]))\n                if float(len(set.union(*[set(true_seq_set), set(pred_seq_set)]))) > 0:\n                    similarity = len(set.intersection(*[set(true_seq_set), set(pred_seq_set)])) \\\n                              / float(len(set.union(*[set(true_seq_set), set(pred_seq_set)])))\n                else:\n                    similarity = 0.0\n                match_score[pred_id, true_id] = similarity\n        elif type == 'mixed':\n            # similar to jaccard, but addtional to 1+2 grams we also put in the full string, serves like an exact+partial surrogate\n            pred_seq_set = set(pred_seq)\n            pred_seq_set.update(set([pred_seq[i]+'_'+pred_seq[i+1] for i in range(len(pred_seq)-1)]))\n            pred_seq_set.update(set(['_'.join(pred_seq)]))\n            for true_id, true_seq in enumerate(tgt_seqs):\n                true_seq_set = set(true_seq)\n                true_seq_set.update(set([true_seq[i]+'_'+true_seq[i+1] for i in range(len(true_seq)-1)]))\n                true_seq_set.update(set(['_'.join(true_seq)]))\n                if float(len(set.union(*[set(true_seq_set), set(pred_seq_set)]))) > 0:\n                    similarity = len(set.intersection(*[set(true_seq_set), set(pred_seq_set)])) \\\n                              / float(len(set.union(*[set(true_seq_set), set(pred_seq_set)])))\n                else:\n                    similarity = 0.0\n                match_score[pred_id, true_id] = similarity\n\n        elif type == 'bleu':\n            # account for the match of subsequences, like n-gram-based (BLEU) or LCS-based\n            # n-grams precision doesn't work that well\n            for true_id, true_seq in enumerate(tgt_seqs):\n                match_score[pred_id, true_id] = bleu(pred_seq, [true_seq], [0.7, 0.3, 0.0])\n\n    return match_score\n\n\ndef run_classic_metrics(match_list, pred_list, tgt_list, score_names, topk_range, type='exact'):\n    \"\"\"\n    Return a dict of scores containing len(score_names) * len(topk_range) items\n    score_names and topk_range actually only define the names of each score in score_dict.\n    :param match_list:\n    :param pred_list:\n    :param tgt_list:\n    :param score_names:\n    :param topk_range:\n    :param type: exact or partial\n    :return:\n    \"\"\"\n    score_dict = {}\n    if len(tgt_list) == 0:\n        for topk in topk_range:\n            for score_name in score_names:\n                score_dict['{}@{}'.format(score_name, topk)] = 0.0\n        return score_dict\n\n    assert len(match_list) == len(pred_list)\n    for topk in topk_range:\n        if topk == 'k':\n            cutoff = len(tgt_list)\n        elif topk == 'M':\n            cutoff = len(pred_list)\n        else:\n            cutoff = topk\n\n        if len(pred_list) > cutoff:\n            pred_list_k = np.asarray(pred_list[:cutoff])\n            match_list_k = match_list[:cutoff]\n        else:\n            pred_list_k = np.asarray(pred_list)\n            match_list_k = match_list\n\n        if type == 'partial':\n            cost_matrix = np.asarray(match_list_k, dtype=float)\n            if len(match_list_k) > 0:\n                # convert to a negative matrix because linear_sum_assignment() looks for minimal assignment\n                row_ind, col_ind = scipy.optimize.linear_sum_assignment(-cost_matrix)\n                match_list_k = cost_matrix[row_ind, col_ind]\n                overall_cost = cost_matrix[row_ind, col_ind].sum()\n            '''\n            print(\"\\n%d\" % topk)\n            print(row_ind, col_ind)\n            print(\"Pred\" + str(np.asarray(pred_list)[row_ind].tolist()))\n            print(\"Target\" + str(tgt_list))\n            print(\"Maximum Score: %f\" % overall_cost)\n\n            print(\"Pred list\")\n            for p_id, (pred, cost) in enumerate(zip(pred_list, cost_matrix)):\n                print(\"\\t%d \\t %s - %s\" % (p_id, pred, str(cost)))\n            '''\n\n        # Micro-Averaged Method\n        correct_num = int(sum(match_list_k))\n        # Precision, Recall and F-score, with flexible cutoff (if number of pred is smaller)\n        micro_p = float(sum(match_list_k)) / float(len(pred_list_k)) if len(pred_list_k) > 0 else 0.0\n        micro_r = float(sum(match_list_k)) / float(len(tgt_list)) if len(tgt_list) > 0 else 0.0\n\n        if micro_p + micro_r > 0:\n            micro_f1 = float(2 * (micro_p * micro_r)) / (micro_p + micro_r)\n        else:\n            micro_f1 = 0.0\n        # F-score, with a hard cutoff on precision, offset the favor towards fewer preds\n        micro_p_hard = float(sum(match_list_k)) / cutoff if len(pred_list_k) > 0 else 0.0\n        if micro_p_hard + micro_r > 0:\n            micro_f1_hard = float(2 * (micro_p_hard * micro_r)) / (micro_p_hard + micro_r)\n        else:\n            micro_f1_hard = 0.0\n\n        for score_name, v in zip(['correct', 'precision', 'recall', 'f_score', 'precision_hard', 'f_score_hard'], [correct_num, micro_p, micro_r, micro_f1, micro_p_hard, micro_f1_hard]):\n            score_dict['{}@{}'.format(score_name, topk)] = v\n\n    # return only the specified scores\n    return_scores = {}\n    for topk in topk_range:\n        for score_name in score_names:\n            return_scores['{}@{}'.format(score_name, topk)] = score_dict['{}@{}'.format(score_name, topk)]\n\n    return return_scores\n\n\ndef run_advanced_metrics(match_scores, pred_list, tgt_list):\n    score_dict = {}\n    corrects, precisions, recalls, fscores = compute_PRF1(match_scores, pred_list, tgt_list)\n    auc = compute_PR_AUC(precisions, recalls)\n    ap = compute_AP(match_scores, precisions, tgt_list)\n    mrr = compute_MRR(match_scores)\n    sadr = compute_SizeAdjustedDiscountedRecall(match_scores, tgt_list)\n    ndcg = compute_NormalizedDiscountedCumulativeGain(match_scores, tgt_list)\n    alpha_ndcg_5 = compute_alphaNormalizedDiscountedCumulativeGain(pred_list, tgt_list, k=5, alpha=0.5)\n    alpha_ndcg_10 = compute_alphaNormalizedDiscountedCumulativeGain(pred_list, tgt_list, k=10, alpha=0.5)\n\n    score_dict['auc'] = auc\n    score_dict['ap'] = ap\n    score_dict['mrr'] = mrr\n    score_dict['sadr'] = sadr\n    score_dict['ndcg'] = ndcg\n    score_dict['alpha_ndcg@5'] = alpha_ndcg_5\n    score_dict['alpha_ndcg@10'] = alpha_ndcg_10\n\n    # print('\\nMatch[#=%d]=%s' % (len(match_scores), str(match_scores)))\n    # print('Accum Corrects=' + str(corrects))\n    # print('P@x=' + str(precisions))\n    # print('R@x=' + str(recalls))\n    # print('F-score@x=' + str(fscores))\n    #\n    # print('F-score@5=%f' % fscores[4])\n    # print('F-score@10=%f' % (fscores[9] if len(fscores) > 9 else -9999))\n    # print('F-score@O=%f' % fscores[len(tgt_list) - 1])\n    # print('F-score@M=%f' % fscores[len(match_scores) - 1])\n    #\n    # print('AUC=%f' % auc)\n    # print('AP=%f' % ap)\n    # print('MRR=%f' % mrr)\n    # print('SADR=%f' % sadr)\n    # print('nDCG=%f' % ndcg)\n    # print('α-nDCG@5=%f' % alpha_ndcg_5)\n    # print('α-nDCG@10=%f' % alpha_ndcg_10)\n\n    return score_dict\n\n\ndef compute_PRF1(match_scores, preds, tgts):\n    corrects, precisions, recalls, fscores = [], [], [], []\n\n    for pred_id, score in enumerate(match_scores):\n        _corr = corrects[-1] + score if len(corrects) > 0 else score\n        _p = _corr / (pred_id + 1) if pred_id + 1 > 0 else 0.0\n        _r = _corr / len(tgts) if len(tgts) > 0 else 0.0\n        _f1 = float(2 * (_p * _r)) / (_p + _r) if (_p + _r) > 0 else 0.0\n        corrects += [_corr]\n        precisions += [_p]\n        recalls += [_r]\n        fscores += [_f1]\n\n    return corrects, precisions, recalls, fscores\n\n\ndef compute_MRR(match_scores):\n    # A modified mean reciprocal rank for KP eval\n    # MRR in IR uses the rank of first correct result. We use the rank of all correctly recalled results.\n    # But it doesn't consider the missing predictions, so it's a precision-like metric\n    mrr = 0.0\n    count = 0.0\n\n    for idx, match_score in enumerate(match_scores):\n        if match_score == 0.0:\n            continue\n        mrr += match_score / (idx + 1)\n        count += 1.0\n\n    if count > 0:\n        mrr /= count\n    else:\n        mrr = 0.0\n    return mrr\n\n\ndef compute_AP(match_scores, precisions, tgts):\n    # Average Precision: Note that the average is over all relevant documents and the relevant documents not retrieved get a precision score of zero.\n    # Updated on March 4, 2020. Previously we average over the number of correct predictions.\n    ap = 0.0\n    tgt_count = len(tgts)\n\n    for idx, (match_score, precision) in enumerate(zip(match_scores, precisions)):\n        if match_score == 0.0:\n            continue\n        ap += precision\n\n    if tgt_count > 0:\n        ap /= tgt_count\n    else:\n        ap = 0.0\n    return ap\n\n\ndef compute_PR_AUC(precisions, recalls):\n    # we need to pad two values as the begin/end point of the curve\n    p = [1.0] + precisions + [0.0]\n    r = [0.0] + recalls + [(recalls[-1] if len(recalls) > 0 else 0.0)]\n    pr_auc = metrics.auc(r, p)\n\n    return pr_auc\n\n\ndef compute_SizeAdjustedDiscountedRecall(match_scores, tgts):\n    # add a log2(pos-num_tgt+2) discount to correct predictions out of the top-k list\n    cumulated_gain = 0.0\n    num_tgts = len(tgts)\n\n    for idx, match_score in enumerate(match_scores):\n        if match_score == 0.0:\n            continue\n        if idx + 1 > num_tgts:\n            gain = 1.0 / math.log(idx - num_tgts + 3, 2)\n        else:\n            gain = 1.0\n        # print('gain@%d=%f' % (idx + 1, gain))\n        cumulated_gain += gain\n\n    if num_tgts > 0:\n        ndr = cumulated_gain / num_tgts\n    else:\n        ndr = 0.0\n\n    return ndr\n\n\ndef compute_NormalizedDiscountedCumulativeGain(match_scores, tgts):\n    # add a positional discount to all predictions\n    def _compute_dcg(match_scores):\n        cumulated_gain = 0.0\n        for idx, match_score in enumerate(match_scores):\n            gain = match_score / math.log(idx + 2, 2)\n            #             print('gain@%d=%f' % (idx + 1, gain))\n            cumulated_gain += gain\n        return cumulated_gain\n\n    num_tgts = len(tgts)\n    assert sum(match_scores) <= num_tgts, \"Sum of relevance scores shouldn't exceed number of targets.\"\n    if num_tgts > 0:\n        dcg = _compute_dcg(match_scores)\n        #         print('DCG=%f' % dcg)\n        idcg = _compute_dcg([1.0] * num_tgts)\n        #         print('IDCG=%f' % idcg)\n        ndcg = dcg / idcg\n    else:\n        ndcg = 0.0\n\n    #     print('nDCG=%f' % ndcg)\n    return ndcg\n\n\ndef compute_alphaNormalizedDiscountedCumulativeGain(preds, tgts, k=5, alpha=0.5):\n    # α-nDCG@k\n    # add a positional discount to all predictions, and penalize repetive predictions\n    def _compute_dcg(match_scores, novelty_scores, alpha):\n        cumulated_gain = 0.0\n        for idx, (match_score, novelty_score) in enumerate(zip(match_scores, novelty_scores)):\n            gain = match_score * ((1 - alpha) ** (novelty_score)) / math.log(idx + 2, 2)\n            # print('gain@%d=%f' % (idx + 1, gain))\n            cumulated_gain += gain\n        return cumulated_gain\n\n    def _compute_matching_novelty_scores(preds, tgts):\n        preds = [set(stem_word_list(seq)) for seq in preds]\n        tgts = [set(stem_word_list(seq)) for seq in tgts]\n        match_scores = [0.0] * len(preds)\n        novelty_discounts = [0.0] * len(preds)\n        rel_matrix = np.asarray([[0.0] * len(preds)] * len(tgts))\n\n        for pred_id, pred in enumerate(preds):\n            match_score = 0.0\n            novelty_discount = 0.0\n            for tgt_id, tgt in enumerate(tgts):\n                if tgt.issubset(pred) or pred.issubset(tgt):\n                    rel_matrix[tgt_id][pred_id] = 1.0\n                    match_score = 1.0\n                    if pred_id > 0 and sum(rel_matrix[tgt_id][: pred_id]) > novelty_discount:\n                        novelty_discount = sum(rel_matrix[tgt_id][: pred_id])\n            match_scores[pred_id] = match_score\n            novelty_discounts[pred_id] = novelty_discount\n\n        #         print('PRED[%d]=%s' % (len(preds), str(preds)))\n        #         print('GT[%d]=%s' % (len(tgts), str(tgts)))\n        #         print(match_scores)\n        #         print(novelty_discounts)\n        #         print(np.asarray(rel_matrix))\n        return match_scores, novelty_discounts\n\n    num_tgts = len(tgts)\n    k = min(k, num_tgts)\n    preds = preds[: k] if len(preds) > k else preds\n\n    if num_tgts > 0:\n        match_scores, novelty_discounts = _compute_matching_novelty_scores(preds, tgts)\n        dcg = _compute_dcg(match_scores, novelty_discounts, alpha=alpha)\n        idcg = _compute_dcg([1.0] * num_tgts, [0.0] * num_tgts, alpha=alpha)\n        ndcg = dcg / idcg\n    else:\n        ndcg, dcg, idcg = 0.0, 0.0, 0.0\n\n    # print('DCG=%f' % dcg)\n    # print('IDCG=%f' % idcg)\n    # print('nDCG=%f' % ndcg)\n    return ndcg\n\n\ndef f1_score(prediction, ground_truth):\n    # both prediction and grount_truth should be list of words\n    common = Counter(prediction) & Counter(ground_truth)\n    num_same = sum(common.values())\n    if num_same == 0:\n        return 0\n    precision = 1.0 * num_same / len(prediction) if len(prediction) > 0 else 0.0\n    recall = 1.0 * num_same / len(ground_truth) if len(ground_truth) > 0 else 0.0\n    f1 = (2 * precision * recall) / (precision + recall) if len(precision + recall) > 0 else 0.0\n    return f1\n\n\ndef macro_averaged_score(precisionlist, recalllist):\n    precision = np.average(precisionlist)\n    recall = np.average(recalllist)\n    f_score = 0\n    if(precision or recall):\n        f_score = round((2 * (precision * recall)) / (precision + recall), 2)\n    return precision, recall, f_score\n\n\ndef self_redundancy(_input):\n    # _input shoule be list of list of words\n    if len(_input) == 0:\n        return None\n    _len = len(_input)\n    scores = np.ones((_len, _len), dtype=\"float32\") * -1.0\n    for i in range(_len):\n        for j in range(_len):\n            if scores[i][j] != -1:\n                continue\n            elif i == j:\n                scores[i][j] = 0.0\n            else:\n                f1 = f1_score(_input[i], _input[j])\n                scores[i][j] = f1\n                scores[j][i] = f1\n    res = np.max(scores, 1)\n    res = np.mean(res)\n    return res\n\n\ndef eval_and_print(src_text, tgt_kps, pred_kps, pred_scores, unk_token='<unk>', return_eval=False):\n    src_seq = [t for t in re.split(r'\\W', src_text) if len(t) > 0]\n    tgt_seqs = [[t for t in re.split(r'\\W', p) if len(t) > 0] for p in tgt_kps]\n    pred_seqs = [[t for t in re.split(r'\\W', p) if len(t) > 0] for p in pred_kps]\n\n    topk_range = [5, 10, 'k']\n    absent_topk_range = [50, 'M']\n    metric_names = ['f_score']\n\n    # 1st filtering, ignore phrases having <unk> and puncs\n    valid_pred_flags = validate_phrases(pred_seqs, unk_token)\n    # 2nd filtering: filter out phrases that don't appear in text, and keep unique ones after stemming\n    present_pred_flags, _, duplicate_flags = if_present_duplicate_phrases(src_seq, pred_seqs)\n    # treat duplicates as invalid\n    valid_pred_flags = valid_pred_flags * ~duplicate_flags if len(valid_pred_flags) > 0 else []\n    valid_and_present_flags = valid_pred_flags * present_pred_flags if len(valid_pred_flags) > 0 else []\n    valid_and_absent_flags = valid_pred_flags * ~present_pred_flags if len(valid_pred_flags) > 0 else []\n\n    # compute match scores (exact, partial and mixed), for exact it's a list otherwise matrix\n    match_scores_exact = compute_match_scores(tgt_seqs=tgt_seqs, pred_seqs=pred_seqs,\n                                              do_lower=True, do_stem=True, type='exact')\n    # split tgts by present/absent\n    present_tgt_flags, _, _ = if_present_duplicate_phrases(src_seq, tgt_seqs)\n    present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\n    absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if ~present]\n\n    # filter out results of invalid preds\n    valid_preds = [seq for seq, valid in zip(pred_seqs, valid_pred_flags) if valid]\n    valid_present_pred_flags = present_pred_flags[valid_pred_flags]\n\n    valid_match_scores_exact = match_scores_exact[valid_pred_flags]\n\n    # split preds by present/absent and exact/partial/mixed\n    valid_present_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if present]\n    valid_absent_preds = [pred for pred, present in zip(valid_preds, valid_present_pred_flags) if ~present]\n    present_exact_match_scores = valid_match_scores_exact[valid_present_pred_flags]\n    absent_exact_match_scores = valid_match_scores_exact[~valid_present_pred_flags]\n\n    all_exact_results = run_classic_metrics(valid_match_scores_exact, valid_preds, tgt_seqs, metric_names, topk_range)\n    present_exact_results = run_classic_metrics(present_exact_match_scores, valid_present_preds, present_tgts, metric_names, topk_range)\n    absent_exact_results = run_classic_metrics(absent_exact_match_scores, valid_absent_preds, absent_tgts, metric_names, absent_topk_range)\n\n    eval_results_names = ['all_exact', 'present_exact', 'absent_exact']\n    eval_results_list = [all_exact_results, present_exact_results, absent_exact_results]\n\n    print_out = print_predeval_result(src_text,\n                                      tgt_seqs, present_tgt_flags,\n                                      pred_seqs, pred_scores, present_pred_flags, valid_pred_flags,\n                                      valid_and_present_flags, valid_and_absent_flags, match_scores_exact,\n                                      eval_results_names, eval_results_list)\n    count_dict = {\n        'num_gold': len(tgt_seqs),\n        'num_present_gold': sum(present_tgt_flags),\n        'num_absent_gold': len(present_tgt_flags)-sum(present_tgt_flags),\n        'num_pred': len(pred_seqs),\n        'num_valid_pred': sum(valid_pred_flags),\n        'num_present_pred': sum(present_pred_flags),\n        'num_present_valid_pred': sum(valid_and_present_flags),\n        'num_absent_valid_pred': sum(valid_and_absent_flags)\n    }\n\n    if return_eval:\n        eval_results_dict = {\n            'count': count_dict,\n            'all_exact': all_exact_results,\n            'present_exact': present_exact_results,\n            'absent_exact': absent_exact_results\n        }\n        return print_out, eval_results_dict\n    else:\n        return print_out\n\n\ndef print_predeval_result(src_text,\n                          tgt_seqs, present_tgt_flags,\n                          pred_seqs, pred_scores, present_pred_flags, valid_pred_flags,\n                          valid_and_present_flags, valid_and_absent_flags, match_scores_exact,\n                          results_names, results_list):\n    print_out = '=' * 50\n    print_out += '\\n[Source]: %s \\n' % (src_text)\n\n    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\n' % \\\n                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\n    print_out += '\\n'.join(\n        ['\\t\\t[%s]' % ' '.join(phrase) if is_present else '\\t\\t%s' % ' '.join(phrase) for phrase, is_present in\n         zip(tgt_seqs, present_tgt_flags)])\n\n    print_out += '\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\n                 '#(valid&present)=%d, #(valid&absent)=%d\\n' % (\n        len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags),\n        sum(valid_and_present_flags), sum(valid_and_absent_flags))\n    print_out += ''\n    preds_out = ''\n    for p_id, (word, match, is_valid, is_present) in enumerate(\n        zip(pred_seqs, match_scores_exact, valid_pred_flags, present_pred_flags)):\n        score = pred_scores[p_id] if pred_scores else \"Score N/A\"\n\n        preds_out += '%s\\n' % (' '.join(word))\n        if is_present:\n            print_phrase = '[%s]' % ' '.join(word)\n        else:\n            print_phrase = ' '.join(word)\n\n        if match == 1.0:\n            correct_str = '[correct!]'\n        else:\n            correct_str = ''\n\n        pred_str = '\\t\\t%s\\t%s \\t%s\\n' % ('[%.4f]' % (-score) if pred_scores else \"Score N/A\",\n                                                print_phrase, correct_str)\n        if not is_valid:\n            pred_str = '\\t%s' % pred_str\n\n        print_out += pred_str\n\n    print_out += \"\\n ======================================================= \\n\"\n\n    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\n' % \\\n                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\n    print_out += '\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\n                 '#(valid&present)=%d, #(valid&absent)=%d\\n' % (\n        len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags),\n        sum(valid_and_present_flags), sum(valid_and_absent_flags))\n\n    for name, results in zip(results_names, results_list):\n        # print @5@10@O@M for present_exact, print @50@M for absent_exact\n        if name in ['all_exact', 'present_exact', 'absent_exact']:\n            if name.startswith('all') or name.startswith('present'):\n                topk_list = ['k', '10']\n            else:\n                topk_list = ['50', 'M']\n\n            for topk in topk_list:\n                print_out += \"\\n --- batch {} F1 @{}: \\t\".format(name, topk) \\\n                             + \"{:.4f}\".format(results['f_score@{}'.format(topk)])\n        else:\n            # ignore partial for now\n            continue\n\n    print_out += \"\\n =======================================================\"\n\n    return print_out\n"
  },
  {
    "path": "onmt/keyphrase/extract_np.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport argparse\nimport json\nimport os\n\nimport tqdm\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\nfrom spacy.symbols import NOUN, PROPN, PRON\n\nimport re\nimport numpy as np\nimport spacy\nimport nltk\n\n# base_dir = '/zfs1/pbrusilovsky/rum20/sum/newssum/'\n# nltk.data.path.append('%s/tools/nltk/' % base_dir)\nstemmer = nltk.stem.porter.PorterStemmer()\nstopword_set = set(nltk.corpus.stopwords.words('english'))\nstopword_set.update(['\\'s', 'doe', 'n\\'t', 'and', 'also', 'whether'])\n\ndef stem_word_list(word_list):\n    return [stemmer.stem(w.strip()) for w in word_list]\n\n\ndef if_present_duplicate_phrases(src_seq, tgt_seqs, stemming=True, lowercase=True):\n    \"\"\"\n    Check if each given target sequence verbatim appears in the source sequence\n    :param src_seq:\n    :param tgt_seqs:\n    :param stemming:\n    :param lowercase:\n    :param check_duplicate:\n    :return:\n    \"\"\"\n    if lowercase:\n        src_seq = [w.lower() for w in src_seq]\n    if stemming:\n        src_seq = stem_word_list(src_seq)\n\n    present_indices = []\n    present_flags = []\n    duplicate_flags = []\n    phrase_set = set()  # some phrases are duplicate after stemming, like \"model\" and \"models\" would be same after stemming, thus we ignore the following ones\n\n    for tgt_seq in tgt_seqs:\n        if lowercase:\n            tgt_seq = [w.lower() for w in tgt_seq]\n        if stemming:\n            tgt_seq = stem_word_list(tgt_seq)\n\n        # check if the phrase appears in source text\n        # iterate each word in source\n        match_flag, match_pos_idx = if_present_phrase(src_seq, tgt_seq)\n\n        # if it reaches the end of source and no match, means it doesn't appear in the source\n        present_flags.append(match_flag)\n        present_indices.append(match_pos_idx)\n\n        # check if it is duplicate\n        if '_'.join(tgt_seq) in phrase_set:\n            duplicate_flags.append(True)\n        else:\n            duplicate_flags.append(False)\n        phrase_set.add('_'.join(tgt_seq))\n\n    assert len(present_flags) == len(present_indices)\n\n    return np.asarray(present_flags), \\\n           np.asarray(present_indices), \\\n           np.asarray(duplicate_flags)\n\n\ndef if_present_phrase(src_str_tokens, phrase_str_tokens):\n    \"\"\"\n\n    :param src_str_tokens: a list of strings (words) of source text\n    :param phrase_str_tokens: a list of strings (words) of a phrase\n    :return:\n    \"\"\"\n    match_flag = False\n    match_pos_idx = -1\n    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\n        match_flag = True\n        # iterate each word in target, if one word does not match, set match=False and break\n        for seq_idx, seq_w in enumerate(phrase_str_tokens):\n            src_w = src_str_tokens[src_start_idx + seq_idx]\n            if src_w != seq_w:\n                match_flag = False\n                break\n        if match_flag:\n            match_pos_idx = src_start_idx\n            break\n\n    return match_flag, match_pos_idx\n\n\ndef meng17_tokenize(text):\n    '''\n    The tokenizer used in Meng et al. ACL 2017\n    parse the feed-in text, filtering and tokenization\n    keep [_<>,\\(\\)\\.\\'%], replace digits to <digit>, split by [^a-zA-Z0-9_<>,\\(\\)\\.\\'%]\n    :param text:\n    :return: a list of tokens\n    '''\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', text)\n    # pad spaces to the left and right of special punctuations\n    text = re.sub(r'[_<>,\\(\\)\\.\\'%]', ' \\g<0> ', text)\n    # tokenize by non-letters (new-added + # & *, but don't pad spaces, to make them as one whole word)\n    tokens = list(filter(lambda w: len(w) > 0, re.split(r'[^a-zA-Z0-9_<>,#&\\+\\*\\(\\)\\.\\']', text)))\n\n    return tokens\n\n\ndef spacy_innate_noun_chunks(doc, remove_duplicate=True):\n    \"\"\"\n    Modified based on spacy noun_chunks() from https://github.com/explosion/spaCy/blob/master/spacy/lang/en/syntax_iterators.py.\n    Detect base noun phrases from a dependency parse. Works on both Doc and Span.\n    \"\"\"\n    labels = [\n        \"oprd\",\n        \"nsubj\",\n        \"dobj\",\n        \"nsubjpass\",\n        \"pcomp\",\n        \"pobj\",\n        \"dative\",\n        \"appos\",\n        \"attr\",\n        \"ROOT\",\n    ]\n    np_deps = [doc.vocab.strings.add(label) for label in labels]\n    conj = doc.vocab.strings.add(\"conj\")\n    np_label = doc.vocab.strings.add(\"NP\")\n    prev_end = -1\n    for i, word in enumerate(doc):\n        if word.pos not in (NOUN, PROPN, PRON):\n            continue\n        # Prevent nested chunks from being produced\n        if word.left_edge.i <= prev_end:\n            continue\n        if word.dep in np_deps:\n            prev_end = word.i\n            yield word.left_edge.i, word.i + 1, np_label\n        elif word.dep == conj:\n            head = word.head\n            while head.dep == conj and head.head.i < head.i:\n                head = head.head\n            # If the head is an NP, and we're coordinated to it, we're an NP\n            if head.dep in np_deps:\n                prev_end = word.i\n                yield word.left_edge.i, word.i + 1, np_label\n\n\ndef all_nested_NPs(span):\n    i = 0\n    for i, word in enumerate(span):\n        if word.pos != 89: # not a DET\n            break\n\n    span = span[i: ]\n    nested_nps = []\n\n    # a two-layer loop to get all possible nested phrases\n    for k in range(1, len(span) + 1):\n        for i in range(len(span) - k + 1):\n            # print(span[i: i + k])\n            np = span[i: i + k]\n            nested_nps.append(np)\n\n    return nested_nps\n\n\ndef spacy_noun_chunks_all_nested(doc, remove_duplicate=True):\n    \"\"\"\n    Detect base noun phrases from a dependency parse. Works on both Doc and Span.\n    \"\"\"\n    noun_chunk_list = []\n    labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj', 'dative', 'appos',\n              'attr', 'ROOT']\n    id2name = {tid: t for tid, t in enumerate(spacy.symbols.NAMES)}\n    np_deps = [doc.vocab.strings.add(label) for label in labels]\n    conj = doc.vocab.strings.add('conj')\n    np_set = set()\n\n    for i, word in enumerate(doc):\n        # print(i, word.text, id2name[word.pos], id2name[word.dep] if word.dep in id2name else 'np_dep')\n        if word.pos not in (NOUN, PROPN, PRON):\n            continue\n        # Prevent nested chunks from being produced\n        if word.dep in np_deps:\n            # print(doc[word.left_edge.i: word.i+1])\n            # print([id2name[t.pos] for t in doc[word.left_edge.i: word.i+1]])\n            if remove_duplicate:\n                for np in all_nested_NPs(doc[word.left_edge.i: word.i+1]):\n                    if np.text not in np_set:\n                        noun_chunk_list.append(np)\n                        np_set.add(np.text)\n            else:\n                noun_chunk_list.extend(all_nested_NPs(doc[word.left_edge.i: word.i+1]))\n        elif word.dep == conj:\n            head = word.head\n            while head.dep == conj and head.head.i < head.i:\n                head = head.head\n            # If the head is an NP, and we're coordinated to it, we're an NP\n            if head.dep in np_deps:\n                # print(doc[word.left_edge.i: word.i + 1])\n                # print([id2name[t.pos] for t in doc[word.left_edge.i: word.i+1]])\n                if remove_duplicate:\n                    for np in all_nested_NPs(doc[word.left_edge.i: word.i+1]):\n                        if np.text not in np_set:\n                            noun_chunk_list.append(np)\n                            np_set.add(np.text)\n                else:\n                    noun_chunk_list.extend(all_nested_NPs(doc[word.left_edge.i: word.i+1]))\n\n    return noun_chunk_list\n\n\ndef noun_chunks_by_pos_regex(text, min_len, max_len):\n    '''\n    https://files.ifi.uzh.ch/cl/hess/classes/ecl1/termerCIE.html\n        (Adjective | Noun)* (Noun Preposition)? (Adjective | Noun)* Noun\n    https://www.aclweb.org/anthology/D09-1027.pdf\n        (JJ)*(NN|NNS|NNP)+\n    :param doc:\n    :param min_len:\n    :param max_len:\n    :return:\n    '''\n    doc = spacy_nlp(text, disable=[\"textcat\"])\n\n    np_regex = r'((^ADJ|^NOUN|^PROPN)(ADP|-|ADJ|NOUN|PROPN)*?)?(NOUN|PROPN)+'\n    cands = []\n    # a two-layer loop to get all n-grams\n    for i in range(0, len(doc) - 1):\n        for k in range(min_len, max_len + 1):\n            if i + k > len(doc): break\n            span = doc[i: i + k]\n            pos = ['-' if t.text == '-' else t.pos_ for t in span]\n            pos_str = ''.join(pos)\n\n            cands.append((span, pos_str, pos))\n\n    #     for np_id, (np, pos_str, pos) in enumerate(cands):\n    #         print('[%d]' % np_id, np, str(pos), '[match]' if re.fullmatch(np_regex, pos_str) else '')\n\n    cands = [span.text for span, pos_str, pos in cands if re.fullmatch(np_regex, pos_str)]\n\n    return cands\n\n\ndef spacy_noun_chunks_wrapper(text, trim_punct=True, remove_stopword=True):\n    spacy_doc = spacy_nlp(text, disable=[\"textcat\"])\n    np_chunks = list(spacy_doc.noun_chunks)\n    np_str_list = []\n    for chunk in np_chunks:\n        np = []\n        for w in chunk:\n            w = w.text\n            if trim_punct:\n                w = w.strip(r\"\"\"!\"#$%&'()*+,-/:;<=>?@[\\]^_`{|}~\"\"\")\n            if remove_stopword:\n                if w.lower() in stopword_set:\n                    continue\n            np.append(w)\n        if len(np) > 0:\n            np_str_list.append(' '.join(np))\n\n    return np_str_list\n\n\ndef get_all_np(text, stem=True, return_set=True):\n    # code to recursively combine nouns\n    # 'We' is actually a pronoun but included in your question\n    # hence the token.pos_ == \"PRON\" part in the last if statement\n    # suggest you extract PRON separately like the noun-chunks above\n\n    doc = spacy_nlp(text)\n    index = 0\n    nounIndices = []\n    for token in doc:\n        # print(token.text, token.pos_, token.dep_, token.head.text)\n        if token.pos_ == 'NOUN':\n            nounIndices.append(index)\n        index = index + 1\n\n    #     print(nounIndices)\n    np_str_list = []\n\n    #     for nc in doc.noun_chunks:\n    #         for np in [nc, doc[nc.root.left_edge.i:nc.root.right_edge.i+1]]:\n    #             print(np.text)\n    # #             np_str_list.append(np)\n    #             np_str_list.append(' '.join([stemmer.stem(w) for w in np.text.split()]))\n\n    for idxValue in nounIndices:\n        doc = spacy_nlp(text)\n        span = doc[doc[idxValue].left_edge.i: doc[idxValue].right_edge.i + 1]\n        span.merge()\n\n        for token in doc:\n            if token.dep_ == 'dobj' or token.dep_ == 'pobj' or token.pos_ == \"PRON\":\n                #                 print(' '.join([stemmer.stem(w) for w in token.text.split()]))\n                if stem:\n                    np_str_list.append(' '.join([stemmer.stem(w) for w in token.text.split()]))\n                else:\n                    np_str_list.append(token.text)\n\n    if return_set:\n        np_str_list = set(np_str_list)\n\n    return np_str_list\n\n\ndef test_np():\n    text = 'An example support-vector machine. A feedback vertex set of a graph G is a set S  of its vertices such that the subgraph induced by V(G)?S is a forest. The cardinality of a minimum feedback vertex set of G  is denoted by ?(G). A graph G is 2-degenerate  if each subgraph G? of G has a vertex v  such that dG?(v)?2. In this paper, we prove that ?(G)?2n/5 for any 2-degenerate n-vertex graph G and moreover, we show that this bound is tight. As a consequence, we derive a polynomial time algorithm, which for a given 2-degenerate n-vertex graph returns its feedback vertex set of cardinality at most 2n/5. Some 40 members of the House joined the Federation for American Immigration Reform in announcing that the suit would be filed Thursday in U.S. District Court in Pittsburgh.'\n    text = 'virtually enhancing the perception of user actions . This paper proposes using virtual reality to enhance the perception of actions by distant users on a shared application. Here, distance may refer either to space ( e.g. in a remote synchronous collaboration) or time ( e.g. during playback of recorded actions). Our approach consists in immersing the application in a virtual inhabited 3D space and mimicking user actions by animating avatars. We illustrate this approach with two applications, the one for remote collaboration on a shared application and the other to playback recorded sequences of user actions. We suggest this could be a low cost enhancement for telepresence'\n    # text = 'An interesting point-of-view. My baby support vector machine. A coalition of members of Congress announced Wednesday that they plan to sue the Census Bureau in an effort to force the agency to delete illegal aliens from its count in 1990. Some 40 members of the House joined the Federation for American Immigration Reform in announcing that the suit would be filed Thursday in U.S. District Court in Pittsburgh, spokesmen said at a news conference here. The group contends that including the estimated 2 million or more illegal aliens in the national head count, which is used to distribute seats in the House of Representatives, will cause unfair shifts of seats from one state to another. Census officials say they are required to count everyone by the U.S. Constitution, which does not mention citizenship but only instructs that the House apportionment be based on the ``whole number of persons'' residing in the various states. That approach was upheld by a federal court in a similar suit, brought by the same immigration reform group, before the 1980 Census. Nonetheless, Dan Stein of the immigration reform federation contended that illegal aliens should not be allowed to be part of determining the political structure of the United States. Rep. Tom Ridge, R-Pa., said the Census Bureau should actually count everyone but that it should develop a method to determine how many people are illegally in the country, and them deduct that number from the figures used for reapportioning Congress. Rep. Jan Meyers, R-Kan., suggested including a question on the Census form asking whether respondents are U.S. citizerns. '\n\n    print(text)\n    spacy_doc = spacy_nlp(text, disable=[\"textcat\"])\n\n    print('*' * 50)\n    print('noun_chunks_by_pos_regex'.upper())\n    nps = list(noun_chunks_by_pos_regex(spacy_doc, min_len=1, max_len=4))\n    print('#np =', len(nps))\n    for np_id, np in enumerate(nps):\n        print('[%d]' % np_id, np.text)\n\n    '''\n    print('*' * 50)\n    print('spacy_noun_chunks_all_nested'.upper())\n    nps = list(spacy_noun_chunks_all_nested(spacy_doc, remove_duplicate=True))\n    print('#np =', len(nps))\n    for np_id, np in enumerate(nps):\n        print('[%d]' % np_id, np.text)\n\n    print('*' * 50)\n    print('get_all_np'.upper())\n    nps = list(get_all_np(text, stem=False, return_set=True))\n    print('#np =', len(nps))\n    for np in nps:\n        print(np)\n\n    print('*' * 50)\n    print('spacy_noun_chunks - raw'.upper())\n    nps = list(spacy_noun_chunks_wrapper(text, trim_punct=False, remove_stopword=False))\n    print('#np =', len(nps))\n    for np_id, np in enumerate(nps):\n        print('[%d]' % np_id, np)\n\n    print('*' * 50)\n    print('spacy_noun_chunks - cleaned'.upper())\n    nps = list(spacy_noun_chunks_wrapper(text, trim_punct=True, remove_stopword=True))\n    print('#np =', len(nps))\n    for np_id, np in enumerate(nps):\n        print('[%d]' % np_id, np)\n    '''\n\ndef spacy_tokenize(text):\n    spacy_doc = spacy_nlp(text, disable=[\"textcat\"])\n    tokens = [token.text for token in spacy_doc]\n\n    return tokens\n\n\ndef check_NP_recallM():\n\n    datasets = ['duc', 'inspec', 'krapivin', 'nus', 'semeval', 'kp20k_valid2k', 'kp20k']\n    datasets = ['duc']\n\n    tokenize_fn = meng17_tokenize\n    tokenize_fn = spacy_tokenize\n\n    for dataset in datasets:\n        input_path = '/zfs1/hdaqing/rum20/kp/data/kp/json/%s/test.json' % (dataset)\n        # input_path = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/%s/%s_test.json' % (dataset, dataset)\n        # output_path = '/Users/memray/project/kp/OpenNMT-kpg/data/keyphrase/json/%s/%s_test_spacynp.json' % (dataset, dataset)\n\n        input_json = open(input_path, 'r')\n        # output_json = open(output_path, 'w')\n\n        present_tgt_num_list, absent_tgt_num_list = [], []\n        np_num_list = []\n        recall_list = []\n\n        for l in tqdm.tqdm(input_json):\n            doc = json.loads(l)\n            src_text = doc[\"title\"] + ' . ' + doc[\"abstract\"]\n\n            src_seq = tokenize_fn(src_text.lower())\n            stemmed_src = stem_word_list(src_seq)\n            tgt_seqs = [tokenize_fn(t) for t in doc[\"keywords\"].lower().split(';')]\n            stemmed_tgt_seqs = [stem_word_list(p) for p in tgt_seqs]\n\n            present_tgt_flags, _, _ = if_present_duplicate_phrases(stemmed_src, stemmed_tgt_seqs)\n            stemmed_present_tgts = [tgt for tgt, present in zip(stemmed_tgt_seqs, present_tgt_flags) if present]\n            stemmed_absent_tgts = [tgt for tgt, present in zip(stemmed_tgt_seqs, present_tgt_flags) if not present]\n            present_tgts_set = set(' '.join(p) for p in stemmed_present_tgts)\n\n            present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\n            absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if not present]\n\n            # np_set = get_all_np(' '.join(src_seq))\n\n            spacy_doc = spacy_nlp(src_text.lower(), disable=[\"textcat\"])\n            # spacy_nps = spacy_noun_chunks_all_nested(spacy_doc, remove_duplicate=True)\n            spacy_nps = noun_chunks_by_pos_regex(spacy_doc, min_len=1, max_len=5)\n            nps = [[t.text.lower() for t in np] for np in spacy_nps]\n            stemmed_nps = [' '.join(stem_word_list(p)) for p in nps]\n            np_set = set(stemmed_nps)\n\n            match_np = [p for p in np_set if p in present_tgts_set]\n            recall = len(match_np) / len(stemmed_present_tgts) if len(stemmed_present_tgts) > 0 else -1.0\n\n            np_num_list.append(len(np_set))\n            recall_list.append(recall)\n\n            present_tgt_num_list.append(len(stemmed_present_tgts))\n            absent_tgt_num_list.append(len(stemmed_absent_tgts))\n\n            output_dict = {'src': src_seq,\n                           'tgt_phrases': tgt_seqs, 'num_tgt': len(tgt_seqs),\n                           'present_tgt_phrases': present_tgts, 'num_present_tgt': len(present_tgts),\n                           'absent_tgt_phrases': absent_tgts, 'num_absent_tgt': len(absent_tgts),\n                           'noun_phrases': nps, 'num_np': len(np_set),\n                           'recall': recall}\n            doc.update(output_dict)\n            # output_json.write(json.dumps(doc)+'\\n')\n\n            print('*' * 50)\n            print(src_text)\n            print('len(tgt_seqs)= %d' % len(tgt_seqs))\n            print(tgt_seqs)\n            print('len(present_tgts)= %d' % len(present_tgts))\n            print(present_tgts)\n            print('len(absent_tgts)= %d' % len(absent_tgts))\n            print(absent_tgts)\n            print('len(np_set)= %d' % len(np_set))\n            print(np_set)\n            print('len(match_np)= %d' % len(match_np))\n            print(match_np)\n            print('recall=%.4f' % recall)\n\n            # break\n\n        present_tgt_num_list = [n for n in present_tgt_num_list if n > 0]\n        absent_tgt_num_list = [n for n in absent_tgt_num_list if n > 0]\n\n        num_data = len(recall_list)\n        recall_list = [r for r in recall_list if r > -1.0]\n        print('%s, #(dp)=%d, #(present_dp)=%d, '\n              'num_present_pred=%.4f, num_absent_pred=%.4f, '\n              'avgnum_NP=%.4f, recall=%.4f' %\n              (dataset, num_data, len(recall_list),\n               np.sum(present_tgt_num_list), np.sum(absent_tgt_num_list),\n               np.mean(np_num_list), np.mean(recall_list)))\n        # output_json.close()\n\n\ndef check_model_recallM():\n    one2one_eval_paths = [\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_46000-duc-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_50000-inspec-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_86000-krapivin-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_26000-nus-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_20000-semeval-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_26000-kp20k_valid2k-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam200-maxlen6/eval/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1_step_36000-kp20k-exhaustive.json\"\n        ]\n\n    one2seq_eval_paths = [\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_65000-duc-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_25000-inspec-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_80000-krapivin-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_45000-nus-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_55000-semeval-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_50000-kp20k_valid2k-exhaustive.json\",\n        \"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/eval/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_75000-kp20k-exhaustive.json\",\n        ]\n\n    for eval_path in one2one_eval_paths:\n\n        pred_basepath = eval_path[: eval_path.rfind('eval/')] + 'pred/'\n        dataset = eval_path[eval_path.rfind('-', 0, eval_path.rfind('-')) + 1: eval_path.rfind('-')]\n        model_name = eval_path[eval_path.rfind('/') + 1: eval_path.rfind(dataset) - 1]\n        pred_path = os.path.join(pred_basepath, model_name, '%s.pred' % dataset)\n        data_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/json/%s/%s_test.json' % (dataset, dataset)\n        print(pred_path)\n\n        tgt_num_list, pred_num_list = [], []\n        present_tgt_num_list, absent_tgt_num_list = [], []\n        present_pred_num_list, absent_pred_num_list = [], []\n        present_recall_list, absent_recall_list = [], []\n\n        with open(data_path, 'r') as data_json, open(pred_path, 'r') as pred_json:\n            for data_line, pred_line in tqdm.tqdm(zip(data_json, pred_json), desc=dataset):\n                doc_dict = json.loads(data_line)\n                pred_dict = json.loads(pred_line)\n\n                src_seq = meng17_tokenize(doc_dict[\"title\"] + ' . ' + doc_dict[\"abstract\"])\n                stemmed_src = stem_word_list(src_seq)\n                tgt_seqs = [meng17_tokenize(t) for t in doc_dict[\"keywords\"].lower().split(';')]\n                stemmed_tgt_seqs = [stem_word_list(p) for p in tgt_seqs]\n\n                present_tgt_flags, _, _ = if_present_duplicate_phrases(stemmed_src, stemmed_tgt_seqs)\n                stemmed_present_tgts = [tgt for tgt, present in zip(stemmed_tgt_seqs, present_tgt_flags) if present]\n                stemmed_absent_tgts = [tgt for tgt, present in zip(stemmed_tgt_seqs, present_tgt_flags) if not present]\n                present_tgts_set = set(' '.join(p) for p in stemmed_present_tgts)\n                absent_tgts_set = set(' '.join(p) for p in stemmed_absent_tgts)\n                present_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if present]\n                absent_tgts = [tgt for tgt, present in zip(tgt_seqs, present_tgt_flags) if not present]\n\n                pred_seqs = pred_dict['pred_sents']\n                stemmed_pred_seqs = [stem_word_list(p) for p in pred_seqs]\n                present_pred_flags, _, _ = if_present_duplicate_phrases(stemmed_src, stemmed_pred_seqs)\n                stemmed_present_preds = [pred for pred, present in zip(stemmed_pred_seqs, present_pred_flags) if present]\n                stemmed_absent_preds = [pred for pred, present in zip(stemmed_pred_seqs, present_pred_flags) if not present]\n                present_preds_set = set(' '.join(p) for p in stemmed_present_preds)\n                absent_preds_set = set(' '.join(p) for p in stemmed_absent_preds)\n                present_preds = [pred for pred, present in zip(pred_seqs, present_pred_flags) if present]\n                absent_preds = [pred for pred, present in zip(pred_seqs, present_pred_flags) if not present]\n\n                match_present_pred = [p for p in present_preds_set if p in present_tgts_set]\n                match_absent_pred = [p for p in absent_preds_set if p in absent_tgts_set]\n\n                present_recall = len(match_present_pred) / len(stemmed_present_tgts) if len(stemmed_present_tgts) > 0 else -1\n                absent_recall = len(match_absent_pred) / len(stemmed_absent_tgts) if len(stemmed_absent_tgts) > 0 else -1\n\n                tgt_num_list.append(len(tgt_seqs))\n                pred_num_list.append(len(pred_seqs))\n                present_tgt_num_list.append(len(stemmed_present_tgts))\n                absent_tgt_num_list.append(len(stemmed_absent_tgts))\n                present_pred_num_list.append(len(present_preds_set))\n                absent_pred_num_list.append(len(absent_preds_set))\n\n                present_recall_list.append(present_recall)\n                absent_recall_list.append(absent_recall)\n\n        num_data = len(present_recall_list)\n        # present_tgt_num_list = [n for n in present_pred_num_list if n > 0.0]\n        # absent_tgt_num_list = [n for n in absent_tgt_num_list if n > 0.0]\n        # present_pred_num_list = [n for n in present_pred_num_list if n > 0.0]\n        # absent_pred_num_list = [n for n in absent_pred_num_list if n > 0.0]\n        present_recall_list = [r for r in present_recall_list if r > -1.0]\n        absent_recall_list = [r for r in absent_recall_list if r > -1.0]\n        present_tgt_num_list = [n for n in present_tgt_num_list if n > 0]\n        absent_tgt_num_list = [n for n in absent_tgt_num_list if n > 0]\n\n        assert len(present_recall_list) == len(present_tgt_num_list)\n        assert len(absent_recall_list) == len(absent_tgt_num_list)\n\n        print('%s, #(dp)=%d, #(tgt)=%.4f, #(pred)=%.4f, \\n'\n              'num_present_doc=%d, avgnum_present_tgt=%.4f, avgnum_present_pred=%.4f, present_recall=%.4f, \\n'\n              'num_absent_doc=%d, avgnum_absent_tgt=%.4f, avgnum_absent_pred=%.4f, absent_recall=%.4f' %\n              (dataset, num_data, np.mean(tgt_num_list), np.mean(pred_num_list),\n               len(present_tgt_num_list), np.mean(present_tgt_num_list), np.mean(present_pred_num_list), np.mean(present_recall_list) if len(present_recall_list) > 0 else 0.0,\n               len(absent_tgt_num_list), np.mean(absent_tgt_num_list), np.mean(absent_pred_num_list), np.mean(absent_recall_list) if len(absent_recall_list) > 0 else 0.0\n               ))\n\n\ndef extract_np_mag():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-input_path', required=True)\n    parser.add_argument('-output_dir', required=True)\n\n    opt = parser.parse_args()\n\n    input_filename = opt.input_path.split('/')[-1]\n    output_path = os.path.join(opt.output_dir, input_filename)\n    if not os.path.exists(opt.output_dir): os.mkdir(opt.output_dir)\n\n    print('Extracting NP for %s' % opt.input_path)\n\n    with open(opt.input_path, 'r') as input_jsonl, open(output_path, 'w') as output_jsonl:\n        for l_id, l in enumerate(input_jsonl):\n            if l_id % 1000 == 0: print('%d' % l_id)\n            ex = json.loads(l)\n            src_text = ex['title'] + ' . ' + ex['abstract']\n            nps = noun_chunks_by_pos_regex(src_text, min_len=2, max_len=6)\n\n#             print(src_text)\n#             for np_id, np in enumerate(nps):\n#                 print('[%d]' % np_id, np)\n\n            # remove duplicates and write to file\n            np_set = set()\n            unique_nps = []\n            for np in nps:\n                _np = np.strip().lower()\n                if _np not in np_set:\n                    unique_nps.append(np)\n                    np_set.add(_np)\n            output_ex = {'pred_sents': unique_nps}\n            output_jsonl.write(json.dumps(output_ex) + '\\n')\n\nif __name__ == '__main__':\n    extract_np_mag()\n    # test_np()\n    # check_NP_recallM()\n    # check_model_recallM()\n"
  },
  {
    "path": "onmt/keyphrase/json_plus_process.py",
    "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport simplejson as json\nfrom nltk.tokenize import sent_tokenize\nimport nltk\nimport re\nfrom stanfordcorenlp import StanfordCoreNLP\nimport random\nfrom transformers import BertTokenizer\n\nstemmer = nltk.stem.porter.PorterStemmer()\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n\nstanfordnlp = None\n\n\ndef BertTokenizer(tokens):\n    bert_tokens = []\n    for token in tokens:\n        token_sp = tokenizer.tokenize(token)\n        for t in token_sp:\n            bert_tokens.append(t)\n    return bert_tokens\n\ndef if_present_phrase(src_str_tokens, phrase_str_tokens):\n    \"\"\"\n    :param src_str_tokens: a list of strings (words) of source text\n    :param phrase_str_tokens: a list of strings (words) of a phrase\n    :return:\n    \"\"\"\n    match_flag = False\n    match_pos_idx = -1\n    match_pos_idxs = []\n    match_pos_ends = []\n    keywords_tokenizes = []\n    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\n        match_flag = True\n        # iterate each word in target, if one word does not match, set match=False and break\n        for seq_idx, seq_w in enumerate(phrase_str_tokens):\n            src_w = src_str_tokens[src_start_idx + seq_idx]\n            if src_w != seq_w:\n                match_flag = False\n                break\n        if match_flag:\n            match_pos_idx = src_start_idx\n            match_pos_idxs.append(match_pos_idx)\n            match_pos_ends.append(match_pos_idx+len(phrase_str_tokens))\n            keywords_tokenizes.append(phrase_str_tokens)\n            #break\n    #print (match_pos_idxs)\n    return match_flag, match_pos_idxs, match_pos_ends, keywords_tokenizes\n\n\ndef recognise_nounchunks(tagged):\n    #from montylingua-2.1/ MontyREChunker.py\n    lookup=[]\n    only_words=[]\n    info_dict = tagged\n    file1 = list(map(lambda filename_dict: filename_dict[0], info_dict))\n    _montylingua_arr = list(map(lambda filename_dict: filename_dict[1], info_dict))\n    # filename_p = \"((PDT )?(DT |PRP[$] |WDT |WP[$] )(VBG |VBD |VBN |JJ |JJR |JJS |, |CC |NN |NNS |NNP |NNPS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    # groupnames1 = \"((PDT )?(JJ |JJR |JJS |, |CC |NN |NNS |NNP |NNPS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    # case1 = \"(\" + filename_p + \"|\" + groupnames1 + \"|EX |PRP |WP |WDT )\"\n    filename_p = \"((PDT )?(VBG |VBD |VBN |JJ |JJR |JJS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    case1 = \"(\" + filename_p+ \")\"\n    case1 = \"(\" + case1 + 'POS )?' + case1\n    case1 = ' ' + case1\n    case1 = re.compile(case1)\n    awk1 = 1\n\n    while awk1:\n        awk1 = 0\n        gawks = ' ' + ' '.join(_montylingua_arr) + ' '\n        groupnames_str = case1.search(gawks)\n\n        if groupnames_str:\n            awk1 = 1\n            info_str = len(gawks[:groupnames_str.start()].split())\n            cleaned_arr = len(gawks[groupnames_str.end():].split())\n            tagged_str = (info_str, len(_montylingua_arr) - cleaned_arr)\n            mores = file1[tagged_str[0]:tagged_str[1]]\n            popd_arr = _montylingua_arr[tagged_str[0]:tagged_str[1]]\n            cron_cleaned = ' '.join(\n                list(map(lambda filename_dict: mores[filename_dict] + '/' + popd_arr[filename_dict], range(len(mores)))))\n            only_word =  ' '.join(\n                list(map(lambda filename_dict: mores[filename_dict], range(len(mores)))))\n            stripped_str = 'NC_' + str(random.randint(0, 1000000000))\n            for stripped_dict in range(len(file1)):\n                if stripped_dict in range(tagged_str[0], tagged_str[1]):\n                    file1[stripped_dict] = 'bar'\n                    _montylingua_arr[stripped_dict] = stripped_str\n            lookup.append(cron_cleaned)\n            only_words.append(only_word)\n    return lookup, list(set(only_words))\n\n\ndef pos(text, model=\"stanfordnlp\", lowercase=False):\n    if model == 'stanfordnlp':\n        global stanfordnlp\n        if not stanfordnlp:\n            nlp = StanfordCoreNLP(r'/home/yingyi/Documents/tool/stanford-corenlp-full-2016-10-31')\n        return nlp.pos_tag(text)\n    else:\n        raise NotImplementedError\n\n\ndef listToStr(tokens):\n    sentence = \"\"\n    for token in tokens:\n        sentence = sentence+token+\" \"\n    return sentence.strip()\n\ndef meng17_tokenize(text):\n    '''\n    The tokenizer used in Meng et al. ACL 2017\n    parse the feed-in text, filtering and tokenization\n    keep [_<>,\\(\\)\\.\\'%], replace digits to <digit>, split by [^a-zA-Z0-9_<>,\\(\\)\\.\\'%]\n    :param text:\n    :return: a list of tokens\n    '''\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', text)\n    # pad spaces to the left and right of special punctuations\n    text = re.sub(r'[_<>,\\(\\)\\.\\'%]', ' \\g<0> ', text)\n    # tokenize by non-letters (new-added + # & *, but don't pad spaces, to make them as one whole word)\n    tokens = list(filter(lambda w: len(w) > 0, re.split(r'[^a-zA-Z0-9_<>,#&\\+\\*\\(\\)\\.\\']', text)))\n\n    return tokens\n\ndef keyword_stemmer(keywords):\n    keywords_tokenize = []\n    for keyword in keywords:\n        keyword_tokenize = meng17_tokenize(keyword.strip())\n        keyword_stemmer = [stemmer.stem(word.lower().strip()) for word in keyword_tokenize]\n        keywords_tokenize.append(keyword_stemmer)\n    return keywords_tokenize\n\ndef nounchunk_stemmer(nouns):\n    nounchunks_tokenize = []\n    for nounchunk in nouns:\n        nounchunk_tokenize = meng17_tokenize(nounchunk.strip())\n        nounchunk_stemmer = [stemmer.stem(word.lower().strip()) for word in nounchunk_tokenize]\n        nounchunks_tokenize.append(nounchunk_stemmer)\n    return nounchunks_tokenize\n\n\ndef macth_word(sentence_stemmer, word_stemmer):\n    match_flags = []\n    match_pos_idxs = []\n    match_pos_ends = []\n    words_tokenize = []\n    for word in word_stemmer:\n        match_flag, match_pos_idx, match_pos_end, word_tokenize = if_present_phrase(sentence_stemmer, word)\n        print (match_pos_idx)\n        match_flags.append(match_flag)\n        match_pos_idxs.append(match_pos_idx)\n        match_pos_ends.append(match_pos_end)\n        words_tokenize.append(word_tokenize)\n    return match_flags, match_pos_idxs, match_pos_ends, words_tokenize\n\nif __name__ == '__main__':\n    #fw = open(\"\",\"w\")\n    with open('/home/yingyi/Documents/kp20k/kp20k_test.json','r') as f:\n        lines = f.readlines()\n        for line in lines:\n            dict = json.loads(line)\n            abstarct = dict['abstract']\n            keywords = dict['keywords'].split(\";\")\n            print (keywords)\n            title = dict['title']\n            sentence = title.strip() + \". \" + abstarct.strip()\n            sentence_tokenize = meng17_tokenize(sentence)\n            pos_sentence = pos(listToStr(sentence_tokenize))\n            nounchunks, nouns = recognise_nounchunks(pos_sentence)\n            sentence_stemmer = [stemmer.stem(word.lower().strip()) for word in sentence_tokenize]\n            keywords_stemmer = keyword_stemmer(keywords)\n            nounchunks_stemmer = nounchunk_stemmer(nouns)\n\n\n            match_flags_key, match_position_idxs_key, match_pos_ends_key, keywords_exist = macth_word(sentence_stemmer, keywords_stemmer)\n            match_flags_noun, match_position_idxs_noun, match_pos_ends_noun, nounwords_exist = macth_word(sentence_stemmer, nounchunks_stemmer)\n\n            keywords_stemmer_bert = []\n            nounchunks_stemmer_bert = []\n            sentence_bert = BertTokenizer(sentence_stemmer)\n\n            for words in keywords_stemmer:\n                keyword_bert = BertTokenizer(words)\n                keywords_stemmer_bert.append(keyword_bert)\n            for words in nounchunks_stemmer:\n                noun_bert = BertTokenizer(words)\n                nounchunks_stemmer_bert.append(noun_bert)\n            print (keywords_stemmer_bert)\n            match_flags_key, match_position_idxs_key, match_pos_ends_key, keywords_exist = macth_word(sentence_bert, keywords_stemmer_bert)\n            match_flags_noun, match_position_idxs_noun, match_pos_ends_noun, nounwords_exist = macth_word(sentence_bert, nounchunks_stemmer_bert)\n\n            print ('match_position_idxs_key', match_position_idxs_key, match_pos_ends_key)\n\n\n\n\n\n\n\n            #keywords_tokenize, sentence_tokenize_ori, match_flags, match_pos_idxs = tokenize(sentence, keywords)\n\n\n\n\n\n"
  },
  {
    "path": "onmt/keyphrase/kpg_example.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport copy\nimport json\n\nfrom onmt.constants import ModelTask\nfrom onmt.keyphrase.eval import eval_and_print\nfrom onmt.keyphrase.pke.utils import compute_document_frequency\nfrom onmt.utils.parse import ArgumentParser\n\nexec('from __future__ import unicode_literals')\n\nimport os\nimport sys\nimport random\n\nmodule_path = os.path.abspath(os.path.join('../'))\nif module_path not in sys.path:\n    sys.path.append(module_path)\nmodule_path = os.path.abspath(os.path.join('../onmt'))\nif module_path not in sys.path:\n    sys.path.append(module_path)\n\nfrom onmt.translate.translator import build_translator\n\nfrom kp_gen_eval_transfer import _get_parser\nimport string\nimport onmt.keyphrase.pke as pke\n\nfrom nltk.corpus import stopwords\nstoplist = stopwords.words('english')\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\ndef extract_bartkp(ex_dict):\n    # Supervised Deep Keyphrase Model, using OpenNMT 2.x pipeline\n    parser = _get_parser()\n    config_path = '/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/config/transfer_kp/infer/keyphrase-one2seq-controlled.yml'\n    opt = parser.parse_args('-config %s' % (config_path))\n\n    ckpt_path = '/zfs1/pbrusilovsky/rum20/kp/openNMT-kpg-release-ckpt/bart-wiki-step40k-bs256.checkpoint_step_40000.pt'\n    opt.__setattr__('models', [ckpt_path])\n    opt.__setattr__('fairseq_model', True)\n    opt.__setattr__('encoder_type', 'bart')\n    opt.__setattr__('decoder_type', 'bart')\n    opt.__setattr__('pretrained_tokenizer', True)\n    opt.__setattr__('copy_attn', False)\n\n    opt.__setattr__('valid_batch_size', 1)\n    opt.__setattr__('batch_size_multiple', 1)\n    opt.__setattr__('bucket_size', 128)\n    opt.__setattr__('pool_factor', 256)\n\n    opt.__setattr__('beam_size', 1)\n    opt.__setattr__('gpu', 0)\n\n    if isinstance(opt.data, str): setattr(opt, 'data', json.loads(opt.data.replace('\\'', '\"')))\n    setattr(opt, 'data_task', ModelTask.SEQ2SEQ)\n    ArgumentParser._get_all_transform(opt)\n\n    translator = build_translator(opt, report_score=False)\n\n    num_pres, num_header, num_cat, num_seealso, num_infill = 5, 5, 5, 2, 0\n\n    control_prefix = '<present>%d<header>%d<category>%d<seealso>%d<infill>%d<s>' \\\n                     % (num_pres, num_header, num_cat, num_seealso, num_infill)\n\n    new_ex_dict = copy.copy(ex_dict)\n    new_ex_dict['src_control_prefix'] = control_prefix\n\n    scores, preds = translator.translate(\n        src=[new_ex_dict],\n        batch_size=opt.batch_size,\n        attn_debug=opt.attn_debug,\n        opt=opt\n    )\n\n    src_text = new_ex_dict['title'] + ' . ' + new_ex_dict['abstract']\n    printout = eval_and_print(src_text, tgt_kps=ex_dict['keywords'], pred_kps=preds[0], pred_scores=scores[0])\n    print(printout)\n\n\ndef extract_deepkp_deprecated(text_to_extract):\n    # Supervised Deep Keyphrase Model, using OpenNMT 1.x pipeline\n    parser = _get_parser()\n    config_path = '../config/translate/config-rnn-keyphrase.yml'\n    one2one_ckpt_path = '../models/keyphrase/meng17-one2one-kp20k-topmodels/kp20k-meng17-one2one-rnn-BS128-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covfalse-Contboth-IF1_step_30000.pt'\n    one2seq_ckpt_path = '../models/keyphrase/meng17-one2seq-kp20k-topmodels/kp20k-meng17-verbatim_append-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_50000.pt'\n    opt = parser.parse_args('-config %s' % (config_path))\n    setattr(opt, 'models', [one2one_ckpt_path])\n\n    # start generating\n    translator = build_translator(opt, report_score=False)\n    scores, predictions = translator.translate(\n        src=[text_to_extract],\n        tgt=None,\n        src_dir=opt.src_dir,\n        batch_size=opt.batch_size,\n        attn_debug=opt.attn_debug,\n        opt=opt\n    )\n    # print results\n    print('Paragraph:\\n\\t' + text_to_extract)\n    print('Top predictions:')\n    keyphrases = [kp.strip() for kp in predictions[0] if (not kp.lower().strip() in stoplist) and (kp != '<unk>')]\n    for kp_id, kp in enumerate(keyphrases[: min(len(keyphrases), 20)]):\n        print('\\t%d: %s' % (kp_id + 1, kp.strip(string.punctuation)))\n\n\ndef extract_pke(text, method, dataset_path=None, df_path=None, top_k=10):\n    method = method.lower()\n    if method == 'tfidf':\n        # 0. check if DF file exists\n        if not os.path.exists(df_path):\n            # stoplist for filtering n-grams\n            stoplist = list(string.punctuation)\n\n            # compute df counts and store as n-stem -> weight values\n            compute_document_frequency(input_dir=dataset_path,\n                                       output_file=df_path,\n                                       extension='xml',  # input file extension\n                                       language='en',  # language of files\n                                       normalization=\"stemming\",  # use porter stemmer\n                                       stoplist=stoplist)\n\n        # 1. create a TfIdf extractor.\n        extractor = pke.unsupervised.TfIdf()\n\n        # 2. load the content of the document.\n        extractor.load_document(input=text,\n                                language='en_core_web_sm',\n                                normalization=None)\n\n        # 3. select {1-3}-grams not containing punctuation marks as candidates.\n        extractor.candidate_selection(n=3, stoplist=list(string.punctuation))\n\n        # 4. weight the candidates using a `tf` x `idf`\n        df = pke.load_document_frequency_file(input_file=df_path)\n        extractor.candidate_weighting(df=df)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=top_k)\n    elif method == 'yake':\n        stoplist = stopwords.words('english')\n        # 1. create a YAKE extractor.\n        extractor = pke.unsupervised.YAKE()\n\n        # 2. load the content of the document.\n        extractor.load_document(input=text,\n                                language='en_core_web_sm',\n                                normalization=None)\n\n        # 3. select {1-3}-grams not containing punctuation marks and not\n        #    beginning/ending with a stopword as candidates.\n        extractor.candidate_selection(n=3, stoplist=stoplist)\n\n        # 4. weight the candidates using YAKE weighting scheme, a window (in\n        #    words) for computing left/right contexts can be specified.\n        window = 2\n        use_stems = False  # use stems instead of words for weighting\n        extractor.candidate_weighting(window=window,\n                                      stoplist=stoplist,\n                                      use_stems=use_stems)\n\n        # 5. get the 10-highest scored candidates as keyphrases.\n        #    redundant keyphrases are removed from the output using levenshtein\n        #    distance and a threshold.\n        threshold = 0.8\n        keyphrases = extractor.get_n_best(n=top_k, threshold=threshold)\n    else:\n        raise NotImplementedError\n\n\n    for kp_id, kp in enumerate(keyphrases):\n        print('\\t%d: %s (%.4f)' % (kp_id + 1, kp[0], kp[1]))\n\n    return keyphrases\n\n\nif __name__ == '__main__':\n    dataset_name = 'stackex'\n    dataset_path = '/zfs1/hdaqing/rum20/kp/data/kp/json/%s/test.json' % dataset_name\n\n    with open(dataset_path, 'r') as f:\n        ex_dicts = [json.loads(l) for l in f.readlines()]\n        for ex in ex_dicts:\n            if dataset_name.startswith('openkp'):\n                ex['title'] = ''\n                ex['abstract'] = ex['text']\n                ex['keywords'] = ex['KeyPhrases']\n                ex['dataset_type'] = 'webpage'\n            elif dataset_name.startswith('stackex'):\n                ex['abstract'] = ex['question']\n                ex['keywords'] = ex['tags'].split(';')\n                ex['dataset_type'] = 'qa'\n            elif dataset_name.startswith('kp20k') or dataset_name.startswith('duc'):\n                ex['keywords'] = ex['keywords'].split(';') if isinstance(ex['keywords'], str) else ex['keywords']\n                ex['dataset_type'] = 'scipaper'\n            elif dataset_name.startswith('kptimes') or dataset_name.startswith('jptimes'):\n                ex['keywords'] = ex['keywords'].split(';') if isinstance(ex['keywords'], str) else ex['keywords']\n                ex['dataset_type'] = 'news'\n            else:\n                raise NotImplementedError(f'Not supported dataset type: {dataset_name}.')\n\n    print('Loaded #(docs)=%d' % (len(ex_dicts)))\n    doc_id = random.randint(0, len(ex_dicts))\n    doc_id = 4399\n    ex_dict = ex_dicts[doc_id]\n    print(doc_id)\n\n    extract_bartkp(ex_dict)\n    # extract_pke(text_to_extract, method='tfidf' , dataset_path=dataset_path,\n    #             df_path=os.path.abspath(dataset_path + '../%s.df.tsv.gz' % dataset_name))\n\n"
  },
  {
    "path": "onmt/keyphrase/kpg_example_hfdatasets.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport copy\nimport json\nfrom collections import defaultdict\n\nfrom onmt.constants import ModelTask\nfrom onmt.keyphrase.eval import eval_and_print\nfrom onmt.utils.parse import ArgumentParser\n\nexec('from __future__ import unicode_literals')\n\nimport os\nimport sys\nimport numpy as np\n\nmodule_path = os.path.abspath(os.path.join('../'))\nif module_path not in sys.path:\n    sys.path.append(module_path)\nmodule_path = os.path.abspath(os.path.join('../onmt'))\nif module_path not in sys.path:\n    sys.path.append(module_path)\n\nfrom onmt.translate.translator import build_translator\n\nfrom kp_gen_eval_transfer import _get_parser\n\nfrom nltk.corpus import stopwords\nstoplist = stopwords.words('english')\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\nimport datasets\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\nif __name__ == '__main__':\n    #########################\n    # 1. specify the config file used for inference, located at config/transfer_kp/infer/\n    #   - One2One models: keyphrase-one2one.yml\n    #   - One2Seq models: keyphrase-one2one.yml\n    #   - wiki-pretrained models: keyphrase-one2seq-controlled.yml\n    #########################\n    config_path = '../../config/transfer_kp/infer/keyphrase-one2seq-controlled.yml'\n    parser = _get_parser()\n    opt = parser.parse_args('-config %s' % (config_path))\n\n    # set up arguments for inference\n    opt.__setattr__('use_given_inputs', True)\n    opt.__setattr__('valid_batch_size', 16)\n    opt.__setattr__('beam_size', 8)\n    opt.__setattr__('gpu', 0) # cuda index, set -1 to use cpu\n\n    # prefix for wiki models, to control the number of phrases of each type. Otherwise, set the prefix to empty\n    num_pres, num_header, num_cat, num_seealso, num_infill = 5, 5, 5, 2, 0\n    control_prefix = '<present>%d<header>%d<category>%d<seealso>%d<infill>%d<s>' \\\n                     % (num_pres, num_header, num_cat, num_seealso, num_infill)\n\n    #########################\n    # 2. load KPG models from pretrained checkpoints\n    #    Some pretrained ckpts are available at https://huggingface.co/memray/opennmt-kpg/tree/main\n    #########################\n    ckpt_path = '/zfs1/pbrusilovsky/rum20/kp/openNMT-kpg-release-ckpt/opennmt-kpg-v2/wiki-pretrained/bart-wiki-step40k-bs256.checkpoint_step_40000.pt'\n    IS_BART_CKPT = True\n    # ckpt_path = '/zfs1/pbrusilovsky/rum20/kp/openNMT-kpg-release-ckpt/opennmt-kpg-v2/wiki-pretrained/transformer-wiki-step200k.checkpoint_step_200000.pt'\n    # ckpt_path = '/zfs1/pbrusilovsky/rum20/kp/openNMT-kpg-release-ckpt/opennmt-kpg-v2/transformer-presabs-step200k/transformer_presabs_kp20k.checkpoint_step_95000.pt'\n    # IS_BART_CKPT = False\n    opt.__setattr__('models', [ckpt_path])\n\n    if IS_BART_CKPT:\n        opt.__setattr__('fairseq_model', True)\n        opt.__setattr__('encoder_type', 'bart')\n        opt.__setattr__('decoder_type', 'bart')\n        opt.__setattr__('pretrained_tokenizer', True)\n        opt.__setattr__('copy_attn', False)\n\n    # initialize translator\n    setattr(opt, 'data_task', ModelTask.SEQ2SEQ)\n    ArgumentParser._get_all_transform(opt)\n    translator = build_translator(opt, report_score=False)\n\n    # load dataset\n    dataset_name = 'midas/inspec'\n    kp_dataset = datasets.load_dataset(dataset_name, name='raw', split='test')\n\n    #########################\n    # 3. start inference\n    #########################\n    print('Loaded #(docs)=%d' % (len(kp_dataset)))\n    srcs, tgts, ex_dicts = [], [], []\n    for eid, ex_dict in enumerate(kp_dataset):\n        ex_dict['src_control_prefix'] = control_prefix\n        src = ' '.join(ex_dict['document'])\n        tgt = ex_dict['extractive_keyphrases'] + ex_dict['abstractive_keyphrases']\n        ex_dict['src'] = src\n        ex_dict['tgt'] = tgt\n        ex_dicts.append(ex_dict)\n        srcs.append(src)\n        tgts.append(tgt)\n\n    scores, preds = translator.translate(\n        ex_dicts, opt=opt\n    )\n\n    #########################\n    # 4. start evaluation\n    #########################\n    eval_results = []\n    avg_scores = defaultdict(list)\n    for src, tgt, score, pred in zip(srcs, tgts, scores, preds):\n        printout, eval_result = eval_and_print(src, tgt_kps=tgt, pred_kps=pred, pred_scores=score, return_eval=True)\n        print(printout)\n        eval_results.append(eval_result)\n        for gname, group in eval_result.items():\n            for metric, score in group.items():\n                avg_scores[f'{gname}-{metric}'].append(score)\n\n    print(\"\\n =======================================================\")\n    print(f'Summary of scores on {dataset_name}')\n    for metric, scores in avg_scores.items():\n        print('\\t{}\\t=\\t\\t{:.4f}'.format(metric, np.mean(scores)))\n    print(\"\\n =======================================================\")\n"
  },
  {
    "path": "onmt/keyphrase/mag/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\n\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    pass"
  },
  {
    "path": "onmt/keyphrase/mag/export_mag_cs.py",
    "content": "'''\nfilter MAG data by fos (field of study)\n'''\nimport argparse\nimport json\nimport os\n\nimport logging\nimport re\nfrom functools import partial\n\nlogging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)\n\ncs_fos = set(\n    ['computer science', 'algorithm', 'artificial intelligence', 'bioinformatics',\n    'computational science', 'computer architecture', 'computer engineering', 'computer graphics',\n    'computer hardware', 'computer network', 'computer security', 'computer vision',\n    'data mining', 'data science', 'database', 'distributed computing',\n    'embedded system', 'embedded system', 'electronic engineering', 'electrical engineering' \n     'human–computer interaction',\n    'information retrieval', 'internet privacy', 'knowledge management', 'library science',\n    'machine learning', 'multimedia', 'natural language processing',\n    'operating system', 'parallel computing', 'pattern recognition', 'programming language',\n    'real-time computing', 'simulation', 'software engineering', 'speech recognition',\n    'telecommunications', 'theoretical computer science', 'text mining',\n     'world wide web']\n)\n\ndef mag2kp(mag_ex, id_field, title_field, text_field, keyword_field):\n    if title_field not in mag_ex or text_field not in mag_ex:\n        return None\n\n    id_str = mag_ex[id_field]\n\n    title = mag_ex[title_field].strip()\n    abstract = mag_ex[text_field].strip()\n\n    # split keywords to a list\n    if keyword_field in mag_ex:\n        keyphrases = [k.strip() for k in mag_ex[keyword_field]]\n    else:\n        keyphrases = []\n\n    example = {\n        \"id\": id_str,\n        \"title\": title,\n        \"abstract\": abstract,\n        \"keywords\": keyphrases,\n        \"fos\": mag_ex['fos'],\n    }\n\n    return example\n\ndef extract_papers(input_dir, output_dir, chunk_size, lang, must_have_kp=False):\n    file_count = 0\n    paper_count = 0\n    domain_paper_count = 0\n    assert chunk_size > 0\n\n    mag2kp_fn = partial(mag2kp,\n                        id_field = 'id',\n                        title_field = 'title',\n                        text_field = 'abstract',\n                        keyword_field = 'keywords')\n\n    if not os.path.exists(output_dir): os.makedirs(output_dir)\n\n    file_list = [fn for fn in os.listdir(input_dir) if fn.startswith('mag_papers_')]\n    file_list = sorted(file_list, key=lambda x: int(re.search('mag_papers_(.*?)\\.txt', x).group(1)))\n    output_file_path = os.path.join(output_dir, 'train_%d.json' % (domain_paper_count // chunk_size))\n    output_file = open(output_file_path, 'w')\n\n    try:\n        for txt_file in file_list:\n            input_file_path = os.path.join(input_dir, txt_file)\n            print(input_file_path)\n            file_count += 1\n\n            with open(input_file_path, 'r') as input_file:\n                for line in input_file:\n                    paper_count+=1\n                    if paper_count % 10000==0:\n                        logging.info('The {:} th File:{:}, total progress: {:}/{:} papers in CS fos'.format(file_count, input_file_path, domain_paper_count, paper_count))\n                    mag_ex = json.loads(line)\n                    if 'fos' not in mag_ex or mag_ex.get('lang') != lang:\n                        continue\n\n                    if must_have_kp and 'keywords' not in mag_ex:\n                        continue\n\n                    is_cs = any([True if f.lower().strip() in cs_fos else False for f in mag_ex['fos']])\n                    if is_cs:\n                        kp_ex = mag2kp_fn(mag_ex)\n                        if kp_ex is None:\n                            continue\n                        output_file.write(json.dumps(kp_ex)+'\\n')\n                        domain_paper_count += 1\n\n                        if domain_paper_count % chunk_size == 0:\n                            output_file.close()\n                            output_file_path = os.path.join(output_dir, 'train_%d.json' % (domain_paper_count // chunk_size))\n                            output_file = open(output_file_path, 'w')\n    finally:\n        output_file.close()\n    logging.info('Process finished \\n\\t'\n                 'Find {:}/{:} CS papers in {:} MAG files: {}\\n\\t'\n                 'Dumped to {}'.format(domain_paper_count, paper_count, file_count, input_dir, output_dir\n    ))\n\n\ndef main():\n    ''' Main function '''\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-mag_input_dir', required=True)\n    parser.add_argument('-mag_output_dir', required=True)\n    parser.add_argument('-must_have_kp', action='store_true')\n    parser.add_argument('-chunk_size', default=1000000, type=int)\n    parser.add_argument('-lang', required=True)\n\n    opt = parser.parse_args()\n\n    extract_papers(opt.mag_input_dir, opt.mag_output_dir, opt.chunk_size, opt.lang, must_have_kp=opt.must_have_kp)\n\n    print('[Info] Dumping the processed data to new text file', opt.mag_output_dir)\n    print('[Info] Finish.')\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "onmt/keyphrase/mag/post_clean.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nRemove noisy items (abstract contains \"Full textFull text is available as a scanned copy of the original print version.\") (around 132561 out of 3114539=2981978) and remove duplicates by title\n\"\"\"\nimport codecs\nimport json\nimport os\nimport re\nimport string\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\ndef example_iterator_from_json(path, dataset_name, id_field, title_field, text_field, keyword_field, trg_delimiter=';', is_train=False):\n    '''\n    Load id/title/abstract/keyword, don't do any preprocessing\n    ID is required to match the original data\n    '''\n    global valid_num\n    print(\"Loading %s\" % os.path.abspath(path))\n\n    with codecs.open(path, \"r\", \"utf-8\") as corpus_file:\n        for idx, line in enumerate(corpus_file):\n            # if(idx == 2000):\n            #     break\n            # print(line)\n\n            _json = json.loads(line)\n\n            if id_field is None or id_field not in _json:\n                id_str = '%s_%d' % (dataset_name, idx)\n            else:\n                id_str = _json[id_field]\n\n            if is_train and title_field not in _json or keyword_field not in _json or text_field not in _json:\n                # print(\"Data is missing:\\n%s\" % (_json))\n                continue\n\n            title_str = _json[title_field].strip(string.punctuation)\n            abstract_str = _json[text_field].strip(string.punctuation)\n\n            # split keywords to a list\n            if trg_delimiter:\n                keyphrase_strs = [k.strip(string.punctuation) for k in re.split(trg_delimiter, _json[keyword_field])\n                                  if len(k.strip(string.punctuation)) > 0]\n            else:\n                keyphrase_strs = [k.strip(string.punctuation) for k in _json[keyword_field]\n                                  if len(k.strip(string.punctuation)) > 0]\n\n            if is_train and abstract_str.startswith('Full textFull text'):\n                continue\n\n            if is_train and len(title_str) == 0 or len(abstract_str) == 0 or len(keyphrase_strs) == 0:\n                continue\n\n            example = {\n                \"id\": id_str,\n                \"title\": title_str,\n                \"abstract\": abstract_str,\n                \"keywords\": keyphrase_strs,\n            }\n\n            valid_num += 1\n            yield example\n\nif __name__ == '__main__':\n    mag_path = \"source_data/mag_output/mag_nodup.json\"\n    mag_output_path = \"source_data/mag_output/mag_nodup_plus.json\"\n    kp20k_train_path = \"source_data/kp20k/kp20k_training.json\"\n\n    train_dataset_name = 'mag'\n    test_dataset_names = ['kp20k_train']\n    id_field = 'id'\n    title_field = 'title'\n    text_field = 'abstract'\n    keyword_field = 'keywords'\n    trg_delimiter = None\n\n    mag_examples_iter = list(example_iterator_from_json(path=mag_path,\n                                                     dataset_name=\"mag\",\n                                                     id_field=id_field,\n                                                     title_field=title_field,\n                                                     text_field=text_field,\n                                                     keyword_field=keyword_field,\n                                                     trg_delimiter=trg_delimiter))\n    print(\"Loaded %d examples from MAG\" % len(mag_examples_iter))\n\n    id_field = None\n    keyword_field = 'keywords'\n    trg_delimiter = ';'\n    kp20k_train_examples = list(example_iterator_from_json(path=kp20k_train_path,\n                                                    dataset_name=\"kp20k_train\",\n                                                    id_field=id_field,\n                                                    title_field=title_field,\n                                                    text_field=text_field,\n                                                    keyword_field=keyword_field,\n                                                    trg_delimiter=trg_delimiter))\n\n    print(\"Loaded %d examples from KP20k train\" % len(kp20k_train_examples))\n\n    title_pool = set()\n    for ex in kp20k_train_examples:\n        title_pool.add(ex[\"title\"].lower().strip())\n\n    non_dup_count = 0\n    with open(mag_output_path, 'w') as mag_output:\n        for ex_id, ex in enumerate(mag_examples_iter):\n            title = ex[\"title\"].lower().strip()\n            if title not in title_pool:\n                non_dup_count += 1\n                title_pool.add(title)\n                mag_output.write(json.dumps(ex) + '\\n')\n                if ex_id % 1000 == 0:\n                    print(\"non-dup/processed/all = %d/%d/%d\" % (non_dup_count, ex_id, len(mag_examples_iter)))\n\n"
  },
  {
    "path": "onmt/keyphrase/migrated/README",
    "content": "Migrated from seq2seq-keyphrase-pytorch\n"
  },
  {
    "path": "onmt/keyphrase/migrated/post_evaluate.py",
    "content": "import json\nimport logging\nfrom nltk.stem.porter import *\nimport numpy as np\n\nimport os\nimport sys\n\nfrom pykp import io\nfrom pykp.io import load_json_data\n\n\ndef check_if_present(source_tokens, targets_tokens):\n    target_present_flags = []\n    for target_tokens in targets_tokens:\n        # whether do filtering on groundtruth phrases.\n        present = False\n        for i in range(len(source_tokens) - len(target_tokens) + 1):\n            match = None\n            for j in range(len(target_tokens)):\n                if target_tokens[j] != source_tokens[i + j]:\n                    match = False\n                    break\n            if len(target_tokens) > 0 and j == len(target_tokens) - 1 and match == None:\n                present = True\n                break\n\n        target_present_flags.append(present)\n    assert len(target_present_flags) == len(targets_tokens)\n\n    return target_present_flags\n\ndef get_match_flags(targets, predictions):\n    match_flags = np.asarray([0] * len(predictions), dtype='int32')\n    for pid, predict in enumerate(predictions):\n        for stemmed_target in targets:\n            if len(stemmed_target) == len(predict):\n                match_flag = True\n                for i, w in enumerate(predict):\n                    if predict[i] != stemmed_target[i]:\n                        match_flag = False\n                if match_flag:\n                    match_flags[pid] = 1\n                    break\n    return match_flags\n\ndef evaluate_(source_str_list, targets_str_list, prediction_str_list,\n              model_name, dataset_name,\n              filter_criteria='present',\n              matching_after_stemming=True,\n              output_path=None):\n    '''\n    '''\n    assert filter_criteria in ['absent', 'present', 'all']\n    stemmer = PorterStemmer()\n\n    if output_path != None:\n        if not os.path.exists(output_path):\n            os.makedirs(output_path)\n        if not os.path.exists(os.path.join(output_path, model_name)):\n            os.makedirs(os.path.join(output_path, model_name))\n\n    json_writer = open(os.path.join(output_path, model_name, '%s.json' % dataset_name), 'w+')\n    score_csv_path = os.path.join(output_path, 'all_scores.csv')\n    csv_writer = open(score_csv_path, 'a')\n\n    print('Evaluating on %s@%s' % (model_name, dataset_name))\n    # Evaluation part\n    macro_metrics = []\n    macro_matches = []\n\n    total_source_length = 0\n    length_groundtruth = []\n    length_groundtruth_for_evaluate = []\n    number_groundtruth = []\n    number_groundtruth_for_evaluate = []\n    total_number_groundtruth = 0\n    total_number_groundtruth_for_evaluate = 0\n    total_groundtruth_set = set()\n    total_groundtruth_set_for_evaluate = set()\n\n    # remove the empty targets first\n    new_targets_str_list = []\n    for targets_str in targets_str_list:\n        new_targets_str = []\n        for target_str in targets_str:\n            if len(target_str.strip()) > 0:\n                new_targets_str.append(target_str.strip())\n        new_targets_str_list.append(new_targets_str)\n\n    targets_str_list = new_targets_str_list\n\n    real_test_size = 0\n\n    \"\"\"\n    Iterate each document\n    \"\"\"\n    for doc_id, (source_text, targets, predictions)\\\n            in enumerate(zip(source_str_list, targets_str_list, prediction_str_list)):\n        # print(targets)\n        # print(predictions)\n        # print('*' * 100)\n\n        # if doc_id > 5:\n        #     break\n        if doc_id + 1 % 1000 == 0:\n            print(doc_id)\n\n        '''\n        stem all texts/targets/predictions\n        '''\n        stemmed_source_text_tokens = [stemmer.stem(t).strip().lower() for t in io.copyseq_tokenize(source_text)]\n        stemmed_targets_tokens = [[stemmer.stem(w).strip().lower() for w in io.copyseq_tokenize(target)] for target in targets]\n        stemmed_predictions_tokens = [[stemmer.stem(w).strip().lower() for w in io.copyseq_tokenize(prediction)] for prediction in predictions]\n\n        '''\n        check and filter targets/predictions by whether it appear in source text\n        '''\n        if filter_criteria != 'all':\n            if matching_after_stemming:\n                source_tokens_to_match = stemmed_source_text_tokens\n                targets_tokens_to_match = stemmed_targets_tokens\n                predictions_tokens_to_match = stemmed_predictions_tokens\n            else:\n                source_tokens_to_match = io.copyseq_tokenize(source_text.strip().lower())\n                targets_tokens_to_match = [io.copyseq_tokenize(target.strip().lower()) for target in targets]\n                predictions_tokens_to_match = [io.copyseq_tokenize(prediction.strip().lower()) for prediction in predictions]\n\n            target_present_flags = check_if_present(source_tokens_to_match, targets_tokens_to_match)\n            prediction_present_flags = check_if_present(source_tokens_to_match, predictions_tokens_to_match)\n\n            if filter_criteria == 'present':\n                targets_valid_flags = target_present_flags\n                prediction_valid_flags = prediction_present_flags\n            elif filter_criteria == 'absent':\n                targets_valid_flags = [not f for f in target_present_flags]\n                prediction_valid_flags = [not f for f in prediction_present_flags]\n\n            targets_for_evaluate = np.asarray(targets)[targets_valid_flags].tolist()\n            stemmed_targets_for_evaluate = np.asarray(stemmed_targets_tokens)[targets_valid_flags].tolist()\n            predictions_for_evaluate = np.asarray(predictions)[prediction_valid_flags].tolist()\n            stemmed_predictions_for_evaluate = np.asarray(stemmed_predictions_tokens)[prediction_valid_flags].tolist()\n\n        else:\n            targets_for_evaluate = targets\n            stemmed_targets_for_evaluate = stemmed_targets_tokens\n            predictions_for_evaluate = predictions\n            stemmed_predictions_for_evaluate = stemmed_predictions_tokens\n\n        total_source_length += len(source_tokens_to_match)\n        total_number_groundtruth += len(targets)\n        total_number_groundtruth_for_evaluate += len(targets_for_evaluate)\n\n        number_groundtruth.append(len(targets))\n        number_groundtruth_for_evaluate.append(len(targets_for_evaluate))\n\n        for target in targets:\n            total_groundtruth_set.add(' '.join(target))\n            length_groundtruth.append(len(target))\n        for target in targets_for_evaluate:\n            total_groundtruth_set_for_evaluate.add(' '.join(target))\n            length_groundtruth_for_evaluate.append(len(target))\n\n        if len(targets_for_evaluate) > 0:\n            real_test_size += 1\n\n        # \"\"\"\n        '''\n        check each prediction if it can match any ground-truth target\n        '''\n        valid_predictions_match_flags = get_match_flags(stemmed_targets_for_evaluate, stemmed_predictions_for_evaluate)\n        predictions_match_flags = get_match_flags(stemmed_targets_for_evaluate, stemmed_predictions_tokens)\n        '''\n        Compute metrics\n        '''\n        metric_dict = {}\n        for number_to_predict in [5, 10]:\n            metric_dict['target_number'] = len(targets_for_evaluate)\n            metric_dict['prediction_number'] = len(predictions_for_evaluate)\n            metric_dict['correct_number@%d' % number_to_predict] = sum(valid_predictions_match_flags[:number_to_predict])\n\n            # Precision\n            metric_dict['p@%d' % number_to_predict] = float(sum(valid_predictions_match_flags[:number_to_predict])) / float(\n                number_to_predict)\n\n            # Recall\n            if len(targets_for_evaluate) != 0:\n                metric_dict['r@%d' % number_to_predict] = float(sum(valid_predictions_match_flags[:number_to_predict])) \\\n                                                          / float(len(targets_for_evaluate))\n            else:\n                metric_dict['r@%d' % number_to_predict] = 0\n\n            # F-score\n            if metric_dict['p@%d' % number_to_predict] + metric_dict['r@%d' % number_to_predict] != 0:\n                metric_dict['f1@%d' % number_to_predict] = 2 * metric_dict['p@%d' % number_to_predict] * metric_dict[\n                    'r@%d' % number_to_predict] / float(\n                    metric_dict['p@%d' % number_to_predict] + metric_dict['r@%d' % number_to_predict])\n            else:\n                metric_dict['f1@%d' % number_to_predict] = 0\n\n            # Bpref: binary preference measure\n            bpref = 0.\n            trunked_match = valid_predictions_match_flags[:number_to_predict].tolist()  # get the first K prediction to evaluate\n            match_indexes = np.nonzero(trunked_match)[0]\n\n            if len(match_indexes) > 0:\n                for mid, mindex in enumerate(match_indexes):\n                    bpref += 1. - float(mindex - mid) / float(\n                        number_to_predict)  # there're mindex elements, and mid elements are correct, before the (mindex+1)-th element\n                metric_dict['bpref@%d' % number_to_predict] = float(bpref) / float(len(match_indexes))\n            else:\n                metric_dict['bpref@%d' % number_to_predict] = 0\n\n            # MRR: mean reciprocal rank\n            rank_first = 0\n            try:\n                rank_first = trunked_match.index(1) + 1\n            except ValueError:\n                pass\n\n            if rank_first > 0:\n                metric_dict['mrr@%d' % number_to_predict] = float(1) / float(rank_first)\n            else:\n                metric_dict['mrr@%d' % number_to_predict] = 0\n\n        macro_metrics.append(metric_dict)\n        macro_matches.append(valid_predictions_match_flags)\n\n        '''\n        Print information on each prediction\n        '''\n        print_out = '[DOC_ID] %d\\n' % doc_id\n        print_out += '[SOURCE][{0}]: {1}\\n'.format(len(source_text) , source_text)\n        print_out += '[STEMMED SOURCE][{0}]: {1}'.format(len(stemmed_source_text_tokens) , ' '.join(stemmed_source_text_tokens))\n        print_out += '\\n'\n\n        print_out += '[TARGET]: %d/%d valid/all targets\\n' % (len(targets_for_evaluate), len(targets))\n        for target, stemmed_target, targets_valid_flag in zip(targets, stemmed_targets_tokens, targets_valid_flags):\n            if targets_valid_flag:\n                print_out += '\\t\\t%s (%s)\\n' % (target, ' '.join(stemmed_target))\n        for target, stemmed_target, targets_valid_flag in zip(targets, stemmed_targets_tokens, targets_valid_flags):\n            if not targets_valid_flag:\n                print_out += '\\t\\t[ABSENT]%s (%s)\\n' % (target, ' '.join(stemmed_target))\n\n        print_out += '\\n'\n\n        num_correct_5 = sum(predictions_match_flags[:5]) if len(predictions_match_flags) >=5 else sum(predictions_match_flags)\n        num_correct_10 = sum(predictions_match_flags[:10]) if len(predictions_match_flags) >=10 else sum(predictions_match_flags)\n        print_out += '[DECODE]: %d/%d valid/all predictions, #(correct@5)=%d, #(correct@10)=%d' \\\n            % (len(predictions_for_evaluate), len(predictions), num_correct_5, num_correct_10)\n        for prediction, stemmed_prediction, prediction_present_flag, predictions_match_flag \\\n                in zip(predictions, stemmed_predictions_tokens, prediction_present_flags, predictions_match_flags):\n            if prediction_present_flag:\n                print_out += ('\\n\\t\\t%s (%s)' % (prediction, ' '.join(stemmed_prediction)))\n            else:\n                print_out += ('\\n\\t\\t[ABSENT]%s (%s)' % (prediction, ' '.join(stemmed_prediction)))\n            if predictions_match_flag == 1:\n                print_out += ' [correct!]'\n        # c += '\\n'\n        # for prediction, stemmed_prediction, prediction_present_flag, predictions_match_flag \\\n        #         in zip(predictions, stemmed_predictions_tokens, prediction_present_flags, predictions_match_flags):\n        #     if not prediction_present_flag:\n        #         c += ('\\n\\t\\t[ABSENT]%s (%s)' % (prediction, ' '.join(stemmed_prediction)))\n        #         if predictions_match_flag == 1:\n        #             c += ' [correct!]'\n\n\n        # c = '[DECODE]: {}'.format(' '.join(cut_zero(phrase, idx2word)))\n        # if inputs_unk is not None:\n        #     k = '[_INPUT]: {}\\n'.format(' '.join(cut_zero(inputs_unk.tolist(),  idx2word, Lmax=len(idx2word))))\n        #     logger.info(k)\n        # a += k\n\n        for number_to_predict in [5, 10]:\n            print_out += '@%d - Precision=%.4f, Recall=%.4f, F1=%.4f, Bpref=%.4f, MRR=%.4f' % (\n            number_to_predict, metric_dict['p@%d' % number_to_predict], metric_dict['r@%d' % number_to_predict],\n            metric_dict['f1@%d' % number_to_predict], metric_dict['bpref@%d' % number_to_predict], metric_dict['mrr@%d' % number_to_predict])\n\n        # logger.info(print_out)\n        # logger.info('*' * 100)\n\n        out_dict = {}\n        out_dict['src_str'] = source_text\n        out_dict['trg_str'] = targets\n        out_dict['trg_present_flag'] = target_present_flags\n        out_dict['pred_str'] = predictions\n        out_dict['pred_score'] = [0.0] * len(predictions)\n        out_dict['present_flag'] = prediction_present_flags\n        out_dict['valid_flag'] = [True] * len(predictions)\n        out_dict['match_flag'] = [float(m) for m in predictions_match_flags]\n\n        # print(out_dict)\n\n        json_writer.write(json.dumps(out_dict)+'\\n')\n\n        assert len(out_dict['trg_str']) == len(out_dict['trg_present_flag'])\n        assert len(out_dict['pred_str']) == len(out_dict['present_flag']) \\\n               == len(out_dict['valid_flag']) == len(out_dict['match_flag']) == len(out_dict['pred_score'])\n        # \"\"\"\n\n    logger.info('Avg(Source Text Length)=%.4f' % (float(total_source_length) / len(source_str_list)))\n    logger.info('#(Target)=%d' % (len(length_groundtruth)))\n    logger.info('Avg(Target Length)=%.4f' % (np.mean(length_groundtruth)))\n    logger.info('#(%s Target)=%d' % (filter_criteria.upper(), len(length_groundtruth_for_evaluate)))\n    logger.info('Avg(%s Target Length)=%.4f' % (filter_criteria.upper(), np.mean(length_groundtruth_for_evaluate)))\n\n    logger.info('#(Ground-truth Keyphrase)=%d' % total_number_groundtruth)\n    logger.info('#(%s Ground-truth Keyphrase)=%d' % (filter_criteria.upper(), total_number_groundtruth_for_evaluate))\n    logger.info('Avg(Ground-truth Keyphrase)=%.4f' % (float(total_number_groundtruth) / len(source_str_list)))\n    logger.info('Avg(%s Ground-truth Keyphrase)=%.4f' % (filter_criteria.upper(),\n                                                     float(total_number_groundtruth_for_evaluate) / len(source_str_list)))\n\n    logger.info('#(Unique Ground-truth Keyphrase)=%d' % (len(total_groundtruth_set)))\n    logger.info('#(Unique %s Ground-truth Keyphrase)=%d' % (filter_criteria.upper(), len(total_groundtruth_set_for_evaluate)))\n\n    logger.info('Avg(Ground-truth Keyphrase)=%.4f' % (np.mean(number_groundtruth)))\n    logger.info('Var(Ground-truth Keyphrase)=%.4f' % (np.var(number_groundtruth)))\n    logger.info('Std(Ground-truth Keyphrase)=%.4f' % (np.std(number_groundtruth)))\n\n    logger.info('Avg(%s Ground-truth Keyphrase)=%.4f' % (filter_criteria.upper(), np.mean(number_groundtruth_for_evaluate)))\n    logger.info('Var(%s Ground-truth Keyphrase)=%.4f' % (filter_criteria.upper(), np.var(number_groundtruth_for_evaluate)))\n    logger.info('Std(%s Ground-truth Keyphrase)=%.4f' % (filter_criteria.upper(), np.std(number_groundtruth_for_evaluate)))\n\n    '''\n    Export the f@5 and f@10 for significance test\n    '''\n    # for k in [5, 10]:\n    #     with open(config['predict_path'] + '/macro-f@%d-' % (k) + model_name+'-'+dataset_name+'.txt', 'w') as writer:\n    #         writer.write('\\n'.join([str(m['f1@%d' % k]) for m in macro_metrics]))\n\n    # \"\"\"\n    '''\n    Compute the corpus evaluation\n    '''\n    overall_score = {}\n\n    for k in [5, 10]:\n        correct_number = sum([m['correct_number@%d' % k] for m in macro_metrics])\n        overall_target_number = sum([m['target_number'] for m in macro_metrics])\n        overall_prediction_number = sum([m['prediction_number'] for m in macro_metrics])\n\n        if real_test_size * k < overall_prediction_number:\n            overall_prediction_number = real_test_size * k\n\n        overall_score['target_number'] = sum([m['target_number'] for m in macro_metrics])\n        overall_score['correct_number@%d' % k] = sum([m['correct_number@%d' % k] for m in macro_metrics])\n        overall_score['prediction_number@%d' % k] = overall_prediction_number\n\n        # Compute the macro Measures, by averaging the macro-score of each prediction\n        overall_score['p@%d' % k] = float(sum([m['p@%d' % k] for m in macro_metrics])) / float(real_test_size)\n        overall_score['r@%d' % k] = float(sum([m['r@%d' % k] for m in macro_metrics])) / float(real_test_size)\n        overall_score['f1@%d' % k] = float(sum([m['f1@%d' % k] for m in macro_metrics])) / float(real_test_size)\n\n        # Print basic statistics\n        logger.info('%s@%s' % (model_name, dataset_name))\n        output_str = 'Overall - valid testing data=%d, Number of Target=%d/%d, ' \\\n                     'Number of Prediction=%d, Number of Correct=%d' % (\n                    real_test_size,\n                    overall_target_number, total_number_groundtruth,\n                    overall_prediction_number, correct_number\n        )\n        logger.info(output_str)\n\n        # Print macro-average performance\n        output_str = 'macro:\\t\\tP@%d=%f, R@%d=%f, F1@%d=%f' % (\n                    k, overall_score['p@%d' % k],\n                    k, overall_score['r@%d' % k],\n                    k, overall_score['f1@%d' % k]\n        )\n        logger.info(output_str)\n\n        # Compute the binary preference measure (Bpref)\n        overall_score['bpref@%d' % k] = float(sum([m['bpref@%d' % k] for m in macro_metrics])) / float(real_test_size)\n\n        # Compute the mean reciprocal rank (MRR)\n        overall_score['mrr@%d' % k] = float(sum([m['mrr@%d' % k] for m in macro_metrics])) / float(real_test_size)\n\n        output_str = '\\t\\t\\tBpref@%d=%f, MRR@%d=%f' % (\n                    k, overall_score['bpref@%d' % k],\n                    k, overall_score['mrr@%d' % k]\n        )\n        logger.info(output_str)\n\n    csv_writer.write('%s, %s, '\n                     '%d, %d, %d, %d, %d, %d, '\n                     '%f, %f, %f, %f, %f, '\n                     '%f, %f, %f, %f, %f\\n' % (\n                model_name, dataset_name,\n                len(source_str_list), real_test_size,\n                total_number_groundtruth, total_number_groundtruth_for_evaluate,\n                overall_score['correct_number@%d' % 5], overall_score['correct_number@%d' % 10],\n\n                overall_score['p@%d' % 5],\n                overall_score['r@%d' % 5],\n                overall_score['f1@%d' % 5],\n                overall_score['bpref@%d' % 5],\n                overall_score['mrr@%d' % 5],\n\n                overall_score['p@%d' % 10],\n                overall_score['r@%d' % 10],\n                overall_score['f1@%d' % 10],\n                overall_score['bpref@%d' % 10],\n                overall_score['mrr@%d' % 10]\n    ))\n\n    json_writer.close()\n    csv_writer.close()\n    # \"\"\"\n\ndef init_logging(logfile):\n    formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(module)s: %(message)s',\n                                  datefmt='%m/%d/%Y %H:%M:%S')\n    fh = logging.FileHandler(logfile)\n    # ch = logging.StreamHandler()\n    # ch = logging.StreamHandler(sys.stdout)\n\n    fh.setFormatter(formatter)\n    # ch.setFormatter(formatter)\n    # fh.setLevel(logging.INFO)\n    # ch.setLevel(logging.INFO)\n    # logging.getLogger().addHandler(ch)\n    logging.getLogger().addHandler(fh)\n    logging.getLogger().setLevel(logging.INFO)\n\n    return logging\n\n\ndef load_predictions_from_file(prediction_dir, file_suffix='.txt'):\n    predictions_str_dict = {}\n\n    for pred_file_name in os.listdir(prediction_dir):\n        if not pred_file_name.endswith(file_suffix):\n            continue\n        doc_id = pred_file_name[: pred_file_name.find(file_suffix)]\n        prediction_str_list = []\n        with open(os.path.join(prediction_dir, pred_file_name), 'r') as pred_file:\n            for line in pred_file:\n                prediction_str_list.append(line.strip())\n        predictions_str_dict[doc_id] = prediction_str_list\n    sorted_predictions_str_dict = sorted(predictions_str_dict.items(), key=lambda k:k[0])\n    doc_ids = [d[0] for d in sorted_predictions_str_dict]\n    predictions_str_list = [d[1] for d in sorted_predictions_str_dict]\n    # print(doc_ids)\n    return predictions_str_list\n\n\ndef load_plain_text(text_dir):\n    source_text_dict = {}\n\n    for source_file_name in os.listdir(text_dir):\n        if not source_file_name.endswith('.txt'):\n            continue\n        doc_id = source_file_name[: source_file_name.find('.txt')]\n        with open(os.path.join(text_dir, source_file_name), 'r') as pred_file:\n            text = ' '.join([l.strip() for l in pred_file.readlines()])\n            # postag = [t.split('_')[1] for t in text_tokens]\n            source_text_dict[doc_id] = text\n\n    sorted_source_text_dict = sorted(source_text_dict.items(), key=lambda k:k[0])\n    doc_ids = [d[0] for d in sorted_source_text_dict]\n    source_text_list = [d[1] for d in sorted_source_text_dict]\n    # print(doc_ids)\n\n    return source_text_list\n\n\ndef load_postag_text(postag_text_dir):\n    source_text_dict = {}\n\n    for source_file_name in os.listdir(postag_text_dir):\n        if not source_file_name.endswith('.txt'):\n            continue\n        doc_id = source_file_name[: source_file_name.find('.txt')]\n        with open(os.path.join(postag_text_dir, source_file_name), 'r') as pred_file:\n            text_tokens = (' '.join(pred_file.readlines())).split()\n            text = ' '.join([t.split('_')[0] for t in text_tokens])\n            # postag = [t.split('_')[1] for t in text_tokens]\n            source_text_dict[doc_id] = text\n\n    sorted_source_text_dict = sorted(source_text_dict.items(), key=lambda k:k[0])\n    doc_ids = [d[0] for d in sorted_source_text_dict]\n    source_text_list = [d[1] for d in sorted_source_text_dict]\n    # print(doc_ids)\n\n    return source_text_list\n\n\ndef evaluate_baselines(models, test_sets, output_dir, filter_criteria, plain_or_postag):\n    '''\n    evaluate baselines' performance\n    plain_or_postag: specify the source of source text. The postag text leads to more bias, but this is the way used in Meng 17.\n    :return:\n    '''\n    base_dir = 'prediction/'\n    print(os.path.abspath(base_dir))\n\n    if not os.path.exists(os.path.join(base_dir, output_dir)):\n        os.makedirs(os.path.join(base_dir, output_dir))\n    score_csv_path = os.path.join(base_dir, output_dir, 'all_scores.csv')\n    with open(score_csv_path, 'w+') as csv_writer:\n        csv_writer.write('model, data, '\n                         '#doc, #valid_doc, #tgt, #valid_tgt, #corr@5, #corr@10, '\n                         'p@5, r@5, f1@5, bpref@5, mrr@5, '\n                         'p@10, r@10, f1@10, bpref@10, mrr@10\\n')\n\n    for model_name in models:\n        for dataset_name in test_sets:\n\n            if dataset_name == 'stackexchange':\n                src_fields = ['title', 'question']\n                trg_fields = ['tags']\n                valid_check = True\n            elif dataset_name == 'twacg':\n                src_fields = ['observation']\n                trg_fields = ['admissible_commands']\n            else:\n                src_fields = ['title', 'abstract']\n                trg_fields = ['keyword']\n\n            if plain_or_postag == 'postag':\n                postag_text_dir = '/Users/memray/Project/keyphrase/seq2seq-keyphrase/dataset/keyphrase/baseline-data/%s/text' % dataset_name\n                source_str_list = load_postag_text(postag_text_dir)\n            elif plain_or_postag == 'plain':\n                plain_text_dir = '/Users/memray/Project/keyphrase/seq2seq-keyphrase/dataset/keyphrase/baseline-data/%s/plain_text' % dataset_name\n                source_str_list = load_plain_text(plain_text_dir)\n            else:\n                raise NotImplementedError\n\n            targets_dir = '/Users/memray/Project/keyphrase/seq2seq-keyphrase/dataset/keyphrase/baseline-data/%s/keyphrase' % dataset_name\n            targets_str_list = load_predictions_from_file(targets_dir, file_suffix='.txt')\n            prediction_dir = os.path.join(base_dir, model_name, dataset_name)\n\n            if not os.path.exists(prediction_dir):\n                print('Folder not found: %s' % prediction_dir)\n                continue\n\n            prediction_str_list = load_predictions_from_file(prediction_dir, file_suffix='.txt.phrases')\n            # prediction_str_list = [[] for i in range(len(targets_str_list))]\n            print(dataset_name)\n            print('#(src)=%d' % len(source_str_list))\n            print('#(tgt)=%d' % len(targets_str_list))\n            print('#(preds)=%d' % len(prediction_str_list))\n            evaluate_(source_str_list, targets_str_list, prediction_str_list, model_name, dataset_name, filter_criteria,\n                      matching_after_stemming = True,\n                      output_path=os.path.join(base_dir, output_dir))\n\n            #if model_name == 'Maui':\n            #    prediction_dir = '/Users/memray/Project/seq2seq-keyphrase/dataset/keyphrase/baseline-data/maui/maui_output/' + dataset_name\n            #if model_name == 'Kea':\n            #    prediction_dir = '/Users/memray/Project/seq2seq-keyphrase/dataset/keyphrase/baseline-data/maui/kea_output/' + dataset_name\n\n\n\"\"\"\ndef significance_test():\n    model1 = 'CopyRNN'\n    models = ['TfIdf', 'TextRank', 'SingleRank', 'ExpandRank', 'RNN', 'CopyRNN']\n\n    test_sets = config['testing_datasets']\n\n    def load_result(filepath):\n        with open(filepath, 'r') as reader:\n            return [float(l.strip()) for l in reader.readlines()]\n\n    for model2 in models:\n        print('*'*20 + '  %s Vs. %s  ' % (model1, model2) + '*' * 20)\n        for dataset_name in test_sets:\n            for k in [5, 10]:\n                print('Evaluating on %s@%d' % (dataset_name, k))\n                filepath = config['predict_path'] + '/macro-f@%d-' % (k) + model1 + '-' + dataset_name + '.txt'\n                val1 = load_result(filepath)\n                filepath = config['predict_path'] + '/macro-f@%d-' % (k) + model2 + '-' + dataset_name + '.txt'\n                val2 = load_result(filepath)\n                s_test = scipy.stats.wilcoxon(val1, val2)\n                print(s_test)\n\"\"\"\n\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger()\nprint('Log path: %s' % (os.path.abspath('prediction/post_evaluate.log')))\nlogger = init_logging(os.path.abspath('prediction/post_evaluate.log'))\n\nif __name__ == '__main__':\n    # 'TfIdf', 'TextRank', 'SingleRank', 'ExpandRank', 'Maui', 'KEA', 'RNN_present', 'CopyRNN_present_singleword=0', 'CopyRNN_present_singleword=1', 'CopyRNN_present_singleword=2'\n    filter_criteria = 'present' # we don't have absent predictions for these models actually\n    # models = ['TfIdf', 'TextRank', 'SingleRank', 'ExpandRank', 'Maui', 'KEA', 'CopyRNN_meng17']\n    models = ['CopyRNN_meng17']\n\n    test_sets = ['duc', 'kp20k']\n    # test_sets = ['duc', 'inspec', 'nus', 'semeval', 'krapivin', 'kp20k']\n    # test_sets = ['stackexchange']\n    # test_sets = ['twacg']\n\n    # plain is only available for ['duc', 'kp20k']\n    plain_or_postag = 'plain'\n    # plain_or_postag = 'postag'\n\n    evaluate_baselines(models, test_sets, output_dir='output_json_%s_20190415' % plain_or_postag,\n                       filter_criteria=filter_criteria, plain_or_postag=plain_or_postag)\n    # significance_test()\n"
  },
  {
    "path": "onmt/keyphrase/move_ckpt_by_devscore.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nLoad averaged results from csv, containing scores of all ckpts.\nFor each exp group, return the best ckpt (ranked by valid performance).\n\"\"\"\nimport shutil\n\nimport configargparse\nimport os\n\nimport pandas as pd\n\nfrom kp_evaluate import gather_eval_results\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\ntrain_test_mappings = {\n    # 'kp20k': ['kp20k', 'kp20k_valid2k', 'inspec', 'krapivin', 'semeval', 'nus', 'duc'],\n    'kp20k': ['kp20k', 'kp20k_valid2k', 'duc'],\n    'openkp': ['openkp', 'openkp_valid2k', 'duc'],\n    'kptimes': ['kptimes', 'kptimes_valid2k', 'jptimes', 'duc'],\n    'stackex': ['stackex', 'stackex_valid2k', 'duc'],\n}\n\ntrain_dev_pairs = [\n    ('kp20k', 'kp20k_valid2k'),\n    ('openkp', 'openkp_valid2k'),\n    ('kptimes', 'kptimes_valid2k'),\n    ('stackex', 'stackex_valid2k'),\n]\n\ndev_test_pairs = [\n    ('kp20k_valid2k', 'kp20k'),\n    ('openkp_valid2k', 'openkp'),\n    ('kptimes_valid2k', 'kptimes'),\n    ('kptimes_valid2k', 'jptimes'),\n    ('kptimes_valid2k', 'duc'),\n    ('stackex_valid2k', 'stackex'),\n]\n\ndef main():\n    parser = configargparse.ArgumentParser()\n    parser.add_argument('-exp_base_dir', type=str, required=True, help='source ckpt/pred/eval files.')\n    parser.add_argument('-export_base_dir', type=str, required=True, help='The best ckpt/pred/eval files will be copied to this place.')\n    parser.add_argument('-decoding_method', type=str, required=True, help='Filter by decoding_method, since there exists results by multiple decoding settings from the same ckpt, like beamsearch-width_50-maxlen_40.')\n    parser.add_argument('-accept_unfinished', action='store_true', help='if inference job is not all done, ignore this group')\n    opt = parser.parse_args()\n\n    for exp_name in os.listdir(opt.exp_base_dir):\n        exp_dir = os.path.join(opt.exp_base_dir, exp_name)\n        ckpt_dir = os.path.join(exp_dir, 'ckpts')\n        if not os.path.exists(ckpt_dir): continue\n        ckpts = [ckpt_file for ckpt_file in os.listdir(ckpt_dir) if ckpt_file.endswith('0.pt')]\n        pred_dir = os.path.join(exp_dir, 'outputs', opt.decoding_method, 'pred')\n\n        print('*' * 50)\n        print('EXP name: %s' % exp_name)\n        print('#ckpts=%d' % len(ckpts))\n\n        export_ckpt_dir = os.path.join(opt.export_base_dir, exp_name, 'ckpts')\n        export_pred_dir = os.path.join(opt.export_base_dir, exp_name, 'outputs', opt.decoding_method, 'pred')\n        # if os.path.exists(export_pred_dir) and len(os.listdir(export_pred_dir)) > 0:\n        #     print('Skip: already found %d files in export dir: \\n\\t\\t\\t%s' % (len(os.listdir(export_pred_dir)), export_pred_dir))\n        #     continue\n\n        train_name, dev_name = None, None\n        for train, dev in train_dev_pairs:\n            if train in exp_name:\n                train_name = train\n                dev_name = dev\n                break\n\n        if train_name is None:\n            print('Trainset not found. Not a common experiment name? %s' % exp_name)\n            continue\n\n        if not os.path.exists(pred_dir):\n            continue\n\n        pred_files = [pred_file for pred_file in os.listdir(pred_dir) if pred_file.endswith('.pred')]\n        dataset_scores_dict = gather_eval_results(pred_dir, report_csv_dir=None, tokenizer='split_nopunc')\n        # for dataset_name, score_df in dataset_scores_dict.items():\n        for dataset_name in train_test_mappings[train_name]:\n            dataset_split_name = dataset_name + '_test'\n            score_df = dataset_scores_dict[dataset_split_name] if dataset_split_name in dataset_scores_dict else []\n            _pred_files = [filename for filename in pred_files if dataset_split_name in filename]\n            print('\\t#pred %s=%d' % (dataset_name, len(_pred_files)))\n            print('\\t#eval %s=%d' % (dataset_name, len(score_df)))\n\n        pred_files = [filename for filename in pred_files if dev_name+'_test' in filename]\n        dev_df = dataset_scores_dict[dev_name+'_test'] if dev_name+'_test' in dataset_scores_dict else None\n        if not opt.accept_unfinished and (dev_df is None or len(dev_df) < len(pred_files) or len(pred_files) < len(ckpts)):\n            print('Inference on devset (%s) has not accomplished: pred=%d/%d, eval=%d/%d '\n                  % (dev_name,\n                     len(pred_files), len(ckpts),\n                     len(dev_df) if dev_df is not None else 0, len(pred_files)))\n            continue\n\n        if dev_df is None or len(dev_df) == 0:\n            print('No inference found on devset (%s)')\n            continue\n\n        # pick up the best ckpt by dev score\n        anchor_metric_name = 'all_exact_f_score@k'\n        dev_df = dev_df.sort_values(by=anchor_metric_name, ascending=False)\n        dev_row = dev_df.iloc[0].to_frame().transpose()\n\n        print('best dev score: %s=%.4f' % (anchor_metric_name, dev_row[anchor_metric_name]))\n        best_step = dev_row.step.item()\n        best_ckpt_name_prefix = 'checkpoint_step_%s-' % best_step\n        best_ckpt_filename = 'checkpoint_step_%s.pt' % best_step\n\n        print('-' * 20)\n\n        if not os.path.exists(export_ckpt_dir): os.makedirs(export_ckpt_dir)\n        if not os.path.exists(export_pred_dir): os.makedirs(export_pred_dir)\n\n        src_ckpt_path = os.path.join(ckpt_dir, best_ckpt_filename)\n        if best_ckpt_filename in ckpts and os.path.exists(src_ckpt_path):\n            print('Copy checkpoint file: %s' % best_ckpt_filename)\n            tgt_ckpt_path = os.path.join(export_ckpt_dir, best_ckpt_filename)\n            if os.path.exists(tgt_ckpt_path):\n                print('Checkpoint file exists, skip: %s' % tgt_ckpt_path)\n            else:\n                shutil.copyfile(src_ckpt_path, tgt_ckpt_path)\n        else:\n            print('Checkpoint file not found: %s, path=%s' % (best_ckpt_filename, src_ckpt_path))\n\n        for predeval_file in os.listdir(pred_dir):\n            if predeval_file.startswith(best_ckpt_name_prefix):\n                print('Copy pred/eval file: %s' % predeval_file)\n                src_pred_path = os.path.join(pred_dir, predeval_file)\n                tgt_pred_path = os.path.join(export_pred_dir, predeval_file)\n                shutil.copyfile(src_pred_path, tgt_pred_path)\n\nif __name__ == '__main__':\n    main()"
  },
  {
    "path": "onmt/keyphrase/pke/__init__.py",
    "content": "from __future__ import absolute_import\n\nfrom onmt.keyphrase.pke.data_structures import Candidate, Document, Sentence\nfrom onmt.keyphrase.pke.readers import MinimalCoreNLPReader, RawTextReader\nfrom onmt.keyphrase.pke.base import LoadFile\nfrom onmt.keyphrase.pke.utils import (load_document_frequency_file, compute_document_frequency,\n                       train_supervised_model, load_references,\n                       compute_lda_model, load_document_as_bos,\n                       compute_pairwise_similarity_matrix)\nimport onmt.keyphrase.pke.unsupervised\nimport onmt.keyphrase.pke.supervised\n"
  },
  {
    "path": "onmt/keyphrase/pke/base.py",
    "content": "# -*- coding: utf-8 -*-\n\n\"\"\"Base classes for the pke module.\"\"\"\n\nfrom collections import defaultdict\n\nfrom onmt.keyphrase.pke.data_structures import Candidate, Document\nfrom onmt.keyphrase.pke.readers import MinimalCoreNLPReader, RawTextReader\n\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk import RegexpParser\nfrom nltk.corpus import stopwords\nfrom nltk.tag.mapping import map_tag\n\nfrom string import punctuation\nimport os\nimport logging\nimport codecs\n\nfrom six import string_types\n\nfrom builtins import str\n\nISO_to_language = {'en': 'english', 'pt': 'portuguese', 'fr': 'french',\n                   'es': 'spanish', 'it': 'italian', 'nl': 'dutch',\n                   'de': 'german',  'en_core_web_sm': 'english'}\n\nescaped_punctuation = {'-lrb-': '(', '-rrb-': ')', '-lsb-': '[', '-rsb-': ']',\n                       '-lcb-': '{', '-rcb-': '}'}\n\n\nclass LoadFile(object):\n    \"\"\"The LoadFile class that provides base functions.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializer for LoadFile class.\"\"\"\n\n        self.input_file = None\n        \"\"\"Path to the input file.\"\"\"\n\n        self.language = None\n        \"\"\"Language of the input file.\"\"\"\n\n        self.normalization = None\n        \"\"\"Word normalization method.\"\"\"\n\n        self.sentences = []\n        \"\"\"Sentence container (list of Sentence objects).\"\"\"\n\n        self.candidates = defaultdict(Candidate)\n        \"\"\"Keyphrase candidates container (dict of Candidate objects).\"\"\"\n\n        self.weights = {}\n        \"\"\"Weight container (can be either word or candidate weights).\"\"\"\n\n        self._models = os.path.join(os.path.dirname(__file__), 'models')\n        \"\"\"Root path of the models.\"\"\"\n\n        self._df_counts = os.path.join(self._models, \"df-semeval2010.tsv.gz\")\n        \"\"\"Path to the document frequency counts provided in pke.\"\"\"\n\n        self.stoplist = None\n        \"\"\"List of stopwords.\"\"\"\n\n    def load_document(self, input, **kwargs):\n        \"\"\"Loads the content of a document/string/stream in a given language.\n\n        Args:\n            input (str): input.\n            language (str): language of the input, defaults to 'en'.\n            encoding (str): encoding of the raw file.\n            normalization (str): word normalization method, defaults to\n                'stemming'. Other possible values are 'lemmatization' or 'None'\n                for using word surface forms instead of stems/lemmas.\n        \"\"\"\n\n        # get the language parameter\n        language = kwargs.get('language', 'en_core_web_sm')\n\n        # test whether the language is known, otherwise fall back to english\n        # if language not in ISO_to_language:\n        #     logging.warning(\n        #         \"ISO 639 code {} is not supported, switching to 'en'.\".format(\n        #             language))\n        #     language = 'en'\n\n        # initialize document\n        doc = Document()\n\n        if isinstance(input, string_types):\n\n            # if input is an input file\n            if os.path.isfile(input):\n\n                # an xml file is considered as a CoreNLP document\n                if input.endswith('xml'):\n                    parser = MinimalCoreNLPReader()\n                    doc = parser.read(path=input, **kwargs)\n                    doc.is_corenlp_file = True\n\n                # other extensions are considered as raw text\n                else:\n                    parser = RawTextReader(language=language)\n                    encoding = kwargs.get('encoding', 'utf-8')\n                    with codecs.open(input, 'r', encoding=encoding) as file:\n                        text = file.read()\n                    doc = parser.read(text=text, path=input, **kwargs)\n\n            # if input is a string\n            else:\n                parser = RawTextReader(language=language)\n                doc = parser.read(text=input, **kwargs)\n\n        elif getattr(input, 'read', None):\n            # check whether it is a compressed CoreNLP document\n            name = getattr(input, 'name', None)\n            if name and name.endswith('xml'):\n                parser = MinimalCoreNLPReader()\n                doc = parser.read(path=input, **kwargs)\n                doc.is_corenlp_file = True\n            else:\n                parser = RawTextReader(language=language)\n                doc = parser.read(text=input.read(), **kwargs)\n\n        else:\n            logging.error('Cannot process {}'.format(type(input)))\n\n        # set the input file\n        self.input_file = doc.input_file\n\n        # set the language of the document\n        self.language = language\n\n        # set the sentences\n        self.sentences = doc.sentences\n\n        # initialize the stoplist\n        self.stoplist = stopwords.words(ISO_to_language[self.language])\n\n        # word normalization\n        self.normalization = kwargs.get('normalization', 'stemming')\n        if self.normalization == 'stemming':\n            self.apply_stemming()\n        elif self.normalization is None:\n            for i, sentence in enumerate(self.sentences):\n                self.sentences[i].stems = sentence.words\n\n        # lowercase the normalized words\n        for i, sentence in enumerate(self.sentences):\n            self.sentences[i].stems = [w.lower() for w in sentence.stems]\n\n        # POS normalization\n        if getattr(doc, 'is_corenlp_file', False):\n            self.normalize_pos_tags()\n            self.unescape_punctuation_marks()\n\n    def apply_stemming(self):\n        \"\"\"Populates the stem containers of sentences.\"\"\"\n\n        if self.language == 'en':\n            # create a new instance of a porter stemmer\n            stemmer = SnowballStemmer(\"porter\")\n        else:\n            # create a new instance of a porter stemmer\n            stemmer = SnowballStemmer(ISO_to_language[self.language],\n                                      ignore_stopwords=True)\n\n        # iterate throughout the sentences\n        for i, sentence in enumerate(self.sentences):\n            self.sentences[i].stems = [stemmer.stem(w) for w in sentence.words]\n\n    def normalize_pos_tags(self):\n        \"\"\"Normalizes the PoS tags from udp-penn to UD.\"\"\"\n\n        if self.language == 'en':\n            # iterate throughout the sentences\n            for i, sentence in enumerate(self.sentences):\n                self.sentences[i].pos = [map_tag('en-ptb', 'universal', tag)\n                                         for tag in sentence.pos]\n\n    def unescape_punctuation_marks(self):\n        \"\"\"Replaces the special punctuation marks produced by CoreNLP.\"\"\"\n\n        for i, sentence in enumerate(self.sentences):\n            for j, word in enumerate(sentence.words):\n                l_word = word.lower()\n                self.sentences[i].words[j] = escaped_punctuation.get(l_word,\n                                                                     word)\n\n    def is_redundant(self, candidate, prev, minimum_length=1):\n        \"\"\"Test if one candidate is redundant with respect to a list of already\n        selected candidates. A candidate is considered redundant if it is\n        included in another candidate that is ranked higher in the list.\n\n        Args:\n            candidate (str): the lexical form of the candidate.\n            prev (list): the list of already selected candidates (lexical\n                forms).\n            minimum_length (int): minimum length (in words) of the candidate\n                to be considered, defaults to 1.\n        \"\"\"\n\n        # get the tokenized lexical form from the candidate\n        candidate = self.candidates[candidate].lexical_form\n\n        # only consider candidate greater than one word\n        if len(candidate) < minimum_length:\n            return False\n\n        # get the tokenized lexical forms from the selected candidates\n        prev = [self.candidates[u].lexical_form for u in prev]\n\n        # loop through the already selected candidates\n        for prev_candidate in prev:\n            for i in range(len(prev_candidate) - len(candidate) + 1):\n                if candidate == prev_candidate[i:i + len(candidate)]:\n                    return True\n        return False\n\n    def get_n_best(self, n=10, redundancy_removal=False, stemming=False):\n        \"\"\"Returns the n-best candidates given the weights.\n\n        Args:\n            n (int): the number of candidates, defaults to 10.\n            redundancy_removal (bool): whether redundant keyphrases are\n                filtered out from the n-best list, defaults to False.\n            stemming (bool): whether to extract stems or surface forms\n                (lowercased, first occurring form of candidate), default to\n                False.\n        \"\"\"\n\n        # sort candidates by descending weight\n        best = sorted(self.weights, key=self.weights.get, reverse=True)\n\n        # remove redundant candidates\n        if redundancy_removal:\n\n            # initialize a new container for non redundant candidates\n            non_redundant_best = []\n\n            # loop through the best candidates\n            for candidate in best:\n\n                # test wether candidate is redundant\n                if self.is_redundant(candidate, non_redundant_best):\n                    continue\n\n                # add the candidate otherwise\n                non_redundant_best.append(candidate)\n\n                # break computation if the n-best are found\n                if len(non_redundant_best) >= n:\n                    break\n\n            # copy non redundant candidates in best container\n            best = non_redundant_best\n\n        # get the list of best candidates as (lexical form, weight) tuples\n        n_best = [(u, self.weights[u]) for u in best[:min(n, len(best))]]\n\n        # replace with surface forms if no stemming\n        if not stemming:\n            n_best = [(' '.join(self.candidates[u].surface_forms[0]).lower(),\n                       self.weights[u]) for u in best[:min(n, len(best))]]\n\n        if len(n_best) < n:\n            logging.warning(\n                'Not enough candidates to choose from '\n                '({} requested, {} given)'.format(n, len(n_best)))\n\n        # return the list of best candidates\n        return n_best\n\n    def add_candidate(self, words, stems, pos, offset, sentence_id):\n        \"\"\"Add a keyphrase candidate to the candidates container.\n\n        Args:\n            words (list): the words (surface form) of the candidate.\n            stems (list): the stemmed words of the candidate.\n            pos (list): the Part-Of-Speeches of the words in the candidate.\n            offset (int): the offset of the first word of the candidate.\n            sentence_id (int): the sentence id of the candidate.\n        \"\"\"\n\n        # build the lexical (canonical) form of the candidate using stems\n        lexical_form = ' '.join(stems)\n\n        # add/update the surface forms\n        self.candidates[lexical_form].surface_forms.append(words)\n\n        # add/update the lexical_form\n        self.candidates[lexical_form].lexical_form = stems\n\n        # add/update the POS patterns\n        self.candidates[lexical_form].pos_patterns.append(pos)\n\n        # add/update the offsets\n        self.candidates[lexical_form].offsets.append(offset)\n\n        # add/update the sentence ids\n        self.candidates[lexical_form].sentence_ids.append(sentence_id)\n\n    def ngram_selection(self, n=3):\n        \"\"\"Select all the n-grams and populate the candidate container.\n\n        Args:\n            n (int): the n-gram length, defaults to 3.\n        \"\"\"\n\n        # loop through the sentences\n        for i, sentence in enumerate(self.sentences):\n\n            # limit the maximum n for short sentence\n            skip = min(n, sentence.length)\n\n            # compute the offset shift for the sentence\n            shift = sum([s.length for s in self.sentences[0:i]])\n\n            # generate the ngrams\n            for j in range(sentence.length):\n                for k in range(j + 1, min(j + 1 + skip, sentence.length + 1)):\n                    # add the ngram to the candidate container\n                    self.add_candidate(words=sentence.words[j:k],\n                                       stems=sentence.stems[j:k],\n                                       pos=sentence.pos[j:k],\n                                       offset=shift + j,\n                                       sentence_id=i)\n\n    def longest_pos_sequence_selection(self, valid_pos=None):\n        self.longest_sequence_selection(\n            key=lambda s: s.pos, valid_values=valid_pos)\n\n    def longest_keyword_sequence_selection(self, keywords):\n        self.longest_sequence_selection(\n            key=lambda s: s.stems, valid_values=keywords)\n\n    def longest_sequence_selection(self, key, valid_values):\n        \"\"\"Select the longest sequences of given POS tags as candidates.\n\n        Args:\n            key (func) : function that given a sentence return an iterable\n            valid_values (set): the set of valid values, defaults to None.\n        \"\"\"\n\n        # loop through the sentences\n        for i, sentence in enumerate(self.sentences):\n\n            # compute the offset shift for the sentence\n            shift = sum([s.length for s in self.sentences[0:i]])\n\n            # container for the sequence (defined as list of offsets)\n            seq = []\n\n            # loop through the tokens\n            for j, value in enumerate(key(self.sentences[i])):\n\n                # add candidate offset in sequence and continue if not last word\n                if value in valid_values:\n                    seq.append(j)\n                    if j < (sentence.length - 1):\n                        continue\n\n                # add sequence as candidate if non empty\n                if seq:\n\n                    # add the ngram to the candidate container\n                    self.add_candidate(words=sentence.words[seq[0]:seq[-1] + 1],\n                                       stems=sentence.stems[seq[0]:seq[-1] + 1],\n                                       pos=sentence.pos[seq[0]:seq[-1] + 1],\n                                       offset=shift + seq[0],\n                                       sentence_id=i)\n\n                # flush sequence container\n                seq = []\n\n    def grammar_selection(self, grammar=None):\n        \"\"\"Select candidates using nltk RegexpParser with a grammar defining\n        noun phrases (NP).\n\n        Args:\n            grammar (str): grammar defining POS patterns of NPs.\n        \"\"\"\n\n        # initialize default grammar if none provided\n        if grammar is None:\n            grammar = r\"\"\"\n                NBAR:\n                    {<NOUN|PROPN|ADJ>*<NOUN|PROPN>} \n                    \n                NP:\n                    {<NBAR>}\n                    {<NBAR><ADP><NBAR>}\n            \"\"\"\n\n        # initialize chunker\n        chunker = RegexpParser(grammar)\n\n        # loop through the sentences\n        for i, sentence in enumerate(self.sentences):\n\n            # compute the offset shift for the sentence\n            shift = sum([s.length for s in self.sentences[0:i]])\n\n            # convert sentence as list of (offset, pos) tuples\n            tuples = [(str(j), sentence.pos[j]) for j in range(sentence.length)]\n\n            # parse sentence\n            tree = chunker.parse(tuples)\n\n            # find candidates\n            for subtree in tree.subtrees():\n                if subtree.label() == 'NP':\n                    leaves = subtree.leaves()\n\n                    # get the first and last offset of the current candidate\n                    first = int(leaves[0][0])\n                    last = int(leaves[-1][0])\n\n                    # add the NP to the candidate container\n                    self.add_candidate(words=sentence.words[first:last + 1],\n                                       stems=sentence.stems[first:last + 1],\n                                       pos=sentence.pos[first:last + 1],\n                                       offset=shift + first,\n                                       sentence_id=i)\n\n    @staticmethod\n    def _is_alphanum(word, valid_punctuation_marks='-'):\n        \"\"\"Check if a word is valid, i.e. it contains only alpha-numeric\n        characters and valid punctuation marks.\n\n        Args:\n            word (string): a word.\n            valid_punctuation_marks (str): punctuation marks that are valid\n                    for a candidate, defaults to '-'.\n        \"\"\"\n        for punct in valid_punctuation_marks.split():\n            word = word.replace(punct, '')\n        return word.isalnum()\n\n    def candidate_filtering(self,\n                            stoplist=None,\n                            minimum_length=3,\n                            minimum_word_size=2,\n                            valid_punctuation_marks='-',\n                            maximum_word_number=5,\n                            only_alphanum=True,\n                            pos_blacklist=None):\n        \"\"\"Filter the candidates containing strings from the stoplist. Only\n        keep the candidates containing alpha-numeric characters (if the\n        non_latin_filter is set to True) and those length exceeds a given\n        number of characters.\n            \n        Args:\n            stoplist (list): list of strings, defaults to None.\n            minimum_length (int): minimum number of characters for a\n                candidate, defaults to 3.\n            minimum_word_size (int): minimum number of characters for a\n                token to be considered as a valid word, defaults to 2.\n            valid_punctuation_marks (str): punctuation marks that are valid\n                for a candidate, defaults to '-'.\n            maximum_word_number (int): maximum length in words of the\n                candidate, defaults to 5.\n            only_alphanum (bool): filter candidates containing non (latin)\n                alpha-numeric characters, defaults to True.\n            pos_blacklist (list): list of unwanted Part-Of-Speeches in\n                candidates, defaults to [].\n        \"\"\"\n\n        if stoplist is None:\n            stoplist = []\n\n        if pos_blacklist is None:\n            pos_blacklist = []\n\n        # loop through the candidates\n        for k in list(self.candidates):\n\n            # get the candidate\n            v = self.candidates[k]\n\n            # get the words from the first occurring surface form\n            words = [u.lower() for u in v.surface_forms[0]]\n\n            # discard if words are in the stoplist\n            if set(words).intersection(stoplist):\n                del self.candidates[k]\n\n            # discard if tags are in the pos_blacklist\n            elif set(v.pos_patterns[0]).intersection(pos_blacklist):\n                del self.candidates[k]\n\n            # discard if containing tokens composed of only punctuation\n            elif any([set(u).issubset(set(punctuation)) for u in words]):\n                del self.candidates[k]\n\n            # discard candidates composed of 1-2 characters\n            elif len(''.join(words)) < minimum_length:\n                del self.candidates[k]\n\n            # discard candidates containing small words (1-character)\n            elif min([len(u) for u in words]) < minimum_word_size:\n                del self.candidates[k]\n\n            # discard candidates composed of more than 5 words\n            elif len(v.lexical_form) > maximum_word_number:\n                del self.candidates[k]\n\n            # discard if not containing only alpha-numeric characters\n            if only_alphanum and k in self.candidates:\n                if not all([self._is_alphanum(w, valid_punctuation_marks)\n                            for w in words]):\n                    del self.candidates[k]\n\n"
  },
  {
    "path": "onmt/keyphrase/pke/data_structures.py",
    "content": "# -*- coding: utf-8 -*-\n\n\"\"\"Data structures for the pke module.\"\"\"\n\n\nclass Sentence(object):\n    \"\"\"The sentence data structure.\"\"\"\n\n    def __init__(self, words):\n\n        self.words = words\n        \"\"\"list of words (tokens) in the sentence.\"\"\"\n\n        self.pos = []\n        \"\"\"list of Part-Of-Speeches.\"\"\"\n\n        self.stems = []\n        \"\"\"list of stems.\"\"\"\n\n        self.length = len(words)\n        \"\"\"length (number of tokens) of the sentence.\"\"\"\n\n        self.meta = {}\n        \"\"\"meta-information of the sentence.\"\"\"\n\n    def __eq__(self, other):\n        \"\"\"Compares two sentences for equality.\"\"\"\n\n        # test whether they are instances of different classes\n        if type(self) != type(other):\n            return False\n\n        # test whether they are of same length\n        if self.length != other.length:\n            return False\n\n        # test whether they have the same words\n        if self.words != other.words:\n            return False\n\n        # test whether they have the same PoS tags\n        if self.pos != other.pos:\n            return False\n\n        # test whether they have the same stem forms\n        if self.stems != other.stems:\n            return False\n\n        # test whether they have the same meta-information\n        if self.meta != other.meta:\n            return False\n\n        # if everything is ok then they are equal\n        return True\n\n\nclass Candidate(object):\n    \"\"\"The keyphrase candidate data structure.\"\"\"\n\n    def __init__(self):\n\n        self.surface_forms = []\n        \"\"\" the surface forms of the candidate. \"\"\"\n\n        self.offsets = []\n        \"\"\" the offsets of the surface forms. \"\"\"\n\n        self.sentence_ids = []\n        \"\"\" the sentence id of each surface form. \"\"\"\n\n        self.pos_patterns = []\n        \"\"\" the Part-Of-Speech patterns of the candidate. \"\"\"\n\n        self.lexical_form = []\n        \"\"\" the lexical form of the candidate. \"\"\"\n\n\nclass Document(object):\n    \"\"\"The Document data structure.\"\"\"\n\n    def __init__(self):\n\n        self.input_file = None\n        \"\"\" The path of the input file. \"\"\"\n\n        self.sentences = []\n        \"\"\" The sentence container (list of Sentence). \"\"\"\n\n    @staticmethod\n    def from_sentences(sentences, **kwargs):\n        \"\"\"Populate the sentence list.\n\n        Args:\n            sentences (Sentence list): content to create the document.\n            input_file (str): path to the input file.\n        \"\"\"\n\n        # initialize document\n        doc = Document()\n\n        # set the input file\n        doc.input_file = kwargs.get('input_file', None)\n\n        # loop through the parsed sentences\n        for i, sentence in enumerate(sentences):\n\n            # add the sentence to the container\n            s = Sentence(words=sentence['words'])\n\n            # add the POS\n            s.pos = sentence['POS']\n\n            # add the lemmas\n            s.stems = sentence['lemmas']\n\n            # add the meta-information\n            for (k, infos) in sentence.items():\n                if k not in {'POS', 'lemmas', 'words'}:\n                    s.meta[k] = infos\n\n            # add the sentence to the document\n            doc.sentences.append(s)\n\n        return doc\n\n    def __eq__(self, other):\n        \"\"\"Compares two documents for equality.\"\"\"\n\n        # test whether they are instances of different classes\n        if type(self) != type(other):\n            return False\n\n        # test whether they have the same language\n        if self.language != other.language:\n            return False\n\n        # test whether they have the same input path\n        if self.input_file != other.input_file:\n            return False\n\n        # test whether they contain the same lists of sentences\n        if self.sentences != other.sentences:\n            return False\n\n        # if everything is ok then they are equal\n        return True\n"
  },
  {
    "path": "onmt/keyphrase/pke/readers.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Readers for the pke module.\"\"\"\n\nimport xml.etree.ElementTree as etree\nimport spacy\n\nfrom onmt.keyphrase.pke.data_structures import Document\n\n\nclass Reader(object):\n    def read(self, path):\n        raise NotImplementedError\n\n\nclass MinimalCoreNLPReader(Reader):\n    \"\"\"Minimal CoreNLP XML Parser.\"\"\"\n\n    def __init__(self):\n        self.parser = etree.XMLParser()\n\n    def read(self, path, **kwargs):\n        sentences = []\n        tree = etree.parse(path, self.parser)\n        for sentence in tree.iterfind('./document/sentences/sentence'):\n            # get the character offsets\n            starts = [int(u.text) for u in\n                      sentence.iterfind(\"tokens/token/CharacterOffsetBegin\")]\n            ends = [int(u.text) for u in\n                    sentence.iterfind(\"tokens/token/CharacterOffsetEnd\")]\n            sentences.append({\n                \"words\": [u.text for u in\n                          sentence.iterfind(\"tokens/token/word\")],\n                \"lemmas\": [u.text for u in\n                           sentence.iterfind(\"tokens/token/lemma\")],\n                \"POS\": [u.text for u in sentence.iterfind(\"tokens/token/POS\")],\n                \"char_offsets\": [(starts[k], ends[k]) for k in\n                                 range(len(starts))]\n            })\n            sentences[-1].update(sentence.attrib)\n\n        doc = Document.from_sentences(sentences, input_file=path, **kwargs)\n\n        return doc\n\n\nclass RawTextReader(Reader):\n    \"\"\"Reader for raw text.\"\"\"\n\n    def __init__(self, language=None):\n        \"\"\"Constructor for RawTextReader.\n\n        Args:\n            language (str): language of text to process.\n        \"\"\"\n\n        self.language = language\n\n        if language is None:\n            self.language = 'en'\n\n    def read(self, text, **kwargs):\n        \"\"\"Read the input file and use spacy to pre-process.\n\n        Args:\n            text (str): raw text to pre-process.\n            max_length (int): maximum number of characters in a single text for\n                spacy, default to 1,000,000 characters (1mb).\n        \"\"\"\n\n        max_length = kwargs.get('max_length', 10**6)\n        try:\n            nlp = spacy.load(self.language, max_length=max_length)\n        except Exception:\n            spacy.cli.download(self.language)\n            nlp = spacy.load(self.language, max_length=max_length)\n        spacy_doc = nlp(text)\n\n        sentences = []\n        for sentence_id, sentence in enumerate(spacy_doc.sents):\n            sentences.append({\n                \"words\": [token.text for token in sentence],\n                \"lemmas\": [token.lemma_ for token in sentence],\n                \"POS\": [token.pos_ for token in sentence],\n                \"char_offsets\": [(token.idx, token.idx + len(token.text))\n                                     for token in sentence]\n            })\n\n        doc = Document.from_sentences(sentences,\n                                      input_file=kwargs.get('input_file', None),\n                                      **kwargs)\n\n        return doc\n\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: unsupervised models\n\nfrom __future__ import absolute_import\n\nfrom onmt.keyphrase.pke.supervised.api import SupervisedLoadFile\nfrom onmt.keyphrase.pke.supervised.feature_based.kea import Kea\nfrom onmt.keyphrase.pke.supervised.feature_based.topiccorank import TopicCoRank\nfrom onmt.keyphrase.pke.supervised.feature_based.wingnus import WINGNUS\nfrom onmt.keyphrase.pke.supervised.neural_based.seq2seq import Seq2Seq\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/api.py",
    "content": "# -*- coding: utf-8 -*-\n\n\"\"\" Abstract base class for Supervised models. \"\"\"\n\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport os\nimport six\n\nfrom onmt.keyphrase.pke.base import LoadFile\nfrom sklearn.preprocessing import MinMaxScaler\nimport joblib\n\n\nclass SupervisedLoadFile(LoadFile):\n    \"\"\" The SupervisedLoadFile class that provides extra base functions for\n        supervised models. \"\"\"\n\n    def __init__(self):\n        \"\"\" Redefining initializer. \"\"\"\n\n        super(SupervisedLoadFile, self).__init__()\n\n        self.instances = {}\n        \"\"\" The instances container. \"\"\"\n\n    def feature_scaling(self):\n        \"\"\" Scale features to [0,1]. \"\"\"\n\n        candidates = self.instances.keys()\n        X = [self.instances[u] for u in candidates]\n        X = MinMaxScaler().fit_transform(X)\n        for i, candidate in enumerate(candidates):\n            self.instances[candidate] = X[i]\n\n    def feature_extraction(self):\n        \"\"\" Skeleton for feature extraction. \"\"\"\n        pass\n\n    def classify_candidates(self, model=None):\n        \"\"\" Classify the candidates as keyphrase or not keyphrase.\n\n            Args:\n                model (str): the path to load the model in pickle format,\n                    default to None.\n        \"\"\"\n\n        # set the default model if none provided\n        if model is None:\n            instance = self.__class__.__name__\n            # model = os.path.join(self._models, instance+\"-semeval2010.pickle\")\n            if six.PY2:\n                model = os.path.join(self._models,\n                                     instance + \"-semeval2010.py2.pickle\")\n            else:\n                model = os.path.join(self._models,\n                                     instance + \"-semeval2010.py3.pickle\")\n\n        # load the model\n        clf = joblib.load(model)\n        # with open(model, 'rb') as f:\n        #     clf = pickle.load(f)\n\n        # get matrix of instances\n        candidates = self.instances.keys()\n        X = [self.instances[u] for u in candidates]\n\n        # classify candidates\n        y = clf.predict_proba(X)\n\n        for i, candidate in enumerate(candidates):\n            self.weights[candidate] = y[i][1]\n\n    def candidate_weighting(self):\n        \"\"\" Extract features and classify candidates with default parameters.\"\"\"\n\n        self.feature_extraction()\n        self.classify_candidates()\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/feature_based/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: supervised feature-based ranking models\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/feature_based/kea.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"Kea supervised keyphrase extraction model.\n\nKea is a supervised model for keyphrase extraction that uses two features,\nnamely TF x IDF and first occurrence, to classify keyphrase candidates as\nkeyphrase or not. The model is described in:\n\n* Ian Witten, Gordon Paynter, Eibe Frank, Carl Gutwin and Craig Nevill-Mannin.\n  KEA: Practical Automatic Keyphrase Extraction.\n  *Proceedings of the 4th ACM Conference on Digital Libraries*, pages 254–255,\n  1999.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport string\nimport logging\n\nimport numpy as np\nimport joblib\nfrom sklearn.naive_bayes import MultinomialNB\n\nfrom onmt.keyphrase.pke.supervised.api import SupervisedLoadFile\nfrom onmt.keyphrase.pke.utils import load_document_frequency_file\n\n\nclass Kea(SupervisedLoadFile):\n    \"\"\"Kea keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        from nltk.corpus import stopwords\n\n        # define a list of stopwords\n        stoplist = stopwords.words('english')\n\n        # 1. create a Kea extractor.\n        extractor = pke.supervised.Kea()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select 1-3 grams that do not start or end with a stopword as\n        #    candidates. Candidates that contain punctuation marks as words\n        #    are discarded.\n        extractor.candidate_selection(stoplist=stoplist)\n\n        # 4. classify candidates as keyphrase or not keyphrase.\n        df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz')\n        model_file = 'path/to/kea_model'\n        extractor.candidate_weighting(model_file=model_file,\n                                      df=df)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for Kea.\"\"\"\n\n        super(Kea, self).__init__()\n\n    def candidate_selection(self, stoplist=None, **kwargs):\n        \"\"\"Select 1-3 grams of `normalized` words as keyphrase candidates.\n        Candidates that start or end with a stopword are discarded. Candidates\n        that contain punctuation marks (from `string.punctuation`) as words are\n        filtered out.\n\n        Args:\n            stoplist (list): the stoplist for filtering candidates, defaults\n                to the nltk stoplist.\n        \"\"\"\n\n        # select ngrams from 1 to 3 grams\n        self.ngram_selection(n=3)\n\n        # filter candidates containing punctuation marks\n        self.candidate_filtering(list(string.punctuation))\n\n        # initialize stoplist list if not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # filter candidates that start or end with a stopword\n        for k in list(self.candidates):\n\n            # get the candidate\n            v = self.candidates[k]\n\n            # delete if candidate contains a stopword in first/last position\n            words = [u.lower() for u in v.surface_forms[0]]\n            if words[0] in stoplist or words[-1] in stoplist:\n                del self.candidates[k]\n\n    def feature_extraction(self, df=None, training=False):\n        \"\"\"Extract features for each keyphrase candidate. Features are the\n        tf*idf of the candidate and its first occurrence relative to the\n        document.\n\n        Args:\n            df (dict): document frequencies, the number of documents should be\n                specified using the \"--NB_DOC--\" key.\n            training (bool): indicates whether features are computed for the\n                training set for computing IDF weights, defaults to false.\n        \"\"\"\n\n        # initialize default document frequency counts if none provided\n        if df is None:\n            logging.warning('LoadFile._df_counts is hard coded to {}'.format(\n                self._df_counts))\n            df = load_document_frequency_file(self._df_counts, delimiter='\\t')\n\n        # initialize the number of documents as --NB_DOC--\n        N = df.get('--NB_DOC--', 0) + 1\n        if training:\n            N -= 1\n\n        # find the maximum offset\n        maximum_offset = float(sum([s.length for s in self.sentences]))\n\n        for k, v in self.candidates.items():\n\n            # get candidate document frequency\n            candidate_df = 1 + df.get(k, 0)\n\n            # hack for handling training documents\n            if training and candidate_df > 1:\n                candidate_df -= 1\n\n            # compute the tf*idf of the candidate\n            idf = math.log(N / candidate_df, 2)\n\n            # add the features to the instance container\n            self.instances[k] = np.array([len(v.surface_forms) * idf,\n                                          v.offsets[0] / maximum_offset])\n\n        # scale features\n        self.feature_scaling()\n\n    def candidate_weighting(self, model_file=None, df=None):\n        \"\"\"Extract features and classify candidates.\n\n        Args:\n            model_file (str): path to the model file.\n            df (dict): document frequencies, the number of documents should\n                    be specified using the \"--NB_DOC--\" key.\n        \"\"\"\n\n        self.feature_extraction(df=df)\n        self.classify_candidates(model=model_file)\n\n    @staticmethod\n    def train(training_instances, training_classes, model_file):\n        \"\"\" Train a Naive Bayes classifier and store the model in a file.\n\n            Args:\n                training_instances (list): list of features.\n                training_classes (list): list of binary values.\n                model_file (str): the model output file.\n        \"\"\"\n\n        clf = MultinomialNB()\n        clf.fit(training_instances, training_classes)\n        joblib.dump(clf, model_file)\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/feature_based/topiccorank.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"TopicCoRank supervised keyphrase extraction model.\n\n\nTopicCoRank is a supervised graph-based ranking approach to keyphrase\nextraction that operates over a unified graph that connects two graphs: the\nformer represents the document and the latter captures how keyphrases are\nassociated with each other in the training data. The model is described in:\n\n* Adrien Bougouin, Florian Boudin, and Beatrice Daille.\n  Keyphrase annotation with graph co-ranking\n  *Proceedings of the COLINGs*, pages 2945–2955, 2016.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom onmt.keyphrase.pke.unsupervised import TopicRank\nfrom onmt.keyphrase.pke.utils import load_references\n\nfrom itertools import combinations\nfrom collections import defaultdict\nimport logging\nimport networkx as nx\nimport math\n\n\nclass TopicCoRank(TopicRank):\n    \"\"\"TopicCoRank keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        import string\n        from nltk.corpus import stopwords\n\n        # 1. create a TopicCoRank extractor.\n        extractor = pke.unsupervised.TopicCoRank()\n\n        # 2. load the content of the document.\n       extractor.load_document(input='path/to/input.xml')\n\n        # 3. select the longest sequences of nouns and adjectives, that do\n        #    not contain punctuation marks or stopwords as candidates.\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n        stoplist = list(string.punctuation)\n        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n        stoplist += stopwords.words('english')\n        extractor.candidate_selection(pos=pos, stoplist=stoplist)\n\n        # 4. build topics by grouping candidates with HAC (average linkage,\n        #    threshold of 1/4 of shared stems). Weight the topics using random\n        #    walk, and select the first occuring candidate from each topic.\n        extractor.candidate_weighting(threshold=0.74, method='average')\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for TopicCoRank.\"\"\"\n\n        super(TopicCoRank, self).__init__()\n\n        self.domain_to_integer = {}\n\n        self.topic_to_integer = {}\n\n    def build_topic_graph(self):\n        \"\"\"Re-define the topic graph construction method.\n\n        Build the topic graph by connecting topics if their candidates\n        co-occur in the same sentence. Edges are weighted by the number of\n        oc-occurrences.\n        \"\"\"\n\n        # adding the nodes to the graph\n        self.graph.add_nodes_from(range(len(self.topics)), src=\"topic\")\n\n        # loop through the topics to connect the nodes\n        for i, j in combinations(range(len(self.topics)), 2):\n\n            # for each candidate in topic i\n            for c_i in self.topics[i]:\n\n                # for each candidate in topic j\n                for c_j in self.topics[j]:\n\n                    weight = len(\n                        set(self.candidates[c_i].sentence_ids).intersection(\n                            self.candidates[c_j].sentence_ids))\n\n                    if weight > 0:\n                        if not self.graph.has_edge(i, j):\n                            self.graph.add_edge(i, j, weight=0, type=\"in\")\n                        self.graph[i][j]['weight'] += weight\n\n    def unify_with_domain_graph(self, input_file, excluded_file=None):\n        \"\"\"Unify the domain graph, built from a reference file, with the topic\n        graph, built from a document.\n\n        Args:\n            input_file (str): path to the reference file.\n            excluded_file (str): file to exclude (for leave-one-out\n                cross-validation), defaults to None.\n        \"\"\"\n\n        if input_file.endswith('.json'):\n            references = load_references(input_file=input_file,\n                                         language=self.language)\n        else:\n            logging.warning(\"{} is not a reference file\".format(input_file))\n            pass\n\n        # remove excluded file if needed\n        if excluded_file is not None:\n            if excluded_file not in references:\n                logging.warning(\"{} is not in reference\".format(excluded_file))\n            else:\n                logging.info(\"{} removed from reference\".format(excluded_file))\n                del references[excluded_file]\n\n        # initialize the topic_to_integer map\n        for i, topic in enumerate(self.topics):\n            for candidate in topic:\n                self.topic_to_integer[candidate] = i\n\n        offset = len(self.topics)\n\n        # loop through the doc_ids\n        for doc_id in references:\n\n            # for each pair of gold keyphrases\n            for gold_1, gold_2 in combinations(references[doc_id], 2):\n\n                # adding nodes to the graph\n                if gold_1 not in self.domain_to_integer:\n                    self.domain_to_integer[gold_1] = offset\n                    self.graph.add_node(offset, src=\"domain\", candidate=gold_1)\n\n                    # checking for out edges with topics\n                    if gold_1 in self.topic_to_integer:\n                        self.graph.add_edge(self.domain_to_integer[gold_1],\n                                            self.topic_to_integer[gold_1],\n                                            weight=0, type=\"out\")\n\n                    offset += 1\n\n                if gold_2 not in self.domain_to_integer:\n                    self.domain_to_integer[gold_2] = offset\n                    self.graph.add_node(offset, src=\"domain\", candidate=gold_2)\n\n                    # checking for out edges with topics\n                    if gold_2 in self.topic_to_integer:\n                        self.graph.add_edge(self.domain_to_integer[gold_2],\n                                            self.topic_to_integer[gold_2],\n                                            weight=0, type=\"out\")\n\n                    offset += 1\n\n                node_1 = self.domain_to_integer[gold_1]\n                node_2 = self.domain_to_integer[gold_2]\n\n                # add/update the edge\n                if not self.graph.has_edge(node_1, node_2):\n                    self.graph.add_edge(node_1, node_2, weight=0, type=\"in\")\n                self.graph[node_1][node_2]['weight'] += 1\n\n    def candidate_weighting(self,\n                            input_file=None,\n                            excluded_file=None,\n                            lambda_t=0.1,\n                            lambda_k=0.5,\n                            nb_iter=100,\n                            convergence_threshold=0.001):\n        \"\"\"Weight candidates using the co-ranking formulae.\n\n        Args:\n            input_file (str): path to the reference file.\n            excluded_file (str): file to exclude (for leave-one-out\n                cross-validation), defaults to None.\n            lambda_t(float): lambda for topics used in the co-ranking formulae,\n                defaults to 0.1.\n            lambda_k(float): lambda for keyphrases used in the co-ranking\n                formulae, defaults to 0.5.\n            nb_iter (int): maximum number of iterations, defaults to 100.\n            convergence_threshold (float): early stop threshold, defaults to\n                0.001.\n        \"\"\"\n\n        # compute topics\n        self.topic_clustering()\n\n        # build graph\n        self.build_topic_graph()\n\n        # unify with domain graph\n        self.unify_with_domain_graph(input_file=input_file,\n                                     excluded_file=excluded_file)\n\n        logging.info(\"resulting graph is {} nodes\".format(\n                                                    len(self.graph.nodes())))\n\n        weights = [1.0] * len(self.graph.nodes)\n\n        # pre-compute the inner/outer normalizations\n        inner_norms = [0.0] * len(self.graph.nodes)\n        outer_norms = [0.0] * len(self.graph.nodes)\n\n        for j in self.graph.nodes():\n            inner_norm = 0\n            outer_norm = 0\n            for k in self.graph.neighbors(j):\n                if self.graph[j][k]['type'] == \"in\":\n                    inner_norm += self.graph[j][k][\"weight\"]\n                else:\n                    outer_norm += 1\n            inner_norms[j] = inner_norm\n            outer_norms[j] = outer_norm\n\n        # ranking nodes in the graph using co-ranking\n        converged = False\n        while nb_iter > 0 and not converged:\n\n            converged = True\n\n            #logging.info(\"{} iter left\".format(nb_iter))\n\n            # save the weights\n            w = weights.copy()\n\n            for i in self.graph.nodes():\n\n                # compute inner/outer recommendations\n                r_in = 0.0\n                r_out = 0.0\n                for j in self.graph.neighbors(i):\n\n                    # inner recommendation\n                    if self.graph[i][j]['type'] == \"in\":\n                        r_in += (self.graph[i][j][\"weight\"] * w[j]) / \\\n                                inner_norms[j]\n\n                    # outer recommendation\n                    else:\n                        r_out += w[j] / outer_norms[j]\n\n                # compute the new weight\n                if self.graph.node[i][\"src\"] == \"topic\":\n                    weights[i] = (1 - lambda_t) * r_out\n                    weights[i] += lambda_t * r_in\n                else:\n                    weights[i] = (1 - lambda_k) * r_out\n                    weights[i] += lambda_k * r_in\n\n                # check for non convergence\n                if math.fabs(weights[i] - w[i]) > convergence_threshold:\n                    converged = False\n\n            nb_iter -= 1\n\n        # get the final ranking\n        for i in self.graph.nodes():\n\n            # if it is a topic candidate\n            if self.graph.node[i][\"src\"] == \"topic\":\n\n                # get the candidates from the topic\n                topic = self.topics[i]\n\n                # get the offsets of the topic candidates\n                offsets = [self.candidates[t].offsets[0] for t in topic]\n\n                first = offsets.index(min(offsets))\n                self.weights[topic[first]] = weights[i]\n\n            # otherwise it is a keyphrase from the domain\n            else:\n\n                gold = self.graph.node[i][\"candidate\"]\n\n                # check if it is acceptable, i.e. if it is directly or\n                # transitively connected to a topic\n                connected = False\n                for j in self.graph.neighbors(i):\n                    if self.graph.node[j][\"src\"] == \"topic\":\n                        connected = True\n                        break\n                    for k in self.graph.neighbors(j):\n                        if self.graph.node[k][\"src\"] == \"topic\":\n                            connected = True\n                            break\n                    if connected:\n                        break\n\n                if connected:\n                    if gold in self.weights:\n                        self.weights[gold] = max(self.weights[gold], weights[i])\n                    else:\n                        self.weights[gold] = weights[i]\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/feature_based/wingnus.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"Kea keyphrase extraction model.\n\nSupervised approach to keyphrase extraction described in:\n\n* Thuy Dung Nguyen and Minh-Thang Luong.\n  WINGNUS: Keyphrase Extraction Utilizing Document Logical Structure.\n  *Proceedings of SemEval*, pages 166–169, 2010.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport logging\n\nimport numpy as np\nimport joblib\nfrom sklearn.naive_bayes import MultinomialNB\n\nfrom onmt.keyphrase.pke.supervised.api import SupervisedLoadFile\nfrom onmt.keyphrase.pke.utils import load_document_frequency_file\n\n\nclass WINGNUS(SupervisedLoadFile):\n    \"\"\"WINGNUS keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n\n        # 1. create a WINGNUS extractor.\n        extractor = pke.supervised.WINGNUS()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input.xml')\n\n        # 3. select simplex noun phrases as candidates.\n        extractor.candidate_selection()\n\n        # 4. classify candidates as keyphrase or not keyphrase.\n        df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz')\n        model_file = 'path/to/wingnus_model'\n        extractor.candidate_weighting(self, model_file=model_file, df=df)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for WINGNUS.\"\"\"\n\n        super(WINGNUS, self).__init__()\n\n    def candidate_selection(self, grammar=None):\n        \"\"\"Select noun phrases (NP) and NP containing a pre-propositional phrase\n        (NP IN NP) as keyphrase candidates.\n\n        Args:\n            grammar (str): grammar defining POS patterns of NPs.\n        \"\"\"\n\n        # initialize default grammar if none provided\n        if grammar is None:\n            grammar = r\"\"\"\n                NBAR:\n                    {<NOUN|PROPN|ADJ>{,2}<NOUN|PROPN>} \n                    \n                NP:\n                    {<NBAR>}\n                    {<NBAR><ADP><NBAR>}\n            \"\"\"\n\n        self.grammar_selection(grammar)\n\n\n    def feature_extraction(self, df=None, training=False, features_set=None):\n        \"\"\"Extract features for each candidate.\n\n        Args:\n            df (dict): document frequencies, the number of documents should be\n                specified using the \"--NB_DOC--\" key.\n            training (bool): indicates whether features are computed for the\n                training set for computing IDF weights, defaults to false.\n            features_set (list): the set of features to use, defaults to\n                [1, 4, 6].\n\n        \"\"\"\n\n        # define the default features_set\n        if features_set is None:\n            features_set = [1, 4, 6]\n\n        # initialize default document frequency counts if none provided\n        if df is None:\n            logging.warning('LoadFile._df_counts is hard coded to {}'.format(\n                self._df_counts))\n            df = load_document_frequency_file(self._df_counts, delimiter='\\t')\n\n        # initialize the number of documents as --NB_DOC--\n        N = df.get('--NB_DOC--', 0) + 1\n        if training:\n            N -= 1\n\n        # find the maximum offset\n        maximum_offset = float(sum([s.length for s in self.sentences]))\n\n        # loop through the candidates\n        for k, v in self.candidates.items():\n\n            # initialize features array\n            feature_array = []\n\n            # get candidate document frequency\n            candidate_df = 1 + df.get(k, 0)\n\n            # hack for handling training documents\n            if training and candidate_df > 1:\n                candidate_df -= 1\n\n            # compute the tf*idf of the candidate\n            idf = math.log(N / candidate_df, 2)\n\n            # [F1] TF*IDF\n            feature_array.append(len(v.surface_forms) * idf)\n\n            # [F2] -> TF\n            feature_array.append(len(v.surface_forms))\n\n            # [F3] -> term frequency of substrings\n            tf_of_substrings = 0\n            stoplist = self.stoplist\n            for i in range(len(v.lexical_form)):\n                for j in range(i, min(len(v.lexical_form), i + 3)):\n                    sub_words = v.lexical_form[i:j + 1]\n                    sub_string = ' '.join(sub_words)\n\n                    # skip if substring is fullstring\n                    if sub_string == ' '.join(v.lexical_form):\n                        continue\n\n                    # skip if substring contains a stopword\n                    if set(sub_words).intersection(stoplist):\n                        continue\n\n                    # check whether the substring occurs \"as it\"\n                    if sub_string in self.candidates:\n\n                        # loop throught substring offsets\n                        for offset_1 in self.candidates[sub_string].offsets:\n                            is_included = False\n                            for offset_2 in v.offsets:\n                                if offset_2 <= offset_1 <= offset_2 + len(v.lexical_form):\n                                    is_included = True\n                            if not is_included:\n                                tf_of_substrings += 1\n\n            feature_array.append(tf_of_substrings)\n\n            # [F4] -> relative first occurrence\n            feature_array.append(v.offsets[0] / maximum_offset)\n\n            # [F5] -> relative last occurrence\n            feature_array.append(v.offsets[-1] / maximum_offset)\n\n            # [F6] -> length of phrases in words\n            feature_array.append(len(v.lexical_form))\n\n            # [F7] -> typeface\n            feature_array.append(0)\n\n            # extract information from sentence meta information\n            meta = [self.sentences[sid].meta for sid in v.sentence_ids]\n\n            # extract meta information of candidate\n            sections = [u['section'] for u in meta if 'section' in u]\n            types = [u['type'] for u in meta if 'type' in u]\n\n            # [F8] -> Is in title\n            feature_array.append('title' in sections)\n\n            # [F9] -> TitleOverlap\n            feature_array.append(0)\n\n            # [F10] -> Header\n            feature_array.append('sectionHeader' in types or\n                                 'subsectionHeader' in types or\n                                 'subsubsectionHeader' in types)\n\n            # [F11] -> abstract\n            feature_array.append('abstract' in sections)\n\n            # [F12] -> introduction\n            feature_array.append('introduction' in sections)\n\n            # [F13] -> related work\n            feature_array.append('related work' in sections)\n\n            # [F14] -> conclusions\n            feature_array.append('conclusions' in sections)\n\n            # [F15] -> HeaderF\n            feature_array.append(types.count('sectionHeader') +\n                                 types.count('subsectionHeader') +\n                                 types.count('subsubsectionHeader'))\n\n            # [F11] -> abstractF\n            feature_array.append(sections.count('abstract'))\n\n            # [F12] -> introductionF\n            feature_array.append(sections.count('introduction'))\n\n            # [F13] -> related workF\n            feature_array.append(sections.count('related work'))\n\n            # [F14] -> conclusionsF\n            feature_array.append(sections.count('conclusions'))\n\n            # add the features to the instance container\n            self.instances[k] = np.array([feature_array[i - 1] for i\n                                          in features_set])\n\n        # scale features\n        self.feature_scaling()\n\n    def candidate_weighting(self, model_file=None, df=None):\n        \"\"\"Extract features and classify candidates.\n\n        Args:\n            model_file (str): path to the model file.\n            df (dict): document frequencies, the number of documents should\n                    be specified using the \"--NB_DOC--\" key.\n        \"\"\"\n\n        self.feature_extraction(df=df)\n        self.classify_candidates(model=model_file)\n\n    @staticmethod\n    def train(training_instances, training_classes, model_file):\n        \"\"\" Train a Naive Bayes classifier and store the model in a file.\n\n            Args:\n                training_instances (list): list of features.\n                training_classes (list): list of binary values.\n                model_file (str): the model output file.\n        \"\"\"\n\n        clf = MultinomialNB()\n        clf.fit(training_instances, training_classes)\n        joblib.dump(clf, model_file)\n        # with open(model_file, 'wb') as f:\n        #     pickle.dump(clf, f)\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/neural_based/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: supervised neural-based ranking models\n"
  },
  {
    "path": "onmt/keyphrase/pke/supervised/neural_based/seq2seq.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 11-11-2018\n\n\"\"\"\nImplementation of the Seq2Seq model for automatic keyphrase extraction.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nfrom onmt.keyphrase.pke.supervised.api import SupervisedLoadFile\n\n\nclass Seq2Seq(SupervisedLoadFile):\n\n    def __init__(self):\n        \"\"\"Redefining initializer for Seq2Seq.\"\"\"\n\n        super(Seq2Seq, self).__init__()\n\n        self.sequence = []\n        \"\"\"Input sequence.\"\"\"\n\n        self.vocabulary = ['<SOS>', '<EOS>', '<UNK>']\n        \"\"\"Vocabulary.\"\"\"\n\n    def document_to_ix(self):\n        \"\"\"Convert the document to a sequence of ix.\"\"\"\n\n        self.sequence.append(self.vocabulary.index('<SOS>'))\n        for i, sentence in enumerate(self.sentences):\n            for word in sentence.stems:\n                try:\n                    self.sequence.append(self.vocabulary.index(word))\n                except ValueError:\n                    self.sequence.append(self.vocabulary.index('<UNK>'))\n        self.sequence.append(self.vocabulary.index('<EOS>'))\n\n    def candidate_selection(self):\n        pass\n\n    def candidate_weighting(self):\n        pass\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: unsupervised models\n\nfrom __future__ import absolute_import\n\nfrom onmt.keyphrase.pke.unsupervised.graph_based.topicrank import TopicRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.singlerank import SingleRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.multipartiterank import MultipartiteRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.positionrank import PositionRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.single_tpr import TopicalPageRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.expandrank import ExpandRank\nfrom onmt.keyphrase.pke.unsupervised.graph_based.textrank import TextRank\n\nfrom onmt.keyphrase.pke.unsupervised.statistical.tfidf import TfIdf\nfrom onmt.keyphrase.pke.unsupervised.statistical.kpminer import KPMiner\nfrom onmt.keyphrase.pke.unsupervised.statistical.yake import YAKE\nfrom onmt.keyphrase.pke.unsupervised.statistical.firstphrases import FirstPhrases\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: unsupervised graph-based ranking models\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/expandrank.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 10-02-2018\n\n\"\"\"ExpandRank keyphrase extraction model.\n\nGraph-based ranking approach to keyphrase extraction described in:\n\n* Xiaojun Wan and Jianguo Xiao.\n  Single Document Keyphrase Extraction Using Neighborhood Knowledge.\n  *In proceedings of AAAI*, pages 855-860, 2008.\n\n\"\"\"\n\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\n\nfrom onmt.keyphrase.pke.unsupervised import SingleRank\nfrom onmt.keyphrase.pke.base import LoadFile\n\nimport networkx as nx\nimport logging\n\n\nclass ExpandRank(SingleRank):\n    \"\"\"ExpandRank keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        import string\n        from nltk.corpus import stopwords\n\n        # 1. create an ExpandRank extractor.\n        extractor = pke.unsupervised.ExpandRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input.xml')\n\n        # 3. select the the longest sequences of nouns and adjectives, that do\n        #    not contain punctuation marks or stopwords as candidates.\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n        stoplist = list(string.punctuation)\n        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n        stoplist += stopwords.words('english')\n        extractor.candidate_selection(pos=pos, stoplist=stoplist)\n\n        # 4. weight the candidates using the sum of their word's scores that are\n        #    computed using random walk. In the graph, nodes are words (nouns\n        #    and adjectives only) that are connected if they occur in a window\n        #    of 10 words. A set of extra documents should be provided to expand\n        #    the graph.\n        expanded_documents = [('path/to/input1.xml', similarity1),\n                              ('path/to/input2.xml', similarity2)]\n        extractor.candidate_weighting(window=10,\n                                      pos=pos,\n                                      expanded_documents=expanded_documents,\n                                      format='corenlp')\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\" Redefining initializer for ExpandRank. \"\"\"\n\n        super(ExpandRank, self).__init__()\n\n    def expand_word_graph(self,\n                          input_file,\n                          similarity,\n                          window=10,\n                          pos=None):\n        \"\"\"Expands the word graph using the given document.\n\n        Args:\n            input_file (str): path to the input file.\n            similarity (float): similarity for weighting edges.\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n        \"\"\"\n\n        # define default pos tags set\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # initialize document loader\n        doc = LoadFile()\n\n        # read document\n        doc.load_document(input=input_file,\n                          language=self.language,\n                          normalization=self.normalization)\n\n        # flatten document and initialize nodes \n        sequence = []\n\n        for sentence in doc.sentences:\n            for j, node in enumerate(sentence.stems):\n                if node not in self.graph and sentence.pos[j] in pos:\n                    self.graph.add_node(node)\n                sequence.append((node, sentence.pos[j]))\n\n        # loop through sequence to build the edges in the graph\n        for j, node_1 in enumerate(sequence):\n            for k in range(j + 1, min(j + window, len(sequence))):\n                node_2 = sequence[k]\n                if node_1[1] in pos and node_2[1] in pos \\\n                        and node_1[0] != node_2[0]:\n                    if not self.graph.has_edge(node_1[0], node_2[0]):\n                        self.graph.add_edge(node_1[0], node_2[0], weight=0)\n                    self.graph[node_1[0]][node_2[0]]['weight'] += similarity\n\n    def candidate_weighting(self,\n                            window=10,\n                            pos=None,\n                            expanded_documents=None,\n                            normalized=False):\n        \"\"\"Candidate ranking using random walk.\n\n        Args:\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n            expanded_documents (list): the set of documents to expand the graph,\n                should be a list of tuples (input_path, similarity). Defaults to\n                empty list, i.e. no expansion.\n            normalized (False): normalize keyphrase score by their length,\n                defaults to False.\n        \"\"\"\n\n        # define default pos tags set\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        if expanded_documents is None:\n            expanded_documents = []\n            logging.warning('No neighbor documents provided for ExpandRank.')\n\n        # build the word graph\n        self.build_word_graph(window=window, pos=pos)\n\n        # expand the word graph\n        for input_file, similarity in expanded_documents:\n            self.expand_word_graph(input_file=input_file,\n                                   similarity=similarity,\n                                   window=window,\n                                   pos=pos)\n\n        # compute the word scores using random walk\n        w = nx.pagerank_scipy(self.graph, alpha=0.85, weight='weight')\n\n        # loop through the candidates\n        for k in self.candidates.keys():\n            tokens = self.candidates[k].lexical_form\n            self.weights[k] = sum([w[t] for t in tokens])\n            if normalized:\n                self.weights[k] /= len(tokens)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/multipartiterank.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-11-2018\n\n\"\"\"Multipartite graph keyphrase extraction model.\n\nGraph-based ranking approach to keyphrase extraction described in:\n\n* Florian Boudin.\n  Unsupervised Keyphrase Extraction with Multipartite Graphs.\n  *In proceedings of NAACL*, pages 667-672, 2018.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nfrom itertools import combinations\n\nimport networkx as nx\nimport numpy as np\nfrom scipy.cluster.hierarchy import linkage, fcluster\nfrom scipy.spatial.distance import pdist\n\nfrom onmt.keyphrase.pke.unsupervised import TopicRank\n\n\nclass MultipartiteRank(TopicRank):\n    \"\"\"Multipartite graph keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        import string\n        from nltk.corpus import stopwords\n\n        # 1. create a MultipartiteRank extractor.\n        extractor = pke.unsupervised.MultipartiteRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input.xml')\n\n        # 3. select the longest sequences of nouns and adjectives, that do\n        #    not contain punctuation marks or stopwords as candidates.\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n        stoplist = list(string.punctuation)\n        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n        stoplist += stopwords.words('english')\n        extractor.candidate_selection(pos=pos, stoplist=stoplist)\n\n        # 4. build the Multipartite graph and rank candidates using random walk,\n        #    alpha controls the weight adjustment mechanism, see TopicRank for\n        #    threshold/method parameters.\n        extractor.candidate_weighting(alpha=1.1,\n                                      threshold=0.74,\n                                      method='average')\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for MultipartiteRank.\n        \"\"\"\n\n        super(MultipartiteRank, self).__init__()\n\n        self.topic_identifiers = {}\n        \"\"\" A container for linking candidates to topic identifiers. \"\"\"\n\n        self.graph = nx.DiGraph()\n        \"\"\" Redefine the graph as a directed graph. \"\"\"\n\n    def topic_clustering(self,\n                         threshold=0.74,\n                         method='average'):\n        \"\"\" Clustering candidates into topics.\n\n            Args:\n                threshold (float): the minimum similarity for clustering,\n                    defaults to 0.74, i.e. more than 1/4 of stem overlap\n                    similarity. \n                method (str): the linkage method, defaults to average.\n        \"\"\"\n\n        # handle document with only one candidate\n        if len(self.candidates) == 1:\n            candidate = list(self.candidates)[0]\n            self.topics.append([candidate])\n            self.topic_identifiers[candidate] = 0\n            return\n\n        # vectorize the candidates\n        candidates, X = self.vectorize_candidates()\n\n        # compute the distance matrix\n        Y = pdist(X, 'jaccard')\n        Y = np.nan_to_num(Y)\n\n        # compute the clusters\n        Z = linkage(Y, method=method)\n\n        # form flat clusters\n        clusters = fcluster(Z, t=threshold, criterion='distance')\n\n        # for each cluster id\n        for cluster_id in range(1, max(clusters) + 1):\n            self.topics.append([candidates[j] for j in range(len(clusters))\n                                if clusters[j] == cluster_id])\n\n        # assign cluster identifiers to candidates\n        for i, cluster_id in enumerate(clusters):\n            self.topic_identifiers[candidates[i]] = cluster_id - 1\n\n    def build_topic_graph(self):\n        \"\"\" Build the Multipartite graph. \"\"\"\n\n        # adding the nodes to the graph\n        self.graph.add_nodes_from(self.candidates.keys())\n\n        # pre-compute edge weights\n        for node_i, node_j in combinations(self.candidates.keys(), 2):\n\n            # discard intra-topic edges\n            if self.topic_identifiers[node_i] == self.topic_identifiers[node_j]:\n                continue\n\n            weights = []\n            for p_i in self.candidates[node_i].offsets:\n                for p_j in self.candidates[node_j].offsets:\n\n                    # compute gap\n                    gap = abs(p_i - p_j)\n\n                    # alter gap according to candidate length\n                    if p_i < p_j:\n                        gap -= len(self.candidates[node_i].lexical_form) - 1\n                    if p_j < p_i:\n                        gap -= len(self.candidates[node_j].lexical_form) - 1\n\n                    weights.append(1.0 / gap)\n\n            # add weighted edges \n            if weights:\n                # node_i -> node_j\n                self.graph.add_edge(node_i, node_j, weight=sum(weights))\n                # node_j -> node_i\n                self.graph.add_edge(node_j, node_i, weight=sum(weights))\n\n    def weight_adjustment(self, alpha=1.1):\n        \"\"\" Adjust edge weights for boosting some candidates.\n\n            Args:\n                alpha (float): hyper-parameter that controls the strength of the\n                    weight adjustment, defaults to 1.1.\n        \"\"\"\n\n        # weighted_edges = defaultdict(list)\n        weighted_edges = {}\n\n        # find the sum of all first positions\n        norm = sum([s.length for s in self.sentences])\n\n        # Topical boosting\n        for variants in self.topics:\n\n            # skip one candidate topics\n            if len(variants) == 1:\n                continue\n\n            # get the offsets\n            offsets = [self.candidates[v].offsets[0] for v in variants]\n\n            # get the first occurring variant\n            first = variants[offsets.index(min(offsets))]\n\n            # find the nodes to which it connects -- Python 2/3 compatible\n            # for start, end in self.graph.edges_iter(first):\n            for start, end in self.graph.edges(first):\n\n                boosters = []\n                for v in variants:\n                    if v != first and self.graph.has_edge(v, end):\n                        boosters.append(self.graph[v][end]['weight'])\n\n                if boosters:\n                    weighted_edges[(start, end)] = np.sum(boosters)\n\n        # update edge weights -- Python 2/3 compatible\n        # for nodes, boosters in weighted_edges.iteritems():\n        for nodes, boosters in weighted_edges.items():\n            node_i, node_j = nodes\n            position_i = 1.0 / (1 + self.candidates[node_i].offsets[0])\n            position_i = math.exp(position_i)\n            self.graph[node_j][node_i]['weight'] += (boosters * alpha * position_i)\n\n    def candidate_weighting(self,\n                            threshold=0.74,\n                            method='average',\n                            alpha=1.1):\n        \"\"\" Candidate weight calculation using random walk.\n\n            Args:\n                threshold (float): the minimum similarity for clustering,\n                    defaults to 0.25.\n                method (str): the linkage method, defaults to average.\n                alpha (float): hyper-parameter that controls the strength of the\n                    weight adjustment, defaults to 1.1.\n        \"\"\"\n\n        # cluster the candidates\n        self.topic_clustering(threshold=threshold, method=method)\n\n        # build the topic graph\n        self.build_topic_graph()\n\n        if alpha > 0.0:\n            self.weight_adjustment(alpha)\n\n        # compute the word scores using random walk\n        self.weights = nx.pagerank_scipy(self.graph)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/positionrank.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-11-2018\n\n\"\"\"PositionRank keyphrase extraction model.\n\nPositionRank is an unsupervised model for keyphrase extraction from scholarly\ndocuments that incorporates information from all positions of a word's\noccurrences into a biased PageRank. The model is described in:\n\n* Corina Florescu and Cornelia Caragea.\n  PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly\n  Documents.\n  *In proceedings of ACL*, pages 1105-1115, 2017.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom onmt.keyphrase.pke.unsupervised import SingleRank\n\nimport networkx as nx\nfrom collections import defaultdict\n\n\nclass PositionRank(SingleRank):\n    \"\"\"PositionRank keyphrase extraction model. \n\n    Parameterized example::\n\n        import pke\n\n        # define the valid Part-of-Speeches to occur in the graph\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # define the grammar for selecting the keyphrase candidates\n        grammar = \"NP: {<ADJ>*<NOUN|PROPN>+}\"\n\n        # 1. create a PositionRank extractor.\n        extractor = pke.unsupervised.PositionRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select the noun phrases up to 3 words as keyphrase candidates.\n        extractor.candidate_selection(grammar=grammar,\n                                      maximum_word_number=3)\n\n        # 4. weight the candidates using the sum of their word's scores that are\n        #    computed using random walk biaised with the position of the words\n        #    in the document. In the graph, nodes are words (nouns and\n        #    adjectives only) that are connected if they occur in a window of\n        #    10 words.\n        extractor.candidate_weighting(window=10,\n                                      pos=pos)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for PositionRank.\"\"\"\n\n        super(PositionRank, self).__init__()\n\n        self.positions = defaultdict(float)\n        \"\"\"Container the sums of word's inverse positions.\"\"\"\n\n    def candidate_selection(self,\n                            grammar=None,\n                            maximum_word_number=3,\n                            **kwargs):\n        \"\"\"Candidate selection heuristic using a syntactic PoS pattern for\n        noun phrase extraction.\n\n        Keyphrase candidates are noun phrases that match the regular expression\n        (adjective)*(noun)+, of length up to three.\n\n        Args:\n            grammar (str): grammar defining POS patterns of NPs, defaults to \n                \"NP: {<ADJ>*<NOUN|PROPN>+}\".\n            maximum_word_number (int): the maximum number of words allowed for\n                keyphrase candidates, defaults to 3.\n        \"\"\"\n\n        if grammar is None:\n            grammar = \"NP:{<ADJ>*<NOUN|PROPN>+}\"\n\n        # select sequence of adjectives and nouns\n        self.grammar_selection(grammar=grammar)\n\n        # filter candidates greater than 3 words\n        for k in list(self.candidates):\n            v = self.candidates[k]\n            if len(v.lexical_form) > maximum_word_number:\n                del self.candidates[k]\n\n    def build_word_graph(self, window=10, pos=None):\n        \"\"\"Build the graph representation of the document.\n\n        In the graph, nodes are words that passes a Part-of-Speech filter. Two\n        nodes are connected if the words corresponding to these nodes co-occur\n        within a `window` of contiguous tokens. The weight of an edge is\n        computed based on the co-occurrence count of the two words within a\n        `window` of successive tokens.\n\n        Args:\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # flatten document as a sequence of only valid (word, position) tuples\n        text = []\n        for i, sentence in enumerate(self.sentences):\n            shift = sum([s.length for s in self.sentences[0:i]])\n            for j, word in enumerate(sentence.stems):\n                if sentence.pos[j] in pos:\n                    text.append((word, shift+j))\n\n        # add nodes to the graph\n        self.graph.add_nodes_from([word for (word, position) in text])\n\n        # add edges to the graph\n        for i, (node1, position1) in enumerate(text):\n            j = i+1\n            while j < len(text) and (text[j][1] - position1) < window:\n                node2, position2 = text[j]\n                if node1 != node2:\n                    if not self.graph.has_edge(node1, node2):\n                        self.graph.add_edge(node1, node2, weight=0)\n                    self.graph[node1][node2]['weight'] += 1\n                j = j + 1\n\n        # compute the sums of the word's inverse positions\n        for word, position in text:\n            self.positions[word] += 1 / (position + 1)\n\n    def candidate_weighting(self, window=10, pos=None, normalized=False):\n        \"\"\"Candidate weight calculation using a biased PageRank.\n\n        Args:\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n            normalized (False): normalize keyphrase score by their length,\n                defaults to False.\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # build the word graph\n        self.build_word_graph(window=window,\n                              pos=pos)\n\n        # normalize cumulated inverse positions\n        norm = sum(self.positions.values())\n        for word in self.positions:\n            self.positions[word] /= norm\n\n        # compute the word scores using biased random walk\n        w = nx.pagerank(G=self.graph,\n                        alpha=0.85,\n                        tol=0.0001,\n                        personalization=self.positions,\n                        weight='weight')\n\n        # loop through the candidates\n        for k in self.candidates.keys():\n            tokens = self.candidates[k].lexical_form\n            self.weights[k] = sum([w.get(t, 0.0) for t in tokens])\n            if normalized:\n                self.weights[k] /= len(tokens)\n\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/single_tpr.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-11-2018\n\n\"\"\"Single Topical PageRank keyphrase extraction model.\n\nThis implementation is an improvement on a keyphrase extraction algorithm,\nTopical PageRank (TPR), incorporating topical information from topic model and\ndescribed in:\n\n* Lucas Sterckx, Thomas Demeester, Johannes Deleu and Chris Develder.\n  Topical Word Importance for Fast Keyphrase Extraction.\n  *In proceedings of WWW*, pages 121-122, 2015.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport gzip\nimport os\nimport pickle\nimport logging\n\nimport networkx as nx\nimport numpy as np\nimport six\nfrom scipy.spatial.distance import cosine\nfrom sklearn.decomposition import LatentDirichletAllocation\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nfrom onmt.keyphrase.pke.unsupervised import SingleRank\n\n\nclass TopicalPageRank(SingleRank):\n    \"\"\"Single TopicalPageRank keyphrase extraction model. \n\n    Parameterized example::\n\n        import pke\n        from nltk.corpus import stopwords\n\n        # define the valid Part-of-Speeches to occur in the graph\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # define the grammar for selecting the keyphrase candidates\n        grammar = \"NP: {<ADJ>*<NOUN|PROPN>+}\"\n\n        # 1. create a TopicalPageRank extractor.\n        extractor = pke.unsupervised.TopicalPageRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select the noun phrases as keyphrase candidates.\n        extractor.candidate_selection(grammar=grammar)\n\n        # 4. weight the keyphrase candidates using Single Topical PageRank.\n        #    Builds a word-graph in which edges connecting two words occurring\n        #    in a window are weighted by co-occurrence counts.\n        extractor.candidate_weighting(window=10,\n                                      pos=pos,\n                                      lda_model='path/to/lda_model')\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for TopicalPageRank.\"\"\"\n\n        super(TopicalPageRank, self).__init__()\n\n    def candidate_selection(self, grammar=None, **kwargs):\n        \"\"\"Candidate selection heuristic.\n\n        Here we select noun phrases that match the regular expression\n        (adjective)*(noun)+, which represents zero or more adjectives followed\n        by one or more nouns (Liu et al., 2010).\n\n        Note that there is no details on this in the Single TPR paper, and these\n        are the only information that can be found:\n\n            ... a set of expressions or noun phrases ...\n\n            ... Adjectives and nouns are then merged into keyphrases and\n            corresponding scores are summed and ranked. ...\n\n        Args:\n            grammar (str): grammar defining POS patterns of NPs, defaults to \n                \"NP: {<ADJ>*<NOUN|PROPN>+}\".\n        \"\"\"\n\n        if grammar is None:\n            grammar = \"NP:{<ADJ>*<NOUN|PROPN>+}\"\n\n        # select sequence of adjectives and nouns\n        self.grammar_selection(grammar=grammar)\n\n    def candidate_weighting(self,\n                            window=10,\n                            pos=None,\n                            lda_model=None,\n                            stoplist=None,\n                            normalized=False):\n        \"\"\"Candidate weight calculation using a biased PageRank towards LDA\n        topic distributions.\n\n        Args:\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as\n                nodes in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n            lda_model (pickle.gz): an LDA model produced by sklearn in\n                pickle compressed (.gz) format\n            stoplist (list): the stoplist for filtering words in LDA, defaults\n                to the nltk stoplist.\n            normalized (False): normalize keyphrase score by their length,\n                defaults to False.\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # initialize stoplist list if not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # build the word graph\n        # ``Since keyphrases are usually noun phrases, we only add adjectives\n        # and nouns in word graph.'' -> (Liu et al., 2010)\n        self.build_word_graph(window=window,\n                              pos=pos)\n\n        # create a blank model\n        model = LatentDirichletAllocation()\n\n        # set the default LDA model if none provided\n        if lda_model is None:\n            if six.PY2:\n                lda_model = os.path.join(self._models,\n                                         \"lda-1000-semeval2010.py2.pickle.gz\")\n            else:\n                lda_model = os.path.join(self._models,\n                                         \"lda-1000-semeval2010.py3.pickle.gz\")\n            logging.warning('LDA model is hard coded to {}'.format(lda_model))\n\n        # load parameters from file\n        with gzip.open(lda_model, 'rb') as f:\n            (dictionary,\n             model.components_,\n             model.exp_dirichlet_component_,\n             model.doc_topic_prior_) = pickle.load(f)\n\n        # build the document representation\n        doc = []\n        for s in self.sentences:\n            doc.extend([s.stems[i] for i in range(s.length)])\n\n        # vectorize document\n        tf_vectorizer = CountVectorizer(stop_words=stoplist,\n                                        vocabulary=dictionary)\n\n        tf = tf_vectorizer.fit_transform([' '.join(doc)])\n\n        # compute the topic distribution over the document\n        distribution_topic_document = model.transform(tf)[0]\n\n        # compute the word distributions over topics\n        distributions = model.components_ / model.components_.sum(axis=1)[:,\n                                            np.newaxis]\n\n        # Computing W(w_i) indicating the full topical importance of each word\n        # w_i in the PageRank\n\n        # First, we determine the cosine similarity between the vector of\n        # word-topic probabilities P(w_i, Z) and the document-topic\n        # probabilities of the document P(Z, d)\n        K = len(distribution_topic_document)\n        W = {}\n        for word in self.graph.nodes():\n            if word in dictionary:\n                index = dictionary.index(word)\n                distribution_word_topic = [distributions[k][index] for k\n                                           in range(K)]\n                W[word] = 1 - cosine(distribution_word_topic,\n                                     distribution_topic_document)\n\n        # get the default probability for OOV words\n        default_similarity = min(W.values())\n        for word in self.graph.nodes():\n            if word not in W:\n                W[word] = 0.0\n\n        # Normalize the topical word importance of words\n        norm = sum(W.values())\n        for word in W:\n            W[word] /= norm\n\n        # compute the word scores using biased random walk\n        w = nx.pagerank(G=self.graph,\n                        personalization=W,\n                        alpha=0.85,\n                        tol=0.0001,\n                        weight='weight')\n\n        # loop through the candidates\n        for k in self.candidates.keys():\n            tokens = self.candidates[k].lexical_form\n            self.weights[k] = sum([w[t] for t in tokens])\n            if normalized:\n                self.weights[k] /= len(tokens)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/singlerank.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-11-2018\n\n\"\"\"SingleRank keyphrase extraction model.\n\nSimple extension of the TextRank model described in:\n\n* Xiaojun Wan and Jianguo Xiao.\n  CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase\n  Extraction.\n  *In proceedings of the COLING*, pages 969-976, 2008.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport networkx as nx\n\nfrom onmt.keyphrase.pke.unsupervised.graph_based.textrank import TextRank\n\n\nclass SingleRank(TextRank):\n    \"\"\"SingleRank keyphrase extraction model.\n\n    This model is an extension of the TextRank model that uses the number of\n    co-occurrences to weigh edges in the graph.\n\n    Parameterized example::\n\n        import pke\n\n        # define the set of valid Part-of-Speeches\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # 1. create a SingleRank extractor.\n        extractor = pke.unsupervised.SingleRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select the longest sequences of nouns and adjectives as candidates.\n        extractor.candidate_selection(pos=pos)\n\n        # 4. weight the candidates using the sum of their word's scores that are\n        #    computed using random walk. In the graph, nodes are words of\n        #    certain part-of-speech (nouns and adjectives) that are connected if\n        #    they occur in a window of 10 words.\n        extractor.candidate_weighting(window=10,\n                                      pos=pos)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for SingleRank.\"\"\"\n\n        super(SingleRank, self).__init__()\n\n    def build_word_graph(self, window=10, pos=None):\n        \"\"\"Build a graph representation of the document in which nodes/vertices\n        are words and edges represent co-occurrence relation. Syntactic filters\n        can be applied to select only words of certain Part-of-Speech.\n        Co-occurrence relations can be controlled using the distance (window)\n        between word occurrences in the document.\n\n        The number of times two words co-occur in a window is encoded as *edge\n        weights*. Sentence boundaries **are not** taken into account in the\n        window.\n\n        Args:\n            window (int): the window for connecting two words in the graph,\n                defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # flatten document as a sequence of (word, pass_syntactic_filter) tuples\n        text = [(word, sentence.pos[i] in pos) for sentence in self.sentences\n                for i, word in enumerate(sentence.stems)]\n\n        # add nodes to the graph\n        self.graph.add_nodes_from([word for word, valid in text if valid])\n\n        # add edges to the graph\n        for i, (node1, is_in_graph1) in enumerate(text):\n\n            # speed up things\n            if not is_in_graph1:\n                continue\n\n            for j in range(i + 1, min(i + window, len(text))):\n                node2, is_in_graph2 = text[j]\n                if is_in_graph2 and node1 != node2:\n                    if not self.graph.has_edge(node1, node2):\n                        self.graph.add_edge(node1, node2, weight=0.0)\n                    self.graph[node1][node2]['weight'] += 1.0\n\n    def candidate_weighting(self, window=10, pos=None, normalized=False):\n        \"\"\"Keyphrase candidate ranking using the weighted variant of the\n        TextRank formulae. Candidates are scored by the sum of the scores of\n        their words.\n\n        Args:\n            window (int): the window within the sentence for connecting two\n                words in the graph, defaults to 10.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n            normalized (False): normalize keyphrase score by their length,\n                defaults to False.\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # build the word graph\n        self.build_word_graph(window=window, pos=pos)\n\n        # compute the word scores using random walk\n        w = nx.pagerank_scipy(self.graph,\n                              alpha=0.85,\n                              tol=0.0001,\n                              weight='weight')\n\n        # loop through the candidates\n        for k in self.candidates.keys():\n            tokens = self.candidates[k].lexical_form\n            self.weights[k] = sum([w[t] for t in tokens])\n            if normalized:\n                self.weights[k] /= len(tokens)\n\n            # use position to break ties\n            self.weights[k] += (self.candidates[k].offsets[0] * 1e-8)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/textrank.py",
    "content": "# -*- coding: utf-8 -*-\n# Authors: Ygor Gallina, Florian Boudin\n# Date: 10-18-2018\n\n\"\"\"TextRank keyphrase extraction model.\n\nImplementation of the TextRank model for keyword extraction described in:\n\n* Rada Mihalcea and Paul Tarau.\n  TextRank: Bringing Order into Texts\n  *In Proceedings of EMNLP*, 2004.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport logging\n\nimport networkx as nx\n\nfrom onmt.keyphrase.pke.base import LoadFile\n\n\nclass TextRank(LoadFile):\n    \"\"\"TextRank for keyword extraction.\n\n    This model builds a graph that represents the text. A graph based ranking\n    algorithm is then applied to extract the lexical units (here the words) that\n    are most important in the text.\n\n    In this implementation, nodes are words of certain part-of-speech (nouns\n    and adjectives) and edges represent co-occurrence relation, controlled by\n    the distance between word occurrences (here a window of 2 words). Nodes\n    are ranked by the TextRank graph-based ranking algorithm in its unweighted\n    variant.\n\n    Parameterized example::\n\n        import pke\n\n        # define the set of valid Part-of-Speeches\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # 1. create a TextRank extractor.\n        extractor = pke.unsupervised.TextRank()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. build the graph representation of the document and rank the words.\n        #    Keyphrase candidates are composed from the 33-percent\n        #    highest-ranked words.\n        extractor.candidate_weighting(window=2,\n                                      pos=pos,\n                                      top_percent=0.33)\n\n        # 4. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for TextRank.\"\"\"\n\n        super(TextRank, self).__init__()\n\n        self.graph = nx.Graph()\n        \"\"\"The word graph.\"\"\"\n\n    def candidate_selection(self, pos=None):\n        \"\"\"Candidate selection using longest sequences of PoS.\n\n        Args:\n            pos (set): set of valid POS tags, defaults to ('NOUN', 'PROPN',\n                'ADJ').\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # select sequence of adjectives and nouns\n        self.longest_pos_sequence_selection(valid_pos=pos)\n\n    def build_word_graph(self, window=2, pos=None):\n        \"\"\"Build a graph representation of the document in which nodes/vertices\n        are words and edges represent co-occurrence relation. Syntactic filters\n        can be applied to select only words of certain Part-of-Speech.\n        Co-occurrence relations can be controlled using the distance between\n        word occurrences in the document.\n\n        As the original paper does not give precise details on how the word\n        graph is constructed, we make the following assumptions from the example\n        given in Figure 2: 1) sentence boundaries **are not** taken into account\n        and, 2) stopwords and punctuation marks **are** considered as words when\n        computing the window.\n\n        Args:\n            window (int): the window for connecting two words in the graph,\n                defaults to 2.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # flatten document as a sequence of (word, pass_syntactic_filter) tuples\n        text = [(word, sentence.pos[i] in pos) for sentence in self.sentences\n                for i, word in enumerate(sentence.stems)]\n\n        # add nodes to the graph\n        self.graph.add_nodes_from([word for word, valid in text if valid])\n\n        # add edges to the graph\n        for i, (node1, is_in_graph1) in enumerate(text):\n\n            # speed up things\n            if not is_in_graph1:\n                continue\n\n            for j in range(i + 1, min(i + window, len(text))):\n                node2, is_in_graph2 = text[j]\n                if is_in_graph2 and node1 != node2:\n                    self.graph.add_edge(node1, node2)\n\n    def candidate_weighting(self,\n                            window=2,\n                            pos=None,\n                            top_percent=None,\n                            normalized=False):\n        \"\"\"Tailored candidate ranking method for TextRank. Keyphrase candidates\n        are either composed from the T-percent highest-ranked words as in the\n        original paper or extracted using the `candidate_selection()` method.\n        Candidates are ranked using the sum of their (normalized?) words.\n\n        Args:\n            window (int): the window for connecting two words in the graph,\n                defaults to 2.\n            pos (set): the set of valid pos for words to be considered as nodes\n                in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').\n            top_percent (float): percentage of top vertices to keep for phrase\n                generation.\n            normalized (False): normalize keyphrase score by their length,\n                defaults to False.\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # build the word graph\n        self.build_word_graph(window=window, pos=pos)\n\n        # compute the word scores using the unweighted PageRank formulae\n        w = nx.pagerank_scipy(self.graph, alpha=0.85, tol=0.0001, weight=None)\n\n        # generate the phrases from the T-percent top ranked words\n        if top_percent is not None:\n\n            # warn user as this is not the pke way of doing it\n            logging.warning(\"Candidates are generated using {}-top\".format(\n                            top_percent))\n\n            # computing the number of top keywords\n            nb_nodes = self.graph.number_of_nodes()\n            to_keep = min(math.floor(nb_nodes * top_percent), nb_nodes)\n\n            # sorting the nodes by decreasing scores\n            top_words = sorted(w, key=w.get, reverse=True)\n\n            # creating keyphrases from the T-top words\n            self.longest_keyword_sequence_selection(top_words[:int(to_keep)])\n\n        # weight candidates using the sum of their word scores\n        for k in self.candidates.keys():\n            tokens = self.candidates[k].lexical_form\n            self.weights[k] = sum([w[t] for t in tokens])\n            if normalized:\n                self.weights[k] /= len(tokens)\n\n            # use position to break ties\n            self.weights[k] += (self.candidates[k].offsets[0]*1e-8)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/graph_based/topicrank.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"TopicRank keyphrase extraction model.\n\nGraph-based ranking approach to keyphrase extraction described in:\n\n* Adrien Bougouin, Florian Boudin and Béatrice Daille.\n  TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction.\n  *In proceedings of IJCNLP*, pages 543-551, 2013.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport string\nfrom itertools import combinations\n\nimport networkx as nx\nimport numpy as np\nfrom scipy.cluster.hierarchy import linkage, fcluster\nfrom scipy.spatial.distance import pdist\n\nfrom onmt.keyphrase.pke.base import LoadFile\n\n\nclass TopicRank(LoadFile):\n    \"\"\"TopicRank keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        import string\n        from nltk.corpus import stopwords\n\n        # 1. create a TopicRank extractor.\n        extractor = pke.unsupervised.TopicRank()\n\n        # 2. load the content of the document.\n       extractor.load_document(input='path/to/input.xml')\n\n        # 3. select the longest sequences of nouns and adjectives, that do\n        #    not contain punctuation marks or stopwords as candidates.\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n        stoplist = list(string.punctuation)\n        stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n        stoplist += stopwords.words('english')\n        extractor.candidate_selection(pos=pos, stoplist=stoplist)\n\n        # 4. build topics by grouping candidates with HAC (average linkage,\n        #    threshold of 1/4 of shared stems). Weight the topics using random\n        #    walk, and select the first occuring candidate from each topic.\n        extractor.candidate_weighting(threshold=0.74, method='average')\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for TopicRank.\n        \"\"\"\n\n        super(TopicRank, self).__init__()\n\n        self.graph = nx.Graph()\n        \"\"\" The topic graph. \"\"\"\n\n        self.topics = []\n        \"\"\" The topic container. \"\"\"\n\n    def candidate_selection(self, pos=None, stoplist=None):\n        \"\"\"Selects longest sequences of nouns and adjectives as keyphrase\n        candidates.\n\n        Args:\n            pos (set): the set of valid POS tags, defaults to ('NOUN',\n                'PROPN', 'ADJ').\n            stoplist (list): the stoplist for filtering candidates, defaults to\n                the nltk stoplist. Words that are punctuation marks from\n                string.punctuation are not allowed.\n\n        \"\"\"\n\n        # define default pos tags set\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # select sequence of adjectives and nouns\n        self.longest_pos_sequence_selection(valid_pos=pos)\n\n        # initialize stoplist list if not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # filter candidates containing stopwords or punctuation marks\n        self.candidate_filtering(stoplist=list(string.punctuation) +\n                                          ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-'] +\n                                          stoplist)\n\n    def vectorize_candidates(self):\n        \"\"\"Vectorize the keyphrase candidates.\n\n        Returns:\n            C (list): the list of candidates.\n            X (matrix): vectorized representation of the candidates.\n\n        \"\"\"\n\n        # build the vocabulary, i.e. setting the vector dimensions\n        dim = set([])\n        # for k, v in self.candidates.iteritems():\n        # iterate Python 2/3 compatible\n        for (k, v) in self.candidates.items():\n            for w in v.lexical_form:\n                dim.add(w)\n        dim = list(dim)\n\n        # vectorize the candidates Python 2/3 + sort for random issues\n        C = list(self.candidates)  # .keys()\n        C.sort()\n\n        X = np.zeros((len(C), len(dim)))\n        for i, k in enumerate(C):\n            for w in self.candidates[k].lexical_form:\n                X[i, dim.index(w)] += 1\n\n        return C, X\n\n    def topic_clustering(self, threshold=0.74, method='average'):\n        \"\"\"Clustering candidates into topics.\n\n        Args:\n            threshold (float): the minimum similarity for clustering, defaults\n                to 0.74, i.e. more than 1/4 of stem overlap similarity.\n            method (str): the linkage method, defaults to average.\n\n        \"\"\"\n\n        # handle document with only one candidate\n        if len(self.candidates) == 1:\n            self.topics.append([list(self.candidates)[0]])\n            return\n\n        # vectorize the candidates\n        candidates, X = self.vectorize_candidates()\n\n        # compute the distance matrix\n        Y = pdist(X, 'jaccard')\n\n        # compute the clusters\n        Z = linkage(Y, method=method)\n\n        # form flat clusters\n        clusters = fcluster(Z, t=threshold, criterion='distance')\n\n        # for each topic identifier\n        for cluster_id in range(1, max(clusters) + 1):\n            self.topics.append([candidates[j] for j in range(len(clusters))\n                                if clusters[j] == cluster_id])\n\n    def build_topic_graph(self):\n        \"\"\"Build topic graph.\"\"\"\n\n        # adding the nodes to the graph\n        self.graph.add_nodes_from(range(len(self.topics)))\n\n        # loop through the topics to connect the nodes\n        for i, j in combinations(range(len(self.topics)), 2):\n            self.graph.add_edge(i, j, weight=0.0)\n            for c_i in self.topics[i]:\n                for c_j in self.topics[j]:\n                    for p_i in self.candidates[c_i].offsets:\n                        for p_j in self.candidates[c_j].offsets:\n                            gap = abs(p_i - p_j)\n                            if p_i < p_j:\n                                gap -= len(self.candidates[c_i].lexical_form) - 1\n                            if p_j < p_i:\n                                gap -= len(self.candidates[c_j].lexical_form) - 1\n                            self.graph[i][j]['weight'] += 1.0 / gap\n\n    def candidate_weighting(self,\n                            threshold=0.74,\n                            method='average',\n                            heuristic=None):\n        \"\"\"Candidate ranking using random walk.\n\n        Args:\n            threshold (float): the minimum similarity for clustering, defaults\n                to 0.74.\n            method (str): the linkage method, defaults to average.\n            heuristic (str): the heuristic for selecting the best candidate for\n                each topic, defaults to first occurring candidate. Other options\n                are 'frequent' (most frequent candidate, position is used for\n                ties).\n\n        \"\"\"\n\n        # cluster the candidates\n        self.topic_clustering(threshold=threshold, method=method)\n\n        # build the topic graph\n        self.build_topic_graph()\n\n        # compute the word scores using random walk\n        w = nx.pagerank_scipy(self.graph, alpha=0.85, weight='weight')\n\n        # loop through the topics\n        for i, topic in enumerate(self.topics):\n\n            # get the offsets of the topic candidates\n            offsets = [self.candidates[t].offsets[0] for t in topic]\n\n            # get first candidate from topic\n            if heuristic == 'frequent':\n\n                # get frequencies for each candidate within the topic\n                freq = [len(self.candidates[t].surface_forms) for t in topic]\n\n                # get the indexes of the most frequent candidates\n                indexes = [j for j, f in enumerate(freq) if f == max(freq)]\n\n                # offsets of the indexes\n                indexes_offsets = [offsets[j] for j in indexes]\n                most_frequent = indexes_offsets.index(min(indexes_offsets))\n                self.weights[topic[most_frequent]] = w[i]\n\n            else:\n                first = offsets.index(min(offsets))\n                self.weights[topic[first]] = w[i]\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/statistical/__init__.py",
    "content": "# -*- coding: utf-8 -*-\n# Python Keyphrase Extraction toolkit: unsupervised statistical ranking models\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/statistical/firstphrases.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: ygor Gallina\n# Date: 19-10-2018\n\n\"\"\"StupidKE keyphrase extraction model.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom onmt.keyphrase.pke.base import LoadFile\n\n\nclass FirstPhrases(LoadFile):\n    \"\"\"Baseline model that extracts the first phrases of a document.\n\n    Parameterized example::\n\n        import pke\n\n        # define the set of valid Part-of-Speeches\n        pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # 1. create a FirstPhrases baseline extractor.\n        extractor = pke.unsupervised.FirstPhrases()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select the longest sequences of nouns and adjectives as candidates.\n        extractor.candidate_selection(pos=pos)\n\n        # 4. weight the candidates using their position\n        extractor.candidate_weighting()\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n\n    \"\"\"\n\n    def candidate_selection(self, pos=None):\n        \"\"\"Candidate selection using longest sequences of PoS.\n\n        Args:\n            pos (set): set of valid POS tags, defaults to ('NOUN', 'PROPN',\n                'ADJ').\n        \"\"\"\n\n        if pos is None:\n            pos = {'NOUN', 'PROPN', 'ADJ'}\n\n        # select sequence of adjectives and nouns\n        self.longest_pos_sequence_selection(valid_pos=pos)\n\n    def candidate_weighting(self):\n        \"\"\"Candidate weighting function using position.\"\"\"\n\n        # rank candidates using inverse position\n        for k in self.candidates.keys():\n            # the '-' ensures that the first item will have the higher weight\n            self.weights[k] = -min(self.candidates[k].offsets)\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/statistical/kpminer.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"KP-Miner keyphrase extraction model.\n\nStatistical approach to keyphrase extraction described in:\n\n* Samhaa R. El-Beltagy and Ahmed Rafea.\n  KP-Miner: Participation in SemEval-2.\n  *Proceedings of SemEval*, pages 190-193, 2010.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport string\nimport logging\n\nfrom onmt.keyphrase.pke.base import LoadFile\nfrom onmt.keyphrase.pke.utils import load_document_frequency_file\n\n\nclass KPMiner(LoadFile):\n    \"\"\"KP-Miner keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n\n        # 1. create a KPMiner extractor. \n        extractor = pke.unsupervised.KPMiner()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n\n        # 3. select {1-5}-grams that do not contain punctuation marks or\n        #    stopwords as keyphrase candidates. Set the least allowable seen\n        #    frequency to 5 and the number of words after which candidates are\n        #    filtered out to 200.\n        lasf = 5\n        cutoff = 200\n        extractor.candidate_selection(lasf=lasf, cutoff=cutoff)\n\n        # 4. weight the candidates using KPMiner weighting function.\n        df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz')\n        alpha = 2.3\n        sigma = 3.0\n        extractor.candidate_weighting(df=df, alpha=alpha, sigma=sigma)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n    \"\"\"\n\n    def candidate_selection(self, lasf=3, cutoff=400, stoplist=None, **kwargs):\n        \"\"\"The candidate selection as described in the KP-Miner paper.\n\n        Args:\n            lasf (int): least allowable seen frequency, defaults to 3.\n            cutoff (int): the number of words after which candidates are\n                filtered out, defaults to 400.\n            stoplist (list): the stoplist for filtering candidates, defaults\n                to the nltk stoplist. Words that are punctuation marks from\n                string.punctuation are not allowed.\n        \"\"\"\n\n        # select ngrams from 1 to 5 grams\n        self.ngram_selection(n=5)\n\n        # initialize stoplist list if not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # filter candidates containing stopwords or punctuation marks\n        self.candidate_filtering(stoplist=list(string.punctuation) + stoplist)\n\n        # further filter candidates using lasf and cutoff\n        # Python 2/3 compatible\n        for k in list(self.candidates):\n\n            # get the candidate\n            v = self.candidates[k]\n\n            # delete if first candidate offset is greater than cutoff\n            if v.offsets[0] > cutoff:\n                del self.candidates[k]\n\n            # delete if frequency is lower than lasf\n            elif len(v.surface_forms) < lasf:\n                del self.candidates[k]\n\n    def candidate_weighting(self, df=None, sigma=3.0, alpha=2.3):\n        \"\"\"Candidate weight calculation as described in the KP-Miner paper.\n\n        Note:\n            w = tf * idf * B * P_f\n            with\n            \n              * B = N_d / (P_d * alpha) and B = min(sigma, B)\n              * N_d = the number of all candidate terms\n              * P_d = number of candidates whose length exceeds one\n              * P_f = 1\n\n        Args:\n            df (dict): document frequencies, the number of documents should\n                be specified using the \"--NB_DOC--\" key.\n            sigma (int): parameter for boosting factor, defaults to 3.0.\n            alpha (int): parameter for boosting factor, defaults to 2.3.\n        \"\"\"\n\n        # initialize default document frequency counts if none provided\n        if df is None:\n            logging.warning('LoadFile._df_counts is hard coded to {}'.format(\n                self._df_counts))\n            df = load_document_frequency_file(self._df_counts, delimiter='\\t')\n\n        # initialize the number of documents as --NB_DOC-- + 1 (current)\n        N = 1 + df.get('--NB_DOC--', 0)\n\n        # compute the number of candidates whose length exceeds one\n        P_d = sum([len(v.surface_forms) for v in self.candidates.values()\n                   if len(v.lexical_form) > 1])\n\n        # fall back to 1 if all candidates are words\n        P_d = max(1, P_d)\n\n        # compute the number of all candidate terms\n        N_d = sum([len(v.surface_forms) for v in self.candidates.values()])\n\n        # compute the boosting factor\n        B = min(N_d / (P_d * alpha), sigma)\n\n        # loop throught the candidates\n        for k, v in self.candidates.items():\n\n            # get candidate document frequency\n            candidate_df = 1\n\n            # get the df for unigram only\n            if len(v.lexical_form) == 1:\n                candidate_df += df.get(k, 0)\n\n            # compute the idf score\n            idf = math.log(N / candidate_df, 2)\n\n            self.weights[k] = len(v.surface_forms) * B * idf\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/statistical/tfidf.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin\n# Date: 09-10-2018\n\n\"\"\"TF-IDF keyphrase extraction model.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport string\nimport logging\n\nfrom onmt.keyphrase.pke.base import LoadFile\nfrom onmt.keyphrase.pke.utils import load_document_frequency_file\n\n\nclass TfIdf(LoadFile):\n    \"\"\"TF*IDF keyphrase extraction model.\n\n    Parameterized example::\n\n        import string\n        import pke\n\n        # 1. create a TfIdf extractor.\n        extractor = pke.unsupervised.TfIdf()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n        # 3. select {1-3}-grams not containing punctuation marks as candidates.\n        extractor.candidate_selection(n=3,\n                                      stoplist=list(string.punctuation))\n\n        # 4. weight the candidates using a `tf` x `idf`\n        df = pke.load_document_frequency_file(input_file='path/to/df.tsv.gz')\n        extractor.candidate_weighting(df=df)\n\n        # 5. get the 10-highest scored candidates as keyphrases\n        keyphrases = extractor.get_n_best(n=10)\n    \"\"\"\n\n    def candidate_selection(self, n=3, stoplist=None, **kwargs):\n        \"\"\"Select 1-3 grams as keyphrase candidates.\n\n        Args:\n            n (int): the length of the n-grams, defaults to 3.\n            stoplist (list): the stoplist for filtering candidates, defaults to\n                `None`. Words that are punctuation marks from\n                `string.punctuation` are not allowed.\n\n        \"\"\"\n\n        # select ngrams from 1 to 3 grams\n        self.ngram_selection(n=n)\n\n        # initialize empty list if stoplist is not provided\n        if stoplist is None:\n            stoplist = list(string.punctuation)\n\n        # filter candidates containing punctuation marks\n        self.candidate_filtering(stoplist=stoplist)\n\n    def candidate_weighting(self, df=None):\n        \"\"\"Candidate weighting function using document frequencies.\n\n        Args:\n            df (dict): document frequencies, the number of documents should be\n                specified using the \"--NB_DOC--\" key.\n        \"\"\"\n\n        # initialize default document frequency counts if none provided\n        if df is None:\n            logging.warning('LoadFile._df_counts is hard coded to {}'.format(\n                self._df_counts))\n            df = load_document_frequency_file(self._df_counts, delimiter='\\t')\n\n        # initialize the number of documents as --NB_DOC-- + 1 (current)\n        N = 1 + df.get('--NB_DOC--', 0)\n\n        # loop throught the candidates\n        for k, v in self.candidates.items():\n\n            # get candidate document frequency\n            candidate_df = 1 + df.get(k, 0)\n\n            # compute the idf score\n            idf = math.log(N / candidate_df, 2)\n\n            # add the idf score to the weights container\n            self.weights[k] = len(v.surface_forms) * idf\n"
  },
  {
    "path": "onmt/keyphrase/pke/unsupervised/statistical/yake.py",
    "content": "# -*- coding: utf-8 -*-\n# Author: Florian Boudin and Vítor Mangaravite\n# Date: 09-10-2018\n\n\"\"\"YAKE keyphrase extraction model.\n\nStatistical approach to keyphrase extraction described in:\n\n* Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Mário Jorge,\n  Célia Nunes and Adam Jatowt.\n  YAKE! Keyword extraction from single documents using multiple local features.\n  *Information Sciences*, pages 257-289, 2020.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport re\nimport string\nfrom collections import defaultdict\n\nimport numpy\nfrom nltk.metrics import edit_distance\n\nfrom onmt.keyphrase.pke.base import LoadFile\n\n\nclass YAKE(LoadFile):\n    \"\"\"YAKE keyphrase extraction model.\n\n    Parameterized example::\n\n        import pke\n        from nltk.corpus import stopwords\n\n        # 1. create a YAKE extractor.\n        extractor = pke.unsupervised.YAKE()\n\n        # 2. load the content of the document.\n        extractor.load_document(input='path/to/input',\n                                language='en',\n                                normalization=None)\n\n\n        # 3. select {1-3}-grams not containing punctuation marks and not\n        #    beginning/ending with a stopword as candidates.\n        stoplist = stopwords.words('english')\n        extractor.candidate_selection(n=3, stoplist=stoplist)\n\n        # 4. weight the candidates using YAKE weighting scheme, a window (in\n        #    words) for computing left/right contexts can be specified.\n        window = 2\n        use_stems = False # use stems instead of words for weighting\n        extractor.candidate_weighting(window=window,\n                                      stoplist=stoplist,\n                                      use_stems=use_stems)\n\n        # 5. get the 10-highest scored candidates as keyphrases.\n        #    redundant keyphrases are removed from the output using levenshtein\n        #    distance and a threshold.\n        threshold = 0.8\n        keyphrases = extractor.get_n_best(n=10, threshold=threshold)\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Redefining initializer for YAKE.\n        \"\"\"\n\n        super(YAKE, self).__init__()\n\n        self.words = defaultdict(set)\n        \"\"\" Container for the vocabulary. \"\"\"\n\n        self.contexts = defaultdict(lambda: ([], []))\n        \"\"\" Container for word contexts. \"\"\"\n\n        self.features = defaultdict(dict)\n        \"\"\" Container for word features. \"\"\"\n\n        self.surface_to_lexical = {}\n        \"\"\" Mapping from surface form to lexical form. \"\"\"\n\n    def candidate_selection(self, n=3, stoplist=None, **kwargs):\n        \"\"\"Select 1-3 grams as keyphrase candidates. Candidates beginning or\n        ending with a stopword are filtered out. Words that do not contain\n        at least one alpha-numeric character are not allowed.\n\n        Args:\n            n (int): the n-gram length, defaults to 3.\n            stoplist (list): the stoplist for filtering candidates, defaults to\n                the nltk stoplist.\n        \"\"\"\n\n        # select ngrams from 1 to 3 grams\n        self.ngram_selection(n=n)\n\n        # filter candidates containing punctuation marks\n        self.candidate_filtering(stoplist=list(string.punctuation))\n\n        # initialize empty list if stoplist is not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # further filter candidates\n        for k in list(self.candidates):\n\n            # get the candidate\n            v = self.candidates[k]\n\n            # filter candidates starting/ending with a stopword or containing\n            # a first/last word with less than 3 characters\n            if v.surface_forms[0][0].lower() in stoplist or v.surface_forms[0][\n                -1].lower() in stoplist or len(\n                    v.surface_forms[0][0]) < 3 or len(\n                    v.surface_forms[0][-1]) < 3:\n                del self.candidates[k]\n\n    def _vocabulary_building(self, use_stems=False):\n        \"\"\"Build the vocabulary that will be used to weight candidates. Only\n        words containing at least one alpha-numeric character are kept.\n\n        Args:\n            use_stems (bool): whether to use stems instead of lowercase words\n                for weighting, defaults to False.\n        \"\"\"\n\n        # loop through sentences\n        for i, sentence in enumerate(self.sentences):\n\n            # compute the offset shift for the sentence\n            shift = sum([s.length for s in self.sentences[0:i]])\n\n            # loop through words in sentence\n            for j, word in enumerate(sentence.words):\n\n                # consider words containing at least one alpha-numeric character\n                if self._is_alphanum(word) and \\\n                        not re.search('(?i)^-[lr][rcs]b-$', word):\n\n                    # get the word or stem\n                    index = word.lower()\n                    if use_stems:\n                        index = sentence.stems[j]\n\n                    # add the word occurrence\n                    self.words[index].add((shift + j, shift, i, word))\n\n    def _contexts_building(self, use_stems=False, window=2):\n        \"\"\"Build the contexts of the words for computing the relatedness\n        feature. Words that occur within a window of n words are considered as\n        context words. Only words co-occurring in a block (sequence of words\n        that appear in the vocabulary) are considered.\n\n        Args:\n            use_stems (bool): whether to use stems instead of lowercase words\n                for weighting, defaults to False.\n            window (int): the size in words of the window used for computing\n                co-occurrence counts, defaults to 2.\n        \"\"\"\n\n        # loop through sentences\n        for i, sentence in enumerate(self.sentences):\n\n            # lowercase the words\n            words = [w.lower() for w in sentence.words]\n\n            # replace with stems if needed\n            if use_stems:\n                words = sentence.stems\n\n            # block container\n            block = []\n\n            # loop through words in sentence\n            for j, word in enumerate(words):\n\n                # skip and flush block if word is not in vocabulary\n                if word not in self.words:\n                    block = []\n                    continue\n\n                # add the left context\n                self.contexts[word][0].extend(\n                    [w for w in block[max(0, len(block) - window):len(block)]]\n                )\n\n                # add the right context\n                for w in block[max(0, len(block) - window):len(block)]:\n                    self.contexts[w][1].append(word)\n\n                # add word to the current block\n                block.append(word)\n\n    def _feature_extraction(self, stoplist=None):\n        \"\"\"Compute the weight of individual words using the following five\n        features:\n\n            1. CASING: gives importance to acronyms or words starting with a\n               capital letter.\n\n               CASING(w) = max(TF(U(w)), TF(A(w))) / (1 + log(TF(w)))\n\n               with TF(U(w) being the # times the word starts with an uppercase\n               letter, excepts beginning of sentences. TF(A(w)) is the # times\n               the word is marked as an acronym.\n\n            2. POSITION: gives importance to words occurring at the beginning of\n               the document.\n\n               POSITION(w) = log( log( 3 + Median(Sen(w)) ) )\n\n               with Sen(w) contains the position of the sentences where w\n               occurs.\n\n            3. FREQUENCY: gives importance to frequent words.\n\n               FREQUENCY(w) = TF(w) / ( MEAN_TF + STD_TF)\n\n               with MEAN_TF and STD_TF computed on valid_tfs which are words\n               that are not stopwords.\n\n            4. RELATEDNESS: gives importance to words that do not have the\n               characteristics of stopwords.\n\n               RELATEDNESS(w) = 1 + (WR+WL)*(TF(w)/MAX_TF) + PL + PR\n\n            5. DIFFERENT: gives importance to words that occurs in multiple\n               sentences.\n\n               DIFFERENT(w) = SF(w) / # sentences\n\n               with SF(w) being the sentence frequency of word w.\n\n        Args:\n            stoplist (list): the stoplist for filtering candidates, defaults to\n                the nltk stoplist.\n        \"\"\"\n\n        # initialize stoplist list if not provided\n        if stoplist is None:\n            stoplist = self.stoplist\n\n        # get the Term Frequency of each word\n        TF = [len(self.words[w]) for w in self.words]\n\n        # get the Term Frequency of non-stop words\n        TF_nsw = [len(self.words[w]) for w in self.words if w not in stoplist]\n\n        # compute statistics\n        mean_TF = numpy.mean(TF_nsw)\n        std_TF = numpy.std(TF_nsw)\n        max_TF = max(TF)\n\n        # Loop through the words\n        for word in self.words:\n\n            # Indicating whether the word is a stopword (vitordouzi change)\n            self.features[word]['isstop'] = word in stoplist or len(word) < 3\n\n            # Term Frequency\n            self.features[word]['TF'] = len(self.words[word])\n\n            # Uppercase/Acronym Term Frequencies\n            self.features[word]['TF_A'] = 0\n            self.features[word]['TF_U'] = 0\n            for (offset, shift, sent_id, surface_form) in self.words[word]:\n                if surface_form.isupper() and len(word) > 1:\n                    self.features[word]['TF_A'] += 1\n                elif surface_form[0].isupper() and offset != shift:\n                    self.features[word]['TF_U'] += 1\n\n            # 1. CASING feature\n            self.features[word]['CASING'] = max(self.features[word]['TF_A'],\n                                                self.features[word]['TF_U'])\n            self.features[word]['CASING'] /= 1.0 + math.log(\n                self.features[word]['TF'])\n\n            # 2. POSITION feature\n            sentence_ids = list(set([t[2] for t in self.words[word]]))\n            self.features[word]['POSITION'] = math.log(\n                3.0 + numpy.median(sentence_ids))\n            self.features[word]['POSITION'] = math.log(\n                self.features[word]['POSITION'])\n\n            # 3. FREQUENCY feature\n            self.features[word]['FREQUENCY'] = self.features[word]['TF']\n            self.features[word]['FREQUENCY'] /= (mean_TF + std_TF)\n\n            # 4. RELATEDNESS feature\n            self.features[word]['WL'] = 0.0\n            if len(self.contexts[word][0]):\n                self.features[word]['WL'] = len(set(self.contexts[word][0]))\n                self.features[word]['WL'] /= len(self.contexts[word][0])\n            self.features[word]['PL'] = len(set(self.contexts[word][0])) / max_TF\n\n            self.features[word]['WR'] = 0.0\n            if len(self.contexts[word][1]):\n                self.features[word]['WR'] = len(set(self.contexts[word][1]))\n                self.features[word]['WR'] /= len(self.contexts[word][1])\n            self.features[word]['PR'] = len(set(self.contexts[word][1])) / max_TF\n\n            self.features[word]['RELATEDNESS'] = 1\n            #self.features[word]['RELATEDNESS'] += self.features[word]['PL']\n            #self.features[word]['RELATEDNESS'] += self.features[word]['PR']\n            self.features[word]['RELATEDNESS'] += (self.features[word]['WR'] +\n                                                   self.features[word]['WL']) * \\\n                                                  (self.features[word]['TF'] / max_TF)\n\n            # 5. DIFFERENT feature\n            self.features[word]['DIFFERENT'] = len(set(sentence_ids))\n            self.features[word]['DIFFERENT'] /= len(self.sentences)\n\n            # assemble the features to weight words\n            A = self.features[word]['CASING']\n            B = self.features[word]['POSITION']\n            C = self.features[word]['FREQUENCY']\n            D = self.features[word]['RELATEDNESS']\n            E = self.features[word]['DIFFERENT']\n            self.features[word]['weight'] = (D * B) / (A + (C / D) + (E / D))\n\n    def candidate_weighting(self, window=2, stoplist=None, use_stems=False):\n        \"\"\"Candidate weight calculation as described in the YAKE paper.\n\n        Args:\n            stoplist (list): the stoplist for filtering candidates, defaults to\n                the nltk stoplist.\n            use_stems (bool): whether to use stems instead of lowercase words\n                for weighting, defaults to False.\n            window (int): the size in words of the window used for computing\n                co-occurrence counts, defaults to 2.\n        \"\"\"\n\n        # build the vocabulary\n        self._vocabulary_building(use_stems=use_stems)\n\n        # extract the contexts\n        self._contexts_building(use_stems=use_stems, window=window)\n\n        # compute the word features\n        self._feature_extraction(stoplist=stoplist)\n\n        # compute candidate weights\n        for k, v in self.candidates.items():\n\n            # use stems\n            if use_stems:\n                weights = [self.features[t]['weight'] for t in v.lexical_form]\n                self.weights[k] = numpy.prod(weights)\n                self.weights[k] /= len(v.offsets) * (1 + sum(weights))\n\n            # use words\n            else:\n                lowercase_forms = [' '.join(t).lower() for t in v.surface_forms]\n                for i, candidate in enumerate(lowercase_forms):\n                    TF = lowercase_forms.count(candidate)\n\n                    # computing differentiated weights for words and stopwords\n                    # (vitordouzi change)\n                    tokens = [t.lower() for t in v.surface_forms[i]]\n                    prod_ = 1.\n                    sum_ = 0.\n                    for j, token in enumerate(tokens):\n                        if self.features[token]['isstop']:\n                            term_left = tokens[j-1]\n                            term_right = tokens[j+1]\n                            term_stop = token\n                            prob_t1 = self.contexts[term_left][1].count(\n                                term_stop) / self.features[term_left]['TF']\n                            prob_t2 = self.contexts[term_stop][0].count(\n                                term_right) / self.features[term_right]['TF']\n\n                            prob = prob_t1 * prob_t2\n                            prod_ *= (1 + (1 - prob))\n                            sum_ -= (1 - prob)\n                        else:\n                            prod_ *= self.features[token]['weight']\n                            sum_ += self.features[token]['weight']\n\n                    self.weights[candidate] = prod_\n                    self.weights[candidate] /= TF * (1 + sum_)\n                    self.surface_to_lexical[candidate] = k\n\n                    # weights = [self.features[t.lower()]['weight'] for t\n                    #          in v.surface_forms[i]]\n                    # self.weights[candidate] = numpy.prod(weights)\n                    # self.weights[candidate] /= TF * (1 + sum(weights))\n                    # self.surface_to_lexical[candidate] = k\n\n    def is_redundant(self, candidate, prev, threshold=0.8):\n        \"\"\"Test if one candidate is redundant with respect to a list of already\n        selected candidates. A candidate is considered redundant if its\n        levenshtein distance, with another candidate that is ranked higher in\n        the list, is greater than a threshold.\n\n        Args:\n            candidate (str): the lexical form of the candidate.\n            prev (list): the list of already selected candidates.\n            threshold (float): the threshold used when computing the\n                levenshtein distance, defaults to 0.8.\n        \"\"\"\n\n        # loop through the already selected candidates\n        for prev_candidate in prev:\n            dist = edit_distance(candidate, prev_candidate)\n            dist /= max(len(candidate), len(prev_candidate))\n            if (1.0 - dist) > threshold:\n                return True\n        return False\n\n    def get_n_best(self,\n                   n=10,\n                   redundancy_removal=True,\n                   stemming=False,\n                   threshold=0.8):\n        \"\"\" Returns the n-best candidates given the weights.\n\n            Args:\n                n (int): the number of candidates, defaults to 10.\n                redundancy_removal (bool): whether redundant keyphrases are\n                    filtered out from the n-best list using levenshtein\n                    distance, defaults to True.\n                stemming (bool): whether to extract stems or surface forms\n                    (lowercased, first occurring form of candidate), default to\n                    stems.\n                threshold (float): the threshold used when computing the\n                    levenshtein distance, defaults to 0.8.\n        \"\"\"\n\n        # sort candidates by ascending weight\n        best = sorted(self.weights, key=self.weights.get, reverse=False)\n\n        # remove redundant candidates\n        if redundancy_removal:\n\n            # initialize a new container for non redundant candidates\n            non_redundant_best = []\n\n            # loop through the best candidates\n            for candidate in best:\n\n                # test wether candidate is redundant\n                if self.is_redundant(candidate, non_redundant_best):\n                    continue\n\n                # add the candidate otherwise\n                non_redundant_best.append(candidate)\n\n                # break computation if the n-best are found\n                if len(non_redundant_best) >= n:\n                    break\n\n            # copy non redundant candidates in best container\n            best = non_redundant_best\n\n        # get the list of best candidates as (lexical form, weight) tuples\n        n_best = [(u, self.weights[u]) for u in best[:min(n, len(best))]]\n\n        # replace with surface forms if no stemming\n        if stemming:\n            for i, (candidate, weight) in enumerate(n_best):\n\n                if candidate not in self.candidates:\n                    candidate = self.surface_to_lexical[candidate]\n\n                candidate = ' '.join(self.candidates[candidate].lexical_form)\n                n_best[i] = (candidate, weight)\n\n        # return the list of best candidates\n        return n_best\n"
  },
  {
    "path": "onmt/keyphrase/pke/utils.py",
    "content": "# -*- coding: utf-8 -*-\n\n\"\"\"Useful functions for the pke module.\"\"\"\n\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport csv\nimport math\nimport glob\nimport pickle\nimport gzip\nimport json\nimport codecs\nimport logging\n\nfrom collections import defaultdict\n\nfrom onmt.keyphrase.pke.base import LoadFile\nfrom onmt.keyphrase.pke.base import ISO_to_language\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import LatentDirichletAllocation\n\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.corpus import stopwords\n\n\ndef load_document_frequency_file(input_file,\n                                 delimiter='\\t'):\n    \"\"\"Load a tsv (tab-separated-values) file containing document frequencies.\n    Automatically detects if input file is compressed (gzip) by looking at its\n    extension (.gz).\n\n    Args:\n        input_file (str): the input file containing document frequencies in\n            csv format.\n        delimiter (str): the delimiter used for separating term-document\n            frequencies tuples, defaults to '\\t'.\n\n    Returns:\n        dict: a dictionary of the form {term_1: freq}, freq being an integer.\n    \"\"\"\n\n    # initialize the DF dictionary\n    frequencies = {}\n\n    # open the input file\n    with gzip.open(input_file, 'rt') if input_file.endswith('.gz') else \\\n            codecs.open(input_file, 'rt') as f:\n        # read the csv file\n        df_reader = csv.reader(f, delimiter=delimiter)\n\n        # populate the dictionary\n        for row in df_reader:\n            frequencies[row[0]] = int(row[1])\n\n    # return the populated dictionary\n    return frequencies\n\n\ndef compute_document_frequency(input_dir,\n                               output_file,\n                               extension='xml',\n                               language='en',\n                               normalization=\"stemming\",\n                               stoplist=None,\n                               delimiter='\\t',\n                               n=3):\n    \"\"\"Compute the n-gram document frequencies from a set of input documents. An\n    extra row is added to the output file for specifying the number of\n    documents from which the document frequencies were computed\n    (--NB_DOC-- tab XXX). The output file is compressed using gzip.\n\n    Args:\n        input_dir (str): the input directory.\n        output_file (str): the output file.\n        extension (str): file extension for input documents, defaults to xml.\n        language (str): language of the input documents (used for computing the\n            n-stem or n-lemma forms), defaults to 'en' (english).\n        normalization (str): word normalization method, defaults to 'stemming'.\n            Other possible values are 'lemmatization' or 'None' for using word\n            surface forms instead of stems/lemmas.\n        stoplist (list): the stop words for filtering n-grams, default to None.\n        delimiter (str): the delimiter between n-grams and document frequencies,\n            defaults to tabulation (\\t).\n        n (int): the size of the n-grams, defaults to 3.\n    \"\"\"\n\n    # document frequency container\n    frequencies = defaultdict(int)\n\n    # initialize number of documents\n    nb_documents = 0\n\n    # loop through the documents\n    for input_file in glob.iglob(input_dir + '/*.' + extension):\n\n        #logging.info('reading file {}'.format(input_file))\n\n        # initialize load file object\n        doc = LoadFile()\n\n        # read the input file\n        doc.load_document(input=input_file,\n                          language=language,\n                          normalization=normalization)\n\n        # candidate selection\n        doc.ngram_selection(n=n)\n\n        # filter candidates containing punctuation marks\n        doc.candidate_filtering(stoplist=stoplist)\n\n        # loop through candidates\n        for lexical_form in doc.candidates:\n            frequencies[lexical_form] += 1\n\n        nb_documents += 1\n\n        if nb_documents % 1000 == 0:\n            logging.info(\"{} docs, memory used: {} mb\".format(nb_documents,\n                                                           sys.getsizeof(\n                                                               frequencies)\n                                                           / 1024 / 1024 ))\n\n    # create directories from path if not exists\n    if os.path.dirname(output_file):\n        os.makedirs(os.path.dirname(output_file), exist_ok=True)\n\n    # dump the df container\n    with gzip.open(output_file, 'wb') as f:\n\n        # add the number of documents as special token\n        first_line = '--NB_DOC--' + delimiter + str(nb_documents)\n        f.write(first_line.encode('utf-8') + b'\\n')\n\n        for ngram in frequencies:\n            line = ngram + delimiter + str(frequencies[ngram])\n            f.write(line.encode('utf-8') + b'\\n')\n\n\ndef train_supervised_model(input_dir,\n                           reference_file,\n                           model_file,\n                           extension='xml',\n                           language='en',\n                           normalization=\"stemming\",\n                           df=None,\n                           model=None,\n                           sep_doc_id=':',\n                           sep_ref_keyphrases=',',\n                           normalize_reference=False,\n                           leave_one_out=False):\n    \"\"\"Build a supervised keyphrase extraction model from a set of documents and\n    a reference file.\n\n    Args:\n        input_dir (str): the input directory.\n        reference_file (str): the reference file.\n        model_file (str): the model output file.\n        extension (str): file extension for input documents, defaults to xml.\n        language (str): language of the input documents (used for computing the\n            n-stem or n-lemma forms), defaults to 'en' (english).\n        normalization (str): word normalization method, defaults to 'stemming'.\n            Other possible values are 'lemmatization' or 'None' for using word\n            surface forms instead of stems/lemmas.\n        df (dict): df weights dictionary.\n        model (object): the supervised model to train, defaults to Kea.\n        sep_doc_id (str): the separator used for doc_id in reference file,\n            defaults to ':'.\n        sep_ref_keyphrases (str): the separator used for keyphrases in\n            reference file, defaults to ','.\n        normalize_reference (bool): whether to normalize the reference\n            keyphrases, default to False.\n        leave_one_out (bool): whether to use a leave-one-out procedure for\n            training, creating one model per input, defaults to False.\n    \"\"\"\n\n    logging.info('building model {} from {}'.format(model, input_dir))\n\n    references = load_references(reference_file,\n                                 sep_doc_id=sep_doc_id,\n                                 sep_ref_keyphrases=sep_ref_keyphrases,\n                                 normalize_reference=normalize_reference,\n                                 language=language)\n    training_instances = []\n    training_classes = []\n    masks = {}\n    offsets = []\n    sizes = []\n\n    # get the input files from the input directory\n    for input_file in glob.iglob(input_dir + '/*.' + extension):\n\n        logging.info('reading file {}'.format(input_file))\n\n        # get the document id from file name\n        doc_id = '.'.join(input_file.split('/')[-1].split('.')[0:-1])\n\n        # initialize the input file\n        model.__init__()\n\n        # load the document\n        model.load_document(input=input_file,\n                            language=language,\n                            normalization=normalization)\n\n        # candidate selection\n        model.candidate_selection()\n\n        # skipping documents without candidates\n        if not len(model.candidates):\n            continue\n\n        # extract features\n        model.feature_extraction(df=df, training=True)\n\n        # add the first offset for leave-one-out masking\n        masks[doc_id] = [len(training_classes)]\n\n        # annotate the reference keyphrases in the instances\n        for candidate in model.instances:\n            if candidate in references[doc_id]:\n                training_classes.append(1)\n            else:\n                training_classes.append(0)\n            training_instances.append(model.instances[candidate])\n\n        # add the last offset for leave-one-out masking\n        masks[doc_id].append(len(training_classes))\n\n    if not leave_one_out:\n        logging.info('writing model to {}'.format(model_file))\n        model.train(training_instances=training_instances,\n                    training_classes=training_classes,\n                    model_file=model_file)\n    else:\n        logging.info('leave-one-out training procedure')\n\n        for doc_id in masks:\n            logging.info('writing model to {}'.format(doc_id))\n            ind = masks[doc_id]\n            fold = training_instances[:ind[0]] + training_instances[ind[1]:]\n            gold = training_classes[:ind[0]] + training_classes[ind[1]:]\n            model.train(training_instances=fold,\n                        training_classes=gold,\n                        model_file=model_file+\".\"+doc_id+\".pickle\")\n\n\ndef load_references(input_file,\n                    sep_doc_id=':',\n                    sep_ref_keyphrases=',',\n                    normalize_reference=False,\n                    language=\"en\",\n                    encoding='utf-8'):\n    \"\"\"Load a reference file. Reference file can be either in json format or in\n    the SemEval-2010 official format.\n\n    Args:\n        input_file (str): path to the reference file.\n        sep_doc_id (str): the separator used for doc_id in reference file,\n            defaults to ':'.\n        sep_ref_keyphrases (str): the separator used for keyphrases in\n            reference file, defaults to ','.\n        normalize_reference (bool): whether to normalize the reference\n            keyphrases using stemming, default to False.\n        language (str): language of the input documents (used for computing the\n            stems), defaults to 'en' (english).\n        encoding (str): file encoding, default to utf-8.\n    \"\"\"\n\n    logging.info('loading reference keyphrases from {}'.format(input_file))\n\n    references = defaultdict(list)\n\n    # open input file\n    with codecs.open(input_file, 'r', encoding) as f:\n\n        # load json data\n        if input_file.endswith('.json'):\n            references = json.load(f)\n            for doc_id in references:\n                references[doc_id] = [keyphrase for variants in\n                                      references[doc_id] for keyphrase in\n                                      variants]\n        # or load SemEval-2010 file\n        else:\n            for line in f:\n                cols = line.strip().split(sep_doc_id)\n                doc_id = cols[0].strip()\n                keyphrases = cols[1].strip().split(sep_ref_keyphrases)\n                for v in keyphrases:\n                    if '+' in v:\n                        for s in v.split('+'):\n                            references[doc_id].append(s)\n                    else:\n                        references[doc_id].append(v)\n\n        # normalize reference if needed\n        if normalize_reference:\n\n            # initialize stemmer\n            stemmer = SnowballStemmer(\"porter\")\n            if language != 'en':\n                stemmer = SnowballStemmer(ISO_to_language[language],\n                                          ignore_stopwords=True)\n\n            for doc_id in references:\n                for i, keyphrase in enumerate(references[doc_id]):\n                    stems = [stemmer.stem(w) for w in keyphrase.split()]\n                    references[doc_id][i] = ' '.join(stems)\n\n    return references\n\n\ndef compute_lda_model(input_dir,\n                      output_file,\n                      n_topics=500,\n                      extension=\"xml\",\n                      language=\"en\",\n                      normalization=\"stemming\"):\n    \"\"\"Compute a LDA model from a collection of documents. Latent Dirichlet\n    Allocation is computed using sklearn module.\n\n    Args:\n        input_dir (str): the input directory.\n        output_file (str): the output file.\n        n_topics (int): number of topics for the LDA model, defaults to 500.\n        extension (str): file extension for input documents, defaults to xml.\n        language (str): language of the input documents, used for stop_words\n            in sklearn CountVectorizer, defaults to 'en'.\n        normalization (str): word normalization method, defaults to 'stemming'.\n            Other possible values are 'lemmatization' or 'None' for using word\n            surface forms instead of stems/lemmas.\n    \"\"\"\n\n    # texts container\n    texts = []\n\n    # loop throught the documents\n    for input_file in glob.iglob(input_dir + '/*.' + extension):\n\n        logging.info('reading file {}'.format(input_file))\n\n        # initialize load file object\n        doc = LoadFile()\n\n        # read the input file\n        doc.load_document(input=input_file,\n                          language=language,\n                          normalization=normalization)\n\n        # container for current document\n        text = []\n\n        # loop through sentences\n        for sentence in doc.sentences:\n            # get the tokens (stems) from the sentence if they are not\n            # punctuation marks \n            text.extend([sentence.stems[i] for i in range(sentence.length)\n                         if sentence.pos[i] != 'PUNCT' and\n                         sentence.pos[i].isalpha()])\n\n        # add the document to the texts container\n        texts.append(' '.join(text))\n\n    # vectorize dataset\n    # get the stoplist from nltk because CountVectorizer only contains english\n    # stopwords atm\n    tf_vectorizer = CountVectorizer(\n        stop_words=stopwords.words(ISO_to_language[language]))\n    tf = tf_vectorizer.fit_transform(texts)\n\n    # extract vocabulary\n    vocabulary = tf_vectorizer.get_feature_names()\n\n    # create LDA model and train\n    lda_model = LatentDirichletAllocation(n_components=n_topics,\n                                          random_state=0,\n                                          learning_method='batch')\n    lda_model.fit(tf)\n\n    # save all data necessary for later prediction\n    saved_model = (vocabulary,\n                   lda_model.components_,\n                   lda_model.exp_dirichlet_component_,\n                   lda_model.doc_topic_prior_)\n\n    # Dump the df container\n    logging.info('writing LDA model to {}'.format(output_file))\n\n    # create directories from path if not exists\n    if os.path.dirname(output_file):\n        os.makedirs(os.path.dirname(output_file), exist_ok=True)\n\n    # dump the LDA model\n    with gzip.open(output_file, 'wb') as fp:\n        pickle.dump(saved_model, fp)\n\n\ndef load_document_as_bos(input_file,\n                         language=\"en\",\n                         normalization=\"stemming\",\n                         stoplist=None):\n    \"\"\"Load a document as a bag of words/stems/lemmas.\n\n    Args:\n        input_file (str): path to input file.\n        language (str): language of the input documents, used for stop_words\n            in sklearn CountVectorizer, defaults to 'en'.\n        normalization (str): word normalization method, defaults to 'stemming'.\n            Other possible values are 'lemmatization' or 'None' for using word\n            surface forms instead of stems/lemmas.\n        stoplist (list): the stop words for filtering tokens, default to [].\n    \"\"\"\n\n    # initialize empty stoplist is None provided\n    if stoplist is None:\n        stoplist = []\n\n    # initialize load file object\n    doc = LoadFile()\n\n    # read the input file\n    doc.load_document(input=input_file,\n                      language=language,\n                      normalization=normalization)\n\n    # initialize document vector\n    vector = defaultdict(int)\n\n    # loop through the sentences and add the stems to the vector\n    for i, sentence in enumerate(doc.sentences):\n        for j, stem in enumerate(sentence.stems):\n            if stem in stoplist:\n                continue\n            vector[stem] += 1\n\n    return vector\n\n\ndef compute_pairwise_similarity_matrix(input_dir,\n                                       output_file,\n                                       collection_dir=None,\n                                       df=None,\n                                       extension=\"xml\",\n                                       language=\"en\",\n                                       normalization=\"stemming\",\n                                       stoplist=None):\n    \"\"\"Compute the pairwise similarity between documents in `input_dir` and\n    documents in `collection_dir`. Similarity scores are computed using a cosine\n    similarity over TF x IDF term weights. If there is no collection to compute\n    those scores, the similarities between documents in input_dir are returned\n    instead.\n\n    Args:\n        input_dir (str): path to the input directory.\n        output_file (str): path to the output file.\n        collection_dir (str): path to the collection of documents, defaults to\n            None.\n        df (dict): df weights dictionary.\n        extension (str): file extension for input documents, defaults to xml.\n        language (str): language of the input documents, used for stop_words\n            in sklearn CountVectorizer, defaults to 'en'.\n        normalization (str): word normalization method, defaults to 'stemming'.\n            Other possible values are 'lemmatization' or 'None' for using word\n            surface forms instead of stems/lemmas.\n        stoplist (list): the stop words for filtering tokens, default to [].\n    \"\"\"\n\n    # containers\n    collection = {}\n    documents = {}\n\n    # initialize the number of documents\n    N = df.get('--NB_DOC--', 1)\n\n    # initialize stoplist as empty if None provided\n    if stoplist is None:\n        stoplist = []\n\n    # build collection tf*idf vectors\n    if collection_dir is not None:\n\n        # loop throught the documents in the collection\n        for input_file in glob.iglob(collection_dir + '/*.' + extension):\n\n            logging.info('Reading file from {}'.format(input_file))\n\n            # initialize document vector\n            collection[input_file] = load_document_as_bos(input_file=input_file,\n                                                          language=language,\n                                                          normalization=normalization,\n                                                          stoplist=stoplist)\n\n            # compute TF*IDF weights\n            for stem in collection[input_file]:\n                collection[input_file][stem] *= math.log(N / df.get(stem, 1), 2)\n\n        # update N if a collection of documents is provided\n        N += 1\n\n    # loop throught the documents in the input directory\n    for input_file in glob.iglob(input_dir + '/*.' + extension):\n\n        logging.info('Reading file from {}'.format(input_file))\n\n        # initialize document vector\n        documents[input_file] = load_document_as_bos(input_file=input_file,\n                                                     language=language,\n                                                     normalization=normalization,\n                                                     stoplist=stoplist)\n\n        # compute TF*IDF weights\n        for stem in documents[input_file]:\n            documents[input_file][stem] *= math.log(N / (1+df.get(stem, 1)), 2)\n\n    # consider input documents as collection if None provided\n    if not collection:\n        collection = documents\n\n\n    # create directories from path if not exists\n    if os.path.dirname(output_file):\n        os.makedirs(os.path.dirname(output_file), exist_ok=True)\n\n    # open the output file in gzip mode\n    with gzip.open(output_file, 'wb') as f:\n\n        # compute pairwise similarity scores\n        for doc_i in documents:\n            for doc_j in collection:\n                if doc_i == doc_j:\n                    continue\n\n                # inner product\n                inner = 0.0\n                for stem in set(documents[doc_i]) & set(collection[doc_j]):\n                    inner += documents[doc_i][stem] * collection[doc_j][stem]\n\n                # norms\n                norm_i = sum([math.pow(documents[doc_i][t], 2) for t in\n                              documents[doc_i]])\n                norm_i = math.sqrt(norm_i)\n                norm_j = sum([math.pow(collection[doc_j][t], 2) for t in\n                              collection[doc_j]])\n                norm_j = math.sqrt(norm_j)\n\n                # compute cosine\n                cosine = inner / (norm_i * norm_j)\n\n                # encode line and write to output file\n                line = doc_i + '\\t' + doc_j + '\\t' + str(cosine) + '\\n'\n                f.write(line.encode('utf-8'))\n"
  },
  {
    "path": "onmt/keyphrase/preprocess.py",
    "content": "import argparse\nimport datetime\nimport json\nimport os\nfrom functools import partial\nimport tqdm\nimport numpy as np\nimport string\n\nimport sys\nsys.path.append(\"/home/yingyi/Documents/OpenNMT-kpg\")\nprint(\"python  path\",sys.path)\nfrom onmt.utils.logging import init_logger\nfrom onmt import opts\nfrom onmt.newssum import docutils\nfrom onmt.inputters.news_dataset import load_pretrained_tokenizer\nimport random\nimport re\nfrom stanfordcorenlp import StanfordCoreNLP\nimport nltk\nnltk.download('stopwords')\n\nTOKENIZER_NAMES = ['roberta-base', 'bert-base-uncased', 'word']\nstemmer = nltk.stem.porter.PorterStemmer()\nnlp = StanfordCoreNLP(r'/home/yingyi/Documents/tool/stanford-corenlp-full-2016-10-31')\n\ndef init_opt():\n    parser = argparse.ArgumentParser()\n    # Input/output options\n    parser.add_argument('--json_dir', '-json_dir', default='/home/yingyi/Documents/kp20k', help='Path to jsonl files.')\n    parser.add_argument('--output_dir', '-output_dir', default='/home/yingyi/Documents/output/kp20k',\n                        help='The path of the output json files, final path is like /export/share/rmeng/output/bert-base-cased/tokenized/'\n                             'folder name will be dataset_tgt, like cnndm_summary, and insides are train.jsonl, valid.jsonl, test.jsonl.')\n    parser.add_argument('--tokenizer', '-tokenizer', default='roberta-base', choices=TOKENIZER_NAMES, help='.')\n    parser.add_argument('--partition', '-partition', default='kp20k_train', type=str, choices=['kp20k_train', 'kp20k_valid', 'kp20k_test'],\n                        help='Specify which partition of dataset to process: train/test/valid/all')\n    parser.add_argument('--shard_filename', '-shard_filename', type=str, help='.')\n    parser.add_argument('--verbose', '-verbose', action='store_true', help='.')\n    parser.add_argument('--special_vocab_path', '-opt.special_vocab_path', default=None, action='store_true', help='.')\n\n    opt = parser.parse_args()\n\n    return opt\n\ndef meng17_tokenize(text):\n    '''\n    The tokenizer used in Meng et al. ACL 2017\n    parse the feed-in text, filtering and tokenization\n    keep [_<>,\\(\\)\\.\\'%], replace digits to <digit>, split by [^a-zA-Z0-9_<>,\\(\\)\\.\\'%]\n    :param text:\n    :return: a list of tokens\n    '''\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', text)\n    # pad spaces to the left and right of special punctuations\n    text = re.sub(r'[_<>,\\(\\)\\.\\'%]', ' \\g<0> ', text)\n    # tokenize by non-letters (new-added + # & *, but don't pad spaces, to make them as one whole word)\n    tokens = list(filter(lambda w: len(w) > 0, re.split(r'[^a-zA-Z0-9_<>,#&\\+\\*\\(\\)\\.\\']', text)))\n\n    return tokens\n\ndef start_end_re(match_position_idxs_key, match_pos_ends_key, keywords_exist, posi_dict, poss, cate):\n    position_lists = []\n    if cate=='noun':\n        for starts, ends, words, pos in zip(match_position_idxs_key, match_pos_ends_key, keywords_exist, poss):\n            position_list = []\n            for start, end in zip(starts, ends):\n                sta = posi_dict[start][0]\n                en = posi_dict[end-1][-1]\n                position_list.append([sta, en])\n            position_lists.append({'position': position_list, 'phrase': words, 'pos': pos})\n    else:\n        for starts, ends, words in zip(match_position_idxs_key, match_pos_ends_key, keywords_exist):\n            position_list = []\n            for start, end in zip(starts, ends):\n                sta = posi_dict[start][0]\n                en = posi_dict[end-1][-1]\n                position_list.append([sta, en])\n            position_lists.append({'position': position_list, 'phrase': words})\n    return position_lists\n\ndef start_end(match_position_idxs_key, match_pos_ends_key, keywords_exist, poss, cate):\n    position_lists = []\n    if cate=='noun':\n        for starts, ends, words, pos in zip(match_position_idxs_key, match_pos_ends_key, keywords_exist, poss):\n            position_list = []\n            for start, end in zip(starts, ends):\n                position_list.append([start, end])\n            position_lists.append({'position': position_list, 'phrase': words, 'pos': pos})\n    else:\n        for starts, ends, words in zip(match_position_idxs_key, match_pos_ends_key, keywords_exist):\n            position_list = []\n            for start, end in zip(starts, ends):\n                position_list.append([start, end])\n            position_lists.append({'position': position_list, 'phrase': words})\n    return position_lists\n\ndef prepend_space_to_words(words):\n    new_words = []\n\n    # prepend a space for all non-head and non-punctuation words\n    for word in words:\n        if len(new_words) == 0 or (len(word) == 1 and word in string.punctuation + string.whitespace):\n            new_words.append(word)\n        else:\n            new_words.append(' ' + word)\n\n    return new_words\n\n\ndef position_dict(position, subwords, length):\n    posi_dict = [i for i in range(length, length+len(subwords))]\n    return posi_dict\n\ndef words_to_subwords(tokenizer, words, pos = None):\n    all_subwords = []\n    all_codes = []\n    all_pos = []\n    all_posi = []\n    spaced_words = prepend_space_to_words(words)\n    length = 0\n    if pos==None:\n        for i, word in enumerate(spaced_words):\n            if opt.tokenizer==\"roberta-base\":\n                subwords = tokenizer.tokenize(word, add_prefix_space=True)\n            else:\n                subwords = tokenizer.tokenize(word)\n            codes = tokenizer.convert_tokens_to_ids(subwords)\n            posi_dict = position_dict(i, subwords, length)\n            length = length + len(subwords)\n            all_subwords.extend(subwords)\n            all_codes.extend(codes)\n            all_posi.append(posi_dict)\n        return all_subwords, all_codes, all_posi\n    else:\n        new_po = []\n        i = 0\n        for word, po in zip(spaced_words, pos):\n            if opt.tokenizer==\"roberta-base\":\n                subwords = tokenizer.tokenize(word, add_prefix_space=True)\n            else:\n                subwords = tokenizer.tokenize(word)\n            codes = tokenizer.convert_tokens_to_ids(subwords)\n            posi_dict = position_dict(i, subwords, length)\n            length = length + len(subwords)\n            for _ in range(0,len(subwords)):\n                new_po.append(po)\n            all_subwords.extend(subwords)\n            all_codes.extend(codes)\n            all_pos.extend(new_po)\n            all_posi.append(posi_dict)\n            new_po = []\n            i=i+1\n        return all_subwords, all_codes, all_pos, all_posi\n\ndef if_present_phrase(src_str_tokens, phrase_str_tokens):\n    \"\"\"\n    :param src_str_tokens: a list of strings (words) of source text\n    :param phrase_str_tokens: a list of strings (words) of a phrase\n    :return:\n    \"\"\"\n    match_flag = False\n    match_pos_idx = -1\n    match_pos_idxs = []\n    match_pos_ends = []\n    keywords_tokenizes = []\n    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\n        match_flag = True\n        # iterate each word in target, if one word does not match, set match=False and break\n        for seq_idx, seq_w in enumerate(phrase_str_tokens):\n            src_w = src_str_tokens[src_start_idx + seq_idx]\n            if src_w != seq_w:\n                match_flag = False\n                break\n        if match_flag:\n            match_pos_idx = src_start_idx\n            match_pos_idxs.append(match_pos_idx)\n            match_pos_ends.append(match_pos_idx+len(phrase_str_tokens))\n            keywords_tokenizes.append(phrase_str_tokens)\n    return match_flag, match_pos_idxs, match_pos_ends, keywords_tokenizes\n\ndef macth_word(sentence_stemmer, word_stemmer, word_origin, poss):\n    match_flags = []\n    match_pos_idxs = []\n    match_pos_ends = []\n    words_tokenize = []\n    absent_toeknize = []\n    exist_pos = []\n    for word, origin, pos in zip(word_stemmer, word_origin, poss):\n        match_flag, match_pos_idx, match_pos_end, word_tokenize = if_present_phrase(sentence_stemmer, word)\n        if len(match_pos_idx)>0:\n            match_flags.append(match_flag)\n            match_pos_idxs.append(match_pos_idx)\n            match_pos_ends.append(match_pos_end)\n            words_tokenize.append(origin)\n            exist_pos.append(pos)\n        else:\n            absent_toeknize.append(origin)\n    return match_flags, match_pos_idxs, match_pos_ends, words_tokenize, absent_toeknize, exist_pos\n\ndef keyword_stemmer(keywords):\n    keywords_tokenize = []\n    for keyword in keywords:\n        keyword_stemmer = [stemmer.stem(word.lower().strip()) for word in keyword]\n        keywords_tokenize.append(keyword_stemmer)\n    return keywords_tokenize\n\ndef nounchunk_stemmer(nouns):\n    nounchunks_tokenize = []\n    for nounchunk in nouns:\n        nounchunk_stemmer = [stemmer.stem(word.lower().strip()) for word in nounchunk]\n        nounchunks_tokenize.append(nounchunk_stemmer)\n    return nounchunks_tokenize\n\ndef pos_judge(text, model=\"stanfordnlp\", lowercase=False):\n    #print ('pos_tag', text)\n    return nlp.pos_tag(text)\n\ndef listToStr(tokens):\n    sentence = \"\"\n    for token in tokens:\n        sentence = sentence+str(token)+\" \"\n    return sentence.strip()\n\ndef tokenize_doc(doc):\n    # process title and abstract\n    title_tokens = meng17_tokenize(doc['title'])\n\n    abstract_tokens = meng17_tokenize(doc['abstract'])\n\n    all_tokens = title_tokens[:]\n    all_tokens.append(\".\")\n    for tokens in abstract_tokens:\n        all_tokens.append(tokens)\n\n    keywords_tokens = []\n    for keyword in doc['keywords']:\n        keyword_tokenize = meng17_tokenize(keyword.strip())\n        if keyword_tokenize!=[]:\n            keywords_tokens.append(keyword_tokenize)\n    return title_tokens, abstract_tokens, all_tokens, keywords_tokens\n\n\ndef label(sentence, starts, ends):\n    zero = np.zeros(len(sentence))\n    for start, end in zip(starts, ends):\n        for st, en in zip(start, end):\n            zero[st]=1\n            zero[st+1:en]=2\n    return [int (i) for i in zero]\n\ndef recognise_nounchunks(tagged):\n    #from montylingua-2.1/ MontyREChunker.py\n    lookup=[]\n    words_poss = {}\n    info_dict = tagged\n    file1 = list(map(lambda filename_dict: filename_dict[0], info_dict))\n    _montylingua_arr = list(map(lambda filename_dict: filename_dict[1], info_dict))\n    # filename_p = \"((PDT )?(DT |PRP[$] |WDT |WP[$] )(VBG |VBD |VBN |JJ |JJR |JJS |, |CC |NN |NNS |NNP |NNPS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    # groupnames1 = \"((PDT )?(JJ |JJR |JJS |, |CC |NN |NNS |NNP |NNPS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    # case1 = \"(\" + filename_p + \"|\" + groupnames1 + \"|EX |PRP |WP |WDT )\"\n    filename_p = \"((PDT )?(VBG |VBD |VBN |JJ |JJR |JJS |CD )*(NN |NNS |NNP |NNPS |CD )+)\"\n    case1 = \"(\" + filename_p+ \")\"\n    case1 = \"(\" + case1 + 'POS )?' + case1\n    case1 = ' ' + case1\n    case1 = re.compile(case1)\n    awk1 = 1\n\n    while awk1:\n        awk1 = 0\n        gawks = ' ' + ' '.join(_montylingua_arr) + ' '\n        groupnames_str = case1.search(gawks)\n\n        if groupnames_str:\n            awk1 = 1\n            info_str = len(gawks[:groupnames_str.start()].split())\n            cleaned_arr = len(gawks[groupnames_str.end():].split())\n            tagged_str = (info_str, len(_montylingua_arr) - cleaned_arr)\n            mores = file1[tagged_str[0]:tagged_str[1]]\n            popd_arr = _montylingua_arr[tagged_str[0]:tagged_str[1]]\n            cron_cleaned = ' '.join(\n                list(map(lambda filename_dict: mores[filename_dict] + '/' + popd_arr[filename_dict], range(len(mores)))))\n            only_word =  ' '.join(\n                list(map(lambda filename_dict: mores[filename_dict], range(len(mores)))))\n            only_pos =  ' '.join(\n                list(map(lambda filename_dict: popd_arr[filename_dict], range(len(popd_arr)))))\n            stripped_str = 'NC_' + str(random.randint(0, 1000000000))\n            for stripped_dict in range(len(file1)):\n                if stripped_dict in range(tagged_str[0], tagged_str[1]):\n                    file1[stripped_dict] = 'bar'\n                    _montylingua_arr[stripped_dict] = stripped_str\n            lookup.append(cron_cleaned)\n            words_poss[only_word] = only_pos\n    noun_phrases = [only_word.split() for only_word in words_poss.keys()]\n    pos_phrases = [only_pos.split() for only_pos in words_poss.values()]\n    return lookup, noun_phrases, pos_phrases\n\n\nif __name__ == '__main__':\n    opt = init_opt()\n\n    current_time = datetime.datetime.now().strftime('%Y-%m-%d')  # '%Y-%m-%d_%H:%M:%S'\n    logger = init_logger(opt.output_dir + '/tokenize.%s.log' % (current_time))\n\n    # determine whether to lowercase the text\n    if opt.tokenizer == 'word' or '-base' in opt.tokenizer:\n        lowercase = False\n    else:\n        lowercase = False\n\n    if opt.tokenizer == 'word':\n        # initialize tokenizer (for testset, only word tokenization should be applied)\n        #tokenizer_fn = partial(docutils.word_tokenize, model=\"spacy\", lowercase=lowercase)\n        tokenizer_fn = nlp\n    else:\n        # Load pre-trained model tokenizer (vocabulary)\n        pretrained_tokenizer = load_pretrained_tokenizer(opt.tokenizer, None,\n                                                         special_vocab_path=opt.special_vocab_path)\n        tokenizer_fn = pretrained_tokenizer.tokenize\n\n    if opt.shard_filename:\n        input_jsonl_path = os.path.join(opt.json_dir, opt.shard_filename)\n        logger.info('Tokenizing dataset. Loaded data from jsonl: %s ' % (input_jsonl_path))\n        output_dir = os.path.join(opt.output_dir, opt.tokenizer, 'sharded_1000')\n        output_jsonl_path = os.path.join(output_dir, opt.shard_filename)\n        logger.info('Exporting tokenized data to %s' % output_jsonl_path)\n    else:\n        input_jsonl_path = os.path.join(opt.json_dir, '%s.json' % (opt.partition))\n        logger.info(\n            'Tokenizing dataset [%s]. Loaded data from jsonl: %s ' % (opt.partition, input_jsonl_path))\n\n        output_dir = os.path.join(opt.output_dir, opt.tokenizer, 'tokenized')\n        output_jsonl_path = os.path.join(output_dir, opt.partition + '7.json')\n        logger.info('Exporting tokenized data to %s' % output_jsonl_path)\n\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    with open(output_jsonl_path, 'w') as output_jsonl_writer:\n        counter = 0\n        src_lengths = []\n        tgt_lengths = []\n        keyphrase_num = 0\n        keyphrase_num_roberta = 0\n        noun_num = 0\n        noun_roberta_num = 0\n        fw_error = open(\"/home/yingyi/Documents/output/kp20k/error.txt\",\"w\")\n        for line in tqdm.tqdm(open(input_jsonl_path, 'r', encoding='utf-8', errors='ignore'),\n                              desc=\"Processing %s\" % (\n                              opt.partition if opt.partition else opt.shard_filename)):\n            counter += 1\n            if counter>460000: #40000, 80000, 140000, 270000, 320000, 460000\n                doc = json.loads(line)\n                word_doc = {}\n                roberta_doc = {}\n                doc['word'] = word_doc\n                doc[opt.tokenizer] = roberta_doc\n\n                title_tokens, abstract_tokens, all_tokens, keywords_tokens = tokenize_doc(doc)\n                #print(all_tokens)\n                pos_sentence = pos_judge(listToStr(all_tokens))\n                nounchunks, nouns, poss = recognise_nounchunks(pos_sentence)\n                sentence_stemmer = [stemmer.stem(word.lower().strip()) for word in all_tokens]\n                keywords_stemmer = keyword_stemmer(keywords_tokens)\n                nounchunks_stemmer = nounchunk_stemmer(nouns)\n                word_doc[\"token\"] = all_tokens\n\n                match_flags_key, match_position_idxs_key, match_pos_ends_key, keywords_exist, keyword_absent, _ = macth_word(\n                            sentence_stemmer, keywords_stemmer, keywords_tokens, poss)\n                match_flags_noun, match_position_idxs_noun, match_pos_ends_noun, nounwords_exist, nounwords_absent, exist_poss = macth_word(\n                            sentence_stemmer, nounchunks_stemmer, nouns, poss)\n\n                word_doc['keyword_absent'] = keyword_absent\n                key_positions = start_end(match_position_idxs_key, match_pos_ends_key, keywords_exist, exist_poss, 'key')\n                noun_positions = start_end(match_position_idxs_noun, match_pos_ends_noun, nounwords_exist, exist_poss, 'noun')\n                word_doc['keywords_position'] = key_positions\n                word_doc['nouns_position'] = noun_positions\n\n                keyphrase_num = keyphrase_num + len(key_positions)\n                noun_num = noun_num + len(noun_positions)\n                assert  len(exist_poss)==len(nounwords_exist)\n\n                # tokenize text\n                keywords_exist_pre = []\n                keywords_absent_pre = []\n                nounchunks_pre = []\n                poss_re = []\n                sentence_pre, _, posi_dict = words_to_subwords(pretrained_tokenizer, all_tokens)\n\n                for words in keywords_exist:\n                    keyword_pre, _, _ = words_to_subwords(pretrained_tokenizer, words)\n                    keywords_exist_pre.append(keyword_pre)\n                for words in keyword_absent:\n                    keyword_pre, _, _ = words_to_subwords(pretrained_tokenizer, words)\n                    keywords_absent_pre.append(keyword_pre)\n                for words, _pos in zip(nounwords_exist, exist_poss):\n                    noun_pre, _, pos_pre, _ = words_to_subwords(pretrained_tokenizer, words, pos = _pos)\n                    nounchunks_pre.append(noun_pre)\n                    poss_re.append(pos_pre)\n\n                roberta_doc[\"token\"] = sentence_pre\n\n                roberta_doc['keyword_absent'] = keywords_absent_pre\n                re_key_positions = start_end_re(match_position_idxs_key, match_pos_ends_key, keywords_exist_pre, posi_dict, exist_poss, 'key')\n                re_noun_positions = start_end_re(match_position_idxs_noun, match_pos_ends_noun, nounchunks_pre, posi_dict, poss_re, 'noun')\n\n                keyphrase_num_roberta = keyphrase_num_roberta + len(re_key_positions)\n                noun_roberta_num = noun_roberta_num + len(re_noun_positions)\n\n                roberta_doc[\"keywords_position\"] = re_key_positions\n                roberta_doc[\"nouns_position\"] = re_noun_positions\n\n                output_jsonl_writer.write(json.dumps(doc)+'\\n')\n                #print (keyphrase_num, keyphrase_num_roberta, noun_num, noun_roberta_num)\n                #print (doc)\n\n\n        print (keyphrase_num)\n        print (keyphrase_num_roberta)\n        print (noun_num)\n        print (noun_roberta_num)\n\n\n\n"
  },
  {
    "path": "onmt/keyphrase/run_infer_hfkpg.py",
    "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2021 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nFine-tuning the library models for sequence to sequence.\n\"\"\"\n# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.\nimport json\nimport logging\nimport os\nimport sys\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n\nimport datasets\nimport nltk  # Here to have a nice missing dependency error message early on\nfrom nltk.stem import *\nimport numpy as np\nfrom datasets import load_dataset, load_metric\n\nfrom filelock import FileLock\nfrom transformers import (\n    AutoConfig,\n    AutoModelForSeq2SeqLM,\n    AutoTokenizer,\n    DataCollatorForSeq2Seq,\n    HfArgumentParser,\n    Seq2SeqTrainer,\n    Seq2SeqTrainingArguments,\n    set_seed, TrainingArguments, TrainerState, TrainerControl,\n    TrainerCallback\n)\n\nfrom transformers.file_utils import is_offline_mode\nfrom transformers.utils import check_min_version\nfrom transformers.utils.versions import require_version\n\n\n# Will error if the minimal version of Transformers is not installed. Remove at your own risks.\ncheck_min_version(\"4.17.0.dev0\")\n\nrequire_version(\"datasets>=1.8.0\", \"To fix: pip install -r examples/pytorch/summarization/requirements.txt\")\n\nlogger = logging.getLogger(__name__)\n\ntry:\n    nltk.data.find(\"tokenizers/punkt\")\nexcept (LookupError, OSError):\n    if is_offline_mode():\n        raise LookupError(\n            \"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files\"\n        )\n    with FileLock(\".lock\") as lock:\n        nltk.download(\"punkt\", quiet=True)\n\n\n@dataclass\nclass ModelArguments:\n    \"\"\"\n    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n    \"\"\"\n\n    model_name_or_path: str = field(\n        metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n    )\n    config_name: Optional[str] = field(\n        default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n    )\n    tokenizer_name: Optional[str] = field(\n        default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n    )\n    cache_dir: Optional[str] = field(\n        default=None,\n        metadata={\"help\": \"Where to store the pretrained models downloaded from huggingface.co\"},\n    )\n    use_fast_tokenizer: bool = field(\n        default=True,\n        metadata={\"help\": \"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\"},\n    )\n    model_revision: str = field(\n        default=\"main\",\n        metadata={\"help\": \"The specific model version to use (can be a branch name, tag name or commit id).\"},\n    )\n    use_auth_token: bool = field(\n        default=False,\n        metadata={\n            \"help\": \"Will use the token generated when running `transformers-cli login` (necessary to use this script \"\n            \"with private models).\"\n        },\n    )\n    resize_position_embeddings: Optional[bool] = field(\n        default=None,\n        metadata={\n            \"help\": \"Whether to automatically resize the position embeddings if `max_source_length` exceeds \"\n            \"the model's position embeddings.\"\n        },\n    )\n\n\n@dataclass\nclass DataTrainingArguments:\n    \"\"\"\n    Arguments pertaining to what data we are going to input our model for training and eval.\n    \"\"\"\n\n    lang: str = field(default=None, metadata={\"help\": \"Language id for summarization.\"})\n\n    dataset_name: Optional[str] = field(\n        default=None, metadata={\"help\": \"The name of the dataset to use (via the datasets library).\"}\n    )\n    dataset_config_name: Optional[str] = field(\n        default=None, metadata={\"help\": \"The configuration name of the dataset to use (via the datasets library).\"}\n    )\n    text_column: Optional[str] = field(\n        default=None,\n        metadata={\"help\": \"The name of the column in the datasets containing the full texts (for summarization).\"},\n    )\n    keyphrase_column: Optional[str] = field(\n        default=None,\n        metadata={\"help\": \"The name of the column in the datasets containing the summaries (for summarization).\"},\n    )\n    train_file: Optional[str] = field(\n        default=None, metadata={\"help\": \"The input training data file (a jsonlines or csv file).\"}\n    )\n    validation_file: Optional[str] = field(\n        default=None,\n        metadata={\n            \"help\": \"An optional input evaluation data file to evaluate the metrics (rouge) on \"\n            \"(a jsonlines or csv file).\"\n        },\n    )\n    test_file: Optional[str] = field(\n        default=None,\n        metadata={\n            \"help\": \"An optional input test data file to evaluate the metrics (rouge) on \" \"(a jsonlines or csv file).\"\n        },\n    )\n    overwrite_cache: bool = field(\n        default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\n    )\n    preprocessing_num_workers: Optional[int] = field(\n        default=None,\n        metadata={\"help\": \"The number of processes to use for the preprocessing.\"},\n    )\n    max_source_length: Optional[int] = field(\n        default=1024,\n        metadata={\n            \"help\": \"The maximum total input sequence length after tokenization. Sequences longer \"\n            \"than this will be truncated, sequences shorter will be padded.\"\n        },\n    )\n    max_target_length: Optional[int] = field(\n        default=128,\n        metadata={\n            \"help\": \"The maximum total sequence length for target text after tokenization. Sequences longer \"\n            \"than this will be truncated, sequences shorter will be padded.\"\n        },\n    )\n    val_max_target_length: Optional[int] = field(\n        default=None,\n        metadata={\n            \"help\": \"The maximum total sequence length for validation target text after tokenization. Sequences longer \"\n            \"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`.\"\n            \"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used \"\n            \"during ``evaluate`` and ``predict``.\"\n        },\n    )\n    pad_to_max_length: bool = field(\n        default=False,\n        metadata={\n            \"help\": \"Whether to pad all samples to model maximum sentence length. \"\n            \"If False, will pad the samples dynamically when batching to the maximum length in the batch. More \"\n            \"efficient on GPU but very bad for TPU.\"\n        },\n    )\n    max_train_samples: Optional[int] = field(\n        default=None,\n        metadata={\n            \"help\": \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n            \"value if set.\"\n        },\n    )\n    max_eval_samples: Optional[int] = field(\n        default=None,\n        metadata={\n            \"help\": \"For debugging purposes or quicker training, truncate the number of evaluation examples to this \"\n            \"value if set.\"\n        },\n    )\n    max_predict_samples: Optional[int] = field(\n        default=None,\n        metadata={\n            \"help\": \"For debugging purposes or quicker training, truncate the number of prediction examples to this \"\n            \"value if set.\"\n        },\n    )\n    num_beams: Optional[int] = field(\n        default=None,\n        metadata={\n            \"help\": \"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, \"\n            \"which is used during ``evaluate`` and ``predict``.\"\n        },\n    )\n    ignore_pad_token_for_loss: bool = field(\n        default=True,\n        metadata={\n            \"help\": \"Whether to ignore the tokens corresponding to padded labels in the loss computation or not.\"\n        },\n    )\n    source_prefix: Optional[str] = field(\n        default=\"\", metadata={\"help\": \"A prefix to add before every source text (useful for T5 models).\"}\n    )\n\n    forced_bos_token: Optional[str] = field(\n        default=None,\n        metadata={\n            \"help\": \"The token to force as the first generated token after the decoder_start_token_id.\"\n            \"Useful for multilingual models like mBART where the first generated token\"\n            \"needs to be the target language token (Usually it is the target language token)\"\n        },\n    )\n\n    def __post_init__(self):\n        if self.dataset_name is None and self.train_file is None and self.validation_file is None:\n            raise ValueError(\"Need either a dataset name or a training/validation file.\")\n        else:\n            if self.train_file is not None:\n                extension = self.train_file.split(\".\")[-1]\n                assert extension in [\"csv\", \"json\"], \"`train_file` should be a csv or a json file.\"\n            if self.validation_file is not None:\n                extension = self.validation_file.split(\".\")[-1]\n                assert extension in [\"csv\", \"json\"], \"`validation_file` should be a csv or a json file.\"\n        if self.val_max_target_length is None:\n            self.val_max_target_length = self.max_target_length\n\n\nsummarization_name_mapping = {\n    \"amazon_reviews_multi\": (\"review_body\", \"review_title\"),\n    \"big_patent\": (\"description\", \"abstract\"),\n    \"cnn_dailymail\": (\"article\", \"highlights\"),\n    \"orange_sum\": (\"text\", \"summary\"),\n    \"pn_summary\": (\"article\", \"summary\"),\n    \"psc\": (\"extract_text\", \"summary_text\"),\n    \"samsum\": (\"dialogue\", \"summary\"),\n    \"thaisum\": (\"body\", \"summary\"),\n    \"xglue\": (\"news_body\", \"news_title\"),\n    \"xsum\": (\"document\", \"summary\"),\n    \"wiki_summary\": (\"article\", \"highlights\"),\n}\n\n\nclass CheckOutputCallback(TrainerCallback):\n    def on_evaluate(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):\n        pass\n\n    def on_prediction_step(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):\n        pass\n\ndef main():\n    # See all possible arguments in src/transformers/training_args.py\n    # or by passing the --help flag to this script.\n    # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))\n    if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n        # If we pass only one argument to the script and it's the path to a json file,\n        # let's parse it to get our arguments.\n        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n    else:\n        model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n    # Setup logging\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        handlers=[logging.StreamHandler(sys.stdout)],\n    )\n    log_level = training_args.get_process_log_level()\n    logger.setLevel(log_level)\n    datasets.utils.logging.set_verbosity(log_level)\n\n    # Log on each process the small summary:\n    logger.warning(\n        f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\"\n        + f\"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}\"\n    )\n    logger.info(f\"Training/evaluation parameters {training_args}\")\n\n    # Set seed before initializing model.\n    set_seed(training_args.seed)\n\n    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)\n    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/\n    # (the dataset will be downloaded automatically from the datasets Hub).\n    #\n    # For CSV/JSON files this script will use the first column for the full texts and the second column for the\n    # summaries (unless you specify column names for this with the `text_column` and `keyphrase_column` arguments).\n    #\n    # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n    # download the dataset.\n    if data_args.dataset_name is not None:\n        # Downloading and loading a dataset from the hub.\n        raw_datasets = load_dataset(\n            data_args.dataset_name, data_args.dataset_config_name, \"raw\", cache_dir=model_args.cache_dir\n        )\n    else:\n        data_files = {}\n        if data_args.train_file is not None:\n            data_files[\"train\"] = data_args.train_file\n            extension = data_args.train_file.split(\".\")[-1]\n        if data_args.validation_file is not None:\n            data_files[\"validation\"] = data_args.validation_file\n            extension = data_args.validation_file.split(\".\")[-1]\n        if data_args.test_file is not None:\n            data_files[\"test\"] = data_args.test_file\n            extension = data_args.test_file.split(\".\")[-1]\n        raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)\n    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at\n    # https://huggingface.co/docs/datasets/loading_datasets.html.\n\n    # Load pretrained model and tokenizer\n    #\n    # Distributed training:\n    # The .from_pretrained methods guarantee that only one local process can concurrently\n    # download model & vocab.\n    config = AutoConfig.from_pretrained(\n        model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        use_auth_token=True if model_args.use_auth_token else None,\n    )\n    tokenizer = AutoTokenizer.from_pretrained(\n        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n        cache_dir=model_args.cache_dir,\n        use_fast=model_args.use_fast_tokenizer,\n        revision=model_args.model_revision,\n        use_auth_token=True if model_args.use_auth_token else None,\n    )\n    # tokenizer = AutoTokenizer.from_pretrained('/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/hf_ckpts/tokenizer')\n    model = AutoModelForSeq2SeqLM.from_pretrained(\n        model_args.model_name_or_path,\n        from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n        config=config,\n        cache_dir=model_args.cache_dir,\n        revision=model_args.model_revision,\n        use_auth_token=True if model_args.use_auth_token else None,\n    )\n\n    model.resize_token_embeddings(len(tokenizer))\n\n    if (\n        hasattr(model.config, \"max_position_embeddings\")\n        and model.config.max_position_embeddings < data_args.max_source_length\n    ):\n        if model_args.resize_position_embeddings is None:\n            logger.warning(\n                f\"Increasing the model's number of position embedding vectors from {model.config.max_position_embeddings} \"\n                f\"to {data_args.max_source_length}.\"\n            )\n            model.resize_position_embeddings(data_args.max_source_length)\n        elif model_args.resize_position_embeddings:\n            model.resize_position_embeddings(data_args.max_source_length)\n        else:\n            raise ValueError(\n                f\"`--max_source_length` is set to {data_args.max_source_length}, but the model only has {model.config.max_position_embeddings}\"\n                f\" position encodings. Consider either reducing `--max_source_length` to {model.config.max_position_embeddings} or to automatically \"\n                \"resize the model's position encodings by passing `--resize_position_embeddings`.\"\n            )\n\n    prefix = data_args.source_prefix if data_args.source_prefix is not None else \"\"\n\n    # Preprocessing the datasets.\n    # We need to tokenize inputs and targets.\n    if training_args.do_train:\n        column_names = raw_datasets[\"train\"].column_names\n    elif training_args.do_eval:\n        column_names = raw_datasets[\"validation\"].column_names\n    elif training_args.do_predict:\n        column_names = raw_datasets[\"test\"].column_names\n    else:\n        logger.info(\"There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.\")\n        return\n\n    # Get the column names for input/target.\n    dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)\n    if data_args.text_column is None:\n        text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n    else:\n        text_column = data_args.text_column\n        if text_column not in column_names:\n            raise ValueError(\n                f\"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n    if data_args.keyphrase_column is None:\n        keyphrase_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n    else:\n        keyphrase_column = data_args.keyphrase_column\n        if keyphrase_column not in column_names:\n            raise ValueError(\n                f\"--keyphrase_column' value '{data_args.keyphrase_column}' needs to be one of: {', '.join(column_names)}\"\n            )\n\n    # Temporarily set max_target_length for training.\n    max_target_length = data_args.max_target_length\n    padding = \"max_length\" if data_args.pad_to_max_length else False\n\n    def preprocess_function(examples):\n        # remove pairs where at least one record is None\n\n        inputs, targets = [], []\n        for i in range(len(examples[text_column])):\n            if examples[text_column][i] is not None and examples[keyphrase_column][i] is not None:\n                inputs.append(examples[text_column][i])\n                targets.append(examples[keyphrase_column][i])\n\n        inputs = examples[text_column]\n        targets = ['<sep>'.join(kps) for kps in examples[keyphrase_column]]\n        inputs = [prefix + ' '.join(inp) for inp in inputs]\n        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)\n\n        # Setup the tokenizer for targets\n        with tokenizer.as_target_tokenizer():\n            labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)\n\n        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n        # padding in the loss.\n        if padding == \"max_length\" and data_args.ignore_pad_token_for_loss:\n            labels[\"input_ids\"] = [\n                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n            ]\n\n        model_inputs[\"labels\"] = labels[\"input_ids\"]\n        return model_inputs\n\n    if training_args.do_train:\n        if \"train\" not in raw_datasets:\n            raise ValueError(\"--do_train requires a train dataset\")\n        train_dataset = raw_datasets[\"train\"]\n        if data_args.max_train_samples is not None:\n            train_dataset = train_dataset.select(range(data_args.max_train_samples))\n        with training_args.main_process_first(desc=\"train dataset map pre-processing\"):\n            train_dataset = train_dataset.map(\n                preprocess_function,\n                batched=True,\n                num_proc=data_args.preprocessing_num_workers,\n                remove_columns=column_names,\n                load_from_cache_file=not data_args.overwrite_cache,\n                desc=\"Running tokenizer on train dataset\",\n            )\n\n    if training_args.do_eval:\n        max_target_length = data_args.val_max_target_length\n        if \"validation\" not in raw_datasets:\n            raise ValueError(\"--do_eval requires a validation dataset\")\n        eval_dataset = raw_datasets[\"validation\"]\n        if data_args.max_eval_samples is not None:\n            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))\n        with training_args.main_process_first(desc=\"validation dataset map pre-processing\"):\n            eval_dataset = eval_dataset.map(\n                preprocess_function,\n                batched=True,\n                num_proc=data_args.preprocessing_num_workers,\n                remove_columns=column_names,\n                load_from_cache_file=not data_args.overwrite_cache,\n                desc=\"Running tokenizer on validation dataset\",\n            )\n\n    if training_args.do_predict:\n        max_target_length = data_args.val_max_target_length\n        if \"test\" not in raw_datasets:\n            raise ValueError(\"--do_predict requires a test dataset\")\n        predict_dataset = raw_datasets[\"test\"]\n        if data_args.max_predict_samples is not None:\n            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))\n        with training_args.main_process_first(desc=\"prediction dataset map pre-processing\"):\n            predict_dataset = predict_dataset.map(\n                preprocess_function,\n                batched=True,\n                num_proc=data_args.preprocessing_num_workers,\n                remove_columns=column_names,\n                load_from_cache_file=not data_args.overwrite_cache,\n                desc=\"Running tokenizer on prediction dataset\",\n            )\n\n    # Data collator\n    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id\n    data_collator = DataCollatorForSeq2Seq(\n        tokenizer,\n        model=model,\n        label_pad_token_id=label_pad_token_id,\n        pad_to_multiple_of=8 if training_args.fp16 else None,\n    )\n\n    # Metric\n    def postprocess_text(preds, labels, sep_token):\n        stemmer = PorterStemmer()\n        preds = [pred.lower().replace('</s>', '').replace('<pad>', '').split(sep_token) for pred in preds]\n        labels = [label.lower().replace('<s>', '').replace('<pad>', '').split(sep_token) for label in labels]\n        preds = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in pred] for pred in preds]\n        labels = [[' '.join([stemmer.stem(w) for w in p.split()]) for p in label] for label in labels]\n        preds = [[p.strip() for p in pred if len(p.strip()) > 0] for pred in preds]\n        labels = [[p.strip() for p in label if len(p.strip()) > 0] for label in labels]\n\n        return preds, labels\n\n    def compute_metrics(eval_preds):\n        preds = eval_preds.predictions\n        labels = eval_preds.label_ids\n        if isinstance(preds, tuple):\n            preds = preds[0]\n        if len(preds.shape) == 3:\n            preds = preds.argmax(axis=-1)\n\n        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=False)\n        if data_args.ignore_pad_token_for_loss:\n            # Replace -100 in the labels as we can't decode them.\n            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=False)\n\n        # Some simple post-processing\n        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels, tokenizer.sep_token)\n\n        precs, recalls, f_scores = [], [], []\n        num_match, num_pred, num_gold = [], [], []\n        for pred, label in zip(decoded_preds, decoded_labels):\n            pred_set = set(pred)\n            label_set = set(label)\n            match_set = label_set.intersection(pred_set)\n            p = float(len(match_set)) / float(len(pred_set)) if len(pred_set) > 0 else 0.0\n            r = float(len(match_set)) / float(len(label_set)) if len(label_set) > 0 else 0.0\n            f1 = float(2 * (p * r)) / (p + r) if (p + r) > 0 else 0.0\n            precs.append(p)\n            recalls.append(r)\n            f_scores.append(f1)\n            num_match.append(len(match_set))\n            num_pred.append(len(pred_set))\n            num_gold.append(len(label_set))\n\n            print(f'PRED: num={len(pred_set)} - {pred_set}')\n            print(f'GT: num={len(label_set)} - {label_set}')\n            print(f'p={p}, r={r}, f1={f1}')\n            print('-' * 20)\n\n        result = {\n            'precision@M': np.mean(precs) * 100.0,\n            'recall@M': np.mean(recalls) * 100.0,\n            'fscore@M': np.mean(f_scores) * 100.0,\n            'num_match': np.mean(num_match),\n            'num_pred': np.mean(num_pred),\n            'num_gold': np.mean(num_gold),\n        }\n\n        result = {k: round(v, 4) for k, v in result.items()}\n        return result\n\n    # Initialize our Trainer\n    trainer = Seq2SeqTrainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset if training_args.do_train else None,\n        eval_dataset=eval_dataset if training_args.do_eval else None,\n        tokenizer=tokenizer,\n        data_collator=data_collator,\n        compute_metrics=compute_metrics if training_args.predict_with_generate else None,\n        callbacks=[CheckOutputCallback]\n    )\n\n    # Training\n    if training_args.do_train:\n        checkpoint = None\n        if training_args.resume_from_checkpoint is not None:\n            checkpoint = training_args.resume_from_checkpoint\n        train_result = trainer.train(resume_from_checkpoint=checkpoint)\n        trainer.save_model()  # Saves the tokenizer too for easy upload\n\n        metrics = train_result.metrics\n        max_train_samples = (\n            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)\n        )\n        metrics[\"train_samples\"] = min(max_train_samples, len(train_dataset))\n\n        trainer.log_metrics(\"train\", metrics)\n        trainer.save_metrics(\"train\", metrics)\n        trainer.save_state()\n\n    # Evaluation\n    results = {}\n    max_length = (\n        training_args.generation_max_length\n        if training_args.generation_max_length is not None\n        else data_args.val_max_target_length\n    )\n    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams\n    if training_args.do_eval:\n        logger.info(\"*** Evaluate ***\")\n        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix=\"eval\")\n        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)\n        metrics[\"eval_samples\"] = min(max_eval_samples, len(eval_dataset))\n\n        trainer.log_metrics(\"eval\", metrics)\n        trainer.save_metrics(\"eval\", metrics)\n\n    if training_args.do_predict:\n        logger.info(\"*** Predict ***\")\n\n        predict_results = trainer.predict(\n            predict_dataset, metric_key_prefix=\"predict\", max_length=max_length, num_beams=num_beams,\n        )\n        metrics = predict_results.metrics\n        max_predict_samples = (\n            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)\n        )\n        metrics[\"predict_samples\"] = min(max_predict_samples, len(predict_dataset))\n\n        trainer.log_metrics(\"predict\", metrics)\n        trainer.save_metrics(\"predict\", metrics)\n\n        if trainer.is_world_process_zero():\n            if training_args.predict_with_generate:\n                predictions = tokenizer.batch_decode(predict_results.predictions)\n                predictions = [pred.lower().replace('</s>', '').replace('<pad>', '').strip().split(tokenizer.sep_token) for pred in predictions]\n                output_prediction_file = os.path.join(training_args.output_dir, \"generated_predictions.txt\")\n                with open(output_prediction_file, \"w\") as writer:\n                    writer.write(\"\\n\".join([json.dumps(pred) for pred in predictions]))\n\n    kwargs = {\"finetuned_from\": model_args.model_name_or_path, \"tasks\": \"keyphrasification\"}\n    if data_args.dataset_name is not None:\n        kwargs[\"dataset_tags\"] = data_args.dataset_name\n        if data_args.dataset_config_name is not None:\n            kwargs[\"dataset_args\"] = data_args.dataset_config_name\n            kwargs[\"dataset\"] = f\"{data_args.dataset_name} {data_args.dataset_config_name}\"\n        else:\n            kwargs[\"dataset\"] = data_args.dataset_name\n\n    if data_args.lang is not None:\n        kwargs[\"language\"] = data_args.lang\n\n    if training_args.push_to_hub:\n        trainer.push_to_hub(**kwargs)\n    else:\n        trainer.create_model_card(**kwargs)\n\n    return results\n\n\ndef _mp_fn(index):\n    # For xla_spawn (TPUs)\n    main()\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "onmt/keyphrase/shrink_pred_files.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nSome pred files use up too much space, e.g. /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/pred/kp20k-meng17-verbatim_prepend-rnn-BS64-LR0.05-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1_step_95000/kp20k.pred is 8.3GB, beam=10 size=2.0GB.\n\nSo this\n\"\"\"\nimport json\nimport os\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    # root_path = ' /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2o/'\n    # root_path = '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/'\n    # root_path = '/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k'\n    # root_path = '/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2'\n    root_path = '/zfs1/hdaqing/rum20/kp/transfer_exps/bart_DAFT-v1-DA1e6_FT1e5'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/'\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/'\n\n    # root_path = '/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/order_matters/transformer/meng17-one2seq-beam50-maxlen40/'\n\n    print(root_path)\n    dataset_line_counts = {\n                     'kp20k': 19987,\n                     # 'kp20k_valid2k': 2000,\n                     'inspec': 500,\n                     'krapivin': 460,\n                     'nus': 211,\n                     'semeval': 100,\n                     # 'duc': 308,\n                     'kp20k_test': 19987,\n                     'openkp_test': 6614,\n                     'kptimes_test': 10000,\n                     'jptimes_test': 10000,\n                     'stackex_test': 16000,\n\n                     'kp20k_valid2k_test': 2000,\n                     'openkp_valid2k_test': 2000,\n                     'kptimes_valid2k_test': 2000,\n                     'stackex_valid2k_test': 2000,\n                     }\n\n    total_size_shrinked = 0\n    for root, dirs, files in os.walk(root_path, topdown=True):\n        for filename in files:\n            # print()\n            # print('-=' * 50)\n            # print(filename)\n            # print('-=' * 50)\n            '''\n            Delete report\n            '''\n            if filename.endswith('.report'):\n                dataset_name = filename[:-7].split('-')[-1][5:]\n                if dataset_name in dataset_line_counts:\n                    report_path = os.path.join(root, filename)\n                    print('Deleting .report: [%s] %s' % (dataset_name, report_path))\n                    ori_size = os.stat(report_path).st_size // 1024 // 1024\n                    print('\\t file size = %d MB' % (ori_size))\n                    total_size_shrinked += ori_size\n                    os.remove(report_path)\n            if filename.endswith('.report.txt'):\n                dataset_name = filename[:-11]\n                if dataset_name in dataset_line_counts:\n                    report_path = os.path.join(root, filename)\n                    print('Deleting .report: [%s] %s' % (dataset_name, report_path))\n                    ori_size = os.stat(report_path).st_size // 1024 // 1024\n                    print('\\t file size = %d MB' % (ori_size))\n                    total_size_shrinked += ori_size\n                    os.remove(report_path)\n            '''\n            Reduce .pred file size\n            '''\n            if not filename.endswith('.pred'):\n                continue\n\n            dataset_name = filename[:-5].split('-')[-1][5:]\n            if dataset_name not in dataset_line_counts: continue\n\n            pred_path = os.path.join(root, filename)\n\n            print('Shrinking .pred: [%s] %s' % (dataset_name, pred_path))\n            ori_size = os.stat(pred_path).st_size // 1024 // 1024\n            print('\\t file size = %d MB' % (ori_size))\n\n            # ensure the pred is complete\n            with open(pred_path, 'r') as pred_file:\n                lines = [l if lid==0 else '' for lid, l in enumerate(pred_file)]\n                if len(lines) != dataset_line_counts[dataset_name]:\n                    # print('Prediction ongoing, skip!')\n                    continue\n\n                pred_dict = json.loads(lines[0])\n                # not a model output\n                if 'attns' not in pred_dict:\n                    continue\n                # indicating it's already shrinked, skip\n                if pred_dict['src'] == None:\n                # if pred_dict['attns'] == None and pred_dict['dup_pred_tuples'] == None:\n                    # print('This pred file has been shrinked, skip!')\n                    continue\n\n            tmp_pred_path = pred_path + '.tmp'\n            tmp_pred_file = open(tmp_pred_path, 'w')\n            with open(pred_path, 'r') as pred_file:\n                for lid, line in enumerate(pred_file):\n                    try:\n                        pred_dict = json.loads(line)\n                    except:\n                        tmp_pred_file.write(line.strip() + '\\n')\n                        print(\"Error occurs while loading line %d in %s\" % (lid, pred_path))\n                        continue\n                    # for k,v in pred_dict.items():\n                    #     print('%s' % k)\n\n                    pred_dict['src'] = None\n                    pred_dict['preds'] = None\n                    # pred_dict['pred_scores'] = None\n                    pred_dict['attns'] = None\n                    pred_dict['copied_flags'] = None\n                    pred_dict['ori_pred_sents'] = None\n                    pred_dict['ori_pred_scores'] = None\n                    pred_dict['ori_preds'] = None\n                    pred_dict['dup_pred_tuples'] = None\n                    tmp_pred_file.write(json.dumps(pred_dict)+'\\n')\n\n            # tmp_pred_file.close()\n            print('\\tDumped to: ' + pred_path + '.tmp')\n            new_size = os.stat(tmp_pred_path).st_size // 1024 // 1024\n            print('\\t new file size = %d MB' % (new_size))\n            print('\\t reduced size = %d MB' % (ori_size-new_size))\n\n            total_size_shrinked += (ori_size - new_size)\n\n            # replace the original file to release space\n            os.remove(pred_path)\n            os.rename(tmp_pred_path, pred_path)\n\n    print('Total shrinked size = %d MB' % (total_size_shrinked))\n"
  },
  {
    "path": "onmt/keyphrase/utils.py",
    "content": "import contextlib\nimport re\nimport string\nfrom os.path import join, dirname\nimport numpy as np\nimport time\nimport sys,logging\nimport matplotlib\n\nSEP_token = \"<sep>\"\nDIGIT_token = \"<digit>\"\nimport time\n\nfrom nltk.stem.porter import *\nstemmer = PorterStemmer()\n\n# matplotlib.use('agg')\n# import matplotlib.pyplot as plt\n\ndef stem_word_list(word_list):\n    return [stemmer.stem(w.strip()) for w in word_list]\n\n\ndef validate_phrases(pred_seqs, unk_token):\n    '''\n    :param pred_seqs:\n    :param src_str:\n    :param oov:\n    :param id2word:\n    :param opt:\n    :return:\n    '''\n    valid_flags = []\n\n    for seq in pred_seqs:\n        keep_flag = True\n\n        if len(seq) == 0:\n            keep_flag = False\n\n        if keep_flag and any([w == unk_token for w in seq]):\n            keep_flag = False\n\n        if keep_flag and any([w == '.' or w == ',' for w in seq]):\n            keep_flag = False\n\n        valid_flags.append(keep_flag)\n\n    return np.asarray(valid_flags)\n\n\ndef if_present_duplicate_phrases(src_seq, tgt_seqs, stemming=True, lowercase=True):\n    \"\"\"\n    Check if each given target sequence verbatim appears in the source sequence\n    :param src_seq:\n    :param tgt_seqs:\n    :param stemming:\n    :param lowercase:\n    :param check_duplicate:\n    :return:\n    \"\"\"\n    if lowercase:\n        src_seq = [w.lower() for w in src_seq]\n    if stemming:\n        src_seq = stem_word_list(src_seq)\n\n    present_indices = []\n    present_flags = []\n    duplicate_flags = []\n    phrase_set = set()  # some phrases are duplicate after stemming, like \"model\" and \"models\" would be same after stemming, thus we ignore the following ones\n\n    for tgt_seq in tgt_seqs:\n        if lowercase:\n            tgt_seq = [w.lower() for w in tgt_seq]\n        if stemming:\n            tgt_seq = stem_word_list(tgt_seq)\n\n        # check if the phrase appears in source text\n        # iterate each word in source\n        match_flag, match_pos_idx = if_present_phrase(src_seq, tgt_seq)\n\n        # if it reaches the end of source and no match, means it doesn't appear in the source\n        present_flags.append(match_flag)\n        present_indices.append(match_pos_idx)\n\n        # check if it is duplicate\n        if '_'.join(tgt_seq) in phrase_set:\n            duplicate_flags.append(True)\n        else:\n            duplicate_flags.append(False)\n        phrase_set.add('_'.join(tgt_seq))\n\n    assert len(present_flags) == len(present_indices)\n\n    return np.asarray(present_flags), \\\n           np.asarray(present_indices), \\\n           np.asarray(duplicate_flags)\n\n\ndef if_present_phrase(src_str_tokens, phrase_str_tokens):\n    \"\"\"\n\n    :param src_str_tokens: a list of strings (words) of source text\n    :param phrase_str_tokens: a list of strings (words) of a phrase\n    :return:\n    \"\"\"\n    match_flag = False\n    match_pos_idx = -1\n    for src_start_idx in range(len(src_str_tokens) - len(phrase_str_tokens) + 1):\n        match_flag = True\n        # iterate each word in target, if one word does not match, set match=False and break\n        for seq_idx, seq_w in enumerate(phrase_str_tokens):\n            src_w = src_str_tokens[src_start_idx + seq_idx]\n            if src_w != seq_w:\n                match_flag = False\n                break\n        if match_flag:\n            match_pos_idx = src_start_idx\n            break\n\n    return match_flag, match_pos_idx\n\n\ndef gather_scores(gathered_scores, results_names, results_dicts):\n    for result_name, result_dict in zip(results_names, results_dicts):\n        for metric_name, score in result_dict.items():\n            if metric_name.endswith('_num'):\n                # if it's 'present_tgt_num' or 'absent_tgt_num', leave as is\n                field_name = result_name\n            else:\n                # if it's other score like 'precision@5' is renamed to like 'present_exact_precision@'\n                field_name = result_name + '_' + metric_name\n\n            if field_name not in gathered_scores:\n                gathered_scores[field_name] = []\n\n            gathered_scores[field_name].append(score)\n\n    return gathered_scores\n\n\ndef print_predeval_result(i, src_text, tgt_seqs, present_tgt_flags,\n                          pred_seqs, pred_scores, pred_idxs, copied_flags,\n                          present_pred_flags, valid_pred_flags,\n                          valid_and_present_flags, valid_and_absent_flags,\n                          match_scores_exact, match_scores_partial,\n                          results_names, results_list, score_dict):\n    '''\n    Print and export predictions\n    '''\n    # src, src_str, tgt, tgt_str_seqs, tgt_copy, pred_seq, oov\n    print_out = '======================  %d =========================' % (i)\n    print_out += '\\n[Source]: %s \\n' % (src_text) if src_text is not None else ''\n    # print_out += '[Abstract]: %s \\n' % (src_dict[\"abstract\"])\n    # print_out += '[Source tokenized][%d]: %s \\n' % (len(src_seq), ' '.join(src_seq))\n    # print_out += 'Real Target [%d] \\n\\t\\t%s \\n' % (len(tgt_seqs), str(tgt_seqs))\n\n    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\n' % \\\n                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\n    print_out += '\\n'.join(\n        ['\\t\\t[%s]' % ' '.join(phrase) if is_present else '\\t\\t%s' % ' '.join(phrase) for phrase, is_present in\n         zip(tgt_seqs, present_tgt_flags)])\n\n    print_out += '\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\n                 '#(valid&present)=%d, #(valid&absent)=%d\\n' % (\n        len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags),\n        sum(valid_and_present_flags), sum(valid_and_absent_flags))\n    print_out += ''\n    preds_out = ''\n    for p_id, (word, match, match_soft,\n               is_valid, is_present) in enumerate(\n        zip(pred_seqs, match_scores_exact, match_scores_partial,\n            valid_pred_flags, present_pred_flags)):\n        score = pred_scores[p_id] if pred_scores else \"Score N/A\"\n        pred_idx = pred_idxs[p_id] if pred_idxs else \"Index N/A\"\n\n        preds_out += '%s\\n' % (' '.join(word))\n        if is_present:\n            print_phrase = '[%s]' % ' '.join(word)\n        else:\n            print_phrase = ' '.join(word)\n\n        if match == 1.0:\n            correct_str = '[correct!]'\n        else:\n            correct_str = ''\n\n        if copied_flags and any(copied_flags[p_id]):\n            copy_str = '[copied!]'\n        else:\n            copy_str = ''\n\n        pred_str = '\\t\\t%s\\t%s \\t %s %s%s\\n' % ('[%.4f]' % (-score) if pred_scores else \"Score N/A\",\n                                                print_phrase, str(pred_idx),\n                                                correct_str, copy_str)\n        if not is_valid:\n            pred_str = '\\t%s' % pred_str\n\n        print_out += pred_str\n\n    print_out += \"\\n ======================================================= \\n\"\n\n    print_out += '[GROUND-TRUTH] #(all)=%d, #(present)=%d, #(absent)=%d\\n' % \\\n                 (len(present_tgt_flags), sum(present_tgt_flags), len(present_tgt_flags)-sum(present_tgt_flags))\n    print_out += '\\n[PREDICTION] #(all)=%d, #(valid)=%d, #(present)=%d, ' \\\n                 '#(valid&present)=%d, #(valid&absent)=%d\\n' % (\n        len(pred_seqs), sum(valid_pred_flags), sum(present_pred_flags),\n        sum(valid_and_present_flags), sum(valid_and_absent_flags))\n\n    for name, results in zip(results_names, results_list):\n        # print @5@10@O@M for present_exact, print @50@M for absent_exact\n        if name in ['all_exact', 'present_exact', 'absent_exact']:\n            if name.startswith('all') or name.startswith('present'):\n                topk_list = ['1', '3', '10', 'k']\n            else:\n                topk_list = ['50', 'M']\n\n            for topk in topk_list:\n                print_out += \"\\n --- batch {} Corr/P/R/F1 @{}: \\t\".format(name, topk) \\\n                             + \" {:6} , {:.4f} , {:.4f} , {:.4f}\".format(int(results['correct@{}'.format(topk)]),\n                                                                          results['precision@{}'.format(topk)],\n                                                                          results['recall@{}'.format(topk)],\n                                                                          results['f_score@{}'.format(topk)],\n                                                                          )\n                # note the reported results might be different from the numbers here\n                #   since we remove data points that have zero valid targets in average (see kp_report.summarize_scores)\n                print_out += \"\\n --- total {} Corr/P/R/F1 @{}: \\t\".format(name, topk) \\\n                             + \" {:6} , {:.4f} , {:.4f} , {:.4f}\".format(\n                                int(np.sum(score_dict['{}_correct@{}'.format(name, topk)])),\n                                np.average(score_dict['{}_precision@{}'.format(name, topk)]),\n                                np.average(score_dict['{}_recall@{}'.format(name, topk)]),\n                                np.average(score_dict['{}_f_score@{}'.format(name, topk)]),)\n        elif name in ['present_exact_advanced', 'absent_exact_advanced']:\n            print_out += \"\\n --- batch {} AUC/SADR/α-nDCG@5/α-nDCG@10/nDCG/AP/MRR: \\t\".format(name[: name.rfind('_')]) \\\n                         + \" {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f}\".format(\n                results['auc'], results['sadr'], results['alpha_ndcg@5'], results['alpha_ndcg@10'],\n                results['ndcg'], results['ap'], results['mrr'],)\n\n            print_out += \"\\n --- total {} AUC/SADR/α-nDCG@5/α-nDCG@10/nDCG/AP/MRR: \\t\".format(name[: name.rfind('_')]) \\\n                         + \" {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f}\".format(\n                np.average(score_dict['{}_{}'.format(name, 'auc')]),\n                np.average(score_dict['{}_{}'.format(name, 'sadr')]),\n                np.average(score_dict['{}_{}'.format(name, 'alpha_ndcg@5')]),\n                np.average(score_dict['{}_{}'.format(name, 'alpha_ndcg@10')]),\n                np.average(score_dict['{}_{}'.format(name, 'ndcg')]),\n                np.average(score_dict['{}_{}'.format(name, 'ap')]),\n                np.average(score_dict['{}_{}'.format(name, 'mrr')]),\n            )\n        else:\n            # ignore partial for now\n            continue\n\n    print_out += \"\\n =======================================================\"\n\n    return print_out\n\n\ndef meng17_tokenize(text):\n    '''\n    The tokenizer used in Meng et al. ACL 2017\n    parse the feed-in text, filtering and tokenization\n    keep [_<>,\\(\\)\\.\\'%], replace digits with <digit>, split by [^a-zA-Z0-9_<>,\\(\\)\\.\\'%]\n    :param text:\n    :return: a list of tokens\n    '''\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', text)\n    # pad spaces to the left and right of special punctuations\n    text = re.sub(r'[_<>,\\(\\)\\.\\'%]', ' \\g<0> ', text)\n    # tokenize by non-letters (new-added + # & *, but don't pad spaces, to make them as one whole word)\n    tokens = list(filter(lambda w: len(w) > 0, re.split(r'[^a-zA-Z0-9_<>,#&\\+\\*\\(\\)\\.\\']', text)))\n\n    return tokens\n\n\ndef retain_punc_tokenize(raw_text):\n    '''\n    Keep almost all punctuations except ?, as ? is often caused by encoding error.\n    Pad underlines before and after each punctuation.\n    :param text:\n    :return: a list of tokens\n    '''\n    puncs = string.punctuation\n    pattern = r\"[{}]\".format(puncs)  # create the pattern\n\n    # remove line breakers\n    text = re.sub(r'[\\r\\n\\t]', ' ', raw_text)\n    # pad spaces&underlines to the left and right of special punctuations\n    text = re.sub(pattern, ' _\\g<0>_ ', text)\n    # tokenize by whitespaces\n    tokens = []\n    for token in re.split(r'\\s', text):\n        # split strings that contain letters and digits\n        if re.match(r'[A-Za-z]+\\d+|\\d+[A-Za-z]+', token):\n            token = re.findall(r'[A-Za-z]+|\\d+', token)\n        else:\n            token = [token]\n        tokens.extend(token)\n\n    tokens = list(filter(lambda w: len(w) > 0 and w!='_?_', tokens))\n    tokens = [t[1] if len(t)==3 and t[0]=='_' and t[2]=='_' else t for t in tokens]\n\n    return tokens\n\n\ndef replace_numbers_to_DIGIT(tokens, k=2):\n    # replace big numbers (contain more than k digit) with <digit>\n    tokens = [w if not re.match('^\\d{%d,}$' % k, w) else DIGIT_token for w in tokens]\n\n    return tokens\n\n\ndef time_usage(func):\n    def wrapper(*args, **kwargs):\n        beg_ts = time.time()\n        retval = func(*args, **kwargs)\n        end_ts = time.time()\n        print(\"elapsed time: %f\" % (end_ts - beg_ts))\n        return retval\n    return wrapper\n\nDATA_DIR = join(dirname(dirname(__file__)), 'data')\nMODELS_DIR = join(dirname(dirname(__file__)), 'models')\nMODEL_NAME = (\"{:s}_model.{:s}.{:s}_contextsize.{:d}_numnoisewords.{:d}\"\n              \"_vecdim.{:d}_batchsize.{:d}_lr.{:f}_epoch.{:d}_loss.{:f}\"\n              \".pth.tar\")\n\ndef current_milli_time():\n    return int(round(time.time() * 1000))\n\nclass LoggerWriter:\n    def __init__(self, level):\n        # self.level is really like using log.debug(message)\n        # at least in my case\n        self.level = level\n\n    def write(self, message):\n        # if statement reduces the amount of newlines that are\n        # printed to the logger\n        if message != '\\n':\n            self.level(message)\n\n    def flush(self):\n        # create a flush method so things can be flushed when\n        # the system wants to. Not sure if simply 'printing'\n        # sys.stderr is the correct way to do it, but it seemed\n        # to work properly for me.\n        self.level(sys.stderr)\n\ndef tally_parameters(model):\n    if logging.getLogger() == None:\n        printer = print\n    else:\n        printer = logging.getLogger().info\n\n    n_params = sum([p.nelement() for p in model.parameters()])\n    printer('Model name: %s' % type(model).__name__)\n    printer('number of parameters: %d' % n_params)\n    enc = 0\n    dec = 0\n    for name, param in model.named_parameters():\n        if 'encoder' in name:\n            enc += param.nelement()\n        elif 'decoder' or 'generator' in name:\n            dec += param.nelement()\n    printer('encoder: %d' % enc)\n    printer('decoder: %d' % dec)\n\ndef _print_progress(epoch_i, batch_i, num_batches):\n    progress = round((batch_i + 1) / num_batches * 100)\n    print(\"\\rEpoch {:d}\".format(epoch_i + 1), end='')\n    sys.stdout.write(\" - {:d}%\".format(progress))\n    sys.stdout.flush()\n\nclass Progbar(object):\n    def __init__(self, logger, title, target, width=30, batch_size = None, total_examples = None, verbose=1):\n        '''\n            @param target: total number of steps expected\n        '''\n        self.logger = logger\n        self.title = title\n        self.width = width\n        self.target = target\n        self.sum_values = {}\n        self.unique_values = []\n        self.start = time.time()\n        self.total_width = 0\n        self.seen_so_far = 0\n        self.verbose = verbose\n\n        self.batch_size = batch_size\n        self.last_batch = 0\n        self.total_examples = total_examples\n        self.start_time = time.time() - 0.00001\n        self.last_time  = self.start_time\n        self.report_delay = 10\n        self.last_report  = self.start_time\n\n    def update(self, current_epoch, current, values=[]):\n        '''\n        @param current: index of current step\n        @param values: list of tuples (name, value_for_last_step).\n        The progress bar will display averages for these values.\n        '''\n        for k, v in values:\n            if k not in self.sum_values:\n                self.sum_values[k] = [v * (current - self.seen_so_far), current - self.seen_so_far]\n                self.unique_values.append(k)\n            else:\n                self.sum_values[k][0] += v * (current - self.seen_so_far)\n                self.sum_values[k][1] += (current - self.seen_so_far)\n        self.seen_so_far = current\n\n        now = time.time()\n        if self.verbose == 1:\n            prev_total_width = self.total_width\n            sys.stdout.write(\"\\b\" * prev_total_width)\n            sys.stdout.write(\"\\r\")\n\n            numdigits = int(np.floor(np.log10(self.target))) + 1\n\n            epoch_info = '%s Epoch=%d -' % (self.title, current_epoch) if current_epoch else '%s -' % (self.title)\n\n            barstr = epoch_info + '%%%dd/%%%dd' % (numdigits, numdigits, ) + ' (%.2f%%)['\n            bar = barstr % (current, self.target, float(current)/float(self.target) * 100.0)\n            prog = float(current)/self.target\n            prog_width = int(self.width*prog)\n            if prog_width > 0:\n                bar += ('.'*(prog_width-1))\n                if current < self.target:\n                    bar += '(-w-)'\n                else:\n                    bar += '(-v-)!!'\n            bar += ('~' * (self.width-prog_width))\n            bar += ']'\n            # sys.stdout.write(bar)\n            self.total_width = len(bar)\n\n            if current:\n                time_per_unit = (now - self.start) / current\n            else:\n                time_per_unit = 0\n            eta = time_per_unit*(self.target - current)\n\n            # info = ''\n            info = bar\n            if current < self.target:\n                info += ' - Run-time: %ds - ETA: %ds' % (now - self.start, eta)\n            else:\n                info += ' - %ds' % (now - self.start)\n            for k in self.unique_values:\n                # info += ' - %s: %.4f' % (k, self.sum_values[k][0] / max(1, self.sum_values[k][1]))\n                if k == 'perplexity' or k == 'PPL':\n                    info += ' - %s: %.4f' % (k, np.exp(self.sum_values[k][0] / max(1, self.sum_values[k][1])))\n                else:\n                    info += ' - %s: %.4f' % (k, self.sum_values[k][0] / max(1, self.sum_values[k][1]))\n\n            # update progress stats\n            '''\n            current_time = time.time()\n            elapsed = current_time - self.last_report\n            if elapsed > self.report_delay:\n                trained_word_count = self.batch_size * current  # only words in vocab & sampled\n                new_trained_word_count = self.batch_size * (current - self.last_batch)  # only words in vocab & sampled\n\n                info += \" - new processed %d words, %.0f words/s\" % (new_trained_word_count, new_trained_word_count / elapsed)\n                self.last_time   = current_time\n                self.last_report = current_time\n                self.last_batch  = current\n            '''\n\n            self.total_width += len(info)\n            if prev_total_width > self.total_width:\n                info += ((prev_total_width-self.total_width) * \" \")\n\n            # sys.stdout.write(info)\n            # sys.stdout.flush()\n\n            self.logger.info(info)\n\n            if current >= self.target:\n                sys.stdout.write(\"\\n\")\n\n        if self.verbose == 2:\n            if current >= self.target:\n                info = '%ds' % (now - self.start)\n                for k in self.unique_values:\n                    info += ' - %s: %.4f' % (k, self.sum_values[k][0] / max(1, self.sum_values[k][1]))\n                # sys.stdout.write(info + \"\\n\")\n                self.logger.critical(info + \"\\n\")\n                print(info + \"\\n\")\n\n    def add(self, n, values=[]):\n        self.update(self.seen_so_far + n, values)\n\n    def clear(self):\n        self.sum_values = {}\n        self.unique_values = []\n        self.total_width = 0\n        self.seen_so_far = 0\n\n\n'''\ndef plot_learning_curve_and_write_csv(scores, curve_names, checkpoint_names, title, ylim=None, save_path=None):\n    \"\"\"\n    Generate a simple plot of the test and training learning curve.\n\n    Parameters\n    ----------\n    title : string\n        Title for the chart.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n    \"\"\"\n    train_sizes=np.linspace(1, len(scores[0]), len(scores[0]))\n    plt.figure(dpi=500)\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\"Training examples\")\n    plt.ylabel(\"Score\")\n\n    # print(train_scores)\n    # print(test_scores)\n    plt.grid()\n    means   = {}\n    stds    = {}\n\n    # colors = \"rgbcmykw\"\n    colors = matplotlib.cm.rainbow(np.linspace(0, 1, len(curve_names)))\n\n    for i, (name, score) in enumerate(zip(curve_names, scores)):\n        # get the mean and std of score along the time step\n        mean = np.asarray([np.mean(s) for s in score])\n        means[name] = mean\n        std  = np.asarray([np.std(s) for s in score])\n        stds[name] = std\n\n        if name.lower().startswith('training ml'):\n            score_ = [np.asarray(s) / 20.0 for s in score]\n            mean = np.asarray([np.mean(s) for s in score_])\n            std  = np.asarray([np.std(s) for s in score_])\n\n        plt.fill_between(train_sizes, mean - std,\n                         mean + std, alpha=0.1,\n                         color=colors[i])\n        plt.plot(train_sizes, mean, 'o-', color=colors[i],\n                 label=name)\n\n    plt.legend(loc=\"best\", prop={'size': 6})\n    # plt.show()\n    if save_path:\n        plt.savefig(save_path + '.png', bbox_inches='tight')\n\n        csv_lines = ['time, ' + ','.join(curve_names)]\n        for t_id, time in enumerate(checkpoint_names):\n            csv_line = time + ',' + ','.join([str(means[c_name][t_id]) for c_name in curve_names])\n            csv_lines.append(csv_line)\n\n        with open(save_path + '.csv', 'w') as result_csv:\n            result_csv.write('\\n'.join(csv_lines))\n\n    plt.close()\n    return plt\n'''\n\n@contextlib.contextmanager\ndef numpy_seed(seed, *addl_seeds):\n    \"\"\"Context manager which seeds the NumPy PRNG with the specified seed and\n    restores the state afterward\"\"\"\n    if seed is None:\n        yield\n        return\n    if len(addl_seeds) > 0:\n        seed = int(hash((seed, *addl_seeds)) % 1e6)\n    state = np.random.get_state()\n    np.random.seed(seed)\n    try:\n        yield\n    finally:\n        np.random.set_state(state)\n"
  },
  {
    "path": "onmt/model_builder.py",
    "content": "\"\"\"\nThis file is for models creation, which consults options\nand creates each encoder and decoder accordingly.\n\"\"\"\nimport os\nimport re\nimport torch\nimport torch.nn as nn\nfrom torch.nn.init import xavier_uniform_\n\nimport onmt.modules\nfrom onmt.encoders import str2enc\n\nfrom onmt.decoders import str2dec\nfrom onmt.inputters.inputter import reload_keyphrase_fields, load_roberta_kp_tokenizer, get_fields\n\nfrom onmt.modules import Embeddings, CopyGenerator\nfrom onmt.modules.util_class import Cast\nfrom onmt.utils.misc import use_gpu\nfrom onmt.utils.logging import logger\nfrom onmt.utils.parse import ArgumentParser\nfrom onmt.constants import ModelTask\nfrom fairseq.models.bart import BARTModel\n\n\ndef build_embeddings(opt, text_field, for_encoder=True):\n    \"\"\"\n    Args:\n        opt: the option in current environment.\n        text_field(TextMultiField): word and feats field.\n        for_encoder(bool): build Embeddings for encoder or decoder?\n    \"\"\"\n    emb_dim = opt.src_word_vec_size if for_encoder else opt.tgt_word_vec_size\n\n    pad_indices = [f.vocab.stoi[f.pad_token] for _, f in text_field]\n    word_padding_idx, feat_pad_indices = pad_indices[0], pad_indices[1:]\n\n    num_embs = [len(f.vocab) for _, f in text_field]\n    num_word_embeddings, num_feat_embeddings = num_embs[0], num_embs[1:]\n\n    freeze_word_vecs_dec = opt.freeze_word_vecs_dec if hasattr(opt, 'freeze_word_vecs_dec') else False\n    freeze_word_vecs = freeze_word_vecs_dec if for_encoder else freeze_word_vecs_dec\n\n    emb = Embeddings(\n        word_vec_size=emb_dim,\n        position_encoding=opt.position_encoding,\n        feat_merge=opt.feat_merge,\n        feat_vec_exponent=opt.feat_vec_exponent,\n        feat_vec_size=opt.feat_vec_size,\n        dropout=opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n        word_padding_idx=word_padding_idx,\n        feat_padding_idx=feat_pad_indices,\n        word_vocab_size=num_word_embeddings,\n        feat_vocab_sizes=num_feat_embeddings,\n        sparse=(hasattr(opt, 'optim') and opt.optim == \"sparseadam\"),\n        freeze_word_vecs=freeze_word_vecs\n    )\n    return emb\n\n\ndef build_encoder(opt, embeddings, **kwargs):\n    \"\"\"\n    Various encoder dispatcher function.\n    Args:\n        opt: the option in current environment.\n        embeddings (Embeddings): vocab embeddings for this encoder.\n    \"\"\"\n    enc_type = opt.encoder_type \\\n        if opt.model_type == \"text\" or opt.model_type == \"keyphrase\" \\\n        else opt.model_type\n    return str2enc[enc_type].from_opt(opt, embeddings, **kwargs)\n\n\ndef build_decoder(opt, embeddings, **kwargs):\n    \"\"\"\n    Various decoder dispatcher function.\n    Args:\n        opt: the option in current environment.\n        embeddings (Embeddings): vocab embeddings for this decoder.\n    \"\"\"\n    dec_type = \"ifrnn\" if opt.decoder_type == \"rnn\" and opt.input_feed \\\n               else opt.decoder_type\n    return str2dec[dec_type].from_opt(opt, embeddings, **kwargs)\n\n\ndef load_test_model(opt, model_path=None):\n    if model_path is None:\n        model_path = opt.models[0]\n    print('Load checkpoint from %s' % model_path)\n    checkpoint = torch.load(model_path,\n                            map_location=lambda storage, loc: storage)\n\n    if opt.fairseq_model:\n        # load a Fairseq-trained model, such as BART\n        tokenizer = None\n        # fairseq models have no previous fields\n        fields = get_fields(opt.data_type,\n                            n_src_feats=0, n_tgt_feats=0,\n                            dynamic_dict=True, # always build src_ex_vocab\n                            src_truncate=opt.src_seq_length_trunc,\n                            tgt_truncate=opt.tgt_seq_length_trunc,\n                            with_align=False)\n\n        if opt.pretrained_tokenizer:\n            tokenizer = load_roberta_kp_tokenizer(opt.src_vocab, bpe_dropout=opt.bpe_dropout)\n            setattr(opt, 'vocab_size', len(tokenizer))\n        else:\n            tokenizer = None\n        fields = reload_keyphrase_fields(fields, opt, tokenizer=tokenizer)\n\n        # @memray, to make tgt_field be aware of format of targets (multiple phrases)\n        setattr(fields[\"tgt\"], 'type', opt.kp_concat_type)\n        model_opt = opt\n    else:\n        # load an ordinary OpenNMT model\n        model_opt = ArgumentParser.ckpt_model_opts(checkpoint['opt'])\n        ArgumentParser.update_model_opts(model_opt)\n        ArgumentParser.validate_model_opts(model_opt)\n        fields = checkpoint['vocab']\n        if hasattr(model_opt, 'copy_attn'):\n            setattr(opt, 'copy_attn', model_opt.copy_attn)\n\n    model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint,\n                             opt.gpu)\n    if opt.fp32:\n        model.float()\n    elif opt.int8:\n        if opt.gpu >= 0:\n            raise ValueError(\n                \"Dynamic 8-bit quantization is not supported on GPU\")\n        torch.quantization.quantize_dynamic(model, inplace=True)\n    model.eval()\n    model.generator.eval()\n    return fields, model, model_opt\n\n\ndef build_src_emb(model_opt, fields):\n    # Build embeddings.\n    if model_opt.model_type == \"text\" or  model_opt.model_type == \"keyphrase\":\n        src_field = fields[\"src\"]\n        src_emb = build_embeddings(model_opt, src_field)\n    else:\n        src_emb = None\n    return src_emb\n\n\ndef build_encoder_with_embeddings(model_opt, fields):\n    # Build encoder.\n    src_emb = build_src_emb(model_opt, fields)\n    encoder = build_encoder(model_opt, src_emb)\n    return encoder, src_emb\n\n\ndef build_decoder_with_embeddings(\n    model_opt, fields, share_embeddings=False, src_emb=None\n):\n    # Build embeddings.\n    tgt_field = fields[\"tgt\"]\n    tgt_emb = build_embeddings(model_opt, tgt_field, for_encoder=False)\n\n    if share_embeddings:\n        tgt_emb.word_lut.weight = src_emb.word_lut.weight\n\n    # Build decoder.\n    decoder = build_decoder(model_opt, tgt_emb)\n    return decoder, tgt_emb\n\n\ndef build_task_specific_model(model_opt, fields):\n    # Share the embedding matrix - preprocess with share_vocab required.\n    if model_opt.share_embeddings:\n        # src/tgt vocab should be the same if `-share_vocab` is specified.\n        assert (\n            fields[\"src\"].base_field.vocab == fields[\"tgt\"].base_field.vocab\n        ), \"preprocess with -share_vocab if you use share_embeddings\"\n\n    if model_opt.model_task == ModelTask.SEQ2SEQ:\n        encoder, src_emb = build_encoder_with_embeddings(model_opt, fields)\n        decoder, _ = build_decoder_with_embeddings(\n            model_opt,\n            fields,\n            share_embeddings=model_opt.share_embeddings,\n            src_emb=src_emb,\n        )\n        return onmt.models.NMTModel(encoder=encoder, decoder=decoder)\n    elif model_opt.model_task == ModelTask.LANGUAGE_MODEL:\n        src_emb = build_src_emb(model_opt, fields)\n        decoder, _ = build_decoder_with_embeddings(\n            model_opt, fields, share_embeddings=True, src_emb=src_emb\n        )\n        return onmt.models.LanguageModel(decoder=decoder)\n    else:\n        raise ValueError(f\"No model defined for {model_opt.model_task} task\")\n\n\ndef build_base_model(model_opt, fields, gpu, checkpoint=None, gpu_id=None):\n    \"\"\"Build a model from opts.\n\n    Args:\n        model_opt: the option loaded from checkpoint. It's important that\n            the opts have been updated and validated. See\n            :class:`onmt.utils.parse.ArgumentParser`.\n        fields (dict[str, torchtext.data.Field]):\n            `Field` objects for the model.\n        gpu (bool): whether to use gpu.\n        checkpoint: the model gnerated by train phase, or a resumed snapshot\n                    model from a stopped training.\n        gpu_id (int or NoneType): Which GPU to use.\n\n    Returns:\n        the NMTModel.\n    \"\"\"\n\n    if gpu and gpu_id is not None:\n        device = torch.device(\"cuda\", gpu_id)\n    elif gpu and not gpu_id:\n        device = torch.device(\"cuda\")\n    elif not gpu:\n        device = torch.device(\"cpu\")\n\n    # Build Model\n    # OpenNMT models\n    if not hasattr(model_opt, 'fairseq_model') or not model_opt.fairseq_model:\n        # for back compat when attention_dropout was not defined\n        try:\n            model_opt.attention_dropout\n        except AttributeError:\n            model_opt.attention_dropout = model_opt.dropout\n\n        model = build_task_specific_model(model_opt, fields)\n\n        # Build Generator.\n        if not model_opt.copy_attn:\n            if model_opt.generator_function == \"sparsemax\":\n                gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)\n            else:\n                gen_func = nn.LogSoftmax(dim=-1)\n            generator = nn.Sequential(\n                nn.Linear(model_opt.dec_rnn_size,\n                          len(fields[\"tgt\"].base_field.vocab)),\n                Cast(torch.float32),\n                gen_func\n            )\n            if model_opt.share_decoder_embeddings:\n                generator[0].weight = model.decoder.embeddings.word_lut.weight\n        else:\n            tgt_base_field = fields[\"tgt\"].base_field\n            vocab_size = len(tgt_base_field.vocab)\n            pad_idx = tgt_base_field.vocab.stoi[tgt_base_field.pad_token]\n            generator = CopyGenerator(model_opt.dec_rnn_size, vocab_size, pad_idx)\n            if model_opt.share_decoder_embeddings:\n                generator.linear.weight = model.decoder.embeddings.word_lut.weight\n\n        # Load the model states from checkpoint or initialize them.\n        if checkpoint is not None:\n            # This preserves backward-compat for models using customed layernorm\n            def fix_key(s):\n                s = re.sub(r'(.*)\\.layer_norm((_\\d+)?)\\.b_2',\n                           r'\\1.layer_norm\\2.bias', s)\n                s = re.sub(r'(.*)\\.layer_norm((_\\d+)?)\\.a_2',\n                           r'\\1.layer_norm\\2.weight', s)\n                return s\n\n            checkpoint['model'] = {fix_key(k): v\n                                   for k, v in checkpoint['model'].items()}\n            # end of patch for backward compatibility\n\n            model.load_state_dict(checkpoint['model'], strict=False)\n            generator.load_state_dict(checkpoint['generator'], strict=False)\n        else:\n            if model_opt.param_init != 0.0:\n                for p in model.parameters():\n                    p.data.uniform_(-model_opt.param_init, model_opt.param_init)\n                for p in generator.parameters():\n                    p.data.uniform_(-model_opt.param_init, model_opt.param_init)\n            if model_opt.param_init_glorot:\n                for p in model.parameters():\n                    if p.dim() > 1:\n                        xavier_uniform_(p)\n                for p in generator.parameters():\n                    if p.dim() > 1:\n                        xavier_uniform_(p)\n\n        if hasattr(model, \"encoder\") and hasattr(model.encoder, \"embeddings\"):\n            model.encoder.embeddings.load_pretrained_vectors(\n                model_opt.pre_word_vecs_enc)\n        if hasattr(model.decoder, 'embeddings'):\n            model.decoder.embeddings.load_pretrained_vectors(\n                model_opt.pre_word_vecs_dec)\n\n    # FairSeq models\n    else:\n        # Build encoder.\n        bart_dir = os.path.join(model_opt.cache_dir, 'bart.large')\n        bart_path = os.path.join(bart_dir, 'model.pt')\n        assert os.path.exists(bart_path), 'BART checkpoint is not found! %s ' % bart_path\n\n        bart_model = BARTModel.from_pretrained(bart_dir, checkpoint_file='model.pt')\n        encoder = build_encoder(model_opt, embeddings=None, bart_model=bart_model, prev_checkpoint=checkpoint)\n\n        # Build decoder.\n        decoder = build_decoder(model_opt, embeddings=None, bart_model=bart_model, prev_checkpoint=checkpoint)\n\n        # Build NMTModel(= encoder + decoder).\n        model = onmt.models.NMTModel(encoder=encoder, decoder=decoder)\n\n        # Build Generator.\n        gen_func = nn.LogSoftmax(dim=-1)\n        generator = nn.Sequential(\n            nn.Linear(model.decoder.model.output_projection.in_features,\n                      model.decoder.model.output_projection.out_features,\n                      bias=False),\n            Cast(torch.float32),\n            gen_func\n        )\n        generator[0].weight = model.decoder.model.output_projection.weight\n\n    model.generator = generator\n    model.to(device)\n    # if model_opt.model_dtype == 'fp16' and (hasattr(model_opt, 'optim') and model_opt.optim == 'fusedadam'):\n    if model_opt.model_dtype == 'fp16':\n        model.half()\n    return model\n\n\ndef build_model(model_opt, opt, fields, checkpoint):\n    logger.info('Building model...')\n    model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)\n    logger.info(model)\n    return model\n"
  },
  {
    "path": "onmt/models/__init__.py",
    "content": "\"\"\"Module defining models.\"\"\"\nfrom onmt.models.model_saver import build_model_saver, ModelSaver\nfrom onmt.models.model import NMTModel, LanguageModel\n\n__all__ = [\"build_model_saver\", \"ModelSaver\", \"NMTModel\", \"LanguageModel\"]\n"
  },
  {
    "path": "onmt/models/model.py",
    "content": "\"\"\" Onmt NMT Model base class definition \"\"\"\nimport torch.nn as nn\n\n\nclass BaseModel(nn.Module):\n    \"\"\"\n    Core trainable object in OpenNMT. Implements a trainable interface\n    for a simple, generic encoder / decoder or decoder only model.\n    \"\"\"\n\n    def __init__(self, encoder, decoder):\n        super(BaseModel, self).__init__()\n\n    def forward(self, src, tgt, lengths, bptt=False, with_align=False):\n        \"\"\"Forward propagate a `src` and `tgt` pair for training.\n        Possible initialized with a beginning decoder state.\n\n        Args:\n            src (Tensor): A source sequence passed to encoder.\n                typically for inputs this will be a padded `LongTensor`\n                of size ``(len, batch, features)``. However, may be an\n                image or other generic input depending on encoder.\n            tgt (LongTensor): A target sequence passed to decoder.\n                Size ``(tgt_len, batch, features)``.\n            lengths(LongTensor): The src lengths, pre-padding ``(batch,)``.\n            bptt (Boolean): A flag indicating if truncated bptt is set.\n                If reset then init_state\n            with_align (Boolean): A flag indicating whether output alignment,\n                Only valid for transformer decoder.\n\n        Returns:\n            (FloatTensor, dict[str, FloatTensor]):\n\n            * decoder output ``(tgt_len, batch, hidden)``\n            * dictionary attention dists of ``(tgt_len, batch, src_len)``\n        \"\"\"\n        raise NotImplementedError\n\n    def update_dropout(self, dropout):\n        raise NotImplementedError\n\n    def count_parameters(self, log=print):\n        raise NotImplementedError\n\n\nclass NMTModel(BaseModel):\n    \"\"\"\n    Core trainable object in OpenNMT. Implements a trainable interface\n    for a simple, generic encoder + decoder model.\n    Args:\n      encoder (onmt.encoders.EncoderBase): an encoder object\n      decoder (onmt.decoders.DecoderBase): a decoder object\n    \"\"\"\n\n    def __init__(self, encoder, decoder):\n        super(NMTModel, self).__init__(encoder, decoder)\n        self.encoder = encoder\n        self.decoder = decoder\n\n    def forward(self, src, tgt, lengths, bptt=False, with_align=False):\n        dec_in = tgt[:-1]  # exclude last target from inputs\n        # enc_state=[src_len, B, enc_dim], memory_bank=[src_len, B, enc_dim], lengths=[B]\n        enc_state, memory_bank, lengths, encoder_output = self.encoder(src, lengths)\n\n        if not bptt:\n            self.decoder.init_state(src, memory_bank, enc_state)\n        # dec_out=[tgt_len, B, dec_dim], attns=[tgt_len, B, src_len]\n        dec_out, attns = self.decoder(dec_in, memory_bank,\n                                      memory_lengths=lengths,\n                                      with_align=with_align,\n                                      encoder_output=encoder_output,\n                                      incremental_state=None)\n        return dec_out, attns\n\n    def update_dropout(self, dropout):\n        self.encoder.update_dropout(dropout)\n        self.decoder.update_dropout(dropout)\n\n    def count_parameters(self, log=print):\n        \"\"\"Count number of parameters in model (& print with `log` callback).\n\n        Returns:\n            (int, int):\n            * encoder side parameter count\n            * decoder side parameter count\n        \"\"\"\n\n        enc, dec = 0, 0\n        for name, param in self.named_parameters():\n            if 'encoder' in name:\n                enc += param.nelement()\n            else:\n                dec += param.nelement()\n        if callable(log):\n            log('encoder: {}'.format(enc))\n            log('decoder: {}'.format(dec))\n            log('* number of parameters: {}'.format(enc + dec))\n        return enc, dec\n\n\nclass LanguageModel(BaseModel):\n    \"\"\"\n    Core trainable object in OpenNMT. Implements a trainable interface\n    for a simple, generic decoder only model.\n    Currently TransformerLMDecoder is the only LM decoder implemented\n    Args:\n      decoder (onmt.decoders.TransformerLMDecoder): a transformer decoder\n    \"\"\"\n\n    def __init__(self, encoder=None, decoder=None):\n        super(LanguageModel, self).__init__(encoder, decoder)\n        if encoder is not None:\n            raise ValueError(\"LanguageModel should not be used\"\n                             \"with an encoder\")\n        self.decoder = decoder\n\n    def forward(self, src, tgt, lengths, bptt=False, with_align=False):\n        \"\"\"Forward propagate a `src` and `tgt` pair for training.\n        Possible initialized with a beginning decoder state.\n        Args:\n            src (Tensor): A source sequence passed to decoder.\n                typically for inputs this will be a padded `LongTensor`\n                of size ``(len, batch, features)``. However, may be an\n                image or other generic input depending on decoder.\n            tgt (LongTensor): A target sequence passed to decoder.\n                Size ``(tgt_len, batch, features)``.\n            lengths(LongTensor): The src lengths, pre-padding ``(batch,)``.\n            bptt (Boolean): A flag indicating if truncated bptt is set.\n                If reset then init_state\n            with_align (Boolean): A flag indicating whether output alignment,\n                Only valid for transformer decoder.\n        Returns:\n            (FloatTensor, dict[str, FloatTensor]):\n            * decoder output ``(tgt_len, batch, hidden)``\n            * dictionary attention dists of ``(tgt_len, batch, src_len)``\n        \"\"\"\n\n        if not bptt:\n            self.decoder.init_state()\n        dec_out, attns = self.decoder(\n            src, memory_bank=None, memory_lengths=lengths,\n            with_align=with_align\n        )\n        return dec_out, attns\n\n    def update_dropout(self, dropout):\n        self.decoder.update_dropout(dropout)\n\n    def count_parameters(self, log=print):\n        \"\"\"Count number of parameters in model (& print with `log` callback).\n        Returns:\n            (int, int):\n            * encoder side parameter count\n            * decoder side parameter count\n        \"\"\"\n\n        enc, dec = 0, 0\n        for name, param in self.named_parameters():\n            if \"decoder\" in name:\n                dec += param.nelement()\n\n        if callable(log):\n            # No encoder in LM, seq2seq count formatting kept\n            log(\"encoder: {}\".format(enc))\n            log(\"decoder: {}\".format(dec))\n            log(\"* number of parameters: {}\".format(enc + dec))\n        return enc, dec\n"
  },
  {
    "path": "onmt/models/model_saver.py",
    "content": "import os\nimport torch\n\nfrom collections import deque\nfrom onmt.utils.logging import logger\n\nfrom copy import deepcopy\n\n\ndef build_model_saver(model_opt, opt, model, fields, optim):\n    # _check_save_model_path\n    save_model_path = os.path.abspath(opt.save_model)\n    os.makedirs(os.path.dirname(save_model_path), exist_ok=True)\n\n    model_saver = ModelSaver(opt.save_model,\n                             model,\n                             model_opt,\n                             fields,\n                             optim,\n                             opt.keep_checkpoint)\n    return model_saver\n\n\ndef load_checkpoint(ckpt_path):\n    \"\"\"Load checkpoint from `ckpt_path` if any else return `None`.\"\"\"\n    checkpoint = None\n    if ckpt_path:\n        logger.info('Loading checkpoint from %s' % ckpt_path)\n        checkpoint = torch.load(ckpt_path,\n                                map_location=lambda storage, loc: storage)\n    return checkpoint\n\n\nclass ModelSaverBase(object):\n    \"\"\"Base class for model saving operations\n\n    Inherited classes must implement private methods:\n    * `_save`\n    * `_rm_checkpoint\n    \"\"\"\n\n    def __init__(self, base_path, model, model_opt, fields, optim,\n                 keep_checkpoint=-1):\n        self.base_path = base_path\n        self.model = model\n        self.model_opt = model_opt\n        self.fields = fields\n        self.optim = optim\n        self.last_saved_step = None\n        self.keep_checkpoint = keep_checkpoint\n        if keep_checkpoint > 0:\n            self.checkpoint_queue = deque([], maxlen=keep_checkpoint)\n\n    def save(self, step, moving_average=None):\n        \"\"\"Main entry point for model saver\n\n        It wraps the `_save` method with checks and apply `keep_checkpoint`\n        related logic\n        \"\"\"\n\n        if self.keep_checkpoint == 0 or step == self.last_saved_step:\n            return\n\n        save_model = self.model\n        if moving_average:\n            model_params_data = []\n            for avg, param in zip(moving_average, save_model.parameters()):\n                model_params_data.append(param.data)\n                param.data = avg.data\n\n        chkpt, chkpt_name = self._save(step, save_model)\n        self.last_saved_step = step\n\n        if moving_average:\n            for param_data, param in zip(model_params_data,\n                                         save_model.parameters()):\n                param.data = param_data\n\n        if self.keep_checkpoint > 0:\n            if len(self.checkpoint_queue) == self.checkpoint_queue.maxlen:\n                todel = self.checkpoint_queue.popleft()\n                self._rm_checkpoint(todel)\n            self.checkpoint_queue.append(chkpt_name)\n\n    def _save(self, step, model):\n        \"\"\"Save a resumable checkpoint.\n\n        Args:\n            step (int): step number\n            model (nn.Module): torch model to save\n\n        Returns:\n            (object, str):\n\n            * checkpoint: the saved object\n            * checkpoint_name: name (or path) of the saved checkpoint\n        \"\"\"\n\n        raise NotImplementedError()\n\n    def _rm_checkpoint(self, name):\n        \"\"\"Remove a checkpoint\n\n        Args:\n            name(str): name that indentifies the checkpoint\n                (it may be a filepath)\n        \"\"\"\n\n        raise NotImplementedError()\n\n\nclass ModelSaver(ModelSaverBase):\n    \"\"\"Simple model saver to filesystem\"\"\"\n\n    def _save(self, step, model):\n        model_state_dict = model.state_dict()\n        model_state_dict = {k: v for k, v in model_state_dict.items()\n                            if 'generator' not in k}\n        generator_state_dict = model.generator.state_dict()\n\n        # NOTE: We need to trim the vocab to remove any unk tokens that\n        # were not originally here.\n\n        vocab = deepcopy(self.fields)\n        for side in [\"src\", \"tgt\"]:\n            keys_to_pop = []\n            if hasattr(vocab[side], \"fields\"):\n                unk_token = vocab[side].fields[0][1].vocab.itos[0]\n                for key, value in vocab[side].fields[0][1].vocab.stoi.items():\n                    if value == 0 and key != unk_token:\n                        keys_to_pop.append(key)\n                for key in keys_to_pop:\n                    vocab[side].fields[0][1].vocab.stoi.pop(key, None)\n\n        checkpoint = {\n            'model': model_state_dict,\n            'generator': generator_state_dict,\n            'vocab': vocab,\n            'opt': self.model_opt,\n            'optim': self.optim.state_dict(),\n        }\n\n        logger.info(\"Saving checkpoint %s_step_%d.pt\" % (self.base_path, step))\n        checkpoint_path = '%s_step_%d.pt' % (self.base_path, step)\n        torch.save(checkpoint, checkpoint_path)\n        return checkpoint, checkpoint_path\n\n    def _rm_checkpoint(self, name):\n        if os.path.exists(name):\n            os.remove(name)\n"
  },
  {
    "path": "onmt/models/sru.py",
    "content": "\"\"\" SRU Implementation \"\"\"\n# flake8: noqa\n\nimport subprocess\nimport platform\nimport os\nimport re\nimport configargparse\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Function\nfrom torch.cuda.amp import custom_fwd, custom_bwd\nfrom collections import namedtuple\n\n\n# For command-line option parsing\nclass CheckSRU(configargparse.Action):\n    def __init__(self, option_strings, dest, **kwargs):\n        super(CheckSRU, self).__init__(option_strings, dest, **kwargs)\n\n    def __call__(self, parser, namespace, values, option_string=None):\n        if values == 'SRU':\n            check_sru_requirement(abort=True)\n        # Check pass, set the args.\n        setattr(namespace, self.dest, values)\n\n\n# This SRU version implements its own cuda-level optimization,\n# so it requires that:\n# 1. `cupy` and `pynvrtc` python package installed.\n# 2. pytorch is built with cuda support.\n# 3. library path set: export LD_LIBRARY_PATH=<cuda lib path>.\ndef check_sru_requirement(abort=False):\n    \"\"\"\n    Return True if check pass; if check fails and abort is True,\n    raise an Exception, othereise return False.\n    \"\"\"\n\n    # Check 1.\n    try:\n        if platform.system() == 'Windows':\n            subprocess.check_output('pip freeze | findstr cupy', shell=True)\n            subprocess.check_output('pip freeze | findstr pynvrtc',\n                                    shell=True)\n        else:  # Unix-like systems\n            subprocess.check_output('pip freeze | grep -w cupy', shell=True)\n            subprocess.check_output('pip freeze | grep -w pynvrtc',\n                                    shell=True)\n    except subprocess.CalledProcessError:\n        if not abort:\n            return False\n        raise AssertionError(\"Using SRU requires 'cupy' and 'pynvrtc' \"\n                             \"python packages installed.\")\n\n    # Check 2.\n    if torch.cuda.is_available() is False:\n        if not abort:\n            return False\n        raise AssertionError(\"Using SRU requires pytorch built with cuda.\")\n\n    # Check 3.\n    pattern = re.compile(\".*cuda/lib.*\")\n    ld_path = os.getenv('LD_LIBRARY_PATH', \"\")\n    if re.match(pattern, ld_path) is None:\n        if not abort:\n            return False\n        raise AssertionError(\"Using SRU requires setting cuda lib path, e.g. \"\n                             \"export LD_LIBRARY_PATH=/usr/local/cuda/lib64.\")\n\n    return True\n\n\nSRU_CODE = \"\"\"\nextern \"C\" {\n    __forceinline__ __device__ float sigmoidf(float x)\n    {\n        return 1.f / (1.f + expf(-x));\n    }\n    __forceinline__ __device__ float reluf(float x)\n    {\n        return (x > 0.f) ? x : 0.f;\n    }\n    __global__ void sru_fwd(const float * __restrict__ u,\n                            const float * __restrict__ x,\n                            const float * __restrict__ bias,\n                            const float * __restrict__ init,\n                            const float * __restrict__ mask_h,\n                            const int len, const int batch,\n                            const int d, const int k,\n                            float * __restrict__ h,\n                            float * __restrict__ c,\n                            const int activation_type)\n    {\n        assert ((k == 3) || (x == NULL));\n        int ncols = batch*d;\n        int col = blockIdx.x * blockDim.x + threadIdx.x;\n        if (col >= ncols) return;\n        int ncols_u = ncols*k;\n        int ncols_x = (k == 3) ? ncols : ncols_u;\n        const float bias1 = *(bias + (col%d));\n        const float bias2 = *(bias + (col%d) + d);\n        const float mask = (mask_h == NULL) ? 1.0 : (*(mask_h + col));\n        float cur = *(init + col);\n        const float *up = u + (col*k);\n        const float *xp = (k == 3) ? (x + col) : (up + 3);\n        float *cp = c + col;\n        float *hp = h + col;\n        for (int row = 0; row < len; ++row)\n        {\n            float g1 = sigmoidf((*(up+1))+bias1);\n            float g2 = sigmoidf((*(up+2))+bias2);\n            cur = (cur-(*up))*g1 + (*up);\n            *cp = cur;\n            float val = (activation_type == 1) ? tanh(cur) : (\n                (activation_type == 2) ? reluf(cur) : cur\n            );\n            *hp = (val*mask-(*xp))*g2 + (*xp);\n            up += ncols_u;\n            xp += ncols_x;\n            cp += ncols;\n            hp += ncols;\n        }\n    }\n    __global__ void sru_bwd(const float * __restrict__ u,\n                            const float * __restrict__ x,\n                            const float * __restrict__ bias,\n                            const float * __restrict__ init,\n                            const float * __restrict__ mask_h,\n                            const float * __restrict__ c,\n                            const float * __restrict__ grad_h,\n                            const float * __restrict__ grad_last,\n                            const int len,\n                            const int batch, const int d, const int k,\n                            float * __restrict__ grad_u,\n                            float * __restrict__ grad_x,\n                            float * __restrict__ grad_bias,\n                            float * __restrict__ grad_init,\n                            int activation_type)\n    {\n        assert((k == 3) || (x == NULL));\n        assert((k == 3) || (grad_x == NULL));\n        int ncols = batch*d;\n        int col = blockIdx.x * blockDim.x + threadIdx.x;\n        if (col >= ncols) return;\n        int ncols_u = ncols*k;\n        int ncols_x = (k == 3) ? ncols : ncols_u;\n        const float bias1 = *(bias + (col%d));\n        const float bias2 = *(bias + (col%d) + d);\n        const float mask = (mask_h == NULL) ? 1.0 : (*(mask_h + col));\n        float gbias1 = 0;\n        float gbias2 = 0;\n        float cur = *(grad_last + col);\n        const float *up = u + (col*k) + (len-1)*ncols_u;\n        const float *xp = (k == 3) ? (x + col + (len-1)*ncols) : (up + 3);\n        const float *cp = c + col + (len-1)*ncols;\n        const float *ghp = grad_h + col + (len-1)*ncols;\n        float *gup = grad_u + (col*k) + (len-1)*ncols_u;\n        float *gxp = (k == 3) ? (grad_x + col + (len-1)*ncols) : (gup + 3);\n        for (int row = len-1; row >= 0; --row)\n        {\n            const float g1 = sigmoidf((*(up+1))+bias1);\n            const float g2 = sigmoidf((*(up+2))+bias2);\n            const float c_val = (activation_type == 1) ? tanh(*cp) : (\n                (activation_type == 2) ? reluf(*cp) : (*cp)\n            );\n            const float x_val = *xp;\n            const float u_val = *up;\n            const float prev_c_val = (row>0) ? (*(cp-ncols)) : (*(init+col));\n            const float gh_val = *ghp;\n            // h = c*g2 + x*(1-g2) = (c-x)*g2 + x\n            // c = c'*g1 + g0*(1-g1) = (c'-g0)*g1 + g0\n            // grad wrt x\n            *gxp = gh_val*(1-g2);\n            // grad wrt g2, u2 and bias2\n            float gg2 = gh_val*(c_val*mask-x_val)*(g2*(1-g2));\n            *(gup+2) = gg2;\n            gbias2 += gg2;\n            // grad wrt c\n            const float tmp = (activation_type == 1) ? (g2*(1-c_val*c_val)) : (\n                ((activation_type == 0) || (c_val > 0)) ? g2 : 0.f\n            );\n            const float gc = gh_val*mask*tmp + cur;\n            // grad wrt u0\n            *gup = gc*(1-g1);\n            // grad wrt g1, u1, and bias1\n            float gg1 = gc*(prev_c_val-u_val)*(g1*(1-g1));\n            *(gup+1) = gg1;\n            gbias1 += gg1;\n            // grad wrt c'\n            cur = gc*g1;\n            up -= ncols_u;\n            xp -= ncols_x;\n            cp -= ncols;\n            gup -= ncols_u;\n            gxp -= ncols_x;\n            ghp -= ncols;\n        }\n        *(grad_bias + col) = gbias1;\n        *(grad_bias + col + ncols) = gbias2;\n        *(grad_init +col) = cur;\n    }\n    __global__ void sru_bi_fwd(const float * __restrict__ u,\n                               const float * __restrict__ x,\n                               const float * __restrict__ bias,\n                               const float * __restrict__ init,\n                               const float * __restrict__ mask_h,\n                               const int len, const int batch,\n                               const int d, const int k,\n                               float * __restrict__ h,\n                               float * __restrict__ c,\n                               const int activation_type)\n    {\n        assert ((k == 3) || (x == NULL));\n        assert ((k == 3) || (k == 4));\n        int ncols = batch*d*2;\n        int col = blockIdx.x * blockDim.x + threadIdx.x;\n        if (col >= ncols) return;\n        int ncols_u = ncols*k;\n        int ncols_x = (k == 3) ? ncols : ncols_u;\n        const float mask = (mask_h == NULL) ? 1.0 : (*(mask_h + col));\n        float cur = *(init + col);\n        const int d2 = d*2;\n        const bool flip = (col%d2) >= d;\n        const float bias1 = *(bias + (col%d2));\n        const float bias2 = *(bias + (col%d2) + d2);\n        const float *up = u + (col*k);\n        const float *xp = (k == 3) ? (x + col) : (up + 3);\n        float *cp = c + col;\n        float *hp = h + col;\n        if (flip) {\n            up += (len-1)*ncols_u;\n            xp += (len-1)*ncols_x;\n            cp += (len-1)*ncols;\n            hp += (len-1)*ncols;\n        }\n        int ncols_u_ = flip ? -ncols_u : ncols_u;\n        int ncols_x_ = flip ? -ncols_x : ncols_x;\n        int ncols_ = flip ? -ncols : ncols;\n        for (int cnt = 0; cnt < len; ++cnt)\n        {\n            float g1 = sigmoidf((*(up+1))+bias1);\n            float g2 = sigmoidf((*(up+2))+bias2);\n            cur = (cur-(*up))*g1 + (*up);\n            *cp = cur;\n            float val = (activation_type == 1) ? tanh(cur) : (\n                (activation_type == 2) ? reluf(cur) : cur\n            );\n            *hp = (val*mask-(*xp))*g2 + (*xp);\n            up += ncols_u_;\n            xp += ncols_x_;\n            cp += ncols_;\n            hp += ncols_;\n        }\n    }\n    __global__ void sru_bi_bwd(const float * __restrict__ u,\n                               const float * __restrict__ x,\n                               const float * __restrict__ bias,\n                               const float * __restrict__ init,\n                               const float * __restrict__ mask_h,\n                               const float * __restrict__ c,\n                               const float * __restrict__ grad_h,\n                               const float * __restrict__ grad_last,\n                               const int len, const int batch,\n                               const int d, const int k,\n                               float * __restrict__ grad_u,\n                               float * __restrict__ grad_x,\n                               float * __restrict__ grad_bias,\n                               float * __restrict__ grad_init,\n                               int activation_type)\n    {\n        assert((k == 3) || (x == NULL));\n        assert((k == 3) || (grad_x == NULL));\n        assert((k == 3) || (k == 4));\n        int ncols = batch*d*2;\n        int col = blockIdx.x * blockDim.x + threadIdx.x;\n        if (col >= ncols) return;\n        int ncols_u = ncols*k;\n        int ncols_x = (k == 3) ? ncols : ncols_u;\n        const float mask = (mask_h == NULL) ? 1.0 : (*(mask_h + col));\n        float gbias1 = 0;\n        float gbias2 = 0;\n        float cur = *(grad_last + col);\n        const int d2 = d*2;\n        const bool flip = ((col%d2) >= d);\n        const float bias1 = *(bias + (col%d2));\n        const float bias2 = *(bias + (col%d2) + d2);\n        const float *up = u + (col*k);\n        const float *xp = (k == 3) ? (x + col) : (up + 3);\n        const float *cp = c + col;\n        const float *ghp = grad_h + col;\n        float *gup = grad_u + (col*k);\n        float *gxp = (k == 3) ? (grad_x + col) : (gup + 3);\n        if (!flip) {\n            up += (len-1)*ncols_u;\n            xp += (len-1)*ncols_x;\n            cp += (len-1)*ncols;\n            ghp += (len-1)*ncols;\n            gup += (len-1)*ncols_u;\n            gxp += (len-1)*ncols_x;\n        }\n        int ncols_u_ = flip ? -ncols_u : ncols_u;\n        int ncols_x_ = flip ? -ncols_x : ncols_x;\n        int ncols_ = flip ? -ncols : ncols;\n        for (int cnt = 0; cnt < len; ++cnt)\n        {\n            const float g1 = sigmoidf((*(up+1))+bias1);\n            const float g2 = sigmoidf((*(up+2))+bias2);\n            const float c_val = (activation_type == 1) ? tanh(*cp) : (\n                (activation_type == 2) ? reluf(*cp) : (*cp)\n            );\n            const float x_val = *xp;\n            const float u_val = *up;\n            const float prev_c_val = (cnt<len-1)?(*(cp-ncols_)):(*(init+col));\n            const float gh_val = *ghp;\n            // h = c*g2 + x*(1-g2) = (c-x)*g2 + x\n            // c = c'*g1 + g0*(1-g1) = (c'-g0)*g1 + g0\n            // grad wrt x\n            *gxp = gh_val*(1-g2);\n            // grad wrt g2, u2 and bias2\n            float gg2 = gh_val*(c_val*mask-x_val)*(g2*(1-g2));\n            *(gup+2) = gg2;\n            gbias2 += gg2;\n            // grad wrt c\n            const float tmp = (activation_type == 1) ? (g2*(1-c_val*c_val)) : (\n                ((activation_type == 0) || (c_val > 0)) ? g2 : 0.f\n            );\n            const float gc = gh_val*mask*tmp + cur;\n            // grad wrt u0\n            *gup = gc*(1-g1);\n            // grad wrt g1, u1, and bias1\n            float gg1 = gc*(prev_c_val-u_val)*(g1*(1-g1));\n            *(gup+1) = gg1;\n            gbias1 += gg1;\n            // grad wrt c'\n            cur = gc*g1;\n            up -= ncols_u_;\n            xp -= ncols_x_;\n            cp -= ncols_;\n            gup -= ncols_u_;\n            gxp -= ncols_x_;\n            ghp -= ncols_;\n        }\n        *(grad_bias + col) = gbias1;\n        *(grad_bias + col + ncols) = gbias2;\n        *(grad_init +col) = cur;\n    }\n}\n\"\"\"\nSRU_FWD_FUNC, SRU_BWD_FUNC = None, None\nSRU_BiFWD_FUNC, SRU_BiBWD_FUNC = None, None\nSRU_STREAM = None\n\n\ndef load_sru_mod():\n    global SRU_FWD_FUNC, SRU_BWD_FUNC, SRU_BiFWD_FUNC, SRU_BiBWD_FUNC\n    global SRU_STREAM\n    if check_sru_requirement():\n        from cupy.cuda import function\n        from pynvrtc.compiler import Program\n\n        # This sets up device to use.\n        device = torch.device(\"cuda\")\n        tmp_ = torch.rand(1, 1).to(device)\n\n        sru_prog = Program(SRU_CODE.encode('utf-8'),\n                           'sru_prog.cu'.encode('utf-8'))\n        sru_ptx = sru_prog.compile()\n        sru_mod = function.Module()\n        sru_mod.load(bytes(sru_ptx.encode()))\n\n        SRU_FWD_FUNC = sru_mod.get_function('sru_fwd')\n        SRU_BWD_FUNC = sru_mod.get_function('sru_bwd')\n        SRU_BiFWD_FUNC = sru_mod.get_function('sru_bi_fwd')\n        SRU_BiBWD_FUNC = sru_mod.get_function('sru_bi_bwd')\n\n        stream = namedtuple('Stream', ['ptr'])\n        SRU_STREAM = stream(ptr=torch.cuda.current_stream().cuda_stream)\n\n\nclass SRU_Compute(Function):\n\n    def __init__(self, activation_type, d_out, bidirectional=False):\n        SRU_Compute.maybe_load_sru_mod()\n        super(SRU_Compute, self).__init__()\n        self.activation_type = activation_type\n        self.d_out = d_out\n        self.bidirectional = bidirectional\n\n    @staticmethod\n    def maybe_load_sru_mod():\n        global SRU_FWD_FUNC\n\n        if SRU_FWD_FUNC is None:\n            load_sru_mod()\n\n    @custom_fwd\n    def forward(self, u, x, bias, init=None, mask_h=None):\n        bidir = 2 if self.bidirectional else 1\n        length = x.size(0) if x.dim() == 3 else 1\n        batch = x.size(-2)\n        d = self.d_out\n        k = u.size(-1) // d\n        k_ = k // 2 if self.bidirectional else k\n        ncols = batch * d * bidir\n        thread_per_block = min(512, ncols)\n        num_block = (ncols - 1) // thread_per_block + 1\n\n        init_ = x.new(ncols).zero_() if init is None else init\n        size = (length, batch, d * bidir) if x.dim() == 3 else (batch, d * bidir)\n        c = x.new(*size)\n        h = x.new(*size)\n\n        FUNC = SRU_FWD_FUNC if not self.bidirectional else SRU_BiFWD_FUNC\n        FUNC(args=[\n            u.contiguous().data_ptr(),\n            x.contiguous().data_ptr() if k_ == 3 else 0,\n            bias.data_ptr(),\n            init_.contiguous().data_ptr(),\n            mask_h.data_ptr() if mask_h is not None else 0,\n            length,\n            batch,\n            d,\n            k_,\n            h.data_ptr(),\n            c.data_ptr(),\n            self.activation_type],\n            block=(thread_per_block, 1, 1), grid=(num_block, 1, 1),\n            stream=SRU_STREAM\n        )\n\n        self.save_for_backward(u, x, bias, init, mask_h)\n        self.intermediate = c\n        if x.dim() == 2:\n            last_hidden = c\n        elif self.bidirectional:\n            # -> directions x batch x dim\n            last_hidden = torch.stack((c[-1, :, :d], c[0, :, d:]))\n        else:\n            last_hidden = c[-1]\n        return h, last_hidden\n\n    @custom_bwd\n    def backward(self, grad_h, grad_last):\n        if self.bidirectional:\n            grad_last = torch.cat((grad_last[0], grad_last[1]), 1)\n        bidir = 2 if self.bidirectional else 1\n        u, x, bias, init, mask_h = self.saved_tensors\n        c = self.intermediate\n        length = x.size(0) if x.dim() == 3 else 1\n        batch = x.size(-2)\n        d = self.d_out\n        k = u.size(-1) // d\n        k_ = k // 2 if self.bidirectional else k\n        ncols = batch * d * bidir\n        thread_per_block = min(512, ncols)\n        num_block = (ncols - 1) // thread_per_block + 1\n\n        init_ = x.new(ncols).zero_() if init is None else init\n        grad_u = u.new(*u.size())\n        grad_bias = x.new(2, batch, d * bidir)\n        grad_init = x.new(batch, d * bidir)\n\n        # For DEBUG\n        # size = (length, batch, x.size(-1)) \\\n        #         if x.dim() == 3 else (batch, x.size(-1))\n        # grad_x = x.new(*x.size()) if k_ == 3 else x.new(*size).zero_()\n\n        # Normal use\n        grad_x = x.new(*x.size()) if k_ == 3 else None\n\n        FUNC = SRU_BWD_FUNC if not self.bidirectional else SRU_BiBWD_FUNC\n        FUNC(args=[\n            u.contiguous().data_ptr(),\n            x.contiguous().data_ptr() if k_ == 3 else 0,\n            bias.data_ptr(),\n            init_.contiguous().data_ptr(),\n            mask_h.data_ptr() if mask_h is not None else 0,\n            c.data_ptr(),\n            grad_h.contiguous().data_ptr(),\n            grad_last.contiguous().data_ptr(),\n            length,\n            batch,\n            d,\n            k_,\n            grad_u.data_ptr(),\n            grad_x.data_ptr() if k_ == 3 else 0,\n            grad_bias.data_ptr(),\n            grad_init.data_ptr(),\n            self.activation_type],\n            block=(thread_per_block, 1, 1), grid=(num_block, 1, 1),\n            stream=SRU_STREAM\n        )\n        return grad_u, grad_x, grad_bias.sum(1).view(-1), grad_init, None\n\n\nclass SRUCell(nn.Module):\n    def __init__(self, n_in, n_out, dropout=0, rnn_dropout=0,\n                 bidirectional=False, use_tanh=1, use_relu=0):\n        super(SRUCell, self).__init__()\n        self.n_in = n_in\n        self.n_out = n_out\n        self.rnn_dropout = rnn_dropout\n        self.dropout = dropout\n        self.bidirectional = bidirectional\n        self.activation_type = 2 if use_relu else (1 if use_tanh else 0)\n\n        out_size = n_out * 2 if bidirectional else n_out\n        k = 4 if n_in != out_size else 3\n        self.size_per_dir = n_out * k\n        self.weight = nn.Parameter(torch.Tensor(\n            n_in,\n            self.size_per_dir * 2 if bidirectional else self.size_per_dir\n        ))\n        self.bias = nn.Parameter(torch.Tensor(\n            n_out * 4 if bidirectional else n_out * 2\n        ))\n        self.init_weight()\n\n    def init_weight(self):\n        val_range = (3.0 / self.n_in)**0.5\n        self.weight.data.uniform_(-val_range, val_range)\n        self.bias.data.zero_()\n\n    def set_bias(self, bias_val=0):\n        n_out = self.n_out\n        if self.bidirectional:\n            self.bias.data[n_out * 2:].zero_().add_(bias_val)\n        else:\n            self.bias.data[n_out:].zero_().add_(bias_val)\n\n    def forward(self, input, c0=None):\n        assert input.dim() == 2 or input.dim() == 3\n        n_in, n_out = self.n_in, self.n_out\n        batch = input.size(-2)\n        if c0 is None:\n            c0 = input.data.new(\n                batch, n_out if not self.bidirectional else n_out * 2\n            ).zero_()\n\n        if self.training and (self.rnn_dropout > 0):\n            mask = self.get_dropout_mask_((batch, n_in), self.rnn_dropout)\n            x = input * mask.expand_as(input)\n        else:\n            x = input\n\n        x_2d = x if x.dim() == 2 else x.contiguous().view(-1, n_in)\n        u = x_2d.mm(self.weight)\n\n        if self.training and (self.dropout > 0):\n            bidir = 2 if self.bidirectional else 1\n            mask_h = self.get_dropout_mask_(\n                (batch, n_out * bidir), self.dropout)\n            h, c = SRU_Compute(self.activation_type, n_out,\n                               self.bidirectional)(\n                                   u, input, self.bias, c0, mask_h\n            )\n        else:\n            h, c = SRU_Compute(self.activation_type, n_out,\n                               self.bidirectional)(\n                                   u, input, self.bias, c0\n            )\n\n        return h, c\n\n    def get_dropout_mask_(self, size, p):\n        w = self.weight.data\n        return w.new(*size).bernoulli_(1 - p).div_(1 - p)\n\n\nclass SRU(nn.Module):\n    \"\"\"\n    Implementation of \"Training RNNs as Fast as CNNs\"\n    :cite:`DBLP:journals/corr/abs-1709-02755`\n\n    TODO: turn to pytorch's implementation when it is available.\n\n    This implementation is adpoted from the author of the paper:\n    https://github.com/taolei87/sru/blob/master/cuda_functional.py.\n\n    Args:\n      input_size (int): input to model\n      hidden_size (int): hidden dimension\n      num_layers (int): number of layers\n      dropout (float): dropout to use (stacked)\n      rnn_dropout (float): dropout to use (recurrent)\n      bidirectional (bool): bidirectional\n      use_tanh (bool): activation\n      use_relu (bool): activation\n    \"\"\"\n\n    def __init__(self, input_size, hidden_size,\n                 num_layers=2, dropout=0, rnn_dropout=0,\n                 bidirectional=False, use_tanh=1, use_relu=0):\n        # An entry check here, will catch on train side and translate side\n        # if requirements are not satisfied.\n        check_sru_requirement(abort=True)\n        super(SRU, self).__init__()\n        self.n_in = input_size\n        self.n_out = hidden_size\n        self.depth = num_layers\n        self.dropout = dropout\n        self.rnn_dropout = rnn_dropout\n        self.rnn_lst = nn.ModuleList()\n        self.bidirectional = bidirectional\n        self.out_size = hidden_size * 2 if bidirectional else hidden_size\n\n        for i in range(num_layers):\n            sru_cell = SRUCell(\n                n_in=self.n_in if i == 0 else self.out_size,\n                n_out=self.n_out,\n                dropout=dropout if i + 1 != num_layers else 0,\n                rnn_dropout=rnn_dropout,\n                bidirectional=bidirectional,\n                use_tanh=use_tanh,\n                use_relu=use_relu,\n            )\n            self.rnn_lst.append(sru_cell)\n\n    def set_bias(self, bias_val=0):\n        for l in self.rnn_lst:\n            l.set_bias(bias_val)\n\n    def forward(self, input, c0=None, return_hidden=True):\n        assert input.dim() == 3  # (len, batch, n_in)\n        dir_ = 2 if self.bidirectional else 1\n        if c0 is None:\n            zeros = input.data.new(\n                input.size(1), self.n_out * dir_\n            ).zero_()\n            c0 = [zeros for i in range(self.depth)]\n        else:\n            if isinstance(c0, tuple):\n                # RNNDecoderState wraps hidden as a tuple.\n                c0 = c0[0]\n            assert c0.dim() == 3    # (depth, batch, dir_*n_out)\n            c0 = [h.squeeze(0) for h in c0.chunk(self.depth, 0)]\n\n        prevx = input\n        lstc = []\n        for i, rnn in enumerate(self.rnn_lst):\n            h, c = rnn(prevx, c0[i])\n            prevx = h\n            lstc.append(c)\n\n        if self.bidirectional:\n            # fh -> (layers*directions) x batch x dim\n            fh = torch.cat(lstc)\n        else:\n            fh = torch.stack(lstc)\n\n        if return_hidden:\n            return prevx, fh\n        else:\n            return prevx\n"
  },
  {
    "path": "onmt/models/stacked_rnn.py",
    "content": "\"\"\" Implementation of ONMT RNN for Input Feeding Decoding \"\"\"\nimport torch\nimport torch.nn as nn\n\n\nclass StackedLSTM(nn.Module):\n    \"\"\"\n    Our own implementation of stacked LSTM.\n    Needed for the decoder, because we do input feeding.\n    \"\"\"\n\n    def __init__(self, num_layers, input_size, rnn_size, dropout):\n        super(StackedLSTM, self).__init__()\n        self.dropout = nn.Dropout(dropout)\n        self.num_layers = num_layers\n        self.layers = nn.ModuleList()\n\n        for _ in range(num_layers):\n            self.layers.append(nn.LSTMCell(input_size, rnn_size))\n            input_size = rnn_size\n\n    def forward(self, input_feed, hidden):\n        h_0, c_0 = hidden\n        h_1, c_1 = [], []\n        for i, layer in enumerate(self.layers):\n            h_1_i, c_1_i = layer(input_feed, (h_0[i], c_0[i]))\n            input_feed = h_1_i\n            if i + 1 != self.num_layers:\n                input_feed = self.dropout(input_feed)\n            h_1 += [h_1_i]\n            c_1 += [c_1_i]\n\n        h_1 = torch.stack(h_1)\n        c_1 = torch.stack(c_1)\n\n        return input_feed, (h_1, c_1)\n\n\nclass StackedGRU(nn.Module):\n    \"\"\"\n    Our own implementation of stacked GRU.\n    Needed for the decoder, because we do input feeding.\n    \"\"\"\n\n    def __init__(self, num_layers, input_size, rnn_size, dropout):\n        super(StackedGRU, self).__init__()\n        self.dropout = nn.Dropout(dropout)\n        self.num_layers = num_layers\n        self.layers = nn.ModuleList()\n\n        for _ in range(num_layers):\n            self.layers.append(nn.GRUCell(input_size, rnn_size))\n            input_size = rnn_size\n\n    def forward(self, input_feed, hidden):\n        h_1 = []\n        for i, layer in enumerate(self.layers):\n            h_1_i = layer(input_feed, hidden[0][i])\n            input_feed = h_1_i\n            if i + 1 != self.num_layers:\n                input_feed = self.dropout(input_feed)\n            h_1 += [h_1_i]\n\n        h_1 = torch.stack(h_1)\n        return input_feed, (h_1,)\n"
  },
  {
    "path": "onmt/modules/__init__.py",
    "content": "\"\"\"  Attention and normalization modules  \"\"\"\nfrom onmt.modules.util_class import Elementwise\nfrom onmt.modules.gate import context_gate_factory, ContextGate\nfrom onmt.modules.global_attention import GlobalAttention\nfrom onmt.modules.conv_multi_step_attention import ConvMultiStepAttention\nfrom onmt.modules.copy_generator import CopyGenerator, CopyGeneratorLoss, \\\n    CopyGeneratorLossCompute, CopyGeneratorLMLossCompute\nfrom onmt.modules.multi_headed_attn import MultiHeadedAttention\nfrom onmt.modules.embeddings import Embeddings, PositionalEncoding\nfrom onmt.modules.weight_norm import WeightNormConv2d\nfrom onmt.modules.average_attn import AverageAttention\n\n__all__ = [\"Elementwise\", \"context_gate_factory\", \"ContextGate\",\n           \"GlobalAttention\", \"ConvMultiStepAttention\", \"CopyGenerator\",\n           \"CopyGeneratorLoss\", \"CopyGeneratorLossCompute\",\n           \"MultiHeadedAttention\", \"Embeddings\", \"PositionalEncoding\",\n           \"WeightNormConv2d\", \"AverageAttention\",\n           \"CopyGeneratorLMLossCompute\"]\n"
  },
  {
    "path": "onmt/modules/average_attn.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"Average Attention module.\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.modules.position_ffn import PositionwiseFeedForward\n\n\nclass AverageAttention(nn.Module):\n    \"\"\"\n    Average Attention module from\n    \"Accelerating Neural Transformer via an Average Attention Network\"\n    :cite:`DBLP:journals/corr/abs-1805-00631`.\n\n    Args:\n       model_dim (int): the dimension of keys/values/queries,\n           must be divisible by head_count\n       dropout (float): dropout parameter\n    \"\"\"\n\n    def __init__(self, model_dim, dropout=0.1, aan_useffn=False):\n        self.model_dim = model_dim\n        self.aan_useffn = aan_useffn\n        super(AverageAttention, self).__init__()\n        if aan_useffn:\n            self.average_layer = PositionwiseFeedForward(model_dim, model_dim,\n                                                         dropout)\n        self.gating_layer = nn.Linear(model_dim * 2, model_dim * 2)\n\n    def cumulative_average_mask(self, batch_size, inputs_len, device):\n        \"\"\"\n        Builds the mask to compute the cumulative average as described in\n        :cite:`DBLP:journals/corr/abs-1805-00631` -- Figure 3\n\n        Args:\n            batch_size (int): batch size\n            inputs_len (int): length of the inputs\n\n        Returns:\n            (FloatTensor):\n\n            * A Tensor of shape ``(batch_size, input_len, input_len)``\n        \"\"\"\n\n        triangle = torch.tril(torch.ones(inputs_len, inputs_len,\n                              dtype=torch.float, device=device))\n        weights = torch.ones(1, inputs_len, dtype=torch.float, device=device) \\\n            / torch.arange(1, inputs_len + 1, dtype=torch.float, device=device)\n        mask = triangle * weights.transpose(0, 1)\n\n        return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)\n\n    def cumulative_average(self, inputs, mask_or_step,\n                           layer_cache=None, step=None):\n        \"\"\"\n        Computes the cumulative average as described in\n        :cite:`DBLP:journals/corr/abs-1805-00631` -- Equations (1) (5) (6)\n\n        Args:\n            inputs (FloatTensor): sequence to average\n                ``(batch_size, input_len, dimension)``\n            mask_or_step: if cache is set, this is assumed\n                to be the current step of the\n                dynamic decoding. Otherwise, it is the mask matrix\n                used to compute the cumulative average.\n            layer_cache: a dictionary containing the cumulative average\n                of the previous step.\n\n        Returns:\n            a tensor of the same shape and type as ``inputs``.\n        \"\"\"\n\n        if layer_cache is not None:\n            step = mask_or_step\n            average_attention = (inputs + step *\n                                 layer_cache[\"prev_g\"]) / (step + 1)\n            layer_cache[\"prev_g\"] = average_attention\n            return average_attention\n        else:\n            mask = mask_or_step\n            return torch.matmul(mask.to(inputs.dtype), inputs)\n\n    def forward(self, inputs, mask=None, layer_cache=None, step=None):\n        \"\"\"\n        Args:\n            inputs (FloatTensor): ``(batch_size, input_len, model_dim)``\n\n        Returns:\n            (FloatTensor, FloatTensor):\n\n            * gating_outputs ``(batch_size, input_len, model_dim)``\n            * average_outputs average attention\n                ``(batch_size, input_len, model_dim)``\n        \"\"\"\n\n        batch_size = inputs.size(0)\n        inputs_len = inputs.size(1)\n        average_outputs = self.cumulative_average(\n          inputs, self.cumulative_average_mask(batch_size,\n                                               inputs_len, inputs.device)\n          if layer_cache is None else step, layer_cache=layer_cache)\n        if self.aan_useffn:\n            average_outputs = self.average_layer(average_outputs)\n        gating_outputs = self.gating_layer(torch.cat((inputs,\n                                                      average_outputs), -1))\n        input_gate, forget_gate = torch.chunk(gating_outputs, 2, dim=2)\n        gating_outputs = torch.sigmoid(input_gate) * inputs + \\\n            torch.sigmoid(forget_gate) * average_outputs\n\n        return gating_outputs, average_outputs\n"
  },
  {
    "path": "onmt/modules/conv_multi_step_attention.py",
    "content": "\"\"\" Multi Step Attention for CNN \"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom onmt.utils.misc import aeq\n\n\nSCALE_WEIGHT = 0.5 ** 0.5\n\n\ndef seq_linear(linear, x):\n    \"\"\" linear transform for 3-d tensor \"\"\"\n    batch, hidden_size, length, _ = x.size()\n    h = linear(torch.transpose(x, 1, 2).contiguous().view(\n        batch * length, hidden_size))\n    return torch.transpose(h.view(batch, length, hidden_size, 1), 1, 2)\n\n\nclass ConvMultiStepAttention(nn.Module):\n    \"\"\"\n    Conv attention takes a key matrix, a value matrix and a query vector.\n    Attention weight is calculated by key matrix with the query vector\n    and sum on the value matrix. And the same operation is applied\n    in each decode conv layer.\n    \"\"\"\n\n    def __init__(self, input_size):\n        super(ConvMultiStepAttention, self).__init__()\n        self.linear_in = nn.Linear(input_size, input_size)\n        self.mask = None\n\n    def apply_mask(self, mask):\n        \"\"\" Apply mask \"\"\"\n        self.mask = mask\n\n    def forward(self, base_target_emb, input_from_dec, encoder_out_top,\n                encoder_out_combine):\n        \"\"\"\n        Args:\n            base_target_emb: target emb tensor\n            input_from_dec: output of decode conv\n            encoder_out_top: the key matrix for calculation of attetion weight,\n                which is the top output of encode conv\n            encoder_out_combine:\n                the value matrix for the attention-weighted sum,\n                which is the combination of base emb and top output of encode\n        \"\"\"\n\n        # checks\n        # batch, channel, height, width = base_target_emb.size()\n        batch, _, height, _ = base_target_emb.size()\n        # batch_, channel_, height_, width_ = input_from_dec.size()\n        batch_, _, height_, _ = input_from_dec.size()\n        aeq(batch, batch_)\n        aeq(height, height_)\n\n        # enc_batch, enc_channel, enc_height = encoder_out_top.size()\n        enc_batch, _, enc_height = encoder_out_top.size()\n        # enc_batch_, enc_channel_, enc_height_ = encoder_out_combine.size()\n        enc_batch_, _, enc_height_ = encoder_out_combine.size()\n\n        aeq(enc_batch, enc_batch_)\n        aeq(enc_height, enc_height_)\n\n        preatt = seq_linear(self.linear_in, input_from_dec)\n        target = (base_target_emb + preatt) * SCALE_WEIGHT\n        target = torch.squeeze(target, 3)\n        target = torch.transpose(target, 1, 2)\n        pre_attn = torch.bmm(target, encoder_out_top)\n\n        if self.mask is not None:\n            pre_attn.data.masked_fill_(self.mask, -float('inf'))\n\n        attn = F.softmax(pre_attn, dim=2)\n\n        context_output = torch.bmm(\n            attn, torch.transpose(encoder_out_combine, 1, 2))\n        context_output = torch.transpose(\n            torch.unsqueeze(context_output, 3), 1, 2)\n        return context_output, attn\n"
  },
  {
    "path": "onmt/modules/copy_generator.py",
    "content": "import torch\nimport torch.nn as nn\n\nfrom onmt.utils.misc import aeq\nfrom onmt.utils.loss import CommonLossCompute\n\n\ndef collapse_copy_scores(scores, batch, tgt_vocab, src_vocabs=None,\n                         batch_dim=1, batch_offset=None):\n    \"\"\"\n    Given scores from an expanded dictionary\n    corresponeding to a batch, sums together copies,\n    with a dictionary word when it is ambiguous.\n    \"\"\"\n    offset = len(tgt_vocab)  # vocab_size\n    for b in range(scores.size(batch_dim)):\n        blank = []\n        fill = []\n\n        if src_vocabs is None:\n            src_vocab = batch.src_ex_vocab[b]\n        else:\n            batch_id = batch_offset[b] if batch_offset is not None else b\n            index = batch.indices.data[batch_id]\n            src_vocab = src_vocabs[index]\n\n        for i in range(1, len(src_vocab)):\n            sw = src_vocab.itos[i]\n            #  (deprecated) normalize subwords to reduce mismatches, but it causes many unmergable words such as '2-deg enerATE' ['Ġ2', '-', 'deg', 'Ġener', 'ATE']\n            # if hasattr(tgt_vocab, 'norm_stoi'):\n            #     ti = tgt_vocab.norm_stoi[sw]\n            # else:\n            #     ti = tgt_vocab.stoi[sw]\n            ti = tgt_vocab.stoi[sw]\n\n            # @memray, for pretrained tokenizer, pad_index can be non-zero\n            pad_index = tgt_vocab.pad_index if hasattr(tgt_vocab, 'pad_index') else 0\n            if ti != pad_index:\n                blank.append(offset + i)\n                fill.append(ti)\n        if blank:\n            blank = torch.Tensor(blank).type_as(batch.indices.data)\n            fill = torch.Tensor(fill).type_as(batch.indices.data)\n            score = scores[:, b] if batch_dim == 1 else scores[b]\n            score.index_add_(1, fill, score.index_select(1, blank))\n            score.index_fill_(1, blank, 1e-10)\n    return scores\n\n\nclass CopyGenerator(nn.Module):\n    \"\"\"An implementation of pointer-generator networks\n    :cite:`DBLP:journals/corr/SeeLM17`.\n\n    These networks consider copying words\n    directly from the source sequence.\n\n    The copy generator is an extended version of the standard\n    generator that computes three values.\n\n    * :math:`p_{softmax}` the standard softmax over `tgt_dict`\n    * :math:`p(z)` the probability of copying a word from\n      the source\n    * :math:`p_{copy}` the probility of copying a particular word.\n      taken from the attention distribution directly.\n\n    The model returns a distribution over the extend dictionary,\n    computed as\n\n    :math:`p(w) = p(z=1)  p_{copy}(w)  +  p(z=0)  p_{softmax}(w)`\n\n\n    .. mermaid::\n\n       graph BT\n          A[input]\n          S[src_map]\n          B[softmax]\n          BB[switch]\n          C[attn]\n          D[copy]\n          O[output]\n          A --> B\n          A --> BB\n          S --> D\n          C --> D\n          D --> O\n          B --> O\n          BB --> O\n\n\n    Args:\n       input_size (int): size of input representation\n       output_size (int): size of output vocabulary\n       pad_idx (int)\n    \"\"\"\n\n    def __init__(self, input_size, output_size, pad_idx):\n        super(CopyGenerator, self).__init__()\n        self.linear = nn.Linear(input_size, output_size)\n        self.linear_copy = nn.Linear(input_size, 1)\n        self.pad_idx = pad_idx\n\n    def forward(self, hidden, attn, src_map):\n        \"\"\"\n        Compute a distribution over the target dictionary\n        extended by the dynamic dictionary implied by copying\n        source words.\n\n        Args:\n           hidden (FloatTensor): hidden outputs ``(batch x tlen, input_size)``\n           attn (FloatTensor): attn for each ``(batch x tlen, input_size)``\n           src_map (FloatTensor):\n               A sparse indicator matrix mapping each source word to\n               its index in the \"extended\" vocab containing.\n               ``(src_len, batch, extra_words)``\n        \"\"\"\n\n        # CHECKS\n        batch_by_tlen, _ = hidden.size()\n        batch_by_tlen_, slen = attn.size()\n        slen_, batch, cvocab = src_map.size()\n        aeq(batch_by_tlen, batch_by_tlen_)\n        aeq(slen, slen_)\n\n        # Original probabilities.\n        logits = self.linear(hidden)\n        logits[:, self.pad_idx] = -float('inf')\n        prob = torch.softmax(logits, 1)\n\n        # Probability of copying p(z=1) batch.\n        p_copy = torch.sigmoid(self.linear_copy(hidden))\n        # Probability of not copying: p_{word}(w) * (1 - p(z))\n        out_prob = torch.mul(prob, 1 - p_copy)\n        mul_attn = torch.mul(attn, p_copy)\n        copy_prob = torch.bmm(\n            mul_attn.view(-1, batch, slen).transpose(0, 1),\n            src_map.transpose(0, 1)\n        ).transpose(0, 1)\n        copy_prob = copy_prob.contiguous().view(-1, cvocab)\n        return torch.cat([out_prob, copy_prob], 1)\n\n\nclass CopyGeneratorLoss(nn.Module):\n    \"\"\"Copy generator criterion.\"\"\"\n    def __init__(self, vocab_size, force_copy, unk_index=0,\n                 ignore_index=-100, eps=1e-20):\n        super(CopyGeneratorLoss, self).__init__()\n        self.force_copy = force_copy\n        self.eps = eps\n        self.vocab_size = vocab_size\n        self.ignore_index = ignore_index\n        self.unk_index = unk_index\n\n    def forward(self, scores, align, target):\n        \"\"\"\n        Args:\n            scores (FloatTensor): ``(batch_size*tgt_len)`` x dynamic vocab size\n                whose sum along dim 1 is less than or equal to 1, i.e. cols\n                softmaxed.\n            align (LongTensor): ``(batch_size x tgt_len)``\n            target (LongTensor): ``(batch_size x tgt_len)``\n        \"\"\"\n        # probabilities assigned by the model to the gold targets\n        vocab_probs = scores.gather(1, target.unsqueeze(1)).squeeze(1)\n\n        # probability of tokens copied from source\n        copy_ix = align.unsqueeze(1) + self.vocab_size\n        copy_tok_probs = scores.gather(1, copy_ix).squeeze(1)\n        # Set scores for unk to 0 and add eps\n        copy_tok_probs[align == self.unk_index] = 0\n        copy_tok_probs += self.eps  # to avoid -inf logs\n\n        # find the indices in which you do not use the copy mechanism\n        non_copy = align == self.unk_index\n        if not self.force_copy:\n            non_copy = non_copy | (target != self.unk_index)\n\n        probs = torch.where(\n            non_copy, copy_tok_probs + vocab_probs, copy_tok_probs\n        )\n\n        loss = -probs.log()  # just NLLLoss; can the module be incorporated?\n        # Drop padding.\n        loss[target == self.ignore_index] = 0\n        return loss\n\n\nclass CommonCopyGeneratorLossCompute(CommonLossCompute):\n    \"\"\"Common Copy Generator Loss Computation.\"\"\"\n    def __init__(self, criterion, generator, tgt_vocab, normalize_by_length,\n                 lambda_coverage=0.0, lambda_align=0.0, tgt_shift_index=1,\n                 lambda_orth_reg=0.0, lambda_sem_cov=0.0,\n                 n_neg=32, semcov_ending_state=False,\n                 sep_idx=None, eos_idx=None,\n                 **kwargs):\n        super(CommonCopyGeneratorLossCompute, self).__init__(\n            criterion, generator, lambda_coverage, lambda_align, tgt_shift_index, **kwargs\n        )\n        self.sep_idx = sep_idx\n        self.eos_idx = eos_idx\n        self.tgt_vocab = tgt_vocab\n        self.normalize_by_length = normalize_by_length\n        self.lambda_coverage = lambda_coverage\n        self.tgt_shift_index = tgt_shift_index\n        self.lambda_orth_reg = lambda_orth_reg\n        self.lambda_sem_cov = lambda_sem_cov\n        self.n_neg = n_neg\n        self.semcov_ending_state = semcov_ending_state\n\n    def _compute_loss(self, batch, output, target, copy_attn, align,\n                      std_attn=None, coverage_attn=None,\n                      src_states=None, dec_states=None, tgtenc_states=None,\n                      model=None\n                      ):\n        \"\"\"Compute the loss.\n\n        The args must match :func:`self._make_shard_state()`.\n\n        Args:\n            batch: the current batch.\n            output: the predict output from the model, shape=[T-1, B, H].\n            target: the validate target to compare output with, shape=[T-1, B, S].\n            copy_attn: the copy attention value, shape=[T-1, B, S].\n            align: the align info, shape=[T-1, B].\n        \"\"\"\n        target_indices = target # before flattening\n\n        target = target.contiguous().view(-1) # [T-1, B] -> (T-1)*B\n        align = align.view(-1) # (T-1)*B\n\n        scores = self.generator( # [(T-1)*B, V+tmp_vocab_size]\n            self._bottle(output), self._bottle(copy_attn), batch.src_map\n        )\n\n        loss = self.criterion(scores, align, target) # (T-1)*B\n        # print(\"loss=%.5f\" % loss.mean().item())\n\n        if self.lambda_coverage != 0.0:\n            coverage_loss = self._compute_coverage_loss(std_attn,\n                                                        coverage_attn)\n            loss += coverage_loss\n\n        # compute orthogonal penalty loss\n        if self.lambda_orth_reg > 0.0:\n            assert dec_states is not None\n            # decoder hidden state: output of decoder\n            orthogonal_penalty = self._compute_orthogonal_regularization_loss(target_indices, dec_states, self.sep_idx)\n            loss += orthogonal_penalty\n            # print(\"Orth_reg=%.5f\" % orthogonal_penalty)\n\n        # compute semantic coverage loss for target encoder\n        if self.lambda_sem_cov > 0.0:\n            assert model is not None\n            assert src_states is not None\n            assert tgtenc_states is not None\n            semantic_coverage_loss = self._compute_semantic_coverage_loss(model,\n                                                                          src_states, tgtenc_states,\n                                                                          target_indices,\n                                                                          num_negative=self.n_neg,\n                                                                          semcov_ending_state=self.semcov_ending_state,\n                                                                          sep_idx=self.sep_idx, eos_idx=self.eos_idx,\n                                                                          )\n            loss += semantic_coverage_loss\n            # print(\"Sem_cov=%.5f\\n\" % semantic_coverage_loss)\n\n        # this block does not depend on the loss value computed above\n        # and is used only for stats\n        scores_data = collapse_copy_scores(\n            self._unbottle(scores.clone(), batch.batch_size),\n            batch, self.tgt_vocab, None)\n        scores_data = self._bottle(scores_data)\n\n        # this block does not depend on the loss value computed above\n        # and is used only for stats\n        # Correct target copy token instead of <unk>\n        # tgt[i] = align[i] + len(tgt_vocab)\n        # for i such that tgt[i] == 0 and align[i] != 0\n        target_data = target.clone()\n        unk = self.criterion.unk_index\n        correct_mask = (target_data == unk) & (align != unk)\n        offset_align = align[correct_mask] + len(self.tgt_vocab)\n        target_data[correct_mask] += offset_align\n\n        # Compute sum of perplexities for stats\n        stats = self._stats(loss.sum().clone(), scores_data, target_data, batch_size=batch.batch_size)\n\n        # this part looks like it belongs in CopyGeneratorLoss\n        if self.normalize_by_length:\n            # Compute Loss as NLL divided by seq length\n            tgt_lens = batch.tgt[:, :, 0].ne(self.padding_idx).sum(0).float()\n            # Compute Total Loss per sequence in batch\n            loss = loss.view(-1, batch.batch_size).sum(0)\n            # Divide by length of each sequence and sum\n            loss = torch.div(loss, tgt_lens).sum()\n        else:\n            loss = loss.sum()\n\n        return loss, stats\n\n    def _make_shard_state(self, batch, output, range_, attns):\n        \"\"\"See base class for args description.\"\"\"\n        shard_state = super(CommonCopyGeneratorLossCompute,\n                            self)._make_shard_state(batch, output,\n                                                    range_, attns)\n\n        start_range = range_[0] + self.tgt_shift_index\n        end_range = range_[1]\n        shard_state.update({\n            \"copy_attn\": attns.get(\"copy\"),\n            \"align\": batch.alignment[start_range: end_range]\n        })\n        return shard_state\n\n\nclass CopyGeneratorLossCompute(CommonCopyGeneratorLossCompute):\n    \"\"\"Copy Generator Loss Computation.\"\"\"\n    def __init__(self, criterion, generator, tgt_vocab, normalize_by_length, **kwargs):\n        super(CopyGeneratorLossCompute, self).__init__(criterion, generator,\n                                                       tgt_vocab,\n                                                       normalize_by_length,\n                                                       # lambda_coverage=0.0, # @memray\n                                                       # lambda_align=0.0,\n                                                       tgt_shift_index=1,\n                                                       **kwargs)\n\n\nclass CopyGeneratorLMLossCompute(CommonCopyGeneratorLossCompute):\n    \"\"\"Copy Generator LM Loss Computation.\"\"\"\n    def __init__(self, criterion, generator, tgt_vocab, normalize_by_length,\n                 lambda_coverage=0.0):\n        super(CopyGeneratorLMLossCompute, self).__init__(criterion, generator,\n                                                         tgt_vocab,\n                                                         normalize_by_length,\n                                                         lambda_coverage=0.0,\n                                                         tgt_shift_index=0)\n"
  },
  {
    "path": "onmt/modules/embeddings.py",
    "content": "\"\"\" Embeddings module \"\"\"\nimport six\nimport math\nimport warnings\n\nimport torch\nimport torch.nn as nn\n\nfrom onmt.modules.util_class import Elementwise\nfrom onmt.utils.logging import logger\n\n\nclass SequenceTooLongError(Exception):\n    pass\n\n\nclass PositionalEncoding(nn.Module):\n    \"\"\"Sinusoidal positional encoding for non-recurrent neural networks.\n\n    Implementation based on \"Attention Is All You Need\"\n    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`\n\n    Args:\n       dropout (float): dropout parameter\n       dim (int): embedding size\n    \"\"\"\n\n    def __init__(self, dropout, dim, max_len=5000):\n        if dim % 2 != 0:\n            raise ValueError(\"Cannot use sin/cos positional encoding with \"\n                             \"odd dim (got dim={:d})\".format(dim))\n        pe = torch.zeros(max_len, dim)\n        position = torch.arange(0, max_len).unsqueeze(1)\n        div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) *\n                             -(math.log(10000.0) / dim)))\n        pe[:, 0::2] = torch.sin(position.float() * div_term)\n        pe[:, 1::2] = torch.cos(position.float() * div_term)\n        pe = pe.unsqueeze(1)\n        super(PositionalEncoding, self).__init__()\n        self.register_buffer('pe', pe)\n        self.dropout = nn.Dropout(p=dropout)\n        self.dim = dim\n\n    def forward(self, emb, step=None):\n        \"\"\"Embed inputs.\n\n        Args:\n            emb (FloatTensor): Sequence of word vectors\n                ``(seq_len, batch_size, self.dim)``\n            step (int or NoneType): If stepwise (``seq_len = 1``), use\n                the encoding for this position.\n        \"\"\"\n\n        emb = emb * math.sqrt(self.dim)\n        if step is None:\n            if self.pe.size(0) < emb.size(0):\n                raise SequenceTooLongError(\n                    f\"Sequence is {emb.size(0)} but PositionalEncoding is\"\n                    f\" limited to {self.pe.size(0)}. See max_len argument.\"\n                )\n            emb = emb + self.pe[:emb.size(0)]\n        else:\n            emb = emb + self.pe[step]\n        emb = self.dropout(emb)\n        return emb\n\n\nclass Embeddings(nn.Module):\n    \"\"\"Words embeddings for encoder/decoder.\n\n    Additionally includes ability to add sparse input features\n    based on \"Linguistic Input Features Improve Neural Machine Translation\"\n    :cite:`sennrich2016linguistic`.\n\n\n    .. mermaid::\n\n       graph LR\n          A[Input]\n          C[Feature 1 Lookup]\n          A-->B[Word Lookup]\n          A-->C\n          A-->D[Feature N Lookup]\n          B-->E[MLP/Concat]\n          C-->E\n          D-->E\n          E-->F[Output]\n\n    Args:\n        word_vec_size (int): size of the dictionary of embeddings.\n        word_padding_idx (int): padding index for words in the embeddings.\n        feat_padding_idx (List[int]): padding index for a list of features\n                                   in the embeddings.\n        word_vocab_size (int): size of dictionary of embeddings for words.\n        feat_vocab_sizes (List[int], optional): list of size of dictionary\n            of embeddings for each feature.\n        position_encoding (bool): see :class:`~onmt.modules.PositionalEncoding`\n        feat_merge (string): merge action for the features embeddings:\n            concat, sum or mlp.\n        feat_vec_exponent (float): when using `-feat_merge concat`, feature\n            embedding size is N^feat_dim_exponent, where N is the\n            number of values the feature takes.\n        feat_vec_size (int): embedding dimension for features when using\n            `-feat_merge mlp`\n        dropout (float): dropout probability.\n        freeze_word_vecs (bool): freeze weights of word vectors.\n    \"\"\"\n\n    def __init__(self, word_vec_size,\n                 word_vocab_size,\n                 word_padding_idx,\n                 position_encoding=False,\n                 feat_merge=\"concat\",\n                 feat_vec_exponent=0.7,\n                 feat_vec_size=-1,\n                 feat_padding_idx=[],\n                 feat_vocab_sizes=[],\n                 dropout=0,\n                 sparse=False,\n                 freeze_word_vecs=False):\n        self._validate_args(feat_merge, feat_vocab_sizes, feat_vec_exponent,\n                            feat_vec_size, feat_padding_idx)\n\n        if feat_padding_idx is None:\n            feat_padding_idx = []\n        self.word_padding_idx = word_padding_idx\n\n        self.word_vec_size = word_vec_size\n\n        # Dimensions and padding for constructing the word embedding matrix\n        vocab_sizes = [word_vocab_size]\n        emb_dims = [word_vec_size]\n        pad_indices = [word_padding_idx]\n\n        # Dimensions and padding for feature embedding matrices\n        # (these have no effect if feat_vocab_sizes is empty)\n        if feat_merge == 'sum':\n            feat_dims = [word_vec_size] * len(feat_vocab_sizes)\n        elif feat_vec_size > 0:\n            feat_dims = [feat_vec_size] * len(feat_vocab_sizes)\n        else:\n            feat_dims = [int(vocab ** feat_vec_exponent)\n                         for vocab in feat_vocab_sizes]\n        vocab_sizes.extend(feat_vocab_sizes)\n        emb_dims.extend(feat_dims)\n        pad_indices.extend(feat_padding_idx)\n\n        # The embedding matrix look-up tables. The first look-up table\n        # is for words. Subsequent ones are for features, if any exist.\n        emb_params = zip(vocab_sizes, emb_dims, pad_indices)\n        embeddings = [nn.Embedding(vocab, dim, padding_idx=pad, sparse=sparse)\n                      for vocab, dim, pad in emb_params]\n        emb_luts = Elementwise(feat_merge, embeddings)\n\n        # The final output size of word + feature vectors. This can vary\n        # from the word vector size if and only if features are defined.\n        # This is the attribute you should access if you need to know\n        # how big your embeddings are going to be.\n        self.embedding_size = (sum(emb_dims) if feat_merge == 'concat'\n                               else word_vec_size)\n\n        # The sequence of operations that converts the input sequence\n        # into a sequence of embeddings. At minimum this consists of\n        # looking up the embeddings for each word and feature in the\n        # input. Model parameters may require the sequence to contain\n        # additional operations as well.\n        super(Embeddings, self).__init__()\n        self.make_embedding = nn.Sequential()\n        self.make_embedding.add_module('emb_luts', emb_luts)\n\n        if feat_merge == 'mlp' and len(feat_vocab_sizes) > 0:\n            in_dim = sum(emb_dims)\n            mlp = nn.Sequential(nn.Linear(in_dim, word_vec_size), nn.ReLU())\n            self.make_embedding.add_module('mlp', mlp)\n\n        self.position_encoding = position_encoding\n\n        if self.position_encoding:\n            pe = PositionalEncoding(dropout, self.embedding_size)\n            self.make_embedding.add_module('pe', pe)\n\n        if freeze_word_vecs:\n            self.word_lut.weight.requires_grad = False\n\n    def _validate_args(self, feat_merge, feat_vocab_sizes, feat_vec_exponent,\n                       feat_vec_size, feat_padding_idx):\n        if feat_merge == \"sum\":\n            # features must use word_vec_size\n            if feat_vec_exponent != 0.7:\n                warnings.warn(\"Merging with sum, but got non-default \"\n                              \"feat_vec_exponent. It will be unused.\")\n            if feat_vec_size != -1:\n                warnings.warn(\"Merging with sum, but got non-default \"\n                              \"feat_vec_size. It will be unused.\")\n        elif feat_vec_size > 0:\n            # features will use feat_vec_size\n            if feat_vec_exponent != -1:\n                warnings.warn(\"Not merging with sum and positive \"\n                              \"feat_vec_size, but got non-default \"\n                              \"feat_vec_exponent. It will be unused.\")\n        else:\n            if feat_vec_exponent <= 0:\n                raise ValueError(\"Using feat_vec_exponent to determine \"\n                                 \"feature vec size, but got feat_vec_exponent \"\n                                 \"less than or equal to 0.\")\n        n_feats = len(feat_vocab_sizes)\n        if n_feats != len(feat_padding_idx):\n            raise ValueError(\"Got unequal number of feat_vocab_sizes and \"\n                             \"feat_padding_idx ({:d} != {:d})\".format(\n                                n_feats, len(feat_padding_idx)))\n\n    @property\n    def word_lut(self):\n        \"\"\"Word look-up table.\"\"\"\n        return self.make_embedding[0][0]\n\n    @property\n    def emb_luts(self):\n        \"\"\"Embedding look-up table.\"\"\"\n        return self.make_embedding[0]\n\n    def load_pretrained_vectors(self, emb_file):\n        \"\"\"Load in pretrained embeddings.\n\n        Args:\n          emb_file (str) : path to torch serialized embeddings\n        \"\"\"\n\n        if emb_file:\n            pretrained = torch.load(emb_file)\n            pretrained_vec_size = pretrained.size(1)\n            if self.word_vec_size > pretrained_vec_size:\n                self.word_lut.weight.data[:, :pretrained_vec_size] = pretrained\n            elif self.word_vec_size < pretrained_vec_size:\n                self.word_lut.weight.data \\\n                    .copy_(pretrained[:, :self.word_vec_size])\n            else:\n                self.word_lut.weight.data.copy_(pretrained)\n\n    def forward(self, source, step=None):\n        \"\"\"Computes the embeddings for words and features.\n\n        Args:\n            source (LongTensor): index tensor ``(len, batch, nfeat)``\n\n        Returns:\n            FloatTensor: Word embeddings ``(len, batch, embedding_size)``\n        \"\"\"\n\n        if self.position_encoding:\n            for i, module in enumerate(self.make_embedding._modules.values()):\n                if i == len(self.make_embedding._modules.values()) - 1:\n                    source = module(source, step=step)\n                else:\n                    source = module(source)\n        else:\n            source = self.make_embedding(source)\n\n        return source\n\n    def update_dropout(self, dropout):\n        if self.position_encoding:\n            self._modules['make_embedding'][1].dropout.p = dropout\n\n\n# Some utilitary functions for pretrained embeddings\n\ndef read_embeddings(path, skip_lines=0, filter_set=None):\n    \"\"\"\n    Read an embeddings file in the glove format.\n    \"\"\"\n    embs = dict()\n    total_vectors_in_file = 0\n    with open(path, 'rb') as f:\n        for i, line in enumerate(f):\n            if i < skip_lines:\n                continue\n            if not line:\n                break\n            if len(line) == 0:\n                # is this reachable?\n                continue\n\n            l_split = line.decode('utf8').strip().split(' ')\n            if len(l_split) == 2:\n                continue\n            total_vectors_in_file += 1\n            if filter_set is not None and l_split[0] not in filter_set:\n                continue\n            embs[l_split[0]] = [float(em) for em in l_split[1:]]\n    return embs, total_vectors_in_file\n\n\ndef calc_vocab_load_stats(vocab, loaded_embed_dict):\n    matching_count = len(\n        set(vocab.stoi.keys()) & set(loaded_embed_dict.keys()))\n    missing_count = len(vocab) - matching_count\n    percent_matching = matching_count / len(vocab) * 100\n    return matching_count, missing_count, percent_matching\n\n\ndef convert_to_torch_tensor(word_to_float_list_dict, vocab):\n    dim = len(six.next(six.itervalues(word_to_float_list_dict)))\n    tensor = torch.zeros((len(vocab), dim))\n    for word, values in word_to_float_list_dict.items():\n        tensor[vocab.stoi[word]] = torch.Tensor(values)\n    return tensor\n\n\ndef prepare_pretrained_embeddings(opt, fields):\n    if all([opt.both_embeddings is None,\n            opt.src_embeddings is None,\n            opt.tgt_embeddings is None]):\n        return\n\n    assert opt.save_data, \"-save_data is required when using \\\n        pretrained embeddings.\"\n\n    vocs = []\n    for side in ['src', 'tgt']:\n        try:\n            vocab = fields[side].base_field.vocab\n        except AttributeError:\n            vocab = fields[side].vocab\n        vocs.append(vocab)\n    enc_vocab, dec_vocab = vocs\n\n    skip_lines = 1 if opt.embeddings_type == \"word2vec\" else 0\n    if opt.both_embeddings is not None:\n        set_of_src_and_tgt_vocab = \\\n            set(enc_vocab.stoi.keys()) | set(dec_vocab.stoi.keys())\n        logger.info(\"Reading encoder and decoder embeddings from {}\".format(\n            opt.both_embeddings))\n        src_vectors, total_vec_count = \\\n            read_embeddings(opt.both_embeddings, skip_lines,\n                            set_of_src_and_tgt_vocab)\n        tgt_vectors = src_vectors\n        logger.info(\"\\tFound {} total vectors in file\".format(total_vec_count))\n    else:\n        if opt.src_embeddings is not None:\n            logger.info(\"Reading encoder embeddings from {}\".format(\n                opt.src_embeddings))\n            src_vectors, total_vec_count = read_embeddings(\n                opt.src_embeddings, skip_lines,\n                filter_set=enc_vocab.stoi\n            )\n            logger.info(\"\\tFound {} total vectors in file.\".format(\n                total_vec_count))\n        else:\n            src_vectors = None\n        if opt.tgt_embeddings is not None:\n            logger.info(\"Reading decoder embeddings from {}\".format(\n                opt.tgt_embeddings))\n            tgt_vectors, total_vec_count = read_embeddings(\n                opt.tgt_embeddings, skip_lines,\n                filter_set=dec_vocab.stoi\n            )\n            logger.info(\n                \"\\tFound {} total vectors in file\".format(total_vec_count))\n        else:\n            tgt_vectors = None\n    logger.info(\"After filtering to vectors in vocab:\")\n    if opt.src_embeddings is not None or opt.both_embeddings is not None:\n        logger.info(\"\\t* enc: %d match, %d missing, (%.2f%%)\"\n                    % calc_vocab_load_stats(enc_vocab, src_vectors))\n    if opt.tgt_embeddings is not None or opt.both_embeddings is not None:\n        logger.info(\"\\t* dec: %d match, %d missing, (%.2f%%)\"\n                    % calc_vocab_load_stats(dec_vocab, tgt_vectors))\n\n    # Write to file\n    enc_output_file = opt.save_data + \".enc_embeddings.pt\"\n    dec_output_file = opt.save_data + \".dec_embeddings.pt\"\n    if opt.src_embeddings is not None or opt.both_embeddings is not None:\n        logger.info(\"\\nSaving encoder embeddings as:\\n\\t* enc: %s\"\n                    % enc_output_file)\n        torch.save(\n            convert_to_torch_tensor(src_vectors, enc_vocab),\n            enc_output_file\n        )\n        # set the opt in place\n        opt.pre_word_vecs_enc = enc_output_file\n    if opt.tgt_embeddings is not None or opt.both_embeddings is not None:\n        logger.info(\"\\nSaving decoder embeddings as:\\n\\t* dec: %s\"\n                    % dec_output_file)\n        torch.save(\n            convert_to_torch_tensor(tgt_vectors, dec_vocab),\n            dec_output_file\n        )\n        # set the opt in place\n        opt.pre_word_vecs_dec = dec_output_file\n"
  },
  {
    "path": "onmt/modules/gate.py",
    "content": "\"\"\" ContextGate module \"\"\"\nimport torch\nimport torch.nn as nn\n\n\ndef context_gate_factory(gate_type, embeddings_size, decoder_size,\n                         attention_size, output_size):\n    \"\"\"Returns the correct ContextGate class\"\"\"\n\n    gate_types = {'source': SourceContextGate,\n                  'target': TargetContextGate,\n                  'both': BothContextGate}\n\n    assert gate_type in gate_types, \"Not valid ContextGate type: {0}\".format(\n        gate_type)\n    return gate_types[gate_type](embeddings_size, decoder_size, attention_size,\n                                 output_size)\n\n\nclass ContextGate(nn.Module):\n    \"\"\"\n    Context gate is a decoder module that takes as input the previous word\n    embedding, the current decoder state and the attention state, and\n    produces a gate.\n    The gate can be used to select the input from the target side context\n    (decoder state), from the source context (attention state) or both.\n    \"\"\"\n\n    def __init__(self, embeddings_size, decoder_size,\n                 attention_size, output_size):\n        super(ContextGate, self).__init__()\n        input_size = embeddings_size + decoder_size + attention_size\n        self.gate = nn.Linear(input_size, output_size, bias=True)\n        self.sig = nn.Sigmoid()\n        self.source_proj = nn.Linear(attention_size, output_size)\n        self.target_proj = nn.Linear(embeddings_size + decoder_size,\n                                     output_size)\n\n    def forward(self, prev_emb, dec_state, attn_state):\n        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)\n        z = self.sig(self.gate(input_tensor))\n        proj_source = self.source_proj(attn_state)\n        proj_target = self.target_proj(\n            torch.cat((prev_emb, dec_state), dim=1))\n        return z, proj_source, proj_target\n\n\nclass SourceContextGate(nn.Module):\n    \"\"\"Apply the context gate only to the source context\"\"\"\n\n    def __init__(self, embeddings_size, decoder_size,\n                 attention_size, output_size):\n        super(SourceContextGate, self).__init__()\n        self.context_gate = ContextGate(embeddings_size, decoder_size,\n                                        attention_size, output_size)\n        self.tanh = nn.Tanh()\n\n    def forward(self, prev_emb, dec_state, attn_state):\n        z, source, target = self.context_gate(\n            prev_emb, dec_state, attn_state)\n        return self.tanh(target + z * source)\n\n\nclass TargetContextGate(nn.Module):\n    \"\"\"Apply the context gate only to the target context\"\"\"\n\n    def __init__(self, embeddings_size, decoder_size,\n                 attention_size, output_size):\n        super(TargetContextGate, self).__init__()\n        self.context_gate = ContextGate(embeddings_size, decoder_size,\n                                        attention_size, output_size)\n        self.tanh = nn.Tanh()\n\n    def forward(self, prev_emb, dec_state, attn_state):\n        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)\n        return self.tanh(z * target + source)\n\n\nclass BothContextGate(nn.Module):\n    \"\"\"Apply the context gate to both contexts\"\"\"\n\n    def __init__(self, embeddings_size, decoder_size,\n                 attention_size, output_size):\n        super(BothContextGate, self).__init__()\n        self.context_gate = ContextGate(embeddings_size, decoder_size,\n                                        attention_size, output_size)\n        self.tanh = nn.Tanh()\n\n    def forward(self, prev_emb, dec_state, attn_state):\n        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)\n        return self.tanh((1. - z) * target + z * source)\n"
  },
  {
    "path": "onmt/modules/global_attention.py",
    "content": "\"\"\"Global attention modules (Luong / Bahdanau)\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom onmt.modules.sparse_activations import sparsemax\nfrom onmt.utils.misc import aeq, sequence_mask\n\n# This class is mainly used by decoder.py for RNNs but also\n# by the CNN / transformer decoder when copy attention is used\n# CNN has its own attention mechanism ConvMultiStepAttention\n# Transformer has its own MultiHeadedAttention\n\n\nclass GlobalAttention(nn.Module):\n    r\"\"\"\n    Global attention takes a matrix and a query vector. It\n    then computes a parameterized convex combination of the matrix\n    based on the input query.\n\n    Constructs a unit mapping a query `q` of size `dim`\n    and a source matrix `H` of size `n x dim`, to an output\n    of size `dim`.\n\n\n    .. mermaid::\n\n       graph BT\n          A[Query]\n          subgraph RNN\n            C[H 1]\n            D[H 2]\n            E[H N]\n          end\n          F[Attn]\n          G[Output]\n          A --> F\n          C --> F\n          D --> F\n          E --> F\n          C -.-> G\n          D -.-> G\n          E -.-> G\n          F --> G\n\n    All models compute the output as\n    :math:`c = \\sum_{j=1}^{\\text{SeqLength}} a_j H_j` where\n    :math:`a_j` is the softmax of a score function.\n    Then then apply a projection layer to [q, c].\n\n    However they\n    differ on how they compute the attention score.\n\n    * Luong Attention (dot, general):\n       * dot: :math:`\\text{score}(H_j,q) = H_j^T q`\n       * general: :math:`\\text{score}(H_j, q) = H_j^T W_a q`\n\n\n    * Bahdanau Attention (mlp):\n       * :math:`\\text{score}(H_j, q) = v_a^T \\text{tanh}(W_a q + U_a h_j)`\n\n\n    Args:\n       dim (int): dimensionality of query and key\n       coverage (bool): use coverage term\n       attn_type (str): type of attention to use, options [dot,general,mlp]\n       attn_func (str): attention function to use, options [softmax,sparsemax]\n\n    \"\"\"\n\n    def __init__(self, dim, coverage=False, attn_type=\"dot\",\n                 attn_func=\"softmax\"):\n        super(GlobalAttention, self).__init__()\n\n        self.dim = dim\n        assert attn_type in [\"dot\", \"general\", \"mlp\"], (\n            \"Please select a valid attention type (got {:s}).\".format(\n                attn_type))\n        self.attn_type = attn_type\n        assert attn_func in [\"softmax\", \"sparsemax\"], (\n            \"Please select a valid attention function.\")\n        self.attn_func = attn_func\n\n        if self.attn_type == \"general\":\n            self.linear_in = nn.Linear(dim, dim, bias=False)\n        elif self.attn_type == \"mlp\":\n            self.linear_context = nn.Linear(dim, dim, bias=False)\n            self.linear_query = nn.Linear(dim, dim, bias=True)\n            self.v = nn.Linear(dim, 1, bias=False)\n        # mlp wants it with bias\n        out_bias = self.attn_type == \"mlp\"\n        self.linear_out = nn.Linear(dim * 2, dim, bias=out_bias)\n\n        if coverage:\n            self.linear_cover = nn.Linear(1, dim, bias=False)\n\n    def score(self, h_t, h_s):\n        \"\"\"\n        Args:\n          h_t (FloatTensor): sequence of queries ``(batch, tgt_len, dim)``\n          h_s (FloatTensor): sequence of sources ``(batch, src_len, dim``\n\n        Returns:\n          FloatTensor: raw attention scores (unnormalized) for each src index\n            ``(batch, tgt_len, src_len)``\n        \"\"\"\n\n        # Check input sizes\n        src_batch, src_len, src_dim = h_s.size()\n        tgt_batch, tgt_len, tgt_dim = h_t.size()\n        aeq(src_batch, tgt_batch)\n        aeq(src_dim, tgt_dim)\n        aeq(self.dim, src_dim)\n\n        if self.attn_type in [\"general\", \"dot\"]:\n            if self.attn_type == \"general\":\n                h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)\n                h_t_ = self.linear_in(h_t_)\n                h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)\n            h_s_ = h_s.transpose(1, 2)\n            # (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len)\n            return torch.bmm(h_t, h_s_)\n        else:\n            dim = self.dim\n            wq = self.linear_query(h_t.view(-1, dim))\n            wq = wq.view(tgt_batch, tgt_len, 1, dim)\n            wq = wq.expand(tgt_batch, tgt_len, src_len, dim)\n\n            uh = self.linear_context(h_s.contiguous().view(-1, dim))\n            uh = uh.view(src_batch, 1, src_len, dim)\n            uh = uh.expand(src_batch, tgt_len, src_len, dim)\n\n            # (batch, t_len, s_len, d)\n            wquh = torch.tanh(wq + uh)\n\n            return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)\n\n    def forward(self, source, memory_bank, memory_lengths=None, coverage=None):\n        \"\"\"\n\n        Args:\n          source (FloatTensor): query vectors ``(batch, tgt_len, dim)``\n          memory_bank (FloatTensor): source vectors ``(batch, src_len, dim)``\n          memory_lengths (LongTensor): the source context lengths ``(batch,)``\n          coverage (FloatTensor): None (not supported yet)\n\n        Returns:\n          (FloatTensor, FloatTensor):\n\n          * Computed vector ``(tgt_len, batch, dim)``\n          * Attention distribtutions for each query\n            ``(tgt_len, batch, src_len)``\n        \"\"\"\n\n        # one step input\n        if source.dim() == 2:\n            one_step = True\n            source = source.unsqueeze(1)\n        else:\n            one_step = False\n\n        batch, source_l, dim = memory_bank.size()\n        batch_, target_l, dim_ = source.size()\n        aeq(batch, batch_)\n        aeq(dim, dim_)\n        aeq(self.dim, dim)\n        if coverage is not None:\n            batch_, source_l_ = coverage.size()\n            aeq(batch, batch_)\n            aeq(source_l, source_l_)\n\n        # @memray: the implementation seems not very correct\n        # https://github.com/OpenNMT/OpenNMT-py/issues/867\n        if coverage is not None:\n            cover = coverage.view(-1).unsqueeze(1)\n            memory_bank += self.linear_cover(cover).view_as(memory_bank)\n            memory_bank = torch.tanh(memory_bank)\n\n        # compute attention scores, as in Luong et al.\n        align = self.score(source, memory_bank)\n\n        if memory_lengths is not None:\n            mask = sequence_mask(memory_lengths, max_len=align.size(-1))\n            mask = mask.unsqueeze(1)  # Make it broadcastable.\n            align.masked_fill_(~mask, -float('inf'))\n\n        # Softmax or sparsemax to normalize attention weights\n        if self.attn_func == \"softmax\":\n            align_vectors = F.softmax(align.view(batch*target_l, source_l), -1)\n        else:\n            align_vectors = sparsemax(align.view(batch*target_l, source_l), -1)\n        align_vectors = align_vectors.view(batch, target_l, source_l)\n\n        # each context vector c_t is the weighted average\n        # over all the source hidden states\n        c = torch.bmm(align_vectors, memory_bank)\n\n        # concatenate\n        concat_c = torch.cat([c, source], 2).view(batch*target_l, dim*2)\n        attn_h = self.linear_out(concat_c).view(batch, target_l, dim)\n        if self.attn_type in [\"general\", \"dot\"]:\n            attn_h = torch.tanh(attn_h)\n\n        if one_step:\n            attn_h = attn_h.squeeze(1)\n            align_vectors = align_vectors.squeeze(1)\n\n            # Check output sizes\n            batch_, dim_ = attn_h.size()\n            aeq(batch, batch_)\n            aeq(dim, dim_)\n            batch_, source_l_ = align_vectors.size()\n            aeq(batch, batch_)\n            aeq(source_l, source_l_)\n\n        else:\n            attn_h = attn_h.transpose(0, 1).contiguous()\n            align_vectors = align_vectors.transpose(0, 1).contiguous()\n            # Check output sizes\n            target_l_, batch_, dim_ = attn_h.size()\n            aeq(target_l, target_l_)\n            aeq(batch, batch_)\n            aeq(dim, dim_)\n            target_l_, batch_, source_l_ = align_vectors.size()\n            aeq(target_l, target_l_)\n            aeq(batch, batch_)\n            aeq(source_l, source_l_)\n\n        return attn_h, align_vectors\n"
  },
  {
    "path": "onmt/modules/multi_headed_attn.py",
    "content": "\"\"\" Multi-Head Attention module \"\"\"\nimport math\nimport torch\nimport torch.nn as nn\n\nfrom onmt.utils.misc import generate_relative_positions_matrix,\\\n                            relative_matmul\n# from onmt.utils.misc import aeq\n\n\nclass MultiHeadedAttention(nn.Module):\n    \"\"\"Multi-Head Attention module from \"Attention is All You Need\"\n    :cite:`DBLP:journals/corr/VaswaniSPUJGKP17`.\n\n    Similar to standard `dot` attention but uses\n    multiple attention distributions simulataneously\n    to select relevant items.\n\n    .. mermaid::\n\n       graph BT\n          A[key]\n          B[value]\n          C[query]\n          O[output]\n          subgraph Attn\n            D[Attn 1]\n            E[Attn 2]\n            F[Attn N]\n          end\n          A --> D\n          C --> D\n          A --> E\n          C --> E\n          A --> F\n          C --> F\n          D --> O\n          E --> O\n          F --> O\n          B --> O\n\n    Also includes several additional tricks.\n\n    Args:\n       head_count (int): number of parallel heads\n       model_dim (int): the dimension of keys/values/queries,\n           must be divisible by head_count\n       dropout (float): dropout parameter\n    \"\"\"\n\n    def __init__(self, head_count, model_dim, dropout=0.1,\n                 max_relative_positions=0):\n        assert model_dim % head_count == 0\n        self.dim_per_head = model_dim // head_count\n        self.model_dim = model_dim\n\n        super(MultiHeadedAttention, self).__init__()\n        self.head_count = head_count\n\n        self.linear_keys = nn.Linear(model_dim,\n                                     head_count * self.dim_per_head)\n        self.linear_values = nn.Linear(model_dim,\n                                       head_count * self.dim_per_head)\n        self.linear_query = nn.Linear(model_dim,\n                                      head_count * self.dim_per_head)\n        self.softmax = nn.Softmax(dim=-1)\n        self.dropout = nn.Dropout(dropout)\n        self.final_linear = nn.Linear(model_dim, model_dim)\n\n        self.max_relative_positions = max_relative_positions\n\n        if max_relative_positions > 0:\n            vocab_size = max_relative_positions * 2 + 1\n            self.relative_positions_embeddings = nn.Embedding(\n                vocab_size, self.dim_per_head)\n\n    def forward(self, key, value, query, mask=None,\n                layer_cache=None, attn_type=None):\n        \"\"\"\n        Compute the context vector and the attention vectors.\n\n        Args:\n           key (FloatTensor): set of `key_len`\n               key vectors ``(batch, key_len, dim)``\n           value (FloatTensor): set of `key_len`\n               value vectors ``(batch, key_len, dim)``\n           query (FloatTensor): set of `query_len`\n               query vectors  ``(batch, query_len, dim)``\n           mask: binary mask 1/0 indicating which keys have\n               zero / non-zero attention ``(batch, query_len, key_len)``\n        Returns:\n           (FloatTensor, FloatTensor):\n\n           * output context vectors ``(batch, query_len, dim)``\n           * Attention vector in heads ``(batch, head, query_len, key_len)``.\n        \"\"\"\n\n        # CHECKS\n        # batch, k_len, d = key.size()\n        # batch_, k_len_, d_ = value.size()\n        # aeq(batch, batch_)\n        # aeq(k_len, k_len_)\n        # aeq(d, d_)\n        # batch_, q_len, d_ = query.size()\n        # aeq(batch, batch_)\n        # aeq(d, d_)\n        # aeq(self.model_dim % 8, 0)\n        # if mask is not None:\n        #    batch_, q_len_, k_len_ = mask.size()\n        #    aeq(batch_, batch)\n        #    aeq(k_len_, k_len)\n        #    aeq(q_len_ == q_len)\n        # END CHECKS\n\n        batch_size = key.size(0)\n        dim_per_head = self.dim_per_head\n        head_count = self.head_count\n        key_len = key.size(1)\n        query_len = query.size(1)\n\n        def shape(x):\n            \"\"\"Projection.\"\"\"\n            return x.view(batch_size, -1, head_count, dim_per_head) \\\n                .transpose(1, 2)\n\n        def unshape(x):\n            \"\"\"Compute context.\"\"\"\n            return x.transpose(1, 2).contiguous() \\\n                    .view(batch_size, -1, head_count * dim_per_head)\n\n        # 1) Project key, value, and query.\n        if layer_cache is not None:\n            if attn_type == \"self\":\n                query, key, value = self.linear_query(query),\\\n                                    self.linear_keys(query),\\\n                                    self.linear_values(query)\n                key = shape(key)\n                value = shape(value)\n                if layer_cache[\"self_keys\"] is not None:\n                    key = torch.cat(\n                        (layer_cache[\"self_keys\"], key),\n                        dim=2)\n                if layer_cache[\"self_values\"] is not None:\n                    value = torch.cat(\n                        (layer_cache[\"self_values\"], value),\n                        dim=2)\n                layer_cache[\"self_keys\"] = key\n                layer_cache[\"self_values\"] = value\n            elif attn_type == \"context\":\n                query = self.linear_query(query)\n                if layer_cache[\"memory_keys\"] is None:\n                    key, value = self.linear_keys(key),\\\n                                 self.linear_values(value)\n                    key = shape(key)\n                    value = shape(value)\n                else:\n                    key, value = layer_cache[\"memory_keys\"],\\\n                               layer_cache[\"memory_values\"]\n                layer_cache[\"memory_keys\"] = key\n                layer_cache[\"memory_values\"] = value\n        else:\n            key = self.linear_keys(key)\n            value = self.linear_values(value)\n            query = self.linear_query(query)\n            key = shape(key)\n            value = shape(value)\n\n        if self.max_relative_positions > 0 and attn_type == \"self\":\n            key_len = key.size(2)\n            # 1 or key_len x key_len\n            relative_positions_matrix = generate_relative_positions_matrix(\n                key_len, self.max_relative_positions,\n                cache=True if layer_cache is not None else False)\n            #  1 or key_len x key_len x dim_per_head\n            relations_keys = self.relative_positions_embeddings(\n                relative_positions_matrix.to(key.device))\n            #  1 or key_len x key_len x dim_per_head\n            relations_values = self.relative_positions_embeddings(\n                relative_positions_matrix.to(key.device))\n\n        query = shape(query)\n\n        key_len = key.size(2)\n        query_len = query.size(2)\n\n        # 2) Calculate and scale scores.\n        query = query / math.sqrt(dim_per_head)\n        # batch x num_heads x query_len x key_len\n        query_key = torch.matmul(query, key.transpose(2, 3))\n\n        if self.max_relative_positions > 0 and attn_type == \"self\":\n            scores = query_key + relative_matmul(query, relations_keys, True)\n        else:\n            scores = query_key\n        scores = scores.float()\n\n        if mask is not None:\n            mask = mask.unsqueeze(1)  # [B, 1, 1, T_values]\n            scores = scores.masked_fill(mask, -1e18)\n\n        # 3) Apply attention dropout and compute context vectors.\n        attn = self.softmax(scores).to(query.dtype)\n        drop_attn = self.dropout(attn)\n\n        context_original = torch.matmul(drop_attn, value)\n\n        if self.max_relative_positions > 0 and attn_type == \"self\":\n            context = unshape(context_original\n                              + relative_matmul(drop_attn,\n                                                relations_values,\n                                                False))\n        else:\n            context = unshape(context_original)\n\n        output = self.final_linear(context)\n        # CHECK\n        # batch_, q_len_, d_ = output.size()\n        # aeq(q_len, q_len_)\n        # aeq(batch, batch_)\n        # aeq(d, d_)\n\n        # Return multi-head attn\n        attns = attn \\\n            .view(batch_size, head_count,\n                  query_len, key_len)\n\n        return output, attns\n\n    def update_dropout(self, dropout):\n        self.dropout.p = dropout\n"
  },
  {
    "path": "onmt/modules/position_ffn.py",
    "content": "\"\"\"Position feed-forward network from \"Attention is All You Need\".\"\"\"\n\nimport torch.nn as nn\n\n\nclass PositionwiseFeedForward(nn.Module):\n    \"\"\" A two-layer Feed-Forward-Network with residual layer norm.\n\n    Args:\n        d_model (int): the size of input for the first-layer of the FFN.\n        d_ff (int): the hidden layer size of the second-layer\n            of the FNN.\n        dropout (float): dropout probability in :math:`[0, 1)`.\n    \"\"\"\n\n    def __init__(self, d_model, d_ff, dropout=0.1):\n        super(PositionwiseFeedForward, self).__init__()\n        self.w_1 = nn.Linear(d_model, d_ff)\n        self.w_2 = nn.Linear(d_ff, d_model)\n        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n        self.dropout_1 = nn.Dropout(dropout)\n        self.relu = nn.ReLU()\n        self.dropout_2 = nn.Dropout(dropout)\n\n    def forward(self, x):\n        \"\"\"Layer definition.\n\n        Args:\n            x: ``(batch_size, input_len, model_dim)``\n\n        Returns:\n            (FloatTensor): Output ``(batch_size, input_len, model_dim)``.\n        \"\"\"\n\n        inter = self.dropout_1(self.relu(self.w_1(self.layer_norm(x))))\n        output = self.dropout_2(self.w_2(inter))\n        return output + x\n\n    def update_dropout(self, dropout):\n        self.dropout_1.p = dropout\n        self.dropout_2.p = dropout\n"
  },
  {
    "path": "onmt/modules/sparse_activations.py",
    "content": "\"\"\"\nAn implementation of sparsemax (Martins & Astudillo, 2016). See\n:cite:`DBLP:journals/corr/MartinsA16` for detailed description.\n\nBy Ben Peters and Vlad Niculae\n\"\"\"\n\nimport torch\nfrom torch.autograd import Function\nfrom torch.cuda.amp import custom_fwd, custom_bwd\nimport torch.nn as nn\n\n\ndef _make_ix_like(input, dim=0):\n    d = input.size(dim)\n    rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)\n    view = [1] * input.dim()\n    view[0] = -1\n    return rho.view(view).transpose(0, dim)\n\n\ndef _threshold_and_support(input, dim=0):\n    \"\"\"Sparsemax building block: compute the threshold\n\n    Args:\n        input: any dimension\n        dim: dimension along which to apply the sparsemax\n\n    Returns:\n        the threshold value\n    \"\"\"\n\n    input_srt, _ = torch.sort(input, descending=True, dim=dim)\n    input_cumsum = input_srt.cumsum(dim) - 1\n    rhos = _make_ix_like(input, dim)\n    support = rhos * input_srt > input_cumsum\n\n    support_size = support.sum(dim=dim).unsqueeze(dim)\n    tau = input_cumsum.gather(dim, support_size - 1)\n    tau /= support_size.to(input.dtype)\n    return tau, support_size\n\n\nclass SparsemaxFunction(Function):\n\n    @staticmethod\n    @custom_fwd\n    def forward(ctx, input, dim=0):\n        \"\"\"sparsemax: normalizing sparse transform (a la softmax)\n\n        Parameters:\n            input (Tensor): any shape\n            dim: dimension along which to apply sparsemax\n\n        Returns:\n            output (Tensor): same shape as input\n        \"\"\"\n        ctx.dim = dim\n        max_val, _ = input.max(dim=dim, keepdim=True)\n        input -= max_val  # same numerical stability trick as for softmax\n        tau, supp_size = _threshold_and_support(input, dim=dim)\n        output = torch.clamp(input - tau, min=0)\n        ctx.save_for_backward(supp_size, output)\n        return output\n\n    @staticmethod\n    @custom_bwd\n    def backward(ctx, grad_output):\n        supp_size, output = ctx.saved_tensors\n        dim = ctx.dim\n        grad_input = grad_output.clone()\n        grad_input[output == 0] = 0\n\n        v_hat = grad_input.sum(dim=dim) / supp_size.to(output.dtype).squeeze()\n        v_hat = v_hat.unsqueeze(dim)\n        grad_input = torch.where(output != 0, grad_input - v_hat, grad_input)\n        return grad_input, None\n\n\nsparsemax = SparsemaxFunction.apply\n\n\nclass Sparsemax(nn.Module):\n\n    def __init__(self, dim=0):\n        self.dim = dim\n        super(Sparsemax, self).__init__()\n\n    def forward(self, input):\n        return sparsemax(input, self.dim)\n\n\nclass LogSparsemax(nn.Module):\n\n    def __init__(self, dim=0):\n        self.dim = dim\n        super(LogSparsemax, self).__init__()\n\n    def forward(self, input):\n        return torch.log(sparsemax(input, self.dim))\n"
  },
  {
    "path": "onmt/modules/sparse_losses.py",
    "content": "import torch\nimport torch.nn as nn\nfrom torch.autograd import Function\nfrom torch.cuda.amp import custom_fwd, custom_bwd\nfrom onmt.modules.sparse_activations import _threshold_and_support\nfrom onmt.utils.misc import aeq\n\n\nclass SparsemaxLossFunction(Function):\n\n    @staticmethod\n    @custom_fwd\n    def forward(ctx, input, target):\n        \"\"\"\n        input (FloatTensor): ``(n, num_classes)``.\n        target (LongTensor): ``(n,)``, the indices of the target classes\n        \"\"\"\n        input_batch, classes = input.size()\n        target_batch = target.size(0)\n        aeq(input_batch, target_batch)\n\n        z_k = input.gather(1, target.unsqueeze(1)).squeeze()\n        tau_z, support_size = _threshold_and_support(input, dim=1)\n        support = input > tau_z\n        x = torch.where(\n            support, input**2 - tau_z**2,\n            torch.tensor(0.0, device=input.device)\n        ).sum(dim=1)\n        ctx.save_for_backward(input, target, tau_z)\n        # clamping necessary because of numerical errors: loss should be lower\n        # bounded by zero, but negative values near zero are possible without\n        # the clamp\n        return torch.clamp(x / 2 - z_k + 0.5, min=0.0)\n\n    @staticmethod\n    @custom_bwd\n    def backward(ctx, grad_output):\n        input, target, tau_z = ctx.saved_tensors\n        sparsemax_out = torch.clamp(input - tau_z, min=0)\n        delta = torch.zeros_like(sparsemax_out)\n        delta.scatter_(1, target.unsqueeze(1), 1)\n        return sparsemax_out - delta, None\n\n\nsparsemax_loss = SparsemaxLossFunction.apply\n\n\nclass SparsemaxLoss(nn.Module):\n    \"\"\"\n    An implementation of sparsemax loss, first proposed in\n    :cite:`DBLP:journals/corr/MartinsA16`. If using\n    a sparse output layer, it is not possible to use negative log likelihood\n    because the loss is infinite in the case the target is assigned zero\n    probability. Inputs to SparsemaxLoss are arbitrary dense real-valued\n    vectors (like in nn.CrossEntropyLoss), not probability vectors (like in\n    nn.NLLLoss).\n    \"\"\"\n\n    def __init__(self, weight=None, ignore_index=-100,\n                 reduction='elementwise_mean'):\n        assert reduction in ['elementwise_mean', 'sum', 'none']\n        self.reduction = reduction\n        self.weight = weight\n        self.ignore_index = ignore_index\n        super(SparsemaxLoss, self).__init__()\n\n    def forward(self, input, target):\n        loss = sparsemax_loss(input, target)\n        if self.ignore_index >= 0:\n            ignored_positions = target == self.ignore_index\n            size = float((target.size(0) - ignored_positions.sum()).item())\n            loss.masked_fill_(ignored_positions, 0.0)\n        else:\n            size = float(target.size(0))\n        if self.reduction == 'sum':\n            loss = loss.sum()\n        elif self.reduction == 'elementwise_mean':\n            loss = loss.sum() / size\n        return loss\n"
  },
  {
    "path": "onmt/modules/structured_attention.py",
    "content": "import torch.nn as nn\nimport torch\nimport torch.cuda\n\n\nclass MatrixTree(nn.Module):\n    \"\"\"Implementation of the matrix-tree theorem for computing marginals\n    of non-projective dependency parsing. This attention layer is used\n    in the paper \"Learning Structured Text Representations\"\n    :cite:`DBLP:journals/corr/LiuL17d`.\n    \"\"\"\n\n    def __init__(self, eps=1e-5):\n        self.eps = eps\n        super(MatrixTree, self).__init__()\n\n    def forward(self, input):\n        laplacian = input.exp() + self.eps\n        output = input.clone()\n        for b in range(input.size(0)):\n            lap = laplacian[b].masked_fill(\n                torch.eye(input.size(1), device=input.device).ne(0), 0)\n            lap = -lap + torch.diag(lap.sum(0))\n            # store roots on diagonal\n            lap[0] = input[b].diag().exp()\n            inv_laplacian = lap.inverse()\n\n            factor = inv_laplacian.diag().unsqueeze(1)\\\n                                         .expand_as(input[b]).transpose(0, 1)\n            term1 = input[b].exp().mul(factor).clone()\n            term2 = input[b].exp().mul(inv_laplacian.transpose(0, 1)).clone()\n            term1[:, 0] = 0\n            term2[0] = 0\n            output[b] = term1 - term2\n            roots_output = input[b].diag().exp().mul(\n                inv_laplacian.transpose(0, 1)[0])\n            output[b] = output[b] + torch.diag(roots_output)\n        return output\n"
  },
  {
    "path": "onmt/modules/util_class.py",
    "content": "\"\"\" Misc classes \"\"\"\nimport torch\nimport torch.nn as nn\n\n\n# At the moment this class is only used by embeddings.Embeddings look-up tables\nclass Elementwise(nn.ModuleList):\n    \"\"\"\n    A simple network container.\n    Parameters are a list of modules.\n    Inputs are a 3d Tensor whose last dimension is the same length\n    as the list.\n    Outputs are the result of applying modules to inputs elementwise.\n    An optional merge parameter allows the outputs to be reduced to a\n    single Tensor.\n    \"\"\"\n\n    def __init__(self, merge=None, *args):\n        assert merge in [None, 'first', 'concat', 'sum', 'mlp']\n        self.merge = merge\n        super(Elementwise, self).__init__(*args)\n\n    def forward(self, inputs):\n        inputs_ = [feat.squeeze(2) for feat in inputs.split(1, dim=2)]\n        assert len(self) == len(inputs_)\n        outputs = [f(x) for f, x in zip(self, inputs_)]\n        if self.merge == 'first':\n            return outputs[0]\n        elif self.merge == 'concat' or self.merge == 'mlp':\n            return torch.cat(outputs, 2)\n        elif self.merge == 'sum':\n            return sum(outputs)\n        else:\n            return outputs\n\n\nclass Cast(nn.Module):\n    \"\"\"\n    Basic layer that casts its input to a specific data type. The same tensor\n    is returned if the data type is already correct.\n    \"\"\"\n\n    def __init__(self, dtype):\n        super(Cast, self).__init__()\n        self._dtype = dtype\n\n    def forward(self, x):\n        return x.to(self._dtype)\n"
  },
  {
    "path": "onmt/modules/weight_norm.py",
    "content": "\"\"\"  Weights normalization modules  \"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import Parameter\n\n\ndef get_var_maybe_avg(namespace, var_name, training, polyak_decay):\n    \"\"\" utility for retrieving polyak averaged params\n        Update average\n    \"\"\"\n    v = getattr(namespace, var_name)\n    v_avg = getattr(namespace, var_name + '_avg')\n    v_avg -= (1 - polyak_decay) * (v_avg - v.data)\n\n    if training:\n        return v\n    else:\n        return v_avg\n\n\ndef get_vars_maybe_avg(namespace, var_names, training, polyak_decay):\n    \"\"\" utility for retrieving polyak averaged params \"\"\"\n    vars = []\n    for vn in var_names:\n        vars.append(get_var_maybe_avg(\n            namespace, vn, training, polyak_decay))\n    return vars\n\n\nclass WeightNormLinear(nn.Linear):\n    \"\"\"\n    Implementation of \"Weight Normalization: A Simple Reparameterization\n    to Accelerate Training of Deep Neural Networks\"\n    :cite:`DBLP:journals/corr/SalimansK16`\n\n    As a reparameterization method, weight normalization is same\n    as BatchNormalization, but it doesn't depend on minibatch.\n\n    NOTE: This is used nowhere in the code at this stage\n          Vincent Nguyen 05/18/2018\n    \"\"\"\n\n    def __init__(self, in_features, out_features,\n                 init_scale=1., polyak_decay=0.9995):\n        super(WeightNormLinear, self).__init__(\n            in_features, out_features, bias=True)\n\n        self.V = self.weight\n        self.g = Parameter(torch.Tensor(out_features))\n        self.b = self.bias\n\n        self.register_buffer(\n            'V_avg', torch.zeros(out_features, in_features))\n        self.register_buffer('g_avg', torch.zeros(out_features))\n        self.register_buffer('b_avg', torch.zeros(out_features))\n\n        self.init_scale = init_scale\n        self.polyak_decay = polyak_decay\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        return\n\n    def forward(self, x, init=False):\n        if init is True:\n            # out_features * in_features\n            self.V.data.copy_(torch.randn(self.V.data.size()).type_as(\n                self.V.data) * 0.05)\n            # norm is out_features * 1\n            v_norm = self.V.data / \\\n                self.V.data.norm(2, 1).expand_as(self.V.data)\n            # batch_size * out_features\n            x_init = F.linear(x, v_norm).data\n            # out_features\n            m_init, v_init = x_init.mean(0).squeeze(\n                0), x_init.var(0).squeeze(0)\n            # out_features\n            scale_init = self.init_scale / \\\n                torch.sqrt(v_init + 1e-10)\n            self.g.data.copy_(scale_init)\n            self.b.data.copy_(-m_init * scale_init)\n            x_init = scale_init.view(1, -1).expand_as(x_init) \\\n                * (x_init - m_init.view(1, -1).expand_as(x_init))\n            self.V_avg.copy_(self.V.data)\n            self.g_avg.copy_(self.g.data)\n            self.b_avg.copy_(self.b.data)\n            return x_init\n        else:\n            v, g, b = get_vars_maybe_avg(self, ['V', 'g', 'b'],\n                                         self.training,\n                                         polyak_decay=self.polyak_decay)\n            # batch_size * out_features\n            x = F.linear(x, v)\n            scalar = g / torch.norm(v, 2, 1).squeeze(1)\n            x = scalar.view(1, -1).expand_as(x) * x + \\\n                b.view(1, -1).expand_as(x)\n            return x\n\n\nclass WeightNormConv2d(nn.Conv2d):\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n                 padding=0, dilation=1, groups=1, init_scale=1.,\n                 polyak_decay=0.9995):\n        super(WeightNormConv2d, self).__init__(in_channels, out_channels,\n                                               kernel_size, stride, padding,\n                                               dilation, groups)\n\n        self.V = self.weight\n        self.g = Parameter(torch.Tensor(out_channels))\n        self.b = self.bias\n\n        self.register_buffer('V_avg', torch.zeros(self.V.size()))\n        self.register_buffer('g_avg', torch.zeros(out_channels))\n        self.register_buffer('b_avg', torch.zeros(out_channels))\n\n        self.init_scale = init_scale\n        self.polyak_decay = polyak_decay\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        return\n\n    def forward(self, x, init=False):\n        if init is True:\n            # out_channels, in_channels // groups, * kernel_size\n            self.V.data.copy_(torch.randn(self.V.data.size()\n                                          ).type_as(self.V.data) * 0.05)\n            v_norm = self.V.data / self.V.data.view(self.out_channels, -1)\\\n                .norm(2, 1).view(self.out_channels, *(\n                    [1] * (len(self.kernel_size) + 1))).expand_as(self.V.data)\n            x_init = F.conv2d(x, v_norm, None, self.stride,\n                              self.padding, self.dilation, self.groups).data\n            t_x_init = x_init.transpose(0, 1).contiguous().view(\n                self.out_channels, -1)\n            m_init, v_init = t_x_init.mean(1).squeeze(\n                1), t_x_init.var(1).squeeze(1)\n            # out_features\n            scale_init = self.init_scale / \\\n                torch.sqrt(v_init + 1e-10)\n            self.g.data.copy_(scale_init)\n            self.b.data.copy_(-m_init * scale_init)\n            scale_init_shape = scale_init.view(\n                1, self.out_channels, *([1] * (len(x_init.size()) - 2)))\n            m_init_shape = m_init.view(\n                1, self.out_channels, *([1] * (len(x_init.size()) - 2)))\n            x_init = scale_init_shape.expand_as(\n                x_init) * (x_init - m_init_shape.expand_as(x_init))\n            self.V_avg.copy_(self.V.data)\n            self.g_avg.copy_(self.g.data)\n            self.b_avg.copy_(self.b.data)\n            return x_init\n        else:\n            v, g, b = get_vars_maybe_avg(\n                self, ['V', 'g', 'b'], self.training,\n                polyak_decay=self.polyak_decay)\n\n            scalar = torch.norm(v.view(self.out_channels, -1), 2, 1)\n            if len(scalar.size()) == 2:\n                scalar = g / scalar.squeeze(1)\n            else:\n                scalar = g / scalar\n\n            w = scalar.view(self.out_channels, *\n                            ([1] * (len(v.size()) - 1))).expand_as(v) * v\n\n            x = F.conv2d(x, w, b, self.stride,\n                         self.padding, self.dilation, self.groups)\n            return x\n\n# This is used nowhere in the code at the moment (Vincent Nguyen 05/18/2018)\n\n\nclass WeightNormConvTranspose2d(nn.ConvTranspose2d):\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n                 padding=0, output_padding=0, groups=1, init_scale=1.,\n                 polyak_decay=0.9995):\n        super(WeightNormConvTranspose2d, self).__init__(\n            in_channels, out_channels,\n            kernel_size, stride,\n            padding, output_padding,\n            groups)\n        # in_channels, out_channels, *kernel_size\n        self.V = self.weight\n        self.g = Parameter(torch.Tensor(out_channels))\n        self.b = self.bias\n\n        self.register_buffer('V_avg', torch.zeros(self.V.size()))\n        self.register_buffer('g_avg', torch.zeros(out_channels))\n        self.register_buffer('b_avg', torch.zeros(out_channels))\n\n        self.init_scale = init_scale\n        self.polyak_decay = polyak_decay\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        return\n\n    def forward(self, x, init=False):\n        if init is True:\n            # in_channels, out_channels, *kernel_size\n            self.V.data.copy_(torch.randn(self.V.data.size()).type_as(\n                self.V.data) * 0.05)\n            v_norm = self.V.data / self.V.data.transpose(0, 1).contiguous() \\\n                .view(self.out_channels, -1).norm(2, 1).view(\n                    self.in_channels, self.out_channels,\n                    *([1] * len(self.kernel_size))).expand_as(self.V.data)\n            x_init = F.conv_transpose2d(\n                x, v_norm, None, self.stride,\n                self.padding, self.output_padding, self.groups).data\n            # self.out_channels, 1\n            t_x_init = x_init.tranpose(0, 1).contiguous().view(\n                self.out_channels, -1)\n            # out_features\n            m_init, v_init = t_x_init.mean(1).squeeze(\n                1), t_x_init.var(1).squeeze(1)\n            # out_features\n            scale_init = self.init_scale / \\\n                torch.sqrt(v_init + 1e-10)\n            self.g.data.copy_(scale_init)\n            self.b.data.copy_(-m_init * scale_init)\n            scale_init_shape = scale_init.view(\n                1, self.out_channels, *([1] * (len(x_init.size()) - 2)))\n            m_init_shape = m_init.view(\n                1, self.out_channels, *([1] * (len(x_init.size()) - 2)))\n\n            x_init = scale_init_shape.expand_as(x_init)\\\n                * (x_init - m_init_shape.expand_as(x_init))\n            self.V_avg.copy_(self.V.data)\n            self.g_avg.copy_(self.g.data)\n            self.b_avg.copy_(self.b.data)\n            return x_init\n        else:\n            v, g, b = get_vars_maybe_avg(\n                self, ['V', 'g', 'b'], self.training,\n                polyak_decay=self.polyak_decay)\n            scalar = g / \\\n                torch.norm(v.transpose(0, 1).contiguous().view(\n                    self.out_channels, -1), 2, 1).squeeze(1)\n            w = scalar.view(self.in_channels, self.out_channels,\n                            *([1] * (len(v.size()) - 2))).expand_as(v) * v\n\n            x = F.conv_transpose2d(x, w, b, self.stride,\n                                   self.padding, self.output_padding,\n                                   self.groups)\n            return x\n"
  },
  {
    "path": "onmt/newssum/__init__.py",
    "content": ""
  },
  {
    "path": "onmt/newssum/bart/__init__.py",
    "content": ""
  },
  {
    "path": "onmt/newssum/bart/example.py",
    "content": "# Load the model in fairseq\nimport torch\nfrom fairseq.models.bart import BARTModel\nfrom fairseq.data.data_utils import collate_tokens\nfrom transformers import AutoTokenizer\n\ndef run_model(model, tokens, return_all_hiddens=False, features_only=False):\n    src_lengths = None\n    if tokens.dim() == 1:\n        tokens = tokens.unsqueeze(0)\n        src_lengths = torch.Tensor([tokens.shape[1]]).long()\n    if tokens.size(-1) > min(model.max_positions()):\n        raise ValueError('tokens exceeds maximum length: {} > {}'.format(\n            tokens.size(-1), model.max_positions()\n        ))\n    prev_output_tokens = tokens.clone()\n    prev_output_tokens[:, 0] = tokens[:, -1]\n    prev_output_tokens[:, 1:] = tokens[:, :-1]\n    features, extra = model(\n        src_tokens=tokens,\n        src_lengths=src_lengths,\n        prev_output_tokens=prev_output_tokens,\n        features_only=features_only,\n        return_all_hiddens=return_all_hiddens,\n    )\n    if return_all_hiddens:\n        # convert from T x B x C -> B x T x C\n        inner_states = extra['inner_states']\n        return [inner_state.transpose(0, 1) for inner_state in inner_states]\n    else:\n        return features  # just the last layer's features\n\n\nbart = BARTModel.from_pretrained('/export/share/rmeng/tools/torchhub/bart.large', checkpoint_file='model.pt')\nbart_cnndm = BARTModel.from_pretrained('/export/share/rmeng/tools/torchhub/bart.large.cnn', checkpoint_file='model.pt')\ngpt2_tokenizer = AutoTokenizer.from_pretrained('gpt2', cache_dir='/export/share/rmeng/output/pretrain_cache/')\nroberta_tokenizer = AutoTokenizer.from_pretrained('roberta-base', cache_dir='/export/share/rmeng/output/pretrain_cache/')\n\nbart.eval()  # disable dropout (or leave in train mode to finetune)\nbart_cnndm.eval()  # disable dropout (or leave in train mode to finetune)\n\ntest_str = 'Helloworld!'\nbart_tokens = bart.encode(test_str) # BART will add <s> and </s>\nbart_cnndm_tokens = bart_cnndm.encode(test_str)\nroberta_tokens = roberta_tokenizer.encode(test_str)\n\n# assert tokens.tolist() == [0, 31414, 232, 328, 2]\nprint(bart_tokens)\nprint(bart_cnndm_tokens)\nprint(roberta_tokens)\n\n# Extract the last layer's features\noutput = run_model(bart.model, bart_tokens)\nlast_layer_features = bart.extract_features(bart_tokens)\nassert last_layer_features.size() == torch.Size([1, 5, 1024])\n\n# Extract all layer's features from decoder (layer 0 is the embedding layer)\nall_layers = bart.extract_features(bart_tokens, return_all_hiddens=True)\nassert len(all_layers) == 13\nassert torch.all(all_layers[-1] == last_layer_features)\n\n# Download BART already finetuned for MNLI\nbart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')\nbart.eval()  # disable dropout for evaluation\n\n# Encode a pair of sentences and make a prediction\ntokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.')\nbart.predict('mnli', tokens).argmax()  # 0: contradiction\n\n# Encode another pair of sentences\ntokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.')\nbart.predict('mnli', tokens).argmax()  # 2: entailment\n\n\nbart.register_classification_head('new_task', num_classes=3)\nlogprobs = bart.predict('new_task', tokens)\n\n\nbart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli')\nbart.eval()\n\nbatch_of_pairs = [\n    ['BART is a seq2seq model.', 'BART is not sequence to sequence.'],\n    ['BART is denoising autoencoder.', 'BART is version of autoencoder.'],\n]\n\nbatch = collate_tokens(\n    [bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1\n)\n\nlogprobs = bart.predict('mnli', batch)\nprint(logprobs.argmax(dim=1))\n# tensor([0, 2])\n\n\nbart.cuda()\nbart.predict('new_task', tokens)\n\n\n\nlabel_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}\nncorrect, nsamples = 0, 0\nbart.cuda()\nbart.eval()\nwith open('glue_data/MNLI/dev_matched.tsv') as fin:\n    fin.readline()\n    for index, line in enumerate(fin):\n        tokens = line.strip().split('\\t')\n        sent1, sent2, target = tokens[8], tokens[9], tokens[-1]\n        tokens = bart.encode(sent1, sent2)\n        prediction = bart.predict('mnli', tokens).argmax().item()\n        prediction_label = label_map[prediction]\n        ncorrect += int(prediction_label == target)\n        nsamples += 1\n        print('| Accuracy: ', float(ncorrect)/float(nsamples))\n# Expected output: 0.9010"
  },
  {
    "path": "onmt/newssum/cnndm/docid_url_mapping.py",
    "content": "'''\nThe data used by previous studies is sorted following the order as in wayback_test_urls.txt, where only urls are available.\nOur data is sorted in a different order but the original .story filename and doc id are preserved.\nHere we read urls in wayback_test_urls.txt, encode them with hashhex, and sort our data to be consistent with others.\nSo this script is not useful any more.\nNote that CNN and DM are processed separately and merged manually. And it seems .question is hashed in a different way.\n(deprecated) ```Instead, our data is generated directly from questions/test/, which is not deliberately ordered and only doc_id is available. Here we read each .question in dir `questions/test/` and extract the mapping between `id` and `url`, then we can put our data in the same order.```\n'''\nimport hashlib\nimport json\nimport os\nfrom collections import defaultdict\n\nimport tqdm\n\n# dataset = 'cnn'\ndataset = 'dailymail'\nbase_dir = '/export/share/rmeng/data/raw/cnndm/original/%s/' % dataset\ntest_url_path = os.path.join(base_dir, 'wayback_test_urls.txt')\ntest_questions_dir = os.path.join(base_dir, 'questions', 'test')\ntest_story_dir = os.path.join(base_dir, 'stories')\n\noutput_path = '/export/share/rmeng/data/json/cnndm/'\n\n\ndef hashhex(s):\n    '''Returns a heximal formated SHA1 hash of the input string.'''\n    h = hashlib.sha1()\n    h.update(s.encode('utf-8'))\n    return h.hexdigest()\n\ndef get_url_hashes(url_list):\n    return [hashhex(url) for url in url_list]\n\nif __name__ == '__main__':\n    print('Processing dataset %s' % dataset)\n    # load all urls from wayback_test_urls.txt, order of which we should map our data to\n    url_list = [u.strip() for u in open(test_url_path, 'r').readlines()]\n    print('#(url)=%d' % len(url_list))\n\n    # obtain the hash code of url\n    hash_list = get_url_hashes(url_list)\n    for hashid in hash_list:\n        story_path = os.path.join(test_story_dir, hashid + '.story')\n        if os.path.exists(story_path):\n            # print('Yay')\n            pass\n        else:\n            print('Nay')\n    print('Done')\n\n    \"\"\"\n    # load .question files to build the mapping between unique docids (a hash code) and urls\n    # one url can have multiple hashcodes (don't know why)\n    url2id_map = defaultdict(list)\n    for q_file in tqdm.tqdm(os.listdir(test_questions_dir), desc='Loading question files'):\n        if not q_file.endswith('.question'):\n            continue\n        docid = '-'.join([dataset, 'test', q_file.split('.')[0]])\n        url = open(os.path.join(test_questions_dir, q_file), 'r').readline().strip()\n        url2id_map[url].append(docid)\n\n    print('#(map)=%d' % len(url2id_map))\n\n    # we write the mapping to file ($output_path/cnn_test_url2id.jsonl) following the order of wayback_test_urls.txt\n    # note that dailymail has 53,182 questions, more than len(wayback_test_urls.txt), so needs filtering first\n    mapping_dicts = []\n    for url in url_list:\n        if url not in url2id_map:\n            print(url)\n        mapping_dicts.append({'url': url, 'id': url2id_map[url]})\n\n    output_jsonl_path = os.path.join(output_path, '%s_test_urlhash_mapping.jsonl' % dataset)\n    with open(output_jsonl_path, 'w') as output_jsonl:\n        for md in mapping_dicts:\n            output_jsonl.write(json.dumps(md) + '\\n')\n\n    print('Done!')\n    \"\"\""
  },
  {
    "path": "onmt/newssum/cnndm/evaluate_bart_cnn.py",
    "content": "import codecs\nimport json\nimport re\nimport numpy as np\n\nimport tqdm\nfrom onmt.newssum import docutils\nfrom tools.files2rouge import files2rouge\nfrom tools.test_rouge import evaluate_chunk_coverage, rouge_results_to_str, test_rouge_perl, test_rouge_py\n\nrouge_impl = 'py'\nstanford_token = True\nhyp = '/export/share/rmeng/tools/torchhub/bart.large.cnn/bart.large.cnndm.test.hyp'\nref = '/export/share/rmeng/output/word/tokenized/cnndm/test.sorted.jsonl'\n\nhyp_path = '/export/share/rmeng/tools/torchhub/bart.large.cnn/bart.large.cnndm.files2rouge.eval.hyp'\nref_path = '/export/share/rmeng/tools/torchhub/bart.large.cnn/bart.large.cnndm.files2rouge.eval.ref'\n\nhyp_file = codecs.open(hyp, encoding=\"utf-8\")\nref_file = codecs.open(ref, encoding=\"utf-8\")\n\norigin_candidates = [line.strip() for line in tqdm.tqdm(hyp_file, desc='Loading generated summaries')]\norigin_references = [line.strip() for line in tqdm.tqdm(ref_file, desc='Loading ground-truth summaries')]\n\ncandidates = origin_candidates\nreferences = origin_references\n\n# post-process references (ground-truth summaries)\nprint('Post-processing references (ground-truth summaries)')\ngt_dicts = [json.loads(s) for s in references]\nreferences = [s['word']['token']['summary'] for s in gt_dicts]\nreferences = [' '.join(l) for l in references]\n\n# remove special tokens like '[SEP_SUM]', '\\n', '</s>'\ncandidates = [re.sub(r'\\[.*?\\]', '', c) for c in candidates]\ncandidates = [c.replace('\\n', ' ').replace('[SEP_SUM]', ' ').replace('</s>', ' ') for c in candidates]\ncandidates = [re.sub(r'-(\\s+)-', '--', c.replace('-', ' - ')) for c in candidates]\nreferences = [r.replace('\\n', ' ').replace('[SEP_SUM]', ' ') for r in references]\nreferences = [re.sub(r'-(\\s+)-', '--', r.replace('-', ' - ')) for r in references]\n\n# whether to apply another round of Stanford Tokenization\nprint(\"Checking sequence length...\")\nif stanford_token:\n    print(\"Stanford Tokenizing...\")\n    candidates = [docutils.word_tokenize(c, model=\"stanfordnlp\") for c in\n                  tqdm.tqdm(candidates, desc='Stanford tokenizing generated summaries')]\n    references = [docutils.word_tokenize(r, model=\"stanfordnlp\") for r in\n                  tqdm.tqdm(references, desc='Stanford tokenizing ground-truth summaries')]\n    leng_stat = {'ref_avg_len': np.mean([len(r) for r in references]),\n                 'hyp_avg_len': np.mean([len(c) for c in candidates])}\nelse:\n    candidates = [c.split() for c in candidates]\n    references = [r.split() for r in references]\n    leng_stat = {'ref_avg_len': np.mean([len(r) for r in references]),\n                 'hyp_avg_len': np.mean([len(c) for c in candidates])}\nprint(leng_stat)\ncandidates = [' '.join(c) for c in candidates]\nreferences = [' '.join(r) for r in references]\n\nprint(\"Lowercasing the input text...\")\ncandidates = [c.lower() for c in candidates]\nreferences = [r.lower() for r in references]\n\n# add NER and NP coverage\nner_coverage = evaluate_chunk_coverage(pred_strs=candidates, gt_dicts=gt_dicts, key='ner')\nnp_coverage = evaluate_chunk_coverage(pred_strs=candidates, gt_dicts=gt_dicts, key='noun_phrase')\nprint(ner_coverage)\nprint(np_coverage)\n\nprint(\"Start calculating rouge with %s...\" % rouge_impl)\nif rouge_impl == 'perl':\n    # pyrouge is pathetically slow once number of docs is large (on newsroom), due to three file exporting steps:\n    #   (1) temp files 1 (~17min); (2)  temp files 2 to /tmp (~2min);\n    #   (3) generating config file (at line 504 __get_model_filenames_for_id() very very slow, >>30min),\n    #       bcuz it's doing n^2 file scanning. Change to no list_dir() version.\n    results_dict = test_rouge_perl(candidates, references,\n                                   rouge_path='/export/share/rmeng/project/OpenNMT-summary/tools/ROUGE-1.5.5/')\nelif rouge_impl == 'py':\n    # a pure python implementation\n    results_dict = test_rouge_py(candidates, references)\nelif rouge_impl == 'files2rouge':\n    # dump output to files and run files2rouge\n    print(\"Dumping candidates and references to: %s \\nand: %s\" % (hyp_path, ref_path))\n    with open(hyp_path, 'w') as h_file:\n        for cl in candidates:\n            h_file.write(cl + '\\n')\n    with open(ref_path, 'w') as r_file:\n        for rl in references:\n            r_file.write(rl + '\\n')\n    results_dict = files2rouge.run(hyp_path, ref_path,\n                                   path='/export/share/rmeng/project/OpenNMT-summary/tools/files2rouge/settings.json')\n\nresults_dict.update(leng_stat)\nif 'ner_coverage' in locals():\n    results_dict.update(ner_coverage)\nif 'np_coverage' in locals():\n    results_dict.update(np_coverage)\n\nprint(rouge_results_to_str(results_dict))\n\nprint(results_dict)\n\n\"\"\"\nrouge_impl = 'files2rouge'\nstanford_token = True\nlower = True\n{'ref_avg_len': 60.00661444734552, 'hyp_avg_len': 74.15596170583116}\n---------------------------------------------\n1 ROUGE-1 Average_R: 0.51361 (95%-conf.int. 0.51104 - 0.51626)\n1 ROUGE-1 Average_P: 0.40549 (95%-conf.int. 0.40308 - 0.40808)\n1 ROUGE-1 Average_F: 0.44235 (95%-conf.int. 0.44011 - 0.44458)\n---------------------------------------------\n1 ROUGE-2 Average_R: 0.24611 (95%-conf.int. 0.24338 - 0.24883)\n1 ROUGE-2 Average_P: 0.19476 (95%-conf.int. 0.19245 - 0.19701)\n1 ROUGE-2 Average_F: 0.21209 (95%-conf.int. 0.20971 - 0.21441)\n---------------------------------------------\n1 ROUGE-3 Average_R: 0.14789 (95%-conf.int. 0.14524 - 0.15057)\n1 ROUGE-3 Average_P: 0.11727 (95%-conf.int. 0.11499 - 0.11937)\n1 ROUGE-3 Average_F: 0.12748 (95%-conf.int. 0.12518 - 0.12969)\n---------------------------------------------\n1 ROUGE-L Average_R: 0.47636 (95%-conf.int. 0.47370 - 0.47896)\n1 ROUGE-L Average_P: 0.37638 (95%-conf.int. 0.37393 - 0.37880)\n1 ROUGE-L Average_F: 0.41045 (95%-conf.int. 0.40809 - 0.41264)\n\nElapsed time: 220.838 seconds\nrouge_1_recall: 0.51361\nrouge_1_precision: 0.40549\nrouge_1_f_score: 0.44235\nrouge_2_recall: 0.24611\nrouge_2_precision: 0.19476\nrouge_2_f_score: 0.21209\nrouge_3_recall: 0.14789\nrouge_3_precision: 0.11727\nrouge_3_f_score: 0.12748\nrouge_l_recall: 0.47636\nrouge_l_precision: 0.37638\nrouge_l_f_score: 0.41045\n\nref_avg_len: 60.00661444734552\nhyp_avg_len: 74.15596170583116\n\nner_corr_num: 3.5452567449956485\nner_all_num: 5.872149695387293\nner_cov: 0.6122657040375573\n\nnoun_phrase_corr_num: 6.301044386422976\nnoun_phrase_all_num: 13.929503916449086\nnoun_phrase_cov: 0.4558219337567122\n\"\"\"\n\n\"\"\"\nrouge_impl = 'py'\nstanford_token = True\nlower = True\n{'ref_avg_len': 60.00661444734552, 'hyp_avg_len': 74.15596170583116}\n{'ner_corr_num': 3.5452567449956485, 'ner_all_num': 5.872149695387293, 'ner_cov': 0.6122657040375573}\n{'noun_phrase_corr_num': 6.301044386422976, 'noun_phrase_all_num': 13.929503916449086, 'noun_phrase_cov': 0.4558219337567122}\nrouge_1_recall: 0.51702\nrouge_1_precision: 0.40819\nrouge_1_f_score: 0.44528\n\nrouge_2_recall: 0.24702\nrouge_2_precision: 0.19546\nrouge_2_f_score: 0.21287\n\nrouge_3_recall: 0.14847\nrouge_3_precision: 0.11771\nrouge_3_f_score: 0.12797\n\nrouge_l_recall: 0.35933\nrouge_l_precision: 0.28188\nrouge_l_f_score: 0.30834\n\nrouge_w_recall: 0.13187\nrouge_w_precision: 0.22244\nrouge_w_f_score: 0.16036\n\nref_avg_len: 60.00661444734552\nhyp_avg_len: 74.15596170583116\n\nner_corr_num: 3.5452567449956485\nner_all_num: 5.872149695387293\nner_cov: 0.6122657040375573\n\nnoun_phrase_corr_num: 6.301044386422976\nnoun_phrase_all_num: 13.929503916449086\nnoun_phrase_cov: 0.4558219337567122\n\"\"\"\n\n\n\"\"\"\nrouge_impl = 'files2rouge'\nstanford_token = False\nlower = True\n{'ref_avg_len': 59.97023498694517, 'hyp_avg_len': 66.10382941688425}\n{'ner_corr_num': 3.529068755439513, 'ner_all_num': 5.872149695387293, 'ner_cov': 0.6092303707411274}\n{'noun_phrase_corr_num': 6.216100957354221, 'noun_phrase_all_num': 13.929503916449086, 'noun_phrase_cov': 0.4495942981856436}\n---------------------------------------------\n1 ROUGE-1 Average_R: 0.51246 (95%-conf.int. 0.50989 - 0.51517)\n1 ROUGE-1 Average_P: 0.40520 (95%-conf.int. 0.40281 - 0.40780)\n1 ROUGE-1 Average_F: 0.44174 (95%-conf.int. 0.43946 - 0.44392)\n---------------------------------------------\n1 ROUGE-2 Average_R: 0.24477 (95%-conf.int. 0.24203 - 0.24753)\n1 ROUGE-2 Average_P: 0.19400 (95%-conf.int. 0.19169 - 0.19622)\n1 ROUGE-2 Average_F: 0.21112 (95%-conf.int. 0.20877 - 0.21343)\n---------------------------------------------\n1 ROUGE-3 Average_R: 0.14664 (95%-conf.int. 0.14402 - 0.14926)\n1 ROUGE-3 Average_P: 0.11647 (95%-conf.int. 0.11419 - 0.11856)\n1 ROUGE-3 Average_F: 0.12652 (95%-conf.int. 0.12422 - 0.12871)\n---------------------------------------------\n1 ROUGE-L Average_R: 0.43126 (95%-conf.int. 0.42863 - 0.43390)\n1 ROUGE-L Average_P: 0.34166 (95%-conf.int. 0.33930 - 0.34404)\n1 ROUGE-L Average_F: 0.37212 (95%-conf.int. 0.36998 - 0.37432)\n\nElapsed time: 218.586 seconds\nrouge_1_recall: 0.51246\nrouge_1_precision: 0.4052\nrouge_1_f_score: 0.44174\nrouge_2_recall: 0.24477\nrouge_2_precision: 0.194\nrouge_2_f_score: 0.21112\nrouge_3_recall: 0.14664\nrouge_3_precision: 0.11647\nrouge_3_f_score: 0.12652\nrouge_l_recall: 0.43126\nrouge_l_precision: 0.34166\nrouge_l_f_score: 0.37212\n\nref_avg_len: 59.97023498694517\nhyp_avg_len: 66.10382941688425\n\nner_corr_num: 3.529068755439513\nner_all_num: 5.872149695387293\nner_cov: 0.6092303707411274\n\nnoun_phrase_corr_num: 6.216100957354221\nnoun_phrase_all_num: 13.929503916449086\nnoun_phrase_cov: 0.4495942981856436\n\"\"\""
  },
  {
    "path": "onmt/newssum/cnndm/resort_examples.py",
    "content": "import hashlib\nimport json\n\nimport tqdm\n\nimport onmt.newssum.docutils as docutils\n\n\ndef load_groundtruth_data(groundtruth_jsonl_path):\n    all_gt_ex_dicts = []\n    for line_id, jsonl in enumerate(open(groundtruth_jsonl_path, 'r')):\n        ex = json.loads(jsonl)\n        all_gt_ex_dicts.append(ex)\n    print('Loaded %s groundtruth data examples' % len(all_gt_ex_dicts))\n\n    # fix the bug that doc['oracle']['bottomup']['oracle_text'] is set as sentence oracle by mistake\n    for ex in tqdm.tqdm(all_gt_ex_dicts):\n        title_tokens = ex['word']['token']['title']\n        sentences_tokens = ex['word']['token']['sents']\n        title_sents_tokens = [title_tokens] + [['\\n']] + sentences_tokens\n        summary_tokens = ex['word']['token']['summary']\n        #     print('[SUMMARY]:' + str(summary_tokens))\n\n        oracle_bottomup_mask = ex['word']['oracle']['bottomup']['mask']\n        oracle_bottomup_text = docutils.mask_to_text(title_sents_tokens, oracle_bottomup_mask)\n        ex['word']['oracle']['bottomup']['oracle_text'] = oracle_bottomup_text\n        #     print('[BOTTOMUP]:' + str(oracle_bottomup_text))\n        #     print('[BOTTOMUP]:' + str(ex['word']['oracle']['bottomup']['oracle_rouge']))\n\n        oracle_sent_ids = ex['word']['oracle']['sentence']['target']\n        oracle_sent_text = ' '.join([w for sid in oracle_sent_ids for w in title_sents_tokens[sid]])\n        oracle_sent_text = oracle_sent_text.replace('\\n', ' ')\n        ex['word']['oracle']['sentence']['oracle_text'] = oracle_sent_text\n    #     print('[SENT=%d]: %s' % (len(oracle_sent_ids), str(oracle_sent_text)))\n    #     print('[SENT]:' + str(ex['word']['oracle']['sentence']['oracle_rouge']))\n\n    return all_gt_ex_dicts\n\ndef hashhex(s):\n    '''Returns a heximal formated SHA1 hash of the input string.'''\n    h = hashlib.sha1()\n    h.update(s.encode('utf-8'))\n    return h.hexdigest()\n\ngroundtruth_jsonl_path = \"/export/share/rmeng/output/word/tokenized/cnndm/test.jsonl\"\nall_gt_ex_dicts = load_groundtruth_data(groundtruth_jsonl_path)\nprint('Loaded %s groundtruth data examples' % len(all_gt_ex_dicts))\n\n# load urls from '/export/share/rmeng/data/raw/cnndm/original/cnn/wayback_test_urls.txt' and hash it with hashhex()\ncnn_url_path = '/export/share/rmeng/data/raw/cnndm/original/cnn/wayback_test_urls.txt'\ndm_url_path = '/export/share/rmeng/data/raw/cnndm/original/dailymail/wayback_test_urls.txt'\n\nurls = [l.strip() for l in open(cnn_url_path, 'r').readlines()]\ncnn_docids = ['cnn-test-' + hashhex(url) for url in urls]\n\nurls = [l.strip() for l in open(dm_url_path, 'r').readlines()]\ndm_docids = ['dailymail-test-' + hashhex(url) for url in urls]\n\ndoc_ids = cnn_docids + dm_docids\nprint('#(doc_id)=%d' % len(doc_ids))\n\nsrcidx2id = {}\nid2ex_dict = {}\nfor srcidx, ex in enumerate(all_gt_ex_dicts):\n    id2ex_dict[ex['id']] = ex\n\nid2tgtidx = {}\ngt_ex_dicts = []\n# resort all_gt_ex_dicts following the order of mappings\nfor tgtidx, doc_id in enumerate(doc_ids):\n    ex = id2ex_dict[doc_id]\n    gt_ex_dicts.append(ex)\n    id2tgtidx[doc_id] = tgtidx\n\nprint('Resorted %s groundtruth data examples' % len(gt_ex_dicts))\n\n# our order -> original order\nmap_our2tgt = []\nfor srcidx, ex in enumerate(all_gt_ex_dicts):\n    map_our2tgt.append(id2tgtidx[ex['id']])\n# original order -> our order\nmap_tgt2our = [None] * len(map_our2tgt)\nfor our_idx, tgt_idx in enumerate(map_our2tgt):\n    map_tgt2our[tgt_idx] = our_idx\nfor i in map_tgt2our:\n    assert i != None\n\nprint(\"Dumping sorted index mappings\")\ndump_path = '/export/share/rmeng/data/json/cnndm/our2tgt.idxmap.json'\njson.dump(map_our2tgt, open(dump_path, 'w'))\ndump_path = '/export/share/rmeng/data/json/cnndm/tgt2our.idxmap.json'\njson.dump(map_tgt2our, open(dump_path, 'w'))\n\n\nprint(\"Dumping sorted word test.jsonl\")\nour_jsonl_path = \"/export/share/rmeng/output/word/tokenized/cnndm/test.jsonl\"\nour_resorted_path = \"/export/share/rmeng/output/word/tokenized/cnndm/test.sorted.jsonl\"\nour_lines = open(our_jsonl_path, 'r').readlines()\nwith open(our_resorted_path, 'w') as our_resorted:\n    for tgt_idx in range(len(our_lines)):\n        our_resorted.write(our_lines[map_tgt2our[tgt_idx]])\n\nprint(\"Dumping sorted roberta-base test.jsonl\")\nour_jsonl_path = \"/export/share/rmeng/output/roberta-base/tokenized/cnndm/test.jsonl\"\nour_resorted_path = \"/export/share/rmeng/output/roberta-base/tokenized/cnndm/test.sorted.jsonl\"\nour_lines = open(our_jsonl_path, 'r').readlines()\nwith open(our_resorted_path, 'w') as our_resorted:\n    for tgt_idx in range(len(our_lines)):\n        our_resorted.write(our_lines[map_tgt2our[tgt_idx]])\n"
  },
  {
    "path": "onmt/newssum/docutils.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport copy\nimport string\nfrom collections import Counter\n\nimport spacy\nimport re\nimport numpy as np\n\nfrom onmt.newssum import fragutils\nfrom tools.stanfordcorenlp import StanfordCoreNLP\n\nfrom nltk.tokenize import sent_tokenize\nimport onmt.newssum.rouge_eval.rouge as rouge\nmetric_keys = [\"rouge-1\", \"rouge-2\", \"rouge-l\", \"entity_cov\"]\n\nimport nltk\nnltk.download('stopwords')\nnltk.data.path.append('/export/share/rmeng/tools/nltk')\nstemmer = nltk.stem.porter.PorterStemmer()\nstopword_set = set(nltk.corpus.stopwords.words('english'))\nstopword_set.update(['\\'s', 'doe', 'n\\'t', 'and', 'also', 'whether'])\nstanfordnlp = None\nspacy_nlp = spacy.load('en_core_web_sm')\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\ndef word_tokenize(text, model=\"spacy\", lowercase=False):\n    if model == 'stanfordnlp':\n        global stanfordnlp\n        if not stanfordnlp:\n            stanfordnlp = StanfordCoreNLP('/export/share/rmeng/tools/stanford-corenlp/stanford-corenlp-full-2018-10-05/',\n                                          memory='8g', port=6666)\n        return stanfordnlp.word_tokenize(text)\n    elif model == 'spacy':\n        spacy_doc = spacy_nlp(text, disable=[\"tagger\", \"parser\", \"ner\", \"textcat\"])\n        tokens = [t.text.strip() for t in spacy_doc if len(t.text.strip()) > 0]\n        tokens = [\n            str(t).lower()\n            if lowercase\n            else str(t)\n            for t in tokens\n        ]\n        return tokens\n    else:\n        raise NotImplementedError\n\n\ndef get_noun_chunks(text, trim_punct=True, remove_stopword=True):\n    spacy_doc = spacy_nlp(text, disable=[\"textcat\"])\n    np_chunks = list(spacy_doc.noun_chunks)\n    np_str_list = []\n    for chunk in np_chunks:\n        np = []\n        for w in chunk:\n            w = w.text\n            if trim_punct:\n                w = w.strip(r\"\"\"!\"#$%&'()*+,-/:;<=>?@[\\]^_`{|}~\"\"\")\n            if remove_stopword:\n                if w.lower() in stopword_set:\n                    continue\n            np.append(w)\n        if len(np) > 0:\n            np_str_list.append(np)\n    np_chunks = [{'start': np.start, 'end': np.end, 'text': np.text, 'label_': np.label_} for np in np_chunks]\n\n    return np_str_list, np_chunks\n\n\ndef get_NERs(text):\n    spacy_doc = spacy_nlp(text, disable=[\"textcat\"])\n    ner_chunks = list(spacy_doc.ents)\n    ner_str_list = []\n    for chunk in ner_chunks:\n        ner = []\n        for w in chunk:\n            w = w.text\n            ner.append(w)\n        if len(ner) > 0:\n            ner_str_list.append(ner)\n\n    ner_chunks = [{'start': ner.start, 'end': ner.end, 'text': ner.text, 'label_': ner.label_} for ner in ner_chunks]\n\n    return ner_str_list, ner_chunks\n\ndef get_verbs(text):\n    spacy_doc = spacy_nlp(text, disable=[\"parser\", \"ner\", \"textcat\"])\n    verb_tokens = [token for token in spacy_doc if token.pos_ == \"VERB\"]\n    verb_tokens = [{'text': token.text, 'i': token.i, 'lemma_': token.lemma_, 'pos_': token.pos_} for token in verb_tokens]\n    return verb_tokens\n\n\ndef build_matching_mask(text_tokens, match_tokens,\n                        lowercase=False, stem=False,\n                        remove_punct=False, remove_stopwords=False):\n    def _preprocess(tokens, lowercase, stem, remove_punct, remove_stopwords):\n        if lowercase:\n            tokens = [w for w in tokens]\n        if stem:\n            tokens = [stemmer.stem(w) for w in tokens]\n        if remove_punct:\n            tokens = [w for w in tokens if w not in string.whitespace+string.punctuation]\n        if remove_stopwords:\n            tokens = [w for w in tokens if w.lower() not in stopword_set]\n\n        return tokens\n\n    match_set = set(_preprocess(match_tokens,\n                                lowercase=lowercase, stem=stem,\n                                remove_punct=remove_punct, remove_stopwords=remove_stopwords))\n    processed_text_tokens = _preprocess(text_tokens, lowercase=lowercase, stem=stem,\n                                        remove_punct=False, remove_stopwords=False)\n    mask = []\n    for t in processed_text_tokens:\n        mask.append(1 if t in match_set else 0)\n\n    assert len(mask) == len(text_tokens)\n    return  mask\n\n\ndef build_bottomup_mask(src_sents, tgt_tokens):\n    \"\"\"\n    Refactored from https://github.com/sebastianGehrmann/bottom-up-summary\n    :param src_sents: a list of sentences, each sentence is a list of words\n    :param tgt_tokens:\n    :return:\n    \"\"\"\n    def _compile_substring(start, end, split):\n        if start == end:\n            return split[start]\n        return ' '.join(split[start: end + 1])\n\n    # flatten sentences to a list, will be stacked back in the end\n    src_tokens = [w for s in src_sents for w in s]\n    tgt_str = (' '.join(tgt_tokens)).replace('\\n', '[IGNORE]')\n    startix = 0\n    endix = 0\n    matches = []\n    match_fragments = []\n    matchstrings = Counter()\n    while endix < len(src_tokens):\n        # last check is to make sure that phrases at end can be copied\n        searchstring = _compile_substring(startix, endix, src_tokens)\n        if searchstring in tgt_str and endix < len(src_tokens)-1:\n            endix +=1\n        else:\n            # only phrases, not words\n            # uncomment the -1 if you only want phrases > len 1\n            if startix >= endix:#-1:\n                matches.extend([0] * (endix - startix + 1))\n                endix += 1\n            else:\n                # if you want phrases not words, change if-condition to startix>=endix-1\n                full_string = _compile_substring(startix, endix - 1, src_tokens)\n                # if the same substring appears before, ignore it\n                if matchstrings[full_string] >= 1:\n                    matches.extend([0] * (endix - startix))\n                else:\n                    matches.extend([1] * (endix - startix))\n                    matchstrings[full_string] += 1\n                    fragment =  {'src_start': startix,\n                                 'src_end': endix,\n                                 'match_score': endix - startix,\n                                 'words': src_tokens[startix: endix],\n                                 'full_string': full_string,\n                                 'src_sentid': None,}\n                    match_fragments.append(fragment)\n                #endix += 1\n            startix = endix\n\n    # convert mask back to seperate sentences\n    sents_mask = []\n    start_offset = 0\n    for sent in src_sents:\n        sents_mask.append(matches[start_offset: start_offset + len(sent)])\n        start_offset += len(sent)\n\n    # convert indexes in fragments\n    src_sent_lengths = [len(s) for s in src_sents]\n    accum_sent_lengths = [sum(src_sent_lengths[: sid + 1]) for sid, _ in enumerate(src_sent_lengths)]\n    for fragment in match_fragments:\n        sent_id = 0\n        ori_start = fragment['src_start']\n        ori_end = fragment['src_end']\n        while ori_end > accum_sent_lengths[sent_id]:\n            sent_id += 1\n        # print(fragment['full_string'])\n        # print(src_sents[sent_id])\n        fragment['src_sentid'] = sent_id\n        fragment['src_start'] = (ori_start - accum_sent_lengths[sent_id-1]) if sent_id > 0 else ori_start\n        fragment['src_end'] = (ori_end - accum_sent_lengths[sent_id-1]) if sent_id > 0 else ori_end\n        # print(src_tokens[ori_start: ori_end])\n        # print(src_sents[sent_id][fragment['src_start']: fragment['src_end']])\n\n    assert len(matches) == len(src_tokens)\n    assert len(src_sents) == len(sents_mask)\n\n    return match_fragments, sents_mask\n\n\ndef build_oracle_sentence_mask(src_sents, tgt_sents, metric='avg'):\n    best_sent_ids = []\n\n    for tgt_sent in tgt_sents:\n        tgt_str = ' '.join(tgt_sent)\n        best_score = 0.0\n        best_sent_id = 0\n\n        for src_sent_id, src_sent in enumerate(src_sents):\n            if len(src_sent) < 3:\n                continue\n            src_str = ' '.join(src_sent)\n            rouge_dict = calc_rouge(src_str, tgt_str)\n            if metric == 'avg':\n                score = np.mean([float(v) for v in rouge_dict.values()])\n            else:\n                score = rouge_dict[metric]\n            if score > best_score and src_sent_id not in best_sent_ids:\n                best_score = score\n                best_sent_id = src_sent_id\n\n        best_sent_ids.append(best_sent_id)\n\n    sent_masks = [0] * len(src_sents)\n    for best_sent_id in best_sent_ids:\n        sent_masks[best_sent_id] = 1\n\n    word_masks = []\n    for src_sent_id, src_sent in enumerate(src_sents):\n        if src_sent_id in best_sent_ids:\n            word_masks.append([1] * len(src_sent))\n        else:\n            word_masks.append([0] * len(src_sent))\n\n    assert len(sent_masks) == len(src_sents)\n\n    extracted_str = ' '.join([w for sid in best_sent_ids for w in src_sents[sid]])\n    tgt_str = ' '.join([w for s in tgt_sents for w in s])\n    extracted_str = extracted_str.replace('\\n', ' ')\n    tgt_str = tgt_str.replace('\\n', ' ')\n    rouge_score = calc_rouge(extracted_str, tgt_str)\n\n    return best_sent_ids, sent_masks, word_masks, extracted_str, rouge_score\n\n\ndef mask_to_text(sents_tokens, token_masks):\n    tokens = []\n    for sent, sent_mask in zip(sents_tokens, token_masks):\n        for token, token_mask in zip(sent, sent_mask):\n            if token_mask == 1:\n                tokens.append(token)\n\n    return ' '.join(tokens)\n\n\ndef summary_sents_to_text(tgt_sents):\n    tgt_tokens = []\n    for sent in tgt_sents:\n        tgt_tokens.extend(sent)\n        if sent[-1] != '.':\n            tgt_tokens.append('.')\n    tgt_str = ' '.join(tgt_tokens)\n    return tgt_str\n\n\ndef fragments_to_mask(flat_fragments, src_sents):\n    '''\n    Given extracted fragments, generate corresponding source text masks\n    :return:\n    '''\n    mask = [[0] * len(s) for s in src_sents]\n\n    for f in flat_fragments:\n        for i in range(f['src_start'], f['src_end'] + 1):\n            mask[f['src_sentid']][i] = 1\n        # print(f['match_score'])\n        # print(f['words'])\n        # print(src_sents[f['src_sentid']][f['src_start']: f['src_end'] + 1])\n        words = []\n        for i, m in enumerate(mask[f['src_sentid']]):\n            if m:\n                words.append(src_sents[f['src_sentid']][i])\n        # print(words)\n\n    return mask\n\n\ndef fragment_to_text(top_fragments_list, src_sents):\n    ex_summary_text = []\n    word_set = set()\n    for sent_frags in top_fragments_list:\n        if len(ex_summary_text) > 0:\n            ex_summary_text.append(' . ')\n        for f in sent_frags:\n            sentid = f['src_sentid']\n            for wordid in range(f['src_start'], f['src_end'] + 1):\n                if '%d_%d' % (sentid, wordid) not in word_set:\n                    ex_summary_text.append(src_sents[sentid][wordid])\n                    word_set.add('%d_%d' % (sentid, wordid))\n\n    ex_summary_text = ' '.join(ex_summary_text)\n\n    # flat_fragments = [f for frags in top_fragments_list for f in frags if len(f) > 0]\n    # ex_summary_text = ' . '.join([' '.join(src_sents[f['src_sentid']][f['src_start']: f['src_end'] + 1]) for f in flat_fragments])\n\n    return ex_summary_text\n\ndef build_oracle_fragment_mask(src_sents, tgt_sents,\n                               ignore_punct=True,\n                               ignore_stopword=True,\n                               stemming=True):\n    \"\"\"\n    Iteratively find the best matching fragments\n    for each summary sentence, return a segment from the most similar sentence\n    :param src_sents:\n    :param tgt_sents:\n    :param match_method: ['lcs', 'word']\n    :param smoothing_window: only useful for word-based matching\n    :param extend_to_boundary:\n    :return:\n        extracted_fragments: a list of fragments corresponding to each summary sent,\n        each fragment is a dict, containing {'src_sentid', 'src_start', 'src_end', 'frag_words', 'sum_start', 'sum_end'}\n    \"\"\"\n    tgt_sents_to_match = copy.copy(tgt_sents)\n    if ignore_punct:\n        tgt_sents_to_match =  [[w if w not in string.punctuation else '[IGNORE]' for w in s] for s in tgt_sents_to_match]\n    if ignore_stopword:\n        tgt_sents_to_match = [[w if w not in stopword_set else '[IGNORE]' for w in s] for s in tgt_sents_to_match]\n\n    top_fragments_list, all_fragments_list = fragutils.extract_multiple_fragments(tgt_sents_to_match, src_sents,\n                                                          match_method='lcs', smoothing_window=5,\n                                                          min_sent_len=2, min_match_words=2, density_threshold=0.3,\n                                                          stemming=stemming,\n                                                          max_depth=10, extend_to_boundary=False)\n\n    ex_summary_text = fragment_to_text(top_fragments_list, src_sents)\n    tgt_str = summary_sents_to_text(tgt_sents)\n    # print('Summary  :' + tgt_str)\n    # print('Extracted:' + ex_summary_text)\n    rouge_score = calc_rouge(ex_summary_text, tgt_str)\n    # print(rouge_score)\n\n    top_flat_fragments = [f for frags in top_fragments_list for f in frags if len(f) > 0]\n    top_fragment_masks = fragments_to_mask(top_flat_fragments, src_sents)\n    all_flat_fragments = [f for frags in all_fragments_list for f in frags if len(f) > 0]\n    all_filtered_fragments = []\n    for f in all_flat_fragments:\n        count = 0\n        for w in f['words']:\n            if w.lower() not in stopword_set:\n                count += 1\n        if count >= 1:\n            all_filtered_fragments.append(f)\n    # print('%d/%d' % (len(filtered_fragments), len(all_flat_fragments)))\n    all_fragment_masks = fragments_to_mask(all_filtered_fragments, src_sents)\n\n    return top_fragments_list, all_fragments_list, top_flat_fragments, all_filtered_fragments, top_fragment_masks, all_fragment_masks, ex_summary_text, rouge_score\n\n\ndef prepend_space_to_words(words):\n    new_words = []\n\n    # prepend a space for all non-head and non-punctuation words\n    for word in words:\n        if len(new_words) == 0 or (len(word) == 1 and word in string.punctuation + string.whitespace):\n            new_words.append(word)\n        else:\n            new_words.append(' ' + word)\n\n    return new_words\n\n\ndef words_to_subwords(tokenizer, words):\n    all_subwords = []\n    all_codes = []\n    spaced_words = prepend_space_to_words(words)\n\n    for word in spaced_words:\n        subwords = tokenizer.tokenize(word, add_prefix_space=True)\n        codes = tokenizer.convert_tokens_to_ids(subwords)\n\n        all_subwords.extend(subwords)\n        all_codes.extend(codes)\n\n    return all_subwords, all_codes\n\n\ndef wordmasks_to_subwords(tokenizer, title_sents_words, oracle_masks):\n    mask_keys = oracle_masks.keys()\n    mask_values = oracle_masks.values()\n\n    subword_sents = []\n    code_sents = []\n    mask_sents = {}\n    for k in mask_keys: mask_sents[k]=[]\n\n    for sent_mask_tuple in zip(title_sents_words, *list(mask_values)):\n        sent = sent_mask_tuple[0]\n        spaced_words = prepend_space_to_words(sent)\n        sent_masks = sent_mask_tuple[1:]\n        # print(spaced_words)\n        # print(len(sent_masks))\n\n        subword_sent = []\n        code_sent = []\n        mask_sent = {}\n        for k in mask_keys: mask_sent[k]=[]\n\n        for word_mask_tuple in list(zip(spaced_words, *sent_masks)):\n            word = word_mask_tuple[0]\n            mask = word_mask_tuple[1:]\n            subwords = tokenizer.tokenize(word, add_prefix_space=True)\n            codes = tokenizer.convert_tokens_to_ids(subwords)\n\n            subword_sent.extend(subwords)\n            code_sent.extend(codes)\n            # print(subwords)\n            # print(codes)\n\n            for mid, mkey in enumerate(mask_keys):\n                mask_sent[mkey].extend([mask[mid]] * len(codes))\n                # print([mask[mid]] * len(codes))\n            # pass\n\n        subword_sents.append(subword_sent)\n        code_sents.append(code_sent)\n        for k,v in mask_sent.items():\n            mask_sents[k].append(v)\n        pass\n\n    return subword_sents, code_sents, mask_sents\n\ndef sentence_split(text, model=\"spacy\"):\n    if model == \"spacy\":\n        spacy_doc = spacy_nlp(text)\n        segmented_sents = [sent.text for sent in spacy_doc.sents]\n    elif  model == \"nltk\":\n        segmented_sents = sent_tokenize(text)\n\n    return segmented_sents\n\n\ndef calc_rouge(pred_sent, gt_sent, stopwords_removal=False, stemming=True, lowercase=True):\n    if lowercase:\n        pred_sent = pred_sent.lower()\n        gt_sent = gt_sent.lower()\n    rouge_metric = rouge.Rouge(stopwords_removal=stopwords_removal, stemming=stemming)\n    if pred_sent == None or gt_sent == None \\\n            or len(pred_sent.strip()) == 0 or len(gt_sent.strip()) == 0:\n        fscores = {k: 0.0 for k in metric_keys}\n    else:\n        try:\n            scores = rouge_metric.get_scores(pred_sent, gt_sent)\n            fscores = {k: v['f'] for k, v in scores[0].items()}\n        except Exception:\n            fscores = {k: 0.0 for k in metric_keys}\n\n    if 'entity_cov' in fscores:\n        del fscores['entity_cov']\n\n    return fscores\n\n\ndef eval_rouge(doc, extract_sent_idx, number_to_cutoff=3, stopwords_removal=False, stemming=True, logger=None):\n    rouge_metric = rouge.Rouge(stopwords_removal=stopwords_removal, stemming=stemming)\n    extract_sent_idx = extract_sent_idx[: min(number_to_cutoff, len(extract_sent_idx))]\n    # sort extracted sentences in the order of their appearance\n    extract_sent_idx = sorted(extract_sent_idx)\n    extracted_sents = [doc.get_sentences()[idx] for idx in extract_sent_idx if idx < len(doc.get_sentences())]\n    hypothesis = ' '.join(extracted_sents)\n    reference = doc.summary\n\n    if hypothesis == None or reference == None or len(hypothesis.strip()) == 0 or len(reference.strip()) == 0:\n        fscores = {k: 0.0 for k in metric_keys}\n    else:\n        scores = rouge_metric.get_scores(hypothesis, reference)\n        fscores = {k: v['f'] for k, v in scores[0].items()}\n\n    # if logger:\n    #     logger.info(\"Scores: %s\" % fscores)\n    # else:\n    #     print(\"Scores: %s\" % fscores)\n\n    return fscores\n\n\ndef extract_entities(sentence, return_text=False):\n    parsed_doc = spacy_nlp(sentence)\n    if return_text:\n        entities = [ent.text for ent in parsed_doc.ents]\n    else:\n        entities = parsed_doc.ents\n\n    return entities\n\n\ndef calc_entity_coverage(pred_entities, gt_entities):\n    correct_entities = set(gt_entities) & set(pred_entities)\n    entity_cov = float(len(correct_entities)) / len(gt_entities) if len(gt_entities) > 0 else 0.0\n    return entity_cov\n\n\ndef eval_entity_coverage(doc, extract_sent_idx, number_to_cutoff=3, logger=None):\n    extract_sent_idx = extract_sent_idx[: min(number_to_cutoff, len(extract_sent_idx))]\n    extract_sent_idx = sorted(extract_sent_idx)\n    extracted_sents = [doc.get_sentences()[idx] for idx in extract_sent_idx if idx < len(doc.get_sentences())]\n    hypothesis = '. '.join(extracted_sents)\n    reference = doc.summary\n\n    parsed_doc = spacy_nlp(hypothesis)\n    hypothesis_entities = set([ent.text.lower() for ent in parsed_doc.ents])\n    parsed_doc = spacy_nlp(reference)\n    reference_entities = set([ent.text.lower() for ent in parsed_doc.ents])\n\n    if logger:\n        print_fn=logger.info\n    else:\n        print_fn=print\n    print_fn(\"-\" * 50)\n    print_fn(\"Hypothesis: \\n\\tSummary: %s\" % hypothesis)\n    print_fn(\"\\t Entities in summary: %s\" % hypothesis_entities)\n    print_fn(\"Ground-truth: \\n\\tSummary: %s\" % reference)\n    print_fn(\"\\t Entities in summary: %s\" % reference_entities)\n    correct_entities = hypothesis_entities & reference_entities\n    entity_cov = float(len(correct_entities)) / len(reference_entities) if len(reference_entities) > 0 else 0.0\n    print_fn(\"Recall: %d/%d=%s\" % (len(correct_entities), len(reference_entities), \"%.4f\" % entity_cov if entity_cov != None else \"N/A\"))\n\n    return {\"entity_cov\": entity_cov}\n\n\ndef print_hypothesis(doc, extract_sent_idx, extract_sent_scores=None, logger=None):\n    for sent_num, extract_sent_id in enumerate(extract_sent_idx):\n        score = str(extract_sent_scores[sent_num]) if extract_sent_scores else 'N/A'\n        if logger:\n            logger.info(\"\\t[%d][index=%d][score=%s] %s\" % (sent_num + 1, extract_sent_id, score, doc.text_sents[extract_sent_id]))\n        else:\n            print(\"\\t[%d][index=%d][score=%s] %s\" % (sent_num + 1, extract_sent_id, score, doc.text_sents[extract_sent_id]))\n\n\ndef update_score(score_dict, new_score_dict):\n    for metric, score in new_score_dict.items():\n        score_dict[metric].append(score)\n\n\ndef tokenize(text):\n    \"\"\"\n    Tokenizes input using the fastest possible SpaCy configuration.\n    This is optional, can be disabled in constructor.\n    \"\"\"\n    tokens = spacy_nlp(text, disable=[\"tagger\", \"parser\", \"ner\", \"textcat\"])\n    return [t.text for t in tokens]\n\n\ndef normalize(tokens, case=False):\n    \"\"\"\n    Lowercases and turns tokens into distinct words.\n    \"\"\"\n    tokens = [\n        str(t).lower()\n        if not case\n        else str(t)\n        for t in tokens\n    ]\n    tokens = [t.strip() for t in tokens if len(t.strip()) > 0]\n    return tokens\n\ndef clean_text(text):\n    text = re.sub(r\"\\W\", \" \", text)\n    return text\n\ndef summary_sentence_segment(summary, dataset='cnndm'):\n    if dataset.lower() == 'cnndm' or dataset.lower() == 'cnn' or dataset.lower() == 'dailymail':\n        return [s.strip() for s in summary.split('\\n')]\n    elif dataset.lower() == 'nyt' or dataset.lower() == 'newyorktimes':\n        sents = []\n        for s in summary.split(';'):\n            s = s.rstrip('(MS) ')\n            tokens = s.split()\n            if len(tokens) < 2:\n                continue\n            if len(tokens) < 5 and (s.startswith('photo') or s.startswith('graph') or s.startswith('chart')\n                                    or s.startswith('map') or s.startswith('table') or s.startswith('drawing')\n                                    or s.startswith('excerpt') or s.endswith('photo') or s.endswith('photos')\n                                    or s.endswith('column') or s.endswith('comments') or s.endswith('recipe')\n                                    or s.endswith('recipes')):\n                continue\n            s = s.strip()\n            sents.append(s)\n        return sents\n    elif dataset.lower() == 'newsroom':\n        summary = summary.replace('%20', ' ')\n        sents = []\n        # embarrassingly, nltk works much better than spacy on noisy data\n        for s in sentence_split(summary, model=\"nltk\"):\n            s = s.strip()\n            tokens = s.split()\n            if len(tokens) < 4:\n                continue\n            sents.append(s)\n        return sents\n    elif dataset.lower() == 'xsum':\n        # per my observation it's all one-sent summary\n        return [summary.strip()]\n    else:\n        raise NotImplementedError\n\ndef tokenize_source_summary(source, summary, title, dataset):\n    tokenized_title = normalize(tokenize(title), case=True)\n    # process source text\n    if source:\n        source_sents = sentence_split(source)\n        tokenized_source_sents = [normalize(tokenize(s), case=True) for s in source_sents]\n    else:\n        source_sents, tokenized_source_sents = None, None\n\n    # process summary text\n    summary_sents = summary_sentence_segment(summary, dataset)\n    tokenized_summary_sents = [normalize(tokenize(s), case=True) for s in summary_sents]\n\n    return tokenized_source_sents, tokenized_summary_sents, tokenized_title\n\n\ndef preprocess_sents(sents, case=False, stemming=True,\n                     ignore_punc=True, ignore_stopword=False):\n    if ignore_punc:\n        sents = [[t if t.lower() not in string.punctuation else '[IGNORE]' for t in s] for s in sents]\n    if ignore_stopword:\n        sents = [[t if t.lower() not in stopword_set else '[IGNORE]' for t in s] for s in sents]\n    if not case:\n        sents = [[t.lower() for t in s] for s in sents]\n    if stemming:\n        sents = [[stemmer.stem(t) for t in s] for s in sents]\n\n    return sents\n\n\ndef concat_sents(sents):\n    tokens = []\n    for s in sents:\n        if len(tokens) > 0:\n            tokens.append('[SEP]')\n        tokens.extend(s)\n    return tokens"
  },
  {
    "path": "onmt/newssum/files2rouge.py",
    "content": "import os\n\nfrom tools.files2rouge import files2rouge\n\nhyp_path = '/export/share/rmeng/output/roberta-base/exps/bart_released_cnndm/bart-released-cnndm/preds/beam_size5-min_length20-max_length140-stepwise_penaltyfalse-length_penaltynone-alpha2.0-coverage_penaltynone-beta0.0-block_ngram_repeat3/model_step_0.cnndm.test.summary.files2rouge.eval.hyp'\nref_path = '/export/share/rmeng/output/roberta-base/exps/bart_released_cnndm/bart-released-cnndm/preds/beam_size5-min_length20-max_length140-stepwise_penaltyfalse-length_penaltynone-alpha2.0-coverage_penaltynone-beta0.0-block_ngram_repeat3/model_step_0.cnndm.test.summary.files2rouge.eval.ref'\n\nrouge_setting_path = '/export/share/rmeng/project/OpenNMT-summary/tools/files2rouge/settings.json'\nfiles2rouge.run(hyp_path, ref_path, path=rouge_setting_path)\n"
  },
  {
    "path": "onmt/newssum/fragutils.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\nimport copy\nfrom collections import defaultdict\nimport numpy as np\n\nimport nltk\nnltk.data.path.append('/export/share/rmeng/tools/nltk')\nstemmer = nltk.stem.porter.PorterStemmer()\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\n\ndef extend_fragment_to_boundary(tokenized_sent, lend, rend):\n    while lend > 0 and \\\n            tokenized_sent[lend] not in [',', '.', '!', '?', '-']:\n        lend -= 1\n    while rend < len(tokenized_sent) and \\\n            tokenized_sent[rend] not in [',', '.', '!', '?', '-']:\n        rend += 1\n    return lend, rend\n\n\ndef obtain_matching_fragments(summary_sent, src_sents,\n                              match_method, smoothing_window,\n                              min_frag_len, min_match_words, density_threshold, stemming\n                              ):\n    \"\"\"\n\n    :param summary_sent:\n    :param src_sents:\n    :param match_method:\n    :param smoothing_window:\n    :param min_frag_len:\n    :param min_match_words:\n    :param density_threshold: defined as #(match_words)/len(subsequence), 0 means return all results\n    :return:\n    \"\"\"\n    if match_method == 'word':\n        matching_fragment_tuples = extract_fragments_by_wordmatch(summary_sent, src_sents,\n                                                            smoothing_window=smoothing_window\n                                                            )\n    elif match_method == 'lcs':\n        matching_fragment_tuples = extract_fragments_by_LCS(summary_sent, src_sents, stemming)\n\n    filered_fragments = []\n    for frag_tuple in matching_fragment_tuples:\n        frag = frag_tuple[1]\n        if frag['match_score'] >= min_match_words and (frag['src_end']-frag['src_start'] + 1 >= min_frag_len) \\\n            and frag['match_score']/(frag['src_end']-frag['src_start'] + 1) >= density_threshold:\n            frag['src_sentid'] = frag_tuple[0]\n            filered_fragments.append(frag)\n\n    top_fragment = filered_fragments[0] if len(filered_fragments) > 0 else None\n\n    return top_fragment, filered_fragments\n\n\ndef extract_fragments_iterative(summary_sent_id, summary_sent, src_sents,\n                                match_method, smoothing_window,\n                                min_frag_len, min_match_words, density_threshold, stemming,\n                                cur_depth, max_depth,\n                                extend_to_boundary=False):\n    # obtain the primary fragment\n    cent_best_frag, cent_frags = obtain_matching_fragments(summary_sent, src_sents,\n                                         match_method, smoothing_window,\n                                         min_frag_len, min_match_words, density_threshold, stemming\n                                         )\n\n    if extend_to_boundary:\n        lend, rend = extend_fragment_to_boundary(src_sents[cent_best_frag['src_sentid']], cent_best_frag['src_start'], cent_best_frag['src_end'])\n        cent_best_frag['src_start'] = lend\n        cent_best_frag['src_end'] = rend\n\n    # in case that no matching source sentence (mostly due to a noisy sentence in the groundtruth)\n    if cent_best_frag is None:\n        return [], []\n\n    # obtain fragments from left remaining part\n    top_fragments = []\n    all_fragments = []\n\n    if cent_best_frag['sum_start'] >= min_frag_len and cur_depth < max_depth:\n        left_summary_sent = copy.copy(summary_sent)\n        left_summary_sent = [t if tid < cent_best_frag['sum_start'] else '[IGNORE]' for tid, t in\n                             enumerate(left_summary_sent)]\n        left_best_frags, left_frags = extract_fragments_iterative(summary_sent_id, left_summary_sent, src_sents,\n                                                 match_method, smoothing_window,\n                                                 min_frag_len, min_match_words, density_threshold, stemming,\n                                                 cur_depth + 1, max_depth)\n    else:\n        left_best_frags, left_frags = [], []\n\n    top_fragments.extend(left_best_frags)\n    all_fragments.extend(left_frags)\n    top_fragments.append(cent_best_frag)\n    all_fragments.extend(cent_frags)\n\n    # obtain fragments from right remaining part\n    if len(summary_sent) - cent_best_frag['sum_end'] - 1 >= min_frag_len and cur_depth < max_depth:\n        right_summary_sent = copy.copy(summary_sent)\n        right_summary_sent = [t if tid > cent_best_frag['sum_end'] else '[IGNORE]' for tid, t in\n                              enumerate(right_summary_sent)]\n        right_best_frags, right_frags = extract_fragments_iterative(summary_sent_id, right_summary_sent, src_sents,\n                                                  match_method, smoothing_window,\n                                                  min_frag_len, min_match_words, density_threshold, stemming,\n                                                  cur_depth + 1, max_depth)\n    else:\n        right_best_frags, right_frags = [], []\n\n    top_fragments.extend(right_best_frags)\n    all_fragments.extend(right_frags)\n\n    return top_fragments, all_fragments\n\n\ndef extract_multiple_fragments(tgt_sents, src_sents,\n                               match_method='lcs', smoothing_window=5,\n                               min_sent_len=2, min_match_words=2, density_threshold=0.5,\n                               stemming=False,\n                               max_depth=10, extend_to_boundary=False\n                               ):\n    \"\"\"\n    Iteratively find the best matching fragments\n    for each summary sentence, return a segment from the most similar sentence\n    :param tgt_sents: tokenized summary sentences\n    :param src_sents: tokenized source sentences\n    :param smoothing_window:\n    :param min_sent_len: minimum length of each fragment\n    :param min_match_words: minimum number of matching words in each fragment\n    :param density_threshold: defined as #(match_words)/len(subsequence), 0 means return all results\n    :param max_depth:\n    :param extend_to_boundary:\n    :return:\n        extracted_fragments: a list of fragments corresponding to each summary sent,\n            each fragment is a dict, containing {'src_sentid', 'src_start', 'src_end', 'frag_words', 'sum_start', 'sum_end'}\n    \"\"\"\n    top_fragments_list = []\n    all_fragments_list = []\n    for summary_sent_id, summary_sent in enumerate(tgt_sents):\n        top_fragments, all_fragments = extract_fragments_iterative(summary_sent_id, summary_sent, src_sents, match_method,\n                                                smoothing_window, min_sent_len, min_match_words, density_threshold,\n                                                stemming=stemming,\n                                                cur_depth=1, max_depth=max_depth,\n                                                extend_to_boundary=extend_to_boundary)\n        # ex_summary_text = '\\n'.join(\n        #     [' '.join(src_sents[f['src_sentid']][f['src_start']: f['src_end'] + 1]) for f in top_fragments])\n        # print(ex_summary_text)\n\n        top_fragments_list.append(top_fragments)\n        all_fragments_list.append(all_fragments)\n\n    return top_fragments_list, all_fragments_list\n\n\ndef extract_singleton_fragments(sum_sents, src_sents,\n                                match_method='word',\n                                smoothing_window=5,\n                                extend_to_boundary=False):\n    \"\"\"\n    basically based on the extractive methods by Towards Annotating and Creating Summary Highlights at Sub-sentence Level\n    for each summary sentence, return a segment from the most similar sentence\n    :param sum_sents:\n    :param src_sents:\n    :param case:\n    :param stemming:\n    :param ignore_punc:\n    :param smoothing_window:\n    :param extend_to_boundary:\n    :return:\n        extracted_fragments: a list of fragments corresponding to each summary sent,\n            each fragment is a dict, containing {'src_sentid', 'src_start', 'src_end', 'frag_words', 'sum_start', 'sum_end'}\n    \"\"\"\n    extracted_fragments = []\n    for summary_sent_id, summary_sent in enumerate(sum_sents):\n        if match_method == 'word':\n            matching_fragments = extract_fragments_by_wordmatch(summary_sent, src_sents,\n                                                                smoothing_window = smoothing_window\n                                                                )\n        elif match_method == 'lcs':\n            matching_fragments = extract_fragments_by_LCS(summary_sent, src_sents)\n        # in case that no matching source sentence (mostly due to a noisy sentence in the groundtruth)\n        best_sent_id, best_fragment = matching_fragments[0]\n        if best_fragment is None:\n            print(summary_sent)\n            continue\n        if extend_to_boundary:\n            lend, rend = extend_fragment_to_boundary(src_sents[best_sent_id], best_fragment['src_start'], best_fragment['src_end'])\n            best_fragment['src_start'] = lend\n            best_fragment['src_end'] = rend\n\n        best_fragment_words = src_sents[best_sent_id][best_fragment['src_start']: best_fragment['src_end'] + 1]\n        best_fragment['src_sentid'] = best_sent_id\n        best_fragment['frag_words'] = best_fragment_words\n        best_fragment['sum_sentid'] = summary_sent_id\n\n        extracted_fragments.append(best_fragment)\n\n    return extracted_fragments\n\n\ndef locate_sum_fragment(sum_sent, src_sent, src_start, src_end):\n    src_set = set(src_sent[src_start: src_end + 1])\n    src_start, src_end = None, -1\n    for wid, w in enumerate(sum_sent):\n        if w in src_set:\n            if src_start is None:\n                src_start = wid\n            if wid > src_end:\n                src_end = wid\n    return src_start, src_end\n\n\ndef smooth_fragments(fragments, smoothing_window):\n    # stop until no more fragments can be merged\n    while True:\n        merged = False\n        new_fragments = []\n        fid = 0\n        while fid < len(fragments):\n            if fid == len(fragments) - 1:\n                new_fragments.append(fragments[fid])\n                fid += 1\n            else:\n                # check if the (fid+1) can be merged\n                cur_fragment = fragments[fid]\n                next_fragment = fragments[fid + 1]\n                if next_fragment['src_start'] - cur_fragment['src_end'] - 1 <= smoothing_window:\n                    new_fragment = {'src_start': cur_fragment['src_start'],\n                                    'src_end': next_fragment['src_end'],\n                                    'match_score': cur_fragment['match_score'] + next_fragment['match_score']}\n                    new_fragments.append(new_fragment)\n                    fid += 2\n                    merged = True\n                else:\n                    new_fragments.append(cur_fragment)\n                    fid += 1\n\n        fragments = new_fragments\n        if not merged:\n            break\n\n    return fragments\n\n\ndef _lcs(sum_sent, src_sent, src_sent_id, stemming):\n    val_mat = np.zeros(shape=(len(sum_sent), len(src_sent)))\n    # store triplets of (sum_start, sum_end, src_start, src_end, match_score)\n    idx_mat = [[None] * len(src_sent)] * len(sum_sent)\n\n    if stemming:\n        src_sent = [stemmer.stem(t) for t in src_sent]\n        sum_sent = [stemmer.stem(t) for t in sum_sent]\n\n    for sum_t_id, sum_t in enumerate(sum_sent):\n        for src_t_id, src_t in enumerate(src_sent):\n            cur_val = 1 if src_t in sum_t == src_t else 0\n            max_val = 0\n            max_idx = None\n            min_len_sum = 99999\n\n            if cur_val > 0:\n                prev_val = 0.0\n                prev_len_sum = 0\n                prev_idx = None\n                if sum_t_id > 0 and src_t_id > 0:\n                    prev_val = val_mat[sum_t_id - 1][src_t_id - 1]\n                    prev_idx = idx_mat[sum_t_id - 1][src_t_id - 1]\n                    if prev_idx:\n                        prev_len_sum = prev_idx['sum_end'] - prev_idx['sum_start'] + prev_idx['src_end'] - prev_idx['src_start'] + 2\n                    else:\n                        prev_len_sum = 0\n                max_val = prev_val + cur_val\n                min_len_sum = prev_len_sum + 2\n                max_idx = {'sum_start': prev_idx['sum_start'] if prev_idx else sum_t_id,\n                           'sum_end': sum_t_id,\n                           'src_sentid': src_sent_id,\n                           'src_start': prev_idx['src_start'] if prev_idx else src_t_id,\n                           'src_end': src_t_id,\n                           'match_score': val_mat[sum_t_id - 1][src_t_id - 1] + cur_val,\n                           'words': prev_idx['words']+[src_t] if prev_idx else [src_t],\n                           'sum_wordidx': prev_idx['sum_wordidx']+[sum_t_id] if prev_idx else [sum_t_id],\n                           'src_wordidx': prev_idx['src_wordidx']+[src_t_id] if prev_idx else [src_t_id]}\n            else:\n                if sum_t_id > 0:\n                    prev_val = val_mat[sum_t_id - 1][src_t_id]\n                    prev_idx = idx_mat[sum_t_id - 1][src_t_id]\n                    if prev_idx:\n                        prev_len_sum = prev_idx['sum_end'] - prev_idx['sum_start'] + prev_idx['src_end'] - prev_idx['src_start']\n                    else:\n                        prev_len_sum = 0\n                    if (prev_val > max_val) or (prev_val == max_val and prev_len_sum < min_len_sum):\n                        max_val = val_mat[sum_t_id - 1][src_t_id]\n                        max_idx = idx_mat[sum_t_id - 1][src_t_id]\n                        min_len_sum = prev_len_sum\n\n                if src_t_id > 0:\n                    prev_val = val_mat[sum_t_id][src_t_id - 1]\n                    prev_idx = idx_mat[sum_t_id][src_t_id - 1]\n                    if prev_idx:\n                        prev_len_sum = prev_idx['sum_end'] - prev_idx['sum_start'] + prev_idx['src_end'] - prev_idx['src_start']\n                    else:\n                        prev_len_sum = 0\n                    if (prev_val > max_val) or (prev_val == max_val and prev_len_sum < min_len_sum):\n                        max_val = val_mat[sum_t_id][src_t_id - 1]\n                        max_idx = idx_mat[sum_t_id][src_t_id - 1]\n\n            val_mat[sum_t_id][src_t_id] = max_val\n            idx_mat[sum_t_id][src_t_id] = max_idx\n\n    fragment = idx_mat[len(sum_sent) - 1][len(src_sent) - 1]\n    return fragment\n\n\ndef extract_fragments_by_LCS(sum_sent, src_sents, stemming):\n    \"\"\"\n    LCS is not compatible with ignore_punc/ignore_stopword\n    :param sum_sent:\n    :param src_sents:\n    :return:\n    \"\"\"\n    match_fragments = []\n    for src_sent_id, src_sent in enumerate(src_sents):\n        try:\n            if src_sent and len(src_sent) > 0:\n                fragment = _lcs(sum_sent, src_sent, src_sent_id, stemming)\n                match_fragments.append(fragment)\n            else:\n                match_fragments.append(None)\n        except Exception:\n            print(\"Error when doing LCS\")\n            print(\"sum_sent:\" + str(sum_sent))\n            print(\"src_sent:\" + str(src_sent))\n            match_fragments.append(None)\n            pass\n\n    ranked_fragments = sorted(enumerate(match_fragments), key=lambda k:k[1]['match_score'] if k[1] else 0, reverse=True)\n    ranked_fragments = [t for t in ranked_fragments if t[1] is not None]\n\n    return ranked_fragments\n\n\ndef extract_fragments_by_wordmatch(sum_sent, src_sents,\n                                   smoothing_window=5):\n    \"\"\"\n    Given one sum_sent, check each sent in src_sents and return one most matching fragment\n    :param src_tokens:\n    :param sum_tokens:\n    :param smoothing_window: if no more than smoothing_window words between two fragments, merge them\n    :return:\n        matching_fragments: a list of triple (start_idx, end_idx, num_word) indicating the info of a fragment\n        best_sent_id: id of the most matching sent\n        best_fragment: triple of the most matching sent\n    \"\"\"\n    match_fragments = []\n    sum_token_set = set(sum_sent)\n    for src_sent in src_sents:\n        match_list = []\n        # get the word match result\n        for t in src_sent:\n            match_flag = 1 if t in sum_token_set else 0\n            match_list.append(match_flag)\n\n        # get fragments by concatenating consecutive matching words\n        fragments = []\n        start = 0\n        while True:\n            while start < len(match_list) - 1 and match_list[start] == 0:\n                start += 1\n            if start >= len(match_list) - 1:\n                break\n            end = start\n            while end < len(match_list) - 1 and match_list[end] == 1:\n                end += 1\n            fragments.append({'src_start': start,\n                              'src_end': end - 1,\n                              'match_score': end - start})\n            start = end\n\n        # smooth fragments\n        if smoothing_window > 0:\n            fragments = smooth_fragments(fragments, smoothing_window)\n\n        # check if any valid fragment exists\n        best_fragment = None\n        best_fragment_value = 0\n        for fragment in fragments:\n            if fragment['match_score'] >= best_fragment_value:\n                best_fragment_value = fragment['match_score']\n                best_fragment = fragment\n\n        # add sum_start and sum_end\n        if best_fragment:\n            sum_start, sum_end = locate_sum_fragment(sum_sent, src_sent,\n                                                     best_fragment['src_start'], best_fragment['src_end'])\n            best_fragment['sum_start'] = sum_start\n            best_fragment['sum_end'] = sum_end\n\n        match_fragments.append(best_fragment)\n\n    ranked_fragments = sorted(enumerate(match_fragments), key=lambda k:k[1]['match_score'] if k[1] else 0, reverse=True)\n\n    return ranked_fragments\n\n\ndef get_top_results(fragments):\n    ranked_results = sorted(enumerate(fragments), key=lambda k:k[1]['match_score'] if k[1] else 0, reverse=True)\n    first_sent_id, first_fragment = ranked_results[0]\n    second_sent_id, second_fragment = ranked_results[1]\n    return first_sent_id, first_fragment, second_sent_id, second_fragment\n\n\ndef match_matrix(sum_tokens, src_tokens):\n    \"\"\"\n    :param src_tokens:\n    :param sum_tokens:\n    :return:\n    \"\"\"\n    m = np.zeros((len(sum_tokens), len(src_tokens)))\n    sum_idx = defaultdict(list)\n    for t_id, t in enumerate(sum_tokens):\n        if t.lower() == '[sep]':\n            continue\n        sum_idx[t].append(t_id)\n\n    for srct_id, srct in enumerate(src_tokens):\n        if srct in sum_idx:\n            for sumt_id in sum_idx[srct]:\n                m[sumt_id, srct_id] = 1\n\n    return m\n\n\ndef shrink_matrix(matrix, src_tokens, ngram_size=2):\n    \"\"\"\n    :param remove_shorts: due to the sparsity,\n           this removes the large consecutive zero parts of matrix (along 2nd dim, src_tokens)\n           also removes short n-grams which don't have a (>n+1)-grams within a window of size w\n    \"\"\"\n    # squash it to a 1D array\n    sum_matrix = np.sum(matrix, axis=0)\n    fragments = []\n    f_start = 0\n    while True:\n        # find a fragment\n        if f_start >= len(sum_matrix) - 1:\n            break\n        while f_start < len(sum_matrix) - 1 and sum_matrix[f_start] == 0:\n            f_start += 1\n        f_end = f_start\n        while f_end < len(sum_matrix) - 1 and sum_matrix[f_end + 1] != 0:\n            f_end += 1\n        # check validity of this fragment (size)\n        if f_end - f_start + 1 < ngram_size:\n            # print('Too short fragment (%d,%d)=%s' % (f_start, f_end, src_tokens[f_start: f_end+1]))\n            f_start = f_end + 1\n            continue\n        fragments.append((f_start, f_end))\n        # print('Find fragment (%d,%d)=%s' % (f_start, f_end, src_tokens[f_start: f_end+1]))\n        f_start = f_end + 1\n\n    # print('Find %d candidate fragments, len=%d' % (len(fragments), sum([e-s+1 for s,e in fragments])))\n\n    # remove zero chunks\n    new_m = np.array([])\n    new_src = []\n    ydim = matrix.shape[0]\n    zero_col = np.zeros((ydim, 1))\n\n    for f_id, (f_start, f_end) in enumerate(fragments):\n        if new_m.size > 0:\n            new_m = np.concatenate([new_m, zero_col, matrix[:, f_start: f_end + 1]], axis=1)\n            # insert the length of segment as separator\n            new_src.append('[%d]' % (f_start-fragments[f_id-1][1]))\n            new_src.extend(src_tokens[f_start: f_end + 1])\n        else:\n            new_m = matrix[:, f_start: f_end + 1]\n            new_src = src_tokens[f_start: f_end + 1]\n\n    return new_m, new_src, fragments\n\n\ndef merge_fragments(src_tokens, fragments, window_size=3):\n\n    # check if each fragment has a valid neighbour (should be used for merger)\n    valid_fragments = []\n    for f_id, (f_start, f_end) in enumerate(fragments):\n        if f_id > 0:\n            lf_start, lf_end = fragments[f_id - 1]\n            if f_start - lf_end + 1 > window_size:\n                continue\n        if f_id < len(fragments) - 1:\n            rf_start, rf_end = fragments[f_id + 1]\n            if rf_start - f_end + 1 > window_size:\n                continue\n        valid_fragments.append((f_start, f_end))\n        print('Valid fragment (%d,%d)=%s' % (f_start, f_end, src_tokens[f_start: f_end+1]))\n    print('Find %d valid fragments, len=%d' % (len(valid_fragments), sum([e-s+1 for s,e in valid_fragments])))\n\n\ndef insert_values_to_matrix(matrix, indices, value):\n    for i,j in indices:\n        matrix[i, j] = value\n    return matrix\n\n\ndef locate_fragments_in_matrix(summary_sents, source_sents, matrix, fragments):\n    indices = []\n    for fragment in fragments:\n        sum_offset = 0\n        src_offset = 0\n        if fragment['sum_sentid'] > 0:\n            sum_offset = fragment['sum_sentid'] + sum([len(s) for s in summary_sents[: fragment['sum_sentid']]])\n        if fragment['src_sentid'] > 0:\n            src_offset = fragment['src_sentid'] + sum([len(s) for s in source_sents[: fragment['src_sentid']]])\n\n        for sum_idx in range(sum_offset + fragment['sum_start'], sum_offset + fragment['sum_end'] + 1):\n            for src_idx in range(src_offset + fragment['src_start'], src_offset + fragment['src_end'] + 1):\n                if matrix[sum_idx][src_idx] > 0:\n                    indices.append((sum_idx, src_idx))\n    return indices\n"
  },
  {
    "path": "onmt/newssum/json_to_shards.py",
    "content": "\nimport argparse\nimport os\n\n\ndef init_opt():\n    parser = argparse.ArgumentParser()\n    # Input/output options\n    parser.add_argument('--input_json', '-input_json', type=str, required=True, help='Path to jsonl files.')\n    parser.add_argument('--output_dir', '-output_dir', default='/export/share/rmeng/data/sharded_json/')\n    parser.add_argument('--output_file', '-output_file', required=True, help='such as `/cnndm/train_%d.jsonl`')\n    parser.add_argument('--shard_size', '-shard_size', type=int, required=True, help='.')\n    opt = parser.parse_args()\n\n    return opt\n\nif __name__ == '__main__':\n    opt = init_opt()\n    output_path = opt.output_dir + '/shard_'+str(opt.shard_size) + opt.output_file\n    output_dir = os.path.dirname(os.path.abspath(output_path))\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n    lines_to_write = []\n    shard_count = 0\n    for line in open(opt.input_json, 'r'):\n        if len(lines_to_write) > 0 and len(lines_to_write) % opt.shard_size == 0:\n            with open(output_path % shard_count, 'w') as output_file:\n                print('Writing to %s' % (output_path % shard_count))\n                for l in lines_to_write:\n                    output_file.write(l)\n            lines_to_write = []\n            shard_count += 1\n        lines_to_write.append(line)\n\n    # flush the rest lines\n    with open(output_path % shard_count, 'w') as output_file:\n        print('Writing to %s' % (output_path % shard_count))\n        for l in lines_to_write:\n            output_file.write(l)\n"
  },
  {
    "path": "onmt/newssum/news_preprocess.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nIt tokenizes src and tgt, and inserts the tokenized information into another json\nA field token is appended to each token as a feature and they are splitted with a delimiter '￨'\nAn extension of bert_tokenize.py, supports word tokenization with Stanford CoreNLP and text labels.\nMultiple-datasets support is done by OpenNMT.\nFrom https://github.com/huggingface/pytorch-pretrained-BERT\n\"\"\"\nimport argparse\nimport datetime\nimport json\nimport os\nfrom functools import partial\n\nimport tqdm\n\nfrom onmt import opts\nfrom onmt.inputters.news_dataset import load_pretrained_tokenizer\nimport logging\n\nfrom onmt.newssum import docutils\nfrom onmt.utils.logging import init_logger\n\n\n__author__ = 'Rui Meng'\n__email__ = 'rui.meng@pitt.edu'\n\nTOKENIZER_NAMES = ['roberta-base', 'bert-base-cased', 'word']\nDATASET_TOKEN_MAP = {'cnndm': '[DATASET_CNNDM]',\n                     'nyt': '[DATASET_NYT]',\n                     'newsroom': '[DATASET_NEWSROOM]',\n                     'xsum': '[DATASET_XSUM]',\n                     'gigaword5': '[DATASET_GIGAWORD5]',\n                     'newscrawl': '[DATASET_NEWSCRAWL]'\n                     }\n\nDENSITY_BIN_MAP = {'extractive': '[BIN_DENSITY_EXT]',\n                     'abstractive': '[BIN_DENSITY_ABS]',\n                     'mixed': '[BIN_DENSITY_MIX]',\n                     'unknown': '[BIN_DENSITY_UNK]'\n                     }\n\ndef init_opt():\n    parser = argparse.ArgumentParser()\n    # Input/output options\n    parser.add_argument('--json_dir', '-json_dir', default='/export/share/rmeng/data/json/', help='Path to jsonl files.')\n    parser.add_argument('--output_dir', '-output_dir', default='/export/share/rmeng/output',\n                        help='The path of the output json files, final path is like /export/share/rmeng/output/bert-base-cased/tokenized/'\n                             'folder name will be dataset_tgt, like cnndm_summary, and insides are train.jsonl, valid.jsonl, test.jsonl.')\n    parser.add_argument('--datasets', '-datasets', type=str, nargs='+',\n                        default=['cnndm', 'nyt', 'newsroom', 'xsum'], choices=['cnndm', 'nyt', 'newsroom', 'xsum'],\n                        help='Specify which datasets to process. '\n                             'Currently only support 6 datasets: cnndm nyt newsroom xsum gigaword5 newscrawl')\n    parser.add_argument('--tokenizer', '-tokenizer', choices=TOKENIZER_NAMES, required=True, help='.')\n    parser.add_argument('--partition', '-partition', type=str, choices=['train', 'valid', 'test'],\n                        help='Specify which partition of dataset to process: train/test/valid/all')\n    parser.add_argument('--shard_filename', '-shard_filename', type=str, help='.')\n    parser.add_argument('--verbose', '-verbose', action='store_true', help='.')\n    opts.pretrained_opts(parser) # @memray\n    opt = parser.parse_args()\n\n    return opt\n\nlogging.basicConfig(level=logging.INFO)\n\n\nclass Token():\n    def __init__(self, token, field):\n        self.token = token\n        self.field = field\n\n\ndef tokenize_doc(doc, tokenizer_fn):\n    # process title and mainbody\n    title_tokens = tokenizer_fn(doc['title'])\n    # tokenize each paragraph and put together as full text\n    paragraphs = [l.strip() for l in doc['text'].split('\\n') if len(l.strip()) > 0]\n    sent_pars = [[s for s in docutils.sentence_split(p.strip(), model=\"nltk\")\n                  if len(s.strip()) > 0] for p in paragraphs]\n    # flatten sentences to 1D, paragraphs are seperated with '\\n', TODO add sentence delimiter\n    sentences = []\n    for sents in sent_pars:\n        if len(sentences) > 0:\n            sentences.append(['\\n'])\n        sentences.extend(sents)\n\n    sentences_tokens = [tokenizer_fn(s) if s[0] != '\\n' else ['\\n'] for s in sentences]\n\n    # concatenate paragraphs to get fulltext\n    title_sents_tokens = [title_tokens] + [['\\n']] + sentences_tokens\n\n    # split summaries according to the dataset\n    summary_sents = docutils.summary_sentence_segment(doc['summary'], doc['source'])\n    summary_sents_tokens = [[t for t in tokenizer_fn(s)] for s in summary_sents]\n\n    summary_tokens = []\n    for p_id, p in enumerate(summary_sents_tokens):\n        if p_id > 0:\n            summary_tokens.append('\\n')\n        summary_tokens.extend(p)\n\n    if ('description' in doc and doc['description']):\n        desc_tokens = tokenizer_fn(doc['description'])\n    elif 'metadata' in doc and 'description' in doc['metadata']:\n        desc_tokens = tokenizer_fn(doc['metadata']['description'])\n        doc['description'] = doc['metadata']['description']\n    else:\n        desc_tokens = None\n\n    return title_tokens, sentences_tokens, title_sents_tokens, summary_sents_tokens, summary_tokens, desc_tokens\n\n\nif __name__ == '__main__':\n    opt = init_opt()\n\n    current_time = datetime.datetime.now().strftime('%Y-%m-%d')  # '%Y-%m-%d_%H:%M:%S'\n    logger = init_logger(opt.output_dir + '/tokenize.%s.log' % (current_time))\n\n    # determine whether to lowercase the text\n    if opt.tokenizer == 'word' or '-cased' in opt.tokenizer:\n        lowercase = False\n    else:\n        lowercase = True\n\n    if opt.tokenizer == 'word':\n        # initialize tokenizer (for testset, only word tokenization should be applied)\n        tokenizer_fn = partial(docutils.word_tokenize, model=\"spacy\", lowercase=lowercase)\n    else:\n        # Load pre-trained model tokenizer (vocabulary)\n        pretrained_tokenizer = load_pretrained_tokenizer(opt.tokenizer, opt.cache_dir,\n                                                         special_vocab_path=opt.special_vocab_path)\n        tokenizer_fn = pretrained_tokenizer.tokenize\n\n    for dataset in opt.datasets:\n        if opt.shard_filename:\n            input_jsonl_path = os.path.join(opt.json_dir, dataset, opt.shard_filename)\n            logger.info('Tokenizing dataset [%s]. Loaded data from jsonl: %s ' % (dataset, input_jsonl_path))\n            output_dir = os.path.join(opt.output_dir, opt.tokenizer, 'sharded_1000', dataset)\n            output_jsonl_path = os.path.join(output_dir, opt.shard_filename)\n            logger.info('Exporting tokenized data to %s' % output_jsonl_path)\n        else:\n            input_jsonl_path = os.path.join(opt.json_dir, dataset, '%s.jsonl' % (opt.partition))\n            logger.info('Tokenizing dataset [%s - %s]. Loaded data from jsonl: %s ' % (dataset, opt.partition, input_jsonl_path))\n\n            output_dir = os.path.join(opt.output_dir, opt.tokenizer, 'tokenized', dataset)\n            output_jsonl_path = os.path.join(output_dir, opt.partition + '.jsonl')\n            logger.info('Exporting tokenized data to %s' % output_jsonl_path)\n\n        if not os.path.exists(output_dir):\n            os.makedirs(output_dir)\n        with open(output_jsonl_path, 'w') as output_jsonl_writer:\n            counter = 0\n            src_lengths = []\n            tgt_lengths = []\n\n            for line in tqdm.tqdm(open(input_jsonl_path, 'r'), desc=\"Processing %s %s\" % (dataset, opt.partition if opt.partition else opt.shard_filename)):\n                counter += 1\n                # print(counter)\n                # if counter < 108120:\n                #     continue\n                doc = json.loads(line)\n\n                # dump the processed data into this dict\n                processed_doc = {}\n                doc[opt.tokenizer] = processed_doc\n\n                # word tokenization and extractive groundtruth should be preliminary for other tokenization\n                if opt.tokenizer != 'word' and 'word' not in doc:\n                    print('Word tokenization and extractive groundtruth should be preliminary for other tokenization')\n                    raise AssertionError\n\n                if opt.tokenizer == 'word':\n                    # add description to 1st level\n                    doc['description'] = doc['metadata']['description'] if dataset == 'cnndm' or dataset == 'xsum' else None\n                    # tokenize text\n                    title_tokens, sents_tokens, title_sents_tokens, \\\n                    summary_sents_tokens, summary_tokens, description_tokens \\\n                        = tokenize_doc(doc, tokenizer_fn)\n                    processed_doc['token'] = {}\n                    processed_doc['token']['title'] = title_tokens\n                    processed_doc['token']['sents'] = sents_tokens\n                    processed_doc['token']['summary'] = summary_tokens\n                    processed_doc['token']['description'] = description_tokens\n\n                    # extract different oracle parts. for consistency, let's use summary only as target to match\n                    tgt_sents = docutils.summary_sentence_segment(doc['summary'], doc['source'])\n                    tgt_text = docutils.summary_sents_to_text(summary_sents_tokens)\n                    if opt.verbose:\n                        print('[target]')\n                        print(tgt_text)\n                    # if doc['description']:\n                    #     tgt_sents += [doc['description']]\n\n                    processed_doc['oracle'] = {}\n\n                    # oracle words\n                    oracle_words = summary_tokens + description_tokens if description_tokens else summary_tokens\n                    oracle_word_mask = [docutils.build_matching_mask(s, oracle_words,\n                                                                     lowercase=True, stem=True,\n                                                                     remove_punct=True, remove_stopwords=True)\n                                        for s in title_sents_tokens]\n                    processed_doc['oracle']['word'] = {}\n                    processed_doc['oracle']['word']['target'] = oracle_words\n                    processed_doc['oracle']['word']['mask'] = oracle_word_mask\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, oracle_word_mask)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['word']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['word']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[word]=%d' % len(oracle_words))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle verbs\n                    oracle_verb_tokens = [[verbs for verbs in docutils.get_verbs(s)] for s in tgt_sents]\n                    oracle_verb_texts = [token['text'] for s in oracle_verb_tokens for token in s]\n                    oracle_verb_mask = [docutils.build_matching_mask(s, oracle_verb_texts,\n                                                                     lowercase=True, stem=True,\n                                                                     remove_punct=True, remove_stopwords=True)\n                                        for s in title_sents_tokens]\n                    processed_doc['oracle']['verb'] = {}\n                    processed_doc['oracle']['verb']['target'] = oracle_verb_tokens\n                    processed_doc['oracle']['verb']['mask'] = oracle_verb_mask\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, oracle_verb_mask)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['verb']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['verb']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[Verb]=%d' % len(oracle_verb_texts))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle noun phrases\n                    sent_np_list = [docutils.get_noun_chunks(s) for s in tgt_sents]\n                    np_text_list = [s[0] for s in sent_np_list]\n                    np_chunk_list = [s[1] for s in sent_np_list]\n                    oracle_np_words = [w for np_list in np_text_list for np in np_list for w in np]\n                    oracle_np_mask = [docutils.build_matching_mask(s, oracle_np_words,\n                                                                   lowercase=True, stem=False,\n                                                                   remove_punct=True, remove_stopwords=True)\n                                      for s in title_sents_tokens]\n                    processed_doc['oracle']['noun_phrase'] = {}\n                    processed_doc['oracle']['noun_phrase']['target'] = np_chunk_list\n                    processed_doc['oracle']['noun_phrase']['mask'] = oracle_np_mask\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, oracle_np_mask)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['noun_phrase']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['noun_phrase']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[NP]=%d' % len([np for np_list in np_text_list for np in np_list]))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle NERs\n                    sent_ner_list = [docutils.get_NERs(s) for s in tgt_sents]\n                    ner_text_list = [s[0] for s in sent_ner_list]\n                    ner_chunk_list = [s[1] for s in sent_ner_list]\n                    oracle_ner_words = [w for ner_list in ner_text_list for ner in ner_list for w in ner]\n                    oracle_ner_masks = [docutils.build_matching_mask(s, oracle_ner_words,\n                                                                     lowercase=True, stem=False,\n                                                                     remove_punct=True, remove_stopwords=True)\n                                        for s in title_sents_tokens]\n                    processed_doc['oracle']['ner'] = {}\n                    processed_doc['oracle']['ner']['target'] = ner_chunk_list\n                    processed_doc['oracle']['ner']['mask'] = oracle_ner_masks\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, oracle_ner_masks)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['ner']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['ner']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[NER]=%d' % len([ner for ner_list in ner_text_list for ner in ner_list]))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle bottom-up style fragments\n                    oracle_bottomup_fragments, oracle_bottomup_mask = docutils.build_bottomup_mask(title_sents_tokens, summary_tokens)\n                    processed_doc['oracle']['bottomup'] = {}\n                    processed_doc['oracle']['bottomup']['target'] = oracle_bottomup_fragments\n                    processed_doc['oracle']['bottomup']['mask'] = oracle_bottomup_mask\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, oracle_bottomup_mask)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['bottomup']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['bottomup']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[Bottom-up]=%d' % len(oracle_bottomup_fragments))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle matching sentences\n                    oracle_sent_ids, oracle_sentence_sent_masks, oracle_sentence_word_masks, extracted_text, rouge_score = \\\n                        docutils.build_oracle_sentence_mask(title_sents_tokens, summary_sents_tokens)\n                    processed_doc['oracle']['sentence'] = {}\n                    processed_doc['oracle']['sentence']['target'] = oracle_sent_ids\n                    processed_doc['oracle']['sentence']['mask'] = oracle_sentence_word_masks\n                    processed_doc['oracle']['sentence']['sentence_mask'] = oracle_sentence_sent_masks\n                    processed_doc['oracle']['sentence']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['sentence']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[Sentence]=%d' % len(oracle_sent_ids))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    # oracle LCS matching fragments\n                    top_fragments_list, all_fragments_list, top_flat_fragments, all_flat_fragments, top_fragment_masks, all_fragment_masks, extracted_text, rouge_score\\\n                        = docutils.build_oracle_fragment_mask(title_sents_tokens, summary_sents_tokens,\n                                                              ignore_punct=True, ignore_stopword=False, stemming=True)\n                    processed_doc['oracle']['best_fragment'] = {}\n                    processed_doc['oracle']['all_fragment'] = {}\n                    processed_doc['oracle']['best_fragment']['target'] = top_fragments_list\n                    processed_doc['oracle']['best_fragment']['mask'] = top_fragment_masks\n                    processed_doc['oracle']['best_fragment']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['best_fragment']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[Top-Fragments]=%d' % len(top_flat_fragments))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                    processed_doc['oracle']['all_fragment']['target'] = all_fragments_list\n                    processed_doc['oracle']['all_fragment']['mask'] = all_fragment_masks\n                    extracted_text = docutils.mask_to_text(title_sents_tokens, all_fragment_masks)\n                    rouge_score = docutils.calc_rouge(extracted_text, tgt_text)\n                    processed_doc['oracle']['all_fragment']['oracle_text'] = extracted_text\n                    processed_doc['oracle']['all_fragment']['oracle_rouge'] = rouge_score\n                    if opt.verbose:\n                        print('[All-Fragments]=%d' % len(all_flat_fragments))\n                        print(extracted_text)\n                        print(rouge_score)\n\n                else:\n                    # get corresponding masks from `word`\n                    word_tokenized_doc = doc['word']['token']\n                    title_words = word_tokenized_doc['title']\n                    sents_words = word_tokenized_doc['sents']\n                    summary_words = word_tokenized_doc['summary']\n                    description_words = word_tokenized_doc['description']\n                    title_sents_words = [title_words] + [['\\n']] + sents_words\n\n                    # tokenize summary/description\n                    summary_words = [w if w != '\\n' else '[SEP_SUM]' for w in summary_words]\n                    if description_words:\n                        description_words = [w if w != '\\n' else '[SEP_SUM]' for w in description_words]\n                    summary_tokens, summary_codes = docutils.words_to_subwords(pretrained_tokenizer, summary_words)\n                    if description_words:\n                        description_tokens, description_codes = docutils.words_to_subwords(pretrained_tokenizer, description_words)\n                    else:\n                        description_tokens, description_codes = None, None\n\n                        # tokenize title/text and masks\n                    oracle_masks = {k:v['mask'] for k,v in doc['word']['oracle'].items()}\n                    title_sents_tokens = [s if s != ['\\n'] else ['[SEP_PAR]'] for s in title_sents_words]\n                    title_sents_tokens, title_sents_codes, oracle_sub_masks = \\\n                        docutils.wordmasks_to_subwords(pretrained_tokenizer, title_sents_words, oracle_masks)\n\n                    processed_doc['token'] = {}\n                    processed_doc['token']['title'] = title_sents_tokens[0]\n                    processed_doc['token']['sents'] = title_sents_tokens[2:]\n                    processed_doc['token']['title_sents'] = title_sents_tokens\n                    processed_doc['token']['summary'] = summary_tokens\n                    processed_doc['token']['description'] = description_tokens\n\n                    processed_doc['code'] = {}\n                    processed_doc['code']['title'] = title_sents_codes[0]\n                    processed_doc['code']['sents'] = title_sents_codes[2:]\n                    processed_doc['code']['title_sents'] = title_sents_codes\n                    processed_doc['code']['summary'] = summary_codes\n                    processed_doc['code']['description'] = description_codes\n\n                    processed_doc['oracle_mask'] = {}\n                    for k,v in oracle_sub_masks.items():\n                        processed_doc['oracle_mask'][k] = v\n\n                    processed_doc['oracle_sent_ids'] = doc['word']['oracle']['sentence']['target']\n                    processed_doc['oracle_sent_mask'] = doc['word']['oracle']['sentence']['sentence_mask']\n\n                # pass\n                output_jsonl_writer.write(json.dumps(doc)+'\\n')\n\n            output_jsonl_writer.close()\n\n"
  },
  {
    "path": "onmt/newssum/rouge_eval/__init__.py",
    "content": "from __future__ import absolute_import\nfrom onmt.newssum.rouge_eval.rouge import FilesRouge, Rouge\n\n__version__ = \"0.3.1\"\n__all__ = [\"FilesRouge\", \"Rouge\", \"rouge_score\"]\n"
  },
  {
    "path": "onmt/newssum/rouge_eval/rouge.py",
    "content": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nimport six\nfrom onmt.newssum.rouge_eval import rouge_score\nimport io\nimport os\n\n\nclass FilesRouge:\n    def __init__(self, hyp_path, ref_path, metrics=None, stats=None,\n                 batch_lines=None):\n        assert(os.path.isfile(hyp_path))\n        assert(os.path.isfile(ref_path))\n\n        self.rouge = Rouge(metrics=metrics, stats=stats)\n\n        def line_count(path):\n            count = 0\n            with open(path, \"rb\") as f:\n                for line in f:\n                    count += 1\n            return count\n\n        hyp_lc = line_count(hyp_path)\n        ref_lc = line_count(ref_path)\n        assert(hyp_lc == ref_lc)\n\n        assert(batch_lines is None or type(batch_lines) == int)\n\n        self.hyp_path = hyp_path\n        self.ref_path = ref_path\n        self.batch_lines = batch_lines\n\n    def get_scores(self, avg=False, ignore_empty=False):\n        \"\"\"Calculate ROUGE scores between each pair of\n        lines (hyp_file[i], ref_file[i]).\n        Args:\n          * hyp_path: hypothesis file path\n          * ref_path: references file path\n          * avg (False): whether to get an average scores or a list\n        \"\"\"\n        hyp_path, ref_path = self.hyp_path, self.ref_path\n\n        with io.open(hyp_path, encoding=\"utf-8\", mode=\"r\") as hyp_file:\n            hyps = [line[:-1] for line in hyp_file]\n        with io.open(ref_path, encoding=\"utf-8\", mode=\"r\") as ref_file:\n            refs = [line[:-1] for line in ref_file]\n\n        return self.rouge.get_scores(hyps, refs, avg=avg,\n                                     ignore_empty=ignore_empty)\n\n\nclass Rouge:\n    DEFAULT_METRICS = [\"rouge-1\", \"rouge-2\", \"rouge-l\"]\n    AVAILABLE_METRICS = {\n        \"rouge-1\": lambda hyp, ref, stopwords_removal, stemming, punctuations_removal: rouge_score.rouge_n(hyp, ref, 1, stopwords_removal, stemming, punctuations_removal),\n        \"rouge-2\": lambda hyp, ref, stopwords_removal, stemming, punctuations_removal: rouge_score.rouge_n(hyp, ref, 2, stopwords_removal, stemming, punctuations_removal),\n        \"rouge-l\": lambda hyp, ref, stopwords_removal, stemming, punctuations_removal: rouge_score.rouge_l_summary_level(hyp, ref, stopwords_removal, stemming, punctuations_removal),\n    }\n    DEFAULT_STATS = [\"f\", \"p\", \"r\"]\n    AVAILABLE_STATS = [\"f\", \"p\", \"r\"]\n\n    def __init__(self, metrics=None, stats=None, stopwords_removal=False, stemming=True, punctuations_removal=True):\n        if metrics is not None:\n            self.metrics = [m.lower() for m in metrics]\n\n            for m in self.metrics:\n                if m not in Rouge.AVAILABLE_METRICS:\n                    raise ValueError(\"Unknown metric '%s'\" % m)\n        else:\n            self.metrics = Rouge.DEFAULT_METRICS\n\n        if stats is not None:\n            self.stats = [s.lower() for s in stats]\n\n            for s in self.stats:\n                if s not in Rouge.AVAILABLE_STATS:\n                    raise ValueError(\"Unknown stat '%s'\" % s)\n        else:\n            self.stats = Rouge.DEFAULT_STATS\n\n        self.stopwords_removal = stopwords_removal\n        self.stemming = stemming\n        self.punctuations_removal = punctuations_removal\n\n    def get_scores(self, hyps, refs, avg=False, ignore_empty=False):\n        if isinstance(hyps, six.string_types):\n            hyps, refs = [hyps], [refs]\n\n        if ignore_empty:\n            # Filter out hyps of 0 length\n            hyps_and_refs = zip(hyps, refs)\n            hyps_and_refs = [_ for _ in hyps_and_refs if len(_[0]) > 0]\n            hyps, refs = zip(*hyps_and_refs)\n\n        assert(type(hyps) == type(refs))\n        assert(len(hyps) == len(refs))\n\n        if not avg:\n            return self._get_scores(hyps, refs)\n        return self._get_avg_scores(hyps, refs)\n\n    def _get_scores(self, hyps, refs):\n        scores = []\n        for hyp, ref in zip(hyps, refs):\n            sen_score = {}\n            hyp = [\" \".join([_ for _ in hyp.split() if len(_) > 0])]\n            ref = [\" \".join([_ for _ in ref.split() if len(_) > 0])]\n            # hyp = [\" \".join(_.split()) for _ in hyp.split(\".\") if len(_) > 0]\n            # ref = [\" \".join(_.split()) for _ in ref.split(\".\") if len(_) > 0]\n\n            for m in self.metrics:\n                fn = Rouge.AVAILABLE_METRICS[m]\n                sc = fn(hyp, ref, stopwords_removal=self.stopwords_removal, stemming=self.stemming, punctuations_removal=self.punctuations_removal)\n                sen_score[m] = {s: sc[s] for s in self.stats}\n            scores.append(sen_score)\n        return scores\n\n    def _get_avg_scores(self, hyps, refs):\n        scores = {m: {s: 0 for s in self.stats} for m in self.metrics}\n\n        count = 0\n        for (hyp, ref) in zip(hyps, refs):\n            hyp = [\" \".join(_.split()) for _ in hyp.split(\".\") if len(_) > 0]\n            ref = [\" \".join(_.split()) for _ in ref.split(\".\") if len(_) > 0]\n\n            for m in self.metrics:\n                fn = Rouge.AVAILABLE_METRICS[m]\n                sc = fn(hyp, ref)\n                scores[m] = {s: scores[m][s] + sc[s] for s in self.stats}\n            count += 1\n        scores = {m: {s: scores[m][s] / count for s in scores[m]}\n                  for m in scores}\n        return scores\n\n\nif __name__ == '__main__':\n    ref = [\"originally fined # 120 for the absence from sweyne park school in essex . . unsuccessfully appealed the decision but parents still refused to pay . . grandmother paid the bailiffs and said it was a ` sober warning to others\"]\n    hyp = [\"couple took their son out of sweyne park school in essex without permission . . they were forced to pay a # 1,230 fine after bailiffs were called in . . they told her goods would be sold to cover # 3,500 for the fine and charges . . essex county council spokesman said parents legally had to ensure attendance .\"]\n    r1 = rouge_score.rouge_n(hyp, ref, 1, stopwords_removal=True, stemming=False, punctuations_removal=True)\n    r2 = rouge_score.rouge_n(hyp, ref, 2, stopwords_removal=True, stemming=False, punctuations_removal=True)\n    rl = rouge_score.rouge_l_summary_level(hyp, ref, stopwords_removal=True, stemming=False, punctuations_removal=True)\n\n    print(r1)\n    print(r2)\n    print(rl)\n    \"\"\"\n    hyp_sent = \"marseille , france -lrb- cnn -rrb- the french prosecutor leading an investigation into the crash of germanwings flight 9525 insisted wednesday that he was not aware of any video footage from on board the plane . marseille prosecutor brice robin told cnn that `` so far no videos were used in the crash investigation . '' he added , `` a person who has such a video needs to immediately give it to the investigators . ''\"\n    ref_sent = \"marseille prosecutor says `` so far no videos were used in the crash investigation '' despite media reports . journalists at bild and paris match are `` very confident '' the video clip is real , an editor says . andreas lubitz had informed his lufthansa training school of an episode of severe depression , airline says .\"\n\n    stopwords_removal = False\n    stemming = True\n    punctuations_removal = True\n\n    rouge = Rouge(stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    score = rouge.get_scores(hyps=[hyp_sent], refs=[ref_sent])\n    print(score)\n\n    stopwords_removal = False\n    stemming = True\n    rouge = Rouge(stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    score = rouge.get_scores(hyps=[hyp_sent], refs=[ref_sent])\n    print(score)\n\n    stopwords_removal = True\n    stemming = False\n    rouge = Rouge(stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    score = rouge.get_scores(hyps=[hyp_sent], refs=[ref_sent])\n    print(score)\n\n    stopwords_removal = False\n    stemming = False\n    rouge = Rouge(stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    score = rouge.get_scores(hyps=[hyp_sent], refs=[ref_sent])\n    print(score)\n    \"\"\"\n\n\n"
  },
  {
    "path": "onmt/newssum/rouge_eval/rouge_score.py",
    "content": "# -*- coding: utf-8 -*-\n# Copyright 2017 Google Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"ROUGE Metric Implementation\n\nThis is a very slightly version of:\nhttps://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py\n\n---\n\nROUGe metric implementation.\n\nThis is a modified and slightly extended verison of\nhttps://github.com/miso-belica/sumy/blob/dev/sumy/evaluation/rouge.py.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division, print_function, unicode_literals\nimport itertools\nimport os\nimport re\nimport string\n\nfrom nltk.stem.porter import PorterStemmer\nstemmer = PorterStemmer()\n\nstopwords = set([l.strip() for l in open(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"smart_common_words.txt\"), \"r\") if len(l.strip()) > 0])\npunctuations = set([p for p in string.punctuation])\npunc_regex = re.compile('[%s]' % re.escape(string.punctuation))\n\ndef _get_ngrams(n, text):\n    \"\"\"Calcualtes n-grams.\n\n    Args:\n      n: which n-grams to calculate\n      text: An array of tokens\n\n    Returns:\n      A set of n-grams\n    \"\"\"\n    ngram_set = set()\n    text_length = len(text)\n    max_index_ngram_start = text_length - n\n    for i in range(max_index_ngram_start + 1):\n        ngram_set.add(tuple(text[i:i + n]))\n    return ngram_set\n\n\ndef _split_into_words(sentences, stopwords_removal=True, stemming=True, punctuations_removal=True):\n    \"\"\"Splits multiple sentences into words and flattens the result\"\"\"\n    sent_words = []\n    for sent in sentences:\n        words = sent.split(\" \")\n\n        if stopwords_removal:\n            words = [w for w in words if w not in stopwords]\n        if stemming:\n            words = [stemmer.stem(w) for w in words]\n        if punctuations_removal:\n            words = [w for w in words if len(punc_regex.sub('', w)) > 0]\n\n        sent_words.extend(words)\n\n    return sent_words\n    # return list(itertools.chain(*[_.split(\" \") for _ in sentences]))\n\n\ndef _get_word_ngrams(n, sentences, stopwords_removal=True, stemming=True, punctuations_removal=True):\n    \"\"\"Calculates word n-grams for multiple sentences.\n    \"\"\"\n    assert len(sentences) > 0\n    assert n > 0\n\n    words = _split_into_words(sentences, stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    # print(len(words))\n    # print(words)\n\n    return _get_ngrams(n, words)\n\n\ndef _len_lcs(x, y):\n    \"\"\"\n    Returns the length of the Longest Common Subsequence between sequences x\n    and y.\n    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n    Args:\n      x: sequence of words\n      y: sequence of words\n\n    Returns\n      integer: Length of LCS between x and y\n    \"\"\"\n    table = _lcs(x, y)\n    n, m = len(x), len(y)\n    return table[n, m]\n\n\ndef _lcs(x, y):\n    \"\"\"\n    Computes the length of the longest common subsequence (lcs) between two\n    strings. The implementation below uses a DP programming algorithm and runs\n    in O(nm) time where n = len(x) and m = len(y).\n    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n    Args:\n      x: collection of words\n      y: collection of words\n\n    Returns:\n      Table of dictionary of coord and len lcs\n    \"\"\"\n    n, m = len(x), len(y)\n    table = dict()\n    for i in range(n + 1):\n        for j in range(m + 1):\n            if i == 0 or j == 0:\n                table[i, j] = 0\n            elif x[i - 1] == y[j - 1]:\n                table[i, j] = table[i - 1, j - 1] + 1\n            else:\n                table[i, j] = max(table[i - 1, j], table[i, j - 1])\n    return table\n\n\ndef _recon_lcs(x, y):\n    \"\"\"\n    Returns the Longest Subsequence between x and y.\n    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n    Args:\n      x: sequence of words\n      y: sequence of words\n\n    Returns:\n      sequence: LCS of x and y\n    \"\"\"\n    i, j = len(x), len(y)\n    table = _lcs(x, y)\n\n    def _recon(i, j):\n        \"\"\"private recon calculation\"\"\"\n        if i == 0 or j == 0:\n            return []\n        elif x[i - 1] == y[j - 1]:\n            return _recon(i - 1, j - 1) + [(x[i - 1], i)]\n        elif table[i - 1, j] > table[i, j - 1]:\n            return _recon(i - 1, j)\n        else:\n            return _recon(i, j - 1)\n\n    recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))\n    return recon_tuple\n\n\ndef multi_rouge_n(sequences, scores_ids, n=2):\n    \"\"\"\n    Efficient way to compute highly repetitive scoring\n    i.e. sequences are involved multiple time\n\n    Args:\n        sequences(list[str]): list of sequences (either hyp or ref)\n        scores_ids(list[tuple(int)]): list of pairs (hyp_id, ref_id)\n            ie. scores[i] = rouge_n(scores_ids[i][0],\n                                    scores_ids[i][1])\n\n    Returns:\n        scores: list of length `len(scores_ids)` containing rouge `n`\n                scores as a dict with 'f', 'r', 'p'\n    Raises:\n        KeyError: if there's a value of i in scores_ids that is not in\n                  [0, len(sequences)[\n    \"\"\"\n    ngrams = [_get_word_ngrams(n, sequence) for sequence in sequences]\n    counts = [len(ngram) for ngram in ngrams]\n\n    scores = []\n    for hyp_id, ref_id in scores_ids:\n        evaluated_ngrams = ngrams[hyp_id]\n        evaluated_count = counts[hyp_id]\n\n        reference_ngrams = ngrams[ref_id]\n        reference_count = counts[ref_id]\n\n        overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)\n        overlapping_count = len(overlapping_ngrams)\n\n        scores += [f_r_p_rouge_n(evaluated_count,\n                                 reference_count, overlapping_count)]\n    return scores\n\n\ndef rouge_n(evaluated_sentences, reference_sentences, n=2, stopwords_removal=True, stemming=True, punctuations_removal=True):\n    \"\"\"\n    Computes ROUGE-N of two text collections of sentences.\n    Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/\n    papers/rouge-working-note-v1.3.1.pdf\n\n    Args:\n      evaluated_sentences: The sentences that have been picked by the\n                           summarizer\n      reference_sentences: The sentences from the referene set\n      n: Size of ngram.  Defaults to 2.\n\n    Returns:\n      A tuple (f1, precision, recall) for ROUGE-N\n\n    Raises:\n      ValueError: raises exception if a param has len <= 0\n    \"\"\"\n    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:\n        raise ValueError(\"Collections must contain at least 1 sentence.\")\n\n    evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences, stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n    reference_ngrams = _get_word_ngrams(n, reference_sentences, stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n\n    reference_count = len(reference_ngrams)\n    evaluated_count = len(evaluated_ngrams)\n\n    # Gets the overlapping ngrams between evaluated and reference\n    overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)\n    overlapping_count = len(overlapping_ngrams)\n\n    # print(len(overlapping_ngrams))\n    # print(overlapping_ngrams)\n    # print(len(evaluated_ngrams))\n    # print(evaluated_ngrams)\n    # print(len(reference_ngrams))\n    # print(reference_ngrams)\n\n    return f_r_p_rouge_n(evaluated_count, reference_count, overlapping_count)\n\n\ndef f_r_p_rouge_n(evaluated_count, reference_count, overlapping_count):\n    # Handle edge case. This isn't mathematically correct, but it's good enough\n    if evaluated_count == 0:\n        precision = 0.0\n    else:\n        precision = overlapping_count / evaluated_count\n\n    if reference_count == 0:\n        recall = 0.0\n    else:\n        recall = overlapping_count / reference_count\n\n    f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))\n\n    return {\"f\": f1_score, \"p\": precision, \"r\": recall}\n\n\ndef _union_lcs(evaluated_sentences, reference_sentence, prev_union=None, stopwords_removal=True, stemming=True, punctuations_removal=True):\n    \"\"\"\n    Returns LCS_u(r_i, C) which is the LCS score of the union longest common\n    subsequence between reference sentence ri and candidate summary C.\n    For example:\n    if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8\n    and c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1\n    is \"w1 w2\" and the longest common subsequence of r_i and c2 is \"w1 w3 w5\".\n    The union longest common subsequence of r_i, c1, and c2 is \"w1 w2 w3 w5\"\n    and LCS_u(r_i, C) = 4/5.\n\n    Args:\n      evaluated_sentences: The sentences that have been picked by the\n                           summarizer\n      reference_sentence: One of the sentences in the reference summaries\n\n    Returns:\n      float: LCS_u(r_i, C)\n\n    ValueError:\n      Raises exception if a param has len <= 0\n    \"\"\"\n    if prev_union is None:\n        prev_union = set()\n\n    if len(evaluated_sentences) <= 0:\n        raise ValueError(\"Collections must contain at least 1 sentence.\")\n\n    lcs_union = prev_union\n    prev_count = len(prev_union)\n    reference_words = _split_into_words([reference_sentence], stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n\n    combined_lcs_length = 0\n    for eval_s in evaluated_sentences:\n        evaluated_words = _split_into_words([eval_s], stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)\n\n        lcs = set(_recon_lcs(reference_words, evaluated_words))\n        combined_lcs_length += len(lcs)\n        lcs_union = lcs_union.union(lcs)\n\n    new_lcs_count = len(lcs_union) - prev_count\n    return new_lcs_count, lcs_union\n\n\ndef rouge_l_summary_level(evaluated_sentences, reference_sentences, stopwords_removal=True, stemming=True, punctuations_removal=True):\n    \"\"\"\n    Computes ROUGE-L (summary level) of two text collections of sentences.\n    http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n    rouge-working-note-v1.3.1.pdf\n\n    Calculated according to:\n    R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m\n    P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n\n    F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)\n\n    where:\n    SUM(i,u) = SUM from i through u\n    u = number of sentences in reference summary\n    C = Candidate summary made up of v sentences\n    m = number of words in reference summary\n    n = number of words in candidate summary\n\n    Args:\n      evaluated_sentences: The sentences that have been picked by the\n                           summarizer\n      reference_sentence: One of the sentences in the reference summaries\n\n    Returns:\n      A float: F_lcs\n\n    Raises:\n      ValueError: raises exception if a param has len <= 0\n    \"\"\"\n    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:\n        raise ValueError(\"Collections must contain at least 1 sentence.\")\n\n    # total number of words in reference sentences\n    m = len(set(_split_into_words(reference_sentences, stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)))\n\n    # total number of words in evaluated sentences\n    n = len(set(_split_into_words(evaluated_sentences, stopwords_removal=stopwords_removal, stemming=stemming, punctuations_removal=punctuations_removal)))\n\n    # print(\"m,n %d %d\" % (m, n))\n    union_lcs_sum_across_all_references = 0\n    union = set()\n    for ref_s in reference_sentences:\n        lcs_count, union = _union_lcs(evaluated_sentences,\n                                      ref_s,\n                                      prev_union=union,\n                                      stopwords_removal = stopwords_removal,\n                                      stemming = stemming)\n        union_lcs_sum_across_all_references += lcs_count\n\n    llcs = union_lcs_sum_across_all_references\n    r_lcs = llcs / m if m > 0 else 0.0\n    p_lcs = llcs / n if n > 0 else 0.0\n    beta = p_lcs / (r_lcs + 1e-12)\n    num = (1 + (beta**2)) * r_lcs * p_lcs\n    denom = r_lcs + ((beta**2) * p_lcs)\n    f_lcs = num / (denom + 1e-12)\n    return {\"f\": f_lcs, \"p\": p_lcs, \"r\": r_lcs}\n\n"
  },
  {
    "path": "onmt/newssum/rouge_eval/smart_common_words.txt",
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    "path": "onmt/newssum/torchviz/__init__.py",
    "content": ""
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    "path": "onmt/newssum/torchviz/example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"PyTorchViz examples\\n\",\n    \"==========\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch import nn\\n\",\n    \"from torchviz import make_dot, make_dot_from_trace\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualize gradients of simple MLP\\n\",\n    \"\\n\",\n    \"The method below is for building directed graphs of PyTorch operations, built during forward propagation and showing which operations will be called on backward. It omits subgraphs which do not require gradients.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/svg+xml\": [\n       \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n\",\n       \"<!DOCTYPE svg PUBLIC \\\"-//W3C//DTD SVG 1.1//EN\\\"\\n\",\n       \" \\\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\\\">\\n\",\n       \"<!-- Generated by graphviz version 2.40.1 (20161225.0304)\\n\",\n       \" -->\\n\",\n       \"<!-- Title: %3 Pages: 1 -->\\n\",\n       \"<svg width=\\\"312pt\\\" height=\\\"276pt\\\"\\n\",\n       \" viewBox=\\\"0.00 0.00 312.14 276.00\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">\\n\",\n       \"<g id=\\\"graph0\\\" class=\\\"graph\\\" transform=\\\"scale(1 1) rotate(0) translate(4 272)\\\">\\n\",\n       \"<title>%3</title>\\n\",\n       \"<polygon fill=\\\"#ffffff\\\" 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\"\\n\",\n    \"An alternative to the above is `torch.jit.trace`, which gives more information about operations, and shows all operations performed during forward\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/svg+xml\": [\n       \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n\",\n       \"<!DOCTYPE svg PUBLIC \\\"-//W3C//DTD SVG 1.1//EN\\\"\\n\",\n       \" \\\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\\\">\\n\",\n       \"<!-- Generated by graphviz version 2.40.1 (20161225.0304)\\n\",\n       \" -->\\n\",\n       \"<!-- Title: %3 Pages: 1 -->\\n\",\n       \"<svg width=\\\"352pt\\\" height=\\\"292pt\\\"\\n\",\n       \" viewBox=\\\"0.00 0.00 351.63 292.00\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">\\n\",\n       \"<g id=\\\"graph0\\\" class=\\\"graph\\\" transform=\\\"scale(1 1) rotate(0) 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The idea is to minimize the loss:\\n\",\n    \"\\n\",\n    \"$$f(x, \\\\theta) = f(x, \\\\theta) + g(\\\\frac{\\\\partial f(x, \\\\theta)}{\\\\partial x})$$\\n\",\n    \"\\n\",\n    \"where $x$ and $\\\\theta$ are input and parameter vectors, $f(x, \\\\theta)$ is the original loss function, and $g$ is a function of gradient w.r.t. input.\\n\",\n    \"\\n\",\n    \"This is used in [Improved Wasserstein GAN](https://arxiv.org/abs/1704.00028) and [Attention Transfer](https://arxiv.org/abs/1612.03928).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/svg+xml\": [\n       \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n\",\n       \"<!DOCTYPE svg PUBLIC \\\"-//W3C//DTD SVG 1.1//EN\\\"\\n\",\n       \" \\\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\\\">\\n\",\n       \"<!-- Generated by graphviz version 2.40.1 (20161225.0304)\\n\",\n       \" 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AlexNet\\n\",\n    \"\\n\",\n    \"model = AlexNet()\\n\",\n    \"\\n\",\n    \"x = torch.randn(1, 3, 227, 227).requires_grad_(True)\\n\",\n    \"y = model(x)\\n\",\n    \"make_dot(y, params=dict(list(model.named_parameters()) + [('x', x)]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/svg+xml\": [\n       \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\"?>\\n\",\n       \"<!DOCTYPE svg PUBLIC \\\"-//W3C//DTD SVG 1.1//EN\\\"\\n\",\n       \" \\\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\\\">\\n\",\n       \"<!-- Generated by graphviz version 2.40.1 (20161225.0304)\\n\",\n       \" -->\\n\",\n       \"<!-- Title: %3 Pages: 1 -->\\n\",\n       \"<svg width=\\\"554pt\\\" height=\\\"972pt\\\"\\n\",\n       \" viewBox=\\\"0.00 0.00 554.09 972.00\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">\\n\",\n      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\"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "onmt/newssum/wandb/example_mnist.py",
    "content": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport wandb\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n\n\ndef test(args, model, device, test_loader):\n    model.eval()\n    test_loss = 0\n    correct = 0\n\n    example_images = []\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            # sum up batch loss\n            test_loss += F.nll_loss(output, target, reduction='sum').item()\n            # get the index of the max log-probability\n            pred = output.max(1, keepdim=True)[1]\n            correct += pred.eq(target.view_as(pred)).sum().item()\n            example_images.append(wandb.Image(\n                data[0], caption=\"Pred: {} Truth: {}\".format(pred[0].item(), target[0])))\n\n    test_loss /= len(test_loader.dataset)\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        100. * correct / len(test_loader.dataset)))\n    wandb.log({\n        \"Examples\": example_images,\n        \"Test Accuracy\": 100. * correct / len(test_loader.dataset),\n        \"Test Loss\": test_loss})\n\n\ndef main():\n    wandb.login(key='c338136c195ab221b8c7cfaa446db16b2e86c6db')\n    wandb.init(project=\"mnist-test\", id=\"mnist-test\", resume=True)\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n    parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                        help='input batch size for training (default: 64)')\n    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                        help='input batch size for testing (default: 1000)')\n    parser.add_argument('--epochs', type=int, default=10, metavar='N',\n                        help='number of epochs to train (default: 10)')\n    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n                        help='learning rate (default: 0.01)')\n    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n                        help='SGD momentum (default: 0.5)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                        help='how many batches to wait before logging training status')\n    args = parser.parse_args()\n    use_cuda = not args.no_cuda and torch.cuda.is_available()\n\n    wandb.config.update(args)\n\n    torch.manual_seed(args.seed)\n\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n    train_loader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=True, download=True,\n                       transform=transforms.Compose([\n                           transforms.ToTensor(),\n                           transforms.Normalize((0.1307,), (0.3081,))\n                       ])),\n        batch_size=args.batch_size, shuffle=True, **kwargs)\n    test_loader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=False, transform=transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize((0.1307,), (0.3081,))\n        ])),\n        batch_size=args.test_batch_size, shuffle=True, **kwargs)\n\n    model = Net().to(device)\n    optimizer = optim.SGD(model.parameters(), lr=args.lr,\n                          momentum=args.momentum)\n    wandb.watch(model)\n\n    for epoch in range(1, args.epochs + 1):\n        train(args, model, device, train_loader, optimizer, epoch)\n        test(args, model, device, test_loader)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "onmt/opts.py",
    "content": "\"\"\" Implementation of all available options \"\"\"\nfrom __future__ import print_function\n\nfrom onmt.utils.misc import str2bool\n\nimport configargparse\n\nfrom onmt.models.sru import CheckSRU\nfrom onmt.transforms import AVAILABLE_TRANSFORMS\nfrom onmt.constants import ModelTask\n\ndef config_opts(parser):\n    group = parser.add_argument_group(\"Configuration\")\n    group.add('-config', '--config', required=False,\n              is_config_file_arg=True,\n              help='Path of the main YAML config file.')\n    group.add('-save_config', '--save_config', required=False,\n              is_write_out_config_file_arg=True,\n              help='Path where to save the config.')\n\n\ndef _add_logging_opts(parser, is_train=True):\n    group = parser.add_argument_group('Logging')\n    group.add('--log_file', '-log_file', type=str, default=\"\",\n              help=\"Output logs to a file under this path.\")\n    group.add('--log_file_level', '-log_file_level', type=str,\n              action=StoreLoggingLevelAction,\n              choices=StoreLoggingLevelAction.CHOICES,\n              default=\"0\")\n\n    if is_train:\n        group.add('--report_every', '-report_every', type=int, default=50,\n                  help=\"Print stats at this interval.\")\n        group.add('--exp_host', '-exp_host', type=str, default=\"\",\n                  help=\"Send logs to this crayon server.\")\n        group.add('--exp', '-exp', type=str, default=\"\",\n                  help=\"Name of the experiment for logging.\")\n        group.add('--exp_dir', '-exp_dir', type=str, default=\"\",\n                  help=\"Directory of outputs.\")\n        # Use Tensorboard for visualization during training\n        group.add('--tensorboard', '-tensorboard', action=\"store_true\",\n                  help=\"Use tensorboard for visualization during training. \"\n                       \"Must have the library tensorboard >= 1.14.\")\n        group.add(\"--tensorboard_log_dir\", \"-tensorboard_log_dir\",\n                  type=str, default=\"runs/onmt\",\n                  help=\"Log directory for Tensorboard. \"\n                       \"This is also the name of the run.\")\n        # Use WANDB\n        group.add('--wandb', '-wandb', default=False, type=str2bool,\n                  help=\"Use wandb.\")\n        group.add('--wandb_project', '-wandb_project', type=str,\n                  help=\"wandb project name.\")\n        group.add('--wandb_key', '-wandb_key',\n                  type=str, help=\"Use wandb.\")\n        group.add(\"--wandb_log_dir\", \"-wandb_log_dir\",\n                  type=str, default=None,\n                  help=\"Log directory for Wandb. \"\n                       \"This is also the name of the run.\")\n    else:\n        # Options only during inference\n        group.add('--verbose', '-verbose', action=\"store_true\",\n                  help='Print scores and predictions for each sentence')\n        group.add('--attn_debug', '-attn_debug', action=\"store_true\",\n                  help='Print best attn for each word')\n        group.add('--align_debug', '-align_debug', action=\"store_true\",\n                  help='Print best align for each word')\n        group.add('--dump_beam', '-dump_beam', type=str, default=\"\",\n                  help='File to dump beam information to.')\n        group.add('--n_best', '-n_best', type=int, default=1,\n                  help=\"If verbose is set, will output the n_best \"\n                       \"decoded sentences\")\n\n\ndef _add_reproducibility_opts(parser):\n    group = parser.add_argument_group('Reproducibility')\n    group.add('--seed', '-seed', type=int, default=-1,\n              help=\"Set random seed used for better \"\n                   \"reproducibility between experiments.\")\n\n\ndef _add_dynamic_corpus_opts(parser, build_vocab_only=False):\n    \"\"\"Options related to training corpus, type: a list of dictionary.\"\"\"\n    group = parser.add_argument_group('Data')\n    group.add(\"-data\", \"--data\", # required=True,\n              help=\"List of datasets and their specifications. \"\n                   \"See examples/*.yaml for further details.\")\n    group.add(\"-skip_empty_level\", \"--skip_empty_level\", default=\"warning\",\n              choices=[\"silent\", \"warning\", \"error\"],\n              help=\"Security level when encounter empty examples.\"\n                   \"silent: silently ignore/skip empty example;\"\n                   \"warning: warning when ignore/skip empty example;\"\n                   \"error: raise error & stop excution when encouter empty.)\")\n    group.add(\"-transforms\", \"--transforms\", default=[], nargs=\"+\",\n              choices=AVAILABLE_TRANSFORMS.keys(),\n              help=\"Default transform pipeline to apply to data. \"\n                   \"Can be specified in each corpus of data to override.\")\n\n    group.add(\"-save_data\", \"--save_data\", required=build_vocab_only,\n              help=\"Output base path for objects that will \"\n                   \"be saved (vocab, transforms, embeddings, ...).\")\n    group.add(\"-overwrite\", \"--overwrite\", action=\"store_true\",\n              help=\"Overwrite existing objects if any.\")\n    group.add(\n        '-n_sample', '--n_sample',\n        type=int, default=(5000 if build_vocab_only else 0),\n        help=(\"Build vocab using \" if build_vocab_only else \"Stop after save \")\n        + \"this number of transformed samples/corpus. Can be [-1, 0, N>0]. \"\n        \"Set to -1 to go full corpus, 0 to skip.\")\n\n    # (deprecated) Data settings added by @memray for multiple news datasets and feature control\n    group.add('--shuffle_shards', '-shuffle_shards', action='store_true',\n              help=\"Shuffle the order of shards for each epoch.\")\n\n    if not build_vocab_only:\n        group.add('-dump_fields', '--dump_fields', action='store_true',\n                  help=\"Dump fields `*.vocab.pt` to disk.\"\n                  \" -save_data should be set as saving prefix.\")\n        group.add('-dump_transforms', '--dump_transforms', action='store_true',\n                  help=\"Dump transforms `*.transforms.pt` to disk.\"\n                  \" -save_data should be set as saving prefix.\")\n    else:\n        group.add('-dump_samples', '--dump_samples', action='store_true',\n                  help=\"Dump samples when building vocab. \"\n                  \"Warning: this may slow down the process.\")\n        group.add('-num_threads', '--num_threads', type=int, default=1,\n                  help=\"Number of parallel threads to build the vocab.\")\n        group.add('-vocab_sample_queue_size', '--vocab_sample_queue_size',\n                  type=int, default=20,\n                  help=\"Size of queues used in the build_vocab dump path.\")\n\n\ndef _add_dynamic_fields_opts(parser, build_vocab_only=False):\n    \"\"\"Options related to vocabulary and fields.\n\n    Add all options relate to vocabulary or fields to parser.\n    If `build_vocab_only` set to True, do not contain fields\n    related options which won't be used in `bin/build_vocab.py`.\n    \"\"\"\n    group = parser.add_argument_group(\"Vocab\")\n    group.add(\"-src_vocab\", \"--src_vocab\", # required=True,\n              help=(\"Path to save\" if build_vocab_only else \"Path to\")\n              + \" src (or shared) vocabulary file. \"\n              \"Format: one <word> or <word>\\t<count> per line.\")\n    group.add(\"-tgt_vocab\", \"--tgt_vocab\",\n              help=(\"Path to save\" if build_vocab_only else \"Path to\")\n              + \" tgt vocabulary file. \"\n              \"Format: one <word> or <word>\\t<count> per line.\")\n    group.add(\"-share_vocab\", \"--share_vocab\", action=\"store_true\",\n              help=\"Share source and target vocabulary.\")\n\n    if not build_vocab_only:\n        group.add(\"-src_vocab_size\", \"--src_vocab_size\",\n                  type=int, default=50000,\n                  help=\"Maximum size of the source vocabulary.\")\n        group.add(\"-tgt_vocab_size\", \"--tgt_vocab_size\",\n                  type=int, default=50000,\n                  help=\"Maximum size of the target vocabulary\")\n        group.add(\"-vocab_size_multiple\", \"--vocab_size_multiple\",\n                  type=int, default=1,\n                  help=\"Make the vocabulary size a multiple of this value.\")\n\n        group.add(\"-src_words_min_frequency\", \"--src_words_min_frequency\",\n                  type=int, default=0,\n                  help=\"Discard source words with lower frequency.\")\n        group.add(\"-tgt_words_min_frequency\", \"--tgt_words_min_frequency\",\n                  type=int, default=0,\n                  help=\"Discard target words with lower frequency.\")\n\n        # Truncation options, for text corpus\n        group = parser.add_argument_group(\"Pruning\")\n        group.add(\"--src_seq_length_trunc\", \"-src_seq_length_trunc\",\n                  type=int, default=None,\n                  help=\"Truncate source sequence length.\")\n        group.add(\"--tgt_seq_length_trunc\", \"-tgt_seq_length_trunc\",\n                  type=int, default=None,\n                  help=\"Truncate target sequence length.\")\n\n        group = parser.add_argument_group('Embeddings')\n        group.add('-both_embeddings', '--both_embeddings',\n                  help=\"Path to the embeddings file to use \"\n                  \"for both source and target tokens.\")\n        group.add('-src_embeddings', '--src_embeddings',\n                  help=\"Path to the embeddings file to use for source tokens.\")\n        group.add('-tgt_embeddings', '--tgt_embeddings',\n                  help=\"Path to the embeddings file to use for target tokens.\")\n        group.add('-embeddings_type', '--embeddings_type',\n                  choices=[\"GloVe\", \"word2vec\"],\n                  help=\"Type of embeddings file.\")\n\n    \"\"\" @memray Related to external resources \"\"\"\n    group.add('--data_format', '-data_format', default='jsonl',\n              choices=['text', 'jsonl'],\n              help=\"\"\"Format of input data, specifying loading data from plain text (OpenNMT default) \n              or .jsonl\"\"\")\n    group.add('--fairseq_model', '-fairseq_model',\n              type=str2bool, default=False, help=\"If true, indicating the checkpoint is trained from FairSeq.\")\n    group.add('--pretrained_tokenizer', '-pretrained_tokenizer',\n              type=str2bool, default=False)\n    # group.add('--pretrained_tokenizer_name', '-pretrained_tokenizer_name',\n    #           type=str, default=\"\")\n    group.add('--cache_dir', '-cache_dir', default=\"\",\n              help=\"Path to HF cache.\")\n    # group.add('--special_vocab_path', '-special_vocab_path', default=\"\",\n    #           help=\"Path to HF special_vocab_path.\")\n    # group.add('--bpe_merges', '-bpe_merges', default=\"\",\n    #           help=\"Required for loading huggingface tokenizer.\")\n\n\ndef _add_dynamic_transform_opts(parser):\n    \"\"\"Options related to transforms.\n\n    Options that specified in the definitions of each transform class\n    at `onmt/transforms/*.py`.\n    \"\"\"\n    for name, transform_cls in AVAILABLE_TRANSFORMS.items():\n        transform_cls.add_options(parser)\n\n\ndef dynamic_prepare_opts(parser, build_vocab_only=False):\n    \"\"\"Options related to data prepare in dynamic mode.\n\n    Add all dynamic data prepare related options to parser.\n    If `build_vocab_only` set to True, then only contains options that\n    will be used in `onmt/bin/build_vocab.py`.\n    \"\"\"\n    config_opts(parser)\n    _add_dynamic_corpus_opts(parser, build_vocab_only=build_vocab_only)\n    _add_dynamic_fields_opts(parser, build_vocab_only=build_vocab_only)\n    _add_dynamic_transform_opts(parser)\n\n    if build_vocab_only:\n        _add_reproducibility_opts(parser)\n        # as for False, this will be added in _add_train_general_opts\n\n\ndef model_opts(parser):\n    \"\"\"\n    These options are passed to the construction of the model.\n    Be careful with these as they will be used during translation.\n    \"\"\"\n\n    # Embedding Options\n    group = parser.add_argument_group('Model-Embeddings')\n    group.add('--src_word_vec_size', '-src_word_vec_size',\n              type=int, default=500,\n              help='Word embedding size for src.')\n    group.add('--tgt_word_vec_size', '-tgt_word_vec_size',\n              type=int, default=500,\n              help='Word embedding size for tgt.')\n    group.add('--word_vec_size', '-word_vec_size', type=int, default=-1,\n              help='Word embedding size for src and tgt.')\n\n    group.add('--share_decoder_embeddings', '-share_decoder_embeddings',\n              action='store_true',\n              help=\"Use a shared weight matrix for the input and \"\n                   \"output word  embeddings in the decoder.\")\n    group.add('--share_embeddings', '-share_embeddings', action='store_true',\n              help=\"Share the word embeddings between encoder \"\n                   \"and decoder. Need to use shared dictionary for this \"\n                   \"option.\")\n    group.add('--position_encoding', '-position_encoding', action='store_true',\n              help=\"Use a sin to mark relative words positions. \"\n                   \"Necessary for non-RNN style models.\")\n\n    group = parser.add_argument_group('Model-Embedding Features')\n    group.add('--feat_merge', '-feat_merge', type=str, default='concat',\n              choices=['concat', 'sum', 'mlp'],\n              help=\"Merge action for incorporating features embeddings. \"\n                   \"Options [concat|sum|mlp].\")\n    group.add('--feat_vec_size', '-feat_vec_size', type=int, default=-1,\n              help=\"If specified, feature embedding sizes \"\n                   \"will be set to this. Otherwise, feat_vec_exponent \"\n                   \"will be used.\")\n    group.add('--feat_vec_exponent', '-feat_vec_exponent',\n              type=float, default=0.7,\n              help=\"If -feat_merge_size is not set, feature \"\n                   \"embedding sizes will be set to N^feat_vec_exponent \"\n                   \"where N is the number of values the feature takes.\")\n    # @memray moved from train group\n    group.add('--dropout', '-dropout', type=float, default=[0.3], nargs='+',\n              help=\"Dropout probability; applied in LSTM stacks.\")\n    group.add('--attention_dropout', '-attention_dropout', type=float,\n              default=[0.1], nargs='+',\n              help=\"Attention Dropout probability.\")\n\n    # Model Task Options\n    group = parser.add_argument_group(\"Model-Task\")\n    group.add(\n        \"-model_task\",\n        \"--model_task\",\n        default=ModelTask.SEQ2SEQ,\n        choices=[ModelTask.SEQ2SEQ, ModelTask.LANGUAGE_MODEL],\n        help=\"Type of task for the model either seq2seq or lm\",\n    )\n\n    # Encoder-Decoder Options\n    group = parser.add_argument_group('Model- Encoder-Decoder')\n    group.add('--model_type', '-model_type', default='text',\n              choices=['text', 'keyphrase'],\n              help=\"Type of source model to use. Allows \"\n                   \"the system to incorporate non-text inputs. \"\n                   \"Options are [text].\")\n    group.add('--model_dtype', '-model_dtype', default='fp32',\n              choices=['fp32', 'fp16'],\n              help='Data type of the model.')\n\n    group.add('--encoder_type', '-encoder_type', type=str, default='rnn',\n              choices=['rnn', 'brnn', 'ggnn', 'mean', 'transformer', 'cnn',\n                       'transformer_lm', 'huggingface', 'fairseq_bart'],\n              help=\"Type of encoder layer to use. Non-RNN layers \"\n                   \"are experimental. Options are \"\n                   \"[rnn|brnn|ggnn|mean|transformer|cnn|transformer_lm].\")\n    group.add('--decoder_type', '-decoder_type', type=str, default='rnn',\n              choices=['rnn', 'transformer', 'cnn', 'transformer_lm', 'huggingface', 'fairseq_bart'],\n              help=\"Type of decoder layer to use. Non-RNN layers \"\n                   \"are experimental. Options are \"\n                   \"[rnn|transformer|cnn|transformer].\")\n\n    group.add('--layers', '-layers', type=int, default=-1,\n              help='Number of layers in enc/dec.')\n    group.add('--enc_layers', '-enc_layers', type=int, default=2,\n              help='Number of layers in the encoder')\n    group.add('--dec_layers', '-dec_layers', type=int, default=2,\n              help='Number of layers in the decoder')\n    group.add('--rnn_size', '-rnn_size', type=int, default=-1,\n              help=\"Size of rnn hidden states. Overwrites \"\n                   \"enc_rnn_size and dec_rnn_size\")\n    group.add('--enc_rnn_size', '-enc_rnn_size', type=int, default=500,\n              help=\"Size of encoder rnn hidden states.\")\n    group.add('--dec_rnn_size', '-dec_rnn_size', type=int, default=500,\n              help=\"Size of decoder rnn hidden states.\")\n    group.add('--cnn_kernel_width', '-cnn_kernel_width', type=int, default=3,\n              help=\"Size of windows in the cnn, the kernel_size is \"\n                   \"(cnn_kernel_width, 1) in conv layer\")\n\n    group.add('--input_feed', '-input_feed', type=int, default=1,\n              help=\"Feed the context vector at each time step as \"\n                   \"additional input (via concatenation with the word \"\n                   \"embeddings) to the decoder.\")\n    group.add('--bridge', '-bridge', action=\"store_true\",\n              help=\"Have an additional layer between the last encoder \"\n                   \"state and the first decoder state\")\n    group.add('--rnn_type', '-rnn_type', type=str, default='LSTM',\n              choices=['LSTM', 'GRU', 'SRU'],\n              action=CheckSRU,\n              help=\"The gate type to use in the RNNs\")\n    # group.add('--residual', '-residual',   action=\"store_true\",\n    #                     help=\"Add residual connections between RNN layers.\")\n\n    group.add('--brnn', '-brnn', action=DeprecateAction,\n              help=\"Deprecated, use `encoder_type`.\")\n\n    group.add('--context_gate', '-context_gate', type=str, default=None,\n              choices=['source', 'target', 'both'],\n              help=\"Type of context gate to use. \"\n                   \"Do not select for no context gate.\")\n\n    # The following options (bridge_extra_node to src_vocab) are used\n    # for training with --encoder_type ggnn (Gated Graph Neural Network).\n    group.add('--bridge_extra_node', '-bridge_extra_node',\n              type=bool, default=True,\n              help='Graph encoder bridges only extra node to decoder as input')\n    group.add('--bidir_edges', '-bidir_edges', type=bool, default=True,\n              help='Graph encoder autogenerates bidirectional edges')\n    group.add('--state_dim', '-state_dim', type=int, default=512,\n              help='Number of state dimensions in the graph encoder')\n    group.add('--n_edge_types', '-n_edge_types', type=int, default=2,\n              help='Number of edge types in the graph encoder')\n    group.add('--n_node', '-n_node', type=int, default=2,\n              help='Number of nodes in the graph encoder')\n    group.add('--n_steps', '-n_steps', type=int, default=2,\n              help='Number of steps to advance graph encoder')\n\n    # Attention options\n    group = parser.add_argument_group('Model- Attention')\n    group.add('--global_attention', '-global_attention',\n              type=str, default='general',\n              choices=['dot', 'general', 'mlp', 'none'],\n              help=\"The attention type to use: \"\n                   \"dotprod or general (Luong) or MLP (Bahdanau)\")\n    group.add('--global_attention_function', '-global_attention_function',\n              type=str, default=\"softmax\", choices=[\"softmax\", \"sparsemax\"])\n    group.add('--self_attn_type', '-self_attn_type',\n              type=str, default=\"scaled-dot\",\n              help='Self attention type in Transformer decoder '\n                   'layer -- currently \"scaled-dot\" or \"average\" ')\n    group.add('--max_relative_positions', '-max_relative_positions',\n              type=int, default=0,\n              help=\"Maximum distance between inputs in relative \"\n                   \"positions representations. \"\n                   \"For more detailed information, see: \"\n                   \"https://arxiv.org/pdf/1803.02155.pdf\")\n    group.add('--heads', '-heads', type=int, default=8,\n              help='Number of heads for transformer self-attention')\n    group.add('--transformer_ff', '-transformer_ff', type=int, default=2048,\n              help='Size of hidden transformer feed-forward')\n    group.add('--aan_useffn', '-aan_useffn', action=\"store_true\",\n              help='Turn on the FFN layer in the AAN decoder')\n\n    # Alignement options\n    group = parser.add_argument_group('Model - Alignement')\n    group.add('--lambda_align', '-lambda_align', type=float, default=0.0,\n              help=\"Lambda value for alignement loss of Garg et al (2019)\"\n                   \"For more detailed information, see: \"\n                   \"https://arxiv.org/abs/1909.02074\")\n    group.add('--alignment_layer', '-alignment_layer', type=int, default=-3,\n              help='Layer number which has to be supervised.')\n    group.add('--alignment_heads', '-alignment_heads', type=int, default=0,\n              help='N. of cross attention heads per layer to supervised with')\n    group.add('--full_context_alignment', '-full_context_alignment',\n              action=\"store_true\",\n              help='Whether alignment is conditioned on full target context.')\n\n    # Generator and loss options.\n    group = parser.add_argument_group('Generator')\n    group.add('--copy_attn', '-copy_attn', action=\"store_true\",\n              help='Train copy attention layer.')\n    group.add('--copy_attn_type', '-copy_attn_type',\n              type=str, default=None,\n              choices=['dot', 'general', 'mlp', 'none'],\n              help=\"The copy attention type to use. Leave as None to use \"\n                   \"the same as -global_attention.\")\n    group.add('--generator_function', '-generator_function', default=\"softmax\",\n              choices=[\"softmax\", \"sparsemax\"],\n              help=\"Which function to use for generating \"\n                   \"probabilities over the target vocabulary (choices: \"\n                   \"softmax, sparsemax)\")\n    group.add('--copy_attn_force', '-copy_attn_force', action=\"store_true\",\n              help='When available, train to copy.')\n    group.add('--reuse_copy_attn', '-reuse_copy_attn', action=\"store_true\",\n              help=\"Reuse standard attention for copy\")\n    group.add('--copy_loss_by_seqlength', '-copy_loss_by_seqlength',\n              action=\"store_true\",\n              help=\"Divide copy loss by length of sequence\")\n    group.add('--coverage_attn', '-coverage_attn', action=\"store_true\",\n              help='Train a coverage attention layer.')\n    group.add('--lambda_coverage', '-lambda_coverage', type=float, default=0.0,\n              help='Lambda value for coverage loss of See et al (2017)')\n    group.add('--loss_scale', '-loss_scale', type=float, default=0,\n              help=\"For FP16 training, the static loss scale to use. If not \"\n                   \"set, the loss scale is dynamically computed.\")\n    group.add('--apex_opt_level', '-apex_opt_level', type=str, default=\"O1\",\n              choices=[\"O0\", \"O1\", \"O2\", \"O3\"],\n              help=\"For FP16 training, the opt_level to use.\"\n                   \"See https://nvidia.github.io/apex/amp.html#opt-levels.\")\n    # keyphrase\n    group.add('--orth_reg', '-orth_reg', type=str2bool, default=False,\n              help='Train with orth_reg.')\n    group.add('--lambda_orth_reg', '-lambda_orth_reg', type=float, default=0.0,\n              help='Train with Orthogonal Regularization (3.5.1).')\n    group.add('--sem_cov', '-sem_cov', type=str2bool, default=False,\n              help='Train with semantic coverage.')\n    group.add('--lambda_sem_cov', '-lambda_sem_cov', type=float, default=0.0,\n              help='Train with Target Encoding (3.5.2).')\n    group.add('--num_negsample', '-num_negsample', type=int, default=32,\n              help='Number of negative samples for semantic coverage.')\n    group.add('--use_ending_state', '-use_ending_state', action=\"store_true\",\n              help='Use the ending state of target encoder instead of each <SEP> state for semantic coverage.'\n                   'The former is more reasonable since there is not pooling for all <SEP> states.')\n    group.add('--target_encoder_type', '-target_encoder_type',\n              type=str, default=None,\n              choices=['rnn', 'dot', 'general', 'mlp', 'none'],\n              help=\"Train with Target Encoding.\"\n                   \"rnn means a GRU encoder\")\n    group.add('--detach_target_encoder', '-detach_target_encoder', action=\"store_true\",\n              help='Whether to detach the target encoder and train with additional loss only, or train with decoder together.')\n    group.add('--target_encoder_layers', '-target_encoder_layers', type=int, default=0,\n              help='Only relevant to Transformer, specifying how many layers of transformer will be used as target encoder.')\n\n\ndef _add_train_general_opts(parser):\n    \"\"\" General options for training \"\"\"\n    group = parser.add_argument_group('General')\n    group.add('--data_type', '-data_type', default=\"text\",\n              help=\"Type of the source input. \"\n                   \"Options are [text].\")\n\n    group.add('--save_model', '-save_model', default='model',\n              help=\"Model filename (the model will be saved as \"\n                   \"<save_model>_N.pt where N is the number \"\n                   \"of steps\")\n\n    group.add('--save_checkpoint_steps', '-save_checkpoint_steps',\n              type=int, default=5000,\n              help=\"\"\"Save a checkpoint every X steps\"\"\")\n    group.add('--keep_checkpoint', '-keep_checkpoint', type=int, default=-1,\n              help=\"Keep X checkpoints (negative: keep all)\")\n\n    # GPU\n    group.add('--gpuid', '-gpuid', default=[], nargs='*', type=int,\n              help=\"Deprecated see world_size and gpu_ranks.\")\n    group.add('--gpu_ranks', '-gpu_ranks', default=[], nargs='*', type=int,\n              help=\"list of ranks of each process.\")\n    group.add('--world_size', '-world_size', default=1, type=int,\n              help=\"total number of distributed processes.\")\n    group.add('--gpu_backend', '-gpu_backend',\n              default=\"nccl\", type=str,\n              help=\"Type of torch distributed backend\")\n    group.add('--gpu_verbose_level', '-gpu_verbose_level', default=0, type=int,\n              help=\"Gives more info on each process per GPU.\")\n    group.add('--master_ip', '-master_ip', default=\"localhost\", type=str,\n              help=\"IP of master for torch.distributed training.\")\n    group.add('--master_port', '-master_port', default=10000, type=int,\n              help=\"Port of master for torch.distributed training.\")\n    group.add('--queue_size', '-queue_size', default=40, type=int,\n              help=\"Size of queue for each process in producer/consumer\")\n\n    _add_reproducibility_opts(parser)\n\n    # Init options\n    group = parser.add_argument_group('Initialization')\n    group.add('--param_init', '-param_init', type=float, default=0.1,\n              help=\"Parameters are initialized over uniform distribution \"\n                   \"with support (-param_init, param_init). \"\n                   \"Use 0 to not use initialization\")\n    group.add('--param_init_glorot', '-param_init_glorot', action='store_true',\n              help=\"Init parameters with xavier_uniform. \"\n                   \"Required for transformer.\")\n\n    group.add('--train_from', '-train_from', default='', type=str,\n              help=\"If training from a checkpoint then this is the \"\n                   \"path to the pretrained model's state_dict.\")\n    group.add('--reset_optim', '-reset_optim', default='none',\n              choices=['none', 'all', 'states', 'keep_states'],\n              help=\"Optimization resetter when train_from.\")\n\n    # Pretrained word vectors\n    group.add('--pre_word_vecs_enc', '-pre_word_vecs_enc',\n              help=\"If a valid path is specified, then this will load \"\n                   \"pretrained word embeddings on the encoder side. \"\n                   \"See README for specific formatting instructions.\")\n    group.add('--pre_word_vecs_dec', '-pre_word_vecs_dec',\n              help=\"If a valid path is specified, then this will load \"\n                   \"pretrained word embeddings on the decoder side. \"\n                   \"See README for specific formatting instructions.\")\n    # Freeze word vectors\n    group.add('--freeze_word_vecs_enc', '-freeze_word_vecs_enc',\n              action='store_true',\n              help=\"Freeze word embeddings on the encoder side.\")\n    group.add('--freeze_word_vecs_dec', '-freeze_word_vecs_dec',\n              action='store_true',\n              help=\"Freeze word embeddings on the decoder side.\")\n\n    # Optimization options\n    group = parser.add_argument_group('Optimization- Type')\n    group.add('--batch_size', '-batch_size', type=int, default=64,\n              help='Maximum batch size for training')\n    group.add('--batch_size_multiple', '-batch_size_multiple',\n              type=int, default=None,\n              help='Batch size multiple for token batches.')\n    group.add('--batch_type', '-batch_type', default='sents',\n              choices=[\"sents\", \"tokens\"],\n              help=\"Batch grouping for batch_size. Standard \"\n                   \"is sents. Tokens will do dynamic batching\")\n    group.add('--pool_factor', '-pool_factor', type=int, default=8192,\n              help=\"\"\"Factor used in data loading and batch creations.\n              It will load the equivalent of `pool_factor` batches,\n              sort them by the according `sort_key` to produce\n              homogeneous batches and reduce padding, and yield\n              the produced batches in a shuffled way.\n              Inspired by torchtext's pool mechanism.\"\"\")\n    group.add('--normalization', '-normalization', default='sents',\n              choices=[\"sents\", \"tokens\"],\n              help='Normalization method of the gradient.')\n    group.add('--accum_count', '-accum_count', type=int, nargs='+',\n              default=[1],\n              help=\"Accumulate gradient this many times. \"\n                   \"Approximately equivalent to updating \"\n                   \"batch_size * accum_count batches at once. \"\n                   \"Recommended for Transformer.\")\n    group.add('--accum_steps', '-accum_steps', type=int, nargs='+',\n              default=[0], help=\"Steps at which accum_count values change\")\n\n    group.add('--valid', '-valid', action='store_true',\n              help=\"Whether perform validation or not during training.\")\n    group.add('--valid_steps', '-valid_steps', type=int, default=10000,\n              help='Perfom validation every X steps')\n    group.add('--valid_batch_size', '-valid_batch_size', type=int, default=32,\n              help='Maximum batch size for validation')\n    group.add('--max_generator_batches', '-max_generator_batches',\n              type=int, default=32,\n              help=\"Maximum batches of words in a sequence to run \"\n                   \"the generator on in parallel. Higher is faster, but \"\n                   \"uses more memory. Set to 0 to disable.\")\n    group.add('--train_steps', '-train_steps', type=int, default=100000,\n              help='Number of training steps')\n    group.add('--single_pass', '-single_pass', action='store_true',\n              help=\"Make a single pass over the training dataset.\")\n    group.add('--epochs', '-epochs', type=int, default=0,\n              help='Deprecated epochs see train_steps')\n    group.add('--early_stopping', '-early_stopping', type=int, default=0,\n              help='Number of validation steps without improving.')\n    group.add('--early_stopping_criteria', '-early_stopping_criteria',\n              nargs=\"*\", default=None,\n              help='Criteria to use for early stopping.')\n    group.add('--optim', '-optim', default='sgd',\n              choices=['sgd', 'adagrad', 'adadelta', 'adam',\n                       'sparseadam', 'adafactor', 'fusedadam'],\n              help=\"Optimization method.\")\n    group.add('--adagrad_accumulator_init', '-adagrad_accumulator_init',\n              type=float, default=0,\n              help=\"Initializes the accumulator values in adagrad. \"\n                   \"Mirrors the initial_accumulator_value option \"\n                   \"in the tensorflow adagrad (use 0.1 for their default).\")\n    group.add('--max_grad_norm', '-max_grad_norm', type=float, default=5,\n              help=\"If the norm of the gradient vector exceeds this, \"\n                   \"renormalize it to have the norm equal to \"\n                   \"max_grad_norm\")\n    group.add('--dropout_steps', '-dropout_steps', type=int, nargs='+',\n              default=[0], help=\"Steps at which dropout changes.\")\n    group.add('--truncated_decoder', '-truncated_decoder', type=int, default=0,\n              help=\"\"\"Truncated bptt.\"\"\")\n    group.add('--adam_beta1', '-adam_beta1', type=float, default=0.9,\n              help=\"The beta1 parameter used by Adam. \"\n                   \"Almost without exception a value of 0.9 is used in \"\n                   \"the literature, seemingly giving good results, \"\n                   \"so we would discourage changing this value from \"\n                   \"the default without due consideration.\")\n    group.add('--adam_beta2', '-adam_beta2', type=float, default=0.999,\n              help='The beta2 parameter used by Adam. '\n                   'Typically a value of 0.999 is recommended, as this is '\n                   'the value suggested by the original paper describing '\n                   'Adam, and is also the value adopted in other frameworks '\n                   'such as Tensorflow and Keras, i.e. see: '\n                   'https://www.tensorflow.org/api_docs/python/tf/train/Adam'\n                   'Optimizer or https://keras.io/optimizers/ . '\n                   'Whereas recently the paper \"Attention is All You Need\" '\n                   'suggested a value of 0.98 for beta2, this parameter may '\n                   'not work well for normal models / default '\n                   'baselines.')\n    group.add('--label_smoothing', '-label_smoothing', type=float, default=0.0,\n              help=\"Label smoothing value epsilon. \"\n                   \"Probabilities of all non-true labels \"\n                   \"will be smoothed by epsilon / (vocab_size - 1). \"\n                   \"Set to zero to turn off label smoothing. \"\n                   \"For more detailed information, see: \"\n                   \"https://arxiv.org/abs/1512.00567\")\n    group.add('--average_decay', '-average_decay', type=float, default=0,\n              help=\"Moving average decay. \"\n                   \"Set to other than 0 (e.g. 1e-4) to activate. \"\n                   \"Similar to Marian NMT implementation: \"\n                   \"http://www.aclweb.org/anthology/P18-4020 \"\n                   \"For more detail on Exponential Moving Average: \"\n                   \"https://en.wikipedia.org/wiki/Moving_average\")\n    group.add('--average_every', '-average_every', type=int, default=1,\n              help=\"Step for moving average. \"\n                   \"Default is every update, \"\n                   \"if -average_decay is set.\")\n\n    # learning rate\n    group = parser.add_argument_group('Optimization-Rate')\n    group.add('--learning_rate', '-learning_rate', type=float, default=1.0,\n              help=\"Starting learning rate. \"\n                   \"Recommended settings: sgd = 1, adagrad = 0.1, \"\n                   \"adadelta = 1, adam = 0.001\")\n    group.add('--learning_rate_decay', '-learning_rate_decay',\n              type=float, default=0.5,\n              help=\"If update_learning_rate, decay learning rate by \"\n                   \"this much if steps have gone past \"\n                   \"start_decay_steps\")\n    group.add('--start_decay_steps', '-start_decay_steps',\n              type=int, default=50000,\n              help=\"Start decaying every decay_steps after \"\n                   \"start_decay_steps\")\n    group.add('--decay_steps', '-decay_steps', type=int, default=10000,\n              help=\"Decay every decay_steps\")\n\n    group.add('--decay_method', '-decay_method', type=str, default=\"none\",\n              choices=['noam', 'noam_simple', 'noamwd', 'rsqrt', 'none', 'linear'],\n              help=\"Use a custom decay rate.\")\n    group.add('--warmup_steps', '-warmup_steps', type=int, default=4000,\n              help=\"Number of warmup steps for custom decay.\")\n    _add_logging_opts(parser, is_train=True)\n\n\ndef _add_train_dynamic_data(parser):\n    group = parser.add_argument_group(\"Dynamic data\")\n    group.add(\"-bucket_size\", \"--bucket_size\", type=int, default=2048,\n              help=\"Examples per dynamically generated torchtext Dataset.\")\n\n\ndef train_opts(parser):\n    \"\"\"All options used in train.\"\"\"\n    # options relate to data preprare\n    dynamic_prepare_opts(parser, build_vocab_only=False)\n    # options relate to train\n    model_opts(parser)\n    _add_train_general_opts(parser)\n    _add_train_dynamic_data(parser)\n\n\ndef _add_decoding_opts(parser):\n    group = parser.add_argument_group('Decoding tricks')\n    group.add('--block_ngram_repeat', '-block_ngram_repeat',\n              type=int, default=0,\n              help='Block repetition of ngrams during decoding.')\n    group.add('--ignore_when_blocking', '-ignore_when_blocking',\n              nargs='+', type=str, default=[],\n              help=\"Ignore these strings when blocking repeats. \"\n                   \"You want to block sentence delimiters.\")\n    group.add('--replace_unk', '-replace_unk', action=\"store_true\",\n              help=\"Replace the generated UNK tokens with the \"\n                   \"source token that had highest attention weight. If \"\n                   \"phrase_table is provided, it will look up the \"\n                   \"identified source token and give the corresponding \"\n                   \"target token. If it is not provided (or the identified \"\n                   \"source token does not exist in the table), then it \"\n                   \"will copy the source token.\")\n    group.add('--phrase_table', '-phrase_table', type=str, default=\"\",\n              help=\"If phrase_table is provided (with replace_unk), it will \"\n                   \"look up the identified source token and give the \"\n                   \"corresponding target token. If it is not provided \"\n                   \"(or the identified source token does not exist in \"\n                   \"the table), then it will copy the source token.\")\n\n    group = parser.add_argument_group('Random Sampling')\n    group.add('--random_sampling_topk', '-random_sampling_topk',\n              default=1, type=int,\n              help=\"Set this to -1 to do random sampling from full \"\n                   \"distribution. Set this to value k>1 to do random \"\n                   \"sampling restricted to the k most likely next tokens. \"\n                   \"Set this to 1 to use argmax or for doing beam \"\n                   \"search.\")\n    group.add('--random_sampling_temp', '-random_sampling_temp',\n              default=1., type=float,\n              help=\"If doing random sampling, divide the logits by \"\n                   \"this before computing softmax during decoding.\")\n    _add_reproducibility_opts(parser)\n\n    group = parser.add_argument_group('Beam Search')\n    group.add('--beam_size', '-beam_size', type=int, default=5,\n              help='Beam size')\n    group.add('--min_length', '-min_length', type=int, default=0,\n              help='Minimum prediction length')\n    group.add('--max_length', '-max_length', type=int, default=100,\n              help='Maximum prediction length.')\n    group.add('--max_sent_length', '-max_sent_length', action=DeprecateAction,\n              help=\"Deprecated, use `-max_length` instead\")\n    # configs for Keyphrase Decoding\n    group.add('--beam_terminate', '-beam_terminate', default='full',\n              choices=['topbeam', 'full'],\n              help=\"Termination condition on when beam search stops\"\n                   \"`topbeam` means the beam search stops once the topbeam is done (top score)\"\n                   \"`full` means all beams will be explored exhaustively until reaching max_length, default for one2one but would cause waste on one2seq kp generation\"\n              )\n\n    # Alpha and Beta values for Google Length + Coverage penalty\n    # Described here: https://arxiv.org/pdf/1609.08144.pdf, Section 7\n    group.add('--stepwise_penalty', '-stepwise_penalty', action='store_true',\n              help=\"Apply penalty at every decoding step. \"\n                   \"Helpful for summary penalty.\")\n    group.add('--length_penalty', '-length_penalty', default='none',\n              choices=['none', 'wu', 'avg'],\n              help=\"Length Penalty to use.\")\n    group.add('--ratio', '-ratio', type=float, default=-0.,\n              help=\"Ratio based beam stop condition\")\n    group.add('--coverage_penalty', '-coverage_penalty', default='none',\n              choices=['none', 'wu', 'summary'],\n              help=\"Coverage Penalty to use.\")\n    group.add('--alpha', '-alpha', type=float, default=0.,\n              help=\"Google NMT length penalty parameter \"\n                   \"(higher = longer generation)\")\n    group.add('--beta', '-beta', type=float, default=-0.,\n              help=\"Coverage penalty parameter\")\n\n\ndef translate_opts(parser):\n    \"\"\" Translation / inference options \"\"\"\n    group = parser.add_argument_group('Model')\n    group.add('--model', '-model', dest='models', metavar='MODEL',\n              nargs='+', type=str, default=[], #required=True,\n              help=\"Path to model .pt file(s). \"\n                   \"Multiple models can be specified, \"\n                   \"for ensemble decoding.\")\n    group.add('--fp32', '-fp32', action='store_true',\n              help=\"Force the model to be in FP32 \"\n                   \"because FP16 is very slow on GTX1080(ti).\")\n    group.add('--int8', '-int8', action='store_true',\n              help=\"Enable dynamic 8-bit quantization (CPU only).\")\n    group.add('--avg_raw_probs', '-avg_raw_probs', action='store_true',\n              help=\"If this is set, during ensembling scores from \"\n                   \"different models will be combined by averaging their \"\n                   \"raw probabilities and then taking the log. Otherwise, \"\n                   \"the log probabilities will be averaged directly. \"\n                   \"Necessary for models whose output layers can assign \"\n                   \"zero probability.\")\n\n    group = parser.add_argument_group('Data')\n    group.add('--data_type', '-data_type', default=\"text\",\n              help=\"Type of the source input. Options: [text].\")\n\n    group.add('--src', '-src', # required=True,\n              help=\"Source sequence to decode (one line per \"\n                   \"sequence)\")\n    group.add('--tgt', '-tgt',\n              help='True target sequence (optional)')\n    group.add('--tgt_prefix', '-tgt_prefix', action='store_true',\n              help='Generate predictions using provided `-tgt` as prefix.')\n    group.add('--shard_size', '-shard_size', type=int, default=10000,\n              help=\"Divide src and tgt (if applicable) into \"\n                   \"smaller multiple src and tgt files, then \"\n                   \"build shards, each shard will have \"\n                   \"opt.shard_size samples except last shard. \"\n                   \"shard_size=0 means no segmentation \"\n                   \"shard_size>0 means segment dataset into multiple shards, \"\n                   \"each shard has shard_size samples\")\n    group.add('--output', '-output', default='pred.txt',\n              help=\"Path to output the predictions (each line will \"\n                   \"be the decoded sequence\")\n    group.add('--report_align', '-report_align', action='store_true',\n              help=\"Report alignment for each translation.\")\n    group.add('--report_time', '-report_time', action='store_true',\n              help=\"Report some translation time metrics\")\n\n    # Adding options relate to decoding strategy\n    _add_decoding_opts(parser)\n\n    # Adding option for logging\n    _add_logging_opts(parser, is_train=False)\n\n    group = parser.add_argument_group('Efficiency')\n    group.add('--batch_size', '-batch_size', type=int, default=30,\n              help='Batch size')\n    group.add('--batch_type', '-batch_type', default='sents',\n              choices=[\"sents\", \"tokens\"],\n              help=\"Batch grouping for batch_size. Standard \"\n                   \"is sents. Tokens will do dynamic batching\")\n    group.add('--gpu', '-gpu', type=int, default=-1,\n              help=\"Device to run on\")\n\n\n# Copyright 2016 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\n\nclass StoreLoggingLevelAction(configargparse.Action):\n    \"\"\" Convert string to logging level \"\"\"\n    import logging\n    LEVELS = {\n        \"CRITICAL\": logging.CRITICAL,\n        \"ERROR\": logging.ERROR,\n        \"WARNING\": logging.WARNING,\n        \"INFO\": logging.INFO,\n        \"DEBUG\": logging.DEBUG,\n        \"NOTSET\": logging.NOTSET\n    }\n\n    CHOICES = list(LEVELS.keys()) + [str(_) for _ in LEVELS.values()]\n\n    def __init__(self, option_strings, dest, help=None, **kwargs):\n        super(StoreLoggingLevelAction, self).__init__(\n            option_strings, dest, help=help, **kwargs)\n\n    def __call__(self, parser, namespace, value, option_string=None):\n        # Get the key 'value' in the dict, or just use 'value'\n        level = StoreLoggingLevelAction.LEVELS.get(value, value)\n        setattr(namespace, self.dest, level)\n\n\nclass DeprecateAction(configargparse.Action):\n    \"\"\" Deprecate action \"\"\"\n\n    def __init__(self, option_strings, dest, help=None, **kwargs):\n        super(DeprecateAction, self).__init__(option_strings, dest, nargs=0,\n                                              help=help, **kwargs)\n\n    def __call__(self, parser, namespace, values, flag_name):\n        help = self.help if self.help is not None else \"\"\n        msg = \"Flag '%s' is deprecated. %s\" % (flag_name, help)\n        raise configargparse.ArgumentTypeError(msg)\n"
  },
  {
    "path": "onmt/tests/__init__.py",
    "content": ""
  },
  {
    "path": "onmt/tests/output_hyp.txt",
    "content": "<unk> Parlament <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> , in der <unk> <unk> , in der <unk> <unk> , in der <unk> der <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> .\nDie <unk> , die die <unk> <unk> <unk> <unk> hat , war es in <unk> <unk> <unk> der <unk> für <unk> <unk> .\nIn <unk> wurde <unk> <unk> in <unk> <unk> in <unk> <unk> <unk> <unk> .\nDie <unk> ist nicht <unk> die <unk> <unk> der <unk> in <unk> .\n<unk> <unk> die <unk> ist eine politische <unk> der <unk> in der <unk> , die <unk> <unk> <unk> <unk> .\nDie Vorschlag der <unk> <unk> von der <unk> von <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> wurde , war er <unk> <unk> <unk> <unk> .\n<unk> wäre <unk> <unk> <unk> .\n<unk>\nDie <unk> von <unk> , <unk> oder <unk> , ist bereits <unk> .\n<unk> <unk> ist ein <unk> <unk> in der <unk> .\n<unk> ist die <unk> .\nWie eine <unk> , <unk> <unk> in der <unk> ist , ob sie die <unk> oder die <unk> <unk> .\nDie <unk> <unk> <unk> <unk> sich eine <unk> der <unk> <unk> , die wir bereits bereits <unk> haben .\n<unk> wird ein <unk> <unk> Land , als die <unk> für <unk> ist .\n<unk> können <unk> <unk> <unk> <unk> <unk> <unk> werden .\n<unk> eines <unk> , ein <unk> <unk> von <unk> <unk> werden .\n<unk> wird ein <unk> <unk> , <unk> , <unk> , <unk> nicht für <unk> .\n<unk> ist unser <unk> <unk> , die <unk> <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> ist es ein <unk> , dass die <unk> <unk> von der <unk> <unk> <unk> <unk> .\n<unk> nur nur <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnsere <unk> sind <unk> für <unk> , die <unk> ist nicht <unk> .\nWir <unk> in einer <unk> , <unk> , <unk> und unsere <unk> <unk> <unk> von einer <unk> , die politische <unk> , ohne die politische <unk> zu <unk> .\n<unk> die <unk> <unk> mit <unk> <unk> , <unk> wir gegen <unk> <unk> <unk> .\nDie <unk> sind nicht über die <unk> der &quot; <unk> &quot; .\n<unk> die <unk> <unk> , wird die <unk> der <unk> <unk> <unk> für <unk> <unk> <unk> .\nEs ist <unk> , dass sie <unk> nicht <unk> wird .\n<unk> die <unk> der <unk> <unk> eine <unk> <unk> .\n<unk> <unk>\nFrankreich und die <unk> Europas Europas <unk> <unk> .\nEin <unk> <unk> <unk> über Europa auf <unk> .\n<unk> Länder <unk> ihre <unk> <unk> <unk> .\n<unk> <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> ist <unk> <unk> .\n<unk> sind kein <unk> <unk> <unk> , <unk> sie <unk> , <unk> , <unk> und anderen .\n<unk> <unk> <unk> in Deutschland , <unk> , <unk> , <unk> , <unk> und <unk> Länder .\nDie <unk> hat durch die <unk> <unk> und <unk> <unk> .\n<unk> <unk> auch <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIn diesem Jahr <unk> <unk> die <unk> <unk> eine <unk> <unk> .\n<unk> die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> der <unk> <unk> <unk> .\nDie Ergebnisse sind <unk> als <unk> <unk> .\n<unk>\nIn diesem Jahr <unk> <unk> die <unk> <unk> eine <unk> <unk> der <unk> <unk> .\nDie <unk> und <unk> Daten , in <unk> <unk> , die <unk> und <unk> <unk> .\n<unk> <unk> und <unk> , die Wirtschaft der <unk> und der Europäischen Union <unk> , um die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> mit <unk> .\nDer <unk> ist eine <unk> <unk> von <unk> .\nDie <unk> der <unk> sind <unk> <unk> .\n<unk> <unk> und <unk> sind in <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , Frankreich , Frankreich , <unk> sind <unk> <unk> .\n<unk> der Tatsache , dass die Situation in der Region <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> sind die <unk> <unk> , die <unk> <unk> <unk> <unk> .\n<unk> für <unk> .\n<unk> <unk> sie <unk> <unk> und <unk> ein <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> der <unk> !\n<unk> <unk> und <unk> <unk> <unk> , <unk> sie <unk> <unk> .\nIm zweiten <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> aus dem <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> in einer <unk> Bericht .\nDie <unk> <unk> in ersten <unk> , und eine <unk> <unk> in der <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> auf der Europäischen <unk> .\n<unk> <unk> <unk> <unk> <unk> nur die <unk> <unk> in der <unk> <unk> .\n<unk> und <unk> <unk> <unk> die <unk> des <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> ist eine der <unk> <unk> der <unk> <unk> .\n<unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> Sie sich in der <unk> <unk> , um die <unk> von <unk> zu <unk> .\nDas war auch <unk> , die von der ersten <unk> in den ersten <unk> im ersten <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nSie <unk> eine <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> eine <unk> <unk> in <unk> .\nDie <unk> ist <unk> , ihre <unk> zu <unk> , die <unk> <unk> <unk> zu <unk> .\n<unk> <unk> , <unk> &quot; <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> , sie <unk> ihre <unk> zu <unk> , ihre Land zu <unk> .\nIn ihrer Bericht , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , dass es sich nicht <unk> für eine <unk> <unk> <unk> .\n<unk> <unk> auf <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> &quot; <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn einer <unk> , <unk> im <unk> <unk> <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ein <unk> Kinder zwischen <unk> und <unk> .\nAls <unk> Kinder , <unk> Kinder <unk> <unk> <unk> mit <unk> <unk> .\n<unk> können wir die Bedeutung der <unk> und <unk> eine <unk> <unk> für seine <unk> <unk> .\nFür <unk> <unk> sich <unk> <unk> auf die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> Kinder <unk> <unk> <unk> für <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> Kinder <unk> <unk> <unk> nicht <unk> .\nDer <unk> war der <unk> <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs wurde <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> .\n<unk> ist die <unk> von <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> und <unk> in <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn den <unk> <unk> , nur <unk> von <unk> <unk> .\nIn einer Reihe von <unk> <unk> alle <unk> .\nDie <unk> <unk> wurde <unk> , dass in einer <unk> <unk> Menschen nicht <unk> haben .\n<unk> ist die <unk> <unk> <unk> <unk> .\nEin <unk> hat sich <unk> , dass <unk> viele in der <unk> Kinder <unk> werden .\n<unk> alle alle <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> über <unk> Kinder <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> und <unk> .\n&quot; <unk> <unk> ist die <unk> <unk> <unk> <unk> <unk> , &quot; die <unk> <unk> der <unk> <unk> .\nAls <unk> für <unk> , <unk> <unk> <unk> <unk> <unk> .\nKinder sind <unk> sehr <unk> , <unk> und <unk> <unk> .\n<unk> in der Entwicklung des <unk>\nDas <unk> auch <unk> <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> werden .\n&quot; <unk> <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> wurde <unk> <unk> <unk> , wo <unk> <unk> <unk> <unk> werden .\n<unk> <unk> Kinder <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIm <unk> <unk> <unk> <unk> ich ein <unk> über die <unk> <unk> in der <unk> <unk> .\nIch habe nicht <unk> , was ich <unk> .\nDie drei <unk> von drei <unk> von drei <unk> <unk> für <unk> <unk> und eine <unk> <unk> , wie er in <unk> <unk> .\nUnd <unk> ist <unk> .\n<unk> <unk> wir die <unk> <unk> und <unk> <unk> <unk> .\nDie <unk> <unk> für <unk> und <unk> , aber es ist wirklich <unk> <unk> .\nDiese Zeit , die mit <unk> , <unk> , <unk> , ist nicht ein <unk> <unk> in der <unk> <unk> , wie wir die <unk> nicht <unk> .\n<unk> <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> <unk> , ohne <unk> <unk> , <unk> , <unk> ist <unk> .\nEin <unk> muss wissen , wie zu <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWir haben <unk> über die <unk> <unk> , die die <unk> <unk> <unk> , <unk> mit <unk> und <unk> <unk> und <unk> <unk> , und nicht <unk> <unk> .\nFür die <unk> und <unk> ist die <unk> zwischen der <unk> und <unk> <unk> , dass die <unk> oder <unk> nicht einer <unk> <unk> <unk> .\nSie <unk> nicht die <unk> , wie <unk> <unk> <unk> werden .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> oder <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> , <unk> ich <unk> , ein <unk> Woche zu <unk> .\n<unk> , ich bin <unk> , mit dem wir <unk> <unk> haben .\n<unk> <unk> war ein <unk> , zwei <unk> zu <unk> und <unk> .\nWir müssen alle <unk> <unk> , die <unk> <unk> , die <unk> , <unk> und <unk> .\nIch <unk> mich <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> .\n<unk> <unk> ich <unk> , wie sie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nWir müssen die <unk> <unk> oder <unk> sie auf die Internet und <unk> <unk> .\n<unk> , wie die <unk> <unk> ist , <unk> unsere <unk> <unk> und <unk> zu <unk> .\nEs ist wichtig , die <unk> Teil der <unk> .\n<unk> <unk> <unk> nicht nur <unk> <unk> , sondern <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDies ist <unk> , was eine <unk> und <unk> , <unk> <unk> , <unk> wir über <unk> .\nAber die <unk> von <unk> ist nicht <unk> .\nEine <unk> <unk>\n<unk> <unk> , <unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> uns , dass die <unk> ein <unk> , und eine <unk> sollten <unk> werden .\nEin <unk> <unk> kann mit einem <unk> <unk> <unk> .\nIn diesem Fall ist <unk> <unk> .\nFür eine <unk> <unk> können in der <unk> <unk> <unk> werden , <unk> <unk> und <unk> , wie eine <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> ist die <unk> oder <unk> , die <unk> <unk> <unk> .\n&quot; <unk> mit <unk> , <unk> er <unk> .\nIch <unk> die <unk> <unk> der <unk> &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot;\n<unk> können Sie <unk> <unk> , <unk> sie uns .\n<unk> ich <unk> <unk> , <unk> wir alle alle <unk> und <unk> , die mit Russland und <unk> , während der <unk> in <unk> <unk> <unk> <unk> werden .\n<unk> <unk> <unk> <unk> .\n<unk> <unk> , die <unk> , <unk> ich , dass eine <unk> <unk> haben , müssen er mehr <unk> <unk> als die Zahl der <unk> <unk> ist .\n<unk> ist ein <unk> <unk> <unk> <unk> <unk> <unk> ich für <unk> <unk> .\n<unk> ich , dass in der <unk> , eine <unk> <unk> <unk> <unk> und <unk> in <unk> mit anderen <unk> <unk> <unk> .\nWir müssen <unk> , dass <unk> eine <unk> <unk> .\n<unk> <unk> <unk> <unk> , dass <unk> und <unk> <unk> wird .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> , die in einer <unk> <unk> <unk> .\nDie <unk> zwischen <unk> ist ein <unk> <unk> in der <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nUnd wenn <unk> <unk> uns <unk> , <unk> wir <unk> , dann müssen wir die <unk> uns <unk> <unk> , die uns <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , wir sind <unk> , wenn die <unk> <unk> in einer <unk> <unk> / <unk> <unk> , <unk> .\nAm <unk> , <unk> und <unk> wurde die Tatsache , dass die <unk> nicht <unk> , <unk> und <unk> <unk> .\nUnsere <unk> <unk> von <unk> mit Menschen , die wir <unk> ist , ist es wichtig , ein <unk> zu <unk> .\nFür <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n&quot; <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> <unk> , <unk> und <unk> , was sie mit &quot; <unk> &quot; <unk> &quot; .\nEr <unk> uns , was <unk> eine <unk> <unk> müssen .\nDas <unk> ist der <unk> <unk> , die Sie bitte <unk> , die <unk> <unk> können .\n<unk> <unk> , wie <unk> eine <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nUm einige <unk> von <unk> und <unk> <unk> <unk> , <unk> wir <unk> <unk> <unk> , wie <unk> <unk> <unk> <unk> .\n<unk> , wenn die <unk> in einer <unk> , wo die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd <unk> sind wir <unk> .\nSie müssen die <unk> mit der <unk> <unk> , um <unk> zu <unk> .\nDas <unk> <unk> <unk> .\n<unk> <unk> mir mir <unk> <unk> .\nDie <unk> sind wirklich nicht <unk> .\nDas ist nur eine <unk> , die wir in der <unk> <unk> <unk> .\nIch <unk> mich , dass ein <unk> <unk> ist , nicht <unk> , die <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\nEin <unk> <unk> <unk> <unk> <unk> in einer <unk> <unk> , weil von einer <unk> <unk> <unk> und <unk> <unk> werden .\nDie <unk> sind eine <unk> <unk> , aber es ist nicht als <unk> <unk> , aber es ist keine <unk> <unk> .\n<unk> ist es <unk> <unk> , die <unk> <unk> <unk> <unk> .\nEs ist <unk> , die <unk> <unk> für <unk> <unk> <unk> <unk> in einer neuen <unk> <unk> <unk> <unk> <unk> .\nWir haben <unk> die <unk> , wo die <unk> <unk> der <unk> und <unk> <unk> .\n<unk> für die <unk> <unk> , die in der <unk> <unk> , <unk> wir <unk> .\nDie <unk> <unk> ein <unk> <unk> , aber die gesamte <unk> ist der <unk> von <unk> und <unk> <unk> in der <unk> <unk> .\n<unk> nicht nicht <unk> , sondern die <unk> einer <unk> <unk> der <unk> .\nDie <unk> <unk> sehr gut <unk> .\nSie <unk> in einer <unk> , wo der <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nSie können im <unk> auf die <unk> <unk> , in <unk> <unk> , und Ihre <unk> ist für <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist in <unk> <unk> <unk> , <unk> in <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> die <unk> <unk> die <unk> <unk> .\n<unk> Sie die <unk> auf der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas ist geschlossen .\n<unk> <unk> , <unk> , <unk> , <unk> oder <unk> in <unk> <unk> <unk> <unk> .\n<unk> sind die <unk> von <unk> , die von einer <unk> <unk> <unk> .\nFür die <unk> finden Sie die <unk> in der <unk> , <unk> die <unk> .\nAuf der anderen Seite , in der <unk> Sie die <unk> <unk> , nicht zu <unk> .\n<unk> sind <unk> <unk> <unk> .\nEs ist ein <unk> <unk> , aber es ist <unk> .\n<unk> Sie eine Reihe von <unk> <unk> <unk> , <unk> Sie die <unk> <unk> <unk> <unk> <unk> oder <unk> <unk> .\nDie <unk> von <unk> sind Ihnen <unk> , die Ihre <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> <unk> <unk> .\nSie sind nur <unk> <unk> , wo die <unk> der <unk> <unk> ist , ist der <unk> von <unk> und <unk> <unk> .\n<unk> <unk> .\nEs ist ein <unk> <unk> , die <unk> , die drei <unk> <unk> <unk> , <unk> <unk> <unk> werden .\nEs ist <unk> <unk> , um <unk> zu <unk> , weil es bereits gesagt ist , nicht in den <unk> mehr <unk> .\nDie <unk> <unk> die <unk> <unk> <unk> .\nDies bedeutet , dass es wichtig ist , <unk> <unk> , aber es kann nicht <unk> werden .\nWenn Sie eine <unk> <unk> <unk> , werden Sie <unk> <unk> .\nDie <unk> <unk> ist <unk> <unk> für die <unk> <unk> <unk> <unk> , <unk> wir die <unk> <unk> .\nEs ist <unk> .\nDie Frage <unk> Probleme .\nIn <unk> mit der <unk> <unk> die <unk> <unk> nicht , dass <unk> <unk> .\n<unk> eine <unk> von diesem <unk> <unk> , aber wenn Sie keine <unk> <unk> und <unk> , aber wenn Sie keine <unk> haben .\n<unk> , wenn <unk> oder <unk> <unk> in <unk> <unk> , <unk> <unk> die anderen <unk> .\n<unk> <unk> <unk> <unk> <unk> .\nDies ist kein <unk> <unk> <unk> , aber ist <unk> die <unk> .\nDie <unk> sind <unk> , dass Sie <unk> oder <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> .\n<unk> sind nur wenige <unk> <unk> pro <unk> .\nWenn Sie diese <unk> <unk> , haben Sie die <unk> von <unk> <unk> .\nDies ist es eine <unk> in <unk> , aber Sie können nicht <unk> .\nDie <unk> sind nicht <unk> und Sie müssen eine Reihe von <unk> <unk> .\nEin <unk> <unk> der <unk> ist der Tatsache , dass die <unk> oft <unk> <unk> in der <unk> <unk> .\nFür einige <unk> haben einige <unk> zu <unk> <unk> <unk> <unk> , <unk> Sie <unk> nicht in <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> ist für <unk> , wo Sie die beiden <unk> <unk> <unk> , die <unk> <unk> <unk> , während Sie sich in <unk> <unk> <unk> , während Sie sich in <unk> <unk> <unk> .\nEs ist nicht <unk> , wenn Sie <unk> werden , wenn Sie <unk> , wenn die Situation immer nur in <unk> <unk> .\nDie <unk> haben eine <unk> <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDies ist die <unk> <unk> des <unk> , <unk> <unk> , wo Sie in den <unk> <unk> <unk> .\n<unk> <unk> , die <unk> <unk> hat , ist es <unk> .\nDie <unk> ist , dass er <unk> ist , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> kann es die <unk> <unk> , <unk> <unk> und <unk> , aber es ist es <unk> <unk> <unk> .\nEs ist eine <unk> , weil die <unk> <unk> , die <unk> <unk> <unk> , ist <unk> <unk> und <unk> <unk> .\nEs ist nicht wichtig , ob die <unk> mit <unk> <unk> <unk> , <unk> von <unk> oder <unk> <unk> .\nDie <unk> von <unk> ist <unk> <unk> .\nSie haben die <unk> für <unk> <unk> <unk> <unk> der <unk> , wie Sie die <unk> <unk> <unk> <unk> , dass es sich in der <unk> der <unk> <unk> <unk> .\n<unk> sind in drei <unk> <unk> .\n<unk> sind <unk> für <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> ist <unk> <unk> mit dem <unk> <unk> .\n<unk> , <unk> , <unk> und <unk> <unk> für Ihre <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , die <unk> <unk> mit einer <unk> <unk> , <unk> die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> mit <unk> <unk> , <unk> und <unk> <unk> .\n<unk> anderen <unk> <unk> , ist die <unk> von <unk> in der <unk> .\nSie und Ihre <unk> in einem <unk> in einem <unk> <unk> <unk> , <unk> Sie die <unk> in der <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nFür <unk> Tage <unk> unsere <unk> <unk> , ob die <unk> kein <unk> für eine <unk> <unk> oder ob die <unk> nicht <unk> .\nDie <unk> Teil der <unk> ist nichts <unk> , aber die <unk> <unk> ist <unk> <unk> , aber die <unk> <unk> ist <unk> <unk> .\nEs ist ein <unk> <unk> , das im <unk> <unk> , <unk> <unk> , <unk> .\nDiese <unk> <unk> <unk> mit <unk> <unk> , <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist alle <unk> <unk> .\nIn <unk> <unk> Sie die <unk> <unk> <unk> , ist die <unk> des <unk> in allen <unk> <unk> , weil es <unk> nicht mehr <unk> <unk> , weil sie <unk> werden .\n<unk> von <unk> für <unk> <unk> ist <unk> .\nWenn die <unk> <unk> <unk> <unk> , können wir <unk> <unk> , dass die <unk> der <unk> für die <unk> Jahren <unk> <unk> .\nDie <unk> <unk> <unk> .\nDie <unk> ist ein <unk> <unk> , aber die <unk> mit der <unk> von <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> bietet die internationale <unk> <unk> <unk> <unk> <unk> und <unk> <unk> mit einem <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\nDie <unk> der <unk> ist auf zwei <unk> <unk> <unk> .\n&quot; <unk> <unk> hat in den <unk> .\n&quot; <unk> <unk> ist die <unk> der <unk> <unk> und <unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> <unk> der <unk> <unk> .\nDie <unk> werden ein <unk> <unk> von <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> <unk> , eine <unk> von einer <unk> <unk> , <unk> <unk> ist die <unk> .\nIn der <unk> ist der <unk> <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> der <unk> ist <unk> , die internationale <unk> &quot; <unk> und <unk> die <unk> der <unk> mit <unk> <unk> <unk> , <unk> und <unk> <unk> <unk> <unk> .\nDie <unk> <unk> ist <unk> <unk> <unk> <unk> in der Zusammenarbeit mit <unk> <unk> .\nDie gesamte <unk> ist im <unk> des <unk> <unk> , <unk> <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> .\nFür <unk> Informationen finden Sie <unk> <unk> .\n<unk> <unk>\n<unk> haben die <unk> von <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> zwischen <unk> .\n&quot; <unk> ist nicht .\nDie <unk> oder <unk> Jahre <unk> die Situation <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> wie <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , <unk> <unk> die <unk> <unk> <unk> <unk> der Europäischen Rates <unk> <unk> <unk> <unk>\n&quot; <unk> &quot; <unk> <unk> hat er <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> er <unk> <unk> .\n&quot; <unk> ist eine Vielzahl , die alle <unk> <unk> , <unk> <unk> , <unk> mit ihnen <unk> , und die <unk> <unk> <unk> .\nDie neue <unk> der <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> über die EU für die EU für die EU , <unk> und <unk> .\n<unk> <unk> <unk> <unk> von <unk> <unk> <unk> <unk> .\n<unk> wurden mit der <unk> <unk> mit dem <unk> <unk> <unk> .\n<unk> <unk> , <unk> die <unk> des <unk> <unk> <unk> <unk> , um die <unk> <unk> <unk> .\n<unk> <unk> in der EU ist sehr <unk> .\nWas die <unk> <unk> , <unk> der <unk> und die <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> <unk> <unk> , die <unk> , die <unk> Krise <unk> .\n<unk> die <unk> <unk> werden <unk> und <unk> , die <unk> werden , die <unk> <unk> , die <unk> werden , die <unk> <unk> .\n<unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , <unk> <unk> , und <unk> keine <unk> .\nWie gibt es <unk> und <unk> , dass in <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> auch die EU <unk> , die <unk> <unk> , <unk> <unk> zu <unk> .\n<unk> Europa <unk>\nDie <unk> <unk> sind <unk> <unk> , wie <unk> die <unk> <unk> als <unk> <unk> .\n<unk> gibt es <unk> <unk> , die in der Institutionen <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> mit der <unk> <unk> <unk> , <unk> die <unk> und Zusammenarbeit des <unk> .\n<unk> <unk> <unk> ich , dass ich <unk> von <unk> und <unk> <unk> , und eine <unk> ist <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> .\nIn der Europäischen Union <unk> <unk> <unk> neue <unk> für die <unk> des <unk> <unk> .\n<unk> als eine neue europäischen <unk> , <unk> die <unk> der <unk> , wenn nicht <unk> <unk> ist , nicht <unk> .\n<unk> sind <unk> und <unk> die <unk> .\n&quot; Wir haben <unk> <unk> , und wir haben bereits ein <unk> <unk> <unk> .\n&quot; <unk> <unk> war es schon gesagt , dass die Länder , die nicht <unk> , während die <unk> nicht <unk> kann .\n&quot; <unk> &quot; <unk> wird es ein <unk> <unk> <unk> , <unk> <unk> <unk> .\nIn diesem Zusammenhang <unk> die <unk> der <unk> <unk> und <unk> <unk> in <unk> auf die <unk> <unk> , die alle <unk> <unk> werden sollten .\n<unk>\nDie <unk> <unk> die <unk> <unk> der <unk> <unk> <unk>\n<unk> <unk> <unk> für die <unk> , dass die <unk> der <unk> <unk> <unk> <unk> <unk> werden .\n<unk> ist ein <unk> von <unk> <unk> , <unk> ist ein <unk> <unk> der <unk> <unk> .\nDie <unk> <unk> <unk> sich auf die ersten <unk> , ein <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEr <unk> in seiner <unk> <unk> <unk> , dass eine <unk> <unk> <unk> <unk> werden , wenn <unk> <unk> werden .\n<unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; , die <unk> <unk> , die <unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> zu <unk> , die <unk> in <unk> zu <unk> , die <unk> in <unk> <unk> zu <unk> , in <unk> <unk> zu <unk> , die <unk> <unk> in <unk> <unk> , in <unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> wurde der <unk> Kultur <unk> <unk> <unk> .\n&quot; <unk> <unk> bedeutet uns , dass <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> und <unk> für eine <unk> Jahren , weil es sich nicht <unk> <unk> <unk> , während sie sich nicht <unk> .\nIn unserer <unk> Jahrhundert wir uns die <unk> <unk> , die uns <unk> , <unk> und <unk> .\nIn der <unk> Jahrhundert ist wir , dass wir mehr mehr <unk> , &quot; <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> als <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> mit dem <unk> <unk> <unk> <unk> eine <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn der letzten Jahren wurde <unk> <unk> <unk> , <unk> <unk> , die viele Zukunft <unk> und <unk> <unk> .\nEs ist keine <unk> , dass die <unk> <unk> - die <unk> <unk> <unk> <unk> <unk> .\n<unk> mit einem <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> ist nicht ein <unk> <unk> , es ist ein <unk> <unk> , es ist ein <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nAls <unk> ist es ein <unk> zwischen <unk> und <unk> .\nWährend der <unk> Jahren <unk> , <unk> , <unk> oder <unk> <unk> , ist es <unk> in der USA , weil die <unk> der <unk> in einer der Welt <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> die <unk> <unk> .\n<unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nMit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\n<unk> <unk> die <unk> <unk> , die <unk> <unk> , die <unk> mit ihrem eigenen <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas ist <unk> <unk> <unk> seine <unk> in vielen <unk> - wenn sie nicht <unk> , wenn sie nicht <unk> .\nDies ist , wie es <unk> , die <unk> , die <unk> , die <unk> , die <unk> und <unk> auf <unk> , die <unk> <unk> .\n<unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> in <unk> , mit dem <unk> <unk> , <unk> er <unk> <unk> .\n<unk> hat <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> hat die <unk> <unk> , wenn die <unk> <unk> , wenn die <unk> der <unk> <unk> .\n<unk> <unk> die <unk> der ersten <unk> , <unk> <unk> , die <unk> der <unk> in der <unk> <unk> .\n<unk> ist <unk> <unk> , um die <unk> Krise zu <unk> .\nDie <unk> des <unk> ist der <unk> der <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> ist die <unk> <unk> , um <unk> zu <unk> .\nWir können uns von <unk> <unk> , wenn es <unk> ist , <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs <unk> die <unk> <unk> in <unk> .\n<unk> für die <unk> <unk> wurde <unk> von vier <unk> <unk> , die <unk> <unk> , die <unk> <unk> .\nSie <unk> <unk> wie die <unk> &quot; <unk> &quot; <unk> <unk> &quot; eine <unk> <unk> der <unk> und <unk> .\nDie <unk> der neuen <unk> wurde <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> ein <unk> <unk> <unk> , <unk> <unk> , <unk> mit <unk> <unk> .\nDie <unk> kann die <unk> der <unk> in der <unk> - <unk> der <unk> <unk> der <unk> <unk> und <unk> .\n<unk> <unk> die <unk> und <unk> .\n<unk> , wie seine <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> .\nIn den ersten Jahren des <unk> <unk> der <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> , <unk> und <unk> <unk> <unk> .\n<unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> seine <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> und <unk> , <unk> und <unk> von <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> und in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> , <unk> <unk> <unk> die ersten <unk> - die <unk> <unk> / <unk> .\nDie <unk> <unk> , dass die <unk> der <unk> <unk> des <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> Jahren <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nIm <unk> drei Jahren wurde <unk> <unk> mit dem <unk> <unk> <unk> .\n<unk> <unk> ist <unk> <unk> von <unk> in <unk> , dass keine <unk> nicht <unk> sind und die <unk> des <unk> <unk> .\nDie <unk> hat <unk> <unk> eine <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> ist <unk> <unk> <unk> <unk> <unk> .\nIm <unk> <unk> <unk> , wie die erste Land in <unk> und <unk> <unk> , <unk> <unk> seine <unk> in <unk> .\n<unk> <unk> auch eine <unk> <unk> in <unk> .\nDie <unk> für die <unk> und <unk> Europa in <unk> , wo man auch eine <unk> <unk> <unk> <unk> .\n<unk>\n<unk> <unk> die <unk> , <unk> ich nicht !\n<unk> <unk> <unk> <unk> , es ist sehr <unk> !\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> darf sie nicht mehr <unk> und <unk> <unk> <unk> .\n&quot; <unk> <unk> können nicht mehr als <unk> <unk> , &quot; <unk> <unk> <unk> .\nWenn <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> kann sie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , er <unk> <unk> <unk> er <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> ist nicht <unk> , die seine <unk> <unk> mit <unk> <unk> .\nEin <unk> , wenn ein Tag <unk> und <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist der <unk> <unk> <unk> , wenn er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n&quot; <unk> <unk> wird er nicht als <unk> <unk> , <unk> , <unk> und <unk> <unk> , aber ein <unk> <unk> , aber auch ein <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist <unk> nicht , dass seine <unk> <unk> der <unk> <unk> .\n<unk> <unk> ich <unk> <unk> .\n&quot; <unk> wird mit <unk> , und ich <unk> nicht , dass die <unk> &quot; <unk> &quot; .\n&quot; <unk> <unk> <unk> für <unk> , und ich <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> und ich <unk> im <unk> <unk> <unk> , <unk> wir die <unk> <unk> <unk> , &quot; <unk> <unk> <unk> für <unk> .\nEr <unk> , dass <unk> eine neue <unk> , <unk> er nicht in den <unk> <unk> .\n<unk> &quot; Wir <unk> nicht , und ich <unk> nicht , wo sie nicht <unk> , wo sie <unk> , mit <unk> , und wenn sie eine <unk> <unk> .\nIch weiß es , <unk> <unk> , <unk> ich in <unk> , und ich <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> <unk>\nWenn Sie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> ich Ihnen <unk> <unk> in der <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\n<unk> Sie die <unk> <unk> <unk> <unk> .\n<unk> Sie müssen eine <unk> von <unk> <unk> <unk> ?\n<unk> Sie mit <unk> <unk> <unk> ?\nWenn Sie Ihre <unk> <unk> können , können Sie Ihre <unk> <unk> .\n<unk> <unk> <unk> sie die <unk> <unk> , die <unk> für <unk> <unk> .\n<unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> kann ein <unk> für Frauen <unk> , aber für <unk> , es kann <unk> <unk> werden .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\n<unk> <unk> ich &quot; <unk> ?\n<unk> <unk> <unk> <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> ?\n&quot; <unk> <unk> Sie mir <unk> ?\n<unk> Sie diesen <unk> <unk> ?\nSie <unk> ?\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> sollten Sie die <unk> <unk> , sondern viele <unk> <unk> , sondern viele <unk> <unk> <unk> zu <unk> .\nDie <unk> <unk> auch die <unk> , die die <unk> der <unk> <unk> , die mehr <unk> <unk> .\n<unk> und <unk> <unk>\nDas ist ein <unk> <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> Sie Ihre <unk> für <unk> <unk> die <unk> <unk> in <unk> <unk> <unk> .\nIn diesem <unk> <unk> &quot; <unk> &quot; <unk> &quot; , die nicht <unk> sind und die <unk> <unk> nicht <unk> .\nDiese <unk> <unk> für <unk> <unk> , wie die gesamte <unk> .\n<unk>\nDie <unk> in der <unk> .\n&quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; ?\n&quot; <unk> <unk> ist nicht auf den <unk> .\nWenn Sie Ihre <unk> <unk> <unk> können , können Sie Ihre <unk> sehr <unk> <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> !\n<unk> , wenn <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk>\n<unk> <unk> <unk> <unk> , <unk> , <unk> und <unk> .\n<unk> sind die <unk> <unk> , die <unk> , wo sie <unk> , die mit <unk> , wie <unk> und <unk> .\n<unk> jetzt <unk> <unk> , <unk> <unk> <unk> <unk> oder <unk> .\n<unk> <unk> , <unk> ist der <unk> alle Frauen .\nEs gibt <unk> <unk> , die <unk> <unk> und <unk> alle <unk> <unk> .\n<unk> <unk> und <unk> <unk>\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> in der <unk> auch die <unk> des <unk> , <unk> <unk> .\n<unk> der <unk> , <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> möchte ich den <unk> <unk> <unk> .\n<unk> <unk> ich <unk> .\n&quot; Sie <unk> <unk> , &quot; <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> ist ein <unk> <unk> für die <unk> <unk> .\nDie <unk> <unk> &quot; <unk> und <unk> ist es für <unk> , die <unk> zwischen den <unk> .\nEin <unk> <unk> , ein <unk> <unk> , ein <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> .\nDie <unk> mit <unk> , <unk> er die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; Ich <unk> <unk> <unk> .\n&quot; Ich <unk> <unk> , dass er die <unk> <unk> <unk> , aber er <unk> die <unk> <unk> <unk> , aber er <unk> <unk> , seine <unk> <unk> .\n<unk> <unk> ich , dass die <unk> meiner <unk> ich <unk> <unk> <unk> .\n<unk> <unk> Dank .\n<unk> Dank .\n&quot; <unk> &quot; <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> die <unk> <unk> mit <unk> <unk> und <unk> der <unk> von <unk> <unk> , <unk> sie eine <unk> <unk> .\n<unk> <unk> wurde <unk> von <unk> <unk> , die die <unk> <unk> <unk> .\n&quot; <unk> <unk> <unk> in unserem <unk> .\n<unk> <unk> die <unk> , <unk> ich <unk> , und <unk> <unk> über die <unk> <unk> .\n&quot; Ich <unk> <unk> , und ich <unk> <unk> , dass er sich <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> die <unk> der <unk> <unk> .\n<unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> mit den <unk> <unk> , die <unk> <unk> <unk> <unk> und <unk> 3 von <unk> .\n<unk> <unk> <unk> auf einer <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie Situation ist <unk> .\n&quot; Wir <unk> die <unk> mit einem <unk> <unk> , die <unk> <unk> .\n&quot; <unk> ist es wichtig , dass wir über <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> ist es <unk> und wir nicht <unk> .\n&quot; <unk> sollte die <unk> <unk> , <unk> die <unk> der <unk> .\n&quot; <unk> ist unser <unk> , <unk> <unk> , <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk>\n<unk> <unk> nur 10 <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAls <unk> war die <unk> <unk> <unk> <unk> , <unk> <unk> der <unk> <unk> <unk> <unk> .\n<unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> wurde die <unk> von <unk> , wenn wir die Möglichkeit , <unk> zu <unk> , wenn wir die Möglichkeit , <unk> <unk> zu <unk> .\nEin <unk> <unk> hat die <unk> <unk> , dass die Bürger der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nWenn ein <unk> <unk> <unk> , wie ein <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> <unk> .\n<unk> , dass <unk> nicht alle <unk> ist .\nEin <unk> <unk> <unk> war <unk> für <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> war nicht <unk> , ohne die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> , eine <unk> <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> in Ländern über die <unk> .\nEin <unk> <unk> , <unk> wir nicht <unk> , ist es <unk> , dass die <unk> <unk> nicht die <unk> <unk> <unk> , wie er <unk> ist .\n<unk> auf der <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> können nicht <unk> <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> in <unk> <unk> <unk> <unk> in <unk> , weil ihre <unk> in <unk> <unk> nicht <unk> <unk> .\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> wichtig hat die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnsere <unk> <unk> und <unk> wurde <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> nicht <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAls <unk> <unk> in <unk> <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> einer anderen <unk> der <unk> , unsere Entwicklung , die unsere Entwicklung , <unk> in <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> die <unk> <unk> für <unk> und <unk> .\nUm die ersten <unk> in <unk> <unk> <unk> als eine <unk> der <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , <unk> <unk> <unk> hat keine <unk> <unk> , dass die <unk> der <unk> der <unk> <unk> <unk> <unk> <unk> wird , nicht nur <unk> <unk> .\nEs ist noch nicht , wie <unk> zu <unk> .\nDie drei <unk> wird nicht <unk> .\n<unk> <unk> , <unk> , <unk> und <unk> sind <unk> <unk> <unk> .\nDie <unk> <unk> von <unk> in <unk> <unk> , <unk> er als <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> der <unk> <unk> sich in <unk> zwischen <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDies war <unk> <unk> auf <unk> <unk> , wenn eine neue <unk> <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> hat <unk> <unk> als <unk> , ob die <unk> <unk> - ob die <unk> nur <unk> , ob die <unk> nur <unk> <unk> .\nDiese <unk> ist <unk> <unk> <unk> seit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> auf die <unk> <unk> <unk> <unk> .\nDie <unk> <unk> hat keine <unk> <unk> , <unk> , <unk> und <unk> , es ist <unk> <unk> .\nDie <unk> ist <unk> <unk> <unk> <unk> , aber die <unk> ist der <unk> <unk> .\nDie Frage <unk> werden in der <unk> <unk> , <unk> zu <unk> ?\n&quot; <unk> <unk> <unk> auf , ob die <unk> <unk> oder <unk> <unk> <unk> , <unk> <unk> , die <unk> <unk> in der <unk> <unk> .\n<unk> auf die <unk> von <unk> , die <unk> des <unk> <unk> , ist ein <unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist sehr <unk> , ein <unk> mit der <unk> <unk> von <unk> von <unk> von <unk> <unk> <unk> .\nDie <unk> <unk> eine <unk> , die in der <unk> der <unk> <unk> , müssen die <unk> <unk> <unk> .\n<unk> &quot; <unk> <unk> würde die beiden <unk> , die die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> mit seiner <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Sie die <unk> <unk> , um den <unk> zu <unk> .\n&quot; Ich möchte alle <unk> <unk> , wie in der <unk> <unk> , <unk> die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> .\nEr <unk> die neue Präsidentin <unk> <unk> in <unk> er auch <unk> , dass die <unk> <unk> wird .\n&quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; mit der <unk> <unk> in der Europäischen <unk> .\nDie <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist <unk> .\n&quot; Ich hoffe für eine Zeit , aber die Situation ist sehr <unk> .\n<unk> ist nicht zwischen <unk> und <unk> , aber es gibt es im <unk> <unk> <unk> .\nDas <unk> <unk> eine <unk> <unk> der <unk> .\n&quot; Ich hoffe , dass die <unk> <unk> , &quot; <unk> <unk> <unk> .\nEs gibt keine <unk> <unk> , die zwischen der <unk> .\n<unk> Verhandlungen <unk> in <unk> <unk> , aber die <unk> , die <unk> , wenn die <unk> <unk> wird .\nEin <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> und <unk> zu <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , eine <unk> in <unk> <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nEin <unk> ist in der <unk> Regierung <unk> <unk> <unk> <unk> <unk> und die <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nWenn es die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> ist ein <unk> <unk> , <unk> ich mich für mich und nicht <unk> .\n&quot; <unk> ist kein <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> .\n<unk> der <unk> , die die <unk> mit <unk> <unk> und <unk> <unk> ist , ist die <unk> der <unk> , der <unk> <unk> der <unk> <unk> .\n<unk> , <unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIn Zukunft , würde er die <unk> <unk> ein <unk> <unk> , nicht <unk> zu <unk> .\n&quot; Ich <unk> <unk> , einige Leute , dass <unk> <unk> werden .\n<unk> <unk> ich mir einige , einige <unk> , <unk> , <unk> , <unk> nicht <unk> <unk> .\n&quot; Ich <unk> mich <unk> , diese Frage , die er <unk> ist .\n<unk> <unk> , <unk> der <unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIch <unk> es <unk> <unk> , die <unk> zu <unk> .\n&quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> und <unk> <unk> <unk> <unk> werden .\n&quot; <unk> mit Unterstützung von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nEs ist <unk> <unk> , dass einige Jahre <unk> <unk> <unk> in <unk> <unk> <unk> <unk> <unk> ist , ohne eine <unk> .\n&quot; Ich <unk> nicht , was <unk> <unk> <unk> , <unk> <unk> <unk> .\nDie <unk> <unk> und <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn der Zukunft <unk> <unk> <unk> er <unk> die Zusammenarbeit mit <unk> in der Zukunft .\nDiese <unk> ist der <unk> von einer <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> möchte ich <unk> <unk> .\n<unk> ist sehr <unk>\n&quot; Ich kann mich <unk> , wie die <unk> <unk> .\n<unk> er nicht <unk> .\n&quot; Ich <unk> nicht <unk> , was ich <unk> , was ich nicht <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> <unk> er auf <unk> <unk> .\nEr <unk> die <unk> von <unk> aus <unk> <unk> <unk> , <unk> er hat die <unk> <unk> , die <unk> <unk> in <unk> <unk> .\n&quot; Ich habe die <unk> , die die <unk> <unk> , ihre <unk> <unk> zu unterstützen .\n&quot; <unk> &quot; <unk> die <unk> <unk> <unk> die <unk> <unk> , die die <unk> und <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> würde die <unk> zwischen <unk> und die <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> zu <unk> .\n<unk> der <unk> wird der <unk> der <unk> <unk> .\n<unk> sind <unk> <unk> , wie die politische <unk> <unk> , <unk> ich auch <unk> , aber sie müssen <unk> , wo die <unk> &quot; <unk> &quot; <unk> wird .\n<unk> <unk> nicht seine <unk> mit dem <unk> <unk> , <unk> er <unk> , dass eine <unk> <unk> <unk> .\n<unk> ist <unk> , und <unk> zu <unk> .\nEs ist nicht <unk> .\n&quot; <unk> , <unk> ich nicht ein <unk> in der <unk> <unk> <unk> , während die <unk> <unk> <unk> , während die <unk> in <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nUm die <unk> von <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> die Verhandlungen in <unk> <unk> , eine <unk> und <unk> , dass die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> in <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , in den letzten <unk> des letzten <unk> , um die <unk> <unk> in der letzten <unk> .\n<unk> auf alle <unk> haben , haben sie in <unk> <unk> , <unk> und <unk> .\nDie <unk> <unk> <unk> die <unk> <unk> , &quot; die <unk> <unk> mit den <unk> <unk> , aber er <unk> viele Jahre in <unk> und <unk> <unk> .\nDies gibt es <unk> , dass er <unk> ist , <unk> <unk> zu <unk> .\n<unk> <unk> Probleme mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n&quot; <unk> <unk> hat keine <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nEr <unk> die <unk> der <unk> <unk> der <unk> als <unk> für die <unk> .\n<unk> <unk> möchte ich <unk> , wie meine <unk> <unk> , und ich <unk> <unk> , die <unk> zu <unk> .\n&quot; <unk> ist eine <unk> Frage , die <unk> <unk> .\n<unk> als <unk> <unk> er <unk> , <unk> er nicht gegen den <unk> .\nIn der <unk> hat er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nFür eine <unk> Zeit <unk> er nicht <unk> <unk> , die <unk> <unk> <unk> .\nDie <unk> <unk> in <unk> für <unk> , und <unk> Kinder <unk> .\n<unk> hat seine <unk> nicht <unk> und viele Menschen <unk> , die von der <unk> <unk> <unk> <unk> .\nDie <unk> ist <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nEr <unk> seine <unk> <unk> in <unk> <unk> .\nDie <unk> sollte die <unk> <unk> <unk> .\n&quot; <unk> wäre auch <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> möchte ich auf <unk> und <unk> <unk> und <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEr kann <unk> <unk> , dass die <unk> der <unk> <unk> nicht <unk> <unk> .\n&quot; Ich <unk> <unk> , um die Arbeit der <unk> der <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> die <unk> <unk> <unk> , <unk> er <unk> , dass die <unk> nicht <unk> wird .\n<unk> <unk> <unk> ist , wie die <unk> in der <unk> <unk> <unk> ist , dass wir über <unk> &quot; .\n&quot; <unk> <unk> wird es nur durch <unk> <unk> <unk> , &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; , der <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> ist die <unk> , die <unk> <unk> <unk> <unk> .\nWenn die <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> , ist der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , dass er <unk> ist , <unk> <unk> , <unk> <unk> werden .\nEr <unk> die <unk> <unk> , <unk> <unk> und die <unk> der <unk> <unk> <unk> <unk> .\n<unk> <unk> auch die <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> .\n<unk> <unk> ich auf <unk> und <unk> <unk> .\n&quot; <unk> \" <unk> mit <unk> und <unk> , <unk> , <unk> , <unk> und <unk> für die <unk> .\n&quot; <unk> <unk> ich nicht <unk> , dass ich <unk> , &quot; <unk> <unk> in seiner <unk> <unk> .\n<unk> ein <unk> <unk> , und ich möchte die <unk> in der <unk> <unk> , dass die <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; Das wäre nicht <unk> <unk> , wie sie <unk> <unk> , nicht nur die Arbeit der <unk> , <unk> er <unk> .\n<unk> mit <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> ich 20 Tag .\n<unk> muss ich wissen , wenn es <unk> hat , wenn es <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> mir mich nicht <unk> , <unk> ich mich <unk> <unk> .\n&quot; <unk> eine <unk> <unk> ich nicht nur nur <unk> .\n<unk> , <unk> er seine <unk> <unk> ist , ist das <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\nDie <unk> sind <unk> , dass <unk> <unk> in einer <unk> Jahren <unk> <unk> , und ich bin nicht <unk> , dass die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> <unk> , die <unk> <unk> , ist <unk> nicht <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n&quot; Ich kann <unk> <unk> <unk> für viele <unk> .\n<unk> <unk> <unk> .\nDie <unk> für alle , weil sie ihre <unk> <unk> , nicht nur die <unk> <unk> , &quot; <unk> <unk> <unk> .\n<unk> , er <unk> die <unk> <unk> , <unk> er nur ein <unk> Präsidentin .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist es für <unk> <unk> , die zweite <unk> <unk> in <unk> und <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> würde die <unk> <unk> , wo es nur drei <unk> <unk> haben , <unk> <unk> in <unk> , <unk> , <unk> <unk> .\nWas die <unk> <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> nicht , seine <unk> zu <unk> .\n&quot; <unk> , <unk> ich ein <unk> <unk> , in der <unk> <unk> , dass mir seine <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nAuf der anderen Seite , er <unk> die Tatsache , dass die <unk> nicht <unk> als seine eigenen <unk> <unk> .\n<unk> <unk> , dass die <unk> von <unk> von <unk> <unk> , <unk> <unk> <unk> , er <unk> die anderen <unk> , die <unk> <unk> <unk> .\n&quot; <unk> eine <unk> kann , dass er ein <unk> <unk> <unk> <unk> ist .\n&quot; <unk> &quot; <unk> Ich hoffe , ich hoffe , dass er sich in der <unk> <unk> <unk> <unk> , ohne <unk> <unk> zu <unk> .\n&quot; <unk> <unk> ist nicht , um seine eigenen <unk> <unk> , &quot; <unk> <unk> zu <unk> .\nEuropa der <unk> <unk>\n<unk> in der <unk>\n<unk>\n<unk> <unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> , <unk> , <unk> , <unk> <unk> .\nEs war ein <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> mit <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> in <unk> als <unk> <unk> <unk> - und hat jetzt in den <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> ist ein <unk> <unk> Kultur <unk> .\n<unk> ist <unk> für <unk> , <unk> <unk> - aber in der Nähe des <unk> .\nDie <unk> <unk> ist , dass die <unk> von <unk> mit <unk> <unk> <unk> und die <unk> der <unk> in der <unk> <unk> können .\n&quot; <unk> Tag <unk> ich <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n&quot; <unk> <unk> <unk> ich <unk> <unk> .\n<unk> <unk>\n<unk> in der <unk> und <unk> <unk> <unk> - die <unk> <unk> in <unk> ist <unk> , weil alle <unk> <unk> sind .\nUnd die <unk> <unk> können für die <unk> , wenn die <unk> die <unk> <unk> , die <unk> <unk> , die <unk> der <unk> in <unk> <unk> .\nDie <unk> ist sehr <unk> , aber auch die <unk> des <unk> .\n<unk> ist <unk> <unk> , <unk> <unk> und <unk> <unk> .\nDie <unk> <unk> die <unk> und <unk> Informationen über die <unk> und <unk> zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> <unk> in <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas <unk> <unk> in drei <unk> <unk> <unk> die <unk> <unk> <unk> <unk> .\n<unk> in der <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist der <unk> von der <unk> in der <unk> <unk> <unk> .\n<unk> <unk> hat sich in einer <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> , die von der <unk> <unk> in <unk> , haben sich <unk> .\nWenn die <unk> durch die <unk> <unk> der <unk> <unk> , <unk> und <unk> <unk> <unk> und <unk> <unk> <unk> .\nDie <unk> <unk> in den <unk> ist ein <unk> <unk> im <unk> - die <unk> in <unk> <unk> .\nDie <unk> haben sich über drei <unk> <unk> der <unk> <unk> .\n<unk> seine <unk> <unk> in <unk> , ist die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> - und nicht nur als <unk> <unk> .\n&quot; <unk> ist ein <unk> <unk> , die <unk> <unk> <unk> , <unk> <unk> <unk> .\nEin <unk> in der <unk> <unk> in <unk> <unk> ein <unk> <unk> <unk> .\n<unk> <unk>\n<unk> hat <unk> <unk> <unk>\n<unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> ?\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> .\n\nDie <unk> ist <unk> <unk> , die <unk> <unk> , die <unk> <unk> , dass <unk> <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> der <unk> <unk> , dass <unk> <unk> und andere <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> waren in einer <unk> <unk> von der <unk> <unk> für <unk> und <unk> <unk> auf <unk> <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> in <unk> .\nDie <unk> der Bericht , <unk> <unk> , dass <unk> , dass die <unk> auf <unk> <unk> <unk> <unk> <unk> - und die <unk> sind <unk> in <unk> <unk> .\nDie <unk> von <unk> <unk> ist <unk> im <unk> des <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> wurde <unk> <unk> als eine <unk> <unk> gegen <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> <unk> , dass die <unk> der <unk> und <unk> in der <unk> der <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , eine internationale <unk> <unk> <unk> , ist eine internationale <unk> in der <unk> <unk> .\nDie <unk> sollte die <unk> der <unk> <unk> , wenn die <unk> der <unk> von <unk> <unk> <unk> <unk> - mit <unk> für <unk> <unk> .\nDie <unk> der <unk> kann die <unk> der <unk> <unk> <unk> <unk> .\n<unk> <unk> werden <unk> oder <unk> <unk> .\n<unk> auf dem <unk>\nDie <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> , <unk> hat eine <unk> <unk> .\n&quot; <unk> <unk> müssen alle alle <unk> <unk> , die <unk> <unk> , <unk> <unk> .\n<unk> ist nicht , ob dieses <unk> oder die <unk> der <unk> <unk> .\n<unk> <unk> ist die <unk> <unk> , aber die <unk> würde der <unk> <unk> , die <unk> <unk> <unk> zu <unk> .\n<unk> <unk> <unk> auf <unk> , dass die <unk> nicht die <unk> <unk> als <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> müssen die <unk> - es <unk> , die <unk> <unk> zu <unk> .\n<unk> im <unk> <unk> <unk> <unk>\n<unk> ist die <unk> der <unk> .\nUm mehr als <unk> <unk> <unk> sind die <unk> des <unk> .\n<unk> <unk> die <unk> auf <unk> <unk> .\n<unk> mehr als <unk> <unk> , ist es <unk> für <unk> <unk> , dass die <unk> der <unk> <unk> .\n<unk> <unk> <unk> , wenn sie <unk> , wenn sie <unk> , dass die <unk> <unk> , die <unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> in zwei beiden <unk> <unk> .\n<unk> <unk> würde die <unk> <unk> , <unk> <unk> , <unk> sie <unk> , die <unk> und <unk> zu <unk> .\n<unk> in <unk> <unk>\nDie <unk> <unk>\n<unk>\nEin <unk> <unk> mit <unk> , <unk> und <unk> .\nDas ist <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> , ist ein <unk> <unk> <unk> mit dem <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> .\nDie <unk> ist ein <unk> in <unk> <unk> .\nEs ist die <unk> <unk> , dass er auch die <unk> des <unk> <unk> <unk> <unk> .\nDas <unk> <unk> <unk> <unk> in <unk> mit seiner <unk> <unk> <unk> und seine <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> als <unk> der <unk> <unk> in <unk> - <unk> und <unk> .\nDie <unk> wurde nicht <unk> wie eine <unk> und <unk> <unk> - es hat keine <unk> <unk> und <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> von <unk> und <unk> in einer <unk> und <unk> <unk> der <unk> .\nDie <unk> <unk> <unk> hat nun die <unk> , die die &quot; <unk> <unk> &quot; und wird <unk> <unk> <unk> <unk> .\nDie <unk> ist ein <unk> der <unk> , die <unk> <unk> , die ersten <unk> <unk> - eine <unk> in allen <unk> .\n<unk> <unk> , die <unk> der neuen <unk> der <unk> in 2007 <unk> <unk> wurde .\n<unk> , ein <unk> <unk> mit einem <unk> <unk> in den <unk> als eine <unk> <unk> , die <unk> <unk> , die <unk> <unk> und <unk> <unk> <unk> und <unk> <unk> <unk> .\nDie neue <unk> war <unk> <unk> , die <unk> <unk> <unk> , eine <unk> und eine <unk> <unk> <unk> .\n<unk> &amp; <unk> <unk>\n<unk> <unk> wurde die <unk> <unk> , die <unk> <unk> <unk> .\n<unk> war <unk> , <unk> und <unk> <unk> der <unk> in <unk> , und <unk> die <unk> , aber er auch die <unk> in der <unk> <unk> .\nEr <unk> die <unk> <unk> und <unk> die Probleme der <unk> <unk> , die bereits bereits <unk> <unk> hat , <unk> <unk> und <unk> .\nDie Probleme <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> der <unk> wurde <unk> <unk> , <unk> <unk> <unk> wurde und die <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> oder <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> für die neue <unk> und der <unk> <unk> , eine <unk> <unk> zwischen den <unk> und der <unk> <unk> .\nDie <unk> <unk> haben <unk> und die <unk> <unk> in der <unk> <unk> <unk> .\nUnd die <unk> <unk> <unk> ist es <unk> , um <unk> zu <unk> .\n<unk> der <unk> <unk> der <unk> <unk> und mit einem <unk> <unk> .\n<unk> <unk> , <unk> und <unk> mit der <unk> der <unk> <unk> , <unk> und <unk> mit dem <unk> des <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> hat <unk> und <unk> <unk> die <unk> , <unk> und <unk> .\n<unk> <unk> <unk> <unk> , dass die <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; in der <unk> &quot; <unk> .\n<unk> <unk> <unk> ist ein <unk> in der <unk> &quot; und <unk> <unk> <unk> .\nDas <unk> <unk> ist sehr <unk> in der <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; mit <unk> und <unk> <unk> .\nEs gibt auch ein <unk> <unk> der <unk> .\n<unk>\n<unk> <unk> hat sich mit einer Krise <unk> <unk> <unk> in der <unk> .\nDie Reihe von <unk> ist für <unk> .\nDie <unk> werden in <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> zu einer <unk> , die <unk> <unk> <unk> , hat die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist <unk> , die <unk> von <unk> und die <unk> <unk> , <unk> in der <unk> <unk> <unk> .\nDie Reihe von <unk> und <unk> <unk> , in <unk> <unk> und <unk> ist <unk> , um <unk> zu <unk> .\nDie <unk> ist <unk> <unk> und <unk> von <unk> und <unk> von <unk> in vier <unk> <unk> .\n<unk> <unk> , <unk> der <unk> , <unk> <unk> , <unk> <unk> und <unk> dieses <unk> auf eine <unk> mit <unk> und <unk> zwei <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> in seiner <unk> <unk> und <unk> <unk> in der <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs ist auch <unk> <unk> in der <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\n<unk> <unk> <unk> <unk> <unk> auf der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIn der <unk> waren nicht <unk> <unk> , die in der <unk> <unk> nicht <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie Frage <unk> die <unk> <unk> <unk> .\nDer <unk> <unk> <unk> <unk> eine <unk> <unk> , die eine <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> in der <unk> und der <unk> .\n&quot; Wir freuen uns , die Frage ohne <unk> <unk> zu <unk> , &quot; <unk> <unk> in neuen <unk> <unk> .\nDer <unk> ist nicht <unk> <unk> in der <unk> .\n<unk> <unk> ist von <unk> , die <unk> , die <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , die <unk> der beiden <unk> .\nDas <unk> <unk> <unk> <unk> ist für die USA <unk> und <unk> in <unk> zu <unk> .\nDie <unk> ist <unk> für <unk> , die <unk> in den <unk> <unk> .\nDie <unk> sind die erste <unk> .\nDas <unk> <unk> auch <unk> <unk> <unk> <unk> und <unk> <unk> , die <unk> <unk> und <unk> <unk> in der <unk> .\nDie <unk> <unk> sind <unk> <unk> .\nSie sind von <unk> <unk> <unk> .\nDies <unk> die <unk> einer Reihe von <unk> , wenn die <unk> <unk> <unk> in 2007 <unk> .\nDie neue <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie <unk> <unk> gegen eine <unk> <unk> in den <unk> Jahren .\n<unk> und <unk> haben eine <unk> von <unk> für <unk> oder <unk> .\nDie <unk> <unk> <unk> <unk> <unk> hat die <unk> <unk> <unk> .\nDas <unk> <unk> <unk> <unk> internationale <unk> in den USA <unk> und <unk> <unk> auf <unk> <unk> .\n<unk> <unk> auch die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist <unk> , dass die <unk> <unk> für &quot; <unk> <unk> &quot; <unk> ist , aber nicht <unk> die <unk> <unk> .\n<unk> <unk> hat die <unk> als <unk> <unk> und hat seine <unk> auf <unk> <unk> <unk> .\n<unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> in der <unk> <unk> <unk> .\n<unk> hat er <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDas <unk> <unk> <unk> <unk> seine <unk> in der <unk> .\n<unk> möchte er <unk> <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> von <unk> <unk> ist ein <unk> im <unk> <unk> .\nDie <unk> <unk> hat nun die <unk> &quot; <unk> <unk> &quot; .\n<unk> <unk> , dass er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> seine <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> ich , dass sie <unk> <unk> <unk> <unk> <unk> <unk> .\nEr <unk> die <unk> , die in <unk> als auch die wirtschaftliche Krise <unk> .\n<unk> ist ein <unk> <unk> , &quot; <unk> , <unk> , <unk> , <unk> <unk> .\nUm eine <unk> Zeit <unk> , hat <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> - auch <unk> <unk> - auch <unk> <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEr <unk> nur auf <unk> <unk> , deren <unk> er <unk> <unk> .\n<unk> und <unk> in einer <unk> <unk> , für eine <unk> <unk> und eine <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> über die <unk> <unk> <unk> in der <unk> und <unk> <unk> <unk> in der Welt .\n<unk> <unk> , Herr <unk>\nIch <unk> ein <unk> <unk> für <unk> <unk> .\n<unk> <unk> <unk> <unk> für <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> , <unk> <unk> <unk> die <unk> der <unk> .\n&quot; <unk> <unk> , &quot; <unk> , <unk> und <unk> ist auch <unk> , ist <unk> <unk> <unk> <unk> .\nHerr Präsident , Herr <unk> sagte , er hat keine <unk> in <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , ist ein <unk> .\n<unk>\n<unk> die <unk> des <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> von <unk> <unk> <unk> <unk> , dass jetzt <unk> <unk> .\nDie <unk> von <unk> und eine <unk> zwischen <unk> und einer <unk> zwischen <unk> und <unk> war der <unk> <unk> <unk> .\n<unk> und <unk> hat die <unk> in Deutschland und in diesem Bereich in Deutschland .\n<unk> <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs war nicht <unk> , dass <unk> <unk> in Deutschland <unk> .\nDie <unk> <unk> <unk> , die jetzt <unk> hat , ist jedoch auf eine <unk> , die nicht <unk> ist .\nDie <unk> der <unk> <unk> <unk> <unk> ist die <unk> des <unk> <unk> <unk> .\nDie <unk> <unk> , die eine <unk> <unk> , <unk> <unk> , dass eine <unk> der <unk> <unk> <unk> für die <unk> und <unk> <unk> , dass die <unk> Behörden <unk> wird .\nDie <unk> der <unk> <unk> <unk> <unk> , die die <unk> der <unk> in <unk> <unk> <unk> <unk> .\n<unk> zwei <unk> <unk> , die <unk> <unk> in <unk> <unk> die <unk> des <unk> <unk> <unk> in <unk> und <unk> .\n<unk> zwischen zwei Ländern <unk> wurde <unk> <unk> - wie ein Erfolg <unk> .\nDie <unk> von <unk> <unk> hat <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> auch nicht die <unk> des <unk> .\n&quot; <unk> ist der <unk> der <unk> der <unk> <unk> .\nEr <unk> <unk> , dass Menschen <unk> , die <unk> <unk> und <unk> eine wichtige Beitrag zu <unk> .\nDie <unk> der <unk> und der <unk> <unk> ist <unk> <unk> <unk> <unk> .\nDie <unk> sind <unk> , die die <unk> Behörden nicht <unk> , in <unk> <unk> zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> wird die <unk> <unk> .\n&quot; <unk> in Deutschland <unk> <unk> <unk> , wie die gesamte <unk> ist , <unk> <unk> und <unk> <unk> .\nDie <unk> auf <unk> und <unk> in der <unk> sind ein Beispiel für <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> ist <unk> in <unk> und <unk> <unk> .\n<unk> anderen <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> die <unk> , die <unk> und <unk> zu <unk> .\nDie <unk> <unk> die <unk> der <unk> <unk> .\n<unk> der <unk> der <unk> <unk>\nDie <unk> <unk> auf der Zeit <unk> der <unk> <unk> der <unk> <unk> <unk> <unk> , um die <unk> in <unk> zu <unk> .\nEr <unk> eine <unk> von <unk> , um die <unk> <unk> in <unk> , aber es wurde <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> , wenn er die <unk> der <unk> eines anderen <unk> <unk> .\nDie <unk> der <unk> <unk> in <unk> <unk> , wie die <unk> der <unk> <unk> , wie <unk> die <unk> <unk> in der <unk> <unk> wird .\n<unk> <unk> ist , was jetzt <unk> , <unk> <unk> , <unk> , <unk> , <unk> und <unk> <unk> in Deutschland .\nDie <unk> <unk> <unk>\nDie <unk> <unk> in Europa ist keine <unk> oder Frankreich , aber in <unk> .\nDie <unk> <unk> <unk> <unk> in der <unk> und hat von einer Krise <unk> <unk> <unk> .\n<unk> ist nun in <unk> <unk> .\n<unk> <unk> hat <unk> nicht , dass die Rolle der Rolle <unk> <unk> <unk> .\n<unk> seine <unk> <unk> von der <unk> <unk> <unk> , war es <unk> in <unk> , dass er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> wurde <unk> <unk> , <unk> <unk> für seine <unk> und <unk> für ihre <unk> <unk> <unk> und <unk> <unk> <unk> .\n&quot; <unk> ist ein <unk> <unk> .\n&quot; Ich <unk> mich über <unk> und <unk> <unk> .\nAber der <unk> war es bereits <unk> , dass es sehr <unk> ist .\nEin <unk> <unk> in <unk> <unk> <unk> die <unk> des <unk> und <unk> .\n<unk> <unk> , <unk> <unk> - ein <unk> nicht nur <unk> in <unk> - <unk> .\nDie <unk> <unk> hat <unk> seit seiner <unk> <unk> .\n<unk> ist die <unk> von <unk> <unk> <unk> .\nUnd er wird <unk> der Rolle der <unk> in <unk> <unk> , und nicht in <unk> , die <unk> zu <unk> .\nDas wird für <unk> <unk> <unk> .\nEr ist ein <unk> <unk> und <unk> <unk> , nicht ein <unk> <unk> zu <unk> .\n<unk> auf <unk> , <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist es in der <unk> <unk> , die <unk> <unk> in der Welt , die <unk> - und hat <unk> von einer <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , ist es zu <unk> <unk> .\nEs ist im <unk> <unk> .\n<unk> <unk> hat eine <unk> <unk> und 2007 .\nDie <unk> <unk> die <unk> der <unk> und <unk> , aber <unk> <unk> <unk> <unk> mit <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> wurde <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn der <unk> des <unk> <unk> <unk> <unk> als <unk> <unk> <unk> .\nDas ist eine <unk> .\nDie <unk> <unk> und <unk> <unk> in der <unk> <unk> der <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , was wir heute heute haben , haben wir heute <unk> <unk> .\nEr war sehr <unk> .\nEs ist klar <unk> , dass es <unk> ist , um <unk> zu <unk> .\nIm <unk> der <unk> <unk> <unk> <unk> <unk> - eine <unk> <unk> und <unk> <unk> - <unk> <unk> .\n<unk> gibt es <unk> , dass die <unk> <unk> <unk> <unk> .\nDie <unk> <unk> der <unk> <unk> <unk> auf <unk> <unk> und wurde <unk> <unk> <unk> .\n<unk> <unk> eine <unk> <unk> , ist es <unk> oder nicht , die <unk> <unk> zu <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas war nur die ersten <unk> .\nEs ist ein <unk> <unk> im <unk> <unk> .\nDie <unk> wurde <unk> <unk> in der <unk> und <unk> der <unk> <unk> <unk> , <unk> <unk> - die zweite <unk> <unk> <unk> in <unk> <unk> .\n<unk> <unk> im <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> <unk> <unk> von <unk> für <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie gesamte <unk> wurde <unk> <unk> - die <unk> <unk> für eine <unk> Land zu <unk> .\n<unk> alle alle <unk> <unk> in 2007 <unk> <unk> die wirtschaftliche <unk> des <unk> <unk> <unk>\nDie zweite <unk> sind nicht zu <unk> <unk> , &quot; <unk> <unk> <unk> <unk> .\nDie zweite <unk> <unk> <unk> auf die <unk> der ersten <unk> .\nEin <unk> <unk> war <unk> <unk> , <unk> <unk> in der <unk> und <unk> , dass es in der <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> auch <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Sie mehr oder weniger <unk> .\n<unk> <unk> sind <unk> <unk> , dass eine <unk> für die <unk> <unk> <unk> <unk> - die <unk> <unk> <unk> .\n<unk> <unk> <unk> war nicht <unk> .\nDie <unk> und die <unk> <unk> <unk> und der <unk> kann <unk> und <unk> <unk> .\nDie <unk> sind <unk> <unk> .\nDie <unk> <unk> seine <unk> in seiner <unk> <unk> <unk> in <unk> <unk> <unk> , in <unk> <unk> und <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist <unk> <unk> , <unk> in <unk> <unk> ist nicht <unk> .\n<unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> von einer <unk> <unk> <unk> auch keine <unk> <unk> .\nEs war <unk> <unk> , dass die <unk> der <unk> der <unk> <unk> <unk> <unk> <unk> <unk> die <unk> der <unk> <unk> - nicht <unk> <unk> .\nDie <unk> von <unk> <unk> hat eine <unk> von <unk> <unk> , nur als <unk> und <unk> <unk> .\nDie <unk> <unk> in <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> - nur eine <unk> <unk> .\nDie <unk> <unk> ist <unk> .\nDie <unk> hat die <unk> <unk> <unk> auf der <unk> der <unk> <unk> , die von der <unk> <unk> <unk> <unk> <unk> .\nDie <unk> sind <unk> und die <unk> <unk> in Europa .\n<unk> <unk> wie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> sind hier für die <unk> Jahren <unk> - von der <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> .\nEin <unk> <unk> <unk> <unk> die <unk> <unk> .\nDie <unk> <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , dass die <unk> <unk> <unk> für <unk> - <unk> .\nDoch die <unk> <unk> <unk> , <unk> und <unk> <unk> , um die <unk> <unk> der <unk> <unk> - oder <unk> sie die <unk> von <unk> <unk> <unk> .\nEr <unk> keine <unk> <unk> in der <unk> - er mit dem <unk> <unk> <unk> <unk> <unk> .\nDas <unk> <unk> <unk> <unk> <unk> <unk> - besonders in <unk> <unk> , wo die <unk> auch sehr <unk> <unk> .\nIm <unk> haben die <unk> noch <unk> .\nDie <unk> <unk> und wenn sie <unk> , haben sie <unk> , <unk> <unk> <unk> <unk> .\nEs war sehr <unk> <unk> <unk> , dass <unk> <unk> , dass <unk> <unk> , <unk> <unk> , <unk> in den ersten <unk> und <unk> <unk> <unk> .\nDie <unk> wurde <unk> <unk> <unk> .\nDie <unk> , die <unk> der <unk> <unk> , der <unk> der <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> in seiner <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> sich auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWie kann es <unk> ?\n<unk> zu <unk> , <unk> sie die Frage der <unk> <unk> .\n<unk> des <unk> <unk> <unk> <unk> in einer <unk> , <unk> <unk> in <unk> .\nEin <unk> <unk> war <unk> , dass ich bereits <unk> <unk> für <unk> <unk> <unk> , wenn die <unk> <unk> <unk> .\nDie Tatsache , dass die beiden <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> und <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> sind mit der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> und <unk> wurde <unk> , dass die <unk> und <unk> eine <unk> <unk> , <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> mit einem <unk> <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nAber <unk> in <unk> <unk> auf <unk> <unk> .\nUnd <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas ist <unk> .\n<unk> kann es <unk> werden .\n<unk> ist ein <unk> .\n<unk> von <unk> <unk> <unk> die <unk> , wo die <unk> immer <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> war der <unk> des <unk> <unk> .\nEs <unk> die &quot; <unk> <unk> &quot; , dass <unk> die <unk> in der Zeit <unk> .\n<unk> <unk> sich auf die <unk> der <unk> .\nEs ist einige der <unk> <unk> .\n<unk> war ein <unk> <unk> in <unk> <unk> und <unk> .\n<unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> in den USA <unk> .\nUm die <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> für die <unk> und eine <unk> <unk> von den <unk> <unk> in <unk> <unk> <unk> .\nEs ist ein <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> <unk> und <unk> <unk> <unk> .\n<unk> , wenn andere <unk> <unk> <unk> <unk> <unk> für eine <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> wurde <unk> als <unk> <unk> , als die <unk> 2007 in <unk> , &quot; <unk> <unk> <unk> .\nDie <unk> der <unk> , die <unk> der <unk> .\nNach der nächsten <unk> haben die <unk> <unk> , die <unk> <unk> zu <unk> als <unk> .\n<unk> <unk> ist ein <unk> von <unk> <unk> und <unk> <unk> .\n<unk> <unk> die <unk> der <unk> <unk> jetzt <unk> <unk> .\nEr muss ein <unk> für eine <unk> <unk> , die ihre <unk> <unk> <unk> .\n<unk> <unk> hat eine <unk> - <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> .\nDie <unk> , Herr <unk> , <unk> keine <unk> zu <unk> .\nEs ist <unk> , dass <unk> <unk> <unk> werden .\nDie neue <unk> der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDie <unk> <unk> <unk> war nur <unk> als <unk> Lösung , wenn die <unk> <unk> mit seiner <unk> <unk> <unk> .\nFür die <unk> hat er seine <unk> <unk> und ist nun <unk> <unk> , die <unk> <unk> zu <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEr ist <unk> <unk> <unk> .\nAber die <unk> <unk> <unk> .\n<unk> ist ein <unk> .\nWas ist <unk> ?\nWie kann es <unk> <unk> werden ?\n<unk> sind die <unk> <unk> <unk> <unk> .\n<unk> , er hat <unk> <unk> , dass er nur eine <unk> in <unk> <unk> .\n<unk> <unk> .\n<unk> in Europa <unk> seine <unk> <unk>\nDie <unk> <unk> in Europa hat die <unk> der <unk> in Europa <unk> .\nDie <unk> der Regierung in <unk> und <unk> wurde <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> , dass die <unk> in der <unk> <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> <unk> des <unk> in <unk> und <unk> nicht <unk> .\nDie <unk> <unk> gegen die <unk> .\nDie <unk> und <unk> <unk> war immer <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> in <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> mit <unk> <unk> .\nDie <unk> <unk> zwischen <unk> und <unk> in der <unk> .\n<unk> <unk> &amp; <unk> <unk> <unk> <unk> <unk> .\nDas <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm <unk> <unk> <unk> die <unk> <unk> <unk> <unk> .\n<unk> in den <unk> Ländern hat <unk> <unk> und ist <unk> <unk> der <unk> .\nDie <unk> <unk> <unk> <unk> , die in <unk> <unk> <unk> <unk> <unk> als in der <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> und <unk> sind nicht eine <unk> .\nEr <unk> auch <unk> , dass Europa keine <unk> <unk> und die <unk> <unk> in der <unk> <unk> .\nDie <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nIm <unk> ist es auch <unk> , um <unk> zu <unk> oder <unk> .\n<unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> von <unk> <unk> in <unk> <unk> die <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> sind <unk> auf <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> pro <unk> und <unk> hat eine <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> <unk> <unk> <unk> <unk> <unk> als die <unk> von <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> in China <unk> <unk> in <unk> , <unk> in <unk> <unk> .\nDie <unk> <unk> <unk> auf <unk> <unk> mit dem <unk> des <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> , <unk> <unk> auf die <unk> .\nDie <unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> in <unk> <unk> .\nDie <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> auf der <unk> <unk> <unk> .\n<unk> <unk> <unk> , <unk> <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> , <unk> <unk> und <unk> auf die <unk> auf <unk> <unk> .\n<unk> sind in <unk> <unk> .\n<unk> sind <unk> Problem in vielen <unk> .\n<unk> wie die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nEine Lösung von <unk> .\n<unk> im <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> von allen <unk> über <unk> <unk> Lösungen .\n<unk> in der <unk>\n<unk> <unk> , dass <unk> der <unk> <unk> der <unk> in <unk> , <unk> , die <unk> , dass die <unk> der <unk> in <unk> <unk> <unk> wurde .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> sind <unk> .\nEs gibt eine <unk> <unk> mit <unk> <unk> in den <unk> <unk> und in der <unk> in <unk> , <unk> , <unk> , <unk> und <unk> .\nEine <unk> Liste von <unk> in <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> der <unk> in <unk> <unk> und wie sie <unk> <unk> , wie <unk> mit <unk> wie <unk> <unk> .\n&quot; <unk> <unk> und <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> <unk> mit <unk> oder <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> .\n<unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> nur als <unk> in <unk> , <unk> und <unk> .\n&quot; Wir haben <unk> mit ihnen <unk> .\n<unk> <unk> die <unk> <unk> , <unk> <unk> , <unk> , <unk> <unk> .\nEin <unk> der <unk> <unk> von <unk> <unk> und <unk> <unk> für diese <unk> <unk> <unk> <unk> und <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> war ein <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist <unk> <unk> .\n<unk> auch die <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> und <unk> der <unk> sind <unk> <unk> , <unk> <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> auf der <unk> der <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> .\nIn einer <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , um eine <unk> zu <unk> .\nSie <unk> die <unk> und <unk> von <unk> <unk> , ohne die <unk> zu <unk> .\nIn einer <unk> <unk> <unk> die <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> ein <unk> mit einem <unk> <unk> in einer <unk> <unk> .\nDie <unk> wird nicht <unk> , mit dem <unk> <unk> .\n<unk> <unk> hat die <unk> <unk> <unk> .\nAlle <unk> <unk> die <unk> <unk> , um die <unk> zu <unk> , um die <unk> der <unk> <unk> .\n<unk> der vier <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\nDie <unk> der <unk> und <unk> für eine <unk> <unk> wurde <unk> .\nDie <unk> <unk> , <unk> und die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> , die <unk> , <unk> und <unk> haben die <unk> <unk> für die <unk> <unk> in <unk> und <unk> .\n<unk> von <unk> <unk> <unk> , <unk> , <unk> in der <unk> und <unk> ...\nDie <unk> nur <unk> <unk> <unk> in der <unk> <unk> der <unk> <unk> gegen <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk>\n<unk> <unk> hat nicht ein <unk> mit <unk> <unk> gegen <unk> und <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> die <unk> für <unk> .\nDas <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> ein <unk> <unk> in der <unk> <unk> .\n<unk> <unk> wurde <unk> des <unk> <unk> <unk> .\n<unk> <unk> <unk> sich in Lissabon <unk> <unk> <unk> <unk> .\n<unk> <unk> ein <unk> <unk> in der <unk> von <unk> <unk> .\nEin <unk> <unk> von einem <unk> <unk> <unk> seine <unk> <unk> , um die <unk> von der <unk> <unk> .\nDie <unk> , mit dem <unk> <unk> in erster Linie mit <unk> <unk> <unk> .\nDie <unk> <unk> <unk> von der <unk> <unk> <unk> <unk> <unk> eine <unk> <unk> der <unk> in der <unk> , aber die <unk> <unk> nicht nur die <unk> <unk> .\n<unk> für die <unk>\nDie <unk> <unk> <unk> von <unk> <unk> <unk> <unk> .\n<unk> eine <unk> auf <unk> <unk> in der <unk> <unk> <unk> , <unk> die <unk> der <unk> <unk> <unk> und <unk> <unk> <unk> .\n<unk> auch ein <unk> <unk> für <unk> , <unk> - <unk> <unk> .\n<unk> <unk> , die <unk> <unk> der <unk> <unk> der <unk> mit <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> für die <unk> in der <unk> .\nDie Europäische <unk> sind die <unk> <unk> für die <unk> <unk> in einer <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\n<unk> hat seine <unk> <unk> <unk> , wenn die <unk> <unk> und <unk> <unk> <unk> .\nDie <unk> <unk> der <unk> <unk> <unk> , dass nur die <unk> des <unk> im <unk> <unk> <unk> .\nDie <unk> <unk> , die <unk> <unk> die <unk> <unk> <unk> <unk> , <unk> in der <unk> der <unk> <unk> .\nDie <unk> wurde <unk> <unk> <unk> .\n<unk> wurde <unk> <unk> in <unk> <unk> .\n<unk> Tage über <unk> <unk> in <unk> , kann die <unk> nicht als <unk> <unk> <unk> .\n<unk> ist es eine <unk> , dass die nationalen <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> die <unk> <unk> in <unk> der <unk> <unk> .\nDie <unk> <unk> <unk> <unk> die <unk> in der <unk> und <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> und <unk> <unk> .\n<unk> <unk> in <unk> <unk> .\nDas <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\nDie <unk> in <unk> wurden ihre ersten <unk> in Deutschland <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> für die <unk> in der <unk> .\n<unk> <unk> <unk> die <unk> , aber <unk> <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> , die <unk> in den letzten <unk> im <unk> <unk> .\nDie <unk> <unk> in der <unk> <unk> .\n<unk> ein <unk> <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\nEin <unk> <unk> war <unk> <unk> in <unk> auf <unk> <unk> .\nDie <unk> <unk> in einer <unk> auf die <unk> , die <unk> <unk> <unk> <unk> <unk> in <unk> .\nDie <unk> wurde <unk> <unk> auf <unk> , wenn ein <unk> <unk> in <unk> <unk> <unk> .\nDie <unk> , <unk> und <unk> ist - die <unk> <unk> <unk> .\nDie andere <unk> auf die <unk> <unk> <unk> .\n<unk> <unk>\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , dass er ein <unk> <unk> mit einem <unk> und <unk> <unk> werden .\nDie <unk> <unk> wurde über <unk> , die <unk> <unk> und mit einem <unk> <unk> <unk> .\nDie <unk> <unk> <unk> von <unk> .\n<unk> <unk>\nEin <unk> <unk> wurde <unk> <unk> auf <unk> <unk> in <unk> .\nEr <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> der <unk> von <unk> <unk> .\n<unk> <unk> , <unk> die <unk> in <unk> <unk> und <unk> in <unk> <unk> .\nDie <unk> wurde <unk> <unk> .\nDie <unk> <unk> wurde <unk> <unk> .\n<unk> <unk> und <unk> <unk>\nEin <unk> <unk> war <unk> <unk> und <unk> <unk> von <unk> in <unk> .\nDie <unk> <unk> in einer <unk> auf die <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> zu <unk> , die <unk> zu <unk> und <unk> <unk> .\nEin <unk> <unk> <unk> in den <unk> <unk> <unk> .\nDie <unk> wurde <unk> <unk> und <unk> für <unk> <unk> in einer <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> war eine <unk> in <unk> .\nEr <unk> ein <unk> <unk> und <unk> <unk> <unk> , wenn die <unk> <unk> .\nEin <unk> <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> - die <unk> - <unk>\n<unk> <unk> !\nEr <unk> gegen eine <unk> <unk> <unk> und <unk> <unk> der <unk> .\nDie <unk> , eine <unk> und die <unk> <unk> auf <unk> .\n<unk> <unk> mit einem <unk> <unk> .\nEin <unk> <unk> in <unk> hat <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die die <unk> <unk> wurde und über die <unk> in der <unk> <unk> , wenn sie <unk> in der <unk> <unk> .\n<unk> <unk> wurde <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> - eine <unk> <unk> .\nEin <unk> <unk> <unk> es ist keine <unk> zwischen der <unk> und der <unk> .\n<unk> <unk> in einer <unk> <unk>\nEin <unk> <unk> ist <unk> <unk> in der <unk> <unk> mit einem <unk> .\nDie <unk> <unk> in einer <unk> , dass die <unk> <unk> <unk> <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> <unk> .\nDie <unk> wurde <unk> <unk> der <unk> <unk> und <unk> mit einem <unk> .\nDie <unk> hat <unk> <unk> , die <unk> <unk> <unk> .\nEr war <unk> <unk> <unk> <unk> .\n<unk> in <unk> auf <unk>\nEin <unk> <unk> <unk> in <unk> auf <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die in einer <unk> <unk> , <unk> <unk> , <unk> die <unk> auf <unk> <unk> <unk> .\n<unk> war <unk> <unk> .\nDie <unk> war <unk> <unk> , die <unk> <unk> in der <unk> <unk> <unk> .\n<unk> <unk> auf die <unk> <unk> und <unk> <unk> .\n<unk> <unk> <unk> von <unk> <unk>\nEin <unk> ist <unk> <unk> von <unk> in der <unk> <unk> .\nDie <unk> <unk> in einer <unk> , dass die <unk> <unk> die <unk> in einer <unk> <unk> <unk> <unk> .\nWenn die <unk> <unk> <unk> , <unk> er die <unk> und <unk> mit einer <unk> .\n<unk> <unk> <unk> <unk> .\nDie <unk> wurde mit <unk> <unk> <unk> .\n<unk>\n<unk> <unk> wurden in der <unk> eines <unk> in <unk> <unk> .\nDie <unk> <unk> in einer <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> <unk> in <unk> <unk> .\nDie <unk> wurde <unk> <unk> Frauen die <unk> <unk> .\n<unk> <unk> oder <unk> <unk> mit <unk> <unk> .\nDie <unk> <unk> <unk> in <unk> <unk> in <unk> <unk> .\n<unk> <unk>\nDie <unk> haben <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , dass die <unk> <unk> <unk> in <unk> <unk> .\nDie <unk> , <unk> in <unk> <unk> , <unk> eine <unk> <unk> auf <unk> .\nSie <unk> eine <unk> , um die <unk> und <unk> <unk> zu <unk> .\n<unk> <unk> die <unk> .\nDie <unk> <unk> <unk> wenn <unk> <unk> .\n<unk> hat sie in einer <unk> <unk> <unk> .\n<unk> <unk> von einer <unk> und <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> .\nDie <unk> <unk> in einer <unk> , die die <unk> <unk> , eine <unk> auf die <unk> der <unk> und <unk> nicht <unk> .\nDie <unk> <unk> die <unk> und die <unk> <unk> <unk> die <unk> und <unk> <unk> .\nEr <unk> <unk> zu <unk> <unk> <unk> .\n<unk> <unk> und <unk> von einer <unk> in <unk> <unk>\nEin <unk> <unk> war <unk> <unk> und <unk> <unk> von <unk> in <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die er <unk> hat , mit <unk> zu <unk> und <unk> .\nDie <unk> <unk> <unk> <unk> die <unk> in <unk> eines <unk> <unk> .\nDie <unk> war nicht , in <unk> und die <unk> <unk> der <unk> .\nDie <unk> war nicht von <unk> .\n<unk> <unk> in <unk> <unk>\nEin <unk> <unk> war <unk> <unk> und <unk> von <unk> in <unk> <unk> .\nDie <unk> <unk> in einer <unk> auf die <unk> , die er <unk> hat , mit <unk> <unk> und <unk> nicht <unk> .\nDie <unk> der <unk> mit der <unk> von <unk> ist nicht <unk> .\n<unk>\nEin <unk> war <unk> <unk> auf <unk> <unk> in <unk> .\nEin <unk> <unk> <unk> <unk> ein <unk> <unk> , um die <unk> <unk> .\nEr war <unk> und die <unk> <unk> <unk> , die die <unk> des <unk> <unk> .\nDie <unk> war ein <unk> <unk> .\n<unk> ist <unk> , aber ein <unk> <unk> .\n<unk> und <unk> in <unk> in <unk>\nEin <unk> <unk> in einem <unk> <unk> in <unk> auf <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die die <unk> <unk> <unk> , <unk> in einer <unk> <unk> und <unk> auf die <unk> .\nEin <unk> <unk> wurde in <unk> und <unk> die <unk> <unk> .\nDie <unk> <unk> wurde <unk> und <unk> ein <unk> <unk> von der <unk> und <unk> <unk> <unk> .\nDie <unk> der <unk> wurde <unk> <unk> , dass sich die <unk> <unk> <unk> <unk> <unk> .\n<unk> in einer <unk> in <unk> <unk>\nEs ist eine <unk> in einer <unk> auf ein <unk> <unk> in <unk> <unk> .\nDie <unk> <unk> in einer <unk> , dass die <unk> der <unk> auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> , die <unk> <unk> in den <unk> <unk> wurde <unk> <unk> .\n<unk> hat er <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> in <unk> <unk> <unk> <unk> <unk>\nIn einer <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die vier <unk> <unk> , <unk> <unk> und <unk> <unk> mit zwei <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> <unk> und <unk> von den <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> .\nDie vier <unk> <unk> in den <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> .\n<unk> <unk> die <unk> <unk> auf <unk> und <unk> <unk> in seiner <unk> <unk> <unk> .\nDie erste <unk> <unk> die <unk> .\nDie <unk> <unk> in einer <unk> , dass ein <unk> <unk> ist , <unk> <unk> .\nEin <unk> <unk> ist die <unk> der <unk> .\n<unk> Sie die <unk> <unk> in einem <unk> <unk> .\nDie <unk> <unk> wurde <unk> für <unk> <unk> .\n<unk> in <unk> in der <unk>\nEin <unk> <unk> , in einem <unk> <unk> , in einem <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , dass die <unk> <unk> <unk> auf <unk> <unk> <unk> werden .\nDie <unk> <unk> eine große <unk> von <unk> , die <unk> <unk> <unk> werden .\nDie <unk> <unk> <unk> mit vier <unk> <unk> und <unk> <unk> <unk> .\nEin <unk> <unk> <unk> und eine <unk> von <unk> .\nDie <unk> <unk> der <unk> würde jetzt auch eine <unk> <unk> .\n<unk> ein <unk> <unk> in der <unk> .\n<unk> war ein <unk> und <unk> <unk> .\n<unk> <unk>\n<unk> <unk> und <unk> <unk> eine <unk> <unk> in <unk> .\nDas <unk> <unk> in einer <unk> auf <unk> <unk> , die die <unk> <unk> <unk> und <unk> <unk> <unk> , <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> werden <unk> , <unk> und <unk> ihre <unk> und <unk> mit seiner <unk> und <unk> .\n<unk> <unk> Sie die <unk> <unk> <unk> <unk> auf die <unk> und <unk> .\nDie <unk> wurde <unk> <unk> .\n<unk> <unk> <unk> <unk> - <unk> <unk> auf <unk> <unk> .\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> auf <unk> , die die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> der <unk> in der <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> <unk> .\nDie <unk> <unk> wurde <unk> <unk> .\n<unk>\n<unk> <unk> <unk> <unk> <unk> auf <unk> in einer <unk> <unk> <unk> .\nDie <unk> <unk> in einer <unk> , die ein <unk> <unk> , in der eine <unk> <unk> <unk> und auch zwei <unk> <unk> .\nDie <unk> wurde <unk> <unk> und <unk> <unk> .\n<unk> <unk> auf <unk> <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> ist ein <unk> im <unk> / <unk> .\nDie <unk> <unk> in einer <unk> auf die <unk> , die die <unk> <unk> , um die <unk> zu <unk> .\nDie <unk> muss eine <unk> und <unk> auf die <unk> .\n<unk> auf die <unk> <unk> <unk> .\nDie <unk> <unk> wurde <unk> <unk> <unk> .\nDie <unk> <unk> von <unk> <unk> .\nDie <unk> der <unk> , die in der <unk> des <unk> <unk> ist , ist <unk> für <unk> <unk> .\n<unk>\n<unk> <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> wurde nicht <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\n<unk> <unk> in China <unk> <unk> .\nEuropa <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> auf China in China in China Jahren <unk> .\nDie <unk> <unk> <unk> auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> <unk> ist <unk> zu <unk> .\nDie <unk> <unk> in <unk> <unk> <unk> auf <unk> <unk> zu <unk> oder <unk> .\n<unk> ist im <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> für die <unk> in China sind auch eine wichtige <unk> des <unk> .\n<unk> <unk> auf <unk> <unk> <unk> <unk> <unk> .\nDie <unk> von <unk> ist <unk> <unk> <unk> <unk> .\n<unk> für die <unk> <unk> ist <unk> als <unk> <unk> .\n<unk> ist China für 20 Jahren <unk> .\nDie <unk> , die unsere <unk> , <unk> <unk> , <unk> <unk> .\n&quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; <unk> &quot; , um <unk> <unk> in China .\n<unk> auf ein <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> und <unk> neue <unk> <unk> , &quot; <unk> mit einem <unk> <unk> der <unk> <unk> , <unk> <unk> <unk> .\n<unk> ist <unk> für <unk> <unk> diese Seite <unk> .\nSie werden <unk> , diese Menschen zu <unk> <unk> <unk> , und dieses <unk> wird die <unk> <unk> , die <unk> <unk> , <unk> und <unk> in der <unk> <unk> .\n<unk> <unk> ist ein <unk> <unk> und <unk> .\n<unk> ist auch die neue <unk> der <unk> , die die <unk> in <unk> <unk> .\n<unk> hat es hier ein <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDas <unk> <unk> <unk> , <unk> <unk> oder <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs ist nicht mehr <unk> , was <unk> , was er &quot; <unk> <unk> ist , als <unk> <unk> , was es &quot; <unk> ist .\n&quot; Die <unk> und der <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> und <unk> <unk> , <unk> , <unk> und <unk> .\n<unk> dieser <unk> werden <unk> .\nEin <unk> wird <unk> von <unk> oder <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> ist ein <unk> von <unk> <unk> <unk> .\nEs ist ein <unk> <unk> , die <unk> <unk> , die ich nur die <unk> <unk> <unk> <unk> .\n&quot; <unk> und die <unk> &quot; <unk> <unk> , die <unk> , <unk> , <unk> , <unk> .\n<unk> in diesem <unk> haben <unk> auf <unk> <unk> in der <unk> <unk> <unk> .\nDas ist eine <unk> <unk> , <unk> von der <unk> .\n<unk> <unk> diese Menschen , <unk> <unk> <unk> , <unk> <unk> , dass <unk> <unk> <unk> <unk> .\nWann sind <unk> in der <unk> , und Sie sind in <unk> mit Ihrem <unk> oder <unk> <unk> ?\n<unk> ist die <unk> ?\n<unk> keine <unk> , was die <unk> , <unk> <unk> , <unk> diese <unk> ist .\n&quot; <unk> <unk> &quot; <unk> <unk> &quot; hat mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nDie <unk> und die <unk> &quot; <unk> <unk> und der <unk> &quot; <unk> &quot; <unk> <unk> und <unk> <unk> , eine <unk> <unk> <unk> .\n<unk> <unk> sind <unk> von <unk> , <unk> und andere <unk> <unk> .\nDie <unk> <unk> können <unk> oder <unk> <unk> , aber <unk> werden in <unk> <unk> <unk> <unk> <unk> .\n<unk> sind <unk> und <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nEr <unk> auf die <unk> <unk> in Deutschland .\n<unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> sind er <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> sind <unk> und <unk> .\n<unk> <unk> seine eigenen <unk> <unk> und <unk> über <unk> &quot; <unk> &quot; <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> eine <unk> <unk> <unk> , <unk> er <unk> <unk> <unk> , und die <unk> <unk> oder <unk> .\n&quot; <unk> eine <unk> <unk> , um die <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> .\n<unk> ist es <unk> und <unk> <unk> <unk> .\n<unk> sind ein <unk> <unk> <unk> , und über neue <unk> von <unk> .\n&quot; <unk> ist ein <unk> <unk> , &quot; <unk> <unk> , &quot; ein <unk> zwischen <unk> .\nEr <unk> die <unk> , was er <unk> &quot; <unk> <unk> &quot; eine neue <unk> in der <unk> der <unk> und <unk> <unk> .\n<unk> <unk> in <unk> <unk> <unk> <unk> ist als <unk> <unk> .\n<unk> <unk> <unk> die <unk> , die <unk> <unk> , eine <unk> <unk> von <unk> zu <unk> .\n&quot; Die <unk> <unk> dieses <unk> können in den <unk> <unk> <unk> werden , die <unk> <unk> und <unk> in den <unk> <unk> , <unk> <unk> <unk> .\nDie <unk> wirklich <unk> ist es <unk> , dass es in <unk> <unk> <unk> <unk> werden .\nIn diesem <unk> wird die <unk> von <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIm <unk> <unk> <unk> er die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> eine <unk> <unk> .\n<unk> ist die <unk> <unk> , <unk> ich in einer <unk> <unk> .\nIch <unk> nicht .\nDoch es ist der <unk> , wie er <unk> hat , die <unk> <unk> , die <unk> <unk> von <unk> , <unk> und <unk> mit einem <unk> .\n<unk> ein <unk> <unk> , ein <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> zwischen <unk> in <unk> waren <unk> , weil <unk> <unk> <unk> <unk> werden .\nDie <unk> war <unk> <unk> von allen <unk> , mit anderen <unk> , dass sie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Herr Kommissar , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs gibt keine <unk> von <unk> in den <unk> , wie die <unk> <unk> , die <unk> <unk> und <unk> <unk> <unk> , die <unk> <unk> und <unk> zu <unk> .\nUnd so so <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> .\n<unk> <unk> <unk> , wie <unk> in anderen <unk> <unk> .\nEin <unk> <unk> war <unk> <unk> , mit einem <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> wurde <unk> <unk> , um die <unk> <unk> des <unk> <unk> .\nDie <unk> der <unk> in den <unk> <unk> , &quot; <unk> <unk> mit dem <unk> , der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n&quot; Die <unk> <unk> , die ich in diesem <unk> haben , haben sie nicht <unk> , dass sie sich nicht in <unk> <unk> <unk> , &quot; <unk> <unk> <unk> .\n&quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> .\n<unk> <unk> war ein <unk> <unk> der <unk> .\n<unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie <unk> <unk> , <unk> von <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> war es <unk> , wenn die <unk> von Menschen in der <unk> &quot; <unk> <unk> &quot; <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> und <unk> <unk> <unk> von der <unk> <unk> <unk> <unk> .\nWie die <unk> <unk> <unk> in den <unk> in ihrer <unk> <unk> , <unk> die <unk> ihre <unk> und <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> der <unk> <unk> hat <unk> auf die <unk> , <unk> <unk> <unk> <unk> in der <unk> .\n<unk> in der <unk> wurde <unk> <unk> .\n<unk> <unk> <unk> , die <unk> der <unk> <unk> <unk> , <unk> er sich für ihre <unk> .\nAber viele <unk> <unk> , dass sie von <unk> <unk> <unk> hat , <unk> die <unk> in der <unk> , und die <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWie die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> auf .\nEin <unk> <unk> <unk> <unk> in der <unk> des <unk> .\n<unk> ist ein <unk> , und die <unk> <unk> in <unk> <unk> .\n<unk> <unk> <unk> , <unk> <unk> , <unk> <unk> .\nUnd zwei <unk> zu <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> wurden in <unk> die <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> war <unk> <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> in der <unk> .\n<unk> hat die <unk> <unk> , <unk> <unk> und <unk> <unk> , <unk> er <unk> .\nDie <unk> <unk> <unk> wurde <unk> <unk> , die <unk> <unk> , die <unk> <unk> in der <unk> .\n<unk> <unk> <unk> , die <unk> der <unk> <unk> , war auch <unk> .\n<unk> <unk> , während der <unk> <unk> ist nicht <unk> <unk> , &quot; <unk> <unk> <unk> <unk> .\nEin zweiten <unk> der <unk> <unk> der <unk> <unk> der <unk> <unk> <unk> , zwischen <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> der <unk> <unk> <unk> <unk> der <unk> .\n<unk> &#124; <unk> <unk> , <unk> der <unk> <unk> , eine <unk> <unk> <unk> , <unk> und andere <unk> <unk> der <unk> <unk> .\n<unk> ist ein <unk> , ein <unk> <unk> von der <unk> .\nIn der <unk> ist eine <unk> <unk> .\n<unk> <unk> \" <unk> \" <unk>\nDie <unk> <unk> <unk> der <unk> der <unk> <unk> , die ihre <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> und <unk> <unk> <unk> , weil die <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist der <unk> <unk> , die <unk> , <unk> <unk> , <unk> <unk> , in den <unk> zu <unk> .\n<unk> <unk> <unk> <unk> <unk> , <unk> <unk> und andere <unk> , die die <unk> des <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> haben <unk> , dass <unk> <unk> .\nIm <unk> der <unk> <unk> <unk> <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> die <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> , <unk> ich , dass es in der <unk> <unk> <unk> , die <unk> <unk> <unk> werden .\n<unk> Rat <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> werden <unk> <unk> und die <unk> des <unk> als <unk> <unk> , und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , eine <unk> für die <unk> der <unk> <unk> , <unk> die <unk> <unk> , nicht <unk> <unk> <unk> .\n<unk> Sie eine <unk> der <unk> <unk> , die <unk> <unk> <unk> , hat die <unk> in <unk> <unk> <unk> <unk> , weil sie sich <unk> <unk> <unk> .\nEs gibt <unk> <unk> , dass es keine <unk> <unk> , <unk> <unk> und <unk> <unk> in <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> die <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> sich <unk> , dass die <unk> in einer <unk> <unk> der <unk> <unk> <unk> werden .\nDie <unk> <unk> die <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> &quot; <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> über <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk>\nEs ist <unk> <unk> , <unk> und <unk> auf <unk> <unk> zu <unk> .\n<unk> <unk> war es für die <unk> , <unk> <unk> <unk> <unk> zu <unk> und <unk> zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n&quot; <unk> ist <unk> <unk> , die <unk> Menschen , die <unk> , die <unk> , die <unk> , die <unk> , <unk> , <unk> <unk> .\n<unk> ist ein <unk> von <unk> für ihre <unk> <unk> .\nDie <unk> sind <unk> <unk> , aber auch die Aussprache Aussprache <unk> hat .\nDiese <unk> , <unk> von <unk> <unk> mit <unk> und die <unk> <unk> über die <unk> der <unk> in <unk> .\nIm Juni <unk> und <unk> <unk> ein <unk> <unk> die <unk> <unk> in <unk> .\nDie <unk> war im <unk> der <unk> <unk> <unk> , und es ist nicht für eine Abstimmung in der <unk> .\nAber die <unk> Tag , dass die <unk> auch die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDies ist die <unk> <unk> in einer <unk> <unk> - mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> wurde <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> werden auf <unk> <unk> .\nDie <unk> <unk> <unk> &quot; <unk> die Menschen für ihre <unk> <unk> und <unk> <unk> in <unk> , die <unk> in <unk> <unk> und <unk> <unk> <unk> .\nDie Entschließung und <unk> &quot; <unk> die nationalen <unk> die nationalen <unk> in einer <unk> und <unk> .\n<unk> die neue <unk> <unk> die <unk> der <unk> <unk> <unk> <unk> oder werden die <unk> <unk> <unk> <unk> ?\nDie <unk> wurde von <unk> <unk> <unk> <unk> <unk> <unk> die <unk> <unk> .\n<unk> der <unk> <unk> <unk> <unk> , dass er die <unk> Version des neuen <unk> <unk> .\n<unk> der <unk> , die <unk> <unk> ist , ist dieser Debatte auf eine <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\nDie <unk> , die von <unk> <unk> <unk> <unk> <unk> wird , wird erste ersten <unk> in der <unk> oder andere <unk> <unk> .\n<unk> <unk> haben sich <unk> <unk> , die <unk> <unk> , aber nicht für <unk> von <unk> .\n<unk> ist ein <unk> von <unk> <unk> , die in diesem <unk> <unk> , <unk> <unk> .\n<unk> sind <unk> - die <unk> <unk> in seiner <unk> und <unk> .\nEs gibt eine <unk> <unk> der <unk> <unk> in der <unk> <unk> und anderen <unk> , dass die <unk> &quot; <unk> &quot; <unk> .\n<unk> dieser <unk> sind <unk> <unk> , aber sie <unk> ihre <unk> - besonders <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> , die in <unk> <unk> , eine <unk> <unk> , um die <unk> <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> die <unk> <unk> ein <unk> <unk> .\n<unk> Sie die <unk> , <unk> und <unk> , die <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> <unk> <unk> <unk> <unk> ist ein <unk> <unk> , als <unk> <unk> .\n<unk> <unk> auch für die <unk> der <unk> <unk> , wenn sie durch die <unk> des <unk> <unk> .\nWir <unk> als <unk> <unk> , <unk> sie nicht auf <unk> <unk> <unk> als in <unk> Jahren <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> oder <unk> in der <unk> .\nDie <unk> <unk> in <unk> auf die <unk> der <unk> , wenn sie <unk> als <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAber sie <unk> nicht mehr <unk> als sie <unk> .\nDie letzte <unk> , die <unk> - die <unk> - war der <unk> - war es <unk> , der <unk> in <unk> .\n&quot; <unk> &quot; <unk> &quot; , nicht <unk> <unk> , <unk> <unk> <unk> <unk> in ersten <unk> seit <unk> <unk> .\n<unk> <unk> ich ein <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> über <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> sich auf die <unk> <unk> von <unk> , die <unk> <unk> , dass die <unk> <unk> und die <unk> der <unk> <unk> , <unk> von der <unk> <unk> .\nIch <unk> eine <unk> <unk> , um die <unk> <unk> zu <unk> .\nDas <unk> <unk> seit <unk> seit <unk> <unk> , die <unk> <unk> und <unk> <unk> , <unk> , <unk> und <unk> .\n<unk> von <unk> , wie sie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDie <unk> mit <unk> und <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> der <unk> <unk> .\nIm ersten <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist <unk> nur von einer <unk> <unk> als <unk> <unk> .\n<unk> <unk> ist das <unk> in einer <unk> als <unk> <unk> <unk> , wie <unk> <unk> <unk> .\n<unk> der <unk> .\n<unk> der <unk> .\n<unk> <unk> &quot; <unk> &quot; <unk> .\n<unk> Tage <unk> <unk> , <unk> <unk> <unk> <unk> <unk> &quot; <unk> <unk> &quot; <unk> <unk> <unk> &quot; <unk> <unk> <unk> &quot; <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn der <unk> mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> hat sie keine <unk> <unk> , der <unk> <unk> <unk> .\n<unk> <unk> <unk> , dass er sich <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> , <unk> , <unk> .\n<unk> in seiner <unk> <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> , <unk> ich <unk> <unk> .\n&quot; <unk> möchte ich &quot; <unk> , <unk> , <unk> , <unk> <unk> <unk> .\n<unk> <unk> <unk> .\nUnd Sie werden mit dieser <unk> <unk> und Ihnen <unk> <unk> , um <unk> zu <unk> .\n<unk> <unk> <unk> .\n<unk> <unk> war ein <unk> <unk> <unk> , die <unk> <unk> <unk> für <unk> <unk> in <unk> <unk> .\n<unk> <unk> <unk> ein <unk> über <unk> , <unk> und <unk> .\nAm <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> in <unk> auf die <unk> .\nDie <unk> <unk> <unk> auf die <unk> und <unk> .\nDie <unk> und Frauen <unk> <unk> , <unk> , <unk> , <unk> , <unk> , die <unk> <unk> .\nEin <unk> <unk> <unk> in der <unk> , <unk> und <unk> von den <unk> <unk> <unk> <unk> , <unk> die <unk> &quot; <unk> &quot; <unk> .\n&quot; Sie <unk> <unk> , wie einige <unk> von <unk> <unk> , &quot; die <unk> in einer <unk> .\nIch <unk> die <unk> <unk> gut .\nIch weiß , <unk> <unk> .\nDoch es <unk> nicht .\nDie <unk> <unk> <unk> <unk> die <unk> , dass die <unk> und eine <unk> <unk> , <unk> <unk> <unk> <unk> .\nDie <unk> sind mit <unk> für ihre <unk> .\nAber die <unk> <unk> mit einem <unk> , <unk> <unk> .\nDie <unk> <unk> als <unk> <unk> , wenn sie in <unk> <unk> <unk> .\n<unk> &quot; <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> ist es , dass einige <unk> <unk> <unk> werden kann , uns in der Rolle der <unk> <unk> .\nDie <unk> ist ein <unk> für die <unk> <unk> , die nicht zu <unk> <unk> oder <unk> mit dem <unk> <unk> .\nSie <unk> auch , dass die <unk> , die <unk> <unk> in den <unk> Jahren <unk> <unk> wurde .\nAls <unk> der <unk> über <unk> <unk> , <unk> und <unk> <unk> .\n<unk> seine <unk> <unk> <unk> von <unk> <unk> mit dem <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> wäre es mit einem <unk> <unk> , weil sie <unk> <unk> und <unk> <unk> <unk> .\n<unk> wäre , ob es eine <unk> <unk> , <unk> <unk> , <unk> <unk> die <unk> , <unk> <unk> , die <unk> in <unk> und <unk> zu <unk> .\nIm <unk> <unk> <unk> er <unk> <unk> , dass er einige <unk> <unk> <unk> , ob er <unk> <unk> , ob er <unk> werden , um <unk> <unk> zu <unk> .\n&quot; Wenn Sie über Ihre <unk> über Ihre <unk> <unk> , müssen Sie die <unk> <unk> , &quot; <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> , in einem <unk> <unk> , der <unk> <unk> <unk> .\nDie <unk> sind <unk> , die von einem Land <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> in <unk> .\n<unk> , <unk> , <unk> und <unk> <unk> auf der <unk> eines <unk> .\n<unk> sind <unk> für die Anzahl der <unk> <unk> , <unk> sie <unk> oder die <unk> ihrer <unk> .\n<unk>\nDiese Woche ist eine <unk> <unk> , ob die <unk> <unk> , ob die <unk> der <unk> für die <unk> <unk> <unk> <unk> .\nWenn nicht von <unk> <unk> werden , sind nicht <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs war <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEr <unk> , wenn er sich <unk> , wenn er die <unk> für eine <unk> <unk> .\n<unk> &quot; <unk> <unk> <unk> <unk> &quot; <unk> <unk> <unk> .\nDoch die <unk> <unk> <unk> die <unk> <unk> <unk> .\nIch <unk> keine <unk> , nicht <unk> .\n<unk> &quot; <unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> .\n&quot; Ich möchte meine <unk> <unk> .\n<unk> <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese Woche muss über <unk> <unk> , <unk> und <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> , dass <unk> und <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> in der <unk> von <unk> <unk> , eine <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> sie <unk> <unk> , weil die <unk> <unk> mit ihnen <unk> .\n&quot; <unk> ist ein <unk> <unk> , &quot; sie <unk> <unk> .\n&quot; <unk> sind die <unk> , die in der Nähe des <unk> <unk> <unk> <unk> .\n<unk> auch die <unk> , die seit <unk> <unk> , hat sie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> die <unk> in diesem Fall - <unk> die <unk> in einem <unk> , <unk> , <unk> - <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> <unk> für die <unk> von <unk> , <unk> von <unk> <unk> , hat eine <unk> , die <unk> <unk> <unk> .\nDie <unk> <unk> , die von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> ein <unk> <unk> <unk> <unk> die <unk> <unk> <unk> , ist die <unk> nicht für <unk> <unk> .\n&quot; <unk> <unk> sind <unk> , <unk> <unk> , &quot; <unk> &quot; <unk> &quot; .\nDie <unk> der <unk> <unk> auf dieser <unk> .\n<unk> haben die <unk> der <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> in <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn <unk> und <unk> , <unk> von <unk> <unk> sind <unk> <unk> , besonders in <unk> <unk> <unk> <unk> .\nDie <unk> zwischen den beiden <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs ist für die <unk> , die <unk> , <unk> <unk> .\n<unk> <unk> nicht <unk> in den <unk> drei <unk> <unk> .\nIm <unk> <unk> <unk> <unk> die <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , die <unk> <unk> , ist eine <unk> gegen die <unk> , die die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> ein <unk> in <unk> gegen die <unk> <unk> , <unk> sie mit <unk> <unk> in den <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> in der <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> gibt es <unk> <unk> <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> sind <unk> <unk> , und von der <unk> der <unk> <unk> , die <unk> <unk> , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie Bericht , <unk> auch in <unk> <unk> , <unk> auch <unk> <unk> in <unk> <unk> <unk> , <unk> die <unk> der <unk> <unk> <unk> .\nDie <unk> in <unk> <unk> ihre <unk> als nur die ersten <unk> der <unk> .\nIn der Nähe des <unk> <unk> <unk> die <unk> <unk> weniger als eine <unk> von <unk> .\nDas <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> <unk> , und die <unk> der <unk> - der <unk> - <unk> im <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> wie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> ist die <unk> <unk> , wenn die <unk> <unk> <unk> .\nDie <unk> <unk> ich in <unk> - ich <unk> von der <unk> .\n<unk> in den <unk> <unk> , und wenn ich <unk> , <unk> ich <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> für ob die <unk> und ihre <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> hat er <unk> &quot; <unk> <unk> <unk> &quot; <unk> <unk> <unk> <unk> .\nWas <unk> , <unk> und eine <unk> ist dieses <unk> <unk> , die er <unk> wurde .\nIch <unk> eine <unk> <unk> der <unk> .\nDie <unk> ist <unk> <unk> <unk> , weil sie nicht die <unk> <unk> und <unk> <unk> werden .\nEr <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , aber es für <unk> <unk> .\nDiese Frage nur nur wenn <unk> Menschen zu <unk> .\nWenn die <unk> nicht <unk> , sind <unk> , <unk> , <unk> , <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; für <unk> <unk> .\n&quot; <unk> <unk> wird die <unk> auf diesem Thema <unk> , <unk> &quot; <unk> <unk> .\n<unk> , wenn wir <unk> , <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> er die neuen <unk> in Europa , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , seine <unk> <unk> <unk> .\n<unk> <unk> sind <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Sie diese <unk> <unk> , <unk> <unk> , <unk> , <unk> .\nAber wie sie es ?\n<unk> - <unk> auf <unk> .\n<unk> &quot; Wir <unk> , dass die <unk> nicht alle <unk> , <unk> &quot; <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> <unk> ein <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> von <unk> , <unk> und seine Kollegen <unk> <unk> , <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> in <unk> <unk> in <unk> <unk> .\n&quot; <unk> ist ein neue <unk> <unk> , die <unk> <unk> .\n<unk> des <unk> <unk> nicht alle , dass <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> und <unk> .\nAber in einer <unk> , <unk> , die die <unk> mit <unk> <unk> sind , die <unk> <unk> mit <unk> <unk> sind , <unk> <unk> und weniger <unk> .\n<unk> <unk> ist eine <unk> <unk> , wie <unk> ist eine <unk> neuen <unk> in der <unk> .\n<unk> <unk> , <unk> <unk> und politische <unk> sind die <unk> , <unk> und politische <unk> zu <unk> , wie <unk> <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nFür einige <unk> , die <unk> der <unk> ist , ist es <unk> , dass Sie nicht <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , wenn Sie <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n&quot; <unk> <unk> <unk> sich auf den <unk> und <unk> <unk> <unk> .\n&quot; <unk> <unk> <unk> alle , die <unk> , <unk> <unk> .\n<unk> würde die <unk> <unk> ein <unk> <unk> und <unk> <unk> <unk> .\nEin <unk> der <unk> von <unk> <unk> , <unk> und <unk> <unk> , die <unk> , die ihre <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , dass die <unk> <unk> , so <unk> <unk> , so dass die <unk> und <unk> <unk> , um eine vielen <unk> zu <unk> .\n&quot; <unk> &quot; <unk> , <unk> <unk> , <unk> , <unk> , <unk> und <unk> der <unk> für <unk> <unk> , <unk> , <unk> und <unk> der <unk> für <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n&quot; Wir <unk> <unk> eine <unk> , die <unk> zu <unk> , die nicht <unk> werden .\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> in seiner eigenen <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nAber <unk> <unk> , <unk> die <unk> , ob sie <unk> , ob sie <unk> ist , ob sie <unk> , ob sie <unk> <unk> , <unk> <unk> <unk> <unk> .\nDoch was die <unk> <unk> <unk> <unk> ist , <unk> sie nicht über <unk> , <unk> , <unk> , <unk> <unk> werden ?\n<unk> sind <unk> <unk> , wie sie die <unk> <unk> , <unk> sie <unk> , <unk> , <unk> <unk> .\n&quot; Ich <unk> nicht ein , dass eine <unk> von <unk> zu <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist es als <unk> , die <unk> und ihre <unk> &quot; <unk> &quot; .\n<unk> sind die Frage der <unk> .\n<unk> <unk> ist die <unk> der <unk> , die eine <unk> , <unk> <unk> <unk> .\nAm <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist <unk> <unk> , die eine <unk> <unk> , <unk> <unk> , <unk> <unk> und eine <unk> <unk> <unk> <unk> .\n&quot; <unk> ist nicht für die <unk> in der <unk> <unk> und <unk> <unk> , er <unk> <unk> .\n&quot; <unk> <unk> ist <unk> , um die <unk> und eine <unk> von <unk> zu <unk> .\n<unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; Ich bin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; Ich <unk> die <unk> , weil Sie Ihre <unk> von <unk> <unk> , die <unk> <unk> und weil es <unk> <unk> .\n&quot; Ich <unk> die <unk> , aber ich bin nicht <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> in der <unk> , weil er &quot; <unk> <unk> ist , nicht <unk> , die <unk> <unk> , die <unk> <unk> zu <unk> .\n<unk> er er <unk> <unk> , <unk> <unk> , die eine <unk> <unk> mit dem <unk> des <unk> <unk> .\n<unk> auch <unk> <unk> , <unk> ich <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> für <unk> <unk> .\n<unk> <unk> und <unk> &quot; <unk> <unk> die <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> mit dem <unk> <unk> <unk> <unk> .\nDie <unk> <unk> er <unk> <unk> und <unk> , &quot; eine <unk> <unk> <unk> <unk> .\nDie <unk> war <unk> , aber für mich <unk> , die <unk> <unk> .\n<unk> in einer <unk> <unk> und <unk> <unk> , <unk> <unk> und <unk> in der <unk> und <unk> <unk> und <unk> in der <unk> als <unk> <unk> .\n<unk> <unk> <unk> der <unk> <unk> der <unk> <unk> <unk> <unk> , die er nicht <unk> hat , und ich , dass es keine <unk> <unk> ist .\nEs kann einige <unk> <unk> , dass die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> werden .\nDoch die <unk> nicht <unk> <unk> die <unk> und <unk> , dass ein <unk> <unk> <unk> .\nDie <unk> <unk> ich ein <unk> , aber er <unk> nicht <unk> , &quot; <unk> <unk> <unk> .\n<unk> würde <unk> , wenn er <unk> ist .\n<unk>\n<unk> die <unk> der <unk> <unk> in <unk> 5 , die <unk> , die ich <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nAber was <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> , <unk> und <unk> <unk> , aber er war auch ein <unk> <unk> und <unk> <unk> .\n<unk> in <unk> , <unk> <unk> <unk> die <unk> <unk> der <unk> <unk> , und er <unk> die <unk> <unk> der <unk> <unk> , und er <unk> <unk> für eine <unk> , <unk> , <unk> .\n<unk> <unk> hat es <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ein <unk> <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs war <unk> <unk> <unk> &quot; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDiese <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> dieses <unk> und <unk> .\nDie <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEs gibt auch auch <unk> <unk> , wo er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> von <unk> , die <unk> oder die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEs ist eine <unk> , die <unk> <unk> <unk> <unk> &quot; <unk> <unk> , aber es gibt der <unk> und <unk> über die <unk> <unk> .\nEr hat eine <unk> für die <unk> <unk> <unk> werden .\n<unk> gibt es in <unk> <unk> , es <unk> <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> er ein <unk> <unk> , ein <unk> <unk> zu <unk> .\nDie <unk> in <unk> in <unk> , <unk> ich <unk> &quot; <unk> &quot; <unk> und <unk> <unk> <unk> <unk> , dass die <unk> der <unk> <unk> ist .\n<unk> <unk> sie <unk> , wie sie <unk> , <unk> <unk> <unk> in <unk> .\nDas <unk> ich mich .\n<unk> <unk> auf <unk> seine <unk> zu <unk> .\n<unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk>\n<unk> <unk> die &quot; <unk> &quot; <unk> &quot; , die er mit <unk> <unk> <unk> <unk> , dass er mit <unk> <unk> <unk> <unk> und <unk> <unk> <unk> für <unk> .\nDie <unk> wurde <unk> <unk> über die <unk> , aber <unk> &quot; <unk> hier in <unk> <unk> .\nEr <unk> , wie er <unk> und <unk> <unk> , würde er die <unk> von <unk> ein <unk> <unk> , die eine <unk> <unk> und <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , was wir <unk> haben , dass wir uns <unk> <unk> , dass <unk> <unk> , dass die <unk> von <unk> <unk> <unk> werden .\n<unk> <unk> , <unk> wir nicht mehr , aber wir wissen , aber wir wissen , dass eine <unk> <unk> , die eine <unk> <unk> - und ich <unk> nicht <unk> .\nDie <unk> von <unk> <unk> , dass er <unk> und <unk> <unk> <unk> , <unk> die <unk> über die <unk> der <unk> in der <unk> und <unk> zu <unk> .\nAls <unk> der <unk> <unk> <unk> wurde in der <unk> der <unk> <unk> <unk> , um die <unk> <unk> in <unk> zu <unk> .\n<unk> in <unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIn der <unk> des <unk> <unk> die <unk> für die <unk> <unk> , die eine <unk> <unk> <unk> , <unk> er <unk> <unk> in der <unk> und <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> <unk> auf , es war es in der <unk> und <unk> <unk> <unk> , wie sie <unk> <unk> und <unk> <unk> .\n<unk> hat eine der <unk> <unk> , die die <unk> <unk> <unk> , die <unk> <unk> <unk> , wo er <unk> &quot; <unk> &quot; <unk> &quot; <unk> .\nEs war <unk> als <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> , und ich in <unk> mit dem <unk> .\nIch möchte ein <unk> <unk> <unk> , wie ein <unk> und <unk> <unk> für die <unk> <unk> .\n<unk> <unk>\n<unk> in der <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> in seiner <unk> , wie er <unk> werden , eine <unk> von <unk> <unk> , <unk> er <unk> <unk> , <unk> er <unk> <unk> , <unk> <unk> <unk> , <unk> er <unk> <unk> .\n<unk> auf nur 4 Tage <unk> , <unk> er <unk> , <unk> <unk> und <unk> der <unk> <unk> .\n<unk> <unk> , <unk> er die <unk> <unk> , <unk> er in <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> Tage <unk> wir eine <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk>\n<unk> <unk> er die <unk> von <unk> , <unk> <unk> <unk> die <unk> in der <unk> , die viele <unk> <unk> , <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nSie haben eine <unk> der <unk> <unk> , so die <unk> nicht <unk> .\n<unk> <unk> und <unk> <unk> ist es <unk> der <unk> , die <unk> <unk> , die <unk> <unk> und die <unk> <unk> <unk> .\nDie <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDas <unk> <unk> ...\n<unk> wäre die gesamte <unk> .\nEs ist <unk> <unk> , die <unk> , die <unk> <unk> der <unk> ...\nSie <unk> <unk> von <unk> .\n<unk> <unk> und <unk> <unk>\n<unk> ist ein <unk> <unk> von <unk> zwischen einem <unk> und einem <unk> , <unk> , <unk> , <unk> und <unk> .\nSie <unk> eine <unk> über die <unk> der <unk> .\nEs gibt <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nSie haben <unk> <unk> <unk> - diese <unk> - diese <unk> - <unk> .\n<unk> ist es in <unk> , um <unk> zu <unk> , um die <unk> <unk> zu <unk> , <unk> und <unk> zu <unk> .\n<unk> <unk> , dass <unk> <unk> <unk> , <unk> <unk> <unk> .\nWir <unk> eine <unk> <unk> über <unk> <unk> .\nEs ist <unk> <unk> , dass die Menschen , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nWenn nicht für <unk> <unk> , würde sie in anderen <unk> <unk> .\n<unk> und <unk> <unk> mit <unk> <unk> wie <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> die <unk> der <unk> <unk> auf <unk> <unk> <unk> die <unk> <unk> , wenn es eine <unk> <unk> , <unk> <unk> , <unk> und <unk> <unk> <unk> .\nDie <unk> war <unk> , wenn alle <unk> <unk> <unk> für eine <unk> <unk> <unk> .\n<unk> des <unk> ist ein <unk> <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> , die <unk> , eine <unk> <unk> <unk> , ist eine <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , dass die <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> wurde <unk> <unk> <unk> , die <unk> <unk> in den <unk> der <unk> .\nDie <unk> wurde in einer <unk> <unk> <unk> <unk> <unk> .\nIn <unk> wurde die <unk> der <unk> <unk> und eine <unk> <unk> der <unk> <unk> .\nDie <unk> <unk> im <unk> <unk> und <unk> .\nDie <unk> <unk> haben <unk> <unk> <unk> <unk> <unk> für <unk> .\nUnd das <unk> <unk> <unk> <unk> .\n<unk> sind <unk> von der <unk> <unk> , dass die <unk> der <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , die <unk> <unk> der <unk> <unk> und <unk> auf <unk> der <unk> <unk> in den <unk> <unk> <unk> <unk> .\nDie <unk> <unk> der <unk> und <unk> auf die <unk> <unk> von <unk> und <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> .\nDie <unk> <unk> <unk> und <unk> auf eine <unk> <unk> mit <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> und <unk> in <unk> für diese <unk> <unk> <unk> und ist <unk> .\n<unk> und <unk> haben sich <unk> <unk> , die <unk> <unk> <unk> die <unk> mit <unk> <unk> wie die <unk> <unk> .\nUnd <unk> &quot; <unk> , <unk> , <unk> , <unk> , <unk> ist ein <unk> <unk> .\n<unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> als <unk> <unk> in der <unk> <unk> , wenn die erste <unk> <unk> , die <unk> <unk> <unk> <unk> .\n<unk> haben die <unk> für die <unk> <unk> von <unk> von <unk> <unk> , <unk> , dass sie sich in <unk> <unk> <unk> .\n<unk> hat keine <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs ist <unk> <unk> , wenn ein <unk> <unk> für 2 <unk> <unk> <unk> .\nDoch es ist der <unk> <unk> , wenn die <unk> <unk> <unk> und die <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> wie <unk> ist , <unk> <unk> die <unk> des <unk> <unk> <unk> <unk> .\nIn einer <unk> <unk> <unk> die <unk> <unk> <unk> , dass die <unk> in der <unk> drei Jahre <unk> <unk> <unk> .\n<unk> über die <unk> <unk> seine <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> von <unk> <unk> in <unk> .\nDie <unk> <unk> auch eine <unk> <unk> , <unk> <unk> , <unk> die <unk> der <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> ist wichtig , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> &quot; <unk> <unk> &quot; .\n<unk> <unk> sind <unk> von den <unk> <unk> .\n<unk> nicht nur nicht nur <unk> , sondern eine <unk> in <unk> <unk> , sondern eine <unk> in <unk> <unk> , weil es die <unk> der <unk> <unk> , die <unk> <unk> <unk> .\nDas ist <unk> wichtig , weil sie <unk> werden , die <unk> zu <unk> oder <unk> <unk> zu <unk> , wenn <unk> <unk> , <unk> <unk> .\n<unk> ist ein <unk> <unk> , &quot; er <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> in der <unk> für <unk> und <unk> auf <unk> <unk> <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> <unk> <unk> \" <unk> <unk> \" <unk> \" <unk> <unk> \" <unk> <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> <unk>\n<unk> der <unk> in der <unk> befindet sich im <unk> <unk> in vier <unk> <unk> , <unk> <unk> , <unk> mit dem <unk> .\nDie <unk> , <unk> <unk> , <unk> <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> der <unk> <unk> in <unk> <unk> .\n<unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nHerr Präsident , <unk> <unk> <unk> , die <unk> für <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> die <unk> in der <unk> für <unk> des <unk> <unk> <unk> , oder <unk> .\n&quot; Die <unk> von <unk> <unk> <unk> die gesamte <unk> und besonders <unk> ist <unk> , nicht nur für die <unk> , aber die <unk> , nicht nur für die <unk> , sondern in einer <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n&quot; Sie <unk> unsere <unk> , unsere <unk> , <unk> <unk> , <unk> , <unk> und <unk> , <unk> , <unk> und <unk> , <unk> , <unk> und <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> <unk> für <unk> <unk> , <unk> die <unk> in 2000 .\nDoch es ist eine <unk> , und <unk> in <unk> ist ein wichtiger Schritt in der <unk> .\nDie <unk> ist die <unk> <unk> in <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> .\n<unk> , ein <unk> für die <unk> der <unk> <unk> <unk> , oder <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> , die <unk> für die <unk> <unk> <unk> <unk> , oder <unk> , die die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> auch die <unk> <unk> <unk> .\n<unk> <unk> <unk> der <unk> <unk> der <unk> <unk> , <unk> <unk> und <unk> die anderen <unk> , um die Ergebnisse zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> , <unk> und <unk> .\nEs ist <unk> der <unk> <unk> , und er <unk> <unk> , eine <unk> gegen <unk> in <unk> zu <unk> .\n<unk> hat <unk> <unk> .\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nFür die <unk> ist jedoch die <unk> <unk> <unk> , eine <unk> der <unk> <unk> .\nDie <unk> <unk> , die <unk> in den <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> auf <unk> <unk>\nDie <unk> <unk> <unk> hat <unk> <unk> , China zu <unk> und China zu <unk> , um die <unk> der <unk> <unk> zu <unk> .\n<unk> <unk> , die <unk> <unk> der <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIch <unk> gegen <unk> &#91; <unk> &#93; , <unk> , China , China , China , <unk> , China , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> in diesem Bereich .\n&quot; <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n&quot; <unk> <unk> werden auf einige <unk> <unk> , sondern es nicht <unk> .\n&quot; <unk> <unk> wenn China <unk> und <unk> <unk> werden .\nEuropa ist der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm <unk> Tagen <unk> die <unk> <unk> <unk> <unk> <unk> und die <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> <unk> in Europa hat <unk> , dass es die <unk> der <unk> in Europa <unk> , die <unk> <unk> , die <unk> <unk> zu <unk> , um <unk> zu <unk> .\nHerr <unk> <unk> , dass ein <unk> <unk> <unk> werden , um die <unk> von <unk> zu <unk> , sondern auch <unk> <unk> <unk> , die Europäische <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> die <unk> , die <unk> und <unk> , <unk> er <unk> .\n&quot; <unk> <unk> und <unk> haben wir eine <unk> <unk> .\n<unk> ist nicht eine <unk> , weil sie <unk> , weil wir die <unk> für <unk> <unk> <unk> und Frauen <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , eine <unk> auf <unk> <unk> in den USA <unk> , <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> &quot; <unk> <unk> werden , dass die <unk> von 50 und <unk> auf <unk> und <unk> <unk> <unk> .\n<unk> <unk> , die <unk> <unk> des <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nHerr <unk> , <unk> <unk> <unk> , dass die <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> .\nDie <unk> der <unk> von <unk> pro <unk> in <unk> und <unk> <unk> <unk> <unk> <unk> <unk> .\nIch <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> .\n<unk> <unk> von <unk> über <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> hat <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> <unk> <unk> <unk> für die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nEr <unk> ich , dass <unk> <unk> und die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> , <unk> in diesem <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> wäre <unk> mit <unk> , die unsere <unk> und <unk> <unk> <unk> .\nDie <unk> wäre <unk> <unk> , <unk> <unk> <unk> .\n&quot; <unk> <unk> <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n&quot; <unk> <unk> wäre <unk> , <unk> in der <unk> und der <unk> <unk> , dass die <unk> der <unk> <unk> .\n&quot; <unk> <unk> wäre <unk> , <unk> in <unk> <unk> , um <unk> <unk> und <unk> zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> eines <unk> auf die <unk> <unk> würde eine <unk> <unk> des <unk> <unk> .\nDie <unk> sind <unk> , &quot; die <unk> <unk> in einer <unk> <unk> <unk> <unk> , wenn sie die <unk> <unk> .\n&quot; <unk> ist keine <unk> , dass wir mit , und es ist <unk> nicht <unk> , dass wir die <unk> nicht <unk> .\n&quot; <unk> ist eine <unk> , die nationalen <unk> der nationalen <unk> , und es sollte nicht <unk> werden .\nDie <unk> <unk> und die <unk> von <unk> und die <unk> von <unk> wird <unk> <unk> , um die <unk> von <unk> zu <unk> .\n<unk> hat die <unk> <unk> <unk> <unk> , <unk> er <unk> <unk> , <unk> er die <unk> <unk> und <unk> <unk> , die er <unk> wurde .\nIm <unk> Woche <unk> die <unk> <unk> ein <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist ein <unk> ohne <unk> .\n<unk> ist ein <unk> ohne <unk> .\n<unk> ist ein <unk> <unk> ohne <unk> <unk> .\nDie <unk> ist ein <unk> <unk> , ein <unk> <unk> , <unk> und <unk> ohne <unk> <unk> <unk> .\n<unk> ist ein <unk> , die <unk> <unk> , <unk> , <unk> und <unk> <unk> zu <unk> .\n<unk> ist es <unk> <unk> .\nIm <unk> und <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nUnd <unk> Kolleginnen und Kollegen <unk> , dass einige der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDas Liste der <unk> <unk> <unk> , eine <unk> <unk> <unk> , eine neue <unk> , die <unk> <unk> , die neue <unk> <unk> und die neue <unk> <unk> .\n<unk> <unk> und <unk> die <unk> , die einige <unk> <unk> <unk> <unk> in <unk> <unk> in <unk> <unk> , aber seine <unk> ist ein <unk> <unk> .\n<unk>\nEin <unk> im <unk> <unk> <unk> , wie <unk> und <unk> sind in <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Woche <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> hat <unk> und <unk> Länder .\n<unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd dann werden nicht einer <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> von <unk> und <unk> sind <unk> <unk> .\nEs gibt <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> in <unk> zu <unk> .\n<unk> , die die <unk> <unk> <unk> werden , sind eine <unk> , die <unk> und <unk> <unk> .\nWie <unk> <unk> <unk> wir <unk> ?\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , viele <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\n<unk> <unk> oder &quot; <unk> <unk> , &quot; <unk> ist ein <unk> <unk> für alle <unk> , <unk> und <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> der <unk> ist , dass die <unk> <unk> über <unk> <unk> haben .\n<unk> <unk> <unk> Sie in <unk> .\nSie sind nicht als <unk> , <unk> , <unk> oder <unk> .\n<unk> <unk> nicht , dass die <unk> <unk> <unk> und <unk> <unk> werden .\nEin <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIch <unk> <unk> <unk> und <unk> die <unk> <unk> in der <unk> und <unk> <unk> von <unk> , <unk> , <unk> oder <unk> .\n<unk> sind <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWir wissen , dass die <unk> mehr als <unk> <unk> , die <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nWir wissen , dass Sie auch <unk> , dass Sie eine <unk> als <unk> <unk> , die Sie <unk> haben , um <unk> zu <unk> .\nWie Sie <unk> , wenn <unk> <unk> ist ?\n<unk> <unk> Sie Ihre <unk> <unk> .\nDas ist <unk> <unk> .\nEs ist ein <unk> <unk> für <unk> <unk> .\n<unk> Sie Ihre <unk> , <unk> , <unk> , <unk> .\nSie <unk> wissen , wenn die <unk> alle <unk> die <unk> alle <unk> <unk> , so dass Sie nicht <unk> <unk> <unk> , wenn Sie <unk> <unk> in der <unk> <unk> .\n<unk> <unk> <unk> für <unk> <unk> und <unk> .\n<unk> diese <unk> <unk> <unk> , nicht <unk> .\n<unk> <unk> , die <unk> <unk> , wenn die <unk> <unk> , wenn die <unk> ihre <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> oft <unk> <unk> , dass eine <unk> in <unk> <unk> ist .\n<unk> , die <unk> auf Ihre <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> sind <unk> .\n<unk> <unk> <unk> <unk> &quot; , wenn <unk> eine <unk> <unk> <unk> &quot; <unk> &quot; , wenn es <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDies <unk> die Antwort <unk> , wenn <unk> und <unk> oft eine <unk> <unk> .\nEs <unk> keine <unk> <unk> für <unk> <unk> , sondern <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> wird die <unk> der <unk> von <unk> <unk> <unk> in ihrer <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , die <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> er <unk> <unk> , wird die <unk> <unk> der <unk> <unk> <unk> <unk> , und ist das <unk> <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> des <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas <unk> der <unk> <unk> in <unk> Woche würde es eine Entscheidung , die <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> und die <unk> <unk> <unk> .\n&quot; <unk> ist ein <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> .\nDie neue <unk> <unk> der <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , in einem <unk> <unk> <unk> wurde , <unk> die Entscheidung , die <unk> , und <unk> unterstützen .\nDie <unk> würde von <unk> auf die <unk> <unk> .\nEs wäre <unk> <unk> pro Tag von der <unk> in <unk> .\nFür <unk> wäre es <unk> <unk> <unk> .\nEs ist <unk> von <unk> , aber <unk> von <unk> <unk> <unk> , die auf der <unk> <unk> <unk> <unk> .\nDoch <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> der <unk> <unk> <unk> <unk> <unk> die <unk> nur nur <unk> <unk> <unk> , sondern die <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist ob eine <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie anderen ist , ob eine <unk> <unk> , die die <unk> <unk> hat , die <unk> <unk> <unk> , weil er <unk> <unk> ist .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> in einer <unk> in der <unk> der <unk> , wo sie die <unk> in <unk> <unk> .\n<unk> &quot; <unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs war <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> <unk> die <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> de <unk> <unk> de <unk> de <unk> de <unk> de <unk> de <unk> de <unk> de <unk> .\n&quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> .\n&quot; <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n&quot; <unk> &quot; <unk> &quot; <unk> Ich habe nicht <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> hat eine <unk> von <unk> und <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> in der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie <unk> war <unk> <unk> , dass sie <unk> , <unk> <unk> .\n<unk> , die <unk> <unk> ein <unk> <unk> für eine <unk> von <unk> , aber <unk> nicht <unk> .\n<unk> <unk> und <unk> ist nicht <unk> für <unk> .\nDie <unk> <unk> <unk> , aber die <unk> <unk> , <unk> er <unk> <unk> , <unk> er <unk> <unk> , <unk> er <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> ist die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist <unk> <unk> , besonders wenn die <unk> eines <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk>\nIm ersten Jahr der <unk> <unk> <unk> <unk> , die <unk> <unk> die <unk> und die <unk> , <unk> <unk> und <unk> <unk> .\n<unk> ist ein Land , die in der <unk> <unk> <unk> <unk> , <unk> mit dem ersten Jahr der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> <unk> , nicht nur <unk> <unk> von <unk> für die <unk> <unk> , aber sie <unk> die <unk> des <unk> <unk> in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> Sie die Bericht der <unk> von <unk> , <unk> von den <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> in der <unk> <unk> hat <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk>\n<unk> in <unk> in der <unk> ist <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> wir heute eine <unk> der <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\n<unk> <unk> <unk> , dass in der <unk> der <unk> <unk> ist , ist auch ein <unk> <unk> .\n&quot; <unk> Teil der Menschen , die die <unk> in einem <unk> <unk> , in der <unk> der <unk> in den Vereinigten Staaten hat <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\nDie <unk> <unk> von <unk> <unk> , wenn die <unk> <unk> <unk> <unk> <unk> , wenn <unk> die <unk> <unk> , die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> und <unk> <unk> eine <unk> <unk> <unk> , ist der <unk> der <unk> <unk> <unk> <unk> der <unk> <unk> und <unk> <unk> .\nDie <unk> <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk>\n<unk> der <unk> in der <unk> <unk> und <unk> <unk> , in der <unk> in <unk> <unk> und <unk> <unk> in <unk> <unk> .\nDie <unk> der <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> der <unk> <unk> der <unk> <unk> <unk> .\n<unk>\n<unk> die <unk> der <unk> <unk> , der <unk> <unk> , in der <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\n<unk> <unk> , die erste Jahr der <unk> <unk> <unk> <unk> , <unk> eine <unk> der Regierung .\n<unk> <unk> <unk> , <unk> der <unk> <unk> der <unk> <unk> <unk> <unk> <unk> , dass eine <unk> <unk> der <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> .\n<unk> <unk> die <unk> der Wirtschaft in <unk> dieses Jahr <unk> , um die <unk> <unk> dieses Jahr in <unk> zu <unk> , dass wir unsere <unk> <unk> , die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk>\n<unk> <unk> <unk> <unk> , dass die <unk> so <unk> <unk> <unk> der <unk> der <unk> <unk> und <unk> <unk> .\n&quot; <unk> <unk> <unk> ist die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\n<unk> <unk> <unk> , <unk> der <unk> &quot; <unk> \" <unk> \" <unk> in <unk> , <unk> , dass die <unk> eine <unk> <unk> <unk> .\nDie <unk> , <unk> wir <unk> , dass es <unk> ist , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\n<unk> Herr Präsident , die Bedeutung der <unk> von <unk> zu <unk> <unk> die <unk> der Regierung .\n&quot; <unk> möchte ich die Regierung <unk> , um <unk> <unk> in verschiedenen <unk> zu <unk> , <unk> er <unk> <unk> .\nEr <unk> , dass die <unk> <unk> <unk> in seiner eigenen <unk> der <unk> <unk> <unk> .\n&quot; <unk> gibt es <unk> , dass die Regierung <unk> <unk> <unk> <unk> , und was ich <unk> <unk> , dass es <unk> ist .\n<unk> <unk> <unk>\n<unk> Jahren <unk> in <unk> und ein <unk> von <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , dass die <unk> <unk> <unk> ist , ist <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , für die <unk> gegen <unk> <unk> <unk> .\n<unk> <unk> <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> / <unk> <unk> wurde der <unk> <unk> .\n<unk> <unk> wurde ein <unk> <unk> <unk> , wo er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nFür diesen <unk> wurde die <unk> der <unk> <unk> <unk> und die <unk> zwischen den <unk> der <unk> <unk> und der <unk> <unk> <unk> .\nEs muss <unk> <unk> werden .\nDie <unk> <unk> , in der <unk> <unk> wie <unk> , <unk> der <unk> und <unk> für <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEr <unk> , dass die <unk> in diese drei drei <unk> <unk> ist , ist der <unk> von <unk> 2007 <unk> <unk> , die <unk> <unk> in der <unk> <unk> .\nSie haben <unk> mit einem <unk> <unk> und viele <unk> <unk> , da sie sich nicht eine <unk> <unk> haben , weil es <unk> <unk> , die uns <unk> und sie sind <unk> .\nIch <unk> , dass in der <unk> der <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> , dass die <unk> der <unk> nicht <unk> .\n<unk> <unk> , dass die <unk> nicht die <unk> <unk> , weil die <unk> <unk> nicht die <unk> <unk> und die <unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> ,\n<unk> gibt es eine <unk> der <unk> <unk> , mit der <unk> dieser <unk> <unk> , <unk> <unk> , dass die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> muss die <unk> mit <unk> <unk> und viele <unk> <unk> .\n<unk> <unk> <unk> &quot; <unk> in <unk> in <unk> in <unk> .\nDie <unk> von <unk> mit <unk> ist eine der <unk> wichtige <unk> , die die <unk> in den letzten Jahren <unk> <unk> hat .\nDie <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> und <unk> des <unk> gegen die <unk> der <unk> und <unk> des <unk> gegen die <unk> <unk> <unk> <unk> .\nEin <unk> <unk> , <unk> die <unk> der <unk> , <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nMit diesem <unk> <unk> die <unk> <unk> von der <unk> <unk> für die <unk> von <unk> <unk> für die <unk> von <unk> <unk> für die <unk> , <unk> und <unk> .\nDie <unk> <unk> von der <unk> <unk> <unk> <unk> , <unk> die <unk> der <unk> , weil die <unk> der <unk> und <unk> <unk> ist .\n<unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> für die <unk> .\nMit diesem <unk> , die die <unk> und die <unk> der <unk> zwei <unk> <unk> , um die <unk> , die <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> .\nIn der <unk> &quot; <unk> &quot; war auch die <unk> <unk> <unk> , ohne die <unk> der <unk> <unk> , ohne <unk> in den <unk> der <unk> <unk> .\nIn seiner <unk> , die <unk> <unk> er nicht mit der <unk> des <unk> in der <unk> , dass die <unk> keine <unk> nicht <unk> werden kann .\n<unk> <unk> ich , dass die <unk> der <unk> <unk> , &quot; und <unk> , dass die <unk> der <unk> <unk> , <unk> und <unk> , dass die <unk> der <unk> <unk> .\nDie <unk> von <unk> , <unk> ist eine der <unk> wichtige <unk> , die die <unk> in den letzten Jahren <unk> <unk> , <unk> und <unk> <unk> .\n<unk> die <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> Fragen .\nDiese Probleme , die <unk> <unk> , <unk> <unk> die <unk> der <unk> <unk> \" \" \" \" \" \" <unk> \" <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , dass ein <unk> Zahl der Länder über die <unk> <unk> hat , und hat jetzt die <unk> , die <unk> <unk> <unk> <unk> <unk> .\nEs kann nicht <unk> , dass die Regierung von <unk> <unk> <unk> , dass die <unk> <unk> , dass die <unk> der <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> die Möglichkeit eines &quot; <unk> in <unk> .\n<unk> Sie die Daten von <unk> , die <unk> zwischen den ersten und <unk> <unk> zu <unk> .\nDie Möglichkeit von <unk> <unk> &quot; <unk> &quot; ist eine <unk> wie die <unk> <unk> dieses Jahr in <unk> , wo man die <unk> von <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIn diesem Fall hat die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nWie wird es <unk> werden , <unk> nicht die <unk> oder die <unk> <unk> der <unk> oder die <unk> <unk> <unk> , weil er <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> der <unk> <unk> und <unk> &quot; <unk> und <unk> &quot; <unk> &quot; .\n<unk> über die Möglichkeit der &quot; <unk> &quot; &quot; <unk> &quot; , die mit der <unk> der <unk> <unk> <unk> <unk> , die mit dem <unk> der <unk> <unk> <unk> wird .\n<unk> , die <unk> <unk> von einer oder <unk> <unk> <unk> <unk> <unk> die Ergebnisse des <unk> <unk> nicht <unk> <unk> .\n<unk> <unk> die <unk> in allen <unk> des <unk> <unk> <unk> <unk> , wenn es eine <unk> der <unk> zwischen der <unk> und / / oder der <unk> der <unk> der <unk> <unk> ist , die <unk> der <unk> <unk> ist .\n<unk> <unk> wird die <unk> <unk> , wenn die <unk> zwischen den <unk> und der <unk> <unk> <unk> , <unk> <unk> , dass die <unk> zwischen den <unk> und die <unk> der <unk> <unk> <unk> <unk> .\nEs sollte <unk> <unk> , dass in diesem Situation die <unk> <unk> , wo man die <unk> zwischen den ersten und der ersten <unk> <unk> <unk> .\n<unk>\n<unk> <unk> werden in der <unk> von <unk> , <unk> die <unk> .\nDie <unk> der <unk> <unk> <unk> <unk> in <unk> , seine <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> Sie die <unk> der <unk> <unk> <unk> <unk> in der <unk> des <unk> .\n<unk> ein <unk> <unk> mit der anderen <unk> , die <unk> in der <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIm <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm <unk> <unk> <unk> <unk> die <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> sollte <unk> , und seine <unk> zu <unk> , um <unk> zu <unk> und seine <unk> zu <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> <unk> <unk> alle seine <unk> <unk> , <unk> <unk> , dass er in <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> hat nicht nicht <unk> , dass er <unk> <unk> <unk> in der <unk> <unk> , aber immer <unk> , dass eine <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , dass er <unk> wurde , <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk>\n<unk> <unk> hat die <unk> <unk> der <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk>\n<unk> <unk> die <unk> Tage <unk> <unk> , wenn er <unk> <unk> <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> sind <unk> in der <unk> <unk> <unk> <unk>\nDie <unk> <unk> die <unk> <unk> in <unk> <unk> , die <unk> <unk> in <unk> <unk> <unk> , die <unk> in <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> des <unk> <unk> , um die <unk> des <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> <unk> eine <unk> <unk> mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> ist ein <unk> <unk> mit <unk> <unk> und <unk> <unk> .\nDie gesamte <unk> sind <unk> .\n<unk> Sie die <unk> <unk> <unk> <unk> <unk> in einer anderen <unk> , da es sich nicht auf die <unk> <unk> .\nIn einer <unk> der <unk> <unk> sind <unk> von einer <unk> <unk> <unk> <unk> und <unk> <unk> <unk> in <unk> und <unk> .\n<unk>\nDie <unk> <unk> <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> , <unk> eine <unk> , die <unk> <unk> , die <unk> <unk> <unk> , <unk> <unk> und <unk> <unk> .\n<unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nSie haben drei <unk> <unk> und ein <unk> <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> sie <unk> und <unk> die <unk> , eine <unk> und <unk> <unk> .\n<unk> , in diesen <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs ist <unk> der <unk> <unk> in der <unk> , China und <unk> .\n<unk> sind die neue <unk> Regierung <unk> <unk> .\nDie <unk> <unk> <unk> , <unk> <unk> , wird morgen mit der <unk> der <unk> in <unk> <unk> , um die neue Regierung , die die <unk> <unk> , die <unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die\nDie <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> und <unk> mit dem Präsident <unk> in <unk> <unk> , um die <unk> der <unk> <unk> , die <unk> <unk> zu <unk> .\nEin <unk> von der <unk> <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nEs ist <unk> <unk> , eine <unk> <unk> von <unk> , <unk> es ist <unk> , wenn die neue <unk> wird .\n<unk> <unk> ich die <unk> <unk> , die <unk> , die <unk> mit den politischen <unk> in seiner <unk> <unk> zu <unk> .\n<unk> möchte ich meine <unk> <unk> in der <unk> der <unk> <unk> , <unk> , <unk> , <unk> , die <unk> europäische <unk> zu <unk> .\n<unk> hat nicht <unk> , dass er eine Regierung <unk> <unk> , aber die <unk> der <unk> <unk> <unk> , dass die <unk> der <unk> <unk> <unk> .\n<unk> ist nun <unk> , <unk> er sehr <unk> .\n<unk> hat die <unk> ein <unk> <unk> <unk> <unk> , <unk> <unk> als die <unk> , während der <unk> eine politischen und <unk> <unk> <unk> <unk> .\nDie neue <unk> der <unk> von <unk> <unk> müssen <unk> <unk> , muss die <unk> eine <unk> <unk> <unk> , um die <unk> in <unk> zu <unk> .\n<unk> der EU auf <unk> , die <unk> <unk> , <unk> die <unk> der <unk> <unk> <unk> <unk> <unk> , die <unk> , die <unk> und <unk> <unk> <unk> .\n<unk> <unk> , <unk> der <unk> <unk> \" <unk> <unk> \" , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nWir <unk> , dass dieses Vorschlag der <unk> für die <unk> dieses <unk> <unk> .\n<unk> der <unk> von <unk> <unk> <unk> <unk> <unk> der <unk> <unk> <unk> <unk> , die <unk> , die <unk> , die <unk> zu <unk> .\n&quot; <unk> ist , dass die <unk> der <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie Unterstützung der <unk> , die <unk> in <unk> , ist <unk> <unk> , weil jetzt <unk> , weil jetzt viele <unk> <unk> <unk> hat .\n<unk>\n<unk> der Regierung muss die neue Regierung <unk> <unk> , die die <unk> <unk> , die <unk> <unk> <unk> werden .\n<unk> <unk> oder <unk> in den <unk> , um ein <unk> und <unk> zu <unk> .\nDie <unk> <unk> der <unk> <unk> <unk> <unk> , dass eine <unk> von <unk> <unk> <unk> wird .\n<unk> <unk> auf <unk> seine <unk> mit dem <unk> <unk> , <unk> und <unk> , als auch mit <unk> und <unk> <unk> .\n<unk> <unk> wird seine <unk> <unk> .\n<unk> <unk> wurde <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> ein <unk> <unk> in der vergangenen Woche wurde <unk> , dass die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> mit einer <unk> von <unk> <unk> <unk> <unk> <unk> <unk> werden .\n<unk> hat <unk> , dass er die <unk> <unk> , die <unk> <unk> <unk> , <unk> die <unk> <unk> , die <unk> <unk> , die mir <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> er er <unk> , <unk> er in seiner <unk> <unk> , aber die <unk> <unk> <unk> , dass es nur von <unk> <unk> <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> , dass die <unk> <unk> und <unk> zwischen den <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> ist <unk> als das <unk> der <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm <unk> <unk> <unk> nur ein <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> <unk> , <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> die <unk> und Europa <unk> <unk> , ist eine <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie Reihe von <unk> wurde <unk> <unk> in <unk> <unk> .\n<unk> <unk> <unk> in der <unk> und in <unk> <unk> der <unk> <unk> und in <unk> <unk> <unk> und <unk> <unk> .\n<unk> <unk> die <unk> <unk> <unk> <unk> in <unk> <unk> für <unk> und <unk> .\nDie <unk> <unk> , die eine <unk> internationalen <unk> in einem Bereich <unk> , <unk> , die die <unk> <unk> in <unk> <unk> ist .\nWir <unk> , dass die <unk> der <unk> <unk> ist , ist die <unk> <unk> , die <unk> <unk> <unk> mit <unk> in <unk> <unk> <unk> .\n<unk> wird heute gegen die <unk> und <unk> ein <unk> <unk> für <unk> <unk> .\n<unk> <unk> , die <unk> <unk> , hat <unk> <unk> .\n<unk> und <unk> wurde <unk> <unk> mit den anderen <unk> der <unk> <unk> , <unk> mit <unk> und <unk> <unk> .\n<unk> im <unk> <unk> <unk> <unk> , <unk> <unk> .\n<unk> <unk> <unk> sie mit <unk> , <unk> , <unk> , <unk> und <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nIm <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> nur 10 <unk> <unk> <unk> <unk> <unk> , <unk> <unk> mit <unk> , <unk> mit <unk> , <unk> mit <unk> und <unk> <unk> mit <unk> .\n<unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> nicht <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> von <unk> ist <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> mit <unk> .\nDie <unk> <unk> , <unk> <unk> ist die <unk> , zwei mehr als <unk> <unk> und drei <unk> .\n<unk> <unk> der <unk> für <unk> <unk>\n<unk> <unk> ist ein <unk> <unk> .\nIn einer <unk> mit <unk> <unk> die <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs gibt noch mehr als 20 Jahren <unk> , aber die <unk> <unk> , dass die <unk> für die <unk> in den Land <unk> .\nDas war <unk> der <unk> und <unk> <unk> , die <unk> mit <unk> , die <unk> mit <unk> , <unk> .\nDie <unk> ist der <unk> <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDas Hotel <unk> <unk> wird in der <unk> <unk> <unk> <unk> <unk> mit dem <unk> der <unk> <unk> <unk> <unk> .\nDies ist eine <unk> der <unk> , deren <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\n<unk> Sie die <unk> von <unk> <unk> , ist ein <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> werden ?\n<unk> <unk> ist ein <unk> <unk> für uns <unk> .\nDies ist nur unsere zweiten <unk> in <unk> und <unk> <unk> .\nIch habe mich <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> sind wir <unk> , unsere <unk> zu <unk> .\n<unk> ist es , dass es ein <unk> <unk> , um die <unk> der <unk> mit <unk> <unk> zu <unk> .\n<unk> Sie <unk> <unk> <unk> <unk> ?\n<unk> mit uns mit <unk> <unk> .\nEs ist uns <unk> <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> und <unk> <unk> , dass wir uns <unk> <unk> .\n<unk> <unk> , wie Sie Ihre <unk> <unk> , wie die <unk> des <unk> <unk> .\n<unk> dieses <unk> eine <unk> der <unk> ?\n<unk> der <unk> <unk> wir über die <unk> <unk> in der letzten <unk> .\nWir haben ein <unk> <unk> <unk> in <unk> und <unk> , dass wir uns in <unk> <unk> <unk> <unk> .\n<unk> sind wir sehr <unk> <unk> .\nWir <unk> dieses Land , die <unk> , <unk> <unk> , <unk> <unk> ist eine der <unk> Länder in der Welt .\nWie <unk> Sie <unk> <unk> und seine <unk> für <unk> ?\nWir haben einige <unk> , die <unk> und <unk> <unk> , die in <unk> und die <unk> <unk> .\n<unk> <unk> sind ein <unk> <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> der <unk> , eine <unk> , <unk> , <unk> mit <unk> <unk> .\n<unk> haben Sie die <unk> in <unk> <unk> ?\nAls wir haben nicht <unk> , wenn wir keine <unk> nicht <unk> können .\nWir werden die <unk> <unk> <unk> <unk> mit einem <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nSie <unk> die <unk> .\nWie <unk> <unk> ?\nIch glaube , die <unk> , weil wir mehr als <unk> <unk> und <unk> <unk> können .\nWenn wir zwei zwei oder drei <unk> <unk> haben , <unk> wir <unk> , dass wir keine <unk> und <unk> <unk> haben , weil wir uns die <unk> <unk> , die uns <unk> haben , die <unk> , die wir <unk> haben , die uns <unk> haben , die <unk> , die wir <unk> haben , die <unk> <unk> , die wir <unk> haben , die <unk> <unk> , die wir <unk> haben , die uns <unk> haben , die <unk> , die wir <unk> haben , die uns <unk> haben , die <unk> , die wir <unk> haben ,\nDas ist <unk> <unk> für uns , und ich glaube , dass es mehr <unk> für die Menschen .\nMit diesem <unk> werden Sie 20 Jahren <unk> <unk> .\n<unk> alle <unk> haben wir <unk> ?\n<unk> .\n<unk> <unk> ich <unk> , dass wir in einer <unk> <unk> und <unk> dieses <unk> <unk> werden .\nWir <unk> unseren <unk> und wir sind sehr <unk> , dass wir <unk> <unk> können .\nWir <unk> alle <unk> und <unk> <unk> <unk> .\nWir <unk> uns auf die <unk> in der Welt , wo wir eine <unk> <unk> <unk> .\n<unk> würde uns nicht <unk> .\n<unk> ist es für Ihre <unk> Teil der <unk> <unk> ?\n<unk> ist eine <unk> , die <unk> <unk> <unk> .\nWir sind sehr <unk> , <unk> und <unk> in <unk> .\nDie <unk> , wenn eine <unk> <unk> von <unk> und es ist <unk> .\n<unk> <unk> , <unk> eine <unk> von <unk> , aber es ist <unk> <unk> , wenn <unk> oder <unk> <unk> .\nWir haben <unk> und <unk> für <unk> Teil der <unk> .\nIch <unk> mich mit einigen <unk> <unk> .\n<unk> <unk> wir in einer <unk> mit <unk> in <unk> und haben wir in <unk> mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nEs ist auch <unk> , dass eine <unk> und <unk> <unk> mit dem <unk> <unk> <unk> <unk> .\n<unk> , vier <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> ich , dass wir alle <unk> <unk> und <unk> <unk> , <unk> wir den anderen <unk> .\nWir möchten alle sehr <unk> und <unk> <unk> , <unk> <unk> , dass die <unk> <unk> <unk> <unk> .\nWir <unk> die <unk> mit der <unk> , die <unk> <unk> .\n<unk> Sie die <unk> <unk> <unk> <unk> <unk> , aber <unk> <unk> <unk> <unk> <unk> .\nWie <unk> Sie eine <unk> ?\nWie wir <unk> haben , hat es eine <unk> <unk> .\nWir haben <unk> , dass es <unk> ist , weil es <unk> ist , in diesem Bereich <unk> und Menschen , die Ihre <unk> <unk> .\n<unk> einer <unk> , dass Sie die <unk> , dass Sie die <unk> und die <unk> <unk> , dass Sie die <unk> und die <unk> <unk> , ohne die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nWie können Sie Ihre <unk> <unk> <unk> <unk> ?\nIch glaube , dass wir sehr sehr <unk> und <unk> <unk> <unk> .\nWir haben eine <unk> für eine drei Jahren <unk> <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEs gibt viele <unk> <unk> , weil die <unk> <unk> <unk> <unk> und die <unk> <unk> , dass wir uns noch eine <unk> oder <unk> <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nEs war ein <unk> <unk> und wir <unk> <unk> .\nDie nationalen <unk> <unk> <unk> wird für <unk> die <unk> in <unk> <unk> .\n<unk> es <unk> , dass Sie die <unk> , besonders <unk> , <unk> , <unk> und <unk> , die Ihnen für die <unk> ?\n<unk> , die <unk> <unk> uns <unk> <unk> und <unk> <unk> <unk> , dass wir die <unk> <unk> , <unk> wir <unk> <unk> , was wir <unk> <unk> und <unk> <unk> werden .\n<unk> Sie eine <unk> <unk> <unk> der <unk> <unk> <unk> ?\nDas ist eine Frage für die <unk> <unk> <unk> <unk> ich nicht .\nEr <unk> es , und ich glaube , er ist es , dass es <unk> hat .\nWie Sie ein <unk> , was Sie Ihre <unk> ?\nEs gibt es viele <unk> .\n<unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEs gibt es viele <unk> , alle <unk> , die ich <unk> <unk> .\nWie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIch glaube , dass alle <unk> eine <unk> von der <unk> <unk> .\nWenn ich der <unk> , dass es <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> und alle <unk> , dass es jetzt <unk> ist , <unk> <unk> und alle <unk> <unk> .\nIm ersten <unk> <unk> die <unk> <unk> eine <unk> <unk> , und nun hat mehr <unk> <unk> .\nWie können Sie diese <unk> <unk> ?\nEs ist <unk> <unk> , weil ich es eine <unk> von <unk> über die <unk> <unk> , aber es sind <unk> , die <unk> oder <unk> <unk> <unk> , <unk> sie <unk> <unk> .\nMit dem <unk> <unk> <unk> sie <unk> , wenn ich sie <unk> , und diese <unk> sind <unk> .\nIch glaube , dass Sie sich , wenn Sie einige <unk> und <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> und <unk> <unk> .\n<unk> , ich glaube , dass Sie eine <unk> ® und <unk> <unk> werden , dass <unk> <unk> ist .\n<unk>\n<unk> <unk> wurde <unk> <unk> .\nDie <unk> Herr Präsident , <unk> war <unk> im <unk> <unk> , um die <unk> von <unk> zu <unk> , um die <unk> von <unk> zu <unk> .\n<unk> <unk> , <unk> Jahre , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> <unk> , dass in seiner Fällen <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> ist in <unk> als <unk> 2007 in der <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> auf <unk> politischen <unk>\nEin <unk> <unk> <unk> der <unk> <unk> <unk> , <unk> und <unk> <unk> und <unk> , wird diese <unk> ein <unk> <unk> , um <unk> zu <unk> , um <unk> zu <unk> und politischen <unk> zu <unk> .\nAls <unk> wird seine <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> zwischen <unk> , in der <unk> <unk> , wird der <unk> &quot; <unk> für die <unk> <unk> , wird die <unk> <unk> und die <unk> der <unk> <unk> <unk> <unk> .\n<unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIn seiner <unk> <unk> <unk> <unk> die internationale Gemeinschaft , die <unk> <unk> , die <unk> <unk> , eine <unk> , die die <unk> der <unk> und der <unk> der <unk> in der Europäischen Union <unk> .\n<unk> <unk> hat die <unk> eine <unk> <unk> des <unk> der <unk> <unk> <unk> und <unk> <unk> .\nDie <unk> Regierung <unk> <unk> in <unk> Jahren auf die <unk> der <unk> <unk> <unk> , um die &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nIn der <unk> <unk> , <unk> und <unk> haben die <unk> ein <unk> <unk> <unk> <unk> , dass die <unk> zwischen <unk> <unk> , <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> , die in der <unk> in der Welt befinden , hat sich die <unk> <unk> , die <unk> der <unk> <unk> <unk> <unk> , die <unk> in drei <unk> ohne die Notwendigkeit <unk> .\n&quot; <unk> ist die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , in der <unk> in <unk> .\n<unk> <unk> <unk> , dass <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist die <unk> <unk> in der <unk> <unk> &quot; oder eine <unk> <unk> <unk> <unk> &quot; <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> <unk> <unk>\nDiese <unk> <unk> <unk> in drei <unk> <unk> auch die <unk> des <unk> , <unk> , <unk> , <unk> , <unk> nicht als <unk> .\nIn diesem Bereich <unk> <unk> die <unk> der <unk> und <unk> die <unk> <unk> der <unk> , die mit <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk>\nHerr Präsident , <unk> <unk> <unk> <unk> <unk> , dass die <unk> <unk> der <unk> <unk> <unk> <unk> hat , um die <unk> in der <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> die <unk> für die <unk> , um die <unk> zu <unk> , die <unk> <unk> zu <unk> , <unk> er <unk> .\nIn einem <unk> <unk> , <unk> <unk> , dass seine Entscheidung <unk> ist , <unk> <unk> und <unk> <unk> , die in der <unk> <unk> <unk> hat .\nEr <unk> , dass die <unk> er nicht &quot; <unk> &quot; in <unk> <unk> und <unk> alle <unk> <unk> hat .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> eine <unk> <unk> , <unk> alle <unk> und <unk> <unk> zu <unk> .\nIn <unk> mit der <unk> der <unk> <unk> und der <unk> <unk> <unk> <unk> werden seine <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> wurde <unk> von <unk> Millionen <unk> in der <unk> <unk> , wenn er ein <unk> <unk> , die <unk> , die <unk> , die <unk> , <unk> und <unk> <unk> .\n<unk> hat <unk> <unk> <unk> <unk> <unk>\nDie <unk> war <unk> , dass die <unk> auf die <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> des <unk> <unk> des <unk> ist <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> gibt es <unk> über eine <unk> und <unk> <unk> <unk> .\nDie <unk> <unk> auf eine <unk> <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> wurde in der <unk> von <unk> <unk> <unk> in <unk> mit dem <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> <unk> <unk> .\nDie politische <unk> , in den <unk> <unk> , in den <unk> <unk> , <unk> die <unk> <unk> der <unk> <unk> .\n<unk> Sie die <unk> der <unk> , <unk> , <unk> und seine <unk> <unk> <unk> , <unk> die <unk> der <unk> und der <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> war <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> wie ein Beispiel für die <unk> <unk> und <unk> <unk> , dass die Menschen mit <unk> <unk> .\nDie Krise ist nicht <unk> , die <unk> <unk> und die Arbeit von <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk>\nDie neue <unk> <unk> die <unk> <unk> , ohne eine <unk> <unk> zu <unk> .\nEs scheint , dass die <unk> <unk> der <unk> in <unk> <unk> <unk> <unk> hat , hat sich ein <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWenn die <unk> in <unk> <unk> die <unk> in <unk> <unk> , um eine <unk> von <unk> zu <unk> .\n&quot; <unk> <unk> wird eine neue <unk> , &quot; <unk> <unk> .\nDie <unk> , die <unk> <unk> der <unk> <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie erste drei <unk> haben die <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> und die <unk> .\nDie <unk> ist ein <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nDie <unk> , <unk> <unk> , <unk> <unk> , dass die <unk> in den <unk> der <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> , dass die <unk> der <unk> der <unk> <unk> <unk> , weil sie sich <unk> <unk> für drei <unk> <unk> , während sie sich in der <unk> <unk> <unk> .\n<unk> <unk> die <unk> der <unk> <unk> , um die <unk> <unk> von <unk> zu <unk> , während sie <unk> <unk> hat , nicht <unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> der <unk> <unk> <unk> , dass die <unk> <unk> <unk> , dass sie sich mit dem <unk> der <unk> und <unk> <unk> sind .\nDie <unk> <unk> auch die <unk> , die in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , dass die <unk> nur eine <unk> der <unk> <unk> ist , aber in den letzten Jahren <unk> die <unk> <unk> .\nIn diesem <unk> <unk> er <unk> <unk> , dass es <unk> ist , um die <unk> zu <unk> und <unk> zu <unk> , dass die <unk> der <unk> <unk> .\n<unk> <unk> , die <unk> der <unk> <unk> eine <unk> in <unk> <unk> .\nIm <unk> <unk> <unk> die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie vier <unk> im <unk> <unk> .\n<unk> oder <unk> ?\nDie <unk> <unk> <unk> <unk> auf <unk> <unk> .\nDie <unk> <unk> auf den <unk> <unk> <unk> die Aussprache über die Aussprache in <unk> <unk> .\nIn der <unk> des <unk> der <unk> <unk> der <unk> <unk> <unk> der <unk> <unk> dieses <unk> <unk> <unk> .\nDie <unk> <unk> die <unk> <unk> <unk> .\n<unk> ist es <unk> von einer <unk> , im <unk> mit dem <unk> <unk> <unk> .\n<unk> der <unk> der <unk> , in der <unk> , in der <unk> <unk> wurde , war der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> <unk> als <unk> <unk> in diesem <unk> .\nDie <unk> wurde nicht <unk> <unk> , aber <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDas war alle <unk> , in <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> für <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> <unk> , <unk> <unk> , <unk> <unk> .\n<unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie Frage <unk> <unk> <unk> oder <unk> ?\nDie <unk> , wie für ihre <unk> <unk> <unk> ist , ist eine <unk> der <unk> der <unk> <unk> .\nEs ist eine <unk> <unk> , um die <unk> dieses <unk> zu <unk> , wenn die <unk> dieser <unk> <unk> <unk> <unk> wird .\nDie <unk> ist <unk> <unk> , die drei <unk> zwischen den drei <unk> <unk> <unk> <unk> und die <unk> , die <unk> <unk> .\n<unk> <unk> , <unk> <unk> und <unk> <unk> die <unk> ist <unk> <unk> <unk> <unk> <unk> .\nFür <unk> , <unk> die <unk> &quot; <unk> &quot; <unk> &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot;\n<unk> <unk> <unk> , dass die <unk> in <unk> <unk> ist .\n<unk> <unk> für die <unk> <unk> .\n<unk> ist <unk> <unk> , &quot; <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , wie <unk> und <unk> mit <unk> in der <unk> des <unk> <unk> die Vorschlag &quot; <unk> &quot; .\n<unk> <unk> von der <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie erste <unk> ist die <unk> , wo man sich auf <unk> <unk> .\n&quot; <unk> <unk> ist <unk> , <unk> , <unk> , <unk> .\nDie <unk> ist der <unk> <unk> <unk> , weil die <unk> der <unk> in <unk> <unk> <unk> werden kann .\nDie Frage , <unk> <unk> , dass die <unk> der <unk> <unk> <unk> <unk> <unk> werden kann , als <unk> , <unk> , <unk> nicht <unk> <unk> .\n<unk> <unk> , dass ein <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> , dass die Antwort die Frage der Frage der <unk> <unk> <unk> , weil die <unk> der <unk> ein <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> , die in diesem Fall haben , <unk> sich in diesem Fall <unk> .\n<unk> , ein <unk> für einen der <unk> <unk> in den <unk> und <unk> mit dem <unk> des <unk> <unk> .\nIm <unk> sollten die <unk> <unk> <unk> , dass die <unk> der <unk> der <unk> <unk> <unk> <unk> <unk> <unk> in den <unk> und der <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk>\n<unk> <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> des <unk> des <unk> des <unk> des <unk> des <unk> der <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> der <unk> <unk> , die <unk> <unk> <unk> eine <unk> Fall gegen die <unk> der <unk> und <unk> <unk> , er wurde <unk> <unk> .\nDie vier <unk> <unk> der <unk> <unk> für <unk> <unk> , <unk> er <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIn diesem Zusammenhang <unk> er die <unk> von <unk> <unk> , <unk> die <unk> der <unk> und <unk> <unk> .\n<unk> <unk> er <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie Richtlinie <unk> <unk> , die in der <unk> <unk> , die <unk> in der <unk> <unk> <unk> <unk> .\nIm <unk> <unk> <unk> er ein <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIn <unk> , die <unk> <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nWährend der <unk> <unk> , die <unk> <unk> , <unk> <unk> , ob er <unk> , ob er <unk> <unk> , ob er die <unk> und die <unk> der <unk> <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> .\n<unk> auf die <unk> <unk> / <unk> <unk> <unk> ist die <unk> <unk> und <unk> <unk> nicht <unk> .\nDie <unk> der <unk> der <unk> <unk> der <unk> , der <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> hat er <unk> <unk> und <unk> <unk> .\n<unk> <unk> er <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> und <unk> <unk> <unk> , dass die <unk> <unk> auf die <unk> in der <unk> <unk> <unk> <unk> , ohne die <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> , dass die <unk> <unk> für die <unk> <unk> .\nDie <unk> , dass die <unk> <unk> <unk> , dass die <unk> in <unk> mit dem <unk> <unk> , die <unk> in <unk> mit dem <unk> <unk> <unk> .\nEr <unk> auch <unk> , dass die <unk> <unk> für <unk> <unk> , <unk> es die <unk> <unk> <unk> .\nEr <unk> auch <unk> , dass die <unk> <unk> in den <unk> der <unk> <unk> nicht der <unk> <unk> .\n&quot; <unk> ist es <unk> , dass es keine <unk> <unk> sind oder <unk> der <unk> in einer anderen <unk> <unk> , ist die <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> \" für <unk> <unk> , <unk> die <unk> <unk> für <unk> <unk> .\nDie <unk> hat eine <unk> von <unk> <unk> <unk> .\nFür jetzt ist es eine <unk> <unk> , die sich die Notwendigkeit <unk> , die Ergebnisse als <unk> <unk> <unk> oder <unk> <unk> .\n<unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> Informationen <unk> die <unk> der <unk> eines <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , die <unk> der <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> in der <unk> .\n<unk>\n<unk> Sie die <unk> <unk> <unk> <unk> in <unk> <unk> <unk> <unk> , dass die <unk> in der <unk> und der <unk> <unk> .\n<unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\nEs ist <unk> , dass die <unk> <unk> die <unk> der <unk> <unk> und <unk> <unk> in der <unk> <unk> .\n<unk> der <unk> .\nDie <unk> der <unk> und der letzten <unk> der <unk> <unk> <unk> , weil sich die <unk> <unk> nicht <unk> , die <unk> <unk> ist .\n<unk> <unk> auf ein <unk> <unk>\n<unk> Informationen zu <unk> <unk> <unk> , dass die <unk> in einer <unk> und <unk> <unk> .\nDie <unk> der <unk> <unk> , dass die <unk> die <unk> in <unk> <unk> <unk> .\nEs ist nicht <unk> <unk> von <unk> oder <unk> <unk> .\nIn der Nähe des <unk> gibt es <unk> <unk> <unk> <unk> <unk> in der <unk> des <unk> <unk> <unk> .\n<unk> sind die <unk> <unk> <unk> <unk> <unk> mit dem <unk> .\nIn der <unk> befinden sich die <unk> in einer <unk> von <unk> <unk> <unk> <unk> .\nDie <unk> ist die <unk> in der <unk> , die <unk> .\nDie Informationen sind <unk> , dass die <unk> <unk> , dass die <unk> mit dem <unk> <unk> <unk> <unk> werden .\nDie <unk> eines <unk> <unk> in einer <unk> <unk> als die <unk> <unk> <unk> , <unk> die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> ist , die <unk> ist , die <unk> <unk> , die <unk> <unk> ist .\n<unk> <unk> ein <unk> für <unk> <unk>\n<unk> <unk> <unk> <unk> ein <unk> <unk> für <unk> in <unk> und <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> hat , dass die <unk> ein <unk> <unk> für <unk> und <unk> <unk> , die die <unk> der <unk> in <unk> und <unk> &quot; <unk> &quot; <unk> wird .\nIn einer <unk> , die <unk> der <unk> <unk> in <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist von <unk> und <unk> <unk> von der <unk> <unk> <unk> und der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> der <unk> oder nicht , die durch die <unk> von <unk> oder nicht <unk> werden , durch die <unk> der <unk> in den <unk> <unk> , wo es <unk> werden , <unk> <unk> <unk> .\n<unk> <unk> , dass die <unk> in der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> der <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , dass die <unk> dieses <unk> <unk> , die die <unk> und die Bedeutung der <unk> in <unk> und <unk> <unk> der <unk> für <unk> <unk> <unk> .\nIn <unk> <unk> werden die <unk> <unk> <unk> , und die <unk> ist die <unk> <unk> <unk> <unk> und <unk> <unk> und <unk> <unk> und <unk> <unk> .\nSie werden auch <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> , dass dieses <unk> des <unk> für die <unk> <unk> in Kinder <unk> .\n<unk> <unk> <unk> <unk> zwischen <unk> und 2006 , dass die in nur <unk> Jahren <unk> <unk> ist , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIch <unk> die <unk> für <unk> die <unk> <unk> <unk> , ist die <unk> <unk> der <unk> <unk> .\nEr <unk> , dass die <unk> eine <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEr <unk> , dass die <unk> <unk> , dass die <unk> der <unk> <unk> in der <unk> der <unk> <unk> <unk> , die wir <unk> <unk> und <unk> ist .\nDie <unk> <unk> , dass die <unk> <unk> für die <unk> der <unk> <unk> <unk> , ist eine <unk> <unk> der Kinder , die <unk> <unk> oder <unk> .\n<unk> <unk> er <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie Informationen ist <unk> <unk> mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> ist es , die <unk> <unk> , <unk> , <unk> und die <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> mit <unk> und <unk> und <unk> in diesem <unk> .\n<unk> würde <unk> <unk> <unk> <unk>\n<unk> <unk> , eine <unk> auf die <unk> der <unk> von <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> und <unk> <unk> .\nWenn die <unk> von der <unk> <unk> <unk> <unk> , würde es <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIn seiner <unk> <unk> <unk> der <unk> <unk> der <unk> , die die <unk> <unk> <unk> , <unk> <unk> , dass die <unk> er <unk> ist , <unk> und <unk> <unk> .\n<unk> <unk> <unk> , dass dieses Vorschlag der <unk> in <unk> , wo die <unk> in <unk> , wo die <unk> <unk> ist , als <unk> in der <unk> und <unk> <unk> .\n<unk> <unk> , dass die <unk> , die <unk> <unk> <unk> ist , ist ein <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> der <unk> der <unk> in der <unk> , dass die <unk> von <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDies bedeutet , dass die <unk> der <unk> <unk> nicht <unk> , aber ist der <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> <unk> , dass die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> der <unk> <unk> ist die <unk> , die <unk> <unk> <unk> in der <unk> und <unk> <unk> .\n<unk> der <unk> , die diese <unk> <unk> <unk> , ist es <unk> mit <unk> .\nWenn die <unk> und <unk> <unk> <unk> , ist es die <unk> <unk> sehr gut <unk> .\nWenn es sich <unk> <unk> <unk> , wäre es <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDiese <unk> ist die <unk> , die in <unk> mit dem <unk> der <unk> <unk> , muss es eine <unk> <unk> als die <unk> <unk> <unk> .\nAls <unk> der <unk> , dass die <unk> und die <unk> , die <unk> , die <unk> <unk> und die <unk> , die <unk> <unk> und die <unk> <unk> ist .\nDie <unk> , besonders <unk> und <unk> sind auch eine sehr <unk> <unk> , besonders <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> ist <unk> <unk> , <unk> <unk> die <unk> und <unk> <unk> , die eine <unk> der <unk> und <unk> <unk> .\nIn einer <unk> eines <unk> , er habe ich <unk> , dass die ersten <unk> und nur nur in <unk> <unk> , ist es <unk> , dass die <unk> <unk> nicht <unk> wird .\n<unk> ein <unk> der <unk> <unk> der <unk> <unk> , dass die <unk> <unk> <unk> wichtig ist , dass die <unk> für die <unk> und die <unk> der <unk> <unk> <unk> <unk> <unk> werden .\n<unk> <unk> <unk> <unk> , dass es keine <unk> und Arbeit <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nWas die <unk> <unk> <unk> , um die neue <unk> <unk> <unk> ?\n<unk> der <unk> , die <unk> <unk> , <unk> <unk> <unk> für die <unk> des <unk> <unk> .\nDie <unk> <unk> seine <unk> <unk> von <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDies ist <unk> <unk> <unk> , <unk> <unk> mit dem <unk> <unk> und die <unk> , dass die <unk> die neuen <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> die Frage <unk> <unk> <unk>\n<unk> <unk> hat die <unk> eines <unk> <unk> , die in <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie erste <unk> in den <unk> , die die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , dass sie nicht mit dem <unk> <unk> ist , ist die <unk> , die Sie <unk> , und <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk>\n<unk> <unk> ich eine <unk> in <unk> , glaube ich , dass die <unk> <unk> , und ich glaube , dass die <unk> mein <unk> <unk> ist .\n<unk> möchte ich &quot; <unk> und <unk> &quot; <unk> und <unk> &quot; ist eine <unk> <unk> <unk> , die wir <unk> <unk> , eine <unk> <unk> .\nEine Frage , die die <unk> <unk> , nicht <unk> , dass die <unk> &quot; <unk> &quot; und eine <unk> für die <unk> der <unk> <unk> <unk> <unk> .\nAber nach der <unk> <unk> <unk> <unk> er <unk> <unk> und <unk> mit der <unk> ist es <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk>\n<unk> die <unk> <unk> , es ist sehr <unk> , dass die <unk> von <unk> <unk> .\nEin <unk> von <unk> <unk> , die für die <unk> , die <unk> , und die <unk> , die alle <unk> <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> <unk> .\nUnd die <unk> <unk> in den ersten <unk> in der <unk> <unk> und der <unk> der <unk> <unk> .\n<unk> in seiner <unk> <unk> <unk> , war es <unk> , dass sie die <unk> und die <unk> <unk> <unk> <unk> .\n<unk> <unk> , eine <unk> der <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; , der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> .\n<unk> <unk> ist , dass <unk> <unk> die <unk> über die <unk> <unk> , die die <unk> der <unk> <unk> <unk> .\nDies ist <unk> , <unk> <unk> die internationale <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> ist nicht für eine <unk> <unk> <unk> , müssen wir über die <unk> <unk> <unk> und die <unk> <unk> , um die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nEs ist auch eine <unk> , dass die <unk> für die <unk> und die <unk> der <unk> <unk> , dass die <unk> der <unk> <unk> <unk> werden kann , weil die <unk> der <unk> <unk> , <unk> und <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> <unk> <unk> , dass die <unk> <unk> nicht die <unk> <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> , die <unk> <unk> , <unk> <unk> , <unk> <unk> , dass die <unk> der <unk> der <unk> <unk> <unk> <unk> .\nDie <unk> sind <unk> , aber <unk> <unk> nicht , wie sie nicht <unk> , wie sie nicht <unk> .\nDie <unk> <unk> wurde nicht nur <unk> <unk> und hier ist , wo sie <unk> <unk> .\nDie <unk> , die für die erste Zeit ist , <unk> die <unk> <unk> <unk> <unk> , in der die <unk> der <unk> <unk> <unk> wurde .\n<unk> oder nicht <unk> , ist die Tatsache , dass die Frage der <unk> <unk> , nicht als <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> werden sie in <unk> <unk> als eine der <unk> <unk> , die die <unk> <unk> , <unk> <unk> in <unk> <unk> .\n<unk> war der <unk> des <unk> <unk> <unk> .\nEin <unk> <unk> in <unk> als <unk> , die die <unk> der <unk> <unk> <unk> .\nDie Arbeit dieser <unk> ist eine <unk> Arbeit .\nIm <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> <unk> , die die <unk> der <unk> <unk> <unk> , <unk> <unk> , die <unk> der <unk> <unk> .\nDie <unk> <unk> in den <unk> <unk> , die in der <unk> des <unk> <unk> <unk> , ist es <unk> <unk> <unk> , die <unk> <unk> in der Region <unk> .\n<unk> der <unk> , <unk> <unk> in <unk> , <unk> er oft <unk> <unk> <unk> <unk> , <unk> er <unk> , dass die <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> .\nUm die <unk> der <unk> und nach dem <unk> <unk> , <unk> er <unk> in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nDie Woche ist der <unk> der <unk> des <unk> gegen <unk> <unk> <unk> <unk> er <unk> <unk> .\n<unk> <unk> wurde <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> von <unk> mit einem <unk> , <unk> .\n<unk> er <unk> er zwei <unk> und eine <unk> <unk> , die die <unk> des <unk> <unk> , so <unk> sie <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> , wenn er mit dem <unk> <unk> , die <unk> war , <unk> er nicht <unk> , dass er nicht <unk> hat , dass es in <unk> <unk> ist .\nEr <unk> ohne <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nAber die <unk> <unk> nicht <unk> .\n<unk> einer <unk> <unk> <unk> , <unk> , eine <unk> und eine <unk> , <unk> die <unk> der <unk> mit <unk> .\n<unk> <unk> hat nicht <unk> <unk> <unk> <unk> &quot; <unk> &quot; .\n<unk> <unk> , die <unk> von <unk> <unk> wurde , <unk> die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> ist .\n<unk> der <unk>\nFür eine <unk> <unk> von <unk> von <unk> <unk> <unk> die <unk> der <unk> <unk> <unk> <unk> , ist sehr <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDie <unk> <unk> ist ein <unk> <unk> , ohne <unk> , die in <unk> <unk> werden kann .\n<unk> von der <unk> <unk> <unk> die <unk> Unterstützung für <unk> und <unk> und <unk> die <unk> von <unk> .\nWann haben viele <unk> <unk> oder <unk> nicht <unk> .\nWir werden alle <unk> und <unk> sie in der <unk> .\nDie <unk> <unk> , die in einem Fall <unk> hat , hat er eine <unk> <unk> der <unk> <unk> , weil die <unk> der <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\n&quot; <unk> sind <unk> <unk> , weil sie nicht <unk> , eine <unk> <unk> zu <unk> , sondern in diesem Fall können wir die <unk> des <unk> <unk> und die <unk> der <unk> <unk> , die in <unk> <unk> <unk> <unk> wird .\nDie <unk> kann auch <unk> <unk> oder <unk> <unk> <unk> .\nSie haben eine <unk> <unk> <unk> mit <unk> <unk> <unk> .\n<unk> der <unk> ist es <unk> , dass eine <unk> <unk> der <unk> und der <unk> der <unk> <unk> <unk> .\n<unk> dieses <unk> ist nicht , dass es sich die <unk> <unk> ist , es ist es im <unk> <unk> .\nEin <unk> <unk> des <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWenn sie sich <unk> <unk> oder <unk> <unk> werden .\nDie <unk> sind <unk> , die <unk> <unk> , die <unk> <unk> oder in der <unk> .\n&quot; <unk> Problem ist , dass es viele <unk> <unk> , <unk> <unk> <unk> .\nEr <unk> , dass nicht mehr als <unk> <unk> ist .\n<unk> wie nicht nicht <unk> , dass <unk> <unk> , nicht mehr <unk> <unk> .\n<unk> kann es <unk> werden .\n<unk> <unk> &quot; <unk> \" <unk> \" <unk> \" <unk> \" \" <unk> \" <unk> \" \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\nEin Teil der <unk> <unk> , die <unk> <unk> und <unk> zu einer <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> <unk> <unk> auf der <unk> der <unk> <unk> <unk> <unk> , wenn sie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> die <unk> <unk> , wo sie die <unk> in <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist , dass die <unk> nicht <unk> , die uns in den <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> , <unk> <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk> , <unk> , <unk> und <unk> , <unk>\n<unk> <unk> , dass die Frage der &quot; <unk> &quot; <unk> &quot; , um die <unk> der <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> , die &quot; <unk> <unk> &quot; <unk> &quot; , um die <unk> der <unk> <unk> <unk> und <unk> <unk> , <unk> <unk> , <unk> <unk> und <unk> <unk> .\n<unk> <unk> sie sich für eine <unk> <unk> , sondern die <unk> <unk> , aber die <unk> <unk> und <unk> zu <unk> , dass er die <unk> <unk> <unk> , während er sich <unk> <unk> .\n<unk> <unk> , die <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> der <unk> <unk> sind <unk> und <unk> in der Zeit <unk> und <unk> in der Zeit , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> <unk> <unk> die Richtlinie auf <unk> <unk> <unk> und <unk> &quot; <unk> , dass die <unk> in der <unk> und anderen <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> , weil der <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> , nicht <unk> , <unk> und <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> er die <unk> , die <unk> , weil es <unk> <unk> ist , um die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> dieser <unk> wurde <unk> und <unk> , dass <unk> <unk> <unk> ist , ist <unk> <unk> in <unk> der <unk> , die die <unk> der <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> &quot; auch die <unk> , dass sie <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> werden in einer <unk> <unk> <unk> <unk> .\n<unk> mit <unk> <unk> und <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> hat die <unk> der <unk> <unk> in <unk> &quot; <unk> &quot; in &quot; <unk> &quot; in <unk> <unk> &quot; <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> , dass <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> <unk> der <unk> <unk> in der <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIm <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> und <unk> , die <unk> <unk> <unk> <unk> <unk> die <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; &quot; <unk> &quot;\n<unk>\n<unk> <unk> von <unk> , <unk> , <unk> , <unk> und <unk> <unk> , dass <unk> in <unk> <unk> <unk> <unk> <unk> <unk>\nDie letzte <unk> wurde <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie erste <unk> der <unk> Regierung über die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> die <unk> <unk> <unk> <unk> .\n<unk> drei <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> zwischen <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> in <unk> <unk> .\nDie <unk> in <unk> <unk> , <unk> die <unk> der <unk> <unk> in <unk> - <unk> auch die <unk> der <unk> von <unk> , <unk> .\nDie <unk> können im <unk> des <unk> in <unk> <unk> <unk> , um die <unk> der <unk> und der <unk> <unk> .\n<unk> <unk> die <unk> von <unk> <unk> in <unk> <unk> , die <unk> von <unk> <unk> in <unk> <unk> .\nDie <unk> <unk> der <unk> Wirtschaft ist nicht <unk> , dass die <unk> nicht die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> hat <unk> <unk> , besonders <unk> <unk> der <unk> <unk> .\nDiese <unk> <unk> <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> der <unk>\n<unk> hat die <unk> <unk> , wenn die <unk> <unk> <unk> <unk> <unk> <unk> werden , die <unk> <unk> , die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nMit einem <unk> <unk> , die <unk> <unk> <unk> , eine <unk> <unk> für eine <unk> <unk> <unk> <unk> .\n<unk> oder <unk> , <unk> die <unk> für ihre <unk> Ergebnisse zwischen <unk> und <unk> .\nAls <unk> <unk> der <unk> Länder in der EU , die <unk> <unk> , <unk> nicht die <unk> .\n<unk> müssen wir die <unk> <unk> , daß die <unk> einige <unk> <unk> , die <unk> <unk> , die <unk> <unk> in <unk> und die <unk> <unk> der <unk> , <unk> <unk> <unk> .\nUnd die <unk> sind nicht <unk> .\nDie <unk> für <unk> und <unk> <unk> <unk> <unk> <unk> , dass die <unk> Länder in <unk> <unk> <unk> <unk> .\nDie <unk> , die die <unk> in der <unk> Wirtschaft <unk> , die <unk> der <unk> <unk> .\nDie <unk> <unk> auf <unk> <unk> , um <unk> in <unk> .\nDie <unk> <unk> <unk> <unk> <unk> die <unk> , weil er sich <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\n<unk> <unk> , dass <unk> <unk> werden , aber <unk> wird nicht <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nMit der <unk> <unk> <unk> <unk> <unk> und <unk> <unk> in den <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> , dass die <unk> &quot; <unk> &quot; \" \" \" <unk> \" <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> die neue <unk> <unk> , <unk> <unk> <unk> , dass es keine <unk> <unk> <unk> , ist <unk> <unk> , dass die <unk> der <unk> <unk> nicht <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> dieser Regierung ist <unk> der <unk> der <unk> <unk> <unk> , &quot; <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nEin <unk> von <unk> <unk> , die <unk> der <unk> <unk> , die <unk> <unk> <unk> <unk> und neue Maßnahmen gegen <unk> <unk> werden .\nDie <unk> der <unk> ist nicht der <unk> <unk> , das <unk> <unk> <unk> , aber wir sind nicht <unk> .\nDie <unk> <unk> war ein Teil der <unk> und der EU , die die <unk> <unk> , die <unk> <unk> , die die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nAber die Europäische Kommission noch noch eine <unk> <unk> der <unk> <unk> oder <unk> der <unk> <unk> , <unk> <unk> , dass die <unk> nicht die <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDie <unk> <unk> noch eine <unk> für die <unk> .\n<unk> <unk> &quot; <unk> <unk>\nDie <unk> <unk> die &quot; <unk> &quot; in <unk> in <unk> , um diese <unk> von <unk> zu <unk> .\n<unk> &quot; <unk> ist eine <unk> <unk> in den <unk> .\n<unk> <unk> <unk> , dass die <unk> in den letzten zwei beiden <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> <unk> in <unk> mit dem <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> ist <unk> <unk> , um die <unk> <unk> zu <unk> , <unk> und <unk> die <unk> mit &quot; <unk> &quot; <unk> , der <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nEs gibt <unk> &quot; <unk> und &quot; <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> nicht nur als <unk> <unk> , sondern auch <unk> , dass <unk> &quot; <unk> &quot; <unk> <unk> .\n<unk> , <unk> <unk> , die <unk> <unk> , dass sie <unk> , dass <unk> <unk> , dass sie in der <unk> <unk> , <unk> <unk> <unk> <unk> werden .\nDie <unk> für die <unk> <unk> in <unk> und in der <unk> der Welt <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd <unk> <unk> dieses <unk> als &quot; <unk> <unk> der <unk> <unk> der <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> zu <unk> .\n&quot; <unk> &quot; <unk>\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> ist ein <unk> <unk> <unk> .\nIn diesem Punkt <unk> sie sehr <unk> , dass es in der <unk> Zusammenarbeit mit der <unk> der <unk> <unk> , wie es eine <unk> <unk> ist .\n<unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist eine der <unk> <unk> der Entwicklung der Zusammenarbeit .\nDie <unk> der <unk> für <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> <unk> der Europäischen Präsident <unk>\nDie erste Tag der <unk> der <unk> <unk> der Europäischen Union über <unk> <unk> <unk> , ohne <unk> <unk> zu <unk> , ohne <unk> <unk> zu <unk> , die auch der <unk> <unk> <unk> .\nDie Zukunft der Zukunft ist nicht <unk> , um die <unk> , aber auch in den <unk> , in dem <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> der <unk> <unk> zu <unk> und die <unk> der <unk> <unk> .\nDie <unk> auf die <unk> der Europäischen Kommission , die von der <unk> <unk> <unk> <unk> hat .\n<unk> <unk> <unk> , nur <unk> <unk> gegen <unk> <unk> .\n<unk> <unk> <unk> , <unk> sie <unk> , <unk> , <unk> und <unk> <unk> .\nDie <unk> <unk> der <unk> und der <unk> ist in der <unk> <unk> <unk> <unk> <unk> .\n<unk> nur weil Europa <unk> , weil der <unk> <unk> <unk> <unk> <unk> <unk> <unk> , weil er &quot; <unk> \" <unk> \" <unk> .\nFür die <unk> , &quot; die <unk> <unk> für eine <unk> in Europa .\nDiese <unk> ist <unk> der Vertrag von Lissabon , die <unk> zu <unk> , die <unk> der Europäischen Union zu <unk> , sondern auch die <unk> der Europäischen Union <unk> .\nDies ist <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , es ist nicht <unk> , dass die <unk> <unk> <unk> werden , dass die <unk> <unk> <unk> werden , und die <unk> der <unk> , die <unk> <unk> ist .\nAber die <unk> in <unk> .\n<unk> , die <unk> der <unk> von einer <unk> von <unk> von <unk> <unk> , <unk> die Abstimmung der <unk> , die die <unk> der <unk> der <unk> <unk> .\n<unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> und <unk> <unk> <unk> <unk> .\nDie <unk> auf Europa <unk> eine <unk> , um die Bedeutung des <unk> <unk> , &quot; <unk> <unk> <unk> nicht <unk> .\n<unk> , wenn in einer <unk> <unk> .\nDie <unk> , die zwei <unk> war , war <unk> <unk> <unk> .\n<unk> war es in der <unk> <unk> der <unk> <unk> .\nWas <unk> <unk> <unk> ?\n<unk> <unk> , die <unk> und die Regierung <unk> <unk> eine <unk> gegen <unk> <unk> .\n<unk> <unk> , <unk> <unk> wird in <unk> ein <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> .\nUnd diese <unk> <unk> von einer <unk> <unk> in der <unk> der <unk> , aber auch die <unk> <unk> der <unk> <unk> , aber auch <unk> <unk> <unk> .\n<unk> <unk> und <unk> , mit einem <unk> <unk> , die <unk> <unk> , um <unk> zu <unk> , um <unk> <unk> zu <unk> .\n<unk> ohne <unk> , ohne die <unk> <unk> des <unk> , <unk> <unk> <unk> <unk> <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk>\nEin <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nUnd die <unk> <unk> <unk> mit einem <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk>\n<unk> <unk> werden diese <unk> <unk> , die von <unk> <unk> , <unk> <unk> , <unk> <unk> zu <unk> .\nDie <unk> werden <unk> und in <unk> , wenn Sie <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nWie in anderen Fällen , <unk> die <unk> der <unk> <unk> eine <unk> <unk> <unk> <unk> .\nDies ist <unk> <unk> <unk> seit <unk> über die politische <unk> , die <unk> <unk> .\nDie <unk> sind keine <unk> als Menschen in Europa .\n<unk> uns <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> .\n<unk> <unk> de <unk> <unk> de <unk> <unk> de <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd meine <unk> <unk> <unk> <unk> .\nDie <unk> <unk> eine <unk> <unk> von <unk> Tage in <unk> <unk> , <unk> mit <unk> <unk> in <unk> <unk> .\nEin <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> <unk> in 2007 der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> diese <unk> mit den europäischen <unk> , <unk> <unk> sind <unk> <unk> , die in <unk> anderen Land <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> ist sehr <unk> , <unk> mit &quot; <unk> &quot; .\nEs ist sehr <unk> .\nDie <unk> des <unk> ist seit <unk> <unk> <unk> für <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> , die <unk> <unk> , dass <unk> der <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm Falle <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> <unk> <unk> , <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> müssen ein <unk> <unk> <unk>\n<unk> ist eine neue <unk> des <unk> zu <unk> .\nDies war <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> die <unk> <unk> der Wirtschaft , wir müssen die Unterstützung unserer Europäischen <unk> und eine neue <unk> der europäischen <unk> &quot; <unk> &quot; <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> <unk>\nDer <unk> der <unk> <unk> in der <unk> des <unk> <unk> <unk> werden die &quot; <unk> &quot; <unk> \" <unk> \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk> \" <unk>\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , dass die <unk> der <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> in <unk> .\nDas <unk> <unk> <unk> <unk>\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n&quot; Wir werden nicht der neuen Behörden <unk> , <unk> er in einer <unk> mit dem <unk> <unk> <unk> <unk> .\nEr <unk> seine <unk> <unk> mit der Ziele der <unk> und der <unk> und der <unk> der <unk> gegen <unk> , aber auch seine <unk> , die <unk> <unk> .\n<unk> <unk> , die Europäische <unk> für die <unk> und <unk> <unk> , die <unk> , dass die <unk> und die EU <unk> , die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> und <unk> <unk> , <unk> eine <unk> von <unk> <unk> .\n<unk> sind <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> , die <unk> , die <unk> , die <unk> , <unk> und <unk> <unk> , die <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> ist <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , wenn sie <unk> und <unk> <unk> werden .\nDies ist eine der <unk> <unk> <unk> Bericht von der <unk> <unk> , in der <unk> <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> und <unk> .\nDies <unk> die Regierung zu <unk> , die die <unk> <unk> , <unk> <unk> und <unk> <unk> , wenn es <unk> ist , eine <unk> <unk> zu <unk> .\nIm <unk> , die <unk> <unk> ein <unk> <unk> , die <unk> <unk> , die die <unk> in <unk> Ländern <unk> .\n<unk> <unk> ist <unk> <unk> , die <unk> <unk> , die <unk> <unk> , dass die <unk> der <unk> in <unk> <unk> <unk> werden .\n<unk> oder nicht <unk> , <unk> von <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUnd <unk>\n<unk> , es ist in <unk> , dass die <unk> <unk> ist , mit <unk> der <unk> <unk> in <unk> .\nAuf der <unk> Frage der <unk> <unk> und <unk> <unk> , die <unk> der <unk> der <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> <unk> .\n<unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIm <unk> , <unk> die <unk> in einem <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , nicht <unk> .\n<unk> ist in <unk> , wo es <unk> ist .\n&quot; <unk> <unk> ist <unk> in <unk> , wo sie <unk> <unk> .\n<unk> in <unk> <unk>\nEs sollte <unk> <unk> , dass die <unk> in der <unk> <unk> , weil sie in einer <unk> <unk> <unk> <unk> , <unk> sie <unk> , die <unk> , die <unk> , die <unk> , die <unk> , ihre <unk> zu <unk> .\nUnd die <unk> <unk> würde nicht <unk> , dass die <unk> von <unk> oder <unk> <unk> <unk> <unk> .\n<unk> <unk> , die <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> oder die <unk> der <unk> oder der <unk> der <unk> ist eine <unk> <unk> , die die <unk> der Bericht <unk> .\nSie <unk> die <unk> zwischen anderen <unk> , die die <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> , ist nicht <unk> .\nDie <unk> der <unk> <unk> auch die Gesundheit , die in der <unk> der <unk> und <unk> <unk> werden werden .\n<unk> <unk> <unk> , die <unk> <unk> , <unk> <unk> , <unk> und <unk> , um die <unk> <unk> <unk> und nicht nur in <unk> <unk> .\n<unk> , <unk> <unk> bereits <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\n<unk> <unk> in der Region <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> , eine <unk> in <unk> und <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; &quot; <unk> &quot; &quot; <unk> &quot; &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> , der die <unk> der <unk> <unk> <unk> .\n&quot; <unk> <unk> die <unk> auf einer Situation , wo sie die <unk> <unk> , und die <unk> in <unk> Jahren <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> auch die <unk> , dass die <unk> der <unk> <unk> , die <unk> <unk> , die <unk> , die <unk> , die <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der <unk> der\nEr <unk> , dass <unk> <unk> in der EU sind und <unk> <unk> .\nDie <unk> in Gesundheit und Entwicklung , <unk> auch <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , die eine <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> der <unk> <unk> auf <unk> <unk> , um <unk> zu <unk> .\n<unk>\n<unk> ist <unk> <unk>\nDie <unk> der <unk> <unk> <unk> <unk> in <unk> der <unk> des <unk> des <unk> <unk> .\n<unk> <unk> <unk> <unk> auf die <unk> von <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> hat eine &quot; <unk> &quot; .\n<unk> Tage mit der <unk> von <unk> von <unk> von <unk> <unk> , der Europäische Union hat seine <unk> gegen <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> von <unk> <unk> <unk> die ersten <unk> , um <unk> zu <unk> , <unk> <unk> und <unk> der <unk> <unk> der <unk> <unk> .\n&quot; <unk> <unk> <unk> die politische <unk> , <unk> er <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nIm <unk> , die <unk> <unk> , <unk> <unk> die Tatsache , dass <unk> <unk> , dass <unk> , dass <unk> , die <unk> , die <unk> der <unk> <unk> , <unk> nicht <unk> .\n&quot; <unk> <unk> in der <unk> Osten , die in <unk> mit ihrem Menschen , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nFür die <unk> sind die internationale Mitgliedstaaten <unk> , um <unk> und die <unk> des <unk> <unk> zu <unk> und der Europäischen Union <unk> .\n&quot; Die internationale Gemeinschaft , die die Europäische Union , die <unk> , <unk> und Länder als <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> in <unk> und <unk> <unk> in der <unk> von <unk> und <unk> .\nIn einer <unk> <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> , dass die <unk> der Krise <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> wird nicht <unk> , <unk> er <unk> , dass <unk> gegen <unk> <unk> <unk> .\nDie Entscheidung , die <unk> , die <unk> <unk> ist , ist ein <unk> <unk> für die <unk> und <unk> die <unk> von <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEine neue <unk> <unk> der <unk> ist <unk> für den <unk> des <unk> von <unk> <unk> und <unk> in <unk> , die <unk> der <unk> in <unk> und <unk> <unk> .\nDie <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk>\n<unk> <unk> <unk>\nDie Zukunft <unk> der <unk> Regierung <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> , <unk>\nDie <unk> <unk> wurde in <unk> mit dem <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\n<unk> werden ein <unk> <unk> <unk> von <unk> <unk> <unk> .\n&quot; <unk> <unk> <unk> über eine wichtige <unk> , ein <unk> <unk> , <unk> <unk> <unk> , eine <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> oder <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , ob der neue Regierung <unk> <unk> .\nDie <unk> wird in der <unk> von <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> auf der <unk> <unk> <unk> <unk> .\n<unk> eines &quot; Demokratie ist <unk> <unk> und <unk> <unk> , dass er <unk> , dass die <unk> <unk> und <unk> <unk> wird .\n<unk> <unk> der <unk> war der <unk> <unk> , aber <unk> <unk> , in der <unk> in <unk> <unk> in <unk> <unk> .\nDie <unk> <unk> hat eine <unk> , die <unk> <unk> , <unk> er <unk> , dass er auch <unk> <unk> , <unk> <unk> .\nDie neue Regierung <unk> in <unk> , die <unk> der <unk> <unk> , <unk> ich <unk> .\n<unk> die <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> <unk> <unk> , <unk> er <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEin <unk> für die Europäische Kommissarin , <unk> <unk> <unk> , dass <unk> <unk> , dass die <unk> der <unk> nicht <unk> ist .\nDie Europäische Union ist <unk> , dass <unk> <unk> nicht die <unk> der <unk> in <unk> <unk> <unk> <unk> hat , <unk> , <unk> , <unk> und <unk> .\nDie <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist der <unk> der <unk> .\nHerr Präsident , <unk> möchte ein <unk> <unk> , weil der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> werden .\n<unk> <unk> hat ein <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> der <unk> von <unk> <unk> <unk> sich auf die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> Woche ist <unk> , die <unk> <unk> zu <unk> <unk> <unk> &quot; <unk> \" <unk> \" <unk> <unk> \" .\n<unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nIm <unk> oder <unk> <unk> er die <unk> , ein <unk> <unk> <unk> &quot; in <unk> .\nDie größte <unk> der <unk> der <unk> <unk> ich <unk> , die <unk> in <unk> in <unk> .\nEr kann <unk> <unk> auf eine <unk> <unk> des <unk> <unk> , um <unk> zu <unk> .\n<unk> <unk> <unk> &quot; <unk> <unk> <unk> , dass die Entwicklung von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> haben <unk> <unk> <unk>\n<unk> <unk> <unk> und 2010 , <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> auf diese <unk> <unk> , <unk> die <unk> von <unk> gegen <unk> in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> eine <unk> in <unk> <unk> , ist es <unk> , diese <unk> zu <unk> , die <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , die <unk> in einer <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> Regierung <unk> und müssen mehr mit seiner <unk> <unk> , <unk> <unk> , dass die <unk> der <unk> <unk> auf <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> &quot; in <unk> <unk> , ist eine <unk> von der <unk> für die <unk> .\nDas <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> seine <unk> und <unk> <unk>\nIm <unk> , die <unk> <unk> <unk> <unk> <unk> <unk> in der <unk> der <unk> <unk> <unk> <unk> <unk> , die <unk> der <unk> , die <unk> , die <unk> <unk> , die <unk> <unk> .\nEin <unk> &quot; und eine <unk> &quot; <unk> &quot; und eine <unk> &quot; <unk> &quot; <unk> und <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDiese <unk> , wenn sie <unk> , <unk> <unk> , wenn sie neue <unk> <unk> , wenn sie neue <unk> <unk> , wenn sie <unk> werden , <unk> .\nDies <unk> <unk> <unk> <unk> <unk> &quot; <unk> auf ein <unk> <unk> \" in <unk> &quot; <unk> &quot; <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAm <unk> , <unk> Sie sich über die <unk> der <unk> <unk> &quot; <unk> &quot; .\n<unk> ist <unk> , <unk> und <unk> <unk> .\n&quot; <unk> &quot; <unk> &quot; &quot; <unk> &quot; <unk> &quot; , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\n<unk> einer <unk> &quot; <unk> &quot; in der <unk>\nDie Regierung Regierung <unk> <unk> / <unk> / <unk> <unk> in <unk> in <unk> in <unk> auf <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEs war die <unk> <unk> , seine <unk> <unk> , <unk> <unk> , die dieses <unk> in der <unk> der <unk> <unk> , <unk> , <unk> oder <unk> .\n<unk> &quot; <unk> <unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> mit einer <unk> von der <unk> <unk> der <unk> der <unk> und der <unk> von <unk> <unk> <unk> die <unk> der <unk> und einer <unk> von <unk> in der <unk> <unk> .\nDie Entschließung hat den <unk> Regierung der <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; .\n<unk> , die <unk> <unk> <unk> müssen , müssen die <unk> der <unk> und der <unk> der <unk> <unk> , die <unk> <unk> , die <unk> <unk> , <unk> <unk> <unk> .\nDie <unk> ist , dass der <unk> die <unk> <unk> der <unk> von <unk> / <unk> / <unk> <unk> .\n<unk>\n<unk> <unk> auf den <unk> <unk>\nDie <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> , <unk> wir <unk> , wenn wir <unk> in der <unk> der <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> , die <unk> <unk> .\nAm <unk> , die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDie <unk> , <unk> <unk> die <unk> der <unk> gegen den <unk> in <unk> , um die <unk> der <unk> <unk> , die <unk> <unk> <unk> <unk> .\nDoch die <unk> <unk> <unk> seit <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> in Europa .\nIm <unk> , die <unk> <unk> <unk> <unk> <unk> und die <unk> <unk> des <unk> <unk> <unk> , <unk> die <unk> der <unk> .\nDie <unk> , die <unk> <unk> wurde <unk> und die <unk> über <unk> <unk> .\nDiese <unk> der <unk> <unk> <unk> <unk> , weil sie <unk> <unk> <unk> <unk> und <unk> die <unk> von <unk> <unk> <unk> , wenn <unk> in den nationalen <unk> <unk> .\n&quot; <unk> ist <unk> <unk> , um die <unk> und die <unk> <unk> <unk> , die die Region <unk> der <unk> <unk> <unk> .\nDie <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDoch die <unk> <unk> <unk> , dass die <unk> <unk> der <unk> <unk> <unk> <unk> <unk> .\nDie <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nAber die <unk> <unk> der <unk> , <unk> <unk> , dass die <unk> der <unk> <unk> hat , <unk> <unk> , dass die <unk> der <unk> <unk> <unk> wurde , die keine <unk> <unk> wurde .\n<unk> seine <unk> in der <unk> des <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk>\nDie <unk> sind <unk> , die <unk> <unk> <unk> sind .\nDie <unk> <unk> der <unk> , die europäische <unk> ist <unk> <unk> .\n<unk> der <unk> <unk> des <unk> in <unk> und <unk> die <unk> <unk> in Frankreich und <unk> die <unk> in <unk> .\n<unk> <unk> über die <unk> von <unk> , europäischen <unk> haben eine <unk> <unk> in diesem <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\nDie <unk> <unk> <unk> <unk> und <unk> <unk> .\nDie neue <unk> über die <unk> der <unk> in <unk> , die <unk> <unk> , die <unk> <unk> , eine <unk> <unk> zu <unk> .\n&quot; <unk> <unk> sind nur <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> sind mit einem <unk> von <unk> , <unk> <unk> <unk> der <unk> der <unk> Europas <unk> .\n&quot; <unk> , wie die <unk> der <unk> des <unk> <unk> <unk> <unk> auf Europa und die <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> <unk> über die Zukunft der Zukunft der Zukunft <unk> , die <unk> zu <unk> .\nDie <unk> ist die <unk> der <unk> <unk> für eine <unk> und <unk> .\n<unk> <unk> <unk> das Europäische <unk> ist <unk> <unk> , <unk> <unk> .\nDie <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> und <unk> <unk> <unk> <unk>\nDie <unk> <unk> von <unk> und <unk> ist <unk> <unk> <unk> .\nDiese <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> , dass der <unk> der <unk> <unk> <unk> <unk> <unk> , <unk> .\nDiese <unk> der <unk> der <unk> Länder , aber auch <unk> , <unk> die <unk> der <unk> .\nDie <unk> der <unk> Regierung <unk> Regierung , <unk> <unk> , die <unk> für die <unk> in einer <unk> <unk> von <unk> zu <unk> .\nDie neue <unk> <unk> <unk> <unk> <unk> <unk> , dass die <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; .\n<unk> <unk> <unk> sich in der Nähe des <unk> <unk> <unk> .\nDie <unk> der <unk> zwischen <unk> und <unk> hat <unk> <unk> <unk> <unk> .\n<unk> der <unk>\nFrankreich hat eine <unk> <unk> des <unk> in der <unk> <unk> , ein <unk> <unk> als die <unk> <unk> <unk> .\nDie <unk> <unk> <unk> die <unk> <unk> , mit einem <unk> von <unk> der <unk> <unk> .\n<unk> ist wirklich der <unk> <unk> , die die <unk> <unk> .\n<unk> <unk> ein <unk> <unk> für die <unk> <unk> <unk> .\nEin <unk> <unk> in der <unk> der <unk> ist der <unk> in <unk> <unk> <unk> <unk> , die europäische <unk> <unk> .\n<unk>\n<unk> müssen <unk> <unk>\n<unk> ist ein <unk> , die mit dem <unk> <unk> , <unk> oder in der <unk> <unk> , ist es <unk> <unk> .\nDie <unk> und <unk> der <unk> <unk> zwischen den <unk> und der <unk> .\nEs ist <unk> , dass ein <unk> <unk> für <unk> <unk> und die <unk> von <unk> <unk> <unk> werden .\nDie Aussprache wird nicht <unk> werden , dass sie alle <unk> <unk> , die <unk> und die <unk> der <unk> <unk> .\nDie <unk> müssen ihre <unk> <unk> <unk> <unk> .\n<unk> , dass wir <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWährend der <unk> <unk> , wird es die <unk> <unk> , die <unk> zu <unk> , um die <unk> <unk> zu <unk> , um die <unk> <unk> zu <unk> , um die <unk> der <unk> <unk> zu <unk> .\nDie <unk> <unk> ist nicht <unk> .\nDas ist <unk> <unk> im <unk> <unk> <unk> .\nDies ist nicht der <unk> <unk> , die sich auf die Notwendigkeit <unk> , um eine <unk> <unk> zu <unk> , <unk> oder die <unk> , die nur die <unk> <unk> .\n<unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> mit <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> mit <unk>\nDer <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> in einer <unk> der <unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> <unk> diese <unk> <unk> werden .\nDie Fall sollte <unk> werden .\n<unk> Europa in der <unk> der <unk> , der der <unk> <unk> <unk> , ist auch eine der <unk> <unk> der <unk> und der <unk> , die ihre <unk> <unk> und der <unk> <unk> .\nAuf den anderen Seite , <unk> Sie die <unk> , unsere <unk> zu <unk> , die <unk> <unk> <unk> <unk> .\nSeit der <unk> Wirtschaft der <unk> Wirtschaft der <unk> ist eine <unk> <unk> von <unk> und eine <unk> in der <unk> der <unk> in Europa .\n<unk> <unk> in diesem <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> , die eine <unk> Mehrheit der <unk> <unk> , die <unk> <unk> , weil sie sich nicht <unk> werden .\nEs sollte <unk> <unk> , dass die <unk> unserer Land <unk> , eine <unk> von <unk> zu <unk> und <unk> <unk> <unk> .\n<unk> <unk> , es <unk> eine <unk> von <unk> , <unk> und <unk> <unk> <unk> , die nicht <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> würde ein <unk> <unk> für <unk> Jahren <unk> , wenn die <unk> ihrer <unk> war ?\nUm die <unk> <unk> , muss es nicht <unk> werden , dass es keine <unk> oder <unk> <unk> werden .\nDie Entwicklung von <unk> <unk> <unk> über <unk> <unk> , <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nFür die <unk> des <unk> ist <unk> <unk> <unk> .\n<unk> haben wir eine <unk> <unk> , die eine <unk> und <unk> der <unk> <unk> <unk> <unk> <unk> <unk> .\nUnsere <unk> , deren <unk> ist <unk> , sind <unk> , die <unk> <unk> <unk> .\nIn einem <unk> Jahren <unk> die <unk> drei <unk> <unk> .\n<unk> einer <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\nDie <unk> und <unk> <unk> die <unk> und <unk> <unk> über die <unk> .\nDie <unk> , einige Menschen zu <unk> , dass die <unk> von zwei <unk> <unk> <unk> <unk> <unk> , die die <unk> der <unk> <unk> <unk> <unk> , die <unk> der <unk> <unk> .\nDie <unk> wird von diesem <unk> <unk> , wie es von zwei <unk> <unk> .\nDie <unk> dieser Schritt <unk> und der <unk> der <unk> und der <unk> , die <unk> und <unk> <unk> , die <unk> eine <unk> <unk> .\n<unk> <unk> die <unk> von allen <unk> , ein <unk> <unk> , ein <unk> <unk> , ein <unk> zu <unk> , ein <unk> zu <unk> und <unk> zu <unk> .\nEs wäre <unk> und <unk> , <unk> für die <unk> <unk> von <unk> für unsere <unk> , sondern es <unk> , <unk> , <unk> und <unk> .\n<unk> ein <unk> oder ein System für die Wirtschaft ?\n<unk> hat <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , es ist <unk> <unk> , <unk> <unk> <unk> , die <unk> , <unk> , <unk> und <unk> .\nDoch es ist nur <unk> <unk> von der <unk> <unk> .\nEs ist ein <unk> <unk> zwischen der <unk> und der <unk> <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDer <unk> , der <unk> <unk>\nIn <unk> , die <unk> der <unk> der nationalen <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> in der <unk> <unk> , die eine <unk> , um <unk> zu <unk> .\nIm <unk> , <unk> <unk> <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese <unk> <unk> eine <unk> und ein Problem <unk> , aber es ist <unk> sehr <unk> .\nWas die <unk> <unk> und <unk> <unk> , <unk> wir uns in einer <unk> <unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> die <unk> <unk> und <unk> <unk> .\n<unk> über die <unk> der <unk> sind <unk> , dass sie <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAber <unk> <unk> , die in <unk> <unk> sind , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> <unk> , <unk> , <unk> , <unk> , <unk> nicht <unk> und die <unk> <unk> <unk> in <unk> , <unk> , <unk> ist nicht <unk> .\n<unk> <unk> <unk> auf <unk> <unk> .\nIn einer <unk> wäre wie eine <unk> <unk> <unk> , die <unk> zwischen <unk> und <unk> <unk> , die <unk> zwischen <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> hat sich <unk> , dass eine <unk> von <unk> und <unk> in <unk> <unk> <unk> <unk> .\n<unk> ohne <unk> <unk> die Tatsache , dass die <unk> der <unk> von <unk> und <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> sind auch die <unk> <unk> , <unk> <unk> der <unk> <unk> der Europäischen Union .\n<unk> <unk> <unk> <unk> <unk> , dass die <unk> in Deutschland <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> im <unk> des <unk> <unk> <unk> <unk> <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> .\nDie <unk> in Frage , um <unk> zu <unk> , <unk> die <unk> <unk> .\nEuropa <unk> Demokratie , <unk> in <unk> .\n<unk> ist ein <unk> <unk> .\nDie Europäische <unk> sollte <unk> zu <unk> <unk> <unk> <unk> , dass es in <unk> über zwei <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> , dass es <unk> war , war es <unk> für die <unk> <unk> über ihre <unk> , um die Abstimmung , die <unk> und <unk> zu <unk> .\nEs ist <unk> , dass die Zeit der <unk> es <unk> ist , <unk> <unk> .\n<unk> <unk> , in <unk> , um die <unk> <unk> <unk> <unk> , ist nicht <unk> und kann nicht <unk> <unk> <unk> .\n<unk> , Demokratie <unk> , ein <unk> , ein <unk> , ein <unk> , ein <unk> , ein <unk> und <unk> <unk> , <unk> , <unk> und <unk> .\nDie Europäische Krise , die wir über <unk> sind , ist ein <unk> <unk> , aber es ist <unk> eine politische Krise und die Demokratie .\nIn diesem <unk> ist die <unk> einer <unk> <unk> <unk> <unk> , die eine <unk> <unk> von <unk> und <unk> <unk> .\nDiese <unk> muss über <unk> , dass es <unk> werden kann .\nDie <unk> ist , dass die <unk> der <unk> <unk> <unk> <unk> <unk> , und die <unk> <unk> sind nicht mehr <unk> .\nWir haben eine <unk> von <unk> ein <unk> <unk> <unk> und in einer <unk> <unk> .\n<unk> &quot; <unk> &quot; <unk> &quot; <unk> \" <unk> \" eine <unk> <unk> und eine <unk> mit <unk> <unk> in den <unk> <unk> .\n<unk> wollen wir nicht die <unk> einer <unk> , aber eine <unk> , <unk> und <unk> , der <unk> und Demokratie , der <unk> und Demokratie , der <unk> und Demokratie , <unk> .\nEs gibt keine <unk> in <unk> , <unk> , <unk> oder <unk> , weil es nur eine <unk> Krise <unk> , weil es nur eine <unk> Krise und <unk> ist , weil es nur eine <unk> Krise und <unk> ist .\nEs ist <unk> der <unk> Europas , die <unk> und <unk> <unk> , die von ihrer <unk> und eine <unk> der <unk> <unk> .\nEs ist <unk> , die internationale <unk> <unk> .\nEs ist <unk> <unk> , die <unk> zu Europa , aber es ist die gesamte <unk> , die die <unk> , die die <unk> <unk> , und mit ihrem <unk> .\nDie <unk> hat die <unk> <unk> <unk> , die <unk> des Europäischen <unk> .\nEs ist von allen <unk> , die <unk> <unk> , die die <unk> der <unk> <unk> , die <unk> , die <unk> und <unk> für die Zukunft der Zukunft <unk> .\n<unk> von der Krise <unk> <unk> auf der <unk> , die nicht <unk> werden , ohne <unk> <unk> zu <unk> .\nDie <unk> <unk> dieser Vorschlag von <unk> und <unk> <unk> , die <unk> <unk> des <unk> für eine europäische <unk> <unk> .\n<unk> ist <unk> .\nDoch die <unk> ohne <unk> <unk> <unk> <unk> und <unk> <unk> und <unk> in diesem <unk> werden ein <unk> <unk> und eine neue <unk> <unk> .\nWir müssen eine neue <unk> europäischen <unk> .\nUm die <unk> in Frankreich <unk> , ist es <unk> <unk> <unk> .\nDer <unk> wäre nicht <unk> .\nDiese <unk> ist <unk> .\nWir müssen ein <unk> <unk> über die <unk> <unk> .\nDies kann nur durch <unk> <unk> <unk> werden , um eine <unk> <unk> europäische <unk> .\nFür Europa <unk> uns <unk> <unk> .\n<unk> <unk> ohne <unk> werden .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> in <unk> <unk> .\nDie <unk> sollte eine <unk> <unk> von <unk> <unk> <unk> <unk> , <unk> mit einem <unk> <unk> mit <unk> <unk> <unk> , <unk> , <unk> .\nDie <unk> ist , dass die <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> oder <unk> für die <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nFür eine <unk> Zeit <unk> die <unk> <unk> in <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDoch die gesamte <unk> sind <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nUm <unk> zu <unk> , ist es jetzt zu <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> sind <unk> von <unk> <unk>\nDoch die <unk> nur ein <unk> <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> sind mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nSie haben <unk> .\n&quot; <unk> <unk> sollte sie <unk> werden .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDoch die <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> von <unk> <unk> und <unk> sind <unk> , um <unk> zu <unk> .\nDie <unk> hat in <unk> <unk> und in <unk> <unk> .\nUnd die <unk> <unk> auch auch <unk> <unk> , dass es die <unk> <unk> eines <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk>\n<unk> ist <unk> von <unk> zwischen der <unk> und der <unk> <unk> .\n<unk> für eine <unk> Zeit <unk> die <unk> zwischen der <unk> in der <unk> und der <unk> <unk> <unk> <unk> .\n<unk> , die <unk> <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDie <unk> in der beiden <unk> , die zwei <unk> <unk> und die <unk> von <unk> <unk> <unk> , ist es eine <unk> der Zusammenarbeit , die er nicht <unk> .\n<unk> <unk> sich ein <unk> <unk> .\nDie <unk> in der <unk> zwei <unk> in <unk> <unk> zwei <unk> <unk> .\nDie <unk> <unk> in einer <unk> von <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> <unk> .\n<unk> der <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk>\nDie <unk> von <unk> <unk> <unk> <unk> in <unk> und in <unk> .\n<unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> ist ein <unk> , aber es ist <unk> <unk> , um <unk> Zusammenarbeit zu <unk> und <unk> zu <unk> .\n<unk> ist es möglich .\nDie <unk> ist sehr <unk> , dass die <unk> von <unk> <unk> und <unk> <unk> und <unk> <unk> ist , <unk> <unk> <unk> .\nIn der Unterstützung der <unk> <unk> <unk> der <unk> und der <unk> von <unk> und der <unk> von <unk> und der <unk> <unk> <unk> , die <unk> , die <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\nDie internationale Gemeinschaft , <unk> mit <unk> , ist jetzt die internationale <unk> , um die Probleme von <unk> zu <unk> .\n<unk> Behörden haben eine <unk> <unk> , die <unk> <unk> <unk> , nicht <unk> .\n<unk> ?\nDie <unk> in <unk> auf <unk> <unk> <unk> , ein <unk> und eine <unk> <unk> <unk> <unk> , <unk> <unk> , die die <unk> einer <unk> <unk> <unk> .\nIn diesem <unk> ist die <unk> <unk> <unk> <unk> .\nEs ist <unk> und <unk> <unk> <unk> , die ihre <unk> in einer <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , <unk> und <unk> .\nIm <unk> 2009 die <unk> von <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> ist als <unk> , ohne <unk> und <unk> .\nDie <unk> <unk> eine <unk> von <unk> und <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> wird die <unk> <unk> und werden ihre <unk> <unk> <unk> .\n<unk> <unk> es <unk> ein <unk> , die <unk> <unk> und <unk> <unk> .\nIm Juni <unk> <unk> <unk> die <unk> ein <unk> <unk> in <unk> <unk> .\nIn <unk> wird die <unk> internationale internationalen <unk> <unk> .\nDie <unk> eines <unk> in <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\nDie <unk> hat <unk> <unk> und <unk> und die <unk> von <unk> in <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> Woche in einer <unk> mit <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> des <unk> <unk>\n<unk> <unk> <unk> <unk> , <unk> <unk> , <unk> , <unk> und <unk> <unk> in <unk> und <unk> <unk> .\n<unk> eine <unk> von <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ?\nUm die <unk> von <unk> <unk> , die <unk> <unk> ist , ist es <unk> .\nDie <unk> Tage <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und anderen <unk> und <unk> <unk> <unk> <unk> , die eine <unk> <unk> <unk> .\n<unk> <unk> China , <unk> und <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> .\n<unk> <unk> , die <unk> <unk> für die <unk> des <unk> <unk> <unk> , &quot; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> <unk> sich die <unk> , wenn diese <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> ist ein <unk> , aber eine <unk> , die mit <unk> <unk> <unk> <unk> <unk> <unk>\nDoch <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> der <unk>\n<unk> auch <unk> <unk> <unk> <unk> <unk> <unk> , wenn <unk> <unk> <unk> werden .\n<unk> <unk> ich <unk> , dass er die <unk> <unk> <unk> .\n<unk> ist <unk> , die <unk> <unk> .\n<unk> <unk> auch <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> , die eine <unk> <unk> <unk> , <unk> <unk> der <unk> .\n<unk> <unk> er sich <unk> , dass die <unk> <unk> <unk> <unk> <unk> , weil er <unk> ist , <unk> <unk> , dass China er <unk> hat !\nDie <unk> <unk> <unk> <unk> , die <unk> und <unk> <unk> <unk> gegen <unk> , <unk> , dass er <unk> ist , die <unk> <unk> in <unk> <unk> <unk> .\n<unk> Sie die <unk> von <unk> <unk> in <unk> <unk> , ist auch die <unk> <unk> <unk> .\n<unk> ist von der <unk> <unk> <unk> <unk> <unk> , ein <unk> <unk> .\nDie <unk> der <unk> <unk> <unk> , dass die <unk> auch auch die <unk> <unk> <unk> <unk> .\nSie <unk> , dass die <unk> <unk> eine <unk> und die <unk> <unk> <unk> <unk> <unk> .\n<unk> mit <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDoch die <unk> ist nicht <unk> .\n<unk> als <unk> <unk> <unk> die <unk> des <unk> , die <unk> <unk> .\n<unk> <unk> es <unk> <unk> , die <unk> in <unk> .\nUnd in der <unk> ist es zu <unk> , um ihre <unk> zu <unk> , die <unk> zu <unk> , ist <unk> <unk> .\n<unk> <unk> , eine neue <unk> für die <unk> der <unk> in Europa in Europa .\nDas <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> und <unk> <unk> .\nEs ist <unk> , <unk> für die <unk> des <unk> <unk> .\n<unk> , die <unk> <unk> <unk> <unk> , <unk> die <unk> mit &quot; <unk> &quot; .\n<unk> <unk> <unk> <unk> pro <unk> und der Regierung <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> , die <unk> <unk> <unk> , die <unk> <unk> <unk> , die <unk> <unk> , die <unk> <unk> <unk> und seine <unk> <unk> <unk> .\nMit diesem neuen <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nEin <unk> <unk> ist es , die <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nUnd wenn die Möglichkeit einer <unk> <unk> <unk> <unk> <unk> ist .\nFür die <unk> <unk> dieses <unk> ist eine <unk> .\n<unk> war <unk> <unk> für die <unk> und die <unk> .\nDie <unk> werden <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDiese zwei Länder wird <unk> über die <unk> <unk> , es wird <unk> <unk> , die <unk> , Russland zu <unk> , <unk> zu <unk> .\nIm <unk> <unk> <unk> die Aussprache , die <unk> nicht <unk> <unk> <unk> <unk> <unk> , &quot; <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\nDas ist der <unk> <unk> <unk> .\n<unk> <unk> hat diese <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nAber in der Nähe des <unk> hat die <unk> eine <unk> zwischen <unk> und <unk> Europa .\nRussland hat <unk> <unk> als 5 Jahren <unk> , die <unk> <unk> zwischen Russland und die <unk> <unk> , die <unk> <unk> <unk> , und deren <unk> sind <unk> .\nDie <unk> ist eine <unk> von <unk> mit Russland , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> der <unk> <unk> auf der <unk> der <unk> <unk> , <unk> <unk> , <unk> <unk> der <unk> .\nDie <unk> dieses <unk> ist auch eine <unk> <unk> .\nDie <unk> <unk> ist von <unk> <unk> in <unk> , um die <unk> von <unk> &quot; <unk> &quot; .\n<unk> <unk> auf die <unk> des <unk> , in einem <unk> <unk> von <unk> <unk> .\nUm die <unk> <unk> ist , <unk> zu <unk> <unk> zu <unk> .\n<unk> &quot; <unk> <unk> <unk> oder <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nEs war auch <unk> , <unk> der <unk> und die <unk> .\nDie <unk> ist ein <unk> <unk> <unk> , der <unk> <unk> , die <unk> <unk> , <unk> <unk> und keine <unk> <unk> .\nEin <unk> war <unk> <unk> in der <unk> <unk> <unk> <unk> , aber die <unk> <unk> <unk> sie nicht auf <unk> <unk> .\n<unk> &quot; <unk> <unk> wir die <unk> für eine <unk> <unk> , &quot; <unk> <unk> <unk> .\nIn der Fall eines <unk> , die <unk> <unk> in den <unk> <unk> .\nDoch dies ist der <unk> <unk> , durch die <unk> der <unk> <unk> <unk> <unk> <unk> .\nDie <unk> der <unk> ist durch eine <unk> <unk> der <unk> <unk> <unk> , die von einer <unk> <unk> <unk> <unk> , als <unk> <unk> <unk> .\n<unk> <unk> , es ist <unk> der <unk> <unk> , und es ist <unk> <unk> der <unk> .\nIm <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> wurde <unk> <unk> , um die <unk> <unk> <unk> .\nDie <unk> <unk> ist <unk> durch ihre <unk> .\nDas <unk> <unk> bietet <unk> <unk> und <unk> <unk> .\n<unk> Unternehmen ihre <unk> zu <unk> .\nDie <unk> ist <unk> , die <unk> <unk> der <unk> .\nDie <unk> sind <unk> und <unk> <unk> <unk> und <unk> <unk> und die <unk> <unk> <unk> <unk> <unk> <unk> <unk>\n<unk> <unk> <unk> ein <unk> <unk> <unk> <unk> <unk> , <unk> , <unk> und <unk> .\nDie <unk> in <unk> in <unk> , wo <unk> ist <unk> <unk> .\nAls <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> , die gesamte <unk> <unk> .\n&quot; <unk> wird die <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> .\nDie <unk> der <unk> wurde der <unk> der <unk> <unk> und <unk> von <unk> <unk> .\n<unk> <unk> die <unk> werden <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> werden <unk> <unk> <unk> .\nDoch die <unk> <unk> sind <unk> , die <unk> , weil die <unk> <unk> <unk> wird .\nDie <unk> <unk> von der <unk> mit <unk> auf <unk> <unk> .\n<unk> <unk> <unk> <unk> die <unk> <unk> .\n<unk> <unk> <unk> , <unk> und die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nIm <unk> werden 3 <unk> ein <unk> <unk> und in <unk> <unk> <unk> <unk> .\n<unk> , eine <unk> <unk> .\n<unk> <unk> Jahren <unk> <unk> <unk> , wird die <unk> der Richtlinie .\nEs ist eine <unk> der <unk> , der zwischen einer der <unk> <unk> <unk> .\nDie <unk> <unk> ist <unk> <unk> , durch ihre <unk> <unk> <unk> , ihre <unk> <unk> <unk> .\n<unk> , die <unk> <unk> , <unk> wir wissen , war es in <unk> .\nDas <unk> <unk> <unk> <unk> , <unk> in <unk> , <unk> , <unk> , eine <unk> <unk> .\n<unk> <unk> , die eine <unk> der <unk> <unk> , hat <unk> eine <unk> des <unk> .\nDie <unk> <unk> die <unk> von einer <unk> <unk> , die <unk> , die <unk> , die <unk> , die <unk> <unk> <unk> für <unk> <unk> , <unk> .\nDiese <unk> ist sehr <unk> , <unk> und <unk> .\n&quot; <unk> ist <unk> <unk> , die <unk> <unk> für eine <unk> und <unk> <unk> , die mit <unk> <unk> <unk> , <unk> <unk> <unk> .\n<unk> ist es <unk> .\n<unk> 2006 und 2010 , <unk> <unk> von <unk> <unk> <unk> <unk> .\n<unk> hat die <unk> der ersten Jahre <unk> , wenn nur <unk> <unk> wurde .\n<unk> ist die <unk> <unk> und ist die <unk> <unk> .\nEs ist <unk> <unk> , eine <unk> der <unk> <unk> , weil die <unk> eine <unk> und eine <unk> .\n<unk> sind <unk> , <unk> und <unk> .\nDie neue <unk> <unk> , die <unk> <unk> <unk> , hat jetzt <unk> <unk> <unk> und zwei <unk> <unk> , eine in <unk> und <unk> <unk> .\n<unk> <unk> seine <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; Wir <unk> die <unk> des <unk> und der Notwendigkeit der <unk> <unk> , die die <unk> <unk> , &quot; <unk> <unk> <unk> .\nDiese <unk> <unk> <unk> die Entwicklung von <unk> , <unk> , <unk> , <unk> oder <unk> <unk> .\nIn <unk> ist es auch ein <unk> <unk> auf die <unk> der <unk> in <unk> .\n<unk> seine Erfolg und die <unk> <unk> , <unk> ihre <unk> zu <unk> , <unk> , <unk> , <unk> .\nEin Erfolg , die <unk> in der <unk> 2006 <unk> , <unk> <unk> wurde der <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> ist der <unk> der <unk> <unk> <unk> <unk> <unk> ?\n<unk> <unk> von <unk> sind <unk> für <unk> .\nDie <unk> Woche für die <unk> des <unk> <unk> heute .\n<unk> ein <unk> <unk> in <unk> , <unk> der <unk> der <unk> ist noch <unk> als <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\n<unk> ?\n&quot; <unk> , <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> und <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> <unk> war , dass die <unk> der <unk> nicht <unk> , sondern <unk> <unk> .\n&quot; <unk> ist jedoch noch ein <unk> <unk> .\nEs muss sehr <unk> <unk> , dass für die <unk> , die noch viele <unk> <unk> sind .\n<unk> sind <unk> <unk> .\nSie haben ein <unk> <unk> von <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> ist <unk> , <unk> für die <unk> <unk> <unk> <unk> .\nDie <unk> sind <unk> , die <unk> <unk> , die eine <unk> <unk> , und die <unk> der <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk>\n<unk> sind einige der <unk> .\nEs ist <unk> <unk> , <unk> , <unk> , <unk> , aber auch <unk> .\nEin <unk> und <unk> in <unk> von der <unk> <unk> <unk> , dass die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> - <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nDie <unk> <unk> <unk> sich auf eine <unk> <unk> .\n<unk> <unk> , &quot; <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> &quot; <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk>\nDie <unk> der <unk> , die die <unk> , die eine <unk> <unk> , <unk> <unk> , <unk> <unk> <unk> .\nDie <unk> <unk> auch eine <unk> von <unk> , in <unk> in <unk> <unk> .\n<unk> , <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> &quot; <unk> &quot; <unk>\n<unk> <unk> die <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\nWir <unk> nicht als <unk> , dass es <unk> <unk> , aber die <unk> ist <unk> .\nDie <unk> von <unk> <unk> <unk> <unk> , <unk> <unk> und <unk> <unk> <unk> .\n<unk> <unk> die <unk> der <unk> der Türkei , die <unk> kann nur <unk> <unk> werden .\n&quot; Ich bin sehr <unk> <unk> mit <unk> , weil er <unk> ist .\n&quot; Ich möchte die <unk> <unk> .\nIm <unk> der <unk> und <unk> <unk> , &quot; es ist der <unk> , wenn es <unk> ist .\nDie <unk> , die die <unk> nicht <unk> , ist ich <unk> , die nicht die Fall <unk> .\n<unk> möchte ich <unk> .\n<unk> auf <unk> auf die <unk> <unk> in den <unk> gegen <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> die <unk> der <unk> .\n<unk> <unk> die <unk> von <unk> Jahren <unk> und <unk> <unk> .\n&quot; Ich glaube auch <unk> , er <unk> <unk> .\nDas <unk> <unk> in <unk> <unk> <unk> die <unk> dieses <unk> .\nDer <unk> <unk> ist seit <unk> <unk> <unk> <unk> <unk> und <unk> <unk> <unk> er <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk>\n&quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> <unk> &quot; <unk> &quot; <unk> &quot; .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n&quot; <unk> &quot; <unk> \" <unk> in &quot; <unk> <unk> .\n&quot; <unk> &quot; auch in <unk> <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot; <unk> &quot;\n&quot; <unk> <unk> war ein <unk> , er <unk> <unk> .\n<unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> .\n<unk> <unk> <unk> <unk> , <unk> er <unk> .\nDie <unk> sind <unk> <unk> , sie sind <unk> <unk> .\n&quot; <unk> wird diese als <unk> der <unk> <unk> .\n<unk> ist sehr <unk>\nDie <unk> der <unk> <unk> <unk> seine <unk> gegen <unk> <unk> <unk> , <unk> dieses <unk> in Lissabon .\nAm <unk> gibt es auch <unk> , <unk> <unk> zu <unk> .\n&quot; <unk> &quot; <unk> <unk> , <unk> , <unk> , <unk> , <unk> , <unk> .\n<unk> <unk> bereits in <unk> und in der <unk> .\nDie <unk> ist nicht <unk> von einer <unk> , die <unk> , die er <unk> hat , seit <unk> <unk> <unk> .\n<unk> ist nur <unk> <unk> .\n<unk> ist es <unk> , um <unk> zu <unk> .\nIn der <unk> über die <unk> in der <unk> in der <unk> ist <unk> , dass die <unk> nicht nur nicht <unk> .\n<unk> <unk> <unk> die <unk> für eine <unk> , die in diesem <unk> nicht <unk> haben , <unk> <unk> , <unk> , <unk> .\n<unk> ist eine <unk> <unk> , die eine <unk> <unk> &quot; <unk> &quot; <unk> &quot; .\nDoch die <unk> <unk> , in den <unk> <unk> , <unk> die <unk> <unk> , dass sie <unk> werden , dass sie <unk> in <unk> <unk> werden , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> , <unk> ,\nDie <unk> sind eine sehr <unk> <unk> , aber wir sind <unk> , dass wir die <unk> <unk> <unk> <unk> .\n&quot; Wir haben eine <unk> <unk> von <unk> , <unk> &quot; <unk> &quot; <unk> &quot; <unk> .\nDie <unk> der <unk> von <unk> aus <unk> <unk> die <unk> zu <unk> .\nEin <unk> <unk> werden für die <unk> .\nDie <unk> ist ein <unk> <unk> , die <unk> <unk> , <unk> <unk> .\n"
  },
  {
    "path": "onmt/tests/pull_request_chk.sh",
    "content": "#!/bin/bash\n# Run this script and fix *any* error before sending PR.\n# For repeated runs, set the environment variables\n# SKIP_DOWNLOADS=1  If files/uncompressed dirs exist don't download (if compressed files exist, just untar).\n# SKIP_FULL_CLEAN=1  Don't remove anything downloaded/uncompressed.\n\nLOG_FILE=/tmp/$$_pull_request_chk.log\necho > ${LOG_FILE} # Empty the log file.\n\nPROJECT_ROOT=`dirname \"$0\"`\"/../../\"\nDATA_DIR=\"$PROJECT_ROOT/data\"\nTEST_DIR=\"$PROJECT_ROOT/onmt/tests\"\nPYTHON=\"python3\"\nTMP_OUT_DIR=\"/tmp/onmt_prchk\"\n\nclean_up()\n{\n    if [[ \"$1\" != \"error\" ]]; then\n        rm ${LOG_FILE}\n    fi\n    if [[ \"${SKIP_FULL_CLEAN}\" == \"1\" ]]; then\n        # delete any .pt's that weren't downloaded\n        ls $TMP_OUT_DIR/*.pt | xargs -I {} rm -f $TMP_OUT_DIR/{}\n    else\n        # delete all .pt's\n        rm -f $TMP_OUT_DIR/*.pt\n        rm -rf $TMP_OUT_DIR/sample\n        rm $TMP_OUT_DIR/onmt.vocab*\n        rm -d $TMP_OUT_DIR\n    fi\n}\ntrap clean_up SIGINT SIGQUIT SIGKILL\n\nerror_exit()\n{\n    echo \"Failed !\" | tee -a ${LOG_FILE}\n    echo \"[!] Check ${LOG_FILE} for detail.\"\n    clean_up error\n    exit 1\n}\n\n# environment_prepare()\n# {\n\n# }\n\n# flake8 check\necho -n \"[+] Doing flake8 check...\"\n${PYTHON} -m flake8 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\n\n# unittest\necho -n \"[+] Doing unittest test...\"\n${PYTHON} -m unittest discover >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\n\n#\n# Get Vocabulary test\n#\necho -n \"[+] Testing vocabulary building...\"\nPYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} onmt/bin/build_vocab.py \\\n            -config ${DATA_DIR}/data.yaml \\\n            -save_data $TMP_OUT_DIR/onmt \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -n_sample 5000 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm -r $TMP_OUT_DIR/sample\n\n#\n# Training test\n#\necho -n \"[+] Testing NMT fields/transforms prepare...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/data.yaml \\\n            -save_data $TMP_OUT_DIR/onmt.train.check \\\n            -dump_fields -dump_transforms -n_sample 30 \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n# rm $TMP_OUT_DIR/onmt.train.check*  # used in tool testing\n\necho \"[+] Doing Training test...\"\n\necho -n \"  [+] Testing NMT training...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -rnn_size 2 -batch_size 10 \\\n            -word_vec_size 5 -report_every 5        \\\n            -rnn_size 10 -train_steps 10 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\necho -n \"  [+] Testing NMT training w/ copy...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -rnn_size 2 -batch_size 10 \\\n            -word_vec_size 5 -report_every 5        \\\n            -rnn_size 10 -train_steps 10 \\\n            -copy_attn >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\necho -n \"  [+] Testing NMT training w/ align...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/align_data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -max_generator_batches 0 \\\n            -encoder_type transformer -decoder_type transformer \\\n            -layers 4 -word_vec_size 16 -rnn_size 16 -heads 2 -transformer_ff 64 \\\n            -lambda_align 0.05 -alignment_layer 2 -alignment_heads 0 \\\n            -report_every 5 -train_steps 10 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\necho -n \"  [+] Testing NMT training w/ coverage...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -rnn_size 2 -batch_size 10 \\\n            -word_vec_size 5 -report_every 5        \\\n            -coverage_attn true -lambda_coverage 0.1 \\\n            -rnn_size 10 -train_steps 10 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\necho -n \"  [+] Testing LM training...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/lm_data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -model_task lm \\\n            -encoder_type transformer_lm \\\n            -decoder_type transformer_lm \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -dec_layers 2 -batch_size 10 \\\n            -heads 4 -transformer_ff 64 \\\n            -word_vec_size 16 -report_every 5        \\\n            -rnn_size 16 -train_steps 10 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\necho -n \"  [+] Testing LM training w/ copy...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/lm_data.yaml \\\n            -src_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -tgt_vocab $TMP_OUT_DIR/onmt.vocab.src \\\n            -model_task lm \\\n            -encoder_type transformer_lm \\\n            -decoder_type transformer_lm \\\n            -src_vocab_size 1000 \\\n            -tgt_vocab_size 1000 \\\n            -dec_layers 2 -batch_size 10 \\\n            -heads 4 -transformer_ff 64 \\\n            -word_vec_size 16 -report_every 5        \\\n            -rnn_size 16 -train_steps 10 \\\n            -copy_attn >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}*\nrm $TMP_OUT_DIR/onmt.vocab*\n\necho -n \"  [+] Testing Graph Neural Network training...\"\n${PYTHON} onmt/bin/train.py \\\n            -config ${DATA_DIR}/ggnn_data.yaml \\\n            -src_seq_length 1000 -tgt_seq_length 30 \\\n            -encoder_type ggnn -layers 2 \\\n            -decoder_type rnn -rnn_size 256 \\\n            -learning_rate 0.1 -learning_rate_decay 0.8 \\\n            -global_attention general -batch_size 32 -word_vec_size 256 \\\n            -bridge -train_steps 10 -n_edge_types 9 -state_dim 256 \\\n            -n_steps 10 -n_node 64 >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\n#\n# Translation test\n#\necho \"[+] Doing translation test...\"\n\necho -n \"  [+] Testing NMT translation...\"\nhead ${DATA_DIR}/src-test.txt > $TMP_OUT_DIR/src-test.txt\n${PYTHON} translate.py -model ${TEST_DIR}/test_model.pt -src $TMP_OUT_DIR/src-test.txt -verbose >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/src-test.txt\n\necho -n \"  [+] Testing NMT ensemble translation...\"\nhead ${DATA_DIR}/src-test.txt > $TMP_OUT_DIR/src-test.txt\n${PYTHON} translate.py -model ${TEST_DIR}/test_model.pt ${TEST_DIR}/test_model.pt \\\n            -src $TMP_OUT_DIR/src-test.txt -verbose >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/src-test.txt\n\necho -n \"  [+] Testing NMT translation w/ Beam search...\"\n${PYTHON} translate.py -model ${TEST_DIR}/test_model2.pt  \\\n            -src ${DATA_DIR}/morph/src.valid   \\\n            -verbose -batch_size 10     \\\n            -beam_size 10               \\\n            -tgt ${DATA_DIR}/morph/tgt.valid   \\\n            -out $TMP_OUT_DIR/trans_beam  >> ${LOG_FILE} 2>&1\ndiff ${DATA_DIR}/morph/tgt.valid $TMP_OUT_DIR/trans_beam\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/trans_beam\n\necho -n \"  [+] Testing NMT translation w/ Random Sampling...\"\n${PYTHON} translate.py -model ${TEST_DIR}/test_model2.pt  \\\n            -src ${DATA_DIR}/morph/src.valid   \\\n            -verbose -batch_size 10     \\\n            -beam_size 1                \\\n            -seed 1                     \\\n            -random_sampling_topk=-1    \\\n            -random_sampling_temp=0.0001    \\\n            -tgt ${DATA_DIR}/morph/tgt.valid   \\\n            -out $TMP_OUT_DIR/trans_sampling  >> ${LOG_FILE} 2>&1\ndiff ${DATA_DIR}/morph/tgt.valid $TMP_OUT_DIR/trans_sampling\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/trans_sampling\n\necho -n \"  [+] Testing LM generation...\"\nhead ${DATA_DIR}/src-test.txt > $TMP_OUT_DIR/src-test.txt\n${PYTHON} translate.py -model ${TEST_DIR}/test_model_lm.pt -src $TMP_OUT_DIR/src-test.txt -verbose >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/src-test.txt\n\necho -n \"  [+] Testing LM generation w/ Beam search...\"\n${PYTHON} translate.py -model ${TEST_DIR}/test_model_lm.pt  \\\n            -src ${DATA_DIR}/data_lm/src-gen.txt   \\\n            -verbose -batch_size 10     \\\n            -beam_size 10               \\\n            -out $TMP_OUT_DIR/gen_beam  >> ${LOG_FILE} 2>&1\ndiff ${DATA_DIR}/data_lm/gen-beam-sol.txt $TMP_OUT_DIR/gen_beam\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/gen_beam\n\necho -n \"  [+] Testing LM generation w/ Random Sampling...\"\n${PYTHON} translate.py -model ${TEST_DIR}/test_model_lm.pt  \\\n            -src ${DATA_DIR}/data_lm/src-gen.txt   \\\n            -verbose -batch_size 10     \\\n            -beam_size 1                \\\n            -seed 1                     \\\n            -random_sampling_topk=-1    \\\n            -random_sampling_temp=0.0001    \\\n            -out $TMP_OUT_DIR/gen_sampling  >> ${LOG_FILE} 2>&1\ndiff ${DATA_DIR}/data_lm/gen-sampling-sol.txt $TMP_OUT_DIR/gen_sampling\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/gen_sampling\n\n#\n# Tools test\n#\necho \"[+] Doing tools test...\"\necho -n \"  [+] Doing extract vocabulary test...\"\nPYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} ./tools/extract_vocabulary.py \\\n            -file $TMP_OUT_DIR/onmt.train.check.vocab.pt -file_type field -side src \\\n            -out_file $TMP_OUT_DIR/vocab.txt >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\nif ! wc -l $TMP_OUT_DIR/vocab.txt | grep -qF  \"1002\"; then\n    echo -n \"wrong word count\\n\" >> ${LOG_FILE}\n    wc -l $TMP_OUT_DIR/vocab.txt >> ${LOG_FILE}\n    error_exit\nfi\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/vocab.txt\n\necho -n \"  [+] Doing embeddings to torch test...\"\nPYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} ./tools/embeddings_to_torch.py \\\n        -emb_file_enc ${TEST_DIR}/sample_glove.txt \\\n        -emb_file_dec ${TEST_DIR}/sample_glove.txt \\\n        -dict_file $TMP_OUT_DIR/onmt.train.check.vocab.pt \\\n        -output_file $TMP_OUT_DIR/q_gloveembeddings        >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\nrm $TMP_OUT_DIR/q_gloveembeddings*\n\necho -n \"  [+] Doing extract embeddings test...\"\nPYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} tools/extract_embeddings.py \\\n        -model onmt/tests/test_model.pt  >> ${LOG_FILE} 2>&1\n[ \"$?\" -eq 0 ] || error_exit\necho \"Succeeded\" | tee -a ${LOG_FILE}\n\n# Finally, clean up\nclean_up\n"
  },
  {
    "path": "onmt/tests/rebuild_test_models.sh",
    "content": "# # Retrain the models used for CI.\n# # Should be done rarely, indicates a major breaking change. \nmy_python=python\n\n############### TEST regular RNN choose either -rnn_type LSTM / GRU / SRU and set input_feed 0 for SRU\nif false; then\n$my_python build_vocab.py \\\n    -config data/data.yaml -save_data data/data \\\n    -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -overwrite true\n$my_python train.py \\\n    -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -src_vocab_size 1000 -tgt_vocab_size 1000 \\\n    -save_model tmp -world_size 1 -gpu_ranks 0 \\\n    -rnn_type LSTM -input_feed 0 \\\n    -rnn_size 256 -word_vec_size 256 \\\n    -layers 1 -train_steps 10000 \\\n    -optim adam  -learning_rate 0.001\n    # -truncated_decoder 5 \n    # -label_smoothing 0.1\n\nmv tmp*10000.pt onmt/tests/test_model.pt\nrm tmp*.pt\nfi\n\n\n############### TEST CNN \nif false; then\n$my_python build_vocab.py \\\n    -config data/data.yaml -save_data data/data \\\n    -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -overwrite true\n$my_python train.py \\\n    -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -src_vocab_size 1000 -tgt_vocab_size 1000 \\\n    -save_model /tmp/tmp -world_size 1 -gpu_ranks 0 \\\n    -encoder_type cnn -decoder_type cnn \\\n    -rnn_size 256 -word_vec_size 256 \\\n    -layers 2 -train_steps 10000 \\\n    -optim adam  -learning_rate 0.001\n\nmv /tmp/tmp*10000.pt onmt/tests/test_model.pt\nrm /tmp/tmp*.pt\nfi\n\n################# MORPH DATA\nif false; then\n$my_python build_vocab.py \\\n    -config data/morph_data.yaml -save_data data/data \\\n    -src_vocab data/morph_data.vocab.src -tgt_vocab data/morph_data.vocab.tgt \\\n    -overwrite true\n$my_python train.py \\\n    -config data/morph_data.yaml -src_vocab data/morph_data.vocab.src -tgt_vocab data/morph_data.vocab.tgt \\\n    -save_model tmp -world_size 1 -gpu_ranks 0 \\\n    -rnn_size 400 -word_vec_size 100 \\\n    -layers 1 -train_steps 8000 \\\n    -optim adam  -learning_rate 0.001\n\n\nmv tmp*8000.pt onmt/tests/test_model2.pt\n\nrm tmp*.pt\nfi\n\n\n############### TEST TRANSFORMER\nif false; then\n$my_python build_vocab.py \\\n    -config data/data.yaml -save_data data/data \\\n    -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -overwrite true -share_vocab\n\n$my_python train.py \\\n    -config data/data.yaml -src_vocab data/data.vocab.src -tgt_vocab data/data.vocab.tgt \\\n    -save_model /tmp/tmp \\\n    -batch_type tokens -batch_size 1024 -accum_count 4 \\\n    -layers 4 -rnn_size 256 -word_vec_size 256 \\\n    -encoder_type transformer -decoder_type transformer \\\n    -share_embedding -share_vocab \\\n    -train_steps 10000 -world_size 1 -gpu_ranks 0 \\\n    -max_generator_batches 4 -dropout 0.1 \\\n    -normalization tokens \\\n    -max_grad_norm 0 -optim adam -decay_method noam \\\n    -learning_rate 2 -label_smoothing 0.1 \\\n    -position_encoding -param_init 0 \\\n    -warmup_steps 100 -param_init_glorot -adam_beta2 0.998\n\nmv /tmp/tmp*10000.pt onmt/tests/test_model.pt\nrm /tmp/tmp*.pt\nfi\n\n\nif false; then\n$my_python translate.py -gpu 0 -model onmt/tests/test_model.pt \\\n  -src data/src-val.txt -output onmt/tests/output_hyp.txt -beam 5 -batch_size 16\n\nfi\n\n############### TEST LANGUAGE MODEL\nif false; then\nrm data/data_lm/*.python\n\n$my_python build_vocab.py \\\n    -config data/lm_data.yaml -save_data data/data_lm -share_vocab \\\n    -src_vocab data/data_lm/data.vocab.src -tgt_vocab data/data_lm/data.vocab.tgt \\\n    -overwrite true\n\n$my_python train.py -config data/lm_data.yaml -save_model /tmp/tmp \\\n -accum_count 2 -dec_layers 2 -rnn_size 64 -word_vec_size 64 -batch_size 256 \\\n -encoder_type transformer_lm -decoder_type transformer_lm -share_embedding \\\n -train_steps 2000 -max_generator_batches 4 -dropout 0.1 -normalization tokens \\\n -share_vocab -transformer_ff 256 -max_grad_norm 0 -optim adam -decay_method noam \\\n -learning_rate 2 -label_smoothing 0.1 -model_task lm -world_size 1 -gpu_ranks 0 \\\n -attention_dropout 0.1 -heads 2 -position_encoding -param_init 0 -warmup_steps 100 \\\n -param_init_glorot -adam_beta2 0.998 -src_vocab data/data_lm/data.vocab.src\n#\nmv /tmp/tmp*2000.pt onmt/tests/test_model_lm_2.pt\nrm /tmp/tmp*.pt\nfi\n#\nif false; then\n$my_python translate.py -gpu 0 -model onmt/tests/test_model_lm.pt \\\n  -src data/src-val.txt -output onmt/tests/output_hyp.txt -beam 5 -batch_size 16\n\nfi\n\n"
  },
  {
    "path": "onmt/tests/sample_glove.txt",
    "content": "the -0.038194 -0.24487 0.72812 -0.39961 0.083172 0.043953 -0.39141 0.3344 -0.57545 0.087459 0.28787 -0.06731 0.30906 -0.26384 -0.13231 -0.20757 0.33395 -0.33848 -0.31743 -0.48336 0.1464 -0.37304 0.34577 0.052041 0.44946 -0.46971 0.02628 -0.54155 -0.15518 -0.14107 -0.039722 0.28277 0.14393 0.23464 -0.31021 0.086173 0.20397 0.52624 0.17164 -0.082378 -0.71787 -0.41531 0.20335 -0.12763 0.41367 0.55187 0.57908 -0.33477 -0.36559 -0.54857 -0.062892 0.26584 0.30205 0.99775 -0.80481 -3.0243 0.01254 -0.36942 2.2167 0.72201 -0.24978 0.92136 0.034514 0.46745 1.1079 -0.19358 -0.074575 0.23353 -0.052062 -0.22044 0.057162 -0.15806 -0.30798 -0.41625 0.37972 0.15006 -0.53212 -0.2055 -1.2526 0.071624 0.70565 0.49744 -0.42063 0.26148 -1.538 -0.30223 -0.073438 -0.28312 0.37104 -0.25217 0.016215 -0.017099 -0.38984 0.87424 -0.72569 -0.51058 -0.52028 -0.1459 0.8278 0.27062\n, -0.10767 0.11053 0.59812 -0.54361 0.67396 0.10663 0.038867 0.35481 0.06351 -0.094189 0.15786 -0.81665 0.14172 0.21939 0.58505 -0.52158 0.22783 -0.16642 -0.68228 0.3587 0.42568 0.19021 0.91963 0.57555 0.46185 0.42363 -0.095399 -0.42749 -0.16567 -0.056842 -0.29595 0.26037 -0.26606 -0.070404 -0.27662 0.15821 0.69825 0.43081 0.27952 -0.45437 -0.33801 -0.58184 0.22364 -0.5778 -0.26862 -0.20425 0.56394 -0.58524 -0.14365 -0.64218 0.0054697 -0.35248 0.16162 1.1796 -0.47674 -2.7553 -0.1321 -0.047729 1.0655 1.1034 -0.2208 0.18669 0.13177 0.15117 0.7131 -0.35215 0.91348 0.61783 0.70992 0.23955 -0.14571 -0.37859 -0.045959 -0.47368 0.2385 0.20536 -0.18996 0.32507 -1.1112 -0.36341 0.98679 -0.084776 -0.54008 0.11726 -1.0194 -0.24424 0.12771 0.013884 0.080374 -0.35414 0.34951 -0.7226 0.37549 0.4441 -0.99059 0.61214 -0.35111 -0.83155 0.45293 0.082577\n. -0.33979 0.20941 0.46348 -0.64792 -0.38377 0.038034 0.17127 0.15978 0.46619 -0.019169 0.41479 -0.34349 0.26872 0.04464 0.42131 -0.41032 0.15459 0.022239 -0.64653 0.25256 0.043136 -0.19445 0.46516 0.45651 0.68588 0.091295 0.21875 -0.70351 0.16785 -0.35079 -0.12634 0.66384 -0.2582 0.036542 -0.13605 0.40253 0.14289 0.38132 -0.12283 -0.45886 -0.25282 -0.30432 -0.11215 -0.26182 -0.22482 -0.44554 0.2991 -0.85612 -0.14503 -0.49086 0.0082973 -0.17491 0.27524 1.4401 -0.21239 -2.8435 -0.27958 -0.45722 1.6386 0.78808 -0.55262 0.65 0.086426 0.39012 1.0632 -0.35379 0.48328 0.346 0.84174 0.098707 -0.24213 -0.27053 0.045287 -0.40147 0.11395 0.0062226 0.036673 0.018518 -1.0213 -0.20806 0.64072 -0.068763 -0.58635 0.33476 -1.1432 -0.1148 -0.25091 -0.45907 -0.096819 -0.17946 -0.063351 -0.67412 -0.068895 0.53604 -0.87773 0.31802 -0.39242 -0.23394 0.47298 -0.028803\nof -0.1529 -0.24279 0.89837 0.16996 0.53516 0.48784 -0.58826 -0.17982 -1.3581 0.42541 0.15377 0.24215 0.13474 0.41193 0.67043 -0.56418 0.42985 -0.012183 -0.11677 0.31781 0.054177 -0.054273 0.35516 -0.30241 0.31434 -0.33846 0.71715 -0.26855 -0.15837 -0.47467 0.051581 -0.33252 0.15003 -0.1299 -0.54617 -0.37843 0.64261 0.82187 -0.080006 0.078479 -0.96976 -0.57741 0.56491 -0.39873 -0.057099 0.19743 0.065706 -0.48092 -0.20125 -0.40834 0.39456 -0.02642 -0.11838 1.012 -0.53171 -2.7474 -0.042981 -0.74849 1.7574 0.59085 0.04885 0.78267 0.38497 0.42097 0.67882 0.10337 0.6328 -0.026595 0.58647 -0.44332 0.33057 -0.12022 -0.55645 0.073611 0.20915 0.43395 -0.012761 0.089874 -1.7991 0.084808 0.77112 0.63105 -0.90685 0.60326 -1.7515 0.18596 -0.50687 -0.70203 0.66578 -0.81304 0.18712 -0.018488 -0.26757 0.727 -0.59363 -0.34839 -0.56094 -0.591 1.0039 0.20664\nto -0.1897 0.050024 0.19084 -0.049184 -0.089737 0.21006 -0.54952 0.098377 -0.20135 0.34241 -0.092677 0.161 -0.13268 -0.2816 0.18737 -0.42959 0.96039 0.13972 -1.0781 0.40518 0.50539 -0.55064 0.4844 0.38044 -0.0029055 -0.34942 -0.099696 -0.78368 1.0363 -0.2314 -0.47121 0.57126 -0.21454 0.35958 -0.48319 1.0875 0.28524 0.12447 -0.039248 -0.076732 -0.76343 -0.32409 -0.5749 -1.0893 -0.41811 0.4512 0.12112 -0.51367 -0.13349 -1.1378 -0.28768 0.16774 0.55804 1.5387 0.018859 -2.9721 -0.24216 -0.92495 2.1992 0.28234 -0.3478 0.51621 -0.43387 0.36852 0.74573 0.072102 0.27931 0.92569 -0.050336 -0.85856 -0.1358 -0.92551 -0.33991 -1.0394 -0.067203 -0.21379 -0.4769 0.21377 -0.84008 0.052536 0.59298 0.29604 -0.67644 0.13916 -1.5504 -0.20765 0.7222 0.52056 -0.076221 -0.15194 -0.13134 0.058617 -0.31869 -0.61419 -0.62393 -0.41548 -0.038175 -0.39804 0.47647 -0.15983\nand -0.071953 0.23127 0.023731 -0.50638 0.33923 0.1959 -0.32943 0.18364 -0.18057 0.28963 0.20448 -0.5496 0.27399 0.58327 0.20468 -0.49228 0.19974 -0.070237 -0.88049 0.29485 0.14071 -0.1009 0.99449 0.36973 0.44554 0.28998 -0.1376 -0.56365 -0.029365 -0.4122 -0.25269 0.63181 -0.44767 0.24363 -0.10813 0.25164 0.46967 0.3755 -0.23613 -0.14129 -0.44537 -0.65737 -0.042421 -0.28636 -0.28811 0.063766 0.20281 -0.53542 0.41307 -0.59722 -0.38614 0.19389 -0.17809 1.6618 -0.011819 -2.3737 0.058427 -0.2698 1.2823 0.81925 -0.22322 0.72932 -0.053211 0.43507 0.85011 -0.42935 0.92664 0.39051 1.0585 -0.24561 -0.18265 -0.5328 0.059518 -0.66019 0.18991 0.28836 -0.2434 0.52784 -0.65762 -0.14081 1.0491 0.5134 -0.23816 0.69895 -1.4813 -0.2487 -0.17936 -0.059137 -0.08056 -0.48782 0.014487 -0.6259 -0.32367 0.41862 -1.0807 0.46742 -0.49931 -0.71895 0.86894 0.19539\nin 0.085703 -0.22201 0.16569 0.13373 0.38239 0.35401 0.01287 0.22461 -0.43817 0.50164 -0.35874 -0.34983 0.055156 0.69648 -0.17958 0.067926 0.39101 0.16039 -0.26635 -0.21138 0.53698 0.49379 0.9366 0.66902 0.21793 -0.46642 0.22383 -0.36204 -0.17656 0.1748 -0.20367 0.13931 0.019832 -0.10413 -0.20244 0.55003 -0.1546 0.98655 -0.26863 -0.2909 -0.32866 -0.34188 -0.16943 -0.42001 -0.046727 -0.16327 0.70824 -0.74911 -0.091559 -0.96178 -0.19747 0.10282 0.55221 1.3816 -0.65636 -3.2502 -0.31556 -1.2055 1.7709 0.4026 -0.79827 1.1597 -0.33042 0.31382 0.77386 0.22595 0.52471 -0.034053 0.32048 0.079948 0.17752 -0.49426 -0.70045 -0.44569 0.17244 0.20278 0.023292 -0.20677 -1.0158 0.18325 0.56752 0.31821 -0.65011 0.68277 -0.86585 -0.059392 -0.29264 -0.55668 -0.34705 -0.32895 0.40215 -0.12746 -0.20228 0.87368 -0.545 0.79205 -0.20695 -0.074273 0.75808 -0.34243\na -0.27086 0.044006 -0.02026 -0.17395 0.6444 0.71213 0.3551 0.47138 -0.29637 0.54427 -0.72294 -0.0047612 0.040611 0.043236 0.29729 0.10725 0.40156 -0.53662 0.033382 0.067396 0.64556 -0.085523 0.14103 0.094539 0.74947 -0.194 -0.68739 -0.41741 -0.22807 0.12 -0.48999 0.80945 0.045138 -0.11898 0.20161 0.39276 -0.20121 0.31354 0.75304 0.25907 -0.11566 -0.029319 0.93499 -0.36067 0.5242 0.23706 0.52715 0.22869 -0.51958 -0.79349 -0.20368 -0.50187 0.18748 0.94282 -0.44834 -3.6792 0.044183 -0.26751 2.1997 0.241 -0.033425 0.69553 -0.64472 -0.0072277 0.89575 0.20015 0.46493 0.61933 -0.1066 0.08691 -0.4623 0.18262 -0.15849 0.020791 0.19373 0.063426 -0.31673 -0.48177 -1.3848 0.13669 0.96859 0.049965 -0.2738 -0.035686 -1.0577 -0.24467 0.90366 -0.12442 0.080776 -0.83401 0.57201 0.088945 -0.42532 -0.018253 -0.079995 -0.28581 -0.01089 -0.4923 0.63687 0.23642\n\" -0.30457 -0.23645 0.17576 -0.72854 -0.28343 -0.2564 0.26587 0.025309 -0.074775 -0.3766 -0.057774 0.12159 0.34384 0.41928 -0.23236 -0.31547 0.60939 0.25117 -0.68667 0.70873 1.2162 -0.1824 -0.48442 -0.33445 0.30343 1.086 0.49992 -0.20198 0.27959 0.68352 -0.33566 -0.12405 0.059656 0.33617 0.37501 0.56552 0.44867 0.11284 -0.16196 -0.94346 -0.67961 0.18581 0.060653 0.43776 0.13834 -0.48207 -0.56141 -0.25422 -0.52445 0.097003 -0.48925 0.19077 0.21481 1.4969 -0.86665 -3.2846 0.56854 0.41971 1.2294 0.78522 -0.29369 0.63803 -1.5926 -0.20437 1.5306 0.13548 0.50722 0.18742 0.48552 -0.28995 0.19573 0.0046515 0.092879 -0.42444 0.64987 0.52839 0.077908 0.8263 -1.2208 -0.34955 0.49855 -0.64155 -0.72308 0.26566 -1.3643 -0.46364 -0.52048 -1.0525 0.22895 -0.3456 -0.658 -0.16735 0.35158 0.74337 0.26074 0.061104 -0.39079 -0.84557 -0.035432 0.17036\n's 0.58854 -0.2025 0.73479 -0.68338 -0.19675 -0.1802 -0.39177 0.34172 -0.60561 0.63816 -0.26695 0.36486 -0.40379 -0.1134 -0.58718 0.2838 0.8025 -0.35303 0.30083 0.078935 0.44416 -0.45906 0.79294 0.50365 0.32805 0.28027 -0.4933 -0.38482 -0.039284 -0.2483 -0.1988 1.1469 0.13228 0.91691 -0.36739 0.89425 0.5426 0.61738 -0.62205 -0.31132 -0.50933 0.23335 1.0826 -0.044637 -0.12767 0.27628 -0.032617 -0.27397 0.77764 -0.50861 0.038307 -0.33679 0.42344 1.2271 -0.53826 -3.2411 0.42626 0.025189 1.3948 0.65085 0.03325 0.37141 0.4044 0.35558 0.98265 -0.61724 0.53901 0.76219 0.30689 0.33065 0.30956 -0.15161 -0.11313 -0.81281 0.6145 -0.44341 -0.19163 -0.089551 -1.5927 0.37405 0.85857 0.54613 -0.31928 0.52598 -1.4802 -0.97931 -0.2939 -0.14724 0.25803 -0.1817 1.0149 0.77649 0.12598 0.54779 -1.0316 0.064599 -0.37523 -0.94475 0.61802 0.39591\nfor -0.14401 0.32554 0.14257 -0.099227 0.72536 0.19321 -0.24188 0.20223 -0.89599 0.15215 0.035963 -0.59513 -0.051635 -0.014428 0.35475 -0.31859 0.76984 -0.087369 -0.24762 0.65059 -0.15138 -0.42703 0.18813 0.091562 0.15192 0.11303 -0.15222 -0.62786 -0.23923 0.096009 -0.46147 0.41526 -0.30475 0.1371 0.16758 0.53301 -0.043658 0.85924 -0.41192 -0.21394 -0.51228 -0.31945 0.12662 -0.3151 0.0031429 0.27129 0.17328 -1.3159 -0.42414 -0.69126 0.019017 -0.13375 -0.096057 1.7069 -0.65291 -2.6111 0.26518 -0.61178 2.095 0.38148 -0.55823 0.2036 -0.33704 0.37354 0.6951 -0.001637 0.81885 0.51793 0.27746 -0.37177 -0.43345 -0.42732 -0.54912 -0.30715 0.18101 0.2709 -0.29266 0.30834 -1.4624 -0.18999 0.92277 -0.099217 -0.25165 0.49197 -1.525 0.15326 0.2827 0.12102 -0.36766 -0.61275 -0.18884 0.10907 0.12315 0.090066 -0.65447 -0.17252 2.6336e-05 0.25398 1.1078 -0.073074\n- -1.2557 0.61036 0.56793 -0.96596 -0.45249 -0.071696 0.57122 -0.31292 -0.43814 0.90622 0.06961 -0.053104 0.25029 0.27841 0.77724 0.26329 0.56874 -1.1171 -0.078268 -0.51317 0.8071 0.99214 0.22753 1.0847 0.88292 0.17221 -0.68686 -0.86467 -0.80003 -0.34738 -0.044074 -0.30444 0.23406 0.28592 0.060548 -0.65477 -0.039738 0.74878 -0.46471 0.063023 -0.16519 -1.2217 -0.089479 -0.8125 0.27615 -0.13841 -0.76667 -0.96974 0.83123 -0.77639 -1.3327 -0.28732 -0.053684 1.1735 -1.1795 -2.7519 0.45359 1.1984 2.8203 0.060114 0.32296 0.19097 0.3459 -0.41503 0.1515 0.38148 1.619 0.9929 -0.82549 -0.098692 0.74449 -0.38602 -1.0004 -1.305 -0.31269 -0.57625 0.14095 -0.80269 -1.4714 -0.48014 1.1993 -0.48561 0.40496 -0.032867 -2.051 0.18284 -0.2723 0.043287 0.066801 -0.62832 -0.05854 0.28253 -0.083276 -0.022234 -0.55914 0.24586 0.36052 -1.5877 0.76984 -0.64998\nthat -0.093337 0.19043 0.68457 -0.41548 -0.22777 -0.11803 -0.095434 0.19613 0.17785 -0.020244 -0.055409 0.33867 0.79396 -0.047126 0.44281 -0.061266 0.20796 0.034094 -0.64751 0.35874 0.13936 -0.6831 0.25596 -0.12911 0.2608 -0.11674 0.024925 -0.60259 -0.41474 -0.51104 0.14936 0.79977 -0.12716 0.40474 -0.21435 0.47031 0.49 0.48886 -0.17772 -0.18861 -0.78391 -0.14158 0.22169 -0.22078 -0.30509 -0.10837 0.57168 -0.7832 -0.16328 -0.76131 0.080873 0.00067217 0.44713 1.3434 -0.20014 -2.868 -0.002647 -0.39858 1.8379 1.2211 -0.16066 0.65853 0.26946 0.27212 0.94735 0.24372 0.8194 0.6774 0.063485 -0.55934 0.45541 -0.64684 -0.034702 -0.45566 0.21847 -0.051689 0.32299 -0.022961 -1.7955 0.31217 0.76227 -0.23191 -1.0133 -0.0064374 -1.8135 -0.75221 0.28362 -0.30815 -0.43853 -0.62654 0.13213 -0.54725 -0.47478 -0.0079727 -0.15112 -0.29326 -0.35118 -0.68175 0.28804 0.54893\non -0.21863 -0.42664 0.5196 0.0043103 0.58045 -0.10873 -0.37726 0.4566 -0.60627 -0.075773 0.11306 0.17703 0.1605 0.074514 0.63649 -0.078852 0.75268 -0.24962 -0.51628 -0.33348 0.66754 -0.34183 0.61316 0.31668 0.64846 -0.079312 -0.065219 -0.17718 -0.32439 0.51868 -0.23424 0.34381 0.046851 0.74025 -0.47005 0.53685 -0.35549 0.40737 -0.093421 -0.13439 -0.41969 -0.30041 0.28646 0.37419 -0.46054 -0.307 -0.3858 -0.69317 -0.00092461 -0.61984 0.11978 0.1495 0.17833 1.5313 -0.92445 -3.0428 0.030761 -0.64359 2.3824 0.56219 -0.56021 1.0264 -0.45143 0.14117 0.65944 0.37572 0.098334 0.38304 -0.076882 -0.21781 -0.29892 -0.49458 0.095239 -0.63059 -0.061311 0.17767 -0.14051 0.47182 -0.95891 0.045334 0.808 -0.026867 -0.27483 0.35541 -0.82896 -0.78838 -0.079732 0.22941 -0.45013 -0.3004 -0.52716 0.11358 -0.49906 0.827 -0.56991 0.25143 -0.40266 -0.29146 1.3816 0.18084\nis -0.54264 0.41476 1.0322 -0.40244 0.46691 0.21816 -0.074864 0.47332 0.080996 -0.22079 -0.12808 -0.1144 0.50891 0.11568 0.028211 -0.3628 0.43823 0.047511 0.20282 0.49857 -0.10068 0.13269 0.16972 0.11653 0.31355 0.25713 0.092783 -0.56826 -0.52975 -0.051456 -0.67326 0.92533 0.2693 0.22734 0.66365 0.26221 0.19719 0.2609 0.18774 -0.3454 -0.42635 0.13975 0.56338 -0.56907 0.12398 -0.12894 0.72484 -0.26105 -0.26314 -0.43605 0.078908 -0.84146 0.51595 1.3997 -0.7646 -3.1453 -0.29202 -0.31247 1.5129 0.52435 0.21456 0.42452 -0.088411 -0.17805 1.1876 0.10579 0.76571 0.21914 0.35824 -0.11636 0.093261 -0.62483 -0.21898 0.21796 0.74056 -0.43735 0.14343 0.14719 -1.1605 -0.050508 0.12677 -0.014395 -0.98676 -0.091297 -1.2054 -0.11974 0.047847 -0.54001 0.52457 -0.70963 -0.32528 -0.1346 -0.41314 0.33435 -0.0072412 0.32253 -0.044219 -1.2969 0.76217 0.46349\nwas 0.13717 -0.54287 0.19419 -0.29953 0.17545 0.084672 0.67752 0.098295 -0.035611 0.21334 0.51663 0.20687 0.44082 -0.33655 0.56025 -0.6879 0.51957 -0.21258 -0.52708 -0.12249 0.33099 0.026448 0.59007 0.0065469 0.45405 -0.33884 -0.28261 -0.24633 0.10847 0.3164 -0.15368 0.73503 0.11858 0.70842 0.075081 0.29738 -0.11395 0.40807 -0.042531 -0.21301 -0.79849 -0.12703 0.752 -0.41746 0.46615 -0.039097 0.65961 -0.32336 0.442 -0.94137 -0.23125 -0.30604 0.79912 1.4581 -0.88199 -3.0041 -0.75243 -0.20503 1.1998 0.94881 0.30649 0.48411 -0.7572 0.65856 0.70107 -0.93141 0.52928 0.23323 0.18857 0.38691 0.011489 -0.31937 0.011858 0.22944 0.17764 0.16868 0.14003 0.58647 -1.5447 -0.064425 -0.00064711 0.13606 -0.32695 0.10043 -1.546 -0.5476 0.21027 -0.67195 -0.1597 -0.68271 -0.22043 -0.87088 -0.16248 0.83086 -0.23045 0.19864 -0.051892 -0.52057 0.25434 -0.23759\nsaid -0.13128 -0.452 0.043399 -0.99798 -0.21053 -0.95868 -0.24609 0.48413 0.18178 0.475 -0.22305 0.30064 0.43496 -0.3605 0.20245 -0.52594 -0.34708 0.0075873 -1.0497 0.18673 0.57369 0.43814 0.098659 0.3877 -0.2258 0.41911 0.043602 -0.7352 -0.53583 0.19276 -0.21961 0.42515 -0.19082 0.47187 0.18826 0.13357 0.41839 1.3138 0.35678 -0.32172 -1.2257 -0.26635 0.36716 -0.27586 -0.53246 0.16786 -0.11253 -0.99959 -0.60706 -0.89271 0.65156 -0.88784 0.049233 0.67111 -0.27553 -2.4005 -0.36989 0.29136 1.3498 1.7353 0.27 0.021299 0.14422 0.023784 0.33643 -0.35476 1.0921 1.4845 0.4943 0.15688 0.34679 -0.57221 0.12093 -1.2616 1.0541 0.064335 -0.002732 0.19038 -1.7643 0.055068 1.4737 -0.41782 -0.57342 -0.12129 -1.3169 -0.73883 0.17682 -0.019991 -0.49176 -0.55247 1.0623 -0.62879 0.29098 0.13238 -0.70414 0.67128 -0.085462 -0.30526 -0.045495 0.56509\nwith -0.43608 0.39104 0.51657 -0.13861 0.2029 0.50723 -0.012544 0.22948 -0.6316 0.21199 -0.018043 -0.39364 0.74164 0.30221 0.51792 -0.25191 0.25373 -0.65184 -0.42963 0.0093622 0.023334 -0.39245 0.34948 0.21217 0.7346 -0.21962 -0.028611 -0.34641 -0.20934 -0.27091 -0.17637 0.82396 -0.082339 -0.034869 0.079722 0.34841 0.60887 0.22811 -0.29633 0.18633 0.234 -0.70966 0.16312 -0.20857 0.092369 -0.075435 -0.13905 -0.35121 -0.19972 -0.41687 -0.31485 0.16123 0.038882 1.6654 -0.12401 -3.3419 0.10929 -0.026199 1.244 0.84374 -0.15679 0.79041 -0.042433 0.18884 0.064345 -0.11683 1.0467 0.71813 0.57834 0.27014 -0.50908 -0.083995 -0.1437 -0.76408 0.27418 0.56814 -0.39375 -0.32558 -0.92854 -0.13098 1.3277 0.11851 -0.15551 0.5972 -1.084 -0.058137 0.23886 0.14558 -0.59303 -0.47511 -0.22064 -0.37591 -0.79649 0.013465 -0.44595 -0.34623 -0.75398 -0.3517 0.99456 0.088196\nhe 0.1225 -0.058833 0.23658 -0.28877 -0.028181 0.31524 0.070229 0.16447 -0.027623 0.25214 0.21174 -0.059674 0.36133 0.13607 0.18755 -0.1487 0.31315 0.13368 -0.59703 -0.030161 0.080656 0.26162 -0.055924 -0.35351 0.34722 -0.0055801 -0.57935 -0.88007 0.42931 -0.15695 -0.51256 1.2684 -0.25228 0.35265 -0.46419 0.55648 -0.57556 0.32574 -0.21893 -0.13178 -1.1027 -0.039591 0.89643 -0.9845 -0.47393 -0.12855 0.63506 -0.94888 0.40088 -0.77542 -0.35153 -0.27788 0.68747 1.458 -0.38474 -2.8937 -0.29523 -0.38836 0.94881 1.3891 0.054591 0.70486 -0.65699 0.075648 0.7655 -0.63365 0.86556 0.42441 0.14796 0.4156 0.29354 -0.51295 0.19635 -0.45568 0.0080246 0.14528 -0.15395 0.11406 -1.2167 -0.1111 0.8264 0.21738 -0.63776 -0.074874 -1.713 -0.8827 -0.0073058 -0.37623 -0.50209 -0.58844 -0.24943 -1.0425 0.27678 0.64142 -0.64605 0.43559 -0.37276 -0.0032068 0.18744 0.30702\nas -0.32721 0.096446 0.34244 -0.44327 0.30535 -0.042016 -0.071235 -0.31036 -0.22557 -0.181 -0.29088 -0.61542 0.29751 0.030491 0.41504 -0.51489 0.68628 -0.020302 -0.18486 0.31605 0.59472 -0.2147 0.29256 0.43262 0.35466 -0.29659 -0.27086 -0.48953 -0.047391 0.24521 -0.15783 0.59742 -0.41664 0.057632 0.1233 0.62326 -0.08844 0.3077 -0.15742 -0.28381 -0.58058 -0.022824 0.26689 -0.22565 0.47548 0.11134 0.37263 -0.14554 -0.16775 -0.79377 -0.30593 -0.10671 0.44199 1.5698 -0.73062 -2.7314 -0.19366 -0.32983 1.2881 0.62126 -0.255 0.8416 -0.23658 0.42594 0.86589 -0.35904 0.78162 0.20396 0.82898 0.0016123 -0.24008 -0.72735 -0.053671 -0.22264 0.31034 -0.21243 -0.14335 0.317 -0.80478 -0.49311 0.88023 -0.24147 -0.3922 0.15997 -1.5854 -0.25824 0.052834 -0.11983 -0.018874 -0.77356 0.049285 -0.25332 -0.3073 0.51295 -0.56802 -0.21239 -0.39741 -0.38165 0.43994 0.24683\n"
  },
  {
    "path": "onmt/tests/test_attention.py",
    "content": "\"\"\"\nHere come the tests for attention types and their compatibility\n\"\"\"\nimport unittest\nimport torch\nfrom torch.autograd import Variable\n\nimport onmt\n\n\nclass TestAttention(unittest.TestCase):\n\n    def test_masked_global_attention(self):\n\n        source_lengths = torch.IntTensor([7, 3, 5, 2])\n        # illegal_weights_mask = torch.ByteTensor([\n        #     [0, 0, 0, 0, 0, 0, 0],\n        #     [0, 0, 0, 1, 1, 1, 1],\n        #     [0, 0, 0, 0, 0, 1, 1],\n        #     [0, 0, 1, 1, 1, 1, 1]])\n\n        batch_size = source_lengths.size(0)\n        dim = 20\n\n        memory_bank = Variable(torch.randn(batch_size,\n                                           source_lengths.max(), dim))\n        hidden = Variable(torch.randn(batch_size, dim))\n\n        attn = onmt.modules.GlobalAttention(dim)\n\n        _, alignments = attn(hidden, memory_bank,\n                             memory_lengths=source_lengths)\n        # TODO: fix for pytorch 0.3\n        # illegal_weights = alignments.masked_select(illegal_weights_mask)\n\n        # self.assertEqual(0.0, illegal_weights.data.sum())\n"
  },
  {
    "path": "onmt/tests/test_beam.py",
    "content": "import unittest\nfrom onmt.translate.beam import Beam, GNMTGlobalScorer\n\nimport torch\n\n\nclass GlobalScorerStub(object):\n    def update_global_state(self, beam):\n        pass\n\n    def score(self, beam, scores):\n        return scores\n\n\nclass TestBeam(unittest.TestCase):\n    BLOCKED_SCORE = -10e20\n\n    def test_advance_with_all_repeats_gets_blocked(self):\n        # all beams repeat (beam >= 1 repeat dummy scores)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47\n        ngram_repeat = 3\n        beam = Beam(beam_sz, 0, 1, 2, n_best=2,\n                    exclusion_tokens=set(),\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=ngram_repeat)\n        for i in range(ngram_repeat + 4):\n            # predict repeat_idx over and over again\n            word_probs = torch.full((beam_sz, n_words), -float('inf'))\n            word_probs[0, repeat_idx] = 0\n            attns = torch.randn(beam_sz)\n            beam.advance(word_probs, attns)\n            if i <= ngram_repeat:\n                self.assertTrue(\n                    beam.scores.equal(\n                        torch.tensor(\n                            [0] + [-float('inf')] * (beam_sz - 1))))\n            else:\n                self.assertTrue(\n                    beam.scores.equal(torch.tensor(\n                        [self.BLOCKED_SCORE] * beam_sz)))\n\n    def test_advance_with_some_repeats_gets_blocked(self):\n        # beam 0 and beam >=2 will repeat (beam >= 2 repeat dummy scores)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47\n        ngram_repeat = 3\n        beam = Beam(beam_sz, 0, 1, 2, n_best=2,\n                    exclusion_tokens=set(),\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=ngram_repeat)\n        for i in range(ngram_repeat + 4):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full((beam_sz, n_words), -float('inf'))\n            if i == 0:\n                # on initial round, only predicted scores for beam 0\n                # matter. Make two predictions. Top one will be repeated\n                # in beam zero, second one will live on in beam 1.\n                word_probs[0, repeat_idx] = -0.1\n                word_probs[0, repeat_idx + i + 1] = -2.3\n            else:\n                # predict the same thing in beam 0\n                word_probs[0, repeat_idx] = 0\n                # continue pushing around what beam 1 predicts\n                word_probs[1, repeat_idx + i + 1] = 0\n            attns = torch.randn(beam_sz)\n            beam.advance(word_probs, attns)\n            if i <= ngram_repeat:\n                self.assertFalse(beam.scores[0].eq(self.BLOCKED_SCORE))\n                self.assertFalse(beam.scores[1].eq(self.BLOCKED_SCORE))\n            else:\n                # now beam 0 dies (along with the others), beam 1 -> beam 0\n                self.assertFalse(beam.scores[0].eq(self.BLOCKED_SCORE))\n                self.assertTrue(\n                    beam.scores[1:].equal(torch.tensor(\n                        [self.BLOCKED_SCORE] * (beam_sz - 1))))\n\n    def test_repeating_excluded_index_does_not_die(self):\n        # beam 0 and beam >= 2 will repeat (beam 2 repeats excluded idx)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47  # will be repeated and should be blocked\n        repeat_idx_ignored = 7  # will be repeated and should not be blocked\n        ngram_repeat = 3\n        beam = Beam(beam_sz, 0, 1, 2, n_best=2,\n                    exclusion_tokens=set([repeat_idx_ignored]),\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=ngram_repeat)\n        for i in range(ngram_repeat + 4):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full((beam_sz, n_words), -float('inf'))\n            if i == 0:\n                word_probs[0, repeat_idx] = -0.1\n                word_probs[0, repeat_idx + i + 1] = -2.3\n                word_probs[0, repeat_idx_ignored] = -5.0\n            else:\n                # predict the same thing in beam 0\n                word_probs[0, repeat_idx] = 0\n                # continue pushing around what beam 1 predicts\n                word_probs[1, repeat_idx + i + 1] = 0\n                # predict the allowed-repeat again in beam 2\n                word_probs[2, repeat_idx_ignored] = 0\n            attns = torch.randn(beam_sz)\n            beam.advance(word_probs, attns)\n            if i <= ngram_repeat:\n                self.assertFalse(beam.scores[0].eq(self.BLOCKED_SCORE))\n                self.assertFalse(beam.scores[1].eq(self.BLOCKED_SCORE))\n                self.assertFalse(beam.scores[2].eq(self.BLOCKED_SCORE))\n            else:\n                # now beam 0 dies, beam 1 -> beam 0, beam 2 -> beam 1\n                # and the rest die\n                self.assertFalse(beam.scores[0].eq(self.BLOCKED_SCORE))\n                # since all preds after i=0 are 0, we can check\n                # that the beam is the correct idx by checking that\n                # the curr score is the initial score\n                self.assertTrue(beam.scores[0].eq(-2.3))\n                self.assertFalse(beam.scores[1].eq(self.BLOCKED_SCORE))\n                self.assertTrue(beam.scores[1].eq(-5.0))\n                self.assertTrue(\n                    beam.scores[2:].equal(torch.tensor(\n                        [self.BLOCKED_SCORE] * (beam_sz - 2))))\n\n    def test_doesnt_predict_eos_if_shorter_than_min_len(self):\n        # beam 0 will always predict EOS. The other beams will predict\n        # non-eos scores.\n        # this is also a test that when block_ngram_repeat=0,\n        # repeating is acceptable\n        beam_sz = 5\n        n_words = 100\n        _non_eos_idxs = [47, 51, 13, 88, 99]\n        valid_score_dist = torch.log_softmax(torch.tensor(\n            [6., 5., 4., 3., 2., 1.]), dim=0)\n        min_length = 5\n        eos_idx = 2\n        beam = Beam(beam_sz, 0, 1, eos_idx, n_best=2,\n                    exclusion_tokens=set(),\n                    min_length=min_length,\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=0)\n        for i in range(min_length + 4):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full((beam_sz, n_words), -float('inf'))\n            if i == 0:\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                # include at least beam_sz predictions OTHER than EOS\n                # that are greater than -1e20\n                for j, score in zip(_non_eos_idxs, valid_score_dist[1:]):\n                    word_probs[0, j] = score\n            else:\n                # predict eos in beam 0\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz-1, k)\n                    word_probs[beam_idx, j] = score\n\n            attns = torch.randn(beam_sz)\n            beam.advance(word_probs, attns)\n            if i < min_length:\n                expected_score_dist = (i+1) * valid_score_dist[1:]\n                self.assertTrue(beam.scores.allclose(expected_score_dist))\n            elif i == min_length:\n                # now the top beam has ended and no others have\n                # first beam finished had length beam.min_length\n                self.assertEqual(beam.finished[0][1], beam.min_length + 1)\n                # first beam finished was 0\n                self.assertEqual(beam.finished[0][2], 0)\n            else:  # i > min_length\n                # not of interest, but want to make sure it keeps running\n                # since only beam 0 terminates and n_best = 2\n                pass\n\n    def test_beam_is_done_when_n_best_beams_eos_using_min_length(self):\n        # this is also a test that when block_ngram_repeat=0,\n        # repeating is acceptable\n        beam_sz = 5\n        n_words = 100\n        _non_eos_idxs = [47, 51, 13, 88, 99]\n        valid_score_dist = torch.log_softmax(torch.tensor(\n            [6., 5., 4., 3., 2., 1.]), dim=0)\n        min_length = 5\n        eos_idx = 2\n        beam = Beam(beam_sz, 0, 1, eos_idx, n_best=2,\n                    exclusion_tokens=set(),\n                    min_length=min_length,\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=0)\n        for i in range(min_length + 4):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full((beam_sz, n_words), -float('inf'))\n            if i == 0:\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                # include at least beam_sz predictions OTHER than EOS\n                # that are greater than -1e20\n                for j, score in zip(_non_eos_idxs, valid_score_dist[1:]):\n                    word_probs[0, j] = score\n            elif i <= min_length:\n                # predict eos in beam 1\n                word_probs[1, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz-1, k)\n                    word_probs[beam_idx, j] = score\n            else:\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                word_probs[1, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz-1, k)\n                    word_probs[beam_idx, j] = score\n\n            attns = torch.randn(beam_sz)\n            beam.advance(word_probs, attns)\n            if i < min_length:\n                self.assertFalse(beam.done)\n            elif i == min_length:\n                # beam 1 dies on min_length\n                self.assertEqual(beam.finished[0][1], beam.min_length + 1)\n                self.assertEqual(beam.finished[0][2], 1)\n                self.assertFalse(beam.done)\n            else:  # i > min_length\n                # beam 0 dies on the step after beam 1 dies\n                self.assertEqual(beam.finished[1][1], beam.min_length + 2)\n                self.assertEqual(beam.finished[1][2], 0)\n                self.assertTrue(beam.done)\n\n\nclass TestBeamAgainstReferenceCase(unittest.TestCase):\n    BEAM_SZ = 5\n    EOS_IDX = 2  # don't change this - all the scores would need updated\n    N_WORDS = 8  # also don't change for same reason\n    N_BEST = 3\n    DEAD_SCORE = -1e20\n    INP_SEQ_LEN = 53\n\n    def init_step(self, beam):\n        # init_preds: [4, 3, 5, 6, 7] - no EOS's\n        init_scores = torch.log_softmax(torch.tensor(\n            [[0, 0, 0, 4, 5, 3, 2, 1]], dtype=torch.float), dim=1)\n        expected_beam_scores, expected_preds_0 = init_scores.topk(self.BEAM_SZ)\n        beam.advance(init_scores, torch.randn(self.BEAM_SZ, self.INP_SEQ_LEN))\n        self.assertTrue(beam.scores.allclose(expected_beam_scores))\n        self.assertTrue(beam.next_ys[-1].equal(expected_preds_0[0]))\n        self.assertFalse(beam.eos_top)\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def first_step(self, beam, expected_beam_scores, expected_len_pen):\n        # no EOS's yet\n        assert len(beam.finished) == 0\n        scores_1 = torch.log_softmax(torch.tensor(\n            [[0, 0,  0, .3,   0, .51, .2, 0],\n             [0, 0, 1.5,  0,   0,   0,  0, 0],\n             [0, 0,  0,  0, .49, .48,  0, 0],\n             [0, 0, 0, .2, .2, .2, .2, .2],\n             [0, 0, 0, .2, .2, .2, .2, .2]]\n        ), dim=1)\n\n        beam.advance(scores_1, torch.randn(self.BEAM_SZ, self.INP_SEQ_LEN))\n\n        new_scores = scores_1 + expected_beam_scores.t()\n        expected_beam_scores, unreduced_preds = new_scores.view(-1).topk(\n            self.BEAM_SZ, 0, True, True)\n        expected_bptr_1 = unreduced_preds / self.N_WORDS\n        # [5, 3, 2, 6, 0], so beam 2 predicts EOS!\n        expected_preds_1 = unreduced_preds - expected_bptr_1 * self.N_WORDS\n\n        self.assertTrue(beam.scores.allclose(expected_beam_scores))\n        self.assertTrue(beam.next_ys[-1].equal(expected_preds_1))\n        self.assertTrue(beam.prev_ks[-1].equal(expected_bptr_1))\n        self.assertEqual(len(beam.finished), 1)\n        self.assertEqual(beam.finished[0][2], 2)  # beam 2 finished\n        self.assertEqual(beam.finished[0][1], 2)  # finished on second step\n        self.assertEqual(beam.finished[0][0],  # finished with correct score\n                         expected_beam_scores[2] / expected_len_pen)\n        self.assertFalse(beam.eos_top)\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def second_step(self, beam, expected_beam_scores, expected_len_pen):\n        # assumes beam 2 finished on last step\n        scores_2 = torch.log_softmax(torch.tensor(\n            [[0, 0,  0, .3,   0, .51, .2, 0],\n             [0, 0, 0,  0,   0,   0,  0, 0],\n             [0, 0,  0,  0, 5000, .48,  0, 0],  # beam 2 shouldn't continue\n             [0, 0, 50, .2, .2, .2, .2, .2],  # beam 3 -> beam 0 should die\n             [0, 0, 0, .2, .2, .2, .2, .2]]\n        ), dim=1)\n\n        beam.advance(scores_2, torch.randn(self.BEAM_SZ, self.INP_SEQ_LEN))\n\n        new_scores = scores_2 + expected_beam_scores.unsqueeze(1)\n        new_scores[2] = self.DEAD_SCORE  # ended beam 2 shouldn't continue\n        expected_beam_scores, unreduced_preds = new_scores.view(-1).topk(\n            self.BEAM_SZ, 0, True, True)\n        expected_bptr_2 = unreduced_preds / self.N_WORDS\n        # [2, 5, 3, 6, 0], so beam 0 predicts EOS!\n        expected_preds_2 = unreduced_preds - expected_bptr_2 * self.N_WORDS\n        # [-2.4879, -3.8910, -4.1010, -4.2010, -4.4010]\n        self.assertTrue(beam.scores.allclose(expected_beam_scores))\n        self.assertTrue(beam.next_ys[-1].equal(expected_preds_2))\n        self.assertTrue(beam.prev_ks[-1].equal(expected_bptr_2))\n        self.assertEqual(len(beam.finished), 2)\n        # new beam 0 finished\n        self.assertEqual(beam.finished[1][2], 0)\n        # new beam 0 is old beam 3\n        self.assertEqual(expected_bptr_2[0], 3)\n        self.assertEqual(beam.finished[1][1], 3)  # finished on third step\n        self.assertEqual(beam.finished[1][0],  # finished with correct score\n                         expected_beam_scores[0] / expected_len_pen)\n        self.assertTrue(beam.eos_top)\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def third_step(self, beam, expected_beam_scores, expected_len_pen):\n        # assumes beam 0 finished on last step\n        scores_3 = torch.log_softmax(torch.tensor(\n            [[0, 0,  5000, 0,   5000, .51, .2, 0],  # beam 0 shouldn't cont\n             [0, 0, 0,  0,   0,   0,  0, 0],\n             [0, 0,  0,  0, 0, 5000,  0, 0],\n             [0, 0, 0, .2, .2, .2, .2, .2],\n             [0, 0, 50, 0, .2, .2, .2, .2]]  # beam 4 -> beam 1 should die\n        ), dim=1)\n\n        beam.advance(scores_3, torch.randn(self.BEAM_SZ, self.INP_SEQ_LEN))\n\n        new_scores = scores_3 + expected_beam_scores.unsqueeze(1)\n        new_scores[0] = self.DEAD_SCORE  # ended beam 2 shouldn't continue\n        expected_beam_scores, unreduced_preds = new_scores.view(-1).topk(\n            self.BEAM_SZ, 0, True, True)\n        expected_bptr_3 = unreduced_preds / self.N_WORDS\n        # [5, 2, 6, 1, 0], so beam 1 predicts EOS!\n        expected_preds_3 = unreduced_preds - expected_bptr_3 * self.N_WORDS\n        self.assertTrue(beam.scores.allclose(expected_beam_scores))\n        self.assertTrue(beam.next_ys[-1].equal(expected_preds_3))\n        self.assertTrue(beam.prev_ks[-1].equal(expected_bptr_3))\n        self.assertEqual(len(beam.finished), 3)\n        # new beam 1 finished\n        self.assertEqual(beam.finished[2][2], 1)\n        # new beam 1 is old beam 4\n        self.assertEqual(expected_bptr_3[1], 4)\n        self.assertEqual(beam.finished[2][1], 4)  # finished on fourth step\n        self.assertEqual(beam.finished[2][0],  # finished with correct score\n                         expected_beam_scores[1] / expected_len_pen)\n        self.assertTrue(beam.eos_top)\n        self.assertTrue(beam.done)\n        return expected_beam_scores\n\n    def test_beam_advance_against_known_reference(self):\n        beam = Beam(self.BEAM_SZ, 0, 1, self.EOS_IDX, n_best=self.N_BEST,\n                    exclusion_tokens=set(),\n                    min_length=0,\n                    global_scorer=GlobalScorerStub(),\n                    block_ngram_repeat=0)\n\n        expected_beam_scores = self.init_step(beam)\n        expected_beam_scores = self.first_step(beam, expected_beam_scores, 1)\n        expected_beam_scores = self.second_step(beam, expected_beam_scores, 1)\n        self.third_step(beam, expected_beam_scores, 1)\n\n\nclass TestBeamWithLengthPenalty(TestBeamAgainstReferenceCase):\n    # this could be considered an integration test because it tests\n    # interactions between the GNMT scorer and the beam\n\n    def test_beam_advance_against_known_reference(self):\n        scorer = GNMTGlobalScorer(0.7, 0., \"avg\", \"none\")\n        beam = Beam(self.BEAM_SZ, 0, 1, self.EOS_IDX, n_best=self.N_BEST,\n                    exclusion_tokens=set(),\n                    min_length=0,\n                    global_scorer=scorer,\n                    block_ngram_repeat=0)\n        expected_beam_scores = self.init_step(beam)\n        expected_beam_scores = self.first_step(beam, expected_beam_scores, 3)\n        expected_beam_scores = self.second_step(beam, expected_beam_scores, 4)\n        self.third_step(beam, expected_beam_scores, 5)\n"
  },
  {
    "path": "onmt/tests/test_beam_search.py",
    "content": "import unittest\nfrom onmt.translate.beam_search import BeamSearch, GNMTGlobalScorer\nfrom onmt.translate.beam_search import BeamSearchLM\n\nfrom copy import deepcopy\n\nimport torch\n\n\nclass GlobalScorerStub(object):\n    alpha = 0\n    beta = 0\n\n    def __init__(self):\n        self.length_penalty = lambda x, alpha: 1.\n        self.cov_penalty = lambda cov, beta: torch.zeros(\n            (1, cov.shape[-2]), device=cov.device, dtype=torch.float)\n        self.has_cov_pen = False\n        self.has_len_pen = False\n\n    def update_global_state(self, beam):\n        pass\n\n    def score(self, beam, scores):\n        return scores\n\n\nclass TestBeamSearch(unittest.TestCase):\n    BLOCKED_SCORE = -10e20\n\n    def test_advance_with_all_repeats_gets_blocked(self):\n        # all beams repeat (beam >= 1 repeat dummy scores)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47\n        ngram_repeat = 3\n        device_init = torch.zeros(1, 1)\n        for batch_sz in [1, 3]:\n            beam = BeamSearch(\n                beam_sz, batch_sz, 0, 1, 2, 2,\n                GlobalScorerStub(), 0, 30,\n                False, ngram_repeat, set(),\n                False, 0.)\n            beam.initialize(device_init, torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                # predict repeat_idx over and over again\n                word_probs = torch.full(\n                    (batch_sz * beam_sz, n_words), -float('inf'))\n                word_probs[0::beam_sz, repeat_idx] = 0\n\n                attns = torch.randn(1, batch_sz * beam_sz, 53)\n                beam.advance(word_probs, attns)\n\n                if i < ngram_repeat:\n                    # before repeat, scores are either 0 or -inf\n                    expected_scores = torch.tensor(\n                        [0] + [-float('inf')] * (beam_sz - 1))\\\n                        .repeat(batch_sz, 1)\n                    self.assertTrue(beam.topk_log_probs.equal(expected_scores))\n                elif i % ngram_repeat == 0:\n                    # on repeat, `repeat_idx` score is BLOCKED_SCORE\n                    # (but it's still the best score, thus we have\n                    # [BLOCKED_SCORE, -inf, -inf, -inf, -inf]\n                    expected_scores = torch.tensor(\n                        [self.BLOCKED_SCORE] + [-float('inf')] * (beam_sz - 1)\n                    ).repeat(batch_sz, 1)\n                    self.assertTrue(beam.topk_log_probs.equal(expected_scores))\n                else:\n                    # repetitions keeps maximizing score\n                    # index 0 has been blocked, so repeating=>+0.0 score\n                    # other indexes are -inf so repeating=>BLOCKED_SCORE\n                    # which is higher\n                    expected_scores = torch.tensor(\n                        [self.BLOCKED_SCORE] + [-float('inf')] * (beam_sz - 1)\n                    ).repeat(batch_sz, 1)\n                    self.assertTrue(beam.topk_log_probs.equal(expected_scores))\n\n    def test_advance_with_some_repeats_gets_blocked(self):\n        # beam 0 and beam >=2 will repeat (beam >= 2 repeat dummy scores)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47\n        ngram_repeat = 3\n        no_repeat_score = -2.3\n        repeat_score = -0.1\n        device_init = torch.zeros(1, 1)\n        for batch_sz in [1, 3]:\n            beam = BeamSearch(\n                beam_sz, batch_sz, 0, 1, 2, 2,\n                GlobalScorerStub(), 0, 30,\n                False, ngram_repeat, set(),\n                False, 0.)\n            beam.initialize(device_init, torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                # non-interesting beams are going to get dummy values\n                word_probs = torch.full(\n                    (batch_sz * beam_sz, n_words), -float('inf'))\n                if i == 0:\n                    # on initial round, only predicted scores for beam 0\n                    # matter. Make two predictions. Top one will be repeated\n                    # in beam zero, second one will live on in beam 1.\n                    word_probs[0::beam_sz, repeat_idx] = repeat_score\n                    word_probs[0::beam_sz, repeat_idx +\n                               i + 1] = no_repeat_score\n                else:\n                    # predict the same thing in beam 0\n                    word_probs[0::beam_sz, repeat_idx] = 0\n                    # continue pushing around what beam 1 predicts\n                    word_probs[1::beam_sz, repeat_idx + i + 1] = 0\n                attns = torch.randn(1, batch_sz * beam_sz, 53)\n                beam.advance(word_probs, attns)\n                if i < ngram_repeat:\n                    self.assertFalse(\n                        beam.topk_log_probs[0::beam_sz].eq(\n                            self.BLOCKED_SCORE).any())\n                    self.assertFalse(\n                        beam.topk_log_probs[1::beam_sz].eq(\n                            self.BLOCKED_SCORE).any())\n                elif i == ngram_repeat:\n                    # now beam 0 dies (along with the others), beam 1 -> beam 0\n                    self.assertFalse(\n                        beam.topk_log_probs[:, 0].eq(\n                            self.BLOCKED_SCORE).any())\n\n                    expected = torch.full([batch_sz, beam_sz], float(\"-inf\"))\n                    expected[:, 0] = no_repeat_score\n                    expected[:, 1] = self.BLOCKED_SCORE\n                    self.assertTrue(\n                        beam.topk_log_probs[:, :].equal(expected))\n                else:\n                    # now beam 0 dies (along with the others), beam 1 -> beam 0\n                    self.assertFalse(\n                        beam.topk_log_probs[:, 0].eq(\n                            self.BLOCKED_SCORE).any())\n\n                    expected = torch.full([batch_sz, beam_sz], float(\"-inf\"))\n                    expected[:, 0] = no_repeat_score\n                    expected[:, 1:3] = self.BLOCKED_SCORE\n                    expected[:, 3:] = float(\"-inf\")\n                    self.assertTrue(\n                        beam.topk_log_probs.equal(expected))\n\n    def test_repeating_excluded_index_does_not_die(self):\n        # beam 0 and beam >= 2 will repeat (beam 2 repeats excluded idx)\n        beam_sz = 5\n        n_words = 100\n        repeat_idx = 47  # will be repeated and should be blocked\n        repeat_idx_ignored = 7  # will be repeated and should not be blocked\n        ngram_repeat = 3\n        device_init = torch.zeros(1, 1)\n        for batch_sz in [1, 3]:\n            beam = BeamSearch(\n                beam_sz, batch_sz, 0, 1, 2, 2,\n                GlobalScorerStub(), 0, 30,\n                False, ngram_repeat, {repeat_idx_ignored},\n                False, 0.)\n            beam.initialize(device_init, torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                # non-interesting beams are going to get dummy values\n                word_probs = torch.full(\n                    (batch_sz * beam_sz, n_words), -float('inf'))\n                if i == 0:\n                    word_probs[0::beam_sz, repeat_idx] = -0.1\n                    word_probs[0::beam_sz, repeat_idx + i + 1] = -2.3\n                    word_probs[0::beam_sz, repeat_idx_ignored] = -5.0\n                else:\n                    # predict the same thing in beam 0\n                    word_probs[0::beam_sz, repeat_idx] = 0\n                    # continue pushing around what beam 1 predicts\n                    word_probs[1::beam_sz, repeat_idx + i + 1] = 0\n                    # predict the allowed-repeat again in beam 2\n                    word_probs[2::beam_sz, repeat_idx_ignored] = 0\n                attns = torch.randn(1, batch_sz * beam_sz, 53)\n                beam.advance(word_probs, attns)\n                if i < ngram_repeat:\n                    self.assertFalse(beam.topk_log_probs[:, 0].eq(\n                        self.BLOCKED_SCORE).any())\n                    self.assertFalse(beam.topk_log_probs[:, 1].eq(\n                        self.BLOCKED_SCORE).any())\n                    self.assertFalse(beam.topk_log_probs[:, 2].eq(\n                        self.BLOCKED_SCORE).any())\n                else:\n                    # now beam 0 dies, beam 1 -> beam 0, beam 2 -> beam 1\n                    # and the rest die\n                    self.assertFalse(beam.topk_log_probs[:, 0].eq(\n                        self.BLOCKED_SCORE).any())\n                    # since all preds after i=0 are 0, we can check\n                    # that the beam is the correct idx by checking that\n                    # the curr score is the initial score\n                    self.assertTrue(beam.topk_log_probs[:, 0].eq(-2.3).all())\n                    self.assertFalse(beam.topk_log_probs[:, 1].eq(\n                        self.BLOCKED_SCORE).all())\n                    self.assertTrue(beam.topk_log_probs[:, 1].eq(-5.0).all())\n\n                    self.assertTrue(beam.topk_log_probs[:, 2].eq(\n                        self.BLOCKED_SCORE).all())\n\n    def test_doesnt_predict_eos_if_shorter_than_min_len(self):\n        # beam 0 will always predict EOS. The other beams will predict\n        # non-eos scores.\n        for batch_sz in [1, 3]:\n            beam_sz = 5\n            n_words = 100\n            _non_eos_idxs = [47, 51, 13, 88, 99]\n            valid_score_dist = torch.log_softmax(torch.tensor(\n                [6., 5., 4., 3., 2., 1.]), dim=0)\n            min_length = 5\n            eos_idx = 2\n            lengths = torch.randint(0, 30, (batch_sz,))\n            beam = BeamSearch(beam_sz, batch_sz, 0, 1, 2, 2,\n                              GlobalScorerStub(),\n                              min_length, 30, False, 0, set(),\n                              False, 0.)\n            device_init = torch.zeros(1, 1)\n            beam.initialize(device_init, lengths)\n            all_attns = []\n            for i in range(min_length + 4):\n                # non-interesting beams are going to get dummy values\n                word_probs = torch.full(\n                    (batch_sz * beam_sz, n_words), -float('inf'))\n                if i == 0:\n                    # \"best\" prediction is eos - that should be blocked\n                    word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                    # include at least beam_sz predictions OTHER than EOS\n                    # that are greater than -1e20\n                    for j, score in zip(_non_eos_idxs, valid_score_dist[1:]):\n                        word_probs[0::beam_sz, j] = score\n                else:\n                    # predict eos in beam 0\n                    word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                    # provide beam_sz other good predictions\n                    for k, (j, score) in enumerate(\n                            zip(_non_eos_idxs, valid_score_dist[1:])):\n                        beam_idx = min(beam_sz - 1, k)\n                        word_probs[beam_idx::beam_sz, j] = score\n\n                attns = torch.randn(1, batch_sz * beam_sz, 53)\n                all_attns.append(attns)\n                beam.advance(word_probs, attns)\n                if i < min_length:\n                    expected_score_dist = \\\n                        (i + 1) * valid_score_dist[1:].unsqueeze(0)\n                    self.assertTrue(\n                        beam.topk_log_probs.allclose(\n                            expected_score_dist))\n                elif i == min_length:\n                    # now the top beam has ended and no others have\n                    self.assertTrue(beam.is_finished[:, 0].eq(1).all())\n                    self.assertTrue(beam.is_finished[:, 1:].eq(0).all())\n                else:  # i > min_length\n                    # not of interest, but want to make sure it keeps running\n                    # since only beam 0 terminates and n_best = 2\n                    pass\n\n    def test_beam_is_done_when_n_best_beams_eos_using_min_length(self):\n        # this is also a test that when block_ngram_repeat=0,\n        # repeating is acceptable\n        beam_sz = 5\n        batch_sz = 3\n        n_words = 100\n        _non_eos_idxs = [47, 51, 13, 88, 99]\n        valid_score_dist = torch.log_softmax(torch.tensor(\n            [6., 5., 4., 3., 2., 1.]), dim=0)\n        min_length = 5\n        eos_idx = 2\n        beam = BeamSearch(\n            beam_sz, batch_sz, 0, 1, 2, 2,\n            GlobalScorerStub(),\n            min_length, 30, False, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        beam.initialize(device_init, torch.randint(0, 30, (batch_sz,)))\n        for i in range(min_length + 4):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full(\n                (batch_sz * beam_sz, n_words), -float('inf'))\n            if i == 0:\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                # include at least beam_sz predictions OTHER than EOS\n                # that are greater than -1e20\n                for j, score in zip(_non_eos_idxs, valid_score_dist[1:]):\n                    word_probs[0::beam_sz, j] = score\n            elif i <= min_length:\n                # predict eos in beam 1\n                word_probs[1::beam_sz, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz - 1, k)\n                    word_probs[beam_idx::beam_sz, j] = score\n            else:\n                word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                word_probs[1::beam_sz, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz - 1, k)\n                    word_probs[beam_idx::beam_sz, j] = score\n\n            attns = torch.randn(1, batch_sz * beam_sz, 53)\n            beam.advance(word_probs, attns)\n            if i < min_length:\n                self.assertFalse(beam.done)\n            elif i == min_length:\n                # beam 1 dies on min_length\n                self.assertTrue(beam.is_finished[:, 1].all())\n                beam.update_finished()\n                self.assertFalse(beam.done)\n            else:  # i > min_length\n                # beam 0 dies on the step after beam 1 dies\n                self.assertTrue(beam.is_finished[:, 0].all())\n                beam.update_finished()\n                self.assertTrue(beam.done)\n\n    def test_beam_returns_attn_with_correct_length(self):\n        beam_sz = 5\n        batch_sz = 3\n        n_words = 100\n        _non_eos_idxs = [47, 51, 13, 88, 99]\n        valid_score_dist = torch.log_softmax(torch.tensor(\n            [6., 5., 4., 3., 2., 1.]), dim=0)\n        min_length = 5\n        eos_idx = 2\n        inp_lens = torch.randint(1, 30, (batch_sz,))\n        beam = BeamSearch(\n            beam_sz, batch_sz, 0, 1, 2, 2,\n            GlobalScorerStub(),\n            min_length, 30, True, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        _, _, inp_lens, _ = beam.initialize(device_init, inp_lens)\n        # inp_lens is tiled in initialize, reassign to make attn match\n        for i in range(min_length + 2):\n            # non-interesting beams are going to get dummy values\n            word_probs = torch.full(\n                (batch_sz * beam_sz, n_words), -float('inf'))\n            if i == 0:\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                # include at least beam_sz predictions OTHER than EOS\n                # that are greater than -1e20\n                for j, score in zip(_non_eos_idxs, valid_score_dist[1:]):\n                    word_probs[0::beam_sz, j] = score\n            elif i <= min_length:\n                # predict eos in beam 1\n                word_probs[1::beam_sz, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz - 1, k)\n                    word_probs[beam_idx::beam_sz, j] = score\n            else:\n                word_probs[0::beam_sz, eos_idx] = valid_score_dist[0]\n                word_probs[1::beam_sz, eos_idx] = valid_score_dist[0]\n                # provide beam_sz other good predictions in other beams\n                for k, (j, score) in enumerate(\n                        zip(_non_eos_idxs, valid_score_dist[1:])):\n                    beam_idx = min(beam_sz - 1, k)\n                    word_probs[beam_idx::beam_sz, j] = score\n\n            attns = torch.randn(1, batch_sz * beam_sz, 53)\n            beam.advance(word_probs, attns)\n            if i < min_length:\n                self.assertFalse(beam.done)\n                # no top beams are finished yet\n                for b in range(batch_sz):\n                    self.assertEqual(beam.attention[b], [])\n            elif i == min_length:\n                # beam 1 dies on min_length\n                self.assertTrue(beam.is_finished[:, 1].all())\n                beam.update_finished()\n                self.assertFalse(beam.done)\n                # no top beams are finished yet\n                for b in range(batch_sz):\n                    self.assertEqual(beam.attention[b], [])\n            else:  # i > min_length\n                # beam 0 dies on the step after beam 1 dies\n                self.assertTrue(beam.is_finished[:, 0].all())\n                beam.update_finished()\n                self.assertTrue(beam.done)\n                # top beam is finished now so there are attentions\n                for b in range(batch_sz):\n                    # two beams are finished in each batch\n                    self.assertEqual(len(beam.attention[b]), 2)\n                    for k in range(2):\n                        # second dim is cut down to the non-padded src length\n                        self.assertEqual(beam.attention[b][k].shape[-1],\n                                         inp_lens[b])\n                    # first dim is equal to the time of death\n                    # (beam 0 died at current step - adjust for SOS)\n                    self.assertEqual(beam.attention[b][0].shape[0], i + 1)\n                    # (beam 1 died at last step - adjust for SOS)\n                    self.assertEqual(beam.attention[b][1].shape[0], i)\n                # behavior gets weird when beam is already done so just stop\n                break\n\n\nclass TestBeamSearchAgainstReferenceCase(unittest.TestCase):\n    # this is just test_beam.TestBeamAgainstReferenceCase repeated\n    # in each batch.\n    BEAM_SZ = 5\n    EOS_IDX = 2  # don't change this - all the scores would need updated\n    N_WORDS = 8  # also don't change for same reason\n    N_BEST = 3\n    DEAD_SCORE = -1e20\n    BATCH_SZ = 3\n    INP_SEQ_LEN = 53\n\n    def random_attn(self):\n        return torch.randn(1, self.BATCH_SZ * self.BEAM_SZ, self.INP_SEQ_LEN)\n\n    def init_step(self, beam, expected_len_pen):\n        # init_preds: [4, 3, 5, 6, 7] - no EOS's\n        init_scores = torch.log_softmax(torch.tensor(\n            [[0, 0, 0, 4, 5, 3, 2, 1]], dtype=torch.float), dim=1)\n        init_scores = deepcopy(init_scores.repeat(\n            self.BATCH_SZ * self.BEAM_SZ, 1))\n        new_scores = init_scores + beam.topk_log_probs.view(-1).unsqueeze(1)\n        expected_beam_scores, expected_preds_0 = new_scores \\\n            .view(self.BATCH_SZ, self.BEAM_SZ * self.N_WORDS) \\\n            .topk(self.BEAM_SZ, dim=-1)\n        beam.advance(deepcopy(init_scores), self.random_attn())\n        self.assertTrue(beam.topk_log_probs.allclose(expected_beam_scores))\n        self.assertTrue(beam.topk_ids.equal(expected_preds_0))\n        self.assertFalse(beam.is_finished.any())\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def first_step(self, beam, expected_beam_scores, expected_len_pen):\n        # no EOS's yet\n        assert beam.is_finished.sum() == 0\n        scores_1 = torch.log_softmax(torch.tensor(\n            [[0, 0, 0, .3, 0, .51, .2, 0],\n             [0, 0, 1.5, 0, 0, 0, 0, 0],\n             [0, 0, 0, 0, .49, .48, 0, 0],\n             [0, 0, 0, .2, .2, .2, .2, .2],\n             [0, 0, 0, .2, .2, .2, .2, .2]]\n        ), dim=1)\n        scores_1 = scores_1.repeat(self.BATCH_SZ, 1)\n\n        beam.advance(deepcopy(scores_1), self.random_attn())\n\n        new_scores = scores_1 + expected_beam_scores.view(-1).unsqueeze(1)\n        expected_beam_scores, unreduced_preds = new_scores\\\n            .view(self.BATCH_SZ, self.BEAM_SZ * self.N_WORDS)\\\n            .topk(self.BEAM_SZ, -1)\n        expected_bptr_1 = unreduced_preds // self.N_WORDS\n        # [5, 3, 2, 6, 0], so beam 2 predicts EOS!\n        expected_preds_1 = unreduced_preds - expected_bptr_1 * self.N_WORDS\n        self.assertTrue(beam.topk_log_probs.allclose(expected_beam_scores))\n        self.assertTrue(beam.topk_scores.allclose(\n            expected_beam_scores / expected_len_pen))\n        self.assertTrue(beam.topk_ids.equal(expected_preds_1))\n        self.assertTrue(beam.current_backptr.equal(expected_bptr_1))\n        self.assertEqual(beam.is_finished.sum(), self.BATCH_SZ)\n        self.assertTrue(beam.is_finished[:, 2].all())  # beam 2 finished\n        beam.update_finished()\n        self.assertFalse(beam.top_beam_finished.any())\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def second_step(self, beam, expected_beam_scores, expected_len_pen):\n        # assumes beam 2 finished on last step\n        scores_2 = torch.log_softmax(torch.tensor(\n            [[0, 0, 0, .3, 0, .51, .2, 0],\n             [0, 0, 0, 0, 0, 0, 0, 0],\n             [0, 0, 0, 0, 5000, .48, 0, 0],  # beam 2 shouldn't continue\n             [0, 0, 50, .2, .2, .2, .2, .2],  # beam 3 -> beam 0 should die\n             [0, 0, 0, .2, .2, .2, .2, .2]]\n        ), dim=1)\n        scores_2 = scores_2.repeat(self.BATCH_SZ, 1)\n\n        beam.advance(deepcopy(scores_2), self.random_attn())\n\n        # ended beam 2 shouldn't continue\n        expected_beam_scores[:, 2::self.BEAM_SZ] = self.DEAD_SCORE\n        new_scores = scores_2 + expected_beam_scores.view(-1).unsqueeze(1)\n        expected_beam_scores, unreduced_preds = new_scores\\\n            .view(self.BATCH_SZ, self.BEAM_SZ * self.N_WORDS)\\\n            .topk(self.BEAM_SZ, -1)\n        expected_bptr_2 = unreduced_preds // self.N_WORDS\n        # [2, 5, 3, 6, 0] repeat self.BATCH_SZ, so beam 0 predicts EOS!\n        expected_preds_2 = unreduced_preds - expected_bptr_2 * self.N_WORDS\n        # [-2.4879, -3.8910, -4.1010, -4.2010, -4.4010] repeat self.BATCH_SZ\n        self.assertTrue(beam.topk_log_probs.allclose(expected_beam_scores))\n        self.assertTrue(beam.topk_scores.allclose(\n            expected_beam_scores / expected_len_pen))\n        self.assertTrue(beam.topk_ids.equal(expected_preds_2))\n        self.assertTrue(beam.current_backptr.equal(expected_bptr_2))\n        # another beam is finished in all batches\n        self.assertEqual(beam.is_finished.sum(), self.BATCH_SZ)\n        # new beam 0 finished\n        self.assertTrue(beam.is_finished[:, 0].all())\n        # new beam 0 is old beam 3\n        self.assertTrue(expected_bptr_2[:, 0].eq(3).all())\n        beam.update_finished()\n        self.assertTrue(beam.top_beam_finished.all())\n        self.assertFalse(beam.done)\n        return expected_beam_scores\n\n    def third_step(self, beam, expected_beam_scores, expected_len_pen):\n        # assumes beam 0 finished on last step\n        scores_3 = torch.log_softmax(torch.tensor(\n            [[0, 0, 5000, 0, 5000, .51, .2, 0],  # beam 0 shouldn't cont\n             [0, 0, 0, 0, 0, 0, 0, 0],\n             [0, 0, 0, 0, 0, 5000, 0, 0],\n             [0, 0, 0, .2, .2, .2, .2, .2],\n             [0, 0, 50, 0, .2, .2, .2, .2]]  # beam 4 -> beam 1 should die\n        ), dim=1)\n        scores_3 = scores_3.repeat(self.BATCH_SZ, 1)\n\n        beam.advance(deepcopy(scores_3), self.random_attn())\n\n        expected_beam_scores[:, 0::self.BEAM_SZ] = self.DEAD_SCORE\n        new_scores = scores_3 + expected_beam_scores.view(-1).unsqueeze(1)\n        expected_beam_scores, unreduced_preds = new_scores\\\n            .view(self.BATCH_SZ, self.BEAM_SZ * self.N_WORDS)\\\n            .topk(self.BEAM_SZ, -1)\n        expected_bptr_3 = unreduced_preds // self.N_WORDS\n        # [5, 2, 6, 1, 0] repeat self.BATCH_SZ, so beam 1 predicts EOS!\n        expected_preds_3 = unreduced_preds - expected_bptr_3 * self.N_WORDS\n        self.assertTrue(beam.topk_log_probs.allclose(\n            expected_beam_scores))\n        self.assertTrue(beam.topk_scores.allclose(\n            expected_beam_scores / expected_len_pen))\n        self.assertTrue(beam.topk_ids.equal(expected_preds_3))\n        self.assertTrue(beam.current_backptr.equal(expected_bptr_3))\n        self.assertEqual(beam.is_finished.sum(), self.BATCH_SZ)\n        # new beam 1 finished\n        self.assertTrue(beam.is_finished[:, 1].all())\n        # new beam 1 is old beam 4\n        self.assertTrue(expected_bptr_3[:, 1].eq(4).all())\n        beam.update_finished()\n        self.assertTrue(beam.top_beam_finished.all())\n        self.assertTrue(beam.done)\n        return expected_beam_scores\n\n    def test_beam_advance_against_known_reference(self):\n        beam = BeamSearch(\n            self.BEAM_SZ, self.BATCH_SZ, 0, 1, 2, self.N_BEST,\n            GlobalScorerStub(),\n            0, 30, False, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        beam.initialize(device_init, torch.randint(0, 30, (self.BATCH_SZ,)))\n        expected_beam_scores = self.init_step(beam, 1)\n        expected_beam_scores = self.first_step(beam, expected_beam_scores, 1)\n        expected_beam_scores = self.second_step(beam, expected_beam_scores, 1)\n        self.third_step(beam, expected_beam_scores, 1)\n\n\nclass TestBeamWithLengthPenalty(TestBeamSearchAgainstReferenceCase):\n    # this could be considered an integration test because it tests\n    # interactions between the GNMT scorer and the beam\n\n    def test_beam_advance_against_known_reference(self):\n        scorer = GNMTGlobalScorer(0.7, 0., \"avg\", \"none\")\n        beam = BeamSearch(\n            self.BEAM_SZ, self.BATCH_SZ, 0, 1, 2, self.N_BEST,\n            scorer,\n            0, 30, False, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        beam.initialize(device_init, torch.randint(0, 30, (self.BATCH_SZ,)))\n        expected_beam_scores = self.init_step(beam, 1.)\n        expected_beam_scores = self.first_step(beam, expected_beam_scores, 3)\n        expected_beam_scores = self.second_step(beam, expected_beam_scores, 4)\n        self.third_step(beam, expected_beam_scores, 5)\n\n\nclass TestBeamSearchLM(TestBeamSearchAgainstReferenceCase):\n    def finish_first_beam_step(self, beam):\n        scores_finish = torch.log_softmax(torch.tensor(\n            [[0, 0, 10000, 0, 5000, .51, .2, 0],  # beam 0 shouldn't cont\n             [100000, 100001, 0, 0, 0, 0, 0, 0],\n             [0,100000, 0, 0, 0, 5000, 0, 0],\n             [0, 0, 0, .2, .2, .2, .2, .2],\n             [0, 0, 0, 0, .2, .2, .2, .2]]  # beam 4 -> beam 1 should die\n        ), dim=1)\n        scores_finish = scores_finish.repeat(self.BATCH_SZ, 1)\n        scores_finish[:self.BEAM_SZ, beam.eos] = 0\n        beam.advance( scores_finish, None)\n        \n        any_finished = beam.is_finished.any()\n        if any_finished:\n            beam.update_finished()\n\n    def test_beam_lm_increase_memory_length(self):\n        beam = BeamSearchLM(\n            self.BEAM_SZ, self.BATCH_SZ, 0, 1, 2, self.N_BEST,\n            GlobalScorerStub(),\n            0, 30, False, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        src_lengths = torch.randint(0, 30, (self.BATCH_SZ,))\n        fn_map_state, _, _, _ = beam.initialize(device_init, src_lengths)\n        expected_beam_scores = self.init_step(beam, 1)\n        expected_beam_scores = self.first_step(beam, expected_beam_scores, 1)\n        expected_beam_scores = self.second_step(beam, expected_beam_scores, 1)\n        self.third_step(beam, expected_beam_scores, 1)\n\n        n_steps = beam.alive_seq.shape[-1] - 1\n        self.assertTrue(beam.memory_lengths.equal(n_steps+fn_map_state(src_lengths, dim=0)))\n    \n    \n    def test_beam_lm_update_memory_length_when_finished(self):\n        beam = BeamSearchLM(\n            self.BEAM_SZ, self.BATCH_SZ, 0, 1, 2, self.N_BEST,\n            GlobalScorerStub(),\n            0, 30, False, 0, set(),\n            False, 0.)\n        device_init = torch.zeros(1, 1)\n        src_lengths = torch.randint(0, 30, (self.BATCH_SZ,))\n        fn_map_state, _, _, _ = beam.initialize(device_init, src_lengths)\n        expected_beam_scores = self.init_step(beam, 1)\n        self.finish_first_beam_step(beam)\n        \n        n_steps = beam.alive_seq.shape[-1] - 1\n        self.assertTrue(beam.memory_lengths.equal(n_steps+fn_map_state(src_lengths[1:], dim=0)))\n"
  },
  {
    "path": "onmt/tests/test_copy_generator.py",
    "content": "import unittest\nfrom onmt.modules.copy_generator import CopyGenerator, CopyGeneratorLoss\n\nimport itertools\nfrom copy import deepcopy\n\nimport torch\nfrom torch.nn.functional import softmax\n\nfrom onmt.tests.utils_for_tests import product_dict\n\n\nclass TestCopyGenerator(unittest.TestCase):\n    INIT_CASES = list(product_dict(\n        input_size=[172],\n        output_size=[319],\n        pad_idx=[0, 39],\n    ))\n    PARAMS = list(product_dict(\n        batch_size=[1, 14],\n        max_seq_len=[23],\n        tgt_max_len=[50],\n        n_extra_words=[107]\n    ))\n\n    @classmethod\n    def dummy_inputs(cls, params, init_case):\n        hidden = torch.randn((params[\"batch_size\"] * params[\"tgt_max_len\"],\n                              init_case[\"input_size\"]))\n        attn = torch.randn((params[\"batch_size\"] * params[\"tgt_max_len\"],\n                            params[\"max_seq_len\"]))\n        src_map = torch.randn((params[\"max_seq_len\"], params[\"batch_size\"],\n                               params[\"n_extra_words\"]))\n        return hidden, attn, src_map\n\n    @classmethod\n    def expected_shape(cls, params, init_case):\n        return params[\"tgt_max_len\"] * params[\"batch_size\"], \\\n               init_case[\"output_size\"] + params[\"n_extra_words\"]\n\n    def test_copy_gen_forward_shape(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            cgen = CopyGenerator(**init_case)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = cgen(*dummy_in)\n            expected_shape = self.expected_shape(params, init_case)\n            self.assertEqual(res.shape, expected_shape, init_case.__str__())\n\n    def test_copy_gen_outp_has_no_prob_of_pad(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            cgen = CopyGenerator(**init_case)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = cgen(*dummy_in)\n            self.assertTrue(\n                res[:, init_case[\"pad_idx\"]].allclose(torch.tensor(0.0)))\n\n    def test_copy_gen_trainable_params_update(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            cgen = CopyGenerator(**init_case)\n            trainable_params = {n: p for n, p in cgen.named_parameters()\n                                if p.requires_grad}\n            assert len(trainable_params) > 0  # sanity check\n            old_weights = deepcopy(trainable_params)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = cgen(*dummy_in)\n            pretend_loss = res.sum()\n            pretend_loss.backward()\n            dummy_optim = torch.optim.SGD(trainable_params.values(), 1)\n            dummy_optim.step()\n            for param_name in old_weights.keys():\n                self.assertTrue(\n                    trainable_params[param_name]\n                    .ne(old_weights[param_name]).any(),\n                    param_name + \" \" + init_case.__str__())\n\n\nclass TestCopyGeneratorLoss(unittest.TestCase):\n    INIT_CASES = list(product_dict(\n        vocab_size=[172],\n        unk_index=[0, 39],\n        ignore_index=[1, 17],  # pad idx\n        force_copy=[True, False]\n    ))\n    PARAMS = list(product_dict(\n        batch_size=[1, 14],\n        tgt_max_len=[50],\n        n_extra_words=[107]\n    ))\n\n    @classmethod\n    def dummy_inputs(cls, params, init_case):\n        n_unique_src_words = 13\n        scores = torch.randn((params[\"batch_size\"] * params[\"tgt_max_len\"],\n                              init_case[\"vocab_size\"] + n_unique_src_words))\n        scores = softmax(scores, dim=1)\n        align = torch.randint(0, n_unique_src_words,\n                              (params[\"batch_size\"] * params[\"tgt_max_len\"],))\n        target = torch.randint(0, init_case[\"vocab_size\"],\n                               (params[\"batch_size\"] * params[\"tgt_max_len\"],))\n        target[0] = init_case[\"unk_index\"]\n        target[1] = init_case[\"ignore_index\"]\n        return scores, align, target\n\n    @classmethod\n    def expected_shape(cls, params, init_case):\n        return (params[\"batch_size\"] * params[\"tgt_max_len\"],)\n\n    def test_copy_loss_forward_shape(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            loss = CopyGeneratorLoss(**init_case)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = loss(*dummy_in)\n            expected_shape = self.expected_shape(params, init_case)\n            self.assertEqual(res.shape, expected_shape, init_case.__str__())\n\n    def test_copy_loss_ignore_index_is_ignored(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            loss = CopyGeneratorLoss(**init_case)\n            scores, align, target = self.dummy_inputs(params, init_case)\n            res = loss(scores, align, target)\n            should_be_ignored = (target == init_case[\"ignore_index\"]).nonzero(\n                as_tuple=False)\n            assert len(should_be_ignored) > 0  # otherwise not testing anything\n            self.assertTrue(res[should_be_ignored].allclose(torch.tensor(0.0)))\n\n    def test_copy_loss_output_range_is_positive(self):\n        for params, init_case in itertools.product(\n                self.PARAMS, self.INIT_CASES):\n            loss = CopyGeneratorLoss(**init_case)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = loss(*dummy_in)\n            self.assertTrue((res >= 0).all())\n"
  },
  {
    "path": "onmt/tests/test_data_prepare.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\nimport copy\nimport unittest\nimport glob\nimport os\n\nfrom onmt.utils.parse import ArgumentParser\nfrom onmt.opts import dynamic_prepare_opts\nfrom onmt.bin.train import prepare_fields_transforms\nfrom onmt.constants import CorpusName\n\n\nSAVE_DATA_PREFIX = 'data/test_data_prepare'\n\n\ndef get_default_opts():\n    parser = ArgumentParser(description='data sample prepare')\n    dynamic_prepare_opts(parser)\n\n    default_opts = [\n        '-config', 'data/data.yaml',\n        '-src_vocab', 'data/vocab-train.src',\n        '-tgt_vocab', 'data/vocab-train.tgt'\n    ]\n\n    opt = parser.parse_known_args(default_opts)[0]\n    # Inject some dummy training options that may needed when build fields\n    opt.copy_attn = False\n    ArgumentParser.validate_prepare_opts(opt)\n    return opt\n\n\ndefault_opts = get_default_opts()\n\n\nclass TestData(unittest.TestCase):\n    def __init__(self, *args, **kwargs):\n        super(TestData, self).__init__(*args, **kwargs)\n        self.opt = default_opts\n\n    def dataset_build(self, opt):\n        try:\n            prepare_fields_transforms(opt)\n        except SystemExit as err:\n            print(err)\n        except IOError as err:\n            if opt.skip_empty_level != 'error':\n                raise err\n            else:\n                print(f\"Catched IOError: {err}\")\n        finally:\n            # Remove the generated *pt files.\n            for pt in glob.glob(SAVE_DATA_PREFIX + '*.pt'):\n                os.remove(pt)\n            if self.opt.save_data:\n                # Remove the generated data samples\n                sample_path = os.path.join(\n                    os.path.dirname(self.opt.save_data),\n                    CorpusName.SAMPLE)\n                if os.path.exists(sample_path):\n                    for f in glob.glob(sample_path + '/*'):\n                        os.remove(f)\n                    os.rmdir(sample_path)\n\n\ndef _add_test(param_setting, methodname):\n    \"\"\"\n    Adds a Test to TestData according to settings\n\n    Args:\n        param_setting: list of tuples of (param, setting)\n        methodname: name of the method that gets called\n    \"\"\"\n\n    def test_method(self):\n        if param_setting:\n            opt = copy.deepcopy(self.opt)\n            for param, setting in param_setting:\n                setattr(opt, param, setting)\n        else:\n            opt = self.opt\n        getattr(self, methodname)(opt)\n    if param_setting:\n        name = 'test_' + methodname + \"_\" + \"_\".join(\n            str(param_setting).split())\n    else:\n        name = 'test_' + methodname + '_standard'\n    setattr(TestData, name, test_method)\n    test_method.__name__ = name\n\n\ntest_databuild = [[],\n                  [('src_vocab_size', 1),\n                   ('tgt_vocab_size', 1)],\n                  [('src_vocab_size', 10000),\n                   ('tgt_vocab_size', 10000)],\n                  [('src_seq_len', 1)],\n                  [('src_seq_len', 5000)],\n                  [('src_seq_length_trunc', 1)],\n                  [('src_seq_length_trunc', 5000)],\n                  [('tgt_seq_len', 1)],\n                  [('tgt_seq_len', 5000)],\n                  [('tgt_seq_length_trunc', 1)],\n                  [('tgt_seq_length_trunc', 5000)],\n                  [('copy_attn', True)],\n                  [('share_vocab', True)],\n                  [('n_sample', 30),\n                   ('save_data', SAVE_DATA_PREFIX)],\n                  [('n_sample', 30),\n                   ('save_data', SAVE_DATA_PREFIX),\n                   ('skip_empty_level', 'error')]\n                  ]\n\nfor p in test_databuild:\n    _add_test(p, 'dataset_build')\n"
  },
  {
    "path": "onmt/tests/test_embeddings.py",
    "content": "import unittest\nfrom onmt.modules.embeddings import Embeddings\n\nimport itertools\nfrom copy import deepcopy\n\nimport torch\n\nfrom onmt.tests.utils_for_tests import product_dict\n\n\nclass TestEmbeddings(unittest.TestCase):\n    INIT_CASES = list(product_dict(\n        word_vec_size=[172],\n        word_vocab_size=[319],\n        word_padding_idx=[17],\n        position_encoding=[False, True],\n        feat_merge=[\"first\", \"concat\", \"sum\", \"mlp\"],\n        feat_vec_exponent=[-1, 1.1, 0.7],\n        feat_vec_size=[0, 200],\n        feat_padding_idx=[[], [29], [0, 1]],\n        feat_vocab_sizes=[[], [39], [401, 39]],\n        dropout=[0, 0.5],\n        freeze_word_vecs=[False, True]\n    ))\n    PARAMS = list(product_dict(\n        batch_size=[1, 14],\n        max_seq_len=[23]\n    ))\n\n    @classmethod\n    def case_is_degenerate(cls, case):\n        no_feats = len(case[\"feat_vocab_sizes\"]) == 0\n        if case[\"feat_merge\"] != \"first\" and no_feats:\n            return True\n        if case[\"feat_merge\"] == \"first\" and not no_feats:\n            return True\n        if case[\"feat_merge\"] == \"concat\" and case[\"feat_vec_exponent\"] != -1:\n            return True\n        if no_feats and case[\"feat_vec_exponent\"] != -1:\n            return True\n        if len(case[\"feat_vocab_sizes\"]) != len(case[\"feat_padding_idx\"]):\n            return True\n        if case[\"feat_vec_size\"] == 0 and case[\"feat_vec_exponent\"] <= 0:\n            return True\n        if case[\"feat_merge\"] == \"sum\":\n            if case[\"feat_vec_exponent\"] != -1:\n                return True\n            if case[\"feat_vec_size\"] != 0:\n                return True\n        if case[\"feat_vec_size\"] != 0 and case[\"feat_vec_exponent\"] != -1:\n            return True\n        return False\n\n    @classmethod\n    def cases(cls):\n        for case in cls.INIT_CASES:\n            if not cls.case_is_degenerate(case):\n                yield case\n\n    @classmethod\n    def dummy_inputs(cls, params, init_case):\n        max_seq_len = params[\"max_seq_len\"]\n        batch_size = params[\"batch_size\"]\n        fv_sizes = init_case[\"feat_vocab_sizes\"]\n        n_words = init_case[\"word_vocab_size\"]\n        voc_sizes = [n_words] + fv_sizes\n        pad_idxs = [init_case[\"word_padding_idx\"]] + \\\n            init_case[\"feat_padding_idx\"]\n        lengths = torch.randint(0, max_seq_len, (batch_size,))\n        lengths[0] = max_seq_len\n        inps = torch.empty((max_seq_len, batch_size, len(voc_sizes)),\n                           dtype=torch.long)\n        for f, (voc_size, pad_idx) in enumerate(zip(voc_sizes, pad_idxs)):\n            for b, len_ in enumerate(lengths):\n                inps[:len_, b, f] = torch.randint(0, voc_size-1, (len_,))\n                inps[len_:, b, f] = pad_idx\n        return inps\n\n    @classmethod\n    def expected_shape(cls, params, init_case):\n        wvs = init_case[\"word_vec_size\"]\n        fvs = init_case[\"feat_vec_size\"]\n        nf = len(init_case[\"feat_vocab_sizes\"])\n        size = wvs\n        if init_case[\"feat_merge\"] not in {\"sum\", \"mlp\"}:\n            size += nf * fvs\n        return params[\"max_seq_len\"], params[\"batch_size\"], size\n\n    def test_embeddings_forward_shape(self):\n        for params, init_case in itertools.product(self.PARAMS, self.cases()):\n            emb = Embeddings(**init_case)\n            dummy_in = self.dummy_inputs(params, init_case)\n            res = emb(dummy_in)\n            expected_shape = self.expected_shape(params, init_case)\n            self.assertEqual(res.shape, expected_shape, init_case.__str__())\n\n    def test_embeddings_trainable_params(self):\n        for params, init_case in itertools.product(self.PARAMS,\n                                                   self.cases()):\n            emb = Embeddings(**init_case)\n            trainable_params = {n: p for n, p in emb.named_parameters()\n                                if p.requires_grad}\n            # first check there's nothing unexpectedly not trainable\n            for key in emb.state_dict():\n                if key not in trainable_params:\n                    if key.endswith(\"emb_luts.0.weight\") and \\\n                            init_case[\"freeze_word_vecs\"]:\n                        # ok: word embeddings shouldn't be trainable\n                        # if word vecs are freezed\n                        continue\n                    if key.endswith(\".pe.pe\"):\n                        # ok: positional encodings shouldn't be trainable\n                        assert init_case[\"position_encoding\"]\n                        continue\n                    else:\n                        self.fail(\"Param {:s} is unexpectedly not \"\n                                  \"trainable.\".format(key))\n            # then check nothing unexpectedly trainable\n            if init_case[\"freeze_word_vecs\"]:\n                self.assertFalse(\n                    any(trainable_param.endswith(\"emb_luts.0.weight\")\n                        for trainable_param in trainable_params),\n                    \"Word embedding is trainable but word vecs are freezed.\")\n            if init_case[\"position_encoding\"]:\n                self.assertFalse(\n                    any(trainable_p.endswith(\".pe.pe\")\n                        for trainable_p in trainable_params),\n                    \"Positional encoding is trainable.\")\n\n    def test_embeddings_trainable_params_update(self):\n        for params, init_case in itertools.product(self.PARAMS, self.cases()):\n            emb = Embeddings(**init_case)\n            trainable_params = {n: p for n, p in emb.named_parameters()\n                                if p.requires_grad}\n            if len(trainable_params) > 0:\n                old_weights = deepcopy(trainable_params)\n                dummy_in = self.dummy_inputs(params, init_case)\n                res = emb(dummy_in)\n                pretend_loss = res.sum()\n                pretend_loss.backward()\n                dummy_optim = torch.optim.SGD(trainable_params.values(), 1)\n                dummy_optim.step()\n                for param_name in old_weights.keys():\n                    self.assertTrue(\n                        trainable_params[param_name]\n                        .ne(old_weights[param_name]).any(),\n                        param_name + \" \" + init_case.__str__())\n"
  },
  {
    "path": "onmt/tests/test_greedy_search.py",
    "content": "import unittest\nfrom onmt.translate.greedy_search import GreedySearch\n\nimport torch\n\n\nclass TestGreedySearch(unittest.TestCase):\n    BATCH_SZ = 3\n    INP_SEQ_LEN = 53\n    DEAD_SCORE = -1e20\n\n    BLOCKED_SCORE = -10e20\n\n    def test_doesnt_predict_eos_if_shorter_than_min_len(self):\n        # batch 0 will always predict EOS. The other batches will predict\n        # non-eos scores.\n        for batch_sz in [1, 3]:\n            n_words = 100\n            _non_eos_idxs = [47]\n            valid_score_dist = torch.log_softmax(torch.tensor(\n                [6., 5.]), dim=0)\n            min_length = 5\n            eos_idx = 2\n            lengths = torch.randint(0, 30, (batch_sz,))\n            samp = GreedySearch(\n                0, 1, 2, batch_sz, min_length,\n                False, set(), False, 30, 1., 1)\n            samp.initialize(torch.zeros(1), lengths)\n            all_attns = []\n            for i in range(min_length + 4):\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                # include at least one prediction OTHER than EOS\n                # that is greater than -1e20\n                word_probs[0, _non_eos_idxs[0]] = valid_score_dist[1]\n                word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                all_attns.append(attns)\n                samp.advance(word_probs, attns)\n                if i < min_length:\n                    self.assertTrue(\n                        samp.topk_scores[0].allclose(valid_score_dist[1]))\n                    self.assertTrue(\n                        samp.topk_scores[1:].eq(0).all())\n                elif i == min_length:\n                    # now batch 0 has ended and no others have\n                    self.assertTrue(samp.is_finished[0, :].eq(1).all())\n                    self.assertTrue(samp.is_finished[1:, 1:].eq(0).all())\n                else:  # i > min_length\n                    break\n\n    def test_returns_correct_scores_deterministic(self):\n        for batch_sz in [1, 13]:\n            for temp in [1., 3.]:\n                n_words = 100\n                _non_eos_idxs = [47, 51, 13, 88, 99]\n                valid_score_dist_1 = torch.log_softmax(torch.tensor(\n                    [6., 5., 4., 3., 2., 1.]), dim=0)\n                valid_score_dist_2 = torch.log_softmax(torch.tensor(\n                    [6., 1.]), dim=0)\n                eos_idx = 2\n                lengths = torch.randint(0, 30, (batch_sz,))\n                samp = GreedySearch(\n                    0, 1, 2, batch_sz, 0,\n                    False, set(), False, 30, temp, 1)\n                samp.initialize(torch.zeros(1), lengths)\n                # initial step\n                i = 0\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                # batch 0 dies on step 0\n                word_probs[0, eos_idx] = valid_score_dist_1[0]\n                # include at least one prediction OTHER than EOS\n                # that is greater than -1e20\n                word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]\n                word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n                self.assertTrue(samp.is_finished[0].eq(1).all())\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[0], [valid_score_dist_1[0] / temp])\n                if batch_sz == 1:\n                    self.assertTrue(samp.done)\n                    continue\n                else:\n                    self.assertFalse(samp.done)\n\n                # step 2\n                i = 1\n                word_probs = torch.full(\n                    (batch_sz - 1, n_words), -float('inf'))\n                # (old) batch 8 dies on step 1\n                word_probs[7, eos_idx] = valid_score_dist_2[0]\n                word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2\n                word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n\n                self.assertTrue(samp.is_finished[7].eq(1).all())\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[8], [valid_score_dist_2[0] / temp])\n\n                # step 3\n                i = 2\n                word_probs = torch.full(\n                    (batch_sz - 2, n_words), -float('inf'))\n                # everything dies\n                word_probs[:, eos_idx] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n\n                self.assertTrue(samp.is_finished.eq(1).all())\n                samp.update_finished()\n                for b in range(batch_sz):\n                    if b != 0 and b != 8:\n                        self.assertEqual(samp.scores[b], [0])\n                self.assertTrue(samp.done)\n\n    def test_returns_correct_scores_non_deterministic(self):\n        for batch_sz in [1, 13]:\n            for temp in [1., 3.]:\n                n_words = 100\n                _non_eos_idxs = [47, 51, 13, 88, 99]\n                valid_score_dist_1 = torch.log_softmax(torch.tensor(\n                    [6., 5., 4., 3., 2., 1.]), dim=0)\n                valid_score_dist_2 = torch.log_softmax(torch.tensor(\n                    [6., 1.]), dim=0)\n                eos_idx = 2\n                lengths = torch.randint(0, 30, (batch_sz,))\n                samp = GreedySearch(\n                    0, 1, 2, batch_sz, 0,\n                    False, set(), False, 30, temp, 2)\n                samp.initialize(torch.zeros(1), lengths)\n                # initial step\n                i = 0\n                for _ in range(100):\n                    word_probs = torch.full(\n                        (batch_sz, n_words), -float('inf'))\n                    # batch 0 dies on step 0\n                    word_probs[0, eos_idx] = valid_score_dist_1[0]\n                    # include at least one prediction OTHER than EOS\n                    # that is greater than -1e20\n                    word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]\n                    word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished[0].eq(1).all():\n                        break\n                else:\n                    self.fail(\"Batch 0 never ended (very unlikely but maybe \"\n                              \"due to stochasticisty. If so, please increase \"\n                              \"the range of the for-loop.\")\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[0], [valid_score_dist_1[0] / temp])\n                if batch_sz == 1:\n                    self.assertTrue(samp.done)\n                    continue\n                else:\n                    self.assertFalse(samp.done)\n\n                # step 2\n                i = 1\n                for _ in range(100):\n                    word_probs = torch.full(\n                        (batch_sz - 1, n_words), -float('inf'))\n                    # (old) batch 8 dies on step 1\n                    word_probs[7, eos_idx] = valid_score_dist_2[0]\n                    word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2\n                    word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished[7].eq(1).all():\n                        break\n                else:\n                    self.fail(\"Batch 8 never ended (very unlikely but maybe \"\n                              \"due to stochasticisty. If so, please increase \"\n                              \"the range of the for-loop.\")\n\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[8], [valid_score_dist_2[0] / temp])\n\n                # step 3\n                i = 2\n                for _ in range(250):\n                    word_probs = torch.full(\n                        (samp.alive_seq.shape[0], n_words), -float('inf'))\n                    # everything dies\n                    word_probs[:, eos_idx] = 0\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished.any():\n                        samp.update_finished()\n                    if samp.is_finished.eq(1).all():\n                        break\n                else:\n                    self.fail(\"All batches never ended (very unlikely but \"\n                              \"maybe due to stochasticisty. If so, please \"\n                              \"increase the range of the for-loop.\")\n\n                for b in range(batch_sz):\n                    if b != 0 and b != 8:\n                        self.assertEqual(samp.scores[b], [0])\n                self.assertTrue(samp.done)\n"
  },
  {
    "path": "onmt/tests/test_models.py",
    "content": "import copy\nimport unittest\n\nimport torch\n\nimport onmt\nimport onmt.inputters\nimport onmt.opts\nfrom onmt.model_builder import build_embeddings, \\\n    build_encoder, build_decoder\nfrom onmt.utils.parse import ArgumentParser\n\nparser = ArgumentParser(description='train.py')\nonmt.opts.model_opts(parser)\nonmt.opts._add_train_general_opts(parser)\n\n# -data option is required, but not used in this test, so dummy.\nopt = parser.parse_known_args(['-data', 'dummy'])[0]\n\n\nclass TestModel(unittest.TestCase):\n\n    def __init__(self, *args, **kwargs):\n        super(TestModel, self).__init__(*args, **kwargs)\n        self.opt = opt\n\n    def get_field(self):\n        src = onmt.inputters.get_fields(\"text\", 0, 0)[\"src\"]\n        src.base_field.build_vocab([])\n        return src\n\n    def get_batch(self, source_l=3, bsize=1):\n        # len x batch x nfeat\n        test_src = torch.ones(source_l, bsize, 1).long()\n        test_tgt = torch.ones(source_l, bsize, 1).long()\n        test_length = torch.ones(bsize).fill_(source_l).long()\n        return test_src, test_tgt, test_length\n\n    def embeddings_forward(self, opt, source_l=3, bsize=1):\n        '''\n        Tests if the embeddings works as expected\n\n        args:\n            opt: set of options\n            source_l: Length of generated input sentence\n            bsize: Batchsize of generated input\n        '''\n        word_field = self.get_field()\n        emb = build_embeddings(opt, word_field)\n        test_src, _, __ = self.get_batch(source_l=source_l, bsize=bsize)\n        if opt.decoder_type == 'transformer':\n            input = torch.cat([test_src, test_src], 0)\n            res = emb(input)\n            compare_to = torch.zeros(source_l * 2, bsize,\n                                     opt.src_word_vec_size)\n        else:\n            res = emb(test_src)\n            compare_to = torch.zeros(source_l, bsize, opt.src_word_vec_size)\n\n        self.assertEqual(res.size(), compare_to.size())\n\n    def encoder_forward(self, opt, source_l=3, bsize=1):\n        '''\n        Tests if the encoder works as expected\n\n        args:\n            opt: set of options\n            source_l: Length of generated input sentence\n            bsize: Batchsize of generated input\n        '''\n        if opt.rnn_size > 0:\n            opt.enc_rnn_size = opt.rnn_size\n        word_field = self.get_field()\n        embeddings = build_embeddings(opt, word_field)\n        enc = build_encoder(opt, embeddings)\n\n        test_src, test_tgt, test_length = self.get_batch(source_l=source_l,\n                                                         bsize=bsize)\n\n        hidden_t, outputs, test_length = enc(test_src, test_length)\n\n        # Initialize vectors to compare size with\n        test_hid = torch.zeros(self.opt.enc_layers, bsize, opt.enc_rnn_size)\n        test_out = torch.zeros(source_l, bsize, opt.dec_rnn_size)\n\n        # Ensure correct sizes and types\n        self.assertEqual(test_hid.size(),\n                         hidden_t[0].size(),\n                         hidden_t[1].size())\n        self.assertEqual(test_out.size(), outputs.size())\n        self.assertEqual(type(outputs), torch.Tensor)\n\n    def nmtmodel_forward(self, opt, source_l=3, bsize=1):\n        \"\"\"\n        Creates a nmtmodel with a custom opt function.\n        Forwards a testbatch and checks output size.\n\n        Args:\n            opt: Namespace with options\n            source_l: length of input sequence\n            bsize: batchsize\n        \"\"\"\n        if opt.rnn_size > 0:\n            opt.enc_rnn_size = opt.rnn_size\n            opt.dec_rnn_size = opt.rnn_size\n        word_field = self.get_field()\n\n        embeddings = build_embeddings(opt, word_field)\n        enc = build_encoder(opt, embeddings)\n\n        embeddings = build_embeddings(opt, word_field, for_encoder=False)\n        dec = build_decoder(opt, embeddings)\n\n        model = onmt.models.model.NMTModel(enc, dec)\n\n        test_src, test_tgt, test_length = self.get_batch(source_l=source_l,\n                                                         bsize=bsize)\n        outputs, attn = model(test_src, test_tgt, test_length)\n        outputsize = torch.zeros(source_l - 1, bsize, opt.dec_rnn_size)\n        # Make sure that output has the correct size and type\n        self.assertEqual(outputs.size(), outputsize.size())\n        self.assertEqual(type(outputs), torch.Tensor)\n\n\ndef _add_test(param_setting, methodname):\n    \"\"\"\n    Adds a Test to TestModel according to settings\n\n    Args:\n        param_setting: list of tuples of (param, setting)\n        methodname: name of the method that gets called\n    \"\"\"\n\n    def test_method(self):\n        opt = copy.deepcopy(self.opt)\n        if param_setting:\n            for param, setting in param_setting:\n                setattr(opt, param, setting)\n        ArgumentParser.update_model_opts(opt)\n        getattr(self, methodname)(opt)\n    if param_setting:\n        name = 'test_' + methodname + \"_\" + \"_\".join(\n            str(param_setting).split())\n    else:\n        name = 'test_' + methodname + '_standard'\n    setattr(TestModel, name, test_method)\n    test_method.__name__ = name\n\n\n'''\nTEST PARAMETERS\n'''\nopt.brnn = False\n\ntest_embeddings = [[],\n                   [('decoder_type', 'transformer')]\n                   ]\n\nfor p in test_embeddings:\n    _add_test(p, 'embeddings_forward')\n\ntests_encoder = [[],\n                 [('encoder_type', 'mean')],\n                 # [('encoder_type', 'transformer'),\n                 # ('word_vec_size', 16), ('rnn_size', 16)],\n                 []\n                 ]\n\nfor p in tests_encoder:\n    _add_test(p, 'encoder_forward')\n\ntests_nmtmodel = [[('rnn_type', 'GRU')],\n                  [('layers', 10)],\n                  [('input_feed', 0)],\n                  [('decoder_type', 'transformer'),\n                   ('encoder_type', 'transformer'),\n                   ('src_word_vec_size', 16),\n                   ('tgt_word_vec_size', 16),\n                   ('rnn_size', 16)],\n                  [('decoder_type', 'transformer'),\n                   ('encoder_type', 'transformer'),\n                   ('src_word_vec_size', 16),\n                   ('tgt_word_vec_size', 16),\n                   ('rnn_size', 16),\n                   ('position_encoding', True)],\n                  [('coverage_attn', True)],\n                  [('copy_attn', True)],\n                  [('global_attention', 'mlp')],\n                  [('context_gate', 'both')],\n                  [('context_gate', 'target')],\n                  [('context_gate', 'source')],\n                  [('encoder_type', \"brnn\"),\n                   ('brnn_merge', 'sum')],\n                  [('encoder_type', \"brnn\")],\n                  [('decoder_type', 'cnn'),\n                   ('encoder_type', 'cnn')],\n                  [('encoder_type', 'rnn'),\n                   ('global_attention', None)],\n                  [('encoder_type', 'rnn'),\n                   ('global_attention', None),\n                   ('copy_attn', True),\n                   ('copy_attn_type', 'general')],\n                  [('encoder_type', 'rnn'),\n                   ('global_attention', 'mlp'),\n                   ('copy_attn', True),\n                   ('copy_attn_type', 'general')],\n                  [],\n                  ]\n\nif onmt.models.sru.check_sru_requirement():\n    #   \"\"\" Only do SRU test if requirment is safisfied. \"\"\"\n    # SRU doesn't support input_feed.\n    tests_nmtmodel.append([('rnn_type', 'SRU'), ('input_feed', 0)])\n\nfor p in tests_nmtmodel:\n    _add_test(p, 'nmtmodel_forward')\n"
  },
  {
    "path": "onmt/tests/test_models.sh",
    "content": "#!/bin/bash\n### Utility to test models by using command lines actions to run\n### Actions are typically model training / translation\n### or options setter.\n###\n### Actions are executed in the order its provided, therefore setters must\n### be first\n###  \n### Example: \n###     - Run all tests: \n###             ./test_models.sh all\n###             or\n###             ./test_models.sh\n###\n###     - Run all tests using GPU (i.e. -gpuid 0):\n###             ./test_models.sh set_gpu all\n###             (note that set_gpu comes first!!!)\n###             you can set all GPU (i.e. to match CUDA_VISIBLE_DEVICES)!\n###             ./test_models.sh set_all_gpu all\n###  \n###     - Train each models, and run translation (for each!):\n###             ./test_models.sh translate_each all\n###             (note that translate_each comes first!!!)\n###     \n###     - Train and translate a specific model (e.g. lstm):\n###             ./test_models.sh lstm translate\n###             note that translate only consider the last model therefore:\n###             ./test_models.sh lstm cnn translate\n###             would actually use CNN model for translation\n###  \n###     - Run in debug mode (stops on first error) \n###             ./test_models set_debug all \n###  \n\n\nPYTHON_BIN=python\n\n\nMODEL_DIR=\"/tmp\"\nMODEL_NAME=\"onmt_tmp_model\"\nMODEL_PATH=\"$MODEL_DIR/$MODEL_NAME\"\nMODEL_FILES_PREFIX=\"${MODEL_NAME}_acc_\"\n\nTEST_DIR=\"./onmt/tests\"\nTEST_MODEL_NAME=\"test_model.pt\"\nTEST_MODEL_PATH=\"$TEST_DIR/$TEST_MODEL_NAME\"\n\nDATA_DIR=\"./data\"\nDATA_PATH=\"$DATA_DIR/data\"\n\n\n# Do not edit directly, use calls 'set_gpu' and 'translate_each'\nGPUID=-1\nTRANSLATE_EACH=0\n\n### Some setters\n###############################################\nset_gpu(){\n    GPUID=0\n}\n\n\nset_all_gpu(){\n    GPUID=$(sed 's/,/ /g' <(echo $CUDA_VISIBLE_DEVICES) >&1)\n}\nprint_gpuid(){\n    echo \"$GPUID\"\n}\n\nset_debug(){\n    set -e\n}\n\ntranslate_each(){\n    TRANSLATE_EACH=1\n}\n### Some utils functions\n###############################################\nmv_best_checkpoint(){\n    best_model=\"$(ls -lsrt $MODEL_DIR | grep \"${MODEL_FILES_PREFIX}*\" | tail -n 1 | awk '{print $NF}')\"\n    mv \"$MODEL_DIR/$best_model\" \"$TEST_MODEL_PATH\"\n}\n\nrm_tmp_checkpoints(){\n    rm -f \"$MODEL_DIR/${MODEL_FILES_PREFIX}\"*\n}\n\n\n### RNNLM\n###############################################\nlstm(){\n    rm -f \"$DATA_DIR\"/*.pt\n    $PYTHON_BIN preprocess.py -train_src \"$DATA_DIR\"/src-train.txt \\\n                         -train_tgt \"$DATA_DIR\"/tgt-train.txt \\\n                         -valid_src \"$DATA_DIR\"/src-val.txt \\\n                         -valid_tgt \"$DATA_DIR\"/tgt-val.txt \\\n                         -save_data \"$DATA_PATH\" \\\n                         -src_vocab_size 1000 \\\n                         -tgt_vocab_size 1000\n\n    $PYTHON_BIN train.py -data \"$DATA_PATH\" \\\n                    -save_model \"$MODEL_PATH\" \\\n                    -gpuid $GPUID \\\n                    -rnn_size 512 \\\n                    -word_vec_size 512 \\\n                    -layers 1 \\\n                    -train_steps 10000 \\\n                    -optim adam  \\\n                    -learning_rate 0.001 \\\n                    -rnn_type LSTM\n    mv_best_checkpoint\n    maybe_translate\n    rm_tmp_checkpoints\n}\n\n\n\n### SRU\n###############################################\nsru(){\n    rm -f \"$DATA_DIR\"/*.pt\n    $PYTHON_BIN preprocess.py -train_src \"$DATA_DIR\"/src-train.txt \\\n                         -train_tgt \"$DATA_DIR\"/tgt-train.txt \\\n                         -valid_src \"$DATA_DIR\"/src-val.txt \\\n                         -valid_tgt \"$DATA_DIR\"/tgt-val.txt \\\n                         -save_data \"$DATA_PATH\" \\\n                         -src_vocab_size 1000 \\\n                         -tgt_vocab_size 1000 \\\n                         -rnn_type \"SRU\" \\\n                         -input_feed 0\n\n    $PYTHON_BIN train.py -data \"$DATA_PATH\" \\\n                    -save_model \"$MODEL_PATH\" \\\n                    -gpuid $GPUID \\\n                    -rnn_size 512 \\\n                    -word_vec_size 512 \\\n                    -layers 1 \\\n                    -train_steps 10000 \\\n                    -optim adam  \\\n                    -learning_rate 0.001 \\\n                    -rnn_type LSTM\n    mv_best_checkpoint\n    maybe_translate\n    rm_tmp_checkpoints\n}\n### CNN\n###############################################\ncnn(){\n    rm -f \"$DATA_DIR\"/*.pt\n    $PYTHON_BIN preprocess.py -train_src \"$DATA_DIR\"/src-train.txt\\\n                         -train_tgt \"$DATA_DIR\"/tgt-train.txt \\\n                         -valid_src \"$DATA_DIR\"/src-val.txt \\\n                         -valid_tgt \"$DATA_DIR\"/tgt-val.txt \\\n                         -save_data \"$DATA_PATH\" \\\n                         -src_vocab_size 1000 \\\n                         -tgt_vocab_size 1000 \n    \n    $PYTHON_BIN train.py -data \"$DATA_PATH\" \\\n                    -save_model \"$MODEL_PATH\" \\\n                    -gpuid $GPUID \\\n                    -rnn_size 256 \\\n                    -word_vec_size 256 \\\n                    -layers 2 \\\n                    -train_steps 10000 \\\n                    -optim adam  \\\n                    -learning_rate 0.001 \\\n                    -encoder_type cnn \\\n                    -decoder_type cnn\n    mv_best_checkpoint\n    maybe_translate\n    rm_tmp_checkpoints\n}\n\n\n### MORPH DATA\n###############################################\nmorph(){\n    ################# MORPH DATA\n    rm -f \"$DATA_DIR\"/morph/*.pt\n    $PYTHON_BIN preprocess.py -train_src \"$DATA_DIR\"/morph/src.train \\\n                         -train_tgt \"$DATA_DIR\"/morph/tgt.train \\\n                         -valid_src \"$DATA_DIR\"/morph/src.valid \\\n                         -valid_tgt \"$DATA_DIR\"/morph/tgt.valid \\\n                         -save_data \"$DATA_DIR\"/morph/data \n\n    $PYTHON_BIN train.py -data \"$DATA_DIR\"/morph/data \\\n                    -save_model \"$MODEL_PATH\" \\\n                    -gpuid $GPUID \\\n                    -rnn_size 400 \\\n                    -word_vec_size 100 \\\n                    -layers 1 \\\n                    -train_steps 10000 \\\n                    -optim adam  \\\n                    -learning_rate 0.001\n\n    mv_best_checkpoint\n    maybe_translate\n    rm_tmp_checkpoints\n}\n\n\n### TRANSFORMER\n###############################################\ntransformer(){\n    rm -f \"$DATA_DIR\"/*.pt\n    $PYTHON_BIN preprocess.py -train_src \"$DATA_DIR\"/src-train.txt \\\n                         -train_tgt \"$DATA_DIR\"/tgt-train.txt \\\n                         -valid_src \"$DATA_DIR\"/src-val.txt \\\n                         -valid_tgt \"$DATA_DIR\"/tgt-val.txt \\\n                         -save_data \"$DATA_PATH\" \\\n                         -src_vocab_size 1000 \\\n                         -tgt_vocab_size 1000 \\\n                         -share_vocab\n\n\n    $PYTHON_BIN train.py -data \"$DATA_PATH\" \\\n                    -save_model \"$MODEL_PATH\" \\\n                    -share_embedding \\\n                    -batch_type tokens \\\n                    -batch_size 1024 \\\n                    -accum_count 4 \\\n                    -layers 1 \\\n                    -rnn_size 256 \\\n                    -word_vec_size 256 \\\n                    -encoder_type transformer \\\n                    -decoder_type transformer \\\n                    -train_steps 10000 \\\n                    -gpuid $GPUID \\\n                    -max_generator_batches 4 \\\n                    -dropout 0.1 \\\n                    -normalization tokens \\\n                    -max_grad_norm 0 \\\n                    -optim adam \\\n                    -decay_method noam \\\n                    -learning_rate 2 \\\n                    -position_encoding \\\n                    -param_init 0 \\\n                    -warmup_steps 100 \\\n                    -param_init_glorot \\\n                    -adam_beta2 0.998\n\n    mv_best_checkpoint\n    maybe_translate\n    rm_tmp_checkpoints\n\n}\n\n\n### TRANSLATION\n###############################################\ntranslate(){\n    $PYTHON_BIN translate.py -gpu \"$GPUID\" \\\n                        -model \"$TEST_MODEL_PATH\" \\\n                        -output \"$TEST_DIR\"/output_hyp.txt \\\n                        -beam 5 \\\n                        -batch_size 32 \\\n                        -src \"$DATA_DIR\"/src-val.txt\n}\n\nmaybe_translate(){\n    if [ $TRANSLATE_EACH -eq 1 ]\n    then\n        translate\n    fi\n}\n\nall(){\n    lstm\n    sru\n    cnn\n    morph\n    transformer\n    translate\n\n}\n\nactions=\"$@\"\n\n# set the default action\nif [ -z \"$1\" ]; then\n    actions=\"all\"\nfi\n\n# Process actions (in order)\nfor action in $actions; do\n    echo \"Running: $action\"\n    eval \"$action\"\ndone\n\necho \"Done.\"\n"
  },
  {
    "path": "onmt/tests/test_random_sampling.py",
    "content": "import unittest\nfrom onmt.translate.random_sampling import RandomSampling\n\nimport torch\n\n\nclass TestRandomSampling(unittest.TestCase):\n    BATCH_SZ = 3\n    INP_SEQ_LEN = 53\n    DEAD_SCORE = -1e20\n\n    BLOCKED_SCORE = -10e20\n\n    def test_advance_with_repeats_gets_blocked(self):\n        n_words = 100\n        repeat_idx = 47\n        ngram_repeat = 3\n        for batch_sz in [1, 3]:\n            samp = RandomSampling(\n                0, 1, 2, batch_sz, torch.device(\"cpu\"), 0, ngram_repeat, set(),\n                False, 30, 1., 5, torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                # predict repeat_idx over and over again\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                word_probs[:, repeat_idx] = 0\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n                if i <= ngram_repeat:\n                    expected_scores = torch.zeros((batch_sz, 1))\n                    self.assertTrue(samp.topk_scores.equal(expected_scores))\n                else:\n                    self.assertTrue(\n                        samp.topk_scores.equal(\n                            torch.tensor(self.BLOCKED_SCORE)\n                            .repeat(batch_sz, 1)))\n\n    def test_advance_with_some_repeats_gets_blocked(self):\n        # batch 0 and 7 will repeat, the rest will advance\n        n_words = 100\n        repeat_idx = 47\n        other_repeat_idx = 12\n        ngram_repeat = 3\n        for batch_sz in [1, 3, 13]:\n            samp = RandomSampling(\n                0, 1, 2, batch_sz, torch.device(\"cpu\"), 0, ngram_repeat, set(),\n                False, 30, 1., 5, torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                # predict the same thing in batch 0 and 7 every i\n                word_probs[0, repeat_idx] = 0\n                if batch_sz > 7:\n                    word_probs[7, other_repeat_idx] = 0\n                # push around what the other batches predict\n                word_probs[1:7, repeat_idx + i] = 0\n                if batch_sz > 7:\n                    word_probs[8:, repeat_idx + i] = 0\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n                if i <= ngram_repeat:\n                    self.assertFalse(\n                        samp.topk_scores.eq(\n                            self.BLOCKED_SCORE).any())\n                else:\n                    # now batch 0 and 7 die\n                    self.assertTrue(samp.topk_scores[0].eq(self.BLOCKED_SCORE))\n                    if batch_sz > 7:\n                        self.assertTrue(samp.topk_scores[7].eq(\n                            self.BLOCKED_SCORE))\n                    self.assertFalse(\n                        samp.topk_scores[1:7].eq(\n                            self.BLOCKED_SCORE).any())\n                    if batch_sz > 7:\n                        self.assertFalse(\n                            samp.topk_scores[8:].eq(\n                                self.BLOCKED_SCORE).any())\n\n    def test_repeating_excluded_index_does_not_die(self):\n        # batch 0 will repeat excluded idx, batch 1 will repeat\n        n_words = 100\n        repeat_idx = 47  # will be repeated and should be blocked\n        repeat_idx_ignored = 7  # will be repeated and should not be blocked\n        ngram_repeat = 3\n        for batch_sz in [1, 3, 17]:\n            samp = RandomSampling(\n                0, 1, 2, batch_sz, torch.device(\"cpu\"), 0, ngram_repeat,\n                {repeat_idx_ignored}, False, 30, 1., 5,\n                torch.randint(0, 30, (batch_sz,)))\n            for i in range(ngram_repeat + 4):\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                word_probs[0, repeat_idx_ignored] = 0\n                if batch_sz > 1:\n                    word_probs[1, repeat_idx] = 0\n                    word_probs[2:, repeat_idx + i] = 0\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n                if i <= ngram_repeat:\n                    self.assertFalse(samp.topk_scores.eq(\n                        self.BLOCKED_SCORE).any())\n                else:\n                    # now batch 1 dies\n                    self.assertFalse(samp.topk_scores[0].eq(\n                        self.BLOCKED_SCORE).any())\n                    if batch_sz > 1:\n                        self.assertTrue(samp.topk_scores[1].eq(\n                            self.BLOCKED_SCORE).all())\n                        self.assertFalse(samp.topk_scores[2:].eq(\n                            self.BLOCKED_SCORE).any())\n\n    def test_doesnt_predict_eos_if_shorter_than_min_len(self):\n        # batch 0 will always predict EOS. The other batches will predict\n        # non-eos scores.\n        for batch_sz in [1, 3]:\n            n_words = 100\n            _non_eos_idxs = [47]\n            valid_score_dist = torch.log_softmax(torch.tensor(\n                [6., 5.]), dim=0)\n            min_length = 5\n            eos_idx = 2\n            lengths = torch.randint(0, 30, (batch_sz,))\n            samp = RandomSampling(\n                0, 1, 2, batch_sz, torch.device(\"cpu\"), min_length,\n                False, set(), False, 30, 1., 1, lengths)\n            all_attns = []\n            for i in range(min_length + 4):\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                # \"best\" prediction is eos - that should be blocked\n                word_probs[0, eos_idx] = valid_score_dist[0]\n                # include at least one prediction OTHER than EOS\n                # that is greater than -1e20\n                word_probs[0, _non_eos_idxs[0]] = valid_score_dist[1]\n                word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                all_attns.append(attns)\n                samp.advance(word_probs, attns)\n                if i < min_length:\n                    self.assertTrue(\n                        samp.topk_scores[0].allclose(valid_score_dist[1]))\n                    self.assertTrue(\n                        samp.topk_scores[1:].eq(0).all())\n                elif i == min_length:\n                    # now batch 0 has ended and no others have\n                    self.assertTrue(samp.is_finished[0, :].eq(1).all())\n                    self.assertTrue(samp.is_finished[1:, 1:].eq(0).all())\n                else:  # i > min_length\n                    break\n\n    def test_returns_correct_scores_deterministic(self):\n        for batch_sz in [1, 13]:\n            for temp in [1., 3.]:\n                n_words = 100\n                _non_eos_idxs = [47, 51, 13, 88, 99]\n                valid_score_dist_1 = torch.log_softmax(torch.tensor(\n                    [6., 5., 4., 3., 2., 1.]), dim=0)\n                valid_score_dist_2 = torch.log_softmax(torch.tensor(\n                    [6., 1.]), dim=0)\n                eos_idx = 2\n                lengths = torch.randint(0, 30, (batch_sz,))\n                samp = RandomSampling(\n                    0, 1, 2, batch_sz, torch.device(\"cpu\"), 0,\n                    False, set(), False, 30, temp, 1, lengths)\n\n                # initial step\n                i = 0\n                word_probs = torch.full(\n                    (batch_sz, n_words), -float('inf'))\n                # batch 0 dies on step 0\n                word_probs[0, eos_idx] = valid_score_dist_1[0]\n                # include at least one prediction OTHER than EOS\n                # that is greater than -1e20\n                word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]\n                word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n                self.assertTrue(samp.is_finished[0].eq(1).all())\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[0], [valid_score_dist_1[0] / temp])\n                if batch_sz == 1:\n                    self.assertTrue(samp.done)\n                    continue\n                else:\n                    self.assertFalse(samp.done)\n\n                # step 2\n                i = 1\n                word_probs = torch.full(\n                    (batch_sz - 1, n_words), -float('inf'))\n                # (old) batch 8 dies on step 1\n                word_probs[7, eos_idx] = valid_score_dist_2[0]\n                word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2\n                word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n\n                self.assertTrue(samp.is_finished[7].eq(1).all())\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[8], [valid_score_dist_2[0] / temp])\n\n                # step 3\n                i = 2\n                word_probs = torch.full(\n                    (batch_sz - 2, n_words), -float('inf'))\n                # everything dies\n                word_probs[:, eos_idx] = 0\n\n                attns = torch.randn(1, batch_sz, 53)\n                samp.advance(word_probs, attns)\n\n                self.assertTrue(samp.is_finished.eq(1).all())\n                samp.update_finished()\n                for b in range(batch_sz):\n                    if b != 0 and b != 8:\n                        self.assertEqual(samp.scores[b], [0])\n                self.assertTrue(samp.done)\n\n    def test_returns_correct_scores_non_deterministic(self):\n        for batch_sz in [1, 13]:\n            for temp in [1., 3.]:\n                n_words = 100\n                _non_eos_idxs = [47, 51, 13, 88, 99]\n                valid_score_dist_1 = torch.log_softmax(torch.tensor(\n                    [6., 5., 4., 3., 2., 1.]), dim=0)\n                valid_score_dist_2 = torch.log_softmax(torch.tensor(\n                    [6., 1.]), dim=0)\n                eos_idx = 2\n                lengths = torch.randint(0, 30, (batch_sz,))\n                samp = RandomSampling(\n                    0, 1, 2, batch_sz, torch.device(\"cpu\"), 0,\n                    False, set(), False, 30, temp, 2, lengths)\n\n                # initial step\n                i = 0\n                for _ in range(100):\n                    word_probs = torch.full(\n                        (batch_sz, n_words), -float('inf'))\n                    # batch 0 dies on step 0\n                    word_probs[0, eos_idx] = valid_score_dist_1[0]\n                    # include at least one prediction OTHER than EOS\n                    # that is greater than -1e20\n                    word_probs[0, _non_eos_idxs] = valid_score_dist_1[1:]\n                    word_probs[1:, _non_eos_idxs[0] + i] = 0\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished[0].eq(1).all():\n                        break\n                else:\n                    self.fail(\"Batch 0 never ended (very unlikely but maybe \"\n                              \"due to stochasticisty. If so, please increase \"\n                              \"the range of the for-loop.\")\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[0], [valid_score_dist_1[0] / temp])\n                if batch_sz == 1:\n                    self.assertTrue(samp.done)\n                    continue\n                else:\n                    self.assertFalse(samp.done)\n\n                # step 2\n                i = 1\n                for _ in range(100):\n                    word_probs = torch.full(\n                        (batch_sz - 1, n_words), -float('inf'))\n                    # (old) batch 8 dies on step 1\n                    word_probs[7, eos_idx] = valid_score_dist_2[0]\n                    word_probs[0:7, _non_eos_idxs[:2]] = valid_score_dist_2\n                    word_probs[8:, _non_eos_idxs[:2]] = valid_score_dist_2\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished[7].eq(1).all():\n                        break\n                else:\n                    self.fail(\"Batch 8 never ended (very unlikely but maybe \"\n                              \"due to stochasticisty. If so, please increase \"\n                              \"the range of the for-loop.\")\n\n                samp.update_finished()\n                self.assertEqual(\n                    samp.scores[8], [valid_score_dist_2[0] / temp])\n\n                # step 3\n                i = 2\n                for _ in range(250):\n                    word_probs = torch.full(\n                        (samp.alive_seq.shape[0], n_words), -float('inf'))\n                    # everything dies\n                    word_probs[:, eos_idx] = 0\n\n                    attns = torch.randn(1, batch_sz, 53)\n                    samp.advance(word_probs, attns)\n                    if samp.is_finished.any():\n                        samp.update_finished()\n                    if samp.is_finished.eq(1).all():\n                        break\n                else:\n                    self.fail(\"All batches never ended (very unlikely but \"\n                              \"maybe due to stochasticisty. If so, please \"\n                              \"increase the range of the for-loop.\")\n\n                for b in range(batch_sz):\n                    if b != 0 and b != 8:\n                        self.assertEqual(samp.scores[b], [0])\n                self.assertTrue(samp.done)\n"
  },
  {
    "path": "onmt/tests/test_simple.py",
    "content": "import onmt\n\n\ndef test_load():\n    onmt\n    pass\n"
  },
  {
    "path": "onmt/tests/test_structured_attention.py",
    "content": "import unittest\nfrom onmt.modules.structured_attention import MatrixTree\n\nimport torch\n\n\nclass TestStructuredAttention(unittest.TestCase):\n    def test_matrix_tree_marg_pdfs_sum_to_1(self):\n        dtree = MatrixTree()\n        q = torch.rand(1, 5, 5)\n        marg = dtree.forward(q)\n        self.assertTrue(\n            marg.sum(1).allclose(torch.tensor(1.0)))\n"
  },
  {
    "path": "onmt/tests/test_text_dataset.py",
    "content": "import unittest\nfrom onmt.inputters.text_dataset import TextMultiField, TextDataReader\n\nimport itertools\nimport os\nfrom copy import deepcopy\n\nfrom torchtext.data import Field\n\nfrom onmt.tests.utils_for_tests import product_dict\n\n\nclass TestTextMultiField(unittest.TestCase):\n    INIT_CASES = list(product_dict(\n        base_name=[\"base_field\", \"zbase_field\"],\n        base_field=[Field],\n        feats_fields=[\n            [],\n            [(\"a\", Field)],\n            [(\"r\", Field), (\"b\", Field)]]))\n\n    PARAMS = list(product_dict(\n        include_lengths=[False, True]))\n\n    @classmethod\n    def initialize_case(cls, init_case, params):\n        # initialize fields at the top of each unit test to prevent\n        # any undesired stateful effects\n        case = deepcopy(init_case)\n        case[\"base_field\"] = case[\"base_field\"](\n            include_lengths=params[\"include_lengths\"])\n        for i, (n, f_cls) in enumerate(case[\"feats_fields\"]):\n            case[\"feats_fields\"][i] = (n, f_cls(sequential=True))\n        return case\n\n    def test_process_shape(self):\n        dummy_input_bs_1 = [[\n                [\"this\", \"is\", \"for\", \"the\", \"unittest\"],\n                [\"NOUN\", \"VERB\", \"PREP\", \"ART\", \"NOUN\"],\n                [\"\", \"\", \"\", \"\", \"MODULE\"]]]\n        dummy_input_bs_5 = [\n                [[\"this\", \"is\", \"for\", \"the\", \"unittest\"],\n                 [\"NOUN\", \"VERB\", \"PREP\", \"ART\", \"NOUN\"],\n                 [\"\", \"\", \"\", \"\", \"MODULE\"]],\n                [[\"batch\", \"2\"],\n                 [\"NOUN\", \"NUM\"],\n                 [\"\", \"\"]],\n                [[\"batch\", \"3\", \"is\", \"the\", \"longest\", \"batch\"],\n                 [\"NOUN\", \"NUM\", \"VERB\", \"ART\", \"ADJ\", \"NOUN\"],\n                 [\"\", \"\", \"\", \"\", \"\", \"\"]],\n                [[\"fourth\", \"batch\"],\n                 [\"ORD\", \"NOUN\"],\n                 [\"\", \"\"]],\n                [[\"and\", \"another\", \"one\"],\n                 [\"CONJ\", \"?\", \"NUM\"],\n                 [\"\", \"\", \"\"]]]\n        for bs, max_len, dummy_input in [\n                (1, 5, dummy_input_bs_1), (5, 6, dummy_input_bs_5)]:\n            for init_case, params in itertools.product(\n                    self.INIT_CASES, self.PARAMS):\n                init_case = self.initialize_case(init_case, params)\n                mf = TextMultiField(**init_case)\n                fields = [init_case[\"base_field\"]] \\\n                    + [f for _, f in init_case[\"feats_fields\"]]\n                nfields = len(fields)\n                for i, f in enumerate(fields):\n                    all_sents = [b[i] for b in dummy_input]\n                    f.build_vocab(all_sents)\n                inp_only_desired_fields = [b[:nfields] for b in dummy_input]\n                data = mf.process(inp_only_desired_fields)\n                if params[\"include_lengths\"]:\n                    data, lengths = data\n                    self.assertEqual(lengths.shape, (bs,))\n                expected_shape = (max_len, bs, nfields)\n                self.assertEqual(data.shape, expected_shape)\n\n    def test_preprocess_shape(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            sample_str = \"dummy input here .\"\n            proc = mf.preprocess(sample_str)\n            self.assertEqual(len(proc), len(init_case[\"feats_fields\"]) + 1)\n\n    def test_base_field(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            self.assertIs(mf.base_field, init_case[\"base_field\"])\n\n    def test_correct_n_fields(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            self.assertEqual(len(mf.fields),\n                             len(init_case[\"feats_fields\"]) + 1)\n\n    def test_fields_order_correct(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            fnames = [name for name, _ in init_case[\"feats_fields\"]]\n            correct_order = [init_case[\"base_name\"]] + list(sorted(fnames))\n            self.assertEqual([name for name, _ in mf.fields], correct_order)\n\n    def test_getitem_0_returns_correct_field(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            self.assertEqual(mf[0][0], init_case[\"base_name\"])\n            self.assertIs(mf[0][1], init_case[\"base_field\"])\n\n    def test_getitem_nonzero_returns_correct_field(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            fnames = [name for name, _ in init_case[\"feats_fields\"]]\n            if len(fnames) > 0:\n                ordered_names = list(sorted(fnames))\n                name2field = dict(init_case[\"feats_fields\"])\n                for i, name in enumerate(ordered_names, 1):\n                    expected_field = name2field[name]\n                    self.assertIs(mf[i][1], expected_field)\n\n    def test_getitem_has_correct_number_of_indexes(self):\n        for init_case, params in itertools.product(\n                self.INIT_CASES, self.PARAMS):\n            init_case = self.initialize_case(init_case, params)\n            mf = TextMultiField(**init_case)\n            nfields = len(init_case[\"feats_fields\"]) + 1\n            with self.assertRaises(IndexError):\n                mf[nfields]\n\n\nclass TestTextDataReader(unittest.TestCase):\n    def test_read(self):\n        strings = [\n            \"hello world\".encode(\"utf-8\"),\n            \"this's a string with punctuation .\".encode(\"utf-8\"),\n            \"ThIs Is A sTrInG wItH oDD CapitALIZAtion\".encode(\"utf-8\")\n        ]\n        rdr = TextDataReader()\n        for i, ex in enumerate(rdr.read(strings, \"src\")):\n            self.assertEqual(ex[\"src\"], strings[i].decode(\"utf-8\"))\n\n\nclass TestTextDataReaderFromFS(unittest.TestCase):\n    # this test touches the file system, so it could be considered an\n    # integration test\n    STRINGS = [\n            \"hello world\\n\".encode(\"utf-8\"),\n            \"this's a string with punctuation . \\n\".encode(\"utf-8\"),\n            \"ThIs Is A sTrInG wItH oDD CapitALIZAtion\\n\".encode(\"utf-8\")\n    ]\n    FILE_NAME = \"test_strings.txt\"\n\n    @classmethod\n    def setUpClass(cls):\n        # write utf-8 bytes\n        with open(cls.FILE_NAME, \"wb\") as f:\n            for str_ in cls.STRINGS:\n                f.write(str_)\n\n    @classmethod\n    def tearDownClass(cls):\n        os.remove(cls.FILE_NAME)\n\n    def test_read(self):\n        rdr = TextDataReader()\n        for i, ex in enumerate(rdr.read(self.FILE_NAME, \"src\")):\n            self.assertEqual(ex[\"src\"], self.STRINGS[i].decode(\"utf-8\"))\n"
  },
  {
    "path": "onmt/tests/test_translation_server.py",
    "content": "import unittest\nfrom onmt.translate.translation_server import ServerModel, TranslationServer\n\nimport os\nfrom six import string_types\nfrom textwrap import dedent\n\nimport torch\n\nfrom onmt.translate.translator import Translator\n\n\nTEST_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass TestServerModel(unittest.TestCase):\n    def test_deferred_loading_model_and_unload(self):\n        model_id = 0\n        opt = {\"models\": [\"test_model.pt\"]}\n        model_root = TEST_DIR\n        sm = ServerModel(opt, model_id, model_root=model_root, load=False)\n        self.assertFalse(sm.loaded)\n        sm.load()\n        self.assertTrue(sm.loaded)\n        self.assertIsInstance(sm.translator, Translator)\n        sm.unload()\n        self.assertFalse(sm.loaded)\n\n    def test_load_model_on_init_and_unload(self):\n        model_id = 0\n        opt = {\"models\": [\"test_model.pt\"]}\n        model_root = TEST_DIR\n        sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n        self.assertTrue(sm.loaded)\n        self.assertIsInstance(sm.translator, Translator)\n        sm.unload()\n        self.assertFalse(sm.loaded)\n\n    def test_tokenizing_with_no_tokenizer_fails(self):\n        model_id = 0\n        opt = {\"models\": [\"test_model.pt\"]}\n        model_root = TEST_DIR\n        sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n        with self.assertRaises(ValueError):\n            sm.tokenize(\"hello world\")\n\n    def test_detokenizing_with_no_tokenizer_fails(self):\n        model_id = 0\n        opt = {\"models\": [\"test_model.pt\"]}\n        model_root = TEST_DIR\n        sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n        with self.assertRaises(ValueError):\n            sm.detokenize(\"hello world\")\n\n    if torch.cuda.is_available():\n        def test_moving_to_gpu_and_back(self):\n            torch.cuda.set_device(torch.device(\"cuda\", 0))\n            model_id = 0\n            opt = {\"models\": [\"test_model.pt\"]}\n            model_root = TEST_DIR\n            sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cpu\")\n            sm.to_gpu()\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cuda\")\n                self.assertEqual(p.device.index, 0)\n            sm.to_cpu()\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cpu\")\n\n        def test_initialize_on_gpu_and_move_back(self):\n            torch.cuda.set_device(torch.device(\"cuda\", 0))\n            model_id = 0\n            opt = {\"models\": [\"test_model.pt\"], \"gpu\": 0}\n            model_root = TEST_DIR\n            sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cuda\")\n                self.assertEqual(p.device.index, 0)\n            sm.to_gpu()\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cuda\")\n                self.assertEqual(p.device.index, 0)\n            sm.to_cpu()\n            for p in sm.translator.model.parameters():\n                self.assertEqual(p.device.type, \"cpu\")\n\n        if torch.cuda.device_count() > 1:\n            def test_initialize_on_nonzero_gpu_and_back(self):\n                torch.cuda.set_device(torch.device(\"cuda\", 1))\n                model_id = 0\n                opt = {\"models\": [\"test_model.pt\"], \"gpu\": 1}\n                model_root = TEST_DIR\n                sm = ServerModel(opt, model_id, model_root=model_root,\n                                 load=True)\n                for p in sm.translator.model.parameters():\n                    self.assertEqual(p.device.type, \"cuda\")\n                    self.assertEqual(p.device.index, 1)\n                sm.to_gpu()\n                for p in sm.translator.model.parameters():\n                    self.assertEqual(p.device.type, \"cuda\")\n                    self.assertEqual(p.device.index, 1)\n                sm.to_cpu()\n                for p in sm.translator.model.parameters():\n                    self.assertEqual(p.device.type, \"cpu\")\n\n    def test_run(self):\n        model_id = 0\n        opt = {\"models\": [\"test_model.pt\"]}\n        model_root = TEST_DIR\n        sm = ServerModel(opt, model_id, model_root=model_root, load=True)\n        inp = [{\"src\": \"hello how are you today\"},\n               {\"src\": \"good morning to you .\"}]\n        results, scores, n_best, time, aligns = sm.run(inp)\n        self.assertIsInstance(results, list)\n        for sentence_string in results:\n            self.assertIsInstance(sentence_string, string_types)\n        self.assertIsInstance(scores, list)\n        for elem in scores:\n            self.assertIsInstance(elem, float)\n        self.assertIsInstance(aligns, list)\n        for align_list in aligns:\n            for align_string in align_list:\n                if align_string is not None:\n                    self.assertIsInstance(align_string, string_types)\n        self.assertEqual(len(results), len(scores))\n        self.assertEqual(len(scores), len(inp) * n_best)\n        self.assertEqual(len(time), 1)\n        self.assertIsInstance(time, dict)\n        self.assertIn(\"translation\", time)\n\n\nclass TestTranslationServer(unittest.TestCase):\n    # this could be considered an integration test because it touches\n    # the filesystem for the config file (and the models)\n\n    CFG_F = os.path.join(\n        TEST_DIR, \"test_translation_server_config_file.json\")\n\n    def tearDown(self):\n        if os.path.exists(self.CFG_F):\n            os.remove(self.CFG_F)\n\n    def write(self, cfg):\n        with open(self.CFG_F, \"w\") as f:\n            f.write(cfg)\n\n    CFG_NO_LOAD = dedent(\"\"\"\\\n        {\n            \"models_root\": \"%s\",\n            \"models\": [\n                {\n                    \"id\": 100,\n                    \"model\": \"test_model.pt\",\n                    \"timeout\": -1,\n                    \"on_timeout\": \"to_cpu\",\n                    \"load\": false,\n                    \"opt\": {\n                        \"beam_size\": 5\n                    }\n                }\n            ]\n        }\n        \"\"\" % TEST_DIR)\n\n    def test_start_without_initial_loading(self):\n        self.write(self.CFG_NO_LOAD)\n        sv = TranslationServer()\n        sv.start(self.CFG_F)\n        self.assertFalse(sv.models[100].loaded)\n        self.assertEqual(set(sv.models.keys()), {100})\n\n    CFG_LOAD = dedent(\"\"\"\\\n        {\n            \"models_root\": \"%s\",\n            \"models\": [\n                {\n                    \"id\": 100,\n                    \"model\": \"test_model.pt\",\n                    \"timeout\": -1,\n                    \"on_timeout\": \"to_cpu\",\n                    \"load\": true,\n                    \"opt\": {\n                        \"beam_size\": 5\n                    }\n                }\n            ]\n        }\n        \"\"\" % TEST_DIR)\n\n    def test_start_with_initial_loading(self):\n        self.write(self.CFG_LOAD)\n        sv = TranslationServer()\n        sv.start(self.CFG_F)\n        self.assertTrue(sv.models[100].loaded)\n        self.assertEqual(set(sv.models.keys()), {100})\n\n    CFG_2_MODELS = dedent(\"\"\"\\\n        {\n            \"models_root\": \"%s\",\n            \"models\": [\n                {\n                    \"id\": 100,\n                    \"model\": \"test_model.pt\",\n                    \"timeout\": -1,\n                    \"on_timeout\": \"to_cpu\",\n                    \"load\": true,\n                    \"opt\": {\n                        \"beam_size\": 5\n                    }\n                },\n                {\n                    \"id\": 1000,\n                    \"model\": \"test_model2.pt\",\n                    \"timeout\": -1,\n                    \"on_timeout\": \"to_cpu\",\n                    \"load\": false,\n                    \"opt\": {\n                        \"beam_size\": 5\n                    }\n                }\n            ]\n        }\n        \"\"\" % TEST_DIR)\n\n    def test_start_with_two_models(self):\n        self.write(self.CFG_2_MODELS)\n        sv = TranslationServer()\n        sv.start(self.CFG_F)\n        self.assertTrue(sv.models[100].loaded)\n        self.assertFalse(sv.models[1000].loaded)\n        self.assertEqual(set(sv.models.keys()), {100, 1000})\n"
  },
  {
    "path": "onmt/tests/utils_for_tests.py",
    "content": "import itertools\n\n\ndef product_dict(**kwargs):\n    keys = kwargs.keys()\n    vals = kwargs.values()\n    for instance in itertools.product(*vals):\n        yield dict(zip(keys, instance))\n"
  },
  {
    "path": "onmt/train_single.py",
    "content": "#!/usr/bin/env python\n\"\"\"Training on a single process.\"\"\"\nimport torch\n\nfrom onmt.inputters.inputter import IterOnDevice\nfrom onmt.model_builder import build_model\nfrom onmt.utils.optimizers import Optimizer\nfrom onmt.utils.misc import set_random_seed\nfrom onmt.trainer import build_trainer\nfrom onmt.models import build_model_saver\nfrom onmt.utils.logging import init_logger, logger\nfrom onmt.utils.parse import ArgumentParser\n\nfrom onmt.inputters.dynamic_iterator import build_dynamic_dataset_iter, build_dynamic_dataset_iter_given_examples\n\n\ndef configure_process(opt, device_id):\n    if device_id >= 0:\n        torch.cuda.set_device(device_id)\n    set_random_seed(opt.seed, device_id >= 0)\n\n\ndef _get_model_opts(opt, checkpoint=None):\n    \"\"\"Get `model_opt` to build model, may load from `checkpoint` if any.\"\"\"\n    if checkpoint is not None:\n        model_opt = ArgumentParser.ckpt_model_opts(checkpoint[\"opt\"])\n        ArgumentParser.update_model_opts(model_opt)\n        ArgumentParser.validate_model_opts(model_opt)\n        if (opt.tensorboard_log_dir == model_opt.tensorboard_log_dir and\n                hasattr(model_opt, 'tensorboard_log_dir_dated')):\n            # ensure tensorboard output is written in the directory\n            # of previous checkpoints\n            opt.tensorboard_log_dir_dated = model_opt.tensorboard_log_dir_dated\n    else:\n        model_opt = opt\n    return model_opt\n\n\ndef _build_iter_given_examples(examples, opt, fields, transforms_cls, is_train=False):\n    \"\"\"Build iterator used for validation.\"\"\"\n    test_iter = build_dynamic_dataset_iter_given_examples(\n        examples, fields, transforms_cls, opt, is_train=is_train)\n    return test_iter\n\n\ndef _build_valid_iter(opt, fields, transforms_cls):\n    \"\"\"Build iterator used for validation.\"\"\"\n    valid_iter = build_dynamic_dataset_iter(\n        fields, transforms_cls, opt, is_train=False)\n    return valid_iter\n\n\ndef _build_train_iter(opt, fields, transforms_cls, stride=1, offset=0):\n    \"\"\"Build training iterator.\"\"\"\n    train_iter = build_dynamic_dataset_iter(\n        fields, transforms_cls, opt, is_train=True,\n        stride=stride, offset=offset)\n    return train_iter\n\n\ndef main(opt, fields, transforms_cls, checkpoint, device_id,\n         batch_queue=None, semaphore=None):\n    \"\"\"Start training on `device_id`.\"\"\"\n    # NOTE: It's important that ``opt`` has been validated and updated\n    # at this point.\n    configure_process(opt, device_id)\n    init_logger(opt.log_file)\n\n    model_opt = _get_model_opts(opt, checkpoint=checkpoint)\n\n    # Build model.\n    model = build_model(model_opt, opt, fields, checkpoint)\n    model.count_parameters(log=logger.info)\n\n    # Build optimizer.\n    optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)\n\n    # Build model saver\n    model_saver = build_model_saver(model_opt, opt, model, fields, optim)\n\n    trainer = build_trainer(\n        opt, device_id, model, fields, optim, model_saver=model_saver)\n\n    if batch_queue is None:\n        _train_iter = _build_train_iter(opt, fields, transforms_cls)\n        train_iter = IterOnDevice(_train_iter, device_id)\n    else:\n        assert semaphore is not None, \\\n            \"Using batch_queue requires semaphore as well\"\n\n        def _train_iter():\n            while True:\n                batch = batch_queue.get()\n                semaphore.release()\n                # Move batch to specified device\n                IterOnDevice.batch_to_device(batch, device_id)\n                yield batch\n\n        train_iter = _train_iter()\n\n    valid_iter = _build_valid_iter(opt, fields, transforms_cls) if opt.valid else None\n    if valid_iter is not None:\n        valid_iter = IterOnDevice(valid_iter, device_id)\n\n    if len(opt.gpu_ranks):\n        logger.info('Starting training on GPU: %s' % opt.gpu_ranks)\n    else:\n        logger.info('Starting training on CPU, could be very slow')\n    train_steps = opt.train_steps\n    if opt.single_pass and train_steps > 0:\n        logger.warning(\"Option single_pass is enabled, ignoring train_steps.\")\n        train_steps = 0\n\n    trainer.train(\n        train_iter,\n        train_steps,\n        save_checkpoint_steps=opt.save_checkpoint_steps,\n        valid_iter=valid_iter,\n        valid_steps=opt.valid_steps)\n\n    if trainer.report_manager.tensorboard_writer is not None:\n        trainer.report_manager.tensorboard_writer.close()\n\n"
  },
  {
    "path": "onmt/trainer.py",
    "content": "\"\"\"\n    This is the loadable seq2seq trainer library that is\n    in charge of training details, loss compute, and statistics.\n    See train.py for a use case of this library.\n\n    Note: To make this a general library, we implement *only*\n          mechanism things here(i.e. what to do), and leave the strategy\n          things to users(i.e. how to do it). Also see train.py(one of the\n          users of this library) for the strategy things we do.\n\"\"\"\n\nimport torch\nimport wandb\nimport traceback\n\nimport onmt.utils\nfrom onmt.utils.logging import logger\n\n\ndef build_trainer(opt, device_id, model, fields, optim, model_saver=None):\n    \"\"\"\n    Simplify `Trainer` creation based on user `opt`s*\n\n    Args:\n        opt (:obj:`Namespace`): user options (usually from argument parsing)\n        model (:obj:`onmt.models.NMTModel`): the model to train\n        fields (dict): dict of fields\n        optim (:obj:`onmt.utils.Optimizer`): optimizer used during training\n        data_type (str): string describing the type of data\n            e.g. \"text\"\n        model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object\n            used to save the model\n    \"\"\"\n\n    tgt_field = dict(fields)[\"tgt\"].base_field\n    train_loss = onmt.utils.loss.build_loss_compute(model, tgt_field, opt)\n    valid_loss = onmt.utils.loss.build_loss_compute(\n        model, tgt_field, opt, train=False)\n\n    trunc_size = opt.truncated_decoder  # Badly named...\n    shard_size = opt.max_generator_batches if opt.model_dtype == 'fp32' else 0\n\n    # @memray: BPTT is not compatible with Orth and SemCov,\n    # Otherwise will trigger error: raise RuntimeError(\"grad can be implicitly created only for scalar outputs\")\n    #    at function shards() in loss.py (torch.autograd.backward(inputs, grads))\n    if opt.data_type == 'keyphrase' or opt.model_type == 'keyphrase':\n        trunc_size = 0\n        shard_size = 0\n\n    norm_method = opt.normalization\n    accum_count = opt.accum_count\n    accum_steps = opt.accum_steps\n    n_gpu = opt.world_size\n    average_decay = opt.average_decay\n    average_every = opt.average_every\n    dropout = opt.dropout\n    dropout_steps = opt.dropout_steps\n    if device_id >= 0:\n        gpu_rank = opt.gpu_ranks[device_id]\n    else:\n        gpu_rank = -1\n        n_gpu = 0\n    gpu_verbose_level = opt.gpu_verbose_level\n\n    earlystopper = onmt.utils.EarlyStopping(\n        opt.early_stopping, scorers=onmt.utils.scorers_from_opts(opt)) \\\n        if opt.early_stopping > 0 else None\n\n    report_manager = onmt.utils.build_report_manager(opt, gpu_rank)\n\n    # setup wandb if applicable\n    if opt.wandb and gpu_rank == 0:\n        from datetime import datetime\n        now = datetime.now()\n        datetime = now.strftime(\"-%Y%m%d-%H%M%S\")\n        wandb.login(key=opt.wandb_key)\n        wandb.init(project=opt.wandb_project, id=opt.exp+datetime, resume=True, dir=opt.wandb_log_dir)\n        # @memray, set is not JSON serializable\n        for k, v in opt.__dict__.items():\n            if isinstance(v, set):\n                setattr(opt, k, list(v))\n        wandb.config.update(opt, allow_val_change=True)\n        wandb.watch(model, log='all')\n\n    trainer = onmt.Trainer(model, train_loss, valid_loss, optim, trunc_size,\n                           shard_size, norm_method,\n                           accum_count, accum_steps,\n                           n_gpu, gpu_rank,\n                           gpu_verbose_level, report_manager,\n                           with_align=True if opt.lambda_align > 0 else False,\n                           model_saver=model_saver if gpu_rank <= 0 else None,\n                           average_decay=average_decay,\n                           average_every=average_every,\n                           model_dtype=opt.model_dtype,\n                           earlystopper=earlystopper,\n                           dropout=dropout,\n                           dropout_steps=dropout_steps)\n    return trainer\n\n\nclass Trainer(object):\n    \"\"\"\n    Class that controls the training process.\n\n    Args:\n            model(:py:class:`onmt.models.model.NMTModel`): translation model\n                to train\n            train_loss(:obj:`onmt.utils.loss.LossComputeBase`):\n               training loss computation\n            valid_loss(:obj:`onmt.utils.loss.LossComputeBase`):\n               training loss computation\n            optim(:obj:`onmt.utils.optimizers.Optimizer`):\n               the optimizer responsible for update\n            trunc_size(int): length of truncated back propagation through time\n            shard_size(int): compute loss in shards of this size for efficiency\n            data_type(string): type of the source input: [text]\n            norm_method(string): normalization methods: [sents|tokens]\n            accum_count(list): accumulate gradients this many times.\n            accum_steps(list): steps for accum gradients changes.\n            report_manager(:obj:`onmt.utils.ReportMgrBase`):\n                the object that creates reports, or None\n            model_saver(:obj:`onmt.models.ModelSaverBase`): the saver is\n                used to save a checkpoint.\n                Thus nothing will be saved if this parameter is None\n    \"\"\"\n\n    def __init__(self, model, train_loss, valid_loss, optim,\n                 trunc_size=0, shard_size=32,\n                 norm_method=\"sents\", accum_count=[1],\n                 accum_steps=[0],\n                 n_gpu=1, gpu_rank=1, gpu_verbose_level=0,\n                 report_manager=None, with_align=False, model_saver=None,\n                 average_decay=0, average_every=1, model_dtype='fp32',\n                 earlystopper=None, dropout=[0.3], dropout_steps=[0]):\n        # Basic attributes.\n        self.model = model\n        self.train_loss = train_loss\n        self.valid_loss = valid_loss\n        self.optim = optim\n        self.trunc_size = trunc_size\n        self.shard_size = shard_size\n        self.norm_method = norm_method\n        self.accum_count_l = accum_count\n        self.accum_count = accum_count[0]\n        self.accum_steps = accum_steps\n        self.n_gpu = n_gpu\n        self.gpu_rank = gpu_rank\n        self.gpu_verbose_level = gpu_verbose_level\n        self.report_manager = report_manager\n        self.with_align = with_align\n        self.model_saver = model_saver\n        self.average_decay = average_decay\n        self.moving_average = None\n        self.average_every = average_every\n        self.model_dtype = model_dtype\n        self.earlystopper = earlystopper\n        self.dropout = dropout\n        self.dropout_steps = dropout_steps\n\n        for i in range(len(self.accum_count_l)):\n            assert self.accum_count_l[i] > 0\n            if self.accum_count_l[i] > 1:\n                assert self.trunc_size == 0, \\\n                    \"\"\"To enable accumulated gradients,\n                       you must disable target sequence truncating.\"\"\"\n\n        # Set model in training mode.\n        self.model.train()\n\n    def _accum_count(self, step):\n        for i in range(len(self.accum_steps)):\n            if step > self.accum_steps[i]:\n                _accum = self.accum_count_l[i]\n        return _accum\n\n    def _maybe_update_dropout(self, step):\n        for i in range(len(self.dropout_steps)):\n            if step > 1 and step == self.dropout_steps[i] + 1:\n                self.model.update_dropout(self.dropout[i])\n                logger.info(\"Updated dropout to %f from step %d\"\n                            % (self.dropout[i], step))\n\n    def _accum_batches(self, iterator):\n        batches = []\n        normalization = 0\n        self.accum_count = self._accum_count(self.optim.training_step)\n        for batch in iterator:\n            batches.append(batch)\n            if self.norm_method == \"tokens\":\n                num_tokens = batch.tgt[1:, :, 0].ne(\n                    self.train_loss.padding_idx).sum()\n                normalization += num_tokens.item()\n            else:\n                normalization += batch.batch_size\n            if len(batches) == self.accum_count:\n                yield batches, normalization\n                self.accum_count = self._accum_count(self.optim.training_step)\n                batches = []\n                normalization = 0\n        if batches:\n            yield batches, normalization\n\n    def _update_average(self, step):\n        if self.moving_average is None:\n            copy_params = [params.detach().float()\n                           for params in self.model.parameters()]\n            self.moving_average = copy_params\n        else:\n            average_decay = max(self.average_decay,\n                                1 - (step + 1) / (step + 10))\n            for (i, avg), cpt in zip(enumerate(self.moving_average),\n                                     self.model.parameters()):\n                self.moving_average[i] = \\\n                    (1 - average_decay) * avg + \\\n                    cpt.detach().float() * average_decay\n\n    def train(self,\n              train_iter,\n              train_steps,\n              save_checkpoint_steps=5000,\n              valid_iter=None,\n              valid_steps=10000):\n        \"\"\"\n        The main training loop by iterating over `train_iter` and possibly\n        running validation on `valid_iter`.\n\n        Args:\n            train_iter: A generator that returns the next training batch.\n            train_steps: Run training for this many iterations.\n            save_checkpoint_steps: Save a checkpoint every this many\n              iterations.\n            valid_iter: A generator that returns the next validation batch.\n            valid_steps: Run evaluation every this many iterations.\n\n        Returns:\n            The gathered statistics.\n        \"\"\"\n        if valid_iter is None:\n            logger.info('Start training loop without validation...')\n        else:\n            logger.info('Start training loop and validate every %d steps...',\n                        valid_steps)\n\n        total_stats = onmt.utils.Statistics()\n        report_stats = onmt.utils.Statistics()\n        self._start_report_manager(start_time=total_stats.start_time)\n\n        for i, (batches, normalization) in enumerate(\n                self._accum_batches(train_iter)):\n            step = self.optim.training_step\n            # UPDATE DROPOUT\n            self._maybe_update_dropout(step)\n\n            if self.gpu_verbose_level > 1:\n                logger.info(\"GpuRank %d: index: %d\", self.gpu_rank, i)\n            if self.gpu_verbose_level > 0:\n                logger.info(\"GpuRank %d: reduce_counter: %d \\\n                            \\t n_minibatch=%d,\\t total_batch_size=%d\"\n                            % (self.gpu_rank, i + 1, len(batches), sum([b.batch_size for b in batches])))\n\n            if self.n_gpu > 1:\n                normalization = sum(onmt.utils.distributed\n                                    .all_gather_list\n                                    (normalization))\n\n            self._gradient_accumulation(\n                batches, normalization, total_stats,\n                report_stats)\n\n            if self.average_decay > 0 and i % self.average_every == 0:\n                self._update_average(step)\n\n            report_stats = self._maybe_report_training(\n                step, train_steps,\n                self.optim.learning_rate(),\n                report_stats)\n\n            if valid_iter is not None and step % valid_steps == 0:\n                if self.gpu_verbose_level > 0:\n                    logger.info('GpuRank %d: validate step %d'\n                                % (self.gpu_rank, step))\n                valid_stats = self.validate(\n                    valid_iter, moving_average=self.moving_average)\n                if self.gpu_verbose_level > 0:\n                    logger.info('GpuRank %d: gather valid stat \\\n                                step %d' % (self.gpu_rank, step))\n                valid_stats = self._maybe_gather_stats(valid_stats)\n                if self.gpu_verbose_level > 0:\n                    logger.info('GpuRank %d: report stat step %d'\n                                % (self.gpu_rank, step))\n                self._report_step(self.optim.learning_rate(),\n                                  step, valid_stats=valid_stats)\n                # Run patience mechanism\n                if self.earlystopper is not None:\n                    self.earlystopper(valid_stats, step)\n                    # If the patience has reached the limit, stop training\n                    if self.earlystopper.has_stopped():\n                        break\n\n            if (self.model_saver is not None\n                    and (save_checkpoint_steps != 0\n                         and step % save_checkpoint_steps == 0)):\n                self.model_saver.save(step, moving_average=self.moving_average)\n\n            if train_steps > 0 and step >= train_steps:\n                break\n\n        if self.model_saver is not None:\n            self.model_saver.save(step, moving_average=self.moving_average)\n        return total_stats\n\n    def validate(self, valid_iter, moving_average=None):\n        \"\"\" Validate model.\n            valid_iter: validate data iterator\n        Returns:\n            :obj:`nmt.Statistics`: validation loss statistics\n        \"\"\"\n        valid_model = self.model\n        if moving_average:\n            # swap model params w/ moving average\n            # (and keep the original parameters)\n            model_params_data = []\n            for avg, param in zip(self.moving_average,\n                                  valid_model.parameters()):\n                model_params_data.append(param.data)\n                param.data = avg.data.half() if self.optim._fp16 == \"legacy\" \\\n                    else avg.data\n\n        # Set model in validating mode.\n        valid_model.eval()\n\n        with torch.no_grad():\n            stats = onmt.utils.Statistics()\n\n            for batch in valid_iter:\n                src, src_lengths = batch.src if isinstance(batch.src, tuple) \\\n                    else (batch.src, None)\n                tgt = batch.tgt\n\n                with torch.cuda.amp.autocast(enabled=self.optim.amp):\n                    # F-prop through the model.\n                    outputs, attns = valid_model(src, tgt, src_lengths,\n                                                 with_align=self.with_align)\n\n                    # Compute loss.\n                    _, batch_stats = self.valid_loss(batch, outputs, attns, model=valid_model)\n\n                # Update statistics.\n                stats.update(batch_stats)\n        if moving_average:\n            for param_data, param in zip(model_params_data,\n                                         self.model.parameters()):\n                param.data = param_data\n\n        # Set model back to training mode.\n        valid_model.train()\n\n        return stats\n\n    def _gradient_accumulation(self, true_batches, normalization, total_stats,\n                               report_stats):\n        if self.accum_count > 1:\n            self.optim.zero_grad()\n\n        for k, batch in enumerate(true_batches):\n            # @memray issue with dynamic data pipeline (same in translator._translate_batch_with_strategy())\n            #   OpenNMT expects:\n            #       src.shape [src_len, batch_size, num_feature], src_lengths.shape=[batch_size]\n            #   but somehow, it turns out to be: src.shape [S,B,1 or F], src_lengths.shape=[B], so permute it\n            #   transformed in onmt.inputters.text_dataset.py L127\n            if batch.src[0].shape[0] == batch.batch_size:\n                if isinstance(batch.src, tuple):\n                    batch.src = (batch.src[0].permute([1, 0, 2]), batch.src[1])\n                else:\n                    batch.src = batch.src.permute([1, 0, 2]) # [src_len, B, 1]\n                batch.tgt = batch.tgt.permute([1, 0, 2]) # [tgt_len, B, 1]\n\n            target_size = batch.tgt.size(0)\n            # Truncated BPTT: reminder not compatible with accum > 1\n            if self.trunc_size:\n                trunc_size = self.trunc_size\n            else:\n                trunc_size = target_size\n            src, src_lengths = batch.src if isinstance(batch.src, tuple) \\\n                else (batch.src, None)\n            if src_lengths is not None:\n                report_stats.n_src_words += src_lengths.sum().item()\n            # tgt.shape = [tgt_len, batch_size, 1]\n            tgt_outer = batch.tgt\n\n            bptt = False\n            for j in range(0, target_size - 1, trunc_size):\n                # 1. Create truncated target.\n                tgt = tgt_outer[j: j + trunc_size]\n\n                # 2. F-prop all but generator.\n                if self.accum_count == 1:\n                    self.optim.zero_grad()\n\n                with torch.cuda.amp.autocast(enabled=self.optim.amp):\n                    # outputs.shape=[tgt_len-1,B,hid_dim], attn.shape=[tgt_len-1,B,src_len]\n                    outputs, attns = self.model(\n                        src, tgt, src_lengths, bptt=bptt,\n                        with_align=self.with_align)\n                    bptt = True\n\n                    # 3. Compute loss.\n                    loss, batch_stats = self.train_loss(\n                        batch,\n                        outputs,\n                        attns,\n                        normalization=normalization,\n                        shard_size=self.shard_size,\n                        trunc_start=j,\n                        trunc_size=trunc_size,\n                        model=self.model\n                    )\n\n                try:\n                    if loss is not None:\n                        self.optim.backward(loss)\n\n                    total_stats.update(batch_stats)\n                    report_stats.update(batch_stats)\n\n                except Exception:\n                    traceback.print_exc()\n                    logger.info(\"At step %d, we removed a batch - accum %d\",\n                                self.optim.training_step, k)\n\n                # 4. Update the parameters and statistics.\n                if self.accum_count == 1:\n                    # Multi GPU gradient gather\n                    if self.n_gpu > 1:\n                        grads = [p.grad.data for p in self.model.parameters()\n                                 if p.requires_grad\n                                 and p.grad is not None]\n                        onmt.utils.distributed.all_reduce_and_rescale_tensors(\n                            grads, float(1))\n                    self.optim.step()\n\n                # If truncated, don't backprop fully.\n                # TO CHECK\n                # if dec_state is not None:\n                #    dec_state.detach()\n                if hasattr(self.model.decoder, 'state') and self.model.decoder.state is not None:\n                    self.model.decoder.detach_state()\n\n        # in case of multi step gradient accumulation,\n        # update only after accum batches\n        if self.accum_count > 1:\n            if self.n_gpu > 1:\n                grads = [p.grad.data for p in self.model.parameters()\n                         if p.requires_grad\n                         and p.grad is not None]\n                onmt.utils.distributed.all_reduce_and_rescale_tensors(\n                    grads, float(1))\n            self.optim.step()\n\n    def _start_report_manager(self, start_time=None):\n        \"\"\"\n        Simple function to start report manager (if any)\n        \"\"\"\n        if self.report_manager is not None:\n            if start_time is None:\n                self.report_manager.start()\n            else:\n                self.report_manager.start_time = start_time\n\n    def _maybe_gather_stats(self, stat):\n        \"\"\"\n        Gather statistics in multi-processes cases\n\n        Args:\n            stat(:obj:onmt.utils.Statistics): a Statistics object to gather\n                or None (it returns None in this case)\n\n        Returns:\n            stat: the updated (or unchanged) stat object\n        \"\"\"\n        if stat is not None and self.n_gpu > 1:\n            return onmt.utils.Statistics.all_gather_stats(stat)\n        return stat\n\n    def _maybe_report_training(self, step, num_steps, learning_rate,\n                               report_stats):\n        \"\"\"\n        Simple function to report training stats (if report_manager is set)\n        see `onmt.utils.ReportManagerBase.report_training` for doc\n        \"\"\"\n        if self.report_manager is not None:\n            return self.report_manager.report_training(\n                step,\n                num_steps,\n                learning_rate,\n                None if self.earlystopper is None\n                else self.earlystopper.current_tolerance,\n                report_stats,\n                multigpu=self.n_gpu > 1)\n\n    def _report_step(self, learning_rate, step, train_stats=None,\n                     valid_stats=None):\n        \"\"\"\n        Simple function to report stats (if report_manager is set)\n        see `onmt.utils.ReportManagerBase.report_step` for doc\n        \"\"\"\n        if self.report_manager is not None:\n            return self.report_manager.report_step(\n                learning_rate,\n                None if self.earlystopper is None\n                else self.earlystopper.current_tolerance,\n                step, train_stats=train_stats,\n                valid_stats=valid_stats)\n"
  },
  {
    "path": "onmt/transforms/__init__.py",
    "content": "\"\"\"Module for dynamic data transfrom.\"\"\"\nimport os\nimport importlib\n\nfrom .transform import make_transforms, get_specials,\\\n    save_transforms, load_transforms, TransformPipe,\\\n    Transform\n\n\nAVAILABLE_TRANSFORMS = {}\n\n\ndef get_transforms_cls(transform_names):\n    \"\"\"Return valid transform class indicated in `transform_names`.\"\"\"\n    transforms_cls = {}\n    for name in transform_names:\n        if name not in AVAILABLE_TRANSFORMS:\n            raise ValueError(\"specified tranform not supported!\")\n        transforms_cls[name] = AVAILABLE_TRANSFORMS[name]\n    return transforms_cls\n\n\n__all__ = [\"get_transforms_cls\", \"get_specials\", \"make_transforms\",\n           \"load_transforms\", \"save_transforms\", \"TransformPipe\"]\n\n\ndef register_transform(name):\n    \"\"\"Transform register that can be used to add new transform class.\"\"\"\n\n    def register_transfrom_cls(cls):\n        if name in AVAILABLE_TRANSFORMS:\n            raise ValueError(\n                'Cannot register duplicate transform ({})'.format(name))\n        if not issubclass(cls, Transform):\n            raise ValueError('transform ({}: {}) must extend Transform'.format(\n                name, cls.__name__))\n        AVAILABLE_TRANSFORMS[name] = cls\n        return cls\n\n    return register_transfrom_cls\n\n\n# Auto import python files in this directory\ntransform_dir = os.path.dirname(__file__)\nfor file in os.listdir(transform_dir):\n    path = os.path.join(transform_dir, file)\n    if not file.startswith('_') and not file.startswith('.') and (\n            file.endswith('.py') or os.path.isdir(path)):\n        file_name = file[:file.find('.py')] if file.endswith('.py') else file\n        module = importlib.import_module(\n            'onmt.transforms.' + file_name)\n"
  },
  {
    "path": "onmt/transforms/bart.py",
    "content": "\"\"\"Transforms relate to noising from BART: based on code of fairseq.\"\"\"\nimport math\nimport numpy as np\nimport torch\nfrom functools import partial\nfrom onmt.constants import DefaultTokens, SubwordMarker\nfrom onmt.transforms import register_transform\nfrom .transform import Transform\n\n\ndef word_start(x, ignore_subword=False, is_joiner=False):\n    \"\"\"Return if a token is the word start or not.\"\"\"\n    if not ignore_subword:\n        if is_joiner:\n            return not x.startswith(SubwordMarker.JOINER)\n        else:\n            return x.startswith(SubwordMarker.SPACER)\n    else:\n        return True\n\n\nclass BARTNoising(object):\n    \"\"\"Noise from BART.\"\"\"\n\n    def __init__(self, vocab, mask_tok=DefaultTokens.MASK, mask_ratio=0.0,\n                 insert_ratio=0.0, permute_sent_ratio=0.0, poisson_lambda=3.0,\n                 replace_length=-1, rotate_ratio=0.0, mask_length='subword',\n                 random_ratio=0.0, is_joiner=False,\n                 full_stop_token=DefaultTokens.SENT_FULL_STOPS):\n        self.vocab = vocab\n\n        self.mask_tok = mask_tok\n\n        self.mask_ratio = mask_ratio\n        self.random_ratio = random_ratio\n        self.insert_ratio = insert_ratio\n        self.rotate_ratio = rotate_ratio\n        self.permute_sent_ratio = permute_sent_ratio\n\n        self.full_stop_token = full_stop_token\n\n        # -1: keep everything (i.e. 1 mask per token)\n        #  0: replace everything (i.e. no mask)\n        #  1: 1 mask per span\n        if replace_length not in [-1, 0, 1]:\n            raise ValueError(f'invalid arg: replace_length={replace_length}')\n        self.replace_length = replace_length\n\n        if mask_length not in ['subword', 'word', 'span-poisson']:\n            raise ValueError(f'invalid arg: mask-length={mask_length}')\n        if mask_length == 'subword' and replace_length not in [0, 1]:\n            raise ValueError('if using subwords, use replace-length=1 or 0')\n\n        if mask_length == 'subword' or is_joiner is None:\n            # view each subword as word start / input is word level token\n            self.__is_word_start = partial(word_start, ignore_subword=True)\n        else:\n            self.__is_word_start = partial(word_start, is_joiner=is_joiner)\n\n        self.mask_span_distribution = None\n        if mask_length == 'span-poisson':\n            self.mask_span_distribution = self._make_poisson(poisson_lambda)\n        self.mask_length = mask_length\n        self.poisson_lambda = poisson_lambda\n\n    def _make_poisson(self, poisson_lambda):\n        lambda_to_the_k = 1\n        e_to_the_minus_lambda = math.exp(-poisson_lambda)\n        k_factorial = 1\n        ps = []\n        for k in range(0, 128):\n            ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)\n            lambda_to_the_k *= poisson_lambda\n            k_factorial *= (k + 1)\n            if ps[-1] < 0.0000001:\n                break\n        ps = torch.FloatTensor(ps)\n        return torch.distributions.Categorical(ps)\n\n    def _is_full_stop(self, token):\n        return True if token in self.full_stop_token else False\n\n    def permute_sentences(self, tokens, p=1.0):\n        if len(tokens) == 1:\n            return tokens\n        full_stops = np.array([self._is_full_stop(token) for token in tokens])\n        # Pretend it ends with a full stop so last span is a sentence\n        full_stops[-1] = True\n\n        # Tokens that are full stops, where the previous token is not\n        sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero()[0] + 2\n\n        n_sentences = sentence_ends.size\n        if n_sentences == 1:\n            return tokens\n\n        n_to_permute = math.ceil((n_sentences * 2 * p) / 2.0)\n\n        substitutions = np.random.permutation(n_sentences)[:n_to_permute]\n        ordering = np.arange(0, n_sentences)\n        ordering[substitutions] = substitutions[np.random.permutation(\n            n_to_permute)]\n\n        result = [tok for tok in tokens]\n        index = 0\n        for i in ordering:\n            sentence = tokens[(sentence_ends[i - 1] if i > 0 else 0):\n                              sentence_ends[i]]\n            result[index:index + len(sentence)] = sentence\n            index += len(sentence)\n        assert len(result) == len(tokens), \"Error when permute sentences.\"\n        return result\n\n    def _is_word_start(self, token):\n        return self.__is_word_start(token)\n\n    def whole_word_mask(self, tokens, p=1.0):  # text span mask/infilling\n        is_word_start = torch.tensor(\n            [self._is_word_start(token) for token in tokens]).int()\n        n_mask = int(math.ceil(is_word_start.sum() * p))\n        n_insert = 0\n        if n_mask == 0:\n            return tokens\n\n        if self.mask_span_distribution is not None:  # Text (span) Infilling\n            lengths = self.mask_span_distribution.sample(\n                sample_shape=(n_mask,))\n\n            # Make sure we have enough to mask\n            cum_length = torch.cumsum(lengths, 0)\n            while cum_length[-1] < n_mask:\n                lengths = torch.cat([\n                    lengths,\n                    self.mask_span_distribution.sample(\n                        sample_shape=(n_mask,))\n                ], dim=0)\n                cum_length = torch.cumsum(lengths, 0)\n\n            # Trim to masking budget\n            i = 0\n            while cum_length[i] < n_mask:\n                i += 1\n            lengths[i] = n_mask - (0 if i == 0 else cum_length[i - 1])\n            n_mask = i + 1\n            lengths = lengths[:n_mask]\n\n            # Handle 0-length mask (inserts) separately\n            lengths = lengths[lengths > 0]\n            n_insert = n_mask - lengths.size(0)\n            n_mask -= n_insert\n            if n_mask == 0:\n                return self.insertion_noise(tokens, n_insert / len(tokens))\n\n            assert (lengths > 0).all()\n        else:  # Token Masking\n            lengths = torch.ones((n_mask,)).long()\n        # assert is_word_start[-1] == 0\n        word_starts = is_word_start.nonzero(as_tuple=False)\n        indices = word_starts[\n            torch.randperm(word_starts.size(0))[:n_mask]\n        ].squeeze(1)\n        mask_random = torch.FloatTensor(n_mask).uniform_() < self.random_ratio\n\n        tokens_length = len(tokens)\n        # assert tokens_length - 1 not in indices\n        to_keep = torch.ones(tokens_length, dtype=torch.bool)\n\n        if self.replace_length == 0:\n            to_keep[indices] = 0\n        else:\n            # keep index, but replace it with [MASK]\n            for i in indices.tolist():\n                tokens[i] = self.mask_tok\n            random_tok_ids = torch.randint(\n                0, len(self.vocab), size=(mask_random.sum(),)).tolist()\n            for i, rid in zip(indices[mask_random].tolist(), random_tok_ids):\n                tokens[i] = self.vocab[rid]\n\n        if tokens_length - 1 in indices:\n            uncompleted = (indices != tokens_length - 1)\n            indices = indices[uncompleted]\n            mask_random = mask_random[uncompleted]\n            lengths = lengths[uncompleted]\n\n        # acts as a long length, so spans don't go over the end of doc\n        is_word_start[-1] = 255\n\n        if self.mask_span_distribution is not None:\n            assert len(lengths.size()) == 1\n            assert lengths.size() == indices.size()\n            lengths -= 1  # 1 for the position already masked\n            while indices.size(0) > 0:\n                assert lengths.size() == indices.size()\n                # next position from each word_start\n                lengths -= is_word_start[indices + 1].long()\n                uncompleted = lengths >= 0\n                indices = indices[uncompleted] + 1\n                mask_random = mask_random[uncompleted]\n                lengths = lengths[uncompleted]\n                if self.replace_length != -1:\n                    # delete token: 1 mask/remove per span\n                    to_keep[indices] = 0\n                else:\n                    # keep index, but replace it with [MASK]: 1 mask per token\n                    for i in indices.tolist():\n                        tokens[i] = self.mask_tok\n                    random_tok_ids = torch.randint(\n                        0, len(self.vocab), size=(mask_random.sum(),)).tolist()\n                    for i, rid in zip(\n                            indices[mask_random].tolist(), random_tok_ids):\n                        tokens[i] = self.vocab[rid]\n        else:\n            # A bit faster when all lengths are 1\n            while indices.size(0) > 0:\n                # to cover whole token\n                uncompleted = is_word_start[indices + 1] == 0\n                indices = indices[uncompleted] + 1\n                mask_random = mask_random[uncompleted]\n                if self.replace_length != -1:\n                    # delete token\n                    to_keep[indices] = 0\n                else:\n                    # keep index, but replace it with [MASK]\n                    for i in indices.tolist():\n                        tokens[i] = self.mask_tok\n                    random_tok_ids = torch.randint(\n                        0, len(self.vocab), size=(mask_random.sum(),)).tolist()\n                    for i, rid in zip(\n                            indices[mask_random].tolist(), random_tok_ids):\n                        tokens[i] = self.vocab[rid]\n\n                # assert tokens_length - 1 not in indices\n\n        tokens = [tok for tok, keep in zip(tokens, to_keep.tolist())\n                  if keep is True]\n\n        if n_insert > 0:\n            tokens = self.insertion_noise(tokens, n_insert / len(tokens))\n\n        return tokens\n\n    def insertion_noise(self, tokens, p=1.0):\n        n_tokens = len(tokens)\n        n_insert = math.ceil(n_tokens * p)\n        if n_insert == 0:\n            return tokens\n        n_random = math.ceil(n_insert * self.random_ratio)\n\n        noise_indices = np.random.permutation(n_tokens + n_insert)[:n_insert]\n        noise_mask = np.zeros(shape=(n_tokens + n_insert,), dtype=bool)\n        noise_mask[noise_indices] = 1\n\n        result = np.empty(shape=(n_tokens + n_insert,), dtype=object)\n        result[noise_indices[n_random:]] = self.mask_tok\n        if n_random > 0:\n            result[noise_indices[:n_random]] = np.random.choice(\n                self.vocab, size=n_random)\n        result[~noise_mask] = tokens\n\n        assert all([item is not None for item in result]),\\\n            \"Error when inserting noise.\"\n        return result.tolist()\n\n    def rolling_noise(self, tokens, p=1.0):\n        if np.random.random() >= p:\n            return tokens\n        offset = np.random.randint(0, max(1, len(tokens) - 1) + 1)\n        return tokens[offset:] + tokens[0:offset]\n\n    def apply(self, tokens):\n        if self.vocab is None:\n            raise ValueError(\"Inject BART noise requires a valid vocabulary.\")\n\n        if self.permute_sent_ratio > 0.0:\n            tokens = self.permute_sentences(tokens, self.permute_sent_ratio)\n\n        if self.mask_ratio > 0.0:\n            tokens = self.whole_word_mask(tokens, self.mask_ratio)\n\n        if self.insert_ratio > 0.0:\n            tokens = self.insertion_noise(tokens, self.insert_ratio)\n\n        if self.rotate_ratio > 0.0:\n            tokens = self.rolling_noise(tokens, self.rotate_ratio)\n        return tokens\n\n    def __repr__(self):\n        cls_name = type(self).__name__\n        kwargs = {}\n        if self.permute_sent_ratio > 0.0:\n            kwargs['permute_sent_ratio'] = self.permute_sent_ratio\n            kwargs['full_stop_token'] = self.full_stop_token\n        if self.insert_ratio > 0.0:\n            kwargs['insert_ratio'] = self.insert_ratio\n        if self.rotate_ratio > 0.0:\n            kwargs['rotate_ratio'] = self.rotate_ratio\n        if self.random_ratio > 0.0:\n            kwargs['random_ratio'] = self.random_ratio\n        if self.mask_ratio > 0.0:\n            kwargs['mask_ratio'] = self.mask_ratio\n            kwargs['mask_length'] = self.mask_length\n            kwargs['poisson_lambda'] = self.poisson_lambda\n            kwargs['replace_length'] = self.replace_length\n        cls_args = ', '.join(\n            [f'{kw}={arg}' for kw, arg in kwargs.items()])\n        return '{}({})'.format(cls_name, cls_args)\n\n\n@register_transform(name='bart')\nclass BARTNoiseTransform(Transform):\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to BART.\"\"\"\n        group = parser.add_argument_group(\"Transform/BART\")\n        group.add(\"--permute_sent_ratio\", \"-permute_sent_ratio\",\n                  type=float, default=0.0,\n                  help=\"Permute this proportion of sentences \"\n                       \"(boundaries defined by {}) in all inputs.\".format(\n                        DefaultTokens.SENT_FULL_STOPS))\n        group.add(\"--rotate_ratio\", \"-rotate_ratio\", type=float, default=0.0,\n                  help=\"Rotate this proportion of inputs.\")\n        group.add(\"--insert_ratio\", \"-insert_ratio\", type=float, default=0.0,\n                  help=\"Insert this percentage of additional random tokens.\")\n        group.add(\"--random_ratio\", \"-random_ratio\", type=float, default=0.0,\n                  help=\"Instead of using {}, use random token \"\n                       \"this often.\".format(DefaultTokens.MASK))\n\n        group.add(\"--mask_ratio\", \"-mask_ratio\", type=float, default=0.0,\n                  help=\"Fraction of words/subwords that will be masked.\")\n        group.add(\"--mask_length\", \"-mask_length\", type=str, default=\"subword\",\n                  choices=[\"subword\", \"word\", \"span-poisson\"],\n                  help=\"Length of masking window to apply.\")\n        group.add(\"--poisson_lambda\", \"-poisson_lambda\",\n                  type=float, default=0.0,\n                  help=\"Lambda for Poisson distribution to sample span length \"\n                       \"if `-mask_length` set to span-poisson.\")\n        group.add(\"--replace_length\", \"-replace_length\",\n                  type=int, default=-1, choices=[-1, 0, 1],\n                  help=\"When masking N tokens, replace with 0, 1, \"\n                       \"or N tokens. (use -1 for N)\")\n\n    def warm_up(self, vocabs):\n        super().warm_up(None)\n        if vocabs is None:\n            self.bart_noise = None\n            return\n        self.vocabs = vocabs\n\n        subword_type = self.opts.src_subword_type\n        if self.opts.mask_length == 'subword':\n            if subword_type == 'none':\n                raise ValueError(\n                    f'src_subword_type={subword_type} incompatible with '\n                    f'mask_length={self.opts.mask_length}!')\n        is_joiner = (subword_type == 'bpe') if subword_type != 'none' else None\n        self.bart_noise = BARTNoising(\n            self.vocabs['src'].itos,\n            mask_tok=DefaultTokens.MASK,\n            mask_ratio=self.opts.mask_ratio,\n            insert_ratio=self.opts.insert_ratio,\n            permute_sent_ratio=self.opts.permute_sent_ratio,\n            poisson_lambda=self.opts.poisson_lambda,\n            replace_length=self.opts.replace_length,\n            rotate_ratio=self.opts.rotate_ratio,\n            mask_length=self.opts.mask_length,\n            random_ratio=self.opts.random_ratio,\n            is_joiner=is_joiner\n        )\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply BART noise to src side tokens.\"\"\"\n        if is_train and self.vocabs is not None:\n            src = self.bart_noise.apply(example['src'])\n            example['src'] = src\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for BART.\"\"\"\n        return repr(self.bart_noise)\n"
  },
  {
    "path": "onmt/transforms/keyphrase.py",
    "content": "# -*- coding: utf-8 -*-\nimport copy\nimport random\nimport re\nimport warnings\n\nimport numpy as np\nimport torch\n\nfrom onmt.constants import DefaultTokens\nfrom onmt.inputters.keyphrase_dataset import KP_DATASET_FIELDS, KP_CONCAT_TYPES, obtain_sorted_indices, \\\n    parse_src_fn\nfrom onmt.keyphrase import utils\nfrom onmt.transforms import register_transform\nfrom onmt.utils.misc import str2bool\nfrom .transform import Transform\n\nimport spacy\nspacy_nlp = spacy.load('en_core_web_sm')\n\n\n@register_transform(name='keyphrase')\nclass KeyphraseTransform(Transform):\n    '''\n    A preprocessing transform for keyphrase data point.\n    Basic steps:\n        (1) concatenate title and abstract (or fulltext in other data type)\n        (2) reorder target keyphrases\n        (3) tokenize strings if specified\n    '''\n    SEP_TOK = DefaultTokens.SEP\n    BOS_TOK = DefaultTokens.BOS\n    EOS_TOK = DefaultTokens.EOS\n\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.kp_concat_type = self.opts.kp_concat_type\n        self.max_target_phrases = self.opts.max_target_phrases\n        self.lowercase = self.opts.lowercase\n        self.return_tokens = self.opts.return_tokens\n        self.keep_punctuations = self.opts.keep_punctuations\n        self.add_src_boseos = self.opts.add_src_boseos\n        self.use_given_inputs = self.opts.use_given_inputs\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to BART.\"\"\"\n        group = parser.add_argument_group(\"Transform/keyphrase\")\n        group.add('--kp_concat_type', '-kp_concat_type', default=None,\n                   choices=KP_CONCAT_TYPES,\n                   help=\"\"\"Format of targets for model to learn/output, 'multiple' is used during test phase.\n                   Note that the names have been changed after the empirical paper, e.g. verbatim_append is changed to pres_abs. \n                   \"\"\")\n        group.add(\"--max_target_phrases\", \"-max_target_phrases\",\n                  type=int, default=-1,\n                  help=\"Number of phrases allowed on the target side, -1 denotes no limit.\")\n        group.add(\"--lowercase\", \"-lowercase\",\n                  type=str2bool, default=False,\n                  help=\"whether to apply lowercase to both source and target text.\")\n        group.add(\"--return_tokens\", \"-return_tokens\",\n                  type=str2bool, default=False,\n                  help=\"return a list of tokens (w/ simple tokenization) or a string after the data point preprocessing.\")\n        group.add(\"--keep_punctuations\", \"-keep_punctuations\",\n                  type=str2bool, default=False,\n                  help=\"keep [_-<>,\\(\\)\\[\\]\\.\\'%].\")\n        group.add(\"--add_src_boseos\", \"-add_src_boseos\",\n                  type=str2bool, default=False,\n                  help=\"add <bos> and <eos> to the source text.\")\n        group.add(\"--use_given_inputs\", \"-use_given_inputs\",\n                  type=str2bool, default=False,\n                  help=\"Only designated to ease the out-of-the-box inference,\"\n                       \"using src and tgt that are directly given in ex['src'] and ex['tgt'] rather than processing them on-the-fly.\")\n\n    @classmethod\n    def get_specials(cls, opts):\n        return ({cls.SEP_TOK, cls.BOS_TOK, cls.EOS_TOK}, set())\n\n    def warm_up(self, vocabs):\n        super().warm_up(None)\n        self.vocabs = vocabs\n        # for now it is hard-coded, vocabs does not contain useful info if pre-trained tokenizer is used\n        self.sep_token = self.SEP_TOK\n\n    def kpdict_parse_fn(self, ex_dict, kp_concat_type, dataset_type='scipaper'):\n        \"\"\"\n        Similar to the function used on\n        :param ex_dict:\n        :param kp_concat_type:\n        :param dataset_type:\n        :param max_target_phrases:\n        :param lowercase:\n        :return:\n        \"\"\"\n        assert dataset_type in KP_DATASET_FIELDS\n        title_field, text_field, keyword_field, category_field = KP_DATASET_FIELDS[dataset_type]\n\n        src_str = parse_src_fn(ex_dict, title_field, text_field)\n\n        # make sure each phrase is a separate text string, not a list nor concatenated multiple phrases\n        if isinstance(ex_dict[keyword_field], str):\n            tgt_kps = ex_dict[keyword_field].split(';')\n        elif isinstance(ex_dict[keyword_field], list) and len(ex_dict[keyword_field]) > 0 \\\n            and isinstance(ex_dict[keyword_field][0], list):\n            tgt_kps = [' '.join(p) for p in ex_dict[keyword_field]]\n        else:\n            tgt_kps = ex_dict[keyword_field]\n\n        if self.keep_punctuations:\n            src_str = re.sub(r'[-_<>,\\(\\)\\[\\]\\.\\'%]', ' \\g<0> ', src_str)\n            tgt_kps = [re.sub(r'[-_<>,\\(\\)\\[\\]\\.\\'%]', ' \\g<0> ', kp) for kp in tgt_kps]\n\n        if self.lowercase:\n            src_str = src_str.lower()\n            tgt_kps = [kp.lower() for kp in tgt_kps]\n\n        src_tokens = [t for t in re.split(r'\\s', src_str) if len(t) > 0]\n        tgt_seqs = [[t for t in re.split(r'\\s', p) if len(t) > 0] for p in tgt_kps]\n        if kp_concat_type == 'one2one':\n            # sample one tgt from multiple tgts and use it as the only tgt\n            rand_idx = np.random.randint(len(tgt_kps))\n            tgt_str = tgt_kps[rand_idx]\n            tgt_tokens = [t for t in re.split(r'\\s', tgt_str) if len(t) > 0]\n        elif kp_concat_type in KP_CONCAT_TYPES:\n            tgt_tokens = None\n            tgt_str = ''\n            # (deprecated, extremely slow)generate one2seq training data points, use spacy tokenization\n            # src_seq = [t.text.lower() for t in spacy_nlp(src_str, disable=[\"textcat\"])]\n            # tgt_seqs = [[t.text.lower() for t in spacy_nlp(p, disable=[\"textcat\"])] for p in tgt_kps]\n            if len(tgt_seqs) > 0:\n                order = obtain_sorted_indices(src_tokens, tgt_seqs, sort_by=kp_concat_type)\n                if self.max_target_phrases > 0 and len(order) > self.max_target_phrases:\n                    order = order[: self.max_target_phrases]\n                tgt = [tgt_kps[idx] for idx in order]\n                tgt_str = self.sep_token.join(tgt)\n                tgt_tokens = copy.copy(tgt_seqs[0])\n                for i in range(1, len(order)):\n                    tgt_tokens.append(self.sep_token)\n                    tgt_tokens += tgt_seqs[i]\n            else:\n                tgt_str = ''\n        else:\n            raise NotImplementedError('Unsupported target concatenation type ' + kp_concat_type)\n\n        ex_dict['keywords_tokens'] = tgt_seqs\n\n        if self.add_src_boseos:\n            src_tokens = [DefaultTokens.BOS] + src_tokens + [DefaultTokens.EOS]\n            src_str = DefaultTokens.BOS + ' ' + src_str + ' ' + DefaultTokens.EOS\n\n        return src_tokens, tgt_tokens, src_str, tgt_str\n\n    @classmethod\n    def infer_dataset_type(self, example):\n        dataset_type = None\n        if 'dataset_type' in example:\n            dataset_type = example['dataset_type']\n        elif 'question' in example:\n            dataset_type = 'qa'\n        elif 'url' in example:\n            dataset_type = 'webpage'\n        elif 'date' in example:\n            dataset_type = 'news'\n        elif 'abstract' in example:\n            dataset_type = 'scipaper'\n\n        assert dataset_type is not None, 'Fail to detect the data type of the given input file.' \\\n                                         'Accecpted values:' + KP_DATASET_FIELDS.keys()\n        return dataset_type\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"\n        Source text: concatenating title and body text.\n        Target text: concatenating phrases according to the given phrase order.\n        \"\"\"\n        if self.use_given_inputs and 'src' in example and 'tgt' in example and example['src'] and example['tgt']:\n            # print('WARNING: using src and tgt that are directly given rather than processed on-the-fly.\\n '\n            #       'This is only designated to ease the out-of-the-box inference. Ensure this behavior is wanted.')\n            return example\n\n        dataset_type = self.infer_dataset_type(example)\n        src_tokens, tgt_tokens, src_str, tgt_str = self.kpdict_parse_fn(example, self.kp_concat_type, dataset_type=dataset_type)\n        if self.return_tokens:\n            example['src'] = src_tokens\n            example['tgt'] = tgt_tokens\n        else:\n            example['src'] = src_str\n            example['tgt'] = tgt_str\n\n        example['src_str'] = src_str\n        example['tgt_str'] = tgt_str\n\n        return example\n\n\n@register_transform(name='kp_random_span')\nclass KeyphraseRandomSpanTargetTransform(Transform):\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.seed = self.opts.seed\n        self.max_phrase_len = self.opts.max_phrase_len\n        self.max_target_phrases = self.opts.max_target_phrases\n        self.lowercase = self.opts.lowercase\n\n        self.span_lens = list(range(1, self.max_phrase_len + 1))\n        geometric_p = 0.2\n        len_distrib = [geometric_p * (1 - geometric_p) ** (i - 1) for i in\n                       range(1, self.max_phrase_len + 1)] if geometric_p >= 0 else None\n        self.len_distrib = [x / (sum(len_distrib)) for x in len_distrib]\n\n    @classmethod\n    def add_options(cls, parser):\n        group = parser.add_argument_group(\"Transform/keyphrase_random_span\")\n        group.add(\"--max_phrase_len\", \"-max_phrase_len\",\n                  type=int, default=-1,\n                  help=\"Max length of target phrases.\")\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n\n    def warm_up(self, vocabs):\n        super().warm_up(None)\n\n    def random_span_parse_fn(self, ex, sep_token,\n                             num_spans=None,\n                             max_target_phrases=8,\n                             span_len_opts=None,\n                             len_distrib=None,\n                             lowercase=False,\n                             return_masked_source=True,\n                             seed=0):\n        \"\"\"\n        :param ex:\n        :param num_spans: if set, will sample this many spans, otherwise it samples a random number of spans\n        :param sep_token:\n        :param max_target_phrases:\n        :param lowercase:\n        :return:\n        \"\"\"\n        assert self.max_phrase_len > 0, 'max_phrase_len must be larger than 0'\n        assert max_target_phrases > 0, 'max_target_phrases must be a positive integer'\n        src_text = ex['src']\n\n        with utils.numpy_seed(seed):\n            # mask random spans\n            src_tokens = src_text.split()\n\n            span_lens = []\n            if not num_spans:\n                num_spans = np.random.random_integers(max_target_phrases)\n            for i in range(num_spans):\n                span_len = max(1, np.random.choice(span_len_opts, p=len_distrib))\n                span_lens.append(span_len)\n\n            span_lens = sorted(span_lens, reverse=True)  # ensure larger spans get processed first\n\n            spans = []\n            is_masked = [False] * len(src_tokens)\n            span_idx = 0\n            num_try, max_try = 0, len(span_lens) * 4\n            while span_idx < len(span_lens):\n                # in case there's not much unmasked tokens left\n                num_try += 1\n                if num_try > max_try: break\n                # sample a span start\n                span_len = span_lens[span_idx]\n                span_left = np.random.random_integers(low=0, high=len(src_tokens)-span_len)\n                # some tokens have been masked, skip. Also to ensure no two spans are contiguous\n                has_overlap = False\n                l_idx = span_left-1 if span_left > 0 else 0\n                r_idx = span_left+span_len+1 if span_left+span_len+1 < len(src_tokens) else len(src_tokens)\n                for i in range(l_idx, r_idx):\n                    if is_masked[i]: has_overlap = True\n                if has_overlap: continue\n                # a new span\n                spans.append((span_left, span_left + span_len))\n                for i in range(span_len):\n                    is_masked[span_left+i] = True\n                span_idx += 1\n\n            spans = sorted(spans, key=lambda k:k[0]) # order spans by their positions\n            masked_src_tokens, infill_spans = [], []\n            prev_span_end = 0\n            for s in spans:\n                masked_src_tokens.extend(src_tokens[prev_span_end: s[0]])\n                masked_src_tokens.append('<infill>')\n                infill_spans.append(src_tokens[s[0]: s[1]])\n                prev_span_end = s[1]\n            masked_src_tokens.extend(src_tokens[prev_span_end:])\n\n            span_texts = [' '.join(s) for s in infill_spans]\n            if return_masked_source:\n                src_text = ' '.join(masked_src_tokens)\n            else:\n                src_text = src_text\n\n        tgt_text = sep_token.join(span_texts)\n\n        if lowercase:\n            return src_text.lower(), tgt_text.lower(), [p.lower() for p in span_texts]\n\n        return src_text, tgt_text, span_texts\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"\n        Source text: concatenating title and body text.\n        Target text: concatenating phrases according to the given phrase order.\n        \"\"\"\n        src_str, tgt_str, _ = self.random_span_parse_fn(example,\n                                                     sep_token=DefaultTokens.SEP,\n                                                     num_spans=None,\n                                                     max_target_phrases=self.max_target_phrases,\n                                                     span_len_opts=self.span_lens, len_distrib=self.len_distrib,\n                                                     lowercase=self.lowercase,\n                                                     seed=self.seed\n                                                     )\n        example['src'] = src_str\n        example['tgt'] = tgt_str\n        example['src_str'] = src_str\n        example['tgt_str'] = tgt_str\n\n        return example\n\n\n@register_transform(name='kp_replace_target')\nclass KeyphraseReplaceTargetTransform(KeyphraseRandomSpanTargetTransform):\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.kp_concat_type = self.opts.kp_concat_type\n        self.max_target_phrases = self.opts.max_target_phrases\n        self.max_phrase_len = self.opts.max_phrase_len\n        self.lowercase = self.opts.lowercase\n        self.use_given_inputs = self.opts.use_given_inputs\n        self.label_sample_ratio = self.opts.label_sample_ratio\n        self.seed = self.opts.seed\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to BART.\"\"\"\n        group = parser.add_argument_group(\"Transform/kp_replace_target\")\n        group.add(\"--label_sample_ratio\", type=float, nargs='+', help=\"Sampling proportion of labels from each label file.\")\n\n    def warm_up(self, vocabs):\n        super().warm_up(None)\n\n    def maybe_replace_target(self, example,\n                             label_sample_ratio,\n                             max_target_phrases,\n                             max_phrase_len=-1,\n                             add_control_prefix_prob=0.0,\n                             fix_target_number=False,\n                             allow_duplicate=False,\n                             sep_token=DefaultTokens.SEP, seed=0):\n        '''\n        If additional target label sets are given, we replace example['target'] with new labels\n        :param example:\n        :param label_sample_ratio: sampling ratio of each extra label set\n        :param max_target_phrases:\n        :param add_control_prefix_prob: if given, we append the number of phrases as a prefix to source string\n        :param fix_target_number: if True, target always contains `max_target_phrases` phrases, otherwise it's sampled in (0, max_target_phrases]\n        :param allow_duplicate: if True, target can contain duplicate phrases, otherwise duplicate phrases are removed\n        :param seed:\n        :return:\n        '''\n        if not label_sample_ratio:  # label set is not given, directly return\n            warnings.warn(\"label_sample_ratio is not set in KeyphraseReplaceTargetTransform\", RuntimeWarning)\n            return example\n        if max_target_phrases < 0:\n            max_target_phrases = 100000 # a very large number\n        src_str, tgt_str = example['src'], example['tgt']\n        with utils.numpy_seed(seed):\n            tgts = []\n            for labelset_id, ratio in enumerate(label_sample_ratio):\n                candicate_tgts = example['target%d' % labelset_id]\n                if isinstance(candicate_tgts, list):\n                    # ensure each phrase has less than 70 characters and max_phrase_len words\n                    if max_phrase_len > 0:\n                        candicate_tgts = [p for p in candicate_tgts\n                                            if len(p) < 70 and len(re.findall(r\"\\w+|[^\\w\\s]\", p, re.UNICODE)) <= max_phrase_len]\n                    # remove punctuations\n                    candicate_tgts = [p.strip() for p in candicate_tgts if len(p.strip()) > 0]\n                    if len(candicate_tgts) == 0:\n                        continue\n                    num_to_sample = max(1, min(len(candicate_tgts), int(ratio * max_target_phrases)))\n                    candicate_tgts = np.random.choice(candicate_tgts, num_to_sample, replace=False)\n                    np.random.shuffle(candicate_tgts)\n                elif isinstance(candicate_tgts, str) and candicate_tgts == '__annotated_kp':\n                    # ground-truth keyphrases\n                    assert 'keywords_tokens' in example, 'keywords_tokens not found in example, ' \\\n                                                         'please ensure the keyphrase transform has run precedingly in the pipeline'\n                    candicate_tgts = example['keywords_tokens']\n                    candicate_tgts = [' '.join(p) for p in candicate_tgts]\n                    if len(candicate_tgts) == 0:\n                        continue\n                    num_to_sample = max(1, min(len(candicate_tgts), int(ratio * max_target_phrases)))\n                    candicate_tgts = np.random.choice(candicate_tgts, num_to_sample, replace=False)\n                    np.random.shuffle(candicate_tgts)\n                elif isinstance(candicate_tgts, str) and candicate_tgts == '__random_span':\n                    num_to_sample = max(1, int(ratio * max_target_phrases))\n                    if num_to_sample > 20:\n                        warnings.warn(\"current number of random span is %d, please ensure that max_target_phrases is properly set, rather than -1\", RuntimeWarning)\n                    # random spans\n                    src_str, tgt_str, candicate_tgts = self.random_span_parse_fn(example,\n                                                                 sep_token=DefaultTokens.SEP,\n                                                                 num_spans=num_to_sample,\n                                                                 span_len_opts=self.span_lens, len_distrib=self.len_distrib,\n                                                                 lowercase=self.lowercase,\n                                                                 seed=self.seed\n                                                                 )\n                else:\n                    raise NotImplementedError('Not supported type:' + candicate_tgts)\n\n                tgts.extend(candicate_tgts)\n\n            # deduplicate\n            if not allow_duplicate:\n                tgts = list(set(tgts))\n\n            # a problematic data example\n            if len(tgts) == 0:\n                return '', ''\n\n            if not fix_target_number:\n                tgts = np.random.choice(tgts, size=np.random.randint(len(tgts)) + 1, replace=False).tolist()\n\n            tgt_str = sep_token.join(tgts)\n\n            # add control prefix (number of phrases to output)\n            if add_control_prefix_prob > 0.0 and np.random.rand() < add_control_prefix_prob:\n                prefix_str = '<mixed><number>%d<s>' % (len(tgts))\n                src_str = prefix_str + src_str\n            else:\n                src_str = src_str\n\n        return src_str, tgt_str\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"\n        Source text: concatenating title and body text.\n        Target text: concatenating phrases according to the given phrase order.\n        \"\"\"\n        src_str, tgt_str = self.maybe_replace_target(example,\n                                                     label_sample_ratio=self.label_sample_ratio,\n                                                     max_target_phrases=self.max_target_phrases,\n                                                     max_phrase_len=self.max_phrase_len,\n                                                     add_control_prefix_prob=0.0,\n                                                     fix_target_number=False,\n                                                     allow_duplicate=False,\n                                                     sep_token=DefaultTokens.SEP,\n                                                     seed=self.seed\n                                                     )\n        # occasionally candidate tgt is empty, skip\n        if len(tgt_str) > 0:\n            example['src'] = src_str\n            example['tgt'] = tgt_str\n            example['src_str'] = src_str\n            example['tgt_str'] = tgt_str\n\n        return example\n\n\n@register_transform(name='add_control_prefix')\nclass ControlPrefixTransform(Transform):\n\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.src_control_prefix = self.opts.src_control_prefix\n        self.tgt_control_prefix = self.opts.tgt_control_prefix\n\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to BART.\"\"\"\n        group = parser.add_argument_group(\"Transform/keyphrase\")\n        group.add(\"--src_control_prefix\", \"-src_control_prefix\",\n                  type=str, default='', help=\"It will be appended to the source text to control the output.\")\n        group.add(\"--tgt_control_prefix\", \"-tgt_control_prefix\",\n                  type=str, default='', help=\"It will be appended to the target text to control the output.\")\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"\n        Source text: concatenating title and body text.\n        Target text: concatenating phrases according to the given phrase order.\n        \"\"\"\n        # if control-prefix is given, prepend it\n        if 'src_control_prefix' in example:\n            src_control_prefix = example['src_control_prefix']\n        elif self.src_control_prefix:\n            src_control_prefix = self.src_control_prefix\n        else:\n            src_control_prefix = ''\n\n        if 'tgt_control_prefix' in example:\n            tgt_control_prefix = example['tgt_control_prefix']\n        elif self.tgt_control_prefix:\n            tgt_control_prefix = self.tgt_control_prefix\n        else:\n            tgt_control_prefix = ''\n\n        example['src'] = src_control_prefix + example['src']\n        example['src_str'] = example['src']\n        if is_train:\n            example['tgt'] = tgt_control_prefix + example['tgt']\n            example['tgt_str'] = example['tgt']\n\n        return example\n\n\ndef findBalanced(text, openDelim=['[['], closeDelim=[']]']):\n    \"\"\"\n    Assuming that text contains a properly balanced expression using\n    :param openDelim: as opening delimiters and\n    :param closeDelim: as closing delimiters.\n    :return: an iterator producing pairs (start, end) of start and end\n    positions in text containing a balanced expression.\n    \"\"\"\n    openPat = '|'.join([re.escape(x) for x in openDelim])\n    # pattern for delimiters expected after each opening delimiter\n    afterPat = {o: re.compile(openPat + '|' + c, re.DOTALL) for o, c in zip(openDelim, closeDelim)}\n    stack = []\n    start = 0\n    cur = 0\n    # end = len(text)\n    startSet = False\n    startPat = re.compile(openPat)\n    nextPat = startPat\n    while True:\n        next = nextPat.search(text, cur)\n        if not next:\n            return\n        if not startSet:\n            start = next.start()\n            startSet = True\n        delim = next.group(0)\n        if delim in openDelim:\n            stack.append(delim)\n            nextPat = afterPat[delim]\n        else:\n            opening = stack.pop()\n            # assert opening == openDelim[closeDelim.index(next.group(0))]\n            if stack:\n                nextPat = afterPat[stack[-1]]\n            else:\n                yield start, next.end()\n                nextPat = startPat\n                start = next.end()\n                startSet = False\n        cur = next.end()\n\n\ndef replaceInternalLinks(text, return_anchor_text=False):\n    \"\"\"\n    Replaces internal links of the form:\n    [[title |...|label]]trail\n\n    with title concatenated with trail, when present, e.g. 's' for plural.\n\n    See https://www.mediawiki.org/wiki/Help:Links#Internal_links\n    \"\"\"\n    # call this after removal of external links, so we need not worry about\n    # triple closing ]]].\n    cur = 0\n    res = ''\n    phrase_list = []\n    for s, e in findBalanced(text):\n        m = tailRE.match(text, e)\n        if m:\n            trail = m.group(0)\n            end = m.end()\n        else:\n            trail = ''\n            end = e\n        inner = text[s + 2:e - 2]\n        # find first |\n        pipe = inner.find('|')\n        if pipe < 0:\n            title = inner\n            label = title\n        else:\n            title = inner[:pipe].rstrip()\n            # find last |\n            curp = pipe + 1\n            for s1, e1 in findBalanced(inner):\n                last = inner.rfind('|', curp, s1)\n                if last >= 0:\n                    pipe = last  # advance\n                curp = e1\n            label = inner[pipe + 1:].strip()\n\n        # phrase_list.append(title.strip())\n        phrase_list.append(label.strip())\n        res += text[cur:s] + label + trail\n        cur = end\n    if return_anchor_text:\n        return res + text[cur:], phrase_list\n    else:\n        return res + text[cur:]\n\nbold_italic = re.compile(r\"'''''(.*?)'''''\")\nbold = re.compile(r\"'''(.*?)'''\")\nitalic_quote = re.compile(r\"''\\\"([^\\\"]*?)\\\"''\")\nitalic = re.compile(r\"''(.*?)''\")\nquote_quote = re.compile(r'\"\"([^\"]*?)\"\"')\ntailRE = re.compile('\\w+')\n\ndef extract_phrases(text):\n    # Extract bold/italic text and internal links\n\n    # Extract bold/anchor texts\n    font_phrases = bold_italic.findall(text)\n    font_phrases += bold.findall(text)\n    font_phrases += italic_quote.findall(text)\n    font_phrases += italic.findall(text)\n    font_phrases += quote_quote.findall(text)\n    font_phrases = [p.strip('\\',\\\"') for p in font_phrases]\n    font_phrases = list(set(font_phrases))\n\n    # Handle bold/italic/quote\n    text = bold_italic.sub(r'\\1', text)\n    text = bold.sub(r'\\1', text)\n    text = italic_quote.sub(r'\"\\1\"', text)\n    text = italic.sub(r'\"\\1\"', text)\n    text = quote_quote.sub(r'\"\\1\"', text)\n    # replace internal links\n    text, anchor_phrases = replaceInternalLinks(text, return_anchor_text=True)\n    anchor_phrases = [p.strip('\\',\\\"') for p in anchor_phrases]\n    anchor_phrases = list(set(anchor_phrases))\n\n    return text, font_phrases, anchor_phrases\n\n\n@register_transform(name='wiki_phrase')\nclass WikiPhraseTransform(Transform):\n    def __init__(self, opts):\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.kp_concat_type = self.opts.kp_concat_type\n        self.max_target_phrases = self.opts.max_target_phrases\n        self.max_phrase_len = self.opts.max_phrase_len\n        self.lowercase = self.opts.lowercase\n        self.seed = self.opts.seed\n        self.phrase_corr_rate = self.opts.phrase_corr_rate\n        self.random_span_rate = self.opts.random_span_rate\n        # span distribution follows SpanBERT (https://arxiv.org/pdf/1907.10529.pdf)\n        self.span_len_opts = list(range(1, 8 + 1))\n        geometric_p = 0.2\n        len_distrib = [geometric_p * (1 - geometric_p) ** (i - 1) for i in\n                       range(1, 8 + 1)] if geometric_p >= 0 else None\n        self.len_distrib = [x / (sum(len_distrib)) for x in len_distrib]\n        self.sep_token = DefaultTokens.SEP\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to BART.\"\"\"\n        group = parser.add_argument_group(\"Transform/keyphrase\")\n        group.add_argument(\"--phrase_corr_rate\", default=0.0, type=float,\n                            help='.')\n        group.add_argument(\"--random_span_rate\", default=0.0, type=float,\n                            help='.')\n\n    def wiki_ex_parse_fn(self,\n                         ex_dict, sep_token,\n                         max_phrase_len=8,\n                         max_target_phrases=-1,\n                         phrase_corr_rate=0.0,\n                         random_span_rate=0.0,\n                         lowercase=False,\n                         seed=0):\n        \"\"\"\n        max_tgt_len=max_phrase_len*max_target_phrases + src_len*random_span_rate = 6*16+512*5%=96+25.6=121.6\n        masked_word=6*8*0.1+512*5%=30.4 (30.4/512=5.9%)\n        :param ex_dict:\n        :param max_phrase_len:\n        :param max_target_phrases:\n        :param phrase_corr_rate: replace p% * num_present_phrase present phrases from src_text with <present>\n        :param random_span_rate: replace p% * num_word spans from src_text with <mask>\n        :param lowercase:\n        :return:\n        \"\"\"\n        assert max_target_phrases > 0, 'max_target_phrases must be a positive integer'\n        text_field = 'text'\n\n        src_text = ex_dict[text_field]\n        src_text, font_phrases, anchor_phrases = extract_phrases(src_text)\n\n        pres_phrases = set(font_phrases + anchor_phrases)\n        header_phrases = [ex_dict['title']] + ex_dict['headers']\n        category_phrases = ex_dict['categories']\n        seealso_phrases = ex_dict['seealso']\n\n        if max_phrase_len:\n            pres_phrases = [p for p in pres_phrases if len(p.split()) <= max_phrase_len]\n            header_phrases = [p for p in header_phrases if len(p.split()) <= max_phrase_len]\n            category_phrases = [p for p in category_phrases if len(p.split()) <= max_phrase_len]\n            seealso_phrases = [p for p in seealso_phrases if len(p.split()) <= max_phrase_len]\n\n        with utils.numpy_seed(seed):\n            # present phrases\n            if max_target_phrases > 0 and len(pres_phrases) > max_target_phrases / 2:\n                pres_phrases = np.random.choice(pres_phrases, int(max_target_phrases / 2), replace=False).tolist()\n\n            num_pres = len(pres_phrases)\n            num_header = len(header_phrases)\n            num_cat = len(category_phrases)\n            num_seealso = len(seealso_phrases)\n\n            # absent phrases\n            abs_phrases = header_phrases + category_phrases + seealso_phrases\n            if max_target_phrases > 0 and len(abs_phrases) > max_target_phrases / 2:\n                num_cat = min(len(category_phrases), random.randint(0, int(max_target_phrases / 2 - len(header_phrases))))\n                num_seealso = min(len(seealso_phrases), int(max_target_phrases / 2) - len(header_phrases) - num_cat)\n                abs_phrases = header_phrases \\\n                              + np.random.choice(category_phrases, num_cat, replace=False).tolist()\\\n                              + np.random.choice(seealso_phrases, num_seealso, replace=False).tolist()\n\n            # mask random spans\n            num_infill = 0\n            if random_span_rate > 0.0:\n                src_tokens = src_text.split()\n                num_word_left = max(1, int(random_span_rate * len(src_tokens)))\n\n                span_lens = []\n                while num_word_left > 0:\n                    span_len = np.random.choice(self.span_len_opts, p=self.len_distrib).tolist()\n                    if span_len <= num_word_left:\n                        span_lens.append(span_len)\n                    else:\n                        span_lens.append(num_word_left)\n                    num_word_left -= span_len\n                span_lens = sorted(span_lens, reverse=True) # ensure larger spans get processed first\n\n                spans = []\n                uncovered_spans = [(0, len(src_tokens))]\n                for span_len in span_lens:\n                    candicate_spans, noncandicate_spans = [], []\n                    for s in uncovered_spans:\n                        if s[1] - s[0] >= span_len:\n                            candicate_spans.append(s)\n                        else:\n                            noncandicate_spans.append(s)\n\n                    if len(candicate_spans) == 0:\n                        # not possible to fit this span\n                        continue\n                    candicate_span_id = random.choice(range(len(candicate_spans)))\n                    candicate_span = candicate_spans[candicate_span_id]\n                    candicate_span_len = candicate_span[1] - candicate_span[0]\n\n                    # sample a span start given the candidate\n                    span_start_offset = random.randint(0, candicate_span_len - span_len + 1)\n                    span_left = candicate_span[0] + span_start_offset\n                    spans.append((span_left, span_left + span_len))\n\n                    # maintain the new candidate lists\n                    if span_start_offset == 0:\n                        leftover_spans = [(candicate_span[0] + span_len, candicate_span[1] + 1)]\n                    elif span_start_offset == candicate_span_len - span_len:\n                        leftover_spans = [(candicate_span[0], candicate_span[1] - span_len)]\n                    else:\n                        leftover_spans = [(candicate_span[0], span_left), (span_left + span_len, candicate_span[1] + 1)]\n\n                    uncovered_spans = noncandicate_spans + leftover_spans\n\n                spans = sorted(spans, key=lambda x: x[0], reverse=False)\n                masked_src_tokens = []\n                prev_span_end = 0\n                for s in spans:\n                    masked_src_tokens.extend(src_tokens[prev_span_end: s[0]])\n                    masked_src_tokens.append('<infill>')\n                    prev_span_end = s[1]\n                masked_src_tokens.extend(src_tokens[prev_span_end:])\n\n                infill_phrases = [' '.join(src_tokens[s[0]: s[1]]) for s in spans]\n                num_infill = len(infill_phrases)\n                src_text = ' '.join(masked_src_tokens)\n\n            # mask random present phrases\n            if phrase_corr_rate > 0.0 and len(pres_phrases) > 0:\n                num_mask_kp = min(1, int(len(pres_phrases) * phrase_corr_rate))\n                mask_pres_phrases = np.random.choice(pres_phrases, num_mask_kp, replace=False).tolist()\n                for p in mask_pres_phrases:\n                    src_text = re.sub(p, '<present>', src_text, flags=re.IGNORECASE)\n\n        prefix_str = '<present>%d<header>%d<category>%d<seealso>%d<infill>%d<s>' \\\n                     % (num_pres, num_header, num_cat, num_seealso, num_infill)\n\n        src_text = prefix_str + src_text\n        tgt_text = sep_token.join(pres_phrases + abs_phrases + infill_phrases)\n\n        if lowercase:\n            return src_text.lower(), tgt_text.lower()\n        return src_text, tgt_text\n\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"\n        Source text: concatenating title and body text.\n        Target text: concatenating phrases according to the given phrase order.\n        \"\"\"\n        src_str, tgt_str = self.wiki_ex_parse_fn(example, sep_token=self.sep_token,\n                                                 max_phrase_len=self.max_phrase_len,\n                                                 max_target_phrases=self.max_target_phrases,\n                                                 phrase_corr_rate=self.phrase_corr_rate,\n                                                 random_span_rate=self.random_span_rate,\n                                                 lowercase=self.lowercase,\n                                                 seed=self.seed)\n\n        example['src'] = src_str\n        example['tgt'] = tgt_str\n        example['src_str'] = src_str\n        example['tgt_str'] = tgt_str\n\n        return example\n"
  },
  {
    "path": "onmt/transforms/misc.py",
    "content": "from onmt.utils.logging import logger\nfrom onmt.transforms import register_transform\nfrom .transform import Transform\n\n\n@register_transform(name='filtertoolong')\nclass FilterTooLongTransform(Transform):\n    \"\"\"Filter out sentence that are too long.\"\"\"\n\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to this Transform.\"\"\"\n        group = parser.add_argument_group(\"Transform/Filter\")\n        group.add(\"--src_seq_length\", \"-src_seq_length\", type=int, default=200,\n                  help=\"Maximum source sequence length.\")\n        group.add(\"--tgt_seq_length\", \"-tgt_seq_length\", type=int, default=200,\n                  help=\"Maximum target sequence length.\")\n\n    def _parse_opts(self):\n        self.src_seq_length = self.opts.src_seq_length\n        self.tgt_seq_length = self.opts.tgt_seq_length\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Return None if too long else return as is.\"\"\"\n        if (len(example['src']) > self.src_seq_length or\n                len(example['tgt']) > self.tgt_seq_length):\n            if stats is not None:\n                stats.filter_too_long()\n            return None\n        else:\n            return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}, {}={}'.format(\n            'src_seq_length', self.src_seq_length,\n            'tgt_seq_length', self.tgt_seq_length\n        )\n\n\n@register_transform(name='prefix')\nclass PrefixTransform(Transform):\n    \"\"\"Add Prefix to src (& tgt) sentence.\"\"\"\n\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    @staticmethod\n    def _get_prefix(corpus):\n        \"\"\"Get prefix string of a `corpus`.\"\"\"\n        if 'prefix' in corpus['transforms']:\n            prefix = {\n                'src': corpus['src_prefix'],\n                'tgt': corpus['tgt_prefix']\n            }\n        else:\n            prefix = None\n        return prefix\n\n    @classmethod\n    def get_prefix_dict(cls, opts):\n        \"\"\"Get all needed prefix correspond to corpus in `opts`.\"\"\"\n        prefix_dict = {}\n        for c_name, corpus in opts.data.items():\n            prefix = cls._get_prefix(corpus)\n            if prefix is not None:\n                logger.info(f\"Get prefix for {c_name}: {prefix}\")\n                prefix_dict[c_name] = prefix\n        return prefix_dict\n\n    @classmethod\n    def get_specials(cls, opts):\n        \"\"\"Get special vocabs added by prefix transform.\"\"\"\n        prefix_dict = cls.get_prefix_dict(opts)\n        src_specials, tgt_specials = set(), set()\n        for _, prefix in prefix_dict.items():\n            src_specials.update(prefix['src'].split())\n            tgt_specials.update(prefix['tgt'].split())\n        return (src_specials, tgt_specials)\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Warm up to get prefix dictionary.\"\"\"\n        super().warm_up(None)\n        self.prefix_dict = self.get_prefix_dict(self.opts)\n\n    def _prepend(self, example, prefix):\n        \"\"\"Prepend `prefix` to `tokens`.\"\"\"\n        for side, side_prefix in prefix.items():\n            example[side] = side_prefix.split() + example[side]\n        return example\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply prefix prepend to example.\n\n        Should provide `corpus_name` to get correspond prefix.\n        \"\"\"\n        corpus_name = kwargs.get('corpus_name', None)\n        if corpus_name is None:\n            raise ValueError('corpus_name is required.')\n        corpus_prefix = self.prefix_dict.get(corpus_name, None)\n        if corpus_prefix is None:\n            raise ValueError(f'prefix for {corpus_name} does not exist.')\n        return self._prepend(example, corpus_prefix)\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}'.format('prefix_dict', self.prefix_dict)\n"
  },
  {
    "path": "onmt/transforms/sampling.py",
    "content": "\"\"\"Transforms relate to hamming distance sampling.\"\"\"\nimport random\nimport numpy as np\nfrom onmt.utils.logging import logger\nfrom onmt.constants import DefaultTokens\nfrom onmt.transforms import register_transform\nfrom .transform import Transform\n\n\nclass HammingDistanceSampling(object):\n    \"\"\"Functions related to (negative) Hamming Distance Sampling.\"\"\"\n\n    def _softmax(self, x):\n        softmax = np.exp(x)/sum(np.exp(x))\n        return softmax\n\n    def _sample_replace(self, vocab, reject):\n        \"\"\"Sample a token from `vocab` other than `reject`.\"\"\"\n        token = reject\n        while token == reject:\n            token = random.choice(vocab)\n        return token\n\n    def _sample_distance(self, tokens, temperature):\n        \"\"\"Sample number of tokens to corrupt from `tokens`.\"\"\"\n        n_tokens = len(tokens)\n        indices = np.arange(n_tokens)\n        logits = indices * -1 * temperature\n        probs = self._softmax(logits)\n        distance = np.random.choice(indices, p=probs)\n        return distance\n\n    def _sample_position(self, tokens, distance):\n        n_tokens = len(tokens)\n        chosen_indices = random.sample(range(n_tokens), k=distance)\n        return chosen_indices\n\n\nclass HammingDistanceSamplingTransform(Transform, HammingDistanceSampling):\n    \"\"\"Abstract Transform class based on HammingDistanceSampling.\"\"\"\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        np.random.seed(seed)\n        random.seed(seed)\n\n\n@register_transform(name='switchout')\nclass SwitchOutTransform(HammingDistanceSamplingTransform):\n    \"\"\"\n    SwitchOut.\n    :cite:`DBLP:journals/corr/abs-1808-07512`\n    \"\"\"\n\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    def warm_up(self, vocabs):\n        super().warm_up(None)\n        self.vocabs = vocabs\n        if vocabs is None:\n            logger.warning(\n                \"Switchout disable as no vocab, shouldn't happen in training!\")\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to this Transform.\"\"\"\n        group = parser.add_argument_group(\"Transform/SwitchOut\")\n        group.add(\"-switchout_temperature\", \"--switchout_temperature\",\n                  type=float, default=1.0,\n                  help=\"Sampling temperature for SwitchOut. :math:`\\\\tau^{-1}`\"\n                       \" in :cite:`DBLP:journals/corr/abs-1808-07512`. \"\n                       \"Smaller value makes data more diverse.\")\n\n    def _parse_opts(self):\n        self.temperature = self.opts.switchout_temperature\n\n    def _switchout(self, tokens, vocab, stats=None):\n        assert vocab is not None, \"vocab can not be None for SwitchOut.\"\n        # 1. sample number of tokens to corrupt\n        n_chosen = self._sample_distance(tokens, self.temperature)\n        # 2. sample positions to corrput\n        chosen_indices = self._sample_position(tokens, distance=n_chosen)\n        # 3. sample corrupted values\n        out = []\n        for (i, tok) in enumerate(tokens):\n            if i in chosen_indices:\n                tok = self._sample_replace(vocab, reject=tok)\n                out.append(tok)\n            else:\n                out.append(tok)\n        if stats is not None:\n            stats.switchout(n_switchout=n_chosen, n_total=len(tokens))\n        return out\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply switchout to both src and tgt side tokens.\"\"\"\n        if is_train and self.vocabs is not None:\n            src = self._switchout(\n                example['src'], self.vocabs['src'].itos, stats)\n            tgt = self._switchout(\n                example['tgt'], self.vocabs['tgt'].itos, stats)\n            example['src'], example['tgt'] = src, tgt\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}'.format('switchout_temperature', self.temperature)\n\n\n@register_transform(name='tokendrop')\nclass TokenDropTransform(HammingDistanceSamplingTransform):\n    \"\"\"Random drop tokens from sentence.\"\"\"\n\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to this Transform.\"\"\"\n        group = parser.add_argument_group(\"Transform/Token_Drop\")\n        group.add(\"-tokendrop_temperature\", \"--tokendrop_temperature\",\n                  type=float, default=1.0,\n                  help=\"Sampling temperature for token deletion.\")\n\n    def _parse_opts(self):\n        self.temperature = self.opts.tokendrop_temperature\n\n    def _token_drop(self, tokens, stats=None):\n        # 1. sample number of tokens to corrupt\n        n_chosen = self._sample_distance(tokens, self.temperature)\n        # 2. sample positions to corrput\n        chosen_indices = self._sample_position(tokens, distance=n_chosen)\n        # 3. Drop token on chosen position\n        out = [tok for (i, tok) in enumerate(tokens)\n               if i not in chosen_indices]\n        if stats is not None:\n            stats.token_drop(n_dropped=n_chosen, n_total=len(tokens))\n        return out\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply token drop to both src and tgt side tokens.\"\"\"\n        if is_train:\n            src = self._token_drop(example['src'], stats)\n            tgt = self._token_drop(example['tgt'], stats)\n            example['src'], example['tgt'] = src, tgt\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}'.format('worddrop_temperature', self.temperature)\n\n\n@register_transform(name='tokenmask')\nclass TokenMaskTransform(HammingDistanceSamplingTransform):\n    \"\"\"Random mask tokens from src sentence.\"\"\"\n    MASK_TOK = DefaultTokens.MASK\n\n    def __init__(self, opts):\n        super().__init__(opts)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Avalilable options relate to this Transform.\"\"\"\n        group = parser.add_argument_group(\"Transform/Token_Mask\")\n        group.add('-tokenmask_temperature', '--tokenmask_temperature',\n                  type=float, default=1.0,\n                  help=\"Sampling temperature for token masking.\")\n\n    def _parse_opts(self):\n        self.temperature = self.opts.tokenmask_temperature\n\n    @classmethod\n    def get_specials(cls, opts):\n        \"\"\"Get special vocabs added by prefix transform.\"\"\"\n        return ({cls.MASK_TOK}, set())\n\n    def _token_mask(self, tokens, stats=None):\n        # 1. sample number of tokens to corrupt\n        n_chosen = self._sample_distance(tokens, self.temperature)\n        # 2. sample positions to corrput\n        chosen_indices = self._sample_position(tokens, distance=n_chosen)\n        # 3. mask word on chosen position\n        out = []\n        for (i, tok) in enumerate(tokens):\n            tok = self.MASK_TOK if i in chosen_indices else tok\n            out.append(tok)\n        if stats is not None:\n            stats.token_mask(n_masked=n_chosen, n_total=len(tokens))\n        return out\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply word drop to both src and tgt side tokens.\"\"\"\n        if is_train:\n            src = self._token_mask(example['src'], stats)\n            tgt = self._token_mask(example['tgt'], stats)\n            example['src'], example['tgt'] = src, tgt\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return '{}={}'.format('tokenmask_temperature', self.temperature)\n"
  },
  {
    "path": "onmt/transforms/tokenize.py",
    "content": "\"\"\"Transforms relate to tokenization/subword.\"\"\"\nimport os\n\nimport torch\n\nfrom onmt.inputters.inputter import load_roberta_kp_tokenizer\nfrom onmt.utils.logging import logger\nfrom onmt.transforms import register_transform\nfrom .transform import Transform, ObservableStats\n\n\nclass TokenizerTransform(Transform):\n    \"\"\"Tokenizer transform abstract class.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize necessary options for Tokenizer.\"\"\"\n        super().__init__(opts)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to Subword.\"\"\"\n        # Sharing options among `TokenizerTransform`s, same name conflict in\n        # this scope will be resolved by remove previous occurrence in parser\n        group = parser.add_argument_group(\n            'Transform/Subword/Common', conflict_handler='resolve',\n            description=\".. Attention:: Common options shared by all subword transforms. \"  # noqa: E501\n            \"Including options for indicate subword model path, \"\n            \"`Subword Regularization <https://arxiv.org/abs/1804.10959>`_\"\n            \"/`BPE-Dropout <https://arxiv.org/abs/1910.13267>`_, \"\n            \"and `Vocabulary Restriction <https://github.com/rsennrich/subword-nmt#best-practice-advice-for-byte-pair-encoding-in-nmt>`__.\")  # noqa: E501\n        group.add('-src_subword_model', '--src_subword_model',\n                  help=\"Path of subword model for src (or shared).\")\n        group.add(\"-tgt_subword_model\", \"--tgt_subword_model\",\n                  help=\"Path of subword model for tgt.\")\n\n        # subword regularization(or BPE dropout) options:\n        group.add('-src_subword_nbest', '--src_subword_nbest',\n                  type=int, default=1,\n                  help=\"Number of candidates in subword regularization. \"\n                       \"Valid for unigram sampling, \"\n                       \"invalid for BPE-dropout. \"\n                       \"(source side)\")\n        group.add('-tgt_subword_nbest', '--tgt_subword_nbest',\n                  type=int, default=1,\n                  help=\"Number of candidates in subword regularization. \"\n                       \"Valid for unigram sampling, \"\n                       \"invalid for BPE-dropout. \"\n                       \"(target side)\")\n        group.add('-src_subword_alpha', '--src_subword_alpha',\n                  type=float, default=0,\n                  help=\"Smoothing parameter for sentencepiece unigram \"\n                       \"sampling, and dropout probability for BPE-dropout. \"\n                       \"(source side)\")\n        group.add('-tgt_subword_alpha', '--tgt_subword_alpha',\n                  type=float, default=0,\n                  help=\"Smoothing parameter for sentencepiece unigram \"\n                       \"sampling, and dropout probability for BPE-dropout. \"\n                       \"(target side)\")\n\n        # subword vocabulary restriction options:\n        group.add('-src_subword_vocab', '--src_subword_vocab',\n                  type=str, default=\"\",\n                  help=\"Path to the vocabulary file for src subword. \"\n                  \"Format: <word>\\t<count> per line.\")\n        group.add(\"-tgt_subword_vocab\", \"--tgt_subword_vocab\",\n                  type=str, default=\"\",\n                  help=\"Path to the vocabulary file for tgt subword. \"\n                  \"Format: <word>\\t<count> per line.\")\n        group.add('-src_vocab_threshold', '--src_vocab_threshold',\n                  type=int, default=0,\n                  help=\"Only produce src subword in src_subword_vocab with \"\n                  \" frequency >= src_vocab_threshold.\")\n        group.add(\"-tgt_vocab_threshold\", \"--tgt_vocab_threshold\",\n                  type=int, default=0,\n                  help=\"Only produce tgt subword in tgt_subword_vocab with \"\n                  \" frequency >= tgt_vocab_threshold.\")\n\n    @classmethod\n    def _validate_options(cls, opts):\n        \"\"\"Extra checks for Subword options.\"\"\"\n        assert 0 <= opts.src_subword_alpha <= 1, \\\n            \"src_subword_alpha should be in the range [0, 1]\"\n        assert 0 <= opts.tgt_subword_alpha <= 1, \\\n            \"tgt_subword_alpha should be in the range [0, 1]\"\n\n    def _parse_opts(self):\n        self.share_vocab = self.opts.share_vocab\n        self.src_subword_model = self.opts.src_subword_model\n        self.tgt_subword_model = self.opts.tgt_subword_model\n        self.src_subword_nbest = self.opts.src_subword_nbest\n        self.tgt_subword_nbest = self.opts.tgt_subword_nbest\n        self.src_subword_alpha = self.opts.src_subword_alpha\n        self.tgt_subword_alpha = self.opts.tgt_subword_alpha\n        self.src_subword_vocab = self.opts.src_subword_vocab\n        self.tgt_subword_vocab = self.opts.tgt_subword_vocab\n        self.src_vocab_threshold = self.opts.src_vocab_threshold\n        self.tgt_vocab_threshold = self.opts.tgt_vocab_threshold\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for TokenizerTransform.\"\"\"\n        kwargs = {\n            'share_vocab': self.share_vocab,\n            'src_subword_model': self.src_subword_model,\n            'tgt_subword_model': self.tgt_subword_model,\n            'src_subword_alpha': self.src_subword_alpha,\n            'tgt_subword_alpha': self.tgt_subword_alpha,\n            'src_subword_vocab': self.src_subword_vocab,\n            'tgt_subword_vocab': self.tgt_subword_vocab,\n            'src_vocab_threshold': self.src_vocab_threshold,\n            'tgt_vocab_threshold': self.tgt_vocab_threshold\n        }\n        return ', '.join([f'{kw}={arg}' for kw, arg in kwargs.items()])\n\n\nclass SubwordStats(ObservableStats):\n    \"\"\"Runing statistics for counting tokens before/after subword transform.\"\"\"\n\n    __slots__ = [\"subwords\", \"words\"]\n\n    def __init__(self, subwords: int, words: int):\n        self.subwords = subwords\n        self.words = words\n\n    def update(self, other: \"SubwordStats\"):\n        self.subwords += other.subwords\n        self.words += other.words\n\n    def __str__(self) -> str:\n        return \"{}: {} -> {} tokens\".format(\n            self.name(), self.words, self.subwords\n        )\n\n\n@register_transform(name='sentencepiece')\nclass SentencePieceTransform(TokenizerTransform):\n    \"\"\"SentencePiece subword transform class.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize necessary options for sentencepiece.\"\"\"\n        super().__init__(opts)\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        import sentencepiece as spm\n        spm.set_random_generator_seed(seed)\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Load subword models.\"\"\"\n        super().warm_up(None)\n        import sentencepiece as spm\n        load_src_model = spm.SentencePieceProcessor()\n        load_src_model.Load(self.src_subword_model)\n        _diff_vocab = self.src_subword_vocab != self.tgt_subword_vocab or \\\n            self.src_vocab_threshold != self.tgt_vocab_threshold\n        if self.src_subword_vocab != \"\" and self.src_vocab_threshold > 0:\n            load_src_model.LoadVocabulary(\n                self.src_subword_vocab, self.src_vocab_threshold)\n        if self.share_vocab and not _diff_vocab:\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_src_model\n            }\n        else:\n            load_tgt_model = spm.SentencePieceProcessor()\n            load_tgt_model.Load(self.tgt_subword_model)\n            if self.tgt_subword_vocab != \"\" and self.tgt_vocab_threshold > 0:\n                load_tgt_model.LoadVocabulary(\n                    self.tgt_subword_vocab, self.tgt_vocab_threshold)\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_tgt_model\n            }\n\n    def _tokenize(self, tokens, side='src', is_train=False):\n        \"\"\"Do sentencepiece subword tokenize.\"\"\"\n        sp_model = self.load_models[side]\n        sentence = ' '.join(tokens)\n        nbest_size = self.tgt_subword_nbest if side == 'tgt' else \\\n            self.src_subword_nbest\n        if is_train is False or nbest_size in [0, 1]:\n            # derterministic subwording\n            segmented = sp_model.encode(sentence, out_type=str)\n        else:\n            # subword sampling when nbest_size > 1 or -1\n            # alpha should be 0.0 < alpha < 1.0\n            alpha = self.tgt_subword_alpha if side == 'tgt' else \\\n                self.src_subword_alpha\n            segmented = sp_model.encode(\n                sentence, out_type=str, enable_sampling=True,\n                alpha=alpha, nbest_size=nbest_size)\n        return segmented\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply sentencepiece subword encode to src & tgt.\"\"\"\n        src_out = self._tokenize(example['src'], 'src', is_train)\n        tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)\n        if stats is not None:\n            n_words = len(example['src']) + len(example['tgt'])\n            n_subwords = len(src_out) + len(tgt_out)\n            stats.update(SubwordStats(n_subwords, n_words))\n        example['src'], example['tgt'] = src_out, tgt_out\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        kwargs_str = super()._repr_args()\n        additional_str = 'src_subword_nbest={}, tgt_subword_nbest={}'.format(\n            self.src_subword_nbest, self.tgt_subword_nbest\n        )\n        return kwargs_str + ', ' + additional_str\n\n\n@register_transform(name='bpe')\nclass BPETransform(TokenizerTransform):\n    \"\"\"subword_nmt: official BPE subword transform class.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize necessary options for subword_nmt.\"\"\"\n        super().__init__(opts)\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.dropout = {'src': self.src_subword_alpha,\n                        'tgt': self.tgt_subword_alpha}\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        import random\n        random.seed(seed)\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Load subword models.\"\"\"\n        super().warm_up(None)\n        from subword_nmt.apply_bpe import BPE, read_vocabulary\n        # Load vocabulary file if provided and set threshold\n        src_vocabulary, tgt_vocabulary = None, None\n        if self.src_subword_vocab != \"\" and self.src_vocab_threshold > 0:\n            with open(self.src_subword_vocab, encoding='utf-8') as _sv:\n                src_vocabulary = read_vocabulary(_sv, self.src_vocab_threshold)\n        if self.tgt_subword_vocab != \"\" and self.tgt_vocab_threshold > 0:\n            with open(self.tgt_subword_vocab, encoding='utf-8') as _tv:\n                tgt_vocabulary = read_vocabulary(_tv, self.tgt_vocab_threshold)\n        # Load Subword Model\n        with open(self.src_subword_model, encoding='utf-8') as src_codes:\n            load_src_model = BPE(codes=src_codes, vocab=src_vocabulary)\n        if self.share_vocab and (src_vocabulary == tgt_vocabulary):\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_src_model\n            }\n        else:\n            with open(self.tgt_subword_model, encoding='utf-8') as tgt_codes:\n                load_tgt_model = BPE(codes=tgt_codes, vocab=tgt_vocabulary)\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_tgt_model\n            }\n\n    def _tokenize(self, tokens, side='src', is_train=False):\n        \"\"\"Do bpe subword tokenize.\"\"\"\n        bpe_model = self.load_models[side]\n        dropout = self.dropout[side] if is_train else 0.0\n        segmented = bpe_model.segment_tokens(tokens, dropout=dropout)\n        return segmented\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply bpe subword encode to src & tgt.\"\"\"\n        src_out = self._tokenize(example['src'], 'src', is_train)\n        tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)\n        if stats is not None:\n            n_words = len(example['src']) + len(example['tgt'])\n            n_subwords = len(src_out) + len(tgt_out)\n            stats.update(SubwordStats(n_subwords, n_words))\n        example['src'], example['tgt'] = src_out, tgt_out\n        return example\n\n\n@register_transform(name='roberta_tokenize_kpg')\nclass RoBERTaTransform(TokenizerTransform):\n    \"\"\"RoBERTa subword transform class.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize necessary options for subword_nmt.\"\"\"\n        super().__init__(opts)\n        self.opts = opts\n        self._parse_opts()\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.bpe_dropout = self.opts.bpe_dropout\n        self.src_vocab = self.opts.src_vocab\n        self.tgt_vocab = self.opts.tgt_vocab\n        self.seq_length_trunc = {}\n        self.seq_length_trunc['src'] = self.opts.src_seq_length_trunc\n        self.seq_length_trunc['tgt'] = self.opts.tgt_seq_length_trunc\n        self.dropout = {'src': self.src_subword_alpha,\n                        'tgt': self.tgt_subword_alpha}\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to Subword.\"\"\"\n        super().add_options(parser)\n        group = parser.add_argument_group('Transform/Subword/HFRobertaTokenizer')\n        group.add('--bpe_dropout', '-bpe_dropout',\n                  type=float, default=0.0,\n                  help=\"Dropout rate for BPE.\")\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        import random\n        random.seed(seed)\n\n    def _load_tokenizer(self, vocab_path):\n        return load_roberta_kp_tokenizer(vocab_path, self.bpe_dropout)\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Load subword models.\"\"\"\n        super().warm_up(None)\n        load_src_model = self._load_tokenizer(self.src_vocab)\n        if self.share_vocab:\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_src_model\n            }\n        else:\n            load_tgt_model = self._load_tokenizer(self.tgt_vocab)\n            self.load_models = {\n                'src': load_src_model,\n                'tgt': load_tgt_model\n            }\n        # test_str = load_src_model.bos_token+'what is wrong with <mask> <sep> I do not know either <present>, <absent>, <category>'+load_src_model.eos_token\n        # print('tokenize:', str(load_src_model.tokenize(test_str, add_special_tokens=False)))\n        # print('encoding:', str(load_src_model.encode(test_str, add_special_tokens=False)))\n        # print('decoded encoding:', str(load_src_model.decode(load_src_model.encode(test_str, add_special_tokens=False))))\n        # print()\n\n    def _tokenize(self, tokens_str, side='src', is_train=False):\n        \"\"\"Do bpe subword tokenize.\"\"\"\n        bpe_model = self.load_models[side]\n        seq_length_trunc = self.seq_length_trunc[side]\n        # bos/eos is later added by OpenNMT pipeline\n        # As mentioned in https://github.com/huggingface/transformers/issues/7199:\n        #   \"Please note that the RoBERTa tokenizer is built using only\n        #   <s> (the BOS token) and </s> (the SEP token), with two </s></s> as the separator.\"\n        if seq_length_trunc is not None:\n            segmented = bpe_model.tokenize(tokens_str, add_special_tokens=False,\n                                         truncation=True,\n                                         max_length=seq_length_trunc - 2) # account for bos/eos\n        else:\n            segmented = bpe_model.tokenize(tokens_str, add_special_tokens=False)\n\n        # print(side)\n        # notruncate_segmented = bpe_model.tokenize(tokens_str, add_special_tokens=False)\n        # notruncate_segmented = [bpe_model.bos_token] + notruncate_segmented + [bpe_model.eos_token]\n        # print('no-truncate length:', len(notruncate_segmented))\n        # print('truncated length:', len(segmented))\n        return segmented\n\n    def _encode(self, tokens_str, side='src', is_train=False):\n        \"\"\"Do bpe subword encoding.\"\"\"\n        # TODO, bpe_dropout should be disabled if is_train is True. Current tokenizers don't support this\n        bpe_model = self.load_models[side]\n        seq_length_trunc = self.seq_length_trunc[side]\n        # bos/eos is later added by OpenNMT pipeline\n        # As mentioned in https://github.com/huggingface/transformers/issues/7199:\n        #   \"Please note that the RoBERTa tokenizer is built using only\n        #   <s> (the BOS token) and </s> (the SEP token), with two </s></s> as the separator.\"\n        if seq_length_trunc is not None:\n            segmented = bpe_model.encode(tokens_str, add_special_tokens=False,\n                                         truncation=True,\n                                         max_length=seq_length_trunc - 2) # account for bos/eos\n        else:\n            segmented = bpe_model.encode(tokens_str, add_special_tokens=False)\n\n        # print(side)\n        # notruncate_segmented = bpe_model.encode(tokens_str, add_special_tokens=False)\n        # notruncate_segmented = [bpe_model.bos_token_id] + notruncate_segmented + [bpe_model.eos_token_id]\n        # print('no-truncate length:', len(notruncate_segmented))\n        # print('truncated length:', len(segmented))\n        return segmented\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply bpe subword encode to src & tgt.\"\"\"\n        src_out = self._tokenize(example['src'], 'src', is_train)\n        if is_train:\n            tgt_out = self._tokenize(example['tgt'], 'tgt', is_train)\n        else:\n            tgt_out = None\n\n        if stats is not None:\n            n_words = len(example['src']) + len(example['tgt']) if tgt_out else len(example['src'])\n            n_subwords = len(src_out) + len(tgt_out) if tgt_out else len(src_out)\n            stats.update(SubwordStats(n_subwords, n_words))\n        example['src'], example['tgt'] = src_out, tgt_out\n        return example\n\n\n@register_transform(name='onmt_tokenize')\nclass ONMTTokenizerTransform(TokenizerTransform):\n    \"\"\"OpenNMT Tokenizer transform class.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize necessary options for OpenNMT Tokenizer.\"\"\"\n        super().__init__(opts)\n\n    def _set_seed(self, seed):\n        \"\"\"set seed to ensure reproducibility.\"\"\"\n        import pyonmttok\n        pyonmttok.set_random_seed(seed)\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to Subword.\"\"\"\n        super().add_options(parser)\n        group = parser.add_argument_group('Transform/Subword/ONMTTOK')\n        group.add('-src_subword_type', '--src_subword_type',\n                  type=str, default='none',\n                  choices=['none', 'sentencepiece', 'bpe'],\n                  help=\"Type of subword model for src (or shared) \"\n                       \"in pyonmttok.\")\n        group.add('-tgt_subword_type', '--tgt_subword_type',\n                  type=str, default='none',\n                  choices=['none', 'sentencepiece', 'bpe'],\n                  help=\"Type of subword model for tgt in  pyonmttok.\")\n        group.add('-src_onmttok_kwargs', '--src_onmttok_kwargs', type=str,\n                  default=\"{'mode': 'none'}\",\n                  help=\"Other pyonmttok options for src in dict string, \"\n                  \"except subword related options listed earlier.\")\n        group.add('-tgt_onmttok_kwargs', '--tgt_onmttok_kwargs', type=str,\n                  default=\"{'mode': 'none'}\",\n                  help=\"Other pyonmttok options for tgt in dict string, \"\n                  \"except subword related options listed earlier.\")\n\n    @classmethod\n    def _validate_options(cls, opts):\n        \"\"\"Extra checks for OpenNMT Tokenizer options.\"\"\"\n        super()._validate_options(opts)\n        src_kwargs_dict = eval(opts.src_onmttok_kwargs)\n        tgt_kwargs_dict = eval(opts.tgt_onmttok_kwargs)\n        if not isinstance(src_kwargs_dict, dict):\n            raise ValueError(\"-src_onmttok_kwargs isn't a dict valid string.\")\n        if not isinstance(tgt_kwargs_dict, dict):\n            raise ValueError(\"-tgt_onmttok_kwargs isn't a dict valid string.\")\n        opts.src_onmttok_kwargs = src_kwargs_dict\n        opts.tgt_onmttok_kwargs = tgt_kwargs_dict\n\n    def _parse_opts(self):\n        super()._parse_opts()\n        self.src_subword_type = self.opts.src_subword_type\n        self.tgt_subword_type = self.opts.tgt_subword_type\n        logger.info(\"Parsed pyonmttok kwargs for src: {}\".format(\n            self.opts.src_onmttok_kwargs))\n        logger.info(\"Parsed pyonmttok kwargs for tgt: {}\".format(\n            self.opts.tgt_onmttok_kwargs))\n        self.src_other_kwargs = self.opts.src_onmttok_kwargs\n        self.tgt_other_kwargs = self.opts.tgt_onmttok_kwargs\n\n    @classmethod\n    def get_specials(cls, opts):\n        src_specials, tgt_specials = set(), set()\n        if opts.src_onmttok_kwargs.get(\"case_markup\", False):\n            _case_specials = ['｟mrk_case_modifier_C｠',\n                              '｟mrk_begin_case_region_U｠',\n                              '｟mrk_end_case_region_U｠']\n            src_specials.update(_case_specials)\n        if opts.tgt_onmttok_kwargs.get(\"case_markup\", False):\n            _case_specials = ['｟mrk_case_modifier_C｠',\n                              '｟mrk_begin_case_region_U｠',\n                              '｟mrk_end_case_region_U｠']\n            tgt_specials.update(_case_specials)\n        return (set(), set())\n\n    def _get_subword_kwargs(self, side='src'):\n        \"\"\"Return a dict containing kwargs relate to `side` subwords.\"\"\"\n        subword_type = self.tgt_subword_type if side == 'tgt' \\\n            else self.src_subword_type\n        subword_model = self.tgt_subword_model if side == 'tgt' \\\n            else self.src_subword_model\n        subword_nbest = self.tgt_subword_nbest if side == 'tgt' \\\n            else self.src_subword_nbest\n        subword_alpha = self.tgt_subword_alpha if side == 'tgt' \\\n            else self.src_subword_alpha\n        kwopts = dict()\n        if subword_type == 'bpe':\n            kwopts['bpe_model_path'] = subword_model\n            kwopts['bpe_dropout'] = subword_alpha\n        elif subword_type == 'sentencepiece':\n            kwopts['sp_model_path'] = subword_model\n            kwopts['sp_nbest_size'] = subword_nbest\n            kwopts['sp_alpha'] = subword_alpha\n        else:\n            logger.warning('No subword method will be applied.')\n        vocabulary_threshold = self.tgt_vocab_threshold if side == 'tgt' \\\n            else self.src_vocab_threshold\n        vocabulary_path = self.tgt_subword_vocab if side == 'tgt' \\\n            else self.src_subword_vocab\n        if vocabulary_threshold > 0 and vocabulary_path != \"\":\n            kwopts['vocabulary_path'] = vocabulary_path\n            kwopts['vocabulary_threshold'] = vocabulary_threshold\n        return kwopts\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Initialize Tokenizer models.\"\"\"\n        super().warm_up(None)\n        import pyonmttok\n        src_subword_kwargs = self._get_subword_kwargs(side='src')\n        src_tokenizer = pyonmttok.Tokenizer(\n            **src_subword_kwargs, **self.src_other_kwargs\n        )\n        tgt_subword_kwargs = self._get_subword_kwargs(side='tgt')\n        _diff_vocab = (\n            src_subword_kwargs.get('vocabulary_path', '') !=\n            tgt_subword_kwargs.get('vocabulary_path', '') or\n            src_subword_kwargs.get('vocabulary_threshold', 0) !=\n            tgt_subword_kwargs.get('vocabulary_threshold', 0))\n        if self.share_vocab and not _diff_vocab:\n            self.load_models = {\n                'src': src_tokenizer,\n                'tgt': src_tokenizer\n            }\n        else:\n            tgt_subword_kwargs = self._get_subword_kwargs(side='tgt')\n            tgt_tokenizer = pyonmttok.Tokenizer(\n                **tgt_subword_kwargs, **self.tgt_other_kwargs\n            )\n            self.load_models = {\n                'src': src_tokenizer,\n                'tgt': tgt_tokenizer\n            }\n\n    def _tokenize(self, tokens, side='src', is_train=False):\n        \"\"\"Do OpenNMT Tokenizer's tokenize.\"\"\"\n        tokenizer = self.load_models[side]\n        sentence = ' '.join(tokens)\n        segmented, _ = tokenizer.tokenize(sentence)\n        return segmented\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply OpenNMT Tokenizer to src & tgt.\"\"\"\n        src_out = self._tokenize(example['src'], 'src')\n        tgt_out = self._tokenize(example['tgt'], 'tgt')\n        if stats is not None:\n            n_words = len(example['src']) + len(example['tgt'])\n            n_subwords = len(src_out) + len(tgt_out)\n            stats.update(SubwordStats(n_subwords, n_words))\n        example['src'], example['tgt'] = src_out, tgt_out\n        return example\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        repr_str = '{}={}'.format('share_vocab', self.share_vocab)\n        repr_str += ', src_subword_kwargs={}'.format(\n            self._get_subword_kwargs(side='src'))\n        repr_str += ', src_onmttok_kwargs={}'.format(self.src_other_kwargs)\n        repr_str += ', tgt_subword_kwargs={}'.format(\n            self._get_subword_kwargs(side='tgt'))\n        repr_str += ', tgt_onmttok_kwargs={}'.format(self.tgt_other_kwargs)\n        return repr_str\n"
  },
  {
    "path": "onmt/transforms/transform.py",
    "content": "\"\"\"Base Transform class and relate utils.\"\"\"\nimport torch\nfrom onmt.utils.logging import logger\nfrom onmt.utils.misc import check_path\nfrom onmt.inputters.fields import get_vocabs\n\n\nclass Transform(object):\n    \"\"\"A Base class that every transform method should derived from.\"\"\"\n\n    def __init__(self, opts):\n        \"\"\"Initialize Transform by parsing `opts` and add them as attribute.\"\"\"\n        self.opts = opts\n        self._parse_opts()\n\n    def _set_seed(self, seed):\n        \"\"\"Reproducibility: Set seed for non-deterministic transform.\"\"\"\n        pass\n\n    @classmethod\n    def require_vocab(cls):\n        \"\"\"Override this method to inform it need vocab to start.\"\"\"\n        return False\n\n    def warm_up(self, vocabs=None):\n        \"\"\"Procedure needed after initialize and before apply.\n\n        This should be override if there exist any procedure necessary\n        before `apply`, like setups based on parsed options or load models,\n        etc.\n        \"\"\"\n        if self.opts.seed > 0:\n            self._set_seed(self.opts.seed)\n        if self.require_vocab():\n            if vocabs is None:\n                raise ValueError(f\"{type(self).__name__} requires vocabs!\")\n            self.vocabs = vocabs\n\n    @classmethod\n    def add_options(cls, parser):\n        \"\"\"Available options relate to this Transform.\"\"\"\n        pass\n\n    @classmethod\n    def _validate_options(cls, opts):\n        \"\"\"Extra checks to validate options added from `add_options`.\"\"\"\n        pass\n\n    @classmethod\n    def get_specials(cls, opts):\n        return (set(), set())\n\n    def apply(self, example, is_train=False, stats=None, **kwargs):\n        \"\"\"Apply transform to `example`.\n\n        Args:\n            example (dict): a dict of field value, ex. src, tgt;\n            is_train (bool): Indicate if src/tgt is training data;\n            stats (TransformStatistics): a statistic object.\n        \"\"\"\n        raise NotImplementedError\n\n    def __getstate__(self):\n        \"\"\"Pickling following for rebuild.\"\"\"\n        state = {\"opts\": self.opts}\n        if hasattr(self, 'vocabs'):\n            state['vocabs'] = self.vocabs\n        return state\n\n    def _parse_opts(self):\n        \"\"\"Parse opts to set/reset instance's attributes.\n\n        This should be override if there are attributes other than self.opts.\n        To make sure we recover from picked state.\n        (This should only contain attribute assignment, other routine is\n        suggest to define in `warm_up`.)\n        \"\"\"\n        pass\n\n    def __setstate__(self, state):\n        \"\"\"Reload when unpickling from save file.\"\"\"\n        self.opts = state[\"opts\"]\n        self._parse_opts()\n        vocabs = state.get('vocabs', None)\n        self.warm_up(vocabs=vocabs)\n\n    def stats(self):\n        \"\"\"Return statistic message.\"\"\"\n        return ''\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        return ''\n\n    def __repr__(self):\n        cls_name = type(self).__name__\n        cls_args = self._repr_args()\n        return '{}({})'.format(cls_name, cls_args)\n\n\nclass ObservableStats:\n    \"\"\"A running observable statistics.\"\"\"\n\n    __slots__ = []\n\n    def name(self) -> str:\n        \"\"\"Return class name as name for statistics.\"\"\"\n        return type(self).__name__\n\n    def update(self, other: \"ObservableStats\"):\n        \"\"\"Update current statistics with others.\"\"\"\n        raise NotImplementedError\n\n    def __str__(self) -> str:\n        return \"{}({})\".format(\n            self.name(),\n            \", \".join(\n                f\"{name}={getattr(self, name)}\" for name in self.__slots__\n            )\n        )\n\n\nclass TransformStatistics:\n    \"\"\"A observer containing runing statistics.\"\"\"\n    def __init__(self):\n        self.observables = {}\n\n    def update(self, observable: ObservableStats):\n        \"\"\"Adding observable to observe/updating existing observable.\"\"\"\n        name = observable.name()\n        if name not in self.observables:\n            self.observables[name] = observable\n        else:\n            self.observables[name].update(observable)\n\n    def report(self):\n        \"\"\"Pop out all observing statistics and reporting them.\"\"\"\n        msgs = []\n        report_ids = list(self.observables.keys())\n        for name in report_ids:\n            observable = self.observables.pop(name)\n            msgs.append(f\"\\t\\t\\t* {str(observable)}\")\n        if len(msgs) != 0:\n            return \"\\n\".join(msgs)\n        else:\n            return \"\"\n\n\nclass TransformPipe(Transform):\n    \"\"\"Pipeline built by a list of Transform instance.\"\"\"\n\n    def __init__(self, opts, transform_list):\n        \"\"\"Initialize pipeline by a list of transform instance.\"\"\"\n        self.opts = None  # opts is not required\n        self.transforms = transform_list\n        self.statistics = TransformStatistics()\n\n    @classmethod\n    def build_from(cls, transform_list):\n        \"\"\"Return a `TransformPipe` instance build from `transform_list`.\"\"\"\n        for transform in transform_list:\n            assert isinstance(transform, Transform), \\\n                \"transform should be a instance of Transform.\"\n        transform_pipe = cls(None, transform_list)\n        return transform_pipe\n\n    def warm_up(self, vocabs):\n        \"\"\"Warm up Pipeline by iterate over all transfroms.\"\"\"\n        for transform in self.transforms:\n            transform.warm_up(vocabs)\n\n    @classmethod\n    def get_specials(cls, opts, transforms):\n        \"\"\"Return all specials introduced by `transforms`.\"\"\"\n        src_specials, tgt_specials = set(), set()\n        for transform in transforms:\n            _src_special, _tgt_special = transform.get_specials(transform.opts)\n            src_specials.update(_src_special)\n            tgt_specials.update(tgt_specials)\n        return (src_specials, tgt_specials)\n\n    def apply(self, example, is_train=False, **kwargs):\n        \"\"\"Apply transform pipe to `example`.\n\n        Args:\n            example (dict): a dict of field value, ex. src, tgt.\n\n        \"\"\"\n        for transform in self.transforms:\n            example = transform.apply(\n                example, is_train=is_train, stats=self.statistics, **kwargs)\n            if example is None:\n                break\n        return example\n\n    def __getstate__(self):\n        \"\"\"Pickling following for rebuild.\"\"\"\n        return (self.opts, self.transforms, self.statistics)\n\n    def __setstate__(self, state):\n        \"\"\"Reload when unpickling from save file.\"\"\"\n        self.opts, self.transforms, self.statistics = state\n\n    def stats(self):\n        \"\"\"Return statistic message.\"\"\"\n        return self.statistics.report()\n\n    def _repr_args(self):\n        \"\"\"Return str represent key arguments for class.\"\"\"\n        info_args = []\n        for transform in self.transforms:\n            info_args.append(repr(transform))\n        return ', '.join(info_args)\n\n\ndef make_transforms(opts, transforms_cls, fields):\n    \"\"\"Build transforms in `transforms_cls` with vocab of `fields`.\"\"\"\n    vocabs = get_vocabs(fields) if fields is not None else None\n    transforms = {}\n    for name, transform_cls in transforms_cls.items():\n        if transform_cls.require_vocab() and vocabs is None:\n            logger.warning(\n                f\"{transform_cls.__name__} require vocab to apply, skip it.\"\n            )\n            continue\n        transform_obj = transform_cls(opts)\n        transform_obj.warm_up(vocabs)\n        transforms[name] = transform_obj\n    return transforms\n\n\ndef get_specials(opts, transforms_cls_dict):\n    \"\"\"Get specials of transforms that should be registed in Vocab.\"\"\"\n    all_specials = {'src': set(), 'tgt': set()}\n    for name, transform_cls in transforms_cls_dict.items():\n        src_specials, tgt_specials = transform_cls.get_specials(opts)\n        all_specials['src'].update(src_specials)\n        all_specials['tgt'].update(tgt_specials)\n    logger.info(f\"Get special vocabs from Transforms: {all_specials}.\")\n    return all_specials\n\n\ndef save_transforms(transforms, save_data, overwrite=True):\n    \"\"\"Dump `transforms` object.\"\"\"\n    transforms_path = \"{}.transforms.pt\".format(save_data)\n    check_path(transforms_path, exist_ok=overwrite, log=logger.warning)\n    logger.info(f\"Saving Transforms to {transforms_path}.\")\n    torch.save(transforms, transforms_path)\n\n\ndef load_transforms(opts):\n    \"\"\"Load dumped `transforms` object.\"\"\"\n    transforms_path = \"{}.transforms.pt\".format(opts.save_data)\n    transforms = torch.load(transforms_path)\n    logger.info(\"Transforms loaded.\")\n    return transforms\n"
  },
  {
    "path": "onmt/translate/__init__.py",
    "content": "\"\"\" Modules for translation \"\"\"\nfrom onmt.translate.translator import Translator, GeneratorLM\nfrom onmt.translate.translation import Translation, TranslationBuilder\nfrom onmt.translate.beam_search import BeamSearch, GNMTGlobalScorer\nfrom onmt.translate.beam_search import BeamSearchLM\nfrom onmt.translate.decode_strategy import DecodeStrategy\nfrom onmt.translate.greedy_search import GreedySearch, GreedySearchLM\nfrom onmt.translate.penalties import PenaltyBuilder\nfrom onmt.translate.translation_server import TranslationServer, \\\n    ServerModelError\n\n__all__ = ['Translator', 'Translation', 'BeamSearch',\n           'GNMTGlobalScorer', 'TranslationBuilder',\n           'PenaltyBuilder', 'TranslationServer', 'ServerModelError',\n           \"DecodeStrategy\", \"GreedySearch\", \"GreedySearchLM\",\n           \"BeamSearchLM\", \"GeneratorLM\"]\n"
  },
  {
    "path": "onmt/translate/beam.py",
    "content": "from __future__ import division\nimport torch\nfrom onmt.translate import penalties\n\nimport warnings\n\n\nclass Beam(object):\n    \"\"\"Class for managing the internals of the beam search process.\n\n    Takes care of beams, back pointers, and scores.\n\n    Args:\n        size (int): Number of beams to use.\n        pad (int): Magic integer in output vocab.\n        bos (int): Magic integer in output vocab.\n        eos (int): Magic integer in output vocab.\n        n_best (int): Don't stop until at least this many beams have\n            reached EOS.\n        cuda (bool): use gpu\n        global_scorer (onmt.translate.GNMTGlobalScorer): Scorer instance.\n        min_length (int): Shortest acceptable generation, not counting\n            begin-of-sentence or end-of-sentence.\n        stepwise_penalty (bool): Apply coverage penalty at every step.\n        block_ngram_repeat (int): Block beams where\n            ``block_ngram_repeat``-grams repeat.\n        exclusion_tokens (set[int]): If a gram contains any of these\n            token indices, it may repeat.\n    \"\"\"\n\n    def __init__(self, size, pad, bos, eos,\n                 n_best=1, cuda=False,\n                 global_scorer=None,\n                 min_length=0,\n                 stepwise_penalty=False,\n                 block_ngram_repeat=0,\n                 exclusion_tokens=set()):\n\n        self.size = size\n        self.tt = torch.cuda if cuda else torch\n\n        # The score for each translation on the beam.\n        self.scores = self.tt.FloatTensor(size).zero_()\n        self.all_scores = []\n\n        # The backpointers at each time-step.\n        self.prev_ks = []\n\n        # The outputs at each time-step.\n        self.next_ys = [self.tt.LongTensor(size)\n                        .fill_(pad)]\n        self.next_ys[0][0] = bos\n\n        # Has EOS topped the beam yet.\n        self._eos = eos\n        self.eos_top = False\n\n        # The attentions (matrix) for each time.\n        self.attn = []\n\n        # Time and k pair for finished.\n        self.finished = []\n        self.n_best = n_best\n\n        # Information for global scoring.\n        self.global_scorer = global_scorer\n        self.global_state = {}\n\n        # Minimum prediction length\n        self.min_length = min_length\n\n        # Apply Penalty at every step\n        self.stepwise_penalty = stepwise_penalty\n        self.block_ngram_repeat = block_ngram_repeat\n        self.exclusion_tokens = exclusion_tokens\n\n    @property\n    def current_predictions(self):\n        return self.next_ys[-1]\n\n    @property\n    def current_origin(self):\n        \"\"\"Get the backpointers for the current timestep.\"\"\"\n        return self.prev_ks[-1]\n\n    def advance(self, word_probs, attn_out):\n        \"\"\"\n        Given prob over words for every last beam `wordLk` and attention\n        `attn_out`: Compute and update the beam search.\n\n        Args:\n            word_probs (FloatTensor): probs of advancing from the last step\n                ``(K, words)``\n            attn_out (FloatTensor): attention at the last step\n\n        Returns:\n            bool: True if beam search is complete.\n        \"\"\"\n\n        num_words = word_probs.size(1)\n        if self.stepwise_penalty:\n            self.global_scorer.update_score(self, attn_out)\n        # force the output to be longer than self.min_length\n        cur_len = len(self.next_ys)\n        if cur_len <= self.min_length:\n            # assumes there are len(word_probs) predictions OTHER\n            # than EOS that are greater than -1e20\n            for k in range(len(word_probs)):\n                word_probs[k][self._eos] = -1e20\n\n        # Sum the previous scores.\n        if len(self.prev_ks) > 0:\n            beam_scores = word_probs + self.scores.unsqueeze(1)\n            # Don't let EOS have children.\n            for i in range(self.next_ys[-1].size(0)):\n                if self.next_ys[-1][i] == self._eos:\n                    beam_scores[i] = -1e20\n\n            # Block ngram repeats\n            if self.block_ngram_repeat > 0:\n                le = len(self.next_ys)\n                for j in range(self.next_ys[-1].size(0)):\n                    hyp, _ = self.get_hyp(le - 1, j)\n                    ngrams = set()\n                    fail = False\n                    gram = []\n                    for i in range(le - 1):\n                        # Last n tokens, n = block_ngram_repeat\n                        gram = (gram +\n                                [hyp[i].item()])[-self.block_ngram_repeat:]\n                        # Skip the blocking if it is in the exclusion list\n                        if set(gram) & self.exclusion_tokens:\n                            continue\n                        if tuple(gram) in ngrams:\n                            fail = True\n                        ngrams.add(tuple(gram))\n                    if fail:\n                        beam_scores[j] = -10e20\n        else:\n            beam_scores = word_probs[0]\n        flat_beam_scores = beam_scores.view(-1)\n        best_scores, best_scores_id = flat_beam_scores.topk(self.size, 0,\n                                                            True, True)\n\n        self.all_scores.append(self.scores)\n        self.scores = best_scores\n\n        # best_scores_id is flattened beam x word array, so calculate which\n        # word and beam each score came from\n        prev_k = best_scores_id / num_words\n        self.prev_ks.append(prev_k)\n        self.next_ys.append((best_scores_id - prev_k * num_words))\n        self.attn.append(attn_out.index_select(0, prev_k))\n        self.global_scorer.update_global_state(self)\n\n        for i in range(self.next_ys[-1].size(0)):\n            if self.next_ys[-1][i] == self._eos:\n                global_scores = self.global_scorer.score(self, self.scores)\n                s = global_scores[i]\n                self.finished.append((s, len(self.next_ys) - 1, i))\n\n        # End condition is when top-of-beam is EOS and no global score.\n        if self.next_ys[-1][0] == self._eos:\n            self.all_scores.append(self.scores)\n            self.eos_top = True\n\n    @property\n    def done(self):\n        return self.eos_top and len(self.finished) >= self.n_best\n\n    def sort_finished(self, minimum=None):\n        if minimum is not None:\n            i = 0\n            # Add from beam until we have minimum outputs.\n            while len(self.finished) < minimum:\n                global_scores = self.global_scorer.score(self, self.scores)\n                s = global_scores[i]\n                self.finished.append((s, len(self.next_ys) - 1, i))\n                i += 1\n\n        self.finished.sort(key=lambda a: -a[0])\n        scores = [sc for sc, _, _ in self.finished]\n        ks = [(t, k) for _, t, k in self.finished]\n        return scores, ks\n\n    def get_hyp(self, timestep, k):\n        \"\"\"Walk back to construct the full hypothesis.\"\"\"\n        hyp, attn = [], []\n        for j in range(len(self.prev_ks[:timestep]) - 1, -1, -1):\n            hyp.append(self.next_ys[j + 1][k])\n            attn.append(self.attn[j][k])\n            k = self.prev_ks[j][k]\n        return hyp[::-1], torch.stack(attn[::-1])\n\n\nclass GNMTGlobalScorer(object):\n    \"\"\"NMT re-ranking.\n\n    Args:\n       alpha (float): Length parameter.\n       beta (float):  Coverage parameter.\n       length_penalty (str): Length penalty strategy.\n       coverage_penalty (str): Coverage penalty strategy.\n\n    Attributes:\n        alpha (float): See above.\n        beta (float): See above.\n        length_penalty (callable): See :class:`penalties.PenaltyBuilder`.\n        coverage_penalty (callable): See :class:`penalties.PenaltyBuilder`.\n        has_cov_pen (bool): See :class:`penalties.PenaltyBuilder`.\n        has_len_pen (bool): See :class:`penalties.PenaltyBuilder`.\n    \"\"\"\n\n    @classmethod\n    def from_opt(cls, opt):\n        return cls(\n            opt.alpha,\n            opt.beta,\n            opt.length_penalty,\n            opt.coverage_penalty)\n\n    def __init__(self, alpha, beta, length_penalty, coverage_penalty):\n        self._validate(alpha, beta, length_penalty, coverage_penalty)\n        self.alpha = alpha\n        self.beta = beta\n        penalty_builder = penalties.PenaltyBuilder(coverage_penalty,\n                                                   length_penalty)\n        self.has_cov_pen = penalty_builder.has_cov_pen\n        # Term will be subtracted from probability\n        self.cov_penalty = penalty_builder.coverage_penalty\n\n        self.has_len_pen = penalty_builder.has_len_pen\n        # Probability will be divided by this\n        self.length_penalty = penalty_builder.length_penalty\n\n    @classmethod\n    def _validate(cls, alpha, beta, length_penalty, coverage_penalty):\n        # these warnings indicate that either the alpha/beta\n        # forces a penalty to be a no-op, or a penalty is a no-op but\n        # the alpha/beta would suggest otherwise.\n        if length_penalty is None or length_penalty == \"none\":\n            if alpha != 0:\n                warnings.warn(\"Non-default `alpha` with no length penalty. \"\n                              \"`alpha` has no effect.\")\n        else:\n            # using some length penalty\n            if length_penalty == \"wu\" and alpha == 0.:\n                warnings.warn(\"Using length penalty Wu with alpha==0 \"\n                              \"is equivalent to using length penalty none.\")\n        if coverage_penalty is None or coverage_penalty == \"none\":\n            if beta != 0:\n                warnings.warn(\"Non-default `beta` with no coverage penalty. \"\n                              \"`beta` has no effect.\")\n        else:\n            # using some coverage penalty\n            if beta == 0.:\n                warnings.warn(\"Non-default coverage penalty with beta==0 \"\n                              \"is equivalent to using coverage penalty none.\")\n\n    def score(self, beam, logprobs):\n        \"\"\"Rescore a prediction based on penalty functions.\"\"\"\n        len_pen = self.length_penalty(len(beam.next_ys), self.alpha)\n        normalized_probs = logprobs / len_pen\n        if not beam.stepwise_penalty:\n            penalty = self.cov_penalty(beam.global_state[\"coverage\"],\n                                       self.beta)\n            normalized_probs -= penalty\n\n        return normalized_probs\n\n    def update_score(self, beam, attn):\n        \"\"\"Update scores of a Beam that is not finished.\"\"\"\n        if \"prev_penalty\" in beam.global_state.keys():\n            beam.scores.add_(beam.global_state[\"prev_penalty\"])\n            penalty = self.cov_penalty(beam.global_state[\"coverage\"] + attn,\n                                       self.beta)\n            beam.scores.sub_(penalty)\n\n    def update_global_state(self, beam):\n        \"\"\"Keeps the coverage vector as sum of attentions.\"\"\"\n        if len(beam.prev_ks) == 1:\n            beam.global_state[\"prev_penalty\"] = beam.scores.clone().fill_(0.0)\n            beam.global_state[\"coverage\"] = beam.attn[-1]\n            self.cov_total = beam.attn[-1].sum(1)\n        else:\n            self.cov_total += torch.min(beam.attn[-1],\n                                        beam.global_state['coverage']).sum(1)\n            beam.global_state[\"coverage\"] = beam.global_state[\"coverage\"] \\\n                .index_select(0, beam.prev_ks[-1]).add(beam.attn[-1])\n\n            prev_penalty = self.cov_penalty(beam.global_state[\"coverage\"],\n                                            self.beta)\n            beam.global_state[\"prev_penalty\"] = prev_penalty\n"
  },
  {
    "path": "onmt/translate/beam_search.py",
    "content": "import torch\nfrom onmt.translate import penalties\nfrom onmt.translate.decode_strategy import DecodeStrategy\nfrom onmt.utils.misc import tile\n\nimport warnings\n\n\nclass BeamSearchBase(DecodeStrategy):\n    \"\"\"Generation beam search.\n\n    Note that the attributes list is not exhaustive. Rather, it highlights\n    tensors to document their shape. (Since the state variables' \"batch\"\n    size decreases as beams finish, we denote this axis with a B rather than\n    ``batch_size``).\n\n    Args:\n        beam_size (int): Number of beams to use (see base ``parallel_paths``).\n        batch_size (int): See base.\n        pad (int): See base.\n        bos (int): See base.\n        eos (int): See base.\n        n_best (int): Don't stop until at least this many beams have\n            reached EOS.\n        global_scorer (onmt.translate.GNMTGlobalScorer): Scorer instance.\n        min_length (int): See base.\n        max_length (int): See base.\n        return_attention (bool): See base.\n        block_ngram_repeat (int): See base.\n        exclusion_tokens (set[int]): See base.\n\n    Attributes:\n        top_beam_finished (ByteTensor): Shape ``(B,)``.\n        _batch_offset (LongTensor): Shape ``(B,)``.\n        _beam_offset (LongTensor): Shape ``(batch_size x beam_size,)``.\n        alive_seq (LongTensor): See base.\n        topk_log_probs (FloatTensor): Shape ``(B x beam_size,)``. These\n            are the scores used for the topk operation.\n        memory_lengths (LongTensor): Lengths of encodings. Used for\n            masking attentions.\n        select_indices (LongTensor or NoneType): Shape\n            ``(B x beam_size,)``. This is just a flat view of the\n            ``_batch_index``.\n        topk_scores (FloatTensor): Shape\n            ``(B, beam_size)``. These are the\n            scores a sequence will receive if it finishes.\n        topk_ids (LongTensor): Shape ``(B, beam_size)``. These are the\n            word indices of the topk predictions.\n        _batch_index (LongTensor): Shape ``(B, beam_size)``.\n        _prev_penalty (FloatTensor or NoneType): Shape\n            ``(B, beam_size)``. Initialized to ``None``.\n        _coverage (FloatTensor or NoneType): Shape\n            ``(1, B x beam_size, inp_seq_len)``.\n        hypotheses (list[list[Tuple[Tensor]]]): Contains a tuple\n            of score (float), sequence (long), and attention (float or None).\n    \"\"\"\n    def __init__(self, beam_size, batch_size, pad, bos, eos, n_best,\n                 global_scorer, min_length, max_length, return_attention,\n                 block_ngram_repeat, exclusion_tokens,\n                 stepwise_penalty, ratio, beam_terminate):\n        super(BeamSearchBase, self).__init__(\n            pad, bos, eos, batch_size, beam_size, min_length,\n            block_ngram_repeat, exclusion_tokens, return_attention,\n            max_length)\n        # beam parameters\n        self.global_scorer = global_scorer\n        self.beam_size = beam_size\n        self.n_best = n_best\n        self.batch_size = batch_size\n        self.ratio = ratio\n        # @memray: beam search termination condition. `topbeam` means search stops once the top-score beam is done. `full` means all beams will be finished until reaching the max_length.\n        self.beam_terminate = beam_terminate\n\n        # result caching\n        self.hypotheses = [[] for _ in range(batch_size)]\n\n        # beam state\n        self.top_beam_finished = torch.zeros([batch_size], dtype=torch.uint8)\n        # BoolTensor was introduced in pytorch 1.2\n        try:\n            self.top_beam_finished = self.top_beam_finished.bool()\n        except AttributeError:\n            pass\n        self._batch_offset = torch.arange(batch_size, dtype=torch.long)\n\n        self.select_indices = None\n        self.done = False\n        # \"global state\" of the old beam\n        self._prev_penalty = None\n        self._coverage = None\n\n        self._stepwise_cov_pen = (\n            stepwise_penalty and self.global_scorer.has_cov_pen)\n        self._vanilla_cov_pen = (\n            not stepwise_penalty and self.global_scorer.has_cov_pen)\n        self._cov_pen = self.global_scorer.has_cov_pen\n\n        self.memory_lengths = None\n\n    def initialize(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def initialize_(self, memory_bank, memory_lengths, src_map, device,\n                    target_prefix):\n        super(BeamSearchBase, self).initialize(\n            memory_bank, memory_lengths, src_map, device, target_prefix)\n\n        self.best_scores = torch.full(\n            [self.batch_size], -1e10, dtype=torch.float, device=device)\n        self._beam_offset = torch.arange(\n            0, self.batch_size * self.beam_size, step=self.beam_size,\n            dtype=torch.long, device=device)\n        self.topk_log_probs = torch.tensor(\n            [0.0] + [float(\"-inf\")] * (self.beam_size - 1), device=device\n        ).repeat(self.batch_size)\n        # buffers for the topk scores and 'backpointer'\n        self.topk_scores = torch.empty((self.batch_size, self.beam_size),\n                                       dtype=torch.float, device=device)\n        self.topk_ids = torch.empty((self.batch_size, self.beam_size),\n                                    dtype=torch.long, device=device)\n        self._batch_index = torch.empty([self.batch_size, self.beam_size],\n                                        dtype=torch.long, device=device)\n\n    @property\n    def current_predictions(self):\n        # self.alive_seq is Shape ``(B x beam_size, step)``, return shape is ``(B x beam_size, 1)``\n        return self.alive_seq[:, -1]\n\n    @property\n    def current_backptr(self):\n        # for testing\n        return self.select_indices.view(self.batch_size, self.beam_size)\\\n            .fmod(self.beam_size)\n\n    @property\n    def batch_offset(self):\n        return self._batch_offset\n\n    def _pick(self, log_probs):\n        \"\"\"Return token decision for a step.\n\n        Args:\n            log_probs (FloatTensor): (B, vocab_size)\n\n        Returns:\n            topk_scores (FloatTensor): (B, beam_size)\n            topk_ids (LongTensor): (B, beam_size)\n        \"\"\"\n        vocab_size = log_probs.size(-1)\n        # maybe fix some prediction at this step by modifying log_probs\n        log_probs = self.target_prefixing(log_probs)\n\n        # Flatten probs into a list of possibilities.\n        curr_scores = log_probs.reshape(-1, self.beam_size * vocab_size)\n        topk_scores, topk_ids = torch.topk(curr_scores, self.beam_size, dim=-1)\n        return topk_scores, topk_ids\n\n    def update_finished(self, last_step=None):\n        \"\"\"\n        @memray\n        A last_step is required from the outside\n        :param last_step: boolean, indicating whether beam search reaches the max_length, otherwise it returns no results (finished beams are abandoned)\n        :return:\n        \"\"\"\n        # Penalize beams that finished.\n        _B_old = self.topk_log_probs.shape[0]\n        step = self.alive_seq.shape[-1]  # 1 greater than the step in advance\n        self.topk_log_probs.masked_fill_(self.is_finished, -1e10)\n        # on real data (newstest2017) with the pretrained transformer,\n        # it's faster to not move this back to the original device\n        self.is_finished = self.is_finished.to('cpu')\n        # TODO @memray: extend to topK beam finished?\n        self.top_beam_finished = self.top_beam_finished.bool() | self.is_finished[:, 0].eq(1)\n        predictions = self.alive_seq.view(_B_old, self.beam_size, step)\n        attention = (\n            self.alive_attn.view(\n                step - 1, _B_old, self.beam_size, self.alive_attn.size(-1))\n            if self.alive_attn is not None else None)\n        non_finished_batch = []\n        # iterate each batch\n        for i in range(self.is_finished.size(0)):  # Batch level\n            b = self._batch_offset[i]\n            finished_hyp = self.is_finished[i].nonzero(as_tuple=False).view(-1)\n            # Store finished hypotheses for this batch.\n            for j in finished_hyp:  # Beam level: finished beam j in batch i\n                if self.ratio > 0:\n                    s = self.topk_scores[i, j] / (step + 1)\n                    if self.best_scores[b] < s:\n                        self.best_scores[b] = s\n                self.hypotheses[b].append((\n                    self.topk_scores[i, j],\n                    predictions[i, j, 1:],  # Ignore start_token.\n                    attention[:, i, j, :self.memory_lengths[i]]\n                    if attention is not None else None))\n            # End condition is the top beam finished and we can return\n            # n_best hypotheses.\n            # @memray: beam_terminate is specific to keyphrase task\n            if self.ratio > 0:\n                pred_len = self.memory_lengths[i] * self.ratio\n                finish_flag = ((self.topk_scores[i, 0] / pred_len)\n                               <= self.best_scores[b]) or \\\n                    self.is_finished[i].all()\n            else:\n                # @memray: 20190520 consider have both strategies: end until collect enough predictions\n                if self.beam_terminate == 'topbeam':\n                    # @memray: top beam finished means no more better \"one sequence\" will be generated\n                    finish_flag = self.top_beam_finished[i] != 0\n                elif self.beam_terminate == 'full':\n                    # @memray: it's the ending step, return all finished beams\n                    if last_step is not None and last_step:\n                        finish_flag = True\n                    else:\n                        finish_flag = False\n                else:\n                    raise NotImplementedError(\"param not recognized: beam_terminate=%s\" % self.beam_terminate)\n            if finish_flag or len(self.hypotheses[b]) >= self.n_best:\n                best_hyp = sorted(\n                    self.hypotheses[b], key=lambda x: x[0], reverse=True)\n                for n, (score, pred, attn) in enumerate(best_hyp):\n                    if n >= self.n_best:\n                        break\n                    self.scores[b].append(score)\n                    self.predictions[b].append(pred)  # ``(batch, n_best,)``\n                    self.attention[b].append(\n                        attn if attn is not None else [])\n            else:\n                non_finished_batch.append(i)\n        non_finished = torch.tensor(non_finished_batch)\n        # If all sentences are translated, no need to go further.\n        if len(non_finished) == 0:\n            self.done = True\n            return\n\n        _B_new = non_finished.shape[0]\n        self.remove_finished_batches(_B_new, _B_old, non_finished,\n                                     predictions, attention, step)\n\n    def remove_finished_batches(self, _B_new, _B_old, non_finished,\n                                predictions, attention, step):\n        # Remove finished batches for the next step.\n        self.top_beam_finished = self.top_beam_finished.index_select(\n            0, non_finished)\n        self._batch_offset = self._batch_offset.index_select(0, non_finished)\n        non_finished = non_finished.to(self.topk_ids.device)\n        self.topk_log_probs = self.topk_log_probs.index_select(0,\n                                                               non_finished)\n        self._batch_index = self._batch_index.index_select(0, non_finished)\n        self.select_indices = self._batch_index.view(_B_new * self.beam_size)\n        self.alive_seq = predictions.index_select(0, non_finished) \\\n            .view(-1, self.alive_seq.size(-1))\n        self.topk_scores = self.topk_scores.index_select(0, non_finished)\n        self.topk_ids = self.topk_ids.index_select(0, non_finished)\n        self.maybe_update_target_prefix(self.select_indices)\n        if self.alive_attn is not None:\n            inp_seq_len = self.alive_attn.size(-1)\n            self.alive_attn = attention.index_select(1, non_finished) \\\n                .view(step - 1, _B_new * self.beam_size, inp_seq_len)\n            if self._cov_pen:\n                self._coverage = self._coverage \\\n                    .view(1, _B_old, self.beam_size, inp_seq_len) \\\n                    .index_select(1, non_finished) \\\n                    .view(1, _B_new * self.beam_size, inp_seq_len)\n                if self._stepwise_cov_pen:\n                    self._prev_penalty = self._prev_penalty.index_select(\n                        0, non_finished)\n\n    def advance(self, log_probs, attn):\n        vocab_size = log_probs.size(-1)\n\n        # using integer division to get an integer _B without casting\n        _B = log_probs.shape[0] // self.beam_size\n\n        if self._stepwise_cov_pen and self._prev_penalty is not None:\n            self.topk_log_probs += self._prev_penalty\n            self.topk_log_probs -= self.global_scorer.cov_penalty(\n                self._coverage + attn, self.global_scorer.beta).view(\n                _B, self.beam_size)\n\n        # force the output to be longer than self.min_length\n        step = len(self)\n        self.ensure_min_length(log_probs)\n\n        # Multiply probs by the beam probability.\n        log_probs += self.topk_log_probs.view(_B * self.beam_size, 1)\n\n        # if the sequence ends now, then the penalty is the current\n        # length + 1, to include the EOS token\n        length_penalty = self.global_scorer.length_penalty(\n            step + 1, alpha=self.global_scorer.alpha)\n\n        curr_scores = log_probs / length_penalty\n\n        # Avoid any direction that would repeat unwanted ngrams\n        self.block_ngram_repeats(curr_scores)\n\n        # Pick up candidate token by curr_scores\n        self.topk_scores, self.topk_ids = self._pick(curr_scores)\n\n        # Recover log probs.\n        # Length penalty is just a scalar. It doesn't matter if it's applied\n        # before or after the topk.\n        torch.mul(self.topk_scores, length_penalty, out=self.topk_log_probs)\n\n        # Resolve beam origin and map to batch index flat representation.\n        self._batch_index = self.topk_ids // vocab_size\n        self._batch_index += self._beam_offset[:_B].unsqueeze(1)\n        self.select_indices = self._batch_index.view(_B * self.beam_size)\n        self.topk_ids.fmod_(vocab_size)  # resolve true word ids\n\n        # Append last prediction.\n        self.alive_seq = torch.cat(\n            [self.alive_seq.index_select(0, self.select_indices),\n             self.topk_ids.view(_B * self.beam_size, 1)], -1)\n\n        self.maybe_update_forbidden_tokens()\n\n        if self.return_attention or self._cov_pen:\n            current_attn = attn.index_select(1, self.select_indices)\n            if step == 1:\n                self.alive_attn = current_attn\n                # update global state (step == 1)\n                if self._cov_pen:  # coverage penalty\n                    self._prev_penalty = torch.zeros_like(self.topk_log_probs)\n                    self._coverage = current_attn\n            else:\n                self.alive_attn = self.alive_attn.index_select(\n                    1, self.select_indices)\n                self.alive_attn = torch.cat([self.alive_attn, current_attn], 0)\n                # update global state (step > 1)\n                if self._cov_pen:\n                    self._coverage = self._coverage.index_select(\n                        1, self.select_indices)\n                    self._coverage += current_attn\n                    self._prev_penalty = self.global_scorer.cov_penalty(\n                        self._coverage, beta=self.global_scorer.beta).view(\n                            _B, self.beam_size)\n\n        if self._vanilla_cov_pen:\n            # shape: (batch_size x beam_size, 1)\n            cov_penalty = self.global_scorer.cov_penalty(\n                self._coverage,\n                beta=self.global_scorer.beta)\n            self.topk_scores -= cov_penalty.view(_B, self.beam_size).float()\n\n        self.is_finished = self.topk_ids.eq(self.eos)\n        self.ensure_max_length()\n\n\nclass BeamSearch(BeamSearchBase):\n    \"\"\"\n        Beam search for seq2seq/encoder-decoder models\n    \"\"\"\n    def initialize(self, memory_bank, src_lengths, src_map=None, device=None,\n                   target_prefix=None):\n        \"\"\"Initialize for decoding.\n        Repeat src objects `beam_size` times.\n        \"\"\"\n\n        def fn_map_state(state, dim):\n            return tile(state, self.beam_size, dim=dim)\n\n        if isinstance(memory_bank, tuple):\n            memory_bank = tuple(tile(x, self.beam_size, dim=1)\n                                for x in memory_bank)\n            mb_device = memory_bank[0].device\n        else:\n            memory_bank = tile(memory_bank, self.beam_size, dim=1)\n            mb_device = memory_bank.device\n        if src_map is not None:\n            src_map = tile(src_map, self.beam_size, dim=1)\n        if device is None:\n            device = mb_device\n\n        self.memory_lengths = tile(src_lengths, self.beam_size)\n        if target_prefix is not None:\n            target_prefix = tile(target_prefix, self.beam_size, dim=1)\n\n        super(BeamSearch, self).initialize_(\n            memory_bank, self.memory_lengths, src_map, device, target_prefix)\n\n        return fn_map_state, memory_bank, self.memory_lengths, src_map\n\n\nclass BeamSearchLM(BeamSearchBase):\n    \"\"\"\n        Beam search for language/decoder only models\n    \"\"\"\n    def initialize(self, src, src_lengths, src_map=None, device=None,\n                   target_prefix=None):\n        \"\"\"Initialize for decoding.\n        Repeat src objects `beam_size` times.\n        \"\"\"\n        def fn_map_state(state, dim):\n            return tile(state, self.beam_size, dim=dim)\n\n        src = fn_map_state(src, dim=1)\n        if src_map is not None:\n            src_map = tile(src_map, self.beam_size, dim=1)\n        if device is None:\n            device = src.device\n\n        self.memory_lengths = tile(src_lengths, self.beam_size)\n        if target_prefix is not None:\n            target_prefix = tile(target_prefix, self.beam_size, dim=1)\n\n        super(BeamSearchLM, self).initialize_(\n            None, self.memory_lengths, src_map=src_map, device=device,\n            target_prefix=target_prefix)\n\n        return fn_map_state, src, self.memory_lengths, src_map\n\n    def advance(self, log_probs, attn):\n        super(BeamSearchLM, self).advance(log_probs, attn)\n\n        # in LM task memory_lengths is associated with currently generated src\n        # and therefore needs to follow the generation\n        self.memory_lengths += 1\n\n    def remove_finished_batches(self, _B_new, _B_old, non_finished,\n                                predictions, attention, step):\n        super(BeamSearchLM, self).remove_finished_batches(\n            _B_new, _B_old, non_finished, predictions, attention, step)\n\n        # in LM task memory_lengths is associated with currently generated src\n        # and therefore needs to follow the generation\n        non_finished = non_finished.to(self.topk_ids.device)\n        self.memory_lengths = self.memory_lengths.view(\n            _B_old, self.beam_size) \\\n            .index_select(0, non_finished) \\\n            .view(_B_new * self.beam_size)\n\n\nclass GNMTGlobalScorer(object):\n    \"\"\"NMT re-ranking.\n\n    Args:\n       alpha (float): Length parameter.\n       beta (float):  Coverage parameter.\n       length_penalty (str): Length penalty strategy.\n       coverage_penalty (str): Coverage penalty strategy.\n\n    Attributes:\n        alpha (float): See above.\n        beta (float): See above.\n        length_penalty (callable): See :class:`penalties.PenaltyBuilder`.\n        coverage_penalty (callable): See :class:`penalties.PenaltyBuilder`.\n        has_cov_pen (bool): See :class:`penalties.PenaltyBuilder`.\n        has_len_pen (bool): See :class:`penalties.PenaltyBuilder`.\n    \"\"\"\n\n    @classmethod\n    def from_opt(cls, opt):\n        return cls(\n            opt.alpha,\n            opt.beta,\n            opt.length_penalty,\n            opt.coverage_penalty)\n\n    def __init__(self, alpha, beta, length_penalty, coverage_penalty):\n        self._validate(alpha, beta, length_penalty, coverage_penalty)\n        self.alpha = alpha\n        self.beta = beta\n        penalty_builder = penalties.PenaltyBuilder(coverage_penalty,\n                                                   length_penalty)\n        self.has_cov_pen = penalty_builder.has_cov_pen\n        # Term will be subtracted from probability\n        self.cov_penalty = penalty_builder.coverage_penalty\n\n        self.has_len_pen = penalty_builder.has_len_pen\n        # Probability will be divided by this\n        self.length_penalty = penalty_builder.length_penalty\n\n    @classmethod\n    def _validate(cls, alpha, beta, length_penalty, coverage_penalty):\n        # these warnings indicate that either the alpha/beta\n        # forces a penalty to be a no-op, or a penalty is a no-op but\n        # the alpha/beta would suggest otherwise.\n        if length_penalty is None or length_penalty == \"none\":\n            if alpha != 0:\n                warnings.warn(\"Non-default `alpha` with no length penalty. \"\n                              \"`alpha` has no effect.\")\n        else:\n            # using some length penalty\n            if length_penalty == \"wu\" and alpha == 0.:\n                warnings.warn(\"Using length penalty Wu with alpha==0 \"\n                              \"is equivalent to using length penalty none.\")\n        if coverage_penalty is None or coverage_penalty == \"none\":\n            if beta != 0:\n                warnings.warn(\"Non-default `beta` with no coverage penalty. \"\n                              \"`beta` has no effect.\")\n        else:\n            # using some coverage penalty\n            if beta == 0.:\n                warnings.warn(\"Non-default coverage penalty with beta==0 \"\n                              \"is equivalent to using coverage penalty none.\")\n"
  },
  {
    "path": "onmt/translate/decode_strategy.py",
    "content": "import torch\nfrom copy import deepcopy\n\n\nclass DecodeStrategy(object):\n    \"\"\"Base class for generation strategies.\n\n    Args:\n        pad (int): Magic integer in output vocab.\n        bos (int): Magic integer in output vocab.\n        eos (int): Magic integer in output vocab.\n        batch_size (int): Current batch size.\n        parallel_paths (int): Decoding strategies like beam search\n            use parallel paths. Each batch is repeated ``parallel_paths``\n            times in relevant state tensors.\n        min_length (int): Shortest acceptable generation, not counting\n            begin-of-sentence or end-of-sentence.\n        max_length (int): Longest acceptable sequence, not counting\n            begin-of-sentence (presumably there has been no EOS\n            yet if max_length is used as a cutoff).\n        block_ngram_repeat (int): Block beams where\n            ``block_ngram_repeat``-grams repeat.\n        exclusion_tokens (set[int]): If a gram contains any of these\n            tokens, it may repeat.\n        return_attention (bool): Whether to work with attention too. If this\n            is true, it is assumed that the decoder is attentional.\n\n    Attributes:\n        pad (int): See above.\n        bos (int): See above.\n        eos (int): See above.\n        predictions (list[list[LongTensor]]): For each batch, holds a\n            list of beam prediction sequences.\n        scores (list[list[FloatTensor]]): For each batch, holds a\n            list of scores.\n        attention (list[list[FloatTensor or list[]]]): For each\n            batch, holds a list of attention sequence tensors\n            (or empty lists) having shape ``(step, inp_seq_len)`` where\n            ``inp_seq_len`` is the length of the sample (not the max\n            length of all inp seqs).\n        alive_seq (LongTensor): Shape ``(B x parallel_paths, step)``.\n            This sequence grows in the ``step`` axis on each call to\n            :func:`advance()`.\n        is_finished (ByteTensor or NoneType): Shape\n            ``(B, parallel_paths)``. Initialized to ``None``.\n        alive_attn (FloatTensor or NoneType): If tensor, shape is\n            ``(step, B x parallel_paths, inp_seq_len)``, where ``inp_seq_len``\n            is the (max) length of the input sequence.\n        target_prefix (LongTensor or NoneType): If tensor, shape is\n            ``(B x parallel_paths, prefix_seq_len)``, where ``prefix_seq_len``\n            is the (max) length of the pre-fixed prediction.\n        min_length (int): See above.\n        max_length (int): See above.\n        block_ngram_repeat (int): See above.\n        exclusion_tokens (set[int]): See above.\n        return_attention (bool): See above.\n        done (bool): See above.\n    \"\"\"\n\n    def __init__(self, pad, bos, eos, batch_size, parallel_paths,\n                 min_length, block_ngram_repeat, exclusion_tokens,\n                 return_attention, max_length):\n\n        # magic indices\n        self.pad = pad\n        self.bos = bos\n        self.eos = eos\n\n        self.batch_size = batch_size\n        self.parallel_paths = parallel_paths\n        # result caching\n        self.predictions = [[] for _ in range(batch_size)]\n        self.scores = [[] for _ in range(batch_size)]\n        self.attention = [[] for _ in range(batch_size)]\n\n        self.alive_attn = None\n\n        self.min_length = min_length\n        self.max_length = max_length\n\n        self.block_ngram_repeat = block_ngram_repeat\n        n_paths = batch_size * parallel_paths\n        self.forbidden_tokens = [dict() for _ in range(n_paths)]\n\n        self.exclusion_tokens = exclusion_tokens\n        self.return_attention = return_attention\n\n        self.done = False\n\n    def initialize(self, memory_bank, src_lengths, src_map=None, device=None,\n                   target_prefix=None):\n        \"\"\"DecodeStrategy subclasses should override :func:`initialize()`.\n\n        `initialize` should be called before all actions.\n        used to prepare necessary ingredients for decode.\n        \"\"\"\n        if device is None:\n            device = torch.device('cpu')\n        self.alive_seq = torch.full(\n            [self.batch_size * self.parallel_paths, 1], self.bos,\n            dtype=torch.long, device=device)\n        self.is_finished = torch.zeros(\n            [self.batch_size, self.parallel_paths],\n            dtype=torch.uint8, device=device)\n        if target_prefix is not None:\n            seq_len, batch_size, n_feats = target_prefix.size()\n            assert batch_size == self.batch_size * self.parallel_paths,\\\n                \"forced target_prefix should've extend to same number of path!\"\n            target_prefix_words = target_prefix[:, :, 0].transpose(0, 1)\n            target_prefix = target_prefix_words[:, 1:]  # remove bos\n            # fix length constraint\n            prefix_non_pad = target_prefix.ne(self.pad).sum(dim=-1).tolist()\n            self.max_length += max(prefix_non_pad)\n            self.min_length += min(prefix_non_pad)\n        self.target_prefix = target_prefix  # NOTE: forced prefix words\n        return None, memory_bank, src_lengths, src_map\n\n    def __len__(self):\n        return self.alive_seq.shape[1]\n\n    def ensure_min_length(self, log_probs):\n        if len(self) <= self.min_length:\n            log_probs[:, self.eos] = -1e20\n\n    def ensure_max_length(self):\n        # add one to account for BOS. Don't account for EOS because hitting\n        # this implies it hasn't been found.\n        if len(self) == self.max_length + 1:\n            self.is_finished.fill_(1)\n\n    def block_ngram_repeats(self, log_probs):\n        \"\"\"\n        We prevent the beam from going in any direction that would repeat any\n        ngram of size <block_ngram_repeat> more thant once.\n\n        The way we do it: we maintain a list of all ngrams of size\n        <block_ngram_repeat> that is updated each time the beam advances, and\n        manually put any token that would lead to a repeated ngram to 0.\n\n        This improves on the previous version's complexity:\n           - previous version's complexity: batch_size * beam_size * len(self)\n           - current version's complexity: batch_size * beam_size\n\n        This improves on the previous version's accuracy;\n           - Previous version blocks the whole beam, whereas here we only\n            block specific tokens.\n           - Before the translation would fail when all beams contained\n            repeated ngrams. This is sure to never happen here.\n        \"\"\"\n\n        # we don't block nothing if the user doesn't want it\n        if self.block_ngram_repeat <= 0:\n            return\n\n        # we can't block nothing beam's too short\n        if len(self) < self.block_ngram_repeat:\n            return\n\n        n = self.block_ngram_repeat - 1\n        for path_idx in range(self.alive_seq.shape[0]):\n            # we check paths one by one\n\n            current_ngram = tuple(self.alive_seq[path_idx, -n:].tolist())\n            forbidden_tokens = self.forbidden_tokens[path_idx].get(\n                current_ngram, None)\n            if forbidden_tokens is not None:\n                log_probs[path_idx, list(forbidden_tokens)] = -10e20\n\n    def maybe_update_forbidden_tokens(self):\n        \"\"\"We complete and reorder the list of forbidden_tokens\"\"\"\n\n        # we don't forbid nothing if the user doesn't want it\n        if self.block_ngram_repeat <= 0:\n            return\n\n        # we can't forbid nothing if beam's too short\n        if len(self) < self.block_ngram_repeat:\n            return\n\n        n = self.block_ngram_repeat\n\n        forbidden_tokens = list()\n        for path_idx, seq in zip(self.select_indices, self.alive_seq):\n\n            # Reordering forbidden_tokens following beam selection\n            # We rebuild a dict to ensure we get the value and not the pointer\n            forbidden_tokens.append(\n                deepcopy(self.forbidden_tokens[path_idx]))\n\n            # Grabing the newly selected tokens and associated ngram\n            current_ngram = tuple(seq[-n:].tolist())\n\n            # skip the blocking if any token in current_ngram is excluded\n            if set(current_ngram) & self.exclusion_tokens:\n                continue\n\n            forbidden_tokens[-1].setdefault(current_ngram[:-1], set())\n            forbidden_tokens[-1][current_ngram[:-1]].add(current_ngram[-1])\n\n        self.forbidden_tokens = forbidden_tokens\n\n    def target_prefixing(self, log_probs):\n        \"\"\"Fix the first part of predictions with `self.target_prefix`.\n\n        Args:\n            log_probs (FloatTensor): logits of size ``(B, vocab_size)``.\n\n        Returns:\n            log_probs (FloatTensor): modified logits in ``(B, vocab_size)``.\n        \"\"\"\n        _B, vocab_size = log_probs.size()\n        step = len(self)\n        if (self.target_prefix is not None and\n                step <= self.target_prefix.size(1)):\n            pick_idx = self.target_prefix[:, step - 1].tolist()  # (B)\n            pick_coo = [[path_i, pick] for path_i, pick in enumerate(pick_idx)\n                        if pick not in [self.eos, self.pad]]\n            mask_pathid = [path_i for path_i, pick in enumerate(pick_idx)\n                           if pick in [self.eos, self.pad]]\n            if len(pick_coo) > 0:\n                pick_coo = torch.tensor(pick_coo).to(self.target_prefix)\n                pick_fill_value = torch.ones(\n                    [pick_coo.size(0)], dtype=log_probs.dtype)\n                # pickups: Tensor where specified index were set to 1, others 0\n                pickups = torch.sparse_coo_tensor(\n                    pick_coo.t(), pick_fill_value,\n                    size=log_probs.size(), device=log_probs.device).to_dense()\n                # dropdowns: opposite of pickups, 1 for those shouldn't pick\n                dropdowns = torch.ones_like(pickups) - pickups\n                if len(mask_pathid) > 0:\n                    path_mask = torch.zeros(_B).to(self.target_prefix)\n                    path_mask[mask_pathid] = 1\n                    path_mask = path_mask.unsqueeze(1).to(dtype=bool)\n                    dropdowns = dropdowns.masked_fill(path_mask, 0)\n                # Minus dropdowns to log_probs making probabilities of\n                # unspecified index close to 0\n                log_probs -= 10000*dropdowns\n        return log_probs\n\n    def maybe_update_target_prefix(self, select_index):\n        \"\"\"We update / reorder `target_prefix` for alive path.\"\"\"\n        if self.target_prefix is None:\n            return\n        # prediction step have surpass length of given target_prefix,\n        # no need to further change this attr\n        if len(self) > self.target_prefix.size(1):\n            return\n        self.target_prefix = self.target_prefix.index_select(0, select_index)\n\n    def advance(self, log_probs, attn):\n        \"\"\"DecodeStrategy subclasses should override :func:`advance()`.\n\n        Advance is used to update ``self.alive_seq``, ``self.is_finished``,\n        and, when appropriate, ``self.alive_attn``.\n        \"\"\"\n\n        raise NotImplementedError()\n\n    def update_finished(self):\n        \"\"\"DecodeStrategy subclasses should override :func:`update_finished()`.\n\n        ``update_finished`` is used to update ``self.predictions``,\n        ``self.scores``, and other \"output\" attributes.\n        \"\"\"\n\n        raise NotImplementedError()\n"
  },
  {
    "path": "onmt/translate/greedy_search.py",
    "content": "import torch\n\nfrom onmt.translate.decode_strategy import DecodeStrategy\n\n\ndef sample_with_temperature(logits, sampling_temp, keep_topk):\n    \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n    Samples from a categorical distribution over the ``keep_topk`` words using\n    the category probabilities ``logits / sampling_temp``.\n\n    Args:\n        logits (FloatTensor): Shaped ``(batch_size, vocab_size)``.\n            These can be logits (``(-inf, inf)``) or log-probs (``(-inf, 0]``).\n            (The distribution actually uses the log-probabilities\n            ``logits - logits.logsumexp(-1)``, which equals the logits if\n            they are log-probabilities summing to 1.)\n        sampling_temp (float): Used to scale down logits. The higher the\n            value, the more likely it is that a non-max word will be\n            sampled.\n        keep_topk (int): This many words could potentially be chosen. The\n            other logits are set to have probability 0.\n\n    Returns:\n        (LongTensor, FloatTensor):\n\n        * topk_ids: Shaped ``(batch_size, 1)``. These are\n          the sampled word indices in the output vocab.\n        * topk_scores: Shaped ``(batch_size, 1)``. These\n          are essentially ``(logits / sampling_temp)[topk_ids]``.\n    \"\"\"\n\n    if sampling_temp == 0.0 or keep_topk == 1:\n        # For temp=0.0, take the argmax to avoid divide-by-zero errors.\n        # keep_topk=1 is also equivalent to argmax.\n        topk_scores, topk_ids = logits.topk(1, dim=-1)\n        if sampling_temp > 0:\n            topk_scores /= sampling_temp\n    else:\n        logits = torch.div(logits, sampling_temp)\n\n        if keep_topk > 0:\n            top_values, top_indices = torch.topk(logits, keep_topk, dim=1)\n            kth_best = top_values[:, -1].view([-1, 1])\n            kth_best = kth_best.repeat([1, logits.shape[1]]).float()\n\n            # Set all logits that are not in the top-k to -10000.\n            # This puts the probabilities close to 0.\n            ignore = torch.lt(logits, kth_best)\n            logits = logits.masked_fill(ignore, -10000)\n\n        dist = torch.distributions.Multinomial(\n            logits=logits, total_count=1)\n        topk_ids = torch.argmax(dist.sample(), dim=1, keepdim=True)\n        topk_scores = logits.gather(dim=1, index=topk_ids)\n    return topk_ids, topk_scores\n\n\nclass GreedySearch(DecodeStrategy):\n    \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n    The ``scores`` attribute's lists are the score, after applying temperature,\n    of the final prediction (either EOS or the final token in the event\n    that ``max_length`` is reached)\n\n    Args:\n        pad (int): See base.\n        bos (int): See base.\n        eos (int): See base.\n        batch_size (int): See base.\n        min_length (int): See base.\n        max_length (int): See base.\n        block_ngram_repeat (int): See base.\n        exclusion_tokens (set[int]): See base.\n        return_attention (bool): See base.\n        max_length (int): See base.\n        sampling_temp (float): See\n            :func:`~onmt.translate.greedy_search.sample_with_temperature()`.\n        keep_topk (int): See\n            :func:`~onmt.translate.greedy_search.sample_with_temperature()`.\n    \"\"\"\n\n    def __init__(self, pad, bos, eos, batch_size, min_length,\n                 block_ngram_repeat, exclusion_tokens, return_attention,\n                 max_length, sampling_temp, keep_topk):\n        assert block_ngram_repeat == 0\n        super(GreedySearch, self).__init__(\n            pad, bos, eos, batch_size, 1, min_length, block_ngram_repeat,\n            exclusion_tokens, return_attention, max_length)\n        self.sampling_temp = sampling_temp\n        self.keep_topk = keep_topk\n        self.topk_scores = None\n\n    def initialize(self, memory_bank, src_lengths, src_map=None, device=None,\n                   target_prefix=None):\n        \"\"\"Initialize for decoding.\"\"\"\n        fn_map_state = None\n\n        if isinstance(memory_bank, tuple):\n            mb_device = memory_bank[0].device\n        else:\n            mb_device = memory_bank.device\n        if device is None:\n            device = mb_device\n\n        self.memory_lengths = src_lengths\n        super(GreedySearch, self).initialize(\n            memory_bank, src_lengths, src_map, device, target_prefix)\n        self.select_indices = torch.arange(\n            self.batch_size, dtype=torch.long, device=device)\n        self.original_batch_idx = torch.arange(\n            self.batch_size, dtype=torch.long, device=device)\n        return fn_map_state, memory_bank, self.memory_lengths, src_map\n\n    @property\n    def current_predictions(self):\n        return self.alive_seq[:, -1]\n\n    @property\n    def batch_offset(self):\n        return self.select_indices\n\n    def _pick(self, log_probs):\n        \"\"\"Function used to pick next tokens.\n\n        Args:\n            log_probs (FloatTensor): ``(batch_size, vocab_size)``.\n        \"\"\"\n        # maybe fix some prediction at this step by modifying log_probs\n        log_probs = self.target_prefixing(log_probs)\n        topk_ids, topk_scores = sample_with_temperature(\n            log_probs, self.sampling_temp, self.keep_topk)\n        return topk_ids, topk_scores\n\n    def advance(self, log_probs, attn):\n        \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n        Args:\n            log_probs (FloatTensor): Shaped ``(batch_size, vocab_size)``.\n                These can be logits (``(-inf, inf)``) or log-probs\n                (``(-inf, 0]``). (The distribution actually uses the\n                log-probabilities ``logits - logits.logsumexp(-1)``,\n                which equals the logits if they are log-probabilities summing\n                to 1.)\n            attn (FloatTensor): Shaped ``(1, B, inp_seq_len)``.\n        \"\"\"\n\n        self.ensure_min_length(log_probs)\n        self.block_ngram_repeats(log_probs)\n\n        topk_ids, self.topk_scores = self._pick(log_probs)\n\n        self.is_finished = topk_ids.eq(self.eos)\n\n        self.alive_seq = torch.cat([self.alive_seq, topk_ids], -1)\n        if self.return_attention:\n            if self.alive_attn is None:\n                self.alive_attn = attn\n            else:\n                self.alive_attn = torch.cat([self.alive_attn, attn], 0)\n        self.ensure_max_length()\n\n    def update_finished(self, last_step=None):\n        \"\"\"Finalize scores and predictions.\"\"\"\n        # shape: (sum(~ self.is_finished), 1)\n        finished_batches = self.is_finished.view(-1).nonzero(as_tuple=False)\n        for b in finished_batches.view(-1):\n            b_orig = self.original_batch_idx[b]\n            self.scores[b_orig].append(self.topk_scores[b, 0])\n            self.predictions[b_orig].append(self.alive_seq[b, 1:])\n            self.attention[b_orig].append(\n                self.alive_attn[:, b, :self.memory_lengths[b]]\n                if self.alive_attn is not None else [])\n        self.done = self.is_finished.all()\n        if self.done:\n            return\n        is_alive = ~self.is_finished.view(-1)\n        self.alive_seq = self.alive_seq[is_alive]\n        if self.alive_attn is not None:\n            self.alive_attn = self.alive_attn[:, is_alive]\n        self.select_indices = is_alive.nonzero(as_tuple=False).view(-1)\n        self.original_batch_idx = self.original_batch_idx[is_alive]\n        self.maybe_update_target_prefix(self.select_indices)\n\n\nclass GreedySearchLM(GreedySearch):\n    def update_finished(self):\n        super(GreedySearchLM, self).update_finished()\n        self.update_memory_lengths()\n\n    def update_memory_lengths(self):\n        is_alive = ~self.is_finished.view(-1)\n        self.memory_lengths = self.memory_lengths[is_alive]\n\n    def advance(self, log_probs, attn):\n        super(GreedySearchLM, self).advance(log_probs, attn)\n\n        # in LM task memory_lengths is associated with currently generated src\n        # and therefore needs to follow the generation\n        self.memory_lengths += 1\n"
  },
  {
    "path": "onmt/translate/penalties.py",
    "content": "from __future__ import division\nimport torch\n\n\nclass PenaltyBuilder(object):\n    \"\"\"Returns the Length and Coverage Penalty function for Beam Search.\n\n    Args:\n        length_pen (str): option name of length pen\n        cov_pen (str): option name of cov pen\n\n    Attributes:\n        has_cov_pen (bool): Whether coverage penalty is None (applying it\n            is a no-op). Note that the converse isn't true. Setting beta\n            to 0 should force coverage length to be a no-op.\n        has_len_pen (bool): Whether length penalty is None (applying it\n            is a no-op). Note that the converse isn't true. Setting alpha\n            to 1 should force length penalty to be a no-op.\n        coverage_penalty (callable[[FloatTensor, float], FloatTensor]):\n            Calculates the coverage penalty.\n        length_penalty (callable[[int, float], float]): Calculates\n            the length penalty.\n    \"\"\"\n\n    def __init__(self, cov_pen, length_pen):\n        self.has_cov_pen = not self._pen_is_none(cov_pen)\n        self.coverage_penalty = self._coverage_penalty(cov_pen)\n        self.has_len_pen = not self._pen_is_none(length_pen)\n        self.length_penalty = self._length_penalty(length_pen)\n\n    @staticmethod\n    def _pen_is_none(pen):\n        return pen == \"none\" or pen is None\n\n    def _coverage_penalty(self, cov_pen):\n        if cov_pen == \"wu\":\n            return self.coverage_wu\n        elif cov_pen == \"summary\":\n            return self.coverage_summary\n        elif self._pen_is_none(cov_pen):\n            return self.coverage_none\n        else:\n            raise NotImplementedError(\"No '{:s}' coverage penalty.\".format(\n                cov_pen))\n\n    def _length_penalty(self, length_pen):\n        if length_pen == \"wu\":\n            return self.length_wu\n        elif length_pen == \"avg\":\n            return self.length_average\n        elif self._pen_is_none(length_pen):\n            return self.length_none\n        else:\n            raise NotImplementedError(\"No '{:s}' length penalty.\".format(\n                length_pen))\n\n    # Below are all the different penalty terms implemented so far.\n    # Subtract coverage penalty from topk log probs.\n    # Divide topk log probs by length penalty.\n\n    def coverage_wu(self, cov, beta=0.):\n        \"\"\"GNMT coverage re-ranking score.\n\n        See \"Google's Neural Machine Translation System\" :cite:`wu2016google`.\n        ``cov`` is expected to be sized ``(*, seq_len)``, where ``*`` is\n        probably ``batch_size x beam_size`` but could be several\n        dimensions like ``(batch_size, beam_size)``. If ``cov`` is attention,\n        then the ``seq_len`` axis probably sums to (almost) 1.\n        \"\"\"\n\n        penalty = -torch.min(cov, cov.clone().fill_(1.0)).log().sum(-1)\n        return beta * penalty\n\n    def coverage_summary(self, cov, beta=0.):\n        \"\"\"Our summary penalty.\"\"\"\n        penalty = torch.max(cov, cov.clone().fill_(1.0)).sum(-1)\n        penalty -= cov.size(-1)\n        return beta * penalty\n\n    def coverage_none(self, cov, beta=0.):\n        \"\"\"Returns zero as penalty\"\"\"\n        none = torch.zeros((1,), device=cov.device,\n                           dtype=torch.float)\n        if cov.dim() == 3:\n            none = none.unsqueeze(0)\n        return none\n\n    def length_wu(self, cur_len, alpha=0.):\n        \"\"\"GNMT length re-ranking score.\n\n        See \"Google's Neural Machine Translation System\" :cite:`wu2016google`.\n        \"\"\"\n\n        return ((5 + cur_len) / 6.0) ** alpha\n\n    def length_average(self, cur_len, alpha=0.):\n        \"\"\"Returns the current sequence length.\"\"\"\n        return cur_len\n\n    def length_none(self, cur_len, alpha=0.):\n        \"\"\"Returns unmodified scores.\"\"\"\n        return 1.0\n"
  },
  {
    "path": "onmt/translate/process_zh.py",
    "content": "from pyhanlp import HanLP\nfrom snownlp import SnowNLP\nimport pkuseg\n\n\ndef wrap_str_func(func):\n    \"\"\"\n    Wrapper to apply str function to the proper key of return_dict.\n    \"\"\"\n    def wrapper(some_dict):\n        some_dict[\"seg\"] = [func(item) for item in some_dict[\"seg\"]]\n        return some_dict\n    return wrapper\n\n\n# Chinese segmentation\n@wrap_str_func\ndef zh_segmentator(line, server_model):\n    return \" \".join(pkuseg.pkuseg().cut(line))\n\n\n# Chinese simplify -> Chinese traditional standard\n@wrap_str_func\ndef zh_traditional_standard(line, server_model):\n    return HanLP.convertToTraditionalChinese(line)\n\n\n# Chinese simplify -> Chinese traditional (HongKong)\n@wrap_str_func\ndef zh_traditional_hk(line, server_model):\n    return HanLP.s2hk(line)\n\n\n# Chinese simplify -> Chinese traditional (Taiwan)\n@wrap_str_func\ndef zh_traditional_tw(line, server_model):\n    return HanLP.s2tw(line)\n\n\n# Chinese traditional -> Chinese simplify (v1)\n@wrap_str_func\ndef zh_simplify(line, server_model):\n    return HanLP.convertToSimplifiedChinese(line)\n\n\n# Chinese traditional -> Chinese simplify (v2)\n@wrap_str_func\ndef zh_simplify_v2(line, server_model):\n    return SnowNLP(line).han\n"
  },
  {
    "path": "onmt/translate/random_sampling.py",
    "content": "import torch\n\nfrom onmt.translate.decode_strategy import DecodeStrategy\n\n\ndef sample_with_temperature(logits, sampling_temp, keep_topk):\n    \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n    Samples from a categorical distribution over the ``keep_topk`` words using\n    the category probabilities ``logits / sampling_temp``.\n\n    Args:\n        logits (FloatTensor): Shaped ``(batch_size, vocab_size)``.\n            These can be logits (``(-inf, inf)``) or log-probs (``(-inf, 0]``).\n            (The distribution actually uses the log-probabilities\n            ``logits - logits.logsumexp(-1)``, which equals the logits if\n            they are log-probabilities summing to 1.)\n        sampling_temp (float): Used to scale down logits. The higher the\n            value, the more likely it is that a non-max word will be\n            sampled.\n        keep_topk (int): This many words could potentially be chosen. The\n            other logits are set to have probability 0.\n\n    Returns:\n        (LongTensor, FloatTensor):\n\n        * topk_ids: Shaped ``(batch_size, 1)``. These are\n          the sampled word indices in the output vocab.\n        * topk_scores: Shaped ``(batch_size, 1)``. These\n          are essentially ``(logits / sampling_temp)[topk_ids]``.\n    \"\"\"\n\n    if sampling_temp == 0.0 or keep_topk == 1:\n        # For temp=0.0, take the argmax to avoid divide-by-zero errors.\n        # keep_topk=1 is also equivalent to argmax.\n        topk_scores, topk_ids = logits.topk(1, dim=-1)\n        if sampling_temp > 0:\n            topk_scores /= sampling_temp\n    else:\n        logits = torch.div(logits, sampling_temp)\n\n        if keep_topk > 0:\n            top_values, top_indices = torch.topk(logits, keep_topk, dim=1)\n            kth_best = top_values[:, -1].view([-1, 1])\n            kth_best = kth_best.repeat([1, logits.shape[1]]).float()\n\n            # Set all logits that are not in the top-k to -10000.\n            # This puts the probabilities close to 0.\n            ignore = torch.lt(logits, kth_best)\n            logits = logits.masked_fill(ignore, -10000)\n\n        dist = torch.distributions.Multinomial(\n            logits=logits, total_count=1)\n        topk_ids = torch.argmax(dist.sample(), dim=1, keepdim=True)\n        topk_scores = logits.gather(dim=1, index=topk_ids)\n    return topk_ids, topk_scores\n\n\nclass RandomSampling(DecodeStrategy):\n    \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n    The ``scores`` attribute's lists are the score, after applying temperature,\n    of the final prediction (either EOS or the final token in the event\n    that ``max_length`` is reached)\n\n    Args:\n        pad (int): See base.\n        bos (int): See base.\n        eos (int): See base.\n        batch_size (int): See base.\n        device (torch.device or str): See base ``device``.\n        min_length (int): See base.\n        max_length (int): See base.\n        block_ngram_repeat (int): See base.\n        exclusion_tokens (set[int]): See base.\n        return_attention (bool): See base.\n        max_length (int): See base.\n        sampling_temp (float): See\n            :func:`~onmt.translate.random_sampling.sample_with_temperature()`.\n        keep_topk (int): See\n            :func:`~onmt.translate.random_sampling.sample_with_temperature()`.\n        memory_length (LongTensor): Lengths of encodings. Used for\n            masking attention.\n    \"\"\"\n\n    def __init__(self, pad, bos, eos, batch_size, device,\n                 min_length, block_ngram_repeat, exclusion_tokens,\n                 return_attention, max_length, sampling_temp, keep_topk,\n                 memory_length):\n        super(RandomSampling, self).__init__(\n            pad, bos, eos, batch_size, device, 1,\n            min_length, block_ngram_repeat, exclusion_tokens,\n            return_attention, max_length)\n        self.sampling_temp = sampling_temp\n        self.keep_topk = keep_topk\n        self.topk_scores = None\n        self.memory_length = memory_length\n        self.batch_size = batch_size\n        self.select_indices = torch.arange(self.batch_size,\n                                           dtype=torch.long, device=device)\n        self.original_batch_idx = torch.arange(self.batch_size,\n                                               dtype=torch.long, device=device)\n\n    def advance(self, log_probs, attn):\n        \"\"\"Select next tokens randomly from the top k possible next tokens.\n\n        Args:\n            log_probs (FloatTensor): Shaped ``(batch_size, vocab_size)``.\n                These can be logits (``(-inf, inf)``) or log-probs\n                (``(-inf, 0]``). (The distribution actually uses the\n                log-probabilities ``logits - logits.logsumexp(-1)``,\n                which equals the logits if they are log-probabilities summing\n                to 1.)\n            attn (FloatTensor): Shaped ``(1, B, inp_seq_len)``.\n        \"\"\"\n\n        self.ensure_min_length(log_probs)\n        self.block_ngram_repeats(log_probs)\n        topk_ids, self.topk_scores = sample_with_temperature(\n            log_probs, self.sampling_temp, self.keep_topk)\n\n        self.is_finished = topk_ids.eq(self.eos)\n\n        self.alive_seq = torch.cat([self.alive_seq, topk_ids], -1)\n        if self.return_attention:\n            if self.alive_attn is None:\n                self.alive_attn = attn\n            else:\n                self.alive_attn = torch.cat([self.alive_attn, attn], 0)\n        self.ensure_max_length()\n\n    def update_finished(self, last_step=None):\n        \"\"\"Finalize scores and predictions.\"\"\"\n        # shape: (sum(~ self.is_finished), 1)\n        finished_batches = self.is_finished.view(-1).nonzero()\n        for b in finished_batches.view(-1):\n            b_orig = self.original_batch_idx[b]\n            self.scores[b_orig].append(self.topk_scores[b, 0])\n            self.predictions[b_orig].append(self.alive_seq[b, 1:])\n            self.attention[b_orig].append(\n                self.alive_attn[:, b, :self.memory_length[b]]\n                if self.alive_attn is not None else [])\n        self.done = self.is_finished.all()\n        if self.done:\n            return\n        is_alive = ~self.is_finished.view(-1)\n        self.alive_seq = self.alive_seq[is_alive]\n        if self.alive_attn is not None:\n            self.alive_attn = self.alive_attn[:, is_alive]\n        self.select_indices = is_alive.nonzero().view(-1)\n        self.original_batch_idx = self.original_batch_idx[is_alive]\n"
  },
  {
    "path": "onmt/translate/translation.py",
    "content": "\"\"\" Translation main class \"\"\"\nfrom __future__ import unicode_literals, print_function\n\nimport os\nimport torch\nfrom onmt.constants import DefaultTokens\nfrom onmt.inputters import KeyphraseDataset\nfrom onmt.inputters.text_dataset import TextMultiField\nfrom onmt.utils.alignment import build_align_pharaoh\n\n\nclass TranslationBuilder(object):\n    \"\"\"\n    Build a word-based translation from the batch output\n    of translator and the underlying dictionaries.\n\n    Replacement based on \"Addressing the Rare Word\n    Problem in Neural Machine Translation\" :cite:`Luong2015b`\n\n    Args:\n       data (onmt.inputters.Dataset): Data.\n       fields (List[Tuple[str, torchtext.data.Field]]): data fields\n       n_best (int): number of translations produced\n       replace_unk (bool): replace unknown words using attention\n       has_tgt (bool): will the batch have gold targets\n    \"\"\"\n\n    def __init__(self, data, fields, n_best=1, replace_unk=False,\n                 has_tgt=False, phrase_table=\"\", use_dynamic_transform=False):\n        self.data = data\n        self.fields = fields\n        self._has_text_src = (self.data is not None) and \\\n                             (isinstance(dict(self.fields)[\"src\"], TextMultiField))\n        self.use_dynamic_transform = use_dynamic_transform\n        if use_dynamic_transform:\n            self._has_text_src = True\n        self.n_best = n_best\n        self.replace_unk = replace_unk\n        self.phrase_table_dict = {}\n        if phrase_table != \"\" and os.path.exists(phrase_table):\n            with open(phrase_table) as phrase_table_fd:\n                for line in phrase_table_fd:\n                    phrase_src, phrase_trg = line.rstrip(\"\\n\").split(\n                        DefaultTokens.PHRASE_TABLE_SEPARATOR)\n                    self.phrase_table_dict[phrase_src] = phrase_trg\n        self.has_tgt = has_tgt\n\n    def _build_target_tokens(self, src, src_vocab, src_raw, pred, attn):\n        tgt_field = dict(self.fields)[\"tgt\"].base_field if hasattr(dict(self.fields)[\"tgt\"], 'base_field') else dict(self.fields)[\"tgt\"]\n        pretrained_tokenizer = None\n        if hasattr(tgt_field, 'pretrained_tokenizer'):\n            pretrained_tokenizer = tgt_field.pretrained_tokenizer\n        vocab = tgt_field.vocab\n        tokens = []\n\n        for tok in pred:\n            if tok < len(vocab):\n                if pretrained_tokenizer:\n                    # directly use the pretrained tokenizer for decoding if applicable\n                    token = pretrained_tokenizer.convert_ids_to_tokens([tok.item()])\n                    tokens.append(token[0])\n                else:\n                    tokens.append(vocab.itos[tok])\n            else:\n                tokens.append(src_vocab.itos[tok - len(vocab)])\n            if tokens[-1] == tgt_field.eos_token:\n                tokens = tokens[:-1]\n        if pretrained_tokenizer:\n            sep = pretrained_tokenizer.sep_token\n            tokens = pretrained_tokenizer.convert_tokens_to_string(tokens).replace(sep, ' %s ' % sep).split()\n        if self.replace_unk and attn is not None and src is not None:\n            for i in range(len(tokens)):\n                if tokens[i] == tgt_field.unk_token:\n                    _, max_index = attn[i][:len(src_raw)].max(0)\n                    tokens[i] = src_raw[max_index.item()]\n                    if self.phrase_table_dict:\n                        src_tok = src_raw[max_index.item()]\n                        if src_tok in self.phrase_table_dict:\n                            tokens[i] = self.phrase_table_dict[src_tok]\n        return tokens\n\n    def from_batch(self, translation_batch):\n        batch = translation_batch[\"batch\"]\n        assert(len(translation_batch[\"gold_score\"]) ==\n               len(translation_batch[\"predictions\"]))\n        batch_size = batch.batch_size\n\n        preds, pred_score, attn, align, gold_score, indices = list(zip(\n            *sorted(zip(translation_batch[\"predictions\"],\n                        translation_batch[\"scores\"],\n                        translation_batch[\"attention\"],\n                        translation_batch[\"alignment\"],\n                        translation_batch[\"gold_score\"],\n                        batch.indices.data),\n                    key=lambda x: x[-1])))\n\n        if not any(align):  # when align is a empty nested list\n            align = [None] * batch_size\n\n        # batch has been sorted by src length, now sort it back to recover\n        inds, perm = torch.sort(batch.indices)\n        if self._has_text_src:\n            if isinstance(batch.src, tuple):\n                src = batch.src[0][:, :, 0].index_select(1, perm)\n            else:\n                src = batch.src[:, :, 0].index_select(1, perm) # src_len, batch_size\n        else:\n            src = None\n\n        # if only one tgt (one2seq): batch.tgt.dim=[max_seq_len, batch_size, 1]\n        # if multiple tgts (one2one): batch.tgt.dim=[max_seq_len, batch_size, max_seq_num, 1]\n        if self.has_tgt:\n            if len(batch.tgt.size()) == 3:\n                tgt = batch.tgt[:, :, 0].index_select(1, perm)\n            else:\n                tgt = batch.tgt[:, :, :, 0].index_select(1, perm)\n        else:\n            tgt = None\n\n        if self.use_dynamic_transform and hasattr(batch, 'src_ex_vocab'):\n            _perm = perm.cpu().numpy().tolist() if perm.is_cuda else perm.numpy().tolist()\n            src_vocabs = [batch.src_ex_vocab[i] for i in _perm]\n        else:\n            src_vocabs = None\n\n        # print('src.shape=%s' % str(src.shape), src.cpu().numpy().tolist())\n        # print('tgt.shape=%s'% str(tgt.shape), tgt.cpu().numpy().tolist())\n\n        translations = []\n        for b in range(batch_size):\n            if self.use_dynamic_transform:\n                src_vocab = src_vocabs[b] if src_vocabs else None\n                src_raw = None\n            elif self._has_text_src:\n                src_vocab = self.data.src_vocabs[inds[b]] \\\n                    if self.data.src_vocabs else None\n                src_raw = self.data.examples[inds[b]].src[0]\n            else:\n                src_vocab = None\n                src_raw = None\n            # @memray: len(preds[b]) can be smaller than n_best\n            pred_sents = [self._build_target_tokens(\n                src[:, b] if src is not None else None,\n                src_vocab, src_raw,\n                preds[b][n],\n                align[b][n] if align[b] is not None else attn[b][n])\n                for n in range(min(self.n_best, len(preds[b])))]\n\n            # for pred, pred_sent in zip(preds[0], pred_sents):\n            #     print('[%d]' % pred.shape, preds[0][0].cpu().numpy().tolist())\n            #     print('[%d]' % len(pred_sent), pred_sent)\n\n            gold_sent = None\n            if tgt is not None:\n                if tgt.dim() == 2:\n                    gold_sent = self._build_target_tokens(\n                        src[:, b] if src is not None else None,\n                        src_vocab, src_raw,\n                        tgt[1:, b] if tgt is not None else None, None)\n                else:\n                    gold_sent = [self._build_target_tokens(\n                        src[:, b] if src is not None else None,\n                        src_vocab, src_raw,\n                        tgt[1:, b, n], None)\n                        for n in range(tgt.size(2))]\n                    gold_sent = [s for s in gold_sent if all([t != '<blank>' for t in s])]\n            translation = Translation(\n                src[:, b] if src is not None else None,\n                src_raw, pred_sents, attn[b], pred_score[b],\n                gold_sent, gold_score[b], preds[b], align[b],\n                index=inds[b].item()\n            )\n            translations.append(translation)\n\n        return translations\n\n\nclass Translation(object):\n    \"\"\"Container for a translated sentence.\n\n    Attributes:\n        src (LongTensor): Source word IDs.\n        src_raw (List[str]): Raw source words.\n        pred_sents (List[List[str]]): Words from the n-best translations.\n        pred_scores (List[List[float]]): Log-probs of n-best translations.\n        attns (List[FloatTensor]) : Attention distribution for each\n            translation.\n        gold_sent (List[str]): Words from gold translation.\n        gold_score (List[float]): Log-prob of gold translation.\n        preds (List[LongTensor]): Original indices of predicted words, added by # @memray\n        word_aligns (List[FloatTensor]): Words Alignment distribution for\n            each translation.\n    \"\"\"\n\n    __slots__ = [\"index\", \"src\", \"src_raw\", \"gold_sent\", \"gold_score\", \"word_aligns\",\n                 \"attns\", \"copied_flags\",\n                 \"unique_pred_num\", \"dup_pred_num\", \"beam_num\", \"beamstep_num\",\n                 \"pred_sents\", \"pred_scores\", \"preds\",\n                 \"ori_pred_sents\", \"ori_pred_scores\", \"ori_preds\",\n                 \"topseq_pred_sents\", \"topseq_pred_scores\", \"topseq_preds\",\n                 \"dup_pred_tuples\"\n                 ]\n\n    def __init__(self, src, src_raw, pred_sents,\n                 attn, pred_scores, tgt_sent, gold_score, preds, word_aligns, index):\n        self.index = index # original index in the dataset\n        self.src = src\n        self.src_raw = src_raw\n        if tgt_sent:\n            _tgt_sent = []\n            for t in tgt_sent:\n                _t = t\n                while _t.endswith('<pad>'):\n                    _t = _t[: -5]\n                _tgt_sent.append(_t)\n            tgt_sent = _tgt_sent\n        self.gold_sent = tgt_sent\n        self.gold_score = gold_score\n        self.attns = attn\n        self.preds = preds\n        self.pred_sents = pred_sents\n        self.pred_scores = pred_scores\n        self.gold_sent = tgt_sent\n        self.gold_score = gold_score\n        self.word_aligns = word_aligns\n\n        self.dup_pred_num = 0 # number of all predicted phrases\n        self.unique_pred_num = 0 # number of unique phrases\n        self.beam_num = 0 # number of effective beams\n        self.beamstep_num = 0 # number of effective beam search steps\n\n        self.copied_flags = None\n        self.ori_pred_sents = None\n        self.ori_pred_scores = None\n        self.ori_preds = None\n        self.topseq_pred_sents = None\n        self.topseq_pred_scores = None\n        self.topseq_preds = None\n        self.dup_pred_tuples = None\n\n    def log(self, sent_number):\n        \"\"\"\n        Log translation.\n        \"\"\"\n        msg = ['\\nSENT {}: {}\\n'.format(sent_number, self.src_raw)]\n\n        best_pred = self.pred_sents[0]\n        best_score = self.pred_scores[0]\n        pred_sent = ' '.join(best_pred)\n        msg.append('PRED {}: {}\\n'.format(sent_number, pred_sent))\n        msg.append(\"PRED SCORE: {:.4f}\\n\".format(best_score))\n\n        if self.word_aligns is not None:\n            pred_align = self.word_aligns[0]\n            pred_align_pharaoh = build_align_pharaoh(pred_align)\n            pred_align_sent = ' '.join(pred_align_pharaoh)\n            msg.append(\"ALIGN: {}\\n\".format(pred_align_sent))\n\n        if self.gold_sent is not None:\n            tgt_sent = ' '.join(self.gold_sent)\n            msg.append('GOLD {}: {}\\n'.format(sent_number, tgt_sent))\n            msg.append((\"GOLD SCORE: {:.4f}\\n\".format(self.gold_score)))\n        if len(self.pred_sents) > 1:\n            msg.append('\\nBEST HYP:\\n')\n            for score, sent in zip(self.pred_scores, self.pred_sents):\n                msg.append(\"[{:.4f}] {}\\n\".format(score, sent))\n\n        return \"\".join(msg)\n\n    def __dict__(self):\n        \"\"\"\n        Added by @memray to facilitate exporting json\n        :return:\n        \"\"\"\n        ret = {slot: getattr(self, slot) for slot in self.__slots__}\n\n        for slot in self.__slots__:\n            if ret[slot] is None:\n                continue\n            if slot == 'gold_score':\n                ret[slot] = ret[slot].item()\n                continue\n            if torch.cuda.is_available():\n                if slot.endswith('src'):\n                    ret[slot] = ret[slot].cpu().numpy().tolist()\n                elif slot.endswith('pred_scores'):\n                    ret[slot] = [t.cpu().numpy().tolist() for t in ret[slot]]\n                elif slot.endswith('preds'):\n                    ret[slot] = [t.cpu().numpy().tolist() for t in ret[slot]]\n            else:\n                if slot.endswith('src'):\n                    ret[slot] = ret[slot].numpy().tolist()\n                elif slot.endswith('pred_scores'):\n                    ret[slot] = [t.numpy().tolist() for t in ret[slot]]\n                elif slot.endswith('preds'):\n                    ret[slot] = [t.numpy().tolist() for t in ret[slot]]\n\n            if slot == \"dup_pred_tuples\":\n                # to save disk storage\n                ret[\"dup_pred_tuples\"] = None\n                # for tid, t in enumerate(ret[\"dup_pred_tuples\"]):\n                #     if torch.cuda.is_available():\n                #         nt = (t[0].cpu().numpy().tolist() if isinstance(t[0], torch.Tensor) else t[0],\n                #               t[1],\n                #               t[2].cpu().item() if isinstance(t[2], torch.Tensor) else t[2])\n                #     else:\n                #         nt = (t[0].numpy().tolist() if isinstance(t[0], torch.Tensor) else t[0],\n                #               t[1],\n                #               t[2].item() if isinstance(t[2], torch.Tensor) else t[2])\n                #     ret[\"dup_pred_tuples\"][tid] = nt\n\n        return ret\n\n\n    def log_kp(self, sent_number):\n        \"\"\"\n        Log keyphrase generation.\n        \"\"\"\n\n        msg = ['\\nSENT {}: {}\\n'.format(sent_number, self.src_raw)]\n\n        best_pred = self.pred_sents[0]\n        best_score = self.pred_scores[0]\n        pred_sent = ' '.join(best_pred) if isinstance(best_pred, list) else best_pred\n        msg.append('PRED {}: {}\\n'.format(sent_number, pred_sent))\n        msg.append(\"PRED SCORE: {:.4f}\\n\".format(best_score))\n\n\n        if self.gold_sent is not None:\n            if len(self.gold_sent) > 0 and isinstance(self.gold_sent[0], str):\n                tgt_sent = ' '.join(self.gold_sent)\n            else:\n                tgt_sent = '\\n\\t'.join([' '.join(tgt) for tgt in self.gold_sent])\n            msg.append('GOLD {}: \\n\\t{}\\n'.format(sent_number, tgt_sent))\n            msg.append((\"GOLD SCORE: {:.4f}\\n\".format(self.gold_score)))\n        if len(self.pred_sents) > 1:\n            msg.append('\\nBEST HYP:\\n')\n            for t_id, (score, sent, pred, copied_flag) in enumerate(zip(self.pred_scores, self.pred_sents, self.preds, self.copied_flags)):\n                tmp_pred = pred.cpu().numpy().tolist() if torch.cuda.is_available() else pred.numpy().tolist()\n                msg.append(\"[{}][{:.4f}] {} {} {}\\n\".format(t_id + 1, score, sent, tmp_pred, '[Copy!]' if any(copied_flag) else ''))\n\n        msg.append(\"#Original sequence: {}\\n\".format(len(self.ori_pred_sents)))\n        unique_first_words = set([s[0] for s in self.ori_pred_sents if len(s)>0])\n        msg.append(\"#Unique 1st words in original sequence: [{}] {}\\n\".format(len(unique_first_words), unique_first_words))\n        unique_first_words = set([s[0] for s in self.pred_sents if len(s) > 0])\n        msg.append(\"#Unique 1st words in splitted sequence: [{}] {}\\n\".format(len(unique_first_words), unique_first_words))\n\n        unique_first_ids = set([t[0].item() for t in self.ori_preds if t.size(0) > 0])\n        msg.append(\"#Unique 1st index in original sequence: [{}] {}\\n\".format(len(unique_first_ids), unique_first_ids))\n        unique_first_ids = set([t[0].item() for t in self.preds if t.size(0) > 0])\n        msg.append(\"#Unique 1st index in splitted sequence: [{}] {}\\n\".format(len(unique_first_ids), unique_first_ids))\n\n        return \"\".join(msg)\n\n\n    def add_copied_flags(self, vocab_size):\n        copied_flags = [pred.ge(vocab_size) for pred in self.preds]\n        if torch.cuda.is_available():\n            copied_flags = [t.cpu().numpy().tolist() for t in copied_flags]\n        else:\n            copied_flags = [t.numpy().tolist() for t in copied_flags]\n\n        self.copied_flags = copied_flags"
  },
  {
    "path": "onmt/translate/translation_server.py",
    "content": "#!/usr/bin/env python\n\"\"\"REST Translation server.\"\"\"\nfrom __future__ import print_function\nimport codecs\nimport sys\nimport os\nimport time\nimport json\nimport threading\nimport re\nimport traceback\nimport importlib\nimport torch\nimport onmt.opts\n\nfrom itertools import islice, zip_longest\nfrom copy import deepcopy\n\nfrom onmt.constants import DefaultTokens\nfrom onmt.utils.logging import init_logger\nfrom onmt.utils.misc import set_random_seed\nfrom onmt.utils.misc import check_model_config\nfrom onmt.utils.alignment import to_word_align\nfrom onmt.utils.parse import ArgumentParser\nfrom onmt.translate.translator import build_translator\n\n\ndef critical(func):\n    \"\"\"Decorator for critical section (mutually exclusive code)\"\"\"\n    def wrapper(server_model, *args, **kwargs):\n        if sys.version_info[0] == 3:\n            if not server_model.running_lock.acquire(True, 120):\n                raise ServerModelError(\"Model %d running lock timeout\"\n                                       % server_model.model_id)\n        else:\n            # semaphore doesn't have a timeout arg in Python 2.7\n            server_model.running_lock.acquire(True)\n        try:\n            o = func(server_model, *args, **kwargs)\n        except (Exception, RuntimeError):\n            server_model.running_lock.release()\n            raise\n        server_model.running_lock.release()\n        return o\n    return wrapper\n\n\nclass Timer:\n    def __init__(self, start=False):\n        self.stime = -1\n        self.prev = -1\n        self.times = {}\n        if start:\n            self.start()\n\n    def start(self):\n        self.stime = time.time()\n        self.prev = self.stime\n        self.times = {}\n\n    def tick(self, name=None, tot=False):\n        t = time.time()\n        if not tot:\n            elapsed = t - self.prev\n        else:\n            elapsed = t - self.stime\n        self.prev = t\n\n        if name is not None:\n            self.times[name] = elapsed\n        return elapsed\n\n\nclass ServerModelError(Exception):\n    pass\n\n\nclass CTranslate2Translator(object):\n    \"\"\"\n    This class wraps the ctranslate2.Translator object to\n    reproduce the onmt.translate.translator API.\n    \"\"\"\n\n    def __init__(self, model_path, device, device_index, batch_size,\n                 beam_size, n_best, target_prefix=False, preload=False):\n        import ctranslate2\n        self.translator = ctranslate2.Translator(\n            model_path,\n            device=device,\n            device_index=device_index,\n            inter_threads=1,\n            intra_threads=1,\n            compute_type=\"default\")\n        self.batch_size = batch_size\n        self.beam_size = beam_size\n        self.n_best = n_best\n        self.target_prefix = target_prefix\n        if preload:\n            # perform a first request to initialize everything\n            dummy_translation = self.translate([\"a\"])\n            print(\"Performed a dummy translation to initialize the model\",\n                  dummy_translation)\n            time.sleep(1)\n            self.translator.unload_model(to_cpu=True)\n\n    def translate(self, texts_to_translate, batch_size=8, tgt=None):\n        batch = [item.split(\" \") for item in texts_to_translate]\n        if tgt is not None:\n            tgt = [item.split(\" \") for item in tgt]\n        preds = self.translator.translate_batch(\n            batch,\n            target_prefix=tgt if self.target_prefix else None,\n            max_batch_size=self.batch_size,\n            beam_size=self.beam_size,\n            num_hypotheses=self.n_best\n        )\n        scores = [[item[\"score\"] for item in ex] for ex in preds]\n        predictions = [[\" \".join(item[\"tokens\"]) for item in ex]\n                       for ex in preds]\n        return scores, predictions\n\n    def to_cpu(self):\n        self.translator.unload_model(to_cpu=True)\n\n    def to_gpu(self):\n        self.translator.load_model()\n\n\nclass TranslationServer(object):\n    def __init__(self):\n        self.models = {}\n        self.next_id = 0\n\n    def start(self, config_file):\n        \"\"\"Read the config file and pre-/load the models.\"\"\"\n        self.config_file = config_file\n        with open(self.config_file) as f:\n            self.confs = json.load(f)\n\n        self.models_root = self.confs.get('models_root', './available_models')\n        for i, conf in enumerate(self.confs[\"models\"]):\n            if \"models\" not in conf:\n                if \"model\" in conf:\n                    # backwards compatibility for confs\n                    conf[\"models\"] = [conf[\"model\"]]\n                else:\n                    raise ValueError(\"\"\"Incorrect config file: missing 'models'\n                                        parameter for model #%d\"\"\" % i)\n            check_model_config(conf, self.models_root)\n            kwargs = {'timeout': conf.get('timeout', None),\n                      'load': conf.get('load', None),\n                      'preprocess_opt': conf.get('preprocess', None),\n                      'tokenizer_opt': conf.get('tokenizer', None),\n                      'postprocess_opt': conf.get('postprocess', None),\n                      'custom_opt': conf.get('custom_opt', None),\n                      'on_timeout': conf.get('on_timeout', None),\n                      'model_root': conf.get('model_root', self.models_root),\n                      'ct2_model': conf.get('ct2_model', None)\n                      }\n            kwargs = {k: v for (k, v) in kwargs.items() if v is not None}\n            model_id = conf.get(\"id\", None)\n            opt = conf[\"opt\"]\n            opt[\"models\"] = conf[\"models\"]\n            self.preload_model(opt, model_id=model_id, **kwargs)\n\n    def clone_model(self, model_id, opt, timeout=-1):\n        \"\"\"Clone a model `model_id`.\n\n        Different options may be passed. If `opt` is None, it will use the\n        same set of options\n        \"\"\"\n        if model_id in self.models:\n            if opt is None:\n                opt = self.models[model_id].user_opt\n            opt[\"models\"] = self.models[model_id].opt.models\n            return self.load_model(opt, timeout)\n        else:\n            raise ServerModelError(\"No such model '%s'\" % str(model_id))\n\n    def load_model(self, opt, model_id=None, **model_kwargs):\n        \"\"\"Load a model given a set of options\n        \"\"\"\n        model_id = self.preload_model(opt, model_id=model_id, **model_kwargs)\n        load_time = self.models[model_id].load_time\n\n        return model_id, load_time\n\n    def preload_model(self, opt, model_id=None, **model_kwargs):\n        \"\"\"Preloading the model: updating internal datastructure\n\n        It will effectively load the model if `load` is set\n        \"\"\"\n        if model_id is not None:\n            if model_id in self.models.keys():\n                raise ValueError(\"Model ID %d already exists\" % model_id)\n        else:\n            model_id = self.next_id\n            while model_id in self.models.keys():\n                model_id += 1\n            self.next_id = model_id + 1\n        print(\"Pre-loading model %d\" % model_id)\n        model = ServerModel(opt, model_id, **model_kwargs)\n        self.models[model_id] = model\n\n        return model_id\n\n    def run(self, inputs):\n        \"\"\"Translate `inputs`\n\n        We keep the same format as the Lua version i.e.\n        ``[{\"id\": model_id, \"src\": \"sequence to translate\"},{ ...}]``\n\n        We use inputs[0][\"id\"] as the model id\n        \"\"\"\n\n        model_id = inputs[0].get(\"id\", 0)\n        if model_id in self.models and self.models[model_id] is not None:\n            return self.models[model_id].run(inputs)\n        else:\n            print(\"Error No such model '%s'\" % str(model_id))\n            raise ServerModelError(\"No such model '%s'\" % str(model_id))\n\n    def unload_model(self, model_id):\n        \"\"\"Manually unload a model.\n\n        It will free the memory and cancel the timer\n        \"\"\"\n\n        if model_id in self.models and self.models[model_id] is not None:\n            self.models[model_id].unload()\n        else:\n            raise ServerModelError(\"No such model '%s'\" % str(model_id))\n\n    def list_models(self):\n        \"\"\"Return the list of available models\n        \"\"\"\n        models = []\n        for _, model in self.models.items():\n            models += [model.to_dict()]\n        return models\n\n\nclass ServerModel(object):\n    \"\"\"Wrap a model with server functionality.\n\n    Args:\n        opt (dict): Options for the Translator\n        model_id (int): Model ID\n        preprocess_opt (list): Options for preprocess processus or None\n        tokenizer_opt (dict): Options for the tokenizer or None\n        postprocess_opt (list): Options for postprocess processus or None\n        custom_opt (dict): Custom options, can be used within preprocess or\n            postprocess, default None\n        load (bool): whether to load the model during :func:`__init__()`\n        timeout (int): Seconds before running :func:`do_timeout()`\n            Negative values means no timeout\n        on_timeout (str): Options are [\"to_cpu\", \"unload\"]. Set what to do on\n            timeout (see :func:`do_timeout()`.)\n        model_root (str): Path to the model directory\n            it must contain the model and tokenizer file\n    \"\"\"\n\n    def __init__(self, opt, model_id, preprocess_opt=None, tokenizer_opt=None,\n                 postprocess_opt=None, custom_opt=None, load=False, timeout=-1,\n                 on_timeout=\"to_cpu\", model_root=\"./\", ct2_model=None):\n        self.model_root = model_root\n        self.opt = self.parse_opt(opt)\n        self.custom_opt = custom_opt\n\n        self.model_id = model_id\n        self.preprocess_opt = preprocess_opt\n        self.tokenizers_opt = tokenizer_opt\n        self.postprocess_opt = postprocess_opt\n        self.timeout = timeout\n        self.on_timeout = on_timeout\n\n        self.ct2_model = os.path.join(model_root, ct2_model) \\\n            if ct2_model is not None else None\n\n        self.unload_timer = None\n        self.user_opt = opt\n        self.tokenizers = None\n\n        if len(self.opt.log_file) > 0:\n            log_file = os.path.join(model_root, self.opt.log_file)\n        else:\n            log_file = None\n        self.logger = init_logger(log_file=log_file,\n                                  log_file_level=self.opt.log_file_level,\n                                  rotate=True)\n\n        self.loading_lock = threading.Event()\n        self.loading_lock.set()\n        self.running_lock = threading.Semaphore(value=1)\n\n        set_random_seed(self.opt.seed, self.opt.cuda)\n\n        if self.preprocess_opt is not None:\n            self.logger.info(\"Loading preprocessor\")\n            self.preprocessor = []\n\n            for function_path in self.preprocess_opt:\n                function = get_function_by_path(function_path)\n                self.preprocessor.append(function)\n\n        if self.tokenizers_opt is not None:\n            if \"src\" in self.tokenizers_opt and \"tgt\" in self.tokenizers_opt:\n                self.logger.info(\"Loading src & tgt tokenizer\")\n                self.tokenizers = {\n                    'src': self.build_tokenizer(tokenizer_opt['src']),\n                    'tgt': self.build_tokenizer(tokenizer_opt['tgt'])\n                }\n            else:\n                self.logger.info(\"Loading tokenizer\")\n                self.tokenizers_opt = {\n                    'src': tokenizer_opt,\n                    'tgt': tokenizer_opt\n                }\n                tokenizer = self.build_tokenizer(tokenizer_opt)\n                self.tokenizers = {\n                    'src': tokenizer,\n                    'tgt': tokenizer\n                }\n\n        if self.postprocess_opt is not None:\n            self.logger.info(\"Loading postprocessor\")\n            self.postprocessor = []\n\n            for function_path in self.postprocess_opt:\n                function = get_function_by_path(function_path)\n                self.postprocessor.append(function)\n\n        if load:\n            self.load(preload=True)\n            self.stop_unload_timer()\n\n    def parse_opt(self, opt):\n        \"\"\"Parse the option set passed by the user using `onmt.opts`\n\n       Args:\n           opt (dict): Options passed by the user\n\n       Returns:\n           opt (argparse.Namespace): full set of options for the Translator\n        \"\"\"\n\n        prec_argv = sys.argv\n        sys.argv = sys.argv[:1]\n        parser = ArgumentParser()\n        onmt.opts.translate_opts(parser)\n\n        models = opt['models']\n        if not isinstance(models, (list, tuple)):\n            models = [models]\n        opt['models'] = [os.path.join(self.model_root, model)\n                         for model in models]\n        opt['src'] = \"dummy_src\"\n\n        for (k, v) in opt.items():\n            if k == 'models':\n                sys.argv += ['-model']\n                sys.argv += [str(model) for model in v]\n            elif type(v) == bool:\n                sys.argv += ['-%s' % k]\n            else:\n                sys.argv += ['-%s' % k, str(v)]\n\n        opt = parser.parse_args()\n        ArgumentParser.validate_translate_opts(opt)\n        opt.cuda = opt.gpu > -1\n\n        sys.argv = prec_argv\n        return opt\n\n    @property\n    def loaded(self):\n        return hasattr(self, 'translator')\n\n    def load(self, preload=False):\n        self.loading_lock.clear()\n\n        timer = Timer()\n        self.logger.info(\"Loading model %d\" % self.model_id)\n        timer.start()\n\n        try:\n            if self.ct2_model is not None:\n                self.translator = CTranslate2Translator(\n                    self.ct2_model,\n                    device=\"cuda\" if self.opt.cuda else \"cpu\",\n                    device_index=self.opt.gpu if self.opt.cuda else 0,\n                    batch_size=self.opt.batch_size,\n                    beam_size=self.opt.beam_size,\n                    n_best=self.opt.n_best,\n                    target_prefix=self.opt.tgt_prefix,\n                    preload=preload)\n            else:\n                self.translator = build_translator(\n                    self.opt, report_score=False,\n                    out_file=codecs.open(os.devnull, \"w\", \"utf-8\"))\n        except RuntimeError as e:\n            raise ServerModelError(\"Runtime Error: %s\" % str(e))\n\n        timer.tick(\"model_loading\")\n        self.load_time = timer.tick()\n        self.reset_unload_timer()\n        self.loading_lock.set()\n\n    @critical\n    def run(self, inputs):\n        \"\"\"Translate `inputs` using this model\n\n        Args:\n            inputs (List[dict[str, str]]): [{\"src\": \"...\"},{\"src\": ...}]\n\n        Returns:\n            result (list): translations\n            times (dict): containing times\n        \"\"\"\n\n        self.stop_unload_timer()\n\n        timer = Timer()\n        timer.start()\n\n        self.logger.info(\"Running translation using %d\" % self.model_id)\n\n        if not self.loading_lock.is_set():\n            self.logger.info(\n                \"Model #%d is being loaded by another thread, waiting\"\n                % self.model_id)\n            if not self.loading_lock.wait(timeout=30):\n                raise ServerModelError(\"Model %d loading timeout\"\n                                       % self.model_id)\n\n        else:\n            if not self.loaded:\n                self.load()\n                timer.tick(name=\"load\")\n            elif self.opt.cuda:\n                self.to_gpu()\n                timer.tick(name=\"to_gpu\")\n\n        texts = []\n        head_spaces = []\n        tail_spaces = []\n        all_preprocessed = []\n        for i, inp in enumerate(inputs):\n            src = inp['src']\n            whitespaces_before, whitespaces_after = \"\", \"\"\n            match_before = re.search(r'^\\s+', src)\n            match_after = re.search(r'\\s+$', src)\n            if match_before is not None:\n                whitespaces_before = match_before.group(0)\n            if match_after is not None:\n                whitespaces_after = match_after.group(0)\n            head_spaces.append(whitespaces_before)\n            # every segment becomes a dict for flexibility purposes\n            seg_dict = self.maybe_preprocess(inp)\n            all_preprocessed.append(seg_dict)\n            for seg, ref in zip_longest(seg_dict[\"seg\"], seg_dict[\"ref\"]):\n                tok = self.maybe_tokenize(seg)\n                if ref is not None:\n                    ref = self.maybe_tokenize(ref, side='tgt')\n                texts.append((tok, ref))\n            tail_spaces.append(whitespaces_after)\n\n        empty_indices = []\n        texts_to_translate, texts_ref = [], []\n        for i, (tok, ref_tok) in enumerate(texts):\n            if tok == \"\":\n                empty_indices.append(i)\n            else:\n                texts_to_translate.append(tok)\n                texts_ref.append(ref_tok)\n        if any([item is None for item in texts_ref]):\n            texts_ref = None\n\n        scores = []\n        predictions = []\n\n        if len(texts_to_translate) > 0:\n            try:\n                scores, predictions = self.translator.translate(\n                    texts_to_translate,\n                    tgt=texts_ref,\n                    batch_size=len(texts_to_translate)\n                    if self.opt.batch_size == 0\n                    else self.opt.batch_size)\n            except (RuntimeError, Exception) as e:\n                err = \"Error: %s\" % str(e)\n                self.logger.error(err)\n                self.logger.error(\"repr(text_to_translate): \"\n                                  + repr(texts_to_translate))\n                self.logger.error(\"model: #%s\" % self.model_id)\n                self.logger.error(\"model opt: \" + str(self.opt.__dict__))\n                self.logger.error(traceback.format_exc())\n\n                raise ServerModelError(err)\n\n        timer.tick(name=\"translation\")\n        self.logger.info(\"\"\"Using model #%d\\t%d inputs\n               \\ttranslation time: %f\"\"\" % (self.model_id, len(texts),\n                                            timer.times['translation']))\n        self.reset_unload_timer()\n\n        # NOTE: translator returns lists of `n_best` list\n        def flatten_list(_list): return sum(_list, [])\n        tiled_texts = [t for t in texts_to_translate\n                       for _ in range(self.opt.n_best)]\n        results = flatten_list(predictions)\n\n        def maybe_item(x): return x.item() if type(x) is torch.Tensor else x\n        scores = [maybe_item(score_tensor)\n                  for score_tensor in flatten_list(scores)]\n\n        results = [self.maybe_detokenize_with_align(result, src)\n                   for result, src in zip(results, tiled_texts)]\n\n        aligns = [align for _, align in results]\n        results = [tokens for tokens, _ in results]\n\n        # build back results with empty texts\n        for i in empty_indices:\n            j = i * self.opt.n_best\n            results = results[:j] + [\"\"] * self.opt.n_best + results[j:]\n            aligns = aligns[:j] + [None] * self.opt.n_best + aligns[j:]\n            scores = scores[:j] + [0] * self.opt.n_best + scores[j:]\n\n        rebuilt_segs, scores, aligns = self.rebuild_seg_packages(\n            all_preprocessed, results, scores, aligns, self.opt.n_best)\n\n        results = [self.maybe_postprocess(seg) for seg in rebuilt_segs]\n\n        head_spaces = [h for h in head_spaces for i in range(self.opt.n_best)]\n        tail_spaces = [h for h in tail_spaces for i in range(self.opt.n_best)]\n        results = [\"\".join(items)\n                   for items in zip(head_spaces, results, tail_spaces)]\n\n        self.logger.info(\"Translation Results: %d\", len(results))\n\n        return results, scores, self.opt.n_best, timer.times, aligns\n\n    def rebuild_seg_packages(self, all_preprocessed, results,\n                             scores, aligns, n_best):\n        \"\"\"\n        Rebuild proper segment packages based on initial n_seg.\n        \"\"\"\n        offset = 0\n        rebuilt_segs = []\n        avg_scores = []\n        merged_aligns = []\n        for i, seg_dict in enumerate(all_preprocessed):\n            n_seg = seg_dict[\"n_seg\"]\n            sub_results = results[n_best * offset: (offset + n_seg) * n_best]\n            sub_scores = scores[n_best * offset: (offset + n_seg) * n_best]\n            sub_aligns = aligns[n_best * offset: (offset + n_seg) * n_best]\n            for j in range(n_best):\n                _seg_dict = deepcopy(seg_dict)\n                _seg_dict[\"seg\"] = list(islice(sub_results, j, None, n_best))\n                rebuilt_segs.append(_seg_dict)\n                sub_sub_scores = list(islice(sub_scores, j, None, n_best))\n                avg_score = sum(sub_sub_scores)/n_seg if n_seg != 0 else 0\n                avg_scores.append(avg_score)\n                sub_sub_aligns = list(islice(sub_aligns, j, None, n_best))\n                merged_aligns.append(sub_sub_aligns)\n            offset += n_seg\n        return rebuilt_segs, avg_scores, merged_aligns\n\n    def do_timeout(self):\n        \"\"\"Timeout function that frees GPU memory.\n\n        Moves the model to CPU or unloads it; depending on\n        attr`self.on_timemout` value\n        \"\"\"\n\n        if self.on_timeout == \"unload\":\n            self.logger.info(\"Timeout: unloading model %d\" % self.model_id)\n            self.unload()\n        if self.on_timeout == \"to_cpu\":\n            self.logger.info(\"Timeout: sending model %d to CPU\"\n                             % self.model_id)\n            self.to_cpu()\n\n    @critical\n    def unload(self):\n        self.logger.info(\"Unloading model %d\" % self.model_id)\n        del self.translator\n        if self.opt.cuda:\n            torch.cuda.empty_cache()\n        self.stop_unload_timer()\n        self.unload_timer = None\n\n    def stop_unload_timer(self):\n        if self.unload_timer is not None:\n            self.unload_timer.cancel()\n\n    def reset_unload_timer(self):\n        if self.timeout < 0:\n            return\n\n        self.stop_unload_timer()\n        self.unload_timer = threading.Timer(self.timeout, self.do_timeout)\n        self.unload_timer.start()\n\n    def to_dict(self):\n        hide_opt = [\"models\", \"src\"]\n        d = {\"model_id\": self.model_id,\n             \"opt\": {k: self.user_opt[k] for k in self.user_opt.keys()\n                     if k not in hide_opt},\n             \"models\": self.user_opt[\"models\"],\n             \"loaded\": self.loaded,\n             \"timeout\": self.timeout,\n             }\n        if self.tokenizers_opt is not None:\n            d[\"tokenizer\"] = self.tokenizers_opt\n        return d\n\n    @critical\n    def to_cpu(self):\n        \"\"\"Move the model to CPU and clear CUDA cache.\"\"\"\n        if type(self.translator) == CTranslate2Translator:\n            self.translator.to_cpu()\n        else:\n            self.translator.model.cpu()\n            if self.opt.cuda:\n                torch.cuda.empty_cache()\n\n    def to_gpu(self):\n        \"\"\"Move the model to GPU.\"\"\"\n        if type(self.translator) == CTranslate2Translator:\n            self.translator.to_gpu()\n        else:\n            torch.cuda.set_device(self.opt.gpu)\n            self.translator.model.cuda()\n\n    def maybe_preprocess(self, sequence):\n        \"\"\"Preprocess the sequence (or not)\n\n        \"\"\"\n        if sequence.get(\"src\", None) is not None:\n            sequence = deepcopy(sequence)\n            sequence[\"seg\"] = [sequence[\"src\"].strip()]\n            sequence.pop(\"src\")\n            sequence[\"ref\"] = [sequence.get('ref', None)]\n            sequence[\"n_seg\"] = 1\n        if self.preprocess_opt is not None:\n            return self.preprocess(sequence)\n        return sequence\n\n    def preprocess(self, sequence):\n        \"\"\"Preprocess a single sequence.\n\n        Args:\n            sequence (str): The sequence to preprocess.\n\n        Returns:\n            sequence (str): The preprocessed sequence.\n        \"\"\"\n        if self.preprocessor is None:\n            raise ValueError(\"No preprocessor loaded\")\n        for function in self.preprocessor:\n            sequence = function(sequence, self)\n        return sequence\n\n    def build_tokenizer(self, tokenizer_opt):\n        \"\"\"Build tokenizer described by `tokenizer_opt`.\"\"\"\n        if \"type\" not in tokenizer_opt:\n            raise ValueError(\n                \"Missing mandatory tokenizer option 'type'\")\n\n        if tokenizer_opt['type'] == 'sentencepiece':\n            if \"model\" not in tokenizer_opt:\n                raise ValueError(\n                    \"Missing mandatory tokenizer option 'model'\")\n            import sentencepiece as spm\n            tokenizer = spm.SentencePieceProcessor()\n            model_path = os.path.join(self.model_root,\n                                      tokenizer_opt['model'])\n            tokenizer.Load(model_path)\n        elif tokenizer_opt['type'] == 'pyonmttok':\n            if \"params\" not in tokenizer_opt:\n                raise ValueError(\n                    \"Missing mandatory tokenizer option 'params'\")\n            import pyonmttok\n            if tokenizer_opt[\"mode\"] is not None:\n                mode = tokenizer_opt[\"mode\"]\n            else:\n                mode = None\n            # load can be called multiple times: modify copy\n            tokenizer_params = dict(tokenizer_opt[\"params\"])\n            for key, value in tokenizer_opt[\"params\"].items():\n                if key.endswith(\"path\"):\n                    tokenizer_params[key] = os.path.join(\n                        self.model_root, value)\n            tokenizer = pyonmttok.Tokenizer(mode,\n                                            **tokenizer_params)\n        else:\n            raise ValueError(\"Invalid value for tokenizer type\")\n        return tokenizer\n\n    def maybe_tokenize(self, sequence, side='src'):\n        \"\"\"Tokenize the sequence (or not).\n\n        Same args/returns as `tokenize`\n        \"\"\"\n\n        if self.tokenizers_opt is not None:\n            return self.tokenize(sequence, side)\n        return sequence\n\n    def tokenize(self, sequence, side='src'):\n        \"\"\"Tokenize a single sequence.\n\n        Args:\n            sequence (str): The sequence to tokenize.\n\n        Returns:\n            tok (str): The tokenized sequence.\n        \"\"\"\n\n        if self.tokenizers is None:\n            raise ValueError(\"No tokenizer loaded\")\n\n        if self.tokenizers_opt[side][\"type\"] == \"sentencepiece\":\n            tok = self.tokenizers[side].EncodeAsPieces(sequence)\n            tok = \" \".join(tok)\n        elif self.tokenizers_opt[side][\"type\"] == \"pyonmttok\":\n            tok, _ = self.tokenizers[side].tokenize(sequence)\n            tok = \" \".join(tok)\n        return tok\n\n    def tokenizer_marker(self, side='src'):\n        \"\"\"Return marker used in `side` tokenizer.\"\"\"\n        marker = None\n        if self.tokenizers_opt is not None:\n            tokenizer_type = self.tokenizers_opt[side].get('type', None)\n            if tokenizer_type == \"pyonmttok\":\n                params = self.tokenizers_opt[side].get('params', None)\n                if params is not None:\n                    if params.get(\"joiner_annotate\", None) is not None:\n                        marker = 'joiner'\n                    elif params.get(\"spacer_annotate\", None) is not None:\n                        marker = 'spacer'\n            elif tokenizer_type == \"sentencepiece\":\n                marker = 'spacer'\n        return marker\n\n    def maybe_detokenize_with_align(self, sequence, src, side='tgt'):\n        \"\"\"De-tokenize (or not) the sequence (with alignment).\n\n        Args:\n            sequence (str): The sequence to detokenize, possible with\n                alignment seperate by ` ||| `.\n\n        Returns:\n            sequence (str): The detokenized sequence.\n            align (str): The alignment correspand to detokenized src/tgt\n                sorted or None if no alignment in output.\n        \"\"\"\n        align = None\n        if self.opt.report_align:\n            # output contain alignment\n            sequence, align = sequence.split(DefaultTokens.ALIGNMENT_SEPARATOR)\n            if align != '':\n                align = self.maybe_convert_align(src, sequence, align)\n        sequence = self.maybe_detokenize(sequence, side)\n        return (sequence, align)\n\n    def maybe_detokenize(self, sequence, side='tgt'):\n        \"\"\"De-tokenize the sequence (or not)\n\n        Same args/returns as :func:`tokenize()`\n        \"\"\"\n\n        if self.tokenizers_opt is not None and ''.join(sequence.split()) != '':\n            return self.detokenize(sequence, side)\n        return sequence\n\n    def detokenize(self, sequence, side='tgt'):\n        \"\"\"Detokenize a single sequence\n\n        Same args/returns as :func:`tokenize()`\n        \"\"\"\n\n        if self.tokenizers is None:\n            raise ValueError(\"No tokenizer loaded\")\n\n        if self.tokenizers_opt[side][\"type\"] == \"sentencepiece\":\n            detok = self.tokenizers[side].DecodePieces(sequence.split())\n        elif self.tokenizers_opt[side][\"type\"] == \"pyonmttok\":\n            detok = self.tokenizers[side].detokenize(sequence.split())\n\n        return detok\n\n    def maybe_convert_align(self, src, tgt, align):\n        \"\"\"Convert alignment to match detokenized src/tgt (or not).\n\n        Args:\n            src (str): The tokenized source sequence.\n            tgt (str): The tokenized target sequence.\n            align (str): The alignment correspand to src/tgt pair.\n\n        Returns:\n            align (str): The alignment correspand to detokenized src/tgt.\n        \"\"\"\n        if self.tokenizers_opt is not None:\n            src_marker = self.tokenizer_marker(side='src')\n            tgt_marker = self.tokenizer_marker(side='tgt')\n            if src_marker is None or tgt_marker is None:\n                raise ValueError(\"To get decoded alignment, joiner/spacer \"\n                                 \"should be used in both side's tokenizer.\")\n            elif ''.join(tgt.split()) != '':\n                align = to_word_align(src, tgt, align, src_marker, tgt_marker)\n        return align\n\n    def maybe_postprocess(self, sequence):\n        \"\"\"Postprocess the sequence (or not)\n\n        \"\"\"\n        if self.postprocess_opt is not None:\n            return self.postprocess(sequence)\n        else:\n            return sequence[\"seg\"][0]\n\n    def postprocess(self, sequence):\n        \"\"\"Preprocess a single sequence.\n\n        Args:\n            sequence (str): The sequence to process.\n\n        Returns:\n            sequence (str): The postprocessed sequence.\n        \"\"\"\n        if self.postprocessor is None:\n            raise ValueError(\"No postprocessor loaded\")\n        for function in self.postprocessor:\n            sequence = function(sequence, self)\n        return sequence\n\n\ndef get_function_by_path(path, args=[], kwargs={}):\n    module_name = \".\".join(path.split(\".\")[:-1])\n    function_name = path.split(\".\")[-1]\n    try:\n        module = importlib.import_module(module_name)\n    except ValueError as e:\n        print(\"Cannot import module '%s'\" % module_name)\n        raise e\n    function = getattr(module, function_name)\n    return function\n"
  },
  {
    "path": "onmt/translate/translator.py",
    "content": "#!/usr/bin/env python\n\"\"\" Translator Class and builder \"\"\"\nfrom __future__ import print_function\nimport codecs\nimport json\nimport os\nimport time\nimport numpy as np\nfrom itertools import count, zip_longest\n\nimport torch\nimport tqdm\n\nfrom onmt.bin.train import prepare_fields_transforms\nfrom onmt.constants import DefaultTokens\nimport onmt.model_builder\nimport onmt.inputters as inputters\nimport onmt.decoders.ensemble\n\nfrom onmt.inputters import KeyphraseDataset\nfrom onmt.decoders import BARTDecoder\nfrom onmt.encoders import PretrainedEncoder, BARTEncoder\nfrom onmt.inputters.inputter import IterOnDevice\nfrom onmt.keyphrase.eval import eval_and_print\nfrom onmt.train_single import _build_valid_iter, _build_iter_given_examples\n\nfrom onmt.translate.beam_search import BeamSearch, BeamSearchLM\nfrom onmt.translate.greedy_search import GreedySearch, GreedySearchLM\nfrom onmt.utils.misc import tile, set_random_seed, report_matrix\nfrom onmt.utils.alignment import extract_alignment, build_align_pharaoh\nfrom onmt.modules.copy_generator import collapse_copy_scores\nfrom onmt.constants import ModelTask\n\n\ndef build_translator(opt, report_score=True, logger=None, out_file=None):\n    if out_file is None and opt.data_type != 'keyphrase':\n        out_file = codecs.open(opt.output, 'w+', 'utf-8')\n\n    load_test_model = (\n        onmt.decoders.ensemble.load_test_model\n        if len(opt.models) > 1\n        else onmt.model_builder.load_test_model\n    )\n    fields, model, model_opt = load_test_model(opt)\n\n    # (deprecated after dynamic data loading) added by @memray, ignore alignment field during testing for keyphrase task\n    # if opt.data_type == 'keyphrase' and 'alignment' in fields:\n    #     del fields['alignment']\n\n    scorer = onmt.translate.GNMTGlobalScorer.from_opt(opt)\n\n    if model_opt.model_task == ModelTask.LANGUAGE_MODEL:\n        translator = GeneratorLM.from_opt(\n            model,\n            fields,\n            opt,\n            model_opt,\n            global_scorer=scorer,\n            out_file=out_file,\n            report_align=opt.report_align,\n            report_score=report_score,\n            logger=logger,\n        )\n    else:\n        translator = Translator.from_opt(\n            model,\n            fields,\n            opt,\n            model_opt,\n            global_scorer=scorer,\n            out_file=out_file,\n            report_align=opt.report_align,\n            report_score=report_score,\n            logger=logger,\n        )\n    return translator\n\n\ndef max_tok_len(new, count, sofar):\n    \"\"\"\n    In token batching scheme, the number of sequences is limited\n    such that the total number of src/tgt tokens (including padding)\n    in a batch <= batch_size\n    \"\"\"\n    # Maintains the longest src and tgt length in the current batch\n    global max_src_in_batch  # this is a hack\n    # Reset current longest length at a new batch (count=1)\n    if count == 1:\n        max_src_in_batch = 0\n        # max_tgt_in_batch = 0\n    # Src: [<bos> w1 ... wN <eos>]\n    max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2)\n    # Tgt: [w1 ... wM <eos>]\n    src_elements = count * max_src_in_batch\n    return src_elements\n\n\nclass Inference(object):\n    \"\"\"Translate a batch of sentences with a saved model.\n\n    Args:\n        model (onmt.modules.NMTModel): NMT model to use for translation\n        fields (dict[str, torchtext.data.Field]): A dict\n            mapping each side to its list of name-Field pairs.\n        src_reader (onmt.inputters.DataReaderBase): Source reader.\n        tgt_reader (onmt.inputters.TextDataReader): Target reader.\n        gpu (int): GPU device. Set to negative for no GPU.\n        n_best (int): How many beams to wait for.\n        min_length (int): See\n            :class:`onmt.translate.decode_strategy.DecodeStrategy`.\n        max_length (int): See\n            :class:`onmt.translate.decode_strategy.DecodeStrategy`.\n        beam_size (int): Number of beams.\n        random_sampling_topk (int): See\n            :class:`onmt.translate.greedy_search.GreedySearch`.\n        random_sampling_temp (int): See\n            :class:`onmt.translate.greedy_search.GreedySearch`.\n        stepwise_penalty (bool): Whether coverage penalty is applied every step\n            or not.\n        dump_beam (bool): Debugging option.\n        block_ngram_repeat (int): See\n            :class:`onmt.translate.decode_strategy.DecodeStrategy`.\n        ignore_when_blocking (set or frozenset): See\n            :class:`onmt.translate.decode_strategy.DecodeStrategy`.\n        replace_unk (bool): Replace unknown token.\n        tgt_prefix (bool): Force the predictions begin with provided -tgt.\n        data_type (str): Source data type.\n        verbose (bool): Print/log every translation.\n        report_time (bool): Print/log total time/frequency.\n        copy_attn (bool): Use copy attention.\n        global_scorer (onmt.translate.GNMTGlobalScorer): Translation\n            scoring/reranking object.\n        out_file (TextIO or codecs.StreamReaderWriter): Output file.\n        report_score (bool) : Whether to report scores\n        logger (logging.Logger or NoneType): Logger.\n    \"\"\"\n\n    def __init__(\n        self,\n        model,\n        fields,\n        src_reader,\n        tgt_reader,\n        gpu=-1,\n        n_best=1,\n        min_length=0,\n        max_length=100,\n        ratio=0.0,\n        beam_size=30,\n        random_sampling_topk=1,\n        random_sampling_temp=1,\n        stepwise_penalty=None,\n        dump_beam=False,\n        block_ngram_repeat=0,\n        ignore_when_blocking=frozenset(),\n        replace_unk=False,\n        tgt_prefix=False,\n        phrase_table=\"\",\n        data_type=\"text\",\n        verbose=False,\n        report_time=False,\n        copy_attn=False,\n        global_scorer=None,\n        out_file=None,\n        report_align=False,\n        report_score=True,\n        logger=None,\n        seed=-1,\n        kp_concat_type=None,\n        model_kp_concat_type=None,\n        beam_terminate=None,\n        # **kwargs\n        opt=None,\n        model_opt=None\n    ):\n        self.model = model\n        self.fields = fields\n        tgt_field = dict(self.fields)[\"tgt\"]\n        if hasattr(tgt_field, 'base_field'):\n            tgt_field = tgt_field.base_field\n        self._tgt_vocab = tgt_field.vocab\n        self._tgt_eos_idx = self._tgt_vocab.stoi[tgt_field.eos_token]\n        self._tgt_pad_idx = self._tgt_vocab.stoi[tgt_field.pad_token]\n        self._tgt_bos_idx = self._tgt_vocab.stoi[tgt_field.init_token]\n        self._tgt_unk_idx = self._tgt_vocab.stoi[tgt_field.unk_token]\n        self._tgt_vocab_len = len(self._tgt_vocab)\n\n        self._gpu = gpu\n        self._use_cuda = gpu > -1\n        self._dev = (\n            torch.device(\"cuda\", self._gpu)\n            if self._use_cuda\n            else torch.device(\"cpu\")\n        )\n\n        self.n_best = n_best\n        self.max_length = max_length\n\n        self.beam_size = beam_size\n        self.random_sampling_temp = random_sampling_temp\n        self.sample_from_topk = random_sampling_topk\n\n        self.min_length = min_length\n        self.ratio = ratio\n        self.stepwise_penalty = stepwise_penalty\n        self.dump_beam = dump_beam\n        self.block_ngram_repeat = block_ngram_repeat\n        self.ignore_when_blocking = ignore_when_blocking\n        self._exclusion_idxs = {\n            self._tgt_vocab.stoi[t] for t in self.ignore_when_blocking\n        }\n        self.src_reader = src_reader\n        self.tgt_reader = tgt_reader\n        self.replace_unk = replace_unk\n        if self.replace_unk and not self.model.decoder.attentional:\n            raise ValueError(\"replace_unk requires an attentional decoder.\")\n        self.tgt_prefix = tgt_prefix\n        self.phrase_table = phrase_table\n        self.data_type = data_type\n        self.verbose = verbose\n        self.report_time = report_time\n\n        self.copy_attn = copy_attn\n\n        self.global_scorer = global_scorer\n        if (\n            self.global_scorer.has_cov_pen\n            and not self.model.decoder.attentional\n        ):\n            raise ValueError(\n                \"Coverage penalty requires an attentional decoder.\"\n            )\n        self.out_file = out_file\n        self.report_align = report_align\n        self.report_score = report_score\n        self.logger = logger\n\n        self.use_filter_pred = False\n        self._filter_pred = None\n\n        # added by @memray, to accommodate multiple targets\n        self.kp_concat_type = kp_concat_type\n        self.model_kp_concat_type = model_kp_concat_type\n        # beam search termination condition\n        self.beam_terminate = beam_terminate\n        self.opt = opt\n        self.model_opt = model_opt\n\n        # for debugging\n        self.beam_trace = self.dump_beam != \"\"\n        self.beam_accum = None\n        if self.beam_trace:\n            self.beam_accum = {\n                \"predicted_ids\": [],\n                \"beam_parent_ids\": [],\n                \"scores\": [],\n                \"log_probs\": [],\n            }\n\n        set_random_seed(seed, self._use_cuda)\n\n    @classmethod\n    def from_opt(\n        cls,\n        model,\n        fields,\n        opt,\n        model_opt,\n        global_scorer=None,\n        out_file=None,\n        report_align=False,\n        report_score=True,\n        logger=None,\n    ):\n        \"\"\"Alternate constructor.\n\n        Args:\n            model (onmt.modules.NMTModel): See :func:`__init__()`.\n            fields (dict[str, torchtext.data.Field]): See\n                :func:`__init__()`.\n            opt (argparse.Namespace): Command line options\n            model_opt (argparse.Namespace): Command line options saved with\n                the model checkpoint.\n            global_scorer (onmt.translate.GNMTGlobalScorer): See\n                :func:`__init__()`..\n            out_file (TextIO or codecs.StreamReaderWriter): See\n                :func:`__init__()`.\n            report_align (bool) : See :func:`__init__()`.\n            report_score (bool) : See :func:`__init__()`.\n            logger (logging.Logger or NoneType): See :func:`__init__()`.\n        \"\"\"\n        # TODO: maybe add dynamic part\n        cls.validate_task(model_opt.model_task)\n\n        src_reader = inputters.str2reader[opt.data_type].from_opt(opt)\n        # @memray a different tgt_reader for keyphrase\n        if opt.data_type == 'keyphrase':\n            data_type = 'keyphrase'\n        else:\n            data_type = 'text'\n        tgt_reader = inputters.str2reader[data_type].from_opt(opt)\n        return cls(\n            model,\n            fields,\n            src_reader,\n            tgt_reader,\n            gpu=opt.gpu,\n            n_best=opt.n_best,\n            min_length=opt.min_length,\n            max_length=opt.max_length,\n            ratio=opt.ratio,\n            beam_size=opt.beam_size,\n            random_sampling_topk=opt.random_sampling_topk,\n            random_sampling_temp=opt.random_sampling_temp,\n            stepwise_penalty=opt.stepwise_penalty,\n            dump_beam=opt.dump_beam,\n            block_ngram_repeat=opt.block_ngram_repeat,\n            ignore_when_blocking=set(opt.ignore_when_blocking),\n            replace_unk=opt.replace_unk,\n            tgt_prefix=opt.tgt_prefix,\n            phrase_table=opt.phrase_table,\n            data_type=opt.data_type,\n            verbose=opt.verbose,\n            report_time=opt.report_time,\n            copy_attn=model_opt.copy_attn,\n            global_scorer=global_scorer,\n            out_file=out_file,\n            report_align=report_align,\n            report_score=report_score,\n            logger=logger,\n            seed=opt.seed,\n            kp_concat_type=opt.kp_concat_type,\n            model_kp_concat_type=model_opt.kp_concat_type,\n            beam_terminate=opt.beam_terminate,\n            opt=opt,\n            model_opt=model_opt\n        )\n\n    def _log(self, msg):\n        if self.logger:\n            self.logger.info(msg)\n        else:\n            print(msg)\n\n    def _gold_score(\n        self,\n        batch,\n        memory_bank,\n        src_lengths,\n        src_vocabs,\n        use_src_map,\n        enc_states,\n        batch_size,\n        src,\n        encoder_output=None\n    ):\n        if \"tgt\" in batch.__dict__:\n            gs = self._score_target(\n                batch,\n                memory_bank,\n                src_lengths,\n                src_vocabs,\n                batch.src_map if use_src_map else None,\n                encoder_output=encoder_output)\n            self.model.decoder.init_state(src, memory_bank, enc_states)\n        else:\n            gs = [0] * batch_size\n        return gs\n\n    def translate(\n        self,\n        src,\n        tgt=None,\n        batch_size=32,\n        batch_type=\"sents\",\n        attn_debug=False,\n        align_debug=False,\n        phrase_table=\"\",\n        opt=None\n    ):\n        \"\"\"Translate content of ``src`` and get gold scores from ``tgt``.\n\n        Args:\n            src: See :func:`self.src_reader.read()`.\n            tgt: See :func:`self.tgt_reader.read()`.\n            batch_size (int): size of examples per mini-batch\n            attn_debug (bool): enables the attention logging\n            align_debug (bool): enables the word alignment logging\n\n        Returns:\n            (`list`, `list`)\n\n            * all_scores is a list of `batch_size` lists of `n_best` scores\n            * all_predictions is a list of `batch_size` lists\n                of `n_best` predictions\n        \"\"\"\n        if batch_size is None:\n            raise ValueError(\"batch_size must be set\")\n\n        if self.tgt_prefix and tgt is None:\n            raise ValueError(\"Prefix should be feed to tgt if -tgt_prefix.\")\n\n        if self.data_type == 'text':\n            use_dynamic_transform = False\n            # original testing data pipeline\n            src_data = {\"reader\": self.src_reader, \"data\": src}\n            tgt_data = {\"reader\": self.tgt_reader, \"data\": tgt}\n            _readers, _data = inputters.Dataset.config(\n                [(\"src\", src_data), (\"tgt\", tgt_data)]\n            )\n\n            # modified by @memray to accommodate keyphrase\n            data = inputters.str2dataset[self.data_type](\n                self.fields,\n                readers=_readers,\n                data=_data,\n                sort_key=inputters.str2sortkey[self.data_type],\n                filter_pred=self._filter_pred,\n                data_format=opt.data_format,\n                tgt_concat_type=opt.kp_concat_type\n            )\n\n            # @memray, as Dataset is only instantiated here, having to use this plugin setter\n            if isinstance(data, KeyphraseDataset):\n                data.kp_concat_type=self.kp_concat_type\n\n            data_iter = inputters.OrderedIterator(\n                dataset=data,\n                device=self._dev,\n                batch_size=batch_size,\n                batch_size_fn=max_tok_len if batch_type == \"tokens\" else None,\n                train=False,\n                sort=False,\n                sort_within_batch=False, #@memray: set False to keep the original order\n                shuffle=False\n            )\n            src_vocabs = data.src_vocabs\n            has_tgt = True if tgt is not None else False\n        elif self.data_type == 'keyphrase':\n            # dynamic data pipeline, lowercase must be inherited from model_opt\n            use_dynamic_transform = True\n            if hasattr(self.model_opt, 'lowercase'):\n                setattr(opt, 'lowercase', self.model_opt.lowercase)\n                setattr(self.opt, 'lowercase', self.model_opt.lowercase)\n\n            _, transforms_cls = prepare_fields_transforms(opt)\n            _data_iter = _build_iter_given_examples(src, opt, self.fields, transforms_cls, is_train=False)\n            data_iter = IterOnDevice(_data_iter, device_id=self._gpu)\n            data = src\n            # src_vocabs will be used in collapse_copy_scores and Translator.py\n            src_vocabs = None\n            has_tgt = True if tgt is not None else False\n        else:\n            raise NotImplementedError('Currently only support data type=text/keyphrase.')\n\n        xlation_builder = onmt.translate.TranslationBuilder(\n            data,\n            self.fields,\n            self.n_best,\n            self.replace_unk,\n            has_tgt,\n            self.phrase_table,\n            use_dynamic_transform=use_dynamic_transform\n        )\n        # Statistics\n        counter = count(1)\n        pred_score_total, pred_words_total = 0, 0\n        gold_score_total, gold_words_total = 0, 0\n\n        all_scores = []\n        all_predictions = []\n\n        start_time = time.time()\n\n        num_examples = 0\n        for batch_idx, batch in tqdm.tqdm(enumerate(data_iter), desc='Translating in batches'):\n            _batch_size = batch.batch_size\n            num_examples += _batch_size\n\n            # if batch_idx > 0:\n            #     break\n\n            # @memray reshaping for dynamic preprocessing and keyphrase dataset\n            if self.data_type == 'keyphrase':\n                src, _ = (batch.src if isinstance(batch.src, tuple) else (batch.src, None))\n                # for compatibility with previous versions, make src/tgt's dim to 3\n                if len(src.shape) == 2:\n                    src = src.unsqueeze(2)\n                    tgt = batch.tgt.unsqueeze(2)\n                    # required in generator\n                    batch.src = src\n                    batch.tgt = tgt\n            # Output of dynamic batching is src.shape=[batch_size, length, num_feat]\n            #   but OpenNMT expects [length, batch_size, num_feat]\n            if batch.src[0].shape[0] == _batch_size:\n                if isinstance(batch.src, tuple):\n                    batch.src = (batch.src[0].permute([1, 0, 2]), batch.src[1])\n                else:\n                    batch.src = batch.src.permute([1, 0, 2])  # [src_len, B, 1]\n                batch.tgt = batch.tgt.permute([1, 0, 2])  # [tgt_len, B, 1]\n\n            batch_data = self.translate_batch(\n                batch, src_vocabs, attn_debug\n            )\n            translations = xlation_builder.from_batch(batch_data)\n\n            # @memray\n            if self.data_type == \"keyphrase\":\n                # post-process for one2seq outputs, split seq into individual phrases\n                if self.model_kp_concat_type != 'one2one':\n                    translations = self.segment_one2seq_trans(translations)\n                # add statistics of kps(pred_num, beamstep_num etc.)\n                translations = self.add_trans_stats(translations, self.kp_concat_type)\n\n                # add copied flag\n                if hasattr(self.fields['src'], 'base_field'):\n                    vocab_size = len(self.fields['src'].base_field.vocab.itos)\n                else:\n                    vocab_size = len(self.fields['src'].vocab.itos)\n                for t in translations:\n                    t.add_copied_flags(vocab_size)\n\n            for tid, trans in enumerate(translations):\n                all_scores += [trans.pred_scores[: self.n_best]]\n                pred_score_total += trans.pred_scores[0]\n                pred_words_total += len(trans.pred_sents[0])\n                if tgt is not None:\n                    gold_score_total += trans.gold_score\n                    gold_words_total += len(trans.gold_sent) + 1\n\n                n_best_preds = [\n                    \" \".join(pred) for pred in trans.pred_sents[: self.n_best]\n                ]\n                if self.report_align:\n                    align_pharaohs = [\n                        build_align_pharaoh(align)\n                        for align in trans.word_aligns[: self.n_best]\n                    ]\n                    n_best_preds_align = [\n                        \" \".join(align) for align in align_pharaohs\n                    ]\n                    n_best_preds = [\n                        pred + DefaultTokens.ALIGNMENT_SEPARATOR + align\n                        for pred, align in zip(\n                            n_best_preds, n_best_preds_align\n                        )\n                    ]\n                all_predictions += [n_best_preds]\n\n                if self.out_file:\n                    import json\n                    if self.data_type == \"keyphrase\":\n                        self.out_file.write(json.dumps(trans.__dict__()) + '\\n')\n                        self.out_file.flush()\n                    else:\n                        self.out_file.write('\\n'.join(n_best_preds) + '\\n')\n                        self.out_file.flush()\n\n                if self.verbose:\n                    sent_number = next(counter)\n                    if self.data_type == \"keyphrase\":\n                        # output = trans.log_kp(sent_number)\n                        src_text = data[trans.index]['src_str']\n                        tgt_list = data[trans.index]['tgt_str'].split('<sep>')\n                        output = eval_and_print(src_text, tgt_kps=tgt_list, pred_kps=n_best_preds, pred_scores=trans.pred_scores[: self.n_best])\n                    else:\n                        output = trans.log(sent_number)\n\n                    if self.logger:\n                        self.logger.info(output)\n                    else:\n                        os.write(1, output.encode(\"utf-8\"))\n\n                if attn_debug:\n                    preds = trans.pred_sents[0]\n                    preds.append(DefaultTokens.EOS)\n                    attns = trans.attns[0].tolist()\n                    if self.data_type == \"text\":\n                        srcs = trans.src_raw\n                    else:\n                        srcs = [str(item) for item in range(len(attns[0]))]\n                    output = report_matrix(srcs, preds, attns)\n                    if self.logger:\n                        self.logger.info(output)\n                    else:\n                        os.write(1, output.encode(\"utf-8\"))\n\n                if align_debug:\n                    tgts = trans.pred_sents[0]\n                    align = trans.word_aligns[0].tolist()\n                    if self.data_type == \"text\":\n                        srcs = trans.src_raw\n                    else:\n                        srcs = [str(item) for item in range(len(align[0]))]\n                    output = report_matrix(srcs, tgts, align)\n                    if self.logger:\n                        self.logger.info(output)\n                    else:\n                        os.write(1, output.encode(\"utf-8\"))\n\n        end_time = time.time()\n\n        if self.report_score:\n            msg = self._report_score(\n                \"PRED\", pred_score_total, pred_words_total\n            )\n            self._log(msg)\n            if tgt is not None:\n                msg = self._report_score(\n                    \"GOLD\", gold_score_total, gold_words_total\n                )\n                self._log(msg)\n\n        if self.report_time:\n            total_time = end_time - start_time\n            self._log(\"Total translation time (s): %f\" % total_time)\n            self._log(\n                \"Average translation time (s): %f\"\n                % (total_time / len(all_predictions))\n            )\n            self._log(\n                \"Tokens per second: %f\" % (pred_words_total / total_time)\n            )\n\n        if self.dump_beam:\n            import json\n\n            json.dump(\n                self.translator.beam_accum,\n                codecs.open(self.dump_beam, \"w\", \"utf-8\"),\n            )\n        return all_scores, all_predictions\n\n    def _align_pad_prediction(self, predictions, bos, pad):\n        \"\"\"\n        Padding predictions in batch and add BOS.\n\n        Args:\n            predictions (List[List[Tensor]]): `(batch, n_best,)`, for each src\n                sequence contain n_best tgt predictions all of which ended with\n                eos id.\n            bos (int): bos index to be used.\n            pad (int): pad index to be used.\n\n        Return:\n            batched_nbest_predict (torch.LongTensor): `(batch, n_best, tgt_l)`\n        \"\"\"\n        dtype, device = predictions[0][0].dtype, predictions[0][0].device\n        flatten_tgt = [\n            best.tolist() for bests in predictions for best in bests\n        ]\n        paded_tgt = torch.tensor(\n            list(zip_longest(*flatten_tgt, fillvalue=pad)),\n            dtype=dtype,\n            device=device,\n        ).T\n        bos_tensor = torch.full(\n            [paded_tgt.size(0), 1], bos, dtype=dtype, device=device\n        )\n        full_tgt = torch.cat((bos_tensor, paded_tgt), dim=-1)\n        batched_nbest_predict = full_tgt.view(\n            len(predictions), -1, full_tgt.size(-1)\n        )  # (batch, n_best, tgt_l)\n        return batched_nbest_predict\n\n    def _report_score(self, name, score_total, words_total):\n        if words_total == 0:\n            msg = \"%s No words predicted\" % (name,)\n        else:\n            avg_score = score_total / words_total\n            ppl = np.exp(-score_total.item() / words_total)\n            msg = \"%s AVG SCORE: %.4f, %s PPL: %.4f\" % (\n                name,\n                avg_score,\n                name,\n                ppl,\n            )\n        return msg\n\n    def _decode_and_generate(\n        self,\n        decoder_in,\n        memory_bank,\n        batch,\n        src_vocabs,\n        memory_lengths,\n        src_map=None,\n        step=None,\n        batch_offset=None,\n    ):\n        if self.copy_attn:\n            # Turn any copied words into UNKs.\n            decoder_in = decoder_in.masked_fill(\n                decoder_in.gt(self._tgt_vocab_len - 1), self._tgt_unk_idx\n            )\n\n        # Decoder forward, takes [tgt_len, batch, nfeats] as input\n        # and [src_len, batch, hidden] as memory_bank\n        # in case of inference tgt_len = 1, batch = beam times batch_size\n        # in case of Gold Scoring tgt_len = actual length, batch = 1 batch\n        dec_out, dec_attn = self.model.decoder(\n            decoder_in, memory_bank, memory_lengths=memory_lengths, step=step\n        )\n\n        # Generator forward.\n        if not self.copy_attn:\n            if \"std\" in dec_attn:\n                attn = dec_attn[\"std\"]\n            else:\n                attn = None\n            log_probs = self.model.generator(dec_out.squeeze(0))\n            # returns [(batch_size x beam_size) , vocab ] when 1 step\n            # or [ tgt_len, batch_size, vocab ] when full sentence\n        else:\n            attn = dec_attn[\"copy\"]\n            scores = self.model.generator(\n                dec_out.view(-1, dec_out.size(2)),\n                attn.view(-1, attn.size(2)),\n                src_map,\n            )\n            # here we have scores [tgt_lenxbatch, vocab] or [beamxbatch, vocab]\n            if batch_offset is None:\n                scores = scores.view(-1, batch.batch_size, scores.size(-1))\n                scores = scores.transpose(0, 1).contiguous()\n            else:\n                scores = scores.view(-1, self.beam_size, scores.size(-1))\n            scores = collapse_copy_scores(\n                scores,\n                batch,\n                self._tgt_vocab,\n                src_vocabs,\n                batch_dim=0,\n                batch_offset=batch_offset,\n            )\n            scores = scores.view(decoder_in.size(0), -1, scores.size(-1))\n            log_probs = scores.squeeze(0).log()\n            # returns [(batch_size x beam_size) , vocab ] when 1 step\n            # or [ tgt_len, batch_size, vocab ] when full sentence\n        return log_probs, attn\n\n    def translate_batch(self, batch, src_vocabs, attn_debug):\n        \"\"\"Translate a batch of sentences.\"\"\"\n        raise NotImplementedError\n\n    def _score_target(\n        self, batch, memory_bank, src_lengths, src_vocabs, src_map\n    ):\n        raise NotImplementedError\n\n    def report_results(\n        self,\n        gold_score,\n        batch,\n        batch_size,\n        src,\n        src_lengths,\n        src_vocabs,\n        use_src_map,\n        decode_strategy,\n    ):\n        results = {\n            \"predictions\": None,\n            \"scores\": None,\n            \"attention\": None,\n            \"batch\": batch,\n            \"gold_score\": gold_score,\n        }\n\n        results[\"scores\"] = decode_strategy.scores\n        results[\"predictions\"] = decode_strategy.predictions\n        results[\"attention\"] = decode_strategy.attention\n        if self.report_align:\n            results[\"alignment\"] = self._align_forward(\n                batch, decode_strategy.predictions\n            )\n        else:\n            results[\"alignment\"] = [[] for _ in range(batch_size)]\n        return results\n\n\nclass Translator(Inference):\n    @classmethod\n    def validate_task(cls, task):\n        if task != ModelTask.SEQ2SEQ:\n            raise ValueError(\n                f\"Translator does not support task {task}.\"\n                f\" Tasks supported: {ModelTask.SEQ2SEQ}\"\n            )\n\n    def _align_forward(self, batch, predictions):\n        \"\"\"\n        For a batch of input and its prediction, return a list of batch predict\n        alignment src indice Tensor in size ``(batch, n_best,)``.\n        \"\"\"\n        # (0) add BOS and padding to tgt prediction\n        batch_tgt_idxs = self._align_pad_prediction(\n            predictions, bos=self._tgt_bos_idx, pad=self._tgt_pad_idx\n        )\n        tgt_mask = (\n            batch_tgt_idxs.eq(self._tgt_pad_idx)\n            | batch_tgt_idxs.eq(self._tgt_eos_idx)\n            | batch_tgt_idxs.eq(self._tgt_bos_idx)\n        )\n\n        n_best = batch_tgt_idxs.size(1)\n        # (1) Encoder forward.\n        src, enc_states, memory_bank, src_lengths = self._run_encoder(batch)\n\n        # (2) Repeat src objects `n_best` times.\n        # We use batch_size x n_best, get ``(src_len, batch * n_best, nfeat)``\n        src = tile(src, n_best, dim=1)\n        enc_states = tile(enc_states, n_best, dim=1)\n        if isinstance(memory_bank, tuple):\n            memory_bank = tuple(tile(x, n_best, dim=1) for x in memory_bank)\n        else:\n            memory_bank = tile(memory_bank, n_best, dim=1)\n        src_lengths = tile(src_lengths, n_best)  # ``(batch * n_best,)``\n\n        # (3) Init decoder with n_best src,\n        self.model.decoder.init_state(src, memory_bank, enc_states)\n        # reshape tgt to ``(len, batch * n_best, nfeat)``\n        tgt = batch_tgt_idxs.view(-1, batch_tgt_idxs.size(-1)).T.unsqueeze(-1)\n        dec_in = tgt[:-1]  # exclude last target from inputs\n        _, attns = self.model.decoder(\n            dec_in, memory_bank, memory_lengths=src_lengths, with_align=True\n        )\n\n        alignment_attn = attns[\"align\"]  # ``(B, tgt_len-1, src_len)``\n        # masked_select\n        align_tgt_mask = tgt_mask.view(-1, tgt_mask.size(-1))\n        prediction_mask = align_tgt_mask[:, 1:]  # exclude bos to match pred\n        # get aligned src id for each prediction's valid tgt tokens\n        alignement = extract_alignment(\n            alignment_attn, prediction_mask, src_lengths, n_best\n        )\n        return alignement\n\n    def translate_batch(self, batch, src_vocabs, attn_debug):\n        \"\"\"Translate a batch of sentences.\"\"\"\n        with torch.no_grad():\n            if self.beam_size == 1:\n                decode_strategy = GreedySearch(\n                    pad=self._tgt_pad_idx,\n                    bos=self._tgt_bos_idx,\n                    eos=self._tgt_eos_idx,\n                    batch_size=batch.batch_size,\n                    min_length=self.min_length,\n                    max_length=self.max_length,\n                    block_ngram_repeat=self.block_ngram_repeat,\n                    exclusion_tokens=self._exclusion_idxs,\n                    return_attention=attn_debug or self.replace_unk,\n                    sampling_temp=self.random_sampling_temp,\n                    keep_topk=self.sample_from_topk,\n                )\n            else:\n                # TODO: support these blacklisted features\n                assert not self.dump_beam\n                decode_strategy = BeamSearch(\n                    self.beam_size,\n                    batch_size=batch.batch_size,\n                    pad=self._tgt_pad_idx,\n                    bos=self._tgt_bos_idx,\n                    eos=self._tgt_eos_idx,\n                    n_best=self.n_best,\n                    global_scorer=self.global_scorer,\n                    min_length=self.min_length,\n                    max_length=self.max_length,\n                    return_attention=attn_debug or self.replace_unk,\n                    block_ngram_repeat=self.block_ngram_repeat,\n                    exclusion_tokens=self._exclusion_idxs,\n                    stepwise_penalty=self.stepwise_penalty,\n                    ratio=self.ratio,\n                    beam_terminate=self.beam_terminate\n                )\n            return self._translate_batch_with_strategy(\n                batch, src_vocabs, decode_strategy)\n\n    def _run_encoder(self, batch):\n        src, src_lengths = (\n            batch.src if isinstance(batch.src, tuple) else (batch.src, None)\n        )\n        if src_lengths is None and hasattr(batch, 'src_length'):\n            src_lengths = batch.src_length\n\n        if isinstance(self.model.encoder, BARTEncoder):\n            enc_states, memory_bank, src_lengths, encoder_output = self.model.encoder(src, src_lengths)\n        elif isinstance(self.model.encoder, PretrainedEncoder):\n            # for Transformer Decoder, only memory_bank is useful\n            enc_states, memory_bank, ext_logits = self.model.encoder(src, batch.src_mask)\n        else:\n            # enc_state is used for initializing OpenNMT decoders\n            enc_states, memory_bank, src_lengths, encoder_output = self.model.encoder(\n                src, src_lengths\n            )\n\n        if src_lengths is None:\n            assert not isinstance(\n                memory_bank, tuple\n            ), \"Ensemble decoding only supported for text data\"\n            src_lengths = (\n                torch.Tensor(batch.batch_size)\n                .type_as(memory_bank)\n                .long()\n                .fill_(memory_bank.size(0))\n            )\n        return src, enc_states, memory_bank, src_lengths, encoder_output\n\n    def _decode_and_generate(\n            self,\n            decoder_in,\n            memory_bank,\n            batch,\n            src_vocabs,\n            memory_lengths,\n            src_map=None,\n            step=None,\n            batch_offset=None,\n            encoder_output=None,\n            incremental_state=None,\n            # @memray in general we just need the prediction of last word,\n            #   but for _gold_score/_score_target we need to return the whole sequence\n            last_word=True,\n    ):\n        if self.copy_attn:\n            # Turn any copied words into UNKs.\n            decoder_in = decoder_in.masked_fill(\n                decoder_in.gt(self._tgt_vocab_len - 1), self._tgt_unk_idx\n            )\n        # Inputs:\n        #     decoder_in: [tgt_len, batch, nfeats]\n        #     memory_bank: [src_len, batch, hidden]\n        # Outputs:\n        #    In case of inference tgt_len = 1, batch = beam x batch_size\n        #        dec_out is in shape of [1, beam_size * batch_size, hidden]\n        #        dec_attn is a dict which values are in shape of [1, beam_size * batch_size, src_len]\n        #    In case of Gold Scoring tgt_len = actual length, batch = 1 x batch\n        #        dec_out: [tgt_len, batch_size, dec_dim]\n        #        dec_attn['std']: [tgt_len, batch_size, src_len]\n        if isinstance(self.model.decoder, BARTDecoder):\n            # BARTDecoder only uses decoder_in and encoder_output\n            #    decoder_in.shape = (tgt_len, batch_size, 1)\n            #    output=(tgt_len, batch_size, dec_dim), attn=(tgt_len, batch_size, src_len)\n            dec_out, dec_attn = self.model.decoder(tgt=decoder_in, memory_bank=memory_bank,\n                                                   memory_lengths=memory_lengths,\n                                                   encoder_output=encoder_output,\n                                                   incremental_state=incremental_state,\n                                                   )\n            # only preserve the output of last word, [tgt_len, B, D] -> [1, B, D]\n            if last_word:\n                dec_out = dec_out[-1, :, :].unsqueeze(0)\n                if dec_attn:\n                    dec_attn = {k:v[-1, :, :].unsqueeze(0) for k,v in dec_attn.items()}\n        else:\n            dec_out, dec_attn = self.model.decoder(\n                decoder_in, memory_bank, memory_lengths=memory_lengths, step=step\n            )\n\n        # print(\"dec_out=\")\n        # print('max=', dec_out.cpu().numpy().max())\n        # print('min=', dec_out.cpu().numpy().min())\n        # print('mean=', dec_out.cpu().numpy().mean())\n        # if dec_attn:\n        #     print(\"dec_attn=\", dec_attn['std'][0][0].cpu().numpy().tolist())\n\n        # Generator forward.\n        if not self.copy_attn:\n            if dec_attn and \"std\" in dec_attn:\n                attn = dec_attn[\"std\"]\n            else:\n                attn = None\n            log_probs = self.model.generator(dec_out.squeeze(0))\n            # returns [(batch_size x beam_size) , vocab ] when 1 step\n            # or [ tgt_len, batch_size, vocab ] when full sentence\n        else:\n            assert dec_attn is not None, 'for copy generator, attention is required'\n            attn = dec_attn[\"copy\"]\n            # for beam search, here we have scores [tgt_len x batch, vocab/cvocab] or [beam x batch, vocab/cvocab]\n            scores = self.model.generator(dec_out.view(-1, dec_out.size(2)),\n                                          attn.view(-1, attn.size(2)),\n                                          src_map)\n            # gold_score [tgt_lenxbatch, tmp_vocab] -> [tgt_len, batch, tmp_vocab]???\n            # beam search [beamxbatch, tmp_vocab] -> [batch, beam, tmp_vocab]\n            if batch_offset is None:\n                scores = scores.view(-1, batch.batch_size, scores.size(-1))\n                scores = scores.transpose(0, 1).contiguous()\n            else:\n                scores = scores.view(-1, self.beam_size, scores.size(-1))\n            scores = collapse_copy_scores(\n                scores,\n                batch,\n                self._tgt_vocab,\n                src_vocabs,\n                batch_dim=0,\n                batch_offset=batch_offset\n            )\n            # Gold score: [batch, tgt_len, merged_vocab] -> [tgt_len, batch, merged_vocab]\n            # Beam search: [batch, beam, merged_vocab] -> [1, batch*beam, merged_vocab]\n            scores = scores.view(-1, decoder_in.size(1), scores.size(-1)) # to be compatible with BART. previous: scores.view(decoder_in.size(0), -1, scores.size(-1))\n            # Gold score: [tgt_len, batch_size, vocab]\n            # Beam search: [batch_size x beam_size, vocab]\n            log_probs = scores.squeeze(0).log()\n\n        # attn=(tgt_len, batch_size, dec_dim)\n        return log_probs, attn\n\n    def _translate_batch_with_strategy(\n        self, batch, src_vocabs, decode_strategy\n    ):\n        \"\"\"Translate a batch of sentences step by step using cache.\n\n        Args:\n            batch: a batch of sentences, yield by data iterator.\n            src_vocabs (list): list of torchtext.data.Vocab if can_copy.\n            decode_strategy (DecodeStrategy): A decode strategy to use for\n                generate translation step by step.\n\n        Returns:\n            results (dict): The translation results.\n        \"\"\"\n        # (0) Prep the components of the search.\n        use_src_map = self.copy_attn\n        parallel_paths = decode_strategy.parallel_paths  # beam_size\n        batch_size = batch.batch_size\n\n        # (1) Run the encoder on the src.\n        src, enc_states, memory_bank, src_lengths, encoder_output = self._run_encoder(batch)\n\n        # print(\"src.shape=\", src.shape)\n        # print(\"encoder_output.encoder_out=\")\n        # print('max=', encoder_output['encoder_out'][0].data.cpu().numpy().max())\n        # print('min=', encoder_output['encoder_out'][0].data.cpu().numpy().min())\n        # print('mean=', encoder_output['encoder_out'][0].data.cpu().numpy().mean())\n        # print(\"encoder_output.encoder_embedding=\")\n        # print('max=', encoder_output['encoder_embedding'][0].data.cpu().numpy().max())\n        # print('min=', encoder_output['encoder_embedding'][0].data.cpu().numpy().min())\n        # print('mean=', encoder_output['encoder_embedding'][0].data.cpu().numpy().mean())\n\n        self.model.decoder.init_state(src, memory_bank, enc_states)\n\n        gold_score = self._gold_score(\n            batch,\n            memory_bank,\n            src_lengths,\n            src_vocabs,\n            use_src_map,\n            enc_states,\n            batch_size,\n            src,\n        )\n        # print('gold_score=', gold_score.data.cpu().numpy().mean())\n\n        # (2) prep decode_strategy. Possibly repeat src objects.\n        src_map = batch.src_map if use_src_map else None\n        target_prefix = batch.tgt if self.tgt_prefix else None\n        (\n            fn_map_state,\n            memory_bank,\n            memory_lengths,\n            src_map,\n        ) = decode_strategy.initialize(\n            memory_bank, src_lengths, src_map, target_prefix=target_prefix\n        )\n        incremental_state = {}\n        if isinstance(self.model.decoder, BARTDecoder):\n            new_order = torch.arange(batch_size).view(-1, 1).repeat(1, parallel_paths).view(-1)\n            new_order = new_order.to(src.device).long()\n            encoder_output = self.model.encoder.model.reorder_encoder_out(encoder_output, new_order)\n        else:\n            if fn_map_state is not None:\n                self.model.decoder.map_state(fn_map_state)\n\n        # (3) Begin decoding step by step:\n        for step in range(decode_strategy.max_length):\n            # print('=' * 50)\n            # print('step=%d' % step)\n\n            if isinstance(self.model.decoder, BARTDecoder):\n                # keep all predicted tokens, length is used for position embedding, (pred_len, batch_size * beam_size, 1)\n                decoder_input = decode_strategy.alive_seq.permute(1, 0).unsqueeze(2)\n            else:\n                decoder_input = decode_strategy.current_predictions.view(1, -1, 1)\n\n            # log_probs: [(batch_size x beam_size) , vocab] when 1 step or [ tgt_len, batch_size, vocab] when full sentence\n            # attn: [1, (batch_size x beam_size), src_len]\n            log_probs, attn = self._decode_and_generate(\n                decoder_input,\n                memory_bank,\n                batch,\n                src_vocabs,\n                memory_lengths=memory_lengths,\n                src_map=src_map,\n                step=step,\n                batch_offset=decode_strategy.batch_offset,\n                encoder_output=encoder_output,\n                incremental_state=incremental_state,\n            )\n\n            # print(\"log_probs=\")\n            # print('max=', log_probs.cpu().numpy().max())\n            # print('min=', log_probs.cpu().numpy().min())\n            # print('mean=', log_probs.cpu().numpy().mean())\n\n            decode_strategy.advance(log_probs, attn)\n            any_finished = decode_strategy.is_finished.any()\n            if any_finished:\n                decode_strategy.update_finished(last_step=(step+1==decode_strategy.max_length))\n                if decode_strategy.done:\n                    break\n\n            select_indices = decode_strategy.select_indices\n\n            if any_finished:\n                # Reorder states.\n                if isinstance(memory_bank, tuple):\n                    memory_bank = tuple(\n                        x.index_select(1, select_indices) for x in memory_bank\n                    )\n                else:\n                    memory_bank = memory_bank.index_select(1, select_indices)\n\n                memory_lengths = memory_lengths.index_select(0, select_indices)\n\n                if src_map is not None:\n                    src_map = src_map.index_select(1, select_indices)\n            if parallel_paths > 1 or any_finished:\n                # @memray re-order decoder internal states based on the prev choice of beams\n                # and drop the finished batches\n                if isinstance(self.model.decoder, BARTDecoder):\n                    self.model.decoder.model.reorder_incremental_state_scripting(incremental_state, select_indices)\n                    encoder_output = self.model.encoder.model.reorder_encoder_out(encoder_output, select_indices)\n                    # print('step=', step)\n                    # print('#select_indices=%d' % len(select_indices))\n                    # print(select_indices)\n                    # print('#finished=%d' % (torch.sum(decode_strategy.is_finished.int()).item()))\n                    # print('incremental_state[0].shape=%s' % str(list(incremental_state.values())[0]['prev_key'].shape))\n                    # print('encoder_output[0].shape=%s' % str(encoder_output[0].shape))\n                else:\n                    self.model.decoder.map_state(\n                        lambda state, dim: state.index_select(dim, select_indices)\n                    )\n            # print('decode_strategy.alive_seq:', decode_strategy.alive_seq.cpu().numpy().tolist())\n            # print('decode_strategy.topk_scores:', decode_strategy.topk_scores.cpu().numpy().tolist())\n\n        return self.report_results(\n            gold_score,\n            batch,\n            batch_size,\n            src,\n            src_lengths,\n            src_vocabs,\n            use_src_map,\n            decode_strategy,\n        )\n\n    def _score_target(\n        self, batch, memory_bank, src_lengths, src_vocabs, src_map, encoder_output\n    ):\n        tgt = batch.tgt\n        tgt_in = tgt[:-1] # trim EOS\n\n        try:\n            # score target sequence (ground-truth), log_probs=[tgt_len-1, batch_size, vocab], attn=[tgt_len-1, batch_size, src_len]\n            log_probs, attn = self._decode_and_generate(\n                tgt_in,\n                memory_bank,\n                batch,\n                src_vocabs,\n                memory_lengths=src_lengths,\n                src_map=src_map,\n                encoder_output=encoder_output,\n                last_word=False\n            )\n\n            log_probs[:, :, self._tgt_pad_idx] = 0\n            gold = tgt[1:] # trim BOS\n            gold_scores = log_probs.gather(2, gold)\n            gold_scores = gold_scores.sum(dim=0).view(-1)\n        except Exception:\n            # occasionally it errors out (dim of log_probs.shape is 2, should be 3), may due to an invalid input\n            gold_scores = torch.Tensor([-np.Inf] * batch.batch_size)\n\n        return gold_scores\n\n\n    def _report_kpeval(self, src_path, tgt_path, pred_path):\n        import subprocess\n        path = os.path.abspath(__file__ + \"/../../..\")\n        msg = subprocess.check_output(\n            \"python %s/tools/kp_eval.py -src %s -tgt %s -pred %s\"\n            % (path, src_path, tgt_path, pred_path),\n            shell=True, stdin=self.out_file\n        ).decode(\"utf-8\").strip()\n        return msg\n\n    def add_trans_stats(self, trans, kp_concat_type):\n        for tran in trans:\n            if kp_concat_type == 'one2one':\n                tran.unique_pred_num = len(tran.preds)\n                tran.dup_pred_num = len(tran.preds)\n                tran.beam_num = len(tran.preds)\n                tran.beamstep_num = sum([len(t) for t in tran.preds])\n            else:\n                tran.beam_num = len(tran.ori_preds)\n                tran.beamstep_num = sum([len(t) for t in tran.ori_preds])\n\n        return trans\n\n    def segment_one2seq_trans(self, trans):\n        \"\"\"\n        For keyphrase generation tasks, one2seq models output sequences consisting of multiple phrases. Split them by delimiters and rerank them\n        :param trans: a list of translations, length=batch_size, each translation contains multiple beams corresponding to one source text\n        :return: a list of translations, each beam in each translation (multiple phrases delimited by <sep>) is a phrase\n        \"\"\"\n        for tran in trans:\n            dup_pred_tuples = []\n\n            new_preds = []\n            new_pred_sents = []\n            new_pred_scores = []\n            new_pred_counter = {}\n\n            topseq_preds = []\n            topseq_pred_sents = []\n            topseq_pred_scores = []\n            for sent_i in range(len(tran.pred_sents)):\n                pred_sent = tran.pred_sents[sent_i]\n                sep_indices = [i for i in range(len(pred_sent)) if pred_sent[i] == DefaultTokens.SEP]\n                sep_indices = [-1] + sep_indices + [len(pred_sent)]\n\n                for kp_i in range(len(sep_indices)-1):\n                    start_idx = sep_indices[kp_i] + 1\n                    end_idx = sep_indices[kp_i + 1]\n                    new_kp = pred_sent[start_idx: end_idx]\n                    new_kp_str = '_'.join(new_kp)\n\n                    # keep all preds, even duplicate\n                    dup_pred_tuples.append((tran.preds[sent_i][start_idx: end_idx],\n                                      tran.pred_sents[sent_i][start_idx: end_idx],\n                                      tran.pred_scores[sent_i]))\n\n                    # skip duplicate\n                    if new_kp_str in new_pred_counter:\n                        new_pred_counter[new_kp_str] += 1\n                        continue\n\n                    # TODO, no account for attns and copies\n                    new_pred_counter[new_kp_str] = 1\n                    new_preds.append(tran.preds[sent_i][start_idx: end_idx])\n                    new_pred_sents.append(tran.pred_sents[sent_i][start_idx: end_idx])\n                    new_pred_scores.append(tran.pred_scores[sent_i])\n\n                    # first beam (top-rank sequence)\n                    if sent_i == 0:\n                        topseq_preds.append(tran.preds[sent_i][start_idx: end_idx])\n                        topseq_pred_sents.append(tran.pred_sents[sent_i][start_idx: end_idx])\n                        topseq_pred_scores.append(tran.pred_scores[sent_i])\n\n            # print('#(unique)/#(kp) = %d/%d' % (len(new_pred_counter), sum(new_pred_counter.values())))\n            # print(new_pred_counter)\n\n            # one2seq-specific stats\n            tran.unique_pred_num = len(new_pred_counter)\n            tran.dup_pred_num = sum(new_pred_counter.values())\n\n            # still keep the original pred beams\n            tran.ori_preds = tran.preds\n            tran.ori_pred_sents = tran.pred_sents\n            tran.ori_pred_scores = tran.pred_scores\n\n            # segmented predictions from the top-score sequence\n            tran.topseq_preds = topseq_preds\n            tran.topseq_pred_sents = topseq_pred_sents\n            tran.topseq_pred_scores = topseq_pred_scores\n\n            # all segmented predictions\n            tran.preds = new_preds\n            tran.pred_sents = new_pred_sents\n            tran.pred_scores = new_pred_scores\n\n            tran.dup_pred_tuples = dup_pred_tuples\n\n        return trans\n\n\nclass GeneratorLM(Inference):\n    @classmethod\n    def validate_task(cls, task):\n        if task != ModelTask.LANGUAGE_MODEL:\n            raise ValueError(\n                f\"GeneratorLM does not support task {task}.\"\n                f\" Tasks supported: {ModelTask.LANGUAGE_MODEL}\"\n            )\n\n    def _align_forward(self, batch, predictions):\n        \"\"\"\n        For a batch of input and its prediction, return a list of batch predict\n        alignment src indice Tensor in size ``(batch, n_best,)``.\n        \"\"\"\n        raise NotImplementedError\n\n    def translate(\n        self,\n        src,\n        tgt=None,\n        batch_size=None,\n        batch_type=\"sents\",\n        attn_debug=False,\n        align_debug=False,\n        phrase_table=\"\",\n    ):\n        if batch_size != 1:\n            warning_msg = (\"GeneratorLM does not support batch_size != 1\"\n                           \" nicely. You can remove this limitation here.\"\n                           \" With batch_size > 1 the end of each input is\"\n                           \" repeated until the input is finished. Then\"\n                           \" generation will start.\")\n            if self.logger:\n                self.logger.info(warning_msg)\n            else:\n                os.write(1, warning_msg.encode(\"utf-8\"))\n\n        return super(GeneratorLM, self).translate(\n            src,\n            tgt,\n            batch_size=batch_size,\n            batch_type=batch_type,\n            attn_debug=attn_debug,\n            align_debug=align_debug,\n            phrase_table=phrase_table,\n        )\n\n    def translate_batch(self, batch, src_vocabs, attn_debug):\n        \"\"\"Translate a batch of sentences.\"\"\"\n        with torch.no_grad():\n            if self.beam_size == 1:\n                decode_strategy = GreedySearchLM(\n                    pad=self._tgt_pad_idx,\n                    bos=self._tgt_bos_idx,\n                    eos=self._tgt_eos_idx,\n                    batch_size=batch.batch_size,\n                    min_length=self.min_length,\n                    max_length=self.max_length,\n                    block_ngram_repeat=self.block_ngram_repeat,\n                    exclusion_tokens=self._exclusion_idxs,\n                    return_attention=attn_debug or self.replace_unk,\n                    sampling_temp=self.random_sampling_temp,\n                    keep_topk=self.sample_from_topk,\n                )\n            else:\n                # TODO: support these blacklisted features\n                assert not self.dump_beam\n                decode_strategy = BeamSearchLM(\n                    self.beam_size,\n                    batch_size=batch.batch_size,\n                    pad=self._tgt_pad_idx,\n                    bos=self._tgt_bos_idx,\n                    eos=self._tgt_eos_idx,\n                    n_best=self.n_best,\n                    global_scorer=self.global_scorer,\n                    min_length=self.min_length,\n                    max_length=self.max_length,\n                    return_attention=attn_debug or self.replace_unk,\n                    block_ngram_repeat=self.block_ngram_repeat,\n                    exclusion_tokens=self._exclusion_idxs,\n                    stepwise_penalty=self.stepwise_penalty,\n                    ratio=self.ratio,\n                )\n            return self._translate_batch_with_strategy(\n                batch, src_vocabs, decode_strategy\n            )\n\n    def split_src_to_prevent_padding(self, src, src_lengths):\n        min_len_batch = torch.min(src_lengths).item()\n        target_prefix = None\n        if min_len_batch > 0 and min_len_batch <= src.size(0):\n            # hack [min_len_batch-1:] because expect <bos>\n            target_prefix = (\n                src[min_len_batch - 1:]\n                if min_len_batch > 0 and min_len_batch <= src.size(0)\n                else None\n            )\n            src = src[:min_len_batch]\n            src_lengths[:] = min_len_batch\n        return src, src_lengths, target_prefix\n\n    def _translate_batch_with_strategy(\n        self, batch, src_vocabs, decode_strategy\n    ):\n        \"\"\"Translate a batch of sentences step by step using cache.\n\n        Args:\n            batch: a batch of sentences, yield by data iterator.\n            src_vocabs (list): list of torchtext.data.Vocab if can_copy.\n            decode_strategy (DecodeStrategy): A decode strategy to use for\n                generate translation step by step.\n\n        Returns:\n            results (dict): The translation results.\n        \"\"\"\n        # (0) Prep the components of the search.\n        use_src_map = self.copy_attn\n        parallel_paths = decode_strategy.parallel_paths  # beam_size\n        batch_size = batch.batch_size\n\n        # (1) split src into src and target_prefix to avoid padding.\n        src, src_lengths = (\n            batch.src if isinstance(batch.src, tuple) else (batch.src, None)\n        )\n\n        src, src_lengths, target_prefix = self.split_src_to_prevent_padding(\n            src, src_lengths\n        )\n\n        # (2) init decoder\n        self.model.decoder.init_state(src, None, None)\n        gold_score = self._gold_score(\n            batch,\n            None,\n            src_lengths,\n            src_vocabs,\n            use_src_map,\n            None,\n            batch_size,\n            src,\n        )\n\n        # (3) prep decode_strategy. Possibly repeat src objects.\n        src_map = batch.src_map if use_src_map else None\n        (\n            fn_map_state,\n            src,\n            memory_lengths,\n            src_map,\n        ) = decode_strategy.initialize(\n            src,\n            src_lengths,\n            src_map,\n            target_prefix=target_prefix,\n        )\n        if fn_map_state is not None:\n            self.model.decoder.map_state(fn_map_state)\n\n        # (4) Begin decoding step by step:\n        for step in range(decode_strategy.max_length):\n            decoder_input = (\n                src\n                if step == 0\n                else decode_strategy.current_predictions.view(1, -1, 1)\n            )\n\n            log_probs, attn = self._decode_and_generate(\n                decoder_input,\n                None,\n                batch,\n                src_vocabs,\n                memory_lengths=memory_lengths.clone(),\n                src_map=src_map,\n                step=step,\n                batch_offset=decode_strategy.batch_offset,\n            )\n\n            if step == 0:\n                log_probs = log_probs[-1]\n\n            decode_strategy.advance(log_probs, attn)\n            any_finished = decode_strategy.is_finished.any()\n            if any_finished:\n                decode_strategy.update_finished()\n                if decode_strategy.done:\n                    break\n\n            select_indices = decode_strategy.select_indices\n            memory_lengths += 1\n            if any_finished:\n                # Reorder states.\n                memory_lengths = memory_lengths.index_select(0, select_indices)\n\n                if src_map is not None:\n                    src_map = src_map.index_select(1, select_indices)\n\n            if parallel_paths > 1 or any_finished:\n                # select indexes in model state/cache\n                self.model.decoder.map_state(\n                    lambda state, dim: state.index_select(dim, select_indices)\n                )\n\n        return self.report_results(\n            gold_score,\n            batch,\n            batch_size,\n            src,\n            src_lengths,\n            src_vocabs,\n            use_src_map,\n            decode_strategy,\n        )\n\n    def _score_target(\n        self, batch, memory_bank, src_lengths, src_vocabs, src_map\n    ):\n        tgt = batch.tgt\n        src, src_lengths = (\n            batch.src if isinstance(batch.src, tuple) else (batch.src, None)\n        )\n\n        log_probs, attn = self._decode_and_generate(\n            src,\n            None,\n            batch,\n            src_vocabs,\n            memory_lengths=src_lengths,\n            src_map=src_map,\n        )\n\n        log_probs[:, :, self._tgt_pad_idx] = 0\n        gold_scores = log_probs.gather(2, tgt)\n        gold_scores = gold_scores.sum(dim=0).view(-1)\n\n        return gold_scores\n"
  },
  {
    "path": "onmt/utils/__init__.py",
    "content": "\"\"\"Module defining various utilities.\"\"\"\nfrom onmt.utils.misc import split_corpus, aeq, use_gpu, set_random_seed\nfrom onmt.utils.alignment import make_batch_align_matrix\nfrom onmt.utils.report_manager import ReportMgr, build_report_manager\nfrom onmt.utils.statistics import Statistics\nfrom onmt.utils.optimizers import MultipleOptimizer, \\\n    Optimizer, AdaFactor\nfrom onmt.utils.earlystopping import EarlyStopping, scorers_from_opts\n\n__all__ = [\"split_corpus\", \"aeq\", \"use_gpu\", \"set_random_seed\", \"ReportMgr\",\n           \"build_report_manager\", \"Statistics\",\n           \"MultipleOptimizer\", \"Optimizer\", \"AdaFactor\", \"EarlyStopping\",\n           \"scorers_from_opts\", \"make_batch_align_matrix\"]\n"
  },
  {
    "path": "onmt/utils/alignment.py",
    "content": "# -*- coding: utf-8 -*-\n\nimport torch\nfrom itertools import accumulate\nfrom onmt.constants import SubwordMarker\n\n\ndef make_batch_align_matrix(index_tensor, size=None, normalize=False):\n    \"\"\"\n    Convert a sparse index_tensor into a batch of alignment matrix,\n    with row normalize to the sum of 1 if set normalize.\n\n    Args:\n        index_tensor (LongTensor): ``(N, 3)`` of [batch_id, tgt_id, src_id]\n        size (List[int]): Size of the sparse tensor.\n        normalize (bool): if normalize the 2nd dim of resulting tensor.\n    \"\"\"\n    n_fill, device = index_tensor.size(0), index_tensor.device\n    value_tensor = torch.ones([n_fill], dtype=torch.float)\n    dense_tensor = torch.sparse_coo_tensor(\n        index_tensor.t(), value_tensor, size=size, device=device).to_dense()\n    if normalize:\n        row_sum = dense_tensor.sum(-1, keepdim=True)  # sum by row(tgt)\n        # threshold on 1 to avoid div by 0\n        torch.nn.functional.threshold(row_sum, 1, 1, inplace=True)\n        dense_tensor.div_(row_sum)\n    return dense_tensor\n\n\ndef extract_alignment(align_matrix, tgt_mask, src_lens, n_best):\n    \"\"\"\n    Extract a batched align_matrix into its src indice alignment lists,\n    with tgt_mask to filter out invalid tgt position as EOS/PAD.\n    BOS already excluded from tgt_mask in order to match prediction.\n\n    Args:\n        align_matrix (Tensor): ``(B, tgt_len, src_len)``,\n            attention head normalized by Softmax(dim=-1)\n        tgt_mask (BoolTensor): ``(B, tgt_len)``, True for EOS, PAD.\n        src_lens (LongTensor): ``(B,)``, containing valid src length\n        n_best (int): a value indicating number of parallel translation.\n        * B: denote flattened batch as B = batch_size * n_best.\n\n    Returns:\n        alignments (List[List[FloatTensor|None]]): ``(batch_size, n_best,)``,\n         containing valid alignment matrix (or None if blank prediction)\n         for each translation.\n    \"\"\"\n    batch_size_n_best = align_matrix.size(0)\n    assert batch_size_n_best % n_best == 0\n\n    alignments = [[] for _ in range(batch_size_n_best // n_best)]\n\n    # treat alignment matrix one by one as each have different lengths\n    for i, (am_b, tgt_mask_b, src_len) in enumerate(\n            zip(align_matrix, tgt_mask, src_lens)):\n        valid_tgt = ~tgt_mask_b\n        valid_tgt_len = valid_tgt.sum()\n        if valid_tgt_len == 0:\n            # No alignment if not exist valid tgt token\n            valid_alignment = None\n        else:\n            # get valid alignment (sub-matrix from full paded aligment matrix)\n            am_valid_tgt = am_b.masked_select(valid_tgt.unsqueeze(-1)) \\\n                               .view(valid_tgt_len, -1)\n            valid_alignment = am_valid_tgt[:, :src_len]  # only keep valid src\n        alignments[i // n_best].append(valid_alignment)\n\n    return alignments\n\n\ndef build_align_pharaoh(valid_alignment):\n    \"\"\"Convert valid alignment matrix to i-j (from 0) Pharaoh format pairs,\n    or empty list if it's None.\n    \"\"\"\n    align_pairs = []\n    if isinstance(valid_alignment, torch.Tensor):\n        tgt_align_src_id = valid_alignment.argmax(dim=-1)\n\n        for tgt_id, src_id in enumerate(tgt_align_src_id.tolist()):\n            align_pairs.append(str(src_id) + \"-\" + str(tgt_id))\n        align_pairs.sort(key=lambda x: int(x.split('-')[-1]))  # sort by tgt_id\n        align_pairs.sort(key=lambda x: int(x.split('-')[0]))  # sort by src_id\n    return align_pairs\n\n\ndef to_word_align(src, tgt, subword_align, m_src='joiner', m_tgt='joiner'):\n    \"\"\"Convert subword alignment to word alignment.\n\n    Args:\n        src (string): tokenized sentence in source language.\n        tgt (string): tokenized sentence in target language.\n        subword_align (string): align_pharaoh correspond to src-tgt.\n        m_src (string): tokenization mode used in src,\n            can be [\"joiner\", \"spacer\"].\n        m_tgt (string): tokenization mode used in tgt,\n            can be [\"joiner\", \"spacer\"].\n\n    Returns:\n        word_align (string): converted alignments correspand to\n            detokenized src-tgt.\n    \"\"\"\n    assert m_src in [\"joiner\", \"spacer\"], \"Invalid value for argument m_src!\"\n    assert m_tgt in [\"joiner\", \"spacer\"], \"Invalid value for argument m_tgt!\"\n\n    src, tgt = src.strip().split(), tgt.strip().split()\n    subword_align = {(int(a), int(b)) for a, b in (x.split(\"-\")\n                     for x in subword_align.split())}\n\n    src_map = (subword_map_by_spacer(src) if m_src == 'spacer'\n               else subword_map_by_joiner(src))\n\n    tgt_map = (subword_map_by_spacer(src) if m_tgt == 'spacer'\n               else subword_map_by_joiner(src))\n\n    word_align = list({\"{}-{}\".format(src_map[a], tgt_map[b])\n                       for a, b in subword_align})\n    word_align.sort(key=lambda x: int(x.split('-')[-1]))  # sort by tgt_id\n    word_align.sort(key=lambda x: int(x.split('-')[0]))  # sort by src_id\n    return \" \".join(word_align)\n\n\ndef subword_map_by_joiner(subwords, marker=SubwordMarker.JOINER):\n    \"\"\"Return word id for each subword token (annotate by joiner).\"\"\"\n    flags = [0] * len(subwords)\n    for i, tok in enumerate(subwords):\n        if tok.endswith(marker):\n            flags[i] = 1\n        if tok.startswith(marker):\n            assert i >= 1 and flags[i-1] != 1, \\\n                \"Sentence `{}` not correct!\".format(\" \".join(subwords))\n            flags[i-1] = 1\n    marker_acc = list(accumulate([0] + flags[:-1]))\n    word_group = [(i - maker_sofar) for i, maker_sofar\n                  in enumerate(marker_acc)]\n    return word_group\n\n\ndef subword_map_by_spacer(subwords, marker=SubwordMarker.SPACER):\n    \"\"\"Return word id for each subword token (annotate by spacer).\"\"\"\n    word_group = list(accumulate([int(marker in x) for x in subwords]))\n    if word_group[0] == 1:  # when dummy prefix is set\n        word_group = [item - 1 for item in word_group]\n    return word_group\n"
  },
  {
    "path": "onmt/utils/cnn_factory.py",
    "content": "\"\"\"\nImplementation of \"Convolutional Sequence to Sequence Learning\"\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\n\nimport onmt.modules\n\nSCALE_WEIGHT = 0.5 ** 0.5\n\n\ndef shape_transform(x):\n    \"\"\" Tranform the size of the tensors to fit for conv input. \"\"\"\n    return torch.unsqueeze(torch.transpose(x, 1, 2), 3)\n\n\nclass GatedConv(nn.Module):\n    \"\"\" Gated convolution for CNN class \"\"\"\n\n    def __init__(self, input_size, width=3, dropout=0.2, nopad=False):\n        super(GatedConv, self).__init__()\n        self.conv = onmt.modules.WeightNormConv2d(\n            input_size, 2 * input_size, kernel_size=(width, 1), stride=(1, 1),\n            padding=(width // 2 * (1 - nopad), 0))\n        init.xavier_uniform_(self.conv.weight, gain=(4 * (1 - dropout))**0.5)\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, x_var):\n        x_var = self.dropout(x_var)\n        x_var = self.conv(x_var)\n        out, gate = x_var.split(int(x_var.size(1) / 2), 1)\n        out = out * torch.sigmoid(gate)\n        return out\n\n\nclass StackedCNN(nn.Module):\n    \"\"\" Stacked CNN class \"\"\"\n\n    def __init__(self, num_layers, input_size, cnn_kernel_width=3,\n                 dropout=0.2):\n        super(StackedCNN, self).__init__()\n        self.dropout = dropout\n        self.num_layers = num_layers\n        self.layers = nn.ModuleList()\n        for _ in range(num_layers):\n            self.layers.append(\n                GatedConv(input_size, cnn_kernel_width, dropout))\n\n    def forward(self, x):\n        for conv in self.layers:\n            x = x + conv(x)\n            x *= SCALE_WEIGHT\n        return x\n"
  },
  {
    "path": "onmt/utils/distributed.py",
    "content": "\"\"\" Pytorch Distributed utils\n    This piece of code was heavily inspired by the equivalent of Fairseq-py\n    https://github.com/pytorch/fairseq\n\"\"\"\n\n\nfrom __future__ import print_function\n\nimport os\nimport signal\nimport math\nimport pickle\n\nimport torch.distributed\n\nfrom onmt.utils.misc import set_random_seed\nfrom onmt.utils.logging import init_logger, logger\n\n\ndef is_master(opt, device_id):\n    return opt.gpu_ranks[device_id] == 0\n\n\ndef multi_init(opt, device_id):\n    dist_init_method = 'tcp://{master_ip}:{master_port}'.format(\n        master_ip=opt.master_ip,\n        master_port=opt.master_port)\n    dist_world_size = opt.world_size\n    torch.distributed.init_process_group(\n        backend=opt.gpu_backend, init_method=dist_init_method,\n        world_size=dist_world_size, rank=opt.gpu_ranks[device_id])\n    gpu_rank = torch.distributed.get_rank()\n    if not is_master(opt, device_id):\n        logger.disabled = True\n\n    return gpu_rank\n\n\ndef all_reduce_and_rescale_tensors(tensors, rescale_denom,\n                                   buffer_size=10485760):\n    \"\"\"All-reduce and rescale tensors in chunks of the specified size.\n\n    Args:\n        tensors: list of Tensors to all-reduce\n        rescale_denom: denominator for rescaling summed Tensors\n        buffer_size: all-reduce chunk size in bytes\n    \"\"\"\n    # buffer size in bytes, determine equiv. # of elements based on data type\n    buffer_t = tensors[0].new(\n        math.ceil(buffer_size / tensors[0].element_size())).zero_()\n    buffer = []\n\n    def all_reduce_buffer():\n        # copy tensors into buffer_t\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            buffer_t[offset:offset+numel].copy_(t.view(-1))\n            offset += numel\n\n        # all-reduce and rescale\n        torch.distributed.all_reduce(buffer_t[:offset])\n        buffer_t.div_(rescale_denom)\n\n        # copy all-reduced buffer back into tensors\n        offset = 0\n        for t in buffer:\n            numel = t.numel()\n            t.view(-1).copy_(buffer_t[offset:offset+numel])\n            offset += numel\n\n    filled = 0\n    for t in tensors:\n        sz = t.numel() * t.element_size()\n        if sz > buffer_size:\n            # tensor is bigger than buffer, all-reduce and rescale directly\n            torch.distributed.all_reduce(t)\n            t.div_(rescale_denom)\n        elif filled + sz > buffer_size:\n            # buffer is full, all-reduce and replace buffer with grad\n            all_reduce_buffer()\n            buffer = [t]\n            filled = sz\n        else:\n            # add tensor to buffer\n            buffer.append(t)\n            filled += sz\n\n    if len(buffer) > 0:\n        all_reduce_buffer()\n\n\ndef all_gather_list(data, max_size=4096):\n    \"\"\"Gathers arbitrary data from all nodes into a list.\"\"\"\n    world_size = torch.distributed.get_world_size()\n    if not hasattr(all_gather_list, '_in_buffer') or \\\n            max_size != all_gather_list._in_buffer.size():\n        all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)\n        all_gather_list._out_buffers = [\n            torch.cuda.ByteTensor(max_size)\n            for i in range(world_size)\n        ]\n    in_buffer = all_gather_list._in_buffer\n    out_buffers = all_gather_list._out_buffers\n\n    enc = pickle.dumps(data)\n    enc_size = len(enc)\n    if enc_size + 2 > max_size:\n        raise ValueError(\n            'encoded data exceeds max_size: {}'.format(enc_size + 2))\n    assert max_size < 255*256\n    in_buffer[0] = enc_size // 255  # this encoding works for max_size < 65k\n    in_buffer[1] = enc_size % 255\n    in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc))\n\n    torch.distributed.all_gather(out_buffers, in_buffer.cuda())\n\n    results = []\n    for i in range(world_size):\n        out_buffer = out_buffers[i]\n        size = (255 * out_buffer[0].item()) + out_buffer[1].item()\n\n        bytes_list = bytes(out_buffer[2:size+2].tolist())\n        result = pickle.loads(bytes_list)\n        results.append(result)\n    return results\n\n\nclass ErrorHandler(object):\n    \"\"\"A class that listens for exceptions in children processes and propagates\n    the tracebacks to the parent process.\"\"\"\n\n    def __init__(self, error_queue):\n        \"\"\" init error handler \"\"\"\n        import signal\n        import threading\n        self.error_queue = error_queue\n        self.children_pids = []\n        self.error_thread = threading.Thread(\n            target=self.error_listener, daemon=True)\n        self.error_thread.start()\n        signal.signal(signal.SIGUSR1, self.signal_handler)\n\n    def add_child(self, pid):\n        \"\"\" error handler \"\"\"\n        self.children_pids.append(pid)\n\n    def error_listener(self):\n        \"\"\" error listener \"\"\"\n        (rank, original_trace) = self.error_queue.get()\n        self.error_queue.put((rank, original_trace))\n        os.kill(os.getpid(), signal.SIGUSR1)\n\n    def signal_handler(self, signalnum, stackframe):\n        \"\"\" signal handler \"\"\"\n        for pid in self.children_pids:\n            os.kill(pid, signal.SIGINT)  # kill children processes\n        (rank, original_trace) = self.error_queue.get()\n        msg = \"\"\"\\n\\n-- Tracebacks above this line can probably\n                 be ignored --\\n\\n\"\"\"\n        msg += original_trace\n        raise Exception(msg)\n\n\ndef batch_producer(generator_to_serve, queue, semaphore, opt):\n    \"\"\"Produce batches to `queues` from `generator_to_serve`.\"\"\"\n    init_logger(opt.log_file)\n    set_random_seed(opt.seed, False)\n\n    def pred(x):\n        \"\"\"\n        Filters batches that belong only\n        to gpu_ranks of current node\n        \"\"\"\n        for rank in opt.gpu_ranks:\n            if x[0] % opt.world_size == rank:\n                return True\n\n    generator_to_serve = filter(\n        pred, enumerate(generator_to_serve))\n\n    def next_batch():\n        # NOTE: stride (if needed) is handled at the\n        # generator (train_iter) level\n        new_batch = next(generator_to_serve)\n        semaphore.acquire()\n        return new_batch[1]\n\n    b = next_batch()\n\n    while True:\n        b.dataset = None\n        # Move batch to correspond device_id when consumer iterate\n\n        # hack to dodge unpicklable `dict_keys`\n        b.fields = list(b.fields)\n        queue.put(b)\n        b = next_batch()\n\n\ndef consumer(process_fn, opt, device_id, error_queue, batch_queue, semaphore):  # noqa: E501\n    \"\"\"Run `process_fn` on `device_id` with data from `batch_queue`.\"\"\"\n    try:\n        gpu_rank = multi_init(opt, device_id)\n        if gpu_rank != opt.gpu_ranks[device_id]:\n            raise AssertionError(\"An error occurred in \\\n                  Distributed initialization\")\n        process_fn(opt, device_id=device_id,\n                   batch_queue=batch_queue, semaphore=semaphore)\n    except KeyboardInterrupt:\n        pass  # killed by parent, do nothing\n    except Exception:\n        # propagate exception to parent process, keeping original traceback\n        import traceback\n        error_queue.put((opt.gpu_ranks[device_id], traceback.format_exc()))\n"
  },
  {
    "path": "onmt/utils/earlystopping.py",
    "content": "\nfrom enum import Enum\nfrom onmt.utils.logging import logger\n\n\nclass PatienceEnum(Enum):\n    IMPROVING = 0\n    DECREASING = 1\n    STOPPED = 2\n\n\nclass Scorer(object):\n    def __init__(self, best_score, name):\n        self.best_score = best_score\n        self.name = name\n\n    def is_improving(self, stats):\n        raise NotImplementedError()\n\n    def is_decreasing(self, stats):\n        raise NotImplementedError()\n\n    def update(self, stats):\n        self.best_score = self._caller(stats)\n\n    def __call__(self, stats, **kwargs):\n        return self._caller(stats)\n\n    def _caller(self, stats):\n        raise NotImplementedError()\n\n\nclass PPLScorer(Scorer):\n\n    def __init__(self):\n        super(PPLScorer, self).__init__(float(\"inf\"), \"ppl\")\n\n    def is_improving(self, stats):\n        return stats.ppl() < self.best_score\n\n    def is_decreasing(self, stats):\n        return stats.ppl() > self.best_score\n\n    def _caller(self, stats):\n        return stats.ppl()\n\n\nclass AccuracyScorer(Scorer):\n\n    def __init__(self):\n        super(AccuracyScorer, self).__init__(float(\"-inf\"), \"acc\")\n\n    def is_improving(self, stats):\n        return stats.accuracy() > self.best_score\n\n    def is_decreasing(self, stats):\n        return stats.accuracy() < self.best_score\n\n    def _caller(self, stats):\n        return stats.accuracy()\n\n\nDEFAULT_SCORERS = [PPLScorer(), AccuracyScorer()]\n\n\nSCORER_BUILDER = {\n    \"ppl\": PPLScorer,\n    \"accuracy\": AccuracyScorer\n}\n\n\ndef scorers_from_opts(opt):\n    if opt.early_stopping_criteria is None:\n        return DEFAULT_SCORERS\n    else:\n        scorers = []\n        for criterion in set(opt.early_stopping_criteria):\n            assert criterion in SCORER_BUILDER.keys(), \\\n                \"Criterion {} not found\".format(criterion)\n            scorers.append(SCORER_BUILDER[criterion]())\n        return scorers\n\n\nclass EarlyStopping(object):\n\n    def __init__(self, tolerance, scorers=DEFAULT_SCORERS):\n        \"\"\"\n            Callable class to keep track of early stopping.\n\n            Args:\n                tolerance(int): number of validation steps without improving\n                scorer(fn): list of scorers to validate performance on dev\n        \"\"\"\n\n        self.tolerance = tolerance\n        self.stalled_tolerance = self.tolerance\n        self.current_tolerance = self.tolerance\n        self.early_stopping_scorers = scorers\n        self.status = PatienceEnum.IMPROVING\n        self.current_step_best = 0\n\n    def __call__(self, valid_stats, step):\n        \"\"\"\n            Update the internal state of early stopping mechanism, whether to\n        continue training or stop the train procedure.\n\n            Checks whether the scores from all pre-chosen scorers improved. If\n        every metric improve, then the status is switched to improving and the\n        tolerance is reset. If every metric deteriorate, then the status is\n        switched to decreasing and the tolerance is also decreased; if the\n        tolerance reaches 0, then the status is changed to stopped.\n        Finally, if some improved and others not, then it's considered stalled;\n        after tolerance number of stalled, the status is switched to stopped.\n\n        :param valid_stats: Statistics of dev set\n        \"\"\"\n\n        if self.status == PatienceEnum.STOPPED:\n            # Don't do anything\n            return\n\n        if all([scorer.is_improving(valid_stats) for scorer\n                in self.early_stopping_scorers]):\n            self._update_increasing(valid_stats, step)\n\n        elif all([scorer.is_decreasing(valid_stats) for scorer\n                  in self.early_stopping_scorers]):\n            self._update_decreasing()\n\n        else:\n            self._update_stalled()\n\n    def _update_stalled(self):\n        self.stalled_tolerance -= 1\n\n        logger.info(\n            \"Stalled patience: {}/{}\".format(self.stalled_tolerance,\n                                             self.tolerance))\n\n        if self.stalled_tolerance == 0:\n            logger.info(\n                \"Training finished after stalled validations. Early Stop!\"\n            )\n            self._log_best_step()\n\n        self._decreasing_or_stopped_status_update(self.stalled_tolerance)\n\n    def _update_increasing(self, valid_stats, step):\n        self.current_step_best = step\n        for scorer in self.early_stopping_scorers:\n            logger.info(\n                \"Model is improving {}: {:g} --> {:g}.\".format(\n                    scorer.name, scorer.best_score, scorer(valid_stats))\n            )\n            # Update best score of each criteria\n            scorer.update(valid_stats)\n\n        # Reset tolerance\n        self.current_tolerance = self.tolerance\n        self.stalled_tolerance = self.tolerance\n\n        # Update current status\n        self.status = PatienceEnum.IMPROVING\n\n    def _update_decreasing(self):\n        # Decrease tolerance\n        self.current_tolerance -= 1\n\n        # Log\n        logger.info(\n            \"Decreasing patience: {}/{}\".format(self.current_tolerance,\n                                                self.tolerance)\n        )\n        # Log\n        if self.current_tolerance == 0:\n            logger.info(\"Training finished after not improving. Early Stop!\")\n            self._log_best_step()\n\n        self._decreasing_or_stopped_status_update(self.current_tolerance)\n\n    def _log_best_step(self):\n        logger.info(\"Best model found at step {}\".format(\n            self.current_step_best))\n\n    def _decreasing_or_stopped_status_update(self, tolerance):\n        self.status = PatienceEnum.DECREASING \\\n            if tolerance > 0 \\\n            else PatienceEnum.STOPPED\n\n    def is_improving(self):\n        return self.status == PatienceEnum.IMPROVING\n\n    def has_stopped(self):\n        return self.status == PatienceEnum.STOPPED\n"
  },
  {
    "path": "onmt/utils/logging.py",
    "content": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\n\nimport logging\nimport sys\nfrom logging.handlers import RotatingFileHandler\nimport os\nlogger = logging.getLogger()\n\n\ndef init_logger(log_file=None, log_file_level=logging.NOTSET, rotate=False):\n    log_format = logging.Formatter(\"[%(asctime)s %(levelname)s] %(message)s\")\n    logger = logging.getLogger()\n    logger.setLevel(logging.INFO)\n\n    console_handler = logging.StreamHandler()\n    console_handler.setFormatter(log_format)\n    logger.handlers = [console_handler]\n\n    if log_file and log_file != '':\n        log_dir = os.path.dirname(log_file)\n        if not os.path.exists(log_dir):\n            os.makedirs(log_dir)\n        if rotate:\n            file_handler = RotatingFileHandler(\n                log_file, maxBytes=1000000, backupCount=10)\n        else:\n            file_handler = logging.FileHandler(log_file)\n        file_handler.setLevel(log_file_level)\n        file_handler.setFormatter(log_format)\n        logger.addHandler(file_handler)\n\n    return logger\n"
  },
  {
    "path": "onmt/utils/loss.py",
    "content": "\"\"\"\nThis includes: LossComputeBase and the standard NMTLossCompute, and\n               sharded loss compute stuff.\n\"\"\"\nfrom __future__ import division\n\nimport random\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport onmt\nfrom onmt.modules.sparse_losses import SparsemaxLoss\nfrom onmt.modules.sparse_activations import LogSparsemax\nfrom onmt.constants import ModelTask, DefaultTokens\n\n\ndef build_loss_compute(model, tgt_field, opt, train=True):\n    \"\"\"\n    Returns a LossCompute subclass which wraps around an nn.Module subclass\n    (such as nn.NLLLoss) which defines the loss criterion. The LossCompute\n    object allows this loss to be computed in shards and passes the relevant\n    data to a Statistics object which handles training/validation logging.\n    Currently, the NMTLossCompute class handles all loss computation except\n    for when using a copy mechanism.\n    \"\"\"\n    device = torch.device(\"cuda\" if onmt.utils.misc.use_gpu(opt) else \"cpu\")\n\n    padding_idx = tgt_field.vocab.stoi[tgt_field.pad_token]\n    unk_idx = tgt_field.vocab.stoi[tgt_field.unk_token]\n    sep_idx = tgt_field.vocab.stoi[DefaultTokens.SEP]\n    eos_idx = tgt_field.vocab.stoi[DefaultTokens.EOS]\n\n    if opt.lambda_coverage != 0:\n        assert opt.coverage_attn, \"--coverage_attn needs to be set in \" \\\n            \"order to use --lambda_coverage != 0\"\n\n    if opt.copy_attn:\n        criterion = onmt.modules.CopyGeneratorLoss(\n            len(tgt_field.vocab), opt.copy_attn_force,\n            unk_index=unk_idx, ignore_index=padding_idx\n        )\n    elif opt.label_smoothing > 0 and train:\n        criterion = LabelSmoothingLoss(\n            opt.label_smoothing, len(tgt_field.vocab), ignore_index=padding_idx, sep_idx=sep_idx\n        )\n    elif isinstance(model.generator[-1], LogSparsemax):\n        criterion = SparsemaxLoss(ignore_index=padding_idx, reduction='sum')\n    else:\n        criterion = nn.NLLLoss(ignore_index=padding_idx, reduction='sum')\n\n    # if the loss function operates on vectors of raw logits instead of\n    # probabilities, only the first part of the generator needs to be\n    # passed to the NMTLossCompute. At the moment, the only supported\n    # loss function of this kind is the sparsemax loss.\n    use_raw_logits = isinstance(criterion, SparsemaxLoss)\n    loss_gen = model.generator[0] if use_raw_logits else model.generator\n\n    # force lambda to 0.0 when disabled\n    if not opt.orth_reg:\n        opt.lambda_orth_reg = 0.0\n    if not opt.sem_cov:\n        opt.lambda_sem_cov = 0.0\n\n    if opt.copy_attn:\n        if opt.model_task == ModelTask.SEQ2SEQ:\n            compute = onmt.modules.CopyGeneratorLossCompute(\n                criterion, loss_gen, tgt_field.vocab,\n                opt.copy_loss_by_seqlength,\n                lambda_coverage=opt.lambda_coverage,\n                lambda_align=opt.lambda_align,\n                lambda_orth_reg=opt.lambda_orth_reg,\n                lambda_sem_cov=opt.lambda_sem_cov,\n                n_neg=opt.num_negsample,\n                semcov_ending_state=opt.use_ending_state,\n                sep_idx=sep_idx,\n                eos_idx=eos_idx\n            )\n        elif opt.model_task == ModelTask.LANGUAGE_MODEL:\n            compute = onmt.modules.CopyGeneratorLMLossCompute(\n                criterion, loss_gen, tgt_field.vocab,\n                opt.copy_loss_by_seqlength,\n                lambda_coverage=opt.lambda_coverage\n            )\n        else:\n            raise ValueError(\n                f\"No copy generator loss defined for task {opt.model_task}\"\n            )\n    else:\n        if opt.model_task == ModelTask.SEQ2SEQ:\n            compute = NMTLossCompute(\n                criterion,\n                loss_gen,\n                lambda_coverage=opt.lambda_coverage,\n                lambda_align=opt.lambda_align,\n            )\n        elif opt.model_task == ModelTask.LANGUAGE_MODEL:\n            assert (\n                opt.lambda_align == 0.0\n            ), \"lamdba_align not supported in LM loss\"\n            compute = LMLossCompute(\n                criterion,\n                loss_gen,\n                lambda_coverage=opt.lambda_coverage,\n                lambda_align=opt.lambda_align,\n            )\n        else:\n            raise ValueError(\n                f\"No compute loss defined for task {opt.model_task}\"\n            )\n    compute.to(device)\n\n    return compute\n\n\nclass LossComputeBase(nn.Module):\n    \"\"\"\n    Class for managing efficient loss computation. Handles\n    sharding next step predictions and accumulating multiple\n    loss computations\n\n    Users can implement their own loss computation strategy by making\n    subclass of this one.  Users need to implement the _compute_loss()\n    and make_shard_state() methods.\n\n    Args:\n        generator (:obj:`nn.Module`) :\n             module that maps the output of the decoder to a\n             distribution over the target vocabulary.\n        tgt_vocab (:obj:`Vocab`) :\n             torchtext vocab object representing the target output\n        normalzation (str): normalize by \"sents\" or \"tokens\"\n    \"\"\"\n\n    def __init__(self, criterion, generator):\n        super(LossComputeBase, self).__init__()\n        self.criterion = criterion\n        self.generator = generator\n\n    @property\n    def padding_idx(self):\n        return self.criterion.ignore_index\n\n    def _make_shard_state(self, batch, output, range_, attns=None):\n        \"\"\"\n        Make shard state dictionary for shards() to return iterable\n        shards for efficient loss computation. Subclass must define\n        this method to match its own _compute_loss() interface.\n        Args:\n            batch: the current batch.\n            output: the predict output from the model.\n            range_: the range of examples for computing, the whole\n                    batch or a trunc of it?\n            attns: the attns dictionary returned from the model.\n        \"\"\"\n        return NotImplementedError\n\n    def _compute_loss(self, batch, output, target, **kwargs):\n        \"\"\"\n        Compute the loss. Subclass must define this method.\n\n        Args:\n\n            batch: the current batch.\n            output: the predict output from the model.\n            target: the validate target to compare output with.\n            **kwargs(optional): additional info for computing loss.\n        \"\"\"\n        return NotImplementedError\n\n    def __call__(self,\n                 batch,\n                 output,\n                 attns,\n                 normalization=1.0,\n                 shard_size=0,\n                 trunc_start=0,\n                 trunc_size=None,\n                 model=None):\n        \"\"\"Compute the forward loss, possibly in shards in which case this\n        method also runs the backward pass and returns ``None`` as the loss\n        value.\n\n        Also supports truncated BPTT for long sequences by taking a\n        range in the decoder output sequence to back propagate in.\n        Range is from `(trunc_start, trunc_start + trunc_size)`.\n\n        Note sharding is an exact efficiency trick to relieve memory\n        required for the generation buffers. Truncation is an\n        approximate efficiency trick to relieve the memory required\n        in the RNN buffers.\n\n        Args:\n          batch (batch) : batch of labeled examples\n          output (:obj:`FloatTensor`) :\n              output of decoder model `[tgt_len x batch x hidden]`\n          attns (dict) : dictionary of attention distributions\n              `[tgt_len x batch x src_len]`\n          normalization: Optional normalization factor.\n          shard_size (int) : maximum number of examples in a shard\n          trunc_start (int) : starting position of truncation window\n          trunc_size (int) : length of truncation window\n          model (int) : @memray: to accommodate the needs of sem_cov\n\n        Returns:\n            A tuple with the loss and a :obj:`onmt.utils.Statistics` instance.\n        \"\"\"\n        if trunc_size is None:\n            trunc_size = batch.tgt.size(0) - trunc_start\n        trunc_range = (trunc_start, trunc_start + trunc_size)\n        shard_state = self._make_shard_state(batch, output, trunc_range, attns)\n        # @memray semcov/orth\n        shard_state['model'] = model\n        if shard_size == 0:\n            loss, stats = self._compute_loss(batch, **shard_state)\n            return loss / float(normalization), stats\n        batch_stats = onmt.utils.Statistics()\n        for shard in shards(shard_state, shard_size):\n            loss, stats = self._compute_loss(batch, **shard)\n            loss.div(float(normalization)).backward()\n            batch_stats.update(stats)\n        return None, batch_stats\n\n    def _stats(self, loss, scores, target, batch_size):\n        \"\"\"\n        Args:\n            loss (:obj:`FloatTensor`): the loss computed by the loss criterion.\n            scores (:obj:`FloatTensor`): a score for each possible output\n            target (:obj:`FloatTensor`): true targets\n\n        Returns:\n            :obj:`onmt.utils.Statistics` : statistics for this batch.\n        \"\"\"\n        pred = scores.max(1)[1]\n        non_padding = target.ne(self.padding_idx)\n        num_correct = pred.eq(target).masked_select(non_padding).sum().item()\n        num_non_padding = non_padding.sum().item()\n        return onmt.utils.Statistics(loss.item(), num_non_padding, num_correct, batch_size)\n\n    def _bottle(self, _v):\n        return _v.view(-1, _v.size(2))\n\n    def _unbottle(self, _v, batch_size):\n        return _v.view(-1, batch_size, _v.size(1))\n\n\nclass LabelSmoothingLoss(nn.Module):\n    \"\"\"\n    With label smoothing,\n    KL-divergence between q_{smoothed ground truth prob.}(w)\n    and p_{prob. computed by model}(w) is minimized.\n    \"\"\"\n    def __init__(self, label_smoothing, tgt_vocab_size, ignore_index=-100):\n        assert 0.0 < label_smoothing <= 1.0\n        self.ignore_index = ignore_index\n        super(LabelSmoothingLoss, self).__init__()\n\n        smoothing_value = label_smoothing / (tgt_vocab_size - 2)\n        one_hot = torch.full((tgt_vocab_size,), smoothing_value)\n        one_hot[self.ignore_index] = 0\n        self.register_buffer('one_hot', one_hot.unsqueeze(0))\n\n        self.confidence = 1.0 - label_smoothing\n\n    def forward(self, output, target):\n        \"\"\"\n        output (FloatTensor): batch_size x n_classes\n        target (LongTensor): batch_size\n        \"\"\"\n        model_prob = self.one_hot.repeat(target.size(0), 1)\n        model_prob.scatter_(1, target.unsqueeze(1), self.confidence)\n        model_prob.masked_fill_((target == self.ignore_index).unsqueeze(1), 0)\n\n        return F.kl_div(output, model_prob, reduction='sum')\n\n\nclass ReplayMemory(object):\n\n    def __init__(self, capacity=300):\n        # vanilla replay memory\n        self.capacity = capacity\n        self.memory = []\n\n    def push(self, stuff):\n        \"\"\"Saves a transition.\"\"\"\n        self.memory.append(stuff)\n        if len(self.memory) > self.capacity:\n            self.memory = self.memory[-self.capacity:]\n\n    def sample(self, batch_size):\n        return random.sample(self.memory, batch_size)\n\n    def __len__(self):\n        return len(self.memory)\n\n\nclass CommonLossCompute(LossComputeBase):\n    \"\"\"\n    Loss Computation parent for NMTLossCompute and LMLossCompute\n\n    Implement loss compatible with coverage and alignement shards\n    \"\"\"\n    def __init__(self, criterion, generator, lambda_coverage, lambda_align, tgt_shift_index, **kwargs):\n        super(CommonLossCompute, self).__init__(criterion, generator, **kwargs)\n        self.lambda_coverage = lambda_coverage\n        self.lambda_align = lambda_align\n        self.tgt_shift_index = tgt_shift_index\n        # self.sep_idx = sep_idx\n        # self.lambda_orth_reg = lambda_orth_reg\n        # self.lambda_sem_cov = lambda_sem_cov\n        # self.n_neg = n_neg\n        # self.semcov_ending_state= semcov_ending_state\n        self.semcov_criterion = nn.NLLLoss()\n\n    def _make_shard_state(self, batch, output, range_, attns=None):\n        shard_state = {\n            \"output\": output,\n            \"target\": batch.tgt[range_[0] + 1: range_[1], :, 0],\n        }\n        if self.lambda_coverage != 0.0:\n            coverage = attns.get(\"coverage\", None)\n            std = attns.get(\"std\", None)\n            assert attns is not None\n            assert std is not None, \"lambda_coverage != 0.0 requires \" \\\n                \"attention mechanism\"\n            assert coverage is not None, \"lambda_coverage != 0.0 requires \" \\\n                \"coverage attention\"\n\n            shard_state.update({\n                \"std_attn\": attns.get(\"std\"),\n                \"coverage_attn\": coverage\n            })\n        if self.lambda_align != 0.0:\n            # attn_align should be in (batch_size, pad_tgt_size, pad_src_size)\n            attn_align = attns.get(\"align\", None)\n            # align_idx should be a Tensor in size([N, 3]), N is total number\n            # of align src-tgt pair in current batch, each as\n            # ['sent_N°_in_batch', 'tgt_id+1', 'src_id'] (check AlignField)\n            align_idx = batch.align\n            assert attns is not None\n            assert attn_align is not None, \"lambda_align != 0.0 requires \" \\\n                \"alignement attention head\"\n            assert align_idx is not None, \"lambda_align != 0.0 requires \" \\\n                \"provide guided alignement\"\n            pad_tgt_size, batch_size, _ = batch.tgt.size()\n            pad_src_size = batch.src[0].size(0)\n            align_matrix_size = [batch_size, pad_tgt_size, pad_src_size]\n            ref_align = onmt.utils.make_batch_align_matrix(\n                align_idx, align_matrix_size, normalize=True)\n            # NOTE: tgt-src ref alignement that in range_ of shard\n            # (coherent with batch.tgt)\n            shard_state.update({\n                \"align_head\": attn_align,\n                \"ref_align\": ref_align[:, range_[0] + 1: range_[1], :]\n            })\n        return shard_state\n\n    def _add_coverage_shard_state(self, shard_state, attns):\n        coverage = attns.get(\"coverage\", None)\n        std = attns.get(\"std\", None)\n        assert attns is not None\n        assert coverage is not None, (\n            \"lambda_coverage != 0.0 requires coverage attention\"\n            \" that could not be found in the model.\"\n            \" Transformer decoders do not implement coverage\"\n        )\n        assert std is not None, (\n            \"lambda_coverage != 0.0 requires attention mechanism\"\n            \" that could not be found in the model.\"\n        )\n        shard_state.update({\"std_attn\": attns.get(\"std\"),\n                            \"coverage_attn\": coverage})\n\n    def _compute_loss(self, batch, output, target,\n                      std_attn=None, coverage_attn=None,\n                      align_head=None, ref_align=None,\n                      src_states=None, dec_states=None, tgtenc_states=None,\n                      model=None):\n        batch_size = batch.batch_size\n        # output=[tgt_len, B, D], bottled_output=[tgt_len*B, D]\n        bottled_output = self._bottle(output)\n        # [tgt_len*B, V]\n        scores = self.generator(bottled_output)\n        # [tgt_len*B]\n        gtruth = target.contiguous().view(-1)\n\n        loss = self.criterion(scores, gtruth)\n        if self.lambda_coverage != 0.0:\n            coverage_loss = self._compute_coverage_loss(\n                std_attn=std_attn, coverage_attn=coverage_attn)\n            loss += coverage_loss\n\n        if self.lambda_align != 0.0:\n            if align_head.dtype != loss.dtype:  # Fix FP16\n                align_head = align_head.to(loss.dtype)\n            if ref_align.dtype != loss.dtype:\n                ref_align = ref_align.to(loss.dtype)\n            align_loss = self._compute_alignement_loss(\n                align_head=align_head, ref_align=ref_align)\n            loss += align_loss\n\n        # compute orthogonal penalty loss\n        if self.lambda_orth_reg > 0.0:\n            target_sep_idx = batch.sep_indices\n            assert dec_states is not None\n            assert target_sep_idx is not None\n            # decoder hidden state: output of decoder\n            orthogonal_penalty = self._compute_orthogonal_regularization_loss(target, dec_states, target_sep_idx)\n            loss += orthogonal_penalty\n            # print(\"Orth_reg=%.5f\" % orthogonal_penalty)\n        if self.lambda_sem_cov > 0.0:\n            target_sep_idx = batch.sep_indices\n            assert model is not None\n            assert src_states is not None\n            assert tgtenc_states is not None\n            assert target_sep_idx is not None\n            # model: model, has to include\n            #   target_encoding_mlp: an mlp with parameter (target_encoder_dim, target_encoding_mlp_hidden_dim), with non-linearity function\n            #   bilinear_layer: nn.Bilinear(source_hid, target_encoding_mlp_hidden_dim, 1), without non-linearity function\n            # source_representations: batch x source_len x source_hid\n            # target_representations: output of target encoder (last state), batch x target_hid\n            semantic_coverage_loss = self._compute_semantic_coverage_loss(model,\n                                                                          src_states, dec_states, tgtenc_states,\n                                                                          target, target_sep_idx,\n                                                                          num_negative=self.n_neg,\n                                                                          semcov_ending_state=self.semcov_ending_state)\n            loss += semantic_coverage_loss\n            # print(\"Sem_cov=%.5f\\n\" % semantic_coverage_loss)\n\n        stats = self._stats(loss.clone(), scores, gtruth, batch_size)\n\n        return loss, stats\n\n    def _compute_coverage_loss(self, std_attn, coverage_attn):\n        covloss = torch.min(std_attn, coverage_attn).sum()\n        covloss *= self.lambda_coverage\n        return covloss\n\n    def _add_align_shard_state(self, shard_state, batch, range_start,\n                               range_end, attns):\n        # attn_align should be in (batch_size, pad_tgt_size, pad_src_size)\n        attn_align = attns.get(\"align\", None)\n        # align_idx should be a Tensor in size([N, 3]), N is total number\n        # of align src-tgt pair in current batch, each as\n        # ['sent_N°_in_batch', 'tgt_id+1', 'src_id'] (check AlignField)\n        align_idx = batch.align\n        assert attns is not None\n        assert attn_align is not None, (\n            \"lambda_align != 0.0 requires \" \"alignement attention head\"\n        )\n        assert align_idx is not None, (\n            \"lambda_align != 0.0 requires \" \"provide guided alignement\"\n        )\n        pad_tgt_size, batch_size, _ = batch.tgt.size()\n        pad_src_size = batch.src[0].size(0)\n        align_matrix_size = [batch_size, pad_tgt_size, pad_src_size]\n        ref_align = onmt.utils.make_batch_align_matrix(\n            align_idx, align_matrix_size, normalize=True\n        )\n        # NOTE: tgt-src ref alignement that in range_ of shard\n        # (coherent with batch.tgt)\n        shard_state.update(\n            {\n                \"align_head\": attn_align,\n                \"ref_align\": ref_align[:, range_start:range_end, :],\n            }\n        )\n\n    def _compute_alignement_loss(self, align_head, ref_align):\n        \"\"\"Compute loss between 2 partial alignment matrix.\"\"\"\n        # align_head contains value in [0, 1) presenting attn prob,\n        # 0 was resulted by the context attention src_pad_mask\n        # So, the correspand position in ref_align should also be 0\n        # Therefore, clip align_head to > 1e-18 should be bias free.\n        align_loss = -align_head.clamp(min=1e-18).log().mul(ref_align).sum()\n        align_loss *= self.lambda_align\n        return align_loss\n\n    def orthogonal_penalty(self, _m, l_n_norm=2):\n        # _m: h x n\n        # I:  n x n\n        m = torch.mm(torch.t(_m), _m)  # n x n\n        _ones = torch.ones([m.size(0), m.size(0)])  # n x n\n        _eyes = torch.eye(m.size(0))  # n x n\n        if m.is_cuda:\n            _ones = _ones.cuda()\n            _eyes = _eyes.cuda()\n        # mask off the diagonal elements\n        m = torch.mul(m, _ones-_eyes)\n        # compute the element-wise norm and return average\n        return torch.pow(m, l_n_norm).sum() / (m.size(0) * (m.size(0) - 1))\n\n    def _compute_orthogonal_regularization_loss(self, tgt_indices, decoder_hidden_states, sep_idx):\n        \"\"\"\n        aux loss: make the decoder outputs at all <sep>s to be orthogonal.\n        The last phrase is actually ignored since there's no trailing <sep>\n\n        :param tgt_indices: T x B\n        :param decoder_hidden_states: B x T x H\n        :param sep_idx: max_num_sep x batch_size\n        :return:\n        \"\"\"\n        penalties = []\n        # make batch first\n        tgt_indices = tgt_indices.permute((1, 0)) # B x T\n        sep_masks = tgt_indices.eq(sep_idx).int() # B x T\n\n        # per data point in a batch\n        for i in range(tgt_indices.size(0)):\n            # if sep_idx.max().item() > decoder_hidden_states.size(1):\n            #     # this error occurs if shard_size is set (BPTT enabled)\n            #     print(\"BUG!\")\n            # if there's at least two <sep> or <eos> (> 2 phrases)\n            if sep_masks[i].sum() > 1:\n                sep_indices = torch.nonzero(sep_masks[i]).squeeze()\n                sep_hiddens = decoder_hidden_states[i].index_select(dim=0, index=sep_indices) # [num_sep, hidden_dim]\n                sep_hiddens = sep_hiddens.permute((1, 0)) # hidden_dim x num_sep\n                penalty = self.orthogonal_penalty(sep_hiddens, 2)  # 1\n                penalties.append(penalty)\n\n        if len(penalties) > 0 and sep_masks.size(0) > 0:\n            penalties = torch.sum(torch.stack(penalties, -1)) / float(len(penalties))\n        else:\n            penalties = 0.0\n\n        penalties = penalties * self.lambda_orth_reg\n        return penalties\n\n    def random_insert(self, _list, elem):\n        insert_before_this = np.random.randint(low=0, high=len(_list) + 1)\n        return _list[:insert_before_this] + [elem] + _list[insert_before_this:], insert_before_this\n\n    def _compute_semantic_coverage_loss(self, model, src_states, tgtenc_states,\n                                        tgt_indices, num_negative=None,\n                                        semcov_ending_state=False,\n                                        sep_idx=None, eos_idx=None\n                                        ):\n        # src_states: B x H\n        # tgtenc_states: B x T x H\n        # target_indices: T x B\n        batch_size = src_states.size(0)\n        # make batch first\n        tgt_indices = tgt_indices.permute((1, 0)) # [B, T]\n        sep_masks = tgt_indices.eq(sep_idx) # [B, T]\n        eos_masks = tgt_indices.eq(eos_idx) # [B, T]\n\n        # n_neg is how many negative samples to sample\n        if num_negative is None or num_negative > batch_size:\n            num_negative = batch_size\n\n        # input for computing the loss, expected size=[n_sep*(1+n_neg), src_hid/tgtenc_hid/1]\n        batch_src_states, batch_tgtenc_states, batch_labels = None, None, None\n        src_states = src_states.detach()\n\n        # per data point in a batch\n        for i in range(batch_size):\n            if semcov_ending_state:\n                if eos_masks[i].ne(0).sum() == 0:\n                    continue\n                eos_indices = torch.nonzero(eos_masks[i]).squeeze()\n                end_index = eos_indices if len(eos_indices.shape) == 0 else eos_indices[-1]\n                sep_tgtenc_states = tgtenc_states[i].index_select(dim=0, index=end_index) # 1 x tgtenc_hid\n                n_sep = 1\n            else:\n                if sep_masks[i].ne(0).sum() == 0:\n                    continue\n                sep_indices = torch.nonzero(sep_masks[i]).squeeze()\n                sep_tgtenc_states = tgtenc_states[i].index_select(dim=0, index=sep_indices) # n_sep x tgtenc_hid\n                n_sep = sep_indices.size(0)\n\n            # [n_sep*(n_neg+1), H]\n            input_tgtenc_states = sep_tgtenc_states.expand((num_negative + 1), -1, -1).reshape(-1, tgtenc_states.size(-1))\n            # i-th example is positive class\n            pos_idx = torch.Tensor([i] * n_sep).long()\n            # negative sampling from the rest examples in the same batch\n            neg_idx = np.random.randint(0, batch_size - 1, size=(n_sep * num_negative))\n            for idx, neg_id in enumerate(neg_idx): # ensure no positive was sampled\n                if neg_id >= i:\n                    neg_idx[idx] = (neg_idx[idx] + 1) % batch_size\n            neg_idx = torch.from_numpy(neg_idx).long()\n            input_src_idx = torch.cat((pos_idx, neg_idx), dim=0) # n_sep*(n_neg+1)\n            if src_states.is_cuda:\n                input_src_idx = input_src_idx.cuda()\n            # n_sep*(n_neg+1) x H\n            input_src_states = src_states.index_select(dim=0, index=input_src_idx)\n            # n_sep*1, the pos example is always at the 1st place\n            input_labels = torch.from_numpy(np.asarray([0] * n_sep)).long()\n\n            if batch_tgtenc_states is None:\n                batch_tgtenc_states = input_tgtenc_states\n                batch_src_states = input_src_states\n                batch_labels = input_labels\n            else:\n                batch_tgtenc_states = torch.cat((batch_tgtenc_states, input_tgtenc_states), dim=0)\n                batch_src_states = torch.cat((batch_src_states, input_src_states), dim=0)\n                batch_labels = torch.cat((batch_labels, input_labels), dim=0)\n\n        pred = model.decoder.bilinear_layer(batch_tgtenc_states, batch_src_states).squeeze(-1).reshape((-1, num_negative + 1))  # [n_sep, n_neg+1]\n        pred = torch.nn.functional.log_softmax(pred, dim=-1) # [n_sep, n_neg+1]\n\n        # loss compute\n        if src_states.is_cuda:\n            batch_labels = batch_labels.cuda()\n        loss = self.semcov_criterion(pred, batch_labels) # pred=[n_sep, n_neg+1], label=[n_sep]\n        loss = loss * self.lambda_sem_cov\n        return loss\n\n\n    def _make_shard_state(self, batch, output, range_, attns=None):\n        range_start = range_[0] + self.tgt_shift_index\n        range_end = range_[1]\n        shard_state = {\n            \"output\": output,\n            \"target\": batch.tgt[range_start:range_end, :, 0],\n            \"src_states\": attns.get(\"enc_states\"), # @memray: dec_hidden_states, [bs, hid_size]\n            \"dec_states\": attns.get(\"dec_states\"), # @memray: dec_hidden_states, [bs, tgt_len, hid_size]\n            \"tgtenc_states\": attns.get(\"tgtenc_states\") # @memray: target_encoder_hidden_states, [bs, tgt_len, hid_size]\n        }\n        if self.lambda_coverage != 0.0:\n            self._add_coverage_shard_state(shard_state, attns)\n        if self.lambda_align != 0.0:\n            self._add_align_shard_state(\n                shard_state, batch, range_start, range_end, attns\n            )\n        return shard_state\n\n\nclass NMTLossCompute(CommonLossCompute):\n    \"\"\"\n    Standard NMT Loss Computation.\n    \"\"\"\n    def __init__(self, criterion, generator, normalization=\"sents\",\n                 lambda_coverage=0.0, lambda_align=0.0):\n        super(NMTLossCompute, self).__init__(criterion, generator,\n                                             normalization=normalization,\n                                             lambda_coverage=lambda_coverage,\n                                             lambda_align=lambda_align,\n                                             tgt_shift_index=1)\n\n\nclass LMLossCompute(CommonLossCompute):\n    \"\"\"\n    Standard LM Loss Computation.\n    \"\"\"\n    def __init__(self, criterion, generator, normalization=\"sents\",\n                 lambda_coverage=0.0, lambda_align=0.0):\n        super(LMLossCompute, self).__init__(criterion, generator,\n                                            normalization=normalization,\n                                            lambda_coverage=lambda_coverage,\n                                            lambda_align=lambda_align,\n                                            tgt_shift_index=0)\n\n\ndef filter_shard_state(state, shard_size=None):\n    for k, v in state.items():\n        if shard_size is None:\n            yield k, v\n\n        if v is not None:\n            v_split = []\n            if isinstance(v, torch.Tensor):\n                for v_chunk in torch.split(v, shard_size):\n                    v_chunk = v_chunk.data.clone()\n                    v_chunk.requires_grad = v.requires_grad\n                    v_split.append(v_chunk)\n            yield k, (v, v_split)\n\n\ndef shards(state, shard_size, eval_only=False):\n    \"\"\"\n    Args:\n        state: A dictionary which corresponds to the output of\n               *LossCompute._make_shard_state(). The values for\n               those keys are Tensor-like or None.\n        shard_size: The maximum size of the shards yielded by the model.\n        eval_only: If True, only yield the state, nothing else.\n              Otherwise, yield shards.\n\n    Yields:\n        Each yielded shard is a dict.\n\n    Side effect:\n        After the last shard, this function does back-propagation.\n    \"\"\"\n    if eval_only:\n        yield filter_shard_state(state)\n    else:\n        # non_none: the subdict of the state dictionary where the values\n        # are not None.\n        non_none = dict(filter_shard_state(state, shard_size))\n\n        # Now, the iteration:\n        # state is a dictionary of sequences of tensor-like but we\n        # want a sequence of dictionaries of tensors.\n        # First, unzip the dictionary into a sequence of keys and a\n        # sequence of tensor-like sequences.\n        keys, values = zip(*((k, [v_chunk for v_chunk in v_split])\n                             for k, (_, v_split) in non_none.items()))\n\n        # Now, yield a dictionary for each shard. The keys are always\n        # the same. values is a sequence of length #keys where each\n        # element is a sequence of length #shards. We want to iterate\n        # over the shards, not over the keys: therefore, the values need\n        # to be re-zipped by shard and then each shard can be paired\n        # with the keys.\n        for shard_tensors in zip(*values):\n            yield dict(zip(keys, shard_tensors))\n\n        # Assumed backprop'd\n        variables = []\n        for k, (v, v_split) in non_none.items():\n            if isinstance(v, torch.Tensor) and state[k].requires_grad:\n                variables.extend(zip(torch.split(state[k], shard_size),\n                                     [v_chunk.grad for v_chunk in v_split]))\n        inputs, grads = zip(*variables)\n        torch.autograd.backward(inputs, grads)\n"
  },
  {
    "path": "onmt/utils/misc.py",
    "content": "# -*- coding: utf-8 -*-\nimport argparse\n\nimport torch\nimport random\nimport inspect\nimport numpy as np\nfrom itertools import islice, repeat\nimport os\n\ndef str2bool(v):\n    if isinstance(v, bool):\n       return v\n    if v.lower() in (True, 'true', 't', 'yes', 'y', '1', 1):\n        return True\n    elif v.lower() in (False, 'false', 'f', 'no', 'n', '0', 0):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\ndef check_path(path, exist_ok=False, log=print):\n    \"\"\"Check if `path` exists, makedirs if not else warning/IOError.\"\"\"\n    if os.path.exists(path):\n        if exist_ok:\n            log(f\"path {path} exists, may overwrite...\")\n        else:\n            raise IOError(f\"path {path} exists, stop.\")\n    else:\n        os.makedirs(os.path.dirname(path), exist_ok=True)\n\n\ndef split_corpus(path, shard_size, default=None):\n    \"\"\"yield a `list` containing `shard_size` line of `path`,\n    or repeatly generate `default` if `path` is None.\n    \"\"\"\n    if path is not None:\n        return _split_corpus(path, shard_size)\n    else:\n        return repeat(default)\n\n\ndef _split_corpus(path, shard_size):\n    \"\"\"Yield a `list` containing `shard_size` line of `path`.\n    \"\"\"\n    with open(path, \"rb\") as f:\n        if shard_size <= 0:\n            yield f.readlines()\n        else:\n            while True:\n                shard = list(islice(f, shard_size))\n                if not shard:\n                    break\n                yield shard\n\n\ndef aeq(*args):\n    \"\"\"\n    Assert all arguments have the same value\n    \"\"\"\n    arguments = (arg for arg in args)\n    first = next(arguments)\n    assert all(arg == first for arg in arguments), \\\n        \"Not all arguments have the same value: \" + str(args)\n\n\ndef sequence_mask(lengths, max_len=None):\n    \"\"\"\n    Creates a boolean mask from sequence lengths.\n    \"\"\"\n    batch_size = lengths.numel()\n    max_len = max_len or lengths.max()\n    return (torch.arange(0, max_len, device=lengths.device)\n            .type_as(lengths)\n            .repeat(batch_size, 1)\n            .lt(lengths.unsqueeze(1)))\n\n\ndef tile(x, count, dim=0):\n    \"\"\"\n    Tiles x on dimension dim count times.\n    \"\"\"\n    perm = list(range(len(x.size())))\n    if dim != 0:\n        perm[0], perm[dim] = perm[dim], perm[0]\n        x = x.permute(perm).contiguous()\n    out_size = list(x.size())\n    out_size[0] *= count\n    batch = x.size(0)\n    x = x.view(batch, -1) \\\n         .transpose(0, 1) \\\n         .repeat(count, 1) \\\n         .transpose(0, 1) \\\n         .contiguous() \\\n         .view(*out_size)\n    if dim != 0:\n        x = x.permute(perm).contiguous()\n    return x\n\n\ndef use_gpu(opt):\n    \"\"\"\n    Creates a boolean if gpu used\n    \"\"\"\n    return (hasattr(opt, 'gpu_ranks') and len(opt.gpu_ranks) > 0) or \\\n        (hasattr(opt, 'gpu') and opt.gpu > -1)\n\n\ndef set_random_seed(seed, is_cuda):\n    \"\"\"Sets the random seed.\"\"\"\n    if seed > 0:\n        torch.manual_seed(seed)\n        # this one is needed for torchtext random call (shuffled iterator)\n        # in multi gpu it ensures datasets are read in the same order\n        random.seed(seed)\n        # some cudnn methods can be random even after fixing the seed\n        # unless you tell it to be deterministic\n        torch.backends.cudnn.deterministic = True\n        # This one is needed for various tranfroms\n        np.random.seed(seed)\n\n    if is_cuda and seed > 0:\n        # These ensure same initialization in multi gpu mode\n        torch.cuda.manual_seed(seed)\n\n\ndef generate_relative_positions_matrix(length, max_relative_positions,\n                                       cache=False):\n    \"\"\"Generate the clipped relative positions matrix\n       for a given length and maximum relative positions\"\"\"\n    if cache:\n        distance_mat = torch.arange(-length+1, 1, 1).unsqueeze(0)\n    else:\n        range_vec = torch.arange(length)\n        range_mat = range_vec.unsqueeze(-1).expand(-1, length).transpose(0, 1)\n        distance_mat = range_mat - range_mat.transpose(0, 1)\n    distance_mat_clipped = torch.clamp(distance_mat,\n                                       min=-max_relative_positions,\n                                       max=max_relative_positions)\n    # Shift values to be >= 0\n    final_mat = distance_mat_clipped + max_relative_positions\n    return final_mat\n\n\ndef relative_matmul(x, z, transpose):\n    \"\"\"Helper function for relative positions attention.\"\"\"\n    batch_size = x.shape[0]\n    heads = x.shape[1]\n    length = x.shape[2]\n    x_t = x.permute(2, 0, 1, 3)\n    x_t_r = x_t.reshape(length, heads * batch_size, -1)\n    if transpose:\n        z_t = z.transpose(1, 2)\n        x_tz_matmul = torch.matmul(x_t_r, z_t)\n    else:\n        x_tz_matmul = torch.matmul(x_t_r, z)\n    x_tz_matmul_r = x_tz_matmul.reshape(length, batch_size, heads, -1)\n    x_tz_matmul_r_t = x_tz_matmul_r.permute(1, 2, 0, 3)\n    return x_tz_matmul_r_t\n\n\ndef fn_args(fun):\n    \"\"\"Returns the list of function arguments name.\"\"\"\n    return inspect.getfullargspec(fun).args\n\n\ndef report_matrix(row_label, column_label, matrix):\n    header_format = \"{:>10.10} \" + \"{:>10.7} \" * len(row_label)\n    row_format = \"{:>10.10} \" + \"{:>10.7f} \" * len(row_label)\n    output = header_format.format(\"\", *row_label) + '\\n'\n    for word, row in zip(column_label, matrix):\n        max_index = row.index(max(row))\n        row_format = row_format.replace(\n            \"{:>10.7f} \", \"{:*>10.7f} \", max_index + 1)\n        row_format = row_format.replace(\n            \"{:*>10.7f} \", \"{:>10.7f} \", max_index)\n        output += row_format.format(word, *row) + '\\n'\n        row_format = \"{:>10.10} \" + \"{:>10.7f} \" * len(row_label)\n    return output\n\n\ndef check_model_config(model_config, root):\n    # we need to check the model path + any tokenizer path\n    for model in model_config[\"models\"]:\n        model_path = os.path.join(root, model)\n        if not os.path.exists(model_path):\n            raise FileNotFoundError(\n                \"{} from model {} does not exist\".format(\n                    model_path, model_config[\"id\"]))\n    if \"tokenizer\" in model_config.keys():\n        if \"params\" in model_config[\"tokenizer\"].keys():\n            for k, v in model_config[\"tokenizer\"][\"params\"].items():\n                if k.endswith(\"path\"):\n                    tok_path = os.path.join(root, v)\n                    if not os.path.exists(tok_path):\n                        raise FileNotFoundError(\n                            \"{} from model {} does not exist\".format(\n                                tok_path, model_config[\"id\"]))\n"
  },
  {
    "path": "onmt/utils/optimizers.py",
    "content": "\"\"\" Optimizers class \"\"\"\nimport torch\nimport torch.optim as optim\nfrom torch.nn.utils import clip_grad_norm_\nimport operator\nimport functools\nfrom copy import copy\nfrom math import sqrt\nimport types\nimport importlib\nfrom onmt.utils.misc import fn_args\n\n\ndef build_torch_optimizer(model, opt):\n    \"\"\"Builds the PyTorch optimizer.\n\n    We use the default parameters for Adam that are suggested by\n    the original paper https://arxiv.org/pdf/1412.6980.pdf\n    These values are also used by other established implementations,\n    e.g. https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer\n    https://keras.io/optimizers/\n    Recently there are slightly different values used in the paper\n    \"Attention is all you need\"\n    https://arxiv.org/pdf/1706.03762.pdf, particularly the value beta2=0.98\n    was used there however, beta2=0.999 is still arguably the more\n    established value, so we use that here as well\n\n    Args:\n      model: The model to optimize.\n      opt. The dictionary of options.\n\n    Returns:\n      A ``torch.optim.Optimizer`` instance.\n    \"\"\"\n    params = [p for p in model.parameters() if p.requires_grad]\n    betas = [opt.adam_beta1, opt.adam_beta2]\n    if opt.optim == 'sgd':\n        optimizer = optim.SGD(params, lr=opt.learning_rate)\n    elif opt.optim == 'adagrad':\n        optimizer = optim.Adagrad(\n            params,\n            lr=opt.learning_rate,\n            initial_accumulator_value=opt.adagrad_accumulator_init)\n    elif opt.optim == 'adadelta':\n        optimizer = optim.Adadelta(params, lr=opt.learning_rate)\n    elif opt.optim == 'adafactor':\n        optimizer = AdaFactor(\n            params,\n            non_constant_decay=True,\n            enable_factorization=True,\n            weight_decay=0)\n    elif opt.optim == 'adam':\n        optimizer = optim.Adam(\n            params,\n            lr=opt.learning_rate,\n            betas=betas,\n            eps=1e-9)\n    elif opt.optim == 'sparseadam':\n        dense = []\n        sparse = []\n        for name, param in model.named_parameters():\n            if not param.requires_grad:\n                continue\n            # TODO: Find a better way to check for sparse gradients.\n            if 'embed' in name:\n                sparse.append(param)\n            else:\n                dense.append(param)\n        optimizer = MultipleOptimizer(\n            [optim.Adam(\n                dense,\n                lr=opt.learning_rate,\n                betas=betas,\n                eps=1e-8),\n             optim.SparseAdam(\n                 sparse,\n                 lr=opt.learning_rate,\n                 betas=betas,\n                 eps=1e-8)])\n    elif opt.optim == 'fusedadam':\n        # we use here a FusedAdam() copy of an old Apex repo\n        optimizer = FusedAdam(\n            params,\n            lr=opt.learning_rate,\n            betas=betas)\n        if opt.model_dtype == 'fp16':\n            import apex\n            # In this case use the old FusedAdam with FP16_optimizer wrapper\n            static_loss_scale = opt.loss_scale\n            dynamic_loss_scale = opt.loss_scale == 0\n            optimizer = apex.contrib.optimizers.FP16_Optimizer(\n                optimizer,\n                static_loss_scale=static_loss_scale,\n                dynamic_loss_scale=dynamic_loss_scale)\n    else:\n        raise ValueError('Invalid optimizer type: ' + opt.optim)\n\n    return optimizer\n\n\ndef make_learning_rate_decay_fn(opt):\n    \"\"\"Returns the learning decay function from options.\"\"\"\n    if opt.decay_method == 'noam':\n        return functools.partial(\n            noam_decay,\n            warmup_steps=opt.warmup_steps,\n            model_size=opt.rnn_size)\n    elif opt.decay_method == 'noam_simple':\n        # @memray\n        return functools.partial(\n            noam_simple_decay,\n            warmup_steps=opt.warmup_steps)\n    elif opt.decay_method == 'noamwd':\n        return functools.partial(\n            noamwd_decay,\n            warmup_steps=opt.warmup_steps,\n            model_size=opt.rnn_size,\n            rate=opt.learning_rate_decay,\n            decay_steps=opt.decay_steps,\n            start_step=opt.start_decay_steps)\n    elif opt.decay_method == 'rsqrt':\n        return functools.partial(\n            rsqrt_decay, warmup_steps=opt.warmup_steps)\n    elif opt.decay_method == 'linear':\n        return functools.partial(\n            linear_decay, warmup_steps=opt.warmup_steps, train_steps=opt.train_steps)\n    elif opt.start_decay_steps is not None:\n        return functools.partial(\n            exponential_decay,\n            rate=opt.learning_rate_decay,\n            decay_steps=opt.decay_steps,\n            start_step=opt.start_decay_steps)\n\n\ndef noam_decay(step, warmup_steps, model_size):\n    \"\"\"Learning rate schedule described in\n    https://arxiv.org/pdf/1706.03762.pdf.\n    \"\"\"\n    return (\n        model_size ** (-0.5) *\n        min(step ** (-0.5), step * warmup_steps**(-1.5)))\n\n\ndef noam_simple_decay(step, warmup_steps):\n    \"\"\"Learning rate schedule described in\n    https://arxiv.org/pdf/1706.03762.pdf.\n    \"\"\"\n    return (\n        min(step ** (-0.5), step * warmup_steps**(-1.5)))\n\n\ndef noamwd_decay(step, warmup_steps,\n                 model_size, rate, decay_steps, start_step=0):\n    \"\"\"Learning rate schedule optimized for huge batches\n    \"\"\"\n    return (\n        model_size ** (-0.5) *\n        min(step ** (-0.5), step * warmup_steps**(-1.5)) *\n        rate ** (max(step - start_step + decay_steps, 0) // decay_steps))\n\n\ndef exponential_decay(step, rate, decay_steps, start_step=0):\n    \"\"\"A standard exponential decay, scaling the learning rate by :obj:`rate`\n    every :obj:`decay_steps` steps.\n    \"\"\"\n    return rate ** (max(step - start_step + decay_steps, 0) // decay_steps)\n\n\ndef rsqrt_decay(step, warmup_steps):\n    \"\"\"Decay based on the reciprocal of the step square root.\"\"\"\n    return 1.0 / sqrt(max(step, warmup_steps))\n\ndef linear_decay(step, warmup_steps, train_steps):\n    \"\"\"Decay based on the reciprocal of the step square root.\"\"\"\n    assert warmup_steps < train_steps, 'warmup_steps must be smaller than train_steps'\n    if step <= warmup_steps:\n        return step / warmup_steps\n    else:\n        return (step - train_steps) / (warmup_steps - train_steps)\n\n\nclass MultipleOptimizer(object):\n    \"\"\" Implement multiple optimizers needed for sparse adam \"\"\"\n\n    def __init__(self, op):\n        \"\"\" ? \"\"\"\n        self.optimizers = op\n\n    @property\n    def param_groups(self):\n        param_groups = []\n        for optimizer in self.optimizers:\n            param_groups.extend(optimizer.param_groups)\n        return param_groups\n\n    def zero_grad(self):\n        \"\"\" ? \"\"\"\n        for op in self.optimizers:\n            op.zero_grad()\n\n    def step(self):\n        \"\"\" ? \"\"\"\n        for op in self.optimizers:\n            op.step()\n\n    @property\n    def state(self):\n        \"\"\" ? \"\"\"\n        return {k: v for op in self.optimizers for k, v in op.state.items()}\n\n    def state_dict(self):\n        \"\"\" ? \"\"\"\n        return [op.state_dict() for op in self.optimizers]\n\n    def load_state_dict(self, state_dicts):\n        \"\"\" ? \"\"\"\n        assert len(state_dicts) == len(self.optimizers)\n        for i in range(len(state_dicts)):\n            self.optimizers[i].load_state_dict(state_dicts[i])\n\n\nclass Optimizer(object):\n    \"\"\"\n    Controller class for optimization. Mostly a thin\n    wrapper for `optim`, but also useful for implementing\n    rate scheduling beyond what is currently available.\n    Also implements necessary methods for training RNNs such\n    as grad manipulations.\n    \"\"\"\n\n    def __init__(self,\n                 optimizer,\n                 learning_rate,\n                 learning_rate_decay_fn=None,\n                 max_grad_norm=None):\n        \"\"\"Initializes the controller.\n\n       Args:\n         optimizer: A ``torch.optim.Optimizer`` instance.\n         learning_rate: The initial learning rate.\n         learning_rate_decay_fn: An optional callable taking the current step\n           as argument and return a learning rate scaling factor.\n         max_grad_norm: Clip gradients to this global norm.\n        \"\"\"\n        self._optimizer = optimizer\n        self._learning_rate = learning_rate\n        self._learning_rate_decay_fn = learning_rate_decay_fn\n        self._max_grad_norm = max_grad_norm or 0\n        self._training_step = 1\n        self._decay_step = 1\n        self._fp16 = None\n        self._scaler = None\n\n    @classmethod\n    def from_opt(cls, model, opt, checkpoint=None):\n        \"\"\"Builds the optimizer from options.\n\n        Args:\n          cls: The ``Optimizer`` class to instantiate.\n          model: The model to optimize.\n          opt: The dict of user options.\n          checkpoint: An optional checkpoint to load states from.\n\n        Returns:\n          An ``Optimizer`` instance.\n        \"\"\"\n        optim_opt = opt\n        optim_state_dict = None\n\n        if opt.train_from and checkpoint is not None:\n            optim = checkpoint['optim']\n            ckpt_opt = checkpoint['opt']\n            ckpt_state_dict = {}\n            if isinstance(optim, Optimizer):  # Backward compatibility.\n                ckpt_state_dict['training_step'] = optim._step + 1\n                ckpt_state_dict['decay_step'] = optim._step + 1\n                ckpt_state_dict['optimizer'] = optim.optimizer.state_dict()\n            else:\n                ckpt_state_dict = optim\n\n            if opt.reset_optim == 'none':\n                # Load everything from the checkpoint.\n                optim_opt = ckpt_opt\n                optim_state_dict = ckpt_state_dict\n            elif opt.reset_optim == 'all':\n                # Build everything from scratch.\n                pass\n            elif opt.reset_optim == 'states':\n                # Reset optimizer, keep options.\n                optim_opt = ckpt_opt\n                optim_state_dict = ckpt_state_dict\n                del optim_state_dict['optimizer']\n            elif opt.reset_optim == 'keep_states':\n                # Reset options, keep optimizer.\n                optim_state_dict = ckpt_state_dict\n\n        optimizer = cls(\n            build_torch_optimizer(model, optim_opt),\n            optim_opt.learning_rate,\n            learning_rate_decay_fn=make_learning_rate_decay_fn(optim_opt),\n            max_grad_norm=optim_opt.max_grad_norm)\n        if opt.model_dtype == \"fp16\":\n            if opt.optim == \"fusedadam\":\n                optimizer._fp16 = \"legacy\"\n            else:\n                optimizer._fp16 = \"amp\"\n                from torch.cuda.amp import GradScaler\n                optimizer._scaler = GradScaler()\n        if optim_state_dict:\n            optimizer.load_state_dict(optim_state_dict)\n        return optimizer\n\n    @property\n    def training_step(self):\n        \"\"\"The current training step.\"\"\"\n        return self._training_step\n\n    @property\n    def amp(self):\n        \"\"\"True if use torch amp mix precision training.\"\"\"\n        return self._fp16 == \"amp\"\n\n    def learning_rate(self):\n        \"\"\"Returns the current learning rate.\"\"\"\n        if self._learning_rate_decay_fn is None:\n            return self._learning_rate\n        scale = self._learning_rate_decay_fn(self._decay_step)\n        return scale * self._learning_rate\n\n    def state_dict(self):\n        return {\n            'training_step': self._training_step,\n            'decay_step': self._decay_step,\n            'optimizer': self._optimizer.state_dict()\n        }\n\n    def load_state_dict(self, state_dict):\n        self._training_step = state_dict['training_step']\n        # State can be partially restored.\n        if 'decay_step' in state_dict:\n            self._decay_step = state_dict['decay_step']\n        if 'optimizer' in state_dict:\n            self._optimizer.load_state_dict(state_dict['optimizer'])\n\n    def zero_grad(self):\n        \"\"\"Zero the gradients of optimized parameters.\"\"\"\n        self._optimizer.zero_grad()\n\n    def backward(self, loss):\n        \"\"\"Wrapper for backward pass. Some optimizer requires ownership of the\n        backward pass.\"\"\"\n        if self.amp:\n            self._scaler.scale(loss).backward()\n        elif self._fp16 == \"legacy\":\n            kwargs = {}\n            if \"update_master_grads\" in fn_args(self._optimizer.backward):\n                kwargs[\"update_master_grads\"] = True\n            self._optimizer.backward(loss, **kwargs)\n        else:\n            loss.backward()\n\n    def step(self):\n        \"\"\"Update the model parameters based on current gradients.\n\n        Optionally, will employ gradient modification or update learning\n        rate.\n        \"\"\"\n        learning_rate = self.learning_rate()\n\n        if self.amp:\n            self._scaler.unscale_(self._optimizer)\n        elif self._fp16 == \"legacy\":\n            if hasattr(self._optimizer, \"update_master_grads\"):\n                self._optimizer.update_master_grads()\n            if hasattr(self._optimizer, \"clip_master_grads\") and \\\n               self._max_grad_norm > 0:\n                self._optimizer.clip_master_grads(self._max_grad_norm)\n\n        for group in self._optimizer.param_groups:\n            group['lr'] = learning_rate\n            if self._max_grad_norm > 0 and self._fp16 != \"legacy\":\n                clip_grad_norm_(group['params'], self._max_grad_norm)\n\n        if self.amp:\n            # unscaled optimizer's gradients (already done therefore skip),\n            # skips optimizer.step() if gradients contain infs/NaNs.\n            self._scaler.step(self._optimizer)\n            # Updates the scale for next iteration.\n            self._scaler.update()\n        else:\n            self._optimizer.step()\n        self._decay_step += 1\n        self._training_step += 1\n\n# Code below is an implementation of https://arxiv.org/pdf/1804.04235.pdf\n# inspired but modified from https://github.com/DeadAt0m/adafactor-pytorch\n\n\nclass AdaFactor(torch.optim.Optimizer):\n\n    def __init__(self, params, lr=None, beta1=0.9, beta2=0.999, eps1=1e-30,\n                 eps2=1e-3, cliping_threshold=1, non_constant_decay=True,\n                 enable_factorization=True, ams_grad=True, weight_decay=0):\n\n        enable_momentum = beta1 != 0\n\n        if non_constant_decay:\n            ams_grad = False\n\n        defaults = dict(lr=lr, beta1=beta1, beta2=beta2, eps1=eps1,\n                        eps2=eps2, cliping_threshold=cliping_threshold,\n                        weight_decay=weight_decay, ams_grad=ams_grad,\n                        enable_factorization=enable_factorization,\n                        enable_momentum=enable_momentum,\n                        non_constant_decay=non_constant_decay)\n\n        super(AdaFactor, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(AdaFactor, self).__setstate__(state)\n\n    def _experimental_reshape(self, shape):\n        temp_shape = shape[2:]\n        if len(temp_shape) == 1:\n            new_shape = (shape[0], shape[1]*shape[2])\n        else:\n            tmp_div = len(temp_shape) // 2 + len(temp_shape) % 2\n            new_shape = (shape[0]*functools.reduce(operator.mul,\n                                                   temp_shape[tmp_div:], 1),\n                         shape[1]*functools.reduce(operator.mul,\n                                                   temp_shape[:tmp_div], 1))\n        return new_shape, copy(shape)\n\n    def _check_shape(self, shape):\n        '''\n        output1 - True - algorithm for matrix, False - vector;\n        output2 - need reshape\n        '''\n        if len(shape) > 2:\n            return True, True\n        elif len(shape) == 2:\n            return True, False\n        elif len(shape) == 2 and (shape[0] == 1 or shape[1] == 1):\n            return False, False\n        else:\n            return False, False\n\n    def _rms(self, x):\n        return sqrt(torch.mean(x.pow(2)))\n\n    def step(self, closure=None):\n        loss = None\n        if closure is not None:\n            loss = closure()\n        for group in self.param_groups:\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n\n                if grad.is_sparse:\n                    raise RuntimeError('Adam does not support sparse \\\n                                       gradients, use SparseAdam instead')\n\n                is_matrix, is_need_reshape = self._check_shape(grad.size())\n                new_shape = p.data.size()\n                if is_need_reshape and group['enable_factorization']:\n                    new_shape, old_shape = \\\n                        self._experimental_reshape(p.data.size())\n                    grad = grad.view(new_shape)\n\n                state = self.state[p]\n                if len(state) == 0:\n                    state['step'] = 0\n                    if group['enable_momentum']:\n                        state['exp_avg'] = torch.zeros(new_shape,\n                                                       dtype=torch.float32,\n                                                       device=p.grad.device)\n\n                    if is_matrix and group['enable_factorization']:\n                        state['exp_avg_sq_R'] = \\\n                            torch.zeros((1, new_shape[1]),\n                                        dtype=torch.float32,\n                                        device=p.grad.device)\n                        state['exp_avg_sq_C'] = \\\n                            torch.zeros((new_shape[0], 1),\n                                        dtype=torch.float32,\n                                        device=p.grad.device)\n                    else:\n                        state['exp_avg_sq'] = torch.zeros(new_shape,\n                                                          dtype=torch.float32,\n                                                          device=p.grad.device)\n                    if group['ams_grad']:\n                        state['exp_avg_sq_hat'] = \\\n                            torch.zeros(new_shape, dtype=torch.float32,\n                                        device=p.grad.device)\n\n                if group['enable_momentum']:\n                    exp_avg = state['exp_avg']\n\n                if is_matrix and group['enable_factorization']:\n                    exp_avg_sq_r = state['exp_avg_sq_R']\n                    exp_avg_sq_c = state['exp_avg_sq_C']\n                else:\n                    exp_avg_sq = state['exp_avg_sq']\n\n                if group['ams_grad']:\n                    exp_avg_sq_hat = state['exp_avg_sq_hat']\n\n                state['step'] += 1\n                lr_t = group['lr']\n                lr_t *= max(group['eps2'], self._rms(p.data))\n\n                if group['enable_momentum']:\n                    if group['non_constant_decay']:\n                        beta1_t = group['beta1'] * \\\n                                  (1 - group['beta1'] ** (state['step'] - 1)) \\\n                                  / (1 - group['beta1'] ** state['step'])\n                    else:\n                        beta1_t = group['beta1']\n                    exp_avg.mul_(beta1_t).add_(1 - beta1_t, grad)\n\n                if group['non_constant_decay']:\n                    beta2_t = group['beta2'] * \\\n                              (1 - group['beta2'] ** (state['step'] - 1)) / \\\n                              (1 - group['beta2'] ** state['step'])\n                else:\n                    beta2_t = group['beta2']\n\n                if is_matrix and group['enable_factorization']:\n                    exp_avg_sq_r.mul_(beta2_t). \\\n                        add_(1 - beta2_t, torch.sum(torch.mul(grad, grad).\n                                                    add_(group['eps1']),\n                                                    dim=0, keepdim=True))\n                    exp_avg_sq_c.mul_(beta2_t). \\\n                        add_(1 - beta2_t, torch.sum(torch.mul(grad, grad).\n                                                    add_(group['eps1']),\n                                                    dim=1, keepdim=True))\n                    v = torch.mul(exp_avg_sq_c,\n                                  exp_avg_sq_r).div_(torch.sum(exp_avg_sq_r))\n                else:\n                    exp_avg_sq.mul_(beta2_t). \\\n                        addcmul_(1 - beta2_t, grad, grad). \\\n                        add_((1 - beta2_t)*group['eps1'])\n                    v = exp_avg_sq\n\n                g = grad\n                if group['enable_momentum']:\n                    g = torch.div(exp_avg, 1 - beta1_t ** state['step'])\n\n                if group['ams_grad']:\n                    torch.max(exp_avg_sq_hat, v, out=exp_avg_sq_hat)\n                    v = exp_avg_sq_hat\n                    u = torch.div(g, (torch.div(v, 1 - beta2_t **\n                                  state['step'])).sqrt().add_(group['eps1']))\n                else:\n                    u = torch.div(g, v.sqrt())\n\n                u.div_(max(1, self._rms(u) / group['cliping_threshold']))\n                p.data.add_(-lr_t * (u.view(old_shape) if is_need_reshape and\n                            group['enable_factorization'] else u))\n\n                if group['weight_decay'] != 0:\n                    p.data.add_(-group['weight_decay'] * lr_t, p.data)\n\n        return loss\n\n\nclass FusedAdam(torch.optim.Optimizer):\n\n    \"\"\"Implements Adam algorithm. Currently GPU-only.\n       Requires Apex to be installed via\n    ``python setup.py install --cuda_ext --cpp_ext``.\n    It has been proposed in `Adam: A Method for Stochastic Optimization`_.\n    Arguments:\n        params (iterable): iterable of parameters to optimize or dicts defining\n            parameter groups.\n        lr (float, optional): learning rate. (default: 1e-3)\n        betas (Tuple[float, float], optional): coefficients used for computing\n            running averages of gradient and its square.\n            (default: (0.9, 0.999))\n        eps (float, optional): term added to the denominator to improve\n            numerical stability. (default: 1e-8)\n        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n        amsgrad (boolean, optional): whether to use the AMSGrad variant of this\n            algorithm from the paper `On the Convergence of Adam and Beyond`_\n            (default: False) NOT SUPPORTED in FusedAdam!\n        eps_inside_sqrt (boolean, optional): in the 'update parameters' step,\n            adds eps to the bias-corrected second moment estimate before\n            evaluating square root instead of adding it to the square root of\n            second moment estimate as in the original paper. (default: False)\n    .. _Adam: A Method for Stochastic Optimization:\n        https://arxiv.org/abs/1412.6980\n    .. _On the Convergence of Adam and Beyond:\n        https://openreview.net/forum?id=ryQu7f-RZ\n    \"\"\"\n\n    def __init__(self, params,\n                 lr=1e-3, bias_correction=True,\n                 betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False,\n                 weight_decay=0., max_grad_norm=0., amsgrad=False):\n        global fused_adam_cuda\n        fused_adam_cuda = importlib.import_module(\"fused_adam_cuda\")\n\n        if amsgrad:\n            raise RuntimeError('AMSGrad variant not supported.')\n        defaults = dict(lr=lr, bias_correction=bias_correction,\n                        betas=betas, eps=eps, weight_decay=weight_decay,\n                        max_grad_norm=max_grad_norm)\n        super(FusedAdam, self).__init__(params, defaults)\n        self.eps_mode = 0 if eps_inside_sqrt else 1\n\n    def step(self, closure=None, grads=None, output_params=None,\n             scale=1., grad_norms=None):\n        \"\"\"Performs a single optimization step.\n        Arguments:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n            grads (list of tensors, optional): weight gradient to use for the\n                optimizer update. If gradients have type torch.half, parameters\n                are expected to be in type torch.float. (default: None)\n            output params (list of tensors, optional): A reduced precision copy\n                of the updated weights written out in addition to the regular\n                updated weights. Have to be of same type as gradients.\n                (default: None)\n            scale (float, optional): factor to divide gradient tensor values\n                by before applying to weights. (default: 1)\n        \"\"\"\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        if grads is None:\n            grads_group = [None]*len(self.param_groups)\n        # backward compatibility\n        # assuming a list/generator of parameter means single group\n        elif isinstance(grads, types.GeneratorType):\n            grads_group = [grads]\n        elif type(grads[0]) != list:\n            grads_group = [grads]\n        else:\n            grads_group = grads\n\n        if output_params is None:\n            output_params_group = [None]*len(self.param_groups)\n        elif isinstance(output_params, types.GeneratorType):\n            output_params_group = [output_params]\n        elif type(output_params[0]) != list:\n            output_params_group = [output_params]\n        else:\n            output_params_group = output_params\n\n        if grad_norms is None:\n            grad_norms = [None]*len(self.param_groups)\n\n        for group, grads_this_group, output_params_this_group, \\\n            grad_norm in zip(self.param_groups, grads_group,\n                             output_params_group, grad_norms):\n            if grads_this_group is None:\n                grads_this_group = [None]*len(group['params'])\n            if output_params_this_group is None:\n                output_params_this_group = [None]*len(group['params'])\n\n            # compute combined scale factor for this group\n            combined_scale = scale\n            if group['max_grad_norm'] > 0:\n                # norm is in fact norm*scale\n                clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']\n                if clip > 1:\n                    combined_scale = clip * scale\n\n            bias_correction = 1 if group['bias_correction'] else 0\n\n            for p, grad, output_param in zip(group['params'],\n                                             grads_this_group,\n                                             output_params_this_group):\n                # note: p.grad should not ever be set for correct operation of\n                # mixed precision optimizer that sometimes sends None gradients\n                if p.grad is None and grad is None:\n                    continue\n                if grad is None:\n                    grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError('FusedAdam does not support sparse \\\n                                       gradients, please consider \\\n                                       SparseAdam instead')\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    state['exp_avg'] = torch.zeros_like(p.data)\n                    # Exponential moving average of squared gradient values\n                    state['exp_avg_sq'] = torch.zeros_like(p.data)\n\n                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n                beta1, beta2 = group['betas']\n\n                state['step'] += 1\n\n                out_p = torch.tensor([], dtype=torch.float) if output_param \\\n                    is None else output_param\n                fused_adam_cuda.adam(p.data,\n                                     out_p,\n                                     exp_avg,\n                                     exp_avg_sq,\n                                     grad,\n                                     group['lr'],\n                                     beta1,\n                                     beta2,\n                                     group['eps'],\n                                     combined_scale,\n                                     state['step'],\n                                     self.eps_mode,\n                                     bias_correction,\n                                     group['weight_decay'])\n        return loss\n"
  },
  {
    "path": "onmt/utils/parse.py",
    "content": "import configargparse as cfargparse\nimport os\nimport torch\n\nimport onmt.opts as opts\nfrom onmt.utils.logging import logger\nfrom onmt.constants import CorpusName, ModelTask\nfrom onmt.transforms import AVAILABLE_TRANSFORMS\n\n\nclass DataOptsCheckerMixin(object):\n    \"\"\"Checker with methods for validate data related options.\"\"\"\n\n    @staticmethod\n    def _validate_file(file_path, info):\n        \"\"\"Check `file_path` is valid or raise `IOError`.\"\"\"\n        if not os.path.isfile(file_path):\n            raise IOError(f\"Please check path of your {info} file!\")\n\n    @classmethod\n    def _validate_data(cls, opt):\n        \"\"\"Parse corpora specified in data field of YAML file.\"\"\"\n        import yaml\n        default_transforms = opt.transforms\n        if len(default_transforms) != 0:\n            logger.info(f\"Default transforms: {default_transforms}.\")\n        corpora = yaml.safe_load(opt.data)\n\n        for cname, corpus in corpora.items():\n            # Check Transforms\n            _transforms = corpus.get('transforms', None)\n            if _transforms is None:\n                logger.info(f\"Missing transforms field for {cname} data, \"\n                            f\"set to default: {default_transforms}.\")\n                corpus['transforms'] = default_transforms\n            # Check path\n            path_src = corpus.get('path_src', None)\n            path_tgt = corpus.get('path_tgt', None)\n            if path_src is None:\n                raise ValueError(f'Corpus {cname} src path is required.'\n                                 'tgt path is also required for non language'\n                                 ' modeling tasks.')\n            else:\n                opt.data_task = ModelTask.SEQ2SEQ\n                if path_tgt is None:\n                    logger.warning(\n                        \"path_tgt is None, it should be set unless the task\"\n                        \" is language modeling\"\n                    )\n                    opt.data_task = ModelTask.LANGUAGE_MODEL\n                    # tgt is src for LM task\n                    corpus[\"path_tgt\"] = path_src\n                    corpora[cname] = corpus\n                    path_tgt = path_src\n                # cls._validate_file(path_src, info=f'{cname}/path_src')\n                # cls._validate_file(path_tgt, info=f'{cname}/path_tgt')\n            path_align = corpus.get('path_align', None)\n            if path_align is None:\n                if hasattr(opt, 'lambda_align') and opt.lambda_align > 0.0:\n                    raise ValueError(f'Corpus {cname} alignment file path are '\n                                     'required when lambda_align > 0.0')\n                corpus['path_align'] = None\n            else:\n                cls._validate_file(path_align, info=f'{cname}/path_align')\n            # Check prefix: will be used when use prefix transform\n            src_prefix = corpus.get('src_prefix', None)\n            tgt_prefix = corpus.get('tgt_prefix', None)\n            if src_prefix is None or tgt_prefix is None:\n                if 'prefix' in corpus['transforms']:\n                    raise ValueError(f'Corpus {cname} prefix are required.')\n            # Check weight\n            weight = corpus.get('weight', None)\n            if weight is None:\n                if cname != CorpusName.VALID:\n                    logger.warning(f\"Corpus {cname}'s weight should be given.\"\n                                   \" We default it to 1 for you.\")\n                corpus['weight'] = 1\n        logger.info(f\"Parsed {len(corpora)} corpora from -data.\")\n        opt.data = corpora\n\n    @classmethod\n    def _validate_transforms_opts(cls, opt):\n        \"\"\"Check options used by transforms.\"\"\"\n        for name, transform_cls in AVAILABLE_TRANSFORMS.items():\n            if name in opt._all_transform:\n                transform_cls._validate_options(opt)\n\n    @classmethod\n    def _get_all_transform(cls, opt):\n        \"\"\"Should only called after `_validate_data`.\"\"\"\n        all_transforms = set(opt.transforms)\n        if isinstance(opt.data, str):\n            opt.data = eval(opt.data)\n        for cname, corpus in opt.data.items():\n            _transforms = set(corpus['transforms'])\n            if len(_transforms) != 0:\n                all_transforms.update(_transforms)\n        if hasattr(opt, 'lambda_align') and opt.lambda_align > 0.0:\n            if not all_transforms.isdisjoint(\n                    {'sentencepiece', 'bpe', 'onmt_tokenize'}):\n                raise ValueError('lambda_align is not compatible with'\n                                 ' on-the-fly tokenization.')\n            if not all_transforms.isdisjoint(\n                    {'tokendrop', 'prefix', 'bart'}):\n                raise ValueError('lambda_align is not compatible yet with'\n                                 ' potentiel token deletion/addition.')\n        opt._all_transform = all_transforms\n\n    @classmethod\n    def _validate_fields_opts(cls, opt, build_vocab_only=False):\n        \"\"\"Check options relate to vocab and fields.\"\"\"\n        if build_vocab_only:\n            if not opt.share_vocab:\n                assert opt.tgt_vocab, \\\n                    \"-tgt_vocab is required if not -share_vocab.\"\n            return\n        # validation when train:\n        cls._validate_file(opt.src_vocab, info='src vocab')\n        if not opt.share_vocab:\n            cls._validate_file(opt.tgt_vocab, info='tgt vocab')\n\n        if opt.dump_fields or opt.dump_transforms:\n            assert opt.save_data, \"-save_data should be set if set \\\n                -dump_fields or -dump_transforms.\"\n        # Check embeddings stuff\n        if opt.both_embeddings is not None:\n            assert (opt.src_embeddings is None\n                    and opt.tgt_embeddings is None), \\\n                \"You don't need -src_embeddings or -tgt_embeddings \\\n                if -both_embeddings is set.\"\n\n        if any([opt.both_embeddings is not None,\n                opt.src_embeddings is not None,\n                opt.tgt_embeddings is not None]):\n            assert opt.embeddings_type is not None, \\\n                \"You need to specify an -embedding_type!\"\n            assert opt.save_data, \"-save_data should be set if use \\\n                pretrained embeddings.\"\n\n    @classmethod\n    def _validate_language_model_compatibilities_opts(cls, opt):\n        if opt.model_task != ModelTask.LANGUAGE_MODEL:\n            return\n\n        logger.info(\"encoder is not used for LM task\")\n\n        assert opt.share_vocab and (\n            opt.tgt_vocab is None\n        ), \"vocab must be shared for LM task\"\n\n        assert (\n            opt.decoder_type == \"transformer\"\n        ), \"Only transformer decoder is supported for LM task\"\n\n    @classmethod\n    def validate_prepare_opts(cls, opt, build_vocab_only=False):\n        \"\"\"Validate all options relate to prepare (data/transform/vocab).\"\"\"\n        if opt.n_sample != 0:\n            assert opt.save_data, \"-save_data should be set if \\\n                want save samples.\"\n        cls._validate_data(opt)\n        cls._get_all_transform(opt)\n        cls._validate_transforms_opts(opt)\n        cls._validate_fields_opts(opt, build_vocab_only=build_vocab_only)\n\n    @classmethod\n    def validate_model_opts(cls, opt):\n        cls._validate_language_model_compatibilities_opts(opt)\n\n\nclass ArgumentParser(cfargparse.ArgumentParser, DataOptsCheckerMixin):\n    \"\"\"OpenNMT option parser powered with option check methods.\"\"\"\n\n    def __init__(\n            self,\n            config_file_parser_class=cfargparse.YAMLConfigFileParser,\n            formatter_class=cfargparse.ArgumentDefaultsHelpFormatter,\n            **kwargs):\n        super(ArgumentParser, self).__init__(\n            config_file_parser_class=config_file_parser_class,\n            formatter_class=formatter_class,\n            **kwargs)\n\n    @classmethod\n    def defaults(cls, *args):\n        \"\"\"Get default arguments added to a parser by all ``*args``.\"\"\"\n        dummy_parser = cls()\n        for callback in args:\n            callback(dummy_parser)\n        defaults = dummy_parser.parse_known_args([])[0]\n        return defaults\n\n    @classmethod\n    def update_model_opts(cls, model_opt):\n        if model_opt.word_vec_size > 0:\n            model_opt.src_word_vec_size = model_opt.word_vec_size\n            model_opt.tgt_word_vec_size = model_opt.word_vec_size\n\n        # Backward compatibility with \"fix_word_vecs_*\" opts\n        if hasattr(model_opt, 'fix_word_vecs_enc'):\n            model_opt.freeze_word_vecs_enc = model_opt.fix_word_vecs_enc\n        if hasattr(model_opt, 'fix_word_vecs_dec'):\n            model_opt.freeze_word_vecs_dec = model_opt.fix_word_vecs_dec\n\n        if model_opt.layers > 0:\n            model_opt.enc_layers = model_opt.layers\n            model_opt.dec_layers = model_opt.layers\n\n        if model_opt.rnn_size > 0:\n            model_opt.enc_rnn_size = model_opt.rnn_size\n            model_opt.dec_rnn_size = model_opt.rnn_size\n\n        model_opt.brnn = model_opt.encoder_type == \"brnn\"\n\n        if model_opt.copy_attn_type is None:\n            model_opt.copy_attn_type = model_opt.global_attention\n\n        if model_opt.alignment_layer is None:\n            model_opt.alignment_layer = -2\n            model_opt.lambda_align = 0.0\n            model_opt.full_context_alignment = False\n\n    @classmethod\n    def validate_model_opts(cls, model_opt):\n        assert model_opt.model_type in [\"text\", \"keyphrase\"], \\\n            \"Unsupported model type %s\" % model_opt.model_type\n\n        # encoder and decoder should be same sizes\n        same_size = model_opt.enc_rnn_size == model_opt.dec_rnn_size\n        assert same_size, \\\n            \"The encoder and decoder rnns must be the same size for now\"\n\n        assert model_opt.rnn_type != \"SRU\" or model_opt.gpu_ranks, \\\n            \"Using SRU requires -gpu_ranks set.\"\n        if model_opt.share_embeddings:\n            if model_opt.model_type != \"text\" and model_opt.model_type != \"keyphrase\":\n                raise AssertionError(\n                    \"--share_embeddings requires --model_type text.\")\n        if model_opt.lambda_align > 0.0:\n            assert model_opt.decoder_type == 'transformer', \\\n                \"Only transformer is supported to joint learn alignment.\"\n            assert model_opt.alignment_layer < model_opt.dec_layers and \\\n                model_opt.alignment_layer >= -model_opt.dec_layers, \\\n                \"N° alignment_layer should be smaller than number of layers.\"\n            logger.info(\"Joint learn alignment at layer [{}] \"\n                        \"with {} heads in full_context '{}'.\".format(\n                            model_opt.alignment_layer,\n                            model_opt.alignment_heads,\n                            model_opt.full_context_alignment))\n\n    @classmethod\n    def ckpt_model_opts(cls, ckpt_opt):\n        # Load default opt values, then overwrite with the opts in\n        # the checkpoint. That way, if there are new options added,\n        # the defaults are used.\n        opt = cls.defaults(opts.model_opts)\n        opt.__dict__.update(ckpt_opt.__dict__)\n        return opt\n\n    @classmethod\n    def validate_train_opts(cls, opt):\n        if opt.epochs:\n            raise AssertionError(\n                  \"-epochs is deprecated please use -train_steps.\")\n        if opt.truncated_decoder > 0 and max(opt.accum_count) > 1:\n            raise AssertionError(\"BPTT is not compatible with -accum > 1\")\n\n        if opt.gpuid:\n            raise AssertionError(\n                  \"gpuid is deprecated see world_size and gpu_ranks\")\n        if torch.cuda.is_available() and not opt.gpu_ranks:\n            logger.warn(\"You have a CUDA device, should run with -gpu_ranks\")\n        if opt.world_size < len(opt.gpu_ranks):\n            raise AssertionError(\n                  \"parameter counts of -gpu_ranks must be less or equal \"\n                  \"than -world_size.\")\n        if opt.world_size == len(opt.gpu_ranks) and \\\n                min(opt.gpu_ranks) > 0:\n            raise AssertionError(\n                  \"-gpu_ranks should have master(=0) rank \"\n                  \"unless -world_size is greater than len(gpu_ranks).\")\n\n        assert len(opt.dropout) == len(opt.dropout_steps), \\\n            \"Number of dropout values must match accum_steps values\"\n\n        assert len(opt.attention_dropout) == len(opt.dropout_steps), \\\n            \"Number of attention_dropout values must match accum_steps values\"\n\n        assert len(opt.accum_count) == len(opt.accum_steps), \\\n            'Number of accum_count values must match number of accum_steps'\n\n    @classmethod\n    def validate_translate_opts(cls, opt):\n        if opt.beam_size != 1 and opt.random_sampling_topk != 1:\n            raise ValueError('Can either do beam search OR random sampling.')\n"
  },
  {
    "path": "onmt/utils/report_manager.py",
    "content": "\"\"\" Report manager utility \"\"\"\nfrom __future__ import print_function\nimport time\nfrom datetime import datetime\n\nimport onmt\nimport wandb\n\nfrom onmt.utils.logging import logger\n\n\ndef build_report_manager(opt, gpu_rank):\n    if opt.tensorboard and gpu_rank <= 0:\n        from torch.utils.tensorboard import SummaryWriter\n        if not hasattr(opt, 'tensorboard_log_dir_dated'):\n            opt.tensorboard_log_dir_dated = (\n                opt.tensorboard_log_dir +\n                datetime.now().strftime(\"/%b-%d_%H-%M-%S\")\n            )\n        writer = SummaryWriter(opt.tensorboard_log_dir_dated, comment=\"Unmt\")\n    else:\n        writer = None\n\n    if opt.wandb == True and gpu_rank == 0:\n        report_wandb = True\n    else:\n        report_wandb = False\n\n    report_mgr = ReportMgr(opt.report_every, start_time=-1,\n                           tensorboard_writer=writer, report_wandb=report_wandb)\n    return report_mgr\n\n\nclass ReportMgrBase(object):\n    \"\"\"\n    Report Manager Base class\n    Inherited classes should override:\n        * `_report_training`\n        * `_report_step`\n    \"\"\"\n\n    def __init__(self, report_every, start_time=-1.):\n        \"\"\"\n        Args:\n            report_every(int): Report status every this many sentences\n            start_time(float): manually set report start time. Negative values\n                means that you will need to set it later or use `start()`\n        \"\"\"\n        self.report_every = report_every\n        self.start_time = start_time\n\n    def start(self):\n        self.start_time = time.time()\n\n    def log(self, *args, **kwargs):\n        logger.info(*args, **kwargs)\n\n    def report_training(self, step, num_steps, learning_rate, patience,\n                        report_stats, multigpu=False):\n        \"\"\"\n        This is the user-defined batch-level traing progress\n        report function.\n\n        Args:\n            step(int): current step count.\n            num_steps(int): total number of batches.\n            learning_rate(float): current learning rate.\n            report_stats(Statistics): old Statistics instance.\n        Returns:\n            report_stats(Statistics): updated Statistics instance.\n        \"\"\"\n        if self.start_time < 0:\n            raise ValueError(\"\"\"ReportMgr needs to be started\n                                (set 'start_time' or use 'start()'\"\"\")\n\n        if step % self.report_every == 0:\n            if multigpu:\n                report_stats = \\\n                    onmt.utils.Statistics.all_gather_stats(report_stats)\n            self._report_training(\n                step, num_steps, learning_rate, patience, report_stats)\n            return onmt.utils.Statistics()\n        else:\n            return report_stats\n\n    def _report_training(self, *args, **kwargs):\n        \"\"\" To be overridden \"\"\"\n        raise NotImplementedError()\n\n    def report_step(self, lr, patience, step, train_stats=None,\n                    valid_stats=None):\n        \"\"\"\n        Report stats of a step\n\n        Args:\n            lr(float): current learning rate\n            patience(int): current patience\n            step(int): current step\n            train_stats(Statistics): training stats\n            valid_stats(Statistics): validation stats\n        \"\"\"\n        self._report_step(\n            lr, patience, step,\n            train_stats=train_stats,\n            valid_stats=valid_stats)\n\n    def _report_step(self, *args, **kwargs):\n        raise NotImplementedError()\n\n\nclass ReportMgr(ReportMgrBase):\n    def __init__(self, report_every, start_time=-1.,\n                 tensorboard_writer=None, report_wandb=False):\n        \"\"\"\n        A report manager that writes statistics on standard output as well as\n        (optionally) TensorBoard\n\n        Args:\n            report_every(int): Report status every this many sentences\n            tensorboard_writer(:obj:`tensorboard.SummaryWriter`):\n                The TensorBoard Summary writer to use or None\n        \"\"\"\n        super(ReportMgr, self).__init__(report_every, start_time)\n        self.tensorboard_writer = tensorboard_writer\n        self.report_wandb = report_wandb\n\n    def maybe_log_tensorboard(self, stats, prefix, learning_rate,\n                              patience, step):\n        if self.tensorboard_writer is not None:\n            stats.log_tensorboard(\n                prefix, self.tensorboard_writer, learning_rate, patience, step)\n\n    def _report_training(self, step, num_steps, learning_rate, patience,\n                         report_stats):\n        \"\"\"\n        See base class method `ReportMgrBase.report_training`.\n        \"\"\"\n        report_stats.output(step, num_steps,\n                            learning_rate, self.start_time)\n\n        self.maybe_log_tensorboard(report_stats,\n                                   \"progress\",\n                                   learning_rate,\n                                   patience,\n                                   step)\n\n        if self.report_wandb:\n            wandb.log(report_stats.to_dict(learning_rate), step=step)\n\n        report_stats = onmt.utils.Statistics()\n\n        return report_stats\n\n    def _report_step(self, lr, patience, step,\n                     train_stats=None,\n                     valid_stats=None):\n        \"\"\"\n        See base class method `ReportMgrBase.report_step`.\n        \"\"\"\n        if train_stats is not None:\n            self.log('Train perplexity: %g' % train_stats.ppl())\n            self.log('Train accuracy: %g' % train_stats.accuracy())\n\n            self.maybe_log_tensorboard(train_stats,\n                                       \"train\",\n                                       lr,\n                                       patience,\n                                       step)\n\n            if self.report_wandb:\n                wandb.log(train_stats.to_dict(learning_rate=lr, prefix='train_'), step=step)\n\n        if valid_stats is not None:\n            self.log('Validation perplexity: %g' % valid_stats.ppl())\n            self.log('Validation accuracy: %g' % valid_stats.accuracy())\n\n            self.maybe_log_tensorboard(valid_stats,\n                                       \"valid\",\n                                       lr,\n                                       patience,\n                                       step)\n\n            if self.report_wandb:\n                wandb.log(valid_stats.to_dict(learning_rate=None, prefix='valid_'), step=step)\n\n"
  },
  {
    "path": "onmt/utils/rnn_factory.py",
    "content": "\"\"\"\n RNN tools\n\"\"\"\nimport torch.nn as nn\nimport onmt.models\n\n\ndef rnn_factory(rnn_type, **kwargs):\n    \"\"\" rnn factory, Use pytorch version when available. \"\"\"\n    no_pack_padded_seq = False\n    if rnn_type == \"SRU\":\n        # SRU doesn't support PackedSequence.\n        no_pack_padded_seq = True\n        rnn = onmt.models.sru.SRU(**kwargs)\n    else:\n        rnn = getattr(nn, rnn_type)(**kwargs)\n    return rnn, no_pack_padded_seq\n"
  },
  {
    "path": "onmt/utils/statistics.py",
    "content": "\"\"\" Statistics calculation utility \"\"\"\nfrom __future__ import division\nimport time\nimport math\nimport sys\n\nfrom onmt.utils.logging import logger\nfrom decimal import Decimal\n\n\nclass Statistics(object):\n    \"\"\"\n    Accumulator for loss statistics.\n    Currently calculates:\n\n    * accuracy\n    * perplexity\n    * elapsed time\n    \"\"\"\n    def __init__(self, loss=0, n_words=0, n_correct=0, n_examples=0):\n        self.loss = loss\n        self.n_examples = n_examples\n        self.n_words = n_words\n        self.n_correct = n_correct\n        self.n_src_words = 0\n        self.start_time = time.time()\n\n    @staticmethod\n    def all_gather_stats(stat, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` object accross multiple process/nodes\n\n        Args:\n            stat(:obj:Statistics): the statistics object to gather\n                accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            `Statistics`, the update stats object\n        \"\"\"\n        stats = Statistics.all_gather_stats_list([stat], max_size=max_size)\n        return stats[0]\n\n    @staticmethod\n    def all_gather_stats_list(stat_list, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` list accross all processes/nodes\n\n        Args:\n            stat_list(list([`Statistics`])): list of statistics objects to\n                gather accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            our_stats(list([`Statistics`])): list of updated stats\n        \"\"\"\n        from torch.distributed import get_rank\n        from onmt.utils.distributed import all_gather_list\n\n        # Get a list of world_size lists with len(stat_list) Statistics objects\n        all_stats = all_gather_list(stat_list, max_size=max_size)\n\n        our_rank = get_rank()\n        our_stats = all_stats[our_rank]\n        for other_rank, stats in enumerate(all_stats):\n            if other_rank == our_rank:\n                continue\n            for i, stat in enumerate(stats):\n                our_stats[i].update(stat, update_n_src_words=True)\n        return our_stats\n\n    def update(self, stat, update_n_src_words=False):\n        \"\"\"\n        Update statistics by suming values with another `Statistics` object\n\n        Args:\n            stat: another statistic object\n            update_n_src_words(bool): whether to update (sum) `n_src_words`\n                or not\n\n        \"\"\"\n        self.loss += stat.loss\n        self.n_words += stat.n_words\n        self.n_correct += stat.n_correct\n        self.n_examples += stat.n_examples\n\n        if update_n_src_words:\n            self.n_src_words += stat.n_src_words\n\n    def accuracy(self):\n        \"\"\" compute accuracy \"\"\"\n        return 100 * (self.n_correct / self.n_words) if self.n_words > 0 else 0.0\n\n    def xent(self):\n        \"\"\" compute cross entropy \"\"\"\n        return self.loss / self.n_words if self.n_words > 0 else 0.0\n\n    def ppl(self):\n        \"\"\" compute perplexity \"\"\"\n        return math.exp(min(self.loss / self.n_words if self.n_words > 0 else 0.0, 100))\n\n    def elapsed_time(self):\n        \"\"\" compute elapsed time \"\"\"\n        return time.time() - self.start_time\n\n    def to_dict(self, learning_rate, prefix=''):\n        t = self.elapsed_time()\n        report = {}\n        report['%saccuracy' % prefix] = self.accuracy()\n        report['%sppl' % prefix] = self.ppl()\n        report['%sxent' % prefix] = self.xent()\n        report['%sloss' % prefix] = self.loss\n        report['%slr' % prefix] = learning_rate\n        report['%sexamples' % prefix] = self.n_examples\n        report['%ssrcpersec' % prefix] = self.n_src_words / (t + 1e-5)\n        report['%sallpersec' % prefix] = self.n_words / (t + 1e-5)\n\n        return report\n\n    def output(self, step, num_steps, learning_rate, start):\n        \"\"\"Write out statistics to stdout.\n\n        Args:\n           step (int): current step\n           n_batch (int): total batches\n           start (int): start time of step.\n        \"\"\"\n        t = self.elapsed_time()\n        step_fmt = \"%2d\" % step\n        if num_steps > 0:\n            step_fmt = \"%s/%5d\" % (step_fmt, num_steps)\n        logger.info(\n            (\"Step %s; acc: %6.2f; ppl: %5.2f; xent: %4.2f; \" +\n             \"lr: %7.5E; num_example: %d; %3.0f/%3.0f tok/s; %6.0f sec\")\n            % (step_fmt,\n               self.accuracy(),\n               self.ppl(),\n               self.xent(),\n               Decimal(learning_rate),\n               self.n_examples,\n               self.n_src_words / (t + 1e-5),\n               self.n_words / (t + 1e-5),\n               time.time() - start))\n        sys.stdout.flush()\n\n    def log_tensorboard(self, prefix, writer, learning_rate, patience, step):\n        \"\"\" display statistics to tensorboard \"\"\"\n        t = self.elapsed_time()\n        writer.add_scalar(prefix + \"/xent\", self.xent(), step)\n        writer.add_scalar(prefix + \"/ppl\", self.ppl(), step)\n        writer.add_scalar(prefix + \"/accuracy\", self.accuracy(), step)\n        writer.add_scalar(prefix + \"/tgtper\", self.n_words / t, step)\n        writer.add_scalar(prefix + \"/lr\", learning_rate, step)\n        if patience is not None:\n            writer.add_scalar(prefix + \"/patience\", patience, step)\n"
  },
  {
    "path": "requirements.opt.txt",
    "content": "cffi==1.14.3\njoblib==0.17.0\nnumba==0.43.0\nllvmlite==0.32.1\npyrouge==0.1.3\ngit+git://github.com/NVIDIA/apex.git@700d6825e205732c1d6be511306ca4e595297070\nsentencepiece==0.1.94\nsubword-nmt==0.3.7\n"
  },
  {
    "path": "requirements.txt",
    "content": "six\ntqdm==4.30.*\ntorch>=1.1\ngit+https://github.com/pytorch/text.git@master#wheel=torchtext\nfuture\nconfigargparse\n"
  },
  {
    "path": "script/batch_kill_by_cluster.sh",
    "content": "#!/usr/bin/env bash\ncluster=\"htc\"\ncluster=\"smp\"\n\n#ids=$(crc-squeue.py | awk  '{printf \"%s %s\", $1 $2}')\n#pars=$(crc-squeue.py | awk  '{printf \"%s\",  $2}')\n\n#ids=(`squeue -M smp -u rum20 -o '%.7i'`)\n#echo ${#ids[@]}\n#echo $ids\n\nsqueue -M $cluster -u rum20 -o '%.7i' | while IFS= read -r line ; do\n    echo \"$line\"\n    echo \"scancel -M $cluster $line\"\n    scancel -M $cluster $line\ndone\n\n"
  },
  {
    "path": "script/batch_kill_by_range.sh",
    "content": "#!/usr/bin/env bash\ncluster=\"htc\"\nfor i in {196622..196670}\ndo\n   echo \"scancel -M $cluster $i\"\n   scancel -M $cluster $i\ndone\n"
  },
  {
    "path": "script/empirical_study/diverse/kpeval_gpu_o2o_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/diversity/keyphrase-one2one-diversity.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl --pred_trained_only -gpu 0\"\n\n#echo $cmd\n#echo $PWD\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/kpeval_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/diversity/keyphrase-one2seq-diversity.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl --pred_trained_only -gpu 0\"\n\n#echo $cmd\n#echo $PWD\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC00.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR00-SC00\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR00-SC00.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC00.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC00.yml",
    "content": "exp: rnn-presabs-kp20k-OR00-SC00\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC00\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC00/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC00/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'false'\nnum_negsample: 32\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'false'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC01.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR00-SC01\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR00-SC01.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC01.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC01.yml",
    "content": "exp: rnn-presabs-kp20k-OR00-SC01\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC01\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC01/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC01/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'true'\nlambda_sem_cov: 0.1\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC05.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR00-SC05\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR00-SC05.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC05.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR00-SC05.yml",
    "content": "exp: rnn-presabs-kp20k-OR00-SC05\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC05\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC05/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR00-SC05/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'true'\nlambda_sem_cov: 0.5\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR01-SC00.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR01-SC00\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR01-SC00.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR01-SC00.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR01-SC00.yml",
    "content": "exp: rnn-presabs-kp20k-OR01-SC00\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC00\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC00/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC00/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR01-SC01.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR01-SC01\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR01-SC01.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR01-SC01.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR01-SC01.yml",
    "content": "exp: rnn-presabs-kp20k-OR01-SC01\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC01\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC01/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR01-SC01/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'true'\nlambda_sem_cov: 0.1\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR05-SC00.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR05-SC00\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR05-SC00.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR05-SC00.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR05-SC00.yml",
    "content": "exp: rnn-presabs-kp20k-OR05-SC00\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC00\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC00/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC00/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.5\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR05-SC05.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR05-SC05\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR05-SC05.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR05-SC05.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR05-SC05.yml",
    "content": "exp: rnn-presabs-kp20k-OR05-SC05\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC05\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC05/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR05-SC05/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.5\nsem_cov: 'true'\nlambda_sem_cov: 0.5\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR10-SC10.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR10-SC10\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR10-SC10.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR10-SC10.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/done/one2seq-rnn-presabs-kp20k-OR10-SC10.yml",
    "content": "exp: rnn-presabs-kp20k-OR10-SC10\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR10-SC10\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR10-SC10/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/rnn-presabs-kp20k-OR10-SC10/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 1.0\nsem_cov: 'true'\nlambda_sem_cov: 1.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR001-SC001.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR001-SC001\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR001-SC001.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR001-SC001.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR001-SC001.yml",
    "content": "exp: rnn-presabs-kp20k-OR001-SC001\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR001-SC001\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR001-SC001/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR001-SC001/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.01\nsem_cov: 'true'\nlambda_sem_cov: 0.01\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC005.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR005-SC005\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR005-SC005.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC005.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC005.yml",
    "content": "exp: rnn-presabs-kp20k-OR005-SC005\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC005\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC005/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC005/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.05\nsem_cov: 'true'\nlambda_sem_cov: 0.05\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC05.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR005-SC05\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR005-SC05.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC05.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC05.yml",
    "content": "exp: rnn-presabs-kp20k-OR005-SC05\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC05\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC05/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR005-SC05/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.05\nsem_cov: 'true'\nlambda_sem_cov: 0.5\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR05-SC005.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR05-SC005\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR05-SC005.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR05-SC005.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR05-SC005.yml",
    "content": "exp: rnn-presabs-kp20k-OR05-SC005\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR05-SC005\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR05-SC005/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR05-SC005/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 0.5\nsem_cov: 'true'\nlambda_sem_cov: 0.05\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR10-SC005.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-presabs-kp20k-OR10-SC005\n#SBATCH --output=slurm_output/one2seq-rnn-presabs-kp20k-OR10-SC005.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR10-SC005.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR10-SC005.yml",
    "content": "exp: rnn-presabs-kp20k-OR10-SC005\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR10-SC005\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR10-SC005/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-OR10-SC005/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\ntarget_encoder_type: 'rnn'\ndetach_target_encoder: 'true'\nnum_negsample: 32\north_reg: 'true'\nlambda_orth_reg: 1.0\nsem_cov: 'true'\nlambda_sem_cov: 0.05\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 1000000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-one2one-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=rnn-one2one-kp20k\n#SBATCH --output=slurm_output/rnn-one2one-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/rnn-one2one-kp20k.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-one2one-kp20k.yml",
    "content": "exp: rnn-one2one-kp20k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-one2one-kp20k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-one2one-kp20k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-one2one-kp20k/log.txt\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: false\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E1D2.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=rnn-presabs-kp20k-E1D2\n#SBATCH --output=slurm_output/rnn-presabs-kp20k-E1D2.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E1D2.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E1D2.yml",
    "content": "exp: rnn-presabs-kp20k-E1D2\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E1D2\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E1D2/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E1D2/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nenc_layers: 1\ndec_layers: 2\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=rnn-presabs-kp20k-E2D1\n#SBATCH --output=slurm_output/rnn-presabs-kp20k-E2D1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D1.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D1.yml",
    "content": "exp: rnn-presabs-kp20k-E2D1\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D1\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D1/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D1/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nenc_layers: 2\ndec_layers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D2.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=rnn-presabs-kp20k-E2D2\n#SBATCH --output=slurm_output/rnn-presabs-kp20k-E2D2.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D2.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D2.yml",
    "content": "exp: rnn-presabs-kp20k-E2D2\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D2\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D2/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k-E2D2/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nenc_layers: 2\ndec_layers: 2\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=rnn-presabs-kp20k\n#SBATCH --output=slurm_output/rnn-presabs-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/rnn/rnn-presabs-kp20k.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/rnn/rnn-presabs-kp20k.yml",
    "content": "exp: rnn-presabs-kp20k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/rnn-presabs-kp20k/log.txt\nwandb_project: kp20k-meng17-presabs\n\n### KP parameters\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n# Model opts:\nencoder_type: brnn\nrnn_type: GRU\ndecoder_type: rnn\ninput_feed: 1\nword_vec_size: 100\nrnn_size: 150\nlayers: 1\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\nparam_init_glorot: 'true'\nposition_encoding: 'false'\nglobal_attention: mlp\n\noptim: adagrad\nlearning_rate: 0.05\nadagrad_accumulator_init: 0.1\nmax_grad_norm: 1.0\n\nbatch_size: 32\naccum_count: 2\nvalid_batch_size: 64\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.rnn/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/run_pred_o2o_dev.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_o2o_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"gtx1080\" # titanx gtx1080 v100\ndays=\"3\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nbatch_size=2\nbeam_size=200\nmax_length=6\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k stackex_valid2k)\ndatasets=(kp20k_valid2k)\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot\"\n\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/\"\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"diverse-bs$beam_size-$partition-$random-devset\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/empirical_study/diverse/run_pred_o2s_dev.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"gtx1080\" # titanx gtx1080 v100\ndays=\"1\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k stackex_valid2k)\ndatasets=(kp20k_valid2k)\ndatasets=(kp20k kp20k_valid2k duc inspec krapivin nus semeval)\n\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot\"\n\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps_devbest/\"\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"diverse-bs$beam_size-$partition-$random-devset\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D6.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-E6D6\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-E6D6.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D6.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D6.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-E6D6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D6/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 0\nnum_negsample: 0\ndetach_target_encoder: 'false'\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'false'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nenc_layers: 6\ndec_layers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D9.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-E6D9\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-E6D9.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D9.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D9.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-E6D9\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D9\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D9/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E6D9/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 0\nnum_negsample: 0\ndetach_target_encoder: 'false'\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'false'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nenc_layers: 6\ndec_layers: 9\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E9D6.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-E9D6\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-E9D6.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E9D6.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E9D6.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-E9D6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E9D6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E9D6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-E9D6/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 0\nnum_negsample: 0\ndetach_target_encoder: 'false'\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'false'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nenc_layers: 9\ndec_layers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC01.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR00-SC01\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR00-SC01.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC01.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC01.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR00-SC01\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC01\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC01/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC01/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'false'\nlambda_orth_reg: 0.0\nsem_cov: 'true'\nlambda_sem_cov: 0.1\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC05.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR00-SC05\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR00-SC05.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC05.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC05.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR00-SC05\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC05\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC05/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC05/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.0\nsem_cov: 'true'\nlambda_sem_cov: 0.5\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR001-SC001.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR001-SC001\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR001-SC001.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR001-SC001.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR001-SC001.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR001-SC001\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR001-SC001\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR001-SC001/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR001-SC001/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.01\nsem_cov: 'true'\nlambda_sem_cov: 0.01\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR005-SC005.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR005-SC005\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR005-SC005.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR005-SC005.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR005-SC005.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR005-SC005\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR005-SC005\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR005-SC005/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR005-SC005/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.05\nsem_cov: 'true'\nlambda_sem_cov: 0.05\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC00.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR01-SC00\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR01-SC00.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC00.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC00.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR01-SC00\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC00\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC00/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC00/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'false'\nlambda_sem_cov: 0.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC01.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR01-SC01\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR01-SC01.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC01.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC01.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR01-SC01\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC01\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC01/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR01-SC01/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.1\nsem_cov: 'true'\nlambda_sem_cov: 0.1\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC00.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR05-SC00\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR05-SC00.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC00.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC00.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR00-SC00\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC00\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC00/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR00-SC00/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.5\nsem_cov: 'true'\nlambda_sem_cov: 0.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC05.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR05-SC05\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR05-SC05.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC05.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC05.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR05-SC05\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR05-SC05\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR05-SC05/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR05-SC05/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 0.5\nsem_cov: 'true'\nlambda_sem_cov: 0.5\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR10-SC10.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-transformer-presabs-kp20k-OR10-SC10\n#SBATCH --output=slurm_output/one2seq-transformer-presabs-kp20k-OR10-SC10.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR10-SC10.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR10-SC10.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-OR10-SC10\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR10-SC10\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR10-SC10/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer-presabs-kp20k-OR10-SC10/log.txt\nwandb_project: transfer_kp\n\n### KP parameters\ntarget_encoder_layers: 3\nnum_negsample: 16\ndetach_target_encoder: 'true'\north_reg: 'true'\nlambda_orth_reg: 1.0\nsem_cov: 'true'\nlambda_sem_cov: 1.0\nuse_ending_state: 'true'\n\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n#        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\nmax_target_phrases: 8\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Subword and vocab\n#src_subword_model: roberta_tokenize\n#src_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\n#bpe_dropout: 0.0\nw\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'false'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/transformer-one2one-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-one2one-kp20k\n#SBATCH --output=slurm_output/transformer-one2one-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/transformer-one2one-kp20k.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/transformer-one2one-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer_one2one_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-one2one-kp20k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_one2one_kp20k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_one2one_kp20k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_one2one_kp20k/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: false\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/diverse/transformer/transformer-presabs-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-presabs-kp20k\n#SBATCH --output=slurm_output/transformer-presabs-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/empirical_study/diverse/transformer/transformer-presabs-kp20k.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/diverse/transformer/transformer-presabs-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_presabs_kp20k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_presabs_kp20k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/diverse_exps/exps/transformer_presabs_kp20k/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\n### Data opts:\nmodel_task: seq2seq\ndata_type: keyphrase\ndata_format: jsonl\npretrained_tokenizer: False\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, onmt_tokenize]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n\nshare_vocab: True\n#### Word and vocab\nsrc_vocab: /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/data/keyphrase/meng17/magkp20k.vocab.json\nsrc_vocab_size: 50000\nlowercase: True\nreturn_tokens: True\nkeep_punctuations: True\nadd_src_boseos: False\n\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\nmax_grad_norm: 2.0\n\nbatch_size: 16\naccum_count: 4\nvalid_batch_size: 64\nmax_generator_batches: 200\n\nbatch_type: sents\nnormalization: sents\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/empirical_study/empirical_cmd.txt",
    "content": "# one2seq-diverse\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nsource script/empirical_study/diverse/run_pred_o2s_dev.sh\nsource script/empirical_study/diverse/run_pred_o2o_dev.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\n\nsbatch script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D2.sh\nsbatch script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E2D1.sh\nsbatch script/empirical_study/diverse/rnn/rnn-presabs-kp20k-E1D2.sh\n\nsbatch script/empirical_study/diverse/rnn/rnn-one2one-kp20k.sh\nsbatch script/empirical_study/diverse/rnn/rnn-presabs-kp20k.sh\n\nsbatch script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC05.sh\nsbatch script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR005-SC005.sh\nsbatch script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR001-SC001.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC00.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC01.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR00-SC05.sh\n\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR01-SC00.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR01-SC01.sh\n\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR05-SC00.sh\nsbatch script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR05-SC005.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR05-SC05.sh\nsbatch script/empirical_study/diverse/rnn/one2seq-rnn-presabs-kp20k-OR10-SC005.sh\nsbatch script/empirical_study/diverse/one2seq-rnn-presabs-kp20k-OR10-SC10.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC01.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC00.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR001-SC001.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR005-SC005.sh\n\nsbatch script/empirical_study/diverse/transformer/transformer-one2one-kp20k.sh\nsbatch script/empirical_study/diverse/transformer/transformer-presabs-kp20k.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D6.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E6D9.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-E9D6.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR01-SC01.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR00-SC05.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC00.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR05-SC05.sh\nsbatch script/empirical_study/diverse/transformer/one2seq-transformer-presabs-kp20k-OR10-SC10.sh\n\n\n# one2one\nsource script/srun_one2one/run_predeval_v2_pred_cpu.sh\nsource script/srun_one2one/run_predeval_v2_pred_gpu.sh\nsource script/srun_one2one/run_predeval_v2_eval.sh\n\n# one2seq, pred\nsource script/srun_one2seq/run_pred_v2.sh\nsource script/srun_one2seq/run_pred_v2_gpu.sh\n# one2seq, eval\nsource script/srun_one2seq/run_eval_exhaustive.sh\nsource script/srun_one2seq/run_eval_selfterminating.sh\n\n\nDATASET: duc inspec semeval krapivin nus  kp20k kp20k_valid2k\n\n\n# clean bad outputs (due to out of space, jobs failed to write into disk)\n# V1 one2one (222 ckpts)\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -output_dir  output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -testsets kp20k -tasks clean\n# V2 one2one (90 ckpts)\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets kp20k kp20k_valid2k -tasks clean\n\n\n# V1 one2seq (580 ckpts)\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2seq.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam10-maxlen40/ -testsets kp20k kp20k_valid2k -tasks clean\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2seq.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam25-maxlen40/ -testsets kp20k kp20k_valid2k -tasks clean\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2seq.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -testsets kp20k kp20k_valid2k -tasks clean\n\n# V2 one2seq (200 ckpts)\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam10-maxlen40/ -testsets duc inspec semeval krapivin nus kp20k kp20k_valid2k -tasks clean\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam25-maxlen40/ -testsets duc inspec semeval krapivin nus kp20k kp20k_valid2k -tasks clean\nsrun python kp_gen_eval.py --onepass -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/ -output_dir output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -testsets duc inspec semeval krapivin nus kp20k kp20k_valid2k -tasks clean\n\n\n\n# One2One\n## Check #ckpt\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -name '*.pt' | wc -l\n\n## Check #pred\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'kp20k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'kp20k_valid2k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'inspec.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'krapivin.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'nus.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'semeval.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name 'duc.pred' | wc -l\n\n## Check #eval\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-kp20k-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-kp20k_valid2k-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-inspec-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-krapivin-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-nus-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-semeval-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/ -name '*-duc-exhaustive.json' | wc -l\n\n\n\n# One2Seq\n## Check #ckpt\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -name '*.pt' | wc -l\n\n## Check #pred\n### fullbeam\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'kp20k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'kp20k_valid2k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'inspec.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'krapivin.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'nus.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'semeval.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name 'duc.pred' | wc -l\n\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'kp20k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'kp20k_valid2k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'inspec.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'krapivin.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'nus.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'semeval.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/meng17-one2seq-beam50-maxlen40/ -name 'duc.pred' | wc -l\n\n\n### topbeamends\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'kp20k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'kp20k_valid2k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'inspec.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'krapivin.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'nus.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'semeval.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/ -name 'duc.pred' | wc -l\n\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'kp20k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'kp20k_valid2k.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'inspec.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'krapivin.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'nus.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'semeval.pred' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-topbeamends/meng17-one2seq-beam50-maxlen40/ -name 'duc.pred' | wc -l\n\n## Check #eval\n### exhaustive\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k_valid2k-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-inspec-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-krapivin-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-nus-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-semeval-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-duc-exhaustive.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k-exhaustive.json' | wc -l\n\n### selfterminating\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k_valid2k-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-inspec-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-krapivin-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-nus-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-semeval-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-duc-selfterminating.json' | wc -l\nfind /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v2/meng17-one2seq-fullbeam/ -name '*-kp20k-selfterminating.json' | wc -l\n\n\n\n20200928 MagKP exps\nsbatch script/srun_one2seq/train/3rd/kpgen-one2one-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2one-transformer-copycovfalse-kp20k.sh\n\non 1080ti\nsbatch script/srun_one2seq/train/3rd/kpgen-one2one-transformer-magkp.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2one-transformer-magkp20k.sh\n\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-length-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-no_sort-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-random-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_prepend-transformer-kp20k.sh\n\non Titanx\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_LN.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp20k.sh\n\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-copycovfalse-kp20k.sh\n\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nsmall.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nlarge.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_LN.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-copycovfalse-magkp20k.sh\n\nFinetune KP20k on top of MagKP\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_LN+kp20kFT.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall+kp20kFT.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge+kp20kFT.sh\n\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp+kp20kFT.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp20k+kp20kFT.sh\n\n\n# new order-matters exps\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical_reverse-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-length_reverse-transformer-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-no_sort_reverse-transformer-kp20k.sh\n\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical_reverse-rnn-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-length_reverse-rnn-kp20k.sh\nsbatch script/srun_one2seq/train/3rd/kpgen-one2seq-no_sort_reverse-rnn-kp20k.sh\n"
  },
  {
    "path": "script/empirical_study/preprocess/kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=preprocess_kp20k\n#SBATCH --output=slurm_output/preprocess_kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\ncmd=\"srun python -m preprocess -config config/preprocess/config-preprocess-keyphrase-kp20k.yml\"\n\necho $cmd\n$cmd\n"
  },
  {
    "path": "script/empirical_study/preprocess/magkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=preprocess_magkp\n#SBATCH --output=slurm_output/preprocess_magkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\ncmd=\"srun python -m preprocess -config config/preprocess/config-preprocess-keyphrase-magkp.yml\"\n\necho $cmd\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_duc.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_duc\n#SBATCH --output=slurm_output/eval_kp_one2one_duc.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets duc -gpu -1\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_inspec.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_inspec\n#SBATCH --output=slurm_output/eval_kp_one2one_inspec.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets inspec -gpu -1\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=high-mem\n#SBATCH --job-name=eval_kp_one2one_kp20k\n#SBATCH --output=slurm_output/eval_kp_one2one_kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n# do not change, 32gb will OOM for one2one\n#SBATCH --mem=64GB\n#SBATCH --time=4-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\nsrun python kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -testsets kp20k -gpu -1 -tasks pred\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_kp20k_gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=eval_kp_one2one_kp20k-v1-gpu\n#SBATCH --output=slurm_output/eval_kp_one2one_kp20k-v1-gpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\nsrun python kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -testsets kp20k -gpu 0 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_kp20k_valid2k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_kp20k_valid2k-evalonly\n#SBATCH --output=slurm_output/eval_kp_one2one_kp20k_valid2k-evalonly.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/ -output_dir output/keyphrase/meng17-one2one/ -testsets kp20k_valid2k -gpu -1 -tasks eval report\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_krapivin.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_krapivin\n#SBATCH --output=slurm_output/eval_kp_one2one_krapivin.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets krapivin -gpu -1\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_nus.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_nus\n#SBATCH --output=slurm_output/eval_kp_one2one_nus.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets nus -gpu -1\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_semeval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_semeval\n#SBATCH --output=slurm_output/eval_kp_one2one_semeval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets semeval -gpu -1\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_stackexchange.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=eval_kp_one2one_stackexchange\n#SBATCH --output=slurm_output/eval_kp_one2one_stackexchange.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one-stackexchange-topmodels/ -output_dir output/keyphrase/meng17-one2one-stackexchange-topmodels/ -testsets stackexchange -gpu 0 -tasks pred eval report\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval/kpeval_stackexchange_valid2k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2one_stackexchange_valid2k\n#SBATCH --output=slurm_output/eval_kp_one2one_stackexchange_valid2k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17/ -testsets stackexchange_valid2k -gpu -1 -tasks pred eval report\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_cpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=htc\n#SBATCH --partition=scavenger\n#SBATCH --partition=htc\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\ncmd=\"python kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -tasks {task_args} -data_dir data/keyphrase/meng17/ -ckpt_dir {ckpt_dir} -output_dir {output_dir} -gpu -1 -testsets {dataset_args} -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full\"\n\necho $cmd\necho $PWD\n$cmd\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\ncmd=\"python kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -tasks {task_args} -data_dir data/keyphrase/meng17/ -ckpt_dir {ckpt_dir} -output_dir {output_dir} -gpu 0 -testsets {dataset_args} -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full\"\n\necho $cmd\necho $PWD\n$cmd\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_duc.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_duc\n#SBATCH --output=slurm_output/eval_one2one_v2_duc.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets duc -gpu -1 -tasks pred\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_inspec.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_inspec\n#SBATCH --output=slurm_output/eval_one2one_v2_inspec.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets inspec -gpu -1 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_kp20k\n#SBATCH --output=slurm_output/eval_one2one_v2_kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n# do not change, 32gb will OOM for one2one\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets kp20k -gpu -1 -tasks pred\n\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_kp20k_gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=eval_one2one_kp20k-v2-gpu\n#SBATCH --output=slurm_output/eval_one2one_kp20k-v2-gpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets kp20k -gpu 0 -batch_size 4 -tasks pred\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_kp20k_topmodel_gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=eval_one2one_kp20k-top-gpu\n#SBATCH --output=slurm_output/eval_one2one_kp20k-top-gpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/ -testsets kp20k -gpu 0 -batch_size 4 -tasks pred\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_kp20k_valid2k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_kp20k_valid2k\n#SBATCH --output=slurm_output/eval_one2one_v2_kp20k_valid2k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets kp20k_valid2k -gpu -1 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_kp20k_valid2k_gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --constraint=ti\n#SBATCH --job-name=eval_one2one_kp20k_valid2k-v2-gpu\n#SBATCH --output=slurm_output/eval_one2one_kp20k_valid2k-v2-gpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets kp20k_valid2k -gpu 0 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_krapivin.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_krapivin\n#SBATCH --output=slurm_output/eval_one2one_v2_krapivin.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets krapivin -gpu -1 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_nus.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_nus\n#SBATCH --output=slurm_output/eval_one2one_v2_nus.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets nus -gpu -1 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/eval_v2/kpeval_semeval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_one2one_v2_semeval\n#SBATCH --output=slurm_output/eval_one2one_v2_semeval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_gen_eval.py -config config/test/config-test-keyphrase-one2one.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -output_dir output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/ -testsets semeval -gpu -1 -tasks pred\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\nbatch_size=\"8\"\nmax_length=\"6\"\ntask_args=\"eval\" # pred or eval\n\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\n#datasets=(duc inspec semeval kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\nbeam_size=\"200\" # 8, 16, 32, 64\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    echo $EXP_NAME\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-v1top-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n#    # V2 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-v2-beamsearch-$task_args-$dataset-beam$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n    sbatch ${DUMP_SCRIPT_PATH}\n\n#         CPU+evaluate with predictions in top sequences -e (no pred necessity)\n#        sbatch \"$PROJECT_DIR/\"kpeval_cpu.sh -a eval -a report -c /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels -o $output_dir -g 0 -b 32 -s $beam_width -l 40 -t topbeam -p true -d $dataset -e true\n\ndone\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval_v1_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\nbatch_size=\"2\"\nmax_length=\"6\"\ntask_args=\"pred\" # pred or eval\n\n# evaluate with all predictions\n#datasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\nbeam_size=\"200\" # 8, 16, 32, 64\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#\n#    echo $EXP_NAME\n#    echo $DUMP_SCRIPT_PATH\n#    sbatch $DUMP_SCRIPT_PATH\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    EXP_NAME=\"one2one-v1top-$TASKS-$dataset\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n\n    # V2 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v2/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval_v2.sh",
    "content": "#!/usr/bin/env bash\n\ntask_args=\"${1}\" # pred or eval\nbatch_size=\"4\"\nmax_length=\"6\"\n\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\n#datasets=(kp20k kp20k_valid2k)\ndatasets=(semeval kp20k_valid2k)\n\nbeam_size=\"200\" # 8, 16, 32, 64\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#\n#    echo $EXP_NAME\n#    echo $DUMP_SCRIPT_PATH\n#    sbatch $DUMP_SCRIPT_PATH\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    EXP_NAME=\"one2one-v1top-$TASKS-$dataset\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n\n    # V2/V3 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval_v2_eval.sh",
    "content": "#!/usr/bin/env bash\n\ntask_args=\"eval\" # pred or eval\nbatch_size=\"4\"\nmax_length=\"6\"\n\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\n\nbeam_size=\"32\" # 8, 16, 32, 64, 200\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#\n#    echo $EXP_NAME\n#    echo $DUMP_SCRIPT_PATH\n#    sbatch $DUMP_SCRIPT_PATH\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    EXP_NAME=\"one2one-v1top-$TASKS-$dataset\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n\n    # V2/V3 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME-v3/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval_v2_pred_cpu.sh",
    "content": "#!/usr/bin/env bash\n\ntask_args=\"pred\" # pred or eval\nbatch_size=\"4\"\nmax_length=\"6\"\n\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\n\nbeam_size=\"16\" # 8, 16, 32, 64, 200\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#\n#    echo $EXP_NAME\n#    echo $DUMP_SCRIPT_PATH\n#    sbatch $DUMP_SCRIPT_PATH\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    EXP_NAME=\"one2one-v1top-$TASKS-$dataset\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n\n    # V2/V3 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/run_predeval_v2_pred_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/eval_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\necho $CURDIR\n\ntask_args=\"pred\" # pred or eval\nbatch_size=\"2\"\nmax_length=\"6\"\n\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\n\nbeam_size=\"32\" # 8, 16, 32, 64, 200\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    # V1 models ( -i true means ignore_existing pred or eval, -p true means onepass)\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n#    EXP_NAME=\"one2one-beamsearch-$task_args-$dataset-beam$beam_size\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n#    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n#    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n#    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#\n#    echo $EXP_NAME\n#    echo $DUMP_SCRIPT_PATH\n#    sbatch $DUMP_SCRIPT_PATH\n\n    # selected V1 models\n#    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-topmodels/\"\n#    EXP_NAME=\"one2one-v1top-$TASKS-$dataset\"\n#    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n#    replaces=\"s/{job_name}/$EXP_NAME/;\";\n#    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n#    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n#    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n#    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n#    replaces=\"$replaces s|{slurm_output_dir}|$PROJECT_DIR/slurm_output|g;\";\n#    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n#    sbatch ${DUMP_SCRIPT_PATH}\n\n\n    # V2/V3 models\n    ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/\"\n    output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2one/meng17-one2one-kp20k-v3/meng17-one2one-fullbeam/meng17-one2one-beam$beam_size-maxlen6/\"\n    EXP_NAME=\"one2one-$task_args-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{ckpt_dir}|$ckpt_dir|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/1st/run_train.txt",
    "content": "#!/usr/bin/env bash\n# train\nCUDA_VISIBLE_DEVICES=0,1 nohup python train.py -config config/config-transformer-keyphrase.yml > output/nohup.kp20k.one2one.transformer.log &\nCUDA_VISIBLE_DEVICES=2,3 nohup python train.py -config config/config-transformer-keyphrase-magkp.yml > output/nohup.mag.one2one.transformer.log &\nCUDA_VISIBLE_DEVICES=4,5 nohup python train.py -config config/config-rnn-keyphrase.yml > output/nohup.kp20k.one2one.rnn.log &\nCUDA_VISIBLE_DEVICES=6,7 nohup python train.py -config config/config-rnn-keyphrase-magkp.yml > output/nohup.mag.one2one.rnn.log &\n\nCUDA_VISIBLE_DEVICES=4 nohup python train.py -config config/train/config-rnn-keyphrase.drop00.yml > output/keyphrase/meng17/nohup.kp20k.one2one.birnn.Dropout00.log &\nCUDA_VISIBLE_DEVICES=5 nohup python train.py -config config/train/config-rnn-keyphrase.drop05.yml > output/keyphrase/meng17/nohup.kp20k.one2one.birnn.Dropout05.log &\nCUDA_VISIBLE_DEVICES=6 nohup python train.py -config config/train/config-rnn-keyphrase.drop05.coverage.yml > output/keyphrase/meng17/nohup.kp20k.one2one.birnn.Dropout05.CovATT.log &\nCUDA_VISIBLE_DEVICES=7 nohup python train.py -config config/train/config-rnn-keyphrase.drop05.coverage.noreuse.yml > output/keyphrase/meng17/nohup.kp20k.one2one.birnn.Dropout05.CovATT.NoReuse.log &\n\n\n# Opal\nCUDA_VISIBLE_DEVICES=0 nohup sh run_script/kp-transformer-1gpu-bs4096-train300k.sh > output/keyphrase/meng17/nohup_magkp-meng17-on2one-transformer-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth.log &\nCUDA_VISIBLE_DEVICES=1 nohup sh run_script/kp-transformer-1gpu-bs4096-train300k.sh > output/keyphrase/meng17/nohup_magkp-meng17-on2one-transformer-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth.log &\nCUDA_VISIBLE_DEVICES=2 nohup sh run_script/kp-transformer-1gpu-bs4096-train300k.sh > output/keyphrase/meng17/nohup_magkp-meng17-on2one-transformer-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth.log &\n\n# test (use CPU is safer)\n# kp20k kp20k_valid500 duc inspec krapivin nus semeval\n# nus & semeval\nmkdir -p \"output/keyphrase/meng17-bs32/\"\nnohup python kp_run_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-bs32/ -testsets nus semeval -batch_size 64 > output/keyphrase/meng17-bs32/kp_run_eval-nus_semeval.log &\n# DUC\nnohup python kp_run_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-bs32/ -testsets duc -batch_size 64 > output/keyphrase/meng17-bs32/kp_run_eval-duc.log &\n# inspec\nnohup python kp_run_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-bs32/ -testsets inspec -batch_size 64 > output/keyphrase/meng17-bs32/kp_run_eval-inspec.log &\n#krapivin\nnohup python kp_run_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-bs32/ -testsets krapivin -batch_size 64 > output/keyphrase/meng17-bs32/kp_run_eval-krapivin.log &\n# kp20k_valid500\nnohup python kp_run_eval.py -config config/test/config-test-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-bs32/ -testsets kp20k_valid500 -batch_size 64 > output/keyphrase/meng17-bs32/kp_run_eval-kp20k_valid500.log &\n\n# kp20k\nnohup python kp_run_eval.py -config config/test/config-rnn-keyphrase.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/selected/ -output_dir output/keyphrase/meng17/ -testsets kp20k -batch_size 64 > output/keyphrase/meng17/kp_run_eval-kp20k.log &\n\n\n# CRC one2one\nsbatch script/srun_kp-transformer-1gpu-bs4096-train300k.sh\n\nsbatch script/kpeval_duc.sh\nsbatch script/kpeval_nus.sh\nsbatch script/kpeval_inspec.sh\nsbatch script/kpeval_krapivin.sh\nsbatch script/kpeval_semeval.sh\nsbatch script/kpeval_kp20k_valid500.sh\n\n# CRC one2many\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-alphabetical.sh\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-length.sh\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-no_sort.sh\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-random.sh\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-verbatim_append.sh\nsbatch script/srun_one2many/srun_kp-rnn-1gpu-verbatim_prepend.sh\n\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-alphabetical.sh\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-length.sh\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-no_sort.sh\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-random.sh\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-verbatim_append.sh\nsbatch script/srun_one2many/srun_kp-transformer-1gpu-verbatim_prepend.sh\n\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_semeval.sh\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_nus.sh\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_duc.sh\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_krapivin.sh\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_inspec.sh\nsbatch script/srun_one2many/kpeval-beam10-maxlen40/kpeval_kp20k_valid500.sh\n\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_semeval.sh\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_nus.sh\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_duc.sh\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_krapivin.sh\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_inspec.sh\nsbatch script/srun_one2many/kpeval-beam25-maxlen40/kpeval_kp20k_valid500.sh\n\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_semeval.sh\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_nus.sh\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_duc.sh\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_krapivin.sh\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_inspec.sh\nsbatch script/srun_one2many/kpeval-beam50-maxlen40/kpeval_kp20k_valid500.sh\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/1st/srun_kp-rnn-1gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-kp20k-rnn-DIM150-EMB100-LR005-DO00-TFFF-TFB1\n#SBATCH --output=slurm_output/train-kp20k-rnn-DIM150-EMB100-LR005-DO00-TFFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=false\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/1st/srun_kp-transformer-1gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-magkp-transformer-L2H4-DIM128-LR05-DO00-TTTT-TFB1\n#SBATCH --output=slurm_output/train-magkp-transformer-L2H4-DIM128-LR05-DO00-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=2\nexport HEADS=4\nexport EMBED=128\nexport HIDDEN=128\nexport BatchSize=4096\nexport TrainSteps=300000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"#\nexport Dropout=\"0.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/1st/srun_stackexchange-rnn-1gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-stackexchange-rnn-DIM150-EMB100-LR005-DO00-TFFF-TFB1\n#SBATCH --output=slurm_output/train-stackexchange-rnn-DIM150-EMB100-LR005-DO00-TFFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"stackexchange\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=false\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-rnn-BS128-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse-Covfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-one2one-rnn-kp20k-DIM150-EMB100-DO01-FFFF-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-kp20k-DIM150-EMB100-DO01-FFFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=5000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=false\nexport ReuseCopy=false\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-LR$LearningRate-L$LAYER-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-rnn-BS128-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-one2one-rnn-kp20k-DIM512-EMB128-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-kp20k-DIM512-EMB128-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=5000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-LR$LearningRate-L$LAYER-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-transformer-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-transformer-kp20k-L2H4-DIM128-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-kp20k-L2H4-DIM128-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=2\nexport HEADS=4\nexport EMBED=128\nexport HIDDEN=128\nexport BatchSize=4096\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-transformer-BS4096-Layer4-Heads8-Dim128-Emb128-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-transformer-kp20k-L4H8-DIM128-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-kp20k-L4H8-DIM128-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=128\nexport HIDDEN=128\nexport BatchSize=4096\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-transformer-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-transformer-kp20k-L4H8-DIM512-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-kp20k-L4H8-DIM512-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/kp20k-transformer-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-one2one-transformer-kp20k-L6H8-DIM512-EMB512-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-kp20k-L6H8-DIM512-EMB512-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport TrainSteps=200000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp-rnn-BS128-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-rnn-magkp-DIM150-EMB100-DO00-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-magkp-DIM150-EMB100-DO00-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp-rnn-BS128-Layer1-Dim150-Emb100-Dropout0.1-Copytrue-Covtrue-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-rnn-magkp-DIM150-EMB100-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-magkp-DIM150-EMB100-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport Optimizer=\"adagrad\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-OPT$Optimizer-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -optim $Optimizer -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp-transformer-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-transformer-magkp-L6H8-DIM512-EMB512-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-magkp-L6H8-DIM512-EMB512-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp20k-rnn-BS128-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Covtrue-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-one2one-rnn-magkp20k-DIM150-EMB100-DO00-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-magkp20k-DIM150-EMB100-DO00-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"srun python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp20k-rnn-BS128-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Covtrue-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-one2one-rnn-magkp20k-DIM512-EMB128-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-rnn-magkp20k-DIM512-EMB128-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=128\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-BS$BatchSize-LR$LearningRate-L$LAYER-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2one/train/2nd/magkp20k-transformer-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Covtrue-Contextboth.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-one2one-transformer-magkp20k-L6H8-DIM512-EMB512-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-one2one-transformer-magkp20k-L6H8-DIM512-EMB512-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"one2one\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-D$HIDDEN-E$EMBED-DO$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2one/$TOKEN_NAME-one2one-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/kpeval_cpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=scavenger\n#SBATCH --partition=smp\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=eval_kp_one2seq\n#SBATCH --output=slurm_output/eval_kp_one2seq_cpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-0:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\n\n# Run the job\nif (($# == 0)); then\n  echo -e \"Please pass argumensts -a <task1> <task2> ... -c <ckpt_dir> -o <output_dir> -g <gpu_id> -b <batch_size> -s <beam_size> -l <max_length> -t full/topbeam -e -p -d <dataset1> <dataset2>...\"\n  exit 2\nfi\n\nbeam_terminate=\"false\"\neval_topbeam=\"false\"\nonepass=\"false\"\n\nwhile getopts \":a:c:o:g:b:s:l:t:e:p:d:\" opt; do\n  case $opt in\n    a)\n      echo \"-a (tasks) was triggered, Parameter: $OPTARG\" >&2\n      tasks+=(\"$OPTARG\")\n      task_args=()\n      for TASK in \"${tasks[@]}\"\n      do\n        task_args=$task_args\" $TASK\"\n      done\n      ;;\n    c)\n      echo \"-c (ckpt_dir) was triggered, Parameter: $OPTARG\" >&2\n      ckpt_dir=$OPTARG\n      ;;\n    o)\n      echo \"-o (output_dir) was triggered, Parameter: $OPTARG\" >&2\n      output_dir=$OPTARG\n      ;;\n    g)\n      echo \"-g (gpu_id, -1 means using cpu) was triggered, Parameter: $OPTARG\" >&2\n      gpu_id=$OPTARG\n      ;;\n    b)\n      echo \"-b (batch_size) was triggered, Parameter: $OPTARG\" >&2\n      batch_size=$OPTARG\n      ;;\n    s)\n      echo \"-s (beam_size) was triggered, Parameter: $OPTARG\" >&2\n      beam_size=$OPTARG\n      ;;\n    l)\n      echo \"-l (max_search_length) was triggered, Parameter: $OPTARG\" >&2\n      max_length=$OPTARG\n      ;;\n    t)\n      echo \"-t (beam_terminate=full/topbeam) was triggered, Parameter: $OPTARG\" >&2\n      beam_terminate=$OPTARG\n      ;;\n    e)\n      echo \"-e (eval_topbeam) was triggered, Parameter: $OPTARG\" >&2\n      eval_topbeam=\"true\"\n      ;;\n    p)\n      echo \"-p (onepass) was triggered, Parameter: $OPTARG\" >&2\n      onepass=\"true\"\n      ;;\n    i)\n      echo \"-i (ignore_existing) was triggered, Parameter: $OPTARG\" >&2\n      ignore_existing=\"true\"\n      ;;\n    d)\n      echo \"-d (datasets) was triggered, Parameter: $OPTARG\" >&2\n      datasets+=(\"$OPTARG\")\n\n      dataset_args=()\n      for DATASET in \"${datasets[@]}\"\n      do\n        dataset_args=$dataset_args\" $DATASET\"\n      done\n      ;;\n    \\?)\n      echo \"Invalid option: -$OPTARG\" >&2\n      exit 1\n      ;;\n    :)\n      echo \"Option -$OPTARG requires an argument.\" >&2\n      exit 1\n      ;;\n  esac\ndone\n\ncmd=\"python kp_gen_eval.py -config config/test/config-test-keyphrase-one2seq.yml -tasks $task_args -data_dir data/keyphrase/meng17/ -ckpt_dir $ckpt_dir -output_dir $output_dir -gpu -1 -batch_size $batch_size -beam_size $beam_size -max_length $max_length -beam_terminate $beam_terminate -testsets $dataset_args\"\n\nif [ \"$eval_topbeam\" = true ]; then\n  cmd=\"${cmd} --eval_topbeam\"\nfi\nif [ \"$onepass\" = true ]; then\n  cmd=\"${cmd} --onepass\"\nfi\nif [ \"$ignore_existing\" = true ]; then\n  cmd=\"${cmd} --ignore_existing\"\nfi\nif [ -z \"$beam_terminate\" ]; then\n  echo \"-t beam_terminate must be given, full/topbeam, exiting\"\nfi\n\necho $cmd\necho $PWD\n$cmd\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/kpeval_gpu.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=scavenger\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n\n#SBATCH --job-name=eval_kp_one2seq_gpu\n#SBATCH --output=slurm_output/eval_kp_one2seq_gpu.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\nif (($# == 0)); then\n  echo -e \"Please pass argumensts -a <task1> <task2> ... -c <ckpt_dir> -o <output_dir> -g <gpu_id> -b <batch_size> -s <beam_size> -l <max_length> -t full/topbeam -e -p -d <dataset1> <dataset2>...\"\n  exit 2\nfi\nwhile getopts \":a:c:o:g:b:s:l:t:e:p:d:\" opt; do\n  case $opt in\n    a)\n      echo \"-a (tasks) was triggered, Parameter: $OPTARG\" >&2\n      tasks+=(\"$OPTARG\")\n      task_args=()\n      for TASK in \"${tasks[@]}\"\n      do\n        task_args=$task_args\" $TASK\"\n      done\n      ;;\n    c)\n      echo \"-c (ckpt_dir) was triggered, Parameter: $OPTARG\" >&2\n      ckpt_dir=$OPTARG\n      ;;\n    o)\n      echo \"-o (output_dir) was triggered, Parameter: $OPTARG\" >&2\n      output_dir=$OPTARG\n      ;;\n    g)\n      echo \"-g (gpu_id, -1 means using cpu) was triggered, Parameter: $OPTARG\" >&2\n      gpu_id=$OPTARG\n      ;;\n    b)\n      echo \"-b (batch_size) was triggered, Parameter: $OPTARG\" >&2\n      batch_size=$OPTARG\n      ;;\n    s)\n      echo \"-s (beam_size) was triggered, Parameter: $OPTARG\" >&2\n      beam_size=$OPTARG\n      ;;\n    l)\n      echo \"-l (max_search_length) was triggered, Parameter: $OPTARG\" >&2\n      max_length=$OPTARG\n      ;;\n    t)\n      echo \"-t (beam_terminate=full/topbeam) was triggered, Parameter: $OPTARG\" >&2\n      beam_terminate=$OPTARG\n      ;;\n    e)\n      echo \"-e (eval_topbeam) was triggered, Parameter: $OPTARG\" >&2\n      eval_topbeam=true\n      ;;\n    p)\n      echo \"-p (onepass) was triggered, Parameter: $OPTARG\" >&2\n      onepass=true\n      ;;\n    i)\n      echo \"-i (ignore_existing) was triggered, Parameter: $OPTARG\" >&2\n      ignore_existing=true\n      ;;\n    d)\n      echo \"-d (datasets) was triggered, Parameter: $OPTARG\" >&2\n      datasets+=(\"$OPTARG\")\n\n      dataset_args=()\n      for DATASET in \"${datasets[@]}\"\n      do\n        dataset_args=$dataset_args\" $DATASET\"\n      done\n      ;;\n    \\?)\n      echo \"Invalid option: -$OPTARG\" >&2\n      exit 1\n      ;;\n    :)\n      echo \"Option -$OPTARG requires an argument.\" >&2\n      exit 1\n      ;;\n  esac\ndone\n\ncmd=\"python kp_gen_eval.py -config config/test/config-test-keyphrase-one2seq.yml -tasks $task_args -data_dir data/keyphrase/meng17/ -ckpt_dir $ckpt_dir -output_dir $output_dir -gpu 0 -batch_size $batch_size -beam_size $beam_size -max_length $max_length -beam_terminate $beam_terminate -testsets $dataset_args\"\n\nif [ \"$eval_topbeam\" = true ]; then\n  cmd=\"${cmd} --eval_topbeam\"\nfi\nif [ \"$onepass\" = true ]; then\n  cmd=\"${cmd} --onepass\"\nfi\nif [ \"$ignore_existing\" = true ]; then\n  cmd=\"${cmd} --ignore_existing\"\nfi\nif [ -z \"$beam_terminate\" ]; then\n  echo \"-t beam_terminate must be given, full/topbeam, exiting\"\nfi\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml",
    "content": "# Translation / inference options\n\n# Data options\ndata_type: keyphrase\nshard_size: 0\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'false'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\n#beam_size: 10\nmax_length: 40\nmin_length: 1\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\nbatch_size: 32\n#gpu: 0\ngpu: -1\n\ntgt_type: multiple"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_duc.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_duc\n#SBATCH --output=slurm_output/eval_kp_one2many_duc.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets duc\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_inspec.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_inspec\n#SBATCH --output=slurm_output/eval_kp_one2many_inspec.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets inspec\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_kp20k_valid500.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_kp20k_valid500\n#SBATCH --output=slurm_output/eval_kp_one2many_kp20k_valid500.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets kp20k_valid500\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_krapivin.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_krapivin\n#SBATCH --output=slurm_output/eval_kp_one2many_krapivin.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets krapivin\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_nus.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_nus\n#SBATCH --output=slurm_output/eval_kp_one2many_nus.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets nus\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam10-maxlen40/kpeval_semeval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_semeval\n#SBATCH --output=slurm_output/eval_kp_one2many_semeval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam10-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam10-maxlen40/ -gpu -1 -testsets semeval\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml",
    "content": "# Translation / inference options\n\n# Data options\ndata_type: keyphrase\nshard_size: 0\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'false'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\nbeam_size: 25\nmax_length: 40\nmin_length: 1\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\nbatch_size: 32\n#gpu: 0\ngpu: -1\n\ntgt_type: multiple"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_duc.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_duc\n#SBATCH --output=slurm_output/eval_kp_one2many_duc.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets duc\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_inspec.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_inspec\n#SBATCH --output=slurm_output/eval_kp_one2many_inspec.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets inspec\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_kp20k_valid500.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_kp20k_valid500\n#SBATCH --output=slurm_output/eval_kp_one2many_kp20k_valid500.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets kp20k_valid500\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_krapivin.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_krapivin\n#SBATCH --output=slurm_output/eval_kp_one2many_krapivin.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets krapivin\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_nus.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_nus\n#SBATCH --output=slurm_output/eval_kp_one2many_nus.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets nus\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam25-maxlen40/kpeval_semeval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_semeval\n#SBATCH --output=slurm_output/eval_kp_one2many_semeval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam25-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam25-maxlen40/ -gpu -1 -testsets semeval\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml",
    "content": "# Translation / inference options\n\n# Data options\ndata_type: keyphrase\nshard_size: 0\n\n# Evaluation options\nreport_bleu: 'false'\nreport_rouge: 'false'\nreport_kpeval: 'false'\nreport_time: 'true'\n\n# Options most relevant to summarization.\n#dynamic_dict: 'true'\nshare_vocab: 'true'\n\n# Beam search\nbeam_size: 50\nmax_length: 40\nmin_length: 1\n\n# Alpha and Beta values for Google Length + Coverage penalty\nstepwise_penalty: 'false'\n\n# Logging\nverbose: 'false'\n#log_file: output/pred/kp20k/kp20k.one2one_step_40000.pred.log\nlog_file_level: 'DEBUG'\nn_best: 500\n\n# Efficiency\nbatch_size: 32\n#gpu: 0\ngpu: -1\n\ntgt_type: multiple"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_duc.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_duc\n#SBATCH --output=slurm_output/eval_kp_one2many_duc.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets duc\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_inspec.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_inspec\n#SBATCH --output=slurm_output/eval_kp_one2many_inspec.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets inspec\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_kp20k_valid500.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_kp20k_valid500\n#SBATCH --output=slurm_output/eval_kp_one2many_kp20k_valid500.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets kp20k_valid500\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_krapivin.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_krapivin\n#SBATCH --output=slurm_output/eval_kp_one2many_krapivin.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets krapivin\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_nus.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_nus\n#SBATCH --output=slurm_output/eval_kp_one2many_nus.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets nus\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/old/kpeval-beam50-maxlen40/kpeval_semeval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --partition=smp\n#SBATCH --job-name=eval_kp_one2many_semeval\n#SBATCH --output=slurm_output/eval_kp_one2many_semeval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Run the job\npython kp_run_eval.py -config script/srun_one2many/kpeval-beam50-maxlen40/config-test-keyphrase-one2many.yml -data_dir data/keyphrase/meng17/ -ckpt_dir models/keyphrase/meng17/ -output_dir output/keyphrase/meng17-one2many-beam50-maxlen40/ -gpu -1 -testsets semeval\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_eval_exhaustive.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\nbeam_widths=(1 10 25 50)\n\ndatasets=(kp20k)\nbeam_widths=(1 10 25 50)\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # V1 models\n        # topbeam_terminate ( -i true means ignore_existing pred or eval, -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n\n        # we selected some topmodels and run fullbeam\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n\n        # V2/V3 models\n        # topbeam_terminate ( -i true means ignore_existing pred or eval, -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n\n#         fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/\"\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3_orders/\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n\n#         CPU+evaluate with predictions in top sequences -e (no pred necessity)\n#        sbatch \"$PROJECT_DIR/\"kpeval_cpu.sh -a eval -a report -c /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels -o $output_dir -g 0 -b 32 -s $beam_width -l 40 -t topbeam -p true -d $dataset -e true\n\n    done\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_eval_selfterminating.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\nbeam_widths=(1 10 25 50)\n\n# it exceeds 64gb memory, so change to 128gbs\ndatasets=(kp20k)\nbeam_widths=(1 10 25 50)\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # eval self-terminating based on fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/\"\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3_orders/\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset} -e true\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset} -e true\n\n    done\ndone\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_pred.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\ndatasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\n#beam_widths=(25 50)\nbeam_widths=(1)\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # topbeam_terminate ( -i true means ignore_existing pred or eval, -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a eval -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n        # CPU+fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n#\n#         CPU+evaluate with predictions in top sequences -e (no pred necessity)\n#        sbatch \"$PROJECT_DIR/\"kpeval_cpu.sh -a eval -a report -c /zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels -o $output_dir -g 0 -b 32 -s $beam_width -l 40 -t topbeam -p true -d $dataset -e true\n\n    done\ndone\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_pred_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\n#datasets=(duc inspec semeval kp20k kp20k_valid2k krapivin nus)\ndatasets=(kp20k)\nbeam_widths=(50)\n\n\n#beam_widths=(1 10 25 50)\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # topbeam_terminate ( -i true means ignore_existing pred or eval,  -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n        # CPU+fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n\n    done\ndone\n\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_pred_v2.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\n\ndatasets=(duc inspec semeval krapivin nus kp20k_valid2k kp20k)\ndatasets=(duc inspec semeval krapivin nus)\n\nbeam_widths=(1 10 25 50)\nbeam_widths=(50)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # topbeam_terminate ( -i true means ignore_existing pred or eval,  -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 16 -s $beam_width -l 40 -t topbeam -d ${dataset}\n\n        # CPU+fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_cpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 16 -s $beam_width -l 40 -t full -d ${dataset}\n    done\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/run_pred_v2_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/\"\n\necho $0\necho $PROJECT_DIR\n# evaluate with all predictions\n#datasets=(duc inspec semeval kp20k_valid2k kp20k krapivin nus)\ndatasets=(kp20k)\nbeam_widths=(1 10 25 50)\n#beam_widths=(1 10 25 50)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    for beam_width in \"${beam_widths[@]}\"\n    do\n        # topbeam_terminate ( -i true means ignore_existing pred or eval,  -p true means onepass)\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-topbeamends/meng17-one2seq-beam$beam_width-maxlen40\"\n#        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 4 -s $beam_width -l 40 -t topbeam -d ${dataset}\n#        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 4 -s $beam_width -l 40 -t topbeam -d ${dataset}\n\n        # fullbeam\n        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3\"\n        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-v3/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n\n#        ckpt_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/models/keyphrase/meng17-one2seq/meng17-one2seq-kp20k_orders\"\n#        output_dir=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg/output/keyphrase/meng17-one2seq/meng17-one2seq-kp20k-topmodels/meng17-one2seq-fullbeam/meng17-one2seq-beam$beam_width-maxlen40\"\n\n        echo \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 2 -s $beam_width -l 40 -t full -d ${dataset}\n        sbatch \"$PROJECT_DIR/script/srun_one2seq/\"kpeval_gpu.sh -a pred -c $ckpt_dir -o $output_dir -g -1 -b 2 -s $beam_width -l 40 -t full -d ${dataset}\n    done\ndone\n\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-alphabetical.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-alphabetical-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-alphabetical-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"alphabetical\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-length.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-length-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-length-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"length\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-no_sort.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-no_sort-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-no_sort-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"no_sort\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-random.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-random-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-random-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"random\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-verbatim_append.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-rnn-1gpu-verbatim_prepend.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_prepend-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_prepend-kp20k-rnn-DIM512-EMB128-LR005-DO03-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_prepend\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=5000\n\n#export LearningRate=\"0.15\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.3\"\nexport MaxGradNorm=\"2.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Cont$ContextGate-IF$InputFeed\"\n\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-alphabetical.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-alphabetical-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-alphabetical-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"alphabetical\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-length.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-length-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-length-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"length\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-no_sort.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-no_sort-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-no_sort-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"no_sort\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-random.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-random-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-random-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"random\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-verbatim_append.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_append-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/1st/srun_kp-transformer-1gpu-verbatim_prepend.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_prepend-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_prepend-kp20k-transformer-L4H8-DIM512-LR05-DO02-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_prepend\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.5\"\nexport Dropout=\"0.2\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-Layer$LAYER-Heads$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Reuse$ReuseCopy-Cov$Cov-PE$PositionEncoding-Context$ContextGate-IF$InputFeed\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME/$EXP_NAME\"\nexport LOG_PATH=\"output/keyphrase/$TOKEN_NAME/$EXP_NAME.log\"\nmkdir -p \"output/keyphrase/$TOKEN_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\n\ncmd=\"python train.py -config config/train/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -save_model $MODEL_PATH -log_file $LOG_PATH -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -valid_batch_size $ValidBatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/kp20k-verbatim_append-rnn-BS64-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Reusetrue-Covfalse-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM100-EMB100-LR005-DO00-TTFF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM100-EMB100-LR005-DO00-TTFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=100\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/kp20k-verbatim_append-rnn-BS64-Layer1-Dim100-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM100-EMB100-LR005-DO00-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM100-EMB100-LR005-DO00-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=100\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/kp20k-verbatim_append-rnn-BS64-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covfalse-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO00-TTFF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO00-TTFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/kp20k-verbatim_append-rnn-BS64-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-OPT$Optimizer-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/magkp20k-verbatim_append-rnn-BS64-Layer1-Dim150-Emb100-Dropout0.0-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-magkp20k-rnn-DIM150-EMB100-LR005-DO00-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-magkp20k-rnn-DIM150-EMB100-LR005-DO00-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/magkp20k-verbatim_append-rnn-BS64-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-magkp20k-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-magkp20k-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/magkp20k-verbatim_append-transformer-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --account=pbrusilovsky\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-transformer-magkp20k-L6H8-DIM512-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-magkp20k-L6H8-DIM512-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=300000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-rnn-kp20k-BS64-Layer1-Dim150-Emb100-Dropout0.0-Copyfalse-Reusefalse-Covfalse-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO01-FFFF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO01-FFFF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=20GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport Optimizer=\"adagrad\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.0\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=false\nexport ReuseCopy=false\nexport Cov=false\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-OPT$Optimizer-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -optim $Optimizer -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-rnn-kp20k-BS64-Layer1-Dim150-Emb100-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO01-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-kp20k-rnn-DIM150-EMB100-LR005-DO01-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=100000\nexport CheckpointSteps=10000\n\nexport Optimizer=\"adagrad\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-OPT$Optimizer-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -optim $Optimizer -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-rnn-magkp-BS64-Layer1-Dim150-Emb100-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-magkp-rnn-DIM150-EMB100-LR005-DO01-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-magkp-rnn-DIM150-EMB100-LR005-DO01-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=100\nexport HIDDEN=150\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport Optimizer=\"adagrad\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-OPT$Optimizer-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -optim $Optimizer -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-rnn-magkp-BS64-Layer1-Dim512-Emb128-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEfalse-Contboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-magkp-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-magkp-rnn-DIM512-EMB128-LR005-DO01-TTTF-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=5000\n\nexport LAYER=1\nexport EMBED=128\nexport HIDDEN=512\nexport BatchSize=64\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\nexport Optimizer=\"adagrad\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\nexport MaxGradNorm=\"1.0\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=false\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-rnn-BS$BatchSize-OPT$Optimizer-LR$LearningRate-Layer$LAYER-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport TENSORBOARD_PATH=\"runs/keyphrase/$TOKEN_NAME/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-rnn-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tensorboard_log_dir $TENSORBOARD_PATH -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -word_vec_size $EMBED -rnn_size $HIDDEN -optim $Optimizer -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate  -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-kp20k-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-transformer-kp20k-L2H4-DIM128-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-kp20k-L2H4-DIM128-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=24GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=2\nexport HEADS=4\nexport EMBED=128\nexport HIDDEN=128\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-kp20k-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_append-transformer-kp20k-L4H8-DIM512-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-kp20k-L4H8-DIM512-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=24GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-kp20k-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=train-verbatim_append-transformer-kp20k-L6H8-DIM512-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-kp20k-L6H8-DIM512-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=24GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"kp20k\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=200000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy-Cov$Cov\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-magkp-BS4096-Layer2-Heads4-Dim128-Emb128-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-transformer-magkp-L2H4-DIM128-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-magkp-L2H4-DIM128-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=2\nexport HEADS=4\nexport EMBED=128\nexport HIDDEN=128\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=300000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-magkp-BS4096-Layer4-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-transformer-magkp-L4H8-DIM512-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-magkp-L4H8-DIM512-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=4\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=300000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/2nd/verbatim_append-transformer-magkp-BS4096-Layer6-Heads8-Dim512-Emb512-Dropout0.1-Copytrue-Reusetrue-Covtrue-PEtrue-Contextboth-IF1.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=train-verbatim_append-transformer-magkp-L6H8-DIM512-LR005-DO01-TTTT-TFB1\n#SBATCH --output=slurm_output/train-verbatim_append-transformer-magkp-L6H8-DIM512-LR005-DO01-TTTT-TFB1.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport DATA_NAME=\"magkp\"\nexport TOKEN_NAME=\"meng17\"\nexport TARGET_TYPE=\"verbatim_append\"\nexport MASTER_PORT=10000\n\nexport LAYER=6\nexport HEADS=8\nexport EMBED=512\nexport HIDDEN=512\nexport BatchSize=4096\nexport ValidBatchSize=64\nexport TrainSteps=300000\nexport CheckpointSteps=10000\n\n#export LearningRate=\"2.0\"\nexport LearningRate=\"0.05\"\nexport Dropout=\"0.1\"\n\nexport Copy=true\nexport ReuseCopy=true\nexport Cov=true\nexport PositionEncoding=true\n\nexport ShareEmbeddings=true\nexport CopyLossBySeqLength=false\n\nexport ContextGate=\"both\"\nexport InputFeed=1\n\nexport EXP_NAME=\"$DATA_NAME-$TOKEN_NAME-$TARGET_TYPE-transformer-BS$BatchSize-LR$LearningRate-L$LAYER-H$HEADS-Dim$HIDDEN-Emb$EMBED-Dropout$Dropout-Copy$Copy\"\n\nexport PATHON_PATH=\"/ihome/pbrusilovsky/rum20/.conda/envs/py36/bin/\"\nexport ROOT_PATH=\"/zfs1/pbrusilovsky/rum20/kp/OpenNMT-kpg\"\nexport DATA_PATH=\"data/keyphrase/$TOKEN_NAME/$DATA_NAME\"\nexport MODEL_PATH=\"models/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME\"\nexport EXP_DIR=\"output/keyphrase/$TOKEN_NAME-one2seq/$TOKEN_NAME-one2seq-kp20k-v2/$EXP_NAME/\"\nexport WANDB_PROJECT_NAME=\"kp20k-meng17-one2one\"\n\ncmd=\"python train.py -config config/train/pt/config-transformer-keyphrase-crc.yml -exp $EXP_NAME -data $DATA_PATH -vocab $DATA_PATH.vocab.pt -save_model $MODEL_PATH -exp_dir $EXP_DIR -tgt_type $TARGET_TYPE -batch_size $BatchSize -train_steps $TrainSteps -save_checkpoint_steps $CheckpointSteps -layers $LAYER -heads $HEADS -word_vec_size $EMBED -rnn_size $HIDDEN -learning_rate $LearningRate -dropout $Dropout -context_gate $ContextGate -input_feed $InputFeed -master_port $MASTER_PORT -wandb_project $WANDB_PROJECT_NAME\"\n\nif [ \"$Copy\" = true ] ; then\n    cmd+=\" -copy_attn\"\nfi\nif [ \"$ReuseCopy\" = true ] ; then\n    cmd+=\" -reuse_copy_attn\"\nfi\nif [ \"$Cov\" = true ] ; then\n    cmd+=\" -coverage_attn\"\nfi\nif [ \"$PositionEncoding\" = true ] ; then\n    cmd+=\" -position_encoding\"\nfi\nif [ \"$ShareEmbeddings\" = true ] ; then\n    cmd+=\" -share_embeddings\"\nfi\nif [ \"$CopyLossBySeqLength\" = true ] ; then\n    cmd+=\" -copy_loss_by_seqlength\"\nfi\n\n#cmd+=\" > output/keyphrase/$TOKEN_NAME/nohup_$EXP_NAME.log &\"\n\necho $TARGET_TYPE\necho $cmd\n\n$cmd\n#$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2one-transformer-copycovfalse-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2one-kp20k-copycovfalse\n#SBATCH --output=slurm_output/one2one-kp20k-copycovfalse.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2one-transformer-copycovfalse-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2one-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2one-kp20k\n#SBATCH --output=slurm_output/one2one-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2one-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2one-transformer-magkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2one-magkp\n#SBATCH --output=slurm_output/one2one-magkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2one-transformer-magkp.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2one-transformer-magkp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2one-magkp20k\n#SBATCH --output=slurm_output/one2one-magkp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2one-transformer-magkp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-alphabetical-kp20k\n#SBATCH --output=slurm_output/one2seq-alphabetical-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-alphabetical-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical_reverse-rnn-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-alphabetical_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-rnn-alphabetical_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-alphabetical_reverse-rnn-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-alphabetical_reverse-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-alphabetical_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-alphabetical_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-alphabetical_reverse-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-length-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-length-kp20k\n#SBATCH --output=slurm_output/one2seq-length-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-length-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-length_reverse-rnn-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-length_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-rnn-length_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-length_reverse-rnn-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-length_reverse-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-length_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-length_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-length_reverse-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-no_sort-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-no_sort-kp20k\n#SBATCH --output=slurm_output/one2seq-no_sort-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-no_sort-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-no_sort_reverse-rnn-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-rnn-no_sort_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-rnn-no_sort_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-no_sort_reverse-rnn-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-no_sort_reverse-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=one2seq-no_sort_reverse-kp20k\n#SBATCH --output=slurm_output/one2seq-no_sort_reverse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-no_sort_reverse-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-random-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-random-kp20k\n#SBATCH --output=slurm_output/one2seq-random-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-random-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_LN+kp20kFT.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-MagKP_LN+kp20kFT\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_LN+kp20kFT.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_LN+kp20kFT.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_LN.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-MagKP_LN\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_LN.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_LN.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge+kp20kFT.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-MagKP_Nlarge+kp20kFT\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_Nlarge+kp20kFT.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge+kp20kFT.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-MagKP_Nlarge\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_Nlarge.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nlarge.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall+kp20kFT.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-MagKP_Nsmall+kp20kFT\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_Nsmall+kp20kFT.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall+kp20kFT.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-MagKP_Nsmall\n#SBATCH --output=slurm_output/one2seq-preabs-MagKP_Nsmall.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-MagKP_Nsmall.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-copycovfalse-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-copycovfalse-kp20k\n#SBATCH --output=slurm_output/one2seq-preabs-copycovfalse-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-copycovfalse-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-copycovfalse-magkp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-copycovfalse-magkp20k\n#SBATCH --output=slurm_output/one2seq-preabs-copycovfalse-magkp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-copycovfalse-magkp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_LN.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-kp20k+MagKP_LN\n#SBATCH --output=slurm_output/one2seq-preabs-kp20k+MagKP_LN.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_LN.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nlarge.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-kp20k+MagKP_Nlarge\n#SBATCH --output=slurm_output/one2seq-preabs-kp20k+MagKP_Nlarge.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nlarge.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nsmall.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-kp20k+MagKP_Nsmall\n#SBATCH --output=slurm_output/one2seq-preabs-kp20k+MagKP_Nsmall.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k+MagKP_Nsmall.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-kp20k\n#SBATCH --output=slurm_output/one2seq-preabs-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp+kp20kFT.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-magkp+kp20kFT\n#SBATCH --output=slurm_output/one2seq-preabs-magkp+kp20kFT.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp+kp20kFT.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-magkp\n#SBATCH --output=slurm_output/one2seq-preabs-magkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp20k+kp20kFT.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-preabs-magkp20k+kp20kFT\n#SBATCH --output=slurm_output/one2seq-preabs-magkp20k+kp20kFT.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp20k+kp20kFT.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_append-transformer-magkp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --job-name=one2seq-preabs-magkp20k\n#SBATCH --output=slurm_output/one2seq-preabs-magkp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_append-transformer-magkp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/empirical_study/srun_one2seq/train/3rd/kpgen-one2seq-verbatim_prepend-transformer-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --job-name=one2seq-abspre-kp20k\n#SBATCH --output=slurm_output/one2seq-abspre-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=64GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/train/pt/3rd/config-kpgen-one2seq-verbatim_prepend-transformer-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/release/bart/DA-kp20k_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/bart-DA_kp20k_tlrs55-lr1e5-bs256-step5k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.98) --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 16 --update-freq 8 --save-interval-updates 1000 --warmup-updates 300 --max-update 5000 --total-num-update 5000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki"
  },
  {
    "path": "script/release/bart/PTDA-kp20k_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/bart-PT_wiki_step40k-DA_kp20k_tlrs55-lr1e5-bs256-step5k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/ckpts/checkpoint_step_40000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.98) --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 16 --update-freq 8 --save-interval-updates 1000 --warmup-updates 300 --max-update 5000 --total-num-update 5000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki"
  },
  {
    "path": "script/release/bart/PTDAFT-kp20k-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT/bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/bart-PT_wiki_step40k-DA_kp20k_tlrs55-lr1e5-bs256-step5k/ckpts/checkpoint_step_5000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 0.1 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 16 --update-freq 1 --save-interval-updates 100 --warmup-updates 200 --max-update 2000 --total-num-update 2000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_v2_bart\n"
  },
  {
    "path": "script/release/bart/PTDAFT-kp20k-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT/bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot1k-bs16_step4k_clip01_labelsmooth01/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/bart-PT_wiki_step40k-DA_kp20k_tlrs55-lr1e5-bs256-step5k/ckpts/checkpoint_step_5000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 0.1 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 16 --update-freq 1 --save-interval-updates 200 --warmup-updates 400 --max-update 4000 --total-num-update 4000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_v2_bart"
  },
  {
    "path": "script/release/bart/PTDAFT-kp20k-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT/bart-PT_step40k-DA_kp20k_step5k-FT1e5_fewshot100-bs16_step2k_clip01_labelsmooth01/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA/bart-PT_wiki_step40k-DA_kp20k_tlrs55-lr1e5-bs256-step5k/ckpts/checkpoint_step_5000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 0.1 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 16 --update-freq 1 --save-interval-updates 100 --warmup-updates 200 --max-update 2000 --total-num-update 2000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_v2_bart\n"
  },
  {
    "path": "script/release/bart/nohup-bart-PTDA-magcs12m-lr1e5-step20k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_12m_v2/:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PT_wiki_step40k-DA_magcs12m_tlrs55-lr1e5-step20k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/ckpts/checkpoint_step_40000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9,0.98)\" --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 8 --update-freq 8 --save-interval-updates 2000 --warmup-updates 1200 --max-update 20000 --total-num-update 20000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PT_wiki_step40k-DA_magcs12m_tlrs55-lr1e5-step20k/train.nohup.out &\n"
  },
  {
    "path": "script/release/bart/nohup-bart-wiki-lr1e5-step40k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/wiki/phrase --disable-validation --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/ckpts --task keyphrasification_pretrain --dataset-type wiki --max-source-length 512 --max-target-length 256 --max-phrase-len 6 --max-target-phrases 16 --phrase-corr-rate 0.1 --random-span-rate 0.05 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9,0.98)\" --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --batch-size 16 --update-freq 4 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 100 --report-accuracy --seed 7 --fixed-validation-seed 7 --save-interval-updates 5000 --warmup-updates 2400 --total-num-update 40000 --num-workers 16 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/train.nohup.out &\n\n"
  },
  {
    "path": "script/release/mag/mag_transfer_labelling_bart.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080 # titanx gtx1080 v100\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n\n#SBATCH --job-name=mag_transfer_labelling_bart-gtx10803d\n#SBATCH --output=slurm_output/mag_transfer_labelling-gtx10803d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k -data_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_13m/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_13m_v2/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/release/mag/mag_transfer_labelling_tf.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx # titanx gtx1080 v100\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n\n#SBATCH --job-name=mag_transfer_labelling_tf-titanx3d\n#SBATCH --output=slurm_output/mag_transfer_labelling-titanx3d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k -data_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_13m/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLtf_13m_v2/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/release/mag/nohup-transformer-PTDA-magcs12m-tlrs55-step200k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer_v2/mag/transformer-PTDA-magcs12m-tlrs55-step200k.yml --report_every 100 > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/release/mag/prev/nohup-bart-DA_MagTL100k-lr1e5-step20k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nexp_dir=/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_mag/bart-DA_MagTL100k-lr1e5-step20k\nmkdir -p $exp_dir\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_100k/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_100k/ --label-sample-ratio [1.0] --save-dir ${exp_dir}/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 2000 --max-update 20000 --total-num-update 20000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > ${exp_dir}/train.nohup.out &\n"
  },
  {
    "path": "script/release/mag/prev/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\n#sleep 14400\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot/bart-MagTL_step200k-kp20k_full_lr1e5_step100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts/checkpoint_step_200000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9, 0.999)\" --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1920 --update-freq 2 --save-interval-updates 5000 --warmup-updates 10000 --max-update 100000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/release/mag/prev/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_13m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_13m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/release/mag/prev/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_3m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_3m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/train.nohup.out &\n\n"
  },
  {
    "path": "script/release/mag/transformer-PTDA-magcs12m-tlrs55-step200k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLtf_12m_v2/, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 200000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs12m-FT100k-step20k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDAFT-magcs12m-FT100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 1000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 15000"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs12m-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs12m-full-step100k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 15000\n"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs1m-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/mag/transformer-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/release/mag/transformer-PTFT-kp20k_100k-step20k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Exp meta\nexp: transformer-PTFT-FT100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 1000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 16000\n"
  },
  {
    "path": "script/release/mag/transformer-kp20k_100k-step100k.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Exp meta\nexp: transformer-kp20k_100k-step100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 500\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 16000\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-kp20k-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 17700\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10000\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-kptimes-tlrs55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_60000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kptimes_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#- 3\nmaster_port: 17745"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-kptimes.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_60000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kptimes_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10001"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-openkp-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_openkp_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10772\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-openkp.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_openkp_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100'\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10002\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-stackex-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_stackex_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#- 3\nmaster_port: 17773\n"
  },
  {
    "path": "script/release/tf/DA/transformer-DA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_stackex_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10003\n"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot100_step1k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot10k_step4k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot1k_step2k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10010\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-NP.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-NP\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-NP.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-NP.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_15000.pt\n\n### Exp meta\nexp: transformer-DA_NP-kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10770"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-RS.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_RS-kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_random_span, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10550"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-TL.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-nprs_55.yml",
    "content": "### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-nprs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 16921\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam10.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_10-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10120\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam25.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam25\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_25-maxlen_40/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam3.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_3-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10120\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_5-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10150\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam50.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam50\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlnp_28\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlnp_28.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_28\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.8,0.2]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 15521\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlnp_55\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlnp_55.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_30000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 16921\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_82.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_82\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 19921\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlrs_28\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlrs_28.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_28\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 17021\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlrs_55\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlrs_55.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10491\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_82.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_82\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.8, 0.2]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11321\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-NP.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_NP-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10770"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-RS.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_RS-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [__random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10550"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-TL.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-nprs55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_kp20k_step20k-nprs55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k-nprs55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-tlnp55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompare/transformer-PTDA-kp20k-step20k-tlrs55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-kp20k_step20k-tlrs55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 8\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-NP-step75k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_NP_kp20k_step100k/ckpts/checkpoint_step_75000.pt\n\n### Exp meta\nexp: transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-RS-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_RS_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLbart-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_TLbart_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLbart-tlnp55-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_TLbart_kp20k_step100k-tlnp_55/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-beam25-step40k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-beam50-step40k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-step20k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/release/tf/FT/transformer-kp20k-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot100/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-kp20k-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot10k/ckpts/checkpoint_step_12500.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-kp20k-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot1k/ckpts/checkpoint_step_14000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-kptimes-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot100/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-kptimes-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot10k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-kptimes-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot1k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-openkp-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot100/ckpts/checkpoint_step_8500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-openkp-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot10k/ckpts/checkpoint_step_7500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-openkp-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot1k/ckpts/checkpoint_step_8000.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-stackex-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot100/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-stackex-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot10k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/FT/transformer-stackex-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot1k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PT/transformer-PT-wiki.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_105000.pt\n\n### Exp meta\nexp: transformer-wiki-step200k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nmax_phrase_len: 16\nmax_target_phrases: 16\nphrase_corr_rate: 0.1\nrandom_span_rate: 0.05\n\nshuffle_shards: false\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [wiki_phrase, roberta_tokenize_kpg]\n#        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/AA/\n        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\nwarmup_steps: 20000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 4480\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 4479\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-kp20k-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-kptimes-tlrs55.yml",
    "content": "### Exp meta\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kptimes_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n\nmaster_port: 10789\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-kptimes.yml",
    "content": "### Exp meta\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_kptimes_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 10789\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-openkp-tlrs55.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_openkp_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n\nmaster_port: 11110\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-openkp.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_openkp_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 11110\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-stackex-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_30000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_stackex_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11156\n\n"
  },
  {
    "path": "script/release/tf/PTDA/transformer-PTDA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_30000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_stackex_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11156\n\n\n"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round10.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round2.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round3.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round4.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round5.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round6.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round7.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round8.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round9.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round10.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-kp20k-TLtf-round10.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round1.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-wiki-step200k_round0/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k_round1\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-wiki-step200k_round0/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round10.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round2.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round2\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round3.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round4.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round4\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round5.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round6.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round7.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/transformer-PTDA-magkp1m-TLtf-round7.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-magkp1m-TLtf-round7.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round7.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round7\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round8.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round8\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round9.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round9\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/release/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round10.yml\"\nCUDA_VISIBLE_DEVICES=2 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-magkp1m-TLtf-round10.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round10-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-kp20k-round10-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round1-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round10-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round2-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round3-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15300"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round4-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15400"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round5-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round6-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-kp20k-round7-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round8-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-kp20k-round9-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round10-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=3 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round10-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round1-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round10-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round2-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round3-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round4-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round5-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round6-fewshot10k.nohup.out &\n\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round7-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round8-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round9-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/release/tf/selftrain/kppred_selftrain_kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx\n#SBATCH --job-name=titanx_pred-magkp1m-3d\n#SBATCH --output=slurm_output/titanx_pred-magkp1m-3d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=8GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts -gpu 0 -batch_size 32 -beam_size 1 -max_length 60 -testsets magkpcs_1m -splits train --wait_time 0 --step_base 1 --data_format jsonl\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/release/tf/selftrain/kppred_selftrain_kp20k100k.sh",
    "content": "#!/usr/bin/env bash\n\nCUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts -gpu 0 -batch_size 64 -beam_size 1 -max_length 60 -testsets kp20k_train100k -splits train --wait_time 0 --step_base 1 --data_format jsonl > slurm_output/kppred_selftrain_kp20k100k_round9.nohup.out &\n"
  },
  {
    "path": "script/release/tf/selftrain/kppred_selftrain_magkp1m.sh",
    "content": "#!/usr/bin/env bash\n\nCUDA_VISIBLE_DEVICES=1 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts -gpu 0 -batch_size 64 -beam_size 1 -max_length 60 -testsets magkpcs_1m -splits train --wait_time 0 --step_base 1 --data_format jsonl > slurm_output/kppred_selftrain_magkpcs1m_round9.nohup.out &\n"
  },
  {
    "path": "script/release/tf/tf_tlgenerate_pseudokp.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nmkdir -p $CURDIR/tmp\nTEMPLATE_PATH=\"$CURDIR/tl_gpu_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"titanx\" # titanx gtx1080 v100\ndays=\"3\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nsplit=\"train\"\nbatch_size=64\nbeam_size=1\nmax_length=40\nstep_base=1\n\n#datasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k inspec krapivin nus semeval)\ndatasets=(kp20k_train100k openkp_train100k kptimes_train100k stackex_train100k)\n\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl\"\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"TF-TL-$partition-$random-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{split}|$split|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\necho \"${slurm_output_dir}/$EXP_NAME.out\"\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/release/tf/tl_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits {split} -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl -gpu 0\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/release/transfer_cmd.txt",
    "content": "## Generate pseudo keyphrases from wiki-pretrained checkpoints\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2_paper/tf/tf_tlgenerate_pseudokp.sh\n\n## Pred and Eval\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsource script/transfer/run_pred_o2s_gpu_fewshot_dev.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot_scavenger.sh\n\n## PT\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsource script/transfer_v2/bart/nohup-bart-PTDA-magcs12m-lr1e5-step20k-bs256.sh\nsource script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step40k-bs256.sh\nsource script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step200k.sh\n\n## DA compare\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.sh\n\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.sh\n\n\n## scale-up with MAG\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2/mag/nohup-transformer-PTDA-magcs12m-tlrs55-step200k.sh\nbash script/transfer_v2/mag/nohup-bart-PTDA_MagTL12m-lr1e5-step200k.sh\n\n### Generate pseudo labels\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer_v2/mag/mag_transfer_labelling_tf.sh\nsbatch script/transfer_v2/mag/mag_transfer_labelling_bart.sh\n\n### BART\n\n#### DA\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL100k-lr1e5-step20k.sh\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh\n\n#### FT\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh\n\n### Transformer\n\n#### selftrain\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\n##### pred\nsource script/transfer_v2/tf/selftrain/kppred_selftrain_kp20k100k.sh\nsource script/transfer_v2/tf/selftrain/kppred_selftrain_magkp1m.sh\n\n##### selftrain-DA\nsource script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf.sh\nsource script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf.sh\n\n##### selftrain-DAFT\nsource script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-fewshot10k.sh\nsource script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-fewshot10k.sh\n\n"
  },
  {
    "path": "script/transfer/kpeval_cpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --cluster=htc\n\n#SBATCH --account=hdaqing\n#SBATCH --partition=scavenger\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl\"\n\n\necho $cmd\necho $PWD\n$cmd\n\n"
  },
  {
    "path": "script/transfer/kpeval_gpu_scavenger_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=scavenger\n#SBATCH --constraint={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=8GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl --pred_trained_only -gpu 0\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/transfer/kpeval_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl --pred_trained_only -gpu 0\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/transfer/kpeval_o2o_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"python kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2one.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits test -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl --pred_trained_only -gpu 0\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/transfer/kppred_selftrain_magkp1m.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx\n#SBATCH --job-name=titanx_pred-magkp1m-3d\n#SBATCH --output=slurm_output/titanx_pred-magkp1m-3d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=8GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts -gpu 0 -batch_size 32 -beam_size 1 -max_length 60 -testsets magkpcs_1m -splits train --wait_time 0 --step_base 1 --data_format jsonl\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/transfer/kppred_v100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx\n#SBATCH --partition=scavenger\n#SBATCH --constraint=titanx\n\n#SBATCH --partition=v100\n#SBATCH --partition=scavenger\n#SBATCH --constraint=v100\n\n#SBATCH --job-name=v100_pred-3d\n#SBATCH --output=slurm_output/v100_pred.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=8GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_fewshot-v1_DA1e6_FT1e5_devbest/ -gpu 0 -batch_size 1 -beam_size 50 -max_length 40 -testsets kp20k openkp kptimes jptimes stackex kp20k_valid2k openkp_valid2k kptimes_valid2k jptimes_valid2k stackex_valid2k duc -splits test --wait_time 360000 -sleep_time 360000 --step_base 1 --data_format jsonl -gpu 0\"\n#cmd=\"python kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/ -gpu 0 -batch_size 1 -beam_size 50 -max_length 40 -testsets kp20k openkp kptimes jptimes stackex -splits test --wait_time 0 --step_base 1 --data_format jsonl --pred_trained_only -gpu 0\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/transfer/mag/bart/nohup-bart-DA_MagTL12m-lr1e5-step20k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\nmkdir -p /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs12m_tlrs55-lr1e5-bs256-step20k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_12m_v2/:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs12m_tlrs55-lr1e5-bs256-step20k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.98)' --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 8 --update-freq 8 --save-interval-updates 2000 --warmup-updates 1200 --max-update 20000 --total-num-update 20000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs12m_tlrs55-lr1e5-bs256-step20k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/bart/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\n#sleep 14400\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot/bart-MagTL_step200k-kp20k_full_lr1e5_step100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts/checkpoint_step_200000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9, 0.999)\" --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1920 --update-freq 2 --save-interval-updates 5000 --warmup-updates 10000 --max-update 100000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/bart/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_13m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_13m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/bart/nohup-bart-DA_MagTL1m-lr1e5-step5k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\nmkdir -p /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs1m_tlrs55-lr1e5-bs256-step5k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_1m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_1m/:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs1m_tlrs55-lr1e5-bs256-step5k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.98)' --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 8 --update-freq 8 --save-interval-updates 1000 --warmup-updates 300 --max-update 5000 --total-num-update 5000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-DA_magcs1m_tlrs55-lr1e5-bs256-step5k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/bart/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_3m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_3m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer/mag/mag_np.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --cluster=htc\n#SBATCH --partition=scavenger\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=mag_np\n#SBATCH --output=slurm_output/mag_np.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ninput_dir=\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/\"\noutput_dir=\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\"\n\nfilenames=(train_101.json train_102.json train_103.json train_104.json train_105.json train_106.json train_107.json train_108.json train_109.json train_110.json train_111.json train_112.json train_113.json train_114.json train_115.json train_116.json train_117.json train_118.json train_119.json train_120.json)\n# train_0.json train_1.json train_2.json train_3.json train_4.json train_5.json train_6.json train_7.json train_8.json train_9.json train_10.json train_11.json train_12.json train_13.json train_14.json train_15.json train_16.json train_17.json train_18.json train_19.json train_20.json train_21.json train_22.json train_23.json train_24.json train_25.json train_26.json train_27.json train_28.json train_29.json train_30.json train_31.json train_32.json train_33.json train_34.json train_35.json train_36.json train_37.json train_38.json train_39.json train_40.json train_41.json train_42.json train_43.json train_44.json train_45.json train_46.json train_47.json train_48.json train_49.json train_50.json train_51.json train_52.json train_53.json train_54.json train_55.json train_56.json train_57.json train_58.json train_59.json train_60.json train_61.json train_62.json train_63.json train_64.json train_65.json train_66.json train_67.json train_68.json train_69.json train_70.json train_71.json train_72.json train_73.json train_74.json train_75.json train_76.json train_77.json train_78.json train_79.json train_80.json train_81.json train_82.json train_83.json train_84.json train_85.json train_86.json train_87.json train_88.json train_89.json train_90.json train_91.json train_92.json train_93.json train_94.json train_95.json train_96.json train_97.json train_98.json train_99.json train_100.json train_101.json train_102.json train_103.json train_104.json train_105.json train_106.json train_107.json train_108.json train_109.json train_110.json train_111.json train_112.json train_113.json train_114.json train_115.json train_116.json train_117.json train_118.json train_119.json train_120.json\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/mag_np_sbatch.sh\"\n\nfor filename in \"${filenames[@]}\"\ndo\n    echo $filename\n\n    EXP_NAME=\"mag-np-$filename\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\n    rm -f $DUMP_SCRIPT_PATH\n\n    input_path=\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/$filename\"\n    output_path=\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{input_path}|$input_path|g;\";\n    replaces=\"$replaces s|{output_dir}|$output_dir|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n"
  },
  {
    "path": "script/transfer/mag/mag_np_sbatch.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --cluster=htc\n#SBATCH --partition=scavenger\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name={job_name}\n#SBATCH --output=slurm_output/mag_np.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python onmt/keyphrase/extract_np.py -input_path {input_path} -output_dir {output_dir}\"\n\necho $cmd\n$cmd\n"
  },
  {
    "path": "script/transfer/mag/mag_transfer_labelling.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080 # titanx gtx1080 v100\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n\n#SBATCH --job-name=mag_transfer_labelling\n#SBATCH --output=slurm_output/mag_transfer_labelling.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k -data_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_13m/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_13m_v2/ -gpu 0 -batch_size 64 -beam_size 1 -max_length 60\"\n\necho $cmd\n$cmd\n"
  },
  {
    "path": "script/transfer/mag/run_pred_o2s_scipaper.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/script/transfer/kpeval_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"gtx1080\" # titanx gtx1080 v100\ndays=\"1\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k kp20k_valid2k inspec krapivin nus semeval duc)\ndataset_list=\"\"\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\necho $dataset_list\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot_devbest/\"\n\n\nEXP_NAME=\"predeval-$partition-$random-scipaper-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\nsbatch $DUMP_SCRIPT_PATH\n\n"
  },
  {
    "path": "script/transfer/mag/tf/nohup_train_transformer-kp20k-PTDA_100k-FT_full-step100k-lr5e5.sh",
    "content": "#!/usr/bin/env bash\n\nsleep 7200\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PTDA_100k-FT_full-step100k-lr5e5.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-DA-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-kp20k-TL\n#SBATCH --output=slurm_output/train-tf-DA-kp20k-TL.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-kp20k-TL.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/mag/tf/transformer-DA_mag_TLbart-step500k-lr3e4-tlnp82.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_390000.pt\n\n### Exp meta\nexp: transformer-MagTL-step500k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 64\nmax_phrase_len: 16\nmax_target_phrases: 16\n\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/\"]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 3840\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 500000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-MagTL-singleshard.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint_step_45000.pt\n\n### Exp meta\nexp: transformer-MagTL-step500k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 64\nmax_phrase_len: 16\nmax_target_phrases: 16\n\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.0.json\"\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.0.np.json\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.0.tl.json\"]\n    corpus_2:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.1.json\"\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.1.np.json\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_test/train_0.1.tl.json\"]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 5120\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 500000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 3\ngpu_ranks:\n- 0\n- 1\n- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-PT-MagDA-step300k-lr1e5-tlnp82-nohup.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-PT-MagDA-step300k-lr1e5-tlnp82.yml --report_every 100 > /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-PT-MagDA-step300k-lr1e5-tlnp82.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer if load from a pretrained ckpt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 64\nmax_phrase_len: 16\nmax_target_phrases: 16\n\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/\"]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 5120 # 3840\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 1000\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-PT-MagTL-lr5e5-step100k-tl:np=8:2.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/ckpts/checkpoint_step_70000.pt\n\n### Exp meta\nexp: transformer-PT-DA_MagTL-step100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 64\nmax_phrase_len: 16\nmax_target_phrases: 16\n\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/\"]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 3840\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-PT-MagTL-lr5e5-step300k-tl:np=8:2.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:4\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-mag_PTDA-step300k-lr1e5-step300k\n#SBATCH --output=slurm_output/train-tf-mag_PTDA-step300k-lr1e5-step300k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/mag/train/transformer-PT-MagTL-lr1e5-step300k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-PT-MagTL-lr5e5-step300k-tl:np=8:2.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-MagTL-step300k/ckpts/checkpoint_step_70000.pt\n\n### Exp meta\nexp: transformer-PT-MagTL-step300k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-MagTL-step300k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-MagTL-step300k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-MagTL-step300k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 64\nmax_phrase_len: 16\nmax_target_phrases: 16\n\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp/\n        label_data: [\"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_np/\", \"/zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL/\"]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 3840\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 300000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot100-step1k-lr5e6.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr5e6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr5e6\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot100_step1k_lr5e6/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot10k-step4k-lr5e6.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr5e6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr5e6\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot10k_step4k_lr5e6/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-DAFT-fewshot1k-step2k-lr5e6.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr5e6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr5e6\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_fewshot1k_step2k_lr5e6/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/ckpts/checkpoint_step_300000.pt\n#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/ckpts/checkpoint_step_100000.pt\n### Reset all states in the optimizer if load from a pretrained ckpt\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot100_step2k_lr1e5\n#exp: transformer-kp20k-PT_step200k-DA_mag_step100k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot100_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot100_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot100_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240 # 10240*2, or 5120*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/ckpts/checkpoint_step_300000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot10k_step8k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot10k_step8k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot10k_step8k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot10k_step8k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step300k-lr1e5-tlnp82/ckpts/checkpoint_step_300000.pt\n#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/ckpts/checkpoint_step_100000.pt\n### Reset all states in the optimizer if load from a pretrained ckpt\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot1k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot1k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot1k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/transformer-kp20k-PT_step200k-MagDA_step300k-FT_fewshot1k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240 # 10240*2, or 5120*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDA_kp20k_20k-FT_full-step100k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr1e5_warmup5k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\nwarmup_steps: 5000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDA_kp20k_20k-FT_full-step20k-lr1e5-warmup2k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nmkdir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k/\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PTDA_kp20k_20k-FT_full-step20k-lr1e5-warmup2k.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDA_kp20k_20k-FT_full-step20k-lr1e5-warmup2k.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step20k_lr1e5_warmup2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 1000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDA_kp20k_20k-FT_full-step50k-lr1e4-warmup5k.sh",
    "content": "#!/usr/bin/env bash\n\nsleep 9000\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nmkdir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PTDA_kp20k_20k-FT_full-step50k-lr1e4-warmup5k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PTDA_kp20k_20k-FT_full-step50k-lr1e4-warmup5k.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PTDA_kp20k_20k-FT_full-step50k-lr1e4-warmup5k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PTDA_kp20k_20k-FT_full-step50k-lr1e4-warmup5k.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step50k_lr1e4_warmup5k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 50000\nwarmup_steps: 5000\nvalid_steps: 10000\nsave_checkpoint_steps: 2500\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 9000\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nmkdir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n\nmaster_port: 10100\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 9000\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nmkdir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n\nmaster_port: 10100\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nmkdir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer/mag/train/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k.yml --report_every 100 > /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 1000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k_DAFT-fulldata-step100k-lr1e5.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:3\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-kp20k_DAFT-fulldata-step100k-lr1e5\n#SBATCH --output=slurm_output/train-tf-kp20k_DAFT-fulldata-step100k-lr1e5.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/mag/train/transformer-kp20k_DAFT-fulldata-step100k-lr1e5.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k_DAFT-fulldata-step100k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/ckpts/checkpoint_step_500000.pt\n### Reset all states in the optimizer if load from a pretrained ckpt\nreset_optim: all\n\n#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5/ckpts/checkpoint_step_30000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\nwarmup_steps: 10000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 1280 # 4096\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 3\ngpu_ranks:\n- 0\n- 1\n- 2\n#- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/mag/tf/transformer-kp20k_PTDAFT-fulldata-step100k-lr5e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-PT-DA_MagTL-step100k/ckpts/checkpoint_step_100000.pt\n### Reset all states in the optimizer if load from a pretrained ckpt\nreset_optim: all\n\n#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-DA_mag_step500k-FT_full_step100k_lr1e5/ckpts/checkpoint_step_30000.pt\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_mag_step100k-FT_full_step100k_lr5e5_warmup10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-PT_step200k-DA_mag_step100k-FT_full_step100k_lr5e5_warmup10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-PT_step200k-DA_mag_step100k-FT_full_step100k_lr5e5_warmup10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/transformer-kp20k-PT_step200k-DA_mag_step100k-FT_full_step100k_lr5e5_warmup10k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 5120 # 10240*2, or 5120*4\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/run_eval.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=smp\n#SBATCH --cluster=htc\n#SBATCH --partition=scavenger\n\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=kp_eval\n#SBATCH --output=slurm_output/kp_eval.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=1-0:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2o/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot10k_devbest/\"\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot\"\n\ndataset_args=\"kp20k kp20k_valid2k kptimes kptimes_valid2k jptimes duc openkp openkp_valid2k stackex stackex_valid2k\"\n\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks eval -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir $exp_root_dir -testsets $dataset_args -splits test -batch_size 8 -beam_size 50 -max_length 40 --step_base 1 --data_format jsonl\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/transfer/run_eval_o2o.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\ntask_args=\"eval\" # pred or eval\nbatch_size=8\n# beam_size=200\n# max_length=6\nbeam_size=128\nmax_length=8\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k kp20k_valid2k kptimes kptimes_valid2k jptimes jptimes_valid2k openkp openkp_valid2k stackex stackex_valid2k duc)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    exp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/\"\n    EXP_NAME=\"$task_args-o2o-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    replaces=\"$replaces s|{step_base}|$step_base|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/transfer/run_eval_o2s.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\ntask_args=\"eval\" # pred or eval\nbatch_size=8\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k kp20k_valid2k kptimes kptimes_valid2k jptimes jptimes_valid2k duc openkp openkp_valid2k stackex stackex_valid2k duc)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    exp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/\"\n    EXP_NAME=\"$task_args-o2s-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    replaces=\"$replaces s|{step_base}|$step_base|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/transfer/run_eval_o2s_fewshot.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_cpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\ntask_args=\"eval\" # pred or eval\nbatch_size=8\nbeam_size=1\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k kp20k_valid2k kptimes kptimes_valid2k jptimes duc openkp openkp_valid2k stackex stackex_valid2k)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    exp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/\"\n    EXP_NAME=\"$task_args-o2sfs-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    replaces=\"$replaces s|{step_base}|$step_base|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/transfer/run_pred_o2o_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_o2o_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\ntask_args=\"pred\" # pred or eval\nbatch_size=1\nbeam_size=128\nmax_length=8\nstep_base=5000\n\n# GPU mem usage\n# beam_size=200, max_length=6, even with BS=1, OOM on TitanX, usage > 13g\n# beam_size=128, max_length=8, BS=1, on TitanX, usage=10114MiB\n\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k duc stackex_valid2k)\ndatasets=(kp20k kp20k_valid2k openkp kptimes jptimes duc stackex stackex_valid2k)\ndatasets=(kp20k)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k duc stackex_valid2k)\ndatasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k)\ndatasets=(kp20k kptimes jptimes stackex)\n\nfor dataset in \"${datasets[@]}\"\ndo\n    exp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/\"\n    EXP_NAME=\"$task_args-o2o-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    replaces=\"$replaces s|{step_base}|$step_base|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n\n"
  },
  {
    "path": "script/transfer/run_pred_o2s_gpu.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\ntask_args=\"pred\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=5000\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k duc stackex_valid2k)\ndatasets=(kp20k kp20k_valid2k openkp kptimes jptimes duc stackex stackex_valid2k)\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k duc stackex_valid2k)\ndatasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k)\ndatasets=(kp20k_valid2k openkp openkp_valid2k kptimes_valid2k stackex stackex_valid2k duc)\n\n\nfor dataset in \"${datasets[@]}\"\ndo\n    exp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/\"\n    EXP_NAME=\"$task_args-o2s-$dataset-bs$beam_size\"\n    DUMP_SCRIPT_PATH=\"$CURDIR/$EXP_NAME.sh\"\n    replaces=\"s/{job_name}/$EXP_NAME/;\";\n    replaces=\"$replaces s|{task_args}|$task_args|g;\";\n    replaces=\"$replaces s|{dataset_args}|$dataset|g;\";\n    replaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\n    replaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\n    replaces=\"$replaces s|{batch_size}|$batch_size|g;\";\n    replaces=\"$replaces s|{max_length}|$max_length|g;\";\n    replaces=\"$replaces s|{beam_size}|$beam_size|g;\";\n    replaces=\"$replaces s|{step_base}|$step_base|g;\";\n    cat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\n    echo $EXP_NAME\n    echo $DUMP_SCRIPT_PATH\n    sbatch $DUMP_SCRIPT_PATH\n\ndone\n"
  },
  {
    "path": "script/transfer/run_pred_o2s_gpu_debug.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_template.sh\"\n\necho $0\necho $PROJECT_DIR\npartition=\"v100\" # titanx gtx1080 v100\ndays=\"1\"\nrandom=$RANDOM\n\ntask_args=\"pred\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k_valid2k duc)\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\necho $dataset_list\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_debug/bartwikikp_DAFT_kp20k_fewshot100_$partition\"\nslurm_output_dir=\"$exp_root_dir\"\n\nEXP_NAME=\"$task_args-$partition-$random-bs$beam_size-debug\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\n#cat $DUMP_SCRIPT_PATH\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/transfer/run_pred_o2s_gpu_fewshot.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"titanx\" # titanx gtx1080 v100\ndays=\"1\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k kp20k_valid2k duc inspec krapivin nus semeval)\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k kp20k_valid2k duc)\ndatasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k)\n\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_o2s_fulldata_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot_devbest/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3_devbest/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/\"\nexp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot_devbest/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT_devbest\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTFT_devbest\"\n\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"predeval-$partition-$random-all-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\necho \"${slurm_output_dir}/$EXP_NAME.out\"\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/transfer/run_pred_o2s_gpu_fewshot_dev.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"titanx\" # gtx1080 titanx v100\ndays=\"1\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k stackex_valid2k)\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_fewshot-v3/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot/\"\n#exp_root_dir=\"/zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/tf_mag/\"\n#exp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot\"\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/\"\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDA\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_DAFT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTFT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_DAFT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/\"\n\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"predeval-$partition-$random-devset-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/transfer/run_pred_o2s_gpu_fewshot_scavenger.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nTEMPLATE_PATH=\"$CURDIR/kpeval_gpu_scavenger_template.sh\"\n\necho $0\necho $PROJECT_DIR\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"titanx\" # titanx gtx1080 v100\ndays=\"0\"\nrandom=$RANDOM\ntask_args=\"pred\" # pred or eval\nbatch_size=1\nbeam_size=50\nmax_length=40\nstep_base=1\n\n# evaluate with all predictions\ndatasets=(kp20k openkp kptimes jptimes stackex)\ndatasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k)\ndatasets=(kp20k kp20k_valid2k duc inspec krapivin nus semeval)\ndatasets=(kp20k_valid2k openkp_valid2k kptimes_valid2k stackex_valid2k)\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\necho $dataset_list\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag_fewshot_devbest/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_PTDAFT\"\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_FT\"\n\n\nEXP_NAME=\"sca_$task_args-$partition-$random-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/transfer/train_bart_DA/wikida-kp20k_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_kp20k-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=9\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/kp20k_train100k/train.json --dataset-type scipaper --label-data /home/ubuntu/efs/rum20/data/kp/json/kp20k_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kp20k_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_kp20k-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer/train_bart_DA/wikida-kptimes_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=24\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type news --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 768 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n\n"
  },
  {
    "path": "script/transfer/train_bart_DA/wikida-openkp_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=24\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type webpage --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer/train_bart_DA/wikida-stackex_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\n# doesn't work on AWS V100*4\nUPDATE_FREQ=14\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type qa --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 768 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kp20k-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kp20k-fewshot100\n#SBATCH --output=slurm_output/train-bart-kp20k-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kp20k_fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kp20k_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 8 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kp20k-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kp20k-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bart-kp20k-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kp20k_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kp20k_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 6 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kp20k-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kp20k-fewshot1k\n#SBATCH --output=slurm_output/train-bart-kp20k-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kp20k_fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kp20k_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 6 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kptimes-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kptimes-fewshot100\n#SBATCH --output=slurm_output/train-bart-kptimes-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kptimes_fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kptimes_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 10 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kptimes-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kptimes-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bart-kptimes-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kptimes_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kptimes_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 8 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-kptimes-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kptimes-fewshot1k\n#SBATCH --output=slurm_output/train-bart-kptimes-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kptimes_fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_kptimes_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 8 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-openkp-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp-fewshot100\n#SBATCH --output=slurm_output/train-bart-openkp-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_openkp_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 14 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-openkp-fewshot10k-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp-fewshot10k-step10k-rerun-resume\n#SBATCH --output=slurm_output/train-bart-openkp-fewshot10k-step10k-rerun-resume.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=0-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_fewshot10k_step10k_rerun-resume\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_openkp_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-openkp-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bart-openkp-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_openkp_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-openkp-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp-fewshot1k\n#SBATCH --output=slurm_output/train-bart-openkp-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_openkp_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-stackex-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-stackex-fewshot100\n#SBATCH --output=slurm_output/train-bart-stackex-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_stackex_fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_stackex_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7  --max-tokens 1536 --update-freq 14 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-stackex-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-stackex-fewshot10k-step10k\n#SBATCH --output=slurm_output/train-bart-stackex-fewshot10k-step10k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_stackex_fewshot10k_step10k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_stackex_fewshot10k_step10k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7  --max-tokens 1344 --update-freq 14 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bart-presabs-stackex-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-stackex-fewshot1k\n#SBATCH --output=slurm_output/train-bart-stackex-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_stackex_fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bart_presabs_stackex_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1536 --update-freq 12 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bartwikikp-presabs-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-kp20k-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bartwikikp-kp20k-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp_presabs_kp20k_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_presabs_kp20k_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/bart_kppretrain_wiki_1e5/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 6 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bartwikikp-presabs-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-kptimes-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bartwikikp-kptimes-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp_presabs_kptimes_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_presabs_kptimes_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/bart_kppretrain_wiki_1e5/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 8 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bartwikikp-presabs-openkp-fewshot10k-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-openkp-fewshot10k-step10k-rerun-resume\n#SBATCH --output=slurm_output/train-bartwikikp-openkp-fewshot10k-step10k-rerun-resume.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=0-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp_presabs_openkp_fewshot10k_step10k_rerun-resume\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_presabs_openkp_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bartwikikp-presabs-openkp-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-openkp-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bartwikikp-openkp-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp_presabs_openkp_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_presabs_openkp_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/bart_kppretrain_wiki_1e5/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bart_fewshot/kpgen-bartwikikp-presabs-stackex.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-stackex-fewshot10k-step10k-rerun\n#SBATCH --output=slurm_output/train-bartwikikp-stackex-fewshot10k-step10k-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp_presabs_stackex_fewshot10k_step10k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_presabs_stackex_fewshot10k_step10k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/bart_kppretrain_wiki_1e5/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7  --max-tokens 1344 --update-freq 14 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kp20k-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-DAFT-kp20k-fewshot100\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kp20k-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kp20k-fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kp20k_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kp20k-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 8 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kp20k-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-kp20k-fewshot10k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kp20k-fewshot10k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kp20k-fewshot10k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kp20k_fewshot10k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kp20k-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 6 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kp20k-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-DAFT-kp20k-fewshot1k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kp20k-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kp20k-fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kp20k_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kp20k-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2752 --update-freq 6 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kptimes-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-kptimes-fewshot100\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kptimes-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kptimes-fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kptimes_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kptimes-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 10 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kptimes-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-kptimes-fewshot10k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kptimes-fewshot10k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kptimes-fewshot10k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kptimes_fewshot10k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kptimes-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 8 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-kptimes-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-kptimes-fewshot1k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-kptimes-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-kptimes-fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json --dataset-type news --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_kptimes_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_kptimes-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 5760 --update-freq 8 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-openkp-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-openkp-fewshot100\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-openkp-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-openkp-fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_openkp_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_openkp-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 14 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-openkp-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-openkp-fewshot10k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-openkp-fewshot10k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-12:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-openkp-fewshot10k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_openkp_fewshot10k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_openkp-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-openkp-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-openkp-fewshot1k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-openkp-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-openkp-fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json --dataset-type webpage --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_openkp_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_openkp-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3584 --update-freq 12 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-stackex-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-stackex-fewshot100\n#SBATCH --output=/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/slurm_output/train-bartwikikp-DAFT-stackex-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-stackex-fewshot100\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_stackex_fewshot100/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_stackex-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 10 --fixed-validation-seed 7  --max-tokens 1536 --update-freq 14 --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-stackex-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-stackex-fewshot10k\n#SBATCH --output=slurm_output/train-bartwikikp-DAFT-stackex-fewshot10k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-stackex-fewshot10k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_stackex_fewshot10k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_stackex-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7  --max-tokens 1344 --update-freq 14 --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_bartwikikp_DAFT_fewshot/kpgen-bartwikikp-DAFT-presabs-stackex-fewshot1k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bartwikikp-DAFT-stackex-fewshot1k\n#SBATCH --output=/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/slurm_output/train-bartwikikp-DAFT-stackex-fewshot1k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.w130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartwikikp-DAFT-stackex-fewshot1k\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json --dataset-type qa --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_fewshot10k/bartwikikp_DAFT_stackex_fewshot1k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_wiki/wiki-DA_stackex-NP_TL/ckpts/checkpoint_step_20000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1536 --update-freq 12 --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000 --num-workers 2 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_fewshot\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-one2one-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-one2one-kp20k\n#SBATCH --output=slurm_output/train-bart-one2one-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=3\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: 31kM / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_one2one_kp20k_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/bartFT_one2one_kp20k_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type one2one --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2048 --update-freq 4 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-one2one-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-one2one-kptimes\n#SBATCH --output=slurm_output/train-bart-one2one-kptimes.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=3\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: 31kM / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_one2one_kptimes_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/bartFT_one2one_kptimes_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type one2one --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 4096 --update-freq 8 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-one2one-openkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-one2one-openkp\n#SBATCH --output=slurm_output/train-bart-one2one-openkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=3\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: 31kM / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_one2one_openkp_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/bartFT_one2one_openkp_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type one2one --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 3072 --update-freq 8 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-one2one-stackex.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-one2one-stackex\n#SBATCH --output=slurm_output/train-bart-one2one-stackex.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=3\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: 31kM / 32480MiB, still often OOM\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_one2one_stackex_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/bartFT_one2one_stackex_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type one2one --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1280 --update-freq 8 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-kp20k-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kp20k-resume\n#SBATCH --output=/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/slurm_output/train-bart-kp20k-resume.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: 16000ish MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kp20k_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_kp20k_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1024 --update-freq 4 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kp20k_100k_rerun\n#SBATCH --output=slurm_output/train-bart-kp20k_100k_rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=4\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage:\n#   max_token=2048, update_freq=4, OOM\n#   max_token=1536, update_freq=6, 27xxxMiB / 32480MiB, OOM...\n#   max_token=1024, update_freq=8,  / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kp20k_100k_rerun\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_kp20k_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --update-freq 8 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1024 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-kptimes-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kptimes-rerun\n#SBATCH --output=slurm_output/train-bart-kptimes-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kptimes_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_kptimes_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1536 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-kptimes-rerun\n#SBATCH --output=slurm_output/train-bart-kptimes-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=0-20:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110: k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_kptimes_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_kptimes_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\n\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-openkp-rerun.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp-rerun\n#SBATCH --output=slurm_output/train-bart-openkp-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=0-20:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=2048,--update-freq=10, bsz~=95: 32k+MiB / 32480MiB, OOMed after 11k steps\n# GPU usage: --max-tokens=1536,--update-freq=16, bsz=110~115: 32k+MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_openkp_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 2048 --update-freq 10 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-openkp-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp\n#SBATCH --output=slurm_output/train-bart-openkp-100k-resume.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: BS=2048: 21k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_openkp_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1536 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-openkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-openkp\n#SBATCH --output=slurm_output/train-bart-openkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: BS=2048: 21k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_openkp_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_openkp_100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 3e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 768 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-stackex-rerun.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-stackex\n#SBATCH --output=slurm_output/train-bart-stackex-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=1-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: --max-tokens=512, --update-freq=16, bsz=80~84: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_stackex_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_stackex_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-bart-presabs-stackex-resume.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:2\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080s\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-bart-stackex\n#SBATCH --output=slurm_output/train-bart-stackex-rerun.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# GPU usage: bsz=~90: 31k+MiB / 32480MiB\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg/fairseq_cli/\nexport WANDB_NAME=bartFT_presabs_stackex_100kstep\nexport TOKENIZERS_PARALLELISM=false\ncmd=\"python train.py /zfs1/hdaqing/rum20/kp/data/kp/json/stackex/ --save-dir /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/bartFT_presabs_stackex_100k_rerun/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file checkpoint_last.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --optimizer adam --adam-betas (0.9,0.999) --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq 16 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 100000 --max-update 100000 --num-workers 16 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-one2one-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TF-one2one-kp20k\n#SBATCH --output=slurm_output/train-TF-one2one-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-one2one-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-one2one-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TF-one2one-kptimes\n#SBATCH --output=slurm_output/train-TF-one2one-kptimes.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-one2one-kptimes.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-one2one-openkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TF-one2one-openkp\n#SBATCH --output=slurm_output/train-TF-one2one-openkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-one2one-openkp.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-one2one-stackex.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TF-one2one-stackex\n#SBATCH --output=slurm_output/train-TF-one2one-stackex.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-one2one-stackex.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-kp20k-lower.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-kp20k-lower\n#SBATCH --output=slurm_output/train-TFpresabs-kp20k-lower.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-kp20k-lower.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-kp20k-nocopy-lower.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-kp20k-nocopy-lower\n#SBATCH --output=slurm_output/train-TFpresabs-kp20k-nocopy-lower.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-kp20k-nocopy-lower.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-kp20k-nocopy.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-kp20k-nocopy\n#SBATCH --output=slurm_output/train-TFpresabs-kp20k-nocopy.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-kp20k-nocopy.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --partition=v100\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-kp20k\n#SBATCH --output=slurm_output/train-TFpresabs-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-kptimes\n#SBATCH --output=slurm_output/train-TFpresabs-kptimes.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-kptimes.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-openkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-openkp\n#SBATCH --output=slurm_output/train-TFpresabs-openkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-openkp.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/kpgen-transformer-presabs-stackex.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-TFpresabs-stackex\n#SBATCH --output=slurm_output/train-TFpresabs-stackex.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=32GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"config/transfer_kp/train/transformer-presabs-stackex.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-one2one-kp20k.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-one2one-kp20k\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kp20k\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kp20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kp20k/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-one2one-kptimes.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kptimes/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-one2one-kptimes\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kptimes\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kptimes/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_kptimes/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-one2one-openkp.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_openkp/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-one2one-openkp\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_openkp\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_openkp/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_openkp/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-one2one-stackex.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_stackex/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-one2one-stackex\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_stackex\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_stackex/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp_o2o/transformer_one2one_stackex/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: one2one\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-kp20k-lower.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_lower/ckpts/checkpoint_step_110000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-lower\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_lower\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_lower/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_lower/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\nlowercase: true\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-kp20k-nocopy-lower.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy_lower/ckpts/checkpoint_step_125000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-nocopy-lower\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy_lower\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy_lower/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy_lower/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\nlowercase: true\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'false'\nreuse_copy_attn: 'false'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-kp20k-nocopy.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy/ckpts/checkpoint_step_150000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k-nocopy\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k_nocopy/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'false'\nreuse_copy_attn: 'false'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-kp20k.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n\n### Exp meta\nexp: transformer-presabs-kp20k\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-kptimes.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kptimes/ckpts/checkpoint_step_160000.pt\n\n### Exp meta\nexp: transformer-presabs-kptimes\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kptimes\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kptimes/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kptimes/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-openkp.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_openkp/ckpts/checkpoint_step_140000.pt\n\n### Exp meta\nexp: transformer-presabs-openkp\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_openkp\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_openkp/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_openkp/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_fulldata/transformer-presabs-stackex.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_stackex/ckpts/checkpoint_step_175000.pt\n\n### Exp meta\nexp: transformer-presabs-stackex\nexp_dir: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_stackex\nsave_model: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_stackex/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_stackex/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k-NP.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-kp20k-NP\n#SBATCH --output=slurm_output/train-tf-DA-kp20k-NP.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-kp20k-NP.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k-NP.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k/ckpts/checkpoint_step_15000.pt\n\n### Exp meta\nexp: transformer-DA_NP-kp20k_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer_DA-NP_kp20k_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k-RS.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_RS-kp20k_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer_DA-RS_kp20k_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_random_span, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k-TL.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-kp20k-TL\n#SBATCH --output=slurm_output/train-tf-DA-kp20k-TL.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-kp20k-TL.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k-TL.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint_step_45000.pt\n\n### Exp meta\nexp: transformer-DA_TL-kp20k_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer_DA-TL_kp20k_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-kp20k\n#SBATCH --output=slurm_output/train-tf-DA-kp20k.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-kp20k.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kp20k.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-kp20k_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer_DA_kp20k_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kptimes.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-kptimes\n#SBATCH --output=slurm_output/train-tf-DA-kptimes.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-kptimes.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-kptimes.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint_step_70000.pt\n\n### Exp meta\nexp: transformer-DA-kptimes_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kptimes_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10001"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-openkp.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-openkp\n#SBATCH --output=slurm_output/train-tf-DA-openkp.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-openkp.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-openkp.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-openkp_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100'\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 2\n#master_port: 10002\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-stackex.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-DA-stackex\n#SBATCH --output=slurm_output/train-tf-DA-stackex.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=6-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_DA/transformer-DA-stackex.yml\"\n\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-DA-stackex.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-stackex_step100k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_stackex_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10003\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-PT-wiki.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_105000.pt\n\n### Exp meta\nexp: transformer-wiki-step200k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nmax_phrase_len: 16\nmax_target_phrases: 16\nphrase_corr_rate: 0.1\nrandom_span_rate: 0.05\n\nshuffle_shards: false\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [wiki_phrase, roberta_tokenize_kpg]\n#        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/AA/\n        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\nwarmup_steps: 20000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 4480\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 4479\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-PTDA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 2000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 8192\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-PTDA-kptimes.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kptimes_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 2000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 16384\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 10789\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-PTDA-openkp.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 2000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 16384\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 10100\n"
  },
  {
    "path": "script/transfer/train_tf_DA/transformer-PTDA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_30000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps/kp_bart_DA/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_stackex_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\nwarmup_steps: 2000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 9216\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11000\n\n"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot100-DA-NP-step50k-FT-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-DA_NP_step50k-FT_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_NP_step50k-FT_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_NP_step50k-FT_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_NP_step50k-FT_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot100-DA-RS-step50k-FT-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-DA_RS_step50k-FT_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_RS_step50k-FT_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_RS_step50k-FT_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_RS_step50k-FT_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot100-DA-TL-step50k-FT-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-DA_TL_step50k-FT_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_TL_step50k-FT_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_TL_step50k-FT_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-DA_TL_step50k-FT_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot10k-DA-NP-step50k-FT-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-DA_NP_step50k-FT_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_NP_step50k-FT_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_NP_step50k-FT_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_NP_step50k-FT_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot10k-DA-RS-step50k-FT-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-DA_RS_step50k-FT_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_RS_step50k-FT_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_RS_step50k-FT_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_RS_step50k-FT_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot10k-DA-TL-step50k-FT-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-DA_TL_step50k-FT_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_TL_step50k-FT_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_TL_step50k-FT_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-DA_TL_step50k-FT_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot1k-DA-NP-step50k-FT-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-NP_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-DA_NP_step50k-FT_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_NP_step50k-FT_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_NP_step50k-FT_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_NP_step50k-FT_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot1k-DA-RS-step50k-FT-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-RS_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-DA_RS_step50k-FT_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_RS_step50k-FT_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_RS_step50k-FT_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_RS_step50k-FT_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_ablation/transformer-kp20k-fewshot1k-DA-TL-step50k-FT-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA-TL_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-DA_TL_step50k-FT_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_TL_step50k-FT_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_TL_step50k-FT_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-DA_TL_step50k-FT_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-MagTL-step500k/checkpoint_step_500000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step50k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr1e3.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e3\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e3\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e3/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e3/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-3\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr1e4.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e4\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e4\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e4/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e4/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr3e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr3e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr3e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr3e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr3e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr5e4.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e4\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e4\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e4/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e4/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr5e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot100_step100_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 100\nwarmup_steps: 10\nsave_checkpoint_steps: 5\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-kp20k-DAFT-fewshot100\n#SBATCH --output=slurm_output/train-tf-kp20k-DAFT-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100.yml\"\ncmd=\"TOKENIZERS_PARALLELISM=false python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot100.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-kp20k-DAFT-fewshot100-lr5e4\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_DAFT_fewshot100_lr5e4\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_DAFT_fewshot100_lr5e4/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_DAFT_fewshot100_lr5e4/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot10k-step4k-lr1e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr1e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot10k-step4k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot10k_step4k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot1k-step2k-lr1e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr1e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-DAFT-fewshot1k-step2k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-DA_step10k-FT_fewshot1k_step2k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-kp20k-fewshot100\n#SBATCH --output=slurm_output/train-tf-kp20k-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_fewshot/transformer-kp20k-fewshot100.yml\"\ncmd=\"TOKENIZERS_PARALLELISM=false python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot100/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot10k/ckpts/checkpoint_step_12500.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kp20k-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot1k/ckpts/checkpoint_step_14000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kptimes-DA_step50k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kptimes-DA_step50k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-DA-step50k-FT-fewshot10k-step8k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kptimes-DA_step50k-FT_fewshot10k_step8k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step8k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step8k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot10k_step8k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-kptimes-DA_step50k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-DA_step50k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-kptimes-fewshot100\n#SBATCH --output=slurm_output/train-tf-kptimes-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_fewshot/transformer-kptimes-fewshot100.yml\"\ncmd=\"TOKENIZERS_PARALLELISM=false python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot100/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot10k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-kptimes-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot1k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-openkp-DA_step50k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-openkp-DA_step50k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-openkp-DA_step50k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step50k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-DAFT-fewshot10k-step4k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_openkp_step100k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-openkp-DA_step5k-FT_fewshot10k_step4k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot10k_step4k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot10k_step4k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot10k_step4k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-DAFT-fewshot1k-step2k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_openkp_step100k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-openkp-DA_step5k-FT_fewshot1k_step2k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot1k_step2k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot1k_step2k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-DA_step5k-FT_fewshot1k_step2k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\nw\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-openkp-fewshot100\n#SBATCH --output=slurm_output/train-tf-openkp-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_fewshot/transformer-openkp-fewshot100.yml\"\ncmd=\"TOKENIZERS_PARALLELISM=false python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot100/ckpts/checkpoint_step_8500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot10k/ckpts/checkpoint_step_7500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-openkp-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot1k/ckpts/checkpoint_step_8000.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-stackex-DA_step50k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-stackex-DA_step50k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-stackex-DA_step50k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step50k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-DAFT-fewshot10k-step4k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_stackex_step100k/ckpts/checkpoint_step_15000.pt\n\n### Exp meta\nexp: transformer-stackex-DA_step15k-FT_fewshot10k_step4k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot10k_step4k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot10k_step4k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot10k_step4k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-DAFT-fewshot1k-step2k-lr5e6.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA_devbest/transformer_DA_stackex_step100k/ckpts/checkpoint_step_15000.pt\n\n### Exp meta\nexp: transformer-stackex-DA_step15k-FT_fewshot1k_step2k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot1k_step2k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot1k_step2k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-DA_step15k-FT_fewshot1k_step2k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-fewshot100.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=train-tf-stackex-fewshot100\n#SBATCH --output=slurm_output/train-tf-stackex-fewshot100.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer/train_tf_fewshot/transformer-stackex-fewshot100.yml\"\ncmd=\"TOKENIZERS_PARALLELISM=false python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot100/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot10k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot/transformer-stackex-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot1k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr1e4.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e4/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr1e6.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr1e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr3e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr3e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr5e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot100_step1k-lr5e6.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot100_step1k_lr5e6/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PT-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 5120\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PTDA-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 2560 # 10240*2, or 5120*4\naccum_count: 8\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PTDA-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 2560 # 10240*2, or 5120*4\naccum_count: 8\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kp20k-PTDA-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kp20k-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10240\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PT-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PT-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5/ckpts/checkpoint_step_3200.pt\n\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot10k_step8k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 22528 # 24576\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PT-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-FT_fewshot1k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PTDA-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-FT_fewshot100_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot100_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PTDA-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot10k_step8k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 22528 # 24576\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-kptimes-PTDA-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kptimes_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-kptimes-PT_step200k-DA_step20k-FT_fewshot1k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PT-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PT-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20480\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PT-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PTDA-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PTDA-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20480\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-openkp-PTDA-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_openkp_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-openkp-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 12288\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PT-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PT-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PT-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PTDA-FT_fewshot100_step1k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PTDA-FT_fewshot10k_step4k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/train_tf_fewshot_v2/transformer-stackex-PTDA-FT_fewshot1k_step2k-lr1e5.yml",
    "content": "train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_20000.pt\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Exp meta\nexp: transformer-stackex-PT_step200k-FT_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot_v2/transformer-stackex-PT_step200k-DA_step20k-FT_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 7168\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer/transfer_cmd.txt",
    "content": "Mag\nsource script/transfer/mag/run_pred_o2s_scipaper.sh\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL12m-lr1e5-step20k-bs256.sh\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL1m-lr1e5-step5k-bs256.sh\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh\nbash script/transfer/mag/train/transformer-PT-MagDA-step300k-lr1e5-tlnp82-nohup.sh\n\nsbatch script/transfer/mag/train/transformer-PT-MagTL-lr1e5-step300k.sh\nsbatch script/transfer/mag/train/transformer-kp20k_DAFT-fulldata-step100k-lr1e5.sh\nbash script/transfer/mag/train/transformer-kp20k-PT_step200k-DA_step20k-FT_full_step100k_lr5e5_warmup10k.sh\nbash script/transfer/mag/train/transformer-kp20k-PT_step200k-FT_full_step20k-lr1e5-warmup2k.sh\nbash script/transfer/mag/train/transformer-kp20k-PTDA_kp20k_20k-FT_full-step20k-lr1e5-warmup2k.sh\nbash script/transfer/mag/train/transformer-kp20k-PT_step200k-FT_full_step100k_lr5e5_warmup10k.sh\n\n\n\nNote\nwandb doesn't work with multigpu case, may be due to the Huggingface Tokenizer is too large to be passed the children thread thru pickle\n\n- 2gpu, TOKENIZERS_PARALLELISM=false\n    15646/653 tok/s; 2.7 sec/step\n- 2gpu TOKENIZERS_PARALLELISM=true\n    15681/809 tok/s; 2.8 sec/step\n    report warning: huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n- 1gpu, TOKENIZERS_PARALLELISM=true\n    9241/448 tok/s, 4.5 sec/step\n\n# transfer, pred & eval\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsource script/transfer/run_pred_o2s_gpu_fewshot_dev.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot_scavenger.sh\nsource script/transfer/mag/train/nohup_train_transformer-kp20k-PTDA_100k-FT_full-step100k-lr5e5.sh\nsbatch script/transfer/kppred_selftrain_magkp1m.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nCUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred eval -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/ -testsets  kp20k_valid2k openkp_valid2k kptimes_valid2k stackex_valid2k -splits test -batch_size 1 -beam_size 50 -max_length 40 -beam_terminate full --step_base 1 --data_format jsonl --pred_trained_only -gpu 0  > slurm_output/pred-kp_transformer_fewshot.nohup.out &\n\nCUDA_VISIBLE_DEVICES=2 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred eval -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/ -testsets  kp20k_valid2k openkp_valid2k kptimes_valid2k stackexw_valid2k -splits test -batch_size 1 -beam_size 50 -max_length 40 -beam_terminate full --step_base 1 --data_format jsonl --pred_trained_only -gpu 0  > slurm_output/pred-kp_transformer_fewshot.nohup.out &\n\n\nsource script/transfer/run_pred_o2o_gpu.sh\nsource script/transfer/run_pred_o2s_gpu.sh\nsource script/transfer/run_pred_o2s_gpu_debug.sh\n\nsource script/transfer/run_eval_o2o.sh\nsource script/transfer/run_eval_o2s.sh\nsource script/transfer/run_eval_o2s_fewshot.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nsbatch script/transfer/kppred_v100.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nsbatch script/transfer/run_eval.sh\n\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nbash script/batch_kill_by_range.sh\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nbash script/batch_kill_by_cluster.sh\n\n\n\n# MagKP process\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\nsbatch script/transfer/mag/mag_transfer_labelling.sh\n\nsource script/transfer/mag/mag_np.sh\nMagKP\n\tFind 12058583/166192182 CS papers in 167 MAG files: /zfs1/hdaqing/rum20/kp/data/kp/oag_v1\n\n\n\n\n\n# TF-o2s\nsbatch script/transfer/train/kpgen-transformer-presabs-kp20k-nocopy.sh\nsbatch script/transfer/train/kpgen-transformer-presabs-kp20k-lower.sh\n\nsbatch script/transfer/train/kpgen-transformer-presabs-kp20k.sh\nsbatch script/transfer/train/kpgen-transformer-presabs-kp20k-nocopy-lower.sh\n\nsbatch script/transfer/train/kpgen-transformer-presabs-kptimes.sh\nsbatch script/transfer/train/kpgen-transformer-presabs-openkp.sh\nsbatch script/transfer/train/kpgen-transformer-presabs-stackex.sh\n\n\n# TF-DA\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer/train_tf_DA/transformer-DA-kptimes.sh\nsbatch script/transfer/train_tf_DA/transformer-DA-openkp.sh\nsbatch script/transfer/train_tf_DA/transformer-DA-kp20k.sh\nsbatch script/transfer/train_tf_DA/transformer-DA-stackex.sh\nsbatch script/transfer/train_tf_DA/transformer-DA-kp20k-NP.sh\nsbatch script/transfer/train_tf_DA/transformer-DA-kp20k-TL.sh\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config script/transfer/train_tf_DA/transformer-DA-kp20k.yml > slurm_output/transformer-DA-kp20k.nohup.out &\n\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config script/transfer/train_tf_DA/transformer-DA-kp20k-NP.yml > slurm_output/transformer-DA-kp20k-NP.nohup.out &\nCUDA_VISIBLE_DEVICES=3 nohup python train.py -config script/transfer/train_tf_DA/transformer-DA-kp20k-TL.yml > slurm_output/transformer-DA-kp20k-TL.nohup.out &\n\n\n# TF-fewshot\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer/train_tf_fewshot/transformer-kptimes-fewshot100.sh\nsbatch script/transfer/train_tf_fewshot/transformer-kp20k-fewshot100.sh\nsbatch script/transfer/train_tf_fewshot/transformer-openkp-fewshot100.sh\nsbatch script/transfer/train_tf_fewshot/transformer-stackex-fewshot100.sh\n\n\n# BART-o2s\nsbatch script/transfer/train/kpgen-bart-presabs-kp20k.sh\nsbatch script/transfer/train/kpgen-bart-presabs-kp20k-resume.sh\nsbatch script/transfer/train/kpgen-bart-presabs-openkp.sh\nsbatch script/transfer/train/kpgen-bart-presabs-openkp-resume.sh\nsbatch script/transfer/train/kpgen-bart-presabs-openkp-rerun.sh\nsbatch script/transfer/train/kpgen-bart-presabs-stackex-rerun.sh\n\nsbatch script/transfer/train/kpgen-bart-presabs-kptimes.sh\n\n\n\n# BART-o2o\nsbatch script/transfer/train/kpgen-bart-one2one-kp20k.sh\nsbatch script/transfer/train/kpgen-bart-one2one-openkp.sh\nsbatch script/transfer/train/kpgen-bart-one2one-kptimes.sh\nsbatch script/transfer/train/kpgen-bart-one2one-stackex.sh\n\n# TF-o2o\nsbatch script/transfer/train/kpgen-transformer-one2one-kp20k.sh\nsbatch script/transfer/train/kpgen-transformer-one2one-openkp.sh\nsbatch script/transfer/train/kpgen-transformer-one2one-kptimes.sh\nsbatch script/transfer/train/kpgen-transformer-one2one-stackex.sh\n\n\n\n# fewshot-10k\n#ckpt=20, #step_per_epoch=100, #epoch_per_ckpt=5, --save-interval-updates 500 --warmup-updates 1000 --total-num-update 10000\n### BART-FT\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-kp20k.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-openkp.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-openkp-resume.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-stackex.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-kptimes.sh\n\n### BART-wikikp-FT\nsbatch script/transfer/train_fewshot/kpgen-bartwikikp-presabs-kp20k.sh\nsbatch script/transfer/train_fewshot/kpgen-bartwikikp-presabs-openkp.sh\nsbatch script/transfer/train_fewshot/kpgen-bartwikikp-presabs-openkp-resume.sh\nsbatch script/transfer/train_fewshot/kpgen-bartwikikp-presabs-stackex.sh\nsbatch script/transfer/train_fewshot/kpgen-bartwikikp-presabs-kptimes.sh\n\n### BART-wikikp-DAFT\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-kp20k_10k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-kptimes_10k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-openkp_10k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-stackex_10k.sh\n\n\n# fewshot-1k\n#ckpt=20, #step_per_epoch=10, #epoch_per_ckpt=20, --save-interval-updates 200 --warmup-updates 400 --total-num-update 4000\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-kp20k-fewshot1k.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-openkp-fewshot1k.sh\n\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-kp20k-fewshot1k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-openkp-fewshot1k.sh\n\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-kptimes-fewshot1k.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-stackex-fewshot1k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-kptimes-fewshot1k.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-stackex-fewshot1k.sh\n\n# fewshot-100\n#ckpt=20, #step_per_epoch=1, #epoch_per_ckpt=100, --save-interval-updates 100 --warmup-updates 200 --total-num-update 2000\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-kp20k_100.sh\nsbatch script/transfer/train_fewshot/kpgen-bart-presabs-openkp_100.sh\n\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-kp20k-fewshot100.sh\nsbatch script/transfer/train_wikikp_DAFT/kpgen-bartwikikp-DAFT-presabs-openkp-fewshot100.sh\n\n"
  },
  {
    "path": "script/transfer/transformer-presabs-kp20k-test.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/fairseq-kpg/exps/kp/transformer_presabs_kp20k/ckpts/checkpoint_step_135000.pt\n### Exp meta\nexp: transformer-presabs-kp20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_debug/transformer_presabs_kp20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_debug/transformer_presabs_kp20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_debug/transformer_presabs_kp20k/log.txt\nwandb_project: transfer_kp\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\ndata:\n    corpus_1:\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 0.05\nparam_init: 0\nwarmup_steps: 8000\ndecay_method: noam_simple\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\nbatch_type: tokens\nnormalization: tokens\nmax_generator_batches: 200\naccum_count: 4\nmax_grad_norm: 2.0\n\n\n# batch_size is actually: num_example * max(#word in src/tgt)\nbatch_size: 4096 # 4096\nvalid_batch_size: 64\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 50\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/bart/DA/bart-DA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10000\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/bart-DA-kptimes.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_60000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kptimes_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10001"
  },
  {
    "path": "script/transfer_v2/bart/DA/bart-DA-openkp.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_openkp_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100'\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10002\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/bart-DA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_stackex_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10003\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/wikida-kp20k_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_kp20k-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=9\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/kp20k_train100k/train.json --dataset-type scipaper --label-data /home/ubuntu/efs/rum20/data/kp/json/kp20k_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_kp20k_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_kp20k-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/wikida-kptimes_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=24\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type news --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 768 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/wikida-openkp_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\nUPDATE_FREQ=24\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type webpage --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 512 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer_v2/bart/DA/wikida-stackex_4gpu.sh",
    "content": "#!/usr/bin/env bash\n\n#/home/ubuntu/anaconda3/bin/conda config --set env_prompt '({name})'\n#/home/ubuntu/anaconda3/bin/conda activate /home/ubuntu/efs/.conda/kp\n\nexport TOKENIZERS_PARALLELISM=false\nexport WANDB_NAME=bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL-step100k\nexport WANDB_API_KEY=72618587b1afa7c116440deb53224bd999919d0f\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ncd /home/ubuntu/efs/rum20/fairseq-kpg/fairseq_cli\n\n# doesn't work on AWS V100*4\nUPDATE_FREQ=14\n\n/home/ubuntu/efs/.conda/kp/bin/python3.7 train.py /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.json --dataset-type qa --label-data /home/ubuntu/efs/rum20/data/kp/json/openkp_train100k/train.noun_phrase.json:/home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/outputs/beamsearch-width_1-maxlen_40/pred/checkpoint_step_100000-data_openkp_train100k_train.pred --label-sample-ratio '(0.5,0.5)' --save-dir /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled-DA_openkp-NP_TL/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --add-control-prefix-prob 0.8 --max-target-phrases 30 --max-phrase-len 8 --arch bart_large --restore-file /home/ubuntu/efs/rum20/exps/bart_kppretrain_wiki_1e5_controlled/ckpts/checkpoint_step_100000.pt --bpe hf_pretrained_bpe --bpe-vocab /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /home/ubuntu/efs/rum20/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 768 --update-freq $UPDATE_FREQ --save-interval-updates 2000 --warmup-updates 2000 --total-num-update 20000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki\n"
  },
  {
    "path": "script/transfer_v2/bart/nohup-bart-PTDA-magcs12m-lr1e5-step20k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_12m_v2/:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PT_wiki_step40k-DA_magcs12m_tlrs55-lr1e5-step20k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 256 --kp-concat-type pres_abs --max-target-phrases 16 --max-phrase-len 8 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/ckpts/checkpoint_step_40000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9,0.98)\" --adam-eps 1e-08 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --batch-size 8 --update-freq 8 --save-interval-updates 2000 --warmup-updates 1200 --max-update 20000 --total-num-update 20000 --num-workers 4 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PT_wiki_step40k-DA_magcs12m_tlrs55-lr1e5-step20k/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\n#sleep 14400\n#kill -9 192000\n#kill -9 192001\n#kill -9 192002\n#kill -9 192003\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/wiki/phrase --disable-validation --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k/ckpts --task keyphrasification_pretrain --dataset-type wiki --max-source-length 512 --max-target-length 256 --max-phrase-len 6 --max-target-phrases 16 --phrase-corr-rate 0.1 --random-span-rate 0.05 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9, 0.999)\" --adam-eps 1e-08 --lr 1e-5 --update-freq 6 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --seed 7 --fixed-validation-seed 7 --max-tokens 1024 --clip-norm 1.0 --save-interval-updates 5000 --warmup-updates 10000 --total-num-update 200000 --num-workers 12 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step40k-bs256.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/wiki/phrase --disable-validation --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/ckpts --task keyphrasification_pretrain --dataset-type wiki --max-source-length 512 --max-target-length 256 --max-phrase-len 6 --max-target-phrases 16 --phrase-corr-rate 0.1 --random-span-rate 0.05 --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9,0.98)\" --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --batch-size 16 --update-freq 4 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 100 --report-accuracy --seed 7 --fixed-validation-seed 7 --save-interval-updates 5000 --warmup-updates 2400 --total-num-update 40000 --num-workers 16 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_wiki > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step40k-bs256/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/mag/mag_transfer_labelling_bart.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=gtx1080 # titanx gtx1080 v100\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n\n#SBATCH --job-name=mag_transfer_labelling_bart-gtx10803d\n#SBATCH --output=slurm_output/mag_transfer_labelling-gtx10803d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k -data_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_13m/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_13m_v2/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/transfer_v2/mag/mag_transfer_labelling_tf.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx # titanx gtx1080 v100\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n\n#SBATCH --job-name=mag_transfer_labelling_tf-titanx3d\n#SBATCH --output=slurm_output/mag_transfer_labelling-titanx3d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_magkp_transfer_labelling.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks pred -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k -data_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_13m/ -output_dir /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLtf_13m_v2/ -gpu 0 -batch_size 32 -beam_size 1 -max_length 60\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/transfer_v2/mag/nohup-bart-PTDA_magcs12m-tlrs55-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLbart_12m_v2/:__random_span --label-sample-ratio [0.5,0.5] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PTDA_magcs12m_tlrs-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/bart-wiki-step200k/ckpts/checkpoint_step_200000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 4 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/bart-PTDA_magcs12m_tlrs-lr1e5-step200k/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/mag/nohup-transformer-PTDA-magcs12m-tlrs55-step200k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py -config script/transfer_v2/mag/transformer-PTDA-magcs12m-tlrs55-step200k.yml --report_every 100 > /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/mag/prev/nohup-bart-DA_MagTL100k-lr1e5-step20k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nexp_dir=/zfs1/hdaqing/rum20/kp/transfer_exps_v2/bart_mag/bart-DA_MagTL100k-lr1e5-step20k\nmkdir -p $exp_dir\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_100k/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_100k/ --label-sample-ratio [1.0] --save-dir ${exp_dir}/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 2000 --max-update 20000 --total-num-update 20000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > ${exp_dir}/train.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/mag/prev/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh",
    "content": "#!/usr/bin/env bash\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\n#sleep 14400\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json --dataset-type scipaper --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag_fewshot/bart-MagTL_step200k-kp20k_full_lr1e5_step100k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 128 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts/checkpoint_step_200000.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.0 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas \"(0.9, 0.999)\" --adam-eps 1e-08 --clip-norm 0.1 --lr 1e-5 --lr-scheduler polynomial_decay --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 --log-format simple --log-interval 100 --fixed-validation-seed 7 --max-tokens 1920 --update-freq 2 --save-interval-updates 5000 --warmup-updates 10000 --max-update 100000 --total-num-update 100000 --num-workers 8 --find-unused-parameters --fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/mag/prev/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\n\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_13m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_13m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL13m-lr1e5-step200k/train.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/mag/prev/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh",
    "content": "#!/usr/bin/env bash\n\n#sleep 14400\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncd /zfs1/hdaqing/rum20/kp/fairseq-kpg\n\nPYTHONUNBUFFERED=1;TOKENIZERS_PARALLELISM=false;CUDA_VISIBLE_DEVICES=0,1,2,3 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 train.py /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_3m/ --dataset-type scipaper --label-data /zfs1/hdaqing/rum20/kp/data/kp/oag_v1_cs_nokp_wikiTL_3m/ --label-sample-ratio [1.0] --save-dir /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/ckpts --disable-validation --task keyphrasification --max-source-length 512 --max-target-length 64 --max-target-phrases 16 --kp-concat-type pres_abs --arch bart_large --restore-file /zfs1/hdaqing/rum20/kp/data/kp/cache/bart.large/model.pt --bpe hf_pretrained_bpe --bpe-vocab /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json --bpe-merges /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/merges.txt --dict-path /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/dict.txt --bpe-dropout 0.1 --ddp-backend=no_c10d --criterion label_smoothed_cross_entropy --share-all-embeddings --layernorm-embedding --share-all-embeddings --share-decoder-input-output-embed --reset-optimizer --reset-dataloader --reset-meters --required-batch-size-multiple 1 --optimizer adam --adam-betas '(0.9,0.999)' --adam-eps 1e-06 --weight-decay 0.01 --lr 1e-5 --lr-scheduler polynomial_decay --clip-norm 1.0 --label-smoothing 0.1 --dropout 0.1 --attention-dropout 0.1 --log-format simple --log-interval 10 --fixed-validation-seed 7 --max-tokens 640 --update-freq 5 --save-interval-updates 5000 --warmup-updates 10000 --max-update 200000 --total-num-update 200000 --num-workers 20 --find-unused-parameters --memory-efficient-fp16 --ddp-backend=no_c10d --wandb-project transfer_kp_mag  > /zfs1/hdaqing/rum20/kp/transfer_exps/bart_mag/bart-DA_MagTL3m-lr1e5-step200k/train.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDA-magcs12m-tlrs55-step200k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_12m/\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/magkp_cs/oag_v1_cs_nokp_TLtf_12m_v2/, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 200000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs12m-FT100k-step20k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDAFT-magcs12m-FT100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-magcs12m-FT100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 1000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs12m-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDAFT-kp20k-magcs12m-fewshot100-step1k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot10k-step4k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs12m-fewshot1k-step2k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs12m-full-step100k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magcs12m_tlrs55-lr1e5-step200k/ckpts/checkpoint_step_200000.pt\n\n### Exp meta\nexp: transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTDA_magcs12m_tlrs55-FT_kp20k_full/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 15000\n"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs1m-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot100-step1k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot10k-step4k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DA/transformer-PTDA_magkp1m_tlrs55-lr1e5_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k-PTDAFT-magcs1m-fewshot1k-step2k-lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/mag/transformer-PTFT-kp20k_100k-step20k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Exp meta\nexp: transformer-PTFT-FT100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-PTFT-FT100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 1000\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 16000\n"
  },
  {
    "path": "script/transfer_v2/mag/transformer-kp20k_100k-step100k.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\n### Exp meta\nexp: transformer-kp20k_100k-step100k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/mag_DAFT/transformer-kp20k_100k-step100k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 100000\nwarmup_steps: 10000\nsave_checkpoint_steps: 5000\nvalid_steps: 500\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\nmaster_port: 16000\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-kp20k-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 17700\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10000\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-kptimes-tlrs55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_60000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kptimes_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#- 3\nmaster_port: 17745"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-kptimes.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_60000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kptimes_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10001"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-openkp-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_openkp_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10772\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-openkp.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_openkp_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_openkp_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_openkp_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100'\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10002\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-stackex-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_stackex_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step20k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#- 3\nmaster_port: 17773\n"
  },
  {
    "path": "script/transfer_v2/tf/DA/transformer-DA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_stackex_step100k/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_stackex_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_stackex_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 17*6 20*5 25*4\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n#- 2\n#- 3\nmaster_port: 10003\n"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot100_step1k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot10k_step4k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kp20k-DA_step100k-FT_fewshot1k_step2k_lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_100k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step100k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-kptimes-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-openkp-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/deprecated/transformer-stackex-DA-step50k-FT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step100k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_50k-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_TLtf_step50k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kp20k-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-kptimes-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_kptimes_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kptimes.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-openkp-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_openkp_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/openkp.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAFT/transformer-stackex-DA-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-DA_TLtf_stackex_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAFT/transformer-DA_tlrs55_40k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10010\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-NP\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-NP.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_15000.pt\n\n### Exp meta\nexp: transformer-DA_NP-kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-NP_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10770"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-RS.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_50000.pt\n\n### Exp meta\nexp: transformer-DA_RS-kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer_DA-RS_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_random_span, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10550"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-TL.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-DA_TLtf_kp20k_step40k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-nprs_55.yml",
    "content": "### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-nprs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 16921\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam10.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_10-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10120\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam25.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam25\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_25-maxlen_40/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam3.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_3-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10120\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_5-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10150\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tl_beam50.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-beam50\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_50-maxlen_40/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlnp_28\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlnp_28.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_35000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_28\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.8,0.2]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 15521\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlnp_55\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlnp_55.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_30000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 16921\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_82.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer_DA_kp20k_step100k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlnp_82\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2,0.8]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 19921\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=gtx1080\n#SBATCH --partition=titanx\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlrs_28\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlrs_28.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_28\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.2, 0.8]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 17021\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --partition=v100\n#SBATCH --partition=titanx\n#SBATCH --partition=gtx1080\n#SBATCH --account=hdaqing\n\n#SBATCH --job-name=transformer-DA-kp20k-step40k-tlrs_55\n#SBATCH --output=slurm_output/transformer-DA-kp20k-step40k-tlrs_55.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=2\n#SBATCH --mem=32GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n# Load modules\n#module restore\n#module load cuda/10.0.130\n#module load gcc/6.3.0\n#module load python/anaconda3.6-5.2.0\n#source activate py36\n#module unload python/anaconda3.6-5.2.0\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.yml\"\ncmd=\"python train.py -config $CONFIG_PATH\"\n\necho $CONFIG_PATH\necho $cmd\n\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.yml",
    "content": "#train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10491\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_82.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA-TLtf_kp20k_step40k-tlrs_82\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.8, 0.2]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 40000\nwarmup_steps: 4000\nvalid_steps: 10000\nsave_checkpoint_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11321\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-NP.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_NP-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10770"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-RS.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_RS-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [__random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10550"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-TL.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-TLtf_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-nprs55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_kp20k_step20k-nprs55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k-nprs55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5,0.5]\nmin_target_phrases: 2\nmax_target_phrases: 8\nmax_phrase_len: 8\n\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-tlnp55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.noun_phrase.json]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompare/transformer-PTDA-kp20k-step20k-tlrs55.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA-kp20k_step20k-tlrs55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 8\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 10020\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-NP-step75k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_NP_kp20k_step100k/ckpts/checkpoint_step_75000.pt\n\n### Exp meta\nexp: transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_50k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-RS-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_RS_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLbart-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_TLbart_kp20k_step100k/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLbart-tlnp55-step100k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA_TLbart_kp20k_step100k-tlnp_55/ckpts/checkpoint_step_100000.pt\n\n### Exp meta\nexp: transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLbart_tlnp55_step100k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-beam25-step40k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam25/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam25-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-beam50-step40k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam50/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam50-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/deprecated/transformer-kp20k-DA-TLtf-step20k-FT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-NP-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-NP_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_NP_40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_NP_40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-RS-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_DA-RS_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_RS_40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_RS_40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam10-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam10/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam10-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam3-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam3/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam3-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-beam5-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-beam5/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_kp20k_step40k_beam5-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-nprs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-nprs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_nprs_55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp28-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp28_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlnp82-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlnp_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlnp82_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs28-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_28/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs28_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs55-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_55/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs55_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-DA-TLtf-tlrs82-step40k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-DA_TLtf_kp20k_step40k-tlrs_82/ckpts/checkpoint_step_40000.pt\n\n### Exp meta\nexp: transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-DA_TLtf_tlrs82_step40k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-NP-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-NP_kp20k_step20k//ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_NP_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_NP_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-RS-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer_PTDA-RS_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_RS_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_RS_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-TL-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-nprs55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-nprs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_nprs55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlnp55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlnp55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlnp55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot100.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot100/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11200\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot10k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/DAcompareFT/transformer-kp20k-PTDA-tlrs55-step20k-FT-fewshot1k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompare/transformer-PTDA_kp20k_step20k-tlrs55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DAcompareFT/transformer-PTDA_TLtf_tlrs55_step20k-FT_kp20k_fewshot1k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0w\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 11000\n"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kp20k-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot100/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kp20k-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot10k/ckpts/checkpoint_step_12500.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kp20k-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kp20k_fewshot1k/ckpts/checkpoint_step_14000.pt\n\n### Exp meta\nexp: transformer-kp20k-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kp20k-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kptimes-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot100/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kptimes-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot10k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-kptimes-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_kptimes_fewshot1k/ckpts/checkpoint_step_5000.pt\n\n### Exp meta\nexp: transformer-kptimes-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-kptimes-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-openkp-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot100/ckpts/checkpoint_step_8500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-openkp-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot10k/ckpts/checkpoint_step_7500.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-openkp-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_openkp_fewshot1k/ckpts/checkpoint_step_8000.pt\n\n### Exp meta\nexp: transformer-openkp-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-openkp-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-stackex-fewshot100.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot100/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot100-lr3e4-step2k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot100-lr3e4-step2k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-stackex-fewshot10k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot10k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot10k-lr3e4-step8k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot10k-lr3e4-step8k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 8000\nwarmup_steps: 800\nsave_checkpoint_steps: 400\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/FT/transformer-stackex-fewshot1k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer_stackex_fewshot1k/ckpts/checkpoint_step_9000.pt\n\n### Exp meta\nexp: transformer-stackex-fewshot1k-lr3e4-step4k\nexp_dir: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k\nsave_model: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/ckpts/checkpoint\nlog_file: /zfs1/pbrusilovsky/rum20/kp/transfer_exps//kp_transformer_fewshot/transformer-stackex-fewshot1k-lr3e4-step4k/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/stackex.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PT/transformer-PT-wiki.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint_step_105000.pt\n\n### Exp meta\nexp: transformer-wiki-step200k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps/kp_mag/transformer-wiki-step200k/log.txt\nwandb_project: transfer_kp_mag\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nmax_phrase_len: 16\nmax_target_phrases: 16\nphrase_corr_rate: 0.1\nrandom_span_rate: 0.05\n\nshuffle_shards: false\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [wiki_phrase, roberta_tokenize_kpg]\n#        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/AA/\n        path_src: /zfs1/hdaqing/rum20/kp/data/wiki/phrase/\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.1\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 3e-4\nparam_init: 0\nwarmup_steps: 20000\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 4480\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: tokens\nnormalization: tokens\nmax_grad_norm: 2.0\nmax_generator_batches: 1000\n\ntrain_steps: 200000\nvalid_steps: 10000\nsave_checkpoint_steps: 5000\nreport_every: 100\nseed: 4479\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\n#wandb: 'true'\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 4\ngpu_ranks:\n- 0\n- 1\n- 2\n- 3\n#master_port: 10000\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-kp20k-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred, __random_span]\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-kp20k.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_kp20k_step20k-lr5e5/ckpts/checkpoint_step_35000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_kp20k_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kp20k_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kp20k_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-kptimes-tlrs55.yml",
    "content": "### Exp meta\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kptimes_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n\nmaster_port: 10789\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-kptimes.yml",
    "content": "### Exp meta\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_kptimes_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_kptimes_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_kptimes_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 10789\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-openkp-tlrs55.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_openkp_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n\nmaster_port: 11110\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-openkp.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_openkp_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_openkp_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_openkp_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\n\nmaster_port: 11110\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-stackex-tlrs55.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_30000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_stackex_step20k-tlrs_55\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11156\n\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDA/transformer-PTDA-stackex.yml",
    "content": "#train_from: /zfs1/pbrusilovsky/rum20/kp/transfer_exps/kp_transformer_DA/transformer-PT_wiki_step200k-DA_stackex_step20k-lr5e5/ckpts/checkpoint_step_30000.pt\n\n### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_PT/transformer-wiki-step200k/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA-tfTL_stackex_step20k\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA_FT/transformer-PT_wiki_step200k-DA_tfTL_stackex_step20k/log.txt\n\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [1.0]\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl/transformer-wiki-step200k/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_stackex_train100k_train.pred]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11156\n\n\n"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kp20k-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PT_wiki_step200k-DA_TLtf_kp20k_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-kptimes-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_kptimes_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-openkp-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_openkp_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tl/transformer-stackex-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_DA/transformer-PT_wiki_step200k-DA_TLtf_stackex_step20k/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PT_wiki_step200k-DA_TLtf_step20k-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'true'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kp20k-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kp20k_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-kptimes-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_kptimes_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_kptimes_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kptimes_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-openkp-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_openkp_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_openkp_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/openkp_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot100-step1k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot100_step1k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train100/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 2.0\nmax_generator_batches: 200\n\ntrain_steps: 1000\nwarmup_steps: 100\nsave_checkpoint_steps: 50\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot10k-step4k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/PTDAFT_tlrs55/transformer-stackex-PTDAFT-fewshot1k-step2k-lr1e5.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA/transformer-PTDA_TLtf_stackex_step20k-tlrs_55/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT/transformer-PTDA_tlrs55-FT_stackex_fewshot1k_step2k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/stackex_train1k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 2000\nwarmup_steps: 200\nsave_checkpoint_steps: 100\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\n#master_port: 10000"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round10.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round2.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round3.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round4.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round5.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round6.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round7.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round8.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round9.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train100k/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_kp20k_train100k_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf-round10.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-kp20k-TLtf-round10.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round1.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-wiki-step200k_round0/ckpts/checkpoint_step_200000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k_round1\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-wiki-step200k_round0/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_200000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round10.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round10\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round2.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round2\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round3.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round3\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round4.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round4\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round5.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 20000\nwarmup_steps: 2000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11100\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round6.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round6\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_20000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 1\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 2\ngpu_ranks:\n- 0\n- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round7.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/transformer-PTDA-magkp1m-TLtf-round7.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-magkp1m-TLtf-round7.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round7.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round7\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round8.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round8\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round9.yml",
    "content": "### Reset all states in the optimizer:\ntrain_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\nreset_optim: all\n\n### Exp meta\nexp: transformer-PT_wiki_step200k-DA_TLtf_magkp1m_step20k-round9\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/log.txt\nwandb_project: transfer_kp_transformer\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nlabel_sample_ratio: [0.5, 0.5]\nmax_target_phrases: 10\nmax_phrase_len: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, kp_replace_target, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/magkpcs_1m/train.json\n        label_data: [/zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/outputs/beamsearch-width_1-maxlen_60/pred/checkpoint_step_10000-data_magkpcs_1m_train.pred, __random_span]\n\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 5e-6\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 10000\nwarmup_steps: 1000\nsave_checkpoint_steps: 5000\nvalid_steps: 10000\nreport_every: 100\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\n#tensorboard_log_dir: runs/kp20k.one2one.transformer/\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\nmaster_port: 11122\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf-round10.yml\"\nCUDA_VISIBLE_DEVICES=2 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDA-magkp1m-TLtf-round10.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round10-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-kp20k-round10-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round1-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round1-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round10-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round10/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round2-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round2-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15200"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round3-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15300"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round4-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round4-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 25 # 4096\naccum_count: 4\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15400"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round5-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round6-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round6-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-kp20k-round7-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round7-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round7-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round8-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round8-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-round9-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_kp20k_round9-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15500\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round10-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=3 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round10-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round1-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round1/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round1-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round10-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round10/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round10-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round2-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round2/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round2-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round3-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round3/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round3-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round4-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round4/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round4-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 50 # 4096\naccum_count: 2\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round5-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts/checkpoint_step_20000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round5-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=0 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round6-fewshot10k.nohup.out &\n\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round6-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round6/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round6-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.sh",
    "content": "#!/usr/bin/env bash\n\n# Run the job\nexport CONFIG_PATH=\"script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.yml\"\nCUDA_VISIBLE_DEVICES=1 nohup python train.py -config $CONFIG_PATH > slurm_output/transformer-PTDAFT-magkp1m-round7-fewshot10k.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round7-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round7/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round7-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 20 # 4096\naccum_count: 5\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round8-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round8/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round8-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-round9-fewshot10k.yml",
    "content": "train_from: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts/checkpoint_step_10000.pt\n\n### Exp meta\nexp: transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nexp_dir: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5\nsave_model: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5/ckpts/checkpoint\nlog_file: /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDAFT_selftrain/transformer-PTDA_magkp1m_round9-FT_kp20k_fewshot10k_step4k_lr1e5/log.txt\nwandb_project: transfer_kp_transformer_fewshot\n\n### Data opts:\ndata_type: keyphrase\npretrained_tokenizer: true # using roberta_tokenize_kpg transform\ndata_format: jsonl\nsave_data: /zfs1/hdaqing/rum20/kp/data/kp/generated/dynamic.ex0\noverwrite: False\ncache_dir: /zfs1/hdaqing/rum20/kp/data/kp/cache/\n\nsrc_seq_length_trunc: 512\ntgt_seq_length_trunc: 128\nshuffle_shards: false\nmax_target_phrases: 8\ndata:\n    corpus_1:\n        type: keyphrase\n        transforms: [keyphrase, roberta_tokenize_kpg]\n        path_src: /zfs1/hdaqing/rum20/kp/data/kp/json/kp20k_train10k/train.json\n\n### Reset all states in the optimizer:\nreset_optim: all\n\n### Transform related opts:\n#### Keyphrase specific\nkp_concat_type: pres_abs\n#### Subword and vocab\nsrc_subword_model: roberta_tokenize\nsrc_vocab: /zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/vocab.json\nshare_vocab: True\nbpe_dropout: 0.0\n#### Sampling\nswitchout_temperature: 1.0\ntokendrop_temperature: 1.0\ntokenmask_temperature: 1.0\n#### Filter, default is 1024\nsrc_seq_length: -1\ntgt_seq_length: -1\n#### BART\npermute_sent_ratio: 0.0\nrotate_ratio: 0.0\ninsert_ratio: 0.0\nrandom_ratio: 0.0\nmask_ratio: 0.0\nmask_length: subword\npoisson_lambda: 3.0\nreplace_length: 1\n\n# Model opts:\nencoder_type: transformer\ndecoder_type: transformer\nword_vec_size: 512\nrnn_size: 512\nlayers: 6\nheads: 8\ndropout: 0.1\n\nshare_embeddings: 'true'\ncopy_attn: 'true'\nreuse_copy_attn: 'true'\ncoverage_attn: 'true'\ncontext_gate: both\ninput_feed: 1\nparam_init_glorot: 'true'\nposition_encoding: 'true'\n\noptim: adam\nlearning_rate: 1e-5\nparam_init: 0\ndecay_method: linear\nlabel_smoothing: 0.1\nadam_beta2: 0.998\n\n# batch_size - token: num_example * max(#word in src/tgt) * accum_count\n# batch_size - sent: batch_size * accum_count\nbatch_size: 10 # 4096\naccum_count: 10\nvalid_batch_size: 64\nbatch_type: sents\nnormalization: sents\nmax_grad_norm: 1.0\nmax_generator_batches: 200\n\ntrain_steps: 4000\nwarmup_steps: 400\nsave_checkpoint_steps: 200\nvalid_steps: 500\nreport_every: 10\nseed: 3435\n\nlog_file_level: DEBUG\ntensorboard: 'false'\n\nwandb: 'false'\nwandb_key: 'c338136c195ab221b8c7cfaa446db16b2e86c6db'\n\nworld_size: 1\ngpu_ranks:\n- 0\n#- 1\n#- 2\nmaster_port: 15100"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/kppred_selftrain_kp20k.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition=titanx\n#SBATCH --job-name=titanx_pred-magkp1m-3d\n#SBATCH --output=slurm_output/titanx_pred-magkp1m-3d.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=8GB\n#SBATCH --time=3-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round5/ckpts -gpu 0 -batch_size 32 -beam_size 1 -max_length 60 -testsets magkpcs_1m -splits train --wait_time 0 --step_base 1 --data_format jsonl\"\n\necho $cmd\n$cmd\n\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/kppred_selftrain_kp20k100k.sh",
    "content": "#!/usr/bin/env bash\n\nCUDA_VISIBLE_DEVICES=0 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_kp20k_step20k-tlrs_55-round9/ckpts -gpu 0 -batch_size 64 -beam_size 1 -max_length 60 -testsets kp20k_train100k -splits train --wait_time 0 --step_base 1 --data_format jsonl > slurm_output/kppred_selftrain_kp20k100k_round9.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/selftrain/kppred_selftrain_magkp1m.sh",
    "content": "#!/usr/bin/env bash\n\nCUDA_VISIBLE_DEVICES=1 nohup /ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq.yml -tasks pred -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir /zfs1/hdaqing/rum20/kp/transfer_exps_v2/tf_PTDA_selftrain/transformer-PTDA_TLtf_magkp1m_step20k-tlrs_55-round9/ckpts -gpu 0 -batch_size 64 -beam_size 1 -max_length 60 -testsets magkpcs_1m -splits train --wait_time 0 --step_base 1 --data_format jsonl > slurm_output/kppred_selftrain_magkpcs1m_round9.nohup.out &\n"
  },
  {
    "path": "script/transfer_v2/tf/tf_tlgenerate_pseudokp.sh",
    "content": "#!/usr/bin/env bash\nPROJECT_DIR=\"/zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer\"\n\nCURDIR=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )\" >/dev/null 2>&1 && pwd )\"\nmkdir -p $CURDIR/tmp\nTEMPLATE_PATH=\"$CURDIR/tl_gpu_template.sh\"\n\nslurm_output_dir=\"$PROJECT_DIR/slurm_output\"\n\npartition=\"titanx\" # titanx gtx1080 v100\ndays=\"3\"\nrandom=$RANDOM\n\ntask_args=\"pred eval\" # pred or eval\nsplit=\"train\"\nbatch_size=64\nbeam_size=1\nmax_length=40\nstep_base=1\n\n#datasets=(kp20k kp20k_valid2k openkp openkp_valid2k kptimes kptimes_valid2k jptimes duc stackex stackex_valid2k inspec krapivin nus semeval)\ndatasets=(kp20k_train100k openkp_train100k kptimes_train100k stackex_train100k)\n\ndataset_list=\"\"\n\nfor dataset in \"${datasets[@]}\"\ndo\n    dataset_list+=\" ${dataset}\"\ndone\n\nexp_root_dir=\"/zfs1/hdaqing/rum20/kp/transfer_exps_v2/kp_tl\"\n\necho $0\necho $PROJECT_DIR\necho $dataset_list\necho $exp_root_dir\n\nEXP_NAME=\"TF-TL-$partition-$random-bs$beam_size\"\nDUMP_SCRIPT_PATH=\"$CURDIR/tmp/$EXP_NAME.sh\"\nrm -f $DUMP_SCRIPT_PATH\n\nreplaces=\"s/{job_name}/$EXP_NAME/;\";\nreplaces=\"$replaces s|{partition}|$partition|g;\";\nreplaces=\"$replaces s|{days}|$days|g;\";\nreplaces=\"$replaces s|{task_args}|$task_args|g;\";\nreplaces=\"$replaces s|{dataset_args}|$dataset_list|g;\";\nreplaces=\"$replaces s|{split}|$split|g;\";\nreplaces=\"$replaces s|{exp_root_dir}|$exp_root_dir|g;\";\nreplaces=\"$replaces s|{slurm_output_dir}|$slurm_output_dir|g;\";\nreplaces=\"$replaces s|{batch_size}|$batch_size|g;\";\nreplaces=\"$replaces s|{max_length}|$max_length|g;\";\nreplaces=\"$replaces s|{beam_size}|$beam_size|g;\";\nreplaces=\"$replaces s|{step_base}|$step_base|g;\";\ncat ${TEMPLATE_PATH} | sed -e \"$replaces\" > ${DUMP_SCRIPT_PATH}\n\necho $EXP_NAME\necho $DUMP_SCRIPT_PATH\necho \"${slurm_output_dir}/$EXP_NAME.out\"\n\nsbatch $DUMP_SCRIPT_PATH\n"
  },
  {
    "path": "script/transfer_v2/tf/tl_gpu_template.sh",
    "content": "#!/usr/bin/env bash\n#SBATCH --cluster=gpu\n#SBATCH --gres=gpu:1\n#SBATCH --account=hdaqing\n\n#SBATCH --partition={partition}\n\n#SBATCH --job-name={job_name}\n#SBATCH --output={slurm_output_dir}/{job_name}.out\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --cpus-per-task=1\n#SBATCH --mem=16GB\n#SBATCH --time={days}-00:00:00 # 6 days walltime in dd-hh:mm format\n#SBATCH --qos=long\n\nsource ~/.bash_profile # reload LD_LIBRARY due to error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found\ncmd=\"/ihome/hdaqing/rum20/anaconda3/envs/kp/bin/python3.7 kp_gen_eval_transfer.py -config config/transfer_kp/infer/keyphrase-one2seq-controlled.yml -tasks {task_args} -data_dir /zfs1/hdaqing/rum20/kp/data/kp/json/ -exp_root_dir {exp_root_dir} -testsets {dataset_args} -splits {split} -batch_size {batch_size} -beam_size {beam_size} -max_length {max_length} -beam_terminate full --step_base {step_base} --data_format jsonl -gpu 0\"\n\necho $cmd\necho $PWD\n$cmd\n"
  },
  {
    "path": "script/transfer_v2/transfer_cmd.txt",
    "content": "## Generate pseudo keyphrases from wiki-pretrained checkpoints\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2_paper/tf/tf_tlgenerate_pseudokp.sh\n\n## Pred and Eval\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsource script/transfer/run_pred_o2s_gpu_fewshot_dev.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot.sh\nsource script/transfer/run_pred_o2s_gpu_fewshot_scavenger.sh\n\n## PT\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsource script/transfer_v2/bart/nohup-bart-PTDA-magcs12m-lr1e5-step20k-bs256.sh\nsource script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step40k-bs256.sh\nsource script/transfer_v2/bart/nohup-bart-wiki-lr1e5-step200k.sh\n\n## DA compare\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-NP.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_55.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_55.sh\n\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlnp_28.sh\nsbatch script/transfer_v2/tf/DAcompare/transformer-DA-kp20k-step40k-tlrs_28.sh\n\n\n## scale-up with MAG\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2/mag/nohup-transformer-PTDA-magcs12m-tlrs55-step200k.sh\nbash script/transfer_v2/mag/nohup-bart-PTDA_MagTL12m-lr1e5-step200k.sh\n\n### Generate pseudo labels\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nsbatch script/transfer_v2/mag/mag_transfer_labelling_tf.sh\nsbatch script/transfer_v2/mag/mag_transfer_labelling_bart.sh\n\n### BART\n\n#### DA\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL100k-lr1e5-step20k.sh\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL3m-lr1e5-step200k.sh\nbash script/transfer_v2_paper/mag/bart/nohup-bart-DA_MagTL13m-lr1e5-step200k.sh\n\n#### FT\nbash script/transfer/mag/bart/nohup-bart-DA_MagTL13m-FT_full-lr1e5-step100k.sh\n\n### Transformer\n\n#### selftrain\ncd /zfs1/hdaqing/rum20/kp/OpenNMT-kpg-transfer/\n\n##### pred\nsource script/transfer_v2/tf/selftrain/kppred_selftrain_kp20k100k.sh\nsource script/transfer_v2/tf/selftrain/kppred_selftrain_magkp1m.sh\n\n##### selftrain-DA\nsource script/transfer_v2/tf/selftrain/DA/transformer-PTDA-kp20k-TLtf.sh\nsource script/transfer_v2/tf/selftrain/DA/transformer-PTDA-magkp1m-TLtf.sh\n\n##### selftrain-DAFT\nsource script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-kp20k-fewshot10k.sh\nsource script/transfer_v2/tf/selftrain/FT/transformer-PTDAFT-magkp1m-fewshot10k.sh\n\n"
  },
  {
    "path": "server.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.server import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "setup.py",
    "content": "#!/usr/bin/env python\nfrom setuptools import setup, find_packages\nfrom os import path\n\nthis_directory = path.abspath(path.dirname(__file__))\nwith open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(\n    name='OpenNMT-py',\n    description='A python implementation of OpenNMT',\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    version='2.0.0rc2',\n    packages=find_packages(),\n    project_urls={\n        \"Documentation\": \"http://opennmt.net/OpenNMT-py/\",\n        \"Forum\": \"http://forum.opennmt.net/\",\n        \"Gitter\": \"https://gitter.im/OpenNMT/OpenNMT-py\",\n        \"Source\": \"https://github.com/OpenNMT/OpenNMT-py/\"\n    },\n    install_requires=[\n        \"numpy==1.19.3\",\n        \"six==1.15.0\",\n        \"tqdm==4.51.0\",\n        \"torch==1.6.0\",\n        \"torchtext==0.4.0\",\n        \"future==0.18.2\",\n        \"configargparse==1.2.3\",\n        \"tensorboard==2.3.0\",\n        \"flask==1.1.2\",\n        \"waitress==1.4.4\",\n        \"pyonmttok==1.22.1;platform_system=='Linux'\",\n        \"pyyaml==5.4\",\n    ],\n    entry_points={\n        \"console_scripts\": [\n            \"onmt_server=onmt.bin.server:main\",\n            \"onmt_train=onmt.bin.train:main\",\n            \"onmt_translate=onmt.bin.translate:main\",\n            \"onmt_release_model=onmt.bin.release_model:main\",\n            \"onmt_average_models=onmt.bin.average_models:main\",\n            \"onmt_build_vocab=onmt.bin.build_vocab:main\"\n        ],\n    }\n)\n"
  },
  {
    "path": "tokenizer_test.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nPython File Template \n\"\"\"\n\nimport os\n\nfrom transformers import RobertaTokenizer, RobertaTokenizerFast, AddedToken, convert_slow_tokenizer\n\n__author__ = \"Rui Meng\"\n__email__ = \"rui.meng@pitt.edu\"\n\nif __name__ == '__main__':\n    original_tokenizer_path = '/Users/memray/project/kp/hf_vocab/roberta-base/'\n    tokenizer_path = '/Users/memray/project/kp/hf_vocab/roberta-base-kp/'\n    # original_tokenizer_path = '/zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base/'\n    # tokenizer_path = '/zfs1/hdaqing/rum20/kp/data/kp/hf_vocab/roberta-base-kp/'\n\n    tokenizer_file = 'tokenizer.json'\n    bpe_vocab = 'vocab.json'\n    bpe_merges = 'merges.txt'\n\n    original_tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\", dropout=0.5)\n\n    sep_token = '<sep>'\n    kp_special_tokens = ['<present>', '<absent>', '<category>']\n    tokenizer = RobertaTokenizer(vocab_file=tokenizer_path+bpe_vocab,\n                                 merges_file=tokenizer_path+bpe_merges,\n                                 sep=sep_token, # doesn't work\n                                 additional_special_tokens=kp_special_tokens)\n    tokenizer1 = RobertaTokenizer.from_pretrained(\"roberta-base\")\n\n    print(tokenizer.tokenize(tokenizer.bos_token + 'what is wrong with <mask> <sep> I do not know either </s>'))\n    print(tokenizer1.tokenize(tokenizer.bos_token + 'what is wrong with <mask> <sep> I do not know either </s>'))\n\n    print('Vocab size=%d, base vocab size=%d' % (len(tokenizer), tokenizer.vocab_size))\n    # <sep> must be additionally hard-coded\n    sep_token_id = tokenizer.convert_tokens_to_ids(sep_token)\n    added_sep_token = AddedToken(sep_token, lstrip=False, rstrip=False)\n    tokenizer.sep_token = sep_token\n    tokenizer._sep_token = added_sep_token\n    tokenizer.init_kwargs['sep_token'] = sep_token\n    tokenizer.all_special_ids.append(sep_token_id)\n    tokenizer.all_special_tokens.append(sep_token)\n    tokenizer.all_special_tokens_extended.append(added_sep_token)\n    tokenizer.special_tokens_map['sep_token'] = sep_token\n    tokenizer.special_tokens_map_extended['sep_token'] = added_sep_token\n\n    tokenizer.unique_no_split_tokens = tokenizer.all_special_tokens\n\n    print(tokenizer.tokenize('<s> what is wrong with <mask> <sep> I do not know either </s> <present> <absent> <category>'))\n    print(tokenizer1.tokenize('<s> what is wrong with <mask> <sep> I do not know either </s> <present> <absent> <category>'))\n\n    # finally, both __slow_tokenizer and tokenizer_file must be set as follows\n    fast_tokenizer3 = RobertaTokenizerFast.from_pretrained(\"roberta-base\",\n                                                           __slow_tokenizer=tokenizer, tokenizer_file=None,\n                                                           vocab_file=tokenizer_path+bpe_vocab,\n                                                           merges_file=tokenizer_path+bpe_merges)\n\n    test_str = fast_tokenizer3.bos_token+'what is wrong with <mask> <sep> I do not know either </s> <present> <absent> <category>'+fast_tokenizer3.eos_token\n    print(fast_tokenizer3.tokenize(test_str))\n    encoding = fast_tokenizer3.encode_plus(test_str, add_special_tokens=False)\n    decoded = fast_tokenizer3.decode(encoding[\"input_ids\"])\n    print(encoding[\"input_ids\"])\n    print(encoding.tokens())\n    print(decoded)\n\n    encoding = original_tokenizer.encode_plus(test_str)\n    print(encoding[\"input_ids\"])\n    print(original_tokenizer.tokenize(test_str))\n    decoded = fast_tokenizer3.decode(encoding[\"input_ids\"])\n    print(decoded)\n"
  },
  {
    "path": "tools/README.md",
    "content": "This directly contains scripts and tools adopted from other open source projects such as Apache Joshua and Moses Decoder.\n\nTODO: credit the authors and resolve license issues (if any)\n"
  },
  {
    "path": "tools/apply_bpe.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: Rico Sennrich\n# flake8: noqa\n\n\"\"\"Use operations learned with learn_bpe.py to encode a new text.\nThe text will not be smaller, but use only a fixed vocabulary, with rare words\nencoded as variable-length sequences of subword units.\n\nReference:\nRico Sennrich, Barry Haddow and Alexandra Birch (2015). Neural Machine Translation of Rare Words with Subword Units.\nProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.\n\"\"\"\n# This file is retrieved from https://github.com/rsennrich/subword-nmt\n\nfrom __future__ import unicode_literals, division\n\nimport sys\nimport codecs\nimport io\nimport argparse\nimport json\nimport re\nfrom collections import defaultdict\n\n# hack for python2/3 compatibility\nfrom io import open\nargparse.open = open\n\n\nclass BPE(object):\n\n    def __init__(self, codes, separator='@@', vocab=None, glossaries=None):\n\n        # check version information\n        firstline = codes.readline()\n        if firstline.startswith('#version:'):\n            self.version = tuple([int(x) for x in re.sub(\n                r'(\\.0+)*$', '', firstline.split()[-1]).split(\".\")])\n        else:\n            self.version = (0, 1)\n            codes.seek(0)\n\n        self.bpe_codes = [tuple(item.split()) for item in codes]\n\n        # some hacking to deal with duplicates (only consider first instance)\n        self.bpe_codes = dict(\n            [(code, i) for (i, code) in reversed(list(enumerate(self.bpe_codes)))])\n\n        self.bpe_codes_reverse = dict(\n            [(pair[0] + pair[1], pair) for pair, i in self.bpe_codes.items()])\n\n        self.separator = separator\n\n        self.vocab = vocab\n\n        self.glossaries = glossaries if glossaries else []\n\n        self.cache = {}\n\n    def segment(self, sentence):\n        \"\"\"segment single sentence (whitespace-tokenized string) with BPE encoding\"\"\"\n        output = []\n        for word in sentence.split():\n            new_word = [out for segment in self._isolate_glossaries(word)\n                        for out in encode(segment,\n                                          self.bpe_codes,\n                                          self.bpe_codes_reverse,\n                                          self.vocab,\n                                          self.separator,\n                                          self.version,\n                                          self.cache,\n                                          self.glossaries)]\n\n            for item in new_word[:-1]:\n                output.append(item + self.separator)\n            output.append(new_word[-1])\n\n        return ' '.join(output)\n\n    def _isolate_glossaries(self, word):\n        word_segments = [word]\n        for gloss in self.glossaries:\n            word_segments = [out_segments for segment in word_segments\n                             for out_segments in isolate_glossary(segment, gloss)]\n        return word_segments\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=\"learn BPE-based word segmentation\")\n\n    parser.add_argument(\n        '--input', '-i', type=argparse.FileType('r'), default=sys.stdin,\n        metavar='PATH',\n        help=\"Input file (default: standard input).\")\n    parser.add_argument(\n        '--codes', '-c', type=argparse.FileType('r'), metavar='PATH',\n        required=True,\n        help=\"File with BPE codes (created by learn_bpe.py).\")\n    parser.add_argument(\n        '--output', '-o', type=argparse.FileType('w'), default=sys.stdout,\n        metavar='PATH',\n        help=\"Output file (default: standard output)\")\n    parser.add_argument(\n        '--separator', '-s', type=str, default='@@', metavar='STR',\n        help=\"Separator between non-final subword units (default: '%(default)s'))\")\n    parser.add_argument(\n        '--vocabulary', type=argparse.FileType('r'), default=None,\n        metavar=\"PATH\",\n        help=\"Vocabulary file (built with get_vocab.py). If provided, this script reverts any merge operations that produce an OOV.\")\n    parser.add_argument(\n        '--vocabulary-threshold', type=int, default=None,\n        metavar=\"INT\",\n        help=\"Vocabulary threshold. If vocabulary is provided, any word with frequency < threshold will be treated as OOV\")\n    parser.add_argument(\n        '--glossaries', type=str, nargs='+', default=None,\n        metavar=\"STR\",\n        help=\"Glossaries. The strings provided in glossaries will not be affected\" +\n             \"by the BPE (i.e. they will neither be broken into subwords, nor concatenated with other subwords\")\n\n    return parser\n\n\ndef get_pairs(word):\n    \"\"\"Return set of symbol pairs in a word.\n\n    word is represented as tuple of symbols (symbols being variable-length strings)\n    \"\"\"\n    pairs = set()\n    prev_char = word[0]\n    for char in word[1:]:\n        pairs.add((prev_char, char))\n        prev_char = char\n    return pairs\n\n\ndef encode(orig, bpe_codes, bpe_codes_reverse, vocab, separator, version, cache, glossaries=None):\n    \"\"\"Encode word based on list of BPE merge operations, which are applied consecutively\n    \"\"\"\n\n    if orig in cache:\n        return cache[orig]\n\n    if orig in glossaries:\n        cache[orig] = (orig,)\n        return (orig,)\n\n    if version == (0, 1):\n        word = tuple(orig) + ('</w>',)\n    elif version == (0, 2):  # more consistent handling of word-final segments\n        word = tuple(orig[:-1]) + (orig[-1] + '</w>',)\n    else:\n        raise NotImplementedError\n\n    pairs = get_pairs(word)\n\n    if not pairs:\n        return orig\n\n    while True:\n        bigram = min(pairs, key=lambda pair: bpe_codes.get(pair, float('inf')))\n        if bigram not in bpe_codes:\n            break\n        first, second = bigram\n        new_word = []\n        i = 0\n        while i < len(word):\n            try:\n                j = word.index(first, i)\n                new_word.extend(word[i:j])\n                i = j\n            except:\n                new_word.extend(word[i:])\n                break\n\n            if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n                new_word.append(first + second)\n                i += 2\n            else:\n                new_word.append(word[i])\n                i += 1\n        new_word = tuple(new_word)\n        word = new_word\n        if len(word) == 1:\n            break\n        else:\n            pairs = get_pairs(word)\n\n    # don't print end-of-word symbols\n    if word[-1] == '</w>':\n        word = word[:-1]\n    elif word[-1].endswith('</w>'):\n        word = word[:-1] + (word[-1].replace('</w>', ''),)\n\n    if vocab:\n        word = check_vocab_and_split(word, bpe_codes_reverse, vocab, separator)\n\n    cache[orig] = word\n    return word\n\n\ndef recursive_split(segment, bpe_codes, vocab, separator, final=False):\n    \"\"\"Recursively split segment into smaller units (by reversing BPE merges)\n    until all units are either in-vocabulary, or cannot be split futher.\"\"\"\n\n    try:\n        if final:\n            left, right = bpe_codes[segment + '</w>']\n            right = right[:-4]\n        else:\n            left, right = bpe_codes[segment]\n    except:\n        #sys.stderr.write('cannot split {0} further.\\n'.format(segment))\n        yield segment\n        return\n\n    if left + separator in vocab:\n        yield left\n    else:\n        for item in recursive_split(left, bpe_codes, vocab, separator, False):\n            yield item\n\n    if (final and right in vocab) or (not final and right + separator in vocab):\n        yield right\n    else:\n        for item in recursive_split(right, bpe_codes, vocab, separator, final):\n            yield item\n\n\ndef check_vocab_and_split(orig, bpe_codes, vocab, separator):\n    \"\"\"Check for each segment in word if it is in-vocabulary,\n    and segment OOV segments into smaller units by reversing the BPE merge operations\"\"\"\n\n    out = []\n\n    for segment in orig[:-1]:\n        if segment + separator in vocab:\n            out.append(segment)\n        else:\n            #sys.stderr.write('OOV: {0}\\n'.format(segment))\n            for item in recursive_split(segment, bpe_codes, vocab, separator, False):\n                out.append(item)\n\n    segment = orig[-1]\n    if segment in vocab:\n        out.append(segment)\n    else:\n        #sys.stderr.write('OOV: {0}\\n'.format(segment))\n        for item in recursive_split(segment, bpe_codes, vocab, separator, True):\n            out.append(item)\n\n    return out\n\n\ndef read_vocabulary(vocab_file, threshold):\n    \"\"\"read vocabulary file produced by get_vocab.py, and filter according to frequency threshold.\n    \"\"\"\n\n    vocabulary = set()\n\n    for line in vocab_file:\n        word, freq = line.split()\n        freq = int(freq)\n        if threshold == None or freq >= threshold:\n            vocabulary.add(word)\n\n    return vocabulary\n\n\ndef isolate_glossary(word, glossary):\n    \"\"\"\n    Isolate a glossary present inside a word.\n\n    Returns a list of subwords. In which all 'glossary' glossaries are isolated \n\n    For example, if 'USA' is the glossary and '1934USABUSA' the word, the return value is:\n        ['1934', 'USA', 'B', 'USA']\n    \"\"\"\n    if word == glossary or glossary not in word:\n        return [word]\n    else:\n        splits = word.split(glossary)\n        segments = [segment.strip() for split in splits[:-1]\n                    for segment in [split, glossary] if segment != '']\n        return segments + [splits[-1].strip()] if splits[-1] != '' else segments\n\n\nif __name__ == '__main__':\n\n    # python 2/3 compatibility\n    if sys.version_info < (3, 0):\n        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)\n        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)\n        sys.stdin = codecs.getreader('UTF-8')(sys.stdin)\n    else:\n        sys.stdin = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')\n        sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')\n        sys.stdout = io.TextIOWrapper(\n            sys.stdout.buffer, encoding='utf-8', write_through=True, line_buffering=True)\n\n    parser = create_parser()\n    args = parser.parse_args()\n\n    # read/write files as UTF-8\n    args.codes = codecs.open(args.codes.name, encoding='utf-8')\n    if args.input.name != '<stdin>':\n        args.input = codecs.open(args.input.name, encoding='utf-8')\n    if args.output.name != '<stdout>':\n        args.output = codecs.open(args.output.name, 'w', encoding='utf-8')\n    if args.vocabulary:\n        args.vocabulary = codecs.open(args.vocabulary.name, encoding='utf-8')\n\n    if args.vocabulary:\n        vocabulary = read_vocabulary(\n            args.vocabulary, args.vocabulary_threshold)\n    else:\n        vocabulary = None\n\n    bpe = BPE(args.codes, args.separator, vocabulary, args.glossaries)\n\n    for line in args.input:\n        args.output.write(bpe.segment(line).strip())\n        args.output.write('\\n')\n"
  },
  {
    "path": "tools/average_models.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.average_models import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "tools/bpe_pipeline.sh",
    "content": "#!/usr/bin/env bash\n# Author : Thamme Gowda\n# Created : Nov 06, 2017\n\nONMT=\"$( cd \"$( dirname \"${BASH_SOURCE[0]}\" )/..\" && pwd )\"\n\n#======= EXPERIMENT SETUP ======\n# Activate python environment if needed\nsource ~/.bashrc\n# source activate py3\n\n# update these variables\nNAME=\"run1\"\nOUT=\"onmt-runs/$NAME\"\n\nDATA=\"$ONMT/onmt-runs/data\"\nTRAIN_SRC=$DATA/*train.src\nTRAIN_TGT=$DATA/*train.tgt\nVALID_SRC=$DATA/*dev.src\nVALID_TGT=$DATA/*dev.tgt\nTEST_SRC=$DATA/*test.src\nTEST_TGT=$DATA/*test.tgt\n\nBPE=\"\" # default\nBPE=\"src\" # src, tgt, src+tgt\n\n# applicable only when BPE=\"src\" or \"src+tgt\"\nBPE_SRC_OPS=10000\n\n# applicable only when BPE=\"tgt\" or \"src+tgt\"\nBPE_TGT_OPS=10000\n\nGPUARG=\"\" # default\nGPUARG=\"0\"\n\n\n#====== EXPERIMENT BEGIN ======\n\n# Check if input exists\nfor f in $TRAIN_SRC $TRAIN_TGT $VALID_SRC $VALID_TGT $TEST_SRC $TEST_TGT; do\n    if [[ ! -f \"$f\" ]]; then\n        echo \"Input File $f doesnt exist. Please fix the paths\"\n        exit 1\n    fi\ndone\n\nfunction lines_check {\n    l1=`wc -l $1`\n    l2=`wc -l $2`\n    if [[ $l1 != $l2 ]]; then\n        echo \"ERROR: Record counts doesnt match between: $1 and $2\"\n        exit 2\n    fi\n}\nlines_check $TRAIN_SRC $TRAIN_TGT\nlines_check $VALID_SRC $VALID_TGT\nlines_check $TEST_SRC $TEST_TGT\n\n\necho \"Output dir = $OUT\"\n[ -d $OUT ] || mkdir -p $OUT\n[ -d $OUT/data ] || mkdir -p $OUT/data\n[ -d $OUT/models ] || mkdir $OUT/models\n[ -d $OUT/test ] || mkdir -p  $OUT/test\n\n\necho \"Step 1a: Preprocess inputs\"\nif [[ \"$BPE\" == *\"src\"* ]]; then\n    echo \"BPE on source\"\n    # Here we could use more  monolingual data\n    $ONMT/tools/learn_bpe.py -s $BPE_SRC_OPS < $TRAIN_SRC > $OUT/data/bpe-codes.src\n\n    $ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.src <  $TRAIN_SRC > $OUT/data/train.src\n    $ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.src <  $VALID_SRC > $OUT/data/valid.src\n    $ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.src <  $TEST_SRC > $OUT/data/test.src\nelse\n    ln -sf $TRAIN_SRC $OUT/data/train.src\n    ln -sf $VALID_SRC $OUT/data/valid.src\n    ln -sf $TEST_SRC $OUT/data/test.src\nfi\n\n\nif [[ \"$BPE\" == *\"tgt\"* ]]; then\n    echo \"BPE on target\"\n    # Here we could use more  monolingual data\n    $ONMT/tools/learn_bpe.py -s $BPE_SRC_OPS < $TRAIN_TGT > $OUT/data/bpe-codes.tgt\n\n    $ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.tgt <  $TRAIN_TGT > $OUT/data/train.tgt\n    $ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.tgt <  $VALID_TGT > $OUT/data/valid.tgt\n    #$ONMT/tools/apply_bpe.py -c $OUT/data/bpe-codes.tgt <  $TEST_TGT > $OUT/data/test.tgt\n    # We dont touch the test References, No BPE on them!\n    ln -sf $TEST_TGT $OUT/data/test.tgt\nelse\n    ln -sf $TRAIN_TGT $OUT/data/train.tgt\n    ln -sf $VALID_TGT $OUT/data/valid.tgt\n    ln -sf $TEST_TGT $OUT/data/test.tgt\nfi\n\n\n#: <<EOF\necho \"Step 1b: Preprocess\"\npython $ONMT/preprocess.py \\\n    -train_src $OUT/data/train.src \\\n    -train_tgt $OUT/data/train.tgt \\\n    -valid_src $OUT/data/valid.src \\\n    -valid_tgt $OUT/data/valid.tgt \\\n    -save_data $OUT/data/processed\n\n\necho \"Step 2: Train\"\nGPU_OPTS=\"\"\nif [[ ! -z $GPUARG ]]; then\n    GPU_OPTS=\"-gpu_ranks $GPUARG\"\nfi\nCMD=\"python $ONMT/train.py -data $OUT/data/processed -save_model $OUT/models/$NAME $GPU_OPTS\"\necho \"Training command :: $CMD\"\neval \"$CMD\"\n\n#EOF\n\n# select a model with high accuracy and low perplexity\n# TODO: currently using linear scale, maybe not be the best\nmodel=`ls $OUT/models/*.pt| awk -F '_' 'BEGIN{maxv=-1000000} {score=$(NF-3)-$(NF-1); if (score > maxv) {maxv=score; max=$0}}  END{ print max}'`\necho \"Chosen Model = $model\"\nif [[ -z \"$model\" ]]; then\n    echo \"Model not found. Looked in $OUT/models/\"\n    exit 1\nfi\n\nGPU_OPTS=\"\"\nif [ ! -z $GPUARG ]; then\n    GPU_OPTS=\"-gpu $GPUARG\"\nfi\n\necho \"Step 3a: Translate Test\"\npython $ONMT/translate.py -model $model \\\n    -src $OUT/data/test.src \\\n    -output $OUT/test/test.out \\\n    -replace_unk  -verbose $GPU_OPTS > $OUT/test/test.log\n\necho \"Step 3b: Translate Dev\"\npython $ONMT/translate.py -model $model \\\n    -src $OUT/data/valid.src \\\n    -output $OUT/test/valid.out \\\n    -replace_unk -verbose $GPU_OPTS > $OUT/test/valid.log\n\nif [[ \"$BPE\" == *\"tgt\"* ]]; then\n    echo \"BPE decoding/detokenising target to match with references\"\n    mv $OUT/test/test.out{,.bpe}\n    mv $OUT/test/valid.out{,.bpe} \n    cat $OUT/test/valid.out.bpe | sed -E 's/(@@ )|(@@ ?$)//g' > $OUT/test/valid.out\n    cat $OUT/test/test.out.bpe | sed -E 's/(@@ )|(@@ ?$)//g' > $OUT/test/test.out\nfi\n\necho \"Step 4a: Evaluate Test\"\n$ONMT/tools/multi-bleu-detok.perl $OUT/data/test.tgt < $OUT/test/test.out > $OUT/test/test.tc.bleu\n$ONMT/tools/multi-bleu-detok.perl -lc $OUT/data/test.tgt < $OUT/test/test.out > $OUT/test/test.lc.bleu\n\necho \"Step 4b: Evaluate Dev\"\n$ONMT/tools/multi-bleu-detok.perl $OUT/data/valid.tgt < $OUT/test/valid.out > $OUT/test/valid.tc.bleu\n$ONMT/tools/multi-bleu-detok.perl -lc $OUT/data/valid.tgt < $OUT/test/valid.out > $OUT/test/valid.lc.bleu\n\n#===== EXPERIMENT END ======\n"
  },
  {
    "path": "tools/detokenize.perl",
    "content": "#!/usr/bin/env perl\n\n# Note: retrieved from https://github.com/apache/incubator-joshua/blob/master/scripts/preparation/detokenize.pl\n\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements.  See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nuse warnings;\nuse strict;\n\n# Sample De-Tokenizer\n# written by Josh Schroeder, based on code by Philipp Koehn\n# modified later by ByungGyu Ahn, bahn@cs.jhu.edu, Luke Orland\n\nbinmode(STDIN, \":utf8\");\nbinmode(STDOUT, \":utf8\");\n\nmy $language = \"en\";\nmy $QUIET = 1;\nmy $HELP = 0;\n\nwhile (@ARGV) {\n  $_ = shift;\n  /^-l$/ && ($language = shift, next);\n  /^-v$/ && ($QUIET = 0, next);\n  /^-h$/ && ($HELP = 1, next);\n}\n\nif ($HELP) {\n  print \"Usage ./detokenizer.perl (-l [en|de|...]) < tokenizedfile > detokenizedfile\\n\";\n  exit;\n}\nif (!$QUIET) {\n  print STDERR \"Detokenizer Version 1.1\\n\";\n  print STDERR \"Language: $language\\n\";\n}\n\nwhile(<STDIN>) {\n  if (/^<.+>$/ || /^\\s*$/) {\n    #don't try to detokenize XML/HTML tag lines\n    print $_;\n  }\n  else {\n    print &detokenize($_);\n  }\n}\n\nsub detokenize {\n  my($text) = @_;\n  chomp($text);\n  $text = \" $text \";\n\n  #  convert curly quotes to ASCII e.g. ‘“”’\n  $text =~ s/\\x{2018}/'/gs;\n  $text =~ s/\\x{2019}/'/gs;\n  $text =~ s/\\x{201c}/\"/gs;\n  $text =~ s/\\x{201d}/\"/gs;\n  $text =~ s/\\x{e2}\\x{80}\\x{98}/'/gs;\n  $text =~ s/\\x{e2}\\x{80}\\x{99}/'/gs;\n  $text =~ s/\\x{e2}\\x{80}\\x{9c}/\"/gs;\n  $text =~ s/\\x{e2}\\x{80}\\x{9d}/\"/gs;\n\n  $text =~ s/ '\\s+' / \" /g;\n  $text =~ s/ ` / ' /g;\n  $text =~ s/ ' / ' /g;\n  $text =~ s/ `` / \" /g;\n  $text =~ s/ '' / \" /g;\n\n  # replace the pipe character, which is\n  # a special reserved character in Moses\n  $text =~ s/ -PIPE- / \\| /g;\n\n  $text =~ s/ -LRB- / \\( /g;\n  $text =~ s/ -RRB- / \\) /g;\n  $text =~ s/ -LSB- / \\[ /g;\n  $text =~ s/ -RSB- / \\] /g;\n  $text =~ s/ -LCB- / \\{ /g;\n  $text =~ s/ -RCB- / \\} /g;\n  $text =~ s/ -lrb- / \\( /g;\n  $text =~ s/ -rrb- / \\) /g;\n  $text =~ s/ -lsb- / \\[ /g;\n  $text =~ s/ -rsb- / \\] /g;\n  $text =~ s/ -lcb- / \\{ /g;\n  $text =~ s/ -rcb- / \\} /g;\n\n  $text =~ s/ 'll /'ll /g;\n  $text =~ s/ 're /'re /g;\n  $text =~ s/ 've /'ve /g;\n  $text =~ s/ n't /n't /g;\n  $text =~ s/ 'LL /'LL /g;\n  $text =~ s/ 'RE /'RE /g;\n  $text =~ s/ 'VE /'VE /g;\n  $text =~ s/ N'T /N'T /g;\n  $text =~ s/ can not / cannot /g;\n  $text =~ s/ Can not / Cannot /g;\n\n  # just in case the contraction was not properly treated\n  $text =~ s/ ' ll /'ll /g;\n  $text =~ s/ ' re /'re /g;\n  $text =~ s/ ' ve /'ve /g;\n  $text =~ s/n ' t /n't /g;\n  $text =~ s/ ' LL /'LL /g;\n  $text =~ s/ ' RE /'RE /g;\n  $text =~ s/ ' VE /'VE /g;\n  $text =~ s/N ' T /N'T /g;\n\n  my $word;\n  my $i;\n  my @words = split(/ /,$text);\n  $text = \"\";\n  my %quoteCount =  (\"\\'\"=>0,\"\\\"\"=>0);\n  my $prependSpace = \" \";\n  for ($i=0;$i<(scalar(@words));$i++) {\n    if ($words[$i] =~ /^[\\p{IsSc}]+$/) {\n      #perform shift on currency\n      if (($i<(scalar(@words)-1)) && ($words[$i+1] =~ /^[0-9]/)) {\n        $text = $text.$prependSpace.$words[$i];\n        $prependSpace = \"\";\n      } else {\n        $text=$text.$words[$i];\n        $prependSpace = \" \";\n      }\n    } elsif ($words[$i] =~ /^[\\(\\[\\{\\¿\\¡]+$/) {\n      #perform right shift on random punctuation items\n      $text = $text.$prependSpace.$words[$i];\n      $prependSpace = \"\";\n    } elsif ($words[$i] =~ /^[\\,\\.\\?\\!\\:\\;\\\\\\%\\}\\]\\)]+$/){\n      #perform left shift on punctuation items\n      $text=$text.$words[$i];\n      $prependSpace = \" \";\n    } elsif (($language eq \"en\") && ($i>0) && ($words[$i] =~ /^[\\'][\\p{IsAlpha}]/) && ($words[$i-1] =~ /[\\p{IsAlnum}]$/)) {\n      #left-shift the contraction for English\n      $text=$text.$words[$i];\n      $prependSpace = \" \";\n    } elsif (($language eq \"en\") && ($i>0) && ($i<(scalar(@words)-1)) && ($words[$i] eq \"&\") && ($words[$i-1] =~ /^[A-Z]$/) && ($words[$i+1] =~ /^[A-Z]$/)) {\n      #some contraction with an ampersand e.g. \"R&D\"\n      $text .= $words[$i];\n      $prependSpace = \"\";\n    }  elsif (($language eq \"fr\") && ($i<(scalar(@words)-1)) && ($words[$i] =~ /[\\p{IsAlpha}][\\']$/) && ($words[$i+1] =~ /^[\\p{IsAlpha}]/)) {\n      #right-shift the contraction for French\n      $text = $text.$prependSpace.$words[$i];\n      $prependSpace = \"\";\n    } elsif ($words[$i] =~ /^[\\'\\\"]+$/) {\n      #combine punctuation smartly\n      if (($quoteCount{$words[$i]} % 2) eq 0) {\n        if(($language eq \"en\") && ($words[$i] eq \"'\") && ($i > 0) && ($words[$i-1] =~ /[s]$/)) {\n          #single quote for posesssives ending in s... \"The Jones' house\"\n          #left shift\n          $text=$text.$words[$i];\n          $prependSpace = \" \";\n        } elsif (($language eq \"en\") && ($words[$i] eq \"'\") && ($i < (scalar(@words)-1)) && ($words[$i+1] eq \"s\")) {\n          #single quote for possessive construction. \"John's\"\n          $text .= $words[$i];\n          $prependSpace = \"\";\n        } elsif (($quoteCount{$words[$i]} == 0) &&\n          ($language eq \"en\") && ($words[$i] eq '\"') && ($i>1) && ($words[$i-1] =~ /^[,.]$/) && ($words[$i-2] ne \"said\")) {\n          #emergency case in which the opening quote is missing\n          #ending double quote for direct quotes. e.g. Blah,\" he said. but not like he said, \"Blah.\n          $text .= $words[$i];\n          $prependSpace = \" \";\n        } elsif (($language eq \"en\") && ($words[$i] eq '\"') && ($i < (scalar(@words)-1)) && ($words[$i+1] =~ /^[,.]$/)) {\n          $text .= $words[$i];\n          $prependSpace = \" \";\n        } else {\n          #right shift\n          $text = $text.$prependSpace.$words[$i];\n          $prependSpace = \"\";\n          $quoteCount{$words[$i]} = $quoteCount{$words[$i]} + 1;\n\n        }\n      } else {\n        #left shift\n        $text=$text.$words[$i];\n        $prependSpace = \" \";\n        $quoteCount{$words[$i]} = $quoteCount{$words[$i]} + 1;\n\n      }\n\n    } else {\n      $text=$text.$prependSpace.$words[$i];\n      $prependSpace = \" \";\n    }\n  }\n\n  #clean continuing spaces\n  $text =~ s/ +/ /g;\n\n  #delete spaces around double angle brackets «»\n  # Uh-oh. not a good idea. it is not consistent.\n  $text =~ s/(\\x{c2}\\x{ab}|\\x{ab}) /$1/g;\n  $text =~ s/ (\\x{c2}\\x{bb}|\\x{bb})/$1/g;\n\n  # delete spaces around all other special characters\n  # Uh-oh. not a good idea. \"Men&Women\"\n  #$text =~ s/ ([^\\p{IsAlnum}\\s\\.\\'\\`\\,\\-\\\"\\|]) /$1/g;\n  $text =~ s/ \\/ /\\//g;\n\n  # clean up spaces at head and tail of each line as well as any double-spacing\n  $text =~ s/\\n /\\n/g;\n  $text =~ s/ \\n/\\n/g;\n  $text =~ s/^ //g;\n  $text =~ s/ $//g;\n\n  #add trailing break\n  $text .= \"\\n\" unless $text =~ /\\n$/;\n\n  return $text;\n}\n"
  },
  {
    "path": "tools/embeddings_to_torch.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import division\nimport six\nimport argparse\nimport torch\nfrom onmt.utils.logging import init_logger, logger\n\n\ndef get_vocabs(dict_path):\n    fields = torch.load(dict_path)\n\n    vocs = []\n    for side in ['src', 'tgt']:\n        try:\n            vocab = fields[side].base_field.vocab\n        except AttributeError:\n            vocab = fields[side].vocab\n        vocs.append(vocab)\n    enc_vocab, dec_vocab = vocs\n\n    logger.info(\"From: %s\" % dict_path)\n    logger.info(\"\\t* source vocab: %d words\" % len(enc_vocab))\n    logger.info(\"\\t* target vocab: %d words\" % len(dec_vocab))\n\n    return enc_vocab, dec_vocab\n\n\ndef read_embeddings(file_enc, skip_lines=0, filter_set=None):\n    embs = dict()\n    total_vectors_in_file = 0\n    with open(file_enc, 'rb') as f:\n        for i, line in enumerate(f):\n            if i < skip_lines:\n                continue\n            if not line:\n                break\n            if len(line) == 0:\n                # is this reachable?\n                continue\n\n            l_split = line.decode('utf8').strip().split(' ')\n            if len(l_split) == 2:\n                continue\n            total_vectors_in_file += 1\n            if filter_set is not None and l_split[0] not in filter_set:\n                continue\n            embs[l_split[0]] = [float(em) for em in l_split[1:]]\n    return embs, total_vectors_in_file\n\n\ndef convert_to_torch_tensor(word_to_float_list_dict, vocab):\n    dim = len(six.next(six.itervalues(word_to_float_list_dict)))\n    tensor = torch.zeros((len(vocab), dim))\n    for word, values in word_to_float_list_dict.items():\n        tensor[vocab.stoi[word]] = torch.Tensor(values)\n    return tensor\n\n\ndef calc_vocab_load_stats(vocab, loaded_embed_dict):\n    matching_count = len(\n        set(vocab.stoi.keys()) & set(loaded_embed_dict.keys()))\n    missing_count = len(vocab) - matching_count\n    percent_matching = matching_count / len(vocab) * 100\n    return matching_count, missing_count, percent_matching\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='embeddings_to_torch.py')\n    parser.add_argument('-emb_file_both', required=False,\n                        help=\"loads Embeddings for both source and target \"\n                             \"from this file.\")\n    parser.add_argument('-emb_file_enc', required=False,\n                        help=\"source Embeddings from this file\")\n    parser.add_argument('-emb_file_dec', required=False,\n                        help=\"target Embeddings from this file\")\n    parser.add_argument('-output_file', required=True,\n                        help=\"Output file for the prepared data\")\n    parser.add_argument('-dict_file', required=True,\n                        help=\"Dictionary file\")\n    parser.add_argument('-verbose', action=\"store_true\", default=False)\n    parser.add_argument('-skip_lines', type=int, default=0,\n                        help=\"Skip first lines of the embedding file\")\n    parser.add_argument('-type', choices=[\"GloVe\", \"word2vec\"],\n                        default=\"GloVe\")\n    opt = parser.parse_args()\n\n    enc_vocab, dec_vocab = get_vocabs(opt.dict_file)\n\n    # Read in embeddings\n    skip_lines = 1 if opt.type == \"word2vec\" else opt.skip_lines\n    if opt.emb_file_both is not None:\n        if opt.emb_file_enc is not None:\n            raise ValueError(\"If --emb_file_both is passed in, you should not\"\n                             \"set --emb_file_enc.\")\n        if opt.emb_file_dec is not None:\n            raise ValueError(\"If --emb_file_both is passed in, you should not\"\n                             \"set --emb_file_dec.\")\n        set_of_src_and_tgt_vocab = \\\n            set(enc_vocab.stoi.keys()) | set(dec_vocab.stoi.keys())\n        logger.info(\"Reading encoder and decoder embeddings from {}\".format(\n            opt.emb_file_both))\n        src_vectors, total_vec_count = \\\n            read_embeddings(opt.emb_file_both, skip_lines,\n                            set_of_src_and_tgt_vocab)\n        tgt_vectors = src_vectors\n        logger.info(\"\\tFound {} total vectors in file\".format(total_vec_count))\n    else:\n        if opt.emb_file_enc is None:\n            raise ValueError(\"If --emb_file_enc not provided. Please specify \"\n                             \"the file with encoder embeddings, or pass in \"\n                             \"--emb_file_both\")\n        if opt.emb_file_dec is None:\n            raise ValueError(\"If --emb_file_dec not provided. Please specify \"\n                             \"the file with encoder embeddings, or pass in \"\n                             \"--emb_file_both\")\n        logger.info(\"Reading encoder embeddings from {}\".format(\n            opt.emb_file_enc))\n        src_vectors, total_vec_count = read_embeddings(\n            opt.emb_file_enc, skip_lines,\n            filter_set=enc_vocab.stoi\n        )\n        logger.info(\"\\tFound {} total vectors in file.\".format(\n            total_vec_count))\n        logger.info(\"Reading decoder embeddings from {}\".format(\n            opt.emb_file_dec))\n        tgt_vectors, total_vec_count = read_embeddings(\n            opt.emb_file_dec, skip_lines,\n            filter_set=dec_vocab.stoi\n        )\n        logger.info(\"\\tFound {} total vectors in file\".format(total_vec_count))\n    logger.info(\"After filtering to vectors in vocab:\")\n    logger.info(\"\\t* enc: %d match, %d missing, (%.2f%%)\"\n                % calc_vocab_load_stats(enc_vocab, src_vectors))\n    logger.info(\"\\t* dec: %d match, %d missing, (%.2f%%)\"\n                % calc_vocab_load_stats(dec_vocab, tgt_vectors))\n\n    # Write to file\n    enc_output_file = opt.output_file + \".enc.pt\"\n    dec_output_file = opt.output_file + \".dec.pt\"\n    logger.info(\"\\nSaving embedding as:\\n\\t* enc: %s\\n\\t* dec: %s\"\n                % (enc_output_file, dec_output_file))\n    torch.save(\n        convert_to_torch_tensor(src_vectors, enc_vocab),\n        enc_output_file\n    )\n    torch.save(\n        convert_to_torch_tensor(tgt_vectors, dec_vocab),\n        dec_output_file\n    )\n    logger.info(\"\\nDone.\")\n\n\nif __name__ == \"__main__\":\n    init_logger('embeddings_to_torch.log')\n    main()\n"
  },
  {
    "path": "tools/extract_embeddings.py",
    "content": "import argparse\n\nimport torch\n\nimport onmt\nimport onmt.model_builder\n\nfrom onmt.utils.parse import ArgumentParser\nimport onmt.opts\n\nfrom onmt.utils.misc import use_gpu\nfrom onmt.utils.logging import init_logger, logger\n\nparser = argparse.ArgumentParser(description='translate.py')\n\nparser.add_argument('-model', required=True,\n                    help='Path to model .pt file')\nparser.add_argument('-output_dir', default='.',\n                    help=\"\"\"Path to output the embeddings\"\"\")\nparser.add_argument('-gpu', type=int, default=-1,\n                    help=\"Device to run on\")\n\n\ndef write_embeddings(filename, dict, embeddings):\n    with open(filename, 'wb') as file:\n        for i in range(min(len(embeddings), len(dict.itos))):\n            str = dict.itos[i].encode(\"utf-8\")\n            for j in range(len(embeddings[0])):\n                str = str + (\" %5f\" % (embeddings[i][j])).encode(\"utf-8\")\n            file.write(str + b\"\\n\")\n\n\ndef main():\n    dummy_parser = argparse.ArgumentParser(description='train.py')\n    onmt.opts.model_opts(dummy_parser)\n    dummy_opt = dummy_parser.parse_known_args([])[0]\n    opt = parser.parse_args()\n    opt.cuda = opt.gpu > -1\n    if opt.cuda:\n        torch.cuda.set_device(opt.gpu)\n\n    # Add in default model arguments, possibly added since training.\n    checkpoint = torch.load(opt.model,\n                            map_location=lambda storage, loc: storage)\n    model_opt = checkpoint['opt']\n\n    fields = checkpoint['vocab']\n    src_dict = fields['src'].base_field.vocab  # assumes src is text\n    tgt_dict = fields['tgt'].base_field.vocab\n\n    model_opt = checkpoint['opt']\n    for arg in dummy_opt.__dict__:\n        if arg not in model_opt:\n            model_opt.__dict__[arg] = dummy_opt.__dict__[arg]\n\n    # build_base_model expects updated and validated opts\n    ArgumentParser.update_model_opts(model_opt)\n    ArgumentParser.validate_model_opts(model_opt)\n\n    model = onmt.model_builder.build_base_model(\n        model_opt, fields, use_gpu(opt), checkpoint)\n    encoder = model.encoder  # no encoder for LM task\n    decoder = model.decoder\n\n    encoder_embeddings = encoder.embeddings.word_lut.weight.data.tolist()\n    decoder_embeddings = decoder.embeddings.word_lut.weight.data.tolist()\n\n    logger.info(\"Writing source embeddings\")\n    write_embeddings(opt.output_dir + \"/src_embeddings.txt\", src_dict,\n                     encoder_embeddings)\n\n    logger.info(\"Writing target embeddings\")\n    write_embeddings(opt.output_dir + \"/tgt_embeddings.txt\", tgt_dict,\n                     decoder_embeddings)\n\n    logger.info('... done.')\n    logger.info('Converting model...')\n\n\nif __name__ == \"__main__\":\n    init_logger('extract_embeddings.log')\n    main()\n"
  },
  {
    "path": "tools/extract_vocabulary.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport argparse\nimport sys\n\n\ndef read_files_batch(file_list):\n    \"\"\"Reads the provided files in batches\"\"\"\n    batch = []  # Keep batch for each file\n    fd_list = []  # File descriptor list\n\n    exit = False  # Flag used for quitting the program in case of error\n    try:\n        for filename in file_list:\n            fd_list.append(open(filename))\n\n        for lines in zip(*fd_list):\n            for i, line in enumerate(lines):\n                line = line.rstrip(\"\\n\").split(\" \")\n                batch.append(line)\n\n            yield batch\n            batch = []  # Reset batch\n\n    except IOError:\n        print(\"Error reading file \" + filename + \".\")\n        exit = True  # Flag to exit the program\n\n    finally:\n        for fd in fd_list:\n            fd.close()\n\n        if exit:  # An error occurred, end execution\n            sys.exit(-1)\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-file_type', default='text',\n                        choices=['text', 'field'], required=True,\n                        help=\"\"\"Options for vocabulary extraction.\n                               The default is 'text' where the user passes\n                               a corpus or a list of corpora files for which\n                               they want to create a vocabulary from.\n                               If choosing the option 'field', we assume\n                               the file passed is a torch file created during\n                               the preprocessing stage of an already\n                               preprocessed corpus. The vocabulary file created\n                               will just be the vocabulary inside the field\n                               corresponding to the argument 'side'.\"\"\")\n    parser.add_argument(\"-file\", type=str, nargs=\"+\", required=True)\n    parser.add_argument(\"-out_file\", type=str, required=True)\n    parser.add_argument(\"-side\", choices=['src', 'tgt'], help=\"\"\"Specifies\n                               'src' or 'tgt' side for 'field' file_type.\"\"\")\n\n    opt = parser.parse_args()\n\n    vocabulary = {}\n    if opt.file_type == 'text':\n        print(\"Reading input file...\")\n        for batch in read_files_batch(opt.file):\n            for sentence in batch:\n                for w in sentence:\n                    if w in vocabulary:\n                        vocabulary[w] += 1\n                    else:\n                        vocabulary[w] = 1\n\n        print(\"Writing vocabulary file...\")\n        with open(opt.out_file, \"w\") as f:\n            for w, count in sorted(vocabulary.items(), key=lambda x: x[1],\n                                   reverse=True):\n                f.write(\"{0}\\n\".format(w))\n    else:\n        if opt.side not in ['src', 'tgt']:\n            raise ValueError(\"If using -file_type='field', specifies \"\n                             \"'src' or 'tgt' argument for -side.\")\n        import torch\n\n        print(\"Reading input file...\")\n        if not len(opt.file) == 1:\n            raise ValueError(\"If using -file_type='field', only pass one \"\n                             \"argument for -file.\")\n        vocabs = torch.load(opt.file[0])\n        voc = dict(vocabs)[opt.side]\n\n        try:\n            word_list = voc[0][1].base_field.vocab.itos\n        except AttributeError:\n            word_list = voc[0][1].vocab.itos\n\n        print(\"Writing vocabulary file...\")\n        with open(opt.out_file, \"wb\") as f:\n            for w in word_list:\n                f.write(u\"{0}\\n\".format(w).encode(\"utf-8\"))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "tools/learn_bpe.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: Rico Sennrich\n# flake8: noqa\n\n\"\"\"Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.\nUnlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary\nof a text to a configurable number of symbols, with only a small increase in the number of tokens.\n\nReference:\nRico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.\nProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.\n\"\"\"\n# This file is retrieved from https://github.com/rsennrich/subword-nmt\n\nfrom __future__ import unicode_literals\n\nimport sys\nimport codecs\nimport re\nimport copy\nimport argparse\nfrom collections import defaultdict, Counter\n\n# hack for python2/3 compatibility\nfrom io import open\nargparse.open = open\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=\"learn BPE-based word segmentation\")\n\n    parser.add_argument(\n        '--input', '-i', type=argparse.FileType('r'), default=sys.stdin,\n        metavar='PATH',\n        help=\"Input text (default: standard input).\")\n\n    parser.add_argument(\n        '--output', '-o', type=argparse.FileType('w'), default=sys.stdout,\n        metavar='PATH',\n        help=\"Output file for BPE codes (default: standard output)\")\n    parser.add_argument(\n        '--symbols', '-s', type=int, default=10000,\n        help=\"Create this many new symbols (each representing a character n-gram) (default: %(default)s))\")\n    parser.add_argument(\n        '--min-frequency', type=int, default=2, metavar='FREQ',\n        help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s))')\n    parser.add_argument('--dict-input', action=\"store_true\",\n                        help=\"If set, input file is interpreted as a dictionary where each line contains a word-count pair\")\n    parser.add_argument(\n        '--verbose', '-v', action=\"store_true\",\n        help=\"verbose mode.\")\n\n    return parser\n\n\ndef get_vocabulary(fobj, is_dict=False):\n    \"\"\"Read text and return dictionary that encodes vocabulary\n    \"\"\"\n    vocab = Counter()\n    for line in fobj:\n        if is_dict:\n            word, count = line.strip().split()\n            vocab[word] = int(count)\n        else:\n            for word in line.split():\n                vocab[word] += 1\n    return vocab\n\n\ndef update_pair_statistics(pair, changed, stats, indices):\n    \"\"\"Minimally update the indices and frequency of symbol pairs\n\n    if we merge a pair of symbols, only pairs that overlap with occurrences\n    of this pair are affected, and need to be updated.\n    \"\"\"\n    stats[pair] = 0\n    indices[pair] = defaultdict(int)\n    first, second = pair\n    new_pair = first + second\n    for j, word, old_word, freq in changed:\n\n        # find all instances of pair, and update frequency/indices around it\n        i = 0\n        while True:\n            # find first symbol\n            try:\n                i = old_word.index(first, i)\n            except ValueError:\n                break\n            # if first symbol is followed by second symbol, we've found an occurrence of pair (old_word[i:i+2])\n            if i < len(old_word) - 1 and old_word[i + 1] == second:\n                # assuming a symbol sequence \"A B C\", if \"B C\" is merged, reduce the frequency of \"A B\"\n                if i:\n                    prev = old_word[i - 1:i + 1]\n                    stats[prev] -= freq\n                    indices[prev][j] -= 1\n                if i < len(old_word) - 2:\n                    # assuming a symbol sequence \"A B C B\", if \"B C\" is merged, reduce the frequency of \"C B\".\n                    # however, skip this if the sequence is A B C B C, because the frequency of \"C B\" will be reduced by the previous code block\n                    if old_word[i + 2] != first or i >= len(old_word) - 3 or old_word[i + 3] != second:\n                        nex = old_word[i + 1:i + 3]\n                        stats[nex] -= freq\n                        indices[nex][j] -= 1\n                i += 2\n            else:\n                i += 1\n\n        i = 0\n        while True:\n            try:\n                # find new pair\n                i = word.index(new_pair, i)\n            except ValueError:\n                break\n            # assuming a symbol sequence \"A BC D\", if \"B C\" is merged, increase the frequency of \"A BC\"\n            if i:\n                prev = word[i - 1:i + 1]\n                stats[prev] += freq\n                indices[prev][j] += 1\n            # assuming a symbol sequence \"A BC B\", if \"B C\" is merged, increase the frequency of \"BC B\"\n            # however, if the sequence is A BC BC, skip this step because the count of \"BC BC\" will be incremented by the previous code block\n            if i < len(word) - 1 and word[i + 1] != new_pair:\n                nex = word[i:i + 2]\n                stats[nex] += freq\n                indices[nex][j] += 1\n            i += 1\n\n\ndef get_pair_statistics(vocab):\n    \"\"\"Count frequency of all symbol pairs, and create index\"\"\"\n\n    # data structure of pair frequencies\n    stats = defaultdict(int)\n\n    # index from pairs to words\n    indices = defaultdict(lambda: defaultdict(int))\n\n    for i, (word, freq) in enumerate(vocab):\n        prev_char = word[0]\n        for char in word[1:]:\n            stats[prev_char, char] += freq\n            indices[prev_char, char][i] += 1\n            prev_char = char\n\n    return stats, indices\n\n\ndef replace_pair(pair, vocab, indices):\n    \"\"\"Replace all occurrences of a symbol pair ('A', 'B') with a new symbol 'AB'\"\"\"\n    first, second = pair\n    pair_str = ''.join(pair)\n    pair_str = pair_str.replace('\\\\', '\\\\\\\\')\n    changes = []\n    pattern = re.compile(\n        r'(?<!\\S)' + re.escape(first + ' ' + second) + r'(?!\\S)')\n    if sys.version_info < (3, 0):\n        iterator = indices[pair].iteritems()\n    else:\n        iterator = indices[pair].items()\n    for j, freq in iterator:\n        if freq < 1:\n            continue\n        word, freq = vocab[j]\n        new_word = ' '.join(word)\n        new_word = pattern.sub(pair_str, new_word)\n        new_word = tuple(new_word.split())\n\n        vocab[j] = (new_word, freq)\n        changes.append((j, new_word, word, freq))\n\n    return changes\n\n\ndef prune_stats(stats, big_stats, threshold):\n    \"\"\"Prune statistics dict for efficiency of max()\n\n    The frequency of a symbol pair never increases, so pruning is generally safe\n    (until we the most frequent pair is less frequent than a pair we previously pruned)\n    big_stats keeps full statistics for when we need to access pruned items\n    \"\"\"\n    for item, freq in list(stats.items()):\n        if freq < threshold:\n            del stats[item]\n            if freq < 0:\n                big_stats[item] += freq\n            else:\n                big_stats[item] = freq\n\n\ndef main(infile, outfile, num_symbols, min_frequency=2, verbose=False, is_dict=False):\n    \"\"\"Learn num_symbols BPE operations from vocabulary, and write to outfile.\n    \"\"\"\n\n    # version 0.2 changes the handling of the end-of-word token ('</w>');\n    # version numbering allows bckward compatibility\n    outfile.write('#version: 0.2\\n')\n\n    vocab = get_vocabulary(infile, is_dict)\n    vocab = dict([(tuple(x[:-1]) + (x[-1] + '</w>',), y)\n                  for (x, y) in vocab.items()])\n    sorted_vocab = sorted(vocab.items(), key=lambda x: x[1], reverse=True)\n\n    stats, indices = get_pair_statistics(sorted_vocab)\n    big_stats = copy.deepcopy(stats)\n    # threshold is inspired by Zipfian assumption, but should only affect speed\n    threshold = max(stats.values()) / 10\n    for i in range(num_symbols):\n        if stats:\n            most_frequent = max(stats, key=lambda x: (stats[x], x))\n\n        # we probably missed the best pair because of pruning; go back to full statistics\n        if not stats or (i and stats[most_frequent] < threshold):\n            prune_stats(stats, big_stats, threshold)\n            stats = copy.deepcopy(big_stats)\n            most_frequent = max(stats, key=lambda x: (stats[x], x))\n            # threshold is inspired by Zipfian assumption, but should only affect speed\n            threshold = stats[most_frequent] * i / (i + 10000.0)\n            prune_stats(stats, big_stats, threshold)\n\n        if stats[most_frequent] < min_frequency:\n            sys.stderr.write(\n                'no pair has frequency >= {0}. Stopping\\n'.format(min_frequency))\n            break\n\n        if verbose:\n            sys.stderr.write('pair {0}: {1} {2} -> {1}{2} (frequency {3})\\n'.format(\n                i, most_frequent[0], most_frequent[1], stats[most_frequent]))\n        outfile.write('{0} {1}\\n'.format(*most_frequent))\n        changes = replace_pair(most_frequent, sorted_vocab, indices)\n        update_pair_statistics(most_frequent, changes, stats, indices)\n        stats[most_frequent] = 0\n        if not i % 100:\n            prune_stats(stats, big_stats, threshold)\n\n\nif __name__ == '__main__':\n\n    # python 2/3 compatibility\n    if sys.version_info < (3, 0):\n        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)\n        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)\n        sys.stdin = codecs.getreader('UTF-8')(sys.stdin)\n    else:\n        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)\n        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)\n        sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)\n\n    parser = create_parser()\n    args = parser.parse_args()\n\n    # read/write files as UTF-8\n    if args.input.name != '<stdin>':\n        args.input = codecs.open(args.input.name, encoding='utf-8')\n    if args.output.name != '<stdout>':\n        args.output = codecs.open(args.output.name, 'w', encoding='utf-8')\n\n    main(args.input, args.output, args.symbols,\n         args.min_frequency, args.verbose, is_dict=args.dict_input)\n"
  },
  {
    "path": "tools/multi-bleu-detok.perl",
    "content": "#!/usr/bin/env perl\n#\n# This file is part of moses.  Its use is licensed under the GNU Lesser General\n# Public License version 2.1 or, at your option, any later version.\n\n# This file uses the internal tokenization of mteval-v13a.pl,\n# giving the exact same (case-sensitive) results on untokenized text.\n# Using this script with detokenized output and untokenized references is\n# preferrable over multi-bleu.perl, since scores aren't affected by tokenization differences.\n# \n# like multi-bleu.perl , it supports plain text input and multiple references.\n\n# This file is retrieved from Moses Decoder ::  https://github.com/moses-smt/mosesdecoder\n# $Id$\nuse warnings;\nuse strict;\n\nmy $lowercase = 0;\nif ($ARGV[0] eq \"-lc\") {\n  $lowercase = 1;\n  shift;\n}\n\nmy $stem = $ARGV[0];\nif (!defined $stem) {\n  print STDERR \"usage: multi-bleu-detok.pl [-lc] reference < hypothesis\\n\";\n  print STDERR \"Reads the references from reference or reference0, reference1, ...\\n\";\n  exit(1);\n}\n\n$stem .= \".ref\" if !-e $stem && !-e $stem.\"0\" && -e $stem.\".ref0\";\n\nmy @REF;\nmy $ref=0;\nwhile(-e \"$stem$ref\") {\n    &add_to_ref(\"$stem$ref\",\\@REF);\n    $ref++;\n}\n&add_to_ref($stem,\\@REF) if -e $stem;\ndie(\"ERROR: could not find reference file $stem\") unless scalar @REF;\n\n# add additional references explicitly specified on the command line\nshift;\nforeach my $stem (@ARGV) {\n    &add_to_ref($stem,\\@REF) if -e $stem;\n}\n\n\n\nsub add_to_ref {\n    my ($file,$REF) = @_;\n    my $s=0;\n    if ($file =~ /.gz$/) {\n\topen(REF,\"gzip -dc $file|\") or die \"Can't read $file\";\n    } else { \n\topen(REF,$file) or die \"Can't read $file\";\n    }\n    while(<REF>) {\n\tchop;\n\t$_ = tokenization($_);\n\tpush @{$$REF[$s++]}, $_;\n    }\n    close(REF);\n}\n\nmy(@CORRECT,@TOTAL,$length_translation,$length_reference);\nmy $s=0;\nwhile(<STDIN>) {\n    chop;\n    $_ = lc if $lowercase;\n    $_ = tokenization($_);\n    my @WORD = split;\n    my %REF_NGRAM = ();\n    my $length_translation_this_sentence = scalar(@WORD);\n    my ($closest_diff,$closest_length) = (9999,9999);\n    foreach my $reference (@{$REF[$s]}) {\n#      print \"$s $_ <=> $reference\\n\";\n  $reference = lc($reference) if $lowercase;\n\tmy @WORD = split(' ',$reference);\n\tmy $length = scalar(@WORD);\n        my $diff = abs($length_translation_this_sentence-$length);\n\tif ($diff < $closest_diff) {\n\t    $closest_diff = $diff;\n\t    $closest_length = $length;\n\t    # print STDERR \"$s: closest diff \".abs($length_translation_this_sentence-$length).\" = abs($length_translation_this_sentence-$length), setting len: $closest_length\\n\";\n\t} elsif ($diff == $closest_diff) {\n            $closest_length = $length if $length < $closest_length;\n            # from two references with the same closeness to me\n            # take the *shorter* into account, not the \"first\" one.\n        }\n\tfor(my $n=1;$n<=4;$n++) {\n\t    my %REF_NGRAM_N = ();\n\t    for(my $start=0;$start<=$#WORD-($n-1);$start++) {\n\t\tmy $ngram = \"$n\";\n\t\tfor(my $w=0;$w<$n;$w++) {\n\t\t    $ngram .= \" \".$WORD[$start+$w];\n\t\t}\n\t\t$REF_NGRAM_N{$ngram}++;\n\t    }\n\t    foreach my $ngram (keys %REF_NGRAM_N) {\n\t\tif (!defined($REF_NGRAM{$ngram}) ||\n\t\t    $REF_NGRAM{$ngram} < $REF_NGRAM_N{$ngram}) {\n\t\t    $REF_NGRAM{$ngram} = $REF_NGRAM_N{$ngram};\n#\t    print \"$i: REF_NGRAM{$ngram} = $REF_NGRAM{$ngram}<BR>\\n\";\n\t\t}\n\t    }\n\t}\n    }\n    $length_translation += $length_translation_this_sentence;\n    $length_reference += $closest_length;\n    for(my $n=1;$n<=4;$n++) {\n\tmy %T_NGRAM = ();\n\tfor(my $start=0;$start<=$#WORD-($n-1);$start++) {\n\t    my $ngram = \"$n\";\n\t    for(my $w=0;$w<$n;$w++) {\n\t\t$ngram .= \" \".$WORD[$start+$w];\n\t    }\n\t    $T_NGRAM{$ngram}++;\n\t}\n\tforeach my $ngram (keys %T_NGRAM) {\n\t    $ngram =~ /^(\\d+) /;\n\t    my $n = $1;\n            # my $corr = 0;\n#\tprint \"$i e $ngram $T_NGRAM{$ngram}<BR>\\n\";\n\t    $TOTAL[$n] += $T_NGRAM{$ngram};\n\t    if (defined($REF_NGRAM{$ngram})) {\n\t\tif ($REF_NGRAM{$ngram} >= $T_NGRAM{$ngram}) {\n\t\t    $CORRECT[$n] += $T_NGRAM{$ngram};\n                    # $corr =  $T_NGRAM{$ngram};\n#\t    print \"$i e correct1 $T_NGRAM{$ngram}<BR>\\n\";\n\t\t}\n\t\telse {\n\t\t    $CORRECT[$n] += $REF_NGRAM{$ngram};\n                    # $corr =  $REF_NGRAM{$ngram};\n#\t    print \"$i e correct2 $REF_NGRAM{$ngram}<BR>\\n\";\n\t\t}\n\t    }\n            # $REF_NGRAM{$ngram} = 0 if !defined $REF_NGRAM{$ngram};\n            # print STDERR \"$ngram: {$s, $REF_NGRAM{$ngram}, $T_NGRAM{$ngram}, $corr}\\n\"\n\t}\n    }\n    $s++;\n}\nmy $brevity_penalty = 1;\nmy $bleu = 0;\n\nmy @bleu=();\n\nfor(my $n=1;$n<=4;$n++) {\n  if (defined ($TOTAL[$n])){\n    $bleu[$n]=($TOTAL[$n])?$CORRECT[$n]/$TOTAL[$n]:0;\n    # print STDERR \"CORRECT[$n]:$CORRECT[$n] TOTAL[$n]:$TOTAL[$n]\\n\";\n  }else{\n    $bleu[$n]=0;\n  }\n}\n\nif ($length_reference==0){\n  printf \"BLEU = 0, 0/0/0/0 (BP=0, ratio=0, hyp_len=0, ref_len=0)\\n\";\n  exit(1);\n}\n\nif ($length_translation<$length_reference) {\n  $brevity_penalty = exp(1-$length_reference/$length_translation);\n}\n$bleu = $brevity_penalty * exp((my_log( $bleu[1] ) +\n\t\t\t\tmy_log( $bleu[2] ) +\n\t\t\t\tmy_log( $bleu[3] ) +\n\t\t\t\tmy_log( $bleu[4] ) ) / 4) ;\nprintf \"BLEU = %.2f, %.1f/%.1f/%.1f/%.1f (BP=%.3f, ratio=%.3f, hyp_len=%d, ref_len=%d)\\n\",\n    100*$bleu,\n    100*$bleu[1],\n    100*$bleu[2],\n    100*$bleu[3],\n    100*$bleu[4],\n    $brevity_penalty,\n    $length_translation / $length_reference,\n    $length_translation,\n    $length_reference;\n\nsub my_log {\n  return -9999999999 unless $_[0];\n  return log($_[0]);\n}\n\n\n\nsub tokenization\n{\n\tmy ($norm_text) = @_;\n\n# language-independent part:\n\t$norm_text =~ s/<skipped>//g; # strip \"skipped\" tags\n\t$norm_text =~ s/-\\n//g; # strip end-of-line hyphenation and join lines\n\t$norm_text =~ s/\\n/ /g; # join lines\n\t$norm_text =~ s/&quot;/\"/g;  # convert SGML tag for quote to \"\n\t$norm_text =~ s/&amp;/&/g;   # convert SGML tag for ampersand to &\n\t$norm_text =~ s/&lt;/</g;    # convert SGML tag for less-than to >\n\t$norm_text =~ s/&gt;/>/g;    # convert SGML tag for greater-than to <\n\n# language-dependent part (assuming Western languages):\n\t$norm_text = \" $norm_text \";\n\t$norm_text =~ s/([\\{-\\~\\[-\\` -\\&\\(-\\+\\:-\\@\\/])/ $1 /g;   # tokenize punctuation\n\t$norm_text =~ s/([^0-9])([\\.,])/$1 $2 /g; # tokenize period and comma unless preceded by a digit\n\t$norm_text =~ s/([\\.,])([^0-9])/ $1 $2/g; # tokenize period and comma unless followed by a digit\n\t$norm_text =~ s/([0-9])(-)/$1 $2 /g; # tokenize dash when preceded by a digit\n\t$norm_text =~ s/\\s+/ /g; # one space only between words\n\t$norm_text =~ s/^\\s+//;  # no leading space\n\t$norm_text =~ s/\\s+$//;  # no trailing space\n\n\treturn $norm_text;\n}\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/README.txt",
    "content": "The language suffix can be found here:\n\nhttp://www.loc.gov/standards/iso639-2/php/code_list.php\n\nThis code includes data from Daniel Naber's Language Tools (czech abbreviations).\nThis code includes data from czech wiktionary (also czech abbreviations).\n\n\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.ca",
    "content": "Dr\nDra\npàg\np\nc\nav\nSr\nSra\nadm\nesq\nProf\nS.A\nS.L\np.e\nptes\nSta\nSt\npl\nmàx\ncast\ndir\nnre\nfra\nadmdora\nEmm\nExcma\nespf\ndc\nadmdor\ntel\nangl\naprox\nca\ndept\ndj\ndl\ndt\nds\ndg\ndv\ned\nentl\nal\ni.e\nmaj\nsmin\nn\nnúm\npta\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.cs",
    "content": "Bc\nBcA\nIng\nIng.arch\nMUDr\nMVDr\nMgA\nMgr\nJUDr\nPhDr\nRNDr\nPharmDr\nThLic\nThDr\nPh.D\nTh.D\nprof\ndoc\nCSc\nDrSc\ndr. h. c\nPaedDr\nDr\nPhMr\nDiS\nabt\nad\na.i\naj\nangl\nanon\napod\natd\natp\naut\nbd\nbiogr\nb.m\nb.p\nb.r\ncca\ncit\ncizojaz\nc.k\ncol\nčes\nčín\nčj\ned\nfacs\nfasc\nfol\nfot\nfranc\nh.c\nhist\nhl\nhrsg\nibid\nil\nind\ninv.č\njap\njhdt\njv\nkoed\nkol\nkorej\nkl\nkrit\nlat\nlit\nm.a\nmaď\nmj\nmp\nnásl\nnapř\nnepubl\nněm\nno\nnr\nn.s\nokr\nodd\nodp\nobr\nopr\norig\nphil\npl\npokrač\npol\nport\npozn\npř.kr\npř.n.l\npřel\npřeprac\npříl\npseud\npt\nred\nrepr\nresp\nrevid\nrkp\nroč\nroz\nrozš\nsamost\nsect\nsest\nseš\nsign\nsl\nsrv\nstol\nsv\nšk\nšk.ro\nšpan\ntab\nt.č\ntis\ntj\ntř\ntzv\nuniv\nuspoř\nvol\nvl.jm\nvs\nvyd\nvyobr\nzal\nzejm\nzkr\nzprac\nzvl\nn.p\nnapř\nnež\nMUDr\nabl\nabsol\nadj\nadv\nak\nak. sl\nakt\nalch\namer\nanat\nangl\nanglosas\narab\narch\narchit\narg\nastr\nastrol\natt\nbás\nbelg\nbibl\nbiol\nboh\nbot\nbulh\ncírk\ncsl\nč\nčas\nčes\ndat\nděj\ndep\ndět\ndial\ndór\ndopr\ndosl\nekon\nepic\netnonym\neufem\nf\nfam\nfem\nfil\nfilm\nform\nfot\nfr\nfut\nfyz\ngen\ngeogr\ngeol\ngeom\ngerm\ngram\nhebr\nherald\nhist\nhl\nhovor\nhud\nhut\nchcsl\nchem\nie\nimp\nimpf\nind\nindoevr\ninf\ninstr\ninterj\nión\niron\nit\nkanad\nkatalán\nklas\nkniž\nkomp\nkonj\n \nkonkr\nkř\nkuch\nlat\nlék\nles\nlid\nlit\nliturg\nlok\nlog\nm\nmat\nmeteor\nmetr\nmod\nms\nmysl\nn\nnáb\nnámoř\nneklas\nněm\nnesklon\nnom\nob\nobch\nobyč\nojed\nopt\npart\npas\npejor\npers\npf\npl\nplpf\n \npráv\nprep\npředl\npřivl\nr\nrcsl\nrefl\nreg\nrkp\nř\nřec\ns\nsamohl\nsg\nsl\nsouhl\nspec\nsrov\nstfr\nstřv\nstsl\nsubj\nsubst\nsuperl\nsv\nsz\ntáz\ntech\ntelev\nteol\ntrans\ntypogr\nvar\nvedl\nverb\nvl. jm\nvoj\nvok\nvůb\nvulg\nvýtv\nvztaž\nzahr\nzájm\nzast\nzejm\n \nzeměd\nzkr\nzř\nmj\ndl\natp\nsport\nMgr\nhorn\nMVDr\nJUDr\nRSDr\nBc\nPhDr\nThDr\nIng\naj\napod\nPharmDr\npomn\nev\nslang\nnprap\nodp\ndop\npol\nst\nstol\np. n. l\npřed n. l\nn. l\npř. Kr\npo Kr\npř. n. l\nodd\nRNDr\ntzv\natd\ntzn\nresp\ntj\np\nbr\nč. j\nčj\nč. p\nčp\na. s\ns. r. o\nspol. s r. o\np. o\ns. p\nv. o. s\nk. s\no. p. s\no. s\nv. r\nv z\nml\nvč\nkr\nmld\nhod\npopř\nap\nevent\nrus\nslov\nrum\nšvýc\nP. T\nzvl\nhor\ndol\nS.O.S"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.de",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\n#no german words end in single lower-case letters, so we throw those in too.\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\na\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n\n\n#Roman Numerals. A dot after one of these is not a sentence break in German.\nI\nII\nIII\nIV\nV\nVI\nVII\nVIII\nIX\nX\nXI\nXII\nXIII\nXIV\nXV\nXVI\nXVII\nXVIII\nXIX\nXX\ni\nii\niii\niv\nv\nvi\nvii\nviii\nix\nx\nxi\nxii\nxiii\nxiv\nxv\nxvi\nxvii\nxviii\nxix\nxx\n\n#Titles and Honorifics\nAdj\nAdm\nAdv\nAsst\nBart\nBldg\nBrig\nBros\nCapt\nCmdr\nCol\nComdr\nCon\nCorp\nCpl\nDR\nDr\nEns\nGen\nGov\nHon\nHosp\nInsp\nLt\nMM\nMR\nMRS\nMS\nMaj\nMessrs\nMlle\nMme\nMr\nMrs\nMs\nMsgr\nOp\nOrd\nPfc\nPh\nProf\nPvt\nRep\nReps\nRes\nRev\nRt\nSen\nSens\nSfc\nSgt\nSr\nSt\nSupt\nSurg\n\n#Misc symbols\nMio\nMrd\nbzw\nv\nvs\nusw\nd.h\nz.B\nu.a\netc\nMrd\nMwSt\nggf\nd.J\nD.h\nm.E\nvgl\nI.F\nz.T\nsogen\nff\nu.E\ng.U\ng.g.A\nc.-à-d\nBuchst\nu.s.w\nsog\nu.ä\nStd\nevtl\nZt\nChr\nu.U\no.ä\nLtd\nb.A\nz.Zt\nspp\nsen\nSA\nk.o\njun\ni.H.v\ndgl\ndergl\nCo\nzzt\nusf\ns.p.a\nDkr\nCorp\nbzgl\nBSE\n\n#Number indicators\n# add #NUMERIC_ONLY# after the word if it should ONLY be non-breaking when a 0-9 digit follows it\nNo\nNos\nArt\nNr\npp\nca\nCa\n\n#Ordinals are done with . in German - \"1.\" = \"1st\" in English\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n74\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n91\n92\n93\n94\n95\n96\n97\n98\n99\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.el",
    "content": "# Sigle letters in upper-case are usually abbreviations of names\nΑ\nΒ\nΓ\nΔ\nΕ\nΖ\nΗ\nΘ\nΙ\nΚ\nΛ\nΜ\nΝ\nΞ\nΟ\nΠ\nΡ\nΣ\nΤ\nΥ\nΦ\nΧ\nΨ\nΩ\n\n# Includes abbreviations for the Greek language compiled from various sources (Greek grammar books, Greek language related web content).\nΆθαν\nΈγχρ\nΈκθ\nΈσδ\nΈφ\nΌμ\nΑ΄Έσδρ\nΑ΄Έσδ\nΑ΄Βασ\nΑ΄Θεσ\nΑ΄Ιω\nΑ΄Κορινθ\nΑ΄Κορ\nΑ΄Μακκ\nΑ΄Μακ\nΑ΄Πέτρ\nΑ΄Πέτ\nΑ΄Παραλ\nΑ΄Πε\nΑ΄Σαμ\nΑ΄Τιμ\nΑ΄Χρον\nΑ΄Χρ\nΑ.Β.Α\nΑ.Β\nΑ.Ε\nΑ.Κ.Τ.Ο\nΑέθλ\nΑέτ\nΑίλ.Δ\nΑίλ.Τακτ\nΑίσ\nΑββακ\nΑβυδ\nΑβ\nΑγάκλ\nΑγάπ\nΑγάπ.Αμαρτ.Σ\nΑγάπ.Γεωπ\nΑγαθάγγ\nΑγαθήμ\nΑγαθιν\nΑγαθοκλ\nΑγαθρχ\nΑγαθ\nΑγαθ.Ιστ\nΑγαλλ\nΑγαπητ\nΑγγ\nΑγησ\nΑγλ\nΑγορ.Κ\nΑγρο.Κωδ\nΑγρ.Εξ\nΑγρ.Κ\nΑγ.Γρ\nΑδριαν\nΑδρ\nΑετ\nΑθάν\nΑθήν\nΑθήν.Επιγρ\nΑθήν.Επιτ\nΑθήν.Ιατρ\nΑθήν.Μηχ\nΑθανάσ\nΑθαν\nΑθηνί\nΑθηναγ\nΑθηνόδ\nΑθ\nΑθ.Αρχ\nΑιλ\nΑιλ.Επιστ\nΑιλ.ΖΙ\nΑιλ.ΠΙ\nΑιλ.απ\nΑιμιλ\nΑιν.Γαζ\nΑιν.Τακτ\nΑισχίν\nΑισχίν.Επιστ\nΑισχ\nΑισχ.Αγαμ\nΑισχ.Αγ\nΑισχ.Αλ\nΑισχ.Ελεγ\nΑισχ.Επτ.Θ\nΑισχ.Ευμ\nΑισχ.Ικέτ\nΑισχ.Ικ\nΑισχ.Περσ\nΑισχ.Προμ.Δεσμ\nΑισχ.Πρ\nΑισχ.Χοηφ\nΑισχ.Χο\nΑισχ.απ\nΑιτΕ\nΑιτ\nΑλκ\nΑλχιας\nΑμ.Π.Ο\nΑμβ\nΑμμών\nΑμ.\nΑν.Πειθ.Συμβ.Δικ\nΑνακρ\nΑνακ\nΑναμν.Τόμ\nΑναπλ\nΑνδ\nΑνθλγος\nΑνθστης\nΑντισθ\nΑνχης\nΑν\nΑποκ\nΑπρ\nΑπόδ\nΑπόφ\nΑπόφ.Νομ\nΑπ\nΑπ.Δαπ\nΑπ.Διατ\nΑπ.Επιστ\nΑριθ\nΑριστοτ\nΑριστοφ\nΑριστοφ.Όρν\nΑριστοφ.Αχ\nΑριστοφ.Βάτρ\nΑριστοφ.Ειρ\nΑριστοφ.Εκκλ\nΑριστοφ.Θεσμ\nΑριστοφ.Ιππ\nΑριστοφ.Λυσ\nΑριστοφ.Νεφ\nΑριστοφ.Πλ\nΑριστοφ.Σφ\nΑριστ\nΑριστ.Αθ.Πολ\nΑριστ.Αισθ\nΑριστ.Αν.Πρ\nΑριστ.Ζ.Ι\nΑριστ.Ηθ.Ευδ\nΑριστ.Ηθ.Νικ\nΑριστ.Κατ\nΑριστ.Μετ\nΑριστ.Πολ\nΑριστ.Φυσιογν\nΑριστ.Φυσ\nΑριστ.Ψυχ\nΑριστ.Ρητ\nΑρμεν\nΑρμ\nΑρχ.Εκ.Καν.Δ\nΑρχ.Ευβ.Μελ\nΑρχ.Ιδ.Δ\nΑρχ.Νομ\nΑρχ.Ν\nΑρχ.Π.Ε\nΑρ\nΑρ.Φορ.Μητρ\nΑσμ\nΑσμ.ασμ\nΑστ.Δ\nΑστ.Χρον\nΑσ\nΑτομ.Γνωμ\nΑυγ\nΑφρ\nΑχ.Νομ\nΑ\nΑ.Εγχ.Π\nΑ.Κ.΄Υδρας\nΒ΄Έσδρ\nΒ΄Έσδ\nΒ΄Βασ\nΒ΄Θεσ\nΒ΄Ιω\nΒ΄Κορινθ\nΒ΄Κορ\nΒ΄Μακκ\nΒ΄Μακ\nΒ΄Πέτρ\nΒ΄Πέτ\nΒ΄Πέ\nΒ΄Παραλ\nΒ΄Σαμ\nΒ΄Τιμ\nΒ΄Χρον\nΒ΄Χρ\nΒ.Ι.Π.Ε\nΒ.Κ.Τ\nΒ.Κ.Ψ.Β\nΒ.Μ\nΒ.Ο.Α.Κ\nΒ.Ο.Α\nΒ.Ο.Δ\nΒίβλ\nΒαρ\nΒεΘ\nΒι.Περ\nΒιπερ\nΒιργ\nΒλγ\nΒούλ\nΒρ\nΓ΄Βασ\nΓ΄Μακκ\nΓΕΝμλ\nΓέν\nΓαλ\nΓεν\nΓλ\nΓν.Ν.Σ.Κρ\nΓνωμ\nΓν\nΓράμμ\nΓρηγ.Ναζ\nΓρηγ.Νύσ\nΓ Νοσ\nΓ' Ογκολ\nΓ.Ν\nΔ΄Βασ\nΔ.Β\nΔ.Δίκη\nΔ.Δίκ\nΔ.Ε.Σ\nΔ.Ε.Φ.Α\nΔ.Ε.Φ\nΔ.Εργ.Ν\nΔαμ\nΔαμ.μνημ.έργ\nΔαν\nΔασ.Κ\nΔεκ\nΔελτ.Δικ.Ε.Τ.Ε\nΔελτ.Νομ\nΔελτ.Συνδ.Α.Ε\nΔερμ\nΔευτ\nΔεύτ\nΔημοσθ\nΔημόκρ\nΔι.Δικ\nΔιάτ\nΔιαιτ.Απ\nΔιαιτ\nΔιαρκ.Στρατ\nΔικ\nΔιοίκ.Πρωτ\nΔιοικΔνη\nΔιοικ.Εφ\nΔιον.Αρ\nΔιόρθ.Λαθ\nΔ.κ.Π\nΔνη\nΔν\nΔογμ.Όρος\nΔρ\nΔ.τ.Α\nΔτ\nΔωδΝομ\nΔ.Περ\nΔ.Στρ\nΕΔΠολ\nΕΕυρΚ\nΕΙΣ\nΕΝαυτΔ\nΕΣΑμΕΑ\nΕΣΘ\nΕΣυγκΔ\nΕΤρΑξΧρΔ\nΕ.Φ.Ε.Τ\nΕ.Φ.Ι\nΕ.Φ.Ο.Επ.Α\nΕβδ\nΕβρ\nΕγκύκλ.Επιστ\nΕγκ\nΕε.Αιγ\nΕθν.Κ.Τ\nΕθν\nΕιδ.Δικ.Αγ.Κακ\nΕικ\nΕιρ.Αθ\nΕιρην.Αθ\nΕιρην\nΈλεγχ\nΕιρ\nΕισ.Α.Π\nΕισ.Ε\nΕισ.Ν.Α.Κ\nΕισ.Ν.Κ.Πολ.Δ\nΕισ.Πρωτ\nΕισηγ.Έκθ\nΕισ\nΕκκλ\nΕκκ\nΕκ\nΕλλ.Δνη\nΕν.Ε\nΕξ\nΕπ.Αν\nΕπ.Εργ.Δ\nΕπ.Εφ\nΕπ.Κυπ.Δ\nΕπ.Μεσ.Αρχ\nΕπ.Νομ\nΕπίκτ\nΕπίκ\nΕπι.Δ.Ε\nΕπιθ.Ναυτ.Δικ\nΕπικ\nΕπισκ.Ε.Δ\nΕπισκ.Εμπ.Δικ\nΕπιστ.Επετ.Αρμ\nΕπιστ.Επετ\nΕπιστ.Ιερ\nΕπιτρ.Προστ.Συνδ.Στελ\nΕπιφάν\nΕπτ.Εφ\nΕπ.Ιρ\nΕπ.Ι\nΕργ.Ασφ.Νομ\nΕρμ.Α.Κ\nΕρμη.Σ\nΕσθ\nΕσπερ\nΕτρ.Δ\nΕυκλ\nΕυρ.Δ.Δ.Α\nΕυρ.Σ.Δ.Α\nΕυρ.ΣτΕ\nΕυρατόμ\nΕυρ.Άλκ\nΕυρ.Ανδρομ\nΕυρ.Βάκχ\nΕυρ.Εκ\nΕυρ.Ελ\nΕυρ.Ηλ\nΕυρ.Ηρακ\nΕυρ.Ηρ\nΕυρ.Ηρ.Μαιν\nΕυρ.Ικέτ\nΕυρ.Ιππόλ\nΕυρ.Ιφ.Α\nΕυρ.Ιφ.Τ\nΕυρ.Ι.Τ\nΕυρ.Κύκλ\nΕυρ.Μήδ\nΕυρ.Ορ\nΕυρ.Ρήσ\nΕυρ.Τρωάδ\nΕυρ.Φοίν\nΕφ.Αθ\nΕφ.Εν\nΕφ.Επ\nΕφ.Θρ\nΕφ.Θ\nΕφ.Ι\nΕφ.Κερ\nΕφ.Κρ\nΕφ.Λ\nΕφ.Ν\nΕφ.Πατ\nΕφ.Πειρ\nΕφαρμ.Δ.Δ\nΕφαρμ\nΕφεσ\nΕφημ\nΕφ\nΖαχ\nΖιγ\nΖυ\nΖχ\nΗΕ.Δ\nΗμερ\nΗράκλ\nΗροδ\nΗσίοδ\nΗσ\nΗ.Ε.Γ\nΘΗΣ\nΘΡ\nΘαλ\nΘεοδ\nΘεοφ\nΘεσ\nΘεόδ.Μοψ\nΘεόκρ\nΘεόφιλ\nΘουκ\nΘρ\nΘρ.Ε\nΘρ.Ιερ\nΘρ.Ιρ\nΙακ\nΙαν\nΙβ\nΙδθ\nΙδ\nΙεζ\nΙερ\nΙζ\nΙησ\nΙησ.Ν\nΙκ\nΙλ\nΙν\nΙουδ\nΙουστ\nΙούδα\nΙούλ\nΙούν\nΙπποκρ\nΙππόλ\nΙρ\nΙσίδ.Πηλ\nΙσοκρ\nΙσ.Ν\nΙωβ\nΙωλ\nΙων\nΙω\nΚΟΣ\nΚΟ.ΜΕ.ΚΟΝ\nΚΠοινΔ\nΚΠολΔ\nΚαΒ\nΚαλ\nΚαλ.Τέχν\nΚανΒ\nΚαν.Διαδ\nΚατάργ\nΚλ\nΚοινΔ\nΚολσ\nΚολ\nΚον\nΚορ\nΚος\nΚριτΕπιθ\nΚριτΕ\nΚριτ\nΚρ\nΚτΒ\nΚτΕ\nΚτΠ\nΚυβ\nΚυπρ\nΚύριλ.Αλεξ\nΚύριλ.Ιερ\nΛεβ\nΛεξ.Σουίδα\nΛευϊτ\nΛευ\nΛκ\nΛογ\nΛουκΑμ\nΛουκιαν\nΛουκ.Έρωτ\nΛουκ.Ενάλ.Διάλ\nΛουκ.Ερμ\nΛουκ.Εταιρ.Διάλ\nΛουκ.Ε.Δ\nΛουκ.Θε.Δ\nΛουκ.Ικ.\nΛουκ.Ιππ\nΛουκ.Λεξιφ\nΛουκ.Μεν\nΛουκ.Μισθ.Συν\nΛουκ.Ορχ\nΛουκ.Περ\nΛουκ.Συρ\nΛουκ.Τοξ\nΛουκ.Τυρ\nΛουκ.Φιλοψ\nΛουκ.Φιλ\nΛουκ.Χάρ\nΛουκ.\nΛουκ.Αλ\nΛοχ\nΛυδ\nΛυκ\nΛυσ\nΛωζ\nΛ1\nΛ2\nΜΟΕφ\nΜάρκ\nΜέν\nΜαλ\nΜατθ\nΜα\nΜιχ\nΜκ\nΜλ\nΜμ\nΜον.Δ.Π\nΜον.Πρωτ\nΜον\nΜρ\nΜτ\nΜχ\nΜ.Βασ\nΜ.Πλ\nΝΑ\nΝαυτ.Χρον\nΝα\nΝδικ\nΝεεμ\nΝε\nΝικ\nΝκΦ\nΝμ\nΝοΒ\nΝομ.Δελτ.Τρ.Ελ\nΝομ.Δελτ\nΝομ.Σ.Κ\nΝομ.Χρ\nΝομ\nΝομ.Διεύθ\nΝοσ\nΝτ\nΝόσων\nΝ1\nΝ2\nΝ3\nΝ4\nΝtot\nΞενοφ\nΞεν\nΞεν.Ανάβ\nΞεν.Απολ\nΞεν.Απομν\nΞεν.Απομ\nΞεν.Ελλ\nΞεν.Ιέρ\nΞεν.Ιππαρχ\nΞεν.Ιππ\nΞεν.Κυρ.Αν\nΞεν.Κύρ.Παιδ\nΞεν.Κ.Π\nΞεν.Λακ.Πολ\nΞεν.Οικ\nΞεν.Προσ\nΞεν.Συμπόσ\nΞεν.Συμπ\nΟ΄\nΟβδ\nΟβ\nΟικΕ\nΟικ\nΟικ.Πατρ\nΟικ.Σύν.Βατ\nΟλομ\nΟλ\nΟλ.Α.Π\nΟμ.Ιλ\nΟμ.Οδ\nΟπΤοιχ\nΟράτ\nΟρθ\nΠΡΟ.ΠΟ\nΠίνδ\nΠίνδ.Ι\nΠίνδ.Νεμ\nΠίνδ.Ν\nΠίνδ.Ολ\nΠίνδ.Παθ\nΠίνδ.Πυθ\nΠίνδ.Π\nΠαγΝμλγ\nΠαν\nΠαρμ\nΠαροιμ\nΠαρ\nΠαυσ\nΠειθ.Συμβ\nΠειρΝ\nΠελ\nΠεντΣτρ\nΠεντ\nΠεντ.Εφ\nΠερΔικ\nΠερ.Γεν.Νοσ\nΠετ\nΠλάτ\nΠλάτ.Αλκ\nΠλάτ.Αντ\nΠλάτ.Αξίοχ\nΠλάτ.Απόλ\nΠλάτ.Γοργ\nΠλάτ.Ευθ\nΠλάτ.Θεαίτ\nΠλάτ.Κρατ\nΠλάτ.Κριτ\nΠλάτ.Λύσ\nΠλάτ.Μεν\nΠλάτ.Νόμ\nΠλάτ.Πολιτ\nΠλάτ.Πολ\nΠλάτ.Πρωτ\nΠλάτ.Σοφ.\nΠλάτ.Συμπ\nΠλάτ.Τίμ\nΠλάτ.Φαίδρ\nΠλάτ.Φιλ\nΠλημ\nΠλούτ\nΠλούτ.Άρατ\nΠλούτ.Αιμ\nΠλούτ.Αλέξ\nΠλούτ.Αλκ\nΠλούτ.Αντ\nΠλούτ.Αρτ\nΠλούτ.Ηθ\nΠλούτ.Θεμ\nΠλούτ.Κάμ\nΠλούτ.Καίσ\nΠλούτ.Κικ\nΠλούτ.Κράσ\nΠλούτ.Κ\nΠλούτ.Λυκ\nΠλούτ.Μάρκ\nΠλούτ.Μάρ\nΠλούτ.Περ\nΠλούτ.Ρωμ\nΠλούτ.Σύλλ\nΠλούτ.Φλαμ\nΠλ\nΠοιν.Δικ\nΠοιν.Δ\nΠοιν.Ν\nΠοιν.Χρον\nΠοιν.Χρ\nΠολ.Δ\nΠολ.Πρωτ\nΠολ\nΠολ.Μηχ\nΠολ.Μ\nΠρακτ.Αναθ\nΠρακτ.Ολ\nΠραξ\nΠρμ\nΠρξ\nΠρωτ\nΠρ\nΠρ.Αν\nΠρ.Λογ\nΠταισμ\nΠυρ.Καλ\nΠόλη\nΠ.Δ\nΠ.Δ.Άσμ\nΡΜ.Ε\nΡθ\nΡμ\nΡωμ\nΣΠλημ\nΣαπφ\nΣειρ\nΣολ\nΣοφ\nΣοφ.Αντιγ\nΣοφ.Αντ\nΣοφ.Αποσ\nΣοφ.Απ\nΣοφ.Ηλέκ\nΣοφ.Ηλ\nΣοφ.Οιδ.Κολ\nΣοφ.Οιδ.Τύρ\nΣοφ.Ο.Τ\nΣοφ.Σειρ\nΣοφ.Σολ\nΣοφ.Τραχ\nΣοφ.Φιλοκτ\nΣρ\nΣ.τ.Ε\nΣ.τ.Π\nΣτρ.Π.Κ\nΣτ.Ευρ\nΣυζήτ\nΣυλλ.Νομολ\nΣυλ.Νομ\nΣυμβΕπιθ\nΣυμπ.Ν\nΣυνθ.Αμ\nΣυνθ.Ε.Ε\nΣυνθ.Ε.Κ\nΣυνθ.Ν\nΣφν\nΣφ\nΣφ.Σλ\nΣχ.Πολ.Δ\nΣχ.Συντ.Ε\nΣωσ\nΣύντ\nΣ.Πληρ\nΤΘ\nΤΣ.Δ\nΤίτ\nΤβ\nΤελ.Ενημ\nΤελ.Κ\nΤερτυλ\nΤιμ\nΤοπ.Α\nΤρ.Ο\nΤριμ\nΤριμ.Πλ\nΤρ.Πλημ\nΤρ.Π.Δ\nΤ.τ.Ε\nΤτ\nΤωβ\nΥγ\nΥπερ\nΥπ\nΥ.Γ\nΦιλήμ\nΦιλιπ\nΦιλ\nΦλμ\nΦλ\nΦορ.Β\nΦορ.Δ.Ε\nΦορ.Δνη\nΦορ.Δ\nΦορ.Επ\nΦώτ\nΧρ.Ι.Δ\nΧρ.Ιδ.Δ\nΧρ.Ο\nΧρυσ\nΨήφ\nΨαλμ\nΨαλ\nΨλ\nΩριγ\nΩσ\nΩ.Ρ.Λ\nάγν\nάγν.ετυμολ\nάγ\nάκλ\nάνθρ\nάπ\nάρθρ\nάρν\nάρ\nάτ\nάψ\nά\nέκδ\nέκφρ\nέμψ\nένθ.αν\nέτ\nέ.α\nίδ\nαβεστ\nαβησσ\nαγγλ\nαγγ\nαδημ\nαεροναυτ\nαερον\nαεροπ\nαθλητ\nαθλ\nαθροιστ\nαιγυπτ\nαιγ\nαιτιολ\nαιτ\nαι\nακαδ\nακκαδ\nαλβ\nαλλ\nαλφαβητ\nαμα\nαμερικ\nαμερ\nαμετάβ\nαμτβ\nαμφιβ\nαμφισβ\nαμφ\nαμ\nανάλ\nανάπτ\nανάτ\nαναβ\nαναδαν\nαναδιπλασ\nαναδιπλ\nαναδρ\nαναλ\nαναν\nανασυλλ\nανατολ\nανατομ\nανατυπ\nανατ\nαναφορ\nαναφ\nανα.ε\nανδρων\nανθρωπολ\nανθρωπ\nανθ\nανομ\nαντίτ\nαντδ\nαντιγρ\nαντιθ\nαντικ\nαντιμετάθ\nαντων\nαντ\nανωτ\nανόργ\nανών\nαορ\nαπαρέμφ\nαπαρφ\nαπαρχ\nαπαρ\nαπλολ\nαπλοπ\nαποβ\nαποηχηροπ\nαποθ\nαποκρυφ\nαποφ\nαπρμφ\nαπρφ\nαπρόσ\nαπόδ\nαπόλ\nαπόσπ\nαπόφ\nαραβοτουρκ\nαραβ\nαραμ\nαρβαν\nαργκ\nαριθμτ\nαριθμ\nαριθ\nαρκτικόλ\nαρκ\nαρμεν\nαρμ\nαρνητ\nαρσ\nαρχαιολ\nαρχιτεκτ\nαρχιτ\nαρχκ\nαρχ\nαρωμουν\nαρωμ\nαρ\nαρ.μετρ\nαρ.φ\nασσυρ\nαστρολ\nαστροναυτ\nαστρον\nαττ\nαυστραλ\nαυτοπ\nαυτ\nαφγαν\nαφηρ\nαφομ\nαφρικ\nαχώρ\nαόρ\nα.α\nα/α\nα0\nβαθμ\nβαθ\nβαπτ\nβασκ\nβεβαιωτ\nβεβ\nβεδ\nβενετ\nβεν\nβερβερ\nβιβλγρ\nβιολ\nβιομ\nβιοχημ\nβιοχ\nβλάχ\nβλ\nβλ.λ\nβοταν\nβοτ\nβουλγαρ\nβουλγ\nβούλ\nβραζιλ\nβρετον\nβόρ\nγαλλ\nγενικότ\nγενοβ\nγεν\nγερμαν\nγερμ\nγεωγρ\nγεωλ\nγεωμετρ\nγεωμ\nγεωπ\nγεωργ\nγλυπτ\nγλωσσολ\nγλωσσ\nγλ\nγνμδ\nγνμ\nγνωμ\nγοτθ\nγραμμ\nγραμ\nγρμ\nγρ\nγυμν\nδίδες\nδίκ\nδίφθ\nδαν\nδεικτ\nδεκατ\nδηλ\nδημογρ\nδημοτ\nδημώδ\nδημ\nδιάγρ\nδιάκρ\nδιάλεξ\nδιάλ\nδιάσπ\nδιαλεκτ\nδιατρ\nδιαφ\nδιαχ\nδιδα\nδιεθν\nδιεθ\nδικον\nδιστ\nδισύλλ\nδισ\nδιφθογγοπ\nδογμ\nδολ\nδοτ\nδρμ\nδρχ\nδρ(α)\nδωρ\nδ\nεβρ\nεγκλπ\nεδ\nεθνολ\nεθν\nειδικότ\nειδ\nειδ.β\nεικ\nειρ\nεισ\nεκατοστμ\nεκατοστ\nεκατστ.2\nεκατστ.3\nεκατ\nεκδ\nεκκλησ\nεκκλ\nεκ\nελλην\nελλ\nελνστ\nελπ\nεμβ\nεμφ\nεναλλ\nενδ\nενεργ\nενεστ\nενικ\nενν\nεν\nεξέλ\nεξακολ\nεξομάλ\nεξ\nεο\nεπέκτ\nεπίδρ\nεπίθ\nεπίρρ\nεπίσ\nεπαγγελμ\nεπανάλ\nεπανέκδ\nεπιθ\nεπικ\nεπιμ\nεπιρρ\nεπιστ\nεπιτατ\nεπιφ\nεπών\nεπ\nεργ\nερμ\nερρινοπ\nερωτ\nετρουσκ\nετυμ\nετ\nευφ\nευχετ\nεφ\nεύχρ\nε.α\nε/υ\nε0\nζωγρ\nζωολ\nηθικ\nηθ\nηλεκτρολ\nηλεκτρον\nηλεκτρ\nημίτ\nημίφ\nημιφ\nηχηροπ\nηχηρ\nηχομιμ\nηχ\nη\nθέατρ\nθεολ\nθετ\nθηλ\nθρακ\nθρησκειολ\nθρησκ\nθ\nιαπων\nιατρ\nιδιωμ\nιδ\nινδ\nιραν\nισπαν\nιστορ\nιστ\nισχυροπ\nιταλ\nιχθυολ\nιων\nκάτ\nκαθ\nκακοσ\nκαν\nκαρ\nκατάλ\nκατατ\nκατωτ\nκατ\nκα\nκελτ\nκεφ\nκινεζ\nκινημ\nκλητ\nκλιτ\nκλπ\nκλ\nκν\nκοινωνιολ\nκοινων\nκοπτ\nκουτσοβλαχ\nκουτσοβλ\nκπ\nκρ.γν\nκτγ\nκτην\nκτητ\nκτλ\nκτ\nκυριολ\nκυρ\nκύρ\nκ\nκ.ά\nκ.ά.π\nκ.α\nκ.εξ\nκ.επ\nκ.ε\nκ.λπ\nκ.λ.π\nκ.ού.κ\nκ.ο.κ\nκ.τ.λ\nκ.τ.τ\nκ.τ.ό\nλέξ\nλαογρ\nλαπ\nλατιν\nλατ\nλαϊκότρ\nλαϊκ\nλετ\nλιθ\nλογιστ\nλογοτ\nλογ\nλουβ\nλυδ\nλόγ\nλ\nλ.χ\nμέλλ\nμέσ\nμαθημ\nμαθ\nμαιευτ\nμαλαισ\nμαλτ\nμαμμων\nμεγεθ\nμεε\nμειωτ\nμελ\nμεξ\nμεσν\nμεσογ\nμεσοπαθ\nμεσοφ\nμετάθ\nμεταβτ\nμεταβ\nμετακ\nμεταπλ\nμεταπτωτ\nμεταρ\nμεταφορ\nμετβ\nμετεπιθ\nμετεπιρρ\nμετεωρολ\nμετεωρ\nμετον\nμετουσ\nμετοχ\nμετρ\nμετ\nμητρων\nμηχανολ\nμηχ\nμικροβιολ\nμογγολ\nμορφολ\nμουσ\nμπενελούξ\nμσνλατ\nμσν\nμτβ\nμτγν\nμτγ\nμτφρδ\nμτφρ\nμτφ\nμτχ\nμυθ\nμυκην\nμυκ\nμφ\nμ\nμ.ε\nμ.μ\nμ.π.ε\nμ.π.π\nμ0\nναυτ\nνεοελλ\nνεολατιν\nνεολατ\nνεολ\nνεότ\nνλατ\nνομ\nνορβ\nνοσ\nνότ\nν\nξ.λ\nοικοδ\nοικολ\nοικον\nοικ\nολλανδ\nολλ\nομηρ\nομόρρ\nονομ\nον\nοπτ\nορθογρ\nορθ\nοριστ\nορυκτολ\nορυκτ\nορ\nοσετ\nοσκ\nουαλ\nουγγρ\nουδ\nουσιαστικοπ\nουσιαστ\nουσ\nπίν\nπαθητ\nπαθολ\nπαθ\nπαιδ\nπαλαιοντ\nπαλαιότ\nπαλ\nπαππων\nπαράγρ\nπαράγ\nπαράλλ\nπαράλ\nπαραγ\nπαρακ\nπαραλ\nπαραπ\nπαρατ\nπαρβ\nπαρετυμ\nπαροξ\nπαρων\nπαρωχ\nπαρ\nπαρ.φρ\nπατριδων\nπατρων\nπβ\nπεριθ\nπεριλ\nπεριφρ\nπερσ\nπερ\nπιθ\nπληθ\nπληροφ\nποδ\nποιητ\nπολιτ\nπολλαπλ\nπολ\nπορτογαλ\nπορτ\nποσ\nπρακριτ\nπρβλ\nπρβ\nπργ\nπρκμ\nπρκ\nπρλ\nπροέλ\nπροβηγκ\nπροελλ\nπροηγ\nπροθεμ\nπροπαραλ\nπροπαροξ\nπροπερισπ\nπροσαρμ\nπροσηγορ\nπροσταχτ\nπροστ\nπροσφών\nπροσ\nπροτακτ\nπροτ.Εισ\nπροφ\nπροχωρ\nπρτ\nπρόθ\nπρόσθ\nπρόσ\nπρότ\nπρ\nπρ.Εφ\nπτ\nπυ\nπ\nπ.Χ\nπ.μ\nπ.χ\nρήμ\nρίζ\nρηματ\nρητορ\nριν\nρουμ\nρωμ\nρωσ\nρ\nσανσκρ\nσαξ\nσελ\nσερβοκρ\nσερβ\nσημασιολ\nσημδ\nσημειολ\nσημερ\nσημιτ\nσημ\nσκανδ\nσκυθ\nσκωπτ\nσλαβ\nσλοβ\nσουηδ\nσουμερ\nσουπ\nσπάν\nσπανιότ\nσπ\nσσ\nστατ\nστερ\nστιγμ\nστιχ\nστρέμ\nστρατιωτ\nστρατ\nστ\nσυγγ\nσυγκρ\nσυγκ\nσυμπερ\nσυμπλεκτ\nσυμπλ\nσυμπροφ\nσυμφυρ\nσυμφ\nσυνήθ\nσυνίζ\nσυναίρ\nσυναισθ\nσυνδετ\nσυνδ\nσυνεκδ\nσυνηρ\nσυνθετ\nσυνθ\nσυνοπτ\nσυντελ\nσυντομογρ\nσυντ\nσυν\nσυρ\nσχημ\nσχ\nσύγκρ\nσύμπλ\nσύμφ\nσύνδ\nσύνθ\nσύντμ\nσύντ\nσ\nσ.π\nσ/β\nτακτ\nτελ\nτετρ\nτετρ.μ\nτεχνλ\nτεχνολ\nτεχν\nτεύχ\nτηλεπικ\nτηλεόρ\nτιμ\nτιμ.τομ\nτοΣ\nτον\nτοπογρ\nτοπων\nτοπ\nτοσκ\nτουρκ\nτοχ\nτριτοπρόσ\nτροποπ\nτροπ\nτσεχ\nτσιγγ\nττ\nτυπ\nτόμ\nτόνν\nτ\nτ.μ\nτ.χλμ\nυβρ\nυπερθ\nυπερσ\nυπερ\nυπεύθ\nυποθ\nυποκορ\nυποκ\nυποσημ\nυποτ\nυποφ\nυποχωρ\nυπόλ\nυπόχρ\nυπ\nυστλατ\nυψόμ\nυψ\nφάκ\nφαρμακολ\nφαρμ\nφιλολ\nφιλοσ\nφιλοτ\nφινλ\nφοινικ\nφράγκ\nφρανκον\nφριζ\nφρ\nφυλλ\nφυσιολ\nφυσ\nφωνηεντ\nφωνητ\nφωνολ\nφων\nφωτογρ\nφ\nφ.τ.μ\nχαμιτ\nχαρτόσ\nχαρτ\nχασμ\nχαϊδ\nχγφ\nχειλ\nχεττ\nχημ\nχιλ\nχλγρ\nχλγ\nχλμ\nχλμ.2\nχλμ.3\nχλσγρ\nχλστγρ\nχλστμ\nχλστμ.2\nχλστμ.3\nχλ\nχργρ\nχρημ\nχρον\nχρ\nχφ\nχ.ε\nχ.κ\nχ.ο\nχ.σ\nχ.τ\nχ.χ\nψευδ\nψυχαν\nψυχιατρ\nψυχολ\nψυχ\nωκεαν\nόμ\nόν\nόπ.παρ\nόπ.π\nό.π\nύψ\n1Βσ\n1Εσ\n1Θσ\n1Ιν\n1Κρ\n1Μκ\n1Πρ\n1Πτ\n1Τμ\n2Βσ\n2Εσ\n2Θσ\n2Ιν\n2Κρ\n2Μκ\n2Πρ\n2Πτ\n2Τμ\n3Βσ\n3Ιν\n3Μκ\n4Βσ\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.en",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nAdj\nAdm\nAdv\nAsst\nBart\nBldg\nBrig\nBros\nCapt\nCmdr\nCol\nComdr\nCon\nCorp\nCpl\nDR\nDr\nDrs\nEns\nGen\nGov\nHon\nHr\nHosp\nInsp\nLt\nMM\nMR\nMRS\nMS\nMaj\nMessrs\nMlle\nMme\nMr\nMrs\nMs\nMsgr\nOp\nOrd\nPfc\nPh\nProf\nPvt\nRep\nReps\nRes\nRev\nRt\nSen\nSens\nSfc\nSgt\nSr\nSt\nSupt\nSurg\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall into this category - it sometimes ends a sentence)\nv\nvs\ni.e\nrev\ne.g\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNo #NUMERIC_ONLY# \nNos\nArt #NUMERIC_ONLY#\nNr\npp #NUMERIC_ONLY#\n\n#month abbreviations\nJan\nFeb\nMar\nApr\n#May is a full word\nJun\nJul\nAug\nSep\nOct\nNov\nDec\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.es",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender\n#usually upper case letters are initials in a name\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n\n# Period-final abbreviation list from http://www.ctspanish.com/words/abbreviations.htm\n\nA.C\nApdo\nAv\nBco\nCC.AA\nDa\nDep\nDn\nDr\nDra\nEE.UU\nExcmo\nFF.CC\nFil \nGral\nJ.C\nLet\nLic\nN.B\nP.D\nP.V.P\nProf\nPts\nRte\nS.A\nS.A.R\nS.E\nS.L\nS.R.C\nSr\nSra\nSrta\nSta\nSto\nT.V.E\nTel\nUd\nUds\nV.B\nV.E\nVd\nVds\na/c\nadj\nadmón\nafmo\napdo\nav\nc\nc.f\nc.g\ncap\ncm\ncta\ndcha\ndoc\nej\nentlo\nesq\netc\nf.c\ngr \ngrs\nizq\nkg\nkm\nmg\nmm\nnúm\nnúm\np\np.a\np.ej\nptas\npág \npágs\npág\npágs\nq.e.g.e\nq.e.s.m\ns\ns.s.s\nvid\nvol\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.fi",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT\n#indicate an end-of-sentence marker.  Special cases are included for prefixes\n#that ONLY appear before 0-9 numbers.\n\n#This list is compiled from omorfi <http://code.google.com/p/omorfi> database\n#by Tommi A Pirinen.\n\n\n#any single upper case letter  followed by a period is not a sentence ender\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nÅ\nÄ\nÖ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nalik\nalil\namir\napul\napul.prof\narkkit\nass\nassist\ndipl\ndipl.arkkit\ndipl.ekon\ndipl.ins\ndipl.kielenk\ndipl.kirjeenv\ndipl.kosm\ndipl.urk\ndos\nerikoiseläinl\nerikoishammasl\nerikoisl\nerikoist\nev.luutn\nevp\nfil\nft\nhallinton\nhallintot\nhammaslääket\njatk\njääk\nkansaned\nkapt\nkapt.luutn\nkenr\nkenr.luutn\nkenr.maj\nkers\nkirjeenv\nkom\nkom.kapt\nkomm\nkonst\nkorpr\nluutn\nmaist\nmaj\nMr\nMrs\nMs\nM.Sc\nneuv\nnimim\nPh.D\nprof\npuh.joht\npääll\nres\nsan\nsiht\nsuom\nsähköp\nsäv\ntoht\ntoim\ntoim.apul\ntoim.joht\ntoim.siht\ntuom\nups\nvänr\nvääp\nye.ups\nylik\nylil\nylim\nylimatr\nyliop\nyliopp\nylip\nyliv\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall\n#into this category - it sometimes ends a sentence)\ne.g\nent\nesim\nhuom\ni.e\nilm\nl\nmm\nmyöh\nnk\nnyk\npar\npo\nt\nv\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.fr",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n#\n#any single upper case letter  followed by a period is not a sentence ender\n#usually upper case letters are initials in a name\n#no French words end in single lower-case letters, so we throw those in too?\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n#a\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n\n# Period-final abbreviation list for French\nA.C.N\nA.M\nart\nann\napr\nav\nauj\nlib\nB.P\nboul\nca\nc.-à-d\ncf\nch.-l\nchap\ncontr\nC.P.I\nC.Q.F.D\nC.N\nC.N.S\nC.S\ndir\néd\ne.g\nenv\nal\netc\nE.V\nex\nfasc\nfém\nfig\nfr\nhab\nibid\nid\ni.e\ninf\nLL.AA\nLL.AA.II\nLL.AA.RR\nLL.AA.SS\nL.D\nLL.EE\nLL.MM\nLL.MM.II.RR\nloc.cit\nmasc\nMM\nms\nN.B\nN.D.A\nN.D.L.R\nN.D.T\nn/réf\nNN.SS\nN.S\nN.D\nN.P.A.I\np.c.c\npl\npp\np.ex\np.j\nP.S\nR.A.S\nR.-V\nR.P\nR.I.P\nSS\nS.S\nS.A\nS.A.I\nS.A.R\nS.A.S\nS.E\nsec\nsect\nsing\nS.M\nS.M.I.R\nsq\nsqq\nsuiv\nsup\nsuppl\ntél\nT.S.V.P\nvb\nvol\nvs\nX.O\nZ.I\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.ga",
    "content": "\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nÁ\nÉ\nÍ\nÓ\nÚ\n\nUacht\nDr\nB.Arch\n\nm.sh\n.i\nCo\nCf\ncf\ni.e\nr\nChr\nlch #NUMERIC_ONLY#\nlgh #NUMERIC_ONLY#\nuimh #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.hu",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nÁ\nÉ\nÍ\nÓ\nÖ\nŐ\nÚ\nÜ\nŰ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nDr\ndr\nkb\nKb\nvö\nVö\npl\nPl\nca\nCa\nmin\nMin\nmax\nMax\nún\nÚn\nprof\nProf\nde\nDe\ndu\nDu\nSzt\nSt\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\n\n# Month name abbreviations\njan #NUMERIC_ONLY#\nJan #NUMERIC_ONLY#\nFeb #NUMERIC_ONLY#\nfeb #NUMERIC_ONLY#\nmárc #NUMERIC_ONLY#\nMárc #NUMERIC_ONLY#\nápr #NUMERIC_ONLY#\nÁpr #NUMERIC_ONLY#\nmáj #NUMERIC_ONLY#\nMáj #NUMERIC_ONLY#\njún #NUMERIC_ONLY#\nJún #NUMERIC_ONLY#\nJúl #NUMERIC_ONLY#\njúl #NUMERIC_ONLY#\naug #NUMERIC_ONLY#\nAug #NUMERIC_ONLY#\nSzept #NUMERIC_ONLY#\nszept #NUMERIC_ONLY#\nokt #NUMERIC_ONLY#\nOkt #NUMERIC_ONLY#\nnov #NUMERIC_ONLY#\nNov #NUMERIC_ONLY#\ndec #NUMERIC_ONLY#\nDec #NUMERIC_ONLY#\n\n# Other abbreviations\ntel #NUMERIC_ONLY#\nTel #NUMERIC_ONLY#\nFax #NUMERIC_ONLY#\nfax #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.is",
    "content": "no #NUMERIC_ONLY#\nNo #NUMERIC_ONLY#\nnr #NUMERIC_ONLY#\nNr #NUMERIC_ONLY#\nnR #NUMERIC_ONLY#\nNR #NUMERIC_ONLY#\na\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n^\ní\ná\nó\næ\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nab.fn\na.fn\nafs\nal\nalm\nalg\nandh\nath\naths\natr\nao\nau\naukaf\náfn\náhrl.s\náhrs\nákv.gr\nákv\nbh\nbls\ndr\ne.Kr\net\nef\nefn\nennfr\neink\nend\ne.st\nerl\nfél\nfskj\nfh\nf.hl\nfísl\nfl\nfn\nfo\nforl\nfrb\nfrl\nfrh\nfrt\nfsl\nfsh\nfs\nfsk\nfst\nf.Kr\nft\nfv\nfyrrn\nfyrrv\ngerm\ngm\ngr\nhdl\nhdr\nhf\nhl\nhlsk\nhljsk\nhljv\nhljóðv\nhr\nhv\nhvk\nholl\nHos\nhöf\nhk\nhrl\nísl\nkaf\nkap\nKhöfn\nkk\nkg\nkk\nkm\nkl\nklst\nkr\nkt\nkgúrsk\nkvk\nleturbr\nlh\nlh.nt\nlh.þt\nlo\nltr\nmlja\nmljó\nmillj\nmm\nmms\nm.fl\nmiðm\nmgr\nmst\nmín\nnf\nnh\nnhm\nnl\nnk\nnmgr\nno\nnúv\nnt\no.áfr\no.m.fl\nohf\no.fl\no.s.frv\nófn\nób\nóákv.gr\nóákv\npfn\nPR\npr\nRitstj\nRvík\nRvk\nsamb\nsamhlj\nsamn\nsamn\nsbr\nsek\nsérn\nsf\nsfn\nsh\nsfn\nsh\ns.hl\nsk\nskv\nsl\nsn\nso\nss.us\ns.st\nsamþ\nsbr\nshlj\nsign\nskál\nst\nst.s\nstk\nsþ\nteg\ntbl\ntfn\ntl\ntvíhlj\ntvt\ntill\nto\numr\nuh\nus\nuppl\nútg\nvb\nVf\nvh\nvkf\nVl\nvl\nvlf\nvmf\n8vo\nvsk\nvth\nþt\nþf\nþjs\nþgf\nþlt\nþolm\nþm\nþml\nþýð\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.it",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nAdj\nAdm\nAdv\nAmn \nArch \nAsst\nAvv\nBart\nBcc\nBldg\nBrig\nBros\nC.A.P\nC.P\nCapt\nCc\nCmdr\nCo\nCol\nComdr\nCon\nCorp\nCpl\nDR\nDott\nDr\nDrs\nEgr\nEns\nGen\nGeom\nGov\nHon\nHosp\nHr\nId\nIng\nInsp\nLt\nMM\nMR\nMRS\nMS\nMaj\nMessrs\nMlle\nMme\nMo\nMons\nMr\nMrs\nMs\nMsgr\nN.B\nOp\nOrd\nP.S\nP.T\nPfc\nPh\nProf\nPvt\nRP\nRSVP\nRag\nRep\nReps\nRes\nRev\nRif\nRt\nS.A\nS.B.F\nS.P.M\nS.p.A\nS.r.l\nSen\nSens\nSfc\nSgt\nSig\nSigg\nSoc\nSpett\nSr\nSt\nSupt\nSurg\nV.P\n\n# other\na.c \nacc\nall \nbanc\nc.a\nc.c.p\nc.m\nc.p\nc.s\nc.v\ncorr\ndott\ne.p.c\necc\nes \nfatt\ngg\nint\nlett\nogg\non\np.c\np.c.c\np.es\np.f\np.r\np.v\npost\npp\nracc\nric\ns.n.c\nseg\nsgg\nss\ntel\nu.s\nv.r\nv.s\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall into this category - it sometimes ends a sentence)\nv\nvs\ni.e\nrev\ne.g\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNo #NUMERIC_ONLY# \nNos\nArt #NUMERIC_ONLY#\nNr\npp #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.lt",
    "content": "# Anything in this file, followed by a period (and an upper-case word),\n# does NOT indicate an end-of-sentence marker.\n# Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n# Any single upper case letter  followed by a period is not a sentence ender\n# (excluding I occasionally, but we leave it in)\n# usually upper case letters are initials in a name\nA\nĀ\nB\nC\nČ\nD\nE\nĒ\nF\nG\nĢ\nH\nI\nĪ\nJ\nK\nĶ\nL\nĻ\nM\nN\nŅ\nO\nP\nQ\nR\nS\nŠ\nT\nU\nŪ\nV\nW\nX\nY\nZ\nŽ\n\n# Initialis -- Džonas\nDz\nDž\nJust\n\n# Day and month abbreviations\n# m. menesis d. diena  g. gimes\nm\nmėn\nd\ng\ngim\n# Pirmadienis Penktadienis\nPr\nPn\nPirm\nAntr\nTreč\nKetv\nPenkt\nŠešt\nSekm\nSaus\nVas\nKov\nBal\nGeg\nBirž\nLiep\nRugpj\nRugs\nSpal\nLapkr\nGruod\n\n# Business, governmental, geographical terms\na\n# aikštė\nadv\n# advokatas\nakad\n# akademikas\naklg\n# akligatvis\nakt\n# aktorius\nal\n# alėja\nA.V\n# antspaudo vieta\naps\napskr\n# apskritis\napyg\n# apygarda\naps\napskr\n# apskritis\nasist\n# asistentas\nasmv\navd\n# asmenvardis\na.k\nasm\nasm.k\n# asmens kodas\natsak\n# atsakingasis\natsisk\nsąsk\n# atsiskaitomoji sąskaita\naut\n# autorius\nb\nk\nb.k\n# banko kodas\nbkl\n# bakalauras\nbt\n# butas\nbuv\n# buvęs, -usi\ndail\n# dailininkas\ndek\n# dekanas\ndėst\n# dėstytojas\ndir\n# direktorius\ndirig\n# dirigentas\ndoc\n# docentas\ndrp\n# durpynas\ndš\n# dešinysis\negz\n# egzempliorius\neil\n# eilutė\nekon\n# ekonomika\nel\n# elektroninis\netc\než\n# ežeras\nfaks\n# faksas\nfak\n# fakultetas\ngen\n# generolas\ngyd\n# gydytojas\ngv\n# gyvenvietė\nįl\n# įlanka\nĮn\n# įnagininkas\ninsp\n# inspektorius\npan\n# ir panašiai\nt.t\n# ir taip toliau\nk.a\n# kaip antai\nkand\n# kandidatas\nkat\n# katedra\nkyš\n# kyšulys\nkl\n# klasė\nkln\n# kalnas\nkn\n# knyga\nkoresp\n# korespondentas\nkpt\n# kapitonas\nkr\n# kairysis\nkt\n# kitas\nkun\n# kunigas\nl\ne\np\nl.e.p\n# laikinai einantis pareigas\nltn\n# leitenantas\nm\nmst\n# miestas\nm.e\n# mūsų eros\nm.m\n# mokslo metai\nmot\n# moteris\nmstl\n# miestelis\nmgr\n# magistras\nmgnt\n# magistrantas\nmjr\n# majoras\nmln\n# milijonas\nmlrd\n# milijardas\nmok\n# mokinys\nmokyt\n# mokytojas\nmoksl\n# mokslinis\nnkt\n# nekaitomas\nntk\n# neteiktinas\nNr\nnr\n# numeris\np\n# ponas\np.d\na.d\n# pašto dėžutė, abonentinė dėžutė\np.m.e\n# prieš mūsų erą\npan\n# ir panašiai\npav\n# paveikslas\npavad\n# pavaduotojas\npirm\n# pirmininkas\npl\n# plentas\nplg\n# palygink\nplk\n# pulkininkas; pelkė\npr\n# prospektas\nKr\npr.Kr\n# prieš Kristų\nprok\n# prokuroras\nprot\n# protokolas\npss\n# pusiasalis\npšt\n# paštas\npvz\n# pavyzdžiui\nr\n# rajonas\nred\n# redaktorius\nrš\n# raštų kalbos\nsąs\n# sąsiuvinis\nsaviv\nsav\n# savivaldybė\nsekr\n# sekretorius\nsen\n# seniūnija, seniūnas\nsk\n# skaityk; skyrius\nskg\n# skersgatvis\nskyr\nsk\n# skyrius\nskv\n# skveras\nsp\n# spauda; spaustuvė\nspec\n# specialistas\nsr\n# sritis\nst\n# stotis\nstr\n# straipsnis\nstud\n# studentas\nš\nš.m\n# šių metų\nšnek\n# šnekamosios\ntir\n# tiražas\ntūkst\n# tūkstantis\nup\n# upė\nupl\n# upelis\nvad\n# vadinamasis, -oji\nvlsč\n# valsčius\nved\n# vedėjas\nvet\n# veterinarija\nvirš\n# viršininkas, viršaitis\nvyr\n# vyriausiasis, -ioji; vyras\nvyresn\n# vyresnysis\nvlsč\n# valsčius\nvs\n# viensėdis\nVt\nvt\n# vietininkas\nvtv\nvv\n# vietovardis\nžml\n# žemėlapis\n\n# Technical terms, abbreviations used in guidebooks, advertisments, etc.\n# Generally lower-case.\nair\n# airiškai\namer\n# amerikanizmas\nanat\n# anatomija\nangl\n# angl. angliskai\narab\n# arabų\narcheol\narchit\nasm\n# asmuo\nastr\n# astronomija\naustral\n# australiškai\naut\n# automobilis\nav\n# aviacija\nbažn\nbdv\n# būdvardis\nbibl\n# Biblija\nbiol\n# biologija\nbot\n# botanika\nbrt\n# burtai, burtažodis.\nbrus\n# baltarusių\nbuh\n# buhalterija\nchem\n# chemija\ncol\n# collectivum\ncon\nconj\n# conjunctivus, jungtukas\ndab\n# dab. dabartine\ndgs\n# daugiskaita\ndial\n# dialektizmas\ndipl\ndktv\n# daiktavardis\ndžn\n# dažnai\nekon\nel\n# elektra\nesam\n# esamasis laikas\neuf\n# eufemizmas\nfam\n# familiariai\nfarm\n# farmacija\nfilol\n# filologija\nfilos\n# filosofija\nfin\n# finansai\nfiz\n# fizika\nfiziol\n# fiziologija\nflk\n# folkloras\nfon\n# fonetika\nfot\n# fotografija\ngeod\n# geodezija\ngeogr\ngeol\n# geologija\ngeom\n# geometrija\nglžk\ngr\n# graikų\ngram\nher\n# heraldika\nhidr\n# hidrotechnika\nind\n# Indų\niron\n# ironiškai\nisp\n# ispanų\nist\nistor\n# istorija\nit\n# italų\nįv\nreikšm\nįv.reikšm\n# įvairiomis reikšmėmis\njap\n# japonų\njuok\n# juokaujamai\njūr\n# jūrininkystė\nkalb\n# kalbotyra\nkar\n# karyba\nkas\n# kasyba\nkin\n# kinematografija\nklaus\n# klausiamasis\nknyg\n# knyginis\nkom\n# komercija\nkomp\n# kompiuteris\nkosm\n# kosmonautika\nkt\n# kitas\nkul\n# kulinarija\nkuop\n# kuopine\nl\n# laikas\nlit\n# literatūrinis\nlingv\n# lingvistika\nlog\n# logika\nlot\n# lotynų\nmat\n# matematika\nmaž\n# mažybinis\nmed\n# medicina\nmedž\n# medžioklė\nmen\n# menas\nmenk\n# menkinamai\nmetal\n# metalurgija\nmeteor\nmin\n# mineralogija\nmit\n# mitologija\nmok\n# mokyklinis\nms\n# mįslė\nmuz\n# muzikinis\nn\n# naujasis\nneig\n# neigiamasis\nneol\n# neologizmas\nniek\n# niekinamai\nofic\n# oficialus\nopt\n# optika\norig\n# original\np\n# pietūs\npan\n# panašiai\nparl\n# parlamentas\npat\n# patarlė\npaž\n# pažodžiui\nplg\n# palygink\npoet\n# poetizmas\npoez\n#  poezija\npoligr\n# poligrafija\npolit\n# politika\nppr\n# paprastai\npranc\npr\n# prancūzų, prūsų\npriet\n# prietaras\nprek\n# prekyba\nprk\n# perkeltine\nprs\n# persona, asmuo\npsn\n# pasenęs žodis\npsich\n# psichologija\npvz\n# pavyzdžiui\nr\n# rytai\nrad\n# radiotechnika\nrel\n# religija\nret\n# retai\nrus\n# rusų\nsen\n# senasis\nsl\n# slengas, slavų\nsov\n# sovietinis\nspec\n# specialus\nsport\nstat\n# statyba\nsudurt\n# sudurtinis\nsutr\n# sutrumpintas\nsuv\n# suvalkiečių\nš\n# šiaurė\nšach\n# šachmatai\nšiaur\nškot\n# škotiškai\nšnek\n# šnekamoji\nteatr\ntech\ntechn\n# technika\nteig\n# teigiamas\nteis\n# teisė\ntekst\n# tekstilė\ntel\n# telefonas\nteol\n# teologija\nv\n# tik vyriškosios, vakarai\nt.p\nt\np\n# ir taip pat\nt.t\n# ir taip toliau\nt.y\n# tai yra\nvaik\n# vaikų\nvart\n# vartojama\nvet\n# veterinarija\nvid\n# vidurinis\nvksm\n# veiksmažodis\nvns\n# vienaskaita\nvok\n# vokiečių\nvulg\n# vulgariai\nzool\n# zoologija\nžr\n# žiūrėk\nž.ū\nž\nū\n# žemės ūkis\n\n# List of titles. These are often followed by upper-case names, but do\n# not indicate sentence breaks\n#\n# Jo Eminencija\nEm.\n# Gerbiamasis\nGerb\ngerb\n#  malonus\nmalon\n# profesorius\nProf\nprof\n# daktaras (mokslų)\nDr\ndr\nhabil\nmed\n# inž inžinierius\ninž\nInž\n\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNo #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.lv",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nA\nĀ\nB\nC\nČ\nD\nE\nĒ\nF\nG\nĢ\nH\nI\nĪ\nJ\nK\nĶ\nL\nĻ\nM\nN\nŅ\nO\nP\nQ\nR\nS\nŠ\nT\nU\nŪ\nV\nW\nX\nY\nZ\nŽ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\ndr\nDr\nmed\nprof\nProf\ninž\nInž\nist.loc\nIst.loc\nkor.loc\nKor.loc\nv.i\nvietn\nVietn\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall into this category - it sometimes ends a sentence)\na.l\nt.p\npārb\nPārb\nvec\nVec\ninv\nInv\nsk\nSk\nspec\nSpec\nvienk\nVienk\nvirz\nVirz\nmāksl\nMāksl\nmūz\nMūz\nakad\nAkad\nsoc\nSoc\ngalv\nGalv\nvad\nVad\nsertif\nSertif\nfolkl\nFolkl\nhum\nHum\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNr #NUMERIC_ONLY# \n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.nl",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n#Sources: http://nl.wikipedia.org/wiki/Lijst_van_afkortingen \n#         http://nl.wikipedia.org/wiki/Aanspreekvorm\n#         http://nl.wikipedia.org/wiki/Titulatuur_in_het_Nederlands_hoger_onderwijs\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nbacc\nbc\nbgen\nc.i\ndhr\ndr\ndr.h.c\ndrs\ndrs\nds\neint\nfa\nFa\nfam\ngen\ngenm\ning\nir\njhr\njkvr\njr\nkand\nkol\nlgen\nlkol\nLt\nmaj\nMej\nmevr\nMme\nmr\nmr\nMw\no.b.s\nplv\nprof\nritm\ntint\nVz\nZ.D\nZ.D.H\nZ.E\nZ.Em\nZ.H\nZ.K.H\nZ.K.M\nZ.M\nz.v\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall into this category - it sometimes ends a sentence)\n#we seem to have a lot of these in dutch i.e.: i.p.v - in plaats van (in stead of) never ends a sentence\na.g.v\nbijv\nbijz\nbv\nd.w.z\ne.c\ne.g\ne.k\nev\ni.p.v\ni.s.m\ni.t.t\ni.v.m\nm.a.w\nm.b.t\nm.b.v\nm.h.o\nm.i\nm.i.v\nv.w.t\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNr #NUMERIC_ONLY# \nNrs \nnrs\nnr #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.pl",
    "content": "adw\nafr\nakad\nal\nAl\nam\namer\narch\nart\nArt\nartyst\nastr\naustr\nbałt\nbdb\nbł\nbm\nbr\nbryg\nbryt\ncentr\nces\nchem\nchiń\nchir\nc.k\nc.o\ncyg\ncyw\ncyt\nczes\nczw\ncd\nCd\nczyt\nćw\nćwicz\ndaw\ndcn\ndekl\ndemokr\ndet\ndiec\ndł\ndn\ndot\ndol\ndop\ndost\ndosł\nh.c\nds\ndst\nduszp\ndypl\negz\nekol\nekon\nelektr\nem\new\nfab\nfarm\nfot\nfr\ngat\ngastr\ngeogr\ngeol\ngimn\ngłęb\ngm\ngodz\ngórn\ngosp\ngr\ngram\nhist\nhiszp\nhr\nHr\nhot\nid\nin\nim\niron\njn\nkard\nkat\nkatol\nk.k\nkk\nkol\nkl\nk.p.a\nkpc\nk.p.c\nkpt\nkr\nk.r\nkrak\nk.r.o\nkryt\nkult\nlaic\nłac\nniem\nwoj\nnb\nnp\nNb\nNp\npol\npow\nm.in\npt\nps\nPt\nPs\ncdn\njw\nryc\nrys\nRyc\nRys\ntj\ntzw\nTzw\ntzn\nzob\nang\nub\nul\npw\npn\npl\nal\nk\nn\nnr #NUMERIC_ONLY#\nNr #NUMERIC_ONLY#\nww\nwł\nur\nzm\nżyd\nżarg\nżyw\nwył\nbp\nbp\nwyst\ntow\nTow\no\nsp\nSp\nst\nspółdz\nSpółdz\nspoł\nspółgł\nstoł\nstow\nStoł\nStow\nzn\nzew\nzewn\nzdr\nzazw\nzast\nzaw\nzał\nzal\nzam\nzak\nzakł\nzagr\nzach\nadw\nAdw\nlek\nLek\nmed\nmec\nMec\ndoc\nDoc\ndyw\ndyr\nDyw\nDyr\ninż\nInż\nmgr\nMgr\ndh\ndr\nDh\nDr\np\nP\nred\nRed\nprof\nprok\nProf\nProk\nhab\npłk\nPłk\nnadkom\nNadkom\npodkom\nPodkom\nks\nKs\ngen\nGen\npor\nPor\nreż\nReż\nprzyp\nPrzyp\nśp\nśw\nśW\nŚp\nŚw\nŚW\nszer\nSzer\npkt #NUMERIC_ONLY#\nstr #NUMERIC_ONLY#\ntab #NUMERIC_ONLY#\nTab #NUMERIC_ONLY#\ntel\nust #NUMERIC_ONLY#\npar #NUMERIC_ONLY#\npoz\npok\noo\noO\nOo\nOO\nr #NUMERIC_ONLY#\nl #NUMERIC_ONLY#\ns #NUMERIC_ONLY#\nnajśw\nNajśw\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nŚ\nĆ\nŻ\nŹ\nDz\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.ro",
    "content": "A\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\ndpdv\netc\nșamd\nM.Ap.N\ndl\nDl\nd-na\nD-na\ndvs\nDvs\npt\nPt\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.ru",
    "content": "# added Cyrillic uppercase letters [А-Я]\n# removed 000D carriage return (this is not removed by chomp in tokenizer.perl, and prevents recognition of the prefixes)\n# edited by Kate Young (nspaceanalysis@earthlink.net) 21 May 2013\nА\nБ\nВ\nГ\nД\nЕ\nЖ\nЗ\nИ\nЙ\nК\nЛ\nМ\nН\nО\nП\nР\nС\nТ\nУ\nФ\nХ\nЦ\nЧ\nШ\nЩ\nЪ\nЫ\nЬ\nЭ\nЮ\nЯ\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n0гг\n1гг\n2гг\n3гг\n4гг\n5гг\n6гг\n7гг\n8гг\n9гг\n0г\n1г\n2г\n3г\n4г\n5г\n6г\n7г\n8г\n9г\nXвв\nVвв\nIвв\nLвв\nMвв\nCвв\nXв\nVв\nIв\nLв\nMв\nCв\n0м\n1м\n2м\n3м\n4м\n5м\n6м\n7м\n8м\n9м\n0мм\n1мм\n2мм\n3мм\n4мм\n5мм\n6мм\n7мм\n8мм\n9мм\n0см\n1см\n2см\n3см\n4см\n5см\n6см\n7см\n8см\n9см\n0дм\n1дм\n2дм\n3дм\n4дм\n5дм\n6дм\n7дм\n8дм\n9дм\n0л\n1л\n2л\n3л\n4л\n5л\n6л\n7л\n8л\n9л\n0км\n1км\n2км\n3км\n4км\n5км\n6км\n7км\n8км\n9км\n0га\n1га\n2га\n3га\n4га\n5га\n6га\n7га\n8га\n9га\n0кг\n1кг\n2кг\n3кг\n4кг\n5кг\n6кг\n7кг\n8кг\n9кг\n0т\n1т\n2т\n3т\n4т\n5т\n6т\n7т\n8т\n9т\n0г\n1г\n2г\n3г\n4г\n5г\n6г\n7г\n8г\n9г\n0мг\n1мг\n2мг\n3мг\n4мг\n5мг\n6мг\n7мг\n8мг\n9мг\nбульв\nв\nвв\nг\nга\nгг\nгл\nгос\nд\nдм\nдоп\nдр\nе\nед\nед\nзам\nи\nинд\nисп\nИсп\nк\nкап\nкг\nкв\nкл\nкм\nкол\nкомн\nкоп\nкуб\nл\nлиц\nлл\nм\nмакс\nмг\nмин\nмл\nмлн\nмлрд\nмм\nн\nнаб\nнач\nнеуд\nном\nо\nобл\nобр\nобщ\nок\nост\nотл\nп\nпер\nперераб\nпл\nпос\nпр\nпросп\nпроф\nр\nред\nруб\nс\nсб\nсв\nсм\nсоч\nср\nст\nстр\nт\nтел\nТел\nтех\nтт\nтуп\nтыс\nуд\nул\nуч\nфиз\nх\nхор\nч\nчел\nшт\nэкз\nэ\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.sk",
    "content": "Bc\nMgr\nRNDr\nPharmDr\nPhDr\nJUDr\nPaedDr\nThDr\nIng\nMUDr\nMDDr\nMVDr\nDr\nThLic\nPhD\nArtD\nThDr\nDr\nDrSc\nCSs\nprof\nobr\nObr\nČ\nč\nabsol\nadj\nadmin\nadr\nAdr\nadv\nadvok\nafr\nak\nakad\nakc\nakuz\net\nal\nalch\namer\nanat\nangl\nAngl\nanglosas\nanorg\nap\napod\narch\narcheol\narchit\narg\nart\nastr\nastrol\nastron\natp\natď\naustr\nAustr\naut\nbelg\nBelg\nbibl\nBibl\nbiol\nbot\nbud\nbás\nbýv\ncest\nchem\ncirk\ncsl\nčs\nČs\ndat\ndep\ndet\ndial\ndiaľ\ndipl\ndistrib\ndokl\ndosl\ndopr\ndram\nduš\ndv\ndvojčl\ndór\nekol\nekon\nel\nelektr\nelektrotech\nenerget\nepic\nest\netc\netonym\neufem\neuróp\nEuróp\nev\nevid\nexpr\nfa\nfam\nfarm\nfem\nfeud\nfil\nfilat\nfiloz\nfi\nfon\nform\nfot\nfr\nFr\nfranc\nFranc\nfraz\nfut\nfyz\nfyziol\ngarb\ngen\ngenet\ngenpor\ngeod\ngeogr\ngeol\ngeom\ngerm\ngr\nGr\ngréc\nGréc\ngréckokat\nhebr\nherald\nhist\nhlav\nhosp\nhromad\nhud\nhypok\nident\ni.e\nident\nimp\nimpf\nindoeur\ninf\ninform\ninstr\nint\ninterj\ninšt\ninštr\niron\njap\nJap\njaz\njedn\njuhoamer\njuhových\njuhozáp\njuž\nkanad\nKanad\nkanc\nkapit\nkpt\nkart\nkatastr\nknih\nkniž\nkomp\nkonj\nkonkr\nkozmet\nkrajč\nkresť\nkt\nkuch\nlat\nlatinskoamer\nlek\nlex\nlingv\nlit\nlitur\nlog\nlok\nmax\nMax\nmaď\nMaď\nmedzinár\nmest\nmetr\nmil\nMil\nmin\nMin\nminer\nml\nmld\nmn\nmod\nmytol\nnapr\nnar\nNar\nnasl\nnedok\nneg\nnegat\nneklas\nnem\nNem\nneodb\nneos\nneskl\nnesklon\nnespis\nnespráv\nneved\nnež\nniekt\nniž\nnom\nnáb\nnákl\nnámor\nnár\nobch\nobj\nobv\nobyč\nobč\nobčian\nodb\nodd\nods\nojed\nokr\nOkr\nopt\nopyt\norg\nos\nosob\not\novoc\npar\npart\npejor\npers\npf\nPf \nP.f\np.f\npl\nPlk\npod\npodst\npokl\npolit\npolitol\npolygr\npomn\npopl\npor\nporad\nporov\nposch\npotrav\npouž\npoz\npozit\npoľ\npoľno\npoľnohosp\npoľov\npošt\npož\nprac\npredl\npren\nprep\npreuk\npriezv\nPriezv\nprivl\nprof\npráv\npríd\npríj\nprík\npríp\nprír\nprísl\npríslov\npríč\npsych\npubl\npís\npísm\npôv\nrefl\nreg\nrep\nresp\nrozk\nrozlič\nrozpráv\nroč\nRoč\nryb\nrádiotech\nrím\nsamohl\nsemest\nsev\nseveroamer\nseverových\nseverozáp\nsg\nskr\nskup\nsl\nSloven\nsoc\nsoch\nsociol\nsp\nspol\nSpol\nspoloč\nspoluhl\nspráv\nspôs\nst\nstar\nstarogréc\nstarorím\ns.r.o\nstol\nstor\nstr\nstredoamer\nstredoškol\nsubj\nsubst\nsuperl\nsv\nsz\nsúkr\nsúp\nsúvzť\ntal\nTal\ntech\ntel\nTel\ntelef\nteles\ntelev\nteol\ntrans\nturist\ntuzem\ntypogr\ntzn\ntzv\nukaz\nul\nUl\numel\nuniv\nust\nved\nvedľ\nverb\nveter\nvin\nviď\nvl\nvod\nvodohosp\npnl\nvulg\nvyj\nvys\nvysokoškol\nvzťaž\nvôb\nvých\nvýd\nvýrob\nvýsk\nvýsl\nvýtv\nvýtvar\nvýzn\nvčel\nvš\nvšeob\nzahr\nzar\nzariad\nzast\nzastar\nzastaráv\nzb\nzdravot\nzdruž\nzjemn\nzlat\nzn\nZn\nzool\nzr\nzried\nzv\nzáhr\nzák\nzákl\nzám\nzáp\nzápadoeur\nzázn\núzem\núčt\nčast\nčes\nČes\nčl\nčísl\nživ\npr\nfak\nKr\np.n.l\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.sl",
    "content": "dr\nDr\nitd\nitn\nšt #NUMERIC_ONLY#\nŠt #NUMERIC_ONLY#\nd\njan\nJan\nfeb\nFeb\nmar\nMar\napr\nApr\njun\nJun\njul\nJul\navg\nAvg\nsept\nSept\nsep\nSep\nokt\nOkt\nnov\nNov\ndec\nDec\ntj\nTj\nnpr\nNpr\nsl\nSl\nop\nOp\ngl\nGl\noz\nOz\nprev\ndipl\ning\nprim\nPrim\ncf\nCf\ngl\nGl\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.sv",
    "content": "#single upper case letter are usually initials\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\n#misc abbreviations\nAB\nG\nVG\ndvs\netc\nfrom\niaf\njfr\nkl\nkr\nmao\nmfl\nmm\nosv\npga\ntex\ntom\nvs\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.ta",
    "content": "#Anything in this file, followed by a period (and an upper-case word), does NOT indicate an end-of-sentence marker.\n#Special cases are included for prefixes that ONLY appear before 0-9 numbers.\n\n#any single upper case letter  followed by a period is not a sentence ender (excluding I occasionally, but we leave it in)\n#usually upper case letters are initials in a name\nஅ\nஆ\nஇ\nஈ\nஉ\nஊ\nஎ\nஏ\nஐ\nஒ\nஓ\nஔ\nஃ\nக\nகா\nகி\nகீ\nகு\nகூ\nகெ\nகே\nகை\nகொ\nகோ\nகௌ\nக்\nச\nசா\nசி\nசீ\nசு\nசூ\nசெ\nசே\nசை\nசொ\nசோ\nசௌ\nச்\nட\nடா\nடி\nடீ\nடு\nடூ\nடெ\nடே\nடை\nடொ\nடோ\nடௌ\nட்\nத\nதா\nதி\nதீ\nது\nதூ\nதெ\nதே\nதை\nதொ\nதோ\nதௌ\nத்\nப\nபா\nபி\nபீ\nபு\nபூ\nபெ\nபே\nபை\nபொ\nபோ\nபௌ\nப்\nற\nறா\nறி\nறீ\nறு\nறூ\nறெ\nறே\nறை\nறொ\nறோ\nறௌ\nற்\nய\nயா\nயி\nயீ\nயு\nயூ\nயெ\nயே\nயை\nயொ\nயோ\nயௌ\nய்\nர\nரா\nரி\nரீ\nரு\nரூ\nரெ\nரே\nரை\nரொ\nரோ\nரௌ\nர்\nல\nலா\nலி\nலீ\nலு\nலூ\nலெ\nலே\nலை\nலொ\nலோ\nலௌ\nல்\nவ\nவா\nவி\nவீ\nவு\nவூ\nவெ\nவே\nவை\nவொ\nவோ\nவௌ\nவ்\nள\nளா\nளி\nளீ\nளு\nளூ\nளெ\nளே\nளை\nளொ\nளோ\nளௌ\nள்\nழ\nழா\nழி\nழீ\nழு\nழூ\nழெ\nழே\nழை\nழொ\nழோ\nழௌ\nழ்\nங\nஙா\nஙி\nஙீ\nஙு\nஙூ\nஙெ\nஙே\nஙை\nஙொ\nஙோ\nஙௌ\nங்  \nஞ\nஞா\nஞி\nஞீ\nஞு\nஞூ\nஞெ\nஞே\nஞை\nஞொ\nஞோ\nஞௌ\nஞ் \nண\nணா\nணி\nணீ\nணு\nணூ\nணெ\nணே\nணை\nணொ\nணோ\nணௌ\nண்\nந\nநா\nநி\nநீ\nநு\nநூ\nநெ\nநே\nநை\nநொ\nநோ\nநௌ\nந் \t\nம\nமா\nமி\nமீ\nமு\nமூ\nமெ\nமே\nமை\nமொ\nமோ\nமௌ\nம் \t\nன\nனா\nனி\nனீ\nனு\nனூ\nனெ\nனே\nனை\nனொ\nனோ\nனௌ\nன்\n\n\n#List of titles. These are often followed by upper-case names, but do not indicate sentence breaks\nதிரு\nதிருமதி\nவண\nகௌரவ\n\n\n#misc - odd period-ending items that NEVER indicate breaks (p.m. does NOT fall into this category - it sometimes ends a sentence)\nஉ.ம்\n#கா.ம்\n#எ.ம்\n\n\n#Numbers only. These should only induce breaks when followed by a numeric sequence\n# add NUMERIC_ONLY after the word for this function\n#This case is mostly for the english \"No.\" which can either be a sentence of its own, or\n#if followed by a number, a non-breaking prefix\nNo #NUMERIC_ONLY# \nNos\nArt #NUMERIC_ONLY#\nNr\npp #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.yue",
    "content": "#\n# Cantonese (Chinese)\n#\n# Anything in this file, followed by a period, \n# does NOT indicate an end-of-sentence marker.\n#\n# English/Euro-language given-name initials (appearing in\n# news, periodicals, etc.)\nA\nĀ\nB\nC\nČ\nD\nE\nĒ\nF\nG\nĢ\nH\nI\nĪ\nJ\nK\nĶ\nL\nĻ\nM\nN\nŅ\nO\nP\nQ\nR\nS\nŠ\nT\nU\nŪ\nV\nW\nX\nY\nZ\nŽ\n\n# Numbers only. These should only induce breaks when followed by\n# a numeric sequence.\n# Add NUMERIC_ONLY after the word for this function. This case is\n# mostly for the english \"No.\" which can either be a sentence of its\n# own, or if followed by a number, a non-breaking prefix.\nNo #NUMERIC_ONLY#\nNr #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/nonbreaking_prefixes/nonbreaking_prefix.zh",
    "content": "#\n# Mandarin (Chinese)\n#\n# Anything in this file, followed by a period, \n# does NOT indicate an end-of-sentence marker.\n#\n# English/Euro-language given-name initials (appearing in\n# news, periodicals, etc.)\nA\nĀ\nB\nC\nČ\nD\nE\nĒ\nF\nG\nĢ\nH\nI\nĪ\nJ\nK\nĶ\nL\nĻ\nM\nN\nŅ\nO\nP\nQ\nR\nS\nŠ\nT\nU\nŪ\nV\nW\nX\nY\nZ\nŽ\n\n# Numbers only. These should only induce breaks when followed by\n# a numeric sequence.\n# Add NUMERIC_ONLY after the word for this function. This case is\n# mostly for the english \"No.\" which can either be a sentence of its\n# own, or if followed by a number, a non-breaking prefix.\nNo #NUMERIC_ONLY#\nNr #NUMERIC_ONLY#\n"
  },
  {
    "path": "tools/release_model.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.release_model import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "tools/spm_to_vocab.py",
    "content": "# converts a SentencePiece vocabulary to the format expected by dynamic data\n# (essentially converts float expected counts to \"fixed precision\" int pseudo\n# counts)\nimport sys\nimport math\nfrom onmt.constants import DefaultTokens\n\nOMIT = (DefaultTokens.UNK, DefaultTokens.BOS, DefaultTokens.EOS)\n\n\ndef convert(lines):\n    for line in lines:\n        w, c = line.rstrip('\\n').split(None, 1)\n        if w in OMIT:\n            continue\n        c = math.exp(float(c)) * 1000000\n        c = int(c) + 1\n        yield w, c\n\n\nif __name__ == '__main__':\n    for c, w in convert(sys.stdin):\n        print('{}\\t{}'.format(c, w))\n"
  },
  {
    "path": "tools/stanfordcorenlp/__init__.py",
    "content": "from tools.stanfordcorenlp.corenlp import StanfordCoreNLP"
  },
  {
    "path": "tools/stanfordcorenlp/corenlp.py",
    "content": "# _*_coding:utf-8_*_\nfrom __future__ import print_function\n\nimport glob\nimport json\nimport logging\nimport os\nimport re\nimport socket\nimport subprocess\nimport sys\nimport time\n\nimport psutil\n\ntry:\n    from urlparse import urlparse\nexcept ImportError:\n    from urllib.parse import urlparse\n\nimport requests\n\n\nclass StanfordCoreNLP:\n    def __init__(self, path_or_host, port=None, memory='4g', lang='en', timeout=1500, quiet=True,\n                 logging_level=logging.WARNING):\n        self.path_or_host = path_or_host\n        self.port = port\n        self.memory = memory\n        self.lang = lang\n        self.timeout = timeout\n        self.quiet = quiet\n        self.logging_level = logging_level\n\n        logging.basicConfig(level=self.logging_level)\n\n        # Check args\n        self._check_args()\n\n        if path_or_host.startswith('http'):\n            self.url = path_or_host + ':' + str(port)\n            logging.info('Using an existing server {}'.format(self.url))\n        else:\n\n            # Check Java\n            if not subprocess.call(['java', '-version'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) == 0:\n                raise RuntimeError('Java not found.')\n\n            # Check if the dir exists\n            if not os.path.isdir(self.path_or_host):\n                raise IOError(str(self.path_or_host) + ' is not a directory.')\n            directory = os.path.normpath(self.path_or_host) + os.sep\n            self.class_path_dir = directory\n\n            # Check if the language specific model file exists\n            switcher = {\n                'en': 'stanford-corenlp-[0-9].[0-9].[0-9]-models.jar',\n                'zh': 'stanford-chinese-corenlp-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-models.jar',\n                'ar': 'stanford-arabic-corenlp-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-models.jar',\n                'fr': 'stanford-french-corenlp-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-models.jar',\n                'de': 'stanford-german-corenlp-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-models.jar',\n                'es': 'stanford-spanish-corenlp-[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]-models.jar'\n            }\n            jars = {\n                'en': 'stanford-corenlp-x.x.x-models.jar',\n                'zh': 'stanford-chinese-corenlp-yyyy-MM-dd-models.jar',\n                'ar': 'stanford-arabic-corenlp-yyyy-MM-dd-models.jar',\n                'fr': 'stanford-french-corenlp-yyyy-MM-dd-models.jar',\n                'de': 'stanford-german-corenlp-yyyy-MM-dd-models.jar',\n                'es': 'stanford-spanish-corenlp-yyyy-MM-dd-models.jar'\n            }\n            if len(glob.glob(directory + switcher.get(self.lang))) <= 0:\n                raise IOError(jars.get(\n                    self.lang) + ' not exists. You should download and place it in the ' + directory + ' first.')\n\n            # If port not set, auto select\n            if self.port is None:\n                for port_candidate in range(9000, 65535):\n                    if port_candidate not in [conn.laddr[1] for conn in psutil.net_connections()]:\n                        self.port = port_candidate\n                        break\n\n            # Check if the port is in use\n            # if self.port in [conn.laddr[1] for conn in psutil.net_connections()]:\n            #     raise IOError('Port ' + str(self.port) + ' is already in use.')\n\n            # Start native server\n            logging.info('Initializing native server...')\n            cmd = \"java\"\n            java_args = \"-Xmx{}\".format(self.memory)\n            java_class = \"edu.stanford.nlp.pipeline.StanfordCoreNLPServer\"\n            class_path = '\"{}*\"'.format(directory)\n\n            args = [cmd, java_args, '-cp', class_path, java_class, '-port', str(self.port), '-maxCharLength', '2000000']\n\n            args = ' '.join(args)\n\n            logging.info(args)\n\n            # Silence\n            with open(os.devnull, 'w') as null_file:\n                out_file = None\n                if self.quiet:\n                    out_file = null_file\n\n                self.p = subprocess.Popen(args, shell=True, stdout=out_file, stderr=subprocess.STDOUT)\n                logging.info('Server shell PID: {}'.format(self.p.pid))\n\n            self.url = 'http://localhost:' + str(self.port)\n\n        # Wait until server starts\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        host_name = urlparse(self.url).hostname\n        time.sleep(1)  # OSX, not tested\n        while sock.connect_ex((host_name, self.port)):\n            logging.info('Waiting until the server is available.')\n            time.sleep(1)\n        logging.info('The server is available.')\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.close()\n\n    def close(self):\n        logging.info('Cleanup...')\n        if hasattr(self, 'p'):\n            try:\n                parent = psutil.Process(self.p.pid)\n            except psutil.NoSuchProcess:\n                logging.info('No process: {}'.format(self.p.pid))\n                return\n\n            if self.class_path_dir not in ' '.join(parent.cmdline()):\n                logging.info('Process not in: {}'.format(parent.cmdline()))\n                return\n\n            children = parent.children(recursive=True)\n            for process in children:\n                logging.info('Killing pid: {}, cmdline: {}'.format(process.pid, process.cmdline()))\n                # process.send_signal(signal.SIGTERM)\n                process.kill()\n\n            logging.info('Killing shell pid: {}, cmdline: {}'.format(parent.pid, parent.cmdline()))\n            # parent.send_signal(signal.SIGTERM)\n            parent.kill()\n\n    def annotate(self, text, properties=None):\n        if sys.version_info.major >= 3:\n            text = text.encode('utf-8')\n\n        r = requests.post(self.url, params={'properties': str(properties)}, data=text,\n                          headers={'Connection': 'close'})\n        return r.text\n\n    def tregex(self, sentence, pattern):\n        tregex_url = self.url + '/tregex'\n        r_dict = self._request(tregex_url, pattern, \"tokenize,ssplit,depparse,parse\", sentence)\n        return r_dict\n\n    def tokensregex(self, sentence, pattern):\n        tokensregex_url = self.url + '/tokensregex'\n        r_dict = self._request(tokensregex_url, pattern, \"tokenize,ssplit,depparse\", sentence)\n        return r_dict\n\n    def semgrex(self, sentence, pattern):\n        semgrex_url = self.url + '/semgrex'\n        r_dict = self._request(semgrex_url, pattern, \"tokenize,ssplit,depparse\", sentence)\n        return r_dict\n\n    def sentence_segment(self, text):\n        r_dict = self._request('ssplit', text)\n        return r_dict\n\n    def word_tokenize(self, sentence, span=False):\n        r_dict = self._request('ssplit,tokenize', sentence)\n        tokens = [token['originalText'] for s in r_dict['sentences'] for token in s['tokens']]\n\n        # Whether return token span\n        if span:\n            spans = [(token['characterOffsetBegin'], token['characterOffsetEnd']) for s in r_dict['sentences'] for token\n                     in s['tokens']]\n            return tokens, spans\n        else:\n            return tokens\n\n    def pos_tag(self, sentence):\n        r_dict = self._request('pos', sentence)\n        words = []\n        tags = []\n        for s in r_dict['sentences']:\n            for token in s['tokens']:\n                words.append(token['originalText'])\n                tags.append(token['pos'])\n        return list(zip(words, tags))\n\n    def ner(self, sentence):\n        r_dict = self._request('ner', sentence)\n        words = []\n        ner_tags = []\n        for s in r_dict['sentences']:\n            for token in s['tokens']:\n                words.append(token['originalText'])\n                ner_tags.append(token['ner'])\n        return list(zip(words, ner_tags))\n\n    def parse(self, sentence):\n        r_dict = self._request('pos,parse', sentence)\n        return [s['parse'] for s in r_dict['sentences']][0]\n\n    def dependency_parse(self, sentence):\n        r_dict = self._request('depparse', sentence)\n        return [(dep['dep'], dep['governor'], dep['dependent']) for s in r_dict['sentences'] for dep in\n                s['basicDependencies']]\n\n    def coref(self, text):\n        r_dict = self._request('coref', text)\n\n        corefs = []\n        for k, mentions in r_dict['corefs'].items():\n            simplified_mentions = []\n            for m in mentions:\n                simplified_mentions.append((m['sentNum'], m['startIndex'], m['endIndex'], m['text']))\n            corefs.append(simplified_mentions)\n        return corefs\n\n    def switch_language(self, language=\"en\"):\n        self._check_language(language)\n        self.lang = language\n\n    def _request(self, annotators=None, data=None, *args, **kwargs):\n        if sys.version_info.major >= 3:\n            data = data.encode('utf-8')\n\n        properties = {'annotators': annotators, 'outputFormat': 'json'}\n        params = {'properties': str(properties), 'pipelineLanguage': self.lang}\n        if 'pattern' in kwargs:\n            params = {\"pattern\": kwargs['pattern'], 'properties': str(properties), 'pipelineLanguage': self.lang}\n\n        # logging.info(params)\n        r = requests.post(self.url, params=params, data=data, headers={'Connection': 'close'})\n        r_dict = json.loads(r.text)\n\n        return r_dict\n\n    def _check_args(self):\n        self._check_language(self.lang)\n        if not re.match('\\dg', self.memory):\n            raise ValueError('memory=' + self.memory + ' not supported. Use 4g, 6g, 8g and etc. ')\n\n    def _check_language(self, lang):\n        if lang not in ['en', 'zh', 'ar', 'fr', 'de', 'es']:\n            raise ValueError('lang=' + self.lang + ' not supported. Use English(en), Chinese(zh), Arabic(ar), '\n                                                   'French(fr), German(de), Spanish(es).')\n"
  },
  {
    "path": "tools/test_rouge.py",
    "content": "# -*- encoding: utf-8 -*-\nimport argparse\nimport os\nimport time\nimport pyrouge\nimport shutil\nimport sys\nimport codecs\n\nfrom onmt.utils.logging import init_logger, logger\n\n\ndef test_rouge(cand, ref):\n    \"\"\"Calculate ROUGE scores of sequences passed as an iterator\n       e.g. a list of str, an open file, StringIO or even sys.stdin\n    \"\"\"\n    current_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())\n    tmp_dir = \".rouge-tmp-{}\".format(current_time)\n    try:\n        if not os.path.isdir(tmp_dir):\n            os.mkdir(tmp_dir)\n            os.mkdir(tmp_dir + \"/candidate\")\n            os.mkdir(tmp_dir + \"/reference\")\n        candidates = [line.strip() for line in cand]\n        references = [line.strip() for line in ref]\n        assert len(candidates) == len(references)\n        cnt = len(candidates)\n        for i in range(cnt):\n            if len(references[i]) < 1:\n                continue\n            with open(tmp_dir + \"/candidate/cand.{}.txt\".format(i), \"w\",\n                      encoding=\"utf-8\") as f:\n                f.write(candidates[i])\n            with open(tmp_dir + \"/reference/ref.{}.txt\".format(i), \"w\",\n                      encoding=\"utf-8\") as f:\n                f.write(references[i])\n        r = pyrouge.Rouge155()\n        r.model_dir = tmp_dir + \"/reference/\"\n        r.system_dir = tmp_dir + \"/candidate/\"\n        r.model_filename_pattern = 'ref.#ID#.txt'\n        r.system_filename_pattern = r'cand.(\\d+).txt'\n        rouge_results = r.convert_and_evaluate()\n        results_dict = r.output_to_dict(rouge_results)\n        return results_dict\n    finally:\n        pass\n        if os.path.isdir(tmp_dir):\n            shutil.rmtree(tmp_dir)\n\n\ndef rouge_results_to_str(results_dict):\n    return \">> ROUGE(1/2/3/L/SU4): {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}\".format(\n        results_dict[\"rouge_1_f_score\"] * 100,\n        results_dict[\"rouge_2_f_score\"] * 100,\n        results_dict[\"rouge_3_f_score\"] * 100,\n        results_dict[\"rouge_l_f_score\"] * 100,\n        results_dict[\"rouge_su*_f_score\"] * 100)\n\n\nif __name__ == \"__main__\":\n    init_logger('test_rouge.log')\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', type=str, default=\"candidate.txt\",\n                        help='candidate file')\n    parser.add_argument('-r', type=str, default=\"reference.txt\",\n                        help='reference file')\n    args = parser.parse_args()\n    if args.c.upper() == \"STDIN\":\n        candidates = sys.stdin\n    else:\n        candidates = codecs.open(args.c, encoding=\"utf-8\")\n    references = codecs.open(args.r, encoding=\"utf-8\")\n\n    results_dict = test_rouge(candidates, references)\n    logger.info(rouge_results_to_str(results_dict))\n"
  },
  {
    "path": "tools/tokenizer.perl",
    "content": "#!/usr/bin/env perl\n#\n# This file is part of moses.  Its use is licensed under the GNU Lesser General\n# Public License version 2.1 or, at your option, any later version.\n\nuse warnings;\n\n# Sample Tokenizer\n### Version 1.1\n# written by Pidong Wang, based on the code written by Josh Schroeder and Philipp Koehn\n# Version 1.1 updates:\n#       (1) add multithreading option \"-threads NUM_THREADS\" (default is 1);\n#       (2) add a timing option \"-time\" to calculate the average speed of this tokenizer;\n#       (3) add an option \"-lines NUM_SENTENCES_PER_THREAD\" to set the number of lines for each thread (default is 2000), and this option controls the memory amount needed: the larger this number is, the larger memory is required (the higher tokenization speed);\n### Version 1.0\n# $Id: tokenizer.perl 915 2009-08-10 08:15:49Z philipp $\n# written by Josh Schroeder, based on code by Philipp Koehn\n\nbinmode(STDIN, \":utf8\");\nbinmode(STDOUT, \":utf8\");\n\nuse warnings;\nuse FindBin qw($RealBin);\nuse strict;\nuse Time::HiRes;\n\nif  (eval {require Thread;1;}) {\n  #module loaded\n  Thread->import();\n}\n\nmy $mydir = \"$RealBin/nonbreaking_prefixes\";\n\nmy %NONBREAKING_PREFIX = ();\nmy @protected_patterns = ();\nmy $protected_patterns_file = \"\";\nmy $language = \"en\";\nmy $QUIET = 0;\nmy $HELP = 0;\nmy $AGGRESSIVE = 0;\nmy $SKIP_XML = 0;\nmy $TIMING = 0;\nmy $NUM_THREADS = 1;\nmy $NUM_SENTENCES_PER_THREAD = 2000;\nmy $PENN = 0;\nmy $NO_ESCAPING = 0;\nwhile (@ARGV)\n{\n\t$_ = shift;\n\t/^-b$/ && ($| = 1, next);\n\t/^-l$/ && ($language = shift, next);\n\t/^-q$/ && ($QUIET = 1, next);\n\t/^-h$/ && ($HELP = 1, next);\n\t/^-x$/ && ($SKIP_XML = 1, next);\n\t/^-a$/ && ($AGGRESSIVE = 1, next);\n\t/^-time$/ && ($TIMING = 1, next);\n  # Option to add list of regexps to be protected\n  /^-protected/ && ($protected_patterns_file = shift, next);\n\t/^-threads$/ && ($NUM_THREADS = int(shift), next);\n\t/^-lines$/ && ($NUM_SENTENCES_PER_THREAD = int(shift), next);\n\t/^-penn$/ && ($PENN = 1, next);\n\t/^-no-escape/ && ($NO_ESCAPING = 1, next);\n}\n\n# for time calculation\nmy $start_time;\nif ($TIMING)\n{\n    $start_time = [ Time::HiRes::gettimeofday( ) ];\n}\n\n# print help message\nif ($HELP)\n{\n\tprint \"Usage ./tokenizer.perl (-l [en|de|...]) (-threads 4) < textfile > tokenizedfile\\n\";\n        print \"Options:\\n\";\n        print \"  -q     ... quiet.\\n\";\n        print \"  -a     ... aggressive hyphen splitting.\\n\";\n        print \"  -b     ... disable Perl buffering.\\n\";\n        print \"  -time  ... enable processing time calculation.\\n\";\n        print \"  -penn  ... use Penn treebank-like tokenization.\\n\";\n        print \"  -protected FILE  ... specify file with patters to be protected in tokenisation.\\n\";\n\tprint \"  -no-escape ... don't perform HTML escaping on apostrophy, quotes, etc.\\n\";\n\texit;\n}\n\nif (!$QUIET)\n{\n\tprint STDERR \"Tokenizer Version 1.1\\n\";\n\tprint STDERR \"Language: $language\\n\";\n\tprint STDERR \"Number of threads: $NUM_THREADS\\n\";\n}\n\n# load the language-specific non-breaking prefix info from files in the directory nonbreaking_prefixes\nload_prefixes($language,\\%NONBREAKING_PREFIX);\n\nif (scalar(%NONBREAKING_PREFIX) eq 0)\n{\n\tprint STDERR \"Warning: No known abbreviations for language '$language'\\n\";\n}\n\n# Load protected patterns\nif ($protected_patterns_file)\n{\n  open(PP,$protected_patterns_file) || die \"Unable to open $protected_patterns_file\";\n  while(<PP>) {\n    chomp;\n    push @protected_patterns, $_;\n  }\n}\n\nmy @batch_sentences = ();\nmy @thread_list = ();\nmy $count_sentences = 0;\n\nif ($NUM_THREADS > 1)\n{# multi-threading tokenization\n    while(<STDIN>)\n    {\n        $count_sentences = $count_sentences + 1;\n        push(@batch_sentences, $_);\n        if (scalar(@batch_sentences)>=($NUM_SENTENCES_PER_THREAD*$NUM_THREADS))\n        {\n            # assign each thread work\n            for (my $i=0; $i<$NUM_THREADS; $i++)\n            {\n                my $start_index = $i*$NUM_SENTENCES_PER_THREAD;\n                my $end_index = $start_index+$NUM_SENTENCES_PER_THREAD-1;\n                my @subbatch_sentences = @batch_sentences[$start_index..$end_index];\n                my $new_thread = new Thread \\&tokenize_batch, @subbatch_sentences;\n                push(@thread_list, $new_thread);\n            }\n            foreach (@thread_list)\n            {\n                my $tokenized_list = $_->join;\n                foreach (@$tokenized_list)\n                {\n                    print $_;\n                }\n            }\n            # reset for the new run\n            @thread_list = ();\n            @batch_sentences = ();\n        }\n    }\n    # the last batch\n    if (scalar(@batch_sentences)>0)\n    {\n        # assign each thread work\n        for (my $i=0; $i<$NUM_THREADS; $i++)\n        {\n            my $start_index = $i*$NUM_SENTENCES_PER_THREAD;\n            if ($start_index >= scalar(@batch_sentences))\n            {\n                last;\n            }\n            my $end_index = $start_index+$NUM_SENTENCES_PER_THREAD-1;\n            if ($end_index >= scalar(@batch_sentences))\n            {\n                $end_index = scalar(@batch_sentences)-1;\n            }\n            my @subbatch_sentences = @batch_sentences[$start_index..$end_index];\n            my $new_thread = new Thread \\&tokenize_batch, @subbatch_sentences;\n            push(@thread_list, $new_thread);\n        }\n        foreach (@thread_list)\n        {\n            my $tokenized_list = $_->join;\n            foreach (@$tokenized_list)\n            {\n                print $_;\n            }\n        }\n    }\n}\nelse\n{# single thread only\n    while(<STDIN>)\n    {\n        if (($SKIP_XML && /^<.+>$/) || /^\\s*$/)\n        {\n            #don't try to tokenize XML/HTML tag lines\n            print $_;\n        }\n        else\n        {\n            print &tokenize($_);\n        }\n    }\n}\n\nif ($TIMING)\n{\n    my $duration = Time::HiRes::tv_interval( $start_time );\n    print STDERR (\"TOTAL EXECUTION TIME: \".$duration.\"\\n\");\n    print STDERR (\"TOKENIZATION SPEED: \".($duration/$count_sentences*1000).\" milliseconds/line\\n\");\n}\n\n#####################################################################################\n# subroutines afterward\n\n# tokenize a batch of texts saved in an array\n# input: an array containing a batch of texts\n# return: another array containing a batch of tokenized texts for the input array\nsub tokenize_batch\n{\n    my(@text_list) = @_;\n    my(@tokenized_list) = ();\n    foreach (@text_list)\n    {\n        if (($SKIP_XML && /^<.+>$/) || /^\\s*$/)\n        {\n            #don't try to tokenize XML/HTML tag lines\n            push(@tokenized_list, $_);\n        }\n        else\n        {\n            push(@tokenized_list, &tokenize($_));\n        }\n    }\n    return \\@tokenized_list;\n}\n\n# the actual tokenize function which tokenizes one input string\n# input: one string\n# return: the tokenized string for the input string\nsub tokenize\n{\n    my($text) = @_;\n\n    if ($PENN) {\n      return tokenize_penn($text);\n    }\n\n    chomp($text);\n    $text = \" $text \";\n\n    # remove ASCII junk\n    $text =~ s/\\s+/ /g;\n    $text =~ s/[\\000-\\037]//g;\n\n    # Find protected patterns\n    my @protected = ();\n    foreach my $protected_pattern (@protected_patterns) {\n      my $t = $text;\n      while ($t =~ /(?<PATTERN>$protected_pattern)(?<TAIL>.*)$/) {\n        push @protected, $+{PATTERN};\n        $t = $+{TAIL};\n      }\n    }\n\n    for (my $i = 0; $i < scalar(@protected); ++$i) {\n      my $subst = sprintf(\"THISISPROTECTED%.3d\", $i);\n      $text =~ s,\\Q$protected[$i], $subst ,g;\n    }\n    $text =~ s/ +/ /g;\n    $text =~ s/^ //g;\n    $text =~ s/ $//g;\n\n    # seperate out all \"other\" special characters\n    $text =~ s/([^\\p{IsAlnum}\\s\\.\\'\\`\\,\\-])/ $1 /g;\n\n    # aggressive hyphen splitting\n    if ($AGGRESSIVE)\n    {\n        $text =~ s/([\\p{IsAlnum}])\\-(?=[\\p{IsAlnum}])/$1 \\@-\\@ /g;\n    }\n\n    #multi-dots stay together\n    $text =~ s/\\.([\\.]+)/ DOTMULTI$1/g;\n    while($text =~ /DOTMULTI\\./)\n    {\n        $text =~ s/DOTMULTI\\.([^\\.])/DOTDOTMULTI $1/g;\n        $text =~ s/DOTMULTI\\./DOTDOTMULTI/g;\n    }\n\n    # seperate out \",\" except if within numbers (5,300)\n    #$text =~ s/([^\\p{IsN}])[,]([^\\p{IsN}])/$1 , $2/g;\n\n    # separate out \",\" except if within numbers (5,300)\n    # previous \"global\" application skips some:  A,B,C,D,E > A , B,C , D,E\n    # first application uses up B so rule can't see B,C\n    # two-step version here may create extra spaces but these are removed later\n    # will also space digit,letter or letter,digit forms (redundant with next section)\n    $text =~ s/([^\\p{IsN}])[,]/$1 , /g;\n    $text =~ s/[,]([^\\p{IsN}])/ , $1/g;\n    \n    # separate \",\" after a number if it's the end of a sentence\n    $text =~ s/([\\p{IsN}])[,]$/$1 ,/g;\n\n    # separate , pre and post number\n    #$text =~ s/([\\p{IsN}])[,]([^\\p{IsN}])/$1 , $2/g;\n    #$text =~ s/([^\\p{IsN}])[,]([\\p{IsN}])/$1 , $2/g;\n\n    # turn `into '\n    #$text =~ s/\\`/\\'/g;\n\n    #turn '' into \"\n    #$text =~ s/\\'\\'/ \\\" /g;\n\n    if ($language eq \"en\")\n    {\n        #split contractions right\n        $text =~ s/([^\\p{IsAlpha}])[']([^\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([^\\p{IsAlpha}\\p{IsN}])[']([\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([\\p{IsAlpha}])[']([^\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([\\p{IsAlpha}])[']([\\p{IsAlpha}])/$1 '$2/g;\n        #special case for \"1990's\"\n        $text =~ s/([\\p{IsN}])[']([s])/$1 '$2/g;\n    }\n    elsif (($language eq \"fr\") or ($language eq \"it\") or ($language eq \"ga\"))\n    {\n        #split contractions left\n        $text =~ s/([^\\p{IsAlpha}])[']([^\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([^\\p{IsAlpha}])[']([\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([\\p{IsAlpha}])[']([^\\p{IsAlpha}])/$1 ' $2/g;\n        $text =~ s/([\\p{IsAlpha}])[']([\\p{IsAlpha}])/$1' $2/g;\n    }\n    else\n    {\n        $text =~ s/\\'/ \\' /g;\n    }\n\n    #word token method\n    my @words = split(/\\s/,$text);\n    $text = \"\";\n    for (my $i=0;$i<(scalar(@words));$i++)\n    {\n        my $word = $words[$i];\n        if ( $word =~ /^(\\S+)\\.$/)\n        {\n            my $pre = $1;\n            if (($pre =~ /\\./ && $pre =~ /\\p{IsAlpha}/) || ($NONBREAKING_PREFIX{$pre} && $NONBREAKING_PREFIX{$pre}==1) || ($i<scalar(@words)-1 && ($words[$i+1] =~ /^[\\p{IsLower}]/)))\n            {\n                #no change\n\t\t\t}\n            elsif (($NONBREAKING_PREFIX{$pre} && $NONBREAKING_PREFIX{$pre}==2) && ($i<scalar(@words)-1 && ($words[$i+1] =~ /^[0-9]+/)))\n            {\n                #no change\n            }\n            else\n            {\n                $word = $pre.\" .\";\n            }\n        }\n        $text .= $word.\" \";\n    }\n\n    # clean up extraneous spaces\n    $text =~ s/ +/ /g;\n    $text =~ s/^ //g;\n    $text =~ s/ $//g;\n\n    # .' at end of sentence is missed\n    $text =~ s/\\.\\' ?$/ . ' /;\n\n    # restore protected\n    for (my $i = 0; $i < scalar(@protected); ++$i) {\n      my $subst = sprintf(\"THISISPROTECTED%.3d\", $i);\n      $text =~ s/$subst/$protected[$i]/g;\n    }\n\n    #restore multi-dots\n    while($text =~ /DOTDOTMULTI/)\n    {\n        $text =~ s/DOTDOTMULTI/DOTMULTI./g;\n    }\n    $text =~ s/DOTMULTI/./g;\n\n    #escape special chars\n    if (!$NO_ESCAPING)\n      {\n\t$text =~ s/\\&/\\&amp;/g;   # escape escape\n\t$text =~ s/\\|/\\&#124;/g;  # factor separator\n\t$text =~ s/\\</\\&lt;/g;    # xml\n\t$text =~ s/\\>/\\&gt;/g;    # xml\n\t$text =~ s/\\'/\\&apos;/g;  # xml\n\t$text =~ s/\\\"/\\&quot;/g;  # xml\n\t$text =~ s/\\[/\\&#91;/g;   # syntax non-terminal\n\t$text =~ s/\\]/\\&#93;/g;   # syntax non-terminal\n      }\n\n    #ensure final line break\n    $text .= \"\\n\" unless $text =~ /\\n$/;\n\n    return $text;\n}\n\nsub tokenize_penn\n{\n    # Improved compatibility with Penn Treebank tokenization.  Useful if\n    # the text is to later be parsed with a PTB-trained parser.\n    #\n    # Adapted from Robert MacIntyre's sed script:\n    #   http://www.cis.upenn.edu/~treebank/tokenizer.sed\n\n    my($text) = @_;\n    chomp($text);\n\n    # remove ASCII junk\n    $text =~ s/\\s+/ /g;\n    $text =~ s/[\\000-\\037]//g;\n\n    # attempt to get correct directional quotes\n    $text =~ s/^``/`` /g;\n    $text =~ s/^\"/`` /g;\n    $text =~ s/^`([^`])/` $1/g;\n    $text =~ s/^'/`  /g;\n    $text =~ s/([ ([{<])\"/$1 `` /g;\n    $text =~ s/([ ([{<])``/$1 `` /g;\n    $text =~ s/([ ([{<])`([^`])/$1 ` $2/g;\n    $text =~ s/([ ([{<])'/$1 ` /g;\n    # close quotes handled at end\n\n    $text =~ s=\\.\\.\\.= _ELLIPSIS_ =g;\n\n    # separate out \",\" except if within numbers (5,300)\n    $text =~ s/([^\\p{IsN}])[,]([^\\p{IsN}])/$1 , $2/g;\n    # separate , pre and post number\n    $text =~ s/([\\p{IsN}])[,]([^\\p{IsN}])/$1 , $2/g;\n    $text =~ s/([^\\p{IsN}])[,]([\\p{IsN}])/$1 , $2/g;\n\n    #$text =~ s=([;:@#\\$%&\\p{IsSc}])= $1 =g;\n$text =~ s=([;:@#\\$%&\\p{IsSc}\\p{IsSo}])= $1 =g;\n\n    # Separate out intra-token slashes.  PTB tokenization doesn't do this, so\n    # the tokens should be merged prior to parsing with a PTB-trained parser\n    # (see syntax-hyphen-splitting.perl).\n    $text =~ s/([\\p{IsAlnum}])\\/([\\p{IsAlnum}])/$1 \\@\\/\\@ $2/g;\n\n    # Assume sentence tokenization has been done first, so split FINAL periods\n    # only.\n    $text =~ s=([^.])([.])([\\]\\)}>\"']*) ?$=$1 $2$3 =g;\n    # however, we may as well split ALL question marks and exclamation points,\n    # since they shouldn't have the abbrev.-marker ambiguity problem\n    $text =~ s=([?!])= $1 =g;\n\n    # parentheses, brackets, etc.\n    $text =~ s=([\\]\\[\\(\\){}<>])= $1 =g;\n    $text =~ s/\\(/-LRB-/g;\n    $text =~ s/\\)/-RRB-/g;\n    $text =~ s/\\[/-LSB-/g;\n    $text =~ s/\\]/-RSB-/g;\n    $text =~ s/{/-LCB-/g;\n    $text =~ s/}/-RCB-/g;\n\n    $text =~ s=--= -- =g;\n\n    # First off, add a space to the beginning and end of each line, to reduce\n    # necessary number of regexps.\n    $text =~ s=$= =;\n    $text =~ s=^= =;\n\n    $text =~ s=\"= '' =g;\n    # possessive or close-single-quote\n    $text =~ s=([^'])' =$1 ' =g;\n    # as in it's, I'm, we'd\n    $text =~ s='([sSmMdD]) = '$1 =g;\n    $text =~ s='ll = 'll =g;\n    $text =~ s='re = 're =g;\n    $text =~ s='ve = 've =g;\n    $text =~ s=n't = n't =g;\n    $text =~ s='LL = 'LL =g;\n    $text =~ s='RE = 'RE =g;\n    $text =~ s='VE = 'VE =g;\n    $text =~ s=N'T = N'T =g;\n\n    $text =~ s= ([Cc])annot = $1an not =g;\n    $text =~ s= ([Dd])'ye = $1' ye =g;\n    $text =~ s= ([Gg])imme = $1im me =g;\n    $text =~ s= ([Gg])onna = $1on na =g;\n    $text =~ s= ([Gg])otta = $1ot ta =g;\n    $text =~ s= ([Ll])emme = $1em me =g;\n    $text =~ s= ([Mm])ore'n = $1ore 'n =g;\n    $text =~ s= '([Tt])is = '$1 is =g;\n    $text =~ s= '([Tt])was = '$1 was =g;\n    $text =~ s= ([Ww])anna = $1an na =g;\n\n    #word token method\n    my @words = split(/\\s/,$text);\n    $text = \"\";\n    for (my $i=0;$i<(scalar(@words));$i++)\n    {\n        my $word = $words[$i];\n        if ( $word =~ /^(\\S+)\\.$/)\n        {\n            my $pre = $1;\n            if (($pre =~ /\\./ && $pre =~ /\\p{IsAlpha}/) || ($NONBREAKING_PREFIX{$pre} && $NONBREAKING_PREFIX{$pre}==1) || ($i<scalar(@words)-1 && ($words[$i+1] =~ /^[\\p{IsLower}]/)))\n            {\n                #no change\n            }\n            elsif (($NONBREAKING_PREFIX{$pre} && $NONBREAKING_PREFIX{$pre}==2) && ($i<scalar(@words)-1 && ($words[$i+1] =~ /^[0-9]+/)))\n            {\n                #no change\n            }\n            else\n            {\n                $word = $pre.\" .\";\n            }\n        }\n        $text .= $word.\" \";\n    }\n\n    # restore ellipses\n    $text =~ s=_ELLIPSIS_=\\.\\.\\.=g;\n\n    # clean out extra spaces\n    $text =~ s=  *= =g;\n    $text =~ s=^ *==g;\n    $text =~ s= *$==g;\n\n    #escape special chars\n    $text =~ s/\\&/\\&amp;/g;   # escape escape\n    $text =~ s/\\|/\\&#124;/g;  # factor separator\n    $text =~ s/\\</\\&lt;/g;    # xml\n    $text =~ s/\\>/\\&gt;/g;    # xml\n    $text =~ s/\\'/\\&apos;/g;  # xml\n    $text =~ s/\\\"/\\&quot;/g;  # xml\n    $text =~ s/\\[/\\&#91;/g;   # syntax non-terminal\n    $text =~ s/\\]/\\&#93;/g;   # syntax non-terminal\n\n    #ensure final line break\n    $text .= \"\\n\" unless $text =~ /\\n$/;\n\n    return $text;\n}\n\nsub load_prefixes\n{\n    my ($language, $PREFIX_REF) = @_;\n\n    my $prefixfile = \"$mydir/nonbreaking_prefix.$language\";\n\n    #default back to English if we don't have a language-specific prefix file\n    if (!(-e $prefixfile))\n    {\n        $prefixfile = \"$mydir/nonbreaking_prefix.en\";\n        print STDERR \"WARNING: No known abbreviations for language '$language', attempting fall-back to English version...\\n\";\n        die (\"ERROR: No abbreviations files found in $mydir\\n\") unless (-e $prefixfile);\n    }\n\n    if (-e \"$prefixfile\")\n    {\n        open(PREFIX, \"<:utf8\", \"$prefixfile\");\n        while (<PREFIX>)\n        {\n            my $item = $_;\n            chomp($item);\n            if (($item) && (substr($item,0,1) ne \"#\"))\n            {\n                if ($item =~ /(.*)[\\s]+(\\#NUMERIC_ONLY\\#)/)\n                {\n                    $PREFIX_REF->{$1} = 2;\n                }\n                else\n                {\n                    $PREFIX_REF->{$item} = 1;\n                }\n            }\n        }\n        close(PREFIX);\n    }\n}\n"
  },
  {
    "path": "train.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.train import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "translate.py",
    "content": "#!/usr/bin/env python\nfrom onmt.bin.translate import main\n\n\nif __name__ == \"__main__\":\n    main()\n"
  }
]